Migration test of Bisphenol A from polycarbonate cups using excitation-emission fluorescence data with parallel factor analysis.

PubMed

Spagnuolo, M L; Marini, F; Sarabia, L A; Ortiz, M C

2017-05-15

Bisphenol A (BPA) is one of the most largely produced chemical in the world; it is used to make plastics and epoxy resins. The endocrine disruptor potential of BPA is well known, but recent researches suggest a relationship between chronic exposure to BPA, genotoxic activity and epigenetic modifications. The main source of exposure to BPA includes food contact materials (FCM). Thus simple and robust test methods are needed to improve the migration test of BPA. In this work, a non-separative, easy, fast and inexpensive spectrofluorimetric method based on the second order calibration of excitation-emission fluorescence matrices (EEMs) was proposed for the determination of BPA. For the first time, molecular fluorescence was used to identify unequivocally and quantify BPA. Trilinearity of the data tensor guarantees the uniqueness of the solution obtained through parallel factor analysis (PARAFAC), so one factor of the decomposition matches up with BPA even if other fluorophores are in the test sample. The effect of four experimental factors of the procedure on the figures of merit and the unequivocally identification was investigated by means of a D-optimal design and PARAFAC calibration. The method is linear and accurate in the range 0-720µgL -1 . The decision limit CCα and detection capability CCβ are 6.63µgL -1 and 18.85µgL -1 respectively (with probabilities of false positive and false negative fixed at 0.05). Finally the proposed method was applied to carry out a migration test from two polycarbonate cups, using 3% (w/v) acetic acid in aqueous solution as food simulant. The migrated amount of BPA was found to be 688.7µgL -1 (n=5) for the first cup and 710.5µgL -1 (n=4) for the second one, above the specific migration limit set by EFSA (European Food Safety Authority). Copyright © 2017 Elsevier B.V. All rights reserved.

A novel approach combining self-organizing map and parallel factor analysis for monitoring water quality of watersheds under non-point source pollution

PubMed Central

Zhang, Yixiang; Liang, Xinqiang; Wang, Zhibo; Xu, Lixian

2015-01-01

High content of organic matter in the downstream of watersheds underscored the severity of non-point source (NPS) pollution. The major objectives of this study were to characterize and quantify dissolved organic matter (DOM) in watersheds affected by NPS pollution, and to apply self-organizing map (SOM) and parallel factor analysis (PARAFAC) to assess fluorescence properties as proxy indicators for NPS pollution and labor-intensive routine water quality indicators. Water from upstreams and downstreams was sampled to measure dissolved organic carbon (DOC) concentrations and excitation-emission matrix (EEM). Five fluorescence components were modeled with PARAFAC. The regression analysis between PARAFAC intensities (Fmax) and raw EEM measurements indicated that several raw fluorescence measurements at target excitation-emission wavelength region could provide similar DOM information to massive EEM measurements combined with PARAFAC. Regression analysis between DOC concentration and raw EEM measurements suggested that some regions in raw EEM could be used as surrogates for labor-intensive routine indicators. SOM can be used to visualize the occurrence of pollution. Relationship between DOC concentration and PARAFAC components analyzed with SOM suggested that PARAFAC component 2 might be the major part of bulk DOC and could be recognized as a proxy indicator to predict the DOC concentration. PMID:26526140

PARAFAC Modeling of Irradiation- and Oxidation-Induced Changes in Fluorescent Dissolved Organic Matter Extracted from Poultry Litter.

PubMed

Mangalgiri, Kiranmayi P; Timko, Stephen A; Gonsior, Michael; Blaney, Lee

2017-07-18

Parallel factor analysis (PARAFAC) applied to fluorescence excitation emission matrices (EEMs) allows quantitative assessment of the composition of fluorescent dissolved organic matter (DOM). In this study, we fit a four-component EEM-PARAFAC model to characterize DOM extracted from poultry litter. The data set included fluorescence EEMs from 291 untreated, irradiated (253.7 nm, 310-410 nm), and oxidized (UV-H 2 O 2 , ozone) poultry litter extracts. The four components were identified as microbial humic-, terrestrial humic-, tyrosine-, and tryptophan-like fluorescent signatures. The Tucker's congruence coefficients for components from the global (i.e., aggregated sample set) model and local (i.e., single poultry litter source) models were greater than 0.99, suggesting that the global EEM-PARAFAC model may be suitable to study poultry litter DOM from individual sources. In general, the transformation trends of the four fluorescence components were comparable for all poultry litter sources tested. For irradiation at 253.7 nm, ozonation, and UV-H 2 O 2 advanced oxidation, transformation of the humic-like components was slower than that of the tryptophan-like component. The opposite trend was observed for irradiation at 310-410 nm, due to differences in UV absorbance properties of components. Compared to the other EEM-PARAFAC components, the tyrosine-like component was fairly recalcitrant in irradiation and oxidation processes. This novel application of EEM-PARAFAC modeling provides insight into the composition and fate of agricultural DOM in natural and engineered systems.

Second-order data obtained by beta-cyclodextrin complexes: a novel approach for multicomponent analysis with three-way multivariate calibration methods.

PubMed

Khani, Rouhollah; Ghasemi, Jahan B; Shemirani, Farzaneh

2014-10-01

This research reports the first application of β-cyclodextrin (β-CD) complexes as a new method for generation of three way data, combined with second-order calibration methods for quantification of a binary mixture of caffeic (CA) and vanillic (VA) acids, as model compounds in fruit juices samples. At first, the basic experimental parameters affecting the formation of inclusion complexes between target analytes and β-CD were investigated and optimized. Then under the optimum conditions, parallel factor analysis (PARAFAC) and bilinear least squares/residual bilinearization (BLLS/RBL) were applied for deconvolution of trilinear data to get spectral and concentration profiles of CA and VA as a function of β-CD concentrations. Due to severe concentration profile overlapping between CA and VA in β-CD concentration dimension, PARAFAC could not be successfully applied to the studied samples. So, BLLS/RBL performed better than PARAFAC. The resolution of the model compounds was possible due to differences in the spectral absorbance changes of the β-CD complexes signals of the investigated analytes, opening a new approach for second-order data generation. The proposed method was validated by comparison with a reference method based on high-performance liquid chromatography photodiode array detection (HPLC-PDA), and no significant differences were found between the reference values and the ones obtained with the proposed method. Such a chemometrics-based protocol may be a very promising tool for more analytical applications in real samples monitoring, due to its advantages of simplicity, rapidity, accuracy, sufficient spectral resolution and concentration prediction even in the presence of unknown interferents. Copyright © 2014 Elsevier B.V. All rights reserved.

Comprehensive analysis of yeast metabolite GC x GC-TOFMS data: combining discovery-mode and deconvolution chemometric software.

PubMed

Mohler, Rachel E; Dombek, Kenneth M; Hoggard, Jamin C; Pierce, Karisa M; Young, Elton T; Synovec, Robert E

2007-08-01

The first extensive study of yeast metabolite GC x GC-TOFMS data from cells grown under fermenting, R, and respiring, DR, conditions is reported. In this study, recently developed chemometric software for use with three-dimensional instrumentation data was implemented, using a statistically-based Fisher ratio method. The Fisher ratio method is fully automated and will rapidly reduce the data to pinpoint two-dimensional chromatographic peaks differentiating sample types while utilizing all the mass channels. The effect of lowering the Fisher ratio threshold on peak identification was studied. At the lowest threshold (just above the noise level), 73 metabolite peaks were identified, nearly three-fold greater than the number of previously reported metabolite peaks identified (26). In addition to the 73 identified metabolites, 81 unknown metabolites were also located. A Parallel Factor Analysis graphical user interface (PARAFAC GUI) was applied to selected mass channels to obtain a concentration ratio, for each metabolite under the two growth conditions. Of the 73 known metabolites identified by the Fisher ratio method, 54 were statistically changing to the 95% confidence limit between the DR and R conditions according to the rigorous Student's t-test. PARAFAC determined the concentration ratio and provided a fully-deconvoluted (i.e. mathematically resolved) mass spectrum for each of the metabolites. The combination of the Fisher ratio method with the PARAFAC GUI provides high-throughput software for discovery-based metabolomics research, and is novel for GC x GC-TOFMS data due to the use of the entire data set in the analysis (640 MB x 70 runs, double precision floating point).

Chemical structure of the Chromophoric Dissolved Organic Matter (CDOM) fluorescent matter.

NASA Astrophysics Data System (ADS)

Blough, N. V.; Del Vecchio, R.; Cartisano, C. M.; Bianca, M.

2017-12-01

The structure(s), distribution and dynamics of CDOM have been investigated over the last several decades largely through optical spectroscopy (including both absorption and fluorescence) due to the fairly inexpensive instrumentation and the easy-to-gather data (over thousands published papers from 1990-2016). Yet, the chemical structure(s) of the light absorbing and emitting species or constituents within CDOM has only recently being proposed and tested through chemical manipulation of selected functional groups (such as carbonyl and carboxylic/phenolic containing molecules) naturally occurring within the organic matter pool. Similarly, fitting models (among which the PArallel FACtor analysis, PARAFAC) have been developed to better understand the nature of a subset of DOM, the CDOM fluorescent matter (FDOM). Fluorescence spectroscopy coupled with chemical tests and PARAFAC analyses could potentially provide valuable insights on CDOM sources and chemical nature of the FDOM pool. However, despite that applications (and publications) of PARAFAC model to FDOM have grown exponentially since its first application/publication (2003), a large fraction of such publications has misinterpreted the chemical meaning of the delivered PARAFAC `components' leading to more confusion than clarification on the nature, distribution and dynamics of the FDOM pool. In this context, we employed chemical manipulation of selected functional groups to gain further insights on the chemical structure of the FDOM and we tested to what extent the PARAFAC `components' represent true fluorophores through a controlled chemical approach with the ultimate goal to provide insights on the chemical nature of such `components' (as well as on the chemical nature of the FDOM) along with the advantages and limitations of the PARAFAC application.

Prebiotic Low Sugar Chocolate Dairy Desserts: Physical and Optical Characteristics and Performance of PARAFAC and PCA Preference Map.

PubMed

Morais, E C; Esmerino, E A; Monteiro, R A; Pinheiro, C M; Nunes, C A; Cruz, A G; Bolini, Helena M A

2016-01-01

The addition of prebiotic and sweeteners in chocolate dairy desserts opens up new opportunities to develop dairy desserts that besides having a lower calorie intake still has functional properties. In this study, prebiotic low sugar dairy desserts were evaluated by 120 consumers using a 9-point hedonic scale, in relation to the attributes of appearance, aroma, flavor, texture, and overall liking. Internal preference map using parallel factor analysis (PARAFAC) and principal component analysis (PCA) was performed using the consumer data. In addition, physical (texture profile) and optical (instrumental color) analyses were also performed. Prebiotic dairy desserts containing sucrose and sucralose were equally liked by the consumers. These samples were characterized by firmness and gumminess, which can be considered drivers of liking by the consumers. Optimization of the prebiotic low sugar dessert formulation should take in account the choice of ingredients that contribute in a positive manner for these parameters. PARAFAC allowed the extraction of more relevant information in relation to PCA, demonstrating that consumer acceptance analysis can be evaluated by simultaneously considering several attributes. Multiple factor analysis reported Rv value of 0.964, suggesting excellent concordance for both methods. © 2015 Institute of Food Technologists®

Identification and quantification of ciprofloxacin in urine through excitation-emission fluorescence and three-way PARAFAC calibration.

PubMed

Ortiz, M C; Sarabia, L A; Sánchez, M S; Giménez, D

2009-05-29

Due to the second-order advantage, calibration models based on parallel factor analysis (PARAFAC) decomposition of three-way data are becoming important in routine analysis. This work studies the possibility of fitting PARAFAC models with excitation-emission fluorescence data for the determination of ciprofloxacin in human urine. The finally chosen PARAFAC decomposition is built with calibration samples spiked with ciprofloxacin, and with other series of urine samples that were also spiked. One of the series of samples has also another drug because the patient was taking mesalazine. The mesalazine is a fluorescent substance that interferes with the ciprofloxacin. Finally, the procedure is applied to samples of a patient who was being treated with ciprofloxacin. The trueness has been established by the regression "predicted concentration versus added concentration". The recovery factor is 88.3% for ciprofloxacin in urine, and the mean of the absolute value of the relative errors is 4.2% for 46 test samples. The multivariate sensitivity of the fit calibration model is evaluated by a regression between the loadings of PARAFAC linked to ciprofloxacin versus the true concentration in spiked samples. The multivariate capability of discrimination is near 8 microg L(-1) when the probabilities of false non-compliance and false compliance are fixed at 5%.

Correction: Humic-like substances from urban waste as auxiliaries for photo-Fenton treatment: a fluorescence EEM-PARAFAC study.

PubMed

García Ballesteros, S; Costante, M; Vicente, R; Mora, M; Amat, A M; Arques, A; Carlos, L; García Einschlag, F S

2018-06-13

Correction for 'Humic-like substances from urban waste as auxiliaries for photo-Fenton treatment: a fluorescence EEM-PARAFAC study' by S. García Ballesteros et al., Photochem. Photobiol. Sci., 2017, 16, 38-45.

Application of fluorescence and PARAFAC to assess vertical distribution of subsurface hydrocarbons and dispersant during the Deepwater Horizon oil spill.

PubMed

Mendoza, Wilson G; Riemer, Daniel D; Zika, Rod G

2013-05-01

We evaluated the use of excitation and emission matrix (EEM) fluorescence and parallel factorial analysis (PARAFAC) modeling techniques for monitoring crude oil components in the water column. Four of the seven derived PARAFAC loadings were associated with the Macondo crude oil components. The other three components were associated with the dispersant, an unresolved component and colored dissolved organic matter (CDOM). The fluorescence of the associated benzene and naphthalene-like components of crude oil exhibited a maximum at ∼1200 m. The maximum fluorescence of the component associated with the dispersant (i.e., Corexit EC9500A) was observed at the same depth. The plume observed at this depth was attributed to the dispersed crude oil from the Deepwater Horizon oil spill. Results demonstrate the application of EEM and PARAFAC to simultaneously monitor selected PAH, dispersant-containing and humic-like fluorescence components in the oil spill region in the Gulf of Mexico.

Detection and quantification of extra virgin olive oil adulteration by means of autofluorescence excitation-emission profiles combined with multi-way classification.

PubMed

Durán Merás, Isabel; Domínguez Manzano, Jaime; Airado Rodríguez, Diego; Muñoz de la Peña, Arsenio

2018-02-01

Within olive oils, extra virgin olive oil is the highest quality and, in consequence, the most expensive one. Because of that, it is common that some merchants attempt to take economic advantage by mixing it up with other less expensive oils, like olive oil or olive pomace oil. In consequence, the characterization and authentication of extra virgin olive oils is a subject of great interest, both for industry and consumers. This paper reports the potential of front-face total fluorescence spectroscopy combined with second-order chemometric methods for the detection of extra virgin olive oils adulteration with other olive oils. Excitation-emission matrices (EEMs) of extra virgin olive oils and extra virgin olive oils adulterated with olive oils or with olive pomace oils were recorded using front-face fluorescence spectroscopy. The full information content in these fluorescence images was analyzed with the aid of unsupervised parallel factor analysis (PARAFAC), PARAFAC supervised by linear discriminant analysis (LDA-PARAFAC), and discriminant unfolded partial least-squares (DA-UPLS). The discriminant ability of LDA-PARAFAC was studied through the tridimensional plots of the canonical vectors, defining a surface separating the established categories. For DA-UPLS, the discriminant ability was established through the bidimensional plots of predicted values of calibration and validation samples, in order to assign each sample to a given class. The models demonstrated the possibility of detecting adulterations of extra virgin olive oils with percentages of around 15% and 3% of olive and olive pomace oils, respectively. Also, UPLS regression was used to quantify the adulteration level of extra virgin olive oils with olive oils or with olive pomace oils. Copyright © 2017 Elsevier B.V. All rights reserved.

Trace Norm Regularized CANDECOMP/PARAFAC Decomposition With Missing Data.

PubMed

Liu, Yuanyuan; Shang, Fanhua; Jiao, Licheng; Cheng, James; Cheng, Hong

2015-11-01

In recent years, low-rank tensor completion (LRTC) problems have received a significant amount of attention in computer vision, data mining, and signal processing. The existing trace norm minimization algorithms for iteratively solving LRTC problems involve multiple singular value decompositions of very large matrices at each iteration. Therefore, they suffer from high computational cost. In this paper, we propose a novel trace norm regularized CANDECOMP/PARAFAC decomposition (TNCP) method for simultaneous tensor decomposition and completion. We first formulate a factor matrix rank minimization model by deducing the relation between the rank of each factor matrix and the mode- n rank of a tensor. Then, we introduce a tractable relaxation of our rank function, and then achieve a convex combination problem of much smaller-scale matrix trace norm minimization. Finally, we develop an efficient algorithm based on alternating direction method of multipliers to solve our problem. The promising experimental results on synthetic and real-world data validate the effectiveness of our TNCP method. Moreover, TNCP is significantly faster than the state-of-the-art methods and scales to larger problems.

Seasonal characterization of CDOM for lakes in semiarid regions of Northeast China using excitation-emission matrix fluorescence and parallel factor analysis (EEM-PARAFAC)

NASA Astrophysics Data System (ADS)

Zhao, Ying; Song, Kaishan; Wen, Zhidan; Li, Lin; Zang, Shuying; Shao, Tiantian; Li, Sijia; Du, Jia

2016-03-01

The seasonal characteristics of fluorescent components in chromophoric dissolved organic matter (CDOM) for lakes in the semiarid region of Northeast China were examined by excitation-emission matrix (EEM) spectra and parallel factor analysis (PARAFAC). Two humic-like (C1 and C2) and protein-like (C3 and C4) components were identified using PARAFAC. The average fluorescence intensity of the four components differed under seasonal variation from June and August 2013 to February and April 2014. Components 1 and 2 exhibited a strong linear correlation (R2 = 0.628). Significantly positive linear relationships between CDOM absorption coefficients a(254) (R2 = 0.72, 0.46, p < 0.01), a(280) (R2 = 0.77, 0.47, p < 0.01), a(350) (R2 = 0.76, 0.78, p < 0.01) and Fmax for two humic-like components (C1 and C2) were exhibited, respectively. A significant relationship (R2 = 0.930) was found between salinity and dissolved organic carbon (DOC). However, almost no obvious correlation was found between salinity and EEM-PARAFAC-extracted components except for C3 (R2 = 0.469). Results from this investigation demonstrate that the EEM-PARAFAC technique can be used to evaluate the seasonal dynamics of CDOM fluorescent components for inland waters in the semiarid regions of Northeast China, and to quantify CDOM components for other waters with similar environmental conditions.

Characterization of chromophoric dissolved organic matter and relationships among PARAFAC components and water quality parameters in Heilongjiang, China.

PubMed

Cui, Hongyang; Shi, Jianhong; Qiu, Linlin; Zhao, Yue; Wei, Zimin; Wang, Xinglei; Jia, Liming; Li, Jiming

2016-05-01

Chromophoric dissolved organic matter (CDOM) is an important optically active substance that can transports nutrients and pollutants from terrestrial to aquatic systems. Additionally, it is used as a measure of water quality. To investigate the source and composition of CDOM, we used chemical and fluorescent analyses to characterize CDOM in Heilongjiang. The composition of CDOM can be investigated by excitation-emission matrix (EEM) fluorescence and parallel factor analysis (PARAFAC). PARAFAC identified four individual components that were attributed to microbial humic-like (C1) and terrestrial humic-like (C2-4) in water samples collected from the Heilongjiang River. The relationships between the maximum fluorescence intensities of the four PARAFAC components and the water quality parameters indicate that the dynamic of the four components is related to nutrients in the Heilongjiang River. The relationships between the fluorescence component C3 and the biochemical oxygen demand (BOD5) indicates that component C3 makes a great contribution to BOD5 and it can be used as a carbon source for microbes in the Heilongjiang River. Furthermore, the relationships between component C3, the particulate organic carbon (POC), and the chemical oxygen demand (CODMn) show that component C3 and POC make great contributions to BOD5 and CODMn. The use of these indexes along with PARAFAC results would be of help to characterize the co-variation between the CDOM and water quality parameters in the Heilongjiang River.

Simultaneous measurement of two enzyme activities using infrared spectroscopy: A comparative evaluation of PARAFAC, TUCKER and N-PLS modeling.

PubMed

Baum, Andreas; Hansen, Per Waaben; Meyer, Anne S; Mikkelsen, Jørn Dalgaard

2013-08-06

Enzymes are used in many processes to release fermentable sugars for green production of biofuel, or the refinery of biomass for extraction of functional food ingredients such as pectin or prebiotic oligosaccharides. The complex biomasses may, however, require a multitude of specific enzymes which are active on specific substrates generating a multitude of products. In this paper we use the plant polymer, pectin, to present a method to quantify enzyme activity of two pectolytic enzymes by monitoring their superimposed spectral evolutions simultaneously. The data is analyzed by three chemometric multiway methods, namely PARAFAC, TUCKER3 and N-PLS, to establish simultaneous enzyme activity assays for pectin lyase and pectin methyl esterase. Correlation coefficients Rpred(2) for prediction test sets are 0.48, 0.96 and 0.96 for pectin lyase and 0.70, 0.89 and 0.89 for pectin methyl esterase, respectively. The retrieved models are compared and prediction test sets show that especially TUCKER3 performs well, even in comparison to the supervised regression method N-PLS. Copyright © 2013 Elsevier B.V. All rights reserved.

Occurrence and behaviors of fluorescence EEM-PARAFAC components in drinking water and wastewater treatment systems and their applications: a review.

PubMed

Yang, Liyang; Hur, Jin; Zhuang, Wane

2015-05-01

Fluorescence excitation emission matrices-parallel factor analysis (EEM-PARAFAC) is a powerful tool for characterizing dissolved organic matter (DOM), and it is applied in a rapidly growing number of studies on drinking water and wastewater treatments. This paper presents an overview of recent findings about the occurrence and behavior of PARAFAC components in drinking water and wastewater treatments, as well as their feasibility for assessing the treatment performance and water quality including disinfection by-product formation potentials (DBPs FPs). A variety of humic-like, protein-like, and unique (e.g., pyrene-like) fluorescent components have been identified, providing valuable insights into the chemical composition of DOM and the effects of various treatment processes in engineered systems. Coagulation/flocculation-clarification preferentially removes humic-like components, and additional treatments such as biological activated carbon filtration, anion exchange, and UV irradiation can further remove DOM from drinking water. In contrast, biological treatments are more effective for protein-like components in wastewater treatments. PARAFAC components have been proven to be valuable as surrogates for conventional water quality parameter, to track the changes of organic matter quantity and quality in drinking water and wastewater treatments. They are also feasible for assessing formations of trihalomethanes and other DBPs and evaluating treatment system performance. Further studies of EEM-PARAFAC for assessing the effects of the raw water quality and variable treatment conditions on the removal of DOM, and the formation potentials of various emerging DBPs, are essential for optimizing the treatment processes to ensure treated water quality.

New light on a dark subject: On the use of fluorescence data to deduce redox states of natural organic matter (NOM)

USGS Publications Warehouse

Macalady, Donald L.; Walton-Day, Katherine

2009-01-01

This paper reports the use of excitation-emission matrix fluorescence spectroscopy (EEMS), parallel factor statistical analysis (PARAFAC), and oxidation-reduction experiments to examine the effect of redox conditions on PARAFAC model results for aqueous samples rich in natural organic matter. Fifty-four aqueous samples from 11 different geographic locations and two plant extracts were analyzed untreated and after chemical treatments or irradiation were used in attempts to change the redox status of the natural organic matter. The EEMS spectra were generated and modeled using a PARAFAC package developed by Cory and McKnight (2005). The PARAFAC model output was examined for consistency with previously reported relations and with changes expected to occur upon experimental oxidation and reduction of aqueous samples. Results indicate the implied fraction of total sample fluorescence attributed to quinone-like moieties was consistent (0.64 to 0.78) and greater than that observed by Cory and McKnight (2005). The fraction of the quinone-like moieties that was reduced (the reducing index, RI) showed relatively little variation (0.46 to 0.71) despite attempts to alter the redox status of the natural organic matter. The RI changed little after reducing samples using zinc metal, oxidizing at high pH with air, or irradiating with a Xenon lamp. Our results, however, are consistent with the correlations between the fluorescence indices (FI) of samples and the ratio of PARAFAC fitting parameters suggested by Cory and McKnight (2005), though we used samples with a much narrower range of FI values.

Assessing factorial invariance of two-way rating designs using three-way methods

PubMed Central

Kroonenberg, Pieter M.

2015-01-01

Assessing the factorial invariance of two-way rating designs such as ratings of concepts on several scales by different groups can be carried out with three-way models such as the Parafac and Tucker models. By their definitions these models are double-metric factorially invariant. The differences between these models lie in their handling of the links between the concept and scale spaces. These links may consist of unrestricted linking (Tucker2 model), invariant component covariances but variable variances per group and per component (Parafac model), zero covariances and variances different per group but not per component (Replicated Tucker3 model) and strict invariance (Component analysis on the average matrix). This hierarchy of invariant models, and the procedures by which to evaluate the models against each other, is illustrated in some detail with an international data set from attachment theory. PMID:25620936

SandiaMRCR

DOE Office of Scientific and Technical Information (OSTI.GOV)

2012-01-05

SandiaMCR was developed to identify pure components and their concentrations from spectral data. This software efficiently implements the multivariate calibration regression alternating least squares (MCR-ALS), principal component analysis (PCA), and singular value decomposition (SVD). Version 3.37 also includes the PARAFAC-ALS Tucker-1 (for trilinear analysis) algorithms. The alternating least squares methods can be used to determine the composition without or with incomplete prior information on the constituents and their concentrations. It allows the specification of numerous preprocessing, initialization and data selection and compression options for the efficient processing of large data sets. The software includes numerous options including the definition ofmore » equality and non-negativety constraints to realistically restrict the solution set, various normalization or weighting options based on the statistics of the data, several initialization choices and data compression. The software has been designed to provide a practicing spectroscopist the tools required to routinely analysis data in a reasonable time and without requiring expert intervention.« less

Characterizing dissolved organic matter using PARAFAC modeling of fluorescence spectroscopy: a comparison of two models

Treesearch

Jason B. Fellman; Mathew P. Miller; Rose M. Cory; David V. D' Amore; Dan White

2009-01-01

We evaluated whether fitting fluorescence excitation-emission matrices (EEMs) to a previously validated PARAFAC model is an acceptable alternative to building an original model. To do this, we built a l0-component model using 307 EEMscollected from southeast Alaskan soil and streamwater. All 307 EEMs were then fit to the existing model (CM) presented in Cory and...

Cross-language information retrieval using PARAFAC2.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bader, Brett William; Chew, Peter; Abdelali, Ahmed

A standard approach to cross-language information retrieval (CLIR) uses Latent Semantic Analysis (LSA) in conjunction with a multilingual parallel aligned corpus. This approach has been shown to be successful in identifying similar documents across languages - or more precisely, retrieving the most similar document in one language to a query in another language. However, the approach has severe drawbacks when applied to a related task, that of clustering documents 'language-independently', so that documents about similar topics end up closest to one another in the semantic space regardless of their language. The problem is that documents are generally more similar tomore » other documents in the same language than they are to documents in a different language, but on the same topic. As a result, when using multilingual LSA, documents will in practice cluster by language, not by topic. We propose a novel application of PARAFAC2 (which is a variant of PARAFAC, a multi-way generalization of the singular value decomposition [SVD]) to overcome this problem. Instead of forming a single multilingual term-by-document matrix which, under LSA, is subjected to SVD, we form an irregular three-way array, each slice of which is a separate term-by-document matrix for a single language in the parallel corpus. The goal is to compute an SVD for each language such that V (the matrix of right singular vectors) is the same across all languages. Effectively, PARAFAC2 imposes the constraint, not present in standard LSA, that the 'concepts' in all documents in the parallel corpus are the same regardless of language. Intuitively, this constraint makes sense, since the whole purpose of using a parallel corpus is that exactly the same concepts are expressed in the translations. We tested this approach by comparing the performance of PARAFAC2 with standard LSA in solving a particular CLIR problem. From our results, we conclude that PARAFAC2 offers a very promising alternative to LSA not only for multilingual document clustering, but also for solving other problems in cross-language information retrieval.« less

Molecular characterization of dissolved organic matter in contrasted freshwater environments by electrospray ionization mass spectrometry and EEM-PARAFAC

NASA Astrophysics Data System (ADS)

Parot, Jérémie; Parlanti, Edith; Guéguen, Céline

2015-04-01

Dissolved organic matter (DOM) is a key parameter in the fate, transport and mobility of inorganic and organic pollutants in natural waters. Excitation emission matrix (EEM) spectra coupled to parallel factor analysis (PARAFAC) provide insights on the main fluorescent DOM constituents. However, the molecular structures associated with PARAFAC DOM remain poorly understood. In this study, DOM from rivers, marshes and algal culture was characterized by EEM-PARAFAC and electrospray ionization Fourier transform mass spectrometry (ESI-FT-MS, Orbitrap Q Exactive). The high resolution of the Orbitrap (i.e. 140,000) allowed us to separate unique molecular species from the complex DOM mixtures. The majority of chemical species were found within the mass to charge ratio (m/z) 200 to 400. Weighted averages of neutral mass were 271.254, 236.480, 213.992Da for river, marsh and algal-derived DOM, respectively, congruent with previous studies. The assigned formula were dominated by CHO in humic-rich river waters whereas N- and S-containing compounds were predominant in marsh and algal samples. Marsh consisted of N and S-containing compounds, which were presumed to be linear alkylbenzene sulfonates. And the double bond equivalent (DBE) was higher in the marsh and in comparison was lower in the algal culture. Kendrick masses, used to identify homologous compounds differing only by a number of base units in high resolution mass spectra, and Van Krevelen diagrams, plot of molar ratio of hydrogen to carbon (H/C) versus oxygen to carbon (O/C), will be discussed in relation to PARAFAC components to further discriminate freshwater systems based on the origin and maturity of DOM. Together, these results showed that ESI-FT-MS has a great potential to distinguish freshwater DOM at the molecular level without any fractionation.

Supercritical Fluid Chromatography of Drugs: Parallel Factor Analysis for Column Testing in a Wide Range of Operational Conditions

PubMed Central

Al-Degs, Yahya; Andri, Bertyl; Thiébaut, Didier; Vial, Jérôme

2017-01-01

Retention mechanisms involved in supercritical fluid chromatography (SFC) are influenced by interdependent parameters (temperature, pressure, chemistry of the mobile phase, and nature of the stationary phase), a complexity which makes the selection of a proper stationary phase for a given separation a challenging step. For the first time in SFC studies, Parallel Factor Analysis (PARAFAC) was employed to evaluate the chromatographic behavior of eight different stationary phases in a wide range of chromatographic conditions (temperature, pressure, and gradient elution composition). Design of Experiment was used to optimize experiments involving 14 pharmaceutical compounds present in biological and/or environmental samples and with dissimilar physicochemical properties. The results showed the superiority of PARAFAC for the analysis of the three-way (column × drug × condition) data array over unfolding the multiway array to matrices and performing several classical principal component analyses. Thanks to the PARAFAC components, similarity in columns' function, chromatographic trend of drugs, and correlation between separation conditions could be simply depicted: columns were grouped according to their H-bonding forces, while gradient composition was dominating for condition classification. Also, the number of drugs could be efficiently reduced for columns classification as some of them exhibited a similar behavior, as shown by hierarchical clustering based on PARAFAC components. PMID:28695040

Gas chromatography - mass spectrometry data processing made easy.

PubMed

Johnsen, Lea G; Skou, Peter B; Khakimov, Bekzod; Bro, Rasmus

2017-06-23

Evaluation of GC-MS data may be challenging due to the high complexity of data including overlapped, embedded, retention time shifted and low S/N ratio peaks. In this work, we demonstrate a new approach, PARAFAC2 based Deconvolution and Identification System (PARADISe), for processing raw GC-MS data. PARADISe is a computer platform independent freely available software incorporating a number of newly developed algorithms in a coherent framework. It offers a solution for analysts dealing with complex chromatographic data. It allows extraction of chemical/metabolite information directly from the raw data. Using PARADISe requires only few inputs from the analyst to process GC-MS data and subsequently converts raw netCDF data files into a compiled peak table. Furthermore, the method is generally robust towards minor variations in the input parameters. The method automatically performs peak identification based on deconvoluted mass spectra using integrated NIST search engine and generates an identification report. In this paper, we compare PARADISe with AMDIS and ChromaTOF in terms of peak quantification and show that PARADISe is more robust to user-defined settings and that these are easier (and much fewer) to set. PARADISe is based on non-proprietary scientifically evaluated approaches and we here show that PARADISe can handle more overlapping signals, lower signal-to-noise peaks and do so in a manner that requires only about an hours worth of work regardless of the number of samples. We also show that there are no non-detects in PARADISe, meaning that all compounds are detected in all samples. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Ozonation effects on emerging micropollutants and effluent organic matter in wastewater: characterization using changes of three-dimensional HP-SEC and EEM fluorescence data.

PubMed

Liu, Chen; Li, Penghui; Tang, Xiangyu; Korshin, Gregory V

2016-10-01

The degradation of effluent organic matter (EfOM) in a municipal wastewater treated by ozonation was characterized using the methods of high-performance size-exclusion chromatography (HP-SEC) and excitation/emission matrix (EEM) fluorescence combined with parallel factor analysis (PARAFAC). The removal of 40 diverse trace-level contaminants of emerging concern (CEC) present in the wastewater was determined as well. Ozonation caused a rapid decrease of the absorbance and fluorescence of the wastewater, which was associated primarily with the oxidation of high and low apparent molecular weight (AMW) EfOM fractions. PARAFAC analysis also showed that components C1 and C2 decreased prominently in these conditions. The EfOM fraction of intermediate molecular weight ascribable to a terrestrial humic-like component (C3) tended to be less reactive toward ozone. Relative changes of EEM fluorescence were quantified using F max values of PARAFAC-identified components (∆F/F 0 max ). Unambiguous relationships between ∆F/F 0 max values and the extent of the degradation of the examined CECs (∆C/C 0 ) were established. This allowed correlating main parameters of the ∆C/C 0 vs. ∆F/F 0 max relationships with the rates of oxidation of these CECs. The results demonstrate the potential of online measurements of EEM fluorescence for quantitating effects of ozonation on EfOM and micropollutants in wastewater effluents.

Use of fluorescence EEM to monitor the removal of emerging contaminants in full scale wastewater treatment plants.

PubMed

Sgroi, Massimiliano; Roccaro, Paolo; Korshin, Gregory V; Greco, Valentina; Sciuto, Sebastiano; Anumol, Tarun; Snyder, Shane A; Vagliasindi, Federico G A

2017-02-05

This study investigated the applicability of different techniques for fluorescence excitation/emission matrices data interpretations, including peak-picking method, fluorescence regional integration and PARAFAC modelling, to act as surrogates in predicting emerging trace organic compounds (ETOrCs) removal during conventional wastewater treatments that usually comprise primary and secondary treatments. Results showed that fluorescence indexes developed using alternative methodologies but indicative of a same dissolved organic matter component resulted in similar predictions of the removal of the target compounds. The peak index defined by the excitation/emission wavelength positions (λ ex/ λ em ) 225/290nm and related to aromatic proteins and tyrosine-like fluorescence was determined to be a particularly suitable surrogate for monitoring ETOrCs that had very high removal rates (average removal >70%) (i.e., triclosan, caffeine and ibuprofen). The peak index defined by λ ex/ λ em =245/440nm and the PARAFAC component with wavelength of the maxima λ ex/ λ em =245, 350/450, both identified as humic-like fluorescence, were found remarkably well correlated with ETOrCs such as atenolol, naproxen and gemfibrozil that were moderately removed (51-70% average removal). Finally, the PARAFAC component with wavelength of the maxima λ ex/ λ em =<240, 315/380 identified as microbial humic-like fluorescence was the only index correlated with the removal of the antibiotic trimethoprim (average removal 68%). Copyright © 2016 Elsevier B.V. All rights reserved.

Chromatographic background drift correction coupled with parallel factor analysis to resolve coelution problems in three-dimensional chromatographic data: quantification of eleven antibiotics in tap water samples by high-performance liquid chromatography coupled with a diode array detector.

PubMed

Yu, Yong-Jie; Wu, Hai-Long; Fu, Hai-Yan; Zhao, Juan; Li, Yuan-Na; Li, Shu-Fang; Kang, Chao; Yu, Ru-Qin

2013-08-09

Chromatographic background drift correction has been an important field of research in chromatographic analysis. In the present work, orthogonal spectral space projection for background drift correction of three-dimensional chromatographic data was described in detail and combined with parallel factor analysis (PARAFAC) to resolve overlapped chromatographic peaks and obtain the second-order advantage. This strategy was verified by simulated chromatographic data and afforded significant improvement in quantitative results. Finally, this strategy was successfully utilized to quantify eleven antibiotics in tap water samples. Compared with the traditional methodology of introducing excessive factors for the PARAFAC model to eliminate the effect of background drift, clear improvement in the quantitative performance of PARAFAC was observed after background drift correction by orthogonal spectral space projection. Copyright © 2013 Elsevier B.V. All rights reserved.

A new approach for SSVEP detection using PARAFAC and canonical correlation analysis.

PubMed

Tello, Richard; Pouryazdian, Saeed; Ferreira, Andre; Beheshti, Soosan; Krishnan, Sridhar; Bastos, Teodiano

2015-01-01

This paper presents a new way for automatic detection of SSVEPs through correlation analysis between tensor models. 3-way EEG tensor of channel × frequency × time is decomposed into constituting factor matrices using PARAFAC model. PARAFAC analysis of EEG tensor enables us to decompose multichannel EEG into constituting temporal, spectral and spatial signatures. SSVEPs characterized with localized spectral and spatial signatures are then detected exploiting a correlation analysis between extracted signatures of the EEG tensor and the corresponding simulated signatures of all target SSVEP signals. The SSVEP that has the highest correlation is selected as the intended target. Two flickers blinking at 8 and 13 Hz were used as visual stimuli and the detection was performed based on data packets of 1 second without overlapping. Five subjects participated in the experiments and the highest classification rate of 83.34% was achieved, leading to the Information Transfer Rate (ITR) of 21.01 bits/min.

Towards a better control of the wastewater treatment process: excitation-emission matrix fluorescence spectroscopy of dissolved organic matter as a predictive tool of soluble BOD5 in influents of six Parisian wastewater treatment plants.

PubMed

Goffin, Angélique; Guérin, Sabrina; Rocher, Vincent; Varrault, Gilles

2018-03-01

The online monitoring of dissolved organic matter (DOM) in raw sewage water is expected to better control wastewater treatment processes. Fluorescence spectroscopy offers one possibility for both the online and real-time monitoring of DOM, especially as regards the DOM biodegradability assessment. In this study, three-dimensional fluorescence spectroscopy combined with a parallel factor analysis (PARAFAC) has been investigated as a predictive tool of the soluble biological oxygen demand in 5 days (BOD 5 ) for raw sewage water. Six PARAFAC components were highlighted in 69 raw sewage water samples: C2, C5, and C6 related to humic-like compounds, along with C1, C3, and C4 related to protein-like compounds. Since the PARAFAC methodology is not available for online monitoring, a peak-picking approach based on maximum excitation-emission (Ex-Em) localization of the PARAFAC components identified in this study has been used. A good predictive model of soluble BOD 5 using fluorescence spectroscopy parameters was obtained (r 2  = 0.846, adjusted r 2  = 0.839, p < 0.0001). This model is quite straightforward, easy to automate, and applicable to the operational field of wastewater treatment for online monitoring purposes.

Influence of soil conditions on dissolved organic matter leached from forest and wetland soils: a controlled growth chamber study.

PubMed

Kim, Eun-Ah; Nguyen, Hang Vo-Minh; Oh, Hae Sung; Hur, Jin; Choi, Jung Hyun

2016-03-01

This study investigated the effects of various soil conditions, including drying-rewetting, nitrogen deposition, and temperature rise, on the quantities and the composition of dissolved organic matter leached from forest and wetland soils. A set of forest and wetland soils with and without the nitrogen deposition were incubated in the growth chambers under three different temperatures. The moisture contents were kept constant, except for two-week drying intervals. Comparisons between the original and the treated samples revealed that drying-rewetting was a crucial environmental factor driving changes in the amount of dissolved organic carbon (DOC). The DOC was also notably increased by the nitrogen deposition to the dry forest soil and was affected by the temperature of the dry wetland soil. A parallel factor (PARAFAC) analysis identified three sub-fractions of the fluorescent dissolved organic matter (FDOM) from the fluorescence excitation-emission matrices (EEMs), and their compositions depended on drying-rewetting. The data as a whole, including the DOC and PARAFAC components and other optical indices, were possibly explained by the two main variables, which were closely related with the PARAFAC components and DOC based on principal component analysis (PCA). Our results suggested that the DOC and PARAFAC component information could provide a comprehensive interpretation of the changes in the soil-leached DOM in response to the different environmental conditions.

Fluorescence spectroscopy for monitoring reduction of natural organic matter and halogenated furanone precursors by biofiltration.

PubMed

Peleato, Nicolás M; McKie, Michael; Taylor-Edmonds, Lizbeth; Andrews, Susan A; Legge, Raymond L; Andrews, Robert C

2016-06-01

The application of fluorescence spectroscopy to monitor natural organic matter (NOM) reduction as a function of biofiltration performance was investigated. This study was conducted at pilot-scale where a conventional media filter was compared to six biofilters employing varying enhancement strategies. Overall reductions of NOM were identified by measuring dissolved organic carbon (DOC), and UV absorbance at 254 nm, as well as characterization of organic sub-fractions by liquid chromatography-organic carbon detection (LC-OCD) and parallel factors analysis (PARAFAC) of fluorescence excitation-emission matrices (FEEM). The biofilter using granular activated carbon media, with exhausted absorptive capacity, was found to provide the highest removal of all identified PARAFAC components. A microbial or processed humic-like component was found to be most amenable to biodegradation by biofilters and removal by conventional treatment. One refractory humic-like component, detectable only by FEEM-PARAFAC, was not well removed by biofiltration or conventional treatment. All biofilters removed protein-like material to a high degree relative to conventional treatment. The formation potential of two halogenated furanones, 3-chloro-4(dichloromethyl)-2(5H)-furanone (MX) and mucochloric acid (MCA), as well as overall treated water genotoxicity are also reported. Using the organic characterization results possible halogenated furanone and genotoxicity precursors are identified. Comparison of FEEM-PARAFAC and LC-OCD results revealed polysaccharides as potential MX/MCA precursors. Copyright © 2016 Elsevier Ltd. All rights reserved.

Prediction of BOD, COD, and total nitrogen concentrations in a typical urban river using a fluorescence excitation-emission matrix with PARAFAC and UV absorption indices.

PubMed

Hur, Jin; Cho, Jinwoo

2012-01-01

The development of a real-time monitoring tool for the estimation of water quality is essential for efficient management of river pollution in urban areas. The Gap River in Korea is a typical urban river, which is affected by the effluent of a wastewater treatment plant (WWTP) and various anthropogenic activities. In this study, fluorescence excitation-emission matrices (EEM) with parallel factor analysis (PARAFAC) and UV absorption values at 220 nm and 254 nm were applied to evaluate the estimation capabilities for biochemical oxygen demand (BOD), chemical oxygen demand (COD), and total nitrogen (TN) concentrations of the river samples. Three components were successfully identified by the PARAFAC modeling from the fluorescence EEM data, in which each fluorophore group represents microbial humic-like (C1), terrestrial humic-like organic substances (C2), and protein-like organic substances (C3), and UV absorption indices (UV(220) and UV(254)), and the score values of the three PARAFAC components were selected as the estimation parameters for the nitrogen and the organic pollution of the river samples. Among the selected indices, UV(220), C3 and C1 exhibited the highest correlation coefficients with BOD, COD, and TN concentrations, respectively. Multiple regression analysis using UV(220) and C3 demonstrated the enhancement of the prediction capability for TN.

Effects of sampling methods on the quantity and quality of dissolved organic matter in sediment pore waters as revealed by absorption and fluorescence spectroscopy.

PubMed

Chen, Meilian; Lee, Jong-Hyeon; Hur, Jin

2015-10-01

Despite literature evidence suggesting the importance of sampling methods on the properties of sediment pore waters, their effects on the dissolved organic matter (PW-DOM) have been unexplored to date. Here, we compared the effects of two commonly used sampling methods (i.e., centrifuge and Rhizon sampler) on the characteristics of PW-DOM for the first time. The bulk dissolved organic carbon (DOC), ultraviolet-visible (UV-Vis) absorption, and excitation-emission matrixes coupled with parallel factor analysis (EEM-PARAFAC) of the PW-DOM samples were compared for the two sampling methods with the sediments from minimal to severely contaminated sites. The centrifuged samples were found to have higher average values of DOC, UV absorption, and protein-like EEM-PARAFAC components. The samples collected with the Rhizon sampler, however, exhibited generally more humified characteristics than the centrifuged ones, implying a preferential collection of PW-DOM with respect to the sampling methods. Furthermore, the differences between the two sampling methods seem more pronounced in relatively more polluted sites. Our observations were possibly explained by either the filtration effect resulting from the smaller pore size of the Rhizon sampler or the desorption of DOM molecules loosely bound to minerals during centrifugation, or both. Our study suggests that consistent use of one sampling method is crucial for PW-DOM studies and also that caution should be taken in the comparison of data collected with different sampling methods.

Investigation of adsorptive fractionation of humic acid on graphene oxide using fluorescence EEM-PARAFAC.

PubMed

Lee, Bo-Mi; Seo, Young-Soo; Hur, Jin

2015-04-15

In this study, the adsorptive fractionation of a humic acid (HA, Elliott soil humic acid) on graphene oxide (GO) was examined at pH 4 and 6 using absorption spectroscopy and fluorescence excitation-emission matrix (EEM)-parallel factor analysis (PARAFAC). The extent of the adsorption was greater at pH 4.0 than at pH 6.0. Aromatic molecules within the HA were preferentially adsorbed onto the GO surface, and the preferential adsorption was more pronounced at pH 6, which is above the zero point of charge of GO. A relative ratio of two PARAFAC humic-like components (ex/em maxima at 270/510 nm and at (250, 265)/440 nm) presented an increasing trend with larger sizes of ultrafiltered humic acid fractions, suggesting the potential for using fluorescence EEM-PARAFAC for tracking the changes in molecular sizes of aromatic HA molecules. The individual adsorption behaviors of the two humic-like components revealed that larger sized aromatic components within HA had a higher adsorption affinity and more nonlinear isotherms compared to smaller sized fractions. Our results demonstrated that adsorptive fractionation of HA occurred on the GO surface with respect to their aromaticity and the sizes, but the degree was highly dependent on solution pH as well as the amount of adsorbed HS (or available surface sites). The observed adsorption behaviors were reasonably explained by a combination of different mechanisms previously suggested. Copyright © 2015 Elsevier Ltd. All rights reserved.

Insight into the heavy metal binding potential of dissolved organic matter in MSW leachate using EEM quenching combined with PARAFAC analysis.

PubMed

Wu, Jun; Zhang, Hua; He, Pin-Jing; Shao, Li-Ming

2011-02-01

Dissolved organic matter (DOM) plays an important role in heavy metal migration from municipal solid waste (MSW) to aquatic environments via the leachate pathway. In this study, fluorescence excitation-emission matrix (EEM) quenching combined with parallel factor (PARAFAC) analysis was adopted to characterize the binding properties of four heavy metals (Cu, Pb, Zn and Cd) and DOM in MSW leachate. Nine leachate samples were collected from various stages of MSW management, including collection, transportation, incineration, landfill and subsequent leachate treatment. Three humic-like components and one protein-like component were identified in the MSW-derived DOM by PARAFAC. Significant differences in quenching effects were observed between components and metal ions, and a relatively consistent trend in metal quenching curves was observed among various leachate samples. Among the four heavy metals, Cu(II) titration led to fluorescence quenching of all four PARAFAC-derived components. Additionally, strong quenching effects were only observed in protein-like and fulvic acid (FA)-like components with the addition of Pb(II), which suggested that these fractions are mainly responsible for Pb(II) binding in MSW-derived DOM. Moreover, the significant quenching effects of the FA-like component by the four heavy metals revealed that the FA-like fraction in MSW-derived DOM plays an important role in heavy metal speciation; therefore, it may be useful as an indicator to assess the potential ability of heavy metal binding and migration. © 2010 Elsevier Ltd. All rights reserved.

Heterogeneous adsorption behavior of landfill leachate on granular activated carbon revealed by fluorescence excitation emission matrix (EEM)-parallel factor analysis (PARAFAC).

PubMed

Lee, Sonmin; Hur, Jin

2016-04-01

Heterogeneous adsorption behavior of landfill leachate on granular activated carbon (GAC) was investigated by fluorescence excitation-emission matrix (EEM) combined with parallel factor analysis (PARAFAC). The equilibrium adsorption of two leachates on GAC was well described by simple Langmuir and Freundlich isotherm models. More nonlinear isotherm and a slower adsorption rate were found for the leachate with the higher values of specific UV absorbance and humification index, suggesting that the leachate containing more aromatic content and condensed structures might have less accessible sites of GAC surface and a lower degree of diffusive adsorption. Such differences in the adsorption behavior were found even within the bulk leachate as revealed by the dissimilarity in the isotherm and kinetic model parameters between two identified PARAFAC components. For both leachates, terrestrial humic-like fluorescence (C1) component, which is likely associated with relatively large sized and condensed aromatic structures, exhibited a higher isotherm nonlinearity and a slower kinetic rate for GAC adsorption than microbial humic-like (C2) component. Our results were consistent with size exclusion effects, a well-known GAC adsorption mechanism. This study demonstrated the promising benefit of using EEM-PARAFAC for GAC adsorption processes of landfill leachate through fast monitoring of the influent and treated leachate, which can provide valuable information on optimizing treatment processes and predicting further environmental impacts of the treated effluent. Copyright © 2016 Elsevier Ltd. All rights reserved.

Unveiling the transformation and bioavailability of dissolved organic matter in contrasting hydrothermal vents using fluorescence EEM-PARAFAC.

PubMed

Yang, Liyang; Zhuang, Wan-E; Chen, Chen-Tung Arthur; Wang, Bing-Jye; Kuo, Fu-Wen

2017-03-15

The submarine hydrothermal systems are extreme environments where active cycling of dissolved organic matter (DOM) may occur. However, little is known about the optical properties and bioavailability of hydrothermal DOM, which could provide valuable insights into its transformation processes and biogeochemical reactivity. The quantity, quality, and bioavailability of DOM were investigated for four very different hydrothermal vents east of Taiwan, using dissolved organic carbon (DOC), absorption spectroscopy, and fluorescence excitation-emission matrices-parallel factor analysis (EEM-PARAFAC). The DOC and absorption coefficient a 280 were both lower in the two hydrothermal vents off the Orchid Island and on the Green Island than in the surrounding seawater and the two vents off the Kueishantao Island, indicating effective removals of DOM in the former two hydrothermal systems owing to possible adsorption/co-precipitation and thermal degradation respectively. The four hydrothermal DOM showed notable differences in the absorption spectral slope S 275-295 , humification index HIX, biological index BIX, EEM spectra, and the relative distributions of seven PARAFAC components. The results demonstrated a high diversity of chemical composition and transformation history of DOM under contrasting hydrothermal conditions. The little change in the hydrothermal DOC after 28-day microbial incubations indicated a low bioavailability of the bulk DOM, and different PARAFAC components showed contrasting bioavailability. The results have profound implications for understanding the biogeochemical cycling and environmental effects of hydrothermal DOM in the marine environments. Copyright © 2017 Elsevier Ltd. All rights reserved.

Photo- and bio-reactivity patterns of dissolved organic matter from biomass and soil leachates and surface waters in a subtropical wetland.

PubMed

Chen, Meilian; Jaffé, Rudolf

2014-09-15

Dissolved organic carbon (DOC) measurements and optical properties were applied to assess the photo- and bio-reactivity of dissolved organic matter (DOM) from different sources, including biomass leaching, soil leaching and surface waters in a subtropical wetland ecosystem. Samples were exposed to light and/or dark incubated through controlled laboratory experiments. Changes in DOC, ultraviolet (UV-Vis) visible absorbance, and excitation-emission matrix (EEM) fluorescence combined with parallel factor analysis (PARAFAC) were performed to assess sample degradation. Degradation experiments showed that while significant amounts of DOC were consumed during bio-incubation for biomass leachates, a higher degree of bio-recalcitrance for soil leachate and particularly surface waters was displayed. Photo- and bio-humification transformations were suggested for sawgrass, mangrove, and seagrass leachates, as compared to substantial photo-degradation and very little to almost no change after bio-incubation for the other samples. During photo-degradation in most cases the EEM-PARAFAC components displayed photo-decay as compared to a few cases which featured photo-production. In contrast during bio-incubation most EEM-PARAFAC components proved to be mostly bio-refractory although some increases and decreases in abundance were also observed. Furthermore, the sequential photo- followed by bio-degradation showed, with some exceptions, a "priming effect" of light exposure on the bio-degradation of DOM, and the combination of these two processes resulted in a DOM composition more similar to that of the natural surface water for the different sub-environments. In addition, for leachate samples there was a general enrichment of one of the EEM-PARAFAC humic-like component (Ex/Em: <260(305)/416 nm) during photo-degradation and an enrichment of a microbial humc-like component (Ex/Em: <260(325)/406 nm and of a tryptophan-like component (Ex/Em: 300/342 nm) during the bio-degradation process. This study exemplifies the effectiveness of optical property and EEM-PARAFAC in the assessment of DOM reactivity and highlights the importance of the coupling of photo- and bio-degradation processes in DOM degradation. Copyright © 2014 Elsevier Ltd. All rights reserved.

A green chemometrics-assisted fluorimetric detection method for the direct and simultaneous determination of six polycyclic aromatic hydrocarbons in oil-field wastewaters

NASA Astrophysics Data System (ADS)

Gu, Hui-Wen; Zhang, Shan-Hui; Wu, Bai-Chun; Chen, Wu; Wang, Jing-Bo; Liu, Yang

2018-07-01

Oil-field wastewaters contain high level of polycyclic aromatic hydrocarbons (PAHs), which have to be analyzed to assess the environmental effects before discharge. In this work, a green fluorimetric detection method that combines excitation-emission matrix (EEM) fluorescence with parallel factor analysis (PARAFAC) algorithm was firstly developed to achieve the direct and simultaneous determination of six U.S. EPA PAHs in two different kinds of complex oil-field wastewaters. Due to the distinctive "second-order advantage", neither time-consuming sample pretreatments nor toxic organic reagents were involved in the determination. By using the environment-friendly "mathematical separation" of PARAFAC, satisfactory quantitative results and reasonable spectral profiles for six PAHs were successfully extracted from the total EEM signals of oil-field wastewaters without need of chromatographic separation. The limits of detection of six PAHs were in the range of 0.09-0.72 ng mL-1, and the average spiked recoveries were between (89.4 ± 4.8)% and (109.1 ± 5.8)%, with average relative predictive errors <2.93%. In order to further confirm the accuracy of the proposed method, the same batch oil-field wastewater samples were analyzed by the recognized GC-MS method. t-test demonstrated that no significant differences exist between the quantitative results of the two methods. Given the advantages of green, fast, low-cost and high-sensitivity, the proposed method is expected to be broadened as an appealing alternative method for multi-residue analysis of overlapped PAHs in complex wastewater samples.

Assessment on the leakage hazard of landfill leachate using three-dimensional excitation-emission fluorescence and parallel factor analysis method.

PubMed

Pan, Hongwei; Lei, Hongjun; Liu, Xin; Wei, Huaibin; Liu, Shufang

2017-09-01

A large number of simple and informal landfills exist in developing countries, which pose as tremendous soil and groundwater pollution threats. Early warning and monitoring of landfill leachate pollution status is of great importance. However, there is a shortage of affordable and effective tools and methods. In this study, a soil column experiment was performed to simulate the pollution status of leachate using three-dimensional excitation-emission fluorescence (3D-EEMF) and parallel factor analysis (PARAFAC) models. Sum of squared residuals (SSR) and principal component analysis (PCA) were used to determine the optimal components for PARAFAC. A one-way analysis of variance showed that the component scores of the soil column leachate were significant influenced by landfill leachate (p<0.05). Therefore, the ratio of the component scores of the soil under the landfill to that of natural soil could be used to evaluate the leakage status of landfill leachate. Furthermore, a hazard index (HI) and a hazard evaluation standard were established. A case study of Kaifeng landfill indicated a low hazard (level 5) by the use of HI. In summation, HI is presented as a tool to evaluate landfill pollution status and for the guidance of municipal solid waste management. Copyright © 2017 Elsevier Ltd. All rights reserved.

Insight into photocatalytic degradation of dissolved organic matter in UVA/TiO₂ systems revealed by fluorescence EEM-PARAFAC.

PubMed

Phong, Diep Dinh; Hur, Jin

2015-12-15

Photocatalytic degradation of dissolved organic matter (DOM) using TiO2 as a catalyst and UVA as a light source was examined under various experimental settings with different TiO2 doses, solution pH, and the light intensities. The changes in UV absorbance and fluorescence with the irradiation time followed a pseudo-first order model much better than those of dissolved organic carbon. In general, the degradation rates were increased by higher TiO2 doses and light intensities. However, the exact photocatalytic responses of DOM to the irradiation were affected by many other factors such as aggregation of TiO2, light scattering, hydroxyl radicals produced, and DOM sorption on TiO2. Fluorescence excitation-emission matrix (EEM) coupled with parallel factor analysis (PARAFAC) revealed that the DOM changes in fluorescence could be described by the combinations of four dissimilar components including one protein-like, two humic-like, and one terrestrial humic-like components, each of which followed well the pseudo-first order model. The photocatalytic degradation rates were higher for protein-like versus humic-like component, whereas the opposite order was displayed for the degradation rates in the absence of TiO2, suggesting different dominant mechanisms operating between the systems with and without TiO2. Our results based on EEM-PARAFAC provided new insights into the underlying mechanisms associated with the photocatalytic degradation of DOM as well as the potential environmental impact of the treated water. This study demonstrated a successful application of EEM-PARAFAC for photocatalytic systems via directly comparing the kinetic rates of the individual DOM components with different compositions. Copyright © 2015 Elsevier Ltd. All rights reserved.

Chromophoric dissolved organic matter (CDOM) variability in Barataria Basin using excitation-emission matrix (EEM) fluorescence and parallel factor analysis (PARAFAC).

PubMed

Singh, Shatrughan; D'Sa, Eurico J; Swenson, Erick M

2010-07-15

Chromophoric dissolved organic matter (CDOM) variability in Barataria Basin, Louisiana, USA,was examined by excitation emission matrix (EEM) fluorescence combined with parallel factor analysis (PARAFAC). CDOM optical properties of absorption and fluorescence at 355nm along an axial transect (36 stations) during March, April, and May 2008 showed an increasing trend from the marine end member to the upper basin with mean CDOM absorption of 11.06 + or - 5.01, 10.05 + or - 4.23, 11.67 + or - 6.03 (m(-)(1)) and fluorescence 0.80 + or - 0.37, 0.78 + or - 0.39, 0.75 + or - 0.51 (RU), respectively. PARAFAC analysis identified two terrestrial humic-like (component 1 and 2), one non-humic like (component 3), and one soil derived humic acid like (component 4) components. The spatial variation of the components showed an increasing trend from station 1 (near the mouth of basin) to station 36 (end member of bay; upper basin). Deviations from this increasing trend were observed at a bayou channel with very high chlorophyll-a concentrations especially for component 3 in May 2008 that suggested autochthonous production of CDOM. The variability of components with salinity indicated conservative mixing along the middle part of the transect. Component 1 and 4 were found to be relatively constant, while components 2 and 3 revealed an inverse relationship for the sampling period. Total organic carbon showed increasing trend for each of the components. An increase in humification and a decrease in fluorescence indices along the transect indicated an increase in terrestrial derived organic matter and reduced microbial activity from lower to upper basin. The use of these indices along with PARAFAC results improved dissolved organic matter characterization in the Barataria Basin. Copyright 2010 Elsevier B.V. All rights reserved.

Quantifying aflatoxins in peanuts using fluorescence spectroscopy coupled with multi-way methods: Resurrecting second-order advantage in excitation-emission matrices with rank overlap problem

NASA Astrophysics Data System (ADS)

Sajjadi, S. Maryam; Abdollahi, Hamid; Rahmanian, Reza; Bagheri, Leila

2016-03-01

A rapid, simple and inexpensive method using fluorescence spectroscopy coupled with multi-way methods for the determination of aflatoxins B1 and B2 in peanuts has been developed. In this method, aflatoxins are extracted with a mixture of water and methanol (90:10), and then monitored by fluorescence spectroscopy producing EEMs. Although the combination of EEMs and multi-way methods is commonly used to determine analytes in complex chemical systems with unknown interference(s), rank overlap problem in excitation and emission profiles may restrain the application of this strategy. If there is rank overlap in one mode, there are several three-way algorithms such as PARAFAC under some constraints that can resolve this kind of data successfully. However, the analysis of EEM data is impossible when some species have rank overlap in both modes because the information of the data matrix is equivalent to a zero-order data for that species, which is the case in our study. Aflatoxins B1 and B2 have the same shape of spectral profiles in both excitation and emission modes and we propose creating a third order data for each sample using solvent as a new additional selectivity mode. This third order data, in turn, converted to the second order data by augmentation, a fact which resurrects the second order advantage in original EEMs. The three-way data is constructed by stacking augmented data in the third way, and then analyzed by two powerful second order calibration methods (BLLS-RBL and PARAFAC) to quantify the analytes in four kinds of peanut samples. The results of both methods are in good agreement and reasonable recoveries are obtained.

Tracking oil and gas wastewater-derived organic matter in a hybrid biofilter membrane treatment system: A multi-analytical approach.

PubMed

Riley, Stephanie M; Ahoor, Danika C; Regnery, Julia; Cath, Tzahi Y

2018-02-01

Dissolved organic matter (DOM) present in oil and gas (O&G) produced water and fracturing flowback was characterized and quantified by multiple analytical techniques throughout a hybrid biological-physical treatment process. Quantitative and qualitative analysis of DOM by liquid chromatography - organic carbon detection (LC-OCD), liquid chromatography-high-resolution mass spectrometry (LC-HRMS), gas chromatography-mass spectrometry (GC-MS), and 3D fluorescence spectroscopy, demonstrated increasing removal of all groups of DOM throughout the treatment train, with most removal occurring during biological pretreatment and some subsequent removal achieved during membrane treatment. Parallel factor analysis (PARAFAC) further validated these results and identified five fluorescent components, including DOM described as humic acids, fulvic acids, proteins, and aromatics. Tryptophan-like compounds bound by complexation to humics/fulvics were most difficult to remove biologically, while aromatics (particularly low molecular weight neutrals) were more challenging to remove with membranes. Strong correlation among PARAFAC, LC-OCD, LC-HRMS, and GC-MS suggests that PARAFAC can be a quick, affordable, and accurate tool for evaluating the presence or removal of specific DOM groups in O&G wastewater. Copyright © 2017 Elsevier B.V. All rights reserved.

Characterization of atrazine binding to dissolved organic matter of soil under different types of land use.

PubMed

Zhu, Long-Ji; Zhao, Yue; Chen, Yan-Ni; Cui, Hong-Yang; Wei, Yu-Quan; Liu, Hai-Long; Chen, Xiao-Meng; Wei, Zi-Min

2018-01-01

Atrazine is widely used in agriculture. In this study, dissolved organic matter (DOM) from soils under four types of land use (forest (F), meadow (M), cropland (C) and wetland (W)) was used to investigate the binding characteristics of atrazine. Fluorescence excitation-emission matrix-parallel factor (EEM-PARAFAC) analysis, two-dimensional correlation spectroscopy (2D-COS) and Stern-Volmer model were combined to explore the complexation between DOM and atrazine. The EEM-PARAFAC indicated that DOM from different sources had different structures, and humic-like components had more obvious quenching effects than protein-like components. The Stern-Volmer model combined with correlation analysis showed that log K values of PARAFAC components had a significant correlation with the humification of DOM, especially for C3 component, and they were all in the same order as follows: meadow soil (5.68)>wetland soil (5.44)>cropland soil (5.35)>forest soil (5.04). The 2D-COS further confirmed that humic-like components firstly combined with atrazine followed by protein-like components. These findings suggest that DOM components can significantly influence the bioavailability, mobility and migration of atrazine in different land uses. Copyright © 2016 Elsevier Inc. All rights reserved.

A new modeling strategy for third-order fast high-performance liquid chromatographic data with fluorescence detection. Quantitation of fluoroquinolones in water samples.

PubMed

Alcaráz, Mirta R; Bortolato, Santiago A; Goicoechea, Héctor C; Olivieri, Alejandro C

2015-03-01

Matrix augmentation is regularly employed in extended multivariate curve resolution-alternating least-squares (MCR-ALS), as applied to analytical calibration based on second- and third-order data. However, this highly useful concept has almost no correspondence in parallel factor analysis (PARAFAC) of third-order data. In the present work, we propose a strategy to process third-order chromatographic data with matrix fluorescence detection, based on an Augmented PARAFAC model. The latter involves decomposition of a three-way data array augmented along the elution time mode with data for the calibration samples and for each of the test samples. A set of excitation-emission fluorescence matrices, measured at different chromatographic elution times for drinking water samples, containing three fluoroquinolones and uncalibrated interferences, were evaluated using this approach. Augmented PARAFAC exploits the second-order advantage, even in the presence of significant changes in chromatographic profiles from run to run. The obtained relative errors of prediction were ca. 10 % for ofloxacin, ciprofloxacin, and danofloxacin, with a significant enhancement in analytical figures of merit in comparison with previous reports. The results are compared with those furnished by MCR-ALS.

Kronecker-Basis-Representation Based Tensor Sparsity and Its Applications to Tensor Recovery.

PubMed

Xie, Qi; Zhao, Qian; Meng, Deyu; Xu, Zongben

2017-08-02

It is well known that the sparsity/low-rank of a vector/matrix can be rationally measured by nonzero-entries-number ($l_0$ norm)/nonzero- singular-values-number (rank), respectively. However, data from real applications are often generated by the interaction of multiple factors, which obviously cannot be sufficiently represented by a vector/matrix, while a high order tensor is expected to provide more faithful representation to deliver the intrinsic structure underlying such data ensembles. Unlike the vector/matrix case, constructing a rational high order sparsity measure for tensor is a relatively harder task. To this aim, in this paper we propose a measure for tensor sparsity, called Kronecker-basis-representation based tensor sparsity measure (KBR briefly), which encodes both sparsity insights delivered by Tucker and CANDECOMP/PARAFAC (CP) low-rank decompositions for a general tensor. Then we study the KBR regularization minimization (KBRM) problem, and design an effective ADMM algorithm for solving it, where each involved parameter can be updated with closed-form equations. Such an efficient solver makes it possible to extend KBR to various tasks like tensor completion and tensor robust principal component analysis. A series of experiments, including multispectral image (MSI) denoising, MSI completion and background subtraction, substantiate the superiority of the proposed methods beyond state-of-the-arts.

Tensor-driven extraction of developmental features from varying paediatric EEG datasets.

PubMed

Kinney-Lang, Eli; Spyrou, Loukianos; Ebied, Ahmed; Chin, Richard Fm; Escudero, Javier

2018-05-21

Constant changes in developing children's brains can pose a challenge in EEG dependant technologies. Advancing signal processing methods to identify developmental differences in paediatric populations could help improve function and usability of such technologies. Taking advantage of the multi-dimensional structure of EEG data through tensor analysis may offer a framework for extracting relevant developmental features of paediatric datasets. A proof of concept is demonstrated through identifying latent developmental features in resting-state EEG. Approach. Three paediatric datasets (n = 50, 17, 44) were analyzed using a two-step constrained parallel factor (PARAFAC) tensor decomposition. Subject age was used as a proxy measure of development. Classification used support vector machines (SVM) to test if PARAFAC identified features could predict subject age. The results were cross-validated within each dataset. Classification analysis was complemented by visualization of the high-dimensional feature structures using t-distributed Stochastic Neighbour Embedding (t-SNE) maps. Main Results. Development-related features were successfully identified for the developmental conditions of each dataset. SVM classification showed the identified features could accurately predict subject at a significant level above chance for both healthy and impaired populations. t-SNE maps revealed suitable tensor factorization was key in extracting the developmental features. Significance. The described methods are a promising tool for identifying latent developmental features occurring throughout childhood EEG. © 2018 IOP Publishing Ltd.

Ethanol- and trifluoroethanol-induced changes in phase states of DPPC membranes. Prodan emission-excitation fluorescence spectroscopy supported by PARAFAC analysis

NASA Astrophysics Data System (ADS)

Horochowska, Martyna; Cieślik-Boczula, Katarzyna; Rospenk, Maria

2018-03-01

It has been shown that Prodan emission-excitation fluorescence spectroscopy supported by Parallel Factor (PARAFAC) analysis is a fast, simple and sensitive method used in the study of the phase transition from the noninterdigitated gel (Lβ‧) state to the interdigitated gel (LβI) phase, triggered by ethanol and 2,2,2-trifluoroethanol (TFE) molecules in dipalmitoylphosphatidylcholines (DPPC) membranes. The relative contribution of lipid phases with spectral characteristics of each pure phase component has been presented as a function of an increase in alcohol concentration. It has been stated that both alcohol molecules can induce a formation of the LβI phase, but TFE is over six times stronger inducer of the interdigitated phase in DPPC membranes than ethanol molecules. Moreover, in the TFE-mixed DPPC membranes, the transition from the Lβ‧ to LβI phase is accompanied by a formation of the fluid phase, which most probably serves as a boundary phase between the Lβ‧ and LβI regions. Contrary to the three phase-state model of TFE-mixed DPPC membranes, in ethanol-mixed DPPC membranes only the two phase-state model has been detected.

[Resolving excitation emission matrix spectroscopy of estuarine CDOM with parallel factor analysis and its application in organic pollution monitoring].

PubMed

Guo, Wei-Dong; Huang, Jian-Ping; Hong, Hua-Sheng; Xu, Jing; Deng, Xun

2010-06-01

The distribution and estuarine behavior of fluorescent components of chromophoric dissolved organic matter (CDOM) from Jiulong Estuary were determined by fluorescence excitation emission matrix spectroscopy (EEMs) combined with parallel factor analysis (PARAFAC). The feasibility of these components as tracers for organic pollution in estuarine environments was also evaluated. Four separate fluorescent components were identified by PARAFAC, including three humic-like components (C1: 240, 310/382 nm; C2: 230, 250, 340/422 nm; C4: 260, 390/482 nm) and one protein-like components (C3: 225, 275/342 nm). These results indicated that UV humic-like peak A area designated by traditional "peak-picking method" was not a single peak but actually a combination of several fluorescent components, and it also had inherent links to so-called marine humic-like peak M or terrestrial humic-like peak C. Component C2 which include peak M decreased with increase of salinity in Jiulong Estuary, demonstrating that peak M can not be thought as the specific indicator of the "marine" humic-like component. Two humic-like components C1 and C2 showed additional behavior in the turbidity maximum region (salinity < 6) and then conservative mixing behavior for the rest estuarine region, while humic-like components C4 showed conservative mixing behavior for the whole estuarine region. However, the protein-like component C3 showed nonconservative mixing behavior, suggesting it had autochthonous estuarine origin. EEMs-PARAFAC can provide fluorescent fingerprint to differentiate the DOM features for three tributaries of Jiulong River. The observed linear relationships between humic-like components and absorption coefficient a (280) with chemical oxygen demand (COD) and biological oxygen demand (BOD5) suggest that the optical properties of CDOM may provide a fast in-situ way to monitor the variation of the degree of organic pollution in estuarine environments.

What can be found in scalp EEG spectrum beyond common frequency bands. EEG-fMRI study

NASA Astrophysics Data System (ADS)

Marecek, R.; Lamos, M.; Mikl, M.; Barton, M.; Fajkus, J.; I, Rektor; Brazdil, M.

2016-08-01

Objective. The scalp EEG spectrum is a frequently used marker of neural activity. Commonly, the preprocessing of EEG utilizes constraints, e.g. dealing with a predefined subset of electrodes or a predefined frequency band of interest. Such treatment of the EEG spectrum neglects the fact that particular neural processes may be reflected in several frequency bands and/or several electrodes concurrently, and can overlook the complexity of the structure of the EEG spectrum. Approach. We showed that the EEG spectrum structure can be described by parallel factor analysis (PARAFAC), a method which blindly uncovers the spatial-temporal-spectral patterns of EEG. We used an algorithm based on variational Bayesian statistics to reveal nine patterns from the EEG of 38 healthy subjects, acquired during a semantic decision task. The patterns reflected neural activity synchronized across theta, alpha, beta and gamma bands and spread over many electrodes, as well as various EEG artifacts. Main results. Specifically, one of the patterns showed significant correlation with the stimuli timing. The correlation was higher when compared to commonly used models of neural activity (power fluctuations in distinct frequency band averaged across a subset of electrodes) and we found significantly correlated hemodynamic fluctuations in simultaneously acquired fMRI data in regions known to be involved in speech processing. Further, we show that the pattern also occurs in EEG data which were acquired outside the MR machine. Two other patterns reflected brain rhythms linked to the attentional and basal ganglia large scale networks. The other patterns were related to various EEG artifacts. Significance. These results show that PARAFAC blindly identifies neural activity in the EEG spectrum and that it naturally handles the correlations among frequency bands and electrodes. We conclude that PARAFAC seems to be a powerful tool for analysis of the EEG spectrum and might bring novel insight to the relationships between EEG activity and brain hemodynamics.

Anthropogenic signature of sediment organic matter probed by UV-Visible and fluorescence spectroscopy and the association with heavy metal enrichment.

PubMed

He, Wei; Lee, Jong-Hyun; Hur, Jin

2016-05-01

Sediment organic matter (SOM) was extracted in an alkaline solution from 43 stream sediments in order to explore the anthropogenic signatures. The SOM spectroscopic characteristics including excitation-emission matrix (EEM)-parallel factor analysis (PARAFAC) were compared for five sampling site groups classified by the anthropogenic variables of land use, population density, the loadings of organics and nutrients, and metal enrichment. The conventional spectroscopic characteristics including specific UV absorbance, absorbance ratio, and humification index did not properly discriminate among the different cluster groups except in the case of metal enrichment. Of the four decomposed PARAFAC components, humic-like and tryptophan-like fluorescence responded negatively and positively, respectively, to increasing degrees of the anthropogenic variables except for land use. The anthropogenic enrichment of heavy metals was positively associated with the abundance of tryptophan-like component. In contrast, humic-like component, known to be mostly responsible for metal binding, exhibited a decreasing trend corresponding with metal enrichment. These conflicting trends can be attributed to the overwhelmed effects of the coupled discharges of heavy metals and organic pollutants into sediments. Our study suggests that the PARAFAC components can be used as functional signatures to probe the anthropogenic influences on sediments. Copyright © 2016 Elsevier Ltd. All rights reserved.

Assessing trihalomethanes (THMs) and N-nitrosodimethylamine (NDMA) formation potentials in drinking water treatment plants using fluorescence spectroscopy and parallel factor analysis.

PubMed

Yang, Liyang; Kim, Daekyun; Uzun, Habibullah; Karanfil, Tanju; Hur, Jin

2015-02-01

The formation of disinfection byproducts (DBPs) is a major challenge in drinking water treatments. This study explored the applicability of fluorescence excitation-emission matrices and parallel factor analysis (EEM-PARAFAC) for assessing the formation potentials (FPs) of trihalomethanes (THMs) and N-nitrosodimethylamine (NDMA), and the treatability of THM and NDMA precursors in nine drinking water treatment plants. Two humic-like and one tryptophan-like components were identified for the samples using PARAFAC. The total THM FP (TTHM FP) correlated strongly with humic-like component C2 (r=0.874), while NDMA FP showed a moderate and significant correlation with the tryptophan-like component C3 (r=0.628). The reduction by conventional treatment was more effective for C2 than C3, and for TTHM FP than NDMA FP. The treatability of DOM and TTHM FP correlated negatively with the absorption spectral slope (S275-295) and biological index (BIX) of the raw water, but it correlated positively with humification index (HIX). Our results demonstrated that PARAFAC components were valuable for assessing DBPs FP in drinking water treatments, and also that the raw water quality could affect the treatment efficiency. Copyright © 2014 Elsevier Ltd. All rights reserved.

Characterisation of landfill leachate by EEM-PARAFAC-SOM during physical-chemical treatment by coagulation-flocculation, activated carbon adsorption and ion exchange.

