Object-Based Dense Matching Method for Maintaining Structure Characteristics of Linear Buildings

PubMed Central

Yan, Yiming; Qiu, Mingjie; Zhao, Chunhui; Wang, Liguo

2018-01-01

In this paper, we proposed a novel object-based dense matching method specially for the high-precision disparity map of building objects in urban areas, which can maintain accurate object structure characteristics. The proposed framework mainly includes three stages. Firstly, an improved edge line extraction method is proposed for the edge segments to fit closely to building outlines. Secondly, a fusion method is proposed for the outlines under the constraint of straight lines, which can maintain the building structural attribute with parallel or vertical edges, which is very useful for the dense matching method. Finally, we proposed an edge constraint and outline compensation (ECAOC) dense matching method to maintain building object structural characteristics in the disparity map. In the proposed method, the improved edge lines are used to optimize matching search scope and matching template window, and the high-precision building outlines are used to compensate the shape feature of building objects. Our method can greatly increase the matching accuracy of building objects in urban areas, especially at building edges. For the outline extraction experiments, our fusion method verifies the superiority and robustness on panchromatic images of different satellites and different resolutions. For the dense matching experiments, our ECOAC method shows great advantages for matching accuracy of building objects in urban areas compared with three other methods. PMID:29596393

Scene-based nonuniformity correction with reduced ghosting using a gated LMS algorithm.

PubMed

Hardie, Russell C; Baxley, Frank; Brys, Brandon; Hytla, Patrick

2009-08-17

In this paper, we present a scene-based nouniformity correction (NUC) method using a modified adaptive least mean square (LMS) algorithm with a novel gating operation on the updates. The gating is designed to significantly reduce ghosting artifacts produced by many scene-based NUC algorithms by halting updates when temporal variation is lacking. We define the algorithm and present a number of experimental results to demonstrate the efficacy of the proposed method in comparison to several previously published methods including other LMS and constant statistics based methods. The experimental results include simulated imagery and a real infrared image sequence. We show that the proposed method significantly reduces ghosting artifacts, but has a slightly longer convergence time. (c) 2009 Optical Society of America

Modeling and quantification of repolarization feature dependency on heart rate.

PubMed

Minchole, A; Zacur, E; Pueyo, E; Laguna, P

2014-01-01

This article is part of the Focus Theme of Methods of Information in Medicine on "Biosignal Interpretation: Advanced Methods for Studying Cardiovascular and Respiratory Systems". This work aims at providing an efficient method to estimate the parameters of a non linear model including memory, previously proposed to characterize rate adaptation of repolarization indices. The physiological restrictions on the model parameters have been included in the cost function in such a way that unconstrained optimization techniques such as descent optimization methods can be used for parameter estimation. The proposed method has been evaluated on electrocardiogram (ECG) recordings of healthy subjects performing a tilt test, where rate adaptation of QT and Tpeak-to-Tend (Tpe) intervals has been characterized. The proposed strategy results in an efficient methodology to characterize rate adaptation of repolarization features, improving the convergence time with respect to previous strategies. Moreover, Tpe interval adapts faster to changes in heart rate than the QT interval. In this work an efficient estimation of the parameters of a model aimed at characterizing rate adaptation of repolarization features has been proposed. The Tpe interval has been shown to be rate related and with a shorter memory lag than the QT interval.

Towards discrete wavelet transform-based human activity recognition

NASA Astrophysics Data System (ADS)

Khare, Manish; Jeon, Moongu

2017-06-01

Providing accurate recognition of human activities is a challenging problem for visual surveillance applications. In this paper, we present a simple and efficient algorithm for human activity recognition based on a wavelet transform. We adopt discrete wavelet transform (DWT) coefficients as a feature of human objects to obtain advantages of its multiresolution approach. The proposed method is tested on multiple levels of DWT. Experiments are carried out on different standard action datasets including KTH and i3D Post. The proposed method is compared with other state-of-the-art methods in terms of different quantitative performance measures. The proposed method is found to have better recognition accuracy in comparison to the state-of-the-art methods.

An efficient algorithm for measurement of retinal vessel diameter from fundus images based on directional filtering

NASA Astrophysics Data System (ADS)

Wang, Xuchu; Niu, Yanmin

2011-02-01

Automatic measurement of vessels from fundus images is a crucial step for assessing vessel anomalies in ophthalmological community, where the change in retinal vessel diameters is believed to be indicative of the risk level of diabetic retinopathy. In this paper, a new retinal vessel diameter measurement method by combining vessel orientation estimation and filter response is proposed. Its interesting characteristics include: (1) different from the methods that only fit the vessel profiles, the proposed method extracts more stable and accurate vessel diameter by casting this problem as a maximal response problem of a variation of Gabor filter; (2) the proposed method can directly and efficiently estimate the vessel's orientation, which is usually captured by time-consuming multi-orientation fitting techniques in many existing methods. Experimental results shows that the proposed method both retains the computational simplicity and achieves stable and accurate estimation results.

Self-Organizing Hierarchical Particle Swarm Optimization with Time-Varying Acceleration Coefficients for Economic Dispatch with Valve Point Effects and Multifuel Options

NASA Astrophysics Data System (ADS)

Polprasert, Jirawadee; Ongsakul, Weerakorn; Dieu, Vo Ngoc

2011-06-01

This paper proposes a self-organizing hierarchical particle swarm optimization (SPSO) with time-varying acceleration coefficients (TVAC) for solving economic dispatch (ED) problem with non-smooth functions including multiple fuel options (MFO) and valve-point loading effects (VPLE). The proposed SPSO with TVAC is the new approach optimizer and good performance for solving ED problems. It can handle the premature convergence of the problem by re-initialization of velocity whenever particles are stagnated in the search space. To properly control both local and global explorations of the swarm during the optimization process, the performance of TVAC is included. The proposed method is tested in different ED problems with non-smooth cost functions and the obtained results are compared to those from many other methods in the literature. The results have revealed that the proposed SPSO with TVAC is effective in finding higher quality solutions for non-smooth ED problems than many other methods.

An efficient mosaic algorithm considering seasonal variation: application to KOMPSAT-2 satellite images.

PubMed

Choi, Jaewon; Jung, Hyung-Sup; Yun, Sang-Ho

2015-03-09

As the aerospace industry grows, images obtained from Earth observation satellites have been successfully used in various fields. Specifically, the demand for a high-resolution (HR) optical images is gradually increasing, and hence the generation of a high-quality mosaic image is being magnified as an interesting issue. In this paper, we have proposed an efficient mosaic algorithm for HR optical images that are significantly different due to seasonal change. The algorithm includes main steps such as: (1) seamline extraction from gradient magnitude and seam images; (2) histogram matching; and (3) image feathering. Eleven Kompsat-2 images characterized by seasonal variations are used for the performance validation of the proposed method. The results of the performance test show that the proposed method effectively mosaics Kompsat-2 adjacent images including severe seasonal changes. Moreover, the results reveal that the proposed method is applicable to HR optic images such as GeoEye, IKONOS, QuickBird, RapidEye, SPOT, WorldView, etc.

Estimation of Comfort/Disconfort Based on EEG in Massage by Use of Clustering according to Correration and Incremental Learning type NN

NASA Astrophysics Data System (ADS)

Teramae, Tatsuya; Kushida, Daisuke; Takemori, Fumiaki; Kitamura, Akira

Authors proposed the estimation method combining k-means algorithm and NN for evaluating massage. However, this estimation method has a problem that discrimination ratio is decreased to new user. There are two causes of this problem. One is that generalization of NN is bad. Another one is that clustering result by k-means algorithm has not high correlation coefficient in a class. Then, this research proposes k-means algorithm according to correlation coefficient and incremental learning for NN. The proposed k-means algorithm is method included evaluation function based on correlation coefficient. Incremental learning is method that NN is learned by new data and initialized weight based on the existing data. The effect of proposed methods are verified by estimation result using EEG data when testee is given massage.

A non-iterative extension of the multivariate random effects meta-analysis.

PubMed

Makambi, Kepher H; Seung, Hyunuk

2015-01-01

Multivariate methods in meta-analysis are becoming popular and more accepted in biomedical research despite computational issues in some of the techniques. A number of approaches, both iterative and non-iterative, have been proposed including the multivariate DerSimonian and Laird method by Jackson et al. (2010), which is non-iterative. In this study, we propose an extension of the method by Hartung and Makambi (2002) and Makambi (2001) to multivariate situations. A comparison of the bias and mean square error from a simulation study indicates that, in some circumstances, the proposed approach perform better than the multivariate DerSimonian-Laird approach. An example is presented to demonstrate the application of the proposed approach.

Design and application of an inertial impactor in combination with an ATP bioluminescence detector for in situ rapid estimation of the efficacies of air controlling devices on removal of bioaerosols.

PubMed

Yoon, Ki Young; Park, Chul Woo; Byeon, Jeong Hoon; Hwang, Jungho

2010-03-01

We proposed a rapid method to estimate the efficacies of air controlling devices in situ using ATP bioluminescence in combination with an inertial impactor. The inertial impactor was designed to have 1 mum of cutoff diameter, and its performance was estimated analytically, numerically, and experimentally. The proposed method was characterized using Staphylococcus epidermidis, which was aerosolized with a nebulizer. The bioaerosol concentrations were estimated within 25 min using the proposed method without a culturing process, which requires several days for colony formation. A linear relationship was obtained between the results of the proposed ATP method (RLU/m(3)) and the conventional culture-based method (CFU/m(3)), with R(2) 0.9283. The proposed method was applied to estimate the concentration of indoor bioaerosols, which were identified as a mixture of various microbial species including bacteria, fungi, and actinomycetes, in an occupational indoor environment, controlled by mechanical ventilation and an air cleaner. Consequently, the proposed method showed a linearity with the culture-based method for indoor bioaerosols with R(2) 0.8189, even though various kinds of microorganisms existed in the indoor air. The proposed method may be effective in monitoring the changes of relative concentration of indoor bioaerosols and estimating the effectiveness of air control devices in indoor environments.

Robust volcano plot: identification of differential metabolites in the presence of outliers.

PubMed

Kumar, Nishith; Hoque, Md Aminul; Sugimoto, Masahiro

2018-04-11

The identification of differential metabolites in metabolomics is still a big challenge and plays a prominent role in metabolomics data analyses. Metabolomics datasets often contain outliers because of analytical, experimental, and biological ambiguity, but the currently available differential metabolite identification techniques are sensitive to outliers. We propose a kernel weight based outlier-robust volcano plot for identifying differential metabolites from noisy metabolomics datasets. Two numerical experiments are used to evaluate the performance of the proposed technique against nine existing techniques, including the t-test and the Kruskal-Wallis test. Artificially generated data with outliers reveal that the proposed method results in a lower misclassification error rate and a greater area under the receiver operating characteristic curve compared with existing methods. An experimentally measured breast cancer dataset to which outliers were artificially added reveals that our proposed method produces only two non-overlapping differential metabolites whereas the other nine methods produced between seven and 57 non-overlapping differential metabolites. Our data analyses show that the performance of the proposed differential metabolite identification technique is better than that of existing methods. Thus, the proposed method can contribute to analysis of metabolomics data with outliers. The R package and user manual of the proposed method are available at https://github.com/nishithkumarpaul/Rvolcano .

Pairwise Classifier Ensemble with Adaptive Sub-Classifiers for fMRI Pattern Analysis.

PubMed

Kim, Eunwoo; Park, HyunWook

2017-02-01

The multi-voxel pattern analysis technique is applied to fMRI data for classification of high-level brain functions using pattern information distributed over multiple voxels. In this paper, we propose a classifier ensemble for multiclass classification in fMRI analysis, exploiting the fact that specific neighboring voxels can contain spatial pattern information. The proposed method converts the multiclass classification to a pairwise classifier ensemble, and each pairwise classifier consists of multiple sub-classifiers using an adaptive feature set for each class-pair. Simulated and real fMRI data were used to verify the proposed method. Intra- and inter-subject analyses were performed to compare the proposed method with several well-known classifiers, including single and ensemble classifiers. The comparison results showed that the proposed method can be generally applied to multiclass classification in both simulations and real fMRI analyses.

A New Pixels Flipping Method for Huge Watermarking Capacity of the Invoice Font Image

PubMed Central

Li, Li; Hou, Qingzheng; Lu, Jianfeng; Dai, Junping; Mao, Xiaoyang; Chang, Chin-Chen

2014-01-01

Invoice printing just has two-color printing, so invoice font image can be seen as binary image. To embed watermarks into invoice image, the pixels need to be flipped. The more huge the watermark is, the more the pixels need to be flipped. We proposed a new pixels flipping method in invoice image for huge watermarking capacity. The pixels flipping method includes one novel interpolation method for binary image, one flippable pixels evaluation mechanism, and one denoising method based on gravity center and chaos degree. The proposed interpolation method ensures that the invoice image keeps features well after scaling. The flippable pixels evaluation mechanism ensures that the pixels keep better connectivity and smoothness and the pattern has highest structural similarity after flipping. The proposed denoising method makes invoice font image smoother and fiter for human vision. Experiments show that the proposed flipping method not only keeps the invoice font structure well but also improves watermarking capacity. PMID:25489606

Multiview echocardiography fusion using an electromagnetic tracking system.

PubMed

Punithakumar, Kumaradevan; Hareendranathan, Abhilash R; Paakkanen, Riitta; Khan, Nehan; Noga, Michelle; Boulanger, Pierre; Becher, Harald

2016-08-01

Three-dimensional ultrasound is an emerging modality for the assessment of complex cardiac anatomy and function. The advantages of this modality include lack of ionizing radiation, portability, low cost, and high temporal resolution. Major limitations include limited field-of-view, reliance on frequently limited acoustic windows, and poor signal to noise ratio. This study proposes a novel approach to combine multiple views into a single image using an electromagnetic tracking system in order to improve the field-of-view. The novel method has several advantages: 1) it does not rely on image information for alignment, and therefore, the method does not require image overlap; 2) the alignment accuracy of the proposed approach is not affected by any poor image quality as in the case of image registration based approaches; 3) in contrast to previous optical tracking based system, the proposed approach does not suffer from line-of-sight limitation; and 4) it does not require any initial calibration. In this pilot project, we were able to show that using a heart phantom, our method can fuse multiple echocardiographic images and improve the field-of view. Quantitative evaluations showed that the proposed method yielded a nearly optimal alignment of image data sets in three-dimensional space. The proposed method demonstrates the electromagnetic system can be used for the fusion of multiple echocardiography images with a seamless integration of sensors to the transducer.

25 CFR 900.125 - What shall a construction contract proposal contain?

Code of Federal Regulations, 2012 CFR

2012-04-01

... tribal building codes and engineering standards; (4) Structural integrity; (5) Accountability of funds..., standards and methods (including national, regional, state, or tribal building codes or construction... methods (including national, regional, state, or tribal building codes or construction industry standards...

25 CFR 900.125 - What shall a construction contract proposal contain?

Code of Federal Regulations, 2014 CFR

2014-04-01

... tribal building codes and engineering standards; (4) Structural integrity; (5) Accountability of funds..., standards and methods (including national, regional, state, or tribal building codes or construction... methods (including national, regional, state, or tribal building codes or construction industry standards...

25 CFR 900.125 - What shall a construction contract proposal contain?

Code of Federal Regulations, 2013 CFR

2013-04-01

... tribal building codes and engineering standards; (4) Structural integrity; (5) Accountability of funds..., standards and methods (including national, regional, state, or tribal building codes or construction... methods (including national, regional, state, or tribal building codes or construction industry standards...

25 CFR 900.125 - What shall a construction contract proposal contain?

Code of Federal Regulations, 2011 CFR

2011-04-01

... tribal building codes and engineering standards; (4) Structural integrity; (5) Accountability of funds..., standards and methods (including national, regional, state, or tribal building codes or construction... methods (including national, regional, state, or tribal building codes or construction industry standards...

25 CFR 900.125 - What shall a construction contract proposal contain?

Code of Federal Regulations, 2010 CFR

2010-04-01

... tribal building codes and engineering standards; (4) Structural integrity; (5) Accountability of funds..., standards and methods (including national, regional, state, or tribal building codes or construction... methods (including national, regional, state, or tribal building codes or construction industry standards...

15 CFR 971.401 - Proposal to issue or transfer and proposed terms, conditions and restrictions.

Code of Federal Regulations, 2011 CFR

2011-01-01

... FOR COMMERCIAL RECOVERY PERMITS Issuance/Transfer: Terms, Conditions and Restrictions Issuance... proposed terms and conditions for, and restrictions on, a commercial recovery permit that will be included... location and may employ such additional methods as he deems appropriate to inform interested persons about...

76 FR 36493 - Endangered and Threatened Wildlife and Plants; Proposed Rule To Establish a Manatee Refuge in...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-22

...). Copies of Documents: The proposed rule and draft EA are available by the following methods. In addition... possible and explain the basis for them. In addition, please include sufficient information with your... waterborne activities and included the Banana Island Sanctuary, the Sunset Shores Sanctuary, and the Magnolia...

Review of technical justification of assumptions and methods used by the Environmental Protection Agency for estimating risks avoided by implementing MCLs for radionuclides

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

Morris, S.C.; Rowe, M.D.; Holtzman, S.

1992-11-01

The Environmental Protection Agency (EPA) has proposed regulations for allowable levels of radioactive material in drinking water (40 CFR Part 141, 56 FR 33050, July 18, 1991). This review examined the assumptions and methods used by EPA in calculating risks that would be avoided by implementing the proposed Maximum Contaminant Levels for uranium, radium, and radon. Proposed limits on gross alpha and beta-gamma emitters were not included in this review.

Max-margin multiattribute learning with low-rank constraint.

PubMed

Zhang, Qiang; Chen, Lin; Li, Baoxin

2014-07-01

Attribute learning has attracted a lot of interests in recent years for its advantage of being able to model high-level concepts with a compact set of midlevel attributes. Real-world objects often demand multiple attributes for effective modeling. Most existing methods learn attributes independently without explicitly considering their intrinsic relatedness. In this paper, we propose max margin multiattribute learning with low-rank constraint, which learns a set of attributes simultaneously, using only relative ranking of the attributes for the data. By learning all the attributes simultaneously through low-rank constraint, the proposed method is able to capture their intrinsic correlation for improved learning; by requiring only relative ranking, the method avoids restrictive binary labels of attributes that are often assumed by many existing techniques. The proposed method is evaluated on both synthetic data and real visual data including a challenging video data set. Experimental results demonstrate the effectiveness of the proposed method.

A framework for the social valuation of ecosystem services.

PubMed

Felipe-Lucia, María R; Comín, Francisco A; Escalera-Reyes, Javier

2015-05-01

Methods to assess ecosystem services using ecological or economic approaches are considerably better defined than methods for the social approach. To identify why the social approach remains unclear, we reviewed current trends in the literature. We found two main reasons: (i) the cultural ecosystem services are usually used to represent the whole social approach, and (ii) the economic valuation based on social preferences is typically included in the social approach. Next, we proposed a framework for the social valuation of ecosystem services that provides alternatives to economics methods, enables comparison across studies, and supports decision-making in land planning and management. The framework includes the agreements emerged from the review, such as considering spatial-temporal flows, including stakeholders from all social ranges, and using two complementary methods to value ecosystem services. Finally, we provided practical recommendations learned from the application of the proposed framework in a case study.

Applying Behavioral Conditioning to Identify Anticipatory Behaviors.

PubMed

Krebs, Bethany L; Torres, Erika; Chesney, Charlie; Kantoniemi Moon, Veronica; Watters, Jason V

2017-01-01

The ability to predict regular events can be adaptive for nonhuman animals living in an otherwise unpredictable environment. Animals may exhibit behavioral changes preceding a predictable event; such changes reflect anticipatory behavior. Anticipatory behavior is broadly defined as a goal-directed increase in activity preceding a predictable event and can be useful for assessing well being in animals in captivity. Anticipation may look different in different animals, however, necessitating methods to generate and study anticipatory behaviors across species. This article includes a proposed method for generating and describing anticipatory behavior in zoos using behavioral conditioning. The article also includes discussion of case studies of the proposed method with 2 animals at the San Francisco Zoo: a silverback gorilla (Gorilla gorilla gorilla) and a red panda (Ailurus fulgens). The study evidence supports anticipation in both animals. As behavioral conditioning can be used with many animals, the proposed method provides a practical approach for using anticipatory behavior to assess animal well being in zoos.

A text zero-watermarking method based on keyword dense interval

NASA Astrophysics Data System (ADS)

Yang, Fan; Zhu, Yuesheng; Jiang, Yifeng; Qing, Yin

2017-07-01

Digital watermarking has been recognized as a useful technology for the copyright protection and authentication of digital information. However, rarely did the former methods focus on the key content of digital carrier. The idea based on the protection of key content is more targeted and can be considered in different digital information, including text, image and video. In this paper, we use text as research object and a text zero-watermarking method which uses keyword dense interval (KDI) as the key content is proposed. First, we construct zero-watermarking model by introducing the concept of KDI and giving the method of KDI extraction. Second, we design detection model which includes secondary generation of zero-watermark and the similarity computing method of keyword distribution. Besides, experiments are carried out, and the results show that the proposed method gives better performance than other available methods especially in the attacks of sentence transformation and synonyms substitution.

Monte Carlo based statistical power analysis for mediation models: methods and software.

PubMed

Zhang, Zhiyong

2014-12-01

The existing literature on statistical power analysis for mediation models often assumes data normality and is based on a less powerful Sobel test instead of the more powerful bootstrap test. This study proposes to estimate statistical power to detect mediation effects on the basis of the bootstrap method through Monte Carlo simulation. Nonnormal data with excessive skewness and kurtosis are allowed in the proposed method. A free R package called bmem is developed to conduct the power analysis discussed in this study. Four examples, including a simple mediation model, a multiple-mediator model with a latent mediator, a multiple-group mediation model, and a longitudinal mediation model, are provided to illustrate the proposed method.

A highly precise frequency-based method for estimating the tension of an inclined cable with unknown boundary conditions

NASA Astrophysics Data System (ADS)

Ma, Lin

2017-11-01

This paper develops a method for precisely determining the tension of an inclined cable with unknown boundary conditions. First, the nonlinear motion equation of an inclined cable is derived, and a numerical model of the motion of the cable is proposed using the finite difference method. The proposed numerical model includes the sag-extensibility, flexural stiffness, inclination angle and rotational stiffness at two ends of the cable. Second, the influence of the dynamic parameters of the cable on its frequencies is discussed in detail, and a method for precisely determining the tension of an inclined cable is proposed based on the derivatives of the eigenvalues of the matrices. Finally, a multiparameter identification method is developed that can simultaneously identify multiple parameters, including the rotational stiffness at two ends. This scheme is applicable to inclined cables with varying sag, varying flexural stiffness and unknown boundary conditions. Numerical examples indicate that the method provides good precision. Because the parameters of cables other than tension (e.g., the flexural stiffness and rotational stiffness at the ends) are not accurately known in practical engineering, the multiparameter identification method could further improve the accuracy of cable tension measurements.

A Real-Time Infrared Ultra-Spectral Signature Classification Method via Spatial Pyramid Matching

PubMed Central

Mei, Xiaoguang; Ma, Yong; Li, Chang; Fan, Fan; Huang, Jun; Ma, Jiayi

2015-01-01

The state-of-the-art ultra-spectral sensor technology brings new hope for high precision applications due to its high spectral resolution. However, it also comes with new challenges, such as the high data dimension and noise problems. In this paper, we propose a real-time method for infrared ultra-spectral signature classification via spatial pyramid matching (SPM), which includes two aspects. First, we introduce an infrared ultra-spectral signature similarity measure method via SPM, which is the foundation of the matching-based classification method. Second, we propose the classification method with reference spectral libraries, which utilizes the SPM-based similarity for the real-time infrared ultra-spectral signature classification with robustness performance. Specifically, instead of matching with each spectrum in the spectral library, our method is based on feature matching, which includes a feature library-generating phase. We calculate the SPM-based similarity between the feature of the spectrum and that of each spectrum of the reference feature library, then take the class index of the corresponding spectrum having the maximum similarity as the final result. Experimental comparisons on two publicly-available datasets demonstrate that the proposed method effectively improves the real-time classification performance and robustness to noise. PMID:26205263

Crystallization mosaic effect generation by superpixels

NASA Astrophysics Data System (ADS)

Xie, Yuqi; Bo, Pengbo; Yuan, Ye; Wang, Kuanquan

2015-03-01

Art effect generation from digital images using computational tools has been a hot research topic in recent years. We propose a new method for generating crystallization mosaic effects from color images. Two key problems in generating pleasant mosaic effect are studied: grouping pixels into mosaic tiles and arrangement of mosaic tiles adapting to image features. To give visually pleasant mosaic effect, we propose to create mosaic tiles by pixel clustering in feature space of color information, taking compactness of tiles into consideration as well. Moreover, we propose a method for processing feature boundaries in images which gives guidance for arranging mosaic tiles near image features. This method gives nearly uniform shape of mosaic tiles, adapting to feature lines in an esthetic way. The new approach considers both color distance and Euclidean distance of pixels, and thus is capable of giving mosaic tiles in a more pleasing manner. Some experiments are included to demonstrate the computational efficiency of the present method and its capability of generating visually pleasant mosaic tiles. Comparisons with existing approaches are also included to show the superiority of the new method.

Validation of Proposed "DSM-5" Criteria for Autism Spectrum Disorder

ERIC Educational Resources Information Center

Frazier, Thomas W.; Youngstrom, Eric A.; Speer, Leslie; Embacher, Rebecca; Law, Paul; Constantino, John; Findling, Robert L.; Hardan, Antonio Y.; Eng, Charis

2012-01-01

Objective: The primary aim of the present study was to evaluate the validity of proposed "DSM-5" criteria for autism spectrum disorder (ASD). Method: We analyzed symptoms from 14,744 siblings (8,911 ASD and 5,863 non-ASD) included in a national registry, the Interactive Autism Network. Youth 2 through 18 years of age were included if at least one…

Multiview road sign detection via self-adaptive color model and shape context matching

NASA Astrophysics Data System (ADS)

Liu, Chunsheng; Chang, Faliang; Liu, Chengyun

2016-09-01

The multiview appearance of road signs in uncontrolled environments has made the detection of road signs a challenging problem in computer vision. We propose a road sign detection method to detect multiview road signs. This method is based on several algorithms, including the classical cascaded detector, the self-adaptive weighted Gaussian color model (SW-Gaussian model), and a shape context matching method. The classical cascaded detector is used to detect the frontal road signs in video sequences and obtain the parameters for the SW-Gaussian model. The proposed SW-Gaussian model combines the two-dimensional Gaussian model and the normalized red channel together, which can largely enhance the contrast between the red signs and background. The proposed shape context matching method can match shapes with big noise, which is utilized to detect road signs in different directions. The experimental results show that compared with previous detection methods, the proposed multiview detection method can reach higher detection rate in detecting signs with different directions.

Group Management Method of RFID Passwords for Privacy Protection

NASA Astrophysics Data System (ADS)

Kobayashi, Yuichi; Kuwana, Toshiyuki; Taniguchi, Yoji; Komoda, Norihisa

When RFID tag is used in the whole item lifecycle including a consumer scene or a recycle scene, we have to protect consumer privacy in the state that RFID tag is stuck on an item. We use the low cost RFID tag that has the access control function using a password, and we propose a method which manages RFID tags by passwords identical to each group of RFID tags. This proposal improves safety of RFID system because the proposal method is able to reduce the traceability for a RFID tag, and hold down the influence for disclosure of RFID passwords in the both scenes.

Flow-gated radial phase-contrast imaging in the presence of weak flow.

PubMed

Peng, Hsu-Hsia; Huang, Teng-Yi; Wang, Fu-Nien; Chung, Hsiao-Wen

2013-01-01

To implement a flow-gating method to acquire phase-contrast (PC) images of carotid arteries without use of an electrocardiography (ECG) signal to synchronize the acquisition of imaging data with pulsatile arterial flow. The flow-gating method was realized through radial scanning and sophisticated post-processing methods including downsampling, complex difference, and correlation analysis to improve the evaluation of flow-gating times in radial phase-contrast scans. Quantitatively comparable results (R = 0.92-0.96, n = 9) of flow-related parameters, including mean velocity, mean flow rate, and flow volume, with conventional ECG-gated imaging demonstrated that the proposed method is highly feasible. The radial flow-gating PC imaging method is applicable in carotid arteries. The proposed flow-gating method can potentially avoid the setting up of ECG-related equipment for brain imaging. This technique has potential use in patients with arrhythmia or weak ECG signals.

78 FR 44563 - Proposed Data Collections Submitted for Public Comment and Recommendations

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-24

... audiences for this research: First responders, including police, fire fighters and emergency medical service... obtain data using two qualitative data collection methods. The first method includes focus groups to... Director for Science, Office of the Director, Centers for Disease Control and Prevention. [FR Doc. 2013...

A Novel Multilayered RFID Tagged Cargo Integrity Assurance Scheme

PubMed Central

Yang, Ming Hour; Luo, Jia Ning; Lu, Shao Yong

2015-01-01

To minimize cargo theft during transport, mobile radio frequency identification (RFID) grouping proof methods are generally employed to ensure the integrity of entire cargo loads. However, conventional grouping proofs cannot simultaneously generate grouping proofs for a specific group of RFID tags. The most serious problem of these methods is that nonexistent tags are included in the grouping proofs because of the considerable amount of time it takes to scan a high number of tags. Thus, applying grouping proof methods in the current logistics industry is difficult. To solve this problem, this paper proposes a method for generating multilayered offline grouping proofs. The proposed method provides tag anonymity; moreover, resolving disputes between recipients and transporters over the integrity of cargo deliveries can be expedited by generating grouping proofs and automatically authenticating the consistency between the receipt proof and pick proof. The proposed method can also protect against replay attacks, multi-session attacks, and concurrency attacks. Finally, experimental results verify that, compared with other methods for generating grouping proofs, the proposed method can efficiently generate offline grouping proofs involving several parties in a supply chain using mobile RFID. PMID:26512673

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.

Improved Quasi-Newton method via PSB update for solving systems of nonlinear equations

NASA Astrophysics Data System (ADS)

Mamat, Mustafa; Dauda, M. K.; Waziri, M. Y.; Ahmad, Fadhilah; Mohamad, Fatma Susilawati

2016-10-01

The Newton method has some shortcomings which includes computation of the Jacobian matrix which may be difficult or even impossible to compute and solving the Newton system in every iteration. Also, the common setback with some quasi-Newton methods is that they need to compute and store an n × n matrix at each iteration, this is computationally costly for large scale problems. To overcome such drawbacks, an improved Method for solving systems of nonlinear equations via PSB (Powell-Symmetric-Broyden) update is proposed. In the proposed method, the approximate Jacobian inverse Hk of PSB is updated and its efficiency has improved thereby require low memory storage, hence the main aim of this paper. The preliminary numerical results show that the proposed method is practically efficient when applied on some benchmark problems.

Comparative study between derivative spectrophotometry and multivariate calibration as analytical tools applied for the simultaneous quantitation of Amlodipine, Valsartan and Hydrochlorothiazide

NASA Astrophysics Data System (ADS)

Darwish, Hany W.; Hassan, Said A.; Salem, Maissa Y.; El-Zeany, Badr A.

2013-09-01

Four simple, accurate and specific methods were developed and validated for the simultaneous estimation of Amlodipine (AML), Valsartan (VAL) and Hydrochlorothiazide (HCT) in commercial tablets. The derivative spectrophotometric methods include Derivative Ratio Zero Crossing (DRZC) and Double Divisor Ratio Spectra-Derivative Spectrophotometry (DDRS-DS) methods, while the multivariate calibrations used are Principal Component Regression (PCR) and Partial Least Squares (PLSs). The proposed methods were applied successfully in the determination of the drugs in laboratory-prepared mixtures and in commercial pharmaceutical preparations. The validity of the proposed methods was assessed using the standard addition technique. The linearity of the proposed methods is investigated in the range of 2-32, 4-44 and 2-20 μg/mL for AML, VAL and HCT, respectively.

Similarity indices based on link weight assignment for link prediction of unweighted complex networks

NASA Astrophysics Data System (ADS)

Liu, Shuxin; Ji, Xinsheng; Liu, Caixia; Bai, Yi

2017-01-01

Many link prediction methods have been proposed for predicting the likelihood that a link exists between two nodes in complex networks. Among these methods, similarity indices are receiving close attention. Most similarity-based methods assume that the contribution of links with different topological structures is the same in the similarity calculations. This paper proposes a local weighted method, which weights the strength of connection between each pair of nodes. Based on the local weighted method, six local weighted similarity indices extended from unweighted similarity indices (including Common Neighbor (CN), Adamic-Adar (AA), Resource Allocation (RA), Salton, Jaccard and Local Path (LP) index) are proposed. Empirical study has shown that the local weighted method can significantly improve the prediction accuracy of these unweighted similarity indices and that in sparse and weakly clustered networks, the indices perform even better.

NERC Policy 10: Measurement of two generation and load balancing IOS

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

Spicer, P.J.; Galow, G.G.

1999-11-01

Policy 10 will describe specific standards and metrics for most of the reliability functions described in the Interconnected Operations Services Working Group (IOS WG) report. The purpose of this paper is to discuss, in detail, the proposed metrics for two generation and load balancing IOSs: Regulation; Load Following. For purposes of this paper, metrics include both measurement and performance evaluation. The measurement methods discussed are included in the current draft of the proposed Policy 10. The performance evaluation method discussed is offered by the authors for consideration by the IOS ITF (Implementation Task Force) for inclusion into Policy 10.

Operations concepts for Mars missions with multiple mobile spacecraft

NASA Technical Reports Server (NTRS)

Dias, William C.

1993-01-01

Missions are being proposed which involve landing a varying number (anywhere from one to 24) of small mobile spacecraft on Mars. Mission proposals include sample returns, in situ geochemistry and geology, and instrument deployment functions. This paper discusses changes needed in traditional space operations methods for support of rover operations. Relevant differences include more frequent commanding, higher risk acceptance, streamlined procedures, and reliance on additional spacecraft autonomy, advanced fault protection, and prenegotiated decisions. New methods are especially important for missions with several Mars rovers operating concurrently against time limits. This paper also discusses likely mission design limits imposed by operations constraints .

Coronary arteries segmentation based on the 3D discrete wavelet transform and 3D neutrosophic transform.

PubMed

Chen, Shuo-Tsung; Wang, Tzung-Dau; Lee, Wen-Jeng; Huang, Tsai-Wei; Hung, Pei-Kai; Wei, Cheng-Yu; Chen, Chung-Ming; Kung, Woon-Man

2015-01-01

Most applications in the field of medical image processing require precise estimation. To improve the accuracy of segmentation, this study aimed to propose a novel segmentation method for coronary arteries to allow for the automatic and accurate detection of coronary pathologies. The proposed segmentation method included 2 parts. First, 3D region growing was applied to give the initial segmentation of coronary arteries. Next, the location of vessel information, HHH subband coefficients of the 3D DWT, was detected by the proposed vessel-texture discrimination algorithm. Based on the initial segmentation, 3D DWT integrated with the 3D neutrosophic transformation could accurately detect the coronary arteries. Each subbranch of the segmented coronary arteries was segmented correctly by the proposed method. The obtained results are compared with those ground truth values obtained from the commercial software from GE Healthcare and the level-set method proposed by Yang et al., 2007. Results indicate that the proposed method is better in terms of efficiency analyzed. Based on the initial segmentation of coronary arteries obtained from 3D region growing, one-level 3D DWT and 3D neutrosophic transformation can be applied to detect coronary pathologies accurately.

Fully-automated segmentation of fluid regions in exudative age-related macular degeneration subjects: Kernel graph cut in neutrosophic domain

PubMed Central

Rashno, Abdolreza; Nazari, Behzad; Koozekanani, Dara D.; Drayna, Paul M.; Sadri, Saeed; Rabbani, Hossein

2017-01-01

A fully-automated method based on graph shortest path, graph cut and neutrosophic (NS) sets is presented for fluid segmentation in OCT volumes for exudative age related macular degeneration (EAMD) subjects. The proposed method includes three main steps: 1) The inner limiting membrane (ILM) and the retinal pigment epithelium (RPE) layers are segmented using proposed methods based on graph shortest path in NS domain. A flattened RPE boundary is calculated such that all three types of fluid regions, intra-retinal, sub-retinal and sub-RPE, are located above it. 2) Seed points for fluid (object) and tissue (background) are initialized for graph cut by the proposed automated method. 3) A new cost function is proposed in kernel space, and is minimized with max-flow/min-cut algorithms, leading to a binary segmentation. Important properties of the proposed steps are proven and quantitative performance of each step is analyzed separately. The proposed method is evaluated using a publicly available dataset referred as Optima and a local dataset from the UMN clinic. For fluid segmentation in 2D individual slices, the proposed method outperforms the previously proposed methods by 18%, 21% with respect to the dice coefficient and sensitivity, respectively, on the Optima dataset, and by 16%, 11% and 12% with respect to the dice coefficient, sensitivity and precision, respectively, on the local UMN dataset. Finally, for 3D fluid volume segmentation, the proposed method achieves true positive rate (TPR) and false positive rate (FPR) of 90% and 0.74%, respectively, with a correlation of 95% between automated and expert manual segmentations using linear regression analysis. PMID:29059257

Improving protein–protein interactions prediction accuracy using protein evolutionary information and relevance vector machine model

PubMed Central

An, Ji‐Yong; Meng, Fan‐Rong; Chen, Xing; Yan, Gui‐Ying; Hu, Ji‐Pu

2016-01-01

Abstract Predicting protein–protein interactions (PPIs) is a challenging task and essential to construct the protein interaction networks, which is important for facilitating our understanding of the mechanisms of biological systems. Although a number of high‐throughput technologies have been proposed to predict PPIs, there are unavoidable shortcomings, including high cost, time intensity, and inherently high false positive rates. For these reasons, many computational methods have been proposed for predicting PPIs. However, the problem is still far from being solved. In this article, we propose a novel computational method called RVM‐BiGP that combines the relevance vector machine (RVM) model and Bi‐gram Probabilities (BiGP) for PPIs detection from protein sequences. The major improvement includes (1) Protein sequences are represented using the Bi‐gram probabilities (BiGP) feature representation on a Position Specific Scoring Matrix (PSSM), in which the protein evolutionary information is contained; (2) For reducing the influence of noise, the Principal Component Analysis (PCA) method is used to reduce the dimension of BiGP vector; (3) The powerful and robust Relevance Vector Machine (RVM) algorithm is used for classification. Five‐fold cross‐validation experiments executed on yeast and Helicobacter pylori datasets, which achieved very high accuracies of 94.57 and 90.57%, respectively. Experimental results are significantly better than previous methods. To further evaluate the proposed method, we compare it with the state‐of‐the‐art support vector machine (SVM) classifier on the yeast dataset. The experimental results demonstrate that our RVM‐BiGP method is significantly better than the SVM‐based method. In addition, we achieved 97.15% accuracy on imbalance yeast dataset, which is higher than that of balance yeast dataset. The promising experimental results show the efficiency and robust of the proposed method, which can be an automatic decision support tool for future proteomics research. For facilitating extensive studies for future proteomics research, we developed a freely available web server called RVM‐BiGP‐PPIs in Hypertext Preprocessor (PHP) for predicting PPIs. The web server including source code and the datasets are available at http://219.219.62.123:8888/BiGP/. PMID:27452983

A method to reproduce alpha-particle spectra measured with semiconductor detectors.

PubMed

Timón, A Fernández; Vargas, M Jurado; Sánchez, A Martín

2010-01-01

A method is proposed to reproduce alpha-particle spectra measured with silicon detectors, combining analytical and computer simulation techniques. The procedure includes the use of the Monte Carlo method to simulate the tracks of alpha-particles within the source and in the detector entrance window. The alpha-particle spectrum is finally obtained by the convolution of this simulated distribution and the theoretical distributions representing the contributions of the alpha-particle spectrometer to the spectrum. Experimental spectra from (233)U and (241)Am sources were compared with the predictions given by the proposed procedure, showing good agreement. The proposed method can be an important aid for the analysis and deconvolution of complex alpha-particle spectra. Copyright 2009 Elsevier Ltd. All rights reserved.

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.

A new frequency approach for light flicker evaluation in electric power systems

NASA Astrophysics Data System (ADS)

Feola, Luigi; Langella, Roberto; Testa, Alfredo

2015-12-01

In this paper, a new analytical estimator for light flicker in frequency domain, which is able to take into account also the frequency components neglected by the classical methods proposed in literature, is proposed. The analytical solutions proposed apply for any generic stationary signal affected by interharmonic distortion. The light flicker analytical estimator proposed is applied to numerous numerical case studies with the goal of showing i) the correctness and the improvements of the analytical approach proposed with respect to the other methods proposed in literature and ii) the accuracy of the results compared to those obtained by means of the classical International Electrotechnical Commission (IEC) flickermeter. The usefulness of the proposed analytical approach is that it can be included in signal processing tools for interharmonic penetration studies for the integration of renewable energy sources in future smart grids.

Efficient reliability analysis of structures with the rotational quasi-symmetric point- and the maximum entropy methods

NASA Astrophysics Data System (ADS)

Xu, Jun; Dang, Chao; Kong, Fan

2017-10-01

This paper presents a new method for efficient structural reliability analysis. In this method, a rotational quasi-symmetric point method (RQ-SPM) is proposed for evaluating the fractional moments of the performance function. Then, the derivation of the performance function's probability density function (PDF) is carried out based on the maximum entropy method in which constraints are specified in terms of fractional moments. In this regard, the probability of failure can be obtained by a simple integral over the performance function's PDF. Six examples, including a finite element-based reliability analysis and a dynamic system with strong nonlinearity, are used to illustrate the efficacy of the proposed method. All the computed results are compared with those by Monte Carlo simulation (MCS). It is found that the proposed method can provide very accurate results with low computational effort.

Intra-generational Redistribution under Public Pension Planning Based on Generation-based Funding Scheme

NASA Astrophysics Data System (ADS)

Banjo, Daisuke; Tamura, Hiroyuki; Murata, Tadahiko

In this paper, we propose a method of determining the pension in the generation-based funding scheme. In this proposal, we include two types of pensions in the scheme. One is the payment-amount related pension and the other is the payment-frequency related pension. We set the ratio of the total amount of payment-amount related pension to the total amount of both pensions, and simulate income gaps and the relationship between contributions and benefits for each individual when the proposed method is applied.

Robust Mediation Analysis Based on Median Regression

PubMed Central

Yuan, Ying; MacKinnon, David P.

2014-01-01

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

High-throughput ocular artifact reduction in multichannel electroencephalography (EEG) using component subspace projection.

PubMed

Ma, Junshui; Bayram, Sevinç; Tao, Peining; Svetnik, Vladimir

2011-03-15

After a review of the ocular artifact reduction literature, a high-throughput method designed to reduce the ocular artifacts in multichannel continuous EEG recordings acquired at clinical EEG laboratories worldwide is proposed. The proposed method belongs to the category of component-based methods, and does not rely on any electrooculography (EOG) signals. Based on a concept that all ocular artifact components exist in a signal component subspace, the method can uniformly handle all types of ocular artifacts, including eye-blinks, saccades, and other eye movements, by automatically identifying ocular components from decomposed signal components. This study also proposes an improved strategy to objectively and quantitatively evaluate artifact reduction methods. The evaluation strategy uses real EEG signals to synthesize realistic simulated datasets with different amounts of ocular artifacts. The simulated datasets enable us to objectively demonstrate that the proposed method outperforms some existing methods when no high-quality EOG signals are available. Moreover, the results of the simulated datasets improve our understanding of the involved signal decomposition algorithms, and provide us with insights into the inconsistency regarding the performance of different methods in the literature. The proposed method was also applied to two independent clinical EEG datasets involving 28 volunteers and over 1000 EEG recordings. This effort further confirms that the proposed method can effectively reduce ocular artifacts in large clinical EEG datasets in a high-throughput fashion. Copyright © 2011 Elsevier B.V. All rights reserved.

3D Sound Techniques for Sound Source Elevation in a Loudspeaker Listening Environment

NASA Astrophysics Data System (ADS)

Kim, Yong Guk; Jo, Sungdong; Kim, Hong Kook; Jang, Sei-Jin; Lee, Seok-Pil

In this paper, we propose several 3D sound techniques for sound source elevation in stereo loudspeaker listening environments. The proposed method integrates a head-related transfer function (HRTF) for sound positioning and early reflection for adding reverberant circumstance. In addition, spectral notch filtering and directional band boosting techniques are also included for increasing elevation perception capability. In order to evaluate the elevation performance of the proposed method, subjective listening tests are conducted using several kinds of sound sources such as white noise, sound effects, speech, and music samples. It is shown from the tests that the degrees of perceived elevation by the proposed method are around the 17º to 21º when the stereo loudspeakers are located on the horizontal plane.

Breast Cancer Recognition Using a Novel Hybrid Intelligent Method

PubMed Central

Addeh, Jalil; Ebrahimzadeh, Ata

2012-01-01

Breast cancer is the second largest cause of cancer deaths among women. At the same time, it is also among the most curable cancer types if it can be diagnosed early. This paper presents a novel hybrid intelligent method for recognition of breast cancer tumors. The proposed method includes three main modules: the feature extraction module, the classifier module, and the optimization module. In the feature extraction module, fuzzy features are proposed as the efficient characteristic of the patterns. In the classifier module, because of the promising generalization capability of support vector machines (SVM), a SVM-based classifier is proposed. In support vector machine training, the hyperparameters have very important roles for its recognition accuracy. Therefore, in the optimization module, the bees algorithm (BA) is proposed for selecting appropriate parameters of the classifier. The proposed system is tested on Wisconsin Breast Cancer database and simulation results show that the recommended system has a high accuracy. PMID:23626945

77 FR 32038 - Energy Conservation Program: Alternative Efficiency Determination Methods and Alternative Rating...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-05-31

... Determination Methods and Alternative Rating Methods AGENCY: Office of Energy Efficiency and Renewable Energy... proposing to revise and expand its existing regulations governing the use of particular methods as...- TP-0024, by any of the following methods: Email: to AED/[email protected] . Include EERE...

A Comparison of Two Methods for Boolean Query Relevancy Feedback.

ERIC Educational Resources Information Center

Salton, G.; And Others

1984-01-01

Evaluates and compares two recently proposed automatic methods for relevance feedback of Boolean queries (Dillon method, which uses probabilistic approach as basis, and disjunctive normal form method). Conclusions are drawn concerning the use of effective feedback methods in a Boolean query environment. Nineteen references are included. (EJS)

A Proposed Methodology for the Conceptualization, Operationalization, and Empirical Validation of the Concept of Information Need

ERIC Educational Resources Information Center

Afzal, Waseem

2017-01-01

Introduction: The purpose of this paper is to propose a methodology to conceptualize, operationalize, and empirically validate the concept of information need. Method: The proposed methodology makes use of both qualitative and quantitative perspectives, and includes a broad array of approaches such as literature reviews, expert opinions, focus…

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

Automatic peak selection by a Benjamini-Hochberg-based algorithm.

PubMed

Abbas, Ahmed; Kong, Xin-Bing; Liu, Zhi; Jing, Bing-Yi; Gao, Xin

2013-01-01

A common issue in bioinformatics is that computational methods often generate a large number of predictions sorted according to certain confidence scores. A key problem is then determining how many predictions must be selected to include most of the true predictions while maintaining reasonably high precision. In nuclear magnetic resonance (NMR)-based protein structure determination, for instance, computational peak picking methods are becoming more and more common, although expert-knowledge remains the method of choice to determine how many peaks among thousands of candidate peaks should be taken into consideration to capture the true peaks. Here, we propose a Benjamini-Hochberg (B-H)-based approach that automatically selects the number of peaks. We formulate the peak selection problem as a multiple testing problem. Given a candidate peak list sorted by either volumes or intensities, we first convert the peaks into [Formula: see text]-values and then apply the B-H-based algorithm to automatically select the number of peaks. The proposed approach is tested on the state-of-the-art peak picking methods, including WaVPeak [1] and PICKY [2]. Compared with the traditional fixed number-based approach, our approach returns significantly more true peaks. For instance, by combining WaVPeak or PICKY with the proposed method, the missing peak rates are on average reduced by 20% and 26%, respectively, in a benchmark set of 32 spectra extracted from eight proteins. The consensus of the B-H-selected peaks from both WaVPeak and PICKY achieves 88% recall and 83% precision, which significantly outperforms each individual method and the consensus method without using the B-H algorithm. The proposed method can be used as a standard procedure for any peak picking method and straightforwardly applied to some other prediction selection problems in bioinformatics. The source code, documentation and example data of the proposed method is available at http://sfb.kaust.edu.sa/pages/software.aspx.

Automatic Peak Selection by a Benjamini-Hochberg-Based Algorithm

PubMed Central

Abbas, Ahmed; Kong, Xin-Bing; Liu, Zhi; Jing, Bing-Yi; Gao, Xin

2013-01-01

A common issue in bioinformatics is that computational methods often generate a large number of predictions sorted according to certain confidence scores. A key problem is then determining how many predictions must be selected to include most of the true predictions while maintaining reasonably high precision. In nuclear magnetic resonance (NMR)-based protein structure determination, for instance, computational peak picking methods are becoming more and more common, although expert-knowledge remains the method of choice to determine how many peaks among thousands of candidate peaks should be taken into consideration to capture the true peaks. Here, we propose a Benjamini-Hochberg (B-H)-based approach that automatically selects the number of peaks. We formulate the peak selection problem as a multiple testing problem. Given a candidate peak list sorted by either volumes or intensities, we first convert the peaks into -values and then apply the B-H-based algorithm to automatically select the number of peaks. The proposed approach is tested on the state-of-the-art peak picking methods, including WaVPeak [1] and PICKY [2]. Compared with the traditional fixed number-based approach, our approach returns significantly more true peaks. For instance, by combining WaVPeak or PICKY with the proposed method, the missing peak rates are on average reduced by 20% and 26%, respectively, in a benchmark set of 32 spectra extracted from eight proteins. The consensus of the B-H-selected peaks from both WaVPeak and PICKY achieves 88% recall and 83% precision, which significantly outperforms each individual method and the consensus method without using the B-H algorithm. The proposed method can be used as a standard procedure for any peak picking method and straightforwardly applied to some other prediction selection problems in bioinformatics. The source code, documentation and example data of the proposed method is available at http://sfb.kaust.edu.sa/pages/software.aspx. PMID:23308147

Statistical methods to estimate treatment effects from multichannel electroencephalography (EEG) data in clinical trials.

PubMed

Ma, Junshui; Wang, Shubing; Raubertas, Richard; Svetnik, Vladimir

2010-07-15

With the increasing popularity of using electroencephalography (EEG) to reveal the treatment effect in drug development clinical trials, the vast volume and complex nature of EEG data compose an intriguing, but challenging, topic. In this paper the statistical analysis methods recommended by the EEG community, along with methods frequently used in the published literature, are first reviewed. A straightforward adjustment of the existing methods to handle multichannel EEG data is then introduced. In addition, based on the spatial smoothness property of EEG data, a new category of statistical methods is proposed. The new methods use a linear combination of low-degree spherical harmonic (SPHARM) basis functions to represent a spatially smoothed version of the EEG data on the scalp, which is close to a sphere in shape. In total, seven statistical methods, including both the existing and the newly proposed methods, are applied to two clinical datasets to compare their power to detect a drug effect. Contrary to the EEG community's recommendation, our results suggest that (1) the nonparametric method does not outperform its parametric counterpart; and (2) including baseline data in the analysis does not always improve the statistical power. In addition, our results recommend that (3) simple paired statistical tests should be avoided due to their poor power; and (4) the proposed spatially smoothed methods perform better than their unsmoothed versions. Copyright 2010 Elsevier B.V. All rights reserved.

An adaptive cubature formula for efficient reliability assessment of nonlinear structural dynamic systems

NASA Astrophysics Data System (ADS)

Xu, Jun; Kong, Fan

2018-05-01

Extreme value distribution (EVD) evaluation is a critical topic in reliability analysis of nonlinear structural dynamic systems. In this paper, a new method is proposed to obtain the EVD. The maximum entropy method (MEM) with fractional moments as constraints is employed to derive the entire range of EVD. Then, an adaptive cubature formula is proposed for fractional moments assessment involved in MEM, which is closely related to the efficiency and accuracy for reliability analysis. Three point sets, which include a total of 2d2 + 1 integration points in the dimension d, are generated in the proposed formula. In this regard, the efficiency of the proposed formula is ensured. Besides, a "free" parameter is introduced, which makes the proposed formula adaptive with the dimension. The "free" parameter is determined by arranging one point set adjacent to the boundary of the hyper-sphere which contains the bulk of total probability. In this regard, the tail distribution may be better reproduced and the fractional moments could be evaluated with accuracy. Finally, the proposed method is applied to a ten-storey shear frame structure under seismic excitations, which exhibits strong nonlinearity. The numerical results demonstrate the efficacy of the proposed method.

The review and results of different methods for facial recognition

NASA Astrophysics Data System (ADS)

Le, Yifan

2017-09-01

In recent years, facial recognition draws much attention due to its wide potential applications. As a unique technology in Biometric Identification, facial recognition represents a significant improvement since it could be operated without cooperation of people under detection. Hence, facial recognition will be taken into defense system, medical detection, human behavior understanding, etc. Several theories and methods have been established to make progress in facial recognition: (1) A novel two-stage facial landmark localization method is proposed which has more accurate facial localization effect under specific database; (2) A statistical face frontalization method is proposed which outperforms state-of-the-art methods for face landmark localization; (3) It proposes a general facial landmark detection algorithm to handle images with severe occlusion and images with large head poses; (4) There are three methods proposed on Face Alignment including shape augmented regression method, pose-indexed based multi-view method and a learning based method via regressing local binary features. The aim of this paper is to analyze previous work of different aspects in facial recognition, focusing on concrete method and performance under various databases. In addition, some improvement measures and suggestions in potential applications will be put forward.

A new generalized exponential rational function method to find exact special solutions for the resonance nonlinear Schrödinger equation

NASA Astrophysics Data System (ADS)

Ghanbari, Behzad; Inc, Mustafa

2018-04-01

The present paper suggests a novel technique to acquire exact solutions of nonlinear partial differential equations. The main idea of the method is to generalize the exponential rational function method. In order to examine the ability of the method, we consider the resonant nonlinear Schrödinger equation (R-NLSE). Many variants of exact soliton solutions for the equation are derived by the proposed method. Physical interpretations of some obtained solutions is also included. One can easily conclude that the new proposed method is very efficient and finds the exact solutions of the equation in a relatively easy way.

A Novel Residual Frequency Estimation Method for GNSS Receivers.

PubMed

Nguyen, Tu Thi-Thanh; La, Vinh The; Ta, Tung Hai

2018-01-04

In Global Navigation Satellite System (GNSS) receivers, residual frequency estimation methods are traditionally applied in the synchronization block to reduce the transient time from acquisition to tracking, or they are used within the frequency estimator to improve its accuracy in open-loop architectures. There are several disadvantages in the current estimation methods, including sensitivity to noise and wide search space size. This paper proposes a new residual frequency estimation method depending on differential processing. Although the complexity of the proposed method is higher than the one of traditional methods, it can lead to more accurate estimates, without increasing the size of the search space.

Community Detection in Complex Networks via Clique Conductance.

PubMed

Lu, Zhenqi; Wahlström, Johan; Nehorai, Arye

2018-04-13

Network science plays a central role in understanding and modeling complex systems in many areas including physics, sociology, biology, computer science, economics, politics, and neuroscience. One of the most important features of networks is community structure, i.e., clustering of nodes that are locally densely interconnected. Communities reveal the hierarchical organization of nodes, and detecting communities is of great importance in the study of complex systems. Most existing community-detection methods consider low-order connection patterns at the level of individual links. But high-order connection patterns, at the level of small subnetworks, are generally not considered. In this paper, we develop a novel community-detection method based on cliques, i.e., local complete subnetworks. The proposed method overcomes the deficiencies of previous similar community-detection methods by considering the mathematical properties of cliques. We apply the proposed method to computer-generated graphs and real-world network datasets. When applied to networks with known community structure, the proposed method detects the structure with high fidelity and sensitivity. When applied to networks with no a priori information regarding community structure, the proposed method yields insightful results revealing the organization of these complex networks. We also show that the proposed method is guaranteed to detect near-optimal clusters in the bipartition case.

Modified Mixed Lagrangian-Eulerian Method Based on Numerical Framework of MT3DMS on Cauchy Boundary.

PubMed

Suk, Heejun

2016-07-01

MT3DMS, a modular three-dimensional multispecies transport model, has long been a popular model in the groundwater field for simulating solute transport in the saturated zone. However, the method of characteristics (MOC), modified MOC (MMOC), and hybrid MOC (HMOC) included in MT3DMS did not treat Cauchy boundary conditions in a straightforward or rigorous manner, from a mathematical point of view. The MOC, MMOC, and HMOC regard the Cauchy boundary as a source condition. For the source, MOC, MMOC, and HMOC calculate the Lagrangian concentration by setting it equal to the cell concentration at an old time level. However, the above calculation is an approximate method because it does not involve backward tracking in MMOC and HMOC or allow performing forward tracking at the source cell in MOC. To circumvent this problem, a new scheme is proposed that avoids direct calculation of the Lagrangian concentration on the Cauchy boundary. The proposed method combines the numerical formulations of two different schemes, the finite element method (FEM) and the Eulerian-Lagrangian method (ELM), into one global matrix equation. This study demonstrates the limitation of all MT3DMS schemes, including MOC, MMOC, HMOC, and a third-order total-variation-diminishing (TVD) scheme under Cauchy boundary conditions. By contrast, the proposed method always shows good agreement with the exact solution, regardless of the flow conditions. Finally, the successful application of the proposed method sheds light on the possible flexibility and capability of the MT3DMS to deal with the mass transport problems of all flow regimes. © 2016, National Ground Water Association.

