Adaptive DSPI phase denoising using mutual information and 2D variational mode decomposition

NASA Astrophysics Data System (ADS)

Xiao, Qiyang; Li, Jian; Wu, Sijin; Li, Weixian; Yang, Lianxiang; Dong, Mingli; Zeng, Zhoumo

2018-04-01

In digital speckle pattern interferometry (DSPI), noise interference leads to a low peak signal-to-noise ratio (PSNR) and measurement errors in the phase map. This paper proposes an adaptive DSPI phase denoising method based on two-dimensional variational mode decomposition (2D-VMD) and mutual information. Firstly, the DSPI phase map is subjected to 2D-VMD in order to obtain a series of band-limited intrinsic mode functions (BLIMFs). Then, on the basis of characteristics of the BLIMFs and in combination with mutual information, a self-adaptive denoising method is proposed to obtain noise-free components containing the primary phase information. The noise-free components are reconstructed to obtain the denoising DSPI phase map. Simulation and experimental results show that the proposed method can effectively reduce noise interference, giving a PSNR that is higher than that of two-dimensional empirical mode decomposition methods.

Thermal Decomposition Behavior of Hydroxytyrosol (HT) in Nitrogen Atmosphere Based on TG-FTIR Methods.

PubMed

Tu, Jun-Ling; Yuan, Jiao-Jiao

2018-02-13

The thermal decomposition behavior of olive hydroxytyrosol (HT) was first studied using thermogravimetry (TG). Cracked chemical bond and evolved gas analysis during the thermal decomposition process of HT were also investigated using thermogravimetry coupled with infrared spectroscopy (TG-FTIR). Thermogravimetry-Differential thermogravimetry (TG-DTG) curves revealed that the thermal decomposition of HT began at 262.8 °C and ended at 409.7 °C with a main mass loss. It was demonstrated that a high heating rate (over 20 K·min -1 ) restrained the thermal decomposition of HT, resulting in an obvious thermal hysteresis. Furthermore, a thermal decomposition kinetics investigation of HT indicated that the non-isothermal decomposition mechanism was one-dimensional diffusion (D1), integral form g ( x ) = x ², and differential form f ( x ) = 1/(2 x ). The four combined approaches were employed to calculate the activation energy ( E = 128.50 kJ·mol -1 ) and Arrhenius preexponential factor (ln A = 24.39 min -1 ). In addition, a tentative mechanism of HT thermal decomposition was further developed. The results provide a theoretical reference for the potential thermal stability of HT.

A Simple Application of Compressed Sensing to Further Accelerate Partially Parallel Imaging

PubMed Central

Miao, Jun; Guo, Weihong; Narayan, Sreenath; Wilson, David L.

2012-01-01

Compressed Sensing (CS) and partially parallel imaging (PPI) enable fast MR imaging by reducing the amount of k-space data required for reconstruction. Past attempts to combine these two have been limited by the incoherent sampling requirement of CS, since PPI routines typically sample on a regular (coherent) grid. Here, we developed a new method, “CS+GRAPPA,” to overcome this limitation. We decomposed sets of equidistant samples into multiple random subsets. Then, we reconstructed each subset using CS, and averaging the results to get a final CS k-space reconstruction. We used both a standard CS, and an edge and joint-sparsity guided CS reconstruction. We tested these intermediate results on both synthetic and real MR phantom data, and performed a human observer experiment to determine the effectiveness of decomposition, and to optimize the number of subsets. We then used these CS reconstructions to calibrate the GRAPPA complex coil weights. In vivo parallel MR brain and heart data sets were used. An objective image quality evaluation metric, Case-PDM, was used to quantify image quality. Coherent aliasing and noise artifacts were significantly reduced using two decompositions. More decompositions further reduced coherent aliasing and noise artifacts but introduced blurring. However, the blurring was effectively minimized using our new edge and joint-sparsity guided CS using two decompositions. Numerical results on parallel data demonstrated that the combined method greatly improved image quality as compared to standard GRAPPA, on average halving Case-PDM scores across a range of sampling rates. The proposed technique allowed the same Case-PDM scores as standard GRAPPA, using about half the number of samples. We conclude that the new method augments GRAPPA by combining it with CS, allowing CS to work even when the k-space sampling pattern is equidistant. PMID:22902065

Grouping individual independent BOLD effects: a new way to ICA group analysis

NASA Astrophysics Data System (ADS)

Duann, Jeng-Ren; Jung, Tzyy-Ping; Sejnowski, Terrence J.; Makeig, Scott

2009-04-01

A new group analysis method to summarize the task-related BOLD responses based on independent component analysis (ICA) was presented. As opposite to the previously proposed group ICA (gICA) method, which first combined multi-subject fMRI data in either temporal or spatial domain and applied ICA decomposition only once to the combined fMRI data to extract the task-related BOLD effects, the method presented here applied ICA decomposition to the individual subjects' fMRI data to first find the independent BOLD effects specifically for each individual subject. Then, the task-related independent BOLD component was selected among the resulting independent components from the single-subject ICA decomposition and hence grouped across subjects to derive the group inference. In this new ICA group analysis (ICAga) method, one does not need to assume that the task-related BOLD time courses are identical across brain areas and subjects as used in the grand ICA decomposition on the spatially concatenated fMRI data. Neither does one need to assume that after spatial normalization, the voxels at the same coordinates represent exactly the same functional or structural brain anatomies across different subjects. These two assumptions have been problematic given the recent BOLD activation evidences. Further, since the independent BOLD effects were obtained from each individual subject, the ICAga method can better account for the individual differences in the task-related BOLD effects. Unlike the gICA approach whereby the task-related BOLD effects could only be accounted for by a single unified BOLD model across multiple subjects. As a result, the newly proposed method, ICAga, was able to better fit the task-related BOLD effects at individual level and thus allow grouping more appropriate multisubject BOLD effects in the group analysis.

Decomposition of metabolic network into functional modules based on the global connectivity structure of reaction graph.

PubMed

Ma, Hong-Wu; Zhao, Xue-Ming; Yuan, Ying-Jin; Zeng, An-Ping

2004-08-12

Metabolic networks are organized in a modular, hierarchical manner. Methods for a rational decomposition of the metabolic network into relatively independent functional subsets are essential to better understand the modularity and organization principle of a large-scale, genome-wide network. Network decomposition is also necessary for functional analysis of metabolism by pathway analysis methods that are often hampered by the problem of combinatorial explosion due to the complexity of metabolic network. Decomposition methods proposed in literature are mainly based on the connection degree of metabolites. To obtain a more reasonable decomposition, the global connectivity structure of metabolic networks should be taken into account. In this work, we use a reaction graph representation of a metabolic network for the identification of its global connectivity structure and for decomposition. A bow-tie connectivity structure similar to that previously discovered for metabolite graph is found also to exist in the reaction graph. Based on this bow-tie structure, a new decomposition method is proposed, which uses a distance definition derived from the path length between two reactions. An hierarchical classification tree is first constructed from the distance matrix among the reactions in the giant strong component of the bow-tie structure. These reactions are then grouped into different subsets based on the hierarchical tree. Reactions in the IN and OUT subsets of the bow-tie structure are subsequently placed in the corresponding subsets according to a 'majority rule'. Compared with the decomposition methods proposed in literature, ours is based on combined properties of the global network structure and local reaction connectivity rather than, primarily, on the connection degree of metabolites. The method is applied to decompose the metabolic network of Escherichia coli. Eleven subsets are obtained. More detailed investigations of the subsets show that reactions in the same subset are really functionally related. The rational decomposition of metabolic networks, and subsequent studies of the subsets, make it more amenable to understand the inherent organization and functionality of metabolic networks at the modular level. http://genome.gbf.de/bioinformatics/

Non-invasive quantitative pulmonary V/Q imaging using Fourier decomposition MRI at 1.5T.

PubMed

Kjørstad, Åsmund; Corteville, Dominique M R; Henzler, Thomas; Schmid-Bindert, Gerald; Zöllner, Frank G; Schad, Lothar R

2015-12-01

Techniques for quantitative pulmonary perfusion and ventilation using the Fourier Decomposition method were recently demonstrated. We combine these two techniques and show that ventilation-perfusion (V/Q) imaging is possible using only a single MR acquisition of less than thirty seconds. The Fourier Decomposition method is used in combination with two quantification techniques, which extract baselines from within the images themselves and thus allows quantification. For the perfusion, a region assumed to consist of 100% blood is utilized, while for the ventilation the zero-frequency component is used. V/Q-imaging is then done by dividing the quantified ventilation map with the quantified perfusion map. The techniques were used on ten healthy volunteers and fifteen patients diagnosed with lung cancer. A mean V/Q-ratio of 1.15 ± 0.22 was found for the healthy volunteers and a mean V/Q-ratio of 1.93 ± 0.83 for the non-afflicted lung in the patients. Mean V/Q-ratio in the afflicted (tumor-bearing) lung was found to be 1.61 ± 1.06. Functional defects were clearly visible in many of the patient images, but 5 of 15 patient images had to be excluded due to artifacts or low SNR, indicating a lack of robustness. Non-invasive, quantitative V/Q-imaging is possible using Fourier Decomposition MRI. The method requires only a single acquisition of less than 30 seconds, but robustness in patients remains an issue. Copyright © 2015. Published by Elsevier GmbH.

High-frequency Total Focusing Method (TFM) imaging in strongly attenuating materials with the decomposition of the time reversal operator associated with orthogonal coded excitations

NASA Astrophysics Data System (ADS)

Villaverde, Eduardo Lopez; Robert, Sébastien; Prada, Claire

2017-02-01

In the present work, the Total Focusing Method (TFM) is used to image defects in a High Density Polyethylene (HDPE) pipe. The viscoelastic attenuation of this material corrupts the images with a high electronic noise. In order to improve the image quality, the Decomposition of the Time Reversal Operator (DORT) filtering is combined with spatial Walsh-Hadamard coded transmissions before calculating the images. Experiments on a complex HDPE joint demonstrate that this method improves the signal-to-noise ratio by more than 40 dB in comparison with the conventional TFM.

Quantitative analysis of microbial biomass yield in aerobic bioreactor.

PubMed

Watanabe, Osamu; Isoda, Satoru

2013-12-01

We have studied the integrated model of reaction rate equations with thermal energy balance in aerobic bioreactor for food waste decomposition and showed that the integrated model has the capability both of monitoring microbial activity in real time and of analyzing biodegradation kinetics and thermal-hydrodynamic properties. On the other hand, concerning microbial metabolism, it was known that balancing catabolic reactions with anabolic reactions in terms of energy and electron flow provides stoichiometric metabolic reactions and enables the estimation of microbial biomass yield (stoichiometric reaction model). We have studied a method for estimating real-time microbial biomass yield in the bioreactor during food waste decomposition by combining the integrated model with the stoichiometric reaction model. As a result, it was found that the time course of microbial biomass yield in the bioreactor during decomposition can be evaluated using the operational data of the bioreactor (weight of input food waste and bed temperature) by the combined model. The combined model can be applied to manage a food waste decomposition not only for controlling system operation to keep microbial activity stable, but also for producing value-added products such as compost on optimum condition. Copyright © 2013 The Research Centre for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.

A Four-Stage Hybrid Model for Hydrological Time Series Forecasting

PubMed Central

Di, Chongli; Yang, Xiaohua; Wang, Xiaochao

2014-01-01

Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of ‘denoising, decomposition and ensemble’. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models. PMID:25111782

A four-stage hybrid model for hydrological time series forecasting.

PubMed

Di, Chongli; Yang, Xiaohua; Wang, Xiaochao

2014-01-01

Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of 'denoising, decomposition and ensemble'. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models.

Combined empirical mode decomposition and texture features for skin lesion classification using quadratic support vector machine.

PubMed

Wahba, Maram A; Ashour, Amira S; Napoleon, Sameh A; Abd Elnaby, Mustafa M; Guo, Yanhui

2017-12-01

Basal cell carcinoma is one of the most common malignant skin lesions. Automated lesion identification and classification using image processing techniques is highly required to reduce the diagnosis errors. In this study, a novel technique is applied to classify skin lesion images into two classes, namely the malignant Basal cell carcinoma and the benign nevus. A hybrid combination of bi-dimensional empirical mode decomposition and gray-level difference method features is proposed after hair removal. The combined features are further classified using quadratic support vector machine (Q-SVM). The proposed system has achieved outstanding performance of 100% accuracy, sensitivity and specificity compared to other support vector machine procedures as well as with different extracted features. Basal Cell Carcinoma is effectively classified using Q-SVM with the proposed combined features.

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.

Fault feature analysis of cracked gear based on LOD and analytical-FE method

NASA Astrophysics Data System (ADS)

Wu, Jiateng; Yang, Yu; Yang, Xingkai; Cheng, Junsheng

2018-01-01

At present, there are two main ideas for gear fault diagnosis. One is the model-based gear dynamic analysis; the other is signal-based gear vibration diagnosis. In this paper, a method for fault feature analysis of gear crack is presented, which combines the advantages of dynamic modeling and signal processing. Firstly, a new time-frequency analysis method called local oscillatory-characteristic decomposition (LOD) is proposed, which has the attractive feature of extracting fault characteristic efficiently and accurately. Secondly, an analytical-finite element (analytical-FE) method which is called assist-stress intensity factor (assist-SIF) gear contact model, is put forward to calculate the time-varying mesh stiffness (TVMS) under different crack states. Based on the dynamic model of the gear system with 6 degrees of freedom, the dynamic simulation response was obtained for different tooth crack depths. For the dynamic model, the corresponding relation between the characteristic parameters and the degree of the tooth crack is established under a specific condition. On the basis of the methods mentioned above, a novel gear tooth root crack diagnosis method which combines the LOD with the analytical-FE is proposed. Furthermore, empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) are contrasted with the LOD by gear crack fault vibration signals. The analysis results indicate that the proposed method performs effectively and feasibility for the tooth crack stiffness calculation and the gear tooth crack fault diagnosis.

In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development.

PubMed

Ozerov, Ivan V; Lezhnina, Ksenia V; Izumchenko, Evgeny; Artemov, Artem V; Medintsev, Sergey; Vanhaelen, Quentin; Aliper, Alexander; Vijg, Jan; Osipov, Andreyan N; Labat, Ivan; West, Michael D; Buzdin, Anton; Cantor, Charles R; Nikolsky, Yuri; Borisov, Nikolay; Irincheeva, Irina; Khokhlovich, Edward; Sidransky, David; Camargo, Miguel Luiz; Zhavoronkov, Alex

2016-11-16

Signalling pathway activation analysis is a powerful approach for extracting biologically relevant features from large-scale transcriptomic and proteomic data. However, modern pathway-based methods often fail to provide stable pathway signatures of a specific phenotype or reliable disease biomarkers. In the present study, we introduce the in silico Pathway Activation Network Decomposition Analysis (iPANDA) as a scalable robust method for biomarker identification using gene expression data. The iPANDA method combines precalculated gene coexpression data with gene importance factors based on the degree of differential gene expression and pathway topology decomposition for obtaining pathway activation scores. Using Microarray Analysis Quality Control (MAQC) data sets and pretreatment data on Taxol-based neoadjuvant breast cancer therapy from multiple sources, we demonstrate that iPANDA provides significant noise reduction in transcriptomic data and identifies highly robust sets of biologically relevant pathway signatures. We successfully apply iPANDA for stratifying breast cancer patients according to their sensitivity to neoadjuvant therapy.

In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development

PubMed Central

Ozerov, Ivan V.; Lezhnina, Ksenia V.; Izumchenko, Evgeny; Artemov, Artem V.; Medintsev, Sergey; Vanhaelen, Quentin; Aliper, Alexander; Vijg, Jan; Osipov, Andreyan N.; Labat, Ivan; West, Michael D.; Buzdin, Anton; Cantor, Charles R.; Nikolsky, Yuri; Borisov, Nikolay; Irincheeva, Irina; Khokhlovich, Edward; Sidransky, David; Camargo, Miguel Luiz; Zhavoronkov, Alex

2016-01-01

Signalling pathway activation analysis is a powerful approach for extracting biologically relevant features from large-scale transcriptomic and proteomic data. However, modern pathway-based methods often fail to provide stable pathway signatures of a specific phenotype or reliable disease biomarkers. In the present study, we introduce the in silico Pathway Activation Network Decomposition Analysis (iPANDA) as a scalable robust method for biomarker identification using gene expression data. The iPANDA method combines precalculated gene coexpression data with gene importance factors based on the degree of differential gene expression and pathway topology decomposition for obtaining pathway activation scores. Using Microarray Analysis Quality Control (MAQC) data sets and pretreatment data on Taxol-based neoadjuvant breast cancer therapy from multiple sources, we demonstrate that iPANDA provides significant noise reduction in transcriptomic data and identifies highly robust sets of biologically relevant pathway signatures. We successfully apply iPANDA for stratifying breast cancer patients according to their sensitivity to neoadjuvant therapy. PMID:27848968

Synthesis of porous sheet-like Co{sub 3}O{sub 4} microstructure by precipitation method and its potential applications in the thermal decomposition of ammonium perchlorate

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

Lu Shanshan; Jing Xiaoyan; Liu Jingyuan

2013-01-15

Porous sheet-like cobalt oxide (Co{sub 3}O{sub 4}) were successfully synthesized by precipitation method combined with calcination of cobalt hydroxide precursors. The structure, morphology and porosity properties of the products were characterized by X-ray powder diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and nitrogen adsorption-desorption measurement. The as-prepared sheet-like microstructures were approximately 2-3 {mu}m in average diameter, and the morphology of the cobalt hydroxide precursors was retained after the calcination process. However, it appeared a large number of uniform pores in the sheets after calcination. In order to calculate the potential catalytic activity, the thermal decomposition of ammoniummore » perchlorate (AP) has been analyzed, in which cobalt oxide played a role of an additive and the porous sheet-like Co{sub 3}O{sub 4} microstructures exhibited high catalytic performance and considerable decrease in the thermal decomposition temperature of AP. Moreover, a formation mechanism for the sheet-like microstructures has been discussed. - Graphical abstract: Porous sheet-like Co{sub 3}O{sub 4} were synthesized by facile precipitation method combined with calcination of {beta}-Co(OH){sub 2} precursors. Thermogravimetric-differential scanning calorimetric analysis indicates potential catalytic activity in the thermal decomposition of ammonium perchlorate. Highlights: Black-Right-Pointing-Pointer Synthesis of sheet-like {beta}-Co(OH){sub 2} precursors by precipitation method. Black-Right-Pointing-Pointer Porous sheet-like Co{sub 3}O{sub 4} were obtained by calcining {beta}-Co(OH){sub 2} precursors. Black-Right-Pointing-Pointer The possible formation mechanism of porous sheet-like Co{sub 3}O{sub 4} has been discussed. Black-Right-Pointing-Pointer Porous sheet-like Co{sub 3}O{sub 4} decrease the thermal decomposition temperature of ammonium perchlorate.« less

An improved EMD method for modal identification and a combined static-dynamic method for damage detection

NASA Astrophysics Data System (ADS)

Yang, Jinping; Li, Peizhen; Yang, Youfa; Xu, Dian

2018-04-01

Empirical mode decomposition (EMD) is a highly adaptable signal processing method. However, the EMD approach has certain drawbacks, including distortions from end effects and mode mixing. In the present study, these two problems are addressed using an end extension method based on the support vector regression machine (SVRM) and a modal decomposition method based on the characteristics of the Hilbert transform. The algorithm includes two steps: using the SVRM, the time series data are extended at both endpoints to reduce the end effects, and then, a modified EMD method using the characteristics of the Hilbert transform is performed on the resulting signal to reduce mode mixing. A new combined static-dynamic method for identifying structural damage is presented. This method combines the static and dynamic information in an equilibrium equation that can be solved using the Moore-Penrose generalized matrix inverse. The combination method uses the differences in displacements of the structure with and without damage and variations in the modal force vector. Tests on a four-story, steel-frame structure were conducted to obtain static and dynamic responses of the structure. The modal parameters are identified using data from the dynamic tests and improved EMD method. The new method is shown to be more accurate and effective than the traditional EMD method. Through tests with a shear-type test frame, the higher performance of the proposed static-dynamic damage detection approach, which can detect both single and multiple damage locations and the degree of the damage, is demonstrated. For structures with multiple damage, the combined approach is more effective than either the static or dynamic method. The proposed EMD method and static-dynamic damage detection method offer improved modal identification and damage detection, respectively, in structures.

Multidimensional k-nearest neighbor model based on EEMD for financial time series forecasting

NASA Astrophysics Data System (ADS)

Zhang, Ningning; Lin, Aijing; Shang, Pengjian

2017-07-01

In this paper, we propose a new two-stage methodology that combines the ensemble empirical mode decomposition (EEMD) with multidimensional k-nearest neighbor model (MKNN) in order to forecast the closing price and high price of the stocks simultaneously. The modified algorithm of k-nearest neighbors (KNN) has an increasingly wide application in the prediction of all fields. Empirical mode decomposition (EMD) decomposes a nonlinear and non-stationary signal into a series of intrinsic mode functions (IMFs), however, it cannot reveal characteristic information of the signal with much accuracy as a result of mode mixing. So ensemble empirical mode decomposition (EEMD), an improved method of EMD, is presented to resolve the weaknesses of EMD by adding white noise to the original data. With EEMD, the components with true physical meaning can be extracted from the time series. Utilizing the advantage of EEMD and MKNN, the new proposed ensemble empirical mode decomposition combined with multidimensional k-nearest neighbor model (EEMD-MKNN) has high predictive precision for short-term forecasting. Moreover, we extend this methodology to the case of two-dimensions to forecast the closing price and high price of the four stocks (NAS, S&P500, DJI and STI stock indices) at the same time. The results indicate that the proposed EEMD-MKNN model has a higher forecast precision than EMD-KNN, KNN method and ARIMA.

Basis material decomposition method for material discrimination with a new spectrometric X-ray imaging detector

NASA Astrophysics Data System (ADS)

Brambilla, A.; Gorecki, A.; Potop, A.; Paulus, C.; Verger, L.

2017-08-01

Energy sensitive photon counting X-ray detectors provide energy dependent information which can be exploited for material identification. The attenuation of an X-ray beam as a function of energy depends on the effective atomic number Zeff and the density. However, the measured attenuation is degraded by the imperfections of the detector response such as charge sharing or pile-up. These imperfections lead to non-linearities that limit the benefits of energy resolved imaging. This work aims to implement a basis material decomposition method which overcomes these problems. Basis material decomposition is based on the fact that the attenuation of any material or complex object can be accurately reproduced by a combination of equivalent thicknesses of basis materials. Our method is based on a calibration phase to learn the response of the detector for different combinations of thicknesses of the basis materials. The decomposition algorithm finds the thicknesses of basis material whose spectrum is closest to the measurement, using a maximum likelihood criterion assuming a Poisson law distribution of photon counts for each energy bin. The method was used with a ME100 linear array spectrometric X-ray imager to decompose different plastic materials on a Polyethylene and Polyvinyl Chloride base. The resulting equivalent thicknesses were used to estimate the effective atomic number Zeff. The results are in good agreement with the theoretical Zeff, regardless of the plastic sample thickness. The linear behaviour of the equivalent lengths makes it possible to process overlapped materials. Moreover, the method was tested with a 3 materials base by adding gadolinium, whose K-edge is not taken into account by the other two materials. The proposed method has the advantage that it can be used with any number of energy channels, taking full advantage of the high energy resolution of the ME100 detector. Although in principle two channels are sufficient, experimental measurements show that the use of a high number of channels significantly improves the accuracy of decomposition by reducing noise and systematic bias.

Urban-area extraction from polarimetric SAR image using combination of target decomposition and orientation angle

NASA Astrophysics Data System (ADS)

Zou, Bin; Lu, Da; Wu, Zhilu; Qiao, Zhijun G.

2016-05-01

The results of model-based target decomposition are the main features used to discriminate urban and non-urban area in polarimetric synthetic aperture radar (PolSAR) application. Traditional urban-area extraction methods based on modelbased target decomposition usually misclassified ground-trunk structure as urban-area or misclassified rotated urbanarea as forest. This paper introduces another feature named orientation angle to improve urban-area extraction scheme for the accurate mapping in urban by PolSAR image. The proposed method takes randomness of orientation angle into account for restriction of urban area first and, subsequently, implements rotation angle to improve results that oriented urban areas are recognized as double-bounce objects from volume scattering. ESAR L-band PolSAR data of the Oberpfaffenhofen Test Site Area was used to validate the proposed algorithm.

Modeling Complex Phenomena Using Multiscale Time Sequences

DTIC Science & Technology

2009-08-24

measures based on Hurst and Holder exponents , auto-regressive methods and Fourier and wavelet decomposition methods. The applications for this technology...relate to each other. This can be done by combining a set statistical fractal measures based on Hurst and Holder exponents , auto-regressive...different scales and how these scales relate to each other. This can be done by combining a set statistical fractal measures based on Hurst and

Error analysis of multipoint flux domain decomposition methods for evolutionary diffusion problems

NASA Astrophysics Data System (ADS)

Arrarás, A.; Portero, L.; Yotov, I.

2014-01-01

We study space and time discretizations for mixed formulations of parabolic problems. The spatial approximation is based on the multipoint flux mixed finite element method, which reduces to an efficient cell-centered pressure system on general grids, including triangles, quadrilaterals, tetrahedra, and hexahedra. The time integration is performed by using a domain decomposition time-splitting technique combined with multiterm fractional step diagonally implicit Runge-Kutta methods. The resulting scheme is unconditionally stable and computationally efficient, as it reduces the global system to a collection of uncoupled subdomain problems that can be solved in parallel without the need for Schwarz-type iteration. Convergence analysis for both the semidiscrete and fully discrete schemes is presented.

Decomposition of intact chicken feathers by a thermophile in combination with an acidulocomposting garbage-treatment process.

PubMed

Shigeri, Yasushi; Matsui, Tatsunobu; Watanabe, Kunihiko

2009-11-01

In order to develop a practical method for the decomposition of intact chicken feathers, a moderate thermophile strain, Meiothermus ruber H328, having strong keratinolytic activity, was used in a bio-type garbage-treatment machine working with an acidulocomposting process. The addition of strain H328 cells (15 g) combined with acidulocomposting in the garbage machine resulted in 70% degradation of intact chicken feathers (30 g) within 14 d. This degradation efficiency is comparable to a previous result employing the strain as a single bacterium in flask culture, and it indicates that strain H328 can promote intact feather degradation activity in a garbage machine currently on the market.

Image fusion method based on regional feature and improved bidimensional empirical mode decomposition

NASA Astrophysics Data System (ADS)

Qin, Xinqiang; Hu, Gang; Hu, Kai

2018-01-01

The decomposition of multiple source images using bidimensional empirical mode decomposition (BEMD) often produces mismatched bidimensional intrinsic mode functions, either by their number or their frequency, making image fusion difficult. A solution to this problem is proposed using a fixed number of iterations and a union operation in the sifting process. By combining the local regional features of the images, an image fusion method has been developed. First, the source images are decomposed using the proposed BEMD to produce the first intrinsic mode function (IMF) and residue component. Second, for the IMF component, a selection and weighted average strategy based on local area energy is used to obtain a high-frequency fusion component. Third, for the residue component, a selection and weighted average strategy based on local average gray difference is used to obtain a low-frequency fusion component. Finally, the fused image is obtained by applying the inverse BEMD transform. Experimental results show that the proposed algorithm provides superior performance over methods based on wavelet transform, line and column-based EMD, and complex empirical mode decomposition, both in terms of visual quality and objective evaluation criteria.

Kinetic analysis of overlapping multistep thermal decomposition comprising exothermic and endothermic processes: thermolysis of ammonium dinitramide.

PubMed

Muravyev, Nikita V; Koga, Nobuyoshi; Meerov, Dmitry B; Pivkina, Alla N

2017-01-25

This study focused on kinetic modeling of a specific type of multistep heterogeneous reaction comprising exothermic and endothermic reaction steps, as exemplified by the practical kinetic analysis of the experimental kinetic curves for the thermal decomposition of molten ammonium dinitramide (ADN). It is known that the thermal decomposition of ADN occurs as a consecutive two step mass-loss process comprising the decomposition of ADN and subsequent evaporation/decomposition of in situ generated ammonium nitrate. These reaction steps provide exothermic and endothermic contributions, respectively, to the overall thermal effect. The overall reaction process was deconvoluted into two reaction steps using simultaneously recorded thermogravimetry and differential scanning calorimetry (TG-DSC) curves by considering the different physical meanings of the kinetic data derived from TG and DSC by P value analysis. The kinetic data thus separated into exothermic and endothermic reaction steps were kinetically characterized using kinetic computation methods including isoconversional method, combined kinetic analysis, and master plot method. The overall kinetic behavior was reproduced as the sum of the kinetic equations for each reaction step considering the contributions to the rate data derived from TG and DSC. During reproduction of the kinetic behavior, the kinetic parameters and contributions of each reaction step were optimized using kinetic deconvolution analysis. As a result, the thermal decomposition of ADN was successfully modeled as partially overlapping exothermic and endothermic reaction steps. The logic of the kinetic modeling was critically examined, and the practical usefulness of phenomenological modeling for the thermal decomposition of ADN was illustrated to demonstrate the validity of the methodology and its applicability to similar complex reaction processes.

DFT study of hydrogen production from formic acid decomposition on Pd-Au alloy nanoclusters

NASA Astrophysics Data System (ADS)

Liu, D.; Gao, Z. Y.; Wang, X. C.; Zeng, J.; Li, Y. M.

2017-12-01

Recently, it has been reported that the hydrogen production rate of formic acid decomposition can be significantly increased using Pd-Au binary alloy nano-catalysts [Wang et al. J. Mater. Chem. A 1 (2013) 12721-12725]. To explain the reaction mechanism of this alloy catalysis method, formic acid decomposition reactions on pure Pd and Pd-Au alloy nanoclusters are studied via density functional theory simulations. The simulation results indicate that the addition of inert element Au would not influence formic acid decomposition on Pd surface sites of Pd-Au alloy nanoclusters. On the other hand, the existence of Au surface sites brings relative weak hydrogen atom adsorption. On Pd-Au alloy nanoclusters, the dissociated hydrogen atoms from formic acid are easier to combine as hydrogen molecules than that on pure Pd clusters. Via the synergetic effect between Pd and Au, both formic acid decomposition and hydrogen production are events with large probability, which eventually results in high hydrogen production rate.

Matrix decomposition graphics processing unit solver for Poisson image editing

NASA Astrophysics Data System (ADS)

Lei, Zhao; Wei, Li

2012-10-01

In recent years, gradient-domain methods have been widely discussed in the image processing field, including seamless cloning and image stitching. These algorithms are commonly carried out by solving a large sparse linear system: the Poisson equation. However, solving the Poisson equation is a computational and memory intensive task which makes it not suitable for real-time image editing. A new matrix decomposition graphics processing unit (GPU) solver (MDGS) is proposed to settle the problem. A matrix decomposition method is used to distribute the work among GPU threads, so that MDGS will take full advantage of the computing power of current GPUs. Additionally, MDGS is a hybrid solver (combines both the direct and iterative techniques) and has two-level architecture. These enable MDGS to generate identical solutions with those of the common Poisson methods and achieve high convergence rate in most cases. This approach is advantageous in terms of parallelizability, enabling real-time image processing, low memory-taken and extensive applications.

Circular Mixture Modeling of Color Distribution for Blind Stain Separation in Pathology Images.

PubMed

Li, Xingyu; Plataniotis, Konstantinos N

2017-01-01

In digital pathology, to address color variation and histological component colocalization in pathology images, stain decomposition is usually performed preceding spectral normalization and tissue component segmentation. This paper examines the problem of stain decomposition, which is a naturally nonnegative matrix factorization (NMF) problem in algebra, and introduces a systematical and analytical solution consisting of a circular color analysis module and an NMF-based computation module. Unlike the paradigm of existing stain decomposition algorithms where stain proportions are computed from estimated stain spectra using a matrix inverse operation directly, the introduced solution estimates stain spectra and stain depths via probabilistic reasoning individually. Since the proposed method pays extra attentions to achromatic pixels in color analysis and stain co-occurrence in pixel clustering, it achieves consistent and reliable stain decomposition with minimum decomposition residue. Particularly, aware of the periodic and angular nature of hue, we propose the use of a circular von Mises mixture model to analyze the hue distribution, and provide a complete color-based pixel soft-clustering solution to address color mixing introduced by stain overlap. This innovation combined with saturation-weighted computation makes our study effective for weak stains and broad-spectrum stains. Extensive experimentation on multiple public pathology datasets suggests that our approach outperforms state-of-the-art blind stain separation methods in terms of decomposition effectiveness.

A data-driven method to enhance vibration signal decomposition for rolling bearing fault analysis

NASA Astrophysics Data System (ADS)

Grasso, M.; Chatterton, S.; Pennacchi, P.; Colosimo, B. M.

2016-12-01

Health condition analysis and diagnostics of rotating machinery requires the capability of properly characterizing the information content of sensor signals in order to detect and identify possible fault features. Time-frequency analysis plays a fundamental role, as it allows determining both the existence and the causes of a fault. The separation of components belonging to different time-frequency scales, either associated to healthy or faulty conditions, represents a challenge that motivates the development of effective methodologies for multi-scale signal decomposition. In this framework, the Empirical Mode Decomposition (EMD) is a flexible tool, thanks to its data-driven and adaptive nature. However, the EMD usually yields an over-decomposition of the original signals into a large number of intrinsic mode functions (IMFs). The selection of most relevant IMFs is a challenging task, and the reference literature lacks automated methods to achieve a synthetic decomposition into few physically meaningful modes by avoiding the generation of spurious or meaningless modes. The paper proposes a novel automated approach aimed at generating a decomposition into a minimal number of relevant modes, called Combined Mode Functions (CMFs), each consisting in a sum of adjacent IMFs that share similar properties. The final number of CMFs is selected in a fully data driven way, leading to an enhanced characterization of the signal content without any information loss. A novel criterion to assess the dissimilarity between adjacent CMFs is proposed, based on probability density functions of frequency spectra. The method is suitable to analyze vibration signals that may be periodically acquired within the operating life of rotating machineries. A rolling element bearing fault analysis based on experimental data is presented to demonstrate the performances of the method and the provided benefits.

High-purity Cu nanocrystal synthesis by a dynamic decomposition method.

PubMed

Jian, Xian; Cao, Yu; Chen, Guozhang; Wang, Chao; Tang, Hui; Yin, Liangjun; Luan, Chunhong; Liang, Yinglin; Jiang, Jing; Wu, Sixin; Zeng, Qing; Wang, Fei; Zhang, Chengui

2014-12-01

Cu nanocrystals are applied extensively in several fields, particularly in the microelectron, sensor, and catalysis. The catalytic behavior of Cu nanocrystals depends mainly on the structure and particle size. In this work, formation of high-purity Cu nanocrystals is studied using a common chemical vapor deposition precursor of cupric tartrate. This process is investigated through a combined experimental and computational approach. The decomposition kinetics is researched via differential scanning calorimetry and thermogravimetric analysis using Flynn-Wall-Ozawa, Kissinger, and Starink methods. The growth was found to be influenced by the factors of reaction temperature, protective gas, and time. And microstructural and thermal characterizations were performed by X-ray diffraction, scanning electron microscopy, transmission electron microscopy, and differential scanning calorimetry. Decomposition of cupric tartrate at different temperatures was simulated by density functional theory calculations under the generalized gradient approximation. High crystalline Cu nanocrystals without floccules were obtained from thermal decomposition of cupric tartrate at 271°C for 8 h under Ar. This general approach paves a way to controllable synthesis of Cu nanocrystals with high purity.

High-purity Cu nanocrystal synthesis by a dynamic decomposition method

NASA Astrophysics Data System (ADS)

Jian, Xian; Cao, Yu; Chen, Guozhang; Wang, Chao; Tang, Hui; Yin, Liangjun; Luan, Chunhong; Liang, Yinglin; Jiang, Jing; Wu, Sixin; Zeng, Qing; Wang, Fei; Zhang, Chengui

2014-12-01

Cu nanocrystals are applied extensively in several fields, particularly in the microelectron, sensor, and catalysis. The catalytic behavior of Cu nanocrystals depends mainly on the structure and particle size. In this work, formation of high-purity Cu nanocrystals is studied using a common chemical vapor deposition precursor of cupric tartrate. This process is investigated through a combined experimental and computational approach. The decomposition kinetics is researched via differential scanning calorimetry and thermogravimetric analysis using Flynn-Wall-Ozawa, Kissinger, and Starink methods. The growth was found to be influenced by the factors of reaction temperature, protective gas, and time. And microstructural and thermal characterizations were performed by X-ray diffraction, scanning electron microscopy, transmission electron microscopy, and differential scanning calorimetry. Decomposition of cupric tartrate at different temperatures was simulated by density functional theory calculations under the generalized gradient approximation. High crystalline Cu nanocrystals without floccules were obtained from thermal decomposition of cupric tartrate at 271°C for 8 h under Ar. This general approach paves a way to controllable synthesis of Cu nanocrystals with high purity.

Scalable and fast heterogeneous molecular simulation with predictive parallelization schemes

NASA Astrophysics Data System (ADS)

Guzman, Horacio V.; Junghans, Christoph; Kremer, Kurt; Stuehn, Torsten

2017-11-01

Multiscale and inhomogeneous molecular systems are challenging topics in the field of molecular simulation. In particular, modeling biological systems in the context of multiscale simulations and exploring material properties are driving a permanent development of new simulation methods and optimization algorithms. In computational terms, those methods require parallelization schemes that make a productive use of computational resources for each simulation and from its genesis. Here, we introduce the heterogeneous domain decomposition approach, which is a combination of an heterogeneity-sensitive spatial domain decomposition with an a priori rearrangement of subdomain walls. Within this approach, the theoretical modeling and scaling laws for the force computation time are proposed and studied as a function of the number of particles and the spatial resolution ratio. We also show the new approach capabilities, by comparing it to both static domain decomposition algorithms and dynamic load-balancing schemes. Specifically, two representative molecular systems have been simulated and compared to the heterogeneous domain decomposition proposed in this work. These two systems comprise an adaptive resolution simulation of a biomolecule solvated in water and a phase-separated binary Lennard-Jones fluid.

Signal processing method and system for noise removal and signal extraction

DOEpatents

Fu, Chi Yung; Petrich, Loren

2009-04-14

A signal processing method and system combining smooth level wavelet pre-processing together with artificial neural networks all in the wavelet domain for signal denoising and extraction. Upon receiving a signal corrupted with noise, an n-level decomposition of the signal is performed using a discrete wavelet transform to produce a smooth component and a rough component for each decomposition level. The n.sup.th level smooth component is then inputted into a corresponding neural network pre-trained to filter out noise in that component by pattern recognition in the wavelet domain. Additional rough components, beginning at the highest level, may also be retained and inputted into corresponding neural networks pre-trained to filter out noise in those components also by pattern recognition in the wavelet domain. In any case, an inverse discrete wavelet transform is performed on the combined output from all the neural networks to recover a clean signal back in the time domain.

Iterative image-domain decomposition for dual-energy CT

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

Niu, Tianye; Dong, Xue; Petrongolo, Michael

2014-04-15

Purpose: Dual energy CT (DECT) imaging plays an important role in advanced imaging applications due to its capability of material decomposition. Direct decomposition via matrix inversion suffers from significant degradation of image signal-to-noise ratios, which reduces clinical values of DECT. Existing denoising algorithms achieve suboptimal performance since they suppress image noise either before or after the decomposition and do not fully explore the noise statistical properties of the decomposition process. In this work, the authors propose an iterative image-domain decomposition method for noise suppression in DECT, using the full variance-covariance matrix of the decomposed images. Methods: The proposed algorithm ismore » formulated in the form of least-square estimation with smoothness regularization. Based on the design principles of a best linear unbiased estimator, the authors include the inverse of the estimated variance-covariance matrix of the decomposed images as the penalty weight in the least-square term. The regularization term enforces the image smoothness by calculating the square sum of neighboring pixel value differences. To retain the boundary sharpness of the decomposed images, the authors detect the edges in the CT images before decomposition. These edge pixels have small weights in the calculation of the regularization term. Distinct from the existing denoising algorithms applied on the images before or after decomposition, the method has an iterative process for noise suppression, with decomposition performed in each iteration. The authors implement the proposed algorithm using a standard conjugate gradient algorithm. The method performance is evaluated using an evaluation phantom (Catphan©600) and an anthropomorphic head phantom. The results are compared with those generated using direct matrix inversion with no noise suppression, a denoising method applied on the decomposed images, and an existing algorithm with similar formulation as the proposed method but with an edge-preserving regularization term. Results: On the Catphan phantom, the method maintains the same spatial resolution on the decomposed images as that of the CT images before decomposition (8 pairs/cm) while significantly reducing their noise standard deviation. Compared to that obtained by the direct matrix inversion, the noise standard deviation in the images decomposed by the proposed algorithm is reduced by over 98%. Without considering the noise correlation properties in the formulation, the denoising scheme degrades the spatial resolution to 6 pairs/cm for the same level of noise suppression. Compared to the edge-preserving algorithm, the method achieves better low-contrast detectability. A quantitative study is performed on the contrast-rod slice of Catphan phantom. The proposed method achieves lower electron density measurement error as compared to that by the direct matrix inversion, and significantly reduces the error variation by over 97%. On the head phantom, the method reduces the noise standard deviation of decomposed images by over 97% without blurring the sinus structures. Conclusions: The authors propose an iterative image-domain decomposition method for DECT. The method combines noise suppression and material decomposition into an iterative process and achieves both goals simultaneously. By exploring the full variance-covariance properties of the decomposed images and utilizing the edge predetection, the proposed algorithm shows superior performance on noise suppression with high image spatial resolution and low-contrast detectability.« less

Low-Rank Correction Methods for Algebraic Domain Decomposition Preconditioners

DOE PAGES

Li, Ruipeng; Saad, Yousef

2017-08-01

This study presents a parallel preconditioning method for distributed sparse linear systems, based on an approximate inverse of the original matrix, that adopts a general framework of distributed sparse matrices and exploits domain decomposition (DD) and low-rank corrections. The DD approach decouples the matrix and, once inverted, a low-rank approximation is applied by exploiting the Sherman--Morrison--Woodbury formula, which yields two variants of the preconditioning methods. The low-rank expansion is computed by the Lanczos procedure with reorthogonalizations. Numerical experiments indicate that, when combined with Krylov subspace accelerators, this preconditioner can be efficient and robust for solving symmetric sparse linear systems. Comparisonsmore » with pARMS, a DD-based parallel incomplete LU (ILU) preconditioning method, are presented for solving Poisson's equation and linear elasticity problems.« less

Low-Rank Correction Methods for Algebraic Domain Decomposition Preconditioners

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

Li, Ruipeng; Saad, Yousef

This study presents a parallel preconditioning method for distributed sparse linear systems, based on an approximate inverse of the original matrix, that adopts a general framework of distributed sparse matrices and exploits domain decomposition (DD) and low-rank corrections. The DD approach decouples the matrix and, once inverted, a low-rank approximation is applied by exploiting the Sherman--Morrison--Woodbury formula, which yields two variants of the preconditioning methods. The low-rank expansion is computed by the Lanczos procedure with reorthogonalizations. Numerical experiments indicate that, when combined with Krylov subspace accelerators, this preconditioner can be efficient and robust for solving symmetric sparse linear systems. Comparisonsmore » with pARMS, a DD-based parallel incomplete LU (ILU) preconditioning method, are presented for solving Poisson's equation and linear elasticity problems.« less

Highly Efficient and Scalable Compound Decomposition of Two-Electron Integral Tensor and Its Application in Coupled Cluster Calculations

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

Peng, Bo; Kowalski, Karol

The representation and storage of two-electron integral tensors are vital in large- scale applications of accurate electronic structure methods. Low-rank representation and efficient storage strategy of integral tensors can significantly reduce the numerical overhead and consequently time-to-solution of these methods. In this paper, by combining pivoted incomplete Cholesky decomposition (CD) with a follow-up truncated singular vector decomposition (SVD), we develop a decomposition strategy to approximately represent the two-electron integral tensor in terms of low-rank vectors. A systematic benchmark test on a series of 1-D, 2-D, and 3-D carbon-hydrogen systems demonstrates high efficiency and scalability of the compound two-step decomposition ofmore » the two-electron integral tensor in our implementation. For the size of atomic basis set N_b ranging from ~ 100 up to ~ 2, 000, the observed numerical scaling of our implementation shows O(N_b^{2.5~3}) versus O(N_b^{3~4}) of single CD in most of other implementations. More importantly, this decomposition strategy can significantly reduce the storage requirement of the atomic-orbital (AO) two-electron integral tensor from O(N_b^4) to O(N_b^2 log_{10}(N_b)) with moderate decomposition thresholds. The accuracy tests have been performed using ground- and excited-state formulations of coupled- cluster formalism employing single and double excitations (CCSD) on several bench- mark systems including the C_{60} molecule described by nearly 1,400 basis functions. The results show that the decomposition thresholds can be generally set to 10^{-4} to 10^{-3} to give acceptable compromise between efficiency and accuracy.« less

Highly Efficient and Scalable Compound Decomposition of Two-Electron Integral Tensor and Its Application in Coupled Cluster Calculations.

PubMed

Peng, Bo; Kowalski, Karol

2017-09-12

The representation and storage of two-electron integral tensors are vital in large-scale applications of accurate electronic structure methods. Low-rank representation and efficient storage strategy of integral tensors can significantly reduce the numerical overhead and consequently time-to-solution of these methods. In this work, by combining pivoted incomplete Cholesky decomposition (CD) with a follow-up truncated singular vector decomposition (SVD), we develop a decomposition strategy to approximately represent the two-electron integral tensor in terms of low-rank vectors. A systematic benchmark test on a series of 1-D, 2-D, and 3-D carbon-hydrogen systems demonstrates high efficiency and scalability of the compound two-step decomposition of the two-electron integral tensor in our implementation. For the size of the atomic basis set, N b , ranging from ∼100 up to ∼2,000, the observed numerical scaling of our implementation shows [Formula: see text] versus [Formula: see text] cost of performing single CD on the two-electron integral tensor in most of the other implementations. More importantly, this decomposition strategy can significantly reduce the storage requirement of the atomic orbital (AO) two-electron integral tensor from [Formula: see text] to [Formula: see text] with moderate decomposition thresholds. The accuracy tests have been performed using ground- and excited-state formulations of coupled cluster formalism employing single and double excitations (CCSD) on several benchmark systems including the C 60 molecule described by nearly 1,400 basis functions. The results show that the decomposition thresholds can be generally set to 10 -4 to 10 -3 to give acceptable compromise between efficiency and accuracy.

Structural system identification based on variational mode decomposition

NASA Astrophysics Data System (ADS)

Bagheri, Abdollah; Ozbulut, Osman E.; Harris, Devin K.

2018-03-01

In this paper, a new structural identification method is proposed to identify the modal properties of engineering structures based on dynamic response decomposition using the variational mode decomposition (VMD). The VMD approach is a decomposition algorithm that has been developed as a means to overcome some of the drawbacks and limitations of the empirical mode decomposition method. The VMD-based modal identification algorithm decomposes the acceleration signal into a series of distinct modal responses and their respective center frequencies, such that when combined their cumulative modal responses reproduce the original acceleration response. The decaying amplitude of the extracted modal responses is then used to identify the modal damping ratios using a linear fitting function on modal response data. Finally, after extracting modal responses from available sensors, the mode shape vector for each of the decomposed modes in the system is identified from all obtained modal response data. To demonstrate the efficiency of the algorithm, a series of numerical, laboratory, and field case studies were evaluated. The laboratory case study utilized the vibration response of a three-story shear frame, whereas the field study leveraged the ambient vibration response of a pedestrian bridge to characterize the modal properties of the structure. The modal properties of the shear frame were computed using analytical approach for a comparison with the experimental modal frequencies. Results from these case studies demonstrated that the proposed method is efficient and accurate in identifying modal data of the structures.

Domain decomposition methods in computational fluid dynamics

NASA Technical Reports Server (NTRS)

Gropp, William D.; Keyes, David E.

1991-01-01

The divide-and-conquer paradigm of iterative domain decomposition, or substructuring, has become a practical tool in computational fluid dynamic applications because of its flexibility in accommodating adaptive refinement through locally uniform (or quasi-uniform) grids, its ability to exploit multiple discretizations of the operator equations, and the modular pathway it provides towards parallelism. These features are illustrated on the classic model problem of flow over a backstep using Newton's method as the nonlinear iteration. Multiple discretizations (second-order in the operator and first-order in the preconditioner) and locally uniform mesh refinement pay dividends separately, and they can be combined synergistically. Sample performance results are included from an Intel iPSC/860 hypercube implementation.

CT Image Sequence Restoration Based on Sparse and Low-Rank Decomposition

PubMed Central

Gou, Shuiping; Wang, Yueyue; Wang, Zhilong; Peng, Yong; Zhang, Xiaopeng; Jiao, Licheng; Wu, Jianshe

2013-01-01

Blurry organ boundaries and soft tissue structures present a major challenge in biomedical image restoration. In this paper, we propose a low-rank decomposition-based method for computed tomography (CT) image sequence restoration, where the CT image sequence is decomposed into a sparse component and a low-rank component. A new point spread function of Weiner filter is employed to efficiently remove blur in the sparse component; a wiener filtering with the Gaussian PSF is used to recover the average image of the low-rank component. And then we get the recovered CT image sequence by combining the recovery low-rank image with all recovery sparse image sequence. Our method achieves restoration results with higher contrast, sharper organ boundaries and richer soft tissue structure information, compared with existing CT image restoration methods. The robustness of our method was assessed with numerical experiments using three different low-rank models: Robust Principle Component Analysis (RPCA), Linearized Alternating Direction Method with Adaptive Penalty (LADMAP) and Go Decomposition (GoDec). Experimental results demonstrated that the RPCA model was the most suitable for the small noise CT images whereas the GoDec model was the best for the large noisy CT images. PMID:24023764

Applying Novel Time-Frequency Moments Singular Value Decomposition Method and Artificial Neural Networks for Ballistocardiography

NASA Astrophysics Data System (ADS)

Akhbardeh, Alireza; Junnila, Sakari; Koivuluoma, Mikko; Koivistoinen, Teemu; Värri, Alpo

2006-12-01

As we know, singular value decomposition (SVD) is designed for computing singular values (SVs) of a matrix. Then, if it is used for finding SVs of an [InlineEquation not available: see fulltext.]-by-1 or 1-by- [InlineEquation not available: see fulltext.] array with elements representing samples of a signal, it will return only one singular value that is not enough to express the whole signal. To overcome this problem, we designed a new kind of the feature extraction method which we call ''time-frequency moments singular value decomposition (TFM-SVD).'' In this new method, we use statistical features of time series as well as frequency series (Fourier transform of the signal). This information is then extracted into a certain matrix with a fixed structure and the SVs of that matrix are sought. This transform can be used as a preprocessing stage in pattern clustering methods. The results in using it indicate that the performance of a combined system including this transform and classifiers is comparable with the performance of using other feature extraction methods such as wavelet transforms. To evaluate TFM-SVD, we applied this new method and artificial neural networks (ANNs) for ballistocardiogram (BCG) data clustering to look for probable heart disease of six test subjects. BCG from the test subjects was recorded using a chair-like ballistocardiograph, developed in our project. This kind of device combined with automated recording and analysis would be suitable for use in many places, such as home, office, and so forth. The results show that the method has high performance and it is almost insensitive to BCG waveform latency or nonlinear disturbance.

Decomposition of the complex system into nonlinear spatio-temporal modes: algorithm and application to climate data mining

NASA Astrophysics Data System (ADS)

Feigin, Alexander; Gavrilov, Andrey; Loskutov, Evgeny; Mukhin, Dmitry

2015-04-01

Proper decomposition of the complex system into well separated "modes" is a way to reveal and understand the mechanisms governing the system behaviour as well as discover essential feedbacks and nonlinearities. The decomposition is also natural procedure that provides to construct adequate and concurrently simplest models of both corresponding sub-systems, and of the system in whole. In recent works two new methods of decomposition of the Earth's climate system into well separated modes were discussed. The first method [1-3] is based on the MSSA (Multichannel Singular Spectral Analysis) [4] for linear expanding vector (space-distributed) time series and makes allowance delayed correlations of the processes recorded in spatially separated points. The second one [5-7] allows to construct nonlinear dynamic modes, but neglects delay of correlations. It was demonstrated [1-3] that first method provides effective separation of different time scales, but prevent from correct reduction of data dimension: slope of variance spectrum of spatio-temporal empirical orthogonal functions that are "structural material" for linear spatio-temporal modes, is too flat. The second method overcomes this problem: variance spectrum of nonlinear modes falls essentially sharply [5-7]. However neglecting time-lag correlations brings error of mode selection that is uncontrolled and increases with growth of mode time scale. In the report we combine these two methods in such a way that the developed algorithm allows constructing nonlinear spatio-temporal modes. The algorithm is applied for decomposition of (i) multi hundreds years globally distributed data generated by the INM RAS Coupled Climate Model [8], and (ii) 156 years time series of SST anomalies distributed over the globe [9]. We compare efficiency of different methods of decomposition and discuss the abilities of nonlinear spatio-temporal modes for construction of adequate and concurrently simplest ("optimal") models of climate systems. 1. Feigin A.M., Mukhin D., Gavrilov A., Volodin E.M., and Loskutov E.M. (2013) "Separation of spatial-temporal patterns ("climatic modes") by combined analysis of really measured and generated numerically vector time series", AGU 2013 Fall Meeting, Abstract NG33A-1574. 2. Alexander Feigin, Dmitry Mukhin, Andrey Gavrilov, Evgeny Volodin, and Evgeny Loskutov (2014) "Approach to analysis of multiscale space-distributed time series: separation of spatio-temporal modes with essentially different time scales", Geophysical Research Abstracts, Vol. 16, EGU2014-6877. 3. Dmitry Mukhin, Dmitri Kondrashov, Evgeny Loskutov, Andrey Gavrilov, Alexander Feigin, and Michael Ghil (2014) "Predicting critical transitions in ENSO models, Part II: Spatially dependent models", Journal of Climate (accepted, doi: 10.1175/JCLI-D-14-00240.1). 4. Ghil, M., R. M. Allen, M. D. Dettinger, K. Ide, D. Kondrashov, et al. (2002) "Advanced spectral methods for climatic time series", Rev. Geophys. 40(1), 3.1-3.41. 5. Dmitry Mukhin, Andrey Gavrilov, Evgeny M Loskutov and Alexander M Feigin (2014) "Nonlinear Decomposition of Climate Data: a New Method for Reconstruction of Dynamical Modes", AGU 2014 Fall Meeting, Abstract NG43A-3752. 6. Andrey Gavrilov, Dmitry Mukhin, Evgeny Loskutov, and Alexander Feigin (2015) "Empirical decomposition of climate data into nonlinear dynamic modes", Geophysical Research Abstracts, Vol. 17, EGU2015-627. 7. Dmitry Mukhin, Andrey Gavrilov, Evgeny Loskutov, Alexander Feigin, and Juergen Kurths (2015) "Reconstruction of principal dynamical modes from climatic variability: nonlinear approach", Geophysical Research Abstracts, Vol. 17, EGU2015-5729. 8. http://83.149.207.89/GCM_DATA_PLOTTING/GCM_INM_DATA_XY_en.htm. 9. http://iridl.ldeo.columbia.edu/SOURCES/.KAPLAN/.EXTENDED/.v2/.ssta/.

Nonlinear mode decomposition: A noise-robust, adaptive decomposition method

NASA Astrophysics Data System (ADS)

Iatsenko, Dmytro; McClintock, Peter V. E.; Stefanovska, Aneta

2015-09-01

The signals emanating from complex systems are usually composed of a mixture of different oscillations which, for a reliable analysis, should be separated from each other and from the inevitable background of noise. Here we introduce an adaptive decomposition tool—nonlinear mode decomposition (NMD)—which decomposes a given signal into a set of physically meaningful oscillations for any wave form, simultaneously removing the noise. NMD is based on the powerful combination of time-frequency analysis techniques—which, together with the adaptive choice of their parameters, make it extremely noise robust—and surrogate data tests used to identify interdependent oscillations and to distinguish deterministic from random activity. We illustrate the application of NMD to both simulated and real signals and demonstrate its qualitative and quantitative superiority over other approaches, such as (ensemble) empirical mode decomposition, Karhunen-Loève expansion, and independent component analysis. We point out that NMD is likely to be applicable and useful in many different areas of research, such as geophysics, finance, and the life sciences. The necessary matlab codes for running NMD are freely available for download.

Distance descending ordering method: An O(n) algorithm for inverting the mass matrix in simulation of macromolecules with long branches

NASA Astrophysics Data System (ADS)

Xu, Xiankun; Li, Peiwen

2017-11-01

Fixman's work in 1974 and the follow-up studies have developed a method that can factorize the inverse of mass matrix into an arithmetic combination of three sparse matrices-one of them is positive definite and needs to be further factorized by using the Cholesky decomposition or similar methods. When the molecule subjected to study is of serial chain structure, this method can achieve O (n) time complexity. However, for molecules with long branches, Cholesky decomposition about the corresponding positive definite matrix will introduce massive fill-in due to its nonzero structure. Although there are several methods can be used to reduce the number of fill-in, none of them could strictly guarantee for zero fill-in for all molecules according to our test, and thus cannot obtain O (n) time complexity by using these traditional methods. In this paper we present a new method that can guarantee for no fill-in in doing the Cholesky decomposition, which was developed based on the correlations between the mass matrix and the geometrical structure of molecules. As a result, the inverting of mass matrix will remain the O (n) time complexity, no matter the molecule structure has long branches or not.

Decomposition and correction overlapping peaks of LIBS using an error compensation method combined with curve fitting.

PubMed

Tan, Bing; Huang, Min; Zhu, Qibing; Guo, Ya; Qin, Jianwei

2017-09-01

The laser induced breakdown spectroscopy (LIBS) technique is an effective method to detect material composition by obtaining the plasma emission spectrum. The overlapping peaks in the spectrum are a fundamental problem in the qualitative and quantitative analysis of LIBS. Based on a curve fitting method, this paper studies an error compensation method to achieve the decomposition and correction of overlapping peaks. The vital step is that the fitting residual is fed back to the overlapping peaks and performs multiple curve fitting processes to obtain a lower residual result. For the quantitative experiments of Cu, the Cu-Fe overlapping peaks in the range of 321-327 nm obtained from the LIBS spectrum of five different concentrations of CuSO 4 ·5H 2 O solution were decomposed and corrected using curve fitting and error compensation methods. Compared with the curve fitting method, the error compensation reduced the fitting residual about 18.12-32.64% and improved the correlation about 0.86-1.82%. Then, the calibration curve between the intensity and concentration of the Cu was established. It can be seen that the error compensation method exhibits a higher linear correlation between the intensity and concentration of Cu, which can be applied to the decomposition and correction of overlapping peaks in the LIBS spectrum.

The study of combining Latin Hypercube Sampling method and LU decomposition method (LULHS method) for constructing spatial random field

NASA Astrophysics Data System (ADS)

WANG, P. T.

2015-12-01

Groundwater modeling requires to assign hydrogeological properties to every numerical grid. Due to the lack of detailed information and the inherent spatial heterogeneity, geological properties can be treated as random variables. Hydrogeological property is assumed to be a multivariate distribution with spatial correlations. By sampling random numbers from a given statistical distribution and assigning a value to each grid, a random field for modeling can be completed. Therefore, statistics sampling plays an important role in the efficiency of modeling procedure. Latin Hypercube Sampling (LHS) is a stratified random sampling procedure that provides an efficient way to sample variables from their multivariate distributions. This study combines the the stratified random procedure from LHS and the simulation by using LU decomposition to form LULHS. Both conditional and unconditional simulations of LULHS were develpoed. The simulation efficiency and spatial correlation of LULHS are compared to the other three different simulation methods. The results show that for the conditional simulation and unconditional simulation, LULHS method is more efficient in terms of computational effort. Less realizations are required to achieve the required statistical accuracy and spatial correlation.

Scalable and fast heterogeneous molecular simulation with predictive parallelization schemes

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

Guzman, Horacio V.; Junghans, Christoph; Kremer, Kurt

Multiscale and inhomogeneous molecular systems are challenging topics in the field of molecular simulation. In particular, modeling biological systems in the context of multiscale simulations and exploring material properties are driving a permanent development of new simulation methods and optimization algorithms. In computational terms, those methods require parallelization schemes that make a productive use of computational resources for each simulation and from its genesis. Here, we introduce the heterogeneous domain decomposition approach, which is a combination of an heterogeneity-sensitive spatial domain decomposition with an a priori rearrangement of subdomain walls. Within this approach and paper, the theoretical modeling and scalingmore » laws for the force computation time are proposed and studied as a function of the number of particles and the spatial resolution ratio. We also show the new approach capabilities, by comparing it to both static domain decomposition algorithms and dynamic load-balancing schemes. Specifically, two representative molecular systems have been simulated and compared to the heterogeneous domain decomposition proposed in this work. Finally, these two systems comprise an adaptive resolution simulation of a biomolecule solvated in water and a phase-separated binary Lennard-Jones fluid.« less

Scalable and fast heterogeneous molecular simulation with predictive parallelization schemes

DOE PAGES

Guzman, Horacio V.; Junghans, Christoph; Kremer, Kurt; ...

2017-11-27

Multiscale and inhomogeneous molecular systems are challenging topics in the field of molecular simulation. In particular, modeling biological systems in the context of multiscale simulations and exploring material properties are driving a permanent development of new simulation methods and optimization algorithms. In computational terms, those methods require parallelization schemes that make a productive use of computational resources for each simulation and from its genesis. Here, we introduce the heterogeneous domain decomposition approach, which is a combination of an heterogeneity-sensitive spatial domain decomposition with an a priori rearrangement of subdomain walls. Within this approach and paper, the theoretical modeling and scalingmore » laws for the force computation time are proposed and studied as a function of the number of particles and the spatial resolution ratio. We also show the new approach capabilities, by comparing it to both static domain decomposition algorithms and dynamic load-balancing schemes. Specifically, two representative molecular systems have been simulated and compared to the heterogeneous domain decomposition proposed in this work. Finally, these two systems comprise an adaptive resolution simulation of a biomolecule solvated in water and a phase-separated binary Lennard-Jones fluid.« less

GPR random noise reduction using BPD and EMD

NASA Astrophysics Data System (ADS)

Ostoori, Roya; Goudarzi, Alireza; Oskooi, Behrooz

2018-04-01

Ground-penetrating radar (GPR) exploration is a new high-frequency technology that explores near-surface objects and structures accurately. The high-frequency antenna of the GPR system makes it a high-resolution method compared to other geophysical methods. The frequency range of recorded GPR is so wide that random noise recording is inevitable due to acquisition. This kind of noise comes from unknown sources and its correlation to the adjacent traces is nearly zero. This characteristic of random noise along with the higher accuracy of GPR system makes denoising very important for interpretable results. The main objective of this paper is to reduce GPR random noise based on pursuing denoising using empirical mode decomposition. Our results showed that empirical mode decomposition in combination with basis pursuit denoising (BPD) provides satisfactory outputs due to the sifting process compared to the time-domain implementation of the BPD method on both synthetic and real examples. Our results demonstrate that because of the high computational costs, the BPD-empirical mode decomposition technique should only be used for heavily noisy signals.

Time-dependent VOC-profile of decomposed human and animal remains in laboratory environment.

PubMed

Rosier, E; Loix, S; Develter, W; Van de Voorde, W; Tytgat, J; Cuypers, E

2016-09-01

A validated method using a thermal desorber combined with a gas chromatograph coupled to a mass spectrometer was used to identify the volatile organic compounds released in decomposed human and animal remains after 9 and 12 months in glass jars in a laboratory environment. This is a follow-up study on a previous report where the first 6 months of decomposition of 6 human and 26 animal remains was investigated. In the first report, out of 452 identified compounds, a combination of 8 compounds was proposed as human and pig specific. The goal of the current study was to investigate if these 8 compounds were still released after 9 and 12 months. The next results were noticed: 287 compounds were identified; only 9 new compounds were detected and 173 were no longer seen. Sulfur-containing compounds were less prevalent as compared to the first month of decomposition. The appearance of nitrogen-containing compounds and alcohols was increasingly evident during the first 6 months, and the same trend was seen in the following 6 months. Esters became less important after 6 months. From the proposed human and pig specific compounds, diethyl disulfide was only detected during the first months of decomposition. Interestingly, the 4 proposed human and pig specific esters, as well as pyridine, 3-methylthio-1-propanol and methyl(methylthio)ethyl disulfide were still present after 9 and 12 months of decomposition. This means that these 7 human and pig specific markers can be used in the development of training aids for cadaver dogs during the whole decomposition process. Diethyl disulfide can be used in training aids for the first month of decomposition. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A reduced-order model for compressible flows with buffeting condition using higher order dynamic mode decomposition with a mode selection criterion

NASA Astrophysics Data System (ADS)

Kou, Jiaqing; Le Clainche, Soledad; Zhang, Weiwei

2018-01-01

This study proposes an improvement in the performance of reduced-order models (ROMs) based on dynamic mode decomposition to model the flow dynamics of the attractor from a transient solution. By combining higher order dynamic mode decomposition (HODMD) with an efficient mode selection criterion, the HODMD with criterion (HODMDc) ROM is able to identify dominant flow patterns with high accuracy. This helps us to develop a more parsimonious ROM structure, allowing better predictions of the attractor dynamics. The method is tested in the solution of a NACA0012 airfoil buffeting in a transonic flow, and its good performance in both the reconstruction of the original solution and the prediction of the permanent dynamics is shown. In addition, the robustness of the method has been successfully tested using different types of parameters, indicating that the proposed ROM approach is a tool promising for using in both numerical simulations and experimental data.

Multi-variants synthesis of Petri nets for FPGA devices

NASA Astrophysics Data System (ADS)

Bukowiec, Arkadiusz; Doligalski, Michał

2015-09-01

There is presented new method of synthesis of application specific logic controllers for FPGA devices. The specification of control algorithm is made with use of control interpreted Petri net (PT type). It allows specifying parallel processes in easy way. The Petri net is decomposed into state-machine type subnets. In this case, each subnet represents one parallel process. For this purpose there are applied algorithms of coloring of Petri nets. There are presented two approaches of such decomposition: with doublers of macroplaces or with one global wait place. Next, subnets are implemented into two-level logic circuit of the controller. The levels of logic circuit are obtained as a result of its architectural decomposition. The first level combinational circuit is responsible for generation of next places and second level decoder is responsible for generation output symbols. There are worked out two variants of such circuits: with one shared operational memory or with many flexible distributed memories as a decoder. Variants of Petri net decomposition and structures of logic circuits can be combined together without any restrictions. It leads to existence of four variants of multi-variants synthesis.

Eigenvalue Solvers for Modeling Nuclear Reactors on Leadership Class Machines

DOE PAGES

Slaybaugh, R. N.; Ramirez-Zweiger, M.; Pandya, Tara; ...

2018-02-20

In this paper, three complementary methods have been implemented in the code Denovo that accelerate neutral particle transport calculations with methods that use leadership-class computers fully and effectively: a multigroup block (MG) Krylov solver, a Rayleigh quotient iteration (RQI) eigenvalue solver, and a multigrid in energy (MGE) preconditioner. The MG Krylov solver converges more quickly than Gauss Seidel and enables energy decomposition such that Denovo can scale to hundreds of thousands of cores. RQI should converge in fewer iterations than power iteration (PI) for large and challenging problems. RQI creates shifted systems that would not be tractable without the MGmore » Krylov solver. It also creates ill-conditioned matrices. The MGE preconditioner reduces iteration count significantly when used with RQI and takes advantage of the new energy decomposition such that it can scale efficiently. Each individual method has been described before, but this is the first time they have been demonstrated to work together effectively. The combination of solvers enables the RQI eigenvalue solver to work better than the other available solvers for large reactors problems on leadership-class machines. Using these methods together, RQI converged in fewer iterations and in less time than PI for a full pressurized water reactor core. These solvers also performed better than an Arnoldi eigenvalue solver for a reactor benchmark problem when energy decomposition is needed. The MG Krylov, MGE preconditioner, and RQI solver combination also scales well in energy. Finally, this solver set is a strong choice for very large and challenging problems.« less

Eigenvalue Solvers for Modeling Nuclear Reactors on Leadership Class Machines

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

Slaybaugh, R. N.; Ramirez-Zweiger, M.; Pandya, Tara

In this paper, three complementary methods have been implemented in the code Denovo that accelerate neutral particle transport calculations with methods that use leadership-class computers fully and effectively: a multigroup block (MG) Krylov solver, a Rayleigh quotient iteration (RQI) eigenvalue solver, and a multigrid in energy (MGE) preconditioner. The MG Krylov solver converges more quickly than Gauss Seidel and enables energy decomposition such that Denovo can scale to hundreds of thousands of cores. RQI should converge in fewer iterations than power iteration (PI) for large and challenging problems. RQI creates shifted systems that would not be tractable without the MGmore » Krylov solver. It also creates ill-conditioned matrices. The MGE preconditioner reduces iteration count significantly when used with RQI and takes advantage of the new energy decomposition such that it can scale efficiently. Each individual method has been described before, but this is the first time they have been demonstrated to work together effectively. The combination of solvers enables the RQI eigenvalue solver to work better than the other available solvers for large reactors problems on leadership-class machines. Using these methods together, RQI converged in fewer iterations and in less time than PI for a full pressurized water reactor core. These solvers also performed better than an Arnoldi eigenvalue solver for a reactor benchmark problem when energy decomposition is needed. The MG Krylov, MGE preconditioner, and RQI solver combination also scales well in energy. Finally, this solver set is a strong choice for very large and challenging problems.« less

Placement-aware decomposition of a digital standard cells library for double patterning lithography

NASA Astrophysics Data System (ADS)

Wassal, Amr G.; Sharaf, Heba; Hammouda, Sherif

2012-11-01

To continue scaling the circuit features down, Double Patterning (DP) technology is needed in 22nm technologies and lower. DP requires decomposing the layout features into two masks for pitch relaxation, such that the spacing between any two features on each mask is greater than the minimum allowed mask spacing. The relaxed pitches of each mask are then processed on two separate exposure steps. In many cases, post-layout decomposition fails to decompose the layout into two masks due to the presence of conflicts. Post-layout decomposition of a standard cells block can result in native conflicts inside the cells (internal conflict), or native conflicts on the boundary between two cells (boundary conflict). Resolving native conflicts requires a redesign and/or multiple iterations for the placement and routing phases to get a clean decomposition. Therefore, DP compliance must be considered in earlier phases, before getting the final placed cell block. The main focus of this paper is generating a library of decomposed standard cells to be used in a DP-aware placer. This library should contain all possible decompositions for each standard cell, i.e., these decompositions consider all possible combinations of boundary conditions. However, the large number of combinations of boundary conditions for each standard cell will significantly increase the processing time and effort required to obtain all possible decompositions. Therefore, an efficient methodology is required to reduce this large number of combinations. In this paper, three different reduction methodologies are proposed to reduce the number of different combinations processed to get the decomposed library. Experimental results show a significant reduction in the number of combinations and decompositions needed for the library processing. To generate and verify the proposed flow and methodologies, a prototype for a placement-aware DP-ready cell-library is developed with an optimized number of cell views.

Decomposition techniques

USGS Publications Warehouse

Chao, T.T.; Sanzolone, R.F.