PubMed

Oloibiri, Violet; De Coninck, Sam; Chys, Michael; Demeestere, Kristof; Van Hulle, Stijn W H

2017-11-01

The combination of fluorescence excitation-emission matrices (EEM), parallel factor analysis (PARAFAC) and self-organizing maps (SOM) is shown to be a powerful tool in the follow up of dissolved organic matter (DOM) removal from landfill leachate by physical-chemical treatment consisting of coagulation, granular activated carbon (GAC) and ion exchange. Using PARAFAC, three DOM components were identified: C1 representing humic/fulvic-like compounds; C2 representing tryptophan-like compounds; and C3 representing humic-like compounds. Coagulation with ferric chloride (FeCl 3 ) at a dose of 7 g/L reduced the maximum fluorescence of C1, C2 and C3 by 52%, 17% and 15% respectively, while polyaluminium chloride (PACl) reduced C1 only by 7% at the same dose. DOM removal during GAC and ion exchange treatment of raw and coagulated leachate exhibited different profiles. At less than 2 bed volumes (BV) of treatment, the humic components C1 and C3 were rapidly removed, whereas at BV ≥ 2 the tryptophan-like component C2 was preferentially removed. Overall, leachate treated with coagulation +10.6 BV GAC +10.6 BV ion exchange showed the highest removal of C1 (39% - FeCl 3 , 8% - PACl), C2 (74% - FeCl 3 , 68% - PACl) and no C3 removal; whereas only 52% C2 and no C1 and C3 removal was observed in raw leachate treated with 10.6 BV GAC + 10.6 BV ion exchange only. Analysis of PARAFAC-derived components with SOM revealed that coagulation, GAC and ion exchange can treat leachate at least 50% longer than only GAC and ion exchange before the fluorescence composition of leachate remains unchanged. Copyright © 2017 Elsevier Ltd. All rights reserved.

Photochemical reactivities of dissolved organic matter (DOM) in a sub-alpine lake revealed by EEM-PARAFAC: An insight into the fate of allochthonous DOM in alpine lakes affected by climate change.

PubMed

Du, Yingxun; Zhang, Yuanyuan; Chen, Feizhou; Chang, Yuguang; Liu, Zhengwen

2016-10-15

Due to climate change, tree line advance is occurring in many alpine regions. Within the next 50 to 100years, alpine lake catchments are expected to develop increased vegetation cover similar to that of sub-alpine lake catchments which currently exist below the tree line. Such changes in vegetation could trigger increased allochthonous DOM inputs to alpine lakes. To understand the fate of allochthonous DOM in alpine lakes impacted by climate change, the photochemical reactivity of DOM in sub-alpine Lake Tiancai (located 200m below the tree line) was investigated by excitation emission matrix fluorescence combined with parallel factor analysis (EEM-PARAFAC) and UV-Vis spectra analysis. With photo-exposure, a decrease in apparent DOM molecular weight was observed and 32% DOM was photomineralized to CO2. Interestingly, the aromaticity of DOM increased after photodegradation, as evidenced by increases in both the specific UV absorbance at 254nm (SUVA254) and the humification index (HIX). Five EEM-PARAFAC components were identified, including four terrestrially-derived substances (C1, C2, C3 and C4; allochthonous) and one tryptophan-like substance (C5; autochthonous). Generally, allochthonous DOM represented by C2 and C3 exhibited greater photoreactivity than autochthonous DOM represented by C5. C4 was identified as a possible photoproduct with relatively high aromaticity and photorefractive tendencies and contributed to the observed increase in SUVA254 and HIX. UV light facilitated the photodegradation of DOM and had the greatest effect on the removal of C3. This study provides information on the transformation of EEM-PARAFAC components in a sub-alpine lake, which is important in understanding the fate of increased allochthonous DOM inputs to alpine lakes impacted by climate change. Copyright © 2016 Elsevier B.V. All rights reserved.

Seasonal characterization of CDOM for lakes in semi-arid regions of Northeast China using excitation-emission matrices fluorescence and parallel factor analysis (EEM-PARAFAC)

NASA Astrophysics Data System (ADS)

Zhao, Y.; Song, K.; Wen, Z.; Li, L.; Zang, S.; Shao, T.; Li, S.; Du, J.

2015-04-01

The seasonal characteristics of fluorescence components in CDOM for lakes in the semi-arid region of Northeast China were examined by excitation-emission matrices fluorescence and parallel factor analysis (EEM-PARAFAC). Two humic-like peaks C1 (Ex/Em = 230, 300/425 nm) and C2 (Ex/Em = 255, 350/460 nm) and two protein-like B (Ex/Em = 220, 275/320 nm) and T (Ex/Em = 225, 290/360 nm) peaks were identified using PARAFAC. The average fluorescence intensity of the four components differed with seasonal variation from June and August 2013 to February and April 2014. The total fluorescence intensity significantly varied from 2.54 ± 0.68 nm-1 in June to the mean value 1.93 ± 0.70 nm-1 in August 2013, and then increased to 2.34 ± 0.92 nm-1 in February and reduced to the lowest 1.57 ± 0.55 nm-1 in April 2014. In general, the fluorescence intensity was dominated by peak C1, indicating that most part of CDOM for inland waters being investigated in this study was originated from phytoplankton degradation. The lowest C2 represents only a small portion of CDOM from terrestrial imported organic matter to water bodies through rainwash and soil leaching. The two protein-like intensities (B and T) formed in situ through microbial activity have almost the same intensity. Especially, in August 2013 and February 2014, the two protein-like peaks showed obviously difference from other seasons and the highest C1 (1.02 nm-1) was present in February 2014. Components 1 and 2 exhibited strong linear correlation (R2 = 0.633). There were significantly positive linear relationships between CDOM absorption coefficients a(254) (R2 = 0.72, 0.46, p < 0.01), a(280) (R2 = 0.77, 0.47, p < 0.01), a(350) (R2 = 0.76, 0.78, p < 0.01) and Fmax for two humic-like components (C1 and C2), respectively. A close relationship (R2 = 0.931) was found between salinity and DOC. However, almost no obvious correlation was found between salinity and EEM-PARAFAC extracted components except for C3 (R2 = 0.469). Results from this investigation demonstrate that the EEM-PARAFAC technique can be used to evaluate the seasonal dynamics of CDOM fluorescence components for inland waters in semi-arid regions of Northeast China.

Neural networks for dimensionality reduction of fluorescence spectra and prediction of drinking water disinfection by-products.

PubMed

Peleato, Nicolas M; Legge, Raymond L; Andrews, Robert C

2018-06-01

The use of fluorescence data coupled with neural networks for improved predictability of drinking water disinfection by-products (DBPs) was investigated. Novel application of autoencoders to process high-dimensional fluorescence data was related to common dimensionality reduction techniques of parallel factors analysis (PARAFAC) and principal component analysis (PCA). The proposed method was assessed based on component interpretability as well as for prediction of organic matter reactivity to formation of DBPs. Optimal prediction accuracies on a validation dataset were observed with an autoencoder-neural network approach or by utilizing the full spectrum without pre-processing. Latent representation by an autoencoder appeared to mitigate overfitting when compared to other methods. Although DBP prediction error was minimized by other pre-processing techniques, PARAFAC yielded interpretable components which resemble fluorescence expected from individual organic fluorophores. Through analysis of the network weights, fluorescence regions associated with DBP formation can be identified, representing a potential method to distinguish reactivity between fluorophore groupings. However, distinct results due to the applied dimensionality reduction approaches were observed, dictating a need for considering the role of data pre-processing in the interpretability of the results. In comparison to common organic measures currently used for DBP formation prediction, fluorescence was shown to improve prediction accuracies, with improvements to DBP prediction best realized when appropriate pre-processing and regression techniques were applied. The results of this study show promise for the potential application of neural networks to best utilize fluorescence EEM data for prediction of organic matter reactivity. Copyright © 2018 Elsevier Ltd. All rights reserved.

Assessment of phytoplankton class abundance using fluorescence excitation-emission matrix by parallel factor analysis and nonnegative least squares

NASA Astrophysics Data System (ADS)

Su, Rongguo; Chen, Xiaona; Wu, Zhenzhen; Yao, Peng; Shi, Xiaoyong

2015-07-01

The feasibility of using fluorescence excitation-emission matrix (EEM) along with parallel factor analysis (PARAFAC) and nonnegative least squares (NNLS) method for the differentiation of phytoplankton taxonomic groups was investigated. Forty-one phytoplankton species belonging to 28 genera of five divisions were studied. First, the PARAFAC model was applied to EEMs, and 15 fluorescence components were generated. Second, 15 fluorescence components were found to have a strong discriminating capability based on Bayesian discriminant analysis (BDA). Third, all spectra of the fluorescence component compositions for the 41 phytoplankton species were spectrographically sorted into 61 reference spectra using hierarchical cluster analysis (HCA), and then, the reference spectra were used to establish a database. Finally, the phytoplankton taxonomic groups was differentiated by the reference spectra database using the NNLS method. The five phytoplankton groups were differentiated with the correct discrimination ratios (CDRs) of 100% for single-species samples at the division level. The CDRs for the mixtures were above 91% for the dominant phytoplankton species and above 73% for the subdominant phytoplankton species. Sixteen of the 85 field samples collected from the Changjiang River estuary were analyzed by both HPLC-CHEMTAX and the fluorometric technique developed. The results of both methods reveal that Bacillariophyta was the dominant algal group in these 16 samples and that the subdominant algal groups comprised Dinophyta, Chlorophyta and Cryptophyta. The differentiation results by the fluorometric technique were in good agreement with those from HPLC-CHEMTAX. The results indicate that the fluorometric technique could differentiate algal taxonomic groups accurately at the division level.

An Exact Model-Based Method for Near-Field Sources Localization with Bistatic MIMO System.

PubMed

Singh, Parth Raj; Wang, Yide; Chargé, Pascal

2017-03-30

In this paper, we propose an exact model-based method for near-field sources localization with a bistatic multiple input, multiple output (MIMO) radar system, and compare it with an approximated model-based method. The aim of this paper is to propose an efficient way to use the exact model of the received signals of near-field sources in order to eliminate the systematic error introduced by the use of approximated model in most existing near-field sources localization techniques. The proposed method uses parallel factor (PARAFAC) decomposition to deal with the exact model. Thanks to the exact model, the proposed method has better precision and resolution than the compared approximated model-based method. The simulation results show the performance of the proposed method.

Characterization of extracellular polymeric substances in biofilms under long-term exposure to ciprofloxacin antibiotic using fluorescence excitation-emission matrix and parallel factor analysis.

PubMed

Gu, Chaochao; Gao, Pin; Yang, Fan; An, Dongxuan; Munir, Mariya; Jia, Hanzhong; Xue, Gang; Ma, Chunyan

2017-05-01

The presence of antibiotic residues in the environment has been regarded as an emerging concern due to their potential adverse environmental consequences such as antibiotic resistance. However, the interaction between antibiotics and extracellular polymeric substances (EPSs) of biofilms in wastewater treatment systems is not entirely clear. In this study, the effect of ciprofloxacin (CIP) antibiotic on biofilm EPS matrix was investigated and characterized using fluorescence excitation-emission matrix (EEM) and parallel factor (PARAFAC) analysis. Physicochemical analysis showed that the proteins were the major EPS fraction, and their contents increased gradually with an increase in CIP concentration (0-300 μg/L). Based on the characterization of biofilm tightly bound EPS (TB-EPS) by EEM, three fluorescent components were identified by PARAFAC analysis. Component C1 was associated with protein-like substances, and components C2 and C3 belonged to humic-like substances. Component C1 exhibited an increasing trend as the CIP addition increased. Pearson's correlation results showed that CIP correlated significantly with the protein contents and component C1, while strong correlations were also found among UV 254 , dissolved organic carbon, humic acids, and component C3. A combined use of EEM-PARAFAC analysis and chemical measurements was demonstrated as a favorable approach for the characterization of variations in biofilm EPS in the presence of CIP antibiotic.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kolda, Tamara Gibson

We propose two new multilinear operators for expressing the matrix compositions that are needed in the Tucker and PARAFAC (CANDECOMP) decompositions. The first operator, which we call the Tucker operator, is shorthand for performing an n-mode matrix multiplication for every mode of a given tensor and can be employed to concisely express the Tucker decomposition. The second operator, which we call the Kruskal operator, is shorthand for the sum of the outer-products of the columns of N matrices and allows a divorce from a matricized representation and a very concise expression of the PARAFAC decomposition. We explore the properties ofmore » the Tucker and Kruskal operators independently of the related decompositions. Additionally, we provide a review of the matrix and tensor operations that are frequently used in the context of tensor decompositions.« less

[Research on optimal modeling strategy for licorice extraction process based on near-infrared spectroscopy technology].

PubMed

Wang, Hai-Xia; Suo, Tong-Chuan; Yu, He-Shui; Li, Zheng

2016-10-01

The manufacture of traditional Chinese medicine (TCM) products is always accompanied by processing complex raw materials and real-time monitoring of the manufacturing process. In this study, we investigated different modeling strategies for the extraction process of licorice. Near-infrared spectra associate with the extraction time was used to detemine the states of the extraction processes. Three modeling approaches, i.e., principal component analysis (PCA), partial least squares regression (PLSR) and parallel factor analysis-PLSR (PARAFAC-PLSR), were adopted for the prediction of the real-time status of the process. The overall results indicated that PCA, PLSR and PARAFAC-PLSR can effectively detect the errors in the extraction procedure and predict the process trajectories, which has important significance for the monitoring and controlling of the extraction processes. Copyright© by the Chinese Pharmaceutical Association.

Linking groundwater dissolved organic matter to sedimentary organic matter from a fluvio-lacustrine aquifer at Jianghan Plain, China by EEM-PARAFAC and hydrochemical analyses.

PubMed

Huang, Shuang-bing; Wang, Yan-xin; Ma, Teng; Tong, Lei; Wang, Yan-yan; Liu, Chang-rong; Zhao, Long

2015-10-01

The sources of dissolved organic matter (DOM) in groundwater are important to groundwater chemistry and quality. This study examined similarities in the nature of DOM and investigated the link between groundwater DOM (GDOM) and sedimentary organic matter (SOM) from a lacustrine-alluvial aquifer at Jianghan Plain. Sediment, groundwater and surface water samples were employed for SOM extraction, optical and/or chemical characterization, and subsequent fluorescence excitation-emission matrix (EEM) and parallel factor analyses (PARAFAC). Spectroscopic properties of bulk DOM pools showed that indices indicative of GDOM (e.g., biological source properties, humification level, aromaticity and molecule mobility) varied within the ranges of those of two extracted end-members of SOM: humic-like materials and microbe-associated materials. The coexistence of PARAFAC compositions and the sustaining internal relationship between GDOM and extracted SOM indicate a similar source. The results from principal component analyses with selected spectroscopic indices showed that GDOM exhibited a transition trend regarding its nature: from refractory high-humification DOM to intermediate humification DOM and then to microbe-associated DOM, with decreasing molecular weight. Correlations of spectroscopic indices with physicochemical parameters of the groundwater suggested that GDOM was released from SOM and was modified by microbial diagenetic processes. The current study demonstrated the associations of GDOM with SOM from a spectroscopic viewpoint and provided new evidence supporting SOM as the source of GDOM. Copyright © 2015 Elsevier B.V. All rights reserved.

The relationship of chromophoric dissolved organic matter parallel factor analysis fluorescence and polycyclic aromatic hydrocarbons in natural surface waters.

PubMed

Li, Sijia; Chen, Ya'nan; Zhang, Jiquan; Song, Kaishan; Mu, Guangyi; Sun, Caiyun; Ju, Hanyu; Ji, Meichen

2018-01-01

Polycyclic aromatic hydrocarbons (PAHs), a large group of persistent organic pollutants (POPs), have caused wide environmental pollution and ecological effects. Chromophoric dissolved organic matter (CDOM), which consists of complex compounds, was seen as a proxy of water quality. An attempt was made to understand the relationships of CDOM absorption parameters and parallel factor analysis (PARAFAC) components with PAHs under seasonal variation in the riverine, reservoir, and urban waters of the Yinma River watershed in 2016. These different types of water bodies provided wide CDOM and PAHs concentration ranges with CDOM absorption coefficients at a wavelength of 350 nm (a CDOM (350)) of 1.17-20.74 m -1 and total PAHs of 0-1829 ng/L. CDOM excitation-emission matrix (EEM) presented two fluorescent components, e.g., terrestrial humic-like (C1) and tryptophan-like (C2) were identified using PARAFAC. Tryptophan-like associated protein-like fluorescence often dominates the EEM signatures of sewage samples. Our finding is that seasonal CDOM EEM-PARAFAC and PAHs concentration showed consistent tendency indicated that PAHs were un-ignorable pollutants. However, the disparities in seasonal CDOM-PAH relationships relate to the similar sources of CDOM and PAHs, and the proportion of PAHs in CDOM. Overlooked and poorly appreciated, quantifying the relationship between CDOM and PAHs has important implications, because these results simplify ecological and health-based risk assessment of pollutants compared to the traditional chemical measurements.

Characterization of Chromophoric Dissolved Organic Matter across the Eastern and the Central Arctic Regions using PARAFAC Modelling

NASA Astrophysics Data System (ADS)

Molodtsova, T.; Amon, R. M. W.

2016-12-01

In this study the optical properties (absorption and fluorescence intensity) of chromophoric dissolved organic matter (CDOM) were investigated in water samples collected during the cruise conducted in August and September 2007 across the Eastern and Central Arctic regions. The fluorescence spectroscopy analysis was complimented with the parallel factor analysis (PARAFAC) and the identified six components were compared to other water properties including salinity, in situ fluorescence, dissolved organic carbon, and specific ultraviolet absorbance at 254 nm. The principal component analysis was conducted to distinguish between the water masses and identify the features such as the Trans Polar Drift and the North Atlantic Current. The preliminary results indicate that investigation of the optical properties of CDOM are able to provide better understanding of Arctic Ocean circulation and environmental changes such as the loss of the perennial sea ice and more light penetrating the water column.

Tracking senescence-induced patterns in leaf litter leachate using parallel factor analysis (PARAFAC) modeling and self-organizing maps

NASA Astrophysics Data System (ADS)

Wheeler, K. I.; Levia, D. F.; Hudson, J. E.

2017-09-01

In autumn, the dissolved organic matter (DOM) contribution of leaf litter leachate to streams in forested watersheds changes as trees undergo resorption, senescence, and leaf abscission. Despite its biogeochemical importance, little work has investigated how leaf litter leachate DOM changes throughout autumn and how any changes might differ interspecifically and intraspecifically. Since climate change is expected to cause vegetation migration, it is necessary to learn how changes in forest composition could affect DOM inputs via leaf litter leachate. We examined changes in leaf litter leachate fluorescent DOM (FDOM) from American beech (Fagus grandifolia Ehrh.) leaves in Maryland, Rhode Island, Vermont, and North Carolina and from yellow poplar (Liriodendron tulipifera L.) leaves from Maryland. FDOM in leachate samples was characterized by excitation-emission matrices (EEMs). A six-component parallel factor analysis (PARAFAC) model was created to identify components that accounted for the majority of the variation in the data set. Self-organizing maps (SOM) compared the PARAFAC component proportions of leachate samples. Phenophase and species exerted much stronger influence on the determination of a sample's SOM placement than geographic origin. As expected, FDOM from all trees transitioned from more protein-like components to more humic-like components with senescence. Percent greenness of sampled leaves and the proportion of tyrosine-like component 1 were found to be significantly different between the two genetic beech clusters, suggesting differences in photosynthesis and resorption. Our results highlight the need to account for interspecific and intraspecific variations in leaf litter leachate FDOM throughout autumn when examining the influence of allochthonous inputs to streams.

Investigation of ozone and peroxone impacts on natural organic matter character and biofiltration performance using fluorescence spectroscopy.

PubMed

Peleato, Nicolás M; Sidhu, Balsher Singh; Legge, Raymond L; Andrews, Robert C

2017-04-01

Impacts of ozonation alone as well as an advanced oxidation process of ozone plus hydrogen peroxide (H 2 O 2  + O 3 ) on organic matter prior to and following biofiltration were studied at pilot-scale. Three biofilters were operated in parallel to assess the effects of varying pre-treatment types and dosages. Conventionally treated water (coagulation/flocculation/sedimentation) was fed to one control biofilter, while the remaining two received water with varying applied doses of O 3 or H 2 O 2  + O 3 . Changes in organic matter were characterized using parallel factors analysis (PARAFAC) and fluorescence peak shifts. Intensities of all PARAFAC components were reduced by pre-oxidation, however, individual humic-like components were observed to be impacted to varying degrees upon exposure to O 3 or H 2 O 2  + O 3 . While the control biofilter uniformly reduced fluorescence of all PARAFAC components, three of the humic-like components were produced by biofiltration only when pre-oxidation was applied. A fluorescence red shift, which occurred with the application of O 3 or H 2 O 2  + O 3 , was attributed to a relative increase in carbonyl-containing components based on previously reported results. A subsequent blue shift in fluorescence caused by biofiltration which received pre-oxidized water indicated that biological treatment readily utilized organics produced by pre-oxidation. The results provide an understanding as to the impacts of organic matter character and pre-oxidation on biofiltration efficiency for organic matter removal. Copyright © 2017 Elsevier Ltd. All rights reserved.

HPSEC reveals ubiquitous components in fluorescent dissolved organic matter across aquatic ecosystems

NASA Astrophysics Data System (ADS)

Wünsch, Urban; Murphy, Kathleen; Stedmon, Colin

2017-04-01

Absorbance and fluorescence spectroscopy are efficient tools for tracing the supply, turnover and fate of dissolved organic matter (DOM). The fluorescent fraction of DOM (FDOM) can be characterized by measuring excitation-emission matrices and decomposing the combined fluorescence signal into independent underlying fraction using Parallel Factor Analysis (PARAFAC). Comparisons between studies, facilitated by the OpenFluor database, reveal highly similar components across different aquatic systems and between studies. To obtain PARAFAC models in sufficient quality, scientists traditionally rely on analyzing dozens to hundreds of samples spanning environmental gradients. A cross-validation of this approach using different analytical tools has not yet been accomplished. In this study, we applied high-performance size-exclusion chromatography (HPSEC) to characterize the size-dependent optical properties of dissolved organic matter of samples from contrasting aquatic environments with online absorbance and fluorescence detectors. Each sample produced hundreds of absorbance spectra of colored DOM (CDOM) and hundreds of matrices of FDOM intensities. This approach facilitated the detailed study of CDOM spectral slopes and further allowed the reliable implementation of PARAFAC on individual samples. This revealed a high degree of overlap in the spectral properties of components identified from different sites. Moreover, many of the model components showed significant spectral congruence with spectra in the OpenFluor database. Our results provide evidence of the presence of ubiquitous FDOM components and additionally provide further evidence for the supramolecular assembly hypothesis. They demonstrate the potential for HPSEC to provide a wealth of new insights into the relationship between optical and chemical properties of DOM.

Fluorescence characterization of fractionated dissolved organic matter in the five tributaries of Poyang Lake, China.

PubMed

Yan, Caixia; Liu, Huihui; Sheng, Yanru; Huang, Xian; Nie, Minghua; Huang, Qi; Baalousha, Mohammed

2018-10-01

Characterization of natural colloids is the key to understand pollutant fate and transport in the environment. The present study investigates the relationship between size and fluorescence properties of colloidal organic matter (COM) from five tributaries of Poyang Lake. Colloids were size-fractionated using cross-flow ultrafiltration and their fluorescence properties were measured by three-dimensional excitation-emission matrix fluorescence spectroscopy (3D-EEM). Parallel factor analysis (PARAFAC) and/or Self-organizing map (SOM) were applied to assess fluorescence properties as proxy indicators for the different size of colloids. PARAFAC analysis identified four fluorescence components including three humic-like components (C1-C3) and a protein-like component (C4). These four fluorescence components, and in particular the protein-like component, are primarily present in <1 kDa phase. For the colloidal fractions (1-10 kDa, 10-100 kDa, and 100 kDa-0.7 μm), the majority of fluorophores are associated with the smallest size fraction. SOM analysis demonstrated that relatively high fluorescence intensity and aromaticity occur primarily in <1 kDa phase, followed by 1-10 kDa colloids. Coupling PARAFAC and SOM facilitate the visualization and interpretation of the relationship between colloidal size and fluorescence properties with fewer input variables, shorter running time, higher reliability, and nondestructive results. Fluorescence indices analysis reveals that the smallest colloidal fraction (1-10 kDa) was dominated by higher humified and less autochthonous COM. Copyright © 2018 Elsevier B.V. All rights reserved.

Tracking variations in fluorescent-dissolved organic matter in an aerobic submerged membrane bioreactor using excitation-emission matrix spectra combined with parallel factor analysis.

PubMed

Hur, Jin; Shin, Jaewon; Kang, Minsun; Cho, Jinwoo

2014-08-01

In this study, the variations in the fluorescent components of dissolved organic matter (DOM) were tracked for an aerobic submerged membrane bioreactor (MBR) at three different operation stages (cake layer formation, condensation, and after cleaning). The fluorescent DOM was characterized using excitation-emission matrix (EEM) spectroscopy combined with parallel factor analysis (PARAFAC). Non-aromatic carbon structures appear to be actively involved in the membrane fouling for the cake layer formation stage as revealed by much higher UV-absorbing DOM per organic carbon found in the effluent versus those inside the reactor. Four fluorescent components were successfully identified from the reactor and the effluent DOMs by EEM-PARAFAC modeling. Among those in the reactor, microbial humic-like fluorescence was the most abundant component at the cake layer formation stage and tryptophan-like fluorescence at the condensation stage. In contrast to the reactor, relatively similar composition of the PARAFAC components was exhibited for the effluent at all three stages. Tryptophan-like fluorescence displayed the largest difference between the reactor and the effluent, suggesting that this component could be a good tracer for membrane fouling. It appears that the fluorescent DOM was involved in membrane fouling by cake layer formation rather than by internal pore adsorption because its difference between the reactor and the effluent was the highest among all the four components, even after the membrane cleaning. Our study provided an insight into the fate and the behavior fluorescent DOM components for an MBR system, which could be an indicator of the membrane fouling.

Evaluation of CDOM sources and their links with water quality in the lakes of Northeast China using fluorescence spectroscopy

NASA Astrophysics Data System (ADS)

Zhao, Ying; Song, Kaishan; Wen, Zhidan; Fang, Chong; Shang, Yingxin; Lv, Lili

2017-07-01

The spatial distributions of the fluorescence intensities Fmax for chromophoric dissolved organic matter (CDOM) components, the fluorescence indices (FI370 and FI310) and their correlations with water quality of 19 lakes in the Songhua River Basin (SHRB) across semiarid regions of Northeast China were examined with the data collected in September 2012 and 2015. The 19 lakes were divided into two groups according to EC (threshold value = 800 μS cm-1): fresh water (N = 13) and brackish water lakes (N = 6). The fluorescent characteristics of CDOM in the 19 lakes were investigated using excitation-emission matrix fluorescence spectroscopy (EEM) coupled with parallel factor (PARAFAC) and multivariate analysis. Two humic-like components (C1 and C3), one tryptophan-like component (C2), and one tyrosine-like component (C4) were identified by PARAFAC. The component C4 was not included in subsequent analyses due to the strong scatter in some colloidal water samples from brackish water lakes. The correlations between Fmax for the three EEM-PARAFAC extracted CDOM components C1-C3, the fluorescence indices (FI370 and FI310) and the water quality parameters (i.e., TN, TP, Chl-a, pH, EC, turbidity (Turb) and dissolved organic carbon (DOC)) were determined by redundancy analysis (RDA). The results of RDA analysis showed that spatial variation in land cover, pollution sources, and salinity/EC gradients in water quality affected Fmax for the fluorescent components C1-C3 and the fluorescence indices (FI370 and FI310). Further examination indicated that the CDOM fluorescent components and the fluorescence indices (FI370 and FI310) did not significantly differ (t-test, p > 0.05) in fresh water (N = 13) and brackish water lakes (N = 6). There was a difference in the distribution of the average Fmax for the CDOM fluorescent components between C1 to C3 from agricultural sources and urban wastewater sources in hypereutrophic brackish water lakes. The Fmax for humic-like components C1 and C3 spatially varied with land cover among the 19 lakes. Our results indicated that the spatial distributions of Fmax for CDOM fluorescent components and their correlations with water quality can be evaluated by EEM-PARAFAC and multivariate analysis among the 19 lakes across semiarid regions of Northeast China, which has potential implication for lakes with similar genesis.

Characterization of CDOM from urban waters in Northern-Northeastern China using excitation-emission matrix fluorescence and parallel factor analysis.

PubMed

Zhao, Ying; Song, Kaishan; Li, Sijia; Ma, Jianhang; Wen, Zhidan

2016-08-01

Chromophoric dissolved organic matter (CDOM) plays an important role in aquatic systems, but high concentrations of organic materials are considered pollutants. The fluorescent component characteristics of CDOM in urban waters sampled from Northern and Northeastern China were examined by excitation-emission matrix fluorescence and parallel factor analysis (EEM-PARAFAC) to investigate the source and compositional changes of CDOM on both space and pollution levels. One humic-like (C1), one tryptophan-like component (C2), and one tyrosine-like component (C3) were identified by PARAFAC. Mean fluorescence intensities of the three CDOM components varied spatially and by pollution level in cities of Northern and Northeastern China during July-August, 2013 and 2014. Principal components analysis (PCA) was conducted to identify the relative distribution of all water samples. Cluster analysis (CA) was also used to categorize the samples into groups of similar pollution levels within a study area. Strong positive linear relationships were revealed between the CDOM absorption coefficients a(254) (R (2) = 0.89, p < 0.01); a(355) (R (2) = 0.94, p < 0.01); and the fluorescence intensity (F max) for the humic-like C1 component. A positive linear relationship (R (2) = 0.77) was also exhibited between dissolved organic carbon (DOC) and the F max for the humic-like C1 component, but a relatively weak correlation (R (2) = 0.56) was detected between DOC and the F max for the tryptophan-like component (C2). A strong positive correlation was observed between the F max for the tryptophan-like component (C2) and total nitrogen (TN) (R (2) = 0.78), but moderate correlations were observed with ammonium-N (NH4-N) (R (2) = 0.68), and chemical oxygen demand (CODMn) (R (2) = 0.52). Therefore, the fluorescence intensities of CDOM components can be applied to monitor water quality in real time compared to that of traditional approaches. These results demonstrate that EEM-PARAFAC is useful to evaluate the dynamics of CDOM fluorescent components in urban waters from Northern and Northeastern China and this method has potential applications for monitoring urban water quality in different regions with various hydrological conditions and pollution levels.

Seasonal variation in the quality of dissolved and particulate organic matter exchanged between a salt marsh and its adjacent estuary

NASA Astrophysics Data System (ADS)

Osburn, Christopher L.; Mikan, Molly P.; Etheridge, J. Randall; Burchell, Michael R.; Birgand, François

2015-07-01

Fluorescence was used to examine the quality of dissolved and particulate organic matter (DOM and POM) exchanging between a tidal creek in a created salt marsh and its adjacent estuary in eastern North Carolina, USA. Samples from the creek were collected hourly over four tidal cycles in May, July, August, and October 2011. Absorbance and fluorescence of chromophoric DOM (CDOM) and of base-extracted POM (BEPOM) served as the tracers for organic matter quality while dissolved organic carbon (DOC) and base-extracted particulate organic carbon (BEPOC) were used to compute fluxes. Fluorescence was modeled using parallel factor analysis (PARAFAC) and principle components analysis (PCA) of the PARAFAC results. Of nine PARAFAC components (C) modeled, C3 represented recalcitrant DOM and C4 represented fresher soil-derived source DOM. Component 1 represented detrital POM, and C6 represented planktonic POM. Based on mass balance, recalcitrant DOC export was 86 g C m-2 yr-1 and labile DOC export was 49 g C m-2 yr-1; no planktonic DOC was exported. The marsh also exported 41 g C m-2 yr-1 of detrital terrestrial POC, which likely originated from lands adjacent to the North River estuary. Planktonic POC export from the marsh was 6 g C m-2 yr-1. Assuming the exported organic matter was oxidized to CO2 and scaled up to global salt marsh area, respiration of salt marsh DOC and POC transported to estuaries could amount to a global CO2 flux of 11 Tg C yr-1, roughly 4% of the recently estimated CO2 release for marshes and estuaries globally.

Tracking fluorescent dissolved organic matter in multistage rivers using EEM-PARAFAC analysis: implications of the secondary tributary remediation for watershed management.

PubMed

Nie, Zeyu; Wu, Xiaodong; Huang, Haomin; Fang, Xiaomin; Xu, Chen; Wu, Jianyu; Liang, Xinqiang; Shi, Jiyan

2016-05-01

Profound understanding of behaviors of organic matter from sources to multistage rivers assists watershed management for improving water quality of river networks in rural areas. Ninety-one water samples were collected from the three orders of receiving rivers in a typical combined polluted subcatchment (diffuse agricultural pollutants and domestic sewage) located in China. Then, the fluorescent dissolved organic matter (FDOM) information for these samples was determined by the excitation-emission matrix coupled with parallel factor analysis (EEM-PARAFAC). Consequently, two typical humic-like (C1 and C2) and other two protein-like (C3 and C4) components were separated. Their fluorescence peaks were located at λ ex/em = 255(360)/455, <250(320)/395, 275/335, and <250/305 nm, which resembled the traditional peaks of A + C, A + M, T, and B, respectively. In addition, C1 and C2 accounted for the dominant contributions to FDOM (>60 %). Principal component analysis (PCA) further demonstrated that, except for the autochthonous produced C4, the allochthonous components (C1 and C2) had the same terrestrial origins, but C3 might possess the separate anthropogenic and biological sources. Moreover, the spatial heterogeneity of contamination levels was noticeable in multistage rivers, and the allochthonous FDOM was gradually homogenized along the migration directions. Interestingly, the average content of the first three PARAFAC components in secondary tributaries and source pollutants had significantly higher levels than that in subsequent receiving rivers, thus suggesting that the supervision and remediation for secondary tributaries would play a prominent role in watershed management works.

Evaluating the distribution of terrestrial dissolved organic matter in a complex coastal ecosystem using fluorescence spectroscopy

NASA Astrophysics Data System (ADS)

Yamashita, Youhei; Boyer, Joseph N.; Jaffé, Rudolf

2013-09-01

The coastal zone of the Florida Keys features the only living coral reef in the continental United States and as such represents a unique regional environmental resource. Anthropogenic pressures combined with climate disturbances such as hurricanes can affect the biogeochemistry of the region and threaten the health of this unique ecosystem. As such, water quality monitoring has historically been implemented in the Florida Keys, and six spatially distinct zones have been identified. In these studies however, dissolved organic matter (DOM) has only been studied as a quantitative parameter, and DOM composition can be a valuable biogeochemical parameter in assessing environmental change in coastal regions. Here we report the first data of its kind on the application of optical properties of DOM, in particular excitation emission matrix fluorescence with parallel factor analysis (EEM-PARAFAC), throughout these six Florida Keys regions in an attempt to assess spatial differences in DOM sources. Our data suggests that while DOM in the Florida Keys can be influenced by distant terrestrial environments such as the Everglades, spatial differences in DOM distribution were also controlled in part by local surface runoff/fringe mangroves, contributions from seasgrass communities, as well as the reefs and waters from the Florida Current. Application of principal component analysis (PCA) of the relative abundance of EEM-PARAFAC components allowed for a clear distinction between the sources of DOM (allochthonous vs. autochthonous), between different autochthonous sources and/or the diagenetic status of DOM, and further clarified contribution of terrestrial DOM in zones where levels of DOM were low in abundance. The combination between EEM-PARAFAC and PCA proved to be ideally suited to discern DOM composition and source differences in coastal zones with complex hydrology and multiple DOM sources.

Interactions between stepwise-eluted sub-fractions of fulvic acids and protons revealed by fluorescence titration combined with EEM-PARAFAC.

PubMed

Song, Fanhao; Wu, Fengchang; Guo, Fei; Wang, Hao; Feng, Weiying; Zhou, Min; Deng, Yanghui; Bai, Yingchen; Xing, Baoshan; Giesy, John P

2017-12-15

In aquatic environments, pH can control environmental behaviors of fulvic acid (FA) via regulating hydrolysis of functional groups. Sub-fractions of FA, eluted using pyrophosphate buffers with initial pHs of 3.0 (FA 3 ), 5.0 (FA 5 ), 7.0 (FA 7 ), 9.0 (FA 9 ) and 13.0 (FA 13 ), were used to explore interactions between the various, operationally defined, FA fractions and protons, by use of EEM-PARAFAC analysis. Splitting of peaks (FA 3 and FA 13 ), merging of peaks (FA 7 ), disappearance of peaks (FA 9 and FA 13 ), and red/blue-shifting of peaks were observed during fluorescence titration. Fulvic-like components were identified from FA 3 -FA 13 , and protein-like components were observed in fractions FA 9 and FA 13 . There primary compounds (carboxylic-like, phenolic-like, and protein-like chromophores) in PARAFAC components were distinguished based on acid-base properties. Dissociation constants (pK a ) for fulvic-like components with proton ranged from 2.43 to 4.13 in an acidic pH and from 9.95 to 11.27 at basic pH. These results might be due to protonation of di-carboxylate and phenolic functional groups. At basic pH, pK a values of protein-like components (9.77-10.13) were similar to those of amino acids. However, at acidic pH, pK a values of protein-like components, which ranged from 3.33 to 4.22, were 1-2units greater than those of amino acids. Results presented here, will benefit understanding of environmental behaviors of FA, as well as interactions of FA with environmental contaminants. Copyright © 2017 Elsevier B.V. All rights reserved.

Characterizing chromophoric dissolved organic matter in Lake Tianmuhu and its catchment basin using excitation-emission matrix fluorescence and parallel factor analysis.

PubMed

Zhang, Yunlin; Yin, Yan; Feng, Longqing; Zhu, Guangwei; Shi, Zhiqiang; Liu, Xiaohan; Zhang, Yuanzhi

2011-10-15

Chromophoric dissolved organic matter (CDOM) is an important optically active substance that transports nutrients, heavy metals, and other pollutants from terrestrial to aquatic systems and is used as a measure of water quality. To investigate how the source and composition of CDOM changes in both space and time, we used chemical, spectroscopic, and fluorescence analyses to characterize CDOM in Lake Tianmuhu (a drinking water source) and its catchment in China. Parallel factor analysis (PARAFAC) identified three individual fluorophore moieties that were attributed to humic-like and protein-like materials in 224 water samples collected between December 2008 and September 2009. The upstream rivers contained significantly higher concentrations of CDOM than did the lake water (a(350) of 4.27±2.51 and 2.32±0.59 m(-1), respectively), indicating that the rivers carried a substantial load of organic matter to the lake. Of the three main rivers that flow into Lake Tianmuhu, the Pingqiao River brought in the most CDOM from the catchment to the lake. CDOM absorption and the microbial and terrestrial humic-like components, but not the protein-like component, were significantly higher in the wet season than in other seasons, indicating that the frequency of rainfall and runoff could significantly impact the quantity and quality of CDOM collected from the catchment. The different relationships between the maximum fluorescence intensities of the three PARAFAC components, CDOM absorption, and chemical oxygen demand (COD) concentration in riverine and lake water indicated the difference in the composition of CDOM between Lake Tianmuhu and the rivers that feed it. This study demonstrates the utility of combining excitation-emission matrix fluorescence and PARAFAC to study CDOM dynamics in inland waters. Copyright © 2011 Elsevier Ltd. All rights reserved.

Character and chlorine reactivity of dissolved organic matter from a mountain pine beetle impacted watershed.

PubMed

Beggs, Katherine M H; Summers, R Scott

2011-07-01

Lodgepole pine needle leachates from trees killed by the mountain pine beetle epidemic in Colorado were evaluated for dissolved organic matter (DOM) character, biodegradation, treatability by coagulation and disinfection byproduct (DBP) formation. An average of 8.0 (±0.62) mg-DOC/g-dry weight of litter was leached from three sets of needle samples representing different levels of forest floor degradation. Fluorescence analysis included collection of excitation and emission matrices, examination of peak intensities and development of a 4-component parallel factor (PARAFAC) analysis model. Peak intensity and PARAFAC analyses provided complementary results showing that fresh leachates were initially dominated by polyphenolic/protein-like components (60-70%) and humic-like fluorescence increased (40-70%) after biodegradation. Humic-like components were removed by coagulation (20-64%), while polyphenolic/protein-like components were not, which may create challenges for utilities required to meet OM removal regulations. DBP formation yields after 24 h chlorination were 20.5-26.4 μg/mg-DOC for trihalomethanes and 9.0-14.5 μg/mg-DOC for haloacetic acids for fresh leachates; increased after biodegradation to 19.2-64.2 and 7.1-30.9 μg/mg-DOC, respectively; and decreased after coagulation (fresh: 11.3-17.7;5.7-7.6 μg/mg-DOC, respectively; biodegraded: 12.0-27.3 and 2.9-7.2 μg/mg-DOC, respectively), reflective of changes in concentration of humic material. Humic-like PARAFAC components and peak intensities were positively correlated (R(2) ≥ 0.45) to DBP concentrations, while polyphenolic/protein-like components were not (R(2) ≤ 0.17).

Characterizing fluorescent dissolved organic matter in a membrane bioreactor via excitation-emission matrix combined with parallel factor analysis.

PubMed

Maqbool, Tahir; Quang, Viet Ly; Cho, Jinwoo; Hur, Jin

2016-06-01

In this study, we successfully tracked the dynamic changes in different constitutes of bound extracellular polymeric substances (bEPS), soluble microbial products (SMP), and permeate during the operation of bench scale membrane bioreactors (MBRs) via fluorescence excitation-emission matrix (EEM) combined with parallel factor analysis (PARAFAC). Three fluorescent groups were identified, including two protein-like (tryptophan-like C1 and tyrosine-like C2) and one microbial humic-like components (C3). In bEPS, protein-like components were consistently more dominant than C3 during the MBR operation, while their relative abundance in SMP depended on aeration intensities. C1 of bEPS exhibited a linear correlation (R(2)=0.738; p<0.01) with bEPS amounts in sludge, and C2 was closely related to the stability of sludge. The protein-like components were more greatly responsible for membrane fouling. Our study suggests that EEM-PARAFAC can be a promising monitoring tool to provide further insight into process evaluation and membrane fouling during MBR operation. Copyright © 2016 Elsevier Ltd. All rights reserved.

Absorption and fluorescence properties of colored dissolved organic matter in the Ross Sea during austral summer

NASA Astrophysics Data System (ADS)

D'Sa, E. J.; Kim, H. C.; Ha, S. Y.

2016-12-01

Colored dissolved organic matter (CDOM) spectral absorption and excitation-emission matrix (EEMs) fluorescence with parallel factor analysis (PARAFAC) were examined in the Ross Sea during a survey conducted on board the R/V Araon in the austral summer of 14/15. CDOM absorption at 355 nm ranged from 0.06 to 1.14 m-1 while spectral slope S calculated between 275-295 nm wavelength ranged from 18.83 to 33.32 µm-1 with water masses playing an important role in its variability. Spectral slope S decreased with increasing CDOM absorption indicating the strong role of photo-oxidation on CDOM abundance during the summer. PARAFAC analysis of EEM data identified two humic-like (terrestrial and marine-like) and a protein-like (tryptophan-like) component. The two humic-like components were well correlated with little variability spatially and across the water column ( 0-100 m) likely indicating more refractory material. The protein-like fluorescent component was relatively quite variable supporting the autochthonous production of this fluorescent component in the highly productive Ross Sea waters.

Seasonal Variation in the Quality of Dissolved and Particulate Organic Matter Exchanged Between a Salt Marsh and Its Adjacent Estuary

NASA Astrophysics Data System (ADS)

Osburn, C. L.; Mikan, M.; Etheridge, J. R.; Burchell, M. R.; Birgand, F.

2015-12-01

Salt marshes are transitional ecosystems between terrestrial and marine environments. Along with mangroves and other vegetated coastal habitats, salt marshes rank among the most productive ecosystems on Earth, with critical global importance for the planet's carbon cycle. Fluorescence was used to examine the quality of dissolved and particulate organic matter (DOM and POM) exchanging between a tidal creek in a created salt marsh and its adjacent estuary in eastern North Carolina, USA. Samples from the creek were collected hourly over four tidal cycles in May, July, August, and October of 2011. Absorbance and fluorescence of chromophoric DOM (CDOM) and of base-extracted POM (BEPOM) served as the tracers for organic matter quality while dissolved organic carbon (DOC) and base-extracted particulate organic carbon (BEPOC) were used to compute fluxes. Fluorescence was modeled using parallel factor analysis (PARAFAC) and principle components analysis (PCA) of the PARAFAC results. Of nine PARAFAC components modeled, we used multiple linear regression to identify tracers for recalcitrant DOM; labile soil-derived source DOM; detrital POM; and planktonic POM. Based on mass balance, recalcitrant DOC export was 86 g C m-2 yr-1 and labile DOC export was 49 g C m-2 yr-1. The marsh also exported 41 g C m-2 yr-1 of detrital terrestrial POC, which likely originated from lands adjacent to the North River estuary. Planktonic POC export from the marsh was 6 g C m-2 yr-1. Using the DOM and POM quality results obtained via fluorescence measurements and scaling up to global salt marsh area, we estimated that the potential release of CO2 from the respiration of salt marsh DOC and POC transported to estuaries could be 11 Tg C yr-1, roughly 4% of the recently estimated CO2 release for marshes and estuaries globally.

Using parallel factor analysis modeling (PARAFAC) and self-organizing maps to track senescence-induced patterns in leaf litter leachate

NASA Astrophysics Data System (ADS)

Wheeler, K. I.; Levia, D. F., Jr.; Hudson, J. E.

2017-12-01

As trees undergo autumnal processes such as resorption, senescence, and leaf abscission, the dissolved organic matter (DOM) contribution of leaf litter leachate to streams changes. However, little research has investigated how the fluorescent DOM (FDOM) changes throughout the autumn and how this differs inter- and intraspecifically. Two of the major impacts of global climate change on forested ecosystems include altering phenology and causing forest community species and subspecies composition restructuring. We examined changes in FDOM in leachate from American beech (Fagus grandifolia Ehrh.) leaves in Maryland, Rhode Island, Vermont, and North Carolina and yellow poplar (Liriodendron tulipifera L.) leaves from Maryland throughout three different phenophases: green, senescing, and freshly abscissed. Beech leaves from Maryland and Rhode Island have previously been identified as belonging to the same distinct genetic cluster and beech trees from Vermont and the study site in North Carolina from the other. FDOM in samples was characterized using excitation-emission matrices (EEMs) and a six-component parallel factor analysis (PARAFAC) model was created to identify components. Self-organizing maps (SOMs) were used to visualize variation and patterns in the PARAFAC component proportions of the leachate samples. Phenophase and species had the greatest influence on determining where a sample mapped on the SOM when compared to genetic clusters and geographic origin. Throughout senescence, FDOM from all the trees transitioned from more protein-like components to more humic-like ones. Percent greenness of the sampled leaves and the proportion of the tyrosine-like component 1 were found to significantly differ between the two genetic beech clusters. This suggests possible differences in photosynthesis and resorption between the two genetic clusters of beech. The use of SOMs to visualize differences in patterns of senescence between the different species and genetic populations proved to be useful in ways that other multivariate analysis techniques lack.

Simultaneous determination of α-asarone and β-asarone in Acorus tatarinowii using excitation-emission matrix fluorescence coupled with chemometrics methods

NASA Astrophysics Data System (ADS)

Bai, Xue-Mei; Liu, Tie; Liu, De-Long; Wei, Yong-Ju

2018-02-01

A chemometrics-assisted excitation-emission matrix (EEM) fluorescence method was proposed for simultaneous determination of α-asarone and β-asarone in Acorus tatarinowii. Using the strategy of combining EEM data with chemometrics methods, the simultaneous determination of α-asarone and β-asarone in the complex Traditional Chinese medicine system was achieved successfully, even in the presence of unexpected interferents. The physical or chemical separation step was avoided due to the use of ;mathematical separation;. Six second-order calibration methods were used including parallel factor analysis (PARAFAC), alternating trilinear decomposition (ATLD), alternating penalty trilinear decomposition (APTLD), self-weighted alternating trilinear decomposition (SWATLD), the unfolded partial least-squares (U-PLS) and multidimensional partial least-squares (N-PLS) with residual bilinearization (RBL). In addition, HPLC method was developed to further validate the presented strategy. Consequently, for the validation samples, the analytical results obtained by six second-order calibration methods were almost accurate. But for the Acorus tatarinowii samples, the results indicated a slightly better predictive ability of N-PLS/RBL procedure over other methods.

Spatial Variations of DOM Compositions in the River with Multi-functional Weir

NASA Astrophysics Data System (ADS)

Yoon, S. M.; Choi, J. H.

2017-12-01

With the global trend to construct artificial impoundments over the last decades, there was a Large River Restoration Project conducted in South Korea from 2010 to 2011. The project included enlargement of river channel capacity and construction of multi-functional weirs, which can alter the hydrological flow of the river and cause spatial variations of water quality indicators, especially DOM (Dissolved Organic Matter) compositions. In order to analyze the spatial variations of organic matter, water samples were collected longitudinally (5 points upstream from the weir), horizontally (left, center, right at each point) and vertically (1m interval at each point). The specific UV-visible absorbance (SUVA) and fluorescence excitation-emission matrices (EEMs) have been used as rapid and non-destructive analytical methods for DOM compositions. In addition, parallel factor analysis (PARAFAC) has adopted for extracting a set of representative fluorescence components from EEMs. It was assumed that autochthonous DOM would be dominant near the weir due to the stagnation of hydrological flow. However, the results showed that the values of fluorescence index (FI) were 1.29-1.47, less than 2, indicating DOM of allochthonous origin dominated in the water near the weir. PARAFAC analysis also showed the peak at 450 nm of emission and < 250 nm of excitation which represented the humic substances group with terrestrial origins. There was no significant difference in the values of Biological index (BIX), however, values of humification index (HIX) spatially increased toward the weir. From the results of the water sample analysis, the river with multi-functional weir is influenced by the allochthonous DOM instead of autochthonous DOM and seems to accumulate humic substances near the weir.

Cation-induced coagulation of aquatic plant-derived dissolved organic matter: Investigation by EEM-PARAFAC and FT-IR spectroscopy

USDA-ARS?s Scientific Manuscript database

Interaction and coagulation of plant-derived dissolved organic matter (DOM) by metal cations are important biogeochemical processes of organic matter in lake systems. Thus, coagulation and fractionation of plant-derived DOM by di- and tri-valent Ca, Al, and Fe ions were investigated. Metal ion-induc...

Fluorescence characteristics and biodegradability of dissolved organic matter in forest and wetland soils from coastal temperate watersheds in southeast Alaska

Treesearch

Jason B. Fellman; David V. D' Amore; Eran Hood; Richard D. Boone

2008-01-01

Understanding how the concentration and chemical quality of dissolved organic matter (DOM) varies in soils is critical because DOM influences an array of biological, chemical, and physical processes. We used PARAFAC modeling of excitation-emission fluorescence spectroscopy, specific UV absorbance (SUVA254) and biodegradable dissolved organic...

Tensor-based Dictionary Learning for Spectral CT Reconstruction

PubMed Central

Zhang, Yanbo; Wang, Ge

2016-01-01

Spectral computed tomography (CT) produces an energy-discriminative attenuation map of an object, extending a conventional image volume with a spectral dimension. In spectral CT, an image can be sparsely represented in each of multiple energy channels, and are highly correlated among energy channels. According to this characteristics, we propose a tensor-based dictionary learning method for spectral CT reconstruction. In our method, tensor patches are extracted from an image tensor, which is reconstructed using the filtered backprojection (FBP), to form a training dataset. With the Candecomp/Parafac decomposition, a tensor-based dictionary is trained, in which each atom is a rank-one tensor. Then, the trained dictionary is used to sparsely represent image tensor patches during an iterative reconstruction process, and the alternating minimization scheme is adapted for optimization. The effectiveness of our proposed method is validated with both numerically simulated and real preclinical mouse datasets. The results demonstrate that the proposed tensor-based method generally produces superior image quality, and leads to more accurate material decomposition than the currently popular popular methods. PMID:27541628

Uptake of allochthonous dissolved organic matter from soil and salmon in coastal temperate rainforest streams

Treesearch

Jason B. Fellman; Eran Hood; Richard T. Edwards; Jeremy B. Jones

2009-01-01

Dissolved organic matter (DOM) is an important component of aquatic food webs. We compare the uptake kinetics for NH4-N and different fractions of DOM during soil and salmon leachate additions by evaluating the uptake of organic forms of carbon (DOC) and nitrogen (DON), and proteinaceous DOM, as measured by parallel factor (PARAFAC) modeling of...

Seasonal changes in the chemical quality and biodegradability of dissolved organic matter exported from soils to streams in coastal temperate rainforest watersheds

Treesearch

Jason B. Fellman; Eran Hood; David V. D' Amore; Richard T. Edwards; Dan White

2009-01-01

The composition and biodegradability of streamwater dissolved organic matter (DOM) varies with source material and degree of transformation. We combined PARAFAC modeling of fluorescence excitation-emission spectroscopy and biodegradable dissolved organic carbon (BDOC) incubations to investigate seasonal changes in the lability of DOM along a soil-stream continuum in...

Source analysis of organic matter in swine wastewater after anaerobic digestion with EEM-PARAFAC.

PubMed

Zeng, Zhuo; Zheng, Ping; Ding, Aqiang; Zhang, Meng; Abbas, Ghulam; Li, Wei

2017-03-01

Swine wastewater is one of the most serious pollution sources, and it has attracted a great public concern in China. Anaerobic digestion technology is extensively used in swine wastewater treatment. However, the anaerobic digestion effluents are difficult to meet the discharge standard. The results from batch experiments showed that plenty of refractory organic matter remained in the effluents after mesophilic anaerobic digestion for 30 days. The effluent total COD (tCOD) and soluble COD (sCOD) were 483 and 324 mg/L, respectively, with the sCOD/tCOD ratio of 0.671. Fluorescence excitation-emission matrix (EEM) coupled with parallel factor analysis (PARAFAC) revealed that the dissolved organic matter in the effluents was tryptophan-like substance, humic acid substance, and fulvic acid substance. Based on the appearance time during anaerobic digestion, tryptophan-like substance and humic acid substance were inferred to originate from the raw swine wastewater, and the fulvic acid substance was inferred to be formed in the anaerobic digestion. This work has revealed the source of residual organic matter in anaerobic digestion of swine wastewater and has provided some valuable information for the post-treatment.

A 21 000-year record of fluorescent organic matter markers in the WAIS Divide ice core

NASA Astrophysics Data System (ADS)

D'Andrilli, Juliana; Foreman, Christine M.; Sigl, Michael; Priscu, John C.; McConnell, Joseph R.

2017-05-01

Englacial ice contains a significant reservoir of organic material (OM), preserving a chronological record of materials from Earth's past. Here, we investigate if OM composition surveys in ice core research can provide paleoecological information on the dynamic nature of our Earth through time. Temporal trends in OM composition from the early Holocene extending back to the Last Glacial Maximum (LGM) of the West Antarctic Ice Sheet Divide (WD) ice core were measured by fluorescence spectroscopy. Multivariate parallel factor (PARAFAC) analysis is widely used to isolate the chemical components that best describe the observed variation across three-dimensional fluorescence spectroscopy (excitation-emission matrices; EEMs) assays. Fluorescent OM markers identified by PARAFAC modeling of the EEMs from the LGM (27.0-18.0 kyr BP; before present 1950) through the last deglaciation (LD; 18.0-11.5 kyr BP), to the mid-Holocene (11.5-6.0 kyr BP) provided evidence of different types of fluorescent OM composition and origin in the WD ice core over 21.0 kyr. Low excitation-emission wavelength fluorescent PARAFAC component one (C1), associated with chemical species similar to simple lignin phenols was the greatest contributor throughout the ice core, suggesting a strong signature of terrestrial OM in all climate periods. The component two (C2) OM marker, encompassed distinct variability in the ice core describing chemical species similar to tannin- and phenylalanine-like material. Component three (C3), associated with humic-like terrestrial material further resistant to biodegradation, was only characteristic of the Holocene, suggesting that more complex organic polymers such as lignins or tannins may be an ecological marker of warmer climates. We suggest that fluorescent OM markers observed during the LGM were the result of greater continental dust loading of lignin precursor (monolignol) material in a drier climate, with lower marine influences when sea ice extent was higher and continents had more expansive tundra cover. As the climate warmed, the record of OM markers in the WD ice core changed, reflecting shifts in carbon productivity as a result of global ecosystem response.

[Characterizing chromophoric dissolved organic matter (CDOM) in Lake Honghu, Lake Donghu and Lake Liangzihu using excitation-emission matrices (EEMs) fluorescence and parallel factor analysis (PARAFAC)].

PubMed

Zhou, Yong-Qiang; Zhang, Yun-Lin; Niu, Cheng; Wang, Ming-Zhu

2013-12-01

Little is known about DOM characteristics in medium to large sized lakes located in the middle and lower reaches of Yangtze River, like Lake Honghu, Lake Donghu and Lake Liangzihu. Absorption, fluorescence and composition characteristics of chromophoric dissolved organic matter (CDOM) are presented using the absorption spectroscopy, the excitation-emission ma trices (EEMs) fluorescence and parallel factor analysis (PARAFAC) model based on the data collected in Sep-Oct. 2007 including 15, 9 and 10 samplings in Lake Honghu, Lake Donghu and Lake Liangzihu, respectively. CDOM absorption coefficient at 350 nm a(350) coefficient in Lake Honghu was significantly higher than those in Lake Donghu and Lake Liangzihu (t-test, p< 0. 001). A significant negative correlation was found between CDOM spectral slope in the wavelength range of 280-500 nm (S280-500) and a(350) (R2 =0. 781, p<0. 001). The mean value of S280-500 in Lake Honghu was significantly lower than those in Lake Donghu (t-test, p

Probing dissolved organic matter in the critical zone: a comparison between in situ sampling and aqueous soil extracts

NASA Astrophysics Data System (ADS)

Perdrial, J. N.; Perdrial, N.; Harpold, A. A.; Peterson, A. M.; Vasquez, A.; Chorover, J.

2011-12-01

Analyzing dissolved organic matter (DOM) of soil solution constitutes an integral activity in critical zone science as important insights to nutrient and carbon cycling and mineral weathering processes can be gained. Soil solution can be obtained by a variety of approaches such as by in situ zero-tension and tension samplers or by performing soil extracts in the lab. It is generally preferred to obtain soil solution in situ with the least amount of disturbance. However, in water limited environments, such as in southwestern US, in situ sampling is only possible during few hydrologic events and soil extracts are often employed. In order to evaluate the performance of different sampling approaches for OM analysis, results from aqueous soil extracts were compared with in situ samples obtained from suction cups and passive capillary wick samplers (PCAP's). Soil from an OA-horizon of mixed conifer forest Jemez River Basin Critical Zone Observatory (JRB-CZO) in NM was sampled twice and in situ samples from co-located suction cups and PCAPs were collected 7 times during the 2011 snowmelt period. Dissolved organic carbon and nitrogen concentrations (DOC and DN) as well as OM quality (FTIR, fluorescence spectroscopy and PARAFAC) were analyzed. The aqueous soil extracts (solid:solution = 1:5 mass basis) showed highest DOC and lowest DN concentrations whereas samples collected in-situ had lower DOC and higher DN concentrations. PARAFAC analysis using a four component model showed a dominance of fluorescence in region I and II (protein-like fluorescence) for samples collected in situ indicating the presence of more bio-molecules (proteins). In contrast, the dominant PARAFAC component of the soil extract was found in region 3 (fulvic acid-like fluorescence). FTIR analysis showed high intensity band at 1600 cm-1 in the case of the aqueous soil extract that correspond to asymmetric stretching of carboxyl groups. These preliminary results indicate that aqueous soil extracts likely lead to the underestimation of the amount of biomolecules and the overestimation of fulvic acid contents of soil solutions.

Estimating the Concentration and Biodegradability of Organic Matter in 22 Wastewater Treatment Plants Using Fluorescence Excitation Emission Matrices and Parallel Factor Analysis

PubMed Central

Yang, Liyang; Shin, Hyun-Sang; Hur, Jin

2014-01-01

This study aimed at monitoring the changes of fluorescent components in wastewater samples from 22 Korean biological wastewater treatment plants and exploring their prediction capabilities for total organic carbon (TOC), dissolved organic carbon (DOC), biochemical oxygen demand (BOD), chemical oxygen demand (COD), and the biodegradability of the wastewater using an optical sensing technique based on fluorescence excitation emission matrices and parallel factor analysis (EEM-PARAFAC). Three fluorescent components were identified from the samples by using EEM-PARAFAC, including protein-like (C1), fulvic-like (C2) and humic-like (C3) components. C1 showed the highest removal efficiencies for all the treatment types investigated here (69% ± 26%–81% ± 8%), followed by C2 (37% ± 27%–65% ± 35%), while humic-like component (i.e., C3) tended to be accumulated during the biological treatment processes. The percentage of C1 in total fluorescence (%C1) decreased from 54% ± 8% in the influents to 28% ± 8% in the effluents, while those of C2 and C3 (%C2 and %C3) increased from 43% ± 6% to 62% ± 9% and from 3% ± 7% to 10% ± 8%, respectively. The concentrations of TOC, DOC, BOD, and COD were the most correlated with the fluorescence intensity (Fmax) of C1 (r = 0.790–0.817), as compared with the other two fluorescent components. The prediction capability of C1 for TOC, BOD, and COD were improved by using multiple regression based on Fmax of C1 and suspended solids (SS) (r = 0.856–0.865), both of which can be easily monitored in situ. The biodegradability of organic matter in BOD/COD were significantly correlated with each PARAFAC component and their combinations (r = −0.598–0.613, p < 0.001), with the highest correlation coefficient shown for %C1. The estimation capability was further enhanced by using multiple regressions based on %C1, %C2 and C3/C2 (r = −0.691). PMID:24448170

Identification and quantification of carbamate pesticides in dried lime tree flowers by means of excitation-emission molecular fluorescence and parallel factor analysis when quenching effect exists.

PubMed

Rubio, L; Ortiz, M C; Sarabia, L A

2014-04-11

A non-separative, fast and inexpensive spectrofluorimetric method based on the second order calibration of excitation-emission fluorescence matrices (EEMs) was proposed for the determination of carbaryl, carbendazim and 1-naphthol in dried lime tree flowers. The trilinearity property of three-way data was used to handle the intrinsic fluorescence of lime flowers and the difference in the fluorescence intensity of each analyte. It also made possible to identify unequivocally each analyte. Trilinearity of the data tensor guarantees the uniqueness of the solution obtained through parallel factor analysis (PARAFAC), so the factors of the decomposition match up with the analytes. In addition, an experimental procedure was proposed to identify, with three-way data, the quenching effect produced by the fluorophores of the lime flowers. This procedure also enabled the selection of the adequate dilution of the lime flowers extract to minimize the quenching effect so the three analytes can be quantified. Finally, the analytes were determined using the standard addition method for a calibration whose standards were chosen with a D-optimal design. The three analytes were unequivocally identified by the correlation between the pure spectra and the PARAFAC excitation and emission spectral loadings. The trueness was established by the accuracy line "calculated concentration versus added concentration" in all cases. Better decision limit values (CCα), in x0=0 with the probability of false positive fixed at 0.05, were obtained for the calibration performed in pure solvent: 2.97 μg L(-1) for 1-naphthol, 3.74 μg L(-1) for carbaryl and 23.25 μg L(-1) for carbendazim. The CCα values for the second calibration carried out in matrix were 1.61, 4.34 and 51.75 μg L(-1) respectively; while the values obtained considering only the pure samples as calibration set were: 2.65, 8.61 and 28.7 μg L(-1), respectively. Copyright © 2014 Elsevier B.V. All rights reserved.

Effects of iron on optical properties of dissolved organic matter.

PubMed

Poulin, Brett A; Ryan, Joseph N; Aiken, George R

2014-09-02

Iron is a source of interference in the spectroscopic analysis of dissolved organic matter (DOM); however, its effects on commonly employed ultraviolet and visible (UV-vis) light adsorption and fluorescence measurements are poorly defined. Here, we describe the effects of iron(II) and iron(III) on the UV-vis absorption and fluorescence of solutions containing two DOM fractions and two surface water samples. In each case, regardless of DOM composition, UV-vis absorption increased linearly with increasing iron(III). Correction factors were derived using iron(III) absorption coefficients determined at wavelengths commonly used to characterize DOM. Iron(III) addition increased specific UV absorbances (SUVA) and decreased the absorption ratios (E2:E3) and spectral slope ratios (SR) of DOM samples. Both iron(II) and iron(III) quenched DOM fluorescence at pH 6.7. The degree and region of fluorescence quenching varied with the iron:DOC concentration ratio, DOM composition, and pH. Regions of the fluorescence spectra associated with greater DOM conjugation were more susceptible to iron quenching, and DOM fluorescence indices were sensitive to the presence of both forms of iron. Analyses of the excitation-emission matrices using a 7- and 13-component parallel factor analysis (PARAFAC) model showed low PARAFAC sensitivity to iron addition.

Characterizing chlorine oxidation of dissolved organic matter and disinfection by-product formation with fluorescence spectroscopy and parallel factor analysis

NASA Astrophysics Data System (ADS)

Beggs, Katherine M. H.; Summers, R. Scott; McKnight, Diane M.

2009-12-01

Relationships between chlorine demand and disinfection by-product (DBP) formation during chlorination and fluorescence of dissolved organic matter (DOM) were developed. Fluorescence excitation and emission (EEM) spectroscopy was employed, and parameters including fluorescence index, redox index, and overall fluorescence intensity (OFI) were correlated to chlorine demand and DBP formation. The EEMs were also analyzed using a well established global parallel factor analysis (PARAFAC) model which resolves the fluorescence signal into 13 components, including quinone-like and protein-like components. Over an 8-day chlorination period the OFI and sum of the 13 PARAFAC loadings decreased by more than 70%. The remaining identified quinone-like compounds within the DOM were shifted to a more oxidized state. Quinone fluorescence was strongly correlated to both reduced fluorescence intensity and to chlorine demand which indicates that fluorescence may be used to track the chlorine oxidation of DOM. Quinone fluorescence was also correlated strongly with both classes of regulated DBPs: total trihalomethanes and haloacetic acids. Quinone-like components were found to be strongly correlated to overall, short-term, and long-term specific DBP formation. The results of this study show that fluorescence is a useful tool in tracking both DOM oxidation and DBP formation during chlorination.

Toward understanding the role of individual fluorescent components in DOM-metal binding.

PubMed

Wu, Jun; Zhang, Hua; Yao, Qi-Sheng; Shao, Li-Ming; He, Pin-Jing

2012-05-15

Knowledge on the function of individual fractions in dissolved organic matter (DOM) is essential for understanding the impact of DOM on metal speciation and migration. Herein, fluorescence excitation-emission matrix quenching and parallel factor (PARAFAC) analysis were adopted for bulk DOM and chemically isolated fractions from landfill leachate, i.e., humic acids (HA), fulvic acids and hydrophilic (HyI) fraction, to elucidate the role of individual fluorescent components in metal binding (Cu(II) and Cd(II)). Three components were identified by PARAFAC model, including one humic substance (HS)-like, one protein-like and one component highly correlated with the HyI fraction. Among them, the HS-like and protein-like components were responsible for Cu(II) binding, while the protein-like component was the only fraction involved in Cd(II) complexation. It was further identified that the slight quenching effect of HA fraction by Cd(II) was induced by the presence of proteinaceous materials in HA. Fluorescent substances in the HyI fraction of landfill leachate did not play as important a role as HS did. Therefore, it was suggested that the potential risk of aged leachate (more humified) as a carrier of heavy metal should not be overlooked. Copyright © 2012 Elsevier B.V. All rights reserved.

Fluorescence excitation-emission matrix spectroscopy analysis of landfill leachate DOM in coagulation-flocculation process.

PubMed

Zhu, Guocheng; Wang, Chuang; Dong, Xingwei

2017-06-01

Landfill leachate contains a variety of organic matters, some of which can be excited and emit fluorescence signal. In order to degrade these organic matters, the pretreatment of the leachate is needed, which can improve the degradation performance of post-treatment process. Coagulation-flocculation is one of the important pretreatment processes to treat landfill leachate. Assessing the chemical compositions of landfill leachate is helpful in the understanding of their sources and fates as well as the mechanistic behaviors in the water environment. The present work aimed to use fluorescence excitation-emission matrix spectroscopy (EEMs) to characterize the chemical fractions of landfill leachate dissolved organic matter (DOM) in conjunction with parallel factor analysis (PARAFAC). Results showed that the DOM of landfill leachate tested in this study was identified resulting from microbial input, which included five typical characteristic peaks and four kinds of PARAFAC fractions. These fractions were mainly composed of hydrophobic macromolecule humic acid-like (HM-HA), hydrophilic intermediate molecular fulvic acid-like (HIM-FA), and hydrophilic small molecule protein-like substances (HSM-PS). HM-HA and HIM-FA were found to be easier to remove than HSM-PS. Further research on HSM-PS removal by coagulation-flocculation still needs to be improved.

Time-resolved laser fluorescence spectroscopy of organic ligands by europium: Fluorescence quenching and lifetime properties

NASA Astrophysics Data System (ADS)

Nouhi, A.; Hajjoul, H.; Redon, R.; Gagné, J. P.; Mounier, S.

2018-03-01

Time-resolved Laser Fluorescence Spectroscopy (TRLFS) has proved its usefulness in the fields of biophysics, life science and geochemistry to characterize the fluorescence probe molecule with its chemical environment. The purpose of this study is to demonstrate the applicability of this powerful technique combined with Steady-State (S-S) measurements. A multi-mode factor analysis, in particular CP/PARAFAC, was used to analyze the interaction between Europium (Eu) and Humic substances (HSs) extracted from Saint Lawrence Estuary in Canada. The Saint Lawrence system is a semi-enclosed water stream with connections to the Atlantic Ocean and is an excellent natural laboratory. CP/PARAFAC applied to fluorescence S-S data allows introspecting ligands-metal interactions and the one-site 1:1 modeling gives information about the stability constants. From the spectral signatures and decay lifetimes data given by TRLFS, one can deduce the fluorescence quenching which modifies the fluorescence and discuss its mechanisms. Results indicated a relatively strong binding ability between europium and humic substances samples (Log K value varies from 3.38 to 5.08 at pH 7.00). Using the Stern-Volmer plot, it has been concluded that static and dynamic quenching takes places in the case of salicylic acid and europium interaction while for HSs interaction only a static quenching is observed.

Effects of iron on optical properties of dissolved organic matter

USGS Publications Warehouse

Poulin, Brett; Ryan, Joseph N.; Aiken, George R.

2014-01-01

Iron is a source of interference in the spectroscopic analysis of dissolved organic matter (DOM); however, its effects on commonly employed ultraviolet and visible (UV–vis) light adsorption and fluorescence measurements are poorly defined. Here, we describe the effects of iron(II) and iron(III) on the UV–vis absorption and fluorescence of solutions containing two DOM fractions and two surface water samples. In each case, regardless of DOM composition, UV–vis absorption increased linearly with increasing iron(III). Correction factors were derived using iron(III) absorption coefficients determined at wavelengths commonly used to characterize DOM. Iron(III) addition increased specific UV absorbances (SUVA) and decreased the absorption ratios (E2:E3) and spectral slope ratios (SR) of DOM samples. Both iron(II) and iron(III) quenched DOM fluorescence at pH 6.7. The degree and region of fluorescence quenching varied with the iron:DOC concentration ratio, DOM composition, and pH. Regions of the fluorescence spectra associated with greater DOM conjugation were more susceptible to iron quenching, and DOM fluorescence indices were sensitive to the presence of both forms of iron. Analyses of the excitation–emission matrices using a 7- and 13-component parallel factor analysis (PARAFAC) model showed low PARAFAC sensitivity to iron addition.

Fluorescent properties of a hybrid cadmium sulfide-dendrimer nanocomposite and its quenching with nitromethane.

PubMed

Campos, Bruno B; Algarra, Manuel; Esteves da Silva, Joaquim C G

2010-01-01

A fluorescent hybrid cadmium sulphide quantum dots (QDs) dendrimer nanocomposite (DAB-CdS) synthesised in water and stable in aqueous solution is described. The dendrimer, DAB-G5 dendrimer (polypropylenimine tetrahexacontaamine) generation 5, a diaminobutene core with 64 amine terminal primary groups. The maximum of the excitation and emission spectra, Stokes' shift and the emission full width of half maximum of this nanocomposite are, respectively: 351, 535, 204 and 212 nm. The fluorescence time decay was complex and a four component decay time model originated a good fit (chi = 1.20) with the following lifetimes: tau (1) = 657 ps; tau (2) = 10.0 ns; tau (3) = 59.42 ns; and tau (4) = 265 ns. The fluorescence intensity of the nanocomposite is markedly quenched by the presence of nitromethane with a dynamic Stern-Volmer constant of 25 M(-1). The quenching profiles show that about 81% of the CdS QDs are located in the external layer of the dendrimer accessible to the quencher. PARAFAC analysis of the excitation emission matrices (EEM) acquired as function of the nitromethane concentration showed a trilinear data structure with only one linearly independent component describing the quenching which allows robust estimation of the excitation and emission spectra and of the quenching profiles. This water soluble and fluorescent nanocomposite shows a set of favourable properties to its use in sensor applications.

An optimization approach for fitting canonical tensor decompositions.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dunlavy, Daniel M.; Acar, Evrim; Kolda, Tamara Gibson

Tensor decompositions are higher-order analogues of matrix decompositions and have proven to be powerful tools for data analysis. In particular, we are interested in the canonical tensor decomposition, otherwise known as the CANDECOMP/PARAFAC decomposition (CPD), which expresses a tensor as the sum of component rank-one tensors and is used in a multitude of applications such as chemometrics, signal processing, neuroscience, and web analysis. The task of computing the CPD, however, can be difficult. The typical approach is based on alternating least squares (ALS) optimization, which can be remarkably fast but is not very accurate. Previously, nonlinear least squares (NLS) methodsmore » have also been recommended; existing NLS methods are accurate but slow. In this paper, we propose the use of gradient-based optimization methods. We discuss the mathematical calculation of the derivatives and further show that they can be computed efficiently, at the same cost as one iteration of ALS. Computational experiments demonstrate that the gradient-based optimization methods are much more accurate than ALS and orders of magnitude faster than NLS.« less

Fluorescence excitation-emission matrix (EEM) spectroscopy and cavity ring-down (CRD) absorption spectroscopy of oil-contaminated jet fuel using fiber-optic probes.

PubMed

Omrani, Hengameh; Barnes, Jack A; Dudelzak, Alexander E; Loock, Hans-Peter; Waechter, Helen

2012-06-21

Excitation emission matrix (EEM) and cavity ring-down (CRD) spectral signatures have been used to detect and quantitatively assess contamination of jet fuels with aero-turbine lubricating oil. The EEM spectrometer has been fiber-coupled to permit in situ measurements of jet turbine oil contamination of jet fuel. Parallel Factor (PARAFAC) analysis as well as Principal Component Analysis and Regression (PCA/PCR) were used to quantify oil contamination in a range from the limit of detection (10 ppm) to 1000 ppm. Fiber-loop cavity ring-down spectroscopy using a pulsed 355 nm laser was used to quantify the oil contamination in the range of 400 ppm to 100,000 ppm. Both methods in combination therefore permit the detection of oil contamination with a linear dynamic range of about 10,000.

Suitability of Organic Matter Surrogates to Predict Trihalomethane Formation in Drinking Water Sources

PubMed Central

Pifer, Ashley D.; Fairey, Julian L.