Deghosting based on the transmission matrix method

NASA Astrophysics Data System (ADS)

Wang, Benfeng; Wu, Ru-Shan; Chen, Xiaohong

2017-12-01

As the developments of seismic exploration and subsequent seismic exploitation advance, marine acquisition systems with towed streamers become an important seismic data acquisition method. But the existing air-water reflective interface can generate surface related multiples, including ghosts, which can affect the accuracy and performance of the following seismic data processing algorithms. Thus, we derive a deghosting method from a new perspective, i.e. using the transmission matrix (T-matrix) method instead of inverse scattering series. The T-matrix-based deghosting algorithm includes all scattering effects and is convergent absolutely. Initially, the effectiveness of the proposed method is demonstrated using synthetic data obtained from a designed layered model, and its noise-resistant property is also illustrated using noisy synthetic data contaminated by random noise. Numerical examples on complicated data from the open SMAART Pluto model and field marine data further demonstrate the validity and flexibility of the proposed method. After deghosting, low frequency components are recovered reasonably and the fake high frequency components are attenuated, and the recovered low frequency components will be useful for the subsequent full waveform inversion. The proposed deghosting method is currently suitable for two-dimensional towed streamer cases with accurate constant depth information and its extension into variable-depth streamers in three-dimensional cases will be studied in the future.

Fabrication of PDMS through-holes using the MIMIC method and the surface treatment by atmospheric-pressure CH4/He RF plasma

NASA Astrophysics Data System (ADS)

Choi, Jongchan; Lee, Kyeong-Hwan; Yang, Sung

2011-09-01

This note presents a simple fabrication process for patterning micro through-holes in a PDMS layer by a combination of the micromolding in capillaries (MIMIC) method and the surface treatment by atmospheric-pressure CH4/He RF plasma. The fabrication process is confirmed by forming micro through-holes with various shapes including circle, C-shape, open microfluidic channel and hemisphere. All micro through-holes of various shapes in a wide range of diameters and heights are well fabricated by the proposed method. Also, a 3D micromixer containing a PDMS micro through-hole layer formed by the proposed method is built and its performance is tested as another practical demonstration of the proposed fabrication method. Therefore, we believe that the proposed fabrication process will build a PDMS micro through-hole layer in a simple and easy way and will contribute to developing highly efficient multi-layered microfluidic systems, which may require PDMS micro through-hole layers.

Estimation of Monthly Near Surface Air Temperature Using Geographically Weighted Regression in China

NASA Astrophysics Data System (ADS)

Wang, M. M.; He, G. J.; Zhang, Z. M.; Zhang, Z. J.; Liu, X. G.

2018-04-01

Near surface air temperature (NSAT) is a primary descriptor of terrestrial environment conditions. The availability of NSAT with high spatial resolution is deemed necessary for several applications such as hydrology, meteorology and ecology. In this study, a regression-based NSAT mapping method is proposed. This method is combined remote sensing variables with geographical variables, and uses geographically weighted regression to estimate NSAT. The altitude was selected as geographical variable; and the remote sensing variables include land surface temperature (LST) and Normalized Difference vegetation index (NDVI). The performance of the proposed method was assessed by predict monthly minimum, mean, and maximum NSAT from point station measurements in China, a domain with a large area, complex topography, and highly variable station density, and the NSAT maps were validated against the meteorology observations. Validation results with meteorological data show the proposed method achieved an accuracy of 1.58 °C. It is concluded that the proposed method for mapping NSAT is very operational and has good precision.

A novel, colorimetric method for biogenic amine detection based on arylalkylamine N-acetyltransferase.

PubMed

Leng, Pei-Qiang; Zhao, Feng-Lan; Yin, Bin-Cheng; Ye, Bang-Ce

2015-05-21

We developed a novel colorimetric method for rapid detection of biogenic amines based on arylalkylamine N-acetyltransferase (aaNAT). The proposed method offers distinct advantages including simple handling, high speed, low cost, good sensitivity and selectivity.

NORMALIZATION, GROUPING, AND WEIGHTING IN LIFE CYCLE IMPACT ASSESSMENT

EPA Science Inventory

This chapter includes a comprehensive overview of weighting methods and principles. The authors propose a very interesting and useful system of criteria for the evaluation of weighting methods; and provide a structured way to discuss the characteristics of weighting methods.

Autonomous space target recognition and tracking approach using star sensors based on a Kalman filter.

PubMed

Ye, Tao; Zhou, Fuqiang

2015-04-10

When imaged by detectors, space targets (including satellites and debris) and background stars have similar point-spread functions, and both objects appear to change as detectors track targets. Therefore, traditional tracking methods cannot separate targets from stars and cannot directly recognize targets in 2D images. Consequently, we propose an autonomous space target recognition and tracking approach using a star sensor technique and a Kalman filter (KF). A two-step method for subpixel-scale detection of star objects (including stars and targets) is developed, and the combination of the star sensor technique and a KF is used to track targets. The experimental results show that the proposed method is adequate for autonomously recognizing and tracking space targets.

Time Series Decomposition into Oscillation Components and Phase Estimation.

PubMed

Matsuda, Takeru; Komaki, Fumiyasu

2017-02-01

Many time series are naturally considered as a superposition of several oscillation components. For example, electroencephalogram (EEG) time series include oscillation components such as alpha, beta, and gamma. We propose a method for decomposing time series into such oscillation components using state-space models. Based on the concept of random frequency modulation, gaussian linear state-space models for oscillation components are developed. In this model, the frequency of an oscillator fluctuates by noise. Time series decomposition is accomplished by this model like the Bayesian seasonal adjustment method. Since the model parameters are estimated from data by the empirical Bayes' method, the amplitudes and the frequencies of oscillation components are determined in a data-driven manner. Also, the appropriate number of oscillation components is determined with the Akaike information criterion (AIC). In this way, the proposed method provides a natural decomposition of the given time series into oscillation components. In neuroscience, the phase of neural time series plays an important role in neural information processing. The proposed method can be used to estimate the phase of each oscillation component and has several advantages over a conventional method based on the Hilbert transform. Thus, the proposed method enables an investigation of the phase dynamics of time series. Numerical results show that the proposed method succeeds in extracting intermittent oscillations like ripples and detecting the phase reset phenomena. We apply the proposed method to real data from various fields such as astronomy, ecology, tidology, and neuroscience.

Double inverse-weighted estimation of cumulative treatment effects under nonproportional hazards and dependent censoring.

PubMed

Schaubel, Douglas E; Wei, Guanghui

2011-03-01

In medical studies of time-to-event data, nonproportional hazards and dependent censoring are very common issues when estimating the treatment effect. A traditional method for dealing with time-dependent treatment effects is to model the time-dependence parametrically. Limitations of this approach include the difficulty to verify the correctness of the specified functional form and the fact that, in the presence of a treatment effect that varies over time, investigators are usually interested in the cumulative as opposed to instantaneous treatment effect. In many applications, censoring time is not independent of event time. Therefore, we propose methods for estimating the cumulative treatment effect in the presence of nonproportional hazards and dependent censoring. Three measures are proposed, including the ratio of cumulative hazards, relative risk, and difference in restricted mean lifetime. For each measure, we propose a double inverse-weighted estimator, constructed by first using inverse probability of treatment weighting (IPTW) to balance the treatment-specific covariate distributions, then using inverse probability of censoring weighting (IPCW) to overcome the dependent censoring. The proposed estimators are shown to be consistent and asymptotically normal. We study their finite-sample properties through simulation. The proposed methods are used to compare kidney wait-list mortality by race. © 2010, The International Biometric Society.

Sequence Based Prediction of DNA-Binding Proteins Based on Hybrid Feature Selection Using Random Forest and Gaussian Naïve Bayes

PubMed Central

Lou, Wangchao; Wang, Xiaoqing; Chen, Fan; Chen, Yixiao; Jiang, Bo; Zhang, Hua

2014-01-01

Developing an efficient method for determination of the DNA-binding proteins, due to their vital roles in gene regulation, is becoming highly desired since it would be invaluable to advance our understanding of protein functions. In this study, we proposed a new method for the prediction of the DNA-binding proteins, by performing the feature rank using random forest and the wrapper-based feature selection using forward best-first search strategy. The features comprise information from primary sequence, predicted secondary structure, predicted relative solvent accessibility, and position specific scoring matrix. The proposed method, called DBPPred, used Gaussian naïve Bayes as the underlying classifier since it outperformed five other classifiers, including decision tree, logistic regression, k-nearest neighbor, support vector machine with polynomial kernel, and support vector machine with radial basis function. As a result, the proposed DBPPred yields the highest average accuracy of 0.791 and average MCC of 0.583 according to the five-fold cross validation with ten runs on the training benchmark dataset PDB594. Subsequently, blind tests on the independent dataset PDB186 by the proposed model trained on the entire PDB594 dataset and by other five existing methods (including iDNA-Prot, DNA-Prot, DNAbinder, DNABIND and DBD-Threader) were performed, resulting in that the proposed DBPPred yielded the highest accuracy of 0.769, MCC of 0.538, and AUC of 0.790. The independent tests performed by the proposed DBPPred on completely a large non-DNA binding protein dataset and two RNA binding protein datasets also showed improved or comparable quality when compared with the relevant prediction methods. Moreover, we observed that majority of the selected features by the proposed method are statistically significantly different between the mean feature values of the DNA-binding and the non DNA-binding proteins. All of the experimental results indicate that the proposed DBPPred can be an alternative perspective predictor for large-scale determination of DNA-binding proteins. PMID:24475169

Human Body 3D Posture Estimation Using Significant Points and Two Cameras

PubMed Central

Juang, Chia-Feng; Chen, Teng-Chang; Du, Wei-Chin

2014-01-01

This paper proposes a three-dimensional (3D) human posture estimation system that locates 3D significant body points based on 2D body contours extracted from two cameras without using any depth sensors. The 3D significant body points that are located by this system include the head, the center of the body, the tips of the feet, the tips of the hands, the elbows, and the knees. First, a linear support vector machine- (SVM-) based segmentation method is proposed to distinguish the human body from the background in red, green, and blue (RGB) color space. The SVM-based segmentation method uses not only normalized color differences but also included angle between pixels in the current frame and the background in order to reduce shadow influence. After segmentation, 2D significant points in each of the two extracted images are located. A significant point volume matching (SPVM) method is then proposed to reconstruct the 3D significant body point locations by using 2D posture estimation results. Experimental results show that the proposed SVM-based segmentation method shows better performance than other gray level- and RGB-based segmentation approaches. This paper also shows the effectiveness of the 3D posture estimation results in different postures. PMID:24883422

Efficient l1 -norm-based low-rank matrix approximations for large-scale problems using alternating rectified gradient method.

PubMed

Kim, Eunwoo; Lee, Minsik; Choi, Chong-Ho; Kwak, Nojun; Oh, Songhwai

2015-02-01

Low-rank matrix approximation plays an important role in the area of computer vision and image processing. Most of the conventional low-rank matrix approximation methods are based on the l2 -norm (Frobenius norm) with principal component analysis (PCA) being the most popular among them. However, this can give a poor approximation for data contaminated by outliers (including missing data), because the l2 -norm exaggerates the negative effect of outliers. Recently, to overcome this problem, various methods based on the l1 -norm, such as robust PCA methods, have been proposed for low-rank matrix approximation. Despite the robustness of the methods, they require heavy computational effort and substantial memory for high-dimensional data, which is impractical for real-world problems. In this paper, we propose two efficient low-rank factorization methods based on the l1 -norm that find proper projection and coefficient matrices using the alternating rectified gradient method. The proposed methods are applied to a number of low-rank matrix approximation problems to demonstrate their efficiency and robustness. The experimental results show that our proposals are efficient in both execution time and reconstruction performance unlike other state-of-the-art methods.

Single image super-resolution based on convolutional neural networks

NASA Astrophysics Data System (ADS)

Zou, Lamei; Luo, Ming; Yang, Weidong; Li, Peng; Jin, Liujia

2018-03-01

We present a deep learning method for single image super-resolution (SISR). The proposed approach learns end-to-end mapping between low-resolution (LR) images and high-resolution (HR) images. The mapping is represented as a deep convolutional neural network which inputs the LR image and outputs the HR image. Our network uses 5 convolution layers, which kernels size include 5×5, 3×3 and 1×1. In our proposed network, we use residual-learning and combine different sizes of convolution kernels at the same layer. The experiment results show that our proposed method performs better than the existing methods in reconstructing quality index and human visual effects on benchmarked images.

A new method based on the Butler-Volmer formalism to evaluate voltammetric cation and anion sensors.

PubMed

Cano, Manuel; Rodríguez-Amaro, Rafael; Fernández Romero, Antonio J

2008-12-11

A new method based on the Butler-Volmer formalism is applied to assess the capability of two voltammetric ion sensors based on polypyrrole films: PPy/DBS and PPy/ClO4 modified electrodes were studied as voltammetric cation and anion sensors, respectively. The reversible potential versus electrolyte concentrations semilogarithm plots provided positive calibration slopes for PPy/DBS and negative ones for PPy/ClO4, as was expected from the proposed method and that based on the Nernst equation. The slope expressions deduced from Butler-Volmer include the electron-transfer coefficient, which allows slope values different from the ideal Nernstian value to be explained. Both polymeric films exhibited a degree of ion-selectivity when they were immersed in mixed-analyte solutions. Selectivity coefficients for the two proposed voltammetric cation and anion sensors were obtained by several experimental methods, including the separated solution method (SSM) and matched potential method (MPM). The K values acquired by the different methods were very close for both polymeric sensors.

75 FR 14659 - Proposed Collection: Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2010-03-26

... of information on respondents, including through the use of automated collection techniques or other... to take this opportunity to comment on proposed and/or continuing information collections, as... Regulations Governing Payments by the Automated Clearing House method on Account of United States Securities...

75 FR 18485 - Proposed Collection, Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2010-04-12

... methods: Federal eRulemaking Portal: http://www.regulations.gov . Follow the instructions for submitting... responsibilities for including humans as subjects in research. Principal and co-principal investigators must have the proposed, signed form on file before they may engage in research conducted, sponsored, or...

Computationally efficient method for localizing the spiral rotor source using synthetic intracardiac electrograms during atrial fibrillation.

PubMed

Shariat, M H; Gazor, S; Redfearn, D

2015-08-01

Atrial fibrillation (AF), the most common sustained cardiac arrhythmia, is an extremely costly public health problem. Catheter-based ablation is a common minimally invasive procedure to treat AF. Contemporary mapping methods are highly dependent on the accuracy of anatomic localization of rotor sources within the atria. In this paper, using simulated atrial intracardiac electrograms (IEGMs) during AF, we propose a computationally efficient method for localizing the tip of the electrical rotor with an Archimedean/arithmetic spiral wavefront. The proposed method deploys the locations of electrodes of a catheter and their IEGMs activation times to estimate the unknown parameters of the spiral wavefront including its tip location. The proposed method is able to localize the spiral as soon as the wave hits three electrodes of the catheter. Our simulation results show that the method can efficiently localize the spiral wavefront that rotates either clockwise or counterclockwise.

Comparative study between derivative spectrophotometry and multivariate calibration as analytical tools applied for the simultaneous quantitation of Amlodipine, Valsartan and Hydrochlorothiazide.

PubMed

Darwish, Hany W; Hassan, Said A; Salem, Maissa Y; El-Zeany, Badr A

2013-09-01

Four simple, accurate and specific methods were developed and validated for the simultaneous estimation of Amlodipine (AML), Valsartan (VAL) and Hydrochlorothiazide (HCT) in commercial tablets. The derivative spectrophotometric methods include Derivative Ratio Zero Crossing (DRZC) and Double Divisor Ratio Spectra-Derivative Spectrophotometry (DDRS-DS) methods, while the multivariate calibrations used are Principal Component Regression (PCR) and Partial Least Squares (PLSs). The proposed methods were applied successfully in the determination of the drugs in laboratory-prepared mixtures and in commercial pharmaceutical preparations. The validity of the proposed methods was assessed using the standard addition technique. The linearity of the proposed methods is investigated in the range of 2-32, 4-44 and 2-20 μg/mL for AML, VAL and HCT, respectively. Copyright © 2013 Elsevier B.V. All rights reserved.

Supplement Analysis for the Transmission System Vegetation Management Program FEIS (DOE/EIS-0285/SA-108), Satsop-Aberdeen #2

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

Tippetts, Greg P.

2002-09-05

Vegetation Management along the Satsop-Aberdeen #2 230kV transmission line corridor from structure 1/1 through structure 11/5. BPA proposes to remove unwanted vegetation along the right-of- way, access roads and around tower structures along the subject transmission line corridors. Approximately 11 miles of right-of-way will be treated using selective and non-selective methods that include hand cutting, mowing and herbicide treatments. Approximately 0.8 miles of access roads will be cleared using selective and non-selective methods that include hand cutting, mowing and herbicide treatments. Tower sites will be treated using selective and non-selective methods that include hand cutting, mowing and herbicide treatments. Vegetationmore » management is required for unimpeded operation and maintenance of the subject transmission line. See Section 1of the attached checklist for a complete description of the proposal.« less

40 CFR 228.6 - Specific criteria for site selection.

Code of Federal Regulations, 2012 CFR

2012-07-01

..., and proposed methods of release, including methods of packing the waste, if any; (5) Feasibility of... of special scientific importance and other legitimate uses of the ocean; (9) The existing water...

40 CFR 228.6 - Specific criteria for site selection.

Code of Federal Regulations, 2013 CFR

2013-07-01

..., and proposed methods of release, including methods of packing the waste, if any; (5) Feasibility of... of special scientific importance and other legitimate uses of the ocean; (9) The existing water...

Robust Skull-Stripping Segmentation Based on Irrational Mask for Magnetic Resonance Brain Images.

PubMed

Moldovanu, Simona; Moraru, Luminița; Biswas, Anjan

2015-12-01

This paper proposes a new method for simple, efficient, and robust removal of the non-brain tissues in MR images based on an irrational mask for filtration within a binary morphological operation framework. The proposed skull-stripping segmentation is based on two irrational 3 × 3 and 5 × 5 masks, having the sum of its weights equal to the transcendental number π value provided by the Gregory-Leibniz infinite series. It allows maintaining a lower rate of useful pixel loss. The proposed method has been tested in two ways. First, it has been validated as a binary method by comparing and contrasting with Otsu's, Sauvola's, Niblack's, and Bernsen's binary methods. Secondly, its accuracy has been verified against three state-of-the-art skull-stripping methods: the graph cuts method, the method based on Chan-Vese active contour model, and the simplex mesh and histogram analysis skull stripping. The performance of the proposed method has been assessed using the Dice scores, overlap and extra fractions, and sensitivity and specificity as statistical methods. The gold standard has been provided by two neurologist experts. The proposed method has been tested and validated on 26 image series which contain 216 images from two publicly available databases: the Whole Brain Atlas and the Internet Brain Segmentation Repository that include a highly variable sample population (with reference to age, sex, healthy/diseased). The approach performs accurately on both standardized databases. The main advantage of the proposed method is its robustness and speed.

Noise Suppression Based on Multi-Model Compositions Using Multi-Pass Search with Multi-Label N-gram Models

NASA Astrophysics Data System (ADS)

Jitsuhiro, Takatoshi; Toriyama, Tomoji; Kogure, Kiyoshi

We propose a noise suppression method based on multi-model compositions and multi-pass search. In real environments, input speech for speech recognition includes many kinds of noise signals. To obtain good recognized candidates, suppressing many kinds of noise signals at once and finding target speech is important. Before noise suppression, to find speech and noise label sequences, we introduce multi-pass search with acoustic models including many kinds of noise models and their compositions, their n-gram models, and their lexicon. Noise suppression is frame-synchronously performed using the multiple models selected by recognized label sequences with time alignments. We evaluated this method using the E-Nightingale task, which contains voice memoranda spoken by nurses during actual work at hospitals. The proposed method obtained higher performance than the conventional method.

Multiple disturbances classifier for electric signals using adaptive structuring neural networks

NASA Astrophysics Data System (ADS)

Lu, Yen-Ling; Chuang, Cheng-Long; Fahn, Chin-Shyurng; Jiang, Joe-Air

2008-07-01

This work proposes a novel classifier to recognize multiple disturbances for electric signals of power systems. The proposed classifier consists of a series of pipeline-based processing components, including amplitude estimator, transient disturbance detector, transient impulsive detector, wavelet transform and a brand-new neural network for recognizing multiple disturbances in a power quality (PQ) event. Most of the previously proposed methods usually treated a PQ event as a single disturbance at a time. In practice, however, a PQ event often consists of various types of disturbances at the same time. Therefore, the performances of those methods might be limited in real power systems. This work considers the PQ event as a combination of several disturbances, including steady-state and transient disturbances, which is more analogous to the real status of a power system. Six types of commonly encountered power quality disturbances are considered for training and testing the proposed classifier. The proposed classifier has been tested on electric signals that contain single disturbance or several disturbances at a time. Experimental results indicate that the proposed PQ disturbance classification algorithm can achieve a high accuracy of more than 97% in various complex testing cases.

Learning to Select Supplier Portfolios for Service Supply Chain

PubMed Central

Zhang, Rui; Li, Jingfei; Wu, Shaoyu; Meng, Dabin

2016-01-01

The research on service supply chain has attracted more and more focus from both academia and industrial community. In a service supply chain, the selection of supplier portfolio is an important and difficult problem due to the fact that a supplier portfolio may include multiple suppliers from a variety of fields. To address this problem, we propose a novel supplier portfolio selection method based on a well known machine learning approach, i.e., Ranking Neural Network (RankNet). In the proposed method, we regard the problem of supplier portfolio selection as a ranking problem, which integrates a large scale of decision making features into a ranking neural network. Extensive simulation experiments are conducted, which demonstrate the feasibility and effectiveness of the proposed method. The proposed supplier portfolio selection model can be applied in a real corporation easily in the future. PMID:27195756

A Hybrid 3D Indoor Space Model

NASA Astrophysics Data System (ADS)

Jamali, Ali; Rahman, Alias Abdul; Boguslawski, Pawel

2016-10-01

GIS integrates spatial information and spatial analysis. An important example of such integration is for emergency response which requires route planning inside and outside of a building. Route planning requires detailed information related to indoor and outdoor environment. Indoor navigation network models including Geometric Network Model (GNM), Navigable Space Model, sub-division model and regular-grid model lack indoor data sources and abstraction methods. In this paper, a hybrid indoor space model is proposed. In the proposed method, 3D modeling of indoor navigation network is based on surveying control points and it is less dependent on the 3D geometrical building model. This research proposes a method of indoor space modeling for the buildings which do not have proper 2D/3D geometrical models or they lack semantic or topological information. The proposed hybrid model consists of topological, geometrical and semantical space.

Robust sleep quality quantification method for a personal handheld device.

PubMed

Shin, Hangsik; Choi, Byunghun; Kim, Doyoon; Cho, Jaegeol

2014-06-01

The purpose of this study was to develop and validate a novel method for sleep quality quantification using personal handheld devices. The proposed method used 3- or 6-axes signals, including acceleration and angular velocity, obtained from built-in sensors in a smartphone and applied a real-time wavelet denoising technique to minimize the nonstationary noise. Sleep or wake status was decided on each axis, and the totals were finally summed to calculate sleep efficiency (SE), regarded as sleep quality in general. The sleep experiment was carried out for performance evaluation of the proposed method, and 14 subjects participated. An experimental protocol was designed for comparative analysis. The activity during sleep was recorded not only by the proposed method but also by well-known commercial applications simultaneously; moreover, activity was recorded on different mattresses and locations to verify the reliability in practical use. Every calculated SE was compared with the SE of a clinically certified medical device, the Philips (Amsterdam, The Netherlands) Actiwatch. In these experiments, the proposed method proved its reliability in quantifying sleep quality. Compared with the Actiwatch, accuracy and average bias error of SE calculated by the proposed method were 96.50% and -1.91%, respectively. The proposed method was vastly superior to other comparative applications with at least 11.41% in average accuracy and at least 6.10% in average bias; average accuracy and average absolute bias error of comparative applications were 76.33% and 17.52%, respectively.

78 FR 25533 - Proposed Collection; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-01

... minimize the burden of the collection of information on respondents, including through the use of automated... to take this opportunity to comment on proposed and/or continuing information collections, as... Regulations Governing Payments by the Automated Clearing House Method on Account of United States Securities...

Functional Multiple-Set Canonical Correlation Analysis

ERIC Educational Resources Information Center

Hwang, Heungsun; Jung, Kwanghee; Takane, Yoshio; Woodward, Todd S.

2012-01-01

We propose functional multiple-set canonical correlation analysis for exploring associations among multiple sets of functions. The proposed method includes functional canonical correlation analysis as a special case when only two sets of functions are considered. As in classical multiple-set canonical correlation analysis, computationally, the…

48 CFR 15.605 - Content of unsolicited proposals.

Code of Federal Regulations, 2010 CFR

2010-10-01

... CONTRACTING METHODS AND CONTRACT TYPES CONTRACTING BY NEGOTIATION Unsolicited Proposals 15.605 Content of... and type of organization; e.g., profit, nonprofit, educational, small business; (2) Names and..., including alternates; and (4) Type of support needed from the agency; e.g., Government property or personnel...

78 FR 51173 - Proposed Collection; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2013-08-20

... following methods: Federal eRulemaking Portal: http://www.regulations.gov . Follow the instructions for... pertaining to including humans as subjects in research. Principal and associate investigators must have the proposed, signed form on file before they may engage in research conducted or supported by entities under...

Survey of methods for soil moisture determination

NASA Technical Reports Server (NTRS)

Schmugge, T. J.; Jackson, T. J.; Mckim, H. L.

1979-01-01

Existing and proposed methods for soil moisture determination are discussed. These include: (1) in situ investigations including gravimetric, nuclear, and electromagnetic techniques; (2) remote sensing approaches that use the reflected solar, thermal infrared, and microwave portions of the electromagnetic spectrum; and (3) soil physics models that track the behavior of water in the soil in response to meteorological inputs (precipitation) and demands (evapotranspiration). The capacities of these approaches to satisfy various user needs for soil moisture information vary from application to application, but a conceptual scheme for merging these approaches into integrated systems to provide soil moisture information is proposed that has the potential for meeting various application requirements.

Optimal integral force feedback for active vibration control

NASA Astrophysics Data System (ADS)

Teo, Yik R.; Fleming, Andrew J.

2015-11-01

This paper proposes an improvement to Integral Force Feedback (IFF), which is a popular method for active vibration control of structures and mechanical systems. Benefits of IFF include robustness, guaranteed stability and simplicity. However, the maximum damping performance is dependent on the stiffness of the system; hence, some systems cannot be adequately controlled. In this paper, an improvement to the classical force feedback control scheme is proposed. The improved method achieves arbitrary damping for any mechanical system by introducing a feed-through term. The proposed improvement is experimentally demonstrated by actively damping an objective lens assembly for a high-speed confocal microscope.

Load-Dependent Soft-Switching Method of Half-Bridge Current Doubler for High-Voltage Point-of-Load Converter in Data Center Power Supplies

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

Cui, Yutian; Yang, Fei; Tolbert, Leon M.

With the increased cloud computing and digital information storage, the energy requirement of data centers keeps increasing. A high-voltage point of load (HV POL) with an input series output parallel structure is proposed to convert 400 to 1 VDC within a single stage to increase the power conversion efficiency. The symmetrical controlled half-bridge current doubler is selected as the converter topology in the HV POL. A load-dependent soft-switching method has been proposed with an auxiliary circuit that includes inductor, diode, and MOSFETs so that the hard-switching issue of typical symmetrical controlled half-bridge converters is resolved. The operation principles of themore » proposed soft-switching half-bridge current doubler have been analyzed in detail. Then, the necessity of adjusting the timing with the loading in the proposed method is analyzed based on losses, and a controller is designed to realize the load-dependent operation. A lossless RCD current sensing method is used to sense the output inductor current value in the proposed load-dependent operation. In conclusion, experimental efficiency of a hardware prototype is provided to show that the proposed method can increase the converter's efficiency in both heavy- and light-load conditions.« less

Load-Dependent Soft-Switching Method of Half-Bridge Current Doubler for High-Voltage Point-of-Load Converter in Data Center Power Supplies

DOE PAGES

Cui, Yutian; Yang, Fei; Tolbert, Leon M.; ...

2016-06-14

With the increased cloud computing and digital information storage, the energy requirement of data centers keeps increasing. A high-voltage point of load (HV POL) with an input series output parallel structure is proposed to convert 400 to 1 VDC within a single stage to increase the power conversion efficiency. The symmetrical controlled half-bridge current doubler is selected as the converter topology in the HV POL. A load-dependent soft-switching method has been proposed with an auxiliary circuit that includes inductor, diode, and MOSFETs so that the hard-switching issue of typical symmetrical controlled half-bridge converters is resolved. The operation principles of themore » proposed soft-switching half-bridge current doubler have been analyzed in detail. Then, the necessity of adjusting the timing with the loading in the proposed method is analyzed based on losses, and a controller is designed to realize the load-dependent operation. A lossless RCD current sensing method is used to sense the output inductor current value in the proposed load-dependent operation. In conclusion, experimental efficiency of a hardware prototype is provided to show that the proposed method can increase the converter's efficiency in both heavy- and light-load conditions.« less

Network Modeling and Energy-Efficiency Optimization for Advanced Machine-to-Machine Sensor Networks

PubMed Central

Jung, Sungmo; Kim, Jong Hyun; Kim, Seoksoo

2012-01-01

Wireless machine-to-machine sensor networks with multiple radio interfaces are expected to have several advantages, including high spatial scalability, low event detection latency, and low energy consumption. Here, we propose a network model design method involving network approximation and an optimized multi-tiered clustering algorithm that maximizes node lifespan by minimizing energy consumption in a non-uniformly distributed network. Simulation results show that the cluster scales and network parameters determined with the proposed method facilitate a more efficient performance compared to existing methods. PMID:23202190

DEVELOPMENT AND APPLICATION OF METHODS TO ASSESS HUMAN EXPOSURE TO PESTICIDES

EPA Science Inventory

Note: this task is schedule to end September 2003. Two tasks will take its place: method development for emerging pesticides including chiral chemistry applications, and in-house laboratory operations. Field sampling methods are covered under a new task proposed this year.
<...

CW-SSIM kernel based random forest for image classification

NASA Astrophysics Data System (ADS)

Fan, Guangzhe; Wang, Zhou; Wang, Jiheng

2010-07-01

Complex wavelet structural similarity (CW-SSIM) index has been proposed as a powerful image similarity metric that is robust to translation, scaling and rotation of images, but how to employ it in image classification applications has not been deeply investigated. In this paper, we incorporate CW-SSIM as a kernel function into a random forest learning algorithm. This leads to a novel image classification approach that does not require a feature extraction or dimension reduction stage at the front end. We use hand-written digit recognition as an example to demonstrate our algorithm. We compare the performance of the proposed approach with random forest learning based on other kernels, including the widely adopted Gaussian and the inner product kernels. Empirical evidences show that the proposed method is superior in its classification power. We also compared our proposed approach with the direct random forest method without kernel and the popular kernel-learning method support vector machine. Our test results based on both simulated and realworld data suggest that the proposed approach works superior to traditional methods without the feature selection procedure.

Automatic identification and removal of ocular artifacts in EEG--improved adaptive predictor filtering for portable applications.

PubMed

Zhao, Qinglin; Hu, Bin; Shi, Yujun; Li, Yang; Moore, Philip; Sun, Minghou; Peng, Hong

2014-06-01

Electroencephalogram (EEG) signals have a long history of use as a noninvasive approach to measure brain function. An essential component in EEG-based applications is the removal of Ocular Artifacts (OA) from the EEG signals. In this paper we propose a hybrid de-noising method combining Discrete Wavelet Transformation (DWT) and an Adaptive Predictor Filter (APF). A particularly novel feature of the proposed method is the use of the APF based on an adaptive autoregressive model for prediction of the waveform of signals in the ocular artifact zones. In our test, based on simulated data, the accuracy of noise removal in the proposed model was significantly increased when compared to existing methods including: Wavelet Packet Transform (WPT) and Independent Component Analysis (ICA), Discrete Wavelet Transform (DWT) and Adaptive Noise Cancellation (ANC). The results demonstrate that the proposed method achieved a lower mean square error and higher correlation between the original and corrected EEG. The proposed method has also been evaluated using data from calibration trials for the Online Predictive Tools for Intervention in Mental Illness (OPTIMI) project. The results of this evaluation indicate an improvement in performance in terms of the recovery of true EEG signals with EEG tracking and computational speed in the analysis. The proposed method is well suited to applications in portable environments where the constraints with respect to acceptable wearable sensor attachments usually dictate single channel devices.

Image color reduction method for color-defective observers using a color palette composed of 20 particular colors

NASA Astrophysics Data System (ADS)

Sakamoto, Takashi

2015-01-01

This study describes a color enhancement method that uses a color palette especially designed for protan and deutan defects, commonly known as red-green color blindness. The proposed color reduction method is based on a simple color mapping. Complicated computation and image processing are not required by using the proposed method, and the method can replace protan and deutan confusion (p/d-confusion) colors with protan and deutan safe (p/d-safe) colors. Color palettes for protan and deutan defects proposed by previous studies are composed of few p/d-safe colors. Thus, the colors contained in these palettes are insufficient for replacing colors in photographs. Recently, Ito et al. proposed a p/dsafe color palette composed of 20 particular colors. The author demonstrated that their p/d-safe color palette could be applied to image color reduction in photographs as a means to replace p/d-confusion colors. This study describes the results of the proposed color reduction in photographs that include typical p/d-confusion colors, which can be replaced. After the reduction process is completed, color-defective observers can distinguish these confusion colors.

Automatic movie skimming with general tempo analysis

NASA Astrophysics Data System (ADS)

Lee, Shih-Hung; Yeh, Chia-Hung; Kuo, C. C. J.

2003-11-01

Story units are extracted by general tempo analysis including tempos analysis including tempos of audio and visual information in this research. Although many schemes have been proposed to successfully segment video data into shots using basic low-level features, how to group shots into meaningful units called story units is still a challenging problem. By focusing on a certain type of video such as sport or news, we can explore models with the specific application domain knowledge. For movie contents, many heuristic rules based on audiovisual clues have been proposed with limited success. We propose a method to extract story units using general tempo analysis. Experimental results are given to demonstrate the feasibility and efficiency of the proposed technique.

Social Science: Course Proposal.

ERIC Educational Resources Information Center

Cook, Charles Gene

A proposal is presented for a Community College of Philadelphia course surveying basic social science skills and information, including scientific method, map usage, evolution, native peoples, social groups, and U.S. Government. Following a standard cover form, a statement of purpose for the course indicates that it is designed to provide…

Estimating repetitive spatiotemporal patterns from resting-state brain activity data.

PubMed

Takeda, Yusuke; Hiroe, Nobuo; Yamashita, Okito; Sato, Masa-Aki

2016-06-01

Repetitive spatiotemporal patterns in spontaneous brain activities have been widely examined in non-human studies. These studies have reported that such patterns reflect past experiences embedded in neural circuits. In human magnetoencephalography (MEG) and electroencephalography (EEG) studies, however, spatiotemporal patterns in resting-state brain activities have not been extensively examined. This is because estimating spatiotemporal patterns from resting-state MEG/EEG data is difficult due to their unknown onsets. Here, we propose a method to estimate repetitive spatiotemporal patterns from resting-state brain activity data, including MEG/EEG. Without the information of onsets, the proposed method can estimate several spatiotemporal patterns, even if they are overlapping. We verified the performance of the method by detailed simulation tests. Furthermore, we examined whether the proposed method could estimate the visual evoked magnetic fields (VEFs) without using stimulus onset information. The proposed method successfully detected the stimulus onsets and estimated the VEFs, implying the applicability of this method to real MEG data. The proposed method was applied to resting-state functional magnetic resonance imaging (fMRI) data and MEG data. The results revealed informative spatiotemporal patterns representing consecutive brain activities that dynamically change with time. Using this method, it is possible to reveal discrete events spontaneously occurring in our brains, such as memory retrieval. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

An Adaptive Orientation Estimation Method for Magnetic and Inertial Sensors in the Presence of Magnetic Disturbances

PubMed Central

Fan, Bingfei; Li, Qingguo; Wang, Chao; Liu, Tao

2017-01-01

Magnetic and inertial sensors have been widely used to estimate the orientation of human segments due to their low cost, compact size and light weight. However, the accuracy of the estimated orientation is easily affected by external factors, especially when the sensor is used in an environment with magnetic disturbances. In this paper, we propose an adaptive method to improve the accuracy of orientation estimations in the presence of magnetic disturbances. The method is based on existing gradient descent algorithms, and it is performed prior to sensor fusion algorithms. The proposed method includes stationary state detection and magnetic disturbance severity determination. The stationary state detection makes this method immune to magnetic disturbances in stationary state, while the magnetic disturbance severity determination helps to determine the credibility of magnetometer data under dynamic conditions, so as to mitigate the negative effect of the magnetic disturbances. The proposed method was validated through experiments performed on a customized three-axis instrumented gimbal with known orientations. The error of the proposed method and the original gradient descent algorithms were calculated and compared. Experimental results demonstrate that in stationary state, the proposed method is completely immune to magnetic disturbances, and in dynamic conditions, the error caused by magnetic disturbance is reduced by 51.2% compared with original MIMU gradient descent algorithm. PMID:28534858

Energy-Based Wavelet De-Noising of Hydrologic Time Series

PubMed Central

Sang, Yan-Fang; Liu, Changming; Wang, Zhonggen; Wen, Jun; Shang, Lunyu

2014-01-01

De-noising is a substantial issue in hydrologic time series analysis, but it is a difficult task due to the defect of methods. In this paper an energy-based wavelet de-noising method was proposed. It is to remove noise by comparing energy distribution of series with the background energy distribution, which is established from Monte-Carlo test. Differing from wavelet threshold de-noising (WTD) method with the basis of wavelet coefficient thresholding, the proposed method is based on energy distribution of series. It can distinguish noise from deterministic components in series, and uncertainty of de-noising result can be quantitatively estimated using proper confidence interval, but WTD method cannot do this. Analysis of both synthetic and observed series verified the comparable power of the proposed method and WTD, but de-noising process by the former is more easily operable. The results also indicate the influences of three key factors (wavelet choice, decomposition level choice and noise content) on wavelet de-noising. Wavelet should be carefully chosen when using the proposed method. The suitable decomposition level for wavelet de-noising should correspond to series' deterministic sub-signal which has the smallest temporal scale. If too much noise is included in a series, accurate de-noising result cannot be obtained by the proposed method or WTD, but the series would show pure random but not autocorrelation characters, so de-noising is no longer needed. PMID:25360533

A primitive study of voxel feature generation by multiple stacked denoising autoencoders for detecting cerebral aneurysms on MRA

NASA Astrophysics Data System (ADS)

Nemoto, Mitsutaka; Hayashi, Naoto; Hanaoka, Shouhei; Nomura, Yukihiro; Miki, Soichiro; Yoshikawa, Takeharu; Ohtomo, Kuni

2016-03-01

The purpose of this study is to evaluate the feasibility of a novel feature generation, which is based on multiple deep neural networks (DNNs) with boosting, for computer-assisted detection (CADe). It is hard and time-consuming to optimize the hyperparameters for DNNs such as stacked denoising autoencoder (SdA). The proposed method allows using SdA based features without the burden of the hyperparameter setting. The proposed method was evaluated by an application for detecting cerebral aneurysms on magnetic resonance angiogram (MRA). A baseline CADe process included four components; scaling, candidate area limitation, candidate detection, and candidate classification. Proposed feature generation method was applied to extract the optimal features for candidate classification. Proposed method only required setting range of the hyperparameters for SdA. The optimal feature set was selected from a large quantity of SdA based features by multiple SdAs, each of which was trained using different hyperparameter set. The feature selection was operated through ada-boost ensemble learning method. Training of the baseline CADe process and proposed feature generation were operated with 200 MRA cases, and the evaluation was performed with 100 MRA cases. Proposed method successfully provided SdA based features just setting the range of some hyperparameters for SdA. The CADe process by using both previous voxel features and SdA based features had the best performance with 0.838 of an area under ROC curve and 0.312 of ANODE score. The results showed that proposed method was effective in the application for detecting cerebral aneurysms on MRA.

Particle swarm optimization method for small retinal vessels detection on multiresolution fundus images.

PubMed

Khomri, Bilal; Christodoulidis, Argyrios; Djerou, Leila; Babahenini, Mohamed Chaouki; Cheriet, Farida

2018-05-01

Retinal vessel segmentation plays an important role in the diagnosis of eye diseases and is considered as one of the most challenging tasks in computer-aided diagnosis (CAD) systems. The main goal of this study was to propose a method for blood-vessel segmentation that could deal with the problem of detecting vessels of varying diameters in high- and low-resolution fundus images. We proposed to use the particle swarm optimization (PSO) algorithm to improve the multiscale line detection (MSLD) method. The PSO algorithm was applied to find the best arrangement of scales in the MSLD method and to handle the problem of multiscale response recombination. The performance of the proposed method was evaluated on two low-resolution (DRIVE and STARE) and one high-resolution fundus (HRF) image datasets. The data include healthy (H) and diabetic retinopathy (DR) cases. The proposed approach improved the sensitivity rate against the MSLD by 4.7% for the DRIVE dataset and by 1.8% for the STARE dataset. For the high-resolution dataset, the proposed approach achieved 87.09% sensitivity rate, whereas the MSLD method achieves 82.58% sensitivity rate at the same specificity level. When only the smallest vessels were considered, the proposed approach improved the sensitivity rate by 11.02% and by 4.42% for the healthy and the diabetic cases, respectively. Integrating the proposed method in a comprehensive CAD system for DR screening would allow the reduction of false positives due to missed small vessels, misclassified as red lesions. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Multi-type sensor placement and response reconstruction for building structures: Experimental investigations

NASA Astrophysics Data System (ADS)

Hu, Rong-Pan; Xu, You-Lin; Zhan, Sheng

2018-01-01

Estimation of lateral displacement and acceleration responses is essential to assess safety and serviceability of high-rise buildings under dynamic loadings including earthquake excitations. However, the measurement information from the limited number of sensors installed in a building structure is often insufficient for the complete structural performance assessment. An integrated multi-type sensor placement and response reconstruction method has thus been proposed by the authors to tackle this problem. To validate the feasibility and effectiveness of the proposed method, an experimental investigation using a cantilever beam with multi-type sensors is performed and reported in this paper. The experimental setup is first introduced. The finite element modelling and model updating of the cantilever beam are then performed. The optimal sensor placement for the best response reconstruction is determined by the proposed method based on the updated FE model of the beam. After the sensors are installed on the physical cantilever beam, a number of experiments are carried out. The responses at key locations are reconstructed and compared with the measured ones. The reconstructed responses achieve a good match with the measured ones, manifesting the feasibility and effectiveness of the proposed method. Besides, the proposed method is also examined for the cases of different excitations and unknown excitation, and the results prove the proposed method to be robust and effective. The superiority of the optimized sensor placement scheme is finally demonstrated through comparison with two other different sensor placement schemes: the accelerometer-only scheme and non-optimal sensor placement scheme. The proposed method can be applied to high-rise buildings for seismic performance assessment.

Real-time anomaly detection for very short-term load forecasting

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

Luo, Jian; Hong, Tao; Yue, Meng

Although the recent load information is critical to very short-term load forecasting (VSTLF), power companies often have difficulties in collecting the most recent load values accurately and timely for VSTLF applications. This paper tackles the problem of real-time anomaly detection in most recent load information used by VSTLF. This paper proposes a model-based anomaly detection method that consists of two components, a dynamic regression model and an adaptive anomaly threshold. The case study is developed using the data from ISO New England. This paper demonstrates that the proposed method significantly outperforms three other anomaly detection methods including two methods commonlymore » used in the field and one state-of-the-art method used by a winning team of the Global Energy Forecasting Competition 2014. Lastly, a general anomaly detection framework is proposed for the future research.« less

Real-time anomaly detection for very short-term load forecasting

DOE PAGES

Luo, Jian; Hong, Tao; Yue, Meng

2018-01-06

Although the recent load information is critical to very short-term load forecasting (VSTLF), power companies often have difficulties in collecting the most recent load values accurately and timely for VSTLF applications. This paper tackles the problem of real-time anomaly detection in most recent load information used by VSTLF. This paper proposes a model-based anomaly detection method that consists of two components, a dynamic regression model and an adaptive anomaly threshold. The case study is developed using the data from ISO New England. This paper demonstrates that the proposed method significantly outperforms three other anomaly detection methods including two methods commonlymore » used in the field and one state-of-the-art method used by a winning team of the Global Energy Forecasting Competition 2014. Lastly, a general anomaly detection framework is proposed for the future research.« less

[Haptic tracking control for minimally invasive robotic surgery].

PubMed

Xu, Zhaohong; Song, Chengli; Wu, Wenwu

2012-06-01

Haptic feedback plays a significant role in minimally invasive robotic surgery (MIRS). A major deficiency of the current MIRS is the lack of haptic perception for the surgeon, including the commercially available robot da Vinci surgical system. In this paper, a dynamics model of a haptic robot is established based on Newton-Euler method. Because it took some period of time in exact dynamics solution, we used a digital PID arithmetic dependent on robot dynamics to ensure real-time bilateral control, and it could improve tracking precision and real-time control efficiency. To prove the proposed method, an experimental system in which two Novint Falcon haptic devices acting as master-slave system has been developed. Simulations and experiments showed proposed methods could give instrument force feedbacks to operator, and bilateral control strategy is an effective method to master-slave MIRS. The proposed methods could be used to tele-robotic system.

Development of a classification method for a crack on a pavement surface images using machine learning

NASA Astrophysics Data System (ADS)

Hizukuri, Akiyoshi; Nagata, Takeshi

2017-03-01

The purpose of this study is to develop a classification method for a crack on a pavement surface image using machine learning to reduce a maintenance fee. Our database consists of 3500 pavement surface images. This includes 800 crack and 2700 normal pavement surface images. The pavement surface images first are decomposed into several sub-images using a discrete wavelet transform (DWT) decomposition. We then calculate the wavelet sub-band histogram from each several sub-images at each level. The support vector machine (SVM) with computed wavelet sub-band histogram is employed for distinguishing between a crack and normal pavement surface images. The accuracies of the proposed classification method are 85.3% for crack and 84.4% for normal pavement images. The proposed classification method achieved high performance. Therefore, the proposed method would be useful in maintenance inspection.

Newmark-Beta-FDTD method for super-resolution analysis of time reversal waves

NASA Astrophysics Data System (ADS)

Shi, Sheng-Bing; Shao, Wei; Ma, Jing; Jin, Congjun; Wang, Xiao-Hua

2017-09-01

In this work, a new unconditionally stable finite-difference time-domain (FDTD) method with the split-field perfectly matched layer (PML) is proposed for the analysis of time reversal (TR) waves. The proposed method is very suitable for multiscale problems involving microstructures. The spatial and temporal derivatives in this method are discretized by the central difference technique and Newmark-Beta algorithm, respectively, and the derivation results in the calculation of a banded-sparse matrix equation. Since the coefficient matrix keeps unchanged during the whole simulation process, the lower-upper (LU) decomposition of the matrix needs to be performed only once at the beginning of the calculation. Moreover, the reverse Cuthill-Mckee (RCM) technique, an effective preprocessing technique in bandwidth compression of sparse matrices, is used to improve computational efficiency. The super-resolution focusing of TR wave propagation in two- and three-dimensional spaces is included to validate the accuracy and efficiency of the proposed method.

Economic evaluation: Concepts, selected studies, system costs, and a proposed program

NASA Technical Reports Server (NTRS)

Osterhoudt, F. H. (Principal Investigator)

1979-01-01

The more usual approaches to valuing crop information are reviewed and an integrated approach is recommended. Problems associated with implementation are examined. What has already been accomplished in the economic evaluation of LACIE-type information is reported including various studies of benefits. The costs of the existing and proposed systems are considered. A method and approach is proposed for further studies.

Key Technology of Real-Time Road Navigation Method Based on Intelligent Data Research

PubMed Central

Tang, Haijing; Liang, Yu; Huang, Zhongnan; Wang, Taoyi; He, Lin; Du, Yicong; Ding, Gangyi

2016-01-01

The effect of traffic flow prediction plays an important role in routing selection. Traditional traffic flow forecasting methods mainly include linear, nonlinear, neural network, and Time Series Analysis method. However, all of them have some shortcomings. This paper analyzes the existing algorithms on traffic flow prediction and characteristics of city traffic flow and proposes a road traffic flow prediction method based on transfer probability. This method first analyzes the transfer probability of upstream of the target road and then makes the prediction of the traffic flow at the next time by using the traffic flow equation. Newton Interior-Point Method is used to obtain the optimal value of parameters. Finally, it uses the proposed model to predict the traffic flow at the next time. By comparing the existing prediction methods, the proposed model has proven to have good performance. It can fast get the optimal value of parameters faster and has higher prediction accuracy, which can be used to make real-time traffic flow prediction. PMID:27872637

Contact-free heart rate measurement using multiple video data

NASA Astrophysics Data System (ADS)

Hung, Pang-Chan; Lee, Kual-Zheng; Tsai, Luo-Wei

2013-10-01

In this paper, we propose a contact-free heart rate measurement method by analyzing sequential images of multiple video data. In the proposed method, skin-like pixels are firstly detected from multiple video data for extracting the color features. These color features are synchronized and analyzed by independent component analysis. A representative component is finally selected among these independent component candidates to measure the HR, which achieves under 2% deviation on average compared with a pulse oximeter in the controllable environment. The advantages of the proposed method include: 1) it uses low cost and high accessibility camera device; 2) it eases users' discomfort by utilizing contact-free measurement; and 3) it achieves the low error rate and the high stability by integrating multiple video data.

Probing-error compensation using 5 degree of freedom force/moment sensor for coordinate measuring machine

NASA Astrophysics Data System (ADS)

Lee, Minho; Cho, Nahm-Gyoo

2013-09-01

A new probing and compensation method is proposed to improve the three-dimensional (3D) measuring accuracy of 3D shapes, including irregular surfaces. A new tactile coordinate measuring machine (CMM) probe with a five-degree of freedom (5-DOF) force/moment sensor using carbon fiber plates was developed. The proposed method efficiently removes the anisotropic sensitivity error and decreases the stylus deformation and the actual contact point estimation errors that are major error components of shape measurement using touch probes. The relationship between the measuring force and estimation accuracy of the actual contact point error and stylus deformation error are examined for practical use of the proposed method. The appropriate measuring force condition is presented for the precision measurement.

78 FR 20695 - Walk-Through Metal Detectors and Hand-Held Metal Detectors Test Method Validation

Federal Register 2010, 2011, 2012, 2013, 2014

2013-04-05

... Detectors and Hand-Held Metal Detectors Test Method Validation AGENCY: National Institute of Justice, DOJ... ensure that the test methods in the standards are properly documented, NIJ is requesting proposals (including price quotes) for test method validation efforts from testing laboratories. NIJ is also seeking...

An enhancement of binary particle swarm optimization for gene selection in classifying cancer classes

PubMed Central

2013-01-01

Background Gene expression data could likely be a momentous help in the progress of proficient cancer diagnoses and classification platforms. Lately, many researchers analyze gene expression data using diverse computational intelligence methods, for selecting a small subset of informative genes from the data for cancer classification. Many computational methods face difficulties in selecting small subsets due to the small number of samples compared to the huge number of genes (high-dimension), irrelevant genes, and noisy genes. Methods We propose an enhanced binary particle swarm optimization to perform the selection of small subsets of informative genes which is significant for cancer classification. Particle speed, rule, and modified sigmoid function are introduced in this proposed method to increase the probability of the bits in a particle’s position to be zero. The method was empirically applied to a suite of ten well-known benchmark gene expression data sets. Results The performance of the proposed method proved to be superior to other previous related works, including the conventional version of binary particle swarm optimization (BPSO) in terms of classification accuracy and the number of selected genes. The proposed method also requires lower computational time compared to BPSO. PMID:23617960

A Rapid Coordinate Transformation Method Applied in Industrial Robot Calibration Based on Characteristic Line Coincidence.

PubMed

Liu, Bailing; Zhang, Fumin; Qu, Xinghua; Shi, Xiaojia

2016-02-18

Coordinate transformation plays an indispensable role in industrial measurements, including photogrammetry, geodesy, laser 3-D measurement and robotics. The widely applied methods of coordinate transformation are generally based on solving the equations of point clouds. Despite the high accuracy, this might result in no solution due to the use of ill conditioned matrices. In this paper, a novel coordinate transformation method is proposed, not based on the equation solution but based on the geometric transformation. We construct characteristic lines to represent the coordinate systems. According to the space geometry relation, the characteristic line scan is made to coincide by a series of rotations and translations. The transformation matrix can be obtained using matrix transformation theory. Experiments are designed to compare the proposed method with other methods. The results show that the proposed method has the same high accuracy, but the operation is more convenient and flexible. A multi-sensor combined measurement system is also presented to improve the position accuracy of a robot with the calibration of the robot kinematic parameters. Experimental verification shows that the position accuracy of robot manipulator is improved by 45.8% with the proposed method and robot calibration.

A Rapid Coordinate Transformation Method Applied in Industrial Robot Calibration Based on Characteristic Line Coincidence

PubMed Central

Liu, Bailing; Zhang, Fumin; Qu, Xinghua; Shi, Xiaojia

2016-01-01

Coordinate transformation plays an indispensable role in industrial measurements, including photogrammetry, geodesy, laser 3-D measurement and robotics. The widely applied methods of coordinate transformation are generally based on solving the equations of point clouds. Despite the high accuracy, this might result in no solution due to the use of ill conditioned matrices. In this paper, a novel coordinate transformation method is proposed, not based on the equation solution but based on the geometric transformation. We construct characteristic lines to represent the coordinate systems. According to the space geometry relation, the characteristic line scan is made to coincide by a series of rotations and translations. The transformation matrix can be obtained using matrix transformation theory. Experiments are designed to compare the proposed method with other methods. The results show that the proposed method has the same high accuracy, but the operation is more convenient and flexible. A multi-sensor combined measurement system is also presented to improve the position accuracy of a robot with the calibration of the robot kinematic parameters. Experimental verification shows that the position accuracy of robot manipulator is improved by 45.8% with the proposed method and robot calibration. PMID:26901203

A new method to identify the foot of continental slope based on an integrated profile analysis

NASA Astrophysics Data System (ADS)

Wu, Ziyin; Li, Jiabiao; Li, Shoujun; Shang, Jihong; Jin, Xiaobin

2017-06-01

A new method is proposed to identify automatically the foot of the continental slope (FOS) based on the integrated analysis of topographic profiles. Based on the extremum points of the second derivative and the Douglas-Peucker algorithm, it simplifies the topographic profiles, then calculates the second derivative of the original profiles and the D-P profiles. Seven steps are proposed to simplify the original profiles. Meanwhile, multiple identification methods are proposed to determine the FOS points, including gradient, water depth and second derivative values of data points, as well as the concave and convex, continuity and segmentation of the topographic profiles. This method can comprehensively and intelligently analyze the topographic profiles and their derived slopes, second derivatives and D-P profiles, based on which, it is capable to analyze the essential properties of every single data point in the profile. Furthermore, it is proposed to remove the concave points of the curve and in addition, to implement six FOS judgment criteria.

An Improved Distributed Secondary Control Method for DC Microgrids With Enhanced Dynamic Current Sharing Performance

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

Wang, Panbao; Lu, Xiaonan; Yang, Xu

This paper proposes an improved distributed secondary control scheme for dc microgrids (MGs), aiming at overcoming the drawbacks of conventional droop control method. The proposed secondary control scheme can remove the dc voltage deviation and improve the current sharing accuracy by using voltage-shifting and slope-adjusting approaches simultaneously. Meanwhile, the average value of droop coefficients is calculated, and then it is controlled by an additional controller included in the distributed secondary control layer to ensure that each droop coefficient converges at a reasonable value. Hence, by adjusting the droop coefficient, each participating converter has equal output impedance, and the accurate proportionalmore » load current sharing can be achieved with different line resistances. Furthermore, the current sharing performance in steady and transient states can be enhanced by using the proposed method. The effectiveness of the proposed method is verified by detailed experimental tests based on a 3 × 1 kW prototype with three interface converters.« less

Improved approach for electric vehicle rapid charging station placement and sizing using Google maps and binary lightning search algorithm

PubMed Central

Shareef, Hussain; Mohamed, Azah

2017-01-01

The electric vehicle (EV) is considered a premium solution to global warming and various types of pollution. Nonetheless, a key concern is the recharging of EV batteries. Therefore, this study proposes a novel approach that considers the costs of transportation loss, buildup, and substation energy loss and that incorporates harmonic power loss into optimal rapid charging station (RCS) planning. A novel optimization technique, called binary lightning search algorithm (BLSA), is proposed to solve the optimization problem. BLSA is also applied to a conventional RCS planning method. A comprehensive analysis is conducted to assess the performance of the two RCS planning methods by using the IEEE 34-bus test system as the power grid. The comparative studies show that the proposed BLSA is better than other optimization techniques. The daily total cost in RCS planning of the proposed method, including harmonic power loss, decreases by 10% compared with that of the conventional method. PMID:29220396

Improved approach for electric vehicle rapid charging station placement and sizing using Google maps and binary lightning search algorithm.

