WorldWideScience

Sample records for hybrids discriminant analysis

  1. Rapid discrimination between four seagrass species using hybrid analysis.

    Science.gov (United States)

    Osathanunkul, M; Madesis, P; Ounjai, S; Suwannapoom, C; Jampeetong, A

    2015-04-27

    Biological species are traditionally identified based on their morphological features and the correct identification of species is critical in biological studies. However, some plant types, such as seagrass, are taxonomically problematic and difficult to identify. Furthermore, closely related seagrass species, such as Halophila spp, form a taxonomically unresolved complex. Although some seagrass taxa are easy to recognize, most species are difficult to identify without skilled taxonomic or molecular techniques. Barcoding coupled with High Resolution Melting analysis (BAR-HRM) offers a potentially reliable, rapid, and cost-effective method to confirm species. Here, DNA information of two chloroplast loci was used in combination with HRM analysis to discriminate four species of seagrass collected off the southern coast of Thailand. A distinct melting curve presenting one inflection point was generated for each species using rbcL primers. While the melting profiles of Cymodocea rotundata and Cymodocea serrulata were not statistically different, analysis of the normalized HRM curves produced with the rpoC primers allowed for their discrimination. The Bar-HRM technique showed promise in discriminating seagrass species and with further adaptations and improvements, could make for an effective and power tool for confirming seagrass species.

  2. Hybrid analysis of multiaxis electromagnetic data for discrimination of munitions and explosives of concern

    Science.gov (United States)

    Friedel, M. J.; Asch, T. H.; Oden, C.

    2012-08-01

    The remediation of land containing munitions and explosives of concern, otherwise known as unexploded ordnance, is an ongoing problem facing the U.S. Department of Defense and similar agencies worldwide that have used or are transferring training ranges or munitions disposal areas to civilian control. The expense associated with cleanup of land previously used for military training and war provides impetus for research towards enhanced discrimination of buried unexploded ordnance. Towards reducing that expense, a multiaxis electromagnetic induction data collection and software system, called ALLTEM, was designed and tested with support from the U.S. Department of Defense Environmental Security Technology Certification Program. ALLTEM is an on-time time-domain system that uses a continuous triangle-wave excitation to measure the target-step response rather than traditional impulse response. The system cycles through three orthogonal transmitting loops and records a total of 19 different transmitting and receiving loop combinations with a nominal spatial data sampling interval of 20 cm. Recorded data are pre-processed and then used in a hybrid discrimination scheme involving both data-driven and numerical classification techniques. The data-driven classification scheme is accomplished in three steps. First, field observations are used to train a type of unsupervised artificial neural network, a self-organizing map (SOM). Second, the SOM is used to simultaneously estimate target parameters (depth, azimuth, inclination, item type and weight) by iterative minimization of the topographic error vectors. Third, the target classification is accomplished by evaluating histograms of the estimated parameters. The numerical classification scheme is also accomplished in three steps. First, the Biot-Savart law is used to model the primary magnetic fields from the transmitter coils and the secondary magnetic fields generated by currents induced in the target materials in the

  3. Hybrid analysis of multiaxis electromagnetic data for discrimination of munitions and explosives of concern

    Science.gov (United States)

    Friedel, M.J.; Asch, T.H.; Oden, C.

    2012-01-01

    The remediation of land containing munitions and explosives of concern, otherwise known as unexploded ordnance, is an ongoing problem facing the U.S. Department of Defense and similar agencies worldwide that have used or are transferring training ranges or munitions disposal areas to civilian control. The expense associated with cleanup of land previously used for military training and war provides impetus for research towards enhanced discrimination of buried unexploded ordnance. Towards reducing that expense, a multiaxis electromagnetic induction data collection and software system, called ALLTEM, was designed and tested with support from the U.S. Department of Defense Environmental Security Technology Certification Program. ALLTEM is an on-time time-domain system that uses a continuous triangle-wave excitation to measure the target-step response rather than traditional impulse response. The system cycles through three orthogonal transmitting loops and records a total of 19 different transmitting and receiving loop combinations with a nominal spatial data sampling interval of 20 cm. Recorded data are pre-processed and then used in a hybrid discrimination scheme involving both data-driven and numerical classification techniques. The data-driven classification scheme is accomplished in three steps. First, field observations are used to train a type of unsupervised artificial neural network, a self-organizing map (SOM). Second, the SOM is used to simultaneously estimate target parameters (depth, azimuth, inclination, item type and weight) by iterative minimization of the topographic error vectors. Third, the target classification is accomplished by evaluating histograms of the estimated parameters. The numerical classification scheme is also accomplished in three steps. First, the Biot–Savart law is used to model the primary magnetic fields from the transmitter coils and the secondary magnetic fields generated by currents induced in the target materials in the

  4. Unsupervised Linear Discriminant Analysis

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    An algorithm for unsupervised linear discriminant analysis was presented. Optimal unsupervised discriminant vectors are obtained through maximizing covariance of all samples and minimizing covariance of local k-nearest neighbor samples. The experimental results show our algorithm is effective.

  5. Tomato (Solanum lycopersicum) variety discrimination and hybridization analysis based on the 5S rRNA region.

    Science.gov (United States)

    Sun, Yan-Lin; Kang, Ho-Min; Kim, Young-Sik; Baek, Jun-Pill; Zheng, Shi-Lin; Xiang, Jin-Jun; Hong, Soon-Kwan

    2014-05-04

    The tomato (Solanum lycopersicum) is a major vegetable crop worldwide. To satisfy popular demand, more than 500 tomato varieties have been bred. However, a clear variety identification has not been found. Thorough understanding of the phylogenetic relationship and hybridization information of tomato varieties is very important for further variety breeding. Thus, in this study, we collected 26 tomato varieties and attempted to distinguish them based on the 5S rRNA region, which is widely used in the determination of phylogenetic relations. Sequence analysis of the 5S rRNA region suggested that a large number of nucleotide variations exist among tomato varieties. These variable nucleotide sites were also informative regarding hybridization. Chromas sequencing of Yellow Mountain View and Seuwiteuking varieties indicated three and one variable nucleotide sites in the non-transcribed spacer (NTS) of the 5S rRNA region showing hybridization, respectively. Based on a phylogenetic tree constructed using the 5S rRNA sequences, we observed that 16 tomato varieties were divided into three groups at 95% similarity. Rubiking and Sseommeoking, Lang Selection Procedure and Seuwiteuking, and Acorn Gold and Yellow Mountain View exhibited very high identity with their partners. This work will aid variety authentication and provides a basis for further tomato variety breeding.

  6. Validation of microsatellite multiplexes for parentage analysis and species discrimination in two hybridizing species of coral reef fish (Plectropomus spp., Serranidae).

    Science.gov (United States)

    Harrison, Hugo B; Feldheim, Kevin A; Jones, Geoffrey P; Ma, Kayan; Mansour, Hicham; Perumal, Sadhasivam; Williamson, David H; Berumen, Michael L

    2014-06-01

    Microsatellites are often considered ideal markers to investigate ecological processes in animal populations. They are regularly used as genetic barcodes to identify species, individuals, and infer familial relationships. However, such applications are highly sensitive the number and diversity of microsatellite markers, which are also prone to error. Here, we propose a novel framework to assess the suitability of microsatellite datasets for parentage analysis and species discrimination in two closely related species of coral reef fish, Plectropomus leopardus and P. maculatus (Serranidae). Coral trout are important fisheries species throughout the Indo-Pacific region and have been shown to hybridize in parts of the Great Barrier Reef, Australia. We first describe the development of 25 microsatellite loci and their integration to three multiplex PCRs that co-amplify in both species. Using simulations, we demonstrate that the complete suite of markers provides appropriate power to discriminate between species, detect hybrid individuals, and resolve parent-offspring relationships in natural populations, with over 99.6% accuracy in parent-offspring assignments. The markers were also tested on seven additional species within the Plectropomus genus with polymorphism in 28-96% of loci. The multiplex PCRs developed here provide a reliable and cost-effective strategy to investigate evolutionary and ecological dynamics and will be broadly applicable in studies of wild populations and aquaculture brood stocks for these closely related fish species.

  7. Validation of microsatellite multiplexes for parentage analysis and species discrimination in two hybridizing species of coral reef fish (Plectropomus spp., Serranidae)

    KAUST Repository

    Harrison, H.B.

    2014-04-24

    Microsatellites are often considered ideal markers to investigate ecological processes in animal populations. They are regularly used as genetic barcodes to identify species, individuals, and infer familial relationships. However, such applications are highly sensitive the number and diversity of microsatellite markers, which are also prone to error. Here, we propose a novel framework to assess the suitability of microsatellite datasets for parentage analysis and species discrimination in two closely related species of coral reef fish, Plectropomus leopardus and P. maculatus (Serranidae). Coral trout are important fisheries species throughout the Indo-Pacific region and have been shown to hybridize in parts of the Great Barrier Reef, Australia. We first describe the development of 25 microsatellite loci and their integration to three multiplex PCRs that co-amplify in both species. Using simulations, we demonstrate that the complete suite of markers provides appropriate power to discriminate between species, detect hybrid individuals, and resolve parent-offspring relationships in natural populations, with over 99.6% accuracy in parent-offspring assignments. The markers were also tested on seven additional species within the Plectropomus genus with polymorphism in 28-96% of loci. The multiplex PCRs developed here provide a reliable and cost-effective strategy to investigate evolutionary and ecological dynamics and will be broadly applicable in studies of wild populations and aquaculture brood stocks for these closely related fish species. 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

  8. A comparison of corporate distress prediction models in Brazil: hybrid neural networks, logit models and discriminant analysis

    Directory of Open Access Journals (Sweden)

    Juliana Yim

    2009-06-01

    Full Text Available This paper looks at the ability of a relatively new technique, hybrid ANN’s, to predict corporate distress in Brazil. These models are compared with traditional statistical techniques and conventional ANN models. The results suggest that hybrid neural networks outperform all other models in predicting firms in financial distress one year prior to the event. This suggests that for researchers, policymakers and others interested in early warning systems, hybrid networks may be a useful tool for predicting firm failure.

  9. A comparison of corporate distress prediction models in Brazil: hybrid neural networks, logit models and discriminant analysis

    Directory of Open Access Journals (Sweden)

    Juliana Yim

    2005-01-01

    Full Text Available This paper looks at the ability of a relatively new technique, hybrid ANN's, to predict corporate distress in Brazil. These models are compared with traditional statistical techniques and conventional ANN models. The results suggest that hybrid neural networks outperform all other models in predicting firms in financial distress one year prior to the event. This suggests that for researchers, policymakers and others interested in early warning systems, hybrid networks may be a useful tool for predicting firm failure.

  10. Discriminant Analysis on Land Grading

    Institute of Scientific and Technical Information of China (English)

    LIU Yaolin; HOU Yajuan

    2004-01-01

    This paper proposes the discriminant analysis on land grading after analyzing the common methods and discussing the Fisher's discriminant in detail. Actually this method deduces the dimension from multi to single, thus it makes the feature vectors in n-dimension change to a scalar, and use this scalar to classify samples. This paper illustrates the result by giving an example of the residential land grading by the discriminant analysis.

  11. Discriminant Incoherent Component Analysis.

    Science.gov (United States)

    Georgakis, Christos; Panagakis, Yannis; Pantic, Maja

    2016-05-01

    Face images convey rich information which can be perceived as a superposition of low-complexity components associated with attributes, such as facial identity, expressions, and activation of facial action units (AUs). For instance, low-rank components characterizing neutral facial images are associated with identity, while sparse components capturing non-rigid deformations occurring in certain face regions reveal expressions and AU activations. In this paper, the discriminant incoherent component analysis (DICA) is proposed in order to extract low-complexity components, corresponding to facial attributes, which are mutually incoherent among different classes (e.g., identity, expression, and AU activation) from training data, even in the presence of gross sparse errors. To this end, a suitable optimization problem, involving the minimization of nuclear-and l1 -norm, is solved. Having found an ensemble of class-specific incoherent components by the DICA, an unseen (test) image is expressed as a group-sparse linear combination of these components, where the non-zero coefficients reveal the class(es) of the respective facial attribute(s) that it belongs to. The performance of the DICA is experimentally assessed on both synthetic and real-world data. Emphasis is placed on face analysis tasks, namely, joint face and expression recognition, face recognition under varying percentages of training data corruption, subject-independent expression recognition, and AU detection by conducting experiments on four data sets. The proposed method outperforms all the methods that are compared with all the tasks and experimental settings.

  12. Variable Selection in Discriminant Analysis.

    Science.gov (United States)

    Huberty, Carl J.; Mourad, Salah A.

    Methods for ordering and selecting variables for discriminant analysis in multiple group comparison or group prediction studies include: univariate Fs, stepwise analysis, learning discriminant function (LDF) variable correlations, communalities, LDF standardized coefficients, and weighted standardized coefficients. Five indices based on distance,…

  13. Variable Selection in Discriminant Analysis.

    Science.gov (United States)

    Huberty, Carl J.; Mourad, Salah A.

    Methods for ordering and selecting variables for discriminant analysis in multiple group comparison or group prediction studies include: univariate Fs, stepwise analysis, learning discriminant function (LDF) variable correlations, communalities, LDF standardized coefficients, and weighted standardized coefficients. Five indices based on distance,…

  14. A hybrid mixture discriminant analysis-random forest computational model for the prediction of volume of distribution of drugs in human.

    Science.gov (United States)

    Lombardo, Franco; Obach, R Scott; Dicapua, Frank M; Bakken, Gregory A; Lu, Jing; Potter, David M; Gao, Feng; Miller, Michael D; Zhang, Yao

    2006-04-06

    A computational approach is described that can predict the VD(ss) of new compounds in humans, with an accuracy of within 2-fold of the actual value. A dataset of VD values for 384 drugs in humans was used to train a hybrid mixture discriminant analysis-random forest (MDA-RF) model using 31 computed descriptors. Descriptors included terms describing lipophilicity, ionization, molecular volume, and various molecular fragments. For a test set of 23 proprietary compounds not used in model construction, the geometric mean fold-error (GMFE) was 1.78-fold (+/-11.4%). The model was also tested using a leave-class out approach wherein subsets of drugs based on therapeutic class were removed from the training set of 384, the model was recast, and the VD(ss) values for each of the subsets were predicted. GMFE values ranged from 1.46 to 2.94-fold, depending on the subset. Finally, for an additional set of 74 compounds, VD(ss) predictions made using the computational model were compared to predictions made using previously described methods dependent on animal pharmacokinetic data. Computational VD(ss) predictions were, on average, 2.13-fold different from the VD(ss) predictions from animal data. The computational model described can predict human VD(ss) with an accuracy comparable to predictions requiring substantially greater effort and can be applied in place of animal experimentation.

  15. A hybrid generative-discriminative approach to speaker diarization

    NARCIS (Netherlands)

    Noulas, A.K.; van Kasteren, T.; Kröse, B.J.A.

    2008-01-01

    In this paper we present a sound probabilistic approach to speaker diarization. We use a hybrid framework where a distribution over the number of speakers at each point of a multimodal stream is estimated with a discriminative model. The output of this process is used as input in a generative model

  16. Brain anatomical structure segmentation by hybrid discriminative/generative models.

    Science.gov (United States)

    Tu, Z; Narr, K L; Dollar, P; Dinov, I; Thompson, P M; Toga, A W

    2008-04-01

    In this paper, a hybrid discriminative/generative model for brain anatomical structure segmentation is proposed. The learning aspect of the approach is emphasized. In the discriminative appearance models, various cues such as intensity and curvatures are combined to locally capture the complex appearances of different anatomical structures. A probabilistic boosting tree (PBT) framework is adopted to learn multiclass discriminative models that combine hundreds of features across different scales. On the generative model side, both global and local shape models are used to capture the shape information about each anatomical structure. The parameters to combine the discriminative appearance and generative shape models are also automatically learned. Thus, low-level and high-level information is learned and integrated in a hybrid model. Segmentations are obtained by minimizing an energy function associated with the proposed hybrid model. Finally, a grid-face structure is designed to explicitly represent the 3-D region topology. This representation handles an arbitrary number of regions and facilitates fast surface evolution. Our system was trained and tested on a set of 3-D magnetic resonance imaging (MRI) volumes and the results obtained are encouraging.

  17. Hybrid Generative/Discriminative Learning for Automatic Image Annotation

    CERN Document Server

    Yang, Shuang Hong; Zha, Hongyuan

    2012-01-01

    Automatic image annotation (AIA) raises tremendous challenges to machine learning as it requires modeling of data that are both ambiguous in input and output, e.g., images containing multiple objects and labeled with multiple semantic tags. Even more challenging is that the number of candidate tags is usually huge (as large as the vocabulary size) yet each image is only related to a few of them. This paper presents a hybrid generative-discriminative classifier to simultaneously address the extreme data-ambiguity and overfitting-vulnerability issues in tasks such as AIA. Particularly: (1) an Exponential-Multinomial Mixture (EMM) model is established to capture both the input and output ambiguity and in the meanwhile to encourage prediction sparsity; and (2) the prediction ability of the EMM model is explicitly maximized through discriminative learning that integrates variational inference of graphical models and the pairwise formulation of ordinal regression. Experiments show that our approach achieves both su...

  18. Discriminant and Proximity Analysis in Intercultural Investigation.

    Science.gov (United States)

    Laveault, Dany

    1982-01-01

    Discriminant analysis is applied to data from previous research dealing with assessing the particularities of cognitive development in young (four to nine years old) Montagnais Indians and French Canadians. The most important future contribution of discriminant analysis to intercultural research will be its ability to conceptualize group…

  19. Semisupervised Sparse Multilinear Discriminant Analysis

    Institute of Scientific and Technical Information of China (English)

    黄锴; 张丽清

    2014-01-01

    Various problems are encountered when adopting ordinary vector space algorithms for high-order tensor data input. Namely, one must overcome the Small Sample Size (SSS) and overfitting problems. In addition, the structural information of the original tensor signal is lost during the vectorization process. Therefore, comparable methods using a direct tensor input are more appropriate. In the case of electrocardiograms (ECGs), another problem must be overcome;the manual diagnosis of ECG data is expensive and time consuming, rendering it difficult to acquire data with diagnosis labels. However, when effective features for classification in the original data are very sparse, we propose a semisupervised sparse multilinear discriminant analysis (SSSMDA) method. This method uses the distribution of both the labeled and the unlabeled data together with labels discovered through a label propagation algorithm. In practice, we use 12-lead ECGs collected from a remote diagnosis system and apply a short-time-fourier transformation (STFT) to obtain third-order tensors. The experimental results highlight the sparsity of the ECG data and the ability of our method to extract sparse and effective features that can be used for classification.

  20. Establishment of a Multi-color Genomic in situ Hybridization Technique to Simultaneously Discriminate the Three Interspecific Hybrid Genomes in Gossypium

    Institute of Scientific and Technical Information of China (English)

    Bing Guan; Kai Wang; Bao-Liang Zhou; Wang-Zhen Guo; Tian-Zhen Zhang

    2008-01-01

    To identify alien chromosomes in recipient progenies and to analyze genome components in polyploidy, a genomic In situ hybridization (GISH) technique that is suitable for cotton was developed using increased stringency conditions. The increased stringency conditions were a combination of the four factors in the following optimized state: 100:1 ratio of blocking DNA to probe, 60% formamide wash solution, 43 =C temperature wash and a 13 min wash. Under these specific conditions using gDNA from Gossypium sturtianurn (C1C1) as a probe, strong hybridization signals were only observed on chromosomes from the C1 genome in somatic cells of the hybrid F1 (G. hirsutum×G. sturtianum) (AtDtC1). Therefore, GISH was able to discriminate parental chromosomes in the hybrid. Further, we developed a multi-color GISH to simultaneously discriminate the three genomes of the above hybrid. The results repeatedly displayed the three genomes, At, Dt, and C1, and each set of chromosomes with a unique color, making them easy to identify. The power of the multi-color GISH was proven by analysis of the hexaploid hybrid F1 (G. hirsutum × G. australe) (AtAtDtDtG2G2). We believe that the powerful multi-color GISH technique could be applied extensively to analyze the genome component in polyploidy and to identify alien chromosomes in the recipient progenies.

  1. Legitimating Racial Discrimination: Emotions, Not Beliefs, Best Predict Discrimination in a Meta-Analysis

    OpenAIRE

    Talaska, Cara A.; Fiske, Susan T.; Chaiken, Shelly

    2008-01-01

    Investigations of racial bias have emphasized stereotypes and other beliefs as central explanatory mechanisms and as legitimating discrimination. In recent theory and research, emotional prejudices have emerged as another, more direct predictor of discrimination. A new comprehensive meta-analysis of 57 racial attitude-discrimination studies finds a moderate relationship between overall attitudes and discrimination. Emotional prejudices are twices as closely related to racial discrimination as...

  2. Incremental Discriminant Analysis in Tensor Space

    Science.gov (United States)

    Chang, Liu; Weidong, Zhao; Tao, Yan; Qiang, Pu; Xiaodan, Du

    2015-01-01

    To study incremental machine learning in tensor space, this paper proposes incremental tensor discriminant analysis. The algorithm employs tensor representation to carry on discriminant analysis and combine incremental learning to alleviate the computational cost. This paper proves that the algorithm can be unified into the graph framework theoretically and analyzes the time and space complexity in detail. The experiments on facial image detection have shown that the algorithm not only achieves sound performance compared with other algorithms, but also reduces the computational issues apparently. PMID:26339229

  3. Visual tracker using sequential bayesian learning: discriminative, generative, and hybrid.

    Science.gov (United States)

    Lei, Yun; Ding, Xiaoqing; Wang, Shengjin

    2008-12-01

    This paper presents a novel solution to track a visual object under changes in illumination, viewpoint, pose, scale, and occlusion. Under the framework of sequential Bayesian learning, we first develop a discriminative model-based tracker with a fast relevance vector machine algorithm, and then, a generative model-based tracker with a novel sequential Gaussian mixture model algorithm. Finally, we present a three-level hierarchy to investigate different schemes to combine the discriminative and generative models for tracking. The presented hierarchical model combination contains the learner combination (at level one), classifier combination (at level two), and decision combination (at level three). The experimental results with quantitative comparisons performed on many realistic video sequences show that the proposed adaptive combination of discriminative and generative models achieves the best overall performance. Qualitative comparison with some state-of-the-art methods demonstrates the effectiveness and efficiency of our method in handling various challenges during tracking.

  4. Variable Selection Strategies in Discriminate Analysis.

    Science.gov (United States)

    Tanguma, Jesus

    This paper presents three variable selection strategies in discriminate analysis (all variables in the model, use of stepwise methods, and all possible subsets). All three methods are illustrated through examples. Although the all variables in the model and the stepwise methods are the most widely used, B. Thompson (1996) and C. Huberty (1994)…

  5. Efficient Global Programming Model for Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    M.ANGULAKSHMI

    2011-03-01

    Full Text Available Conventional statistical analysis includes the capacity to systematically assign individuals to groups. We suggest alternative assignment procedures, utilizing a set of interrelated goal programming formulations. This paper represents an effort to suggest ways by which the discriminant problem might reasonably be addressed via straightforward linear goal programming formulations. Simple and direct, such formulations may ultimately compete with conventional approaches - free of the classical assumptions and possessing a stronger intuitive appeal. We further demonstrate via simple illustration the potential of these procedures to play a significant part in addressing the discriminant problem, and indicate fundamental ideas that lay the foundation for other more sophisticated approaches.

  6. Discrimination of Maize Haploid Seeds from Hybrid Seeds Using Vis Spectroscopy and Support Vector Machine Method.

    Science.gov (United States)

    Liu, Jin; Guo, Ting-ting; Li, Hao-chuan; Jia, Shi-qiang; Yan, Yan-lu; An, Dong; Zhang, Yao; Chen, Shao-jiang

    2015-11-01

    Doubled haploid (DH) lines are routinely applied in the hybrid maize breeding programs of many institutes and companies for their advantages of complete homozygosity and short breeding cycle length. A key issue in this approach is an efficient screening system to identify haploid kernels from the hybrid kernels crossed with the inducer. At present, haploid kernel selection is carried out manually using the"red-crown" kernel trait (the haploid kernel has a non-pigmented embryo and pigmented endosperm) controlled by the R1-nj gene. Manual selection is time-consuming and unreliable. Furthermore, the color of the kernel embryo is concealed by the pericarp. Here, we establish a novel approach for identifying maize haploid kernels based on visible (Vis) spectroscopy and support vector machine (SVM) pattern recognition technology. The diffuse transmittance spectra of individual kernels (141 haploid kernels and 141 hybrid kernels from 9 genotypes) were collected using a portable UV-Vis spectrometer and integrating sphere. The raw spectral data were preprocessed using smoothing and vector normalization methods. The desired feature wavelengths were selected based on the results of the Kolmogorov-Smirnov test. The wavelengths with p values above 0. 05 were eliminated because the distributions of absorbance data in these wavelengths show no significant difference between haploid and hybrid kernels. Principal component analysis was then performed to reduce the number of variables. The SVM model was evaluated by 9-fold cross-validation. In each round, samples of one genotype were used as the testing set, while those of other genotypes were used as the training set. The mean rate of correct discrimination was 92.06%. This result demonstrates the feasibility of using Vis spectroscopy to identify haploid maize kernels. The method would help develop a rapid and accurate automated screening-system for haploid kernels.

  7. Semisupervised learning for a hybrid generative/discriminative classifier based on the maximum entropy principle.

    Science.gov (United States)

    Fujino, Akinori; Ueda, Naonori; Saito, Kazumi

    2008-03-01

    This paper presents a method for designing semi-supervised classifiers trained on labeled and unlabeled samples. We focus on probabilistic semi-supervised classifier design for multi-class and single-labeled classification problems, and propose a hybrid approach that takes advantage of generative and discriminative approaches. In our approach, we first consider a generative model trained by using labeled samples and introduce a bias correction model, where these models belong to the same model family, but have different parameters. Then, we construct a hybrid classifier by combining these models based on the maximum entropy principle. To enable us to apply our hybrid approach to text classification problems, we employed naive Bayes models as the generative and bias correction models. Our experimental results for four text data sets confirmed that the generalization ability of our hybrid classifier was much improved by using a large number of unlabeled samples for training when there were too few labeled samples to obtain good performance. We also confirmed that our hybrid approach significantly outperformed generative and discriminative approaches when the performance of the generative and discriminative approaches was comparable. Moreover, we examined the performance of our hybrid classifier when the labeled and unlabeled data distributions were different.

  8. Multivariate analysis applied to tomato hybrid production.

    Science.gov (United States)

    Balasch, S; Nuez, F; Palomares, G; Cuartero, J

    1984-11-01

    Twenty characters were measured on 60 tomato varieties cultivated in the open-air and in polyethylene plastic-house. Data were analyzed by means of principal components, factorial discriminant methods, Mahalanobis D(2) distances and principal coordinate techniques. Factorial discriminant and Mahalanobis D(2) distances methods, both of which require collecting data plant by plant, lead to similar conclusions as the principal components method that only requires taking data by plots. Characters that make up the principal components in both environments studied are the same, although the relative importance of each one of them varies within the principal components. By combining information supplied by multivariate analysis with the inheritance mode of characters, crossings among cultivars can be experimented with that will produce heterotic hybrids showing characters within previously established limits.

  9. A Hybrid Generative/Discriminative Classifier Design for Semi-supervised Learing

    Science.gov (United States)

    Fujino, Akinori; Ueda, Naonori; Saito, Kazumi

    Semi-supervised classifier design that simultaneously utilizes both a small number of labeled samples and a large number of unlabeled samples is a major research issue in machine learning. Existing semi-supervised learning methods for probabilistic classifiers belong to either generative or discriminative approaches. This paper focuses on a semi-supervised probabilistic classifier design for multiclass and single-labeled classification problems and first presents a hybrid approach to take advantage of the generative and discriminative approaches. Our formulation considers a generative model trained on labeled samples and a newly introduced bias correction model, whose belongs to the same model family as the generative model, but whose parameters are different from the generative model. A hybrid classifier is constructed by combining both the generative and bias correction models based on the maximum entropy principle, where the combination weights of these models are determined so that the class labels of labeled samples are as correctly predicted as possible. We apply the hybrid approach to text classification problems by employing naive Bayes as the generative and bias correction models. In our experimental results on three English and one Japanese text data sets, we confirmed that the hybrid classifier significantly outperformed conventional probabilistic generative and discriminative classifiers when the classification performance of the generative classifier was comparable to the discriminative classifier.

  10. Quadrature phase shift keying coherent state discrimination via a hybrid receiver

    DEFF Research Database (Denmark)

    Müller, C. R.; Castaneda, Mario A. Usuga; Wittmann, C.;

    2012-01-01

    We propose and experimentally demonstrate a near-optimal discrimination scheme for the quadrature phase shift keying (QPSK) protocol. We show in theory that the performance of our hybrid scheme is superior to the standard scheme—heterodyne detection—for all signal amplitudes and underpin the pred...

  11. Direct Neighborhood Discriminant Analysis for Face Recognition

    Directory of Open Access Journals (Sweden)

    Miao Cheng

    2008-01-01

    Full Text Available Face recognition is a challenging problem in computer vision and pattern recognition. Recently, many local geometrical structure-based techiniques are presented to obtain the low-dimensional representation of face images with enhanced discriminatory power. However, these methods suffer from the small simple size (SSS problem or the high computation complexity of high-dimensional data. To overcome these problems, we propose a novel local manifold structure learning method for face recognition, named direct neighborhood discriminant analysis (DNDA, which separates the nearby samples of interclass and preserves the local within-class geometry in two steps, respectively. In addition, the PCA preprocessing to reduce dimension to a large extent is not needed in DNDA avoiding loss of discriminative information. Experiments conducted on ORL, Yale, and UMIST face databases show the effectiveness of the proposed method.

  12. A Computational Discriminability Analysis on Twin Fingerprints

    Science.gov (United States)

    Liu, Yu; Srihari, Sargur N.

    Sharing similar genetic traits makes the investigation of twins an important study in forensics and biometrics. Fingerprints are one of the most commonly found types of forensic evidence. The similarity between twins’ prints is critical establish to the reliability of fingerprint identification. We present a quantitative analysis of the discriminability of twin fingerprints on a new data set (227 pairs of identical twins and fraternal twins) recently collected from a twin population using both level 1 and level 2 features. Although the patterns of minutiae among twins are more similar than in the general population, the similarity of fingerprints of twins is significantly different from that between genuine prints of the same finger. Twins fingerprints are discriminable with a 1.5%~1.7% higher EER than non-twins. And identical twins can be distinguished by examine fingerprint with a slightly higher error rate than fraternal twins.

  13. Face Recognition Using Kernel Discriminant Analysis

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    Linear Discrimiant Analysis (LDA) has demonstrated their success in face recognition. But LDA is difficult to handle the high nonlinear problems, such as changes of large viewpoint and illumination in face recognition. In order to overcome these problems, we investigate Kernel Discriminant Analysis (KDA) for face recognition. This approach adopts the kernel functions to replace the dot products of nonlinear mapping in the high dimensional feature space, and then the nonlinear problem can be solved in the input space conveniently without explicit mapping. Two face databases are used to test KDA approach. The results show that our approach outperforms the conventional PCA(Eigenface) and LDA(Fisherface) approaches.

  14. EM-63 Decay Curve Analysis for UXO Discrimination

    Science.gov (United States)

    2016-06-13

    EM-63 Decay Curve Analysis for UXO Discrimination ESTCP Contract # 200035 Final Report NAEVA Geophysics September 7...07 SEP 2001 2. REPORT TYPE 3. DATES COVERED 00-00-2001 to 00-00-2001 4. TITLE AND SUBTITLE EM-63 Decay Curve Analysis for UXO Discrimination ... Discrimination ESTCP Contract # 200035 Final Report NAEVA Geophysics September 7, 2001 Table Of Contents 1 Introduction

  15. Activity recognition using hybrid generative/discriminative models on home environments using binary sensors.

    Science.gov (United States)

    Ordóñez, Fco Javier; de Toledo, Paula; Sanchis, Araceli

    2013-04-24

    Activities of daily living are good indicators of elderly health status, and activity recognition in smart environments is a well-known problem that has been previously addressed by several studies. In this paper, we describe the use of two powerful machine learning schemes, ANN (Artificial Neural Network) and SVM (Support Vector Machines), within the framework of HMM (Hidden Markov Model) in order to tackle the task of activity recognition in a home setting. The output scores of the discriminative models, after processing, are used as observation probabilities of the hybrid approach. We evaluate our approach by comparing these hybrid models with other classical activity recognition methods using five real datasets. We show how the hybrid models achieve significantly better recognition performance, with significance level p < 0.05, proving that the hybrid approach is better suited for the addressed domain.

  16. Activity Recognition Using Hybrid Generative/Discriminative Models on Home Environments Using Binary Sensors

    Directory of Open Access Journals (Sweden)

    Araceli Sanchis

    2013-04-01

    Full Text Available Activities of daily living are good indicators of elderly health status, and activity recognition in smart environments is a well-known problem that has been previously addressed by several studies. In this paper, we describe the use of two powerful machine learning schemes, ANN (Artificial Neural Network and SVM (Support Vector Machines, within the framework of HMM (Hidden Markov Model in order to tackle the task of activity recognition in a home setting. The output scores of the discriminative models, after processing, are used as observation probabilities of the hybrid approach. We evaluate our approach by comparing these hybrid models with other classical activity recognition methods using five real datasets. We show how the hybrid models achieve significantly better recognition performance, with significance level p < 0:05, proving that the hybrid approach is better suited for the addressed domain.

  17. DISCRIMINANT ANALYSIS OF BANK PROFITABILITY LEVELS

    Directory of Open Access Journals (Sweden)

    Ante Rozga

    2013-02-01

    Full Text Available Discriminant analysis has been employed in this paper in order to identify and explain key features of bank profitability levels. Bank profitability is set up in the form of two categorical variables: profit or loss recorded and above or below average return on equity. Predictor variables are selected from various groups of financial indicators usually included in the empirical work on microeconomic determinants of bank profitability. The data from the Croatian banking sector is analyzed using the Enter method. General recommendations for a more profitable business of banking found in the bank management literature and existing empirical framework such as rationalization of overhead costs, asset growth, increase of non-interest income by expanding scale and scope of financial products proved to be important for classification of banks in different profitability levels. A higher market share may bring additional advantages. Classification results, canonical correlation and Wilks’ Lambda test confirm statistical significance of research results. Altogether, discriminant analysis turns out to be a suitable statistical method for solving presented research problem and moving forward from the bankruptcy, credit rating or default issues in finance.

  18. Discriminant analysis with errors in variables

    CERN Document Server

    Loustau, Sébastien

    2012-01-01

    The effect of measurement error in discriminant analysis is investigated. Given observations $Z=X+\\epsilon$, where $\\epsilon$ denotes a random noise, the goal is to predict the density of $X$ among two possible candidates $f$ and $g$. We suppose that we have at our disposal two learning samples. The aim is to approach the best possible decision rule $G^*$ defined as a minimizer of the Bayes risk. In the free-noise case $(\\epsilon=0)$, minimax fast rates of convergence are well-known under the margin assumption in discriminant analysis (see \\cite{mammen}) or in the more general classification framework (see \\cite{tsybakov2004,AT}). In this paper we intend to establish similar results in the noisy case, i.e. when dealing with errors in variables. In particular, we discuss two possible complexity assumptions that can be set on the problem, which may alternatively concern the regularity of $f-g$ or the boundary of $G^*$. We prove minimax lower bounds for these both problems and explain how can these rates be atta...

  19. Discrimination in Recruitment: An Empirical Analysis.

    Science.gov (United States)

    Newman, Jerry M.

    1978-01-01

    To investigate whether recruitment practices of companies with affirmative action programs discriminated against Blacks or resulted in reverse discrimination, qualifications and race of fictitious job applicants were manipulated on resumes sent to a sample of employers. Responses strongly indicate discrimination, with Black applicants favored…

  20. Comparing two methods of univariate discriminant analysis for sex discrimination in an Iberian population.

    Science.gov (United States)

    Jiménez-Arenas, Juan Manuel; Esquivel, José Antonio

    2013-05-10

    This study assesses the performance of two analytical approaches to sex discrimination based on single linear variables: discriminant analysis and the Lubischew's test. Ninety individuals from an archaeological population (La Torrecilla-Arenas del Rey, Granada, southern Spain) and 17 craniometrical variables were included in the analyses. Most craniometrical variables were higher for men. The bizygomatic breadth enabled the highest level of discrimination: 87.5% and 88.5%, using discriminant analysis and Lubischew's test, respectively. Bizygomatic breadth proved highly dimorphic in comparison to other populations reported in the literature. Lubischew's test raised the discrimination percentage in specific craniometrical variables, while others showed a superior performance by means of the discriminant analysis. The inconsistent results across statistical methods resulted from the specific formulation of each procedure. Discriminant analysis accounts both for within-group and between-group variance, while Lubischew's test emphasizes between-group variation only. Therefore, both techniques are recommended, as they provide different means of achieving optimal discrimination percentages.

  1. Discrimination of wild types and hybrids of Duboisia myoporoides and Duboisia leichhardtii at different growth stages using (1)H NMR-based metabolite profiling and tropane alkaloids-targeted HPLC-MS analysis.

    Science.gov (United States)

    Ullrich, Sophie Friederike; Averesch, Nils J H; Castellanos, Leonardo; Choi, Young Hae; Rothauer, Andreas; Kayser, Oliver

    2016-11-01

    Duboisia species, which belong to the family of Solanaceae, are commercially cultivated in large scale, as they are main source of the pharmaceutically-used active compound scopolamine. In this study, (1)H NMR-based metabolite profiling linking primary with secondary metabolism and additional quantification via HPCL-MS with special focus on the tropane alkaloids were applied to compare leaf and root extracts of three wild types and two hybrids of Duboisia myoporoides and D. leichhardtii at different developmental stages grown under controlled conditions in climate chambers and under agricultural field plantation. Based on the leaf extracts, a clear distinction between the Duboisia hybrids and the wild types Duboisia myoporoides and D. leichhardtii using principal component analysis of (1)H NMR data was observed. The average content in scopolamine in the hybrids of Duboisia cultivated in climate chambers increased significantly from month 3-6 after potting of the rooted cuttings, however not so for the examined wild types. The Duboisia hybrids grown in climate chambers showed higher growth and contained more sugars and amino acids than Duboisia hybrids grown in the field, which in contrast showed an enhanced flux towards tropane alkaloids as well as flavonoids. For a more detailed analysis of tropane alkaloids, an appropriate HPLC-MS method was developed and validated. The measurements revealed large differences in the alkaloid pattern within the different genotypes under investigation, especially regarding the last enzymatic step, the conversion from hyoscamine to scopolamine by the hyoscyamine 6β-hydroxylase. Scopolamine was found in highest concentrations in Duboisia hybrids (20.04 ± 4.05 and 17.82 ± 3.52 mg/g dry wt) followed by Duboisia myoporoides (12.71 ± 2.55 mg/g dry wt), both showing a high selectivity for scopolamine in contrast to Duboisia leichhardtii (3.38 ± 0.59 and 5.09 ± 1.24 mg/g dry wt) with hyoscyamine being the

  2. The Discriminant Analysis Flare Forecasting System (DAFFS)

    Science.gov (United States)

    Leka, K. D.; Barnes, Graham; Wagner, Eric; Hill, Frank; Marble, Andrew R.

    2016-05-01

    The Discriminant Analysis Flare Forecasting System (DAFFS) has been developed under NOAA/Small Business Innovative Research funds to quantitatively improve upon the NOAA/SWPC flare prediction. In the Phase-I of this project, it was demonstrated that DAFFS could indeed improve by the requested 25% most of the standard flare prediction data products from NOAA/SWPC. In the Phase-II of this project, a prototype has been developed and is presently running autonomously at NWRA.DAFFS uses near-real-time data from NOAA/GOES, SDO/HMI, and the NSO/GONG network to issue both region- and full-disk forecasts of solar flares, based on multi-variable non-parametric Discriminant Analysis. Presently, DAFFS provides forecasts which match those provided by NOAA/SWPC in terms of thresholds and validity periods (including 1-, 2-, and 3- day forecasts), although issued twice daily. Of particular note regarding DAFFS capabilities are the redundant system design, automatically-generated validation statistics and the large range of customizable options available. As part of this poster, a description of the data used, algorithm, performance and customizable options will be presented, as well as a demonstration of the DAFFS prototype.DAFFS development at NWRA is supported by NOAA/SBIR contracts WC-133R-13-CN-0079 and WC-133R-14-CN-0103, with additional support from NASA contract NNH12CG10C, plus acknowledgment to the SDO/HMI and NSO/GONG facilities and NOAA/SWPC personnel for data products, support, and feedback. DAFFS is presently ready for Phase-III development.

  3. Stochastic model updating using distance discrimination analysis

    Institute of Scientific and Technical Information of China (English)

    Deng Zhongmin; Bi Sifeng; Sez Atamturktur

    2014-01-01

    This manuscript presents a stochastic model updating method, taking both uncertainties in models and variability in testing into account. The updated finite element (FE) models obtained through the proposed technique can aid in the analysis and design of structural systems. The authors developed a stochastic model updating method integrating distance discrimination analysis (DDA) and advanced Monte Carlo (MC) technique to (1) enable more efficient MC by using a response surface model, (2) calibrate parameters with an iterative test-analysis correlation based upon DDA, and (3) utilize and compare different distance functions as correlation metrics. Using DDA, the influence of distance functions on model updating results is analyzed. The proposed sto-chastic method makes it possible to obtain a precise model updating outcome with acceptable cal-culation cost. The stochastic method is demonstrated on a helicopter case study updated using both Euclidian and Mahalanobis distance metrics. It is observed that the selected distance function influ-ences the iterative calibration process and thus, the calibration outcome, indicating that an integra-tion of different metrics might yield improved results.

  4. Hybrid methods for cybersecurity analysis :

    Energy Technology Data Exchange (ETDEWEB)

    Davis, Warren Leon,; Dunlavy, Daniel M.

    2014-01-01

    Early 2010 saw a signi cant change in adversarial techniques aimed at network intrusion: a shift from malware delivered via email attachments toward the use of hidden, embedded hyperlinks to initiate sequences of downloads and interactions with web sites and network servers containing malicious software. Enterprise security groups were well poised and experienced in defending the former attacks, but the new types of attacks were larger in number, more challenging to detect, dynamic in nature, and required the development of new technologies and analytic capabilities. The Hybrid LDRD project was aimed at delivering new capabilities in large-scale data modeling and analysis to enterprise security operators and analysts and understanding the challenges of detection and prevention of emerging cybersecurity threats. Leveraging previous LDRD research e orts and capabilities in large-scale relational data analysis, large-scale discrete data analysis and visualization, and streaming data analysis, new modeling and analysis capabilities were quickly brought to bear on the problems in email phishing and spear phishing attacks in the Sandia enterprise security operational groups at the onset of the Hybrid project. As part of this project, a software development and deployment framework was created within the security analyst work ow tool sets to facilitate the delivery and testing of new capabilities as they became available, and machine learning algorithms were developed to address the challenge of dynamic threats. Furthermore, researchers from the Hybrid project were embedded in the security analyst groups for almost a full year, engaged in daily operational activities and routines, creating an atmosphere of trust and collaboration between the researchers and security personnel. The Hybrid project has altered the way that research ideas can be incorporated into the production environments of Sandias enterprise security groups, reducing time to deployment from months and

  5. Symbolic Algorithmic Analysis of Rectangular Hybrid Systems

    Institute of Scientific and Technical Information of China (English)

    Hai-Bin Zhang; Zhen-Hua Duan

    2009-01-01

    This paper investigates symbolic algorithmic analysis of rectangular hybrid systems. To deal with the symbolic reachability problem, a restricted constraint system called hybrid zone is formalized for the representation and manipulation of rectangular automata state-spaces. Hybrid zones are proved to be closed over symbolic reachability operations of rectangular hybrid systems. They are also applied to model-checking procedures for verifying some important classes of timed computation tree logic formulas. To represent hybrid zones, a data structure called difference constraint matrix is defined.These enable us to deal with the symbolic algorithmic analysis of rectangular hybrid systems in an efficient way.

  6. Discrimination

    National Research Council Canada - National Science Library

    Midtbøen, Arnfinn H; Rogstad, Jon

    2012-01-01

    ... of discrimination in the labour market as well as to the mechanisms involved in discriminatory hiring practices. The design has several advantages compared to -‘single-method’ approaches and provides a more substantial understanding of the processes leading to ethnic inequality in the labour market.

  7. PRICE DISCRIMINATION AND MARKET POWER: A THEORETICAL ANALYSIS

    Directory of Open Access Journals (Sweden)

    Olga Smirnova

    2015-07-01

    Full Text Available This paper analyzes the contemporary theoretical and empirical research in the field of impact assessment of market power and conclusions about the possibilities of the company to implement price discrimination in different market structures. The results of the analysis allow to evaluate current approaches to antitrust regulation of price discrimination.

  8. Mismatch discrimination in fluorescent in situ hybridization using different types of nucleic acids

    DEFF Research Database (Denmark)

    Fontenete, Sílvia; Joana, Barros; Pedro, Madureira

    2015-01-01

    in biological targets, Helicobacter pylori and Helicobacter acinonychis. This is also the first study where unlocked nucleic acids (UNA) were used as chemistry modification in oligonucleotides for FISH methodologies. The effectiveness in detecting the specific target and in mismatch discrimination appears...... acid monomers might be crucial to the success of the analysis. To achieve the expected accuracy in detection, FISH probes should have high binding affinity towards their complementary strands and discriminate effectively the noncomplementary strands. In this study, we investigate the effect...... of different chemical modifications in fluorescent probes on their ability to successfully detect the complementary target and discriminate the mismatched base pairs by FISH. To our knowledge, this paper presents the first study where this analysis is performed with different types of FISH probes directly...

  9. Modeling and analysis using hybrid Petri nets

    CERN Document Server

    Ghomri, Latéfa

    2007-01-01

    This paper is devoted to the use of hybrid Petri nets (PNs) for modeling and control of hybrid dynamic systems (HDS). Modeling, analysis and control of HDS attract ever more of researchers' attention and several works have been devoted to these topics. We consider in this paper the extensions of the PN formalism (initially conceived for modeling and analysis of discrete event systems) in the direction of hybrid modeling. We present, first, the continuous PN models. These models are obtained from discrete PNs by the fluidification of the markings. They constitute the first steps in the extension of PNs toward hybrid modeling. Then, we present two hybrid PN models, which differ in the class of HDS they can deal with. The first one is used for deterministic HDS modeling, whereas the second one can deal with HDS with nondeterministic behavior. Keywords: Hybrid dynamic systems; D-elementary hybrid Petri nets; Hybrid automata; Controller synthesis

  10. A Bayesian Predictive Discriminant Analysis with Screened Data

    OpenAIRE

    Hea-Jung Kim

    2015-01-01

    In the application of discriminant analysis, a situation sometimes arises where individual measurements are screened by a multidimensional screening scheme. For this situation, a discriminant analysis with screened populations is considered from a Bayesian viewpoint, and an optimal predictive rule for the analysis is proposed. In order to establish a flexible method to incorporate the prior information of the screening mechanism, we propose a hierarchical screened scale mixture of normal (HSS...

  11. Cloud type discrimination via multispectral textural analysis

    Science.gov (United States)

    Lamei, Niloufar; Crawford, Melba M.; Hutchison, Keith D.; Khazenie, Nahid

    1993-09-01

    One of the primary interests in digital image processing is the development of robust methods to perform feature detection, extraction, and classification. Until recently, classification methods for cloud discrimination were mainly based on the spectral information of the imagery. However, because of the spectral similarities of certain features (such as ice clouds and snow) and the effects of atmospheric attenuation, multi-spectral rule based classifications do not necessarily produce accurate feature discrimination. Spectral homogeneity of two different features within a scene can lead to misclassification. Furthermore, the opposite problem can occur when one feature exhibits different spectral signatures locally but is homogeneous in its cyclic spatial variation. The exploration of spatial information is often advantageous in these discrimination problems. A texture-based method for feature identification has been investigated. This method uses a set of localized spatial filters known as two dimensional Gabor functions. Gabor filters can be described as a sinusoidal plane wave within a two-dimensional Gaussian envelope. The frequency and orientation of the sine plane and the width of the Gaussian envelope are determined by the Gabor parameters. These tunable channels yield joint optimal information both in the spatial and the frequency domains. The new method has been applied to the thermal channels of the NOAA-advanced very high resolution radiometer (AVHRR) data for cloud type discrimination.

  12. Score-moment combined linear discrimination analysis (SMC-LDA) as an improved discrimination method.

    Science.gov (United States)

    Han, Jintae; Chung, Hoeil; Han, Sung-Hwan; Yoon, Moon-Young

    2007-01-01

    A new discrimination method called the score-moment combined linear discrimination analysis (SMC-LDA) has been developed and its performance has been evaluated using three practical spectroscopic datasets. The key concept of SMC-LDA was to use not only the score from principal component analysis (PCA), but also the moment of the spectrum, as inputs for LDA to improve discrimination. Along with conventional score, moment is used in spectroscopic fields as an effective alternative for spectral feature representation. Three different approaches were considered. Initially, the score generated from PCA was projected onto a two-dimensional feature space by maximizing Fisher's criterion function (conventional PCA-LDA). Next, the same procedure was performed using only moment. Finally, both score and moment were utilized simultaneously for LDA. To evaluate discrimination performances, three different spectroscopic datasets were employed: (1) infrared (IR) spectra of normal and malignant stomach tissue, (2) near-infrared (NIR) spectra of diesel and light gas oil (LGO) and (3) Raman spectra of Chinese and Korean ginseng. For each case, the best discrimination results were achieved when both score and moment were used for LDA (SMC-LDA). Since the spectral representation character of moment was different from that of score, inclusion of both score and moment for LDA provided more diversified and descriptive information.

  13. Small visible energy scalar top iterative discriminant analysis

    Indian Academy of Sciences (India)

    A Sopczak; A Finch; A Freitas; C Milsténe; M Schimtt

    2007-12-01

    Light scalar top quarks with a small mass difference with respect to the neutralino mass are of particular cosmological interest. This study uses an iterative discriminant analysis method to optimize the expected selection efficiency at the international linear collider (ILC).

  14. Discrete Discriminant analysis based on tree-structured graphical models

    DEFF Research Database (Denmark)

    Perez de la Cruz, Gonzalo; Eslava, Guillermina

    The purpose of this paper is to illustrate the potential use of discriminant analysis based on tree{structured graphical models for discrete variables. This is done by comparing its empirical performance using estimated error rates for real and simulated data. The results show that discriminant...... analysis based on tree{structured graphical models is a simple nonlinear method competitive with, and sometimes superior to, other well{known linear methods like those assuming mutual independence between variables and linear logistic regression....

  15. Discrete Discriminant analysis based on tree-structured graphical models

    DEFF Research Database (Denmark)

    Perez de la Cruz, Gonzalo; Eslava, Guillermina

    The purpose of this paper is to illustrate the potential use of discriminant analysis based on tree{structured graphical models for discrete variables. This is done by comparing its empirical performance using estimated error rates for real and simulated data. The results show that discriminant a...... analysis based on tree{structured graphical models is a simple nonlinear method competitive with, and sometimes superior to, other well{known linear methods like those assuming mutual independence between variables and linear logistic regression....

  16. On discriminant analysis techniques and correlation structures in high dimensions

    DEFF Research Database (Denmark)

    Clemmensen, Line Katrine Harder

    This paper compares several recently proposed techniques for performing discriminant analysis in high dimensions, and illustrates that the various sparse methods dier in prediction abilities depending on their underlying assumptions about the correlation structures in the data. The techniques...... generally focus on two things: Obtaining sparsity (variable selection) and regularizing the estimate of the within-class covariance matrix. For high-dimensional data, this gives rise to increased interpretability and generalization ability over standard linear discriminant analysis. Here, we group...

  17. Face Recognition Using Double Sparse Local Fisher Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Zhan Wang

    2015-01-01

    Full Text Available Local Fisher discriminant analysis (LFDA was proposed for dealing with the multimodal problem. It not only combines the idea of locality preserving projections (LPP for preserving the local structure of the high-dimensional data but also combines the idea of Fisher discriminant analysis (FDA for obtaining the discriminant power. However, LFDA also suffers from the undersampled problem as well as many dimensionality reduction methods. Meanwhile, the projection matrix is not sparse. In this paper, we propose double sparse local Fisher discriminant analysis (DSLFDA for face recognition. The proposed method firstly constructs a sparse and data-adaptive graph with nonnegative constraint. Then, DSLFDA reformulates the objective function as a regression-type optimization problem. The undersampled problem is avoided naturally and the sparse solution can be obtained by adding the regression-type problem to a l1 penalty. Experiments on Yale, ORL, and CMU PIE face databases are implemented to demonstrate the effectiveness of the proposed method.

  18. EEG based Autism Diagnosis Using Regularized Fisher Linear Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Mahmoud I. Kamel

    2012-04-01

    Full Text Available Diagnosis of autism is one of the difficult problems facing researchers. To reveal the discriminative pattern between autistic and normal children via electroencephalogram (EEG analysis is a big challenge. The feature extraction is averaged Fast Fourier Transform (FFT with the Regulated Fisher Linear Discriminant (RFLD classifier. Gaussinaty condition for the optimality of Regulated Fisher Linear Discriminant (RFLD has been achieved by a well-conditioned appropriate preprocessing of the data, as well as optimal shrinkage technique for the Lambda parameter. Winsorised Filtered Data gave the best result.

  19. Discrimination analysis of mass spectrometry proteomics for ovarian cancer detection

    Institute of Scientific and Technical Information of China (English)

    Yan-jun HONG; Xiao-dan WANG; David SHEN; Su ZENG

    2008-01-01

    Aim:A discrimination analysis has been explored for the probabilistic classifica-tion of healthy versus ovarian cancer serum samples using proteomics data from mass spectrometry (MS).Methods:The method employs data normalization,clustering,and a linear discriminant analysis on surface-enhanced laser desorp-tion ionization (SELDI) time-of-flight MS data.The probabilistic classification method computes the optimal linear discriminant using the complex human blood serum SELDI spectra.Cross-validation and training/testing data-split experi-ments are conducted to verify the optimal discriminant and demonstrate the accu-racy and robustness of the method.Results:The cluster discrimination method achieves excellent performance.The sensitivity,specificity,and positive predic-tive values are above 97% on ovarian cancer.The protein fraction peaks,which significantly contribute to the classification,can be available from the analysis process.Conclusion:The discrimination analysis helps the molecular identities of differentially expressed proteins and peptides between the healthy and ovarian patients.

  20. Kernel-based fisher discriminant analysis for hyperspectral target detection

    Institute of Scientific and Technical Information of China (English)

    GU Yan-feng; ZHANG Ye; YOU Di

    2007-01-01

    A new method based on kernel Fisher discriminant analysis (KFDA) is proposed for target detection of hyperspectral images. The KFDA combines kernel mapping derived from support vector machine and the classical linear Fisher discriminant analysis (LFDA), and it possesses good ability to process nonlinear data such as hyperspectral images. According to the Fisher rule that the ratio of the between-class and within-class scatters is maximized, the KFDA is used to obtain a set of optimal discriminant basis vectors in high dimensional feature space. All pixels in the hyperspectral images are projected onto the discriminant basis vectors and the target detection is performed according to the projection result. The numerical experiments are performed on hyperspectral data with 126 bands collected by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). The experimental results show the effectiveness of the proposed detection method and prove that this method has good ability to overcome small sample size and spectral variability in the hyperspectral target detection.

  1. Application of Discriminant Analysis on Romanian Insurance Market

    Directory of Open Access Journals (Sweden)

    Constantin Anghelache

    2008-11-01

    Full Text Available Discriminant analysis is a supervised learning technique that can be used in order to determine which variables are the best predictors of the classification of objects belonging to a population into predetermined classes. At the same time, discriminant analysis provides a powerful tool that enables researchers to make predictions regarding the classification of new objects into predefined classes. The main goal of discriminant analysis is to determine which of the N descriptive variables have the most discriminatory power, that is, which of them are the most relevant for the classification of objects into classes. In order to classify objects, we need a mathematical model that provides the rules for optimal allocation. This is the classifier. In this paper we will discuss three of the most important models of classification: the Bayesian criterion, the Mahalanobis criterion and the Fisher criterion. In this paper, we will use discriminant analysis to classify the insurance companies that operated on the Romanian market in 2006. We have selected a number of eigth (8 relevant variables: gross written premium (GR_WRI_PRE, net mathematical reserves (NET_M_PES, gross claims paid (GR_CL_PAID, net premium reserves (NET_PRE_RES, net claim reserves (NET_CL_RES, net income (NE—_INCOME, share capital (SHARE_CAP and gross written premium ceded in Reinsurance (GR_WRI_PRE_CED. Before proceeding to discriminant analysis, we performed cluster analysis on the initial data in order to identify classes (clusters that emerge from the data.

  2. Stochastic Reachability Analysis of Hybrid Systems

    CERN Document Server

    Bujorianu, Luminita Manuela

    2012-01-01

    Stochastic reachability analysis (SRA) is a method of analyzing the behavior of control systems which mix discrete and continuous dynamics. For probabilistic discrete systems it has been shown to be a practical verification method but for stochastic hybrid systems it can be rather more. As a verification technique SRA can assess the safety and performance of, for example, autonomous systems, robot and aircraft path planning and multi-agent coordination but it can also be used for the adaptive control of such systems. Stochastic Reachability Analysis of Hybrid Systems is a self-contained and accessible introduction to this novel topic in the analysis and development of stochastic hybrid systems. Beginning with the relevant aspects of Markov models and introducing stochastic hybrid systems, the book then moves on to coverage of reachability analysis for stochastic hybrid systems. Following this build up, the core of the text first formally defines the concept of reachability in the stochastic framework and then...

  3. High-Order Supervised Discriminant Analysis for Visual Data

    Institute of Scientific and Technical Information of China (English)

    Xiao-Ling Xia; Hang-Hui Huang

    2014-01-01

    In practical applications, we often have to deal with high-order data, for example, a grayscale image and a video clip are intrinsically a 2nd-order tensor and a 3rd-order tensor, respectively. In order to satisty these high-order data, it is conventional to vectorize these data in advance, which often destroys the intrinsic structures of the data and includes the curse of dimensionality. For this reason, we consider the problem of high-order data representation and classification, and propose a tensor based fisher discriminant analysis (FDA), which is a generalized version of FDA, named as GFDA. Experimental results show our GFDA outperforms the existing methods, such as the 2-directional 2-dimensional principal component analysis ((2D)2PCA), 2-directional 2-dimensional linear discriminant analysis ((2D)2LDA), and multilinear discriminant analysis (MDA), in high-order data classification under a lower compression ratio.

  4. Discriminant analysis for repeated measures data: a review

    Directory of Open Access Journals (Sweden)

    Lisa Lix

    2010-09-01

    Full Text Available Discriminant analysis (DA encompasses procedures for classifying observations into groups (i.e., predictive discriminative analysis and describing the relative importance of variables for distinguishing amongst groups (i.e., descriptive discriminative analysis. In recent years, a number of developments have occurred in DA procedures for the analysis of data from repeated measures designs. Specifically, DA procedures have been developed for repeated measures data characterized by missing observations and/or unbalanced measurement occasions, as well as high-dimensional data in which measurements are collected repeatedly on two or more variables. This paper reviews the literature on DA procedures for univariate and multivariate repeated measures data, focusing on covariance pattern and linear mixed-effects models. A numeric example illustrates their implementation using SAS software.

  5. Principal Component Clustering Approach to Teaching Quality Discriminant Analysis

    Science.gov (United States)

    Xian, Sidong; Xia, Haibo; Yin, Yubo; Zhai, Zhansheng; Shang, Yan

    2016-01-01

    Teaching quality is the lifeline of the higher education. Many universities have made some effective achievement about evaluating the teaching quality. In this paper, we establish the Students' evaluation of teaching (SET) discriminant analysis model and algorithm based on principal component clustering analysis. Additionally, we classify the SET…

  6. A Bayesian Predictive Discriminant Analysis with Screened Data

    Directory of Open Access Journals (Sweden)

    Hea-Jung Kim

    2015-09-01

    Full Text Available In the application of discriminant analysis, a situation sometimes arises where individual measurements are screened by a multidimensional screening scheme. For this situation, a discriminant analysis with screened populations is considered from a Bayesian viewpoint, and an optimal predictive rule for the analysis is proposed. In order to establish a flexible method to incorporate the prior information of the screening mechanism, we propose a hierarchical screened scale mixture of normal (HSSMN model, which makes provision for flexible modeling of the screened observations. An Markov chain Monte Carlo (MCMC method using the Gibbs sampler and the Metropolis–Hastings algorithm within the Gibbs sampler is used to perform a Bayesian inference on the HSSMN models and to approximate the optimal predictive rule. A simulation study is given to demonstrate the performance of the proposed predictive discrimination procedure.

  7. Facial Affect Recognition Using Regularized Discriminant Analysis-Based Algorithms

    Directory of Open Access Journals (Sweden)

    Cheng-Yuan Shih

    2010-01-01

    Full Text Available This paper presents a novel and effective method for facial expression recognition including happiness, disgust, fear, anger, sadness, surprise, and neutral state. The proposed method utilizes a regularized discriminant analysis-based boosting algorithm (RDAB with effective Gabor features to recognize the facial expressions. Entropy criterion is applied to select the effective Gabor feature which is a subset of informative and nonredundant Gabor features. The proposed RDAB algorithm uses RDA as a learner in the boosting algorithm. The RDA combines strengths of linear discriminant analysis (LDA and quadratic discriminant analysis (QDA. It solves the small sample size and ill-posed problems suffered from QDA and LDA through a regularization technique. Additionally, this study uses the particle swarm optimization (PSO algorithm to estimate optimal parameters in RDA. Experiment results demonstrate that our approach can accurately and robustly recognize facial expressions.

  8. Discrimination of bacteriophage infected cells using locked nucleic acid fluorescent in situ hybridization (LNA-FISH).

    Science.gov (United States)

    Vilas Boas, Diana; Almeida, Carina; Sillankorva, Sanna; Nicolau, Ana; Azeredo, Joana; Azevedo, Nuno F

    2016-01-01

    Bacteriophage-host interaction studies in biofilm structures are still challenging due to the technical limitations of traditional methods. The aim of this study was to provide a direct fluorescence in situ hybridization (FISH) method based on locked nucleic acid (LNA) probes, which targets the phage replication phase, allowing the study of population dynamics during infection. Bacteriophages specific for two biofilm-forming bacteria, Pseudomonas aeruginosa and Acinetobacter, were selected. Four LNA probes were designed and optimized for phage-specific detection and for bacterial counterstaining. To validate the method, LNA-FISH counts were compared with the traditional plaque forming unit (PFU) technique. To visualize the progression of phage infection within a biofilm, colony-biofilms were formed and infected with bacteriophages. A good correlation (r = 0.707) was observed between LNA-FISH and PFU techniques. In biofilm structures, LNA-FISH provided a good discrimination of the infected cells and also allowed the assessment of the spatial distribution of infected and non-infected populations.

  9. Discrimination of clostridium species using a magnetic bead based hybridization assay

    Science.gov (United States)

    Pahlow, Susanne; Seise, Barbara; Pollok, Sibyll; Seyboldt, Christian; Weber, Karina; Popp, Jürgen

    2014-05-01

    Clostridium chauvoei is the causative agent of blackleg, which is an endogenous bacterial infection. Mainly cattle and other ruminants are affected. The symptoms of blackleg are very similar to those of malignant edema, an infection caused by Clostridium septicum. [1, 2] Therefore a reliable differentiation of Clostridium chauvoei from other Clostridium species is required. Traditional microbiological detection methods are time consuming and laborious. Additionally, the unique identification is hindered by the overgrowing tendency of swarming Clostridium septicum colonies when both species are present. [1, 3, 4] Thus, there is a crucial need to improve and simplify the specific detection of Clostridium chauvoei and Clostridium septicum. Here we present an easy and fast Clostridium species discrimination method combining magnetic beads and fluorescence spectroscopy. Functionalized magnetic particles exhibit plentiful advantages, like their simple manipulation in combination with a large binding capacity of biomolecules. A specific region of the pathogenic DNA is amplified and labelled with biotin by polymerase chain reaction (PCR). These PCR products were then immobilized on magnetic beads exploiting the strong biotin-streptavidin interaction. The specific detection of different Clostridium species is achieved by using fluorescence dye labeled probe DNA for the hybridization with the immobilized PCR products. Finally, the samples were investigated by fluorescence spectroscopy. [5

  10. Discrimination and numerical analysis of human pathogenic ...

    African Journals Online (AJOL)

    SERVER

    2008-02-19

    Feb 19, 2008 ... Numerical analysis of whole-cell protein profiles of all strains revealed 2 .... average linkage method and correlation coefficient distance. ... distance yielded a dendrogam, consisting of two basic .... Candida glabrata: review of.

  11. Visual category recognition using Spectral Regression and Kernel Discriminant Analysis

    NARCIS (Netherlands)

    Tahir, M.A.; Kittler, J.; Mikolajczyk, K.; Yan, F.; van de Sande, K.E.A.; Gevers, T.

    2009-01-01

    Visual category recognition (VCR) is one of the most important tasks in image and video indexing. Spectral methods have recently emerged as a powerful tool for dimensionality reduction and manifold learning. Recently, Spectral Regression combined with Kernel Discriminant Analysis (SR-KDA) has been s

  12. A Critical Analysis of Anti-Discrimination Law and Microaggressions in Academia

    Science.gov (United States)

    Lukes, Robin; Bangs, Joann

    2014-01-01

    This article provides a critical analysis of microaggressions and anti-discrimination law in academia. There are many challenges for faculty claiming discrimination under current civil rights laws. Examples of microaggressions that fall outside of anti-discrimination law will be provided. Traditional legal analysis of discrimination will not end…

  13. Kernel-Based Nonlinear Discriminant Analysis for Face Recognition

    Institute of Scientific and Technical Information of China (English)

    LIU QingShan (刘青山); HUANG Rui (黄锐); LU HanQing (卢汉清); MA SongDe (马颂德)

    2003-01-01

    Linear subspace analysis methods have been successfully applied to extract features for face recognition. But they are inadequate to represent the complex and nonlinear variations of real face images, such as illumination, facial expression and pose variations, because of their linear properties. In this paper, a nonlinear subspace analysis method, Kernel-based Nonlinear Discriminant Analysis (KNDA), is presented for face recognition, which combines the nonlinear kernel trick with the linear subspace analysis method - Fisher Linear Discriminant Analysis (FLDA).First, the kernel trick is used to project the input data into an implicit feature space, then FLDA is performed in this feature space. Thus nonlinear discriminant features of the input data are yielded. In addition, in order to reduce the computational complexity, a geometry-based feature vectors selection scheme is adopted. Another similar nonlinear subspace analysis is Kernel-based Principal Component Analysis (KPCA), which combines the kernel trick with linear Principal Component Analysis (PCA). Experiments are performed with the polynomial kernel, and KNDA is compared with KPCA and FLDA. Extensive experimental results show that KNDA can give a higher recognition rate than KPCA and FLDA.