2014-01-01

Abstract Broadly applicable disinfection by-product (DBP) precursor surrogate parameters could be leveraged at drinking water treatment plants (DWTPs) to curb formation of regulated DBPs, such as trihalomethanes (THMs). In this study, dissolved organic carbon (DOC), ultraviolet absorbance at 254 nm (UV254), fluorescence excitation/emission wavelength pairs (IEx/Em), and the maximum fluorescence intensities (FMAX) of components from parallel factor (PARAFAC) analysis were evaluated as total THM formation potential (TTHMFP) precursor surrogate parameters. A diverse set of source waters from eleven DWTPs located within watersheds underlain by six different soil orders were coagulated with alum at pH 6, 7, and 8, resulting in 44 sample waters. DOC, UV254, IEx/Em, and FMAX values were measured to characterize dissolved organic matter in raw and treated waters and THMs were quantified following formation potential tests with free chlorine. For the 44 sample waters, the linear TTHMFP correlation with UV254 was stronger (r2=0.89) than I240/562 (r2=0.81, the strongest surrogate parameter from excitation/emission matrix pair picking), FMAX from a humic/fulvic acid-like PARAFAC component (r2=0.78), and DOC (r2=0.75). Results indicate that UV254 was the most accurate TTHMFP precursor surrogate parameter assessed for a diverse group of raw and alum-coagulated waters. PMID:24669183

Associations Between the Molecular and Optical Properties of Dissolved Organic Matter in the Florida Everglades, a Model Coastal Wetland System

PubMed Central

Wagner, Sasha; Jaffé, Rudolf; Cawley, Kaelin; Dittmar, Thorsten; Stubbins, Aron

2015-01-01

Optical properties are easy-to-measure proxies for dissolved organic matter (DOM) composition, source, and reactivity. However, the molecular signature of DOM associated with such optical parameters remains poorly defined. The Florida coastal Everglades is a subtropical wetland with diverse vegetation (e.g., sawgrass prairies, mangrove forests, seagrass meadows) and DOM sources (e.g., terrestrial, microbial, and marine). As such, the Everglades is an excellent model system from which to draw samples of diverse origin and composition to allow classically-defined optical properties to be linked to molecular properties of the DOM pool. We characterized a suite of seasonally- and spatially-collected DOM samples using optical measurements (EEM-PARAFAC, SUVA254, S275−295, S350−400, SR, FI, freshness index, and HIX) and ultrahigh resolution mass spectrometry (FTICR-MS). Spearman's rank correlations between FTICR-MS signal intensities of individual molecular formulae and optical properties determined which molecular formulae were associated with each PARAFAC component and optical index. The molecular families that tracked with the optical indices were generally in agreement with conventional biogeochemical interpretations. Therefore, although they represent only a small portion of the bulk DOM pool, absorbance, and fluorescence measurements appear to be appropriate proxies for the aquatic cycling of both optically-active and associated optically-inactive DOM in coastal wetlands. PMID:26636070

Fluorescence quenching effects of antibiotics on the main components of dissolved organic matter.

PubMed

Yan, Peng-Fei; Hu, Zhen-Hu; Yu, Han-Qing; Li, Wei-Hua; Liu, Li

2016-03-01

Dissolved organic matter (DOM) in wastewater can be characterized using fluorescence excitation-emission matrix and parallel factor (EEM-PARAFAC) analysis. Wastewater from animal farms or pharmaceutical plants usually contains high concentration of antibiotics. In this study, the quenching effect of antibiotics on the typical components of DOM was explored using fluorescence EEM-PARAFAC analysis. Four antibiotics (roxarsone, sulfaquinoxaline sodium, oxytetracycline, and erythromycin) at the concentration of 0.5∼4.0 mg/L and three typical components of DOM (tyrosine, tryptophan, and humic acid) were selected. Fluorescence quenching effects were observed with the addition of antibiotics. Among these four antibiotics, roxarsone (2.9∼20.2 %), sulfaquinoxaline sodium (0∼32.0 %), and oxytetracycline (0∼41.8 %) led to a stronger quenching effect than erythromycin (0∼8.0 %). From the side of DOM, tyrosine and tryptophan (0.5∼41.8 %) exhibited a similar quenching effect, but they were higher than humic acids (0∼20.2 %) at the same concentration of antibiotics. For humic acid, a significant quenching effect was observed only with the addition of roxarsone. This might be the first report about the fluorescence quenching effect caused by antibiotics. The results from this study confirmed the interference of antibiotics on the fluorescence intensity of the main components of DOM and highlighted the importance of correcting fluorescence data in the wastewater containing antibiotics.

Changes in dissolved organic matter quality in a northern hardwood forest revealed by excitation emission spectroscopy

NASA Astrophysics Data System (ADS)

Saraceno, J.; Shanley, J. B.; Pellerin, B. A.; Hansen, A. M.

2016-12-01

Changes in dissolved organic matter (DOM) quality may result from unusual and extreme precipitation patterns such as floods and droughts. In order to study DOM quality changes, we collected several hundred surface water samples during the past eight years from the W-9 watershed of the Sleepers River Research Watershed in Danville, Vermont for optical analysis of dissolved organic matter. We present the results of parallel factor (PARAFAC) and principal component analysis (PCA) on excitation emission matrices (EEMs). This analysis revealed that peaks T, C and M as identified by PARAFAC were the most prominent EEM features. The intensity of these peaks varied on inter-annual, seasonal and event time periods and these shifts reflect changes in DOM quality. Likely drivers of this variability in DOM chemistry are seasonal shifts in flow paths, antecedent moisture conditions, and precipitation duration and intensity. For example, during events, the relative proportion of protein-like, peak T fluorophores increased, likely from flushing of fresh polyphenols from surficial and shallow flow paths. During the winter, when groundwater dominates flow, EEMs were strong in humic-like peak C and peak M fluorophores, reflecting deeper soil sources and longer flow paths. Our analyses will reveal how DOM quality responds to climatic drivers, and thus how we can expect DOM quality to evolve under projected climate change scenarios.

Speciation and Sources of Brown Carbon in Precipitation at Seoul, Korea: Insights from Excitation-Emission Matrix Spectroscopy and Carbon Isotopic Analysis.

PubMed

Yan, Ge; Kim, Guebuem

2017-10-17

Brown carbon (BrC) plays a significant role in the Earth's radiative balance, yet its sources and chemical composition remain poorly understood. In this work, we investigated BrC in the atmospheric environment of Seoul by characterizing dissolved organic matter in precipitation using excitation-emission matrix (EEM) fluorescence spectroscopy coupled with parallel factor analysis (PARAFAC). The two independent fluorescent components identified by PARAFAC were attributed to humic-like substance (HULIS) and biologically derived material based on their significant correlations with measured HULIS isolated using solid-phase extraction and total hydrolyzable tyrosine. The year-long observation shows that HULIS contributes to 66 ± 13% of total fluorescence intensity of our samples on average. By using dual carbon ( 13 C and 14 C) isotopic analysis conducted on isolated HULIS, the HULIS fraction of BrC was found to be primarily derived from biomass burning and emission of terrestrial biogenic gases and particles (>70%), with minor contributions from fossil-fuel combustion. The knowledge derived from this study could contribute to the establishment of a characterizing system of BrC components identified by EEM spectroscopy. Our work demonstrates that, EEM fluorescence spectroscopy is a powerful tool in BrC study, on the basis of its chromophore resolving power, allowing investigation into individual components of BrC by other organic matter characterization techniques.

New approach for rapid assessment of trophic status of Yellow Sea and East China Sea using easy-to-measure parameters

NASA Astrophysics Data System (ADS)

Kong, Xianyu; Liu, Yanfang; Jian, Huimin; Su, Rongguo; Yao, Qingzhen; Shi, Xiaoyong

2017-10-01

To realize potential cost savings in coastal monitoring programs and provide timely advice for marine management, there is an urgent need for efficient evaluation tools based on easily measured variables for the rapid and timely assessment of estuarine and offshore eutrophication. In this study, using parallel factor analysis (PARAFAC), principal component analysis (PCA), and discriminant function analysis (DFA) with the trophic index (TRIX) for reference, we developed an approach for rapidly assessing the eutrophication status of coastal waters using easy-to-measure parameters, including chromophoric dissolved organic matter (CDOM), fluorescence excitation-emission matrices, CDOM UV-Vis absorbance, and other water-quality parameters (turbidity, chlorophyll a, and dissolved oxygen). First, we decomposed CDOM excitation-emission matrices (EEMs) by PARAFAC to identify three components. Then, we applied PCA to simplify the complexity of the relationships between the water-quality parameters. Finally, we used the PCA score values as independent variables in DFA to develop a eutrophication assessment model. The developed model yielded classification accuracy rates of 97.1%, 80.5%, 90.3%, and 89.1% for good, moderate, and poor water qualities, and for the overall data sets, respectively. Our results suggest that these easy-to-measure parameters could be used to develop a simple approach for rapid in-situ assessment and monitoring of the eutrophication of estuarine and offshore areas.

Adaptive handling of Rayleigh and Raman scatter of fluorescence data based on evaluation of the degree of spectral overlap

NASA Astrophysics Data System (ADS)

Hu, Yingtian; Liu, Chao; Wang, Xiaoping; Zhao, Dongdong

2018-06-01

At present the general scatter handling methods are unsatisfactory when scatter and fluorescence seriously overlap in excitation emission matrix. In this study, an adaptive method for scatter handling of fluorescence data is proposed. Firstly, the Raman scatter was corrected by subtracting the baseline of deionized water which was collected in each experiment to adapt to the intensity fluctuations. Then, the degrees of spectral overlap between Rayleigh scatter and fluorescence were classified into three categories based on the distance between the spectral peaks. The corresponding algorithms, including setting to zero, fitting on single or both sides, were implemented after the evaluation of the degree of overlap for individual emission spectra. The proposed method minimized the number of fitting and interpolation processes, which reduced complexity, saved time, avoided overfitting, and most importantly assured the authenticity of data. Furthermore, the effectiveness of this procedure on the subsequent PARAFAC analysis was assessed and compared to Delaunay interpolation by conducting experiments with four typical organic chemicals and real water samples. Using this method, we conducted long-term monitoring of tap water and river water near a dyeing and printing plant. This method can be used for improving adaptability and accuracy in the scatter handling of fluorescence data.

Estuarial fingerprinting through multidimensional fluorescence and multivariate analysis.

PubMed

Hall, Gregory J; Clow, Kerin E; Kenny, Jonathan E

2005-10-01

As part of a strategy for preventing the introduction of aquatic nuisance species (ANS) to U.S. estuaries, ballast water exchange (BWE) regulations have been imposed. Enforcing these regulations requires a reliable method for determining the port of origin of water in the ballast tanks of ships entering U.S. waters. This study shows that a three-dimensional fluorescence fingerprinting technique, excitation emission matrix (EEM) spectroscopy, holds great promise as a ballast water analysis tool. In our technique, EEMs are analyzed by multivariate classification and curve resolution methods, such as N-way partial least squares Regression-discriminant analysis (NPLS-DA) and parallel factor analysis (PARAFAC). We demonstrate that classification techniques can be used to discriminate among sampling sites less than 10 miles apart, encompassing Boston Harbor and two tributaries in the Mystic River Watershed. To our knowledge, this work is the first to use multivariate analysis to classify water as to location of origin. Furthermore, it is shown that curve resolution can show seasonal features within the multidimensional fluorescence data sets, which correlate with difficulty in classification.

A novel second-order standard addition analytical method based on data processing with multidimensional partial least-squares and residual bilinearization.

PubMed

Lozano, Valeria A; Ibañez, Gabriela A; Olivieri, Alejandro C

2009-10-05

In the presence of analyte-background interactions and a significant background signal, both second-order multivariate calibration and standard addition are required for successful analyte quantitation achieving the second-order advantage. This report discusses a modified second-order standard addition method, in which the test data matrix is subtracted from the standard addition matrices, and quantitation proceeds via the classical external calibration procedure. It is shown that this novel data processing method allows one to apply not only parallel factor analysis (PARAFAC) and multivariate curve resolution-alternating least-squares (MCR-ALS), but also the recently introduced and more flexible partial least-squares (PLS) models coupled to residual bilinearization (RBL). In particular, the multidimensional variant N-PLS/RBL is shown to produce the best analytical results. The comparison is carried out with the aid of a set of simulated data, as well as two experimental data sets: one aimed at the determination of salicylate in human serum in the presence of naproxen as an additional interferent, and the second one devoted to the analysis of danofloxacin in human serum in the presence of salicylate.

Spectroscopic characterization of dissolved organic matter in coking wastewater during bio-treatment: full-scale plant study.

PubMed

Xu, Ronghua; Ou, Huase; Yu, Xubiao; He, Runsheng; Lin, Chong; Wei, Chaohai

2015-01-01

This paper taking a full-scale coking wastewater (CWW) treatment plant as a case study aimed to characterize removal behaviors of dissolved organic matter (DOM) by UV spectra and fluorescence excitation-emission matrix-parallel factor analysis (PARAFAC), and investigate the correlations between spectroscopic indices and water quality parameters. Efficient removal rates of chemical oxygen demand (COD), dissolved organic carbon (DOC) and total nitrogen (TN) after the bio-treatment were 91.3%, 87.3% and 69.1%, respectively. UV270 was proven to be a stable UV absorption peak of CWW that could reflect the mixture of phenols, heterocyclics, polynuclear aromatic hydrocarbons and their derivatives. Molecular weight and aromaticity were increased, and also the content of polar functional groups was greatly reduced after bio-treatment. Three fluorescent components were identified by PARAFAC: C1 (tyrosine-like), C2 (tryptophan-like) and C3 (humic-like). The removal rate of protein-like was higher than that of humic-like and C1 was identified as biodegradable substance. Correlation analysis showed UV270 had an excellent correlation with COD (r=0.921, n=60, P<0.01) and DOC (r=0.959, n=60, P<0.01) and significant correlation (r=0.875, n=60, P<0.01) was also found between C2 and TN. Therefore, spectroscopic characterization could provide novel insights into removal behaviors of DOM and potential to monitor water quality real-time during CWW bio-treatment.

Interaction of Benzo(a)pyrene with the natural organic matter of soil using three-dimensional (3-D) fluorescence spectroscopy with Parallel Factor Analysis

NASA Astrophysics Data System (ADS)

El Fallah, Rawa

2017-04-01

Benzo(a)pyrene (BaP) is a polycyclic aromatic hydrocarbon arising mainly from the incomplete combustion of organic material. It is toxic and has mutagenic and carcinogenic properties. It is classified as a priority pollutant by The United States Environmental Protection Agency (US-EPA). After it's emission in the atmosphere, and due to its physico-chemical properties, BaP will be deposited in the soil. Its aromaticity gives it the capacity to be studied by fluorescence spectroscopy so that of the Natural Organic Matter (NOM). In this study we used fluorescence excitation-emission-matrix (FEEM) with Parallel Factor analysis (PARAFAC) to study the interaction between NOM of soil and BaP. Soil sample was treated with Tetrasodium pyrophosphate along with Sodium hydroxide to obtain the Humic Substances, which afterwards were physically fractioned under acidic pH into solid Humic Acid and liquid Fulvic Acid. Three concentrations of BaP solution were added to each soil fraction. We compared the results of PARAFAC analysis of the samples containing BaP and the original NOM fractions. In the samples containing BaP, four fluorophores (components) were found, the fourth identified as BaP. Out of the three other fluorophores characteristic of NOM, two were found similar in all NOM fractions whereas only one fluorophore had some variations in its spectral characteristics. The presence of BaP changed the fluorescence of NOM. These modifications were depending on the type of soil fraction.

Spatial and seasonal variations in the composition of dissolved organic matter in a tropical catchment: the Lower Kinabatangan River, Sabah, Malaysia.

PubMed

Harun, Sahana; Baker, Andy; Bradley, Chris; Pinay, Gilles

2016-01-01

Dissolved organic matter (DOM) was characterised in water samples sampled in the Lower Kinabatangan River Catchment, Sabah, Malaysia between October 2009 and May 2010. This study aims at: (i) distinguishing between the quality of DOM in waters draining palm oil plantations (OP), secondary forests (SF) and coastal swamps (CS) and, (ii) identifying the seasonal variability of DOM quantity and quality. Surface waters were sampled during fieldwork campaigns that spanned the wet and dry seasons. DOM was characterised optically by using the fluorescence Excitation Emission Matrix (EEM), the absorption coefficient at 340 nm and the spectral slope coefficient (S). Parallel Factor Analysis (PARAFAC) was undertaken to assess the DOM composition from EEM spectra and five terrestrial derived components were identified: (C1, C2, C3, C4 and C5). Components C1 and C4 contributed the most to DOM fluorescence in all study areas during both the wet and dry seasons. The results suggest that component C4 could be a significant (and common) PARAFAC signal found in similar catchments. Peak M (C2 and C3) was dominant in all samples collected during wet and dry seasons, which could be anthropogenic in origin given the active land use change in the study area. In conclusion, there were significant seasonal and spatial variations in DOM which demonstrated the effects of land use cover and precipitation amounts in the Kinabatangan catchment.

UV-induced photochemical heterogeneity of dissolved and attached organic matter associated with cyanobacterial blooms in a eutrophic freshwater lake.

PubMed

Xu, Huacheng; Jiang, Helong

2013-11-01

Cyanobacterial blooms represent a significant ecological and human health problem worldwide. In aquatic environments, cyanobacterial blooms are actually surrounded by dissolved organic matter (DOM) and attached organic matter (AOM) that bind with algal cells. In this study, DOM and AOM fractionated from blooming cyanobacteria in a eutrophic freshwater lake (Lake Taihu, China) were irradiated with a polychromatic UV lamp, and the photochemical heterogeneity was investigated using fluorescence excitation-emission matrix (EEM)-parallel factor (PARAFAC) analysis and synchronous fluorescence (SF)-two dimensional correlation spectroscopy (2DCOS). It was shown that a 6-day UV irradiation caused more pronounced mineralization for DOM than AOM (59.7% vs. 41.9%). The EEM-PARAFAC analysis identified one tyrosine-, one humic-, and two tryptophan-like components in both DOM and AOM, and high component photodegradation rates were observed for DOM versus AOM (k > 0.554 vs. <0.519). Moreover, SF-2DCOS found that the photodegradation of organic matters followed the sequence of tyrosine-like > humic-like > tryptophan-like substances. Humic-like substances promoted the indirect photochemical reactions, and were responsible for the higher photochemical rate for DOM. The lower photodegradation of AOM benefited the integrality of cells in cyanobacterial blooms against the negative impact of UV irradiation. Therefore, the photochemical behavior of organic matter was related to the adaptation of enhanced-duration cyanobacterial blooms in aquatic environments. Copyright © 2013 Elsevier Ltd. All rights reserved.

Influence of intermittent stream connectivity on water quality and salmonid survivorship.

NASA Astrophysics Data System (ADS)

Hildebrand, J.; Woelfle-Erskine, C. A.; Larsen, L.

2014-12-01

Anthropogenic stress and climate change are causing an increasing number of California streams to become intermittent and are driving earlier and more severe summertime drying. The extent to which emerging water conservation alternatives impact flows or habitat quality (e.g. temperature, DO) for salmonids remains poorly understood. Here, we investigate the proximal drivers of salmonid mortality over a range of connectivity conditions during summertime intermittency in Salmon Creek watershed, Sonoma County, CA. Through extensive sampling in paired subwatersheds over a period of two years, we tested the hypothesis that accumulation of readily bioavailable DOC in poorly flushed pools drives DO decline associated with loss of salmonids. We then traced the origin and flow pathways of DOC throughout the watershed using Parallel Factor Analysis (PARAFAC). We obtained samples for DOC and stable isotope analyses at monthly intervals from 20 piezometers and surface water in the study reaches and from private wells and springs distributed throughout the watersheds. We also obtained in situ DO, conductivity and pH readings within stream study reaches. We determined DOC quality by SUVA (specific UV absorbance) and fluorescence index. We calculated stream metabolism rates using the single station method. In pools instrumented with DO sensors, we compared changing DOC quality during the summer months to changes in DO concentrations and stream metabolism. Our results show that the duration of complete disconnection of pools during the summer months and stream metabolic rates are positively correlated with salmonid mortality. Furthermore, our results indicate that salmonid mortality is greatest in disconnected pools with low DOC fluorescence indices and high SUVA values, indicative of terrestrially derived DOC and little or no groundwater inflow. Conversely low salmonid mortality was found in disconnected pools with high fluorescence index and low SUVA, indicative of microbially derived DOC. These pools showed clear signs of hyporheic inflow during summertime drying despite complete surficial disconnection. PARAFAC analysis pinpointed groundwater sources of hyporheic flow in the watershed, suggesting that targeted aquifer recharge may contribute to salmonid recovery by augmenting flow in summer refugia.

Spectral characterization of the fluorescent components present in humic substances, fulvic acid and humic acid mixed with pure benzo(a)pyrene solution.

PubMed

El Fallah, Rawa; Rouillon, Régis; Vouvé, Florence

2018-06-15

The fate of benzo(a)pyrene (BaP), a ubiquitous contaminant reported to be persistent in the environment, is largely controlled by its interactions with the soil organic matter. In the present study, the spectral characteristics of fluorophores present in the physical fractions of the soil organic matter were investigated in the presence of pure BaP solution. After extraction of humic substances (HSs), and their fractionation into fluvic acid (FA) and humic acid (HA), two fluorescent compounds (C 1 and C 2 ) were identified and characterized in each physical soil fraction, by means of fluorescence excitation-emission matrices (FEEMs) and Parallel Factor Analysis (PARAFAC). Then, to each type of fraction having similar DOC content, was added an increasing volume of pure BaP solution in attempt to assess the behavior of BaP with the fluorophores present in each one. The application of FEEMs-PARAFAC method validated a three-component model that consisted of the two resulted fluorophores from HSs, FA and HA (C 1 and C 2 ) and a BaP-like fluorophore (C 3 ). Spectral modifications were noted for components C 2 HSs (C 2 in humic substances fraction) (λex/λem: 420/490-520 nm), C 2 FA (C 2 in fulvic acid fraction) (λex/λem: 400/487(517) nm) and C 1 HA (C 1 in humic acid fraction) (λex/λem: 350/452(520) nm). We explored the impact of increasing the volume of the added pure BaP solution on the scores of the fluorophores present in the soil fractions. It was found that the scores of C 2 HSs, C 2 FA, and C 1 HA increased when the volume of the added pure BaP solution increased. Superposition of the excitation spectra of these fluorophores with the emission spectrum of BaP showed significant overlaps that might explain the observed interactions between BaP and the fluorescent compounds present in SOM physical fractions. Copyright © 2018 Elsevier B.V. All rights reserved.

Spectral characterization of the fluorescent components present in humic substances, fulvic acid and humic acid mixed with pure benzo(a)pyrene solution

NASA Astrophysics Data System (ADS)

El Fallah, Rawa; Rouillon, Régis; Vouvé, Florence

2018-06-01

The fate of benzo(a)pyrene (BaP), a ubiquitous contaminant reported to be persistent in the environment, is largely controlled by its interactions with the soil organic matter. In the present study, the spectral characteristics of fluorophores present in the physical fractions of the soil organic matter were investigated in the presence of pure BaP solution. After extraction of humic substances (HSs), and their fractionation into fluvic acid (FA) and humic acid (HA), two fluorescent compounds (C1 and C2) were identified and characterized in each physical soil fraction, by means of fluorescence excitation-emission matrices (FEEMs) and Parallel Factor Analysis (PARAFAC). Then, to each type of fraction having similar DOC content, was added an increasing volume of pure BaP solution in attempt to assess the behavior of BaP with the fluorophores present in each one. The application of FEEMs-PARAFAC method validated a three-component model that consisted of the two resulted fluorophores from HSs, FA and HA (C1 and C2) and a BaP-like fluorophore (C3). Spectral modifications were noted for components C2HSs (C2 in humic substances fraction) (λex/λem: 420/490-520 nm), C2FA (C2 in fulvic acid fraction) (λex/λem: 400/487(517) nm) and C1HA (C1 in humic acid fraction) (λex/λem: 350/452(520) nm). We explored the impact of increasing the volume of the added pure BaP solution on the scores of the fluorophores present in the soil fractions. It was found that the scores of C2HSs, C2FA, and C1HA increased when the volume of the added pure BaP solution increased. Superposition of the excitation spectra of these fluorophores with the emission spectrum of BaP showed significant overlaps that might explain the observed interactions between BaP and the fluorescent compounds present in SOM physical fractions.

Exploring the potential of DOC fluorescence as proxy for groundwater contamination by pesticides

NASA Astrophysics Data System (ADS)

Farlin, Julien; Gallé, Tom; Bayerle, Michael; Pittois, Denis; Huck, viola

2017-04-01

Of the different water quality surrogates the fluorescence of dissolved organic content (FDOC) appears particularly promising due to its sensitivity and specificity. A complete spectrum of FDOC can be obtained using bench top instruments scanning a spectral space going from short wavelength UV to visible blue, yielding a so-called an excitation-emission matrix (EEM). The raw EEM can be either used directly for correlation analysis with the variable of interest, or first decomposed into underlying elements corresponding to different groups of organic compounds displaying similar properties using multiway techniques such as Parallel factor analysis (PARAFAC). Fluorescence spectroscopy has up to now only rarely been applied specifically to groundwater environments. The objective of the project was to explore systematically the possibilities offered by FDOC and PARAFAC for the assessment of groundwater contamination by pesticides, taking into account the transit time from the pesticide source to the groundwater outlet. Three sites corresponding to different transit times were sampled: -one spring regularly contaminated by surface water from a nearby stream (sub-daily to daily response to fast-flow generating storm events) -one spring displaying a weekly to monthly response to interflow -sampling along a flowline consisting of a series of springs and an observation well situated upgradient with mean transit times difference of several years Preliminary results show that a three component PARAFAC model is sufficient to decompose the raw EEMs, which is less than the seven or eight component models often encountered in surface water studies. For the first site, one component in the protein-like region 275(excitation)/310 (emission) nm measured in the stream samples was filtrered completely by the aquifer and did not appear in the spring samples. The other two components followed roughly the trend of the DOC and pesticide breakthrough. For the second site, soil sampling of the agricultural plots and DOC extraction also allowed to characterise the spectral signature of the pollution source. A humic-like component (250/450 nm) was correlated with the breakthrough of recent soil water and pesticide concentration. Lastly, the fluorescence intensity of the different components for the third sampling site showed a decrease proportional to the decrease in DOC concentration between the observation well and the springs caused either by dilution, degradation or both. This lack of change in the spectral pattern along a flow line seems to indicate that labile soil DOC fractions have already been degraded by the time water reaches the observation well.

Partial least-squares with residual bilinearization for the spectrofluorimetric determination of pesticides. A solution of the problems of inner-filter effects and matrix interferents.

PubMed

Piccirilli, Gisela N; Escandar, Graciela M

2006-09-01

This paper demonstrates for the first time the power of a chemometric second-order algorithm for predicting, in a simple way and using spectrofluorimetric data, the concentration of analytes in the presence of both the inner-filter effect and unsuspected species. The simultaneous determination of the systemic fungicides carbendazim and thiabendazole was achieved and employed for the discussion of the scopes of the applied second-order chemometric tools: parallel factor analysis (PARAFAC) and partial least-squares with residual bilinearization (PLS/RBL). The chemometric study was performed using fluorescence excitation-emission matrices obtained after the extraction of the analytes over a C18-membrane surface. The ability of PLS/RBL to recognize and overcome the significant changes produced by thiabendazole in both the excitation and emission spectra of carbendazim is demonstrated. The high performance of the selected PLS/RBL method was established with the determination of both pesticides in artificial and real samples.

Robust digital image watermarking using distortion-compensated dither modulation

NASA Astrophysics Data System (ADS)

Li, Mianjie; Yuan, Xiaochen

2018-04-01

In this paper, we propose a robust feature extraction based digital image watermarking method using Distortion- Compensated Dither Modulation (DC-DM). Our proposed local watermarking method provides stronger robustness and better flexibility than traditional global watermarking methods. We improve robustness by introducing feature extraction and DC-DM method. To extract the robust feature points, we propose a DAISY-based Robust Feature Extraction (DRFE) method by employing the DAISY descriptor and applying the entropy calculation based filtering. The experimental results show that the proposed method achieves satisfactory robustness under the premise of ensuring watermark imperceptibility quality compared to other existing methods.

Influence of Flood and Drought Conditions on Dissolved Organic Matter Distribution in Northern Gulf of Mexico Shelf Waters

NASA Astrophysics Data System (ADS)

Shank, G. C.; Liu, Q.; Patterson, L.; Kowalczuk, P.

2012-12-01

DOC, CDOM, and EEM PARAFAC analyses were used to examine DOM distribution along the Louisiana (LA) and Texas (TX) continental shelves in the northern Gulf of Mexico during cruises in May and August of the 2011 Mississippi basin flood year, and May, June, and August of the 2012 Mississippi basin drought year. For both 2011 and 2012, CDOM and DOC levels were well-correlated with salinity on the LA shelf. However, the mixing curves for each parameter were markedly different between 2011 and 2012 and CDOM:DOC ratios, indicative of terrestrial organic matter inputs, were much higher during 2011 than during 2012. EEM PARAFAC results confirmed a much higher terrestrial DOM signature in LA shelf waters for 2011, but also a higher terrestrial DOM signature for TX waters in 2012 as the drought in the western Gulf region subsided. CDOM:DOC ratios were anomalously high offshore of Atchafalaya Bay and the Breton-Chandeleur Sound complex indicating coastal wetlands augment the terrestrial DOM discharged through the Mississippi and Atchafalaya Rivers. At several sites along the LA and TX shelves during both 2011 and 2012, CDOM was higher near bottom than at mid-depth without concomitant DOC increases, possibly due to microbial processing of settling phytoplankton cells, sedimentary fluxes, and benthic algal activity which was especially prevalent along the TX shelf. Results from simulated solar radiation experiments indicate that shelf water CDOM readily photobleaches with losses of >50% likely in surface waters over the summer, while DOC photooxidation is at least an order of magnitude slower than CDOM photobleaching.;

Distinguishing dissolved organic matter at its origin: size and optical properties of leaf-litter leachates.

PubMed

Cuss, C W; Guéguen, C

2013-09-01

Dissolved organic matter (DOM) was leached from eight distinct samples of leaves taken from six distinct trees (red maple, bur oak at three times of the year, two sugar maple and two white spruce trees from disparate soil types). Multiple samples were taken over 72-96h of leaching. The size and optical properties of leachates were assessed using asymmetrical flow field-flow fractionation (AF4) coupled to diode-array ultraviolet/visible absorbance and excitation-emission matrix fluorescence detectors (EEM). The fluorescence of unfractionated samples was also analyzed. EEMs were analyzed using parallel factor analysis (PARAFAC) and principal component analysis (PCA) of proportional component loadings. Both the unfractionated and AF4-fractionated leachates had distinct size and optical properties. The 95% confidence ranges for molecular weight distributions were determined as: 210-440Da for spruce, 540-920Da for sugar maple, 630-800Da for spring oak leaves, 930-950Da for senescent oak, 1490-1670 for senescent red maple, and 3430-4270Da for oak leaves that were collected from the ground after spring thaw. In most cases the fluorescence properties of leachates were different for individuals from different soil types and across seasons; however, PCA of PARAFAC loadings revealed that the observed distinctiveness was chiefly species-based. Strong correlations were found between the molecular weight distribution of both unfractionated and fractionated leachates and their principal component loadings (R(2)=0.85 and 0.95, respectively). It is concluded that results support a species-based origin for differences in optical properties. Copyright © 2013 Elsevier Ltd. All rights reserved.

Coupling asymmetric flow-field flow fractionation and fluorescence parallel factor analysis reveals stratification of dissolved organic matter in a drinking water reservoir.

PubMed

Pifer, Ashley D; Miskin, Daniel R; Cousins, Sarah L; Fairey, Julian L

2011-07-08

Using asymmetrical flow field-flow fractionation (AF4) and fluorescence parallel factor analysis (PARAFAC), we showed physicochemical properties of chromophoric dissolved organic matter (CDOM) in the Beaver Lake Reservoir (Lowell, AR) were stratified by depth. Sampling was performed at a drinking water intake structure from May to July 2010 at three depths (3-, 10-, and 18-m) below the water surface. AF4-fractograms showed that the CDOM had diffusion coefficient peak maximums between 3.5 and 2.8 x 10⁻⁶ cm² s⁻¹, which corresponded to a molecular weight range of 680-1950 Da and a size of 1.6-2.5 nm. Fluorescence excitation-emission matrices of whole water samples and AF4-generated fractions were decomposed with a PARAFAC model into five principal components. For the whole water samples, the average total maximum fluorescence was highest for the 10-m depth samples and lowest (about 40% less) for 18-m depth samples. While humic-like fluorophores comprised the majority of the total fluorescence at each depth, a protein-like fluorophore was in the least abundance at the 10-m depth, indicating stratification of both total fluorescence and the type of fluorophores. The results present a powerful approach to investigate CDOM properties and can be extended to investigate CDOM reactivity, with particular applications in areas such as disinfection byproduct formation and control and evaluating changes in drinking water source quality driven by climate change. Copyright © 2010 Elsevier B.V. All rights reserved.

Differentiation of cumin seeds using a metal-oxide based gas sensor array in tandem with chemometric tools.

PubMed

Ghasemi-Varnamkhasti, Mahdi; Amiri, Zahra Safari; Tohidi, Mojtaba; Dowlati, Majid; Mohtasebi, Seyed Saeid; Silva, Adenilton C; Fernandes, David D S; Araujo, Mário C U

2018-01-01

Cumin is a plant of the Apiaceae family (umbelliferae) which has been used since ancient times as a medicinal plant and as a spice. The difference in the percentage of aromatic compounds in cumin obtained from different locations has led to differentiation of some species of cumin from other species. The quality and price of cumin vary according to the specie and may be an incentive for the adulteration of high value samples with low quality cultivars. An electronic nose simulates the human olfactory sense by using an array of sensors to distinguish complex smells. This makes it an alternative for the identification and classification of cumin species. The data, however, may have a complex structure, difficult to interpret. Given this, chemometric tools can be used to manipulate data with two-dimensional structure (sensor responses in time) obtained by using electronic nose sensors. In this study, an electronic nose based on eight metal oxide semiconductor sensors (MOS) and 2D-LDA (two-dimensional linear discriminant analysis), U-PLS-DA (Partial least square discriminant analysis applied to the unfolded data) and PARAFAC-LDA (Parallel factor analysis with linear discriminant analysis) algorithms were used in order to identify and classify different varieties of both cultivated and wild black caraway and cumin. The proposed methodology presented a correct classification rate of 87.1% for PARAFAC-LDA and 100% for 2D-LDA and U-PLS-DA, indicating a promising strategy for the classification different varieties of cumin, caraway and other seeds. Copyright © 2017 Elsevier B.V. All rights reserved.

Identifying key nodes in multilayer networks based on tensor decomposition.

PubMed

Wang, Dingjie; Wang, Haitao; Zou, Xiufen

2017-06-01

The identification of essential agents in multilayer networks characterized by different types of interactions is a crucial and challenging topic, one that is essential for understanding the topological structure and dynamic processes of multilayer networks. In this paper, we use the fourth-order tensor to represent multilayer networks and propose a novel method to identify essential nodes based on CANDECOMP/PARAFAC (CP) tensor decomposition, referred to as the EDCPTD centrality. This method is based on the perspective of multilayer networked structures, which integrate the information of edges among nodes and links between different layers to quantify the importance of nodes in multilayer networks. Three real-world multilayer biological networks are used to evaluate the performance of the EDCPTD centrality. The bar chart and ROC curves of these multilayer networks indicate that the proposed approach is a good alternative index to identify real important nodes. Meanwhile, by comparing the behavior of both the proposed method and the aggregated single-layer methods, we demonstrate that neglecting the multiple relationships between nodes may lead to incorrect identification of the most versatile nodes. Furthermore, the Gene Ontology functional annotation demonstrates that the identified top nodes based on the proposed approach play a significant role in many vital biological processes. Finally, we have implemented many centrality methods of multilayer networks (including our method and the published methods) and created a visual software based on the MATLAB GUI, called ENMNFinder, which can be used by other researchers.

Identifying key nodes in multilayer networks based on tensor decomposition

NASA Astrophysics Data System (ADS)

Wang, Dingjie; Wang, Haitao; Zou, Xiufen

2017-06-01

The identification of essential agents in multilayer networks characterized by different types of interactions is a crucial and challenging topic, one that is essential for understanding the topological structure and dynamic processes of multilayer networks. In this paper, we use the fourth-order tensor to represent multilayer networks and propose a novel method to identify essential nodes based on CANDECOMP/PARAFAC (CP) tensor decomposition, referred to as the EDCPTD centrality. This method is based on the perspective of multilayer networked structures, which integrate the information of edges among nodes and links between different layers to quantify the importance of nodes in multilayer networks. Three real-world multilayer biological networks are used to evaluate the performance of the EDCPTD centrality. The bar chart and ROC curves of these multilayer networks indicate that the proposed approach is a good alternative index to identify real important nodes. Meanwhile, by comparing the behavior of both the proposed method and the aggregated single-layer methods, we demonstrate that neglecting the multiple relationships between nodes may lead to incorrect identification of the most versatile nodes. Furthermore, the Gene Ontology functional annotation demonstrates that the identified top nodes based on the proposed approach play a significant role in many vital biological processes. Finally, we have implemented many centrality methods of multilayer networks (including our method and the published methods) and created a visual software based on the MATLAB GUI, called ENMNFinder, which can be used by other researchers.

Dissolved organic matter dynamics in the oligo/meso-haline zone of wetland-influenced coastal rivers

NASA Astrophysics Data System (ADS)

Maie, Nagamitsu; Sekiguchi, Satoshi; Watanabe, Akira; Tsutsuki, Kiyoshi; Yamashita, Youhei; Melling, Lulie; Cawley, Kaelin M.; Shima, Eikichi; Jaffé, Rudolf

2014-08-01

Wetlands are key components in the global carbon cycle and export significant amounts of terrestrial carbon to the coastal oceans in the form of dissolved organic carbon (DOC). Conservative behavior along the salinity gradient of DOC and chromophoric dissolved organic matter (CDOM) has often been observed in estuaries from their freshwater end-member (salinity = 0) to the ocean (salinity = 35). While the oligo/meso-haline (salinity < 10) tidal zone of upper estuaries has been suggested to be more complex and locally influenced by geomorphological and hydrological features, the environmental dynamics of dissolved organic matter (DOM) and the environmental drivers controlling its source, transport, and fate have scarcely been evaluated. Here, we investigated the distribution patterns of DOC and CDOM optical properties determined by UV absorbance at 254 nm (A254) and excitation-emission matrix (EEM) fluorescence coupled with parallel factor analysis (PARAFAC) along the lower salinity range (salinity < 10) of the oligo/meso-haline zone for three distinct wetland-influenced rivers; namely the Bekanbeushi River, a cool-temperate river with estuarine lake in Hokkaido, Japan, the Harney River, a subtropical river with tidally-submerged mangrove fringe in Florida, USA, and the Judan River, a small, acidic, tropical rainforest river in Borneo, Malaysia. For the first two rivers, a clear decoupling between DOC and A254 was observed, while these parameters showed similar conservative behavior for the third. Three distinct EEM-PARAFAC models established for each of the rivers provided similar spectroscopic characteristics except for some unique fluorescence features observed for the Judan River. The distribution patterns of PARAFAC components suggested that the inputs from plankton and/or submerged aquatic vegetation can be important in the Bekanbeushi River. Further, DOM photo-products formed in the estuarine lake were also found to be transported upstream. In the Harney River, whereas upriver-derived terrestrial humic-like components were mostly distributed conservatively, some of these components were also derived from mangrove inputs in the oligo/meso-haline zone. Interestingly, fluorescence intensities of some terrestrial humic-like components increased with salinity for the Judan River possibly due to changes in the dissociation state of acidic functional groups and/or increase in the fluorescence quantum yield along the salinity gradient. The protein-like and microbial humic-like components were distributed differently between three wetland rivers, implying that interplay between loss to microbial degradation and inputs from diverse sources are different for the three wetland-influenced rivers. The results presented here indicate that upper estuarine oligo/meso-haline regions of coastal wetland rivers are highly dynamic with regard to the biogeochemical behavior of DOM.

[Characterization of Chromophoric dissolved organic matter (CDOM) in Zhoushan fishery using excitation-emission matrix spectroscopy (EEMs) and parallel factor analysis (PARAFAC)].

PubMed

Zhou, Qian-qian; Su, Rong-guo; Bai, Ying; Zhang, Chuan-song; Shi, Xiao-yong

2015-01-01

The composition, distribution characteristics and sources of chromophoric dissolved organic matter(CDOM) in Zhoushan Fishery in spring were evaluated by fluorescence excitation-emission matrix (EEM) combined with parallel factor analysis (EEMs-PARAFAC). Three humic-like components [C1 (330/420 nm)], C2 [(290) 365/440 nm] and C3 [(260) 370/490 nm)] and two protein-like components [C4(285/340 nm) and C5 (270/310 nm)] were identified by EEMs-PARAFAC. The horizontal distribution patterns of the five components were almost the same with only slight differences, showing decreasing trends with increasing distance from shore. In the surface and middle layers, the high value areas were located in the north of Hangzhou Bay estuary and the outlet of Xiazhimen channel, and the former's was higher in the surface layer while the latter's was higher in the middle layer. In the bottom layer, CDOM decreased gradiently from the inshore to offshore, with higher CDOM near Zhoushan Island. The distributions of fluorescence components showed an opposite trend with salinity, and no significant linear relationship with Chl-a concentration was found, which indicated that CDOM in the surface and middle layers were dominated by terrestrial input and human activities of Zhoushan Island and that of the bottom layer was attribute to human activities of Zhoushan Island. The vertical distribution of five fluorescent components along 30.5 degrees N transect showed a decreasing trend from the surface and middle layers to bottom layer with high values in inshore and offshore areas, which were correlated with the lower salinity and higher Chl-a concentration, respectively. On this transect, CDOM was mainly affected by Yangtze River input in coastal area but by bioactivities in offshore waters. Along the 30 degrees N transect, the vertical distribution patterns of CDOM were similar to those of 30.5 degrees N transect but there was a high value area in the bottom layer near the shore, attributing to the CDOM release from the marine sediment pore water to the water body because of physical force role like tidal, the underlying upwelling and so on. A strong correlation occurred between C1 and C3, C4, indicating that they had similar sources; a weak correlation was found between C1 and C2, C5, reflecting some differences among their sources. CDOM in Zhoushan Fishery in spring had low humification index (HIX) values, which reflected a low degree of humification, poor stability and a short resident time in the environment. For biological index (BIX), its higher values appeared in the offshore waters and the lower values occurred in the inshore area, reflecting a greater influence of human and biological activities, respectively.

Robust design optimization using the price of robustness, robust least squares and regularization methods

NASA Astrophysics Data System (ADS)

Bukhari, Hassan J.

2017-12-01

In this paper a framework for robust optimization of mechanical design problems and process systems that have parametric uncertainty is presented using three different approaches. Robust optimization problems are formulated so that the optimal solution is robust which means it is minimally sensitive to any perturbations in parameters. The first method uses the price of robustness approach which assumes the uncertain parameters to be symmetric and bounded. The robustness for the design can be controlled by limiting the parameters that can perturb.The second method uses the robust least squares method to determine the optimal parameters when data itself is subjected to perturbations instead of the parameters. The last method manages uncertainty by restricting the perturbation on parameters to improve sensitivity similar to Tikhonov regularization. The methods are implemented on two sets of problems; one linear and the other non-linear. This methodology will be compared with a prior method using multiple Monte Carlo simulation runs which shows that the approach being presented in this paper results in better performance.

Depth-dependent variations of sedimentary dissolved organic matter composition in a eutrophic lake: Implications for lake restoration.

PubMed

Xu, Huacheng; Guo, Laodong; Jiang, Helong

2016-02-01

Dissolved organic matter (DOM) plays a significant role in regulating nutrients and carbon cycling and the reactivity of trace metals and other contaminants in the environment. However, the environmental/ecological role of sedimentary DOM is highly dependent on organic composition. In this study, fluorescence excitation emission matrix-parallel factor (EEM-PARAFAC) analysis, two dimensional correlation spectroscopy (2D-COS), and ultrahigh resolution electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) were applied to investigate the depth-dependent variations of sediment-leached DOM components in a eutrophic lake. Results of EEM-PARAFAC and 2D-COS showed that fluorescent humic-like component was preferentially degraded microbially over fulvic-like component at greater sediment depths, and the relative abundance of non-fluorescent components decreased with increasing depth, leaving the removal rate of carbohydrates > lignins. The predominant sedimentary DOM components derived from FT-ICR-MS were lipids (>50%), followed by lignins (∼15%) and proteins (∼15%). The relative abundance of carbohydrates, lignins, and condensed aromatics decreased significantly at greater depths, whereas that of lipids increased in general with depth. There existed a significant negative correlation between the short-range ordered (SRO) minerals and the total dissolved organic carbon concentration or the relative contents of lignins and condensed aromatics (p < 0.05), suggesting that SRO mineral sorption plays a significant role in controlling the composition heterogeneity and releasing of DOM in lake sediments. Higher metal binding potential observed for DOM at deeper sediment depth (e.g., 25-30 cm) supported the ecological safety of sediment dredging technique from the viewpoint of heavy metal de-toxicity. Copyright © 2015 Elsevier Ltd. All rights reserved.

Characteristics and fate of natural organic matter during UV oxidation processes.

PubMed

Ahn, Yongtae; Lee, Doorae; Kwon, Minhwan; Choi, Il-Hwan; Nam, Seong-Nam; Kang, Joon-Wun

2017-10-01

Advanced oxidation processes (AOPs) are widely used in water treatments. During oxidation processes, natural organic matter (NOM) is modified and broken down into smaller compounds that affect the characteristics of the oxidized NOM by AOPs. In this study, NOM was characterized and monitored in the UV/hydrogen peroxide (H 2 O 2 ) and UV/persulfate (PS) processes using a liquid chromatography-organic carbon detector (LC-OCD) technique, and a combination of excitation-emission matrices (EEM) and parallel factor analysis (PARAFAC). The percentages of mineralization of NOM in the UV/H 2 O 2 and UV/PS processes were 20.5 and 83.3%, respectively, with a 10 mM oxidant dose and a contact time of 174 s (UV dose: approximately 30,000 mJ). Low-pressure, Hg UV lamp (254 nm) was applied in this experiment. The steady-state concentration of SO 4 - was 38-fold higher than that of OH at an oxidant dose of 10 mM. With para-chlorobenzoic acid (pCBA) as a radical probe compound, we experimentally determined the rate constants of Suwannee River NOM (SRNOM) with OH (k OH/NOM  = 3.3 × 10 8  M -1 s -1 ) and SO 4 - (k SO4-/NOM  = 4.55 × 10 6  M -1 s -1 ). The hydroxyl radical and sulfate radical showed different mineralization pathways of NOM, which have been verified by the use of LC-OCD and EEM/PARAFAC. Consequently, higher steady-state concentrations of SO 4 - , and different reaction preferences of OH and SO 4 - with the NOM constituent had an effect on the mineralization efficiency. Copyright © 2017 Elsevier Ltd. All rights reserved.

Novel insights into anoxic/aerobic(1)/aerobic(2) biological fluidized-bed system for coke wastewater treatment by fluorescence excitation-emission matrix spectra coupled with parallel factor analysis.

PubMed

Ou, Hua-Se; Wei, Chao-Hai; Mo, Ce-Hui; Wu, Hai-Zhen; Ren, Yuan; Feng, Chun-Hua

2014-10-01

Fluorescence spectroscopy coupled with parallel factor analysis (PARAFAC) was applied to investigate the contaminant removal efficiency and fluorescent characteristic variations in a full scale coke wastewater (CWW) treatment plant with a novel anoxic/aerobic(1)/aerobic(2) (A/O(1)/O(2)) process, which combined with internal-loop fluidized-bed reactor. Routine monitoring results indicated that primary contaminants in CWW, such as phenols and free cyanide, were removed efficiently in A/O(1)/O(2) process (removal efficiency reached 99% and 95%, respectively). Three-dimensional excitation-emission matrix fluorescence spectroscopy and PARAFAC identified three fluorescent components, including two humic-like fluorescence components (C1 and C3) and one protein-like component (C2). Principal component analysis revealed that C1 and C2 correlated with COD (correlation coefficient (r)=0.782, p<0.01 and r=0.921, p<0.01), respectively) and phenols (r=0.796, p<0.01 and r=0.914, p<0.01, respectively), suggesting that C1 and C2 might be associated with the predominating aromatic contaminants in CWW. C3 correlated with mixed liquor suspended solids (r=0.863, p<0.01) in fluidized-bed reactors, suggesting that it might represent the biological dissolved organic matter. In A/O(1)/O(2) process, the fluorescence intensities of C1 and C2 consecutively decreased, indicating the degradation of aromatic contaminants. Correspondingly, the fluorescence intensity of C3 increased in aerobic(1) stage, suggesting an increase of biological dissolved organic matter. Copyright © 2014 Elsevier Ltd. All rights reserved.

Cation-induced coagulation of aquatic plant-derived dissolved organic matter: Investigation by EEM-PARAFAC and FT-IR spectroscopy.

PubMed

Liu, Shasha; Zhu, Yuanrong; Liu, Leizhen; He, Zhongqi; Giesy, John P; Bai, Yingchen; Sun, Fuhong; Wu, Fengchang

2018-03-01

Complexation and coagulation of plant-derived dissolved organic matter (DOM) by metal cations are important biogeochemical processes of organic matter in aquatic systems. Thus, coagulation and fractionation of DOM derived from aquatic plants by Ca(II), Al(III), and Fe(III) ions were investigated. Metal ion-induced removal of DOM was determined by analyzing dissolved organic carbon in supernatants after addition of these metal cations individually. After additions of metal ions, both dissolved and coagulated organic fractions were characterized by use of fluorescence excitation emission matrix-parallel factor (EEM-PARAFAC) analysis and Fourier transform infrared (FT-IR) spectroscopy. Addition of Ca(II), Fe(III) or Al(III) resulted in net removal of aquatic plant-derived DOM. Efficiencies of removal of DOM by Fe(III) or Al(III) were greater than that by Ca(II). However, capacities to remove plant-derived DOM by the three metals were less than which had been previously reported for humic materials. Molecular and structural features of plant-derived DOM fractions in associations with metal cations were characterized by changes in fluorescent components and infrared absorption peaks. Both aromatic and carboxylic-like organic matters could be removed by Ca(II), Al(III) or Fe(III) ions. Whereas organic matters containing amides were preferentially removed by Ca(II), and phenolic materials were selectively removed by Fe(III) or Al(III). These observations indicated that plant-derived DOM might have a long-lasting effect on water quality and organisms due to its poor coagulation with metal cations in aquatic ecosystems. Plant-derived DOM is of different character than natural organic matter and it is not advisable to attempt removal through addition of metal salts during treatment of sewage. Copyright © 2017 Elsevier Ltd. All rights reserved.

Chemodiversity of dissolved organic matter in the Amazon Basin

NASA Astrophysics Data System (ADS)

Gonsior, Michael; Valle, Juliana; Schmitt-Kopplin, Philippe; Hertkorn, Norbert; Bastviken, David; Luek, Jenna; Harir, Mourad; Bastos, Wanderley; Enrich-Prast, Alex

2016-07-01

Regions in the Amazon Basin have been associated with specific biogeochemical processes, but a detailed chemical classification of the abundant and ubiquitous dissolved organic matter (DOM), beyond specific indicator compounds and bulk measurements, has not yet been established. We sampled water from different locations in the Negro, Madeira/Jamari and Tapajós River areas to characterize the molecular DOM composition and distribution. Ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) combined with excitation emission matrix (EEM) fluorescence spectroscopy and parallel factor analysis (PARAFAC) revealed a large proportion of ubiquitous DOM but also unique area-specific molecular signatures. Unique to the DOM of the Rio Negro area was the large abundance of high molecular weight, diverse hydrogen-deficient and highly oxidized molecular ions deviating from known lignin or tannin compositions, indicating substantial oxidative processing of these ultimately plant-derived polyphenols indicative of these black waters. In contrast, unique signatures in the Madeira/Jamari area were defined by presumably labile sulfur- and nitrogen-containing molecules in this white water river system. Waters from the Tapajós main stem did not show any substantial unique molecular signatures relative to those present in the Rio Madeira and Rio Negro, which implied a lower organic molecular complexity in this clear water tributary, even after mixing with the main stem of the Amazon River. Beside ubiquitous DOM at average H / C and O / C elemental ratios, a distinct and significant unique DOM pool prevailed in the black, white and clear water areas that were also highly correlated with EEM-PARAFAC components and define the frameworks for primary production and other aspects of aquatic life.

Relationship between soluble microbial products (SMP) and effluent organic matter (EfOM): characterized by fluorescence excitation emission matrix coupled with parallel factor analysis.

PubMed

Yu, Huarong; Qu, Fangshu; Sun, Lianpeng; Liang, Heng; Han, Zhengshuang; Chang, Haiqing; Shao, Senlin; Li, Guibai

2015-02-01

Effluent organic matter (EfOM) originating from wastewater treatment plant (WWTP) is of significant concern, as it not only influences the discharge quality of WWTP but also exerts a significant effect on the efficiency of the downstream advanced treatment facilities. Soluble microbial products (SMP) is a major part of EfOM. In order to further understand the relationship between soluble microbial products (SMP) and EfOM, and in turn, to propose measures for EfOM control, the formation of SMP and EfOM in identical activated sludge sequencing batch reactors (SBR) with different feed water was investigated using fluorescence excitation and emission spectroscopy matrix coupled with parallel factor analysis (EEM-PARAFAC) as well as other organic matter quantification tools. Results showed that EfOM contained not only SMP but also a considerable amount of allochthonous organic matter that derived not merely from natural organic matter (NOM). Four components in EfOM/SMP were identified by EEM-PARAFAC. Tyrosine-like substances in EfOM (Component 3, λex/em=270/316 nm) were mainly originated from utilization associated products (UAP) of SMP. Tryptophan-like substances (Component 2, λex/em=280/336 nm) as well as fulvic-like and humic-like substances in EfOM (Component 1, λex/em=240(290)/392 nm and Component 4, λex/em=260(365)/444 nm) were majorly derived from the refractory substances introduced along with the influent, among which Component 2 was stemmed from sources other than NOM. As solid retention time (SRT) increased, Component 2 and polysaccharides in SMP/EfOM decreased, while Component 4 in SMP increased. Copyright © 2014 Elsevier Ltd. All rights reserved.

Characterization and spacial distribution variability of chromophoric dissolved organic matter (CDOM) in the Yangtze Estuary.

PubMed

Wang, Ying; Zhang, Di; Shen, Zhenyao; Chen, Jing; Feng, Chenghong

2014-01-01

The spatial characteristics and the quantity and quality of the chromophoric dissolved organic matter (CDOM) in the Yangtze Estuary, based on the abundance, degree of humification and sources, were studied using 3D fluorescence excitation emission matrix spectra (F-EEMs) with parallel factor and principal component analysis (PARAFAC-PCA). The results indicated that the CDOM abundance decreased and the aromaticity increased from the upstream to the downstream areas of the estuary. Higher CDOM abundance and degrees of humification were observed in the pore water than that in the surface and bottom waters. Two humic-like components (C1 and C3) and one tryptophan-like component (C2) were identified using the PARAFAC model. The separation of the samples by PCA highlighted the differences in the DOM properties. Components C1 and C3 concurrently displayed positive factor 1 loadings with nearly zero factor 2 loadings, while C2 showed highly positive factor 2 loadings. The C1 and C3 were very similar and exhibited a direct relationship with A355 and DOC. The CDOM in the pore water increased along the river to the coastal area, which was mainly influenced by C1 and C3 and was significantly derived from sediment remineralization and deposition from the inflow of the Yangtze River. The CDOM in the surface and bottom waters was dominated by C2, especially in the inflows of multiple tributaries that were affected by intensive anthropogenic activities. The microbial degradation of exogenous wastes from the tributary inputs and shoreside discharges were dominant sources of the CDOM in the surface and bottom waters. Copyright © 2013 Elsevier Ltd. All rights reserved.

Does Litter Impart A Detectable Chemical Signal on Soil DOC? DOC Fluorescence Signatures in Soils Undergoing Long-Term Litter Manipulations

NASA Astrophysics Data System (ADS)

Lajtha, K.; Strid, A.; Lee, B. S.

2015-12-01

Soil dissolved organic carbon (DOC) is a small but crucial part of the forest carbon cycle. Characterizing the relationship between organic matter inputs to soil and DOC chemistry is crucial to understanding the ultimate fate of root carbon, fallen wood and needles. Chemical differences in the DOC pool may help to explain whether fractions are sorbed to mineral surfaces and contribute to accumulation of soil organic carbon, respired as CO2, or exported. Soil solution DOC was sampled from the detrital input and removal treatment (DIRT) plots located in the H.J. Andrews Experimental Forest, OR to determine whether detrital inputs impart a detectable signal on DOC in mineral soil. Multiple types of fresh litter extracts, along with lysimeter and soil extracts from DIRT treatment plots were characterized using UV-Vis and fluorescence spectroscopy coupled with the Cory and McKnight (2005) parallel factor analysis (PARAFAC) model. Principal component analysis of 13 unique fluorophores distinguished using PARAFAC show that litter and soil extracts (needles, wood of decomposition Class 1, Class 3 and Class 5, O-horizon, and A-horizon) each have distinct fluorescence signatures. However, while litter-leached DOC chemistry varies by litter type, neither lysimeter-collected DOC or soil extracts show statistically significant differences in fluorescence signatures among treatments, even after 17 years of litter manipulations. The lack of observed differences among DIRT treatments suggests a "Soil Blender" hypothesis whereby both abiotic and biotic mechanisms effectively homogenize organic carbon constituents within the dissolved pool. The results of this work emphasize the ability of sorption and biodegradation to homogenize soil DOC and demonstrate that fluorescence can be an effective fingerprinting technique for soil DOC composition.

Revealing Sources and Distribution Changes of Dissolved Organic Matter (DOM) in Pore Water of Sediment from the Yangtze Estuary

PubMed Central

Wang, Ying; Zhang, Di; Shen, Zhenyao; Feng, Chenghong; Chen, Jing

2013-01-01

Dissolved organic matter (DOM) in sediment pore waters from Yangtze estuary of China based on abundance, UV absorbance, molecular weight distribution and fluorescence were investigated using a combination of various parameters of DOM as well as 3D fluorescence excitation emission matrix spectra (F-EEMS) with the parallel factor and principal component analysis (PARAFAC-PCA). The results indicated that DOM in pore water of Yangtze estuary was very variable which mainly composed of low aromaticity and molecular weight materials. Three humic-like substances (C1, C2, C4) and one protein-like substance (C3) were identified by PARAFAC model. C1, C2 and C4 exhibited same trends and were very similar. The separation of samples on both axes of the PCA showed the difference in DOM properties. C1, C2 and C4 concurrently showed higher positive factor 1 loadings, while C3 showed highly positive factor 2 loadings. The PCA analysis showed a combination contribution of microbial DOM signal and terrestrial DOM signal in the Yangtze estuary. Higher and more variable DOM abundance, aromaticity and molecular weight of surface sediment pore water DOM can be found in the southern nearshore than the other regions primarily due to the influence of frequent and intensive human activities and tributaries inflow in this area. The DOM abundance, aromaticity, molecular weight and fluorescence intensity in core of different depth were relative constant and increased gradually with depth. DOM in core was mainly composed of humic-like material, which was due to higher release of the sedimentary organic material into the porewater during early diagenesis. PMID:24155904

Computing sensitivity and selectivity in parallel factor analysis and related multiway techniques: the need for further developments in net analyte signal theory.

PubMed

Olivieri, Alejandro C

2005-08-01

Sensitivity and selectivity are important figures of merit in multiway analysis, regularly employed for comparison of the analytical performance of methods and for experimental design and planning. They are especially interesting in the second-order advantage scenario, where the latter property allows for the analysis of samples with a complex background, permitting analyte determination even in the presence of unsuspected interferences. Since no general theory exists for estimating the multiway sensitivity, Monte Carlo numerical calculations have been developed for estimating variance inflation factors, as a convenient way of assessing both sensitivity and selectivity parameters for the popular parallel factor (PARAFAC) analysis and also for related multiway techniques. When the second-order advantage is achieved, the existing expressions derived from net analyte signal theory are only able to adequately cover cases where a single analyte is calibrated using second-order instrumental data. However, they fail for certain multianalyte cases, or when third-order data are employed, calling for an extension of net analyte theory. The results have strong implications in the planning of multiway analytical experiments.

Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination.

PubMed

Zhao, Qibin; Zhang, Liqing; Cichocki, Andrzej

2015-09-01

CANDECOMP/PARAFAC (CP) tensor factorization of incomplete data is a powerful technique for tensor completion through explicitly capturing the multilinear latent factors. The existing CP algorithms require the tensor rank to be manually specified, however, the determination of tensor rank remains a challenging problem especially for CP rank . In addition, existing approaches do not take into account uncertainty information of latent factors, as well as missing entries. To address these issues, we formulate CP factorization using a hierarchical probabilistic model and employ a fully Bayesian treatment by incorporating a sparsity-inducing prior over multiple latent factors and the appropriate hyperpriors over all hyperparameters, resulting in automatic rank determination. To learn the model, we develop an efficient deterministic Bayesian inference algorithm, which scales linearly with data size. Our method is characterized as a tuning parameter-free approach, which can effectively infer underlying multilinear factors with a low-rank constraint, while also providing predictive distributions over missing entries. Extensive simulations on synthetic data illustrate the intrinsic capability of our method to recover the ground-truth of CP rank and prevent the overfitting problem, even when a large amount of entries are missing. Moreover, the results from real-world applications, including image inpainting and facial image synthesis, demonstrate that our method outperforms state-of-the-art approaches for both tensor factorization and tensor completion in terms of predictive performance.

Feasibility of the determination of polycyclic aromatic hydrocarbons in edible oils via unfolded partial least-squares/residual bilinearization and parallel factor analysis of fluorescence excitation emission matrices.

PubMed

Alarcón, Francis; Báez, María E; Bravo, Manuel; Richter, Pablo; Escandar, Graciela M; Olivieri, Alejandro C; Fuentes, Edwar

2013-01-15

The possibility of simultaneously determining seven concerned heavy polycyclic aromatic hydrocarbons (PAHs) of the US-EPA priority pollutant list, in extra virgin olive and sunflower oils was examined using unfolded partial least-squares with residual bilinearization (U-PLS/RBL) and parallel factor analysis (PARAFAC). Both of these methods were applied to fluorescence excitation emission matrices. The compounds studied were benzo[a]anthracene, benzo[b]fluoranthene, benzo[k]fluoranthene, benzo[a]pyrene, dibenz[a,h]anthracene, benzo[g,h,i]perylene and indeno[1,2,3-c,d]-pyrene. The analysis was performed using fluorescence spectroscopy after a microwave assisted liquid-liquid extraction and solid-phase extraction on silica. The U-PLS/RBL algorithm exhibited the best performance for resolving the heavy PAH mixture in the presence of both the highly complex oil matrix and other unpredicted PAHs of the US-EPA list. The obtained limit of detection for the proposed method ranged from 0.07 to 2 μg kg(-1). The predicted U-PLS/RBL concentrations were satisfactorily compared with those obtained using high-performance liquid chromatography with fluorescence detection. A simple analysis with a considerable reduction in time and solvent consumption in comparison with chromatography are the principal advantages of the proposed method. Copyright © 2012 Elsevier B.V. All rights reserved.

Robust Methods for Moderation Analysis with a Two-Level Regression Model.

PubMed

Yang, Miao; Yuan, Ke-Hai

2016-01-01

Moderation analysis has many applications in social sciences. Most widely used estimation methods for moderation analysis assume that errors are normally distributed and homoscedastic. When these assumptions are not met, the results from a classical moderation analysis can be misleading. For more reliable moderation analysis, this article proposes two robust methods with a two-level regression model when the predictors do not contain measurement error. One method is based on maximum likelihood with Student's t distribution and the other is based on M-estimators with Huber-type weights. An algorithm for obtaining the robust estimators is developed. Consistent estimates of standard errors of the robust estimators are provided. The robust approaches are compared against normal-distribution-based maximum likelihood (NML) with respect to power and accuracy of parameter estimates through a simulation study. Results show that the robust approaches outperform NML under various distributional conditions. Application of the robust methods is illustrated through a real data example. An R program is developed and documented to facilitate the application of the robust methods.

Structure-seeking multilinear methods for the analysis of fMRI data.

PubMed

Andersen, Anders H; Rayens, William S

2004-06-01

In comprehensive fMRI studies of brain function, the data structures often contain higher-order ways such as trial, task condition, subject, and group in addition to the intrinsic dimensions of time and space. While multivariate bilinear methods such as principal component analysis (PCA) have been used successfully for extracting information about spatial and temporal features in data from a single fMRI run, the need to unfold higher-order data sets into bilinear arrays has led to decompositions that are nonunique and to the loss of multiway linkages and interactions present in the data. These additional dimensions or ways can be retained in multilinear models to produce structures that are unique and which admit interpretations that are neurophysiologically meaningful. Multiway analysis of fMRI data from multiple runs of a bilateral finger-tapping paradigm was performed using the parallel factor (PARAFAC) model. A trilinear model was fitted to a data cube of dimensions voxels by time by run. Similarly, a quadrilinear model was fitted to a higher-way structure of dimensions voxels by time by trial by run. The spatial and temporal response components were extracted and validated by comparison to results from traditional SVD/PCA analyses based on scenarios of unfolding into lower-order bilinear structures.

Characterization and classification of oral tissues using excitation and emission matrix: a statistical modeling approach

NASA Astrophysics Data System (ADS)

Kanniyappan, Udayakumar; Gnanatheepaminstein, Einstein; Prakasarao, Aruna; Dornadula, Koteeswaran; Singaravelu, Ganesan

2017-02-01

Cancer is one of the most common human threats around the world and diagnosis based on optical spectroscopy especially fluorescence technique has been established as the standard approach among scientist to explore the biochemical and morphological changes in tissues. In this regard, the present work aims to extract spectral signatures of the various fluorophores present in oral tissues using parallel factor analysis (PARAFAC). Subsequently, the statistical analysis also to be performed to show its diagnostic potential in distinguishing malignant, premalignant from normal oral tissues. Hence, the present study may lead to the possible and/or alternative tool for oral cancer diagnosis.

The comparison of robust partial least squares regression with robust principal component regression on a real

NASA Astrophysics Data System (ADS)

Polat, Esra; Gunay, Suleyman

2013-10-01

One of the problems encountered in Multiple Linear Regression (MLR) is multicollinearity, which causes the overestimation of the regression parameters and increase of the variance of these parameters. Hence, in case of multicollinearity presents, biased estimation procedures such as classical Principal Component Regression (CPCR) and Partial Least Squares Regression (PLSR) are then performed. SIMPLS algorithm is the leading PLSR algorithm because of its speed, efficiency and results are easier to interpret. However, both of the CPCR and SIMPLS yield very unreliable results when the data set contains outlying observations. Therefore, Hubert and Vanden Branden (2003) have been presented a robust PCR (RPCR) method and a robust PLSR (RPLSR) method called RSIMPLS. In RPCR, firstly, a robust Principal Component Analysis (PCA) method for high-dimensional data on the independent variables is applied, then, the dependent variables are regressed on the scores using a robust regression method. RSIMPLS has been constructed from a robust covariance matrix for high-dimensional data and robust linear regression. The purpose of this study is to show the usage of RPCR and RSIMPLS methods on an econometric data set, hence, making a comparison of two methods on an inflation model of Turkey. The considered methods have been compared in terms of predictive ability and goodness of fit by using a robust Root Mean Squared Error of Cross-validation (R-RMSECV), a robust R2 value and Robust Component Selection (RCS) statistic.

A comparative study of multivariable robustness analysis methods as applied to integrated flight and propulsion control

NASA Technical Reports Server (NTRS)

Schierman, John D.; Lovell, T. A.; Schmidt, David K.

1993-01-01

Three multivariable robustness analysis methods are compared and contrasted. The focus of the analysis is on system stability and performance robustness to uncertainty in the coupling dynamics between two interacting subsystems. Of particular interest is interacting airframe and engine subsystems, and an example airframe/engine vehicle configuration is utilized in the demonstration of these approaches. The singular value (SV) and structured singular value (SSV) analysis methods are compared to a method especially well suited for analysis of robustness to uncertainties in subsystem interactions. This approach is referred to here as the interacting subsystem (IS) analysis method. This method has been used previously to analyze airframe/engine systems, emphasizing the study of stability robustness. However, performance robustness is also investigated here, and a new measure of allowable uncertainty for acceptable performance robustness is introduced. The IS methodology does not require plant uncertainty models to measure the robustness of the system, and is shown to yield valuable information regarding the effects of subsystem interactions. In contrast, the SV and SSV methods allow for the evaluation of the robustness of the system to particular models of uncertainty, and do not directly indicate how the airframe (engine) subsystem interacts with the engine (airframe) subsystem.

A Robust Cooperated Control Method with Reinforcement Learning and Adaptive H∞ Control

NASA Astrophysics Data System (ADS)

Obayashi, Masanao; Uchiyama, Shogo; Kuremoto, Takashi; Kobayashi, Kunikazu

This study proposes a robust cooperated control method combining reinforcement learning with robust control to control the system. A remarkable characteristic of the reinforcement learning is that it doesn't require model formula, however, it doesn't guarantee the stability of the system. On the other hand, robust control system guarantees stability and robustness, however, it requires model formula. We employ both the actor-critic method which is a kind of reinforcement learning with minimal amount of computation to control continuous valued actions and the traditional robust control, that is, H∞ control. The proposed system was compared method with the conventional control method, that is, the actor-critic only used, through the computer simulation of controlling the angle and the position of a crane system, and the simulation result showed the effectiveness of the proposed method.

Real Time Monitoring of Dissolved Organic Carbon Concentration and Disinfection By-Product Formation Potential in a Surface Water Treatment Plant with Simulaneous UV-VIS Absorbance and Fluorescence Excitation-Emission Mapping

NASA Astrophysics Data System (ADS)

Gilmore, A. M.

2015-12-01

This study describes a method based on simultaneous absorbance and fluorescence excitation-emission mapping for rapidly and accurately monitoring dissolved organic carbon concentration and disinfection by-product formation potential for surface water sourced drinking water treatment. The method enables real-time monitoring of the Dissolved Organic Carbon (DOC), absorbance at 254 nm (UVA), the Specific UV Absorbance (SUVA) as well as the Simulated Distribution System Trihalomethane (THM) Formation Potential (SDS-THMFP) for the source and treated water among other component parameters. The method primarily involves Parallel Factor Analysis (PARAFAC) decomposition of the high and lower molecular weight humic and fulvic organic component concentrations. The DOC calibration method involves calculating a single slope factor (with the intercept fixed at 0 mg/l) by linear regression for the UVA divided by the ratio of the high and low molecular weight component concentrations. This method thus corrects for the changes in the molecular weight component composition as a function of the source water composition and coagulation treatment effects. The SDS-THMFP calibration involves a multiple linear regression of the DOC, organic component ratio, chlorine residual, pH and alkalinity. Both the DOC and SDS-THMFP correlations over a period of 18 months exhibited adjusted correlation coefficients with r2 > 0.969. The parameters can be reported as a function of compliance rules associated with required % removals of DOC (as a function of alkalinity) and predicted maximum contaminant levels (MCL) of THMs. The single instrument method, which is compatible with continuous flow monitoring or grab sampling, provides a rapid (2-3 minute) and precise indicator of drinking water disinfectant treatability without the need for separate UV photometric and DOC meter measurements or independent THM determinations.

Image segmentation-based robust feature extraction for color image watermarking

NASA Astrophysics Data System (ADS)

Li, Mianjie; Deng, Zeyu; Yuan, Xiaochen

2018-04-01

This paper proposes a local digital image watermarking method based on Robust Feature Extraction. The segmentation is achieved by Simple Linear Iterative Clustering (SLIC) based on which an Image Segmentation-based Robust Feature Extraction (ISRFE) method is proposed for feature extraction. Our method can adaptively extract feature regions from the blocks segmented by SLIC. This novel method can extract the most robust feature region in every segmented image. Each feature region is decomposed into low-frequency domain and high-frequency domain by Discrete Cosine Transform (DCT). Watermark images are then embedded into the coefficients in the low-frequency domain. The Distortion-Compensated Dither Modulation (DC-DM) algorithm is chosen as the quantization method for embedding. The experimental results indicate that the method has good performance under various attacks. Furthermore, the proposed method can obtain a trade-off between high robustness and good image quality.

A kriging metamodel-assisted robust optimization method based on a reverse model

NASA Astrophysics Data System (ADS)

Zhou, Hui; Zhou, Qi; Liu, Congwei; Zhou, Taotao

2018-02-01

The goal of robust optimization methods is to obtain a solution that is both optimum and relatively insensitive to uncertainty factors. Most existing robust optimization approaches use outer-inner nested optimization structures where a large amount of computational effort is required because the robustness of each candidate solution delivered from the outer level should be evaluated in the inner level. In this article, a kriging metamodel-assisted robust optimization method based on a reverse model (K-RMRO) is first proposed, in which the nested optimization structure is reduced into a single-loop optimization structure to ease the computational burden. Ignoring the interpolation uncertainties from kriging, K-RMRO may yield non-robust optima. Hence, an improved kriging-assisted robust optimization method based on a reverse model (IK-RMRO) is presented to take the interpolation uncertainty of kriging metamodel into consideration. In IK-RMRO, an objective switching criterion is introduced to determine whether the inner level robust optimization or the kriging metamodel replacement should be used to evaluate the robustness of design alternatives. The proposed criterion is developed according to whether or not the robust status of the individual can be changed because of the interpolation uncertainties from the kriging metamodel. Numerical and engineering cases are used to demonstrate the applicability and efficiency of the proposed approach.

SU-E-T-07: 4DCT Robust Optimization for Esophageal Cancer Using Intensity Modulated Proton Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liao, L; Department of Industrial Engineering, University of Houston, Houston, TX; Yu, J

2015-06-15

Purpose: To develop a 4DCT robust optimization method to reduce the dosimetric impact from respiratory motion in intensity modulated proton therapy (IMPT) for esophageal cancer. Methods: Four esophageal cancer patients were selected for this study. The different phases of CT from a set of 4DCT were incorporated into the worst-case dose distribution robust optimization algorithm. 4DCT robust treatment plans were designed and compared with the conventional non-robust plans. Result doses were calculated on the average and maximum inhale/exhale phases of 4DCT. Dose volume histogram (DVH) band graphic and ΔD95%, ΔD98%, ΔD5%, ΔD2% of CTV between different phases were used tomore » evaluate the robustness of the plans. Results: Compare to the IMPT plans optimized using conventional methods, the 4DCT robust IMPT plans can achieve the same quality in nominal cases, while yield a better robustness to breathing motion. The mean ΔD95%, ΔD98%, ΔD5% and ΔD2% of CTV are 6%, 3.2%, 0.9% and 1% for the robustly optimized plans vs. 16.2%, 11.8%, 1.6% and 3.3% from the conventional non-robust plans. Conclusion: A 4DCT robust optimization method was proposed for esophageal cancer using IMPT. We demonstrate that the 4DCT robust optimization can mitigate the dose deviation caused by the diaphragm motion.« less

Determination of the thermal, oxidative and photochemical degradation rates of scintillator liquid by fluorescence EEM spectroscopy.

PubMed

Andrews, N L P; Fan, J Z; Forward, R L; Chen, M C; Loock, H-P

2016-12-21

The thermal, oxidative and photochemical stability of the scintillator liquid proposed for the SNO+ experiment has been tested experimentally using accelerated aging methods. The stability of the scintillator constituents was determined through fluorescence excitation emission matrix (EEM) spectroscopy and absorption spectroscopy, using parallel factor analysis (PARAFAC) as an multivariate analysis tool. By exposing the scintillator liquid to a well-known photon flux at 365 nm and by measuring the decay rate of the fluorescence shifters and the formation rate of their photochemical degradation products, we can place an upper limit on the acceptable photon flux as 1.38 ± 0.09 × 10 -11 photon mol L -1 . Similarly, the oxidative stability of the scintillator liquid was determined by exposure to air at several elevated temperatures. Through measurement of the corresponding activation energy it was determined that the average oxygen concentration would have to be kept below 4.3-7.1 ppb w (headspace partial pressure below 24 ppm v ). On the other hand, the thermal stability of the scintillator cocktail in the absence of light and oxygen was remarkable and poses no concern to the SNO+ experiment.

An Electrochemical Quartz Crystal Microbalance Multisensor System Based on Phthalocyanine Nanostructured Films: Discrimination of Musts

PubMed Central

Garcia-Hernandez, Celia; Medina-Plaza, Cristina; Garcia-Cabezon, Cristina; Martin-Pedrosa, Fernando; del Valle, Isabel; de Saja, Jose Antonio; Rodríguez-Méndez, Maria Luz

2015-01-01

An array of electrochemical quartz crystal electrodes (EQCM) modified with nanostructured films based on phthalocyanines was developed and used to discriminate musts prepared from different varieties of grapes. Nanostructured films of iron, nickel and copper phthalocyanines were deposited on Pt/quartz crystals through the Layer by Layer technique by alternating layers of the corresponding phthalocyanine and poly-allylamine hydrochloride. Simultaneous electrochemical and mass measurements were used to study the mass changes accompanying the oxidation of electroactive species present in must samples obtained from six Spanish varieties of grapes (Juan García, Prieto Picudo, Mencía Regadío, Cabernet Sauvignon, Garnacha and Tempranillo). The mass and voltammetric outputs were processed using three-way models. Parallel Factor Analysis (PARAFAC) was successfully used to discriminate the must samples according to their variety. Multi-way partial least squares (N-PLS) evidenced the correlations existing between the voltammetric data and the polyphenolic content measured by chemical methods. Similarly, N-PLS showed a correlation between mass outputs and parameters related to the sugar content. These results demonstrated that electronic tongues based on arrays of EQCM sensors can offer advantages over arrays of mass or voltammetric sensors used separately. PMID:26610494

The Case Against Charge Transfer Interactions in Dissolved Organic Matter Optical Properties

NASA Astrophysics Data System (ADS)

McKay, G.; Korak, J.; Erickson, P. R.; Latch, D. E.; McNeill, K.; Rosario-Ortiz, F.