PubMed

Islam, Md Mainul; Shareef, Hussain; Mohamed, Azah

2017-01-01

The electric vehicle (EV) is considered a premium solution to global warming and various types of pollution. Nonetheless, a key concern is the recharging of EV batteries. Therefore, this study proposes a novel approach that considers the costs of transportation loss, buildup, and substation energy loss and that incorporates harmonic power loss into optimal rapid charging station (RCS) planning. A novel optimization technique, called binary lightning search algorithm (BLSA), is proposed to solve the optimization problem. BLSA is also applied to a conventional RCS planning method. A comprehensive analysis is conducted to assess the performance of the two RCS planning methods by using the IEEE 34-bus test system as the power grid. The comparative studies show that the proposed BLSA is better than other optimization techniques. The daily total cost in RCS planning of the proposed method, including harmonic power loss, decreases by 10% compared with that of the conventional method.

Optimization of Gear Ratio in the Tidal Current Generation System based on Generated Energy

NASA Astrophysics Data System (ADS)

Naoi, Kazuhisa; Shiono, Mitsuhiro; Suzuki, Katsuyuki

It is possible to predict generating power of the tidal current generation, because of the tidal current's periodicity. Tidal current generation is more advantageous than other renewable energy sources, when the tidal current generation system is connected to the power system and operated. In this paper, we propose a method used to optimize the gear ratio and generator capacity, that is fundamental design items in the tidal current generation system which is composed of Darrieus type water turbine and squirrel-cage induction generator coupled with gear. The proposed method is applied to the tidal current generation system including the most large-sized turbine that we have developed and studied. This paper shows optimum gear ratio and generator capacity that make generated energy maximum, and verify effectiveness of the proposed method. The paper also proposes a method of selecting maximum generating current velocity in order to reduce the generator capacity, from the viewpoint of economics.

A new approach for reducing beam hardening artifacts in polychromatic X-ray computed tomography using more accurate prior image.

PubMed

Wang, Hui; Xu, Yanan; Shi, Hongli

2018-03-15

Metal artifacts severely degrade CT image quality in clinical diagnosis, which are difficult to removed, especially for the beam hardening artifacts. The metal artifact reduction (MAR) based on prior images are the most frequently-used methods. However, there exists a lot misclassification in most prior images caused by absence of prior information such as spectrum distribution of X-ray beam source, especially when multiple or big metal are included. This work aims is to identify a more accurate prior image to improve image quality. The proposed method includes four steps. First, the metal image is segmented by thresholding an initial image, where the metal traces are identified in the initial projection data using the forward projection of the metal image. Second, the accurate absorbent model of certain metal image is calculated according to the spectrum distribution of certain X-ray beam source and energy-dependent attenuation coefficients of metal. Third, a new metal image is reconstructed by the general analytical reconstruction algorithm such as filtered back projection (FPB). The prior image is obtained by segmenting the difference image between the initial image and the new metal image into air, tissue and bone. Fourth, the initial projection data are normalized by dividing the projection data of prior image pixel to pixel. The final corrected image is obtained by interpolation, denormalization and reconstruction. Several clinical images with dental fillings and knee prostheses were used to evaluate the proposed algorithm and normalized metal artifact reduction (NMAR) and linear interpolation (LI) method. The results demonstrate the artifacts were reduced efficiently by the proposed method. The proposed method could obtain an exact prior image using the prior information about X-ray beam source and energy-dependent attenuation coefficients of metal. As a result, better performance of reducing beam hardening artifacts can be achieved. Moreover, the process of the proposed method is rather simple and little extra calculation burden is necessary. It has superiorities over other algorithms when include multiple and/or big implants.

A Multidimensional Analysis Tool for Visualizing Online Interactions

ERIC Educational Resources Information Center

Kim, Minjeong; Lee, Eunchul

2012-01-01

This study proposes and verifies the performance of an analysis tool for visualizing online interactions. A review of the most widely used methods for analyzing online interactions, including quantitative analysis, content analysis, and social network analysis methods, indicates these analysis methods have some limitations resulting from their…

Improving protein-protein interactions prediction accuracy using protein evolutionary information and relevance vector machine model.

PubMed

An, Ji-Yong; Meng, Fan-Rong; You, Zhu-Hong; Chen, Xing; Yan, Gui-Ying; Hu, Ji-Pu

2016-10-01

Predicting protein-protein interactions (PPIs) is a challenging task and essential to construct the protein interaction networks, which is important for facilitating our understanding of the mechanisms of biological systems. Although a number of high-throughput technologies have been proposed to predict PPIs, there are unavoidable shortcomings, including high cost, time intensity, and inherently high false positive rates. For these reasons, many computational methods have been proposed for predicting PPIs. However, the problem is still far from being solved. In this article, we propose a novel computational method called RVM-BiGP that combines the relevance vector machine (RVM) model and Bi-gram Probabilities (BiGP) for PPIs detection from protein sequences. The major improvement includes (1) Protein sequences are represented using the Bi-gram probabilities (BiGP) feature representation on a Position Specific Scoring Matrix (PSSM), in which the protein evolutionary information is contained; (2) For reducing the influence of noise, the Principal Component Analysis (PCA) method is used to reduce the dimension of BiGP vector; (3) The powerful and robust Relevance Vector Machine (RVM) algorithm is used for classification. Five-fold cross-validation experiments executed on yeast and Helicobacter pylori datasets, which achieved very high accuracies of 94.57 and 90.57%, respectively. Experimental results are significantly better than previous methods. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on the yeast dataset. The experimental results demonstrate that our RVM-BiGP method is significantly better than the SVM-based method. In addition, we achieved 97.15% accuracy on imbalance yeast dataset, which is higher than that of balance yeast dataset. The promising experimental results show the efficiency and robust of the proposed method, which can be an automatic decision support tool for future proteomics research. For facilitating extensive studies for future proteomics research, we developed a freely available web server called RVM-BiGP-PPIs in Hypertext Preprocessor (PHP) for predicting PPIs. The web server including source code and the datasets are available at http://219.219.62.123:8888/BiGP/. © 2016 The Authors Protein Science published by Wiley Periodicals, Inc. on behalf of The Protein Society.

A novel hybrid meta-heuristic technique applied to the well-known benchmark optimization problems

NASA Astrophysics Data System (ADS)

Abtahi, Amir-Reza; Bijari, Afsane

2017-03-01

In this paper, a hybrid meta-heuristic algorithm, based on imperialistic competition algorithm (ICA), harmony search (HS), and simulated annealing (SA) is presented. The body of the proposed hybrid algorithm is based on ICA. The proposed hybrid algorithm inherits the advantages of the process of harmony creation in HS algorithm to improve the exploitation phase of the ICA algorithm. In addition, the proposed hybrid algorithm uses SA to make a balance between exploration and exploitation phases. The proposed hybrid algorithm is compared with several meta-heuristic methods, including genetic algorithm (GA), HS, and ICA on several well-known benchmark instances. The comprehensive experiments and statistical analysis on standard benchmark functions certify the superiority of the proposed method over the other algorithms. The efficacy of the proposed hybrid algorithm is promising and can be used in several real-life engineering and management problems.

A Novel Method to Identify Differential Pathways in Hippocampus Alzheimer's Disease.

PubMed

Liu, Chun-Han; Liu, Lian

2017-05-08

BACKGROUND Alzheimer's disease (AD) is the most common type of dementia. The objective of this paper is to propose a novel method to identify differential pathways in hippocampus AD. MATERIAL AND METHODS We proposed a combined method by merging existed methods. Firstly, pathways were identified by four known methods (DAVID, the neaGUI package, the pathway-based co-expressed method, and the pathway network approach), and differential pathways were evaluated through setting weight thresholds. Subsequently, we combined all pathways by a rank-based algorithm and called the method the combined method. Finally, common differential pathways across two or more of five methods were selected. RESULTS Pathways obtained from different methods were also different. The combined method obtained 1639 pathways and 596 differential pathways, which included all pathways gained from the four existing methods; hence, the novel method solved the problem of inconsistent results. Besides, a total of 13 common pathways were identified, such as metabolism, immune system, and cell cycle. CONCLUSIONS We have proposed a novel method by combining four existing methods based on a rank product algorithm, and identified 13 significant differential pathways based on it. These differential pathways might provide insight into treatment and diagnosis of hippocampus AD.

Recovering of images degraded by atmosphere

NASA Astrophysics Data System (ADS)

Lin, Guang; Feng, Huajun; Xu, Zhihai; Li, Qi; Chen, Yueting

2017-08-01

Remote sensing images are seriously degraded by multiple scattering and bad weather. Through the analysis of the radiative transfer procedure in atmosphere, an image atmospheric degradation model considering the influence of atmospheric absorption multiple scattering and non-uniform distribution is proposed in this paper. Based on the proposed model, a novel recovering method is presented to eliminate atmospheric degradation. Mean-shift image segmentation and block-wise deconvolution are used to reduce time cost, retaining a good result. The recovering results indicate that the proposed method can significantly remove atmospheric degradation and effectively improve contrast compared with other removal methods. The results also illustrate that our method is suitable for various degraded remote sensing, including images with large field of view (FOV), images taken in side-glance situations, image degraded by atmospheric non-uniform distribution and images with various forms of clouds.

Adaptive control of a jet turboshaft engine driving a variable pitch propeller using multiple models

NASA Astrophysics Data System (ADS)

Ahmadian, Narjes; Khosravi, Alireza; Sarhadi, Pouria

2017-08-01

In this paper, a multiple model adaptive control (MMAC) method is proposed for a gas turbine engine. The model of a twin spool turbo-shaft engine driving a variable pitch propeller includes various operating points. Variations in fuel flow and propeller pitch inputs produce different operating conditions which force the controller to be adopted rapidly. Important operating points are three idle, cruise and full thrust cases for the entire flight envelope. A multi-input multi-output (MIMO) version of second level adaptation using multiple models is developed. Also, stability analysis using Lyapunov method is presented. The proposed method is compared with two conventional first level adaptation and model reference adaptive control techniques. Simulation results for JetCat SPT5 turbo-shaft engine demonstrate the performance and fidelity of the proposed method.

An Aggregated Method for Determining Railway Defects and Obstacle Parameters

NASA Astrophysics Data System (ADS)

Loktev, Daniil; Loktev, Alexey; Stepanov, Roman; Pevzner, Viktor; Alenov, Kanat

2018-03-01

The method of combining algorithms of image blur analysis and stereo vision to determine the distance to objects (including external defects of railway tracks) and the speed of moving objects-obstacles is proposed. To estimate the deviation of the distance depending on the blur a statistical approach, logarithmic, exponential and linear standard functions are used. The statistical approach includes a method of estimating least squares and the method of least modules. The accuracy of determining the distance to the object, its speed and direction of movement is obtained. The paper develops a method of determining distances to objects by analyzing a series of images and assessment of depth using defocusing using its aggregation with stereoscopic vision. This method is based on a physical effect of dependence on the determined distance to the object on the obtained image from the focal length or aperture of the lens. In the calculation of the blur spot diameter it is assumed that blur occurs at the point equally in all directions. According to the proposed approach, it is possible to determine the distance to the studied object and its blur by analyzing a series of images obtained using the video detector with different settings. The article proposes and scientifically substantiates new and improved existing methods for detecting the parameters of static and moving objects of control, and also compares the results of the use of various methods and the results of experiments. It is shown that the aggregate method gives the best approximation to the real distances.

Segmentation of the pectoral muscle in breast MR images using structure tensor and deformable model

NASA Astrophysics Data System (ADS)

Lee, Myungeun; Kim, Jong Hyo

2012-02-01

Recently, breast MR images have been used in wider clinical area including diagnosis, treatment planning, and treatment response evaluation, which requests quantitative analysis and breast tissue segmentation. Although several methods have been proposed for segmenting MR images, segmenting out breast tissues robustly from surrounding structures in a wide range of anatomical diversity still remains challenging. Therefore, in this paper, we propose a practical and general-purpose approach for segmenting the pectoral muscle boundary based on the structure tensor and deformable model. The segmentation work flow comprises four key steps: preprocessing, detection of the region of interest (ROI) within the breast region, segmenting the pectoral muscle and finally extracting and refining the pectoral muscle boundary. From experimental results we show that the proposed method can segment the pectoral muscle robustly in diverse patient cases. In addition, the proposed method will allow the application of the quantification research for various breast images.

Advanced scatter search approach and its application in a sequencing problem of mixed-model assembly lines in a case company

NASA Astrophysics Data System (ADS)

Liu, Qiong; Wang, Wen-xi; Zhu, Ke-ren; Zhang, Chao-yong; Rao, Yun-qing

2014-11-01

Mixed-model assembly line sequencing is significant in reducing the production time and overall cost of production. To improve production efficiency, a mathematical model aiming simultaneously to minimize overtime, idle time and total set-up costs is developed. To obtain high-quality and stable solutions, an advanced scatter search approach is proposed. In the proposed algorithm, a new diversification generation method based on a genetic algorithm is presented to generate a set of potentially diverse and high-quality initial solutions. Many methods, including reference set update, subset generation, solution combination and improvement methods, are designed to maintain the diversification of populations and to obtain high-quality ideal solutions. The proposed model and algorithm are applied and validated in a case company. The results indicate that the proposed advanced scatter search approach is significant for mixed-model assembly line sequencing in this company.

Automatic construction of subject-specific human airway geometry including trifurcations based on a CT-segmented airway skeleton and surface

PubMed Central

Miyawaki, Shinjiro; Tawhai, Merryn H.; Hoffman, Eric A.; Wenzel, Sally E.; Lin, Ching-Long

2016-01-01

We propose a method to construct three-dimensional airway geometric models based on airway skeletons, or centerlines (CLs). Given a CT-segmented airway skeleton and surface, the proposed CL-based method automatically constructs subject-specific models that contain anatomical information regarding branches, include bifurcations and trifurcations, and extend from the trachea to terminal bronchioles. The resulting model can be anatomically realistic with the assistance of an image-based surface; alternatively a model with an idealized skeleton and/or branch diameters is also possible. This method systematically identifies and classifies trifurcations to successfully construct the models, which also provides the number and type of trifurcations for the analysis of the airways from an anatomical point of view. We applied this method to 16 normal and 16 severe asthmatic subjects using their computed tomography images. The average distance between the surface of the model and the image-based surface was 11% of the average voxel size of the image. The four most frequent locations of trifurcations were the left upper division bronchus, left lower lobar bronchus, right upper lobar bronchus, and right intermediate bronchus. The proposed method automatically constructed accurate subject-specific three-dimensional airway geometric models that contain anatomical information regarding branches using airway skeleton, diameters, and image-based surface geometry. The proposed method can construct (i) geometry automatically for population-based studies, (ii) trifurcations to retain the original airway topology, (iii) geometry that can be used for automatic generation of computational fluid dynamics meshes, and (iv) geometry based only on a skeleton and diameters for idealized branches. PMID:27704229

Characterization of coronary plaque regions in intravascular ultrasound images using a hybrid ensemble classifier.

PubMed

Hwang, Yoo Na; Lee, Ju Hwan; Kim, Ga Young; Shin, Eun Seok; Kim, Sung Min

2018-01-01

The purpose of this study was to propose a hybrid ensemble classifier to characterize coronary plaque regions in intravascular ultrasound (IVUS) images. Pixels were allocated to one of four tissues (fibrous tissue (FT), fibro-fatty tissue (FFT), necrotic core (NC), and dense calcium (DC)) through processes of border segmentation, feature extraction, feature selection, and classification. Grayscale IVUS images and their corresponding virtual histology images were acquired from 11 patients with known or suspected coronary artery disease using 20 MHz catheter. A total of 102 hybrid textural features including first order statistics (FOS), gray level co-occurrence matrix (GLCM), extended gray level run-length matrix (GLRLM), Laws, local binary pattern (LBP), intensity, and discrete wavelet features (DWF) were extracted from IVUS images. To select optimal feature sets, genetic algorithm was implemented. A hybrid ensemble classifier based on histogram and texture information was then used for plaque characterization in this study. The optimal feature set was used as input of this ensemble classifier. After tissue characterization, parameters including sensitivity, specificity, and accuracy were calculated to validate the proposed approach. A ten-fold cross validation approach was used to determine the statistical significance of the proposed method. Our experimental results showed that the proposed method had reliable performance for tissue characterization in IVUS images. The hybrid ensemble classification method outperformed other existing methods by achieving characterization accuracy of 81% for FFT and 75% for NC. In addition, this study showed that Laws features (SSV and SAV) were key indicators for coronary tissue characterization. The proposed method had high clinical applicability for image-based tissue characterization. Copyright © 2017 Elsevier B.V. All rights reserved.

Discrimination of Oil Slicks and Lookalikes in Polarimetric SAR Images Using CNN.

PubMed

Guo, Hao; Wu, Danni; An, Jubai

2017-08-09

Oil slicks and lookalikes (e.g., plant oil and oil emulsion) all appear as dark areas in polarimetric Synthetic Aperture Radar (SAR) images and are highly heterogeneous, so it is very difficult to use a single feature that can allow classification of dark objects in polarimetric SAR images as oil slicks or lookalikes. We established multi-feature fusion to support the discrimination of oil slicks and lookalikes. In the paper, simple discrimination analysis is used to rationalize a preferred features subset. The features analyzed include entropy, alpha, and Single-bounce Eigenvalue Relative Difference (SERD) in the C-band polarimetric mode. We also propose a novel SAR image discrimination method for oil slicks and lookalikes based on Convolutional Neural Network (CNN). The regions of interest are selected as the training and testing samples for CNN on the three kinds of polarimetric feature images. The proposed method is applied to a training data set of 5400 samples, including 1800 crude oil, 1800 plant oil, and 1800 oil emulsion samples. In the end, the effectiveness of the method is demonstrated through the analysis of some experimental results. The classification accuracy obtained using 900 samples of test data is 91.33%. It is here observed that the proposed method not only can accurately identify the dark spots on SAR images but also verify the ability of the proposed algorithm to classify unstructured features.

Discrimination of Oil Slicks and Lookalikes in Polarimetric SAR Images Using CNN

PubMed Central

An, Jubai

2017-01-01

Oil slicks and lookalikes (e.g., plant oil and oil emulsion) all appear as dark areas in polarimetric Synthetic Aperture Radar (SAR) images and are highly heterogeneous, so it is very difficult to use a single feature that can allow classification of dark objects in polarimetric SAR images as oil slicks or lookalikes. We established multi-feature fusion to support the discrimination of oil slicks and lookalikes. In the paper, simple discrimination analysis is used to rationalize a preferred features subset. The features analyzed include entropy, alpha, and Single-bounce Eigenvalue Relative Difference (SERD) in the C-band polarimetric mode. We also propose a novel SAR image discrimination method for oil slicks and lookalikes based on Convolutional Neural Network (CNN). The regions of interest are selected as the training and testing samples for CNN on the three kinds of polarimetric feature images. The proposed method is applied to a training data set of 5400 samples, including 1800 crude oil, 1800 plant oil, and 1800 oil emulsion samples. In the end, the effectiveness of the method is demonstrated through the analysis of some experimental results. The classification accuracy obtained using 900 samples of test data is 91.33%. It is here observed that the proposed method not only can accurately identify the dark spots on SAR images but also verify the ability of the proposed algorithm to classify unstructured features. PMID:28792477

NREL, International Colleagues Propose Strategy for Stable, Commercial

Science.gov Websites

, Commercial Perovskite Solar Cells News Release: NREL, International Colleagues Propose Strategy for Stable , Commercial Perovskite Solar Cells October 17, 2016 Photo of two men in a lab. NREL Scientists Keith Emery and stable commercial PSCs-that includes the following: Developing a reproducible manufacturing method that

48 CFR 15.609 - Limited use of data.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 48 Federal Acquisition Regulations System 1 2010-10-01 2010-10-01 false Limited use of data. 15... METHODS AND CONTRACT TYPES CONTRACTING BY NEGOTIATION Unsolicited Proposals 15.609 Limited use of data. (a) An unsolicited proposal may include data that the offeror does not want disclosed to the public for...

Malware analysis using visualized image matrices.

PubMed

Han, KyoungSoo; Kang, BooJoong; Im, Eul Gyu

2014-01-01

This paper proposes a novel malware visual analysis method that contains not only a visualization method to convert binary files into images, but also a similarity calculation method between these images. The proposed method generates RGB-colored pixels on image matrices using the opcode sequences extracted from malware samples and calculates the similarities for the image matrices. Particularly, our proposed methods are available for packed malware samples by applying them to the execution traces extracted through dynamic analysis. When the images are generated, we can reduce the overheads by extracting the opcode sequences only from the blocks that include the instructions related to staple behaviors such as functions and application programming interface (API) calls. In addition, we propose a technique that generates a representative image for each malware family in order to reduce the number of comparisons for the classification of unknown samples and the colored pixel information in the image matrices is used to calculate the similarities between the images. Our experimental results show that the image matrices of malware can effectively be used to classify malware families both statically and dynamically with accuracy of 0.9896 and 0.9732, respectively.

Multiscale Medical Image Fusion in Wavelet Domain

PubMed Central

Khare, Ashish

2013-01-01

Wavelet transforms have emerged as a powerful tool in image fusion. However, the study and analysis of medical image fusion is still a challenging area of research. Therefore, in this paper, we propose a multiscale fusion of multimodal medical images in wavelet domain. Fusion of medical images has been performed at multiple scales varying from minimum to maximum level using maximum selection rule which provides more flexibility and choice to select the relevant fused images. The experimental analysis of the proposed method has been performed with several sets of medical images. Fusion results have been evaluated subjectively and objectively with existing state-of-the-art fusion methods which include several pyramid- and wavelet-transform-based fusion methods and principal component analysis (PCA) fusion method. The comparative analysis of the fusion results has been performed with edge strength (Q), mutual information (MI), entropy (E), standard deviation (SD), blind structural similarity index metric (BSSIM), spatial frequency (SF), and average gradient (AG) metrics. The combined subjective and objective evaluations of the proposed fusion method at multiple scales showed the effectiveness and goodness of the proposed approach. PMID:24453868

Iterative deep convolutional encoder-decoder network for medical image segmentation.

PubMed

Jung Uk Kim; Hak Gu Kim; Yong Man Ro

2017-07-01

In this paper, we propose a novel medical image segmentation using iterative deep learning framework. We have combined an iterative learning approach and an encoder-decoder network to improve segmentation results, which enables to precisely localize the regions of interest (ROIs) including complex shapes or detailed textures of medical images in an iterative manner. The proposed iterative deep convolutional encoder-decoder network consists of two main paths: convolutional encoder path and convolutional decoder path with iterative learning. Experimental results show that the proposed iterative deep learning framework is able to yield excellent medical image segmentation performances for various medical images. The effectiveness of the proposed method has been proved by comparing with other state-of-the-art medical image segmentation methods.

Multi-frame knowledge based text enhancement for mobile phone captured videos

NASA Astrophysics Data System (ADS)

Ozarslan, Suleyman; Eren, P. Erhan

2014-02-01

In this study, we explore automated text recognition and enhancement using mobile phone captured videos of store receipts. We propose a method which includes Optical Character Resolution (OCR) enhanced by our proposed Row Based Multiple Frame Integration (RB-MFI), and Knowledge Based Correction (KBC) algorithms. In this method, first, the trained OCR engine is used for recognition; then, the RB-MFI algorithm is applied to the output of the OCR. The RB-MFI algorithm determines and combines the most accurate rows of the text outputs extracted by using OCR from multiple frames of the video. After RB-MFI, KBC algorithm is applied to these rows to correct erroneous characters. Results of the experiments show that the proposed video-based approach which includes the RB-MFI and the KBC algorithm increases the word character recognition rate to 95%, and the character recognition rate to 98%.

Intelligent fault recognition strategy based on adaptive optimized multiple centers

NASA Astrophysics Data System (ADS)

Zheng, Bo; Li, Yan-Feng; Huang, Hong-Zhong

2018-06-01

For the recognition principle based optimized single center, one important issue is that the data with nonlinear separatrix cannot be recognized accurately. In order to solve this problem, a novel recognition strategy based on adaptive optimized multiple centers is proposed in this paper. This strategy recognizes the data sets with nonlinear separatrix by the multiple centers. Meanwhile, the priority levels are introduced into the multi-objective optimization, including recognition accuracy, the quantity of optimized centers, and distance relationship. According to the characteristics of various data, the priority levels are adjusted to ensure the quantity of optimized centers adaptively and to keep the original accuracy. The proposed method is compared with other methods, including support vector machine (SVM), neural network, and Bayesian classifier. The results demonstrate that the proposed strategy has the same or even better recognition ability on different distribution characteristics of data.

Transformations based on continuous piecewise-affine velocity fields

DOE PAGES

Freifeld, Oren; Hauberg, Soren; Batmanghelich, Kayhan; ...

2017-01-11

Here, we propose novel finite-dimensional spaces of well-behaved Rn → Rn transformations. The latter are obtained by (fast and highly-accurate) integration of continuous piecewise-affine velocity fields. The proposed method is simple yet highly expressive, effortlessly handles optional constraints (e.g., volume preservation and/or boundary conditions), and supports convenient modeling choices such as smoothing priors and coarse-to-fine analysis. Importantly, the proposed approach, partly due to its rapid likelihood evaluations and partly due to its other properties, facilitates tractable inference over rich transformation spaces, including using Markov-Chain Monte-Carlo methods. Its applications include, but are not limited to: monotonic regression (more generally, optimization overmore » monotonic functions); modeling cumulative distribution functions or histograms; time-warping; image warping; image registration; real-time diffeomorphic image editing; data augmentation for image classifiers. Our GPU-based code is publicly available.« less

Transformations Based on Continuous Piecewise-Affine Velocity Fields

PubMed Central

Freifeld, Oren; Hauberg, Søren; Batmanghelich, Kayhan; Fisher, Jonn W.

2018-01-01

We propose novel finite-dimensional spaces of well-behaved ℝn → ℝn transformations. The latter are obtained by (fast and highly-accurate) integration of continuous piecewise-affine velocity fields. The proposed method is simple yet highly expressive, effortlessly handles optional constraints (e.g., volume preservation and/or boundary conditions), and supports convenient modeling choices such as smoothing priors and coarse-to-fine analysis. Importantly, the proposed approach, partly due to its rapid likelihood evaluations and partly due to its other properties, facilitates tractable inference over rich transformation spaces, including using Markov-Chain Monte-Carlo methods. Its applications include, but are not limited to: monotonic regression (more generally, optimization over monotonic functions); modeling cumulative distribution functions or histograms; time-warping; image warping; image registration; real-time diffeomorphic image editing; data augmentation for image classifiers. Our GPU-based code is publicly available. PMID:28092517

Multilevel filtering elliptic preconditioners

NASA Technical Reports Server (NTRS)

Kuo, C. C. Jay; Chan, Tony F.; Tong, Charles

1989-01-01

A class of preconditioners is presented for elliptic problems built on ideas borrowed from the digital filtering theory and implemented on a multilevel grid structure. They are designed to be both rapidly convergent and highly parallelizable. The digital filtering viewpoint allows the use of filter design techniques for constructing elliptic preconditioners and also provides an alternative framework for understanding several other recently proposed multilevel preconditioners. Numerical results are presented to assess the convergence behavior of the new methods and to compare them with other preconditioners of multilevel type, including the usual multigrid method as preconditioner, the hierarchical basis method and a recent method proposed by Bramble-Pasciak-Xu.

Limitations of the method of complex basis functions

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

Baumel, R.T.; Crocker, M.C.; Nuttall, J.

1975-08-01

The method of complex basis functions proposed by Rescigno and Reinhardt is applied to the calculation of the amplitude in a model problem which can be treated analytically. It is found for an important class of potentials, including some of infinite range and also the square well, that the method does not provide a converging sequence of approximations. However, in some cases, approximations of relatively low order might be close to the correct result. The method is also applied to S-wave e-H elastic scattering above the ionization threshold, and spurious ''convergence'' to the wrong result is found. A procedure whichmore » might overcome the difficulties of the method is proposed.« less

An alternative method for centrifugal compressor loading factor modelling

NASA Astrophysics Data System (ADS)

Galerkin, Y.; Drozdov, A.; Rekstin, A.; Soldatova, K.

2017-08-01

The loading factor at design point is calculated by one or other empirical formula in classical design methods. Performance modelling as a whole is out of consideration. Test data of compressor stages demonstrates that loading factor versus flow coefficient at the impeller exit has a linear character independent of compressibility. Known Universal Modelling Method exploits this fact. Two points define the function - loading factor at design point and at zero flow rate. The proper formulae include empirical coefficients. A good modelling result is possible if the choice of coefficients is based on experience and close analogs. Earlier Y. Galerkin and K. Soldatova had proposed to define loading factor performance by the angle of its inclination to the ordinate axis and by the loading factor at zero flow rate. Simple and definite equations with four geometry parameters were proposed for loading factor performance calculated for inviscid flow. The authors of this publication have studied the test performance of thirteen stages of different types. The equations are proposed with universal empirical coefficients. The calculation error lies in the range of plus to minus 1,5%. The alternative model of a loading factor performance modelling is included in new versions of the Universal Modelling Method.

Landslide Inventory Mapping from Bitemporal 10 m SENTINEL-2 Images Using Change Detection Based Markov Random Field

NASA Astrophysics Data System (ADS)

Qin, Y.; Lu, P.; Li, Z.

2018-04-01

Landslide inventory mapping is essential for hazard assessment and mitigation. In most previous studies, landslide mapping was achieved by visual interpretation of aerial photos and remote sensing images. However, such method is labor-intensive and time-consuming, especially over large areas. Although a number of semi-automatic landslide mapping methods have been proposed over the past few years, limitations remain in terms of their applicability over different study areas and data, and there is large room for improvement in terms of the accuracy and automation degree. For these reasons, we developed a change detection-based Markov Random Field (CDMRF) method for landslide inventory mapping. The proposed method mainly includes two steps: 1) change detection-based multi-threshold for training samples generation and 2) MRF for landslide inventory mapping. Compared with the previous methods, the proposed method in this study has three advantages: 1) it combines multiple image difference techniques with multi-threshold method to generate reliable training samples; 2) it takes the spectral characteristics of landslides into account; and 3) it is highly automatic with little parameter tuning. The proposed method was applied for regional landslides mapping from 10 m Sentinel-2 images in Western China. Results corroborated the effectiveness and applicability of the proposed method especially the capability of rapid landslide mapping. Some directions for future research are offered. This study to our knowledge is the first attempt to map landslides from free and medium resolution satellite (i.e., Sentinel-2) images in China.

77 FR 23231 - National Institute on Disability and Rehabilitation Research; Notice of Proposed Long-Range Plan...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-04-18

... scientific merit of the research and development activities, whatever the method employed, and the... research methods, policy, services and supports, including individuals with disabilities or, as appropriate...-designed research and development activities using a range of appropriate methods. Adopt a stages-of...

Mixed Methods in Intervention Research: Theory to Adaptation

ERIC Educational Resources Information Center

Nastasi, Bonnie K.; Hitchcock, John; Sarkar, Sreeroopa; Burkholder, Gary; Varjas, Kristen; Jayasena, Asoka

2007-01-01

The purpose of this article is to demonstrate the application of mixed methods research designs to multiyear programmatic research and development projects whose goals include integration of cultural specificity when generating or translating evidence-based practices. The authors propose a set of five mixed methods designs related to different…

Complex amplitude reconstruction for dynamic beam quality M² factor measurement with self-referencing interferometer wavefront sensor.

PubMed

Du, Yongzhao; Fu, Yuqing; Zheng, Lixin

2016-12-20

A real-time complex amplitude reconstruction method for determining the dynamic beam quality M² factor based on a Mach-Zehnder self-referencing interferometer wavefront sensor is developed. By using the proposed complex amplitude reconstruction method, full characterization of the laser beam, including amplitude (intensity profile) and phase information, can be reconstructed from a single interference pattern with the Fourier fringe pattern analysis method in a one-shot measurement. With the reconstructed complex amplitude, the beam fields at any position z along its propagation direction can be obtained by first utilizing the diffraction integral theory. Then the beam quality M² factor of the dynamic beam is calculated according to the specified method of the Standard ISO11146. The feasibility of the proposed method is demonstrated with the theoretical analysis and experiment, including the static and dynamic beam process. The experimental method is simple, fast, and operates without movable parts and is allowed in order to investigate the laser beam in inaccessible conditions using existing methods.

Tensor Factorization for Low-Rank Tensor Completion.

PubMed

Zhou, Pan; Lu, Canyi; Lin, Zhouchen; Zhang, Chao

2018-03-01

Recently, a tensor nuclear norm (TNN) based method was proposed to solve the tensor completion problem, which has achieved state-of-the-art performance on image and video inpainting tasks. However, it requires computing tensor singular value decomposition (t-SVD), which costs much computation and thus cannot efficiently handle tensor data, due to its natural large scale. Motivated by TNN, we propose a novel low-rank tensor factorization method for efficiently solving the 3-way tensor completion problem. Our method preserves the low-rank structure of a tensor by factorizing it into the product of two tensors of smaller sizes. In the optimization process, our method only needs to update two smaller tensors, which can be more efficiently conducted than computing t-SVD. Furthermore, we prove that the proposed alternating minimization algorithm can converge to a Karush-Kuhn-Tucker point. Experimental results on the synthetic data recovery, image and video inpainting tasks clearly demonstrate the superior performance and efficiency of our developed method over state-of-the-arts including the TNN and matricization methods.

HIPS: A new hippocampus subfield segmentation method.

PubMed

Romero, José E; Coupé, Pierrick; Manjón, José V

2017-12-01

The importance of the hippocampus in the study of several neurodegenerative diseases such as Alzheimer's disease makes it a structure of great interest in neuroimaging. However, few segmentation methods have been proposed to measure its subfields due to its complex structure and the lack of high resolution magnetic resonance (MR) data. In this work, we present a new pipeline for automatic hippocampus subfield segmentation using two available hippocampus subfield delineation protocols that can work with both high and standard resolution data. The proposed method is based on multi-atlas label fusion technology that benefits from a novel multi-contrast patch match search process (using high resolution T1-weighted and T2-weighted images). The proposed method also includes as post-processing a new neural network-based error correction step to minimize systematic segmentation errors. The method has been evaluated on both high and standard resolution images and compared to other state-of-the-art methods showing better results in terms of accuracy and execution time. Copyright © 2017 Elsevier Inc. All rights reserved.

Robust keyword retrieval method for OCRed text

NASA Astrophysics Data System (ADS)

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

2011-01-01

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

Fuzzy difference-of-Gaussian-based iris recognition method for noisy iris images

NASA Astrophysics Data System (ADS)

Kang, Byung Jun; Park, Kang Ryoung; Yoo, Jang-Hee; Moon, Kiyoung

2010-06-01

Iris recognition is used for information security with a high confidence level because it shows outstanding recognition accuracy by using human iris patterns with high degrees of freedom. However, iris recognition accuracy can be reduced by noisy iris images with optical and motion blurring. We propose a new iris recognition method based on the fuzzy difference-of-Gaussian (DOG) for noisy iris images. This study is novel in three ways compared to previous works: (1) The proposed method extracts iris feature values using the DOG method, which is robust to local variations of illumination and shows fine texture information, including various frequency components. (2) When determining iris binary codes, image noises that cause the quantization error of the feature values are reduced with the fuzzy membership function. (3) The optimal parameters of the DOG filter and the fuzzy membership function are determined in terms of iris recognition accuracy. Experimental results showed that the performance of the proposed method was better than that of previous methods for noisy iris images.

Deep Correlated Holistic Metric Learning for Sketch-Based 3D Shape Retrieval.

PubMed

Dai, Guoxian; Xie, Jin; Fang, Yi

2018-07-01

How to effectively retrieve desired 3D models with simple queries is a long-standing problem in computer vision community. The model-based approach is quite straightforward but nontrivial, since people could not always have the desired 3D query model available by side. Recently, large amounts of wide-screen electronic devices are prevail in our daily lives, which makes the sketch-based 3D shape retrieval a promising candidate due to its simpleness and efficiency. The main challenge of sketch-based approach is the huge modality gap between sketch and 3D shape. In this paper, we proposed a novel deep correlated holistic metric learning (DCHML) method to mitigate the discrepancy between sketch and 3D shape domains. The proposed DCHML trains two distinct deep neural networks (one for each domain) jointly, which learns two deep nonlinear transformations to map features from both domains into a new feature space. The proposed loss, including discriminative loss and correlation loss, aims to increase the discrimination of features within each domain as well as the correlation between different domains. In the new feature space, the discriminative loss minimizes the intra-class distance of the deep transformed features and maximizes the inter-class distance of the deep transformed features to a large margin within each domain, while the correlation loss focused on mitigating the distribution discrepancy across different domains. Different from existing deep metric learning methods only with loss at the output layer, our proposed DCHML is trained with loss at both hidden layer and output layer to further improve the performance by encouraging features in the hidden layer also with desired properties. Our proposed method is evaluated on three benchmarks, including 3D Shape Retrieval Contest 2013, 2014, and 2016 benchmarks, and the experimental results demonstrate the superiority of our proposed method over the state-of-the-art methods.

Evaluation method on steering for the shape-shifting robot in different configurations

NASA Astrophysics Data System (ADS)

Chang, Jian; Li, Bin; Wang, Chong; Zheng, Huaibing; Li, Zhiqiang

2016-01-01

The evaluation method on steering is based on qualitative manner in existence, which causes the result inaccurate and fuzziness. It reduces the efficiency of process execution. So the method by quantitative manner for the shape-shifting robot in different configurations is proposed. Comparing to traditional evaluation method, the most important aspects which can influence the steering abilities of the robot in different configurations are researched in detail, including the energy, angular velocity, time and space. In order to improve the robustness of system, the ideal and slippage conditions are all considered by mathematical model. Comparing to the traditional weighting confirming method, the extent of robot steering method is proposed by the combination of subjective and objective weighting method. The subjective weighting method can show more preferences of the experts and is based on five-grade scale. The objective weighting method is based on information entropy to determine the factors. By the sensors fixed on the robot, the contract force between track grouser and ground, the intrinsic motion characteristics of robot are obtained and the experiment is done to prove the algorithm which is proposed as the robot in different common configurations. Through the method proposed in the article, fuzziness and inaccurate of the evaluation method has been solved, so the operators can choose the most suitable configuration of the robot to fulfil the different tasks more quickly and simply.

Background Registration-Based Adaptive Noise Filtering of LWIR/MWIR Imaging Sensors for UAV Applications

PubMed Central

Kim, Byeong Hak; Kim, Min Young; Chae, You Seong

2017-01-01

Unmanned aerial vehicles (UAVs) are equipped with optical systems including an infrared (IR) camera such as electro-optical IR (EO/IR), target acquisition and designation sights (TADS), or forward looking IR (FLIR). However, images obtained from IR cameras are subject to noise such as dead pixels, lines, and fixed pattern noise. Nonuniformity correction (NUC) is a widely employed method to reduce noise in IR images, but it has limitations in removing noise that occurs during operation. Methods have been proposed to overcome the limitations of the NUC method, such as two-point correction (TPC) and scene-based NUC (SBNUC). However, these methods still suffer from unfixed pattern noise. In this paper, a background registration-based adaptive noise filtering (BRANF) method is proposed to overcome the limitations of conventional methods. The proposed BRANF method utilizes background registration processing and robust principle component analysis (RPCA). In addition, image quality verification methods are proposed that can measure the noise filtering performance quantitatively without ground truth images. Experiments were performed for performance verification with middle wave infrared (MWIR) and long wave infrared (LWIR) images obtained from practical military optical systems. As a result, it is found that the image quality improvement rate of BRANF is 30% higher than that of conventional NUC. PMID:29280970

Background Registration-Based Adaptive Noise Filtering of LWIR/MWIR Imaging Sensors for UAV Applications.

PubMed

Kim, Byeong Hak; Kim, Min Young; Chae, You Seong

2017-12-27

Unmanned aerial vehicles (UAVs) are equipped with optical systems including an infrared (IR) camera such as electro-optical IR (EO/IR), target acquisition and designation sights (TADS), or forward looking IR (FLIR). However, images obtained from IR cameras are subject to noise such as dead pixels, lines, and fixed pattern noise. Nonuniformity correction (NUC) is a widely employed method to reduce noise in IR images, but it has limitations in removing noise that occurs during operation. Methods have been proposed to overcome the limitations of the NUC method, such as two-point correction (TPC) and scene-based NUC (SBNUC). However, these methods still suffer from unfixed pattern noise. In this paper, a background registration-based adaptive noise filtering (BRANF) method is proposed to overcome the limitations of conventional methods. The proposed BRANF method utilizes background registration processing and robust principle component analysis (RPCA). In addition, image quality verification methods are proposed that can measure the noise filtering performance quantitatively without ground truth images. Experiments were performed for performance verification with middle wave infrared (MWIR) and long wave infrared (LWIR) images obtained from practical military optical systems. As a result, it is found that the image quality improvement rate of BRANF is 30% higher than that of conventional NUC.

Automatic generation of the non-holonomic equations of motion for vehicle stability analysis

NASA Astrophysics Data System (ADS)

Minaker, B. P.; Rieveley, R. J.

2010-09-01

The mathematical analysis of vehicle stability has been utilised as an important tool in the design, development, and evaluation of vehicle architectures and stability controls. This paper presents a novel method for automatic generation of the linearised equations of motion for mechanical systems that is well suited to vehicle stability analysis. Unlike conventional methods for generating linearised equations of motion in standard linear second order form, the proposed method allows for the analysis of systems with non-holonomic constraints. In the proposed method, the algebraic constraint equations are eliminated after linearisation and reduction to first order. The described method has been successfully applied to an assortment of classic dynamic problems of varying complexity including the classic rolling coin, the planar truck-trailer, and the bicycle, as well as in more recent problems such as a rotor-stator and a benchmark road vehicle with suspension. This method has also been applied in the design and analysis of a novel three-wheeled narrow tilting vehicle with zero roll-stiffness. An application for determining passively stable configurations using the proposed method together with a genetic search algorithm is detailed. The proposed method and software implementation has been shown to be robust and provides invaluable conceptual insight into the stability of vehicles and mechanical systems.

Analysis Method for Laterally Loaded Pile Groups Using an Advanced Modeling of Reinforced Concrete Sections.

PubMed

Stacul, Stefano; Squeglia, Nunziante

2018-02-15

A Boundary Element Method (BEM) approach was developed for the analysis of pile groups. The proposed method includes: the non-linear behavior of the soil by a hyperbolic modulus reduction curve; the non-linear response of reinforced concrete pile sections, also taking into account the influence of tension stiffening; the influence of suction by increasing the stiffness of shallow portions of soil and modeled using the Modified Kovacs model; pile group shadowing effect, modeled using an approach similar to that proposed in the Strain Wedge Model for pile groups analyses. The proposed BEM method saves computational effort compared to more sophisticated codes such as VERSAT-P3D, PLAXIS 3D and FLAC-3D, and provides reliable results using input data from a standard site investigation. The reliability of this method was verified by comparing results from data from full scale and centrifuge tests on single piles and pile groups. A comparison is presented between measured and computed data on a laterally loaded fixed-head pile group composed by reinforced concrete bored piles. The results of the proposed method are shown to be in good agreement with those obtained in situ.

Analysis Method for Laterally Loaded Pile Groups Using an Advanced Modeling of Reinforced Concrete Sections

PubMed Central

2018-01-01

A Boundary Element Method (BEM) approach was developed for the analysis of pile groups. The proposed method includes: the non-linear behavior of the soil by a hyperbolic modulus reduction curve; the non-linear response of reinforced concrete pile sections, also taking into account the influence of tension stiffening; the influence of suction by increasing the stiffness of shallow portions of soil and modeled using the Modified Kovacs model; pile group shadowing effect, modeled using an approach similar to that proposed in the Strain Wedge Model for pile groups analyses. The proposed BEM method saves computational effort compared to more sophisticated codes such as VERSAT-P3D, PLAXIS 3D and FLAC-3D, and provides reliable results using input data from a standard site investigation. The reliability of this method was verified by comparing results from data from full scale and centrifuge tests on single piles and pile groups. A comparison is presented between measured and computed data on a laterally loaded fixed-head pile group composed by reinforced concrete bored piles. The results of the proposed method are shown to be in good agreement with those obtained in situ. PMID:29462857

A novel knowledge-based potential for RNA 3D structure evaluation

NASA Astrophysics Data System (ADS)

Yang, Yi; Gu, Qi; Zhang, Ben-Gong; Shi, Ya-Zhou; Shao, Zhi-Gang

2018-03-01

Ribonucleic acids (RNAs) play a vital role in biology, and knowledge of their three-dimensional (3D) structure is required to understand their biological functions. Recently structural prediction methods have been developed to address this issue, but a series of RNA 3D structures are generally predicted by most existing methods. Therefore, the evaluation of the predicted structures is generally indispensable. Although several methods have been proposed to assess RNA 3D structures, the existing methods are not precise enough. In this work, a new all-atom knowledge-based potential is developed for more accurately evaluating RNA 3D structures. The potential not only includes local and nonlocal interactions but also fully considers the specificity of each RNA by introducing a retraining mechanism. Based on extensive test sets generated from independent methods, the proposed potential correctly distinguished the native state and ranked near-native conformations to effectively select the best. Furthermore, the proposed potential precisely captured RNA structural features such as base-stacking and base-pairing. Comparisons with existing potential methods show that the proposed potential is very reliable and accurate in RNA 3D structure evaluation. Project supported by the National Science Foundation of China (Grants Nos. 11605125, 11105054, 11274124, and 11401448).

Development of Generation System of Simplified Digital Maps

NASA Astrophysics Data System (ADS)

Uchimura, Keiichi; Kawano, Masato; Tokitsu, Hiroki; Hu, Zhencheng

In recent years, digital maps have been used in a variety of scenarios, including car navigation systems and map information services over the Internet. These digital maps are formed by multiple layers of maps of different scales; the map data most suitable for the specific situation are used. Currently, the production of map data of different scales is done by hand due to constraints related to processing time and accuracy. We conducted research concerning technologies for automatic generation of simplified map data from detailed map data. In the present paper, the authors propose the following: (1) a method to transform data related to streets, rivers, etc. containing widths into line data, (2) a method to eliminate the component points of the data, and (3) a method to eliminate data that lie below a certain threshold. In addition, in order to evaluate the proposed method, a user survey was conducted; in this survey we compared maps generated using the proposed method with the commercially available maps. From the viewpoint of the amount of data reduction and processing time, and on the basis of the results of the survey, we confirmed the effectiveness of the automatic generation of simplified maps using the proposed methods.

Chemometrics-enhanced high performance liquid chromatography-diode array detection strategy for simultaneous determination of eight co-eluted compounds in ten kinds of Chinese teas using second-order calibration method based on alternating trilinear decomposition algorithm.

PubMed

Yin, Xiao-Li; Wu, Hai-Long; Gu, Hui-Wen; Zhang, Xiao-Hua; Sun, Yan-Mei; Hu, Yong; Liu, Lu; Rong, Qi-Ming; Yu, Ru-Qin

2014-10-17

In this work, an attractive chemometrics-enhanced high performance liquid chromatography-diode array detection (HPLC-DAD) strategy was proposed for simultaneous and fast determination of eight co-eluted compounds including gallic acid, caffeine and six catechins in ten kinds of Chinese teas by using second-order calibration method based on alternating trilinear decomposition (ATLD) algorithm. This new strategy proved to be a useful tool for handling the co-eluted peaks, uncalibrated interferences and baseline drifts existing in the process of chromatographic separation, which benefited from the "second-order advantages", making the determination of gallic acid, caffeine and six catechins in tea infusions within 8 min under a simple mobile phase condition. The average recoveries of the analytes on two selected tea samples ranged from 91.7 to 103.1% with standard deviations (SD) ranged from 1.9 to 11.9%. Figures of merit including sensitivity (SEN), selectivity (SEL), root-mean-square error of prediction (RMSEP) and limit of detection (LOD) have been calculated to validate the accuracy of the proposed method. To further confirm the reliability of the method, a multiple reaction monitoring (MRM) method based on LC-MS/MS was employed for comparison and the obtained results of both methods were consistent with each other. Furthermore, as a universal strategy, this new proposed analytical method was applied for the determination of gallic acid, caffeine and catechins in several other kinds of Chinese teas, including different levels and varieties. Finally, based on the quantitative results, principal component analysis (PCA) was used to conduct a cluster analysis for these Chinese teas. The green tea, Oolong tea and Pu-erh raw tea samples were classified successfully. All results demonstrated that the proposed method is accurate, sensitive, fast, universal and ideal for the rapid, routine analysis and discrimination of gallic acid, caffeine and catechins in Chinese tea samples. Copyright © 2014 Elsevier B.V. All rights reserved.

The Method Effect in Communicative Testing.

ERIC Educational Resources Information Center

Canale, Michael

1981-01-01

A focus on test validity includes a consideration of the way a test measures that which it proposes to test; in other words, the validity of a test depends on method as well as content. This paper examines three areas of concern: (1) some features of communication that test method should reflect, (2) the main components of method, and (3) some…

Robust mislabel logistic regression without modeling mislabel probabilities.

PubMed

Hung, Hung; Jou, Zhi-Yu; Huang, Su-Yun

2018-03-01

Logistic regression is among the most widely used statistical methods for linear discriminant analysis. In many applications, we only observe possibly mislabeled responses. Fitting a conventional logistic regression can then lead to biased estimation. One common resolution is to fit a mislabel logistic regression model, which takes into consideration of mislabeled responses. Another common method is to adopt a robust M-estimation by down-weighting suspected instances. In this work, we propose a new robust mislabel logistic regression based on γ-divergence. Our proposal possesses two advantageous features: (1) It does not need to model the mislabel probabilities. (2) The minimum γ-divergence estimation leads to a weighted estimating equation without the need to include any bias correction term, that is, it is automatically bias-corrected. These features make the proposed γ-logistic regression more robust in model fitting and more intuitive for model interpretation through a simple weighting scheme. Our method is also easy to implement, and two types of algorithms are included. Simulation studies and the Pima data application are presented to demonstrate the performance of γ-logistic regression. © 2017, The International Biometric Society.

75 FR 1591 - Green Technology Pilot Program

Federal Register 2010, 2011, 2012, 2013, 2014

2010-01-12

... DEPARTMENT OF COMMERCE Patent and Trademark Office Green Technology Pilot Program ACTION: Proposed... methods: E-mail: [email protected] . Include A0651-0062 Green Technology Pilot Program [email protected] in... green technologies, including greenhouse gas reduction. [[Page 1592

Problems With Risk Reclassification Methods for Evaluating Prediction Models

PubMed Central

Pepe, Margaret S.

2011-01-01

For comparing the performance of a baseline risk prediction model with one that includes an additional predictor, a risk reclassification analysis strategy has been proposed. The first step is to cross-classify risks calculated according to the 2 models for all study subjects. Summary measures including the percentage of reclassification and the percentage of correct reclassification are calculated, along with 2 reclassification calibration statistics. The author shows that interpretations of the proposed summary measures and P values are problematic. The author's recommendation is to display the reclassification table, because it shows interesting information, but to use alternative methods for summarizing and comparing model performance. The Net Reclassification Index has been suggested as one alternative method. The author argues for reporting components of the Net Reclassification Index because they are more clinically relevant than is the single numerical summary measure. PMID:21555714

On unified modeling, theory, and method for solving multi-scale global optimization problems

NASA Astrophysics Data System (ADS)

Gao, David Yang

2016-10-01

A unified model is proposed for general optimization problems in multi-scale complex systems. Based on this model and necessary assumptions in physics, the canonical duality theory is presented in a precise way to include traditional duality theories and popular methods as special applications. Two conjectures on NP-hardness are proposed, which should play important roles for correctly understanding and efficiently solving challenging real-world problems. Applications are illustrated for both nonconvex continuous optimization and mixed integer nonlinear programming.

Reliable Wireless Broadcast with Linear Network Coding for Multipoint-to-Multipoint Real-Time Communications

NASA Astrophysics Data System (ADS)

Kondo, Yoshihisa; Yomo, Hiroyuki; Yamaguchi, Shinji; Davis, Peter; Miura, Ryu; Obana, Sadao; Sampei, Seiichi

This paper proposes multipoint-to-multipoint (MPtoMP) real-time broadcast transmission using network coding for ad-hoc networks like video game networks. We aim to achieve highly reliable MPtoMP broadcasting using IEEE 802.11 media access control (MAC) that does not include a retransmission mechanism. When each node detects packets from the other nodes in a sequence, the correctly detected packets are network-encoded, and the encoded packet is broadcasted in the next sequence as a piggy-back for its native packet. To prevent increase of overhead in each packet due to piggy-back packet transmission, network coding vector for each node is exchanged between all nodes in the negotiation phase. Each user keeps using the same coding vector generated in the negotiation phase, and only coding information that represents which user signal is included in the network coding process is transmitted along with the piggy-back packet. Our simulation results show that the proposed method can provide higher reliability than other schemes using multi point relay (MPR) or redundant transmissions such as forward error correction (FEC). We also implement the proposed method in a wireless testbed, and show that the proposed method achieves high reliability in a real-world environment with a practical degree of complexity when installed on current wireless devices.

Reinforcement learning algorithms for robotic navigation in dynamic environments.

PubMed

Yen, Gary G; Hickey, Travis W

2004-04-01

The purpose of this study was to examine improvements to reinforcement learning (RL) algorithms in order to successfully interact within dynamic environments. The scope of the research was that of RL algorithms as applied to robotic navigation. Proposed improvements include: addition of a forgetting mechanism, use of feature based state inputs, and hierarchical structuring of an RL agent. Simulations were performed to evaluate the individual merits and flaws of each proposal, to compare proposed methods to prior established methods, and to compare proposed methods to theoretically optimal solutions. Incorporation of a forgetting mechanism did considerably improve the learning times of RL agents in a dynamic environment. However, direct implementation of a feature-based RL agent did not result in any performance enhancements, as pure feature-based navigation results in a lack of positional awareness, and the inability of the agent to determine the location of the goal state. Inclusion of a hierarchical structure in an RL agent resulted in significantly improved performance, specifically when one layer of the hierarchy included a feature-based agent for obstacle avoidance, and a standard RL agent for global navigation. In summary, the inclusion of a forgetting mechanism, and the use of a hierarchically structured RL agent offer substantially increased performance when compared to traditional RL agents navigating in a dynamic environment.

Identifying outliers of non-Gaussian groundwater state data based on ensemble estimation for long-term trends

NASA Astrophysics Data System (ADS)

Jeong, Jina; Park, Eungyu; Han, Weon Shik; Kim, Kueyoung; Choung, Sungwook; Chung, Il Moon

2017-05-01

A hydrogeological dataset often includes substantial deviations that need to be inspected. In the present study, three outlier identification methods - the three sigma rule (3σ), inter quantile range (IQR), and median absolute deviation (MAD) - that take advantage of the ensemble regression method are proposed by considering non-Gaussian characteristics of groundwater data. For validation purposes, the performance of the methods is compared using simulated and actual groundwater data with a few hypothetical conditions. In the validations using simulated data, all of the proposed methods reasonably identify outliers at a 5% outlier level; whereas, only the IQR method performs well for identifying outliers at a 30% outlier level. When applying the methods to real groundwater data, the outlier identification performance of the IQR method is found to be superior to the other two methods. However, the IQR method shows limitation by identifying excessive false outliers, which may be overcome by its joint application with other methods (for example, the 3σ rule and MAD methods). The proposed methods can be also applied as potential tools for the detection of future anomalies by model training based on currently available data.

Transformation-invariant and nonparametric monotone smooth estimation of ROC curves.

PubMed

Du, Pang; Tang, Liansheng

2009-01-30

When a new diagnostic test is developed, it is of interest to evaluate its accuracy in distinguishing diseased subjects from non-diseased subjects. The accuracy of the test is often evaluated by receiver operating characteristic (ROC) curves. Smooth ROC estimates are often preferable for continuous test results when the underlying ROC curves are in fact continuous. Nonparametric and parametric methods have been proposed by various authors to obtain smooth ROC curve estimates. However, there are certain drawbacks with the existing methods. Parametric methods need specific model assumptions. Nonparametric methods do not always satisfy the inherent properties of the ROC curves, such as monotonicity and transformation invariance. In this paper we propose a monotone spline approach to obtain smooth monotone ROC curves. Our method ensures important inherent properties of the underlying ROC curves, which include monotonicity, transformation invariance, and boundary constraints. We compare the finite sample performance of the newly proposed ROC method with other ROC smoothing methods in large-scale simulation studies. We illustrate our method through a real life example. Copyright (c) 2008 John Wiley & Sons, Ltd.