  14. ODVBA: optimally-discriminative voxel-based analysis.

    Science.gov (United States)

    Zhang, Tianhao; Davatzikos, Christos

    2011-08-01

    Gaussian smoothing of images prior to applying voxel-based statistics is an important step in voxel-based analysis and statistical parametric mapping (VBA-SPM) and is used to account for registration errors, to Gaussianize the data and to integrate imaging signals from a region around each voxel. However, it has also become a limitation of VBA-SPM based methods, since it is often chosen empirically and lacks spatial adaptivity to the shape and spatial extent of the region of interest, such as a region of atrophy or functional activity. In this paper, we propose a new framework, named optimally-discriminative voxel-based analysis (ODVBA), for determining the optimal spatially adaptive smoothing of images, followed by applying voxel-based group analysis. In ODVBA, nonnegative discriminative projection is applied regionally to get the direction that best discriminates between two groups, e.g., patients and controls; this direction is equivalent to local filtering by an optimal kernel whose coefficients define the optimally discriminative direction. By considering all the neighborhoods that contain a given voxel, we then compose this information to produce the statistic for each voxel. Finally, permutation tests are used to obtain a statistical parametric map of group differences. ODVBA has been evaluated using simulated data in which the ground truth is known and with data from an Alzheimer's disease (AD) study. The experimental results have shown that the proposed ODVBA can precisely describe the shape and location of structural abnormality.

  15. Insulation Defects Discrimination in GIS by Fisher Discriminant Analysis of Partial Discharge

    Institute of Scientific and Technical Information of China (English)

    DING Dengwei; GAO Wensheng; LIU Weidong

    2013-01-01

    To monitor the condition of online power apparatus accurately and provide appropriate guidance on their maintenance,a fundamental ultra-high frequency (UHF) database of partial discharges corresponding to different types of defects is presented for the observation of insulation state of gas insulated switchgear (GIS).In order to imitate the defects in a GIS online,five types of typical partial discharge (PD) sources (i.e.floating metal,protrusion,bouncing particles,void in spacer,and particle on the surface of solid insulation) were designed and fabricated.A wideband UHF sensor and an amplifier were applied to obtain UHF signals.Based on the characteristics of the five different defects,nine meaningful parameters which were independent of the phase of the applied voltage were extracted from the PD samples and then discussed.In this paper,Fisher discriminant analysis (FDA),a pattern recognition algorithm,was applied for the purpose of classifying the defects.In this way,there was no need to set complicated parameters and kernel function,and the total discrimination accuracy of test samples was 97.6%.It indicates that based on the nine especial parameters,the typical defects in GIS can be identified exactly by means of FDA.Hence the analysis method could be possibly regarded as a universal classification method.

  16. Multi spectral imaging analysis for meat spoilage discrimination

    DEFF Research Database (Denmark)

    Christiansen, Asger Nyman; Carstensen, Jens Michael; Papadopoulou, Olga

    ) was performed in parallel with videometer image snapshots and sensory analysis. Odour and colour characteristics of meat were determined by a test panel and attributed into three pre-characterized quality classes, namely Fresh; Semi Fresh and Spoiled during the days of its shelf life. So far, different...... classification methods: Naive Bayes Classifier as a reference model, Canonical Discriminant Analysis (CDA) and Support Vector Classification (SVC). As the final step, generalization of the models was performed using k-fold validation (k=10). Results showed that image analysis provided good discrimination of meat...... samples regarding the spoilage process as evaluated from sensory as well as from microbiological data. The support vector classification (SVC) model outperformed other models. Specifically, the misclassification error rate (MER), derived from odour characteristics, was 18% for both aerobic and MAP meat...

  17. Forensic discrimination of dyed hair color: II. Multivariate statistical analysis.

    Science.gov (United States)

    Barrett, Julie A; Siegel, Jay A; Goodpaster, John V

    2011-01-01

    This research is intended to assess the ability of UV-visible microspectrophotometry to successfully discriminate the color of dyed hair. Fifty-five red hair dyes were analyzed and evaluated using multivariate statistical techniques including agglomerative hierarchical clustering (AHC), principal component analysis (PCA), and discriminant analysis (DA). The spectra were grouped into three classes, which were visually consistent with different shades of red. A two-dimensional PCA observations plot was constructed, describing 78.6% of the overall variance. The wavelength regions associated with the absorbance of hair and dye were highly correlated. Principal components were selected to represent 95% of the overall variance for analysis with DA. A classification accuracy of 89% was observed for the comprehensive dye set, while external validation using 20 of the dyes resulted in a prediction accuracy of 75%. Significant color loss from successive washing of hair samples was estimated to occur within 3 weeks of dye application.

  18. Multi spectral imaging analysis for meat spoilage discrimination

    DEFF Research Database (Denmark)

    Christiansen, Asger Nyman; Carstensen, Jens Michael; Papadopoulou, Olga

    with corresponding sensory data would be of great interest. The purpose of this research was to produce a method capable of quantifying and/or predicting the spoilage status (e.g. express in TVC counts as well as on sensory evaluation) using a multi spectral image of a meat sample and thereby avoid any time...... classification methods: Naive Bayes Classifier as a reference model, Canonical Discriminant Analysis (CDA) and Support Vector Classification (SVC). As the final step, generalization of the models was performed using k-fold validation (k=10). Results showed that image analysis provided good discrimination of meat...... samples. In the case where all data were taken together the misclassification error amounted to 16%. When spoilage status was based on visual sensory data, the model produced a MER of 22% for the combined dataset. These results suggest that it is feasible to employ a multi spectral image...

  19. Quark/gluon jet discrimination: a reproducible analysis using R

    CERN Document Server

    CERN. Geneva

    2017-01-01

    The power to discriminate between light-quark jets and gluon jets would have a huge impact on many searches for new physics at CERN and beyond. This talk will present a walk-through of the development of a prototype machine learning classifier for differentiating between quark and gluon jets at experiments like those at the Large Hadron Collider at CERN. A new fast feature selection method that combines information theory and graph analytics will be outlined. This method has found new variables that promise significant improvements in discrimination power. The prototype jet tagger is simple, interpretable, parsimonious, and computationally extremely cheap, and therefore might be suitable for use in trigger systems for real-time data processing. Nested stratified k-fold cross validation was used to generate robust estimates of model performance. The data analysis was performed entirely in the R statistical programming language, and is fully reproducible. The entire analysis workflow is data-driven, automated a...

  20. Multiple Kernel Learning in Fisher Discriminant Analysis for Face Recognition

    Directory of Open Access Journals (Sweden)

    Xiao-Zhang Liu

    2013-02-01

    Full Text Available Recent applications and developments based on support vector machines (SVMs have shown that using multiple kernels instead of a single one can enhance classifier performance. However, there are few reports on performance of the kernel‐based Fisher discriminant analysis (kernel‐based FDA method with multiple kernels. This paper proposes a multiple kernel construction method for kernel‐based FDA. The constructed kernel is a linear combination of several base kernels with a constraint on their weights. By maximizing the margin maximization criterion (MMC, we present an iterative scheme for weight optimization. The experiments on the FERET and CMU PIE face databases show that, our multiple kernel Fisher discriminant analysis (MKFD achieves high recognition performance, compared with single‐kernel‐based FDA. The experiments also show that the constructed kernel relaxes parameter selection for kernel‐based FDA to some extent.

  1. Discrimination and content analysis of fritillaria using near infrared spectroscopy.

    Science.gov (United States)

    Meng, Yu; Wang, Shisheng; Cai, Rui; Jiang, Bohai; Zhao, Weijie

    2015-01-01

    Fritillaria is a traditional Chinese herbal medicine which can be used to moisten the lungs. The objective of this study is to develop simple, accurate, and solvent-free methods to discriminate and quantify Fritillaria herbs from seven different origins. Near infrared spectroscopy (NIRS) methods are established for the rapid discrimination of seven different Fritillaria samples and quantitative analysis of their total alkaloids. The scaling to first range method and the partial least square (PLS) method are used for the establishment of qualitative and quantitative analysis models. As a result of evaluation for the qualitative NIR model, the selectivity values between groups are always above 2, and the mistaken judgment rate of fifteen samples in prediction sets was zero. This means that the NIR model can be used to distinguish different species of Fritillaria herbs. The established quantitative NIR model can accurately predict the content of total alkaloids from Fritillaria samples.

  2. BUSINESS FAILURE PREDICTION FOR ROMANIAN SMES USING MULTIVARIATE DISCRIMINANT ANALYSIS

    OpenAIRE

    2012-01-01

    Business failure prediction is one of special importance for small and medium sized enterprises (SMEs) due to their increased vulnerability. Consequently, the purpose of this paper is to investigate the utility of financial ratios and other non-financial variables to predict business failure using a sample of Romanian SMEs and applying multiple discriminant analysis. The process that leads to failure is analyzed on a three year time horizon prior to failure and the results showed that failure...

  3. Multivariable Discriminant Analysis for the Differential Diagnosis of Microcytic Anemia

    Directory of Open Access Journals (Sweden)

    Eloísa Urrechaga

    2013-01-01

    Full Text Available Introduction. Iron deficiency anemia and thalassemia are the most common causes of microcytic anemia. Powerful statistical computer programming enables sensitive discriminant analyses to aid in the diagnosis. We aimed at investigating the performance of the multiple discriminant analysis (MDA to the differential diagnosis of microcytic anemia. Methods. The training group was composed of 200 β-thalassemia carriers, 65 α-thalassemia carriers, 170 iron deficiency anemia (IDA, and 45 mixed cases of thalassemia and acute phase response or iron deficiency. A set of potential predictor parameters that could detect differences among groups were selected: Red Blood Cells (RBC, hemoglobin (Hb, mean cell volume (MCV, mean cell hemoglobin (MCH, and RBC distribution width (RDW. The functions obtained with MDA analysis were applied to a set of 628 consecutive patients with microcytic anemia. Results. For classifying patients into two groups (genetic anemia and acquired anemia, only one function was needed; 87.9% β-thalassemia carriers, and 83.3% α-thalassemia carriers, and 72.1% in the mixed group were correctly classified. Conclusion. Linear discriminant functions based on hemogram data can aid in differentiating between IDA and thalassemia, so samples can be efficiently selected for further analysis to confirm the presence of genetic anemia.

  4. Quantum discriminant analysis for dimensionality reduction and classification

    Science.gov (United States)

    Cong, Iris; Duan, Luming

    2016-07-01

    We present quantum algorithms to efficiently perform discriminant analysis for dimensionality reduction and classification over an exponentially large input data set. Compared with the best-known classical algorithms, the quantum algorithms show an exponential speedup in both the number of training vectors M and the feature space dimension N. We generalize the previous quantum algorithm for solving systems of linear equations (2009 Phys. Rev. Lett. 103 150502) to efficiently implement a Hermitian chain product of k trace-normalized N ×N Hermitian positive-semidefinite matrices with time complexity of O({log}(N)). Using this result, we perform linear as well as nonlinear Fisher discriminant analysis for dimensionality reduction over M vectors, each in an N-dimensional feature space, in time O(p {polylog}({MN})/{ε }3), where ɛ denotes the tolerance error, and p is the number of principal projection directions desired. We also present a quantum discriminant analysis algorithm for data classification with time complexity O({log}({MN})/{ε }3).

  5. Discrimination of Species and Hybrid Detection in Myriophyllum Spp.: an Introduction to Biodiversity Conservation and Invasion Avoidance

    Directory of Open Access Journals (Sweden)

    R Ghahramanzadeh

    2014-03-01

    Full Text Available Minimizing economical loss through introduction of invasive alien species (IAS in local ecosystem is one of the most important issues in biosecurity. The hybridization potential between non-indigenous and native species has raised concerns due mainly to introgression, which can cause extirpation of native species through gene contamination. In the present study, 71 samples belonging to 12 species from Myriophyllum genus were assessed in Plant Breeding group of Wageningen University. Internal transcribed spacer (ITS was used for identification of invasive species from related native and possible hybrid plants. The result showed that based on universal application, high sequence divergence and species discrimination, ITS is a powerful sequence for the identification of invasive species from related non-invasive foreign and native species. In contrast to morphological data, ITS grouped suspected hybrid plants in to M. heterophyllum and demonstrated that they have not resulted from hybridization. These observations suggest that multiple introduction and genetic recombination among different introduced genotypes or genetic pools could be reasons of non-flowering in suspected hybrid plants. Results showed that molecular markers enable to distinguish invasive plant species from their most closely related congeners. This could be helpful with enforcing a ban on important of such invasive which can help to plant ecosystem and biodiversity stability.

  6. Discrimination of inflammatory bowel disease using Raman spectroscopy and linear discriminant analysis methods

    Science.gov (United States)

    Ding, Hao; Cao, Ming; DuPont, Andrew W.; Scott, Larry D.; Guha, Sushovan; Singhal, Shashideep; Younes, Mamoun; Pence, Isaac; Herline, Alan; Schwartz, David; Xu, Hua; Mahadevan-Jansen, Anita; Bi, Xiaohong

    2016-03-01

    Inflammatory bowel disease (IBD) is an idiopathic disease that is typically characterized by chronic inflammation of the gastrointestinal tract. Recently much effort has been devoted to the development of novel diagnostic tools that can assist physicians for fast, accurate, and automated diagnosis of the disease. Previous research based on Raman spectroscopy has shown promising results in differentiating IBD patients from normal screening cases. In the current study, we examined IBD patients in vivo through a colonoscope-coupled Raman system. Optical diagnosis for IBD discrimination was conducted based on full-range spectra using multivariate statistical methods. Further, we incorporated several feature selection methods in machine learning into the classification model. The diagnostic performance for disease differentiation was significantly improved after feature selection. Our results showed that improved IBD diagnosis can be achieved using Raman spectroscopy in combination with multivariate analysis and feature selection.

  7. Discriminant analysis of bronchial asthma by linear discriminant function with parameters of flow-volumes: discriminant analysis of bronchial asthma in young male non-smokers

    Directory of Open Access Journals (Sweden)

    Meguro,Tadamichi

    1978-10-01

    Full Text Available With the parameters of a flow-volume and a volume-time curve, the discriminant analysis of bronchial asthma is described. The subjects were classified into three groups (healthy adults, mild asthmatic patients and moderates ones. The difference of the mean vectors of the parameters of the three groups was made clear by the selection methods of the discriminant analysis between any two of the groups both with 6 parameters (%FVC, FEV1.0%, peak flow rate (PF, flow rate at 50% of FVC (V50, flow rate at 25% of FVC (V25, and V50/V25 and with 8 (6 parameters mentioned above and V75, V10. Forced expiratory volume in 1 second percent (FEV1.0% or V50 was selected at the first step with 6 parameters, and V75 was selected at the first step with 8 parameters. Probabilities of misclassification with 8 parameters were lower than those with 6 ones and the probability of misclassification at the discriminant analysis between healthy adults and mild asthmatic patients with 8 parameters was 15.75% at the final step.

  8. Use of discriminant analysis to identify propensity for purchasing properties

    Directory of Open Access Journals (Sweden)

    Ricardo Floriani

    2015-03-01

    Full Text Available Properties usually represent a milestone for people and families due to the high added-value when compared with family income. The objective of this study is the proposition of a discrimination model, by a discriminant analysis of people with characteristics (according to independent variables classified as potential buyers of properties, as well as to identify the interest in the use of such property, if it will be assigned to housing or leisure activities such as a cottage or beach house, and/or for investment. Thus, the following research question is proposed: What are the characteristics that better describe the profile of people which intend to acquire properties? The study justifies itself by its economic relevance in the real estate industry, as well as to the players of the real estate Market that may develop products based on the profile of potential customers. As a statistical technique, discriminant analysis was applied to the data gathered by questionnaire, which was sent via e-mail. Three hundred and thirty four responses were gathered. Based on this study, it was observed that it is possible to identify the intention for acquired properties, as well the purpose for acquiring it, for housing or investments.

  9. Discriminating dysplasia: Optical tomographic texture analysis of colorectal polyps.

    Science.gov (United States)

    Li, Wenqi; Coats, Maria; Zhang, Jianguo; McKenna, Stephen J

    2015-12-01

    Optical projection tomography enables 3-D imaging of colorectal polyps at resolutions of 5-10 µm. This paper investigates the ability of image analysis based on 3-D texture features to discriminate diagnostic levels of dysplastic change from such images, specifically, low-grade dysplasia, high-grade dysplasia and invasive cancer. We build a patch-based recognition system and evaluate both multi-class classification and ordinal regression formulations on a 90 polyp dataset. 3-D texture representations computed with a hand-crafted feature extractor, random projection, and unsupervised image filter learning are compared using a bag-of-words framework. We measure performance in terms of error rates, F-measures, and ROC surfaces. Results demonstrate that randomly projected features are effective. Discrimination was improved by carefully manipulating various important aspects of the system, including class balancing, output calibration and approximation of non-linear kernels.

  10. A Direct Estimation Approach to Sparse Linear Discriminant Analysis

    CERN Document Server

    Cai, Tony

    2011-01-01

    This paper considers sparse linear discriminant analysis of high-dimensional data. In contrast to the existing methods which are based on separate estimation of the precision matrix $\\O$ and the difference $\\de$ of the mean vectors, we introduce a simple and effective classifier by estimating the product $\\O\\de$ directly through constrained $\\ell_1$ minimization. The estimator can be implemented efficiently using linear programming and the resulting classifier is called the linear programming discriminant (LPD) rule. The LPD rule is shown to have desirable theoretical and numerical properties. It exploits the approximate sparsity of $\\O\\de$ and as a consequence allows cases where it can still perform well even when $\\O$ and/or $\\de$ cannot be estimated consistently. Asymptotic properties of the LPD rule are investigated and consistency and rate of convergence results are given. The LPD classifier has superior finite sample performance and significant computational advantages over the existing methods that req...

  11. Sex determination by discriminant function analysis of lumbar vertebrae.

    Science.gov (United States)

    Ostrofsky, Kelly R; Churchill, Steven E

    2015-01-01

    Sex determination is critical for developing the biological profile of unidentified skeletal remains. When more commonly used elements (os coxa, cranium) for sexing are not available, methods utilizing other skeletal elements are needed. This study aims to assess the degree of sexual dimorphism of the lumbar vertebrae and develop discriminant functions for sex determination from them, using a sample of South African blacks from the Raymond A. Dart Collection (47 males, 51 females). Eleven variables at each lumbar level were subjected to univariate and multivariate discriminant function analyses. Univariate equations produced classification rates ranging from 57.7% to 83.5%, with the highest accuracies associated with dimensions of the vertebral body. Multivariate stepwise analysis generated classification rates ranging from 75.9% to 88.7%. These results are comparable to other methods for sexing the skeleton and indicate that measures of the lumbar vertebrae can be used as an effective tool for sex determination.

  12. Analysis and design of hybrid control systems

    Energy Technology Data Exchange (ETDEWEB)

    Malmborg, J.

    1998-05-01

    Different aspects of hybrid control systems are treated: analysis, simulation, design and implementation. A systematic methodology using extended Lyapunov theory for design of hybrid systems is developed. The methodology is based on conventional control designs in separate regions together with a switching strategy. Dynamics are not well defined if the control design methods lead to fast mode switching. The dynamics depend on the salient features of the implementation of the mode switches. A theorem for the stability of second order switching together with the resulting dynamics is derived. The dynamics on an intersection of two sliding sets are defined for two relays working on different time scales. The current simulation packages have problems modeling and simulating hybrid systems. It is shown how fast mode switches can be found before or during simulation. The necessary analysis work is a very small overhead for a modern simulation tool. To get some experience from practical problems with hybrid control the switching strategy is implemented in two different software environments. In one of them a time-optimal controller is added to an existing PID controller on a commercial control system. Successful experiments with this hybrid controller shows the practical use of the method 78 refs, 51 figs, 2 tabs

  13. Sparse discriminant analysis for breast cancer biomarker identification and classification

    Institute of Scientific and Technical Information of China (English)

    Yu Shi; Daoqing Dai; Chaochun Liu; Hong Yan

    2009-01-01

    Biomarker identification and cancer classification are two important procedures in microarray data analysis. We propose a novel uni-fied method to carry out both tasks. We first preselect biomarker candidates by eliminating unrelated genes through the BSS/WSS ratio filter to reduce computational cost, and then use a sparse discriminant analysis method for simultaneous biomarker identification and cancer classification. Moreover, we give a mathematical justification about automatic biomarker identification. Experimental results show that the proposed method can identify key genes that have been verified in biochemical or biomedical research and classify the breast cancer type correctly.

  14. Visual Tracking via Feature Tensor Multimanifold Discriminate Analysis

    Directory of Open Access Journals (Sweden)

    Ting-quan Deng

    2014-01-01

    Full Text Available In the visual tracking scenarios, if there are multiple objects, due to the interference of similar objects, tracking may fail in the progress of occlusion to separation. To address this problem, this paper proposed a visual tracking algorithm with discrimination through multimanifold learning. Color-gradient-based feature tensor was used to describe object appearance for accommodation of partial occlusion. A prior multimanifold tensor dataset is established through the template matching tracking algorithm. For the purpose of discrimination, tensor distance was defined to determine the intramanifold and intermanifold neighborhood relationship in multimanifold space. Then multimanifold discriminate analysis was employed to construct multilinear projection matrices of submanifolds. Finally, object states were obtained by combining with sequence inference. Meanwhile, the multimanifold dataset and manifold learning embedded projection should be updated online. Experiments were conducted on two real visual surveillance sequences to evaluate the proposed algorithm with three state-of-the-art tracking methods qualitatively and quantitatively. Experimental results show that the proposed algorithm can achieve effective and robust effect in multi-similar-object mutual occlusion scenarios.

  15. Discrimination in Macedonian companies: Case studies research and analysis of current discrimination grounds, forms and trends

    OpenAIRE

    Ananiev, Jovan; Poposka, Zaneta

    2013-01-01

    Discrimination which is evident in in companies in the Republic of Macedonia is mostly done by the owners or management and on the ground of personal status, gender, ethnicity and age and mostly in the form of harassment and direct discrimination.

  16. Isokinetic evaluation of knee muscles in soccer players: discriminant analysis

    Directory of Open Access Journals (Sweden)

    Bruno Fles Mazuquin

    2015-10-01

    Full Text Available ABSTRACTIntroduction:Muscle activity in soccer players can be measured by isokinetic dynamometer, which is a reliable tool for assessing human performance.Objectives:To perform isokinetic analyses and to determine which variables differentiate the under-17 (U17 soccer category from the professional (PRO.Methods:Thirty four players were assessed (n=17 for each category. The isokinetic variables used for the knee extension-flexion analysis were: peak torque (Nm, total work (J, average power (W, angle of peak torque (deg., agonist/ antagonist ratio (%, measured for three velocities (60°/s, 120°/s and 300°/s, with each series containing five repetitions. Three Wilks' Lambda discriminant analyses were performed, to identify which variables were more significant for the definition of each of the categories.Results:The discriminative variables at 60°/s in the PRO category were: extension peak torque, flexion total work, extension average power and agonist/antagonist ratio; and for the U17s were: extension total work, flexion peak torque and flexion average power. At 120°/s for the PRO category the discriminant variables were: flexion peak torque and extension average power; for the U17s they were: extension total work and flexion average power. Finally at 300°/s, the variables found in the PRO and U17 categories respectively were: extension average power and extension total work.Conclusion:Isokinetic variables for flexion and extension knee muscles were able to significantly discriminate between PRO and U17 soccer players.

  17. A Comparative Study: Globality versus Locality for Graph Construction in Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Bo Yang

    2014-01-01

    Discriminant Analysis (GmGcDA just based on globality alone, GmLcDA, and LmGcDA, we suggest that the joint of locally constructed intraclass and globally constructed interclass graphs is more discriminant.

  18. Discrimination of healthy and carious teeth using laser-induced breakdown spectroscopy and partial least square discriminant analysis.

    Science.gov (United States)

    Gazmeh, Meisam; Bahreini, Maryam; Tavassoli, Seyed Hassan

    2015-01-01

    In the laser drilling of teeth, a microplasma is generated which may be utilized for elemental analysis of ablated tissue via a laser-induced breakdown spectroscopy (LIBS) technique. In this study, LIBS is used to investigate the possibility of discrimination of healthy and carious tooth tissues. This possibility is examined using multivariate statistical analysis called partial least square discriminant analysis (PLS-DA) based on atomic and ionic emission lines of teeth LIBS spectra belonging to P, Ca, Mg, Zn, K, Sr, C, Na, H, and O elements. Results show an excellent discrimination and prediction of unknown tooth tissues. It is shown that using the PLS-DA method, the spectroscopic analysis of plasma emission during the laser drilling, would be a promising technique for caries detection.

  19. Discrimination of Chinese teas with different fermentation degrees by stepwise linear discriminant analysis (S-LDA) of the chemical compounds.

    Science.gov (United States)

    Wu, Quan-Jin; Dong, Qing-Hua; Sun, Wei-Jiang; Huang, Yan; Wang, Qiong-Qiong; Zhou, Wei-Long

    2014-09-24

    This study aimed to construct objective and accurate analytical models of tea categories based on their polyphenols and caffeine. A total of 522 tea samples of 4 commonly consumed teas with different fermentation degrees (green tea, white tea, oolong tea, and black tea) were analyzed by high-performance liquid chromatography (HPLC) coupled with spectrophotometry, utilizing ISO 14502, as analytical tools. The content of polyphenols and caffeine varied significantly according to differently fermented teas, indicating that these active constituents may discriminate fermentation degrees effectively. By principal component analysis (PCA) and stepwise linear discriminant analysis (S-LDA), the vast majority of tea samples could be successfully differentiated according to their chemical markers. This study yielded three discriminant functions with the capacity to simultaneously discriminate the four tea categories with a 97.8% correct rate. In classification of oolong and other teas, there were one discriminant function and two equations with best discriminant capacity. Furthermore, the classification of different degrees of fermentation of oolong and external validation achieved the desired results. It is suggested that polyphenols and caffeine are the distinct variables to establish internationally recognized models of teas.

  20. Discrimination of cultivation ages and cultivars of ginseng leaves using Fourier transform infrared spectroscopy combined with multivariate analysis

    Science.gov (United States)

    Kwon, Yong-Kook; Ahn, Myung Suk; Park, Jong Suk; Liu, Jang Ryol; In, Dong Su; Min, Byung Whan; Kim, Suk Weon

    2013-01-01

    To determine whether Fourier transform (FT)-IR spectral analysis combined with multivariate analysis of whole-cell extracts from ginseng leaves can be applied as a high-throughput discrimination system of cultivation ages and cultivars, a total of total 480 leaf samples belonging to 12 categories corresponding to four different cultivars (Yunpung, Kumpung, Chunpung, and an open-pollinated variety) and three different cultivation ages (1 yr, 2 yr, and 3 yr) were subjected to FT-IR. The spectral data were analyzed by principal component analysis and partial least squares-discriminant analysis. A dendrogram based on hierarchical clustering analysis of the FT-IR spectral data on ginseng leaves showed that leaf samples were initially segregated into three groups in a cultivation age-dependent manner. Then, within the same cultivation age group, leaf samples were clustered into four subgroups in a cultivar-dependent manner. The overall prediction accuracy for discrimination of cultivars and cultivation ages was 94.8% in a cross-validation test. These results clearly show that the FT-IR spectra combined with multivariate analysis from ginseng leaves can be applied as an alternative tool for discriminating of ginseng cultivars and cultivation ages. Therefore, we suggest that this result could be used as a rapid and reliable F1 hybrid seed-screening tool for accelerating the conventional breeding of ginseng. PMID:24558311

  1. Bundled Hybrid Offset Riser Global Strength Analysis

    Institute of Scientific and Technical Information of China (English)

    William C.Webster; Zhuang Kang; Wenzhou Liang; Youwei Kang; Liping Sun

    2011-01-01

    Bundled hybrid offset riser(BHOR)global strength analysis,which is more complex than single line offset riser global strength analysis,was carried out in this paper.At first,the equivalent theory is used to deal with BHOR,and then its global strength in manifold cases was analyzed,along with the use of a three-dimensional nonlinear time domain finite element program.So the max bending stress,max circumferential stress,and max axial stress in the BHOR bundle main section(BMS)were obtained,and the values of these three stresses in each riser were obtained through the "stress distribution method".Finally,the Max Von Mises stress in each riser was given and a check was made whether or not they met the demand.This paper provides a reference for strength analysis of the bundled hybrid offset riser and some other bundled pipelines.

  2. Fluorometric Discrimination Technique of Phytoplankton Population Based on Wavelet Analysis

    Institute of Scientific and Technical Information of China (English)

    ZHANG Shanshan; SU Rongguo; DUAN Yali; ZHANG Cui; SONG Zhijie; WANG Xiulin

    2012-01-01

    The discrete excitation-emission-matrix fluorescence spectra(EEMS)at 12 excitation wavelengths (400,430,450,460,470,490,500,510,525,550,570,and 590 nm)and emission wavelengths ranging from 600-750 nm were determined for 43 phytoplankton species.A two-rank fluorescence spectra database was established by wavelet analysis and a fluorometric discrimination technique for determining phytoplankton population was developed.For laboratory simulatively mixed samples,the samples mixed from 43 algal species(the algae of one division accounted for 25%,50%,75%,85%,and 100% of the gross biomass,respectively),the average discrimination rates at the level of division were 65.0%,87.5%,98.6%,99.0%,and 99.1%,with average relative contents of 18.9%,44.5%,68.9%,73.4%,and 82.9%,respectively;the samples mixed from 32 red tide algal species(the dominant species accounted for 60%,70%,80%,90%,and 100% of the gross biomass,respectively),the average correct discrimination rates of the dominant species at the level of genus were 63.3%,74.2%,78.8%,83.4%,and 79.4%,respectively.For the 81 laboratory mixed samples with the dominant species accounting for 75% of the gross biomass(chlorophyll),the discrimination rates of the dominant species were 95.1% and 72.8% at the level of division and genus,respectively.For the 12 samples collected from the mesocosm experiment in Maidao Bay of Qingdao in August 2007,the dominant species of the 11 samples were recognized at the division level and the dominant species of four of the five samples in which the dominant species accounted for more than 80% of the gross biomass were discriminated at the genus level;for the 12 samples obtained from Jiaozhou Bay in August 2007,the dominant species of all the 12 samples were recognized at the division level.The technique can be directly applied to fluorescence spectrophotometers and to the developing of an in situ algae fluorescence auto-analyzer for

  3. Analysis of hybrid viscous damper by real time hybrid simulations

    DEFF Research Database (Denmark)

    Brodersen, Mark Laier; Ou, Ge; Høgsberg, Jan Becker

    2016-01-01

    Results from real time hybrid simulations are compared to full numerical simulations for a hybrid viscous damper, composed of a viscous dashpot in series with an active actuator and a load cell. By controlling the actuator displacement via filtered integral force feedback the damping performance...... of the hybrid viscous damper is improved, while for pure integral force feedback the damper stroke is instead increased. In the real time hybrid simulations viscous damping is emulated by a bang-bang controlled Magneto-Rheological (MR) damper. The controller activates high-frequency modes and generates drift...... in the actuator displacement, and only a fraction of the measured damper force can therefore be used as input to the investigated integral force feedback in the real time hybrid simulations....

  4. Analysis of fuel cell hybrid locomotives

    Science.gov (United States)

    Miller, Arnold R.; Peters, John; Smith, Brian E.; Velev, Omourtag A.

    Led by Vehicle Projects LLC, an international industry-government consortium is developing a 109 t, 1.2 MW road-switcher locomotive for commercial and military railway applications. As part of the feasibility and conceptual-design analysis, a study has been made of the potential benefits of a hybrid power plant in which fuel cells comprise the prime mover and a battery or flywheel provides auxiliary power. The potential benefits of a hybrid power plant are: (i) enhancement of transient power and hence tractive effort; (ii) regenerative braking; (iii) reduction of capital cost. Generally, the tractive effort of a locomotive at low speed is limited by wheel adhesion and not by available power. Enhanced transient power is therefore unlikely to benefit a switcher locomotive, but could assist applications that require high acceleration, e.g. subway trains with all axles powered. In most cases, the value of regeneration in locomotives is minimal. For low-speed applications such as switchers, the available kinetic energy and the effectiveness of traction motors as generators are both minimal. For high-speed heavy applications such as freight, the ability of the auxiliary power device to absorb a significant portion of the available kinetic energy is low. Moreover, the hybrid power plant suffers a double efficiency penalty, namely, losses occur in both absorbing and then releasing energy from the auxiliary device, which result in a net storage efficiency of no more than 50% for present battery technology. Capital cost in some applications may be reduced. Based on an observed locomotive duty cycle, a cost model shows that a hybrid power plant for a switcher may indeed reduce capital cost. Offsetting this potential benefit are the increased complexity, weight and volume of the power plant, as well as 20-40% increased fuel consumption that results from lower efficiency. Based on this analysis, the consortium has decided to develop a pure fuel cell road-switcher locomotive

  5. Analysis of hybrid solar systems

    Science.gov (United States)

    Swisher, J.

    1980-10-01

    The TRNSYS simulation program was used to evaluate the performance of active charge/passive discharge solar systems with water as the working fluid. TRNSYS simulations are used to evaluate the heating performance and cooling augmentation provided by systems in several climates. The results of the simulations are used to develop a simplified analysis tool similar to the F-chart and Phi-bar procedures used for active systems. This tool, currently in a preliminary stage, should provide the designer with quantitative performance estimates for comparison with other passive, active, and nonsolar heating and cooling designs.

  6. Probabilistic Analysis Methods for Hybrid Ventilation

    DEFF Research Database (Denmark)

    Brohus, Henrik; Frier, Christian; Heiselberg, Per

    This paper discusses a general approach for the application of probabilistic analysis methods in the design of ventilation systems. The aims and scope of probabilistic versus deterministic methods are addressed with special emphasis on hybrid ventilation systems. A preliminary application of stoc...... of stochastic differential equations is presented comprising a general heat balance for an arbitrary number of loads and zones in a building to determine the thermal behaviour under random conditions....

  7. The intersectionality of discrimination attributes and bullying among youth: an applied latent class analysis.

    Science.gov (United States)

    Garnett, Bernice Raveche; Masyn, Katherine E; Austin, S Bryn; Miller, Matthew; Williams, David R; Viswanath, Kasisomayajula

    2014-08-01

    Discrimination is commonly experienced among adolescents. However, little is known about the intersection of multiple attributes of discrimination and bullying. We used a latent class analysis (LCA) to illustrate the intersections of discrimination attributes and bullying, and to assess the associations of LCA membership to depressive symptoms, deliberate self harm and suicidal ideation among a sample of ethnically diverse adolescents. The data come from the 2006 Boston Youth Survey where students were asked whether they had experienced discrimination based on four attributes: race/ethnicity, immigration status, perceived sexual orientation and weight. They were also asked whether they had been bullied or assaulted for these attributes. A total of 965 (78%) students contributed to the LCA analytic sample (45% Non-Hispanic Black, 29% Hispanic, 58% Female). The LCA revealed that a 4-class solution had adequate relative and absolute fit. The 4-classes were characterized as: low discrimination (51%); racial discrimination (33%); sexual orientation discrimination (7%); racial and weight discrimination with high bullying (intersectional class) (7%). In multivariate models, compared to the low discrimination class, individuals in the sexual orientation discrimination class and the intersectional class had higher odds of engaging in deliberate self-harm. Students in the intersectional class also had higher odds of suicidal ideation. All three discrimination latent classes had significantly higher depressive symptoms compared to the low discrimination class. Multiple attributes of discrimination and bullying co-occur among adolescents. Research should consider the co-occurrence of bullying and discrimination.

  8. Discrimination against Latina/os: A Meta-Analysis of Individual-Level Resources and Outcomes

    Science.gov (United States)

    Lee, Debbiesiu L.; Ahn, Soyeon

    2012-01-01

    This meta-analysis synthesizes the findings of 60 independent samples from 51 studies examining racial/ethnic discrimination against Latina/os in the United States. The purpose was to identify individual-level resources and outcomes that most strongly relate to discrimination. Discrimination against Latina/os significantly results in outcomes…

  9. Discrimination against Latina/os: A Meta-Analysis of Individual-Level Resources and Outcomes

    Science.gov (United States)

    Lee, Debbiesiu L.; Ahn, Soyeon

    2012-01-01

    This meta-analysis synthesizes the findings of 60 independent samples from 51 studies examining racial/ethnic discrimination against Latina/os in the United States. The purpose was to identify individual-level resources and outcomes that most strongly relate to discrimination. Discrimination against Latina/os significantly results in outcomes…

  10. Discriminant possibilities of the Hamilton depression scale: ROC analysis

    Directory of Open Access Journals (Sweden)

    Novović Zdenka

    2005-01-01

    Full Text Available The purpose of this study is to compare discrimination power of original and reconstructed version of Hamilton’s depression scale in separation of depressive vs. anxious patients and to suggest some possibilities which offer ROC analysis. The subjects of the study were 119 patients of Psychiatric clinic in Novi Sad. 67 of them were diagnosed with some of the forms of affective disorders and 52 with an anxious-phobic diagnosis. Results of ROC analysis suggest that both instruments can be used in distinguishing depressive from anxious patients, but reconstructed version shows greater sensitivity and specificity with optimal cut-off score. It also has more significant AUC, which refers to probability of prediction on the basis of the whole spectrum of the results. These data is commented in relation with current debates, between unitaristic and pluralistic oriented authors, about the nature of the anxious-depression relationship.

  11. Genetic molecular analysis of Coffea arabica (Rubiaceae hybrids using SRAP markers

    Directory of Open Access Journals (Sweden)

    Manoj Kumar Mishra

    2011-06-01

    Full Text Available In Coffea arabica (arabica coffee, the phenotypic as well as genetic variability has been found low because of the narrow genetic basis and self fertile nature of the species. Because of high similarity in phenotypic appearance among the majority of arabica collections, selection of parental lines for inter-varietals hybridization and identification of resultant hybrids at an early stage of plant growth is difficult. DNA markers are known to be reliable in identifying closely related cultivars and hybrids. Sequence Related Amplified Polymorphism (SRAP is a new molecular marker technology developed based on PCR. In this paper, sixty arabica-hybrid progenies belonging to six crosses were analyzed using 31 highly polymorphic SRAP markers. The analysis revealed seven types of SRAP marker profiles which are useful in discriminating the parents and hybrids. The number of bands amplified per primer pair ranges from 6.13 to 8.58 with average number of seven bands. Among six hybrid combinations, percentage of bands shared between hybrids and their parents ranged from 66.29% to 85.71% with polymorphic bands varied from 27.64% to 60.0%. Percentage of hybrid specific fragments obtained in various hybrid combinations ranged from 0.71% to 10.86% and ascribed to the consequence of meiotic recombination. Based on the similarity index calculation, it was observed that F1 hybrids share maximum number of bands with the female parent compared to male parent. The results obtained in the present study revealed the effectiveness of SRAP technique in cultivar identification and hybrid analysis in this coffee species. Rev. Biol. Trop. 59 (2: 607-617. Epub 2011 June 01.

  12. SEM-EDS analysis and discrimination of forensic soil.

    Science.gov (United States)

    Cengiz, Salih; Cengiz Karaca, Ali; Cakir, Ismail; Bülent Uner, H; Sevindik, Aytekin

    2004-04-20

    Soils vary among different areas, and have some characteristics because of the natural effects and transfers made by human and other living beings in time. So that forensic examination of soil is not only concerned with the analysis of naturally occurring rocks, minerals, vegetation, and animal matter. It also includes the detection of such manufactured materials such as ions from synthetic fertilizers and from different environments (e.g., nitrate, phosphate, and sulfate) as environmental artifacts (e.g., lead or objects as glass, paint chips, asphalt, brick fragments, and cinders) whose presence may impart soil with characteristics that will make it unique to a particular location. Many screening and analytical methods have been applied for determining the characteristics which differentiate and discriminate the forensic soil samples but none of them easily standardized. Some of the methods that applied in forensic laboratories in forensic soil discrimination are the color comparison of the normal air-dried (dehumidified) and overheated soil samples, macroscopic observation, and low-power stereo-microscopic observation, determination of anionic composition by capillary electrophoresis (CE), and the elemental composition by scanning electron microscope (SEM)-energy dispersive X-ray spectrometer (EDS) and other high sensitivity techniques. The objective of this study was to show the effect of the application of 9 tonnes/cm2 pressure on the elemental compositions obtained by SEM-EDS technique and comparing the discrimination power of the pressed-homogenized and not homogenized forensic soil samples. For this purpose soil samples from 17 different locations of Istanbul were collected. Aliquots of the well mixed samples were dried in an oven at 110-120 degrees C and sieved by using 0.5 mm sieve and then the undersieve fraction(JEO-JSM-5600 equipped with an energy dispersive X-ray spectrometer OXFORD Link-ISIS-300. The samples from top of the sieves were examined with

  13. Semi-supervised learning for ordinal Kernel Discriminant Analysis.

    Science.gov (United States)

    Pérez-Ortiz, M; Gutiérrez, P A; Carbonero-Ruz, M; Hervás-Martínez, C

    2016-12-01

    Ordinal classification considers those classification problems where the labels of the variable to predict follow a given order. Naturally, labelled data is scarce or difficult to obtain in this type of problems because, in many cases, ordinal labels are given by a user or expert (e.g. in recommendation systems). Firstly, this paper develops a new strategy for ordinal classification where both labelled and unlabelled data are used in the model construction step (a scheme which is referred to as semi-supervised learning). More specifically, the ordinal version of kernel discriminant learning is extended for this setting considering the neighbourhood information of unlabelled data, which is proposed to be computed in the feature space induced by the kernel function. Secondly, a new method for semi-supervised kernel learning is devised in the context of ordinal classification, which is combined with our developed classification strategy to optimise the kernel parameters. The experiments conducted compare 6 different approaches for semi-supervised learning in the context of ordinal classification in a battery of 30 datasets, showing (1) the good synergy of the ordinal version of discriminant analysis and the use of unlabelled data and (2) the advantage of computing distances in the feature space induced by the kernel function.

  14. Multitask linear discriminant analysis for view invariant action recognition.

    Science.gov (United States)

    Yan, Yan; Ricci, Elisa; Subramanian, Ramanathan; Liu, Gaowen; Sebe, Nicu

    2014-12-01

    Robust action recognition under viewpoint changes has received considerable attention recently. To this end, self-similarity matrices (SSMs) have been found to be effective view-invariant action descriptors. To enhance the performance of SSM-based methods, we propose multitask linear discriminant analysis (LDA), a novel multitask learning framework for multiview action recognition that allows for the sharing of discriminative SSM features among different views (i.e., tasks). Inspired by the mathematical connection between multivariate linear regression and LDA, we model multitask multiclass LDA as a single optimization problem by choosing an appropriate class indicator matrix. In particular, we propose two variants of graph-guided multitask LDA: 1) where the graph weights specifying view dependencies are fixed a priori and 2) where graph weights are flexibly learnt from the training data. We evaluate the proposed methods extensively on multiview RGB and RGBD video data sets, and experimental results confirm that the proposed approaches compare favorably with the state-of-the-art.

  15. Discriminative Ocular Artifact Correction for Feature Learning in EEG Analysis.

    Science.gov (United States)

    Li, Xinyang; Guan, Cuntai; Zhang, Haihong; Ang, Kai Keng

    2016-11-16

    Electrooculogram (EOG) artifact contamination is a common critical issue in general electroencephalogram (EEG) studies as well as in brain computer interface (BCI) research. It is especially challenging when dedicated EOG channels are unavailable or when there are very few EEG channels available for ICA-based ocular artifact removal. It is even more challenging to avoid loss of the signal of interest during the artifact correction process, where the signal of interest can be multiple magnitudes weaker than the artifact. To address these issues, we propose a novel discriminative ocular artifact correction approach for feature learning in EEG analysis.Without extra ocular movement measurements, the artifact is extracted from raw EEG data, which is totally automatic and requires no visual inspection of artifacts. Then, artifact correction is optimized jointly with feature extraction by maximizing oscillatory correlations between trials from the same class and minimizing them between trials from different classes. We evaluate this approach on a real world EEG data set comprising 68 subjects performing cognitive tasks. The results showed that the approach is capable of not only suppressing the artifact components but also improving the discriminative power of a classifier with statistical significance. We also demonstrate that the proposed method addresses the confounding issues induced by ocular movements in cognitive EEG study.

  16. Linear discriminant analysis of character sequences using occurrences of words

    KAUST Repository

    Dutta, Subhajit

    2014-02-01

    Classification of character sequences, where the characters come from a finite set, arises in disciplines such as molecular biology and computer science. For discriminant analysis of such character sequences, the Bayes classifier based on Markov models turns out to have class boundaries defined by linear functions of occurrences of words in the sequences. It is shown that for such classifiers based on Markov models with unknown orders, if the orders are estimated from the data using cross-validation, the resulting classifier has Bayes risk consistency under suitable conditions. Even when Markov models are not valid for the data, we develop methods for constructing classifiers based on linear functions of occurrences of words, where the word length is chosen by cross-validation. Such linear classifiers are constructed using ideas of support vector machines, regression depth, and distance weighted discrimination. We show that classifiers with linear class boundaries have certain optimal properties in terms of their asymptotic misclassification probabilities. The performance of these classifiers is demonstrated in various simulated and benchmark data sets.

  17. Anti-discrimination Analysis Using Privacy Attack Strategies

    KAUST Repository

    Ruggieri, Salvatore

    2014-09-15

    Social discrimination discovery from data is an important task to identify illegal and unethical discriminatory patterns towards protected-by-law groups, e.g., ethnic minorities. We deploy privacy attack strategies as tools for discrimination discovery under hard assumptions which have rarely tackled in the literature: indirect discrimination discovery, privacy-aware discrimination discovery, and discrimination data recovery. The intuition comes from the intriguing parallel between the role of the anti-discrimination authority in the three scenarios above and the role of an attacker in private data publishing. We design strategies and algorithms inspired/based on Frèchet bounds attacks, attribute inference attacks, and minimality attacks to the purpose of unveiling hidden discriminatory practices. Experimental results show that they can be effective tools in the hands of anti-discrimination authorities.

  18. Sustainable process design & analysis of hybrid separations

    DEFF Research Database (Denmark)

    Kumar Tula, Anjan; Befort, Bridgette; Garg, Nipun

    2016-01-01

    Distillation is an energy intensive operation in chemical process industries. There are around 40,000 distillation columns in operation in the US, requiring approximately 40% of the total energy consumption in US chemical process industries. However, analysis of separations by distillation has...... shown that more than 50% of energy is spent in purifying the last 5-10% of the distillate product. Membrane modules on the other hand can achieve high purity separations at lower energy costs, but if the flux is high, it requires large membrane area. A hybrid scheme where distillation and membrane...... modules are combined such that each operates at its highest efficiency, has the potential for significant energy reduction without significant increase of capital costs. This paper presents a method for sustainable design of hybrid distillation-membrane schemes with guaranteed reduction of energy...

  19. Sentiment Analysis Using Hybrid Approach: A Survey

    Directory of Open Access Journals (Sweden)

    Chauhan Ashish P

    2015-01-01

    Full Text Available Sentiment analysis is the process of identifying people’s attitude and emotional state’s from language. The main objective is realized by identifying a set of potential features in the review and extracting opinion expressions about those features by exploiting their associations. Opinion mining, also known as Sentiment analysis, plays an important role in this process. It is the study of emotions i.e. Sentiments, Expressionsthat are stated in natural language. Natural language techniques are applied to extract emotions from unstructured data. There are several techniques which can be used to analysis such type of data. Here, we are categorizing these techniques broadly as ”supervised learning”, ”unsupervised learning” and ”hybrid techniques”. The objective of this paper is to provide the overview of Sentiment Analysis, their challenges and a comparative analysis of it’s techniques in the field of Natural Language Processing

  20. Discriminating topology in galaxy distributions using network analysis

    Science.gov (United States)

    Hong, Sungryong; Coutinho, Bruno C.; Dey, Arjun; Barabási, Albert-L.; Vogelsberger, Mark; Hernquist, Lars; Gebhardt, Karl

    2016-07-01

    The large-scale distribution of galaxies is generally analysed using the two-point correlation function. However, this statistic does not capture the topology of the distribution, and it is necessary to resort to higher order correlations to break degeneracies. We demonstrate that an alternate approach using network analysis can discriminate between topologically different distributions that have similar two-point correlations. We investigate two galaxy point distributions, one produced by a cosmological simulation and the other by a Lévy walk. For the cosmological simulation, we adopt the redshift z = 0.58 slice from Illustris and select galaxies with stellar masses greater than 108 M⊙. The two-point correlation function of these simulated galaxies follows a single power law, ξ(r) ˜ r-1.5. Then, we generate Lévy walks matching the correlation function and abundance with the simulated galaxies. We find that, while the two simulated galaxy point distributions have the same abundance and two-point correlation function, their spatial distributions are very different; most prominently, filamentary structures, absent in Lévy fractals. To quantify these missing topologies, we adopt network analysis tools and measure diameter, giant component, and transitivity from networks built by a conventional friends-of-friends recipe with various linking lengths. Unlike the abundance and two-point correlation function, these network quantities reveal a clear separation between the two simulated distributions; therefore, the galaxy distribution simulated by Illustris is not a Lévy fractal quantitatively. We find that the described network quantities offer an efficient tool for discriminating topologies and for comparing observed and theoretical distributions.

  1. Dielectrophoresis and shear-enhanced sensitivity and selectivity of DNA hybridization for the rapid discrimination of Candida species.

    Science.gov (United States)

    Cheng, I-Fang; Han, Huan-Wen; Chang, Hsien-Chang

    2012-03-15

    We present a dielectrophoresis (DEP)-based microfluidic chip that is capable of enhancing the sensitivity and selectivity of DNA hybridization using an AC electric field and hydrodynamic shear in a continuous through-flow. Molecular DEP was employed to rapidly trap ssDNA molecules in a flowing solution to a cusp-shaped nanocolloid assembly on a microfluidic chip with a locally amplified AC electric field gradient. The detection time can be accelerated to sub-minute periods, and the sensitivity can reach the pico-molar level due to the AC DEP-enhanced molecule concentration (at an optimal AC frequency of 900 kHz) in a small region (∼100 μm(2)) instead of the broad area used in a tank reactor (∼10(6) μm(2)). Continuous flow in a microchannel provides a constant and high shear rate that can shear off most non-specific target-probe binding to promote the discriminating selectivity. On-chip multi-target discrimination of Candida species can be achieved within a few minutes under optimal conditions.

  2. Regularized generalized eigen-decomposition with applications to sparse supervised feature extraction and sparse discriminant analysis

    DEFF Research Database (Denmark)

    Han, Xixuan; Clemmensen, Line Katrine Harder

    2015-01-01

    techniques, for instance, 2D-Linear Discriminant Analysis (2D-LDA). Furthermore, an iterative algorithm based on the alternating direction method of multipliers is developed. The algorithm approximately solves RGED with monotonically decreasing convergence and at an acceptable speed for results of modest......We propose a general technique for obtaining sparse solutions to generalized eigenvalue problems, and call it Regularized Generalized Eigen-Decomposition (RGED). For decades, Fisher's discriminant criterion has been applied in supervised feature extraction and discriminant analysis...... accuracy. Numerical experiments based on four data sets of different types of images show that RGED has competitive classification performance with existing multidimensional and sparse techniques of discriminant analysis....

  3. An Analysis of Discrimination by Real Estate Brokers.

    Science.gov (United States)

    Yinger, John

    This paper focuses on designing policies to eliminate discrimination in the sale of single-family houses by analyzing the behavior of the agents who actually do most of the discriminating, namely real estate agents. Discriminatory practices are said to be supported by policies of house builders, lending institutions, and government, and by the…

  4. Racial Discrimination and Asian Mental Health: A Meta-Analysis

    Science.gov (United States)

    Lee, Debbiesiu L.; Ahn, Soyeon

    2011-01-01

    Although research on racial discrimination and mental health has proliferated, findings are varied and dispersed. This study explored the critical question of how Asians, in particular, deal with discrimination and how this relates to Asian mental health. With 99 correlations from 23 independent studies, the overall relationship between racial…

  5. Discrimination of red and white rice bran from Indonesia using HPLC fingerprint analysis combined with chemometrics.

    Science.gov (United States)

    Sabir, Aryani; Rafi, Mohamad; Darusman, Latifah K

    2017-04-15

    HPLC fingerprint analysis combined with chemometrics was developed to discriminate between the red and the white rice bran grown in Indonesia. The major component in rice bran is γ-oryzanol which consisted of 4 main compounds, namely cycloartenol ferulate, cyclobranol ferulate, campesterol ferulate and β-sitosterol ferulate. Separation of these four compounds along with other compounds was performed using C18 and methanol-acetonitrile with gradient elution system. By using these intensity variations, principal component and discriminant analysis were performed to discriminate the two samples. Discriminant analysis was successfully discriminated the red from the white rice bran with predictive ability of the model showed a satisfactory classification for the test samples. The results of this study indicated that the developed method was suitable as quality control method for rice bran in terms of identification and discrimination of the red and the white rice bran.

  6. Sparse Regression by Projection and Sparse Discriminant Analysis

    KAUST Repository

    Qi, Xin

    2015-04-03

    © 2015, © American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America. Recent years have seen active developments of various penalized regression methods, such as LASSO and elastic net, to analyze high-dimensional data. In these approaches, the direction and length of the regression coefficients are determined simultaneously. Due to the introduction of penalties, the length of the estimates can be far from being optimal for accurate predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high-dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths and the tuning parameters are determined by a cross-validation procedure to achieve the largest prediction accuracy. We provide a theoretical result for simultaneous model selection consistency and parameter estimation consistency of our method in high dimension. This new framework is then generalized such that it can be applied to principal components analysis, partial least squares, and canonical correlation analysis. We also adapt this framework for discriminant analysis. Compared with the existing methods, where there is relatively little control of the dependency among the sparse components, our method can control the relationships among the components. We present efficient algorithms and related theory for solving the sparse regression by projection problem. Based on extensive simulations and real data analysis, we demonstrate that our method achieves good predictive performance and variable selection in the regression setting, and the ability to control relationships between the sparse components leads to more accurate classification. In supplementary materials available online, the details of the algorithms and theoretical proofs, and R codes for all simulation studies are provided.

  7. Kinematic Analysis of a Hybrid Structure

    Directory of Open Access Journals (Sweden)

    Q.J. Duan

    2012-11-01

    Full Text Available This paper presents a kinematic analysis and simulation of a hybrid structure applied to the new design cable-suspended feed structure (CSFS for the next generation of large spherical radio telescopes. First, considering the requirement that feeds should be tilted from 40° to 60° and that the tracking precision in steady state is 4mm, a novel design of the feed supporting structure including a cable-cabin structure, an AB axis structure and a Stewart platform is performed. Next, kinematic analysis and the simulation of the CSFS are done. Simulations have been developed in combination with the 50m CSFS model, which demonstrate the effectiveness and feasibility of the proposed three-level cable-suspended feed system.

  8. Sex determination of the Acadian Flycatcher using discriminant analysis

    Science.gov (United States)

    Wilson, R.R.

    1999-01-01

    I used five morphometric variables from 114 individuals captured in Arkansas to develop a discriminant model to predict the sex of Acadian Flycatchers (Empidonax virescens). Stepwise discriminant function analyses selected wing chord and tail length as the most parsimonious subset of variables for discriminating sex. This two-variable model correctly classified 80% of females and 97% of males used to develop the model. Validation of the model using 19 individuals from Louisiana and Virginia resulted in 100% correct classification of males and females. This model provides criteria for sexing monomorphic Acadian Flycatchers during the breeding season and possibly during the winter.

  9. Unbiased bootstrap error estimation for linear discriminant analysis.

    Science.gov (United States)

    Vu, Thang; Sima, Chao; Braga-Neto, Ulisses M; Dougherty, Edward R

    2014-12-01

    Convex bootstrap error estimation is a popular tool for classifier error estimation in gene expression studies. A basic question is how to determine the weight for the convex combination between the basic bootstrap estimator and the resubstitution estimator such that the resulting estimator is unbiased at finite sample sizes. The well-known 0.632 bootstrap error estimator uses asymptotic arguments to propose a fixed 0.632 weight, whereas the more recent 0.632+ bootstrap error estimator attempts to set the weight adaptively. In this paper, we study the finite sample problem in the case of linear discriminant analysis under Gaussian populations. We derive exact expressions for the weight that guarantee unbiasedness of the convex bootstrap error estimator in the univariate and multivariate cases, without making asymptotic simplifications. Using exact computation in the univariate case and an accurate approximation in the multivariate case, we obtain the required weight and show that it can deviate significantly from the constant 0.632 weight, depending on the sample size and Bayes error for the problem. The methodology is illustrated by application on data from a well-known cancer classification study.

  10. Discriminating Topology in Galaxy Distributions using Network Analysis

    CERN Document Server

    Hong, Sungryong; Dey, Arjun; Barabási, Albert -L; Vogelsberger, Mark; Hernquist, Lars; Gebhardt, Karl

    2016-01-01

    (abridged) The large-scale distribution of galaxies is generally analyzed using the two-point correlation function. However, this statistic does not capture the topology of the distribution, and it is necessary to resort to higher order correlations to break degeneracies. We demonstrate that an alternate approach using network analysis can discriminate between topologically different distributions that have similar two-point correlations. We investigate two galaxy point distributions, one produced by a cosmological simulation and the other by a L\\'evy walk. For the cosmological simulation, we adopt the redshift $z = 0.58$ slice from Illustris (Vogelsberger et al. 2014A) and select galaxies with stellar masses greater than $10^8$$M_\\odot$. The two point correlation function of these simulated galaxies follows a single power-law, $\\xi(r) \\sim r^{-1.5}$. Then, we generate L\\'evy walks matching the correlation function and abundance with the simulated galaxies. We find that, while the two simulated galaxy point d...

  11. Genetic mapping of complex discrete human diseases by discriminant analysis

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    The objective of the present study is to propose and evaluate a novel multivariate approach for genetic mapping of complex categorical diseases. This approach results from an application of standard stepwise discriminant analysis to detect linkage based on the differential marker identity-by-descent (IBD) distributions among the different groups of sib pairs. Two major advantages of this method are that it allows for simultaneously testing all markers, together with other genetic and environmental factors in a single multivariate setting and it avoids explicitly modeling the complex relationship between the affection status of sib pairs and the underlying genetic determinants. The efficiency and properties of the method are demonstrated via simulations. The proposed multivariate approach has successfully located the true position(s) under various genetic scenarios. The more important finding is that using highly densely spaced markers (1~2 cM) leads to only a marginal loss of statistical efficiency of the proposed methods in terms of gene localization and statistical power. These results have well established its utility and advantages as a fine-mapping tool. A unique property of the proposed method is the ability to map multiple linked trait loci to their precise positions due to its sequential nature, as demonstrated via simulations.

  12. Learning Discriminative Subspaces on Random Contrasts for Image Saliency Analysis.

    Science.gov (United States)

    Fang, Shu; Li, Jia; Tian, Yonghong; Huang, Tiejun; Chen, Xiaowu

    2017-05-01

    In visual saliency estimation, one of the most challenging tasks is to distinguish targets and distractors that share certain visual attributes. With the observation that such targets and distractors can sometimes be easily separated when projected to specific subspaces, we propose to estimate image saliency by learning a set of discriminative subspaces that perform the best in popping out targets and suppressing distractors. Toward this end, we first conduct principal component analysis on massive randomly selected image patches. The principal components, which correspond to the largest eigenvalues, are selected to construct candidate subspaces since they often demonstrate impressive abilities to separate targets and distractors. By projecting images onto various subspaces, we further characterize each image patch by its contrasts against randomly selected neighboring and peripheral regions. In this manner, the probable targets often have the highest responses, while the responses at background regions become very low. Based on such random contrasts, an optimization framework with pairwise binary terms is adopted to learn the saliency model that best separates salient targets and distractors by optimally integrating the cues from various subspaces. Experimental results on two public benchmarks show that the proposed approach outperforms 16 state-of-the-art methods in human fixation prediction.

  13. Face Recognition Using Holistic Features and Simplified Linear Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Gou Koutaki

    2012-08-01

    Full Text Available This paper proposed an alternative approach to face recognition algorithm that is based on global/holistic features of face image and simplified Linear Discriminant Analysis (LDA. The proposed method can overcome main problems of the conventional LDA in terms of large processing time for retraining when a new class data was registered into the training data set. The holistic features of face image were proposed as dimensional reduction of raw face image. While, the simplified LDA which is the redefinition of between class scatter using constant global mean assignment was proposed to decrease time complexity of retraining process. In order to know the performance of the proposed method, several experiments were performed using several challenging face databases: ORL, YALE, ITS-Lab, INDIA, and FERET database. Furthermore, we compared the developed algorithm experimental results to the best traditional subspace methods such as DLDA, 2DLDA, (2D2DLDA, 2DPCA, and (2D22DPCA. The experimental results show that the proposed method can solve the retraining problem of the conventional LDA indicated by requiring short retraining time and stable recognition rate.

  14. Sparse linear discriminant analysis by thresholding for high dimensional data

    CERN Document Server

    Shao, Jun; Deng, Xinwei; Wang, Sijian; 10.1214/10-AOS870

    2011-01-01

    In many social, economical, biological and medical studies, one objective is to classify a subject into one of several classes based on a set of variables observed from the subject. Because the probability distribution of the variables is usually unknown, the rule of classification is constructed using a training sample. The well-known linear discriminant analysis (LDA) works well for the situation where the number of variables used for classification is much smaller than the training sample size. Because of the advance in technologies, modern statistical studies often face classification problems with the number of variables much larger than the sample size, and the LDA may perform poorly. We explore when and why the LDA has poor performance and propose a sparse LDA that is asymptotically optimal under some sparsity conditions on the unknown parameters. For illustration of application, we discuss an example of classifying human cancer into two classes of leukemia based on a set of 7,129 genes and a training ...

  15. A Hybrid Discriminative Approach with Bayesian Prior Constraint%贝叶斯先验约束下的混合判别方法

    Institute of Scientific and Technical Information of China (English)

    姚婷婷; 谢昭; 张骏; 高隽

    2015-01-01

    在有限样本下判别模型对训练样本敏感,易导致分类器学习结果泛化性能较弱,产生过拟合现象。针对上述问题,提出一种贝叶斯先验约束下的混合判别方法。通过在判别计算中引入生成先验分析,构建生成与判别模型在决策层的混合求解框架。该方法采用不同质分类器进行分类预测,并通过定义有效的融合机制进行样本筛选和标签决策,自动扩展训练集以更新模型,弥补训练样本信息的不完全性。有限样本下的场景分类实验结果表明,相比经典算法,该模型能够挖掘出具有高度判别特性的样本,从而进行有效的模型更新,纠正前期由于样本分布不均而导致的错分样本标签,提升场景分类精度。%The discriminative models are sensitive to limited training samples, which usually has poor generalization performances and is easily over-fitting. A hybrid discriminative approach with Bayesian prior constraints is proposed to solve this issue. By introducing the generative prior analysis into the discriminative approach, a complementary learning structure is built to fuse different classification results. The different types of classifiers are trained separately, and an effective fusion decision is defined to obtain the most confident testing samples along with the estimated labels. By enlarging the training set automatically, the model is updated to make up for the incomplete distribution information of training samples. The experimental results show that compared with the classical methods, the proposed approach can effectively update the model by figuring out the discriminating samples and correct the misclassifications caused by the uneven distribution of limited samples. It can improve the performances of scene categorization.

  16. Optimal discrimination of multiple quantum systems: controllability analysis

    Energy Technology Data Exchange (ETDEWEB)

    Turinici, Gabriel [INRIA Rocquencourt, BP 105, 78153 Le Chesnay Cedex (France); Ramakhrishna, Viswanath [Department of Mathematical Sciences and Center for Signals, Systems and Communications, University of Texas at Dallas, PO Box 830688, Richardson, TX 75083 (United States); Li Baiqing [Department of Chemistry, Princeton University, Princeton, NJ 08544 (United States); Rabitz, Herschel [Department of Chemistry, Princeton University, Princeton, NJ 08544 (United States)

    2004-01-09

    A theoretical study is presented concerning the ability to dynamically discriminate between members of a set of different (but possibly similar) quantum systems. This discrimination is analysed in terms of independently and simultaneously steering about the wavefunction of each component system to a target state of interest using a tailored control (i.e. laser) field. Controllability criteria are revealed and their applicability is demonstrated in simple cases. Discussion is also presented in some uncontrollable cases.

  17. Discriminant forest classification method and system

    Science.gov (United States)

    Chen, Barry Y.; Hanley, William G.; Lemmond, Tracy D.; Hiller, Lawrence J.; Knapp, David A.; Mugge, Marshall J.

    2012-11-06

    A hybrid machine learning methodology and system for classification that combines classical random forest (RF) methodology with discriminant analysis (DA) techniques to provide enhanced classification capability. A DA technique which uses feature measurements of an object to predict its class membership, such as linear discriminant analysis (LDA) or Andersen-Bahadur linear discriminant technique (AB), is used to split the data at each node in each of its classification trees to train and grow the trees and the forest. When training is finished, a set of n DA-based decision trees of a discriminant forest is produced for use in predicting the classification of new samples of unknown class.

  18. Links between patterns of racial socialization and discrimination experiences and psychological adjustment: a cluster analysis.

    Science.gov (United States)

    Ajayi, Alex A; Syed, Moin

    2014-10-01

    This study used a person-oriented analytic approach to identify meaningful patterns of barriers-focused racial socialization and perceived racial discrimination experiences in a sample of 295 late adolescents. Using cluster analysis, three distinct groups were identified: Low Barrier Socialization-Low Discrimination, High Barrier Socialization-Low Discrimination, and High Barrier Socialization-High Discrimination clusters. These groups were substantively unique in terms of the frequency of racial socialization messages about bias preparation and out-group mistrust its members received and their actual perceived discrimination experiences. Further, individuals in the High Barrier Socialization-High Discrimination cluster reported significantly higher depressive symptoms than those in the Low Barrier Socialization-Low Discrimination and High Barrier Socialization-Low Discrimination clusters. However, no differences in adjustment were observed between the Low Barrier Socialization-Low Discrimination and High Barrier Socialization-Low Discrimination clusters. Overall, the findings highlight important individual differences in how young people of color experience their race and how these differences have significant implications on psychological adjustment. Copyright © 2014 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

  19. Comparison of linear discriminant analysis methods for the classification of cancer based on gene expression data

    Directory of Open Access Journals (Sweden)

    He Miao

    2009-12-01

    Full Text Available Abstract Background More studies based on gene expression data have been reported in great detail, however, one major challenge for the methodologists is the choice of classification methods. The main purpose of this research was to compare the performance of linear discriminant analysis (LDA and its modification methods for the classification of cancer based on gene expression data. Methods The classification performance of linear discriminant analysis (LDA and its modification methods was evaluated by applying these methods to six public cancer gene expression datasets. These methods included linear discriminant analysis (LDA, prediction analysis for microarrays (PAM, shrinkage centroid regularized discriminant analysis (SCRDA, shrinkage linear discriminant analysis (SLDA and shrinkage diagonal discriminant analysis (SDDA. The procedures were performed by software R 2.80. Results PAM picked out fewer feature genes than other methods from most datasets except from Brain dataset. For the two methods of shrinkage discriminant analysis, SLDA selected more genes than SDDA from most datasets except from 2-class lung cancer dataset. When comparing SLDA with SCRDA, SLDA selected more genes than SCRDA from 2-class lung cancer, SRBCT and Brain dataset, the result was opposite for the rest datasets. The average test error of LDA modification methods was lower than LDA method. Conclusions The classification performance of LDA modification methods was superior to that of traditional LDA with respect to the average error and there was no significant difference between theses modification methods.