2017-12-01

The optical properties of dissolved organic matter influence chemical and biological processes in all aquatic ecosystems. Organic matter optical properties have been used by scientists and engineers for decades for remote sensing, in situ monitoring, and characterizing laboratory samples to track dissolved organic carbon concentration and character. However, there is still a lack of understanding of the origin of organic matter optical properties, which could conflict with other empirical fluorescence interpretation methods (e.g. PARAFAC). Organic matter optical properties have been attributed to a charge-transfer model in which donor-acceptor complexes play a primary role. This model was evaluated by measuring the absorbance and fluorescence response of organic matter isolates to perturbations in solvent temperature, viscosity, and polarity, which affect the position and intensity of spectra for known donor-acceptor complexes of organic molecules. Absorbance and fluorescence spectral shape were unaffected by these perturbations, indicating that the distribution of absorbing and emitting species was unchanged. These results call into question the wide applicability of the charge-transfer model for explaining organic matter optical properties and suggest that future research should explore other models for organic matter photophysics.

3D tensor-based blind multispectral image decomposition for tumor demarcation

NASA Astrophysics Data System (ADS)

Kopriva, Ivica; Peršin, Antun

2010-03-01

Blind decomposition of multi-spectral fluorescent image for tumor demarcation is formulated exploiting tensorial structure of the image. First contribution of the paper is identification of the matrix of spectral responses and 3D tensor of spatial distributions of the materials present in the image from Tucker3 or PARAFAC models of 3D image tensor. Second contribution of the paper is clustering based estimation of the number of the materials present in the image as well as matrix of their spectral profiles. 3D tensor of the spatial distributions of the materials is recovered through 3-mode multiplication of the multi-spectral image tensor and inverse of the matrix of spectral profiles. Tensor representation of the multi-spectral image preserves its local spatial structure that is lost, due to vectorization process, when matrix factorization-based decomposition methods (such as non-negative matrix factorization and independent component analysis) are used. Superior performance of the tensor-based image decomposition over matrix factorization-based decompositions is demonstrated on experimental red-green-blue (RGB) image with known ground truth as well as on RGB fluorescent images of the skin tumor (basal cell carcinoma).

Robust evaluation of performance monitoring options for ozone disinfection in water recycling using Bayesian analysis.

PubMed

Carvajal, Guido; Branch, Amos; Michel, Philipp; Sisson, Scott A; Roser, David J; Drewes, Jörg E; Khan, Stuart J

2017-11-01

Ozonation of wastewater has gained popularity because of its effectiveness in removing colour, UV absorbance, trace organic chemicals, and pathogens. Due to the rapid reaction of ozone with organic compounds, dissolved ozone is often not measurable and therefore, the common disinfection controlling parameter, concentration integrated over contact time (CT) cannot be obtained. In such cases, alternative parameters have been shown to be useful as surrogate measures for microbial removal including change in UV 254 absorbance (ΔUVA), change in total fluorescence (ΔTF), or O 3 :TOC (or O 3 :DOC). Although these measures have shown promise, a number of caveats remain. These include uncertainties in the associations between these measurements and microbial inactivation. Furthermore, previous use of seeded microorganisms with higher disinfection sensitivity compared to autochthonous microorganisms could lead to overestimation of appropriate log credits. In our study, secondary treated wastewater from a full-scale plant was ozonated in a bench-scale reactor using five increasing ozone doses. During the experiments, removal of four indigenous microbial indicators representing viruses, bacteria and protozoa were monitored concurrent with ΔUVA, ΔTF, O 3 :DOC and PARAFAC derived components. Bayesian methods were used to fit linear regression models, and the uncertainty in the posterior predictive distributions and slopes provided a comparison between previously reported results and those reported here. Combined results indicated that all surrogate parameters were useful in predicting the removal of microorganisms, with a better fit to the models using ΔUVA, ΔTF in most cases. Average adjusted determination coefficients for fitted models were high (R 2 adjusted >0.47). With ΔUVA, one unit decrease in LRV corresponded with a UVA mean reduction of 15-20% for coliforms, 59% for C. perfringens spores, and 11% for somatic coliphages. With ΔTF, a one unit decrease in LRV corresponded with a TF mean reduction of 18-23% for coliforms, 71% for C. perfringens spores, and 14% for somatic coliphages. Compared to previous studies also analysed, our results suggest that microbial reductions were more conservative for autochthonous than for seeded microorganisms. The findings of our study suggested that site-specific analyses should be conducted to generate models with lower uncertainty and that indigenous microorganisms are useful for the measurement of system performance even when censored observations are obtained. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Utility of Robust Means in Statistics

ERIC Educational Resources Information Center

Goodwyn, Fara

2012-01-01

Location estimates calculated from heuristic data were examined using traditional and robust statistical methods. The current paper demonstrates the impact outliers have on the sample mean and proposes robust methods to control for outliers in sample data. Traditional methods fail because they rely on the statistical assumptions of normality and…

Investigation of interpolation techniques for the reconstruction of the first dimension of comprehensive two-dimensional liquid chromatography-diode array detector data.

PubMed

Allen, Robert C; Rutan, Sarah C

2011-10-31

Simulated and experimental data were used to measure the effectiveness of common interpolation techniques during chromatographic alignment of comprehensive two-dimensional liquid chromatography-diode array detector (LC×LC-DAD) data. Interpolation was used to generate a sufficient number of data points in the sampled first chromatographic dimension to allow for alignment of retention times from different injections. Five different interpolation methods, linear interpolation followed by cross correlation, piecewise cubic Hermite interpolating polynomial, cubic spline, Fourier zero-filling, and Gaussian fitting, were investigated. The fully aligned chromatograms, in both the first and second chromatographic dimensions, were analyzed by parallel factor analysis to determine the relative area for each peak in each injection. A calibration curve was generated for the simulated data set. The standard error of prediction and percent relative standard deviation were calculated for the simulated peak for each technique. The Gaussian fitting interpolation technique resulted in the lowest standard error of prediction and average relative standard deviation for the simulated data. However, upon applying the interpolation techniques to the experimental data, most of the interpolation methods were not found to produce statistically different relative peak areas from each other. While most of the techniques were not statistically different, the performance was improved relative to the PARAFAC results obtained when analyzing the unaligned data. Copyright © 2011 Elsevier B.V. All rights reserved.

Determination of polycyclic aromatic hydrocarbons by four-way parallel factor analysis in presence of humic acid.

PubMed

Yang, Ruifang; Zhao, Nanjing; Xiao, Xue; Yu, Shaohui; Liu, Jianguo; Liu, Wenqing

2016-01-05

There is not effective method to solve the quenching effect of quencher in fluorescence spectra measurement and recognition of polycyclic aromatic hydrocarbons in aquatic environment. In this work, a four-way dataset combined with four-way parallel factor analysis is used to identify and quantify polycyclic aromatic hydrocarbons in the presence of humic acid, a fluorescent quencher and an ubiquitous substance in aquatic system, through modeling the quenching effect of humic acid by decomposing the four-way dataset into four loading matrices corresponding to relative concentration, excitation spectra, emission spectra and fluorescence quantum yield, respectively. It is found that Phenanthrene, pyrene, anthracene and fluorene can be recognized simultaneously with the similarities all above 0.980 between resolved spectra and reference spectra. Moreover, the concentrations of them ranging from 0 to 8μgL(-1) in the test samples prepared with river water could also be predicted successfully with recovery rate of each polycyclic aromatic hydrocarbon between 100% and 120%, which were higher than those of three-way PARAFAC. These results demonstrate that the combination of four-way dataset with four-way parallel factor analysis could be a promising method to recognize the fluorescence spectra of polycyclic aromatic hydrocarbons in the presence of fluorescent quencher from both qualitative and quantitative perspective. Copyright © 2015 Elsevier B.V. All rights reserved.

Visualizing DOM super-spectrum covariance in vanKrevelen space

NASA Astrophysics Data System (ADS)

Fatland, D. R.; Kalawe, J.; Stubbins, A.; Spencer, R. G.; Sleighter, R. L.; Abdulla, H. A.; Dittmar, T.

2011-12-01

We investigate the fate of terrigenous organic matter, DOM exported to the coastal marine environ. Many methods (fluor., FT-ICR-MS, NMR, 13C, lignin, etc) help characterize this DOM. We define a 'super spectrum' as amalgamation of analyses to a data stack and we search for physically significant patterns therein beginning with covariance across 31 samples from six circum-Arctic rivers: The Ob, Kolyma, Mackenzie, Yukon, Lena, and Yenisey sampled five times throughout the year. A vanKrevelen diagram is convenient to view distributions of molecules provided by Fourier Transform Ion Cyclotron Resonance Mass Spectometry (FT-ICR-MS). We augment this distribution space in the vertical dimension, for example to show peak height, molecular mass, principle component weighting or covariance. We use Worldwide Telescope, a virtual globe with strong data support from Microsoft Research to explore covariance results along 3+ dimensions (adding brightness, color and a parameter slide). The results show interesting covariance e.g. between molecules and PARAFAC peaks, a step towards fluorophore and cohort identification in the terrigenous DOM spectrum. Given the geoscience explosion in data volume and data complexity we feel these results should survive beyond the end point of a journal article. We are building a cloud-based Library on the Microsoft Azure platform to support this and subsequent analyses to enable data and methods to carry over and benefit other research groups and objectives.

Robust Satisficing Decision Making for Unmanned Aerial Vehicle Complex Missions under Severe Uncertainty

PubMed Central

Ji, Xiaoting; Niu, Yifeng; Shen, Lincheng

2016-01-01

This paper presents a robust satisficing decision-making method for Unmanned Aerial Vehicles (UAVs) executing complex missions in an uncertain environment. Motivated by the info-gap decision theory, we formulate this problem as a novel robust satisficing optimization problem, of which the objective is to maximize the robustness while satisfying some desired mission requirements. Specifically, a new info-gap based Markov Decision Process (IMDP) is constructed to abstract the uncertain UAV system and specify the complex mission requirements with the Linear Temporal Logic (LTL). A robust satisficing policy is obtained to maximize the robustness to the uncertain IMDP while ensuring a desired probability of satisfying the LTL specifications. To this end, we propose a two-stage robust satisficing solution strategy which consists of the construction of a product IMDP and the generation of a robust satisficing policy. In the first stage, a product IMDP is constructed by combining the IMDP with an automaton representing the LTL specifications. In the second, an algorithm based on robust dynamic programming is proposed to generate a robust satisficing policy, while an associated robustness evaluation algorithm is presented to evaluate the robustness. Finally, through Monte Carlo simulation, the effectiveness of our algorithms is demonstrated on an UAV search mission under severe uncertainty so that the resulting policy can maximize the robustness while reaching the desired performance level. Furthermore, by comparing the proposed method with other robust decision-making methods, it can be concluded that our policy can tolerate higher uncertainty so that the desired performance level can be guaranteed, which indicates that the proposed method is much more effective in real applications. PMID:27835670

Robust Satisficing Decision Making for Unmanned Aerial Vehicle Complex Missions under Severe Uncertainty.

PubMed

Ji, Xiaoting; Niu, Yifeng; Shen, Lincheng

2016-01-01

This paper presents a robust satisficing decision-making method for Unmanned Aerial Vehicles (UAVs) executing complex missions in an uncertain environment. Motivated by the info-gap decision theory, we formulate this problem as a novel robust satisficing optimization problem, of which the objective is to maximize the robustness while satisfying some desired mission requirements. Specifically, a new info-gap based Markov Decision Process (IMDP) is constructed to abstract the uncertain UAV system and specify the complex mission requirements with the Linear Temporal Logic (LTL). A robust satisficing policy is obtained to maximize the robustness to the uncertain IMDP while ensuring a desired probability of satisfying the LTL specifications. To this end, we propose a two-stage robust satisficing solution strategy which consists of the construction of a product IMDP and the generation of a robust satisficing policy. In the first stage, a product IMDP is constructed by combining the IMDP with an automaton representing the LTL specifications. In the second, an algorithm based on robust dynamic programming is proposed to generate a robust satisficing policy, while an associated robustness evaluation algorithm is presented to evaluate the robustness. Finally, through Monte Carlo simulation, the effectiveness of our algorithms is demonstrated on an UAV search mission under severe uncertainty so that the resulting policy can maximize the robustness while reaching the desired performance level. Furthermore, by comparing the proposed method with other robust decision-making methods, it can be concluded that our policy can tolerate higher uncertainty so that the desired performance level can be guaranteed, which indicates that the proposed method is much more effective in real applications.

Similarities in Photodegradation of Cyanobacteria-Derived and Marine Fluorescent Dissolved Organic Matter

NASA Astrophysics Data System (ADS)

Ianiri, H. L.; Timko, S.; Gonsior, M.

2016-02-01

Marine dissolved organic matter (DOM) is one of the largest reduced carbon reservoirs on Earth, yet we only have a limited understanding of its production, cycling, degradation, and overall structure. It was previously believed that a significant portion of refractory dissolved organic carbon (RDOC) in the ocean was derived from terrestrial sources, however recent studies indicated that the majority of marine DOM might be produced in situ by marine biota. Previous research has found that terrestrial and microbial DOM fluorescent signatures are similar, complicating the identification of the origins of marine fluorescent DOM (FDOM). However, photodegradation kinetics of terrestrial and microbial-derived DOM are expected to be different due to their assumed different chemical compositions. In this study we analyzed for the first time the photodegradation kinetics of microbial-derived DOM originating from different cyanobacteria strains. Cyanobacterial-derived DOM were exposed to simulated sunlight for a total of 20 hours while recording excitation emission matrix (EEM) fluorescence every twenty minutes to observe the photodegradation of this specific FDOM. Parallel Factor Analysis (PARAFAC) was applied to deconvolute the EEM matrices into six separate components. The photodegradation kinetics was then calculated for each component and compared with previously obtained photodegradation data of marine and terrestrial FDOM. This six component PARAFAC model was similar to those generated from open ocean data and global DOM data sets. The "humic-like" FDOM was also found in cyanobacteria FDOM and showed similar fluorescence intensities and percent fluorescence loss when compared to marine DOM. The degradation kinetics of the "humic-like" component of microbial-derived DOM was faster than that of terrestrial-derived DOM, and marine FDOM samples showed degradation kinetics more similar to microbial-derived FDOM. This indicates marine FDOM is more similar in chemical composition to microbial-derived FDOM than terrestrial-derived FDOM, supporting the hypothesis that the majority of marine FDOM is produced in situ.

Spatiotemporal variations in the abundance and composition of bulk and chromophoric dissolved organic matter in seasonally hypoxia-influenced Green Bay, Lake Michigan, USA.

PubMed

DeVilbiss, Stephen E; Zhou, Zhengzhen; Klump, J Val; Guo, Laodong

2016-09-15

Green Bay, Lake Michigan, USA, is the largest freshwater estuary in the Laurentian Great Lakes and receives disproportional terrestrial inputs as a result of a high watershed to bay surface area ratio. While seasonal hypoxia and the formation of "dead zones" in Green Bay have received increasing attention, there are no systematic studies on the dynamics of dissolved organic matter (DOM) and its linkage to the development of hypoxia. During summer 2014, bulk dissolved organic carbon (DOC) analysis, UV-vis spectroscopy, and fluorescence excitation-emission matrices (EEMs) coupled with PARAFAC analysis were used to quantify the abundance, composition and source of DOM and their spatiotemporal variations in Green Bay, Lake Michigan. Concentrations of DOC ranged from 202 to 571μM-C (average=361±73μM-C) in June and from 279 to 610μM-C (average=349±64μM-C) in August. In both months, absorption coefficient at 254nm (a254) was strongly correlated to bulk DOC and was most abundant in the Fox River, attesting a dominant terrestrial input. Non-chromophoric DOC comprised, on average, ~32% of bulk DOC in June with higher terrestrial DOM and ~47% in August with higher aquagenic DOM, indicating that autochthonous and more degraded DOM is of lower optical activity. PARAFAC modeling on EEM data resulted in four major fluorescent DOM components, including two terrestrial humic-like, one aquagenic humic-like, and one protein-like component. Variations in the abundance of DOM components further supported changes in DOM sources. Mixing behavior of DOM components also indicated that while bulk DOM behaved quasi-conservatively, significant compositional changes occurred during transport from the Fox River to the open bay. Copyright © 2016 Elsevier B.V. All rights reserved.

Characterising Event-Based DOM Inputs to an Urban Watershed

NASA Astrophysics Data System (ADS)

Croghan, D.; Bradley, C.; Hannah, D. M.; Van Loon, A.; Sadler, J. P.

2017-12-01

Dissolved Organic Matter (DOM) composition in urban streams is dominated by terrestrial inputs after rainfall events. Urban streams have particularly strong terrestrial-riverine connections due to direct input from terrestrial drainage systems. Event driven DOM inputs can have substantial adverse effects on water quality. Despite this, DOM from important catchment sources such as road drains and Combined Sewage Overflows (CSO's) remains poorly characterised within urban watersheds. We studied DOM sources within an urbanised, headwater watershed in Birmingham, UK. Samples from terrestrial sources (roads, roofs and a CSO), were collected manually after the onset of rainfall events of varying magnitude, and again within 24-hrs of the event ending. Terrestrial samples were analysed for fluorescence, absorbance and Dissolved Organic Carbon (DOC) concentration. Fluorescence and absorbance indices were calculated, and Parallel Factor Analysis (PARAFAC) was undertaken to aid sample characterization. Substantial differences in fluorescence, absorbance, and DOC were observed between source types. PARAFAC-derived components linked to organic pollutants were generally highest within road derived samples, whilst humic-like components tended to be highest within roof samples. Samples taken from the CSO generally contained low fluorescence, however this likely represents a dilution effect. Variation within source groups was particularly high, and local land use seemed to be the driving factor for road and roof drain DOM character and DOC quantity. Furthermore, high variation in fluorescence, absorbance and DOC was apparent between all sources depending on event type. Drier antecedent conditions in particular were linked to greater presence of terrestrially-derived components and higher DOC content. Our study indicates that high variations in DOM character occur between source types, and over small spatial scales. Road drains located on main roads appear to contain the poorest quality DOM of the sources studied due to the presence of hydrocarbons. In order to prevent storm-derived DOM degradation of water quality of urban streams, greater knowledge of links between these drainage sources, and their pathways to streams is required.

Hydrological Controls on Dissolved Organic Matter Quality and Export in a Coastal River System in Southeastern USA

NASA Astrophysics Data System (ADS)

Bhattacharya, R.; Osburn, C. L.

2017-12-01

Dissolved organic matter (DOM) exported from river catchments can influence the biogeochemical processes in coastal environments with implications for water quality and carbon budget. High flow conditions are responsible for most DOM export ("pulses") from watersheds, and these events reduce DOM transformation and production by "shunting" DOM from river networks into coastal waters: the Pulse-Shunt Concept (PSC). Subsequently, the source and quality of DOM is also expected to change as a function of river flow. Here, we used stream dissolved organic carbon concentrations ([DOC]) along with DOM optical properties, such as absorbance at 350 nm (a350) and fluorescence excitation and emission matrices modeled by parallel factor analysis (PARAFAC), to characterize DOM source, quality and fluxes under variable flow conditions for the Neuse River, a coastal river system in the southeastern US. Observations were made at a flow gauged station above head of tide periodically between Aug 2011 and Feb 2013, which captured low flow periods in summer and several high flow events including Hurricane Irene. [DOC] and a350 were correlated and varied positively with river flow, implying that a large portion of the DOM was colored, humic and flow-mobilized. During high flow conditions, PARAFAC results demonstrated the higher influx of terrestrial humic DOM, and lower in-stream phytoplankton production or microbial degradation. However, during low flow, DOM transformation and production increased in response to higher residence times and elevated productivity. Further, 70% of the DOC was exported by above average flows, where 3-4 fold increases in DOC fluxes were observed during episodic events, consistent with PSC. These results imply that storms dramatically affects DOM export to coastal waters, whereby high river flow caused by episodic events primarily shunt terrestrial DOM to coastal waters, whereas low flow promotes in-stream DOM transformation and amendment with microbial DOM.

Caracterisation of anthropogenic contribution to the coastal fluorescent organic matter

NASA Astrophysics Data System (ADS)

El Nahhal, Ibrahim; Nouhi, Ayoub; Mounier, Stéphane

2015-04-01

It is known that most of the coastal fluorescent organic matter is of a terrestrial origin (Parlanti, 2000; Tedetti, Guigue, & Goutx, 2010). However, the contribution of the anthropogenic organic matter to this pool is not well defined and evaluated. In this work the monitoring of little bay (Toulon Bay, France) was done in the way to determine the organic fluorescent response during a winter period. The sampling campaign consisted of different days during the month of December, 2014 ( 12th, 15th, 17th, 19th) on 21 different sampling sites for the fluorescence measurements (without any filtering of the samples) and the whole month of December for the bacterial and the turbidity measurements. Excitation Emission Matrices (EEMs) of fluorescence (from 200 to 400 nm and 220 to 420 nm excitation and emission range) were treated by parallel factor analysis (PARAFAC).The parafac analysis of the EEM datasets was conducted using PROGMEEF software in Matlab langage. On the same time that the turbidity and bacterial measurement (particularly the E.Coli concentration) were determined. The results gives in a short time range, information on the the contribution of the anthropogenic inputs to the coastal fluorescent organic matter. In addition, the effect of salinity on the photochemical degradation of the anthropogenic organic matter (especially those from wastewater treatment plants) will be studied to investigate their fate in the water end member by the way of laboratory experiments. Parlanti, E. (2000). Dissolved organic matter fluorescence spectroscopy as a tool to estimate biological activity in a coastal zone submitted to anthropogenic inputs. Organic Geochemistry, 31(12), 1765-1781. doi:10.1016/S0146-6380(00)00124-8 Tedetti, M., Guigue, C., & Goutx, M. (2010). Utilization of a submersible UV fluorometer for monitoring anthropogenic inputs in the Mediterranean coastal waters. Marine Pollution Bulletin, 60(3), 350-62. doi:10.1016/j.marpolbul.2009.10.018

Quality by Design: Multidimensional exploration of the design space in high performance liquid chromatography method development for better robustness before validation.

PubMed

Monks, K; Molnár, I; Rieger, H-J; Bogáti, B; Szabó, E

2012-04-06

Robust HPLC separations lead to fewer analysis failures and better method transfer as well as providing an assurance of quality. This work presents the systematic development of an optimal, robust, fast UHPLC method for the simultaneous assay of two APIs of an eye drop sample and their impurities, in accordance with Quality by Design principles. Chromatography software is employed to effectively generate design spaces (Method Operable Design Regions), which are subsequently employed to determine the final method conditions and to evaluate robustness prior to validation. Copyright © 2011 Elsevier B.V. All rights reserved.

Robust Variable Selection with Exponential Squared Loss.

PubMed

Wang, Xueqin; Jiang, Yunlu; Huang, Mian; Zhang, Heping

2013-04-01

Robust variable selection procedures through penalized regression have been gaining increased attention in the literature. They can be used to perform variable selection and are expected to yield robust estimates. However, to the best of our knowledge, the robustness of those penalized regression procedures has not been well characterized. In this paper, we propose a class of penalized robust regression estimators based on exponential squared loss. The motivation for this new procedure is that it enables us to characterize its robustness that has not been done for the existing procedures, while its performance is near optimal and superior to some recently developed methods. Specifically, under defined regularity conditions, our estimators are [Formula: see text] and possess the oracle property. Importantly, we show that our estimators can achieve the highest asymptotic breakdown point of 1/2 and that their influence functions are bounded with respect to the outliers in either the response or the covariate domain. We performed simulation studies to compare our proposed method with some recent methods, using the oracle method as the benchmark. We consider common sources of influential points. Our simulation studies reveal that our proposed method performs similarly to the oracle method in terms of the model error and the positive selection rate even in the presence of influential points. In contrast, other existing procedures have a much lower non-causal selection rate. Furthermore, we re-analyze the Boston Housing Price Dataset and the Plasma Beta-Carotene Level Dataset that are commonly used examples for regression diagnostics of influential points. Our analysis unravels the discrepancies of using our robust method versus the other penalized regression method, underscoring the importance of developing and applying robust penalized regression methods.

Robust Variable Selection with Exponential Squared Loss

PubMed Central

Wang, Xueqin; Jiang, Yunlu; Huang, Mian; Zhang, Heping

2013-01-01

Robust variable selection procedures through penalized regression have been gaining increased attention in the literature. They can be used to perform variable selection and are expected to yield robust estimates. However, to the best of our knowledge, the robustness of those penalized regression procedures has not been well characterized. In this paper, we propose a class of penalized robust regression estimators based on exponential squared loss. The motivation for this new procedure is that it enables us to characterize its robustness that has not been done for the existing procedures, while its performance is near optimal and superior to some recently developed methods. Specifically, under defined regularity conditions, our estimators are n-consistent and possess the oracle property. Importantly, we show that our estimators can achieve the highest asymptotic breakdown point of 1/2 and that their influence functions are bounded with respect to the outliers in either the response or the covariate domain. We performed simulation studies to compare our proposed method with some recent methods, using the oracle method as the benchmark. We consider common sources of influential points. Our simulation studies reveal that our proposed method performs similarly to the oracle method in terms of the model error and the positive selection rate even in the presence of influential points. In contrast, other existing procedures have a much lower non-causal selection rate. Furthermore, we re-analyze the Boston Housing Price Dataset and the Plasma Beta-Carotene Level Dataset that are commonly used examples for regression diagnostics of influential points. Our analysis unravels the discrepancies of using our robust method versus the other penalized regression method, underscoring the importance of developing and applying robust penalized regression methods. PMID:23913996

Designing robust control laws using genetic algorithms

NASA Technical Reports Server (NTRS)

Marrison, Chris

1994-01-01

The purpose of this research is to create a method of finding practical, robust control laws. The robustness of a controller is judged by Stochastic Robustness metrics and the level of robustness is optimized by searching for design parameters that minimize a robustness cost function.

Robust Segmentation of Planar and Linear Features of Terrestrial Laser Scanner Point Clouds Acquired from Construction Sites.

PubMed

Maalek, Reza; Lichti, Derek D; Ruwanpura, Janaka Y

2018-03-08

Automated segmentation of planar and linear features of point clouds acquired from construction sites is essential for the automatic extraction of building construction elements such as columns, beams and slabs. However, many planar and linear segmentation methods use scene-dependent similarity thresholds that may not provide generalizable solutions for all environments. In addition, outliers exist in construction site point clouds due to data artefacts caused by moving objects, occlusions and dust. To address these concerns, a novel method for robust classification and segmentation of planar and linear features is proposed. First, coplanar and collinear points are classified through a robust principal components analysis procedure. The classified points are then grouped using a new robust clustering method, the robust complete linkage method. A robust method is also proposed to extract the points of flat-slab floors and/or ceilings independent of the aforementioned stages to improve computational efficiency. The applicability of the proposed method is evaluated in eight datasets acquired from a complex laboratory environment and two construction sites at the University of Calgary. The precision, recall, and accuracy of the segmentation at both construction sites were 96.8%, 97.7% and 95%, respectively. These results demonstrate the suitability of the proposed method for robust segmentation of planar and linear features of contaminated datasets, such as those collected from construction sites.

Robust Segmentation of Planar and Linear Features of Terrestrial Laser Scanner Point Clouds Acquired from Construction Sites

PubMed Central

Maalek, Reza; Lichti, Derek D; Ruwanpura, Janaka Y

2018-01-01

Automated segmentation of planar and linear features of point clouds acquired from construction sites is essential for the automatic extraction of building construction elements such as columns, beams and slabs. However, many planar and linear segmentation methods use scene-dependent similarity thresholds that may not provide generalizable solutions for all environments. In addition, outliers exist in construction site point clouds due to data artefacts caused by moving objects, occlusions and dust. To address these concerns, a novel method for robust classification and segmentation of planar and linear features is proposed. First, coplanar and collinear points are classified through a robust principal components analysis procedure. The classified points are then grouped using a new robust clustering method, the robust complete linkage method. A robust method is also proposed to extract the points of flat-slab floors and/or ceilings independent of the aforementioned stages to improve computational efficiency. The applicability of the proposed method is evaluated in eight datasets acquired from a complex laboratory environment and two construction sites at the University of Calgary. The precision, recall, and accuracy of the segmentation at both construction sites were 96.8%, 97.7% and 95%, respectively. These results demonstrate the suitability of the proposed method for robust segmentation of planar and linear features of contaminated datasets, such as those collected from construction sites. PMID:29518062

Robust, Optimal Subsonic Airfoil Shapes

NASA Technical Reports Server (NTRS)

Rai, Man Mohan

2014-01-01

A method has been developed to create an airfoil robust enough to operate satisfactorily in different environments. This method determines a robust, optimal, subsonic airfoil shape, beginning with an arbitrary initial airfoil shape, and imposes the necessary constraints on the design. Also, this method is flexible and extendible to a larger class of requirements and changes in constraints imposed.

Robust Smoothing: Smoothing Parameter Selection and Applications to Fluorescence Spectroscopy∂

PubMed Central

Lee, Jong Soo; Cox, Dennis D.

2009-01-01

Fluorescence spectroscopy has emerged in recent years as an effective way to detect cervical cancer. Investigation of the data preprocessing stage uncovered a need for a robust smoothing to extract the signal from the noise. Various robust smoothing methods for estimating fluorescence emission spectra are compared and data driven methods for the selection of smoothing parameter are suggested. The methods currently implemented in R for smoothing parameter selection proved to be unsatisfactory, and a computationally efficient procedure that approximates robust leave-one-out cross validation is presented. PMID:20729976

Robust keyword retrieval method for OCRed text

NASA Astrophysics Data System (ADS)

Fujii, Yusaku; Takebe, Hiroaki; Tanaka, Hiroshi; Hotta, Yoshinobu

2011-01-01

Document management systems have become important because of the growing popularity of electronic filing of documents and scanning of books, magazines, manuals, etc., through a scanner or a digital camera, for storage or reading on a PC or an electronic book. Text information acquired by optical character recognition (OCR) is usually added to the electronic documents for document retrieval. Since texts generated by OCR generally include character recognition errors, robust retrieval methods have been introduced to overcome this problem. In this paper, we propose a retrieval method that is robust against both character segmentation and recognition errors. In the proposed method, the insertion of noise characters and dropping of characters in the keyword retrieval enables robustness against character segmentation errors, and character substitution in the keyword of the recognition candidate for each character in OCR or any other character enables robustness against character recognition errors. The recall rate of the proposed method was 15% higher than that of the conventional method. However, the precision rate was 64% lower.

Robust Statistical Approaches for RSS-Based Floor Detection in Indoor Localization.

PubMed

Razavi, Alireza; Valkama, Mikko; Lohan, Elena Simona

2016-05-31

Floor detection for indoor 3D localization of mobile devices is currently an important challenge in the wireless world. Many approaches currently exist, but usually the robustness of such approaches is not addressed or investigated. The goal of this paper is to show how to robustify the floor estimation when probabilistic approaches with a low number of parameters are employed. Indeed, such an approach would allow a building-independent estimation and a lower computing power at the mobile side. Four robustified algorithms are to be presented: a robust weighted centroid localization method, a robust linear trilateration method, a robust nonlinear trilateration method, and a robust deconvolution method. The proposed approaches use the received signal strengths (RSS) measured by the Mobile Station (MS) from various heard WiFi access points (APs) and provide an estimate of the vertical position of the MS, which can be used for floor detection. We will show that robustification can indeed increase the performance of the RSS-based floor detection algorithms.

Robust optimization methods for cardiac sparing in tangential breast IMRT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mahmoudzadeh, Houra, E-mail: houra@mie.utoronto.ca; Lee, Jenny; Chan, Timothy C. Y.

Purpose: In left-sided tangential breast intensity modulated radiation therapy (IMRT), the heart may enter the radiation field and receive excessive radiation while the patient is breathing. The patient’s breathing pattern is often irregular and unpredictable. We verify the clinical applicability of a heart-sparing robust optimization approach for breast IMRT. We compare robust optimized plans with clinical plans at free-breathing and clinical plans at deep inspiration breath-hold (DIBH) using active breathing control (ABC). Methods: Eight patients were included in the study with each patient simulated using 4D-CT. The 4D-CT image acquisition generated ten breathing phase datasets. An average scan was constructedmore » using all the phase datasets. Two of the eight patients were also imaged at breath-hold using ABC. The 4D-CT datasets were used to calculate the accumulated dose for robust optimized and clinical plans based on deformable registration. We generated a set of simulated breathing probability mass functions, which represent the fraction of time patients spend in different breathing phases. The robust optimization method was applied to each patient using a set of dose-influence matrices extracted from the 4D-CT data and a model of the breathing motion uncertainty. The goal of the optimization models was to minimize the dose to the heart while ensuring dose constraints on the target were achieved under breathing motion uncertainty. Results: Robust optimized plans were improved or equivalent to the clinical plans in terms of heart sparing for all patients studied. The robust method reduced the accumulated heart dose (D10cc) by up to 801 cGy compared to the clinical method while also improving the coverage of the accumulated whole breast target volume. On average, the robust method reduced the heart dose (D10cc) by 364 cGy and improved the optBreast dose (D99%) by 477 cGy. In addition, the robust method had smaller deviations from the planned dose to the accumulated dose. The deviation of the accumulated dose from the planned dose for the optBreast (D99%) was 12 cGy for robust versus 445 cGy for clinical. The deviation for the heart (D10cc) was 41 cGy for robust and 320 cGy for clinical. Conclusions: The robust optimization approach can reduce heart dose compared to the clinical method at free-breathing and can potentially reduce the need for breath-hold techniques.« less

Comparison of linear and nonlinear programming approaches for "worst case dose" and "minmax" robust optimization of intensity-modulated proton therapy dose distributions.

PubMed

Zaghian, Maryam; Cao, Wenhua; Liu, Wei; Kardar, Laleh; Randeniya, Sharmalee; Mohan, Radhe; Lim, Gino

2017-03-01

Robust optimization of intensity-modulated proton therapy (IMPT) takes uncertainties into account during spot weight optimization and leads to dose distributions that are resilient to uncertainties. Previous studies demonstrated benefits of linear programming (LP) for IMPT in terms of delivery efficiency by considerably reducing the number of spots required for the same quality of plans. However, a reduction in the number of spots may lead to loss of robustness. The purpose of this study was to evaluate and compare the performance in terms of plan quality and robustness of two robust optimization approaches using LP and nonlinear programming (NLP) models. The so-called "worst case dose" and "minmax" robust optimization approaches and conventional planning target volume (PTV)-based optimization approach were applied to designing IMPT plans for five patients: two with prostate cancer, one with skull-based cancer, and two with head and neck cancer. For each approach, both LP and NLP models were used. Thus, for each case, six sets of IMPT plans were generated and assessed: LP-PTV-based, NLP-PTV-based, LP-worst case dose, NLP-worst case dose, LP-minmax, and NLP-minmax. The four robust optimization methods behaved differently from patient to patient, and no method emerged as superior to the others in terms of nominal plan quality and robustness against uncertainties. The plans generated using LP-based robust optimization were more robust regarding patient setup and range uncertainties than were those generated using NLP-based robust optimization for the prostate cancer patients. However, the robustness of plans generated using NLP-based methods was superior for the skull-based and head and neck cancer patients. Overall, LP-based methods were suitable for the less challenging cancer cases in which all uncertainty scenarios were able to satisfy tight dose constraints, while NLP performed better in more difficult cases in which most uncertainty scenarios were hard to meet tight dose limits. For robust optimization, the worst case dose approach was less sensitive to uncertainties than was the minmax approach for the prostate and skull-based cancer patients, whereas the minmax approach was superior for the head and neck cancer patients. The robustness of the IMPT plans was remarkably better after robust optimization than after PTV-based optimization, and the NLP-PTV-based optimization outperformed the LP-PTV-based optimization regarding robustness of clinical target volume coverage. In addition, plans generated using LP-based methods had notably fewer scanning spots than did those generated using NLP-based methods. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Robust and High Order Computational Method for Parachute and Air Delivery and MAV System

DTIC Science & Technology

2017-11-01

Report: Robust and High Order Computational Method for Parachute and Air Delivery and MAV System The views, opinions and/or findings contained in this...University Title: Robust and High Order Computational Method for Parachute and Air Delivery and MAV System Report Term: 0-Other Email: xiaolin.li...model coupled with an incompressible fluid solver through the impulse method . Our approach to simulating the parachute system is based on the front

Probabilistic BPRRC: Robust Change Detection against Illumination Changes and Background Movements

NASA Astrophysics Data System (ADS)

Yokoi, Kentaro

This paper presents Probabilistic Bi-polar Radial Reach Correlation (PrBPRRC), a change detection method that is robust against illumination changes and background movements. Most of the traditional change detection methods are robust against either illumination changes or background movements; BPRRC is one of the illumination-robust change detection methods. We introduce a probabilistic background texture model into BPRRC and add the robustness against background movements including foreground invasions such as moving cars, walking people, swaying trees, and falling snow. We show the superiority of PrBPRRC in the environment with illumination changes and background movements by using three public datasets and one private dataset: ATON Highway data, Karlsruhe traffic sequence data, PETS 2007 data, and Walking-in-a-room data.

Robust Optimization Design for Turbine Blade-Tip Radial Running Clearance using Hierarchically Response Surface Method

NASA Astrophysics Data System (ADS)

Zhiying, Chen; Ping, Zhou

2017-11-01

Considering the robust optimization computational precision and efficiency for complex mechanical assembly relationship like turbine blade-tip radial running clearance, a hierarchically response surface robust optimization algorithm is proposed. The distribute collaborative response surface method is used to generate assembly system level approximation model of overall parameters and blade-tip clearance, and then a set samples of design parameters and objective response mean and/or standard deviation is generated by using system approximation model and design of experiment method. Finally, a new response surface approximation model is constructed by using those samples, and this approximation model is used for robust optimization process. The analyses results demonstrate the proposed method can dramatic reduce the computational cost and ensure the computational precision. The presented research offers an effective way for the robust optimization design of turbine blade-tip radial running clearance.

Robust path planning for flexible needle insertion using Markov decision processes.

PubMed

Tan, Xiaoyu; Yu, Pengqian; Lim, Kah-Bin; Chui, Chee-Kong

2018-05-11

Flexible needle has the potential to accurately navigate to a treatment region in the least invasive manner. We propose a new planning method using Markov decision processes (MDPs) for flexible needle navigation that can perform robust path planning and steering under the circumstance of complex tissue-needle interactions. This method enhances the robustness of flexible needle steering from three different perspectives. First, the method considers the problem caused by soft tissue deformation. The method then resolves the common needle penetration failure caused by patterns of targets, while the last solution addresses the uncertainty issues in flexible needle motion due to complex and unpredictable tissue-needle interaction. Computer simulation and phantom experimental results show that the proposed method can perform robust planning and generate a secure control policy for flexible needle steering. Compared with a traditional method using MDPs, the proposed method achieves higher accuracy and probability of success in avoiding obstacles under complicated and uncertain tissue-needle interactions. Future work will involve experiment with biological tissue in vivo. The proposed robust path planning method can securely steer flexible needle within soft phantom tissues and achieve high adaptability in computer simulation.

Efficient Variable Selection Method for Exposure Variables on Binary Data

NASA Astrophysics Data System (ADS)

Ohno, Manabu; Tarumi, Tomoyuki

In this paper, we propose a new variable selection method for "robust" exposure variables. We define "robust" as property that the same variable can select among original data and perturbed data. There are few studies of effective for the selection method. The problem that selects exposure variables is almost the same as a problem that extracts correlation rules without robustness. [Brin 97] is suggested that correlation rules are possible to extract efficiently using chi-squared statistic of contingency table having monotone property on binary data. But the chi-squared value does not have monotone property, so it's is easy to judge the method to be not independent with an increase in the dimension though the variable set is completely independent, and the method is not usable in variable selection for robust exposure variables. We assume anti-monotone property for independent variables to select robust independent variables and use the apriori algorithm for it. The apriori algorithm is one of the algorithms which find association rules from the market basket data. The algorithm use anti-monotone property on the support which is defined by association rules. But independent property does not completely have anti-monotone property on the AIC of independent probability model, but the tendency to have anti-monotone property is strong. Therefore, selected variables with anti-monotone property on the AIC have robustness. Our method judges whether a certain variable is exposure variable for the independent variable using previous comparison of the AIC. Our numerical experiments show that our method can select robust exposure variables efficiently and precisely.

Multi-time Scale Joint Scheduling Method Considering the Grid of Renewable Energy

NASA Astrophysics Data System (ADS)

Zhijun, E.; Wang, Weichen; Cao, Jin; Wang, Xin; Kong, Xiangyu; Quan, Shuping

2018-01-01

Renewable new energy power generation prediction error like wind and light, brings difficulties to dispatch the power system. In this paper, a multi-time scale robust scheduling method is set to solve this problem. It reduces the impact of clean energy prediction bias to the power grid by using multi-time scale (day-ahead, intraday, real time) and coordinating the dispatching power output of various power supplies such as hydropower, thermal power, wind power, gas power and. The method adopts the robust scheduling method to ensure the robustness of the scheduling scheme. By calculating the cost of the abandon wind and the load, it transforms the robustness into the risk cost and optimizes the optimal uncertainty set for the smallest integrative costs. The validity of the method is verified by simulation.

Online two-stage association method for robust multiple people tracking

NASA Astrophysics Data System (ADS)

Lv, Jingqin; Fang, Jiangxiong; Yang, Jie

2011-07-01

Robust multiple people tracking is very important for many applications. It is a challenging problem due to occlusion and interaction in crowded scenarios. This paper proposes an online two-stage association method for robust multiple people tracking. In the first stage, short tracklets generated by linking people detection responses grow longer by particle filter based tracking, with detection confidence embedded into the observation model. And, an examining scheme runs at each frame for the reliability of tracking. In the second stage, multiple people tracking is achieved by linking tracklets to generate trajectories. An online tracklet association method is proposed to solve the linking problem, which allows applications in time-critical scenarios. This method is evaluated on the popular CAVIAR dataset. The experimental results show that our two-stage method is robust.

The 32nd CDC: Robust stabilizer synthesis for interval plants using Nevanlina-pick theory

NASA Technical Reports Server (NTRS)

Bhattacharya, Saikat; Keel, L. H.; Bhattacharyya, S. P.

1989-01-01

The synthesis of robustly stabilizing compensators for interval plants, i.e., plants whose parameters vary within prescribed ranges is discussed. Well-known H(sup infinity) methods are used to establish robust stabilizability conditions for a family of plants and also to synthesize controllers that would stabilize the whole family. Though conservative, these methods give a very simple way to come up with a family of robust stabilizers for an interval plant.

Robust time and frequency domain estimation methods in adaptive control

NASA Technical Reports Server (NTRS)

Lamaire, Richard Orville

1987-01-01

A robust identification method was developed for use in an adaptive control system. The type of estimator is called the robust estimator, since it is robust to the effects of both unmodeled dynamics and an unmeasurable disturbance. The development of the robust estimator was motivated by a need to provide guarantees in the identification part of an adaptive controller. To enable the design of a robust control system, a nominal model as well as a frequency-domain bounding function on the modeling uncertainty associated with this nominal model must be provided. Two estimation methods are presented for finding parameter estimates, and, hence, a nominal model. One of these methods is based on the well developed field of time-domain parameter estimation. In a second method of finding parameter estimates, a type of weighted least-squares fitting to a frequency-domain estimated model is used. The frequency-domain estimator is shown to perform better, in general, than the time-domain parameter estimator. In addition, a methodology for finding a frequency-domain bounding function on the disturbance is used to compute a frequency-domain bounding function on the additive modeling error due to the effects of the disturbance and the use of finite-length data. The performance of the robust estimator in both open-loop and closed-loop situations is examined through the use of simulations.

Robust adaptive multichannel SAR processing based on covariance matrix reconstruction

NASA Astrophysics Data System (ADS)

Tan, Zhen-ya; He, Feng

2018-04-01

With the combination of digital beamforming (DBF) processing, multichannel synthetic aperture radar(SAR) systems in azimuth promise well in high-resolution and wide-swath imaging, whereas conventional processing methods don't take the nonuniformity of scattering coefficient into consideration. This paper brings up a robust adaptive Multichannel SAR processing method which utilizes the Capon spatial spectrum estimator to obtain the spatial spectrum distribution over all ambiguous directions first, and then the interference-plus-noise covariance Matrix is reconstructed based on definition to acquire the Multichannel SAR processing filter. The performance of processing under nonuniform scattering coefficient is promoted by this novel method and it is robust again array errors. The experiments with real measured data demonstrate the effectiveness and robustness of the proposed method.

TENSOR DECOMPOSITIONS AND SPARSE LOG-LINEAR MODELS

PubMed Central

Johndrow, James E.; Bhattacharya, Anirban; Dunson, David B.

2017-01-01

Contingency table analysis routinely relies on log-linear models, with latent structure analysis providing a common alternative. Latent structure models lead to a reduced rank tensor factorization of the probability mass function for multivariate categorical data, while log-linear models achieve dimensionality reduction through sparsity. Little is known about the relationship between these notions of dimensionality reduction in the two paradigms. We derive several results relating the support of a log-linear model to nonnegative ranks of the associated probability tensor. Motivated by these findings, we propose a new collapsed Tucker class of tensor decompositions, which bridge existing PARAFAC and Tucker decompositions, providing a more flexible framework for parsimoniously characterizing multivariate categorical data. Taking a Bayesian approach to inference, we illustrate empirical advantages of the new decompositions. PMID:29332971

Variable fidelity robust optimization of pulsed laser orbital debris removal under epistemic uncertainty

NASA Astrophysics Data System (ADS)

Hou, Liqiang; Cai, Yuanli; Liu, Jin; Hou, Chongyuan

2016-04-01

A variable fidelity robust optimization method for pulsed laser orbital debris removal (LODR) under uncertainty is proposed. Dempster-shafer theory of evidence (DST), which merges interval-based and probabilistic uncertainty modeling, is used in the robust optimization. The robust optimization method optimizes the performance while at the same time maximizing its belief value. A population based multi-objective optimization (MOO) algorithm based on a steepest descent like strategy with proper orthogonal decomposition (POD) is used to search robust Pareto solutions. Analytical and numerical lifetime predictors are used to evaluate the debris lifetime after the laser pulses. Trust region based fidelity management is designed to reduce the computational cost caused by the expensive model. When the solutions fall into the trust region, the analytical model is used to reduce the computational cost. The proposed robust optimization method is first tested on a set of standard problems and then applied to the removal of Iridium 33 with pulsed lasers. It will be shown that the proposed approach can identify the most robust solutions with minimum lifetime under uncertainty.

Robust and Imperceptible Watermarking of Video Streams for Low Power Devices

NASA Astrophysics Data System (ADS)

Ishtiaq, Muhammad; Jaffar, M. Arfan; Khan, Muhammad A.; Jan, Zahoor; Mirza, Anwar M.

With the advent of internet, every aspect of life is going online. From online working to watching videos, everything is now available on the internet. With the greater business benefits, increased availability and other online business advantages, there is a major challenge of security and ownership of data. Videos downloaded from an online store can easily be shared among non-intended or unauthorized users. Invisible watermarking is used to hide copyright protection information in the videos. The existing methods of watermarking are less robust and imperceptible and also the computational complexity of these methods does not suit low power devices. In this paper, we have proposed a new method to address the problem of robustness and imperceptibility. Experiments have shown that our method has better robustness and imperceptibility as well as our method is computationally efficient than previous approaches in practice. Hence our method can easily be applied on low power devices.

Schizophrenia patients differentiation based on MR vascular perfusion and volumetric imaging

NASA Astrophysics Data System (ADS)

Spanier, A. B.; Joskowicz, L.; Moshel, S.; Israeli, D.

2015-03-01

Candecomp/Parafac Decomposition (CPD) has emerged as a framework for modeling N-way arrays (higher-order matrices). CPD is naturally well suited for the analysis of data sets comprised of observations of a function of multiple discrete indices. In this study we evaluate the prospects of using CPD for modeling MRI brain properties (i.e. brain volume and gray-level) for schizophrenia diagnosis. Taking into account that 3D imaging data consists of millions of pixels per patient, the diagnosis of a schizophrenia patient based on pixel analysis constitutes a methodological challenge (e.g. multiple comparison problem). We show that the CPD could potentially be used as a dimensionality redaction method and as a discriminator between schizophrenia patients and match control, using the gradient of pre- and post Gd-T1-weighted MRI data, which is strongly correlated with cerebral blood perfusion. Our approach was tested on 68 MRI scans: 40 first-episode schizophrenia patients and 28 matched controls. The CPD subject's scores exhibit statistically significant result (P < 0.001). In the context of diagnosing schizophrenia with MRI, the results suggest that the CPD could potentially be used to discriminate between schizophrenia patients and matched control. In addition, the CPD model suggests for brain regions that might exhibit abnormalities in schizophrenia patients for future research.

Effect of a solar Fered-Fenton system using a recirculation reactor on biologically treated landfill leachate.

PubMed

Ye, Zhihong; Zhang, Hui; Yang, Lin; Wu, Luxue; Qian, Yue; Geng, Jinyao; Chen, Mengmeng

2016-12-05

The effects of electrochemical oxidation (EO), Fered-Fenton and solar Fered-Fenton processes using a recirculation flow system containing an electrochemical cell and a solar photo-reactor on biochemically treated landfill leachate were investigated. The most successful method was solar Fered-Fenton which achieved 66.5% COD removal after 120min treatment utilizing the optimum operating conditions of 47mM H2O2, 0.29mM Fe(2+), pH0 of 3.0 and a current density of 60mA/cm(2). The generation of hydroxyl radicals (OH) are mainly from Fered-Fenton process, which is enhanced by the introduction of renewable solar energy. Moreover, Fe(2+)/chlorine and UV/chlorine processes taking place in this system also result in additional production of OH due to the relatively high concentration of chloride ions contained in the leachate. The energy consumption was 74.5kWh/kg COD and the current efficiency was 36.4% for 2h treatment. In addition, the molecular weight (MW) distribution analysis and PARAFAC analysis of excitation emission matrix (EEM) fluorescence spectroscopy for different leachate samples indicated that the organics in the leachate were significantly degraded into either small molecular weight species or inorganics. Copyright © 2016 Elsevier B.V. All rights reserved.

An adaptive discontinuous Galerkin solver for aerodynamic flows

NASA Astrophysics Data System (ADS)

Burgess, Nicholas K.

This work considers the accuracy, efficiency, and robustness of an unstructured high-order accurate discontinuous Galerkin (DG) solver for computational fluid dynamics (CFD). Recently, there has been a drive to reduce the discretization error of CFD simulations using high-order methods on unstructured grids. However, high-order methods are often criticized for lacking robustness and having high computational cost. The goal of this work is to investigate methods that enhance the robustness of high-order discontinuous Galerkin (DG) methods on unstructured meshes, while maintaining low computational cost and high accuracy of the numerical solutions. This work investigates robustness enhancement of high-order methods by examining effective non-linear solvers, shock capturing methods, turbulence model discretizations and adaptive refinement techniques. The goal is to develop an all encompassing solver that can simulate a large range of physical phenomena, where all aspects of the solver work together to achieve a robust, efficient and accurate solution strategy. The components and framework for a robust high-order accurate solver that is capable of solving viscous, Reynolds Averaged Navier-Stokes (RANS) and shocked flows is presented. In particular, this work discusses robust discretizations of the turbulence model equation used to close the RANS equations, as well as stable shock capturing strategies that are applicable across a wide range of discretization orders and applicable to very strong shock waves. Furthermore, refinement techniques are considered as both efficiency and robustness enhancement strategies. Additionally, efficient non-linear solvers based on multigrid and Krylov subspace methods are presented. The accuracy, efficiency, and robustness of the solver is demonstrated using a variety of challenging aerodynamic test problems, which include turbulent high-lift and viscous hypersonic flows. Adaptive mesh refinement was found to play a critical role in obtaining a robust and efficient high-order accurate flow solver. A goal-oriented error estimation technique has been developed to estimate the discretization error of simulation outputs. For high-order discretizations, it is shown that functional output error super-convergence can be obtained, provided the discretization satisfies a property known as dual consistency. The dual consistency of the DG methods developed in this work is shown via mathematical analysis and numerical experimentation. Goal-oriented error estimation is also used to drive an hp-adaptive mesh refinement strategy, where a combination of mesh or h-refinement, and order or p-enrichment, is employed based on the smoothness of the solution. The results demonstrate that the combination of goal-oriented error estimation and hp-adaptation yield superior accuracy, as well as enhanced robustness and efficiency for a variety of aerodynamic flows including flows with strong shock waves. This work demonstrates that DG discretizations can be the basis of an accurate, efficient, and robust CFD solver. Furthermore, enhancing the robustness of DG methods does not adversely impact the accuracy or efficiency of the solver for challenging and complex flow problems. In particular, when considering the computation of shocked flows, this work demonstrates that the available shock capturing techniques are sufficiently accurate and robust, particularly when used in conjunction with adaptive mesh refinement . This work also demonstrates that robust solutions of the Reynolds Averaged Navier-Stokes (RANS) and turbulence model equations can be obtained for complex and challenging aerodynamic flows. In this context, the most robust strategy was determined to be a low-order turbulence model discretization coupled to a high-order discretization of the RANS equations. Although RANS solutions using high-order accurate discretizations of the turbulence model were obtained, the behavior of current-day RANS turbulence models discretized to high-order was found to be problematic, leading to solver robustness issues. This suggests that future work is warranted in the area of turbulence model formulation for use with high-order discretizations. Alternately, the use of Large-Eddy Simulation (LES) subgrid scale models with high-order DG methods offers the potential to leverage the high accuracy of these methods for very high fidelity turbulent simulations. This thesis has developed the algorithmic improvements that will lay the foundation for the development of a three-dimensional high-order flow solution strategy that can be used as the basis for future LES simulations.

Comparison of robustness to outliers between robust poisson models and log-binomial models when estimating relative risks for common binary outcomes: a simulation study.

PubMed

Chen, Wansu; Shi, Jiaxiao; Qian, Lei; Azen, Stanley P

2014-06-26

To estimate relative risks or risk ratios for common binary outcomes, the most popular model-based methods are the robust (also known as modified) Poisson and the log-binomial regression. Of the two methods, it is believed that the log-binomial regression yields more efficient estimators because it is maximum likelihood based, while the robust Poisson model may be less affected by outliers. Evidence to support the robustness of robust Poisson models in comparison with log-binomial models is very limited. In this study a simulation was conducted to evaluate the performance of the two methods in several scenarios where outliers existed. The findings indicate that for data coming from a population where the relationship between the outcome and the covariate was in a simple form (e.g. log-linear), the two models yielded comparable biases and mean square errors. However, if the true relationship contained a higher order term, the robust Poisson models consistently outperformed the log-binomial models even when the level of contamination is low. The robust Poisson models are more robust (or less sensitive) to outliers compared to the log-binomial models when estimating relative risks or risk ratios for common binary outcomes. Users should be aware of the limitations when choosing appropriate models to estimate relative risks or risk ratios.

Robustness of coevolution in resolving prisoner's dilemma games on interdependent networks subject to attack

NASA Astrophysics Data System (ADS)

Liu, Penghui; Liu, Jing

2017-08-01

Recently, coevolution between strategy and network structure has been established as a rule to resolve social dilemmas and reach optimal situations for cooperation. Many follow-up researches have focused on studying how coevolution helps networks reorganize to deter the defectors and many coevolution methods have been proposed. However, the robustness of the coevolution rules against attacks have not been studied much. Since attacks may directly influence the original evolutionary process of cooperation, the robustness should be an important index while evaluating the quality of a coevolution method. In this paper, we focus on investigating the robustness of an elementary coevolution method in resolving the prisoner's dilemma game upon the interdependent networks. Three different types of time-independent attacks, named as edge attacks, instigation attacks and node attacks have been employed to test its robustness. Through analyzing the simulation results obtained, we find this coevolution method is relatively robust against the edge attack and the node attack as it successfully maintains cooperation in the population over the entire attack range. However, when the instigation probability of the attacked individuals is large or the attack range of instigation attack is wide enough, coevolutionary rule finally fails in maintaining cooperation in the population.

Comparisons of Robustness and Sensitivity between Cancer and Normal Cells by Microarray Data

PubMed Central

Chu, Liang-Hui; Chen, Bor-Sen

2008-01-01

Robustness is defined as the ability to uphold performance in face of perturbations and uncertainties, and sensitivity is a measure of the system deviations generated by perturbations to the system. While cancer appears as a robust but fragile system, few computational and quantitative evidences demonstrate robustness tradeoffs in cancer. Microarrays have been widely applied to decipher gene expression signatures in human cancer research, and quantification of global gene expression profiles facilitates precise prediction and modeling of cancer in systems biology. We provide several efficient computational methods based on system and control theory to compare robustness and sensitivity between cancer and normal cells by microarray data. Measurement of robustness and sensitivity by linear stochastic model is introduced in this study, which shows oscillations in feedback loops of p53 and demonstrates robustness tradeoffs that cancer is a robust system with some extreme fragilities. In addition, we measure sensitivity of gene expression to perturbations in other gene expression and kinetic parameters, discuss nonlinear effects in feedback loops of p53 and extend our method to robustness-based cancer drug design. PMID:19259409

Fast and robust reconstruction for fluorescence molecular tomography via a sparsity adaptive subspace pursuit method.

PubMed

Ye, Jinzuo; Chi, Chongwei; Xue, Zhenwen; Wu, Ping; An, Yu; Xu, Han; Zhang, Shuang; Tian, Jie

2014-02-01

Fluorescence molecular tomography (FMT), as a promising imaging modality, can three-dimensionally locate the specific tumor position in small animals. However, it remains challenging for effective and robust reconstruction of fluorescent probe distribution in animals. In this paper, we present a novel method based on sparsity adaptive subspace pursuit (SASP) for FMT reconstruction. Some innovative strategies including subspace projection, the bottom-up sparsity adaptive approach, and backtracking technique are associated with the SASP method, which guarantees the accuracy, efficiency, and robustness for FMT reconstruction. Three numerical experiments based on a mouse-mimicking heterogeneous phantom have been performed to validate the feasibility of the SASP method. The results show that the proposed SASP method can achieve satisfactory source localization with a bias less than 1mm; the efficiency of the method is much faster than mainstream reconstruction methods; and this approach is robust even under quite ill-posed condition. Furthermore, we have applied this method to an in vivo mouse model, and the results demonstrate the feasibility of the practical FMT application with the SASP method.

The nature of colored dissolved organic matter in the southern Canada Basin and East Siberian Sea

NASA Astrophysics Data System (ADS)

Guéguen, C.; McLaughlin, F. A.; Carmack, E. C.; Itoh, M.; Narita, H.; Nishino, S.

2012-12-01

Distributions of colored dissolved organic matter (CDOM) in the upper 400 m of the southern Canada Basin and East Siberian Sea were determined using an in situ WETStar fluorometer and fluorescence spectroscopy during cruises in 2008 as part of the Canada/US Joint Ocean Ice Study and Japan's International Polar Year program. Despite the low CDOM range (0.009-0.069 r.u.) observed in the upper 400 m of the study area, our results show that CDOM can be quantified from in situ DOM fluorescence sensor measurements. Unlike DOC concentrations, which are known to decrease with increasing depth, a pronounced mid-depth CDOM maximum was associated with the Pacific-derived winter water throughout our study area. Using parallel factor analysis (PARAFAC) to resolve dominant fluorophore components in fluorescence excitation-emission matrices (EEM), we identified three humic-like and two proteinaceous components. The nature and origin of these five fluorophores were investigated based on their fluorescent characteristics as well as their vertical and geographical distributions. The lowest terrestrial humic-like signals in the surface waters were mostly due to photochemical processes, whereas the highest microbial/marine humic-like signal revealed interactions with sediment during the formation of Pacific-origin haloclines over the Arctic shelves. The humic-like fluorophores dominated DOM fluorescence in the Westernmost region in the East Siberian Sea whereas the contribution of protein-like fluorophores was predominant elsewhere. The significant difference in CDOM composition between East and West of the 180° meridian suggests the presence of a front that divides our study area into the Eastern Chukchi—Beaufort and East Siberian sides. This indicates a change in water circulation, and that more than one DOM source affects our study area. Unlike proteinaceous material, the humic-like compounds varied significantly in the halocline. Ten to 20 percent enrichment was observed in terrestrially-derived DOM in the two Pacific-derived haloclines relative to the Atlantic-derived lower halocline. The application of PARAFAC modeling on fluorescent DOM is shown to be an important tool to investigate the dynamics and transport of allochthonous DOM in the Arctic Ocean.

Identification of the function of extracellular polymeric substances (EPS) in denitrifying phosphorus removal sludge in the presence of copper ion.

PubMed

Wang, Yayi; Qin, Jian; Zhou, Shuai; Lin, Ximao; Ye, Liu; Song, Chengkang; Yan, Yuan

2015-04-15

Industrial wastewater containing heavy metals that enters municipal wastewater treatment plants inevitably has a toxic impact on biological treatment processes. In this study, the impact of Cu(II) (0, 1.5, 2, 2.5, 3 mg/L) on the performance of denitrifying phosphorus removal (DPR) and microbial community structures was investigated. Particularly, the dynamic change in the amount and composition of extracellular polymeric substances (EPS), and the role of EPS in P removal, were assessed using three-dimensional excitation-emission matrix fluorescence spectroscopy combined with parallel factor (PARAFAC) analysis. The results showed that, after long-term adjustment, the P removal efficiency was maintained at 95 ± 2.7% at Cu(II) addition up to 2.5 mg/L, but deteriorated when the Cu(II) addition was 3 mg/L. The EPS content, including proteins and humic substances, increased with increasing Cu(II) additions at concentrations ≤2.5 mg/L. This property of EPS was beneficial for protecting phosphate-accumulating organisms (PAOs) against heavy metals, as both proteins and humic substances are strong ligands for Cu(II). Therefore, the PAOs abundance was still relatively high (67 ± 3%) when Cu(II) accumulation in sludge was up to 10 mg/g SS. PARAFAC confirmed that aromatic proteins could be transformed into soluble microbial byproduct-like material when microorganisms were subjected to Cu(II) stress, owing to their strong metal ion complexing capacity. The increase in the percentage of humic-like substances enhanced the detoxification function of the sludge EPS. EPS accounted for approximately 26-47% of P removed by adsorption when Cu(II) additions were between 0 and 2.5 mg/L. The EPS function, including binding toxic heavy metals and P storage, enhanced the operating stability of DPR systems. This study provides us with a better understanding of (1) the tolerance of DPR sludge to copper toxicity and (2) the function of sludge EPS in the presence of heavy metals in biological P removal systems. Copyright © 2015 Elsevier Ltd. All rights reserved.

Experimental design for the optimization of the derivatization reaction in determining chlorophenols and chloroanisoles by headspace-solid-phase microextraction-gas chromatography/mass spectrometry.

PubMed

Morales, Rocío; Sarabia, Luis A; Sánchez, M Sagrario; Ortiz, M Cruz

2013-06-28

The paper shows some tools (its interpretation and usefulness) to optimize a derivatization reaction and to more easily interpret and visualize the effect that some experimental factors exert on several analytical responses of interest when these responses are in conflict. The entire proposed procedure has been applied in the optimization of equilibrium/extraction temperature and extraction time in the acetylation reaction of 2,4,6-trichlorophenol; 2,3,4,6-tetrachlorophenol, pentachlorophenol and 2,4,6-tribromophenol as internal standard (IS) in presence of 2,4,6-trichloroanisole, 2,3,5,6-tetrachloroanisole, pentachloroanisole and 2,4,6-trichloroanisole-d5 as IS. The procedure relies on the second order advantage of PARAFAC (parallel factor analysis) that allows the unequivocal identification and quantification, mandatory according international regulations (in this paper the EU document SANCO/12495/2011), of the acetyl-chlorophenols and chloroanisoles that are determined by means of a HS-SPME-GC/MS automated device. The joint use of a PARAFAC decomposition and a Doehlert design provides the data to fit a response surface for each analyte. With the fitted surfaces, the overall desirability function and the Pareto-optimal front are used to describe the relation between the conditions of the derivatization reaction and the quantity extracted of each analyte. The visualization by using a parallel coordinates plot allows a deeper knowledge about the problem at hand as well as the wise selection of the conditions of the experimental factors for achieving specific goals about the responses. In the optimal experimental conditions (45°C and 25min) the determination by means of an automated HS-SPME-GC/MS system is carried out. By using the regression line fitted between calculated and true concentrations, it has been checked that the procedure has neither proportional nor constant bias. The decision limits, CCa, for probability a of false positive set to 0.05, vary between 0.221 and 0.420µgL(-1). Copyright © 2013 Elsevier B.V. All rights reserved.

The partitioning behavior of persistent toxicant organic contaminants in eutrophic sediments: Coefficients and effects of fluorescent organic matter and particle size.

PubMed

He, Wei; Yang, Chen; Liu, Wenxiu; He, Qishuang; Wang, Qingmei; Li, Yilong; Kong, Xiangzhen; Lan, Xinyu; Xu, Fuliu

2016-12-01

In the shallow lakes, the partitioning of organic contaminants into the water phase from the solid phase might pose a potential hazard to both benthic and planktonic organisms, which would further damage aquatic ecosystems. This study determined the concentrations of polycyclic aromatic hydrocarbons (PAHs), organochlorine pesticides (OCPs), and phthalate esters (PAEs) in both the sediment and the pore water from Lake Chaohu and calculated the sediment - pore water partition coefficient (K D ) and the organic carbon normalized sediment - pore water partition coefficient (K OC ), and explored the effects of particle size, organic matter content, and parallel factor fluorescent organic matter (PARAFAC-FOM) on K D . The results showed that log K D values of PAHs (2.61-3.94) and OCPs (1.75-3.05) were significantly lower than that of PAEs (4.13-5.05) (p < 0.05). The chemicals were ranked by log K OC as follows: PAEs (6.05-6.94) > PAHs (4.61-5.86) > OCPs (3.62-4.97). A modified MCI model can predict K OC values in a range of log 1.5 at a higher frequency, especially for PAEs. The significantly positive correlation between K OC and the octanol - water partition coefficient (K OW ) were observed for PAHs and OCPs. However, significant correlation was found for PAEs only when excluding PAEs with lower K OW . Sediments with smaller particle sizes (clay and silt) and their organic matter would affect distributions of PAHs and OCPs between the sediment and the pore water. Protein-like fluorescent organic matter (C2) was associated with the K D of PAEs. Furthermore, the partitioning of PARAFAC-FOM between the sediment and the pore water could potentially affect the distribution of organic pollutants. The partitioning mechanism of PAEs between the sediment and the pore water might be different from that of PAHs and OCPs, as indicated by their associations with influencing factors and K OW . Copyright © 2016 Elsevier Ltd. All rights reserved.

Differential fluorescence EEMs can be used to assess treatability of DOM during drinking water production

NASA Astrophysics Data System (ADS)

Lavonen, Elin; Kothawala, Dolly; Tranvik, Lars; Köhler, Stephan

2014-05-01

Fluorescence spectroscopy has been widely used to characterize fluorescent dissolved organic matter (FDOM) in various waters including during drinking water production. Commonly used techniques for data treatment include peak picking, indexes calculated from 2D emission spectra and modelling of fluorescence components using parallel factor analysis (PARAFAC). However, peak picking and indexes only use limited information from the fluorescence EEMs and PARAFAC requires a larger dataset and experience to perform. Because DOM is a major issue in drinking water production, and personnel at water treatment plants usually have limited time for advanced analysis we have developed a simple way of assessing the treatability of DOM in different waters using differential fluorescence. With this approach the removed fraction of FDOM is calculated from samples taken before and after a particular treatment process and the percentage of removed material assessed. Samples have been collected from four large water treatment plants in Sweden and analyzed for 3Dfluorescence, absorbance and DOC. The selective removal of DOM during e.g. flocculation and slow sand filtration as well as differences in experienced treatability between the treatment plants was described with differential fluorescence. Chemical flocculation is selective towards FDOM with red-shifted emission across the entire EEM. Red-shift has earlier been connected to condensation (i.e. decrease in H/C) and positively correlated to molecular size indicating that larger, humified molecules are being preferentially removed. During the biological process of slow sand filtration compounds with blue-shifted emission are targeted demonstrating selective removal of more freshly produced, microbial material. Disinfection with UV/NH2Cl and NaOCl was found to only target material with protein-like fluorescence suggesting that FDOM of this nature could be responsible for unwanted consumption of disinfection agent. Targeted removal of this fraction prior to disinfection should optimize the process. Furthermore, the main process at all studied WTPs is flocculation and their experienced treatability could easily be explained through the percentage of FDOM with emission above 450 nm (p<0.0001).

Unsupervised classification of petroleum Certified Reference Materials and other fuels by chemometric analysis of gas chromatography-mass spectrometry data

PubMed Central

de Carvalho Rocha, Werickson Fortunato; Schantz, Michele M.; Sheen, David A.; Chu, Pamela M.; Lippa, Katrice A.

2017-01-01

As feedstocks transition from conventional oil to unconventional petroleum sources and biomass, it will be necessary to determine whether a particular fuel or fuel blend is suitable for use in engines. Certifying a fuel as safe for use is time-consuming and expensive and must be performed for each new fuel. In principle, suitability of a fuel should be completely determined by its chemical composition. This composition can be probed through use of detailed analytical techniques such as gas chromatography-mass spectroscopy (GC-MS). In traditional analysis, chromatograms would be used to determine the details of the composition. In the approach taken in this paper, the chromatogram is assumed to be entirely representative of the composition of a fuel, and is used directly as the input to an algorithm in order to develop a model that is predictive of a fuel's suitability. When a new fuel is proposed for service, its suitability for any application could then be ascertained by using this model to compare its chromatogram with those of the fuels already known to be suitable for that application. In this paper, we lay the mathematical and informatics groundwork for a predictive model of hydrocarbon properties. The objective of this work was to develop a reliable model for unsupervised classification of the hydrocarbons as a prelude to developing a predictive model of their engine-relevant physical and chemical properties. A set of hydrocarbons including biodiesel fuels, gasoline, highway and marine diesel fuels, and crude oils was collected and GC-MS profiles obtained. These profiles were then analyzed using multi-way principal components analysis (MPCA), principal factors analysis (PARAFAC), and a self-organizing map (SOM), which is a kind of artificial neural network. It was found that, while MPCA and PARAFAC were able to recover descriptive models of the fuels, their linear nature obscured some of the finer physical details due to the widely varying composition of the fuels. The SOM was able to find a descriptive classification model which has the potential for practical recognition and perhaps prediction of fuel properties. PMID:28603295

Characterization of Organic Matter Sources within a Matrix of Land Use in Northeast Utah

NASA Astrophysics Data System (ADS)

Kelso, J. E.; Baker, M. A.

2017-12-01

Dynamics of organic matter (OM) sources in natural aquatic systems have been studied for decades, but urban studies have revealed additional, less studied, OM sources such as stormwater, lawn clippings, and wastewater effluent. Traditionally the OM pool in freshwater systems has been defined as a homogenous pool of varying size classes: course particulate, fine particulate and dissolved OM. Our goal was to identify and quantify the composition of fine particulate OM (FPOM), and dissolved OM (DOM) as derived from autochthonous, terrestrial, and potential anthropogenic sources. We hypothesized anthropogenic changes in land use have increased the proportion of autochthonous sources of OM. We sampled OM at 33 sites in four watersheds in northeast Utah that encompass a range of land uses. Stable isotopes of carbon, nitrogen, and deuterium were collected for all size classes of OM, and DOM was analyzed with a spectrofluorometer. Stable isotopes were used to estimate the proportion of autochthonous and terrestrial sources of OM. Fluorescence indices and a PARAFAC model were created from DOM excitation emission matrices (EEMs). FPOM appeared to be a mixture of autochthonous and terrestrial sources but overlap in endmember isotope values made quantifying the proportion of each source difficult. Higher deuterium values (-120 to -80‰) were associated with sites receiving wastewater effluent, while sites with agriculture, forest, and urban land use had lower deuterium isotope values (-200 to -110). DOM Excitation Emission Matrices were resolved into a 5-component PARAFAC model. The percent of protein-like DOM components tended to be higher in urban versus non-urban sites (mean 35%, S.D. 12% versus mean 25%, S.D. 15%). We concluded deuterium isotopes may be used as a tracer or wastewater effluent and DOM is composed of more labile, protein-like DOM with increased wastewater input. A greater understanding of the sources of OM can inform management and policy decisions aimed at mitigating the effects of OM pollution. For example, evaluating tradeoffs between mitigating the effects of OM inputs from cattle grazing versus building or improving waste water treatment facilities can be further explored.

Options for Robust Airfoil Optimization under Uncertainty

NASA Technical Reports Server (NTRS)

Padula, Sharon L.; Li, Wu

2002-01-01

A robust optimization method is developed to overcome point-optimization at the sampled design points. This method combines the best features from several preliminary methods proposed by the authors and their colleagues. The robust airfoil shape optimization is a direct method for drag reduction over a given range of operating conditions and has three advantages: (1) it prevents severe degradation in the off-design performance by using a smart descent direction in each optimization iteration, (2) it uses a large number of spline control points as design variables yet the resulting airfoil shape does not need to be smoothed, and (3) it allows the user to make a tradeoff between the level of optimization and the amount of computing time consumed. For illustration purposes, the robust optimization method is used to solve a lift-constrained drag minimization problem for a two-dimensional (2-D) airfoil in Euler flow with 20 geometric design variables.

Evaluation of Ares-I Control System Robustness to Uncertain Aerodynamics and Flex Dynamics

NASA Technical Reports Server (NTRS)

Jang, Jiann-Woei; VanTassel, Chris; Bedrossian, Nazareth; Hall, Charles; Spanos, Pol

2008-01-01

This paper discusses the application of robust control theory to evaluate robustness of the Ares-I control systems. Three techniques for estimating upper and lower bounds of uncertain parameters which yield stable closed-loop response are used here: (1) Monte Carlo analysis, (2) mu analysis, and (3) characteristic frequency response analysis. All three methods are used to evaluate stability envelopes of the Ares-I control systems with uncertain aerodynamics and flex dynamics. The results show that characteristic frequency response analysis is the most effective of these methods for assessing robustness.

Robust stabilization of the Space Station in the presence of inertia matrix uncertainty

NASA Technical Reports Server (NTRS)

Wie, Bong; Liu, Qiang; Sunkel, John

1993-01-01

This paper presents a robust H-infinity full-state feedback control synthesis method for uncertain systems with D11 not equal to 0. The method is applied to the robust stabilization problem of the Space Station in the face of inertia matrix uncertainty. The control design objective is to find a robust controller that yields the largest stable hypercube in uncertain parameter space, while satisfying the nominal performance requirements. The significance of employing an uncertain plant model with D11 not equal 0 is demonstrated.

A hybrid multi-objective imperialist competitive algorithm and Monte Carlo method for robust safety design of a rail vehicle

NASA Astrophysics Data System (ADS)

Nejlaoui, Mohamed; Houidi, Ajmi; Affi, Zouhaier; Romdhane, Lotfi

2017-10-01

This paper deals with the robust safety design optimization of a rail vehicle system moving in short radius curved tracks. A combined multi-objective imperialist competitive algorithm and Monte Carlo method is developed and used for the robust multi-objective optimization of the rail vehicle system. This robust optimization of rail vehicle safety considers simultaneously the derailment angle and its standard deviation where the design parameters uncertainties are considered. The obtained results showed that the robust design reduces significantly the sensitivity of the rail vehicle safety to the design parameters uncertainties compared to the determinist one and to the literature results.

Robust power spectral estimation for EEG data

PubMed Central

Melman, Tamar; Victor, Jonathan D.

2016-01-01

Background Typical electroencephalogram (EEG) recordings often contain substantial artifact. These artifacts, often large and intermittent, can interfere with quantification of the EEG via its power spectrum. To reduce the impact of artifact, EEG records are typically cleaned by a preprocessing stage that removes individual segments or components of the recording. However, such preprocessing can introduce bias, discard available signal, and be labor-intensive. With this motivation, we present a method that uses robust statistics to reduce dependence on preprocessing by minimizing the effect of large intermittent outliers on the spectral estimates. New method Using the multitaper method[1] as a starting point, we replaced the final step of the standard power spectrum calculation with a quantile-based estimator, and the Jackknife approach to confidence intervals with a Bayesian approach. The method is implemented in provided MATLAB modules, which extend the widely used Chronux toolbox. Results Using both simulated and human data, we show that in the presence of large intermittent outliers, the robust method produces improved estimates of the power spectrum, and that the Bayesian confidence intervals yield close-to-veridical coverage factors. Comparison to existing method The robust method, as compared to the standard method, is less affected by artifact: inclusion of outliers produces fewer changes in the shape of the power spectrum as well as in the coverage factor. Conclusion In the presence of large intermittent outliers, the robust method can reduce dependence on data preprocessing as compared to standard methods of spectral estimation. PMID:27102041

Efficient robust doubly adaptive regularized regression with applications.