1992-01-01

Sample decomposition is a fundamental and integral step in the procedure of geochemical analysis. It is often the limiting factor to sample throughput, especially with the recent application of the fast and modern multi-element measurement instrumentation. The complexity of geological materials makes it necessary to choose the sample decomposition technique that is compatible with the specific objective of the analysis. When selecting a decomposition technique, consideration should be given to the chemical and mineralogical characteristics of the sample, elements to be determined, precision and accuracy requirements, sample throughput, technical capability of personnel, and time constraints. This paper addresses these concerns and discusses the attributes and limitations of many techniques of sample decomposition along with examples of their application to geochemical analysis. The chemical properties of reagents as to their function as decomposition agents are also reviewed. The section on acid dissolution techniques addresses the various inorganic acids that are used individually or in combination in both open and closed systems. Fluxes used in sample fusion are discussed. The promising microwave-oven technology and the emerging field of automation are also examined. A section on applications highlights the use of decomposition techniques for the determination of Au, platinum group elements (PGEs), Hg, U, hydride-forming elements, rare earth elements (REEs), and multi-elements in geological materials. Partial dissolution techniques used for geochemical exploration which have been treated in detail elsewhere are not discussed here; nor are fire-assaying for noble metals and decomposition techniques for X-ray fluorescence or nuclear methods be discussed. ?? 1992.

Near-Infrared Spectrum Detection of Wheat Gluten Protein Content Based on a Combined Filtering Method.

PubMed

Cai, Jian-Hua

2017-09-01

To eliminate the random error of the derivative near-IR (NIR) spectrum and to improve model stability and the prediction accuracy of the gluten protein content, a combined method is proposed for pretreatment of the NIR spectrum based on both empirical mode decomposition and the wavelet soft-threshold method. The principle and the steps of the method are introduced and the denoising effect is evaluated. The wheat gluten protein content is calculated based on the denoised spectrum, and the results are compared with those of the nine-point smoothing method and the wavelet soft-threshold method. Experimental results show that the proposed combined method is effective in completing pretreatment of the NIR spectrum, and the proposed method improves the accuracy of detection of wheat gluten protein content from the NIR spectrum.

Accuracy assessment of a surface electromyogram decomposition system in human first dorsal interosseus muscle

NASA Astrophysics Data System (ADS)

Hu, Xiaogang; Rymer, William Z.; Suresh, Nina L.

2014-04-01

Objective. The aim of this study is to assess the accuracy of a surface electromyogram (sEMG) motor unit (MU) decomposition algorithm during low levels of muscle contraction. Approach. A two-source method was used to verify the accuracy of the sEMG decomposition system, by utilizing simultaneous intramuscular and surface EMG recordings from the human first dorsal interosseous muscle recorded during isometric trapezoidal force contractions. Spike trains from each recording type were decomposed independently utilizing two different algorithms, EMGlab and dEMG decomposition algorithms. The degree of agreement of the decomposed spike timings was assessed for three different segments of the EMG signals, corresponding to specified regions in the force task. A regression analysis was performed to examine whether certain properties of the sEMG and force signal can predict the decomposition accuracy. Main results. The average accuracy of successful decomposition among the 119 MUs that were common to both intramuscular and surface records was approximately 95%, and the accuracy was comparable between the different segments of the sEMG signals (i.e., force ramp-up versus steady state force versus combined). The regression function between the accuracy and properties of sEMG and force signals revealed that the signal-to-noise ratio of the action potential and stability in the action potential records were significant predictors of the surface decomposition accuracy. Significance. The outcomes of our study confirm the accuracy of the sEMG decomposition algorithm during low muscle contraction levels and provide confidence in the overall validity of the surface dEMG decomposition algorithm.

Intelligent diagnosis of short hydraulic signal based on improved EEMD and SVM with few low-dimensional training samples

NASA Astrophysics Data System (ADS)

Zhang, Meijun; Tang, Jian; Zhang, Xiaoming; Zhang, Jiaojiao

2016-03-01

The high accurate classification ability of an intelligent diagnosis method often needs a large amount of training samples with high-dimensional eigenvectors, however the characteristics of the signal need to be extracted accurately. Although the existing EMD(empirical mode decomposition) and EEMD(ensemble empirical mode decomposition) are suitable for processing non-stationary and non-linear signals, but when a short signal, such as a hydraulic impact signal, is concerned, their decomposition accuracy become very poor. An improve EEMD is proposed specifically for short hydraulic impact signals. The improvements of this new EEMD are mainly reflected in four aspects, including self-adaptive de-noising based on EEMD, signal extension based on SVM(support vector machine), extreme center fitting based on cubic spline interpolation, and pseudo component exclusion based on cross-correlation analysis. After the energy eigenvector is extracted from the result of the improved EEMD, the fault pattern recognition based on SVM with small amount of low-dimensional training samples is studied. At last, the diagnosis ability of improved EEMD+SVM method is compared with the EEMD+SVM and EMD+SVM methods, and its diagnosis accuracy is distinctly higher than the other two methods no matter the dimension of the eigenvectors are low or high. The improved EEMD is very propitious for the decomposition of short signal, such as hydraulic impact signal, and its combination with SVM has high ability for the diagnosis of hydraulic impact faults.

Proper orthogonal decomposition-based spectral higher-order stochastic estimation

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

Baars, Woutijn J., E-mail: wbaars@unimelb.edu.au; Tinney, Charles E.

A unique routine, capable of identifying both linear and higher-order coherence in multiple-input/output systems, is presented. The technique combines two well-established methods: Proper Orthogonal Decomposition (POD) and Higher-Order Spectra Analysis. The latter of these is based on known methods for characterizing nonlinear systems by way of Volterra series. In that, both linear and higher-order kernels are formed to quantify the spectral (nonlinear) transfer of energy between the system's input and output. This reduces essentially to spectral Linear Stochastic Estimation when only first-order terms are considered, and is therefore presented in the context of stochastic estimation as spectral Higher-Order Stochastic Estimationmore » (HOSE). The trade-off to seeking higher-order transfer kernels is that the increased complexity restricts the analysis to single-input/output systems. Low-dimensional (POD-based) analysis techniques are inserted to alleviate this void as POD coefficients represent the dynamics of the spatial structures (modes) of a multi-degree-of-freedom system. The mathematical framework behind this POD-based HOSE method is first described. The method is then tested in the context of jet aeroacoustics by modeling acoustically efficient large-scale instabilities as combinations of wave packets. The growth, saturation, and decay of these spatially convecting wave packets are shown to couple both linearly and nonlinearly in the near-field to produce waveforms that propagate acoustically to the far-field for different frequency combinations.« less

Evidence for Early Morphological Decomposition: Combining Masked Priming with Magnetoencephalography

ERIC Educational Resources Information Center

Lehtonen, Minna; Monahan, Philip J.; Poeppel, David

2011-01-01

Are words stored as morphologically structured representations? If so, when during word recognition are morphological pieces accessed? Recent masked priming studies support models that assume early decomposition of (potentially) morphologically complex words. The electrophysiological evidence, however, is inconsistent. We combined masked…

Decomposition of timed automata for solving scheduling problems

NASA Astrophysics Data System (ADS)

Nishi, Tatsushi; Wakatake, Masato

2014-03-01

A decomposition algorithm for scheduling problems based on timed automata (TA) model is proposed. The problem is represented as an optimal state transition problem for TA. The model comprises of the parallel composition of submodels such as jobs and resources. The procedure of the proposed methodology can be divided into two steps. The first step is to decompose the TA model into several submodels by using decomposable condition. The second step is to combine individual solution of subproblems for the decomposed submodels by the penalty function method. A feasible solution for the entire model is derived through the iterated computation of solving the subproblem for each submodel. The proposed methodology is applied to solve flowshop and jobshop scheduling problems. Computational experiments demonstrate the effectiveness of the proposed algorithm compared with a conventional TA scheduling algorithm without decomposition.

An NAFP Project: Use of Object Oriented Methodologies and Design Patterns to Refactor Software Design

NASA Technical Reports Server (NTRS)

Shaykhian, Gholam Ali; Baggs, Rhoda

2007-01-01

In the early problem-solution era of software programming, functional decompositions were mainly used to design and implement software solutions. In functional decompositions, functions and data are introduced as two separate entities during the design phase, and are followed as such in the implementation phase. Functional decompositions make use of refactoring through optimizing the algorithms, grouping similar functionalities into common reusable functions, and using abstract representations of data where possible; all these are done during the implementation phase. This paper advocates the usage of object-oriented methodologies and design patterns as the centerpieces of refactoring software solutions. Refactoring software is a method of changing software design while explicitly preserving its external functionalities. The combined usage of object-oriented methodologies and design patterns to refactor should also benefit the overall software life cycle cost with improved software.

Decomposition of atrazine traces in water by combination of non-thermal electrical discharge and adsorption on nanofiber membrane.

PubMed

Vanraes, Patrick; Willems, Gert; Daels, Nele; Van Hulle, Stijn W H; De Clerck, Karen; Surmont, Pieter; Lynen, Frederic; Vandamme, Jeroen; Van Durme, Jim; Nikiforov, Anton; Leys, Christophe

2015-04-01

In recent decades, several types of persistent substances are detected in the aquatic environment at very low concentrations. Unfortunately, conventional water treatment processes are not able to remove these micropollutants. As such, advanced treatment methods are required to meet both current and anticipated maximally allowed concentrations. Plasma discharge in contact with water is a promising new technology, since it produces a wide spectrum of oxidizing species. In this study, a new type of reactor is tested, in which decomposition by atmospheric pulsed direct barrier discharge (pDBD) plasma is combined with micropollutant adsorption on a nanofiber polyamide membrane. Atrazine is chosen as model micropollutant with an initial concentration of 30 μg/L. While the H2O2 and O3 production in the reactor is not influenced by the presence of the membrane, there is a significant increase in atrazine decomposition when the membrane is added. With membrane, 85% atrazine removal can be obtained in comparison to only 61% removal without membrane, at the same experimental parameters. The by-products of atrazine decomposition identified by HPLC-MS are deethylatrazine and ammelide. Formation of these by-products is more pronounced when the membrane is added. These results indicate the synergetic effect of plasma discharge and pollutant adsorption, which is attractive for future applications of water treatment. Copyright © 2014 Elsevier Ltd. All rights reserved.

Evaluation of global horizontal irradiance to plane-of-array irradiance models at locations across the United States

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

Lave, Matthew; Hayes, William; Pohl, Andrew

2015-02-02

We report an evaluation of the accuracy of combinations of models that estimate plane-of-array (POA) irradiance from measured global horizontal irradiance (GHI). This estimation involves two steps: 1) decomposition of GHI into direct and diffuse horizontal components and 2) transposition of direct and diffuse horizontal irradiance (DHI) to POA irradiance. Measured GHI and coincident measured POA irradiance from a variety of climates within the United States were used to evaluate combinations of decomposition and transposition models. A few locations also had DHI measurements, allowing for decoupled analysis of either the decomposition or the transposition models alone. Results suggest that decompositionmore » models had mean bias differences (modeled versus measured) that vary with climate. Transposition model mean bias differences depended more on the model than the location. Lastly, when only GHI measurements were available and combinations of decomposition and transposition models were considered, the smallest mean bias differences were typically found for combinations which included the Hay/Davies transposition model.« less

Empirical mode decomposition apparatus, method and article of manufacture for analyzing biological signals and performing curve fitting

NASA Technical Reports Server (NTRS)

Huang, Norden E. (Inventor)

2004-01-01

A computer implemented physical signal analysis method includes four basic steps and the associated presentation techniques of the results. The first step is a computer implemented Empirical Mode Decomposition that extracts a collection of Intrinsic Mode Functions (IMF) from nonlinear, nonstationary physical signals. The decomposition is based on the direct extraction of the energy associated with various intrinsic time scales in the physical signal. Expressed in the IMF's, they have well-behaved Hilbert Transforms from which instantaneous frequencies can be calculated. The second step is the Hilbert Transform which produces a Hilbert Spectrum. Thus, the invention can localize any event on the time as well as the frequency axis. The decomposition can also be viewed as an expansion of the data in terms of the IMF's. Then, these IMF's, based on and derived from the data, can serve as the basis of that expansion. The local energy and the instantaneous frequency derived from the IMF's through the Hilbert transform give a full energy-frequency-time distribution of the data which is designated as the Hilbert Spectrum. The third step filters the physical signal by combining a subset of the IMFs. In the fourth step, a curve may be fitted to the filtered signal which may not have been possible with the original, unfiltered signal.

Empirical mode decomposition apparatus, method and article of manufacture for analyzing biological signals and performing curve fitting

NASA Technical Reports Server (NTRS)

Huang, Norden E. (Inventor)

2002-01-01

A computer implemented physical signal analysis method includes four basic steps and the associated presentation techniques of the results. The first step is a computer implemented Empirical Mode Decomposition that extracts a collection of Intrinsic Mode Functions (IMF) from nonlinear, nonstationary physical signals. The decomposition is based on the direct extraction of the energy associated with various intrinsic time scales in the physical signal. Expressed in the IMF's, they have well-behaved Hilbert Transforms from which instantaneous frequencies can be calculated. The second step is the Hilbert Transform which produces a Hilbert Spectrum. Thus, the invention can localize any event on the time as well as the frequency axis. The decomposition can also be viewed as an expansion of the data in terms of the IMF's. Then, these IMF's, based on and derived from the data, can serve as the basis of that expansion. The local energy and the instantaneous frequency derived from the IMF's through the Hilbert transform give a full energy-frequency-time distribution of the data which is designated as the Hilbert Spectrum. The third step filters the physical signal by combining a subset of the IMFs. In the fourth step, a curve may be fitted to the filtered signal which may not have been possible with the original, unfiltered signal.

Thermal Decomposition Behaviors and Burning Characteristics of Composite Propellants Prepared Using Combined Ammonium Perchlorate/Ammonium Nitrate Particles

NASA Astrophysics Data System (ADS)

Kohga, Makoto; Handa, Saori

2018-01-01

The thermal decomposition behaviors and burning characteristics of propellants prepared with combined ammonium perchlorate (AP)/ammonium nitrate (AN) particles greatly depended on the AN content (χ) of the AP/AN sample. The thermal decomposition behaviors of the propellants prepared with the combined samples almost matched those of the propellants prepared by physically mixing AP and AN particles, while their burning characteristics differed. The use of combined AP/AN particles decreased the heterogeneity of the combustion waves of the AP/AN propellants because of the difference in the combustion wave structure. In contrast, the addition of Fe2O3 caused unsteady combustion of the propellants prepared using samples with χ values lower than 8.1%.

Scalable parallel elastic-plastic finite element analysis using a quasi-Newton method with a balancing domain decomposition preconditioner

NASA Astrophysics Data System (ADS)

Yusa, Yasunori; Okada, Hiroshi; Yamada, Tomonori; Yoshimura, Shinobu

2018-04-01

A domain decomposition method for large-scale elastic-plastic problems is proposed. The proposed method is based on a quasi-Newton method in conjunction with a balancing domain decomposition preconditioner. The use of a quasi-Newton method overcomes two problems associated with the conventional domain decomposition method based on the Newton-Raphson method: (1) avoidance of a double-loop iteration algorithm, which generally has large computational complexity, and (2) consideration of the local concentration of nonlinear deformation, which is observed in elastic-plastic problems with stress concentration. Moreover, the application of a balancing domain decomposition preconditioner ensures scalability. Using the conventional and proposed domain decomposition methods, several numerical tests, including weak scaling tests, were performed. The convergence performance of the proposed method is comparable to that of the conventional method. In particular, in elastic-plastic analysis, the proposed method exhibits better convergence performance than the conventional method.

Automating CPM-GOMS

NASA Technical Reports Server (NTRS)

John, Bonnie; Vera, Alonso; Matessa, Michael; Freed, Michael; Remington, Roger

2002-01-01

CPM-GOMS is a modeling method that combines the task decomposition of a GOMS analysis with a model of human resource usage at the level of cognitive, perceptual, and motor operations. CPM-GOMS models have made accurate predictions about skilled user behavior in routine tasks, but developing such models is tedious and error-prone. We describe a process for automatically generating CPM-GOMS models from a hierarchical task decomposition expressed in a cognitive modeling tool called Apex. Resource scheduling in Apex automates the difficult task of interleaving the cognitive, perceptual, and motor resources underlying common task operators (e.g. mouse move-and-click). Apex's UI automatically generates PERT charts, which allow modelers to visualize a model's complex parallel behavior. Because interleaving and visualization is now automated, it is feasible to construct arbitrarily long sequences of behavior. To demonstrate the process, we present a model of automated teller interactions in Apex and discuss implications for user modeling. available to model human users, the Goals, Operators, Methods, and Selection (GOMS) method [6, 21] has been the most widely used, providing accurate, often zero-parameter, predictions of the routine performance of skilled users in a wide range of procedural tasks [6, 13, 15, 27, 28]. GOMS is meant to model routine behavior. The user is assumed to have methods that apply sequences of operators and to achieve a goal. Selection rules are applied when there is more than one method to achieve a goal. Many routine tasks lend themselves well to such decomposition. Decomposition produces a representation of the task as a set of nested goal states that include an initial state and a final state. The iterative decomposition into goals and nested subgoals can terminate in primitives of any desired granularity, the choice of level of detail dependent on the predictions required. Although GOMS has proven useful in HCI, tools to support the construction of GOMS models have not yet come into general use.

Dual energy CT: How well can pseudo-monochromatic imaging reduce metal artifacts?

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

Kuchenbecker, Stefan, E-mail: stefan.kuchenbecker@dkfz.de; Faby, Sebastian; Sawall, Stefan

2015-02-15

Purpose: Dual Energy CT (DECT) provides so-called monoenergetic images based on a linear combination of the original polychromatic images. At certain patient-specific energy levels, corresponding to certain patient- and slice-dependent linear combination weights, e.g., E = 160 keV corresponds to α = 1.57, a significant reduction of metal artifacts may be observed. The authors aimed at analyzing the method for its artifact reduction capabilities to identify its limitations. The results are compared with raw data-based processing. Methods: Clinical DECT uses a simplified version of monochromatic imaging by linearly combining the low and the high kV images and by assigning an energymore » to that linear combination. Those pseudo-monochromatic images can be used by radiologists to obtain images with reduced metal artifacts. The authors analyzed the underlying physics and carried out a series expansion of the polychromatic attenuation equations. The resulting nonlinear terms are responsible for the artifacts, but they are not linearly related between the low and the high kV scan: A linear combination of both images cannot eliminate the nonlinearities, it can only reduce their impact. Scattered radiation yields additional noncanceling nonlinearities. This method is compared to raw data-based artifact correction methods. To quantify the artifact reduction potential of pseudo-monochromatic images, they simulated the FORBILD abdomen phantom with metal implants, and they assessed patient data sets of a clinical dual source CT system (100, 140 kV Sn) containing artifacts induced by a highly concentrated contrast agent bolus and by metal. In each case, they manually selected an optimal α and compared it to a raw data-based material decomposition in case of simulation, to raw data-based material decomposition of inconsistent rays in case of the patient data set containing contrast agent, and to the frequency split normalized metal artifact reduction in case of the metal implant. For each case, the contrast-to-noise ratio (CNR) was assessed. Results: In the simulation, the pseudo-monochromatic images yielded acceptable artifact reduction results. However, the CNR in the artifact-reduced images was more than 60% lower than in the original polychromatic images. In contrast, the raw data-based material decomposition did not significantly reduce the CNR in the virtual monochromatic images. Regarding the patient data with beam hardening artifacts and with metal artifacts from small implants the pseudo-monochromatic method was able to reduce the artifacts, again with the downside of a significant CNR reduction. More intense metal artifacts, e.g., as those caused by an artificial hip joint, could not be suppressed. Conclusions: Pseudo-monochromatic imaging is able to reduce beam hardening, scatter, and metal artifacts in some cases but it cannot remove them. In all cases, the CNR is significantly reduced, thereby rendering the method questionable, unless special post processing algorithms are implemented to restore the high CNR from the original images (e.g., by using a frequency split technique). Raw data-based dual energy decomposition methods should be preferred, in particular, because the CNR penalty is almost negligible.« less

The development of a post-mortem interval estimation for human remains found on land in the Netherlands.

PubMed

Gelderman, H T; Boer, L; Naujocks, T; IJzermans, A C M; Duijst, W L J M

2018-05-01

The decomposition process of human remains can be used to estimate the post-mortem interval (PMI), but decomposition varies due to many factors. Temperature is believed to be the most important and can be connected to decomposition by using the accumulated degree days (ADD). The aim of this research was to develop a decomposition scoring method and to develop a formula to estimate the PMI by using the developed decomposition scoring method and ADD.A decomposition scoring method and a Book of Reference (visual resource) were made. Ninety-one cases were used to develop a method to estimate the PMI. The photographs were scored using the decomposition scoring method. The temperature data was provided by the Royal Netherlands Meteorological Institute. The PMI was estimated using the total decomposition score (TDS) and using the TDS and ADD. The latter required an additional step, namely to calculate the ADD from the finding date back until the predicted day of death.The developed decomposition scoring method had a high interrater reliability. The TDS significantly estimates the PMI (R 2  = 0.67 and 0.80 for indoor and outdoor bodies, respectively). When using the ADD, the R 2 decreased to 0.66 and 0.56.The developed decomposition scoring method is a practical method to measure decomposition for human remains found on land. The PMI can be estimated using this method, but caution is advised in cases with a long PMI. The ADD does not account for all the heat present in a decomposing remain and is therefore a possible bias.

GC × GC-TOFMS and supervised multivariate approaches to study human cadaveric decomposition olfactive signatures.

PubMed

Stefanuto, Pierre-Hugues; Perrault, Katelynn A; Stadler, Sonja; Pesesse, Romain; LeBlanc, Helene N; Forbes, Shari L; Focant, Jean-François

2015-06-01

In forensic thanato-chemistry, the understanding of the process of soft tissue decomposition is still limited. A better understanding of the decomposition process and the characterization of the associated volatile organic compounds (VOC) can help to improve the training of victim recovery (VR) canines, which are used to search for trapped victims in natural disasters or to locate corpses during criminal investigations. The complexity of matrices and the dynamic nature of this process require the use of comprehensive analytical methods for investigation. Moreover, the variability of the environment and between individuals creates additional difficulties in terms of normalization. The resolution of the complex mixture of VOCs emitted by a decaying corpse can be improved using comprehensive two-dimensional gas chromatography (GC × GC), compared to classical single-dimensional gas chromatography (1DGC). This study combines the analytical advantages of GC × GC coupled to time-of-flight mass spectrometry (TOFMS) with the data handling robustness of supervised multivariate statistics to investigate the VOC profile of human remains during early stages of decomposition. Various supervised multivariate approaches are compared to interpret the large data set. Moreover, early decomposition stages of pig carcasses (typically used as human surrogates in field studies) are also monitored to obtain a direct comparison of the two VOC profiles and estimate the robustness of this human decomposition analog model. In this research, we demonstrate that pig and human decomposition processes can be described by the same trends for the major compounds produced during the early stages of soft tissue decomposition.

Simultaneous Tensor Decomposition and Completion Using Factor Priors.

PubMed

Chen, Yi-Lei; Hsu, Chiou-Ting Candy; Liao, Hong-Yuan Mark

2013-08-27

Tensor completion, which is a high-order extension of matrix completion, has generated a great deal of research interest in recent years. Given a tensor with incomplete entries, existing methods use either factorization or completion schemes to recover the missing parts. However, as the number of missing entries increases, factorization schemes may overfit the model because of incorrectly predefined ranks, while completion schemes may fail to interpret the model factors. In this paper, we introduce a novel concept: complete the missing entries and simultaneously capture the underlying model structure. To this end, we propose a method called Simultaneous Tensor Decomposition and Completion (STDC) that combines a rank minimization technique with Tucker model decomposition. Moreover, as the model structure is implicitly included in the Tucker model, we use factor priors, which are usually known a priori in real-world tensor objects, to characterize the underlying joint-manifold drawn from the model factors. We conducted experiments to empirically verify the convergence of our algorithm on synthetic data, and evaluate its effectiveness on various kinds of real-world data. The results demonstrate the efficacy of the proposed method and its potential usage in tensor-based applications. It also outperforms state-of-the-art methods on multilinear model analysis and visual data completion tasks.

Unconditionally energy stable time stepping scheme for Cahn–Morral equation: Application to multi-component spinodal decomposition and optimal space tiling

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

Tavakoli, Rouhollah, E-mail: rtavakoli@sharif.ir

An unconditionally energy stable time stepping scheme is introduced to solve Cahn–Morral-like equations in the present study. It is constructed based on the combination of David Eyre's time stepping scheme and Schur complement approach. Although the presented method is general and independent of the choice of homogeneous free energy density function term, logarithmic and polynomial energy functions are specifically considered in this paper. The method is applied to study the spinodal decomposition in multi-component systems and optimal space tiling problems. A penalization strategy is developed, in the case of later problem, to avoid trivial solutions. Extensive numerical experiments demonstrate themore » success and performance of the presented method. According to the numerical results, the method is convergent and energy stable, independent of the choice of time stepsize. Its MATLAB implementation is included in the appendix for the numerical evaluation of algorithm and reproduction of the presented results. -- Highlights: •Extension of Eyre's convex–concave splitting scheme to multiphase systems. •Efficient solution of spinodal decomposition in multi-component systems. •Efficient solution of least perimeter periodic space partitioning problem. •Developing a penalization strategy to avoid trivial solutions. •Presentation of MATLAB implementation of the introduced algorithm.« less

Error reduction in EMG signal decomposition

PubMed Central

Kline, Joshua C.

2014-01-01

Decomposition of the electromyographic (EMG) signal into constituent action potentials and the identification of individual firing instances of each motor unit in the presence of ambient noise are inherently probabilistic processes, whether performed manually or with automated algorithms. Consequently, they are subject to errors. We set out to classify and reduce these errors by analyzing 1,061 motor-unit action-potential trains (MUAPTs), obtained by decomposing surface EMG (sEMG) signals recorded during human voluntary contractions. Decomposition errors were classified into two general categories: location errors representing variability in the temporal localization of each motor-unit firing instance and identification errors consisting of falsely detected or missed firing instances. To mitigate these errors, we developed an error-reduction algorithm that combines multiple decomposition estimates to determine a more probable estimate of motor-unit firing instances with fewer errors. The performance of the algorithm is governed by a trade-off between the yield of MUAPTs obtained above a given accuracy level and the time required to perform the decomposition. When applied to a set of sEMG signals synthesized from real MUAPTs, the identification error was reduced by an average of 1.78%, improving the accuracy to 97.0%, and the location error was reduced by an average of 1.66 ms. The error-reduction algorithm in this study is not limited to any specific decomposition strategy. Rather, we propose it be used for other decomposition methods, especially when analyzing precise motor-unit firing instances, as occurs when measuring synchronization. PMID:25210159

Chemical stability of molten 2,4,6-trinitrotoluene at high pressure

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

Dattelbaum, Dana M., E-mail: danadat@lanl.gov; Chellappa, Raja S.; Bowden, Patrick R.

2014-01-13

2,4,6-trinitrotoluene (TNT) is a molecular explosive that exhibits chemical stability in the molten phase at ambient pressure. A combination of visual, spectroscopic, and structural (x-ray diffraction) methods coupled to high pressure, resistively heated diamond anvil cells was used to determine the melt and decomposition boundaries to >15 GPa. The chemical stability of molten TNT was found to be limited, existing in a small domain of pressure-temperature conditions below 2 GPa. Decomposition dominates the phase diagram at high temperatures beyond 6 GPa. From the calculated bulk temperature rise, we conclude that it is unlikely that TNT melts on its principal Hugoniot.

Dynamic mode decomposition for plasma diagnostics and validation.

PubMed

Taylor, Roy; Kutz, J Nathan; Morgan, Kyle; Nelson, Brian A

2018-05-01

We demonstrate the application of the Dynamic Mode Decomposition (DMD) for the diagnostic analysis of the nonlinear dynamics of a magnetized plasma in resistive magnetohydrodynamics. The DMD method is an ideal spatio-temporal matrix decomposition that correlates spatial features of computational or experimental data while simultaneously associating the spatial activity with periodic temporal behavior. DMD can produce low-rank, reduced order surrogate models that can be used to reconstruct the state of the system with high fidelity. This allows for a reduction in the computational cost and, at the same time, accurate approximations of the problem, even if the data are sparsely sampled. We demonstrate the use of the method on both numerical and experimental data, showing that it is a successful mathematical architecture for characterizing the helicity injected torus with steady inductive (HIT-SI) magnetohydrodynamics. Importantly, the DMD produces interpretable, dominant mode structures, including a stationary mode consistent with our understanding of a HIT-SI spheromak accompanied by a pair of injector-driven modes. In combination, the 3-mode DMD model produces excellent dynamic reconstructions across the domain of analyzed data.

Dynamic mode decomposition for plasma diagnostics and validation

NASA Astrophysics Data System (ADS)

Taylor, Roy; Kutz, J. Nathan; Morgan, Kyle; Nelson, Brian A.

2018-05-01

We demonstrate the application of the Dynamic Mode Decomposition (DMD) for the diagnostic analysis of the nonlinear dynamics of a magnetized plasma in resistive magnetohydrodynamics. The DMD method is an ideal spatio-temporal matrix decomposition that correlates spatial features of computational or experimental data while simultaneously associating the spatial activity with periodic temporal behavior. DMD can produce low-rank, reduced order surrogate models that can be used to reconstruct the state of the system with high fidelity. This allows for a reduction in the computational cost and, at the same time, accurate approximations of the problem, even if the data are sparsely sampled. We demonstrate the use of the method on both numerical and experimental data, showing that it is a successful mathematical architecture for characterizing the helicity injected torus with steady inductive (HIT-SI) magnetohydrodynamics. Importantly, the DMD produces interpretable, dominant mode structures, including a stationary mode consistent with our understanding of a HIT-SI spheromak accompanied by a pair of injector-driven modes. In combination, the 3-mode DMD model produces excellent dynamic reconstructions across the domain of analyzed data.

Empirical Mode Decomposition and k-Nearest Embedding Vectors for Timely Analyses of Antibiotic Resistance Trends

PubMed Central

Teodoro, Douglas; Lovis, Christian

2013-01-01

Background Antibiotic resistance is a major worldwide public health concern. In clinical settings, timely antibiotic resistance information is key for care providers as it allows appropriate targeted treatment or improved empirical treatment when the specific results of the patient are not yet available. Objective To improve antibiotic resistance trend analysis algorithms by building a novel, fully data-driven forecasting method from the combination of trend extraction and machine learning models for enhanced biosurveillance systems. Methods We investigate a robust model for extraction and forecasting of antibiotic resistance trends using a decade of microbiology data. Our method consists of breaking down the resistance time series into independent oscillatory components via the empirical mode decomposition technique. The resulting waveforms describing intrinsic resistance trends serve as the input for the forecasting algorithm. The algorithm applies the delay coordinate embedding theorem together with the k-nearest neighbor framework to project mappings from past events into the future dimension and estimate the resistance levels. Results The algorithms that decompose the resistance time series and filter out high frequency components showed statistically significant performance improvements in comparison with a benchmark random walk model. We present further qualitative use-cases of antibiotic resistance trend extraction, where empirical mode decomposition was applied to highlight the specificities of the resistance trends. Conclusion The decomposition of the raw signal was found not only to yield valuable insight into the resistance evolution, but also to produce novel models of resistance forecasters with boosted prediction performance, which could be utilized as a complementary method in the analysis of antibiotic resistance trends. PMID:23637796

Study on the mechanism of copper-ammonia complex decomposition in struvite formation process and enhanced ammonia and copper removal.

PubMed

Peng, Cong; Chai, Liyuan; Tang, Chongjian; Min, Xiaobo; Song, Yuxia; Duan, Chengshan; Yu, Cheng

2017-01-01

Heavy metals and ammonia are difficult to remove from wastewater, as they easily combine into refractory complexes. The struvite formation method (SFM) was applied for the complex decomposition and simultaneous removal of heavy metal and ammonia. The results indicated that ammonia deprivation by SFM was the key factor leading to the decomposition of the copper-ammonia complex ion. Ammonia was separated from solution as crystalline struvite, and the copper mainly co-precipitated as copper hydroxide together with struvite. Hydrogen bonding and electrostatic attraction were considered to be the main surface interactions between struvite and copper hydroxide. Hydrogen bonding was concluded to be the key factor leading to the co-precipitation. In addition, incorporation of copper ions into the struvite crystal also occurred during the treatment process. Copyright © 2016. Published by Elsevier B.V.

ARMA Cholesky Factor Models for the Covariance Matrix of Linear Models.

PubMed

Lee, Keunbaik; Baek, Changryong; Daniels, Michael J

2017-11-01

In longitudinal studies, serial dependence of repeated outcomes must be taken into account to make correct inferences on covariate effects. As such, care must be taken in modeling the covariance matrix. However, estimation of the covariance matrix is challenging because there are many parameters in the matrix and the estimated covariance matrix should be positive definite. To overcomes these limitations, two Cholesky decomposition approaches have been proposed: modified Cholesky decomposition for autoregressive (AR) structure and moving average Cholesky decomposition for moving average (MA) structure, respectively. However, the correlations of repeated outcomes are often not captured parsimoniously using either approach separately. In this paper, we propose a class of flexible, nonstationary, heteroscedastic models that exploits the structure allowed by combining the AR and MA modeling of the covariance matrix that we denote as ARMACD. We analyze a recent lung cancer study to illustrate the power of our proposed methods.

An Integrated Centroid Finding and Particle Overlap Decomposition Algorithm for Stereo Imaging Velocimetry

NASA Technical Reports Server (NTRS)

McDowell, Mark

2004-01-01

An integrated algorithm for decomposing overlapping particle images (multi-particle objects) along with determining each object s constituent particle centroid(s) has been developed using image analysis techniques. The centroid finding algorithm uses a modified eight-direction search method for finding the perimeter of any enclosed object. The centroid is calculated using the intensity-weighted center of mass of the object. The overlap decomposition algorithm further analyzes the object data and breaks it down into its constituent particle centroid(s). This is accomplished with an artificial neural network, feature based technique and provides an efficient way of decomposing overlapping particles. Combining the centroid finding and overlap decomposition routines into a single algorithm allows us to accurately predict the error associated with finding the centroid(s) of particles in our experiments. This algorithm has been tested using real, simulated, and synthetic data and the results are presented and discussed.

Use of the Morlet mother wavelet in the frequency-scale domain decomposition technique for the modal identification of ambient vibration responses

NASA Astrophysics Data System (ADS)

Le, Thien-Phu

2017-10-01

The frequency-scale domain decomposition technique has recently been proposed for operational modal analysis. The technique is based on the Cauchy mother wavelet. In this paper, the approach is extended to the Morlet mother wavelet, which is very popular in signal processing due to its superior time-frequency localization. Based on the regressive form and an appropriate norm of the Morlet mother wavelet, the continuous wavelet transform of the power spectral density of ambient responses enables modes in the frequency-scale domain to be highlighted. Analytical developments first demonstrate the link between modal parameters and the local maxima of the continuous wavelet transform modulus. The link formula is then used as the foundation of the proposed modal identification method. Its practical procedure, combined with the singular value decomposition algorithm, is presented step by step. The proposition is finally verified using numerical examples and a laboratory test.

Prediction of S-wave velocity using complete ensemble empirical mode decomposition and neural networks

NASA Astrophysics Data System (ADS)

Gaci, Said; Hachay, Olga; Zaourar, Naima

2017-04-01

One of the key elements in hydrocarbon reservoirs characterization is the S-wave velocity (Vs). Since the traditional estimating methods often fail to accurately predict this physical parameter, a new approach that takes into account its non-stationary and non-linear properties is needed. In this view, a prediction model based on complete ensemble empirical mode decomposition (CEEMD) and a multiple layer perceptron artificial neural network (MLP ANN) is suggested to compute Vs from P-wave velocity (Vp). Using a fine-to-coarse reconstruction algorithm based on CEEMD, the Vp log data is decomposed into a high frequency (HF) component, a low frequency (LF) component and a trend component. Then, different combinations of these components are used as inputs of the MLP ANN algorithm for estimating Vs log. Applications on well logs taken from different geological settings illustrate that the predicted Vs values using MLP ANN with the combinations of HF, LF and trend in inputs are more accurate than those obtained with the traditional estimating methods. Keywords: S-wave velocity, CEEMD, multilayer perceptron neural networks.

Electrocardiogram signal denoising based on empirical mode decomposition technique: an overview

NASA Astrophysics Data System (ADS)

Han, G.; Lin, B.; Xu, Z.

2017-03-01

Electrocardiogram (ECG) signal is nonlinear and non-stationary weak signal which reflects whether the heart is functioning normally or abnormally. ECG signal is susceptible to various kinds of noises such as high/low frequency noises, powerline interference and baseline wander. Hence, the removal of noises from ECG signal becomes a vital link in the ECG signal processing and plays a significant role in the detection and diagnosis of heart diseases. The review will describe the recent developments of ECG signal denoising based on Empirical Mode Decomposition (EMD) technique including high frequency noise removal, powerline interference separation, baseline wander correction, the combining of EMD and Other Methods, EEMD technique. EMD technique is a quite potential and prospective but not perfect method in the application of processing nonlinear and non-stationary signal like ECG signal. The EMD combined with other algorithms is a good solution to improve the performance of noise cancellation. The pros and cons of EMD technique in ECG signal denoising are discussed in detail. Finally, the future work and challenges in ECG signal denoising based on EMD technique are clarified.

Spectral CT of the extremities with a silicon strip photon counting detector

NASA Astrophysics Data System (ADS)

Sisniega, A.; Zbijewski, W.; Stayman, J. W.; Xu, J.; Taguchi, K.; Siewerdsen, J. H.

2015-03-01

Purpose: Photon counting x-ray detectors (PCXDs) are an important emerging technology for spectral imaging and material differentiation with numerous potential applications in diagnostic imaging. We report development of a Si-strip PCXD system originally developed for mammography with potential application to spectral CT of musculoskeletal extremities, including challenges associated with sparse sampling, spectral calibration, and optimization for higher energy x-ray beams. Methods: A bench-top CT system was developed incorporating a Si-strip PCXD, fixed anode x-ray source, and rotational and translational motions to execute complex acquisition trajectories. Trajectories involving rotation and translation combined with iterative reconstruction were investigated, including single and multiple axial scans and longitudinal helical scans. The system was calibrated to provide accurate spectral separation in dual-energy three-material decomposition of soft-tissue, bone, and iodine. Image quality and decomposition accuracy were assessed in experiments using a phantom with pairs of bone and iodine inserts (3, 5, 15 and 20 mm) and an anthropomorphic wrist. Results: The designed trajectories improved the sampling distribution from 56% minimum sampling of voxels to 75%. Use of iterative reconstruction (viz., penalized likelihood with edge preserving regularization) in combination with such trajectories resulted in a very low level of artifacts in images of the wrist. For large bone or iodine inserts (>5 mm diameter), the error in the estimated material concentration was <16% for (50 mg/mL) bone and <8% for (5 mg/mL) iodine with strong regularization. For smaller inserts, errors of 20-40% were observed and motivate improved methods for spectral calibration and optimization of the edge-preserving regularizer. Conclusion: Use of PCXDs for three-material decomposition in joint imaging proved feasible through a combination of rotation-translation acquisition trajectories and iterative reconstruction with optimized regularization.

Trigonometric Integrals via Partial Fractions

ERIC Educational Resources Information Center

Chen, H.; Fulford, M.

2005-01-01

Parametric differentiation is used to derive the partial fractions decompositions of certain rational functions. Those decompositions enable us to integrate some new combinations of trigonometric functions.

Effective brain network analysis with resting-state EEG data: a comparison between heroin abstinent and non-addicted subjects

NASA Astrophysics Data System (ADS)

Hu, Bin; Dong, Qunxi; Hao, Yanrong; Zhao, Qinglin; Shen, Jian; Zheng, Fang

2017-08-01

Objective. Neuro-electrophysiological tools have been widely used in heroin addiction studies. Previous studies indicated that chronic heroin abuse would result in abnormal functional organization of the brain, while few heroin addiction studies have applied the effective connectivity tool to analyze the brain functional system (BFS) alterations induced by heroin abuse. The present study aims to identify the abnormality of resting-state heroin abstinent BFS using source decomposition and effective connectivity tools. Approach. The resting-state electroencephalograph (EEG) signals were acquired from 15 male heroin abstinent (HA) subjects and 14 male non-addicted (NA) controls. Multivariate autoregressive models combined independent component analysis (MVARICA) was applied for blind source decomposition. Generalized partial directed coherence (GPDC) was applied for effective brain connectivity analysis. Effective brain networks of both HA and NA groups were constructed. The two groups of effective cortical networks were compared by the bootstrap method. Abnormal causal interactions between decomposed source regions were estimated in the 1-45 Hz frequency domain. Main results. This work suggested: (a) there were clear effective network alterations in heroin abstinent subject groups; (b) the parietal region was a dominant hub of the abnormally weaker causal pathways, and the left occipital region was a dominant hub of the abnormally stronger causal pathways. Significance. These findings provide direct evidence that chronic heroin abuse induces brain functional abnormalities. The potential value of combining effective connectivity analysis and brain source decomposition methods in exploring brain alterations of heroin addicts is also implied.

Effective brain network analysis with resting-state EEG data: a comparison between heroin abstinent and non-addicted subjects.

PubMed

Hu, Bin; Dong, Qunxi; Hao, Yanrong; Zhao, Qinglin; Shen, Jian; Zheng, Fang

2017-08-01

Neuro-electrophysiological tools have been widely used in heroin addiction studies. Previous studies indicated that chronic heroin abuse would result in abnormal functional organization of the brain, while few heroin addiction studies have applied the effective connectivity tool to analyze the brain functional system (BFS) alterations induced by heroin abuse. The present study aims to identify the abnormality of resting-state heroin abstinent BFS using source decomposition and effective connectivity tools. The resting-state electroencephalograph (EEG) signals were acquired from 15 male heroin abstinent (HA) subjects and 14 male non-addicted (NA) controls. Multivariate autoregressive models combined independent component analysis (MVARICA) was applied for blind source decomposition. Generalized partial directed coherence (GPDC) was applied for effective brain connectivity analysis. Effective brain networks of both HA and NA groups were constructed. The two groups of effective cortical networks were compared by the bootstrap method. Abnormal causal interactions between decomposed source regions were estimated in the 1-45 Hz frequency domain. This work suggested: (a) there were clear effective network alterations in heroin abstinent subject groups; (b) the parietal region was a dominant hub of the abnormally weaker causal pathways, and the left occipital region was a dominant hub of the abnormally stronger causal pathways. These findings provide direct evidence that chronic heroin abuse induces brain functional abnormalities. The potential value of combining effective connectivity analysis and brain source decomposition methods in exploring brain alterations of heroin addicts is also implied.

Forecasting hotspots in East Kutai, Kutai Kartanegara, and West Kutai as early warning information

NASA Astrophysics Data System (ADS)

Wahyuningsih, S.; Goejantoro, R.; Rizki, N. A.

2018-04-01

The aims of this research are to model hotspots and forecast hotspot 2017 in East Kutai, Kutai Kartanegara and West Kutai. The methods which used in this research were Holt exponential smoothing, Holt’s additive dump trend method, Holt-Winters’ additive method, additive decomposition method, multiplicative decomposition method, Loess decomposition method and Box-Jenkins method. For smoothing techniques, additive decomposition is better than Holt’s exponential smoothing. The hotspots model using Box-Jenkins method were Autoregressive Moving Average ARIMA(1,1,0), ARIMA(0,2,1), and ARIMA(0,1,0). Comparing the results from all methods which were used in this research, and based on Root of Mean Squared Error (RMSE), show that Loess decomposition method is the best times series model, because it has the least RMSE. Thus the Loess decomposition model used to forecast the number of hotspot. The forecasting result indicatethat hotspots pattern tend to increase at the end of 2017 in Kutai Kartanegara and West Kutai, but stationary in East Kutai.

Interface conditions for domain decomposition with radical grid refinement

NASA Technical Reports Server (NTRS)

Scroggs, Jeffrey S.

1991-01-01

Interface conditions for coupling the domains in a physically motivated domain decomposition method are discussed. The domain decomposition is based on an asymptotic-induced method for the numerical solution of hyperbolic conservation laws with small viscosity. The method consists of multiple stages. The first stage is to obtain a first approximation using a first-order method, such as the Godunov scheme. Subsequent stages of the method involve solving internal-layer problem via a domain decomposition. The method is derived and justified via singular perturbation techniques.

[A Feature Extraction Method for Brain Computer Interface Based on Multivariate Empirical Mode Decomposition].

PubMed

Wang, Jinjia; Liu, Yuan

2015-04-01

This paper presents a feature extraction method based on multivariate empirical mode decomposition (MEMD) combining with the power spectrum feature, and the method aims at the non-stationary electroencephalogram (EEG) or magnetoencephalogram (MEG) signal in brain-computer interface (BCI) system. Firstly, we utilized MEMD algorithm to decompose multichannel brain signals into a series of multiple intrinsic mode function (IMF), which was proximate stationary and with multi-scale. Then we extracted and reduced the power characteristic from each IMF to a lower dimensions using principal component analysis (PCA). Finally, we classified the motor imagery tasks by linear discriminant analysis classifier. The experimental verification showed that the correct recognition rates of the two-class and four-class tasks of the BCI competition III and competition IV reached 92.0% and 46.2%, respectively, which were superior to the winner of the BCI competition. The experimental proved that the proposed method was reasonably effective and stable and it would provide a new way for feature extraction.

Adomian decomposition method used to solve the one-dimensional acoustic equations

NASA Astrophysics Data System (ADS)

Dispini, Meta; Mungkasi, Sudi

2017-05-01

In this paper we propose the use of Adomian decomposition method to solve one-dimensional acoustic equations. This recursive method can be calculated easily and the result is an approximation of the exact solution. We use the Maple software to compute the series in the Adomian decomposition. We obtain that the Adomian decomposition method is able to solve the acoustic equations with the physically correct behavior.

Final Report, DE-FG01-06ER25718 Domain Decomposition and Parallel Computing

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

Widlund, Olof B.

2015-06-09

The goal of this project is to develop and improve domain decomposition algorithms for a variety of partial differential equations such as those of linear elasticity and electro-magnetics.These iterative methods are designed for massively parallel computing systems and allow the fast solution of the very large systems of algebraic equations that arise in large scale and complicated simulations. A special emphasis is placed on problems arising from Maxwell's equation. The approximate solvers, the preconditioners, are combined with the conjugate gradient method and must always include a solver of a coarse model in order to have a performance which is independentmore » of the number of processors used in the computer simulation. A recent development allows for an adaptive construction of this coarse component of the preconditioner.« less

Experimental and DFT simulation study of a novel felodipine cocrystal: Characterization, dissolving properties and thermal decomposition kinetics.

PubMed

Yang, Caiqin; Guo, Wei; Lin, Yulong; Lin, Qianqian; Wang, Jiaojiao; Wang, Jing; Zeng, Yanli

2018-05-30

In this study, a new cocrystal of felodipine (Fel) and glutaric acid (Glu) with a high dissolution rate was developed using the solvent ultrasonic method. The prepared cocrystal was characterized using X-ray powder diffraction, differential scanning calorimetry, thermogravimetric (TG) analysis, and infrared (IR) spectroscopy. To provide basic information about the optimization of pharmaceutical preparations of Fel-based cocrystals, this work investigated the thermal decomposition kinetics of the Fel-Glu cocrystal through non-isothermal thermogravimetry. Density functional theory (DFT) simulations were also performed on the Fel monomer and the trimolecular cocrystal compound for exploring the mechanisms underlying hydrogen bonding formation and thermal decomposition. Combined results of IR spectroscopy and DFT simulation verified that the Fel-Glu cocrystal formed via the NH⋯OC and CO⋯HO hydrogen bonds between Fel and Glu at the ratio of 1:2. The TG/derivative TG curves indicated that the thermal decomposition of the Fel-Glu cocrystal underwent a two-step process. The apparent activation energy (E a ) and pre-exponential factor (A) of the thermal decomposition for the first stage were 84.90 kJ mol -1 and 7.03 × 10 7  min -1 , respectively. The mechanism underlying thermal decomposition possibly involved nucleation and growth, with the integral mechanism function G(α) of α 3/2 . DFT calculation revealed that the hydrogen bonding between Fel and Glu weakened the terminal methoxyl, methyl, and ethyl groups in the Fel molecule. As a result, these groups were lost along with the Glu molecule in the first thermal decomposition. In conclusion, the formed cocrystal exhibited different thermal decomposition kinetics and showed different E a , A, and shelf life from the intact active pharmaceutical ingredient. Copyright © 2018 Elsevier B.V. All rights reserved.

SU-E-QI-14: Quantitative Variogram Detection of Mild, Unilateral Disease in Elastase-Treated Rats

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

Jacob, R; Carson, J

2014-06-15

Purpose: Determining the presence of mild or early disease in the lungs can be challenging and subjective. We present a rapid and objective method for evaluating lung damage in a rat model of unilateral mild emphysema based on a new approach to heterogeneity assessment. We combined octree decomposition (used in three-dimensional (3D) computer graphics) with variograms (used in geostatistics to assess spatial relationships) to evaluate 3D computed tomography (CT) lung images for disease. Methods: Male, Sprague-Dawley rats (232 ± 7 g) were intratracheally dosed with 50 U/kg of elastase dissolved in 200 μL of saline to a single lobe (n=6)more » or with saline only (n=5). After four weeks, 3D micro-CT images were acquired at end expiration on mechanically ventilated rats using prospective gating. Images were masked, and lungs were decomposed to homogeneous blocks of 2×2×2, 4×4×4, and 8×8×8 voxels using octree decomposition. The spatial variance – the square of the difference of signal intensity – between all pairs of the 8×8×8 blocks was calculated. Variograms – graphs of distance vs. variance - were made, and data were fit to a power law and the exponent determined. The mean HU values, coefficient of variation (CoV), and the emphysema index (EI) were calculated and compared to the variograms. Results: The variogram analysis showed that significant differences between groups existed (p<0.01), whereas the mean HU (p=0.07), CoV (p=0.24), and EI (p=0.08) did not. Calculation time for the variogram for a typical 1000 block decomposition was ∼6 seconds, and octree decomposition took ∼2 minutes. Decomposing the images prior to variogram calculation resulted in a ∼700x decrease in time as compared to other published approaches. Conclusions: Our results suggest that the approach combining octree decomposition and variogram analysis may be a rapid, non-subjective, and sensitive imaging-based biomarker for quantitative characterization of lung disease.« less

Can the biomass-ratio hypothesis predict mixed-species litter decomposition along a climatic gradient?

PubMed Central

Tardif, Antoine; Shipley, Bill; Bloor, Juliette M. G.; Soussana, Jean-François

2014-01-01

Background and Aims The biomass-ratio hypothesis states that ecosystem properties are driven by the characteristics of dominant species in the community. In this study, the hypothesis was operationalized as community-weighted means (CWMs) of monoculture values and tested for predicting the decomposition of multispecies litter mixtures along an abiotic gradient in the field. Methods Decomposition rates (mg g−1 d−1) of litter from four herb species were measured using litter-bed experiments with the same soil at three sites in central France along a correlated climatic gradient of temperature and precipitation. All possible combinations from one to four species mixtures were tested over 28 weeks of incubation. Observed mixture decomposition rates were compared with those predicted by the biomass-ratio hypothesis. Variability of the prediction errors was compared with the species richness of the mixtures, across sites, and within sites over time. Key Results Both positive and negative prediction errors occurred. Despite this, the biomass-ratio hypothesis was true as an average claim for all sites (r = 0·91) and for each site separately, except for the climatically intermediate site, which showed mainly synergistic deviations. Variability decreased with increasing species richness and in less favourable climatic conditions for decomposition. Conclusions Community-weighted mean values provided good predictions of mixed-species litter decomposition, converging to the predicted values with increasing species richness and in climates less favourable to decomposition. Under a context of climate change, abiotic variability would be important to take into account when predicting ecosystem processes. PMID:24482152

Removing volatile organic compounds in cooking fume by nano-sized TiO2 photocatalytic reaction combined with ozone oxidation technique.

PubMed

Li, Yu-Hua; Cheng, Su-Wen; Yuan, Chung-Shin; Lai, Tzu-Fan; Hung, Chung-Hsuang

2018-06-05

Chinese cooking fume is one of the sources of volatile organic compounds (VOCs) in the air. An innovative control technology combining photocatalytic degradation and ozone oxidation (UV/TiO 2 +O 3 ) was developed to decompose VOCs in the cooking fume. Fiberglass filter (FGF) coated with TiO 2 was prepared by an impregnation procedure. A continuous-flow reaction system was self-designed by combining photocatalysis with advanced ozone oxidation technique. By passing the simulated cooking fume through the FGF, the VOC decomposition efficiency in the cooking fume could be increased by about 10%. The decomposition efficiency of VOCs in the cooking fume increased and then decreased with the inlet VOC concentration. A maximum VOC decomposition efficiency of 64% was obtained at 100 ppm. Similar trend was observed for reaction temperature with the VOC decomposition efficiencies ranging from 64 to 68%. Moreover, inlet ozone concentration had a positive effect on the decomposition of VOCs in the cooking fume for inlet ozone≤1000 ppm and leveled off for inlet ozone>1000 ppm. 34% of VOC decomposition efficiency was achieved solely by ozone oxidation with or without near-UV irradiation. A maximum of 75% and 94% VOC decomposition efficiency could be achieved by O 3 +UV/TiO 2 and UV/TiO 2 +O 3 techniques, respectively. The maximum decomposition efficiencies of VOCs decreased to 79% for using UV/TiO 2 +O 3 technique with adding water in the oil fume. Comparing the chromatographical species of VOCs in the oil fume before and after the decomposition of VOCs by using UV/TiO 2 +O 3 technique, we found that both TVOC and VOC species in the oil fume were effectively decomposed. Copyright © 2018 Elsevier Ltd. All rights reserved.

Robust and automated three-dimensional segmentation of densely packed cell nuclei in different biological specimens with Lines-of-Sight decomposition.

PubMed

Mathew, B; Schmitz, A; Muñoz-Descalzo, S; Ansari, N; Pampaloni, F; Stelzer, E H K; Fischer, S C

2015-06-08

Due to the large amount of data produced by advanced microscopy, automated image analysis is crucial in modern biology. Most applications require reliable cell nuclei segmentation. However, in many biological specimens cell nuclei are densely packed and appear to touch one another in the images. Therefore, a major difficulty of three-dimensional cell nuclei segmentation is the decomposition of cell nuclei that apparently touch each other. Current methods are highly adapted to a certain biological specimen or a specific microscope. They do not ensure similarly accurate segmentation performance, i.e. their robustness for different datasets is not guaranteed. Hence, these methods require elaborate adjustments to each dataset. We present an advanced three-dimensional cell nuclei segmentation algorithm that is accurate and robust. Our approach combines local adaptive pre-processing with decomposition based on Lines-of-Sight (LoS) to separate apparently touching cell nuclei into approximately convex parts. We demonstrate the superior performance of our algorithm using data from different specimens recorded with different microscopes. The three-dimensional images were recorded with confocal and light sheet-based fluorescence microscopes. The specimens are an early mouse embryo and two different cellular spheroids. We compared the segmentation accuracy of our algorithm with ground truth data for the test images and results from state-of-the-art methods. The analysis shows that our method is accurate throughout all test datasets (mean F-measure: 91%) whereas the other methods each failed for at least one dataset (F-measure≤69%). Furthermore, nuclei volume measurements are improved for LoS decomposition. The state-of-the-art methods required laborious adjustments of parameter values to achieve these results. Our LoS algorithm did not require parameter value adjustments. The accurate performance was achieved with one fixed set of parameter values. We developed a novel and fully automated three-dimensional cell nuclei segmentation method incorporating LoS decomposition. LoS are easily accessible features that ensure correct splitting of apparently touching cell nuclei independent of their shape, size or intensity. Our method showed superior performance compared to state-of-the-art methods, performing accurately for a variety of test images. Hence, our LoS approach can be readily applied to quantitative evaluation in drug testing, developmental and cell biology.

Wavelet-based unsupervised learning method for electrocardiogram suppression in surface electromyograms.

PubMed

Niegowski, Maciej; Zivanovic, Miroslav

2016-03-01

We present a novel approach aimed at removing electrocardiogram (ECG) perturbation from single-channel surface electromyogram (EMG) recordings by means of unsupervised learning of wavelet-based intensity images. The general idea is to combine the suitability of certain wavelet decomposition bases which provide sparse electrocardiogram time-frequency representations, with the capacity of non-negative matrix factorization (NMF) for extracting patterns from images. In order to overcome convergence problems which often arise in NMF-related applications, we design a novel robust initialization strategy which ensures proper signal decomposition in a wide range of ECG contamination levels. Moreover, the method can be readily used because no a priori knowledge or parameter adjustment is needed. The proposed method was evaluated on real surface EMG signals against two state-of-the-art unsupervised learning algorithms and a singular spectrum analysis based method. The results, expressed in terms of high-to-low energy ratio, normalized median frequency, spectral power difference and normalized average rectified value, suggest that the proposed method enables better ECG-EMG separation quality than the reference methods. Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.

Resonance-Based Sparse Signal Decomposition and its Application in Mechanical Fault Diagnosis: A Review.

PubMed

Huang, Wentao; Sun, Hongjian; Wang, Weijie

2017-06-03

Mechanical equipment is the heart of industry. For this reason, mechanical fault diagnosis has drawn considerable attention. In terms of the rich information hidden in fault vibration signals, the processing and analysis techniques of vibration signals have become a crucial research issue in the field of mechanical fault diagnosis. Based on the theory of sparse decomposition, Selesnick proposed a novel nonlinear signal processing method: resonance-based sparse signal decomposition (RSSD). Since being put forward, RSSD has become widely recognized, and many RSSD-based methods have been developed to guide mechanical fault diagnosis. This paper attempts to summarize and review the theoretical developments and application advances of RSSD in mechanical fault diagnosis, and to provide a more comprehensive reference for those interested in RSSD and mechanical fault diagnosis. Followed by a brief introduction of RSSD's theoretical foundation, based on different optimization directions, applications of RSSD in mechanical fault diagnosis are categorized into five aspects: original RSSD, parameter optimized RSSD, subband optimized RSSD, integrated optimized RSSD, and RSSD combined with other methods. On this basis, outstanding issues in current RSSD study are also pointed out, as well as corresponding instructional solutions. We hope this review will provide an insightful reference for researchers and readers who are interested in RSSD and mechanical fault diagnosis.

Resonance-Based Sparse Signal Decomposition and Its Application in Mechanical Fault Diagnosis: A Review

PubMed Central

Huang, Wentao; Sun, Hongjian; Wang, Weijie

2017-01-01

Mechanical equipment is the heart of industry. For this reason, mechanical fault diagnosis has drawn considerable attention. In terms of the rich information hidden in fault vibration signals, the processing and analysis techniques of vibration signals have become a crucial research issue in the field of mechanical fault diagnosis. Based on the theory of sparse decomposition, Selesnick proposed a novel nonlinear signal processing method: resonance-based sparse signal decomposition (RSSD). Since being put forward, RSSD has become widely recognized, and many RSSD-based methods have been developed to guide mechanical fault diagnosis. This paper attempts to summarize and review the theoretical developments and application advances of RSSD in mechanical fault diagnosis, and to provide a more comprehensive reference for those interested in RSSD and mechanical fault diagnosis. Followed by a brief introduction of RSSD’s theoretical foundation, based on different optimization directions, applications of RSSD in mechanical fault diagnosis are categorized into five aspects: original RSSD, parameter optimized RSSD, subband optimized RSSD, integrated optimized RSSD, and RSSD combined with other methods. On this basis, outstanding issues in current RSSD study are also pointed out, as well as corresponding instructional solutions. We hope this review will provide an insightful reference for researchers and readers who are interested in RSSD and mechanical fault diagnosis. PMID:28587198

Spectral response model for a multibin photon-counting spectral computed tomography detector and its applications.

PubMed

Liu, Xuejin; Persson, Mats; Bornefalk, Hans; Karlsson, Staffan; Xu, Cheng; Danielsson, Mats; Huber, Ben

2015-07-01

Variations among detector channels in computed tomography can lead to ring artifacts in the reconstructed images and biased estimates in projection-based material decomposition. Typically, the ring artifacts are corrected by compensation methods based on flat fielding, where transmission measurements are required for a number of material-thickness combinations. Phantoms used in these methods can be rather complex and require an extensive number of transmission measurements. Moreover, material decomposition needs knowledge of the individual response of each detector channel to account for the detector inhomogeneities. For this purpose, we have developed a spectral response model that binwise predicts the response of a multibin photon-counting detector individually for each detector channel. The spectral response model is performed in two steps. The first step employs a forward model to predict the expected numbers of photon counts, taking into account parameters such as the incident x-ray spectrum, absorption efficiency, and energy response of the detector. The second step utilizes a limited number of transmission measurements with a set of flat slabs of two absorber materials to fine-tune the model predictions, resulting in a good correspondence with the physical measurements. To verify the response model, we apply the model in two cases. First, the model is used in combination with a compensation method which requires an extensive number of transmission measurements to determine the necessary parameters. Our spectral response model successfully replaces these measurements by simulations, saving a significant amount of measurement time. Second, the spectral response model is used as the basis of the maximum likelihood approach for projection-based material decomposition. The reconstructed basis images show a good separation between the calcium-like material and the contrast agents, iodine and gadolinium. The contrast agent concentrations are reconstructed with more than 94% accuracy.

Spectral response model for a multibin photon-counting spectral computed tomography detector and its applications

PubMed Central

Liu, Xuejin; Persson, Mats; Bornefalk, Hans; Karlsson, Staffan; Xu, Cheng; Danielsson, Mats; Huber, Ben

2015-01-01

Abstract. Variations among detector channels in computed tomography can lead to ring artifacts in the reconstructed images and biased estimates in projection-based material decomposition. Typically, the ring artifacts are corrected by compensation methods based on flat fielding, where transmission measurements are required for a number of material-thickness combinations. Phantoms used in these methods can be rather complex and require an extensive number of transmission measurements. Moreover, material decomposition needs knowledge of the individual response of each detector channel to account for the detector inhomogeneities. For this purpose, we have developed a spectral response model that binwise predicts the response of a multibin photon-counting detector individually for each detector channel. The spectral response model is performed in two steps. The first step employs a forward model to predict the expected numbers of photon counts, taking into account parameters such as the incident x-ray spectrum, absorption efficiency, and energy response of the detector. The second step utilizes a limited number of transmission measurements with a set of flat slabs of two absorber materials to fine-tune the model predictions, resulting in a good correspondence with the physical measurements. To verify the response model, we apply the model in two cases. First, the model is used in combination with a compensation method which requires an extensive number of transmission measurements to determine the necessary parameters. Our spectral response model successfully replaces these measurements by simulations, saving a significant amount of measurement time. Second, the spectral response model is used as the basis of the maximum likelihood approach for projection-based material decomposition. The reconstructed basis images show a good separation between the calcium-like material and the contrast agents, iodine and gadolinium. The contrast agent concentrations are reconstructed with more than 94% accuracy. PMID:26839904

Decomposition-Based Failure Mode Identification Method for Risk-Free Design of Large Systems

NASA Technical Reports Server (NTRS)

Tumer, Irem Y.; Stone, Robert B.; Roberts, Rory A.; Clancy, Daniel (Technical Monitor)

2002-01-01

When designing products, it is crucial to assure failure and risk-free operation in the intended operating environment. Failures are typically studied and eliminated as much as possible during the early stages of design. The few failures that go undetected result in unacceptable damage and losses in high-risk applications where public safety is of concern. Published NASA and NTSB accident reports point to a variety of components identified as sources of failures in the reported cases. In previous work, data from these reports were processed and placed in matrix form for all the system components and failure modes encountered, and then manipulated using matrix methods to determine similarities between the different components and failure modes. In this paper, these matrices are represented in the form of a linear combination of failures modes, mathematically formed using Principal Components Analysis (PCA) decomposition. The PCA decomposition results in a low-dimensionality representation of all failure modes and components of interest, represented in a transformed coordinate system. Such a representation opens the way for efficient pattern analysis and prediction of failure modes with highest potential risks on the final product, rather than making decisions based on the large space of component and failure mode data. The mathematics of the proposed method are explained first using a simple example problem. The method is then applied to component failure data gathered from helicopter, accident reports to demonstrate its potential.

Data analysis using a combination of independent component analysis and empirical mode decomposition

NASA Astrophysics Data System (ADS)

Lin, Shih-Lin; Tung, Pi-Cheng; Huang, Norden E.

2009-06-01

A combination of independent component analysis and empirical mode decomposition (ICA-EMD) is proposed in this paper to analyze low signal-to-noise ratio data. The advantages of ICA-EMD combination are these: ICA needs few sensory clues to separate the original source from unwanted noise and EMD can effectively separate the data into its constituting parts. The case studies reported here involve original sources contaminated by white Gaussian noise. The simulation results show that the ICA-EMD combination is an effective data analysis tool.

Predictability of monthly temperature and precipitation using automatic time series forecasting methods

NASA Astrophysics Data System (ADS)

Papacharalampous, Georgia; Tyralis, Hristos; Koutsoyiannis, Demetris

2018-02-01

We investigate the predictability of monthly temperature and precipitation by applying automatic univariate time series forecasting methods to a sample of 985 40-year-long monthly temperature and 1552 40-year-long monthly precipitation time series. The methods include a naïve one based on the monthly values of the last year, as well as the random walk (with drift), AutoRegressive Fractionally Integrated Moving Average (ARFIMA), exponential smoothing state-space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components (BATS), simple exponential smoothing, Theta and Prophet methods. Prophet is a recently introduced model inspired by the nature of time series forecasted at Facebook and has not been applied to hydrometeorological time series before, while the use of random walk, BATS, simple exponential smoothing and Theta is rare in hydrology. The methods are tested in performing multi-step ahead forecasts for the last 48 months of the data. We further investigate how different choices of handling the seasonality and non-normality affect the performance of the models. The results indicate that: (a) all the examined methods apart from the naïve and random walk ones are accurate enough to be used in long-term applications; (b) monthly temperature and precipitation can be forecasted to a level of accuracy which can barely be improved using other methods; (c) the externally applied classical seasonal decomposition results mostly in better forecasts compared to the automatic seasonal decomposition used by the BATS and Prophet methods; and (d) Prophet is competitive, especially when it is combined with externally applied classical seasonal decomposition.

Tropical herbivorous phasmids, but not litter snails, alter decomposition rates by modifying litter bacteria

Treesearch

Chelse M. Prather; Gary E. Belovsky; Sharon A. Cantrell; Grizelle González

2018-01-01

Consumers can alter decomposition rates through both feces and selective feeding in many ecosystems, but these combined effects have seldom been examined in tropical ecosystems. Members of the detrital food web (litter-feeders or microbivores) should presumably have greater effects on decomposition than herbivores, members of the green food web. Using litterbag...

Boreal soil carbon dynamics under a changing climate: a model inversion approach

Treesearch

Zhaosheng Fan; Jason C. Neff; Jennifer W. Harden; Kimberly P. Wickland

2008-01-01

Several fundamental but important factors controlling the feedback of boreal organic carbon (OC) to climate change were examined using a mechanistic model of soil OC dynamics, including the combined effects of temperature and moisture on the decomposition of OC and the factors controlling carbon quality and decomposition with depth. To estimate decomposition rates and...

Mode decomposition and Lagrangian structures of the flow dynamics in orbitally shaken bioreactors

NASA Astrophysics Data System (ADS)

Weheliye, Weheliye Hashi; Cagney, Neil; Rodriguez, Gregorio; Micheletti, Martina; Ducci, Andrea

2018-03-01

In this study, two mode decomposition techniques were applied and compared to assess the flow dynamics in an orbital shaken bioreactor (OSB) of cylindrical geometry and flat bottom: proper orthogonal decomposition and dynamic mode decomposition. Particle Image Velocimetry (PIV) experiments were carried out for different operating conditions including fluid height, h, and shaker rotational speed, N. A detailed flow analysis is provided for conditions when the fluid and vessel motions are in-phase (Fr = 0.23) and out-of-phase (Fr = 0.47). PIV measurements in vertical and horizontal planes were combined to reconstruct low order models of the full 3D flow and to determine its Finite-Time Lyapunov Exponent (FTLE) within OSBs. The combined results from the mode decomposition and the FTLE fields provide a useful insight into the flow dynamics and Lagrangian coherent structures in OSBs and offer a valuable tool to optimise bioprocess design in terms of mixing and cell suspension.

Completed Ensemble Empirical Mode Decomposition: a Robust Signal Processing Tool to Identify Sequence Strata

NASA Astrophysics Data System (ADS)

Purba, H.; Musu, J. T.; Diria, S. A.; Permono, W.; Sadjati, O.; Sopandi, I.; Ruzi, F.

2018-03-01

Well logging data provide many geological information and its trends resemble nonlinear or non-stationary signals. As long well log data recorded, there will be external factors can interfere or influence its signal resolution. A sensitive signal analysis is required to improve the accuracy of logging interpretation which it becomes an important thing to determine sequence stratigraphy. Complete Ensemble Empirical Mode Decomposition (CEEMD) is one of nonlinear and non-stationary signal analysis method which decomposes complex signal into a series of intrinsic mode function (IMF). Gamma Ray and Spontaneous Potential well log parameters decomposed into IMF-1 up to IMF-10 and each of its combination and correlation makes physical meaning identification. It identifies the stratigraphy and cycle sequence and provides an effective signal treatment method for sequence interface. This method was applied to BRK- 30 and BRK-13 well logging data. The result shows that the combination of IMF-5, IMF-6, and IMF-7 pattern represent short-term and middle-term while IMF-9 and IMF-10 represent the long-term sedimentation which describe distal front and delta front facies, and inter-distributary mouth bar facies, respectively. Thus, CEEMD clearly can determine the different sedimentary layer interface and better identification of the cycle of stratigraphic base level.

Preparation of Coaxial-Line and Hollow Mn2O3 Nanofibers by Single-Nozzle Electrospinning and Their Catalytic Performances for Thermal Decomposition of Ammonium Perchlorate.

PubMed

Liang, Jiyuan; Yang, Jie; Cao, Weiguo; Guo, Xiangke; Guo, Xuefeng; Ding, Weiping

2015-09-01

Coaxial-line and hollow Mn2O3 nanofibers have been synthesized by a simple single-nozzle electrospinning method without using a complicated coaxial jet head, combined with final calcination. The crystal structure and morphology of the Mn2O3 nanofibers were investigated by using the X-ray diffraction, scanning electron microscopy and transmission electron microscopy. The results indicate that the electrospinning distance has important influence on the morphology and structure of the obtained Mn2O3 nanofibers, which changes from hollow fibers for short electrospinning distance to coaxial-line structure for long electrospinning distance after calcination in the air. The formation mechanisms of different structured Mn2O3 fibers are discussed in detail. This facile and effective method is easy to scale up and may be versatile for constructing coaxial-line and hollow fibers of other metal oxides. The catalytic activity of the obtained Mn2O3 nanofibers on thermal decomposition of ammonium perchlorate (AP) was studied by differential scanning calorimetry (DSC). The results show that the hollow Mn2O3 nanofibers have good catalytic activity to promote the thermal decomposition of AP.

A Fast Solver for Implicit Integration of the Vlasov--Poisson System in the Eulerian Framework

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

Garrett, C. Kristopher; Hauck, Cory D.

In this paper, we present a domain decomposition algorithm to accelerate the solution of Eulerian-type discretizations of the linear, steady-state Vlasov equation. The steady-state solver then forms a key component in the implementation of fully implicit or nearly fully implicit temporal integrators for the nonlinear Vlasov--Poisson system. The solver relies on a particular decomposition of phase space that enables the use of sweeping techniques commonly used in radiation transport applications. The original linear system for the phase space unknowns is then replaced by a smaller linear system involving only unknowns on the boundary between subdomains, which can then be solvedmore » efficiently with Krylov methods such as GMRES. Steady-state solves are combined to form an implicit Runge--Kutta time integrator, and the Vlasov equation is coupled self-consistently to the Poisson equation via a linearized procedure or a nonlinear fixed-point method for the electric field. Finally, numerical results for standard test problems demonstrate the efficiency of the domain decomposition approach when compared to the direct application of an iterative solver to the original linear system.« less

A Fast Solver for Implicit Integration of the Vlasov--Poisson System in the Eulerian Framework

DOE PAGES

Garrett, C. Kristopher; Hauck, Cory D.