A dimension-wise analysis method for the structural-acoustic system with interval parameters

NASA Astrophysics Data System (ADS)

Xu, Menghui; Du, Jianke; Wang, Chong; Li, Yunlong

2017-04-01

The interval structural-acoustic analysis is mainly accomplished by interval and subinterval perturbation methods. Potential limitations for these intrusive methods include overestimation or interval translation effect for the former and prohibitive computational cost for the latter. In this paper, a dimension-wise analysis method is thus proposed to overcome these potential limitations. In this method, a sectional curve of the system response surface along each input dimensionality is firstly extracted, the minimal and maximal points of which are identified based on its Legendre polynomial approximation. And two input vectors, i.e. the minimal and maximal input vectors, are dimension-wisely assembled by the minimal and maximal points of all sectional curves. Finally, the lower and upper bounds of system response are computed by deterministic finite element analysis at the two input vectors. Two numerical examples are studied to demonstrate the effectiveness of the proposed method and show that, compared to the interval and subinterval perturbation method, a better accuracy is achieved without much compromise on efficiency by the proposed method, especially for nonlinear problems with large interval parameters.

A novel infrared small moving target detection method based on tracking interest points under complicated background

NASA Astrophysics Data System (ADS)

Dong, Xiabin; Huang, Xinsheng; Zheng, Yongbin; Bai, Shengjian; Xu, Wanying

2014-07-01

Infrared moving target detection is an important part of infrared technology. We introduce a novel infrared small moving target detection method based on tracking interest points under complicated background. Firstly, Difference of Gaussians (DOG) filters are used to detect a group of interest points (including the moving targets). Secondly, a sort of small targets tracking method inspired by Human Visual System (HVS) is used to track these interest points for several frames, and then the correlations between interest points in the first frame and the last frame are obtained. Last, a new clustering method named as R-means is proposed to divide these interest points into two groups according to the correlations, one is target points and another is background points. In experimental results, the target-to-clutter ratio (TCR) and the receiver operating characteristics (ROC) curves are computed experimentally to compare the performances of the proposed method and other five sophisticated methods. From the results, the proposed method shows a better discrimination of targets and clutters and has a lower false alarm rate than the existing moving target detection methods.

Multi-Excitation Magnetoacoustic Tomography with Magnetic Induction for Bioimpedance Imaging

PubMed Central

Li, Xu; He, Bin

2011-01-01

Magnetoacoustic tomography with magnetic induction (MAT-MI) is an imaging approach proposed to conduct non-invasive electrical conductivity imaging of biological tissue with high spatial resolution. In the present study, based on the analysis of the relationship between the conductivity distribution and the generated MAT-MI acoustic source, we propose a new multi-excitation MAT-MI approach and the corresponding reconstruction algorithms. In the proposed method, multiple magnetic excitations using different coil configurations are employed and ultrasound measurements corresponding to each excitation are collected to derive the conductivity distribution inside the sample. A modified reconstruction algorithm is also proposed for the multi-excitation MAT-MI imaging approach when only limited bandwidth acoustic measurements are available. Computer simulation and phantom experiment studies have been done to demonstrate the merits of the proposed method. It is shown that if unlimited bandwidth acoustic data is available, we can accurately reconstruct the internal conductivity contrast of an object using the proposed method. With limited bandwidth data and the use of the modified algorithm we can reconstruct the relative conductivity contrast of an object instead of only boundaries at the conductivity heterogeneity. Benefits that come with this new method include better differentiation of tissue types with conductivity contrast using the MAT-MI approach, specifically for potential breast cancer screening application in the future. PMID:20529729

Multiple-3D-object secure information system based on phase shifting method and single interference.

PubMed

Li, Wei-Na; Shi, Chen-Xiao; Piao, Mei-Lan; Kim, Nam

2016-05-20

We propose a multiple-3D-object secure information system for encrypting multiple three-dimensional (3D) objects based on the three-step phase shifting method. During the decryption procedure, five phase functions (PFs) are decreased to three PFs, in comparison with our previous method, which implies that one cross beam splitter is utilized to implement the single decryption interference. Moreover, the advantages of the proposed scheme also include: each 3D object can be decrypted discretionarily without decrypting a series of other objects earlier; the quality of the decrypted slice image of each object is high according to the correlation coefficient values, none of which is lower than 0.95; no iterative algorithm is involved. The feasibility of the proposed scheme is demonstrated by computer simulation results.

Proposal of Classification Method of Time Series Data in International Emissions Trading Market Using Agent-based Simulation

NASA Astrophysics Data System (ADS)

Nakada, Tomohiro; Takadama, Keiki; Watanabe, Shigeyoshi

This paper proposes the classification method using Bayesian analytical method to classify the time series data in the international emissions trading market depend on the agent-based simulation and compares the case with Discrete Fourier transform analytical method. The purpose demonstrates the analytical methods mapping time series data such as market price. These analytical methods have revealed the following results: (1) the classification methods indicate the distance of mapping from the time series data, it is easier the understanding and inference than time series data; (2) these methods can analyze the uncertain time series data using the distance via agent-based simulation including stationary process and non-stationary process; and (3) Bayesian analytical method can show the 1% difference description of the emission reduction targets of agent.

Robust gaze-steering of an active vision system against errors in the estimated parameters

NASA Astrophysics Data System (ADS)

Han, Youngmo

2015-01-01

Gaze-steering is often used to broaden the viewing range of an active vision system. Gaze-steering procedures are usually based on estimated parameters such as image position, image velocity, depth and camera calibration parameters. However, there may be uncertainties in these estimated parameters because of measurement noise and estimation errors. In this case, robust gaze-steering cannot be guaranteed. To compensate for such problems, this paper proposes a gaze-steering method based on a linear matrix inequality (LMI). In this method, we first propose a proportional derivative (PD) control scheme on the unit sphere that does not use depth parameters. This proposed PD control scheme can avoid uncertainties in the estimated depth and camera calibration parameters, as well as inconveniences in their estimation process, including the use of auxiliary feature points and highly non-linear computation. Furthermore, the control gain of the proposed PD control scheme on the unit sphere is designed using LMI such that the designed control is robust in the presence of uncertainties in the other estimated parameters, such as image position and velocity. Simulation results demonstrate that the proposed method provides a better compensation for uncertainties in the estimated parameters than the contemporary linear method and steers the gaze of the camera more steadily over time than the contemporary non-linear method.

An adequacy-constrained integrated planning method for effective accommodation of DG and electric vehicles in smart distribution systems

NASA Astrophysics Data System (ADS)

Tan, Zhukui; Xie, Baiming; Zhao, Yuanliang; Dou, Jinyue; Yan, Tong; Liu, Bin; Zeng, Ming

2018-06-01

This paper presents a new integrated planning framework for effective accommodating electric vehicles in smart distribution systems (SDS). The proposed method incorporates various investment options available for the utility collectively, including distributed generation (DG), capacitors and network reinforcement. Using a back-propagation algorithm combined with cost-benefit analysis, the optimal network upgrade plan, allocation and sizing of the selected components are determined, with the purpose of minimizing the total system capital and operating costs of DG and EV accommodation. Furthermore, a new iterative reliability test method is proposed. It can check the optimization results by subsequently simulating the reliability level of the planning scheme, and modify the generation reserve margin to guarantee acceptable adequacy levels for each year of the planning horizon. Numerical results based on a 32-bus distribution system verify the effectiveness of the proposed method.

Efficient Power Network Analysis with Modeling of Inductive Effects

NASA Astrophysics Data System (ADS)

Zeng, Shan; Yu, Wenjian; Hong, Xianlong; Cheng, Chung-Kuan

In this paper, an efficient method is proposed to accurately analyze large-scale power/ground (P/G) networks, where inductive parasitics are modeled with the partial reluctance. The method is based on frequency-domain circuit analysis and the technique of vector fitting [14], and obtains the time-domain voltage response at given P/G nodes. The frequency-domain circuit equation including partial reluctances is derived, and then solved with the GMRES algorithm with rescaling, preconditioning and recycling techniques. With the merit of sparsified reluctance matrix and iterative solving techniques for the frequency-domain circuit equations, the proposed method is able to handle large-scale P/G networks with complete inductive modeling. Numerical results show that the proposed method is orders of magnitude faster than HSPICE, several times faster than INDUCTWISE [4], and capable of handling the inductive P/G structures with more than 100, 000 wire segments.

A parametric finite element method for solid-state dewetting problems with anisotropic surface energies

NASA Astrophysics Data System (ADS)

Bao, Weizhu; Jiang, Wei; Wang, Yan; Zhao, Quan

2017-02-01

We propose an efficient and accurate parametric finite element method (PFEM) for solving sharp-interface continuum models for solid-state dewetting of thin films with anisotropic surface energies. The governing equations of the sharp-interface models belong to a new type of high-order (4th- or 6th-order) geometric evolution partial differential equations about open curve/surface interface tracking problems which include anisotropic surface diffusion flow and contact line migration. Compared to the traditional methods (e.g., marker-particle methods), the proposed PFEM not only has very good accuracy, but also poses very mild restrictions on the numerical stability, and thus it has significant advantages for solving this type of open curve evolution problems with applications in the simulation of solid-state dewetting. Extensive numerical results are reported to demonstrate the accuracy and high efficiency of the proposed PFEM.

A new web-based framework development for fuzzy multi-criteria group decision-making.

PubMed

Hanine, Mohamed; Boutkhoum, Omar; Tikniouine, Abdessadek; Agouti, Tarik

2016-01-01

Fuzzy multi-criteria group decision making (FMCGDM) process is usually used when a group of decision-makers faces imprecise data or linguistic variables to solve the problems. However, this process contains many methods that require many time-consuming calculations depending on the number of criteria, alternatives and decision-makers in order to reach the optimal solution. In this study, a web-based FMCGDM framework that offers decision-makers a fast and reliable response service is proposed. The proposed framework includes commonly used tools for multi-criteria decision-making problems such as fuzzy Delphi, fuzzy AHP and fuzzy TOPSIS methods. The integration of these methods enables taking advantages of the strengths and complements each method's weakness. Finally, a case study of location selection for landfill waste in Morocco is performed to demonstrate how this framework can facilitate decision-making process. The results demonstrate that the proposed framework can successfully accomplish the goal of this study.

Kernel-aligned multi-view canonical correlation analysis for image recognition

NASA Astrophysics Data System (ADS)

Su, Shuzhi; Ge, Hongwei; Yuan, Yun-Hao

2016-09-01

Existing kernel-based correlation analysis methods mainly adopt a single kernel in each view. However, only a single kernel is usually insufficient to characterize nonlinear distribution information of a view. To solve the problem, we transform each original feature vector into a 2-dimensional feature matrix by means of kernel alignment, and then propose a novel kernel-aligned multi-view canonical correlation analysis (KAMCCA) method on the basis of the feature matrices. Our proposed method can simultaneously employ multiple kernels to better capture the nonlinear distribution information of each view, so that correlation features learned by KAMCCA can have well discriminating power in real-world image recognition. Extensive experiments are designed on five real-world image datasets, including NIR face images, thermal face images, visible face images, handwritten digit images, and object images. Promising experimental results on the datasets have manifested the effectiveness of our proposed method.

System and Method for Monitoring Distributed Asset Data

NASA Technical Reports Server (NTRS)

Gorinevsky, Dimitry (Inventor)

2015-01-01

A computer-based monitoring system and monitoring method implemented in computer software for detecting, estimating, and reporting the condition states, their changes, and anomalies for many assets. The assets are of same type, are operated over a period of time, and outfitted with data collection systems. The proposed monitoring method accounts for variability of working conditions for each asset by using regression model that characterizes asset performance. The assets are of the same type but not identical. The proposed monitoring method accounts for asset-to-asset variability; it also accounts for drifts and trends in the asset condition and data. The proposed monitoring system can perform distributed processing of massive amounts of historical data without discarding any useful information where moving all the asset data into one central computing system might be infeasible. The overall processing is includes distributed preprocessing data records from each asset to produce compressed data.

Autoregressive statistical pattern recognition algorithms for damage detection in civil structures

NASA Astrophysics Data System (ADS)

Yao, Ruigen; Pakzad, Shamim N.

2012-08-01

Statistical pattern recognition has recently emerged as a promising set of complementary methods to system identification for automatic structural damage assessment. Its essence is to use well-known concepts in statistics for boundary definition of different pattern classes, such as those for damaged and undamaged structures. In this paper, several statistical pattern recognition algorithms using autoregressive models, including statistical control charts and hypothesis testing, are reviewed as potentially competitive damage detection techniques. To enhance the performance of statistical methods, new feature extraction techniques using model spectra and residual autocorrelation, together with resampling-based threshold construction methods, are proposed. Subsequently, simulated acceleration data from a multi degree-of-freedom system is generated to test and compare the efficiency of the existing and proposed algorithms. Data from laboratory experiments conducted on a truss and a large-scale bridge slab model are then used to further validate the damage detection methods and demonstrate the superior performance of proposed algorithms.

Adaptive Event-Triggered Control Based on Heuristic Dynamic Programming for Nonlinear Discrete-Time Systems.

PubMed

Dong, Lu; Zhong, Xiangnan; Sun, Changyin; He, Haibo

2017-07-01

This paper presents the design of a novel adaptive event-triggered control method based on the heuristic dynamic programming (HDP) technique for nonlinear discrete-time systems with unknown system dynamics. In the proposed method, the control law is only updated when the event-triggered condition is violated. Compared with the periodic updates in the traditional adaptive dynamic programming (ADP) control, the proposed method can reduce the computation and transmission cost. An actor-critic framework is used to learn the optimal event-triggered control law and the value function. Furthermore, a model network is designed to estimate the system state vector. The main contribution of this paper is to design a new trigger threshold for discrete-time systems. A detailed Lyapunov stability analysis shows that our proposed event-triggered controller can asymptotically stabilize the discrete-time systems. Finally, we test our method on two different discrete-time systems, and the simulation results are included.

Toyota Prius HEV neurocontrol and diagnostics.

PubMed

Prokhorov, Danil V

2008-01-01

A neural network controller for improved fuel efficiency of the Toyota Prius hybrid electric vehicle is proposed. A new method to detect and mitigate a battery fault is also presented. The approach is based on recurrent neural networks and includes the extended Kalman filter. The proposed approach is quite general and applicable to other control systems.

Development of Support Service for Prevention and Recovery from Dementia and Science of Lethe

NASA Astrophysics Data System (ADS)

Otake, Mihoko

This paper proposes multiscale service design method through the development of support service for prevention and recovery from dementia towards science of lethe. Proposed multiscale service model consists of tool, event, human, network, style and rule. Service elements at different scales are developed according to the model. Firstly, the author proposes and practices coimagination method as an ``event'', which is expected to prevent the progress of cognitive impairment. Coimagination support system was developed as a ``tool''. Experimental results suggest the effective activation of episodic memory, division of attention, and planning function of participants by the measurement of cognitive activities during the coimagination. Then, Fonobono Research Institute was established as a ''network'' for ``human'' who studies coimagination, which is a multisector research organization including elderly people living around Kashiwa city, companies including instrument and welfare companies, Kashiwa city and Chiba prefecture, researchers of the University of Tokyo. The institute proposes and realizes lifelong research as a novel life ``style'' for elderly people, and discusses life with two rounds as an innovative ``rule'' for social system of aged society.

Deep feature classification of angiomyolipoma without visible fat and renal cell carcinoma in abdominal contrast-enhanced CT images with texture image patches and hand-crafted feature concatenation.

PubMed

Lee, Hansang; Hong, Helen; Kim, Junmo; Jung, Dae Chul

2018-04-01

To develop an automatic deep feature classification (DFC) method for distinguishing benign angiomyolipoma without visible fat (AMLwvf) from malignant clear cell renal cell carcinoma (ccRCC) from abdominal contrast-enhanced computer tomography (CE CT) images. A dataset including 80 abdominal CT images of 39 AMLwvf and 41 ccRCC patients was used. We proposed a DFC method for differentiating the small renal masses (SRM) into AMLwvf and ccRCC using the combination of hand-crafted and deep features, and machine learning classifiers. First, 71-dimensional hand-crafted features (HCF) of texture and shape were extracted from the SRM contours. Second, 1000-4000-dimensional deep features (DF) were extracted from the ImageNet pretrained deep learning model with the SRM image patches. In DF extraction, we proposed the texture image patches (TIP) to emphasize the texture information inside the mass in DFs and reduce the mass size variability. Finally, the two features were concatenated and the random forest (RF) classifier was trained on these concatenated features to classify the types of SRMs. The proposed method was tested on our dataset using leave-one-out cross-validation and evaluated using accuracy, sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV), and area under receiver operating characteristics curve (AUC). In experiments, the combinations of four deep learning models, AlexNet, VGGNet, GoogleNet, and ResNet, and four input image patches, including original, masked, mass-size, and texture image patches, were compared and analyzed. In qualitative evaluation, we observed the change in feature distributions between the proposed and comparative methods using tSNE method. In quantitative evaluation, we evaluated and compared the classification results, and observed that (a) the proposed HCF + DF outperformed HCF-only and DF-only, (b) AlexNet showed generally the best performances among the CNN models, and (c) the proposed TIPs not only achieved the competitive performances among the input patches, but also steady performance regardless of CNN models. As a result, the proposed method achieved the accuracy of 76.6 ± 1.4% for the proposed HCF + DF with AlexNet and TIPs, which improved the accuracy by 6.6%p and 8.3%p compared to HCF-only and DF-only, respectively. The proposed shape features and TIPs improved the HCFs and DFs, respectively, and the feature concatenation further enhanced the quality of features for differentiating AMLwvf from ccRCC in abdominal CE CT images. © 2018 American Association of Physicists in Medicine.

A New Automated Design Method Based on Machine Learning for CMOS Analog Circuits

NASA Astrophysics Data System (ADS)

Moradi, Behzad; Mirzaei, Abdolreza

2016-11-01

A new simulation based automated CMOS analog circuit design method which applies a multi-objective non-Darwinian-type evolutionary algorithm based on Learnable Evolution Model (LEM) is proposed in this article. The multi-objective property of this automated design of CMOS analog circuits is governed by a modified Strength Pareto Evolutionary Algorithm (SPEA) incorporated in the LEM algorithm presented here. LEM includes a machine learning method such as the decision trees that makes a distinction between high- and low-fitness areas in the design space. The learning process can detect the right directions of the evolution and lead to high steps in the evolution of the individuals. The learning phase shortens the evolution process and makes remarkable reduction in the number of individual evaluations. The expert designer's knowledge on circuit is applied in the design process in order to reduce the design space as well as the design time. The circuit evaluation is made by HSPICE simulator. In order to improve the design accuracy, bsim3v3 CMOS transistor model is adopted in this proposed design method. This proposed design method is tested on three different operational amplifier circuits. The performance of this proposed design method is verified by comparing it with the evolutionary strategy algorithm and other similar methods.

Hesitant Fuzzy Linguistic Preference Utility Set and Its Application in Selection of Fire Rescue Plans

PubMed Central

Si, Guangsen; Xu, Zeshui

2018-01-01

Hesitant fuzzy linguistic term set provides an effective tool to represent uncertain decision information. However, the semantics corresponding to the linguistic terms in it cannot accurately reflect the decision-makers’ subjective cognition. In general, different decision-makers’ sensitivities towards the semantics are different. Such sensitivities can be represented by the cumulative prospect theory value function. Inspired by this, we propose a linguistic scale function to transform the semantics corresponding to linguistic terms into the linguistic preference values. Furthermore, we propose the hesitant fuzzy linguistic preference utility set, based on which, the decision-makers can flexibly express their distinct semantics and obtain the decision results that are consistent with their cognition. For calculations and comparisons over the hesitant fuzzy linguistic preference utility sets, we introduce some distance measures and comparison laws. Afterwards, to apply the hesitant fuzzy linguistic preference utility sets in emergency management, we develop a method to obtain objective weights of attributes and then propose a hesitant fuzzy linguistic preference utility-TOPSIS method to select the best fire rescue plan. Finally, the validity of the proposed method is verified by some comparisons of the method with other two representative methods including the hesitant fuzzy linguistic-TOPSIS method and the hesitant fuzzy linguistic-VIKOR method. PMID:29614019

Automatic registration of fused lidar/digital imagery (texel images) for three-dimensional image creation

NASA Astrophysics Data System (ADS)

Budge, Scott E.; Badamikar, Neeraj S.; Xie, Xuan

2015-03-01

Several photogrammetry-based methods have been proposed that the derive three-dimensional (3-D) information from digital images from different perspectives, and lidar-based methods have been proposed that merge lidar point clouds and texture the merged point clouds with digital imagery. Image registration alone has difficulty with smooth regions with low contrast, whereas point cloud merging alone has difficulty with outliers and a lack of proper convergence in the merging process. This paper presents a method to create 3-D images that uses the unique properties of texel images (pixel-fused lidar and digital imagery) to improve the quality and robustness of fused 3-D images. The proposed method uses both image processing and point-cloud merging to combine texel images in an iterative technique. Since the digital image pixels and the lidar 3-D points are fused at the sensor level, more accurate 3-D images are generated because registration of image data automatically improves the merging of the point clouds, and vice versa. Examples illustrate the value of this method over other methods. The proposed method also includes modifications for the situation where an estimate of position and attitude of the sensor is known, when obtained from low-cost global positioning systems and inertial measurement units sensors.

Comparative study of methods for recognition of an unknown person's action from a video sequence

NASA Astrophysics Data System (ADS)

Hori, Takayuki; Ohya, Jun; Kurumisawa, Jun

2009-02-01

This paper proposes a Tensor Decomposition Based method that can recognize an unknown person's action from a video sequence, where the unknown person is not included in the database (tensor) used for the recognition. The tensor consists of persons, actions and time-series image features. For the observed unknown person's action, one of the actions stored in the tensor is assumed. Using the motion signature obtained from the assumption, the unknown person's actions are synthesized. The actions of one of the persons in the tensor are replaced by the synthesized actions. Then, the core tensor for the replaced tensor is computed. This process is repeated for the actions and persons. For each iteration, the difference between the replaced and original core tensors is computed. The assumption that gives the minimal difference is the action recognition result. For the time-series image features to be stored in the tensor and to be extracted from the observed video sequence, the human body silhouette's contour shape based feature is used. To show the validity of our proposed method, our proposed method is experimentally compared with Nearest Neighbor rule and Principal Component analysis based method. Experiments using 33 persons' seven kinds of action show that our proposed method achieves better recognition accuracies for the seven actions than the other methods.

A novel method for correcting scanline-observational bias of discontinuity orientation

PubMed Central

Huang, Lei; Tang, Huiming; Tan, Qinwen; Wang, Dingjian; Wang, Liangqing; Ez Eldin, Mutasim A. M.; Li, Changdong; Wu, Qiong

2016-01-01

Scanline observation is known to introduce an angular bias into the probability distribution of orientation in three-dimensional space. In this paper, numerical solutions expressing the functional relationship between the scanline-observational distribution (in one-dimensional space) and the inherent distribution (in three-dimensional space) are derived using probability theory and calculus under the independence hypothesis of dip direction and dip angle. Based on these solutions, a novel method for obtaining the inherent distribution (also for correcting the bias) is proposed, an approach which includes two procedures: 1) Correcting the cumulative probabilities of orientation according to the solutions, and 2) Determining the distribution of the corrected orientations using approximation methods such as the one-sample Kolmogorov-Smirnov test. The inherent distribution corrected by the proposed method can be used for discrete fracture network (DFN) modelling, which is applied to such areas as rockmass stability evaluation, rockmass permeability analysis, rockmass quality calculation and other related fields. To maximize the correction capacity of the proposed method, the observed sample size is suggested through effectiveness tests for different distribution types, dispersions and sample sizes. The performance of the proposed method and the comparison of its correction capacity with existing methods are illustrated with two case studies. PMID:26961249

Super-resolution reconstruction of diffusion parameters from diffusion-weighted images with different slice orientations.

PubMed

Van Steenkiste, Gwendolyn; Jeurissen, Ben; Veraart, Jelle; den Dekker, Arnold J; Parizel, Paul M; Poot, Dirk H J; Sijbers, Jan

2016-01-01

Diffusion MRI is hampered by long acquisition times, low spatial resolution, and a low signal-to-noise ratio. Recently, methods have been proposed to improve the trade-off between spatial resolution, signal-to-noise ratio, and acquisition time of diffusion-weighted images via super-resolution reconstruction (SRR) techniques. However, during the reconstruction, these SRR methods neglect the q-space relation between the different diffusion-weighted images. An SRR method that includes a diffusion model and directly reconstructs high resolution diffusion parameters from a set of low resolution diffusion-weighted images was proposed. Our method allows an arbitrary combination of diffusion gradient directions and slice orientations for the low resolution diffusion-weighted images, optimally samples the q- and k-space, and performs motion correction with b-matrix rotation. Experiments with synthetic data and in vivo human brain data show an increase of spatial resolution of the diffusion parameters, while preserving a high signal-to-noise ratio and low scan time. Moreover, the proposed SRR method outperforms the previous methods in terms of the root-mean-square error. The proposed SRR method substantially increases the spatial resolution of MRI that can be obtained in a clinically feasible scan time. © 2015 Wiley Periodicals, Inc.

Hesitant Fuzzy Linguistic Preference Utility Set and Its Application in Selection of Fire Rescue Plans.

PubMed

Liao, Huchang; Si, Guangsen; Xu, Zeshui; Fujita, Hamido

2018-04-03

Hesitant fuzzy linguistic term set provides an effective tool to represent uncertain decision information. However, the semantics corresponding to the linguistic terms in it cannot accurately reflect the decision-makers' subjective cognition. In general, different decision-makers' sensitivities towards the semantics are different. Such sensitivities can be represented by the cumulative prospect theory value function. Inspired by this, we propose a linguistic scale function to transform the semantics corresponding to linguistic terms into the linguistic preference values. Furthermore, we propose the hesitant fuzzy linguistic preference utility set, based on which, the decision-makers can flexibly express their distinct semantics and obtain the decision results that are consistent with their cognition. For calculations and comparisons over the hesitant fuzzy linguistic preference utility sets, we introduce some distance measures and comparison laws. Afterwards, to apply the hesitant fuzzy linguistic preference utility sets in emergency management, we develop a method to obtain objective weights of attributes and then propose a hesitant fuzzy linguistic preference utility-TOPSIS method to select the best fire rescue plan. Finally, the validity of the proposed method is verified by some comparisons of the method with other two representative methods including the hesitant fuzzy linguistic-TOPSIS method and the hesitant fuzzy linguistic-VIKOR method.

Direct simulation of groundwater age

USGS Publications Warehouse

Goode, Daniel J.

1996-01-01

A new method is proposed to simulate groundwater age directly, by use of an advection-dispersion transport equation with a distributed zero-order source of unit (1) strength, corresponding to the rate of aging. The dependent variable in the governing equation is the mean age, a mass-weighted average age. The governing equation is derived from residence-time-distribution concepts for the case of steady flow. For the more general case of transient flow, a transient governing equation for age is derived from mass-conservation principles applied to conceptual “age mass.” The age mass is the product of the water mass and its age, and age mass is assumed to be conserved during mixing. Boundary conditions include zero age mass flux across all noflow and inflow boundaries and no age mass dispersive flux across outflow boundaries. For transient-flow conditions, the initial distribution of age must be known. The solution of the governing transport equation yields the spatial distribution of the mean groundwater age and includes diffusion, dispersion, mixing, and exchange processes that typically are considered only through tracer-specific solute transport simulation. Traditional methods have relied on advective transport to predict point values of groundwater travel time and age. The proposed method retains the simplicity and tracer-independence of advection-only models, but incorporates the effects of dispersion and mixing on volume-averaged age. Example simulations of age in two idealized regional aquifer systems, one homogeneous and the other layered, demonstrate the agreement between the proposed method and traditional particle-tracking approaches and illustrate use of the proposed method to determine the effects of diffusion, dispersion, and mixing on groundwater age.

Automated and simultaneous fovea center localization and macula segmentation using the new dynamic identification and classification of edges model.

PubMed

Onal, Sinan; Chen, Xin; Satamraju, Veeresh; Balasooriya, Maduka; Dabil-Karacal, Humeyra

2016-07-01

Detecting the position of retinal structures, including the fovea center and macula, in retinal images plays a key role in diagnosing eye diseases such as optic nerve hypoplasia, amblyopia, diabetic retinopathy, and macular edema. However, current detection methods are unreliable for infants or certain ethnic populations. Thus, a methodology is proposed here that may be useful for infants and across ethnicities that automatically localizes the fovea center and segments the macula on digital fundus images. First, dark structures and bright artifacts are removed from the input image using preprocessing operations, and the resulting image is transformed to polar space. Second, the fovea center is identified, and the macula region is segmented using the proposed dynamic identification and classification of edges (DICE) model. The performance of the method was evaluated using 1200 fundus images obtained from the relatively large, diverse, and publicly available Messidor database. In 96.1% of these 1200 cases, the distance between the fovea center identified manually by ophthalmologists and automatically using the proposed method remained within 0 to 8 pixels. The dice similarity index comparing the manually obtained results with those of the model for macula segmentation was 96.12% for these 1200 cases. Thus, the proposed method displayed a high degree of accuracy. The methodology using the DICE model is unique and advantageous over previously reported methods because it simultaneously determines the fovea center and segments the macula region without using any structural information, such as optic disc or blood vessel location, and it may prove useful for all populations, including infants.

Estimating two indirect logging costs caused by accelerated erosion.

Treesearch

Glen O. Klock

1976-01-01

In forest areas where high soil erosion potential exists, a comparative yarding cost estimate, including the indirect costs determined by methods proposed here, shows that the total cost of using "advanced" logging methods may be less than that of "traditional" systems.

Event by event analysis and entropy of multiparticle systems

NASA Astrophysics Data System (ADS)

Bialas, A.; Czyz, W.

2000-04-01

The coincidence method of measuring the entropy of a system, proposed some time ago by Ma, is generalized to include systems out of equilibrium. It is suggested that the method can be adapted to analyze multiparticle states produced in high-energy collisions.

Nonparametric Inference of Doubly Stochastic Poisson Process Data via the Kernel Method

PubMed Central

Zhang, Tingting; Kou, S. C.

2010-01-01

Doubly stochastic Poisson processes, also known as the Cox processes, frequently occur in various scientific fields. In this article, motivated primarily by analyzing Cox process data in biophysics, we propose a nonparametric kernel-based inference method. We conduct a detailed study, including an asymptotic analysis, of the proposed method, and provide guidelines for its practical use, introducing a fast and stable regression method for bandwidth selection. We apply our method to real photon arrival data from recent single-molecule biophysical experiments, investigating proteins' conformational dynamics. Our result shows that conformational fluctuation is widely present in protein systems, and that the fluctuation covers a broad range of time scales, highlighting the dynamic and complex nature of proteins' structure. PMID:21258615

Nonparametric Inference of Doubly Stochastic Poisson Process Data via the Kernel Method.

PubMed

Zhang, Tingting; Kou, S C

2010-01-01

Doubly stochastic Poisson processes, also known as the Cox processes, frequently occur in various scientific fields. In this article, motivated primarily by analyzing Cox process data in biophysics, we propose a nonparametric kernel-based inference method. We conduct a detailed study, including an asymptotic analysis, of the proposed method, and provide guidelines for its practical use, introducing a fast and stable regression method for bandwidth selection. We apply our method to real photon arrival data from recent single-molecule biophysical experiments, investigating proteins' conformational dynamics. Our result shows that conformational fluctuation is widely present in protein systems, and that the fluctuation covers a broad range of time scales, highlighting the dynamic and complex nature of proteins' structure.

Systematic review of proposed definitions of nocturnal polyuria and population-based evidence of their diagnostic accuracy.

PubMed

Olesen, Tine Kold; Denys, Marie-Astrid; Vande Walle, Johan; Everaert, Karel

2018-02-06

Background Evidence of diagnostic accuracy for proposed definitions of nocturnal polyuria is currently unclear. Purpose Systematic review to determine population-based evidence of the diagnostic accuracy of proposed definitions of nocturnal polyuria based on data from frequency-volume charts. Methods Seventeen pre-specified search terms identified 351 unique investigations published from 1990 to 2016 in BIOSIS, Embase, Embase Alerts, International Pharmaceutical Abstract, Medline, and Cochrane. Thirteen original communications were included in this review based on pre-specified exclusion criteria. Data were extracted from each paper regarding subject age, sex, ethnicity, health status, sample size, data collection methods, and diagnostic discrimination of proposed definitions including sensitivity, specificity, positive and negative predictive value. Results The sample size of study cohorts, participant age, sex, ethnicity, and health status varied considerably in 13 studies reporting on the diagnostic performance of seven different definitions of nocturnal polyuria using frequency-volume chart data from 4968 participants. Most study cohorts were small, mono-ethnic, including only Caucasian males aged 50 or higher with primary or secondary polyuria that were compared to a control group of healthy men without nocturia in prospective or retrospective settings. Proposed definitions had poor discriminatory accuracy in evaluations based on data from subjects independent from the original study cohorts with findings being similar regarding the most widely evaluated definition endorsed by ICS. Conclusions Diagnostic performance characteristics for proposed definitions of nocturnal polyuria show poor to modest discrimination and are not based on sufficient level of evidence from representative, multi-ethnic population-based data from both females and males of all adult ages.

Modal parameter identification based on combining transmissibility functions and blind source separation techniques

NASA Astrophysics Data System (ADS)

Araújo, Iván Gómez; Sánchez, Jesús Antonio García; Andersen, Palle

2018-05-01

Transmissibility-based operational modal analysis is a recent and alternative approach used to identify the modal parameters of structures under operational conditions. This approach is advantageous compared with traditional operational modal analysis because it does not make any assumptions about the excitation spectrum (i.e., white noise with a flat spectrum). However, common methodologies do not include a procedure to extract closely spaced modes with low signal-to-noise ratios. This issue is relevant when considering that engineering structures generally have closely spaced modes and that their measured responses present high levels of noise. Therefore, to overcome these problems, a new combined method for modal parameter identification is proposed in this work. The proposed method combines blind source separation (BSS) techniques and transmissibility-based methods. Here, BSS techniques were used to recover source signals, and transmissibility-based methods were applied to estimate modal information from the recovered source signals. To achieve this combination, a new method to define a transmissibility function was proposed. The suggested transmissibility function is based on the relationship between the power spectral density (PSD) of mixed signals and the PSD of signals from a single source. The numerical responses of a truss structure with high levels of added noise and very closely spaced modes were processed using the proposed combined method to evaluate its ability to identify modal parameters in these conditions. Colored and white noise excitations were used for the numerical example. The proposed combined method was also used to evaluate the modal parameters of an experimental test on a structure containing closely spaced modes. The results showed that the proposed combined method is capable of identifying very closely spaced modes in the presence of noise and, thus, may be potentially applied to improve the identification of damping ratios.

Bimodal Biometric Verification Using the Fusion of Palmprint and Infrared Palm-Dorsum Vein Images

PubMed Central

Lin, Chih-Lung; Wang, Shih-Hung; Cheng, Hsu-Yung; Fan, Kuo-Chin; Hsu, Wei-Lieh; Lai, Chin-Rong

2015-01-01

In this paper, we present a reliable and robust biometric verification method based on bimodal physiological characteristics of palms, including the palmprint and palm-dorsum vein patterns. The proposed method consists of five steps: (1) automatically aligning and cropping the same region of interest from different palm or palm-dorsum images; (2) applying the digital wavelet transform and inverse wavelet transform to fuse palmprint and vein pattern images; (3) extracting the line-like features (LLFs) from the fused image; (4) obtaining multiresolution representations of the LLFs by using a multiresolution filter; and (5) using a support vector machine to verify the multiresolution representations of the LLFs. The proposed method possesses four advantages: first, both modal images are captured in peg-free scenarios to improve the user-friendliness of the verification device. Second, palmprint and vein pattern images are captured using a low-resolution digital scanner and infrared (IR) camera. The use of low-resolution images results in a smaller database. In addition, the vein pattern images are captured through the invisible IR spectrum, which improves antispoofing. Third, since the physiological characteristics of palmprint and vein pattern images are different, a hybrid fusing rule can be introduced to fuse the decomposition coefficients of different bands. The proposed method fuses decomposition coefficients at different decomposed levels, with different image sizes, captured from different sensor devices. Finally, the proposed method operates automatically and hence no parameters need to be set manually. Three thousand palmprint images and 3000 vein pattern images were collected from 100 volunteers to verify the validity of the proposed method. The results show a false rejection rate of 1.20% and a false acceptance rate of 1.56%. It demonstrates the validity and excellent performance of our proposed method comparing to other methods. PMID:26703596

Bimodal Biometric Verification Using the Fusion of Palmprint and Infrared Palm-Dorsum Vein Images.

PubMed

Lin, Chih-Lung; Wang, Shih-Hung; Cheng, Hsu-Yung; Fan, Kuo-Chin; Hsu, Wei-Lieh; Lai, Chin-Rong

2015-12-12

In this paper, we present a reliable and robust biometric verification method based on bimodal physiological characteristics of palms, including the palmprint and palm-dorsum vein patterns. The proposed method consists of five steps: (1) automatically aligning and cropping the same region of interest from different palm or palm-dorsum images; (2) applying the digital wavelet transform and inverse wavelet transform to fuse palmprint and vein pattern images; (3) extracting the line-like features (LLFs) from the fused image; (4) obtaining multiresolution representations of the LLFs by using a multiresolution filter; and (5) using a support vector machine to verify the multiresolution representations of the LLFs. The proposed method possesses four advantages: first, both modal images are captured in peg-free scenarios to improve the user-friendliness of the verification device. Second, palmprint and vein pattern images are captured using a low-resolution digital scanner and infrared (IR) camera. The use of low-resolution images results in a smaller database. In addition, the vein pattern images are captured through the invisible IR spectrum, which improves antispoofing. Third, since the physiological characteristics of palmprint and vein pattern images are different, a hybrid fusing rule can be introduced to fuse the decomposition coefficients of different bands. The proposed method fuses decomposition coefficients at different decomposed levels, with different image sizes, captured from different sensor devices. Finally, the proposed method operates automatically and hence no parameters need to be set manually. Three thousand palmprint images and 3000 vein pattern images were collected from 100 volunteers to verify the validity of the proposed method. The results show a false rejection rate of 1.20% and a false acceptance rate of 1.56%. It demonstrates the validity and excellent performance of our proposed method comparing to other methods.

Dealing with noise and physiological artifacts in human EEG recordings: empirical mode methods

NASA Astrophysics Data System (ADS)

Runnova, Anastasiya E.; Grubov, Vadim V.; Khramova, Marina V.; Hramov, Alexander E.

2017-04-01

In the paper we propose the new method for removing noise and physiological artifacts in human EEG recordings based on empirical mode decomposition (Hilbert-Huang transform). As physiological artifacts we consider specific oscillatory patterns that cause problems during EEG analysis and can be detected with additional signals recorded simultaneously with EEG (ECG, EMG, EOG, etc.) We introduce the algorithm of the proposed method with steps including empirical mode decomposition of EEG signal, choosing of empirical modes with artifacts, removing these empirical modes and reconstructing of initial EEG signal. We show the efficiency of the method on the example of filtration of human EEG signal from eye-moving artifacts.

A deconvolution extraction method for 2D multi-object fibre spectroscopy based on the regularized least-squares QR-factorization algorithm

NASA Astrophysics Data System (ADS)

Yu, Jian; Yin, Qian; Guo, Ping; Luo, A.-li

2014-09-01

This paper presents an efficient method for the extraction of astronomical spectra from two-dimensional (2D) multifibre spectrographs based on the regularized least-squares QR-factorization (LSQR) algorithm. We address two issues: we propose a modified Gaussian point spread function (PSF) for modelling the 2D PSF from multi-emission-line gas-discharge lamp images (arc images), and we develop an efficient deconvolution method to extract spectra in real circumstances. The proposed modified 2D Gaussian PSF model can fit various types of 2D PSFs, including different radial distortion angles and ellipticities. We adopt the regularized LSQR algorithm to solve the sparse linear equations constructed from the sparse convolution matrix, which we designate the deconvolution spectrum extraction method. Furthermore, we implement a parallelized LSQR algorithm based on graphics processing unit programming in the Compute Unified Device Architecture to accelerate the computational processing. Experimental results illustrate that the proposed extraction method can greatly reduce the computational cost and memory use of the deconvolution method and, consequently, increase its efficiency and practicability. In addition, the proposed extraction method has a stronger noise tolerance than other methods, such as the boxcar (aperture) extraction and profile extraction methods. Finally, we present an analysis of the sensitivity of the extraction results to the radius and full width at half-maximum of the 2D PSF.

A coupling method for a cardiovascular simulation model which includes the Kalman filter.

PubMed

Hasegawa, Yuki; Shimayoshi, Takao; Amano, Akira; Matsuda, Tetsuya

2012-01-01

Multi-scale models of the cardiovascular system provide new insight that was unavailable with in vivo and in vitro experiments. For the cardiovascular system, multi-scale simulations provide a valuable perspective in analyzing the interaction of three phenomenons occurring at different spatial scales: circulatory hemodynamics, ventricular structural dynamics, and myocardial excitation-contraction. In order to simulate these interactions, multiscale cardiovascular simulation systems couple models that simulate different phenomena. However, coupling methods require a significant amount of calculation, since a system of non-linear equations must be solved for each timestep. Therefore, we proposed a coupling method which decreases the amount of calculation by using the Kalman filter. In our method, the Kalman filter calculates approximations for the solution to the system of non-linear equations at each timestep. The approximations are then used as initial values for solving the system of non-linear equations. The proposed method decreases the number of iterations required by 94.0% compared to the conventional strong coupling method. When compared with a smoothing spline predictor, the proposed method required 49.4% fewer iterations.

SU-E-J-89: Deformable Registration Method Using B-TPS in Radiotherapy.

PubMed

Xie, Y

2012-06-01

A novel deformable registration method for four-dimensional computed tomography (4DCT) images is developed in radiation therapy. The proposed method combines the thin plate spline (TPS) and B-spline together to achieve high accuracy and high efficiency. The method consists of two steps. First, TPS is used as a global registration method to deform large unfit regions in the moving image to match counterpart in the reference image. Then B-spline is used for local registration, the previous deformed moving image is further deformed to match the reference image more accurately. Two clinical CT image sets, including one pair of lung and one pair of liver, are simulated using the proposed algorithm, which results in a tremendous improvement in both run-time and registration quality, compared with the conventional methods solely using either TPS or B-spline. The proposed method can combine the efficiency of TPS and the accuracy of B-spline, performing good adaptively and robust in registration of clinical 4DCT image. © 2012 American Association of Physicists in Medicine.

A Robust Gradient Based Method for Building Extraction from LiDAR and Photogrammetric Imagery.

PubMed

Siddiqui, Fasahat Ullah; Teng, Shyh Wei; Awrangjeb, Mohammad; Lu, Guojun

2016-07-19

Existing automatic building extraction methods are not effective in extracting buildings which are small in size and have transparent roofs. The application of large area threshold prohibits detection of small buildings and the use of ground points in generating the building mask prevents detection of transparent buildings. In addition, the existing methods use numerous parameters to extract buildings in complex environments, e.g., hilly area and high vegetation. However, the empirical tuning of large number of parameters reduces the robustness of building extraction methods. This paper proposes a novel Gradient-based Building Extraction (GBE) method to address these limitations. The proposed method transforms the Light Detection And Ranging (LiDAR) height information into intensity image without interpolation of point heights and then analyses the gradient information in the image. Generally, building roof planes have a constant height change along the slope of a roof plane whereas trees have a random height change. With such an analysis, buildings of a greater range of sizes with a transparent or opaque roof can be extracted. In addition, a local colour matching approach is introduced as a post-processing stage to eliminate trees. This stage of our proposed method does not require any manual setting and all parameters are set automatically from the data. The other post processing stages including variance, point density and shadow elimination are also applied to verify the extracted buildings, where comparatively fewer empirically set parameters are used. The performance of the proposed GBE method is evaluated on two benchmark data sets by using the object and pixel based metrics (completeness, correctness and quality). Our experimental results show the effectiveness of the proposed method in eliminating trees, extracting buildings of all sizes, and extracting buildings with and without transparent roof. When compared with current state-of-the-art building extraction methods, the proposed method outperforms the existing methods in various evaluation metrics.

A Robust Gradient Based Method for Building Extraction from LiDAR and Photogrammetric Imagery

PubMed Central

Siddiqui, Fasahat Ullah; Teng, Shyh Wei; Awrangjeb, Mohammad; Lu, Guojun

2016-01-01

Existing automatic building extraction methods are not effective in extracting buildings which are small in size and have transparent roofs. The application of large area threshold prohibits detection of small buildings and the use of ground points in generating the building mask prevents detection of transparent buildings. In addition, the existing methods use numerous parameters to extract buildings in complex environments, e.g., hilly area and high vegetation. However, the empirical tuning of large number of parameters reduces the robustness of building extraction methods. This paper proposes a novel Gradient-based Building Extraction (GBE) method to address these limitations. The proposed method transforms the Light Detection And Ranging (LiDAR) height information into intensity image without interpolation of point heights and then analyses the gradient information in the image. Generally, building roof planes have a constant height change along the slope of a roof plane whereas trees have a random height change. With such an analysis, buildings of a greater range of sizes with a transparent or opaque roof can be extracted. In addition, a local colour matching approach is introduced as a post-processing stage to eliminate trees. This stage of our proposed method does not require any manual setting and all parameters are set automatically from the data. The other post processing stages including variance, point density and shadow elimination are also applied to verify the extracted buildings, where comparatively fewer empirically set parameters are used. The performance of the proposed GBE method is evaluated on two benchmark data sets by using the object and pixel based metrics (completeness, correctness and quality). Our experimental results show the effectiveness of the proposed method in eliminating trees, extracting buildings of all sizes, and extracting buildings with and without transparent roof. When compared with current state-of-the-art building extraction methods, the proposed method outperforms the existing methods in various evaluation metrics. PMID:27447631

Equivalent Circuit Parameter Calculation of Interior Permanent Magnet Motor Involving Iron Loss Resistance Using Finite Element Method

NASA Astrophysics Data System (ADS)

Yamazaki, Katsumi

In this paper, we propose a method to calculate the equivalent circuit parameters of interior permanent magnet motors including iron loss resistance using the finite element method. First, the finite element analysis considering harmonics and magnetic saturation is carried out to obtain time variations of magnetic fields in the stator and the rotor core. Second, the iron losses of the stator and the rotor are calculated from the results of the finite element analysis with the considerations of harmonic eddy current losses and the minor hysteresis losses of the core. As a result, we obtain the equivalent circuit parameters i.e. the d-q axis inductance and the iron loss resistance as functions of operating condition of the motor. The proposed method is applied to an interior permanent magnet motor to calculate the characteristics based on the equivalent circuit obtained by the proposed method. The calculated results are compared with the experimental results to verify the accuracy.

Accelerating an Ordered-Subset Low-Dose X-Ray Cone Beam Computed Tomography Image Reconstruction with a Power Factor and Total Variation Minimization.

PubMed

Huang, Hsuan-Ming; Hsiao, Ing-Tsung

2016-01-01

In recent years, there has been increased interest in low-dose X-ray cone beam computed tomography (CBCT) in many fields, including dentistry, guided radiotherapy and small animal imaging. Despite reducing the radiation dose, low-dose CBCT has not gained widespread acceptance in routine clinical practice. In addition to performing more evaluation studies, developing a fast and high-quality reconstruction algorithm is required. In this work, we propose an iterative reconstruction method that accelerates ordered-subsets (OS) reconstruction using a power factor. Furthermore, we combine it with the total-variation (TV) minimization method. Both simulation and phantom studies were conducted to evaluate the performance of the proposed method. Results show that the proposed method can accelerate conventional OS methods, greatly increase the convergence speed in early iterations. Moreover, applying the TV minimization to the power acceleration scheme can further improve the image quality while preserving the fast convergence rate.

Sensing Methods for Detecting Analog Television Signals

NASA Astrophysics Data System (ADS)

Rahman, Mohammad Azizur; Song, Chunyi; Harada, Hiroshi

This paper introduces a unified method of spectrum sensing for all existing analog television (TV) signals including NTSC, PAL and SECAM. We propose a correlation based method (CBM) with a single reference signal for sensing any analog TV signals. In addition we also propose an improved energy detection method. The CBM approach has been implemented in a hardware prototype specially designed for participating in Singapore TV white space (WS) test trial conducted by Infocomm Development Authority (IDA) of the Singapore government. Analytical and simulation results of the CBM method will be presented in the paper, as well as hardware testing results for sensing various analog TV signals. Both AWGN and fading channels will be considered. It is shown that the theoretical results closely match with those from simulations. Sensing performance of the hardware prototype will also be presented in fading environment by using a fading simulator. We present performance of the proposed techniques in terms of probability of false alarm, probability of detection, sensing time etc. We also present a comparative study of the various techniques.

Point cloud registration from local feature correspondences-Evaluation on challenging datasets.

PubMed

Petricek, Tomas; Svoboda, Tomas

2017-01-01

Registration of laser scans, or point clouds in general, is a crucial step of localization and mapping with mobile robots or in object modeling pipelines. A coarse alignment of the point clouds is generally needed before applying local methods such as the Iterative Closest Point (ICP) algorithm. We propose a feature-based approach to point cloud registration and evaluate the proposed method and its individual components on challenging real-world datasets. For a moderate overlap between the laser scans, the method provides a superior registration accuracy compared to state-of-the-art methods including Generalized ICP, 3D Normal-Distribution Transform, Fast Point-Feature Histograms, and 4-Points Congruent Sets. Compared to the surface normals, the points as the underlying features yield higher performance in both keypoint detection and establishing local reference frames. Moreover, sign disambiguation of the basis vectors proves to be an important aspect in creating repeatable local reference frames. A novel method for sign disambiguation is proposed which yields highly repeatable reference frames.

A MUSIC-based method for SSVEP signal processing.

PubMed

Chen, Kun; Liu, Quan; Ai, Qingsong; Zhou, Zude; Xie, Sheng Quan; Meng, Wei

2016-03-01

The research on brain computer interfaces (BCIs) has become a hotspot in recent years because it offers benefit to disabled people to communicate with the outside world. Steady state visual evoked potential (SSVEP)-based BCIs are more widely used because of higher signal to noise ratio and greater information transfer rate compared with other BCI techniques. In this paper, a multiple signal classification based method was proposed for multi-dimensional SSVEP feature extraction. 2-second data epochs from four electrodes achieved excellent accuracy rates including idle state detection. In some asynchronous mode experiments, the recognition accuracy reached up to 100%. The experimental results showed that the proposed method attained good frequency resolution. In most situations, the recognition accuracy was higher than canonical correlation analysis, which is a typical method for multi-channel SSVEP signal processing. Also, a virtual keyboard was successfully controlled by different subjects in an unshielded environment, which proved the feasibility of the proposed method for multi-dimensional SSVEP signal processing in practical applications.

An integrated condition-monitoring method for a milling process using reduced decomposition features

NASA Astrophysics Data System (ADS)

Liu, Jie; Wu, Bo; Wang, Yan; Hu, Youmin

2017-08-01

Complex and non-stationary cutting chatter affects productivity and quality in the milling process. Developing an effective condition-monitoring approach is critical to accurately identify cutting chatter. In this paper, an integrated condition-monitoring method is proposed, where reduced features are used to efficiently recognize and classify machine states in the milling process. In the proposed method, vibration signals are decomposed into multiple modes with variational mode decomposition, and Shannon power spectral entropy is calculated to extract features from the decomposed signals. Principal component analysis is adopted to reduce feature size and computational cost. With the extracted feature information, the probabilistic neural network model is used to recognize and classify the machine states, including stable, transition, and chatter states. Experimental studies are conducted, and results show that the proposed method can effectively detect cutting chatter during different milling operation conditions. This monitoring method is also efficient enough to satisfy fast machine state recognition and classification.

Accelerating an Ordered-Subset Low-Dose X-Ray Cone Beam Computed Tomography Image Reconstruction with a Power Factor and Total Variation Minimization

PubMed Central

Huang, Hsuan-Ming; Hsiao, Ing-Tsung

2016-01-01

In recent years, there has been increased interest in low-dose X-ray cone beam computed tomography (CBCT) in many fields, including dentistry, guided radiotherapy and small animal imaging. Despite reducing the radiation dose, low-dose CBCT has not gained widespread acceptance in routine clinical practice. In addition to performing more evaluation studies, developing a fast and high-quality reconstruction algorithm is required. In this work, we propose an iterative reconstruction method that accelerates ordered-subsets (OS) reconstruction using a power factor. Furthermore, we combine it with the total-variation (TV) minimization method. Both simulation and phantom studies were conducted to evaluate the performance of the proposed method. Results show that the proposed method can accelerate conventional OS methods, greatly increase the convergence speed in early iterations. Moreover, applying the TV minimization to the power acceleration scheme can further improve the image quality while preserving the fast convergence rate. PMID:27073853

A novel method for identifying disease associated protein complexes based on functional similarity protein complex networks.

PubMed

Le, Duc-Hau

2015-01-01

Protein complexes formed by non-covalent interaction among proteins play important roles in cellular functions. Computational and purification methods have been used to identify many protein complexes and their cellular functions. However, their roles in terms of causing disease have not been well discovered yet. There exist only a few studies for the identification of disease-associated protein complexes. However, they mostly utilize complicated heterogeneous networks which are constructed based on an out-of-date database of phenotype similarity network collected from literature. In addition, they only apply for diseases for which tissue-specific data exist. In this study, we propose a method to identify novel disease-protein complex associations. First, we introduce a framework to construct functional similarity protein complex networks where two protein complexes are functionally connected by either shared protein elements, shared annotating GO terms or based on protein interactions between elements in each protein complex. Second, we propose a simple but effective neighborhood-based algorithm, which yields a local similarity measure, to rank disease candidate protein complexes. Comparing the predictive performance of our proposed algorithm with that of two state-of-the-art network propagation algorithms including one we used in our previous study, we found that it performed statistically significantly better than that of these two algorithms for all the constructed functional similarity protein complex networks. In addition, it ran about 32 times faster than these two algorithms. Moreover, our proposed method always achieved high performance in terms of AUC values irrespective of the ways to construct the functional similarity protein complex networks and the used algorithms. The performance of our method was also higher than that reported in some existing methods which were based on complicated heterogeneous networks. Finally, we also tested our method with prostate cancer and selected the top 100 highly ranked candidate protein complexes. Interestingly, 69 of them were evidenced since at least one of their protein elements are known to be associated with prostate cancer. Our proposed method, including the framework to construct functional similarity protein complex networks and the neighborhood-based algorithm on these networks, could be used for identification of novel disease-protein complex associations.

A Noise Removal Method for Uniform Circular Arrays in Complex Underwater Noise Environments with Low SNR

PubMed Central

Xia, Huijun; Yang, Kunde; Ma, Yuanliang; Wang, Yong; Liu, Yaxiong

2017-01-01

Generally, many beamforming methods are derived under the assumption of white noise. In practice, the actual underwater ambient noise is complex. As a result, the noise removal capacity of the beamforming method may be deteriorated considerably. Furthermore, in underwater environment with extremely low signal-to-noise ratio (SNR), the performances of the beamforming method may be deteriorated. To tackle these problems, a noise removal method for uniform circular array (UCA) is proposed to remove the received noise and improve the SNR in complex noise environments with low SNR. First, the symmetrical noise sources are defined and the spatial correlation of the symmetrical noise sources is calculated. Then, based on the preceding results, the noise covariance matrix is decomposed into symmetrical and asymmetrical components. Analysis indicates that the symmetrical component only affect the real part of the noise covariance matrix. Consequently, the delay-and-sum (DAS) beamforming is performed by using the imaginary part of the covariance matrix to remove the symmetrical component. However, the noise removal method causes two problems. First, the proposed method produces a false target. Second, the proposed method would seriously suppress the signal when it is located in some directions. To solve the first problem, two methods to reconstruct the signal covariance matrix are presented: based on the estimation of signal variance and based on the constrained optimization algorithm. To solve the second problem, we can design the array configuration and select the suitable working frequency. Theoretical analysis and experimental results are included to demonstrate that the proposed methods are particularly effective in complex noise environments with low SNR. The proposed method can be extended to any array. PMID:28598386

Malware Analysis Using Visualized Image Matrices

PubMed Central

Im, Eul Gyu

2014-01-01

This paper proposes a novel malware visual analysis method that contains not only a visualization method to convert binary files into images, but also a similarity calculation method between these images. The proposed method generates RGB-colored pixels on image matrices using the opcode sequences extracted from malware samples and calculates the similarities for the image matrices. Particularly, our proposed methods are available for packed malware samples by applying them to the execution traces extracted through dynamic analysis. When the images are generated, we can reduce the overheads by extracting the opcode sequences only from the blocks that include the instructions related to staple behaviors such as functions and application programming interface (API) calls. In addition, we propose a technique that generates a representative image for each malware family in order to reduce the number of comparisons for the classification of unknown samples and the colored pixel information in the image matrices is used to calculate the similarities between the images. Our experimental results show that the image matrices of malware can effectively be used to classify malware families both statically and dynamically with accuracy of 0.9896 and 0.9732, respectively. PMID:25133202

Image processing pipeline for segmentation and material classification based on multispectral high dynamic range polarimetric images.