  20. 稀疏判别分析%Sparse discriminant analysis

    Institute of Scientific and Technical Information of China (English)

    陈小冬; 林焕祥

    2012-01-01

    Methods for manifold embedding have the following issues: on one hand, neighborhood graph is constructed in such high-dimensionality of original space that it tends to work poorly; on the other hand, appropriate values for the neighborhood size and heat kernel parameter involved in graph construction are generally difficult to be assigned. To address these problems, a new semi-supervised dimensionality reduction algorithm called SparsE Discriminant Analysis (SEDA) was proposed. Firstly, SEDA set up a sparse graph to preserve the global information and geometric structure of the data based on sparse representation. Secondly, it applied both sparse graph and Fisher criterion to seek the optimal projection. The experimental results on a broad range of data sets show that SEDA is superior to many popular dimensionality reduction methods.%针对流形嵌入降维方法中在高维空间构建近邻图无益于后续工作,以及不容易给近邻大小和热核参数赋合适值的问题,提出一种稀疏判别分析算法(SEDA).首先使用稀疏表示构建稀疏图保持数据的全局信息和几何结构,以克服流形嵌入方法的不足;其次,将稀疏保持作为正则化项使用Fisher判别准则,能够得到最优的投影.在一组高维数据集上的实验结果表明,SEDA是非常有效的半监督降维方法.

  1. Declining Bias and Gender Wage Discrimination? A Meta-Regression Analysis

    Science.gov (United States)

    Jarrell, Stephen B.; Stanley, T. D.

    2004-01-01

    The meta-regression analysis reveals that there is a strong tendency for discrimination estimates to fall and wage discrimination exist against the woman. The biasing effect of researchers' gender of not correcting for selection bias has weakened and changes in labor market have made it less important.

  2. Discrimination of Bacillus anthracis from closely related microorganisms by analysis of 16S and 23S rRNA with oligonucleotide microchips

    Science.gov (United States)

    Bavykin, Sergei G.; Mirzabekov, Andrei D.

    2007-10-30

    The present invention is directed to a novel method of discriminating a highly infectious bacterium Bacillus anthracis from a group of closely related microorganisms. Sequence variations in the 16S and 23S rRNA of the B. cereus subgroup including B. anthracis are utilized to construct an array that can detect these sequence variations through selective hybridizations. The identification and analysis of these sequence variations enables positive discrimination of isolates of the B. cereus group that includes B. anthracis. Discrimination of single base differences in rRNA was achieved with a microchip during analysis of B. cereus group isolates from both single and in mixed probes, as well as identification of polymorphic sites. Successful use of a microchip to determine the appropriate subgroup classification using eight reference microorganisms from the B. cereus group as a study set, was demonstrated.

  3. Impact of hybrid GSI analysis using ETR ensembles

    Indian Academy of Sciences (India)

    V S Prasad; C J Johny

    2016-04-01

    Performance of a hybrid assimilation system combining 3D Var based NGFS (NCMRWF Global ForecastSystem) with ETR (Ensemble Transform with Rescaling) based Global Ensemble Forecast (GEFS) ofresolution T-190L28 is investigated. The experiment is conducted for a period of one week in June 2013and forecast skills over different spatial domains are compared with respect to mean analysis state.Rainfall forecast is verified over Indian region against combined observations of IMD and NCMRWF.Hybrid assimilation produced marginal improvements in overall forecast skill in comparison with 3DVar. Hybrid experiment made significant improvement in wind forecasts in all the regions on verificationagainst mean analysis. The verification of forecasts with radiosonde observations also show improvementin wind forecasts with the hybrid assimilation. On verification against observations, hybrid experimentshows more improvement in temperature and wind forecasts at upper levels. Both hybrid and operational3D Var failed in prediction of extreme rainfall event over Uttarakhand on 17 June, 2013.

  4. Optimal measurement position estimation by discriminant analysis based on Wilks' lambda for myoelectric hand control.

    Science.gov (United States)

    Kiso, Atsushi; Taniguchi, Yu; Seki, Hirokazu

    2011-01-01

    This paper describes an optimal measurement position estimation by the discriminant analysis based on Wilks' lambda for the myoelectric hand control. In the past studies, the myoelectric signals were measured from the same positions for the motions discrimination. However, the optimal measurement positions of the myoelectric signals for the motion discrimination are different according to the remaining muscle situation of amputees. Therefore the purpose of this study is to estimate the optimal and fewer measurement positions for the precise motion discrimination of the human forearm. This study proposes the estimation method of the optimal measurement positions by the discriminant analysis based on Wilks' lambda among the myoelectric signal measured from multiple positions. Some experiments on the myoelectric hand simulator show the effectiveness of the proposed optimal measurement position estimation method.

  5. Kernel Model Applied in Kernel Direct Discriminant Analysis for the Recognition of Face with Nonlinear Variations

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    A kernel-based discriminant analysis method called kernel direct discriminant analysis is employed, which combines the merit of direct linear discriminant analysis with that of kernel trick. In order to demonstrate its better robustness to the complex and nonlinear variations of real face images, such as illumination, facial expression, scale and pose variations, experiments are carried out on the Olivetti Research Laboratory, Yale and self-built face databases. The results indicate that in contrast to kernel principal component analysis and kernel linear discriminant analysis, the method can achieve lower (7%) error rate using only a very small set of features. Furthermore, a new corrected kernel model is proposed to improve the recognition performance. Experimental results confirm its superiority (1% in terms of recognition rate) to other polynomial kernel models.

  6. An approach for mechanical fault classification based on generalized discriminant analysis

    Institute of Scientific and Technical Information of China (English)

    LI Wei-hua; SHI Tie-lin; YANG Shu-zi

    2006-01-01

    To deal with pattern classification of complicated mechanical faults,an approach to multi-faults classification based on generalized discriminant analysis is presented.Compared with linear discriminant analysis (LDA),generalized discriminant analysis (GDA),one of nonlinear discriminant analysis methods,is more suitable for classifying the linear non-separable problem.The connection and difference between KPCA (Kernel Principal Component Analysis) and GDA is discussed.KPCA is good at detection of machine abnormality while GDA performs well in multi-faults classification based on the collection of historical faults symptoms.When the proposed method is applied to air compressor condition classification and gear fault classification,an excellent performance in complicated multi-faults classification is presented.

  7. Block-diagonal discriminant analysis and its bias-corrected rules.

    Science.gov (United States)

    Pang, Herbert; Tong, Tiejun; Ng, Michael

    2013-06-01

    High-throughput expression profiling allows simultaneous measure of tens of thousands of genes at once. These data have motivated the development of reliable biomarkers for disease subtypes identification and diagnosis. Many methods have been developed in the literature for analyzing these data, such as diagonal discriminant analysis, support vector machines, and k-nearest neighbor methods. The diagonal discriminant methods have been shown to perform well for high-dimensional data with small sample sizes. Despite its popularity, the independence assumption is unlikely to be true in practice. Recently, a gene module based linear discriminant analysis strategy has been proposed by utilizing the correlation among genes in discriminant analysis. However, the approach can be underpowered when the samples of the two classes are unbalanced. In this paper, we propose to correct the biases in the discriminant scores of block-diagonal discriminant analysis. In simulation studies, our proposed method outperforms other approaches in various settings. We also illustrate our proposed discriminant analysis method for analyzing microarray data studies.

  8. Cloud-type discrimination via multispectral textural analysis

    Science.gov (United States)

    Lamei, Niloufar; Hutchison, Keith D.; Crawford, Melba M.; Khazenie, Nahid

    1994-04-01

    In recent years, with the development of satellite and computer technology, Earth observation and atmospheric research have become highly dependent on digital imagery. One of the primary interests in digital image processing is the development of robust methods to perform feature detection, extraction, and classification. Until recently, classification methods for cloud discrimination were mainly based on the spectral information of the imagery. However, because of the spectral similarities of certain features (such as ice clouds and snow) and the effects of atmospheric attenuation, multispectral rule-based classifications do not necessarily produce accurate feature discrimination. Spectral homogeneity of two different features within a scene can lead to misclassification. Furthermore, the opposite problem can occur when one feature exhibits different spectral signatures locally but is homogeneous in its cyclic spatial variation. The exploration of spatial information is often advantageous in these discrimination problems. A texture- based method for feature identification has been investigated. This method uses a set of localized spatial filters known as 2-D Gabor functions. Gabor filters can be described as a sinusoidal plane wave within a 2-D Gaussian envelope. The frequency and orientation of the sine plane and the width of the Gaussian envelope are determine by the Gabor parameters. These tunable channels yield joint optimal information both in the spatial and the frequency domains. The new method has been applied to the thermal channels of the NOAA Advanced Very High Resolution Radiometer data for cloud-type discrimination. Results show that additional texture information improves discrimination between cloud types (especially thin cirrus).

  9. An Analysis of Visible Racial Discrimination In Invisible Man

    Institute of Scientific and Technical Information of China (English)

    LUO Hui

    2016-01-01

    Invisible Man by Ralph Ellison is regarded as one of the classical works in contemporary Black American Literature. By briefly analyzing the causes and effects of racial discrimination, the study aims to explore the blacks’responses to it. It is concluded that the hero, as an“invisible man”, should accept his black identity, admitting the blacks’tradition and cultural iden-tity, then he can find his self-belonging in the black community.

  10. Analysis of Financial Ratio to Distinguish Indonesia Joint Venture General Insurance Company Performance using Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Subiakto Soekarno

    2012-01-01

    Full Text Available Insurance industry stands as a service business that plays a significant role in Indonesiaeconomical condition. The development of insurance industry in Indonesia, both of generalinsurance and life insurance, has increased very fast. The general insurance industry itselfdivided into two major players which are local private company and Joint Venture Company.Lately, the use of statistical techniques and financial ratios models to asses financial institutionsuch as insurance company have been used as one of the appropriate combination inpredicting the performance of an industry. This research aims to distinguish between JointVenture General Insurance Companies that have a good performance and those who are lessperforming well using Discriminant Analysis. Further, the findings led that DiscriminantAnalysis is able to distinguish Joint Venture General Insurance Companies that have a goodperformance and those who are not performing well. There are also six ratios which are RBC,Technical Reserve to Investment Ratio, Debt Ratio, Return on Equity, Loss Ratio, and ExpenseRatio that stand as the most influential ratios to distinguish the performance of joint venturegeneral insurance companies. In addition, the result suggest business people to be concernedtoward those six ratios, to increase their companies’ performance.Key words: general insurance, financial ratio, discriminant analysis

  11. Analysis of Financial Ratio to Distinguish Indonesia Joint Venture General Insurance Company Performance using Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Subiakto Soekarno

    2012-01-01

    Full Text Available Insurance industry stands as a service business that plays a significant role in Indonesiaeconomical condition. The development of insurance industry in Indonesia, both of generalinsurance and life insurance, has increased very fast. The general insurance industry itselfdivided into two major players which are local private company and Joint Venture Company.Lately, the use of statistical techniques and financial ratios models to asses financial institutionsuch as insurance company have been used as one of the appropriate combination inpredicting the performance of an industry. This research aims to distinguish between JointVenture General Insurance Companies that have a good performance and those who are lessperforming well using Discriminant Analysis. Further, the findings led that DiscriminantAnalysis is able to distinguish Joint Venture General Insurance Companies that have a goodperformance and those who are not performing well. There are also six ratios which are RBC,Technical Reserve to Investment Ratio, Debt Ratio, Return on Equity, Loss Ratio, and ExpenseRatio that stand as the most influential ratios to distinguish the performance of joint venturegeneral insurance companies. In addition, the result suggest business people to be concernedtoward those six ratios, to increase their companies’ performance.Key words: general insurance, financial ratio, discriminant analysis

  12. Mu opioid mediated discriminative-stimulus effects of tramadol: an individual subjects analysis.

    Science.gov (United States)

    Strickland, Justin C; Rush, Craig R; Stoops, William W

    2015-03-01

    Drug discrimination procedures use dose-dependent generalization, substitution, and pretreatment with selective agonists and antagonists to evaluate receptor systems mediating interoceptive effects of drugs. Despite the extensive use of these techniques in the nonhuman animal literature, few studies have used human participants. Specifically, human studies have not routinely used antagonist administration as a pharmacological tool to elucidate the mechanisms mediating the discriminative stimulus effects of drugs. This study evaluated the discriminative-stimulus effects of tramadol, an atypical analgesic with monoamine and mu opioid activity. Three human participants first learned to discriminate 100 mg tramadol from placebo. A range of tramadol doses (25 to 150 mg) and hydromorphone (4 mg) with and without naltrexone pretreatment (50 mg) were then administered to participants after they acquired the discrimination. Tramadol produced dose-dependent increases in drug-appropriate responding and hydromorphone partially or fully substituted for tramadol in all participants. These effects were attenuated by naltrexone. Individual participant records indicated a relationship between mu opioid activity (i.e., miosis) and drug discrimination performance. Our findings indicate that mu opioid activity may mediate the discriminative-stimulus effects of tramadol in humans. The correspondence of generalization, substitution, and pretreatment findings with the animal literature supports the neuropharmacological specificity of the drug discrimination procedure. © Society for the Experimental Analysis of Behavior.

  13. Analysis of Hybrid Hydrogen Systems: Final Report

    Energy Technology Data Exchange (ETDEWEB)

    Dean, J.; Braun, R.; Munoz, D.; Penev, M.; Kinchin, C.

    2010-01-01

    Report on biomass pathways for hydrogen production and how they can be hybridized to support renewable electricity generation. Two hybrid systems were studied in detail for process feasibility and economic performance. The best-performing system was estimated to produce hydrogen at costs ($1.67/kg) within Department of Energy targets ($2.10/kg) for central biomass-derived hydrogen production while also providing value-added energy services to the electric grid.

  14. Local kernel nonparametric discriminant analysis for adaptive extraction of complex structures

    Science.gov (United States)

    Li, Quanbao; Wei, Fajie; Zhou, Shenghan

    2017-05-01

    The linear discriminant analysis (LDA) is one of popular means for linear feature extraction. It usually performs well when the global data structure is consistent with the local data structure. Other frequently-used approaches of feature extraction usually require linear, independence, or large sample condition. However, in real world applications, these assumptions are not always satisfied or cannot be tested. In this paper, we introduce an adaptive method, local kernel nonparametric discriminant analysis (LKNDA), which integrates conventional discriminant analysis with nonparametric statistics. LKNDA is adept in identifying both complex nonlinear structures and the ad hoc rule. Six simulation cases demonstrate that LKNDA have both parametric and nonparametric algorithm advantages and higher classification accuracy. Quartic unilateral kernel function may provide better robustness of prediction than other functions. LKNDA gives an alternative solution for discriminant cases of complex nonlinear feature extraction or unknown feature extraction. At last, the application of LKNDA in the complex feature extraction of financial market activities is proposed.

  15. The discrimination of honey origin using melissopalynology and Raman spectroscopy techniques coupled with multivariate analysis.

    Science.gov (United States)

    Corvucci, Francesca; Nobili, Lara; Melucci, Dora; Grillenzoni, Francesca-Vittoria

    2015-02-15

    Honey traceability to food quality is required by consumers and food control institutions. Melissopalynologists traditionally use percentages of nectariferous pollens to discriminate the botanical origin and the entire pollen spectrum (presence/absence, type and quantities and association of some pollen types) to determinate the geographical origin of honeys. To improve melissopalynological routine analysis, principal components analysis (PCA) was used. A remarkable and innovative result was that the most significant pollens for the traditional discrimination of the botanical and geographical origin of honeys were the same as those individuated with the chemometric model. The reliability of assignments of samples to honey classes was estimated through explained variance (85%). This confirms that the chemometric model properly describes the melissopalynological data. With the aim to improve honey discrimination, FT-microRaman spectrography and multivariate analysis were also applied. Well performing PCA models and good agreement with known classes were achieved. Encouraging results were obtained for botanical discrimination.

  16. Gas Classification Using Combined Features Based on a Discriminant Analysis for an Electronic Nose

    Directory of Open Access Journals (Sweden)

    Sang-Il Choi

    2016-01-01

    Full Text Available This paper proposes a gas classification method for an electronic nose (e-nose system, for which combined features that have been configured through discriminant analysis are used. First, each global feature is extracted from the entire measurement section of the data samples, while the same process is applied to the local features of the section that corresponds to the stabilization, exposure, and purge stages. The discriminative information amounts in the individual features are then measured based on the discriminant analysis, and the combined features are subsequently composed by selecting the features that have a large amount of discriminative information. Regarding a variety of volatile organic compound data, the results of the experiment show that, in a noisy environment, the proposed method exhibits classification performance that is relatively excellent compared to the other feature types.

  17. Discrimination of handlebar grip samples by fourier transform infrared microspectroscopy analysis and statistics

    Directory of Open Access Journals (Sweden)

    Zeyu Lin

    2017-01-01

    Full Text Available In this paper, the authors presented a study on the discrimination of handlebar grip samples, to provide effective forensic science service for hit and run traffic cases. 50 bicycle handlebar grip samples, 49 electric bike handlebar grip samples, and 96 motorcycle handlebar grip samples have been randomly collected by the local police in Beijing (China. Fourier transform infrared microspectroscopy (FTIR was utilized as analytical technology. Then, target absorption selection, data pretreatment, and discrimination of linked samples and unlinked samples were chosen as three steps to improve the discrimination of FTIR spectrums collected from different handlebar grip samples. Principal component analysis and receiver operating characteristic curve were utilized to evaluate different data selection methods and different data pretreatment methods, respectively. It is possible to explore the evidential value of handlebar grip residue evidence through instrumental analysis and statistical treatments. It will provide a universal discrimination method for other forensic science samples as well.

  18. A Theoretical Analysis of Why Hybrid Ensembles Work

    Directory of Open Access Journals (Sweden)

    Kuo-Wei Hsu

    2017-01-01

    Full Text Available Inspired by the group decision making process, ensembles or combinations of classifiers have been found favorable in a wide variety of application domains. Some researchers propose to use the mixture of two different types of classification algorithms to create a hybrid ensemble. Why does such an ensemble work? The question remains. Following the concept of diversity, which is one of the fundamental elements of the success of ensembles, we conduct a theoretical analysis of why hybrid ensembles work, connecting using different algorithms to accuracy gain. We also conduct experiments on classification performance of hybrid ensembles of classifiers created by decision tree and naïve Bayes classification algorithms, each of which is a top data mining algorithm and often used to create non-hybrid ensembles. Therefore, through this paper, we provide a complement to the theoretical foundation of creating and using hybrid ensembles.

  19. Multimodal Discrimination of Schizophrenia Using Hybrid Weighted Feature Concatenation of Brain Functional Connectivity and Anatomical Features with an Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Muhammad Naveed Iqbal Qureshi

    2017-09-01

    Full Text Available Multimodal features of structural and functional magnetic resonance imaging (MRI of the human brain can assist in the diagnosis of schizophrenia. We performed a classification study on age, sex, and handedness-matched subjects. The dataset we used is publicly available from the Center for Biomedical Research Excellence (COBRE and it consists of two groups: patients with schizophrenia and healthy controls. We performed an independent component analysis and calculated global averaged functional connectivity-based features from the resting-state functional MRI data for all the cortical and subcortical anatomical parcellation. Cortical thickness along with standard deviation, surface area, volume, curvature, white matter volume, and intensity measures from the cortical parcellation, as well as volume and intensity from sub-cortical parcellation and overall volume of cortex features were extracted from the structural MRI data. A novel hybrid weighted feature concatenation method was used to acquire maximal 99.29% (P < 0.0001 accuracy which preserves high discriminatory power through the weight of the individual feature type. The classification was performed by an extreme learning machine, and its efficiency was compared to linear and non-linear (radial basis function support vector machines, linear discriminant analysis, and random forest bagged tree ensemble algorithms. This article reports the predictive accuracy of both unimodal and multimodal features after 10-by-10-fold nested cross-validation. A permutation test followed the classification experiment to assess the statistical significance of the classification results. It was concluded that, from a clinical perspective, this feature concatenation approach may assist the clinicians in schizophrenia diagnosis.

  20. Mars Hybrid Propulsion System Trajectory Analysis. Part II; Cargo Missions

    Science.gov (United States)

    Chai, Patrick R.; Merrill, Raymond G.; Qu, Min

    2015-01-01

    NASA's Human Spaceflight Architecture Team is developing a reusable hybrid transportation architecture in which both chemical and electric propulsion systems are used to send crew and cargo to Mars destinations such as Phobos, Deimos, the surface of Mars, and other orbits around Mars. By combining chemical and electrical propulsion into a single spaceship and applying each where it is more effective, the hybrid architecture enables a series of Mars trajectories that are more fuel-efficient than an all chemical architecture without significant increases in flight times. This paper shows the feasibility of the hybrid transportation architecture to pre-deploy cargo to Mars and Phobos in support of the Evolvable Mars Campaign crew missions. The analysis shows that the hybrid propulsion stage is able to deliver all of the current manifested payload to Phobos and Mars through the first three crew missions. The conjunction class trajectory also allows the hybrid propulsion stage to return to Earth in a timely fashion so it can be reused for additional cargo deployment. The 1,100 days total trip time allows the hybrid propulsion stage to deliver cargo to Mars every other Earth-Mars transit opportunity. For the first two Mars surface mission in the Evolvable Mars Campaign, the short trip time allows the hybrid propulsion stage to be reused for three round-trip journeys to Mars, which matches the hybrid propulsion stage's designed lifetime for three round-trip crew missions to the Martian sphere of influence.

  1. Comparison of discriminant analysis methods: Application to occupational exposure to particulate matter

    Science.gov (United States)

    Ramos, M. Rosário; Carolino, E.; Viegas, Carla; Viegas, Sandra

    2016-06-01

    Health effects associated with occupational exposure to particulate matter have been studied by several authors. In this study were selected six industries of five different areas: Cork company 1, Cork company 2, poultry, slaughterhouse for cattle, riding arena and production of animal feed. The measurements tool was a portable device for direct reading. This tool provides information on the particle number concentration for six different diameters, namely 0.3 µm, 0.5 µm, 1 µm, 2.5 µm, 5 µm and 10 µm. The focus on these features is because they might be more closely related with adverse health effects. The aim is to identify the particles that better discriminate the industries, with the ultimate goal of classifying industries regarding potential negative effects on workers' health. Several methods of discriminant analysis were applied to data of occupational exposure to particulate matter and compared with respect to classification accuracy. The selected methods were linear discriminant analyses (LDA); linear quadratic discriminant analysis (QDA), robust linear discriminant analysis with selected estimators (MLE (Maximum Likelihood Estimators), MVE (Minimum Volume Elipsoid), "t", MCD (Minimum Covariance Determinant), MCD-A, MCD-B), multinomial logistic regression and artificial neural networks (ANN). The predictive accuracy of the methods was accessed through a simulation study. ANN yielded the highest rate of classification accuracy in the data set under study. Results indicate that the particle number concentration of diameter size 0.5 µm is the parameter that better discriminates industries.

  2. Mixture subclass discriminant analysis link to restricted Gaussian model and other generalizations.

    Science.gov (United States)

    Gkalelis, Nikolaos; Mezaris, Vasileios; Kompatsiaris, Ioannis; Stathaki, Tania

    2013-01-01

    In this paper, a theoretical link between mixture subclass discriminant analysis (MSDA) and a restricted Gaussian model is first presented. Then, two further discriminant analysis (DA) methods, i.e., fractional step MSDA (FSMSDA) and kernel MSDA (KMSDA) are proposed. Linking MSDA to an appropriate Gaussian model allows the derivation of a new DA method under the expectation maximization (EM) framework (EM-MSDA), which simultaneously derives the discriminant subspace and the maximum likelihood estimates. The two other proposed methods generalize MSDA in order to solve problems inherited from conventional DA. FSMSDA solves the subclass separation problem, that is, the situation in which the dimensionality of the discriminant subspace is strictly smaller than the rank of the inter-between-subclass scatter matrix. This is done by an appropriate weighting scheme and the utilization of an iterative algorithm for preserving useful discriminant directions. On the other hand, KMSDA uses the kernel trick to separate data with nonlinearly separable subclass structure. Extensive experimentation shows that the proposed methods outperform conventional MSDA and other linear discriminant analysis variants.

  3. Electron - nuclear recoil discrimination by pulse shape analysis

    CERN Document Server

    Elbs, J; Collin, E; Godfrin, H; Suvorova, O

    2007-01-01

    In the framework of the ``ULTIMA'' project, we use ultra cold superfluid 3He bolometers for the direct detection of single particle events, aimed for a future use as a dark matter detector. One parameter of the pulse shape observed after such an event is the thermalization time constant. Until now it was believed that this parameter only depends on geometrical factors and superfluid 3He properties, and that it is independent of the nature of the incident particles. In this report we show new results which demonstrate that a difference for muon- and neutron events, as well as events simulated by heater pulses exist. The possibility to use this difference for event discrimination in a future dark matter detector will be discussed.

  4. Justifying direct discrimination: an analysis of the scope for a general justification defence in cases of direct sex discrimination

    OpenAIRE

    Moran, E. R.

    2000-01-01

    The prospect of a justification defence in cases of direct sex discrimination is universally criticised by academic commentators on the ground that it would subvert the goal of equality that underlies sex discrimination and equal treatment legislation. At the outset the thesis examines the differences between the sexes, how these differences can be used to explain the distinction between direct and indirect sex discrimination and considers various concepts of equality. Building...

  5. New techniques for emulsion analysis in a hybrid experiment

    Energy Technology Data Exchange (ETDEWEB)

    Kodama, K. (Aichi University of Education, Kariya 448 (Japan)); Ushida, N. (Aichi University of Education, Kariya 448 (Japan)); Mokhtarani, A. (University of California (Davis), Davis, CA 95616 (United States)); Paolone, V.S. (University of California (Davis), Davis, CA 95616 (United States)); Volk, J.T. (University of California (Davis), Davis, CA 95616 (United States)); Wilcox, J.O. (University of California (Davis), Davis, CA 95616 (United States)); Yager, P.M. (University of California (Davis), Davis, CA 95616 (United States)); Edelstein, R.M. (Carnegie-Mellon University, Pittsburgh, PA 15213 (United States)); Freyberger, A.P. (Carnegie-Mellon University, Pittsburgh, PA 15213 (United States)); Gibaut, D.B. (Carnegie-Mellon University, Pittsburgh, PA 15213 (United States)); Lipton, R.J. (Carnegie-Mellon University, Pittsburgh, PA 15213 (United States)); Nichols, W.R. (Carnegie-Mellon University, Pittsburgh, PA 15213 (United States)); Potter, D.M. (Carnegie-Mellon Univers

    1994-08-01

    A new method, called graphic scanning, was developed by the Nagoya University Group for emulsion analysis in a hybrid experiment. This method enhances both speed and reliability of emulsion analysis. Details of the application of this technique to the analysis of Fermilab experiment E653 are described. ((orig.))

  6. Stability classification model of mine-lane surrounding rock based on distance discriminant analysis method

    Institute of Scientific and Technical Information of China (English)

    ZHANG Wei; LI Xi-bing; GONG Feng-qiang

    2008-01-01

    Based on the principle of Mahalanobis distance discriminant analysis (DDA) theory, a stability classification model for mine-lane surrounding rock was established, including six indexes of discriminant factors that reflect the engineering quality of surrounding rock: lane depth below surface, span of lane, ratio of directly top layer thickness to coal thickness, uniaxial comprehensive strength of surrounding rock, development degree coefficient of surrounding rock joint and range of broken surrounding rock zone. A DDA model was obtained through training 15 practical measuring samples. The re-substitution method was introduced to verify the stability of DDA model and the ratio of mis-discrimination is zero. The DDA model was used to discriminate3 new samples and the results are identical with actual rock kind. Compared with the artificial neural network method and support vector mechanic method, the results show that this model has high prediction accuracy and can be used in practical engineering.

  7. Baseline drift effect on the performance of neutron and gamma ray discrimination using frequency gradient analysis

    CERN Document Server

    Liu, Guofu; Yang, Jun; Lin, Cunbao; Hu, Qingqing; Peng, Jinxian

    2013-01-01

    Frequency gradient analysis (FGA) effectively discriminates neutrons and gamma rays by examining the frequency-domain features of the photomultiplier tube anode signal. This approach is insensitive to noise but is inevitably affected by the baseline drift, similar to other pulse shape discrimination methods. The baseline drift effect is attributed to the factors such as power line fluctuation, dark current, noise disturbances, hum, and pulse tail in front-end electronics. This effect needs to be elucidated and quantified before the baseline shift can be estimated and removed from the captured signal. Therefore, the effect of baseline shift on the discrimination performance of neutrons and gamma rays with organic scintillation detectors using FGA is investigated in this paper. The relationship between the baseline shift and discrimination parameters of FGA is derived and verified by an experimental system consisting of an americium-beryllium source, a BC501A liquid scintillator detector, and a 5 GSPS 8-bit osc...

  8. New class of hybrid EoS and Bayesian M - R data analysis

    Energy Technology Data Exchange (ETDEWEB)

    Alvarez-Castillo, D. [JINR Dubna, Bogoliubov Laboratory of Theoretical Physics, Dubna (Russian Federation); Ayriyan, A.; Grigorian, H. [JINR Dubna, Laboratory of Information Technologies, Dubna (Russian Federation); Benic, S. [University of Zagreb, Department of Physics, Zagreb (Croatia); Blaschke, D. [JINR Dubna, Bogoliubov Laboratory of Theoretical Physics, Dubna (Russian Federation); National Research Nuclear University (MEPhI), Moscow (Russian Federation); Typel, S. [GSI Helmholtzzentrum fuer Schwerionenforschung GmbH, Darmstadt (Germany)

    2016-03-15

    We explore systematically a new class of two-phase equations of state (EoS) for hybrid stars that is characterized by three main features: (1) stiffening of the nuclear EoS at supersaturation densities due to quark exchange effects (Pauli blocking) between hadrons, modelled by an excluded volume correction; (2) stiffening of the quark matter EoS at high densities due to multiquark interactions; and (3) possibility for a strong first-order phase transition with an early onset and large density jump. The third feature results from a Maxwell construction for the possible transition from the nuclear to a quark matter phase and its properties depend on the two parameters used for (1) and (2), respectively. Varying these two parameters, one obtains a class of hybrid EoS that yields solutions of the Tolman-Oppenheimer-Volkoff (TOV) equations for sequences of hadronic and hybrid stars in the mass-radius diagram which cover the full range of patterns according to the Alford-Han-Prakash classification following which a hybrid star branch can be either absent, connected or disconnected with the hadronic one. The latter case often includes a tiny connected branch. The disconnected hybrid star branch, also called ''third family'', corresponds to high-mass twin stars characterized by the same gravitational mass but different radii. We perform a Bayesian analysis and demonstrate that the observation of such a pair of high-mass twin stars would have a sufficient discriminating power to favor hybrid EoS with a strong first-order phase transition over alternative EoS. (orig.)

  9. Analysis of nuclear and organellar DNA in somatic hybrids between solanaceous species.

    NARCIS (Netherlands)

    Wolters, A.M.A.

    1994-01-01

    This thesis describes an analysis of the possibilities and limitations of somatic hybridization of solanaceous species. Emphasis was laid on the elucidation of the interactions between nuclei, chloroplasts and mitochondria in the obtained somatic hybrids. Hybridization experiments between tomato ( L

  10. Vehicle surge detection and pathway discrimination by pedestrians who are blind: Effect of adding an alert sound to hybrid electric vehicles on performance.

    Science.gov (United States)

    Kim, Dae Shik; Emerson, Robert Wall; Naghshineh, Koorosh; Pliskow, Jay; Myers, Kyle

    2012-05-01

    This study examined the effect of adding an artificially generated alert sound to a quiet vehicle on its detectability and localizability with 15 visually impaired adults. When starting from a stationary position, the hybrid electric vehicle with an alert sound was significantly more quickly and reliably detected than either the identical vehicle without such added sound or the comparable internal combustion engine vehicle. However, no significant difference was found between the vehicles in respect to how accurately the participants could discriminate the path of a given vehicle (straight vs. right turn). These results suggest that adding an artificial sound to a hybrid electric vehicle may help reduce delay in street crossing initiation by a blind pedestrian, but the benefit of such alert sound may not be obvious in determining whether the vehicle in his near parallel lane proceeds straight through the intersection or turns right in front of him.

  11. Performance Evaluation of Discriminant Analysis and Decision Tree, for Weed Classification of Potato Fields

    Directory of Open Access Journals (Sweden)

    Farshad Vesali

    2012-09-01

    Full Text Available In present study we tried to recognizing weeds in potato fields to effective use from herbicides. As we know potato is one of the crops which is cultivated vastly all over the world and it is a major world food crop that is consumed by over one billion people world over, but it is threated by weed invade, because of row cropping system applied in potato tillage. Machine vision is used in this research for effective application of herbicides in field. About 300 color images from 3 potato farms of Qorveh city and 2 farms of Urmia University-Iran, was acquired. Images were acquired in different illumination condition from morning to evening in sunny and cloudy days. Because of overlap and shading of plants in farm condition it is hard to use morphologic parameters. In method used for classifying weeds and potato plants, primary color components of each plant were extracted and the relation between them was estimated for determining discriminant function and classifying plants using discrimination analysis. In addition the decision tree method was used to compare results with discriminant analysis. Three different classifications were applied: first, Classification was applied to discriminate potato plant from all other weeds (two groups, the rate of correct classification was 76.67% for discriminant analysis and 83.82% for decision tree; second classification was applied to discriminate potato plant from separate groups of each weed (6 groups, the rate of correct classification was 87%. And the third, Classification of potato plant versus weed species one by one. As the weeds were different, the results of classification were different in this composition. The decision tree in all conditions showed the better result than discriminant analysis.

  12. Hybrid Dynamic Network Data Envelopment Analysis

    Directory of Open Access Journals (Sweden)

    Ling Li

    2015-01-01

    Full Text Available Conventional DEA models make no hypothesis concerning the internal operations in a static situation. To open the “black box” and work with dynamic assessment issues synchronously, we put forward a hybrid model for evaluating the relative efficiencies of a set of DMUs over an observed time period with a composite of network DEA and dynamic DEA. We vertically deal with intermediate products between divisions with assignable inputs in the network structure and, horizontally, we extend network structure by means of a dynamic pattern with unrelated activities between two succeeding periods. The hybrid dynamic network DEA model proposed in this paper enables us to (i pry into the internal operations of DEA by another network structure, (ii obtain dynamic change of period efficiency, and (iii gain the overall dynamic efficiency of DMUs over the entire observed periods. We finally illustrate the calculation procedure of the proposed approach by a numerical example.

  13. Determination of sex by discriminant function analysis of mandibles from a Central Indian population

    Directory of Open Access Journals (Sweden)

    Kanchankumar P Wankhede

    2015-01-01

    Full Text Available Context: Identification of sex from skeletal remains is one of the important forensic considerations. Discriminant function analysis is increasingly used to determine the sex from skeleton. Aims: To develop discriminant function to determine sex from mandible in a Central Indian population. Settings and Design: This was a prospective study done at the Department of Anatomy. Materials and Methods: The mandibles used in the present study were from the museum specimens. Only 82 adult mandibles (55 male and 27 female that had been preserved were selected. Ten mandibular parameters were measured. Statistical Analysis Used: Statistical analysis was conducted using Statistical Package for Social Sciences (SPSS for Windows, version 16. The level of statistical significance was set at P < 0.05. Results: Using stepwise discriminant function analysis, only six variables were selected as the best discriminant between sexes, with the projection length of corpus mandibulae being the most dimorphic. It was observed that sex classification accuracy of the discriminant functions ranged from 57.3 to 80.5% for the individual variables, 81.7% for the stepwise method, and 85.4% for the direct method. Conclusion: The results of the study show that mandibles can be used for determining sex and the results are comparable with other similar studies. The studied mandibular variables showed sexual dimorphism with an accuracy comparable with other skeletal remains, next to cranium and pelvis.

  14. ALE meta-analysis reveals dissociable networks for affective and discriminative aspects of touch.

    Science.gov (United States)

    Morrison, India

    2016-04-01

    Emotionally-laden tactile stimulation-such as a caress on the skin or the feel of velvet-may represent a functionally distinct domain of touch, underpinned by specific cortical pathways. In order to determine whether, and to what extent, cortical functional neuroanatomy supports a distinction between affective and discriminative touch, an activation likelihood estimate (ALE) meta-analysis was performed. This meta-analysis statistically mapped reported functional magnetic resonance imaging (fMRI) activations from 17 published affective touch studies in which tactile stimulation was associated with positive subjective evaluation (n = 291, 34 experimental contrasts). A separate ALE meta-analysis mapped regions most likely to be activated by tactile stimulation during detection and discrimination tasks (n = 1,075, 91 experimental contrasts). These meta-analyses revealed dissociable regions for affective and discriminative touch, with posterior insula (PI) more likely to be activated for affective touch, and primary somatosensory cortices (SI) more likely to be activated for discriminative touch. Secondary somatosensory cortex had a high likelihood of engagement by both affective and discriminative touch. Further, meta-analytic connectivity (MCAM) analyses investigated network-level co-activation likelihoods independent of task or stimulus, across a range of domains and paradigms. Affective-related PI and discriminative-related SI regions co-activated with different networks, implicated in dissociable functions, but sharing somatosensory co-activations. Taken together, these meta-analytic findings suggest that affective and discriminative touch are dissociable both on the regional and network levels. However, their degree of shared activation likelihood in somatosensory cortices indicates that this dissociation reflects functional biases within tactile processing networks, rather than functionally and anatomically distinct pathways.

  15. Discriminant analysis of milk adulteration based on near-infrared spectroscopy and pattern recognition

    Science.gov (United States)

    Liu, Rong; Lv, Guorong; He, Bin; Xu, Kexin

    2011-03-01

    Since the beginning of the 21st century, the issue of food safety is becoming a global concern. It is very important to develop a rapid, cost-effective, and widely available method for food adulteration detection. In this paper, near-infrared spectroscopy techniques and pattern recognition were applied to study the qualitative discriminant analysis method. The samples were prepared and adulterated with one of the three adulterants, urea, glucose and melamine with different concentrations. First, the spectral characteristics of milk and adulterant samples were analyzed. Then, pattern recognition methods were used for qualitative discriminant analysis of milk adulteration. Soft independent modeling of class analogy and partial least squares discriminant analysis (PLSDA) were used to construct discriminant models, respectively. Furthermore, the optimization method of the model was studied. The best spectral pretreatment methods and the optimal band were determined. In the optimal conditions, PLSDA models were constructed respectively for each type of adulterated sample sets (urea, melamine and glucose) and all the three types of adulterated sample sets. Results showed that, the discrimination accuracy of model achieved 93.2% in the classification of different adulterated and unadulterated milk samples. Thus, it can be concluded that near-infrared spectroscopy and PLSDA can be used to identify whether the milk has been adulterated or not and the type of adulterant used.

  16. Chemical Discrimination of Cortex Phellodendri amurensis and Cortex Phellodendri chinensis by Multivariate Analysis Approach.

    Science.gov (United States)

    Sun, Hui; Wang, Huiyu; Zhang, Aihua; Yan, Guangli; Han, Ying; Li, Yuan; Wu, Xiuhong; Meng, Xiangcai; Wang, Xijun

    2016-01-01

    As herbal medicines have an important position in health care systems worldwide, their current assessment, and quality control are a major bottleneck. Cortex Phellodendri chinensis (CPC) and Cortex Phellodendri amurensis (CPA) are widely used in China, however, how to identify species of CPA and CPC has become urgent. In this study, multivariate analysis approach was performed to the investigation of chemical discrimination of CPA and CPC. Principal component analysis showed that two herbs could be separated clearly. The chemical markers such as berberine, palmatine, phellodendrine, magnoflorine, obacunone, and obaculactone were identified through the orthogonal partial least squared discriminant analysis, and were identified tentatively by the accurate mass of quadruple-time-of-flight mass spectrometry. A total of 29 components can be used as the chemical markers for discrimination of CPA and CPC. Of them, phellodenrine is significantly higher in CPC than that of CPA, whereas obacunone and obaculactone are significantly higher in CPA than that of CPC. The present study proves that multivariate analysis approach based chemical analysis greatly contributes to the investigation of CPA and CPC, and showed that the identified chemical markers as a whole should be used to discriminate the two herbal medicines, and simultaneously the results also provided chemical information for their quality assessment. Multivariate analysis approach was performed to the investigate the herbal medicineThe chemical markers were identified through multivariate analysis approachA total of 29 components can be used as the chemical markers. UPLC-Q/TOF-MS-based multivariate analysis method for the herbal medicine samples Abbreviations used: CPC: Cortex Phellodendri chinensis, CPA: Cortex Phellodendri amurensis, PCA: Principal component analysis, OPLS-DA: Orthogonal partial least squares discriminant analysis, BPI: Base peaks ion intensity.

  17. Discriminant analysis of some east Tennessee forest herb niches. Environmental Sciences Division Publication No. 752

    Energy Technology Data Exchange (ETDEWEB)

    Mann, L.K.; Shugart, H.H.; Kitchings, J.T.

    1978-03-01

    The purpose of this study was to evaluate the effectiveness of using discriminant analysis in assessing plant niches. As a component of research by the Environmental Research Park Project at Oak Ridge, Tennessee, five sites were inventoried for herbaceous species. From this inventory, four sympatric species of Galium and seventeen co-occurring herbaceous species were selected for discriminant analysis. The four species of Galium were treated as two data sets: one was composed of information collected at one site (a mesic hardwood area) and the other contained data from two cedar sites of shallow soil over limestone bedrock. The seventeen herbaceous species all occurred in the mesic hardwood area.

  18. Analysis of Synchronization for Coupled Hybrid Systems

    DEFF Research Database (Denmark)

    Li, Zheng; Wisniewski, Rafal

    2006-01-01

    In the control systems with coupled multi-subsystem, the subsystems might be synchronized (i.e. all the subsystems have the same operation states), which results in negative influence to the whole system. For example, in the supermarket refrigeration systems, the synchronized switch of each...... subsystem will cause low efficiency, inferior control performance and a high wear on the compressor. This paper takes the supermarket refrigeration systems as an example to analyze the synchronization and its coupling strengths of coupled hybrid systems, which may provide a base for further research...

  19. Discrimination of whisky brands and counterfeit identification by UV-Vis spectroscopy and multivariate data analysis.

    Science.gov (United States)

    Martins, Angélica Rocha; Talhavini, Márcio; Vieira, Maurício Leite; Zacca, Jorge Jardim; Braga, Jez Willian Batista

    2017-08-15

    The discrimination of whisky brands and counterfeit identification were performed by UV-Vis spectroscopy combined with partial least squares for discriminant analysis (PLS-DA). In the proposed method all spectra were obtained with no sample preparation. The discrimination models were built with the employment of seven whisky brands: Red Label, Black Label, White Horse, Chivas Regal (12years), Ballantine's Finest, Old Parr and Natu Nobilis. The method was validated with an independent test set of authentic samples belonging to the seven selected brands and another eleven brands not included in the training samples. Furthermore, seventy-three counterfeit samples were also used to validate the method. Results showed correct classification rates for genuine and false samples over 98.6% and 93.1%, respectively, indicating that the method can be helpful for the forensic analysis of whisky samples. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. Use of correspondence discriminant analysis to predict the subcellular location of bacterial proteins.

    Science.gov (United States)

    Perrière, Guy; Thioulouse, Jean

    2003-02-01

    Correspondence discriminant analysis (CDA) is a multivariate statistical method derived from discriminant analysis which can be used on contingency tables. We have used CDA to separate Gram negative bacteria proteins according to their subcellular location. The high resolution of the discrimination obtained makes this method a good tool to predict subcellular location when this information is not known. The main advantage of this technique is its simplicity. Indeed, by computing two linear formulae on amino acid composition, it is possible to classify a protein into one of the three classes of subcellular location we have defined. The CDA itself can be computed with the ADE-4 software package that can be downloaded, as well as the data set used in this study, from the Pôle Bio-Informatique Lyonnais (PBIL) server at http://pbil.univ-lyon1.fr.

  1. Combined approach based on principal component analysis and canonical discriminant analysis for investigating hyperspectral plant response

    Directory of Open Access Journals (Sweden)

    Anna Maria Stellacci

    2012-07-01

    Full Text Available Hyperspectral (HS data represents an extremely powerful means for rapidly detecting crop stress and then aiding in the rational management of natural resources in agriculture. However, large volume of data poses a challenge for data processing and extracting crucial information. Multivariate statistical techniques can play a key role in the analysis of HS data, as they may allow to both eliminate redundant information and identify synthetic indices which maximize differences among levels of stress. In this paper we propose an integrated approach, based on the combined use of Principal Component Analysis (PCA and Canonical Discriminant Analysis (CDA, to investigate HS plant response and discriminate plant status. The approach was preliminary evaluated on a data set collected on durum wheat plants grown under different nitrogen (N stress levels. Hyperspectral measurements were performed at anthesis through a high resolution field spectroradiometer, ASD FieldSpec HandHeld, covering the 325-1075 nm region. Reflectance data were first restricted to the interval 510-1000 nm and then divided into five bands of the electromagnetic spectrum [green: 510-580 nm; yellow: 581-630 nm; red: 631-690 nm; red-edge: 705-770 nm; near-infrared (NIR: 771-1000 nm]. PCA was applied to each spectral interval. CDA was performed on the extracted components to identify the factors maximizing the differences among plants fertilised with increasing N rates. Within the intervals of green, yellow and red only the first principal component (PC had an eigenvalue greater than 1 and explained more than 95% of total variance; within the ranges of red-edge and NIR, the first two PCs had an eigenvalue higher than 1. Two canonical variables explained cumulatively more than 81% of total variance and the first was able to discriminate wheat plants differently fertilised, as confirmed also by the significant correlation with aboveground biomass and grain yield parameters. The combined

  2. SPR Detection and Discrimination of the Oligonucleotides Related to the Normal and the Hybrid bcr-abl Genes by Two Stringency Control Strategies

    Science.gov (United States)

    Matsishin, M. J.; Ushenin, Iu. V.; Rachkov, A. E.; Solatkin, A. P.

    2016-01-01

    In this study, we applied two stringency control strategies for surface plasmon resonance (SPR) detection of DNA hybridization and discrimination of completely and partially complementary 24-mer sequences. These sequences are specific to the human normal bcr and the hybrid bcr-abl genes, protein products of which are responsible for some leukemia. SPR sensors based on resonance phenomena in nanoscale gold films are well suited for label-free, real-time investigations of the macromolecule interactions. Thermodynamic parameters obtained using the web server DINAMelt allowed supposing the possibility for realization (a) stringency control based on the ionic strength of the hybridization buffer and (b) stringency control based on the temperature elevation. The first one resulted in that the discrimination index of completely complementary and partially complementary oligonucleotides depending on the target concentration varied from 1.3 to 1.8 in 2 × SSC and from 2.0 to 2.9 in 0.5 × SSC. For implementation of the second stringency control strategy, SPR spectrometer measuring flow cell with built-in high-precision temperature control and regulation as well as corresponding software was created. It is shown that the duplexes formed by the immobilized probes mod-Ph and completely complementary oligonucleotides P1 remained without significant changes until ~50 °C, while the duplexes formed with partially complementary oligonucleotide Bcrex14 almost entirely disrupted at 40 °C. Thus, the absolutely effective thermodiscrimination of this pair of oligonucleotides was achieved in this temperature range (40-50 °C).

  3. Hybrid energy system cost analysis: San Nicolas Island, California

    Energy Technology Data Exchange (ETDEWEB)

    Olsen, T.L.; McKenna, E.

    1996-07-01

    This report analyzes the local wind resource and evaluates the costs and benefits of supplementing the current diesel-powered energy system on San Nicolas Island, California (SNI), with wind turbines. In Section 2.0 the SNI site, naval operations, and current energy system are described, as are the data collection and analysis procedures. Section 3.0 summarizes the wind resource data and analyses that were presented in NREL/TP 442-20231. Sections 4.0 and 5.0 present the conceptual design and cost analysis of a hybrid wind and diesel energy system on SNI, with conclusions following in Section 6. Appendix A presents summary pages of the hybrid system spreadsheet model, and Appendix B contains input and output files for the HYBRID2 program.

  4. Hybrid Ventilation with Innovative Heat Recovery—A System Analysis

    Directory of Open Access Journals (Sweden)

    Bengt Hellström

    2013-02-01

    Full Text Available One of the most important factors when low energy houses are built is to have good heat recovery on the ventilation system. However, standard ventilation units use a considerable amount of electricity. This article discusses the consequences on a system level of using hybrid ventilation with heat recovery. The simulation program TRNSYS was used in order to investigate a ventilation system with heat recovery. The system also includes a ground source storage and waste water heat recovery system. The result of the analysis shows that the annual energy gain from ground source storage is limited. However, this is partly a consequence of the fact that the well functioning hybrid ventilation system leaves little room for improvements. The analysis shows that the hybrid ventilation system has potential to be an attractive solution for low energy buildings with a very low need for electrical energy.

  5. Buckling analysis of a ring stiffened hybrid composite cylinder

    Science.gov (United States)

    Potluri, Rakesh; Eswara Kumar, A.; Navuri, Karteek; Nagaraju, M.; Mojeswara Rao, Duduku

    2016-09-01

    This study aims to understand the response of the ring stiffened cylinders made up of hybrid composites subjected to buckling loads by using the concepts of Design of Experiments (DOE) and optimization by using Finite Element Method (FEM) simulation software Ansys workbench V15. Carbon epoxy and E-glass epoxy composites were used in the hybrid composite. This hybrid composite was analyzed by using different layup angles. Central composite design (CCD) was used to perform design of experiments (D.O.E) and kriging method was used to generate a response surface. The response surface optimization (RSO) was performed by using the method of the multi-objective genetic algorithm (MOGA). After optimization, the best candidate was chosen and applied to the ring stiffened cylinder and eigenvalue buckling analysis was performed to understand the buckling behavior. Best laminate candidates with high buckling strength have been identified. A generalized procedure of the laminate optimization and analysis have been shown.

  6. ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R

    Directory of Open Access Journals (Sweden)

    Tarn Duong

    2007-09-01

    Full Text Available Kernel smoothing is one of the most widely used non-parametric data smoothing techniques. We introduce a new R package ks for multivariate kernel smoothing. Currently it contains functionality for kernel density estimation and kernel discriminant analysis. It is a comprehensive package for bandwidth matrix selection, implementing a wide range of data-driven diagonal and unconstrained bandwidth selectors.

  7. Optimal Class Separation in Hyperspectral Image Data: Iterated Canonical Discriminant Analysis

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Müller, Andreas

    This paper describes canonical discriminant analysis and sketches an iterative version which is then applied to obtain optimal separation between a region, here examplified by either “water” or “wood/trees” and the rest of a HyMap image. We show that the iterative version greatly enhances the sep...

  8. Discrimination of bromodeoxyuridine labelled and unlabelled mitotic cells in flow cytometric bromodeoxyuridine/DNA analysis

    DEFF Research Database (Denmark)

    Jensen, P O; Larsen, J K; Christensen, I J

    1994-01-01

    Bromodeoxyuridine (BrdUrd) labelled and unlabelled mitotic cells, respectively, can be discriminated from interphase cells using a new method, based on immunocytochemical staining of BrdUrd and flow cytometric four-parameter analysis of DNA content, BrdUrd incorporation, and forward and orthogona...

  9. A Research Project Using the Safran Student Interest Inventory (SSII): Discriminant Analysis of University Majors.

    Science.gov (United States)

    Nichols, K. E.

    Safran Student Interest Inventory (SSII) data was gathered on 135 university students registered in five different faculties. A discriminant analysis of the data indicated that the SSII was a good test for separating students into faculties and therefore would make a good counselling instrument. Some results are also present using Differential…

  10. Cloud mask via cumulative discriminant analysis applied to satellite infrared observations: scientific basis and initial evaluation

    Directory of Open Access Journals (Sweden)

    U. Amato

    2014-06-01

    Full Text Available We introduce a classification method (Cumulative Discriminant Analysis of the Discriminant Analysis type to discriminate between cloudy and clear sky satellite observations in the thermal infrared. The tool is intended for the high spectral resolution infrared sounder (IRS planned for the geostationary METEOSAT (Meteorological Satellite Third Generation platform and uses IASI (Infrared Atmospheric Sounding Interferometer data as a proxy. The Cumulative Discriminant Analysis does not introduce biases intrinsic with the approximation of the probability density functions and is flexible enough to adapt to different strategies to optimize the cloud mask. The methodology is based on nine statistics computed from IASI spectral radiances, which exploit the high spectral resolution of the instrument and which effectively summarize information contained within the IASI spectrum. A Principal Component Analysis prior step is also introduced which makes the problem more consistent with the statistical assumptions of the methodology. An initial assessment of the scheme is performed based on global and regional IASI real data sets and cloud masks obtained from AVHRR (Advanced Very High Resolution Radiometer and SEVIRI (Spinning Enhanced Visible and Infrared Imager imagers. The agreement with these independent cloud masks is generally well above 80%, except at high latitudes in their winter seasons.

  11. Cloud mask via cumulative discriminant analysis applied to satellite infrared observations: scientific basis and initial evaluation

    Science.gov (United States)

    Amato, U.; Lavanant, L.; Liuzzi, G.; Masiello, G.; Serio, C.; Stuhlmann, R.; Tjemkes, S. A.

    2014-10-01

    We introduce a classification method (cumulative discriminant analysis) of the discriminant analysis type to discriminate between cloudy and clear-sky satellite observations in the thermal infrared. The tool is intended for the high-spectral-resolution infrared sounder (IRS) planned for the geostationary METEOSAT (Meteorological Satellite) Third Generation platform and uses IASI (Infrared Atmospheric Sounding Interferometer) data as a proxy. The cumulative discriminant analysis does not introduce biases intrinsic with the approximation of the probability density functions and is flexible enough to adapt to different strategies to optimize the cloud mask. The methodology is based on nine statistics computed from IASI spectral radiances, which exploit the high spectral resolution of the instrument and which effectively summarize information contained within the IASI spectrum. A principal component analysis prior step is also introduced, which makes the problem more consistent with the statistical assumptions of the methodology. An initial assessment of the scheme is performed based on global and regional IASI real data sets and cloud masks obtained from AVHRR (Advanced Very High Resolution Radiometer) and SEVIRI (Spinning Enhanced Visible and Infrared Imager) imagers. The agreement with these independent cloud masks is generally well above 80 %, except at high latitudes in the winter seasons.

  12. On the Variable Selection Problem in Multiple Group Discriminant Analysis.

    Science.gov (United States)

    Huberty, Carl J.

    This study was concerned with various schemes for reducing the number of variables in a multivariate analysis. Two sets of illustrative data were used; the numbers of criterion groups were 3 and 5. The proportion of correct classifications was employed as an index of discriminatory power of each subset of variables selected. Of the four procedures…

  13. Mars Hybrid Propulsion System Trajectory Analysis. Part I; Crew Missions

    Science.gov (United States)

    Chai, Patrick R.; Merrill, Raymond G.; Qu, Min

    2015-01-01

    NASAs Human spaceflight Architecture team is developing a reusable hybrid transportation architecture in which both chemical and electric propulsion systems are used to send crew and cargo to Mars destinations such as Phobos, Deimos, the surface of Mars, and other orbits around Mars. By combining chemical and electrical propulsion into a single space- ship and applying each where it is more effective, the hybrid architecture enables a series of Mars trajectories that are more fuel-efficient than an all chemical architecture without significant increases in flight times. This paper provides the analysis of the interplanetary segments of the three Evolvable Mars Campaign crew missions to Mars using the hybrid transportation architecture. The trajectory analysis provides departure and arrival dates and propellant needs for the three crew missions that are used by the campaign analysis team for campaign build-up and logistics aggregation analysis. Sensitivity analyses were performed to investigate the impact of mass growth, departure window, and propulsion system performance on the hybrid transportation architecture. The results and system analysis from this paper contribute to analyses of the other human spaceflight architecture team tasks and feed into the definition of the Evolvable Mars Campaign.

  14. Modelling and analysis of real-time and hybrid systems

    Energy Technology Data Exchange (ETDEWEB)

    Olivero, A.

    1994-09-29

    This work deals with the modelling and analysis of real-time and hybrid systems. We first present the timed-graphs as model for the real-time systems and we recall the basic notions of the analysis of real-time systems. We describe the temporal properties on the timed-graphs using TCTL formulas. We consider two methods for property verification: in one hand we study the symbolic model-checking (based on backward analysis) and in the other hand we propose a verification method derived of the construction of the simulation graph (based on forward analysis). Both methods have been implemented within the KRONOS verification tool. Their application for the automatic verification on several real-time systems confirms the practical interest of our approach. In a second part we study the hybrid systems, systems combining discrete components with continuous ones. As in the general case the analysis of this king of systems is not decidable, we identify two sub-classes of hybrid systems and we give a construction based method for the generation of a timed-graph from an element into the sub-classes. We prove that in one case the timed-graph obtained is bi-similar with the considered system and that there exists a simulation in the other case. These relationships allow the application of the described technics on the hybrid systems into the defined sub-classes. (authors). 60 refs., 43 figs., 8 tabs., 2 annexes.

  15. Comparative analysis of pulse shape discrimination methods in a {sup 6}Li loaded plastic scintillator

    Energy Technology Data Exchange (ETDEWEB)

    Balmer, Matthew J.I., E-mail: m.balmer@lancaster.ac.uk [Department of Engineering, Lancaster University, LA1 4YR (United Kingdom); Gamage, Kelum A.A. [Department of Engineering, Lancaster University, LA1 4YR (United Kingdom); Taylor, Graeme C. [Neutron Metrology Group, National Physical Laboratory, Teddington, TW11 0LW (United Kingdom)

    2015-07-11

    Three algorithms for discriminating between fast neutrons, thermal neutrons and gamma rays in a {sup 6}Li loaded plastic scintillator have been compared. Following a literature review of existing pulse shape discrimination techniques, the performance of the charge comparison method, triangular filtering and frequency gradient analysis were investigated in this work. The scintillator was exposed to three different mixed gamma/neutron radiation fields. The figure of merit of neutron/gamma separation was investigated over a broad energy range, as well as for the neutron capture energy region. After optimisation, all three methods were found to perform similarly in terms of neutron/gamma separation.

  16. Hybrid reliability model for fatigue reliability analysis of steel bridges

    Institute of Scientific and Technical Information of China (English)

    曹珊珊; 雷俊卿

    2016-01-01

    A kind of hybrid reliability model is presented to solve the fatigue reliability problems of steel bridges. The cumulative damage model is one kind of the models used in fatigue reliability analysis. The parameter characteristics of the model can be described as probabilistic and interval. The two-stage hybrid reliability model is given with a theoretical foundation and a solving algorithm to solve the hybrid reliability problems. The theoretical foundation is established by the consistency relationships of interval reliability model and probability reliability model with normally distributed variables in theory. The solving process is combined with the definition of interval reliability index and the probabilistic algorithm. With the consideration of the parameter characteristics of theS−N curve, the cumulative damage model with hybrid variables is given based on the standards from different countries. Lastly, a case of steel structure in the Neville Island Bridge is analyzed to verify the applicability of the hybrid reliability model in fatigue reliability analysis based on the AASHTO.

  17. Regularized discriminant analysis for multi-sensor decision fusion and damage detection with Lamb waves

    Science.gov (United States)

    Mishra, Spandan; Vanli, O. Arda; Huffer, Fred W.; Jung, Sungmoon

    2016-04-01

    In this study we propose a regularized linear discriminant analysis approach for damage detection which does not require an intermediate feature extraction step and therefore more efficient in handling data with high-dimensionality. A robust discriminant model is obtained by shrinking of the covariance matrix to a diagonal matrix and thresholding redundant predictors without hurting the predictive power of the model. The shrinking and threshold parameters of the discriminant function (decision boundary) are estimated to minimize the classification error. Furthermore, it is shown how the damage classification achieved by the proposed method can be extended to multiple sensors by following a Bayesian decision-fusion formulation. The detection probability of each sensor is used as a prior condition to estimate the posterior detection probability of the entire network and the posterior detection probability is used as a quantitative basis to make the final decision about the damage.

  18. Shrinkage-based diagonal discriminant analysis and its applications in high-dimensional data.

    Science.gov (United States)

    Pang, Herbert; Tong, Tiejun; Zhao, Hongyu

    2009-12-01

    High-dimensional data such as microarrays have brought us new statistical challenges. For example, using a large number of genes to classify samples based on a small number of microarrays remains a difficult problem. Diagonal discriminant analysis, support vector machines, and k-nearest neighbor have been suggested as among the best methods for small sample size situations, but none was found to be superior to others. In this article, we propose an improved diagonal discriminant approach through shrinkage and regularization of the variances. The performance of our new approach along with the existing methods is studied through simulations and applications to real data. These studies show that the proposed shrinkage-based and regularization diagonal discriminant methods have lower misclassification rates than existing methods in many cases.

  19. Characterization of local complex structures in a recurrence plot to improve nonlinear dynamic discriminant analysis.

    Science.gov (United States)

    Ding, Hang

    2014-01-01

    Structures in recurrence plots (RPs), preserving the rich information of nonlinear invariants and trajectory characteristics, have been increasingly analyzed in dynamic discrimination studies. The conventional analysis of RPs is mainly focused on quantifying the overall diagonal and vertical line structures through a method, called recurrence quantification analysis (RQA). This study extensively explores the information in RPs by quantifying local complex RP structures. To do this, an approach was developed to analyze the combination of three major RQA variables: determinism, laminarity, and recurrence rate (DLR) in a metawindow moving over a RP. It was then evaluated in two experiments discriminating (1) ideal nonlinear dynamic series emulated from the Lorenz system with different control parameters and (2) data sets of human heart rate regulations with normal sinus rhythms (n = 18) and congestive heart failure (n = 29). Finally, the DLR was compared with seven major RQA variables in terms of discriminatory power, measured by standardized mean difference (DSMD). In the two experiments, DLR resulted in the highest discriminatory power with DSMD = 2.53 and 0.98, respectively, which were 7.41 and 2.09 times the best performance from RQA. The study also revealed that the optimal RP structures for the discriminations were neither typical diagonal structures nor vertical structures. These findings indicate that local complex RP structures contain some rich information unexploited by RQA. Therefore, future research to extensively analyze complex RP structures would potentially improve the effectiveness of the RP analysis in dynamic discrimination studies.

  20. Generalized Discriminant Analysis algorithm for feature reduction in Cyber Attack Detection System

    Directory of Open Access Journals (Sweden)

    Shailendra Singh

    2009-10-01

    Full Text Available This Generalized Discriminant Analysis (GDA has provided an extremely powerful approach to extracting non-linear features. The network traffic data provided for the design of intrusion detection system always are large with ineffective information, thus we need to remove the worthless information from the original high dimensional database. To improve the generalization ability, we usually generate a small set of features from the original input variables by feature extraction. The conventional Linear Discriminant Analysis (LDA feature reduction technique has its limitations. It is not suitable for non-linear dataset. Thus we propose an efficient algorithm based on the Generalized Discriminant Analysis (GDA feature reduction technique which is novel approach used in the area of cyber attack detection. This not only reduces the number of the input features but also increases the classification accuracy and reduces the training and testing time of the classifiers by selecting most discriminating features. We use Artificial Neural Network (ANN and C4.5 classifiers to compare the performance of the proposed technique. The result indicates the superiority of algorithm.

  1. Characterization of local complex structures in a recurrence plot to improve nonlinear dynamic discriminant analysis

    Science.gov (United States)

    Ding, Hang

    2014-01-01

    Structures in recurrence plots (RPs), preserving the rich information of nonlinear invariants and trajectory characteristics, have been increasingly analyzed in dynamic discrimination studies. The conventional analysis of RPs is mainly focused on quantifying the overall diagonal and vertical line structures through a method, called recurrence quantification analysis (RQA). This study extensively explores the information in RPs by quantifying local complex RP structures. To do this, an approach was developed to analyze the combination of three major RQA variables: determinism, laminarity, and recurrence rate (DLR) in a metawindow moving over a RP. It was then evaluated in two experiments discriminating (1) ideal nonlinear dynamic series emulated from the Lorenz system with different control parameters and (2) data sets of human heart rate regulations with normal sinus rhythms (n = 18) and congestive heart failure (n = 29). Finally, the DLR was compared with seven major RQA variables in terms of discriminatory power, measured by standardized mean difference (DSMD). In the two experiments, DLR resulted in the highest discriminatory power with DSMD = 2.53 and 0.98, respectively, which were 7.41 and 2.09 times the best performance from RQA. The study also revealed that the optimal RP structures for the discriminations were neither typical diagonal structures nor vertical structures. These findings indicate that local complex RP structures contain some rich information unexploited by RQA. Therefore, future research to extensively analyze complex RP structures would potentially improve the effectiveness of the RP analysis in dynamic discrimination studies.

  2. The hebridological analysis of productivity traits in pea hybrids

    Directory of Open Access Journals (Sweden)

    Світлана Володимирівна Коблай

    2015-09-01

    Full Text Available To study the nature of inheritance of quantitative traits that influence productivity of pea hybrids were obtained first and second generations for which sample and varieties with different leaf morphotype are served as parental form. As the results of hybridological analysis it is determined the degree of domination and revealed the heterosis combinations

  3. Nuclear-Renewable Hybrid Energy System Market Analysis Plans

    Energy Technology Data Exchange (ETDEWEB)

    Ruth, Mark

    2016-06-09

    This presentation describes nuclear-renewable hybrid energy systems (N-R HESs), states their potential benefits, provides figures for the four tightly coupled N-R HESs that NREL is currently analyzing, and outlines the analysis process that is underway.

  4. Variations in students' perceived reasons for, sources of, and forms of in-school discrimination: A latent class analysis.