2018-04-05

In this paper, we present a domain decomposition algorithm to accelerate the solution of Eulerian-type discretizations of the linear, steady-state Vlasov equation. The steady-state solver then forms a key component in the implementation of fully implicit or nearly fully implicit temporal integrators for the nonlinear Vlasov--Poisson system. The solver relies on a particular decomposition of phase space that enables the use of sweeping techniques commonly used in radiation transport applications. The original linear system for the phase space unknowns is then replaced by a smaller linear system involving only unknowns on the boundary between subdomains, which can then be solvedmore » efficiently with Krylov methods such as GMRES. Steady-state solves are combined to form an implicit Runge--Kutta time integrator, and the Vlasov equation is coupled self-consistently to the Poisson equation via a linearized procedure or a nonlinear fixed-point method for the electric field. Finally, numerical results for standard test problems demonstrate the efficiency of the domain decomposition approach when compared to the direct application of an iterative solver to the original linear system.« less

Improving 3D Wavelet-Based Compression of Hyperspectral Images

NASA Technical Reports Server (NTRS)

Klimesh, Matthew; Kiely, Aaron; Xie, Hua; Aranki, Nazeeh

2009-01-01

Two methods of increasing the effectiveness of three-dimensional (3D) wavelet-based compression of hyperspectral images have been developed. (As used here, images signifies both images and digital data representing images.) The methods are oriented toward reducing or eliminating detrimental effects of a phenomenon, referred to as spectral ringing, that is described below. In 3D wavelet-based compression, an image is represented by a multiresolution wavelet decomposition consisting of several subbands obtained by applying wavelet transforms in the two spatial dimensions corresponding to the two spatial coordinate axes of the image plane, and by applying wavelet transforms in the spectral dimension. Spectral ringing is named after the more familiar spatial ringing (spurious spatial oscillations) that can be seen parallel to and near edges in ordinary images reconstructed from compressed data. These ringing phenomena are attributable to effects of quantization. In hyperspectral data, the individual spectral bands play the role of edges, causing spurious oscillations to occur in the spectral dimension. In the absence of such corrective measures as the present two methods, spectral ringing can manifest itself as systematic biases in some reconstructed spectral bands and can reduce the effectiveness of compression of spatially-low-pass subbands. One of the two methods is denoted mean subtraction. The basic idea of this method is to subtract mean values from spatial planes of spatially low-pass subbands prior to encoding, because (a) such spatial planes often have mean values that are far from zero and (b) zero-mean data are better suited for compression by methods that are effective for subbands of two-dimensional (2D) images. In this method, after the 3D wavelet decomposition is performed, mean values are computed for and subtracted from each spatial plane of each spatially-low-pass subband. The resulting data are converted to sign-magnitude form and compressed in a manner similar to that of a baseline hyperspectral- image-compression method. The mean values are encoded in the compressed bit stream and added back to the data at the appropriate decompression step. The overhead incurred by encoding the mean values only a few bits per spectral band is negligible with respect to the huge size of a typical hyperspectral data set. The other method is denoted modified decomposition. This method is so named because it involves a modified version of a commonly used multiresolution wavelet decomposition, known in the art as the 3D Mallat decomposition, in which (a) the first of multiple stages of a 3D wavelet transform is applied to the entire dataset and (b) subsequent stages are applied only to the horizontally-, vertically-, and spectrally-low-pass subband from the preceding stage. In the modified decomposition, in stages after the first, not only is the spatially-low-pass, spectrally-low-pass subband further decomposed, but also spatially-low-pass, spectrally-high-pass subbands are further decomposed spatially. Either method can be used alone to improve the quality of a reconstructed image (see figure). Alternatively, the two methods can be combined by first performing modified decomposition, then subtracting the mean values from spatial planes of spatially-low-pass subbands.

Canonical decomposition of magnetotelluric responses: Experiment on 1D anisotropic structures

NASA Astrophysics Data System (ADS)

Guo, Ze-qiu; Wei, Wen-bo; Ye, Gao-feng; Jin, Sheng; Jing, Jian-en

2015-08-01

Horizontal electrical heterogeneity of subsurface earth is mostly originated from structural complexity and electrical anisotropy, and local near-surface electrical heterogeneity will severely distort regional electromagnetic responses. Conventional distortion analyses for magnetotelluric soundings are primarily physical decomposition methods with respect to isotropic models, which mostly presume that the geoelectric distribution of geological structures is of local and regional patterns represented by 3D/2D models. Due to the widespread anisotropy of earth media, the confusion between 1D anisotropic responses and 2D isotropic responses, and the defects of physical decomposition methods, we propose to conduct modeling experiments with canonical decomposition in terms of 1D layered anisotropic models, and the method is one of the mathematical decomposition methods based on eigenstate analyses differentiated from distortion analyses, which can be used to recover electrical information such as strike directions, and maximum and minimum conductivity. We tested this method with numerical simulation experiments on several 1D synthetic models, which turned out that canonical decomposition is quite effective to reveal geological anisotropic information. Finally, for the background of anisotropy from previous study by geological and seismological methods, canonical decomposition is applied to real data acquired in North China Craton for 1D anisotropy analyses, and the result shows that, with effective modeling and cautious interpretation, canonical decomposition could be another good method to detect anisotropy of geological media.

Simultaneous tensor decomposition and completion using factor priors.

PubMed

Chen, Yi-Lei; Hsu, Chiou-Ting; Liao, Hong-Yuan Mark

2014-03-01

The success of research on matrix completion is evident in a variety of real-world applications. Tensor completion, which is a high-order extension of matrix completion, has also generated a great deal of research interest in recent years. Given a tensor with incomplete entries, existing methods use either factorization or completion schemes to recover the missing parts. However, as the number of missing entries increases, factorization schemes may overfit the model because of incorrectly predefined ranks, while completion schemes may fail to interpret the model factors. In this paper, we introduce a novel concept: complete the missing entries and simultaneously capture the underlying model structure. To this end, we propose a method called simultaneous tensor decomposition and completion (STDC) that combines a rank minimization technique with Tucker model decomposition. Moreover, as the model structure is implicitly included in the Tucker model, we use factor priors, which are usually known a priori in real-world tensor objects, to characterize the underlying joint-manifold drawn from the model factors. By exploiting this auxiliary information, our method leverages two classic schemes and accurately estimates the model factors and missing entries. We conducted experiments to empirically verify the convergence of our algorithm on synthetic data and evaluate its effectiveness on various kinds of real-world data. The results demonstrate the efficacy of the proposed method and its potential usage in tensor-based applications. It also outperforms state-of-the-art methods on multilinear model analysis and visual data completion tasks.

Adaptive multi-step Full Waveform Inversion based on Waveform Mode Decomposition

NASA Astrophysics Data System (ADS)

Hu, Yong; Han, Liguo; Xu, Zhuo; Zhang, Fengjiao; Zeng, Jingwen

2017-04-01

Full Waveform Inversion (FWI) can be used to build high resolution velocity models, but there are still many challenges in seismic field data processing. The most difficult problem is about how to recover long-wavelength components of subsurface velocity models when seismic data is lacking of low frequency information and without long-offsets. To solve this problem, we propose to use Waveform Mode Decomposition (WMD) method to reconstruct low frequency information for FWI to obtain a smooth model, so that the initial model dependence of FWI can be reduced. In this paper, we use adjoint-state method to calculate the gradient for Waveform Mode Decomposition Full Waveform Inversion (WMDFWI). Through the illustrative numerical examples, we proved that the low frequency which is reconstructed by WMD method is very reliable. WMDFWI in combination with the adaptive multi-step inversion strategy can obtain more faithful and accurate final inversion results. Numerical examples show that even if the initial velocity model is far from the true model and lacking of low frequency information, we still can obtain good inversion results with WMD method. From numerical examples of anti-noise test, we see that the adaptive multi-step inversion strategy for WMDFWI has strong ability to resist Gaussian noise. WMD method is promising to be able to implement for the land seismic FWI, because it can reconstruct the low frequency information, lower the dominant frequency in the adjoint source, and has a strong ability to resist noise.

Time-frequency analysis based on ensemble local mean decomposition and fast kurtogram for rotating machinery fault diagnosis

NASA Astrophysics Data System (ADS)

Wang, Lei; Liu, Zhiwen; Miao, Qiang; Zhang, Xin

2018-03-01

A time-frequency analysis method based on ensemble local mean decomposition (ELMD) and fast kurtogram (FK) is proposed for rotating machinery fault diagnosis. Local mean decomposition (LMD), as an adaptive non-stationary and nonlinear signal processing method, provides the capability to decompose multicomponent modulation signal into a series of demodulated mono-components. However, the occurring mode mixing is a serious drawback. To alleviate this, ELMD based on noise-assisted method was developed. Still, the existing environmental noise in the raw signal remains in corresponding PF with the component of interest. FK has good performance in impulse detection while strong environmental noise exists. But it is susceptible to non-Gaussian noise. The proposed method combines the merits of ELMD and FK to detect the fault for rotating machinery. Primarily, by applying ELMD the raw signal is decomposed into a set of product functions (PFs). Then, the PF which mostly characterizes fault information is selected according to kurtosis index. Finally, the selected PF signal is further filtered by an optimal band-pass filter based on FK to extract impulse signal. Fault identification can be deduced by the appearance of fault characteristic frequencies in the squared envelope spectrum of the filtered signal. The advantages of ELMD over LMD and EEMD are illustrated in the simulation analyses. Furthermore, the efficiency of the proposed method in fault diagnosis for rotating machinery is demonstrated on gearbox case and rolling bearing case analyses.

Domain decomposition method for the Baltic Sea based on theory of adjoint equation and inverse problem.

NASA Astrophysics Data System (ADS)

Lezina, Natalya; Agoshkov, Valery

2017-04-01

Domain decomposition method (DDM) allows one to present a domain with complex geometry as a set of essentially simpler subdomains. This method is particularly applied for the hydrodynamics of oceans and seas. In each subdomain the system of thermo-hydrodynamic equations in the Boussinesq and hydrostatic approximations is solved. The problem of obtaining solution in the whole domain is that it is necessary to combine solutions in subdomains. For this purposes iterative algorithm is created and numerical experiments are conducted to investigate an effectiveness of developed algorithm using DDM. For symmetric operators in DDM, Poincare-Steklov's operators [1] are used, but for the problems of the hydrodynamics, it is not suitable. In this case for the problem, adjoint equation method [2] and inverse problem theory are used. In addition, it is possible to create algorithms for the parallel calculations using DDM on multiprocessor computer system. DDM for the model of the Baltic Sea dynamics is numerically studied. The results of numerical experiments using DDM are compared with the solution of the system of hydrodynamic equations in the whole domain. The work was supported by the Russian Science Foundation (project 14-11-00609, the formulation of the iterative process and numerical experiments). [1] V.I. Agoshkov, Domain Decompositions Methods in the Mathematical Physics Problem // Numerical processes and systems, No 8, Moscow, 1991 (in Russian). [2] V.I. Agoshkov, Optimal Control Approaches and Adjoint Equations in the Mathematical Physics Problem, Institute of Numerical Mathematics, RAS, Moscow, 2003 (in Russian).

Short-term forecasting of urban rail transit ridership based on ARIMA and wavelet decomposition

NASA Astrophysics Data System (ADS)

Wang, Xuemei; Zhang, Ning; Chen, Ying; Zhang, Yunlong

2018-05-01

Due to different functions and land use types, there are significant differences in ridership patterns among different urban rail transit stations. Considering the characteristics of different ridership and coping with the uncertainty, periodical and stochastic natures of short-term passenger flow, and this paper proposes a novel hybrid methodology that combines the autoregressive integrated moving average (ARIMA) model and wavelet decomposition, which has strong strengths in signal processing, to short-term ridership forecasting. The seasonal ARIMA is used to represent the relatively stable and regular ridership patterns while the wavelet decomposition is used to capture the stochastic or sometimes drastic changing characteristics of ridership patterns. The inclusion of wavelet decomposition and reconstruction provides the hybrid model with a unique strength in capturing sudden change in ridership patterns associated with certain rail stations. The case study is carried out by analyzing real ridership data of Metro Line 1 in Nanjing, China. The experimental results indicate that the hybrid method is superior to the individual ARIMA model for all ridership patterns, but particularly advantageous in predicting ridership at stations often associated with sudden pattern changes due to special events.

Application of composite dictionary multi-atom matching in gear fault diagnosis.

PubMed

Cui, Lingli; Kang, Chenhui; Wang, Huaqing; Chen, Peng

2011-01-01

The sparse decomposition based on matching pursuit is an adaptive sparse expression method for signals. This paper proposes an idea concerning a composite dictionary multi-atom matching decomposition and reconstruction algorithm, and the introduction of threshold de-noising in the reconstruction algorithm. Based on the structural characteristics of gear fault signals, a composite dictionary combining the impulse time-frequency dictionary and the Fourier dictionary was constituted, and a genetic algorithm was applied to search for the best matching atom. The analysis results of gear fault simulation signals indicated the effectiveness of the hard threshold, and the impulse or harmonic characteristic components could be separately extracted. Meanwhile, the robustness of the composite dictionary multi-atom matching algorithm at different noise levels was investigated. Aiming at the effects of data lengths on the calculation efficiency of the algorithm, an improved segmented decomposition and reconstruction algorithm was proposed, and the calculation efficiency of the decomposition algorithm was significantly enhanced. In addition it is shown that the multi-atom matching algorithm was superior to the single-atom matching algorithm in both calculation efficiency and algorithm robustness. Finally, the above algorithm was applied to gear fault engineering signals, and achieved good results.

Improving prediction accuracy of cooling load using EMD, PSR and RBFNN

NASA Astrophysics Data System (ADS)

Shen, Limin; Wen, Yuanmei; Li, Xiaohong

2017-08-01

To increase the accuracy for the prediction of cooling load demand, this work presents an EMD (empirical mode decomposition)-PSR (phase space reconstruction) based RBFNN (radial basis function neural networks) method. Firstly, analyzed the chaotic nature of the real cooling load demand, transformed the non-stationary cooling load historical data into several stationary intrinsic mode functions (IMFs) by using EMD. Secondly, compared the RBFNN prediction accuracies of each IMFs and proposed an IMF combining scheme that is combine the lower-frequency components (called IMF4-IMF6 combined) while keep the higher frequency component (IMF1, IMF2, IMF3) and the residual unchanged. Thirdly, reconstruct phase space for each combined components separately, process the highest frequency component (IMF1) by differential method and predict with RBFNN in the reconstructed phase spaces. Real cooling load data of a centralized ice storage cooling systems in Guangzhou are used for simulation. The results show that the proposed hybrid method outperforms the traditional methods.

The Distributed Diagonal Force Decomposition Method for Parallelizing Molecular Dynamics Simulations

PubMed Central

Boršnik, Urban; Miller, Benjamin T.; Brooks, Bernard R.; Janežič, Dušanka

2011-01-01

Parallelization is an effective way to reduce the computational time needed for molecular dynamics simulations. We describe a new parallelization method, the distributed-diagonal force decomposition method, with which we extend and improve the existing force decomposition methods. Our new method requires less data communication during molecular dynamics simulations than replicated data and current force decomposition methods, increasing the parallel efficiency. It also dynamically load-balances the processors' computational load throughout the simulation. The method is readily implemented in existing molecular dynamics codes and it has been incorporated into the CHARMM program, allowing its immediate use in conjunction with the many molecular dynamics simulation techniques that are already present in the program. We also present the design of the Force Decomposition Machine, a cluster of personal computers and networks that is tailored to running molecular dynamics simulations using the distributed diagonal force decomposition method. The design is expandable and provides various degrees of fault resilience. This approach is easily adaptable to computers with Graphics Processing Units because it is independent of the processor type being used. PMID:21793007

TE/TM decomposition of electromagnetic sources

NASA Technical Reports Server (NTRS)

Lindell, Ismo V.

1988-01-01

Three methods are given by which bounded EM sources can be decomposed into two parts radiating transverse electric (TE) and transverse magnetic (TM) fields with respect to a given constant direction in space. The theory applies source equivalence and nonradiating source concepts, which lead to decomposition methods based on a recursive formula or two differential equations for the determination of the TE and TM components of the original source. Decompositions for a dipole in terms of point, line, and plane sources are studied in detail. The planar decomposition is seen to match to an earlier result given by Clemmow (1963). As an application of the point decomposition method, it is demonstrated that the general exact image expression for the Sommerfeld half-space problem, previously derived through heuristic reasoning, can be more straightforwardly obtained through the present decomposition method.

Distinct Cortical Pathways for Music and Speech Revealed by Hypothesis-Free Voxel Decomposition

PubMed Central

Norman-Haignere, Sam

2015-01-01

SUMMARY The organization of human auditory cortex remains unresolved, due in part to the small stimulus sets common to fMRI studies and the overlap of neural populations within voxels. To address these challenges, we measured fMRI responses to 165 natural sounds and inferred canonical response profiles (“components”) whose weighted combinations explained voxel responses throughout auditory cortex. This analysis revealed six components, each with interpretable response characteristics despite being unconstrained by prior functional hypotheses. Four components embodied selectivity for particular acoustic features (frequency, spectrotemporal modulation, pitch). Two others exhibited pronounced selectivity for music and speech, respectively, and were not explainable by standard acoustic features. Anatomically, music and speech selectivity concentrated in distinct regions of non-primary auditory cortex. However, music selectivity was weak in raw voxel responses, and its detection required a decomposition method. Voxel decomposition identifies primary dimensions of response variation across natural sounds, revealing distinct cortical pathways for music and speech. PMID:26687225

Distinct Cortical Pathways for Music and Speech Revealed by Hypothesis-Free Voxel Decomposition.

PubMed

Norman-Haignere, Sam; Kanwisher, Nancy G; McDermott, Josh H

2015-12-16

The organization of human auditory cortex remains unresolved, due in part to the small stimulus sets common to fMRI studies and the overlap of neural populations within voxels. To address these challenges, we measured fMRI responses to 165 natural sounds and inferred canonical response profiles ("components") whose weighted combinations explained voxel responses throughout auditory cortex. This analysis revealed six components, each with interpretable response characteristics despite being unconstrained by prior functional hypotheses. Four components embodied selectivity for particular acoustic features (frequency, spectrotemporal modulation, pitch). Two others exhibited pronounced selectivity for music and speech, respectively, and were not explainable by standard acoustic features. Anatomically, music and speech selectivity concentrated in distinct regions of non-primary auditory cortex. However, music selectivity was weak in raw voxel responses, and its detection required a decomposition method. Voxel decomposition identifies primary dimensions of response variation across natural sounds, revealing distinct cortical pathways for music and speech. Copyright © 2015 Elsevier Inc. All rights reserved.

Wind Farm Flow Modeling using an Input-Output Reduced-Order Model

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

Annoni, Jennifer; Gebraad, Pieter; Seiler, Peter

Wind turbines in a wind farm operate individually to maximize their own power regardless of the impact of aerodynamic interactions on neighboring turbines. There is the potential to increase power and reduce overall structural loads by properly coordinating turbines. To perform control design and analysis, a model needs to be of low computational cost, but retains the necessary dynamics seen in high-fidelity models. The objective of this work is to obtain a reduced-order model that represents the full-order flow computed using a high-fidelity model. A variety of methods, including proper orthogonal decomposition and dynamic mode decomposition, can be used tomore » extract the dominant flow structures and obtain a reduced-order model. In this paper, we combine proper orthogonal decomposition with a system identification technique to produce an input-output reduced-order model. This technique is used to construct a reduced-order model of the flow within a two-turbine array computed using a large-eddy simulation.« less

Gravity Tides Extracted from Relative Gravimeter Data by Combining Empirical Mode Decomposition and Independent Component Analysis

NASA Astrophysics Data System (ADS)

Yu, Hongjuan; Guo, Jinyun; Kong, Qiaoli; Chen, Xiaodong

2018-04-01

The static observation data from a relative gravimeter contain noise and signals such as gravity tides. This paper focuses on the extraction of the gravity tides from the static relative gravimeter data for the first time applying the combined method of empirical mode decomposition (EMD) and independent component analysis (ICA), called the EMD-ICA method. The experimental results from the CG-5 gravimeter (SCINTREX Limited Ontario Canada) data show that the gravity tides time series derived by EMD-ICA are consistent with the theoretical reference (Longman formula) and the RMS of their differences only reaches 4.4 μGal. The time series of the gravity tides derived by EMD-ICA have a strong correlation with the theoretical time series and the correlation coefficient is greater than 0.997. The accuracy of the gravity tides estimated by EMD-ICA is comparable to the theoretical model and is slightly higher than that of independent component analysis (ICA). EMD-ICA could overcome the limitation of ICA having to process multiple observations and slightly improve the extraction accuracy and reliability of gravity tides from relative gravimeter data compared to that estimated with ICA.

Hybrid Upwind Splitting (HUS) by a Field-by-Field Decomposition

NASA Technical Reports Server (NTRS)

Coquel, Frederic; Liou, Meng-Sing

1995-01-01

We introduce and develop a new approach for upwind biasing: the hybrid upwind splitting (HUS) method. This original procedure is based on a suitable hybridization of current prominent flux vector splitting (FVS) and flux difference splitting (FDS) methods. The HUS method is designed to naturally combine the respective strengths of the above methods while excluding their main deficiencies. Specifically, the HUS strategy yields a family of upwind methods that exhibit the robustness of FVS schemes in the capture of nonlinear waves and the accuracy of some FDS schemes in the resolution of linear waves. We give a detailed construction of the HUS methods following a general and systematic procedure directly performed at the basic level of the field by field (i.e. waves) decomposition involved in FDS methods. For such a given decomposition, each field is endowed either with FVS or FDS numerical fluxes, depending on the nonlinear nature of the field under consideration. Such a design principle is made possible thanks to the introduction of a convenient formalism that provides us with a unified framework for upwind methods. The HUS methods we propose bring significant improvements over current methods in terms of accuracy and robustness. They yield entropy-satisfying approximate solutions as they are strongly supported in numerical experiments. Field by field hybrid numerical fluxes also achieve fairly simple and explicit expressions and hence require a computational effort between that of the FVS and FDS. Several numerical experiments ranging from stiff 1D shock-tube to high speed viscous flows problems are displayed, intending to illustrate the benefits of the present approach. We assess in particular the relevance of our HUS schemes to viscous flow calculations.

Pursuing reliable thermal analysis techniques for energetic materials: decomposition kinetics and thermal stability of dihydroxylammonium 5,5'-bistetrazole-1,1'-diolate (TKX-50).

PubMed

Muravyev, Nikita V; Monogarov, Konstantin A; Asachenko, Andrey F; Nechaev, Mikhail S; Ananyev, Ivan V; Fomenkov, Igor V; Kiselev, Vitaly G; Pivkina, Alla N

2016-12-21

Thermal decomposition of a novel promising high-performance explosive dihydroxylammonium 5,5'-bistetrazole-1,1'-diolate (TKX-50) was studied using a number of thermal analysis techniques (thermogravimetry, differential scanning calorimetry, and accelerating rate calorimetry, ARC). To obtain more comprehensive insight into the kinetics and mechanism of TKX-50 decomposition, a variety of complementary thermoanalytical experiments were performed under various conditions. Non-isothermal and isothermal kinetics were obtained at both atmospheric and low (up to 0.3 Torr) pressures. The gas products of thermolysis were detected in situ using IR spectroscopy, and the structure of solid-state decomposition products was determined by X-ray diffraction and scanning electron microscopy. Diammonium 5,5'-bistetrazole-1,1'-diolate (ABTOX) was directly identified to be the most important intermediate of the decomposition process. The important role of bistetrazole diol (BTO) in the mechanism of TKX-50 decomposition was also rationalized by thermolysis experiments with mixtures of TKX-50 and BTO. Several widely used thermoanalytical data processing techniques (Kissinger, isoconversional, formal kinetic approaches, etc.) were independently benchmarked against the ARC data, which are more germane to the real storage and application conditions of energetic materials. Our study revealed that none of the Arrhenius parameters reported before can properly describe the complex two-stage decomposition process of TKX-50. In contrast, we showed the superior performance of the isoconversional methods combined with isothermal measurements, which yielded the most reliable kinetic parameters of TKX-50 thermolysis. In contrast with the existing reports, the thermal stability of TKX-50 was determined in the ARC experiments to be lower than that of hexogen, but close to that of hexanitrohexaazaisowurtzitane (CL-20).

A novel method of identifying motor primitives using wavelet decomposition*

PubMed Central

Popov, Anton; Olesh, Erienne V.; Yakovenko, Sergiy; Gritsenko, Valeriya

2018-01-01

This study reports a new technique for extracting muscle synergies using continuous wavelet transform. The method allows to quantify coincident activation of muscle groups caused by the physiological processes of fixed duration, thus enabling the extraction of wavelet modules of arbitrary groups of muscles. Hierarchical clustering and identification of the repeating wavelet modules across subjects and across movements, was used to identify consistent muscle synergies. Results indicate that the most frequently repeated wavelet modules comprised combinations of two muscles that are not traditional agonists and span different joints. We have also found that these wavelet modules were flexibly combined across different movement directions in a pattern resembling directional tuning. This method is extendable to multiple frequency domains and signal modalities.

Propensity-score matching in economic analyses: comparison with regression models, instrumental variables, residual inclusion, differences-in-differences, and decomposition methods.

PubMed

Crown, William H

2014-02-01

This paper examines the use of propensity score matching in economic analyses of observational data. Several excellent papers have previously reviewed practical aspects of propensity score estimation and other aspects of the propensity score literature. The purpose of this paper is to compare the conceptual foundation of propensity score models with alternative estimators of treatment effects. References are provided to empirical comparisons among methods that have appeared in the literature. These comparisons are available for a subset of the methods considered in this paper. However, in some cases, no pairwise comparisons of particular methods are yet available, and there are no examples of comparisons across all of the methods surveyed here. Irrespective of the availability of empirical comparisons, the goal of this paper is to provide some intuition about the relative merits of alternative estimators in health economic evaluations where nonlinearity, sample size, availability of pre/post data, heterogeneity, and missing variables can have important implications for choice of methodology. Also considered is the potential combination of propensity score matching with alternative methods such as differences-in-differences and decomposition methods that have not yet appeared in the empirical literature.

Three-Component Decomposition of Polarimetric SAR Data Integrating Eigen-Decomposition Results

NASA Astrophysics Data System (ADS)

Lu, Da; He, Zhihua; Zhang, Huan

2018-01-01

This paper presents a novel three-component scattering power decomposition of polarimetric SAR data. There are two problems in three-component decomposition method: volume scattering component overestimation in urban areas and artificially set parameter to be a fixed value. Though volume scattering component overestimation can be partly solved by deorientation process, volume scattering still dominants some oriented urban areas. The speckle-like decomposition results introduced by artificially setting value are not conducive to further image interpretation. This paper integrates the results of eigen-decomposition to solve the aforementioned problems. Two principal eigenvectors are used to substitute the surface scattering model and the double bounce scattering model. The decomposed scattering powers are obtained using a constrained linear least-squares method. The proposed method has been verified using an ESAR PolSAR image, and the results show that the proposed method has better performance in urban area.

Scare Tactics: Evaluating Problem Decompositions Using Failure Scenarios

NASA Technical Reports Server (NTRS)

Helm, B. Robert; Fickas, Stephen

1992-01-01

Our interest is in the design of multi-agent problem-solving systems, which we refer to as composite systems. We have proposed an approach to composite system design by decomposition of problem statements. An automated assistant called Critter provides a library of reusable design transformations which allow a human analyst to search the space of decompositions for a problem. In this paper we describe a method for evaluating and critiquing problem decompositions generated by this search process. The method uses knowledge stored in the form of failure decompositions attached to design transformations. We suggest the benefits of our critiquing method by showing how it could re-derive steps of a published development example. We then identify several open issues for the method.

Parallel pivoting combined with parallel reduction

NASA Technical Reports Server (NTRS)

Alaghband, Gita

1987-01-01

Parallel algorithms for triangularization of large, sparse, and unsymmetric matrices are presented. The method combines the parallel reduction with a new parallel pivoting technique, control over generations of fill-ins and a check for numerical stability, all done in parallel with the work being distributed over the active processes. The parallel technique uses the compatibility relation between pivots to identify parallel pivot candidates and uses the Markowitz number of pivots to minimize fill-in. This technique is not a preordering of the sparse matrix and is applied dynamically as the decomposition proceeds.

A practical material decomposition method for x-ray dual spectral computed tomography.

PubMed

Hu, Jingjing; Zhao, Xing

2016-03-17

X-ray dual spectral CT (DSCT) scans the measured object with two different x-ray spectra, and the acquired rawdata can be used to perform the material decomposition of the object. Direct calibration methods allow a faster material decomposition for DSCT and can be separated in two groups: image-based and rawdata-based. The image-based method is an approximative method, and beam hardening artifacts remain in the resulting material-selective images. The rawdata-based method generally obtains better image quality than the image-based method, but this method requires geometrically consistent rawdata. However, today's clinical dual energy CT scanners usually measure different rays for different energy spectra and acquire geometrically inconsistent rawdata sets, and thus cannot meet the requirement. This paper proposes a practical material decomposition method to perform rawdata-based material decomposition in the case of inconsistent measurement. This method first yields the desired consistent rawdata sets from the measured inconsistent rawdata sets, and then employs rawdata-based technique to perform material decomposition and reconstruct material-selective images. The proposed method was evaluated by use of simulated FORBILD thorax phantom rawdata and dental CT rawdata, and simulation results indicate that this method can produce highly quantitative DSCT images in the case of inconsistent DSCT measurements.

Randomized interpolative decomposition of separated representations

NASA Astrophysics Data System (ADS)

Biagioni, David J.; Beylkin, Daniel; Beylkin, Gregory

2015-01-01

We introduce an algorithm to compute tensor interpolative decomposition (dubbed CTD-ID) for the reduction of the separation rank of Canonical Tensor Decompositions (CTDs). Tensor ID selects, for a user-defined accuracy ɛ, a near optimal subset of terms of a CTD to represent the remaining terms via a linear combination of the selected terms. CTD-ID can be used as an alternative to or in combination with the Alternating Least Squares (ALS) algorithm. We present examples of its use within a convergent iteration to compute inverse operators in high dimensions. We also briefly discuss the spectral norm as a computational alternative to the Frobenius norm in estimating approximation errors of tensor ID. We reduce the problem of finding tensor IDs to that of constructing interpolative decompositions of certain matrices. These matrices are generated via randomized projection of the terms of the given tensor. We provide cost estimates and several examples of the new approach to the reduction of separation rank.

Validating the performance of one-time decomposition for fMRI analysis using ICA with automatic target generation process.

PubMed

Yao, Shengnan; Zeng, Weiming; Wang, Nizhuan; Chen, Lei

2013-07-01

Independent component analysis (ICA) has been proven to be effective for functional magnetic resonance imaging (fMRI) data analysis. However, ICA decomposition requires to optimize the unmixing matrix iteratively whose initial values are generated randomly. Thus the randomness of the initialization leads to different ICA decomposition results. Therefore, just one-time decomposition for fMRI data analysis is not usually reliable. Under this circumstance, several methods about repeated decompositions with ICA (RDICA) were proposed to reveal the stability of ICA decomposition. Although utilizing RDICA has achieved satisfying results in validating the performance of ICA decomposition, RDICA cost much computing time. To mitigate the problem, in this paper, we propose a method, named ATGP-ICA, to do the fMRI data analysis. This method generates fixed initial values with automatic target generation process (ATGP) instead of being produced randomly. We performed experimental tests on both hybrid data and fMRI data to indicate the effectiveness of the new method and made a performance comparison of the traditional one-time decomposition with ICA (ODICA), RDICA and ATGP-ICA. The proposed method demonstrated that it not only could eliminate the randomness of ICA decomposition, but also could save much computing time compared to RDICA. Furthermore, the ROC (Receiver Operating Characteristic) power analysis also denoted the better signal reconstruction performance of ATGP-ICA than that of RDICA. Copyright © 2013 Elsevier Inc. All rights reserved.

Selection theory of free dendritic growth in a potential flow.

PubMed

von Kurnatowski, Martin; Grillenbeck, Thomas; Kassner, Klaus

2013-04-01

The Kruskal-Segur approach to selection theory in diffusion-limited or Laplacian growth is extended via combination with the Zauderer decomposition scheme. This way nonlinear bulk equations become tractable. To demonstrate the method, we apply it to two-dimensional crystal growth in a potential flow. We omit the simplifying approximations used in a preliminary calculation for the same system [Fischaleck, Kassner, Europhys. Lett. 81, 54004 (2008)], thus exhibiting the capability of the method to extend mathematical rigor to more complex problems than hitherto accessible.

A compositional approach to building applications in a computational environment

NASA Astrophysics Data System (ADS)

Roslovtsev, V. V.; Shumsky, L. D.; Wolfengagen, V. E.

2014-04-01

The paper presents an approach to creating an applicative computational environment to feature computational processes and data decomposition, and a compositional approach to application building. The approach in question is based on the notion of combinator - both in systems with variable binding (such as λ-calculi) and those allowing programming without variables (combinatory logic style). We present a computation decomposition technique based on objects' structural decomposition, with the focus on computation decomposition. The computational environment's architecture is based on a network with nodes playing several roles simultaneously.

Research on Ship-Radiated Noise Denoising Using Secondary Variational Mode Decomposition and Correlation Coefficient.

PubMed

Li, Yuxing; Li, Yaan; Chen, Xiao; Yu, Jing

2017-12-26

As the sound signal of ships obtained by sensors contains other many significant characteristics of ships and called ship-radiated noise (SN), research into a denoising algorithm and its application has obtained great significance. Using the advantage of variational mode decomposition (VMD) combined with the correlation coefficient for denoising, a hybrid secondary denoising algorithm is proposed using secondary VMD combined with a correlation coefficient (CC). First, different kinds of simulation signals are decomposed into several bandwidth-limited intrinsic mode functions (IMFs) using VMD, where the decomposition number by VMD is equal to the number by empirical mode decomposition (EMD); then, the CCs between the IMFs and the simulation signal are calculated respectively. The noise IMFs are identified by the CC threshold and the rest of the IMFs are reconstructed in order to realize the first denoising process. Finally, secondary denoising of the simulation signal can be accomplished by repeating the above steps of decomposition, screening and reconstruction. The final denoising result is determined according to the CC threshold. The denoising effect is compared under the different signal-to-noise ratio and the time of decomposition by VMD. Experimental results show the validity of the proposed denoising algorithm using secondary VMD (2VMD) combined with CC compared to EMD denoising, ensemble EMD (EEMD) denoising, VMD denoising and cubic VMD (3VMD) denoising, as well as two denoising algorithms presented recently. The proposed denoising algorithm is applied to feature extraction and classification for SN signals, which can effectively improve the recognition rate of different kinds of ships.

Kinetics of calcium sulfoaluminate formation from tricalcium aluminate, calcium sulfate and calcium oxide

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

Li, Xuerun, E-mail: xuerunli@163.com; Zhang, Yu; Shen, Xiaodong, E-mail: xdshen@njut.edu.cn

The formation kinetics of tricalcium aluminate (C{sub 3}A) and calcium sulfate yielding calcium sulfoaluminate (C{sub 4}A{sub 3}more » $$) and the decomposition kinetics of calcium sulfoaluminate were investigated by sintering a mixture of synthetic C{sub 3}A and gypsum. The quantitative analysis of the phase composition was performed by X-ray powder diffraction analysis using the Rietveld method. The results showed that the formation reaction 3Ca{sub 3}Al{sub 2}O{sub 6} + CaSO{sub 4} → Ca{sub 4}Al{sub 6}O{sub 12}(SO{sub 4}) + 6CaO was the primary reaction < 1350 °C with and activation energy of 231 ± 42 kJ/mol; while the decomposition reaction 2Ca{sub 4}Al{sub 6}O{sub 12}(SO{sub 4}) + 10CaO → 6Ca{sub 3}Al{sub 2}O{sub 6} + 2SO{sub 2} ↑ + O{sub 2} ↑ primarily occurred beyond 1350 °C with an activation energy of 792 ± 64 kJ/mol. The optimal formation region for C{sub 4}A{sub 3}$$ was from 1150 °C to 1350 °C and from 6 h to 1 h, which could provide useful information on the formation of C{sub 4}A{sub 3}$ containing clinkers. The Jander diffusion model was feasible for the formation and decomposition of calcium sulfoaluminate. Ca{sup 2+} and SO{sub 4}{sup 2−} were the diffusive species in both the formation and decomposition reactions. -- Highlights: •Formation and decomposition of calcium sulphoaluminate were studied. •Decomposition of calcium sulphoaluminate combined CaO and yielded C{sub 3}A. •Activation energy for formation was 231 ± 42 kJ/mol. •Activation energy for decomposition was 792 ± 64 kJ/mol. •Both the formation and decomposition were controlled by diffusion.« less

A New Approach of evaluating the damage in simply-supported reinforced concrete beam by Local mean decomposition (LMD)

NASA Astrophysics Data System (ADS)

Zhang, Xuebing; Liu, Ning; Xi, Jiaxin; Zhang, Yunqi; Zhang, Wenchun; Yang, Peipei

2017-08-01

How to analyze the nonstationary response signals and obtain vibration characters is extremely important in the vibration-based structural diagnosis methods. In this work, we introduce a more reasonable time-frequency decomposition method termed local mean decomposition (LMD) to instead the widely-used empirical mode decomposition (EMD). By employing the LMD method, one can derive a group of component signals, each of which is more stationary, and then analyze the vibration state and make the assessment of structural damage of a construction or building. We illustrated the effectiveness of LMD by a synthetic data and an experimental data recorded in a simply-supported reinforced concrete beam. Then based on the decomposition results, an elementary method of damage diagnosis was proposed.

Influence of different fertilizer supplements on decomposition of cereal stubble remains in chernozem soil

NASA Astrophysics Data System (ADS)

Nikolaev, I. V.; Klein, O. I.; Kulikova, N. A.; Stepanova, E. V.; Koroleva, O. V.

2009-04-01

Introduction Recently, many farmers have converted to low-disturbance tillage land cultivation as disk or plow fields can result in water and wind erosion of soil. So, crop residue and plant crowns and roots are left to hold the soil. However, low-disturbance tillage can be a challenge to manage since the key to crop production still requires good seed-to-soil contact. Therefore, decomposition of stubble in agricultural soils in situ is an issue of the day of modern agriculture. The aim of the present study was to compare different organic and inorganic fertilizer supplements on decomposition of cereal stubble remains in chernozem soil. Materials and methods Field trials were conducted in Krasnodar region, Russia. To promote stubble decomposition, a biopreparation that was cultural liquid obtained during cultivation of white-rot fungi Coriolus hirsutus 075 (Wulf Ex. Fr.) Quel. was used at the dosage of 150 ml/ha. The other tested supplements included ammonium nitrate (34 kg/ha), commercially available humate LignohumateTM (0.2 kg/ha) and combination of Lignohumate and biopreparation. Test plots were treated once after wheat harvesting. Non-treated ploughed plot was used as a blank. Soil samples were collected within 2 and 14 weeks after soil treatment. To control soil potential for stubble remains decomposition enzymatic activity is soil was determined. To perform soil analysis, stubble remains were carefully separated from soils followed by soil extraction with 0.14 M phosphate buffer pH 7.1 and analysis of the extracts for laccase and peroxidase activities [1,2]. Estimation of stubble decomposition in soil was performed by cellulose contents determination [3]. Results and discussion The obtained results demonstrated after 14 weeks of treatment increase of soil enzymatic activity due to soil supplementation was observed. Introduction of ammonium nitrate resulted in 108% of peroxidise activity as compared to blank. That value for Lignohumate variant was estimated as 106%. The most pronounced promoting effect on soil enzymatic activity was observed for biopreparation and its combination with Lignohumate where peroxidise activity was as much as 180% and 425% respectively. Moreover, for the above supplements enhancement of laccase activity was also observed, while ammonium nitrate or Lignohumate did not affect that parameter. Treatment of soil with biopreparation led to increase of laccase activity to 265% of blank value, and combined introduction of biopreparation with Lignohumate to 390%. That finding was evident for high potential of soil treated with biopreparation or its combination with Lignohumate to decompose cereal stubble remains. The latter was confirmed by direct measurements of cellulose contents in soil. The most dramatic decrease in plant residue cellulose content was registered for soils treated with biopreparation and Lignohumate where it was 32.4 and 44.3% of blank value respectively. Conclusions Effects of mineral and organic supplements on decomposition of cereal stubble remains in chernozem soil were determined. Among supplements studied, the most efficient treatment was combined application of biopreparation with Lignohumate. Acknowledgements Authors express their deepest appreciation to O.A. Gladkov and SPA RET for the field trials. References 1. Childs R.E., Bardsley W.G. Biochem. J. 1975, 145, 93-103 2. Solomon E.J., Sundaram U.M., Machonkin T.E. Chem. Rev. 1996, 96, 2563-2605 3. Dubois M., Gilles K.A., Hamilton J.K., Rebers P.A., Smith F. Analyt. Chem. 1956, 28, 350-356.

Conceptual design optimization study

NASA Technical Reports Server (NTRS)

Hollowell, S. J.; Beeman, E. R., II; Hiyama, R. M.

1990-01-01

The feasibility of applying multilevel functional decomposition and optimization techniques to conceptual design of advanced fighter aircraft was investigated. Applying the functional decomposition techniques to the conceptual design phase appears to be feasible. The initial implementation of the modified design process will optimize wing design variables. A hybrid approach, combining functional decomposition techniques for generation of aerodynamic and mass properties linear sensitivity derivatives with existing techniques for sizing mission performance and optimization, is proposed.

Extracting fingerprint of wireless devices based on phase noise and multiple level wavelet decomposition

NASA Astrophysics Data System (ADS)

Zhao, Weichen; Sun, Zhuo; Kong, Song

2016-10-01

Wireless devices can be identified by the fingerprint extracted from the signal transmitted, which is useful in wireless communication security and other fields. This paper presents a method that extracts fingerprint based on phase noise of signal and multiple level wavelet decomposition. The phase of signal will be extracted first and then decomposed by multiple level wavelet decomposition. The statistic value of each wavelet coefficient vector is utilized for constructing fingerprint. Besides, the relationship between wavelet decomposition level and recognition accuracy is simulated. And advertised decomposition level is revealed as well. Compared with previous methods, our method is simpler and the accuracy of recognition remains high when Signal Noise Ratio (SNR) is low.

Application of singular value decomposition to structural dynamics systems with constraints

NASA Technical Reports Server (NTRS)

Juang, J.-N.; Pinson, L. D.

1985-01-01

Singular value decomposition is used to construct a coordinate transformation for a linear dynamic system subject to linear, homogeneous constraint equations. The method is compared with two commonly used methods, namely classical Gaussian elimination and Walton-Steeves approach. Although the classical method requires fewer numerical operations, the singular value decomposition method is more accurate and convenient in eliminating the dependent coordinates. Numerical examples are presented to demonstrate the application of the method.

Theoretical studies of the decomposition mechanisms of 1,2,4-butanetriol trinitrate.

PubMed

Pei, Liguan; Dong, Kehai; Tang, Yanhui; Zhang, Bo; Yu, Chang; Li, Wenzuo

2017-12-06

Density functional theory (DFT) and canonical variational transition-state theory combined with a small-curvature tunneling correction (CVT/SCT) were used to explore the decomposition mechanisms of 1,2,4-butanetriol trinitrate (BTTN) in detail. The results showed that the γ-H abstraction reaction is the initial pathway for autocatalytic BTTN decomposition. The three possible hydrogen atom abstraction reactions are all exothermic. The rate constants for autocatalytic BTTN decomposition are 3 to 10 40 times greater than the rate constants for the two unimolecular decomposition reactions (O-NO 2 cleavage and HONO elimination). The process of BTTN decomposition can be divided into two stages according to whether the NO 2 concentration is above a threshold value. HONO elimination is the main reaction channel during the first stage because autocatalytic decomposition requires NO 2 and the concentration of NO 2 is initially low. As the reaction proceeds, the concentration of NO 2 gradually increases; when it exceeds the threshold value, the second stage begins, with autocatalytic decomposition becoming the main reaction channel.

Material decomposition in an arbitrary number of dimensions using noise compensating projection

NASA Astrophysics Data System (ADS)

O'Donnell, Thomas; Halaweish, Ahmed; Cormode, David; Cheheltani, Rabee; Fayad, Zahi A.; Mani, Venkatesh

2017-03-01

Purpose: Multi-energy CT (e.g., dual energy or photon counting) facilitates the identification of certain compounds via data decomposition. However, the standard approach to decomposition (i.e., solving a system of linear equations) fails if - due to noise - a pixel's vector of HU values falls outside the boundary of values describing possible pure or mixed basis materials. Typically, this is addressed by either throwing away those pixels or projecting them onto the closest point on this boundary. However, when acquiring four (or more) energy volumes, the space bounded by three (or more) materials that may be found in the human body (either naturally or through injection) can be quite small. Noise may significantly limit the number of those pixels to be included within. Therefore, projection onto the boundary becomes an important option. But, projection in higher than 3 dimensional space is not possible with standard vector algebra: the cross-product is not defined. Methods: We describe a technique which employs Clifford Algebra to perform projection in an arbitrary number of dimensions. Clifford Algebra describes a manipulation of vectors that incorporates the concepts of addition, subtraction, multiplication, and division. Thereby, vectors may be operated on like scalars forming a true algebra. Results: We tested our approach on a phantom containing inserts of calcium, gadolinium, iodine, gold nanoparticles and mixtures of pairs thereof. Images were acquired on a prototype photon counting CT scanner under a range of threshold combinations. Comparison of the accuracy of different threshold combinations versus ground truth are presented. Conclusions: Material decomposition is possible with three or more materials and four or more energy thresholds using Clifford Algebra projection to mitigate noise.

Domain Decomposition By the Advancing-Partition Method for Parallel Unstructured Grid Generation

NASA Technical Reports Server (NTRS)

Pirzadeh, Shahyar Z.; Zagaris, George

2009-01-01

A new method of domain decomposition has been developed for generating unstructured grids in subdomains either sequentially or using multiple computers in parallel. Domain decomposition is a crucial and challenging step for parallel grid generation. Prior methods are generally based on auxiliary, complex, and computationally intensive operations for defining partition interfaces and usually produce grids of lower quality than those generated in single domains. The new technique, referred to as "Advancing Partition," is based on the Advancing-Front method, which partitions a domain as part of the volume mesh generation in a consistent and "natural" way. The benefits of this approach are: 1) the process of domain decomposition is highly automated, 2) partitioning of domain does not compromise the quality of the generated grids, and 3) the computational overhead for domain decomposition is minimal. The new method has been implemented in NASA's unstructured grid generation code VGRID.

Domain Decomposition By the Advancing-Partition Method

NASA Technical Reports Server (NTRS)

Pirzadeh, Shahyar Z.

2008-01-01

A new method of domain decomposition has been developed for generating unstructured grids in subdomains either sequentially or using multiple computers in parallel. Domain decomposition is a crucial and challenging step for parallel grid generation. Prior methods are generally based on auxiliary, complex, and computationally intensive operations for defining partition interfaces and usually produce grids of lower quality than those generated in single domains. The new technique, referred to as "Advancing Partition," is based on the Advancing-Front method, which partitions a domain as part of the volume mesh generation in a consistent and "natural" way. The benefits of this approach are: 1) the process of domain decomposition is highly automated, 2) partitioning of domain does not compromise the quality of the generated grids, and 3) the computational overhead for domain decomposition is minimal. The new method has been implemented in NASA's unstructured grid generation code VGRID.

Dynamic Responses and Initial Decomposition under Shock Loading: A DFTB Calculation Combined with MSST Method for β-HMX with Molecular Vacancy.

PubMed

He, Zheng-Hua; Chen, Jun; Ji, Guang-Fu; Liu, Li-Min; Zhu, Wen-Jun; Wu, Qiang

2015-08-20

Despite extensive efforts on studying the decomposition mechanism of HMX under extreme condition, an intrinsic understanding of mechanical and chemical response processes, inducing the initial chemical reaction, is not yet achieved. In this work, the microscopic dynamic response and initial decomposition of β-HMX with (1 0 0) surface and molecular vacancy under shock condition, were explored by means of the self-consistent-charge density-functional tight-binding method (SCC-DFTB) in conjunction with multiscale shock technique (MSST). The evolutions of various bond lengths and charge transfers were analyzed to explore and understand the initial reaction mechanism of HMX. Our results discovered that the C-N bond close to major axes had less compression sensitivity and higher stretch activity. The charge was transferred mainly from the N-NO2 group along the minor axes and H atom to C atom during the early compression process. The first reaction of HMX primarily initiated with the fission of the molecular ring at the site of the C-N bond close to major axes. Further breaking of the molecular ring enhanced intermolecular interactions and promoted the cleavage of C-H and N-NO2 bonds. More significantly, the dynamic response behavior clearly depended on the angle between chemical bond and shock direction.

Differential Decomposition Among Pig, Rabbit, and Human Remains.

PubMed

Dautartas, Angela; Kenyhercz, Michael W; Vidoli, Giovanna M; Meadows Jantz, Lee; Mundorff, Amy; Steadman, Dawnie Wolfe

2018-03-30

While nonhuman animal remains are often utilized in forensic research to develop methods to estimate the postmortem interval, systematic studies that directly validate animals as proxies for human decomposition are lacking. The current project compared decomposition rates among pigs, rabbits, and humans at the University of Tennessee's Anthropology Research Facility across three seasonal trials that spanned nearly 2 years. The Total Body Score (TBS) method was applied to quantify decomposition changes and calculate the postmortem interval (PMI) in accumulated degree days (ADD). Decomposition trajectories were analyzed by comparing the estimated and actual ADD for each seasonal trial and by fuzzy cluster analysis. The cluster analysis demonstrated that the rabbits formed one group while pigs and humans, although more similar to each other than either to rabbits, still showed important differences in decomposition patterns. The decomposition trends show that neither nonhuman model captured the pattern, rate, and variability of human decomposition. © 2018 American Academy of Forensic Sciences.

Multi-scale structure and topological anomaly detection via a new network statistic: The onion decomposition.

PubMed

Hébert-Dufresne, Laurent; Grochow, Joshua A; Allard, Antoine

2016-08-18

We introduce a network statistic that measures structural properties at the micro-, meso-, and macroscopic scales, while still being easy to compute and interpretable at a glance. Our statistic, the onion spectrum, is based on the onion decomposition, which refines the k-core decomposition, a standard network fingerprinting method. The onion spectrum is exactly as easy to compute as the k-cores: It is based on the stages at which each vertex gets removed from a graph in the standard algorithm for computing the k-cores. Yet, the onion spectrum reveals much more information about a network, and at multiple scales; for example, it can be used to quantify node heterogeneity, degree correlations, centrality, and tree- or lattice-likeness. Furthermore, unlike the k-core decomposition, the combined degree-onion spectrum immediately gives a clear local picture of the network around each node which allows the detection of interesting subgraphs whose topological structure differs from the global network organization. This local description can also be leveraged to easily generate samples from the ensemble of networks with a given joint degree-onion distribution. We demonstrate the utility of the onion spectrum for understanding both static and dynamic properties on several standard graph models and on many real-world networks.

Atomic-batched tensor decomposed two-electron repulsion integrals

NASA Astrophysics Data System (ADS)

Schmitz, Gunnar; Madsen, Niels Kristian; Christiansen, Ove

2017-04-01

We present a new integral format for 4-index electron repulsion integrals, in which several strategies like the Resolution-of-the-Identity (RI) approximation and other more general tensor-decomposition techniques are combined with an atomic batching scheme. The 3-index RI integral tensor is divided into sub-tensors defined by atom pairs on which we perform an accelerated decomposition to the canonical product (CP) format. In a first step, the RI integrals are decomposed to a high-rank CP-like format by repeated singular value decompositions followed by a rank reduction, which uses a Tucker decomposition as an intermediate step to lower the prefactor of the algorithm. After decomposing the RI sub-tensors (within the Coulomb metric), they can be reassembled to the full decomposed tensor (RC approach) or the atomic batched format can be maintained (ABC approach). In the first case, the integrals are very similar to the well-known tensor hypercontraction integral format, which gained some attraction in recent years since it allows for quartic scaling implementations of MP2 and some coupled cluster methods. On the MP2 level, the RC and ABC approaches are compared concerning efficiency and storage requirements. Furthermore, the overall accuracy of this approach is assessed. Initial test calculations show a good accuracy and that it is not limited to small systems.

Atomic-batched tensor decomposed two-electron repulsion integrals.

PubMed

Schmitz, Gunnar; Madsen, Niels Kristian; Christiansen, Ove

2017-04-07

We present a new integral format for 4-index electron repulsion integrals, in which several strategies like the Resolution-of-the-Identity (RI) approximation and other more general tensor-decomposition techniques are combined with an atomic batching scheme. The 3-index RI integral tensor is divided into sub-tensors defined by atom pairs on which we perform an accelerated decomposition to the canonical product (CP) format. In a first step, the RI integrals are decomposed to a high-rank CP-like format by repeated singular value decompositions followed by a rank reduction, which uses a Tucker decomposition as an intermediate step to lower the prefactor of the algorithm. After decomposing the RI sub-tensors (within the Coulomb metric), they can be reassembled to the full decomposed tensor (RC approach) or the atomic batched format can be maintained (ABC approach). In the first case, the integrals are very similar to the well-known tensor hypercontraction integral format, which gained some attraction in recent years since it allows for quartic scaling implementations of MP2 and some coupled cluster methods. On the MP2 level, the RC and ABC approaches are compared concerning efficiency and storage requirements. Furthermore, the overall accuracy of this approach is assessed. Initial test calculations show a good accuracy and that it is not limited to small systems.

MCTDH on-the-fly: Efficient grid-based quantum dynamics without pre-computed potential energy surfaces

NASA Astrophysics Data System (ADS)

Richings, Gareth W.; Habershon, Scott

2018-04-01

We present significant algorithmic improvements to a recently proposed direct quantum dynamics method, based upon combining well established grid-based quantum dynamics approaches and expansions of the potential energy operator in terms of a weighted sum of Gaussian functions. Specifically, using a sum of low-dimensional Gaussian functions to represent the potential energy surface (PES), combined with a secondary fitting of the PES using singular value decomposition, we show how standard grid-based quantum dynamics methods can be dramatically accelerated without loss of accuracy. This is demonstrated by on-the-fly simulations (using both standard grid-based methods and multi-configuration time-dependent Hartree) of both proton transfer on the electronic ground state of salicylaldimine and the non-adiabatic dynamics of pyrazine.

Acoustical Applications of the HHT Method

NASA Technical Reports Server (NTRS)

Huang, Norden E.

2003-01-01

A document discusses applications of a method based on the Huang-Hilbert transform (HHT). The method was described, without the HHT name, in Analyzing Time Series Using EMD and Hilbert Spectra (GSC-13817), NASA Tech Briefs, Vol. 24, No. 10 (October 2000), page 63. To recapitulate: The method is especially suitable for analyzing time-series data that represent nonstationary and nonlinear physical phenomena. The method involves the empirical mode decomposition (EMD), in which a complicated signal is decomposed into a finite number of functions, called intrinsic mode functions (IMFs), that admit well-behaved Hilbert transforms. The HHT consists of the combination of EMD and Hilbert spectral analysis.

a Novel Two-Component Decomposition for Co-Polar Channels of GF-3 Quad-Pol Data

NASA Astrophysics Data System (ADS)

Kwok, E.; Li, C. H.; Zhao, Q. H.; Li, Y.

2018-04-01

Polarimetric target decomposition theory is the most dynamic and exploratory research area in the field of PolSAR. But most methods of target decomposition are based on fully polarized data (quad pol) and seldom utilize dual-polar data for target decomposition. Given this, we proposed a novel two-component decomposition method for co-polar channels of GF-3 quad-pol data. This method decomposes the data into two scattering contributions: surface, double bounce in dual co-polar channels. To save this underdetermined problem, a criterion for determining the model is proposed. The criterion can be named as second-order averaged scattering angle, which originates from the H/α decomposition. and we also put forward an alternative parameter of it. To validate the effectiveness of proposed decomposition, Liaodong Bay is selected as research area. The area is located in northeastern China, where it grows various wetland resources and appears sea ice phenomenon in winter. and we use the GF-3 quad-pol data as study data, which which is China's first C-band polarimetric synthetic aperture radar (PolSAR) satellite. The dependencies between the features of proposed algorithm and comparison decompositions (Pauli decomposition, An&Yang decomposition, Yamaguchi S4R decomposition) were investigated in the study. Though several aspects of the experimental discussion, we can draw the conclusion: the proposed algorithm may be suitable for special scenes with low vegetation coverage or low vegetation in the non-growing season; proposed decomposition features only using co-polar data are highly correlated with the corresponding comparison decomposition features under quad-polarization data. Moreover, it would be become input of the subsequent classification or parameter inversion.

Pilot study assessing differentiation of steatosis hepatis, hepatic iron overload, and combined disease using two-point dixon MRI at 3 T: in vitro and in vivo results of a 2D decomposition technique.

PubMed

Boll, Daniel T; Marin, Daniele; Redmon, Grace M; Zink, Stephen I; Merkle, Elmar M

2010-04-01

The purpose of our study was to evaluate whether two-point Dixon MRI using a 2D decomposition technique facilitates metabolite differentiation between lipids and iron in standardized in vitro liver phantoms with in vivo patient validation and allows semiquantitative in vitro assessment of metabolites associated with steatosis, iron overload, and combined disease. The acrylamide-based phantoms were made to reproduce the T1- and T2-weighted MRI appearances of physiologic hepatic parenchyma and hepatic steatosis-iron overload by the admixture of triglycerides and ferumoxides. Combined disease was simulated using joint admixtures of triglycerides and ferumoxides at various concentrations. For phantom validation, 30 patients were included, of whom 10 had steatosis, 10 had iron overload, and 10 had no liver disease. For MRI an in-phase/opposed-phase T1-weighted sequence with TR/TE(opposed-phase)/TE(in-phase) of 4.19/1.25/2.46 was used. Fat/water series were obtained by Dixon-based algorithms. In-phase and opposed-phase and fat/water ratios were calculated. Statistical cluster analysis assessed ratio pairs of physiologic liver, steatosis, iron overload, and combined disease in 2D metabolite discrimination plots. Statistical assessment proved that metabolite decomposition in phantoms simulating steatosis (1.77|0.22; in-phase/opposed-phase|fat/water ratios), iron overload (0.75|0.21), and healthy control subjects (1.09|0.05) formed three clusters with distinct ratio pairs. Patient validation for hepatic steatosis (3.29|0.51), iron overload (0.56|0.41), and normal control subjects (0.99|0.05) confirmed this clustering (p < 0.001). One-dimensional analysis assessing in vitro combined disease only with in-phase/opposed-phase ratios would have failed to characterize metabolites. The 2D analysis plotting in-phase/opposed-phase and fat/water ratios (2.16|0.59) provided accurate semiquantitative metabolite decomposition (p < 0.001). MR Dixon imaging facilitates metabolite decomposition of intrahepatic lipids and iron using in vitro phantoms with in vivo patient validation. The proposed decomposition technique identified distinct in-phase/opposed-phase and fat/water ratios for in vitro steatosis, iron overload, and combined disease.

Fast heap transform-based QR-decomposition of real and complex matrices: algorithms and codes

NASA Astrophysics Data System (ADS)

Grigoryan, Artyom M.

2015-03-01

In this paper, we describe a new look on the application of Givens rotations to the QR-decomposition problem, which is similar to the method of Householder transformations. We apply the concept of the discrete heap transform, or signal-induced unitary transforms which had been introduced by Grigoryan (2006) and used in signal and image processing. Both cases of real and complex nonsingular matrices are considered and examples of performing QR-decomposition of square matrices are given. The proposed method of QR-decomposition for the complex matrix is novel and differs from the known method of complex Givens rotation and is based on analytical equations for the heap transforms. Many examples illustrated the proposed heap transform method of QR-decomposition are given, algorithms are described in detail, and MATLAB-based codes are included.

Modal identification of structures by a novel approach based on FDD-wavelet method

NASA Astrophysics Data System (ADS)

Tarinejad, Reza; Damadipour, Majid

2014-02-01

An important application of system identification in structural dynamics is the determination of natural frequencies, mode shapes and damping ratios during operation which can then be used for calibrating numerical models. In this paper, the combination of two advanced methods of Operational Modal Analysis (OMA) called Frequency Domain Decomposition (FDD) and Continuous Wavelet Transform (CWT) based on novel cyclic averaging of correlation functions (CACF) technique are used for identification of dynamic properties. By using this technique, the autocorrelation of averaged correlation functions is used instead of original signals. Integration of FDD and CWT methods is used to overcome their deficiency and take advantage of the unique capabilities of these methods. The FDD method is able to accurately estimate the natural frequencies and mode shapes of structures in the frequency domain. On the other hand, the CWT method is in the time-frequency domain for decomposition of a signal at different frequencies and determines the damping coefficients. In this paper, a new formulation applied to the wavelet transform of the averaged correlation function of an ambient response is proposed. This application causes to accurate estimation of damping ratios from weak (noise) or strong (earthquake) vibrations and long or short duration record. For this purpose, the modified Morlet wavelet having two free parameters is used. The optimum values of these two parameters are obtained by employing a technique which minimizes the entropy of the wavelet coefficients matrix. The capabilities of the novel FDD-Wavelet method in the system identification of various dynamic systems with regular or irregular distribution of mass and stiffness are illustrated. This combined approach is superior to classic methods and yields results that agree well with the exact solutions of the numerical models.

Analysis of cured carbon-phenolic decomposition products to investigate the thermal decomposition of nozzle materials

NASA Technical Reports Server (NTRS)

Thompson, James M.; Daniel, Janice D.

1989-01-01

The development of a mass spectrometer/thermal analyzer/computer (MS/TA/Computer) system capable of providing simultaneous thermogravimetry (TG), differential thermal analysis (DTA), derivative thermogravimetry (DTG) and evolved gas detection and analysis (EGD and EGA) under both atmospheric and high pressure conditions is described. The combined system was used to study the thermal decomposition of the nozzle material that constitutes the throat of the solid rocket boosters (SRB).

Conception of discrete systems decomposition algorithm using p-invariants and hypergraphs

NASA Astrophysics Data System (ADS)

Stefanowicz, Ł.

2016-09-01

In the article author presents an idea of decomposition algorithm of discrete systems described by Petri Nets using pinvariants. Decomposition process is significant from the point of view of discrete systems design, because it allows separation of the smaller sequential parts. Proposed algorithm uses modified Martinez-Silva method as well as author's selection algorithm. The developed method is a good complement of classical decomposition algorithms using graphs and hypergraphs.

Elastic and acoustic wavefield decompositions and application to reverse time migrations

NASA Astrophysics Data System (ADS)

Wang, Wenlong

P- and S-waves coexist in elastic wavefields, and separation between them is an essential step in elastic reverse-time migrations (RTMs). Unlike the traditional separation methods that use curl and divergence operators, which do not preserve the wavefield vector component information, we propose and compare two vector decomposition methods, which preserve the same vector components that exist in the input elastic wavefield. The amplitude and phase information is automatically preserved, so no amplitude or phase corrections are required. The decoupled propagation method is extended from elastic to viscoelastic wavefields. To use the decomposed P and S vector wavefields and generate PP and PS images, we create a new 2D migration context for isotropic, elastic RTM which includes PS vector decomposition; the propagation directions of both incident and reflected P- and S-waves are calculated directly from the stress and particle velocity definitions of the decomposed P- and S-wave Poynting vectors. Then an excitation-amplitude image condition that scales the receiver wavelet by the source vector magnitude produces angle-dependent images of PP and PS reflection coefficients with the correct polarities, polarization, and amplitudes. It thus simplifies the process of obtaining PP and PS angle-domain common-image gathers (ADCIGs); it is less effort to generate ADCIGs from vector data than from scalar data. Besides P- and S-waves decomposition, separations of up- and down-going waves are also a part of processing of multi-component recorded data and propagating wavefields. A complex trace based up/down separation approach is extended from acoustic to elastic, and combined with P- and S-wave decomposition by decoupled propagation. This eliminates the need for a Fourier transform over time, thereby significantly reducing the storage cost and improving computational efficiency. Wavefield decomposition is applied to both synthetic elastic VSP data and propagating wavefield snapshots. Poynting vectors obtained from the particle-velocity and stress fields after P/S and up/down decompositions are much more accurate than those without. The up/down separation algorithm is also applicable in acoustic RTMs, where both (forward-time extrapolated) source and (reverse-time extrapolated) receiver wavefields are decomposed into up-going and down-going parts. Together with the crosscorrelation imaging condition, four images (down-up, up-down, up-up and down-down) are generated, which facilitate the analysis of artifacts and the imaging ability of the four images. Artifacts may exist in all the decomposed images, but their positions and types are different. The causes of artifacts in different images are explained and illustrated with sketches and numerical tests.

TEMPORAL SIGNATURES OF AIR QUALITY OBSERVATIONS AND MODEL OUTPUTS: DO TIME SERIES DECOMPOSITION METHODS CAPTURE RELEVANT TIME SCALES?

EPA Science Inventory

Time series decomposition methods were applied to meteorological and air quality data and their numerical model estimates. Decomposition techniques express a time series as the sum of a small number of independent modes which hypothetically represent identifiable forcings, thereb...

A Features Selection for Crops Classification

NASA Astrophysics Data System (ADS)

Liu, Yifan; Shao, Luyi; Yin, Qiang; Hong, Wen

2016-08-01

The components of the polarimetric target decomposition reflect the differences of target since they linked with the scattering properties of the target and can be imported into SVM as the classification features. The result of decomposition usually concentrate on part of the components. Selecting a combination of components can reduce the features that importing into the SVM. The features reduction can lead to less calculation and targeted classification of one target when we classify a multi-class area. In this research, we import different combinations of features into the SVM and find a better combination for classification with a data of AGRISAR.

Image Analysis Using Quantum Entropy Scale Space and Diffusion Concepts

DTIC Science & Technology

2009-11-01

images using a combination of analytic methods and prototype Matlab and Mathematica programs. We investigated concepts of generalized entropy and...Schmidt strength from quantum logic gate decomposition. This form of entropy gives a measure of the nonlocal content of an entangling logic gate...11 We recall that the Schmidt number is an indicator of entanglement , but not a measure of entanglement . For instance, let us compare

On the reliable and flexible solution of practical subset regression problems

NASA Technical Reports Server (NTRS)

Verhaegen, M. H.

1987-01-01

A new algorithm for solving subset regression problems is described. The algorithm performs a QR decomposition with a new column-pivoting strategy, which permits subset selection directly from the originally defined regression parameters. This, in combination with a number of extensions of the new technique, makes the method a very flexible tool for analyzing subset regression problems in which the parameters have a physical meaning.

A Removal of Eye Movement and Blink Artifacts from EEG Data Using Morphological Component Analysis

PubMed Central

Wagatsuma, Hiroaki

2017-01-01

EEG signals contain a large amount of ocular artifacts with different time-frequency properties mixing together in EEGs of interest. The artifact removal has been substantially dealt with by existing decomposition methods known as PCA and ICA based on the orthogonality of signal vectors or statistical independence of signal components. We focused on the signal morphology and proposed a systematic decomposition method to identify the type of signal components on the basis of sparsity in the time-frequency domain based on Morphological Component Analysis (MCA), which provides a way of reconstruction that guarantees accuracy in reconstruction by using multiple bases in accordance with the concept of “dictionary.” MCA was applied to decompose the real EEG signal and clarified the best combination of dictionaries for this purpose. In our proposed semirealistic biological signal analysis with iEEGs recorded from the brain intracranially, those signals were successfully decomposed into original types by a linear expansion of waveforms, such as redundant transforms: UDWT, DCT, LDCT, DST, and DIRAC. Our result demonstrated that the most suitable combination for EEG data analysis was UDWT, DST, and DIRAC to represent the baseline envelope, multifrequency wave-forms, and spiking activities individually as representative types of EEG morphologies. PMID:28194221

A Combined Methodology to Eliminate Artifacts in Multichannel Electrogastrogram Based on Independent Component Analysis and Ensemble Empirical Mode Decomposition.

PubMed

Sengottuvel, S; Khan, Pathan Fayaz; Mariyappa, N; Patel, Rajesh; Saipriya, S; Gireesan, K

2018-06-01

Cutaneous measurements of electrogastrogram (EGG) signals are heavily contaminated by artifacts due to cardiac activity, breathing, motion artifacts, and electrode drifts whose effective elimination remains an open problem. A common methodology is proposed by combining independent component analysis (ICA) and ensemble empirical mode decomposition (EEMD) to denoise gastric slow-wave signals in multichannel EGG data. Sixteen electrodes are fixed over the upper abdomen to measure the EGG signals under three gastric conditions, namely, preprandial, postprandial immediately, and postprandial 2 h after food for three healthy subjects and a subject with a gastric disorder. Instantaneous frequencies of intrinsic mode functions that are obtained by applying the EEMD technique are analyzed to individually identify and remove each of the artifacts. A critical investigation on the proposed ICA-EEMD method reveals its ability to provide a higher attenuation of artifacts and lower distortion than those obtained by the ICA-EMD method and conventional techniques, like bandpass and adaptive filtering. Characteristic changes in the slow-wave frequencies across the three gastric conditions could be determined from the denoised signals for all the cases. The results therefore encourage the use of the EEMD-based technique for denoising gastric signals to be used in clinical practice.

RESOLVING THE ACTIVE GALACTIC NUCLEUS AND HOST EMISSION IN THE MID-INFRARED USING A MODEL-INDEPENDENT SPECTRAL DECOMPOSITION

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

Hernán-Caballero, Antonio; Alonso-Herrero, Almudena; Hatziminaoglou, Evanthia

2015-04-20

We present results on the spectral decomposition of 118 Spitzer Infrared Spectrograph (IRS) spectra from local active galactic nuclei (AGNs) using a large set of Spitzer/IRS spectra as templates. The templates are themselves IRS spectra from extreme cases where a single physical component (stellar, interstellar, or AGN) completely dominates the integrated mid-infrared emission. We show that a linear combination of one template for each physical component reproduces the observed IRS spectra of AGN hosts with unprecedented fidelity for a template fitting method with no need to model extinction separately. We use full probability distribution functions to estimate expectation values andmore » uncertainties for observables, and find that the decomposition results are robust against degeneracies. Furthermore, we compare the AGN spectra derived from the spectral decomposition with sub-arcsecond resolution nuclear photometry and spectroscopy from ground-based observations. We find that the AGN component derived from the decomposition closely matches the nuclear spectrum with a 1σ dispersion of 0.12 dex in luminosity and typical uncertainties of ∼0.19 in the spectral index and ∼0.1 in the silicate strength. We conclude that the emission from the host galaxy can be reliably removed from the IRS spectra of AGNs. This allows for unbiased studies of the AGN emission in intermediate- and high-redshift galaxies—currently inaccesible to ground-based observations—with archival Spitzer/IRS data and in the future with the Mid-InfraRed Instrument of the James Webb Space Telescope. The decomposition code and templates are available at http://denebola.org/ahc/deblendIRS.« less

Hybrid diversity method utilizing adaptive diversity function for recovering unknown aberrations in an optical system

NASA Technical Reports Server (NTRS)

Dean, Bruce H. (Inventor)

2009-01-01

A method of recovering unknown aberrations in an optical system includes collecting intensity data produced by the optical system, generating an initial estimate of a phase of the optical system, iteratively performing a phase retrieval on the intensity data to generate a phase estimate using an initial diversity function corresponding to the intensity data, generating a phase map from the phase retrieval phase estimate, decomposing the phase map to generate a decomposition vector, generating an updated diversity function by combining the initial diversity function with the decomposition vector, generating an updated estimate of the phase of the optical system by removing the initial diversity function from the phase map. The method may further include repeating the process beginning with iteratively performing a phase retrieval on the intensity data using the updated estimate of the phase of the optical system in place of the initial estimate of the phase of the optical system, and using the updated diversity function in place of the initial diversity function, until a predetermined convergence is achieved.