PubMed

Karunamuni, Rohana J; Kong, Linglong; Tu, Wei

2018-01-01

We consider the problem of estimation and variable selection for general linear regression models. Regularized regression procedures have been widely used for variable selection, but most existing methods perform poorly in the presence of outliers. We construct a new penalized procedure that simultaneously attains full efficiency and maximum robustness. Furthermore, the proposed procedure satisfies the oracle properties. The new procedure is designed to achieve sparse and robust solutions by imposing adaptive weights on both the decision loss and the penalty function. The proposed method of estimation and variable selection attains full efficiency when the model is correct and, at the same time, achieves maximum robustness when outliers are present. We examine the robustness properties using the finite-sample breakdown point and an influence function. We show that the proposed estimator attains the maximum breakdown point. Furthermore, there is no loss in efficiency when there are no outliers or the error distribution is normal. For practical implementation of the proposed method, we present a computational algorithm. We examine the finite-sample and robustness properties using Monte Carlo studies. Two datasets are also analyzed.

Robust statistical methods for impulse noise suppressing of spread spectrum induced polarization data, with application to a mine site, Gansu province, China

NASA Astrophysics Data System (ADS)

Liu, Weiqiang; Chen, Rujun; Cai, Hongzhu; Luo, Weibin

2016-12-01

In this paper, we investigated the robust processing of noisy spread spectrum induced polarization (SSIP) data. SSIP is a new frequency domain induced polarization method that transmits pseudo-random m-sequence as source current where m-sequence is a broadband signal. The potential information at multiple frequencies can be obtained through measurement. Removing the noise is a crucial problem for SSIP data processing. Considering that if the ordinary mean stack and digital filter are not capable of reducing the impulse noise effectively in SSIP data processing, the impact of impulse noise will remain in the complex resistivity spectrum that will affect the interpretation of profile anomalies. We implemented a robust statistical method to SSIP data processing. The robust least-squares regression is used to fit and remove the linear trend from the original data before stacking. The robust M estimate is used to stack the data of all periods. The robust smooth filter is used to suppress the residual noise for data after stacking. For robust statistical scheme, the most appropriate influence function and iterative algorithm are chosen by testing the simulated data to suppress the outliers' influence. We tested the benefits of the robust SSIP data processing using examples of SSIP data recorded in a test site beside a mine in Gansu province, China.

Joint estimation of 2D-DOA and frequency based on space-time matrix and conformal array.

PubMed

Wan, Liang-Tian; Liu, Lu-Tao; Si, Wei-Jian; Tian, Zuo-Xi

2013-01-01

Each element in the conformal array has a different pattern, which leads to the performance deterioration of the conventional high resolution direction-of-arrival (DOA) algorithms. In this paper, a joint frequency and two-dimension DOA (2D-DOA) estimation algorithm for conformal array are proposed. The delay correlation function is used to suppress noise. Both spatial and time sampling are utilized to construct the spatial-time matrix. The frequency and 2D-DOA estimation are accomplished based on parallel factor (PARAFAC) analysis without spectral peak searching and parameter pairing. The proposed algorithm needs only four guiding elements with precise positions to estimate frequency and 2D-DOA. Other instrumental elements can be arranged flexibly on the surface of the carrier. Simulation results demonstrate the effectiveness of the proposed algorithm.

Dissolved organic matter dynamics in surface waters affected by oil spill pollution: Results from the Serious Game exercise

NASA Astrophysics Data System (ADS)

Gonnelli, M.; Galletti, Y.; Marchetti, E.; Mercadante, L.; Retelletti Brogi, S.; Ribotti, A.; Sorgente, R.; Vestri, S.; Santinelli, C.

2016-11-01

Dissolved organic carbon (DOC), chromophoric and fluorescent dissolved organic matter (CDOM and FDOM, respectively) surface distribution was studied during the Serious Game exercise carried out in the Eastern Ligurian Sea, where an oil spill was localized by using satellite images and models. This paper reports the first DOC, CDOM and FDOM data for this area together with an evaluation of fluorescence as a fast and inexpensive tool for early oil spill detection in marine waters. The samples collected in the oil spill showed a fluorescence intensity markedly higher ( 5 fold) than all the other samples. The excitation-emission matrixes, coupled with parallel factor analysis (PARAFAC), allowed for the identification in the FDOM pool of a mixture of polycyclic aromatic hydrocarbons, humic-like and protein-like fluorophores.

Study on fluorescence spectra of thiamine, riboflavin and pyridoxine

NASA Astrophysics Data System (ADS)

Yang, Hui; Xiao, Xue; Zhao, Xuesong; Hu, Lan; Lv, Caofang; Yin, Zhangkun

2016-01-01

This paper presents the intrinsic fluorescence characteristics of vitamin B1, B2 and B6 measured with 3D fluorescence Spectrophotometer. Three strong fluorescence areas of vitamin B2 locate at λex/λem=270/525nm, 370/525nm and 450/525nm, one fluorescence areas of vitamin B1 locates at λex/λem=370/460nm, two fluorescence areas of vitamin B6 locate at λex/λem=250/370nm and 325/370nm were found. The influence of pH of solution to the fluorescence profile was also discussed. Using the PARAFAC algorithm, 10 vitamin B1, B2 and B6 mixed solutions were successfully decomposed, and the emission profiles, excitation profiles, central wavelengths and the concentration of the three components were retrieved precisely through about 5 iteration times.

Primal-dual convex optimization in large deformation diffeomorphic metric mapping: LDDMM meets robust regularizers

NASA Astrophysics Data System (ADS)

Hernandez, Monica

2017-12-01

This paper proposes a method for primal-dual convex optimization in variational large deformation diffeomorphic metric mapping problems formulated with robust regularizers and robust image similarity metrics. The method is based on Chambolle and Pock primal-dual algorithm for solving general convex optimization problems. Diagonal preconditioning is used to ensure the convergence of the algorithm to the global minimum. We consider three robust regularizers liable to provide acceptable results in diffeomorphic registration: Huber, V-Huber and total generalized variation. The Huber norm is used in the image similarity term. The primal-dual equations are derived for the stationary and the non-stationary parameterizations of diffeomorphisms. The resulting algorithms have been implemented for running in the GPU using Cuda. For the most memory consuming methods, we have developed a multi-GPU implementation. The GPU implementations allowed us to perform an exhaustive evaluation study in NIREP and LPBA40 databases. The experiments showed that, for all the considered regularizers, the proposed method converges to diffeomorphic solutions while better preserving discontinuities at the boundaries of the objects compared to baseline diffeomorphic registration methods. In most cases, the evaluation showed a competitive performance for the robust regularizers, close to the performance of the baseline diffeomorphic registration methods.

Efficient and Robust Optimization for Building Energy Simulation

PubMed Central

Pourarian, Shokouh; Kearsley, Anthony; Wen, Jin; Pertzborn, Amanda

2016-01-01

Efficiently, robustly and accurately solving large sets of structured, non-linear algebraic and differential equations is one of the most computationally expensive steps in the dynamic simulation of building energy systems. Here, the efficiency, robustness and accuracy of two commonly employed solution methods are compared. The comparison is conducted using the HVACSIM+ software package, a component based building system simulation tool. The HVACSIM+ software presently employs Powell’s Hybrid method to solve systems of nonlinear algebraic equations that model the dynamics of energy states and interactions within buildings. It is shown here that the Powell’s method does not always converge to a solution. Since a myriad of other numerical methods are available, the question arises as to which method is most appropriate for building energy simulation. This paper finds considerable computational benefits result from replacing the Powell’s Hybrid method solver in HVACSIM+ with a solver more appropriate for the challenges particular to numerical simulations of buildings. Evidence is provided that a variant of the Levenberg-Marquardt solver has superior accuracy and robustness compared to the Powell’s Hybrid method presently used in HVACSIM+. PMID:27325907

Efficient and Robust Optimization for Building Energy Simulation.

PubMed

Pourarian, Shokouh; Kearsley, Anthony; Wen, Jin; Pertzborn, Amanda

2016-06-15

Efficiently, robustly and accurately solving large sets of structured, non-linear algebraic and differential equations is one of the most computationally expensive steps in the dynamic simulation of building energy systems. Here, the efficiency, robustness and accuracy of two commonly employed solution methods are compared. The comparison is conducted using the HVACSIM+ software package, a component based building system simulation tool. The HVACSIM+ software presently employs Powell's Hybrid method to solve systems of nonlinear algebraic equations that model the dynamics of energy states and interactions within buildings. It is shown here that the Powell's method does not always converge to a solution. Since a myriad of other numerical methods are available, the question arises as to which method is most appropriate for building energy simulation. This paper finds considerable computational benefits result from replacing the Powell's Hybrid method solver in HVACSIM+ with a solver more appropriate for the challenges particular to numerical simulations of buildings. Evidence is provided that a variant of the Levenberg-Marquardt solver has superior accuracy and robustness compared to the Powell's Hybrid method presently used in HVACSIM+.

Reader reaction to "a robust method for estimating optimal treatment regimes" by Zhang et al. (2012).

PubMed

Taylor, Jeremy M G; Cheng, Wenting; Foster, Jared C

2015-03-01

A recent article (Zhang et al., 2012, Biometrics 168, 1010-1018) compares regression based and inverse probability based methods of estimating an optimal treatment regime and shows for a small number of covariates that inverse probability weighted methods are more robust to model misspecification than regression methods. We demonstrate that using models that fit the data better reduces the concern about non-robustness for the regression methods. We extend the simulation study of Zhang et al. (2012, Biometrics 168, 1010-1018), also considering the situation of a larger number of covariates, and show that incorporating random forests into both regression and inverse probability weighted based methods improves their properties. © 2014, The International Biometric Society.

Robust Neural Sliding Mode Control of Robot Manipulators

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nguyen Tran Hiep; Pham Thuong Cat

2009-03-05

This paper proposes a robust neural sliding mode control method for robot tracking problem to overcome the noises and large uncertainties in robot dynamics. The Lyapunov direct method has been used to prove the stability of the overall system. Simulation results are given to illustrate the applicability of the proposed method.

Robust Eye Center Localization through Face Alignment and Invariant Isocentric Patterns

PubMed Central

Teng, Dongdong; Chen, Dihu; Tan, Hongzhou

2015-01-01

The localization of eye centers is a very useful cue for numerous applications like face recognition, facial expression recognition, and the early screening of neurological pathologies. Several methods relying on available light for accurate eye-center localization have been exploited. However, despite the considerable improvements that eye-center localization systems have undergone in recent years, only few of these developments deal with the challenges posed by the profile (non-frontal face). In this paper, we first use the explicit shape regression method to obtain the rough location of the eye centers. Because this method extracts global information from the human face, it is robust against any changes in the eye region. We exploit this robustness and utilize it as a constraint. To locate the eye centers accurately, we employ isophote curvature features, the accuracy of which has been demonstrated in a previous study. By applying these features, we obtain a series of eye-center locations which are candidates for the actual position of the eye-center. Among these locations, the estimated locations which minimize the reconstruction error between the two methods mentioned above are taken as the closest approximation for the eye centers locations. Therefore, we combine explicit shape regression and isophote curvature feature analysis to achieve robustness and accuracy, respectively. In practical experiments, we use BioID and FERET datasets to test our approach to obtaining an accurate eye-center location while retaining robustness against changes in scale and pose. In addition, we apply our method to non-frontal faces to test its robustness and accuracy, which are essential in gaze estimation but have seldom been mentioned in previous works. Through extensive experimentation, we show that the proposed method can achieve a significant improvement in accuracy and robustness over state-of-the-art techniques, with our method ranking second in terms of accuracy. According to our implementation on a PC with a Xeon 2.5Ghz CPU, the frame rate of the eye tracking process can achieve 38 Hz. PMID:26426929

TU-AB-BRB-02: Stochastic Programming Methods for Handling Uncertainty and Motion in IMRT Planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Unkelbach, J.

The accepted clinical method to accommodate targeting uncertainties inherent in fractionated external beam radiation therapy is to utilize GTV-to-CTV and CTV-to-PTV margins during the planning process to design a PTV-conformal static dose distribution on the planning image set. Ideally, margins are selected to ensure a high (e.g. >95%) target coverage probability (CP) in spite of inherent inter- and intra-fractional positional variations, tissue motions, and initial contouring uncertainties. Robust optimization techniques, also known as probabilistic treatment planning techniques, explicitly incorporate the dosimetric consequences of targeting uncertainties by including CP evaluation into the planning optimization process along with coverage-based planning objectives. Themore » treatment planner no longer needs to use PTV and/or PRV margins; instead robust optimization utilizes probability distributions of the underlying uncertainties in conjunction with CP-evaluation for the underlying CTVs and OARs to design an optimal treated volume. This symposium will describe CP-evaluation methods as well as various robust planning techniques including use of probability-weighted dose distributions, probability-weighted objective functions, and coverage optimized planning. Methods to compute and display the effect of uncertainties on dose distributions will be presented. The use of robust planning to accommodate inter-fractional setup uncertainties, organ deformation, and contouring uncertainties will be examined as will its use to accommodate intra-fractional organ motion. Clinical examples will be used to inter-compare robust and margin-based planning, highlighting advantages of robust-plans in terms of target and normal tissue coverage. Robust-planning limitations as uncertainties approach zero and as the number of treatment fractions becomes small will be presented, as well as the factors limiting clinical implementation of robust planning. Learning Objectives: To understand robust-planning as a clinical alternative to using margin-based planning. To understand conceptual differences between uncertainty and predictable motion. To understand fundamental limitations of the PTV concept that probabilistic planning can overcome. To understand the major contributing factors to target and normal tissue coverage probability. To understand the similarities and differences of various robust planning techniques To understand the benefits and limitations of robust planning techniques.« less

TU-AB-BRB-00: New Methods to Ensure Target Coverage

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

2015-06-15

The accepted clinical method to accommodate targeting uncertainties inherent in fractionated external beam radiation therapy is to utilize GTV-to-CTV and CTV-to-PTV margins during the planning process to design a PTV-conformal static dose distribution on the planning image set. Ideally, margins are selected to ensure a high (e.g. >95%) target coverage probability (CP) in spite of inherent inter- and intra-fractional positional variations, tissue motions, and initial contouring uncertainties. Robust optimization techniques, also known as probabilistic treatment planning techniques, explicitly incorporate the dosimetric consequences of targeting uncertainties by including CP evaluation into the planning optimization process along with coverage-based planning objectives. Themore » treatment planner no longer needs to use PTV and/or PRV margins; instead robust optimization utilizes probability distributions of the underlying uncertainties in conjunction with CP-evaluation for the underlying CTVs and OARs to design an optimal treated volume. This symposium will describe CP-evaluation methods as well as various robust planning techniques including use of probability-weighted dose distributions, probability-weighted objective functions, and coverage optimized planning. Methods to compute and display the effect of uncertainties on dose distributions will be presented. The use of robust planning to accommodate inter-fractional setup uncertainties, organ deformation, and contouring uncertainties will be examined as will its use to accommodate intra-fractional organ motion. Clinical examples will be used to inter-compare robust and margin-based planning, highlighting advantages of robust-plans in terms of target and normal tissue coverage. Robust-planning limitations as uncertainties approach zero and as the number of treatment fractions becomes small will be presented, as well as the factors limiting clinical implementation of robust planning. Learning Objectives: To understand robust-planning as a clinical alternative to using margin-based planning. To understand conceptual differences between uncertainty and predictable motion. To understand fundamental limitations of the PTV concept that probabilistic planning can overcome. To understand the major contributing factors to target and normal tissue coverage probability. To understand the similarities and differences of various robust planning techniques To understand the benefits and limitations of robust planning techniques.« less

Robust Mediation Analysis Based on Median Regression

PubMed Central

Yuan, Ying; MacKinnon, David P.

2014-01-01

Mediation analysis has many applications in psychology and the social sciences. The most prevalent methods typically assume that the error distribution is normal and homoscedastic. However, this assumption may rarely be met in practice, which can affect the validity of the mediation analysis. To address this problem, we propose robust mediation analysis based on median regression. Our approach is robust to various departures from the assumption of homoscedasticity and normality, including heavy-tailed, skewed, contaminated, and heteroscedastic distributions. Simulation studies show that under these circumstances, the proposed method is more efficient and powerful than standard mediation analysis. We further extend the proposed robust method to multilevel mediation analysis, and demonstrate through simulation studies that the new approach outperforms the standard multilevel mediation analysis. We illustrate the proposed method using data from a program designed to increase reemployment and enhance mental health of job seekers. PMID:24079925

A robust ridge regression approach in the presence of both multicollinearity and outliers in the data

NASA Astrophysics Data System (ADS)

Shariff, Nurul Sima Mohamad; Ferdaos, Nur Aqilah

2017-08-01

Multicollinearity often leads to inconsistent and unreliable parameter estimates in regression analysis. This situation will be more severe in the presence of outliers it will cause fatter tails in the error distributions than the normal distributions. The well-known procedure that is robust to multicollinearity problem is the ridge regression method. This method however is expected to be affected by the presence of outliers due to some assumptions imposed in the modeling procedure. Thus, the robust version of existing ridge method with some modification in the inverse matrix and the estimated response value is introduced. The performance of the proposed method is discussed and comparisons are made with several existing estimators namely, Ordinary Least Squares (OLS), ridge regression and robust ridge regression based on GM-estimates. The finding of this study is able to produce reliable parameter estimates in the presence of both multicollinearity and outliers in the data.

Adaptive Critic Nonlinear Robust Control: A Survey.

PubMed

Wang, Ding; He, Haibo; Liu, Derong

2017-10-01

Adaptive dynamic programming (ADP) and reinforcement learning are quite relevant to each other when performing intelligent optimization. They are both regarded as promising methods involving important components of evaluation and improvement, at the background of information technology, such as artificial intelligence, big data, and deep learning. Although great progresses have been achieved and surveyed when addressing nonlinear optimal control problems, the research on robustness of ADP-based control strategies under uncertain environment has not been fully summarized. Hence, this survey reviews the recent main results of adaptive-critic-based robust control design of continuous-time nonlinear systems. The ADP-based nonlinear optimal regulation is reviewed, followed by robust stabilization of nonlinear systems with matched uncertainties, guaranteed cost control design of unmatched plants, and decentralized stabilization of interconnected systems. Additionally, further comprehensive discussions are presented, including event-based robust control design, improvement of the critic learning rule, nonlinear H ∞ control design, and several notes on future perspectives. By applying the ADP-based optimal and robust control methods to a practical power system and an overhead crane plant, two typical examples are provided to verify the effectiveness of theoretical results. Overall, this survey is beneficial to promote the development of adaptive critic control methods with robustness guarantee and the construction of higher level intelligent systems.

A Robust Shape Reconstruction Method for Facial Feature Point Detection.

PubMed

Tan, Shuqiu; Chen, Dongyi; Guo, Chenggang; Huang, Zhiqi

2017-01-01

Facial feature point detection has been receiving great research advances in recent years. Numerous methods have been developed and applied in practical face analysis systems. However, it is still a quite challenging task because of the large variability in expression and gestures and the existence of occlusions in real-world photo shoot. In this paper, we present a robust sparse reconstruction method for the face alignment problems. Instead of a direct regression between the feature space and the shape space, the concept of shape increment reconstruction is introduced. Moreover, a set of coupled overcomplete dictionaries termed the shape increment dictionary and the local appearance dictionary are learned in a regressive manner to select robust features and fit shape increments. Additionally, to make the learned model more generalized, we select the best matched parameter set through extensive validation tests. Experimental results on three public datasets demonstrate that the proposed method achieves a better robustness over the state-of-the-art methods.

A Robust Image Watermarking in the Joint Time-Frequency Domain

NASA Astrophysics Data System (ADS)

Öztürk, Mahmut; Akan, Aydın; Çekiç, Yalçın

2010-12-01

With the rapid development of computers and internet applications, copyright protection of multimedia data has become an important problem. Watermarking techniques are proposed as a solution to copyright protection of digital media files. In this paper, a new, robust, and high-capacity watermarking method that is based on spatiofrequency (SF) representation is presented. We use the discrete evolutionary transform (DET) calculated by the Gabor expansion to represent an image in the joint SF domain. The watermark is embedded onto selected coefficients in the joint SF domain. Hence, by combining the advantages of spatial and spectral domain watermarking methods, a robust, invisible, secure, and high-capacity watermarking method is presented. A correlation-based detector is also proposed to detect and extract any possible watermarks on an image. The proposed watermarking method was tested on some commonly used test images under different signal processing attacks like additive noise, Wiener and Median filtering, JPEG compression, rotation, and cropping. Simulation results show that our method is robust against all of the attacks.

A Comprehensive Study of Gridding Methods for GPS Horizontal Velocity Fields

NASA Astrophysics Data System (ADS)

Wu, Yanqiang; Jiang, Zaisen; Liu, Xiaoxia; Wei, Wenxin; Zhu, Shuang; Zhang, Long; Zou, Zhenyu; Xiong, Xiaohui; Wang, Qixin; Du, Jiliang

2017-03-01

Four gridding methods for GPS velocities are compared in terms of their precision, applicability and robustness by analyzing simulated data with uncertainties from 0.0 to ±3.0 mm/a. When the input data are 1° × 1° grid sampled and the uncertainty of the additional error is greater than ±1.0 mm/a, the gridding results show that the least-squares collocation method is highly robust while the robustness of the Kriging method is low. In contrast, the spherical harmonics and the multi-surface function are moderately robust, and the regional singular values for the multi-surface function method and the edge effects for the spherical harmonics method become more significant with increasing uncertainty of the input data. When the input data (with additional errors of ±2.0 mm/a) are decimated by 50% from the 1° × 1° grid data and then erased in three 6° × 12° regions, the gridding results in these three regions indicate that the least-squares collocation and the spherical harmonics methods have good performances, while the multi-surface function and the Kriging methods may lead to singular values. The gridding techniques are also applied to GPS horizontal velocities with an average error of ±0.8 mm/a over the Chinese mainland and the surrounding areas, and the results show that the least-squares collocation method has the best performance, followed by the Kriging and multi-surface function methods. Furthermore, the edge effects of the spherical harmonics method are significantly affected by the sparseness and geometric distribution of the input data. In general, the least-squares collocation method is superior in terms of its robustness, edge effect, error distribution and stability, while the other methods have several positive features.

Robust Dynamic Multi-objective Vehicle Routing Optimization Method.

PubMed

Guo, Yi-Nan; Cheng, Jian; Luo, Sha; Gong, Dun-Wei

2017-03-21

For dynamic multi-objective vehicle routing problems, the waiting time of vehicle, the number of serving vehicles, the total distance of routes were normally considered as the optimization objectives. Except for above objectives, fuel consumption that leads to the environmental pollution and energy consumption was focused on in this paper. Considering the vehicles' load and the driving distance, corresponding carbon emission model was built and set as an optimization objective. Dynamic multi-objective vehicle routing problems with hard time windows and randomly appeared dynamic customers, subsequently, were modeled. In existing planning methods, when the new service demand came up, global vehicle routing optimization method was triggered to find the optimal routes for non-served customers, which was time-consuming. Therefore, robust dynamic multi-objective vehicle routing method with two-phase is proposed. Three highlights of the novel method are: (i) After finding optimal robust virtual routes for all customers by adopting multi-objective particle swarm optimization in the first phase, static vehicle routes for static customers are formed by removing all dynamic customers from robust virtual routes in next phase. (ii)The dynamically appeared customers append to be served according to their service time and the vehicles' statues. Global vehicle routing optimization is triggered only when no suitable locations can be found for dynamic customers. (iii)A metric measuring the algorithms' robustness is given. The statistical results indicated that the routes obtained by the proposed method have better stability and robustness, but may be sub-optimum. Moreover, time-consuming global vehicle routing optimization is avoided as dynamic customers appear.

Robust power spectral estimation for EEG data.

PubMed

Melman, Tamar; Victor, Jonathan D

2016-08-01

Typical electroencephalogram (EEG) recordings often contain substantial artifact. These artifacts, often large and intermittent, can interfere with quantification of the EEG via its power spectrum. To reduce the impact of artifact, EEG records are typically cleaned by a preprocessing stage that removes individual segments or components of the recording. However, such preprocessing can introduce bias, discard available signal, and be labor-intensive. With this motivation, we present a method that uses robust statistics to reduce dependence on preprocessing by minimizing the effect of large intermittent outliers on the spectral estimates. Using the multitaper method (Thomson, 1982) as a starting point, we replaced the final step of the standard power spectrum calculation with a quantile-based estimator, and the Jackknife approach to confidence intervals with a Bayesian approach. The method is implemented in provided MATLAB modules, which extend the widely used Chronux toolbox. Using both simulated and human data, we show that in the presence of large intermittent outliers, the robust method produces improved estimates of the power spectrum, and that the Bayesian confidence intervals yield close-to-veridical coverage factors. The robust method, as compared to the standard method, is less affected by artifact: inclusion of outliers produces fewer changes in the shape of the power spectrum as well as in the coverage factor. In the presence of large intermittent outliers, the robust method can reduce dependence on data preprocessing as compared to standard methods of spectral estimation. Copyright © 2016 Elsevier B.V. All rights reserved.

Rapid fingerprinting of spilled petroleum products using fluorescence spectroscopy coupled with parallel factor and principal component analysis.

PubMed

Mirnaghi, Fatemeh S; Soucy, Nicholas; Hollebone, Bruce P; Brown, Carl E

2018-05-19

The characterization of spilled petroleum products in an oil spill is necessary for identifying the spill source, selection of clean-up strategies, and evaluating potential environmental and ecological impacts. Existing standard methods for the chemical characterization of spilled oils are time-consuming due to the lengthy sample preparation for analysis. The main objective of this study is the development of a rapid screening method for the fingerprinting of spilled petroleum products using excitation/emission matrix (EEM) fluorescence spectroscopy, thereby delivering a preliminary evaluation of the petroleum products within hours after a spill. In addition, the developed model can be used for monitoring the changes of aromatic compositions of known spilled oils over time. This study involves establishing a fingerprinting model based on the composition of polycyclic and heterocyclic aromatic hydrocarbons (PAH and HAHs, respectively) of 130 petroleum products at different states of evaporative weathering. The screening model was developed using parallel factor analysis (PARAFAC) of a large EEM dataset. The significant fluorescing components for each sample class were determined. After which, through principal component analysis (PCA), the variation of scores of their modeled factors was discriminated based on the different classes of petroleum products. This model was then validated using gas chromatography-mass spectrometry (GC-MS) analysis. The rapid fingerprinting and the identification of unknown and new spilled oils occurs through matching the spilled product with the products of the developed model. Finally, it was shown that HAH compounds in asphaltene and resins contribute to ≥4-ring PAHs compounds in petroleum products. Copyright © 2018. Published by Elsevier Ltd.

Multivariate analysis of organic acids in fermented food from reversed-phase high-performance liquid chromatography data.

PubMed

Mortera, Pablo; Zuljan, Federico A; Magni, Christian; Bortolato, Santiago A; Alarcón, Sergio H

2018-02-01

Multivariate calibration coupled to RP-HPLC with diode array detection (HPLC-DAD) was applied to the identification and the quantitative evaluation of the short chain organic acids (malic, oxalic, formic, lactic, acetic, citric, pyruvic, succinic, tartaric, propionic and α-cetoglutaric) in fermented food. The goal of the present study was to get the successful resolution of a system in the combined occurrence of strongly coeluting peaks, of distortions in the time sensors among chromatograms, and of the presence of unexpected compounds not included in the calibration step. Second-order HPLC-DAD data matrices were obtained in a short time (10min) on a C18 column with a chromatographic system operating in isocratic mode (mobile phase was 20mmolL -1 phosphate buffer at pH 2.20) and a flow-rate of 1.0mLmin -1 at room temperature. Parallel factor analysis (PARAFAC) and unfolded partial least-squares combined with residual bilinearization (U-PLS/RBL) were the second-order calibration algorithms select for data processing. The performance of the analytical parameters was good with an outstanding limit of detection (LODs) for acids ranging from 0.15 to 10.0mmolL -1 in the validation samples. The improved method was applied to the analysis of many dairy products (yoghurt, cultured milk and cheese) and wine. The method was shown as an effective means for determining and following acid contents in fermented food and was characterized by reducibility with simple, high resolution and rapid procedure without derivatization of analytes. Copyright © 2017 Elsevier B.V. All rights reserved.

A study of the temporal robustness of the growing global container-shipping network

PubMed Central

Wang, Nuo; Wu, Nuan; Dong, Ling-ling; Yan, Hua-kun; Wu, Di

2016-01-01

Whether they thrive as they grow must be determined for all constantly expanding networks. However, few studies have focused on this important network feature or the development of quantitative analytical methods. Given the formation and growth of the global container-shipping network, we proposed the concept of network temporal robustness and quantitative method. As an example, we collected container liner companies’ data at two time points (2004 and 2014) and built a shipping network with ports as nodes and routes as links. We thus obtained a quantitative value of the temporal robustness. The temporal robustness is a significant network property because, for the first time, we can clearly recognize that the shipping network has become more vulnerable to damage over the last decade: When the node failure scale reached 50% of the entire network, the temporal robustness was approximately −0.51% for random errors and −12.63% for intentional attacks. The proposed concept and analytical method described in this paper are significant for other network studies. PMID:27713549

Including robustness in multi-criteria optimization for intensity-modulated proton therapy

NASA Astrophysics Data System (ADS)

Chen, Wei; Unkelbach, Jan; Trofimov, Alexei; Madden, Thomas; Kooy, Hanne; Bortfeld, Thomas; Craft, David

2012-02-01

We present a method to include robustness in a multi-criteria optimization (MCO) framework for intensity-modulated proton therapy (IMPT). The approach allows one to simultaneously explore the trade-off between different objectives as well as the trade-off between robustness and nominal plan quality. In MCO, a database of plans each emphasizing different treatment planning objectives, is pre-computed to approximate the Pareto surface. An IMPT treatment plan that strikes the best balance between the different objectives can be selected by navigating on the Pareto surface. In our approach, robustness is integrated into MCO by adding robustified objectives and constraints to the MCO problem. Uncertainties (or errors) of the robust problem are modeled by pre-calculated dose-influence matrices for a nominal scenario and a number of pre-defined error scenarios (shifted patient positions, proton beam undershoot and overshoot). Objectives and constraints can be defined for the nominal scenario, thus characterizing nominal plan quality. A robustified objective represents the worst objective function value that can be realized for any of the error scenarios and thus provides a measure of plan robustness. The optimization method is based on a linear projection solver and is capable of handling large problem sizes resulting from a fine dose grid resolution, many scenarios, and a large number of proton pencil beams. A base-of-skull case is used to demonstrate the robust optimization method. It is demonstrated that the robust optimization method reduces the sensitivity of the treatment plan to setup and range errors to a degree that is not achieved by a safety margin approach. A chordoma case is analyzed in more detail to demonstrate the involved trade-offs between target underdose and brainstem sparing as well as robustness and nominal plan quality. The latter illustrates the advantage of MCO in the context of robust planning. For all cases examined, the robust optimization for each Pareto optimal plan takes less than 5 min on a standard computer, making a computationally friendly interface possible to the planner. In conclusion, the uncertainty pertinent to the IMPT procedure can be reduced during treatment planning by optimizing plans that emphasize different treatment objectives, including robustness, and then interactively seeking for a most-preferred one from the solution Pareto surface.

Detecting outliers when fitting data with nonlinear regression – a new method based on robust nonlinear regression and the false discovery rate

PubMed Central

Motulsky, Harvey J; Brown, Ronald E

2006-01-01

Background Nonlinear regression, like linear regression, assumes that the scatter of data around the ideal curve follows a Gaussian or normal distribution. This assumption leads to the familiar goal of regression: to minimize the sum of the squares of the vertical or Y-value distances between the points and the curve. Outliers can dominate the sum-of-the-squares calculation, and lead to misleading results. However, we know of no practical method for routinely identifying outliers when fitting curves with nonlinear regression. Results We describe a new method for identifying outliers when fitting data with nonlinear regression. We first fit the data using a robust form of nonlinear regression, based on the assumption that scatter follows a Lorentzian distribution. We devised a new adaptive method that gradually becomes more robust as the method proceeds. To define outliers, we adapted the false discovery rate approach to handling multiple comparisons. We then remove the outliers, and analyze the data using ordinary least-squares regression. Because the method combines robust regression and outlier removal, we call it the ROUT method. When analyzing simulated data, where all scatter is Gaussian, our method detects (falsely) one or more outlier in only about 1–3% of experiments. When analyzing data contaminated with one or several outliers, the ROUT method performs well at outlier identification, with an average False Discovery Rate less than 1%. Conclusion Our method, which combines a new method of robust nonlinear regression with a new method of outlier identification, identifies outliers from nonlinear curve fits with reasonable power and few false positives. PMID:16526949

Analysis and improvements of Adaptive Particle Refinement (APR) through CPU time, accuracy and robustness considerations

NASA Astrophysics Data System (ADS)

Chiron, L.; Oger, G.; de Leffe, M.; Le Touzé, D.

2018-02-01

While smoothed-particle hydrodynamics (SPH) simulations are usually performed using uniform particle distributions, local particle refinement techniques have been developed to concentrate fine spatial resolutions in identified areas of interest. Although the formalism of this method is relatively easy to implement, its robustness at coarse/fine interfaces can be problematic. Analysis performed in [16] shows that the radius of refined particles should be greater than half the radius of unrefined particles to ensure robustness. In this article, the basics of an Adaptive Particle Refinement (APR) technique, inspired by AMR in mesh-based methods, are presented. This approach ensures robustness with alleviated constraints. Simulations applying the new formalism proposed achieve accuracy comparable to fully refined spatial resolutions, together with robustness, low CPU times and maintained parallel efficiency.

Robust design of microchannel cooler

NASA Astrophysics Data System (ADS)

He, Ye; Yang, Tao; Hu, Li; Li, Leimin

2005-12-01

Microchannel cooler has offered a new method for the cooling of high power diode lasers, with the advantages of small volume, high efficiency of thermal dissipation and low cost when mass-produced. In order to reduce the sensitivity of design to manufacture errors or other disturbances, Taguchi method that is one of robust design method was chosen to optimize three parameters important to the cooling performance of roof-like microchannel cooler. The hydromechanical and thermal mathematical model of varying section microchannel was calculated using finite volume method by FLUENT. A special program was written to realize the automation of the design process for improving efficiency. The optimal design is presented which compromises between optimal cooling performance and its robustness. This design method proves to be available.

Robust extraction of the aorta and pulmonary artery from 3D MDCT image data

NASA Astrophysics Data System (ADS)

Taeprasartsit, Pinyo; Higgins, William E.

2010-03-01

Accurate definition of the aorta and pulmonary artery from three-dimensional (3D) multi-detector CT (MDCT) images is important for pulmonary applications. This work presents robust methods for defining the aorta and pulmonary artery in the central chest. The methods work on both contrast enhanced and no-contrast 3D MDCT image data. The automatic methods use a common approach employing model fitting and selection and adaptive refinement. During the occasional event that more precise vascular extraction is desired or the method fails, we also have an alternate semi-automatic fail-safe method. The semi-automatic method extracts the vasculature by extending the medial axes into a user-guided direction. A ground-truth study over a series of 40 human 3D MDCT images demonstrates the efficacy, accuracy, robustness, and efficiency of the methods.

Robust image matching via ORB feature and VFC for mismatch removal

NASA Astrophysics Data System (ADS)

Ma, Tao; Fu, Wenxing; Fang, Bin; Hu, Fangyu; Quan, Siwen; Ma, Jie

2018-03-01

Image matching is at the base of many image processing and computer vision problems, such as object recognition or structure from motion. Current methods rely on good feature descriptors and mismatch removal strategies for detection and matching. In this paper, we proposed a robust image match approach based on ORB feature and VFC for mismatch removal. ORB (Oriented FAST and Rotated BRIEF) is an outstanding feature, it has the same performance as SIFT with lower cost. VFC (Vector Field Consensus) is a state-of-the-art mismatch removing method. The experiment results demonstrate that our method is efficient and robust.

Robust Economic Control Decision Method of Uncertain System on Urban Domestic Water Supply.

PubMed

Li, Kebai; Ma, Tianyi; Wei, Guo

2018-03-31

As China quickly urbanizes, urban domestic water generally presents the circumstances of both rising tendency and seasonal cycle fluctuation. A robust economic control decision method for dynamic uncertain systems is proposed in this paper. It is developed based on the internal model principle and pole allocation method, and it is applied to an urban domestic water supply system with rising tendency and seasonal cycle fluctuation. To achieve this goal, first a multiplicative model is used to describe the urban domestic water demand. Then, a capital stock and a labor stock are selected as the state vector, and the investment and labor are designed as the control vector. Next, the compensator subsystem is devised in light of the internal model principle. Finally, by using the state feedback control strategy and pole allocation method, the multivariable robust economic control decision method is implemented. The implementation with this model can accomplish the urban domestic water supply control goal, with the robustness for the variation of parameters. The methodology presented in this study may be applied to the water management system in other parts of the world, provided all data used in this study are available. The robust control decision method in this paper is also applicable to deal with tracking control problems as well as stabilization control problems of other general dynamic uncertain systems.

Robust Economic Control Decision Method of Uncertain System on Urban Domestic Water Supply

PubMed Central

Li, Kebai; Ma, Tianyi; Wei, Guo

2018-01-01

As China quickly urbanizes, urban domestic water generally presents the circumstances of both rising tendency and seasonal cycle fluctuation. A robust economic control decision method for dynamic uncertain systems is proposed in this paper. It is developed based on the internal model principle and pole allocation method, and it is applied to an urban domestic water supply system with rising tendency and seasonal cycle fluctuation. To achieve this goal, first a multiplicative model is used to describe the urban domestic water demand. Then, a capital stock and a labor stock are selected as the state vector, and the investment and labor are designed as the control vector. Next, the compensator subsystem is devised in light of the internal model principle. Finally, by using the state feedback control strategy and pole allocation method, the multivariable robust economic control decision method is implemented. The implementation with this model can accomplish the urban domestic water supply control goal, with the robustness for the variation of parameters. The methodology presented in this study may be applied to the water management system in other parts of the world, provided all data used in this study are available. The robust control decision method in this paper is also applicable to deal with tracking control problems as well as stabilization control problems of other general dynamic uncertain systems. PMID:29614749

Robust phase retrieval of complex-valued object in phase modulation by hybrid Wirtinger flow method

NASA Astrophysics Data System (ADS)

Wei, Zhun; Chen, Wen; Yin, Tiantian; Chen, Xudong

2017-09-01

This paper presents a robust iterative algorithm, known as hybrid Wirtinger flow (HWF), for phase retrieval (PR) of complex objects from noisy diffraction intensities. Numerical simulations indicate that the HWF method consistently outperforms conventional PR methods in terms of both accuracy and convergence rate in multiple phase modulations. The proposed algorithm is also more robust to low oversampling ratios, loose constraints, and noisy environments. Furthermore, compared with traditional Wirtinger flow, sample complexity is largely reduced. It is expected that the proposed HWF method will find applications in the rapidly growing coherent diffractive imaging field for high-quality image reconstruction with multiple modulations, as well as other disciplines where PR is needed.

RSRE: RNA structural robustness evaluator

PubMed Central

Shu, Wenjie; Zheng, Zhiqiang; Wang, Shengqi

2007-01-01

Biological robustness, defined as the ability to maintain stable functioning in the face of various perturbations, is an important and fundamental topic in current biology, and has become a focus of numerous studies in recent years. Although structural robustness has been explored in several types of RNA molecules, the origins of robustness are still controversial. Computational analysis results are needed to make up for the lack of evidence of robustness in natural biological systems. The RNA structural robustness evaluator (RSRE) web server presented here provides a freely available online tool to quantitatively evaluate the structural robustness of RNA based on the widely accepted definition of neutrality. Several classical structure comparison methods are employed; five randomization methods are implemented to generate control sequences; sub-optimal predicted structures can be optionally utilized to mitigate the uncertainty of secondary structure prediction. With a user-friendly interface, the web application is easy to use. Intuitive illustrations are provided along with the original computational results to facilitate analysis. The RSRE will be helpful in the wide exploration of RNA structural robustness and will catalyze our understanding of RNA evolution. The RSRE web server is freely available at http://biosrv1.bmi.ac.cn/RSRE/ or http://biotech.bmi.ac.cn/RSRE/. PMID:17567615

Robust estimation for ordinary differential equation models.

PubMed

Cao, J; Wang, L; Xu, J

2011-12-01

Applied scientists often like to use ordinary differential equations (ODEs) to model complex dynamic processes that arise in biology, engineering, medicine, and many other areas. It is interesting but challenging to estimate ODE parameters from noisy data, especially when the data have some outliers. We propose a robust method to address this problem. The dynamic process is represented with a nonparametric function, which is a linear combination of basis functions. The nonparametric function is estimated by a robust penalized smoothing method. The penalty term is defined with the parametric ODE model, which controls the roughness of the nonparametric function and maintains the fidelity of the nonparametric function to the ODE model. The basis coefficients and ODE parameters are estimated in two nested levels of optimization. The coefficient estimates are treated as an implicit function of ODE parameters, which enables one to derive the analytic gradients for optimization using the implicit function theorem. Simulation studies show that the robust method gives satisfactory estimates for the ODE parameters from noisy data with outliers. The robust method is demonstrated by estimating a predator-prey ODE model from real ecological data. © 2011, The International Biometric Society.

Closed-loop and robust control of quantum systems.

PubMed

Chen, Chunlin; Wang, Lin-Cheng; Wang, Yuanlong

2013-01-01

For most practical quantum control systems, it is important and difficult to attain robustness and reliability due to unavoidable uncertainties in the system dynamics or models. Three kinds of typical approaches (e.g., closed-loop learning control, feedback control, and robust control) have been proved to be effective to solve these problems. This work presents a self-contained survey on the closed-loop and robust control of quantum systems, as well as a brief introduction to a selection of basic theories and methods in this research area, to provide interested readers with a general idea for further studies. In the area of closed-loop learning control of quantum systems, we survey and introduce such learning control methods as gradient-based methods, genetic algorithms (GA), and reinforcement learning (RL) methods from a unified point of view of exploring the quantum control landscapes. For the feedback control approach, the paper surveys three control strategies including Lyapunov control, measurement-based control, and coherent-feedback control. Then such topics in the field of quantum robust control as H(∞) control, sliding mode control, quantum risk-sensitive control, and quantum ensemble control are reviewed. The paper concludes with a perspective of future research directions that are likely to attract more attention.

Robust EM Continual Reassessment Method in Oncology Dose Finding

PubMed Central

Yuan, Ying; Yin, Guosheng

2012-01-01

The continual reassessment method (CRM) is a commonly used dose-finding design for phase I clinical trials. Practical applications of this method have been restricted by two limitations: (1) the requirement that the toxicity outcome needs to be observed shortly after the initiation of the treatment; and (2) the potential sensitivity to the prespecified toxicity probability at each dose. To overcome these limitations, we naturally treat the unobserved toxicity outcomes as missing data, and use the expectation-maximization (EM) algorithm to estimate the dose toxicity probabilities based on the incomplete data to direct dose assignment. To enhance the robustness of the design, we propose prespecifying multiple sets of toxicity probabilities, each set corresponding to an individual CRM model. We carry out these multiple CRMs in parallel, across which model selection and model averaging procedures are used to make more robust inference. We evaluate the operating characteristics of the proposed robust EM-CRM designs through simulation studies and show that the proposed methods satisfactorily resolve both limitations of the CRM. Besides improving the MTD selection percentage, the new designs dramatically shorten the duration of the trial, and are robust to the prespecification of the toxicity probabilities. PMID:22375092

Robust and Blind 3D Mesh Watermarking in Spatial Domain Based on Faces Categorization and Sorting

NASA Astrophysics Data System (ADS)

Molaei, Amir Masoud; Ebrahimnezhad, Hossein; Sedaaghi, Mohammad Hossein

2016-06-01

In this paper, a 3D watermarking algorithm in spatial domain is presented with blind detection. In the proposed method, a negligible visual distortion is observed in host model. Initially, a preprocessing is applied on the 3D model to make it robust against geometric transformation attacks. Then, a number of triangle faces are determined as mark triangles using a novel systematic approach in which faces are categorized and sorted robustly. In order to enhance the capability of information retrieval by attacks, block watermarks are encoded using Reed-Solomon block error-correcting code before embedding into the mark triangles. Next, the encoded watermarks are embedded in spherical coordinates. The proposed method is robust against additive noise, mesh smoothing and quantization attacks. Also, it is stout next to geometric transformation, vertices and faces reordering attacks. Moreover, the proposed algorithm is designed so that it is robust against the cropping attack. Simulation results confirm that the watermarked models confront very low distortion if the control parameters are selected properly. Comparison with other methods demonstrates that the proposed method has good performance against the mesh smoothing attacks.

Robustness of fit indices to outliers and leverage observations in structural equation modeling.

PubMed

Yuan, Ke-Hai; Zhong, Xiaoling

2013-06-01

Normal-distribution-based maximum likelihood (NML) is the most widely used method in structural equation modeling (SEM), although practical data tend to be nonnormally distributed. The effect of nonnormally distributed data or data contamination on the normal-distribution-based likelihood ratio (LR) statistic is well understood due to many analytical and empirical studies. In SEM, fit indices are used as widely as the LR statistic. In addition to NML, robust procedures have been developed for more efficient and less biased parameter estimates with practical data. This article studies the effect of outliers and leverage observations on fit indices following NML and two robust methods. Analysis and empirical results indicate that good leverage observations following NML and one of the robust methods lead most fit indices to give more support to the substantive model. While outliers tend to make a good model superficially bad according to many fit indices following NML, they have little effect on those following the two robust procedures. Implications of the results to data analysis are discussed, and recommendations are provided regarding the use of estimation methods and interpretation of fit indices. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

Convergence analysis of the alternating RGLS algorithm for the identification of the reduced complexity Volterra model.

PubMed

Laamiri, Imen; Khouaja, Anis; Messaoud, Hassani

2015-03-01

In this paper we provide a convergence analysis of the alternating RGLS (Recursive Generalized Least Square) algorithm used for the identification of the reduced complexity Volterra model describing stochastic non-linear systems. The reduced Volterra model used is the 3rd order SVD-PARAFC-Volterra model provided using the Singular Value Decomposition (SVD) and the Parallel Factor (PARAFAC) tensor decomposition of the quadratic and the cubic kernels respectively of the classical Volterra model. The Alternating RGLS (ARGLS) algorithm consists on the execution of the classical RGLS algorithm in alternating way. The ARGLS convergence was proved using the Ordinary Differential Equation (ODE) method. It is noted that the algorithm convergence canno׳t be ensured when the disturbance acting on the system to be identified has specific features. The ARGLS algorithm is tested in simulations on a numerical example by satisfying the determined convergence conditions. To raise the elegies of the proposed algorithm, we proceed to its comparison with the classical Alternating Recursive Least Squares (ARLS) presented in the literature. The comparison has been built on a non-linear satellite channel and a benchmark system CSTR (Continuous Stirred Tank Reactor). Moreover the efficiency of the proposed identification approach is proved on an experimental Communicating Two Tank system (CTTS). Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Competitive interactions of anti-carcinogens with serum albumin: a spectroscopic study of bendamustine and dexamethasone with the aid of chemometrics.

PubMed

Wang, Yong; Zhu, Ruirui; Ni, Yongnian; Kokot, Serge

2014-04-05

Interactions between the anti-carcinogens, bendamustine (BDM) and dexamethasone (DXM), with bovine serum albumin (BSA) were investigated with the use of fluorescence and UV-vis spectroscopies under pseudo-physiological conditions (Tris-HCl buffer, pH 7.4). The static mechanism was responsible for the fluorescence quenching during the interactions; the binding formation constant of the BSA-BDM complex and the binding number were 5.14×10(5)Lmol(-1) and 1.0, respectively. Spectroscopic studies for the formation of BDM-BSA complex were interpreted with the use of multivariate curve resolution - alternating least squares (MCR-ALS), which supported the complex formation. The BSA samples treated with site markers (warfarin - site I and ibuprofen - site II) were reacted separately with BDM and DXM; while both anti-carcinogens bound to site I, the binding constants suggested that DXM formed a more stable complex. Relative concentration profiles and the fluorescence spectra associated with BDM, DXM and BSA, were recovered simultaneously from the full fluorescence excitation-emission data with the use of the parallel factor analysis (PARAFAC) method. The results confirmed that on addition of DXM to the BDM-BSA complex, the BDM was replaced and the DXM-BSA complex formed; free BDM was released. This finding may have consequences for the transport of these drugs during any anti-cancer treatment. Copyright © 2013 Elsevier B.V. All rights reserved.

Robust numerical solution of the reservoir routing equation

NASA Astrophysics Data System (ADS)

Fiorentini, Marcello; Orlandini, Stefano

2013-09-01

The robustness of numerical methods for the solution of the reservoir routing equation is evaluated. The methods considered in this study are: (1) the Laurenson-Pilgrim method, (2) the fourth-order Runge-Kutta method, and (3) the fixed order Cash-Karp method. Method (1) is unable to handle nonmonotonic outflow rating curves. Method (2) is found to fail under critical conditions occurring, especially at the end of inflow recession limbs, when large time steps (greater than 12 min in this application) are used. Method (3) is computationally intensive and it does not solve the limitations of method (2). The limitations of method (2) can be efficiently overcome by reducing the time step in the critical phases of the simulation so as to ensure that water level remains inside the domains of the storage function and the outflow rating curve. The incorporation of a simple backstepping procedure implementing this control into the method (2) yields a robust and accurate reservoir routing method that can be safely used in distributed time-continuous catchment models.

Profile Optimization Method for Robust Airfoil Shape Optimization in Viscous Flow

NASA Technical Reports Server (NTRS)

Li, Wu

2003-01-01

Simulation results obtained by using FUN2D for robust airfoil shape optimization in transonic viscous flow are included to show the potential of the profile optimization method for generating fairly smooth optimal airfoils with no off-design performance degradation.

2016 KIVA-hpFE Development: A Robust and Accurate Engine Modeling Software

DOE Office of Scientific and Technical Information (OSTI.GOV)

Carrington, David Bradley; Waters, Jiajia

Los Alamos National Laboratory and its collaborators are facilitating engine modeling by improving accuracy and robustness of the modeling, and improving the robustness of software. We also continue to improve the physical modeling methods. We are developing and implementing new mathematical algorithms, those that represent the physics within an engine. We provide software that others may use directly or that they may alter with various models e.g., sophisticated chemical kinetics, different turbulent closure methods or other fuel injection and spray systems.

Robust Classification and Segmentation of Planar and Linear Features for Construction Site Progress Monitoring and Structural Dimension Compliance Control

NASA Astrophysics Data System (ADS)

Maalek, R.; Lichti, D. D.; Ruwanpura, J.

2015-08-01

The application of terrestrial laser scanners (TLSs) on construction sites for automating construction progress monitoring and controlling structural dimension compliance is growing markedly. However, current research in construction management relies on the planned building information model (BIM) to assign the accumulated point clouds to their corresponding structural elements, which may not be reliable in cases where the dimensions of the as-built structure differ from those of the planned model and/or the planned model is not available with sufficient detail. In addition outliers exist in construction site datasets due to data artefacts caused by moving objects, occlusions and dust. In order to overcome the aforementioned limitations, a novel method for robust classification and segmentation of planar and linear features is proposed to reduce the effects of outliers present in the LiDAR data collected from construction sites. First, coplanar and collinear points are classified through a robust principal components analysis procedure. The classified points are then grouped using a robust clustering method. A method is also proposed to robustly extract the points belonging to the flat-slab floors and/or ceilings without performing the aforementioned stages in order to preserve computational efficiency. The applicability of the proposed method is investigated in two scenarios, namely, a laboratory with 30 million points and an actual construction site with over 150 million points. The results obtained by the two experiments validate the suitability of the proposed method for robust segmentation of planar and linear features in contaminated datasets, such as those collected from construction sites.

Auto Regressive Moving Average (ARMA) Modeling Method for Gyro Random Noise Using a Robust Kalman Filter

PubMed Central

Huang, Lei

2015-01-01

To solve the problem in which the conventional ARMA modeling methods for gyro random noise require a large number of samples and converge slowly, an ARMA modeling method using a robust Kalman filtering is developed. The ARMA model parameters are employed as state arguments. Unknown time-varying estimators of observation noise are used to achieve the estimated mean and variance of the observation noise. Using the robust Kalman filtering, the ARMA model parameters are estimated accurately. The developed ARMA modeling method has the advantages of a rapid convergence and high accuracy. Thus, the required sample size is reduced. It can be applied to modeling applications for gyro random noise in which a fast and accurate ARMA modeling method is required. PMID:26437409

Low cost and efficient kurtosis-based deflationary ICA method: application to MRS sources separation problem.

PubMed

Saleh, M; Karfoul, A; Kachenoura, A; Senhadji, L; Albera, L

2016-08-01

Improving the execution time and the numerical complexity of the well-known kurtosis-based maximization method, the RobustICA, is investigated in this paper. A Newton-based scheme is proposed and compared to the conventional RobustICA method. A new implementation using the nonlinear Conjugate Gradient one is investigated also. Regarding the Newton approach, an exact computation of the Hessian of the considered cost function is provided. The proposed approaches and the considered implementations inherit the global plane search of the initial RobustICA method for which a better convergence speed for a given direction is still guaranteed. Numerical results on Magnetic Resonance Spectroscopy (MRS) source separation show the efficiency of the proposed approaches notably the quasi-Newton one using the BFGS method.

Robust Coefficients Alpha and Omega and Confidence Intervals With Outlying Observations and Missing Data: Methods and Software.

PubMed

Zhang, Zhiyong; Yuan, Ke-Hai

2016-06-01

Cronbach's coefficient alpha is a widely used reliability measure in social, behavioral, and education sciences. It is reported in nearly every study that involves measuring a construct through multiple items. With non-tau-equivalent items, McDonald's omega has been used as a popular alternative to alpha in the literature. Traditional estimation methods for alpha and omega often implicitly assume that data are complete and normally distributed. This study proposes robust procedures to estimate both alpha and omega as well as corresponding standard errors and confidence intervals from samples that may contain potential outlying observations and missing values. The influence of outlying observations and missing data on the estimates of alpha and omega is investigated through two simulation studies. Results show that the newly developed robust method yields substantially improved alpha and omega estimates as well as better coverage rates of confidence intervals than the conventional nonrobust method. An R package coefficientalpha is developed and demonstrated to obtain robust estimates of alpha and omega.

A Robust Inner and Outer Loop Control Method for Trajectory Tracking of a Quadrotor

PubMed Central

Xia, Dunzhu; Cheng, Limei; Yao, Yanhong

2017-01-01

In order to achieve the complicated trajectory tracking of quadrotor, a geometric inner and outer loop control scheme is presented. The outer loop generates the desired rotation matrix for the inner loop. To improve the response speed and robustness, a geometric SMC controller is designed for the inner loop. The outer loop is also designed via sliding mode control (SMC). By Lyapunov theory and cascade theory, the closed-loop system stability is guaranteed. Next, the tracking performance is validated by tracking three representative trajectories. Then, the robustness of the proposed control method is illustrated by trajectory tracking in presence of model uncertainty and disturbances. Subsequently, experiments are carried out to verify the method. In the experiment, ultra wideband (UWB) is used for indoor positioning. Extended Kalman Filter (EKF) is used for fusing inertial measurement unit (IMU) and UWB measurements. The experimental results show the feasibility of the designed controller in practice. The comparative experiments with PD and PD loop demonstrate the robustness of the proposed control method. PMID:28925984

Robust estimation procedure in panel data model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shariff, Nurul Sima Mohamad; Hamzah, Nor Aishah

2014-06-19

The panel data modeling has received a great attention in econometric research recently. This is due to the availability of data sources and the interest to study cross sections of individuals observed over time. However, the problems may arise in modeling the panel in the presence of cross sectional dependence and outliers. Even though there are few methods that take into consideration the presence of cross sectional dependence in the panel, the methods may provide inconsistent parameter estimates and inferences when outliers occur in the panel. As such, an alternative method that is robust to outliers and cross sectional dependencemore » is introduced in this paper. The properties and construction of the confidence interval for the parameter estimates are also considered in this paper. The robustness of the procedure is investigated and comparisons are made to the existing method via simulation studies. Our results have shown that robust approach is able to produce an accurate and reliable parameter estimates under the condition considered.« less

Robust Control for Microgravity Vibration Isolation using Fixed Order, Mixed H2/Mu Design

NASA Technical Reports Server (NTRS)

Whorton, Mark

2003-01-01

Many space-science experiments need an active isolation system to provide a sufficiently quiescent microgravity environment. Modern control methods provide the potential for both high-performance and robust stability in the presence of parametric uncertainties that are characteristic of microgravity vibration isolation systems. While H2 and H(infinity) methods are well established, neither provides the levels of attenuation performance and robust stability in a compensator with low order. Mixed H2/H(infinity), controllers provide a means for maximizing robust stability for a given level of mean-square nominal performance while directly optimizing for controller order constraints. This paper demonstrates the benefit of mixed norm design from the perspective of robustness to parametric uncertainties and controller order for microgravity vibration isolation. A nominal performance metric analogous to the mu measure, for robust stability assessment is also introduced in order to define an acceptable trade space from which different control methodologies can be compared.

SU-E-T-625: Robustness Evaluation and Robust Optimization of IMPT Plans Based on Per-Voxel Standard Deviation of Dose Distributions.

PubMed

Liu, W; Mohan, R

2012-06-01

Proton dose distributions, IMPT in particular, are highly sensitive to setup and range uncertainties. We report a novel method, based on per-voxel standard deviation (SD) of dose distributions, to evaluate the robustness of proton plans and to robustly optimize IMPT plans to render them less sensitive to uncertainties. For each optimization iteration, nine dose distributions are computed - the nominal one, and one each for ± setup uncertainties along x, y and z axes and for ± range uncertainty. SD of dose in each voxel is used to create SD-volume histogram (SVH) for each structure. SVH may be considered a quantitative representation of the robustness of the dose distribution. For optimization, the desired robustness may be specified in terms of an SD-volume (SV) constraint on the CTV and incorporated as a term in the objective function. Results of optimization with and without this constraint were compared in terms of plan optimality and robustness using the so called'worst case' dose distributions; which are obtained by assigning the lowest among the nine doses to each voxel in the clinical target volume (CTV) and the highest to normal tissue voxels outside the CTV. The SVH curve and the area under it for each structure were used as quantitative measures of robustness. Penalty parameter of SV constraint may be varied to control the tradeoff between robustness and plan optimality. We applied these methods to one case each of H&N and lung. In both cases, we found that imposing SV constraint improved plan robustness but at the cost of normal tissue sparing. SVH-based optimization and evaluation is an effective tool for robustness evaluation and robust optimization of IMPT plans. Studies need to be conducted to test the methods for larger cohorts of patients and for other sites. This research is supported by National Cancer Institute (NCI) grant P01CA021239, the University Cancer Foundation via the Institutional Research Grant program at the University of Texas MD Anderson Cancer Center, and MD Anderson’s cancer center support grant CA016672. © 2012 American Association of Physicists in Medicine.

Humic like substances for the treatment of scarcely soluble pollutants by mild photo-Fenton process.

PubMed

Caram, Bruno; García-Ballesteros, Sara; Santos-Juanes, Lucas; Arques, Antonio; García-Einschlag, Fernando S

2018-05-01

Humic-like substances (HLS) extracted from urban wastes have been tested as auxiliaries for the photo-Fenton removal of thiabendazole (TBZ) under simulated sunlight. Experimental design methodology based on Doehlert matrices was employed to check the effects of hydrogen peroxide concentration, HLS amount as well as TBZ loading; this last parameter was studied in the range 25-100 mg/L, to include values below and above the limit of solubility at pH = 5. Very satisfactory results were reached when TBZ was above solubility if HLS and H 2 O 2 amounts were high. This could be attributed to an interaction of HLS-TBZ that enhances the solubility of the pollutant. Additional evidence supporting the latter interaction was obtained by fluorescence measurements (excitation emission matrices) and parallel factor analysis (PARAFAC). Copyright © 2018 Elsevier Ltd. All rights reserved.

Exploiting the synergistic effect of concurrent data signals: Low-level fusion of liquid chromatographic with dual detection data.

PubMed

Teglia, Carla M; Azcarate, Silvana M; Alcaráz, Mirta R; Goicoechea, Héctor C; Culzoni, María J

2018-08-15

A low-level data fusion strategy was developed and implemented for data processing of second-order liquid chromatographic data with dual detection, i.e. absorbance and fluorescence monitoring. The synergistic effect of coupling individual information provided by two different detectors was evaluated by analyzing the results gathered after the application of a series of data preprocessing steps and chemometric resolution. The chemometric modeling involved data analysis by MCR-ALS, PARAFAC and N-PLS. Their ability to handle the new data block was assessed through the estimation of the analytical figures of merits achieved in the prediction of a validation set containing fifteen fluorescent and non-fluorescent veterinary active ingredients that can be found in poultry litter. Eventually, the feasibility of the application of the fusion strategy to real poultry litter samples containing the studied compounds was verified. Copyright © 2018 Elsevier B.V. All rights reserved.

Characterization of DOM adsorption of CNTs by using excitation-emission matrix fluorescence spectroscopy and multiway analysis.

PubMed

Peng, Mingguo; Li, Huajie; Li, Dongdong; Du, Erdeng; Li, Zhihong

2017-06-01

Carbon nanotubes (CNTs) were utilized to adsorb DOM in micro-polluted water. The characteristics of DOM adsorption on CNTs were investigated based on UV 254 , TOC, and fluorescence spectrum measurements. Based on PARAFAC (parallel factor) analysis, four fluorescent components were extracted, including one protein-like component (C4) and three humic acid-like components (C1, C2, and C3). The adsorption isotherms, kinetics, and thermodynamics of DOM adsorption on CNTs were further investigated. A Freundlich isotherm model fit the adsorption data well with high values of correlation. As a type of macro-porous and meso-porous adsorbent, CNTs preferably adsorb humic acid-like substances rather than protein-like substances. The increasing temperature will speed up the adsorption process. The self-organizing map (SOM) analysis further explains the fluorescent properties of water samples. The results provide a new insight into the adsorption behaviour of DOM fluorescent components on CNTs.

Intrinsic fluorescence spectra characteristics of vitamin B1, B2, and B6

NASA Astrophysics Data System (ADS)

Yang, Hui; Xiao, Xue; Zhao, Xuesong; Hu, Lan; Lv, Caofang; Yin, Zhangkun

2015-11-01

This paper presents the intrinsic fluorescence characteristics of vitamin B1, B2 and B6 measured with 3D fluorescence Spectrophotometer. Three strong fluorescence areas of vitamin B2 locate at λex/λem=270/525nm, 370/525nm and 450/525nm, one fluorescence areas of vitamin B1 locates at λex/λem=370/460nm, two fluorescence areas of vitamin B6 locates at λex/λem=250/370nm and 325/370nm were found. The influence of pH of solution to the fluorescence profile was also discussed. Using the PARAFAC algorithm, 10 vitamin B1, B2 and B6 mixed solutions were successfully decomposed, and the emission profiles, excitation profiles, central wavelengths and the concentration of the three components were retrieved precisely through about 5 iteration times.

UAV Mission Planning under Uncertainty

DTIC Science & Technology

2006-06-01

algorithm , adapted from [13] . 57 4-5 Robust Optimization considers only a subset of the feasible region . 61 5-1 Overview of simulation with parameter...incorporates the robust optimization method suggested by Bertsimas and Sim [12], and is solved with a standard Branch- and-Cut algorithm . The chapter... algorithms , and the heuristic methods of Local Search methods and Simulated Annealing. With each method, we attempt to give a review of research that has

Real-time simulation of large-scale floods

NASA Astrophysics Data System (ADS)

Liu, Q.; Qin, Y.; Li, G. D.; Liu, Z.; Cheng, D. J.; Zhao, Y. H.

2016-08-01

According to the complex real-time water situation, the real-time simulation of large-scale floods is very important for flood prevention practice. Model robustness and running efficiency are two critical factors in successful real-time flood simulation. This paper proposed a robust, two-dimensional, shallow water model based on the unstructured Godunov- type finite volume method. A robust wet/dry front method is used to enhance the numerical stability. An adaptive method is proposed to improve the running efficiency. The proposed model is used for large-scale flood simulation on real topography. Results compared to those of MIKE21 show the strong performance of the proposed model.

Multi-point objective-oriented sequential sampling strategy for constrained robust design

NASA Astrophysics Data System (ADS)

Zhu, Ping; Zhang, Siliang; Chen, Wei

2015-03-01

Metamodelling techniques are widely used to approximate system responses of expensive simulation models. In association with the use of metamodels, objective-oriented sequential sampling methods have been demonstrated to be effective in balancing the need for searching an optimal solution versus reducing the metamodelling uncertainty. However, existing infilling criteria are developed for deterministic problems and restricted to one sampling point in one iteration. To exploit the use of multiple samples and identify the true robust solution in fewer iterations, a multi-point objective-oriented sequential sampling strategy is proposed for constrained robust design problems. In this article, earlier development of objective-oriented sequential sampling strategy for unconstrained robust design is first extended to constrained problems. Next, a double-loop multi-point sequential sampling strategy is developed. The proposed methods are validated using two mathematical examples followed by a highly nonlinear automotive crashworthiness design example. The results show that the proposed method can mitigate the effect of both metamodelling uncertainty and design uncertainty, and identify the robust design solution more efficiently than the single-point sequential sampling approach.

Robust Mokken Scale Analysis by Means of the Forward Search Algorithm for Outlier Detection

ERIC Educational Resources Information Center

Zijlstra, Wobbe P.; van der Ark, L. Andries; Sijtsma, Klaas

2011-01-01

Exploratory Mokken scale analysis (MSA) is a popular method for identifying scales from larger sets of items. As with any statistical method, in MSA the presence of outliers in the data may result in biased results and wrong conclusions. The forward search algorithm is a robust diagnostic method for outlier detection, which we adapt here to…

Risk assessment for construction projects of transport infrastructure objects

NASA Astrophysics Data System (ADS)

Titarenko, Boris

2017-10-01

The paper analyzes and compares different methods of risk assessment for construction projects of transport objects. The management of such type of projects demands application of special probabilistic methods due to large level of uncertainty of their implementation. Risk management in the projects requires the use of probabilistic and statistical methods. The aim of the work is to develop a methodology for using traditional methods in combination with robust methods that allow obtaining reliable risk assessments in projects. The robust approach is based on the principle of maximum likelihood and in assessing the risk allows the researcher to obtain reliable results in situations of great uncertainty. The application of robust procedures allows to carry out a quantitative assessment of the main risk indicators of projects when solving the tasks of managing innovation-investment projects. Calculation of damage from the onset of a risky event is possible by any competent specialist. And an assessment of the probability of occurrence of a risky event requires the involvement of special probabilistic methods based on the proposed robust approaches. Practice shows the effectiveness and reliability of results. The methodology developed in the article can be used to create information technologies and their application in automated control systems for complex projects.

Robust check loss-based variable selection of high-dimensional single-index varying-coefficient model

NASA Astrophysics Data System (ADS)

Song, Yunquan; Lin, Lu; Jian, Ling

2016-07-01

Single-index varying-coefficient model is an important mathematical modeling method to model nonlinear phenomena in science and engineering. In this paper, we develop a variable selection method for high-dimensional single-index varying-coefficient models using a shrinkage idea. The proposed procedure can simultaneously select significant nonparametric components and parametric components. Under defined regularity conditions, with appropriate selection of tuning parameters, the consistency of the variable selection procedure and the oracle property of the estimators are established. Moreover, due to the robustness of the check loss function to outliers in the finite samples, our proposed variable selection method is more robust than the ones based on the least squares criterion. Finally, the method is illustrated with numerical simulations.

A robust bayesian estimate of the concordance correlation coefficient.

PubMed

Feng, Dai; Baumgartner, Richard; Svetnik, Vladimir

2015-01-01

A need for assessment of agreement arises in many situations including statistical biomarker qualification or assay or method validation. Concordance correlation coefficient (CCC) is one of the most popular scaled indices reported in evaluation of agreement. Robust methods for CCC estimation currently present an important statistical challenge. Here, we propose a novel Bayesian method of robust estimation of CCC based on multivariate Student's t-distribution and compare it with its alternatives. Furthermore, we extend the method to practically relevant settings, enabling incorporation of confounding covariates and replications. The superiority of the new approach is demonstrated using simulation as well as real datasets from biomarker application in electroencephalography (EEG). This biomarker is relevant in neuroscience for development of treatments for insomnia.

Computational methods of robust controller design for aerodynamic flutter suppression

NASA Technical Reports Server (NTRS)

Anderson, L. R.

1981-01-01

The development of Riccati iteration, a tool for the design and analysis of linear control systems is examined. First, Riccati iteration is applied to the problem of pole placement and order reduction in two-time scale control systems. Order reduction, yielding a good approximation to the original system, is demonstrated using a 16th order linear model of a turbofan engine. Next, a numerical method for solving the Riccati equation is presented and demonstrated for a set of eighth order random examples. A literature review of robust controller design methods follows which includes a number of methods for reducing the trajectory and performance index sensitivity in linear regulators. Lastly, robust controller design for large parameter variations is discussed.

SU-E-T-287: Robustness Study of Passive-Scattering Proton Therapy in Lung: Is Range and Setup Uncertainty Calculation On the Initial CT Enough to Predict the Plan Robustness?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ding, X; Dormer, J; Kenton, O

Purpose: Plan robustness of the passive-scattering proton therapy treatment of lung tumors has been studied previously using combined uncertainties of 3.5% in CT number and 3 mm geometric shifts. In this study, we investigate whether this method is sufficient to predict proton plan robustness by comparing to plans performed on weekly verification CT scans. Methods: Ten lung cancer patients treated with passive-scattering proton therapy were randomly selected. All plans were prescribed 6660cGy in 37 fractions. Each initial plan was calculated using +/− 3.5% range and +/− 0.3cm setup uncertainty in x, y and z directions in Eclipse TPS(Method-A). Throughout themore » treatment course, patients received weekly verification CT scans to assess the daily treatment variation(Method-B). After contours and imaging registrations are verified by the physician, the initial plan with the same beamline and compensator was mapped into the verification CT. Dose volume histograms (DVH) were evaluated for robustness study. Results: Differences are observed between method A and B in terms of iCTV coverage and lung dose. Method-A shows all the iCTV D95 are within +/− 1% difference, while 20% of cases fall outside +/−1% range in Method-B. In the worst case scenario(WCS), the iCTV D95 is reduced by 2.5%. All lung V5 and V20 are within +/−5% in Method-A while 15% of V5 and 10% of V20 fall outside of +/−5% in Method-B. In the WCS, Lung V5 increased by 15% and V20 increased by 9%. Method A and B show good agreement with regard to cord maximum and Esophagus mean dose. Conclusion: This study suggests that using range and setup uncertainty calculation (+/−3.5% and +/−3mm) may not be sufficient to predict the WCS. In the absence of regular verification scans, expanding the conventional uncertainty parameters(e.g., to +/−3.5% and +/−4mm) may be needed to better reflect plan actual robustness.« less

Robust Temperature Control of a Thermoelectric Cooler via μ -Synthesis

NASA Astrophysics Data System (ADS)

Kürkçü, Burak; Kasnakoğlu, Coşku

2018-02-01

In this work robust temperature control of a thermoelectric cooler (TEC) via μ -synthesis is studied. An uncertain dynamical model for the TEC that is suitable for robust control methods is derived. The model captures variations in operating point due to current, load and temperature changes. A temperature controller is designed utilizing μ -synthesis, a powerful method guaranteeing robust stability and performance. For comparison two well-known control methods, namely proportional-integral-derivative (PID) and internal model control (IMC), are also realized to benchmark the proposed approach. It is observed that the stability and performance on the nominal model are satisfactory for all cases. On the other hand, under perturbations the responses of PID and IMC deteriorate and even become unstable. In contrast, the μ -synthesis controller succeeds in keeping system stability and achieving good performance under all perturbations within the operating range, while at the same time providing good disturbance rejection.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gilbert, Andrew J.; Miller, Brian W.; Robinson, Sean M.

Imaging technology is generally considered too invasive for arms control inspections due to the concern that it cannot properly secure sensitive features of the inspected item. But, this same sensitive information, which could include direct information on the form and function of the items under inspection, could be used for robust arms control inspections. The single-pixel X-ray imager (SPXI) is introduced as a method to make such inspections, capturing the salient spatial information of an object in a secure manner while never forming an actual image. We built this method on the theory of compressive sensing and the single pixelmore » optical camera. The performance of the system is quantified using simulated inspections of simple objects. Measures of the robustness and security of the method are introduced and used to determine how robust and secure such an inspection would be. Particularly, it is found that an inspection with low noise (<1%) and high undersampling (>256×) exhibits high robustness and security.« less

The comparison of predictive scheduling algorithms for different sizes of job shop scheduling problems

NASA Astrophysics Data System (ADS)

Paprocka, I.; Kempa, W. M.; Grabowik, C.; Kalinowski, K.; Krenczyk, D.

2016-08-01

In the paper a survey of predictive and reactive scheduling methods is done in order to evaluate how the ability of prediction of reliability characteristics influences over robustness criteria. The most important reliability characteristics are: Mean Time to Failure, Mean Time of Repair. Survey analysis is done for a job shop scheduling problem. The paper answers the question: what method generates robust schedules in the case of a bottleneck failure occurrence before, at the beginning of planned maintenance actions or after planned maintenance actions? Efficiency of predictive schedules is evaluated using criteria: makespan, total tardiness, flow time, idle time. Efficiency of reactive schedules is evaluated using: solution robustness criterion and quality robustness criterion. This paper is the continuation of the research conducted in the paper [1], where the survey of predictive and reactive scheduling methods is done only for small size scheduling problems.

Adaptive torque estimation of robot joint with harmonic drive transmission

NASA Astrophysics Data System (ADS)

Shi, Zhiguo; Li, Yuankai; Liu, Guangjun

2017-11-01

Robot joint torque estimation using input and output position measurements is a promising technique, but the result may be affected by the load variation of the joint. In this paper, a torque estimation method with adaptive robustness and optimality adjustment according to load variation is proposed for robot joint with harmonic drive transmission. Based on a harmonic drive model and a redundant adaptive robust Kalman filter (RARKF), the proposed approach can adapt torque estimation filtering optimality and robustness to the load variation by self-tuning the filtering gain and self-switching the filtering mode between optimal and robust. The redundant factor of RARKF is designed as a function of the motor current for tolerating the modeling error and load-dependent filtering mode switching. The proposed joint torque estimation method has been experimentally studied in comparison with a commercial torque sensor and two representative filtering methods. The results have demonstrated the effectiveness of the proposed torque estimation technique.

Replication and robustness in developmental research.

PubMed

Duncan, Greg J; Engel, Mimi; Claessens, Amy; Dowsett, Chantelle J

2014-11-01

Replications and robustness checks are key elements of the scientific method and a staple in many disciplines. However, leading journals in developmental psychology rarely include explicit replications of prior research conducted by different investigators, and few require authors to establish in their articles or online appendices that their key results are robust across estimation methods, data sets, and demographic subgroups. This article makes the case for prioritizing both explicit replications and, especially, within-study robustness checks in developmental psychology. It provides evidence on variation in effect sizes in developmental studies and documents strikingly different replication and robustness-checking practices in a sample of journals in developmental psychology and a sister behavioral science-applied economics. Our goal is not to show that any one behavioral science has a monopoly on best practices, but rather to show how journals from a related discipline address vital concerns of replication and generalizability shared by all social and behavioral sciences. We provide recommendations for promoting graduate training in replication and robustness-checking methods and for editorial policies that encourage these practices. Although some of our recommendations may shift the form and substance of developmental research articles, we argue that they would generate considerable scientific benefits for the field. (PsycINFO Database Record (c) 2014 APA, all rights reserved).

Geometrically robust image watermarking by sector-shaped partitioning of geometric-invariant regions.