    Science.gov (United States)

    Byrd, Christy M; Carter Andrews, Dorinda J

    2016-08-01

    Although there exists a healthy body of literature related to discrimination in schools, this research has primarily focused on racial or ethnic discrimination as perceived and experienced by students of color. Few studies examine students' perceptions of discrimination from a variety of sources, such as adults and peers, their descriptions of the discrimination, or the frequency of discrimination in the learning environment. Middle and high school students in a Midwestern school district (N=1468) completed surveys identifying whether they experienced discrimination from seven sources (e.g., peers, teachers, administrators), for seven reasons (e.g., gender, race/ethnicity, religion), and in eight forms (e.g., punished more frequently, called names, excluded from social groups). The sample was 52% White, 15% Black/African American, 14% Multiracial, and 17% Other. Latent class analysis was used to cluster individuals based on reported sources of, reasons for, and forms of discrimination. Four clusters were found, and ANOVAs were used to test for differences between clusters on perceptions of school climate, relationships with teachers, perceptions that the school was a "good school," and engagement. The Low Discrimination cluster experienced the best outcomes, whereas an intersectional cluster experienced the most discrimination and the worst outcomes. The results confirm existing research on the negative effects of discrimination. Additionally, the paper adds to the literature by highlighting the importance of an intersectional approach to examining students' perceptions of in-school discrimination. Copyright © 2016 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

  5. Validation of a hybrid life-cycle inventory analysis method.

    Science.gov (United States)

    Crawford, Robert H

    2008-08-01

    The life-cycle inventory analysis step of a life-cycle assessment (LCA) may currently suffer from several limitations, mainly concerned with the use of incomplete and unreliable data sources and methods of assessment. Many past LCA studies have used traditional inventory analysis methods, namely process analysis and input-output analysis. More recently, hybrid inventory analysis methods have been developed, combining these two traditional methods in an attempt to minimise their limitations. In light of recent improvements, these hybrid methods need to be compared and validated, as these too have been considered to have several limitations. This paper evaluates a recently developed hybrid inventory analysis method which aims to improve the limitations of previous methods. It was found that the truncation associated with process analysis can be up to 87%, reflecting the considerable shortcomings in the quantity of process data currently available. Capital inputs were found to account for up to 22% of the total inputs to a particular product. These findings suggest that current best-practice methods are sufficiently accurate for most typical applications, but this is heavily dependent upon data quality and availability. The use of input-output data assists in improving the system boundary completeness of life-cycle inventories. However, the use of input-output analysis alone does not always provide an accurate model for replacing process data. Further improvements in the quantity of process data currently available are needed to increase the reliability of life-cycle inventories.

  6. Discrimination of honeys using colorimetric sensor arrays, sensory analysis and gas chromatography techniques.

    Science.gov (United States)

    Tahir, Haroon Elrasheid; Xiaobo, Zou; Xiaowei, Huang; Jiyong, Shi; Mariod, Abdalbasit Adam

    2016-09-01

    Aroma profiles of six honey varieties of different botanical origins were investigated using colorimetric sensor array, gas chromatography-mass spectrometry (GC-MS) and descriptive sensory analysis. Fifty-eight aroma compounds were identified, including 2 norisoprenoids, 5 hydrocarbons, 4 terpenes, 6 phenols, 7 ketones, 9 acids, 12 aldehydes and 13 alcohols. Twenty abundant or active compounds were chosen as key compounds to characterize honey aroma. Discrimination of the honeys was subsequently implemented using multivariate analysis, including hierarchical clustering analysis (HCA) and principal component analysis (PCA). Honeys of the same botanical origin were grouped together in the PCA score plot and HCA dendrogram. SPME-GC/MS and colorimetric sensor array were able to discriminate the honeys effectively with the advantages of being rapid, simple and low-cost. Moreover, partial least squares regression (PLSR) was applied to indicate the relationship between sensory descriptors and aroma compounds.

  7. Cytogenetic analysis from DNA by comparative genomic hybridization.

    Science.gov (United States)

    Tachdjian, G; Aboura, A; Lapierre, J M; Viguié, F

    2000-01-01

    Comparative genomic hybridization (CGH) is a modified in situ hybridization technique which allows detection and mapping of DNA sequence copy differences between two genomes in a single experiment. In CGH analysis, two differentially labelled genomic DNA (study and reference) are co-hybridized to normal metaphase spreads. Chromosomal locations of copy number changes in the DNA segments of the study genome are revealed by a variable fluorescence intensity ratio along each target chromosome. Since its development, CGH has been applied mostly as a research tool in the field of cancer cytogenetics to identify genetic changes in many previously unknown regions. CGH may also have a role in clinical cytogenetics for detection and identification of unbalanced chromosomal abnormalities.

  8. Hybrid vehicle assessment. Phase 1: Petroleum savings analysis

    Science.gov (United States)

    Levin, R.; Liddle, S.; Deshpande, G.; Trummel, M.; Vivian, H. C.

    1984-01-01

    The results of a comprehensive analysis of near term electric hybrid vehicles are presented, with emphasis on their potential to save significant amounts of petroleum on a national scale in the 1990s. Performance requirements and expected annual usage patterns of these vehicles are first modeled. The projected U.S. fleet composition is estimated, and conceptual hybrid vehicle designs are conceived and analyzed for petroleum use when driven in the expected annual patterns. These petroleum consumption estimates are then compared to similar estimates for projected 1990 conventional vehicles having the same performance and driven in the same patterns. Results are presented in the form of three utility functions and comparisons of sevral conceptual designs are made. The Hybrid Vehicle (HV) design and assessment techniques are discussed and a general method is explained for selecting the optimum energy management strategy for any vehicle mission battery combination. Conclusions and recommendations are presented, and development recommendations are identified.

  9. Proteomic Analysis of Pachytene Spermatocytes of Sterile Hybrid Male Mice.

    Science.gov (United States)

    Wang, Lu; Guo, Yueshuai; Liu, Wenjing; Zhao, Weidong; Song, Gendi; Zhou, Tao; Huang, Hefeng; Guo, Xuejiang; Sun, Fei

    2016-09-01

    Incompatibilities in interspecific hybrids, such as reduced hybrid fertility and lethality, are common features resulting from reproductive isolation that lead to speciation. Subspecies crosses of house mice produce offspring in which one sex is infertile or absent, yet the molecular mechanisms of hybrid sterility are poorly understood. In this study, we observed extensive asynapsis of chromosomes and disturbance of the sex body in pachytene spermatocytes of sterile F1 males (PWK/Ph female × C57BL/6J male). We report the high-confidence identification of 4005 proteins in the pachytene spermatocytes of fertile F1 males (PWK/Ph male × C57BL/6J female) and sterile F1 males (PWK/Ph female × C57BL/6J male), of which 215 were upregulated and 381 were downregulated. Bioinformatics analysis of the proteome led to the identification of 43 and 59 proteins known to be essential for male meiosis and spermatogenesis in mice, respectively. Characterization of the proteome of pachytene spermatocytes associated with hybrid male sterility provides an inventory of proteins that is useful for understanding meiosis and the mechanisms of hybrid male infertility.

  10. Free choice profiling sensory analysis to discriminate coffees

    Directory of Open Access Journals (Sweden)

    Cíntia Sorane Good Kitzberger

    2016-12-01

    Full Text Available Sensory attributes were evaluated from Arabica coffee genotypes growing in two places in Brazil, Mandaguari and Londrina. Post-harvest and roasted process was standardized. Free choice profiling sensory analysis was apply to investigate the influence of genetic variability and local cultivation (Londrina and Mandaguari, Brazil on the sensory characteristics of coffee genotypes. A sensory panel evaluated coffees from Mandaguari in two groups: one (Sarchimor derived, IPR100, IPR102, IPR105, IPR106 characterized by transparency, coffee colour, green aroma, taste (green, bitter, fermented, astringent and a watery texture, another group (Catuaí, Sarchimor derived, IPR101, IPR103 was characterized by coffee colour, brightness, aroma (coffee, acid, sweet, chocolate, acidity, bitterness, burnt aroma, sweetness and full-bodied. Coffees from Londrina presented brightness, coffee colour, sweet, green, burnt aroma, astringent, bitter, fermented, green taste; and watery texture (Catuaí, IPR97, IPR98, IPR100. Another group (Sarchimor derived, IPR101, IPR102, IPR103, IPR105, IPR106 were associated with turbidity, aroma (green, coffee, sweet, acidity, astringency, bitterness, sweetness and full-bodied. Catuaí, Iapar59, IPR99, IPR101, IPR103 and IPR108 exhibited positive attributes when grown in either locale. Edaphoclimatic conditions play a major role in the sensory profiles of coffee.

  11. Over-excavation forecast of underground opening by using Bayes discriminant analysis method

    Institute of Scientific and Technical Information of China (English)

    GONG Feng-qiang; LI Xi-bing; ZHANG Wei

    2008-01-01

    A method to forecast the over-excavation of underground opening by using the Bayes discriminant analysis(BDA) theory was presented. The Bayes discriminant analysis theory was introduced. Based on an engineering example, the factors influencing the over-excavation of underground opening were taken into account to build a forecast BDA model, and the prior information about over-excavation of underground opening was also taken into consideration. Five parameters influencing the over-excavation of opening, including 2 groups of joints, 1 group of layer surface, extension and space between structure faces were selected as geometric parameters. Engineering data in an underground opening were used as the training samples. The cross-validation method was introduced to verify the stability of BDA model and the ratio of mistake-discrimination was equal to zero after the BDA model was trained. Data in an underground engineering were used to test the discriminant ability of BDA model. The results show that five forecast results are identical with the actual situation and BDA can be used in practical engineering.

  12. Multivariate Analysis of Laser-Induced Breakdown Spectroscopy for Discrimination between Explosives and Plastics

    Institute of Scientific and Technical Information of China (English)

    WANG Qian-Qian; LIU Kai; ZHAO Hua

    2012-01-01

    A method to distinguish explosives from plastics using laser-induced breakdown spectroscopy is discussed. A model for classification with cross-validation theory is built based on the partial least-square discriminant analysis method. Seven types of plastics and one explosive are used as samples to test the model. The experimental results demonstrate that laser-induced breakdown spectroscopy has the capacity to discriminate explosives from plastics combined with chemometrics methods. The results could be useful for prospective research of laser-induced breakdown spectroscopy on the differentiation of explosives and other materials.%A method to distinguish explosives from plastics using laser-induced breakdown spectroscopy is discussed.A model for classification with cross-validation theory is built based on the partial least-square discriminant analysis method.Seven types of plastics and one explosive are used as samples to test the model.The experimental results demonstrate that laser-induced breakdown spectroscopy has the capacity to discriminate explosives from plastics combined with chemometrics methods.The results could be useful for prospective research of laser-induced breakdown spectroscopy on the differentiation of explosives and other materials.

  13. Comparative analysis of cyanobacterial superoxide dismutases to discriminate canonical forms

    Directory of Open Access Journals (Sweden)

    Prabaharan Dharmar

    2007-11-01

    Full Text Available Abstract Background Superoxide dismutases (SOD are ubiquitous metalloenzymes that catalyze the disproportion of superoxide to peroxide and molecular oxygen through alternate oxidation and reduction of their metal ions. In general, SODs are classified into four forms by their catalytic metals namely; FeSOD, MnSOD, Cu/ZnSOD and NiSOD. In addition, a cambialistic form that uses Fe/Mn in its active site also exists. Cyanobacteria, the oxygen evolving photosynthetic prokaryotes, produce reactive oxygen species that can damage cellular components leading to cell death. Thus, the co-evolution of an antioxidant system was necessary for the survival of photosynthetic organisms with SOD as the initial enzyme evolved to alleviate the toxic effect. Cyanobacteria represent the first oxygenic photoautotrophs and their SOD sequences available in the databases lack clear annotation. Hence, the present study focuses on structure and sequence pattern of subsets of cyanobacterial superoxide dismutases. Result The sequence conservation and structural analysis of Fe (Thermosynechococcus elongatus BP1 and MnSOD (Anabaena sp. PCC7120 reveal the sharing of N and C terminal domains. At the C terminal domain, the metal binding motif in cyanoprokaryotes is DVWEHAYY while it is D-X-[WF]-E-H-[STA]-[FY]-[FY] in other pro- and eukaryotes. The cyanobacterial FeSOD differs from MnSOD at least in three ways viz. (i FeSOD has a metal specific signature F184X3A188Q189.......T280......F/Y303 while, in Mn it is R184X3G188G189......G280......W303, (ii aspartate ligand forms a hydrogen bond from the active site with the outer sphere residue of W243 in Fe where as it is Q262 in MnSOD; and (iii two unique lysine residues at positions 201 and 255 with a photosynthetic role, found only in FeSOD. Further, most of the cyanobacterial Mn metalloforms have a specific transmembrane hydrophobic pocket that distinguishes FeSOD from Mn isoform. Cyanobacterial Cu/ZnSOD has a copper domain and two

  14. Diagnostic support for glaucoma using retinal images: a hybrid image analysis and data mining approach.

    Science.gov (United States)

    Yu, Jin; Abidi, Syed Sibte Raza; Artes, Paul; McIntyre, Andy; Heywood, Malcolm

    2005-01-01

    The availability of modern imaging techniques such as Confocal Scanning Laser Tomography (CSLT) for capturing high-quality optic nerve images offer the potential for developing automatic and objective methods for diagnosing glaucoma. We present a hybrid approach that features the analysis of CSLT images using moment methods to derive abstract image defining features. The features are then used to train classifers for automatically distinguishing CSLT images of normal and glaucoma patient. As a first, in this paper, we present investigations in feature subset selction methods for reducing the relatively large input space produced by the moment methods. We use neural networks and support vector machines to determine a sub-set of moments that offer high classification accuracy. We demonstratee the efficacy of our methods to discriminate between healthy and glaucomatous optic disks based on shape information automatically derived from optic disk topography and reflectance images.

  15. Discriminative Nonlinear Analysis Operator Learning: When Cosparse Model Meets Image Classification

    Science.gov (United States)

    Wen, Zaidao; Hou, Biao; Jiao, Licheng

    2017-07-01

    Linear synthesis model based dictionary learning framework has achieved remarkable performances in image classification in the last decade. Behaved as a generative feature model, it however suffers from some intrinsic deficiencies. In this paper, we propose a novel parametric nonlinear analysis cosparse model (NACM) with which a unique feature vector will be much more efficiently extracted. Additionally, we derive a deep insight to demonstrate that NACM is capable of simultaneously learning the task adapted feature transformation and regularization to encode our preferences, domain prior knowledge and task oriented supervised information into the features. The proposed NACM is devoted to the classification task as a discriminative feature model and yield a novel discriminative nonlinear analysis operator learning framework (DNAOL). The theoretical analysis and experimental performances clearly demonstrate that DNAOL will not only achieve the better or at least competitive classification accuracies than the state-of-the-art algorithms but it can also dramatically reduce the time complexities in both training and testing phases.

  16. Digital discrimination of neutrons and gamma-rays in organic scintillation detectors using moment analysis

    Science.gov (United States)

    Xie, Xufei; Zhang, Xing; Yuan, Xi; Chen, Jinxiang; Li, Xiangqing; Zhang, Guohui; Fan, Tieshuan; Yuan, Guoliang; Yang, Jinwei; Yang, Qingwei

    2012-09-01

    Digital discrimination of neutron and gamma-ray events in an organic scintillator has been investigated by moment analysis. Signals induced by an americium-beryllium (Am/Be) isotropic neutron source in a stilbene crystal detector have been sampled with a flash analogue-to-digital converter (ADC) of 1 GSamples/s sampling rate and 10-bit vertical resolution. Neutrons and gamma-rays have been successfully discriminated with a threshold corresponding to gamma-ray energy about 217 keV. Moment analysis has also been verified against the results assessed by a time-of-flight (TOF) measurement. It is shown that the classification of neutrons and gamma-rays afforded by moment analysis is consistent with that achieved by digital TOF measurement. This method has been applied to analyze the data acquired from the stilbene crystal detector in mixed radiation field of the HL-2A tokamak deuterium plasma discharges and the results are described.

  17. Intelligent Hybrid Cluster Based Classification Algorithm for Social Network Analysis

    Directory of Open Access Journals (Sweden)

    S. Muthurajkumar

    2014-05-01

    Full Text Available In this paper, we propose an hybrid clustering based classification algorithm based on mean approach to effectively classify to mine the ordered sequences (paths from weblog data in order to perform social network analysis. In the system proposed in this work for social pattern analysis, the sequences of human activities are typically analyzed by switching behaviors, which are likely to produce overlapping clusters. In this proposed system, a robust Modified Boosting algorithm is proposed to hybrid clustering based classification for clustering the data. This work is useful to provide connection between the aggregated features from the network data and traditional indices used in social network analysis. Experimental results show that the proposed algorithm improves the decision results from data clustering when combined with the proposed classification algorithm and hence it is proved that of provides better classification accuracy when tested with Weblog dataset. In addition, this algorithm improves the predictive performance especially for multiclass datasets which can increases the accuracy.

  18. Hybrid Expert Systems In Image Analysis

    Science.gov (United States)

    Dixon, Mark J.; Gregory, Paul J.

    1987-04-01

    Vision systems capable of inspecting industrial components and assemblies have a large potential market if they can be easily programmed and produced quickly. Currently, vision application software written in conventional high-level languages such as C or Pascal are produced by experts in program design, image analysis, and process control. Applications written this way are difficult to maintain and modify. Unless other similar inspection problems can be found, the final program is essentially one-off redundant code. A general-purpose vision system targeted for the Visual Machines Ltd. C-VAS 3000 image processing workstation, is described which will make writing image analysis software accessible to the non-expert both in programming computers and image analysis. A significant reduction in the effort required to produce vision systems, will be gained through a graphically-driven interactive application generator. Finally, an Expert System will be layered on top to guide the naive user through the process of generating an application.

  19. DNA hybridization sensing for cytogenetic analysis

    DEFF Research Database (Denmark)

    Kwasny, Dorota; Dapra, Johannes; Brøgger, Anna Line;

    2013-01-01

    Cytogenetic analysis focuses on studying the cell structure, mainly in respect to chromosome content and their structure. Chromosome abnormalities, such as translocations may cause various genetic disorders, but are also associated with heametological malignancies. Chromosome translocations...... for cheaper detection a label-free approach has been investigated using electrochemical impedance spectroscopy as a sensing method. We present here our recent results in regards to DNA sensing on metallic and conductive polymer electrodes for translocation detection. Our sensors are inexpensive and can...... be successfully applied in cytogenetic analysis as a replacement of standard techniques....

  20. Forensic discrimination of blue ballpoint pen inks based on thin layer chromatography and image analysis.

    Science.gov (United States)

    Djozan, Djavanshir; Baheri, Tahmineh; Karimian, Ghader; Shahidi, Masomeh

    2008-08-06

    This article aims to provide a new and fast method for differentiation of inks on a questioned document. The data acquisition was carried out by designing specific image analysis software for evaluating thin layer chromatograms (TLC-IA). The ink spot was extracted from the document using methanol and separated by TLC using plastic sheet silica gel 60 without fluorescent indicator, and a mixture of ethyl acetate, ethanol, and water (70:35:30, v/v/v) as mobile phase. To discriminate between different pen inks, new software was designed on the basis of intensity profile of red, green, and blue (RGB) characteristic. In practice, after development of chromatogram, the chromatograms were scanned by ordinary office scanner, intensity profiles of RGB characteristics on the development straight of each sample were produced and compared with the mentioned software. RGB profiles of ballpoint inks from various manufacturers showed that the patterns in most cases were distinctly different from each other. This new method allowed discriminating among different pen inks with a high reliability and the discriminating power of 92.8%. Blue ballpoint pen inks of 41 different samples available on the local market were successfully analyzed and discriminated.

  1. Optical spectroscopic analysis for the discrimination of extra-virgin olive-oil (Conference Presentation)

    Science.gov (United States)

    McReynolds, Naomi; Auñón Garcia, Juan M.; Guengerich, Zoe; Smith, Terry K.; Dholakia, Kishan

    2017-02-01

    We present an optical spectroscopic technique, making use of both Raman signals and fluorescence spectroscopy, for the identification of five brands of commercially available extra-virgin olive-oil (EVOO). We demonstrate our technique on both a `bulk-optics' free-space system and a compact device. Using the compact device, which is capable of recording both Raman and fluorescence signals, we achieved an average sensitivity and specificity of 98.4% and 99.6% for discrimination, respectively. Our approach demonstrates that both Raman and fluorescence spectroscopy can be used for portable discrimination of EVOOs which obviates the need to use centralised laboratories and opens up the prospect of in-field testing. This technique may enable detection of EVOO that has undergone counterfeiting or adulteration. One of the main challenges facing Raman spectroscopy for use in quality control of EVOOs is that the oxidation of EVOO, which naturally occurs due to aging, causes shifts in Raman spectra with time, which implies regular retraining would be necessary. We present a potential method of analysis to minimize the effect that aging has on discrimination efficiency; we show that by discarding the first principal component, which contains information on the variations due to oxidation, we can improve discrimination efficiency thus improving the robustness of our technique.

  2. Discrimination of closely homologous HPV types by nonisotopic in situ hybridization: definition and derivation of tissue melting temperatures.

    Science.gov (United States)

    Herrington, C S; Graham, A K; Flannery, D M; Burns, J; McGee, J O

    1990-10-01

    It is generally assumed that nucleic acid association during in situ hybridization reactions is similar to that of nucleic acid association in solution. This assumption has been investigated by detecting closely homologous human papillomavirus types 6 and 11 by in situ hybridization as a model for the evaluation of stringency conditions in clinical biopsies. By examining matched and mismatched, labelled and target sequences under various stringency conditions, empirical DNA-DNA stability curves and their derivative equations for tissue melting temperatures (Tmt) were derived. The corresponding values for Tmt are 10-20 degrees C higher than their solution equivalents. These data, supported by polymerase chain reaction experiments, demonstrate that closely homologous viral DNAs cross linked in tissue by formaldehyde fixation do not interact with the corresponding labelled probes as predicted from solution kinetic equations. This not only has theoretical implications but is also relevant to the accuracy of clinical diagnostic testing.

  3. Hybrid optimization for 13C metabolic flux analysis using systems parametrized by compactification

    Directory of Open Access Journals (Sweden)

    Frick Oliver

    2008-03-01

    Full Text Available Abstract Background The importance and power of isotope-based metabolic flux analysis and its contribution to understanding the metabolic network is increasingly recognized. Its application is, however, still limited partly due to computational inefficiency. 13C metabolic flux analysis aims to compute in vivo metabolic fluxes in terms of metabolite balancing extended by carbon isotopomer balances and involves a nonlinear least-squares problem. To solve the problem more efficiently, improved numerical optimization techniques are necessary. Results For flux computation, we developed a gradient-based hybrid optimization algorithm. Here, independent flux variables were compactified into [0, 1-ranged variables using a single transformation rule. The compactified parameters could be discriminated between non-identifiable and identifiable variables after model linearization. The developed hybrid algorithm was applied to the central metabolism of Bacillus subtilis with only succinate and glutamate as carbon sources. This creates difficulties caused by symmetry of succinate leading to limited introduction of 13C labeling information into the system. The algorithm was found to be superior to its parent algorithms and to global optimization methods both in accuracy and speed. The hybrid optimization with tolerance adjustment quickly converged to the minimum with close to zero deviation and exactly re-estimated flux variables. In the metabolic network studied, some fluxes were found to be either non-identifiable or nonlinearly correlated. The non-identifiable fluxes could correctly be predicted a priori using the model identification method applied, whereas the nonlinear flux correlation was revealed only by identification runs using different starting values a posteriori. Conclusion This fast, robust and accurate optimization method is useful for high-throughput metabolic flux analysis, a posteriori identification of possible parameter correlations, and

  4. Multilinear Biased Discriminant Analysis: A Novel Method for Facial Action Unit Representation

    CERN Document Server

    Khademi, Mahmoud; Manzuri-Shalmani, Mohammad T

    2010-01-01

    In this paper a novel efficient method for representation of facial action units by encoding an image sequence as a fourth-order tensor is presented. The multilinear tensor-based extension of the biased discriminant analysis (BDA) algorithm, called multilinear biased discriminant analysis (MBDA), is first proposed. Then, we apply the MBDA and two-dimensional BDA (2DBDA) algorithms, as the dimensionality reduction techniques, to Gabor representations and the geometric features of the input image sequence respectively. The proposed scheme can deal with the asymmetry between positive and negative samples as well as curse of dimensionality dilemma. Extensive experiments on Cohn-Kanade database show the superiority of the proposed method for representation of the subtle changes and the temporal information involved in formation of the facial expressions. As an accurate tool, this representation can be applied to many areas such as recognition of spontaneous and deliberate facial expressions, multi modal/media huma...

  5. Z-score linear discriminant analysis for EEG based brain-computer interfaces.

    Directory of Open Access Journals (Sweden)

    Rui Zhang

    Full Text Available Linear discriminant analysis (LDA is one of the most popular classification algorithms for brain-computer interfaces (BCI. LDA assumes Gaussian distribution of the data, with equal covariance matrices for the concerned classes, however, the assumption is not usually held in actual BCI applications, where the heteroscedastic class distributions are usually observed. This paper proposes an enhanced version of LDA, namely z-score linear discriminant analysis (Z-LDA, which introduces a new decision boundary definition strategy to handle with the heteroscedastic class distributions. Z-LDA defines decision boundary through z-score utilizing both mean and standard deviation information of the projected data, which can adaptively adjust the decision boundary to fit for heteroscedastic distribution situation. Results derived from both simulation dataset and two actual BCI datasets consistently show that Z-LDA achieves significantly higher average classification accuracies than conventional LDA, indicating the superiority of the new proposed decision boundary definition strategy.

  6. Facial Expression Representation Using Heteroscedastic Linear Discriminant Analysis and Gabor Wavelets

    CERN Document Server

    Khademi, Mahmoud; Manzuri, Mohammad T

    2010-01-01

    In this paper, a novel representation for facial expressions in two-dimensional image sequences is presented. We apply a variation of two-dimensional heteroscedastic linear discriminant analysis (2DHLDA) algorithm, as an efficient dimensionality reduction technique, to Gabor representation of the input sequence. 2DHLDA is an extension of the two-dimensional linear discriminant analysis (2DLDA) approach and removes the equal within-class covariance. By applying 2DHLDA in two directions, we eliminate the correlations between both image columns and image rows. Then, we perform a one-dimensional LDA on the new features. This combined method can alleviate the small sample size problem and instability encountered by HLDA. The proposed method is robust to illumination changes and can represent temporal information as well as subtle changes in facial muscles properly. Also, employing both geometric and appearance features and using support vector machines (SVMs) classifier, we provide experiments on Cohn-Kanade datab...

  7. Sparse dimensionality reduction of hyperspectral image based on semi-supervised local Fisher discriminant analysis

    Science.gov (United States)

    Shao, Zhenfeng; Zhang, Lei

    2014-09-01

    This paper presents a novel sparse dimensionality reduction method of hyperspectral image based on semi-supervised local Fisher discriminant analysis (SELF). The proposed method is designed to be especially effective for dealing with the out-of-sample extrapolation to realize advantageous complementarities between SELF and sparsity preserving projections (SPP). Compared to SELF and SPP, the method proposed herein offers highly discriminative ability and produces an explicit nonlinear feature mapping for the out-of-sample extrapolation. This is due to the fact that the proposed method can get an explicit feature mapping for dimensionality reduction and improve the classification performance of classifiers by performing dimensionality reduction. Experimental analysis on the sparsity and efficacy of low dimensional outputs shows that, sparse dimensionality reduction based on SELF can yield good classification results and interpretability in the field of hyperspectral remote sensing.

  8. Z-score linear discriminant analysis for EEG based brain-computer interfaces.

    Science.gov (United States)

    Zhang, Rui; Xu, Peng; Guo, Lanjin; Zhang, Yangsong; Li, Peiyang; Yao, Dezhong

    2013-01-01

    Linear discriminant analysis (LDA) is one of the most popular classification algorithms for brain-computer interfaces (BCI). LDA assumes Gaussian distribution of the data, with equal covariance matrices for the concerned classes, however, the assumption is not usually held in actual BCI applications, where the heteroscedastic class distributions are usually observed. This paper proposes an enhanced version of LDA, namely z-score linear discriminant analysis (Z-LDA), which introduces a new decision boundary definition strategy to handle with the heteroscedastic class distributions. Z-LDA defines decision boundary through z-score utilizing both mean and standard deviation information of the projected data, which can adaptively adjust the decision boundary to fit for heteroscedastic distribution situation. Results derived from both simulation dataset and two actual BCI datasets consistently show that Z-LDA achieves significantly higher average classification accuracies than conventional LDA, indicating the superiority of the new proposed decision boundary definition strategy.

  9. Using discriminant analysis to detect intrusions in external communication for self-driving vehicles

    Directory of Open Access Journals (Sweden)

    Khattab M.Ali Alheeti

    2017-08-01

    Full Text Available Security systems are a necessity for the deployment of smart vehicles in our society. Security in vehicular ad hoc networks is crucial to the reliable exchange of information and control data. In this paper, we propose an intelligent Intrusion Detection System (IDS to protect the external communication of self-driving and semi self-driving vehicles. This technology has the ability to detect Denial of Service (DoS and black hole attacks on vehicular ad hoc networks (VANETs. The advantage of the proposed IDS over existing security systems is that it detects attacks before they causes significant damage. The intrusion prediction technique is based on Linear Discriminant Analysis (LDA and Quadratic Discriminant Analysis (QDA which are used to predict attacks based on observed vehicle behavior. We perform simulations using Network Simulator 2 to demonstrate that the IDS achieves a low rate of false alarms and high accuracy in detection.

  10. Analysis of Linear Hybrid Systems in CLP

    DEFF Research Database (Denmark)

    Banda, Gourinath; Gallagher, John Patrick

    2009-01-01

    notation for specifying real-time systems. The main contributions are (i) a technique for capturing the reachable states of the continuously changing state variables of the LHA as CLP constraints; (ii) a way of representing events in the LHA as constraints in CLP, along with a product construction...... and argue that we contribute to the general field of using static analysis tools for verification...

  11. Fuel cell hybrid taxi life cycle analysis

    Energy Technology Data Exchange (ETDEWEB)

    Baptista, Patricia, E-mail: patricia.baptista@ist.utl.pt [IDMEC-Instituto Superior Tecnico, Universidade Tecnica de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisboa (Portugal); Ribau, Joao; Bravo, Joao; Silva, Carla [IDMEC-Instituto Superior Tecnico, Universidade Tecnica de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisboa (Portugal); Adcock, Paul; Kells, Ashley [Intelligent Energy, Charnwood Building, HolywellPark, Ashby Road, Loughborough, LE11 3GR (United Kingdom)

    2011-09-15

    A small fleet of classic London Taxis (Black cabs) equipped with hydrogen fuel cell power systems is being prepared for demonstration during the 2012 London Olympics. This paper presents a Life Cycle Analysis for these vehicles in terms of energy consumption and CO{sub 2} emissions, focusing on the impacts of alternative vehicle technologies for the Taxi, combining the fuel life cycle (Tank-to-Wheel and Well-to-Tank) and vehicle materials Cradle-to-Grave. An internal combustion engine diesel taxi was used as the reference vehicle for the currently available technology. This is compared to battery and fuel cell vehicle configurations. Accordingly, the following energy pathways are compared: diesel, electricity and hydrogen (derived from natural gas steam reforming). Full Life Cycle Analysis, using the PCO-CENEX drive cycle, (derived from actual London Taxi drive cycles) shows that the fuel cell powered vehicle configurations have lower energy consumption (4.34 MJ/km) and CO{sub 2} emissions (235 g/km) than both the ICE Diesel (9.54 MJ/km and 738 g/km) and the battery electric vehicle (5.81 MJ/km and 269 g/km). - Highlights: > A Life Cycle Analysis of alternative vehicle technologies for the London Taxi was performed. > The hydrogen powered vehicles have the lowest energy consumption and CO{sub 2} emissions results. > A hydrogen powered solution can be a sustainable alternative in a full life cycle framework.

  12. Fisher's Discriminant and Relevant Component Analysis for static facial expression classification

    OpenAIRE

    Sorci, Matteo; Antonini, Gianluca; Thiran, Jean-Philippe

    2007-01-01

    This paper addresses the issue of automatic classification of the six universal emotional categories (joy, surprise, fear, anger, disgust, sadness) in the case of static images. Appearance parameters are extracted by an active appearance model(AAM) representing the input for the classification step. We show how Relevant Component Analysis (RCA) in combination with Fisher's Linear Discriminant (FLD) provides a good "plug-\\&-play" classifier in the context of facial expression recognitio...

  13. Origin of uncontrolled water emissions in Alicante: use of discriminant analysis

    OpenAIRE

    Chinchón Payá, Servando; Blas Bravo, Isabel de; Nueda Roldán, María José; García Andreu, Francisco

    2010-01-01

    A model has been developed to predict the origin of uncontrolled water flows. For this purpose, we analysed a total of 52 elements using ICP techniques on 72 water samples in all: 40 of them from leaks in the drinking water supply network and the remaining 32 from groundwater outcrops. The study focused on the cases registered within the Alicante town limits (Spain). The use of multivariate statistical classification tools such as discriminant analysis made it possible not only to reduce the ...

  14. 2D Face Recognition System Based on Selected Gabor Filters and Linear Discriminant Analysis LDA

    OpenAIRE

    Hafez, Samir F.; Selim, Mazen M.; Hala H. Zayed

    2015-01-01

    We present a new approach for face recognition system. The method is based on 2D face image features using subset of non-correlated and Orthogonal Gabor Filters instead of using the whole Gabor Filter Bank, then compressing the output feature vector using Linear Discriminant Analysis (LDA). The face image has been enhanced using multi stage image processing technique to normalize it and compensate for illumination variation. Experimental results show that the proposed system is effective for ...

  15. Machinery fault diagnosis using joint global and local/nonlocal discriminant analysis with selective ensemble learning

    Science.gov (United States)

    Yu, Jianbo

    2016-11-01

    The vibration signals of faulty machine are generally non-stationary and nonlinear under those complicated working conditions. Thus, it is a big challenge to extract and select the effective features from vibration signals for machinery fault diagnosis. This paper proposes a new manifold learning algorithm, joint global and local/nonlocal discriminant analysis (GLNDA), which aims to extract effective intrinsic geometrical information from the given vibration data. Comparisons with other regular methods, principal component analysis (PCA), local preserving projection (LPP), linear discriminant analysis (LDA) and local LDA (LLDA), illustrate the superiority of GLNDA in machinery fault diagnosis. Based on the extracted information by GLNDA, a GLNDA-based Fisher discriminant rule (FDR) is put forward and applied to machinery fault diagnosis without additional recognizer construction procedure. By importing Bagging into GLNDA score-based feature selection and FDR, a novel manifold ensemble method (selective GLNDA ensemble, SE-GLNDA) is investigated for machinery fault diagnosis. The motivation for developing ensemble of manifold learning components is that it can achieve higher accuracy and applicability than single component in machinery fault diagnosis. The effectiveness of the SE-GLNDA-based fault diagnosis method has been verified by experimental results from bearing full life testers.

  16. How Many Genes Are Needed for a Discriminant Microarray Data Analysis ?

    CERN Document Server

    Li, W; Li, Wentian; Yang, Yaning

    2001-01-01

    The analysis of the leukemia data from Whitehead/MIT group is a discriminant analysis (also called a supervised learning). Among thousands of genes whose expression levels are measured, not all are needed for discriminant analysis: a gene may either not contribute to the separation of two types of tissues/cancers, or it may be redundant because it is highly correlated with other genes. There are two theoretical frameworks in which variable selection (or gene selection in our case) can be addressed. The first is model selection, and the second is model averaging. We have carried out model selection using Akaike information criterion and Bayesian information criterion with logistic regression (discrimination, prediction, or classification) to determine the number of genes that provide the best model. These model selection criteria set upper limits of 22-25 and 12-13 genes for this data set with 38 samples, and the best model consists of only one (no.4847, zyxin) or two genes. We have also carried out model aver...

  17. Spike detection, characterization, and discrimination using feature analysis software written in LabVIEW.

    Science.gov (United States)

    Stewart, C M; Newlands, S D; Perachio, A A

    2004-12-01

    Rapid and accurate discrimination of single units from extracellular recordings is a fundamental process for the analysis and interpretation of electrophysiological recordings. We present an algorithm that performs detection, characterization, discrimination, and analysis of action potentials from extracellular recording sessions. The program was entirely written in LabVIEW (National Instruments), and requires no external hardware devices or a priori information about action potential shapes. Waveform events are detected by scanning the digital record for voltages that exceed a user-adjustable trigger. Detected events are characterized to determine nine different time and voltage levels for each event. Various algebraic combinations of these waveform features are used as axis choices for 2-D Cartesian plots of events. The user selects axis choices that generate distinct clusters. Multiple clusters may be defined as action potentials by manually generating boundaries of arbitrary shape. Events defined as action potentials are validated by visual inspection of overlain waveforms. Stimulus-response relationships may be identified by selecting any recorded channel for comparison to continuous and average cycle histograms of binned unit data. The algorithm includes novel aspects of feature analysis and acquisition, including higher acquisition rates for electrophysiological data compared to other channels. The program confirms that electrophysiological data may be discriminated with high-speed and efficiency using algebraic combinations of waveform features derived from high-speed digital records.

  18. Hybrid vehicle assessment. Phase I. Petroleum savings analysis

    Energy Technology Data Exchange (ETDEWEB)

    Levin, R.; Liddle, S.; Deshpande, G.; Trummel, M.; Vivian, H.

    1984-03-01

    This report presents the results of a comprehensive analysis of near-term electric-hybrid vehicles. Its purpose was to estimate their potential to save significant amounts of petroleum on a national scale in the 1990s. Performance requirements and expected annual usage patterns of these vehicles were first modeled. The projected US fleet composition was estimated, and conceptual hybrid vehicle designs were conceived and analyzed for petroleum use when driven in the expected annual patterns. These petroleum consumption estimates were then compared to similar estimates for projected 1990 conventional vehicles having the same performance and driven in the same patterns. Results are presented in the form of three utility functions and comparisons of several conceptual designs are made. The Hybrid Vehicle (HV) design and assessment techniques are discussed and a general method is explained for selecting the optimum energy management strategy for any vehicle-mission-battery combination. A discussion of lessons learned during the construction and test of the General Electric Hybrid Test Vehicle is also presented. Conclusions and recommendations are presented, and development recommendations are identified.

  19. Performance Analysis of a Hybrid Power Cutting System for Roadheader

    Directory of Open Access Journals (Sweden)

    Yang Yang

    2017-01-01

    Full Text Available An electrohydraulic hybrid power cutting transmission system for roadheader under specific working condition was proposed in this paper. The overall model for the new system composed of an electric motor model, a hydraulic pump-motor model, a torsional planetary set model, and a hybrid power train model was established. The working mode characteristics were simulated under the conditions of taking the effect of cutting picks into account. The advantages of new hybrid power cutting system about the dynamic response under shock load were investigated compared with the traditional cutting system. The results illustrated that the hybrid power system had an obvious cushioning in terms of the dynamic load of cutting electric motor and planetary gear set. Besides, the hydraulic motor could provide an auxiliary power to improve the performance of the electric motor. With further analysis, a dynamic load was found to have a high relation to the stiffness and damping of coupling in the transmission train. The results could be a useful guide for the design of cutting transmission of roadheader.

  20. Dance in mental health nursing: a hybrid concept analysis.

    Science.gov (United States)

    Ravelin, Teija; Kylmä, Jari; Korhonen, Teija

    2006-04-01

    The aim of this concept analysis is to describe the defining attributes and consequences of the concept of dance and to define it in a mental health nursing context using hybrid concept analysis. Dance is a human resource learned from culture. Dance implies body movements, steps, expression, and interaction. The outcomes of dance are mostly functional, including a client's physical and emotional health, well-being, ability to cooperate with other people in activities of daily life, and meeting role expectations within family and community. Based on the findings of this concept analysis, dance can be used as a nursing intervention.

  1. A hybrid monkey search algorithm for clustering analysis.

    Science.gov (United States)

    Chen, Xin; Zhou, Yongquan; Luo, Qifang

    2014-01-01

    Clustering is a popular data analysis and data mining technique. The k-means clustering algorithm is one of the most commonly used methods. However, it highly depends on the initial solution and is easy to fall into local optimum solution. In view of the disadvantages of the k-means method, this paper proposed a hybrid monkey algorithm based on search operator of artificial bee colony algorithm for clustering analysis and experiment on synthetic and real life datasets to show that the algorithm has a good performance than that of the basic monkey algorithm for clustering analysis.

  2. A Hybrid Monkey Search Algorithm for Clustering Analysis

    Directory of Open Access Journals (Sweden)

    Xin Chen

    2014-01-01

    Full Text Available Clustering is a popular data analysis and data mining technique. The k-means clustering algorithm is one of the most commonly used methods. However, it highly depends on the initial solution and is easy to fall into local optimum solution. In view of the disadvantages of the k-means method, this paper proposed a hybrid monkey algorithm based on search operator of artificial bee colony algorithm for clustering analysis and experiment on synthetic and real life datasets to show that the algorithm has a good performance than that of the basic monkey algorithm for clustering analysis.

  3. A hybrid transfinite element approach for nonlinear transient thermal analysis

    Science.gov (United States)

    Tamma, Kumar K.; Railkar, Sudhir B.

    1987-01-01

    A new computational approach for transient nonlinear thermal analysis of structures is proposed. It is a hybrid approach which combines the modeling versatility of contemporary finite elements in conjunction with transform methods and classical Bubnov-Galerkin schemes. The present study is limited to nonlinearities due to temperature-dependent thermophysical properties. Numerical test cases attest to the basic capabilities and therein validate the transfinite element approach by means of comparisons with conventional finite element schemes and/or available solutions.

  4. Estimation of Optimal Measurement Position of Human Forearm EMG Signal by Discriminant Analysis Based on Wilks' lambda

    Science.gov (United States)

    Kiso, Atsushi; Taniguchi, Yu; Seki, Hirokazu

    This paper describes the estimation of the optimal measurement position by discriminant analysis based on Wilks' lambda for myoelectric hand control. In previous studies, for motion discrimination, the myoelectric signals were measured at the same positions. However, the optimal measurement positions of the myoelectric signals for motion discrimination differ depending on the remaining muscles of amputees. Therefore, the purpose of this study is to estimate the optimal and fewer measurement positions for precise motion discrimination of a human forearm. This study proposes a method for estimating the optimal measurement positions by discriminant analysis based on Wilks' lambda, using the myoelectric signals measured at multiple positions. The results of some experiments on the myoelectric hand simulator show the effectiveness of the proposed optimal measurement position estimation method.

  5. Discriminant analysis of principal components: a new method for the analysis of genetically structured populations

    Directory of Open Access Journals (Sweden)

    Balloux François

    2010-10-01

    Full Text Available Abstract Background The dramatic progress in sequencing technologies offers unprecedented prospects for deciphering the organization of natural populations in space and time. However, the size of the datasets generated also poses some daunting challenges. In particular, Bayesian clustering algorithms based on pre-defined population genetics models such as the STRUCTURE or BAPS software may not be able to cope with this unprecedented amount of data. Thus, there is a need for less computer-intensive approaches. Multivariate analyses seem particularly appealing as they are specifically devoted to extracting information from large datasets. Unfortunately, currently available multivariate methods still lack some essential features needed to study the genetic structure of natural populations. Results We introduce the Discriminant Analysis of Principal Components (DAPC, a multivariate method designed to identify and describe clusters of genetically related individuals. When group priors are lacking, DAPC uses sequential K-means and model selection to infer genetic clusters. Our approach allows extracting rich information from genetic data, providing assignment of individuals to groups, a visual assessment of between-population differentiation, and contribution of individual alleles to population structuring. We evaluate the performance of our method using simulated data, which were also analyzed using STRUCTURE as a benchmark. Additionally, we illustrate the method by analyzing microsatellite polymorphism in worldwide human populations and hemagglutinin gene sequence variation in seasonal influenza. Conclusions Analysis of simulated data revealed that our approach performs generally better than STRUCTURE at characterizing population subdivision. The tools implemented in DAPC for the identification of clusters and graphical representation of between-group structures allow to unravel complex population structures. Our approach is also faster than

  6. Performance analysis of a hybrid fingerprint extracted from optical coherence tomography fingertip scans

    CSIR Research Space (South Africa)

    Darlow, Luke N

    2016-06-01

    Full Text Available International Conference on Biometrics (ICB), 13-16 June 2016, Halmstad, Sweden Performance analysis of a hybrid fingerprint extracted from optical coherence tomography fingertip scans Darlow LN Connan J Singh A ABSTRACT: The Hybrid fingerprint is a...

  7. Principal component analysis for neural electron/jet discrimination in highly segmented calorimeters

    CERN Document Server

    Vassali, M R

    2001-01-01

    A neural electron/jet discriminator based on calorimetry is developed for the second-level trigger system of the ATLAS detector. As preprocessing of the calorimeter information, a principal component analysis is performed on each segment of the two sections (electromagnetic and hadronic) of the calorimeter system, in order to reduce significantly the dimension of the input data space and fully explore the detailed energy deposition profile, which is provided by the highly-segmented calorimeter system. It is shown that projecting calorimeter data onto 33 segmented principal components, the discrimination efficiency of the neural classifier reaches 98.9% for electrons (with only 1% of false alarm probability). Furthermore, restricting data projection onto only 9 components, an electron efficiency of 99.1% is achieved (with 3% of false alarm), which confirms that a fast triggering system may be designed using few components. (6 refs).

  8. UV-vis absorption spectroscopy and multivariate analysis as a method to discriminate tequila

    Science.gov (United States)

    Barbosa-García, O.; Ramos-Ortíz, G.; Maldonado, J. L.; Pichardo-Molina, J. L.; Meneses-Nava, M. A.; Landgrave, J. E. A.; Cervantes-Martínez, J.

    2007-01-01

    Based on the UV-vis absorption spectra of commercially bottled tequilas, and with the aid of multivariate analysis, it is proved that different brands of white tequila can be identified from such spectra, and that 100% agave and mixed tequilas can be discriminated as well. Our study was done with 60 tequilas, 58 of them purchased at liquor stores in various Mexican cities, and two directly acquired from a distillery. All the tequilas were of the "white" type, that is, no aged spirits were considered. For the purposes of discrimination and quality control of tequilas, the spectroscopic method that we present here offers an attractive alternative to the traditional methods, like gas chromatography, which is expensive and time-consuming.

  9. Using discriminant analysis to assess polycyclic aromatic hydrocarbons contamination in Yongding New River.

    Science.gov (United States)

    Wang, Xiaojing; Zou, Zhihong; Zou, Hui

    2013-10-01

    Yongding New River has been polluted by polycyclic aromatic hydrocarbons (PAHs) which are carcinogenic and mutagenic. In three periods (the abundant water period, mean water period, dry water period), ten sites (totally 30 samples) in Yongding New River were clustered into four categories by hierarchical cluster analysis (hierarchical CA). In the same cluster, the samples had the same approximate contamination situation. In order to eliminate the dimensional differences, the data in each sample, containing 16 kinds of PAHs, were standardized with normal standardization and maximum difference standardization. According to the results of the cubic clustering criterion, pseudo F, and pseudo t (2) (PST2), the proper number of clustering for the 30 samples is 4. Before conducting hierarchical CA and K-means cluster analysis on the samples, we used principal component analysis to obtain another group data set. This data set was composed of the principal component scores which are uncorrelated variables. Hierarchical CA and K-means cluster analysis were used to classify the two data sets into four categories. With the classification results of hierarchical CA and K-means cluster analysis, discriminant analysis is applied to determine which method was better for normalization of the original data and which one was proper to cluster the samples and establish discriminant functions so that a new sample can be grouped into the right categories.

  10. Theoretical analysis and experimental research on port/starboard discrimination in towed line array

    Institute of Scientific and Technical Information of China (English)

    DU Xuanmin; ZHU Daizhu; ZHAO Rongrong; YAO Lan

    2001-01-01

    The theoretical analysis and experimental research on Port/Starboard (P/S) discrimination in towed line array are proposed. Two methods resolving the P/S ambiguity with hydrophone triplets are introduced. By processing experimental data, the theoretical analysis is verified. The processing algorithm is extended to broadband signal. The research results show that the method based on optimum beamforming with triplets can be used to remove the port/starboard ambiguity. Also because of the simplicity of the method, it is expected to be implemented in practical towed line array sonar.

  11. Independent component feature-based human activity recognition via Linear Discriminant Analysis and Hidden Markov Model.

    Science.gov (United States)

    Uddin, Md; Lee, J J; Kim, T S

    2008-01-01

    In proactive computing, human activity recognition from image sequences is an active research area. This paper presents a novel approach of human activity recognition based on Linear Discriminant Analysis (LDA) of Independent Component (IC) features from shape information. With extracted features, Hidden Markov Model (HMM) is applied for training and recognition. The recognition performance using LDA of IC features has been compared to other approaches including Principle Component Analysis (PCA), LDA of PC, and ICA. The preliminary results show much improved performance in the recognition rate with our proposed method.

  12. Analysis/design of strip reinforced random composites (strip hybrids)

    Science.gov (United States)

    Chamis, C. C.; Sinclair, J. H.

    1978-01-01

    Advanced analysis methods and composite mechanics were applied to a strip-reinforced random composite square panel with fixed ends to illustrate the use of these methods for the a priori assessment of the composite panel when subjected to complex loading conditions. The panel was assumed to be of E-glass random composite. The strips were assumed to be of three advanced unidirectional composites to cover a range of low, intermediate, and high modulus stiffness. The panels were assumed to be subjected to complex loadings to assess their adequacy as load-carrying members in auto body, aircraft engine nacelle and windmill blade applications. The results show that strip hybrid panels can be several times more structurally efficient than the random composite base materials. Some of the results are presented in graphical form and procedures are described for use of these graphs as guides for preliminary design of strip hybrids.

  13. Analysis/design of strip reinforced random composites /strip hybrids/

    Science.gov (United States)

    Chamis, C. C.; Sinclair, J. H.

    1978-01-01

    Results are described which were obtained by applying advanced analysis methods and composite mechanics to a strip-reinforced random composite square panel with fixed ends. This was done in order to illustrate the use of these methods for the apriori assessment of the composite panel when subjected to complex loading conditions. The panel was assumed to be of E-Glass/Random Composite. The strips were assumed to be of three advanced unidirectional composites to cover a range of low, intermediate, and high modulus stiffness. The panels were assumed to be subjected to complex loadings to assess their adequacy as load-carrying members in auto body, aircraft engine nacelle, and windmill blade applications. The results show that strip hybrid panels can be several times more structurally efficient than the random composite base materials. Some of the results are presented in graphical form and procedures are described for use of these graphs as guides for preliminary design of strip hybrids.

  14. Accuracy Analysis and Calibration of Gantry Hybrid Machine Tool

    Institute of Scientific and Technical Information of China (English)

    唐晓强; 李铁民; 尹文生; 汪劲松

    2003-01-01

    The kinematic accuracy is a key factor in the design of parallel or hybrid machine tools. This analysis improved the accuracy of a 4-DOF (degree of freedom) gantry hybrid machine tool based on a 3-DOF planar parallel manipulator by compensating for various positioning errors. The machine tool architecture was described with the inverse kinematic solution. The control parameter error model was used to analyze the accuracy of the 3-DOF planar parallel manipulator and to develop a kinematic calibration method. The experimental results prove that the calibration method reduces the cutter nose errors from ±0.50 mm to ±0.03 mm for a horizontal movement of 600 mm by compensating for errors in the slider home position, the guide way distance and the extensible strut home position. The calibration method will be useful for similar types of parallel kinematic machines.

  15. Plug-In Hybrid Vehicle Analysis (Milestone Report)

    Energy Technology Data Exchange (ETDEWEB)

    Markel, T.; Brooker, A.; Gonder, J.; O' Keefe, M.; Simpson, A.; Thornton, M.

    2006-11-01

    NREL's plug-in hybrid electric vehicle (PHEV) analysis activities made great strides in FY06 to objectively assess PHEV technology, support the larger U.S. Department of Energy PHEV assessment effort, and share technical knowledge with the vehicle research community and vehicle manufacturers. This report provides research papers and presentations developed in FY06 to support these efforts. The report focuses on the areas of fuel economy reporting methods, cost and consumption benefit analysis, real-world performance expectations, and energy management strategies.

  16. Discriminating between cultivars and treatments of broccoli using mass spectral fingerprinting and analysis of variance-principal component analysis.

    Science.gov (United States)

    Luthria, Devanand L; Lin, Long-Ze; Robbins, Rebecca J; Finley, John W; Banuelos, Gary S; Harnly, James M

    2008-11-12

    Metabolite fingerprints, obtained with direct injection mass spectrometry (MS) with both positive and negative ionization, were used with analysis of variance-principal components analysis (ANOVA-PCA) to discriminate between cultivars and growing treatments of broccoli. The sample set consisted of two cultivars of broccoli, Majestic and Legacy, the first grown with four different levels of Se and the second grown organically and conventionally with two rates of irrigation. Chemical composition differences in the two cultivars and seven treatments produced patterns that were visually and statistically distinguishable using ANOVA-PCA. PCA loadings allowed identification of the molecular and fragment ions that provided the most significant chemical differences. A standardized profiling method for phenolic compounds showed that important discriminating ions were not phenolic compounds. The elution times of the discriminating ions and previous results suggest that they were common sugars and organic acids. ANOVA calculations of the positive and negative ionization MS fingerprints showed that 33% of the variance came from the cultivar, 59% from the growing treatment, and 8% from analytical uncertainty. Although the positive and negative ionization fingerprints differed significantly, there was no difference in the distribution of variance. High variance of individual masses with cultivars or growing treatment was correlated with high PCA loadings. The ANOVA data suggest that only variables with high variance for analytical uncertainty should be deleted. All other variables represent discriminating masses that allow separation of the samples with respect to cultivar and treatment.

  17. Fluorescence spectral analysis for the discrimination of complex, similar mixtures with the aid of chemometrics.

    Science.gov (United States)

    Ni, Yongnian; Lai, Yanhua; Kokot, Serge

    2012-07-01

    An analytical method for the classification of complex real-world samples was researched and developed with the use of excitation-emission fluorescence matrix (EEFM) spectroscopy, using the medicinal herbs, Rhizoma corydalis decumbentis (RCD) and Rhizoma corydalis (RC) as example samples. The data set was obtained from various authentic RCD-A and RC-A, adulterated AD, and commercial RCD-C and RC-C samples. The spectra (range: λ(ex) = 215∼395 nm and λ(em) = 290∼560 nm), arranged in two- and three-way data matrix formats, were processed using principal component analysis (PCA) and parallel factor analysis (PARAFAC) to produce two-dimensional component-by-component plots for qualitative data classification. The RCD-A and RC-A object groups were clearly discriminated, but the AD and the RCD-C as well as RC-C samples were less well separated. PARAFAC analysis produced somewhat better discrimination, and loadings plots revealed the presence of the marker compound Protopine-a strongly fluorescing substance-as well as at least two other unidentified fluorescent components. Classification performance of the common K-nearest neighbors (KNN) and linear discrimination analysis (LDA) methods was relatively poor when compared with that of the back propagation- and radial basis function-artificial neural networks (BP-ANN and RBF-ANN) models on the basis of two- and three-way formatted data. The best results were obtained with the three-way fingerprints and the RBF-ANN model. Subsequently, the quality of the commercial samples (RCD-C and RC-C) was classified on the best optimized RBF-ANN model. Thus, EEFM spectroscopy, which provides three-way measured data, is potentially a powerful analytical technique for the analysis of complex real-world substances provided the classification is performed by the RBF-ANN or similar ANN methods.

  18. Marine Fish Hybridization

    KAUST Repository

    He, Song

    2017-04-01

    Natural hybridization is reproduction (without artificial influence) between two or more species/populations which are distinguishable from each other by heritable characters. Natural hybridizations among marine fishes were highly underappreciated due to limited research effort; it seems that this phenomenon occurs more often than is commonly recognized. As hybridization plays an important role in biodiversity processes in the marine environment, detecting hybridization events and investigating hybridization is important to understand and protect biodiversity. The first chapter sets the framework for this disseration study. The Cohesion Species Concept was selected as the working definition of a species for this study as it can handle marine fish hybridization events. The concept does not require restrictive species boundaries. A general history and background of natural hybridization in marine fishes is reviewed during in chapter as well. Four marine fish hybridization cases were examed and documented in Chapters 2 to 5. In each case study, at least one diagnostic nuclear marker, screened from among ~14 candidate markers, was found to discriminate the putative hybridizing parent species. To further investigate genetic evidence to support the hybrid status for each hybrid offspring in each case, haploweb analysis on diagnostic markers (nuclear and/or mitochondrial) and the DAPC/PCA analysis on microsatellite data were used. By combining the genetic evidences, morphological traits, and ecological observations together, the potential reasons that triggered each hybridization events and the potential genetic/ecology effects could be discussed. In the last chapter, sequences from 82 pairs of hybridizing parents species (for which COI barcoding sequences were available either on GenBank or in our lab) were collected. By comparing the COI fragment p-distance between each hybridizing parent species, some general questions about marine fish hybridization were discussed: Is

  19. Hybrid Information Flow Analysis for Programs with Arrays

    Directory of Open Access Journals (Sweden)

    Gergö Barany

    2016-07-01

    Full Text Available Information flow analysis checks whether certain pieces of (confidential data may affect the results of computations in unwanted ways and thus leak information. Dynamic information flow analysis adds instrumentation code to the target software to track flows at run time and raise alarms if a flow policy is violated; hybrid analyses combine this with preliminary static analysis. Using a subset of C as the target language, we extend previous work on hybrid information flow analysis that handled pointers to scalars. Our extended formulation handles arrays, pointers to array elements, and pointer arithmetic. Information flow through arrays of pointers is tracked precisely while arrays of non-pointer types are summarized efficiently. A prototype of our approach is implemented using the Frama-C program analysis and transformation framework. Work on a full machine-checked proof of the correctness of our approach using Isabelle/HOL is well underway; we present the existing parts and sketch the rest of the correctness argument.

  20. Gender identification of Caspian Terns using external morphology and discriminant function analysis

    Science.gov (United States)

    Ackerman, J.T.; Takekawa, J.Y.; Bluso, J.D.; Yee, J.L.; Eagles-Smith, C. A.

    2008-01-01

    Caspian Tern (Sterna caspia) plumage characteristics are sexually monochromatic and gender cannot easily be distinguished in the field without extensive behavioral observations. We assessed sexual size dimorphism and developed a discriminant function to assign gender in Caspian Terns based on external morphology. We collected and measured Caspian Terns in San Francisco Bay, California, and confirmed their gender based on necropsy and genetic analysis. Of the eight morphological measurements we examined, only bill depth at the gonys and head plus bill length differed between males and females with males being larger than females. A discriminant function using both bill depth at the gonys and head plus bill length accurately assigned gender of 83% of terns for which gender was known. We improved the accuracy of our discriminant function to 90% by excluding individuals that had less than a 75% posterior probability of correctly being assigned to gender. Caspian Terns showed little sexual size dimorphism in many morphometries, but our results indicate they can be reliably assigned to gender in the field using two morphological measurements.

  1. Attractor structure discriminates sleep states: recurrence plot analysis applied to infant breathing patterns.

    Science.gov (United States)

    Terrill, Philip Ian; Wilson, Stephen James; Suresh, Sadasivam; Cooper, David M; Dakin, Carolyn

    2010-05-01

    Breathing patterns are characteristically different between infant active sleep (AS) and quiet sleep (QS), and statistical quantifications of interbreath interval (IBI) data have previously been used to discriminate between infant sleep states. It has also been identified that breathing patterns are governed by a nonlinear controller. This study aims to investigate whether nonlinear quantifications of infant IBI data are characteristically different between AS and QS, and whether they may be used to discriminate between these infant sleep states. Polysomnograms were obtained from 24 healthy infants at six months of age. Periods of AS and QS were identified, and IBI data extracted. Recurrence quantification analysis (RQA) was applied to each period, and recurrence calculated for a fixed radius in the range of 0-8 in steps of 0.02, and embedding dimensions of 4, 6, 8, and 16. When a threshold classifier was trained, the RQA variable recurrence was able to correctly classify 94.3% of periods in a test dataset. It was concluded that RQA of IBI data is able to accurately discriminate between infant sleep states. This is a promising step toward development of a minimal-channel automatic sleep state classification system.

  2. Prediction of Depression in Cancer Patients With Different Classification Criteria, Linear Discriminant Analysis versus Logistic Regression.

    Science.gov (United States)

    Shayan, Zahra; Mohammad Gholi Mezerji, Naser; Shayan, Leila; Naseri, Parisa

    2015-11-03

    Logistic regression (LR) and linear discriminant analysis (LDA) are two popular statistical models for prediction of group membership. Although they are very similar, the LDA makes more assumptions about the data. When categorical and continuous variables used simultaneously, the optimal choice between the two models is questionable. In most studies, classification error (CE) is used to discriminate between subjects in several groups, but this index is not suitable to predict the accuracy of the outcome. The present study compared LR and LDA models using classification indices. This cross-sectional study selected 243 cancer patients. Sample sets of different sizes (n = 50, 100, 150, 200, 220) were randomly selected and the CE, B, and Q classification indices were calculated by the LR and LDA models. CE revealed the a lack of superiority for one model over the other, but the results showed that LR performed better than LDA for the B and Q indices in all situations. No significant effect for sample size on CE was noted for selection of an optimal model. Assessment of the accuracy of prediction of real data indicated that the B and Q indices are appropriate for selection of an optimal model. The results of this study showed that LR performs better in some cases and LDA in others when based on CE. The CE index is not appropriate for classification, although the B and Q indices performed better and offered more efficient criteria for comparison and discrimination between groups.

  3. Using confirmatory factor analysis to manage discriminant validity issues in social pharmacy research.

    Science.gov (United States)

    Carter, Stephen R

    2016-06-01

    Background Confirmatory factory analysis (CFA) and structural equation modelling (SEM) are increasingly used in social pharmacy research. One of the key benefits of CFA is that it allows researchers to provide evidence for the validity of internal factor structure of measurement scales. In particular, CFA can be used to provide evidence for the validity of the assertion that a hypothesized multi-dimensional scale discriminates between sub-scales. Aim This manuscript aims to provide guidance for researchers who wish to use CFA to provide evidence for the internal factor structure of measurement scales. Methods The manuscript places discriminant validity in the context of providing overall validity evidence for measurement scales. Four examples from the recent social pharmacy literature are used to critically examine the various methods which are used to establish discriminant validity. Using a hypothetical scenario, the manuscript demonstrates how commonly used output from CFA computer programs can be used to provide evidence for separateness of sub-scales within a multi-dimensional scale. Conclusion The manuscript concludes with recommendations for the conduct and reporting of studies which use CFA to provide evidence of internal factor structure of measurement scales.

  4. Morphological Discrimination of Greek Honey Bee Populations Based on Geometric Morphometrics Analysis of Wing Shape

    Directory of Open Access Journals (Sweden)

    Charistos Leonidas

    2014-06-01

    Full Text Available Honey bees collected from 32 different localities in Greece were studied based on the geometric morphometrics approach using the coordinates of 19 landmarks located at wing vein intersections. Procrustes analysis, principal component analysis, and Canonical variate analysis (CVA detected population variability among the studied samples. According to the Principal component analysis (PCA of pooled data from each locality, the most differentiated populations were the populations from the Aegean island localities Astypalaia, Chios, and Kythira. However, the populations with the most distant according to the canonical variate analysis performed on all measurements were the populations from Heraklion and Chania (both from Crete island. These results can be used as a starting point for the use of geometric morphometrics in the discrimination of honey bee populations in Greece and the establishment of conservation areas for local honey bee populations.

  5. Electric and Hybrid Vehicle System Research and Development Project: Hybrid Vehicle Potential Assessment. Volume VI. Cost analysis

    Energy Technology Data Exchange (ETDEWEB)

    Hardy, K.S.

    1979-09-30

    The purpose of the cost analysis is to determine the economic feasibility of a variety of hybrid vehicles with respect to conventional vehicles specifically designed for the same duty cycle defined by the mission analysis. Several different hybrid configurations including parallel, parallel-flywheel, and series vehicles were evaluated. The ramifications of incorporating examples of advanced batteries, these being the advanced lead-acid, nickel-zinc, and sodium sulfur were also investigated. Vehicles were specifically designed with these batteries and for the driving cycles specified by the mission. Simulated operation on the missions yielded the energy consumption (petroleum and/or electricity) over the driving cycles. It was concluded that: in the event that gasoline prices reach $2.50 to $3.00/gal, hybrid vehicles in many applications will become economically competitive with conventional vehicles without subsidization; in some commercial applications hybrid vehicles could be economically competitive, when the gasoline price ranges from $1.20 to $1.50/gal. The cost per kWh per cycle of the advanced batteries is much more important economically than the specific energy; the series hybrid vehicles were found to be more expensive in comparison to the parallel or parallel-flywheel hybrids when designed as passenger vehicles; and hybrid vehicles designed for private use could become economically competitive and displace up to 50% of the fuel normally used on that mission if subsidies of $500 to $2000 were supplied to the owner/operator. (LCL)

  6. Rapid discrimination of plastic packaging materials using MIR spectroscopy coupled with independent components analysis (ICA).

    Science.gov (United States)

    Kassouf, Amine; Maalouly, Jacqueline; Rutledge, Douglas N; Chebib, Hanna; Ducruet, Violette

    2014-11-01

    Plastic packaging wastes increased considerably in recent decades, raising a major and serious public concern on political, economical and environmental levels. Dealing with this kind of problems is generally done by landfilling and energy recovery. However, these two methods are becoming more and more expensive, hazardous to the public health and the environment. Therefore, recycling is gaining worldwide consideration as a solution to decrease the growing volume of plastic packaging wastes and simultaneously reduce the consumption of oil required to produce virgin resin. Nevertheless, a major shortage is encountered in recycling which is related to the sorting of plastic wastes. In this paper, a feasibility study was performed in order to test the potential of an innovative approach combining mid infrared (MIR) spectroscopy with independent components analysis (ICA), as a simple and fast approach which could achieve high separation rates. This approach (MIR-ICA) gave 100% discrimination rates in the separation of all studied plastics: polyethylene terephthalate (PET), polyethylene (PE), polypropylene (PP), polystyrene (PS) and polylactide (PLA). In addition, some more specific discriminations were obtained separating plastic materials belonging to the same polymer family e.g. high density polyethylene (HDPE) from low density polyethylene (LDPE). High discrimination rates were obtained despite the heterogeneity among samples especially differences in colors, thicknesses and surface textures. The reproducibility of the proposed approach was also tested using two spectrometers with considerable differences in their sensitivities. Discrimination rates were not affected proving that the developed approach could be extrapolated to different spectrometers. MIR combined with ICA is a promising tool for plastic waste separation that can help improve performance in this field; however further technological improvements and developments are required before it can be applied

  7. Analysis of a Hybrid Wing Body Center Section Test Article

    Science.gov (United States)

    Wu, Hsi-Yung T.; Shaw, Peter; Przekop, Adam

    2013-01-01

    The hybrid wing body center section test article is an all-composite structure made of crown, floor, keel, bulkhead, and rib panels utilizing the Pultruded Rod Stitched Efficient Unitized Structure (PRSEUS) design concept. The primary goal of this test article is to prove that PRSEUS components are capable of carrying combined loads that are representative of a hybrid wing body pressure cabin design regime. This paper summarizes the analytical approach, analysis results, and failure predictions of the test article. A global finite element model of composite panels, metallic fittings, mechanical fasteners, and the Combined Loads Test System (COLTS) test fixture was used to conduct linear structural strength and stability analyses to validate the specimen under the most critical combination of bending and pressure loading conditions found in the hybrid wing body pressure cabin. Local detail analyses were also performed at locations with high stress concentrations, at Tee-cap noodle interfaces with surrounding laminates, and at fastener locations with high bearing/bypass loads. Failure predictions for different composite and metallic failure modes were made, and nonlinear analyses were also performed to study the structural response of the test article under combined bending and pressure loading. This large-scale specimen test will be conducted at the COLTS facility at the NASA Langley Research Center.