Domain decomposition: A bridge between nature and parallel computers

NASA Technical Reports Server (NTRS)

Keyes, David E.

1992-01-01

Domain decomposition is an intuitive organizing principle for a partial differential equation (PDE) computation, both physically and architecturally. However, its significance extends beyond the readily apparent issues of geometry and discretization, on one hand, and of modular software and distributed hardware, on the other. Engineering and computer science aspects are bridged by an old but recently enriched mathematical theory that offers the subject not only unity, but also tools for analysis and generalization. Domain decomposition induces function-space and operator decompositions with valuable properties. Function-space bases and operator splittings that are not derived from domain decompositions generally lack one or more of these properties. The evolution of domain decomposition methods for elliptically dominated problems has linked two major algorithmic developments of the last 15 years: multilevel and Krylov methods. Domain decomposition methods may be considered descendants of both classes with an inheritance from each: they are nearly optimal and at the same time efficiently parallelizable. Many computationally driven application areas are ripe for these developments. A progression is made from a mathematically informal motivation for domain decomposition methods to a specific focus on fluid dynamics applications. To be introductory rather than comprehensive, simple examples are provided while convergence proofs and algorithmic details are left to the original references; however, an attempt is made to convey their most salient features, especially where this leads to algorithmic insight.

Smoothing spline ANOVA frailty model for recurrent event data.

PubMed

Du, Pang; Jiang, Yihua; Wang, Yuedong

2011-12-01

Gap time hazard estimation is of particular interest in recurrent event data. This article proposes a fully nonparametric approach for estimating the gap time hazard. Smoothing spline analysis of variance (ANOVA) decompositions are used to model the log gap time hazard as a joint function of gap time and covariates, and general frailty is introduced to account for between-subject heterogeneity and within-subject correlation. We estimate the nonparametric gap time hazard function and parameters in the frailty distribution using a combination of the Newton-Raphson procedure, the stochastic approximation algorithm (SAA), and the Markov chain Monte Carlo (MCMC) method. The convergence of the algorithm is guaranteed by decreasing the step size of parameter update and/or increasing the MCMC sample size along iterations. Model selection procedure is also developed to identify negligible components in a functional ANOVA decomposition of the log gap time hazard. We evaluate the proposed methods with simulation studies and illustrate its use through the analysis of bladder tumor data. © 2011, The International Biometric Society.

Decomposition of persistent pharmaceuticals in wastewater by ionizing radiation

NASA Astrophysics Data System (ADS)

Kimura, Atsushi; Osawa, Misako; Taguchi, Mitsumasa

2012-09-01

Pharmaceuticals in wastewater were treated by the combined method of activated sludge and ionizing radiation in laboratory scale. Oseltamivir, aspirin, and ibuprofen at 5 μmol dm-3 in wastewater were decomposed by the activated sludge at reaction time for 4 h. Carbamazepine, ketoprofen, mefenamic acid, clofibric acid, and diclofenac were not biodegraded completely, but were eliminated by γ-ray irradiation at 2 kGy. The rate constants of the reactions of these pharmaceuticals with hydroxyl radicals were estimated by the competition reaction method to be 4.0-10×109 mol-1 dm3 s-1. Decompositions of the pharmaceuticals in wastewater by ionizing radiation were simulated by use of the rate constants and the amount of total organic carbon as parameters. Simulation curves of concentrations of these pharmaceuticals as a function of dose described the experimental data, and the required dose for the elimination of them in wastewater by ionizing radiation can be estimated by this simulation.

Effect of Copper Oxide, Titanium Dioxide, and Lithium Fluoride on the Thermal Behavior and Decomposition Kinetics of Ammonium Nitrate

NASA Astrophysics Data System (ADS)

Vargeese, Anuj A.; Mija, S. J.; Muralidharan, Krishnamurthi

2014-07-01

Ammonium nitrate (AN) is crystallized along with copper oxide, titanium dioxide, and lithium fluoride. Thermal kinetic constants for the decomposition reaction of the samples were calculated by model-free (Friedman's differential and Vyzovkins nonlinear integral) and model-fitting (Coats-Redfern) methods. To determine the decomposition mechanisms, 12 solid-state mechanisms were tested using the Coats-Redfern method. The results of the Coats-Redfern method show that the decomposition mechanism for all samples is the contracting cylinder mechanism. The phase behavior of the obtained samples was evaluated by differential scanning calorimetry (DSC), and structural properties were determined by X-ray powder diffraction (XRPD). The results indicate that copper oxide modifies the phase transition behavior and can catalyze AN decomposition, whereas LiF inhibits AN decomposition, and TiO2 shows no influence on the rate of decomposition. Possible explanations for these results are discussed. Supplementary materials are available for this article. Go to the publisher's online edition of the Journal of Energetic Materials to view the free supplemental file.

Size-controlled magnetic nanoparticles with lecithin for biomedical applications

NASA Astrophysics Data System (ADS)

Park, S. I.; Kim, J. H.; Kim, C. G.; Kim, C. O.

2007-05-01

Lecithin-adsorbed magnetic nanoparticles were prepared by three-step process that the thermal decomposition was combined with ultrasonication. Experimental parameters were three items—molar ratio between Fe(CO) 5 and oleic acid, keeping time at decomposition temperature and lecithin concentration. As the molar ratio between Fe(CO) 5 and oleic acid, and keeping time at decomposition temperature increased, the particle size increased. However, the change of lecithin concentration did not show the remarkable particle size variation.

Development Of Polarimetric Decomposition Techniques For Indian Forest Resource Assessment Using Radar Imaging Satellite (Risat-1) Images

NASA Astrophysics Data System (ADS)

Sridhar, J.

2015-12-01

The focus of this work is to examine polarimetric decomposition techniques primarily focussed on Pauli decomposition and Sphere Di-Plane Helix (SDH) decomposition for forest resource assessment. The data processing methods adopted are Pre-processing (Geometric correction and Radiometric calibration), Speckle Reduction, Image Decomposition and Image Classification. Initially to classify forest regions, unsupervised classification was applied to determine different unknown classes. It was observed K-means clustering method gave better results in comparison with ISO Data method.Using the algorithm developed for Radar Tools, the code for decomposition and classification techniques were applied in Interactive Data Language (IDL) and was applied to RISAT-1 image of Mysore-Mandya region of Karnataka, India. This region is chosen for studying forest vegetation and consists of agricultural lands, water and hilly regions. Polarimetric SAR data possess a high potential for classification of earth surface.After applying the decomposition techniques, classification was done by selecting region of interests andpost-classification the over-all accuracy was observed to be higher in the SDH decomposed image, as it operates on individual pixels on a coherent basis and utilises the complete intrinsic coherent nature of polarimetric SAR data. Thereby, making SDH decomposition particularly suited for analysis of high-resolution SAR data. The Pauli Decomposition represents all the polarimetric information in a single SAR image however interpretation of the resulting image is difficult. The SDH decomposition technique seems to produce better results and interpretation as compared to Pauli Decomposition however more quantification and further analysis are being done in this area of research. The comparison of Polarimetric decomposition techniques and evolutionary classification techniques will be the scope of this work.

An optimized time varying filtering based empirical mode decomposition method with grey wolf optimizer for machinery fault diagnosis

NASA Astrophysics Data System (ADS)

Zhang, Xin; Liu, Zhiwen; Miao, Qiang; Wang, Lei

2018-03-01

A time varying filtering based empirical mode decomposition (EMD) (TVF-EMD) method was proposed recently to solve the mode mixing problem of EMD method. Compared with the classical EMD, TVF-EMD was proven to improve the frequency separation performance and be robust to noise interference. However, the decomposition parameters (i.e., bandwidth threshold and B-spline order) significantly affect the decomposition results of this method. In original TVF-EMD method, the parameter values are assigned in advance, which makes it difficult to achieve satisfactory analysis results. To solve this problem, this paper develops an optimized TVF-EMD method based on grey wolf optimizer (GWO) algorithm for fault diagnosis of rotating machinery. Firstly, a measurement index termed weighted kurtosis index is constructed by using kurtosis index and correlation coefficient. Subsequently, the optimal TVF-EMD parameters that match with the input signal can be obtained by GWO algorithm using the maximum weighted kurtosis index as objective function. Finally, fault features can be extracted by analyzing the sensitive intrinsic mode function (IMF) owning the maximum weighted kurtosis index. Simulations and comparisons highlight the performance of TVF-EMD method for signal decomposition, and meanwhile verify the fact that bandwidth threshold and B-spline order are critical to the decomposition results. Two case studies on rotating machinery fault diagnosis demonstrate the effectiveness and advantages of the proposed method.

The Search for a Volatile Human Specific Marker in the Decomposition Process

PubMed Central

Rosier, E.; Loix, S.; Develter, W.; Van de Voorde, W.; Tytgat, J.; Cuypers, E.

2015-01-01

In this study, a validated method using a thermal desorber combined with a gas chromatograph coupled to mass spectrometry was used to identify the volatile organic compounds released during decomposition of 6 human and 26 animal remains in a laboratory environment during a period of 6 months. 452 compounds were identified. Among them a human specific marker was sought using principle component analysis. We found a combination of 8 compounds (ethyl propionate, propyl propionate, propyl butyrate, ethyl pentanoate, pyridine, diethyl disulfide, methyl(methylthio)ethyl disulfide and 3-methylthio-1-propanol) that led to the distinction of human and pig remains from other animal remains. Furthermore, it was possible to separate the pig remains from human remains based on 5 esters (3-methylbutyl pentanoate, 3-methylbutyl 3-methylbutyrate, 3-methylbutyl 2-methylbutyrate, butyl pentanoate and propyl hexanoate). Further research in the field with full bodies has to corroborate these results and search for one or more human specific markers. These markers would allow a more efficiently training of cadaver dogs or portable detection devices could be developed. PMID:26375029

Surface EMG decomposition based on K-means clustering and convolution kernel compensation.

PubMed

Ning, Yong; Zhu, Xiangjun; Zhu, Shanan; Zhang, Yingchun

2015-03-01

A new approach has been developed by combining the K-mean clustering (KMC) method and a modified convolution kernel compensation (CKC) method for multichannel surface electromyogram (EMG) decomposition. The KMC method was first utilized to cluster vectors of observations at different time instants and then estimate the initial innervation pulse train (IPT). The CKC method, modified with a novel multistep iterative process, was conducted to update the estimated IPT. The performance of the proposed K-means clustering-Modified CKC (KmCKC) approach was evaluated by reconstructing IPTs from both simulated and experimental surface EMG signals. The KmCKC approach successfully reconstructed all 10 IPTs from the simulated surface EMG signals with true positive rates (TPR) of over 90% with a low signal-to-noise ratio (SNR) of -10 dB. More than 10 motor units were also successfully extracted from the 64-channel experimental surface EMG signals of the first dorsal interosseous (FDI) muscles when a contraction force was held at 8 N by using the KmCKC approach. A "two-source" test was further conducted with 64-channel surface EMG signals. The high percentage of common MUs and common pulses (over 92% at all force levels) between the IPTs reconstructed from the two independent groups of surface EMG signals demonstrates the reliability and capability of the proposed KmCKC approach in multichannel surface EMG decomposition. Results from both simulated and experimental data are consistent and confirm that the proposed KmCKC approach can successfully reconstruct IPTs with high accuracy at different levels of contraction.

Application of an improved spectral decomposition method to examine earthquake source scaling in Southern California

NASA Astrophysics Data System (ADS)

Trugman, Daniel T.; Shearer, Peter M.

2017-04-01

Earthquake source spectra contain fundamental information about the dynamics of earthquake rupture. However, the inherent tradeoffs in separating source and path effects, when combined with limitations in recorded signal bandwidth, make it challenging to obtain reliable source spectral estimates for large earthquake data sets. We present here a stable and statistically robust spectral decomposition method that iteratively partitions the observed waveform spectra into source, receiver, and path terms. Unlike previous methods of its kind, our new approach provides formal uncertainty estimates and does not assume self-similar scaling in earthquake source properties. Its computational efficiency allows us to examine large data sets (tens of thousands of earthquakes) that would be impractical to analyze using standard empirical Green's function-based approaches. We apply the spectral decomposition technique to P wave spectra from five areas of active contemporary seismicity in Southern California: the Yuha Desert, the San Jacinto Fault, and the Big Bear, Landers, and Hector Mine regions of the Mojave Desert. We show that the source spectra are generally consistent with an increase in median Brune-type stress drop with seismic moment but that this observed deviation from self-similar scaling is both model dependent and varies in strength from region to region. We also present evidence for significant variations in median stress drop and stress drop variability on regional and local length scales. These results both contribute to our current understanding of earthquake source physics and have practical implications for the next generation of ground motion prediction assessments.

Field by field hybrid upwind splitting methods

NASA Technical Reports Server (NTRS)

Coquel, Frederic; Liou, Meng-Sing

1993-01-01

A new and general approach to upwind splitting is presented. The design principle combines the robustness of flux vector splitting schemes in the capture of nonlinear waves and the accuracy of some flux difference splitting schemes in the resolution of linear waves. The new schemes are derived following a general hybridization technique performed directly at the basic level of the field by field decomposition involved in FDS methods. The scheme does not use a spatial switch to be tuned up according to the local smoothness of the approximate solution.

An Efficient Local Correlation Matrix Decomposition Approach for the Localization Implementation of Ensemble-Based Assimilation Methods

NASA Astrophysics Data System (ADS)

Zhang, Hongqin; Tian, Xiangjun

2018-04-01

Ensemble-based data assimilation methods often use the so-called localization scheme to improve the representation of the ensemble background error covariance (Be). Extensive research has been undertaken to reduce the computational cost of these methods by using the localized ensemble samples to localize Be by means of a direct decomposition of the local correlation matrix C. However, the computational costs of the direct decomposition of the local correlation matrix C are still extremely high due to its high dimension. In this paper, we propose an efficient local correlation matrix decomposition approach based on the concept of alternating directions. This approach is intended to avoid direct decomposition of the correlation matrix. Instead, we first decompose the correlation matrix into 1-D correlation matrices in the three coordinate directions, then construct their empirical orthogonal function decomposition at low resolution. This procedure is followed by the 1-D spline interpolation process to transform the above decompositions to the high-resolution grid. Finally, an efficient correlation matrix decomposition is achieved by computing the very similar Kronecker product. We conducted a series of comparison experiments to illustrate the validity and accuracy of the proposed local correlation matrix decomposition approach. The effectiveness of the proposed correlation matrix decomposition approach and its efficient localization implementation of the nonlinear least-squares four-dimensional variational assimilation are further demonstrated by several groups of numerical experiments based on the Advanced Research Weather Research and Forecasting model.

Nonconforming mortar element methods: Application to spectral discretizations

NASA Technical Reports Server (NTRS)

Maday, Yvon; Mavriplis, Cathy; Patera, Anthony

1988-01-01

Spectral element methods are p-type weighted residual techniques for partial differential equations that combine the generality of finite element methods with the accuracy of spectral methods. Presented here is a new nonconforming discretization which greatly improves the flexibility of the spectral element approach as regards automatic mesh generation and non-propagating local mesh refinement. The method is based on the introduction of an auxiliary mortar trace space, and constitutes a new approach to discretization-driven domain decomposition characterized by a clean decoupling of the local, structure-preserving residual evaluations and the transmission of boundary and continuity conditions. The flexibility of the mortar method is illustrated by several nonconforming adaptive Navier-Stokes calculations in complex geometry.

Dynamic characterization of a damaged beam using empirical mode decomposition and Hilbert spectrum method

NASA Astrophysics Data System (ADS)

Chang, Chih-Chen; Poon, Chun-Wing

2004-07-01

Recently, the empirical mode decomposition (EMD) in combination with the Hilbert spectrum method has been proposed to identify the dynamic characteristics of linear structures. In this study, this EMD and Hilbert spectrum method is used to analyze the dynamic characteristics of a damaged reinforced concrete (RC) beam in the laboratory. The RC beam is 4m long with a cross section of 200mm X 250mm. The beam is sequentially subjected to a concentrated load of different magnitudes at the mid-span to produce different degrees of damage. An impact load is applied around the mid-span to excite the beam. Responses of the beam are recorded by four accelerometers. Results indicate that the EMD and Hilbert spectrum method can reveal the variation of the dynamic characteristics in the time domain. These results are also compared with those obtained using the Fourier analysis. In general, it is found that the two sets of results correlate quite well in terms of mode counts and frequency values. Some differences, however, can be seen in the damping values, which perhaps can be attributed to the linear assumption of the Fourier transform.

Peak tree: a new tool for multiscale hierarchical representation and peak detection of mass spectrometry data.

PubMed

Zhang, Peng; Li, Houqiang; Wang, Honghui; Wong, Stephen T C; Zhou, Xiaobo

2011-01-01

Peak detection is one of the most important steps in mass spectrometry (MS) analysis. However, the detection result is greatly affected by severe spectrum variations. Unfortunately, most current peak detection methods are neither flexible enough to revise false detection results nor robust enough to resist spectrum variations. To improve flexibility, we introduce peak tree to represent the peak information in MS spectra. Each tree node is a peak judgment on a range of scales, and each tree decomposition, as a set of nodes, is a candidate peak detection result. To improve robustness, we combine peak detection and common peak alignment into a closed-loop framework, which finds the optimal decomposition via both peak intensity and common peak information. The common peak information is derived and loopily refined from the density clustering of the latest peak detection result. Finally, we present an improved ant colony optimization biomarker selection method to build a whole MS analysis system. Experiment shows that our peak detection method can better resist spectrum variations and provide higher sensitivity and lower false detection rates than conventional methods. The benefits from our peak-tree-based system for MS disease analysis are also proved on real SELDI data.

An algorithm for separation of mixed sparse and Gaussian sources

PubMed Central

Akkalkotkar, Ameya

2017-01-01

Independent component analysis (ICA) is a ubiquitous method for decomposing complex signal mixtures into a small set of statistically independent source signals. However, in cases in which the signal mixture consists of both nongaussian and Gaussian sources, the Gaussian sources will not be recoverable by ICA and will pollute estimates of the nongaussian sources. Therefore, it is desirable to have methods for mixed ICA/PCA which can separate mixtures of Gaussian and nongaussian sources. For mixtures of purely Gaussian sources, principal component analysis (PCA) can provide a basis for the Gaussian subspace. We introduce a new method for mixed ICA/PCA which we call Mixed ICA/PCA via Reproducibility Stability (MIPReSt). Our method uses a repeated estimations technique to rank sources by reproducibility, combined with decomposition of multiple subsamplings of the original data matrix. These multiple decompositions allow us to assess component stability as the size of the data matrix changes, which can be used to determinine the dimension of the nongaussian subspace in a mixture. We demonstrate the utility of MIPReSt for signal mixtures consisting of simulated sources and real-word (speech) sources, as well as mixture of unknown composition. PMID:28414814

An algorithm for separation of mixed sparse and Gaussian sources.

PubMed

Akkalkotkar, Ameya; Brown, Kevin Scott

2017-01-01

Independent component analysis (ICA) is a ubiquitous method for decomposing complex signal mixtures into a small set of statistically independent source signals. However, in cases in which the signal mixture consists of both nongaussian and Gaussian sources, the Gaussian sources will not be recoverable by ICA and will pollute estimates of the nongaussian sources. Therefore, it is desirable to have methods for mixed ICA/PCA which can separate mixtures of Gaussian and nongaussian sources. For mixtures of purely Gaussian sources, principal component analysis (PCA) can provide a basis for the Gaussian subspace. We introduce a new method for mixed ICA/PCA which we call Mixed ICA/PCA via Reproducibility Stability (MIPReSt). Our method uses a repeated estimations technique to rank sources by reproducibility, combined with decomposition of multiple subsamplings of the original data matrix. These multiple decompositions allow us to assess component stability as the size of the data matrix changes, which can be used to determinine the dimension of the nongaussian subspace in a mixture. We demonstrate the utility of MIPReSt for signal mixtures consisting of simulated sources and real-word (speech) sources, as well as mixture of unknown composition.

A Type-2 Block-Component-Decomposition Based 2D AOA Estimation Algorithm for an Electromagnetic Vector Sensor Array

PubMed Central

Gao, Yu-Fei; Gui, Guan; Xie, Wei; Zou, Yan-Bin; Yang, Yue; Wan, Qun

2017-01-01

This paper investigates a two-dimensional angle of arrival (2D AOA) estimation algorithm for the electromagnetic vector sensor (EMVS) array based on Type-2 block component decomposition (BCD) tensor modeling. Such a tensor decomposition method can take full advantage of the multidimensional structural information of electromagnetic signals to accomplish blind estimation for array parameters with higher resolution. However, existing tensor decomposition methods encounter many restrictions in applications of the EMVS array, such as the strict requirement for uniqueness conditions of decomposition, the inability to handle partially-polarized signals, etc. To solve these problems, this paper investigates tensor modeling for partially-polarized signals of an L-shaped EMVS array. The 2D AOA estimation algorithm based on rank-(L1,L2,·) BCD is developed, and the uniqueness condition of decomposition is analyzed. By means of the estimated steering matrix, the proposed algorithm can automatically achieve angle pair-matching. Numerical experiments demonstrate that the present algorithm has the advantages of both accuracy and robustness of parameter estimation. Even under the conditions of lower SNR, small angular separation and limited snapshots, the proposed algorithm still possesses better performance than subspace methods and the canonical polyadic decomposition (CPD) method. PMID:28448431

A Type-2 Block-Component-Decomposition Based 2D AOA Estimation Algorithm for an Electromagnetic Vector Sensor Array.

PubMed

Gao, Yu-Fei; Gui, Guan; Xie, Wei; Zou, Yan-Bin; Yang, Yue; Wan, Qun

2017-04-27

This paper investigates a two-dimensional angle of arrival (2D AOA) estimation algorithm for the electromagnetic vector sensor (EMVS) array based on Type-2 block component decomposition (BCD) tensor modeling. Such a tensor decomposition method can take full advantage of the multidimensional structural information of electromagnetic signals to accomplish blind estimation for array parameters with higher resolution. However, existing tensor decomposition methods encounter many restrictions in applications of the EMVS array, such as the strict requirement for uniqueness conditions of decomposition, the inability to handle partially-polarized signals, etc. To solve these problems, this paper investigates tensor modeling for partially-polarized signals of an L-shaped EMVS array. The 2D AOA estimation algorithm based on rank- ( L 1 , L 2 , · ) BCD is developed, and the uniqueness condition of decomposition is analyzed. By means of the estimated steering matrix, the proposed algorithm can automatically achieve angle pair-matching. Numerical experiments demonstrate that the present algorithm has the advantages of both accuracy and robustness of parameter estimation. Even under the conditions of lower SNR, small angular separation and limited snapshots, the proposed algorithm still possesses better performance than subspace methods and the canonical polyadic decomposition (CPD) method.

A novel ECG data compression method based on adaptive Fourier decomposition

NASA Astrophysics Data System (ADS)

Tan, Chunyu; Zhang, Liming

2017-12-01

This paper presents a novel electrocardiogram (ECG) compression method based on adaptive Fourier decomposition (AFD). AFD is a newly developed signal decomposition approach, which can decompose a signal with fast convergence, and hence reconstruct ECG signals with high fidelity. Unlike most of the high performance algorithms, our method does not make use of any preprocessing operation before compression. Huffman coding is employed for further compression. Validated with 48 ECG recordings of MIT-BIH arrhythmia database, the proposed method achieves the compression ratio (CR) of 35.53 and the percentage root mean square difference (PRD) of 1.47% on average with N = 8 decomposition times and a robust PRD-CR relationship. The results demonstrate that the proposed method has a good performance compared with the state-of-the-art ECG compressors.

Trend extraction using empirical mode decomposition and statistical empirical mode decomposition: Case study: Kuala Lumpur stock market

NASA Astrophysics Data System (ADS)

Jaber, Abobaker M.

2014-12-01

Two nonparametric methods for prediction and modeling of financial time series signals are proposed. The proposed techniques are designed to handle non-stationary and non-linearity behave and to extract meaningful signals for reliable prediction. Due to Fourier Transform (FT), the methods select significant decomposed signals that will be employed for signal prediction. The proposed techniques developed by coupling Holt-winter method with Empirical Mode Decomposition (EMD) and it is Extending the scope of empirical mode decomposition by smoothing (SEMD). To show performance of proposed techniques, we analyze daily closed price of Kuala Lumpur stock market index.

Structural optimization by multilevel decomposition

NASA Technical Reports Server (NTRS)

Sobieszczanski-Sobieski, J.; James, B.; Dovi, A.

1983-01-01

A method is described for decomposing an optimization problem into a set of subproblems and a coordination problem which preserves coupling between the subproblems. The method is introduced as a special case of multilevel, multidisciplinary system optimization and its algorithm is fully described for two level optimization for structures assembled of finite elements of arbitrary type. Numerical results are given for an example of a framework to show that the decomposition method converges and yields results comparable to those obtained without decomposition. It is pointed out that optimization by decomposition should reduce the design time by allowing groups of engineers, using different computers to work concurrently on the same large problem.

Calibration methods influence quantitative material decomposition in photon-counting spectral CT

NASA Astrophysics Data System (ADS)

Curtis, Tyler E.; Roeder, Ryan K.

2017-03-01

Photon-counting detectors and nanoparticle contrast agents can potentially enable molecular imaging and material decomposition in computed tomography (CT). Material decomposition has been investigated using both simulated and acquired data sets. However, the effect of calibration methods on material decomposition has not been systematically investigated. Therefore, the objective of this study was to investigate the influence of the range and number of contrast agent concentrations within a modular calibration phantom on quantitative material decomposition. A commerciallyavailable photon-counting spectral micro-CT (MARS Bioimaging) was used to acquire images with five energy bins selected to normalize photon counts and leverage the contrast agent k-edge. Material basis matrix values were determined using multiple linear regression models and material decomposition was performed using a maximum a posteriori estimator. The accuracy of quantitative material decomposition was evaluated by the root mean squared error (RMSE), specificity, sensitivity, and area under the curve (AUC). An increased maximum concentration (range) in the calibration significantly improved RMSE, specificity and AUC. The effects of an increased number of concentrations in the calibration were not statistically significant for the conditions in this study. The overall results demonstrated that the accuracy of quantitative material decomposition in spectral CT is significantly influenced by calibration methods, which must therefore be carefully considered for the intended diagnostic imaging application.

Purification of Hydrogen

DOEpatents

Newton, A S

1950-12-05

Disclosed is a process for purifying hydrogen containing various gaseous impurities by passing the hydrogen over a large surface of uranium metal at a temperature above the decomposition temperature of uranium hydride, and below the decomposition temperature of the compounds formed by the combination of the uranium with the impurities in the hydrogen.

Performance of flapping airfoil propulsion with LBM method and DMD analysis

NASA Astrophysics Data System (ADS)

Li, Bing-Hua; Huang, Xian-Wen; Zheng, Yao; Xie, Fang-Fang; Wang, Jing; Zou, Jian-Feng

2018-05-01

In this work, the performance of flapping airfoil propulsion at low Reynolds number of Re = 100-400 is studied numerically with the lattice Boltzmann method (LBM). Combined with immersed boundary method (IBM), the LBM has been widely used to simulate moving boundary problems. The influences of the reduced frequency on the plunging and pitching airfoil are explored. It is found that the leading-edge vertex separation and inverted wake structures are two main coherent structures, which dominate the flapping airfoil propulsion. However, the two structures play different roles in the flow and the combination effects on the propulsion need to be clarified. To do so, we adopt the dynamic mode decomposition (DMD) algorithm to reveal the underlying physics. The DMD has been proven to be very suitable for analyzing the complex transient systems like the vortex structure of flapping flight.

Preparation of Paraffin@Poly(styrene-co-acrylic acid) Phase Change Nanocapsules via Combined Miniemulsion/Emulsion Polymerization.

PubMed

Zhang, Feng; Liu, Tian-Yu; Hou, Gui-Hua; Guan, Rong-Feng; Zhang, Jun-Hao

2018-06-01

The fast development of solid-liquid phase change materials calls for nanomaterials with large specific surface area for rapid heat transfer and encapsulation of phase change materials to prevent potential leakage. Here we report a combined miniemulsion/emulsion polymerization method to prepare poly(styrene-co-acrylic acid)-encapsulated paraffin (paraffin@P(St-co-AA)) nanocapsules. The method could suppress the shortcomings of common miniemulsion polymerization (such as evaporation of monomer and decomposition of initiator during ultrasonication). The paraffin@P(St-co-AA) nanocapsules are uniform in size and the polymer shell can be controlled by the weight ratio of St to paraffin. The phase change behavior of the nanocapsules is similar to that of pure paraffin. We believe our method can also be utilized to synthesize other core-shell phase change materials.

Rapid water and lipid imaging with T2 mapping using a radial IDEAL-GRASE technique.

PubMed

Li, Zhiqiang; Graff, Christian; Gmitro, Arthur F; Squire, Scott W; Bilgin, Ali; Outwater, Eric K; Altbach, Maria I

2009-06-01

Three-point Dixon methods have been investigated as a means to generate water and fat images without the effects of field inhomogeneities. Recently, an iterative algorithm (IDEAL, iterative decomposition of water and fat with echo asymmetry and least squares estimation) was combined with a gradient and spin-echo acquisition strategy (IDEAL-GRASE) to provide a time-efficient method for lipid-water imaging with correction for the effects of field inhomogeneities. The method presented in this work combines IDEAL-GRASE with radial data acquisition. Radial data sampling offers robustness to motion over Cartesian trajectories as well as the possibility of generating high-resolution T(2) maps in addition to the water and fat images. The radial IDEAL-GRASE technique is demonstrated in phantoms and in vivo for various applications including abdominal, pelvic, and cardiac imaging.

Fundamental Theory of Crystal Decomposition

DTIC Science & Technology

1991-05-01

rather than combine them as is often the case in a computation based on the density functional method.4 In the Case of a cluster embedded in a...classical lattice, special care needs to be taken to ensure that mathematical consistency is achieved between the cluster and the embedding lattice. This has...localizing potential or KKLP. Simulation of a large crystallite or an infinite lattice containing a point defect represented by a cluster and a

Reactivity continuum modeling of leaf, root, and wood decomposition across biomes

NASA Astrophysics Data System (ADS)

Koehler, Birgit; Tranvik, Lars J.

2015-07-01

Large carbon dioxide amounts are released to the atmosphere during organic matter decomposition. Yet the large-scale and long-term regulation of this critical process in global carbon cycling by litter chemistry and climate remains poorly understood. We used reactivity continuum (RC) modeling to analyze the decadal data set of the "Long-term Intersite Decomposition Experiment," in which fine litter and wood decomposition was studied in eight biome types (224 time series). In 32 and 46% of all sites the litter content of the acid-unhydrolyzable residue (AUR, formerly referred to as lignin) and the AUR/nitrogen ratio, respectively, retarded initial decomposition rates. This initial rate-retarding effect generally disappeared within the first year of decomposition, and rate-stimulating effects of nutrients and a rate-retarding effect of the carbon/nitrogen ratio became more prevalent. For needles and leaves/grasses, the influence of climate on decomposition decreased over time. For fine roots, the climatic influence was initially smaller but increased toward later-stage decomposition. The climate decomposition index was the strongest climatic predictor of decomposition. The similar variability in initial decomposition rates across litter categories as across biome types suggested that future changes in decomposition may be dominated by warming-induced changes in plant community composition. In general, the RC model parameters successfully predicted independent decomposition data for the different litter-biome combinations (196 time series). We argue that parameterization of large-scale decomposition models with RC model parameters, as opposed to the currently common discrete multiexponential models, could significantly improve their mechanistic foundation and predictive accuracy across climate zones and litter categories.

Distributed Damage Estimation for Prognostics based on Structural Model Decomposition

NASA Technical Reports Server (NTRS)

Daigle, Matthew; Bregon, Anibal; Roychoudhury, Indranil

2011-01-01

Model-based prognostics approaches capture system knowledge in the form of physics-based models of components, and how they fail. These methods consist of a damage estimation phase, in which the health state of a component is estimated, and a prediction phase, in which the health state is projected forward in time to determine end of life. However, the damage estimation problem is often multi-dimensional and computationally intensive. We propose a model decomposition approach adapted from the diagnosis community, called possible conflicts, in order to both improve the computational efficiency of damage estimation, and formulate a damage estimation approach that is inherently distributed. Local state estimates are combined into a global state estimate from which prediction is performed. Using a centrifugal pump as a case study, we perform a number of simulation-based experiments to demonstrate the approach.

Stochastic shock response spectrum decomposition method based on probabilistic definitions of temporal peak acceleration, spectral energy, and phase lag distributions of mechanical impact pyrotechnic shock test data

NASA Astrophysics Data System (ADS)

Hwang, James Ho-Jin; Duran, Adam

2016-08-01

Most of the times pyrotechnic shock design and test requirements for space systems are provided in Shock Response Spectrum (SRS) without the input time history. Since the SRS does not describe the input or the environment, a decomposition method is used to obtain the source time history. The main objective of this paper is to develop a decomposition method producing input time histories that can satisfy the SRS requirement based on the pyrotechnic shock test data measured from a mechanical impact test apparatus. At the heart of this decomposition method is the statistical representation of the pyrotechnic shock test data measured from the MIT Lincoln Laboratory (LL) designed Universal Pyrotechnic Shock Simulator (UPSS). Each pyrotechnic shock test data measured at the interface of a test unit has been analyzed to produce the temporal peak acceleration, Root Mean Square (RMS) acceleration, and the phase lag at each band center frequency. Maximum SRS of each filtered time history has been calculated to produce a relationship between the input and the response. Two new definitions are proposed as a result. The Peak Ratio (PR) is defined as the ratio between the maximum SRS and the temporal peak acceleration at each band center frequency. The ratio between the maximum SRS and the RMS acceleration is defined as the Energy Ratio (ER) at each band center frequency. Phase lag is estimated based on the time delay between the temporal peak acceleration at each band center frequency and the peak acceleration at the lowest band center frequency. This stochastic process has been applied to more than one hundred pyrotechnic shock test data to produce probabilistic definitions of the PR, ER, and the phase lag. The SRS is decomposed at each band center frequency using damped sinusoids with the PR and the decays obtained by matching the ER of the damped sinusoids to the ER of the test data. The final step in this stochastic SRS decomposition process is the Monte Carlo (MC) simulation. The MC simulation identifies combinations of the PR and decays that can meet the SRS requirement at each band center frequency. Decomposed input time histories are produced by summing the converged damped sinusoids with the MC simulation of the phase lag distribution.

Modeling and implementation of concurrent logic controllers with use of Petri nets, LSMs, and sequent calculus

NASA Astrophysics Data System (ADS)

Tkacz, J.; Bukowiec, A.; Doligalski, M.

2017-08-01

The paper presentes the method of modeling and implementation of concurrent controllers. Concurrent controllers are specified by Petri nets. Then Petri nets are decomposed using symbolic deduction method of analysis. Formal methods like sequent calculus system with considered elements of Thelen's algorithm have been used here. As a result, linked state machines (LSMs) are received. Each FSM is implemented using methods of structural decomposition during process of logic synthesis. The method of multiple encoding of microinstruction has been applied. It leads to decreased number of Boolean function realized by combinational part of FSM. The additional decoder could be implemented with the use of memory blocks.

Waveform LiDAR processing: comparison of classic approaches and optimized Gold deconvolution to characterize vegetation structure and terrain elevation

NASA Astrophysics Data System (ADS)

Zhou, T.; Popescu, S. C.; Krause, K.

2016-12-01

Waveform Light Detection and Ranging (LiDAR) data have advantages over discrete-return LiDAR data in accurately characterizing vegetation structure. However, we lack a comprehensive understanding of waveform data processing approaches under different topography and vegetation conditions. The objective of this paper is to highlight a novel deconvolution algorithm, the Gold algorithm, for processing waveform LiDAR data with optimal deconvolution parameters. Further, we present a comparative study of waveform processing methods to provide insight into selecting an approach for a given combination of vegetation and terrain characteristics. We employed two waveform processing methods: 1) direct decomposition, 2) deconvolution and decomposition. In method two, we utilized two deconvolution algorithms - the Richardson Lucy (RL) algorithm and the Gold algorithm. The comprehensive and quantitative comparisons were conducted in terms of the number of detected echoes, position accuracy, the bias of the end products (such as digital terrain model (DTM) and canopy height model (CHM)) from discrete LiDAR data, along with parameter uncertainty for these end products obtained from different methods. This study was conducted at three study sites that include diverse ecological regions, vegetation and elevation gradients. Results demonstrate that two deconvolution algorithms are sensitive to the pre-processing steps of input data. The deconvolution and decomposition method is more capable of detecting hidden echoes with a lower false echo detection rate, especially for the Gold algorithm. Compared to the reference data, all approaches generate satisfactory accuracy assessment results with small mean spatial difference (<1.22 m for DTMs, < 0.77 m for CHMs) and root mean square error (RMSE) (<1.26 m for DTMs, < 1.93 m for CHMs). More specifically, the Gold algorithm is superior to others with smaller root mean square error (RMSE) (< 1.01m), while the direct decomposition approach works better in terms of the percentage of spatial difference within 0.5 and 1 m. The parameter uncertainty analysis demonstrates that the Gold algorithm outperforms other approaches in dense vegetation areas, with the smallest RMSE, and the RL algorithm performs better in sparse vegetation areas in terms of RMSE.

Surrogate models for sheet metal stamping problem based on the combination of proper orthogonal decomposition and radial basis function

NASA Astrophysics Data System (ADS)

Dang, Van Tuan; Lafon, Pascal; Labergere, Carl

2017-10-01

In this work, a combination of Proper Orthogonal Decomposition (POD) and Radial Basis Function (RBF) is proposed to build a surrogate model based on the Benchmark Springback 3D bending from the Numisheet2011 congress. The influence of the two design parameters, the geometrical parameter of the die radius and the process parameter of the blank holder force, on the springback of the sheet after a stamping operation is analyzed. The classical Design of Experience (DoE) uses Full Factorial to design the parameter space with sample points as input data for finite element method (FEM) numerical simulation of the sheet metal stamping process. The basic idea is to consider the design parameters as additional dimensions for the solution of the displacement fields. The order of the resultant high-fidelity model is reduced through the use of POD method which performs model space reduction and results in the basis functions of the low order model. Specifically, the snapshot method is used in our work, in which the basis functions is derived from snapshot deviation of the matrix of the final displacements fields of the FEM numerical simulation. The obtained basis functions are then used to determine the POD coefficients and RBF is used for the interpolation of these POD coefficients over the parameter space. Finally, the presented POD-RBF approach which is used for shape optimization can be performed with high accuracy.

Recognizing of stereotypic patterns in epileptic EEG using empirical modes and wavelets

NASA Astrophysics Data System (ADS)

Grubov, V. V.; Sitnikova, E.; Pavlov, A. N.; Koronovskii, A. A.; Hramov, A. E.

2017-11-01

Epileptic activity in the form of spike-wave discharges (SWD) appears in the electroencephalogram (EEG) during absence seizures. This paper evaluates two approaches for detecting stereotypic rhythmic activities in EEG, i.e., the continuous wavelet transform (CWT) and the empirical mode decomposition (EMD). The CWT is a well-known method of time-frequency analysis of EEG, whereas EMD is a relatively novel approach for extracting signal's waveforms. A new method for pattern recognition based on combination of CWT and EMD is proposed. It was found that this combined approach resulted to the sensitivity of 86.5% and specificity of 92.9% for sleep spindles and 97.6% and 93.2% for SWD, correspondingly. Considering strong within- and between-subjects variability of sleep spindles, the obtained efficiency in their detection was high in comparison with other methods based on CWT. It is concluded that the combination of a wavelet-based approach and empirical modes increases the quality of automatic detection of stereotypic patterns in rat's EEG.

Method of generating hydrogen by catalytic decomposition of water

DOEpatents

Balachandran, Uthamalingam; Dorris, Stephen E.; Bose, Arun C.; Stiegel, Gary J.; Lee, Tae-Hyun

2002-01-01

A method for producing hydrogen includes providing a feed stream comprising water; contacting at least one proton conducting membrane adapted to interact with the feed stream; splitting the water into hydrogen and oxygen at a predetermined temperature; and separating the hydrogen from the oxygen. Preferably the proton conducting membrane comprises a proton conductor and a second phase material. Preferable proton conductors suitable for use in a proton conducting membrane include a lanthanide element, a Group VIA element and a Group IA or Group IIA element such as barium, strontium, or combinations of these elements. More preferred proton conductors include yttrium. Preferable second phase materials include platinum, palladium, nickel, cobalt, chromium, manganese, vanadium, silver, gold, copper, rhodium, ruthenium, niobium, zirconium, tantalum, and combinations of these. More preferably second phase materials suitable for use in a proton conducting membrane include nickel, palladium, and combinations of these. The method for generating hydrogen is preferably preformed in the range between about 600.degree. C. and 1,700.degree. C.

Flexible Mediation Analysis With Multiple Mediators.

PubMed

Steen, Johan; Loeys, Tom; Moerkerke, Beatrijs; Vansteelandt, Stijn

2017-07-15

The advent of counterfactual-based mediation analysis has triggered enormous progress on how, and under what assumptions, one may disentangle path-specific effects upon combining arbitrary (possibly nonlinear) models for mediator and outcome. However, current developments have largely focused on single mediators because required identification assumptions prohibit simple extensions to settings with multiple mediators that may depend on one another. In this article, we propose a procedure for obtaining fine-grained decompositions that may still be recovered from observed data in such complex settings. We first show that existing analytical approaches target specific instances of a more general set of decompositions and may therefore fail to provide a comprehensive assessment of the processes that underpin cause-effect relationships between exposure and outcome. We then outline conditions for obtaining the remaining set of decompositions. Because the number of targeted decompositions increases rapidly with the number of mediators, we introduce natural effects models along with estimation methods that allow for flexible and parsimonious modeling. Our procedure can easily be implemented using off-the-shelf software and is illustrated using a reanalysis of the World Health Organization's Large Analysis and Review of European Housing and Health Status (WHO-LARES) study on the effect of mold exposure on mental health (2002-2003). © The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2.5 concentration forecasting

NASA Astrophysics Data System (ADS)

Niu, Mingfei; Wang, Yufang; Sun, Shaolong; Li, Yongwu

2016-06-01

To enhance prediction reliability and accuracy, a hybrid model based on the promising principle of "decomposition and ensemble" and a recently proposed meta-heuristic called grey wolf optimizer (GWO) is introduced for daily PM2.5 concentration forecasting. Compared with existing PM2.5 forecasting methods, this proposed model has improved the prediction accuracy and hit rates of directional prediction. The proposed model involves three main steps, i.e., decomposing the original PM2.5 series into several intrinsic mode functions (IMFs) via complementary ensemble empirical mode decomposition (CEEMD) for simplifying the complex data; individually predicting each IMF with support vector regression (SVR) optimized by GWO; integrating all predicted IMFs for the ensemble result as the final prediction by another SVR optimized by GWO. Seven benchmark models, including single artificial intelligence (AI) models, other decomposition-ensemble models with different decomposition methods and models with the same decomposition-ensemble method but optimized by different algorithms, are considered to verify the superiority of the proposed hybrid model. The empirical study indicates that the proposed hybrid decomposition-ensemble model is remarkably superior to all considered benchmark models for its higher prediction accuracy and hit rates of directional prediction.

Dominant modal decomposition method

NASA Astrophysics Data System (ADS)

Dombovari, Zoltan

2017-03-01

The paper deals with the automatic decomposition of experimental frequency response functions (FRF's) of mechanical structures. The decomposition of FRF's is based on the Green function representation of free vibratory systems. After the determination of the impulse dynamic subspace, the system matrix is formulated and the poles are calculated directly. By means of the corresponding eigenvectors, the contribution of each element of the impulse dynamic subspace is determined and the sufficient decomposition of the corresponding FRF is carried out. With the presented dominant modal decomposition (DMD) method, the mode shapes, the modal participation vectors and the modal scaling factors are identified using the decomposed FRF's. Analytical example is presented along with experimental case studies taken from machine tool industry.

A comparison of reduced-order modelling techniques for application in hyperthermia control and estimation.

PubMed

Bailey, E A; Dutton, A W; Mattingly, M; Devasia, S; Roemer, R B

1998-01-01

Reduced-order modelling techniques can make important contributions in the control and state estimation of large systems. In hyperthermia, reduced-order modelling can provide a useful tool by which a large thermal model can be reduced to the most significant subset of its full-order modes, making real-time control and estimation possible. Two such reduction methods, one based on modal decomposition and the other on balanced realization, are compared in the context of simulated hyperthermia heat transfer problems. The results show that the modal decomposition reduction method has three significant advantages over that of balanced realization. First, modal decomposition reduced models result in less error, when compared to the full-order model, than balanced realization reduced models of similar order in problems with low or moderate advective heat transfer. Second, because the balanced realization based methods require a priori knowledge of the sensor and actuator placements, the reduced-order model is not robust to changes in sensor or actuator locations, a limitation not present in modal decomposition. Third, the modal decomposition transformation is less demanding computationally. On the other hand, in thermal problems dominated by advective heat transfer, numerical instabilities make modal decomposition based reduction problematic. Modal decomposition methods are therefore recommended for reduction of models in which advection is not dominant and research continues into methods to render balanced realization based reduction more suitable for real-time clinical hyperthermia control and estimation.

Quantitative evaluation of muscle synergy models: a single-trial task decoding approach

PubMed Central

Delis, Ioannis; Berret, Bastien; Pozzo, Thierry; Panzeri, Stefano

2013-01-01

Muscle synergies, i.e., invariant coordinated activations of groups of muscles, have been proposed as building blocks that the central nervous system (CNS) uses to construct the patterns of muscle activity utilized for executing movements. Several efficient dimensionality reduction algorithms that extract putative synergies from electromyographic (EMG) signals have been developed. Typically, the quality of synergy decompositions is assessed by computing the Variance Accounted For (VAF). Yet, little is known about the extent to which the combination of those synergies encodes task-discriminating variations of muscle activity in individual trials. To address this question, here we conceive and develop a novel computational framework to evaluate muscle synergy decompositions in task space. Unlike previous methods considering the total variance of muscle patterns (VAF based metrics), our approach focuses on variance discriminating execution of different tasks. The procedure is based on single-trial task decoding from muscle synergy activation features. The task decoding based metric evaluates quantitatively the mapping between synergy recruitment and task identification and automatically determines the minimal number of synergies that captures all the task-discriminating variability in the synergy activations. In this paper, we first validate the method on plausibly simulated EMG datasets. We then show that it can be applied to different types of muscle synergy decomposition and illustrate its applicability to real data by using it for the analysis of EMG recordings during an arm pointing task. We find that time-varying and synchronous synergies with similar number of parameters are equally efficient in task decoding, suggesting that in this experimental paradigm they are equally valid representations of muscle synergies. Overall, these findings stress the effectiveness of the decoding metric in systematically assessing muscle synergy decompositions in task space. PMID:23471195

Descent theory for semiorthogonal decompositions

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

Elagin, Alexei D

We put forward a method for constructing semiorthogonal decompositions of the derived category of G-equivariant sheaves on a variety X under the assumption that the derived category of sheaves on X admits a semiorthogonal decomposition with components preserved by the action of the group G on X. This method is used to obtain semiorthogonal decompositions of equivariant derived categories for projective bundles and blow-ups with a smooth centre as well as for varieties with a full exceptional collection preserved by the group action. Our main technical tool is descent theory for derived categories. Bibliography: 12 titles.

About decomposition approach for solving the classification problem

NASA Astrophysics Data System (ADS)

Andrianova, A. A.

2016-11-01

This article describes the features of the application of an algorithm with using of decomposition methods for solving the binary classification problem of constructing a linear classifier based on Support Vector Machine method. Application of decomposition reduces the volume of calculations, in particular, due to the emerging possibilities to build parallel versions of the algorithm, which is a very important advantage for the solution of problems with big data. The analysis of the results of computational experiments conducted using the decomposition approach. The experiment use known data set for binary classification problem.

On the Possibility of Studying the Reactions of the Thermal Decomposition of Energy Substances by the Methods of High-Resolution Terahertz Spectroscopy

NASA Astrophysics Data System (ADS)

Vaks, V. L.; Domracheva, E. G.; Chernyaeva, M. B.; Pripolzin, S. I.; Revin, L. S.; Tretyakov, I. V.; Anfertyev, V. A.; Yablokov, A. A.; Lukyanenko, I. A.; Sheikov, Yu. V.

2018-02-01

We show prospects for using the method of high-resolution terahertz spectroscopy for a continuous analysis of the decomposition products of energy substances in the gas phase (including short-lived ones) in a wide temperature range. The experimental setup, which includes a terahertz spectrometer for studying the thermal decomposition reactions, is described. The results of analysis of the gaseous decomposition products of energy substances by the example of ammonium nitrate heated from room temperature to 167°C are presented.

Exogenous nutrients and carbon resource change the responses of soil organic matter decomposition and nitrogen immobilization to nitrogen deposition

PubMed Central

He, Ping; Wan, Song-Ze; Fang, Xiang-Min; Wang, Fang-Chao; Chen, Fu-Sheng

2016-01-01

It is unclear whether exogenous nutrients and carbon (C) additions alter substrate immobilization to deposited nitrogen (N) during decomposition. In this study, we used laboratory microcosm experiments and 15N isotope tracer techniques with five different treatments including N addition, N+non-N nutrients addition, N+C addition, N+non-N nutrients+C addition and control, to investigate the coupling effects of non-N nutrients, C addition and N deposition on forest floor decomposition in subtropical China. The results indicated that N deposition inhibited soil organic matter and litter decomposition by 66% and 38%, respectively. Soil immobilized 15N following N addition was lowest among treatments. Litter 15N immobilized following N addition was significantly higher and lower than that of combined treatments during the early and late decomposition stage, respectively. Both soil and litter extractable mineral N were lower in combined treatments than in N addition treatment. Since soil N immobilization and litter N release were respectively enhanced and inhibited with elevated non-N nutrient and C resources, it can be speculated that the N leaching due to N deposition decreases with increasing nutrient and C resources. This study should advance our understanding of how forests responds the elevated N deposition. PMID:27020048

Applications of singular value analysis and partial-step algorithm for nonlinear orbit determination

NASA Technical Reports Server (NTRS)

Ryne, Mark S.; Wang, Tseng-Chan

1991-01-01

An adaptive method in which cruise and nonlinear orbit determination problems can be solved using a single program is presented. It involves singular value decomposition augmented with an extended partial step algorithm. The extended partial step algorithm constrains the size of the correction to the spacecraft state and other solve-for parameters. The correction is controlled by an a priori covariance and a user-supplied bounds parameter. The extended partial step method is an extension of the update portion of the singular value decomposition algorithm. It thus preserves the numerical stability of the singular value decomposition method, while extending the region over which it converges. In linear cases, this method reduces to the singular value decomposition algorithm with the full rank solution. Two examples are presented to illustrate the method's utility.

Rank-based decompositions of morphological templates.

PubMed

Sussner, P; Ritter, G X

2000-01-01

Methods for matrix decomposition have found numerous applications in image processing, in particular for the problem of template decomposition. Since existing matrix decomposition techniques are mainly concerned with the linear domain, we consider it timely to investigate matrix decomposition techniques in the nonlinear domain with applications in image processing. The mathematical basis for these investigations is the new theory of rank within minimax algebra. Thus far, only minimax decompositions of rank 1 and rank 2 matrices into outer product expansions are known to the image processing community. We derive a heuristic algorithm for the decomposition of matrices having arbitrary rank.

Integrated control/structure optimization by multilevel decomposition

NASA Technical Reports Server (NTRS)

Zeiler, Thomas A.; Gilbert, Michael G.

1990-01-01

A method for integrated control/structure optimization by multilevel decomposition is presented. It is shown that several previously reported methods were actually partial decompositions wherein only the control was decomposed into a subsystem design. One of these partially decomposed problems was selected as a benchmark example for comparison. The system is fully decomposed into structural and control subsystem designs and an improved design is produced. Theory, implementation, and results for the method are presented and compared with the benchmark example.

Assessing plant residue decomposition in soil using DRIFT spectroscopy

NASA Astrophysics Data System (ADS)

Ouellette, Lance; Van Eerd, Laura; Voroney, Paul

2016-04-01

Assessment of the decomposition of plant residues typically involves the use of tracer techniques combined with measurements of soil respiration. This laboratory study evaluated use of Diffuse Reflectance Fourier Transform (DRIFT) spectroscopy for its potential to assess plant residue decomposition in soil. A sandy loam soil (Orthic Humic Gleysol) obtained from a field research plot was passed through a 4.75 mm sieve moist (~70% of field capacity) to remove larger crop residues. The experimental design consisted of a randomized complete block with four replicates of ten above-ground cover crop residue-corn stover combinations, where sampling time was blocked. Two incubations were set up for 1) Drift analysis: field moist soil (250 g ODW) was placed in 500 mL glass jars, and 2) CO2 evolution: 100 g (ODW) was placed in 2 L jars. Soils were amended with the plant residues (oven-dried at 60°C and ground to <2 mm) at rates equivalent to field mean above-ground biomass yields, then moistened to 60% water holding capacity and incubated in the dark at 22±3°C. Measurements for DRIFT and CO2-C evolved were taken after 0.5, 2, 4, 7, 10, 15, 22, 29, 36, 43, 50 64 and 72 d. DRIFT spectral data (100co-added scans per sample) were recorded with a Varian Cary 660 FT-IR Spectrometer equipped with an EasiDiff Diffuse Reflectance accessory operated at a resolution of 4 cm-1 over the mid-infrared spectrum from 4000 to 400 cm-1. DRIFT spectra of amended soils indicated peak areas of aliphatics at 2930 cm-1, of aromatics at 1620, and 1530 cm-1 and of polysaccharides at 1106 and 1036 cm-1. Evolved CO2 was measured by the alkali trap method (1 M NaOH); the amount of plant residue-C remaining in soil was calculated from the difference in the quantity of plant residue C added and the additional CO2-C evolved from the amended soil. First-order model parameters of the change in polysaccharide peak area over the incubation were related to those generated from the plant residue C decay curves obtained from respiration measurements. The DRIFT method demonstrated that spectral areas consistent with labile aliphatic-C bands (2930 cm-1) can also be used to describe residue C decomposition. This is the first study to demonstrate the usefulness of DRIFT spectroscopy to characterize plant decomposition in soil.

A novel iterative scheme and its application to differential equations.

PubMed

Khan, Yasir; Naeem, F; Šmarda, Zdeněk

2014-01-01

The purpose of this paper is to employ an alternative approach to reconstruct the standard variational iteration algorithm II proposed by He, including Lagrange multiplier, and to give a simpler formulation of Adomian decomposition and modified Adomian decomposition method in terms of newly proposed variational iteration method-II (VIM). Through careful investigation of the earlier variational iteration algorithm and Adomian decomposition method, we find unnecessary calculations for Lagrange multiplier and also repeated calculations involved in each iteration, respectively. Several examples are given to verify the reliability and efficiency of the method.

Artifact removal from EEG data with empirical mode decomposition

NASA Astrophysics Data System (ADS)

Grubov, Vadim V.; Runnova, Anastasiya E.; Efremova, Tatyana Yu.; Hramov, Alexander E.

2017-03-01

In the paper we propose the novel method for dealing with the physiological artifacts caused by intensive activity of facial and neck muscles and other movements in experimental human EEG recordings. The method is based on analysis of EEG signals with empirical mode decomposition (Hilbert-Huang transform). We introduce the mathematical algorithm of the method with following steps: empirical mode decomposition of EEG signal, choosing of empirical modes with artifacts, removing empirical modes with artifacts, reconstruction of the initial EEG signal. We test the method on filtration of experimental human EEG signals from movement artifacts and show high efficiency of the method.

Adaptive Fourier decomposition based ECG denoising.

PubMed

Wang, Ze; Wan, Feng; Wong, Chi Man; Zhang, Liming

2016-10-01

A novel ECG denoising method is proposed based on the adaptive Fourier decomposition (AFD). The AFD decomposes a signal according to its energy distribution, thereby making this algorithm suitable for separating pure ECG signal and noise with overlapping frequency ranges but different energy distributions. A stop criterion for the iterative decomposition process in the AFD is calculated on the basis of the estimated signal-to-noise ratio (SNR) of the noisy signal. The proposed AFD-based method is validated by the synthetic ECG signal using an ECG model and also real ECG signals from the MIT-BIH Arrhythmia Database both with additive Gaussian white noise. Simulation results of the proposed method show better performance on the denoising and the QRS detection in comparing with major ECG denoising schemes based on the wavelet transform, the Stockwell transform, the empirical mode decomposition, and the ensemble empirical mode decomposition. Copyright © 2016 Elsevier Ltd. All rights reserved.

Mixing effects on litter decomposition rates in a young tree diversity experiment

NASA Astrophysics Data System (ADS)

Setiawan, Nuri Nurlaila; Vanhellemont, Margot; De Schrijver, An; Schelfhout, Stephanie; Baeten, Lander; Verheyen, Kris

2016-01-01

Litter decomposition is an essential process for biogeochemical cycling and for the formation of new soil organic matter. Mixing litter from different tree species has been reported to increase litter decomposition rates through synergistic effects. We assessed the decomposition rates of leaf litter from five tree species in a recently established tree diversity experiment on a post-agriculture site in Belgium. We used 20 different leaf litter compositions with diversity levels ranging from 1 up to 4 species. Litter mass loss in litterbags was assessed 10, 20, 25, 35, and 60 weeks after installation in the field. We found that litter decomposition rates were higher for high-quality litters, i.e., with high nitrogen content and low lignin content. The decomposition rates of mixed litter were more affected by the identity of the litter species within the mixture than by the diversity of the litter per se, but the variability in litter decomposition rates decreased as the litter diversity increased. Among the 15 different mixed litter compositions in our study, only three litter combinations showed synergistic effects. Our study suggests that admixing tree species with high-quality litter in post-agricultural plantations helps in increasing the mixture's early-stage litter decomposition rate.

Traits drive global wood decomposition rates more than climate.

PubMed

Hu, Zhenhong; Michaletz, Sean T; Johnson, Daniel J; McDowell, Nate G; Huang, Zhiqun; Zhou, Xuhui; Xu, Chonggang

2018-06-14

Wood decomposition is a major component of the global carbon cycle. Decomposition rates vary across climate gradients, which is thought to reflect the effects of temperature and moisture on the metabolic kinetics of decomposers. However, decomposition rates also vary with wood traits, which may reflect the influence of stoichiometry on decomposer metabolism as well as geometry relating the surface areas that decomposers colonize with the volumes they consume. In this paper, we combined metabolic and geometric scaling theories to formalize hypotheses regarding the drivers of wood decomposition rates, and assessed these hypotheses using a global compilation of data on climate, wood traits, and wood decomposition rates. Our results are consistent with predictions from both metabolic and geometric scaling theories. Approximately half of the global variation in decomposition rates was explained by wood traits (nitrogen content and diameter), while only a fifth was explained by climate variables (air temperature, precipitation, and relative humidity). These results indicate that global variation in wood decomposition rates is best explained by stoichiometric and geometric wood traits. Our findings suggest that inclusion of wood traits in global carbon cycle models can improve predictions of carbon fluxes from wood decomposition. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

High temperature phase decomposition in Ti{sub x}Zr{sub y}Al{sub z}N

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

Lind, Hans; Pilemalm, Robert; Rogström, Lina

2014-12-15

Through a combination of theoretical and experimental observations we study the high temperature decomposition behavior of c-(Ti{sub x}Zr{sub y}Al{sub z}N) alloys. We show that for most concentrations the high formation energy of (ZrAl)N causes a strong tendency for spinodal decomposition between ZrN and AlN while other decompositions tendencies are suppressed. In addition we observe that entropic effects due to configurational disorder favor a formation of a stable Zr-rich (TiZr)N phase with increasing temperature. Our calculations also predict that at high temperatures a Zr rich (TiZrAl)N disordered phase should become more resistant against the spinodal decomposition despite its high and positivemore » formation energy due to the specific topology of the free energy surface at the relevant concentrations. Our experimental observations confirm this prediction by showing strong tendency towards decomposition in a Zr-poor sample while a Zr-rich alloy shows a greatly reduced decomposition rate, which is mostly attributable to binodal decomposition processes. This result highlights the importance of considering the second derivative of the free energy, in addition to its absolute value in predicting decomposition trends of thermodynamically unstable alloys.« less

Reaction behaviors of decomposition of monocrotophos in aqueous solution by UV and UV/O processes.

PubMed

Ku, Y; Wang, W; Shen, Y S

2000-02-01

The decomposition of monocrotophos (cis-3-dimethoxyphosphinyloxy-N-methyl-crotonamide) in aqueous solution by UV and UV/O(3) processes was studied. The experiments were carried out under various solution pH values to investigate the decomposition efficiencies of the reactant and organic intermediates in order to determine the completeness of decomposition. The photolytic decomposition rate of monocrotophos was increased with increasing solution pH because the solution pH affects the distribution and light absorbance of monocrotophos species. The combination of O(3) with UV light apparently promoted the decomposition and mineralization of monocrotophos in aqueous solution. For the UV/O(3) process, the breakage of the >C=C< bond of monocrotophos by ozone molecules was found to occur first, followed by mineralization by hydroxyl radicals to generate CO(3)(2-), PO4(3-), and NO(3)(-) anions in sequence. The quasi-global kinetics based on a simplified consecutive-parallel reaction scheme was developed to describe the temporal behavior of monocrotophos decomposition in aqueous solution by the UV/O(3) process.

A new impedance accounting for short- and long-range effects in mixed substructured formulations of nonlinear problems

NASA Astrophysics Data System (ADS)

Negrello, Camille; Gosselet, Pierre; Rey, Christian

2018-05-01

An efficient method for solving large nonlinear problems combines Newton solvers and Domain Decomposition Methods (DDM). In the DDM framework, the boundary conditions can be chosen to be primal, dual or mixed. The mixed approach presents the advantage to be eligible for the research of an optimal interface parameter (often called impedance) which can increase the convergence rate. The optimal value for this parameter is often too expensive to be computed exactly in practice: an approximate version has to be sought for, along with a compromise between efficiency and computational cost. In the context of parallel algorithms for solving nonlinear structural mechanical problems, we propose a new heuristic for the impedance which combines short and long range effects at a low computational cost.

Kinetics and mechanism of solid decompositions — From basic discoveries by atomic absorption spectrometry and quadrupole mass spectroscopy to thorough thermogravimetric analysis

NASA Astrophysics Data System (ADS)

L'vov, Boris V.

2008-02-01

This paper sums up the evolution of thermochemical approach to the interpretation of solid decompositions for the past 25 years. This period includes two stages related to decomposition studies by different techniques: by ET AAS and QMS in 1981-2001 and by TG in 2002-2007. As a result of ET AAS and QMS investigations, the method for determination of absolute rates of solid decompositions was developed and the mechanism of decompositions through the congruent dissociative vaporization was discovered. On this basis, in the period from 1997 to 2001, the decomposition mechanisms of several classes of reactants were interpreted and some unusual effects observed in TA were explained. However, the thermochemical approach has not received any support by other TA researchers. One of the potential reasons of this distrust was the unreliability of the E values measured by the traditional Arrhenius plot method. The theoretical analysis and comparison of metrological features of different methods used in the determinations of thermochemical quantities permitted to conclude that in comparison with the Arrhenius plot and second-law methods, the third-law method is to be very much preferred. However, this method cannot be used in the kinetic studies by the Arrhenius approach because its use suggests the measuring of the equilibrium pressures of decomposition products. On the contrary, the method of absolute rates is ideally suitable for this purpose. As a result of much higher precision of the third-law method, some quantitative conclusions that follow from the theory were confirmed, and several new effects, which were invisible in the framework of the Arrhenius approach, have been revealed. In spite of great progress reached in the development of reliable methodology, based on the third-law method, the thermochemical approach remains unclaimed as before.

Integrated control/structure optimization by multilevel decomposition

NASA Technical Reports Server (NTRS)

Zeiler, Thomas A.; Gilbert, Michael G.

1990-01-01

A method for integrated control/structure optimization by multilevel decomposition is presented. It is shown that several previously reported methods were actually partial decompositions wherein only the control was decomposed into a subsystem design. One of these partially decomposed problems was selected as a benchmark example for comparison. The present paper fully decomposes the system into structural and control subsystem designs and produces an improved design. Theory, implementation, and results for the method are presented and compared with the benchmark example.

Degradation of organic wastewater by hydrodynamic cavitation combined with acoustic cavitation.

PubMed

Yi, Chunhai; Lu, Qianqian; Wang, Yun; Wang, Yixuan; Yang, Bolun

2018-05-01

In this paper, the decomposition of Rhodamine B (RhB) by hydrodynamic cavitation (HC), acoustic cavitation (AC) and the combination of these individual methods (HAC) have been investigated. The degradation of 20 L RhB aqueous solution was carried out in a self-designed HAC reactor, where hydrodynamic cavitation and acoustic cavitation could take place in the same space simultaneously. The effects of initial concentration, inlet pressure, solution temperature and ultrasonic power were studied and discussed. Obvious synergies were found in the HAC process. The combined method achieved the best conversion, and the synergistic effect in HAC was even up to 119% with the ultrasonic power of 220 W in a treatment time of 30 min. The time-independent synergistic factor based on rate constant was introduced and the maximum value reached 40% in the HAC system. Besides, the hybrid HAC method showed great superiority in energy efficiency at lower ultrasonic power (88-176 W). Therefore, HAC technology can be visualized as a promising method for wastewater treatment with good scale-up possibilities. Copyright © 2018 Elsevier B.V. All rights reserved.

Primary decomposition of zero-dimensional ideals over finite fields

NASA Astrophysics Data System (ADS)

Gao, Shuhong; Wan, Daqing; Wang, Mingsheng

2009-03-01

A new algorithm is presented for computing primary decomposition of zero-dimensional ideals over finite fields. Like Berlekamp's algorithm for univariate polynomials, the new method is based on the invariant subspace of the Frobenius map acting on the quotient algebra. The dimension of the invariant subspace equals the number of primary components, and a basis of the invariant subspace yields a complete decomposition. Unlike previous approaches for decomposing multivariate polynomial systems, the new method does not need primality testing nor any generic projection, instead it reduces the general decomposition problem directly to root finding of univariate polynomials over the ground field. Also, it is shown how Groebner basis structure can be used to get partial primary decomposition without any root finding.

Hybridization of decomposition and local search for multiobjective optimization.

PubMed

Ke, Liangjun; Zhang, Qingfu; Battiti, Roberto

2014-10-01

Combining ideas from evolutionary algorithms, decomposition approaches, and Pareto local search, this paper suggests a simple yet efficient memetic algorithm for combinatorial multiobjective optimization problems: memetic algorithm based on decomposition (MOMAD). It decomposes a combinatorial multiobjective problem into a number of single objective optimization problems using an aggregation method. MOMAD evolves three populations: 1) population P(L) for recording the current solution to each subproblem; 2) population P(P) for storing starting solutions for Pareto local search; and 3) an external population P(E) for maintaining all the nondominated solutions found so far during the search. A problem-specific single objective heuristic can be applied to these subproblems to initialize the three populations. At each generation, a Pareto local search method is first applied to search a neighborhood of each solution in P(P) to update P(L) and P(E). Then a single objective local search is applied to each perturbed solution in P(L) for improving P(L) and P(E), and reinitializing P(P). The procedure is repeated until a stopping condition is met. MOMAD provides a generic hybrid multiobjective algorithmic framework in which problem specific knowledge, well developed single objective local search and heuristics and Pareto local search methods can be hybridized. It is a population based iterative method and thus an anytime algorithm. Extensive experiments have been conducted in this paper to study MOMAD and compare it with some other state-of-the-art algorithms on the multiobjective traveling salesman problem and the multiobjective knapsack problem. The experimental results show that our proposed algorithm outperforms or performs similarly to the best so far heuristics on these two problems.

Extended FDD-WT method based on correcting the errors due to non-synchronous sensing of sensors

NASA Astrophysics Data System (ADS)

Tarinejad, Reza; Damadipour, Majid

2016-05-01

In this research, a combinational non-parametric method called frequency domain decomposition-wavelet transform (FDD-WT) that was recently presented by the authors, is extended for correction of the errors resulting from asynchronous sensing of sensors, in order to extend the application of the algorithm for different kinds of structures, especially for huge structures. Therefore, the analysis process is based on time-frequency domain decomposition and is performed with emphasis on correcting time delays between sensors. Time delay estimation (TDE) methods are investigated for their efficiency and accuracy for noisy environmental records and the Phase Transform - β (PHAT-β) technique was selected as an appropriate method to modify the operation of traditional FDD-WT in order to achieve the exact results. In this paper, a theoretical example (3DOF system) has been provided in order to indicate the non-synchronous sensing effects of the sensors on the modal parameters; moreover, the Pacoima dam subjected to 13 Jan 2001 earthquake excitation was selected as a case study. The modal parameters of the dam obtained from the extended FDD-WT method were compared with the output of the classical signal processing method, which is referred to as 4-Spectral method, as well as other literatures relating to the dynamic characteristics of Pacoima dam. The results comparison indicates that values are correct and reliable.

Adaptive variational mode decomposition method for signal processing based on mode characteristic

NASA Astrophysics Data System (ADS)

Lian, Jijian; Liu, Zhuo; Wang, Haijun; Dong, Xiaofeng

2018-07-01

Variational mode decomposition is a completely non-recursive decomposition model, where all the modes are extracted concurrently. However, the model requires a preset mode number, which limits the adaptability of the method since a large deviation in the number of mode set will cause the discard or mixing of the mode. Hence, a method called Adaptive Variational Mode Decomposition (AVMD) was proposed to automatically determine the mode number based on the characteristic of intrinsic mode function. The method was used to analyze the simulation signals and the measured signals in the hydropower plant. Comparisons have also been conducted to evaluate the performance by using VMD, EMD and EWT. It is indicated that the proposed method has strong adaptability and is robust to noise. It can determine the mode number appropriately without modulation even when the signal frequencies are relatively close.

Self-similar pyramidal structures and signal reconstruction

NASA Astrophysics Data System (ADS)

Benedetto, John J.; Leon, Manuel; Saliani, Sandra

1998-03-01

Pyramidal structures are defined which are locally a combination of low and highpass filtering. The structures are analogous to but different from wavelet packet structures. In particular, new frequency decompositions are obtained; and these decompositions can be parameterized to establish a correspondence with a large class of Cantor sets. Further correspondences are then established to relate such frequency decompositions with more general self- similarities. The role of the filters in defining these pyramidal structures gives rise to signal reconstruction algorithms, and these, in turn, are used in the analysis of speech data.

Effects of calibration methods on quantitative material decomposition in photon-counting spectral computed tomography using a maximum a posteriori estimator.