PubMed

Martínez-Domingo, Miguel Ángel; Valero, Eva M; Hernández-Andrés, Javier; Tominaga, Shoji; Horiuchi, Takahiko; Hirai, Keita

2017-11-27

We propose a method for the capture of high dynamic range (HDR), multispectral (MS), polarimetric (Pol) images of indoor scenes using a liquid crystal tunable filter (LCTF). We have included the adaptive exposure estimation (AEE) method to fully automatize the capturing process. We also propose a pre-processing method which can be applied for the registration of HDR images after they are already built as the result of combining different low dynamic range (LDR) images. This method is applied to ensure a correct alignment of the different polarization HDR images for each spectral band. We have focused our efforts in two main applications: object segmentation and classification into metal and dielectric classes. We have simplified the segmentation using mean shift combined with cluster averaging and region merging techniques. We compare the performance of our segmentation with that of Ncut and Watershed methods. For the classification task, we propose to use information not only in the highlight regions but also in their surrounding area, extracted from the degree of linear polarization (DoLP) maps. We present experimental results which proof that the proposed image processing pipeline outperforms previous techniques developed specifically for MSHDRPol image cubes.

Using SVD on Clusters to Improve Precision of Interdocument Similarity Measure.

PubMed

Zhang, Wen; Xiao, Fan; Li, Bin; Zhang, Siguang

2016-01-01

Recently, LSI (Latent Semantic Indexing) based on SVD (Singular Value Decomposition) is proposed to overcome the problems of polysemy and homonym in traditional lexical matching. However, it is usually criticized as with low discriminative power for representing documents although it has been validated as with good representative quality. In this paper, SVD on clusters is proposed to improve the discriminative power of LSI. The contribution of this paper is three manifolds. Firstly, we make a survey of existing linear algebra methods for LSI, including both SVD based methods and non-SVD based methods. Secondly, we propose SVD on clusters for LSI and theoretically explain that dimension expansion of document vectors and dimension projection using SVD are the two manipulations involved in SVD on clusters. Moreover, we develop updating processes to fold in new documents and terms in a decomposed matrix by SVD on clusters. Thirdly, two corpora, a Chinese corpus and an English corpus, are used to evaluate the performances of the proposed methods. Experiments demonstrate that, to some extent, SVD on clusters can improve the precision of interdocument similarity measure in comparison with other SVD based LSI methods.

Using SVD on Clusters to Improve Precision of Interdocument Similarity Measure

PubMed Central

Xiao, Fan; Li, Bin; Zhang, Siguang

2016-01-01

Recently, LSI (Latent Semantic Indexing) based on SVD (Singular Value Decomposition) is proposed to overcome the problems of polysemy and homonym in traditional lexical matching. However, it is usually criticized as with low discriminative power for representing documents although it has been validated as with good representative quality. In this paper, SVD on clusters is proposed to improve the discriminative power of LSI. The contribution of this paper is three manifolds. Firstly, we make a survey of existing linear algebra methods for LSI, including both SVD based methods and non-SVD based methods. Secondly, we propose SVD on clusters for LSI and theoretically explain that dimension expansion of document vectors and dimension projection using SVD are the two manipulations involved in SVD on clusters. Moreover, we develop updating processes to fold in new documents and terms in a decomposed matrix by SVD on clusters. Thirdly, two corpora, a Chinese corpus and an English corpus, are used to evaluate the performances of the proposed methods. Experiments demonstrate that, to some extent, SVD on clusters can improve the precision of interdocument similarity measure in comparison with other SVD based LSI methods. PMID:27579031

Stroke-model-based character extraction from gray-level document images.

PubMed

Ye, X; Cheriet, M; Suen, C Y

2001-01-01

Global gray-level thresholding techniques such as Otsu's method, and local gray-level thresholding techniques such as edge-based segmentation or the adaptive thresholding method are powerful in extracting character objects from simple or slowly varying backgrounds. However, they are found to be insufficient when the backgrounds include sharply varying contours or fonts in different sizes. A stroke-model is proposed to depict the local features of character objects as double-edges in a predefined size. This model enables us to detect thin connected components selectively, while ignoring relatively large backgrounds that appear complex. Meanwhile, since the stroke width restriction is fully factored in, the proposed technique can be used to extract characters in predefined font sizes. To process large volumes of documents efficiently, a hybrid method is proposed for character extraction from various backgrounds. Using the measurement of class separability to differentiate images with simple backgrounds from those with complex backgrounds, the hybrid method can process documents with different backgrounds by applying the appropriate methods. Experiments on extracting handwriting from a check image, as well as machine-printed characters from scene images demonstrate the effectiveness of the proposed model.

48 CFR 15.306 - Exchanges with offerors after receipt of proposals.

Code of Federal Regulations, 2011 CFR

2011-10-01

... negotiations may include bargaining. Bargaining includes persuasion, alteration of assumptions and positions... ACQUISITION REGULATION CONTRACTING METHODS AND CONTRACT TYPES CONTRACTING BY NEGOTIATION Source Selection 15.... Negotiations are exchanges, in either a competitive or sole source environment, between the Government and...

48 CFR 15.306 - Exchanges with offerors after receipt of proposals.

Code of Federal Regulations, 2010 CFR

2010-10-01

... negotiations may include bargaining. Bargaining includes persuasion, alteration of assumptions and positions... ACQUISITION REGULATION CONTRACTING METHODS AND CONTRACT TYPES CONTRACTING BY NEGOTIATION Source Selection 15.... Negotiations are exchanges, in either a competitive or sole source environment, between the Government and...

Run-Curve Design for Energy Saving Operation in a Modern DC-Electrification

NASA Astrophysics Data System (ADS)

Koseki, Takafumi; Noda, Takashi

Mechanical brakes are often used by electric trains. These brakes have a few problems like response speed, coefficient of friction, maintenance cost and so on. As a result, methods for actively using regenerative brakes are required. In this paper, we propose the useful pure electric braking, which would involve ordinary brakes by only regenerative brakes without any mechanical brakes at high speed. Benefits of our proposal include a DC-electrification system with regenerative substations that can return powers to the commercial power system and a train that can use the full regenerative braking force. We furthermore evaluate the effects on running time and energies saved by regenerative substations in the proposed method.

Time-domain simulation of constitutive relations for nonlinear acoustics including relaxation for frequency power law attenuation media modeling

NASA Astrophysics Data System (ADS)

Jiménez, Noé; Camarena, Francisco; Redondo, Javier; Sánchez-Morcillo, Víctor; Konofagou, Elisa E.

2015-10-01

We report a numerical method for solving the constitutive relations of nonlinear acoustics, where multiple relaxation processes are included in a generalized formulation that allows the time-domain numerical solution by an explicit finite differences scheme. Thus, the proposed physical model overcomes the limitations of the one-way Khokhlov-Zabolotskaya-Kuznetsov (KZK) type models and, due to the Lagrangian density is implicitly included in the calculation, the proposed method also overcomes the limitations of Westervelt equation in complex configurations for medical ultrasound. In order to model frequency power law attenuation and dispersion, such as observed in biological media, the relaxation parameters are fitted to both exact frequency power law attenuation/dispersion media and also empirically measured attenuation of a variety of tissues that does not fit an exact power law. Finally, a computational technique based on artificial relaxation is included to correct the non-negligible numerical dispersion of the finite difference scheme, and, on the other hand, improve stability trough artificial attenuation when shock waves are present. This technique avoids the use of high-order finite-differences schemes leading to fast calculations. The present algorithm is especially suited for practical configuration where spatial discontinuities are present in the domain (e.g. axisymmetric domains or zero normal velocity boundary conditions in general). The accuracy of the method is discussed by comparing the proposed simulation solutions to one dimensional analytical and k-space numerical solutions.

A Novel Interfacing Technique for Distributed Hybrid Simulations Combining EMT and Transient Stability Models

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

Shu, Dewu; Xie, Xiaorong; Jiang, Qirong

With steady increase of power electronic devices and nonlinear dynamic loads in large scale AC/DC systems, the traditional hybrid simulation method, which incorporates these components into a single EMT subsystem and hence causes great difficulty for network partitioning and significant deterioration in simulation efficiency. To resolve these issues, a novel distributed hybrid simulation method is proposed in this paper. The key to realize this method is a distinct interfacing technique, which includes: i) a new approach based on the two-level Schur complement to update the interfaces by taking full consideration of the couplings between different EMT subsystems; and ii) amore » combined interaction protocol to further improve the efficiency while guaranteeing the simulation accuracy. The advantages of the proposed method in terms of both efficiency and accuracy have been verified by using it for the simulation study of an AC/DC hybrid system including a two-terminal VSC-HVDC and nonlinear dynamic loads.« less

Testing the Construct Validity of Proposed Criteria for "DSM-5" Autism Spectrum Disorder

ERIC Educational Resources Information Center

Mandy, William P. L.; Charman, Tony; Skuse, David H.

2012-01-01

Objective: To use confirmatory factor analysis to test the construct validity of the proposed "DSM-5" symptom model of autism spectrum disorder (ASD), in comparison to alternative models, including that described in "DSM-IV-TR." Method: Participants were 708 verbal children and young persons (mean age, 9.5 years) with mild to severe autistic…

An Open Source Agenda for Research Linking Text and Image Content Features.

ERIC Educational Resources Information Center

Goodrum, Abby A.; Rorvig, Mark E.; Jeong, Ki-Tai; Suresh, Chitturi

2001-01-01

Proposes methods to utilize image primitives to support term assignment for image classification. Proposes to release code for image analysis in a common tool set for other researchers to use. Of particular focus is the expansion of work by researchers in image indexing to include image content-based feature extraction capabilities in their work.…

Automatic pole-like object modeling via 3D part-based analysis of point cloud

NASA Astrophysics Data System (ADS)

He, Liu; Yang, Haoxiang; Huang, Yuchun

2016-10-01

Pole-like objects, including trees, lampposts and traffic signs, are indispensable part of urban infrastructure. With the advance of vehicle-based laser scanning (VLS), massive point cloud of roadside urban areas becomes applied in 3D digital city modeling. Based on the property that different pole-like objects have various canopy parts and similar trunk parts, this paper proposed the 3D part-based shape analysis to robustly extract, identify and model the pole-like objects. The proposed method includes: 3D clustering and recognition of trunks, voxel growing and part-based 3D modeling. After preprocessing, the trunk center is identified as the point that has local density peak and the largest minimum inter-cluster distance. Starting from the trunk centers, the remaining points are iteratively clustered to the same centers of their nearest point with higher density. To eliminate the noisy points, cluster border is refined by trimming boundary outliers. Then, candidate trunks are extracted based on the clustering results in three orthogonal planes by shape analysis. Voxel growing obtains the completed pole-like objects regardless of overlaying. Finally, entire trunk, branch and crown part are analyzed to obtain seven feature parameters. These parameters are utilized to model three parts respectively and get signal part-assembled 3D model. The proposed method is tested using the VLS-based point cloud of Wuhan University, China. The point cloud includes many kinds of trees, lampposts and other pole-like posters under different occlusions and overlaying. Experimental results show that the proposed method can extract the exact attributes and model the roadside pole-like objects efficiently.

Breast masses in mammography classification with local contour features.

PubMed

Li, Haixia; Meng, Xianjing; Wang, Tingwen; Tang, Yuchun; Yin, Yilong

2017-04-14

Mammography is one of the most popular tools for early detection of breast cancer. Contour of breast mass in mammography is very important information to distinguish benign and malignant mass. Contour of benign mass is smooth and round or oval, while malignant mass has irregular shape and spiculated contour. Several studies have shown that 1D signature translated from 2D contour can describe the contour features well. In this paper, we propose a new method to translate 2D contour of breast mass in mammography into 1D signature. The method can describe not only the contour features but also the regularity of breast mass. Then we segment the whole 1D signature into different subsections. We extract four local features including a new contour descriptor from the subsections. The new contour descriptor is root mean square (RMS) slope. It can describe the roughness of the contour. KNN, SVM and ANN classifier are used to classify benign breast mass and malignant mass. The proposed method is tested on a set with 323 contours including 143 benign masses and 180 malignant ones from digital database of screening mammography (DDSM). The best accuracy of classification is 99.66% using the feature of root mean square slope with SVM classifier. The performance of the proposed method is better than traditional method. In addition, RMS slope is an effective feature comparable to most of the existing features.

Automatic choroid cells segmentation and counting in fluorescence microscopic image

NASA Astrophysics Data System (ADS)

Fei, Jianjun; Zhu, Weifang; Shi, Fei; Xiang, Dehui; Lin, Xiao; Yang, Lei; Chen, Xinjian

2016-03-01

In this paper, we proposed a method to automatically segment and count the rhesus choroid-retinal vascular endothelial cells (RF/6A) in fluorescence microscopic images which is based on shape classification, bottleneck detection and accelerated Dijkstra algorithm. The proposed method includes four main steps. First, a thresholding filter and morphological operations are applied to reduce the noise. Second, a shape classifier is used to decide whether a connected component is needed to be segmented. In this step, the AdaBoost classifier is applied with a set of shape features. Third, the bottleneck positions are found based on the contours of the connected components. Finally, the cells segmentation and counting are completed based on the accelerated Dijkstra algorithm with the gradient information between the bottleneck positions. The results show the feasibility and efficiency of the proposed method.

Single shot multi-wavelength phase retrieval with coherent modulation imaging.

PubMed

Dong, Xue; Pan, Xingchen; Liu, Cheng; Zhu, Jianqiang

2018-04-15

A single shot multi-wavelength phase retrieval method is proposed by combining common coherent modulation imaging (CMI) and a low rank mixed-state algorithm together. A radiation beam consisting of multi-wavelength is illuminated on the sample to be observed, and the exiting field is incident on a random phase plate to form speckle patterns, which is the incoherent superposition of diffraction patterns of each wavelength. The exiting complex amplitude of the sample including both the modulus and phase of each wavelength can be reconstructed simultaneously from the recorded diffraction intensity using a low rank mixed-state algorithm. The feasibility of this proposed method was verified with visible light experimentally. This proposed method not only makes CMI realizable with partially coherent illumination but also can extend its application to various traditionally unrelated fields, where several wavelengths should be considered simultaneously.

Laser-induced plasma characterization through self-absorption quantification

NASA Astrophysics Data System (ADS)

Hou, JiaJia; Zhang, Lei; Zhao, Yang; Yan, Xingyu; Ma, Weiguang; Dong, Lei; Yin, Wangbao; Xiao, Liantuan; Jia, Suotang

2018-07-01

A self-absorption quantification method is proposed to quantify the self-absorption degree of spectral lines, in which plasma characteristics including electron temperature, elemental concentration ratio, and absolute species number density can be deduced directly. Since there is no spectral intensity involved in the calculation, the analysis results are independent of the self-absorption effects and the additional spectral efficiency calibration is not required. In order to evaluate the practicality, the limitation for application and the precision of this method are also discussed. Experimental results of aluminum-lithium alloy prove that the proposed method is qualified to realize semi-quantitative measurements and fast plasma characteristics diagnostics.

Alignment of chirped-pulse compressor

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

Yakovlev, I V

2012-11-30

An original method of alignment of grating compressors for ultrahigh-power CPA laser systems is proposed. The use of this method for adjustment of the grating compressor of a PEARL subpetawatt laser complex made it possible to align the diffraction gratings with a second accuracy in all three angular degrees of freedom, including alignment of the grooves, and to adjust the angles of beam incidence on the grating with a high accuracy. A simple method for measuring the difference in the groove densities of gratings with accuracy better than 0.005 lines mm{sup -1} is proposed and tested. (control of laser radiationmore » parameters)« less

Passive quantum error correction of linear optics networks through error averaging

NASA Astrophysics Data System (ADS)

Marshman, Ryan J.; Lund, Austin P.; Rohde, Peter P.; Ralph, Timothy C.

2018-02-01

We propose and investigate a method of error detection and noise correction for bosonic linear networks using a method of unitary averaging. The proposed error averaging does not rely on ancillary photons or control and feedforward correction circuits, remaining entirely passive in its operation. We construct a general mathematical framework for this technique and then give a series of proof of principle examples including numerical analysis. Two methods for the construction of averaging are then compared to determine the most effective manner of implementation and probe the related error thresholds. Finally we discuss some of the potential uses of this scheme.

A New Method for Single-Epoch Ambiguity Resolution with Indoor Pseudolite Positioning.

PubMed

Li, Xin; Zhang, Peng; Guo, Jiming; Wang, Jinling; Qiu, Weining

2017-04-21

Ambiguity resolution (AR) is crucial for high-precision indoor pseudolite positioning. Due to the existing characteristics of the pseudolite positioning system, such as the geometry structure of the stationary pseudolite which is consistently invariant, the indoor signal is easy to interrupt and the first order linear truncation error cannot be ignored, and a new AR method based on the idea of the ambiguity function method (AFM) is proposed in this paper. The proposed method is a single-epoch and nonlinear method that is especially well-suited for indoor pseudolite positioning. Considering the very low computational efficiency of conventional AFM, we adopt an improved particle swarm optimization (IPSO) algorithm to search for the best solution in the coordinate domain, and variances of a least squares adjustment is conducted to ensure the reliability of the solving ambiguity. Several experiments, including static and kinematic tests, are conducted to verify the validity of the proposed AR method. Numerical results show that the IPSO significantly improved the computational efficiency of AFM and has a more elaborate search ability compared to the conventional grid searching method. For the indoor pseudolite system, which had an initial approximate coordinate precision better than 0.2 m, the AFM exhibited good performances in both static and kinematic tests. With the corrected ambiguity gained from our proposed method, indoor pseudolite positioning can achieve centimeter-level precision using a low-cost single-frequency software receiver.

Wavelength calibration of dispersive near-infrared spectrometer using relative k-space distribution with low coherence interferometer

NASA Astrophysics Data System (ADS)

Kim, Ji-hyun; Han, Jae-Ho; Jeong, Jichai

2016-05-01

The commonly employed calibration methods for laboratory-made spectrometers have several disadvantages, including poor calibration when the number of characteristic spectral peaks is low. Therefore, we present a wavelength calibration method using relative k-space distribution with low coherence interferometer. The proposed method utilizes an interferogram with a perfect sinusoidal pattern in k-space for calibration. Zero-crossing detection extracts the k-space distribution of a spectrometer from the interferogram in the wavelength domain, and a calibration lamp provides information about absolute wavenumbers. To assign wavenumbers, wavelength-to-k-space conversion is required for the characteristic spectrum of the calibration lamp with the extracted k-space distribution. Then, the wavelength calibration is completed by inverse conversion of the k-space into wavelength domain. The calibration performance of the proposed method was demonstrated with two experimental conditions of four and eight characteristic spectral peaks. The proposed method elicited reliable calibration results in both cases, whereas the conventional method of third-order polynomial curve fitting failed to determine wavelengths in the case of four characteristic peaks. Moreover, for optical coherence tomography imaging, the proposed method could improve axial resolution due to higher suppression of sidelobes in point spread function than the conventional method. We believe that our findings can improve not only wavelength calibration accuracy but also resolution for optical coherence tomography.

Design of a dual-mode electrochemical measurement and analysis system.

PubMed

Yang, Jr-Fu; Wei, Chia-Ling; Wu, Jian-Fu; Liu, Bin-Da

2013-01-01

A dual-mode electrochemical measurement and analysis system is proposed. This system includes a dual-mode chip, which was designed and fabricated by using TSMC 0.35 µm 3.3 V/5 V 2P4M mixed-signal CMOS process. Two electrochemical measurement and analysis methods, chronopotentiometry and voltammetry, can be performed by using the proposed chip and system. The proposed chip and system are verified successfully by performing voltammetry and chronopotentiometry on solutions.

Stress and PTSD Mechanisms as Targets for Pharmacotherapy of Alcohol Abuse, Addiction, and Relapse

DTIC Science & Technology

2014-10-01

hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed , and...new staff person needed for the proposed studies and have also implemented new methods that were not in the original proposal but that make the...in year 3, due to the urgent need for new therapies to prevent PTSD in response to trauma. The remaining proposed studies using rat models to

A double expansion method for the frequency response of finite-length beams with periodic parameters

NASA Astrophysics Data System (ADS)

Ying, Z. G.; Ni, Y. Q.

2017-03-01

A double expansion method for the frequency response of finite-length beams with periodic distribution parameters is proposed. The vibration response of the beam with spatial periodic parameters under harmonic excitations is studied. The frequency response of the periodic beam is the function of parametric period and then can be expressed by the series with the product of periodic and non-periodic functions. The procedure of the double expansion method includes the following two main steps: first, the frequency response function and periodic parameters are expanded by using identical periodic functions based on the extension of the Floquet-Bloch theorem, and the period-parametric differential equation for the frequency response is converted into a series of linear differential equations with constant coefficients; second, the solutions to the linear differential equations are expanded by using modal functions which satisfy the boundary conditions, and the linear differential equations are converted into algebraic equations according to the Galerkin method. The expansion coefficients are obtained by solving the algebraic equations and then the frequency response function is finally determined. The proposed double expansion method can uncouple the effects of the periodic expansion and modal expansion so that the expansion terms are determined respectively. The modal number considered in the second expansion can be reduced remarkably in comparison with the direct expansion method. The proposed double expansion method can be extended and applied to the other structures with periodic distribution parameters for dynamics analysis. Numerical results on the frequency response of the finite-length periodic beam with various parametric wave numbers and wave amplitude ratios are given to illustrate the effective application of the proposed method and the new frequency response characteristics, including the parameter-excited modal resonance, doubling-peak frequency response and remarkable reduction of the maximum frequency response for certain parametric wave number and wave amplitude. The results have the potential application to structural vibration control.

Filter Bank Regularized Common Spatial Pattern Ensemble for Small Sample Motor Imagery Classification.

PubMed

Park, Sang-Hoon; Lee, David; Lee, Sang-Goog

2018-02-01

For the last few years, many feature extraction methods have been proposed based on biological signals. Among these, the brain signals have the advantage that they can be obtained, even by people with peripheral nervous system damage. Motor imagery electroencephalograms (EEG) are inexpensive to measure, offer a high temporal resolution, and are intuitive. Therefore, these have received a significant amount of attention in various fields, including signal processing, cognitive science, and medicine. The common spatial pattern (CSP) algorithm is a useful method for feature extraction from motor imagery EEG. However, performance degradation occurs in a small-sample setting (SSS), because the CSP depends on sample-based covariance. Since the active frequency range is different for each subject, it is also inconvenient to set the frequency range to be different every time. In this paper, we propose the feature extraction method based on a filter bank to solve these problems. The proposed method consists of five steps. First, motor imagery EEG is divided by a using filter bank. Second, the regularized CSP (R-CSP) is applied to the divided EEG. Third, we select the features according to mutual information based on the individual feature algorithm. Fourth, parameter sets are selected for the ensemble. Finally, we classify using ensemble based on features. The brain-computer interface competition III data set IVa is used to evaluate the performance of the proposed method. The proposed method improves the mean classification accuracy by 12.34%, 11.57%, 9%, 4.95%, and 4.47% compared with CSP, SR-CSP, R-CSP, filter bank CSP (FBCSP), and SR-FBCSP. Compared with the filter bank R-CSP ( , ), which is a parameter selection version of the proposed method, the classification accuracy is improved by 3.49%. In particular, the proposed method shows a large improvement in performance in the SSS.

The Mediated MIMIC Model for Understanding the Underlying Mechanism of DIF

ERIC Educational Resources Information Center

Cheng, Ying; Shao, Can; Lathrop, Quinn N.

2016-01-01

Due to its flexibility, the multiple-indicator, multiple-causes (MIMIC) model has become an increasingly popular method for the detection of differential item functioning (DIF). In this article, we propose the mediated MIMIC model method to uncover the underlying mechanism of DIF. This method extends the usual MIMIC model by including one variable…

Climate Change Discourse in Mass Media: Application of Computer-Assisted Content Analysis

ERIC Educational Resources Information Center

Kirilenko, Andrei P.; Stepchenkova, Svetlana O.

2012-01-01

Content analysis of mass media publications has become a major scientific method used to analyze public discourse on climate change. We propose a computer-assisted content analysis method to extract prevalent themes and analyze discourse changes over an extended period in an objective and quantifiable manner. The method includes the following: (1)…

An efficient genome-wide association test for multivariate phenotypes based on the Fisher combination function.

PubMed

Yang, James J; Li, Jia; Williams, L Keoki; Buu, Anne

2016-01-05

In genome-wide association studies (GWAS) for complex diseases, the association between a SNP and each phenotype is usually weak. Combining multiple related phenotypic traits can increase the power of gene search and thus is a practically important area that requires methodology work. This study provides a comprehensive review of existing methods for conducting GWAS on complex diseases with multiple phenotypes including the multivariate analysis of variance (MANOVA), the principal component analysis (PCA), the generalizing estimating equations (GEE), the trait-based association test involving the extended Simes procedure (TATES), and the classical Fisher combination test. We propose a new method that relaxes the unrealistic independence assumption of the classical Fisher combination test and is computationally efficient. To demonstrate applications of the proposed method, we also present the results of statistical analysis on the Study of Addiction: Genetics and Environment (SAGE) data. Our simulation study shows that the proposed method has higher power than existing methods while controlling for the type I error rate. The GEE and the classical Fisher combination test, on the other hand, do not control the type I error rate and thus are not recommended. In general, the power of the competing methods decreases as the correlation between phenotypes increases. All the methods tend to have lower power when the multivariate phenotypes come from long tailed distributions. The real data analysis also demonstrates that the proposed method allows us to compare the marginal results with the multivariate results and specify which SNPs are specific to a particular phenotype or contribute to the common construct. The proposed method outperforms existing methods in most settings and also has great applications in GWAS on complex diseases with multiple phenotypes such as the substance abuse disorders.

40 CFR 160.120 - Protocol.

Code of Federal Regulations, 2010 CFR

2010-07-01

... system. (8) A description of the experimental design, including methods for the control of bias. (9... being conducted. (4) The proposed experimental start and termination dates. (5) Justification for...

40 CFR 160.120 - Protocol.

Code of Federal Regulations, 2013 CFR

2013-07-01

... system. (8) A description of the experimental design, including methods for the control of bias. (9... being conducted. (4) The proposed experimental start and termination dates. (5) Justification for...

40 CFR 160.120 - Protocol.

Code of Federal Regulations, 2012 CFR

2012-07-01

... system. (8) A description of the experimental design, including methods for the control of bias. (9... being conducted. (4) The proposed experimental start and termination dates. (5) Justification for...

40 CFR 160.120 - Protocol.

Code of Federal Regulations, 2011 CFR

2011-07-01

... system. (8) A description of the experimental design, including methods for the control of bias. (9... being conducted. (4) The proposed experimental start and termination dates. (5) Justification for...

40 CFR 160.120 - Protocol.

Code of Federal Regulations, 2014 CFR

2014-07-01

... system. (8) A description of the experimental design, including methods for the control of bias. (9... being conducted. (4) The proposed experimental start and termination dates. (5) Justification for...

An Extended Spectral-Spatial Classification Approach for Hyperspectral Data

NASA Astrophysics Data System (ADS)

Akbari, D.

2017-11-01

In this paper an extended classification approach for hyperspectral imagery based on both spectral and spatial information is proposed. The spatial information is obtained by an enhanced marker-based minimum spanning forest (MSF) algorithm. Three different methods of dimension reduction are first used to obtain the subspace of hyperspectral data: (1) unsupervised feature extraction methods including principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF); (2) supervised feature extraction including decision boundary feature extraction (DBFE), discriminate analysis feature extraction (DAFE), and nonparametric weighted feature extraction (NWFE); (3) genetic algorithm (GA). The spectral features obtained are then fed into the enhanced marker-based MSF classification algorithm. In the enhanced MSF algorithm, the markers are extracted from the classification maps obtained by both SVM and watershed segmentation algorithm. To evaluate the proposed approach, the Pavia University hyperspectral data is tested. Experimental results show that the proposed approach using GA achieves an approximately 8 % overall accuracy higher than the original MSF-based algorithm.

Automated detection of retinal nerve fiber layer defects on fundus images: false positive reduction based on vessel likelihood

NASA Astrophysics Data System (ADS)

Muramatsu, Chisako; Ishida, Kyoko; Sawada, Akira; Hatanaka, Yuji; Yamamoto, Tetsuya; Fujita, Hiroshi

2016-03-01

Early detection of glaucoma is important to slow down or cease progression of the disease and for preventing total blindness. We have previously proposed an automated scheme for detection of retinal nerve fiber layer defect (NFLD), which is one of the early signs of glaucoma observed on retinal fundus images. In this study, a new multi-step detection scheme was included to improve detection of subtle and narrow NFLDs. In addition, new features were added to distinguish between NFLDs and blood vessels, which are frequent sites of false positives (FPs). The result was evaluated with a new test dataset consisted of 261 cases, including 130 cases with NFLDs. Using the proposed method, the initial detection rate was improved from 82% to 98%. At the sensitivity of 80%, the number of FPs per image was reduced from 4.25 to 1.36. The result indicates the potential usefulness of the proposed method for early detection of glaucoma.

A monotonicity preserving conservative sharp interface flow solver for high density ratio two-phase flows

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

Le Chenadec, Vincent, E-mail: vlechena@stanford.edu; Pitsch, Heinz; Institute for Combustion Technology, RWTH Aachen, Templergraben 64, 52056 Aachen

2013-09-15

This paper presents a novel approach for solving the conservative form of the incompressible two-phase Navier–Stokes equations. In order to overcome the numerical instability induced by the potentially large density ratio encountered across the interface, the proposed method includes a Volume-of-Fluid type integration of the convective momentum transport, a monotonicity preserving momentum rescaling, and a consistent and conservative Ghost Fluid projection that includes surface tension effects. The numerical dissipation inherent in the Volume-of-Fluid treatment of the convective transport is localized in the interface vicinity, enabling the use of a kinetic energy conserving discretization away from the singularity. Two- and three-dimensionalmore » tests are presented, and the solutions shown to remain accurate at arbitrary density ratios. The proposed method is then successfully used to perform the detailed simulation of a round water jet emerging in quiescent air, therefore suggesting the applicability of the proposed algorithm to the computation of realistic turbulent atomization.« less

Comparing and combining biomarkers as principle surrogates for time-to-event clinical endpoints.

PubMed

Gabriel, Erin E; Sachs, Michael C; Gilbert, Peter B

2015-02-10

Principal surrogate endpoints are useful as targets for phase I and II trials. In many recent trials, multiple post-randomization biomarkers are measured. However, few statistical methods exist for comparison of or combination of biomarkers as principal surrogates, and none of these methods to our knowledge utilize time-to-event clinical endpoint information. We propose a Weibull model extension of the semi-parametric estimated maximum likelihood method that allows for the inclusion of multiple biomarkers in the same risk model as multivariate candidate principal surrogates. We propose several methods for comparing candidate principal surrogates and evaluating multivariate principal surrogates. These include the time-dependent and surrogate-dependent true and false positive fraction, the time-dependent and the integrated standardized total gain, and the cumulative distribution function of the risk difference. We illustrate the operating characteristics of our proposed methods in simulations and outline how these statistics can be used to evaluate and compare candidate principal surrogates. We use these methods to investigate candidate surrogates in the Diabetes Control and Complications Trial. Copyright © 2014 John Wiley & Sons, Ltd.

Development of Support Service for Prevention and Recovery from Dementia and Science of Lethe

NASA Astrophysics Data System (ADS)

Otake, Mihoko

Purpose of this study is to explore service design method through the development of support service for prevention and recovery from dementia towards science of lethe. We designed and implemented conversation support service via coimagination method based on multiscale service design method, both were proposed by the author. Multiscale service model consists of tool, event, human, network, style and rule. Service elements at different scales are developed according to the model. Interactive conversation supported by coimagination method activates cognitive functions so as to prevent progress of dementia. This paper proposes theoretical bases for science of lethe. Firstly, relationship among coimagination method and three cognitive functions including division of attention, planning, episodic memory which decline at mild cognitive imparement. Secondly, thought state transition model during conversation which describes cognitive enhancement via interactive communication. Thirdly, Set Theoretical Measure of Interaction is proposed for evaluating effectiveness of conversation to cognitive enhancement. Simulation result suggests that the ideas which cannot be explored by each speaker are explored during interactive conversation. Finally, coimagination method compared with reminiscence therapy and its possibility for collaboration is discussed.

Radioisotope identification method for poorly resolved gamma-ray spectrum of nuclear security concern

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

Ninh, Giang Nguyen; Phongphaeth, Pengvanich, E-mail: phongphaeth.p@chula.ac.th; Nares, Chankow

Gamma-ray signal can be used as a fingerprint for radioisotope identification. In the context of radioactive and nuclear materials security at the border control point, the detection task can present a significant challenge due to various constraints such as the limited measurement time, the shielding conditions, and the noise interference. This study proposes a novel method to identify the signal of one or several radioisotopes from a poorly resolved gamma-ray spectrum. In this method, the noise component in the raw spectrum is reduced by the wavelet decomposition approach, and the removal of the continuum background is performed using the baselinemore » determination algorithm. Finally, the identification of radioisotope is completed using the matrix linear regression method. The proposed method has been verified by experiments using the poorly resolved gamma-ray signals from various scenarios including single source, mixing of natural uranium with five of the most common industrial radioactive sources (57Co, 60Co, 133Ba, 137Cs, and 241Am). The preliminary results show that the proposed algorithm is comparable with the commercial method.« less

An Improved Image Matching Method Based on Surf Algorithm

NASA Astrophysics Data System (ADS)

Chen, S. J.; Zheng, S. Z.; Xu, Z. G.; Guo, C. C.; Ma, X. L.

2018-04-01

Many state-of-the-art image matching methods, based on the feature matching, have been widely studied in the remote sensing field. These methods of feature matching which get highly operating efficiency, have a disadvantage of low accuracy and robustness. This paper proposes an improved image matching method which based on the SURF algorithm. The proposed method introduces color invariant transformation, information entropy theory and a series of constraint conditions to increase feature points detection and matching accuracy. First, the model of color invariant transformation is introduced for two matching images aiming at obtaining more color information during the matching process and information entropy theory is used to obtain the most information of two matching images. Then SURF algorithm is applied to detect and describe points from the images. Finally, constraint conditions which including Delaunay triangulation construction, similarity function and projective invariant are employed to eliminate the mismatches so as to improve matching precision. The proposed method has been validated on the remote sensing images and the result benefits from its high precision and robustness.

Determination of Optimal Heat-Storage Thickness of Layer for “Smart Wall” by Methods of Nonlinear Heat Conduction Equations for Phase-transition Materials

NASA Astrophysics Data System (ADS)

Pospelova, I.

2017-11-01

The article suggests an original way of keeping heat load and its compensation for a microclimate system by proposing the “Smart Wall”. The construction consists of specially combined composite materials including phase-transition materials. The method for determination of the layer thickness is proposed for a certain accumulation time. Varying the thickness and composition of the layer it is possible to achieve a low amount of the thermal conductivity coefficient and to obtain various functional characteristics of fences.

Integrated control and health management. Orbit transfer rocket engine technology program

NASA Technical Reports Server (NTRS)

Holzmann, Wilfried A.; Hayden, Warren R.

1988-01-01

To insure controllability of the baseline design for a 7500 pound thrust, 10:1 throttleable, dual expanded cycle, Hydrogen-Oxygen, orbit transfer rocket engine, an Integrated Controls and Health Monitoring concept was developed. This included: (1) Dynamic engine simulations using a TUTSIM derived computer code; (2) analysis of various control methods; (3) Failure Modes Analysis to identify critical sensors; (4) Survey of applicable sensors technology; and, (5) Study of Health Monitoring philosophies. The engine design was found to be controllable over the full throttling range by using 13 valves, including an oxygen turbine bypass valve to control mixture ratio, and a hydrogen turbine bypass valve, used in conjunction with the oxygen bypass to control thrust. Classic feedback control methods are proposed along with specific requirements for valves, sensors, and the controller. Expanding on the control system, a Health Monitoring system is proposed including suggested computing methods and the following recommended sensors: (1) Fiber optic and silicon bearing deflectometers; (2) Capacitive shaft displacement sensors; and (3) Hot spot thermocouple arrays. Further work is needed to refine and verify the dynamic simulations and control algorithms, to advance sensor capabilities, and to develop the Health Monitoring computational methods.

Proportional hazards model with varying coefficients for length-biased data.

PubMed

Zhang, Feipeng; Chen, Xuerong; Zhou, Yong

2014-01-01

Length-biased data arise in many important applications including epidemiological cohort studies, cancer prevention trials and studies of labor economics. Such data are also often subject to right censoring due to loss of follow-up or the end of study. In this paper, we consider a proportional hazards model with varying coefficients for right-censored and length-biased data, which is used to study the interact effect nonlinearly of covariates with an exposure variable. A local estimating equation method is proposed for the unknown coefficients and the intercept function in the model. The asymptotic properties of the proposed estimators are established by using the martingale theory and kernel smoothing techniques. Our simulation studies demonstrate that the proposed estimators have an excellent finite-sample performance. The Channing House data is analyzed to demonstrate the applications of the proposed method.

A semiparametric graphical modelling approach for large-scale equity selection.

PubMed

Liu, Han; Mulvey, John; Zhao, Tianqi

2016-01-01

We propose a new stock selection strategy that exploits rebalancing returns and improves portfolio performance. To effectively harvest rebalancing gains, we apply ideas from elliptical-copula graphical modelling and stability inference to select stocks that are as independent as possible. The proposed elliptical-copula graphical model has a latent Gaussian representation; its structure can be effectively inferred using the regularized rank-based estimators. The resulting algorithm is computationally efficient and scales to large data-sets. To show the efficacy of the proposed method, we apply it to conduct equity selection based on a 16-year health care stock data-set and a large 34-year stock data-set. Empirical tests show that the proposed method is superior to alternative strategies including a principal component analysis-based approach and the classical Markowitz strategy based on the traditional buy-and-hold assumption.

A Method for Extracting Important Segments from Documents Using Support Vector Machines

NASA Astrophysics Data System (ADS)

Suzuki, Daisuke; Utsumi, Akira

In this paper we propose an extraction-based method for automatic summarization. The proposed method consists of two processes: important segment extraction and sentence compaction. The process of important segment extraction classifies each segment in a document as important or not by Support Vector Machines (SVMs). The process of sentence compaction then determines grammatically appropriate portions of a sentence for a summary according to its dependency structure and the classification result by SVMs. To test the performance of our method, we conducted an evaluation experiment using the Text Summarization Challenge (TSC-1) corpus of human-prepared summaries. The result was that our method achieved better performance than a segment-extraction-only method and the Lead method, especially for sentences only a part of which was included in human summaries. Further analysis of the experimental results suggests that a hybrid method that integrates sentence extraction with segment extraction may generate better summaries.

A simple iterative independent component analysis algorithm for vibration source signal identification of complex structures

NASA Astrophysics Data System (ADS)

Lee, Dong-Sup; Cho, Dae-Seung; Kim, Kookhyun; Jeon, Jae-Jin; Jung, Woo-Jin; Kang, Myeng-Hwan; Kim, Jae-Ho

2015-01-01

Independent Component Analysis (ICA), one of the blind source separation methods, can be applied for extracting unknown source signals only from received signals. This is accomplished by finding statistical independence of signal mixtures and has been successfully applied to myriad fields such as medical science, image processing, and numerous others. Nevertheless, there are inherent problems that have been reported when using this technique: instability and invalid ordering of separated signals, particularly when using a conventional ICA technique in vibratory source signal identification of complex structures. In this study, a simple iterative algorithm of the conventional ICA has been proposed to mitigate these problems. The proposed method to extract more stable source signals having valid order includes an iterative and reordering process of extracted mixing matrix to reconstruct finally converged source signals, referring to the magnitudes of correlation coefficients between the intermediately separated signals and the signals measured on or nearby sources. In order to review the problems of the conventional ICA technique and to validate the proposed method, numerical analyses have been carried out for a virtual response model and a 30 m class submarine model. Moreover, in order to investigate applicability of the proposed method to real problem of complex structure, an experiment has been carried out for a scaled submarine mockup. The results show that the proposed method could resolve the inherent problems of a conventional ICA technique.

Fast and simultaneous determination of 12 polyphenols in apple peel and pulp by using chemometrics-assisted high-performance liquid chromatography with diode array detection.

PubMed

Wang, Tong; Wu, Hai-Long; Xie, Li-Xia; Zhu, Li; Liu, Zhi; Sun, Xiao-Dong; Xiao, Rong; Yu, Ru-Qin

2017-04-01

In this work, a smart chemometrics-enhanced strategy, high-performance liquid chromatography, and diode array detection coupled with second-order calibration method based on alternating trilinear decomposition algorithm was proposed to simultaneously quantify 12 polyphenols in different kinds of apple peel and pulp samples. The proposed strategy proved to be a powerful tool to solve the problems of coelution, unknown interferences, and chromatographic shifts in the process of high-performance liquid chromatography analysis, making it possible for the determination of 12 polyphenols in complex apple matrices within 10 min under simple conditions of elution. The average recoveries with standard deviations, and figures of merit including sensitivity, selectivity, limit of detection, and limit of quantitation were calculated to validate the accuracy of the proposed method. Compared to the quantitative analysis results from the classic high-performance liquid chromatography method, the statistical and graphical analysis showed that our proposed strategy obtained more reliable results. All results indicated that our proposed method used in the quantitative analysis of apple polyphenols was an accurate, fast, universal, simple, and green one, and it was expected to be developed as an attractive alternative method for simultaneous determination of multitargeted analytes in complex matrices. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A partial differential equation-based general framework adapted to Rayleigh's, Rician's and Gaussian's distributed noise for restoration and enhancement of magnetic resonance image.

PubMed

Yadav, Ram Bharos; Srivastava, Subodh; Srivastava, Rajeev

2016-01-01

The proposed framework is obtained by casting the noise removal problem into a variational framework. This framework automatically identifies the various types of noise present in the magnetic resonance image and filters them by choosing an appropriate filter. This filter includes two terms: the first term is a data likelihood term and the second term is a prior function. The first term is obtained by minimizing the negative log likelihood of the corresponding probability density functions: Gaussian or Rayleigh or Rician. Further, due to the ill-posedness of the likelihood term, a prior function is needed. This paper examines three partial differential equation based priors which include total variation based prior, anisotropic diffusion based prior, and a complex diffusion (CD) based prior. A regularization parameter is used to balance the trade-off between data fidelity term and prior. The finite difference scheme is used for discretization of the proposed method. The performance analysis and comparative study of the proposed method with other standard methods is presented for brain web dataset at varying noise levels in terms of peak signal-to-noise ratio, mean square error, structure similarity index map, and correlation parameter. From the simulation results, it is observed that the proposed framework with CD based prior is performing better in comparison to other priors in consideration.

The Multi-Attribute Group Decision-Making Method Based on Interval Grey Trapezoid Fuzzy Linguistic Variables.

PubMed

Yin, Kedong; Wang, Pengyu; Li, Xuemei

2017-12-13

With respect to multi-attribute group decision-making (MAGDM) problems, where attribute values take the form of interval grey trapezoid fuzzy linguistic variables (IGTFLVs) and the weights (including expert and attribute weight) are unknown, improved grey relational MAGDM methods are proposed. First, the concept of IGTFLV, the operational rules, the distance between IGTFLVs, and the projection formula between the two IGTFLV vectors are defined. Second, the expert weights are determined by using the maximum proximity method based on the projection values between the IGTFLV vectors. The attribute weights are determined by the maximum deviation method and the priorities of alternatives are determined by improved grey relational analysis. Finally, an example is given to prove the effectiveness of the proposed method and the flexibility of IGTFLV.

An efficient heuristic method for dynamic portfolio selection problem under transaction costs and uncertain conditions

NASA Astrophysics Data System (ADS)

Najafi, Amir Abbas; Pourahmadi, Zahra

2016-04-01

Selecting the optimal combination of assets in a portfolio is one of the most important decisions in investment management. As investment is a long term concept, looking into a portfolio optimization problem just in a single period may cause loss of some opportunities that could be exploited in a long term view. Hence, it is tried to extend the problem from single to multi-period model. We include trading costs and uncertain conditions to this model which made it more realistic and complex. Hence, we propose an efficient heuristic method to tackle this problem. The efficiency of the method is examined and compared with the results of the rolling single-period optimization and the buy and hold method which shows the superiority of the proposed method.

An atlas-based multimodal registration method for 2D images with discrepancy structures.

PubMed

Lv, Wenchao; Chen, Houjin; Peng, Yahui; Li, Yanfeng; Li, Jupeng

2018-06-04

An atlas-based multimodal registration method for 2-dimension images with discrepancy structures was proposed in this paper. Atlas was utilized for complementing the discrepancy structure information in multimodal medical images. The scheme includes three steps: floating image to atlas registration, atlas to reference image registration, and field-based deformation. To evaluate the performance, a frame model, a brain model, and clinical images were employed in registration experiments. We measured the registration performance by the squared sum of intensity differences. Results indicate that this method is robust and performs better than the direct registration for multimodal images with discrepancy structures. We conclude that the proposed method is suitable for multimodal images with discrepancy structures. Graphical Abstract An Atlas-based multimodal registration method schematic diagram.

A repeated measures model for analysis of continuous outcomes in sequential parallel comparison design studies.

PubMed

Doros, Gheorghe; Pencina, Michael; Rybin, Denis; Meisner, Allison; Fava, Maurizio

2013-07-20

Previous authors have proposed the sequential parallel comparison design (SPCD) to address the issue of high placebo response rate in clinical trials. The original use of SPCD focused on binary outcomes, but recent use has since been extended to continuous outcomes that arise more naturally in many fields, including psychiatry. Analytic methods proposed to date for analysis of SPCD trial continuous data included methods based on seemingly unrelated regression and ordinary least squares. Here, we propose a repeated measures linear model that uses all outcome data collected in the trial and accounts for data that are missing at random. An appropriate contrast formulated after the model has been fit can be used to test the primary hypothesis of no difference in treatment effects between study arms. Our extensive simulations show that when compared with the other methods, our approach preserves the type I error even for small sample sizes and offers adequate power and the smallest mean squared error under a wide variety of assumptions. We recommend consideration of our approach for analysis of data coming from SPCD trials. Copyright © 2013 John Wiley & Sons, Ltd.

[Classification of local anesthesia methods].

PubMed

Petricas, A Zh; Medvedev, D V; Olkhovskaya, E B

The traditional classification methods of dental local anesthesia must be modified. In this paper we proved that the vascular mechanism is leading component of spongy injection. It is necessary to take into account the high effectiveness and relative safety of spongy anesthesia, as well as versatility, ease of implementation and the growing prevalence in the world. The essence of the proposed modification is to distinguish the methods in diffusive (including surface anesthesia, infiltration and conductive anesthesia) and vascular-diffusive (including intraosseous, intraligamentary, intraseptal and intrapulpal anesthesia). For the last four methods the common term «spongy (intraosseous) anesthesia» may be used.

Improved Simplified Methods for Effective Seismic Analysis and Design of Isolated and Damped Bridges in Western and Eastern North America

NASA Astrophysics Data System (ADS)

Koval, Viacheslav

The seismic design provisions of the CSA-S6 Canadian Highway Bridge Design Code and the AASHTO LRFD Seismic Bridge Design Specifications have been developed primarily based on historical earthquake events that have occurred along the west coast of North America. For the design of seismic isolation systems, these codes include simplified analysis and design methods. The appropriateness and range of application of these methods are investigated through extensive parametric nonlinear time history analyses in this thesis. It was found that there is a need to adjust existing design guidelines to better capture the expected nonlinear response of isolated bridges. For isolated bridges located in eastern North America, new damping coefficients are proposed. The applicability limits of the code-based simplified methods have been redefined to ensure that the modified method will lead to conservative results and that a wider range of seismically isolated bridges can be covered by this method. The possibility of further improving current simplified code methods was also examined. By transforming the quantity of allocated energy into a displacement contribution, an idealized analytical solution is proposed as a new simplified design method. This method realistically reflects the effects of ground-motion and system design parameters, including the effects of a drifted oscillation center. The proposed method is therefore more appropriate than current existing simplified methods and can be applicable to isolation systems exhibiting a wider range of properties. A multi-level-hazard performance matrix has been adopted by different seismic provisions worldwide and will be incorporated into the new edition of the Canadian CSA-S6-14 Bridge Design code. However, the combined effect and optimal use of isolation and supplemental damping devices in bridges have not been fully exploited yet to achieve enhanced performance under different levels of seismic hazard. A novel Dual-Level Seismic Protection (DLSP) concept is proposed and developed in this thesis which permits to achieve optimum seismic performance with combined isolation and supplemental damping devices in bridges. This concept is shown to represent an attractive design approach for both the upgrade of existing seismically deficient bridges and the design of new isolated bridges.

Tracking and recognition face in videos with incremental local sparse representation model

NASA Astrophysics Data System (ADS)

Wang, Chao; Wang, Yunhong; Zhang, Zhaoxiang

2013-10-01

This paper addresses the problem of tracking and recognizing faces via incremental local sparse representation. First a robust face tracking algorithm is proposed via employing local sparse appearance and covariance pooling method. In the following face recognition stage, with the employment of a novel template update strategy, which combines incremental subspace learning, our recognition algorithm adapts the template to appearance changes and reduces the influence of occlusion and illumination variation. This leads to a robust video-based face tracking and recognition with desirable performance. In the experiments, we test the quality of face recognition in real-world noisy videos on YouTube database, which includes 47 celebrities. Our proposed method produces a high face recognition rate at 95% of all videos. The proposed face tracking and recognition algorithms are also tested on a set of noisy videos under heavy occlusion and illumination variation. The tracking results on challenging benchmark videos demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods. In the case of the challenging dataset in which faces undergo occlusion and illumination variation, and tracking and recognition experiments under significant pose variation on the University of California, San Diego (Honda/UCSD) database, our proposed method also consistently demonstrates a high recognition rate.

Accelerated Edge-Preserving Image Restoration Without Boundary Artifacts

PubMed Central

Matakos, Antonios; Ramani, Sathish; Fessler, Jeffrey A.

2013-01-01

To reduce blur in noisy images, regularized image restoration methods have been proposed that use non-quadratic regularizers (like l1 regularization or total-variation) that suppress noise while preserving edges in the image. Most of these methods assume a circulant blur (periodic convolution with a blurring kernel) that can lead to wraparound artifacts along the boundaries of the image due to the implied periodicity of the circulant model. Using a non-circulant model could prevent these artifacts at the cost of increased computational complexity. In this work we propose to use a circulant blur model combined with a masking operator that prevents wraparound artifacts. The resulting model is non-circulant, so we propose an efficient algorithm using variable splitting and augmented Lagrangian (AL) strategies. Our variable splitting scheme, when combined with the AL framework and alternating minimization, leads to simple linear systems that can be solved non-iteratively using FFTs, eliminating the need for more expensive CG-type solvers. The proposed method can also efficiently tackle a variety of convex regularizers including edge-preserving (e.g., total-variation) and sparsity promoting (e.g., l1 norm) regularizers. Simulation results show fast convergence of the proposed method, along with improved image quality at the boundaries where the circulant model is inaccurate. PMID:23372080

A Sparsity-Promoted Method Based on Majorization-Minimization for Weak Fault Feature Enhancement

PubMed Central

Hao, Yansong; Song, Liuyang; Tang, Gang; Yuan, Hongfang

2018-01-01

Fault transient impulses induced by faulty components in rotating machinery usually contain substantial interference. Fault features are comparatively weak in the initial fault stage, which renders fault diagnosis more difficult. In this case, a sparse representation method based on the Majorzation-Minimization (MM) algorithm is proposed to enhance weak fault features and extract the features from strong background noise. However, the traditional MM algorithm suffers from two issues, which are the choice of sparse basis and complicated calculations. To address these challenges, a modified MM algorithm is proposed in which a sparse optimization objective function is designed firstly. Inspired by the Basis Pursuit (BP) model, the optimization function integrates an impulsive feature-preserving factor and a penalty function factor. Second, a modified Majorization iterative method is applied to address the convex optimization problem of the designed function. A series of sparse coefficients can be achieved through iterating, which only contain transient components. It is noteworthy that there is no need to select the sparse basis in the proposed iterative method because it is fixed as a unit matrix. Then the reconstruction step is omitted, which can significantly increase detection efficiency. Eventually, envelope analysis of the sparse coefficients is performed to extract weak fault features. Simulated and experimental signals including bearings and gearboxes are employed to validate the effectiveness of the proposed method. In addition, comparisons are made to prove that the proposed method outperforms the traditional MM algorithm in terms of detection results and efficiency. PMID:29597280

A Sparsity-Promoted Method Based on Majorization-Minimization for Weak Fault Feature Enhancement.

PubMed

Ren, Bangyue; Hao, Yansong; Wang, Huaqing; Song, Liuyang; Tang, Gang; Yuan, Hongfang

2018-03-28

Fault transient impulses induced by faulty components in rotating machinery usually contain substantial interference. Fault features are comparatively weak in the initial fault stage, which renders fault diagnosis more difficult. In this case, a sparse representation method based on the Majorzation-Minimization (MM) algorithm is proposed to enhance weak fault features and extract the features from strong background noise. However, the traditional MM algorithm suffers from two issues, which are the choice of sparse basis and complicated calculations. To address these challenges, a modified MM algorithm is proposed in which a sparse optimization objective function is designed firstly. Inspired by the Basis Pursuit (BP) model, the optimization function integrates an impulsive feature-preserving factor and a penalty function factor. Second, a modified Majorization iterative method is applied to address the convex optimization problem of the designed function. A series of sparse coefficients can be achieved through iterating, which only contain transient components. It is noteworthy that there is no need to select the sparse basis in the proposed iterative method because it is fixed as a unit matrix. Then the reconstruction step is omitted, which can significantly increase detection efficiency. Eventually, envelope analysis of the sparse coefficients is performed to extract weak fault features. Simulated and experimental signals including bearings and gearboxes are employed to validate the effectiveness of the proposed method. In addition, comparisons are made to prove that the proposed method outperforms the traditional MM algorithm in terms of detection results and efficiency.

Improving the performances of autofocus based on adaptive retina-like sampling model

NASA Astrophysics Data System (ADS)

Hao, Qun; Xiao, Yuqing; Cao, Jie; Cheng, Yang; Sun, Ce

2018-03-01

An adaptive retina-like sampling model (ARSM) is proposed to balance autofocusing accuracy and efficiency. Based on the model, we carry out comparative experiments between the proposed method and the traditional method in terms of accuracy, the full width of the half maxima (FWHM) and time consumption. Results show that the performances of our method are better than that of the traditional method. Meanwhile, typical autofocus functions, including sum-modified-Laplacian (SML), Laplacian (LAP), Midfrequency-DCT (MDCT) and Absolute Tenengrad (ATEN) are compared through comparative experiments. The smallest FWHM is obtained by the use of LAP, which is more suitable for evaluating accuracy than other autofocus functions. The autofocus function of MDCT is most suitable to evaluate the real-time ability.

Comparative study of signalling methods for high-speed backplane transceiver

NASA Astrophysics Data System (ADS)

Wu, Kejun

2017-11-01

A combined analysis of transient simulation and statistical method is proposed for comparative study of signalling methods applied to high-speed backplane transceivers. This method enables fast and accurate signal-to-noise ratio and symbol error rate estimation of a serial link based on a four-dimension design space, including channel characteristics, noise scenarios, equalisation schemes, and signalling methods. The proposed combined analysis method chooses an efficient sampling size for performance evaluation. A comparative study of non-return-to-zero (NRZ), PAM-4, and four-phase shifted sinusoid symbol (PSS-4) using parameterised behaviour-level simulation shows PAM-4 and PSS-4 has substantial advantages over conventional NRZ in most of the cases. A comparison between PAM-4 and PSS-4 shows PAM-4 gets significant bit error rate degradation when noise level is enhanced.

Exposure Models for the Prior Distribution in Bayesian Decision Analysis for Occupational Hygiene Decision Making

PubMed Central

Lee, Eun Gyung; Kim, Seung Won; Feigley, Charles E.; Harper, Martin

2015-01-01

This study introduces two semi-quantitative methods, Structured Subjective Assessment (SSA) and Control of Substances Hazardous to Health (COSHH) Essentials, in conjunction with two-dimensional Monte Carlo simulations for determining prior probabilities. Prior distribution using expert judgment was included for comparison. Practical applications of the proposed methods were demonstrated using personal exposure measurements of isoamyl acetate in an electronics manufacturing facility and of isopropanol in a printing shop. Applicability of these methods in real workplaces was discussed based on the advantages and disadvantages of each method. Although these methods could not be completely independent of expert judgments, this study demonstrated a methodological improvement in the estimation of the prior distribution for the Bayesian decision analysis tool. The proposed methods provide a logical basis for the decision process by considering determinants of worker exposure. PMID:23252451

A direct method to solve optimal knots of B-spline curves: An application for non-uniform B-spline curves fitting.