  8. From hybridization theory to microarray data analysis: performance evaluation

    Directory of Open Access Journals (Sweden)

    Berger Fabrice

    2011-12-01

    Full Text Available Abstract Background Several preprocessing methods are available for the analysis of Affymetrix Genechips arrays. The most popular algorithms analyze the measured fluorescence intensities with statistical methods. Here we focus on a novel algorithm, AffyILM, available from Bioconductor, which relies on inputs from hybridization thermodynamics and uses an extended Langmuir isotherm model to compute transcript concentrations. These concentrations are then employed in the statistical analysis. We compared the performance of AffyILM and other traditional methods both in the old and in the newest generation of GeneChips. Results Tissue mixture and Latin Square datasets (provided by Affymetrix were used to assess the performances of the differential expression analysis depending on the preprocessing strategy. A correlation analysis conducted on the tissue mixture data reveals that the median-polish algorithm allows to best summarize AffyILM concentrations computed at the probe-level. Those correlation results are equivalent to the best correlations observed using popular preprocessing methods relying on intensity values. The performances of each tested preprocessing algorithm were quantified using the Latin Square HG-U133A dataset, thanks to the comparison of differential analysis results with the list of spiked genes. The figures of merit generated illustrates that the performances associated to AffyILM(medianpolish, inferred from the present statistical analysis, are comparable to the best performing strategies previously reported. Conclusions Converting probe intensities to estimates of target concentrations prior to the statistical analysis, AffyILM(medianpolish is one of the best performing strategy currently available. Using hybridization theory, probe-level estimates of target concentrations should be identically distributed. In the future, a probe-level multivariate analysis of the concentrations should be compared to the univariate analysis of

  9. An intercomparison of different topography effects on discrimination performance of fuzzy change vector analysis algorithm

    Science.gov (United States)

    Singh, Sartajvir; Talwar, Rajneesh

    2016-12-01

    Detection of snow cover changes is vital for avalanche hazard analysis and flood flashes that arise due to variation in temperature. Hence, multitemporal change detection is one of the practical mean to estimate the snow cover changes over larger area using remotely sensed data. There have been some previous studies that examined how accuracy of change detection analysis is affected by different topography effects over Northwestern Indian Himalayas. The present work emphases on the intercomparison of different topography effects on discrimination performance of fuzzy based change vector analysis (FCVA) as change detection algorithm that includes extraction of change-magnitude and change-direction from a specific pixel belongs multiple or partial membership. The qualitative and quantitative analysis of the proposed FCVA algorithm is performed under topographic conditions and topographic correction conditions. The experimental outcomes confirmed that in change category discrimination procedure, FCVA with topographic correction achieved 86.8% overall accuracy and 4.8% decay (82% of overall accuracy) is found in FCVA without topographic correction. This study suggests that by incorporating the topographic correction model over mountainous region satellite imagery, performance of FCVA algorithm can be significantly improved up to great extent in terms of determining actual change categories.

  10. Analysis of Non-binary Hybrid LDPC Codes

    CERN Document Server

    Sassatelli, Lucile

    2008-01-01

    In this paper, we analyse asymptotically a new class of LDPC codes called Non-binary Hybrid LDPC codes, which has been recently introduced. We use density evolution techniques to derive a stability condition for hybrid LDPC codes, and prove their threshold behavior. We study this stability condition to conclude on asymptotic advantages of hybrid LDPC codes compared to their non-hybrid counterparts.

  11. Rapid discrimination of bergamot essential oil by paper spray mass spectrometry and chemometric analysis.

    Science.gov (United States)

    Taverna, Domenico; Di Donna, Leonardo; Mazzotti, Fabio; Tagarelli, Antonio; Napoli, Anna; Furia, Emilia; Sindona, Giovanni

    2016-09-01

    A novel approach for the rapid discrimination of bergamot essential oil from other citrus fruits oils is presented. The method was developed using paper spray mass spectrometry (PS-MS) allowing for a rapid molecular profiling coupled with a statistic tool for a precise and reliable discrimination between the bergamot complex matrix and other similar matrices, commonly used for its reconstitution. Ambient mass spectrometry possesses the ability to record mass spectra of ordinary samples, in their native environment, without sample preparation or pre-separation by creating ions outside the instrument. The present study reports a PS-MS method for the determination of oxygen heterocyclic compounds such as furocoumarins, psoralens and flavonoids present in the non-volatile fraction of citrus fruits essential oils followed by chemometric analysis. The volatile fraction of Bergamot is one of the most known and fashionable natural products, which found applications in flavoring industry as ingredient in beverages and flavored foodstuff. The development of the presented method employed bergamot, sweet orange, orange, cedar, grapefruit and mandarin essential oils. PS-MS measurements were carried out in full scan mode for a total run time of 2 min. The capability of PS-MS profiling to act as marker for the classification of bergamot essential oils was evaluated by using multivariate statistical analysis. Two pattern recognition techniques, linear discriminant analysis and soft independent modeling of class analogy, were applied to MS data. The cross-validation procedure has shown excellent results in terms of the prediction ability because both models have correctly classified all samples for each category. Copyright © 2016 John Wiley & Sons, Ltd.

  12. First Lunar Wake Passage of ARTEMIS: Discrimination of Wake Effects and Solar Wind Fluctuations by 3D Hybrid Simulations

    Science.gov (United States)

    Wiehle, S.; Plaschke, F.; Motschmann, U.; Glassmeier, K. H.; Auster, H. U.; Angelopoulos, V.; Mueller, J.; Kriegel, H.; Georgescu, E.; Halekas, J.; Sibeck, D. G.; McFadden, J. P.

    2011-01-01

    The spacecraft P1 of the new ARTEMIS (Acceleration, Reconnection, Turbulence, and Electrodynamics of the Moon's Interaction with the Sun) mission passed the lunar wake for the first time on February 13, 2010. We present magnetic field and plasma data of this event and results of 3D hybrid simulations. As the solar wind magnetic field was highly dynamic during the passage, a simulation with stationary solar wind input cannot distinguish whether distortions were caused by these solar wind variations or by the lunar wake; therefore, a dynamic real-time simulation of the flyby has been performed. The input values of this simulation are taken from NASA OMNI data and adapted to the P1 data, resulting in a good agreement between simulation and measurements. Combined with the stationary simulation showing non-transient lunar wake structures, a separation of solar wind and wake effects is achieved. An anisotropy in the magnitude of the plasma bulk flow velocity caused by a non-vanishing magnetic field component parallel to the solar wind flow and perturbations created by counterstreaming ions in the lunar wake are observed in data and simulations. The simulations help to interpret the data granting us the opportunity to examine the entire lunar plasma environment and, thus, extending the possibilities of measurements alone: A comparison of a simulation cross section to theoretical predictions of MHD wave propagation shows that all three basic MHD modes are present in the lunar wake and that their expansion governs the lunar wake refilling process.

  13. Classification of Error-Diffused Halftone Images Based on Spectral Regression Kernel Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Zhigao Zeng

    2016-01-01

    Full Text Available This paper proposes a novel algorithm to solve the challenging problem of classifying error-diffused halftone images. We firstly design the class feature matrices, after extracting the image patches according to their statistics characteristics, to classify the error-diffused halftone images. Then, the spectral regression kernel discriminant analysis is used for feature dimension reduction. The error-diffused halftone images are finally classified using an idea similar to the nearest centroids classifier. As demonstrated by the experimental results, our method is fast and can achieve a high classification accuracy rate with an added benefit of robustness in tackling noise.

  14. Discourse analysis of gender equality and non-discrimination laws and strategies

    Directory of Open Access Journals (Sweden)

    Antonijević Zorana

    2017-01-01

    Full Text Available Based on the contemporary research on gender and language, using the method of discourse analysis applied to the laws and policies, this article explains how certain linguistic practice, in the context of the administrative discourse, produces meaning that may or may not contribute to its better understanding and more efficient implementation. Through discourse analysis of gender equality and non-discrimination laws and strategies in Serbia, it has been shown how and with what consequences the socio-political and academic elites affect defining and promoting certain concepts (gender, sex, gender equality, discrimination in one social and historical moment. The paper is placed in the theoretical framework of three visions of gender equality: perspective of equal treatment, women‘s perspectives and gender perspective (Booth, Bennett 2002, that are corresponding to the three strategies for achieving gender equality: equal treatment, specific policy of gender equality and gender mainstreaming (Verloo, 2001. The discourse analysis of the Law on Gender Equality (2009, the National Strategy for the Improvement of the Position of Women and Advancement of Gender Equality (2009, the Law on Prohibition of Discrimination (2009 and the Strategy for Prevention and Protection against Discrimination (2013, has shown the context of use and meaning of terms gender and sex, as well as implications it has on their potential to change the existing paradigms and understanding of gender equality, and the implementation of policies in Serbia. Analysis of the use of terms sex and gender in the most important legal and strategic documents for achieving gender equality, showed that the choice of certain categories and terms is always a political choice. The authors show how these documents are written in the key of two gender perspectives and strategies: equal treatment and the specific policy of gender equality, while the third - introduction of a gender perspective

  15. Discriminant Analysis of a Spatially Extensive Landsliding Inventory for the Haida Gwaii, British Columbia, Canada

    Science.gov (United States)

    Sjogren, D.; Martin, Y. E.; Jagielko, L.

    2010-12-01

    Gimbarzevsky (1988) collected an exceptional landsliding inventory for the Haida Gwaii, British Columbia (formerly called the Queen Charlotte Islands). This data base includes more than 8 000 landsliding vectors, with an areal coverage of about 10 000 km2. Unfortunately, this landsliding inventory was never published in the referred literature, despite its regional significance. The data collection occurred prior to widespread use of GIS technologies in landsliding analysis, thus restricting the types of analyses that were undertaken at the time relative to what is possible today. Gimbarzevsky identified the landsliding events from 1:50 000 aerial photographs, and then transferred the landslide vectors to NTS map sheets. In this study, we digitized the landslide vectors from these original map sheets and connected each vector to a digital elevation model. Lengths of landslide vectors were then compared to results of Rood (1984), whose landsliding inventory for the Haida Gwaii relied on larger-scale aerial photographs (~ 1:13 000). A comparison of the two data bases shows that Rood’s inventory contains a more complete record of smaller landslides, whereas Gimbarzevsky’s inventory provides a much better statistical representation of less frequently occurring, medium to large landslide events. We then apply discriminant analysis to the Gimbarzevsky data base to assess which of a set of ten predictor variables, selected on the basis of mechanical theory, best predict failed vs. unfailed locations in the landscape (referred to as the grouping variable in discriminant analysis). Certain predictor variables may be cross-correlated, and any one particular variable may be related to several aspects of mechanical theory (for example, a particular variable may affect various components of shear stress and/or shear strength); it is important to recognize that the significance of particular groupings may reflect this information. Eight of the original variables were found

  16. Estimating the causes of traffic accidents using logistic regression and discriminant analysis.

    Science.gov (United States)

    Karacasu, Murat; Ergül, Barış; Altin Yavuz, Arzu

    2014-01-01

    Factors that affect traffic accidents have been analysed in various ways. In this study, we use the methods of logistic regression and discriminant analysis to determine the damages due to injury and non-injury accidents in the Eskisehir Province. Data were obtained from the accident reports of the General Directorate of Security in Eskisehir; 2552 traffic accidents between January and December 2009 were investigated regarding whether they resulted in injury. According to the results, the effects of traffic accidents were reflected in the variables. These results provide a wealth of information that may aid future measures toward the prevention of undesired results.

  17. Analysis of high velocity impact on hybrid composite fan blades

    Science.gov (United States)

    Chamis, C. C.; Sinclair, J. H.

    1979-01-01

    This paper describes recent developments in the analysis of high velocity impact of composite blades using a computerized capability which consists of coupling a composites mechanics code with the direct-time integration features of NASTRAN. The application of the capability to determine the linear dynamic response of an intraply hybrid composite aircraft engine fan blade is described in detail. The predicted results agree with measured data. The results also show that the impact stresses reach sufficiently high magnitudes to cause failures in the impact region at early times of the impact event.

  18. Analysis and Design of Hybrid Excitation Permanent Magnet Synchronous Generators

    Institute of Scientific and Technical Information of China (English)

    JIN Wan-bing; ZHANG Dong; AN Zhong-liang; TAN Ren-yuan

    2006-01-01

    On the basis of a conventional permanent magnet (PM) synchronous generator's construction,a novel kind of Hybrid Excitation Permanent Magnet Synchronous Generator (HEPMSG) is introduced by inserting exciting winding in the stator or rotor.Firstly,the construction of HEPMSG is improved with the addition of PM excitation on the ferromagnetic pole,and its working principle and design method are studied in detail.Then,an appropriate exciting current control system is presented considering the characteristics of HEPMSG.Finally,a prototype is made,and test results confirm the analysis and design.

  19. Rapid discrimination of plastic packaging materials using MIR spectroscopy coupled with independent components analysis (ICA)

    Energy Technology Data Exchange (ETDEWEB)

    Kassouf, Amine, E-mail: amine.kassouf@agroparistech.fr [ER004 “Lebanese Food Packaging”, Faculty of Sciences II, Lebanese University, 90656 Jdeideth El Matn, Fanar (Lebanon); INRA, UMR1145 Ingénierie Procédés Aliments, 1 Avenue des Olympiades, 91300 Massy (France); AgroParisTech, UMR1145 Ingénierie Procédés Aliments, 16 rue Claude Bernard, 75005 Paris (France); Maalouly, Jacqueline, E-mail: j_maalouly@hotmail.com [ER004 “Lebanese Food Packaging”, Faculty of Sciences II, Lebanese University, 90656 Jdeideth El Matn, Fanar (Lebanon); Rutledge, Douglas N., E-mail: douglas.rutledge@agroparistech.fr [INRA, UMR1145 Ingénierie Procédés Aliments, 1 Avenue des Olympiades, 91300 Massy (France); AgroParisTech, UMR1145 Ingénierie Procédés Aliments, 16 rue Claude Bernard, 75005 Paris (France); Chebib, Hanna, E-mail: hchebib@hotmail.com [ER004 “Lebanese Food Packaging”, Faculty of Sciences II, Lebanese University, 90656 Jdeideth El Matn, Fanar (Lebanon); Ducruet, Violette, E-mail: violette.ducruet@agroparistech.fr [INRA, UMR1145 Ingénierie Procédés Aliments, 1 Avenue des Olympiades, 91300 Massy (France); AgroParisTech, UMR1145 Ingénierie Procédés Aliments, 16 rue Claude Bernard, 75005 Paris (France)

    2014-11-15

    Highlights: • An innovative technique, MIR-ICA, was applied to plastic packaging separation. • This study was carried out on PE, PP, PS, PET and PLA plastic packaging materials. • ICA was applied to discriminate plastics and 100% separation rates were obtained. • Analyses performed on two spectrometers proved the reproducibility of the method. • MIR-ICA is a simple and fast technique allowing plastic identification/classification. - Abstract: Plastic packaging wastes increased considerably in recent decades, raising a major and serious public concern on political, economical and environmental levels. Dealing with this kind of problems is generally done by landfilling and energy recovery. However, these two methods are becoming more and more expensive, hazardous to the public health and the environment. Therefore, recycling is gaining worldwide consideration as a solution to decrease the growing volume of plastic packaging wastes and simultaneously reduce the consumption of oil required to produce virgin resin. Nevertheless, a major shortage is encountered in recycling which is related to the sorting of plastic wastes. In this paper, a feasibility study was performed in order to test the potential of an innovative approach combining mid infrared (MIR) spectroscopy with independent components analysis (ICA), as a simple and fast approach which could achieve high separation rates. This approach (MIR-ICA) gave 100% discrimination rates in the separation of all studied plastics: polyethylene terephthalate (PET), polyethylene (PE), polypropylene (PP), polystyrene (PS) and polylactide (PLA). In addition, some more specific discriminations were obtained separating plastic materials belonging to the same polymer family e.g. high density polyethylene (HDPE) from low density polyethylene (LDPE). High discrimination rates were obtained despite the heterogeneity among samples especially differences in colors, thicknesses and surface textures. The reproducibility of

  20. Discriminant analysis of pulmonary function parameters. Healthy adults versus mild asthmatics and moderate asthmatics.

    Directory of Open Access Journals (Sweden)

    Meguro,Tadamichi

    1982-08-01

    Full Text Available Volume-time (V-T and flow-volume (F-V curves were measured in all the subjects of nonsmoking young males (mean value 26.3 yrs. of age, healthy and asthmatics. Eleven parameters of pulmonary function tests composed of two V-T, six F-V, and three mean time constant (MTC parameters, were calculated from the curves. These parameters were used in the two analyses through the all possible selection procedure (APSP discriminating between healthy adults and mild asthmatics and also between healthy and moderate. Flow rate at 75% of FVC (V75 proved to be the most useful parameter and V50 the next best in both analyses. The probability of misclassification using all eleven parameters was 19.64% in the analysis of healthy adults and mild asthmatics, and 4.29% in the analysis of healthy adults and moderate asthmatics. There was a little difference in the parameters selected at every step. The discriminant analysis proved that the flow-volume patterns were different according to the severity of bronchial asthma. Thus flow-volume recognition was considered to be important in analyzing the severity of bronchial asthma.

  1. Predicting groundwater redox status on a regional scale using linear discriminant analysis

    Science.gov (United States)

    Close, M. E.; Abraham, P.; Humphries, B.; Lilburne, L.; Cuthill, T.; Wilson, S.

    2016-08-01

    Reducing conditions are necessary for denitrification, thus the groundwater redox status can be used to identify subsurface zones where potentially significant nitrate reduction can occur. Groundwater chemistry in two contrasting regions of New Zealand was classified with respect to redox status and related to mappable factors, such as geology, topography and soil characteristics using discriminant analysis. Redox assignment was carried out for water sampled from 568 and 2223 wells in the Waikato and Canterbury regions, respectively. For the Waikato region 64% of wells sampled indicated oxic conditions in the water; 18% indicated reduced conditions and 18% had attributes indicating both reducing and oxic conditions termed "mixed". In Canterbury 84% of wells indicated oxic conditions; 10% were mixed; and only 5% indicated reduced conditions. The analysis was performed over three different well depths, 100 m. For both regions, the percentage of oxidised groundwater decreased with increasing well depth. Linear discriminant analysis was used to develop models to differentiate between the three redox states. Models were derived for each depth and region using 67% of the data, and then subsequently validated on the remaining 33%. The average agreement between predicted and measured redox status was 63% and 70% for the Waikato and Canterbury regions, respectively. The models were incorporated into GIS and the prediction of redox status was extended over the whole region, excluding mountainous land. This knowledge improves spatial prediction of reduced groundwater zones, and therefore, when combined with groundwater flow paths, improves estimates of denitrification.

  2. Classification tree analysis for the discrimination of pleural exudates and transudates.

    Science.gov (United States)

    Esquerda, Aureli; Trujillano, Javier; López de Ullibarri, Ignacio; Bielsa, Silvia; Madroñero, Ana B; Porcel, José M

    2007-01-01

    Classification and regression tree (CART) analysis is a non-parametric technique suitable for the generation of clinical decision rules. We have studied the performance of CART analysis in the separation of pleural exudates and transudates. Basic demographic, radiologic and laboratory data were retrospectively evaluated in 1257 pleural effusions (204 transudates and 1053 exudates, according to standard clinical criteria) and submitted for CART analysis. The model's discriminative ability was compared with that of Light's criteria, in both the original formulation and an abbreviated version, i.e., deleting the pleural fluid (PF)/serum lactate dehydrogenase (LDH) ratio from the triad. A first CART model built starting from all available data identified PF/serum protein ratio and PF LDH ratios as the two best discriminatory parameters. This algorithm achieved a sensitivity of 96.8%, slightly lower than that of classical Light's criteria (98.5%) and comparable to that of the abbreviated Light's criteria (97.0%), and significantly better specificity (85.3%) compared to both classical (74.0%) and abbreviated (79.4%) Light's criteria. A second CART model developed after excluding serum measurements selected PF protein and PF LDH as the most discriminatory variables, and correctly classified 97.2% of exudates and 77.0% of transudates. CART-based algorithms can efficiently discriminate between pleural exudates and transudates.

  3. Rapid discrimination of the geographical origins of an oolong tea (anxi-tieguanyin) by near-infrared spectroscopy and partial least squares discriminant analysis.

    Science.gov (United States)

    Yan, Si-Min; Liu, Jun-Ping; Xu, Lu; Fu, Xian-Shu; Cui, Hai-Feng; Yun, Zhen-Yu; Yu, Xiao-Ping; Ye, Zi-Hong

    2014-01-01

    This paper focuses on a rapid and nondestructive way to discriminate the geographical origin of Anxi-Tieguanyin tea by near-infrared (NIR) spectroscopy and chemometrics. 450 representative samples were collected from Anxi County, the original producing area of Tieguanyin tea, and another 120 Tieguanyin samples with similar appearance were collected from unprotected producing areas in China. All these samples were measured by NIR. The Stahel-Donoho estimates (SDE) outlyingness diagnosis was used to remove the outliers. Partial least squares discriminant analysis (PLSDA) was performed to develop a classification model and predict the authenticity of unknown objects. To improve the sensitivity and specificity of classification, the raw data was preprocessed to reduce unwanted spectral variations by standard normal variate (SNV) transformation, taking second-order derivatives (D2) spectra, and smoothing. As the best model, the sensitivity and specificity reached 0.931 and 1.000 with SNV spectra. Combination of NIR spectrometry and statistical model selection can provide an effective and rapid method to discriminate the geographical producing area of Anxi-Tieguanyin.

  4. Rapid Discrimination of the Geographical Origins of an Oolong Tea (Anxi-Tieguanyin by Near-Infrared Spectroscopy and Partial Least Squares Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Si-Min Yan

    2014-01-01

    Full Text Available This paper focuses on a rapid and nondestructive way to discriminate the geographical origin of Anxi-Tieguanyin tea by near-infrared (NIR spectroscopy and chemometrics. 450 representative samples were collected from Anxi County, the original producing area of Tieguanyin tea, and another 120 Tieguanyin samples with similar appearance were collected from unprotected producing areas in China. All these samples were measured by NIR. The Stahel-Donoho estimates (SDE outlyingness diagnosis was used to remove the outliers. Partial least squares discriminant analysis (PLSDA was performed to develop a classification model and predict the authenticity of unknown objects. To improve the sensitivity and specificity of classification, the raw data was preprocessed to reduce unwanted spectral variations by standard normal variate (SNV transformation, taking second-order derivatives (D2 spectra, and smoothing. As the best model, the sensitivity and specificity reached 0.931 and 1.000 with SNV spectra. Combination of NIR spectrometry and statistical model selection can provide an effective and rapid method to discriminate the geographical producing area of Anxi-Tieguanyin.

  5. Penalized discriminant analysis for the detection of wild-grown and cultivated Ganoderma lucidum using Fourier transform infrared spectroscopy

    Science.gov (United States)

    Zhu, Ying; Tan, Tuck Lee

    2016-04-01

    An effective and simple analytical method using Fourier transform infrared (FTIR) spectroscopy to distinguish wild-grown high-quality Ganoderma lucidum (G. lucidum) from cultivated one is of essential importance for its quality assurance and medicinal value estimation. Commonly used chemical and analytical methods using full spectrum are not so effective for the detection and interpretation due to the complex system of the herbal medicine. In this study, two penalized discriminant analysis models, penalized linear discriminant analysis (PLDA) and elastic net (Elnet),using FTIR spectroscopy have been explored for the purpose of discrimination and interpretation. The classification performances of the two penalized models have been compared with two widely used multivariate methods, principal component discriminant analysis (PCDA) and partial least squares discriminant analysis (PLSDA). The Elnet model involving a combination of L1 and L2 norm penalties enabled an automatic selection of a small number of informative spectral absorption bands and gave an excellent classification accuracy of 99% for discrimination between spectra of wild-grown and cultivated G. lucidum. Its classification performance was superior to that of the PLDA model in a pure L1 setting and outperformed the PCDA and PLSDA models using full wavelength. The well-performed selection of informative spectral features leads to substantial reduction in model complexity and improvement of classification accuracy, and it is particularly helpful for the quantitative interpretations of the major chemical constituents of G. lucidum regarding its anti-cancer effects.

  6. Penalized discriminant analysis for the detection of wild-grown and cultivated Ganoderma lucidum using Fourier transform infrared spectroscopy.

    Science.gov (United States)

    Zhu, Ying; Tan, Tuck Lee

    2016-04-15

    An effective and simple analytical method using Fourier transform infrared (FTIR) spectroscopy to distinguish wild-grown high-quality Ganoderma lucidum (G. lucidum) from cultivated one is of essential importance for its quality assurance and medicinal value estimation. Commonly used chemical and analytical methods using full spectrum are not so effective for the detection and interpretation due to the complex system of the herbal medicine. In this study, two penalized discriminant analysis models, penalized linear discriminant analysis (PLDA) and elastic net (Elnet),using FTIR spectroscopy have been explored for the purpose of discrimination and interpretation. The classification performances of the two penalized models have been compared with two widely used multivariate methods, principal component discriminant analysis (PCDA) and partial least squares discriminant analysis (PLSDA). The Elnet model involving a combination of L1 and L2 norm penalties enabled an automatic selection of a small number of informative spectral absorption bands and gave an excellent classification accuracy of 99% for discrimination between spectra of wild-grown and cultivated G. lucidum. Its classification performance was superior to that of the PLDA model in a pure L1 setting and outperformed the PCDA and PLSDA models using full wavelength. The well-performed selection of informative spectral features leads to substantial reduction in model complexity and improvement of classification accuracy, and it is particularly helpful for the quantitative interpretations of the major chemical constituents of G. lucidum regarding its anti-cancer effects.

  7. Commercial tree species discrimination using airborne AISA Eagle hyperspectral imagery and partial least squares discriminant analysis (PLS-DA) in KwaZulu-Natal, South Africa

    Science.gov (United States)

    Peerbhay, Kabir Yunus; Mutanga, Onisimo; Ismail, Riyad

    2013-05-01

    Discriminating commercial tree species using hyperspectral remote sensing techniques is critical in monitoring the spatial distributions and compositions of commercial forests. However, issues related to data dimensionality and multicollinearity limit the successful application of the technology. The aim of this study was to examine the utility of the partial least squares discriminant analysis (PLS-DA) technique in accurately classifying six exotic commercial forest species (Eucalyptus grandis, Eucalyptus nitens, Eucalyptus smithii, Pinus patula, Pinus elliotii and Acacia mearnsii) using airborne AISA Eagle hyperspectral imagery (393-900 nm). Additionally, the variable importance in the projection (VIP) method was used to identify subsets of bands that could successfully discriminate the forest species. Results indicated that the PLS-DA model that used all the AISA Eagle bands (n = 230) produced an overall accuracy of 80.61% and a kappa value of 0.77, with user's and producer's accuracies ranging from 50% to 100%. In comparison, incorporating the optimal subset of VIP selected wavebands (n = 78) in the PLS-DA model resulted in an improved overall accuracy of 88.78% and a kappa value of 0.87, with user's and producer's accuracies ranging from 70% to 100%. Bands located predominantly within the visible region of the electromagnetic spectrum (393-723 nm) showed the most capability in terms of discriminating between the six commercial forest species. Overall, the research has demonstrated the potential of using PLS-DA for reducing the dimensionality of hyperspectral datasets as well as determining the optimal subset of bands to produce the highest classification accuracies.

  8. Local classification: Locally weighted-partial least squares-discriminant analysis (LW-PLS-DA).

    Science.gov (United States)

    Bevilacqua, Marta; Marini, Federico

    2014-08-01

    The possibility of devising a simple, flexible and accurate non-linear classification method, by extending the locally weighted partial least squares (LW-PLS) approach to the cases where the algorithm is used in a discriminant way (partial least squares discriminant analysis, PLS-DA), is presented. In particular, to assess which category an unknown sample belongs to, the proposed algorithm operates by identifying which training objects are most similar to the one to be predicted and building a PLS-DA model using these calibration samples only. Moreover, the influence of the selected training samples on the local model can be further modulated by adopting a not uniform distance-based weighting scheme which allows the farthest calibration objects to have less impact than the closest ones. The performances of the proposed locally weighted-partial least squares-discriminant analysis (LW-PLS-DA) algorithm have been tested on three simulated data sets characterized by a varying degree of non-linearity: in all cases, a classification accuracy higher than 99% on external validation samples was achieved. Moreover, when also applied to a real data set (classification of rice varieties), characterized by a high extent of non-linearity, the proposed method provided an average correct classification rate of about 93% on the test set. By the preliminary results, showed in this paper, the performances of the proposed LW-PLS-DA approach have proved to be comparable and in some cases better than those obtained by other non-linear methods (k nearest neighbors, kernel-PLS-DA and, in the case of rice, counterpropagation neural networks).

  9. Flow cytometry-based DNA hybridization and polymorphism analysis

    Energy Technology Data Exchange (ETDEWEB)

    Cai, H.; Kommander, K.; White, P.S.; Nolan, J.P.

    1998-07-01

    Functional analysis of the humane genome, including the quantification of differential gene expression and the identification of polymorphic sites and disease genes, is an important element of the Human Genome Project. Current methods of analysis are mainly gel-based assays that are not well-suited to rapid genome-scale analyses. To analyze DNA sequence on a large scale, robust and high throughput assays are needed. The authors are developing a suite of microsphere-based approaches employing fluorescence detection to screen and analyze genomic sequence. The approaches include competitive DNA hybridization to measure DNA or RNA targets in unknown samples, and oligo ligation or extension assays to analyze single-nucleotide polymorphisms. Apart from the advances of sensitivity, simplicity, and low sample consumption, these flow cytometric approaches have the potential for high throughput multiplexed analysis using multicolored microspheres and automated sample handling.

  10. An Application of Discriminant Analysis to Pattern Recognition of Selected Contaminated Soil Features in Thin Sections

    DEFF Research Database (Denmark)

    Ribeiro, Alexandra B.; Nielsen, Allan Aasbjerg

    1997-01-01

    qualitative microprobe results: present elements Al, Si, Cr, Fe, As (associated with others). Selected groups of calibrated images (same light conditions and magnification) submitted to discriminant analysis, in order to find a pattern of recognition in the soil features corresponding to contamination already...... in the soluble and exchangeable phase, these elements being associated primarily with amorphous-crystalline Fe-oxides, organic matter and/or resistant phases. The results obtained with sequential extraction were the prerequisite to the attempt to identify the Cr and As distribution in the solid phase. If high...... concentrations of contaminants are indicated by chemical wet analysis, these contaminants must occur directly in the solid phase. Thin sections of soil aggregates were scanned for Cu, Cr and As using an electron microprobe, and qualitative analysis was made on selected areas. Microphotographs of thin sections...

  11. Assessing Credit Default using Logistic Regression and Multiple Discriminant Analysis: Empirical Evidence from Bosnia and Herzegovina

    Directory of Open Access Journals (Sweden)

    Deni Memić

    2015-01-01

    Full Text Available This article has an aim to assess credit default prediction on the banking market in Bosnia and Herzegovina nationwide as well as on its constitutional entities (Federation of Bosnia and Herzegovina and Republika Srpska. Ability to classify companies info different predefined groups or finding an appropriate tool which would replace human assessment in classifying companies into good and bad buckets has been one of the main interests on risk management researchers for a long time. We investigated the possibility and accuracy of default prediction using traditional statistical methods logistic regression (logit and multiple discriminant analysis (MDA and compared their predictive abilities. The results show that the created models have high predictive ability. For logit models, some variables are more influential on the default prediction than the others. Return on assets (ROA is statistically significant in all four periods prior to default, having very high regression coefficients, or high impact on the model's ability to predict default. Similar results are obtained for MDA models. It is also found that predictive ability differs between logistic regression and multiple discriminant analysis.

  12. Predicting ethnic and racial discrimination: a meta-analysis of IAT criterion studies.

    Science.gov (United States)

    Oswald, Frederick L; Mitchell, Gregory; Blanton, Hart; Jaccard, James; Tetlock, Philip E

    2013-08-01

    This article reports a meta-analysis of studies examining the predictive validity of the Implicit Association Test (IAT) and explicit measures of bias for a wide range of criterion measures of discrimination. The meta-analysis estimates the heterogeneity of effects within and across 2 domains of intergroup bias (interracial and interethnic), 6 criterion categories (interpersonal behavior, person perception, policy preference, microbehavior, response time, and brain activity), 2 versions of the IAT (stereotype and attitude IATs), 3 strategies for measuring explicit bias (feeling thermometers, multi-item explicit measures such as the Modern Racism Scale, and ad hoc measures of intergroup attitudes and stereotypes), and 4 criterion-scoring methods (computed majority-minority difference scores, relative majority-minority ratings, minority-only ratings, and majority-only ratings). IATs were poor predictors of every criterion category other than brain activity, and the IATs performed no better than simple explicit measures. These results have important implications for the construct validity of IATs, for competing theories of prejudice and attitude-behavior relations, and for measuring and modeling prejudice and discrimination.

  13. Correlation analysis of proprioceptive acuity in ipsilateral position-matching and velocity-discrimination.

    Science.gov (United States)

    Djupsjöbacka, Mats; Domkin, Dmitry

    2005-01-01

    In order to plan and control movements the central nervous system (CNS) needs to continuously keep track of the state of the musculoskeletal system. Therefore the CNS constantly uses sensory input from mechanoreceptors in muscles, joints and skin to update information about body configuration on different levels of the CNS. On the conscious level, such representations constitute proprioception. Different tests for assessment of proprioceptive acuity have been described. However, it is unclear if the proprioceptive acuity measurements in these tests correlate within subjects. By using both uni- and multivariate analysis we compared proprioceptive acuity in different variants of ipsilateral active and passive limb position-matching and ipsilateral passive limb movement velocity-discrimination in a group of healthy subjects. The analysis of the position-matching data revealed a higher acuity of matching for active movements in comparison to passive ones. The acuity of matching was negatively correlated to movement extent. There was a lack of correlation between proprioceptive acuity measurements in position-matching and velocity-discrimination.

  14. Prostate lesion detection and localization based on locality alignment discriminant analysis

    Science.gov (United States)

    Lin, Mingquan; Chen, Weifu; Zhao, Mingbo; Gibson, Eli; Bastian-Jordan, Matthew; Cool, Derek W.; Kassam, Zahra; Chow, Tommy W. S.; Ward, Aaron; Chiu, Bernard

    2017-03-01

    Prostatic adenocarcinoma is one of the most commonly occurring cancers among men in the world, and it also the most curable cancer when it is detected early. Multiparametric MRI (mpMRI) combines anatomic and functional prostate imaging techniques, which have been shown to produce high sensitivity and specificity in cancer localization, which is important in planning biopsies and focal therapies. However, in previous investigations, lesion localization was achieved mainly by manual segmentation, which is time-consuming and prone to observer variability. Here, we developed an algorithm based on locality alignment discriminant analysis (LADA) technique, which can be considered as a version of linear discriminant analysis (LDA) localized to patches in the feature space. Sensitivity, specificity and accuracy generated by the proposed algorithm in five prostates by LADA were 52.2%, 89.1% and 85.1% respectively, compared to 31.3%, 85.3% and 80.9% generated by LDA. The delineation accuracy attainable by this tool has a potential in increasing the cancer detection rate in biopsies and in minimizing collateral damage of surrounding tissues in focal therapies.

  15. An item factor analysis and item response theory-based revision of the Everyday Discrimination Scale.

    Science.gov (United States)

    Stucky, Brian D; Gottfredson, Nisha C; Panter, A T; Daye, Charles E; Allen, Walter R; Wightman, Linda F

    2011-04-01

    The Everyday Discrimination Scale (EDS), a widely used measure of daily perceived discrimination, is purported to be unidimensional, to function well among African Americans, and to have adequate construct validity. Two separate studies and data sources were used to examine and cross-validate the psychometric properties of the EDS. In Study 1, an exploratory factor analysis was conducted on a sample of African American law students (N = 589), providing strong evidence of local dependence, or nuisance multidimensionality within the EDS. In Study 2, a separate nationally representative community sample (N = 3,527) was used to model the identified local dependence in an item factor analysis (i.e., bifactor model). Next, item response theory (IRT) calibrations were conducted to obtain item parameters. A five-item, revised-EDS was then tested for gender differential item functioning (in an IRT framework). Based on these analyses, a summed score to IRT-scaled score translation table is provided for the revised-EDS. Our results indicate that the revised-EDS is unidimensional, with minimal differential item functioning, and retains predictive validity consistent with the original scale.

  16. Multiple Subject Barycentric Discriminant Analysis (MUSUBADA: How to Assign Scans to Categories without Using Spatial Normalization

    Directory of Open Access Journals (Sweden)

    Hervé Abdi

    2012-01-01

    Full Text Available We present a new discriminant analysis (DA method called Multiple Subject Barycentric Discriminant Analysis (MUSUBADA suited for analyzing fMRI data because it handles datasets with multiple participants that each provides different number of variables (i.e., voxels that are themselves grouped into regions of interest (ROIs. Like DA, MUSUBADA (1 assigns observations to predefined categories, (2 gives factorial maps displaying observations and categories, and (3 optimally assigns observations to categories. MUSUBADA handles cases with more variables than observations and can project portions of the data table (e.g., subtables, which can represent participants or ROIs on the factorial maps. Therefore MUSUBADA can analyze datasets with different voxel numbers per participant and, so does not require spatial normalization. MUSUBADA statistical inferences are implemented with cross-validation techniques (e.g., jackknife and bootstrap, its performance is evaluated with confusion matrices (for fixed and random models and represented with prediction, tolerance, and confidence intervals. We present an example where we predict the image categories (houses, shoes, chairs, and human, monkey, dog, faces, of images watched by participants whose brains were scanned. This example corresponds to a DA question in which the data table is made of subtables (one per subject and with more variables than observations.

  17. Forensic analysis of Salvia divinorum using multivariate statistical procedures. Part I: discrimination from related Salvia species.

    Science.gov (United States)

    Willard, Melissa A Bodnar; McGuffin, Victoria L; Smith, Ruth Waddell

    2012-01-01

    Salvia divinorum is a hallucinogenic herb that is internationally regulated. In this study, salvinorin A, the active compound in S. divinorum, was extracted from S. divinorum plant leaves using a 5-min extraction with dichloromethane. Four additional Salvia species (Salvia officinalis, Salvia guaranitica, Salvia splendens, and Salvia nemorosa) were extracted using this procedure, and all extracts were analyzed by gas chromatography-mass spectrometry. Differentiation of S. divinorum from other Salvia species was successful based on visual assessment of the resulting chromatograms. To provide a more objective comparison, the total ion chromatograms (TICs) were subjected to principal components analysis (PCA). Prior to PCA, the TICs were subjected to a series of data pretreatment procedures to minimize non-chemical sources of variance in the data set. Successful discrimination of S. divinorum from the other four Salvia species was possible based on visual assessment of the PCA scores plot. To provide a numerical assessment of the discrimination, a series of statistical procedures such as Euclidean distance measurement, hierarchical cluster analysis, Student's t tests, Wilcoxon rank-sum tests, and Pearson product moment correlation were also applied to the PCA scores. The statistical procedures were then compared to determine the advantages and disadvantages for forensic applications.

  18. Semi-automated porosity identification from thin section images using image analysis and intelligent discriminant classifiers

    Science.gov (United States)

    Ghiasi-Freez, Javad; Soleimanpour, Iman; Kadkhodaie-Ilkhchi, Ali; Ziaii, Mansur; Sedighi, Mahdi; Hatampour, Amir

    2012-08-01

    Identification of different types of porosity within a reservoir rock is a functional parameter for reservoir characterization since various pore types play different roles in fluid transport and also, the pore spaces determine the fluid storage capacity of the reservoir. The present paper introduces a model for semi-automatic identification of porosity types within thin section images. To get this goal, a pattern recognition algorithm is followed. Firstly, six geometrical shape parameters of sixteen largest pores of each image are extracted using image analysis techniques. The extracted parameters and their corresponding pore types of 294 pores are used for training two intelligent discriminant classifiers, namely linear and quadratic discriminant analysis. The trained classifiers take the geometrical features of the pores to identify the type and percentage of five types of porosity, including interparticle, intraparticle, oomoldic, biomoldic, and vuggy in each image. The accuracy of classifiers is determined from two standpoints. Firstly, the predicted and measured percentages of each type of porosity are compared with each other. The results indicate reliable performance for predicting percentage of each type of porosity. In the second step, the precisions of classifiers for categorizing the pore spaces are analyzed. The classifiers also took a high acceptance score when used for individual recognition of pore spaces. The proposed methodology is a further promising application for petroleum geologists allowing statistical study of pore types in a rapid and accurate way.

  19. Classification of hand preshaping in persons with stroke using Linear Discriminant Analysis.

    Science.gov (United States)

    Puthenveettil, Saumya; Fluet, Gerard; Qiu, Qinyin; Adamovich, Sergei

    2012-01-01

    This study describes the analysis of hand preshaping using Linear Discriminant Analysis (LDA) to predict hand formation during reaching and grasping tasks of the hemiparetic hand, following a series of upper extremity motor intervention treatments. The purpose of this study is to use classification of hand posture as an additional tool for evaluating the effectiveness of therapies for upper extremity rehabilitation such as virtual reality (VR) therapy and conventional physical therapy. Classification error for discriminating between two objects during hand preshaping is obtained for the hemiparetic and unimpaired hands pre and post training. Eight subjects post stroke participated in a two-week training session consisting of upper extremity motor training. Four subjects trained with interactive VR computer games and four subjects trained with clinical physical therapy procedures of similar intensity. Subjects' finger joint angles were measured during a kinematic reach to grasp test using CyberGlove® and arm joint angles were measured using the trackSTAR™ system prior to training and after training. The unimpaired hand of subjects preshape into the target object with greater accuracy than the hemiparetic hand as indicated by lower classification errors. Hemiparetic hand improved in preshaping accuracy and time to reach minimum error. Classification of hand preshaping may provide insight into improvements in motor performance elicited by robotically facilitated virtually simulated training sessions or conventional physical therapy.

  20. Shape-based discriminative analysis of combined bilateral hippocampi using multiple object alignment

    Science.gov (United States)

    Shen, Li; Makedon, Fillia; Saykin, Andrew

    2004-05-01

    Shape analysis of hippocampi in schizophrenia has been preformed previously using the spherical harmonic SPHARM description. In these studies, the left and right hippocampi are aligned independently and the spatial relation between them is not explored. This paper presents a new SPHARM-based technique which examines not only the individual shape information of the two hippocampi but also the spatial relation between them. The left and right hippocampi are treated as a single shape configuration. A ploy-shape alignment algorithm is developed for aligning configurations of multiple SPHARM surfaces as follows: (1) the total volume is normalized; (2) the parameter space is aligned for creating the surface correspondence; (3) landmarks are created by a uniform sampling of multiple surfaces for each configuration; (4) a quaternion-based algorithm is employed to align each landmark representation to the mean configuration through the least square rotation and translation iteratively until the mean converges. After applying the poly-shape alignment algorithm, a point distribution model is applied to aligned landmarks for feature extraction. Classification is performed using Fisher's linear discriminant with an effective feature selection scheme. Applying the above procedure to our hippocampal data (14 controls versus 25 schizophrenics, all right-handed males), we achieve the best cross-validation accuracy of 92%, supporting the idea that the whole shape configuration of the two hippocampi provides valuable information in detecting schizophrenia. The results of an ROC analysis and a visualization of discriminative patterns are also included.

  1. The differentiation of camel breeds based on meat measurements using discriminant analysis.

    Science.gov (United States)

    Al-Atiyat, Raed Mahmoud; Suliman, Gamal; AlSuhaibani, Entissar; El-Waziry, Ahmad; Al-Owaimer, Abdullah; Basmaeil, Saeid

    2016-06-01

    The meat productivity of camel in the tropics is still under investigation for identification of better meat breed or type. Therefore, four one-humped Saudi Arabian (SA) camel breeds, Majaheem, Maghateer, Hamrah, and Safrah were experimented in order to differentiate them from each other based on meat measurements. The measurements were biometrical meat traits measured on six intact males from each breed. The results showed higher values of the Majaheem breed than that obtained for the other breeds except few cases such dressing percentage and rib-eye area. In differentiation analysis, the most discriminating meat variables were myofibrillar protein index, meat color components (L* and a*, b*), and cooking loss. Consequently, the Safrah and the Majaheem breeds presented the largest dissimilarity as evidenced by their multivariate means. The canonical discriminant analysis allowed an additional understanding of the differentiation between breeds. Furthermore, two large clusters, one formed by Hamrah and Maghateer in one group along with Safrah. These classifications may assign each breed into one cluster considering they are better as meat producers. The Majaheem was clustered alone in another cluster that might be a result of being better as milk producers. Nevertheless, the productivity type of the camel breeds of SA needs further morphology and genetic descriptions.

  2. A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis.

    Science.gov (United States)

    Hayashi, Hideaki; Shibanoki, Taro; Shima, Keisuke; Kurita, Yuichi; Tsuji, Toshio

    2015-12-01

    This paper proposes a probabilistic neural network (NN) developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model with a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into an NN, which is named a time-series discriminant component network (TSDCN), so that parameters of dimensionality reduction and classification can be obtained simultaneously as network coefficients according to a backpropagation through time-based learning algorithm with the Lagrange multiplier method. The TSDCN is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. The validity of the TSDCN is demonstrated for high-dimensional artificial data and electroencephalogram signals in the experiments conducted during the study.

  3. Study of age-related changes in postural control during quiet standing through Linear Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Andrade Adriano O

    2009-11-01

    Full Text Available Abstract Background The human body adopts a number of strategies to maintain an upright position. The analysis of the human balance allows for the understanding and identification of such strategies. The displacement of the centre of pressure (COP is a measure that has been successfully employed in studies regarding the postural control. Most of these investigations are related to the analysis of individuals suffering from neuromuscular disorders. Recent studies have shown that the elderly population is growing very fast in many countries all over the world, and therefore, researches that try to understand changes in this group are required. In this context, this study proposes the analysis of the postural control, measured by the displacement of the COP, in groups of young and elderly adults. Methods In total 59 subjects participated of this study. They were divided into seven groups according to their age. The displacement of the COP was collected for each subject standing on a force plate. Two experimental conditions, of 30 seconds each, were investigated: opened eyes and closed eyes. Traditional and recent digital signal processing tools were employed for feature computation from the displacement of the COP. Statistical analyses were carried out in order to identify significant differences between the features computed from the distinct groups that could allow for their discrimination. Results Our results showed that Linear Discrimination Analysis (LDA, which is one of the most popular feature extraction and classifier design techniques, could be successfully employed as a linear transformation, based on the linear combination of standard features for COP analysis, capable of estimating a unique feature, so-called LDA-value, from which it was possible to discriminate the investigated groups and show a high correlation between this feature and age. Conclusion These results show that the analysis of features computed from the displacement of

  4. Analysis of plug-in hybrid electric vehicle utility factors

    Science.gov (United States)

    Bradley, Thomas H.; Quinn, Casey W.

    Plug-in hybrid electric vehicles (PHEVs) are hybrid electric vehicles that can be fueled from both conventional liquid fuels and grid electricity. To represent the total contribution of both of these fuels to the operation, energy use, and environmental impacts of PHEVs, researchers have developed the concept of the utility factor. As standardized in documents such as SAE J1711 and SAE J2841, the utility factor represents the proportion of vehicle distance travelled that can be allocated to a vehicle test condition so as to represent the real-world driving habits of a vehicle fleet. These standards must be used with care so that the results are understood within the context of the assumptions implicit in the standardized utility factors. This study analyzes and derives alternatives to the standard utility factors from the 2001 National Highway Transportation Survey, so as to understand the sensitivity of PHEV performance to assumptions regarding charging frequency, vehicle characteristics, driver characteristics, and means of defining the utility factor. Through analysis of these alternative utility factors, this study identifies areas where analysis, design, and policy development for PHEVs can be improved by alternative utility factor calculations.

  5. Hybrid Analysis Approach for Stochastic Response of Offshore Jacket Platforms

    Institute of Scientific and Technical Information of China (English)

    金伟良; 郑忠双; 李海波; 张立

    2000-01-01

    The dynamic response of offshore platforms is more serious in hostile sea environment than in shallow sea. In this paper, a hybrid solution combined with analytical and numerical method is proposed to compute the stochastic response of fixed offshore platforms to random waves, considering wave-structure interaction and non-linear drag force. The simulation program includes two steps: the first step is the eigenanalysis aspects associated the structure and the second step is response estimation based on spectral equations. The eigenanalysis could be done through conventional finite element method conveniently and its natural frequency and mode shapes obtained. In the second part of the process, the solution of the offshore structural response is obtained by iteration of a series of coupled spectral equations. Considering the third-order term in the drag force, the evaluation of the three-fold convolution should be demanded for nonlinear stochastic response analysis. To demonstrate this method, a numerical analysis is carried out for both linear and non-linear platform motions. The final response spectra have the typical two peaks in agreement with reality, indicating that the hybrid method is effective and can be applied to offshore engineering.

  6. The role of critical ethnic awareness and social support in the discrimination-depression relationship among Asian Americans: path analysis.

    Science.gov (United States)

    Kim, Isok

    2014-01-01

    This study used a path analytic technique to examine associations among critical ethnic awareness, racial discrimination, social support, and depressive symptoms. Using a convenience sample from online survey of Asian American adults (N = 405), the study tested 2 main hypotheses: First, based on the empowerment theory, critical ethnic awareness would be positively associated with racial discrimination experience; and second, based on the social support deterioration model, social support would partially mediate the relationship between racial discrimination and depressive symptoms. The result of the path analysis model showed that the proposed path model was a good fit based on global fit indices, χ²(2) = 4.70, p = .10; root mean square error of approximation = 0.06; comparative fit index = 0.97; Tucker-Lewis index = 0.92; and standardized root mean square residual = 0.03. The examinations of study hypotheses demonstrated that critical ethnic awareness was directly associated (b = .11, p racial discrimination experience, whereas social support had a significant indirect effect (b = .48; bias-corrected 95% confidence interval [0.02, 1.26]) between the racial discrimination experience and depressive symptoms. The proposed path model illustrated that both critical ethnic awareness and social support are important mechanisms for explaining the relationship between racial discrimination and depressive symptoms among this sample of Asian Americans. This study highlights the usefulness of the critical ethnic awareness concept as a way to better understand how Asian Americans might perceive and recognize racial discrimination experiences in relation to its mental health consequences.

  7. Rapid direct analysis to discriminate geographic origin of extra virgin olive oils by flash gas chromatography electronic nose and chemometrics.

    Science.gov (United States)

    Melucci, Dora; Bendini, Alessandra; Tesini, Federica; Barbieri, Sara; Zappi, Alessandro; Vichi, Stefania; Conte, Lanfranco; Gallina Toschi, Tullia

    2016-08-01

    At present, the geographical origin of extra virgin olive oils can be ensured by documented traceability, although chemical analysis may add information that is useful for possible confirmation. This preliminary study investigated the effectiveness of flash gas chromatography electronic nose and multivariate data analysis to perform rapid screening of commercial extra virgin olive oils characterized by a different geographical origin declared in the label. A comparison with solid phase micro extraction coupled to gas chromatography mass spectrometry was also performed. The new method is suitable to verify the geographic origin of extra virgin olive oils based on principal components analysis and discriminant analysis applied to the volatile profile of the headspace as a fingerprint. The selected variables were suitable in discriminating between "100% Italian" and "non-100% Italian" oils. Partial least squares discriminant analysis also allowed prediction of the degree of membership of unknown samples to the classes examined.

  8. Is it really organic?--multi-isotopic analysis as a tool to discriminate between organic and conventional plants.

    Science.gov (United States)

    Laursen, K H; Mihailova, A; Kelly, S D; Epov, V N; Bérail, S; Schjoerring, J K; Donard, O F X; Larsen, E H; Pedentchouk, N; Marca-Bell, A D; Halekoh, U; Olesen, J E; Husted, S

    2013-12-01

    Novel procedures for analytical authentication of organic plant products are urgently needed. Here we present the first study encompassing stable isotopes of hydrogen, carbon, nitrogen, oxygen, magnesium and sulphur as well as compound-specific nitrogen and oxygen isotope analysis of nitrate for discrimination of organically and conventionally grown plants. The study was based on wheat, barley, faba bean and potato produced in rigorously controlled long-term field trials comprising 144 experimental plots. Nitrogen isotope analysis revealed the use of animal manure, but was unable to discriminate between plants that were fertilised with synthetic nitrogen fertilisers or green manures from atmospheric nitrogen fixing legumes. This limitation was bypassed using oxygen isotope analysis of nitrate in potato tubers, while hydrogen isotope analysis allowed complete discrimination of organic and conventional wheat and barley grains. It is concluded, that multi-isotopic analysis has the potential to disclose fraudulent substitutions of organic with conventionally cultivated plants.

  9. The role of discriminant analysis in the refinement of customer satisfaction assessment

    Directory of Open Access Journals (Sweden)

    Verdessi BD

    2000-01-01

    Full Text Available OBJECTIVE: To test discriminant analysis as a method of turning the information of a routine customer satisfaction survey (CSS into a more accurate decision-making tool. METHODS: A 7-question, 10-multiple choice, self-applied questionnaire was used to study a sample of patients seen in two outpatient care units in Valparaíso, Chile, one of primary care (n=100 and the other of secondary care (n=249. Two cutting points were considered in the dependent variable (final satisfaction score: satisfied versus unsatisfied, and very satisfied versus all others. Results were compared with empirical measures (proportion of satisfied individuals, proportion of unsatisfied individuals and size of the median. RESULTS: The response rate was very high, over 97.0% in both units. A new variable, medical attention, was revealed, as explaining satisfaction at the primary care unit. The proportion of the total variability explained by the model was very high (over 99.4% in both units, when comparing satisfied with unsatisfied customers. In the analysis of very satisfied versus all other customers, significant relationship was identified only in the case of the primary care unit, which explained a small proportion of the variability (41.9%. CONCLUSIONS: Discriminant analysis identified relationships not revealed by the previous analysis. It provided information about the proportion of the variability explained by the model. It identified non-significant relationships suggested by empirical analysis (e.g. the case of the relation very satisfied versus others in the secondary care unit. It measured the contribution of each independent variable to the explanation of the variation of the dependent one.

  10. The role of discriminant analysis in the refinement of customer satisfaction assessment

    Directory of Open Access Journals (Sweden)

    BD Verdessi

    2000-12-01

    Full Text Available OBJECTIVE: To test discriminant analysis as a method of turning the information of a routine customer satisfaction survey (CSS into a more accurate decision-making tool. METHODS: A 7-question, 10-multiple choice, self-applied questionnaire was used to study a sample of patients seen in two outpatient care units in Valparaíso, Chile, one of primary care (n=100 and the other of secondary care (n=249. Two cutting points were considered in the dependent variable (final satisfaction score: satisfied versus unsatisfied, and very satisfied versus all others. Results were compared with empirical measures (proportion of satisfied individuals, proportion of unsatisfied individuals and size of the median. RESULTS: The response rate was very high, over 97.0% in both units. A new variable, medical attention, was revealed, as explaining satisfaction at the primary care unit. The proportion of the total variability explained by the model was very high (over 99.4% in both units, when comparing satisfied with unsatisfied customers. In the analysis of very satisfied versus all other customers, significant relationship was identified only in the case of the primary care unit, which explained a small proportion of the variability (41.9%. CONCLUSIONS: Discriminant analysis identified relationships not revealed by the previous analysis. It provided information about the proportion of the variability explained by the model. It identified non-significant relationships suggested by empirical analysis (e.g. the case of the relation very satisfied versus others in the secondary care unit. It measured the contribution of each independent variable to the explanation of the variation of the dependent one.

  11. Discrimination Against State and Local Government LGBT Employees: An Analysis of Administrative Complaints

    OpenAIRE

    Mallory, Christy; Sears, Brad

    2014-01-01

    This article documents evidence of recent discrimination against lesbian, gay, bisexual, and transgender (LGBT) public sector workers by analyzing employment discrimination complaints filed with state and local administrative agencies. We present information about 589 complaints of sexual orientation and gender identity discrimination filed by public sector workers in 123 jurisdictions. We find that discrimination against LGBT people in the public sector is pervasive and occurs nearly as freq...

  12. Evaluation of hierarchical agglomerative cluster analysis methods for discrimination of primary biological aerosol

    Directory of Open Access Journals (Sweden)

    I. Crawford

    2015-07-01

    Full Text Available In this paper we present improved methods for discriminating and quantifying Primary Biological Aerosol Particles (PBAP by applying hierarchical agglomerative cluster analysis to multi-parameter ultra violet-light induced fluorescence (UV-LIF spectrometer data. The methods employed in this study can be applied to data sets in excess of 1×106 points on a desktop computer, allowing for each fluorescent particle in a dataset to be explicitly clustered. This reduces the potential for misattribution found in subsampling and comparative attribution methods used in previous approaches, improving our capacity to discriminate and quantify PBAP meta-classes. We evaluate the performance of several hierarchical agglomerative cluster analysis linkages and data normalisation methods using laboratory samples of known particle types and an ambient dataset. Fluorescent and non-fluorescent polystyrene latex spheres were sampled with a Wideband Integrated Bioaerosol Spectrometer (WIBS-4 where the optical size, asymmetry factor and fluorescent measurements were used as inputs to the analysis package. It was found that the Ward linkage with z-score or range normalisation performed best, correctly attributing 98 and 98.1 % of the data points respectively. The best performing methods were applied to the BEACHON-RoMBAS ambient dataset where it was found that the z-score and range normalisation methods yield similar results with each method producing clusters representative of fungal spores and bacterial aerosol, consistent with previous results. The z-score result was compared to clusters generated with previous approaches (WIBS AnalysiS Program, WASP where we observe that the subsampling and comparative attribution method employed by WASP results in the overestimation of the fungal spore concentration by a factor of 1.5 and the underestimation of bacterial aerosol concentration by a factor of 5. We suggest that this likely due to errors arising from misatrribution

  13. Evaluation of hierarchical agglomerative cluster analysis methods for discrimination of primary biological aerosol

    Directory of Open Access Journals (Sweden)

    I. Crawford

    2015-11-01

    Full Text Available In this paper we present improved methods for discriminating and quantifying primary biological aerosol particles (PBAPs by applying hierarchical agglomerative cluster analysis to multi-parameter ultraviolet-light-induced fluorescence (UV-LIF spectrometer data. The methods employed in this study can be applied to data sets in excess of 1 × 106 points on a desktop computer, allowing for each fluorescent particle in a data set to be explicitly clustered. This reduces the potential for misattribution found in subsampling and comparative attribution methods used in previous approaches, improving our capacity to discriminate and quantify PBAP meta-classes. We evaluate the performance of several hierarchical agglomerative cluster analysis linkages and data normalisation methods using laboratory samples of known particle types and an ambient data set. Fluorescent and non-fluorescent polystyrene latex spheres were sampled with a Wideband Integrated Bioaerosol Spectrometer (WIBS-4 where the optical size, asymmetry factor and fluorescent measurements were used as inputs to the analysis package. It was found that the Ward linkage with z-score or range normalisation performed best, correctly attributing 98 and 98.1 % of the data points respectively. The best-performing methods were applied to the BEACHON-RoMBAS (Bio–hydro–atmosphere interactions of Energy, Aerosols, Carbon, H2O, Organics and Nitrogen–Rocky Mountain Biogenic Aerosol Study ambient data set, where it was found that the z-score and range normalisation methods yield similar results, with each method producing clusters representative of fungal spores and bacterial aerosol, consistent with previous results. The z-score result was compared to clusters generated with previous approaches (WIBS AnalysiS Program, WASP where we observe that the subsampling and comparative attribution method employed by WASP results in the overestimation of the fungal spore concentration by a factor of 1.5 and the

  14. Discrimination of Aurantii Fructus Immaturus and Fructus Poniciri Trifoliatae Immaturus by Flow Injection UV Spectroscopy (FIUV) and 1H NMR using Partial Least-squares Discriminant Analysis (PLS-DA)

    Science.gov (United States)

    Two simple fingerprinting methods, flow-injection UV spectroscopy (FIUV) and 1H nuclear magnetic resonance (NMR), for discrimination of Aurantii FructusImmaturus and Fructus Poniciri TrifoliataeImmaturususing were described. Both methods were combined with partial least-squares discriminant analysis...

  15. Analysis of Network Parameters Influencing Performance of Hybrid Multimedia Networks

    Directory of Open Access Journals (Sweden)

    Dominik Kovac

    2013-10-01

    Full Text Available Multimedia networks is an emerging subject that currently attracts the attention of research and industrial communities. This environment provides new entertainment services and business opportunities merged with all well-known network services like VoIP calls or file transfers. Such a heterogeneous system has to be able satisfy all network and end-user requirements which are increasing constantly. Therefore the simulation tools enabling deep analysis in order to find the key performance indicators and factors which influence the overall quality for specific network service the most are highly needed. This paper provides a study on the network parameters like communication technology, routing protocol, QoS mechanism, etc. and their effect on the performance of hybrid multimedia network. The analysis was performed in OPNET Modeler environment and the most interesting results are discussed at the end of this paper

  16. Performance Analysis of Hybrid Electric Vehicle over Different Driving Cycles

    Science.gov (United States)

    Panday, Aishwarya; Bansal, Hari Om

    2017-02-01

    Article aims to find the nature and response of a hybrid vehicle on various standard driving cycles. Road profile parameters play an important role in determining the fuel efficiency. Typical parameters of road profile can be reduced to a useful smaller set using principal component analysis and independent component analysis. Resultant data set obtained after size reduction may result in more appropriate and important parameter cluster. With reduced parameter set fuel economies over various driving cycles, are ranked using TOPSIS and VIKOR multi-criteria decision making methods. The ranking trend is then compared with the fuel economies achieved after driving the vehicle over respective roads. Control strategy responsible for power split is optimized using genetic algorithm. 1RC battery model and modified SOC estimation method are considered for the simulation and improved results compared with the default are obtained.

  17. Ensemble regularized linear discriminant analysis classifier for P300-based brain-computer interface.

    Science.gov (United States)

    Onishi, Akinari; Natsume, Kiyohisa

    2013-01-01

    This paper demonstrates a better classification performance of an ensemble classifier using a regularized linear discriminant analysis (LDA) for P300-based brain-computer interface (BCI). The ensemble classifier with an LDA is sensitive to the lack of training data because covariance matrices are estimated imprecisely. One of the solution against the lack of training data is to employ a regularized LDA. Thus we employed the regularized LDA for the ensemble classifier of the P300-based BCI. The principal component analysis (PCA) was used for the dimension reduction. As a result, an ensemble regularized LDA classifier showed significantly better classification performance than an ensemble un-regularized LDA classifier. Therefore the proposed ensemble regularized LDA classifier is robust against the lack of training data.

  18. Colored inks analysis and differentiation: A first step in artistic contemporary prints discrimination

    Energy Technology Data Exchange (ETDEWEB)

    Vila, Anna [Department de Pintura, Conservacio-Restauracio, Facultat de Belles Arts, Universitat de Barcelona, C/Pau Gargallo 4, 08028 Barcelona (Spain)]. E-mail: avila@sct.ub.es; Ferrer, Nuria [Serveis Cientificotecnics, Universitat de Barcelona, C/Lluis Sole i Sabaris 1, 08028 Barcelona (Spain)]. E-mail: nferrer@sctub.es; Garcia, Jose F. [Department de Pintura, Conservacio-Restauracio, Facultat de Belles Arts, Universitat de Barcelona, C/Pau Gargallo 4, 08028 Barcelona (Spain)]. E-mail: ifgarcia@ub.edu

    2007-04-04

    Prints are the most popular artistic technique. Due to their manufacturing procedure, they are also one of the most frequently falsified types of artwork. In terms of their economic and historic value, the chemical analysis and characterisation of coloured inks and their principal constituent materials (pigments), together with the historical and aesthetic information available in the Catalogues Raisonees, are important tools in distinguishing originals from non-original prints. The chemical characterisation and discrimination of coloured inks has test in this study. Analysis using Fourier transform infrared spectroscopy (FTIR), Scanning electron microscopy (SEM) and X-ray diffraction (XRD) has been done on blue pigments and inks, due to this colour is one of the most representative for the presence of organic and inorganic materials in their composition. Conclusion obtained for this colour would demonstrate the capability of the approach when it is applied to any other coloured set of inks.

  19. Bioelectric signal classification using a recurrent probabilistic neural network with time-series discriminant component analysis.

    Science.gov (United States)

    Hayashi, Hideaki; Shima, Keisuke; Shibanoki, Taro; Kurita, Yuichi; Tsuji, Toshio

    2013-01-01

    This paper outlines a probabilistic neural network developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower-dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model that incorporates a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into a neural network so that parameters can be obtained appropriately as network coefficients according to backpropagation-through-time-based training algorithm. The network is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. In the experiments conducted during the study, the validity of the proposed network was demonstrated for EEG signals.

  20. Combining pharmacophore fingerprints and PLS-discriminant analysis for virtual screening and SAR elucidation

    DEFF Research Database (Denmark)

    Askjær, Sune; Langgård, Morten

    2008-01-01

    fingerprints proved superior to the TGT and TGD fingerprints. Examples of SAR elucidation based on PLS-DA model interpretation of model coefficients using a reversible pharmacophore fingerprint are given. In addition, we tested the hypothesis that feature combinations coming from the analysis of two...... the lead optimization toward a final drug candidate. This paper presents a combined approach to solving these two problems of ligand-based virtual screening and elucidation of SAR based on interplay between pharmacophore fingerprints and interpretation of PLS-discriminant analysis (PLS-DA) models....... The virtual screening capability of the PLS-DA method is compared to group fusion maximum similarity searching in a test using four graph-based pharmacophore fingerprints over a range of 10 diverse targets. The PLS-DA method was generally found to do better than the Smax method. The GpiDAPH3 and PCH...