PubMed

Curtis, Tyler E; Roeder, Ryan K

2017-10-01

Advances in photon-counting detectors have enabled quantitative material decomposition using multi-energy or spectral computed tomography (CT). Supervised methods for material decomposition utilize an estimated attenuation for each material of interest at each photon energy level, which must be calibrated based upon calculated or measured values for known compositions. Measurements using a calibration phantom can advantageously account for system-specific noise, but the effect of calibration methods on the material basis matrix and subsequent quantitative material decomposition has not been experimentally investigated. Therefore, the objective of this study was to investigate the influence of the range and number of contrast agent concentrations within a modular calibration phantom on the accuracy of quantitative material decomposition in the image domain. Gadolinium was chosen as a model contrast agent in imaging phantoms, which also contained bone tissue and water as negative controls. The maximum gadolinium concentration (30, 60, and 90 mM) and total number of concentrations (2, 4, and 7) were independently varied to systematically investigate effects of the material basis matrix and scaling factor calibration on the quantitative (root mean squared error, RMSE) and spatial (sensitivity and specificity) accuracy of material decomposition. Images of calibration and sample phantoms were acquired using a commercially available photon-counting spectral micro-CT system with five energy bins selected to normalize photon counts and leverage the contrast agent k-edge. Material decomposition of gadolinium, calcium, and water was performed for each calibration method using a maximum a posteriori estimator. Both the quantitative and spatial accuracy of material decomposition were most improved by using an increased maximum gadolinium concentration (range) in the basis matrix calibration; the effects of using a greater number of concentrations were relatively small in magnitude by comparison. The material basis matrix calibration was more sensitive to changes in the calibration methods than the scaling factor calibration. The material basis matrix calibration significantly influenced both the quantitative and spatial accuracy of material decomposition, while the scaling factor calibration influenced quantitative but not spatial accuracy. Importantly, the median RMSE of material decomposition was as low as ~1.5 mM (~0.24 mg/mL gadolinium), which was similar in magnitude to that measured by optical spectroscopy on the same samples. The accuracy of quantitative material decomposition in photon-counting spectral CT was significantly influenced by calibration methods which must therefore be carefully considered for the intended diagnostic imaging application. © 2017 American Association of Physicists in Medicine.

Wavelet decomposition based principal component analysis for face recognition using MATLAB

NASA Astrophysics Data System (ADS)

Sharma, Mahesh Kumar; Sharma, Shashikant; Leeprechanon, Nopbhorn; Ranjan, Aashish

2016-03-01

For the realization of face recognition systems in the static as well as in the real time frame, algorithms such as principal component analysis, independent component analysis, linear discriminate analysis, neural networks and genetic algorithms are used for decades. This paper discusses an approach which is a wavelet decomposition based principal component analysis for face recognition. Principal component analysis is chosen over other algorithms due to its relative simplicity, efficiency, and robustness features. The term face recognition stands for identifying a person from his facial gestures and having resemblance with factor analysis in some sense, i.e. extraction of the principal component of an image. Principal component analysis is subjected to some drawbacks, mainly the poor discriminatory power and the large computational load in finding eigenvectors, in particular. These drawbacks can be greatly reduced by combining both wavelet transform decomposition for feature extraction and principal component analysis for pattern representation and classification together, by analyzing the facial gestures into space and time domain, where, frequency and time are used interchangeably. From the experimental results, it is envisaged that this face recognition method has made a significant percentage improvement in recognition rate as well as having a better computational efficiency.

Study of thermal decomposition mechanisms and low-level detection of explosives using pulsed photoacoustic technique

NASA Astrophysics Data System (ADS)

Yehya, F.; Chaudhary, A. K.; Srinivas, D.; Muralidharan, K.

2015-11-01

We report a novel time-resolved photoacoustic-based technique for studying the thermal decomposition mechanisms of some secondary explosives such as RDX (hexahydro-1,3,5-trinitro-1,3,5-triazine), picric acid, 4,6-dinitro-5-(4-nitro-1 H-imidazol-1-yl)-1 H-benzo[ d] [1-3] triazole, and 5-chloro-1-(4-nitrophenyl)-1 H-tetrazole. A comparison of the thermal decomposition mechanisms of these secondary explosives was made by detecting NO2 molecules released under controlled pyrolysis between 25 and 350 °C. The results show excellent agreement with the thermogravimetric and differential thermal analysis (TGA-DTA) results. A specially designed PA cell made of stainless steel was filled with explosive vapor and pumped using second harmonic, i.e., λ = 532 nm, pulses of duration 7 ns at a 10 Hz repetition rate, obtained using a Q-switched Nd:YAG laser. The use of a combination of PA and TGA-DTA techniques enables the study of NO2 generation, and this method can be used to scale the performance of these explosives as rocket fuels. The minimum detection limits of the four explosives were 38 ppmv to 69 ppbv, depending on their respective vapor pressures.

Global Solutions to Repulsive Hookean Elastodynamics

NASA Astrophysics Data System (ADS)

Hu, Xianpeng; Masmoudi, Nader

2017-01-01

The global existence of classical solutions to the three dimensional repulsive Hookean elastodynamics around an equilibrium is considered. By linearization and Hodge's decomposition, the compressible part of the velocity, the density, and the compressible part of the transpose of the deformation gradient satisfy Klein-Gordon equations with speed {√{2}}, while the incompressible parts of the velocity and of the transpose of the deformation gradient satisfy wave equations with speed one. The space-time resonance method combined with the vector field method is used in a novel way to obtain the decay of the solution and hence global existence.

Powerline noise elimination in biomedical signals via blind source separation and wavelet analysis.

PubMed

Akwei-Sekyere, Samuel

2015-01-01

The distortion of biomedical signals by powerline noise from recording biomedical devices has the potential to reduce the quality and convolute the interpretations of the data. Usually, powerline noise in biomedical recordings are extinguished via band-stop filters. However, due to the instability of biomedical signals, the distribution of signals filtered out may not be centered at 50/60 Hz. As a result, self-correction methods are needed to optimize the performance of these filters. Since powerline noise is additive in nature, it is intuitive to model powerline noise in a raw recording and subtract it from the raw data in order to obtain a relatively clean signal. This paper proposes a method that utilizes this approach by decomposing the recorded signal and extracting powerline noise via blind source separation and wavelet analysis. The performance of this algorithm was compared with that of a 4th order band-stop Butterworth filter, empirical mode decomposition, independent component analysis and, a combination of empirical mode decomposition with independent component analysis. The proposed method was able to expel sinusoidal signals within powerline noise frequency range with higher fidelity in comparison with the mentioned techniques, especially at low signal-to-noise ratio.

Multispectral image fusion for illumination-invariant palmprint recognition

PubMed Central

Zhang, Xinman; Xu, Xuebin; Shang, Dongpeng

2017-01-01

Multispectral palmprint recognition has shown broad prospects for personal identification due to its high accuracy and great stability. In this paper, we develop a novel illumination-invariant multispectral palmprint recognition method. To combine the information from multiple spectral bands, an image-level fusion framework is completed based on a fast and adaptive bidimensional empirical mode decomposition (FABEMD) and a weighted Fisher criterion. The FABEMD technique decomposes the multispectral images into their bidimensional intrinsic mode functions (BIMFs), on which an illumination compensation operation is performed. The weighted Fisher criterion is to construct the fusion coefficients at the decomposition level, making the images be separated correctly in the fusion space. The image fusion framework has shown strong robustness against illumination variation. In addition, a tensor-based extreme learning machine (TELM) mechanism is presented for feature extraction and classification of two-dimensional (2D) images. In general, this method has fast learning speed and satisfying recognition accuracy. Comprehensive experiments conducted on the PolyU multispectral palmprint database illustrate that the proposed method can achieve favorable results. For the testing under ideal illumination, the recognition accuracy is as high as 99.93%, and the result is 99.50% when the lighting condition is unsatisfied. PMID:28558064

Multispectral image fusion for illumination-invariant palmprint recognition.

PubMed

Lu, Longbin; Zhang, Xinman; Xu, Xuebin; Shang, Dongpeng

2017-01-01

Multispectral palmprint recognition has shown broad prospects for personal identification due to its high accuracy and great stability. In this paper, we develop a novel illumination-invariant multispectral palmprint recognition method. To combine the information from multiple spectral bands, an image-level fusion framework is completed based on a fast and adaptive bidimensional empirical mode decomposition (FABEMD) and a weighted Fisher criterion. The FABEMD technique decomposes the multispectral images into their bidimensional intrinsic mode functions (BIMFs), on which an illumination compensation operation is performed. The weighted Fisher criterion is to construct the fusion coefficients at the decomposition level, making the images be separated correctly in the fusion space. The image fusion framework has shown strong robustness against illumination variation. In addition, a tensor-based extreme learning machine (TELM) mechanism is presented for feature extraction and classification of two-dimensional (2D) images. In general, this method has fast learning speed and satisfying recognition accuracy. Comprehensive experiments conducted on the PolyU multispectral palmprint database illustrate that the proposed method can achieve favorable results. For the testing under ideal illumination, the recognition accuracy is as high as 99.93%, and the result is 99.50% when the lighting condition is unsatisfied.

Reduced quantum dynamics with arbitrary bath spectral densities: hierarchical equations of motion based on several different bath decomposition schemes.

PubMed

Liu, Hao; Zhu, Lili; Bai, Shuming; Shi, Qiang

2014-04-07

We investigated applications of the hierarchical equation of motion (HEOM) method to perform high order perturbation calculations of reduced quantum dynamics for a harmonic bath with arbitrary spectral densities. Three different schemes are used to decompose the bath spectral density into analytical forms that are suitable to the HEOM treatment: (1) The multiple Lorentzian mode model that can be obtained by numerically fitting the model spectral density. (2) The combined Debye and oscillatory Debye modes model that can be constructed by fitting the corresponding classical bath correlation function. (3) A new method that uses undamped harmonic oscillator modes explicitly in the HEOM formalism. Methods to extract system-bath correlations were investigated for the above bath decomposition schemes. We also show that HEOM in the undamped harmonic oscillator modes can give detailed information on the partial Wigner transform of the total density operator. Theoretical analysis and numerical simulations of the spin-Boson dynamics and the absorption line shape of molecular dimers show that the HEOM formalism for high order perturbations can serve as an important tool in studying the quantum dissipative dynamics in the intermediate coupling regime.

Reduced quantum dynamics with arbitrary bath spectral densities: Hierarchical equations of motion based on several different bath decomposition schemes

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

Liu, Hao; Zhu, Lili; Bai, Shuming

2014-04-07

We investigated applications of the hierarchical equation of motion (HEOM) method to perform high order perturbation calculations of reduced quantum dynamics for a harmonic bath with arbitrary spectral densities. Three different schemes are used to decompose the bath spectral density into analytical forms that are suitable to the HEOM treatment: (1) The multiple Lorentzian mode model that can be obtained by numerically fitting the model spectral density. (2) The combined Debye and oscillatory Debye modes model that can be constructed by fitting the corresponding classical bath correlation function. (3) A new method that uses undamped harmonic oscillator modes explicitly inmore » the HEOM formalism. Methods to extract system-bath correlations were investigated for the above bath decomposition schemes. We also show that HEOM in the undamped harmonic oscillator modes can give detailed information on the partial Wigner transform of the total density operator. Theoretical analysis and numerical simulations of the spin-Boson dynamics and the absorption line shape of molecular dimers show that the HEOM formalism for high order perturbations can serve as an important tool in studying the quantum dissipative dynamics in the intermediate coupling regime.« less

Utilization of a balanced steady state free precession signal model for improved fat/water decomposition.

PubMed

Henze Bancroft, Leah C; Strigel, Roberta M; Hernando, Diego; Johnson, Kevin M; Kelcz, Frederick; Kijowski, Richard; Block, Walter F

2016-03-01

Chemical shift based fat/water decomposition methods such as IDEAL are frequently used in challenging imaging environments with large B0 inhomogeneity. However, they do not account for the signal modulations introduced by a balanced steady state free precession (bSSFP) acquisition. Here we demonstrate improved performance when the bSSFP frequency response is properly incorporated into the multipeak spectral fat model used in the decomposition process. Balanced SSFP allows for rapid imaging but also introduces a characteristic frequency response featuring periodic nulls and pass bands. Fat spectral components in adjacent pass bands will experience bulk phase offsets and magnitude modulations that change the expected constructive and destructive interference between the fat spectral components. A bSSFP signal model was incorporated into the fat/water decomposition process and used to generate images of a fat phantom, and bilateral breast and knee images in four normal volunteers at 1.5 Tesla. Incorporation of the bSSFP signal model into the decomposition process improved the performance of the fat/water decomposition. Incorporation of this model allows rapid bSSFP imaging sequences to use robust fat/water decomposition methods such as IDEAL. While only one set of imaging parameters were presented, the method is compatible with any field strength or repetition time. © 2015 Wiley Periodicals, Inc.

Forecasting stochastic neural network based on financial empirical mode decomposition.

PubMed

Wang, Jie; Wang, Jun

2017-06-01

In an attempt to improve the forecasting accuracy of stock price fluctuations, a new one-step-ahead model is developed in this paper which combines empirical mode decomposition (EMD) with stochastic time strength neural network (STNN). The EMD is a processing technique introduced to extract all the oscillatory modes embedded in a series, and the STNN model is established for considering the weight of occurrence time of the historical data. The linear regression performs the predictive availability of the proposed model, and the effectiveness of EMD-STNN is revealed clearly through comparing the predicted results with the traditional models. Moreover, a new evaluated method (q-order multiscale complexity invariant distance) is applied to measure the predicted results of real stock index series, and the empirical results show that the proposed model indeed displays a good performance in forecasting stock market fluctuations. Copyright © 2017 Elsevier Ltd. All rights reserved.

A density functional theory study of the decomposition mechanism of nitroglycerin.

PubMed

Pei, Liguan; Dong, Kehai; Tang, Yanhui; Zhang, Bo; Yu, Chang; Li, Wenzuo

2017-08-21

The detailed decomposition mechanism of nitroglycerin (NG) in the gas phase was studied by examining reaction pathways using density functional theory (DFT) and canonical variational transition state theory combined with a small-curvature tunneling correction (CVT/SCT). The mechanism of NG autocatalytic decomposition was investigated at the B3LYP/6-31G(d,p) level of theory. Five possible decomposition pathways involving NG were identified and the rate constants for the pathways at temperatures ranging from 200 to 1000 K were calculated using CVT/SCT. There was found to be a lower energy barrier to the β-H abstraction reaction than to the α-H abstraction reaction during the initial step in the autocatalytic decomposition of NG. The decomposition pathways for CHOCOCHONO 2 (a product obtained following the abstraction of three H atoms from NG by NO 2 ) include O-NO 2 cleavage or isomer production, meaning that the autocatalytic decomposition of NG has two reaction pathways, both of which are exothermic. The rate constants for these two reaction pathways are greater than the rate constants for the three pathways corresponding to unimolecular NG decomposition. The overall process of NG decomposition can be divided into two stages based on the NO 2 concentration, which affects the decomposition products and reactions. In the first stage, the reaction pathway corresponding to O-NO 2 cleavage is the main pathway, but the rates of the two autocatalytic decomposition pathways increase with increasing NO 2 concentration. However, when a threshold NO 2 concentration is reached, the NG decomposition process enters its second stage, with the two pathways for NG autocatalytic decomposition becoming the main and secondary reaction pathways.

A general framework of noise suppression in material decomposition for dual-energy CT

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

Petrongolo, Michael; Dong, Xue; Zhu, Lei, E-mail: leizhu@gatech.edu

Purpose: As a general problem of dual-energy CT (DECT), noise amplification in material decomposition severely reduces the signal-to-noise ratio on the decomposed images compared to that on the original CT images. In this work, the authors propose a general framework of noise suppression in material decomposition for DECT. The method is based on an iterative algorithm recently developed in their group for image-domain decomposition of DECT, with an extension to include nonlinear decomposition models. The generalized framework of iterative DECT decomposition enables beam-hardening correction with simultaneous noise suppression, which improves the clinical benefits of DECT. Methods: The authors propose tomore » suppress noise on the decomposed images of DECT using convex optimization, which is formulated in the form of least-squares estimation with smoothness regularization. Based on the design principles of a best linear unbiased estimator, the authors include the inverse of the estimated variance–covariance matrix of the decomposed images as the penalty weight in the least-squares term. Analytical formulas are derived to compute the variance–covariance matrix for decomposed images with general-form numerical or analytical decomposition. As a demonstration, the authors implement the proposed algorithm on phantom data using an empirical polynomial function of decomposition measured on a calibration scan. The polynomial coefficients are determined from the projection data acquired on a wedge phantom, and the signal decomposition is performed in the projection domain. Results: On the Catphan{sup ®}600 phantom, the proposed noise suppression method reduces the average noise standard deviation of basis material images by one to two orders of magnitude, with a superior performance on spatial resolution as shown in comparisons of line-pair images and modulation transfer function measurements. On the synthesized monoenergetic CT images, the noise standard deviation is reduced by a factor of 2–3. By using nonlinear decomposition on projections, the authors’ method effectively suppresses the streaking artifacts of beam hardening and obtains more uniform images than their previous approach based on a linear model. Similar performance of noise suppression is observed in the results of an anthropomorphic head phantom and a pediatric chest phantom generated by the proposed method. With beam-hardening correction enabled by their approach, the image spatial nonuniformity on the head phantom is reduced from around 10% on the original CT images to 4.9% on the synthesized monoenergetic CT image. On the pediatric chest phantom, their method suppresses image noise standard deviation by a factor of around 7.5, and compared with linear decomposition, it reduces the estimation error of electron densities from 33.3% to 8.6%. Conclusions: The authors propose a general framework of noise suppression in material decomposition for DECT. Phantom studies have shown the proposed method improves the image uniformity and the accuracy of electron density measurements by effective beam-hardening correction and reduces noise level without noticeable resolution loss.« less

Regularization of nonlinear decomposition of spectral x-ray projection images.

PubMed

Ducros, Nicolas; Abascal, Juan Felipe Perez-Juste; Sixou, Bruno; Rit, Simon; Peyrin, Françoise

2017-09-01

Exploiting the x-ray measurements obtained in different energy bins, spectral computed tomography (CT) has the ability to recover the 3-D description of a patient in a material basis. This may be achieved solving two subproblems, namely the material decomposition and the tomographic reconstruction problems. In this work, we address the material decomposition of spectral x-ray projection images, which is a nonlinear ill-posed problem. Our main contribution is to introduce a material-dependent spatial regularization in the projection domain. The decomposition problem is solved iteratively using a Gauss-Newton algorithm that can benefit from fast linear solvers. A Matlab implementation is available online. The proposed regularized weighted least squares Gauss-Newton algorithm (RWLS-GN) is validated on numerical simulations of a thorax phantom made of up to five materials (soft tissue, bone, lung, adipose tissue, and gadolinium), which is scanned with a 120 kV source and imaged by a 4-bin photon counting detector. To evaluate the method performance of our algorithm, different scenarios are created by varying the number of incident photons, the concentration of the marker and the configuration of the phantom. The RWLS-GN method is compared to the reference maximum likelihood Nelder-Mead algorithm (ML-NM). The convergence of the proposed method and its dependence on the regularization parameter are also studied. We show that material decomposition is feasible with the proposed method and that it converges in few iterations. Material decomposition with ML-NM was very sensitive to noise, leading to decomposed images highly affected by noise, and artifacts even for the best case scenario. The proposed method was less sensitive to noise and improved contrast-to-noise ratio of the gadolinium image. Results were superior to those provided by ML-NM in terms of image quality and decomposition was 70 times faster. For the assessed experiments, material decomposition was possible with the proposed method when the number of incident photons was equal or larger than 10 5 and when the marker concentration was equal or larger than 0.03 g·cm -3 . The proposed method efficiently solves the nonlinear decomposition problem for spectral CT, which opens up new possibilities such as material-specific regularization in the projection domain and a parallelization framework, in which projections are solved in parallel. © 2017 American Association of Physicists in Medicine.

A hybrid method with deviational particles for spatial inhomogeneous plasma

NASA Astrophysics Data System (ADS)

Yan, Bokai

2016-03-01

In this work we propose a Hybrid method with Deviational Particles (HDP) for a plasma modeled by the inhomogeneous Vlasov-Poisson-Landau system. We split the distribution into a Maxwellian part evolved by a grid based fluid solver and a deviation part simulated by numerical particles. These particles, named deviational particles, could be both positive and negative. We combine the Monte Carlo method proposed in [31], a Particle in Cell method and a Macro-Micro decomposition method [3] to design an efficient hybrid method. Furthermore, coarse particles are employed to accelerate the simulation. A particle resampling technique on both deviational particles and coarse particles is also investigated and improved. This method is applicable in all regimes and significantly more efficient compared to a PIC-DSMC method near the fluid regime.

Modeling the Metabolism of Arabidopsis thaliana: Application of Network Decomposition and Network Reduction in the Context of Petri Nets.

PubMed

Koch, Ina; Nöthen, Joachim; Schleiff, Enrico

2017-01-01

Motivation: Arabidopsis thaliana is a well-established model system for the analysis of the basic physiological and metabolic pathways of plants. Nevertheless, the system is not yet fully understood, although many mechanisms are described, and information for many processes exists. However, the combination and interpretation of the large amount of biological data remain a big challenge, not only because data sets for metabolic paths are still incomplete. Moreover, they are often inconsistent, because they are coming from different experiments of various scales, regarding, for example, accuracy and/or significance. Here, theoretical modeling is powerful to formulate hypotheses for pathways and the dynamics of the metabolism, even if the biological data are incomplete. To develop reliable mathematical models they have to be proven for consistency. This is still a challenging task because many verification techniques fail already for middle-sized models. Consequently, new methods, like decomposition methods or reduction approaches, are developed to circumvent this problem. Methods: We present a new semi-quantitative mathematical model of the metabolism of Arabidopsis thaliana . We used the Petri net formalism to express the complex reaction system in a mathematically unique manner. To verify the model for correctness and consistency we applied concepts of network decomposition and network reduction such as transition invariants, common transition pairs, and invariant transition pairs. Results: We formulated the core metabolism of Arabidopsis thaliana based on recent knowledge from literature, including the Calvin cycle, glycolysis and citric acid cycle, glyoxylate cycle, urea cycle, sucrose synthesis, and the starch metabolism. By applying network decomposition and reduction techniques at steady-state conditions, we suggest a straightforward mathematical modeling process. We demonstrate that potential steady-state pathways exist, which provide the fixed carbon to nearly all parts of the network, especially to the citric acid cycle. There is a close cooperation of important metabolic pathways, e.g., the de novo synthesis of uridine-5-monophosphate, the γ-aminobutyric acid shunt, and the urea cycle. The presented approach extends the established methods for a feasible interpretation of biological network models, in particular of large and complex models.

Modeling the Metabolism of Arabidopsis thaliana: Application of Network Decomposition and Network Reduction in the Context of Petri Nets

PubMed Central

Koch, Ina; Nöthen, Joachim; Schleiff, Enrico

2017-01-01

Motivation: Arabidopsis thaliana is a well-established model system for the analysis of the basic physiological and metabolic pathways of plants. Nevertheless, the system is not yet fully understood, although many mechanisms are described, and information for many processes exists. However, the combination and interpretation of the large amount of biological data remain a big challenge, not only because data sets for metabolic paths are still incomplete. Moreover, they are often inconsistent, because they are coming from different experiments of various scales, regarding, for example, accuracy and/or significance. Here, theoretical modeling is powerful to formulate hypotheses for pathways and the dynamics of the metabolism, even if the biological data are incomplete. To develop reliable mathematical models they have to be proven for consistency. This is still a challenging task because many verification techniques fail already for middle-sized models. Consequently, new methods, like decomposition methods or reduction approaches, are developed to circumvent this problem. Methods: We present a new semi-quantitative mathematical model of the metabolism of Arabidopsis thaliana. We used the Petri net formalism to express the complex reaction system in a mathematically unique manner. To verify the model for correctness and consistency we applied concepts of network decomposition and network reduction such as transition invariants, common transition pairs, and invariant transition pairs. Results: We formulated the core metabolism of Arabidopsis thaliana based on recent knowledge from literature, including the Calvin cycle, glycolysis and citric acid cycle, glyoxylate cycle, urea cycle, sucrose synthesis, and the starch metabolism. By applying network decomposition and reduction techniques at steady-state conditions, we suggest a straightforward mathematical modeling process. We demonstrate that potential steady-state pathways exist, which provide the fixed carbon to nearly all parts of the network, especially to the citric acid cycle. There is a close cooperation of important metabolic pathways, e.g., the de novo synthesis of uridine-5-monophosphate, the γ-aminobutyric acid shunt, and the urea cycle. The presented approach extends the established methods for a feasible interpretation of biological network models, in particular of large and complex models. PMID:28713420

xEMD procedures as a data - Assisted filtering method

NASA Astrophysics Data System (ADS)

Machrowska, Anna; Jonak, Józef

2018-01-01

The article presents the possibility of using Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Improved Complete Ensemble Empirical Mode Decomposition (ICEEMD) algorithms for mechanical system condition monitoring applications. There were presented the results of the xEMD procedures used for vibration signals of system in different states of wear.

Phosphorus availability modulates the toxic effect of silver on aquatic fungi and leaf litter decomposition.

PubMed

Funck, J Arce; Clivot, H; Felten, V; Rousselle, P; Guérold, F; Danger, M

2013-11-15

The functioning of forested headwater streams is intimately linked to the decomposition of leaf litter by decomposers, mainly aquatic hyphomycetes, which enables the transfer of allochthonous carbon to higher trophic levels. Evaluation of this process is being increasingly used as an indicator of ecosystem health and ecological integrity. Yet, even though the individual impacts of contaminants and nutrient availability on decomposition have been well studied, the understanding of their combined effects remains limited. In the current study, we investigated whether the toxic effects of a reemerging contaminant, silver (Ag), on leaf litter decomposition could be partly overcome in situations where microorganisms were benefitting from high phosphorus (P) availability, the latter being a key chemical element that often limits detritus decomposition. We also investigated whether these interactive effects were mediated by changes in the structure of the aquatic hyphomycete community. To verify these hypotheses, leaf litter decomposition by a consortium of ten aquatic hyphomycete species was followed in a microcosm experiment combining five Ag contamination levels and three P concentrations. Indirect effects of Ag and P on the consumption of leaf litter by the detritivorous crustacean, Gammarus fossarum, were also evaluated. Ag significantly reduced decomposition but only at the highest concentration tested, independently of P level. By contrast, P and Ag interactively affected fungal biomass. Both P level and Ag concentrations shaped microbial communities without significantly affecting the overall species richness. Finally, the levels of P and Ag interacted significantly on G. fossarum feeding rates, high [Ag] reducing litter consumption and low P availability tending to intensify the feeding rate. Given the high level of contaminant needed to impair the decomposition process, it is unlikely that a direct effect of Ag on leaf litter decomposition could be observed in situ. However, subtle Ag effects in relation to nutrient levels in ecosystems could be expected. In particular, owing to higher consumption of low P leaf litter, shredding invertebrates could increase the ingestion of contaminated resources, which could, in turn, represent an important threat to headwater stream ecosystems. Copyright © 2013 Elsevier B.V. All rights reserved.

Numerical predictions and experiments for optimizing hidden corrosion detection in aircraft structures using Lamb modes.

PubMed

Terrien, N; Royer, D; Lepoutre, F; Déom, A

2007-06-01

To increase the sensitivity of Lamb waves to hidden corrosion in aircraft structures, a preliminary step is to understand the phenomena governing this interaction. A hybrid model combining a finite element approach and a modal decomposition method is used to investigate the interaction of Lamb modes with corrosion pits. The finite element mesh is used to describe the region surrounding the corrosion pits while the modal decomposition method permits to determine the waves reflected and transmitted by the damaged area. Simulations make easier the interpretation of some parts of the measured waveform corresponding to superposition of waves diffracted by the corroded area. Numerical results permit to extract significant information from the transmitted waveform and thus to optimize the signal processing for the detection of corrosion at an early stage. Now, we are able to detect corrosion pits down to 80-mum depth distributed randomly on a square centimeter of an aluminum plate. Moreover, thickness variations present on aircraft structures can be discriminated from a slightly corroded area. Finally, using this experimental setup, aircraft structures have been tested.

Transmission and reflection of terahertz plasmons in two-dimensional plasmonic devices

DOE PAGES

Sydoruk, Oleksiy; Choonee, Kaushal; Dyer, Gregory Conrad

2015-03-10

We found that plasmons in two-dimensional semiconductor devices will be reflected by discontinuities, notably, junctions between gated and non-gated electron channels. The transmitted and reflected plasmons can form spatially- and frequency-varying signals, and their understanding is important for the design of terahertz detectors, oscillators, and plasmonic crystals. Using mode decomposition, we studied terahertz plasmons incident on a junction between a gated and a nongated channel. The plasmon reflection and transmission coefficients were found numerically and analytically and studied between 0.3 and 1 THz for a range of electron densities. At higher frequencies, we could describe the plasmons by a simplifiedmore » model of channels in homogeneous dielectrics, for which the analytical approximations were accurate. At low frequencies, however, the full geometry and mode spectrum had to be taken into account. Moreover, the results agreed with simulations by the finite-element method. As a result, mode decomposition thus proved to be a powerful method for plasmonic devices, combining the rigor of complete solutions of Maxwell's equations with the convenience of analytical expressions.« less

Optimal cost design of water distribution networks using a decomposition approach

NASA Astrophysics Data System (ADS)

Lee, Ho Min; Yoo, Do Guen; Sadollah, Ali; Kim, Joong Hoon

2016-12-01

Water distribution network decomposition, which is an engineering approach, is adopted to increase the efficiency of obtaining the optimal cost design of a water distribution network using an optimization algorithm. This study applied the source tracing tool in EPANET, which is a hydraulic and water quality analysis model, to the decomposition of a network to improve the efficiency of the optimal design process. The proposed approach was tested by carrying out the optimal cost design of two water distribution networks, and the results were compared with other optimal cost designs derived from previously proposed optimization algorithms. The proposed decomposition approach using the source tracing technique enables the efficient decomposition of an actual large-scale network, and the results can be combined with the optimal cost design process using an optimization algorithm. This proves that the final design in this study is better than those obtained with other previously proposed optimization algorithms.

Efficient material decomposition method for dual-energy X-ray cargo inspection system

NASA Astrophysics Data System (ADS)

Lee, Donghyeon; Lee, Jiseoc; Min, Jonghwan; Lee, Byungcheol; Lee, Byeongno; Oh, Kyungmin; Kim, Jaehyun; Cho, Seungryong

2018-03-01

Dual-energy X-ray inspection systems are widely used today for it provides X-ray attenuation contrast of the imaged object and also its material information. Material decomposition capability allows a higher detection sensitivity of potential targets including purposely loaded impurities in agricultural product inspections and threats in security scans for example. Dual-energy X-ray transmission data can be transformed into two basis material thickness data, and its transformation accuracy heavily relies on a calibration of material decomposition process. The calibration process in general can be laborious and time consuming. Moreover, a conventional calibration method is often challenged by the nonuniform spectral characteristics of the X-ray beam in the entire field-of-view (FOV). In this work, we developed an efficient material decomposition calibration process for a linear accelerator (LINAC) based high-energy X-ray cargo inspection system. We also proposed a multi-spot calibration method to improve the decomposition performance throughout the entire FOV. Experimental validation of the proposed method has been demonstrated by use of a cargo inspection system that supports 6 MV and 9 MV dual-energy imaging.

Assessing the Relative Effects of Geographic Location and Soil Type on Microbial Communities Associated with Straw Decomposition

PubMed Central

Wang, Xiaoyue; Wang, Feng; Jiang, Yuji

2013-01-01

Decomposition of plant residues is largely mediated by soil-dwelling microorganisms whose activities are influenced by both climate conditions and properties of the soil. However, a comprehensive understanding of their relative importance remains elusive, mainly because traditional methods, such as soil incubation and environmental surveys, have a limited ability to differentiate between the combined effects of climate and soil. Here, we performed a large-scale reciprocal soil transplantation experiment, whereby microbial communities associated with straw decomposition were examined in three initially identical soils placed in parallel in three climate regions of China (red soil, Chao soil, and black soil, located in midsubtropical, warm-temperate, and cold-temperate zones). Maize straws buried in mesh bags were sampled at 0.5, 1, and 2 years after the burial and subjected to chemical, physical, and microbiological analyses, e.g., phospholipid fatty acid analysis for microbial abundance, community-level physiological profiling, and 16S rRNA gene denaturing gradient gel electrophoresis, respectively, for functional and phylogenic diversity. Results of aggregated boosted tree analysis show that location rather soil is the primary determining factor for the rate of straw decomposition and structures of the associated microbial communities. Principal component analysis indicates that the straw communities are primarily grouped by location at any of the three time points. In contrast, microbial communities in bulk soil remained closely related to one another for each soil. Together, our data suggest that climate (specifically, geographic location) has stronger effects than soil on straw decomposition; moreover, the successive process of microbial communities in soils is slower than those in straw residues in response to climate changes. PMID:23524671

Thermal degradation of sucralose: a combination of analytical methods to determine stability and chlorinated byproducts

NASA Astrophysics Data System (ADS)

de Oliveira, Diogo N.; de Menezes, Maico; Catharino, Rodrigo R.

2015-04-01

In the late years, much attention has been brought to the scientific community regarding the safety of sucralose and its industrial applications. Although it is the most used artificial sweetener in foods and pharmaceuticals, many questions still arise on its potential to form chlorinated byproducts in high temperatures, as demonstrated by several recent studies. In the present contribution, we use a combination of differential scanning calorimetry and thermogravimetric analysis coupled with infrared spectroscopy (DSC/TGA/IR), Hot-stage microscopy (HSM) and high-resolution mass spectrometry (HRMS) on samples submitted to water bath at mild temperatures to evaluate a broad spectrum of hazardous compounds formed in the degradation of this product. TGA/IR has revealed that there is effective decomposition in form of CO2 along with the formation of hydrogen chloride and other minor compounds. HSM results have provided accurate information, where the melting of the crystals was observed, followed by decomposition. Chlorinated derivatives, including polychlorinated aromatic hydrocarbons (PCAHs) were also confirmed by HRMS. These findings not only corroborate the suspected instability of sucralose to high temperatures, but also indicate that even exposed to mild conditions the formation of hazardous polychlorinated compounds is observed.

Closed-form solution of decomposable stochastic models

NASA Technical Reports Server (NTRS)

Sjogren, Jon A.

1990-01-01

Markov and semi-Markov processes are increasingly being used in the modeling of complex reconfigurable systems (fault tolerant computers). The estimation of the reliability (or some measure of performance) of the system reduces to solving the process for its state probabilities. Such a model may exhibit numerous states and complicated transition distributions, contributing to an expensive and numerically delicate solution procedure. Thus, when a system exhibits a decomposition property, either structurally (autonomous subsystems), or behaviorally (component failure versus reconfiguration), it is desirable to exploit this decomposition in the reliability calculation. In interesting cases there can be failure states which arise from non-failure states of the subsystems. Equations are presented which allow the computation of failure probabilities of the total (combined) model without requiring a complete solution of the combined model. This material is presented within the context of closed-form functional representation of probabilities as utilized in the Symbolic Hierarchical Automated Reliability and Performance Evaluator (SHARPE) tool. The techniques adopted enable one to compute such probability functions for a much wider class of systems at a reduced computational cost. Several examples show how the method is used, especially in enhancing the versatility of the SHARPE tool.

Thermal degradation of sucralose: a combination of analytical methods to determine stability and chlorinated byproducts.

PubMed

de Oliveira, Diogo N; de Menezes, Maico; Catharino, Rodrigo R

2015-04-15

In the late years, much attention has been brought to the scientific community regarding the safety of sucralose and its industrial applications. Although it is the most used artificial sweetener in foods and pharmaceuticals, many questions still arise on its potential to form chlorinated byproducts in high temperatures, as demonstrated by several recent studies. In the present contribution, we use a combination of differential scanning calorimetry and thermogravimetric analysis coupled with infrared spectroscopy (DSC/TGA/IR), Hot-stage microscopy (HSM) and high-resolution mass spectrometry (HRMS) on samples submitted to water bath at mild temperatures to evaluate a broad spectrum of hazardous compounds formed in the degradation of this product. TGA/IR has revealed that there is effective decomposition in form of CO2 along with the formation of hydrogen chloride and other minor compounds. HSM results have provided accurate information, where the melting of the crystals was observed, followed by decomposition. Chlorinated derivatives, including polychlorinated aromatic hydrocarbons (PCAHs) were also confirmed by HRMS. These findings not only corroborate the suspected instability of sucralose to high temperatures, but also indicate that even exposed to mild conditions the formation of hazardous polychlorinated compounds is observed.

Thermal degradation of sucralose: a combination of analytical methods to determine stability and chlorinated byproducts

PubMed Central

de Oliveira, Diogo N.; de Menezes, Maico; Catharino, Rodrigo R.

2015-01-01

In the late years, much attention has been brought to the scientific community regarding the safety of sucralose and its industrial applications. Although it is the most used artificial sweetener in foods and pharmaceuticals, many questions still arise on its potential to form chlorinated byproducts in high temperatures, as demonstrated by several recent studies. In the present contribution, we use a combination of differential scanning calorimetry and thermogravimetric analysis coupled with infrared spectroscopy (DSC/TGA/IR), Hot-stage microscopy (HSM) and high-resolution mass spectrometry (HRMS) on samples submitted to water bath at mild temperatures to evaluate a broad spectrum of hazardous compounds formed in the degradation of this product. TGA/IR has revealed that there is effective decomposition in form of CO2 along with the formation of hydrogen chloride and other minor compounds. HSM results have provided accurate information, where the melting of the crystals was observed, followed by decomposition. Chlorinated derivatives, including polychlorinated aromatic hydrocarbons (PCAHs) were also confirmed by HRMS. These findings not only corroborate the suspected instability of sucralose to high temperatures, but also indicate that even exposed to mild conditions the formation of hazardous polychlorinated compounds is observed. PMID:25873245

New spectrophotometric assay for pilocarpine.

PubMed

El-Masry, S; Soliman, R

1980-07-01

A quick method for the determination of pilocarpine in eye drops in the presence of decomposition products is described. The method involves complexation of the alkaloid with bromocresol purple at pH 6. After treatment with 0.1N NaOH, the liberated dye is measured at 580 nm. The method has a relative standard deviation of 1.99%, and has been successfully applied to the analysis of 2 batches of pilocarpine eye drops. The recommended method was also used to monitor the stability of a pilocarpine nitrate solution in 0.05N NaOH at 65 degrees C. The BPC method failed to detect any significant decomposition after 2 h incubation, but the recommended method revealed 87.5% decomposition.

Toward the Elucidation of the Competing Role of Evaporation and Thermal Decomposition in Ionic Liquids: A Multitechnique Study of the Vaporization Behavior of 1-Butyl-3-methylimidazolium Hexafluorophosphate under Effusion Conditions.

PubMed

Volpe, V; Brunetti, B; Gigli, G; Lapi, A; Vecchio Ciprioti, S; Ciccioli, A

2017-11-16

The evaporation/decomposition behavior of the imidazolium ionic liquid 1-butyl-3-methylimidazolium hexafluorophosphate (BMImPF 6 ) was investigated in the overall temperature range 425-551 K by means of the molecular-effusion-based techniques Knudsen effusion mass loss (KEML) and Knudsen effusion mass spectrometry (KEMS), using effusion orifices of different size (from 0.2 to 3 mm in diameter). Specific effusion fluxes measured by KEML were found to depend markedly on the orifice size, suggesting the occurrence of a kinetically delayed evaporation/decomposition process. KEMS experiments revealed that other species are present in the vapor phase besides the intact ion pair BMImPF 6 (g) produced by the simple evaporation BMImPF 6 (l) = BMImPF 6 (g), with relative abundances depending on the orifice size-the larger the orifice, the larger the contribution of the BMImPF 6 (g) species. By combining KEML and KEMS results, the conclusion is drawn that in the investigated temperature range, when small effusion orifices are used, a significant part of the mass loss/volatility of BMImPF 6 is due to molecular products formed by decomposition/dissociation processes rather than to evaporated intact ion pairs. Additional experiments performed by nonisothermal thermogravimetry-differential thermal analysis (TG-DTA) further support the evidence of simultaneous evaporation/decomposition, although the conventional decomposition temperature derived from TG curves is much higher than the temperatures covered in effusion experiments. Partial pressures of the BMImPF 6 (g) species were derived from KEMS spectra and analyzed by second- and third-law methods giving a value of Δ evap H 298K ° = 145.3 ± 2.9 kJ·mol -1 for the standard evaporation enthalpy of BMImPF 6 . A comparison is done with the behavior of the 1-butyl-3-methylimidazolium bis(trifluoromethyl)sulfonylimide (BMImNTf 2 ) ionic liquid.

GENESIS: a hybrid-parallel and multi-scale molecular dynamics simulator with enhanced sampling algorithms for biomolecular and cellular simulations.

PubMed

Jung, Jaewoon; Mori, Takaharu; Kobayashi, Chigusa; Matsunaga, Yasuhiro; Yoda, Takao; Feig, Michael; Sugita, Yuji

2015-07-01

GENESIS (Generalized-Ensemble Simulation System) is a new software package for molecular dynamics (MD) simulations of macromolecules. It has two MD simulators, called ATDYN and SPDYN. ATDYN is parallelized based on an atomic decomposition algorithm for the simulations of all-atom force-field models as well as coarse-grained Go-like models. SPDYN is highly parallelized based on a domain decomposition scheme, allowing large-scale MD simulations on supercomputers. Hybrid schemes combining OpenMP and MPI are used in both simulators to target modern multicore computer architectures. Key advantages of GENESIS are (1) the highly parallel performance of SPDYN for very large biological systems consisting of more than one million atoms and (2) the availability of various REMD algorithms (T-REMD, REUS, multi-dimensional REMD for both all-atom and Go-like models under the NVT, NPT, NPAT, and NPγT ensembles). The former is achieved by a combination of the midpoint cell method and the efficient three-dimensional Fast Fourier Transform algorithm, where the domain decomposition space is shared in real-space and reciprocal-space calculations. Other features in SPDYN, such as avoiding concurrent memory access, reducing communication times, and usage of parallel input/output files, also contribute to the performance. We show the REMD simulation results of a mixed (POPC/DMPC) lipid bilayer as a real application using GENESIS. GENESIS is released as free software under the GPLv2 licence and can be easily modified for the development of new algorithms and molecular models. WIREs Comput Mol Sci 2015, 5:310-323. doi: 10.1002/wcms.1220.

Photoacoustic imaging optimization with raw signal deconvolution and empirical mode decomposition

NASA Astrophysics Data System (ADS)

Guo, Chengwen; Wang, Jing; Qin, Yu; Zhan, Hongchen; Yuan, Jie; Cheng, Qian; Wang, Xueding

2018-02-01

Photoacoustic (PA) signal of an ideal optical absorb particle is a single N-shape wave. PA signals of a complicated biological tissue can be considered as the combination of individual N-shape waves. However, the N-shape wave basis not only complicates the subsequent work, but also results in aliasing between adjacent micro-structures, which deteriorates the quality of the final PA images. In this paper, we propose a method to improve PA image quality through signal processing method directly working on raw signals, which including deconvolution and empirical mode decomposition (EMD). During the deconvolution procedure, the raw PA signals are de-convolved with a system dependent point spread function (PSF) which is measured in advance. Then, EMD is adopted to adaptively re-shape the PA signals with two constraints, positive polarity and spectrum consistence. With our proposed method, the built PA images can yield more detail structural information. Micro-structures are clearly separated and revealed. To validate the effectiveness of this method, we present numerical simulations and phantom studies consist of a densely distributed point sources model and a blood vessel model. In the future, our study might hold the potential for clinical PA imaging as it can help to distinguish micro-structures from the optimized images and even measure the size of objects from deconvolved signals.

Improving surface EMG burst detection in infrahyoid muscles during swallowing using digital filters and discrete wavelet analysis.

PubMed

Restrepo-Agudelo, Sebastian; Roldan-Vasco, Sebastian; Ramirez-Arbelaez, Lina; Cadavid-Arboleda, Santiago; Perez-Giraldo, Estefania; Orozco-Duque, Andres

2017-08-01

The visual inspection is a widely used method for evaluating the surface electromyographic signal (sEMG) during deglutition, a process highly dependent of the examiners expertise. It is desirable to have a less subjective and automated technique to improve the onset detection in swallowing related muscles, which have a low signal-to-noise ratio. In this work, we acquired sEMG measured in infrahyoid muscles with high baseline noise of ten healthy adults during water swallowing tasks. Two methods were applied to find the combination of cutoff frequencies that achieve the most accurate onset detection: discrete wavelet decomposition based method and fixed steps variations of low and high cutoff frequencies of a digital bandpass filter. Teager-Kaiser Energy operator, root mean square and simple threshold method were applied for both techniques. Results show a narrowing of the effective bandwidth vs. the literature recommended parameters for sEMG acquisition. Both level 3 decomposition with mother wavelet db4 and bandpass filter with cutoff frequencies between 130 and 180Hz were optimal for onset detection in infrahyoid muscles. The proposed methodologies recognized the onset time with predictive power above 0.95, that is similar to previous findings but in larger and more superficial muscles in limbs. Copyright © 2017 Elsevier Ltd. All rights reserved.

Effect of Isomorphous Substitution on the Thermal Decomposition Mechanism of Hydrotalcites

PubMed Central

Crosby, Sergio; Tran, Doanh; Cocke, David; Duraia, El-Shazly M.; Beall, Gary W.

2014-01-01

Hydrotalcites have many important applications in catalysis, wastewater treatment, gene delivery and polymer stabilization, all depending on preparation history and treatment scenarios. In catalysis and polymer stabilization, thermal decomposition is of great importance. Hydrotalcites form easily with atmospheric carbon dioxide and often interfere with the study of other anion containing systems, particularly if formed at room temperature. The dehydroxylation and decomposition of carbonate occurs simultaneously, making it difficult to distinguish the dehydroxylation mechanisms directly. To date, the majority of work on understanding the decomposition mechanism has utilized hydrotalcite precipitated at room temperature. In this study, evolved gas analysis combined with thermal analysis has been used to show that CO2 contamination is problematic in materials being formed at RT that are poorly crystalline. This has led to some dispute as to the nature of the dehydroxylation mechanism. In this paper, data for the thermal decomposition of the chloride form of hydrotalcite are reported. In addition, carbonate-free hydrotalcites have been synthesized with different charge densities and at different growth temperatures. This combination of parameters has allowed a better understanding of the mechanism of dehydroxylation and the role that isomorphous substitution plays in these mechanisms to be delineated. In addition, the effect of anion type on thermal stability is also reported. A stepwise dehydroxylation model is proposed that is mediated by the level of aluminum substitution. PMID:28788231

Using Matrix and Tensor Factorizations for the Single-Trial Analysis of Population Spike Trains.

PubMed

Onken, Arno; Liu, Jian K; Karunasekara, P P Chamanthi R; Delis, Ioannis; Gollisch, Tim; Panzeri, Stefano

2016-11-01

Advances in neuronal recording techniques are leading to ever larger numbers of simultaneously monitored neurons. This poses the important analytical challenge of how to capture compactly all sensory information that neural population codes carry in their spatial dimension (differences in stimulus tuning across neurons at different locations), in their temporal dimension (temporal neural response variations), or in their combination (temporally coordinated neural population firing). Here we investigate the utility of tensor factorizations of population spike trains along space and time. These factorizations decompose a dataset of single-trial population spike trains into spatial firing patterns (combinations of neurons firing together), temporal firing patterns (temporal activation of these groups of neurons) and trial-dependent activation coefficients (strength of recruitment of such neural patterns on each trial). We validated various factorization methods on simulated data and on populations of ganglion cells simultaneously recorded in the salamander retina. We found that single-trial tensor space-by-time decompositions provided low-dimensional data-robust representations of spike trains that capture efficiently both their spatial and temporal information about sensory stimuli. Tensor decompositions with orthogonality constraints were the most efficient in extracting sensory information, whereas non-negative tensor decompositions worked well even on non-independent and overlapping spike patterns, and retrieved informative firing patterns expressed by the same population in response to novel stimuli. Our method showed that populations of retinal ganglion cells carried information in their spike timing on the ten-milliseconds-scale about spatial details of natural images. This information could not be recovered from the spike counts of these cells. First-spike latencies carried the majority of information provided by the whole spike train about fine-scale image features, and supplied almost as much information about coarse natural image features as firing rates. Together, these results highlight the importance of spike timing, and particularly of first-spike latencies, in retinal coding.

Using Matrix and Tensor Factorizations for the Single-Trial Analysis of Population Spike Trains

PubMed Central

Onken, Arno; Liu, Jian K.; Karunasekara, P. P. Chamanthi R.; Delis, Ioannis; Gollisch, Tim; Panzeri, Stefano

2016-01-01

Advances in neuronal recording techniques are leading to ever larger numbers of simultaneously monitored neurons. This poses the important analytical challenge of how to capture compactly all sensory information that neural population codes carry in their spatial dimension (differences in stimulus tuning across neurons at different locations), in their temporal dimension (temporal neural response variations), or in their combination (temporally coordinated neural population firing). Here we investigate the utility of tensor factorizations of population spike trains along space and time. These factorizations decompose a dataset of single-trial population spike trains into spatial firing patterns (combinations of neurons firing together), temporal firing patterns (temporal activation of these groups of neurons) and trial-dependent activation coefficients (strength of recruitment of such neural patterns on each trial). We validated various factorization methods on simulated data and on populations of ganglion cells simultaneously recorded in the salamander retina. We found that single-trial tensor space-by-time decompositions provided low-dimensional data-robust representations of spike trains that capture efficiently both their spatial and temporal information about sensory stimuli. Tensor decompositions with orthogonality constraints were the most efficient in extracting sensory information, whereas non-negative tensor decompositions worked well even on non-independent and overlapping spike patterns, and retrieved informative firing patterns expressed by the same population in response to novel stimuli. Our method showed that populations of retinal ganglion cells carried information in their spike timing on the ten-milliseconds-scale about spatial details of natural images. This information could not be recovered from the spike counts of these cells. First-spike latencies carried the majority of information provided by the whole spike train about fine-scale image features, and supplied almost as much information about coarse natural image features as firing rates. Together, these results highlight the importance of spike timing, and particularly of first-spike latencies, in retinal coding. PMID:27814363

SU-F-I-41: Calibration-Free Material Decomposition for Dual-Energy CT

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

Zhao, W; Xing, L; Zhang, Q

2016-06-15

Purpose: To eliminate tedious phantom calibration or manually region of interest (ROI) selection as required in dual-energy CT material decomposition, we establish a new projection-domain material decomposition framework with incorporation of energy spectrum. Methods: Similar to the case of dual-energy CT, the integral of the basis material image in our model is expressed as a linear combination of basis functions, which are the polynomials of high- and low-energy raw projection data. To yield the unknown coefficients of the linear combination, the proposed algorithm minimizes the quadratic error between the high- and low-energy raw projection data and the projection calculated usingmore » material images. We evaluate the algorithm with an iodine concentration numerical phantom at different dose and iodine concentration levels. The x-ray energy spectra of the high and low energy are estimated using an indirect transmission method. The derived monochromatic images are compared with the high- and low-energy CT images to demonstrate beam hardening artifacts reduction. Quantitative results were measured and compared to the true values. Results: The differences between the true density value used for simulation and that were obtained from the monochromatic images, are 1.8%, 1.3%, 2.3%, and 2.9% for the dose levels from standard dose to 1/8 dose, and are 0.4%, 0.7%, 1.5%, and 1.8% for the four iodine concentration levels from 6 mg/mL to 24 mg/mL. For all of the cases, beam hardening artifacts, especially streaks shown between dense inserts, are almost completely removed in the monochromatic images. Conclusion: The proposed algorithm provides an effective way to yield material images and artifacts-free monochromatic images at different dose levels without the need for phantom calibration or ROI selection. Furthermore, the approach also yields accurate results when the concentration of the iodine concentrate insert is very low, suggesting the algorithm is robust with respect to the low-contrast scenario.« less

A projection method for low speed flows

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

Colella, P.; Pao, K.

The authors propose a decomposition applicable to low speed, inviscid flows of all Mach numbers less than 1. By using the Hodge decomposition, they may write the velocity field as the sum of a divergence-free vector field and a gradient of a scalar function. Evolution equations for these parts are presented. A numerical procedure based on this decomposition is designed, using projection methods for solving the incompressible variables and a backward-Euler method for solving the potential variables. Numerical experiments are included to illustrate various aspects of the algorithm.

Shrub encroachment in Arctic tundra: Betula nana effects on above- and belowground litter decomposition.

PubMed

McLaren, Jennie R; Buckeridge, Kate M; van de Weg, Martine J; Shaver, Gaius R; Schimel, Joshua P; Gough, Laura

2017-05-01

Rapid arctic vegetation change as a result of global warming includes an increase in the cover and biomass of deciduous shrubs. Increases in shrub abundance will result in a proportional increase of shrub litter in the litter community, potentially affecting carbon turnover rates in arctic ecosystems. We investigated the effects of leaf and root litter of a deciduous shrub, Betula nana, on decomposition, by examining species-specific decomposition patterns, as well as effects of Betula litter on the decomposition of other species. We conducted a 2-yr decomposition experiment in moist acidic tundra in northern Alaska, where we decomposed three tundra species (Vaccinium vitis-idaea, Rhododendron palustre, and Eriophorum vaginatum) alone and in combination with Betula litter. Decomposition patterns for leaf and root litter were determined using three different measures of decomposition (mass loss, respiration, extracellular enzyme activity). We report faster decomposition of Betula leaf litter compared to other species, with support for species differences coming from all three measures of decomposition. Mixing effects were less consistent among the measures, with negative mixing effects shown only for mass loss. In contrast, there were few species differences or mixing effects for root decomposition. Overall, we attribute longer-term litter mass loss patterns to patterns created by early decomposition processes in the first winter. We note numerous differences for species patterns between leaf and root decomposition, indicating that conclusions from leaf litter experiments should not be extrapolated to below-ground decomposition. The high decomposition rates of Betula leaf litter aboveground, and relatively similar decomposition rates of multiple species below, suggest a potential for increases in turnover in the fast-decomposing carbon pool of leaves and fine roots as the dominance of deciduous shrubs in the Arctic increases, but this outcome may be tempered by negative litter mixing effects during the early stages of encroachment. © 2017 by the Ecological Society of America.

Analyzing nonstationary financial time series via hilbert-huang transform (HHT)

NASA Technical Reports Server (NTRS)

Huang, Norden E. (Inventor)

2008-01-01

An apparatus, computer program product and method of analyzing non-stationary time varying phenomena. A representation of a non-stationary time varying phenomenon is recursively sifted using Empirical Mode Decomposition (EMD) to extract intrinsic mode functions (IMFs). The representation is filtered to extract intrinsic trends by combining a number of IMFs. The intrinsic trend is inherent in the data and identifies an IMF indicating the variability of the phenomena. The trend also may be used to detrend the data.

Reassessment of the NH4 NO3 thermal decomposition technique for calibration of the N2 O isotopic composition.

PubMed

Mohn, Joachim; Gutjahr, Wilhelm; Toyoda, Sakae; Harris, Eliza; Ibraim, Erkan; Geilmann, Heike; Schleppi, Patrick; Kuhn, Thomas; Lehmann, Moritz F; Decock, Charlotte; Werner, Roland A; Yoshida, Naohiro; Brand, Willi A

2016-09-08

In the last few years, the study of N 2 O site-specific nitrogen isotope composition has been established as a powerful technique to disentangle N 2 O emission pathways. This trend has been accelerated by significant analytical progress in the field of isotope-ratio mass-spectrometry (IRMS) and more recently quantum cascade laser absorption spectroscopy (QCLAS). Methods The ammonium nitrate (NH 4 NO 3 ) decomposition technique provides a strategy to scale the 15 N site-specific (SP ≡ δ 15 N α - δ 15 N β ) and bulk (δ 15 N bulk  = (δ 15 N α  + δ 15 N β )/2) isotopic composition of N 2 O against the international standard for the 15 N/ 14 N isotope ratio (AIR-N 2 ). Within the current project 15 N fractionation effects during thermal decomposition of NH 4 NO 3 on the N 2 O site preference were studied using static and dynamic decomposition techniques. The validity of the NH 4 NO 3 decomposition technique to link NH 4 + and NO 3 - moiety-specific δ 15 N analysis by IRMS to the site-specific nitrogen isotopic composition of N 2 O was confirmed. However, the accuracy of this approach for the calibration of δ 15 N α and δ 15 N β values was found to be limited by non-quantitative NH 4 NO 3 decomposition in combination with substantially different isotope enrichment factors for the conversion of the NO 3 - or NH 4 + nitrogen atom into the α or β position of the N 2 O molecule. The study reveals that the completeness and reproducibility of the NH 4 NO 3 decomposition reaction currently confine the anchoring of N 2 O site-specific isotopic composition to the international isotope ratio scale AIR-N 2 . The authors suggest establishing a set of N 2 O isotope reference materials with appropriate site-specific isotopic composition, as community standards, to improve inter-laboratory compatibility. This article is protected by copyright. All rights reserved.

Decomposition analysis of water footprint changes in a water-limited river basin: a case study of the Haihe River basin, China

NASA Astrophysics Data System (ADS)

Zhi, Y.; Yang, Z. F.; Yin, X. A.

2014-05-01

Decomposition analysis of water footprint (WF) changes, or assessing the changes in WF and identifying the contributions of factors leading to the changes, is important to water resource management. Instead of focusing on WF from the perspective of administrative regions, we built a framework in which the input-output (IO) model, the structural decomposition analysis (SDA) model and the generating regional IO tables (GRIT) method are combined to implement decomposition analysis for WF in a river basin. This framework is illustrated in the WF in Haihe River basin (HRB) from 2002 to 2007, which is a typical water-limited river basin. It shows that the total WF in the HRB increased from 4.3 × 1010 m3 in 2002 to 5.6 × 1010 m3 in 2007, and the agriculture sector makes the dominant contribution to the increase. Both the WF of domestic products (internal) and the WF of imported products (external) increased, and the proportion of external WF rose from 29.1 to 34.4%. The technological effect was the dominant contributor to offsetting the increase of WF. However, the growth of WF caused by the economic structural effect and the scale effect was greater, so the total WF increased. This study provides insights about water challenges in the HRB and proposes possible strategies for the future, and serves as a reference for WF management and policy-making in other water-limited river basins.

Modified complementary ensemble empirical mode decomposition and intrinsic mode functions evaluation index for high-speed train gearbox fault diagnosis

NASA Astrophysics Data System (ADS)

Chen, Dongyue; Lin, Jianhui; Li, Yanping

2018-06-01

Complementary ensemble empirical mode decomposition (CEEMD) has been developed for the mode-mixing problem in Empirical Mode Decomposition (EMD) method. Compared to the ensemble empirical mode decomposition (EEMD), the CEEMD method reduces residue noise in the signal reconstruction. Both CEEMD and EEMD need enough ensemble number to reduce the residue noise, and hence it would be too much computation cost. Moreover, the selection of intrinsic mode functions (IMFs) for further analysis usually depends on experience. A modified CEEMD method and IMFs evaluation index are proposed with the aim of reducing the computational cost and select IMFs automatically. A simulated signal and in-service high-speed train gearbox vibration signals are employed to validate the proposed method in this paper. The results demonstrate that the modified CEEMD can decompose the signal efficiently with less computation cost, and the IMFs evaluation index can select the meaningful IMFs automatically.

A new time-frequency method for identification and classification of ball bearing faults

NASA Astrophysics Data System (ADS)

Attoui, Issam; Fergani, Nadir; Boutasseta, Nadir; Oudjani, Brahim; Deliou, Adel

2017-06-01

In order to fault diagnosis of ball bearing that is one of the most critical components of rotating machinery, this paper presents a time-frequency procedure incorporating a new feature extraction step that combines the classical wavelet packet decomposition energy distribution technique and a new feature extraction technique based on the selection of the most impulsive frequency bands. In the proposed procedure, firstly, as a pre-processing step, the most impulsive frequency bands are selected at different bearing conditions using a combination between Fast-Fourier-Transform FFT and Short-Frequency Energy SFE algorithms. Secondly, once the most impulsive frequency bands are selected, the measured machinery vibration signals are decomposed into different frequency sub-bands by using discrete Wavelet Packet Decomposition WPD technique to maximize the detection of their frequency contents and subsequently the most useful sub-bands are represented in the time-frequency domain by using Short Time Fourier transform STFT algorithm for knowing exactly what the frequency components presented in those frequency sub-bands are. Once the proposed feature vector is obtained, three feature dimensionality reduction techniques are employed using Linear Discriminant Analysis LDA, a feedback wrapper method and Locality Sensitive Discriminant Analysis LSDA. Lastly, the Adaptive Neuro-Fuzzy Inference System ANFIS algorithm is used for instantaneous identification and classification of bearing faults. In order to evaluate the performances of the proposed method, different testing data set to the trained ANFIS model by using different conditions of healthy and faulty bearings under various load levels, fault severities and rotating speed. The conclusion resulting from this paper is highlighted by experimental results which prove that the proposed method can serve as an intelligent bearing fault diagnosis system.

Factorization-based texture segmentation

DOE PAGES

Yuan, Jiangye; Wang, Deliang; Cheriyadat, Anil M.

2015-06-17

This study introduces a factorization-based approach that efficiently segments textured images. We use local spectral histograms as features, and construct an M × N feature matrix using M-dimensional feature vectors in an N-pixel image. Based on the observation that each feature can be approximated by a linear combination of several representative features, we factor the feature matrix into two matrices-one consisting of the representative features and the other containing the weights of representative features at each pixel used for linear combination. The factorization method is based on singular value decomposition and nonnegative matrix factorization. The method uses local spectral histogramsmore » to discriminate region appearances in a computationally efficient way and at the same time accurately localizes region boundaries. Finally, the experiments conducted on public segmentation data sets show the promise of this simple yet powerful approach.« less

Adaptive Fourier decomposition based R-peak detection for noisy ECG Signals.

PubMed

Ze Wang; Chi Man Wong; Feng Wan

2017-07-01

An adaptive Fourier decomposition (AFD) based R-peak detection method is proposed for noisy ECG signals. Although lots of QRS detection methods have been proposed in literature, most detection methods require high signal quality. The proposed method extracts the R waves from the energy domain using the AFD and determines the R-peak locations based on the key decomposition parameters, achieving the denoising and the R-peak detection at the same time. Validated by clinical ECG signals in the MIT-BIH Arrhythmia Database, the proposed method shows better performance than the Pan-Tompkin (PT) algorithm in both situations of a native PT and the PT with a denoising process.

The reduced space Sequential Quadratic Programming (SQP) method for calculating the worst resonance response of nonlinear systems

NASA Astrophysics Data System (ADS)

Liao, Haitao; Wu, Wenwang; Fang, Daining

2018-07-01

A coupled approach combining the reduced space Sequential Quadratic Programming (SQP) method with the harmonic balance condensation technique for finding the worst resonance response is developed. The nonlinear equality constraints of the optimization problem are imposed on the condensed harmonic balance equations. Making use of the null space decomposition technique, the original optimization formulation in the full space is mathematically simplified, and solved in the reduced space by means of the reduced SQP method. The transformation matrix that maps the full space to the null space of the constrained optimization problem is constructed via the coordinate basis scheme. The removal of the nonlinear equality constraints is accomplished, resulting in a simple optimization problem subject to bound constraints. Moreover, second order correction technique is introduced to overcome Maratos effect. The combination application of the reduced SQP method and condensation technique permits a large reduction of the computational cost. Finally, the effectiveness and applicability of the proposed methodology is demonstrated by two numerical examples.

The Multiscale Robin Coupled Method for flows in porous media

NASA Astrophysics Data System (ADS)

Guiraldello, Rafael T.; Ausas, Roberto F.; Sousa, Fabricio S.; Pereira, Felipe; Buscaglia, Gustavo C.

2018-02-01

A multiscale mixed method aiming at the accurate approximation of velocity and pressure fields in heterogeneous porous media is proposed. The procedure is based on a new domain decomposition method in which the local problems are subject to Robin boundary conditions. The domain decomposition procedure is defined in terms of two independent spaces on the skeleton of the decomposition, corresponding to interface pressures and fluxes, that can be chosen with great flexibility to accommodate local features of the underlying permeability fields. The well-posedness of the new domain decomposition procedure is established and its connection with the method of Douglas et al. (1993) [12], is identified, also allowing us to reinterpret the known procedure as an optimized Schwarz (or Two-Lagrange-Multiplier) method. The multiscale property of the new domain decomposition method is indicated, and its relation with the Multiscale Mortar Mixed Finite Element Method (MMMFEM) and the Multiscale Hybrid-Mixed (MHM) Finite Element Method is discussed. Numerical simulations are presented aiming at illustrating several features of the new method. Initially we illustrate the possibility of switching from MMMFEM to MHM by suitably varying the Robin condition parameter in the new multiscale method. Then we turn our attention to realistic flows in high-contrast, channelized porous formations. We show that for a range of values of the Robin condition parameter our method provides better approximations for pressure and velocity than those computed with either the MMMFEM and the MHM. This is an indication that our method has the potential to produce more accurate velocity fields in the presence of rough, realistic permeability fields of petroleum reservoirs.

A multiscale decomposition approach to detect abnormal vasculature in the optic disc.

PubMed

Agurto, Carla; Yu, Honggang; Murray, Victor; Pattichis, Marios S; Nemeth, Sheila; Barriga, Simon; Soliz, Peter

2015-07-01

This paper presents a multiscale method to detect neovascularization in the optic disc (NVD) using fundus images. Our method is applied to a manually selected region of interest (ROI) containing the optic disc. All the vessels in the ROI are segmented by adaptively combining contrast enhancement methods with a vessel segmentation technique. Textural features extracted using multiscale amplitude-modulation frequency-modulation, morphological granulometry, and fractal dimension are used. A linear SVM is used to perform the classification, which is tested by means of 10-fold cross-validation. The performance is evaluated using 300 images achieving an AUC of 0.93 with maximum accuracy of 88%. Copyright © 2015 Elsevier Ltd. All rights reserved.

Simple method based on intensity measurements for characterization of aberrations from micro-optical components.

PubMed

Perrin, Stephane; Baranski, Maciej; Froehly, Luc; Albero, Jorge; Passilly, Nicolas; Gorecki, Christophe

2015-11-01

We report a simple method, based on intensity measurements, for the characterization of the wavefront and aberrations produced by micro-optical focusing elements. This method employs the setup presented earlier in [Opt. Express 22, 13202 (2014)] for measurements of the 3D point spread function, on which a basic phase-retrieval algorithm is applied. This combination allows for retrieval of the wavefront generated by the micro-optical element and, in addition, quantification of the optical aberrations through the wavefront decomposition with Zernike polynomials. The optical setup requires only an in-motion imaging system. The technique, adapted for the optimization of micro-optical component fabrication, is demonstrated by characterizing a planoconvex microlens.

An Aquatic Decomposition Scoring Method to Potentially Predict the Postmortem Submersion Interval of Bodies Recovered from the North Sea.

PubMed

van Daalen, Marjolijn A; de Kat, Dorothée S; Oude Grotebevelsborg, Bernice F L; de Leeuwe, Roosje; Warnaar, Jeroen; Oostra, Roelof Jan; M Duijst-Heesters, Wilma L J

2017-03-01

This study aimed to develop an aquatic decomposition scoring (ADS) method and investigated the predictive value of this method in estimating the postmortem submersion interval (PMSI) of bodies recovered from the North Sea. This method, consisting of an ADS item list and a pictorial reference atlas, showed a high interobserver agreement (Krippendorff's alpha ≥ 0.93) and hence proved to be valid. This scoring method was applied to data, collected from closed cases-cases in which the postmortal submersion interval (PMSI) was known-concerning bodies recovered from the North Sea from 1990 to 2013. Thirty-eight cases met the inclusion criteria and were scored by quantifying the observed total aquatic decomposition score (TADS). Statistical analysis demonstrated that TADS accurately predicts the PMSI (p < 0.001), confirming that the decomposition process in the North Sea is strongly correlated to time. © 2017 American Academy of Forensic Sciences.

On simulating flow with multiple time scales using a method of averages

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

Margolin, L.G.

1997-12-31

The author presents a new computational method based on averaging to efficiently simulate certain systems with multiple time scales. He first develops the method in a simple one-dimensional setting and employs linear stability analysis to demonstrate numerical stability. He then extends the method to multidimensional fluid flow. His method of averages does not depend on explicit splitting of the equations nor on modal decomposition. Rather he combines low order and high order algorithms in a generalized predictor-corrector framework. He illustrates the methodology in the context of a shallow fluid approximation to an ocean basin circulation. He finds that his newmore » method reproduces the accuracy of a fully explicit second-order accurate scheme, while costing less than a first-order accurate scheme.« less

Capturing molecular multimode relaxation processes in excitable gases based on decomposition of acoustic relaxation spectra

NASA Astrophysics Data System (ADS)

Zhu, Ming; Liu, Tingting; Wang, Shu; Zhang, Kesheng

2017-08-01

Existing two-frequency reconstructive methods can only capture primary (single) molecular relaxation processes in excitable gases. In this paper, we present a reconstructive method based on the novel decomposition of frequency-dependent acoustic relaxation spectra to capture the entire molecular multimode relaxation process. This decomposition of acoustic relaxation spectra is developed from the frequency-dependent effective specific heat, indicating that a multi-relaxation process is the sum of the interior single-relaxation processes. Based on this decomposition, we can reconstruct the entire multi-relaxation process by capturing the relaxation times and relaxation strengths of N interior single-relaxation processes, using the measurements of acoustic absorption and sound speed at 2N frequencies. Experimental data for the gas mixtures CO2-N2 and CO2-O2 validate our decomposition and reconstruction approach.

White blood cell segmentation by color-space-based k-means clustering.

PubMed

Zhang, Congcong; Xiao, Xiaoyan; Li, Xiaomei; Chen, Ying-Jie; Zhen, Wu; Chang, Jun; Zheng, Chengyun; Liu, Zhi

2014-09-01

White blood cell (WBC) segmentation, which is important for cytometry, is a challenging issue because of the morphological diversity of WBCs and the complex and uncertain background of blood smear images. This paper proposes a novel method for the nucleus and cytoplasm segmentation of WBCs for cytometry. A color adjustment step was also introduced before segmentation. Color space decomposition and k-means clustering were combined for segmentation. A database including 300 microscopic blood smear images were used to evaluate the performance of our method. The proposed segmentation method achieves 95.7% and 91.3% overall accuracy for nucleus segmentation and cytoplasm segmentation, respectively. Experimental results demonstrate that the proposed method can segment WBCs effectively with high accuracy.

Decomposition of Multi-player Games

NASA Astrophysics Data System (ADS)

Zhao, Dengji; Schiffel, Stephan; Thielscher, Michael

Research in General Game Playing aims at building systems that learn to play unknown games without human intervention. We contribute to this endeavour by generalising the established technique of decomposition from AI Planning to multi-player games. To this end, we present a method for the automatic decomposition of previously unknown games into independent subgames, and we show how a general game player can exploit a successful decomposition for game tree search.

Microbial community assembly and metabolic function during mammalian corpse decomposition

USGS Publications Warehouse

Metcalf, Jessica L; Xu, Zhenjiang Zech; Weiss, Sophie; Lax, Simon; Van Treuren, Will; Hyde, Embriette R.; Song, Se Jin; Amir, Amnon; Larsen, Peter; Sangwan, Naseer; Haarmann, Daniel; Humphrey, Greg C; Ackermann, Gail; Thompson, Luke R; Lauber, Christian; Bibat, Alexander; Nicholas, Catherine; Gebert, Matthew J; Petrosino, Joseph F; Reed, Sasha C.; Gilbert, Jack A; Lynne, Aaron M; Bucheli, Sibyl R; Carter, David O; Knight, Rob

2016-01-01

Vertebrate corpse decomposition provides an important stage in nutrient cycling in most terrestrial habitats, yet microbially mediated processes are poorly understood. Here we combine deep microbial community characterization, community-level metabolic reconstruction, and soil biogeochemical assessment to understand the principles governing microbial community assembly during decomposition of mouse and human corpses on different soil substrates. We find a suite of bacterial and fungal groups that contribute to nitrogen cycling and a reproducible network of decomposers that emerge on predictable time scales. Our results show that this decomposer community is derived primarily from bulk soil, but key decomposers are ubiquitous in low abundance. Soil type was not a dominant factor driving community development, and the process of decomposition is sufficiently reproducible to offer new opportunities for forensic investigations.

Microbial community assembly and metabolic function during mammalian corpse decomposition

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

Metcalf, J. L.; Xu, Z. Z.; Weiss, S.