PubMed

Dung, Van Than; Tjahjowidodo, Tegoeh

2017-01-01

B-spline functions are widely used in many industrial applications such as computer graphic representations, computer aided design, computer aided manufacturing, computer numerical control, etc. Recently, there exist some demands, e.g. in reverse engineering (RE) area, to employ B-spline curves for non-trivial cases that include curves with discontinuous points, cusps or turning points from the sampled data. The most challenging task in these cases is in the identification of the number of knots and their respective locations in non-uniform space in the most efficient computational cost. This paper presents a new strategy for fitting any forms of curve by B-spline functions via local algorithm. A new two-step method for fast knot calculation is proposed. In the first step, the data is split using a bisecting method with predetermined allowable error to obtain coarse knots. Secondly, the knots are optimized, for both locations and continuity levels, by employing a non-linear least squares technique. The B-spline function is, therefore, obtained by solving the ordinary least squares problem. The performance of the proposed method is validated by using various numerical experimental data, with and without simulated noise, which were generated by a B-spline function and deterministic parametric functions. This paper also discusses the benchmarking of the proposed method to the existing methods in literature. The proposed method is shown to be able to reconstruct B-spline functions from sampled data within acceptable tolerance. It is also shown that, the proposed method can be applied for fitting any types of curves ranging from smooth ones to discontinuous ones. In addition, the method does not require excessive computational cost, which allows it to be used in automatic reverse engineering applications.

Using Kriging with a heterogeneous measurement error to improve the accuracy of extreme precipitation return level estimation

NASA Astrophysics Data System (ADS)

Yin, Shui-qing; Wang, Zhonglei; Zhu, Zhengyuan; Zou, Xu-kai; Wang, Wen-ting

2018-07-01

Extreme precipitation can cause flooding and may result in great economic losses and deaths. The return level is a commonly used measure of extreme precipitation events and is required for hydrological engineer designs, including those of sewerage systems, dams, reservoirs and bridges. In this paper, we propose a two-step method to estimate the return level and its uncertainty for a study region. In the first step, we use the generalized extreme value distribution, the L-moment method and the stationary bootstrap to estimate the return level and its uncertainty at the site with observations. In the second step, a spatial model incorporating the heterogeneous measurement errors and covariates is trained to estimate return levels at sites with no observations and to improve the estimates at sites with limited information. The proposed method is applied to the daily rainfall data from 273 weather stations in the Haihe river basin of North China. We compare the proposed method with two alternatives: the first one is based on the ordinary Kriging method without measurement error, and the second one smooths the estimated location and scale parameters of the generalized extreme value distribution by the universal Kriging method. Results show that the proposed method outperforms its counterparts. We also propose a novel approach to assess the two-step method by comparing it with the at-site estimation method with a series of reduced length of observations. Estimates of the 2-, 5-, 10-, 20-, 50- and 100-year return level maps and the corresponding uncertainties are provided for the Haihe river basin, and a comparison with those released by the Hydrology Bureau of Ministry of Water Resources of China is made.

A New Framework of Removing Salt and Pepper Impulse Noise for the Noisy Image Including Many Noise-Free White and Black Pixels

NASA Astrophysics Data System (ADS)

Li, Song; Wang, Caizhu; Li, Yeqiu; Wang, Ling; Sakata, Shiro; Sekiya, Hiroo; Kuroiwa, Shingo

In this paper, we propose a new framework of removing salt and pepper impulse noise. In our proposed framework, the most important point is that the number of noise-free white and black pixels in a noisy image can be determined by using the noise rates estimated by Fuzzy Impulse Noise Detection and Reduction Method (FINDRM) and Efficient Detail-Preserving Approach (EDPA). For the noisy image includes many noise-free white and black pixels, the detected noisy pixel from the FINDRM is re-checked by using the alpha-trimmed mean. Finally, the impulse noise filtering phase of the FINDRM is used to restore the image. Simulation results show that for the noisy image including many noise-free white and black pixels, the proposed framework can decrease the False Hit Rate (FHR) efficiently compared with the FINDRM. Therefore, the proposed framework can be used more widely than the FINDRM.

Recommendations for the Avoidance of Delayed-Onset Muscle Soreness.

ERIC Educational Resources Information Center

Szymanski, David J.

2001-01-01

Describes the possible causes of delayed-onset muscle soreness (DOMS), which include buildup of lactic acid in muscle, increased intracellular calcium concentration, increased intramuscular inflammation, and muscle fiber and connective tissue damage. Proposed methods to reduce DOMS include warming up before exercise and performing repeated bouts…

Fine-Tuning Your Ensemble's Jazz Style.

ERIC Educational Resources Information Center

Garcia, Antonio J.

1991-01-01

Proposes instructional strategies for directors of jazz groups, including guidelines for developing of skills necessary for good performance. Includes effective methods for positive changes in ensemble style. Addresses jazz group problems such as beat, tempo, staying in tune, wind power, and solo/ensemble lines. Discusses percussionists, bassists,…

Improving the complementary methods to estimate evapotranspiration under diverse climatic and physical conditions

NASA Astrophysics Data System (ADS)

Anayah, F. M.; Kaluarachchi, J. J.

2014-06-01

Reliable estimation of evapotranspiration (ET) is important for the purpose of water resources planning and management. Complementary methods, including complementary relationship areal evapotranspiration (CRAE), advection aridity (AA) and Granger and Gray (GG), have been used to estimate ET because these methods are simple and practical in estimating regional ET using meteorological data only. However, prior studies have found limitations in these methods especially in contrasting climates. This study aims to develop a calibration-free universal method using the complementary relationships to compute regional ET in contrasting climatic and physical conditions with meteorological data only. The proposed methodology consists of a systematic sensitivity analysis using the existing complementary methods. This work used 34 global FLUXNET sites where eddy covariance (EC) fluxes of ET are available for validation. A total of 33 alternative model variations from the original complementary methods were proposed. Further analysis using statistical methods and simplified climatic class definitions produced one distinctly improved GG-model-based alternative. The proposed model produced a single-step ET formulation with results equal to or better than the recent studies using data-intensive, classical methods. Average root mean square error (RMSE), mean absolute bias (BIAS) and R2 (coefficient of determination) across 34 global sites were 20.57 mm month-1, 10.55 mm month-1 and 0.64, respectively. The proposed model showed a step forward toward predicting ET in large river basins with limited data and requiring no calibration.

Geochemical data from groundwater at the proposed Dewey Burdock uranium in-situ recovery mine, Edgemont, South Dakota

USGS Publications Warehouse

Johnson, Raymond H.

2012-01-01

This report releases groundwater geochemistry data from samples that were collected in June 2011 at the Dewey Burdock proposed uranium in-situ recovery site near Edgemont, South Dakota. The sampling and analytical methods are summarized, and all of the data, including quality assurance/quality control information are provided in data tables.

76 FR 51944 - Proposed Information Collection; Comment Request; Southeast Region Bycatch Reduction Device...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-19

....) fisheries of the exclusive economic zone (EEZ) off the South Atlantic, Caribbean, and Gulf of Mexico under.... Method of Collection Paper applications, electronic reports, and telephone calls are required from participants, and methods of submittal include Internet, electronic forms, and facsimile transmission of paper...

78 FR 36779 - Agency Information Collection Activities: Proposed Collection; Comment Request Re: Information...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-19

... providers through qualitative research methods such as focus groups, in-depth interviews, and/or qualitative... qualitative research methods will also contribute to the FDIC's understanding of how consumers, including... of an array of financial services and products. Qualitative type research does not seek to measure or...

Distributed sensor management for space situational awareness via a negotiation game

NASA Astrophysics Data System (ADS)

Jia, Bin; Shen, Dan; Pham, Khanh; Blasch, Erik; Chen, Genshe

2015-05-01

Space situational awareness (SSA) is critical to many space missions serving weather analysis, communications, and navigation. However, the number of sensors used in space situational awareness is limited which hinders collision avoidance prediction, debris assessment, and efficient routing. Hence, it is critical to use such sensor resources efficiently. In addition, it is desired to develop the SSA sensor management algorithm in a distributed manner. In this paper, a distributed sensor management approach using the negotiation game (NG-DSM) is proposed for the SSA. Specifically, the proposed negotiation game is played by each sensor and its neighboring sensors. The bargaining strategies are developed for each sensor based on negotiating for accurately tracking desired targets (e.g., satellite, debris, etc.) . The proposed NG-DSM method is tested in a scenario which includes eight space objects and three different sensor modalities which include a space based optical sensor, a ground radar, or a ground Electro-Optic sensor. The geometric relation between the sensor, the Sun, and the space object is also considered. The simulation results demonstrate the effectiveness of the proposed NG-DSM sensor management methods, which facilitates an application of multiple-sensor multiple-target tracking for space situational awareness.

Personal identification based on blood vessels of retinal fundus images

NASA Astrophysics Data System (ADS)

Fukuta, Keisuke; Nakagawa, Toshiaki; Hayashi, Yoshinori; Hatanaka, Yuji; Hara, Takeshi; Fujita, Hiroshi

2008-03-01

Biometric technique has been implemented instead of conventional identification methods such as password in computer, automatic teller machine (ATM), and entrance and exit management system. We propose a personal identification (PI) system using color retinal fundus images which are unique to each individual. The proposed procedure for identification is based on comparison of an input fundus image with reference fundus images in the database. In the first step, registration between the input image and the reference image is performed. The step includes translational and rotational movement. The PI is based on the measure of similarity between blood vessel images generated from the input and reference images. The similarity measure is defined as the cross-correlation coefficient calculated from the pixel values. When the similarity is greater than a predetermined threshold, the input image is identified. This means both the input and the reference images are associated to the same person. Four hundred sixty-two fundus images including forty-one same-person's image pairs were used for the estimation of the proposed technique. The false rejection rate and the false acceptance rate were 9.9×10 -5% and 4.3×10 -5%, respectively. The results indicate that the proposed method has a higher performance than other biometrics except for DNA. To be used for practical application in the public, the device which can take retinal fundus images easily is needed. The proposed method is applied to not only the PI but also the system which warns about misfiling of fundus images in medical facilities.

Incipient Fault Detection for Rolling Element Bearings under Varying Speed Conditions.

PubMed

Xue, Lang; Li, Naipeng; Lei, Yaguo; Li, Ningbo

2017-06-20

Varying speed conditions bring a huge challenge to incipient fault detection of rolling element bearings because both the change of speed and faults could lead to the amplitude fluctuation of vibration signals. Effective detection methods need to be developed to eliminate the influence of speed variation. This paper proposes an incipient fault detection method for bearings under varying speed conditions. Firstly, relative residual (RR) features are extracted, which are insensitive to the varying speed conditions and are able to reflect the degradation trend of bearings. Then, a health indicator named selected negative log-likelihood probability (SNLLP) is constructed to fuse a feature set including RR features and non-dimensional features. Finally, based on the constructed SNLLP health indicator, a novel alarm trigger mechanism is designed to detect the incipient fault. The proposed method is demonstrated using vibration signals from bearing tests and industrial wind turbines. The results verify the effectiveness of the proposed method for incipient fault detection of rolling element bearings under varying speed conditions.

Singular boundary method for wave propagation analysis in periodic structures

NASA Astrophysics Data System (ADS)

Fu, Zhuojia; Chen, Wen; Wen, Pihua; Zhang, Chuanzeng

2018-07-01

A strong-form boundary collocation method, the singular boundary method (SBM), is developed in this paper for the wave propagation analysis at low and moderate wavenumbers in periodic structures. The SBM is of several advantages including mathematically simple, easy-to-program, meshless with the application of the concept of origin intensity factors in order to eliminate the singularity of the fundamental solutions and avoid the numerical evaluation of the singular integrals in the boundary element method. Due to the periodic behaviors of the structures, the SBM coefficient matrix can be represented as a block Toeplitz matrix. By employing three different fast Toeplitz-matrix solvers, the computational time and storage requirements are significantly reduced in the proposed SBM analysis. To demonstrate the effectiveness of the proposed SBM formulation for wave propagation analysis in periodic structures, several benchmark examples are presented and discussed The proposed SBM results are compared with the analytical solutions, the reference results and the COMSOL software.

Structured Matrix Completion with Applications to Genomic Data Integration.

PubMed

Cai, Tianxi; Cai, T Tony; Zhang, Anru

2016-01-01

Matrix completion has attracted significant recent attention in many fields including statistics, applied mathematics and electrical engineering. Current literature on matrix completion focuses primarily on independent sampling models under which the individual observed entries are sampled independently. Motivated by applications in genomic data integration, we propose a new framework of structured matrix completion (SMC) to treat structured missingness by design. Specifically, our proposed method aims at efficient matrix recovery when a subset of the rows and columns of an approximately low-rank matrix are observed. We provide theoretical justification for the proposed SMC method and derive lower bound for the estimation errors, which together establish the optimal rate of recovery over certain classes of approximately low-rank matrices. Simulation studies show that the method performs well in finite sample under a variety of configurations. The method is applied to integrate several ovarian cancer genomic studies with different extent of genomic measurements, which enables us to construct more accurate prediction rules for ovarian cancer survival.

Application of the correlation constrained multivariate curve resolution alternating least-squares method for analyte quantitation in the presence of unexpected interferences using first-order instrumental data.

PubMed

Goicoechea, Héctor C; Olivieri, Alejandro C; Tauler, Romà

2010-03-01

Correlation constrained multivariate curve resolution-alternating least-squares is shown to be a feasible method for processing first-order instrumental data and achieve analyte quantitation in the presence of unexpected interferences. Both for simulated and experimental data sets, the proposed method could correctly retrieve the analyte and interference spectral profiles and perform accurate estimations of analyte concentrations in test samples. Since no information concerning the interferences was present in calibration samples, the proposed multivariate calibration approach including the correlation constraint facilitates the achievement of the so-called second-order advantage for the analyte of interest, which is known to be present for more complex higher-order richer instrumental data. The proposed method is tested using a simulated data set and two experimental data systems, one for the determination of ascorbic acid in powder juices using UV-visible absorption spectral data, and another for the determination of tetracycline in serum samples using fluorescence emission spectroscopy.

Complete denture tooth arrangement technology driven by a reconfigurable rule.

PubMed

Dai, Ning; Yu, Xiaoling; Fan, Qilei; Yuan, Fulai; Liu, Lele; Sun, Yuchun

2018-01-01

The conventional technique for the fabrication of complete dentures is complex, with a long fabrication process and difficult-to-control restoration quality. In recent years, digital complete denture design has become a research focus. Digital complete denture tooth arrangement is a challenging issue that is difficult to efficiently implement under the constraints of complex tooth arrangement rules and the patient's individualized functional aesthetics. The present study proposes a complete denture automatic tooth arrangement method driven by a reconfigurable rule; it uses four typical operators, including a position operator, a scaling operator, a posture operator, and a contact operator, to establish the constraint mapping association between the teeth and the constraint set of the individual patient. By using the process reorganization of different constraint operators, this method can flexibly implement different clinical tooth arrangement rules. When combined with a virtual occlusion algorithm based on progressive iterative Laplacian deformation, the proposed method can achieve automatic and individual tooth arrangement. Finally, the experimental results verify that the proposed method is flexible and efficient.

Incipient Fault Detection for Rolling Element Bearings under Varying Speed Conditions

PubMed Central

Xue, Lang; Li, Naipeng; Lei, Yaguo; Li, Ningbo

2017-01-01

Varying speed conditions bring a huge challenge to incipient fault detection of rolling element bearings because both the change of speed and faults could lead to the amplitude fluctuation of vibration signals. Effective detection methods need to be developed to eliminate the influence of speed variation. This paper proposes an incipient fault detection method for bearings under varying speed conditions. Firstly, relative residual (RR) features are extracted, which are insensitive to the varying speed conditions and are able to reflect the degradation trend of bearings. Then, a health indicator named selected negative log-likelihood probability (SNLLP) is constructed to fuse a feature set including RR features and non-dimensional features. Finally, based on the constructed SNLLP health indicator, a novel alarm trigger mechanism is designed to detect the incipient fault. The proposed method is demonstrated using vibration signals from bearing tests and industrial wind turbines. The results verify the effectiveness of the proposed method for incipient fault detection of rolling element bearings under varying speed conditions. PMID:28773035

A Local Agreement Pattern Measure Based on Hazard Functions for Survival Outcomes

PubMed Central

Dai, Tian; Guo, Ying; Peng, Limin; Manatunga, Amita K.

2017-01-01

Summary Assessing agreement is often of interest in biomedical and clinical research when measurements are obtained on the same subjects by different raters or methods. Most classical agreement methods have been focused on global summary statistics, which cannot be used to describe various local agreement patterns. The objective of this work is to study the local agreement pattern between two continuous measurements subject to censoring. In this paper, we propose a new agreement measure based on bivariate hazard functions to characterize the local agreement pattern between two correlated survival outcomes. The proposed measure naturally accommodates censored observations, fully captures the dependence structure between bivariate survival times and provides detailed information on how the strength of agreement evolves over time. We develop a nonparametric estimation method for the proposed local agreement pattern measure and study theoretical properties including strong consistency and asymptotical normality. We then evaluate the performance of the estimator through simulation studies and illustrate the method using a prostate cancer data example. PMID:28724196

A local agreement pattern measure based on hazard functions for survival outcomes.

PubMed

Dai, Tian; Guo, Ying; Peng, Limin; Manatunga, Amita K

2018-03-01

Assessing agreement is often of interest in biomedical and clinical research when measurements are obtained on the same subjects by different raters or methods. Most classical agreement methods have been focused on global summary statistics, which cannot be used to describe various local agreement patterns. The objective of this work is to study the local agreement pattern between two continuous measurements subject to censoring. In this article, we propose a new agreement measure based on bivariate hazard functions to characterize the local agreement pattern between two correlated survival outcomes. The proposed measure naturally accommodates censored observations, fully captures the dependence structure between bivariate survival times and provides detailed information on how the strength of agreement evolves over time. We develop a nonparametric estimation method for the proposed local agreement pattern measure and study theoretical properties including strong consistency and asymptotical normality. We then evaluate the performance of the estimator through simulation studies and illustrate the method using a prostate cancer data example. © 2017, The International Biometric Society.

Incorporating availability/bioavailability in risk assessment and decision making of polluted sites, using Germany as an example.

PubMed

Kördel, Werner; Bernhardt, Cornelia; Derz, Kerstin; Hund-Rinke, Kerstin; Harmsen, Joop; Peijnenburg, Willie; Comans, Rob; Terytze, Konstantin

2013-10-15

Nearly all publications dealing with availability or bioavailability of soil pollutants start with the following statement: the determination of total pollutant content will lead to an over-estimation of risk. However, an assessment of contaminated sites should be based on the determination of mobile fractions of pollutants, and the fractions with potential for mobilisation that threaten groundwater and surface water, and the actual and potential fractions available for uptake by plants, soil microflora and soil organisms. After reviewing the literature for method proposals concerning the determination of available/bioavailable fractions of contaminants with respect to leaching, plants, microorganisms (biodegradation) and soil organisms, we propose a testing and assessment scheme for contaminated sites. The proposal includes (i) already accepted and used methods, (ii) methods which are under standardisation, and (iii) methods for which development has just started in order to promote urgently needed research. Copyright © 2013 Elsevier B.V. All rights reserved.

Imaging of human vertebral surface using ultrasound RF data received at each element of probe for thoracic anesthesia

NASA Astrophysics Data System (ADS)

Takahashi, Kazuki; Taki, Hirofumi; Onishi, Eiko; Yamauchi, Masanori; Kanai, Hiroshi

2017-07-01

Epidural anesthesia is a common technique for perioperative analgesia and chronic pain treatment. Since ultrasonography is insufficient for depicting the human vertebral surface, most examiners apply epidural puncture by body surface landmarks on the back such as the spinous process and scapulae without any imaging, including ultrasonography. The puncture route to the epidural space at thoracic vertebrae is much narrower than that at lumber vertebrae, and therefore, epidural anesthesia at thoracic vertebrae is difficult, especially for a beginner. Herein, a novel imaging method is proposed based on a bi-static imaging technique by making use of the transmit beam width and direction. In an in vivo experimental study on human thoracic vertebrae, the proposed method succeeded in depicting the vertebral surface clearly as compared with conventional B-mode imaging and the conventional envelope method. This indicates the potential of the proposed method in visualizing the vertebral surface for the proper and safe execution of epidural anesthesia.

Induced subgraph searching for geometric model fitting

NASA Astrophysics Data System (ADS)

Xiao, Fan; Xiao, Guobao; Yan, Yan; Wang, Xing; Wang, Hanzi

2017-11-01

In this paper, we propose a novel model fitting method based on graphs to fit and segment multiple-structure data. In the graph constructed on data, each model instance is represented as an induced subgraph. Following the idea of pursuing the maximum consensus, the multiple geometric model fitting problem is formulated as searching for a set of induced subgraphs including the maximum union set of vertices. After the generation and refinement of the induced subgraphs that represent the model hypotheses, the searching process is conducted on the "qualified" subgraphs. Multiple model instances can be simultaneously estimated by solving a converted problem. Then, we introduce the energy evaluation function to determine the number of model instances in data. The proposed method is able to effectively estimate the number and the parameters of model instances in data severely corrupted by outliers and noises. Experimental results on synthetic data and real images validate the favorable performance of the proposed method compared with several state-of-the-art fitting methods.

Facial expression recognition based on weber local descriptor and sparse representation

NASA Astrophysics Data System (ADS)

Ouyang, Yan

2018-03-01

Automatic facial expression recognition has been one of the research hotspots in the area of computer vision for nearly ten years. During the decade, many state-of-the-art methods have been proposed which perform very high accurate rate based on the face images without any interference. Nowadays, many researchers begin to challenge the task of classifying the facial expression images with corruptions and occlusions and the Sparse Representation based Classification framework has been wildly used because it can robust to the corruptions and occlusions. Therefore, this paper proposed a novel facial expression recognition method based on Weber local descriptor (WLD) and Sparse representation. The method includes three parts: firstly the face images are divided into many local patches, and then the WLD histograms of each patch are extracted, finally all the WLD histograms features are composed into a vector and combined with SRC to classify the facial expressions. The experiment results on the Cohn-Kanade database show that the proposed method is robust to occlusions and corruptions.

How They (Should Have) Built the Pyramids

NASA Astrophysics Data System (ADS)

Gallagher, Gregory; West, Joseph; Waters, Kevin

2014-03-01

A novel ``polygon method'' is proposed for moving large stone blocks. The method is implemented by the attachment of rods of analytically chosen radii to the block by means of rope. The chosen rods are placed on each side of the square-prism block in order to transform the square prism into a prism of higher order polygon, i.e. octagon, dodecagon etc. Experimental results are presented and compared to other methods proposed by the authors, including a dragging method and a rail method which includes the idea of dragging the block on rails made from arbitrarily chosen rod-shaped ``tracks,'' and to independent work by another group which utilized wooden attachments providing a cylindrical shape. It is found that the polygon method when used on small scale stone blocks across level open ground has an equivalent of a coefficient of friction order of 0.1. For full scale pyramid blocks, the wooden ``rods'' would need to be of order 30 cm in diameter, certainly within reason, given the diameter of wooden masts used on ships in that region during the relevant time period in Egypt. This project also inspired a ``spin-off'' project in which the behavior or rolling polygons is investigated and preliminary data is presented.

Gene selection for microarray cancer classification using a new evolutionary method employing artificial intelligence concepts.

PubMed

Dashtban, M; Balafar, Mohammadali

2017-03-01

Gene selection is a demanding task for microarray data analysis. The diverse complexity of different cancers makes this issue still challenging. In this study, a novel evolutionary method based on genetic algorithms and artificial intelligence is proposed to identify predictive genes for cancer classification. A filter method was first applied to reduce the dimensionality of feature space followed by employing an integer-coded genetic algorithm with dynamic-length genotype, intelligent parameter settings, and modified operators. The algorithmic behaviors including convergence trends, mutation and crossover rate changes, and running time were studied, conceptually discussed, and shown to be coherent with literature findings. Two well-known filter methods, Laplacian and Fisher score, were examined considering similarities, the quality of selected genes, and their influences on the evolutionary approach. Several statistical tests concerning choice of classifier, choice of dataset, and choice of filter method were performed, and they revealed some significant differences between the performance of different classifiers and filter methods over datasets. The proposed method was benchmarked upon five popular high-dimensional cancer datasets; for each, top explored genes were reported. Comparing the experimental results with several state-of-the-art methods revealed that the proposed method outperforms previous methods in DLBCL dataset. Copyright © 2017 Elsevier Inc. All rights reserved.

A Monocular Vision Sensor-Based Obstacle Detection Algorithm for Autonomous Robots.

PubMed

Lee, Tae-Jae; Yi, Dong-Hoon; Cho, Dong-Il Dan

2016-03-01

This paper presents a monocular vision sensor-based obstacle detection algorithm for autonomous robots. Each individual image pixel at the bottom region of interest is labeled as belonging either to an obstacle or the floor. While conventional methods depend on point tracking for geometric cues for obstacle detection, the proposed algorithm uses the inverse perspective mapping (IPM) method. This method is much more advantageous when the camera is not high off the floor, which makes point tracking near the floor difficult. Markov random field-based obstacle segmentation is then performed using the IPM results and a floor appearance model. Next, the shortest distance between the robot and the obstacle is calculated. The algorithm is tested by applying it to 70 datasets, 20 of which include nonobstacle images where considerable changes in floor appearance occur. The obstacle segmentation accuracies and the distance estimation error are quantitatively analyzed. For obstacle datasets, the segmentation precision and the average distance estimation error of the proposed method are 81.4% and 1.6 cm, respectively, whereas those for a conventional method are 57.5% and 9.9 cm, respectively. For nonobstacle datasets, the proposed method gives 0.0% false positive rates, while the conventional method gives 17.6%.

Multiratio fusion change detection with adaptive thresholding

NASA Astrophysics Data System (ADS)

Hytla, Patrick C.; Balster, Eric J.; Vasquez, Juan R.; Neuroth, Robert M.

2017-04-01

A ratio-based change detection method known as multiratio fusion (MRF) is proposed and tested. The MRF framework builds on other change detection components proposed in this work: dual ratio (DR) and multiratio (MR). The DR method involves two ratios coupled with adaptive thresholds to maximize detected changes and minimize false alarms. The use of two ratios is shown to outperform the single ratio case when the means of the image pairs are not equal. MR change detection builds on the DR method by including negative imagery to produce four total ratios with adaptive thresholds. Inclusion of negative imagery is shown to improve detection sensitivity and to boost detection performance in certain target and background cases. MRF further expands this concept by fusing together the ratio outputs using a routine in which detections must be verified by two or more ratios to be classified as a true changed pixel. The proposed method is tested with synthetically generated test imagery and real datasets with results compared to other methods found in the literature. DR is shown to significantly outperform the standard single ratio method. MRF produces excellent change detection results that exhibit up to a 22% performance improvement over other methods from the literature at low false-alarm rates.

40 CFR 230.94 - Planning and documentation.

Code of Federal Regulations, 2013 CFR

2013-07-01

... waters and uplands; methods for establishing the desired plant community; plans to control invasive plant species; the proposed grading plan, including elevations and slopes of the substrate; soil management; and...

40 CFR 230.94 - Planning and documentation.

Code of Federal Regulations, 2011 CFR

2011-07-01

... waters and uplands; methods for establishing the desired plant community; plans to control invasive plant species; the proposed grading plan, including elevations and slopes of the substrate; soil management; and...

40 CFR 230.94 - Planning and documentation.

Code of Federal Regulations, 2012 CFR

2012-07-01

... waters and uplands; methods for establishing the desired plant community; plans to control invasive plant species; the proposed grading plan, including elevations and slopes of the substrate; soil management; and...

40 CFR 230.94 - Planning and documentation.

Code of Federal Regulations, 2014 CFR

2014-07-01

... waters and uplands; methods for establishing the desired plant community; plans to control invasive plant species; the proposed grading plan, including elevations and slopes of the substrate; soil management; and...

40 CFR 792.120 - Protocol.

Code of Federal Regulations, 2014 CFR

2014-07-01

... the test system. (8) A description of the experimental design, including methods for the control of... at which the study is being conducted. (4) The proposed experimental start and termination dates. (5...

40 CFR 792.120 - Protocol.

Code of Federal Regulations, 2012 CFR

2012-07-01

... the test system. (8) A description of the experimental design, including methods for the control of... at which the study is being conducted. (4) The proposed experimental start and termination dates. (5...

40 CFR 792.120 - Protocol.

Code of Federal Regulations, 2013 CFR

2013-07-01

... the test system. (8) A description of the experimental design, including methods for the control of... at which the study is being conducted. (4) The proposed experimental start and termination dates. (5...

40 CFR 792.120 - Protocol.

Code of Federal Regulations, 2011 CFR

2011-07-01

... the test system. (8) A description of the experimental design, including methods for the control of... at which the study is being conducted. (4) The proposed experimental start and termination dates. (5...

Novel inter-crystal scattering event identification method for PET detectors

NASA Astrophysics Data System (ADS)

Lee, Min Sun; Kang, Seung Kwan; Lee, Jae Sung

2018-06-01

Here, we propose a novel method to identify inter-crystal scattering (ICS) events from a PET detector that is even applicable to light-sharing designs. In the proposed method, the detector observation was considered as a linear problem and ICS events were identified by solving this problem. Two ICS identification methods were suggested for solving the linear problem, pseudoinverse matrix calculation and convex constrained optimization. The proposed method was evaluated based on simulation and experimental studies. For the simulation study, an 8  ×  8 photo sensor was coupled to 8  ×  8, 10  ×  10 and 12  ×  12 crystal arrays to simulate a one-to-one coupling and two light-sharing detectors, respectively. The identification rate, the rate that the identified ICS events correctly include the true first interaction position and the energy linearity were evaluated for the proposed ICS identification methods. For the experimental study, a digital silicon photomultiplier was coupled with 8  ×  8 and 10  ×  10 arrays of 3  ×  3  ×  20 mm3 LGSO crystals to construct the one-to-one coupling and light-sharing detectors, respectively. Intrinsic spatial resolutions were measured for two detector types. The proposed ICS identification methods were implemented, and intrinsic resolutions were compared with and without ICS recovery. As a result, the simulation study showed that the proposed convex optimization method yielded robust energy estimation and high ICS identification rates of 0.93 and 0.87 for the one-to-one and light-sharing detectors, respectively. The experimental study showed a resolution improvement after recovering the identified ICS events into the first interaction position. The average intrinsic spatial resolutions for the one-to-one and light-sharing detector were 1.95 and 2.25 mm in the FWHM without ICS recovery, respectively. These values improved to 1.72 and 1.83 mm after ICS recovery, respectively. In conclusion, our proposed method showed good ICS identification in both one-to-one coupling and light-sharing detectors. We experimentally validated that the ICS recovery based on the proposed identification method led to an improved resolution.

76 FR 21393 - Agency Information Collection Activities: Proposed Collection; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2011-04-15

... startup cost components or annual operation, maintenance, and purchase of service components. You should describe the methods you use to estimate major cost factors, including system and technology acquisition.... Capital and startup costs include, among other items, computers and software you purchase to prepare for...

76 FR 25367 - Agency Information Collection Activities: Proposed Collection, Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2011-05-04

... startup cost components or annual operation, maintenance, and purchase of service components. You should describe the methods you use to estimate major cost factors, including system and technology acquisition.... Capital and startup costs include, among other items, computers and software you purchase to prepare for...

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.

Design and performance evaluation of a distributed OFDMA-based MAC protocol for MANETs.

PubMed

Park, Jaesung; Chung, Jiyoung; Lee, Hyungyu; Lee, Jung-Ryun

2014-01-01

In this paper, we propose a distributed MAC protocol for OFDMA-based wireless mobile ad hoc multihop networks, in which the resource reservation and data transmission procedures are operated in a distributed manner. A frame format is designed considering the characteristics of OFDMA that each node can transmit or receive data to or from multiple nodes simultaneously. Under this frame structure, we propose a distributed resource management method including network state estimation and resource reservation processes. We categorize five types of logical errors according to their root causes and show that two of the logical errors are inevitable while three of them are avoided under the proposed distributed MAC protocol. In addition, we provide a systematic method to determine the advertisement period of each node by presenting a clear relation between the accuracy of estimated network states and the signaling overhead. We evaluate the performance of the proposed protocol in respect of the reservation success rate and the success rate of data transmission. Since our method focuses on avoiding logical errors, it could be easily placed on top of the other resource allocation methods focusing on the physical layer issues of the resource management problem and interworked with them.

Economic Analysis and Optimal Sizing for behind-the-meter Battery Storage

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

Wu, Di; Kintner-Meyer, Michael CW; Yang, Tao

This paper proposes methods to estimate the potential benefits and determine the optimal energy and power capacity for behind-the-meter BSS. In the proposed method, a linear programming is first formulated only using typical load profiles, energy/demand charge rates, and a set of battery parameters to determine the maximum saving in electric energy cost. The optimization formulation is then adapted to include battery cost as a function of its power and energy capacity in order to capture the trade-off between benefits and cost, and therefore to determine the most economic battery size. Using the proposed methods, economic analysis and optimal sizingmore » have been performed for a few commercial buildings and utility rate structures that are representative of those found in the various regions of the Continental United States. The key factors that affect the economic benefits and optimal size have been identified. The proposed methods and case study results cannot only help commercial and industrial customers or battery vendors to evaluate and size the storage system for behind-the-meter application, but can also assist utilities and policy makers to design electricity rate or subsidies to promote the development of energy storage.« less

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

Wu, Chase

A number of Department of Energy (DOE) science applications, involving exascale computing systems and large experimental facilities, are expected to generate large volumes of data, in the range of petabytes to exabytes, which will be transported over wide-area networks for the purpose of storage, visualization, and analysis. The objectives of this proposal are to (1) develop and test the component technologies and their synthesis methods to achieve source-to-sink high-performance flows, and (2) develop tools that provide these capabilities through simple interfaces to users and applications. In terms of the former, we propose to develop (1) optimization methods that align andmore » transition multiple storage flows to multiple network flows on multicore, multibus hosts; and (2) edge and long-haul network path realization and maintenance using advanced provisioning methods including OSCARS and OpenFlow. We also propose synthesis methods that combine these individual technologies to compose high-performance flows using a collection of constituent storage-network flows, and realize them across the storage and local network connections as well as long-haul connections. We propose to develop automated user tools that profile the hosts, storage systems, and network connections; compose the source-to-sink complex flows; and set up and maintain the needed network connections.« less

An exploration for research-oriented teaching model in biology teaching.

PubMed

Xing, Wanjin; Mo, Morigen; Su, Huimin

2014-07-01

Training innovative talents, as one of the major aims for Chinese universities, needs to reform the traditional teaching methods. The research-oriented teaching method has been introduced and its connotation and significance for Chinese university teaching have been discussed for years. However, few practical teaching methods for routine class teaching were proposed. In this paper, a comprehensive and concrete research-oriented teaching model with contents of reference value and evaluation method for class teaching was proposed based on the current teacher-guiding teaching model in China. We proposed that the research-oriented teaching model should include at least seven aspects on: (1) telling the scientific history for the skills to find out scientific questions; (2) replaying the experiments for the skills to solve scientific problems; (3) analyzing experimental data for learning how to draw a conclusion; (4) designing virtual experiments for learning how to construct a proposal; (5) teaching the lesson as the detectives solve the crime for learning the logic in scientific exploration; (6) guiding students how to read and consult the relative references; (7) teaching students differently according to their aptitude and learning ability. In addition, we also discussed how to evaluate the effects of the research-oriented teaching model in examination.

Chemically Patterned Inverse Opal Created by a Selective Photolysis Modification Process.

PubMed

Tian, Tian; Gao, Ning; Gu, Chen; Li, Jian; Wang, Hui; Lan, Yue; Yin, Xianpeng; Li, Guangtao

2015-09-02

Anisotropic photonic crystal materials have long been pursued for their broad applications. A novel method for creating chemically patterned inverse opals is proposed here. The patterning technique is based on selective photolysis of a photolabile polymer together with postmodification on released amine groups. The patterning method allows regioselective modification within an inverse opal structure, taking advantage of selective chemical reaction. Moreover, combined with the unique signal self-reporting feature of the photonic crystal, the fabricated structure is capable of various applications, including gradient photonic bandgap and dynamic chemical patterns. The proposed method provides the ability to extend the structural and chemical complexity of the photonic crystal, as well as its potential applications.

Initialization Method for Grammar-Guided Genetic Programming

NASA Astrophysics Data System (ADS)

García-Arnau, M.; Manrique, D.; Ríos, J.; Rodríguez-Patón, A.

This paper proposes a new tree-generation algorithm for grammarguided genetic programming that includes a parameter to control the maximum size of the trees to be generated. An important feature of this algorithm is that the initial populations generated are adequately distributed in terms of tree size and distribution within the search space. Consequently, genetic programming systems starting from the initial populations generated by the proposed method have a higher convergence speed. Two different problems have been chosen to carry out the experiments: a laboratory test involving searching for arithmetical equalities and the real-world task of breast cancer prognosis. In both problems, comparisons have been made to another five important initialization methods.

78 FR 7939 - Energy Conservation Program: Test Procedures for Microwave Ovens (Active Mode)

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-04

...The U.S. Department of Energy (DOE) proposes to revise its test procedures for microwave ovens established under the Energy Policy and Conservation Act. The proposed amendments would add provisions for measuring the active mode energy use for microwave ovens, including both microwave-only ovens and convection microwave ovens. Specifically, DOE is proposing provisions for measuring the energy use of the microwave-only cooking mode for both microwave-only ovens and convection microwave ovens based on the testing methods in the latest draft version of the International Electrotechnical Commission Standard 60705, ``Household microwave ovens--Methods for measuring performance.'' DOE is proposing provisions for measuring the energy use of the convection-only cooking mode for convection microwave ovens based on the DOE test procedure for conventional ovens in our regulations. DOE is also proposing to calculate the energy use of the convection-microwave cooking mode for convection microwave ovens by apportioning the microwave-only mode and convection-only mode energy consumption measurements based on typical consumer use.

Data Field Modeling and Spectral-Spatial Feature Fusion for Hyperspectral Data Classification.

PubMed

Liu, Da; Li, Jianxun

2016-12-16

Classification is a significant subject in hyperspectral remote sensing image processing. This study proposes a spectral-spatial feature fusion algorithm for the classification of hyperspectral images (HSI). Unlike existing spectral-spatial classification methods, the influences and interactions of the surroundings on each measured pixel were taken into consideration in this paper. Data field theory was employed as the mathematical realization of the field theory concept in physics, and both the spectral and spatial domains of HSI were considered as data fields. Therefore, the inherent dependency of interacting pixels was modeled. Using data field modeling, spatial and spectral features were transformed into a unified radiation form and further fused into a new feature by using a linear model. In contrast to the current spectral-spatial classification methods, which usually simply stack spectral and spatial features together, the proposed method builds the inner connection between the spectral and spatial features, and explores the hidden information that contributed to classification. Therefore, new information is included for classification. The final classification result was obtained using a random forest (RF) classifier. The proposed method was tested with the University of Pavia and Indian Pines, two well-known standard hyperspectral datasets. The experimental results demonstrate that the proposed method has higher classification accuracies than those obtained by the traditional approaches.

Uniform enhancement of optical micro-angiography images using Rayleigh contrast-limited adaptive histogram equalization.

PubMed

Yousefi, Siavash; Qin, Jia; Zhi, Zhongwei; Wang, Ruikang K

2013-02-01

Optical microangiography is an imaging technology that is capable of providing detailed functional blood flow maps within microcirculatory tissue beds in vivo. Some practical issues however exist when displaying and quantifying the microcirculation that perfuses the scanned tissue volume. These issues include: (I) Probing light is subject to specular reflection when it shines onto sample. The unevenness of the tissue surface makes the light energy entering the tissue not uniform over the entire scanned tissue volume. (II) The biological tissue is heterogeneous in nature, meaning the scattering and absorption properties of tissue would attenuate the probe beam. These physical limitations can result in local contrast degradation and non-uniform micro-angiogram images. In this paper, we propose a post-processing method that uses Rayleigh contrast-limited adaptive histogram equalization to increase the contrast and improve the overall appearance and uniformity of optical micro-angiograms without saturating the vessel intensity and changing the physical meaning of the micro-angiograms. The qualitative and quantitative performance of the proposed method is compared with those of common histogram equalization and contrast enhancement methods. We demonstrate that the proposed method outperforms other existing approaches. The proposed method is not limited to optical microangiography and can be used in other image modalities such as photo-acoustic tomography and scanning laser confocal microscopy.

Ultra-Short-Term Wind Power Prediction Using a Hybrid Model

NASA Astrophysics Data System (ADS)

Mohammed, E.; Wang, S.; Yu, J.

2017-05-01

This paper aims to develop and apply a hybrid model of two data analytical methods, multiple linear regressions and least square (MLR&LS), for ultra-short-term wind power prediction (WPP), for example taking, Northeast China electricity demand. The data was obtained from the historical records of wind power from an offshore region, and from a wind farm of the wind power plant in the areas. The WPP achieved in two stages: first, the ratios of wind power were forecasted using the proposed hybrid method, and then the transformation of these ratios of wind power to obtain forecasted values. The hybrid model combines the persistence methods, MLR and LS. The proposed method included two prediction types, multi-point prediction and single-point prediction. WPP is tested by applying different models such as autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA) and artificial neural network (ANN). By comparing results of the above models, the validity of the proposed hybrid model is confirmed in terms of error and correlation coefficient. Comparison of results confirmed that the proposed method works effectively. Additional, forecasting errors were also computed and compared, to improve understanding of how to depict highly variable WPP and the correlations between actual and predicted wind power.

Bayesian Normalization Model for Label-Free Quantitative Analysis by LC-MS

PubMed Central

Nezami Ranjbar, Mohammad R.; Tadesse, Mahlet G.; Wang, Yue; Ressom, Habtom W.

2016-01-01

We introduce a new method for normalization of data acquired by liquid chromatography coupled with mass spectrometry (LC-MS) in label-free differential expression analysis. Normalization of LC-MS data is desired prior to subsequent statistical analysis to adjust variabilities in ion intensities that are not caused by biological differences but experimental bias. There are different sources of bias including variabilities during sample collection and sample storage, poor experimental design, noise, etc. In addition, instrument variability in experiments involving a large number of LC-MS runs leads to a significant drift in intensity measurements. Although various methods have been proposed for normalization of LC-MS data, there is no universally applicable approach. In this paper, we propose a Bayesian normalization model (BNM) that utilizes scan-level information from LC-MS data. Specifically, the proposed method uses peak shapes to model the scan-level data acquired from extracted ion chromatograms (EIC) with parameters considered as a linear mixed effects model. We extended the model into BNM with drift (BNMD) to compensate for the variability in intensity measurements due to long LC-MS runs. We evaluated the performance of our method using synthetic and experimental data. In comparison with several existing methods, the proposed BNM and BNMD yielded significant improvement. PMID:26357332

Identification of ubiquitin/ubiquitin-like protein modification from tandem mass spectra with various PTMs

PubMed Central

2011-01-01

Background Various solutions have been introduced for the identification of post-translational modification (PTM) from tandem mass spectrometry (MS/MS) in proteomics field but the identification of peptide modifiers, such as Ubiquitin (Ub) and ubiquitin-like proteins (Ubls), is still a challenge. The fragmentation of peptide modifier produce complex shifted ion mass patterns in combination with other PTMs, which makes it difficult to identify and locate the PTMs on a protein sequence. Currently, most PTM identification methods do not consider the complex fragmentation of peptide modifier or deals it separately from the other PTMs. Results We developed an advanced PTM identification method that inspects possible ion patterns of the most known peptide modifiers as well as other known biological and chemical PTMs to make more comprehensive and accurate conclusion. The proposed method searches all detectable mass differences of measured peaks from their theoretical values and the mass differences within mass tolerance range are grouped as mass shift classes. The most possible locations of multiple PTMs including peptide modifiers can be determined by evaluating all possible scenarios generated by the combination of the qualified mass shift classes.The proposed method showed excellent performance in the test with simulated spectra having various PTMs including peptide modifiers and in the comparison with recently developed methods such as QuickMod and SUMmOn. In the analysis of HUPO Brain Proteome Project (BPP) datasets, the proposed method could find the ubiquitin modification sites that were not identified by other conventional methods. Conclusions This work presents a novel method for identifying bothpeptide modifiers that generate complex fragmentation patternsand PTMs that are not fragmented during fragmentation processfrom tandem mass spectra. PMID:22373085

Calculation of acoustic field based on laser-measured vibration velocities on ultrasonic transducer surface

NASA Astrophysics Data System (ADS)

Hu, Liang; Zhao, Nannan; Gao, Zhijian; Mao, Kai; Chen, Wenyu; Fu, Xin

2018-05-01

Determination of the distribution of a generated acoustic field is valuable for studying ultrasonic transducers, including providing the guidance for transducer design and the basis for analyzing their performance, etc. A method calculating the acoustic field based on laser-measured vibration velocities on the ultrasonic transducer surface is proposed in this paper. Without knowing the inner structure of the transducer, the acoustic field outside it can be calculated by solving the governing partial differential equation (PDE) of the field based on the specified boundary conditions (BCs). In our study, the BC on the transducer surface, i.e. the distribution of the vibration velocity on the surface, is accurately determined by laser scanning measurement of discrete points and follows a data fitting computation. In addition, to ensure the calculation accuracy for the whole field even in an inhomogeneous medium, a finite element method is used to solve the governing PDE based on the mixed BCs, including the discretely measured velocity data and other specified BCs. The method is firstly validated on numerical piezoelectric transducer models. The acoustic pressure distributions generated by a transducer operating in an homogeneous and inhomogeneous medium, respectively, are both calculated by the proposed method and compared with the results from other existing methods. Then, the method is further experimentally validated with two actual ultrasonic transducers used for flow measurement in our lab. The amplitude change of the output voltage signal from the receiver transducer due to changing the relative position of the two transducers is calculated by the proposed method and compared with the experimental data. This method can also provide the basis for complex multi-physical coupling computations where the effect of the acoustic field should be taken into account.

A Modified Kirchhoff plate theory for Free Vibration analysis of functionally graded material plates using meshfree method

NASA Astrophysics Data System (ADS)

Nguyen Van Do, Vuong

2018-04-01

In this paper, a modified Kirchhoff theory is presented for free vibration analyses of functionally graded material (FGM) plate based on modified radial point interpolation method (RPIM). The shear deformation effects are taken account into modified theory to ignore the locking phenomenon of thin plates. Due to the proposed refined plate theory, the number of independent unknowns reduces one variable and exists with four degrees of freedom per node. The simulated free vibration results employed by the modified RPIM are compared with the other analytical solutions to verify the effectiveness and the accuracy of the developed mesh-free method. Detail parametric studies of the proposed method are then conducted including the effectiveness of thickness ratio, boundary condition and material inhomogeneity on the sample problems of square plates. Results illustrated that the modified mesh-free RPIM can effectively predict the numerical calculation as compared to the exact solutions. The obtained numerical results are indicated that the proposed method are stable and well accurate prediction to evaluate with other published analyses.

Dynamic path planning for autonomous driving on various roads with avoidance of static and moving obstacles

NASA Astrophysics Data System (ADS)

Hu, Xuemin; Chen, Long; Tang, Bo; Cao, Dongpu; He, Haibo

2018-02-01

This paper presents a real-time dynamic path planning method for autonomous driving that avoids both static and moving obstacles. The proposed path planning method determines not only an optimal path, but also the appropriate acceleration and speed for a vehicle. In this method, we first construct a center line from a set of predefined waypoints, which are usually obtained from a lane-level map. A series of path candidates are generated by the arc length and offset to the center line in the s - ρ coordinate system. Then, all of these candidates are converted into Cartesian coordinates. The optimal path is selected considering the total cost of static safety, comfortability, and dynamic safety; meanwhile, the appropriate acceleration and speed for the optimal path are also identified. Various types of roads, including single-lane roads and multi-lane roads with static and moving obstacles, are designed to test the proposed method. The simulation results demonstrate the effectiveness of the proposed method, and indicate its wide practical application to autonomous driving.

Star tracking method based on multiexposure imaging for intensified star trackers.

PubMed

Yu, Wenbo; Jiang, Jie; Zhang, Guangjun

2017-07-20

The requirements for the dynamic performance of star trackers are rapidly increasing with the development of space exploration technologies. However, insufficient knowledge of the angular acceleration has largely decreased the performance of the existing star tracking methods, and star trackers may even fail to track under highly dynamic conditions. This study proposes a star tracking method based on multiexposure imaging for intensified star trackers. The accurate estimation model of the complete motion parameters, including the angular velocity and angular acceleration, is established according to the working characteristic of multiexposure imaging. The estimation of the complete motion parameters is utilized to generate the predictive star image accurately. Therefore, the correct matching and tracking between stars in the real and predictive star images can be reliably accomplished under highly dynamic conditions. Simulations with specific dynamic conditions are conducted to verify the feasibility and effectiveness of the proposed method. Experiments with real starry night sky observation are also conducted for further verification. Simulations and experiments demonstrate that the proposed method is effective and shows excellent performance under highly dynamic conditions.

Standardless quantification by parameter optimization in electron probe microanalysis

NASA Astrophysics Data System (ADS)

Limandri, Silvina P.; Bonetto, Rita D.; Josa, Víctor Galván; Carreras, Alejo C.; Trincavelli, Jorge C.

2012-11-01

A method for standardless quantification by parameter optimization in electron probe microanalysis is presented. The method consists in minimizing the quadratic differences between an experimental spectrum and an analytical function proposed to describe it, by optimizing the parameters involved in the analytical prediction. This algorithm, implemented in the software POEMA (Parameter Optimization in Electron Probe Microanalysis), allows the determination of the elemental concentrations, along with their uncertainties. The method was tested in a set of 159 elemental constituents corresponding to 36 spectra of standards (mostly minerals) that include trace elements. The results were compared with those obtained with the commercial software GENESIS Spectrum® for standardless quantification. The quantifications performed with the method proposed here are better in the 74% of the cases studied. In addition, the performance of the method proposed is compared with the first principles standardless analysis procedure DTSA for a different data set, which excludes trace elements. The relative deviations with respect to the nominal concentrations are lower than 0.04, 0.08 and 0.35 for the 66% of the cases for POEMA, GENESIS and DTSA, respectively.

A Penalized Robust Method for Identifying Gene-Environment Interactions

PubMed Central

Shi, Xingjie; Liu, Jin; Huang, Jian; Zhou, Yong; Xie, Yang; Ma, Shuangge

2015-01-01

In high-throughput studies, an important objective is to identify gene-environment interactions associated with disease outcomes and phenotypes. Many commonly adopted methods assume specific parametric or semiparametric models, which may be subject to model mis-specification. In addition, they usually use significance level as the criterion for selecting important interactions. In this study, we adopt the rank-based estimation, which is much less sensitive to model specification than some of the existing methods and includes several commonly encountered data and models as special cases. Penalization is adopted for the identification of gene-environment interactions. It achieves simultaneous estimation and identification and does not rely on significance level. For computation feasibility, a smoothed rank estimation is further proposed. Simulation shows that under certain scenarios, for example with contaminated or heavy-tailed data, the proposed method can significantly outperform the existing alternatives with more accurate identification. We analyze a lung cancer prognosis study with gene expression measurements under the AFT (accelerated failure time) model. The proposed method identifies interactions different from those using the alternatives. Some of the identified genes have important implications. PMID:24616063

Lamb Wave Damage Quantification Using GA-Based LS-SVM.

PubMed

Sun, Fuqiang; Wang, Ning; He, Jingjing; Guan, Xuefei; Yang, Jinsong

2017-06-12

Lamb waves have been reported to be an efficient tool for non-destructive evaluations (NDE) for various application scenarios. However, accurate and reliable damage quantification using the Lamb wave method is still a practical challenge, due to the complex underlying mechanism of Lamb wave propagation and damage detection. This paper presents a Lamb wave damage quantification method using a least square support vector machine (LS-SVM) and a genetic algorithm (GA). Three damage sensitive features, namely, normalized amplitude, phase change, and correlation coefficient, were proposed to describe changes of Lamb wave characteristics caused by damage. In view of commonly used data-driven methods, the GA-based LS-SVM model using the proposed three damage sensitive features was implemented to evaluate the crack size. The GA method was adopted to optimize the model parameters. The results of GA-based LS-SVM were validated using coupon test data and lap joint component test data with naturally developed fatigue cracks. Cases of different loading and manufacturer were also included to further verify the robustness of the proposed method for crack quantification.

A new EEG synchronization strength analysis method: S-estimator based normalized weighted-permutation mutual information.

PubMed

Cui, Dong; Pu, Weiting; Liu, Jing; Bian, Zhijie; Li, Qiuli; Wang, Lei; Gu, Guanghua

2016-10-01

Synchronization is an important mechanism for understanding information processing in normal or abnormal brains. In this paper, we propose a new method called normalized weighted-permutation mutual information (NWPMI) for double variable signal synchronization analysis and combine NWPMI with S-estimator measure to generate a new method named S-estimator based normalized weighted-permutation mutual information (SNWPMI) for analyzing multi-channel electroencephalographic (EEG) synchronization strength. The performances including the effects of time delay, embedding dimension, coupling coefficients, signal to noise ratios (SNRs) and data length of the NWPMI are evaluated by using Coupled Henon mapping model. The results show that the NWPMI is superior in describing the synchronization compared with the normalized permutation mutual information (NPMI). Furthermore, the proposed SNWPMI method is applied to analyze scalp EEG data from 26 amnestic mild cognitive impairment (aMCI) subjects and 20 age-matched controls with normal cognitive function, who both suffer from type 2 diabetes mellitus (T2DM). The proposed methods NWPMI and SNWPMI are suggested to be an effective index to estimate the synchronization strength. Copyright © 2016 Elsevier Ltd. All rights reserved.

Lamb Wave Damage Quantification Using GA-Based LS-SVM

PubMed Central

Sun, Fuqiang; Wang, Ning; He, Jingjing; Guan, Xuefei; Yang, Jinsong

2017-01-01

Lamb waves have been reported to be an efficient tool for non-destructive evaluations (NDE) for various application scenarios. However, accurate and reliable damage quantification using the Lamb wave method is still a practical challenge, due to the complex underlying mechanism of Lamb wave propagation and damage detection. This paper presents a Lamb wave damage quantification method using a least square support vector machine (LS-SVM) and a genetic algorithm (GA). Three damage sensitive features, namely, normalized amplitude, phase change, and correlation coefficient, were proposed to describe changes of Lamb wave characteristics caused by damage. In view of commonly used data-driven methods, the GA-based LS-SVM model using the proposed three damage sensitive features was implemented to evaluate the crack size. The GA method was adopted to optimize the model parameters. The results of GA-based LS-SVM were validated using coupon test data and lap joint component test data with naturally developed fatigue cracks. Cases of different loading and manufacturer were also included to further verify the robustness of the proposed method for crack quantification. PMID:28773003

Research on adaptive optics image restoration algorithm based on improved joint maximum a posteriori method

NASA Astrophysics Data System (ADS)

Zhang, Lijuan; Li, Yang; Wang, Junnan; Liu, Ying

2018-03-01

In this paper, we propose a point spread function (PSF) reconstruction method and joint maximum a posteriori (JMAP) estimation method for the adaptive optics image restoration. Using the JMAP method as the basic principle, we establish the joint log likelihood function of multi-frame adaptive optics (AO) images based on the image Gaussian noise models. To begin with, combining the observed conditions and AO system characteristics, a predicted PSF model for the wavefront phase effect is developed; then, we build up iterative solution formulas of the AO image based on our proposed algorithm, addressing the implementation process of multi-frame AO images joint deconvolution method. We conduct a series of experiments on simulated and real degraded AO images to evaluate our proposed algorithm. Compared with the Wiener iterative blind deconvolution (Wiener-IBD) algorithm and Richardson-Lucy IBD algorithm, our algorithm has better restoration effects including higher peak signal-to-noise ratio ( PSNR) and Laplacian sum ( LS) value than the others. The research results have a certain application values for actual AO image restoration.

Prediction of Drug-Target Interaction Networks from the Integration of Protein Sequences and Drug Chemical Structures.

PubMed

Meng, Fan-Rong; You, Zhu-Hong; Chen, Xing; Zhou, Yong; An, Ji-Yong

2017-07-05

Knowledge of drug-target interaction (DTI) plays an important role in discovering new drug candidates. Unfortunately, there are unavoidable shortcomings; including the time-consuming and expensive nature of the experimental method to predict DTI. Therefore, it motivates us to develop an effective computational method to predict DTI based on protein sequence. In the paper, we proposed a novel computational approach based on protein sequence, namely PDTPS (Predicting Drug Targets with Protein Sequence) to predict DTI. The PDTPS method combines Bi-gram probabilities (BIGP), Position Specific Scoring Matrix (PSSM), and Principal Component Analysis (PCA) with Relevance Vector Machine (RVM). In order to evaluate the prediction capacity of the PDTPS, the experiment was carried out on enzyme, ion channel, GPCR, and nuclear receptor datasets by using five-fold cross-validation tests. The proposed PDTPS method achieved average accuracy of 97.73%, 93.12%, 86.78%, and 87.78% on enzyme, ion channel, GPCR and nuclear receptor datasets, respectively. The experimental results showed that our method has good prediction performance. Furthermore, in order to further evaluate the prediction performance of the proposed PDTPS method, we compared it with the state-of-the-art support vector machine (SVM) classifier on enzyme and ion channel datasets, and other exiting methods on four datasets. The promising comparison results further demonstrate that the efficiency and robust of the proposed PDTPS method. This makes it a useful tool and suitable for predicting DTI, as well as other bioinformatics tasks.

Local linear estimation of concordance probability with application to covariate effects models on association for bivariate failure-time data.