  1. BIOMETRIC ANALYSIS OF THE ARTIFICIAL HYBRIDIZATION BETWEEN Pangasius djambal BLEEKER, 1846 AND Pangasianodon hypophthalmus SAUVAGE, 1878

    Directory of Open Access Journals (Sweden)

    Rudhy Gustiano

    2016-10-01

    Full Text Available It is really important, since the possible use of these pangasiid hybrids in aquaculture faces the problem of potential impact on wild population. Therefore, it is urgently needed to provide quick identification tools in the field. This study investigated morphological characters of Pangasius djambal and Pangasianodon hypophthalmus and their hybrids. A detailed morphological analysis using 32 morphometric measurements and five meristic counts was done on the hybridization of P. djambal and P. hypophthalmus. Morphometric analysis and meristic counts showed that the reciprocal hybrids have intermediate characters except for gill raker number in which lower than that of parental species. In general, the hybrids have tendency to be like P. hypophthalmus rather than P. djambal. The only typical character of P. djambal appeared on hybrids is teeth shape, both vomerine and palatine. It is clearly defined that the true hybrids have seven pelvic fin rays.

  2. A Unified Factors Analysis Framework for Discriminative Feature Extraction and Object Recognition

    Directory of Open Access Journals (Sweden)

    Ningbo Hao

    2016-01-01

    Full Text Available Various methods for feature extraction and dimensionality reduction have been proposed in recent decades, including supervised and unsupervised methods and linear and nonlinear methods. Despite the different motivations of these methods, we present in this paper a general formulation known as factor analysis to unify them within a common framework. During factor analysis, an object can be seen as being comprised of content and style factors, and the objective of feature extraction and dimensionality reduction is to obtain the content factor without style factor. There are two vital steps in factor analysis framework; one is the design of factor separating objective function, including the design of partition and weight matrix, and the other is the design of space mapping function. In this paper, classical Linear Discriminant Analysis (LDA and Locality Preserving Projection (LPP algorithms are improved based on factor analysis framework, and LDA based on factor analysis (FA-LDA and LPP based on factor analysis (FA-LPP are proposed. Experimental results show the superiority of our proposed approach in classification performance compared to classical LDA and LPP algorithms.

  3. Mass discrimination in elastic recoil detection analysis and its application to Al2O3 on MoS2

    Science.gov (United States)

    Laricchiuta, G.; Vandervorst, W.; Meersschaut, J.

    2017-09-01

    A time of flight-energy (TOF-E) telescope is often used to detect the scattered and recoiled atoms in elastic recoil detection analysis. The experimental two-dimensional TOF-E histogram may be numerically transformed into a time of flight-mass (TOF-M) histogram. The limited mass resolution in the TOF-M histogram, which results from the limited energy resolution of the energy detector, makes it sometimes difficult to discriminate elements with a small difference in atomic mass. We describe a mass discrimination procedure to numerically discriminate the elements in the TOF-M histogram. The procedure is illustrated on a sample consisting of an Al and a Si layer deposited on a MgO substrate. Besides, we apply the procedure to discriminate Al and Si in a sample consisting of Al2O3 deposited on MoS2/SiO2/Si.

  4. Is it really organic? – Multi-isotopic analysis as a tool to discriminate between organic and conventional plants

    DEFF Research Database (Denmark)

    Laursen, K.H.; Mihailova, A.; Kelly, S.D.

    2013-01-01

    for discrimination of organically and conventionally grown plants. The study was based on wheat, barley, faba bean and potato produced in rigorously controlled long-term field trials comprising 144 experimental plots. Nitrogen isotope analysis revealed the use of animal manure, but was unable to discriminate between......Novel procedures for analytical authentication of organic plant products are urgently needed. Here we present the first study encompassing stable isotopes of hydrogen, carbon, nitrogen, oxygen, magnesium and sulphur as well as compound-specific nitrogen and oxygen isotope analysis of nitrate...... plants that were fertilised with synthetic nitrogen fertilisers or green manures from atmospheric nitrogen fixing legumes. This limitation was bypassed using oxygen isotope analysis of nitrate in potato tubers, while hydrogen isotope analysis allowed complete discrimination of organic and conventional...

  5. Discrimination of Semen cassiae from two related species based on the multivariate analysis of high-performance liquid chromatography fingerprints.

    Science.gov (United States)

    Tang, Liying; Wu, Hongwei; Zhou, Xidan; Xu, Yilong; Zhou, Guohong; Wang, Ting; Kou, Zhenzhen; Wang, Zhuju

    2015-07-01

    A simple and efficient high-performance liquid chromatography fingerprint method was developed to discriminate Semen cassiae from two related species: Cassia obtusifolia L. (CO) and Cassia tora L. (CT), the seeds of which are abbreviated as COS and CTS, respectively. 22 major bioactive ingredients in 42 samples (20 COS and 22 CTS) collected from different provinces of China were identified. The statistical methods included similarity analysis and partial least-squares discriminant analysis. The pattern analysis method was specific and could be readily used for the comprehensive evaluation of Semen cassiae samples. Therefore, high-performance liquid chromatography fingerprint in combination with pattern analysis provided a simple and reliable method for discriminating between COS and CTS. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  6. Genetic Discrimination

    Science.gov (United States)

    ... in Genetics Archive Regulation of Genetic Tests Genetic Discrimination Overview Many Americans fear that participating in research ... I) and employment (Title II). Read more Genetic Discrimination and Other Laws Genetic Discrimination and Other Laws ...

  7. Promises of silent salesman to the FMCG industry: an investigation using linear discriminant analysis approach

    Directory of Open Access Journals (Sweden)

    Shekhar Suraj Kushe

    2015-12-01

    Full Text Available Packaging which is often called as the ‘silent salesman’ is an important component of marketing. Today the importance of packaging has risen to such an extent that product packaging is rightly called as the fifth ‘P’ of marketing mix. FMCG are products which are utilized by large number of people. The present study examined the discriminating power of five selected FMCG packaging variables namely ‘picture’, ‘colour’, ‘size’, ‘shape’ and ‘material’ amidst those who purchased FMCG based on these packaging variables and for those who purchased FMCG not based on these packaging variables. Descriptive research was carried out in the study. Respondents (students were asked to rate four packaging variable on a five point Likert’s scale. Discriminant analysis showed that only two variables namely ‘Colour’ (.706 and ‘Shape’ (–.527 were good predictors. Variables ‘Picture’, ‘size’ and ‘material’ were considered as poor predictors as far as the student communities were considered. The cross validated classification showed that out of the 240 samples drawn, 91.8% of the cases were correctly classified.

  8. Sex determination from the talus in a contemporary Greek population using discriminant function analysis.

    Science.gov (United States)

    Peckmann, Tanya R; Orr, Kayla; Meek, Susan; Manolis, Sotiris K

    2015-07-01

    The determination of sex is an important part of building the biological profile for unknown human remains. Many of the bones traditionally used for the determination of sex are often found fragmented or incomplete in forensic and archaeological cases. The goal of the present research was to derive discriminant function equations from the talus, a preservationally favoured bone, for sexing skeletons from a contemporary Greek population. Nine parameters were measured on 182 individuals (96 males and 86 females) from the University of Athens Human Skeletal Reference Collection. The individuals ranged in age from 20 to 99 years old. The statistical analyses showed that all measured parameters were sexually dimorphic. Discriminant function score equations were generated for use in sex determination. The average accuracy of sex classification ranged from 65.2% to 93.4% for the univariate analysis, 90%-96.5% for the direct method and 86.7% for the stepwise method. Comparisons to other populations were made. Overall, the cross-validated accuracies ranged from 65.5% to 83.2% and males were most often correctly identified. The talus was shown to be useful for sex determination in the modern Greek population.

  9. Sex determination from the calcaneus in a 20th century Greek population using discriminant function analysis.

    Science.gov (United States)

    Peckmann, Tanya R; Orr, Kayla; Meek, Susan; Manolis, Sotiris K

    2015-12-01

    The skull and post-cranium have been used for the determination of sex for unknown human remains. However, in forensic cases where skeletal remains often exhibit postmortem damage and taphonomic changes the calcaneus may be used for the determination of sex as it is a preservationally favored bone. The goal of the present research was to derive discriminant function equations from the calcaneus for estimation of sex from a contemporary Greek population. Nine parameters were measured on 198 individuals (103 males and 95 females), ranging in age from 20 to 99 years old, from the University of Athens Human Skeletal Reference Collection. The statistical analyses showed that all variables were sexually dimorphic. Discriminant function score equations were generated for use in sex determination. The average accuracy of sex classification ranged from 70% to 90% for the univariate analysis, 82.9% to 87.5% for the direct method, and 86.2% for the stepwise method. Comparisons to other populations were made. Overall, the cross-validated accuracies ranged from 48.6% to 56.1% with males most often identified correctly and females most often misidentified. The calcaneus was shown to be useful for sex determination in the twentieth century Greek population.

  10. Wave Mode Discrimination of Coded Ultrasonic Guided Waves Using Two-Dimensional Compressed Pulse Analysis.

    Science.gov (United States)

    Malo, Sergio; Fateri, Sina; Livadas, Makis; Mares, Cristinel; Gan, Tat-Hean

    2017-07-01

    Ultrasonic guided waves testing is a technique successfully used in many industrial scenarios worldwide. For many complex applications, the dispersive nature and multimode behavior of the technique still poses a challenge for correct defect detection capabilities. In order to improve the performance of the guided waves, a 2-D compressed pulse analysis is presented in this paper. This novel technique combines the use of pulse compression and dispersion compensation in order to improve the signal-to-noise ratio (SNR) and temporal-spatial resolution of the signals. The ability of the technique to discriminate different wave modes is also highlighted. In addition, an iterative algorithm is developed to identify the wave modes of interest using adaptive peak detection to enable automatic wave mode discrimination. The employed algorithm is developed in order to pave the way for further in situ applications. The performance of Barker-coded and chirp waveforms is studied in a multimodal scenario where longitudinal and flexural wave packets are superposed. The technique is tested in both synthetic and experimental conditions. The enhancements in SNR and temporal resolution are quantified as well as their ability to accurately calculate the propagation distance for different wave modes.

  11. A New Method for Improving the Discrimination Power and Weights Dispersion in the Data Envelopment Analysis

    Directory of Open Access Journals (Sweden)

    S. Kordrostami

    2013-06-01

    Full Text Available The appropriate choice of input-output weights is necessary to have a successful DEA model. Generally, if the number of DMUs i.e., n, is less than number of inputs and outputs i.e., m+s, then many of DMUs are introduced as efficient then the discrimination between DMUs is not possible. Besides, DEA models are free to choose the best weights. For resolving the problems that are resulted from freedom of weights, some constraints are set on the input-output weights. Symmetric weight constraints are a kind of weight constrains. In this paper, we represent a new model based on a multi-criterion data envelopment analysis (MCDEA are developed to moderate the homogeneity of weights distribution by using symmetric weight constrains.Consequently, we show that the improvement of the dispersal of unrealistic input-output weights and the increasing discrimination power for our suggested models. Finally, as an application of the new model, we use this model to evaluate and ranking guilan selected hospitals.

  12. Assessing Reclamation Levels of Coastal Saline Lands with Integrated Stepwise Discriminant Analysis and Laboratory Hyperspectral Data

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    At different times over the past 30 years in Zhejiang Province, China, the coastal tidelands have been successively enclosed and reclaimed for agricultural land use. The purpose of this work was to evaluate whether laboratory hyperspectral data might be used to estimate the physicochemical characteristics of these reclaimed saline soils. A coastal region of Shangyu City (Zhejiang Province), which was grouped into four subzones according to reclamation history, was used as the study area, and soil samples were collected in each subzone. Physicochemical analyses showed that the soils were characterized by high electrical conductivity and sand content with low organic matter; the longer the saline lands had been reclaimed, the lower were the electrical conductivity and sand content and the higher the organic matter content.These changing trends of soil chemical and physical properties were found in laboratory reflectance spectra of soil samples and their first-order derivative curves. Stepwise discriminant analysis (SDA) identified six salient spectral bands at 488,530, 670, 880, 1 400, and 1 900 nm. Using derived discriminant functions for saline lands with different historical years of reclamation, classification revealed an overall accuracy from a self-test of 86.6% and from cross-validation of 89.3%.Therefore, as opposed to time-consuming field investigations, this study suggested that remotely sensed hyperspectral data could serve as a promising measure to assess the reclamation levels of coastal saline lands.

  13. Image analysis of food particles can discriminate deficient mastication of mixed foodstuffs simulating daily meal.

    Science.gov (United States)

    Sugimoto, K; Hashimoto, Y; Fukuike, C; Kodama, N; Minagi, S

    2014-03-01

    Because food texture is regarded as an important factor for smooth deglutition, identification of objective parameters that could provide a basis for food texture selection for elderly or dysphagic patients is of great importance. We aimed to develop an objective evaluation method of mastication using a mixed test food comprising foodstuffs, simulating daily dietary life. The particle size distribution (>2 mm in diameter) in a bolus was analysed using a digital image under dark-field illumination. Ten female participants (mean age ± s.d., 27·6 ± 2·6 years) masticated a mixed test food comprising prescribed amounts of rice, sausage, hard omelette, raw cabbage and raw cucumber with 100%, 75%, 50% and 25% of the number of their masticatory strokes. A single set of coefficient thresholds of 0·10 for the homogeneity index and 1·62 for the particle size index showed excellent discrimination of deficient masticatory conditions with high sensitivity (0·90) and specificity (0·77). Based on the results of this study, normal mastication was discriminated from deficient masticatory conditions using a large particle analysis of mixed foodstuffs, thus showing the possibility of future application of this method for objective decision-making regarding the properties of meals served to dysphagic patients.

  14. Discriminative analysis of brain functional connectivity patterns for mental fatigue classification.

    Science.gov (United States)

    Sun, Yu; Lim, Julian; Meng, Jianjun; Kwok, Kenneth; Thakor, Nitish; Bezerianos, Anastasios

    2014-10-01

    Mental fatigue is a commonly experienced state that can be induced by placing heavy demands on cognitive systems. This often leads to lowered productivity and increased safety risks. In this study, we developed a functional-connectivity based mental fatigue monitoring method. Twenty-six subjects underwent a 20-min mentally demanding test of sustained attention with high-resolution EEG monitoring. Functional connectivity patterns were obtained on the cortical surface via source localization of cortical activities in the first and last 5-min quartiles of the experiment. Multivariate pattern analysis was then adopted to extract the highly discriminative functional connectivity information. The algorithm used in the present study demonstrated an overall accuracy of 81.5% (p fatigue classification through leave-one-out cross validation. Moreover, we found that the most discriminative connectivity features were located in or across middle frontal gyrus and several motor areas, in agreement with the important role that these cortical regions play in the maintenance of sustained attention. This work therefore demonstrates the feasibility of a functional-connectivity-based mental fatigue assessment method, opening up a new avenue for modeling natural brain dynamics under different mental states. Our method has potential applications in several domains, including traffic and industrial safety.

  15. Fatigue Life Analysis of Tapered Hybrid Composite Flexbeams

    Science.gov (United States)

    Murri, Gretchen B.; Schaff, Jeffery R.; Dobyns, Alan L.

    2002-01-01

    Nonlinear-tapered flexbeam laminates from a full-size composite helicopter rotor hub flexbeam were tested under combined constant axial tension and cyclic bending loads. The two different graphite/glass hybrid configurations tested under cyclic loading failed by delamination in the tapered region. A 2-D finite element model was developed which closely approximated the flexbeam geometry, boundary conditions, and loading. The analysis results from two geometrically nonlinear finite element codes, ANSYS and ABAQUS, are presented and compared. Strain energy release rates (G) obtained from the above codes using the virtual crack closure technique (VCCT) at a resin crack location in the flexbeams are presented for both hybrid material types. These results compare well with each other and suggest that the initial delamination growth from the resin crack toward the thick region of the flexbeam is strongly mode II. The peak calculated G values were used with material characterization data to calculate fatigue life curves and compared with test data. A curve relating maximum surface strain to number of loading cycles at delamination onset compared reasonably well with the test results.

  16. Regulatory pathway analysis by high-throughput in situ hybridization.

    Directory of Open Access Journals (Sweden)

    Axel Visel

    2007-10-01

    Full Text Available Automated in situ hybridization enables the construction of comprehensive atlases of gene expression patterns in mammals. Such atlases can become Web-searchable digital expression maps of individual genes and thus offer an entryway to elucidate genetic interactions and signaling pathways. Towards this end, an atlas housing approximately 1,000 spatial gene expression patterns of the midgestation mouse embryo was generated. Patterns were textually annotated using a controlled vocabulary comprising >90 anatomical features. Hierarchical clustering of annotations was carried out using distance scores calculated from the similarity between pairs of patterns across all anatomical structures. This process ordered hundreds of complex expression patterns into a matrix that reflects the embryonic architecture and the relatedness of patterns of expression. Clustering yielded 12 distinct groups of expression patterns. Because of the similarity of expression patterns within a group, members of each group may be components of regulatory cascades. We focused on the group containing Pax6, an evolutionary conserved transcriptional master mediator of development. Seventeen of the 82 genes in this group showed a change of expression in the developing neocortex of Pax6-deficient embryos. Electromobility shift assays were used to test for the presence of Pax6-paired domain binding sites. This led to the identification of 12 genes not previously known as potential targets of Pax6 regulation. These findings suggest that cluster analysis of annotated gene expression patterns obtained by automated in situ hybridization is a novel approach for identifying components of signaling cascades.

  17. [Discriminant analysis of raw milk adulterated with botanical filling material using near infrared spectroscopy].

    Science.gov (United States)

    Li, Liang; Ding, Wu

    2010-05-01

    In order to find out a fast measure method of adulterated milk based on near infrared spectroscopy, milk adulterated with plant butter, vegetable protein and starch was collected respectively. Using Fourier transform near infrared spectroscopy to scan the samples, the spectrum data were obtained. The samples were scanned in the spectral region between 4 000 and 12 000 cm(-1) by FT-NIR spectrometer with an optic fiber of 2 mm path-length and an InGaAs detector. Then all data were analyzed by principal component analysis combined with Fisher line discriminant analysis (FLDA) and partial least squares (PLS). Results show that the accumulative reliabilities of the first six components were more than 99%, so the first six components were applied as FLDA inputs and the values of the type of milk were applied as the outputs. An adulterated milk qualitative discriminant model based on Fisher line discriminant analysis was developed finally. The result indicated that the accuracy of detection of calibration samples is 97.78%. The unknown test samples were tested by this model and the correct identification rate is 94.44%. Partial least square models for detecting the content of material added to raw milk were set up with good veracity. The predictive correlation coefficient (R2) of calibration sets of milk adulterated with plant butter, vegetable protein and starch are 99.08%, 99.96% and 99.39%, respectively, while the root mean square errors of cross validation (RMSECV) of the three calibration sets are 0.304%, 0.013 5% and 0.060%, respectively. The R2 of validation sets of the three kinds of adulterated milk are 98.50%, 99.94% and 98.50%, respectively, while the root mean square errors of prediction (RMSEP) of the three validation sets are 0.323%, 0.028 8% and 0.068%, respectively. All of these suggested that near infrared spectroscopy has good potential for rapid qualitative and quantitative detection of milk adulterated with botanical filling material.

  18. Combined cluster and discriminant analysis: An efficient chemometric approach in diesel fuel characterization.

    Science.gov (United States)

    Novák, Márton; Palya, Dóra; Bodai, Zsolt; Nyiri, Zoltán; Magyar, Norbert; Kovács, József; Eke, Zsuzsanna

    2017-01-01

    Combined cluster and discriminant analysis (CCDA) as a chemometric tool in compound specific isotope analysis of diesel fuels was studied. The stable carbon isotope ratios (δ(13)C) of n-alkanes in diesel fuel can be used to characterize or differentiate diesels originating from different sources. We investigated 25 diesel fuel samples representing 20 different brands. The samples were collected from 25 different service stations in 11 European countries over a 2 year period. The n-alkane fraction of diesel fuels was separated using solid-state urea clathrate formation combined with silica gel fractionation. The stable carbon isotope ratios of C10-C24 n-alkanes were measured with gas chromatography-isotope ratio mass spectrometry (GC-IRMS) using perdeuterated n-alkanes as internal standards. Beside the 25 samples one additional diesel fuel was prepared and measured three times to get totally homogenous samples in order to test the performance of our analytical and statistical routine. Stable isotope ratio data were evaluated with hierarchical cluster analysis (HCA), principal component analysis (PCA) and CCDA. CCDA combines two multivariate data analysis methods hierarchical cluster analysis with linear discriminant analysis (LDA). The main idea behind CCDA is to compare the goodness of preconceived (based on the sample origins) and random groupings. In CCDA all the samples were compared pairwise. The results for the parallel sample preparations showed that the analytical procedure does not have any significant effect on the δ(13)C values of n-alkanes. The three parallels proved to be totally homogenous with CCDA. HCA and PCA can be useful tools when the examining of the relationship among several samples is in question. However, these two techniques cannot be always decisive on the origin of similar samples. The initial hypothesis that all diesel fuel samples are considered chemically unique was verified by CCDA. The main advantage of CCDA is that it gives an

  19. Design, analysis and modeling of a novel hybrid powertrain system based on hybridized automated manual transmission

    Science.gov (United States)

    Wu, Guang; Dong, Zuomin

    2017-09-01

    Hybrid electric vehicles are widely accepted as a promising short to mid-term technical solution due to noticeably improved efficiency and lower emissions at competitive costs. In recent years, various hybrid powertrain systems were proposed and implemented based on different types of conventional transmission. Power-split system, including Toyota Hybrid System and Ford Hybrid System, are well-known examples. However, their relatively low torque capacity, and the drive of alternative and more advanced designs encouraged other innovative hybrid system designs. In this work, a new type of hybrid powertrain system based hybridized automated manual transmission (HAMT) is proposed. By using the concept of torque gap filler (TGF), this new hybrid powertrain type has the potential to overcome issue of torque gap during gearshift. The HAMT design (patent pending) is described in details, from gear layout and design of gear ratios (EV mode and HEV mode) to torque paths at different gears. As an analytical tool, mutli-body model of vehicle equipped with this HAMT was built to analyze powertrain dynamics at various steady and transient modes. A gearshift was decomposed and analyzed based basic modes. Furthermore, a Simulink-SimDriveline hybrid vehicle model was built for the new transmission, driveline and vehicle modular. Control strategy has also been built to harmonically coordinate different powertrain components to realize TGF function. A vehicle launch simulation test has been completed under 30% of accelerator pedal position to reveal details during gearshift. Simulation results showed that this HAMT can eliminate most torque gap that has been persistent issue of traditional AMT, improving both drivability and performance. This work demonstrated a new type of transmission that features high torque capacity, high efficiency and improved drivability.

  20. Variable Selection and Updating In Model-Based Discriminant Analysis for High Dimensional Data with Food Authenticity Applications.

    Science.gov (United States)

    Murphy, Thomas Brendan; Dean, Nema; Raftery, Adrian E

    2010-03-01

    Food authenticity studies are concerned with determining if food samples have been correctly labelled or not. Discriminant analysis methods are an integral part of the methodology for food authentication. Motivated by food authenticity applications, a model-based discriminant analysis method that includes variable selection is presented. The discriminant analysis model is fitted in a semi-supervised manner using both labeled and unlabeled data. The method is shown to give excellent classification performance on several high-dimensional multiclass food authenticity datasets with more variables than observations. The variables selected by the proposed method provide information about which variables are meaningful for classification purposes. A headlong search strategy for variable selection is shown to be efficient in terms of computation and achieves excellent classification performance. In applications to several food authenticity datasets, our proposed method outperformed default implementations of Random Forests, AdaBoost, transductive SVMs and Bayesian Multinomial Regression by substantial margins.

  1. Modeling and Analysis of Hybrid Dynamic Systems Using Hybrid Petri Nets

    OpenAIRE

    GHOMRI Latefa; Alla, Hassane

    2008-01-01

    Some extensions of PNs permitting HDS modeling were presented here. The first models to be presented are continuous PNs. This model may be used for modeling either a continuous system or a discrete system. In this case, it is an approximation that is often satisfactory. Hybrid PNs combine in the same formalism a discrete PN and a continuous PN. Two hybrid PN models were considered in this chapter. The first, called the hybrid PN, has a deterministic behavior; this means that we can predict th...

  2. Local analysis of hybrid systems on polyhedral sets with state-dependent switching

    Directory of Open Access Journals (Sweden)

    Leth John

    2014-06-01

    Full Text Available This paper deals with stability analysis of hybrid systems. Various stability concepts related to hybrid systems are introduced. The paper advocates a local analysis. It involves the equivalence relation generated by reset maps of a hybrid system. To establish a tangible method for stability analysis, we introduce the notion of a chart, which locally reduces the complexity of the hybrid system. In a chart, a hybrid system is particularly simple and can be analyzed with the use of methods borrowed from the theory of differential inclusions. Thus, the main contribution of this paper is to show how stability of a hybrid system can be reduced to a specialization of the well established stability theory of differential inclusions. A number of examples illustrate the concepts introduced in the paper.

  3. Performance Analysis of a Hybrid District Heating System

    DEFF Research Database (Denmark)

    Mikulandric, Robert; Krajačić, Goran; Duic, Neven

    2015-01-01

    Hybridisation of district heating systems can contribute to more efficient heat generation through cogeneration power plants or through the share increase of renewable energy sources in total energy consumption while reducing negative aspects of particular energy source utilisation. In this work......, the performance of a hybrid district energy system for a small town in Croatia has been analysed. Mathematical model for process analysis and optimisation algorithm for optimal system configuration has been developed and described. The main goal of the system optimisation is to reduce heat production costs....... Several energy sources for heat production have been considered in 8 different simulation cases. Simulation results show that the heat production costs could be reduced with introduction of different energy systems into an existing district heating system. Renewable energy based district heating systems...

  4. Second Year Analysis of a Hybrid Schedule High School

    Directory of Open Access Journals (Sweden)

    James B. Schreiber

    2001-11-01

    Full Text Available The current study examined two independent sophomore cohorts from a mid-western high school that had implemented a multi-schedule system (i.e., traditional, block, hybrid. The purpose of the study was to examine differences among the schedule types, gender, and GPA group on a state mandated standardized test. Analysis of covariance was used to examine the differences. Results indicate that a significant difference among schedule types was observed for only one cohort and for only one test (mathematics-computation. Results also indicate that schedule type did not significantly interact with gender or GPA group. The authors conclude that for these cohorts the type of schedule does not negatively or positively influence achievement.

  5. Seedling test and genetic analysis of white poplar hybrid clones

    Institute of Scientific and Technical Information of China (English)

    LI Bo; JIANG Xi-bing; ZHANG You-hui; ZHANG Zhi-yi; LI Shan-wen; AN Xin-min

    2008-01-01

    Cross breeding strategies are very efficient for gaining new and superior genotypes. Ninety-eight new white poplar hybrid clones produced from 12 cross combinations within the Section Leuce Duby were studied using genetic analysis and seedling tests. We exploited the wide variation that exists in this population and found that the differences among diameter at breast height (DBH), root collar diameter (RCD) and height (H) were statistically extremely significant. The repeatability of clones of these measured traits ranged from 0.947-0.967, which indicated that these Waits were strongly controlled by genetic factors. Based on multiple comparisons, a total of 25 clones showed better performance in growth than the conlrol cultivar. These 25 clones were from six different cross combinations, which can guarantee a larger genetic background for future new clone promotion projects. This study provides a simple overview on these clones and can guide us to carry out subsequent selection plans.

  6. ANALYSIS OF A TRANSPORT PROCESS USING HYBRID PETRI NETS

    Directory of Open Access Journals (Sweden)

    Elisabeta Mihaela CIORTEA

    2013-05-01

    Full Text Available Purpose of the paper is to analyze the Petri net model, to describe the transport process, part of amanufacturing system and its dynamics.A hibrid Petri net model is built to describe the dinamics of the transport process manufacturingsystem. Mathematical formulation of the dinamycs processes a detailed description. Based on this model, theanalysis of the transport process is designed to be able to execute a production plan and resolve any conflictsthat may arise in the system.In the analysis dinamics known two stages: in the continuous variables are discrete hybrid system in thehibrid discrete variables are used as safety control with very well defined responsibilities.In terms of the chosen model, analyze transport process is designed to help execute a production planand resolve conflicts that may arise in the process, and then the ones in the system

  7. A longitudinal analysis of Hispanic youth acculturation and cigarette smoking: the roles of gender, culture, family, and discrimination.

    Science.gov (United States)

    Lorenzo-Blanco, Elma I; Unger, Jennifer B; Ritt-Olson, Anamara; Soto, Daniel; Baezconde-Garbanati, Lourdes

    2013-05-01

    Risk for smoking initiation increases as Hispanic youth acculturate to U.S. society, and this association seems to be stronger for Hispanic girls than boys. To better understand the influence of culture, family, and everyday discrimination on cigarette smoking, we tested a process-oriented model of acculturation and cigarette smoking. Data came from Project RED (Reteniendo y Entendiendo Diversidad para Salud), which included 1,436 Hispanic students (54% girls) from Southern California. We used data from 9th to 11th grade (85% were 14 years old, and 86% were U.S. born) to test the influence of acculturation-related experiences on smoking over time. Multigroup structural equation analysis suggested that acculturation was associated with increased familismo and lower traditional gender roles, and enculturation was linked more with familismo and respeto. Familismo, respeto, and traditional gender roles were linked with lower family conflict and increased family cohesion, and these links were stronger for girls. Familismo and respeto were further associated with lower discrimination. Conversely, fatalismo was linked with worse family functioning (especially for boys) and increased discrimination in both the groups. Discrimination was the only predictor of smoking for boys and girls. In all, the results of the current study indicate that reducing discrimination and helping youth cope with discrimination may prevent or reduce smoking in Hispanic boys and girls. This may be achieved by promoting familismo and respeto and by discouraging fatalistic beliefs.

  8. A discrimination technique for extensive air showers based on multiscale, lacunarity and neural network analysis

    Science.gov (United States)

    Pagliaro, Antonio; D'Alí Staiti, G.; D'Anna, F.

    2011-03-01

    We present a new method for the identification of extensive air showers initiated by different primaries. The method uses the multiscale concept and is based on the analysis of multifractal behaviour and lacunarity of secondary particle distributions together with a properly designed and trained artificial neural network. In the present work the method is discussed and applied to a set of fully simulated vertical showers, in the experimental framework of ARGO-YBJ, to obtain hadron to gamma primary separation. We show that the presented approach gives very good results, leading, in the 1-10 TeV energy range, to a clear improvement of the discrimination power with respect to the existing figures for extended shower detectors.

  9. A discrimination technique for extensive air showers based on multiscale, lacunarity and neural network analysis

    Energy Technology Data Exchange (ETDEWEB)

    Pagliaro, Antonio, E-mail: pagliaro@ifc.inaf.it [Istituto di Astrofisica Spaziale e Fisica Cosmica di Palermo - Istituto Nazionale di Astrofisica, Via Ugo La Malfa 153, 90146 Palermo (Italy); Istituto Nazionale di Fisica Nucleare, Sezione di Catania, Viale A. Doria 6, 95125 Catania (Italy); D' Ali Staiti, G. [Universita degli Studi di Palermo, Dipartimento di Fisica e Tecnologie Relative, Viale delle Scienze, Edificio 18, 90128 Palermo (Italy); Istituto Nazionale di Fisica Nucleare, Sezione di Catania, Viale A. Doria 6, 95125 Catania (Italy); D' Anna, F. [Istituto di Astrofisica Spaziale e Fisica Cosmica di Palermo - Istituto Nazionale di Astrofisica, Via Ugo La Malfa 153, 90146 Palermo (Italy)

    2011-03-15

    We present a new method for the identification of extensive air showers initiated by different primaries. The method uses the multiscale concept and is based on the analysis of multifractal behaviour and lacunarity of secondary particle distributions together with a properly designed and trained artificial neural network. In the present work the method is discussed and applied to a set of fully simulated vertical showers, in the experimental framework of ARGO-YBJ, to obtain hadron to gamma primary separation. We show that the presented approach gives very good results, leading, in the 1-10 TeV energy range, to a clear improvement of the discrimination power with respect to the existing figures for extended shower detectors.

  10. GSK-3β polymorphism discriminates bipolar disorder and schizophrenia: a systematic meta-analysis.

    Science.gov (United States)

    Tang, Hui; Shen, Na; Jin, Huijuan; Liu, Dan; Miao, Xiaoping; Zhu, Ling-Qiang

    2013-12-01

    Glycogen synthase kinase 3 (GSK-3) is a well-known conserved and ubiquitous protein kinase and playing a pivotal role in neurodevelopment, neurogenesis, learning/memory, and neuronal cell death. Dysfunction of GSK-3 had been seen in multiple neurodegenerative and psychiatric diseases. Bipolar disorder and schizophrenia are two common psychiatric diseases first occur in adolescence or young adulthood. They share similar risk genes as well as clinical symptoms, which make it is difficult to be discriminated from each other. Here, by using meta-analysis we reported that glycogen synthase kinase 3β promoter inactive mutant rs334558 may contribute to the development of schizophrenia not bipolar disorder. This might be used to distinguish these two diseases.

  11. A Multimodal Biometric System Using Linear Discriminant Analysis For Improved Performance

    CERN Document Server

    Khan, Aamir; Khurshid, Aasim; Akram, Adeel

    2012-01-01

    Essentially a biometric system is a pattern recognition system which recognizes a user by determining the authenticity of a specific anatomical or behavioral characteristic possessed by the user. With the ever increasing integration of computers and Internet into daily life style, it has become necessary to protect sensitive and personal data. This paper proposes a multimodal biometric system which incorporates more than one biometric trait to attain higher security and to handle failure to enroll situations for some users. This paper is aimed at investigating a multimodal biometric identity system using Linear Discriminant Analysis as backbone to both facial and speech recognition and implementing such system in real-time using SignalWAVE.

  12. External Defect classification of Citrus Fruit Images using Linear Discriminant Analysis Clustering and ANN classifiers

    Directory of Open Access Journals (Sweden)

    K.Vijayarekha

    2012-12-01

    Full Text Available Linear Discriminant Analysis (LDA is one technique for transforming raw data into a new feature space in which classification can be carried out more robustly. It is useful where the within-class frequencies are unequal. This method maximizes the ratio of between-class variance to the within-class variance in any particular data set and the maximal separability is guaranteed. LDA clustering models are used to classify object into different category. This study makes use of LDA for clustering the features obtained for the citrus fruit images taken in five different domains. Sub-windows of size 40x40 are cropped from the citrus fruit images having defects such as pitting, splitting and stem end rot. Features are extracted in four domains such as statistical features, fourier transform based features, discrete wavelet transform based features and stationary wavelet transform based features. The results of clustering and classification using LDA and ANN classifiers are reported

  13. Efficient Discriminate Component Analysis using Support Vector Machine Classifier on Invariant Pose and Illumination Face Images

    Directory of Open Access Journals (Sweden)

    R. Rajalakshmi

    2015-03-01

    Full Text Available Face recognition is the process of categorizing a person in an image by evaluating with a known face image library. The pose and illumination variations are two main practical confronts for an automatic face recognition system. This study proposes a novel face recognition algorithm known as Efficient Discriminant Component Analysis (EDCA for face recognition under varying poses and illumination conditions. This EDCA algorithm overcomes the high dimensionality problem in the feature space by extracting features from the low dimensional frequency band of the image. It combines the features of both LDA and PCA algorithms and these features are used in the training set and is classified using Support Vector Machine classifier. The experiments were performed on the CMU-PIE datasets. The experimental results show that the proposed algorithm produces a higher recognition rate than the existing LDA and PCA based face recognition techniques.

  14. Lameness detection challenges in automated milking systems addressed with partial least squares discriminant analysis

    DEFF Research Database (Denmark)

    Garcia, Emanuel; Klaas, Ilka Christine; Amigo Rubio, Jose Manuel;

    2014-01-01

    . Eighty variables retrieved from AMS were summarized week-wise and used to predict 2 defined classes: nonlame and clinically lame cows. Variables were represented with 2 transformations of the week summarized variables, using 2-wk data blocks before gait scoring, totaling 320 variables (2 × 2 × 80......). The reference gait scoring error was estimated in the first week of the study and was, on average, 15%. Two partial least squares discriminant analysis models were fitted to parity 1 and parity 2 groups, respectively, to assign the lameness class according to the predicted probability of being lame (score 3......Lameness is prevalent in dairy herds. It causes decreased animal welfare and leads to higher production costs. This study explored data from an automatic milking system (AMS) to model on-farm gait scoring from a commercial farm. A total of 88 cows were gait scored once per week, for 2 5-wk periods...

  15. On Kolmogorov asymptotics of estimators of the misclassification error rate in linear discriminant analysis

    KAUST Repository

    Zollanvari, Amin

    2013-05-24

    We provide a fundamental theorem that can be used in conjunction with Kolmogorov asymptotic conditions to derive the first moments of well-known estimators of the actual error rate in linear discriminant analysis of a multivariate Gaussian model under the assumption of a common known covariance matrix. The estimators studied in this paper are plug-in and smoothed resubstitution error estimators, both of which have not been studied before under Kolmogorov asymptotic conditions. As a result of this work, we present an optimal smoothing parameter that makes the smoothed resubstitution an unbiased estimator of the true error. For the sake of completeness, we further show how to utilize the presented fundamental theorem to achieve several previously reported results, namely the first moment of the resubstitution estimator and the actual error rate. We provide numerical examples to show the accuracy of the succeeding finite sample approximations in situations where the number of dimensions is comparable or even larger than the sample size.

  16. On Kolmogorov Asymptotics of Estimators of the Misclassification Error Rate in Linear Discriminant Analysis.

    Science.gov (United States)

    Zollanvari, Amin; Genton, Marc G

    2013-08-01

    We provide a fundamental theorem that can be used in conjunction with Kolmogorov asymptotic conditions to derive the first moments of well-known estimators of the actual error rate in linear discriminant analysis of a multivariate Gaussian model under the assumption of a common known covariance matrix. The estimators studied in this paper are plug-in and smoothed resubstitution error estimators, both of which have not been studied before under Kolmogorov asymptotic conditions. As a result of this work, we present an optimal smoothing parameter that makes the smoothed resubstitution an unbiased estimator of the true error. For the sake of completeness, we further show how to utilize the presented fundamental theorem to achieve several previously reported results, namely the first moment of the resubstitution estimator and the actual error rate. We provide numerical examples to show the accuracy of the succeeding finite sample approximations in situations where the number of dimensions is comparable or even larger than the sample size.

  17. Fault Diagnosis in Chemical Process Based on Self-organizing Map Integrated with Fisher Discriminant Analysis

    Institute of Scientific and Technical Information of China (English)

    CHEN Xinyi; YAN Xuefeng

    2013-01-01

    Fault diagnosis and monitoring are very important for complex chemical process.There are numerous methods that have been studied in this field,in which the effective visualization method is still challenging.In order to get a better visualization effect,a novel fault diagnosis method which combines self-organizing map (SOM) with Fisher discriminant analysis (FDA) is proposed.FDA can reduce the dimension of the data in terms of maximizing the separability of the classes.After feature extraction by FDA,SOM can distinguish the different states on the output map clearly and it can also be employed to monitor abnormal states.Tennessee Eastman (TE) process is employed to illustrate the fault diagnosis and monitoring performance of the proposed method.The result shows that the SOM integrated with FDA method is efficient and capable for real-time monitoring and fault diagnosis in complex chemical process.

  18. Study of major volatiles in wines and discriminant analysis applied to classification according to region.

    Science.gov (United States)

    Huerta-Díaz-Regañon, M D; Salinas Fernández, M R; Masoud, T

    1997-12-01

    The major volatiles of eighty eight wines (white, rosè and red) from Madrid were studied. The samples came from the three districts forming the "Vinos de Madrid" DO (Denominación de Origen) region: Arganda, Navalcarnero and San Martín, and were analyzed by gas chromatography. The resulting data were treated by Stepwise Discriminant Analysis (SDA) in order to ascertain the efficacity of these compounds in classifying the wines according to their geographical origin. The results confirm that the above components were of little use in classifying the red and white wines and, although a correct classification percentage of 90.91% was obtained for the rosés when all the variables were used, this too was considered unsatisfactory.

  19. Sex determination using discriminant analysis of the medial and lateral condyles of the femur in Koreans.

    Science.gov (United States)

    Kim, Deog-Im; Kwak, Dai-Soon; Han, Seung-Ho

    2013-12-10

    The proximal and distal parts of the femur show the differences between the sexes. Head diameter and the breadth of the epicondyle of the femur are known to distinguish males from females. The proximal end of the femur is studied to determine sex using discriminant analysis but; its distal end is not done. This study aims to develop an equation specific to Koreans by using the medial and lateral condyles of the femur, and to demonstrate the usefulness of equations for specific population groups. We used three-dimensional images from 202 Korean femurs. Twelve variables were measured with a computer program after the femurs were in alignment. Eleven variables showed a statistically significant difference between the sexes (Psex determination in situations where the skull and pelvis are missing and part of the femur is available. The study also demonstrates the need for different equations for different population groups.

  20. The Importance of the Discriminant Analysis for the Evolution of the Equity Prices

    Directory of Open Access Journals (Sweden)

    Dinca G.

    2014-12-01

    Full Text Available This paper aims to show the correlation between the results obtained using the discriminant analysis and the evolution of stock prices of listed Romanian companies. For this purpose, we have carried out the research on a sample of 32 issuers from categories I and II of the Bucharest Stock Exchange, pertaining to nine economic sectors, for the period 2010- 2012. Our study is based on the Anghel prediction model of bankruptcy, using the stock prices of the 32 listed companies from the first and the last day of trading for each year examined. The results obtained by applying the prediction model allow the classification of issuers into potential bankrupt and non-bankrupt firms and help investors take appropriate decisions on the stock market.

  1. Lameness detection challenges in automated milking systems addressed with partial least squares discriminant analysis

    DEFF Research Database (Denmark)

    Garcia, Emanuel; Klaas, Ilka Christine; Amigo Rubio, Jose Manuel;

    2014-01-01

    Lameness is prevalent in dairy herds. It causes decreased animal welfare and leads to higher production costs. This study explored data from an automatic milking system (AMS) to model on-farm gait scoring from a commercial farm. A total of 88 cows were gait scored once per week, for 2 5-wk periods....... Eighty variables retrieved from AMS were summarized week-wise and used to predict 2 defined classes: nonlame and clinically lame cows. Variables were represented with 2 transformations of the week summarized variables, using 2-wk data blocks before gait scoring, totaling 320 variables (2 × 2 × 80......). The reference gait scoring error was estimated in the first week of the study and was, on average, 15%. Two partial least squares discriminant analysis models were fitted to parity 1 and parity 2 groups, respectively, to assign the lameness class according to the predicted probability of being lame (score 3...

  2. Quantitative Classification of Quartz by Laser Induced Breakdown Spectroscopy in Conjunction with Discriminant Function Analysis

    Directory of Open Access Journals (Sweden)

    A. Ali

    2016-01-01

    Full Text Available A responsive laser induced breakdown spectroscopic system was developed and improved for utilizing it as a sensor for the classification of quartz samples on the basis of trace elements present in the acquired samples. Laser induced breakdown spectroscopy (LIBS in conjunction with discriminant function analysis (DFA was applied for the classification of five different types of quartz samples. The quartz plasmas were produced at ambient pressure using Nd:YAG laser at fundamental harmonic mode (1064 nm. We optimized the detection system by finding the suitable delay time of the laser excitation. This is the first study, where the developed technique (LIBS+DFA was successfully employed to probe and confirm the elemental composition of quartz samples.

  3. Cross View Gait Recognition Using Joint-Direct Linear Discriminant Analysis

    Science.gov (United States)

    Portillo-Portillo, Jose; Leyva, Roberto; Sanchez, Victor; Sanchez-Perez, Gabriel; Perez-Meana, Hector; Olivares-Mercado, Jesus; Toscano-Medina, Karina; Nakano-Miyatake, Mariko

    2016-01-01

    This paper proposes a view-invariant gait recognition framework that employs a unique view invariant model that profits from the dimensionality reduction provided by Direct Linear Discriminant Analysis (DLDA). The framework, which employs gait energy images (GEIs), creates a single joint model that accurately classifies GEIs captured at different angles. Moreover, the proposed framework also helps to reduce the under-sampling problem (USP) that usually appears when the number of training samples is much smaller than the dimension of the feature space. Evaluation experiments compare the proposed framework’s computational complexity and recognition accuracy against those of other view-invariant methods. Results show improvements in both computational complexity and recognition accuracy. PMID:28025484

  4. Cross View Gait Recognition Using Joint-Direct Linear Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Jose Portillo-Portillo

    2016-12-01

    Full Text Available This paper proposes a view-invariant gait recognition framework that employs a unique view invariant model that profits from the dimensionality reduction provided by Direct Linear Discriminant Analysis (DLDA. The framework, which employs gait energy images (GEIs, creates a single joint model that accurately classifies GEIs captured at different angles. Moreover, the proposed framework also helps to reduce the under-sampling problem (USP that usually appears when the number of training samples is much smaller than the dimension of the feature space. Evaluation experiments compare the proposed framework’s computational complexity and recognition accuracy against those of other view-invariant methods. Results show improvements in both computational complexity and recognition accuracy.

  5. A Multimodal Biometric System Using Linear Discriminant Analysis For Improved Performance

    Directory of Open Access Journals (Sweden)

    Aamir Khan

    2011-11-01

    Full Text Available Essentially a biometric system is a pattern recognition system which recognizes a user by determining the authenticity of a specific anatomical or behavioral characteristic possessed by the user. With the ever increasing integration of computers and Internet into daily life style, it has become necessary to protect sensitive and personal data. This paper proposes a multimodal biometric system which incorporates more than one biometric trait to attain higher security and to handle failure to enroll situations for some users. This paper is aimed at investigating a multimodal biometric identity system using Linear Discriminant Analysis as backbone to both facial and speech recognition and implementing such system in real-time using SignalWAVE.

  6. Multiple binary classifications via linear discriminant analysis for improved controllability of a powered prosthesis.

    Science.gov (United States)

    Hargrove, Levi J; Scheme, Erik J; Englehart, Kevin B; Hudgins, Bernard S

    2010-02-01

    This paper describes a novel pattern recognition based myoelectric control system that uses parallel binary classification and class specific thresholds. The system was designed with an intuitive configuration interface, similar to existing conventional myoelectric control systems. The system was assessed quantitatively with a classification error metric and functionally with a clothespin test implemented in a virtual environment. For each case, the proposed system was compared to a state-of-the-art pattern recognition system based on linear discriminant analysis and a conventional myoelectric control scheme with mode switching. These assessments showed that the proposed control system had a higher classification error ( p myoelectric control system ( p myoelectric control system which is robust, easily configured, and highly usable.

  7. Identifying Plant Part Composition of Forest Logging Residue Using Infrared Spectral Data and Linear Discriminant Analysis.

    Science.gov (United States)

    Acquah, Gifty E; Via, Brian K; Billor, Nedret; Fasina, Oladiran O; Eckhardt, Lori G

    2016-08-27

    As new markets, technologies and economies evolve in the low carbon bioeconomy, forest logging residue, a largely untapped renewable resource will play a vital role. The feedstock can however be variable depending on plant species and plant part component. This heterogeneity can influence the physical, chemical and thermochemical properties of the material, and thus the final yield and quality of products. Although it is challenging to control compositional variability of a batch of feedstock, it is feasible to monitor this heterogeneity and make the necessary changes in process parameters. Such a system will be a first step towards optimization, quality assurance and cost-effectiveness of processes in the emerging biofuel/chemical industry. The objective of this study was therefore to qualitatively classify forest logging residue made up of different plant parts using both near infrared spectroscopy (NIRS) and Fourier transform infrared spectroscopy (FTIRS) together with linear discriminant analysis (LDA). Forest logging residue harvested from several Pinus taeda (loblolly pine) plantations in Alabama, USA, were classified into three plant part components: clean wood, wood and bark and slash (i.e., limbs and foliage). Five-fold cross-validated linear discriminant functions had classification accuracies of over 96% for both NIRS and FTIRS based models. An extra factor/principal component (PC) was however needed to achieve this in FTIRS modeling. Analysis of factor loadings of both NIR and FTIR spectra showed that, the statistically different amount of cellulose in the three plant part components of logging residue contributed to their initial separation. This study demonstrated that NIR or FTIR spectroscopy coupled with PCA and LDA has the potential to be used as a high throughput tool in classifying the plant part makeup of a batch of forest logging residue feedstock. Thus, NIR/FTIR could be employed as a tool to rapidly probe/monitor the variability of forest

  8. Neighbor Class Linear Discriminate Analysis%近邻类鉴别分析方法

    Institute of Scientific and Technical Information of China (English)

    王言伟; 丁晓青; 刘长松

    2012-01-01

    提出一种近邻类鉴别分析方法,线性鉴别分析是该方法的一个特例.线性鉴别分析通过最大化类间散度同时最小化类内散度寻找最佳投影,其中类间散度是所有类之间散度的总体平均;而近邻类鉴别分析中类间散度定义为各个类与其k个近邻类之间的平均散度.该方法通过选取适当的近邻类数,能够缓解线性鉴别降维后造成的部分类的重叠.实验结果表明近邻类鉴别分析方法性能稳定且优于传统的线性鉴别分析.%A method of neighbor class linear discriminant analysis (NCLDA) is proposed. Linear discriminant analysis ( LDA) is a special case of this method. LDA finds the optimal projections by maximum between-class scatter while by minimum within-class scatter. The between-class scatter is an average over divergences among all classes. In NCLDA, between-class scatter is defined as average divergences between one class and its k nearest neighbor classes. By selecting proper numbers of neighbor class, NCLDA alleviates overlaps among classes caused by LDA. The experimental results show that the proposed NCLDA is robust and outperforms LDA.

  9. Discrimination in relation to parenthood reported by community psychiatric service users in the UK: a framework analysis.

    Science.gov (United States)

    Jeffery, Debra; Clement, Sarah; Corker, Elizabeth; Howard, Louise M; Murray, Joanna; Thornicroft, Graham

    2013-04-20

    Experienced discrimination refers to an individual's perception that they have been treated unfairly due to an attribute and is an important recent focus within stigma research. A significant proportion of mental health service users report experiencing mental illness-based discrimination in relation to parenthood. Existing studies in this area have not gone beyond prevalence, therefore little is known about the nature of experienced discrimination in relation to parenthood, and how is it constituted. This study aims to generate a typology of community psychiatric service users' reports of mental illness-based discrimination in relation to becoming or being a parent. A secondary aim is to assess the prevalence of these types of experienced discrimination. In a telephone survey 2026 community psychiatric service users in ten UK Mental Health service provider organisations (Trusts) were asked about discrimination experienced in the previous 12 months using the Discrimination and Stigma Scale (DISC). The sample were asked if, due to their mental health problem, they had been treated unfairly in starting a family, or in their role as a parent, and gave examples of this. Prevalence is reported and the examples of experienced discrimination in relation to parenthood were analysed using the framework method of qualitative analysis. Three hundred and four participants (73% female) reported experienced discrimination, with prevalences of 22.5% and 28.3% for starting a family and for the parenting role respectively. Participants gave 89 examples of discrimination about starting a family and 228 about parenting, and these occurred in social and professional contexts. Ten themes were identified. These related to being seen as an unfit parent; people not being understanding; being stopped from having children; not being allowed to see their children; not getting the support needed; children being affected; children avoiding their parents; children's difficulties being blamed

  10. Mid infrared and fluorescence spectroscopies coupled with factorial discriminant analysis technique to identify sheep milk from different feeding systems

    OpenAIRE

    Karoui, Romdhane; Hammami, Moncef; Rouissi, Hamadi; Blecker, Christophe

    2011-01-01

    Mid infrared spectroscopy (MIR) combined with multivariate data analysis was used to discriminate between ewes milk samples according to their feeding systems (controls, ewes fed scotch bean and ewes fed soybean). The MIR spectra were scanned throughout the first 11 weeks of the lactation stage. When factorial discriminant analysis (FDA) with leave one-out cross-validation was applied, separately, to the three spectral regions in the MIR (i.e. 3000-2800, 1700-1500 and 1500-900 cm(-1)), the cl...

  11. Statistical analysis of Thematic Mapper Simulator data for the geobotanical discrimination of rock types in southwest Oregon

    Science.gov (United States)

    Morrissey, L. A.; Weinstock, K. J.; Mouat, D. A.; Card, D. H.

    1984-01-01

    An evaluation of Thematic Mapper Simulator (TMS) data for the geobotanical discrimination of rock types based on vegetative cover characteristics is addressed in this research. A methodology for accomplishing this evaluation utilizing univariate and multivariate techniques is presented. TMS data acquired with a Daedalus DEI-1260 multispectral scanner were integrated with vegetation and geologic information for subsequent statistical analyses, which included a chi-square test, an analysis of variance, stepwise discriminant analysis, and Duncan's multiple range test. Results indicate that ultramafic rock types are spectrally separable from nonultramafics based on vegetative cover through the use of statistical analyses.

  12. Genetic molecular analysis of Coffea arabica (Rubiaceae hybrids using SRAP markers

    Directory of Open Access Journals (Sweden)

    Manoj Kumar Mishra

    2011-06-01

    Full Text Available In Coffea arabica (arabica coffee, the phenotypic as well as genetic variability has been found low because of the narrow genetic basis and self fertile nature of the species. Because of high similarity in phenotypic appearance among the majority of arabica collections, selection of parental lines for inter-varietals hybridization and identification of resultant hybrids at an early stage of plant growth is difficult. DNA markers are known to be reliable in identifying closely related cultivars and hybrids. Sequence Related Amplified Polymorphism (SRAP is a new molecular marker technology developed based on PCR. In this paper, sixty arabica-hybrid progenies belonging to six crosses were analyzed using 31 highly polymorphic SRAP markers. The analysis revealed seven types of SRAP marker profiles which are useful in discriminating the parents and hybrids. The number of bands amplified per primer pair ranges from 6.13 to 8.58 with average number of seven bands. Among six hybrid combinations, percentage of bands shared between hybrids and their parents ranged from 66.29% to 85.71% with polymorphic bands varied from 27.64% to 60.0%. Percentage of hybrid specific fragments obtained in various hybrid combinations ranged from 0.71% to 10.86% and ascribed to the consequence of meiotic recombination. Based on the similarity index calculation, it was observed that F1 hybrids share maximum number of bands with the female parent compared to male parent. The results obtained in the present study revealed the effectiveness of SRAP technique in cultivar identification and hybrid analysis in this coffee species. Rev. Biol. Trop. 59 (2: 607-617. Epub 2011 June 01.En Coffea arabica (café arabica, el fenotipo y la variabilidad genética son bajos debido a la estrecha base genética y la autofecundación de la especie. Por su alta similitud fenotípica entre la mayoría de las colecciones de arábica, la selección de líneas parentales para hibridación entre

  13. Whole-genome single-nucleotide-polymorphism analysis for discrimination of Clostridium botulinum group I strains.

    Science.gov (United States)

    Gonzalez-Escalona, Narjol; Timme, Ruth; Raphael, Brian H; Zink, Donald; Sharma, Shashi K

    2014-04-01

    Clostridium botulinum is a genetically diverse Gram-positive bacterium producing extremely potent neurotoxins (botulinum neurotoxins A through G [BoNT/A-G]). The complete genome sequences of three strains harboring only the BoNT/A1 nucleotide sequence are publicly available. Although these strains contain a toxin cluster (HA(+) OrfX(-)) associated with hemagglutinin genes, little is known about the genomes of subtype A1 strains (termed HA(-) OrfX(+)) that lack hemagglutinin genes in the toxin gene cluster. We sequenced the genomes of three BoNT/A1-producing C. botulinum strains: two strains with the HA(+) OrfX(-) cluster (69A and 32A) and one strain with the HA(-) OrfX(+) cluster (CDC297). Whole-genome phylogenic single-nucleotide-polymorphism (SNP) analysis of these strains along with other publicly available C. botulinum group I strains revealed five distinct lineages. Strains 69A and 32A clustered with the C. botulinum type A1 Hall group, and strain CDC297 clustered with the C. botulinum type Ba4 strain 657. This study reports the use of whole-genome SNP sequence analysis for discrimination of C. botulinum group I strains and demonstrates the utility of this analysis in quickly differentiating C. botulinum strains harboring identical toxin gene subtypes. This analysis further supports previous work showing that strains CDC297 and 657 likely evolved from a common ancestor and independently acquired separate BoNT/A1 toxin gene clusters at distinct genomic locations.

  14. Rapid discrimination of Salmonella isolates by single-strand conformation polymorphism analysis.

    Science.gov (United States)

    Al-Adhami, Batol H; Huby-Chilton, Florence; Blais, Burton W; Martinez-Perez, Amalia; Chilton, Neil B; Gajadhar, Alvin A

    2008-10-01

    A molecular typing technique was developed for the differentiation of Salmonella isolates based on single-strand conformation polymorphism (SSCP) analysis of amplicons generated by PCR. Amplicons from parts of the fimA (both the 5' and 3' ends), mdh, invA, and atpD genes were generated separately from a panel of Salmonella strains representing Salmonella bongori, and four subspecies and 17 serovars of Salmonella enterica. These amplicons were subjected to SSCP analysis for differentiation of the salmonellae on the basis of different conformational forms arising due to nucleotide sequence variations in the target genes. Several distinct SSCP banding patterns (a maximum of 14 each for atpD and fimA 3' end) were observed with this panel of Salmonella strains for amplicons generated from each target gene. The best discrimination of Salmonella subspecies and serovar was achieved from the SSCP analysis of a combination of at least three gene targets: atpD, invA, and either mdh or fimA 3' end. This demonstrates the applicability of SSCP analysis as an important additional method to classical typing approaches for the differentiation of foodborne Salmonella isolates. SSCP is simple to perform and should be readily transferable to food microbiology laboratories with basic PCR capability.

  15. The Analysis of the Ethnical Discrimination on the Manpower’s Market under the Economical Crisis

    Directory of Open Access Journals (Sweden)

    Mihaela Hrisanta DOBRE

    2012-06-01

    Full Text Available Discrimination means any difference, exclusion, restriction, preference or different treatment that brings forth disadvantages for a person or a group as compared to other ones that are in similar situations. The reasons on which discrimination is based can be various, such as race, nationality, ethnics, religion, gender, sexual orientation, language, age, disabilities etc. and in this case we talk about multiple discrimination. In Romania the main forms of discrimination are linked to ethnics and to sexual appurtenance. Within this column we analysed the discrimination amongst the Romany ethnics people, according to a statistical investigation (Access onto the Labour Market – A Chance for You, the research goal being to identify the answer to the following questions: Is there any discrimination inside the Romany ethnic group? What is the correlation between their level of education and their income? What is the correlation between the level of education of the parents and the respondent’s?

  16. Demographic Consequences of Gender Discrimination in China: Simulation Analysis of Policy Options

    OpenAIRE

    Quanbao, Jiang; Shuzhuo, Li; Marcus W., Feldman

    2011-01-01

    The large number of missing females in China, a consequence of gender discrimination, is having and will continue to have a profound effect on the country's population development. In this paper, we analyze the causes of this gender discrimination in terms of institutions, culture and, economy, and suggest public policies that might help eliminate gender discrimination. Using a population simulation model, we study the effect of public policies on the sex ratio at birth and excess female chil...

  17. Limit Cycle Analysis in a Class of Hybrid Systems

    Directory of Open Access Journals (Sweden)

    Antonio Favela-Contreras

    2016-01-01

    Full Text Available Hybrid systems are those that inherently combine discrete and continuous dynamics. This paper considers the hybrid system model to be an extension of the discrete automata associating a continuous evolution with each discrete state. This model is called the hybrid automaton. In this work, we achieve a mathematical formulation of the steady state and we show a way to obtain the initial conditions region to reach a specific limit cycle for a class of uncoupled and coupled continuous-linear hybrid systems. The continuous-linear term is used in the sense of the system theory and, in this sense, continuous-linear hybrid automata will be defined. Thus, some properties and theorems that govern the hybrid automata dynamic behavior to evaluate a limit cycle existence have been established; this content is explained under a theoretical framework.

  18. The convergent and discriminant validity of burnout measures in sport: a multi-trait/multi-method analysis.

    Science.gov (United States)

    Cresswell, Scott L; Eklund, Robert C

    2006-02-01

    Athlete burnout research has been hampered by the lack of an adequate measurement tool. The Athlete Burnout Questionnaire (ABQ) and the Maslach Burnout Inventory General Survey (MBI-GS) are two recently developed self-report instruments designed to assess burnout. The convergent and discriminant validity of the ABQ and MBI-GS were assessed through multi-trait/multi-method analysis with a sporting population. Overall, the ABQ and the MBI-GS displayed acceptable convergent validity with matching subscales highly correlated, and satisfactory internal discriminant validity with lower correlations between non-matching subscales. Both scales also indicated an adequate discrimination between the concepts of burnout and depression. These findings add support to previous findings in non-sporting populations that depression and burnout are separate constructs. Based on the psychometric results, construct validity analysis and practical considerations, the results support the use of the ABQ to assess athlete burnout.

  19. A Brief Analysis of Discrimination in Language Classroom--from the Perspecti ve of Soci oli ngui sti cs

    Institute of Scientific and Technical Information of China (English)

    曹志勇

    2014-01-01

    It is widely acknowledged that sociolinguistics is the study of language in certain context concerned with our soci-ety. Sociolinguistics and linguistics are intrinsically related to each other, but there has been difference as well. Linguistics research deals with language system itself, which belongs to the micro lev-el on the one hand; many phenomena reflect discrimination in language classroom, these discrimination are caused by social fac-tors to a certain degree. This paper makes a brief analysis of dis-crimination in language classroom from the perspective of socio-linguistics, which deals with many issues such as depiction of lan-guage discrimination、analysis of phenomenon and accordingly-solved measures.

  20. Improved Aerodynamic Analysis for Hybrid Wing Body Conceptual Design Optimization

    Science.gov (United States)

    Gern, Frank H.

    2012-01-01

    This paper provides an overview of ongoing efforts to develop, evaluate, and validate different tools for improved aerodynamic modeling and systems analysis of Hybrid Wing Body (HWB) aircraft configurations. Results are being presented for the evaluation of different aerodynamic tools including panel methods, enhanced panel methods with viscous drag prediction, and computational fluid dynamics. Emphasis is placed on proper prediction of aerodynamic loads for structural sizing as well as viscous drag prediction to develop drag polars for HWB conceptual design optimization. Data from transonic wind tunnel tests at the Arnold Engineering Development Center s 16-Foot Transonic Tunnel was used as a reference data set in order to evaluate the accuracy of the aerodynamic tools. Triangularized surface data and Vehicle Sketch Pad (VSP) models of an X-48B 2% scale wind tunnel model were used to generate input and model files for the different analysis tools. In support of ongoing HWB scaling studies within the NASA Environmentally Responsible Aviation (ERA) program, an improved finite element based structural analysis and weight estimation tool for HWB center bodies is currently under development. Aerodynamic results from these analyses are used to provide additional aerodynamic validation data.

  1. Classification of the long-QT syndrome based on discriminant analysis of T-wave morphology

    DEFF Research Database (Denmark)

    Struijk, Johannes; Kanters, J.K.; Andersen, Mads Peter

    2006-01-01

    been shown to be useful discriminators, but no single ECG parameter has been sufficient to solve the diagnostic problem. In this study we present a method for discrimination among persons with a normal genotype and those with mutations in the KCNQ1 (KvLQT1 or LQT1) and KCNH2 (HERG or LQT2) genes...... on the basis of parameters describing T-wave morphology in terms of duration, asymmetry, flatness and amplitude. Discriminant analyses based on 4 or 5 parameters both resulted in perfect discrimination in a learning set of 36 subjects. In both cases cross-validation of the resulting classifiers showed...

  2. Discriminant analysis to classify glioma grading using dynamic contrast-enhanced MRI and immunohistochemical markers

    Energy Technology Data Exchange (ETDEWEB)

    Awasthi, Rishi [Sanjay Gandhi Post Graduate Institute of Medical Sciences, Department of Radiodiagnosis, Lucknow (India); Rathore, Ram K.S.; Sahoo, Prativa [Indian Institute of Technology, Department of Mathematics and Statistics, Kanpur (India); Soni, Priyanka; Husain, Nuzhat [Chhatrapati Sahuji Maharj Medical University, Department of Pathology, Lucknow (India); Awasthi, Ashish; Pandey, Chandra M. [Sanjay Gandhi Post Graduate Institute of Medical Sciences, Department of Biostatistics, Lucknow (India); Behari, Sanjay; Singh, Rohit K. [Sanjay Gandhi Post Graduate Institute of Medical Sciences, Department of Neurosurgery, Lucknow (India); Gupta, Rakesh K. [Sanjay Gandhi Post Graduate Institute of Medical Sciences, MR Section, Department of Radiodiagnosis, Lucknow, UP (India)

    2012-03-15

    The purpose of the present study was to look for the possible predictors which might discriminate between high- and low-grade gliomas by pooling dynamic contrast-enhanced (DCE)-perfusion derived indices and immunohistochemical markers. DCE-MRI was performed in 76 patients with different grades of gliomas. Perfusion indices, i.e., relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), permeability (k{sup trans} and k{sub ep}), and leakage (v{sub e}) were quantified. MMP-9-, PRL-3-, HIF-1{alpha}-, and VEGF-expressing cells were quantified from the excised tumor tissues. Discriminant function analysis using these markers was used to identify discriminatory variables using a stepwise procedure. To look for correlations between immunohistochemical parameters and DCE metrics, Pearson's correlation coefficient was also used. A discriminant function for differentiating between high- and low-grade tumors was constructed using DCE-MRI-derived rCBV, k{sub ep}, and v{sub e}. The form of the functions estimated are ''D{sub 1} = 0.642 x rCBV + 0.591 x k{sub ep} - 1.501 x v{sub e} - 1.550'' and ''D{sub 2} = 1.608 x rCBV + 3.033 x k{sub ep} + 5.508 x v{sub e} - 8.784'' for low- and high-grade tumors, respectively. This function classified overall 92.1% of the cases correctly (89.1% high-grade tumors and 100% low-grade tumors). In addition, VEGF expression correlated with rCBV and rCBF, whereas MMP-9 expression correlated with k{sub ep}. A significant positive correlation of HIF-1{alpha} with rCBV and VEGF expression was also found. DCE-MRI may be used to differentiate between high-grade and low-grade brain tumors non-invasively, which may be helpful in appropriate treatment planning and management of these patients. The correlation of its indices with immunohistochemical markers suggests that this imaging technique is useful in tissue characterization of gliomas. (orig.)