PubMed

Tian, Huawei; Zhao, Yao; Ni, Rongrong; Cao, Gang

2009-11-23

In a feature-based geometrically robust watermarking system, it is a challenging task to detect geometric-invariant regions (GIRs) which can survive a broad range of image processing operations. Instead of commonly used Harris detector or Mexican hat wavelet method, a more robust corner detector named multi-scale curvature product (MSCP) is adopted to extract salient features in this paper. Based on such features, disk-like GIRs are found, which consists of three steps. First, robust edge contours are extracted. Then, MSCP is utilized to detect the centers for GIRs. Third, the characteristic scale selection is performed to calculate the radius of each GIR. A novel sector-shaped partitioning method for the GIRs is designed, which can divide a GIR into several sector discs with the help of the most important corner (MIC). The watermark message is then embedded bit by bit in each sector by using Quantization Index Modulation (QIM). The GIRs and the divided sector discs are invariant to geometric transforms, so the watermarking method inherently has high robustness against geometric attacks. Experimental results show that the scheme has a better robustness against various image processing operations including common processing attacks, affine transforms, cropping, and random bending attack (RBA) than the previous approaches.

Robust climate policies under uncertainty: a comparison of robust decision making and info-gap methods.

PubMed

Hall, Jim W; Lempert, Robert J; Keller, Klaus; Hackbarth, Andrew; Mijere, Christophe; McInerney, David J

2012-10-01

This study compares two widely used approaches for robustness analysis of decision problems: the info-gap method originally developed by Ben-Haim and the robust decision making (RDM) approach originally developed by Lempert, Popper, and Bankes. The study uses each approach to evaluate alternative paths for climate-altering greenhouse gas emissions given the potential for nonlinear threshold responses in the climate system, significant uncertainty about such a threshold response and a variety of other key parameters, as well as the ability to learn about any threshold responses over time. Info-gap and RDM share many similarities. Both represent uncertainty as sets of multiple plausible futures, and both seek to identify robust strategies whose performance is insensitive to uncertainties. Yet they also exhibit important differences, as they arrange their analyses in different orders, treat losses and gains in different ways, and take different approaches to imprecise probabilistic information. The study finds that the two approaches reach similar but not identical policy recommendations and that their differing attributes raise important questions about their appropriate roles in decision support applications. The comparison not only improves understanding of these specific methods, it also suggests some broader insights into robustness approaches and a framework for comparing them. © 2012 RAND Corporation.

Generalized internal model robust control for active front steering intervention

NASA Astrophysics Data System (ADS)

Wu, Jian; Zhao, Youqun; Ji, Xuewu; Liu, Yahui; Zhang, Lipeng

2015-03-01

Because of the tire nonlinearity and vehicle's parameters' uncertainties, robust control methods based on the worst cases, such as H ∞, µ synthesis, have been widely used in active front steering control, however, in order to guarantee the stability of active front steering system (AFS) controller, the robust control is at the cost of performance so that the robust controller is a little conservative and has low performance for AFS control. In this paper, a generalized internal model robust control (GIMC) that can overcome the contradiction between performance and stability is used in the AFS control. In GIMC, the Youla parameterization is used in an improved way. And GIMC controller includes two sections: a high performance controller designed for the nominal vehicle model and a robust controller compensating the vehicle parameters' uncertainties and some external disturbances. Simulations of double lane change (DLC) maneuver and that of braking on split- µ road are conducted to compare the performance and stability of the GIMC control, the nominal performance PID controller and the H ∞ controller. Simulation results show that the high nominal performance PID controller will be unstable under some extreme situations because of large vehicle's parameters variations, H ∞ controller is conservative so that the performance is a little low, and only the GIMC controller overcomes the contradiction between performance and robustness, which can both ensure the stability of the AFS controller and guarantee the high performance of the AFS controller. Therefore, the GIMC method proposed for AFS can overcome some disadvantages of control methods used by current AFS system, that is, can solve the instability of PID or LQP control methods and the low performance of the standard H ∞ controller.

Robust and fast-converging level set method for side-scan sonar image segmentation

NASA Astrophysics Data System (ADS)

Liu, Yan; Li, Qingwu; Huo, Guanying

2017-11-01

A robust and fast-converging level set method is proposed for side-scan sonar (SSS) image segmentation. First, the noise in each sonar image is removed using the adaptive nonlinear complex diffusion filter. Second, k-means clustering is used to obtain the initial presegmentation image from the denoised image, and then the distance maps of the initial contours are reinitialized to guarantee the accuracy of the numerical calculation used in the level set evolution. Finally, the satisfactory segmentation is achieved using a robust variational level set model, where the evolution control parameters are generated by the presegmentation. The proposed method is successfully applied to both synthetic image with speckle noise and real SSS images. Experimental results show that the proposed method needs much less iteration and therefore is much faster than the fuzzy local information c-means clustering method, the level set method using a gamma observation model, and the enhanced region-scalable fitting method. Moreover, the proposed method can usually obtain more accurate segmentation results compared with other methods.

Enhanced robust fractional order proportional-plus-integral controller based on neural network for velocity control of permanent magnet synchronous motor.

PubMed

Zhang, Bitao; Pi, YouGuo

2013-07-01

The traditional integer order proportional-integral-differential (IO-PID) controller is sensitive to the parameter variation or/and external load disturbance of permanent magnet synchronous motor (PMSM). And the fractional order proportional-integral-differential (FO-PID) control scheme based on robustness tuning method is proposed to enhance the robustness. But the robustness focuses on the open-loop gain variation of controlled plant. In this paper, an enhanced robust fractional order proportional-plus-integral (ERFOPI) controller based on neural network is proposed. The control law of the ERFOPI controller is acted on a fractional order implement function (FOIF) of tracking error but not tracking error directly, which, according to theory analysis, can enhance the robust performance of system. Tuning rules and approaches, based on phase margin, crossover frequency specification and robustness rejecting gain variation, are introduced to obtain the parameters of ERFOPI controller. And the neural network algorithm is used to adjust the parameter of FOIF. Simulation and experimental results show that the method proposed in this paper not only achieve favorable tracking performance, but also is robust with regard to external load disturbance and parameter variation. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

Horsetail matching: a flexible approach to optimization under uncertainty

NASA Astrophysics Data System (ADS)

Cook, L. W.; Jarrett, J. P.

2018-04-01

It is important to design engineering systems to be robust with respect to uncertainties in the design process. Often, this is done by considering statistical moments, but over-reliance on statistical moments when formulating a robust optimization can produce designs that are stochastically dominated by other feasible designs. This article instead proposes a formulation for optimization under uncertainty that minimizes the difference between a design's cumulative distribution function and a target. A standard target is proposed that produces stochastically non-dominated designs, but the formulation also offers enough flexibility to recover existing approaches for robust optimization. A numerical implementation is developed that employs kernels to give a differentiable objective function. The method is applied to algebraic test problems and a robust transonic airfoil design problem where it is compared to multi-objective, weighted-sum and density matching approaches to robust optimization; several advantages over these existing methods are demonstrated.

Approach for Input Uncertainty Propagation and Robust Design in CFD Using Sensitivity Derivatives

NASA Technical Reports Server (NTRS)

Putko, Michele M.; Taylor, Arthur C., III; Newman, Perry A.; Green, Lawrence L.

2002-01-01

An implementation of the approximate statistical moment method for uncertainty propagation and robust optimization for quasi 3-D Euler CFD code is presented. Given uncertainties in statistically independent, random, normally distributed input variables, first- and second-order statistical moment procedures are performed to approximate the uncertainty in the CFD output. Efficient calculation of both first- and second-order sensitivity derivatives is required. In order to assess the validity of the approximations, these moments are compared with statistical moments generated through Monte Carlo simulations. The uncertainties in the CFD input variables are also incorporated into a robust optimization procedure. For this optimization, statistical moments involving first-order sensitivity derivatives appear in the objective function and system constraints. Second-order sensitivity derivatives are used in a gradient-based search to successfully execute a robust optimization. The approximate methods used throughout the analyses are found to be valid when considering robustness about input parameter mean values.

Robust nonlinear control of vectored thrust aircraft

NASA Technical Reports Server (NTRS)

Doyle, John C.; Murray, Richard; Morris, John

1993-01-01

An interdisciplinary program in robust control for nonlinear systems with applications to a variety of engineering problems is outlined. Major emphasis will be placed on flight control, with both experimental and analytical studies. This program builds on recent new results in control theory for stability, stabilization, robust stability, robust performance, synthesis, and model reduction in a unified framework using Linear Fractional Transformations (LFT's), Linear Matrix Inequalities (LMI's), and the structured singular value micron. Most of these new advances have been accomplished by the Caltech controls group independently or in collaboration with researchers in other institutions. These recent results offer a new and remarkably unified framework for all aspects of robust control, but what is particularly important for this program is that they also have important implications for system identification and control of nonlinear systems. This combines well with Caltech's expertise in nonlinear control theory, both in geometric methods and methods for systems with constraints and saturations.

Comparative Robustness of Recent Methods for Analyzing Multivariate Repeated Measures Designs

ERIC Educational Resources Information Center

Seco, Guillermo Vallejo; Gras, Jaime Arnau; Garcia, Manuel Ato

2007-01-01

This study evaluated the robustness of two recent methods for analyzing multivariate repeated measures when the assumptions of covariance homogeneity and multivariate normality are violated. Specifically, the authors' work compares the performance of the modified Brown-Forsythe (MBF) procedure and the mixed-model procedure adjusted by the…

Robust range estimation with a monocular camera for vision-based forward collision warning system.

PubMed

Park, Ki-Yeong; Hwang, Sun-Young

2014-01-01

We propose a range estimation method for vision-based forward collision warning systems with a monocular camera. To solve the problem of variation of camera pitch angle due to vehicle motion and road inclination, the proposed method estimates virtual horizon from size and position of vehicles in captured image at run-time. The proposed method provides robust results even when road inclination varies continuously on hilly roads or lane markings are not seen on crowded roads. For experiments, a vision-based forward collision warning system has been implemented and the proposed method is evaluated with video clips recorded in highway and urban traffic environments. Virtual horizons estimated by the proposed method are compared with horizons manually identified, and estimated ranges are compared with measured ranges. Experimental results confirm that the proposed method provides robust results both in highway and in urban traffic environments.

Robust Range Estimation with a Monocular Camera for Vision-Based Forward Collision Warning System

PubMed Central

2014-01-01

We propose a range estimation method for vision-based forward collision warning systems with a monocular camera. To solve the problem of variation of camera pitch angle due to vehicle motion and road inclination, the proposed method estimates virtual horizon from size and position of vehicles in captured image at run-time. The proposed method provides robust results even when road inclination varies continuously on hilly roads or lane markings are not seen on crowded roads. For experiments, a vision-based forward collision warning system has been implemented and the proposed method is evaluated with video clips recorded in highway and urban traffic environments. Virtual horizons estimated by the proposed method are compared with horizons manually identified, and estimated ranges are compared with measured ranges. Experimental results confirm that the proposed method provides robust results both in highway and in urban traffic environments. PMID:24558344

A P-Norm Robust Feature Extraction Method for Identifying Differentially Expressed Genes

PubMed Central

Liu, Jian; Liu, Jin-Xing; Gao, Ying-Lian; Kong, Xiang-Zhen; Wang, Xue-Song; Wang, Dong

2015-01-01

In current molecular biology, it becomes more and more important to identify differentially expressed genes closely correlated with a key biological process from gene expression data. In this paper, based on the Schatten p-norm and Lp-norm, a novel p-norm robust feature extraction method is proposed to identify the differentially expressed genes. In our method, the Schatten p-norm is used as the regularization function to obtain a low-rank matrix and the Lp-norm is taken as the error function to improve the robustness to outliers in the gene expression data. The results on simulation data show that our method can obtain higher identification accuracies than the competitive methods. Numerous experiments on real gene expression data sets demonstrate that our method can identify more differentially expressed genes than the others. Moreover, we confirmed that the identified genes are closely correlated with the corresponding gene expression data. PMID:26201006

A P-Norm Robust Feature Extraction Method for Identifying Differentially Expressed Genes.

PubMed

Liu, Jian; Liu, Jin-Xing; Gao, Ying-Lian; Kong, Xiang-Zhen; Wang, Xue-Song; Wang, Dong

2015-01-01

In current molecular biology, it becomes more and more important to identify differentially expressed genes closely correlated with a key biological process from gene expression data. In this paper, based on the Schatten p-norm and Lp-norm, a novel p-norm robust feature extraction method is proposed to identify the differentially expressed genes. In our method, the Schatten p-norm is used as the regularization function to obtain a low-rank matrix and the Lp-norm is taken as the error function to improve the robustness to outliers in the gene expression data. The results on simulation data show that our method can obtain higher identification accuracies than the competitive methods. Numerous experiments on real gene expression data sets demonstrate that our method can identify more differentially expressed genes than the others. Moreover, we confirmed that the identified genes are closely correlated with the corresponding gene expression data.

Robust control of nonlinear MAGLEV suspension system with mismatched uncertainties via DOBC approach.

PubMed

Yang, Jun; Zolotas, Argyrios; Chen, Wen-Hua; Michail, Konstantinos; Li, Shihua

2011-07-01

Robust control of a class of uncertain systems that have disturbances and uncertainties not satisfying "matching" condition is investigated in this paper via a disturbance observer based control (DOBC) approach. In the context of this paper, "matched" disturbances/uncertainties stand for the disturbances/uncertainties entering the system through the same channels as control inputs. By properly designing a disturbance compensation gain, a novel composite controller is proposed to counteract the "mismatched" lumped disturbances from the output channels. The proposed method significantly extends the applicability of the DOBC methods. Rigorous stability analysis of the closed-loop system with the proposed method is established under mild assumptions. The proposed method is applied to a nonlinear MAGnetic LEViation (MAGLEV) suspension system. Simulation shows that compared to the widely used integral control method, the proposed method provides significantly improved disturbance rejection and robustness against load variation. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

TLE uncertainty estimation using robust weighted differencing

NASA Astrophysics Data System (ADS)

Geul, Jacco; Mooij, Erwin; Noomen, Ron

2017-05-01

Accurate knowledge of satellite orbit errors is essential for many types of analyses. Unfortunately, for two-line elements (TLEs) this is not available. This paper presents a weighted differencing method using robust least-squares regression for estimating many important error characteristics. The method is applied to both classic and enhanced TLEs, compared to previous implementations, and validated using Global Positioning System (GPS) solutions for the GOCE satellite in Low-Earth Orbit (LEO), prior to its re-entry. The method is found to be more accurate than previous TLE differencing efforts in estimating initial uncertainty, as well as error growth. The method also proves more reliable and requires no data filtering (such as outlier removal). Sensitivity analysis shows a strong relationship between argument of latitude and covariance (standard deviations and correlations), which the method is able to approximate. Overall, the method proves accurate, computationally fast, and robust, and is applicable to any object in the satellite catalogue (SATCAT).

A method for detecting nonlinear determinism in normal and epileptic brain EEG signals.

PubMed

Meghdadi, Amir H; Fazel-Rezai, Reza; Aghakhani, Yahya

2007-01-01

A robust method of detecting determinism for short time series is proposed and applied to both healthy and epileptic EEG signals. The method provides a robust measure of determinism through characterizing the trajectories of the signal components which are obtained through singular value decomposition. Robustness of the method is shown by calculating proposed index of determinism at different levels of white and colored noise added to a simulated chaotic signal. The method is shown to be able to detect determinism at considerably high levels of additive noise. The method is then applied to both intracranial and scalp EEG recordings collected in different data sets for healthy and epileptic brain signals. The results show that for all of the studied EEG data sets there is enough evidence of determinism. The determinism is more significant for intracranial EEG recordings particularly during seizure activity.

Closed-Loop and Robust Control of Quantum Systems

PubMed Central

Wang, Lin-Cheng

2013-01-01

For most practical quantum control systems, it is important and difficult to attain robustness and reliability due to unavoidable uncertainties in the system dynamics or models. Three kinds of typical approaches (e.g., closed-loop learning control, feedback control, and robust control) have been proved to be effective to solve these problems. This work presents a self-contained survey on the closed-loop and robust control of quantum systems, as well as a brief introduction to a selection of basic theories and methods in this research area, to provide interested readers with a general idea for further studies. In the area of closed-loop learning control of quantum systems, we survey and introduce such learning control methods as gradient-based methods, genetic algorithms (GA), and reinforcement learning (RL) methods from a unified point of view of exploring the quantum control landscapes. For the feedback control approach, the paper surveys three control strategies including Lyapunov control, measurement-based control, and coherent-feedback control. Then such topics in the field of quantum robust control as H ∞ control, sliding mode control, quantum risk-sensitive control, and quantum ensemble control are reviewed. The paper concludes with a perspective of future research directions that are likely to attract more attention. PMID:23997680

Robust Operation of Soft Open Points in Active Distribution Networks with High Penetration of Photovoltaic Integration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ding, Fei; Ji, Haoran; Wang, Chengshan

Distributed generators (DGs) including photovoltaic panels (PVs) have been integrated dramatically in active distribution networks (ADNs). Due to the strong volatility and uncertainty, the high penetration of PV generation immensely exacerbates the conditions of voltage violation in ADNs. However, the emerging flexible interconnection technology based on soft open points (SOPs) provides increased controllability and flexibility to the system operation. For fully exploiting the regulation ability of SOPs to address the problems caused by PV, this paper proposes a robust optimization method to achieve the robust optimal operation of SOPs in ADNs. A two-stage adjustable robust optimization model is built tomore » tackle the uncertainties of PV outputs, in which robust operation strategies of SOPs are generated to eliminate the voltage violations and reduce the power losses of ADNs. A column-and-constraint generation (C&CG) algorithm is developed to solve the proposed robust optimization model, which are formulated as second-order cone program (SOCP) to facilitate the accuracy and computation efficiency. Case studies on the modified IEEE 33-node system and comparisons with the deterministic optimization approach are conducted to verify the effectiveness and robustness of the proposed method.« less

Development and validation of a single robust HPLC method for the characterization of a pharmaceutical starting material and impurities from three suppliers using three separate synthetic routes.

PubMed

Sheldon, E M; Downar, J B

2000-08-15

Novel approaches to the development of analytical procedures for monitoring incoming starting material in support of chemical/pharmaceutical processes are described. High technology solutions were utilized for timely process development and preparation of high quality clinical supplies. A single robust HPLC method was developed and characterized for the analysis of the key starting material from three suppliers. Each supplier used a different process for the preparation of this material and, therefore, each suppliers' material exhibited a unique impurity profile. The HPLC method utilized standard techniques acceptable for release testing in a QC/manufacturing environment. An automated experimental design protocol was used to characterize the robustness of the HPLC method. The method was evaluated for linearity, limit of quantitation, solution stability, and precision of replicate injections. An LC-MS method that emulated the release HPLC method was developed and the identities of impurities were mapped between the two methods.

TU-AB-BRB-01: Coverage Evaluation and Probabilistic Treatment Planning as a Margin Alternative

DOE Office of Scientific and Technical Information (OSTI.GOV)

Siebers, J.

The accepted clinical method to accommodate targeting uncertainties inherent in fractionated external beam radiation therapy is to utilize GTV-to-CTV and CTV-to-PTV margins during the planning process to design a PTV-conformal static dose distribution on the planning image set. Ideally, margins are selected to ensure a high (e.g. >95%) target coverage probability (CP) in spite of inherent inter- and intra-fractional positional variations, tissue motions, and initial contouring uncertainties. Robust optimization techniques, also known as probabilistic treatment planning techniques, explicitly incorporate the dosimetric consequences of targeting uncertainties by including CP evaluation into the planning optimization process along with coverage-based planning objectives. Themore » treatment planner no longer needs to use PTV and/or PRV margins; instead robust optimization utilizes probability distributions of the underlying uncertainties in conjunction with CP-evaluation for the underlying CTVs and OARs to design an optimal treated volume. This symposium will describe CP-evaluation methods as well as various robust planning techniques including use of probability-weighted dose distributions, probability-weighted objective functions, and coverage optimized planning. Methods to compute and display the effect of uncertainties on dose distributions will be presented. The use of robust planning to accommodate inter-fractional setup uncertainties, organ deformation, and contouring uncertainties will be examined as will its use to accommodate intra-fractional organ motion. Clinical examples will be used to inter-compare robust and margin-based planning, highlighting advantages of robust-plans in terms of target and normal tissue coverage. Robust-planning limitations as uncertainties approach zero and as the number of treatment fractions becomes small will be presented, as well as the factors limiting clinical implementation of robust planning. Learning Objectives: To understand robust-planning as a clinical alternative to using margin-based planning. To understand conceptual differences between uncertainty and predictable motion. To understand fundamental limitations of the PTV concept that probabilistic planning can overcome. To understand the major contributing factors to target and normal tissue coverage probability. To understand the similarities and differences of various robust planning techniques To understand the benefits and limitations of robust planning techniques.« less

TU-AB-BRB-03: Coverage-Based Treatment Planning to Accommodate Organ Deformable Motions and Contouring Uncertainties for Prostate Treatment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xu, H.

The accepted clinical method to accommodate targeting uncertainties inherent in fractionated external beam radiation therapy is to utilize GTV-to-CTV and CTV-to-PTV margins during the planning process to design a PTV-conformal static dose distribution on the planning image set. Ideally, margins are selected to ensure a high (e.g. >95%) target coverage probability (CP) in spite of inherent inter- and intra-fractional positional variations, tissue motions, and initial contouring uncertainties. Robust optimization techniques, also known as probabilistic treatment planning techniques, explicitly incorporate the dosimetric consequences of targeting uncertainties by including CP evaluation into the planning optimization process along with coverage-based planning objectives. Themore » treatment planner no longer needs to use PTV and/or PRV margins; instead robust optimization utilizes probability distributions of the underlying uncertainties in conjunction with CP-evaluation for the underlying CTVs and OARs to design an optimal treated volume. This symposium will describe CP-evaluation methods as well as various robust planning techniques including use of probability-weighted dose distributions, probability-weighted objective functions, and coverage optimized planning. Methods to compute and display the effect of uncertainties on dose distributions will be presented. The use of robust planning to accommodate inter-fractional setup uncertainties, organ deformation, and contouring uncertainties will be examined as will its use to accommodate intra-fractional organ motion. Clinical examples will be used to inter-compare robust and margin-based planning, highlighting advantages of robust-plans in terms of target and normal tissue coverage. Robust-planning limitations as uncertainties approach zero and as the number of treatment fractions becomes small will be presented, as well as the factors limiting clinical implementation of robust planning. Learning Objectives: To understand robust-planning as a clinical alternative to using margin-based planning. To understand conceptual differences between uncertainty and predictable motion. To understand fundamental limitations of the PTV concept that probabilistic planning can overcome. To understand the major contributing factors to target and normal tissue coverage probability. To understand the similarities and differences of various robust planning techniques To understand the benefits and limitations of robust planning techniques.« less

Robust geostatistical analysis of spatial data

NASA Astrophysics Data System (ADS)

Papritz, Andreas; Künsch, Hans Rudolf; Schwierz, Cornelia; Stahel, Werner A.

2013-04-01

Most of the geostatistical software tools rely on non-robust algorithms. This is unfortunate, because outlying observations are rather the rule than the exception, in particular in environmental data sets. Outliers affect the modelling of the large-scale spatial trend, the estimation of the spatial dependence of the residual variation and the predictions by kriging. Identifying outliers manually is cumbersome and requires expertise because one needs parameter estimates to decide which observation is a potential outlier. Moreover, inference after the rejection of some observations is problematic. A better approach is to use robust algorithms that prevent automatically that outlying observations have undue influence. Former studies on robust geostatistics focused on robust estimation of the sample variogram and ordinary kriging without external drift. Furthermore, Richardson and Welsh (1995) proposed a robustified version of (restricted) maximum likelihood ([RE]ML) estimation for the variance components of a linear mixed model, which was later used by Marchant and Lark (2007) for robust REML estimation of the variogram. We propose here a novel method for robust REML estimation of the variogram of a Gaussian random field that is possibly contaminated by independent errors from a long-tailed distribution. It is based on robustification of estimating equations for the Gaussian REML estimation (Welsh and Richardson, 1997). Besides robust estimates of the parameters of the external drift and of the variogram, the method also provides standard errors for the estimated parameters, robustified kriging predictions at both sampled and non-sampled locations and kriging variances. Apart from presenting our modelling framework, we shall present selected simulation results by which we explored the properties of the new method. This will be complemented by an analysis a data set on heavy metal contamination of the soil in the vicinity of a metal smelter. Marchant, B.P. and Lark, R.M. 2007. Robust estimation of the variogram by residual maximum likelihood. Geoderma 140: 62-72. Richardson, A.M. and Welsh, A.H. 1995. Robust restricted maximum likelihood in mixed linear models. Biometrics 51: 1429-1439. Welsh, A.H. and Richardson, A.M. 1997. Approaches to the robust estimation of mixed models. In: Handbook of Statistics Vol. 15, Elsevier, pp. 343-384.

Design for robustness of unique, multi-component engineering systems

NASA Astrophysics Data System (ADS)

Shelton, Kenneth A.

2007-12-01

The purpose of this research is to advance the science of conceptual designing for robustness in unique, multi-component engineering systems. Robustness is herein defined as the ability of an engineering system to operate within a desired performance range even if the actual configuration has differences from specifications within specified tolerances. These differences are caused by three sources, namely manufacturing errors, system degradation (operational wear and tear), and parts availability. Unique, multi-component engineering systems are defined as systems produced in unique or very small production numbers. They typically have design and manufacturing costs on the order of billions of dollars, and have multiple, competing performance objectives. Design time for these systems must be minimized due to competition, high manpower costs, long manufacturing times, technology obsolescence, and limited available manpower expertise. Most importantly, design mistakes cannot be easily corrected after the systems are operational. For all these reasons, robustness of these systems is absolutely critical. This research examines the space satellite industry in particular. Although inherent robustness assurance is absolutely critical, it is difficult to achieve in practice. The current state of the art for robustness in the industry is to overdesign components and subsystems with redundancy and margin. The shortfall is that it is not known if the added margins were either necessary or sufficient given the risk management preferences of the designer or engineering system customer. To address this shortcoming, new assessment criteria to evaluate robustness in design concepts have been developed. The criteria are comprised of the "Value Distance", addressing manufacturing errors and system degradation, and "Component Distance", addressing parts availability. They are based on an evolutionary computation format that uses a string of alleles to describe the components in the design concept. These allele values are unitless themselves, but map to both configuration descriptions and attribute values. The Value Distance and Component Distance are metrics that measure the relative differences between two design concepts using the allele values, and all differences in a population of design concepts are calculated relative to a reference design, called the "base design". The base design is the top-ranked member of the population in weighted terms of robustness and performance. Robustness is determined based on the change in multi-objective performance as Value Distance and Component Distance (and thus differences in design) increases. It is assessed as acceptable if differences in design configurations up to specified tolerances result in performance changes that remain within a specified performance range. The design configuration difference tolerances and performance range together define the designer's risk management preferences for the final design concepts. Additionally, a complementary visualization capability was developed, called the "Design Solution Topography". This concept allows the visualization of a population of design concepts, and is a 3-axis plot where each point represents an entire design concept. The axes are the Value Distance, Component Distance and Performance Objective. The key benefit of the Design Solution Topography is that it allows the designer to visually identify and interpret the overall robustness of the current population of design concepts for a particular performance objective. In a multi-objective problem, each performance objective has its own Design Solution Topography view. These new concepts are implemented in an evolutionary computation-based conceptual designing method called the "Design for Robustness Method" that produces robust design concepts. The design procedures associated with this method enable designers to evaluate and ensure robustness in selected designs that also perform within a desired performance range. The method uses an evolutionary computation-based procedure to generate populations of large numbers of alternative design concepts, which are assessed for robustness using the Value Distance, Component Distance and Design Solution Topography procedures. The Design for Robustness Method provides a working conceptual designing structure in which to implement and gain the benefits of these new concepts. In the included experiments, the method was used on several mathematical examples to demonstrate feasibility, which showed favorable results as compared to existing known methods. Furthermore, it was tested on a real-world satellite conceptual designing problem to illustrate the applicability and benefits to industry. Risk management insights were demonstrated for the robustness-related issues of manufacturing errors, operational degradation, parts availability, and impacts based on selections of particular types of components.

SU-F-T-192: Study of Robustness Analysis Method of Multiple Field Optimized IMPT Plans for Head & Neck Patients

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Y; Wang, X; Li, H

Purpose: Proton therapy is more sensitive to uncertainties than photon treatments due to protons’ finite range depending on the tissue density. Worst case scenario (WCS) method originally proposed by Lomax has been adopted in our institute for robustness analysis of IMPT plans. This work demonstrates that WCS method is sufficient enough to take into account of the uncertainties which could be encountered during daily clinical treatment. Methods: A fast and approximate dose calculation method is developed to calculate the dose for the IMPT plan under different setup and range uncertainties. Effects of two factors, inversed square factor and range uncertainty,more » are explored. WCS robustness analysis method was evaluated using this fast dose calculation method. The worst-case dose distribution was generated by shifting isocenter by 3 mm along x,y and z directions and modifying stopping power ratios by ±3.5%. 1000 randomly perturbed cases in proton range and x, yz directions were created and the corresponding dose distributions were calculated using this approximated method. DVH and dosimetric indexes of all 1000 perturbed cases were calculated and compared with the result using worst case scenario method. Results: The distributions of dosimetric indexes of 1000 perturbed cases were generated and compared with the results using worst case scenario. For D95 of CTVs, at least 97% of 1000 perturbed cases show higher values than the one of worst case scenario. For D5 of CTVs, at least 98% of perturbed cases have lower values than worst case scenario. Conclusion: By extensively calculating the dose distributions under random uncertainties, WCS method was verified to be reliable in evaluating the robustness level of MFO IMPT plans of H&N patients. The extensively sampling approach using fast approximated method could be used in evaluating the effects of different factors on the robustness level of IMPT plans in the future.« less

Multiple-image hiding using super resolution reconstruction in high-frequency domains

NASA Astrophysics Data System (ADS)

Li, Xiao-Wei; Zhao, Wu-Xiang; Wang, Jun; Wang, Qiong-Hua

2017-12-01

In this paper, a robust multiple-image hiding method using the computer-generated integral imaging and the modified super-resolution reconstruction algorithm is proposed. In our work, the host image is first transformed into frequency domains by cellular automata (CA), to assure the quality of the stego-image, the secret images are embedded into the CA high-frequency domains. The proposed method has the following advantages: (1) robustness to geometric attacks because of the memory-distributed property of elemental images, (2) increasing quality of the reconstructed secret images as the scheme utilizes the modified super-resolution reconstruction algorithm. The simulation results show that the proposed multiple-image hiding method outperforms other similar hiding methods and is robust to some geometric attacks, e.g., Gaussian noise and JPEG compression attacks.

Determination of skeleton and sign map for phase obtaining from a single ESPI image

NASA Astrophysics Data System (ADS)

Yang, Xia; Yu, Qifeng; Fu, Sihua

2009-06-01

A robust method of determining the sign map and skeletons for ESPI images is introduced in this paper. ESPI images have high speckle noise which makes it difficult to obtain the fringe information, especially from a single image. To overcome the effects of high speckle noise, local directional computing windows are designed according to the fringe directions. Then by calculating the gradients from the filtered image in directional windows, sign map and good skeletons can be determined robustly. Based on the sign map, single image phase-extracting methods such as quadrature transform can be improved. And based on skeletons, fringe phases can be obtained directly by normalization methods. Experiments show that this new method is robust and effective for extracting phase from a single ESPI fringe image.

Position Accuracy Analysis of a Robust Vision-Based Navigation

NASA Astrophysics Data System (ADS)

Gaglione, S.; Del Pizzo, S.; Troisi, S.; Angrisano, A.

2018-05-01

Using images to determine camera position and attitude is a consolidated method, very widespread for application like UAV navigation. In harsh environment, where GNSS could be degraded or denied, image-based positioning could represent a possible candidate for an integrated or alternative system. In this paper, such method is investigated using a system based on single camera and 3D maps. A robust estimation method is proposed in order to limit the effect of blunders or noisy measurements on position solution. The proposed approach is tested using images collected in an urban canyon, where GNSS positioning is very unaccurate. A previous photogrammetry survey has been performed to build the 3D model of tested area. The position accuracy analysis is performed and the effect of the robust method proposed is validated.

A Robust and Efficient Method for Steady State Patterns in Reaction-Diffusion Systems

PubMed Central

Lo, Wing-Cheong; Chen, Long; Wang, Ming; Nie, Qing

2012-01-01

An inhomogeneous steady state pattern of nonlinear reaction-diffusion equations with no-flux boundary conditions is usually computed by solving the corresponding time-dependent reaction-diffusion equations using temporal schemes. Nonlinear solvers (e.g., Newton’s method) take less CPU time in direct computation for the steady state; however, their convergence is sensitive to the initial guess, often leading to divergence or convergence to spatially homogeneous solution. Systematically numerical exploration of spatial patterns of reaction-diffusion equations under different parameter regimes requires that the numerical method be efficient and robust to initial condition or initial guess, with better likelihood of convergence to an inhomogeneous pattern. Here, a new approach that combines the advantages of temporal schemes in robustness and Newton’s method in fast convergence in solving steady states of reaction-diffusion equations is proposed. In particular, an adaptive implicit Euler with inexact solver (AIIE) method is found to be much more efficient than temporal schemes and more robust in convergence than typical nonlinear solvers (e.g., Newton’s method) in finding the inhomogeneous pattern. Application of this new approach to two reaction-diffusion equations in one, two, and three spatial dimensions, along with direct comparisons to several other existing methods, demonstrates that AIIE is a more desirable method for searching inhomogeneous spatial patterns of reaction-diffusion equations in a large parameter space. PMID:22773849

Robust Control for The G-Limit Microgravity Vibration Isolation System

NASA Technical Reports Server (NTRS)

Whorton, Mark S.

2004-01-01

Many microgravity science experiments need an active isolation system to provide a sufficiently quiescent acceleration environment. The g-LIMIT vibration isolation system will provide isolation for Microgravity Science Glovebox experiments in the International Space Station. While standard control system technologies have been demonstrated for these applications, modern control methods have the potential for meeting performance requirements while providing robust stability in the presence of parametric uncertainties that are characteristic of microgravity vibration isolation systems. While H2 and H infinity methods are well established, neither provides the levels of attenuation performance and robust stability in a compensator with low order. Mixed H2/mu controllers provide a means for maximizing robust stability for a given level of mean-square nominal performance while directly optimizing for controller order constraints. This paper demonstrates the benefit of mixed norm design from the perspective of robustness to parametric uncertainties and controller order for microgravity vibration isolation. A nominal performance metric analogous to the mu measure for robust stability assessment is also introduced in order to define an acceptable trade space from which different control methodologies can be compared.

Towards a robust HDR imaging system

NASA Astrophysics Data System (ADS)

Long, Xin; Zeng, Xiangrong; Huangpeng, Qizi; Zhou, Jinglun; Feng, Jing

2016-07-01

High dynamic range (HDR) images can show more details and luminance information in general display device than low dynamic image (LDR) images. We present a robust HDR imaging system which can deal with blurry LDR images, overcoming the limitations of most existing HDR methods. Experiments on real images show the effectiveness and competitiveness of the proposed method.

Possibility of spoof attack against robustness of multibiometric authentication systems

NASA Astrophysics Data System (ADS)

Hariri, Mahdi; Shokouhi, Shahriar Baradaran

2011-07-01

Multibiometric systems have been recently developed in order to overcome some weaknesses of single biometric authentication systems, but security of these systems against spoofing has not received enough attention. In this paper, we propose a novel practical method for simulation of possibilities of spoof attacks against a biometric authentication system. Using this method, we model matching scores from standard to completely spoofed genuine samples. Sum, product, and Bayes fusion rules are applied for score level combination. The security of multimodal authentication systems are examined and compared with the single systems against various spoof possibilities. However, vulnerability of fused systems is considerably increased against spoofing, but their robustness is generally higher than single matcher systems. In this paper we show that robustness of a combined system is not always higher than a single system against spoof attack. We propose empirical methods for upgrading the security of multibiometric systems, which contain how to organize and select biometric traits and matchers against various possibilities of spoof attack. These methods provide considerable robustness and present an appropriate reason for using combined systems against spoof attacks.

Robust pattern decoding in shape-coded structured light

NASA Astrophysics Data System (ADS)

Tang, Suming; Zhang, Xu; Song, Zhan; Song, Lifang; Zeng, Hai

2017-09-01

Decoding is a challenging and complex problem in a coded structured light system. In this paper, a robust pattern decoding method is proposed for the shape-coded structured light in which the pattern is designed as grid shape with embedded geometrical shapes. In our decoding method, advancements are made at three steps. First, a multi-template feature detection algorithm is introduced to detect the feature point which is the intersection of each two orthogonal grid-lines. Second, pattern element identification is modelled as a supervised classification problem and the deep neural network technique is applied for the accurate classification of pattern elements. Before that, a training dataset is established, which contains a mass of pattern elements with various blurring and distortions. Third, an error correction mechanism based on epipolar constraint, coplanarity constraint and topological constraint is presented to reduce the false matches. In the experiments, several complex objects including human hand are chosen to test the accuracy and robustness of the proposed method. The experimental results show that our decoding method not only has high decoding accuracy, but also owns strong robustness to surface color and complex textures.

Robust synchronization control scheme of a population of nonlinear stochastic synthetic genetic oscillators under intrinsic and extrinsic molecular noise via quorum sensing.

PubMed

Chen, Bor-Sen; Hsu, Chih-Yuan

2012-10-26

Collective rhythms of gene regulatory networks have been a subject of considerable interest for biologists and theoreticians, in particular the synchronization of dynamic cells mediated by intercellular communication. Synchronization of a population of synthetic genetic oscillators is an important design in practical applications, because such a population distributed over different host cells needs to exploit molecular phenomena simultaneously in order to emerge a biological phenomenon. However, this synchronization may be corrupted by intrinsic kinetic parameter fluctuations and extrinsic environmental molecular noise. Therefore, robust synchronization is an important design topic in nonlinear stochastic coupled synthetic genetic oscillators with intrinsic kinetic parameter fluctuations and extrinsic molecular noise. Initially, the condition for robust synchronization of synthetic genetic oscillators was derived based on Hamilton Jacobi inequality (HJI). We found that if the synchronization robustness can confer enough intrinsic robustness to tolerate intrinsic parameter fluctuation and extrinsic robustness to filter the environmental noise, then robust synchronization of coupled synthetic genetic oscillators is guaranteed. If the synchronization robustness of a population of nonlinear stochastic coupled synthetic genetic oscillators distributed over different host cells could not be maintained, then robust synchronization could be enhanced by external control input through quorum sensing molecules. In order to simplify the analysis and design of robust synchronization of nonlinear stochastic synthetic genetic oscillators, the fuzzy interpolation method was employed to interpolate several local linear stochastic coupled systems to approximate the nonlinear stochastic coupled system so that the HJI-based synchronization design problem could be replaced by a simple linear matrix inequality (LMI)-based design problem, which could be solved with the help of LMI toolbox in MATLAB easily. If the synchronization robustness criterion, i.e. the synchronization robustness ≥ intrinsic robustness + extrinsic robustness, then the stochastic coupled synthetic oscillators can be robustly synchronized in spite of intrinsic parameter fluctuation and extrinsic noise. If the synchronization robustness criterion is violated, external control scheme by adding inducer can be designed to improve synchronization robustness of coupled synthetic genetic oscillators. The investigated robust synchronization criteria and proposed external control method are useful for a population of coupled synthetic networks with emergent synchronization behavior, especially for multi-cellular, engineered networks.

Robust synchronization control scheme of a population of nonlinear stochastic synthetic genetic oscillators under intrinsic and extrinsic molecular noise via quorum sensing

PubMed Central

2012-01-01

Background Collective rhythms of gene regulatory networks have been a subject of considerable interest for biologists and theoreticians, in particular the synchronization of dynamic cells mediated by intercellular communication. Synchronization of a population of synthetic genetic oscillators is an important design in practical applications, because such a population distributed over different host cells needs to exploit molecular phenomena simultaneously in order to emerge a biological phenomenon. However, this synchronization may be corrupted by intrinsic kinetic parameter fluctuations and extrinsic environmental molecular noise. Therefore, robust synchronization is an important design topic in nonlinear stochastic coupled synthetic genetic oscillators with intrinsic kinetic parameter fluctuations and extrinsic molecular noise. Results Initially, the condition for robust synchronization of synthetic genetic oscillators was derived based on Hamilton Jacobi inequality (HJI). We found that if the synchronization robustness can confer enough intrinsic robustness to tolerate intrinsic parameter fluctuation and extrinsic robustness to filter the environmental noise, then robust synchronization of coupled synthetic genetic oscillators is guaranteed. If the synchronization robustness of a population of nonlinear stochastic coupled synthetic genetic oscillators distributed over different host cells could not be maintained, then robust synchronization could be enhanced by external control input through quorum sensing molecules. In order to simplify the analysis and design of robust synchronization of nonlinear stochastic synthetic genetic oscillators, the fuzzy interpolation method was employed to interpolate several local linear stochastic coupled systems to approximate the nonlinear stochastic coupled system so that the HJI-based synchronization design problem could be replaced by a simple linear matrix inequality (LMI)-based design problem, which could be solved with the help of LMI toolbox in MATLAB easily. Conclusion If the synchronization robustness criterion, i.e. the synchronization robustness ≥ intrinsic robustness + extrinsic robustness, then the stochastic coupled synthetic oscillators can be robustly synchronized in spite of intrinsic parameter fluctuation and extrinsic noise. If the synchronization robustness criterion is violated, external control scheme by adding inducer can be designed to improve synchronization robustness of coupled synthetic genetic oscillators. The investigated robust synchronization criteria and proposed external control method are useful for a population of coupled synthetic networks with emergent synchronization behavior, especially for multi-cellular, engineered networks. PMID:23101662

Multi-damage identification based on joint approximate diagonalisation and robust distance measure

NASA Astrophysics Data System (ADS)

Cao, S.; Ouyang, H.

2017-05-01

Mode shapes or operational deflection shapes are highly sensitive to damage and can be used for multi-damage identification. Nevertheless, one drawback of this kind of methods is that the extracted spatial shape features tend to be compromised by noise, which degrades their damage identification accuracy, especially for incipient damage. To overcome this, joint approximate diagonalisation (JAD) also known as simultaneous diagonalisation is investigated to estimate mode shapes (MS’s) statistically. The major advantage of JAD method is that it efficiently provides the common Eigen-structure of a set of power spectral density matrices. In this paper, a new criterion in terms of coefficient of variation (CV) is utilised to numerically demonstrate the better noise robustness and accuracy of JAD method over traditional frequency domain decomposition method (FDD). Another original contribution is that a new robust damage index (DI) is proposed, which is comprised of local MS distortions of several modes weighted by their associated vibration participation factors. The advantage of doing this is to include fair contributions from changes of all modes concerned. Moreover, the proposed DI provides a measure of damage-induced changes in ‘modal vibration energy’ in terms of the selected mode shapes. Finally, an experimental study is presented to verify the efficiency and noise robustness of JAD method and the proposed DI. The results show that the proposed DI is effective and robust under random vibration situations, which indicates that it has the potential to be applied to practical engineering structures with ambient excitations.

Limitations of fluorescence spectroscopy to characterize organic matter in engineered systems

NASA Astrophysics Data System (ADS)

Korak, J.

2017-12-01

Fluorescence spectroscopy has been widely used to characterize dissolved organic matter (DOM) in engineered systems, such as drinking water, municipal wastewater and industrial water treatment. While fluorescence data collected in water treatment applications has led to the development of strong empirical relationships between fluorescence responses and process performance, the use of fluorescence to infer changes in the underlying organic matter chemistry is often oversimplified and applied out of context. Fluorescence only measures a small fraction of DOM as fluorescence quantum yields are less than 5% for many DOM sources. Relying on fluorescence as a surrogate for DOM presence, character or reactivity may not be appropriate for systems where small molecular weight, hydrophilic constituents unlikely to fluoresce are important. In addition, some methods rely on interpreting fluorescence signals at different excitation wavelengths as a surrogate for operationally-defined humic- and fulvic-acids in lieu of traditional XAD fractionation techniques, but these approaches cannot be supported by other lines of evidence considering natural abundance and fluorescence quantum yields of these fractions. These approaches also conflict with parallel factor analysis (PARAFAC), a statistical approach that routinely identifies fluorescence components with dual excitation behavior. Lastly, methods developed for natural systems are often applied out of context to engineered systems. Fluorescence signals characteristic of phenols or indoles are often interpreted as indicators for biological activity in natural systems due to fluorescent amino acids and peptides, but this interpretation is may not be appropriate in engineering applications where non-biological sources of phenolic functional groups may be present. This presentation explores common fluorescence interpretation approaches, discusses the limitations and provides recommendations related to engineered systems.

Robust Least-Squares Support Vector Machine With Minimization of Mean and Variance of Modeling Error.

PubMed

Lu, Xinjiang; Liu, Wenbo; Zhou, Chuang; Huang, Minghui

2017-06-13

The least-squares support vector machine (LS-SVM) is a popular data-driven modeling method and has been successfully applied to a wide range of applications. However, it has some disadvantages, including being ineffective at handling non-Gaussian noise as well as being sensitive to outliers. In this paper, a robust LS-SVM method is proposed and is shown to have more reliable performance when modeling a nonlinear system under conditions where Gaussian or non-Gaussian noise is present. The construction of a new objective function allows for a reduction of the mean of the modeling error as well as the minimization of its variance, and it does not constrain the mean of the modeling error to zero. This differs from the traditional LS-SVM, which uses a worst-case scenario approach in order to minimize the modeling error and constrains the mean of the modeling error to zero. In doing so, the proposed method takes the modeling error distribution information into consideration and is thus less conservative and more robust in regards to random noise. A solving method is then developed in order to determine the optimal parameters for the proposed robust LS-SVM. An additional analysis indicates that the proposed LS-SVM gives a smaller weight to a large-error training sample and a larger weight to a small-error training sample, and is thus more robust than the traditional LS-SVM. The effectiveness of the proposed robust LS-SVM is demonstrated using both artificial and real life cases.

Sparse alignment for robust tensor learning.

PubMed

Lai, Zhihui; Wong, Wai Keung; Xu, Yong; Zhao, Cairong; Sun, Mingming

2014-10-01

Multilinear/tensor extensions of manifold learning based algorithms have been widely used in computer vision and pattern recognition. This paper first provides a systematic analysis of the multilinear extensions for the most popular methods by using alignment techniques, thereby obtaining a general tensor alignment framework. From this framework, it is easy to show that the manifold learning based tensor learning methods are intrinsically different from the alignment techniques. Based on the alignment framework, a robust tensor learning method called sparse tensor alignment (STA) is then proposed for unsupervised tensor feature extraction. Different from the existing tensor learning methods, L1- and L2-norms are introduced to enhance the robustness in the alignment step of the STA. The advantage of the proposed technique is that the difficulty in selecting the size of the local neighborhood can be avoided in the manifold learning based tensor feature extraction algorithms. Although STA is an unsupervised learning method, the sparsity encodes the discriminative information in the alignment step and provides the robustness of STA. Extensive experiments on the well-known image databases as well as action and hand gesture databases by encoding object images as tensors demonstrate that the proposed STA algorithm gives the most competitive performance when compared with the tensor-based unsupervised learning methods.

Robust backstepping control of an interlink converter in a hybrid AC/DC microgrid based on feedback linearisation method

NASA Astrophysics Data System (ADS)

Dehkordi, N. Mahdian; Sadati, N.; Hamzeh, M.

2017-09-01

This paper presents a robust dc-link voltage as well as a current control strategy for a bidirectional interlink converter (BIC) in a hybrid ac/dc microgrid. To enhance the dc-bus voltage control, conventional methods strive to measure and feedforward the load or source power in the dc-bus control scheme. However, the conventional feedforward-based approaches require remote measurement with communications. Moreover, conventional methods suffer from stability and performance issues, mainly due to the use of the small-signal-based control design method. To overcome these issues, in this paper, the power from DG units of the dc subgrid imposed on the BIC is considered an unmeasurable disturbance signal. In the proposed method, in contrast to existing methods, using the nonlinear model of BIC, a robust controller that does not need the remote measurement with communications effectively rejects the impact of the disturbance signal imposed on the BIC's dc-link voltage. To avoid communication links, the robust controller has a plug-and-play feature that makes it possible to add a DG/load to or remove it from the dc subgrid without distorting the hybrid microgrid stability. Finally, Monte Carlo simulations are conducted to confirm the effectiveness of the proposed control strategy in MATLAB/SimPowerSystems software environment.

Avoid lost discoveries, because of violations of standard assumptions, by using modern robust statistical methods.

PubMed

Wilcox, Rand; Carlson, Mike; Azen, Stan; Clark, Florence

2013-03-01

Recently, there have been major advances in statistical techniques for assessing central tendency and measures of association. The practical utility of modern methods has been documented extensively in the statistics literature, but they remain underused and relatively unknown in clinical trials. Our objective was to address this issue. STUDY DESIGN AND PURPOSE: The first purpose was to review common problems associated with standard methodologies (low power, lack of control over type I errors, and incorrect assessments of the strength of the association). The second purpose was to summarize some modern methods that can be used to circumvent such problems. The third purpose was to illustrate the practical utility of modern robust methods using data from the Well Elderly 2 randomized controlled trial. In multiple instances, robust methods uncovered differences among groups and associations among variables that were not detected by classic techniques. In particular, the results demonstrated that details of the nature and strength of the association were sometimes overlooked when using ordinary least squares regression and Pearson correlation. Modern robust methods can make a practical difference in detecting and describing differences between groups and associations between variables. Such procedures should be applied more frequently when analyzing trial-based data. Copyright © 2013 Elsevier Inc. All rights reserved.

Robust Control Design for Systems With Probabilistic Uncertainty

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.

2005-01-01

This paper presents a reliability- and robustness-based formulation for robust control synthesis for systems with probabilistic uncertainty. In a reliability-based formulation, the probability of violating design requirements prescribed by inequality constraints is minimized. In a robustness-based formulation, a metric which measures the tendency of a random variable/process to cluster close to a target scalar/function is minimized. A multi-objective optimization procedure, which combines stability and performance requirements in time and frequency domains, is used to search for robustly optimal compensators. Some of the fundamental differences between the proposed strategy and conventional robust control methods are: (i) unnecessary conservatism is eliminated since there is not need for convex supports, (ii) the most likely plants are favored during synthesis allowing for probabilistic robust optimality, (iii) the tradeoff between robust stability and robust performance can be explored numerically, (iv) the uncertainty set is closely related to parameters with clear physical meaning, and (v) compensators with improved robust characteristics for a given control structure can be synthesized.

Wisdom of crowds for robust gene network inference

PubMed Central

Marbach, Daniel; Costello, James C.; Küffner, Robert; Vega, Nicci; Prill, Robert J.; Camacho, Diogo M.; Allison, Kyle R.; Kellis, Manolis; Collins, James J.; Stolovitzky, Gustavo

2012-01-01

Reconstructing gene regulatory networks from high-throughput data is a long-standing problem. Through the DREAM project (Dialogue on Reverse Engineering Assessment and Methods), we performed a comprehensive blind assessment of over thirty network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae, and in silico microarray data. We characterize performance, data requirements, and inherent biases of different inference approaches offering guidelines for both algorithm application and development. We observe that no single inference method performs optimally across all datasets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse datasets. Thereby, we construct high-confidence networks for E. coli and S. aureus, each comprising ~1700 transcriptional interactions at an estimated precision of 50%. We experimentally test 53 novel interactions in E. coli, of which 23 were supported (43%). Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks. PMID:22796662

The scenario-based generalization of radiation therapy margins.

PubMed

Fredriksson, Albin; Bokrantz, Rasmus

2016-03-07

We give a scenario-based treatment plan optimization formulation that is equivalent to planning with geometric margins if the scenario doses are calculated using the static dose cloud approximation. If the scenario doses are instead calculated more accurately, then our formulation provides a novel robust planning method that overcomes many of the difficulties associated with previous scenario-based robust planning methods. In particular, our method protects only against uncertainties that can occur in practice, it gives a sharp dose fall-off outside high dose regions, and it avoids underdosage of the target in 'easy' scenarios. The method shares the benefits of the previous scenario-based robust planning methods over geometric margins for applications where the static dose cloud approximation is inaccurate, such as irradiation with few fields and irradiation with ion beams. These properties are demonstrated on a suite of phantom cases planned for treatment with scanned proton beams subject to systematic setup uncertainty.

A robust ordering strategy for retailers facing a free shipping option.

PubMed

Meng, Qing-chun; Wan, Xiao-le; Rong, Xiao-xia

2015-01-01

Free shipping with conditions has become one of the most effective marketing tools available. An increasing number of companies, especially e-businesses, prefer to offer free shipping with some predetermined condition, such as a minimum purchase amount by the customer. However, in practice, the demands of buyers are uncertain; they are often affected by many factors, such as the weather and season. We begin by modeling the centralized ordering problem in which the supplier offers a free shipping service and retailers face stochastic demands. As these random data are considered, only partial information such as the known mean, support, and deviation is needed. The model is then analyzed via a robust optimization method, and the two types of equivalent sets of uncertainty constraints that are obtained provide good mathematical properties with consideration of the robustness of solutions. Subsequently, a numerical example is used to compare the results achieved from a robust optimization method and the linear decision rules. Additionally, the robustness of the optimal solution is discussed, as it is affected by the minimum quantity parameters. The increasing cost-threshold relationship is divided into three periods. In addition, the case study shows that the proposed method achieves better stability as well as computational complexity.

An improved method for bivariate meta-analysis when within-study correlations are unknown.

PubMed

Hong, Chuan; D Riley, Richard; Chen, Yong

2018-03-01

Multivariate meta-analysis, which jointly analyzes multiple and possibly correlated outcomes in a single analysis, is becoming increasingly popular in recent years. An attractive feature of the multivariate meta-analysis is its ability to account for the dependence between multiple estimates from the same study. However, standard inference procedures for multivariate meta-analysis require the knowledge of within-study correlations, which are usually unavailable. This limits standard inference approaches in practice. Riley et al proposed a working model and an overall synthesis correlation parameter to account for the marginal correlation between outcomes, where the only data needed are those required for a separate univariate random-effects meta-analysis. As within-study correlations are not required, the Riley method is applicable to a wide variety of evidence synthesis situations. However, the standard variance estimator of the Riley method is not entirely correct under many important settings. As a consequence, the coverage of a function of pooled estimates may not reach the nominal level even when the number of studies in the multivariate meta-analysis is large. In this paper, we improve the Riley method by proposing a robust variance estimator, which is asymptotically correct even when the model is misspecified (ie, when the likelihood function is incorrect). Simulation studies of a bivariate meta-analysis, in a variety of settings, show a function of pooled estimates has improved performance when using the proposed robust variance estimator. In terms of individual pooled estimates themselves, the standard variance estimator and robust variance estimator give similar results to the original method, with appropriate coverage. The proposed robust variance estimator performs well when the number of studies is relatively large. Therefore, we recommend the use of the robust method for meta-analyses with a relatively large number of studies (eg, m≥50). When the sample size is relatively small, we recommend the use of the robust method under the working independence assumption. We illustrate the proposed method through 2 meta-analyses. Copyright © 2017 John Wiley & Sons, Ltd.

A Robust Approach to Risk Assessment Based on Species Sensitivity Distributions.

PubMed

Monti, Gianna S; Filzmoser, Peter; Deutsch, Roland C

2018-05-03

The guidelines for setting environmental quality standards are increasingly based on probabilistic risk assessment due to a growing general awareness of the need for probabilistic procedures. One of the commonly used tools in probabilistic risk assessment is the species sensitivity distribution (SSD), which represents the proportion of species affected belonging to a biological assemblage as a function of exposure to a specific toxicant. Our focus is on the inverse use of the SSD curve with the aim of estimating the concentration, HCp, of a toxic compound that is hazardous to p% of the biological community under study. Toward this end, we propose the use of robust statistical methods in order to take into account the presence of outliers or apparent skew in the data, which may occur without any ecological basis. A robust approach exploits the full neighborhood of a parametric model, enabling the analyst to account for the typical real-world deviations from ideal models. We examine two classic HCp estimation approaches and consider robust versions of these estimators. In addition, we also use data transformations in conjunction with robust estimation methods in case of heteroscedasticity. Different scenarios using real data sets as well as simulated data are presented in order to illustrate and compare the proposed approaches. These scenarios illustrate that the use of robust estimation methods enhances HCp estimation. © 2018 Society for Risk Analysis.

Covariate selection with group lasso and doubly robust estimation of causal effects

PubMed Central

Koch, Brandon; Vock, David M.; Wolfson, Julian

2017-01-01

Summary The efficiency of doubly robust estimators of the average causal effect (ACE) of a treatment can be improved by including in the treatment and outcome models only those covariates which are related to both treatment and outcome (i.e., confounders) or related only to the outcome. However, it is often challenging to identify such covariates among the large number that may be measured in a given study. In this paper, we propose GLiDeR (Group Lasso and Doubly Robust Estimation), a novel variable selection technique for identifying confounders and predictors of outcome using an adaptive group lasso approach that simultaneously performs coefficient selection, regularization, and estimation across the treatment and outcome models. The selected variables and corresponding coefficient estimates are used in a standard doubly robust ACE estimator. We provide asymptotic results showing that, for a broad class of data generating mechanisms, GLiDeR yields a consistent estimator of the ACE when either the outcome or treatment model is correctly specified. A comprehensive simulation study shows that GLiDeR is more efficient than doubly robust methods using standard variable selection techniques and has substantial computational advantages over a recently proposed doubly robust Bayesian model averaging method. We illustrate our method by estimating the causal treatment effect of bilateral versus single-lung transplant on forced expiratory volume in one year after transplant using an observational registry. PMID:28636276

Covariate selection with group lasso and doubly robust estimation of causal effects.

PubMed

Koch, Brandon; Vock, David M; Wolfson, Julian

2018-03-01

The efficiency of doubly robust estimators of the average causal effect (ACE) of a treatment can be improved by including in the treatment and outcome models only those covariates which are related to both treatment and outcome (i.e., confounders) or related only to the outcome. However, it is often challenging to identify such covariates among the large number that may be measured in a given study. In this article, we propose GLiDeR (Group Lasso and Doubly Robust Estimation), a novel variable selection technique for identifying confounders and predictors of outcome using an adaptive group lasso approach that simultaneously performs coefficient selection, regularization, and estimation across the treatment and outcome models. The selected variables and corresponding coefficient estimates are used in a standard doubly robust ACE estimator. We provide asymptotic results showing that, for a broad class of data generating mechanisms, GLiDeR yields a consistent estimator of the ACE when either the outcome or treatment model is correctly specified. A comprehensive simulation study shows that GLiDeR is more efficient than doubly robust methods using standard variable selection techniques and has substantial computational advantages over a recently proposed doubly robust Bayesian model averaging method. We illustrate our method by estimating the causal treatment effect of bilateral versus single-lung transplant on forced expiratory volume in one year after transplant using an observational registry. © 2017, The International Biometric Society.

Linear regression based on Minimum Covariance Determinant (MCD) and TELBS methods on the productivity of phytoplankton

NASA Astrophysics Data System (ADS)

Gusriani, N.; Firdaniza

2018-03-01

The existence of outliers on multiple linear regression analysis causes the Gaussian assumption to be unfulfilled. If the Least Square method is forcedly used on these data, it will produce a model that cannot represent most data. For that, we need a robust regression method against outliers. This paper will compare the Minimum Covariance Determinant (MCD) method and the TELBS method on secondary data on the productivity of phytoplankton, which contains outliers. Based on the robust determinant coefficient value, MCD method produces a better model compared to TELBS method.

Modern CACSD using the Robust-Control Toolbox

NASA Technical Reports Server (NTRS)

Chiang, Richard Y.; Safonov, Michael G.

1989-01-01

The Robust-Control Toolbox is a collection of 40 M-files which extend the capability of PC/PRO-MATLAB to do modern multivariable robust control system design. Included are robust analysis tools like singular values and structured singular values, robust synthesis tools like continuous/discrete H(exp 2)/H infinity synthesis and Linear Quadratic Gaussian Loop Transfer Recovery methods and a variety of robust model reduction tools such as Hankel approximation, balanced truncation and balanced stochastic truncation, etc. The capabilities of the toolbox are described and illustated with examples to show how easily they can be used in practice. Examples include structured singular value analysis, H infinity loop-shaping and large space structure model reduction.

Synthesis Methods for Robust Passification and Control

NASA Technical Reports Server (NTRS)

Kelkar, Atul G.; Joshi, Suresh M. (Technical Monitor)

2000-01-01

The research effort under this cooperative agreement has been essentially the continuation of the work from previous grants. The ongoing work has primarily focused on developing passivity-based control techniques for Linear Time-Invariant (LTI) systems. During this period, there has been a significant progress made in the area of passivity-based control of LTI systems and some preliminary results have also been obtained for nonlinear systems, as well. The prior work has addressed optimal control design for inherently passive as well as non- passive linear systems. For exploiting the robustness characteristics of passivity-based controllers the passification methodology was developed for LTI systems that are not inherently passive. Various methods of passification were first proposed in and further developed. The robustness of passification was addressed for multi-input multi-output (MIMO) systems for certain classes of uncertainties using frequency-domain methods. For MIMO systems, a state-space approach using Linear Matrix Inequality (LMI)-based formulation was presented, for passification of non-passive LTI systems. An LMI-based robust passification technique was presented for systems with redundant actuators and sensors. The redundancy in actuators and sensors was used effectively for robust passification using the LMI formulation. The passification was designed to be robust to an interval-type uncertainties in system parameters. The passification techniques were used to design a robust controller for Benchmark Active Control Technology wing under parametric uncertainties. The results on passive nonlinear systems, however, are very limited to date. Our recent work in this area was presented, wherein some stability results were obtained for passive nonlinear systems that are affine in control.

Parametric synthesis of a robust controller on a base of mathematical programming method

NASA Astrophysics Data System (ADS)

Khozhaev, I. V.; Gayvoronskiy, S. A.; Ezangina, T. A.

2018-05-01

Considered paper is dedicated to deriving sufficient conditions, linking root indices of robust control quality with coefficients of interval characteristic polynomial, on the base of mathematical programming method. On the base of these conditions, a method of PI- and PID-controllers, providing aperiodic transient process with acceptable stability degree and, subsequently, acceptable setting time, synthesis was developed. The method was applied to a problem of synthesizing a controller for a depth control system of an unmanned underwater vehicle.

A Hybrid One-Way ANOVA Approach for the Robust and Efficient Estimation of Differential Gene Expression with Multiple Patterns

PubMed Central

Mollah, Mohammad Manir Hossain; Jamal, Rahman; Mokhtar, Norfilza Mohd; Harun, Roslan; Mollah, Md. Nurul Haque

2015-01-01

Background Identifying genes that are differentially expressed (DE) between two or more conditions with multiple patterns of expression is one of the primary objectives of gene expression data analysis. Several statistical approaches, including one-way analysis of variance (ANOVA), are used to identify DE genes. However, most of these methods provide misleading results for two or more conditions with multiple patterns of expression in the presence of outlying genes. In this paper, an attempt is made to develop a hybrid one-way ANOVA approach that unifies the robustness and efficiency of estimation using the minimum β-divergence method to overcome some problems that arise in the existing robust methods for both small- and large-sample cases with multiple patterns of expression. Results The proposed method relies on a β-weight function, which produces values between 0 and 1. The β-weight function with β = 0.2 is used as a measure of outlier detection. It assigns smaller weights (≥ 0) to outlying expressions and larger weights (≤ 1) to typical expressions. The distribution of the β-weights is used to calculate the cut-off point, which is compared to the observed β-weight of an expression to determine whether that gene expression is an outlier. This weight function plays a key role in unifying the robustness and efficiency of estimation in one-way ANOVA. Conclusion Analyses of simulated gene expression profiles revealed that all eight methods (ANOVA, SAM, LIMMA, EBarrays, eLNN, KW, robust BetaEB and proposed) perform almost identically for m = 2 conditions in the absence of outliers. However, the robust BetaEB method and the proposed method exhibited considerably better performance than the other six methods in the presence of outliers. In this case, the BetaEB method exhibited slightly better performance than the proposed method for the small-sample cases, but the the proposed method exhibited much better performance than the BetaEB method for both the small- and large-sample cases in the presence of more than 50% outlying genes. The proposed method also exhibited better performance than the other methods for m > 2 conditions with multiple patterns of expression, where the BetaEB was not extended for this condition. Therefore, the proposed approach would be more suitable and reliable on average for the identification of DE genes between two or more conditions with multiple patterns of expression. PMID:26413858

Tabu Search enhances network robustness under targeted attacks

NASA Astrophysics Data System (ADS)

Sun, Shi-wen; Ma, Yi-lin; Li, Rui-qi; Wang, Li; Xia, Cheng-yi

2016-03-01

We focus on the optimization of network robustness with respect to intentional attacks on high-degree nodes. Given an existing network, this problem can be considered as a typical single-objective combinatorial optimization problem. Based on the heuristic Tabu Search optimization algorithm, a link-rewiring method is applied to reconstruct the network while keeping the degree of every node unchanged. Through numerical simulations, BA scale-free network and two real-world networks are investigated to verify the effectiveness of the proposed optimization method. Meanwhile, we analyze how the optimization affects other topological properties of the networks, including natural connectivity, clustering coefficient and degree-degree correlation. The current results can help to improve the robustness of existing complex real-world systems, as well as to provide some insights into the design of robust networks.

Robust Kalman filter design for predictive wind shear detection

NASA Technical Reports Server (NTRS)

Stratton, Alexander D.; Stengel, Robert F.

1991-01-01

Severe, low-altitude wind shear is a threat to aviation safety. Airborne sensors under development measure the radial component of wind along a line directly in front of an aircraft. In this paper, optimal estimation theory is used to define a detection algorithm to warn of hazardous wind shear from these sensors. To achieve robustness, a wind shear detection algorithm must distinguish threatening wind shear from less hazardous gustiness, despite variations in wind shear structure. This paper presents statistical analysis methods to refine wind shear detection algorithm robustness. Computational methods predict the ability to warn of severe wind shear and avoid false warning. Comparative capability of the detection algorithm as a function of its design parameters is determined, identifying designs that provide robust detection of severe wind shear.

Simplified paraboloid phase model-based phase tracker for demodulation of a single complex fringe.

PubMed

He, A; Deepan, B; Quan, C

2017-09-01

A regularized phase tracker (RPT) is an effective method for demodulation of single closed-fringe patterns. However, lengthy calculation time, specially designed scanning strategy, and sign-ambiguity problems caused by noise and saddle points reduce its effectiveness, especially for demodulating large and complex fringe patterns. In this paper, a simplified paraboloid phase model-based regularized phase tracker (SPRPT) is proposed. In SPRPT, first and second phase derivatives are pre-determined by the density-direction-combined method and discrete higher-order demodulation algorithm, respectively. Hence, cost function is effectively simplified to reduce the computation time significantly. Moreover, pre-determined phase derivatives improve the robustness of the demodulation of closed, complex fringe patterns. Thus, no specifically designed scanning strategy is needed; nevertheless, it is robust against the sign-ambiguity problem. The paraboloid phase model also assures better accuracy and robustness against noise. Both the simulated and experimental fringe patterns (obtained using electronic speckle pattern interferometry) are used to validate the proposed method, and a comparison of the proposed method with existing RPT methods is carried out. The simulation results show that the proposed method has achieved the highest accuracy with less computational time. The experimental result proves the robustness and the accuracy of the proposed method for demodulation of noisy fringe patterns and its feasibility for static and dynamic applications.

A single-pixel X-ray imager concept and its application to secure radiographic inspections

DOE PAGES

Gilbert, Andrew J.; Miller, Brian W.; Robinson, Sean M.; ...

2017-07-01

Imaging technology is generally considered too invasive for arms control inspections due to the concern that it cannot properly secure sensitive features of the inspected item. But, this same sensitive information, which could include direct information on the form and function of the items under inspection, could be used for robust arms control inspections. The single-pixel X-ray imager (SPXI) is introduced as a method to make such inspections, capturing the salient spatial information of an object in a secure manner while never forming an actual image. We built this method on the theory of compressive sensing and the single pixelmore » optical camera. The performance of the system is quantified using simulated inspections of simple objects. Measures of the robustness and security of the method are introduced and used to determine how robust and secure such an inspection would be. Particularly, it is found that an inspection with low noise (<1%) and high undersampling (>256×) exhibits high robustness and security.« less

Robust Coefficients Alpha and Omega and Confidence Intervals With Outlying Observations and Missing Data

PubMed Central

Zhang, Zhiyong; Yuan, Ke-Hai

2015-01-01

Cronbach’s coefficient alpha is a widely used reliability measure in social, behavioral, and education sciences. It is reported in nearly every study that involves measuring a construct through multiple items. With non-tau-equivalent items, McDonald’s omega has been used as a popular alternative to alpha in the literature. Traditional estimation methods for alpha and omega often implicitly assume that data are complete and normally distributed. This study proposes robust procedures to estimate both alpha and omega as well as corresponding standard errors and confidence intervals from samples that may contain potential outlying observations and missing values. The influence of outlying observations and missing data on the estimates of alpha and omega is investigated through two simulation studies. Results show that the newly developed robust method yields substantially improved alpha and omega estimates as well as better coverage rates of confidence intervals than the conventional nonrobust method. An R package coefficientalpha is developed and demonstrated to obtain robust estimates of alpha and omega. PMID:29795870

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gilbert, Andrew J.; Miller, Brian W.; Robinson, Sean M.

Imaging technology is generally considered too invasive for arms control inspections due to the concern that it cannot properly secure sensitive features of the inspected item. However, this same sensitive information, which could include direct information on the form and function of the items under inspection, could be used for robust arms control inspections. The single-pixel X-ray imager (SPXI) is introduced as a method to make such inspections, capturing the salient spatial information of an object in a secure manner while never forming an actual image. The method is built on the theory of compressive sensing and the single pixelmore » optical camera. The performance of the system is quantified here using simulated inspections of simple objects. Measures of the robustness and security of the method are introduced and used to determine how such an inspection would be made which can maintain high robustness and security. In particular, it is found that an inspection with low noise (<1%) and high undersampling (>256×) exhibits high robustness and security.« less

A Robust Control of Two-Wheeled Mobile Manipulator with Underactuated Joint by Nonlinear Backstepping Method

NASA Astrophysics Data System (ADS)

Acar, Cihan; Murakami, Toshiyuki

In this paper, a robust control of two-wheeled mobile manipulator with underactuated joint is considered. Two-wheeled mobile manipulators are dynamically balanced two-wheeled driven systems that do not have any caster or extra wheels to stabilize their body. Two-wheeled mobile manipulators mainly have an important feature that makes them more flexible and agile than the statically stable mobile manipulators. However, two-wheeled mobile manipulator is an underactuated system due to its two-wheeled structure. Therefore, it is required to stabilize the underactuated passive body and, at the same time, control the position of the center of gravity (CoG) of the manipulator in this system. To realize this, nonlinear backstepping based control method with virtual double inverted pendulum model is proposed in this paper. Backstepping is used with sliding mode to increase the robustness of the system against modeling errors and other perturbations. Then robust acceleration control is also achieved by utilizing disturbance observer. Performance of the proposed method is evaluated by several experiments.

Robust Frequency-Domain Constrained Feedback Design via a Two-Stage Heuristic Approach.

PubMed

Li, Xianwei; Gao, Huijun

2015-10-01

Based on a two-stage heuristic method, this paper is concerned with the design of robust feedback controllers with restricted frequency-domain specifications (RFDSs) for uncertain linear discrete-time systems. Polytopic uncertainties are assumed to enter all the system matrices, while RFDSs are motivated by the fact that practical design specifications are often described in restricted finite frequency ranges. Dilated multipliers are first introduced to relax the generalized Kalman-Yakubovich-Popov lemma for output feedback controller synthesis and robust performance analysis. Then a two-stage approach to output feedback controller synthesis is proposed: at the first stage, a robust full-information (FI) controller is designed, which is used to construct a required output feedback controller at the second stage. To improve the solvability of the synthesis method, heuristic iterative algorithms are further formulated for exploring the feedback gain and optimizing the initial FI controller at the individual stage. The effectiveness of the proposed design method is finally demonstrated by the application to active control of suspension systems.

A single-pixel X-ray imager concept and its application to secure radiographic inspections

NASA Astrophysics Data System (ADS)

Gilbert, Andrew J.; Miller, Brian W.; Robinson, Sean M.; White, Timothy A.; Pitts, William Karl; Jarman, Kenneth D.; Seifert, Allen

2017-07-01

Imaging technology is generally considered too invasive for arms control inspections due to the concern that it cannot properly secure sensitive features of the inspected item. However, this same sensitive information, which could include direct information on the form and function of the items under inspection, could be used for robust arms control inspections. The single-pixel X-ray imager (SPXI) is introduced as a method to make such inspections, capturing the salient spatial information of an object in a secure manner while never forming an actual image. The method is built on the theory of compressive sensing and the single pixel optical camera. The performance of the system is quantified using simulated inspections of simple objects. Measures of the robustness and security of the method are introduced and used to determine how robust and secure such an inspection would be. In particular, it is found that an inspection with low noise ( < 1 %) and high undersampling ( > 256 ×) exhibits high robustness and security.

Hypothesis Testing, "p" Values, Confidence Intervals, Measures of Effect Size, and Bayesian Methods in Light of Modern Robust Techniques

ERIC Educational Resources Information Center

Wilcox, Rand R.; Serang, Sarfaraz

2017-01-01

The article provides perspectives on p values, null hypothesis testing, and alternative techniques in light of modern robust statistical methods. Null hypothesis testing and "p" values can provide useful information provided they are interpreted in a sound manner, which includes taking into account insights and advances that have…

EVALUATION OF THE ROBUSTNESS OF THE FATHEAD MINNOW, PIMEPHALES PROMELAS, LARVAL SURVIVAL AND GROWTH TEST, U.S. EPA METHOD 1000.0

EPA Science Inventory

An intralaboratory study was conducted to evaluate the robustness of the Fathead Minnow (Pimephales promelas) Larval Survival and Growth Test, Method 1000.0 Toxicity tests were conducted with the reference toxicants hexavalent chromium (Cr6+) and copper (Cu), and the data were st...

Robust High-Capacity Audio Watermarking Based on FFT Amplitude Modification

NASA Astrophysics Data System (ADS)

Fallahpour, Mehdi; Megías, David

This paper proposes a novel robust audio watermarking algorithm to embed data and extract it in a bit-exact manner based on changing the magnitudes of the FFT spectrum. The key point is selecting a frequency band for embedding based on the comparison between the original and the MP3 compressed/decompressed signal and on a suitable scaling factor. The experimental results show that the method has a very high capacity (about 5kbps), without significant perceptual distortion (ODG about -0.25) and provides robustness against common audio signal processing such as added noise, filtering and MPEG compression (MP3). Furthermore, the proposed method has a larger capacity (number of embedded bits to number of host bits rate) than recent image data hiding methods.

A robust functional-data-analysis method for data recovery in multichannel sensor systems.

PubMed

Sun, Jian; Liao, Haitao; Upadhyaya, Belle R

2014-08-01

Multichannel sensor systems are widely used in condition monitoring for effective failure prevention of critical equipment or processes. However, loss of sensor readings due to malfunctions of sensors and/or communication has long been a hurdle to reliable operations of such integrated systems. Moreover, asynchronous data sampling and/or limited data transmission are usually seen in multiple sensor channels. To reliably perform fault diagnosis and prognosis in such operating environments, a data recovery method based on functional principal component analysis (FPCA) can be utilized. However, traditional FPCA methods are not robust to outliers and their capabilities are limited in recovering signals with strongly skewed distributions (i.e., lack of symmetry). This paper provides a robust data-recovery method based on functional data analysis to enhance the reliability of multichannel sensor systems. The method not only considers the possibly skewed distribution of each channel of signal trajectories, but is also capable of recovering missing data for both individual and correlated sensor channels with asynchronous data that may be sparse as well. In particular, grand median functions, rather than classical grand mean functions, are utilized for robust smoothing of sensor signals. Furthermore, the relationship between the functional scores of two correlated signals is modeled using multivariate functional regression to enhance the overall data-recovery capability. An experimental flow-control loop that mimics the operation of coolant-flow loop in a multimodular integral pressurized water reactor is used to demonstrate the effectiveness and adaptability of the proposed data-recovery method. The computational results illustrate that the proposed method is robust to outliers and more capable than the existing FPCA-based method in terms of the accuracy in recovering strongly skewed signals. In addition, turbofan engine data are also analyzed to verify the capability of the proposed method in recovering non-skewed signals.

Approach for Uncertainty Propagation and Robust Design in CFD Using Sensitivity Derivatives

NASA Technical Reports Server (NTRS)

Putko, Michele M.; Newman, Perry A.; Taylor, Arthur C., III; Green, Lawrence L.