PubMed

Ding, Aidong Adam; Hsieh, Jin-Jian; Wang, Weijing

2015-01-01

Bivariate survival analysis has wide applications. In the presence of covariates, most literature focuses on studying their effects on the marginal distributions. However covariates can also affect the association between the two variables. In this article we consider the latter issue by proposing a nonstandard local linear estimator for the concordance probability as a function of covariates. Under the Clayton copula, the conditional concordance probability has a simple one-to-one correspondence with the copula parameter for different data structures including those subject to independent or dependent censoring and dependent truncation. The proposed method can be used to study how covariates affect the Clayton association parameter without specifying marginal regression models. Asymptotic properties of the proposed estimators are derived and their finite-sample performances are examined via simulations. Finally, for illustration, we apply the proposed method to analyze a bone marrow transplant data set.

New Dandelion Algorithm Optimizes Extreme Learning Machine for Biomedical Classification Problems

PubMed Central

Li, Xiguang; Zhao, Liang; Gong, Changqing; Liu, Xiaojing

2017-01-01

Inspired by the behavior of dandelion sowing, a new novel swarm intelligence algorithm, namely, dandelion algorithm (DA), is proposed for global optimization of complex functions in this paper. In DA, the dandelion population will be divided into two subpopulations, and different subpopulations will undergo different sowing behaviors. Moreover, another sowing method is designed to jump out of local optimum. In order to demonstrate the validation of DA, we compare the proposed algorithm with other existing algorithms, including bat algorithm, particle swarm optimization, and enhanced fireworks algorithm. Simulations show that the proposed algorithm seems much superior to other algorithms. At the same time, the proposed algorithm can be applied to optimize extreme learning machine (ELM) for biomedical classification problems, and the effect is considerable. At last, we use different fusion methods to form different fusion classifiers, and the fusion classifiers can achieve higher accuracy and better stability to some extent. PMID:29085425

A semiparametric graphical modelling approach for large-scale equity selection

PubMed Central

Liu, Han; Mulvey, John; Zhao, Tianqi

2016-01-01

We propose a new stock selection strategy that exploits rebalancing returns and improves portfolio performance. To effectively harvest rebalancing gains, we apply ideas from elliptical-copula graphical modelling and stability inference to select stocks that are as independent as possible. The proposed elliptical-copula graphical model has a latent Gaussian representation; its structure can be effectively inferred using the regularized rank-based estimators. The resulting algorithm is computationally efficient and scales to large data-sets. To show the efficacy of the proposed method, we apply it to conduct equity selection based on a 16-year health care stock data-set and a large 34-year stock data-set. Empirical tests show that the proposed method is superior to alternative strategies including a principal component analysis-based approach and the classical Markowitz strategy based on the traditional buy-and-hold assumption. PMID:28316507

Segmentation of epidermal tissue with histopathological damage in images of haematoxylin and eosin stained human skin

PubMed Central

2014-01-01

Background Digital image analysis has the potential to address issues surrounding traditional histological techniques including a lack of objectivity and high variability, through the application of quantitative analysis. A key initial step in image analysis is the identification of regions of interest. A widely applied methodology is that of segmentation. This paper proposes the application of image analysis techniques to segment skin tissue with varying degrees of histopathological damage. The segmentation of human tissue is challenging as a consequence of the complexity of the tissue structures and inconsistencies in tissue preparation, hence there is a need for a new robust method with the capability to handle the additional challenges materialising from histopathological damage. Methods A new algorithm has been developed which combines enhanced colour information, created following a transformation to the L*a*b* colourspace, with general image intensity information. A colour normalisation step is included to enhance the algorithm’s robustness to variations in the lighting and staining of the input images. The resulting optimised image is subjected to thresholding and the segmentation is fine-tuned using a combination of morphological processing and object classification rules. The segmentation algorithm was tested on 40 digital images of haematoxylin & eosin (H&E) stained skin biopsies. Accuracy, sensitivity and specificity of the algorithmic procedure were assessed through the comparison of the proposed methodology against manual methods. Results Experimental results show the proposed fully automated methodology segments the epidermis with a mean specificity of 97.7%, a mean sensitivity of 89.4% and a mean accuracy of 96.5%. When a simple user interaction step is included, the specificity increases to 98.0%, the sensitivity to 91.0% and the accuracy to 96.8%. The algorithm segments effectively for different severities of tissue damage. Conclusions Epidermal segmentation is a crucial first step in a range of applications including melanoma detection and the assessment of histopathological damage in skin. The proposed methodology is able to segment the epidermis with different levels of histological damage. The basic method framework could be applied to segmentation of other epithelial tissues. PMID:24521154

On the discretization and control of an SEIR epidemic model with a periodic impulsive vaccination

NASA Astrophysics Data System (ADS)

Alonso-Quesada, S.; De la Sen, M.; Ibeas, A.

2017-01-01

This paper deals with the discretization and control of an SEIR epidemic model. Such a model describes the transmission of an infectious disease among a time-varying host population. The model assumes mortality from causes related to the disease. Our study proposes a discretization method including a free-design parameter to be adjusted for guaranteeing the positivity of the resulting discrete-time model. Such a method provides a discrete-time model close to the continuous-time one without the need for the sampling period to be as small as other commonly used discretization methods require. This fact makes possible the design of impulsive vaccination control strategies with less burden of measurements and related computations if one uses the proposed instead of other discretization methods. The proposed discretization method and the impulsive vaccination strategy designed on the resulting discretized model are the main novelties of the paper. The paper includes (i) the analysis of the positivity of the obtained discrete-time SEIR model, (ii) the study of stability of the disease-free equilibrium point of a normalized version of such a discrete-time model and (iii) the existence and the attractivity of a globally asymptotically stable disease-free periodic solution under a periodic impulsive vaccination. Concretely, the exposed and infectious subpopulations asymptotically converge to zero as time tends to infinity while the normalized subpopulations of susceptible and recovered by immunization individuals oscillate in the context of such a solution. Finally, a numerical example illustrates the theoretic results.

Toward a Smartphone Application for Estimation of Pulse Transit Time

PubMed Central

Liu, He; Ivanov, Kamen; Wang, Yadong; Wang, Lei

2015-01-01

Pulse transit time (PTT) is an important physiological parameter that directly correlates with the elasticity and compliance of vascular walls and variations in blood pressure. This paper presents a PTT estimation method based on photoplethysmographic imaging (PPGi). The method utilizes two opposing cameras for simultaneous acquisition of PPGi waveform signals from the index fingertip and the forehead temple. An algorithm for the detection of maxima and minima in PPGi signals was developed, which includes technology for interpolation of the real positions of these points. We compared our PTT measurements with those obtained from the current methodological standards. Statistical results indicate that the PTT measured by our proposed method exhibits a good correlation with the established method. The proposed method is especially suitable for implementation in dual-camera-smartphones, which could facilitate PTT measurement among populations affected by cardiac complications. PMID:26516861

Toward a Smartphone Application for Estimation of Pulse Transit Time.

PubMed

Liu, He; Ivanov, Kamen; Wang, Yadong; Wang, Lei

2015-10-27

Pulse transit time (PTT) is an important physiological parameter that directly correlates with the elasticity and compliance of vascular walls and variations in blood pressure. This paper presents a PTT estimation method based on photoplethysmographic imaging (PPGi). The method utilizes two opposing cameras for simultaneous acquisition of PPGi waveform signals from the index fingertip and the forehead temple. An algorithm for the detection of maxima and minima in PPGi signals was developed, which includes technology for interpolation of the real positions of these points. We compared our PTT measurements with those obtained from the current methodological standards. Statistical results indicate that the PTT measured by our proposed method exhibits a good correlation with the established method. The proposed method is especially suitable for implementation in dual-camera-smartphones, which could facilitate PTT measurement among populations affected by cardiac complications.

A meshless method for solving two-dimensional variable-order time fractional advection-diffusion equation

NASA Astrophysics Data System (ADS)

Tayebi, A.; Shekari, Y.; Heydari, M. H.

2017-07-01

Several physical phenomena such as transformation of pollutants, energy, particles and many others can be described by the well-known convection-diffusion equation which is a combination of the diffusion and advection equations. In this paper, this equation is generalized with the concept of variable-order fractional derivatives. The generalized equation is called variable-order time fractional advection-diffusion equation (V-OTFA-DE). An accurate and robust meshless method based on the moving least squares (MLS) approximation and the finite difference scheme is proposed for its numerical solution on two-dimensional (2-D) arbitrary domains. In the time domain, the finite difference technique with a θ-weighted scheme and in the space domain, the MLS approximation are employed to obtain appropriate semi-discrete solutions. Since the newly developed method is a meshless approach, it does not require any background mesh structure to obtain semi-discrete solutions of the problem under consideration, and the numerical solutions are constructed entirely based on a set of scattered nodes. The proposed method is validated in solving three different examples including two benchmark problems and an applied problem of pollutant distribution in the atmosphere. In all such cases, the obtained results show that the proposed method is very accurate and robust. Moreover, a remarkable property so-called positive scheme for the proposed method is observed in solving concentration transport phenomena.

Structural design of composite rotor blades with consideration of manufacturability, durability, and manufacturing uncertainties

NASA Astrophysics Data System (ADS)

Li, Leihong

A modular structural design methodology for composite blades is developed. This design method can be used to design composite rotor blades with sophisticate geometric cross-sections. This design method hierarchically decomposed the highly-coupled interdisciplinary rotor analysis into global and local levels. In the global level, aeroelastic response analysis and rotor trim are conduced based on multi-body dynamic models. In the local level, variational asymptotic beam sectional analysis methods are used for the equivalent one-dimensional beam properties. Compared with traditional design methodology, the proposed method is more efficient and accurate. Then, the proposed method is used to study three different design problems that have not been investigated before. The first is to add manufacturing constraints into design optimization. The introduction of manufacturing constraints complicates the optimization process. However, the design with manufacturing constraints benefits the manufacturing process and reduces the risk of violating major performance constraints. Next, a new design procedure for structural design against fatigue failure is proposed. This procedure combines the fatigue analysis with the optimization process. The durability or fatigue analysis employs a strength-based model. The design is subject to stiffness, frequency, and durability constraints. Finally, the manufacturing uncertainty impacts on rotor blade aeroelastic behavior are investigated, and a probabilistic design method is proposed to control the impacts of uncertainty on blade structural performance. The uncertainty factors include dimensions, shapes, material properties, and service loads.

Synthesis Gas Demonstration Plant, Baskett, Kentucky: environmental report. [Contains chapter 4 and appendix 4A

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

None

This volume contains chapter 4 and Appendix 4A which include descriptions of use of adjacent land and water (within miles of the proposed site), baseline ecology, air quality, meteorology, noise, hydrology, water quality, geology, soils and socio-economic factors. Appendix 4A includes detailed ecological surveys made in the area including the methods used. (LTN)

Applications of 3D-EDGE Detection for ALS Point Cloud

NASA Astrophysics Data System (ADS)

Ni, H.; Lin, X. G.; Zhang, J. X.

2017-09-01

Edge detection has been one of the major issues in the field of remote sensing and photogrammetry. With the fast development of sensor technology of laser scanning system, dense point clouds have become increasingly common. Precious 3D-edges are able to be detected from these point clouds and a great deal of edge or feature line extraction methods have been proposed. Among these methods, an easy-to-use 3D-edge detection method, AGPN (Analyzing Geometric Properties of Neighborhoods), has been proposed. The AGPN method detects edges based on the analysis of geometric properties of a query point's neighbourhood. The AGPN method detects two kinds of 3D-edges, including boundary elements and fold edges, and it has many applications. This paper presents three applications of AGPN, i.e., 3D line segment extraction, ground points filtering, and ground breakline extraction. Experiments show that the utilization of AGPN method gives a straightforward solution to these applications.

A fast simulation method for radiation maps using interpolation in a virtual environment.

PubMed

Li, Meng-Kun; Liu, Yong-Kuo; Peng, Min-Jun; Xie, Chun-Li; Yang, Li-Qun

2018-05-10

In nuclear decommissioning, virtual simulation technology is a useful tool to achieve an effective work process by using virtual environments to represent the physical and logical scheme of a real decommissioning project. This technology is cost-saving and time-saving, with the capacity to develop various decommissioning scenarios and reduce the risk of retrofitting. The method utilises a radiation map in a virtual simulation as the basis for the assessment of exposure to a virtual human. In this paper, we propose a fast simulation method using a known radiation source. The method has a unique advantage over point kernel and Monte Carlo methods because it generates the radiation map using interpolation in a virtual environment. The simulation of the radiation map including the calculation and the visualisation were realised using UNITY and MATLAB. The feasibility of the proposed method was tested on a hypothetical case and the results obtained are discussed in this paper.

Simultaneous maximum a posteriori longitudinal PET image reconstruction

NASA Astrophysics Data System (ADS)

Ellis, Sam; Reader, Andrew J.

2017-09-01

Positron emission tomography (PET) is frequently used to monitor functional changes that occur over extended time scales, for example in longitudinal oncology PET protocols that include routine clinical follow-up scans to assess the efficacy of a course of treatment. In these contexts PET datasets are currently reconstructed into images using single-dataset reconstruction methods. Inspired by recently proposed joint PET-MR reconstruction methods, we propose to reconstruct longitudinal datasets simultaneously by using a joint penalty term in order to exploit the high degree of similarity between longitudinal images. We achieved this by penalising voxel-wise differences between pairs of longitudinal PET images in a one-step-late maximum a posteriori (MAP) fashion, resulting in the MAP simultaneous longitudinal reconstruction (SLR) method. The proposed method reduced reconstruction errors and visually improved images relative to standard maximum likelihood expectation-maximisation (ML-EM) in simulated 2D longitudinal brain tumour scans. In reconstructions of split real 3D data with inserted simulated tumours, noise across images reconstructed with MAP-SLR was reduced to levels equivalent to doubling the number of detected counts when using ML-EM. Furthermore, quantification of tumour activities was largely preserved over a variety of longitudinal tumour changes, including changes in size and activity, with larger changes inducing larger biases relative to standard ML-EM reconstructions. Similar improvements were observed for a range of counts levels, demonstrating the robustness of the method when used with a single penalty strength. The results suggest that longitudinal regularisation is a simple but effective method of improving reconstructed PET images without using resolution degrading priors.

Systematic construction and control of stereo nerve vision network in intelligent manufacturing

NASA Astrophysics Data System (ADS)

Liu, Hua; Wang, Helong; Guo, Chunjie; Ding, Quanxin; Zhou, Liwei

2017-10-01

A system method of constructing stereo vision by using neural network is proposed, and the operation and control mechanism in actual operation are proposed. This method makes effective use of the neural network in learning and memory function, by after training with samples. Moreover, the neural network can learn the nonlinear relationship in the stereoscopic vision system and the internal and external orientation elements. These considerations are Worthy of attention, which includes limited constraints, the scientific of critical group, the operating speed and the operability in technical aspects. The results support our theoretical forecast.

Evaluating color deficiency simulation and daltonization methods through visual search and sample-to-match: SaMSEM and ViSDEM

NASA Astrophysics Data System (ADS)

Simon-Liedtke, Joschua T.; Farup, Ivar; Laeng, Bruno

2015-01-01

Color deficient people might be confronted with minor difficulties when navigating through daily life, for example when reading websites or media, navigating with maps, retrieving information from public transport schedules and others. Color deficiency simulation and daltonization methods have been proposed to better understand problems of color deficient individuals and to improve color displays for their use. However, it remains unclear whether these color prosthetic" methods really work and how well they improve the performance of color deficient individuals. We introduce here two methods to evaluate color deficiency simulation and daltonization methods based on behavioral experiments that are widely used in the field of psychology. Firstly, we propose a Sample-to-Match Simulation Evaluation Method (SaMSEM); secondly, we propose a Visual Search Daltonization Evaluation Method (ViSDEM). Both methods can be used to validate and allow the generalization of the simulation and daltonization methods related to color deficiency. We showed that both the response times (RT) and the accuracy of SaMSEM can be used as an indicator of the success of color deficiency simulation methods and that performance in the ViSDEM can be used as an indicator for the efficacy of color deficiency daltonization methods. In future work, we will include comparison and analysis of different color deficiency simulation and daltonization methods with the help of SaMSEM and ViSDEM.

A walkthrough solution to the boundary overlap problem

Treesearch

Mark J. Ducey; Jeffrey H. Gove; Harry T. Valentine

2004-01-01

Existing methods for eliminating bias due to boundary overlap suffer some disadvantages in practical use, including the need to work outside the tract, restrictions on the kinds of boundaries to which they are applicable, and the possibility of significantly increased variance as a price for unbiasedness. We propose a new walkthrough method for reducing boundary...

Forestry sector analysis for developing countries: issues and methods.

Treesearch

R.W. Haynes

1993-01-01

A satellite meeting of the 10th Forestry World Congress focused on the methods used for forest sector analysis and their applications in both developed and developing countries. The results of that meeting are summarized, and a general approach for forest sector modeling is proposed. The approach includes models derived from the existing...

Item Response Theory Equating Using Bayesian Informative Priors.

ERIC Educational Resources Information Center

de la Torre, Jimmy; Patz, Richard J.

This paper seeks to extend the application of Markov chain Monte Carlo (MCMC) methods in item response theory (IRT) to include the estimation of equating relationships along with the estimation of test item parameters. A method is proposed that incorporates estimation of the equating relationship in the item calibration phase. Item parameters from…

Factorial Structure of the French Version of the Rosenberg Self-Esteem Scale among the Elderly

ERIC Educational Resources Information Center

Gana, Kamel; Alaphilippe, Daniel; Bailly, Nathalie

2005-01-01

Ten different confirmatory factor analysis models, including ones with correlated traits correlated methods, correlated traits correlated uniqueness, and correlated traits uncorrelated methods, were proposed to examine the factorial structure of the French version of the Rosenberg Self-Esteem Scale (Rosenberg, 1965). In line with previous studies…

An Empirical Comparison of Heterogeneity Variance Estimators in 12,894 Meta-Analyses

ERIC Educational Resources Information Center

Langan, Dean; Higgins, Julian P. T.; Simmonds, Mark

2015-01-01

Heterogeneity in meta-analysis is most commonly estimated using a moment-based approach described by DerSimonian and Laird. However, this method has been shown to produce biased estimates. Alternative methods to estimate heterogeneity include the restricted maximum likelihood approach and those proposed by Paule and Mandel, Sidik and Jonkman, and…

77 FR 1129 - Revisions to Test Methods and Testing Regulations

Federal Register 2010, 2011, 2012, 2013, 2014

2012-01-09

...This action proposes editorial and technical corrections necessary for source testing of emissions and operations. The revisions include the addition of alternative equipment and methods as well as corrections to technical and typographical errors. We also solicit public comment on potential changes to the current procedures for determining emission stratification.

75 FR 51752 - Proposed Information Collection; Comment Request; An Observer Program for Vessels in the Pacific...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-08-23

... training, debriefing or responses to suspension or decertification. II. Method of Collection Respondents have a choice of either electronic or paper forms. Methods of submittal include electronic (Web-based... define observer duties, train and debrief observers, and manage observer data and its release. The...

76 FR 81554 - Notice of Information Collection Under Emergency Review: 60-Day Notice of Proposed Information...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-28

... methods: Email: [email protected] . You must include the DS form number, information collection... review of this information collection is also being undertaken. The submitting agency requests written... Federal Register. You may submit comments by any of the following methods: Email: [email protected] . Mail...

Hybrid method to predict the resonant frequencies and to characterise dual band proximity coupled microstrip antennas

NASA Astrophysics Data System (ADS)

Varma, Ruchi; Ghosh, Jayanta

2018-06-01

A new hybrid technique, which is a combination of neural network (NN) and support vector machine, is proposed for designing of different slotted dual band proximity coupled microstrip antennas. Slots on the patch are employed to produce the second resonance along with size reduction. The proposed hybrid model provides flexibility to design the dual band antennas in the frequency range from 1 to 6 GHz. This includes DCS (1.71-1.88 GHz), PCS (1.88-1.99 GHz), UMTS (1.92-2.17 GHz), LTE2300 (2.3-2.4 GHz), Bluetooth (2.4-2.485 GHz), WiMAX (3.3-3.7 GHz), and WLAN (5.15-5.35 GHz, 5.725-5.825 GHz) bands applications. Also, the comparative study of this proposed technique is done with the existing methods like knowledge based NN and support vector machine. The proposed method is found to be more accurate in terms of % error and root mean square % error and the results are in good accord with the measured values.

Semi-automatic segmentation of brain tumors using population and individual information.

PubMed

Wu, Yao; Yang, Wei; Jiang, Jun; Li, Shuanqian; Feng, Qianjin; Chen, Wufan

2013-08-01

Efficient segmentation of tumors in medical images is of great practical importance in early diagnosis and radiation plan. This paper proposes a novel semi-automatic segmentation method based on population and individual statistical information to segment brain tumors in magnetic resonance (MR) images. First, high-dimensional image features are extracted. Neighborhood components analysis is proposed to learn two optimal distance metrics, which contain population and patient-specific information, respectively. The probability of each pixel belonging to the foreground (tumor) and the background is estimated by the k-nearest neighborhood classifier under the learned optimal distance metrics. A cost function for segmentation is constructed through these probabilities and is optimized using graph cuts. Finally, some morphological operations are performed to improve the achieved segmentation results. Our dataset consists of 137 brain MR images, including 68 for training and 69 for testing. The proposed method overcomes segmentation difficulties caused by the uneven gray level distribution of the tumors and even can get satisfactory results if the tumors have fuzzy edges. Experimental results demonstrate that the proposed method is robust to brain tumor segmentation.

Cellular neural networks, the Navier-Stokes equation, and microarray image reconstruction.

PubMed

Zineddin, Bachar; Wang, Zidong; Liu, Xiaohui

2011-11-01

Although the last decade has witnessed a great deal of improvements achieved for the microarray technology, many major developments in all the main stages of this technology, including image processing, are still needed. Some hardware implementations of microarray image processing have been proposed in the literature and proved to be promising alternatives to the currently available software systems. However, the main drawback of those proposed approaches is the unsuitable addressing of the quantification of the gene spot in a realistic way without any assumption about the image surface. Our aim in this paper is to present a new image-reconstruction algorithm using the cellular neural network that solves the Navier-Stokes equation. This algorithm offers a robust method for estimating the background signal within the gene-spot region. The MATCNN toolbox for Matlab is used to test the proposed method. Quantitative comparisons are carried out, i.e., in terms of objective criteria, between our approach and some other available methods. It is shown that the proposed algorithm gives highly accurate and realistic measurements in a fully automated manner within a remarkably efficient time.

No-Reference Image Quality Assessment by Wide-Perceptual-Domain Scorer Ensemble Method.

PubMed

Liu, Tsung-Jung; Liu, Kuan-Hsien

2018-03-01

A no-reference (NR) learning-based approach to assess image quality is presented in this paper. The devised features are extracted from wide perceptual domains, including brightness, contrast, color, distortion, and texture. These features are used to train a model (scorer) which can predict scores. The scorer selection algorithms are utilized to help simplify the proposed system. In the final stage, the ensemble method is used to combine the prediction results from selected scorers. Two multiple-scale versions of the proposed approach are also presented along with the single-scale one. They turn out to have better performances than the original single-scale method. Because of having features from five different domains at multiple image scales and using the outputs (scores) from selected score prediction models as features for multi-scale or cross-scale fusion (i.e., ensemble), the proposed NR image quality assessment models are robust with respect to more than 24 image distortion types. They also can be used on the evaluation of images with authentic distortions. The extensive experiments on three well-known and representative databases confirm the performance robustness of our proposed model.

Glue detection based on teaching points constraint and tracking model of pixel convolution

NASA Astrophysics Data System (ADS)

Geng, Lei; Ma, Xiao; Xiao, Zhitao; Wang, Wen

2018-01-01

On-line glue detection based on machine version is significant for rust protection and strengthening in car production. Shadow stripes caused by reflect light and unevenness of inside front cover of car reduce the accuracy of glue detection. In this paper, we propose an effective algorithm to distinguish the edges of the glue and shadow stripes. Teaching points are utilized to calculate slope between the two adjacent points. Then a tracking model based on pixel convolution along motion direction is designed to segment several local rectangular regions using distance. The distance is the height of rectangular region. The pixel convolution along the motion direction is proposed to extract edges of gules in local rectangular region. A dataset with different illumination and complexity shape stripes are used to evaluate proposed method, which include 500 thousand images captured from the camera of glue gun machine. Experimental results demonstrate that the proposed method can detect the edges of glue accurately. The shadow stripes are distinguished and removed effectively. Our method achieves the 99.9% accuracies for the image dataset.

An efficient cloud detection method for high resolution remote sensing panchromatic imagery

NASA Astrophysics Data System (ADS)

Li, Chaowei; Lin, Zaiping; Deng, Xinpu

2018-04-01

In order to increase the accuracy of cloud detection for remote sensing satellite imagery, we propose an efficient cloud detection method for remote sensing satellite panchromatic images. This method includes three main steps. First, an adaptive intensity threshold value combined with a median filter is adopted to extract the coarse cloud regions. Second, a guided filtering process is conducted to strengthen the textural features difference and then we conduct the detection process of texture via gray-level co-occurrence matrix based on the acquired texture detail image. Finally, the candidate cloud regions are extracted by the intersection of two coarse cloud regions above and we further adopt an adaptive morphological dilation to refine them for thin clouds in boundaries. The experimental results demonstrate the effectiveness of the proposed method.

A rule-based automatic sleep staging method.

PubMed

Liang, Sheng-Fu; Kuo, Chin-En; Hu, Yu-Han; Cheng, Yu-Shian

2012-03-30

In this paper, a rule-based automatic sleep staging method was proposed. Twelve features including temporal and spectrum analyses of the EEG, EOG, and EMG signals were utilized. Normalization was applied to each feature to eliminating individual differences. A hierarchical decision tree with fourteen rules was constructed for sleep stage classification. Finally, a smoothing process considering the temporal contextual information was applied for the continuity. The overall agreement and kappa coefficient of the proposed method applied to the all night polysomnography (PSG) of seventeen healthy subjects compared with the manual scorings by R&K rules can reach 86.68% and 0.79, respectively. This method can integrate with portable PSG system for sleep evaluation at-home in the near future. Copyright © 2012 Elsevier B.V. All rights reserved.

Hierarchical Adaptive Regression Kernels for Regression with Functional Predictors.

PubMed

Woodard, Dawn B; Crainiceanu, Ciprian; Ruppert, David

2013-01-01

We propose a new method for regression using a parsimonious and scientifically interpretable representation of functional predictors. Our approach is designed for data that exhibit features such as spikes, dips, and plateaus whose frequency, location, size, and shape varies stochastically across subjects. We propose Bayesian inference of the joint functional and exposure models, and give a method for efficient computation. We contrast our approach with existing state-of-the-art methods for regression with functional predictors, and show that our method is more effective and efficient for data that include features occurring at varying locations. We apply our methodology to a large and complex dataset from the Sleep Heart Health Study, to quantify the association between sleep characteristics and health outcomes. Software and technical appendices are provided in online supplemental materials.

The development of methods of analysis of documents on the basis of the methods of Raman spectroscopy and fluorescence analysis

NASA Astrophysics Data System (ADS)

Gorshkova, Kseniia O.; Tumkin, Ilya I.; Kirillova, Elizaveta O.; Panov, Maxim S.; Kochemirovsky, Vladimir A.

2017-05-01

The investigation of natural aging of writing inks printed on paper using Raman spectroscopy was performed. Based on the obtained dependencies of the Raman peak intensities ratios on the exposure time, the dye degradation model was proposed. It was suggested that there are several competing bond breaking and bond forming reactions corresponding to the characteristic vibration frequencies of the dye molecule that simultaneously occur during ink aging process. Also we propose a methodology based on the study of the optical properties of paper, particularly changes in the fluorescence of optical brighteners included in its composition as well as the paper reflectivity using spectrophotometric methods. These results can be implemented to develop the novel and promising method of criminology.

Retrieval of the non-depolarizing components of depolarizing Mueller matrices by using symmetry conditions and least squares minimization

NASA Astrophysics Data System (ADS)

Kuntman, Ertan; Canillas, Adolf; Arteaga, Oriol

2017-11-01

Experimental Mueller matrices contain certain amount of uncertainty in their elements and these uncertainties can create difficulties for decomposition methods based on analytic solutions. In an earlier paper [1], we proposed a decomposition method for depolarizing Mueller matrices by using certain symmetry conditions. However, because of the experimental error, that method creates over-determined systems with non-unique solutions. Here we propose to use least squares minimization approach in order to improve the accuracy of our results. In this method, we are taking into account the number of independent parameters of the corresponding symmetry and the rank constraints on the component matrices to decide on our fitting model. This approach is illustrated with experimental Mueller matrices that include material media with different Mueller symmetries.

Automatic and quantitative measurement of collagen gel contraction using model-guided segmentation

NASA Astrophysics Data System (ADS)

Chen, Hsin-Chen; Yang, Tai-Hua; Thoreson, Andrew R.; Zhao, Chunfeng; Amadio, Peter C.; Sun, Yung-Nien; Su, Fong-Chin; An, Kai-Nan

2013-08-01

Quantitative measurement of collagen gel contraction plays a critical role in the field of tissue engineering because it provides spatial-temporal assessment (e.g., changes of gel area and diameter during the contraction process) reflecting the cell behavior and tissue material properties. So far the assessment of collagen gels relies on manual segmentation, which is time-consuming and suffers from serious intra- and inter-observer variability. In this study, we propose an automatic method combining various image processing techniques to resolve these problems. The proposed method first detects the maximal feasible contraction range of circular references (e.g., culture dish) and avoids the interference of irrelevant objects in the given image. Then, a three-step color conversion strategy is applied to normalize and enhance the contrast between the gel and background. We subsequently introduce a deformable circular model which utilizes regional intensity contrast and circular shape constraint to locate the gel boundary. An adaptive weighting scheme was employed to coordinate the model behavior, so that the proposed system can overcome variations of gel boundary appearances at different contraction stages. Two measurements of collagen gels (i.e., area and diameter) can readily be obtained based on the segmentation results. Experimental results, including 120 gel images for accuracy validation, showed high agreement between the proposed method and manual segmentation with an average dice similarity coefficient larger than 0.95. The results also demonstrated obvious improvement in gel contours obtained by the proposed method over two popular, generic segmentation methods.

Exploiting Quantum Resonance to Solve Combinatorial Problems

NASA Technical Reports Server (NTRS)

Zak, Michail; Fijany, Amir

2006-01-01

Quantum resonance would be exploited in a proposed quantum-computing approach to the solution of combinatorial optimization problems. In quantum computing in general, one takes advantage of the fact that an algorithm cannot be decoupled from the physical effects available to implement it. Prior approaches to quantum computing have involved exploitation of only a subset of known quantum physical effects, notably including parallelism and entanglement, but not including resonance. In the proposed approach, one would utilize the combinatorial properties of tensor-product decomposability of unitary evolution of many-particle quantum systems for physically simulating solutions to NP-complete problems (a class of problems that are intractable with respect to classical methods of computation). In this approach, reinforcement and selection of a desired solution would be executed by means of quantum resonance. Classes of NP-complete problems that are important in practice and could be solved by the proposed approach include planning, scheduling, search, and optimal design.

Deep data fusion method for missile-borne inertial/celestial system

NASA Astrophysics Data System (ADS)

Zhang, Chunxi; Chen, Xiaofei; Lu, Jiazhen; Zhang, Hao

2018-05-01

Strap-down inertial-celestial integrated navigation system has the advantages of autonomy and high precision and is very useful for ballistic missiles. The star sensor installation error and inertial measurement error have a great influence for the system performance. Based on deep data fusion, this paper establishes measurement equations including star sensor installation error and proposes the deep fusion filter method. Simulations including misalignment error, star sensor installation error, IMU error are analyzed. Simulation results indicate that the deep fusion method can estimate the star sensor installation error and IMU error. Meanwhile, the method can restrain the misalignment errors caused by instrument errors.

Preparation Methods: past and Potential Methods of Food Preparation for Space

NASA Technical Reports Server (NTRS)

Huber, C. S.

1985-01-01

The logical progression of development of space food systems during the Mercury, Gemini, Apollo, Skylab and Shuttle programs is outlined. The preparation methods which include no preparation to heating, cooling and freezing are reviewed. The introduction of some new and exciting technological advances is proposed, which should result in a system providing crew members with appetizing, safe, nutritious and convenient food.

Model-based sphere localization (MBSL) in x-ray projections

NASA Astrophysics Data System (ADS)

Sawall, Stefan; Maier, Joscha; Leinweber, Carsten; Funck, Carsten; Kuntz, Jan; Kachelrieß, Marc

2017-08-01

The detection of spherical markers in x-ray projections is an important task in a variety of applications, e.g. geometric calibration and detector distortion correction. Therein, the projection of the sphere center on the detector is of particular interest as the used spherical beads are no ideal point-like objects. Only few methods have been proposed to estimate this respective position on the detector with sufficient accuracy and surrogate positions, e.g. the center of gravity, are used, impairing the results of subsequent algorithms. We propose to estimate the projection of the sphere center on the detector using a simulation-based method matching an artificial projection to the actual measurement. The proposed algorithm intrinsically corrects for all polychromatic effects included in the measurement and absent in the simulation by a polynomial which is estimated simultaneously. Furthermore, neither the acquisition geometry nor any object properties besides the fact that the object is of spherical shape need to be known to find the center of the bead. It is shown by simulations that the algorithm estimates the center projection with an error of less than 1% of the detector pixel size in case of realistic noise levels and that the method is robust to the sphere material, sphere size, and acquisition parameters. A comparison to three reference methods using simulations and measurements indicates that the proposed method is an order of magnitude more accurate compared to these algorithms. The proposed method is an accurate algorithm to estimate the center of spherical markers in CT projections in the presence of polychromatic effects and noise.

The Relationships Between Lost Work Time and Duration of Absence Spells: Proposal for a Payroll Driven Measure of Absenteeism

PubMed Central

Hill, James J.; Slade, Martin D.; Cantley, Linda; Vegso, Sally; Fiellin, Martha; Cullen, Mark R.

2011-01-01

Objective To propose a standard measure of absenteeism (the work lost rate [WLR]) be included in future research to facilitate understanding and allow for translation of findings between scientific disciplines. Methods Hourly payroll data derived from “punch clock” reports was used to compare various measures of absenteeism used in the literature and the application of the proposed metric (N = 4000 workers). Results Unpaid hours and full absent days were highly correlated with the WLR (r = 0.896 to 0.898). The highest percentage of unpaid hours (lost work time) is captured by absence spells of 1 and 2 days duration. Conclusion The proposed WLR metric captures: 1) The range and distribution of the individual WLRs, 2) the percentage of subjects with no unpaid hours, and 3) the population WLR and should be included whenever payroll data is used to measure absenteeism. PMID:18617841

Inverse dynamics of a 3 degree of freedom spatial flexible manipulator

NASA Technical Reports Server (NTRS)

Bayo, Eduardo; Serna, M.

1989-01-01

A technique is presented for solving the inverse dynamics and kinematics of 3 degree of freedom spatial flexible manipulator. The proposed method finds the joint torques necessary to produce a specified end effector motion. Since the inverse dynamic problem in elastic manipulators is closely coupled to the inverse kinematic problem, the solution of the first also renders the displacements and rotations at any point of the manipulator, including the joints. Furthermore the formulation is complete in the sense that it includes all the nonlinear terms due to the large rotation of the links. The Timoshenko beam theory is used to model the elastic characteristics, and the resulting equations of motion are discretized using the finite element method. An iterative solution scheme is proposed that relies on local linearization of the problem. The solution of each linearization is carried out in the frequency domain. The performance and capabilities of this technique are tested through simulation analysis. Results show the potential use of this method for the smooth motion control of space telerobots.

Effective evaluation of privacy protection techniques in visible and thermal imagery

NASA Astrophysics Data System (ADS)

Nawaz, Tahir; Berg, Amanda; Ferryman, James; Ahlberg, Jörgen; Felsberg, Michael

2017-09-01

Privacy protection may be defined as replacing the original content in an image region with a (less intrusive) content having modified target appearance information to make it less recognizable by applying a privacy protection technique. Indeed, the development of privacy protection techniques also needs to be complemented with an established objective evaluation method to facilitate their assessment and comparison. Generally, existing evaluation methods rely on the use of subjective judgments or assume a specific target type in image data and use target detection and recognition accuracies to assess privacy protection. An annotation-free evaluation method that is neither subjective nor assumes a specific target type is proposed. It assesses two key aspects of privacy protection: "protection" and "utility." Protection is quantified as an appearance similarity, and utility is measured as a structural similarity between original and privacy-protected image regions. We performed an extensive experimentation using six challenging datasets (having 12 video sequences), including a new dataset (having six sequences) that contains visible and thermal imagery. The new dataset is made available online for the community. We demonstrate effectiveness of the proposed method by evaluating six image-based privacy protection techniques and also show comparisons of the proposed method over existing methods.

Gait-Event-Based Synchronization Method for Gait Rehabilitation Robots via a Bioinspired Adaptive Oscillator.

PubMed

Chen, Gong; Qi, Peng; Guo, Zhao; Yu, Haoyong

2017-06-01

In the field of gait rehabilitation robotics, achieving human-robot synchronization is very important. In this paper, a novel human-robot synchronization method using gait event information is proposed. This method includes two steps. First, seven gait events in one gait cycle are detected in real time with a hidden Markov model; second, an adaptive oscillator is utilized to estimate the stride percentage of human gait using any one of the gait events. Synchronous reference trajectories for the robot are then generated with the estimated stride percentage. This method is based on a bioinspired adaptive oscillator, which is a mathematical tool, first proposed to explain the phenomenon of synchronous flashing among fireflies. The proposed synchronization method is implemented in a portable knee-ankle-foot robot and tested in 15 healthy subjects. This method has the advantages of simple structure, flexible selection of gait events, and fast adaptation. Gait event is the only information needed, and hence the performance of synchronization holds when an abnormal gait pattern is involved. The results of the experiments reveal that our approach is efficient in achieving human-robot synchronization and feasible for rehabilitation robotics application.

A robust and accurate numerical method for transcritical turbulent flows at supercritical pressure with an arbitrary equation of state

NASA Astrophysics Data System (ADS)

Kawai, Soshi; Terashima, Hiroshi; Negishi, Hideyo

2015-11-01

This paper addresses issues in high-fidelity numerical simulations of transcritical turbulent flows at supercritical pressure. The proposed strategy builds on a tabulated look-up table method based on REFPROP database for an accurate estimation of non-linear behaviors of thermodynamic and fluid transport properties at the transcritical conditions. Based on the look-up table method we propose a numerical method that satisfies high-order spatial accuracy, spurious-oscillation-free property, and capability of capturing the abrupt variation in thermodynamic properties across the transcritical contact surface. The method introduces artificial mass diffusivity to the continuity and momentum equations in a physically-consistent manner in order to capture the steep transcritical thermodynamic variations robustly while maintaining spurious-oscillation-free property in the velocity field. The pressure evolution equation is derived from the full compressible Navier-Stokes equations and solved instead of solving the total energy equation to achieve the spurious pressure oscillation free property with an arbitrary equation of state including the present look-up table method. Flow problems with and without physical diffusion are employed for the numerical tests to validate the robustness, accuracy, and consistency of the proposed approach.

Theoretical and experimental study on active sound transmission control based on single structural mode actuation using point force actuators.

PubMed

Sanada, Akira; Tanaka, Nobuo

2012-08-01

This study deals with the feedforward active control of sound transmission through a simply supported rectangular panel using vibration actuators. The control effect largely depends on the excitation method, including the number and locations of actuators. In order to obtain a large control effect at low frequencies over a wide frequency, an active transmission control method based on single structural mode actuation is proposed. Then, with the goal of examining the feasibility of the proposed method, the (1, 3) mode is selected as the target mode and a modal actuation method in combination with six point force actuators is considered. Assuming that a single input single output feedforward control is used, sound transmission in the case minimizing the transmitted sound power is calculated for some actuation methods. Simulation results showed that the (1, 3) modal actuation is globally effective at reducing the sound transmission by more than 10 dB in the low-frequency range for both normal and oblique incidences. Finally, experimental results also showed that a large reduction could be achieved in the low-frequency range, which proves the validity and feasibility of the proposed method.

A robust and accurate numerical method for transcritical turbulent flows at supercritical pressure with an arbitrary equation of state

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

Kawai, Soshi, E-mail: kawai@cfd.mech.tohoku.ac.jp; Terashima, Hiroshi; Negishi, Hideyo

2015-11-01

This paper addresses issues in high-fidelity numerical simulations of transcritical turbulent flows at supercritical pressure. The proposed strategy builds on a tabulated look-up table method based on REFPROP database for an accurate estimation of non-linear behaviors of thermodynamic and fluid transport properties at the transcritical conditions. Based on the look-up table method we propose a numerical method that satisfies high-order spatial accuracy, spurious-oscillation-free property, and capability of capturing the abrupt variation in thermodynamic properties across the transcritical contact surface. The method introduces artificial mass diffusivity to the continuity and momentum equations in a physically-consistent manner in order to capture themore » steep transcritical thermodynamic variations robustly while maintaining spurious-oscillation-free property in the velocity field. The pressure evolution equation is derived from the full compressible Navier–Stokes equations and solved instead of solving the total energy equation to achieve the spurious pressure oscillation free property with an arbitrary equation of state including the present look-up table method. Flow problems with and without physical diffusion are employed for the numerical tests to validate the robustness, accuracy, and consistency of the proposed approach.« less

48 CFR 731.771 - Bid and proposal costs.

Code of Federal Regulations, 2012 CFR

2012-10-01

... Government and non-Government grants, contracts and other agreements, including the development of scientific... method, the results obtained may be accepted only if found to be reasonable and equitable. (b) Bid and...

48 CFR 731.771 - Bid and proposal costs.

Code of Federal Regulations, 2013 CFR

2013-10-01

... Government and non-Government grants, contracts and other agreements, including the development of scientific... method, the results obtained may be accepted only if found to be reasonable and equitable. (b) Bid and...

48 CFR 731.771 - Bid and proposal costs.

Code of Federal Regulations, 2014 CFR

2014-10-01

... Government and non-Government grants, contracts and other agreements, including the development of scientific... method, the results obtained may be accepted only if found to be reasonable and equitable. (b) Bid and...

48 CFR 731.771 - Bid and proposal costs.

Code of Federal Regulations, 2011 CFR

2011-10-01

... Government and non-Government grants, contracts and other agreements, including the development of scientific... method, the results obtained may be accepted only if found to be reasonable and equitable. (b) Bid and...

48 CFR 731.771 - Bid and proposal costs.

Code of Federal Regulations, 2010 CFR

2010-10-01

... Government and non-Government grants, contracts and other agreements, including the development of scientific... method, the results obtained may be accepted only if found to be reasonable and equitable. (b) Bid and...

Photovoltaic panel extraction from very high-resolution aerial imagery using region-line primitive association analysis and template matching

NASA Astrophysics Data System (ADS)

Wang, Min; Cui, Qi; Sun, Yujie; Wang, Qiao

2018-07-01

In object-based image analysis (OBIA), object classification performance is jointly determined by image segmentation, sample or rule setting, and classifiers. Typically, as a crucial step to obtain object primitives, image segmentation quality significantly influences subsequent feature extraction and analyses. By contrast, template matching extracts specific objects from images and prevents shape defects caused by image segmentation. However, creating or editing templates is tedious and sometimes results in incomplete or inaccurate templates. In this study, we combine OBIA and template matching techniques to address these problems and aim for accurate photovoltaic panel (PVP) extraction from very high-resolution (VHR) aerial imagery. The proposed method is based on the previously proposed region-line primitive association framework, in which complementary information between region (segment) and line (straight line) primitives is utilized to achieve a more powerful performance than routine OBIA. Several novel concepts, including the mutual fitting ratio and best-fitting template based on region-line primitive association analyses, are proposed. Automatic template generation and matching method for PVP extraction from VHR imagery are designed for concept and model validation. Results show that the proposed method can successfully extract PVPs without any user-specified matching template or training sample. High user independency and accuracy are the main characteristics of the proposed method in comparison with routine OBIA and template matching techniques.

Detecting Moving Targets by Use of Soliton Resonances

NASA Technical Reports Server (NTRS)

Zak, Michael; Kulikov, Igor

2003-01-01

A proposed method of detecting moving targets in scenes that include cluttered or noisy backgrounds is based on a soliton-resonance mathematical model. The model is derived from asymptotic solutions of the cubic Schroedinger equation for a one-dimensional system excited by a position-and-time-dependent externally applied potential. The cubic Schroedinger equation has general significance for time-dependent dispersive waves. It has been used to approximate several phenomena in classical as well as quantum physics, including modulated beams in nonlinear optics, and superfluids (in particular, Bose-Einstein condensates). In the proposed method, one would take advantage of resonant interactions between (1) a soliton excited by the position-and-time-dependent potential associated with a moving target and (2) eigen-solitons, which represent dispersive waves and are solutions of the cubic Schroedinger equation for a time-independent potential.

Automatic Synthesis of Panoramic Radiographs from Dental Cone Beam Computed Tomography Data.

PubMed

Luo, Ting; Shi, Changrong; Zhao, Xing; Zhao, Yunsong; Xu, Jinqiu

2016-01-01

In this paper, we propose an automatic method of synthesizing panoramic radiographs from dental cone beam computed tomography (CBCT) data for directly observing the whole dentition without the superimposition of other structures. This method consists of three major steps. First, the dental arch curve is generated from the maximum intensity projection (MIP) of 3D CBCT data. Then, based on this curve, the long axial curves of the upper and lower teeth are extracted to create a 3D panoramic curved surface describing the whole dentition. Finally, the panoramic radiograph is synthesized by developing this 3D surface. Both open-bite shaped and closed-bite shaped dental CBCT datasets were applied in this study, and the resulting images were analyzed to evaluate the effectiveness of this method. With the proposed method, a single-slice panoramic radiograph can clearly and completely show the whole dentition without the blur and superimposition of other dental structures. Moreover, thickened panoramic radiographs can also be synthesized with increased slice thickness to show more features, such as the mandibular nerve canal. One feature of the proposed method is that it is automatically performed without human intervention. Another feature of the proposed method is that it requires thinner panoramic radiographs to show the whole dentition than those produced by other existing methods, which contributes to the clarity of the anatomical structures, including the enamel, dentine and pulp. In addition, this method can rapidly process common dental CBCT data. The speed and image quality of this method make it an attractive option for observing the whole dentition in a clinical setting.

Multi-parametric centrality method for graph network models

NASA Astrophysics Data System (ADS)

Ivanov, Sergei Evgenievich; Gorlushkina, Natalia Nikolaevna; Ivanova, Lubov Nikolaevna

2018-04-01

The graph model networks are investigated to determine centrality, weights and the significance of vertices. For centrality analysis appliesa typical method that includesany one of the properties of graph vertices. In graph theory, methods of analyzing centrality are used: in terms by degree, closeness, betweenness, radiality, eccentricity, page-rank, status, Katz and eigenvector. We have proposed a new method of multi-parametric centrality, which includes a number of basic properties of the network member. The mathematical model of multi-parametric centrality method is developed. Comparison of results for the presented method with the centrality methods is carried out. For evaluate the results for the multi-parametric centrality methodthe graph model with hundreds of vertices is analyzed. The comparative analysis showed the accuracy of presented method, includes simultaneously a number of basic properties of vertices.

Group decision-making approach for flood vulnerability identification using the fuzzy VIKOR method

NASA Astrophysics Data System (ADS)

Lee, G.; Jun, K. S.; Chung, E.-S.

2015-04-01

This study proposes an improved group decision making (GDM) framework that combines the VIKOR method with data fuzzification to quantify the spatial flood vulnerability including multiple criteria. In general, GDM method is an effective tool for formulating a compromise solution that involves various decision makers since various stakeholders may have different perspectives on their flood risk/vulnerability management responses. The GDM approach is designed to achieve consensus building that reflects the viewpoints of each participant. The fuzzy VIKOR method was developed to solve multi-criteria decision making (MCDM) problems with conflicting and noncommensurable criteria. This comprising method can be used to obtain a nearly ideal solution according to all established criteria. This approach effectively can propose some compromising decisions by combining the GDM method and fuzzy VIKOR method. The spatial flood vulnerability of the southern Han River using the GDM approach combined with the fuzzy VIKOR method was compared with the spatial flood vulnerability using general MCDM methods, such as the fuzzy TOPSIS and classical GDM methods (i.e., Borda, Condorcet, and Copeland). As a result, the proposed fuzzy GDM approach can reduce the uncertainty in the data confidence and weight derivation techniques. Thus, the combination of the GDM approach with the fuzzy VIKOR method can provide robust prioritization because it actively reflects the opinions of various groups and considers uncertainty in the input data.

On recursive least-squares filtering algorithms and implementations. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Hsieh, Shih-Fu

1990-01-01

In many real-time signal processing applications, fast and numerically stable algorithms for solving least-squares problems are necessary and important. In particular, under non-stationary conditions, these algorithms must be able to adapt themselves to reflect the changes in the system and take appropriate adjustments to achieve optimum performances. Among existing algorithms, the QR-decomposition (QRD)-based recursive least-squares (RLS) methods have been shown to be useful and effective for adaptive signal processing. In order to increase the speed of processing and achieve high throughput rate, many algorithms are being vectorized and/or pipelined to facilitate high degrees of parallelism. A time-recursive formulation of RLS filtering employing block QRD will be considered first. Several methods, including a new non-continuous windowing scheme based on selectively rejecting contaminated data, were investigated for adaptive processing. Based on systolic triarrays, many other forms of systolic arrays are shown to be capable of implementing different algorithms. Various updating and downdating systolic algorithms and architectures for RLS filtering are examined and compared in details, which include Householder reflector, Gram-Schmidt procedure, and Givens rotation. A unified approach encompassing existing square-root-free algorithms is also proposed. For the sinusoidal spectrum estimation problem, a judicious method of separating the noise from the signal is of great interest. Various truncated QR methods are proposed for this purpose and compared to the truncated SVD method. Computer simulations provided for detailed comparisons show the effectiveness of these methods. This thesis deals with fundamental issues of numerical stability, computational efficiency, adaptivity, and VLSI implementation for the RLS filtering problems. In all, various new and modified algorithms and architectures are proposed and analyzed; the significance of any of the new method depends crucially on specific application.

Identifying key nodes in multilayer networks based on tensor decomposition.

PubMed

Wang, Dingjie; Wang, Haitao; Zou, Xiufen

2017-06-01

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

A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices.

PubMed

Ravi, Daniele; Wong, Charence; Lo, Benny; Yang, Guang-Zhong

2017-01-01

The increasing popularity of wearable devices in recent years means that a diverse range of physiological and functional data can now be captured continuously for applications in sports, wellbeing, and healthcare. This wealth of information requires efficient methods of classification and analysis where deep learning is a promising technique for large-scale data analytics. While deep learning has been successful in implementations that utilize high-performance computing platforms, its use on low-power wearable devices is limited by resource constraints. In this paper, we propose a deep learning methodology, which combines features learned from inertial sensor data together with complementary information from a set of shallow features to enable accurate and real-time activity classification. The design of this combined method aims to overcome some of the limitations present in a typical deep learning framework where on-node computation is required. To optimize the proposed method for real-time on-node computation, spectral domain preprocessing is used before the data are passed onto the deep learning framework. The classification accuracy of our proposed deep learning approach is evaluated against state-of-the-art methods using both laboratory and real world activity datasets. Our results show the validity of the approach on different human activity datasets, outperforming other methods, including the two methods used within our combined pipeline. We also demonstrate that the computation times for the proposed method are consistent with the constraints of real-time on-node processing on smartphones and a wearable sensor platform.

Identifying key nodes in multilayer networks based on tensor decomposition

NASA Astrophysics Data System (ADS)

Wang, Dingjie; Wang, Haitao; Zou, Xiufen

2017-06-01

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

A method for improved accuracy in three dimensions for determining wheel/rail contact points

NASA Astrophysics Data System (ADS)

Yang, Xinwen; Gu, Shaojie; Zhou, Shunhua; Zhou, Yu; Lian, Songliang

2015-11-01

Searching for the contact points between wheels and rails is important because these points represent the points of exerted contact forces. In order to obtain an accurate contact point and an in-depth description of the wheel/rail contact behaviours on a curved track or in a turnout, a method with improved accuracy in three dimensions is proposed to determine the contact points and the contact patches between the wheel and the rail when considering the effect of the yaw angle and the roll angle on the motion of the wheel set. The proposed method, with no need of the curve fitting of the wheel and rail profiles, can accurately, directly, and comprehensively determine the contact interface distances between the wheel and the rail. The range iteration algorithm is used to improve the computation efficiency and reduce the calculation required. The present computation method is applied for the analysis of the contact of rails of CHINA (CHN) 75 kg/m and wheel sets of wearing type tread of China's freight cars. In addition, it can be proved that the results of the proposed method are consistent with that of Kalker's program CONTACT, and the maximum deviation from the wheel/rail contact patch area of this two methods is approximately 5%. The proposed method, can also be used to investigate static wheel/rail contact. Some wheel/rail contact points and contact patch distributions are discussed and assessed, wheel and rail non-worn and worn profiles included.

Correcting groove error in gratings ruled on a 500-mm ruling engine using interferometric control.

PubMed

Mi, Xiaotao; Yu, Haili; Yu, Hongzhu; Zhang, Shanwen; Li, Xiaotian; Yao, Xuefeng; Qi, Xiangdong; Bayinhedhig; Wan, Qiuhua

2017-07-20

Groove error is one of the most important factors affecting grating quality and spectral performance. To reduce groove error, we propose a new ruling-tool carriage system based on aerostatic guideways. We design a new blank carriage system with double piezoelectric actuators. We also propose a completely closed-loop servo-control system with a new optical measurement system that can control the position of the diamond relative to the blank. To evaluate our proposed methods, we produced several gratings, including an echelle grating with 79  grooves/mm, a grating with 768  grooves/mm, and a high-density grating with 6000  grooves/mm. The results show that our methods effectively reduce groove error in ruled gratings.

An adaptive tensor voting algorithm combined with texture spectrum

NASA Astrophysics Data System (ADS)

Wang, Gang; Su, Qing-tang; Lü, Gao-huan; Zhang, Xiao-feng; Liu, Yu-huan; He, An-zhi

2015-01-01

An adaptive tensor voting algorithm combined with texture spectrum is proposed. The image texture spectrum is used to get the adaptive scale parameter of voting field. Then the texture information modifies both the attenuation coefficient and the attenuation field so that we can use this algorithm to create more significant and correct structures in the original image according to the human visual perception. At the same time, the proposed method can improve the edge extraction quality, which includes decreasing the flocculent region efficiently and making image clear. In the experiment for extracting pavement cracks, the original pavement image is processed by the proposed method which is combined with the significant curve feature threshold procedure, and the resulted image displays the faint crack signals submerged in the complicated background efficiently and clearly.

Determination of the transmission coefficients for quantum structures using FDTD method.

PubMed

Peng, Yangyang; Wang, Xiaoying; Sui, Wenquan

2011-12-01

The purpose of this work is to develop a simple method to incorporate quantum effect in traditional finite-difference time-domain (FDTD) simulators. Witch could make it possible to co-simulate systems include quantum structures and traditional components. In this paper, tunneling transmission coefficient is calculated by solving time-domain Schrödinger equation with a developed FDTD technique, called FDTD-S method. To validate the feasibility of the method, a simple resonant tunneling diode (RTD) structure model has been simulated using the proposed method. The good agreement between the numerical and analytical results proves its accuracy. The effectness and accuracy of this approach makes it a potential method for analysis and design of hybrid systems includes quantum structures and traditional components.

The combination of the error correction methods of GAFCHROMIC EBT3 film

PubMed Central

Li, Yinghui; Chen, Lixin; Zhu, Jinhan; Liu, Xiaowei

2017-01-01

Purpose The aim of this study was to combine a set of methods for use of radiochromic film dosimetry, including calibration, correction for lateral effects and a proposed triple-channel analysis. These methods can be applied to GAFCHROMIC EBT3 film dosimetry for radiation field analysis and verification of IMRT plans. Methods A single-film exposure was used to achieve dose calibration, and the accuracy was verified based on comparisons with the square-field calibration method. Before performing the dose analysis, the lateral effects on pixel values were corrected. The position dependence of the lateral effect was fitted by a parabolic function, and the curvature factors of different dose levels were obtained using a quadratic formula. After lateral effect correction, a triple-channel analysis was used to reduce disturbances and convert scanned images from films into dose maps. The dose profiles of open fields were measured using EBT3 films and compared with the data obtained using an ionization chamber. Eighteen IMRT plans with different field sizes were measured and verified with EBT3 films, applying our methods, and compared to TPS dose maps, to check correct implementation of film dosimetry proposed here. Results The uncertainty of lateral effects can be reduced to ±1 cGy. Compared with the results of Micke A et al., the residual disturbances of the proposed triple-channel method at 48, 176 and 415 cGy are 5.3%, 20.9% and 31.4% smaller, respectively. Compared with the ionization chamber results, the difference in the off-axis ratio and percentage depth dose are within 1% and 2%, respectively. For the application of IMRT verification, there were no difference between two triple-channel methods. Compared with only corrected by triple-channel method, the IMRT results of the combined method (include lateral effect correction and our present triple-channel method) show a 2% improvement for large IMRT fields with the criteria 3%/3 mm. PMID:28750023

Fuzzy Finite-Time Command Filtered Control of Nonlinear Systems With Input Saturation.