  3. Analysis of chromosome aberration data by hybrid-scale models

    Energy Technology Data Exchange (ETDEWEB)

    Indrawati, Iwiq [Research and Development on Radiation and Nuclear Biomedical Center, National Nuclear Energy Agency (Indonesia); Kumazawa, Shigeru [Nuclear Technology and Education Center, Japan Atomic Energy Research Institute, Honkomagome, Tokyo (Japan)

    2000-02-01

    This paper presents a new methodology for analyzing data of chromosome aberrations, which is useful to understand the characteristics of dose-response relationships and to construct the calibration curves for the biological dosimetry. The hybrid scale of linear and logarithmic scales brings a particular plotting paper, where the normal section paper, two types of semi-log papers and the log-log paper are continuously connected. The hybrid-hybrid plotting paper may contain nine kinds of linear relationships, and these are conveniently called hybrid scale models. One can systematically select the best-fit model among the nine models by among the conditions for a straight line of data points. A biological interpretation is possible with some hybrid-scale models. In this report, the hybrid scale models were applied to separately reported data on chromosome aberrations in human lymphocytes as well as on chromosome breaks in Tradescantia. The results proved that the proposed models fit the data better than the linear-quadratic model, despite the demerit of the increased number of model parameters. We showed that the hybrid-hybrid model (both variables of dose and response using the hybrid scale) provides the best-fit straight lines to be used as the reliable and readable calibration curves of chromosome aberrations. (author)

  4. Multi-class ERP-based BCI data analysis using a discriminant space self-organizing map.

    Science.gov (United States)

    Onishi, Akinari; Natsume, Kiyohisa

    2014-01-01

    Emotional or non-emotional image stimulus is recently applied to event-related potential (ERP) based brain computer interfaces (BCI). Though the classification performance is over 80% in a single trial, a discrimination between those ERPs has not been considered. In this research we tried to clarify the discriminability of four-class ERP-based BCI target data elicited by desk, seal, spider images and letter intensifications. A conventional self organizing map (SOM) and newly proposed discriminant space SOM (ds-SOM) were applied, then the discriminabilites were visualized. We also classify all pairs of those ERPs by stepwise linear discriminant analysis (SWLDA) and verify the visualization of discriminabilities. As a result, the ds-SOM showed understandable visualization of the data with a shorter computational time than the traditional SOM. We also confirmed the clear boundary between the letter cluster and the other clusters. The result was coherent with the classification performances by SWLDA. The method might be helpful not only for developing a new BCI paradigm, but also for the big data analysis.

  5. Surface Water Quality Assessment of Wular Lake, A Ramsar Site in Kashmir Himalaya, Using Discriminant Analysis and WQI

    Directory of Open Access Journals (Sweden)

    Salim Aijaz Bhat

    2014-01-01

    Full Text Available Multivariate techniques, discriminant analysis, and WQI were applied to analyze a water quality data set including 27 parameters at 5 sites of the Lake Wular in Kashmir Himalaya from 2011 to 2013 to investigate spatiotemporal variations and identify potential pollution sources. Spatial and temporal variations in water quality parameters were evaluated through stepwise discriminant analysis (DA. The first spatial discriminant function (DF accounted for 76.5% of the total spatial variance, and the second DF accounted for 19.1%. The mean values of water temperature, EC, total-N, K, and silicate showed a strong contribution to discriminate the five sampling sites. The mean concentration of NO2-N, total-N, and sulphate showed a strong contribution to discriminate the four sampling seasons and accounted for most of the expected seasonal variations. The order of major cations and anions was Ca2+>Mg2+> Na+>K+ and Cl->SO42->SiO22- respectively. The results of water quality index, employing thirteen core parameters vital for drinking water purposes, showed values of 49.2, 46.5, 47.3, 40.6, and 37.1 for sites I, II, III, IV, and V, respectively. These index values reflect that the water of lake is in good condition for different purposes but increased values alarm us about future repercussions.

  6. An Improved Method for Discriminating ECG Signals using Typical Nonlinear Dynamic Parameters and Recurrence Quantification Analysis in Cardiac Disease Therapy.

    Science.gov (United States)

    Tang, M; Chang, C Q; Fung, P C W; Chau, K T; Chan, F H Y

    2005-01-01

    The discrimination of ECG signals using nonlinear dynamic parameters is of crucial importance in the cardiac disease therapy and chaos control for arrhythmia defibrillation in the cardiac system. However, the discrimination results of previous studies using features such as maximal Lyapunov exponent (λmax) and correlation dimension (D2) alone are somewhat limited in recognition rate. In this paper, improved methods for computing λmaxand D2are purposed. Another parameter from recurrence quantification analysis is incorporated to the new multi-feature Bayesian classifier with λmaxand D2so as to improve the discrimination power. Experimental results have verified the prediction using Fisher discriminant that the maximal vertical line length (Vmax) from recurrence quantification analysis is the best to distinguish different ECG classes. Experimental results using the MIT-BIH Arrhythmia Database show improved and excellent overall accuracy (96.3%), average sensitivity (96.3%) and average specificity (98.15%) for discriminating sinus, premature ventricular contraction and ventricular flutter signals.

  7. Hybrid scintillators for neutron discrimination

    Science.gov (United States)

    Feng, Patrick L; Cordaro, Joseph G; Anstey, Mitchell R; Morales, Alfredo M

    2015-05-12

    A composition capable of producing a unique scintillation response to neutrons and gamma rays, comprising (i) at least one surfactant; (ii) a polar hydrogen-bonding solvent; and (iii) at least one luminophore. A method including combining at least one surfactant, a polar hydrogen-bonding solvent and at least one luminophore in a scintillation cell under vacuum or an inert atmosphere.

  8. BIOMETRIC ANALYSIS OF THE ARTIFICIAL HYBRIDIZATION BETWEEN Pangasius djambal BLEEKER, 1846 AND Pangasianodon hypophthalmus SAUVAGE, 1878

    OpenAIRE

    Rudhy Gustiano

    2016-01-01

    It is really important, since the possible use of these pangasiid hybrids in aquaculture faces the problem of potential impact on wild population. Therefore, it is urgently needed to provide quick identification tools in the field. This study investigated morphological characters of Pangasius djambal and Pangasianodon hypophthalmus and their hybrids. A detailed morphological analysis using 32 morphometric measurements and five meristic counts was done on the hybridization of P. djambal and P....

  9. Multivariate analysis of dermatoglyphics of severe mental retardates: an application of the constellation graphical method for discriminant analysis.

    Directory of Open Access Journals (Sweden)

    Wakita,Yoshiharu

    1988-06-01

    Full Text Available We studied the dermatoglyphics of 353 severe mental retardates (excluding those with chromosomal abnormalities and major limb malformations, using multivariate analysis, to determine how early intrauterine factors are related to the etiology of mental retardation. First, dermatoglyphics were compared between 140 individuals with undefined prenatal factors and 700 normal controls. After 6 and 9 dermatoglyphic traits were chosen as discriminative variables for males and females, respectively, the data were subjected separately for each sex to the constellation graphical method for discriminant analysis. The same formula as obtained in the idiopathic group was subsequently applied to data from cases in other etiological categories. When the misclassification rate was 0.03, the rates of correct classification of the male patients into the etiological categories of undefined prenatal, defined prenatal, perinatal, postnatal and unknown (no anamnestic data available categories were 19.7% (13/66, 20.0% (3/15, 8.8% (5/57, 5.0% (1/20 and 7.7% (2/26, while the correct classification rates of females were 24.3% (18/74, 42.1% (8/19, 18.9% (7/37, 5.1% (1/16 and 13.0% (3/23, respectively. The results suggest that early intrauterine factors such as those producing dermatoglyphic deviations may contribute to the pathogenesis of severe mental retardation not only in patients with undefined prenatal etiological factors but also in those with perinatal factors, especially those of the female sex.

  10. Analysis on Dry Matter Production Characteristics of Super Hybrid Rice

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Six middle-season indica hybrid rice combinations,including five super hybrid rice combinations with the high yield about 10.5 t/ha and a check hybrid rice combination Shanyou 63 with a yield potential about 9.5 t/ha,were used as materials to study the dry matter production characteristics.The super hybrid rice showed a high ability in dry matter production and accumulation and its yield enhanced with the increase of dry matter accumulation.The advantage period of dry matter production in the super hybrid rice was mainly at the middle and late growth stages compared with the check.The grain yield had no significant correlation with the dry matter accumulation before the elongation stage while had a significantly positive correlation with the dry matter accumulation from the elongation to maturity stages in super hybrid rice.There were more dry matter in vegetative organs at the heading stage in the super hybrid rice but its contribution to yield (apparent conversion percentage) was averagely 4.3 percent points lower than that in the check.For crop growth rate (CGR),the comparative advantage of super hybrid rice was at the middle and late stages,especially after flowering.Moreover,as the rising of leaf area index (LAI) and leaf area duration (LAD),CGR enhanced.The total LAD and the mean of LAD per day of super hybrid rice was about 14.79% and 10.31% higher than those of the check,respectively.The results indicate that the high yield of super hybrid rice mostly comes from the products of photosynthesis after heading,which is shown by the increased CGR at middle and later stages.It is suggested that LAD character might be used to better explain the advantage in the dry matter production of super hybrid rice than LAI.

  11. Simultaneous thermal analysis and thermodilatometry of hybrid fiber reinforced UHPC

    Science.gov (United States)

    Scheinherrová, Lenka; Fořt, Jan; Pavlík, Zbyšek; Černý, Robert

    2017-07-01

    Development of concrete technology and the availability of variety of materials such as silica fume, mineral microfillers and high-range water-reducing admixtures make possible to produce Ultra-High Performance Concrete (UHPC) with compressive strength higher than 160 MPa. However, UHPC is prone to spall under high temperatures what limits its use for special applications only, such as offshore and marine structures, industrial floors, security barriers etc. The spalling is caused by the thermal stresses due to the temperature gradient during heating, and by the splitting force owing to the release of water vapour. Hybrid fibre reinforcement based on combination of steel and polymer fibres is generally accepted by concrete community as a functional solution preventing spalling. In this way, Ultra-High Performance Fibre Reinforced Concrete (UHPFRC) is produced possessing high mechanical strength, durability and resistance to water and salt ingress. Since UHPFRC find use in construction industry in tunnel linings, precast tunnel segments, and high-rise buildings, its behaviour during the high-temperature exposure and its residual parameters are of the particular importance. On this account, Simultaneous Thermal Analysis (STA) and Thermodilatometry Analysis (TDA) were done in the paper to identify the structural and chemical changes in UHPFRC during its high-temperature load. Based on the experimental results, several physical and chemical processes that studied material underwent at high-temperatures were recognized. The obtained data revealed changes in the composition of the studied material and allowed identification of critical temperatures for material damage.

  12. Analysis of SMA Hybrid Composite Structures using Commercial Codes

    Science.gov (United States)

    Turner, Travis L.; Patel, Hemant D.

    2004-01-01

    A thermomechanical model for shape memory alloy (SMA) actuators and SMA hybrid composite (SMAHC) structures has been recently implemented in the commercial finite element codes MSC.Nastran and ABAQUS. The model may be easily implemented in any code that has the capability for analysis of laminated composite structures with temperature dependent material properties. The model is also relatively easy to use and requires input of only fundamental engineering properties. A brief description of the model is presented, followed by discussion of implementation and usage in the commercial codes. Results are presented from static and dynamic analysis of SMAHC beams of two types; a beam clamped at each end and a cantilevered beam. Nonlinear static (post-buckling) and random response analyses are demonstrated for the first specimen. Static deflection (shape) control is demonstrated for the cantilevered beam. Approaches for modeling SMAHC material systems with embedded SMA in ribbon and small round wire product forms are demonstrated and compared. The results from the commercial codes are compared to those from a research code as validation of the commercial implementations; excellent correlation is achieved in all cases.

  13. Perceived Discrimination among African American Adolescents and Allostatic Load: A Longitudinal Analysis with Buffering Effects

    Science.gov (United States)

    Brody, Gene H.; Lei, Man-Kit; Chae, David H.; Yu, Tianyi; Kogan, Steven M.; Beach, Steven R. H.

    2014-01-01

    This study was designed to examine the prospective relations of perceived racial discrimination with allostatic load (AL), along with a possible buffer of the association. A sample of 331 African Americans in the rural South provided assessments of perceived discrimination from ages 16 to 18 years. When youth were 18 years, caregivers reported…

  14. Perceived Discrimination among African American Adolescents and Allostatic Load: A Longitudinal Analysis with Buffering Effects

    Science.gov (United States)

    Brody, Gene H.; Lei, Man-Kit; Chae, David H.; Yu, Tianyi; Kogan, Steven M.; Beach, Steven R. H.

    2014-01-01

    This study was designed to examine the prospective relations of perceived racial discrimination with allostatic load (AL), along with a possible buffer of the association. A sample of 331 African Americans in the rural South provided assessments of perceived discrimination from ages 16 to 18 years. When youth were 18 years, caregivers reported…

  15. Qualitative analysis of mental health service users' reported experiences of discrimination.

    Science.gov (United States)

    Hamilton, S; Pinfold, V; Cotney, J; Couperthwaite, L; Matthews, J; Barret, K; Warren, S; Corker, E; Rose, D; Thornicroft, G; Henderson, C

    2016-08-01

    To better understand mental health service users' experiences of stigma and discrimination in different settings. An annual telephone survey of people with a mental health diagnosis conducted to evaluate the Time to Change antistigma campaign in England. Of 985 people who participated in 2013, 84 took part in a qualitative interview which was audio recorded. Of these, 50 interviews were transcribed and thematically analysed to explore accounts of discrimination. We analysed common types of behaviour; motivations ascribed to the discriminators; expectations of what fair treatment would have been; and the impact of discrimination on participants. Discrimination was most common in five contexts: welfare benefits, mental health care, physical health care, family and friends. Participants often found it hard to assess whether a behaviour was discriminatory or not. Lack of support, whether by public services or by friends and family, was often experienced as discrimination, reflecting an expectation that positive behaviours and reasonable adjustments should be offered in response to mental health needs. The impact of discrimination across different settings was often perceived by participants as aggravating their mental health, and there is thus a need to treat discrimination as a health issue, not just a social justice issue. © 2016 The Authors. Acta Psychiatrica Scandinavica Published by John Wiley & Sons Ltd.

  16. A nutritional risk screening model for patients with liver cirrhosis established using discriminant analysis

    Directory of Open Access Journals (Sweden)

    ZHU Binghua

    2017-06-01

    Full Text Available ObjectiveTo establish a nutritional risk screening model for patients with liver cirrhosis using discriminant analysis. MethodsThe clinical data of 273 patients with liver cirrhosis who were admitted to Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine from August 2015 to March 2016 were collected. Body height, body weight, upper arm circumference, triceps skinfold thickness, subscapular skinfold thickness, and hand grip strength were measured and recorded, and then body mass index (BMI and upper arm muscle circumference were calculated. Laboratory markers including liver function parameters, renal function parameters, and vitamins were measured. The patients were asked to complete Nutritional Risk Screening 2002 and Malnutrition Universal Screening Tool (MUST, and a self-developed nutritional risk screening pathway was used for nutritional risk classification. Observation scales of the four diagnostic methods in traditional Chinese medicine were used to collect patients′ symptoms and signs. Continuous data were expressed as mean±SD (x±s; an analysis of variance was used for comparison between multiple groups, and the least significant difference t-test was used for further comparison between two groups. Discriminant analysis was used for model establishment, and cross validation was used for model verification. ResultsThe nutritional risk screening pathway for patients with liver cirrhosis was used for the screening of respondents, and there were 49 patients (17.95% in non-risk group, 49 (17.95% in possible-risk group, and 175 (64.10% in risk group. The distance criterion function was used to establish the nutritional risk screening model for patients with liver cirrhosis: D1=-11.885+0.310×BMI+0150×MAC+0.005×P-Alb-0.001×Vit B12+0.103×Vit D-0.89×ascites-0.404×weakness-0.560×hypochondriac pain+0035×dysphoria with feverish sensation (note: if a patient has ascites, weakness, hypochondriac pain

  17. Lacunarity analysis of spaceborne radar image texture for rock unit discrimination

    Science.gov (United States)

    Dong, Pinliang

    Fractal geometry has led to new understanding of many natural objects and phenomena. As a scale-dependent measure, lacunarity can be used to discriminate different textures that may not be differentiated by fractal dimension. Based on a differential box counting method and a gliding-box algorithm, a new lacunarity estimation method is developed for texture analysis of digital images, and a "Lacunarity Analysis" extension built for ArcView (ESRI) geographical information system software. To reveal the directional properties of textures, the directionality of lacunarity is also defined. The new lacunarity measure is evaluated through quantitative comparison with the Voss lacunarity, the binary lacunarity, the grey level cooccurrence matrix (GLCM) based texture measures (homogeneity, contrast, dissimilarity, entropy), the fractal dimension, and the min-max operator using Brodatz textures. The results from Brodatz textures suggest that the new lacunarity estimation method for grey-scale images provides more accurate texture measurements than the above-mentioned fractal-based and statistical texture measures. In comparison with the Voss lacunarity, the fractal dimension, and the GLCM-based texture measures, the new lacunarity measure is then applied to dual-band (L and C) and dual-polarization (HH and HV) Shuttle Imaging Radar (SIR-C), and C-band HH polarization Radarsat images of two imaging modes for rock unit discrimination in a study area between California and Arizona, USA. Using textural analysis of 36 SIR-C and Radarsat sub-images and classification accuracy assessment of the combined Landsat Thematic Mapper (TM) images and spaceborne radar textural feature images, it has been demonstrated that the new lacunarity measure outperformed other texture measures in comparison, and the L-band HH polarization SIR-C image provides more textural information of the rock units compared with the Radarsat and other SIR-C radar images used in this study. The study shows that

  18. Regularized discriminate analysis for breast mass detection on full field digital mammograms

    Science.gov (United States)

    Wei, Jun; Sahiner, Berkman; Zhang, Yiheng; Chan, Heang-Ping; Hadjiiski, Lubomir M.; Zhou, Chuan; Ge, Jun; Wu, Yi-Ta

    2006-03-01

    In computer-aided detection (CAD) applications, an important step is to design a classifier for the differentiation of the abnormal from the normal structures. We have previously developed a stepwise linear discriminant analysis (LDA) method with simplex optimization for this purpose. In this study, our goal was to investigate the performance of a regularized discriminant analysis (RDA) classifier in combination with a feature selection method for classification of the masses and normal tissues detected on full field digital mammograms (FFDM). The feature selection scheme combined a forward stepwise feature selection process and a backward stepwise feature elimination process to obtain the best feature subset. An RDA classifier and an LDA classifier in combination with this new feature selection method were compared to an LDA classifier with stepwise feature selection. A data set of 130 patients containing 260 mammograms with 130 biopsy-proven masses was used. All cases had two mammographic views. The true locations of the masses were identified by experienced radiologists. To evaluate the performance of the classifiers, we randomly divided the data set into two independent sets of approximately equal size for training and testing. The training and testing were performed using the 2-fold cross validation method. The detection performance of the CAD system was assessed by free response receiver operating characteristic (FROC) analysis. The average test FROC curve was obtained by averaging the FP rates at the same sensitivity along the two corresponding test FROC curves from the 2-fold cross validation. At the case-based sensitivities of 90%, 80% and 70% on the test set, our RDA classifier with the new feature selection scheme achieved an FP rate of 1.8, 1.1, and 0.6 FPs/image, respectively, compared to 2.1, 1.4, and 0.8 FPs/image with stepwise LDA with simplex optimization. Our results indicate that RDA in combination with the sequential forward inclusion

  19. Differential diagnosis of posterior fossa brain tumors: Multiple discriminant analysis of Tl-SPECT and FDG-PET.

    Science.gov (United States)

    Yamauchi, Moritaka; Okada, Tomohisa; Okada, Tsutomu; Yamamoto, Akira; Fushimi, Yasutaka; Arakawa, Yoshiki; Miyamoto, Susumu; Togashi, Kaori

    2017-08-01

    This study investigated the combined capability of thallium-201 (Tl)-SPECT and fluorine-18-fluoro-deoxy-glucose (FDG)-PET for differential diagnosis of posterior fossa brain tumors using multiple discriminant analysis.This retrospective study was conducted under approval of the institutional review board. In the hospital information system, 27 patients with posterior fossa intra-axial tumor between January 2009 and June 2015 were enrolled and grouped as the following 7 entities: low grade glioma (LGG) 6, anaplastic astrocytoma (AA) 2, glioblastoma (GBM) 3, medulloblastoma (MB) 3, hemangioblastoma (HB) 6, metastatic tumor (Mets) 3, and malignant lymphoma (ML) 4. Tl and FDG uptakes were measured at the tumors and control areas, and several indexes were derived. Using indexes selected by the stepwise method, discriminant analysis was conducted with leave-one-out cross-validation.The predicted accuracy for tumor classification was 70.4% at initial analysis and 55.6% at cross-validation to differentiate 7 tumor entities. HB, LGG, and ML were well-discriminated, but AA was located next to LGG. GBM, MB, and Mets largely overlapped and could not be well distinguished even applying multiple discriminant analysis. Correct classification in the original and cross-validation analyses was 44.4% and 33.3% for Tl-SPECT and 55.6% and 48.1% for FDG-PET.

  20. Improvement Distance Discriminant Analysis Method%改进的距离判别分析法

    Institute of Scientific and Technical Information of China (English)

    黄利文

    2011-01-01

    在不对判别变量进行处理的条件下,对传统的距离判别方法进行改进,提出一种新的判别方法,试图解决复杂球形数据的判别问题,以提高判别的正确率.通过实例表明,该方法的判别效果良好,能较好地处理复杂球形数据的判别问题.%This paper presents a new discriminant method through improving the traditional distance discriminant method and tries to improve the accuracy of discriminant problems of complex spherical data in condition of not reducing variables. The example shows that this method has good discriminant effect, and can effectively deal with the discriminant problems of complex spherical data.

  1. The Effectiveness of the Discriminant Analysis Models. Study Based on the Selected Polish Companies Quoted on the Warsaw Stock Exchange

    Directory of Open Access Journals (Sweden)

    Grzegorz Gołębiowski

    2008-07-01

    Full Text Available The article presents the result of research on en effectiveness of discriminant models on the example of selected Polish joint-stock companies which declared bankruptcy. Aside from general results describing an effectiveness of discriminant models on the base of the own research of the authors, a comparison of the received findings with others economists results of research was made. Moreover, an analysis how the relation of an enterprise with the economic situation impacts on an effectiveness of considered models concerning the forecast of a risk of bankruptcy was carried out.

  2. DISCRIMINATIVE ANALYSIS OF MORPHOLOGIC AND MOTORIC PARAMETER TO JUDO AND KARATE SPORTIEST BOYS

    Directory of Open Access Journals (Sweden)

    Lulzim Ibri

    2013-07-01

    Full Text Available In sample from 160 boys from secondary schools of Prizren 16-17 age, separated in two groups were implicated 18 tests, from them 10 test for valuation morphologic characteristic and 8 test, for valuation motoric abilities. Group (A is component from 80 judo athletes’ boys and group (B from 80 karate athletes’ boys. Purpose of this investigation is to verify changes between judo and karate athletes’ boys in morphologic characteristic and motoric abilities. The problem of investigation was to investigate if there are changes between judo and karate athletes’ boys in morphologic characteristic that represent longitudinal dimensionality, body measure and adipose tissue, and in motoric abilities (used is eurofit battery tests. For global analysis of dimension to some changes and variable system (which contribute in changes between judo and karate athletes’ boys were implicated t-test for small independent sample and, canonic discriminative analysis. The results of this study show that judo and karate athletes significantly differ among themselves in motoric abilities, judo athletes are better in the tests: long jump from place (LOJU, squeeze palm (SQPA and support the knuckle (SUKN, while the karate athletes are better in the tests: taping for hands (TAHE, reach sitting down position (RSDP and run there-hire 10x5 meters (R10x5M, but these changes were not noticed and morphological variables.

  3. Discriminant Context Information Analysis for Post-Ranking Person Re-Identification.

    Science.gov (United States)

    Garcia, Jorge; Martinel, Niki; Gardel, Alfredo; Bravo, Ignacio; Foresti, Gian Luca; Micheloni, Christian

    2017-01-16

    Existing approaches for person re-identification are mainly based on creating distinctive representations or on learning optimal metrics. The achieved results are then provided in form of a list of ranked matching persons. It often happens that the true match is not ranked first but it is in the first positions. This is mostly due to the visual ambiguities shared between the true match and other "similar" persons. At the current state, there is a lack of a study of such visual ambiguities which limit the re-identification performance within the first ranks. We believe that an analysis of the similar appearances of the first ranks can be helpful in detecting, hence removing, such visual ambiguities. We propose to achieve such a goal by introducing an unsupervised post-ranking framework. Once the initial ranking is available, content and context sets are extracted. Then, these are exploited to remove the visual ambiguities and to obtain the discriminant feature space which is finally exploited to compute the new ranking. An in-depth analysis of the performance achieved on three public benchmark datasets support our believes. For every dataset, the proposed method remarkably improves the first ranks results and outperforms state-of-the-art approaches.

  4. Enlightening discriminative network functional modules behind Principal Component Analysis separation in differential-omic science studies

    Science.gov (United States)

    Ciucci, Sara; Ge, Yan; Durán, Claudio; Palladini, Alessandra; Jiménez-Jiménez, Víctor; Martínez-Sánchez, Luisa María; Wang, Yuting; Sales, Susanne; Shevchenko, Andrej; Poser, Steven W.; Herbig, Maik; Otto, Oliver; Androutsellis-Theotokis, Andreas; Guck, Jochen; Gerl, Mathias J.; Cannistraci, Carlo Vittorio

    2017-01-01

    Omic science is rapidly growing and one of the most employed techniques to explore differential patterns in omic datasets is principal component analysis (PCA). However, a method to enlighten the network of omic features that mostly contribute to the sample separation obtained by PCA is missing. An alternative is to build correlation networks between univariately-selected significant omic features, but this neglects the multivariate unsupervised feature compression responsible for the PCA sample segregation. Biologists and medical researchers often prefer effective methods that offer an immediate interpretation to complicated algorithms that in principle promise an improvement but in practice are difficult to be applied and interpreted. Here we present PC-corr: a simple algorithm that associates to any PCA segregation a discriminative network of features. Such network can be inspected in search of functional modules useful in the definition of combinatorial and multiscale biomarkers from multifaceted omic data in systems and precision biomedicine. We offer proofs of PC-corr efficacy on lipidomic, metagenomic, developmental genomic, population genetic, cancer promoteromic and cancer stem-cell mechanomic data. Finally, PC-corr is a general functional network inference approach that can be easily adopted for big data exploration in computer science and analysis of complex systems in physics. PMID:28287094

  5. Ultrasonic analysis to discriminate bread dough of different types of flour

    Science.gov (United States)

    García-Álvarez, J.; Rosell, C. M.; García-Hernández, M. J.; Chávez, J. A.; Turó, A.; Salazar, J.

    2012-12-01

    Many varieties of bread are prepared using flour coming from wheat. However, there are other types of flours milled from rice, legumes and some fruits and vegetables that are also suitable for baking purposes, used alone or in combination with wheat flour. The type of flour employed strongly influences the dough consistency, which is a relevant property for determining the dough potential for breadmaking purposes. Traditional methods for dough testing are relatively expensive, time-consuming, off-line and often require skilled operators. In this work, ultrasonic analysis are performed in order to obtain acoustic properties of bread dough samples prepared using two different types of flour, wheat flour and rice flour. The dough acoustic properties can be related to its viscoelastic characteristics, which in turn determine the dough feasibility for baking. The main advantages of the ultrasonic dough testing can be, among others, its low cost, fast, hygienic and on-line performance. The obtained results point out the potential of the ultrasonic analysis to discriminate doughs of different types of flour.

  6. Lateral cephalometric analysis of mandibular morphology: discrimination among subjects with and without temporomandibular joint disk displacement and osteoarthrosis.

    Science.gov (United States)

    Bertram, S; Moriggl, A; Neunteufel, N; Rudisch, A; Emshoff, R

    2012-02-01

    To assess whether in patients with temporomandibular joint (TMJ) arthralgia cephalometric variables of mandibular morphology may discriminate among the magnetic resonance (MR) imaging-based TMJ groups of 'bilateral presence of disk displacement without reduction (DDwoR) and osteoarthrosis (OA)' and 'bilateral absence of bilateral DDwoR and OA'. Bilateral MR imaging of the TMJ was performed in 45 consecutive TMJ arthralgia patients to identify individuals with the specific structural characteristics of bilateral TMJ DDwoR associated with OA. Linear and angular cephalometric measurements were taken from lateral cephalograms to apply selected criteria of mandibular morphology. A discriminant function analysis was used to investigate how cephalometric parameters discriminate among the TMJ groups of 'bilateral presence of DDwoR with OA' and 'bilateral absence of DDwoR and OA'. Ramus height (Ar-Go) and effective mandibular length (Ar-Pog) produced a significant discriminant function that predicted TMJ group membership (P < 0·001). This function correctly classified 80·2% of original and cross-validated grouped cases. This study supports the concept that cephalometric variables of mandibular morphology discriminate among subjects with and without bilateral TMJ DDwoR and OA.

  7. Multivariate analysis of microarray data by principal component discriminant analysis: Prioritizing relevant transcripts linked to the degradation of different carbohydrates in Pseudomonas putida S12

    NARCIS (Netherlands)

    Werf, M.J. van der; Pieterse, B.; Luijk, N. van; Schuren, F.; Werff van der - Vat, B. van der; Overkamp, K.; Jellema, R.H.

    2006-01-01

    The value of the multivariate data analysis tools principal component analysis (PCA) and principal component discriminant analysis (PCDA) for prioritizing leads generated by microarrays was evaluated. To this end, Pseudomonas putida S12 was grown in independent triplicate fermentations on four

  8. Multivariate analysis of microarray data by principal component discriminant analysis: Prioritizing relevant transcripts linked to the degradation of different carbohydrates in Pseudomonas putida S12

    NARCIS (Netherlands)

    Werf, M.J. van der; Pieterse, B.; Luijk, N. van; Schuren, F.; Werff van der - Vat, B. van der; Overkamp, K.; Jellema, R.H.

    2006-01-01

    The value of the multivariate data analysis tools principal component analysis (PCA) and principal component discriminant analysis (PCDA) for prioritizing leads generated by microarrays was evaluated. To this end, Pseudomonas putida S12 was grown in independent triplicate fermentations on four diffe

  9. Identifying Plant Part Composition of Forest Logging Residue Using Infrared Spectral Data and Linear Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Gifty E. Acquah

    2016-08-01

    Full Text Available As new markets, technologies and economies evolve in the low carbon bioeconomy, forest logging residue, a largely untapped renewable resource will play a vital role. The feedstock can however be variable depending on plant species and plant part component. This heterogeneity can influence the physical, chemical and thermochemical properties of the material, and thus the final yield and quality of products. Although it is challenging to control compositional variability of a batch of feedstock, it is feasible to monitor this heterogeneity and make the necessary changes in process parameters. Such a system will be a first step towards optimization, quality assurance and cost-effectiveness of processes in the emerging biofuel/chemical industry. The objective of this study was therefore to qualitatively classify forest logging residue made up of different plant parts using both near infrared spectroscopy (NIRS and Fourier transform infrared spectroscopy (FTIRS together with linear discriminant analysis (LDA. Forest logging residue harvested from several Pinus taeda (loblolly pine plantations in Alabama, USA, were classified into three plant part components: clean wood, wood and bark and slash (i.e., limbs and foliage. Five-fold cross-validated linear discriminant functions had classification accuracies of over 96% for both NIRS and FTIRS based models. An extra factor/principal component (PC was however needed to achieve this in FTIRS modeling. Analysis of factor loadings of both NIR and FTIR spectra showed that, the statistically different amount of cellulose in the three plant part components of logging residue contributed to their initial separation. This study demonstrated that NIR or FTIR spectroscopy coupled with PCA and LDA has the potential to be used as a high throughput tool in classifying the plant part makeup of a batch of forest logging residue feedstock. Thus, NIR/FTIR could be employed as a tool to rapidly probe/monitor the variability

  10. User-interfaces for hybrid systems: Analysis and design through hybrid reachability

    Science.gov (United States)

    Oishi, Meeko Mitsuko Karen

    Hybrid systems combine discrete state dynamics, which model mode switching, with continuous state dynamics, which model the physical processes themselves. Applications of hybrid system theory to automated systems have traditionally assumed that the controller itself is an automaton which runs in parallel with the system under control. We model human interaction with hybrid systems, which involves the user; the automation's discrete mode-logic, and the underlying continuous dynamics of the physical system. Often in safety-critical systems, user-interfaces display a reduced set of information about the entire system, however must still provide adequate information and must not confuse the user. We present (1) a method of designing a discrete event system abstraction of the hybrid system, in order to verify or design user-interfaces for hybrid human-automation systems, and (2) the relationship between user-interfaces and discrete observability properties. Using a hybrid computational tool for reachability, we find the largest region in which the system can always remain---this is the safe region of operation. By implementing a controller which arises from this computation, we mathematically guarantee that this safe region is invariant. Assigning discrete states to the computed invariant regions, we create a discrete event system from this hybrid system with safety restrictions. This abstraction can then be used in existing interface verification and design methods. A user-interface, modeled as a discrete system, must, not only be reduced (extraneous information has been eliminated), but also "immediately observable". We derive conditions for immediate observability, in which the current state can be constructed from the current output and last occurring event. Based on finite state machine state-reduction techniques, we synthesize an output for remote user-interfaces which fulfills this property. Aircraft are prime examples of complex, safety-critical systems. In

  11. Computational analysis on plug-in hybrid electric motorcycle chassis

    Science.gov (United States)

    Teoh, S. J.; Bakar, R. A.; Gan, L. M.

    2013-12-01

    Plug-in hybrid electric motorcycle (PHEM) is an alternative to promote sustainability lower emissions. However, the PHEM overall system packaging is constrained by limited space in a motorcycle chassis. In this paper, a chassis applying the concept of a Chopper is analysed to apply in PHEM. The chassis 3dimensional (3D) modelling is built with CAD software. The PHEM power-train components and drive-train mechanisms are intergraded into the 3D modelling to ensure the chassis provides sufficient space. Besides that, a human dummy model is built into the 3D modelling to ensure the rider?s ergonomics and comfort. The chassis 3D model then undergoes stress-strain simulation. The simulation predicts the stress distribution, displacement and factor of safety (FOS). The data are used to identify the critical point, thus suggesting the chassis design is applicable or need to redesign/ modify to meet the require strength. Critical points mean highest stress which might cause the chassis to fail. This point occurs at the joints at triple tree and bracket rear absorber for a motorcycle chassis. As a conclusion, computational analysis predicts the stress distribution and guideline to develop a safe prototype chassis.

  12. Hybrid pairwise likelihood analysis of animal behavior experiments.

    Science.gov (United States)

    Cattelan, Manuela; Varin, Cristiano

    2013-12-01

    The study of the determinants of fights between animals is an important issue in understanding animal behavior. For this purpose, tournament experiments among a set of animals are often used by zoologists. The results of these tournament experiments are naturally analyzed by paired comparison models. Proper statistical analysis of these models is complicated by the presence of dependence between the outcomes of fights because the same animal is involved in different contests. This paper discusses two different model specifications to account for between-fights dependence. Models are fitted through the hybrid pairwise likelihood method that iterates between optimal estimating equations for the regression parameters and pairwise likelihood inference for the association parameters. This approach requires the specification of means and covariances only. For this reason, the method can be applied also when the computation of the joint distribution is difficult or inconvenient. The proposed methodology is investigated by simulation studies and applied to real data about adult male Cape Dwarf Chameleons. © 2013, The International Biometric Society.

  13. Performance analysis of switching based hybrid FSO/RF transmission

    KAUST Repository

    Usman, Muneer

    2014-09-01

    Hybrid free space optical (FSO)/ radio frequency (RF) systems have emerged as a promising solution for high data rate wireless back haul.We present and analyze a switching based transmission scheme for hybrid FSO/RF system. Specifically, either FSO or RF link will be active at a certain time instance, with FSO link enjoying a higher priority. Analytical expressions have been obtained for the outage probability, average bit error rate and ergodic capacity for the resulting system. Numerical examples are presented to compare the performance of the hybrid scheme with FSO only scenario.

  14. Discriminating micropathogen lineages and their reticulate evolution through graph theory-based network analysis: the case of Trypanosoma cruzi, the agent of Chagas disease.

    Science.gov (United States)

    Arnaud-Haond, Sophie; Moalic, Yann; Barnabé, Christian; Ayala, Francisco José; Tibayrenc, Michel

    2014-01-01

    Micropathogens (viruses, bacteria, fungi, parasitic protozoa) share a common trait, which is partial clonality, with wide variance in the respective influence of clonality and sexual recombination on the dynamics and evolution of taxa. The discrimination of distinct lineages and the reconstruction of their phylogenetic history are key information to infer their biomedical properties. However, the phylogenetic picture is often clouded by occasional events of recombination across divergent lineages, limiting the relevance of classical phylogenetic analysis and dichotomic trees. We have applied a network analysis based on graph theory to illustrate the relationships among genotypes of Trypanosoma cruzi, the parasitic protozoan responsible for Chagas disease, to identify major lineages and to unravel their past history of divergence and possible recombination events. At the scale of T. cruzi subspecific diversity, graph theory-based networks applied to 22 isoenzyme loci (262 distinct Multi-Locus-Enzyme-Electrophoresis -MLEE) and 19 microsatellite loci (66 Multi-Locus-Genotypes -MLG) fully confirms the high clustering of genotypes into major lineages or "near-clades". The release of the dichotomic constraint associated with phylogenetic reconstruction usually applied to Multilocus data allows identifying putative hybrids and their parental lineages. Reticulate topology suggests a slightly different history for some of the main "near-clades", and a possibly more complex origin for the putative hybrids than hitherto proposed. Finally the sub-network of the near-clade T. cruzi I (28 MLG) shows a clustering subdivision into three differentiated lesser near-clades ("Russian doll pattern"), which confirms the hypothesis recently proposed by other investigators. The present study broadens and clarifies the hypotheses previously obtained from classical markers on the same sets of data, which demonstrates the added value of this approach. This underlines the potential of graph

  15. Genetic analysis of agro-morphological traits in promising hybrids of sunflower (Helianthus annuus L.

    Directory of Open Access Journals (Sweden)

    Maryam GOLABADI

    2015-11-01

    Full Text Available The main objective underlying sunflower breeding programs is to develop high-yielding productive F1 hybrid cultivars. This study was conducted to investigate the genetic control of some agro-morphological traits of new sunflower F1 hybrids. For this purpose, fourteen inbred lines of sunflower were crossed with three male sterile inbred lines. Their hybrids (14 hybrids were then evaluated against three control cultivars. The data thus obtained were analyzed using the nested model (North Carolina Design І as a completely randomized block design (CRBD with four replications. Analysis of variance showed that the hybrids were significantly different in all the traits studied, except for head and stem diameters. From among the hybrids evaluated, Cms19 × Rn1-81 was found to have the highest seed yield and oil content. Cluster analysis classified the hybrids into four different groups. Genetic analysis showed that days to maturity, seed weight, and oil content (% were under the additive gene action. Breeding strategies based on selection could be suggested for the improvement of these traits. Head angle, head diameter, seed yield, and oil yield were under the dominance gene action; breeding based on hybridization methods is, therefore, proposed for these traits. Finally, both additive and dominance gene actions were observed to play important roles in the genetic control of plant height and stem diameter.

  16. Price Discrimination

    OpenAIRE

    Armstrong, Mark

    2008-01-01

    This paper surveys recent economic research on price discrimination, both in monopoly and oligopoly markets. Topics include static and dynamic forms of price discrimination, and both final and input markets are considered. Potential antitrust aspects of price discrimination are highlighted throughout the paper. The paper argues that the informational requirements to make accurate policy are very great, and with most forms of price discrimination a laissez-faire policy may be the best availabl...

  17. Epileptic Seizure Detection Using Lacunarity and Bayesian Linear Discriminant Analysis in Intracranial EEG.

    Science.gov (United States)

    Zhou, Weidong; Liu, Yinxia; Yuan, Qi; Li, Xueli

    2013-12-01

    Automatic seizure detection plays an important role in long-term epilepsy monitoring, and seizure detection algorithms have been intensively investigated over the years. This paper proposes an algorithm for seizure detection using lacunarity and Bayesian linear discriminant analysis (BLDA) in long-term intracranial EEG. Lacunarity is a measure of heterogeneity for a fractal. The proposed method first conducts wavelet decomposition on EEGs with five scales, and selects the wavelet coefficients at scale 3, 4, and 5 for subsequent processing. Effective features including lacunarity and fluctuation index are extracted from the selected three scales, and then sent into the BLDA for training and classification. Finally, postprocessing which includes smoothing, threshold judgment, multichannels integration, and collar technique is applied to obtain high sensitivity and low false detection rate. The proposed algorithm is evaluated on 289.14 h intracranial EEG data from 21-patient Freiburg dataset and yields a sensitivity of 96.25% and a false detection rate of 0.13/h with a mean delay time of 13.8 s.

  18. On-line Batch Process Monitoring and Diagnosing Based on Fisher Discriminant Analysis

    Institute of Scientific and Technical Information of China (English)

    ZHAO Xu; SHAO Hui-he

    2006-01-01

    A new on-line batch process monitoring and diagnosing approach based on Fisher discriminant analysis (FDA) was proposed. This method does not need to predict the future observations of variables, so it is more sensitive to fault detection and stronger implement for monitoring. In order to improve the monitoring performance,the variables trajectories of batch process are separated into several blocks. The key to the proposed approach for on-line monitoring is to calculate the distance of block data that project to low-dimension Fisher space between new batch and reference batch. Comparing the distance with the predefine threshold, it can be considered whether the batch process is normal or abnormal. Fault diagnosis is performed based on the weights in fault direction calculated by FDA. The proposed method was applied to the simulation model of fed-batch penicillin fermentation and the results were compared with those obtained using MPCA. The simulation results clearly show that the on-line monitoring method based on FDA is more efficient than the MPCA.

  19. PENDETEKSIAN KANKER PARU–PARU DENGAN MENGGUNAKAN TRANSFORMASI WAVELET DAN METODE LINEAR DISCRIMINANT ANALYSIS

    Directory of Open Access Journals (Sweden)

    Hanung Tyas Saksono

    2010-07-01

    Full Text Available Kanker merupakan pertumbuhan dan penyebaran sel-sel abnormal yang memiliki karakteristik yang khas. Kanker yang sudah menyebar dan tidak dapat terkontrol lagi, biasanya akan menyebabkan kematian. Kanker paru-paru lebih sering menyebabkan pria meninggal dibanding kanker lain, dimana yang sering menjadi penyebab kanker paru-paru adalah merokok. Cara yang digunakan untuk mendeteksi kanker paru-paru ialah melalui pemeriksaan hasil foto rontgen dada. Penelitian ini bertujuan untuk menghasilkan suatu sistem aplikasi yang dapat mendiagnosa citra paru-parudan mengklasifikasikan paru-paruke dalam tipe kanker, normal atau efusi serta menganalisa performansi sistem yang digunakan dalam proses pengenalan citra paru-paru. Proses pendeteksian diawali dengan pemrosesan awal pada citra paru-paru, proses ekstraksi ciri menggunakan Transformasi Wavelet, dan proses klasifikasi menggunakan Linear Discriminant Analysis (LDA. Pemrosesan awal dilakukan untuk membuang informasi yang tidak dibutuhkan dalam pengolahan citra. Proses ekstraksi ciri dilakukan dengan cara mengurangi dimensi citra paru- paru yang akan menjadi masukan pada proses pengenalan menggunakan LDA. Pada penelitian ini citra latih yang digunakan adalah 60 buah citra, yang terdiri dari 20 kelas citra kondisi normal, 20 kelas citra kondisi kanker, dan 20 kelas citra kondisi efusi. Citra uji yang akan digunakan juga berjumlah 60 buah citra, yang tediri dari 20 citra untuk masing-masing kelas. Akurasi yang dihasilkan sistem pada pendeteksian kanker paru-paru ini sebesar 100% untuk citra latih dan 95% untuk citra uji.

  20. Discriminating lava flows of different age within Nyamuragira's volcanic field using spectral mixture analysis

    Science.gov (United States)

    Li, Long; Canters, Frank; Solana, Carmen; Ma, Weiwei; Chen, Longqian; Kervyn, Matthieu

    2015-08-01

    In this study, linear spectral mixture analysis (LSMA) is used to characterize the spectral heterogeneity of lava flows from Nyamuragira volcano, Democratic Republic of Congo, where vegetation and lava are the two main land covers. In order to estimate fractions of vegetation and lava through satellite remote sensing, we made use of 30 m resolution Landsat Enhanced Thematic Mapper Plus (ETM+) and Advanced Land Imager (ALI) imagery. 2 m Pleiades data was used for validation. From the results, we conclude that (1) LSMA is capable of characterizing volcanic fields and discriminating between different types of lava surfaces; (2) three lava endmembers can be identified as lava of old, intermediate and young age, corresponding to different stages in lichen growth and chemical weathering; (3) a strong relationship is observed between vegetation fraction and lava age, where vegetation at Nyamuragira starts to significantly colonize lava flows ∼15 years after eruption and occupies over 50% of the lava surfaces ∼40 years after eruption. Our study demonstrates the capability of spectral unmixing to characterize lava surfaces and vegetation colonization over time, which is particularly useful for poorly known volcanoes or those not accessible for physical or political reasons.

  1. Discrimination of Anemonefish Species by PCR-RFLP Analysis of Mitochondrial Gene Fragments

    Directory of Open Access Journals (Sweden)

    Chuta Boonphakdee

    2008-01-01

    Full Text Available A means of discriminating among species of clown anemonefishes, based on restriction enzyme analysis of partial mitochondrial DNA sequences, was investigated. Mitochondrial 16S rRNA and cytochrome b genes from 6 species (7 strains of anemonefish (Premnas biculeatus, Amphiprion polymnus, A. sandaracinos, A. perideraion, A. ocellaris, A. ocellaris var. and A. percula were PCR-amplified. A 623-bp portion of 16S rRNA gene was obtained from different fishes using the same pair of primers. Further investigation of this 16S rRNA fragment, by restriction endonuclease digestion with BfuCI and RsaI, was not able to distinguish all fishes studied, but did yield 3 different digestion patterns. The first was specific to P. biculaetus, the sole member of the genus Premnas, while the remaining two separated the Amphiprion species into 2 groups: 1 A. polymnas, A. sandaracinos and A. perideraion, and 2 A. ocellaris, A. ocellaris var. and A. percula. In contrast to this, restriction endonuclease digestion of a 786-bp fragment of the cytochrome b gene with HinfI and RsaI, was able to differentiate different 7 anemonefishes. This utility marker is valuable for unambiguous species/strain identification of juvenile anemonefishes.

  2. Recognition for avian influenza virus proteins based on support vector machine and linear discriminant analysis

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Total 200 properties related to structural characteristics were employed to represent structures of 400 HA coded proteins of influenza virus as training samples. Some recognition models for HA proteins of avian influenza virus (AIV) were developed using support vector machine (SVM) and linear discriminant analysis (LDA). The results obtained from LDA are as follows: the identification accuracy (Ria) for training samples is 99.8% and Ria by leave one out cross validation is 99.5%. Both Ria of 99.8% for training samples and Ria of 99.3% by leave one out cross validation are obtained using SVM model, respectively. External 200 HA proteins of influenza virus were used to validate the external predictive power of the resulting model. The external Ria for them is 95.5% by LDA and 96.5% by SVM, respectively, which shows that HA proteins of AIVs are preferably recognized by SVM and LDA, and the performances by SVM are superior to those by LDA.

  3. Comparing discriminant analysis and neural network for the determination of sex using femur head measurements.

    Science.gov (United States)

    Alunni, Véronique; Jardin, Philippe du; Nogueira, Luisa; Buchet, Luc; Quatrehomme, Gérald

    2015-08-01

    The measurement of the femoral head is usually considered an interesting variable for the sex determination of skeletal remains. To date, there are few published reference measurements of the femoral head in a modern European population for the purpose of sex determination. In this study, 116 femurs from 58 individuals of the South of France (Nice Bone Collection, Nice, France) were studied. Three measurements of the femoral head were taken: the vertical head diameter (VHD), the transversal head diameter (THD) and the head circumference (HC). The results show that: (i) there is no statistical difference between the right and left femurs for each of the three measurements (VHD, THD and HC). Therefore we arbitrarily chose to use the measures from the right femurs (N=58) to pursue our experiments; (ii) the measurements of the femoral head are similar to those of contemporary American populations; (iii) the dimensions of the femoral head place the measurements of the French population somewhere between Germany or Croatia, and Spain; (iv) there is no significant secular trend (in contrast with the femoral neck diameter); (v) the femoral head measurement as a single variable is useful for sex determination: a 96.5% rate of accuracy was obtained using THD and HC measurements with the artificial neural network; and a 94.8% rate of accuracy using VHD, both with the discriminant analysis and the neural network.

  4. Analysing breast cancer microarrays from African Americans using shrinkage-based discriminant analysis

    Directory of Open Access Journals (Sweden)

    Pang Herbert

    2010-10-01

    Full Text Available Abstract Breast cancer tumours among African Americans are usually more aggressive than those found in Caucasian populations. African-American patients with breast cancer also have higher mortality rates than Caucasian women. A better understanding of the disease aetiology of these breast cancers can help to improve and develop new methods for cancer prevention, diagnosis and treatment. The main goal of this project was to identify genes that help differentiate between oestrogen receptor-positive and -negative samples among a small group of African-American patients with breast cancer. Breast cancer microarrays from one of the largest genomic consortiums were analysed using 13 African-American and 201 Caucasian samples with oestrogen receptor status. We used a shrinkage-based classification method to identify genes that were informative in discriminating between oestrogen receptor-positive and -negative samples. Subset analysis and permutation were performed to obtain a set of genes unique to the African-American population. We identified a set of 156 probe sets, which gave a misclassification rate of 0.16 in distinguishing between oestrogen receptor-positive and -negative patients. The biological relevance of our findings was explored through literature-mining techniques and pathway mapping. An independent dataset was used to validate our findings and we found that the top ten genes mapped onto this dataset gave a misclassification rate of 0.15. The described method allows us best to utilise the information available from small sample size microarray data in the context of ethnic minorities.

  5. Partial Least Square Discriminant Analysis Discovered a Dietary Pattern Inversely Associated with Nasopharyngeal Carcinoma Risk.

    Science.gov (United States)

    Lo, Yen-Li; Pan, Wen-Harn; Hsu, Wan-Lun; Chien, Yin-Chu; Chen, Jen-Yang; Hsu, Mow-Ming; Lou, Pei-Jen; Chen, I-How; Hildesheim, Allan; Chen, Chien-Jen

    2016-01-01

    Evidence on the association between dietary component, dietary pattern and nasopharyngeal carcinoma (NPC) is scarce. A major challenge is the high degree of correlation among dietary constituents. We aimed to identify dietary pattern associated with NPC and to illustrate the dose-response relationship between the identified dietary pattern scores and the risk of NPC. Taking advantage of a matched NPC case-control study, data from a total of 319 incident cases and 319 matched controls were analyzed. Dietary pattern was derived employing partial least square discriminant analysis (PLS-DA) performed on energy-adjusted food frequencies derived from a 66-item food-frequency questionnaire. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated with multiple conditional logistic regression models, linking pattern scores and NPC risk. A high score of the PLS-DA derived pattern was characterized by high intakes of fruits, milk, fresh fish, vegetables, tea, and eggs ordered by loading values. We observed that one unit increase in the scores was associated with a significantly lower risk of NPC (ORadj = 0.73, 95% CI = 0.60-0.88) after controlling for potential confounders. Similar results were observed among Epstein-Barr virus seropositive subjects. An NPC protective diet is indicated with more phytonutrient-rich plant foods (fruits, vegetables), milk, other protein-rich foods (in particular fresh fish and eggs), and tea. This information may be used to design potential dietary regimen for NPC prevention.

  6. Differentiation of Pueraria lobata and Pueraria thomsonii using partial least square discriminant analysis (PLS-DA).

    Science.gov (United States)

    Wong, Ka H; Razmovski-Naumovski, Valentina; Li, Kong M; Li, George Q; Chan, Kelvin

    2013-10-01

    The aims of the study were to differentiate Pueraria lobata from its related species Pueraria thomsonii and to examine the raw herbal material used in manufacturing kudzu root granules using partial least square discriminant analysis (PLS-DA). Sixty-four raw materials of P. lobata and P. thomsonii and kudzu root-labelled granules were analysed by ultra performance liquid chromatography. To differentiate P. lobata from P. thomsonii, PLS-DA models using the variables selected from the entire chromatograms, genetic algorithm (GA), successive projection algorithm (SPA), puerarin alone and six selected peaks were employed. The models constructed by GA and SPA demonstrated superior classification ability and lower model's complexity as compared to the model based on the entire chromatographic matrix, whilst the model constructed by the six selected peaks was comparable to the entire chromatographic model. The model established by puerarin alone showed inferior classification ability. In addition, the PLS-DA models constructed by the entire chromatographic matrix, GA, SPA and the six selected peaks showed that four brands out of seventeen granules were mislabelled as P. lobata. In conclusion, PLS-DA is a promising procedure for differentiating Pueraria species and determining raw material used in commercial products.

  7. Material decomposition with the multi-energy attenuation coefficient ratio by using a multiple discriminant analysis

    Science.gov (United States)

    Lee, Woo-Jin; Kang, Se-Ryong; Choi, Soon-Chul; Lee, Sam-Sun; Heo, Min-Suk; Huh, Kyung-Hoe; Yi, Won-Jin

    2016-07-01

    The objective of this study was to develop a spectral CT system using a photon counting detector and to decompose materials by applying a multiple discriminant analysis (MDA) to the energy-dependent attenuation coefficient ratios. We imaged cylindrical phantoms of Polymethyl methacrylate (PMMA) with four holes filled with calcium chloride, iodine, and gold nanoparticle contrast agents. The attenuation coefficients were measured via reconstructed multi-energy images, and the linear attenuation ratio was used for material identification. The MDA projection matrix, determined from training phantoms, was used to identify the four materials in the testing phantoms. For quantification purposes, the relationships between the attenuation coefficients at multiple energy bins and the concentrations were characterized by using the least-squares method for each material. The mean identification accuracy for each of the three materials were 0.94 ± 0.09 for iodine, 0.96 ± 0.07 for gold nanoparticles, and 0.92 ± 0.05 for calcium chloride. The mean quantification errors were 1.90 ± 1.58% for iodine, 3.85 ± 3.13% for gold nanoparticle, and 3.40 ± 2.62% for calcium chloride. The developed multi-energy CT system based on the photon-counting detector with MDA can precisely decompose the four materials.

  8. Recognition for avian influenza virus proteins based on support vector machine and linear discriminant analysis

    Institute of Scientific and Technical Information of China (English)

    LIANG GuiZhao; LIAO ChunYang; WU ShiRong; LI GenRong; HE Liu; GAO JianKun; Gan MengYu; LI DeJing; CHEN GuoPing; WANG GuiXue; LONG Sha; CHEN ZeCong; JING JuHua; ZHENG XiaoLin; ZENG Hui; ZHANG QiaoXia; ZHANG MengJun; YANG Qi; TIAN FeiFei; TONG JianBo; WANG JiaoNa; LIU YongHong; YANG ShanBin; LI Bo; QIU LiangJia; CAI ShaoXi; ZHAO Na; YANG Yan; SU XiaLi; SONG Jian; CHEN MeiXia; ZHANG XueJiao; SUN JiaYing; MEI Hu; LI JingWei; CHEN GuoHua; CHEN Gang; DENG Jie; PENG ChuanYou; ZHU WanPing; XU LuoNan; WU YuQuan; LIAO LiMin; LI Zhi; ZHOU Yuan; LI Jun; LU DaJun; SU QinLiang; HUANG ZhengHu; ZHOU Ping; LI ZhiLiang; YANG Li; ZHOU Peng; YANG ShengXi; SHU Mao

    2008-01-01

    Total 200 properties related to structural characteristics were employed to represent structures of 400 HA coded proteins of influenza virus as training samples.Some recognition models for HA proteins of avian influenza virus (AIV) were developed using support vector machine (SVM) and linear discriminant analysis (LDA).The results obtained from LDA are as follows: the identification accuracy (Ria) for training samples is 99.8% and Ria by leave one out cross validation is 99.5%.Both Ria of 99.8% for training samples and Ria of 99.3% by leave one out cross validation are obtained using SVM model, respectively.External 200 HA proteins of influenza virus were used to validate the external predictive power of the resulting model.The external Ria for them is 95.5% by LDA and 96.5% by SVM, respectively, which shows that HA proteins of AIVs are preferably recognized by SVM and LDA, and the performances by SVM are superior to those by LDA.

  9. A comparison of independent component analysis algorithms and measures to discriminate between EEG and artifact components.

    Science.gov (United States)

    Dharmaprani, Dhani; Nguyen, Hoang K; Lewis, Trent W; DeLosAngeles, Dylan; Willoughby, John O; Pope, Kenneth J

    2016-08-01

    Independent Component Analysis (ICA) is a powerful statistical tool capable of separating multivariate scalp electrical signals into their additive independent or source components, specifically EEG or electroencephalogram and artifacts. Although ICA is a widely accepted EEG signal processing technique, classification of the recovered independent components (ICs) is still flawed, as current practice still requires subjective human decisions. Here we build on the results from Fitzgibbon et al. [1] to compare three measures and three ICA algorithms. Using EEG data acquired during neuromuscular paralysis, we tested the ability of the measures (spectral slope, peripherality and spatial smoothness) and algorithms (FastICA, Infomax and JADE) to identify components containing EMG. Spatial smoothness showed differentiation between paralysis and pre-paralysis ICs comparable to spectral slope, whereas peripherality showed less differentiation. A combination of the measures showed better differentiation than any measure alone. Furthermore, FastICA provided the best discrimination between muscle-free and muscle-contaminated recordings in the shortest time, suggesting it may be the most suited to EEG applications of the considered algorithms. Spatial smoothness results suggest that a significant number of ICs are mixed, i.e. contain signals from more than one biological source, and so the development of an ICA algorithm that is optimised to produce ICs that are easily classifiable is warranted.

  10. The LDA beamformer: Optimal estimation of ERP source time series using linear discriminant analysis.

    Science.gov (United States)

    Treder, Matthias S; Porbadnigk, Anne K; Shahbazi Avarvand, Forooz; Müller, Klaus-Robert; Blankertz, Benjamin

    2016-04-01

    We introduce a novel beamforming approach for estimating event-related potential (ERP) source time series based on regularized linear discriminant analysis (LDA). The optimization problems in LDA and linearly-constrained minimum-variance (LCMV) beamformers are formally equivalent. The approaches differ in that, in LCMV beamformers, the spatial patterns are derived from a source model, whereas in an LDA beamformer the spatial patterns are derived directly from the data (i.e., the ERP peak). Using a formal proof and MEG simulations, we show that the LDA beamformer is robust to correlated sources and offers a higher signal-to-noise ratio than the LCMV beamformer and PCA. As an application, we use EEG data from an oddball experiment to show how the LDA beamformer can be harnessed to detect single-trial ERP latencies and estimate connectivity between ERP sources. Concluding, the LDA beamformer optimally reconstructs ERP sources by maximizing the ERP signal-to-noise ratio. Hence, it is a highly suited tool for analyzing ERP source time series, particularly in EEG/MEG studies wherein a source model is not available.

  11. Comparative genomic in situ hybridization analysis on the ...

    African Journals Online (AJOL)

    AJL

    2012-04-10

    Apr 10, 2012 ... different parents/ancestors/genomes in hybrid plants to be distinguished ... sequences in common between the two species. Therefore, cGISH ... genomic organization and genome evolution in plants. (Zoller et al., 2001).

  12. Synchronizability Analysis of Harmonious Unification Hybrid Preferential Model

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    The harmonious unification hybrid preferential model uses the dr ratio to adjust the proportion of deterministic preferential attachment and random preferential attachment, enriched the only deterministic preferential network model,

  13. SIMS: a hybrid method for rapid conformational analysis.

    Directory of Open Access Journals (Sweden)

    Bryant Gipson

    Full Text Available Proteins are at the root of many biological functions, often performing complex tasks as the result of large changes in their structure. Describing the exact details of these conformational changes, however, remains a central challenge for computational biology due the enormous computational requirements of the problem. This has engendered the development of a rich variety of useful methods designed to answer specific questions at different levels of spatial, temporal, and energetic resolution. These methods fall largely into two classes: physically accurate, but computationally demanding methods and fast, approximate methods. We introduce here a new hybrid modeling tool, the Structured Intuitive Move Selector (sims, designed to bridge the divide between these two classes, while allowing the benefits of both to be seamlessly integrated into a single framework. This is achieved by applying a modern motion planning algorithm, borrowed from the field of robotics, in tandem with a well-established protein modeling library. sims can combine precise energy calculations with approximate or specialized conformational sampling routines to produce rapid, yet accurate, analysis of the large-scale conformational variability of protein systems. Several key advancements are shown, including the abstract use of generically defined moves (conformational sampling methods and an expansive probabilistic conformational exploration. We present three example problems that sims is applied to and demonstrate a rapid solution for each. These include the automatic determination of "active" residues for the hinge-based system Cyanovirin-N, exploring conformational changes involving long-range coordinated motion between non-sequential residues in Ribose-Binding Protein, and the rapid discovery of a transient conformational state of Maltose-Binding Protein, previously only determined by Molecular Dynamics. For all cases we provide energetic validations using well

  14. Design and Analysis of the AlNiCo Hybrid Magnet in EMS Maglev Vehicles

    Directory of Open Access Journals (Sweden)

    Lv Chao

    2017-01-01

    Full Text Available In order to solve the problem of hybrid electromagnet lock orbit, we design a new type of AlNiCo-NdFeB hybrid levitation electromagnet. The theoretical analysis has be carried on and mathematical model is established for AlNiCo-NdFeB hybrid levitation electromagnet. Through two dimensional simulation, the electromagnetic characteristics of the suspended electromagnet are analyzed in the 3 typical operating conditions , which are in heavy load at gap 8mm, in full load at gap 16mm and in no-load at gap 3mm. And it’s compared with the traditional electromagnetic magnet and NdFeB hybrid electromagnet. Calculation and analysis show that the new hybrid levitation electromagnet can effectively solve the problems of the electromagnet lock orbit, at the same time, have a good dynamic performance and suspension regulation performance.

  15. Site-specific analysis of hybrid geothermal/fossil power plants

    Energy Technology Data Exchange (ETDEWEB)

    1977-06-01

    A preliminary economic analysis of a hybrid geothermal/coal power plant has been completed for four geothermal Resource areas: Roosevelt Hot Springs, Coso Hot Springs, East Mesa and Long Valley. A hybrid plant would be economically viable at Roosevelt Hot Springs and somewhat less so at Coso Hot Springs. East Mesa and Long Valley show no economic promise. A well-designed hybrid plant could use geothermal energy for boiler feedwater heating, auxiliary power, auxiliary heating, and cooling water. Construction and operation of a hybrid plant at either Roosevelt Hot Springs or Coso Hot Springs is recommended. Brown University provided the theoretical basis for the hybrid study. A modified version of the Lawrence Berkeley Livermore GEOTHM Program is the major analytical tool used in the analysis. The Intermountain Power Project is the reference all coal-fired plant. Costing methods followed recommendations issued by the Energy research and Development Administration.

  16. UV-visible microscope spectrophotometric polarization and dichroism with increased discrimination power in forensic analysis

    Science.gov (United States)

    Purcell, Dale Kevin

    Microanalysis of transfer (Trace) evidence is the application of a microscope and microscopical techniques for the collection, observation, documentation, examination, identification, and discrimination of micrometer sized particles or domains. Microscope spectrophotometry is the union of microscopy and spectroscopy for microanalysis. Analytical microspectroscopy is the science of studying the emission, reflection, transmission, and absorption of electromagnetic radiation to determine the structure or chemical composition of microscopic-size materials. Microscope spectrophotometry instrument designs have evolved from monochromatic illumination which transmitted through the microscope and sample and then is detected by a photometer detector (photomultiplier tube) to systems in which broad-band (white light) illumination falls incident upon a sample followed by a non-scanning grating spectrometer equipped with a solid-state multi-element detector. Most of these small modern spectrometers are configured with either silicon based charged-couple device detectors (200-950 nm) or InGaAs based diode array detectors (850-2300 nm) with computerized data acquisition and signal processing being common. A focus of this research was to evaluate the performance characteristics of various modern forensic (UV-Vis) microscope photometer systems as well as review early model instrumental designs. An important focus of this research was to efficiently measure ultraviolet-visible spectra of microscopically small specimens for classification, differentiation, and possibly individualization. The first stage of the project consisted of the preparation of microscope slides containing neutral density filter reference materials, molecular fluorescence reference materials, and dichroic reference materials. Upon completion of these standard slide preparations analysis began with measurements in order to evaluate figures of merit for comparison of the instruments investigated. The figures of

  17. Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images

    Directory of Open Access Journals (Sweden)

    Shengwen Guo

    2017-05-01

    Full Text Available Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI. Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely, gray matter volume, cortical thickness, surface area, and mean curvature, were explored for discriminative analysis among three groups including the stable MCI (sMCI, the converted MCI (cMCI, and the normal control (NC groups. In this study, 158 subjects (72 NC, 46 sMCI, and 40 cMCI were selected from the Alzheimer's Disease Neuroimaging Initiative. A sparse-constrained regression model based on the l2-1-norm was introduced to reduce the feature dimensionality and retrieve essential features for the discrimination of the three groups by using a support vector machine (SVM. An optimized strategy of feature addition based on the weight of each feature was adopted for the SVM classifier in order to achieve the best classification performance. The baseline cortical features combined with the longitudinal measurements for 2 years of follow-up data yielded prominent classification results. In particular, the cortical thickness produced a classification with 98.84% accuracy, 97.5% sensitivity, and 100% specificity for the sMCI–cMCI comparison; 92.37% accuracy, 84.78% sensitivity, and 97.22% specificity for the cMCI–NC comparison; and 93.75% accuracy, 92.5% sensitivity, and 94.44% specificity for the sMCI–NC comparison. The best performances obtained by the SVM classifier using the essential features were 5–40% more than those using all of the retained features. The feasibility of the cortical features for the recognition of anatomical patterns was certified; thus, the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and

  18. Discrimination and chemical phylogenetic study of seven species of Dendrobium using infrared spectroscopy combined with cluster analysis

    Science.gov (United States)

    Luo, Congpei; He, Tao; Chun, Ze

    2013-04-01

    Dendrobium is a commonly used and precious herb in Traditional Chinese Medicine. The high biodiversity of Dendrobium and the therapeutic needs require tools for the correct and fast discrimination of different Dendrobium species. This study investigates Fourier transform infrared spectroscopy followed by cluster analysis for discrimination and chemical phylogenetic study of seven Dendrobium species. Despite the general pattern of the IR spectra, different intensities, shapes, peak positions were found in the IR spectra of these samples, especially in the range of 1800-800 cm-1. The second derivative transformation and alcoholic extracting procedure obviously enlarged the tiny spectral differences among these samples. The results indicated each Dendrobium species had a characteristic IR spectra profile, which could be used to discriminate them. The similarity coefficients among the samples were analyzed based on their second derivative IR spectra, which ranged from 0.7632 to 0.9700, among the seven Dendrobium species, and from 0.5163 to 0.9615, among the ethanol extracts. A dendrogram was constructed based on cluster analysis the IR spectra for studying the chemical phylogenetic relationships among the samples. The results indicated that D. denneanum and D. crepidatum could be the alternative resources to substitute D. chrysotoxum, D. officinale and D. nobile which were officially recorded in Chinese Pharmacopoeia. In conclusion, with the advantages of high resolution, speediness and convenience, the experimental approach can successfully discriminate and construct the chemical phylogenetic relationships of the seven Dendrobium species.