2015-12-10

Vertebrate corpse decomposition provides an important stage in nutrient cycling in most terrestrial habitats, yet microbially mediated processes are poorly understood. Here we combine deep microbial community characterization, community-level metabolic reconstruction, and soil biogeochemical assessment to understand the principles governing microbial community assembly during decomposition of mouse and human corpses on different soil substrates. We find a suite of bacterial and fungal groups that contribute to nitrogen cycling and a reproducible network of decomposers that emerge on predictable time scales. Our results show that this decomposer community is derived primarily from bulk soil, but key decomposers are ubiquitous in lowmore » abundance. Soil type was not a dominant factor driving community development, and the process of decomposition is sufficiently reproducible to offer new opportunities for forensic investigations.« less

NMR study of methane + ethane structure I hydrate decomposition.

PubMed

Dec, Steven F; Bowler, Kristen E; Stadterman, Laura L; Koh, Carolyn A; Sloan, E Dendy

2007-05-24

The thermally activated decomposition of methane + ethane structure I hydrate was studied with use of 13C magic-angle spinning (MAS) NMR as a function of composition and temperature. The observed higher decomposition rate of large sI cages initially filled with ethane gas can be described in terms of a model where a distribution of sI unit cells exists such that a particular unit cell contains zero, one, or two methane molecules in the unit cell; this distribution of unit cells is combined to form the observed equilibrium composition. In this model, unit cells with zero methane molecules are the least stable and decompose more rapidly than those populated with one or two methane molecules leading to the observed overall faster decomposition rate of the large cages containing ethane molecules.

A discrete structure of the brain waves.

NASA Astrophysics Data System (ADS)

Dabaghian, Yuri; Perotti, Luca; oscillons in biological rhythms Collaboration; physics of biological rhythms Team

A physiological interpretation of the biological rhythms, e.g., of the local field potentials (LFP) depends on the mathematical approaches used for the analysis. Most existing mathematical methods are based on decomposing the signal into a set of ``primitives,'' e.g., sinusoidal harmonics, and correlating them with different cognitive and behavioral phenomena. A common feature of all these methods is that the decomposition semantics is presumed from the onset, and the goal of the subsequent analysis reduces merely to identifying the combination that best reproduces the original signal. We propose a fundamentally new method in which the decomposition components are discovered empirically, and demonstrate that it is more flexible and more sensitive to the signal's structure than the standard Fourier method. Applying this method to the rodent LFP signals reveals a fundamentally new structure of these ``brain waves.'' In particular, our results suggest that the LFP oscillations consist of a superposition of a small, discrete set of frequency modulated oscillatory processes, which we call ``oscillons''. Since these structures are discovered empirically, we hypothesize that they may capture the signal's actual physical structure, i.e., the pattern of synchronous activity in neuronal ensembles. Proving this hypothesis will help to advance our principal understanding of the neuronal synchronization mechanisms and reveal new structure within the LFPs and other biological oscillations. NSF 1422438 Grant, Houston Bioinformatics Endowment Fund.

Sparse Solution of Fiber Orientation Distribution Function by Diffusion Decomposition

PubMed Central

Yeh, Fang-Cheng; Tseng, Wen-Yih Isaac

2013-01-01

Fiber orientation is the key information in diffusion tractography. Several deconvolution methods have been proposed to obtain fiber orientations by estimating a fiber orientation distribution function (ODF). However, the L 2 regularization used in deconvolution often leads to false fibers that compromise the specificity of the results. To address this problem, we propose a method called diffusion decomposition, which obtains a sparse solution of fiber ODF by decomposing the diffusion ODF obtained from q-ball imaging (QBI), diffusion spectrum imaging (DSI), or generalized q-sampling imaging (GQI). A simulation study, a phantom study, and an in-vivo study were conducted to examine the performance of diffusion decomposition. The simulation study showed that diffusion decomposition was more accurate than both constrained spherical deconvolution and ball-and-sticks model. The phantom study showed that the angular error of diffusion decomposition was significantly lower than those of constrained spherical deconvolution at 30° crossing and ball-and-sticks model at 60° crossing. The in-vivo study showed that diffusion decomposition can be applied to QBI, DSI, or GQI, and the resolved fiber orientations were consistent regardless of the diffusion sampling schemes and diffusion reconstruction methods. The performance of diffusion decomposition was further demonstrated by resolving crossing fibers on a 30-direction QBI dataset and a 40-direction DSI dataset. In conclusion, diffusion decomposition can improve angular resolution and resolve crossing fibers in datasets with low SNR and substantially reduced number of diffusion encoding directions. These advantages may be valuable for human connectome studies and clinical research. PMID:24146772

A Comparative Study of the Application of Fluorescence Excitation-Emission Matrices Combined with Parallel Factor Analysis and Nonnegative Matrix Factorization in the Analysis of Zn Complexation by Humic Acids

PubMed Central

Boguta, Patrycja; Pieczywek, Piotr M.; Sokołowska, Zofia

2016-01-01

The main aim of this study was the application of excitation-emission fluorescence matrices (EEMs) combined with two decomposition methods: parallel factor analysis (PARAFAC) and nonnegative matrix factorization (NMF) to study the interaction mechanisms between humic acids (HAs) and Zn(II) over a wide concentration range (0–50 mg·dm−3). The influence of HA properties on Zn(II) complexation was also investigated. Stability constants, quenching degree and complexation capacity were estimated for binding sites found in raw EEM, EEM-PARAFAC and EEM-NMF data using mathematical models. A combination of EEM fluorescence analysis with one of the proposed decomposition methods enabled separation of overlapping binding sites and yielded more accurate calculations of the binding parameters. PARAFAC and NMF processing allowed finding binding sites invisible in a few raw EEM datasets as well as finding totally new maxima attributed to structures of the lowest humification. Decomposed data showed an increase in Zn complexation with an increase in humification, aromaticity and molecular weight of HAs. EEM-PARAFAC analysis also revealed that the most stable compounds were formed by structures containing the highest amounts of nitrogen. The content of oxygen-functional groups did not influence the binding parameters, mainly due to fact of higher competition of metal cation with protons. EEM spectra coupled with NMF and especially PARAFAC processing gave more adequate assessments of interactions as compared to raw EEM data and should be especially recommended for modeling of complexation processes where the fluorescence intensities (FI) changes are weak or where the processes are interfered with by the presence of other fluorophores. PMID:27782078

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

Dong, X; Petrongolo, M; Wang, T

Purpose: A general problem of dual-energy CT (DECT) is that the decomposition is sensitive to noise in the two sets of dual-energy projection data, resulting in severely degraded qualities of decomposed images. We have previously proposed an iterative denoising method for DECT. Using a linear decomposition function, the method does not gain the full benefits of DECT on beam-hardening correction. In this work, we expand the framework of our iterative method to include non-linear decomposition models for noise suppression in DECT. Methods: We first obtain decomposed projections, which are free of beam-hardening artifacts, using a lookup table pre-measured on amore » calibration phantom. First-pass material images with high noise are reconstructed from the decomposed projections using standard filter-backprojection reconstruction. Noise on the decomposed images is then suppressed by an iterative method, which is formulated in the form of least-square estimation with smoothness regularization. Based on the design principles of a best linear unbiased estimator, we include the inverse of the estimated variance-covariance matrix of the decomposed images as the penalty weight in the least-square term. Analytical formulae are derived to compute the variance-covariance matrix from the measured decomposition lookup table. Results: We have evaluated the proposed method via phantom studies. Using non-linear decomposition, our method effectively suppresses the streaking artifacts of beam-hardening and obtains more uniform images than our previous approach based on a linear model. The proposed method reduces the average noise standard deviation of two basis materials by one order of magnitude without sacrificing the spatial resolution. Conclusion: We propose a general framework of iterative denoising for material decomposition of DECT. Preliminary phantom studies have shown the proposed method improves the image uniformity and reduces noise level without resolution loss. In the future, we will perform more phantom studies to further validate the performance of the purposed method. This work is supported by a Varian MRA grant.« less

Gold - A novel deconvolution algorithm with optimization for waveform LiDAR processing

NASA Astrophysics Data System (ADS)

Zhou, Tan; Popescu, Sorin C.; Krause, Keith; Sheridan, Ryan D.; Putman, Eric

2017-07-01

Waveform Light Detection and Ranging (LiDAR) data have advantages over discrete-return LiDAR data in accurately characterizing vegetation structure. However, we lack a comprehensive understanding of waveform data processing approaches under different topography and vegetation conditions. The objective of this paper is to highlight a novel deconvolution algorithm, the Gold algorithm, for processing waveform LiDAR data with optimal deconvolution parameters. Further, we present a comparative study of waveform processing methods to provide insight into selecting an approach for a given combination of vegetation and terrain characteristics. We employed two waveform processing methods: (1) direct decomposition, (2) deconvolution and decomposition. In method two, we utilized two deconvolution algorithms - the Richardson-Lucy (RL) algorithm and the Gold algorithm. The comprehensive and quantitative comparisons were conducted in terms of the number of detected echoes, position accuracy, the bias of the end products (such as digital terrain model (DTM) and canopy height model (CHM)) from the corresponding reference data, along with parameter uncertainty for these end products obtained from different methods. This study was conducted at three study sites that include diverse ecological regions, vegetation and elevation gradients. Results demonstrate that two deconvolution algorithms are sensitive to the pre-processing steps of input data. The deconvolution and decomposition method is more capable of detecting hidden echoes with a lower false echo detection rate, especially for the Gold algorithm. Compared to the reference data, all approaches generate satisfactory accuracy assessment results with small mean spatial difference (<1.22 m for DTMs, <0.77 m for CHMs) and root mean square error (RMSE) (<1.26 m for DTMs, <1.93 m for CHMs). More specifically, the Gold algorithm is superior to others with smaller root mean square error (RMSE) (<1.01 m), while the direct decomposition approach works better in terms of the percentage of spatial difference within 0.5 and 1 m. The parameter uncertainty analysis demonstrates that the Gold algorithm outperforms other approaches in dense vegetation areas, with the smallest RMSE, and the RL algorithm performs better in sparse vegetation areas in terms of RMSE. Additionally, the high level of uncertainty occurs more on areas with high slope and high vegetation. This study provides an alternative and innovative approach for waveform processing that will benefit high fidelity processing of waveform LiDAR data to characterize vegetation structures.

Seismic random noise attenuation method based on empirical mode decomposition of Hausdorff dimension

NASA Astrophysics Data System (ADS)

Yan, Z.; Luan, X.

2017-12-01

Introduction Empirical mode decomposition (EMD) is a noise suppression algorithm by using wave field separation, which is based on the scale differences between effective signal and noise. However, since the complexity of the real seismic wave field results in serious aliasing modes, it is not ideal and effective to denoise with this method alone. Based on the multi-scale decomposition characteristics of the signal EMD algorithm, combining with Hausdorff dimension constraints, we propose a new method for seismic random noise attenuation. First of all, We apply EMD algorithm adaptive decomposition of seismic data and obtain a series of intrinsic mode function (IMF)with different scales. Based on the difference of Hausdorff dimension between effectively signals and random noise, we identify IMF component mixed with random noise. Then we use threshold correlation filtering process to separate the valid signal and random noise effectively. Compared with traditional EMD method, the results show that the new method of seismic random noise attenuation has a better suppression effect. The implementation process The EMD algorithm is used to decompose seismic signals into IMF sets and analyze its spectrum. Since most of the random noise is high frequency noise, the IMF sets can be divided into three categories: the first category is the effective wave composition of the larger scale; the second category is the noise part of the smaller scale; the third category is the IMF component containing random noise. Then, the third kind of IMF component is processed by the Hausdorff dimension algorithm, and the appropriate time window size, initial step and increment amount are selected to calculate the Hausdorff instantaneous dimension of each component. The dimension of the random noise is between 1.0 and 1.05, while the dimension of the effective wave is between 1.05 and 2.0. On the basis of the previous steps, according to the dimension difference between the random noise and effective signal, we extracted the sample points, whose fractal dimension value is less than or equal to 1.05 for the each IMF components, to separate the residual noise. Using the IMF components after dimension filtering processing and the effective wave IMF components after the first selection for reconstruction, we can obtained the results of de-noising.

Comparison of two interpolation methods for empirical mode decomposition based evaluation of radiographic femur bone images.

PubMed

Udhayakumar, Ganesan; Sujatha, Chinnaswamy Manoharan; Ramakrishnan, Swaminathan

2013-01-01

Analysis of bone strength in radiographic images is an important component of estimation of bone quality in diseases such as osteoporosis. Conventional radiographic femur bone images are used to analyze its architecture using bi-dimensional empirical mode decomposition method. Surface interpolation of local maxima and minima points of an image is a crucial part of bi-dimensional empirical mode decomposition method and the choice of appropriate interpolation depends on specific structure of the problem. In this work, two interpolation methods of bi-dimensional empirical mode decomposition are analyzed to characterize the trabecular femur bone architecture of radiographic images. The trabecular bone regions of normal and osteoporotic femur bone images (N = 40) recorded under standard condition are used for this study. The compressive and tensile strength regions of the images are delineated using pre-processing procedures. The delineated images are decomposed into their corresponding intrinsic mode functions using interpolation methods such as Radial basis function multiquadratic and hierarchical b-spline techniques. Results show that bi-dimensional empirical mode decomposition analyses using both interpolations are able to represent architectural variations of femur bone radiographic images. As the strength of the bone depends on architectural variation in addition to bone mass, this study seems to be clinically useful.

Performance of tensor decomposition-based modal identification under nonstationary vibration

NASA Astrophysics Data System (ADS)

Friesen, P.; Sadhu, A.

2017-03-01

Health monitoring of civil engineering structures is of paramount importance when they are subjected to natural hazards or extreme climatic events like earthquake, strong wind gusts or man-made excitations. Most of the traditional modal identification methods are reliant on stationarity assumption of the vibration response and posed difficulty while analyzing nonstationary vibration (e.g. earthquake or human-induced vibration). Recently tensor decomposition based methods are emerged as powerful and yet generic blind (i.e. without requiring a knowledge of input characteristics) signal decomposition tool for structural modal identification. In this paper, a tensor decomposition based system identification method is further explored to estimate modal parameters using nonstationary vibration generated due to either earthquake or pedestrian induced excitation in a structure. The effects of lag parameters and sensor densities on tensor decomposition are studied with respect to the extent of nonstationarity of the responses characterized by the stationary duration and peak ground acceleration of the earthquake. A suite of more than 1400 earthquakes is used to investigate the performance of the proposed method under a wide variety of ground motions utilizing both complete and partial measurements of a high-rise building model. Apart from the earthquake, human-induced nonstationary vibration of a real-life pedestrian bridge is also used to verify the accuracy of the proposed method.

Decomposition of Near-Infrared Spectroscopy Signals Using Oblique Subspace Projections: Applications in Brain Hemodynamic Monitoring.

PubMed

Caicedo, Alexander; Varon, Carolina; Hunyadi, Borbala; Papademetriou, Maria; Tachtsidis, Ilias; Van Huffel, Sabine

2016-01-01

Clinical data is comprised by a large number of synchronously collected biomedical signals that are measured at different locations. Deciphering the interrelationships of these signals can yield important information about their dependence providing some useful clinical diagnostic data. For instance, by computing the coupling between Near-Infrared Spectroscopy signals (NIRS) and systemic variables the status of the hemodynamic regulation mechanisms can be assessed. In this paper we introduce an algorithm for the decomposition of NIRS signals into additive components. The algorithm, SIgnal DEcomposition base on Obliques Subspace Projections (SIDE-ObSP), assumes that the measured NIRS signal is a linear combination of the systemic measurements, following the linear regression model y = Ax + ϵ . SIDE-ObSP decomposes the output such that, each component in the decomposition represents the sole linear influence of one corresponding regressor variable. This decomposition scheme aims at providing a better understanding of the relation between NIRS and systemic variables, and to provide a framework for the clinical interpretation of regression algorithms, thereby, facilitating their introduction into clinical practice. SIDE-ObSP combines oblique subspace projections (ObSP) with the structure of a mean average system in order to define adequate signal subspaces. To guarantee smoothness in the estimated regression parameters, as observed in normal physiological processes, we impose a Tikhonov regularization using a matrix differential operator. We evaluate the performance of SIDE-ObSP by using a synthetic dataset, and present two case studies in the field of cerebral hemodynamics monitoring using NIRS. In addition, we compare the performance of this method with other system identification techniques. In the first case study data from 20 neonates during the first 3 days of life was used, here SIDE-ObSP decoupled the influence of changes in arterial oxygen saturation from the NIRS measurements, facilitating the use of NIRS as a surrogate measure for cerebral blood flow (CBF). The second case study used data from a 3-years old infant under Extra Corporeal Membrane Oxygenation (ECMO), here SIDE-ObSP decomposed cerebral/peripheral tissue oxygenation, as a sum of the partial contributions from different systemic variables, facilitating the comparison between the effects of each systemic variable on the cerebral/peripheral hemodynamics.

A fast combination method in DSmT and its application to recommender system

PubMed Central

Liu, Yihai

2018-01-01

In many applications involving epistemic uncertainties usually modeled by belief functions, it is often necessary to approximate general (non-Bayesian) basic belief assignments (BBAs) to subjective probabilities (called Bayesian BBAs). This necessity occurs if one needs to embed the fusion result in a system based on the probabilistic framework and Bayesian inference (e.g. tracking systems), or if one needs to make a decision in the decision making problems. In this paper, we present a new fast combination method, called modified rigid coarsening (MRC), to obtain the final Bayesian BBAs based on hierarchical decomposition (coarsening) of the frame of discernment. Regarding this method, focal elements with probabilities are coarsened efficiently to reduce computational complexity in the process of combination by using disagreement vector and a simple dichotomous approach. In order to prove the practicality of our approach, this new approach is applied to combine users’ soft preferences in recommender systems (RSs). Additionally, in order to make a comprehensive performance comparison, the proportional conflict redistribution rule #6 (PCR6) is regarded as a baseline in a range of experiments. According to the results of experiments, MRC is more effective in accuracy of recommendations compared to original Rigid Coarsening (RC) method and comparable in computational time. PMID:29351297

Potential macro-detritivore range expansion into the subarctic stimulates litter decomposition: a new positive feedback mechanism to climate change?

PubMed

van Geffen, Koert G; Berg, Matty P; Aerts, Rien

2011-12-01

As a result of low decomposition rates, high-latitude ecosystems store large amounts of carbon. Litter decomposition in these ecosystems is constrained by harsh abiotic conditions, but also by the absence of macro-detritivores. We have studied the potential effects of their climate change-driven northward range expansion on the decomposition of two contrasting subarctic litter types. Litter of Alnus incana and Betula pubescens was incubated in microcosms together with monocultures and all possible combinations of three functionally different macro-detritivores (the earthworm Lumbricus rubellus, isopod Oniscus asellus, and millipede Julus scandinavius). Our results show that these macro-detritivores stimulated decomposition, especially of the high-quality A. incana litter and that the macro-detritivores tested differed in their decomposition-stimulating effects, with earthworms having the largest influence. Decomposition processes increased with increasing number of macro-detritivore species, and positive net diveristy effects occurred in several macro-detritivore treatments. However, after correction for macro-detritivore biomass, all interspecific differences in macro-detritivore effects, as well as the positive effects of species number on subarctic litter decomposition disappeared. The net diversity effects also appeared to be driven by variation in biomass, with a possible exception of net diversity effects in mass loss. Based on these results, we conclude that the expected climate change-induced range expansion of macro-detritivores into subarctic regions is likely to result in accelerated decomposition rates. Our results also indicate that the magnitude of macro-detritivore effects on subarctic decomposition will mainly depend on macro-detritivore biomass, rather than on macro-detritivore species number or identity.

Understanding Litter Input Controls on Soil Organic Matter Turnover and Formation are Essential for Improving Carbon-Climate Feedback Predictions for Arctic, Tundra Ecosystems

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

Wallenstein, Matthew

The Arctic region stored vast amounts of carbon (C) in soils over thousands of years because decomposition has been limited by cold, wet conditions. Arctic soils now contain roughly as much C that is contained in all other soils across the globe combined. However, climate warming could unlock this oil C as decomposition accelerates and permafrost thaws. In addition to temperature-driven acceleration of decomposition, several additional processes could either counteract or augment warming-induced SOM losses. For example, increased plant growth under a warmer climate will increase organic matter inputs to soils, which could fuel further soil decomposition by microbes, butmore » will also increase the production of new SOM. Whether Arctic ecosystems store or release carbon in the future depends in part on the balance between these two counteracting processes. By differentiating SOM decomposition and formation and understanding the drivers of these processes, we will better understand how these systems function. We did not find evidence of priming under current conditions, defined as an increase in the decomposition of native SOM stocks. This suggests that decomposition is unlikely to be further accelerated through this mechanism. We did find that decomposition of native SOM did occur when nitrogen was added to these soils, suggesting that nitrogen limits decomposition in these systems. Our results highlight the resilience and extraordinary C storage capacity of these soils, and suggest shrub expansion may partially mitigate C losses from decomposition of old SOM as Arctic soils warm.« less

Adaptive fault feature extraction from wayside acoustic signals from train bearings

NASA Astrophysics Data System (ADS)

Zhang, Dingcheng; Entezami, Mani; Stewart, Edward; Roberts, Clive; Yu, Dejie

2018-07-01

Wayside acoustic detection of train bearing faults plays a significant role in maintaining safety in the railway transport system. However, the bearing fault information is normally masked by strong background noises and harmonic interferences generated by other components (e.g. axles and gears). In order to extract the bearing fault feature information effectively, a novel method called improved singular value decomposition (ISVD) with resonance-based signal sparse decomposition (RSSD), namely the ISVD-RSSD method, is proposed in this paper. A Savitzky-Golay (S-G) smoothing filter is used to filter singular vectors (SVs) in the ISVD method as an extension of the singular value decomposition (SVD) theorem. Hilbert spectrum entropy and a stepwise optimisation strategy are used to optimize the S-G filter's parameters. The RSSD method is able to nonlinearly decompose the wayside acoustic signal of a faulty train bearing into high and low resonance components, the latter of which contains bearing fault information. However, the high level of noise usually results in poor decomposition results from the RSSD method. Hence, the collected wayside acoustic signal must first be de-noised using the ISVD component of the ISVD-RSSD method. Next, the de-noised signal is decomposed by using the RSSD method. The obtained low resonance component is then demodulated with a Hilbert transform such that the bearing fault can be detected by observing Hilbert envelope spectra. The effectiveness of the ISVD-RSSD method is verified through both laboratory field-based experiments as described in the paper. The results indicate that the proposed method is superior to conventional spectrum analysis and ensemble empirical mode decomposition methods.

Reconstructing multi-mode networks from multivariate time series

NASA Astrophysics Data System (ADS)

Gao, Zhong-Ke; Yang, Yu-Xuan; Dang, Wei-Dong; Cai, Qing; Wang, Zhen; Marwan, Norbert; Boccaletti, Stefano; Kurths, Jürgen

2017-09-01

Unveiling the dynamics hidden in multivariate time series is a task of the utmost importance in a broad variety of areas in physics. We here propose a method that leads to the construction of a novel functional network, a multi-mode weighted graph combined with an empirical mode decomposition, and to the realization of multi-information fusion of multivariate time series. The method is illustrated in a couple of successful applications (a multi-phase flow and an epileptic electro-encephalogram), which demonstrate its powerfulness in revealing the dynamical behaviors underlying the transitions of different flow patterns, and enabling to differentiate brain states of seizure and non-seizure.

Neurotransmitters in the Gas Phase: La-Mb Studies

NASA Astrophysics Data System (ADS)

Cabezas, C.; Mata, S.; López, J. C.; Alonso, J. L.

2011-06-01

LA-MB-FTMW spectroscopy combines laser ablation with Fourier transform microwave spectroscopy in supersonic jets overcoming the problems of thermal decomposition associated with conventional heating methods. We present here the results on LA-MB-FTMW studies of some neurotransmitters. Six conformers of dopamine, four of adrenaline, five of noradrenaline and three conformers of serotonin have been characterized in the gas phase. The rotational and nuclear quadrupole coupling constants extracted from the analysis of the rotational spectrum are directly compared with those predicted by ab initio methods to achieve the conclusive identification of different conformers and the experimental characterization of the intramolecular forces at play which control conformational preferences.

Moisture drives surface decomposition in thawing tundra

NASA Astrophysics Data System (ADS)

Hicks Pries, Caitlin E.; Schuur, E. A. G.; Vogel, Jason G.; Natali, Susan M.

2013-07-01

Permafrost thaw can affect decomposition rates by changing environmental conditions and litter quality. As permafrost thaws, soils warm and thermokarst (ground subsidence) features form, causing some areas to become wetter while other areas become drier. We used a common substrate to measure how permafrost thaw affects decomposition rates in the surface soil in a natural permafrost thaw gradient and a warming experiment in Healy, Alaska. Permafrost thaw also changes plant community composition. We decomposed 12 plant litters in a common garden to test how changing plant litter inputs would affect decomposition. We combined species' tissue-specific decomposition rates with species and tissue-level estimates of aboveground net primary productivity to calculate community-weighted decomposition constants at both the thaw gradient and warming experiment. Moisture, specifically growing season precipitation and water table depth, was the most significant driver of decomposition. At the gradient, an increase in growing season precipitation from 200 to 300 mm increased mass loss of the common substrate by 100%. At the warming experiment, a decrease in the depth to the water table from 30 to 15 cm increased mass loss by 100%. At the gradient, community-weighted decomposition was 21% faster in extensive than in minimal thaw, but was similar when moss production was included. Overall, the effect of climate change and permafrost thaw on surface soil decomposition are driven more by precipitation and soil environment than by changes to plant communities. Increasing soil moisture is thereby another mechanism by which permafrost thaw can become a positive feedback to climate change.

Relationship of host recurrence in fungi to rates of tropical leaf decomposition

Treesearch

Mirna E. Santanaa; JeanD. Lodgeb; Patricia Lebowc

2004-01-01

Here we explore the significance of fungal diversity on ecosystem processes by testing whether microfungal ‘preferences’ for (i.e., host recurrence) different tropical leaf species increases the rate of decomposition. We used pairwise combinations of girradiated litter of five tree species with cultures of two dominant microfungi derived from each plant in a microcosm...

Relationship of host recurrence in fungi to rates of tropical leaf decomposition

Treesearch

Mirna E. Santana; D. Jean Lodge; Patricia Lebow

2005-01-01

Here we explore the significance of fungal diversity on ecosystem processes by testing whether microfungal ‘preferences’ for (i.e., host recurrence) different tropical leaf species increases the rate of decomposition. We used pairwise combinations of [gamma]-irradiated litter of five tree species with cultures of two dominant microfungi derived from each plant in a...

The deconvolution of complex spectra by artificial immune system

NASA Astrophysics Data System (ADS)

Galiakhmetova, D. I.; Sibgatullin, M. E.; Galimullin, D. Z.; Kamalova, D. I.

2017-11-01

An application of the artificial immune system method for decomposition of complex spectra is presented. The results of decomposition of the model contour consisting of three components, Gaussian contours, are demonstrated. The method of artificial immune system is an optimization method, which is based on the behaviour of the immune system and refers to modern methods of search for the engine optimization.

Enhanced decomposition of stable soil organic carbon and microbial catabolic potentials by long-term field warming

DOE PAGES

Feng, Wenting; Liang, Junyi; Hale, Lauren E.; ...

2017-06-09

Quantifying soil organic carbon (SOC) decomposition under warming is critical to predict carbon–climate feedbacks. According to the substrate regulating principle, SOC decomposition would decrease as labile SOC declines under field warming, but observations of SOC decomposition under warming do not always support this prediction. This discrepancy could result from varying changes in SOC components and soil microbial communities under warming. This study aimed to determine the decomposition of SOC components with different turnover times after subjected to long-term field warming and/or root exclusion to limit C input, and to test whether SOC decomposition is driven by substrate lability under warming.more » Taking advantage of a 12-year field warming experiment in a prairie, we assessed the decomposition of SOC components by incubating soils from control and warmed plots, with and without root exclusion for 3 years. We assayed SOC decomposition from these incubations by combining inverse modeling and microbial functional genes during decomposition with a metagenomic technique (GeoChip). The decomposition of SOC components with turnover times of years and decades, which contributed to 95% of total cumulative CO 2 respiration, was greater in soils from warmed plots. But the decomposition of labile SOC was similar in warmed plots compared to the control. The diversity of C-degradation microbial genes generally declined with time during the incubation in all treatments, suggesting shifts of microbial functional groups as substrate composition was changing. Compared to the control, soils from warmed plots showed significant increase in the signal intensities of microbial genes involved in degrading complex organic compounds, implying enhanced potential abilities of microbial catabolism. These are likely responsible for accelerated decomposition of SOC components with slow turnover rates. Overall, the shifted microbial community induced by long-term warming accelerates the decomposition of SOC components with slow turnover rates and thus amplify the positive feedback to climate change.« less

Enhanced decomposition of stable soil organic carbon and microbial catabolic potentials by long-term field warming

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

Feng, Wenting; Liang, Junyi; Hale, Lauren E.

Quantifying soil organic carbon (SOC) decomposition under warming is critical to predict carbon–climate feedbacks. According to the substrate regulating principle, SOC decomposition would decrease as labile SOC declines under field warming, but observations of SOC decomposition under warming do not always support this prediction. This discrepancy could result from varying changes in SOC components and soil microbial communities under warming. This study aimed to determine the decomposition of SOC components with different turnover times after subjected to long-term field warming and/or root exclusion to limit C input, and to test whether SOC decomposition is driven by substrate lability under warming.more » Taking advantage of a 12-year field warming experiment in a prairie, we assessed the decomposition of SOC components by incubating soils from control and warmed plots, with and without root exclusion for 3 years. We assayed SOC decomposition from these incubations by combining inverse modeling and microbial functional genes during decomposition with a metagenomic technique (GeoChip). The decomposition of SOC components with turnover times of years and decades, which contributed to 95% of total cumulative CO 2 respiration, was greater in soils from warmed plots. But the decomposition of labile SOC was similar in warmed plots compared to the control. The diversity of C-degradation microbial genes generally declined with time during the incubation in all treatments, suggesting shifts of microbial functional groups as substrate composition was changing. Compared to the control, soils from warmed plots showed significant increase in the signal intensities of microbial genes involved in degrading complex organic compounds, implying enhanced potential abilities of microbial catabolism. These are likely responsible for accelerated decomposition of SOC components with slow turnover rates. Overall, the shifted microbial community induced by long-term warming accelerates the decomposition of SOC components with slow turnover rates and thus amplify the positive feedback to climate change.« less

Enhanced decomposition of stable soil organic carbon and microbial catabolic potentials by long-term field warming.

PubMed

Feng, Wenting; Liang, Junyi; Hale, Lauren E; Jung, Chang Gyo; Chen, Ji; Zhou, Jizhong; Xu, Minggang; Yuan, Mengting; Wu, Liyou; Bracho, Rosvel; Pegoraro, Elaine; Schuur, Edward A G; Luo, Yiqi

2017-11-01

Quantifying soil organic carbon (SOC) decomposition under warming is critical to predict carbon-climate feedbacks. According to the substrate regulating principle, SOC decomposition would decrease as labile SOC declines under field warming, but observations of SOC decomposition under warming do not always support this prediction. This discrepancy could result from varying changes in SOC components and soil microbial communities under warming. This study aimed to determine the decomposition of SOC components with different turnover times after subjected to long-term field warming and/or root exclusion to limit C input, and to test whether SOC decomposition is driven by substrate lability under warming. Taking advantage of a 12-year field warming experiment in a prairie, we assessed the decomposition of SOC components by incubating soils from control and warmed plots, with and without root exclusion for 3 years. We assayed SOC decomposition from these incubations by combining inverse modeling and microbial functional genes during decomposition with a metagenomic technique (GeoChip). The decomposition of SOC components with turnover times of years and decades, which contributed to 95% of total cumulative CO 2 respiration, was greater in soils from warmed plots. But the decomposition of labile SOC was similar in warmed plots compared to the control. The diversity of C-degradation microbial genes generally declined with time during the incubation in all treatments, suggesting shifts of microbial functional groups as substrate composition was changing. Compared to the control, soils from warmed plots showed significant increase in the signal intensities of microbial genes involved in degrading complex organic compounds, implying enhanced potential abilities of microbial catabolism. These are likely responsible for accelerated decomposition of SOC components with slow turnover rates. Overall, the shifted microbial community induced by long-term warming accelerates the decomposition of SOC components with slow turnover rates and thus amplify the positive feedback to climate change. © 2017 John Wiley & Sons Ltd.

Multi-Centrality Graph Spectral Decompositions and Their Application to Cyber Intrusion Detection

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

Chen, Pin-Yu; Choudhury, Sutanay; Hero, Alfred

Many modern datasets can be represented as graphs and hence spectral decompositions such as graph principal component analysis (PCA) can be useful. Distinct from previous graph decomposition approaches based on subspace projection of a single topological feature, e.g., the centered graph adjacency matrix (graph Laplacian), we propose spectral decomposition approaches to graph PCA and graph dictionary learning that integrate multiple features, including graph walk statistics, centrality measures and graph distances to reference nodes. In this paper we propose a new PCA method for single graph analysis, called multi-centrality graph PCA (MC-GPCA), and a new dictionary learning method for ensembles ofmore » graphs, called multi-centrality graph dictionary learning (MC-GDL), both based on spectral decomposition of multi-centrality matrices. As an application to cyber intrusion detection, MC-GPCA can be an effective indicator of anomalous connectivity pattern and MC-GDL can provide discriminative basis for attack classification.« less

Classification of fully polarimetric F-SAR ( X / S ) airborne radar images using decomposition methods. (Polish Title: Klasyfikacja treści polarymetrycznych obrazów radarowych z wykorzystaniem metod dekompozycji na przykładzie systemu F-SAR ( X / S ))

NASA Astrophysics Data System (ADS)

Mleczko, M.

2014-12-01

Polarimetric SAR data is not widely used in practice, because it is not yet available operationally from the satellites. Currently we can distinguish two approaches in POL - In - SAR technology: alternating polarization imaging (Alt - POL) and fully polarimetric (QuadPol). The first represents a subset of another and is more operational, while the second is experimental because classification of this data requires polarimetric decomposition of scattering matrix in the first stage. In the literature decomposition process is divided in two types: the coherent and incoherent decomposition. In this paper the decomposition methods have been tested using data from the high resolution airborne F - SAR system. Results of classification have been interpreted in the context of the land cover mapping capabilities

Short-term standard litter decomposition across three different ecosystems in middle taiga zone of West Siberia

NASA Astrophysics Data System (ADS)

Filippova, Nina V.; Glagolev, Mikhail V.

2018-03-01

The method of standard litter (tea) decomposition was implemented to compare decomposition rate constants (k) between different peatland ecosystems and coniferous forests in the middle taiga zone of West Siberia (near Khanty-Mansiysk). The standard protocol of TeaComposition initiative was used to make the data usable for comparisons among different sites and zonobiomes worldwide. This article sums up the results of short-term decomposition (3 months) on the local scale. The values of decomposition rate constants differed significantly between three ecosystem types: it was higher in forest compared to bogs, and treed bogs had lower decomposition constant compared to Sphagnum lawns. In general, the decomposition rate constants were close to ones reported earlier for similar climatic conditions and habitats.

Pass-transistor very large scale integration

NASA Technical Reports Server (NTRS)

Maki, Gary K. (Inventor); Bhatia, Prakash R. (Inventor)

2004-01-01

Logic elements are provided that permit reductions in layout size and avoidance of hazards. Such logic elements may be included in libraries of logic cells. A logical function to be implemented by the logic element is decomposed about logical variables to identify factors corresponding to combinations of the logical variables and their complements. A pass transistor network is provided for implementing the pass network function in accordance with this decomposition. The pass transistor network includes ordered arrangements of pass transistors that correspond to the combinations of variables and complements resulting from the logical decomposition. The logic elements may act as selection circuits and be integrated with memory and buffer elements.

Impact of joint statistical dual-energy CT reconstruction of proton stopping power images: Comparison to image- and sinogram-domain material decomposition approaches.

PubMed

Zhang, Shuangyue; Han, Dong; Politte, David G; Williamson, Jeffrey F; O'Sullivan, Joseph A

2018-05-01

The purpose of this study was to assess the performance of a novel dual-energy CT (DECT) approach for proton stopping power ratio (SPR) mapping that integrates image reconstruction and material characterization using a joint statistical image reconstruction (JSIR) method based on a linear basis vector model (BVM). A systematic comparison between the JSIR-BVM method and previously described DECT image- and sinogram-domain decomposition approaches is also carried out on synthetic data. The JSIR-BVM method was implemented to estimate the electron densities and mean excitation energies (I-values) required by the Bethe equation for SPR mapping. In addition, image- and sinogram-domain DECT methods based on three available SPR models including BVM were implemented for comparison. The intrinsic SPR modeling accuracy of the three models was first validated. Synthetic DECT transmission sinograms of two 330 mm diameter phantoms each containing 17 soft and bony tissues (for a total of 34) of known composition were then generated with spectra of 90 and 140 kVp. The estimation accuracy of the reconstructed SPR images were evaluated for the seven investigated methods. The impact of phantom size and insert location on SPR estimation accuracy was also investigated. All three selected DECT-SPR models predict the SPR of all tissue types with less than 0.2% RMS errors under idealized conditions with no reconstruction uncertainties. When applied to synthetic sinograms, the JSIR-BVM method achieves the best performance with mean and RMS-average errors of less than 0.05% and 0.3%, respectively, for all noise levels, while the image- and sinogram-domain decomposition methods show increasing mean and RMS-average errors with increasing noise level. The JSIR-BVM method also reduces statistical SPR variation by sixfold compared to other methods. A 25% phantom diameter change causes up to 4% SPR differences for the image-domain decomposition approach, while the JSIR-BVM method and sinogram-domain decomposition methods are insensitive to size change. Among all the investigated methods, the JSIR-BVM method achieves the best performance for SPR estimation in our simulation phantom study. This novel method is robust with respect to sinogram noise and residual beam-hardening effects, yielding SPR estimation errors comparable to intrinsic BVM modeling error. In contrast, the achievable SPR estimation accuracy of the image- and sinogram-domain decomposition methods is dominated by the CT image intensity uncertainties introduced by the reconstruction and decomposition processes. © 2018 American Association of Physicists in Medicine.

Amplitude-cyclic frequency decomposition of vibration signals for bearing fault diagnosis based on phase editing

NASA Astrophysics Data System (ADS)

Barbini, L.; Eltabach, M.; Hillis, A. J.; du Bois, J. L.

2018-03-01

In rotating machine diagnosis different spectral tools are used to analyse vibration signals. Despite the good diagnostic performance such tools are usually refined, computationally complex to implement and require oversight of an expert user. This paper introduces an intuitive and easy to implement method for vibration analysis: amplitude cyclic frequency decomposition. This method firstly separates vibration signals accordingly to their spectral amplitudes and secondly uses the squared envelope spectrum to reveal the presence of cyclostationarity in each amplitude level. The intuitive idea is that in a rotating machine different components contribute vibrations at different amplitudes, for instance defective bearings contribute a very weak signal in contrast to gears. This paper also introduces a new quantity, the decomposition squared envelope spectrum, which enables separation between the components of a rotating machine. The amplitude cyclic frequency decomposition and the decomposition squared envelope spectrum are tested on real word signals, both at stationary and varying speeds, using data from a wind turbine gearbox and an aircraft engine. In addition a benchmark comparison to the spectral correlation method is presented.

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

The suitability of visual taphonomic methods for digital photographs: An experimental approach with pig carcasses in a tropical climate.

PubMed

Ribéreau-Gayon, Agathe; Rando, Carolyn; Morgan, Ruth M; Carter, David O

2018-05-01

In the context of increased scrutiny of the methods in forensic sciences, it is essential to ensure that the approaches used in forensic taphonomy to measure decomposition and estimate the postmortem interval are underpinned by robust evidence-based data. Digital photographs are an important source of documentation in forensic taphonomic investigations but the suitability of the current approaches for photographs, rather than real-time remains, is poorly studied which can undermine accurate forensic conclusions. The present study aimed to investigate the suitability of 2D colour digital photographs for evaluating decomposition of exposed human analogues (Sus scrofa domesticus) in a tropical savanna environment (Hawaii), using two published scoring methods; Megyesi et al., 2005 and Keough et al., 2017. It was found that there were significant differences between the real-time and photograph decomposition scores when the Megyesi et al. method was used. However, the Keough et al. method applied to photographs reflected real-time decomposition more closely and thus appears more suitable to evaluate pig decomposition from 2D photographs. The findings indicate that the type of scoring method used has a significant impact on the ability to accurately evaluate the decomposition of exposed pig carcasses from photographs. It was further identified that photographic taphonomic analysis can reach high inter-observer reproducibility. These novel findings are of significant importance for the forensic sciences as they highlight the potential for high quality photograph coverage to provide useful complementary information for the forensic taphonomic investigation. New recommendations to develop robust transparent approaches adapted to photographs in forensic taphonomy are suggested based on these findings. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

An investigation on the modelling of kinetics of thermal decomposition of hazardous mercury wastes.

PubMed

Busto, Yailen; M G Tack, Filip; Peralta, Luis M; Cabrera, Xiomara; Arteaga-Pérez, Luis E

2013-09-15

The kinetics of mercury removal from solid wastes generated by chlor-alkali plants were studied. The reaction order and model-free method with an isoconversional approach were used to estimate the kinetic parameters and reaction mechanism that apply to the thermal decomposition of hazardous mercury wastes. As a first approach to the understanding of thermal decomposition for this type of systems (poly-disperse and multi-component), a novel scheme of six reactions was proposed to represent the behaviour of mercury compounds in the solid matrix during the treatment. An integration-optimization algorithm was used in the screening of nine mechanistic models to develop kinetic expressions that best describe the process. The kinetic parameters were calculated by fitting each of these models to the experimental data. It was demonstrated that the D₁-diffusion mechanism appeared to govern the process at 250°C and high residence times, whereas at 450°C a combination of the diffusion mechanism (D₁) and the third order reaction mechanism (F3) fitted the kinetics of the conversions. The developed models can be applied in engineering calculations to dimension the installations and determine the optimal conditions to treat a mercury containing sludge. Copyright © 2013 Elsevier B.V. All rights reserved.

Structural, optical and magnetic properties of gadolinium sesquioxide nanobars synthesized via thermal decomposition of gadolinium oxalate

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

Manigandan, R.; Giribabu, K.; Suresh, R.

2013-10-15

Graphical abstract: - Highlights: • The cubic Gd{sub 2}O{sub 3} nanobars are synthesized by decomposition of C{sub 6}H{sub 20}Gd{sub 2}O{sub 22}. • The nanoparticles are rectangular bar shape with high porous surface. • The combination of magnetic and optical properties within a single particle. • The Gd{sub 2}O{sub 3} nanobars have tailorable nanostructure, wide bandgap and are paramagnetic. - Abstract: Gadolinium oxide nanobars were obtained by thermal decomposition of gadolinium oxalate, which was synthesized by the chemical precipitation method along with glycerol. The functional group analysis and formation of gadolinium oxide from gadolinium oxalate were characterized by the Fourier transformmore » infrared spectroscopy and thermo gravimetric analyzer. The crystal structure, average crystallite size, and lattice parameter were analyzed by X-ray diffraction technique. Moreover, Raman shifts, elemental composition and morphology of the gadolinium oxide was widely investigated by the laser Raman microscope, X-ray photoelectron spectroscopy, FE-SEM-EDAX and HR-TEM, respectively. Furthermore, the optical properties like band gap, absorbance measurement of the gadolinium oxide were extensively examined. In addition, the paramagnetic property of gadolinium oxide nanobars was explored by the vibrating sample magnetometer.« less

Study on Influencing Factors of Carbon Emissions from Energy Consumption of Shandong Province of China from 1995 to 2012

PubMed Central

Song, Jiekun; Song, Qing; Zhang, Dong; Lu, Youyou; Luan, Long

2014-01-01

Carbon emissions from energy consumption of Shandong province from 1995 to 2012 are calculated. Three zero-residual decomposition models (LMDI, MRCI and Shapley value models) are introduced for decomposing carbon emissions. Based on the results, Kendall coordination coefficient method is employed for testing their compatibility, and an optimal weighted combination decomposition model is constructed for improving the objectivity of decomposition. STIRPAT model is applied to evaluate the impact of each factor on carbon emissions. The results show that, using 1995 as the base year, the cumulative effects of population, per capita GDP, energy consumption intensity, and energy consumption structure of Shandong province in 2012 are positive, while the cumulative effect of industrial structure is negative. Per capita GDP is the largest driver of the increasing carbon emissions and has a great impact on carbon emissions; energy consumption intensity is a weak driver and has certain impact on carbon emissions; population plays a weak driving role, but it has the most significant impact on carbon emissions; energy consumption structure is a weak driver of the increasing carbon emissions and has a weak impact on carbon emissions; industrial structure has played a weak inhibitory role, and its impact on carbon emissions is great. PMID:24977216

Effect of CaO on the selectivity of N2O decomposition products: A combined experimental and DFT study

NASA Astrophysics Data System (ADS)

Wu, Lingnan; Hu, Xiaoying; Qin, Wu; Gao, Pan; Dong, Changqing; Yang, Yongping

2016-09-01

The effect of CaO on N2O decomposition and the selectivity of its decomposition products (NO and N2) was investigated using a fixed-bed flow reactor with varying temperatures from 317 °C to 947 °C. The selectivity of NO from CaO-catalyzed N2O decomposition is much lower than the N2 selectivity with the N2/NO products ratio greater than 12.1. Compared to N2O homogeneous decomposition with the minimum N2/NO products ratio of 6.2 at 718 °C, CaO also decreases the NO selectivity from 718 °C to 947 °C. Density functional theory calculations provide possible N2O decomposition routes on the CaO (1 0 0) surface considering both N2 and NO as N2O decomposition products. The N2 formation route is more favorable than the NO formation route in terms of energy barrier and reaction energy, and NO formation on the CaO (1 0 0) surface is likely to proceed via N2O + Osurf2- → N2 + O2 , surf2- and N2O + O2 , surf2- → 2NO + Osurf2-.

MRF energy minimization and beyond via dual decomposition.

PubMed

Komodakis, Nikos; Paragios, Nikos; Tziritas, Georgios

2011-03-01

This paper introduces a new rigorous theoretical framework to address discrete MRF-based optimization in computer vision. Such a framework exploits the powerful technique of Dual Decomposition. It is based on a projected subgradient scheme that attempts to solve an MRF optimization problem by first decomposing it into a set of appropriately chosen subproblems, and then combining their solutions in a principled way. In order to determine the limits of this method, we analyze the conditions that these subproblems have to satisfy and demonstrate the extreme generality and flexibility of such an approach. We thus show that by appropriately choosing what subproblems to use, one can design novel and very powerful MRF optimization algorithms. For instance, in this manner we are able to derive algorithms that: 1) generalize and extend state-of-the-art message-passing methods, 2) optimize very tight LP-relaxations to MRF optimization, and 3) take full advantage of the special structure that may exist in particular MRFs, allowing the use of efficient inference techniques such as, e.g., graph-cut-based methods. Theoretical analysis on the bounds related with the different algorithms derived from our framework and experimental results/comparisons using synthetic and real data for a variety of tasks in computer vision demonstrate the extreme potentials of our approach.

Novel method for preparation of the template DNA and selection of primers to differentiate the material rice cultivars of rice wine by PCR.

PubMed

Ohtsubo, Ken'ichi; Suzuki, Keitaro; Haraguchi, Kazutomo; Nakamura, Sumiko

2008-04-24

As many rice wine brewers label the name of the cultivar of the material rice, authentication technology is necessary. The problems are (1) decomposition of DNAs during the fermentation, (2) contamination of DNAs from microorganisms, (3) co-existence of PCR inhibitors, such as polyphenols. The present authors improved the PCR method by (1) lyophilizing and pulverizing the rice wine to concentrate DNAs, (2) decomposition of starches and proteins so as not to inhibit DNA extraction by the use of heat-resistant amylase and proteinase K, (3) purification of the template DNA by the combination of CTAB method and fractional precipitation by 70% EtOH. To prevent the amplification of microorganism's DNAs during PCR, the present authors selected the suitable plant-specific primers. It became possible to prepare the template DNAs for PCR from the rice wine. The sequences of the amplified DNAs by PCR were ascertained to be same with those of material rice. Mislabeling of material rice cultivar was detected by PCR using the commercial rice wine. It became possible to extract and purify the template DNAs for PCR from the rice wine and to differentiate the material rice cultivars by the PCR using the rice wine as a sample.

Joint detection and tracking of size-varying infrared targets based on block-wise sparse decomposition

NASA Astrophysics Data System (ADS)

Li, Miao; Lin, Zaiping; Long, Yunli; An, Wei; Zhou, Yiyu

2016-05-01

The high variability of target size makes small target detection in Infrared Search and Track (IRST) a challenging task. A joint detection and tracking method based on block-wise sparse decomposition is proposed to address this problem. For detection, the infrared image is divided into overlapped blocks, and each block is weighted on the local image complexity and target existence probabilities. Target-background decomposition is solved by block-wise inexact augmented Lagrange multipliers. For tracking, label multi-Bernoulli (LMB) tracker tracks multiple targets taking the result of single-frame detection as input, and provides corresponding target existence probabilities for detection. Unlike fixed-size methods, the proposed method can accommodate size-varying targets, due to no special assumption for the size and shape of small targets. Because of exact decomposition, classical target measurements are extended and additional direction information is provided to improve tracking performance. The experimental results show that the proposed method can effectively suppress background clutters, detect and track size-varying targets in infrared images.

High performance computation of radiative transfer equation using the finite element method

NASA Astrophysics Data System (ADS)

Badri, M. A.; Jolivet, P.; Rousseau, B.; Favennec, Y.

2018-05-01

This article deals with an efficient strategy for numerically simulating radiative transfer phenomena using distributed computing. The finite element method alongside the discrete ordinate method is used for spatio-angular discretization of the monochromatic steady-state radiative transfer equation in an anisotropically scattering media. Two very different methods of parallelization, angular and spatial decomposition methods, are presented. To do so, the finite element method is used in a vectorial way. A detailed comparison of scalability, performance, and efficiency on thousands of processors is established for two- and three-dimensional heterogeneous test cases. Timings show that both algorithms scale well when using proper preconditioners. It is also observed that our angular decomposition scheme outperforms our domain decomposition method. Overall, we perform numerical simulations at scales that were previously unattainable by standard radiative transfer equation solvers.

GENESIS: a hybrid-parallel and multi-scale molecular dynamics simulator with enhanced sampling algorithms for biomolecular and cellular simulations

PubMed Central

Jung, Jaewoon; Mori, Takaharu; Kobayashi, Chigusa; Matsunaga, Yasuhiro; Yoda, Takao; Feig, Michael; Sugita, Yuji

2015-01-01

GENESIS (Generalized-Ensemble Simulation System) is a new software package for molecular dynamics (MD) simulations of macromolecules. It has two MD simulators, called ATDYN and SPDYN. ATDYN is parallelized based on an atomic decomposition algorithm for the simulations of all-atom force-field models as well as coarse-grained Go-like models. SPDYN is highly parallelized based on a domain decomposition scheme, allowing large-scale MD simulations on supercomputers. Hybrid schemes combining OpenMP and MPI are used in both simulators to target modern multicore computer architectures. Key advantages of GENESIS are (1) the highly parallel performance of SPDYN for very large biological systems consisting of more than one million atoms and (2) the availability of various REMD algorithms (T-REMD, REUS, multi-dimensional REMD for both all-atom and Go-like models under the NVT, NPT, NPAT, and NPγT ensembles). The former is achieved by a combination of the midpoint cell method and the efficient three-dimensional Fast Fourier Transform algorithm, where the domain decomposition space is shared in real-space and reciprocal-space calculations. Other features in SPDYN, such as avoiding concurrent memory access, reducing communication times, and usage of parallel input/output files, also contribute to the performance. We show the REMD simulation results of a mixed (POPC/DMPC) lipid bilayer as a real application using GENESIS. GENESIS is released as free software under the GPLv2 licence and can be easily modified for the development of new algorithms and molecular models. WIREs Comput Mol Sci 2015, 5:310–323. doi: 10.1002/wcms.1220 PMID:26753008

Nanorods, nanospheres, nanocubes: Synthesis, characterization and catalytic activity of nanoferrites of Mn, Co, Ni, Part-89

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

Singh, Supriya; Srivastava, Pratibha; Singh, Gurdip, E-mail: gsingh4us@yahoo.com

2013-02-15

Graphical abstract: Prepared nanoferrites were characterized by FE-SEM and bright field TEM micrographs. The catalytic effect of these nanoferrites was evaluated on the thermal decomposition of ammonium perchlorate using TG and TG–DSC techniques. The kinetics of thermal decomposition of AP was evaluated using isothermal TG data by model fitting as well as isoconversional method. Display Omitted Highlights: ► Synthesis of ferrite nanostructures (∼20.0 nm) by wet-chemical method under different synthetic conditions. ► Characterization using XRD, FE-SEM, EDS, TEM, HRTEM and SAED pattern. ► Catalytic activity of ferrite nanostructures on AP thermal decomposition by thermal techniques. ► Burning rate measurements ofmore » CSPs with ferrite nanostructures. ► Kinetics of thermal decomposition of AP + nanoferrites. -- Abstract: In this paper, the nanoferrites of Mn, Co and Ni were synthesized by wet chemical method and characterized by X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), energy dispersive, X-ray spectra (EDS), transmission electron microscopy (TEM) and high resolution transmission electron microscopy (HR-TEM). It is catalytic activity were investigated on the thermal decomposition of ammonium perchlorate (AP) and composite solid propellants (CSPs) using thermogravimetry (TG), TG coupled with differential scanning calorimetry (TG–DSC) and ignition delay measurements. Kinetics of thermal decomposition of AP + nanoferrites have also been investigated using isoconversional and model fitting approaches which have been applied to data for isothermal TG decomposition. The burning rate of CSPs was considerably enhanced by these nanoferrites. Addition of nanoferrites to AP led to shifting of the high temperature decomposition peak toward lower temperature. All these studies reveal that ferrite nanorods show the best catalytic activity superior to that of nanospheres and nanocubes.« less

Thermal decomposition of ammonium hexachloroosmate.

PubMed

Asanova, T I; Kantor, I; Asanov, I P; Korenev, S V; Yusenko, K V

2016-12-07

Structural changes of (NH 4 ) 2 [OsCl 6 ] occurring during thermal decomposition in a reduction atmosphere have been studied in situ using combined energy-dispersive X-ray absorption spectroscopy (ED-XAFS) and powder X-ray diffraction (PXRD). According to PXRD, (NH 4 ) 2 [OsCl 6 ] transforms directly to metallic Os without the formation of any crystalline intermediates but through a plateau where no reactions occur. XANES and EXAFS data by means of Multivariate Curve Resolution (MCR) analysis show that thermal decomposition occurs with the formation of an amorphous intermediate {OsCl 4 } x with a possible polymeric structure. Being revealed for the first time the intermediate was subjected to determine the local atomic structure around osmium. The thermal decomposition of hexachloroosmate is much more complex and occurs within a minimum two-step process, which has never been observed before.

Spectroscopic study of shock-induced decomposition in ammonium perchlorate single crystals.

PubMed

Gruzdkov, Y A; Winey, J M; Gupta, Y M

2008-05-01

Time-resolved Raman scattering measurements were performed on ammonium perchlorate (AP) single crystals under stepwise shock loading. For particular temperature and pressure conditions, the intensity of the Raman spectra in shocked AP decayed exponentially with time. This decay is attributed to shock-induced chemical decomposition in AP. A series of shock experiments, reaching peak stresses from 10-18 GPa, demonstrated that higher stresses inhibit decomposition while higher temperatures promote it. No orientation dependence was found when AP crystals were shocked normal to the (210) and (001) crystallographic planes. VISAR (velocity interferometer system for any reflector) particle velocity measurements and time-resolved optical extinction measurements carried out to verify these observations are consistent with the Raman data. The combined kinetic and spectroscopic results are consistent with a proton-transfer reaction as the first decomposition step in shocked AP.

Microbial community assembly and metabolic function during mammalian corpse decomposition.

PubMed

Metcalf, Jessica L; Xu, Zhenjiang Zech; Weiss, Sophie; Lax, Simon; Van Treuren, Will; Hyde, Embriette R; Song, Se Jin; Amir, Amnon; Larsen, Peter; Sangwan, Naseer; Haarmann, Daniel; Humphrey, Greg C; Ackermann, Gail; Thompson, Luke R; Lauber, Christian; Bibat, Alexander; Nicholas, Catherine; Gebert, Matthew J; Petrosino, Joseph F; Reed, Sasha C; Gilbert, Jack A; Lynne, Aaron M; Bucheli, Sibyl R; Carter, David O; Knight, Rob

2016-01-08

Vertebrate corpse decomposition provides an important stage in nutrient cycling in most terrestrial habitats, yet microbially mediated processes are poorly understood. Here we combine deep microbial community characterization, community-level metabolic reconstruction, and soil biogeochemical assessment to understand the principles governing microbial community assembly during decomposition of mouse and human corpses on different soil substrates. We find a suite of bacterial and fungal groups that contribute to nitrogen cycling and a reproducible network of decomposers that emerge on predictable time scales. Our results show that this decomposer community is derived primarily from bulk soil, but key decomposers are ubiquitous in low abundance. Soil type was not a dominant factor driving community development, and the process of decomposition is sufficiently reproducible to offer new opportunities for forensic investigations. Copyright © 2016, American Association for the Advancement of Science.

Nuclear driven water decomposition plant for hydrogen production

NASA Technical Reports Server (NTRS)

Parker, G. H.; Brecher, L. E.; Farbman, G. H.

1976-01-01

The conceptual design of a hydrogen production plant using a very-high-temperature nuclear reactor (VHTR) to energize a hybrid electrolytic-thermochemical system for water decomposition has been prepared. A graphite-moderated helium-cooled VHTR is used to produce 1850 F gas for electric power generation and 1600 F process heat for the water-decomposition process which uses sulfur compounds and promises performance superior to normal water electrolysis or other published thermochemical processes. The combined cycle operates at an overall thermal efficiency in excess of 45%, and the overall economics of hydrogen production by this plant have been evaluated predicated on a consistent set of economic ground rules. The conceptual design and evaluation efforts have indicated that development of this type of nuclear-driven water-decomposition plant will permit large-scale economic generation of hydrogen in the 1990s.

Oxidative decomposition of aromatic hydrocarbons by electron beam irradiation

NASA Astrophysics Data System (ADS)

Han, Do-Hung; Stuchinskaya, Tatiana; Won, Yang-Soo; Park, Wan-Sik; Lim, Jae-Kyong

2003-05-01

Decomposition of aromatic volatile organic compounds (VOCs) under electron beam irradiation was studied in order to examine the kinetics of the process, to characterize the reaction product distribution and to develop a process of waste gas control technology. Toluene, ethylbenzene, o-, m-, p-xylenes and chlorobenzene were used as target materials. The experiments were carried out at doses ranging from 0.5 to 10 kGy, using a flow reactor utilized under electron beam irradiation. Maximum degrees of decomposition carried out at 10 kGy in air environment were 55-65% for “non-chlorinated” aromatic VOC and 85% for chlorobenzene. It was found that a combination of aromatic pollutants with chlorobenzene would considerably increase the degradation value up to nearly 50% compared to the same compounds in the absence of chlorine groups. Based on our experimental observation, the degradation mechanism of the aromatic compounds combined with chloro-compound suggests that a chlorine radical, formed from EB irradiation, induces a chain reaction, resulting in an accelerating oxidative destruction of aromatic VOCs.

Native conflict awared layout decomposition in triple patterning lithography using bin-based library matching method

NASA Astrophysics Data System (ADS)

Ke, Xianhua; Jiang, Hao; Lv, Wen; Liu, Shiyuan

2016-03-01

Triple patterning (TP) lithography becomes a feasible technology for manufacturing as the feature size further scale down to sub 14/10 nm. In TP, a layout is decomposed into three masks followed with exposures and etches/freezing processes respectively. Previous works mostly focus on layout decomposition with minimal conflicts and stitches simultaneously. However, since any existence of native conflict will result in layout re-design/modification and reperforming the time-consuming decomposition, the effective method that can be aware of native conflicts (NCs) in layout is desirable. In this paper, a bin-based library matching method is proposed for NCs detection and layout decomposition. First, a layout is divided into bins and the corresponding conflict graph in each bin is constructed. Then, we match the conflict graph in a prebuilt colored library, and as a result the NCs can be located and highlighted quickly.

Measuring Glial Metabolism in Repetitive Brain Trauma and Alzheimer’s Disease

DTIC Science & Technology

2016-09-01

Six methods: Single value decomposition (SVD), wavelet, sliding window, sliding window with Gaussian weighting, spline and spectral improvements...comparison of a range of different denoising methods for dynamic MRS. Six denoising methods were considered: Single value decomposition (SVD), wavelet...project by improving the software required for the data analysis by developing six different denoising methods. He also assisted with the testing

Radar-based Quantitative Precipitation Forecasting using Spatial-scale Decomposition Method for Urban Flood Management

NASA Astrophysics Data System (ADS)

Yoon, S.; Lee, B.; Nakakita, E.; Lee, G.

2016-12-01

Recent climate changes and abnormal weather phenomena have resulted in increased occurrences of localized torrential rainfall. Urban areas in Korea have suffered from localized heavy rainfall, including the notable Seoul flood disaster in 2010 and 2011. The urban hydrological environment has changed in relation to precipitation, such as reduced concentration time, a decreased storage rate, and increased peak discharge. These changes have altered and accelerated the severity of damage to urban areas. In order to prevent such urban flash flood damages, we have to secure the lead time for evacuation through the improvement of radar-based quantitative precipitation forecasting (QPF). The purpose of this research is to improve the QPF products using spatial-scale decomposition method for considering the life time of storm and to assess the accuracy between traditional QPF method and proposed method in terms of urban flood management. The layout of this research is as below. First, this research applies the image filtering to separate the spatial-scale of rainfall field. Second, the separated small and large-scale rainfall fields are extrapolated by each different forecasting method. Third, forecasted rainfall fields are combined at each lead time. Finally, results of this method are evaluated and compared with the results of uniform advection model for urban flood modeling. It is expected that urban flood information using improved QPF will help to reduce casualties and property damage caused by urban flooding through this research.

Two Dimensional Finite Element Based Magnetotelluric Inversion using Singular Value Decomposition Method on Transverse Electric Mode

NASA Astrophysics Data System (ADS)

Tjong, Tiffany; Yihaa’ Roodhiyah, Lisa; Nurhasan; Sutarno, Doddy

2018-04-01

In this work, an inversion scheme was performed using a vector finite element (VFE) based 2-D magnetotelluric (MT) forward modelling. We use an inversion scheme with Singular value decomposition (SVD) method toimprove the accuracy of MT inversion.The inversion scheme was applied to transverse electric (TE) mode of MT. SVD method was used in this inversion to decompose the Jacobian matrices. Singular values which obtained from the decomposition process were analyzed. This enabled us to determine the importance of data and therefore to define a threshold for truncation process. The truncation of singular value in inversion processcould improve the resulted model.

Preparation, non-isothermal decomposition kinetics, heat capacity and adiabatic time-to-explosion of NTOxDNAZ.

PubMed

Ma, Haixia; Yan, Biao; Li, Zhaona; Guan, Yulei; Song, Jirong; Xu, Kangzhen; Hu, Rongzu

2009-09-30

NTOxDNAZ was prepared by mixing 3,3-dinitroazetidine (DNAZ) and 3-nitro-1,2,4-triazol-5-one (NTO) in ethanol solution. The thermal behavior of the title compound was studied under a non-isothermal condition by DSC and TG/DTG methods. The kinetic parameters were obtained from analysis of the DSC and TG/DTG curves by Kissinger method, Ozawa method, the differential method and the integral method. The main exothermic decomposition reaction mechanism of NTOxDNAZ is classified as chemical reaction, and the kinetic parameters of the reaction are E(a)=149.68 kJ mol(-1) and A=10(15.81)s(-1). The specific heat capacity of the title compound was determined with continuous C(p) mode of microcalorimeter. The standard mole specific heat capacity of NTOxDNAZ was 352.56 J mol(-1)K(-1) in 298.15K. Using the relationship between C(p) and T and the thermal decomposition parameters, the time of the thermal decomposition from initialization to thermal explosion (adiabatic time-to-explosion) was obtained.

A Novel Multilevel-SVD Method to Improve Multistep Ahead Forecasting in Traffic Accidents Domain.

PubMed

Barba, Lida; Rodríguez, Nibaldo

2017-01-01

Here is proposed a novel method for decomposing a nonstationary time series in components of low and high frequency. The method is based on Multilevel Singular Value Decomposition (MSVD) of a Hankel matrix. The decomposition is used to improve the forecasting accuracy of Multiple Input Multiple Output (MIMO) linear and nonlinear models. Three time series coming from traffic accidents domain are used. They represent the number of persons with injuries in traffic accidents of Santiago, Chile. The data were continuously collected by the Chilean Police and were weekly sampled from 2000:1 to 2014:12. The performance of MSVD is compared with the decomposition in components of low and high frequency of a commonly accepted method based on Stationary Wavelet Transform (SWT). SWT in conjunction with the Autoregressive model (SWT + MIMO-AR) and SWT in conjunction with an Autoregressive Neural Network (SWT + MIMO-ANN) were evaluated. The empirical results have shown that the best accuracy was achieved by the forecasting model based on the proposed decomposition method MSVD, in comparison with the forecasting models based on SWT.

A Novel Multilevel-SVD Method to Improve Multistep Ahead Forecasting in Traffic Accidents Domain

PubMed Central

Rodríguez, Nibaldo

2017-01-01

Here is proposed a novel method for decomposing a nonstationary time series in components of low and high frequency. The method is based on Multilevel Singular Value Decomposition (MSVD) of a Hankel matrix. The decomposition is used to improve the forecasting accuracy of Multiple Input Multiple Output (MIMO) linear and nonlinear models. Three time series coming from traffic accidents domain are used. They represent the number of persons with injuries in traffic accidents of Santiago, Chile. The data were continuously collected by the Chilean Police and were weekly sampled from 2000:1 to 2014:12. The performance of MSVD is compared with the decomposition in components of low and high frequency of a commonly accepted method based on Stationary Wavelet Transform (SWT). SWT in conjunction with the Autoregressive model (SWT + MIMO-AR) and SWT in conjunction with an Autoregressive Neural Network (SWT + MIMO-ANN) were evaluated. The empirical results have shown that the best accuracy was achieved by the forecasting model based on the proposed decomposition method MSVD, in comparison with the forecasting models based on SWT. PMID:28261267

Some results on numerical methods for hyperbolic conservation laws

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

Yang Huanan.

1989-01-01

This dissertation contains some results on the numerical solutions of hyperbolic conservation laws. (1) The author introduced an artificial compression method as a correction to the basic ENO schemes. The method successfully prevents contact discontinuities from being smeared. This is achieved by increasing the slopes of the ENO reconstructions in such a way that the essentially non-oscillatory property of the schemes is kept. He analyzes the non-oscillatory property of the new artificial compression method by applying it to the UNO scheme which is a second order accurate ENO scheme, and proves that the resulting scheme is indeed non-oscillatory. Extensive 1-Dmore » numerical results and some preliminary 2-D ones are provided to show the strong performance of the method. (2) He combines the ENO schemes and the centered difference schemes into self-adjusting hybrid schemes which will be called the localized ENO schemes. At or near the jumps, he uses the ENO schemes with the field by field decompositions, otherwise he simply uses the centered difference schemes without the field by field decompositions. The method involves a new interpolation analysis. In the numerical experiments on several standard test problems, the quality of the numerical results of this method is close to that of the pure ENO results. The localized ENO schemes can be equipped with the above artificial compression method. In this way, he dramatically improves the resolutions of the contact discontinuities at very little additional costs. (3) He introduces a space-time mesh refinement method for time dependent problems.« less

On-the-fly transition search and applications to temperature-accelerated dynamics

NASA Astrophysics Data System (ADS)

Shim, Yunsic; Amar, Jacques

2015-03-01

Temperature-accelerated dynamics (TAD) is a powerful method to study non-equilibrium processes and has been providing surprising insights for a variety of systems. While serial TAD simulations have been limited by the roughly N3 increase in the computational cost as a function of the number of atoms N in the system, recently we have shown that by carrying out parallel TAD simulations which combine spatial decomposition with our semi-rigorous synchronous sublattice algorithm, significantly improved scaling is possible. However, in this approach the size of activated events is limited by the processor size while the dynamics is not exact. Here we discuss progress in improving the scaling of serial TAD by combining the use of on-the-fly transition searching with our previously developed localized saddle-point method. We demonstrate improved performance for the cases of Ag/Ag(100) annealing and Cu/Cu(100) growth. Supported by NSF DMR-1410840.

Complete ensemble local mean decomposition with adaptive noise and its application to fault diagnosis for rolling bearings

NASA Astrophysics Data System (ADS)

Wang, Lei; Liu, Zhiwen; Miao, Qiang; Zhang, Xin

2018-06-01

Mode mixing resulting from intermittent signals is an annoying problem associated with the local mean decomposition (LMD) method. Based on noise-assisted approach, ensemble local mean decomposition (ELMD) method alleviates the mode mixing issue of LMD to some degree. However, the product functions (PFs) produced by ELMD often contain considerable residual noise, and thus a relatively large number of ensemble trials are required to eliminate the residual noise. Furthermore, since different realizations of Gaussian white noise are added to the original signal, different trials may generate different number of PFs, making it difficult to take ensemble mean. In this paper, a novel method is proposed called complete ensemble local mean decomposition with adaptive noise (CELMDAN) to solve these two problems. The method adds a particular and adaptive noise at every decomposition stage for each trial. Moreover, a unique residue is obtained after separating each PF, and the obtained residue is used as input for the next stage. Two simulated signals are analyzed to illustrate the advantages of CELMDAN in comparison to ELMD and CEEMDAN. To further demonstrate the efficiency of CELMDAN, the method is applied to diagnose faults for rolling bearings in an experimental case and an engineering case. The diagnosis results indicate that CELMDAN can extract more fault characteristic information with less interference than ELMD.

A TV-constrained decomposition method for spectral CT

NASA Astrophysics Data System (ADS)

Guo, Xiaoyue; Zhang, Li; Xing, Yuxiang

2017-03-01

Spectral CT is attracting more and more attention in medicine, industrial nondestructive testing and security inspection field. Material decomposition is an important issue to a spectral CT to discriminate materials. Because of the spectrum overlap of energy channels, as well as the correlation of basis functions, it is well acknowledged that decomposition step in spectral CT imaging causes noise amplification and artifacts in component coefficient images. In this work, we propose materials decomposition via an optimization method to improve the quality of decomposed coefficient images. On the basis of general optimization problem, total variance minimization is constrained on coefficient images in our overall objective function with adjustable weights. We solve this constrained optimization problem under the framework of ADMM. Validation on both a numerical dental phantom in simulation and a real phantom of pig leg on a practical CT system using dual-energy imaging is executed. Both numerical and physical experiments give visually obvious better reconstructions than a general direct inverse method. SNR and SSIM are adopted to quantitatively evaluate the image quality of decomposed component coefficients. All results demonstrate that the TV-constrained decomposition method performs well in reducing noise without losing spatial resolution so that improving the image quality. The method can be easily incorporated into different types of spectral imaging modalities, as well as for cases with energy channels more than two.

Exploring Patterns of Soil Organic Matter Decomposition with Students and the Public Through the Global Decomposition Project (GDP)

NASA Astrophysics Data System (ADS)

Wood, J. H.; Natali, S.