PubMed

Yu, Jinpeng; Zhao, Lin; Yu, Haisheng; Lin, Chong; Dong, Wenjie

2017-08-22

This paper considers the fuzzy finite-time tracking control problem for a class of nonlinear systems with input saturation. A novel fuzzy finite-time command filtered backstepping approach is proposed by introducing the fuzzy finite-time command filter, designing the new virtual control signals and the modified error compensation signals. The proposed approach not only holds the advantages of the conventional command-filtered backstepping control, but also guarantees the finite-time convergence. A practical example is included to show the effectiveness of the proposed method.

Variable selection for zero-inflated and overdispersed data with application to health care demand in Germany

PubMed Central

Wang, Zhu; Shuangge, Ma; Wang, Ching-Yun

2017-01-01

In health services and outcome research, count outcomes are frequently encountered and often have a large proportion of zeros. The zero-inflated negative binomial (ZINB) regression model has important applications for this type of data. With many possible candidate risk factors, this paper proposes new variable selection methods for the ZINB model. We consider maximum likelihood function plus a penalty including the least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP). An EM (expectation-maximization) algorithm is proposed for estimating the model parameters and conducting variable selection simultaneously. This algorithm consists of estimating penalized weighted negative binomial models and penalized logistic models via the coordinated descent algorithm. Furthermore, statistical properties including the standard error formulae are provided. A simulation study shows that the new algorithm not only has more accurate or at least comparable estimation, also is more robust than the traditional stepwise variable selection. The proposed methods are applied to analyze the health care demand in Germany using an open-source R package mpath. PMID:26059498

Comparison of Classification Methods for Detecting Emotion from Mandarin Speech

NASA Astrophysics Data System (ADS)

Pao, Tsang-Long; Chen, Yu-Te; Yeh, Jun-Heng

It is said that technology comes out from humanity. What is humanity? The very definition of humanity is emotion. Emotion is the basis for all human expression and the underlying theme behind everything that is done, said, thought or imagined. Making computers being able to perceive and respond to human emotion, the human-computer interaction will be more natural. Several classifiers are adopted for automatically assigning an emotion category, such as anger, happiness or sadness, to a speech utterance. These classifiers were designed independently and tested on various emotional speech corpora, making it difficult to compare and evaluate their performance. In this paper, we first compared several popular classification methods and evaluated their performance by applying them to a Mandarin speech corpus consisting of five basic emotions, including anger, happiness, boredom, sadness and neutral. The extracted feature streams contain MFCC, LPCC, and LPC. The experimental results show that the proposed WD-MKNN classifier achieves an accuracy of 81.4% for the 5-class emotion recognition and outperforms other classification techniques, including KNN, MKNN, DW-KNN, LDA, QDA, GMM, HMM, SVM, and BPNN. Then, to verify the advantage of the proposed method, we compared these classifiers by applying them to another Mandarin expressive speech corpus consisting of two emotions. The experimental results still show that the proposed WD-MKNN outperforms others.

Decision Support System Based on Computational Collective Intelligence in Campus Information Systems

NASA Astrophysics Data System (ADS)

Saito, Yoshihito; Matsuo, Tokuro

Education institutions such as universities have a lot of information including book information, equipment administrative information, student information, and several others. The institutions also have multiple information in time series. As collective intelligence in campus, integrating and reusing these preserved information regarding career and taking a class, university can effectively support students' decision making of their getting jobs and subjects choice. Our purpose of support is to increase student's motivation. In this paper, we focus on course record and job information included in students' information, and propose the method to analyze correlation between a pattern of taking class and job lined up. Afterwards, we propose a support system regarding getting a job and taking class by using our proposed method. For a student who has his/her favorite job to get, the system supports his/her decision making of lecture choice by recommending a set of appropriate lecture groups. On another hand, for a student who does not have favorite job to get, the system supports his/her decision making of getting job by presenting appropriate job families related with lecture group in which he/she has ever taken. The contribution of this paper is showing a concrete method to reuse the campus collective information, implementing a system, and user perspectives.

Allowable SEM noise for unbiased LER measurement

NASA Astrophysics Data System (ADS)

Papavieros, George; Constantoudis, Vassilios; Gogolides, Evangelos

2018-03-01

Recently, a novel method for the calculation of unbiased Line Edge Roughness based on Power Spectral Density analysis has been proposed. In this paper first an alternative method is discussed and investigated, utilizing the Height-Height Correlation Function (HHCF) of edges. The HHCF-based method enables the unbiased determination of the whole triplet of LER parameters including besides rms the correlation length and roughness exponent. The key of both methods is the sensitivity of PSD and HHCF on noise at high frequencies and short distance respectively. Secondly, we elaborate a testbed of synthesized SEM images with controlled LER and noise to justify the effectiveness of the proposed unbiased methods. Our main objective is to find out the boundaries of the method in respect to noise levels and roughness characteristics, for which the method remains reliable, i.e the maximum amount of noise allowed, for which the output results cope with the controllable known inputs. At the same time, we will also set the extremes of roughness parameters for which the methods hold their accuracy.

A new rational-based optimal design strategy of ship structure based on multi-level analysis and super-element modeling method

NASA Astrophysics Data System (ADS)

Sun, Li; Wang, Deyu

2011-09-01

A new multi-level analysis method of introducing the super-element modeling method, derived from the multi-level analysis method first proposed by O. F. Hughes, has been proposed in this paper to solve the problem of high time cost in adopting a rational-based optimal design method for ship structural design. Furthermore, the method was verified by its effective application in optimization of the mid-ship section of a container ship. A full 3-D FEM model of a ship, suffering static and quasi-static loads, was used as the analyzing object for evaluating the structural performance of the mid-ship module, including static strength and buckling performance. Research results reveal that this new method could substantially reduce the computational cost of the rational-based optimization problem without decreasing its accuracy, which increases the feasibility and economic efficiency of using a rational-based optimal design method in ship structural design.

Comparative Validation of the Determination of Sofosbuvir in Pharmaceuticals by Several Inexpensive Ecofriendly Chromatographic, Electrophoretic, and Spectrophotometric Methods.

PubMed

El-Yazbi, Amira F

2017-07-01

Sofosbuvir (SOFO) was approved by the U.S. Food and Drug Administration in 2013 for the treatment of hepatitis C virus infection with enhanced antiviral potency compared with earlier analogs. Notwithstanding, all current editions of the pharmacopeias still do not present any analytical methods for the quantification of SOFO. Thus, rapid, simple, and ecofriendly methods for the routine analysis of commercial formulations of SOFO are desirable. In this study, five accurate methods for the determination of SOFO in pharmaceutical tablets were developed and validated. These methods include HPLC, capillary zone electrophoresis, HPTLC, and UV spectrophotometric and derivative spectrometry methods. The proposed methods proved to be rapid, simple, sensitive, selective, and accurate analytical procedures that were suitable for the reliable determination of SOFO in pharmaceutical tablets. An analysis of variance test with P-value > 0.05 confirmed that there were no significant differences between the proposed assays. Thus, any of these methods can be used for the routine analysis of SOFO in commercial tablets.

Optimization of magnetic flux density measurement using multiple RF receiver coils and multi-echo in MREIT.

PubMed

Jeong, Woo Chul; Chauhan, Munish; Sajib, Saurav Z K; Kim, Hyung Joong; Serša, Igor; Kwon, Oh In; Woo, Eung Je

2014-09-07

Magnetic Resonance Electrical Impedance Tomography (MREIT) is an MRI method that enables mapping of internal conductivity and/or current density via measurements of magnetic flux density signals. The MREIT measures only the z-component of the induced magnetic flux density B = (Bx, By, Bz) by external current injection. The measured noise of Bz complicates recovery of magnetic flux density maps, resulting in lower quality conductivity and current-density maps. We present a new method for more accurate measurement of the spatial gradient of the magnetic flux density gradient (∇ Bz). The method relies on the use of multiple radio-frequency receiver coils and an interleaved multi-echo pulse sequence that acquires multiple sampling points within each repetition time. The noise level of the measured magnetic flux density Bz depends on the decay rate of the signal magnitude, the injection current duration, and the coil sensitivity map. The proposed method uses three key steps. The first step is to determine a representative magnetic flux density gradient from multiple receiver coils by using a weighted combination and by denoising the measured noisy data. The second step is to optimize the magnetic flux density gradient by using multi-echo magnetic flux densities at each pixel in order to reduce the noise level of ∇ Bz and the third step is to remove a random noise component from the recovered ∇ Bz by solving an elliptic partial differential equation in a region of interest. Numerical simulation experiments using a cylindrical phantom model with included regions of low MRI signal to noise ('defects') verified the proposed method. Experimental results using a real phantom experiment, that included three different kinds of anomalies, demonstrated that the proposed method reduced the noise level of the measured magnetic flux density. The quality of the recovered conductivity maps using denoised ∇ Bz data showed that the proposed method reduced the conductivity noise level up to 3-4 times at each anomaly region in comparison to the conventional method.

Effect-based trigger values for in vitro and in vivo bioassays performed on surface water extracts supporting the environmental quality standards (EQS) of the European Water Framework Directive.

PubMed

Escher, Beate I; Aїt-Aїssa, Selim; Behnisch, Peter A; Brack, Werner; Brion, François; Brouwer, Abraham; Buchinger, Sebastian; Crawford, Sarah E; Du Pasquier, David; Hamers, Timo; Hettwer, Karina; Hilscherová, Klára; Hollert, Henner; Kase, Robert; Kienle, Cornelia; Tindall, Andrew J; Tuerk, Jochen; van der Oost, Ron; Vermeirssen, Etienne; Neale, Peta A

2018-07-01

Effect-based methods including cell-based bioassays, reporter gene assays and whole-organism assays have been applied for decades in water quality monitoring and testing of enriched solid-phase extracts. There is no common EU-wide agreement on what level of bioassay response in water extracts is acceptable. At present, bioassay results are only benchmarked against each other but not against a consented measure of chemical water quality. The EU environmental quality standards (EQS) differentiate between acceptable and unacceptable surface water concentrations for individual chemicals but cannot capture the thousands of chemicals in water and their biological action as mixtures. We developed a method that reads across from existing EQS and includes additional mixture considerations with the goal that the derived effect-based trigger values (EBT) indicate acceptable risk for complex mixtures as they occur in surface water. Advantages and limitations of various approaches to read across from EQS are discussed and distilled to an algorithm that translates EQS into their corresponding bioanalytical equivalent concentrations (BEQ). The proposed EBT derivation method was applied to 48 in vitro bioassays with 32 of them having sufficient information to yield preliminary EBTs. To assess the practicability and robustness of the proposed approach, we compared the tentative EBTs with observed environmental effects. The proposed method only gives guidance on how to derive EBTs but does not propose final EBTs for implementation. The EBTs for some bioassays such as those for estrogenicity are already mature and could be implemented into regulation in the near future, while for others it will still take a few iterations until we can be confident of the power of the proposed EBTs to differentiate good from poor water quality with respect to chemical contamination. Copyright © 2018 Elsevier B.V. All rights reserved.

Military personnel recognition system using texture, colour, and SURF features

NASA Astrophysics Data System (ADS)

Irhebhude, Martins E.; Edirisinghe, Eran A.

2014-06-01

This paper presents an automatic, machine vision based, military personnel identification and classification system. Classification is done using a Support Vector Machine (SVM) on sets of Army, Air Force and Navy camouflage uniform personnel datasets. In the proposed system, the arm of service of personnel is recognised by the camouflage of a persons uniform, type of cap and the type of badge/logo. The detailed analysis done include; camouflage cap and plain cap differentiation using gray level co-occurrence matrix (GLCM) texture feature; classification on Army, Air Force and Navy camouflaged uniforms using GLCM texture and colour histogram bin features; plain cap badge classification into Army, Air Force and Navy using Speed Up Robust Feature (SURF). The proposed method recognised camouflage personnel arm of service on sets of data retrieved from google images and selected military websites. Correlation-based Feature Selection (CFS) was used to improve recognition and reduce dimensionality, thereby speeding the classification process. With this method success rates recorded during the analysis include 93.8% for camouflage appearance category, 100%, 90% and 100% rates of plain cap and camouflage cap categories for Army, Air Force and Navy categories, respectively. Accurate recognition was recorded using SURF for the plain cap badge category. Substantial analysis has been carried out and results prove that the proposed method can correctly classify military personnel into various arms of service. We show that the proposed method can be integrated into a face recognition system, which will recognise personnel in addition to determining the arm of service which the personnel belong. Such a system can be used to enhance the security of a military base or facility.

A technique for setting analytical thresholds in massively parallel sequencing-based forensic DNA analysis

PubMed Central

2017-01-01

Amplicon (targeted) sequencing by massively parallel sequencing (PCR-MPS) is a potential method for use in forensic DNA analyses. In this application, PCR-MPS may supplement or replace other instrumental analysis methods such as capillary electrophoresis and Sanger sequencing for STR and mitochondrial DNA typing, respectively. PCR-MPS also may enable the expansion of forensic DNA analysis methods to include new marker systems such as single nucleotide polymorphisms (SNPs) and insertion/deletions (indels) that currently are assayable using various instrumental analysis methods including microarray and quantitative PCR. Acceptance of PCR-MPS as a forensic method will depend in part upon developing protocols and criteria that define the limitations of a method, including a defensible analytical threshold or method detection limit. This paper describes an approach to establish objective analytical thresholds suitable for multiplexed PCR-MPS methods. A definition is proposed for PCR-MPS method background noise, and an analytical threshold based on background noise is described. PMID:28542338

A technique for setting analytical thresholds in massively parallel sequencing-based forensic DNA analysis.

PubMed

Young, Brian; King, Jonathan L; Budowle, Bruce; Armogida, Luigi

2017-01-01

Amplicon (targeted) sequencing by massively parallel sequencing (PCR-MPS) is a potential method for use in forensic DNA analyses. In this application, PCR-MPS may supplement or replace other instrumental analysis methods such as capillary electrophoresis and Sanger sequencing for STR and mitochondrial DNA typing, respectively. PCR-MPS also may enable the expansion of forensic DNA analysis methods to include new marker systems such as single nucleotide polymorphisms (SNPs) and insertion/deletions (indels) that currently are assayable using various instrumental analysis methods including microarray and quantitative PCR. Acceptance of PCR-MPS as a forensic method will depend in part upon developing protocols and criteria that define the limitations of a method, including a defensible analytical threshold or method detection limit. This paper describes an approach to establish objective analytical thresholds suitable for multiplexed PCR-MPS methods. A definition is proposed for PCR-MPS method background noise, and an analytical threshold based on background noise is described.

Automated bone segmentation from dental CBCT images using patch-based sparse representation and convex optimization

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

Wang, Li; Gao, Yaozong; Shi, Feng

Purpose: Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. Accurate segmentation of CBCT image is an essential step to generate three-dimensional (3D) models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the poor image quality, including very low signal-to-noise ratio and the widespread image artifacts such as noise, beam hardening, and inhomogeneity, it is challenging to segment the CBCT images. In this paper, the authors present a new automatic segmentation method to address these problems. Methods: To segmentmore » CBCT images, the authors propose a new method for fully automated CBCT segmentation by using patch-based sparse representation to (1) segment bony structures from the soft tissues and (2) further separate the mandible from the maxilla. Specifically, a region-specific registration strategy is first proposed to warp all the atlases to the current testing subject and then a sparse-based label propagation strategy is employed to estimate a patient-specific atlas from all aligned atlases. Finally, the patient-specific atlas is integrated into amaximum a posteriori probability-based convex segmentation framework for accurate segmentation. Results: The proposed method has been evaluated on a dataset with 15 CBCT images. The effectiveness of the proposed region-specific registration strategy and patient-specific atlas has been validated by comparing with the traditional registration strategy and population-based atlas. The experimental results show that the proposed method achieves the best segmentation accuracy by comparison with other state-of-the-art segmentation methods. Conclusions: The authors have proposed a new CBCT segmentation method by using patch-based sparse representation and convex optimization, which can achieve considerably accurate segmentation results in CBCT segmentation based on 15 patients.« less

A Hybrid One-Way ANOVA Approach for the Robust and Efficient Estimation of Differential Gene Expression with Multiple Patterns

PubMed Central

Mollah, Mohammad Manir Hossain; Jamal, Rahman; Mokhtar, Norfilza Mohd; Harun, Roslan; Mollah, Md. Nurul Haque

2015-01-01

Background Identifying genes that are differentially expressed (DE) between two or more conditions with multiple patterns of expression is one of the primary objectives of gene expression data analysis. Several statistical approaches, including one-way analysis of variance (ANOVA), are used to identify DE genes. However, most of these methods provide misleading results for two or more conditions with multiple patterns of expression in the presence of outlying genes. In this paper, an attempt is made to develop a hybrid one-way ANOVA approach that unifies the robustness and efficiency of estimation using the minimum β-divergence method to overcome some problems that arise in the existing robust methods for both small- and large-sample cases with multiple patterns of expression. Results The proposed method relies on a β-weight function, which produces values between 0 and 1. The β-weight function with β = 0.2 is used as a measure of outlier detection. It assigns smaller weights (≥ 0) to outlying expressions and larger weights (≤ 1) to typical expressions. The distribution of the β-weights is used to calculate the cut-off point, which is compared to the observed β-weight of an expression to determine whether that gene expression is an outlier. This weight function plays a key role in unifying the robustness and efficiency of estimation in one-way ANOVA. Conclusion Analyses of simulated gene expression profiles revealed that all eight methods (ANOVA, SAM, LIMMA, EBarrays, eLNN, KW, robust BetaEB and proposed) perform almost identically for m = 2 conditions in the absence of outliers. However, the robust BetaEB method and the proposed method exhibited considerably better performance than the other six methods in the presence of outliers. In this case, the BetaEB method exhibited slightly better performance than the proposed method for the small-sample cases, but the the proposed method exhibited much better performance than the BetaEB method for both the small- and large-sample cases in the presence of more than 50% outlying genes. The proposed method also exhibited better performance than the other methods for m > 2 conditions with multiple patterns of expression, where the BetaEB was not extended for this condition. Therefore, the proposed approach would be more suitable and reliable on average for the identification of DE genes between two or more conditions with multiple patterns of expression. PMID:26413858

A general method for the inclusion of radiation chemistry in astrochemical models.

PubMed

Shingledecker, Christopher N; Herbst, Eric

2018-02-21

In this paper, we propose a general formalism that allows for the estimation of radiolysis decomposition pathways and rate coefficients suitable for use in astrochemical models, with a focus on solid phase chemistry. Such a theory can help increase the connection between laboratory astrophysics experiments and astrochemical models by providing a means for modelers to incorporate radiation chemistry into chemical networks. The general method proposed here is targeted particularly at the majority of species now included in chemical networks for which little radiochemical data exist; however, the method can also be used as a starting point for considering better studied species. We here apply our theory to the irradiation of H 2 O ice and compare the results with previous experimental data.

Implementation of the nudged elastic band method in a dislocation dynamics formalism: Application to dislocation nucleation

NASA Astrophysics Data System (ADS)

Geslin, Pierre-Antoine; Gatti, Riccardo; Devincre, Benoit; Rodney, David

2017-11-01

We propose a framework to study thermally-activated processes in dislocation glide. This approach is based on an implementation of the nudged elastic band method in a nodal mesoscale dislocation dynamics formalism. Special care is paid to develop a variational formulation to ensure convergence to well-defined minimum energy paths. We also propose a methodology to rigorously parametrize the model on atomistic data, including elastic, core and stacking fault contributions. To assess the validity of the model, we investigate the homogeneous nucleation of partial dislocation loops in aluminum, recovering the activation energies and loop shapes obtained with atomistic calculations and extending these calculations to lower applied stresses. The present method is also applied to heterogeneous nucleation on spherical inclusions.

Coarse graining for synchronization in directed networks

NASA Astrophysics Data System (ADS)

Zeng, An; Lü, Linyuan

2011-05-01

Coarse-graining model is a promising way to analyze and visualize large-scale networks. The coarse-grained networks are required to preserve statistical properties as well as the dynamic behaviors of the initial networks. Some methods have been proposed and found effective in undirected networks, while the study on coarse-graining directed networks lacks of consideration. In this paper we proposed a path-based coarse-graining (PCG) method to coarse grain the directed networks. Performing the linear stability analysis of synchronization and numerical simulation of the Kuramoto model on four kinds of directed networks, including tree networks and variants of Barabási-Albert networks, Watts-Strogatz networks, and Erdös-Rényi networks, we find our method can effectively preserve the network synchronizability.

Computation of Anisotropic Bi-Material Interfacial Fracture Parameters and Delamination Creteria

NASA Technical Reports Server (NTRS)

Chow, W-T.; Wang, L.; Atluri, S. N.

1998-01-01

This report documents the recent developments in methodologies for the evaluation of the integrity and durability of composite structures, including i) the establishment of a stress-intensity-factor based fracture criterion for bimaterial interfacial cracks in anisotropic materials (see Sec. 2); ii) the development of a virtual crack closure integral method for the evaluation of the mixed-mode stress intensity factors for a bimaterial interfacial crack (see Sec. 3). Analytical and numerical results show that the proposed fracture criterion is a better fracture criterion than the total energy release rate criterion in the characterization of the bimaterial interfacial cracks. The proposed virtual crack closure integral method is an efficient and accurate numerical method for the evaluation of mixed-mode stress intensity factors.

Efficient tiled calculation of over-10-gigapixel holograms using ray-wavefront conversion.

PubMed

Igarashi, Shunsuke; Nakamura, Tomoya; Matsushima, Kyoji; Yamaguchi, Masahiro

2018-04-16

In the calculation of large-scale computer-generated holograms, an approach called "tiling," which divides the hologram plane into small rectangles, is often employed due to limitations on computational memory. However, the total amount of computational complexity severely increases with the number of divisions. In this paper, we propose an efficient method for calculating tiled large-scale holograms using ray-wavefront conversion. In experiments, the effectiveness of the proposed method was verified by comparing its calculation cost with that using the previous method. Additionally, a hologram of 128K × 128K pixels was calculated and fabricated by a laser-lithography system, and a high-quality 105 mm × 105 mm 3D image including complicated reflection and translucency was optically reconstructed.

Face liveness detection for face recognition based on cardiac features of skin color image

NASA Astrophysics Data System (ADS)

Suh, Kun Ha; Lee, Eui Chul

2016-07-01

With the growth of biometric technology, spoofing attacks have been emerged a threat to the security of the system. Main spoofing scenarios in the face recognition system include the printing attack, replay attack, and 3D mask attack. To prevent such attacks, techniques that evaluating liveness of the biometric data can be considered as a solution. In this paper, a novel face liveness detection method based on cardiac signal extracted from face is presented. The key point of proposed method is that the cardiac characteristic is detected in live faces but not detected in non-live faces. Experimental results showed that the proposed method can be effective way for determining printing attack or 3D mask attack.

Graded-Index "Whispering-Gallery" Optical Microresonators

NASA Technical Reports Server (NTRS)

Savchenkov, Anatoliy; Maleki, Lute; Iltchenko, Vladimir; Matsko, Andrey

2006-01-01

Graded-index-of-refraction dielectric optical microresonators have been proposed as a superior alternative to prior dielectric optical microresonators, which include microspheres and microtori wherein electromagnetic waves propagate along circumferential paths in "whispering-gallery" modes. The design and method of fabrication of the proposed microresonators would afford improved performance by exploiting a combination of the propagation characteristics of the whisperinggallery modes and the effect of a graded index of refraction on the modes.

Acyl Meldrum's acid derivatives: application in organic synthesis

NASA Astrophysics Data System (ADS)

Janikowska, K.; Rachoń, J.; Makowiec, S.

2014-07-01

This review is focused on an important class of Meldrum's acid derivatives commonly known as acyl Meldrum's acids. The preparation methods of these compounds are considered including the recently proposed and rather rarely used ones. The chemical properties of acyl Meldrum's acids are described in detail, including thermal stability and reactions with various nucleophiles. The possible mechanisms of these transformations are analyzed. The bibliography includes 134 references.

A proposed protocol for acceptance and constancy control of computed tomography systems: a Nordic Association for Clinical Physics (NACP) work group report.

PubMed

Kuttner, Samuel; Bujila, Robert; Kortesniemi, Mika; Andersson, Henrik; Kull, Love; Østerås, Bjørn Helge; Thygesen, Jesper; Tarp, Ivanka Sojat

2013-03-01

Quality assurance (QA) of computed tomography (CT) systems is one of the routine tasks for medical physicists in the Nordic countries. However, standardized QA protocols do not yet exist and the QA methods, as well as the applied tolerance levels, vary in scope and extent at different hospitals. To propose a standardized protocol for acceptance and constancy testing of CT scanners in the Nordic Region. Following a Nordic Association for Clinical Physics (NACP) initiative, a group of medical physicists, with representatives from four Nordic countries, was formed. Based on international literature and practical experience within the group, a comprehensive standardized test protocol was developed. The proposed protocol includes tests related to the mechanical functionality, X-ray tube, detector, and image quality for CT scanners. For each test, recommendations regarding the purpose, equipment needed, an outline of the test method, the measured parameter, tolerance levels, and the testing frequency are stated. In addition, a number of optional tests are briefly discussed that may provide further information about the CT system. Based on international references and medical physicists' practical experiences, a comprehensive QA protocol for CT systems is proposed, including both acceptance and constancy tests. The protocol may serve as a reference for medical physicists in the Nordic countries.

The system of technical diagnostics of the industrial safety information network

NASA Astrophysics Data System (ADS)

Repp, P. V.

2017-01-01

This research is devoted to problems of safety of the industrial information network. Basic sub-networks, ensuring reliable operation of the elements of the industrial Automatic Process Control System, were identified. The core tasks of technical diagnostics of industrial information safety were presented. The structure of the technical diagnostics system of the information safety was proposed. It includes two parts: a generator of cyber-attacks and the virtual model of the enterprise information network. The virtual model was obtained by scanning a real enterprise network. A new classification of cyber-attacks was proposed. This classification enables one to design an efficient generator of cyber-attacks sets for testing the virtual modes of the industrial information network. The numerical method of the Monte Carlo (with LPτ - sequences of Sobol), and Markov chain was considered as the design method for the cyber-attacks generation algorithm. The proposed system also includes a diagnostic analyzer, performing expert functions. As an integrative quantitative indicator of the network reliability the stability factor (Kstab) was selected. This factor is determined by the weight of sets of cyber-attacks, identifying the vulnerability of the network. The weight depends on the frequency and complexity of cyber-attacks, the degree of damage, complexity of remediation. The proposed Kstab is an effective integral quantitative measure of the information network reliability.

Marginalized zero-altered models for longitudinal count data.

PubMed

Tabb, Loni Philip; Tchetgen, Eric J Tchetgen; Wellenius, Greg A; Coull, Brent A

2016-10-01

Count data often exhibit more zeros than predicted by common count distributions like the Poisson or negative binomial. In recent years, there has been considerable interest in methods for analyzing zero-inflated count data in longitudinal or other correlated data settings. A common approach has been to extend zero-inflated Poisson models to include random effects that account for correlation among observations. However, these models have been shown to have a few drawbacks, including interpretability of regression coefficients and numerical instability of fitting algorithms even when the data arise from the assumed model. To address these issues, we propose a model that parameterizes the marginal associations between the count outcome and the covariates as easily interpretable log relative rates, while including random effects to account for correlation among observations. One of the main advantages of this marginal model is that it allows a basis upon which we can directly compare the performance of standard methods that ignore zero inflation with that of a method that explicitly takes zero inflation into account. We present simulations of these various model formulations in terms of bias and variance estimation. Finally, we apply the proposed approach to analyze toxicological data of the effect of emissions on cardiac arrhythmias.

Marginalized zero-altered models for longitudinal count data

PubMed Central

Tabb, Loni Philip; Tchetgen, Eric J. Tchetgen; Wellenius, Greg A.; Coull, Brent A.

2015-01-01

Count data often exhibit more zeros than predicted by common count distributions like the Poisson or negative binomial. In recent years, there has been considerable interest in methods for analyzing zero-inflated count data in longitudinal or other correlated data settings. A common approach has been to extend zero-inflated Poisson models to include random effects that account for correlation among observations. However, these models have been shown to have a few drawbacks, including interpretability of regression coefficients and numerical instability of fitting algorithms even when the data arise from the assumed model. To address these issues, we propose a model that parameterizes the marginal associations between the count outcome and the covariates as easily interpretable log relative rates, while including random effects to account for correlation among observations. One of the main advantages of this marginal model is that it allows a basis upon which we can directly compare the performance of standard methods that ignore zero inflation with that of a method that explicitly takes zero inflation into account. We present simulations of these various model formulations in terms of bias and variance estimation. Finally, we apply the proposed approach to analyze toxicological data of the effect of emissions on cardiac arrhythmias. PMID:27867423

Sea Ice Concentration Estimation Using Active and Passive Remote Sensing Data Fusion

NASA Astrophysics Data System (ADS)

Zhang, Y.; Li, F.; Zhang, S.; Zhu, T.

2017-12-01

In this abstract, a decision-level fusion method by utilizing SAR and passive microwave remote sensing data for sea ice concentration estimation is investigated. Sea ice concentration product from passive microwave concentration retrieval methods has large uncertainty within thin ice zone. Passive microwave data including SSM/I, AMSR-E, and AMSR-2 provide daily and long time series observations covering whole polar sea ice scene, and SAR images provide rich sea ice details with high spatial resolution including deformation and polarimetric features. In the proposed method, the merits from passive microwave data and SAR data are considered. Sea ice concentration products from ASI and sea ice category label derived from CRF framework in SAR imagery are calibrated under least distance protocol. For SAR imagery, incident angle and azimuth angle were used to correct backscattering values from slant range to ground range in order to improve geocoding accuracy. The posterior probability distribution between category label from SAR imagery and passive microwave sea ice concentration product is modeled and integrated under Bayesian network, where Gaussian statistical distribution from ASI sea ice concentration products serves as the prior term, which represented as an uncertainty of sea ice concentration. Empirical model based likelihood term is constructed under Bernoulli theory, which meets the non-negative and monotonically increasing conditions. In the posterior probability estimation procedure, final sea ice concentration is obtained using MAP criterion, which equals to minimize the cost function and it can be calculated with nonlinear iteration method. The proposed algorithm is tested on multiple satellite SAR data sets including GF-3, Sentinel-1A, RADARSAT-2 and Envisat ASAR. Results show that the proposed algorithm can improve the accuracy of ASI sea ice concentration products and reduce the uncertainty along the ice edge.

On the weight of indels in genomic distances

PubMed Central

2011-01-01

Background Classical approaches to compute the genomic distance are usually limited to genomes with the same content, without duplicated markers. However, differences in the gene content are frequently observed and can reflect important evolutionary aspects. A few polynomial time algorithms that include genome rearrangements, insertions and deletions (or substitutions) were already proposed. These methods often allow a block of contiguous markers to be inserted, deleted or substituted at once but result in distance functions that do not respect the triangular inequality and hence do not constitute metrics. Results In the present study we discuss the disruption of the triangular inequality in some of the available methods and give a framework to establish an efficient correction for two models recently proposed, one that includes insertions, deletions and double cut and join (DCJ) operations, and one that includes substitutions and DCJ operations. Conclusions We show that the proposed framework establishes the triangular inequality in both distances, by summing a surcharge on indel operations and on substitutions that depends only on the number of markers affected by these operations. This correction can be applied a posteriori, without interfering with the already available formulas to compute these distances. We claim that this correction leads to distances that are biologically more plausible. PMID:22151784

A Novel Secure IoT-Based Smart Home Automation System Using a Wireless Sensor Network.

PubMed

Pirbhulal, Sandeep; Zhang, Heye; E Alahi, Md Eshrat; Ghayvat, Hemant; Mukhopadhyay, Subhas Chandra; Zhang, Yuan-Ting; Wu, Wanqing

2016-12-30

Wireless sensor networks (WSNs) provide noteworthy benefits over traditional approaches for several applications, including smart homes, healthcare, environmental monitoring, and homeland security. WSNs are integrated with the Internet Protocol (IP) to develop the Internet of Things (IoT) for connecting everyday life objects to the internet. Hence, major challenges of WSNs include: (i) how to efficiently utilize small size and low-power nodes to implement security during data transmission among several sensor nodes; (ii) how to resolve security issues associated with the harsh and complex environmental conditions during data transmission over a long coverage range. In this study, a secure IoT-based smart home automation system was developed. To facilitate energy-efficient data encryption, a method namely Triangle Based Security Algorithm (TBSA) based on efficient key generation mechanism was proposed. The proposed TBSA in integration of the low power Wi-Fi were included in WSNs with the Internet to develop a novel IoT-based smart home which could provide secure data transmission among several associated sensor nodes in the network over a long converge range. The developed IoT based system has outstanding performance by fulfilling all the necessary security requirements. The experimental results showed that the proposed TBSA algorithm consumed less energy in comparison with some existing methods.

A Novel Secure IoT-Based Smart Home Automation System Using a Wireless Sensor Network

PubMed Central

Pirbhulal, Sandeep; Zhang, Heye; E Alahi, Md Eshrat; Ghayvat, Hemant; Mukhopadhyay, Subhas Chandra; Zhang, Yuan-Ting; Wu, Wanqing

2016-01-01

Wireless sensor networks (WSNs) provide noteworthy benefits over traditional approaches for several applications, including smart homes, healthcare, environmental monitoring, and homeland security. WSNs are integrated with the Internet Protocol (IP) to develop the Internet of Things (IoT) for connecting everyday life objects to the internet. Hence, major challenges of WSNs include: (i) how to efficiently utilize small size and low-power nodes to implement security during data transmission among several sensor nodes; (ii) how to resolve security issues associated with the harsh and complex environmental conditions during data transmission over a long coverage range. In this study, a secure IoT-based smart home automation system was developed. To facilitate energy-efficient data encryption, a method namely Triangle Based Security Algorithm (TBSA) based on efficient key generation mechanism was proposed. The proposed TBSA in integration of the low power Wi-Fi were included in WSNs with the Internet to develop a novel IoT-based smart home which could provide secure data transmission among several associated sensor nodes in the network over a long converge range. The developed IoT based system has outstanding performance by fulfilling all the necessary security requirements. The experimental results showed that the proposed TBSA algorithm consumed less energy in comparison with some existing methods. PMID:28042831

An Integrated Method for Airfoil Optimization

NASA Astrophysics Data System (ADS)

Okrent, Joshua B.

Design exploration and optimization is a large part of the initial engineering and design process. To evaluate the aerodynamic performance of a design, viscous Navier-Stokes solvers can be used. However this method can prove to be overwhelmingly time consuming when performing an initial design sweep. Therefore, another evaluation method is needed to provide accurate results at a faster pace. To accomplish this goal, a coupled viscous-inviscid method is used. This thesis proposes an integrated method for analyzing, evaluating, and optimizing an airfoil using a coupled viscous-inviscid solver along with a genetic algorithm to find the optimal candidate. The method proposed is different from prior optimization efforts in that it greatly broadens the design space, while allowing the optimization to search for the best candidate that will meet multiple objectives over a characteristic mission profile rather than over a single condition and single optimization parameter. The increased design space is due to the use of multiple parametric airfoil families, namely the NACA 4 series, CST family, and the PARSEC family. Almost all possible airfoil shapes can be created with these three families allowing for all possible configurations to be included. This inclusion of multiple airfoil families addresses a possible criticism of prior optimization attempts since by only focusing on one airfoil family, they were inherently limiting the number of possible airfoil configurations. By using multiple parametric airfoils, it can be assumed that all reasonable airfoil configurations are included in the analysis and optimization and that a global and not local maximum is found. Additionally, the method used is amenable to customization to suit any specific needs as well as including the effects of other physical phenomena or design criteria and/or constraints. This thesis found that an airfoil configuration that met multiple objectives could be found for a given set of nominal operational conditions from a broad design space with the use of minimal computational resources on both an absolute and relative scale to traditional analysis techniques. Aerodynamicists, program managers, aircraft configuration specialist, and anyone else in charge of aircraft configuration, design studies, and program level decisions might find the evaluation and optimization method proposed of interest.

76 FR 65192 - Agency Information Collection Activities: Proposed Collection Renewals; Comment Request (3064-0022)

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-20

... methods: http://www.FDIC.gov/regulations/laws/federal/notices.html . E-mail: [email protected] . Include... collection of information: Title: Uniform Application/Uniform Termination for Municipal Securities Principal...

76 FR 67668 - Proposed Collection; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2011-11-02

... variety of collection methods, including interviews and research, to inform the design, development and.... For example, information collected from consumers will help the CFPB to design model forms... used for quantitative information collections [[Page 67669

78 FR 4158 - Notice of Proposed Information Collection: Comment Request Rent Reform Demonstration (Task Order 1)

Federal Register 2010, 2011, 2012, 2013, 2014

2013-01-18

... technology, e.g., permitting electronic submission of responses. This notice also lists the following.... Data will be gathered through a variety of methods including informational interviews, direct...

78 FR 28601 - National Center for Advancing Translational Sciences; Request for Comment on Proposed Methods for...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-15

... Acceleration Network Review Board, which include members of the biotechnology, pharmaceutical, and venture...), the Biotechnology Industry Organization (BIO), and the National Venture Capital Association (NVCA); (5...

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.

Hyperbolic Method for Dispersive PDEs: Same High-Order of Accuracy for Solution, Gradient, and Hessian

NASA Technical Reports Server (NTRS)

Mazaheri, Alireza; Ricchiuto, Mario; Nishikawa, Hiroaki

2016-01-01

In this paper, we introduce a new hyperbolic first-order system for general dispersive partial differential equations (PDEs). We then extend the proposed system to general advection-diffusion-dispersion PDEs. We apply the fourth-order RD scheme of Ref. 1 to the proposed hyperbolic system, and solve time-dependent dispersive equations, including the classical two-soliton KdV and a dispersive shock case. We demonstrate that the predicted results, including the gradient and Hessian (second derivative), are in a very good agreement with the exact solutions. We then show that the RD scheme applied to the proposed system accurately captures dispersive shocks without numerical oscillations. We also verify that the solution, gradient and Hessian are predicted with equal order of accuracy.

A review of recent studies on the mechanisms and analysis methods of sub-synchronous oscillation in wind farms

NASA Astrophysics Data System (ADS)

Wang, Chenggen; Zhou, Qian; Gao, Shuning; Luo, Jia; Diao, Junchao; Zhao, Haoran; Bu, Jing

2018-04-01

This paper reviews the recent studies of Sub-Synchronous Oscillation(SSO) in wind farms. Mechanisms and analysis methods are the main concerns of this article. A classification method including new types of oscillation occurred between wind farms and HVDC systems and oscillation caused by Permanent Magnet Synchronous Generators(PMSG) is proposed. Characteristics of oscillation analysis techniques are summarized.

78 FR 61081 - Endangered and Threatened Wildlife and Plants; Withdrawal of the Proposed Rule To List Coral Pink...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-10-02

... occurs in a manner typical of most tiger beetles, which can include several different methods. For one method, the female is positioned vertically and digs a small hole with the ovipositor at the end of her... at night. These burrows are about 25.4-50.8 mm (1-2 in) deep and 50.8 mm (2 in) long. This method...

Convergence of Numerical Sequences--A Commentary on "The Vice: Some Historically Inspired and Proof Generated Steps to Limits of Sequences" by R. P. Burn

ERIC Educational Resources Information Center

Berge, Analia

2006-01-01

Burn (2005) proposes a "genetic approach" to teaching limits of numerical sequences. The article includes an explanation of the Method of Exhaustion, a generalization of this method, and a description of how this method was used for obtaining areas and lengths in the seventeenth century. The author uses these historical and mathematical analyses…

Incidences from modifications of the computational methods of the psophic index

NASA Technical Reports Server (NTRS)

Francois, J.

1981-01-01

In France, the level of annoyance in areas around airports is represented by the psyphic index N. Various modifications were proposed in the method of calculating this indexing order to improve the index as an annoyance indicator. The quality of the modified N index as a prognosis index for annoyance caused by aircraft noise is included.

A Test Method for Monitoring Modulus Changes during Durability Tests on Building Joint Sealants

Treesearch

Christopher C. White; Donald L. Hunston; Kar Tean Tan; Gregory T. Schueneman

2012-01-01

The durability of building joint sealants is generally assessed using a descriptive methodology involving visual inspection of exposed specimens for defects. It is widely known that this methodology has inherent limitations, including that the results are qualitative. A new test method is proposed that provides more fundamental and quantitative information about...

Teaching Methods and Their Impact on Students' Emotions in Mathematics: An Experience-Sampling Approach

ERIC Educational Resources Information Center

Bieg, Madeleine; Goetz, Thomas; Sticca, Fabio; Brunner, Esther; Becker, Eva; Morger, Vinzenz; Hubbard, Kyle

2017-01-01

Various theoretical approaches propose that emotions in the classroom are elicited by appraisal antecedents, with subjective experiences of control playing a crucial role in this context. Perceptions of control, in turn, are expected to be influenced by the classroom social environment, which can include the teaching methods being employed (e.g.,…

Solving mixed integer nonlinear programming problems using spiral dynamics optimization algorithm

NASA Astrophysics Data System (ADS)

Kania, Adhe; Sidarto, Kuntjoro Adji

2016-02-01

Many engineering and practical problem can be modeled by mixed integer nonlinear programming. This paper proposes to solve the problem with modified spiral dynamics inspired optimization method of Tamura and Yasuda. Four test cases have been examined, including problem in engineering and sport. This method succeeds in obtaining the optimal result in all test cases.

Dynamic Scheduling for Web Monitoring Crawler

DTIC Science & Technology

2009-02-27

researches on static scheduling methods , but they are not included in this project, because this project mainly focuses on the event-driven...pages from public search engines. This research aims to propose various query generation methods using MCRDR knowledge base and evaluates them to...South Wales Professor Hiroshi Motoda/Osaka University Dr. John Salerno, Air Force Research Laboratory/Information Directorate Report

A linear complementarity method for the solution of vertical vehicle-track interaction

NASA Astrophysics Data System (ADS)

Zhang, Jian; Gao, Qiang; Wu, Feng; Zhong, Wan-Xie

2018-02-01

A new method is proposed for the solution of the vertical vehicle-track interaction including a separation between wheel and rail. The vehicle is modelled as a multi-body system using rigid bodies, and the track is treated as a three-layer beam model in which the rail is considered as an Euler-Bernoulli beam and both the sleepers and the ballast are represented by lumped masses. A linear complementarity formulation is directly established using a combination of the wheel-rail normal contact condition and the generalised-α method. This linear complementarity problem is solved using the Lemke algorithm, and the wheel-rail contact force can be obtained. Then the dynamic responses of the vehicle and the track are solved without iteration based on the generalised-α method. The same equations of motion for the vehicle and track are adopted at the different wheel-rail contact situations. This method can remove some restrictions, that is, time-dependent mass, damping and stiffness matrices of the coupled system, multiple equations of motion for the different contact situations and the effect of the contact stiffness. Numerical results demonstrate that the proposed method is effective for simulating the vehicle-track interaction including a separation between wheel and rail.

A Monocular Vision Sensor-Based Obstacle Detection Algorithm for Autonomous Robots

PubMed Central

Lee, Tae-Jae; Yi, Dong-Hoon; Cho, Dong-Il “Dan”

2016-01-01

This paper presents a monocular vision sensor-based obstacle detection algorithm for autonomous robots. Each individual image pixel at the bottom region of interest is labeled as belonging either to an obstacle or the floor. While conventional methods depend on point tracking for geometric cues for obstacle detection, the proposed algorithm uses the inverse perspective mapping (IPM) method. This method is much more advantageous when the camera is not high off the floor, which makes point tracking near the floor difficult. Markov random field-based obstacle segmentation is then performed using the IPM results and a floor appearance model. Next, the shortest distance between the robot and the obstacle is calculated. The algorithm is tested by applying it to 70 datasets, 20 of which include nonobstacle images where considerable changes in floor appearance occur. The obstacle segmentation accuracies and the distance estimation error are quantitatively analyzed. For obstacle datasets, the segmentation precision and the average distance estimation error of the proposed method are 81.4% and 1.6 cm, respectively, whereas those for a conventional method are 57.5% and 9.9 cm, respectively. For nonobstacle datasets, the proposed method gives 0.0% false positive rates, while the conventional method gives 17.6%. PMID:26938540

On-orbit calibration for star sensors without priori information.

PubMed

Zhang, Hao; Niu, Yanxiong; Lu, Jiazhen; Zhang, Chengfen; Yang, Yanqiang

2017-07-24

The star sensor is a prerequisite navigation device for a spacecraft. The on-orbit calibration is an essential guarantee for its operation performance. However, traditional calibration methods rely on ground information and are invalid without priori information. The uncertain on-orbit parameters will eventually influence the performance of guidance navigation and control system. In this paper, a novel calibration method without priori information for on-orbit star sensors is proposed. Firstly, the simplified back propagation neural network is designed for focal length and main point estimation along with system property evaluation, called coarse calibration. Then the unscented Kalman filter is adopted for the precise calibration of all parameters, including focal length, main point and distortion. The proposed method benefits from self-initialization and no attitude or preinstalled sensor parameter is required. Precise star sensor parameter estimation can be achieved without priori information, which is a significant improvement for on-orbit devices. Simulations and experiments results demonstrate that the calibration is easy for operation with high accuracy and robustness. The proposed method can satisfy the stringent requirement for most star sensors.

Seismic noise attenuation using an online subspace tracking algorithm

NASA Astrophysics Data System (ADS)

Zhou, Yatong; Li, Shuhua; Zhang, Dong; Chen, Yangkang

2018-02-01

We propose a new low-rank based noise attenuation method using an efficient algorithm for tracking subspaces from highly corrupted seismic observations. The subspace tracking algorithm requires only basic linear algebraic manipulations. The algorithm is derived by analysing incremental gradient descent on the Grassmannian manifold of subspaces. When the multidimensional seismic data are mapped to a low-rank space, the subspace tracking algorithm can be directly applied to the input low-rank matrix to estimate the useful signals. Since the subspace tracking algorithm is an online algorithm, it is more robust to random noise than traditional truncated singular value decomposition (TSVD) based subspace tracking algorithm. Compared with the state-of-the-art algorithms, the proposed denoising method can obtain better performance. More specifically, the proposed method outperforms the TSVD-based singular spectrum analysis method in causing less residual noise and also in saving half of the computational cost. Several synthetic and field data examples with different levels of complexities demonstrate the effectiveness and robustness of the presented algorithm in rejecting different types of noise including random noise, spiky noise, blending noise, and coherent noise.

Optimal External Wrench Distribution During a Multi-Contact Sit-to-Stand Task.

PubMed

Bonnet, Vincent; Azevedo-Coste, Christine; Robert, Thomas; Fraisse, Philippe; Venture, Gentiane

2017-07-01

This paper aims at developing and evaluating a new practical method for the real-time estimate of joint torques and external wrenches during multi-contact sit-to-stand (STS) task using kinematics data only. The proposed method allows also identifying subject specific body inertial segment parameters that are required to perform inverse dynamics. The identification phase is performed using simple and repeatable motions. Thanks to an accurately identified model the estimate of the total external wrench can be used as an input to solve an under-determined multi-contact problem. It is solved using a constrained quadratic optimization process minimizing a hybrid human-like energetic criterion. The weights of this hybrid cost function are adjusted and a sensitivity analysis is performed in order to reproduce robustly human external wrench distribution. The results showed that the proposed method could successfully estimate the external wrenches under buttocks, feet, and hands during STS tasks (RMS error lower than 20 N and 6 N.m). The simplicity and generalization abilities of the proposed method allow paving the way of future diagnosis solutions and rehabilitation applications, including in-home use.

Urban noise functional stratification for estimating average annual sound level.

PubMed

Rey Gozalo, Guillermo; Barrigón Morillas, Juan Miguel; Prieto Gajardo, Carlos

2015-06-01

Road traffic noise causes many health problems and the deterioration of the quality of urban life; thus, adequate spatial noise and temporal assessment methods are required. Different methods have been proposed for the spatial evaluation of noise in cities, including the categorization method. Until now, this method has only been applied for the study of spatial variability with measurements taken over a week. In this work, continuous measurements of 1 year carried out in 21 different locations in Madrid (Spain), which has more than three million inhabitants, were analyzed. The annual average sound levels and the temporal variability were studied in the proposed categories. The results show that the three proposed categories highlight the spatial noise stratification of the studied city in each period of the day (day, evening, and night) and in the overall indicators (L(And), L(Aden), and L(A24)). Also, significant differences between the diurnal and nocturnal sound levels show functional stratification in these categories. Therefore, this functional stratification offers advantages from both spatial and temporal perspectives by reducing the sampling points and the measurement time.

A new three-dimensional manufacturing service composition method under various structures using improved Flower Pollination Algorithm

NASA Astrophysics Data System (ADS)

Zhang, Wenyu; Yang, Yushu; Zhang, Shuai; Yu, Dejian; Chen, Yong

2018-05-01

With the growing complexity of customer requirements and the increasing scale of manufacturing services, how to select and combine the single services to meet the complex demand of the customer has become a growing concern. This paper presents a new manufacturing service composition method to solve the multi-objective optimization problem based on quality of service (QoS). The proposed model not only presents different methods for calculating the transportation time and transportation cost under various structures but also solves the three-dimensional composition optimization problem, including service aggregation, service selection, and service scheduling simultaneously. Further, an improved Flower Pollination Algorithm (IFPA) is proposed to solve the three-dimensional composition optimization problem using a matrix-based representation scheme. The mutation operator and crossover operator of the Differential Evolution (DE) algorithm are also used to extend the basic Flower Pollination Algorithm (FPA) to improve its performance. Compared to Genetic Algorithm, DE, and basic FPA, the experimental results confirm that the proposed method demonstrates superior performance than other meta heuristic algorithms and can obtain better manufacturing service composition solutions.

Feature extraction based on extended multi-attribute profiles and sparse autoencoder for remote sensing image classification

NASA Astrophysics Data System (ADS)

Teffahi, Hanane; Yao, Hongxun; Belabid, Nasreddine; Chaib, Souleyman

2018-02-01

The satellite images with very high spatial resolution have been recently widely used in image classification topic as it has become challenging task in remote sensing field. Due to a number of limitations such as the redundancy of features and the high dimensionality of the data, different classification methods have been proposed for remote sensing images classification particularly the methods using feature extraction techniques. This paper propose a simple efficient method exploiting the capability of extended multi-attribute profiles (EMAP) with sparse autoencoder (SAE) for remote sensing image classification. The proposed method is used to classify various remote sensing datasets including hyperspectral and multispectral images by extracting spatial and spectral features based on the combination of EMAP and SAE by linking them to kernel support vector machine (SVM) for classification. Experiments on new hyperspectral image "Huston data" and multispectral image "Washington DC data" shows that this new scheme can achieve better performance of feature learning than the primitive features, traditional classifiers and ordinary autoencoder and has huge potential to achieve higher accuracy for classification in short running time.

Heterogeneous Data Fusion Method to Estimate Travel Time Distributions in Congested Road Networks

PubMed Central

Lam, William H. K.; Li, Qingquan

2017-01-01

Travel times in congested urban road networks are highly stochastic. Provision of travel time distribution information, including both mean and variance, can be very useful for travelers to make reliable path choice decisions to ensure higher probability of on-time arrival. To this end, a heterogeneous data fusion method is proposed to estimate travel time distributions by fusing heterogeneous data from point and interval detectors. In the proposed method, link travel time distributions are first estimated from point detector observations. The travel time distributions of links without point detectors are imputed based on their spatial correlations with links that have point detectors. The estimated link travel time distributions are then fused with path travel time distributions obtained from the interval detectors using Dempster-Shafer evidence theory. Based on fused path travel time distribution, an optimization technique is further introduced to update link travel time distributions and their spatial correlations. A case study was performed using real-world data from Hong Kong and showed that the proposed method obtained accurate and robust estimations of link and path travel time distributions in congested road networks. PMID:29210978

Heterogeneous Data Fusion Method to Estimate Travel Time Distributions in Congested Road Networks.

PubMed

Shi, Chaoyang; Chen, Bi Yu; Lam, William H K; Li, Qingquan

2017-12-06

Travel times in congested urban road networks are highly stochastic. Provision of travel time distribution information, including both mean and variance, can be very useful for travelers to make reliable path choice decisions to ensure higher probability of on-time arrival. To this end, a heterogeneous data fusion method is proposed to estimate travel time distributions by fusing heterogeneous data from point and interval detectors. In the proposed method, link travel time distributions are first estimated from point detector observations. The travel time distributions of links without point detectors are imputed based on their spatial correlations with links that have point detectors. The estimated link travel time distributions are then fused with path travel time distributions obtained from the interval detectors using Dempster-Shafer evidence theory. Based on fused path travel time distribution, an optimization technique is further introduced to update link travel time distributions and their spatial correlations. A case study was performed using real-world data from Hong Kong and showed that the proposed method obtained accurate and robust estimations of link and path travel time distributions in congested road networks.

Determination of phthalate esters from environmental water samples by micro-solid-phase extraction using TiO2 nanotube arrays before high-performance liquid chromatography.

PubMed

Zhou, Qingxiang; Fang, Zhi; Liao, Xiangkun

2015-07-01

We describe a highly sensitive micro-solid-phase extraction method for the pre-concentration of six phthalate esters utilizing a TiO2 nanotube array coupled to high-performance liquid chromatography with a variable-wavelength ultraviolet visible detector. The selected phthalate esters included dimethyl phthalate, diethyl phthalate, dibutyl phthalate, butyl benzyl phthalate, bis(2-ethylhexyl)phthalate and dioctyl phthalate. The factors that would affect the enrichment, such as desorption solvent, sample pH, salting-out effect, extraction time and desorption time, were optimized. Under the optimum conditions, the linear range of the proposed method was 0.3-200 μg/L. The limits of detection were 0.04-0.2 μg/L (S/N = 3). The proposed method was successfully applied to the determination of six phthalate esters in water samples and satisfied spiked recoveries were achieved. These results indicated that the proposed method was appropriate for the determination of trace phthalate esters in environmental water samples. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Estimation of color modification in digital images by CFA pattern change.

PubMed

Choi, Chang-Hee; Lee, Hae-Yeoun; Lee, Heung-Kyu

2013-03-10

Extensive studies have been carried out for detecting image forgery such as copy-move, re-sampling, blurring, and contrast enhancement. Although color modification is a common forgery technique, there is no reported forensic method for detecting this type of manipulation. In this paper, we propose a novel algorithm for estimating color modification in images acquired from digital cameras when the images are modified. Most commercial digital cameras are equipped with a color filter array (CFA) for acquiring the color information of each pixel. As a result, the images acquired from such digital cameras include a trace from the CFA pattern. This pattern is composed of the basic red green blue (RGB) colors, and it is changed when color modification is carried out on the image. We designed an advanced intermediate value counting method for measuring the change in the CFA pattern and estimating the extent of color modification. The proposed method is verified experimentally by using 10,366 test images. The results confirmed the ability of the proposed method to estimate color modification with high accuracy. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Drawing Road Networks with Mental Maps.

PubMed

Lin, Shih-Syun; Lin, Chao-Hung; Hu, Yan-Jhang; Lee, Tong-Yee

2014-09-01

Tourist and destination maps are thematic maps designed to represent specific themes in maps. The road network topologies in these maps are generally more important than the geometric accuracy of roads. A road network warping method is proposed to facilitate map generation and improve theme representation in maps. The basic idea is deforming a road network to meet a user-specified mental map while an optimization process is performed to propagate distortions originating from road network warping. To generate a map, the proposed method includes algorithms for estimating road significance and for deforming a road network according to various geometric and aesthetic constraints. The proposed method can produce an iconic mark of a theme from a road network and meet a user-specified mental map. Therefore, the resulting map can serve as a tourist or destination map that not only provides visual aids for route planning and navigation tasks, but also visually emphasizes the presentation of a theme in a map for the purpose of advertising. In the experiments, the demonstrations of map generations show that our method enables map generation systems to generate deformed tourist and destination maps efficiently.

Nested sparse grid collocation method with delay and transformation for subsurface flow and transport problems

NASA Astrophysics Data System (ADS)

Liao, Qinzhuo; Zhang, Dongxiao; Tchelepi, Hamdi

2017-06-01

In numerical modeling of subsurface flow and transport problems, formation properties may not be deterministically characterized, which leads to uncertainty in simulation results. In this study, we propose a sparse grid collocation method, which adopts nested quadrature rules with delay and transformation to quantify the uncertainty of model solutions. We show that the nested Kronrod-Patterson-Hermite quadrature is more efficient than the unnested Gauss-Hermite quadrature. We compare the convergence rates of various quadrature rules including the domain truncation and domain mapping approaches. To further improve accuracy and efficiency, we present a delayed process in selecting quadrature nodes and a transformed process for approximating unsmooth or discontinuous solutions. The proposed method is tested by an analytical function and in one-dimensional single-phase and two-phase flow problems with different spatial variances and correlation lengths. An additional example is given to demonstrate its applicability to three-dimensional black-oil models. It is found from these examples that the proposed method provides a promising approach for obtaining satisfactory estimation of the solution statistics and is much more efficient than the Monte-Carlo simulations.