2001-01-01

This paper presents an implementation of the approximate statistical moment method for uncertainty propagation and robust optimization for a quasi 1-D Euler CFD (computational fluid dynamics) code. Given uncertainties in statistically independent, random, normally distributed input variables, a first- and second-order statistical moment matching procedure is performed to approximate the uncertainty in the CFD output. Efficient calculation of both first- and second-order sensitivity derivatives is required. In order to assess the validity of the approximations, the moments are compared with statistical moments generated through Monte Carlo simulations. The uncertainties in the CFD input variables are also incorporated into a robust optimization procedure. For this optimization, statistical moments involving first-order sensitivity derivatives appear in the objective function and system constraints. Second-order sensitivity derivatives are used in a gradient-based search to successfully execute a robust optimization. The approximate methods used throughout the analyses are found to be valid when considering robustness about input parameter mean values.

Capacity planning for waste management systems: an interval fuzzy robust dynamic programming approach.

PubMed

Nie, Xianghui; Huang, Guo H; Li, Yongping

2009-11-01

This study integrates the concepts of interval numbers and fuzzy sets into optimization analysis by dynamic programming as a means of accounting for system uncertainty. The developed interval fuzzy robust dynamic programming (IFRDP) model improves upon previous interval dynamic programming methods. It allows highly uncertain information to be effectively communicated into the optimization process through introducing the concept of fuzzy boundary interval and providing an interval-parameter fuzzy robust programming method for an embedded linear programming problem. Consequently, robustness of the optimization process and solution can be enhanced. The modeling approach is applied to a hypothetical problem for the planning of waste-flow allocation and treatment/disposal facility expansion within a municipal solid waste (MSW) management system. Interval solutions for capacity expansion of waste management facilities and relevant waste-flow allocation are generated and interpreted to provide useful decision alternatives. The results indicate that robust and useful solutions can be obtained, and the proposed IFRDP approach is applicable to practical problems that are associated with highly complex and uncertain information.

Redundancy relations and robust failure detection

NASA Technical Reports Server (NTRS)

Chow, E. Y.; Lou, X. C.; Verghese, G. C.; Willsky, A. S.

1984-01-01

All failure detection methods are based on the use of redundancy, that is on (possible dynamic) relations among the measured variables. Consequently the robustness of the failure detection process depends to a great degree on the reliability of the redundancy relations given the inevitable presence of model uncertainties. The problem of determining redundancy relations which are optimally robust in a sense which includes the major issues of importance in practical failure detection is addressed. A significant amount of intuition concerning the geometry of robust failure detection is provided.

Robust geostatistical analysis of spatial data

NASA Astrophysics Data System (ADS)

Papritz, A.; Künsch, H. R.; Schwierz, C.; Stahel, W. A.

2012-04-01

Most of the geostatistical software tools rely on non-robust algorithms. This is unfortunate, because outlying observations are rather the rule than the exception, in particular in environmental data sets. Outlying observations may results from errors (e.g. in data transcription) or from local perturbations in the processes that are responsible for a given pattern of spatial variation. As an example, the spatial distribution of some trace metal in the soils of a region may be distorted by emissions of local anthropogenic sources. Outliers affect the modelling of the large-scale spatial variation, the so-called external drift or trend, the estimation of the spatial dependence of the residual variation and the predictions by kriging. Identifying outliers manually is cumbersome and requires expertise because one needs parameter estimates to decide which observation is a potential outlier. Moreover, inference after the rejection of some observations is problematic. A better approach is to use robust algorithms that prevent automatically that outlying observations have undue influence. Former studies on robust geostatistics focused on robust estimation of the sample variogram and ordinary kriging without external drift. Furthermore, Richardson and Welsh (1995) [2] proposed a robustified version of (restricted) maximum likelihood ([RE]ML) estimation for the variance components of a linear mixed model, which was later used by Marchant and Lark (2007) [1] for robust REML estimation of the variogram. We propose here a novel method for robust REML estimation of the variogram of a Gaussian random field that is possibly contaminated by independent errors from a long-tailed distribution. It is based on robustification of estimating equations for the Gaussian REML estimation. Besides robust estimates of the parameters of the external drift and of the variogram, the method also provides standard errors for the estimated parameters, robustified kriging predictions at both sampled and unsampled locations and kriging variances. The method has been implemented in an R package. Apart from presenting our modelling framework, we shall present selected simulation results by which we explored the properties of the new method. This will be complemented by an analysis of the Tarrawarra soil moisture data set [3].

A robust nonparametric framework for reconstruction of stochastic differential equation models

NASA Astrophysics Data System (ADS)

Rajabzadeh, Yalda; Rezaie, Amir Hossein; Amindavar, Hamidreza

2016-05-01

In this paper, we employ a nonparametric framework to robustly estimate the functional forms of drift and diffusion terms from discrete stationary time series. The proposed method significantly improves the accuracy of the parameter estimation. In this framework, drift and diffusion coefficients are modeled through orthogonal Legendre polynomials. We employ the least squares regression approach along with the Euler-Maruyama approximation method to learn coefficients of stochastic model. Next, a numerical discrete construction of mean squared prediction error (MSPE) is established to calculate the order of Legendre polynomials in drift and diffusion terms. We show numerically that the new method is robust against the variation in sample size and sampling rate. The performance of our method in comparison with the kernel-based regression (KBR) method is demonstrated through simulation and real data. In case of real dataset, we test our method for discriminating healthy electroencephalogram (EEG) signals from epilepsy ones. We also demonstrate the efficiency of the method through prediction in the financial data. In both simulation and real data, our algorithm outperforms the KBR method.

Robust feature matching via support-line voting and affine-invariant ratios

NASA Astrophysics Data System (ADS)

Li, Jiayuan; Hu, Qingwu; Ai, Mingyao; Zhong, Ruofei

2017-10-01

Robust image matching is crucial for many applications of remote sensing and photogrammetry, such as image fusion, image registration, and change detection. In this paper, we propose a robust feature matching method based on support-line voting and affine-invariant ratios. We first use popular feature matching algorithms, such as SIFT, to obtain a set of initial matches. A support-line descriptor based on multiple adaptive binning gradient histograms is subsequently applied in the support-line voting stage to filter outliers. In addition, we use affine-invariant ratios computed by a two-line structure to refine the matching results and estimate the local affine transformation. The local affine model is more robust to distortions caused by elevation differences than the global affine transformation, especially for high-resolution remote sensing images and UAV images. Thus, the proposed method is suitable for both rigid and non-rigid image matching problems. Finally, we extract as many high-precision correspondences as possible based on the local affine extension and build a grid-wise affine model for remote sensing image registration. We compare the proposed method with six state-of-the-art algorithms on several data sets and show that our method significantly outperforms the other methods. The proposed method achieves 94.46% average precision on 15 challenging remote sensing image pairs, while the second-best method, RANSAC, only achieves 70.3%. In addition, the number of detected correct matches of the proposed method is approximately four times the number of initial SIFT matches.

Robust estimation approach for blind denoising.

PubMed

Rabie, Tamer

2005-11-01

This work develops a new robust statistical framework for blind image denoising. Robust statistics addresses the problem of estimation when the idealized assumptions about a system are occasionally violated. The contaminating noise in an image is considered as a violation of the assumption of spatial coherence of the image intensities and is treated as an outlier random variable. A denoised image is estimated by fitting a spatially coherent stationary image model to the available noisy data using a robust estimator-based regression method within an optimal-size adaptive window. The robust formulation aims at eliminating the noise outliers while preserving the edge structures in the restored image. Several examples demonstrating the effectiveness of this robust denoising technique are reported and a comparison with other standard denoising filters is presented.

Nonlinear robust control of hypersonic aircrafts with interactions between flight dynamics and propulsion systems.

PubMed

Li, Zhaoying; Zhou, Wenjie; Liu, Hao

2016-09-01

This paper addresses the nonlinear robust tracking controller design problem for hypersonic vehicles. This problem is challenging due to strong coupling between the aerodynamics and the propulsion system, and the uncertainties involved in the vehicle dynamics including parametric uncertainties, unmodeled model uncertainties, and external disturbances. By utilizing the feedback linearization technique, a linear tracking error system is established with prescribed references. For the linear model, a robust controller is proposed based on the signal compensation theory to guarantee that the tracking error dynamics is robustly stable. Numerical simulation results are given to show the advantages of the proposed nonlinear robust control method, compared to the robust loop-shaping control approach. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

A robust automatic phase correction method for signal dense spectra

NASA Astrophysics Data System (ADS)

Bao, Qingjia; Feng, Jiwen; Chen, Li; Chen, Fang; Liu, Zao; Jiang, Bin; Liu, Chaoyang

2013-09-01

A robust automatic phase correction method for Nuclear Magnetic Resonance (NMR) spectra is presented. In this work, a new strategy combining ‘coarse tuning' with ‘fine tuning' is introduced to correct various spectra accurately. In the ‘coarse tuning' procedure, a new robust baseline recognition method is proposed for determining the positions of the tail ends of the peaks, and then the preliminary phased spectra are obtained by minimizing the objective function based on the height difference of these tail ends. After the ‘coarse tuning', the peaks in the preliminary corrected spectra can be categorized into three classes: positive, negative, and distorted. Based on the classification result, a new custom negative penalty function used in the step of ‘fine tuning' is constructed to avoid the negative peak points in the spectra excluded in the negative peaks and distorted peaks. Finally, the fine phased spectra can be obtained by minimizing the custom negative penalty function. This method is proven to be very robust for it is tolerant to low signal-to-noise ratio, large baseline distortion and independent of the starting search points of phasing parameters. The experimental results on both 1D metabonomics spectra with over-crowded peaks and 2D spectra demonstrate the high efficiency of this automatic method.

Optimal robust control strategy of a solid oxide fuel cell system

NASA Astrophysics Data System (ADS)

Wu, Xiaojuan; Gao, Danhui

2018-01-01

Optimal control can ensure system safe operation with a high efficiency. However, only a few papers discuss optimal control strategies for solid oxide fuel cell (SOFC) systems. Moreover, the existed methods ignore the impact of parameter uncertainty on system instantaneous performance. In real SOFC systems, several parameters may vary with the variation of operation conditions and can not be identified exactly, such as load current. Therefore, a robust optimal control strategy is proposed, which involves three parts: a SOFC model with parameter uncertainty, a robust optimizer and robust controllers. During the model building process, boundaries of the uncertain parameter are extracted based on Monte Carlo algorithm. To achieve the maximum efficiency, a two-space particle swarm optimization approach is employed to obtain optimal operating points, which are used as the set points of the controllers. To ensure the SOFC safe operation, two feed-forward controllers and a higher-order robust sliding mode controller are presented to control fuel utilization ratio, air excess ratio and stack temperature afterwards. The results show the proposed optimal robust control method can maintain the SOFC system safe operation with a maximum efficiency under load and uncertainty variations.

Vehicle active steering control research based on two-DOF robust internal model control

NASA Astrophysics Data System (ADS)

Wu, Jian; Liu, Yahui; Wang, Fengbo; Bao, Chunjiang; Sun, Qun; Zhao, Youqun

2016-07-01

Because of vehicle's external disturbances and model uncertainties, robust control algorithms have obtained popularity in vehicle stability control. The robust control usually gives up performance in order to guarantee the robustness of the control algorithm, therefore an improved robust internal model control(IMC) algorithm blending model tracking and internal model control is put forward for active steering system in order to reach high performance of yaw rate tracking with certain robustness. The proposed algorithm inherits the good model tracking ability of the IMC control and guarantees robustness to model uncertainties. In order to separate the design process of model tracking from the robustness design process, the improved 2 degree of freedom(DOF) robust internal model controller structure is given from the standard Youla parameterization. Simulations of double lane change maneuver and those of crosswind disturbances are conducted for evaluating the robust control algorithm, on the basis of a nonlinear vehicle simulation model with a magic tyre model. Results show that the established 2-DOF robust IMC method has better model tracking ability and a guaranteed level of robustness and robust performance, which can enhance the vehicle stability and handling, regardless of variations of the vehicle model parameters and the external crosswind interferences. Contradiction between performance and robustness of active steering control algorithm is solved and higher control performance with certain robustness to model uncertainties is obtained.

A hybrid robust fault tolerant control based on adaptive joint unscented Kalman filter.

PubMed

Shabbouei Hagh, Yashar; Mohammadi Asl, Reza; Cocquempot, Vincent

2017-01-01

In this paper, a new hybrid robust fault tolerant control scheme is proposed. A robust H ∞ control law is used in non-faulty situation, while a Non-Singular Terminal Sliding Mode (NTSM) controller is activated as soon as an actuator fault is detected. Since a linear robust controller is designed, the system is first linearized through the feedback linearization method. To switch from one controller to the other, a fuzzy based switching system is used. An Adaptive Joint Unscented Kalman Filter (AJUKF) is used for fault detection and diagnosis. The proposed method is based on the simultaneous estimation of the system states and parameters. In order to show the efficiency of the proposed scheme, a simulated 3-DOF robotic manipulator is used. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Quality Scalability Aware Watermarking for Visual Content.

PubMed

Bhowmik, Deepayan; Abhayaratne, Charith

2016-11-01

Scalable coding-based content adaptation poses serious challenges to traditional watermarking algorithms, which do not consider the scalable coding structure and hence cannot guarantee correct watermark extraction in media consumption chain. In this paper, we propose a novel concept of scalable blind watermarking that ensures more robust watermark extraction at various compression ratios while not effecting the visual quality of host media. The proposed algorithm generates scalable and robust watermarked image code-stream that allows the user to constrain embedding distortion for target content adaptations. The watermarked image code-stream consists of hierarchically nested joint distortion-robustness coding atoms. The code-stream is generated by proposing a new wavelet domain blind watermarking algorithm guided by a quantization based binary tree. The code-stream can be truncated at any distortion-robustness atom to generate the watermarked image with the desired distortion-robustness requirements. A blind extractor is capable of extracting watermark data from the watermarked images. The algorithm is further extended to incorporate a bit-plane discarding-based quantization model used in scalable coding-based content adaptation, e.g., JPEG2000. This improves the robustness against quality scalability of JPEG2000 compression. The simulation results verify the feasibility of the proposed concept, its applications, and its improved robustness against quality scalable content adaptation. Our proposed algorithm also outperforms existing methods showing 35% improvement. In terms of robustness to quality scalable video content adaptation using Motion JPEG2000 and wavelet-based scalable video coding, the proposed method shows major improvement for video watermarking.

An optimization program based on the method of feasible directions: Theory and users guide

NASA Technical Reports Server (NTRS)

Belegundu, Ashok D.; Berke, Laszlo; Patnaik, Surya N.

1994-01-01

The theory and user instructions for an optimization code based on the method of feasible directions are presented. The code was written for wide distribution and ease of attachment to other simulation software. Although the theory of the method of feasible direction was developed in the 1960's, many considerations are involved in its actual implementation as a computer code. Included in the code are a number of features to improve robustness in optimization. The search direction is obtained by solving a quadratic program using an interior method based on Karmarkar's algorithm. The theory is discussed focusing on the important and often overlooked role played by the various parameters guiding the iterations within the program. Also discussed is a robust approach for handling infeasible starting points. The code was validated by solving a variety of structural optimization test problems that have known solutions obtained by other optimization codes. It has been observed that this code is robust: it has solved a variety of problems from different starting points. However, the code is inefficient in that it takes considerable CPU time as compared with certain other available codes. Further work is required to improve its efficiency while retaining its robustness.

Technical Note: Using dual step-wedge and 2D scintillator to achieve highly precise and robust proton range quality assurance.

PubMed

Deng, Wei; Liu, Wei; Robertson, Daniel G; Bues, Martin; Sio, Terence T; Keole, Sameer R; Shen, Jiajian

2018-05-12

To develop a fast method for proton range quality assurance (QA) using a dual step-wedge and 2D scintillator and to evaluate the robustness, sensitivity, and long term reproducibility of this method. An in-house customized dual step-wedge and a 2D scintillator were developed to measure proton ranges. Proton beams with homogenous fluence were delivered through wedge, and the images captured by the scintillator were used to calculate the proton ranges by a simple trigonometric method. The range measurements of 97 energies, comprising all clinically available synchrotron energies at our facility (ranges varying from 4 to 32 cm) were repeated 10 times in all four gantry rooms for range baseline values. They were then used for evaluating room-to-room range consistencies. The robustness to setup uncertainty was evaluated by measuring ranges with ±2mm setup deviations in the x, y, and z directions. The long term reproducibility was evaluated by one month of daily range measurements by this method. Ranges of all 97 energies were measured in less than 10 minutes including setup time. The reproducibility in a single day and daily over one month is within 0.1 mm and 0.15 mm, respectively. The method was very robust to setup uncertainty, with measured range consistencies within 0.15mm for ±2mm couch shifts. The method was also sensitive enough for validating range consistencies among gantry rooms and for detecting small range variations. The new method of using a dual step-wedge and scintillator for proton range QA was efficient, highly reproducible, and robust. This method of proton range QA was highly feasible, and appealing from a workflow point of view. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Robust Takagi-Sugeno fuzzy control for fractional order hydro-turbine governing system.

PubMed

Wang, Bin; Xue, Jianyi; Wu, Fengjiao; Zhu, Delan

2016-11-01

A robust fuzzy control method for fractional order hydro-turbine governing system (FOHGS) in the presence of random disturbances is investigated in this paper. Firstly, the mathematical model of FOHGS is introduced, and based on Takagi-Sugeno (T-S) fuzzy rules, the generalized T-S fuzzy model of FOHGS is presented. Secondly, based on fractional order Lyapunov stability theory, a novel T-S fuzzy control method is designed for the stability control of FOHGS. Thirdly, the relatively loose sufficient stability condition is acquired, which could be transformed into a group of linear matrix inequalities (LMIs) via Schur complement as well as the strict mathematical derivation is given. Furthermore, the control method could resist random disturbances, which shows the good robustness. Simulation results indicate the designed fractional order T-S fuzzy control scheme works well compared with the existing method. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Integrating info-gap decision theory with robust population management: a case study using the Mountain Plover.

PubMed

van der Burg, Max Post; Tyre, Andrew J

2011-01-01

Wildlife managers often make decisions under considerable uncertainty. In the most extreme case, a complete lack of data leads to uncertainty that is unquantifiable. Information-gap decision theory deals with assessing management decisions under extreme uncertainty, but it is not widely used in wildlife management. So too, robust population management methods were developed to deal with uncertainties in multiple-model parameters. However, the two methods have not, as yet, been used in tandem to assess population management decisions. We provide a novel combination of the robust population management approach for matrix models with the information-gap decision theory framework for making conservation decisions under extreme uncertainty. We applied our model to the problem of nest survival management in an endangered bird species, the Mountain Plover (Charadrius montanus). Our results showed that matrix sensitivities suggest that nest management is unlikely to have a strong effect on population growth rate, confirming previous analyses. However, given the amount of uncertainty about adult and juvenile survival, our analysis suggested that maximizing nest marking effort was a more robust decision to maintain a stable population. Focusing on the twin concepts of opportunity and robustness in an information-gap model provides a useful method of assessing conservation decisions under extreme uncertainty.

A Robust Ordering Strategy for Retailers Facing a Free Shipping Option

PubMed Central

Meng, Qing-chun; Wan, Xiao-le; Rong, Xiao-xia

2015-01-01

Free shipping with conditions has become one of the most effective marketing tools available. An increasing number of companies, especially e-businesses, prefer to offer free shipping with some predetermined condition, such as a minimum purchase amount by the customer. However, in practice, the demands of buyers are uncertain; they are often affected by many factors, such as the weather and season. We begin by modeling the centralized ordering problem in which the supplier offers a free shipping service and retailers face stochastic demands. As these random data are considered, only partial information such as the known mean, support, and deviation is needed. The model is then analyzed via a robust optimization method, and the two types of equivalent sets of uncertainty constraints that are obtained provide good mathematical properties with consideration of the robustness of solutions. Subsequently, a numerical example is used to compare the results achieved from a robust optimization method and the linear decision rules. Additionally, the robustness of the optimal solution is discussed, as it is affected by the minimum quantity parameters. The increasing cost-threshold relationship is divided into three periods. In addition, the case study shows that the proposed method achieves better stability as well as computational complexity. PMID:25993533

A subagging regression method for estimating the qualitative and quantitative state of groundwater

NASA Astrophysics Data System (ADS)

Jeong, Jina; Park, Eungyu; Han, Weon Shik; Kim, Kue-Young

2017-08-01

A subsample aggregating (subagging) regression (SBR) method for the analysis of groundwater data pertaining to trend-estimation-associated uncertainty is proposed. The SBR method is validated against synthetic data competitively with other conventional robust and non-robust methods. From the results, it is verified that the estimation accuracies of the SBR method are consistent and superior to those of other methods, and the uncertainties are reasonably estimated; the others have no uncertainty analysis option. To validate further, actual groundwater data are employed and analyzed comparatively with Gaussian process regression (GPR). For all cases, the trend and the associated uncertainties are reasonably estimated by both SBR and GPR regardless of Gaussian or non-Gaussian skewed data. However, it is expected that GPR has a limitation in applications to severely corrupted data by outliers owing to its non-robustness. From the implementations, it is determined that the SBR method has the potential to be further developed as an effective tool of anomaly detection or outlier identification in groundwater state data such as the groundwater level and contaminant concentration.

Robust and accurate vectorization of line drawings.

PubMed

Hilaire, Xavier; Tombre, Karl

2006-06-01

This paper presents a method for vectorizing the graphical parts of paper-based line drawings. The method consists of separating the input binary image into layers of homogeneous thickness, skeletonizing each layer, segmenting the skeleton by a method based on random sampling, and simplifying the result. The segmentation method is robust with a best bound of 50 percent noise reached for indefinitely long primitives. Accurate estimation of the recognized vector's parameters is enabled by explicitly computing their feasibility domains. Theoretical performance analysis and expression of the complexity of the segmentation method are derived. Experimental results and comparisons with other vectorization systems are also provided.

Developing Appropriate Methods for Cost-Effectiveness Analysis of Cluster Randomized Trials

PubMed Central

Gomes, Manuel; Ng, Edmond S.-W.; Nixon, Richard; Carpenter, James; Thompson, Simon G.

2012-01-01

Aim. Cost-effectiveness analyses (CEAs) may use data from cluster randomized trials (CRTs), where the unit of randomization is the cluster, not the individual. However, most studies use analytical methods that ignore clustering. This article compares alternative statistical methods for accommodating clustering in CEAs of CRTs. Methods. Our simulation study compared the performance of statistical methods for CEAs of CRTs with 2 treatment arms. The study considered a method that ignored clustering—seemingly unrelated regression (SUR) without a robust standard error (SE)—and 4 methods that recognized clustering—SUR and generalized estimating equations (GEEs), both with robust SE, a “2-stage” nonparametric bootstrap (TSB) with shrinkage correction, and a multilevel model (MLM). The base case assumed CRTs with moderate numbers of balanced clusters (20 per arm) and normally distributed costs. Other scenarios included CRTs with few clusters, imbalanced cluster sizes, and skewed costs. Performance was reported as bias, root mean squared error (rMSE), and confidence interval (CI) coverage for estimating incremental net benefits (INBs). We also compared the methods in a case study. Results. Each method reported low levels of bias. Without the robust SE, SUR gave poor CI coverage (base case: 0.89 v. nominal level: 0.95). The MLM and TSB performed well in each scenario (CI coverage, 0.92–0.95). With few clusters, the GEE and SUR (with robust SE) had coverage below 0.90. In the case study, the mean INBs were similar across all methods, but ignoring clustering underestimated statistical uncertainty and the value of further research. Conclusions. MLMs and the TSB are appropriate analytical methods for CEAs of CRTs with the characteristics described. SUR and GEE are not recommended for studies with few clusters. PMID:22016450

Design of optimally normal minimum gain controllers by continuation method

NASA Technical Reports Server (NTRS)

Lim, K. B.; Juang, J.-N.; Kim, Z. C.

1989-01-01

A measure of the departure from normality is investigated for system robustness. An attractive feature of the normality index is its simplicity for pole placement designs. To allow a tradeoff between system robustness and control effort, a cost function consisting of the sum of a norm of weighted gain matrix and a normality index is minimized. First- and second-order necessary conditions for the constrained optimization problem are derived and solved by a Newton-Raphson algorithm imbedded into a one-parameter family of neighboring zero problems. The method presented allows the direct computation of optimal gains in terms of robustness and control effort for pole placement problems.

A Robust and Multi-Weighted Approach to Estimating Topographically Correlated Tropospheric Delays in Radar Interferograms

PubMed Central

Zhu, Bangyan; Li, Jiancheng; Chu, Zhengwei; Tang, Wei; Wang, Bin; Li, Dawei

2016-01-01

Spatial and temporal variations in the vertical stratification of the troposphere introduce significant propagation delays in interferometric synthetic aperture radar (InSAR) observations. Observations of small amplitude surface deformations and regional subsidence rates are plagued by tropospheric delays, and strongly correlated with topographic height variations. Phase-based tropospheric correction techniques assuming a linear relationship between interferometric phase and topography have been exploited and developed, with mixed success. Producing robust estimates of tropospheric phase delay however plays a critical role in increasing the accuracy of InSAR measurements. Meanwhile, few phase-based correction methods account for the spatially variable tropospheric delay over lager study regions. Here, we present a robust and multi-weighted approach to estimate the correlation between phase and topography that is relatively insensitive to confounding processes such as regional subsidence over larger regions as well as under varying tropospheric conditions. An expanded form of robust least squares is introduced to estimate the spatially variable correlation between phase and topography by splitting the interferograms into multiple blocks. Within each block, correlation is robustly estimated from the band-filtered phase and topography. Phase-elevation ratios are multiply- weighted and extrapolated to each persistent scatter (PS) pixel. We applied the proposed method to Envisat ASAR images over the Southern California area, USA, and found that our method mitigated the atmospheric noise better than the conventional phase-based method. The corrected ground surface deformation agreed better with those measured from GPS. PMID:27420066

A Robust and Multi-Weighted Approach to Estimating Topographically Correlated Tropospheric Delays in Radar Interferograms.

PubMed

Zhu, Bangyan; Li, Jiancheng; Chu, Zhengwei; Tang, Wei; Wang, Bin; Li, Dawei

2016-07-12

Spatial and temporal variations in the vertical stratification of the troposphere introduce significant propagation delays in interferometric synthetic aperture radar (InSAR) observations. Observations of small amplitude surface deformations and regional subsidence rates are plagued by tropospheric delays, and strongly correlated with topographic height variations. Phase-based tropospheric correction techniques assuming a linear relationship between interferometric phase and topography have been exploited and developed, with mixed success. Producing robust estimates of tropospheric phase delay however plays a critical role in increasing the accuracy of InSAR measurements. Meanwhile, few phase-based correction methods account for the spatially variable tropospheric delay over lager study regions. Here, we present a robust and multi-weighted approach to estimate the correlation between phase and topography that is relatively insensitive to confounding processes such as regional subsidence over larger regions as well as under varying tropospheric conditions. An expanded form of robust least squares is introduced to estimate the spatially variable correlation between phase and topography by splitting the interferograms into multiple blocks. Within each block, correlation is robustly estimated from the band-filtered phase and topography. Phase-elevation ratios are multiply- weighted and extrapolated to each persistent scatter (PS) pixel. We applied the proposed method to Envisat ASAR images over the Southern California area, USA, and found that our method mitigated the atmospheric noise better than the conventional phase-based method. The corrected ground surface deformation agreed better with those measured from GPS.

Robust and Efficient Biomolecular Clustering of Tumor Based on ${p}$ -Norm Singular Value Decomposition.

PubMed

Kong, Xiang-Zhen; Liu, Jin-Xing; Zheng, Chun-Hou; Hou, Mi-Xiao; Wang, Juan

2017-07-01

High dimensionality has become a typical feature of biomolecular data. In this paper, a novel dimension reduction method named p-norm singular value decomposition (PSVD) is proposed to seek the low-rank approximation matrix to the biomolecular data. To enhance the robustness to outliers, the Lp-norm is taken as the error function and the Schatten p-norm is used as the regularization function in the optimization model. To evaluate the performance of PSVD, the Kmeans clustering method is then employed for tumor clustering based on the low-rank approximation matrix. Extensive experiments are carried out on five gene expression data sets including two benchmark data sets and three higher dimensional data sets from the cancer genome atlas. The experimental results demonstrate that the PSVD-based method outperforms many existing methods. Especially, it is experimentally proved that the proposed method is more efficient for processing higher dimensional data with good robustness, stability, and superior time performance.

A robust collagen scoring method for human liver fibrosis by second harmonic microscopy.

PubMed

Guilbert, Thomas; Odin, Christophe; Le Grand, Yann; Gailhouste, Luc; Turlin, Bruno; Ezan, Frédérick; Désille, Yoann; Baffet, Georges; Guyader, Dominique

2010-12-06

Second Harmonic Generation (SHG) microscopy offers the opportunity to image collagen of type I without staining. We recently showed that a simple scoring method, based on SHG images of histological human liver biopsies, correlates well with the Metavir assessment of fibrosis level (Gailhouste et al., J. Hepatol., 2010). In this article, we present a detailed study of this new scoring method with two different objective lenses. By using measurements of the objectives point spread functions and of the photomultiplier gain, and a simple model of the SHG intensity, we show that our scoring method, applied to human liver biopsies, is robust to the objective's numerical aperture (NA) for low NA, the choice of the reference sample and laser power, and the spatial sampling rate. The simplicity and robustness of our collagen scoring method may open new opportunities in the quantification of collagen content in different organs, which is of main importance in providing diagnostic information and evaluation of therapeutic efficiency.

Chaos and Robustness in a Single Family of Genetic Oscillatory Networks

PubMed Central

Fu, Daniel; Tan, Patrick; Kuznetsov, Alexey; Molkov, Yaroslav I.

2014-01-01

Genetic oscillatory networks can be mathematically modeled with delay differential equations (DDEs). Interpreting genetic networks with DDEs gives a more intuitive understanding from a biological standpoint. However, it presents a problem mathematically, for DDEs are by construction infinitely-dimensional and thus cannot be analyzed using methods common for systems of ordinary differential equations (ODEs). In our study, we address this problem by developing a method for reducing infinitely-dimensional DDEs to two- and three-dimensional systems of ODEs. We find that the three-dimensional reductions provide qualitative improvements over the two-dimensional reductions. We find that the reducibility of a DDE corresponds to its robustness. For non-robust DDEs that exhibit high-dimensional dynamics, we calculate analytic dimension lines to predict the dependence of the DDEs’ correlation dimension on parameters. From these lines, we deduce that the correlation dimension of non-robust DDEs grows linearly with the delay. On the other hand, for robust DDEs, we find that the period of oscillation grows linearly with delay. We find that DDEs with exclusively negative feedback are robust, whereas DDEs with feedback that changes its sign are not robust. We find that non-saturable degradation damps oscillations and narrows the range of parameter values for which oscillations exist. Finally, we deduce that natural genetic oscillators with highly-regular periods likely have solely negative feedback. PMID:24667178

Efficient l1 -norm-based low-rank matrix approximations for large-scale problems using alternating rectified gradient method.

PubMed

Kim, Eunwoo; Lee, Minsik; Choi, Chong-Ho; Kwak, Nojun; Oh, Songhwai

2015-02-01

Low-rank matrix approximation plays an important role in the area of computer vision and image processing. Most of the conventional low-rank matrix approximation methods are based on the l2 -norm (Frobenius norm) with principal component analysis (PCA) being the most popular among them. However, this can give a poor approximation for data contaminated by outliers (including missing data), because the l2 -norm exaggerates the negative effect of outliers. Recently, to overcome this problem, various methods based on the l1 -norm, such as robust PCA methods, have been proposed for low-rank matrix approximation. Despite the robustness of the methods, they require heavy computational effort and substantial memory for high-dimensional data, which is impractical for real-world problems. In this paper, we propose two efficient low-rank factorization methods based on the l1 -norm that find proper projection and coefficient matrices using the alternating rectified gradient method. The proposed methods are applied to a number of low-rank matrix approximation problems to demonstrate their efficiency and robustness. The experimental results show that our proposals are efficient in both execution time and reconstruction performance unlike other state-of-the-art methods.

When Can Categorical Variables Be Treated as Continuous? A Comparison of Robust Continuous and Categorical SEM Estimation Methods under Suboptimal Conditions

ERIC Educational Resources Information Center

Rhemtulla, Mijke; Brosseau-Liard, Patricia E.; Savalei, Victoria

2012-01-01

A simulation study compared the performance of robust normal theory maximum likelihood (ML) and robust categorical least squares (cat-LS) methodology for estimating confirmatory factor analysis models with ordinal variables. Data were generated from 2 models with 2-7 categories, 4 sample sizes, 2 latent distributions, and 5 patterns of category…

Robustness analysis of elastoplastic structure subjected to double impulse

NASA Astrophysics Data System (ADS)

Kanno, Yoshihiro; Takewaki, Izuru

2016-11-01

The double impulse has extensively been used to evaluate the critical response of an elastoplastic structure against a pulse-type input, including near-fault earthquake ground motions. In this paper, we propose a robustness assessment method for elastoplastic single-degree-of-freedom structures subjected to the double impulse input. Uncertainties in the initial velocity of the input, as well as the natural frequency and the strength of the structure, are considered. As fundamental properties of the structural robustness, we show monotonicity of the robustness measure with respect to the natural frequency. In contrast, we show that robustness is not necessarily improved even if the structural strength is increased. Moreover, the robustness preference between two structures with different values of structural strength can possibly reverse when the performance requirement is changed.

WTA estimates using the method of paired comparison: tests of robustness

Treesearch

Patricia A. Champ; John B. Loomis

1998-01-01

The method of paired comparison is modified to allow choices between two alternative gains so as to estimate willingness to accept (WTA) without loss aversion. The robustness of WTA values for two public goods is tested with respect to sensitivity of theWTA measure to the context of the bundle of goods used in the paired comparison exercise and to the scope (scale) of...

Optimal patch code design via device characterization

NASA Astrophysics Data System (ADS)

Wu, Wencheng; Dalal, Edul N.

2012-01-01

In many color measurement applications, such as those for color calibration and profiling, "patch code" has been used successfully for job identification and automation to reduce operator errors. A patch code is similar to a barcode, but is intended primarily for use in measurement devices that cannot read barcodes due to limited spatial resolution, such as spectrophotometers. There is an inherent tradeoff between decoding robustness and the number of code levels available for encoding. Previous methods have attempted to address this tradeoff, but those solutions have been sub-optimal. In this paper, we propose a method to design optimal patch codes via device characterization. The tradeoff between decoding robustness and the number of available code levels is optimized in terms of printing and measurement efforts, and decoding robustness against noises from the printing and measurement devices. Effort is drastically reduced relative to previous methods because print-and-measure is minimized through modeling and the use of existing printer profiles. Decoding robustness is improved by distributing the code levels in CIE Lab space rather than in CMYK space.

Robust Airfoil Optimization in High Resolution Design Space

NASA Technical Reports Server (NTRS)

Li, Wu; Padula, Sharon L.

2003-01-01

The robust airfoil shape optimization is a direct method for drag reduction over a given range of operating conditions and has three advantages: (1) it prevents severe degradation in the off-design performance by using a smart descent direction in each optimization iteration, (2) it uses a large number of B-spline control points as design variables yet the resulting airfoil shape is fairly smooth, and (3) it allows the user to make a trade-off between the level of optimization and the amount of computing time consumed. The robust optimization method is demonstrated by solving a lift-constrained drag minimization problem for a two-dimensional airfoil in viscous flow with a large number of geometric design variables. Our experience with robust optimization indicates that our strategy produces reasonable airfoil shapes that are similar to the original airfoils, but these new shapes provide drag reduction over the specified range of Mach numbers. We have tested this strategy on a number of advanced airfoil models produced by knowledgeable aerodynamic design team members and found that our strategy produces airfoils better or equal to any designs produced by traditional design methods.

Fuzzy robust credibility-constrained programming for environmental management and planning.

PubMed

Zhang, Yimei; Hang, Guohe

2010-06-01

In this study, a fuzzy robust credibility-constrained programming (FRCCP) is developed and applied to the planning for waste management systems. It incorporates the concepts of credibility-based chance-constrained programming and robust programming within an optimization framework. The developed method can reflect uncertainties presented as possibility-density by fuzzy-membership functions. Fuzzy credibility constraints are transformed to the crisp equivalents with different credibility levels, and ordinary fuzzy inclusion constraints are determined by their robust deterministic constraints by setting a-cut levels. The FRCCP method can provide different system costs under different credibility levels (lambda). From the results of sensitivity analyses, the operation cost of the landfill is a critical parameter. For the management, any factors that would induce cost fluctuation during landfilling operation would deserve serious observation and analysis. By FRCCP, useful solutions can be obtained to provide decision-making support for long-term planning of solid waste management systems. It could be further enhanced through incorporating methods of inexact analysis into its framework. It can also be applied to other environmental management problems.

Robust mislabel logistic regression without modeling mislabel probabilities.

PubMed

Hung, Hung; Jou, Zhi-Yu; Huang, Su-Yun

2018-03-01

Logistic regression is among the most widely used statistical methods for linear discriminant analysis. In many applications, we only observe possibly mislabeled responses. Fitting a conventional logistic regression can then lead to biased estimation. One common resolution is to fit a mislabel logistic regression model, which takes into consideration of mislabeled responses. Another common method is to adopt a robust M-estimation by down-weighting suspected instances. In this work, we propose a new robust mislabel logistic regression based on γ-divergence. Our proposal possesses two advantageous features: (1) It does not need to model the mislabel probabilities. (2) The minimum γ-divergence estimation leads to a weighted estimating equation without the need to include any bias correction term, that is, it is automatically bias-corrected. These features make the proposed γ-logistic regression more robust in model fitting and more intuitive for model interpretation through a simple weighting scheme. Our method is also easy to implement, and two types of algorithms are included. Simulation studies and the Pima data application are presented to demonstrate the performance of γ-logistic regression. © 2017, The International Biometric Society.

A probabilistic approach to aircraft design emphasizing stability and control uncertainties

NASA Astrophysics Data System (ADS)

Delaurentis, Daniel Andrew

In order to address identified deficiencies in current approaches to aerospace systems design, a new method has been developed. This new method for design is based on the premise that design is a decision making activity, and that deterministic analysis and synthesis can lead to poor, or misguided decision making. This is due to a lack of disciplinary knowledge of sufficient fidelity about the product, to the presence of uncertainty at multiple levels of the aircraft design hierarchy, and to a failure to focus on overall affordability metrics as measures of goodness. Design solutions are desired which are robust to uncertainty and are based on the maximum knowledge possible. The new method represents advances in the two following general areas. 1. Design models and uncertainty. The research performed completes a transition from a deterministic design representation to a probabilistic one through a modeling of design uncertainty at multiple levels of the aircraft design hierarchy, including: (1) Consistent, traceable uncertainty classification and representation; (2) Concise mathematical statement of the Probabilistic Robust Design problem; (3) Variants of the Cumulative Distribution Functions (CDFs) as decision functions for Robust Design; (4) Probabilistic Sensitivities which identify the most influential sources of variability. 2. Multidisciplinary analysis and design. Imbedded in the probabilistic methodology is a new approach for multidisciplinary design analysis and optimization (MDA/O), employing disciplinary analysis approximations formed through statistical experimentation and regression. These approximation models are a function of design variables common to the system level as well as other disciplines. For aircraft, it is proposed that synthesis/sizing is the proper avenue for integrating multiple disciplines. Research hypotheses are translated into a structured method, which is subsequently tested for validity. Specifically, the implementation involves the study of the relaxed static stability technology for a supersonic commercial transport aircraft. The probabilistic robust design method is exercised resulting in a series of robust design solutions based on different interpretations of "robustness". Insightful results are obtained and the ability of the method to expose trends in the design space are noted as a key advantage.

Developing appropriate methods for cost-effectiveness analysis of cluster randomized trials.

PubMed

Gomes, Manuel; Ng, Edmond S-W; Grieve, Richard; Nixon, Richard; Carpenter, James; Thompson, Simon G

2012-01-01

Cost-effectiveness analyses (CEAs) may use data from cluster randomized trials (CRTs), where the unit of randomization is the cluster, not the individual. However, most studies use analytical methods that ignore clustering. This article compares alternative statistical methods for accommodating clustering in CEAs of CRTs. Our simulation study compared the performance of statistical methods for CEAs of CRTs with 2 treatment arms. The study considered a method that ignored clustering--seemingly unrelated regression (SUR) without a robust standard error (SE)--and 4 methods that recognized clustering--SUR and generalized estimating equations (GEEs), both with robust SE, a "2-stage" nonparametric bootstrap (TSB) with shrinkage correction, and a multilevel model (MLM). The base case assumed CRTs with moderate numbers of balanced clusters (20 per arm) and normally distributed costs. Other scenarios included CRTs with few clusters, imbalanced cluster sizes, and skewed costs. Performance was reported as bias, root mean squared error (rMSE), and confidence interval (CI) coverage for estimating incremental net benefits (INBs). We also compared the methods in a case study. Each method reported low levels of bias. Without the robust SE, SUR gave poor CI coverage (base case: 0.89 v. nominal level: 0.95). The MLM and TSB performed well in each scenario (CI coverage, 0.92-0.95). With few clusters, the GEE and SUR (with robust SE) had coverage below 0.90. In the case study, the mean INBs were similar across all methods, but ignoring clustering underestimated statistical uncertainty and the value of further research. MLMs and the TSB are appropriate analytical methods for CEAs of CRTs with the characteristics described. SUR and GEE are not recommended for studies with few clusters.

Sulfites and the wine metabolome.

PubMed

Roullier-Gall, Chloé; Hemmler, Daniel; Gonsior, Michael; Li, Yan; Nikolantonaki, Maria; Aron, Alissa; Coelho, Christian; Gougeon, Régis D; Schmitt-Kopplin, Philippe

2017-12-15

In a context of societal concern about food preservation, the reduction of sulfite input plays a major role in the wine industry. To improve the understanding of the chemistry involved in the SO 2 protection, a series of bottle aged Chardonnay wines made from the same must, but with different concentrations of SO 2 added at pressing were analyzed by ultrahigh resolution mass spectrometry (FT-ICR-MS) and excitation emission matrix fluorescence (EEMF). Metabolic fingerprints from FT-ICR-MS data could discriminate wines according to the added concentration to the must but they also revealed chemistry-related differences according to the type of stopper, providing a wine metabolomics picture of the impact of distinct stopping strategies. Spearman rank correlation was applied to link the statistically modeled EEMF components (parallel factor analysis (PARAFAC)) and the exact mass information from FT-ICR-MS, and thus revealing the extent of sulfur-containing compounds which could show some correlation with fluorescence fingerprints. Copyright © 2017 Elsevier Ltd. All rights reserved.

The GNAT: A new tool for processing NMR data.

PubMed

Castañar, Laura; Poggetto, Guilherme Dal; Colbourne, Adam A; Morris, Gareth A; Nilsson, Mathias

2018-06-01

The GNAT (General NMR Analysis Toolbox) is a free and open-source software package for processing, visualising, and analysing NMR data. It supersedes the popular DOSY Toolbox, which has a narrower focus on diffusion NMR. Data import of most common formats from the major NMR platforms is supported, as well as a GNAT generic format. Key basic processing of NMR data (e.g., Fourier transformation, baseline correction, and phasing) is catered for within the program, as well as more advanced techniques (e.g., reference deconvolution and pure shift FID reconstruction). Analysis tools include DOSY and SCORE for diffusion data, ROSY T 1 /T 2 estimation for relaxation data, and PARAFAC for multilinear analysis. The GNAT is written for the MATLAB® language and comes with a user-friendly graphical user interface. The standard version is intended to run with a MATLAB installation, but completely free-standing compiled versions for Windows, Mac, and Linux are also freely available. © 2018 The Authors Magnetic Resonance in Chemistry Published by John Wiley & Sons Ltd.

Selective robust optimization: A new intensity-modulated proton therapy optimization strategy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Yupeng; Niemela, Perttu; Siljamaki, Sami

2015-08-15

Purpose: To develop a new robust optimization strategy for intensity-modulated proton therapy as an important step in translating robust proton treatment planning from research to clinical applications. Methods: In selective robust optimization, a worst-case-based robust optimization algorithm is extended, and terms of the objective function are selectively computed from either the worst-case dose or the nominal dose. Two lung cancer cases and one head and neck cancer case were used to demonstrate the practical significance of the proposed robust planning strategy. The lung cancer cases had minimal tumor motion less than 5 mm, and, for the demonstration of the methodology,more » are assumed to be static. Results: Selective robust optimization achieved robust clinical target volume (CTV) coverage and at the same time increased nominal planning target volume coverage to 95.8%, compared to the 84.6% coverage achieved with CTV-based robust optimization in one of the lung cases. In the other lung case, the maximum dose in selective robust optimization was lowered from a dose of 131.3% in the CTV-based robust optimization to 113.6%. Selective robust optimization provided robust CTV coverage in the head and neck case, and at the same time improved controls over isodose distribution so that clinical requirements may be readily met. Conclusions: Selective robust optimization may provide the flexibility and capability necessary for meeting various clinical requirements in addition to achieving the required plan robustness in practical proton treatment planning settings.« less

Geometrically Flexible and Efficient Flow Analysis of High Speed Vehicles Via Domain Decomposition, Part 1: Unstructured-Grid Solver for High Speed Flows

NASA Technical Reports Server (NTRS)

White, Jeffery A.; Baurle, Robert A.; Passe, Bradley J.; Spiegel, Seth C.; Nishikawa, Hiroaki

2017-01-01

The ability to solve the equations governing the hypersonic turbulent flow of a real gas on unstructured grids using a spatially-elliptic, 2nd-order accurate, cell-centered, finite-volume method has been recently implemented in the VULCAN-CFD code. This paper describes the key numerical methods and techniques that were found to be required to robustly obtain accurate solutions to hypersonic flows on non-hex-dominant unstructured grids. The methods and techniques described include: an augmented stencil, weighted linear least squares, cell-average gradient method, a robust multidimensional cell-average gradient-limiter process that is consistent with the augmented stencil of the cell-average gradient method and a cell-face gradient method that contains a cell skewness sensitive damping term derived using hyperbolic diffusion based concepts. A data-parallel matrix-based symmetric Gauss-Seidel point-implicit scheme, used to solve the governing equations, is described and shown to be more robust and efficient than a matrix-free alternative. In addition, a y+ adaptive turbulent wall boundary condition methodology is presented. This boundary condition methodology is deigned to automatically switch between a solve-to-the-wall and a wall-matching-function boundary condition based on the local y+ of the 1st cell center off the wall. The aforementioned methods and techniques are then applied to a series of hypersonic and supersonic turbulent flat plate unit tests to examine the efficiency, robustness and convergence behavior of the implicit scheme and to determine the ability of the solve-to-the-wall and y+ adaptive turbulent wall boundary conditions to reproduce the turbulent law-of-the-wall. Finally, the thermally perfect, chemically frozen, Mach 7.8 turbulent flow of air through a scramjet flow-path is computed and compared with experimental data to demonstrate the robustness, accuracy and convergence behavior of the unstructured-grid solver for a realistic 3-D geometry on a non-hex-dominant grid.

Skin-inspired hydrogel-elastomer hybrids with robust interfaces and functional microstructures

NASA Astrophysics Data System (ADS)

Yuk, Hyunwoo; Zhang, Teng; Parada, German Alberto; Liu, Xinyue; Zhao, Xuanhe

2016-06-01

Inspired by mammalian skins, soft hybrids integrating the merits of elastomers and hydrogels have potential applications in diverse areas including stretchable and bio-integrated electronics, microfluidics, tissue engineering, soft robotics and biomedical devices. However, existing hydrogel-elastomer hybrids have limitations such as weak interfacial bonding, low robustness and difficulties in patterning microstructures. Here, we report a simple yet versatile method to assemble hydrogels and elastomers into hybrids with extremely robust interfaces (interfacial toughness over 1,000 Jm-2) and functional microstructures such as microfluidic channels and electrical circuits. The proposed method is generally applicable to various types of tough hydrogels and diverse commonly used elastomers including polydimethylsiloxane Sylgard 184, polyurethane, latex, VHB and Ecoflex. We further demonstrate applications enabled by the robust and microstructured hydrogel-elastomer hybrids including anti-dehydration hydrogel-elastomer hybrids, stretchable and reactive hydrogel-elastomer microfluidics, and stretchable hydrogel circuit boards patterned on elastomer.

Assessing the Robustness of Complete Bacterial Genome Segmentations

NASA Astrophysics Data System (ADS)

Devillers, Hugo; Chiapello, Hélène; Schbath, Sophie; El Karoui, Meriem

Comparison of closely related bacterial genomes has revealed the presence of highly conserved sequences forming a "backbone" that is interrupted by numerous, less conserved, DNA fragments. Segmentation of bacterial genomes into backbone and variable regions is particularly useful to investigate bacterial genome evolution. Several software tools have been designed to compare complete bacterial chromosomes and a few online databases store pre-computed genome comparisons. However, very few statistical methods are available to evaluate the reliability of these software tools and to compare the results obtained with them. To fill this gap, we have developed two local scores to measure the robustness of bacterial genome segmentations. Our method uses a simulation procedure based on random perturbations of the compared genomes. The scores presented in this paper are simple to implement and our results show that they allow to discriminate easily between robust and non-robust bacterial genome segmentations when using aligners such as MAUVE and MGA.

Robust lane detection and tracking using multiple visual cues under stochastic lane shape conditions

NASA Astrophysics Data System (ADS)

Huang, Zhi; Fan, Baozheng; Song, Xiaolin

2018-03-01

As one of the essential components of environment perception techniques for an intelligent vehicle, lane detection is confronted with challenges including robustness against the complicated disturbance and illumination, also adaptability to stochastic lane shapes. To overcome these issues, we proposed a robust lane detection method named classification-generation-growth-based (CGG) operator to the detected lines, whereby the linear lane markings are identified by synergizing multiple visual cues with the a priori knowledge and spatial-temporal information. According to the quality of linear lane fitting, the linear and linear-parabolic models are dynamically switched to describe the actual lane. The Kalman filter with adaptive noise covariance and the region of interests (ROI) tracking are applied to improve the robustness and efficiency. Experiments were conducted with images covering various challenging scenarios. The experimental results evaluate the effectiveness of the presented method for complicated disturbances, illumination, and stochastic lane shapes.

Robust image watermarking using DWT and SVD for copyright protection

NASA Astrophysics Data System (ADS)

Harjito, Bambang; Suryani, Esti

2017-02-01

The Objective of this paper is proposed a robust combined Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD). The RGB image is called a cover medium, and watermark image is converted into gray scale. Then, they are transformed using DWT so that they can be split into several subbands, namely sub-band LL2, LH2, HL2. The watermark image embeds into the cover medium on sub-band LL2. This scheme aims to obtain the higher robustness level than the previous method which performs of SVD matrix factorization image for copyright protection. The experiment results show that the proposed method has robustness against several image processing attacks such as Gaussian, Poisson and Salt and Pepper Noise. In these attacks, noise has average Normalized Correlation (NC) values of 0.574863 0.889784, 0.889782 respectively. The watermark image can be detected and extracted.

Employing Sensitivity Derivatives for Robust Optimization under Uncertainty in CFD

NASA Technical Reports Server (NTRS)

Newman, Perry A.; Putko, Michele M.; Taylor, Arthur C., III

2004-01-01

A robust optimization is demonstrated on a two-dimensional inviscid airfoil problem in subsonic flow. Given uncertainties in statistically independent, random, normally distributed flow parameters (input variables), an approximate first-order statistical moment method is employed to represent the Computational Fluid Dynamics (CFD) code outputs as expected values with variances. These output quantities are used to form the objective function and constraints. The constraints are cast in probabilistic terms; that is, the probability that a constraint is satisfied is greater than or equal to some desired target probability. Gradient-based robust optimization of this stochastic problem is accomplished through use of both first and second-order sensitivity derivatives. For each robust optimization, the effect of increasing both input standard deviations and target probability of constraint satisfaction are demonstrated. This method provides a means for incorporating uncertainty when considering small deviations from input mean values.

Robust fast controller design via nonlinear fractional differential equations.

PubMed

Zhou, Xi; Wei, Yiheng; Liang, Shu; Wang, Yong

2017-07-01

A new method for linear system controller design is proposed whereby the closed-loop system achieves both robustness and fast response. The robustness performance considered here means the damping ratio of closed-loop system can keep its desired value under system parameter perturbation, while the fast response, represented by rise time of system output, can be improved by tuning the controller parameter. We exploit techniques from both the nonlinear systems control and the fractional order systems control to derive a novel nonlinear fractional order controller. For theoretical analysis of the closed-loop system performance, two comparison theorems are developed for a class of fractional differential equations. Moreover, the rise time of the closed-loop system can be estimated, which facilitates our controller design to satisfy the fast response performance and maintain the robustness. Finally, numerical examples are given to illustrate the effectiveness of our methods. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Optimal design of loudspeaker arrays for robust cross-talk cancellation using the Taguchi method and the genetic algorithm.

PubMed

Bai, Mingsian R; Tung, Chih-Wei; Lee, Chih-Chung

2005-05-01

An optimal design technique of loudspeaker arrays for cross-talk cancellation with application in three-dimensional audio is presented. An array focusing scheme is presented on the basis of the inverse propagation that relates the transducers to a set of chosen control points. Tikhonov regularization is employed in designing the inverse cancellation filters. An extensive analysis is conducted to explore the cancellation performance and robustness issues. To best compromise the performance and robustness of the cross-talk cancellation system, optimal configurations are obtained with the aid of the Taguchi method and the genetic algorithm (GA). The proposed systems are further justified by physical as well as subjective experiments. The results reveal that large number of loudspeakers, closely spaced configuration, and optimal control point design all contribute to the robustness of cross-talk cancellation systems (CCS) against head misalignment.

Robust iterative method for nonlinear Helmholtz equation

NASA Astrophysics Data System (ADS)

Yuan, Lijun; Lu, Ya Yan

2017-08-01

A new iterative method is developed for solving the two-dimensional nonlinear Helmholtz equation which governs polarized light in media with the optical Kerr nonlinearity. In the strongly nonlinear regime, the nonlinear Helmholtz equation could have multiple solutions related to phenomena such as optical bistability and symmetry breaking. The new method exhibits a much more robust convergence behavior than existing iterative methods, such as frozen-nonlinearity iteration, Newton's method and damped Newton's method, and it can be used to find solutions when good initial guesses are unavailable. Numerical results are presented for the scattering of light by a nonlinear circular cylinder based on the exact nonlocal boundary condition and a pseudospectral method in the polar coordinate system.

Noise robustness of a combined phase retrieval and reconstruction method for phase-contrast tomography.

PubMed

Kongskov, Rasmus Dalgas; Jørgensen, Jakob Sauer; Poulsen, Henning Friis; Hansen, Per Christian

2016-04-01

Classical reconstruction methods for phase-contrast tomography consist of two stages: phase retrieval and tomographic reconstruction. A novel algebraic method combining the two was suggested by Kostenko et al. [Opt. Express21, 12185 (2013)OPEXFF1094-408710.1364/OE.21.012185], and preliminary results demonstrated improved reconstruction compared with a given two-stage method. Using simulated free-space propagation experiments with a single sample-detector distance, we thoroughly compare the novel method with the two-stage method to address limitations of the preliminary results. We demonstrate that the novel method is substantially more robust toward noise; our simulations point to a possible reduction in counting times by an order of magnitude.

Robust sensor fault detection and isolation of gas turbine engines subjected to time-varying parameter uncertainties

NASA Astrophysics Data System (ADS)

Pourbabaee, Bahareh; Meskin, Nader; Khorasani, Khashayar

2016-08-01

In this paper, a novel robust sensor fault detection and isolation (FDI) strategy using the multiple model-based (MM) approach is proposed that remains robust with respect to both time-varying parameter uncertainties and process and measurement noise in all the channels. The scheme is composed of robust Kalman filters (RKF) that are constructed for multiple piecewise linear (PWL) models that are constructed at various operating points of an uncertain nonlinear system. The parameter uncertainty is modeled by using a time-varying norm bounded admissible structure that affects all the PWL state space matrices. The robust Kalman filter gain matrices are designed by solving two algebraic Riccati equations (AREs) that are expressed as two linear matrix inequality (LMI) feasibility conditions. The proposed multiple RKF-based FDI scheme is simulated for a single spool gas turbine engine to diagnose various sensor faults despite the presence of parameter uncertainties, process and measurement noise. Our comparative studies confirm the superiority of our proposed FDI method when compared to the methods that are available in the literature.

A scoring mechanism for the rank aggregation of network robustness

NASA Astrophysics Data System (ADS)

Yazdani, Alireza; Dueñas-Osorio, Leonardo; Li, Qilin

2013-10-01

To date, a number of metrics have been proposed to quantify inherent robustness of network topology against failures. However, each single metric usually only offers a limited view of network vulnerability to different types of random failures and targeted attacks. When applied to certain network configurations, different metrics rank network topology robustness in different orders which is rather inconsistent, and no single metric fully characterizes network robustness against different modes of failure. To overcome such inconsistency, this work proposes a multi-metric approach as the basis of evaluating aggregate ranking of network topology robustness. This is based on simultaneous utilization of a minimal set of distinct robustness metrics that are standardized so to give way to a direct comparison of vulnerability across networks with different sizes and configurations, hence leading to an initial scoring of inherent topology robustness. Subsequently, based on the inputs of initial scoring a rank aggregation method is employed to allocate an overall ranking of robustness to each network topology. A discussion is presented in support of the presented multi-metric approach and its applications to more realistically assess and rank network topology robustness.

Adaptive and robust statistical methods for processing near-field scanning microwave microscopy images.

PubMed

Coakley, K J; Imtiaz, A; Wallis, T M; Weber, J C; Berweger, S; Kabos, P

2015-03-01

Near-field scanning microwave microscopy offers great potential to facilitate characterization, development and modeling of materials. By acquiring microwave images at multiple frequencies and amplitudes (along with the other modalities) one can study material and device physics at different lateral and depth scales. Images are typically noisy and contaminated by artifacts that can vary from scan line to scan line and planar-like trends due to sample tilt errors. Here, we level images based on an estimate of a smooth 2-d trend determined with a robust implementation of a local regression method. In this robust approach, features and outliers which are not due to the trend are automatically downweighted. We denoise images with the Adaptive Weights Smoothing method. This method smooths out additive noise while preserving edge-like features in images. We demonstrate the feasibility of our methods on topography images and microwave |S11| images. For one challenging test case, we demonstrate that our method outperforms alternative methods from the scanning probe microscopy data analysis software package Gwyddion. Our methods should be useful for massive image data sets where manual selection of landmarks or image subsets by a user is impractical. Published by Elsevier B.V.

An Optimal Mean Based Block Robust Feature Extraction Method to Identify Colorectal Cancer Genes with Integrated Data.

PubMed

Liu, Jian; Cheng, Yuhu; Wang, Xuesong; Zhang, Lin; Liu, Hui

2017-08-17

It is urgent to diagnose colorectal cancer in the early stage. Some feature genes which are important to colorectal cancer development have been identified. However, for the early stage of colorectal cancer, less is known about the identity of specific cancer genes that are associated with advanced clinical stage. In this paper, we conducted a feature extraction method named Optimal Mean based Block Robust Feature Extraction method (OMBRFE) to identify feature genes associated with advanced colorectal cancer in clinical stage by using the integrated colorectal cancer data. Firstly, based on the optimal mean and L 2,1 -norm, a novel feature extraction method called Optimal Mean based Robust Feature Extraction method (OMRFE) is proposed to identify feature genes. Then the OMBRFE method which introduces the block ideology into OMRFE method is put forward to process the colorectal cancer integrated data which includes multiple genomic data: copy number alterations, somatic mutations, methylation expression alteration, as well as gene expression changes. Experimental results demonstrate that the OMBRFE is more effective than previous methods in identifying the feature genes. Moreover, genes identified by OMBRFE are verified to be closely associated with advanced colorectal cancer in clinical stage.

The comparison between several robust ridge regression estimators in the presence of multicollinearity and multiple outliers

NASA Astrophysics Data System (ADS)

Zahari, Siti Meriam; Ramli, Norazan Mohamed; Moktar, Balkiah; Zainol, Mohammad Said

2014-09-01

In the presence of multicollinearity and multiple outliers, statistical inference of linear regression model using ordinary least squares (OLS) estimators would be severely affected and produces misleading results. To overcome this, many approaches have been investigated. These include robust methods which were reported to be less sensitive to the presence of outliers. In addition, ridge regression technique was employed to tackle multicollinearity problem. In order to mitigate both problems, a combination of ridge regression and robust methods was discussed in this study. The superiority of this approach was examined when simultaneous presence of multicollinearity and multiple outliers occurred in multiple linear regression. This study aimed to look at the performance of several well-known robust estimators; M, MM, RIDGE and robust ridge regression estimators, namely Weighted Ridge M-estimator (WRM), Weighted Ridge MM (WRMM), Ridge MM (RMM), in such a situation. Results of the study showed that in the presence of simultaneous multicollinearity and multiple outliers (in both x and y-direction), the RMM and RIDGE are more or less similar in terms of superiority over the other estimators, regardless of the number of observation, level of collinearity and percentage of outliers used. However, when outliers occurred in only single direction (y-direction), the WRMM estimator is the most superior among the robust ridge regression estimators, by producing the least variance. In conclusion, the robust ridge regression is the best alternative as compared to robust and conventional least squares estimators when dealing with simultaneous presence of multicollinearity and outliers.

TH-CD-209-04: Fuzzy Robust Optimization in Intensity-Modulated Proton Therapy Planning to Account for Range and Patient Setup Uncertainties

DOE Office of Scientific and Technical Information (OSTI.GOV)

An, Y; Bues, M; Schild, S

Purpose: We propose to apply a robust optimization model based on fuzzy-logic constraints in the intensity-modulated proton therapy (IMPT) planning subject to range and patient setup uncertainties. The purpose is to ensure the plan robustness under uncertainty and obtain the best trade-off between tumor dose coverage and organ-at-risk(OAR) sparing. Methods: Two IMPT plans were generated for 3 head-and-neck cancer patients: one used the planning target volume(PTV) method; the other used the fuzzy robust optimization method. In the latter method, nine dose distributions were computed - the nominal one and one each for ±3mm setup uncertainties along three cardinal axes andmore » for ±3.5% range uncertainty. For tumors, these nine dose distributions were explicitly controlled by adding hard constraints with adjustable parameters. For OARs, fuzzy constraints that allow the dose to vary within a certain range were used so that the tumor dose distribution was guaranteed by minimum compromise of that of OARs. We rendered this model tractable by converting the fuzzy constraints to linear constraints. The plan quality was evaluated using dose-volume histogram(DVH) indices such as tumor dose coverage(D95%), homogeneity(D5%-D95%), plan robustness(DVH band at D95%), and OAR sparing like D1% of brain and D1% of brainstem. Results: Our model could yield clinically acceptable plans. The fuzzy-logic robust optimization method produced IMPT plans with comparable target dose coverage and homogeneity compared to the PTV method(unit: Gy[RBE]; average[min, max])(CTV D95%: 59 [52.7, 63.5] vs 53.5[46.4, 60.1], CTV D5% - D95%: 11.1[5.3, 18.6] vs 14.4[9.2, 21.5]). It also generated more robust plans(CTV DVH band at D95%: 3.8[1.2, 5.6] vs 11.5[6.2, 16.7]). The parameters of tumor constraints could be adjusted to control the tradeoff between tumor coverage and OAR sparing. Conclusion: The fuzzy-logic robust optimization generates superior IMPT with minimum compromise of OAR sparing. This research was supported by the National Cancer Institute Career Developmental Award K25CA168984, by the Fraternal Order of Eagles Cancer Research Fund Career Development Award, by The Lawrence W. and Marilyn W. Matteson Fund for Cancer Research, by Mayo Arizona State University Seed Grant, and by The Kemper Marley Foundation. eRA Person ID(s) for the Principal Investigator: 11017970 (Research Supported by National Institutes of Health)« less

Effect of interaction strength on robustness of controlling edge dynamics in complex networks

NASA Astrophysics Data System (ADS)

Pang, Shao-Peng; Hao, Fei

2018-05-01

Robustness plays a critical role in the controllability of complex networks to withstand failures and perturbations. Recent advances in the edge controllability show that the interaction strength among edges plays a more important role than network structure. Therefore, we focus on the effect of interaction strength on the robustness of edge controllability. Using three categories of all edges to quantify the robustness, we develop a universal framework to evaluate and analyze the robustness in complex networks with arbitrary structures and interaction strengths. Applying our framework to a large number of model and real-world networks, we find that the interaction strength is a dominant factor for the robustness in undirected networks. Meanwhile, the strongest robustness and the optimal edge controllability in undirected networks can be achieved simultaneously. Different from the case of undirected networks, the robustness in directed networks is determined jointly by the interaction strength and the network's degree distribution. Moreover, a stronger robustness is usually associated with a larger number of driver nodes required to maintain full control in directed networks. This prompts us to provide an optimization method by adjusting the interaction strength to optimize the robustness of edge controllability.

A robust interrupted time series model for analyzing complex health care intervention data.

PubMed

Cruz, Maricela; Bender, Miriam; Ombao, Hernando

2017-12-20

Current health policy calls for greater use of evidence-based care delivery services to improve patient quality and safety outcomes. Care delivery is complex, with interacting and interdependent components that challenge traditional statistical analytic techniques, in particular, when modeling a time series of outcomes data that might be "interrupted" by a change in a particular method of health care delivery. Interrupted time series (ITS) is a robust quasi-experimental design with the ability to infer the effectiveness of an intervention that accounts for data dependency. Current standardized methods for analyzing ITS data do not model changes in variation and correlation following the intervention. This is a key limitation since it is plausible for data variability and dependency to change because of the intervention. Moreover, present methodology either assumes a prespecified interruption time point with an instantaneous effect or removes data for which the effect of intervention is not fully realized. In this paper, we describe and develop a novel robust interrupted time series (robust-ITS) model that overcomes these omissions and limitations. The robust-ITS model formally performs inference on (1) identifying the change point; (2) differences in preintervention and postintervention correlation; (3) differences in the outcome variance preintervention and postintervention; and (4) differences in the mean preintervention and postintervention. We illustrate the proposed method by analyzing patient satisfaction data from a hospital that implemented and evaluated a new nursing care delivery model as the intervention of interest. The robust-ITS model is implemented in an R Shiny toolbox, which is freely available to the community. Copyright © 2017 John Wiley & Sons, Ltd.

Robust Gain-Scheduled Fault Tolerant Control for a Transport Aircraft

NASA Technical Reports Server (NTRS)

Shin, Jong-Yeob; Gregory, Irene

2007-01-01

This paper presents an application of robust gain-scheduled control concepts using a linear parameter-varying (LPV) control synthesis method to design fault tolerant controllers for a civil transport aircraft. To apply the robust LPV control synthesis method, the nonlinear dynamics must be represented by an LPV model, which is developed using the function substitution method over the entire flight envelope. The developed LPV model associated with the aerodynamic coefficient uncertainties represents nonlinear dynamics including those outside the equilibrium manifold. Passive and active fault tolerant controllers (FTC) are designed for the longitudinal dynamics of the Boeing 747-100/200 aircraft in the presence of elevator failure. Both FTC laws are evaluated in the full nonlinear aircraft simulation in the presence of the elevator fault and the results are compared to show pros and cons of each control law.

Robust Mosaicking of Stereo Digital Elevation Models from the Ames Stereo Pipeline

NASA Technical Reports Server (NTRS)

Kim, Tae Min; Moratto, Zachary M.; Nefian, Ara Victor

2010-01-01

Robust estimation method is proposed to combine multiple observations and create consistent, accurate, dense Digital Elevation Models (DEMs) from lunar orbital imagery. The NASA Ames Intelligent Robotics Group (IRG) aims to produce higher-quality terrain reconstructions of the Moon from Apollo Metric Camera (AMC) data than is currently possible. In particular, IRG makes use of a stereo vision process, the Ames Stereo Pipeline (ASP), to automatically generate DEMs from consecutive AMC image pairs. However, the DEMs currently produced by the ASP often contain errors and inconsistencies due to image noise, shadows, etc. The proposed method addresses this problem by making use of multiple observations and by considering their goodness of fit to improve both the accuracy and robustness of the estimate. The stepwise regression method is applied to estimate the relaxed weight of each observation.

A robust rotation-invariance displacement measurement method for a micro-/nano-positioning system

NASA Astrophysics Data System (ADS)

Zhang, Xiang; Zhang, Xianmin; Wu, Heng; Li, Hai; Gan, Jinqiang

2018-05-01

A robust and high-precision displacement measurement method for a compliant mechanism-based micro-/nano-positioning system is proposed. The method is composed of an integer-pixel and a sub-pixel matching procedure. In the proposed algorithm (Pro-A), an improved ring projection transform (IRPT) and gradient information are used as features for approximating the coarse candidates and fine locations, respectively. Simulations are conducted and the results show that the Pro-A has the ability of rotation-invariance and strong robustness, with a theoretical accuracy of 0.01 pixel. To validate the practical performance, a series of experiments are carried out using a computer micro-vision and laser interferometer system (LIMS). The results demonstrate that both the LIMS and Pro-A can achieve high precision, while the Pro-A has better stability and adaptability.

Robust Surface Reconstruction via Laplace-Beltrami Eigen-Projection and Boundary Deformation

PubMed Central

Shi, Yonggang; Lai, Rongjie; Morra, Jonathan H.; Dinov, Ivo; Thompson, Paul M.; Toga, Arthur W.

2010-01-01

In medical shape analysis, a critical problem is reconstructing a smooth surface of correct topology from a binary mask that typically has spurious features due to segmentation artifacts. The challenge is the robust removal of these outliers without affecting the accuracy of other parts of the boundary. In this paper, we propose a novel approach for this problem based on the Laplace-Beltrami (LB) eigen-projection and properly designed boundary deformations. Using the metric distortion during the LB eigen-projection, our method automatically detects the location of outliers and feeds this information to a well-composed and topology-preserving deformation. By iterating between these two steps of outlier detection and boundary deformation, we can robustly filter out the outliers without moving the smooth part of the boundary. The final surface is the eigen-projection of the filtered mask boundary that has the correct topology, desired accuracy and smoothness. In our experiments, we illustrate the robustness of our method on different input masks of the same structure, and compare with the popular SPHARM tool and the topology preserving level set method to show that our method can reconstruct accurate surface representations without introducing artificial oscillations. We also successfully validate our method on a large data set of more than 900 hippocampal masks and demonstrate that the reconstructed surfaces retain volume information accurately. PMID:20624704

Evaluation of the Bitterness of Traditional Chinese Medicines using an E-Tongue Coupled with a Robust Partial Least Squares Regression Method.

PubMed

Lin, Zhaozhou; Zhang, Qiao; Liu, Ruixin; Gao, Xiaojie; Zhang, Lu; Kang, Bingya; Shi, Junhan; Wu, Zidan; Gui, Xinjing; Li, Xuelin

2016-01-25

To accurately, safely, and efficiently evaluate the bitterness of Traditional Chinese Medicines (TCMs), a robust predictor was developed using robust partial least squares (RPLS) regression method based on data obtained from an electronic tongue (e-tongue) system. The data quality was verified by the Grubb's test. Moreover, potential outliers were detected based on both the standardized residual and score distance calculated for each sample. The performance of RPLS on the dataset before and after outlier detection was compared to other state-of-the-art methods including multivariate linear regression, least squares support vector machine, and the plain partial least squares regression. Both R² and root-mean-squares error (RMSE) of cross-validation (CV) were recorded for each model. With four latent variables, a robust RMSECV value of 0.3916 with bitterness values ranging from 0.63 to 4.78 were obtained for the RPLS model that was constructed based on the dataset including outliers. Meanwhile, the RMSECV, which was calculated using the models constructed by other methods, was larger than that of the RPLS model. After six outliers were excluded, the performance of all benchmark methods markedly improved, but the difference between the RPLS model constructed before and after outlier exclusion was negligible. In conclusion, the bitterness of TCM decoctions can be accurately evaluated with the RPLS model constructed using e-tongue data.

Gradient-based Electrical Properties Tomography (gEPT): a Robust Method for Mapping Electrical Properties of Biological Tissues In Vivo Using Magnetic Resonance Imaging

PubMed Central

Liu, Jiaen; Zhang, Xiaotong; Schmitter, Sebastian; Van de Moortele, Pierre-Francois; He, Bin

2014-01-01

Purpose To develop high-resolution electrical properties tomography (EPT) methods and investigate a gradient-based EPT (gEPT) approach which aims to reconstruct the electrical properties (EP), including conductivity and permittivity, of an imaged sample from experimentally measured B1 maps with improved boundary reconstruction and robustness against measurement noise. Theory and Methods Using a multi-channel transmit/receive stripline head coil, with acquired B1 maps for each coil element, by assuming negligible Bz component compared to transverse B1 components, a theory describing the relationship between B1 field, EP value and their spatial gradient has been proposed. The final EP images were obtained through spatial integration over the reconstructed EP gradient. Numerical simulation, physical phantom and in vivo human experiments at 7 T have been conducted to evaluate the performance of the proposed methods. Results Reconstruction results were compared with target EP values in both simulations and phantom experiments. Human experimental results were compared with EP values in literature. Satisfactory agreement was observed with improved boundary reconstruction. Importantly, the proposed gEPT method proved to be more robust against noise when compared to previously described non-gradient-based EPT approaches. Conclusion The proposed gEPT approach holds promises to improve EP mapping quality by recovering the boundary information and enhancing robustness against noise. PMID:25213371

Robust synthetic biology design: stochastic game theory approach.

PubMed

Chen, Bor-Sen; Chang, Chia-Hung; Lee, Hsiao-Ching

2009-07-15

Synthetic biology is to engineer artificial biological systems to investigate natural biological phenomena and for a variety of applications. However, the development of synthetic gene networks is still difficult and most newly created gene networks are non-functioning due to uncertain initial conditions and disturbances of extra-cellular environments on the host cell. At present, how to design a robust synthetic gene network to work properly under these uncertain factors is the most important topic of synthetic biology. A robust regulation design is proposed for a stochastic synthetic gene network to achieve the prescribed steady states under these uncertain factors from the minimax regulation perspective. This minimax regulation design problem can be transformed to an equivalent stochastic game problem. Since it is not easy to solve the robust regulation design problem of synthetic gene networks by non-linear stochastic game method directly, the Takagi-Sugeno (T-S) fuzzy model is proposed to approximate the non-linear synthetic gene network via the linear matrix inequality (LMI) technique through the Robust Control Toolbox in Matlab. Finally, an in silico example is given to illustrate the design procedure and to confirm the efficiency and efficacy of the proposed robust gene design method. http://www.ee.nthu.edu.tw/bschen/SyntheticBioDesign_supplement.pdf.

Robust Unit Commitment Considering Uncertain Demand Response

DOE PAGES

Liu, Guodong; Tomsovic, Kevin

2014-09-28

Although price responsive demand response has been widely accepted as playing an important role in the reliable and economic operation of power system, the real response from demand side can be highly uncertain due to limited understanding of consumers' response to pricing signals. To model the behavior of consumers, the price elasticity of demand has been explored and utilized in both research and real practice. However, the price elasticity of demand is not precisely known and may vary greatly with operating conditions and types of customers. To accommodate the uncertainty of demand response, alternative unit commitment methods robust to themore » uncertainty of the demand response require investigation. In this paper, a robust unit commitment model to minimize the generalized social cost is proposed for the optimal unit commitment decision taking into account uncertainty of the price elasticity of demand. By optimizing the worst case under proper robust level, the unit commitment solution of the proposed model is robust against all possible realizations of the modeled uncertain demand response. Numerical simulations on the IEEE Reliability Test System show the e ectiveness of the method. Finally, compared to unit commitment with deterministic price elasticity of demand, the proposed robust model can reduce the average Locational Marginal Prices (LMPs) as well as the price volatility.« less

Conditioning and Robustness of RNA Boltzmann Sampling under Thermodynamic Parameter Perturbations.

PubMed

Rogers, Emily; Murrugarra, David; Heitsch, Christine

2017-07-25

Understanding how RNA secondary structure prediction methods depend on the underlying nearest-neighbor thermodynamic model remains a fundamental challenge in the field. Minimum free energy (MFE) predictions are known to be "ill conditioned" in that small changes to the thermodynamic model can result in significantly different optimal structures. Hence, the best practice is now to sample from the Boltzmann distribution, which generates a set of suboptimal structures. Although the structural signal of this Boltzmann sample is known to be robust to stochastic noise, the conditioning and robustness under thermodynamic perturbations have yet to be addressed. We present here a mathematically rigorous model for conditioning inspired by numerical analysis, and also a biologically inspired definition for robustness under thermodynamic perturbation. We demonstrate the strong correlation between conditioning and robustness and use its tight relationship to define quantitative thresholds for well versus ill conditioning. These resulting thresholds demonstrate that the majority of the sequences are at least sample robust, which verifies the assumption of sampling's improved conditioning over the MFE prediction. Furthermore, because we find no correlation between conditioning and MFE accuracy, the presence of both well- and ill-conditioned sequences indicates the continued need for both thermodynamic model refinements and alternate RNA structure prediction methods beyond the physics-based ones. Copyright © 2017. Published by Elsevier Inc.