2014-12-01

The Global Decomposition Project (GDP) is a program designed to introduce and educate students and the general public about soil organic matter and decomposition through a standardized protocol for collecting, reporting, and sharing data. This easy-to-use hands-on activity focuses on questions such as "How do environmental conditions control decomposition of organic matter in soil?" and "Why do some areas accumulate organic matter and others do not?" Soil organic matter is important to local ecosystems because it affects soil structure, regulates soil moisture and temperature, and provides energy and nutrients to soil organisms. It is also important globally because it stores a large amount of carbon, and when microbes "eat", or decompose organic matter they release greenhouse gasses such as carbon dioxide and methane into the atmosphere, which affects the earth's climate. The protocol describes a commonly used method to measure decomposition using a paper made of cellulose, a component of plant cell walls. Participants can receive pre-made cellulose decomposition bags, or make decomposition bags using instructions in the protocol and easily obtained materials (e.g., window screen and lignin-free paper). Individual results will be shared with all participants and the broader public through an online database. We will present decomposition bag results from a research site in Alaskan tundra, as well as from a middle-school-student led experiment in California. The GDP demonstrates how scientific methods can be extended to educate broader audiences, while at the same time, data collected by students and the public can provide new insight into global patterns of soil decomposition. The GDP provides a pathway for scientists and educators to interact and reach meaningful education and research goals.

Numeric Modified Adomian Decomposition Method for Power System Simulations

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

Dimitrovski, Aleksandar D; Simunovic, Srdjan; Pannala, Sreekanth

This paper investigates the applicability of numeric Wazwaz El Sayed modified Adomian Decomposition Method (WES-ADM) for time domain simulation of power systems. WESADM is a numerical method based on a modified Adomian decomposition (ADM) technique. WES-ADM is a numerical approximation method for the solution of nonlinear ordinary differential equations. The non-linear terms in the differential equations are approximated using Adomian polynomials. In this paper WES-ADM is applied to time domain simulations of multimachine power systems. WECC 3-generator, 9-bus system and IEEE 10-generator, 39-bus system have been used to test the applicability of the approach. Several fault scenarios have been tested.more » It has been found that the proposed approach is faster than the trapezoidal method with comparable accuracy.« less

Numerical prediction of the energy efficiency of the three-dimensional fish school using the discretized Adomian decomposition method

NASA Astrophysics Data System (ADS)

Lin, Yinwei

2018-06-01

A three-dimensional modeling of fish school performed by a modified Adomian decomposition method (ADM) discretized by the finite difference method is proposed. To our knowledge, few studies of the fish school are documented due to expensive cost of numerical computing and tedious three-dimensional data analysis. Here, we propose a simple model replied on the Adomian decomposition method to estimate the efficiency of energy saving of the flow motion of the fish school. First, the analytic solutions of Navier-Stokes equations are used for numerical validation. The influences of the distance between the side-by-side two fishes are studied on the energy efficiency of the fish school. In addition, the complete error analysis for this method is presented.

Active listening room compensation for massive multichannel sound reproduction systems using wave-domain adaptive filtering.

PubMed

Spors, Sascha; Buchner, Herbert; Rabenstein, Rudolf; Herbordt, Wolfgang

2007-07-01

The acoustic theory for multichannel sound reproduction systems usually assumes free-field conditions for the listening environment. However, their performance in real-world listening environments may be impaired by reflections at the walls. This impairment can be reduced by suitable compensation measures. For systems with many channels, active compensation is an option, since the compensating waves can be created by the reproduction loudspeakers. Due to the time-varying nature of room acoustics, the compensation signals have to be determined by an adaptive system. The problems associated with the successful operation of multichannel adaptive systems are addressed in this contribution. First, a method for decoupling the adaptation problem is introduced. It is based on a generalized singular value decomposition and is called eigenspace adaptive filtering. Unfortunately, it cannot be implemented in its pure form, since the continuous adaptation of the generalized singular value decomposition matrices to the variable room acoustics is numerically very demanding. However, a combination of this mathematical technique with the physical description of wave propagation yields a realizable multichannel adaptation method with good decoupling properties. It is called wave domain adaptive filtering and is discussed here in the context of wave field synthesis.

Crude oil price analysis and forecasting based on variational mode decomposition and independent component analysis

NASA Astrophysics Data System (ADS)

E, Jianwei; Bao, Yanling; Ye, Jimin

2017-10-01

As one of the most vital energy resources in the world, crude oil plays a significant role in international economic market. The fluctuation of crude oil price has attracted academic and commercial attention. There exist many methods in forecasting the trend of crude oil price. However, traditional models failed in predicting accurately. Based on this, a hybrid method will be proposed in this paper, which combines variational mode decomposition (VMD), independent component analysis (ICA) and autoregressive integrated moving average (ARIMA), called VMD-ICA-ARIMA. The purpose of this study is to analyze the influence factors of crude oil price and predict the future crude oil price. Major steps can be concluded as follows: Firstly, applying the VMD model on the original signal (crude oil price), the modes function can be decomposed adaptively. Secondly, independent components are separated by the ICA, and how the independent components affect the crude oil price is analyzed. Finally, forecasting the price of crude oil price by the ARIMA model, the forecasting trend demonstrates that crude oil price declines periodically. Comparing with benchmark ARIMA and EEMD-ICA-ARIMA, VMD-ICA-ARIMA can forecast the crude oil price more accurately.

Decomposition-based transfer distance metric learning for image classification.

PubMed

Luo, Yong; Liu, Tongliang; Tao, Dacheng; Xu, Chao

2014-09-01

Distance metric learning (DML) is a critical factor for image analysis and pattern recognition. To learn a robust distance metric for a target task, we need abundant side information (i.e., the similarity/dissimilarity pairwise constraints over the labeled data), which is usually unavailable in practice due to the high labeling cost. This paper considers the transfer learning setting by exploiting the large quantity of side information from certain related, but different source tasks to help with target metric learning (with only a little side information). The state-of-the-art metric learning algorithms usually fail in this setting because the data distributions of the source task and target task are often quite different. We address this problem by assuming that the target distance metric lies in the space spanned by the eigenvectors of the source metrics (or other randomly generated bases). The target metric is represented as a combination of the base metrics, which are computed using the decomposed components of the source metrics (or simply a set of random bases); we call the proposed method, decomposition-based transfer DML (DTDML). In particular, DTDML learns a sparse combination of the base metrics to construct the target metric by forcing the target metric to be close to an integration of the source metrics. The main advantage of the proposed method compared with existing transfer metric learning approaches is that we directly learn the base metric coefficients instead of the target metric. To this end, far fewer variables need to be learned. We therefore obtain more reliable solutions given the limited side information and the optimization tends to be faster. Experiments on the popular handwritten image (digit, letter) classification and challenge natural image annotation tasks demonstrate the effectiveness of the proposed method.

One-step synthesis and structural features of CdS/montmorillonite nanocomposites.

PubMed

Han, Zhaohui; Zhu, Huaiyong; Bulcock, Shaun R; Ringer, Simon P

2005-02-24

A novel synthesis method was introduced for the nanocomposites of cadmium sulfide and montmorillonite. This method features the combination of an ion exchange process and an in situ hydrothermal decomposition process of a complex precursor, which is simple in contrast to the conventional synthesis methods that comprise two separate steps for similar nanocomposite materials. Cadmium sulfide species in the composites exist in the forms of pillars and nanoparticles, the crystallized sulfide particles are in the hexagonal phase, and the sizes change when the amount of the complex for the synthesis is varied. Structural features of the nanocomposites are similar to those of the clay host but changed because of the introduction of the sulfide into the clay.

Detonation initiation in a model of explosive: Comparative atomistic and hydrodynamics simulations

NASA Astrophysics Data System (ADS)

Murzov, S. A.; Sergeev, O. V.; Dyachkov, S. A.; Egorova, M. S.; Parshikov, A. N.; Zhakhovsky, V. V.

2016-11-01

Here we extend consistent simulations to reactive materials by the example of AB model explosive. The kinetic model of chemical reactions observed in a molecular dynamics (MD) simulation of self-sustained detonation wave can be used in hydrodynamic simulation of detonation initiation. Kinetic coefficients are obtained by minimization of difference between profiles of species calculated from the kinetic model and observed in MD simulations of isochoric thermal decomposition with a help of downhill simplex method combined with random walk in multidimensional space of fitting kinetic model parameters.

Reducing variation in decomposition odour profiling using comprehensive two-dimensional gas chromatography.

PubMed

Perrault, Katelynn A; Stefanuto, Pierre-Hugues; Stuart, Barbara H; Rai, Tapan; Focant, Jean-François; Forbes, Shari L

2015-01-01

Challenges in decomposition odour profiling have led to variation in the documented odour profile by different research groups worldwide. Background subtraction and use of controls are important considerations given the variation introduced by decomposition studies conducted in different geographical environments. The collection of volatile organic compounds (VOCs) from soil beneath decomposing remains is challenging due to the high levels of inherent soil VOCs, further confounded by the use of highly sensitive instrumentation. This study presents a method that provides suitable chromatographic resolution for profiling decomposition odour in soil by comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry using appropriate controls and field blanks. Logarithmic transformation and t-testing of compounds permitted the generation of a compound list of decomposition VOCs in soil. Principal component analysis demonstrated the improved discrimination between experimental and control soil, verifying the value of the data handling method. Data handling procedures have not been well documented in this field and standardisation would thereby reduce misidentification of VOCs present in the surrounding environment as decomposition byproducts. Uniformity of data handling and instrumental procedures will reduce analytical variation, increasing confidence in the future when investigating the effect of taphonomic variables on the decomposition VOC profile. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Parallelization of PANDA discrete ordinates code using spatial decomposition

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

Humbert, P.

2006-07-01

We present the parallel method, based on spatial domain decomposition, implemented in the 2D and 3D versions of the discrete Ordinates code PANDA. The spatial mesh is orthogonal and the spatial domain decomposition is Cartesian. For 3D problems a 3D Cartesian domain topology is created and the parallel method is based on a domain diagonal plane ordered sweep algorithm. The parallel efficiency of the method is improved by directions and octants pipelining. The implementation of the algorithm is straightforward using MPI blocking point to point communications. The efficiency of the method is illustrated by an application to the 3D-Ext C5G7more » benchmark of the OECD/NEA. (authors)« less

Online Artifact Removal for Brain-Computer Interfaces Using Support Vector Machines and Blind Source Separation

PubMed Central

Halder, Sebastian; Bensch, Michael; Mellinger, Jürgen; Bogdan, Martin; Kübler, Andrea; Birbaumer, Niels; Rosenstiel, Wolfgang

2007-01-01

We propose a combination of blind source separation (BSS) and independent component analysis (ICA) (signal decomposition into artifacts and nonartifacts) with support vector machines (SVMs) (automatic classification) that are designed for online usage. In order to select a suitable BSS/ICA method, three ICA algorithms (JADE, Infomax, and FastICA) and one BSS algorithm (AMUSE) are evaluated to determine their ability to isolate electromyographic (EMG) and electrooculographic (EOG) artifacts into individual components. An implementation of the selected BSS/ICA method with SVMs trained to classify EMG and EOG artifacts, which enables the usage of the method as a filter in measurements with online feedback, is described. This filter is evaluated on three BCI datasets as a proof-of-concept of the method. PMID:18288259

Online artifact removal for brain-computer interfaces using support vector machines and blind source separation.

PubMed

Halder, Sebastian; Bensch, Michael; Mellinger, Jürgen; Bogdan, Martin; Kübler, Andrea; Birbaumer, Niels; Rosenstiel, Wolfgang

2007-01-01

We propose a combination of blind source separation (BSS) and independent component analysis (ICA) (signal decomposition into artifacts and nonartifacts) with support vector machines (SVMs) (automatic classification) that are designed for online usage. In order to select a suitable BSS/ICA method, three ICA algorithms (JADE, Infomax, and FastICA) and one BSS algorithm (AMUSE) are evaluated to determine their ability to isolate electromyographic (EMG) and electrooculographic (EOG) artifacts into individual components. An implementation of the selected BSS/ICA method with SVMs trained to classify EMG and EOG artifacts, which enables the usage of the method as a filter in measurements with online feedback, is described. This filter is evaluated on three BCI datasets as a proof-of-concept of the method.

An eigensystem realization algorithm using data correlations (ERA/DC) for modal parameter identification

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Cooper, J. E.; Wright, J. R.

1987-01-01

A modification to the Eigensystem Realization Algorithm (ERA) for modal parameter identification is presented in this paper. The ERA minimum order realization approach using singular value decomposition is combined with the philosophy of the Correlation Fit method in state space form such that response data correlations rather than actual response values are used for modal parameter identification. This new method, the ERA using data correlations (ERA/DC), reduces bias errors due to noise corruption significantly without the need for model overspecification. This method is tested using simulated five-degree-of-freedom system responses corrupted by measurement noise. It is found for this case that, when model overspecification is permitted and a minimum order solution obtained via singular value truncation, the results from the two methods are of similar quality.

A general range-separated double-hybrid density-functional theory

NASA Astrophysics Data System (ADS)

Kalai, Cairedine; Toulouse, Julien

2018-04-01

A range-separated double-hybrid (RSDH) scheme which generalizes the usual range-separated hybrids and double hybrids is developed. This scheme consistently uses a two-parameter Coulomb-attenuating-method (CAM)-like decomposition of the electron-electron interaction for both exchange and correlation in order to combine Hartree-Fock exchange and second-order Møller-Plesset (MP2) correlation with a density functional. The RSDH scheme relies on an exact theory which is presented in some detail. Several semi-local approximations are developed for the short-range exchange-correlation density functional involved in this scheme. After finding optimal values for the two parameters of the CAM-like decomposition, the RSDH scheme is shown to have a relatively small basis dependence and to provide atomization energies, reaction barrier heights, and weak intermolecular interactions globally more accurate or comparable to range-separated MP2 or standard MP2. The RSDH scheme represents a new family of double hybrids with minimal empiricism which could be useful for general chemical applications.

Asymmetric color image encryption based on singular value decomposition

NASA Astrophysics Data System (ADS)

Yao, Lili; Yuan, Caojin; Qiang, Junjie; Feng, Shaotong; Nie, Shouping

2017-02-01

A novel asymmetric color image encryption approach by using singular value decomposition (SVD) is proposed. The original color image is encrypted into a ciphertext shown as an indexed image by using the proposed method. The red, green and blue components of the color image are subsequently encoded into a complex function which is then separated into U, S and V parts by SVD. The data matrix of the ciphertext is obtained by multiplying orthogonal matrices U and V while implementing phase-truncation. Diagonal entries of the three diagonal matrices of the SVD results are abstracted and scrambling combined to construct the colormap of the ciphertext. Thus, the encrypted indexed image covers less space than the original image. For decryption, the original color image cannot be recovered without private keys which are obtained from phase-truncation and the orthogonality of V. Computer simulations are presented to evaluate the performance of the proposed algorithm. We also analyze the security of the proposed system.

Detecting intrinsic dynamics of traffic flow with recurrence analysis and empirical mode decomposition

NASA Astrophysics Data System (ADS)

Xiong, Hui; Shang, Pengjian; Bian, Songhan

2017-05-01

In this paper, we apply the empirical mode decomposition (EMD) method to the recurrence plot (RP) and recurrence quantification analysis (RQA), to evaluate the frequency- and time-evolving dynamics of the traffic flow. Based on the cumulative intrinsic mode functions extracted by the EMD, the frequency-evolving RP regarding different oscillation of modes suggests that apparent dynamics of the data considered are mainly dominated by its components of medium- and low-frequencies while severely affected by fast oscillated noises contained in the signal. Noises are then eliminated to analyze the intrinsic dynamics and consequently, the denoised time-evolving RQA diversely characterizes the properties of the signal and marks crucial points more accurately where white bands in the RP occur, whereas a strongly qualitative agreement exists between all the non-denoised RQA measures. Generally, the EMD combining with the recurrence analysis sheds more reliable, abundant and inherent lights into the traffic flow, which is meaningful to the empirical analysis of complex systems.

Independent component analysis decomposition of hospital emergency department throughput measures

NASA Astrophysics Data System (ADS)

He, Qiang; Chu, Henry

2016-05-01

We present a method adapted from medical sensor data analysis, viz. independent component analysis of electroencephalography data, to health system analysis. Timely and effective care in a hospital emergency department is measured by throughput measures such as median times patients spent before they were admitted as an inpatient, before they were sent home, before they were seen by a healthcare professional. We consider a set of five such measures collected at 3,086 hospitals distributed across the U.S. One model of the performance of an emergency department is that these correlated throughput measures are linear combinations of some underlying sources. The independent component analysis decomposition of the data set can thus be viewed as transforming a set of performance measures collected at a site to a collection of outputs of spatial filters applied to the whole multi-measure data. We compare the independent component sources with the output of the conventional principal component analysis to show that the independent components are more suitable for understanding the data sets through visualizations.

Method for improved decomposition of metal nitrate solutions

DOEpatents

Haas, P.A.; Stines, W.B.

1981-01-21

A method for co-conversion of aqueous solutions of one or more heavy metal nitrates is described, wherein thermal decomposition within a temperature range of about 300 to 800/sup 0/C is carried out in the presence of about 50 to 500% molar concentration of ammonium nitrate to total metal.

Method for improved decomposition of metal nitrate solutions

DOEpatents

Haas, Paul A.; Stines, William B.

1983-10-11

A method for co-conversion of aqueous solutions of one or more heavy metal nitrates wherein thermal decomposition within a temperature range of about 300.degree. to 800.degree. C. is carried out in the presence of about 50 to 500% molar concentration of ammonium nitrate to total metal.

Method of forming semiconducting amorphous silicon films from the thermal decomposition of fluorohydridodisilanes

DOEpatents

Sharp, Kenneth G.; D'Errico, John J.

1988-01-01

The invention relates to a method of forming amorphous, photoconductive, and semiconductive silicon films on a substrate by the vapor phase thermal decomposition of a fluorohydridodisilane or a mixture of fluorohydridodisilanes. The invention is useful for the protection of surfaces including electronic devices.

On Partial Fraction Decompositions by Repeated Polynomial Divisions

ERIC Educational Resources Information Center

Man, Yiu-Kwong

2017-01-01

We present a method for finding partial fraction decompositions of rational functions with linear or quadratic factors in the denominators by means of repeated polynomial divisions. This method does not involve differentiation or solving linear equations for obtaining the unknown partial fraction coefficients, which is very suitable for either…

Decomposing Achievement Gaps among OECD Countries

ERIC Educational Resources Information Center

Zhang, Liang; Lee, Kristen A.

2011-01-01

In this study, we use decomposition methods on PISA 2006 data to compare student academic performance across OECD countries. We first establish an empirical model to explain the variation in academic performance across individuals, and then use the Oaxaca-Blinder decomposition method to decompose the achievement gap between each of the OECD…

A Dual Super-Element Domain Decomposition Approach for Parallel Nonlinear Finite Element Analysis

NASA Astrophysics Data System (ADS)

Jokhio, G. A.; Izzuddin, B. A.

2015-05-01

This article presents a new domain decomposition method for nonlinear finite element analysis introducing the concept of dual partition super-elements. The method extends ideas from the displacement frame method and is ideally suited for parallel nonlinear static/dynamic analysis of structural systems. In the new method, domain decomposition is realized by replacing one or more subdomains in a "parent system," each with a placeholder super-element, where the subdomains are processed separately as "child partitions," each wrapped by a dual super-element along the partition boundary. The analysis of the overall system, including the satisfaction of equilibrium and compatibility at all partition boundaries, is realized through direct communication between all pairs of placeholder and dual super-elements. The proposed method has particular advantages for matrix solution methods based on the frontal scheme, and can be readily implemented for existing finite element analysis programs to achieve parallelization on distributed memory systems with minimal intervention, thus overcoming memory bottlenecks typically faced in the analysis of large-scale problems. Several examples are presented in this article which demonstrate the computational benefits of the proposed parallel domain decomposition approach and its applicability to the nonlinear structural analysis of realistic structural systems.

Efficient Method for the Determination of the Activation Energy of the Iodide-Catalyzed Decomposition of Hydrogen Peroxide

ERIC Educational Resources Information Center

Sweeney, William; Lee, James; Abid, Nauman; DeMeo, Stephen

2014-01-01

An experiment is described that determines the activation energy (E[subscript a]) of the iodide-catalyzed decomposition reaction of hydrogen peroxide in a much more efficient manner than previously reported in the literature. Hydrogen peroxide, spontaneously or with a catalyst, decomposes to oxygen and water. Because the decomposition reaction is…

MO-FG-204-03: Using Edge-Preserving Algorithm for Significantly Improved Image-Domain Material Decomposition in Dual Energy CT

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

Zhao, W; Niu, T; Xing, L

2015-06-15

Purpose: To significantly improve dual energy CT (DECT) imaging by establishing a new theoretical framework of image-domain material decomposition with incorporation of edge-preserving techniques. Methods: The proposed algorithm, HYPR-NLM, combines the edge-preserving non-local mean filter (NLM) with the HYPR-LR (Local HighlY constrained backPRojection Reconstruction) framework. Image denoising using HYPR-LR framework depends on the noise level of the composite image which is the average of the different energy images. For DECT, the composite image is the average of high- and low-energy images. To further reduce noise, one may want to increase the window size of the filter of the HYPR-LR, leadingmore » resolution degradation. By incorporating the NLM filtering and the HYPR-LR framework, HYPR-NLM reduces the boost material decomposition noise using energy information redundancies as well as the non-local mean. We demonstrate the noise reduction and resolution preservation of the algorithm with both iodine concentration numerical phantom and clinical patient data by comparing the HYPR-NLM algorithm to the direct matrix inversion, HYPR-LR and iterative image-domain material decomposition (Iter-DECT). Results: The results show iterative material decomposition method reduces noise to the lowest level and provides improved DECT images. HYPR-NLM significantly reduces noise while preserving the accuracy of quantitative measurement and resolution. For the iodine concentration numerical phantom, the averaged noise levels are about 2.0, 0.7, 0.2 and 0.4 for direct inversion, HYPR-LR, Iter- DECT and HYPR-NLM, respectively. For the patient data, the noise levels of the water images are about 0.36, 0.16, 0.12 and 0.13 for direct inversion, HYPR-LR, Iter-DECT and HYPR-NLM, respectively. Difference images of both HYPR-LR and Iter-DECT show edge effect, while no significant edge effect is shown for HYPR-NLM, suggesting spatial resolution is well preserved for HYPR-NLM. Conclusion: HYPR-NLM provides an effective way to reduce the generic magnified image noise of dual–energy material decomposition while preserving resolution. This work is supported in part by NIH grants 7R01HL111141 and 1R01-EB016777. This work is also supported by the Natural Science Foundation of China (NSFC Grant No. 81201091), Fundamental Research Funds for the Central Universities in China, and Fund Project for Excellent Abroad Scholar Personnel in Science and Technology.« less

Efficient calculation of full waveform time domain inversion for electromagnetic problem using fictitious wave domain method and cascade decimation decomposition

NASA Astrophysics Data System (ADS)

Imamura, N.; Schultz, A.

2016-12-01

Recently, a full waveform time domain inverse solution has been developed for the magnetotelluric (MT) and controlled-source electromagnetic (CSEM) methods. The ultimate goal of this approach is to obtain a computationally tractable direct waveform joint inversion to solve simultaneously for source fields and earth conductivity structure in three and four dimensions. This is desirable on several grounds, including the improved spatial resolving power expected from use of a multitude of source illuminations, the ability to operate in areas of high levels of source signal spatial complexity, and non-stationarity. This goal would not be obtainable if one were to adopt the pure time domain solution for the inverse problem. This is particularly true for the case of MT surveys, since an enormous number of degrees of freedom are required to represent the observed MT waveforms across a large frequency bandwidth. This means that for the forward simulation, the smallest time steps should be finer than that required to represent the highest frequency, while the number of time steps should also cover the lowest frequency. This leads to a sensitivity matrix that is computationally burdensome to solve a model update. We have implemented a code that addresses this situation through the use of cascade decimation decomposition to reduce the size of the sensitivity matrix substantially, through quasi-equivalent time domain decomposition. We also use a fictitious wave domain method to speed up computation time of the forward simulation in the time domain. By combining these refinements, we have developed a full waveform joint source field/earth conductivity inverse modeling method. We found that cascade decimation speeds computations of the sensitivity matrices dramatically, keeping the solution close to that of the undecimated case. For example, for a model discretized into 2.6x105 cells, we obtain model updates in less than 1 hour on a 4U rack-mounted workgroup Linux server, which is a practical computational time for the inverse problem.

Scoring of Decomposition: A Proposed Amendment to the Method When Using a Pig Model for Human Studies.

PubMed

Keough, Natalie; Myburgh, Jolandie; Steyn, Maryna

2017-07-01

Decomposition studies often use pigs as proxies for human cadavers. However, differences in decomposition sequences/rates relative to humans have not been scientifically examined. Descriptions of five main decomposition stages (humans) were developed and refined by Galloway and later by Megyesi. However, whether these changes/processes are alike in pigs is unclear. Any differences can have significant effects when pig models are used for human PMI estimation. This study compared human decomposition models to the changes observed in pigs. Twenty pigs (50-90 kg) were decomposed over five months and decompositional features recorded. Total body scores (TBS) were calculated. Significant differences were observed during early decomposition between pigs and humans. An amended scoring system to be used in future studies was developed. Standards for PMI estimation derived from porcine models may not directly apply to humans and may need adjustment. Porcine models, however, remain valuable to study variables influencing decomposition. © 2016 American Academy of Forensic Sciences.

A pilot stability study on four-drug fixed-dose combination anti-tuberculosis products.

PubMed

Singh, S; Mohan, B

2003-03-01

A pilot stability study was carried out on four fixed-dose combination anti-tuberculosis products at 40 degrees C and 75% RH. The strip-packed products were stable, while the blister-packed products showed both physical and chemical changes. The products in unpacked conditions showed severe (approximately 60%) decomposition of rifampicin and extensive physical changes. The main decomposition product in the solid state was isonicotinyl hydrazone of 3-formylrifamycin and isoniazid. It is suggested that attention should be paid to the detection and quantitation of this product in the marketed formulations. The packing material used in the manufacture of FDC products should also be of the highest quality.

Seasonal variation of carcass decomposition and gravesoil chemistry in a cold (Dfa) climate.

PubMed

Meyer, Jessica; Anderson, Brianna; Carter, David O

2013-09-01

It is well known that temperature significantly affects corpse decomposition. Yet relatively few taphonomy studies investigate the effects of seasonality on decomposition. Here, we propose the use of the Köppen-Geiger climate classification system and describe the decomposition of swine (Sus scrofa domesticus) carcasses during the summer and winter near Lincoln, Nebraska, USA. Decomposition was scored, and gravesoil chemistry (total carbon, total nitrogen, ninhydrin-reactive nitrogen, ammonium, nitrate, and soil pH) was assessed. Gross carcass decomposition in summer was three to seven times greater than in winter. Initial significant changes in gravesoil chemistry occurred following approximately 320 accumulated degree days, regardless of season. Furthermore, significant (p < 0.05) correlations were observed between ammonium and pH (positive correlation) and between nitrate and pH (negative correlation). We hope that future decomposition studies employ the Köppen-Geiger climate classification system to understand the seasonality of corpse decomposition, to validate taphonomic methods, and to facilitate cross-climate comparisons of carcass decomposition. © 2013 American Academy of Forensic Sciences.

Defect Detection in Textures through the Use of Entropy as a Means for Automatically Selecting the Wavelet Decomposition Level.

PubMed

Navarro, Pedro J; Fernández-Isla, Carlos; Alcover, Pedro María; Suardíaz, Juan

2016-07-27

This paper presents a robust method for defect detection in textures, entropy-based automatic selection of the wavelet decomposition level (EADL), based on a wavelet reconstruction scheme, for detecting defects in a wide variety of structural and statistical textures. Two main features are presented. One of the new features is an original use of the normalized absolute function value (NABS) calculated from the wavelet coefficients derived at various different decomposition levels in order to identify textures where the defect can be isolated by eliminating the texture pattern in the first decomposition level. The second is the use of Shannon's entropy, calculated over detail subimages, for automatic selection of the band for image reconstruction, which, unlike other techniques, such as those based on the co-occurrence matrix or on energy calculation, provides a lower decomposition level, thus avoiding excessive degradation of the image, allowing a more accurate defect segmentation. A metric analysis of the results of the proposed method with nine different thresholding algorithms determined that selecting the appropriate thresholding method is important to achieve optimum performance in defect detection. As a consequence, several different thresholding algorithms depending on the type of texture are proposed.

A statistical approach based on accumulated degree-days to predict decomposition-related processes in forensic studies.

PubMed

Michaud, Jean-Philippe; Moreau, Gaétan

2011-01-01

Using pig carcasses exposed over 3 years in rural fields during spring, summer, and fall, we studied the relationship between decomposition stages and degree-day accumulation (i) to verify the predictability of the decomposition stages used in forensic entomology to document carcass decomposition and (ii) to build a degree-day accumulation model applicable to various decomposition-related processes. Results indicate that the decomposition stages can be predicted with accuracy from temperature records and that a reliable degree-day index can be developed to study decomposition-related processes. The development of degree-day indices opens new doors for researchers and allows for the application of inferential tools unaffected by climatic variability, as well as for the inclusion of statistics in a science that is primarily descriptive and in need of validation methods in courtroom proceedings. © 2010 American Academy of Forensic Sciences.

Water-splitting using photocatalytic porphyrin-nanotube composite devices

DOEpatents

Shelnutt, John A [Tijeras, NM; Miller, James E [Albuquerque, NM; Wang, Zhongchun [Albuquerque, NM; Medforth, Craig J [Winters, CA

2008-03-04

A method for generating hydrogen by photocatalytic decomposition of water using porphyrin nanotube composites. In some embodiments, both hydrogen and oxygen are generated by photocatalytic decomposition of water.

Sparse decomposition of seismic data and migration using Gaussian beams with nonzero initial curvature

NASA Astrophysics Data System (ADS)

Liu, Peng; Wang, Yanfei

2018-04-01

We study problems associated with seismic data decomposition and migration imaging. We first represent the seismic data utilizing Gaussian beam basis functions, which have nonzero curvature, and then consider the sparse decomposition technique. The sparse decomposition problem is an l0-norm constrained minimization problem. In solving the l0-norm minimization, a polynomial Radon transform is performed to achieve sparsity, and a fast gradient descent method is used to calculate the waveform functions. The waveform functions can subsequently be used for sparse Gaussian beam migration. Compared with traditional sparse Gaussian beam methods, the seismic data can be properly reconstructed employing fewer Gaussian beams with nonzero initial curvature. The migration approach described in this paper is more efficient than the traditional sparse Gaussian beam migration.

Kinetics of Thermal Decomposition of Ammonium Perchlorate by TG/DSC-MS-FTIR

NASA Astrophysics Data System (ADS)

Zhu, Yan-Li; Huang, Hao; Ren, Hui; Jiao, Qing-Jie

2014-01-01

The method of thermogravimetry/differential scanning calorimetry-mass spectrometry-Fourier transform infrared (TG/DSC-MS-FTIR) simultaneous analysis has been used to study thermal decomposition of ammonium perchlorate (AP). The processing of nonisothermal data at various heating rates was performed using NETZSCH Thermokinetics. The MS-FTIR spectra showed that N2O and NO2 were the main gaseous products of the thermal decomposition of AP, and there was a competition between the formation reaction of N2O and that of NO2 during the process with an iso-concentration point of N2O and NO2. The dependence of the activation energy calculated by Friedman's iso-conversional method on the degree of conversion indicated that the AP decomposition process can be divided into three stages, which are autocatalytic, low-temperature diffusion and high-temperature, stable-phase reaction. The corresponding kinetic parameters were determined by multivariate nonlinear regression and the mechanism of the AP decomposition process was proposed.

Reconstructing photorealistic 3D models from image sequence using domain decomposition method

NASA Astrophysics Data System (ADS)

Xiong, Hanwei; Pan, Ming; Zhang, Xiangwei

2009-11-01

In the fields of industrial design, artistic design and heritage conservation, physical objects are usually digitalized by reverse engineering through some 3D scanning methods. Structured light and photogrammetry are two main methods to acquire 3D information, and both are expensive. Even if these expensive instruments are used, photorealistic 3D models are seldom available. In this paper, a new method to reconstruction photorealistic 3D models using a single camera is proposed. A square plate glued with coded marks is used to place the objects, and a sequence of about 20 images is taken. From the coded marks, the images are calibrated, and a snake algorithm is used to segment object from the background. A rough 3d model is obtained using shape from silhouettes algorithm. The silhouettes are decomposed into a combination of convex curves, which are used to partition the rough 3d model into some convex mesh patches. For each patch, the multi-view photo consistency constraints and smooth regulations are expressed as a finite element formulation, which can be resolved locally, and the information can be exchanged along the patches boundaries. The rough model is deformed into a fine 3d model through such a domain decomposition finite element method. The textures are assigned to each element mesh, and a photorealistic 3D model is got finally. A toy pig is used to verify the algorithm, and the result is exciting.

A Decomposition Method for Security Constrained Economic Dispatch of a Three-Layer Power System

NASA Astrophysics Data System (ADS)

Yang, Junfeng; Luo, Zhiqiang; Dong, Cheng; Lai, Xiaowen; Wang, Yang

2018-01-01

This paper proposes a new decomposition method for the security-constrained economic dispatch in a three-layer large-scale power system. The decomposition is realized using two main techniques. The first is to use Ward equivalencing-based network reduction to reduce the number of variables and constraints in the high-layer model without sacrificing accuracy. The second is to develop a price response function to exchange signal information between neighboring layers, which significantly improves the information exchange efficiency of each iteration and results in less iterations and less computational time. The case studies based on the duplicated RTS-79 system demonstrate the effectiveness and robustness of the proposed method.

Transition-density-fragment interaction combined with transfer integral approach for excitation-energy transfer via charge-transfer states

NASA Astrophysics Data System (ADS)

Fujimoto, Kazuhiro J.

2012-07-01

A transition-density-fragment interaction (TDFI) combined with a transfer integral (TI) method is proposed. The TDFI method was previously developed for describing electronic Coulomb interaction, which was applied to excitation-energy transfer (EET) [K. J. Fujimoto and S. Hayashi, J. Am. Chem. Soc. 131, 14152 (2009)] and exciton-coupled circular dichroism spectra [K. J. Fujimoto, J. Chem. Phys. 133, 124101 (2010)]. In the present study, the TDFI method is extended to the exchange interaction, and hence it is combined with the TI method for applying to the EET via charge-transfer (CT) states. In this scheme, the overlap correction is also taken into account. To check the TDFI-TI accuracy, several test calculations are performed to an ethylene dimer. As a result, the TDFI-TI method gives a much improved description of the electronic coupling, compared with the previous TDFI method. Based on the successful description of the electronic coupling, the decomposition analysis is also performed with the TDFI-TI method. The present analysis clearly shows a large contribution from the Coulomb interaction in most of the cases, and a significant influence of the CT states at the small separation. In addition, the exchange interaction is found to be small in this system. The present approach is useful for analyzing and understanding the mechanism of EET.

Hydrogen production by photoelectrolytic decomposition of H2O using solar energy

NASA Technical Reports Server (NTRS)

Rauh, R. D.; Alkaitis, S. A.; Buzby, J. M.; Schiff, R.

1980-01-01

Photoelectrochemical systems for the efficient decomposition of water are discussed. Semiconducting d band oxides which would yield the combination of stability, low electron affinity, and moderate band gap essential for an efficient photoanode are sought. The materials PdO and Fe-xRhxO3 appear most likely. Oxygen evolution yields may also be improved by mediation of high energy oxidizing agents, such as CO3(-). Examination of several p type semiconductors as photocathodes revealed remarkable stability for p-GaAs, and also indicated p-CdTe as a stable H2 photoelectrode. Several potentially economical schemes for photoelectrochemical decomposition of water were examined, including photoelectrochemical diodes and two stage, four photon processes.

A hybrid Dantzig-Wolfe, Benders decomposition and column generation procedure for multiple diet production planning under uncertainties

NASA Astrophysics Data System (ADS)

Udomsungworagul, A.; Charnsethikul, P.

2018-03-01

This article introduces methodology to solve large scale two-phase linear programming with a case of multiple time period animal diet problems under both nutrients in raw materials and finished product demand uncertainties. Assumption of allowing to manufacture multiple product formulas in the same time period and assumption of allowing to hold raw materials and finished products inventory have been added. Dantzig-Wolfe decompositions, Benders decomposition and Column generations technique has been combined and applied to solve the problem. The proposed procedure was programmed using VBA and Solver tool in Microsoft Excel. A case study was used and tested in term of efficiency and effectiveness trade-offs.

Comparing and improving proper orthogonal decomposition (POD) to reduce the complexity of groundwater models

NASA Astrophysics Data System (ADS)

Gosses, Moritz; Nowak, Wolfgang; Wöhling, Thomas

2017-04-01

Physically-based modeling is a wide-spread tool in understanding and management of natural systems. With the high complexity of many such models and the huge amount of model runs necessary for parameter estimation and uncertainty analysis, overall run times can be prohibitively long even on modern computer systems. An encouraging strategy to tackle this problem are model reduction methods. In this contribution, we compare different proper orthogonal decomposition (POD, Siade et al. (2010)) methods and their potential applications to groundwater models. The POD method performs a singular value decomposition on system states as simulated by the complex (e.g., PDE-based) groundwater model taken at several time-steps, so-called snapshots. The singular vectors with the highest information content resulting from this decomposition are then used as a basis for projection of the system of model equations onto a subspace of much lower dimensionality than the original complex model, thereby greatly reducing complexity and accelerating run times. In its original form, this method is only applicable to linear problems. Many real-world groundwater models are non-linear, tough. These non-linearities are introduced either through model structure (unconfined aquifers) or boundary conditions (certain Cauchy boundaries, like rivers with variable connection to the groundwater table). To date, applications of POD focused on groundwater models simulating pumping tests in confined aquifers with constant head boundaries. In contrast, POD model reduction either greatly looses accuracy or does not significantly reduce model run time if the above-mentioned non-linearities are introduced. We have also found that variable Dirichlet boundaries are problematic for POD model reduction. An extension to the POD method, called POD-DEIM, has been developed for non-linear groundwater models by Stanko et al. (2016). This method uses spatial interpolation points to build the equation system in the reduced model space, thereby allowing the recalculation of system matrices at every time-step necessary for non-linear models while retaining the speed of the reduced model. This makes POD-DEIM applicable for groundwater models simulating unconfined aquifers. However, in our analysis, the method struggled to reproduce variable river boundaries accurately and gave no advantage for variable Dirichlet boundaries compared to the original POD method. We have developed another extension for POD that targets to address these remaining problems by performing a second POD operation on the model matrix on the left-hand side of the equation. The method aims to at least reproduce the accuracy of the other methods where they are applicable while outperforming them for setups with changing river boundaries or variable Dirichlet boundaries. We compared the new extension with original POD and POD-DEIM for different combinations of model structures and boundary conditions. The new method shows the potential of POD extensions for applications to non-linear groundwater systems and complex boundary conditions that go beyond the current, relatively limited range of applications. References: Siade, A. J., Putti, M., and Yeh, W. W.-G. (2010). Snapshot selection for groundwater model reduction using proper orthogonal decomposition. Water Resour. Res., 46(8):W08539. Stanko, Z. P., Boyce, S. E., and Yeh, W. W.-G. (2016). Nonlinear model reduction of unconfined groundwater flow using pod and deim. Advances in Water Resources, 97:130 - 143.

Can visible light impact litter decomposition under pollution of ZnO nanoparticles?

PubMed

Du, Jingjing; Zhang, Yuyan; Liu, Lina; Qv, Mingxiang; Lv, Yanna; Yin, Yifei; Zhou, Yinfei; Cui, Minghui; Zhu, Yanfeng; Zhang, Hongzhong

2017-11-01

ZnO nanoparticles is one of the most used materials in a wide range including antibacterial coating, electronic device, and personal care products. With the development of nanotechnology, ecotoxicology of ZnO nanoparticles has been received increasing attention. To assess the phototoxicity of ZnO nanoparticles in aquatic ecosystem, microcosm experiments were conducted on Populus nigra L. leaf litter decomposition under combined effect of ZnO nanoparticles and visible light radiation. Litter decomposition rate, pH value, extracellular enzyme activity, as well as the relative contributions of fungal community to litter decomposition were studied. Results showed that long-term exposure to ZnO nanoparticles and visible light led to a significant decrease in litter decomposition rate (0.26 m -1 vs 0.45 m -1 ), and visible light would increase the inhibitory effect (0.24 m -1 ), which caused significant decrease in pH value of litter cultures, fungal sporulation rate, as well as most extracellular enzyme activities. The phototoxicity of ZnO nanoparticles also showed impacts on fungal community composition, especially on the genus of Varicosporium, whose abundance was significantly and positively related to decomposition rate. In conclusion, our study provides the evidence for negatively effects of ZnO NPs photocatalysis on ecological process of litter decomposition and highlights the contribution of visible light radiation to nanoparticles toxicity in freshwater ecosystems. Copyright © 2017 Elsevier Ltd. All rights reserved.

A hybrid algorithm for parallel molecular dynamics simulations

NASA Astrophysics Data System (ADS)

Mangiardi, Chris M.; Meyer, R.

2017-10-01

This article describes algorithms for the hybrid parallelization and SIMD vectorization of molecular dynamics simulations with short-range forces. The parallelization method combines domain decomposition with a thread-based parallelization approach. The goal of the work is to enable efficient simulations of very large (tens of millions of atoms) and inhomogeneous systems on many-core processors with hundreds or thousands of cores and SIMD units with large vector sizes. In order to test the efficiency of the method, simulations of a variety of configurations with up to 74 million atoms have been performed. Results are shown that were obtained on multi-core systems with Sandy Bridge and Haswell processors as well as systems with Xeon Phi many-core processors.

Coil combination for receive array spectroscopy: Are data-driven methods superior to methods using computed field maps?

PubMed

Rodgers, Christopher T; Robson, Matthew D

2016-02-01

Combining spectra from receive arrays, particularly X-nuclear spectra with low signal-to-noise ratios (SNRs), is challenging. We test whether data-driven combination methods are better than using computed coil sensitivities. Several combination algorithms are recast into the notation of Roemer's classic formula, showing that they differ primarily in their estimation of coil receive sensitivities. This viewpoint reveals two extensions of the whitened singular-value decomposition (WSVD) algorithm, using temporal or temporal + spatial apodization to improve the coil sensitivities, and thus the combined spectral SNR. Radiofrequency fields from an array were simulated and used to make synthetic spectra. These were combined with 10 algorithms. The combined spectra were then assessed in terms of their SNR. Validation used phantoms and cardiac (31) P spectra from five subjects at 3T. Combined spectral SNRs from simulations, phantoms, and humans showed the same trends. In phantoms, the combined SNR using computed coil sensitivities was lower than with WSVD combination whenever the WSVD SNR was >14 (or >11 with temporal apodization, or >9 with temporal + spatial apodization). These new apodized WSVD methods gave higher SNRs than other data-driven methods. In the human torso, at frequencies ≥49 MHz, data-driven combination is preferable to using computed coil sensitivities. Magn Reson, 2015. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. Magn Reson Med 75:473-487, 2016. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.

A Posteriori Error Analysis and Uncertainty Quantification for Adaptive Multiscale Operator Decomposition Methods for Multiphysics Problems

DTIC Science & Technology

2014-04-01

Barrier methods for critical exponent problems in geometric analysis and mathematical physics, J. Erway and M. Holst, Submitted for publication ...TR-14-33 A Posteriori Error Analysis and Uncertainty Quantification for Adaptive Multiscale Operator Decomposition Methods for Multiphysics...Problems Approved for public release, distribution is unlimited. April 2014 HDTRA1-09-1-0036 Donald Estep and Michael

Newton-Krylov-Schwarz: An implicit solver for CFD

NASA Technical Reports Server (NTRS)

Cai, Xiao-Chuan; Keyes, David E.; Venkatakrishnan, V.

1995-01-01

Newton-Krylov methods and Krylov-Schwarz (domain decomposition) methods have begun to become established in computational fluid dynamics (CFD) over the past decade. The former employ a Krylov method inside of Newton's method in a Jacobian-free manner, through directional differencing. The latter employ an overlapping Schwarz domain decomposition to derive a preconditioner for the Krylov accelerator that relies primarily on local information, for data-parallel concurrency. They may be composed as Newton-Krylov-Schwarz (NKS) methods, which seem particularly well suited for solving nonlinear elliptic systems in high-latency, distributed-memory environments. We give a brief description of this family of algorithms, with an emphasis on domain decomposition iterative aspects. We then describe numerical simulations with Newton-Krylov-Schwarz methods on aerodynamics applications emphasizing comparisons with a standard defect-correction approach, subdomain preconditioner consistency, subdomain preconditioner quality, and the effect of a coarse grid.

Parallel iterative solution for h and p approximations of the shallow water equations

USGS Publications Warehouse

Barragy, E.J.; Walters, R.A.

1998-01-01

A p finite element scheme and parallel iterative solver are introduced for a modified form of the shallow water equations. The governing equations are the three-dimensional shallow water equations. After a harmonic decomposition in time and rearrangement, the resulting equations are a complex Helmholz problem for surface elevation, and a complex momentum equation for the horizontal velocity. Both equations are nonlinear and the resulting system is solved using the Picard iteration combined with a preconditioned biconjugate gradient (PBCG) method for the linearized subproblems. A subdomain-based parallel preconditioner is developed which uses incomplete LU factorization with thresholding (ILUT) methods within subdomains, overlapping ILUT factorizations for subdomain boundaries and under-relaxed iteration for the resulting block system. The method builds on techniques successfully applied to linear elements by introducing ordering and condensation techniques to handle uniform p refinement. The combined methods show good performance for a range of p (element order), h (element size), and N (number of processors). Performance and scalability results are presented for a field scale problem where up to 512 processors are used. ?? 1998 Elsevier Science Ltd. All rights reserved.

Phase unwrapping with graph cuts optimization and dual decomposition acceleration for 3D high-resolution MRI data.

PubMed

Dong, Jianwu; Chen, Feng; Zhou, Dong; Liu, Tian; Yu, Zhaofei; Wang, Yi

2017-03-01

Existence of low SNR regions and rapid-phase variations pose challenges to spatial phase unwrapping algorithms. Global optimization-based phase unwrapping methods are widely used, but are significantly slower than greedy methods. In this paper, dual decomposition acceleration is introduced to speed up a three-dimensional graph cut-based phase unwrapping algorithm. The phase unwrapping problem is formulated as a global discrete energy minimization problem, whereas the technique of dual decomposition is used to increase the computational efficiency by splitting the full problem into overlapping subproblems and enforcing the congruence of overlapping variables. Using three dimensional (3D) multiecho gradient echo images from an agarose phantom and five brain hemorrhage patients, we compared this proposed method with an unaccelerated graph cut-based method. Experimental results show up to 18-fold acceleration in computation time. Dual decomposition significantly improves the computational efficiency of 3D graph cut-based phase unwrapping algorithms. Magn Reson Med 77:1353-1358, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Accelerating Dynamic Magnetic Resonance Imaging (MRI) for Lung Tumor Tracking Based on Low-Rank Decomposition in the Spatial–Temporal Domain: A Feasibility Study Based on Simulation and Preliminary Prospective Undersampled MRI

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

Sarma, Manoj; Department of Radiation Oncology, University of California, Los Angeles, California; Hu, Peng

Purpose: To evaluate a low-rank decomposition method to reconstruct down-sampled k-space data for the purpose of tumor tracking. Methods and Materials: Seven retrospective lung cancer patients were included in the simulation study. The fully-sampled k-space data were first generated from existing 2-dimensional dynamic MR images and then down-sampled by 5 × -20 × before reconstruction using a Cartesian undersampling mask. Two methods, a low-rank decomposition method using combined dynamic MR images (k-t SLR based on sparsity and low-rank penalties) and a total variation (TV) method using individual dynamic MR frames, were used to reconstruct images. The tumor trajectories were derived on the basis ofmore » autosegmentation of the resultant images. To further test its feasibility, k-t SLR was used to reconstruct prospective data of a healthy subject. An undersampled balanced steady-state free precession sequence with the same undersampling mask was used to acquire the imaging data. Results: In the simulation study, higher imaging fidelity and low noise levels were achieved with the k-t SLR compared with TV. At 10 × undersampling, the k-t SLR method resulted in an average normalized mean square error <0.05, as opposed to 0.23 by using the TV reconstruction on individual frames. Less than 6% showed tracking errors >1 mm with 10 × down-sampling using k-t SLR, as opposed to 17% using TV. In the prospective study, k-t SLR substantially reduced reconstruction artifacts and retained anatomic details. Conclusions: Magnetic resonance reconstruction using k-t SLR on highly undersampled dynamic MR imaging data results in high image quality useful for tumor tracking. The k-t SLR was superior to TV by better exploiting the intrinsic anatomic coherence of the same patient. The feasibility of k-t SLR was demonstrated by prospective imaging acquisition and reconstruction.« less

Linked independent component analysis for multimodal data fusion.

PubMed

Groves, Adrian R; Beckmann, Christian F; Smith, Steve M; Woolrich, Mark W

2011-02-01

In recent years, neuroimaging studies have increasingly been acquiring multiple modalities of data and searching for task- or disease-related changes in each modality separately. A major challenge in analysis is to find systematic approaches for fusing these differing data types together to automatically find patterns of related changes across multiple modalities, when they exist. Independent Component Analysis (ICA) is a popular unsupervised learning method that can be used to find the modes of variation in neuroimaging data across a group of subjects. When multimodal data is acquired for the subjects, ICA is typically performed separately on each modality, leading to incompatible decompositions across modalities. Using a modular Bayesian framework, we develop a novel "Linked ICA" model for simultaneously modelling and discovering common features across multiple modalities, which can potentially have completely different units, signal- and contrast-to-noise ratios, voxel counts, spatial smoothnesses and intensity distributions. Furthermore, this general model can be configured to allow tensor ICA or spatially-concatenated ICA decompositions, or a combination of both at the same time. Linked ICA automatically determines the optimal weighting of each modality, and also can detect single-modality structured components when present. This is a fully probabilistic approach, implemented using Variational Bayes. We evaluate the method on simulated multimodal data sets, as well as on a real data set of Alzheimer's patients and age-matched controls that combines two very different types of structural MRI data: morphological data (grey matter density) and diffusion data (fractional anisotropy, mean diffusivity, and tensor mode). Copyright © 2010 Elsevier Inc. All rights reserved.

The influence of minerals on decomposition of the n-alkyl-α-amino acid norvaline under hydrothermal conditions

NASA Astrophysics Data System (ADS)

McCollom, Thomas M.

2013-03-01

Laboratory experiments were conducted to observe the effect of iron oxide and sulfide minerals on decomposition reactions of norvaline, a representative of a group of alkyl-α-amino acids observed in meteorites and prebiotic synthesis experiments. The primary products observed during heating of aqueous solutions of norvaline at temperatures of 156-186 °C in the presence of minerals included CO2, NH3, butyric acid, and valeric acid. The products indicated that norvaline predominantly decomposed by a combination of pathways that included both decarboxylation followed rapidly by oxidative deamination (norvaline → butanamide + CO2 → butyric acid + NH3) and deamination directly to valeric acid (norvaline → valeric acid + NH3). An experiment performed with alanine under similar conditions showed it decomposed by analogous reactions that produced acetic and propionic acids along with CO2 and NH3. For both amino acids, the presence of minerals accelerated decomposition rates as well as altered the final products of reaction, when compared with decomposition in the absence of mineral substrates. In addition, decomposition of norvaline was found to proceed much faster in the presence of the mineral assemblage hematite-magnetite-pyrite (HMP) than with the assemblage pyrite-pyrrhotite-magnetite (PPM), a trend that has been observed for several other organic compounds. The influence of minerals on decomposition reactions of these amino acids appears to be attributable to a combination of surface catalysis and production of dissolved sulfur compounds. Overall, the results indicate that minerals may exert a substantial influence on amino acid stability in many geologic environments, and emphasize the need to consider the impact of minerals when evaluating the lifetimes and decomposition rates of amino acids in terrestrial and planetary systems. Estimated half-lives for alkyl-α-amino acids based on the experimental results indicate that moderately hot hydrothermal environments (<˜100 °C) would have been the most favorable for accumulation of these amino acids in the early solar system, and that the predominance of alkyl-α-amino acids in some meteorites may only be compatible with temperature remaining below about 60 °C following their formation.

Data decomposition method for parallel polygon rasterization considering load balancing

NASA Astrophysics Data System (ADS)

Zhou, Chen; Chen, Zhenjie; Liu, Yongxue; Li, Feixue; Cheng, Liang; Zhu, A.-xing; Li, Manchun

2015-12-01

It is essential to adopt parallel computing technology to rapidly rasterize massive polygon data. In parallel rasterization, it is difficult to design an effective data decomposition method. Conventional methods ignore load balancing of polygon complexity in parallel rasterization and thus fail to achieve high parallel efficiency. In this paper, a novel data decomposition method based on polygon complexity (DMPC) is proposed. First, four factors that possibly affect the rasterization efficiency were investigated. Then, a metric represented by the boundary number and raster pixel number in the minimum bounding rectangle was developed to calculate the complexity of each polygon. Using this metric, polygons were rationally allocated according to the polygon complexity, and each process could achieve balanced loads of polygon complexity. To validate the efficiency of DMPC, it was used to parallelize different polygon rasterization algorithms and tested on different datasets. Experimental results showed that DMPC could effectively parallelize polygon rasterization algorithms. Furthermore, the implemented parallel algorithms with DMPC could achieve good speedup ratios of at least 15.69 and generally outperformed conventional decomposition methods in terms of parallel efficiency and load balancing. In addition, the results showed that DMPC exhibited consistently better performance for different spatial distributions of polygons.

Domain decomposition for a mixed finite element method in three dimensions

USGS Publications Warehouse

Cai, Z.; Parashkevov, R.R.; Russell, T.F.; Wilson, J.D.; Ye, X.

2003-01-01

We consider the solution of the discrete linear system resulting from a mixed finite element discretization applied to a second-order elliptic boundary value problem in three dimensions. Based on a decomposition of the velocity space, these equations can be reduced to a discrete elliptic problem by eliminating the pressure through the use of substructures of the domain. The practicality of the reduction relies on a local basis, presented here, for the divergence-free subspace of the velocity space. We consider additive and multiplicative domain decomposition methods for solving the reduced elliptic problem, and their uniform convergence is established.

Rotational-path decomposition based recursive planning for spacecraft attitude reorientation

NASA Astrophysics Data System (ADS)

Xu, Rui; Wang, Hui; Xu, Wenming; Cui, Pingyuan; Zhu, Shengying

2018-02-01

The spacecraft reorientation is a common task in many space missions. With multiple pointing constraints, it is greatly difficult to solve the constrained spacecraft reorientation planning problem. To deal with this problem, an efficient rotational-path decomposition based recursive planning (RDRP) method is proposed in this paper. The uniform pointing-constraint-ignored attitude rotation planning process is designed to solve all rotations without considering pointing constraints. Then the whole path is checked node by node. If any pointing constraint is violated, the nearest critical increment approach will be used to generate feasible alternative nodes in the process of rotational-path decomposition. As the planning path of each subdivision may still violate pointing constraints, multiple decomposition is needed and the reorientation planning is designed as a recursive manner. Simulation results demonstrate the effectiveness of the proposed method. The proposed method has been successfully applied in two SPARK microsatellites to solve onboard constrained attitude reorientation planning problem, which were developed by the Shanghai Engineering Center for Microsatellites and launched on 22 December 2016.

A Domain Decomposition Parallelization of the Fast Marching Method

NASA Technical Reports Server (NTRS)

Herrmann, M.

2003-01-01

In this paper, the first domain decomposition parallelization of the Fast Marching Method for level sets has been presented. Parallel speedup has been demonstrated in both the optimal and non-optimal domain decomposition case. The parallel performance of the proposed method is strongly dependent on load balancing separately the number of nodes on each side of the interface. A load imbalance of nodes on either side of the domain leads to an increase in communication and rollback operations. Furthermore, the amount of inter-domain communication can be reduced by aligning the inter-domain boundaries with the interface normal vectors. In the case of optimal load balancing and aligned inter-domain boundaries, the proposed parallel FMM algorithm is highly efficient, reaching efficiency factors of up to 0.98. Future work will focus on the extension of the proposed parallel algorithm to higher order accuracy. Also, to further enhance parallel performance, the coupling of the domain decomposition parallelization to the G(sub 0)-based parallelization will be investigated.

Ab initio kinetics of gas phase decomposition reactions.

PubMed

Sharia, Onise; Kuklja, Maija M

2010-12-09

The thermal and kinetic aspects of gas phase decomposition reactions can be extremely complex due to a large number of parameters, a variety of possible intermediates, and an overlap in thermal decomposition traces. The experimental determination of the activation energies is particularly difficult when several possible reaction pathways coexist in the thermal decomposition. Ab initio calculations intended to provide an interpretation of the experiment are often of little help if they produce only the activation barriers and ignore the kinetics of the decomposition process. To overcome this ambiguity, a theoretical study of a complete picture of gas phase thermo-decomposition, including reaction energies, activation barriers, and reaction rates, is illustrated with the example of the β-octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX) molecule by means of quantum-chemical calculations. We study three types of major decomposition reactions characteristic of nitramines: the HONO elimination, the NONO rearrangement, and the N-NO(2) homolysis. The reaction rates were determined using the conventional transition state theory for the HONO and NONO decompositions and the variational transition state theory for the N-NO(2) homolysis. Our calculations show that the HMX decomposition process is more complex than it was previously believed to be and is defined by a combination of reactions at any given temperature. At all temperatures, the direct N-NO(2) homolysis prevails with the activation barrier at 38.1 kcal/mol. The nitro-nitrite isomerization and the HONO elimination, with the activation barriers at 46.3 and 39.4 kcal/mol, respectively, are slow reactions at all temperatures. The obtained conclusions provide a consistent interpretation for the reported experimental data.

Homotopy decomposition method for solving one-dimensional time-fractional diffusion equation

NASA Astrophysics Data System (ADS)

Abuasad, Salah; Hashim, Ishak

2018-04-01

In this paper, we present the homotopy decomposition method with a modified definition of beta fractional derivative for the first time to find exact solution of one-dimensional time-fractional diffusion equation. In this method, the solution takes the form of a convergent series with easily computable terms. The exact solution obtained by the proposed method is compared with the exact solution obtained by using fractional variational homotopy perturbation iteration method via a modified Riemann-Liouville derivative.

Plasmonic Thermal Decomposition/Digestion of Proteins: A Rapid On-Surface Protein Digestion Technique for Mass Spectrometry Imaging.

PubMed

Zhou, Rong; Basile, Franco

2017-09-05

A method based on plasmon surface resonance absorption and heating was developed to perform a rapid on-surface protein thermal decomposition and digestion suitable for imaging mass spectrometry (MS) and/or profiling. This photothermal process or plasmonic thermal decomposition/digestion (plasmonic-TDD) method incorporates a continuous wave (CW) laser excitation and gold nanoparticles (Au-NPs) to induce known thermal decomposition reactions that cleave peptides and proteins specifically at the C-terminus of aspartic acid and at the N-terminus of cysteine. These thermal decomposition reactions are induced by heating a solid protein sample to temperatures between 200 and 270 °C for a short period of time (10-50 s per 200 μm segment) and are reagentless and solventless, and thus are devoid of sample product delocalization. In the plasmonic-TDD setup the sample is coated with Au-NPs and irradiated with 532 nm laser radiation to induce thermoplasmonic heating and bring about site-specific thermal decomposition on solid peptide/protein samples. In this manner the Au-NPs act as nanoheaters that result in a highly localized thermal decomposition and digestion of the protein sample that is independent of the absorption properties of the protein, making the method universally applicable to all types of proteinaceous samples (e.g., tissues or protein arrays). Several experimental variables were optimized to maximize product yield, and they include heating time, laser intensity, size of Au-NPs, and surface coverage of Au-NPs. Using optimized parameters, proof-of-principle experiments confirmed the ability of the plasmonic-TDD method to induce both C-cleavage and D-cleavage on several peptide standards and the protein lysozyme by detecting their thermal decomposition products with matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). The high spatial specificity of the plasmonic-TDD method was demonstrated by using a mask to digest designated sections of the sample surface with the heating laser and MALDI-MS imaging to map the resulting products. The solventless nature of the plasmonic-TDD method enabled the nonenzymatic on-surface digestion of proteins to proceed with undetectable delocalization of the resulting products from their precursor protein location. The advantages of this novel plasmonic-TDD method include short reaction times (<30 s/200 μm), compatibility with MALDI, universal sample compatibility, high spatial specificity, and localization of the digestion products. These advantages point to potential applications of this method for on-tissue protein digestion and MS-imaging/profiling for the identification of proteins, high-fidelity MS imaging of high molecular weight (>30 kDa) proteins, and the rapid analysis of formalin-fixed paraffin-embedded (FFPE) tissue samples.

Litter Decomposition in a Semiarid Dune Grassland: Neutral Effect of Water Supply and Inhibitory Effect of Nitrogen Addition.

PubMed

Li, Yulin; Ning, Zhiying; Cui, Duo; Mao, Wei; Bi, Jingdong; Zhao, Xueyong

2016-01-01

The decomposition of plant material in arid ecosystems is considered to be substantially controlled by water and N availability. The responses of litter decomposition to external N and water, however, remain controversial, and the interactive effects of supplementary N and water also have been largely unexamined. A 3.5-year field experiment with supplementary nitrogen and water was conducted to assess the effects of N and water addition on mass loss and nitrogen release in leaves and fine roots of three dominant plant species (i.e., Artemisia halondendron, Setaria viridis, and Phragmites australis) with contrasting substrate chemistry (e.g. N concentration, lignin content in this study) in a desertified dune grassland of Inner Mongolia, China. The treatments included N addition, water addition, combination of N and water, and an untreated control. The decomposition rate in both leaves and roots was related to the initial litter N and lignin concentrations of the three species. However, litter quality did not explain the slower mass loss in roots than in leaves in the present study, and thus warrant further research. Nitrogen addition, either alone or in combination with water, significantly inhibited dry mass loss and N release in the leaves and roots of the three species, whereas water input had little effect on the decomposition of leaf litter and fine roots, suggesting that there was no interactive effect of supplementary N and water on litter decomposition in this system. Furthermore, our results clearly indicate that the inhibitory effects of external N on dry mass loss and nitrogen release are relatively strong in high-lignin litter compared with low-lignin litter. These findings suggest that increasing precipitation hardly facilitates ecosystem carbon turnover but atmospheric N deposition can enhance carbon sequestration and nitrogen retention in desertified dune grasslands of northern China. Additionally, litter quality of plant species should be considered when modelling the carbon cycle and nutrient dynamics of this system.

Litter Decomposition in a Semiarid Dune Grassland: Neutral Effect of Water Supply and Inhibitory Effect of Nitrogen Addition

PubMed Central

Li, Yulin; Ning, Zhiying; Cui, Duo; Mao, Wei; Bi, Jingdong; Zhao, Xueyong

2016-01-01

Background The decomposition of plant material in arid ecosystems is considered to be substantially controlled by water and N availability. The responses of litter decomposition to external N and water, however, remain controversial, and the interactive effects of supplementary N and water also have been largely unexamined. Methodology/Principal Findings A 3.5-year field experiment with supplementary nitrogen and water was conducted to assess the effects of N and water addition on mass loss and nitrogen release in leaves and fine roots of three dominant plant species (i.e., Artemisia halondendron, Setaria viridis, and Phragmites australis) with contrasting substrate chemistry (e.g. N concentration, lignin content in this study) in a desertified dune grassland of Inner Mongolia, China. The treatments included N addition, water addition, combination of N and water, and an untreated control. The decomposition rate in both leaves and roots was related to the initial litter N and lignin concentrations of the three species. However, litter quality did not explain the slower mass loss in roots than in leaves in the present study, and thus warrant further research. Nitrogen addition, either alone or in combination with water, significantly inhibited dry mass loss and N release in the leaves and roots of the three species, whereas water input had little effect on the decomposition of leaf litter and fine roots, suggesting that there was no interactive effect of supplementary N and water on litter decomposition in this system. Furthermore, our results clearly indicate that the inhibitory effects of external N on dry mass loss and nitrogen release are relatively strong in high-lignin litter compared with low-lignin litter. Conclusion/Significance These findings suggest that increasing precipitation hardly facilitates ecosystem carbon turnover but atmospheric N deposition can enhance carbon sequestration and nitrogen retention in desertified dune grasslands of northern China. Additionally, litter quality of plant species should be considered when modelling the carbon cycle and nutrient dynamics of this system. PMID:27617439

Long-Term Simulated Atmospheric Nitrogen Deposition Alters ...

EPA Pesticide Factsheets

Atmospheric nitrogen deposition has been suggested to increase forest carbon sequestration across much of the Northern Hemisphere; slower organic matter decomposition could contribute to this increase. At four sugar maple (Acer saccharum)-dominated northern hardwood forests, we previously observed that 10 years of chronic simulated nitrogen deposition (30 kg N ha-1 yr-1) increased soil organic carbon. Over three years at these sites, we investigated the effects of nitrogen additions on decomposition of two substrates with documented differences in biochemistry: leaf litter (more labile) and fine roots (more recalcitrant). Further, we combined decomposition rates with annual leaf and fine root litter production to estimate how nitrogen additions altered the accumulation of soil organic matter. Nitrogen additions marginally stimulated early-stage decomposition of leaf litter, a substrate with little acid-insoluble material (e.g., lignin). In contrast, nitrogen additions inhibited the late stage decomposition of fine roots, a substrate with high amount of acid insoluble material and a change consistent with observed decreases in lignin-degrading enzyme activities with nitrogen additions at these sites. At the ecosystem scale, the slower fine root decomposition led to additional root mass retention (g m-2), which explained 5, 48, and 52 % of previously-documented soil carbon accumulation due to nitrogen additions. Our results demonstrated that nitrogen deposition ha

Transportation Network Analysis and Decomposition Methods

DOT National Transportation Integrated Search

1978-03-01

The report outlines research in transportation network analysis using decomposition techniques as a basis for problem solutions. Two transportation network problems were considered in detail: a freight network flow problem and a scheduling problem fo...

System and methods for determining masking signals for applying empirical mode decomposition (EMD) and for demodulating intrinsic mode functions obtained from application of EMD

DOEpatents

Senroy, Nilanjan [New Delhi, IN; Suryanarayanan, Siddharth [Littleton, CO

2011-03-15

A computer-implemented method of signal processing is provided. The method includes generating one or more masking signals based upon a computed Fourier transform of a received signal. The method further includes determining one or more intrinsic mode functions (IMFs) of the received signal by performing a masking-signal-based empirical mode decomposition (EMD) using the at least one masking signal.

Resolution of singularities for multi-loop integrals

NASA Astrophysics Data System (ADS)

Bogner, Christian; Weinzierl, Stefan

2008-04-01

We report on a program for the numerical evaluation of divergent multi-loop integrals. The program is based on iterated sector decomposition. We improve the original algorithm of Binoth and Heinrich such that the program is guaranteed to terminate. The program can be used to compute numerically the Laurent expansion of divergent multi-loop integrals regulated by dimensional regularisation. The symbolic and the numerical steps of the algorithm are combined into one program. Program summaryProgram title: sector_decomposition Catalogue identifier: AEAG_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEAG_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 47 506 No. of bytes in distributed program, including test data, etc.: 328 485 Distribution format: tar.gz Programming language: C++ Computer: all Operating system: Unix RAM: Depending on the complexity of the problem Classification: 4.4 External routines: GiNaC, available from http://www.ginac.de, GNU scientific library, available from http://www.gnu.org/software/gsl Nature of problem: Computation of divergent multi-loop integrals. Solution method: Sector decomposition. Restrictions: Only limited by the available memory and CPU time. Running time: Depending on the complexity of the problem.

Modeling and Real-Time Process Monitoring of Organometallic Chemical Vapor Deposition of III-V Phosphides and Nitrides at Low and High Pressure

NASA Technical Reports Server (NTRS)

Bachmann, K. J.; Cardelino, B. H.; Moore, C. E.; Cardelino, C. A.; Sukidi, N.; McCall, S.

1999-01-01

The purpose of this paper is to review modeling and real-time monitoring by robust methods of reflectance spectroscopy of organometallic chemical vapor deposition (OMCVD) processes in extreme regimes of pressure. The merits of p-polarized reflectance spectroscopy under the conditions of chemical beam epitaxy (CBE) and of internal transmission spectroscopy and principal angle spectroscopy at high pressure are assessed. In order to extend OMCVD to materials that exhibit large thermal decomposition pressure at their optimum growth temperature we have designed and built a differentially-pressure-controlled (DCP) OMCVD reactor for use at pressures greater than or equal to 6 atm. We also describe a compact hard-shell (CHS) reactor for extending the pressure range to 100 atm. At such very high pressure the decomposition of source vapors occurs in the vapor phase, and is coupled to flow dynamics and transport. Rate constants for homogeneous gas phase reactions can be predicted based on a combination of first principles and semi-empirical calculations. The pressure dependence of unimolecular rate constants is described by RRKM theory, but requires variational and anharmonicity corrections not included in presently available calculations with the exception of ammonia decomposition. Commercial codes that include chemical reactions and transport exist, but do not adequately cover at present the kinetics of heteroepitaxial crystal growth.

The trait contribution to wood decomposition rates of 15 Neotropical tree species.

PubMed

van Geffen, Koert G; Poorter, Lourens; Sass-Klaassen, Ute; van Logtestijn, Richard S P; Cornelissen, Johannes H C

2010-12-01

The decomposition of dead wood is a critical uncertainty in models of the global carbon cycle. Despite this, relatively few studies have focused on dead wood decomposition, with a strong bias to higher latitudes. Especially the effect of interspecific variation in species traits on differences in wood decomposition rates remains unknown. In order to fill these gaps, we applied a novel method to study long-term wood decomposition of 15 tree species in a Bolivian semi-evergreen tropical moist forest. We hypothesized that interspecific differences in species traits are important drivers of variation in wood decomposition rates. Wood decomposition rates (fractional mass loss) varied between 0.01 and 0.31 yr(-1). We measured 10 different chemical, anatomical, and morphological traits for all species. The species' average traits were useful predictors of wood decomposition rates, particularly the average diameter (dbh) of the tree species (R2 = 0.41). Lignin concentration further increased the proportion of explained inter-specific variation in wood decomposition (both negative relations, cumulative R2 = 0.55), although it did not significantly explain variation in wood decomposition rates if considered alone. When dbh values of the actual dead trees sampled for decomposition rate determination were used as a predictor variable, the final model (including dead tree dbh and lignin concentration) explained even more variation in wood decomposition rates (R2 = 0.71), underlining the importance of dbh in wood decomposition. Other traits, including wood density, wood anatomical traits, macronutrient concentrations, and the amount of phenolic extractives could not significantly explain the variation in wood decomposition rates. The surprising results of this multi-species study, in which for the first time a large set of traits is explicitly linked to wood decomposition rates, merits further testing in other forest ecosystems.

Vapor Pressure Data and Analysis for Selected HD Decomposition Products: 1,4-Thioxane, Divinyl Sulfoxide, Chloroethyl Acetylsulfide, and 1,4-Dithiane

DTIC Science & Technology

2018-06-01

decomposition products from bis-(2-chloroethyl) sulfide (HD). These data were measured using an ASTM International method that is based on differential...2.1 Materials and Method ........................................................................................2 2.2 Data Analysis...and Method The source and purity of the materials studied are listed in Table 1. Table 1. Sample Information for Title Compounds Compound

Decomposition of 2,4-dichlorophenoxyacetic acid by ozonation, ionizing radiation as well as ozonation combined with ionizing radiation

NASA Astrophysics Data System (ADS)

Drzewicz, Przemyslaw; Trojanowicz, Marek; Zona, Robert; Solar, Sonja; Gehringer, Peter

2004-03-01

Electron beam (EB), ozone (O 3) and the combination EB/O 3 were used to study the oxidative decomposition of 2,4-dichlorophenoxyacetic acid (2,4-D) in local tap water. Using an EB treatment, a dose of 10 kGy was required for complete 2,4-D degradation, and a 90% conversion of organic chlorine into chloride ions. Using additionally 1.33 mmol dm -3 O 3 during irradiation, the same result was achieved with a dose of 2.7 kGy. The yields of products acetate and formate were almost doubled by the combined EB/O 3 treatment, compared to those obtained with the same dose by EB irradiation. Gamma radiolysis showed that the degradation dose was proportional to the initial concentration of 2,4-D in the range of 50-2260 μmol dm -3.