  19. Discriminant analysis for characterization of hydrochemistry of two mountain river basins of contrasting climates in the southern Western Ghats, India.

    Science.gov (United States)

    Thomas, Jobin; Joseph, Sabu; Thrivikramji, K P

    2015-06-01

    Discriminant analysis (DA) was performed on river hydrochemistry data for three seasons (i.e., monsoon (MON), post-monsoon (POM), and pre-monsoon (PRM)) to examine the spatio-temporal hydrochemical variability of two mountain river basins (Muthirapuzha River Basin (MRB) and Pambar River Basin (PRB)) of the southern Western Ghats, India. Although the river basins drain tropical mountainous terrain, climate and degree of anthropogenic disturbances show significant differences (i.e., humid, more disturbed MRB vs semiarid, less disturbed PRB). In MRB, TDS, Na(+), pH, Mg(2+), and K(+) are the attributes responsible for significant hydrochemical variations between the seasons, while Cl(-), TH, and Na(+) are the predictors in PRB. The temporal discriminant models imply the importance of rainfall pattern, relative contribution of groundwater toward stream discharge and farming activities in hydrochemistry between the seasons. Inclusion of hydrochemical attributes (in the temporal discriminant functions) that can be derived from both natural and anthropogenic sources suggests that ionic enrichment strongly depends on the seasons, and is mainly due to the variability in the intensity of anthropogenic activities as well as fluctuations in river discharge. In spatial discriminant models, Cl(-) is the only variable responsible for hydrochemical variations between the basins (during MON), whereas Si discriminates during POM and PRM, implying the role of atmospheric supply, anthropogenic modifications as well as intensity of weathering. In the spatial discrimination models, misclassification of hydrochemistry data between MRB and PRB can be attributed to the overlapping effect of humid climate of MRB extending toward the upstream of (semiarid) PRB. This study underscores the versatility of DA in deciphering the significance of climatic controls on hydrochemical composition of tropical mountain rivers.

  20. Evaluation of new NS maize hybrids using biplot analysis

    Directory of Open Access Journals (Sweden)

    Stojaković Milisav

    2012-01-01

    Full Text Available The study analyzed two-year results of a testing of 20 new maize hybrids from FAO MG 600 as compared to a standard. Data on the hybrids NS6683, NS6686, NS281633, and NS396432 are discussed in the paper in greater detail. In order to study grain yield, grain moisture, root and stalk lodging, and resistance to pests and diseases, field trials using a RCB design with four replicates were conducted in six locations in 2009 and five locations in 2010. The results were presented in the form of GGE biplots in order to rank hybrids relative to the standard while taking into account the genotype x environment interaction and to identify the highest-yielding genotypes in different environments. It was determined that the new NS hybrids had higher grain yield than the standard by 0.883 to 1.720 tha-1, lower grain moisture by 0.85 to 2.54%, better tolerance to root and stalk lodging, and pest and disease resistance on a par to the standard. The study identified so-called ideal locations for particular hybrids, which may be of use when determining which areas the hybrids are best suited for.

  1. Patterns of Discrimination, Grievances and Political Activity Among Europe's Roma: A Cross-Sectional Analysis

    Directory of Open Access Journals (Sweden)

    Jonathan Fox

    2001-01-01

    Full Text Available The purpose of this study is to analyse in a large-n cross-sectional format the patterns of discrimination, grievances and political activity among European Roma (Gypsies using data from the Minority at Risk project. The model tested here is a two-step model positing that discrimination leads to grievance formation which in turn leads to protest and rebellion. The results show that the Roma, in general, conform to this model but differ in some important specifics.

  2. Coarse-Grained Multifractality Analysis Based on Structure Function Measurements to Discriminate Healthy from Distressed Foetuses

    Directory of Open Access Journals (Sweden)

    Souad Oudjemia

    2013-01-01

    Full Text Available This paper proposes a combined coarse-grained multifractal method to discriminate between distressed and normal foetuses. The coarse-graining operation was performed by means of a coarse-grained procedure and the multifractal operation was based on a structure function. The proposed method was evaluated by one hundred recordings including eighty normal foetuses and twenty distressed foetuses. We found that it was possible to discriminate between distressed and normal foetuses using the Hurst exponent, singularity, and Holder spectra.

  3. Evaluation of sensory panels of consumers of specialty coffee beverages using the boosting method in discriminant analysis

    Directory of Open Access Journals (Sweden)

    Gilberto Rodrigues Liska

    2015-12-01

    Full Text Available Automatic classification methods have been widely used in numerous situations and the boosting method has become known for use of a classification algorithm, which considers a set of training data and, from that set, constructs a classifier with reweighted versions of the training set. Given this characteristic, the aim of this study is to assess a sensory experiment related to acceptance tests with specialty coffees, with reference to both trained and untrained consumer groups. For the consumer group, four sensory characteristics were evaluated, such as aroma, body, sweetness, and final score, attributed to four types of specialty coffees. In order to obtain a classification rule that discriminates trained and untrained tasters, we used the conventional Fisher’s Linear Discriminant Analysis (LDA and discriminant analysis via boosting algorithm (AdaBoost. The criteria used in the comparison of the two approaches were sensitivity, specificity, false positive rate, false negative rate, and accuracy of classification methods. Additionally, to evaluate the performance of the classifiers, the success rates and error rates were obtained by Monte Carlo simulation, considering 100 replicas of a random partition of 70% for the training set, and the remaining for the test set. It was concluded that the boosting method applied to discriminant analysis yielded a higher sensitivity rate in regard to the trained panel, at a value of 80.63% and, hence, reduction in the rate of false negatives, at 19.37%. Thus, the boosting method may be used as a means of improving the LDA classifier for discrimination of trained tasters.

  4. In Vitro Cell Death Discrimination and Screening Method by Simple and Cost-Effective Viability Analysis.

    Science.gov (United States)

    Helm, Katharina; Beyreis, Marlena; Mayr, Christian; Ritter, Markus; Jakab, Martin; Kiesslich, Tobias; Plaetzer, Kristjan

    2017-01-01

    For in vitro cytotoxicity testing, discrimination of apoptosis and necrosis represents valuable information. Viability analysis performed at two different time points post treatment could serve such a purpose because the dynamics of metabolic activity of apoptotic and necrotic cells is different, i.e. a more rapid decline of cellular metabolism during necrosis whereas cellular metabolism is maintained during the entire execution phase of apoptosis. This study describes a straightforward approach to distinguish apoptosis and necrosis. A431 human epidermoid carcinoma cells were treated with different concentrations/doses of actinomycin D (Act-D), 4,5,6,7-tetrabromo-2-azabenzimidazole (TBB), Ro 31-8220, H2O2 and photodynamic treatment (PDT). The resazurin viability signal was recorded at 2 and 24 hrs post treatment. Apoptosis and necrosis were verified by measuring caspase 3/7 and membrane integrity. Calculation of the difference curve between the 2 and 24 hrs resazurin signals yields the following information: a positive difference signal indicates apoptosis (i.e. high metabolic activity at early time points and low signal at 24 hrs post treatment) while an early reduction of the viability signal indicates necrosis. For all treatments, this dose-dependent sequence of cellular responses could be confirmed by independent assays. Simple and cost-effective viability analysis provides reliable information about the dose ranges of a cytotoxic agent where apoptosis or necrosis occurs. This may serve as a starting point for further in-depth characterisation of cytotoxic treatments. © 2017 The Author(s)Published by S. Karger AG, Basel.

  5. Methylation Linear Discriminant Analysis (MLDA for identifying differentially methylated CpG islands

    Directory of Open Access Journals (Sweden)

    Vass J Keith

    2008-08-01

    Full Text Available Abstract Background Hypermethylation of promoter CpG islands is strongly correlated to transcriptional gene silencing and epigenetic maintenance of the silenced state. As well as its role in tumor development, CpG island methylation contributes to the acquisition of resistance to chemotherapy. Differential Methylation Hybridisation (DMH is one technique used for genome-wide DNA methylation analysis. The study of such microarray data sets should ideally account for the specific biological features of DNA methylation and the non-symmetrical distribution of the ratios of unmethylated and methylated sequences hybridised on the array. We have therefore developed a novel algorithm tailored to this type of data, Methylation Linear Discriminant Analysis (MLDA. Results MLDA was programmed in R (version 2.7.0 and the package is available at CRAN 1. This approach utilizes linear regression models of non-normalised hybridisation data to define methylation status. Log-transformed signal intensities of unmethylated controls on the microarray are used as a reference. The signal intensities of DNA samples digested with methylation sensitive restriction enzymes and mock digested are then transformed to the likelihood of a locus being methylated using this reference. We tested the ability of MLDA to identify loci differentially methylated as analysed by DMH between cisplatin sensitive and resistant ovarian cancer cell lines. MLDA identified 115 differentially methylated loci and 23 out of 26 of these loci have been independently validated by Methylation Specific PCR and/or bisulphite pyrosequencing. Conclusion MLDA has advantages for analyzing methylation data from CpG island microarrays, since there is a clear rational for the definition of methylation status, it uses DMH data without between-group normalisation and is less influenced by cross-hybridisation of loci. The MLDA algorithm successfully identified differentially methylated loci between two classes of

  6. Hybrid-Electric and All-Electric Rotorcraft Analysis and Tool Development Project

    Data.gov (United States)

    National Aeronautics and Space Administration — During this Phase I effort ESAero will draw upon its knowledge of hybrid-electric propulsion system design and analysis for fixed wing aircraft to investigate the...

  7. A generalized hybrid transfinite element computational approach for nonlinear/linear unified thermal/structural analysis

    Science.gov (United States)

    Tamma, Kumar K.; Railkar, Sudhir B.

    1987-01-01

    The present paper describes the development of a new hybrid computational approach for applicability for nonlinear/linear thermal structural analysis. The proposed transfinite element approach is a hybrid scheme as it combines the modeling versatility of contemporary finite elements in conjunction with transform methods and the classical Bubnov-Galerkin schemes. Applicability of the proposed formulations for nonlinear analysis is also developed. Several test cases are presented to include nonlinear/linear unified thermal-stress and thermal-stress wave propagations. Comparative results validate the fundamental capablities of the proposed hybrid transfinite element methodology.

  8. Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid FMM-CART model.

    Science.gov (United States)

    Seera, Manjeevan; Lim, Chee Peng; Ishak, Dahaman; Singh, Harapajan

    2012-01-01

    In this paper, a novel approach to detect and classify comprehensive fault conditions of induction motors using a hybrid fuzzy min-max (FMM) neural network and classification and regression tree (CART) is proposed. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. A series of real experiments is conducted, whereby the motor current signature analysis method is applied to form a database comprising stator current signatures under different motor conditions. The signal harmonics from the power spectral density are extracted as discriminative input features for fault detection and classification with FMM-CART. A comprehensive list of induction motor fault conditions, viz., broken rotor bars, unbalanced voltages, stator winding faults, and eccentricity problems, has been successfully classified using FMM-CART with good accuracy rates. The results are comparable, if not better, than those reported in the literature. Useful explanatory rules in the form of a decision tree are also elicited from FMM-CART to analyze and understand different fault conditions of induction motors.

  9. Structural Discrimination

    DEFF Research Database (Denmark)

    Thorsen, Mira Skadegård

    In this article, I discuss structural discrimination, an underrepresented area of study in Danish discrimination and intercultural research. It is defined here as discursive and constitutive, and presented as a central element of my analytical approach. This notion is employed in the with which...... to understand and identify aspects of power and asymmetry in communication and interactions. With this as a defining term, I address how exclusion and discrimination exist, while also being indiscernible, within widely accepted societal norms. I introduce the concepts of microdiscrimination and benevolent...... discrimination as two ways of articulating particular, opaque forms of racial discrimination that occur in everyday Danish (and other) contexts, and have therefore become normalized. I present and discuss discrimination as it surfaces in data from my empirical studies of discrimination in Danish contexts...

  10. In situ hybridization analysis of isodicentric X-chromosomes with short arm fusion

    DEFF Research Database (Denmark)

    Koch, J E; Kølvraa, S; Hertz, Jens Michael;

    1990-01-01

    We present here an alternative approach to the study of mosaic cell lines containing dicentric chromosomes. The approach is based on chromosome-specific non-radioactive in situ hybridization with centromere (alpha satellite DNA) probes. The hybridization analysis may be used as an alternative...... it for the analysis of two cases of isodicentric X-chromosomes. The approach is expected to be generally applicable, so that it may be applied to the scoring of other types of chromosomal mosaicism as well....

  11. Parallel Hybrid Gas-Electric Geared Turbofan Engine Conceptual Design and Benefits Analysis

    Science.gov (United States)

    Lents, Charles; Hardin, Larry; Rheaume, Jonathan; Kohlman, Lee

    2016-01-01

    The conceptual design of a parallel gas-electric hybrid propulsion system for a conventional single aisle twin engine tube and wing vehicle has been developed. The study baseline vehicle and engine technology are discussed, followed by results of the hybrid propulsion system sizing and performance analysis. The weights analysis for the electric energy storage & conversion system and thermal management system is described. Finally, the potential system benefits are assessed.

  12. Functional MRI Representational Similarity Analysis Reveals a Dissociation between Discriminative and Relative Location Information in the Human Visual System

    Directory of Open Access Journals (Sweden)

    Zvi N Roth

    2016-03-01

    Full Text Available Neural responses in visual cortex are governed by a topographic mapping from retinal locations to cortical responses. Moreover, at the voxel population level early visual cortex (EVC activity enables accurate decoding of stimuli locations. However, in many cases information enabling one to discriminate between locations (i.e. discriminative information may be less relevant than information regarding the relative location of two objects (i.e. relative information. For example, when planning to grab a cup, determining whether the cup is located at the same retinal location as the hand is hardly relevant, whereas the location of the cup relative to the hand is crucial for performing the action.We have previously used multivariate pattern analysis techniques to measure discriminative location information, and found the highest levels in early visual cortex, in line with other studies. Here we show, using representational similarity analysis, that availability of discriminative information in fMRI activation patterns does not entail availability of relative information. Specifically, we find that relative location information can be reliably extracted from activity patterns in posterior intraparietal sulcus (pIPS, but not from EVC, where we find the spatial representation to be warped.We further show that this variability in relative information levels between regions can be explained by a computational model based on an array of receptive fields. Moreover, when the model’s receptive fields are extended to include inhibitory surround regions, the model can account for the spatial warping in EVC.These results demonstrate how size and shape properties of receptive fields in human visual cortex contribute to the transformation of discriminative spatial representation into relative spatial representation along the visual stream.

  13. Functional MRI Representational Similarity Analysis Reveals a Dissociation between Discriminative and Relative Location Information in the Human Visual System.

    Science.gov (United States)

    Roth, Zvi N

    2016-01-01

    Neural responses in visual cortex are governed by a topographic mapping from retinal locations to cortical responses. Moreover, at the voxel population level early visual cortex (EVC) activity enables accurate decoding of stimuli locations. However, in many cases information enabling one to discriminate between locations (i.e., discriminative information) may be less relevant than information regarding the relative location of two objects (i.e., relative information). For example, when planning to grab a cup, determining whether the cup is located at the same retinal location as the hand is hardly relevant, whereas the location of the cup relative to the hand is crucial for performing the action. We have previously used multivariate pattern analysis techniques to measure discriminative location information, and found the highest levels in EVC, in line with other studies. Here we show, using representational similarity analysis, that availability of discriminative information in fMRI activation patterns does not entail availability of relative information. Specifically, we find that relative location information can be reliably extracted from activity patterns in posterior intraparietal sulcus (pIPS), but not from EVC, where we find the spatial representation to be warped. We further show that this variability in relative information levels between regions can be explained by a computational model based on an array of receptive fields. Moreover, when the model's receptive fields are extended to include inhibitory surround regions, the model can account for the spatial warping in EVC. These results demonstrate how size and shape properties of receptive fields in human visual cortex contribute to the transformation of discriminative spatial representations into relative spatial representations along the visual stream.

  14. Functional MRI Representational Similarity Analysis Reveals a Dissociation between Discriminative and Relative Location Information in the Human Visual System

    Science.gov (United States)

    Roth, Zvi N.

    2016-01-01

    Neural responses in visual cortex are governed by a topographic mapping from retinal locations to cortical responses. Moreover, at the voxel population level early visual cortex (EVC) activity enables accurate decoding of stimuli locations. However, in many cases information enabling one to discriminate between locations (i.e., discriminative information) may be less relevant than information regarding the relative location of two objects (i.e., relative information). For example, when planning to grab a cup, determining whether the cup is located at the same retinal location as the hand is hardly relevant, whereas the location of the cup relative to the hand is crucial for performing the action. We have previously used multivariate pattern analysis techniques to measure discriminative location information, and found the highest levels in EVC, in line with other studies. Here we show, using representational similarity analysis, that availability of discriminative information in fMRI activation patterns does not entail availability of relative information. Specifically, we find that relative location information can be reliably extracted from activity patterns in posterior intraparietal sulcus (pIPS), but not from EVC, where we find the spatial representation to be warped. We further show that this variability in relative information levels between regions can be explained by a computational model based on an array of receptive fields. Moreover, when the model's receptive fields are extended to include inhibitory surround regions, the model can account for the spatial warping in EVC. These results demonstrate how size and shape properties of receptive fields in human visual cortex contribute to the transformation of discriminative spatial representations into relative spatial representations along the visual stream. PMID:27242455

  15. Application of Discriminant Analysis for Studying the Source Rock Potential of Probable Formations in the Lorestan Basin, Iran

    Directory of Open Access Journals (Sweden)

    Amir Negahdari

    2014-06-01

    Full Text Available Understanding the performance and role of each formation in a petroleum play is crucial for the efficient and precise exploration and exploitation of trapped hydrocarbons in a sedimentary basin. The Lorestan basin is one of the most important hydrocarbon basins of Iran, and it includes various oil-prone potential source rocks and reservoir rocks. Previous geochemical studies of the basin were not accurate and there remain various uncertainties about the potential of the probable source rocks of the basin. In the present research, the geochemical characteristics of four probable source rocks of the Lorestan basin are studied using Rock-Eval pyrolysis and discriminant analysis. In achieving this goal, several discriminant functions are defined to evaluate the discriminant factor for the division of samples into two groups. The function with the highest discriminant factor was selected for the classification of probable source rocks into two groups: weak and strong. Among the studied formations, Garau and Pabdeh had the richest and poorest source rocks of the Lorestan basin, respectively. The comparison of the obtained results with the previous literature shows that the proposed model is more reliable for the recognition of the richness of source rock in the area.

  16. GeoNeoLogical novel and other hybrimedia experiments: or how to use hybrid methods such as Hybrid Discourse Analysis (HDA within a knowledge base

    Directory of Open Access Journals (Sweden)

    Andrade, Pedro

    2016-07-01

    Full Text Available In this essay I will present some results of the project Public Communication of Art, which developed a seminal theory and methodology intended to cope with hybridity and new media literacy in our globalized and inter/transcultural world. Some of the methods used blend vision with touch and are called ‘hybrid methods’ or ‘hybrimethods’. Examples of these are, for instance, a Multitouch Interactive Table, a Multitouch Questionnaire, Trichotomies Game and GeoNeoLogic Novel, this last one being a hybrid novel activated by fusion of vision, touch and GPS coordinates. Another hybrimethod is a sort of discursive analysis, named Hybrid Discourse Analysis (HDA, which uses ‘semantic-logical networks’ organized by concepts and ‘relation-concepts’. HDA is here articulated with Critical Sociology and applied to the analysis of a text on Magic Realism, which is also a hybrid genre within the social field of literature.

  17. Racial/ethnic differences in responses to the everyday discrimination scale: a differential item functioning analysis.

    Science.gov (United States)

    Lewis, Tené T; Yang, Frances M; Jacobs, Elizabeth A; Fitchett, George

    2012-03-01

    The authors examined the impact of race/ethnicity on responses to the Everyday Discrimination Scale, one of the most widely used discrimination scales in epidemiologic and public health research. Participants were 3,295 middle-aged US women (African-American, Caucasian, Chinese, Hispanic, and Japanese) from the Study of Women's Health Across the Nation (SWAN) baseline examination (1996-1997). Multiple-indicator, multiple-cause models were used to examine differential item functioning (DIF) on the Everyday Discrimination Scale by race/ethnicity. After adjustment for age, education, and language of interview, meaningful DIF was observed for 3 (out of 10) items: "receiving poorer service in restaurants or stores," "being treated as if you are dishonest," and "being treated with less courtesy than other people" (all P's discrimination differed slightly for women of different racial/ethnic groups, with certain "public" experiences appearing to have more salience for African-American and Chinese women and "dishonesty" having more salience for racial/ethnic minority women overall. "Courtesy" appeared to have more salience for Hispanic women only in comparison with African-American women. Findings suggest that the Everyday Discrimination Scale could potentially be used across racial/ethnic groups as originally intended. However, researchers should use caution with items that demonstrated DIF.

  18. Racial discrimination mediates race differences in sleep problems: A longitudinal analysis.

    Science.gov (United States)

    Fuller-Rowell, Thomas E; Curtis, David S; El-Sheikh, Mona; Duke, Adrienne M; Ryff, Carol D; Zgierska, Aleksandra E

    2017-04-01

    To examine changes in sleep problems over a 1.5-year period among Black or African American (AA) and White or European American (EA) college students and to consider the role of racial discrimination as a mediator of race differences in sleep problems over time. Students attending a large, predominantly White university (N = 133, 41% AA, 57% female, mean age = 18.8, SD = .90) reported on habitual sleep characteristics and experiences of racial discrimination at baseline and follow-up assessments. A latent variable for sleep problems was assessed from reports of sleep latency, duration, efficiency, and quality. Longitudinal models were used to examine race differences in sleep problems over time and the mediating role of perceived discrimination. Covariates included age, gender, parent education, parent income, body mass index, self-rated physical health, and depressive symptoms. Each of the individual sleep measures was also examined separately, and sensitivity analyses were conducted using alternative formulations of the sleep problems measure. AAs had greater increases in sleep problems than EAs. Perceived discrimination was also associated with increases in sleep problems over time and mediated racial disparities in sleep. This pattern of findings was similar when each of the sleep indicators was considered separately and held with alternative sleep problems measures. The findings highlight the importance of racial disparities in sleep across the college years and suggest that experiences of discrimination contribute to group disparities. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  19. Multi-channel Hybrid Access Femtocells: A Stochastic Geometric Analysis

    CERN Document Server

    Zhong, Yi

    2011-01-01

    For two-tier networks consisting of macrocells and femtocells, the channel access mechanism can be configured to be open access, closed access, or hybrid access. Hybrid access arises as a compromise between open and closed access mechanisms, in which a fraction of available spectrum resource is shared to nonsubscribers while the remaining reserved for subscribers. This paper focuses on a hybrid access mechanism for multi-channel femtocells which employ orthogonal spectrum access schemes. Considering a randomized channel assignment strategy, we analyze the performance in the downlink. Using stochastic geometry as technical tools, we derive the distributions of signal-to-interference-plus-noise ratios, and mean achievable rates, of both nonsubscribers and subscribers. The established expressions are amenable to numerical evaluation, and shed key insights into the performance tradeoff between subscribers and nonsubscribers. The analytical results are corroborated by numerical simulations.

  20. EVOLUTION OF NEUROENDOCRINE CELL POPULATION AND PEPTIDERGIC INNERVATION, ASSESSED BY DISCRIMINANT ANALYSIS, DURING POSTNATAL DEVELOPMENT OF THE RAT PROSTATE

    Directory of Open Access Journals (Sweden)

    Rosario Rodríguez

    2011-05-01

    Full Text Available Serotonin immunoreactive neuroendocrine cells and peptidergic nerves (NPY and VIP could have a role in prostate growth and function. In the present study, rats grouped by stages of postnatal development (prepubertal, pubertal, young and aged adults were employed in order to ascertain whether age causes changes in the number of serotoninergic neuroendocrine cells and the length of NPY and VIP fibres. Discriminant analysis was performed in order to ascertain the classificatory power of stereologic variables (absolute and relative measurements of cell number and fibre length on age groups. The following conclusions were drawn: a discriminant analysis confirms the androgen-dependence of both neuroendocrine cells and NPYVIP innervation during the postnatal development of the rat prostate; b periglandular innervation has more relevance than interglandular innervation in classifying the rats in age groups; and c peptidergic nerves from ventral, ampullar and periductal regions were more age-dependent than nerves from the dorso-lateral region.

  1. Early discrimination of nasopharyngeal carcinoma based on tissue deoxyribose nucleic acid surface-enhanced Raman spectroscopy analysis

    Science.gov (United States)

    Qiu, Sufang; Li, Chao; Lin, Jinyong; Xu, Yuanji; Lu, Jun; Huang, Qingting; Zou, Changyan; Chen, Chao; Xiao, Nanyang; Lin, Duo; Chen, Rong; Pan, Jianji; Feng, Shangyuan

    2016-12-01

    Surface-enhanced Raman spectroscopy (SERS) was employed to detect deoxyribose nucleic acid (DNA) variations associated with the development of nasopharyngeal carcinoma (NPC). Significant SERS spectral differences between the DNA extracted from early NPC, advanced NPC, and normal nasopharyngeal tissue specimens were observed at 678, 729, 788, 1337, 1421, 1506, and 1573 cm-1, which reflects the genetic variations in NPC. Principal component analysis combined with discriminant function analysis for early NPC discrimination yielded a diagnostic accuracy of 86.8%, 92.3%, and 87.9% for early NPC, advanced NPC, and normal nasopharyngeal tissue DNA, respectively. In this exploratory study, we demonstrated the potential of SERS for early detection of NPC based on the DNA molecular study of biopsy tissues.

  2. Towards Maximum Spanning Tree Model in Web 3.0 Design and Development for Students using Discriminant Analysis

    CERN Document Server

    Padma, S

    2012-01-01

    Web 3.0 is an evolving extension of the web 2.0 scenario. The perceptions regarding web 3.0 is different from person to person . Web 3.0 Architecture supports ubiquitous connectivity, network computing, open identity, intelligent web, distributed databases and intelligent applications. Some of the technologies which lead to the design and development of web 3.0 applications are Artificial intelligence, Automated reasoning, Cognitive architecture, Semantic web . An attempt is made to capture the requirements of Students inline with web 3.0 so as to bridge the gap between the design and development of web 3.0 applications and requirements among Students. Maximum Spanning Tree modeling of the requirements facilitate the identification of key areas and key attributes in the design and development of software products for Students in Web 3.0 using Discriminant analysis. Keywords : Web 3.0, Discriminant analysis, Design and Development, Model, Maximum Spanning Tree 1.

  3. Multi-task linear programming discriminant analysis for the identification of progressive MCI individuals.

    Directory of Open Access Journals (Sweden)

    Guan Yu

    Full Text Available Accurately identifying mild cognitive impairment (MCI individuals who will progress to Alzheimer's disease (AD is very important for making early interventions. Many classification methods focus on integrating multiple imaging modalities such as magnetic resonance imaging (MRI and fluorodeoxyglucose positron emission tomography (FDG-PET. However, the main challenge for MCI classification using multiple imaging modalities is the existence of a lot of missing data in many subjects. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI study, almost half of the subjects do not have PET images. In this paper, we propose a new and flexible binary classification method, namely Multi-task Linear Programming Discriminant (MLPD analysis, for the incomplete multi-source feature learning. Specifically, we decompose the classification problem into different classification tasks, i.e., one for each combination of available data sources. To solve all different classification tasks jointly, our proposed MLPD method links them together by constraining them to achieve the similar estimated mean difference between the two classes (under classification for those shared features. Compared with the state-of-the-art incomplete Multi-Source Feature (iMSF learning method, instead of constraining different classification tasks to choose a common feature subset for those shared features, MLPD can flexibly and adaptively choose different feature subsets for different classification tasks. Furthermore, our proposed MLPD method can be efficiently implemented by linear programming. To validate our MLPD method, we perform experiments on the ADNI baseline dataset with the incomplete MRI and PET images from 167 progressive MCI (pMCI subjects and 226 stable MCI (sMCI subjects. We further compared our method with the iMSF method (using incomplete MRI and PET images and also the single-task classification method (using only MRI or only subjects with both MRI and

  4. The use of kernel local Fisher discriminant analysis for the channelization of the Hotelling model observer

    Science.gov (United States)

    Wen, Gezheng; Markey, Mia K.

    2015-03-01

    It is resource-intensive to conduct human studies for task-based assessment of medical image quality and system optimization. Thus, numerical model observers have been developed as a surrogate for human observers. The Hotelling observer (HO) is the optimal linear observer for signal-detection tasks, but the high dimensionality of imaging data results in a heavy computational burden. Channelization is often used to approximate the HO through a dimensionality reduction step, but how to produce channelized images without losing significant image information remains a key challenge. Kernel local Fisher discriminant analysis (KLFDA) uses kernel techniques to perform supervised dimensionality reduction, which finds an embedding transformation that maximizes betweenclass separability and preserves within-class local structure in the low-dimensional manifold. It is powerful for classification tasks, especially when the distribution of a class is multimodal. Such multimodality could be observed in many practical clinical tasks. For example, primary and metastatic lesions may both appear in medical imaging studies, but the distributions of their typical characteristics (e.g., size) may be very different. In this study, we propose to use KLFDA as a novel channelization method. The dimension of the embedded manifold (i.e., the result of KLFDA) is a counterpart to the number of channels in the state-of-art linear channelization. We present a simulation study to demonstrate the potential usefulness of KLFDA for building the channelized HOs (CHOs) and generating reliable decision statistics for clinical tasks. We show that the performance of the CHO with KLFDA channels is comparable to that of the benchmark CHOs.

  5. Multi-task linear programming discriminant analysis for the identification of progressive MCI individuals.

    Science.gov (United States)

    Yu, Guan; Liu, Yufeng; Thung, Kim-Han; Shen, Dinggang

    2014-01-01

    Accurately identifying mild cognitive impairment (MCI) individuals who will progress to Alzheimer's disease (AD) is very important for making early interventions. Many classification methods focus on integrating multiple imaging modalities such as magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET). However, the main challenge for MCI classification using multiple imaging modalities is the existence of a lot of missing data in many subjects. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, almost half of the subjects do not have PET images. In this paper, we propose a new and flexible binary classification method, namely Multi-task Linear Programming Discriminant (MLPD) analysis, for the incomplete multi-source feature learning. Specifically, we decompose the classification problem into different classification tasks, i.e., one for each combination of available data sources. To solve all different classification tasks jointly, our proposed MLPD method links them together by constraining them to achieve the similar estimated mean difference between the two classes (under classification) for those shared features. Compared with the state-of-the-art incomplete Multi-Source Feature (iMSF) learning method, instead of constraining different classification tasks to choose a common feature subset for those shared features, MLPD can flexibly and adaptively choose different feature subsets for different classification tasks. Furthermore, our proposed MLPD method can be efficiently implemented by linear programming. To validate our MLPD method, we perform experiments on the ADNI baseline dataset with the incomplete MRI and PET images from 167 progressive MCI (pMCI) subjects and 226 stable MCI (sMCI) subjects. We further compared our method with the iMSF method (using incomplete MRI and PET images) and also the single-task classification method (using only MRI or only subjects with both MRI and PET images

  6. Studies in genetic discrimination. Final progress report

    Energy Technology Data Exchange (ETDEWEB)

    1994-06-01

    We have screened 1006 respondents in a study of genetic discrimination. Analysis of these responses has produced evidence of the range of institutions engaged in genetic discrimination and demonstrates the impact of this discrimination on the respondents to the study. We have found that both ignorance and policy underlie genetic discrimination and that anti-discrimination laws are being violated.

  7. Analysis of advanced solar hybrid desiccant cooling systems for buildings

    Energy Technology Data Exchange (ETDEWEB)

    Schlepp, D.; Schultz, K.

    1984-10-01

    This report describes an assessment of the energy savings possible from developing hybrid desiccant/vapor-compression air conditioning systems. Recent advances in dehumidifier design for solar desiccant cooling systems have resulted in a dehumidifier with a low pressure drop and high efficiency in heat and mass transfer. A recent study on hybrid desiccant/vapor compression systems showed a 30%-80% savings in resource energy when compared with the best conventional systems with vapor compression. A system consisting of a dehumidifier with vapor compression subsystems in series was found to be the simplest and best overall performer.

  8. Phenotypic and Genotypic Analysis of Newly Obtained Interspecific Hybrids in the Campanula Genus

    Science.gov (United States)

    Röper, Anna-Catharina; Orabi, Jihad; Lütken, Henrik; Christensen, Brian; Thonning Skou, Anne-Marie; Müller, Renate

    2015-01-01

    Interspecific hybridisation creates new phenotypes within several ornamental plant species including the Campanula genus. We have employed phenotypic and genotypic methods to analyse and evaluate interspecific hybridisation among cultivars of four Campanula species, i.e. C. cochleariifolia, C. isophylla, C. medium and C. formanekiana. Hybrids were analysed using amplified fragment length polymorphism (AFLP), flow cytometry and biometrical measurements. Results of correlation matrices demonstrated heterogeneous phenotypes for the parental species, which confirmed our basic premise for new phenotypes of interspecific hybrids. AFLP assays confirmed the hybridity and identified self-pollinated plants. Limitation of flow cytometry analysis detection was observed while detecting the hybridity status of two closely related parents, e.g. C. cochleariiafolia × C. isophylla. Phenotypic characteristics such as shoot habitus and flower colour were strongly influenced by one of the parental species in most crosses. Rooting analysis revealed that inferior rooting quality occurred more often in interspecific hybrids than in the parental species. Only interspecific hybrid lines of C. formanekiana ‘White’ × C. medium ‘Pink’ showed a high rooting level. Phenotype analyses demonstrated a separation from the interspecific hybrid lines of C. formanekiana ‘White’ × C. medium ‘Pink’ to the other clustered hybrids of C. formanekiana and C. medium. In our study we demonstrated that the use of correlation matrices is a suitable tool for identifying suitable cross material. This study presents a comprehensive overview for analysing newly obtained interspecific hybrids. The chosen methods can be used as guidance for analyses for further interspecific hybrids in Campanula, as well as in other ornamental species. PMID:26352688

  9. Sex determination from the mandibular ramus flexure of Koreans by discrimination function analysis using three-dimensional mandible models.

    Science.gov (United States)

    Lin, Chenghe; Jiao, Benzheng; Liu, Shanshan; Guan, Feng; Chung, Nak-Eun; Han, Seung-Ho; Lee, U-Young

    2014-03-01

    It has been known that mandible ramus flexure is an important morphologic trait for sex determination. However, it will be unavailable when mandible is incomplete or fragmented. Therefore, the anthropometric analysis on incomplete or fragmented mandible becomes more important. The aim of this study is to investigate the sex-discriminant potential of mandible ramus flexure on the Korean three-dimensional (3D) mandible models with anthropometric analysis. The sample consists of 240 three dimensional mandibular models obtained from Korean population (M:F; 120:120, mean age 46.2 y), collected by The Catholic Institute for Applied Anatomy, The Catholic University of Korea. Anthropometric information about 11 metric was taken with Mimics, anthropometry libraries toolkit. These parameters were subjected to different discriminant function analyses using SPSS 17.0. Univariate analyses showed that the resubstitution accuracies for sex determination range from 50.4 to 77.1%. Mandibular flexure upper border (MFUB), maximum ramus vertical height (MRVH), and upper ramus vertical height (URVH) expressed the greatest dimorphism, 72.1 to 77.1%. Bivariate analyses indicated that the combination of MFUB and MRVH hold even higher resubstitution accuracy of 81.7%. Furthermore, the direct and stepwise discriminant analyses with the variables on the upper ramus above flexure could predict sex in 83.3 and 85.0%, respectively. When all variables of mandibular ramus flexure were input in stepwise discriminant analysis, the resubstitution accuracy arrived as high as 88.8%. Therefore, we concluded that the upper ramus above flexure hold the larger potentials than the mandibular ramus flexure itself to predict sexes, and that the equations in bivariate and multivariate analysis from our study will be helpful for sex determination on Korean population in forensic science and law.

  10. Functional connectivity in tactile object discrimination: a principal component analysis of an event related fMRI-Study.

    Directory of Open Access Journals (Sweden)

    Susanne Hartmann

    Full Text Available BACKGROUND: Tactile object discrimination is an essential human skill that relies on functional connectivity between the neural substrates of motor, somatosensory and supramodal areas. From a theoretical point of view, such distributed networks elude categorical analysis because subtraction methods are univariate. Thus, the aim of this study was to identify the neural networks involved in somatosensory object discrimination using a voxel-based principal component analysis (PCA of event-related functional magnetic resonance images. METHODOLOGY/PRINCIPAL FINDINGS: Seven healthy, right-handed subjects aged between 22 and 44 years were required to discriminate with their dominant hand the length differences between otherwise identical parallelepipeds in a two-alternative forced-choice paradigm. Of the 34 principal components retained for analysis according to the 'bootstrapped' Kaiser-Guttman criterion, t-tests applied to the subject-condition expression coefficients showed significant mean differences between the object presentation and inter-stimulus phases in PC 1, 3, 26 and 32. Specifically, PC 1 reflected object exploration or manipulation, PC 3 somatosensory and short-term memory processes. PC 26 evinced the perception that certain parallelepipeds could not be distinguished, while PC 32 emerged in those choices when they could be. Among the cerebral regions evident in the PCs are the left posterior parietal lobe and premotor cortex in PC 1, the left superior parietal lobule (SPL and the right cuneus in PC 3, the medial frontal and orbitofrontal cortex bilaterally in PC 26, and the right intraparietal sulcus, anterior SPL and dorsolateral prefrontal cortex in PC 32. CONCLUSIONS/SIGNIFICANCE: The analysis provides evidence for the concerted action of large-scale cortico-subcortical networks mediating tactile object discrimination. Parallel to activity in nodes processing object-related impulses we found activity in key cerebral regions

  11. [Study on the genuineness and producing area of Panax notoginseng based on infrared spectroscopy combined with discriminant analysis].

    Science.gov (United States)

    Liu, Fei; Wang, Yuan-zhong; Yang, Chun-yan; Jin, Hang

    2015-01-01

    The genuineness and producing area of Panax notoginseng were studied based on infrared spectroscopy combined with discriminant analysis. The infrared spectra of 136 taproots of P. notoginseng from 13 planting point in 11 counties were collected and the second derivate spectra were calculated by Omnic 8. 0 software. The infrared spectra and their second derivate spectra in the range 1 800 - 700 cm-1 were used to build model by stepwise discriminant analysis, which was in order to distinguish study on the genuineness of P. notoginseng. The model built based on the second derivate spectra showed the better recognition effect for the genuineness of P. notoginseng. The correct rate of returned classification reached to 100%, and the prediction accuracy was 93. 4%. The stability of model was tested by cross validation and the method was performed extrapolation validation. The second derivate spectra combined with the same discriminant analysis method were used to distinguish the producing area of P. notoginseng. The recognition effect of models built based on different range of spectrum and different numbers of samples were compared and found that when the model was built by collecting 8 samples from each planting point as training sample and the spectrum in the range 1 500 - 1 200 cm-1 , the recognition effect was better, with the correct rate of returned classification reached to 99. 0%, and the prediction accuracy was 76. 5%. The results indicated that infrared spectroscopy combined with discriminant analysis showed good recognition effect for the genuineness of P. notoginseng. The method might be a hopeful new method for identification of genuineness of P. notoginseng in practice. The method could recognize the producing area of P. notoginseng to some extent and could be a new thought for identification of the producing area of P. natoginseng.

  12. Genetic analysis of hybridization and introgression between wild mongoose and brown lemurs

    Directory of Open Access Journals (Sweden)

    Nievergelt Caroline M

    2009-02-01

    Full Text Available Abstract Background Hybrid zones generally represent areas of secondary contact after speciation. The nature of the interaction between genes of individuals in a hybrid zone is of interest in the study of evolutionary processes. In this study, data from nuclear microsatellites and mitochondrial DNA sequences were used to genetically characterize hybridization between wild mongoose lemurs (Eulemur mongoz and brown lemurs (E. fulvus at Anjamena in west Madagascar. Results Two segments of mtDNA have been sequenced and 12 microsatellite loci screened in 162 brown lemurs and mongoose lemurs. Among the mongoose lemur population at Anjamena, we identified two F1 hybrids (one also having the mtDNA haplotype of E. fulvus and six other individuals with putative introgressed alleles in their genotype. Principal component analysis groups both hybrids as intermediate between E. mongoz and E. fulvus and admixture analyses revealed an admixed genotype for both animals. Paternity testing proved one F1 hybrid to be fertile. Of the eight brown lemurs genotyped, all have either putative introgressed microsatellite alleles and/or the mtDNA haplotype of E. mongoz. Conclusion Introgression is bidirectional for the two species, with an indication that it is more frequent in brown lemurs than in mongoose lemurs. We conclude that this hybridization occurs because mongoose lemurs have expanded their range relatively recently. Introgressive hybridization may play an important role in the unique lemur radiation, as has already been shown in other rapidly evolving animals.

  13. Effects of measurement errors on psychometric measurements in ergonomics studies: Implications for correlations, ANOVA, linear regression, factor analysis, and linear discriminant analysis.

    Science.gov (United States)

    Liu, Yan; Salvendy, Gavriel

    2009-05-01

    This paper aims to demonstrate the effects of measurement errors on psychometric measurements in ergonomics studies. A variety of sources can cause random measurement errors in ergonomics studies and these errors can distort virtually every statistic computed and lead investigators to erroneous conclusions. The effects of measurement errors on five most widely used statistical analysis tools have been discussed and illustrated: correlation; ANOVA; linear regression; factor analysis; linear discriminant analysis. It has been shown that measurement errors can greatly attenuate correlations between variables, reduce statistical power of ANOVA, distort (overestimate, underestimate or even change the sign of) regression coefficients, underrate the explanation contributions of the most important factors in factor analysis and depreciate the significance of discriminant function and discrimination abilities of individual variables in discrimination analysis. The discussions will be restricted to subjective scales and survey methods and their reliability estimates. Other methods applied in ergonomics research, such as physical and electrophysiological measurements and chemical and biomedical analysis methods, also have issues of measurement errors, but they are beyond the scope of this paper. As there has been increasing interest in the development and testing of theories in ergonomics research, it has become very important for ergonomics researchers to understand the effects of measurement errors on their experiment results, which the authors believe is very critical to research progress in theory development and cumulative knowledge in the ergonomics field.

  14. Characterization of Fatty Acid Profile of Argan Oil and Other Edible Vegetable Oils by Gas Chromatography and Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Ascensión Rueda

    2014-01-01

    Full Text Available Virgin argan oil is an emergent oil that is being introduced into specialized international markets as a healthy and luxury food. In order to compare the fatty acid composition of argan oil with that of the eleven other vegetable edible oils, a combination of gas chromatography as analytical technique and multivariate discriminant analysis was applied. This analysis takes into account the conjoint effect of all the variables analyzed in the discrimination between oils and also shows the contribution of each variable to oils characterization. The model correctly classified 100% oil samples. According to the fatty acid composition, argan oil showed closest similarity firstly with sesame oil and secondly with high oleic sunflower oil. Olive oil was close to avocado oil and almond oil, followed by argan oil. Thus, similarities and differences between vegetable oils based on their fatty acid profile were established by the application of multivariate discriminant analysis. This method was proven to be a useful tool to study the relationships between oils according to the fat composition and to determine the importance of the fatty acid variables on the oils classification.

  15. [Discriminant analysis of near infrared diffuse reflectance spectra to detect adulteration of non-ruminant meat and bone meal].

    Science.gov (United States)

    Li, Qiong-Fei; Yang, Zeng-Ling; Han, Lu-Jia

    2008-03-01

    In order to study the feasibility of using near infrared (NIR) diffuse reflectance spectroscopy to discriminate adultera tion of non-ruminant meat and bone meal (MBM) with ruminant MBM, a total of 39 MBM samples made up of 15 from pig, 15 from poultry, 5 from cattle and 4 from sheep produced in different areas in China were chosen. The MBM samples were ground with 0. 5 mm sieve. 252 specimens were prepared by non-ruminant MBM deliberately adulterated with different proportion of ruminant MBM. The specimens were scanned by FOSS NIRSystem 6500. A calibration set of 180 specimens and an independent validation set of 72 specimens were randomly selected by the WINISI software. Discriminant analysis model was developed by partial least squares (PLS) on the calibration set and validated with independent validation set. The best discriminant model was obtained using standard normal variate and detrend (SNVD) and second derivative for spectrum pretreatment; this model had a coefficient of determination (R2(CV)) of 0.83 and a standard error of cross-validation (SECV) of 0. 147 1. For the independent validation set, the correct classification rate is 90%. There were a false negative specimen (0.5%) and two uncertain specimens (1%, 1.5%) in validation set. Results showed that it is feasible to use NIR diffuse reflectance spectroscopy to discriminate adulteration of non-ruminant MBM with ruminant MBM, but for specimens adulterated with ruminant MBM at less than 2%, the accuracy of calibration model needs to be improved. NIR was a rapid and non-destructive approach to discriminating adulteration of non-ruminant MBM with ruminant MIBM.

  16. Ray analysis of a class of hybrid cylindrical aircraft wings

    OpenAIRE

    Jha, RM; Bokhari, SA; Sudhakan, V; Mahapatra, PR

    1988-01-01

    A new approach to the modelling of aircraft wings, based on the combination of hybrid quadric (parabolic and circular) cylinders, has been presented for electromagnetic applications. Closed-form expressions have been obtained for ray parameters required in the high-frequency mutual coupling computation of antenna pairs located arbitrarily on an aircraft wing.

  17. Design and Analysis of Hybrid CMOS SRAM Sense Amplifier

    Directory of Open Access Journals (Sweden)

    Karishma Bajaj

    2012-03-01

    Full Text Available Sense amplifiers are one of the very important peripheral components of CMOS memories. In a Hybrid Sense amplifier both current and voltage sensing techniques are used which makes it a better selection than a conventional current or voltage sense amplifiers. The hybrid sense amplifier works in three phases-Offset cancellation (200ps, Access phase (500ps and Evaluation phase. The offset cancellation is done simultaneously with word line decoding, so as to speed up the process. The sensing range of the hybrid sense amplifier is improved from 1.18mV to 92mV. Also hybrid sense amplifier consumes very low energy of about 6.84fj. This sense amplifier is analyzed with a column of 512 SRAM cells at 180nm technology node and compared to CMOS conventional voltage sense amplifier. The circuit consumes an average power of 1.57 µW with a negligible offset of 149.3µV.

  18. Sensorless Suitability Analysis of Hybrid PM Machines for Electric Vehicles

    DEFF Research Database (Denmark)

    Matzen, Torben Nørregaard; Rasmussen, Peter Omand

    2009-01-01

    Electrical machines for traction in electric vehicles are an essential component which attract attention with respect to machine design and control as a part of the emerging renewable industry. For the hybrid electric machine to replace the familiar behaviour of the combustion engine torque...

  19. Combined analysis of cervical smears. Cytopathology, image cytometry and in situ hybridization

    DEFF Research Database (Denmark)

    Multhaupt, H; Bruder, E; Elit, L

    1993-01-01

    This study was an attempt to correlate the Bethesda System of Papanicolaou smear classification with DNA content by image analysis and the presence of human papillomavirus (HPV) as determined by in situ hybridization. DNA histograms were classified as normal diploid, diploid proliferative......, polyploid and aneuploid. HPV in situ hybridization was performed with a cocktail of probes specific to HPV types 6, 11, 16 and 18. There was a good correlation between normal cytology and normal DNA histograms. Cytologically normal smears with bacterial or fungal infections showed a high proliferation index....... Four of five cases cytologically suspicious for HPV infection had HPV by in situ hybridization....

  20. APPLICATION OF PENALTY FUNCTION METHOD IN ISOPARAMETRIC HYBRID FINITE ELEMENT ANALYSIS

    Institute of Scientific and Technical Information of China (English)

    CHEN Dao-zheng; JIAO Zhao-ping

    2005-01-01

    By the aid of the penalty function method, the equilibrium restriction conditions were introduced to the isoparametric hybrid finite element analysis, and the concrete application course of the penalty function method in three-dimensional isoparametric hybrid finite element was discussed. The separated penalty parameters method and the optimal hybrid element model with penalty balance were also presented.The penalty balance method can effectively refrain the parasitical stress on the premise of no additional degrees of freedom. The numeric experiment shows that the presented element not only is effective in improving greatly the numeric calculation precision of distorted grids but also has the universality.

  1. Evaluation of genotype x environment interactions in maize hybrids using GGE biplot analysis

    Directory of Open Access Journals (Sweden)

    Fatma Aykut Tonk

    2011-01-01

    Full Text Available Seventeen hybrid maize genotypes were evaluated at four different locations in 2005 and 2006 cropping seasonsunder irrigated conditions in Turkey. The analysis of variance showed that mean squares of environments (E, genotypes (G andGE interactions (GEI were highly significant and accounted for 74, 7 and 19 % of treatment combination sum squares, respectively.To determine the effects of GEI on grain yield, the data were subjected to the GGE biplot analysis. Maize hybrid G16 can be proposedas reliably growing in test locations for high grain yield. Also, only the Yenisehir location could be best representative of overalllocations for deciding about which experimental hybrids can be recommended for grain yield in this study. Consequently, using ofgrain yield per plant instead of grain yield per plot in hybrid maize breeding programs could be preferred by private companies dueto some advantages.

  2. A Qualitative Analysis of Multiracial Students' Experiences with Prejudice and Discrimination in College

    Science.gov (United States)

    Museus, Samuel D.; Lambe Sariñana, Susan A.; Yee, April L.; Robinson, Thomas E.

    2016-01-01

    Mixed-race persons constitute a substantial and growing population in the United States. We examined multiracial college students' experiences with prejudice and discrimination in college with conducted focus group interviews with 12 mixed-race participants and individual interviews with 22 mixed-race undergraduates to understand how they…

  3. A Qualitative Analysis of Multiracial Students' Experiences with Prejudice and Discrimination in College

    Science.gov (United States)

    Museus, Samuel D.; Lambe Sariñana, Susan A.; Yee, April L.; Robinson, Thomas E.

    2016-01-01

    Mixed-race persons constitute a substantial and growing population in the United States. We examined multiracial college students' experiences with prejudice and discrimination in college with conducted focus group interviews with 12 mixed-race participants and individual interviews with 22 mixed-race undergraduates to understand how they…

  4. Social Status, Discrimination, and Minority Individuals' Mental Health: a Secondary Analysis of US National Surveys.

    Science.gov (United States)

    Lo, Celia C; Cheng, Tyrone C

    2017-08-15

    Our study measured minority individuals' social status factors and frequency of discrimination experiences, in order to delineate social mechanisms linking race/ethnicity to mental status (specifically, to current mood/anxiety disorder and self-rated mental health). In this nationally representative secondary research, our data analyses drew on the cross-sectional "Collaborative Psychiatric Epidemiology Surveys," dating 2001-2003. The sample for the final model numbered 9368 respondents (2016 Asians, 2676 Latinos, 4676 blacks). Across races/ethnicities, better mental health was associated with male gender, higher income, marriage, more education, and less-frequent discrimination experiences; discrimination experiences could impair health, especially among blacks. Marriage's strong contribution to Asians' mental health did not hold among blacks; education's contribution to Latinos' mental health did not hold among blacks either. Blacks' mental health was unaffected by immigration status, but Asian and Latino immigrants showed less-robust mental health than native-born counterparts. Across the three racial/ethnic groups studied, differences were noted in relationships between self-reported mental health status and the employed social status and discrimination factors.

  5. Analysis of gene expression using gene sets discriminates cancer patients with and without late radiation toxicity

    NARCIS (Netherlands)

    J.P. Svensson; L.J.A. Stalpers; R.E.E. Esveldt-van Lange; N.A.P. Franken; J. Haveman; B. Klein; I. Turesson; H. Vrieling; M. Giphart-Gassler

    2006-01-01

    Background Radiation is an effective anti-cancer therapy but leads to severe late radiation toxicity in 5%-10% of patients. Assuming that genetic susceptibility impacts this risk, we hypothesized that the cellular response of normal tissue to X-rays could discriminate patients with and without late

  6. Biometric analysis of interspecific hybrids between Rosa canina L. and Rosa rubiginosa L. (section Caninae DC. em. Christ.

    Directory of Open Access Journals (Sweden)

    Anna Sołtys-Lelek

    2016-06-01

    Full Text Available The article presents the biometric analysis of selected morphological features of interspecies hybrid Rosa canina L. × R. rubiginosa L. This hybrid was the result of spontaneous hybridization between the two species falling into section Caninae DC. em. Christ. So far, it has not been studied in terms of morphological characteristics, in particular with respect to the parental forms.

  7. Otolith shape analysis for stock discrimination of two Collichthys genus croaker (Pieces: Sciaenidae,) from the northern Chinese coast

    Science.gov (United States)

    Zhao, Bo; Liu, Jinhu; Song, Junjie; Cao, Liang; Dou, Shuozeng

    2017-08-01

    The otolith morphology of two croaker species (Collichthys lucidus and Collichthys niveatus) from three areas (Liaodong Bay, LD; Huanghe (Yellow) River estuary, HRE; Jiaozhou Bay, JZ) along the northern Chinese coast were investigated for species identification and stock discrimination. The otolith contour shape described by elliptic Fourier coefficients (EFC) were analysed using principal components analysis (PCA) and stepwise canonical discriminant analysis (CDA) to identify species and stocks. The two species were well differentiated, with an overall classification success rate of 97.8%. And variations in the otolith shapes were significant enough to discriminate among the three geographical samples of C. lucidus (67.7%) or C. niveatus (65.2%). Relatively high mis-assignment occurred between the geographically adjacent LD and HRE samples, which implied that individual mixing may exist between the two samples. This study yielded information complementary to that derived from genetic studies and provided information for assessing the stock structure of C. lucidus and C. niveatus in the Bohai Sea and the Yellow Sea.

  8. Design and analysis of a wheel-legged hybrid locomotion mechanism

    Directory of Open Access Journals (Sweden)

    Change Zheng

    2015-11-01

    Full Text Available A wheel-legged hybrid robot was put forward. First, the mechanical design of this chassis was introduced. Second, based on the kinematic analysis, comparison between the robot with the active transition system and that with the passive transition system was made. Third, the process of overcoming three kinds of obstacles was simulated. Finally, the conclusion can be made that this hybrid locomotion mechanism with the active transition system has good performance on rough terrains.

  9. Periodically-Scheduled Controller Analysis using Hybrid Systems Reachability and Continuization

    Science.gov (United States)

    2015-12-01

    algorithm is run, and actuator outputs are set. The physical world , on the other hand, evolves continuously. Models of the physical world may be given...An extra clock variable, c, is added to the hybrid automaton that ticks at rate one (ċ = 1). When the clock reaches the period, a transition is...Preliminary Reachability Analysis Although hybrid automata can model real-time scheduled controllers and plants as shown above, an important factor is

  10. Discrimination of selected species of pathogenic bacteria using near-infrared Raman spectroscopy and principal components analysis

    Science.gov (United States)

    de Siqueira e Oliveira, Fernanda SantAna; Giana, Hector Enrique; Silveira, Landulfo

    2012-10-01

    A method, based on Raman spectroscopy, for identification of different microorganisms involved in bacterial urinary tract infections has been proposed. Spectra were collected from different bacterial colonies (Gram-negative: Escherichia coli, Klebsiella pneumoniae, Proteus mirabilis, Pseudomonas aeruginosa and Enterobacter cloacae, and Gram-positive: Staphylococcus aureus and Enterococcus spp.), grown on culture medium (agar), using a Raman spectrometer with a fiber Raman probe (830 nm). Colonies were scraped from the agar surface and placed on an aluminum foil for Raman measurements. After preprocessing, spectra were submitted to a principal component analysis and Mahalanobis distance (PCA/MD) discrimination algorithm. We found that the mean Raman spectra of different bacterial species show similar bands, and S. aureus was well characterized by strong bands related to carotenoids. PCA/MD could discriminate Gram-positive bacteria with sensitivity and specificity of 100% and Gram-negative bacteria with sensitivity ranging from 58 to 88% and specificity ranging from 87% to 99%.

  11. Sex determination using discriminant function analysis in Indigenous (Kurubas) children and adolescents of Coorg, Karnataka, India: A lateral cephalometric study.

    Science.gov (United States)

    Devang Divakar, Darshan; John, Jacob; Al Kheraif, Abdulaziz Abdullah; Mavinapalla, Seema; Ramakrishnaiah, Ravikumar; Vellappally, Sajith; Hashem, Mohamed Ibrahim; Dalati, M H N; Durgesh, B H; Safadi, Rima A; Anil, Sukumaran

    2016-11-01

    Aim: To test the validity of sex discrimination using lateral cephalometric radiograph and discriminant function analysis in Indigenous (Kuruba) children and adolescents of Coorg, Karnataka, India. Methods and materials: Six hundred and sixteen lateral cephalograms of 380 male and 236 females of age ranging from 6.5 to 18 years of Indigenous population of Coorg, Karnataka, India called Kurubas having a normal occlusion were included in the study. Lateral cephalograms were obtained in a standard position with teeth in centric occlusion and lips relaxed. Each radiograph was traced and cephalometric landmarks were measured using digital calliper. Calculations of 24 cephalometric measurements were performed. Results: Males exhibited significantly greater mean angular and linear cephalometric measurements as compared to females (p determine other landmarks that can help in sex determination and norms for Indigenous (Kuruba) population and also other Indigenous population of Coorg, Karnataka, India.

  12. Analysis of genetic diversity of maize hybrids in the regional tests in Sichuan and Southwest China

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    In this study,analyses of phenotypic characters,SSR molecular markers and pedigrees were done to study the genetic diversity in 186 maize hybrids that were tested in regional trials in Sichuan and Southwest China.The results showed that there were differences in the variation coefficients of different characteristics,but all of the variation coefficients changed within a narrow range.Sixty pairs of simple sequence repeat (SSR) primer distributed on the ten chromosomes of maize produced stable amplified bands and 608 alleles were detected among the hybrids.The average number of alleles per locus was 10.1 ranging from 3 to 23.The values of polymorphism information content (PIC) for each SSR locus varied from 0.5179 to 0.9256 with an average of 0.7826.The genetic similarities of SSR marker pattern among the 186 hybrids ranged from 0.6067 to 0.9162,with an average of 0.7722.There were 16499 pairs of genetic similarity,in which 96.9% were 0.70000 to 0.9256.The cluster analysis showed that the hybrids could be classified into ten clusters,with 88.2% of the hybrids included in Cluster 4,Cluster 8 and Cluster 10.The analysis of pedigree sources of 51 hybrids showed that 36 hybrids had close genetic relationships with the hybrids developed by the Pioneer Company in the late 1980s and early 1990s in the United States,such as Y78599,Y7865 and Y78698,accounting for 70.58%.Meanwhile,13 hybrids had close genetic relationships with Y78599,accounting for 8.66%.The genetic similarities of SSR marker pattern among the 51 hybrids ranged from 0.66192 to 0.8799,with an average of 0.7686.There were 1196 pairs of genetic similarity ranged between 0.7000 to 0.8796,accounting for 93.80% of all the genetic similarity pairs.The cluster analysis showed that 88.2% of the 51 hybrids were in Cluster 4,Cluster 8 and Cluster 10,which indicated that similarity was high and genetic diversity narrow among the 186 hybrids.This showed that it is necessary to broaden the genetic basis of breeding

  13. Optimal Threshold Determination for Discriminating Driving Anger Intensity Based on EEG Wavelet Features and ROC Curve Analysis

    Directory of Open Access Journals (Sweden)

    Ping Wan

    2016-08-01

    Full Text Available Driving anger, called “road rage”, has become increasingly common nowadays, affecting road safety. A few researches focused on how to identify driving anger, however, there is still a gap in driving anger grading, especially in real traffic environment, which is beneficial to take corresponding intervening measures according to different anger intensity. This study proposes a method for discriminating driving anger states with different intensity based on Electroencephalogram (EEG spectral features. First, thirty drivers were recruited to conduct on-road experiments on a busy route in Wuhan, China where anger could be inducted by various road events, e.g., vehicles weaving/cutting in line, jaywalking/cyclist crossing, traffic congestion and waiting red light if they want to complete the experiments ahead of basic time for extra paid. Subsequently, significance analysis was used to select relative energy spectrum of β band (β% and relative energy spectrum of θ band (θ% for discriminating the different driving anger states. Finally, according to receiver operating characteristic (ROC curve analysis, the optimal thresholds (best cut-off points of β% and θ% for identifying none anger state (i.e., neutral were determined to be 0.2183 ≤ θ% < 1, 0 < β% < 0.2586; low anger state is 0.1539 ≤ θ% < 0.2183, 0.2586 ≤ β% < 0.3269; moderate anger state is 0.1216 ≤ θ% < 0.1539, 0.3269 ≤ β% < 0.3674; high anger state is 0 < θ% < 0.1216, 0.3674 ≤ β% < 1. Moreover, the discrimination performances of verification indicate that, the overall accuracy (Acc of the optimal thresholds of β% for discriminating the four driving anger states is 80.21%, while 75.20% for that of θ%. The results can provide theoretical foundation for developing driving anger detection or warning devices based on the relevant optimal thresholds.

  14. Thermal analysis of solar biomass hybrid co-generation plants

    Science.gov (United States)

    Kaushika, N. D.; Mishra, Anuradha; Chakravarty, M. N.

    2005-12-01

    This article describes a co-generation plant based on the biogas being produced from the waste of distillery plant and highlights the possible configuration in which the plant can be hybridized with auxiliary solar energy source having the advantage of using financial incentives in several countries. In hybridization, the solar heat is used for heating the boiler feed water. The solar heat-generating unit consists of line focus parabolic trough collector, heat transportation system and heat delivery unit such as heat exchanger. The simulation model of heat and mass transfer processes in the solar field as well as the balance of the system is developed to investigate the technological feasibility of the concept in terms of plant yield and matching of subsystems.

  15. Capacity analysis of inhomogeneous hybrid wireless networks using directional antennas

    Institute of Scientific and Technical Information of China (English)

    WU Feng; ZHU Jiang; TIAN Yi-long; ZOU Jian-bin

    2016-01-01

    Most of studies on network capacity are based on the assumption that all the nodes are uniformly distributed, which means that the networks are characterized by homogeneity. However, many realistic networks exhibit inhomogeneity due to natural and man-made reasons. In this work, the capacity of inhomogeneous hybrid networks with directional antennas for the first time is studied. By setting different node distribution probabilities, the whole network can be devided into dense cells and sparse cells. On this basis, an inhomogeneous hybrid network model is proposed. The network can exhibit significant inhomogeneity due to the coexistence of two types of cells. Then, we derive the network capacity and maximize the capacity under different channel allocation schemes. Finally, how the network parameters influence the network capacity is analyzed. It is found that if there are plenty of base stations, the per-node throughput can achieve constant order, and if the beamwidth of directional antenna is small enough, the network capacity can scale.

  16. Hybrid Scenario Based Performance Analysis of DSDV and DSSR

    CERN Document Server

    Majumder, Koushik; 10.5121/ijcsit.2010.2305

    2010-01-01

    The area of mobile ad hoc networking has received considerable attention of the research community in recent years. These networks have gained immense popularity primarily due to their infrastructure-less mode of operation which makes them a suitable candidate for deployment in emergency scenarios like relief operation, battlefield etc., where either the pre-existing infrastructure is totally damaged or it is not possible to establish a new infrastructure quickly. However, MANETs are constrained due to the limited transmission range of the mobile nodes which reduces the total coverage area. Sometimes the infrastructure-less ad hoc network may be combined with a fixed network to form a hybrid network which can cover a wider area with the advantage of having less fixed infrastructure. In such a combined network, for transferring data, we need base stations which act as gateways between the wired and wireless domains. Due to the hybrid nature of these networks, routing is considered a challenging task. Several r...

  17. Coexistence analysis of diploid and triploid hybrid water frogs

    Science.gov (United States)

    Apri, M.; Suandi, D.; Soewono, E.

    2014-02-01

    A dynamical model for genotype distributions of all hybrid populations of Pelophylax esculentus in the absence of differential survival is studied here. Assuming that under natural condition the parental genotypes LL and RR do not survive into adult stage, the dynamic is then reduced into three-dimensional dynamical system of classes LR, LLR, LRR genotypes. Coexistence of diploid (LR) and triploid (LLR and LRR) genotypes is analyzed here.

  18. Delta-Complete Analysis for Bounded Reachability of Hybrid Systems

    Science.gov (United States)

    2014-07-16

    can occur in realistic hybrid sys- tems, such as polynomials, trigonometric functions , and solutions of Lipschitz-continuous ODEs. The goal of this...systems are Type 2 computable, such as polynomials, exponentiation, logarithm, trigonometric functions , and solution functions of Lipschitz-continuous...comes from the need of solving logic formulas over the real numbers with nonlinear functions , which is notoriously hard. Recently, we have defined the δ

  19. Hybrid Threat Center of Gravity Analysis: Cutting the Gordian Knot

    Science.gov (United States)

    2016-04-04

    observed “the response from Jerusalem was both quick and violent, surprising Hezbollah’s leadership and triggering a month-long conflict that, in...urban areas that caught the IDF completely by surprise . Peters notes three distinct advantages employed by Hezbollah in the development of its hybrid...Ukraine. These “green men” had no identifying marks or uniforms yet operated with amazing efficiency and tactical proficiency.2 No shots were fired

  20. Four-state discrimination scheme beyond the heterodyne limit

    DEFF Research Database (Denmark)

    Muller, C. R.; Castaneda, Mario A. Usuga; Wittmann, C.

    2012-01-01

    We propose and experimentally demonstrate a hybrid discrimination scheme for the quadrature phase shift keying protocol, which outperforms heterodyne detection for any signal power. The discrimination is composed of a quadrature measurement, feed forward and photon detection.......We propose and experimentally demonstrate a hybrid discrimination scheme for the quadrature phase shift keying protocol, which outperforms heterodyne detection for any signal power. The discrimination is composed of a quadrature measurement, feed forward and photon detection....