Robust water fat separated dual-echo MRI by phase-sensitive reconstruction.

PubMed

Romu, Thobias; Dahlström, Nils; Leinhard, Olof Dahlqvist; Borga, Magnus

2017-09-01

The purpose of this work was to develop and evaluate a robust water-fat separation method for T1-weighted symmetric two-point Dixon data. A method for water-fat separation by phase unwrapping of the opposite-phase images by phase-sensitive reconstruction (PSR) is introduced. PSR consists of three steps; (1), identification of clusters of tissue voxels; (2), unwrapping of the phase in each cluster by solving Poisson's equation; and (3), finding the correct sign of each unwrapped opposite-phase cluster, so that the water-fat images are assigned the correct identities. Robustness was evaluated by counting the number of water-fat swap artifacts in a total of 733 image volumes. The method was also compared to commercial software. In the water-fat separated image volumes, the PSR method failed to unwrap the phase of one cluster and misclassified 10. One swap was observed in areas affected by motion and was constricted to the affected area. Twenty swaps were observed surrounding susceptibility artifacts, none of which spread outside the artifact affected regions. The PSR method had fewer swaps when compared to commercial software. The PSR method can robustly produce water-fat separated whole-body images based on symmetric two-echo spoiled gradient echo images, under both ideal conditions and in the presence of common artifacts. Magn Reson Med 78:1208-1216, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Automated Robust Image Segmentation: Level Set Method Using Nonnegative Matrix Factorization with Application to Brain MRI.

PubMed

Dera, Dimah; Bouaynaya, Nidhal; Fathallah-Shaykh, Hassan M

2016-07-01

We address the problem of fully automated region discovery and robust image segmentation by devising a new deformable model based on the level set method (LSM) and the probabilistic nonnegative matrix factorization (NMF). We describe the use of NMF to calculate the number of distinct regions in the image and to derive the local distribution of the regions, which is incorporated into the energy functional of the LSM. The results demonstrate that our NMF-LSM method is superior to other approaches when applied to synthetic binary and gray-scale images and to clinical magnetic resonance images (MRI) of the human brain with and without a malignant brain tumor, glioblastoma multiforme. In particular, the NMF-LSM method is fully automated, highly accurate, less sensitive to the initial selection of the contour(s) or initial conditions, more robust to noise and model parameters, and able to detect as small distinct regions as desired. These advantages stem from the fact that the proposed method relies on histogram information instead of intensity values and does not introduce nuisance model parameters. These properties provide a general approach for automated robust region discovery and segmentation in heterogeneous images. Compared with the retrospective radiological diagnoses of two patients with non-enhancing grade 2 and 3 oligodendroglioma, the NMF-LSM detects earlier progression times and appears suitable for monitoring tumor response. The NMF-LSM method fills an important need of automated segmentation of clinical MRI.

Development and application of a robust N-glycan profiling method for heightened characterization of monoclonal antibodies and related glycoproteins.

PubMed

Shang, Tanya Q; Saati, Andrew; Toler, Kelly N; Mo, Jianming; Li, Heyi; Matlosz, Tonya; Lin, Xi; Schenk, Jennifer; Ng, Chee-Keng; Duffy, Toni; Porter, Thomas J; Rouse, Jason C

2014-07-01

A highly robust hydrophilic interaction liquid chromatography (HILIC) method that involves both fluorescence and mass spectrometric detection was developed for profiling and characterizing enzymatically released and 2-aminobenzamide (2-AB)-derivatized mAb N-glycans. Online HILIC/mass spectrometry (MS) with a quadrupole time-of-flight mass spectrometer provides accurate mass identifications of the separated, 2-AB-labeled N-glycans. The method features a high-resolution, low-shedding HILIC column with acetonitrile and water-based mobile phases containing trifluoroacetic acid (TFA) as a modifier. This column and solvent system ensures the combination of robust chromatographic performance and full compatibility and sensitivity with online MS in addition to the baseline separation of all typical mAb N-glycans. The use of TFA provided distinct advantages over conventional ammonium formate as a mobile phase additive, such as, optimal elution order for sialylated N-glycans, reproducible chromatographic profiles, and matching total ion current chromatograms, as well as minimal signal splitting, analyte adduction, and fragmentation during HILIC/MS, maximizing sensitivity for trace-level species. The robustness and selectivity of HILIC for N-glycan analyses allowed for method qualification. The method is suitable for bioprocess development activities, heightened characterization, and clinical drug substance release. Application of this HILIC/MS method to the detailed characterization of a marketed therapeutic mAb, Rituxan(®), is described. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association.

A Gradient Taguchi Method for Engineering Optimization

NASA Astrophysics Data System (ADS)

Hwang, Shun-Fa; Wu, Jen-Chih; He, Rong-Song

2017-10-01

To balance the robustness and the convergence speed of optimization, a novel hybrid algorithm consisting of Taguchi method and the steepest descent method is proposed in this work. Taguchi method using orthogonal arrays could quickly find the optimum combination of the levels of various factors, even when the number of level and/or factor is quite large. This algorithm is applied to the inverse determination of elastic constants of three composite plates by combining numerical method and vibration testing. For these problems, the proposed algorithm could find better elastic constants in less computation cost. Therefore, the proposed algorithm has nice robustness and fast convergence speed as compared to some hybrid genetic algorithms.

Multiple methods integration for structural mechanics analysis and design

NASA Technical Reports Server (NTRS)

Housner, J. M.; Aminpour, M. A.

1991-01-01

A new research area of multiple methods integration is proposed for joining diverse methods of structural mechanics analysis which interact with one another. Three categories of multiple methods are defined: those in which a physical interface are well defined; those in which a physical interface is not well-defined, but selected; and those in which the interface is a mathematical transformation. Two fundamental integration procedures are presented that can be extended to integrate various methods (e.g., finite elements, Rayleigh Ritz, Galerkin, and integral methods) with one another. Since the finite element method will likely be the major method to be integrated, its enhanced robustness under element distortion is also examined and a new robust shell element is demonstrated.

Experimental design methodologies in the optimization of chiral CE or CEC separations: an overview.

PubMed

Dejaegher, Bieke; Mangelings, Debby; Vander Heyden, Yvan

2013-01-01

In this chapter, an overview of experimental designs to develop chiral capillary electrophoresis (CE) and capillary electrochromatographic (CEC) methods is presented. Method development is generally divided into technique selection, method optimization, and method validation. In the method optimization part, often two phases can be distinguished, i.e., a screening and an optimization phase. In method validation, the method is evaluated on its fit for purpose. A validation item, also applying experimental designs, is robustness testing. In the screening phase and in robustness testing, screening designs are applied. During the optimization phase, response surface designs are used. The different design types and their application steps are discussed in this chapter and illustrated by examples of chiral CE and CEC methods.

Robust, optimal subsonic airfoil shapes

NASA Technical Reports Server (NTRS)

Rai, Man Mohan (Inventor)

2008-01-01

Method system, and product from application of the method, for design of a subsonic airfoil shape, beginning with an arbitrary initial airfoil shape and incorporating one or more constraints on the airfoil geometric parameters and flow characteristics. The resulting design is robust against variations in airfoil dimensions and local airfoil shape introduced in the airfoil manufacturing process. A perturbation procedure provides a class of airfoil shapes, beginning with an initial airfoil shape.

Sample manipulation and data assembly for robust microcrystal synchrotron crystallography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Guo, Gongrui; Fuchs, Martin R.; Shi, Wuxian

With the recent developments in microcrystal handling, synchrotron microdiffraction beamline instrumentation and data analysis, microcrystal crystallography with crystal sizes of less than 10 µm is appealing at synchrotrons. However, challenges remain in sample manipulation and data assembly for robust microcrystal synchrotron crystallography. Here, the development of micro-sized polyimide well-mounts for the manipulation of microcrystals of a few micrometres in size and the implementation of a robust data-analysis method for the assembly of rotational microdiffraction data sets from many microcrystals are described. Here, the method demonstrates that microcrystals may be routinely utilized for the acquisition and assembly of complete data setsmore » from synchrotron microdiffraction beamlines.« less

Sample manipulation and data assembly for robust microcrystal synchrotron crystallography

DOE PAGES

Guo, Gongrui; Fuchs, Martin R.; Shi, Wuxian; ...

2018-04-19

With the recent developments in microcrystal handling, synchrotron microdiffraction beamline instrumentation and data analysis, microcrystal crystallography with crystal sizes of less than 10 µm is appealing at synchrotrons. However, challenges remain in sample manipulation and data assembly for robust microcrystal synchrotron crystallography. Here, the development of micro-sized polyimide well-mounts for the manipulation of microcrystals of a few micrometres in size and the implementation of a robust data-analysis method for the assembly of rotational microdiffraction data sets from many microcrystals are described. Here, the method demonstrates that microcrystals may be routinely utilized for the acquisition and assembly of complete data setsmore » from synchrotron microdiffraction beamlines.« less

Early warning of climate tipping points from critical slowing down: comparing methods to improve robustness

PubMed Central

Lenton, T. M.; Livina, V. N.; Dakos, V.; Van Nes, E. H.; Scheffer, M.

2012-01-01

We address whether robust early warning signals can, in principle, be provided before a climate tipping point is reached, focusing on methods that seek to detect critical slowing down as a precursor of bifurcation. As a test bed, six previously analysed datasets are reconsidered, three palaeoclimate records approaching abrupt transitions at the end of the last ice age and three models of varying complexity forced through a collapse of the Atlantic thermohaline circulation. Approaches based on examining the lag-1 autocorrelation function or on detrended fluctuation analysis are applied together and compared. The effects of aggregating the data, detrending method, sliding window length and filtering bandwidth are examined. Robust indicators of critical slowing down are found prior to the abrupt warming event at the end of the Younger Dryas, but the indicators are less clear prior to the Bølling-Allerød warming, or glacial termination in Antarctica. Early warnings of thermohaline circulation collapse can be masked by inter-annual variability driven by atmospheric dynamics. However, rapidly decaying modes can be successfully filtered out by using a long bandwidth or by aggregating data. The two methods have complementary strengths and weaknesses and we recommend applying them together to improve the robustness of early warnings. PMID:22291229

Fast and Robust Registration of Multimodal Remote Sensing Images via Dense Orientated Gradient Feature

NASA Astrophysics Data System (ADS)

Ye, Y.

2017-09-01

This paper presents a fast and robust method for the registration of multimodal remote sensing data (e.g., optical, LiDAR, SAR and map). The proposed method is based on the hypothesis that structural similarity between images is preserved across different modalities. In the definition of the proposed method, we first develop a pixel-wise feature descriptor named Dense Orientated Gradient Histogram (DOGH), which can be computed effectively at every pixel and is robust to non-linear intensity differences between images. Then a fast similarity metric based on DOGH is built in frequency domain using the Fast Fourier Transform (FFT) technique. Finally, a template matching scheme is applied to detect tie points between images. Experimental results on different types of multimodal remote sensing images show that the proposed similarity metric has the superior matching performance and computational efficiency than the state-of-the-art methods. Moreover, based on the proposed similarity metric, we also design a fast and robust automatic registration system for multimodal images. This system has been evaluated using a pair of very large SAR and optical images (more than 20000 × 20000 pixels). Experimental results show that our system outperforms the two popular commercial software systems (i.e. ENVI and ERDAS) in both registration accuracy and computational efficiency.

Robust Path Planning and Feedback Design Under Stochastic Uncertainty

NASA Technical Reports Server (NTRS)

Blackmore, Lars

2008-01-01

Autonomous vehicles require optimal path planning algorithms to achieve mission goals while avoiding obstacles and being robust to uncertainties. The uncertainties arise from exogenous disturbances, modeling errors, and sensor noise, which can be characterized via stochastic models. Previous work defined a notion of robustness in a stochastic setting by using the concept of chance constraints. This requires that mission constraint violation can occur with a probability less than a prescribed value.In this paper we describe a novel method for optimal chance constrained path planning with feedback design. The approach optimizes both the reference trajectory to be followed and the feedback controller used to reject uncertainty. Our method extends recent results in constrained control synthesis based on convex optimization to solve control problems with nonconvex constraints. This extension is essential for path planning problems, which inherently have nonconvex obstacle avoidance constraints. Unlike previous approaches to chance constrained path planning, the new approach optimizes the feedback gain as wellas the reference trajectory.The key idea is to couple a fast, nonconvex solver that does not take into account uncertainty, with existing robust approaches that apply only to convex feasible regions. By alternating between robust and nonrobust solutions, the new algorithm guarantees convergence to a global optimum. We apply the new method to an unmanned aircraft and show simulation results that demonstrate the efficacy of the approach.

Using spatial information about recurrence risk for robust optimization of dose-painting prescription functions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bender, Edward T.

Purpose: To develop a robust method for deriving dose-painting prescription functions using spatial information about the risk for disease recurrence. Methods: Spatial distributions of radiobiological model parameters are derived from distributions of recurrence risk after uniform irradiation. These model parameters are then used to derive optimal dose-painting prescription functions given a constant mean biologically effective dose. Results: An estimate for the optimal dose distribution can be derived based on spatial information about recurrence risk. Dose painting based on imaging markers that are moderately or poorly correlated with recurrence risk are predicted to potentially result in inferior disease control when comparedmore » the same mean biologically effective dose delivered uniformly. A robust optimization approach may partially mitigate this issue. Conclusions: The methods described here can be used to derive an estimate for a robust, patient-specific prescription function for use in dose painting. Two approximate scaling relationships were observed: First, the optimal choice for the maximum dose differential when using either a linear or two-compartment prescription function is proportional to R, where R is the Pearson correlation coefficient between a given imaging marker and recurrence risk after uniform irradiation. Second, the predicted maximum possible gain in tumor control probability for any robust optimization technique is nearly proportional to the square of R.« less

Applying robust variant of Principal Component Analysis as a damage detector in the presence of outliers

NASA Astrophysics Data System (ADS)

Gharibnezhad, Fahit; Mujica, Luis E.; Rodellar, José

2015-01-01

Using Principal Component Analysis (PCA) for Structural Health Monitoring (SHM) has received considerable attention over the past few years. PCA has been used not only as a direct method to identify, classify and localize damages but also as a significant primary step for other methods. Despite several positive specifications that PCA conveys, it is very sensitive to outliers. Outliers are anomalous observations that can affect the variance and the covariance as vital parts of PCA method. Therefore, the results based on PCA in the presence of outliers are not fully satisfactory. As a main contribution, this work suggests the use of robust variant of PCA not sensitive to outliers, as an effective way to deal with this problem in SHM field. In addition, the robust PCA is compared with the classical PCA in the sense of detecting probable damages. The comparison between the results shows that robust PCA can distinguish the damages much better than using classical one, and even in many cases allows the detection where classic PCA is not able to discern between damaged and non-damaged structures. Moreover, different types of robust PCA are compared with each other as well as with classical counterpart in the term of damage detection. All the results are obtained through experiments with an aircraft turbine blade using piezoelectric transducers as sensors and actuators and adding simulated damages.

Integrated direct/indirect adaptive robust motion trajectory tracking control of pneumatic cylinders

NASA Astrophysics Data System (ADS)

Meng, Deyuan; Tao, Guoliang; Zhu, Xiaocong

2013-09-01

This paper studies the precision motion trajectory tracking control of a pneumatic cylinder driven by a proportional-directional control valve. An integrated direct/indirect adaptive robust controller is proposed. The controller employs a physical model based indirect-type parameter estimation to obtain reliable estimates of unknown model parameters, and utilises a robust control method with dynamic compensation type fast adaptation to attenuate the effects of parameter estimation errors, unmodelled dynamics and disturbances. Due to the use of projection mapping, the robust control law and the parameter adaption algorithm can be designed separately. Since the system model uncertainties are unmatched, the recursive backstepping technology is adopted to design the robust control law. Extensive comparative experimental results are presented to illustrate the effectiveness of the proposed controller and its performance robustness to parameter variations and sudden disturbances.

An adaptive proper orthogonal decomposition method for model order reduction of multi-disc rotor system

NASA Astrophysics Data System (ADS)

Jin, Yulin; Lu, Kuan; Hou, Lei; Chen, Yushu

2017-12-01

The proper orthogonal decomposition (POD) method is a main and efficient tool for order reduction of high-dimensional complex systems in many research fields. However, the robustness problem of this method is always unsolved, although there are some modified POD methods which were proposed to solve this problem. In this paper, a new adaptive POD method called the interpolation Grassmann manifold (IGM) method is proposed to address the weakness of local property of the interpolation tangent-space of Grassmann manifold (ITGM) method in a wider parametric region. This method is demonstrated here by a nonlinear rotor system of 33-degrees of freedom (DOFs) with a pair of liquid-film bearings and a pedestal looseness fault. The motion region of the rotor system is divided into two parts: simple motion region and complex motion region. The adaptive POD method is compared with the ITGM method for the large and small spans of parameter in the two parametric regions to present the advantage of this method and disadvantage of the ITGM method. The comparisons of the responses are applied to verify the accuracy and robustness of the adaptive POD method, as well as the computational efficiency is also analyzed. As a result, the new adaptive POD method has a strong robustness and high computational efficiency and accuracy in a wide scope of parameter.

MOCC: A Fast and Robust Correlation-Based Method for Interest Point Matching under Large Scale Changes

NASA Astrophysics Data System (ADS)

Zhao, Feng; Huang, Qingming; Wang, Hao; Gao, Wen

2010-12-01

Similarity measures based on correlation have been used extensively for matching tasks. However, traditional correlation-based image matching methods are sensitive to rotation and scale changes. This paper presents a fast correlation-based method for matching two images with large rotation and significant scale changes. Multiscale oriented corner correlation (MOCC) is used to evaluate the degree of similarity between the feature points. The method is rotation invariant and capable of matching image pairs with scale changes up to a factor of 7. Moreover, MOCC is much faster in comparison with the state-of-the-art matching methods. Experimental results on real images show the robustness and effectiveness of the proposed method.

Defining robustness protocols: a method to include and evaluate robustness in clinical plans

NASA Astrophysics Data System (ADS)

McGowan, S. E.; Albertini, F.; Thomas, S. J.; Lomax, A. J.

2015-04-01

We aim to define a site-specific robustness protocol to be used during the clinical plan evaluation process. Plan robustness of 16 skull base IMPT plans to systematic range and random set-up errors have been retrospectively and systematically analysed. This was determined by calculating the error-bar dose distribution (ebDD) for all the plans and by defining some metrics used to define protocols aiding the plan assessment. Additionally, an example of how to clinically use the defined robustness database is given whereby a plan with sub-optimal brainstem robustness was identified. The advantage of using different beam arrangements to improve the plan robustness was analysed. Using the ebDD it was found range errors had a smaller effect on dose distribution than the corresponding set-up error in a single fraction, and that organs at risk were most robust to the range errors, whereas the target was more robust to set-up errors. A database was created to aid planners in terms of plan robustness aims in these volumes. This resulted in the definition of site-specific robustness protocols. The use of robustness constraints allowed for the identification of a specific patient that may have benefited from a treatment of greater individuality. A new beam arrangement showed to be preferential when balancing conformality and robustness for this case. The ebDD and error-bar volume histogram proved effective in analysing plan robustness. The process of retrospective analysis could be used to establish site-specific robustness planning protocols in proton therapy. These protocols allow the planner to determine plans that, although delivering a dosimetrically adequate dose distribution, have resulted in sub-optimal robustness to these uncertainties. For these cases the use of different beam start conditions may improve the plan robustness to set-up and range uncertainties.

Robust design optimization method for centrifugal impellers under surface roughness uncertainties due to blade fouling

NASA Astrophysics Data System (ADS)

Ju, Yaping; Zhang, Chuhua

2016-03-01

Blade fouling has been proved to be a great threat to compressor performance in operating stage. The current researches on fouling-induced performance degradations of centrifugal compressors are based mainly on simplified roughness models without taking into account the realistic factors such as spatial non-uniformity and randomness of the fouling-induced surface roughness. Moreover, little attention has been paid to the robust design optimization of centrifugal compressor impellers with considerations of blade fouling. In this paper, a multi-objective robust design optimization method is developed for centrifugal impellers under surface roughness uncertainties due to blade fouling. A three-dimensional surface roughness map is proposed to describe the nonuniformity and randomness of realistic fouling accumulations on blades. To lower computational cost in robust design optimization, the support vector regression (SVR) metamodel is combined with the Monte Carlo simulation (MCS) method to conduct the uncertainty analysis of fouled impeller performance. The analyzed results show that the critical fouled region associated with impeller performance degradations lies at the leading edge of blade tip. The SVR metamodel has been proved to be an efficient and accurate means in the detection of impeller performance variations caused by roughness uncertainties. After design optimization, the robust optimal design is found to be more efficient and less sensitive to fouling uncertainties while maintaining good impeller performance in the clean condition. This research proposes a systematic design optimization method for centrifugal compressors with considerations of blade fouling, providing a practical guidance to the design of advanced centrifugal compressors.

Least median of squares and iteratively re-weighted least squares as robust linear regression methods for fluorimetric determination of α-lipoic acid in capsules in ideal and non-ideal cases of linearity.

PubMed

Korany, Mohamed A; Gazy, Azza A; Khamis, Essam F; Ragab, Marwa A A; Kamal, Miranda F

2018-06-01

This study outlines two robust regression approaches, namely least median of squares (LMS) and iteratively re-weighted least squares (IRLS) to investigate their application in instrument analysis of nutraceuticals (that is, fluorescence quenching of merbromin reagent upon lipoic acid addition). These robust regression methods were used to calculate calibration data from the fluorescence quenching reaction (∆F and F-ratio) under ideal or non-ideal linearity conditions. For each condition, data were treated using three regression fittings: Ordinary Least Squares (OLS), LMS and IRLS. Assessment of linearity, limits of detection (LOD) and quantitation (LOQ), accuracy and precision were carefully studied for each condition. LMS and IRLS regression line fittings showed significant improvement in correlation coefficients and all regression parameters for both methods and both conditions. In the ideal linearity condition, the intercept and slope changed insignificantly, but a dramatic change was observed for the non-ideal condition and linearity intercept. Under both linearity conditions, LOD and LOQ values after the robust regression line fitting of data were lower than those obtained before data treatment. The results obtained after statistical treatment indicated that the linearity ranges for drug determination could be expanded to lower limits of quantitation by enhancing the regression equation parameters after data treatment. Analysis results for lipoic acid in capsules, using both fluorimetric methods, treated by parametric OLS and after treatment by robust LMS and IRLS were compared for both linearity conditions. Copyright © 2018 John Wiley & Sons, Ltd.

Combination of a Copper-Ion Selective Electrode and Fluorometric Titration for the Determination of Copper(II) Ion Conditional Stability Constants of Humic Substances.

PubMed

Chen, Juan; Chen, Hao; Zhang, Xing-wen; Lei, Kun; Kenny, Jonathan E

2015-11-01

A fluorescence quenching model using copper(II) ion (Cu(2+)) ion selective electrode (Cu-ISE) is developed. It uses parallel factor analysis (PARAFAC) to model fluorescence excitation-emission matrices (EEMs) of humic acid (HA) samples titrated with Cu(2+) to resolve fluorescence response of fluorescent components to Cu(2+) titration. Meanwhile, Cu-ISE is employed to monitor free Cu(2+) concentration ([Cu]) at each titration step. The fluorescence response of each component is fit individually to a nonlinear function of [Cu] to find the Cu(2+) conditional stability constant for that component. This approach differs from other fluorescence quenching models, including the most up-to-date multi-response model that has a problematic assumption on Cu(2+) speciation, i.e., an assumption that total Cu(2+) present in samples is a sum of [Cu] and those bound by fluorescent components without taking into consideration the contribution of non-fluorescent organic ligands and inorganic ligands to speciation of Cu(2+). This paper employs the new approach to investigate Cu(2+) binding by Pahokee peat HA (PPHA) at pH values of 6.0, 7.0, and 8.0 buffered by phosphate or without buffer. Two fluorescent components (C1 and C2) were identified by PARAFAC. For the new quenching model, the conditional stability constants (logK1 and logK2) of the two components all increased with increasing pH. In buffered solutions, the new quenching model reported logK1 = 7.11, 7.89, 8.04 for C1 and logK2 = 7.04, 7.64, 8.11 for C2 at pH 6.0, 7.0, and 8.0, respectively, nearly two log units higher than the results of the multi-response model. Without buffer, logK1 and logK2 decreased but were still high (>7) at pH 8.0 (logK1 = 7.54, logK2 = 7.95), and all the values were at least 0.5 log unit higher than those (4.83 ~ 5.55) of the multi-response model. These observations indicate that the new quenching model is more intrinsically sensitive than the multi-response model in revealing strong fluorescent binding sites of PPHA in different experimental conditions. The new model was validated by testing it with a mixture of two fluorescing Cu(2+) chelating organic compounds, i.e., l-tryptophan and salicylic acid mixed with one non-fluorescent binding compound oxalic acid titrated with Cu(2+) at pH 5.0.

Influence of land use and land cover on the spatial variability of dissolved organic matter in multiple aquatic environments.

PubMed

Singh, Shatrughan; Dash, Padmanava; Silwal, Saurav; Feng, Gary; Adeli, Ardeshir; Moorhead, Robert J

2017-06-01

Water quality of lakes, estuaries, and coastal areas serves as an indicator of the overall health of aquatic ecosystems as well as the health of the terrestrial ecosystem that drains to the water body. Land use and land cover plays not only a significant role in controlling the quantity of the exported dissolved organic matter (DOM) but also influences the quality of DOM via various biogeochemical and biodegradation processes. We examined the characteristics and spatial distribution of DOM in five major lakes, in an estuary, and in the coastal waters of the Mississippi, USA, and investigated the influence of the land use and land cover of their watersheds on the DOM composition. We employed absorption and fluorescence spectroscopy including excitation-emission matrix (EEM) combined with parallel factor (PARAFAC) analysis modeling techniques to determine optical properties of DOM and its characteristics in this study. We developed a site-specific PARAFAC model to evaluate DOM characteristics resulting in five diverse DOM compositions that included two terrestrial humic-like (C1 and C3), two microbial humic-like (C2 and C5), and one protein-like (C4) DOM. Our results showed elevated fluorescence levels of microbial humic-like or protein-like DOM in the lakes and coastal waters, while the estuarine waters showed relatively high fluorescence levels of terrestrial humic-like DOM. The results also showed that percent forest and wetland coverage explained 68 and 82% variability, respectively, in terrestrial humic-like DOM exports, while 87% variability in microbially derived humiclike DOM was explained by percent agricultural lands. Strong correlations between microbial humic-like DOM and fluorescence-derived DOM indices such as biological index (BIX) and fluorescence index (FI) indicated autochthonous characteristics in the lakes, while the estuary showed largely allochthonous DOM of terrestrial origin. We also observed higher concentrations of total dissolved phosphorous (TDP) and ammonium nitrogen (NH 4 -N) in coastal waters potentially due to photodegradation of refractory DOM derived from the sediment-bound organic matter in the coastal wetlands. This study highlights the relationships between the DOM compositions in the water and the land use and land cover in the watershed. The spatial variability of DOM in three different types of aquatic environments enhances the understanding of the role of land use and land cover in carbon cycling through export of organic matter to the aquatic ecosystems..

Application of EEM fluorescence spectroscopy in understanding of the "LIGA" phenomenon in the Bay of Biscay (France)

NASA Astrophysics Data System (ADS)

Parot, Jérémie; Susperregui, Nicolas; Rouaud, Vanessa; Dubois, Laurent; Anglade, Nathalie; Parlanti, Edith

2014-05-01

Marine mucilage is present in all oceans over the world, and in particular in the Mediterranean Sea and in the Pacific Ocean. Surface water warming and hydrodynamic processes can favor the coalescence of marine mucilage, large marine aggregates representing an ephemeral and extreme habitat for biota. DOM is a heterogeneous, complex mixture of compounds, including extracellular polymeric substances (EPS), with wide ranging chemical properties and it is well known to interact with pollutants and to affect their transport and their fate in aquatic environment. The LIGA French research program focuses on tracing colloidal dissolved organic matter (DOM) sources and cycling in the Bay of Biscay (South Western French coast). This ephemeral phenomenon (called "LIGA" in the South West of France) has been observed more than 750 times since 2010. It presents a great ecological impact on marine ecosystems and has been shown to be concomitant with the development of pathogen organisms. A one-year intensive survey of fluorescent DOM was undertaken. From April 2013 until May 2014, water samples were monthly collected from the Adour River (main fresh water inputs) and from 2 sites in the Bay of Biscay at 3 depths of the water column (surface water, at the maximum of chlorophyll-a, and deep water). Moreover, intensified samplings took place from the appearance of the phenomenon twice a week during 4 weeks. UV/visible absorbance and excitation emission matrix (EEM) fluorescence spectroscopy combined with PARAFAC and PCA analyses have been used to characterize colloidal DOM in the Bay of Biscay in order to estimate DOM sources as well as spatial and temporal variability of DOM properties. The preliminary results, obtained for about 70 samples of this survey, have already highlighted spatial and temporal variations of DOM optical properties and a peculiar fluorescent component (exc300nm/em338nm) was detected while the LIGA phenomenon arises. The appearance of this specific fluorescence signal seems to be correlated with high freshwater and terrestrial DOM inputs combined with physical forcing (flows, swell) as well as a rise in temperature and sunshine. This work already allowed us to identify different sources of colloidal DOM in the Bay of Biscay and highlighted a specific fingerprint of the LIGA phenomenon. The combination of EEM fluorescence spectroscopy with PARAFAC and PCA analyses appears thus to be a very powerful tool for the long term monitoring of such a phenomenon and would be very useful for a better understanding of the biogeochemical processes in marine environments and of the marine colloidal DOM ecodynamics.

Enhancing the chemical selectivity in discovery-based analysis with tandem ionization time-of-flight mass spectrometry detection for comprehensive two-dimensional gas chromatography.

PubMed

Freye, Chris E; Moore, Nicholas R; Synovec, Robert E

2018-02-16

The complementary information provided by tandem ionization time-of-flight mass spectrometry (TI-TOFMS) is investigated for comparative discovery-based analysis, when coupled with comprehensive two-dimensional gas chromatography (GC × GC). The TI conditions implemented were a hard ionization energy (70 eV) concurrently collected with a soft ionization energy (14 eV). Tile-based Fisher ratio (F-ratio) analysis is used to analyze diesel fuel spiked with twelve analytes at a nominal concentration of 50 ppm. F-ratio analysis is a supervised discovery-based technique that compares two different sample classes, in this case spiked and unspiked diesel, to reduce the complex GC × GC-TI-TOFMS data into a hit list of class distinguishing analyte features. Hit lists of the 70 eV and 14 eV data sets, and the single hit list produced when the two data sets are fused together, are all investigated. For the 70 eV hit list, eleven of the twelve analytes were found in the top thirteen hits. For the 14 eV hit list, nine of the twelve analytes were found in the top nine hits, with the other three analytes either not found or well down the hit list. As expected, the F-ratios per m/z used to calculate each average F-ratio per hit were generally smaller fragment ions for the 70 eV data set, while the larger fragment ions were emphasized in the 14 eV data set, supporting the notion that complementary information was provided. The discovery rate was improved when F-ratio analysis was performed on the fused data sets resulted in eleven of the twelve analytes being at the top of the single hit list. Using PARAFAC, analytes that were "discovered" were deconvoluted in order to obtain their identification via match values (MV). Location of the analytes and the "F-ratio spectra" obtained from F-ratio analysis were used to guide the deconvolution. Eight of the twelve analytes where successfully deconvoluted and identified using the in-house library for the 70 eV data set. PARAFAC deconvolution of the two separate data sets provided increased confidence in identification of "discovered" analytes. Herein, we explore the limit of analyte discovery and limit of analyte identification, and demonstrate a general workflow for the investigation of key chemical features in complex samples. Copyright © 2018 Elsevier B.V. All rights reserved.

Characterization of extractable soil organic matter pools from African Dark Earths (AfDE): A case study in historical biochar and organic waste amendments

NASA Astrophysics Data System (ADS)

Fujiu, Manna; Plante, Alain; Ohno, Tsutomu; Solomon, Dawit; Lehmann, Johannes; Fraser, James; Leach, Melissa; Fairhead, James

2014-05-01

Anthropogenic Dark Earths are soils generated through long-term human inputs of organic and pyrogenic materials. These soils were originally discovered in the Amazon, and have since been found in Australia and in this case in Africa. African Dark Earths (AfDE) are black, highly fertile and carbon-rich soils that were formed from the original highly-weathered infertile yellowish to red Oxisols and Ultisols through an extant but hitherto overlooked climate-smart sustainable soil management system that has long been an important feature of the indigenous West African agricultural repertoire. Studies have demonstrated that ADE soils in general have significantly different organic matter properties compared to adjacent non-DE soils, largely attributable to the presence of high concentrations of ash-derived carbon. Quantification and characterization of bulk soil organic matter of several (n=11) AfDE and non-AfDE pairs of surface (0-15 cm) soils using thermal analysis techniques (TG-DSC-EGA) confirmed substantial differences in SOM composition and the presence of pyrogenic C. Such pyrogenic organic matter is generally considered recalcitrant or relatively stable, but the goal of the current study was to characterize the presumably labile, more rapidly cycling, pools of C in AfDEs through the characterization of hot water- and pyrophosphate-extractable fractions, referred to as HWEOC and PyroC respectively. Extracts were analyzed for carbon content, as well as composition using fluorescence (EEM/PARAFAC) and high resolution mass spectrometry (FTICR-MS). The amount of extractable C as a proportion of total soil C was relatively low: less than ~0.8% for HWEOC and 2.8% for PyroC. The proportion of HWEOC did not differ (P = 0.18, paired t-test) between the AfDE and the non-AfDE soils, while the proportions of PyroC were significantly larger (P = 0.001) in the AfDE soils compared to the non-AfDE soils. Preliminary analysis of the EEM/PARAFAC data suggests that AfDE samples had a greater fraction of their DOM that was more humic-like than the paired non-AfDE samples, though differences were small. Similarly, FTICR-MS analysis of hot water extracts suggests that differences among the three sites analyzed were larger than between the paired AfDE and non-AfDE extracts. Overall, in spite of substantial differences in the composition of bulk SOM, the extractable fractions appear to be relatively similar between the AfDE and non-AfDE soils.

Review of LMIs, Interior Point Methods, Complexity Theory, and Robustness Analysis

NASA Technical Reports Server (NTRS)

Mesbahi, M.

1996-01-01

From end of intro: ...We would like to show that for certain problems in systems and control theory, there exist algorithms for which corresponding (xi) can be viewed as a certain measure of robustness, e.g., stability margin.

Influence of robust optimization in intensity-modulated proton therapy with different dose delivery techniques

PubMed Central

Liu, Wei; Li, Yupeng; Li, Xiaoqiang; Cao, Wenhua; Zhang, Xiaodong

2012-01-01

Purpose: The distal edge tracking (DET) technique in intensity-modulated proton therapy (IMPT) allows for high energy efficiency, fast and simple delivery, and simple inverse treatment planning; however, it is highly sensitive to uncertainties. In this study, the authors explored the application of DET in IMPT (IMPT-DET) and conducted robust optimization of IMPT-DET to see if the planning technique’s sensitivity to uncertainties was reduced. They also compared conventional and robust optimization of IMPT-DET with three-dimensional IMPT (IMPT-3D) to gain understanding about how plan robustness is achieved. Methods: They compared the robustness of IMPT-DET and IMPT-3D plans to uncertainties by analyzing plans created for a typical prostate cancer case and a base of skull (BOS) cancer case (using data for patients who had undergone proton therapy at our institution). Spots with the highest and second highest energy layers were chosen so that the Bragg peak would be at the distal edge of the targets in IMPT-DET using 36 equally spaced angle beams; in IMPT-3D, 3 beams with angles chosen by a beam angle optimization algorithm were planned. Dose contributions for a number of range and setup uncertainties were calculated, and a worst-case robust optimization was performed. A robust quantification technique was used to evaluate the plans’ sensitivity to uncertainties. Results: With no uncertainties considered, the DET is less robust to uncertainties than is the 3D method but offers better normal tissue protection. With robust optimization to account for range and setup uncertainties, robust optimization can improve the robustness of IMPT plans to uncertainties; however, our findings show the extent of improvement varies. Conclusions: IMPT’s sensitivity to uncertainties can be improved by using robust optimization. They found two possible mechanisms that made improvements possible: (1) a localized single-field uniform dose distribution (LSFUD) mechanism, in which the optimization algorithm attempts to produce a single-field uniform dose distribution while minimizing the patching field as much as possible; and (2) perturbed dose distribution, which follows the change in anatomical geometry. Multiple-instance optimization has more knowledge of the influence matrices; this greater knowledge improves IMPT plans’ ability to retain robustness despite the presence of uncertainties. PMID:22755694

Atlas-based liver segmentation and hepatic fat-fraction assessment for clinical trials.

PubMed

Yan, Zhennan; Zhang, Shaoting; Tan, Chaowei; Qin, Hongxing; Belaroussi, Boubakeur; Yu, Hui Jing; Miller, Colin; Metaxas, Dimitris N

2015-04-01

Automated assessment of hepatic fat-fraction is clinically important. A robust and precise segmentation would enable accurate, objective and consistent measurement of hepatic fat-fraction for disease quantification, therapy monitoring and drug development. However, segmenting the liver in clinical trials is a challenging task due to the variability of liver anatomy as well as the diverse sources the images were acquired from. In this paper, we propose an automated and robust framework for liver segmentation and assessment. It uses single statistical atlas registration to initialize a robust deformable model to obtain fine segmentation. Fat-fraction map is computed by using chemical shift based method in the delineated region of liver. This proposed method is validated on 14 abdominal magnetic resonance (MR) volumetric scans. The qualitative and quantitative comparisons show that our proposed method can achieve better segmentation accuracy with less variance comparing with two other atlas-based methods. Experimental results demonstrate the promises of our assessment framework. Copyright © 2014 Elsevier Ltd. All rights reserved.

Towards an Automated Acoustic Detection System for Free Ranging Elephants.

PubMed

Zeppelzauer, Matthias; Hensman, Sean; Stoeger, Angela S

The human-elephant conflict is one of the most serious conservation problems in Asia and Africa today. The involuntary confrontation of humans and elephants claims the lives of many animals and humans every year. A promising approach to alleviate this conflict is the development of an acoustic early warning system. Such a system requires the robust automated detection of elephant vocalizations under unconstrained field conditions. Today, no system exists that fulfills these requirements. In this paper, we present a method for the automated detection of elephant vocalizations that is robust to the diverse noise sources present in the field. We evaluate the method on a dataset recorded under natural field conditions to simulate a real-world scenario. The proposed method outperformed existing approaches and robustly and accurately detected elephants. It thus can form the basis for a future automated early warning system for elephants. Furthermore, the method may be a useful tool for scientists in bioacoustics for the study of wildlife recordings.

LOCAL ORTHOGONAL CUTTING METHOD FOR COMPUTING MEDIAL CURVES AND ITS BIOMEDICAL APPLICATIONS

PubMed Central

Einstein, Daniel R.; Dyedov, Vladimir

2010-01-01

Medial curves have a wide range of applications in geometric modeling and analysis (such as shape matching) and biomedical engineering (such as morphometry and computer assisted surgery). The computation of medial curves poses significant challenges, both in terms of theoretical analysis and practical efficiency and reliability. In this paper, we propose a definition and analysis of medial curves and also describe an efficient and robust method called local orthogonal cutting (LOC) for computing medial curves. Our approach is based on three key concepts: a local orthogonal decomposition of objects into substructures, a differential geometry concept called the interior center of curvature (ICC), and integrated stability and consistency tests. These concepts lend themselves to robust numerical techniques and result in an algorithm that is efficient and noise resistant. We illustrate the effectiveness and robustness of our approach with some highly complex, large-scale, noisy biomedical geometries derived from medical images, including lung airways and blood vessels. We also present comparisons of our method with some existing methods. PMID:20628546

Robust estimation of partially linear models for longitudinal data with dropouts and measurement error.

PubMed

Qin, Guoyou; Zhang, Jiajia; Zhu, Zhongyi; Fung, Wing

2016-12-20

Outliers, measurement error, and missing data are commonly seen in longitudinal data because of its data collection process. However, no method can address all three of these issues simultaneously. This paper focuses on the robust estimation of partially linear models for longitudinal data with dropouts and measurement error. A new robust estimating equation, simultaneously tackling outliers, measurement error, and missingness, is proposed. The asymptotic properties of the proposed estimator are established under some regularity conditions. The proposed method is easy to implement in practice by utilizing the existing standard generalized estimating equations algorithms. The comprehensive simulation studies show the strength of the proposed method in dealing with longitudinal data with all three features. Finally, the proposed method is applied to data from the Lifestyle Education for Activity and Nutrition study and confirms the effectiveness of the intervention in producing weight loss at month 9. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Methods for compressible multiphase flows and their applications

NASA Astrophysics Data System (ADS)

Kim, H.; Choe, Y.; Kim, H.; Min, D.; Kim, C.

2018-06-01

This paper presents an efficient and robust numerical framework to deal with multiphase real-fluid flows and their broad spectrum of engineering applications. A homogeneous mixture model incorporated with a real-fluid equation of state and a phase change model is considered to calculate complex multiphase problems. As robust and accurate numerical methods to handle multiphase shocks and phase interfaces over a wide range of flow speeds, the AUSMPW+_N and RoeM_N schemes with a system preconditioning method are presented. These methods are assessed by extensive validation problems with various types of equation of state and phase change models. Representative realistic multiphase phenomena, including the flow inside a thermal vapor compressor, pressurization in a cryogenic tank, and unsteady cavitating flow around a wedge, are then investigated as application problems. With appropriate physical modeling followed by robust and accurate numerical treatments, compressible multiphase flow physics such as phase changes, shock discontinuities, and their interactions are well captured, confirming the suitability of the proposed numerical framework to wide engineering applications.

Doubly robust estimation of generalized partial linear models for longitudinal data with dropouts.

PubMed

Lin, Huiming; Fu, Bo; Qin, Guoyou; Zhu, Zhongyi

2017-12-01

We develop a doubly robust estimation of generalized partial linear models for longitudinal data with dropouts. Our method extends the highly efficient aggregate unbiased estimating function approach proposed in Qu et al. (2010) to a doubly robust one in the sense that under missing at random (MAR), our estimator is consistent when either the linear conditional mean condition is satisfied or a model for the dropout process is correctly specified. We begin with a generalized linear model for the marginal mean, and then move forward to a generalized partial linear model, allowing for nonparametric covariate effect by using the regression spline smoothing approximation. We establish the asymptotic theory for the proposed method and use simulation studies to compare its finite sample performance with that of Qu's method, the complete-case generalized estimating equation (GEE) and the inverse-probability weighted GEE. The proposed method is finally illustrated using data from a longitudinal cohort study. © 2017, The International Biometric Society.

H∞ robust fault-tolerant controller design for an autonomous underwater vehicle's navigation control system

NASA Astrophysics Data System (ADS)

Cheng, Xiang-Qin; Qu, Jing-Yuan; Yan, Zhe-Ping; Bian, Xin-Qian

2010-03-01

In order to improve the security and reliability for autonomous underwater vehicle (AUV) navigation, an H∞ robust fault-tolerant controller was designed after analyzing variations in state-feedback gain. Operating conditions and the design method were then analyzed so that the control problem could be expressed as a mathematical optimization problem. This permitted the use of linear matrix inequalities (LMI) to solve for the H∞ controller for the system. When considering different actuator failures, these conditions were then also mathematically expressed, allowing the H∞ robust controller to solve for these events and thus be fault-tolerant. Finally, simulation results showed that the H∞ robust fault-tolerant controller could provide precise AUV navigation control with strong robustness.

Reconfigurable Flight Control Design using a Robust Servo LQR and Radial Basis Function Neural Networks

NASA Technical Reports Server (NTRS)

Burken, John J.

2005-01-01

This viewgraph presentation reviews the use of a Robust Servo Linear Quadratic Regulator (LQR) and a Radial Basis Function (RBF) Neural Network in reconfigurable flight control designs in adaptation to a aircraft part failure. The method uses a robust LQR servomechanism design with model Reference adaptive control, and RBF neural networks. During the failure the LQR servomechanism behaved well, and using the neural networks improved the tracking.

Robust Hybrid Finite Element Methods for Antennas and Microwave Circuits

NASA Technical Reports Server (NTRS)

Gong, J.; Volakis, John L.

1996-01-01

One of the primary goals in this dissertation is concerned with the development of robust hybrid finite element-boundary integral (FE-BI) techniques for modeling and design of conformal antennas of arbitrary shape. Both the finite element and integral equation methods will be first overviewed in this chapter with an emphasis on recently developed hybrid FE-BI methodologies for antennas, microwave and millimeter wave applications. The structure of the dissertation is then outlined. We conclude the chapter with discussions of certain fundamental concepts and methods in electromagnetics, which are important to this study.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tripathi, Ashish; McNulty, Ian; Munson, Todd

We propose a new approach to robustly retrieve the exit wave of an extended sample from its coherent diffraction pattern by exploiting sparsity of the sample's edges. This approach enables imaging of an extended sample with a single view, without ptychography. We introduce nonlinear optimization methods that promote sparsity, and we derive update rules to robustly recover the sample's exit wave. We test these methods on simulated samples by varying the sparsity of the edge-detected representation of the exit wave. Finally, our tests illustrate the strengths and limitations of the proposed method in imaging extended samples.

Advanced scanning probe lithography.

PubMed

Garcia, Ricardo; Knoll, Armin W; Riedo, Elisa

2014-08-01

The nanoscale control afforded by scanning probe microscopes has prompted the development of a wide variety of scanning-probe-based patterning methods. Some of these methods have demonstrated a high degree of robustness and patterning capabilities that are unmatched by other lithographic techniques. However, the limited throughput of scanning probe lithography has prevented its exploitation in technological applications. Here, we review the fundamentals of scanning probe lithography and its use in materials science and nanotechnology. We focus on robust methods, such as those based on thermal effects, chemical reactions and voltage-induced processes, that demonstrate a potential for applications.

A robust direct-integration method for rotorcraft maneuver and periodic response

NASA Technical Reports Server (NTRS)

Panda, Brahmananda

1992-01-01

The Newmark-Beta method and the Newton-Raphson iteration scheme are combined to develop a direct-integration method for evaluating the maneuver and periodic-response expressions for rotorcraft. The method requires the generation of Jacobians and includes higher derivatives in the formulation of the geometric stiffness matrix to enhance the convergence of the system. The method leads to effective convergence with nonlinear structural dynamics and aerodynamic terms. Singularities in the matrices can be addressed with the method as they arise from a Lagrange multiplier approach for coupling equations with nonlinear constraints. The method is also shown to be general enough to handle singularities from quasisteady control-system models. The method is shown to be more general and robust than the similar 2GCHAS method for analyzing rotorcraft dynamics.

Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems

PubMed Central

Rodriguez-Fernandez, Maria; Egea, Jose A; Banga, Julio R

2006-01-01

Background We consider the problem of parameter estimation (model calibration) in nonlinear dynamic models of biological systems. Due to the frequent ill-conditioning and multi-modality of many of these problems, traditional local methods usually fail (unless initialized with very good guesses of the parameter vector). In order to surmount these difficulties, global optimization (GO) methods have been suggested as robust alternatives. Currently, deterministic GO methods can not solve problems of realistic size within this class in reasonable computation times. In contrast, certain types of stochastic GO methods have shown promising results, although the computational cost remains large. Rodriguez-Fernandez and coworkers have presented hybrid stochastic-deterministic GO methods which could reduce computation time by one order of magnitude while guaranteeing robustness. Our goal here was to further reduce the computational effort without loosing robustness. Results We have developed a new procedure based on the scatter search methodology for nonlinear optimization of dynamic models of arbitrary (or even unknown) structure (i.e. black-box models). In this contribution, we describe and apply this novel metaheuristic, inspired by recent developments in the field of operations research, to a set of complex identification problems and we make a critical comparison with respect to the previous (above mentioned) successful methods. Conclusion Robust and efficient methods for parameter estimation are of key importance in systems biology and related areas. The new metaheuristic presented in this paper aims to ensure the proper solution of these problems by adopting a global optimization approach, while keeping the computational effort under reasonable values. This new metaheuristic was applied to a set of three challenging parameter estimation problems of nonlinear dynamic biological systems, outperforming very significantly all the methods previously used for these benchmark problems. PMID:17081289

Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems.

PubMed

Rodriguez-Fernandez, Maria; Egea, Jose A; Banga, Julio R

2006-11-02

We consider the problem of parameter estimation (model calibration) in nonlinear dynamic models of biological systems. Due to the frequent ill-conditioning and multi-modality of many of these problems, traditional local methods usually fail (unless initialized with very good guesses of the parameter vector). In order to surmount these difficulties, global optimization (GO) methods have been suggested as robust alternatives. Currently, deterministic GO methods can not solve problems of realistic size within this class in reasonable computation times. In contrast, certain types of stochastic GO methods have shown promising results, although the computational cost remains large. Rodriguez-Fernandez and coworkers have presented hybrid stochastic-deterministic GO methods which could reduce computation time by one order of magnitude while guaranteeing robustness. Our goal here was to further reduce the computational effort without loosing robustness. We have developed a new procedure based on the scatter search methodology for nonlinear optimization of dynamic models of arbitrary (or even unknown) structure (i.e. black-box models). In this contribution, we describe and apply this novel metaheuristic, inspired by recent developments in the field of operations research, to a set of complex identification problems and we make a critical comparison with respect to the previous (above mentioned) successful methods. Robust and efficient methods for parameter estimation are of key importance in systems biology and related areas. The new metaheuristic presented in this paper aims to ensure the proper solution of these problems by adopting a global optimization approach, while keeping the computational effort under reasonable values. This new metaheuristic was applied to a set of three challenging parameter estimation problems of nonlinear dynamic biological systems, outperforming very significantly all the methods previously used for these benchmark problems.

A robust real-time surface reconstruction method on point clouds captured from a 3D surface photogrammetry system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Wenyang; Cheung, Yam; Sawant, Amit

2016-05-15

Purpose: To develop a robust and real-time surface reconstruction method on point clouds captured from a 3D surface photogrammetry system. Methods: The authors have developed a robust and fast surface reconstruction method on point clouds acquired by the photogrammetry system, without explicitly solving the partial differential equation required by a typical variational approach. Taking advantage of the overcomplete nature of the acquired point clouds, their method solves and propagates a sparse linear relationship from the point cloud manifold to the surface manifold, assuming both manifolds share similar local geometry. With relatively consistent point cloud acquisitions, the authors propose a sparsemore » regression (SR) model to directly approximate the target point cloud as a sparse linear combination from the training set, assuming that the point correspondences built by the iterative closest point (ICP) is reasonably accurate and have residual errors following a Gaussian distribution. To accommodate changing noise levels and/or presence of inconsistent occlusions during the acquisition, the authors further propose a modified sparse regression (MSR) model to model the potentially large and sparse error built by ICP with a Laplacian prior. The authors evaluated the proposed method on both clinical point clouds acquired under consistent acquisition conditions and on point clouds with inconsistent occlusions. The authors quantitatively evaluated the reconstruction performance with respect to root-mean-squared-error, by comparing its reconstruction results against that from the variational method. Results: On clinical point clouds, both the SR and MSR models have achieved sub-millimeter reconstruction accuracy and reduced the reconstruction time by two orders of magnitude to a subsecond reconstruction time. On point clouds with inconsistent occlusions, the MSR model has demonstrated its advantage in achieving consistent and robust performance despite the introduced occlusions. Conclusions: The authors have developed a fast and robust surface reconstruction method on point clouds captured from a 3D surface photogrammetry system, with demonstrated sub-millimeter reconstruction accuracy and subsecond reconstruction time. It is suitable for real-time motion tracking in radiotherapy, with clear surface structures for better quantifications.« less

A robust real-time surface reconstruction method on point clouds captured from a 3D surface photogrammetry system

PubMed Central

Liu, Wenyang; Cheung, Yam; Sawant, Amit; Ruan, Dan

2016-01-01

Purpose: To develop a robust and real-time surface reconstruction method on point clouds captured from a 3D surface photogrammetry system. Methods: The authors have developed a robust and fast surface reconstruction method on point clouds acquired by the photogrammetry system, without explicitly solving the partial differential equation required by a typical variational approach. Taking advantage of the overcomplete nature of the acquired point clouds, their method solves and propagates a sparse linear relationship from the point cloud manifold to the surface manifold, assuming both manifolds share similar local geometry. With relatively consistent point cloud acquisitions, the authors propose a sparse regression (SR) model to directly approximate the target point cloud as a sparse linear combination from the training set, assuming that the point correspondences built by the iterative closest point (ICP) is reasonably accurate and have residual errors following a Gaussian distribution. To accommodate changing noise levels and/or presence of inconsistent occlusions during the acquisition, the authors further propose a modified sparse regression (MSR) model to model the potentially large and sparse error built by ICP with a Laplacian prior. The authors evaluated the proposed method on both clinical point clouds acquired under consistent acquisition conditions and on point clouds with inconsistent occlusions. The authors quantitatively evaluated the reconstruction performance with respect to root-mean-squared-error, by comparing its reconstruction results against that from the variational method. Results: On clinical point clouds, both the SR and MSR models have achieved sub-millimeter reconstruction accuracy and reduced the reconstruction time by two orders of magnitude to a subsecond reconstruction time. On point clouds with inconsistent occlusions, the MSR model has demonstrated its advantage in achieving consistent and robust performance despite the introduced occlusions. Conclusions: The authors have developed a fast and robust surface reconstruction method on point clouds captured from a 3D surface photogrammetry system, with demonstrated sub-millimeter reconstruction accuracy and subsecond reconstruction time. It is suitable for real-time motion tracking in radiotherapy, with clear surface structures for better quantifications. PMID:27147347

A Novel Adaptive H∞ Filtering Method with Delay Compensation for the Transfer Alignment of Strapdown Inertial Navigation Systems.

PubMed

Lyu, Weiwei; Cheng, Xianghong

2017-11-28

Transfer alignment is always a key technology in a strapdown inertial navigation system (SINS) because of its rapidity and accuracy. In this paper a transfer alignment model is established, which contains the SINS error model and the measurement model. The time delay in the process of transfer alignment is analyzed, and an H∞ filtering method with delay compensation is presented. Then the H∞ filtering theory and the robust mechanism of H∞ filter are deduced and analyzed in detail. In order to improve the transfer alignment accuracy in SINS with time delay, an adaptive H∞ filtering method with delay compensation is proposed. Since the robustness factor plays an important role in the filtering process and has effect on the filtering accuracy, the adaptive H∞ filter with delay compensation can adjust the value of robustness factor adaptively according to the dynamic external environment. The vehicle transfer alignment experiment indicates that by using the adaptive H∞ filtering method with delay compensation, the transfer alignment accuracy and the pure inertial navigation accuracy can be dramatically improved, which demonstrates the superiority of the proposed filtering method.

Robust Angle Estimation for MIMO Radar with the Coexistence of Mutual Coupling and Colored Noise.

PubMed

Wang, Junxiang; Wang, Xianpeng; Xu, Dingjie; Bi, Guoan

2018-03-09

This paper deals with joint estimation of direction-of-departure (DOD) and direction-of- arrival (DOA) in bistatic multiple-input multiple-output (MIMO) radar with the coexistence of unknown mutual coupling and spatial colored noise by developing a novel robust covariance tensor-based angle estimation method. In the proposed method, a third-order tensor is firstly formulated for capturing the multidimensional nature of the received data. Then taking advantage of the temporal uncorrelated characteristic of colored noise and the banded complex symmetric Toeplitz structure of the mutual coupling matrices, a novel fourth-order covariance tensor is constructed for eliminating the influence of both spatial colored noise and mutual coupling. After a robust signal subspace estimation is obtained by using the higher-order singular value decomposition (HOSVD) technique, the rotational invariance technique is applied to achieve the DODs and DOAs. Compared with the existing HOSVD-based subspace methods, the proposed method can provide superior angle estimation performance and automatically jointly perform the DODs and DOAs. Results from numerical experiments are presented to verify the effectiveness of the proposed method.

Automated compromised right lung segmentation method using a robust atlas-based active volume model with sparse shape composition prior in CT.

PubMed

Zhou, Jinghao; Yan, Zhennan; Lasio, Giovanni; Huang, Junzhou; Zhang, Baoshe; Sharma, Navesh; Prado, Karl; D'Souza, Warren

2015-12-01

To resolve challenges in image segmentation in oncologic patients with severely compromised lung, we propose an automated right lung segmentation framework that uses a robust, atlas-based active volume model with a sparse shape composition prior. The robust atlas is achieved by combining the atlas with the output of sparse shape composition. Thoracic computed tomography images (n=38) from patients with lung tumors were collected. The right lung in each scan was manually segmented to build a reference training dataset against which the performance of the automated segmentation method was assessed. The quantitative results of this proposed segmentation method with sparse shape composition achieved mean Dice similarity coefficient (DSC) of (0.72, 0.81) with 95% CI, mean accuracy (ACC) of (0.97, 0.98) with 95% CI, and mean relative error (RE) of (0.46, 0.74) with 95% CI. Both qualitative and quantitative comparisons suggest that this proposed method can achieve better segmentation accuracy with less variance than other atlas-based segmentation methods in the compromised lung segmentation. Published by Elsevier Ltd.

A Fast and Robust UHPLC-MRM-MS Method to Characterize and Quantify Grape Skin Tannins after Chemical Depolymerization.

PubMed

Pinasseau, Lucie; Verbaere, Arnaud; Roques, Maryline; Meudec, Emmanuelle; Vallverdú-Queralt, Anna; Terrier, Nancy; Boulet, Jean-Claude; Cheynier, Véronique; Sommerer, Nicolas

2016-10-21

A rapid, sensitive, and selective analysis method using ultra high performance liquid chromatography coupled with triple-quadrupole mass spectrometry (UHPLC-QqQ-MS) has been developed for the characterization and quantification of grape skin flavan-3-ols after acid-catalysed depolymerization in the presence of phloroglucinol (phloroglucinolysis). The compound detection being based on specific MS transitions in Multiple Reaction Monitoring (MRM) mode, this fast gradient robust method allows analysis of constitutive units of grape skin proanthocyanidins, including some present in trace amounts, in a single injection, with a throughput of 6 samples per hour. This method was applied to a set of 214 grape skin samples from 107 different red and white grape cultivars grown under two conditions in the vineyard, irrigated or non-irrigated. The results of triplicate analyses confirmed the robustness of the method, which was thus proven to be suitable for high-throughput and large-scale metabolomics studies. Moreover, these preliminary results suggest that analysis of tannin composition is relevant to investigate the genetic bases of grape response to drought.

Robust finger vein ROI localization based on flexible segmentation.

PubMed

Lu, Yu; Xie, Shan Juan; Yoon, Sook; Yang, Jucheng; Park, Dong Sun

2013-10-24

Finger veins have been proved to be an effective biometric for personal identification in the recent years. However, finger vein images are easily affected by influences such as image translation, orientation, scale, scattering, finger structure, complicated background, uneven illumination, and collection posture. All these factors may contribute to inaccurate region of interest (ROI) definition, and so degrade the performance of finger vein identification system. To improve this problem, in this paper, we propose a finger vein ROI localization method that has high effectiveness and robustness against the above factors. The proposed method consists of a set of steps to localize ROIs accurately, namely segmentation, orientation correction, and ROI detection. Accurate finger region segmentation and correct calculated orientation can support each other to produce higher accuracy in localizing ROIs. Extensive experiments have been performed on the finger vein image database, MMCBNU_6000, to verify the robustness of the proposed method. The proposed method shows the segmentation accuracy of 100%. Furthermore, the average processing time of the proposed method is 22 ms for an acquired image, which satisfies the criterion of a real-time finger vein identification system.

Robust Finger Vein ROI Localization Based on Flexible Segmentation

PubMed Central

Lu, Yu; Xie, Shan Juan; Yoon, Sook; Yang, Jucheng; Park, Dong Sun

2013-01-01

Finger veins have been proved to be an effective biometric for personal identification in the recent years. However, finger vein images are easily affected by influences such as image translation, orientation, scale, scattering, finger structure, complicated background, uneven illumination, and collection posture. All these factors may contribute to inaccurate region of interest (ROI) definition, and so degrade the performance of finger vein identification system. To improve this problem, in this paper, we propose a finger vein ROI localization method that has high effectiveness and robustness against the above factors. The proposed method consists of a set of steps to localize ROIs accurately, namely segmentation, orientation correction, and ROI detection. Accurate finger region segmentation and correct calculated orientation can support each other to produce higher accuracy in localizing ROIs. Extensive experiments have been performed on the finger vein image database, MMCBNU_6000, to verify the robustness of the proposed method. The proposed method shows the segmentation accuracy of 100%. Furthermore, the average processing time of the proposed method is 22 ms for an acquired image, which satisfies the criterion of a real-time finger vein identification system. PMID:24284769

Robust Angle Estimation for MIMO Radar with the Coexistence of Mutual Coupling and Colored Noise

PubMed Central

Wang, Junxiang; Wang, Xianpeng; Xu, Dingjie; Bi, Guoan

2018-01-01

This paper deals with joint estimation of direction-of-departure (DOD) and direction-of- arrival (DOA) in bistatic multiple-input multiple-output (MIMO) radar with the coexistence of unknown mutual coupling and spatial colored noise by developing a novel robust covariance tensor-based angle estimation method. In the proposed method, a third-order tensor is firstly formulated for capturing the multidimensional nature of the received data. Then taking advantage of the temporal uncorrelated characteristic of colored noise and the banded complex symmetric Toeplitz structure of the mutual coupling matrices, a novel fourth-order covariance tensor is constructed for eliminating the influence of both spatial colored noise and mutual coupling. After a robust signal subspace estimation is obtained by using the higher-order singular value decomposition (HOSVD) technique, the rotational invariance technique is applied to achieve the DODs and DOAs. Compared with the existing HOSVD-based subspace methods, the proposed method can provide superior angle estimation performance and automatically jointly perform the DODs and DOAs. Results from numerical experiments are presented to verify the effectiveness of the proposed method. PMID:29522499

SU-E-T-452: Impact of Respiratory Motion On Robustly-Optimized Intensity-Modulated Proton Therapy to Treat Lung Cancers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, W; Schild, S; Bues, M

Purpose: We compared conventionally optimized intensity-modulated proton therapy (IMPT) treatment plans against the worst-case robustly optimized treatment plans for lung cancer. The comparison of the two IMPT optimization strategies focused on the resulting plans' ability to retain dose objectives under the influence of patient set-up, inherent proton range uncertainty, and dose perturbation caused by respiratory motion. Methods: For each of the 9 lung cancer cases two treatment plans were created accounting for treatment uncertainties in two different ways: the first used the conventional Method: delivery of prescribed dose to the planning target volume (PTV) that is geometrically expanded from themore » internal target volume (ITV). The second employed the worst-case robust optimization scheme that addressed set-up and range uncertainties through beamlet optimization. The plan optimality and plan robustness were calculated and compared. Furthermore, the effects on dose distributions of the changes in patient anatomy due to respiratory motion was investigated for both strategies by comparing the corresponding plan evaluation metrics at the end-inspiration and end-expiration phase and absolute differences between these phases. The mean plan evaluation metrics of the two groups were compared using two-sided paired t-tests. Results: Without respiratory motion considered, we affirmed that worst-case robust optimization is superior to PTV-based conventional optimization in terms of plan robustness and optimality. With respiratory motion considered, robust optimization still leads to more robust dose distributions to respiratory motion for targets and comparable or even better plan optimality [D95% ITV: 96.6% versus 96.1% (p=0.26), D5% - D95% ITV: 10.0% versus 12.3% (p=0.082), D1% spinal cord: 31.8% versus 36.5% (p =0.035)]. Conclusion: Worst-case robust optimization led to superior solutions for lung IMPT. Despite of the fact that robust optimization did not explicitly account for respiratory motion it produced motion-resistant treatment plans. However, further research is needed to incorporate respiratory motion into IMPT robust optimization.« less

A new robust adaptive controller for vibration control of active engine mount subjected to large uncertainties

NASA Astrophysics Data System (ADS)

Fakhari, Vahid; Choi, Seung-Bok; Cho, Chang-Hyun

2015-04-01

This work presents a new robust model reference adaptive control (MRAC) for vibration control caused from vehicle engine using an electromagnetic type of active engine mount. Vibration isolation performances of the active mount associated with the robust controller are evaluated in the presence of large uncertainties. As a first step, an active mount with linear solenoid actuator is prepared and its dynamic model is identified via experimental test. Subsequently, a new robust MRAC based on the gradient method with σ-modification is designed by selecting a proper reference model. In designing the robust adaptive control, structured (parametric) uncertainties in the stiffness of the passive part of the mount and in damping ratio of the active part of the mount are considered to investigate the robustness of the proposed controller. Experimental and simulation results are presented to evaluate performance focusing on the robustness behavior of the controller in the face of large uncertainties. The obtained results show that the proposed controller can sufficiently provide the robust vibration control performance even in the presence of large uncertainties showing an effective vibration isolation.

Robust nonlinear system identification: Bayesian mixture of experts using the t-distribution

NASA Astrophysics Data System (ADS)

Baldacchino, Tara; Worden, Keith; Rowson, Jennifer

2017-02-01

A novel variational Bayesian mixture of experts model for robust regression of bifurcating and piece-wise continuous processes is introduced. The mixture of experts model is a powerful model which probabilistically splits the input space allowing different models to operate in the separate regions. However, current methods have no fail-safe against outliers. In this paper, a robust mixture of experts model is proposed which consists of Student-t mixture models at the gates and Student-t distributed experts, trained via Bayesian inference. The Student-t distribution has heavier tails than the Gaussian distribution, and so it is more robust to outliers, noise and non-normality in the data. Using both simulated data and real data obtained from the Z24 bridge this robust mixture of experts performs better than its Gaussian counterpart when outliers are present. In particular, it provides robustness to outliers in two forms: unbiased parameter regression models, and robustness to overfitting/complex models.

Three-Dimensional Navier-Stokes Calculations Using the Modified Space-Time CESE Method

NASA Technical Reports Server (NTRS)

Chang, Chau-lyan

2007-01-01

The space-time conservation element solution element (CESE) method is modified to address the robustness issues of high-aspect-ratio, viscous, near-wall meshes. In this new approach, the dependent variable gradients are evaluated using element edges and the corresponding neighboring solution elements while keeping the original flux integration procedure intact. As such, the excellent flux conservation property is retained and the new edge-based gradients evaluation significantly improves the robustness for high-aspect ratio meshes frequently encountered in three-dimensional, Navier-Stokes calculations. The order of accuracy of the proposed method is demonstrated for oblique acoustic wave propagation, shock-wave interaction, and hypersonic flows over a blunt body. The confirmed second-order convergence along with the enhanced robustness in handling hypersonic blunt body flow calculations makes the proposed approach a very competitive CFD framework for 3D Navier-Stokes simulations.

AutoCellSeg: robust automatic colony forming unit (CFU)/cell analysis using adaptive image segmentation and easy-to-use post-editing techniques.

PubMed

Khan, Arif Ul Maula; Torelli, Angelo; Wolf, Ivo; Gretz, Norbert

2018-05-08

In biological assays, automated cell/colony segmentation and counting is imperative owing to huge image sets. Problems occurring due to drifting image acquisition conditions, background noise and high variation in colony features in experiments demand a user-friendly, adaptive and robust image processing/analysis method. We present AutoCellSeg (based on MATLAB) that implements a supervised automatic and robust image segmentation method. AutoCellSeg utilizes multi-thresholding aided by a feedback-based watershed algorithm taking segmentation plausibility criteria into account. It is usable in different operation modes and intuitively enables the user to select object features interactively for supervised image segmentation method. It allows the user to correct results with a graphical interface. This publicly available tool outperforms tools like OpenCFU and CellProfiler in terms of accuracy and provides many additional useful features for end-users.

Robust bidirectional links for photonic quantum networks

PubMed Central

Xu, Jin-Shi; Yung, Man-Hong; Xu, Xiao-Ye; Tang, Jian-Shun; Li, Chuan-Feng; Guo, Guang-Can

2016-01-01

Optical fibers are widely used as one of the main tools for transmitting not only classical but also quantum information. We propose and report an experimental realization of a promising method for creating robust bidirectional quantum communication links through paired optical polarization-maintaining fibers. Many limitations of existing protocols can be avoided with the proposed method. In particular, the path and polarization degrees of freedom are combined to deterministically create a photonic decoherence-free subspace without the need for any ancillary photon. This method is input state–independent, robust against dephasing noise, postselection-free, and applicable bidirectionally. To rigorously quantify the amount of quantum information transferred, the optical fibers are analyzed with the tools developed in quantum communication theory. These results not only suggest a practical means for protecting quantum information sent through optical quantum networks but also potentially provide a new physical platform for enriching the structure of the quantum communication theory. PMID:26824069

Sliding mode control method having terminal convergence in finite time

NASA Technical Reports Server (NTRS)

Venkataraman, Subramanian T. (Inventor); Gulati, Sandeep (Inventor)

1994-01-01

An object of this invention is to provide robust nonlinear controllers for robotic operations in unstructured environments based upon a new class of closed loop sliding control methods, sometimes denoted terminal sliders, where the new class will enforce closed-loop control convergence to equilibrium in finite time. Improved performance results from the elimination of high frequency control switching previously employed for robustness to parametric uncertainties. Improved performance also results from the dependence of terminal slider stability upon the rate of change of uncertainties over the sliding surface rather than the magnitude of the uncertainty itself for robust control. Terminal sliding mode control also yields improved convergence where convergence time is finite and is to be controlled. A further object is to apply terminal sliders to robot manipulator control and benchmark performance with the traditional computed torque control method and provide for design of control parameters.

A robust background regression based score estimation algorithm for hyperspectral anomaly detection

NASA Astrophysics Data System (ADS)

Zhao, Rui; Du, Bo; Zhang, Liangpei; Zhang, Lefei

2016-12-01

Anomaly detection has become a hot topic in the hyperspectral image analysis and processing fields in recent years. The most important issue for hyperspectral anomaly detection is the background estimation and suppression. Unreasonable or non-robust background estimation usually leads to unsatisfactory anomaly detection results. Furthermore, the inherent nonlinearity of hyperspectral images may cover up the intrinsic data structure in the anomaly detection. In order to implement robust background estimation, as well as to explore the intrinsic data structure of the hyperspectral image, we propose a robust background regression based score estimation algorithm (RBRSE) for hyperspectral anomaly detection. The Robust Background Regression (RBR) is actually a label assignment procedure which segments the hyperspectral data into a robust background dataset and a potential anomaly dataset with an intersection boundary. In the RBR, a kernel expansion technique, which explores the nonlinear structure of the hyperspectral data in a reproducing kernel Hilbert space, is utilized to formulate the data as a density feature representation. A minimum squared loss relationship is constructed between the data density feature and the corresponding assigned labels of the hyperspectral data, to formulate the foundation of the regression. Furthermore, a manifold regularization term which explores the manifold smoothness of the hyperspectral data, and a maximization term of the robust background average density, which suppresses the bias caused by the potential anomalies, are jointly appended in the RBR procedure. After this, a paired-dataset based k-nn score estimation method is undertaken on the robust background and potential anomaly datasets, to implement the detection output. The experimental results show that RBRSE achieves superior ROC curves, AUC values, and background-anomaly separation than some of the other state-of-the-art anomaly detection methods, and is easy to implement in practice.

Robustness of radiomic breast features of benign lesions and luminal A cancers across MR magnet strengths

NASA Astrophysics Data System (ADS)

Whitney, Heather M.; Drukker, Karen; Edwards, Alexandra; Papaioannou, John; Giger, Maryellen L.

2018-02-01

Radiomics features extracted from breast lesion images have shown potential in diagnosis and prognosis of breast cancer. As clinical institutions transition from 1.5 T to 3.0 T magnetic resonance imaging (MRI), it is helpful to identify robust features across these field strengths. In this study, dynamic contrast-enhanced MR images were acquired retrospectively under IRB/HIPAA compliance, yielding 738 cases: 241 and 124 benign lesions imaged at 1.5 T and 3.0 T and 231 and 142 luminal A cancers imaged at 1.5 T and 3.0 T, respectively. Lesions were segmented using a fuzzy C-means method. Extracted radiomic values for each group of lesions by cancer status and field strength of acquisition were compared using a Kolmogorov-Smirnov test for the null hypothesis that two groups being compared came from the same distribution, with p-values being corrected for multiple comparisons by the Holm-Bonferroni method. Two shape features, one texture feature, and three enhancement variance kinetics features were found to be potentially robust. All potentially robust features had areas under the receiver operating characteristic curve (AUC) statistically greater than 0.5 in the task of distinguishing between lesion types (range of means 0.57-0.78). The significant difference in voxel size between field strength of acquisition limits the ability to affirm more features as robust or not robust according to field strength alone, and inhomogeneities in static field strength and radiofrequency field could also have affected the assessment of kinetic curve features as robust or not. Vendor-specific image scaling could have also been a factor. These findings will contribute to the development of radiomic signatures that use features identified as robust across field strength.

Scout 2008 Version 1.0 User Guide

EPA Science Inventory

The Scout 2008 version 1.0 software package provides a wide variety of classical and robust statistical methods that are not typically available in other commercial software packages. A major part of Scout deals with classical, robust, and resistant univariate and multivariate ou...

Benchmarking of a treatment planning system for spot scanning proton therapy: Comparison and analysis of robustness to setup errors of photon IMRT and proton SFUD treatment plans of base of skull meningioma

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harding, R., E-mail: ruth.harding2@wales.nhs.uk; Trnková, P.; Lomax, A. J.

Purpose: Base of skull meningioma can be treated with both intensity modulated radiation therapy (IMRT) and spot scanned proton therapy (PT). One of the main benefits of PT is better sparing of organs at risk, but due to the physical and dosimetric characteristics of protons, spot scanned PT can be more sensitive to the uncertainties encountered in the treatment process compared with photon treatment. Therefore, robustness analysis should be part of a comprehensive comparison between these two treatment methods in order to quantify and understand the sensitivity of the treatment techniques to uncertainties. The aim of this work was tomore » benchmark a spot scanning treatment planning system for planning of base of skull meningioma and to compare the created plans and analyze their robustness to setup errors against the IMRT technique. Methods: Plans were produced for three base of skull meningioma cases: IMRT planned with a commercial TPS [Monaco (Elekta AB, Sweden)]; single field uniform dose (SFUD) spot scanning PT produced with an in-house TPS (PSI-plan); and SFUD spot scanning PT plan created with a commercial TPS [XiO (Elekta AB, Sweden)]. A tool for evaluating robustness to random setup errors was created and, for each plan, both a dosimetric evaluation and a robustness analysis to setup errors were performed. Results: It was possible to create clinically acceptable treatment plans for spot scanning proton therapy of meningioma with a commercially available TPS. However, since each treatment planning system uses different methods, this comparison showed different dosimetric results as well as different sensitivities to setup uncertainties. The results confirmed the necessity of an analysis tool for assessing plan robustness to provide a fair comparison of photon and proton plans. Conclusions: Robustness analysis is a critical part of plan evaluation when comparing IMRT plans with spot scanned proton therapy plans.« less

Robust H(∞) positional control of 2-DOF robotic arm driven by electro-hydraulic servo system.

PubMed

Guo, Qing; Yu, Tian; Jiang, Dan

2015-11-01

In this paper an H∞ positional feedback controller is developed to improve the robust performance under structural and parametric uncertainty disturbance in electro-hydraulic servo system (EHSS). The robust control model is described as the linear state-space equation by upper linear fractional transformation. According to the solution of H∞ sub-optimal control problem, the robust controller is designed and simplified to lower order linear model which is easily realized in EHSS. The simulation and experimental results can validate the robustness of this proposed method. The comparison result with PI control shows that the robust controller is suitable for this EHSS under the critical condition where the desired system bandwidth is higher and the external load of the hydraulic actuator is closed to its limited capability. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Optimally robust redundancy relations for failure detection in uncertain systems

NASA Technical Reports Server (NTRS)

Lou, X.-C.; Willsky, A. S.; Verghese, G. C.

1986-01-01

All failure detection methods are based, either explicitly or implicitly, on the use of redundancy, i.e. on (possibly dynamic) relations among the measured variables. The robustness of the failure detection process consequently depends to a great degree on the reliability of the redundancy relations, which in turn is affected by the inevitable presence of model uncertainties. In this paper the problem of determining redundancy relations that are optimally robust is addressed in a sense that includes several major issues of importance in practical failure detection and that provides a significant amount of intuition concerning the geometry of robust failure detection. A procedure is given involving the construction of a single matrix and its singular value decomposition for the determination of a complete sequence of redundancy relations, ordered in terms of their level of robustness. This procedure also provides the basis for comparing levels of robustness in redundancy provided by different sets of sensors.