Analytical separations of mammalian decomposition products for forensic science: a review.

PubMed

Swann, L M; Forbes, S L; Lewis, S W

2010-12-03

The study of mammalian soft tissue decomposition is an emerging area in forensic science, with a major focus of the research being the use of various chemical and biological methods to study the fate of human remains in the environment. Decomposition of mammalian soft tissue is a postmortem process that, depending on environmental conditions and physiological factors, will proceed until complete disintegration of the tissue. The major stages of decomposition involve complex reactions which result in the chemical breakdown of the body's main constituents; lipids, proteins, and carbohydrates. The first step to understanding this chemistry is identifying the compounds present in decomposition fluids and determining when they are produced. This paper provides an overview of decomposition chemistry and reviews recent advances in this area utilising analytical separation science. Copyright © 2010 Elsevier B.V. All rights reserved.

Long-term power generation expansion planning with short-term demand response: Model, algorithms, implementation, and electricity policies

NASA Astrophysics Data System (ADS)

Lohmann, Timo

Electric sector models are powerful tools that guide policy makers and stakeholders. Long-term power generation expansion planning models are a prominent example and determine a capacity expansion for an existing power system over a long planning horizon. With the changes in the power industry away from monopolies and regulation, the focus of these models has shifted to competing electric companies maximizing their profit in a deregulated electricity market. In recent years, consumers have started to participate in demand response programs, actively influencing electricity load and price in the power system. We introduce a model that features investment and retirement decisions over a long planning horizon of more than 20 years, as well as an hourly representation of day-ahead electricity markets in which sellers of electricity face buyers. This combination makes our model both unique and challenging to solve. Decomposition algorithms, and especially Benders decomposition, can exploit the model structure. We present a novel method that can be seen as an alternative to generalized Benders decomposition and relies on dynamic linear overestimation. We prove its finite convergence and present computational results, demonstrating its superiority over traditional approaches. In certain special cases of our model, all necessary solution values in the decomposition algorithms can be directly calculated and solving mathematical programming problems becomes entirely obsolete. This leads to highly efficient algorithms that drastically outperform their programming problem-based counterparts. Furthermore, we discuss the implementation of all tailored algorithms and the challenges from a modeling software developer's standpoint, providing an insider's look into the modeling language GAMS. Finally, we apply our model to the Texas power system and design two electricity policies motivated by the U.S. Environment Protection Agency's recently proposed CO2 emissions targets for the power sector.

Automated diagnoses of attention deficit hyperactive disorder using magnetic resonance imaging.

PubMed

Eloyan, Ani; Muschelli, John; Nebel, Mary Beth; Liu, Han; Han, Fang; Zhao, Tuo; Barber, Anita D; Joel, Suresh; Pekar, James J; Mostofsky, Stewart H; Caffo, Brian

2012-01-01

Successful automated diagnoses of attention deficit hyperactive disorder (ADHD) using imaging and functional biomarkers would have fundamental consequences on the public health impact of the disease. In this work, we show results on the predictability of ADHD using imaging biomarkers and discuss the scientific and diagnostic impacts of the research. We created a prediction model using the landmark ADHD 200 data set focusing on resting state functional connectivity (rs-fc) and structural brain imaging. We predicted ADHD status and subtype, obtained by behavioral examination, using imaging data, intelligence quotients and other covariates. The novel contributions of this manuscript include a thorough exploration of prediction and image feature extraction methodology on this form of data, including the use of singular value decompositions (SVDs), CUR decompositions, random forest, gradient boosting, bagging, voxel-based morphometry, and support vector machines as well as important insights into the value, and potentially lack thereof, of imaging biomarkers of disease. The key results include the CUR-based decomposition of the rs-fc-fMRI along with gradient boosting and the prediction algorithm based on a motor network parcellation and random forest algorithm. We conjecture that the CUR decomposition is largely diagnosing common population directions of head motion. Of note, a byproduct of this research is a potential automated method for detecting subtle in-scanner motion. The final prediction algorithm, a weighted combination of several algorithms, had an external test set specificity of 94% with sensitivity of 21%. The most promising imaging biomarker was a correlation graph from a motor network parcellation. In summary, we have undertaken a large-scale statistical exploratory prediction exercise on the unique ADHD 200 data set. The exercise produced several potential leads for future scientific exploration of the neurological basis of ADHD.

Automated diagnoses of attention deficit hyperactive disorder using magnetic resonance imaging

PubMed Central

Eloyan, Ani; Muschelli, John; Nebel, Mary Beth; Liu, Han; Han, Fang; Zhao, Tuo; Barber, Anita D.; Joel, Suresh; Pekar, James J.; Mostofsky, Stewart H.; Caffo, Brian

2012-01-01

Successful automated diagnoses of attention deficit hyperactive disorder (ADHD) using imaging and functional biomarkers would have fundamental consequences on the public health impact of the disease. In this work, we show results on the predictability of ADHD using imaging biomarkers and discuss the scientific and diagnostic impacts of the research. We created a prediction model using the landmark ADHD 200 data set focusing on resting state functional connectivity (rs-fc) and structural brain imaging. We predicted ADHD status and subtype, obtained by behavioral examination, using imaging data, intelligence quotients and other covariates. The novel contributions of this manuscript include a thorough exploration of prediction and image feature extraction methodology on this form of data, including the use of singular value decompositions (SVDs), CUR decompositions, random forest, gradient boosting, bagging, voxel-based morphometry, and support vector machines as well as important insights into the value, and potentially lack thereof, of imaging biomarkers of disease. The key results include the CUR-based decomposition of the rs-fc-fMRI along with gradient boosting and the prediction algorithm based on a motor network parcellation and random forest algorithm. We conjecture that the CUR decomposition is largely diagnosing common population directions of head motion. Of note, a byproduct of this research is a potential automated method for detecting subtle in-scanner motion. The final prediction algorithm, a weighted combination of several algorithms, had an external test set specificity of 94% with sensitivity of 21%. The most promising imaging biomarker was a correlation graph from a motor network parcellation. In summary, we have undertaken a large-scale statistical exploratory prediction exercise on the unique ADHD 200 data set. The exercise produced several potential leads for future scientific exploration of the neurological basis of ADHD. PMID:22969709

Comparison of three-way and four-way calibration for the real-time quantitative analysis of drug hydrolysis in complex dynamic samples by excitation-emission matrix fluorescence.

PubMed

Yin, Xiao-Li; Gu, Hui-Wen; Liu, Xiao-Lu; Zhang, Shan-Hui; Wu, Hai-Long

2018-03-05

Multiway calibration in combination with spectroscopic technique is an attractive tool for online or real-time monitoring of target analyte(s) in complex samples. However, how to choose a suitable multiway calibration method for the resolution of spectroscopic-kinetic data is a troubling problem in practical application. In this work, for the first time, three-way and four-way fluorescence-kinetic data arrays were generated during the real-time monitoring of the hydrolysis of irinotecan (CPT-11) in human plasma by excitation-emission matrix fluorescence. Alternating normalization-weighted error (ANWE) and alternating penalty trilinear decomposition (APTLD) were used as three-way calibration for the decomposition of the three-way kinetic data array, whereas alternating weighted residual constraint quadrilinear decomposition (AWRCQLD) and alternating penalty quadrilinear decomposition (APQLD) were applied as four-way calibration to the four-way kinetic data array. The quantitative results of the two kinds of calibration models were fully compared from the perspective of predicted real-time concentrations, spiked recoveries of initial concentration, and analytical figures of merit. The comparison study demonstrated that both three-way and four-way calibration models could achieve real-time quantitative analysis of the hydrolysis of CPT-11 in human plasma under certain conditions. However, it was also found that both of them possess some critical advantages and shortcomings during the process of dynamic analysis. The conclusions obtained in this paper can provide some helpful guidance for the reasonable selection of multiway calibration models to achieve the real-time quantitative analysis of target analyte(s) in complex dynamic systems. Copyright © 2017 Elsevier B.V. All rights reserved.

A Survey of Singular Value Decomposition Methods and Performance Comparison of Some Available Serial Codes

NASA Technical Reports Server (NTRS)

Plassman, Gerald E.

2005-01-01

This contractor report describes a performance comparison of available alternative complete Singular Value Decomposition (SVD) methods and implementations which are suitable for incorporation into point spread function deconvolution algorithms. The report also presents a survey of alternative algorithms, including partial SVD's special case SVD's, and others developed for concurrent processing systems.

Computing the Partial Fraction Decomposition of Rational Functions with Irreducible Quadratic Factors in the Denominators

ERIC Educational Resources Information Center

Man, Yiu-Kwong

2012-01-01

In this note, a new method for computing the partial fraction decomposition of rational functions with irreducible quadratic factors in the denominators is presented. This method involves polynomial divisions and substitutions only, without having to solve for the complex roots of the irreducible quadratic polynomial or to solve a system of linear…

Quantitative separation of tetralin hydroperoxide from its decomposition products by high performance liquid chromatography

NASA Technical Reports Server (NTRS)

Worstell, J. H.; Daniel, S. R.

1981-01-01

A method for the separation and analysis of tetralin hydroperoxide and its decomposition products by high pressure liquid chromatography has been developed. Elution with a single, mixed solvent from a micron-Porasil column was employed. Constant response factors (internal standard method) over large concentration ranges and reproducible retention parameters are reported.

Multidisciplinary Optimization Methods for Aircraft Preliminary Design

NASA Technical Reports Server (NTRS)

Kroo, Ilan; Altus, Steve; Braun, Robert; Gage, Peter; Sobieski, Ian

1994-01-01

This paper describes a research program aimed at improved methods for multidisciplinary design and optimization of large-scale aeronautical systems. The research involves new approaches to system decomposition, interdisciplinary communication, and methods of exploiting coarse-grained parallelism for analysis and optimization. A new architecture, that involves a tight coupling between optimization and analysis, is intended to improve efficiency while simplifying the structure of multidisciplinary, computation-intensive design problems involving many analysis disciplines and perhaps hundreds of design variables. Work in two areas is described here: system decomposition using compatibility constraints to simplify the analysis structure and take advantage of coarse-grained parallelism; and collaborative optimization, a decomposition of the optimization process to permit parallel design and to simplify interdisciplinary communication requirements.

A novel spatial-temporal detection method of dim infrared moving small target

NASA Astrophysics Data System (ADS)

Chen, Zhong; Deng, Tao; Gao, Lei; Zhou, Heng; Luo, Song

2014-09-01

Moving small target detection under complex background in infrared image sequence is one of the major challenges of modern military in Early Warning Systems (EWS) and the use of Long-Range Strike (LRS). However, because of the low SNR and undulating background, the infrared moving small target detection is a difficult problem in a long time. To solve this problem, a novel spatial-temporal detection method based on bi-dimensional empirical mode decomposition (EMD) and time-domain difference is proposed in this paper. This method is downright self-data decomposition and do not rely on any transition kernel function, so it has a strong adaptive capacity. Firstly, we generalized the 1D EMD algorithm to the 2D case. In this process, the project has solved serial issues in 2D EMD, such as large amount of data operations, define and identify extrema in 2D case, and two-dimensional signal boundary corrosion. The EMD algorithm studied in this project can be well adapted to the automatic detection of small targets under low SNR and complex background. Secondly, considering the characteristics of moving target, we proposed an improved filtering method based on three-frame difference on basis of the original difference filtering in time-domain, which greatly improves the ability of anti-jamming algorithm. Finally, we proposed a new time-space fusion method based on a combined processing of 2D EMD and improved time-domain differential filtering. And, experimental results show that this method works well in infrared small moving target detection under low SNR and complex background.

An Ensemble Multilabel Classification for Disease Risk Prediction

PubMed Central

Liu, Wei; Zhao, Hongling; Zhang, Chaoyang

2017-01-01

It is important to identify and prevent disease risk as early as possible through regular physical examinations. We formulate the disease risk prediction into a multilabel classification problem. A novel Ensemble Label Power-set Pruned datasets Joint Decomposition (ELPPJD) method is proposed in this work. First, we transform the multilabel classification into a multiclass classification. Then, we propose the pruned datasets and joint decomposition methods to deal with the imbalance learning problem. Two strategies size balanced (SB) and label similarity (LS) are designed to decompose the training dataset. In the experiments, the dataset is from the real physical examination records. We contrast the performance of the ELPPJD method with two different decomposition strategies. Moreover, the comparison between ELPPJD and the classic multilabel classification methods RAkEL and HOMER is carried out. The experimental results show that the ELPPJD method with label similarity strategy has outstanding performance. PMID:29065647

Decomposition rates and termite assemblage composition in semiarid Africa

USGS Publications Warehouse

Schuurman, G.

2005-01-01

Outside of the humid tropics, abiotic factors are generally considered the dominant regulators of decomposition, and biotic influences are frequently not considered in predicting decomposition rates. In this study, I examined the effect of termite assemblage composition and abundance on decomposition of wood litter of an indigenous species (Croton megalobotrys) in five terrestrial habitats of the highly seasonal semiarid Okavango Delta region of northern Botswana, to determine whether natural variation in decomposer community composition and abundance influences decomposition rates. 1 conducted the study in two areas, Xudum and Santawani, with the Xudum study preceding the Santawani study. I assessed termite assemblage composition and abundance using a grid of survey baits (rolls of toilet paper) placed on the soil surface and checked 2-4 times/month. I placed a billet (a section of wood litter) next to each survey bait and measured decomposition in a plot by averaging the mass loss of its billets. Decomposition rates varied up to sixfold among plots within the same habitat and locality, despite the fact that these plots experienced the same climate. In addition, billets decomposed significantly faster during the cooler and drier Santawani study, contradicting climate-based predictions. Because termite incidence was generally higher in Santawani plots, termite abundance initially seemed a likely determinant of decomposition in this system. However, no significant effect of termite incidence on billet mass loss rates was observed among the Xudum plots, where decomposition rates remained low even though termite incidence varied considerably. Considering the incidences of fungus-growing termites and non-fungus-growing termites separately resolves this apparent contradiction: in both Santawani and Xudum, only fungus-growing termites play a significant role in decomposition. This result is mirrored in an analysis of the full data set of combined Xudum and Santawani data. The determination that natural variation in the abundance of a single taxonomic group of soil fauna, a termite subfamily, determines almost all observed variation in decomposition rates supports the emerging view that biotic influences may be important in many biomes and that consideration of decomposer community composition and abundance may be critical for accurate prediction of decomposition rates. ?? 2005 by the Ecological Society of America.

Single block three-dimensional volume grids about complex aerodynamic vehicles

NASA Technical Reports Server (NTRS)

Alter, Stephen J.; Weilmuenster, K. James

1993-01-01

This paper presents an alternate approach for the generation of volumetric grids for supersonic and hypersonic flows about complex configurations. The method uses parametric two dimensional block face grid definition within the framework of GRIDGEN2D. The incorporation of face decomposition reduces complex surfaces to simple shapes. These simple shapes are combined to obtain the final face definition. The advantages of this method include the reduction of overall grid generation time through the use of vectorized computer code, the elimination of the need to generate matching block faces, and the implementation of simplified boundary conditions. A simple axisymmetric grid is used to illustrate this method. In addition, volume grids for two complex configurations, the Langley Lifting Body (HL-20) and the Space Shuttle Orbiter, are shown.

Surface infrastructure functions, requirements and subsystems for a manned Mars mission

NASA Technical Reports Server (NTRS)

Fairchild, Kyle

1986-01-01

Planning and development for a permanently manned scientific outpost on Mars requires an in-depth understanding and analysis of the functions the outpost is expected to perform. The optimum configuration that accomplishes these functions then arises during the trade studies process. In a project this complex, it becomes necessary to use a formal methodology to document the design and planning process. The method chosen for this study is called top-down functional decomposition. This method is used to determine the functions that are needed to accomplish the overall mission, then determine what requirements and systems are needed to do each of the functions. This method facilitates automation of the trades and options process. In the example, this was done with an off-the shelf software package called TK! olver. The basic functions that a permanently manned outpost on Mars must accomplish are: (1) Establish the Life Critical Systems; (2) Support Planetary Sciences and Exploration; and (3) Develop and Maintain Long-term Support Functions, including those systems needed towards self-sufficiency. The top-down functional decomposition methology, combined with standard spread sheet software, offers a powerful tool to quickly assess various design trades and analyze options. As the specific subsystems, and the relational rule algorithms are further refined, it will be possible to very accurately determine the implications of continually evolving mission requirements.

Analysis of Self-Associating Proteins by Singular Value Decomposition of Solution Scattering Data

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

Williamson, Tim E.; Craig, Bruce A.; Kondrashkina, Elena

2008-07-08

We describe a method by which a single experiment can reveal both association model (pathway and constants) and low-resolution structures of a self-associating system. Small-angle scattering data are collected from solutions at a range of concentrations. These scattering data curves are mass-weighted linear combinations of the scattering from each oligomer. Singular value decomposition of the data yields a set of basis vectors from which the scattering curve for each oligomer is reconstructed using coefficients that depend on the association model. A search identifies the association pathway and constants that provide the best agreement between reconstructed and observed data. Using simulatedmore » data with realistic noise, our method finds the correct pathway and association constants. Depending on the simulation parameters, reconstructed curves for each oligomer differ from the ideal by 0.050.99% in median absolute relative deviation. The reconstructed scattering curves are fundamental to further analysis, including interatomic distance distribution calculation and low-resolution ab initio shape reconstruction of each oligomer in solution. This method can be applied to x-ray or neutron scattering data from small angles to moderate (or higher) resolution. Data can be taken under physiological conditions, or particular conditions (e.g., temperature) can be varied to extract fundamental association parameters ({Delta}H{sub ass}, S{sub ass}).« less

Disaggregating tree and grass phenology in tropical savannas

NASA Astrophysics Data System (ADS)

Zhou, Qiang

Savannas are mixed tree-grass systems and as one of the world's largest biomes represent an important component of the Earth system affecting water and energy balances, carbon sequestration and biodiversity as well as supporting large human populations. Savanna vegetation structure and its distribution, however, may change because of major anthropogenic disturbances from climate change, wildfire, agriculture, and livestock production. The overstory and understory may have different water use strategies, different nutrient requirements and have different responses to fire and climate variation. The accurate measurement of the spatial distribution and structure of the overstory and understory are essential for understanding the savanna ecosystem. This project developed a workflow for separating the dynamics of the overstory and understory fractional cover in savannas at the continental scale (Australia, South America, and Africa). Previous studies have successfully separated the phenology of Australian savanna vegetation into persistent and seasonal greenness using time series decomposition, and into fractions of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV) and bare soil (BS) using linear unmixing. This study combined these methods to separate the understory and overstory signal in both the green and senescent phenological stages using remotely sensed imagery from the MODIS (MODerate resolution Imaging Spectroradiometer) sensor. The methods and parameters were adjusted based on the vegetation variation. The workflow was first tested at the Australian site. Here the PV estimates for overstory and understory showed best performance, however NPV estimates exhibited spatial variation in validation relationships. At the South American site (Cerrado), an additional method based on frequency unmixing was developed to separate green vegetation components with similar phenology. When the decomposition and frequency methods were compared, the frequency method was better for extracting the green tree phenology, but the original decomposition method was better for retrieval of understory grass phenology. Both methods, however, were less accurate than in the Cerrado than in Australia due to intermingling and intergrading of grass and small woody components. Since African savanna trees are predominantly deciduous, the frequency method was combined with the linear unmixing of fractional cover to attempt to separate the relatively similar phenology of deciduous trees and seasonal grasses. The results for Africa revealed limitations associated with both methods. There was spatial and seasonal variation in the spectral indices used to unmix fractional cover resulting in poor validation for NPV in particular. The frequency analysis revealed significant phase variation indicative of different phenology, but these could not be clearly ascribed to separate grass and tree components. Overall findings indicate that site-specific variation and vegetation structure and composition, along with MODIS pixel resolution, and the simple vegetation index approach used was not robust across the different savanna biomes. The approach showed generally better performance for estimating PV fraction, and separating green phenology, but there were major inconsistencies, errors and biases in estimation of NPV and BS outside of the Australian savanna environment.

Three-pattern decomposition of global atmospheric circulation: part I—decomposition model and theorems

NASA Astrophysics Data System (ADS)

Hu, Shujuan; Chou, Jifan; Cheng, Jianbo

2018-04-01

In order to study the interactions between the atmospheric circulations at the middle-high and low latitudes from the global perspective, the authors proposed the mathematical definition of three-pattern circulations, i.e., horizontal, meridional and zonal circulations with which the actual atmospheric circulation is expanded. This novel decomposition method is proved to accurately describe the actual atmospheric circulation dynamics. The authors used the NCEP/NCAR reanalysis data to calculate the climate characteristics of those three-pattern circulations, and found that the decomposition model agreed with the observed results. Further dynamical analysis indicates that the decomposition model is more accurate to capture the major features of global three dimensional atmospheric motions, compared to the traditional definitions of Rossby wave, Hadley circulation and Walker circulation. The decomposition model for the first time realized the decomposition of global atmospheric circulation using three orthogonal circulations within the horizontal, meridional and zonal planes, offering new opportunities to study the large-scale interactions between the middle-high latitudes and low latitudes circulations.

Pressure-dependent decomposition kinetics of the energetic material HMX up to 3.6 GPa.

PubMed

Glascoe, Elizabeth A; Zaug, Joseph M; Burnham, Alan K

2009-12-03

The effect of pressure on the global thermal decomposition rate of the energetic material HMX was studied. HMX was precompressed in a diamond anvil cell (DAC) and heated at various rates. The parent species population was monitored as a function of time and temperature using Fourier transform infrared (FTIR) spectroscopy. Global decomposition rates were determined by fitting the fraction reacted to the extended-Prout-Tompkins nucleation-growth model and the Friedman isoconversional method. The results of these experiments and analysis indicate that pressure accelerates the decomposition at low-to-moderate pressures (i.e., between ambient pressure and 0.1 GPa) and decelerates the decomposition at higher pressures. The decomposition acceleration is attributed to pressure-enhanced autocatalysis, whereas the deceleration at high pressures is attributed to pressure-inhibiting bond homolysis step(s), which would result in an increase in volume. These results indicate that both the beta- and delta-polymorphs of HMX are sensitive to pressure in the thermally induced decomposition kinetics.

Test program to demonstrate the stability of hydrazine in propellant tanks

NASA Technical Reports Server (NTRS)

Moran, C. M.; Bjorklund, R. A.

1983-01-01

A 24-month coupon test program to evaluate the decomposition of propellant tanks is reported. The propellant fuel evaluated was monopropellant-grade hydrazine (N2H4), which is normally a colorless, fuming, corrosive, strongly reducing liquid. The degree of hydrazine decomposition was determined by means of chemical analyses of the liquid and evolved gases at the end of the test program. The experimental rates of hydrazine decomposition were determined to be within acceptable limits. The propellant tank materials and material combinations were not degraded by a 2-year exposure to hydrazine propellant. This was verified using change-of-weight determinations and microscopic examination of the specimen surface before and after exposure, and by posttest chemical analyses of hydrazine liquid for residual metal content.

Exploring Patterns of Soil Organic Matter Decomposition with Students through the Global Decomposition Project (GDP) and the Interactive Model of Leaf Decomposition (IMOLD)

NASA Astrophysics Data System (ADS)

Steiner, S. M.; Wood, J. H.

2015-12-01

As decomposition rates are affected by climate change, understanding crucial soil interactions that affect plant growth and decomposition becomes a vital part of contributing to the students' knowledge base. The Global Decomposition Project (GDP) is designed to introduce and educate students about soil organic matter and decomposition through a standardized protocol for collecting, reporting, and sharing data. The Interactive Model of Leaf Decomposition (IMOLD) utilizes animations and modeling to learn about the carbon cycle, leaf anatomy, and the role of microbes in decomposition. Paired together, IMOLD teaches the background information and allows simulation of numerous scenarios, and the GDP is a data collection protocol that allows students to gather usable measurements of decomposition in the field. Our presentation will detail how the GDP protocol works, how to obtain or make the materials needed, and how results will be shared. We will also highlight learning objectives from the three animations of IMOLD, and demonstrate how students can experiment with different climates and litter types using the interactive model to explore a variety of decomposition scenarios. The GDP demonstrates how scientific methods can be extended to educate broader audiences, and data collected by students can provide new insight into global patterns of soil decomposition. Using IMOLD, students will gain a better understanding of carbon cycling in the context of litter decomposition, as well as learn to pose questions they can answer with an authentic computer model. Using the GDP protocols and IMOLD provide a pathway for scientists and educators to interact and reach meaningful education and research goals.

Land-use legacies regulate decomposition dynamics following bioenergy crop conversion

DOE PAGES

Kallenbach, Cynthia M.; Stuart Grandy, A.

2014-07-14

Land-use conversion into bioenergy crop production can alter litter decomposition processes tightly coupled to soil carbon and nutrient dynamics. Yet, litter decomposition has been poorly described in bioenergy production systems, especially following land-use conversion. Predicting decomposition dynamics in postconversion bioenergy production systems is challenging because of the combined influence of land-use legacies with current management and litter quality. To evaluate how land-use legacies interact with current bioenergy crop management to influence litter decomposition in different litter types, we conducted a landscape-scale litterbag decomposition experiment. We proposed land-use legacies regulate decomposition, but their effects are weakened under higher quality litter andmore » when current land use intensifies ecosystem disturbance relative to prior land use. We compared sites left in historical land uses of either agriculture (AG) or Conservation Reserve Program grassland (CRP) to those that were converted to corn or switchgrass bioenergy crop production. Enzyme activities, mass loss, microbial biomass, and changes in litter chemistry were monitored in corn stover and switchgrass litter over 485 days, accompanied by similar soil measurements. Across all measured variables, legacy had the strongest effect (P < 0.05) relative to litter type and current management, where CRP sites maintained higher soil and litter enzyme activities and microbial biomass relative to AG sites. Decomposition responses to conversion depended on legacy but also current management and litter type. Within the CRP sites, conversion into corn increased litter enzymes, microbial biomass, and litter protein and lipid abundances, especially on decomposing corn litter, relative to nonconverted CRP. However, conversion into switchgrass from CRP, a moderate disturbance, often had no effect on switchgrass litter decomposition parameters. Thus, legacies shape the direction and magnitude of decomposition responses to bioenergy crop conversion and therefore should be considered a key influence on litter and soil C cycling under bioenergy crop management.« less

Computational Elucidation of Selectivities and Mechanisms Performed by Organometallic and Bioinorganic Catalysts

NASA Astrophysics Data System (ADS)

Grandner, Jessica Marie

Computational methods were used to determine the mechanisms and selectivities of organometallic-catalyzed reactions. The first half of the dissertation focuses on the study of metathesis catalysts in collaboration with the Grubbs group at CalTech. Chapter 1 describes the studies of the decomposition modes of several ruthenium-based metathesis catalysts. These studies were performed to better understand the decomposition of such catalysts in order to prevent decomposition (Chapter 1.2) or utilize decomposed catalysts for alternative reactions (Chapter 1.1). Chapter 2.1 describes the computational investigation of the origins of stereoretentive metathesis with ruthenium-based metathesis catalysts. These findings were then used to computationally design E-selective metathesis catalysts (Chapter 2.2). While the first half of the dissertation was centered around ruthenium catalysts, the second half of the dissertation pertains to iron-catalyzed reaction, in particular, iron-catalyzed reactions by P450 enzymes. The elements of Chapter 3 concentrate on the stereo- and chemo-selectivity of P450-catalyzed C-H hydroxylations. By combining multiple computational methods, the inherent activity of the iron-oxo catalyst and the influence of the active site on such reactions were illuminated. These discoveries allow for the engineering of new substrates and mutant enzymes for tailored C-H hydroxylation. While the mechanism of C-H hydroxylations catalyzed by P450 enzymes has been well studied, there are several P450-catalyzed transformations for which the mechanism is unknown. The components of Chapter 4 describe the use of computations to determine the mechanisms of complex, multi-step reactions catalyzed by P450s. The determination of these mechanisms elucidates how these enzymes react with various functional groups and substrate architectures and allows for a better understanding of how drug-like compounds may be broken down by human P450s.

Integration of multivariate empirical mode decomposition and independent component analysis for fetal ECG separation from abdominal signals.

PubMed

Thanaraj, Palani; Roshini, Mable; Balasubramanian, Parvathavarthini

2016-11-14

The fetal electrocardiogram (FECG) signals are essential to monitor the health condition of the baby. Fetal heart rate (FHR) is commonly used for diagnosing certain abnormalities in the formation of the heart. Usually, non-invasive abdominal electrocardiogram (AbECG) signals are obtained by placing surface electrodes in the abdomen region of the pregnant woman. AbECG signals are often not suitable for the direct analysis of fetal heart activity. Moreover, the strength and magnitude of the FECG signals are low compared to the maternal electrocardiogram (MECG) signals. The MECG signals are often superimposed with the FECG signals that make the monitoring of FECG signals a difficult task. Primary goal of the paper is to separate the fetal electrocardiogram (FECG) signals from the unwanted maternal electrocardiogram (MECG) signals. A multivariate signal processing procedure is proposed here that combines the Multivariate Empirical Mode Decomposition (MEMD) and Independent Component Analysis (ICA). The proposed method is evaluated with clinical abdominal signals taken from three pregnant women (N= 3) recorded during the 38-41 weeks of the gestation period. The number of fetal R-wave detected (NEFQRS), the number of unwanted maternal peaks (NMQRS), the number of undetected fetal R-wave (NUFQRS) and the FHR detection accuracy quantifies the performance of our method. Clinical investigation with three test subjects shows an overall detection accuracy of 92.8%. Comparative analysis with benchmark signal processing method such as ICA suggests the noteworthy performance of our method.

Heterogeneous Decomposition of Volatile Organic Compounds by Visible-Light Activated N, C, S-Embedded Titania.

PubMed

Chun, Ho-Hwan; Jo, Wan-Kuen

2016-05-01

In this study, a N-, C-, and S-doped titania (NCS-TiO2) composite was prepared by combining the titanium precursor with a single dopant source, and the photocatalytic activity of this system for the decomposition of volatile organic compounds (VOCs) at indoor-concentration levels, under exposure to visible light, was examined. The NCS-TiO2 composite and the pure TiO2 photocatalyst, used as a reference, were characterized via X-ray diffraction, scanning electron microscopy, ultraviolet-visible diffuse reflectance spectroscopy, X-ray photoelectron spectroscopy, and Fourier transform infrared spectroscopy. The average efficiencies of benzene, toluene, ethyl benzene, and o-xylene decomposition using NCS-TiO2 for were 70, 87, -100, and -100%, respectively, whereas the values obtained using the pure TiO2 powder were -0, 18, 49, and 51%, respectively. These results suggested that, for the photocatalytic decomposition of toxic VOCs under visible-light exposure conditions, NCS-TiO2 was superior to the reference photocatalyst. The decomposition efficiencies of the target VOCs were inversely related to the initial concentration and relative humidity as well as to the air-flow rate. The decomposition efficiencies of the target chemicals achieved with a conventional lamp/NCS-TiO2 system were higher than those achieved with a light emitting diode/NCS-TiO2 system. Overall, NCS-TiO2 can be used for the efficient decomposition of VOCs under visible-light exposure, if the operational conditions are optimized.

A decentralized linear quadratic control design method for flexible structures

NASA Technical Reports Server (NTRS)

Su, Tzu-Jeng; Craig, Roy R., Jr.

1990-01-01

A decentralized suboptimal linear quadratic control design procedure which combines substructural synthesis, model reduction, decentralized control design, subcontroller synthesis, and controller reduction is proposed for the design of reduced-order controllers for flexible structures. The procedure starts with a definition of the continuum structure to be controlled. An evaluation model of finite dimension is obtained by the finite element method. Then, the finite element model is decomposed into several substructures by using a natural decomposition called substructuring decomposition. Each substructure, at this point, still has too large a dimension and must be reduced to a size that is Riccati-solvable. Model reduction of each substructure can be performed by using any existing model reduction method, e.g., modal truncation, balanced reduction, Krylov model reduction, or mixed-mode method. Then, based on the reduced substructure model, a subcontroller is designed by an LQ optimal control method for each substructure independently. After all subcontrollers are designed, a controller synthesis method called substructural controller synthesis is employed to synthesize all subcontrollers into a global controller. The assembling scheme used is the same as that employed for the structure matrices. Finally, a controller reduction scheme, called the equivalent impulse response energy controller (EIREC) reduction algorithm, is used to reduce the global controller to a reasonable size for implementation. The EIREC reduced controller preserves the impulse response energy of the full-order controller and has the property of matching low-frequency moments and low-frequency power moments. An advantage of the substructural controller synthesis method is that it relieves the computational burden associated with dimensionality. Besides that, the SCS design scheme is also a highly adaptable controller synthesis method for structures with varying configuration, or varying mass and stiffness properties.

Analysis of tasks for dynamic man/machine load balancing in advanced helicopters

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

Jorgensen, C.C.

1987-10-01

This report considers task allocation requirements imposed by advanced helicopter designs incorporating mixes of human pilots and intelligent machines. Specifically, it develops an analogy between load balancing using distributed non-homogeneous multiprocessors and human team functions. A taxonomy is presented which can be used to identify task combinations likely to cause overload for dynamic scheduling and process allocation mechanisms. Designer criteria are given for function decomposition, separation of control from data, and communication handling for dynamic tasks. Possible effects of n-p complete scheduling problems are noted and a class of combinatorial optimization methods are examined.

Communication: Acceleration of coupled cluster singles and doubles via orbital-weighted least-squares tensor hypercontraction

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

Parrish, Robert M.; Sherrill, C. David, E-mail: sherrill@gatech.edu; Hohenstein, Edward G.

2014-05-14

We apply orbital-weighted least-squares tensor hypercontraction decomposition of the electron repulsion integrals to accelerate the coupled cluster singles and doubles (CCSD) method. Using accurate and flexible low-rank factorizations of the electron repulsion integral tensor, we are able to reduce the scaling of the most vexing particle-particle ladder term in CCSD from O(N{sup 6}) to O(N{sup 5}), with remarkably low error. Combined with a T{sub 1}-transformed Hamiltonian, this leads to substantial practical accelerations against an optimized density-fitted CCSD implementation.

Lamb Waves Decomposition and Mode Identification Using Matching Pursuit Method

DTIC Science & Technology

2009-01-01

Wigner - Ville distribution ( WVD ). However, WVD suffers from severe interferences, called cross-terms. Cross- terms are the area of a time-frequency...transform (STFT), wavelet transform, Wigner - Ville distribution , matching pursuit decomposition, etc. 1 Report Documentation Page Form ApprovedOMB No...MP decomposition using chirplet dictionary was applied to a simulated S0 mode Lamb wave shown previously in Figure 2a. Wigner - Ville distribution of

Analysis of Self-Excited Combustion Instabilities Using Decomposition Techniques

DTIC Science & Technology

2016-07-05

are evaluated for the study of self-excited longitudinal combustion instabilities in laboratory-scaled single-element gas turbine and rocket...Air Force Base, California 93524 DOI: 10.2514/1.J054557 Proper orthogonal decomposition and dynamic mode decomposition are evaluated for the study of...instabilities. In addition, we also evaluate the capabilities of the methods to deal with data sets of different spatial extents and temporal resolution

Gas Pressure Monitored Iodide-Catalyzed Decomposition Kinetics of H[subscript 2]O[subscript 2]: Initial-Rate and Integrated-Rate Methods in the General Chemistry Lab

ERIC Educational Resources Information Center

Nyasulu, Frazier; Barlag, Rebecca

2010-01-01

The reaction kinetics of the iodide-catalyzed decomposition of [subscript 2]O[subscript 2] using the integrated-rate method is described. The method is based on the measurement of the total gas pressure using a datalogger and pressure sensor. This is a modification of a previously reported experiment based on the initial-rate approach. (Contains 2…

Semi-Supervised Marginal Fisher Analysis for Hyperspectral Image Classification

NASA Astrophysics Data System (ADS)

Huang, H.; Liu, J.; Pan, Y.

2012-07-01

The problem of learning with both labeled and unlabeled examples arises frequently in Hyperspectral image (HSI) classification. While marginal Fisher analysis is a supervised method, which cannot be directly applied for Semi-supervised classification. In this paper, we proposed a novel method, called semi-supervised marginal Fisher analysis (SSMFA), to process HSI of natural scenes, which uses a combination of semi-supervised learning and manifold learning. In SSMFA, a new difference-based optimization objective function with unlabeled samples has been designed. SSMFA preserves the manifold structure of labeled and unlabeled samples in addition to separating labeled samples in different classes from each other. The semi-supervised method has an analytic form of the globally optimal solution, and it can be computed based on eigen decomposition. Classification experiments with a challenging HSI task demonstrate that this method outperforms current state-of-the-art HSI-classification methods.

A Chebyshev Collocation Method for Moving Boundaries, Heat Transfer, and Convection During Directional Solidification

NASA Technical Reports Server (NTRS)

Zhang, Yiqiang; Alexander, J. I. D.; Ouazzani, J.

1994-01-01

Free and moving boundary problems require the simultaneous solution of unknown field variables and the boundaries of the domains on which these variables are defined. There are many technologically important processes that lead to moving boundary problems associated with fluid surfaces and solid-fluid boundaries. These include crystal growth, metal alloy and glass solidification, melting and name propagation. The directional solidification of semi-conductor crystals by the Bridgman-Stockbarger method is a typical example of such a complex process. A numerical model of this growth method must solve the appropriate heat, mass and momentum transfer equations and determine the location of the melt-solid interface. In this work, a Chebyshev pseudospectra collocation method is adapted to the problem of directional solidification. Implementation involves a solution algorithm that combines domain decomposition, finite-difference preconditioned conjugate minimum residual method and a Picard type iterative scheme.

An improved principal component analysis based region matching method for fringe direction estimation

NASA Astrophysics Data System (ADS)

He, A.; Quan, C.

2018-04-01

The principal component analysis (PCA) and region matching combined method is effective for fringe direction estimation. However, its mask construction algorithm for region matching fails in some circumstances, and the algorithm for conversion of orientation to direction in mask areas is computationally-heavy and non-optimized. We propose an improved PCA based region matching method for the fringe direction estimation, which includes an improved and robust mask construction scheme, and a fast and optimized orientation-direction conversion algorithm for the mask areas. Along with the estimated fringe direction map, filtered fringe pattern by automatic selective reconstruction modification and enhanced fast empirical mode decomposition (ASRm-EFEMD) is used for Hilbert spiral transform (HST) to demodulate the phase. Subsequently, windowed Fourier ridge (WFR) method is used for the refinement of the phase. The robustness and effectiveness of proposed method are demonstrated by both simulated and experimental fringe patterns.

Time-Frequency Analysis of Non-Stationary Biological Signals with Sparse Linear Regression Based Fourier Linear Combiner.

PubMed

Wang, Yubo; Veluvolu, Kalyana C

2017-06-14

It is often difficult to analyze biological signals because of their nonlinear and non-stationary characteristics. This necessitates the usage of time-frequency decomposition methods for analyzing the subtle changes in these signals that are often connected to an underlying phenomena. This paper presents a new approach to analyze the time-varying characteristics of such signals by employing a simple truncated Fourier series model, namely the band-limited multiple Fourier linear combiner (BMFLC). In contrast to the earlier designs, we first identified the sparsity imposed on the signal model in order to reformulate the model to a sparse linear regression model. The coefficients of the proposed model are then estimated by a convex optimization algorithm. The performance of the proposed method was analyzed with benchmark test signals. An energy ratio metric is employed to quantify the spectral performance and results show that the proposed method Sparse-BMFLC has high mean energy (0.9976) ratio and outperforms existing methods such as short-time Fourier transfrom (STFT), continuous Wavelet transform (CWT) and BMFLC Kalman Smoother. Furthermore, the proposed method provides an overall 6.22% in reconstruction error.

Improved accuracy and precision in δ15 NAIR measurements of explosives, urea, and inorganic nitrates by elemental analyzer/isotope ratio mass spectrometry using thermal decomposition.

PubMed

Lott, Michael J; Howa, John D; Chesson, Lesley A; Ehleringer, James R

2015-08-15

Elemental analyzer systems generate N(2) and CO(2) for elemental composition and isotope ratio measurements. As quantitative conversion of nitrogen in some materials (i.e., nitrate salts and nitro-organic compounds) is difficult, this study tests a recently published method - thermal decomposition without the addition of O(2) - for the analysis of these materials. Elemental analyzer/isotope ratio mass spectrometry (EA/IRMS) was used to compare the traditional combustion method (CM) and the thermal decomposition method (TDM), where additional O(2) is eliminated from the reaction. The comparisons used organic and inorganic materials with oxidized and/or reduced nitrogen and included ureas, nitrate salts, ammonium sulfate, nitro esters, and nitramines. Previous TDM applications were limited to nitrate salts and ammonium sulfate. The measurement precision and accuracy were compared to determine the effectiveness of converting materials containing different fractions of oxidized nitrogen into N(2). The δ(13) C(VPDB) values were not meaningfully different when measured via CM or TDM, allowing for the analysis of multiple elements in one sample. For materials containing oxidized nitrogen, (15) N measurements made using thermal decomposition were more precise than those made using combustion. The precision was similar between the methods for materials containing reduced nitrogen. The %N values were closer to theoretical when measured by TDM than by CM. The δ(15) N(AIR) values of purchased nitrate salts and ureas were nearer to the known values when analyzed using thermal decomposition than using combustion. The thermal decomposition method addresses insufficient recovery of nitrogen during elemental analysis in a variety of organic and inorganic materials. Its implementation requires relatively few changes to the elemental analyzer. Using TDM, it is possible to directly calibrate certain organic materials to international nitrate isotope reference materials without off-line preparation. Copyright © 2015 John Wiley & Sons, Ltd.

Algebraic multigrid domain and range decomposition (AMG-DD / AMG-RD)*

DOE PAGES

Bank, R.; Falgout, R. D.; Jones, T.; ...

2015-10-29

In modern large-scale supercomputing applications, algebraic multigrid (AMG) is a leading choice for solving matrix equations. However, the high cost of communication relative to that of computation is a concern for the scalability of traditional implementations of AMG on emerging architectures. This paper introduces two new algebraic multilevel algorithms, algebraic multigrid domain decomposition (AMG-DD) and algebraic multigrid range decomposition (AMG-RD), that replace traditional AMG V-cycles with a fully overlapping domain decomposition approach. While the methods introduced here are similar in spirit to the geometric methods developed by Brandt and Diskin [Multigrid solvers on decomposed domains, in Domain Decomposition Methods inmore » Science and Engineering, Contemp. Math. 157, AMS, Providence, RI, 1994, pp. 135--155], Mitchell [Electron. Trans. Numer. Anal., 6 (1997), pp. 224--233], and Bank and Holst [SIAM J. Sci. Comput., 22 (2000), pp. 1411--1443], they differ primarily in that they are purely algebraic: AMG-RD and AMG-DD trade communication for computation by forming global composite “grids” based only on the matrix, not the geometry. (As is the usual AMG convention, “grids” here should be taken only in the algebraic sense, regardless of whether or not it corresponds to any geometry.) Another important distinguishing feature of AMG-RD and AMG-DD is their novel residual communication process that enables effective parallel computation on composite grids, avoiding the all-to-all communication costs of the geometric methods. The main purpose of this paper is to study the potential of these two algebraic methods as possible alternatives to existing AMG approaches for future parallel machines. As a result, this paper develops some theoretical properties of these methods and reports on serial numerical tests of their convergence properties over a spectrum of problem parameters.« less

Repeated decompositions reveal the stability of infomax decomposition of fMRI data

PubMed Central

Duann, Jeng-Ren; Jung, Tzyy-Ping; Sejnowski, Terrence J.; Makeig, Scott

2010-01-01

In this study, we decomposed 12 fMRI data sets from six subjects each 101 times using the infomax algorithm. The first decomposition was taken as a reference decomposition; the others were used to form a component matrix of 100 by 100 components. Equivalence relations between components in this matrix, defined as maximum spatial correlations to the components of the reference decomposition, were found by the Hungarian sorting method and used to form 100 equivalence classes for each data set. We then tested the reproducibility of the matched components in the equivalence classes using uncertainty measures based on component distributions, time courses, and ROC curves. Infomax ICA rarely failed to derive nearly the same components in different decompositions. Very few components per data set were poorly reproduced, even using vector angle uncertainty measures stricter than correlation and detection theory measures. PMID:17281453

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

Glascoe, E A; Zaug, J M; Burnham, A K

The effect of pressure on the thermal decomposition rate of the energetic material HMX was studied. HMX was precompressed in a diamond anvil cell (DAC) and heated at various rates. The parent species population was monitored as a function of time and temperature using Fourier transform infrared (FTIR) spectroscopy. Decomposition rates were determined by fitting the fraction reacted to the extended-Prout-Tompkins nucleation-growth model and the Friedman isoconversional method. The results of these experiments and analysis indicate that pressure accelerates the decomposition at low to moderate pressures (i.e. between ambient pressure and 1 GPa) and decelerates the decomposition at higher pressures.more » The decomposition acceleration is attributed to pressure enhanced autocatalysis whereas the deceleration at high pressures is attributed pressure inhibiting bond homolysis step(s), which would result in an increase in volume. These results indicate that both {beta} and {delta} phase HMX are sensitive to pressure in the thermally induced decomposition kinetics.« less

Spatial and temporal distribution of 13C labelled plant residues in soil aggregates and Lumbricus terrestris surface casts: A combination of Transmission Electron Microscopy and Nanoscale Secondary Ion Mass Spectrometry

NASA Astrophysics Data System (ADS)

Vidal, Alix; Remusat, Laurent; Watteau, Françoise; Derenne, Sylvie; Quenea, Katell

2016-04-01

Earthworms play a central role in litter decomposition, soil structuration and carbon cycling. They ingest both organic and mineral compounds which are mixed, complexed with mucus and dejected in form of casts at the soil surface and along burrows. Bulk isotopic or biochemical technics have often been used to study the incorporation of litter in soil and casts, but they could not reflect the complex interaction between soil, plant and microorganisms at the microscale. However, the heterogeneous distribution of organic carbon in soil structures induces contrasted microbial activity areas. Nano-scale secondary ion mass spectrometry (NanoSIMS), which is a high spatial resolution method providing elemental and isotopic maps of organic and mineral materials, has recently been applied in soil science (Herrmann et al., 2007; Vogel et al., 2014). The combination of Nano-scale secondary ion mass spectrometry (NanoSIMS) and Transmission Electron Microscopy (TEM) has proven its potential to investigate labelled residues incorporation in earthworm casts (Vidal et al., 2016). In line of this work, we studied the spatial and temporal distribution of plant residues in soil aggregates and earthworm surface casts. This study aimed to (1) identify the decomposition states of labelled plant residues incorporated at different time steps, in casts and soil, (2) identify the microorganisms implied in this decomposition (3) relate the organic matter states of decomposition with their 13C signature. A one year mesocosm experiment was set up to follow the incorporation of 13C labelled Ryegrass (Lolium multiflorum) litter in a soil in the presence of anecic earthworms (Lumbricus terrestris). Soil and surface cast samples were collected after 8 and 54 weeks, embedded in epoxy resin and cut into ultra-thin sections. Soil was fractionated and all and analyzed with TEM and NanoSIMS, obtaining secondary ion images of 12C, 16O, 12C14N, 13C14N and 28Si. The δ13C maps were obtained using the 13C14N-/12C14N- ratio. We identified various states of decomposition within a same sample, associated with a high heterogeneity of δ13C values of plant residues. We also recognized various labelled microorganisms, mainly bacteria and fungi, underlining their participation in residues decomposition. δ13C values were higher in casts than soil aggregates and decreased between 8 and 54 weeks for both samples. Herrmann, A.M., Ritz, K., Nunan, N., Clode, P.L., Pett-Ridge, J., Kilburn, M.R., Murphy, D.V., O'Donnell, A.G., Stockdale, E.A., 2007. Nano-scale secondary ion mass spectrometry - A new analytical tool in biogeochemistry and soil ecology: A review article. Soil Biology and Biochemistry. 39, 1835-1850. Vidal, A., Remusat, L., Watteau, F., Derenne, S., Quenea K., 2016. Incorporation of 13C labelled shoot residues in Lumbricus terrestris casts: A combination of Transmission Electron Microscopy and Nanoscale Secondary Ion Mass Spectrometry. Soil Biology and Biochemistry. Vogel, C., Mueller, C.W., Höschen, C., Buegger, F., Heister, K., Schulz, S., Schloter, M., Kögel-Knabner, I., 2014. Submicron structures provide preferential spots for carbon and nitrogen sequestration in soils. Nature Communications 5.

Method and apparatus for maintaining the pH in zinc-bromine battery systems

DOEpatents

Grimes, Patrick G.

1985-09-10

A method and apparatus for maintaining the pH level in a zinc-bromine battery features reacting decomposition hydrogen with bromine in the presence of a catalyst. The catalyst encourages the formation of hydrogen and bromine ions. The decomposition hydrogen is therefore consumed, alloying the pH of the system to remain substantially at a given value.

Computer implemented empirical mode decomposition method, apparatus and article of manufacture

NASA Technical Reports Server (NTRS)

Huang, Norden E. (Inventor)

1999-01-01

A computer implemented physical signal analysis method is invented. This method includes two essential steps and the associated presentation techniques of the results. All the steps exist only in a computer: there are no analytic expressions resulting from the method. The first step is a computer implemented Empirical Mode Decomposition to extract a collection of Intrinsic Mode Functions (IMF) from nonlinear, nonstationary physical signals. The decomposition is based on the direct extraction of the energy associated with various intrinsic time scales in the physical signal. Expressed in the IMF's, they have well-behaved Hilbert Transforms from which instantaneous frequencies can be calculated. The second step is the Hilbert Transform. The final result is the Hilbert Spectrum. Thus, the invention can localize any event on the time as well as the frequency axis. The decomposition can also be viewed as an expansion of the data in terms of the IMF's. Then, these IMF's, based on and derived from the data, can serve as the basis of that expansion. The local energy and the instantaneous frequency derived from the IMF's through the Hilbert transform give a full energy-frequency-time distribution of the data which is designated as the Hilbert Spectrum.

Full waveform time domain solutions for source and induced magnetotelluric and controlled-source electromagnetic fields using quasi-equivalent time domain decomposition and GPU parallelization

NASA Astrophysics Data System (ADS)

Imamura, N.; Schultz, A.

2015-12-01

Recently, a full waveform time domain solution has been developed for the magnetotelluric (MT) and controlled-source electromagnetic (CSEM) methods. The ultimate goal of this approach is to obtain a computationally tractable direct waveform joint inversion for source fields and earth conductivity structure in three and four dimensions. This is desirable on several grounds, including the improved spatial resolving power expected from use of a multitude of source illuminations of non-zero wavenumber, the ability to operate in areas of high levels of source signal spatial complexity and non-stationarity, etc. This goal would not be obtainable if one were to adopt the finite difference time-domain (FDTD) approach for the forward problem. This is particularly true for the case of MT surveys, since an enormous number of degrees of freedom are required to represent the observed MT waveforms across the large frequency bandwidth. It means that for FDTD simulation, the smallest time steps should be finer than that required to represent the highest frequency, while the number of time steps should also cover the lowest frequency. This leads to a linear system that is computationally burdensome to solve. We have implemented our code that addresses this situation through the use of a fictitious wave domain method and GPUs to speed up the computation time. We also substantially reduce the size of the linear systems by applying concepts from successive cascade decimation, through quasi-equivalent time domain decomposition. By combining these refinements, we have made good progress toward implementing the core of a full waveform joint source field/earth conductivity inverse modeling method. From results, we found the use of previous generation of CPU/GPU speeds computations by an order of magnitude over a parallel CPU only approach. In part, this arises from the use of the quasi-equivalent time domain decomposition, which shrinks the size of the linear system dramatically.

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

Meng, F.; Banks, J. W.; Henshaw, W. D.

We describe a new partitioned approach for solving conjugate heat transfer (CHT) problems where the governing temperature equations in different material domains are time-stepped in a implicit manner, but where the interface coupling is explicit. The new approach, called the CHAMP scheme (Conjugate Heat transfer Advanced Multi-domain Partitioned), is based on a discretization of the interface coupling conditions using a generalized Robin (mixed) condition. The weights in the Robin condition are determined from the optimization of a condition derived from a local stability analysis of the coupling scheme. The interface treatment combines ideas from optimized-Schwarz methods for domain-decomposition problems togethermore » with the interface jump conditions and additional compatibility jump conditions derived from the governing equations. For many problems (i.e. for a wide range of material properties, grid-spacings and time-steps) the CHAMP algorithm is stable and second-order accurate using no sub-time-step iterations (i.e. a single implicit solve of the temperature equation in each domain). In extreme cases (e.g. very fine grids with very large time-steps) it may be necessary to perform one or more sub-iterations. Each sub-iteration generally increases the range of stability substantially and thus one sub-iteration is likely sufficient for the vast majority of practical problems. The CHAMP algorithm is developed first for a model problem and analyzed using normal-mode the- ory. The theory provides a mechanism for choosing optimal parameters in the mixed interface condition. A comparison is made to the classical Dirichlet-Neumann (DN) method and, where applicable, to the optimized- Schwarz (OS) domain-decomposition method. For problems with different thermal conductivities and dif- fusivities, the CHAMP algorithm outperforms the DN scheme. For domain-decomposition problems with uniform conductivities and diffusivities, the CHAMP algorithm performs better than the typical OS scheme with one grid-cell overlap. Lastly, the CHAMP scheme is also developed for general curvilinear grids and CHT ex- amples are presented using composite overset grids that confirm the theory and demonstrate the effectiveness of the approach.« less

Health monitoring of pipeline girth weld using empirical mode decomposition

NASA Astrophysics Data System (ADS)

Rezaei, Davood; Taheri, Farid

2010-05-01

In the present paper the Hilbert-Huang transform (HHT), as a time-series analysis technique, has been combined with a local diagnostic approach in an effort to identify flaws in pipeline girth welds. This method is based on monitoring the free vibration signals of the pipe at its healthy and flawed states, and processing the signals through the HHT and its associated signal decomposition technique, known as empirical mode decomposition (EMD). The EMD method decomposes the vibration signals into a collection of intrinsic mode functions (IMFs). The deviations in structural integrity, measured from a healthy-state baseline, are subsequently evaluated by two damage sensitive parameters. The first is a damage index, referred to as the EM-EDI, which is established based on an energy comparison of the first or second IMF of the vibration signals, before and after occurrence of damage. The second parameter is the evaluation of the lag in instantaneous phase, a quantity derived from the HHT. In the developed methodologies, the pipe's free vibration is monitored by piezoceramic sensors and a laser Doppler vibrometer. The effectiveness of the proposed techniques is demonstrated through a set of numerical and experimental studies on a steel pipe with a mid-span girth weld, for both pressurized and nonpressurized conditions. To simulate a crack, a narrow notch is cut on one side of the girth weld. Several damage scenarios, including notches of different depths and at various locations on the pipe, are investigated. Results from both numerical and experimental studies reveal that in all damage cases the sensor located at the notch vicinity could successfully detect the notch and qualitatively predict its severity. The effect of internal pressure on the damage identification method is also monitored. Overall, the results are encouraging and promise the effectiveness of the proposed approaches as inexpensive systems for structural health monitoring purposes.

Model diagnostics in reduced-rank estimation

PubMed Central

Chen, Kun

2016-01-01

Reduced-rank methods are very popular in high-dimensional multivariate analysis for conducting simultaneous dimension reduction and model estimation. However, the commonly-used reduced-rank methods are not robust, as the underlying reduced-rank structure can be easily distorted by only a few data outliers. Anomalies are bound to exist in big data problems, and in some applications they themselves could be of the primary interest. While naive residual analysis is often inadequate for outlier detection due to potential masking and swamping, robust reduced-rank estimation approaches could be computationally demanding. Under Stein's unbiased risk estimation framework, we propose a set of tools, including leverage score and generalized information score, to perform model diagnostics and outlier detection in large-scale reduced-rank estimation. The leverage scores give an exact decomposition of the so-called model degrees of freedom to the observation level, which lead to exact decomposition of many commonly-used information criteria; the resulting quantities are thus named information scores of the observations. The proposed information score approach provides a principled way of combining the residuals and leverage scores for anomaly detection. Simulation studies confirm that the proposed diagnostic tools work well. A pattern recognition example with hand-writing digital images and a time series analysis example with monthly U.S. macroeconomic data further demonstrate the efficacy of the proposed approaches. PMID:28003860

An improved algorithm for balanced POD through an analytic treatment of impulse response tails

NASA Astrophysics Data System (ADS)

Tu, Jonathan H.; Rowley, Clarence W.

2012-06-01

We present a modification of the balanced proper orthogonal decomposition (balanced POD) algorithm for systems with simple impulse response tails. In this new method, we use dynamic mode decomposition (DMD) to estimate the slowly decaying eigenvectors that dominate the long-time behavior of the direct and adjoint impulse responses. This is done using a new, low-memory variant of the DMD algorithm, appropriate for large datasets. We then formulate analytic expressions for the contribution of these eigenvectors to the controllability and observability Gramians. These contributions can be accounted for in the balanced POD algorithm by simply appending the impulse response snapshot matrices (direct and adjoint, respectively) with particular linear combinations of the slow eigenvectors. Aside from these additions to the snapshot matrices, the algorithm remains unchanged. By treating the tails analytically, we eliminate the need to run long impulse response simulations, lowering storage requirements and speeding up ensuing computations. To demonstrate its effectiveness, we apply this method to two examples: the linearized, complex Ginzburg-Landau equation, and the two-dimensional fluid flow past a cylinder. As expected, reduced-order models computed using an analytic tail match or exceed the accuracy of those computed using the standard balanced POD procedure, at a fraction of the cost.

Mathematical methods to analysis of topology, functional variability and evolution of metabolic systems based on different decomposition concepts.

PubMed

Mrabet, Yassine; Semmar, Nabil

2010-05-01

Complexity of metabolic systems can be undertaken at different scales (metabolites, metabolic pathways, metabolic network map, biological population) and under different aspects (structural, functional, evolutive). To analyse such a complexity, metabolic systems need to be decomposed into different components according to different concepts. Four concepts are presented here consisting in considering metabolic systems as sets of metabolites, chemical reactions, metabolic pathways or successive processes. From a metabolomic dataset, such decompositions are performed using different mathematical methods including correlation, stiochiometric, ordination, classification, combinatorial and kinetic analyses. Correlation analysis detects and quantifies affinities/oppositions between metabolites. Stoichiometric analysis aims to identify the organisation of a metabolic network into different metabolic pathways on the hand, and to quantify/optimize the metabolic flux distribution through the different chemical reactions of the system. Ordination and classification analyses help to identify different metabolic trends and their associated metabolites in order to highlight chemical polymorphism representing different variability poles of the metabolic system. Then, metabolic processes/correlations responsible for such a polymorphism can be extracted in silico by combining metabolic profiles representative of different metabolic trends according to a weighting bootstrap approach. Finally evolution of metabolic processes in time can be analysed by different kinetic/dynamic modelling approaches.

Model diagnostics in reduced-rank estimation.

PubMed

Chen, Kun

2016-01-01

Reduced-rank methods are very popular in high-dimensional multivariate analysis for conducting simultaneous dimension reduction and model estimation. However, the commonly-used reduced-rank methods are not robust, as the underlying reduced-rank structure can be easily distorted by only a few data outliers. Anomalies are bound to exist in big data problems, and in some applications they themselves could be of the primary interest. While naive residual analysis is often inadequate for outlier detection due to potential masking and swamping, robust reduced-rank estimation approaches could be computationally demanding. Under Stein's unbiased risk estimation framework, we propose a set of tools, including leverage score and generalized information score, to perform model diagnostics and outlier detection in large-scale reduced-rank estimation. The leverage scores give an exact decomposition of the so-called model degrees of freedom to the observation level, which lead to exact decomposition of many commonly-used information criteria; the resulting quantities are thus named information scores of the observations. The proposed information score approach provides a principled way of combining the residuals and leverage scores for anomaly detection. Simulation studies confirm that the proposed diagnostic tools work well. A pattern recognition example with hand-writing digital images and a time series analysis example with monthly U.S. macroeconomic data further demonstrate the efficacy of the proposed approaches.

An integrated analysis-synthesis array system for spatial sound fields.

PubMed

Bai, Mingsian R; Hua, Yi-Hsin; Kuo, Chia-Hao; Hsieh, Yu-Hao

2015-03-01

An integrated recording and reproduction array system for spatial audio is presented within a generic framework akin to the analysis-synthesis filterbanks in discrete time signal processing. In the analysis stage, a microphone array "encodes" the sound field by using the plane-wave decomposition. Direction of arrival of plane-wave components that comprise the sound field of interest are estimated by multiple signal classification. Next, the source signals are extracted by using a deconvolution procedure. In the synthesis stage, a loudspeaker array "decodes" the sound field by reconstructing the plane-wave components obtained in the analysis stage. This synthesis stage is carried out by pressure matching in the interior domain of the loudspeaker array. The deconvolution problem is solved by truncated singular value decomposition or convex optimization algorithms. For high-frequency reproduction that suffers from the spatial aliasing problem, vector panning is utilized. Listening tests are undertaken to evaluate the deconvolution method, vector panning, and a hybrid approach that combines both methods to cover frequency ranges below and above the spatial aliasing frequency. Localization and timbral attributes are considered in the subjective evaluation. The results show that the hybrid approach performs the best in overall preference. In addition, there is a trade-off between reproduction performance and the external radiation.

Combining a wavelet transform with a channelized Hotelling observer for tumor detection in 3D PET oncology imaging

NASA Astrophysics Data System (ADS)

Lartizien, Carole; Tomei, Sandrine; Maxim, Voichita; Odet, Christophe

2007-03-01

This study evaluates new observer models for 3D whole-body Positron Emission Tomography (PET) imaging based on a wavelet sub-band decomposition and compares them with the classical constant-Q CHO model. Our final goal is to develop an original method that performs guided detection of abnormal activity foci in PET oncology imaging based on these new observer models. This computer-aided diagnostic method would highly benefit to clinicians for diagnostic purpose and to biologists for massive screening of rodents populations in molecular imaging. Method: We have previously shown good correlation of the channelized Hotelling observer (CHO) using a constant-Q model with human observer performance for 3D PET oncology imaging. We propose an alternate method based on combining a CHO observer with a wavelet sub-band decomposition of the image and we compare it to the standard CHO implementation. This method performs an undecimated transform using a biorthogonal B-spline 4/4 wavelet basis to extract the features set for input to the Hotelling observer. This work is based on simulated 3D PET images of an extended MCAT phantom with randomly located lesions. We compare three evaluation criteria: classification performance using the signal-to-noise ratio (SNR), computation efficiency and visual quality of the derived 3D maps of the decision variable λ. The SNR is estimated on a series of test images for a variable number of training images for both observers. Results: Results show that the maximum SNR is higher with the constant-Q CHO observer, especially for targets located in the liver, and that it is reached with a smaller number of training images. However, preliminary analysis indicates that the visual quality of the 3D maps of the decision variable λ is higher with the wavelet-based CHO and the computation time to derive a 3D λ-map is about 350 times shorter than for the standard CHO. This suggests that the wavelet-CHO observer is a good candidate for use in our guided detection method.

Active sites and mechanisms for H2O2 decomposition over Pd catalysts

PubMed Central

Plauck, Anthony; Stangland, Eric E.; Dumesic, James A.; Mavrikakis, Manos

2016-01-01

A combination of periodic, self-consistent density functional theory (DFT-GGA-PW91) calculations, reaction kinetics experiments on a SiO2-supported Pd catalyst, and mean-field microkinetic modeling are used to probe key aspects of H2O2 decomposition on Pd in the absence of cofeeding H2. We conclude that both Pd(111) and OH-partially covered Pd(100) surfaces represent the nature of the active site for H2O2 decomposition on the supported Pd catalyst reasonably well. Furthermore, all reaction flux in the closed catalytic cycle is predicted to flow through an O–O bond scission step in either H2O2 or OOH, followed by rapid H-transfer steps to produce the H2O and O2 products. The barrier for O–O bond scission is sensitive to Pd surface structure and is concluded to be the central parameter governing H2O2 decomposition activity. PMID:27006504

Research and application of a novel hybrid decomposition-ensemble learning paradigm with error correction for daily PM10 forecasting

NASA Astrophysics Data System (ADS)

Luo, Hongyuan; Wang, Deyun; Yue, Chenqiang; Liu, Yanling; Guo, Haixiang

2018-03-01

In this paper, a hybrid decomposition-ensemble learning paradigm combining error correction is proposed for improving the forecast accuracy of daily PM10 concentration. The proposed learning paradigm is consisted of the following two sub-models: (1) PM10 concentration forecasting model; (2) error correction model. In the proposed model, fast ensemble empirical mode decomposition (FEEMD) and variational mode decomposition (VMD) are applied to disassemble original PM10 concentration series and error sequence, respectively. The extreme learning machine (ELM) model optimized by cuckoo search (CS) algorithm is utilized to forecast the components generated by FEEMD and VMD. In order to prove the effectiveness and accuracy of the proposed model, two real-world PM10 concentration series respectively collected from Beijing and Harbin located in China are adopted to conduct the empirical study. The results show that the proposed model performs remarkably better than all other considered models without error correction, which indicates the superior performance of the proposed model.

Decompositions of large-scale biological systems based on dynamical properties.

PubMed

Soranzo, Nicola; Ramezani, Fahimeh; Iacono, Giovanni; Altafini, Claudio

2012-01-01

Given a large-scale biological network represented as an influence graph, in this article we investigate possible decompositions of the network aimed at highlighting specific dynamical properties. The first decomposition we study consists in finding a maximal directed acyclic subgraph of the network, which dynamically corresponds to searching for a maximal open-loop subsystem of the given system. Another dynamical property investigated is strong monotonicity. We propose two methods to deal with this property, both aimed at decomposing the system into strongly monotone subsystems, but with different structural characteristics: one method tends to produce a single large strongly monotone component, while the other typically generates a set of smaller disjoint strongly monotone subsystems. Original heuristics for the methods investigated are described in the article. altafini@sissa.it

Decomposition of gas-phase trichloroethene by the UV/TiO2 process in the presence of ozone.

PubMed

Shen, Y S; Ku, Y

2002-01-01

The decomposition of gas-phase trichloroethene (TCE) in air streams by direct photolysis, the UV/TiO2 and UV/O3 processes was studied. The experiments were carried out under various UV light intensities and wavelengths, ozone dosages, and initial concentrations of TCE to investigate and compare the removal efficiency of the pollutant. For UV/TiO2 process, the individual contribution to the decomposition of TCE by direct photolysis and hydroxyl radicals destruction was differentiated to discuss the quantum efficiency with 254 and 365 nm UV lamps. The removal of gaseous TCE was found to reduce by UV/TiO2 process in the presence of ozone possibly because of the ozone molecules could scavenge hydroxyl radicals produced from the excitation of TiO2 by UV radiation to inhibit the decomposition of TCE. A photoreactor design equation for the decomposition of gaseous TCE by the UV/TiO2 process in air streams was developed by combining the continuity equation of the pollutant and the surface catalysis reaction rate expression. By the proposed design scheme, the temporal distribution of TCE at various operation conditions by the UV/TiO2 process can be well modeled.

Dual-energy x-ray image decomposition by independent component analysis

NASA Astrophysics Data System (ADS)

Jiang, Yifeng; Jiang, Dazong; Zhang, Feng; Zhang, Dengfu; Lin, Gang

2001-09-01

The spatial distributions of bone and soft tissue in human body are separated by independent component analysis (ICA) of dual-energy x-ray images. It is because of the dual energy imaging modelí-s conformity to the ICA model that we can apply this method: (1) the absorption in body is mainly caused by photoelectric absorption and Compton scattering; (2) they take place simultaneously but are mutually independent; and (3) for monochromatic x-ray sources the total attenuation is achieved by linear combination of these two absorption. Compared with the conventional method, the proposed one needs no priori information about the accurate x-ray energy magnitude for imaging, while the results of the separation agree well with the conventional one.

Evolutionary Algorithm Based Feature Optimization for Multi-Channel EEG Classification.

PubMed

Wang, Yubo; Veluvolu, Kalyana C

2017-01-01

The most BCI systems that rely on EEG signals employ Fourier based methods for time-frequency decomposition for feature extraction. The band-limited multiple Fourier linear combiner is well-suited for such band-limited signals due to its real-time applicability. Despite the improved performance of these techniques in two channel settings, its application in multiple-channel EEG is not straightforward and challenging. As more channels are available, a spatial filter will be required to eliminate the noise and preserve the required useful information. Moreover, multiple-channel EEG also adds the high dimensionality to the frequency feature space. Feature selection will be required to stabilize the performance of the classifier. In this paper, we develop a new method based on Evolutionary Algorithm (EA) to solve these two problems simultaneously. The real-valued EA encodes both the spatial filter estimates and the feature selection into its solution and optimizes it with respect to the classification error. Three Fourier based designs are tested in this paper. Our results show that the combination of Fourier based method with covariance matrix adaptation evolution strategy (CMA-ES) has the best overall performance.

In situ carbon turnover dynamics and the role of soil microorganisms therein: a climate warming study in an Alpine ecosystem.

PubMed

Djukic, Ika; Zehetner, Franz; Watzinger, Andrea; Horacek, Micha; Gerzabek, Martin H

2013-01-01

Litter decomposition represents one of the largest fluxes in the global terrestrial carbon cycle. The aim of this study was to improve our understanding of the factors governing decomposition in alpine ecosystems and how their responses to changing environmental conditions change over time. Our study area stretches over an elevation gradient of 1000 m on the Hochschwab massif in the Northern Limestone Alps of Austria. We used high-to-low elevation soil translocation to simulate the combined effects of changing climatic conditions, shifting vegetation zones, and altered snow cover regimes. In original and translocated soils, we conducted in situ decomposition experiments with maize litter and studied carbon turnover dynamics as well as temporal response patterns of the pathways of carbon during microbial decomposition over a 2-year incubation period. A simulated mean annual soil warming (through down-slope translocation) of 1.5 and 2.7 °C, respectively, resulted in a significantly accelerated turnover of added maize carbon. Changes in substrate quantity and quality in the course of the decomposition appeared to have less influence on the microbial community composition and its substrate utilization than the prevailing environmental/site conditions, to which the microbial community adapted quickly upon change. In general, microbial community composition and function significantly affected substrate decomposition rates only in the later stage of decomposition when the differentiation in substrate use among the microbial groups became more evident. Our study demonstrated that rising temperatures in alpine ecosystems may accelerate decomposition of litter carbon and also lead to a rapid adaptation of the microbial communities to the new environmental conditions. © 2012 Federation of European Microbiological Societies. Published by Blackwell Publishing Ltd. All rights reserved.

Parallel processing methods for space based power systems

NASA Technical Reports Server (NTRS)

Berry, F. C.

1993-01-01

This report presents a method for doing load-flow analysis of a power system by using a decomposition approach. The power system for the Space Shuttle is used as a basis to build a model for the load-flow analysis. To test the decomposition method for doing load-flow analysis, simulations were performed on power systems of 16, 25, 34, 43, 52, 61, 70, and 79 nodes. Each of the power systems was divided into subsystems and simulated under steady-state conditions. The results from these tests have been found to be as accurate as tests performed using a standard serial simulator. The division of the power systems into different subsystems was done by assigning a processor to each area. There were 13 transputers available, therefore, up to 13 different subsystems could be simulated at the same time. This report has preliminary results for a load-flow analysis using a decomposition principal. The report shows that the decomposition algorithm for load-flow analysis is well suited for parallel processing and provides increases in the speed of execution.