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Sample records for hybrid feature set

  1. Glaucoma detection using novel optic disc localization, hybrid feature set and classification techniques.

    Science.gov (United States)

    Akram, M Usman; Tariq, Anam; Khalid, Shehzad; Javed, M Younus; Abbas, Sarmad; Yasin, Ubaid Ullah

    2015-12-01

    Glaucoma is a chronic and irreversible neuro-degenerative disease in which the neuro-retinal nerve that connects the eye to the brain (optic nerve) is progressively damaged and patients suffer from vision loss and blindness. The timely detection and treatment of glaucoma is very crucial to save patient's vision. Computer aided diagnostic systems are used for automated detection of glaucoma that calculate cup to disc ratio from colored retinal images. In this article, we present a novel method for early and accurate detection of glaucoma. The proposed system consists of preprocessing, optic disc segmentation, extraction of features from optic disc region of interest and classification for detection of glaucoma. The main novelty of the proposed method lies in the formation of a feature vector which consists of spatial and spectral features along with cup to disc ratio, rim to disc ratio and modeling of a novel mediods based classier for accurate detection of glaucoma. The performance of the proposed system is tested using publicly available fundus image databases along with one locally gathered database. Experimental results using a variety of publicly available and local databases demonstrate the superiority of the proposed approach as compared to the competitors.

  2. Feature Sets for Screenshot Detection

    Science.gov (United States)

    2013-06-01

    Python Imaging Library [35] and OpenCV [25] provided image processing and feature ex- traction capabilities, NumPy [36] was used for general numeric...www.pythonware.com/products/pil/ [36] NumPy . (2013, Apr.) Scientific computer tools for python - numpy ). [Online]. Available: http://www.numpy.org/ [37] T. Curk, J

  3. Rough set-based feature selection method

    Institute of Scientific and Technical Information of China (English)

    ZHAN Yanmei; ZENG Xiangyang; SUN Jincai

    2005-01-01

    A new feature selection method is proposed based on the discern matrix in rough set in this paper. The main idea of this method is that the most effective feature, if used for classification, can distinguish the most number of samples belonging to different classes. Experiments are performed using this method to select relevant features for artificial datasets and real-world datasets. Results show that the selection method proposed can correctly select all the relevant features of artificial datasets and drastically reduce the number of features at the same time. In addition, when this method is used for the selection of classification features of real-world underwater targets,the number of classification features after selection drops to 20% of the original feature set, and the classification accuracy increases about 6% using dataset after feature selection.

  4. Elderly fall detection using SIFT hybrid features

    Science.gov (United States)

    Wang, Xiaoxiao; Gao, Chao; Guo, Yongcai

    2015-10-01

    With the tendency of aging society, countries all over the world are dealing with the demographic change. Fall had been proven to be of the highest fatality rate among the elderly. To realize the elderly fall detection, the proposed algorithm used the hybrid feature. Based on the rate of centroid change, the algorithm adopted VEI to offer the posture feature, this combined motion feature with posture feature. The algorithm also took advantage of SIFT descriptor of VEI(V-SIFT) to show more details of behaviors with occlusion. An improved motion detection method was proposed to improve the accuracy of front-view motion detection. The experimental results on CASIA database and self-built database showed that the proposed approach has high efficiency and strong robustness which effectively improved the accuracy of fall detection.

  5. Breast Cancer Detection with Reduced Feature Set.

    Science.gov (United States)

    Mert, Ahmet; Kılıç, Niyazi; Bilgili, Erdem; Akan, Aydin

    2015-01-01

    This paper explores feature reduction properties of independent component analysis (ICA) on breast cancer decision support system. Wisconsin diagnostic breast cancer (WDBC) dataset is reduced to one-dimensional feature vector computing an independent component (IC). The original data with 30 features and reduced one feature (IC) are used to evaluate diagnostic accuracy of the classifiers such as k-nearest neighbor (k-NN), artificial neural network (ANN), radial basis function neural network (RBFNN), and support vector machine (SVM). The comparison of the proposed classification using the IC with original feature set is also tested on different validation (5/10-fold cross-validations) and partitioning (20%-40%) methods. These classifiers are evaluated how to effectively categorize tumors as benign and malignant in terms of specificity, sensitivity, accuracy, F-score, Youden's index, discriminant power, and the receiver operating characteristic (ROC) curve with its criterion values including area under curve (AUC) and 95% confidential interval (CI). This represents an improvement in diagnostic decision support system, while reducing computational complexity.

  6. Breast Cancer Detection with Reduced Feature Set

    Directory of Open Access Journals (Sweden)

    Ahmet Mert

    2015-01-01

    Full Text Available This paper explores feature reduction properties of independent component analysis (ICA on breast cancer decision support system. Wisconsin diagnostic breast cancer (WDBC dataset is reduced to one-dimensional feature vector computing an independent component (IC. The original data with 30 features and reduced one feature (IC are used to evaluate diagnostic accuracy of the classifiers such as k-nearest neighbor (k-NN, artificial neural network (ANN, radial basis function neural network (RBFNN, and support vector machine (SVM. The comparison of the proposed classification using the IC with original feature set is also tested on different validation (5/10-fold cross-validations and partitioning (20%–40% methods. These classifiers are evaluated how to effectively categorize tumors as benign and malignant in terms of specificity, sensitivity, accuracy, F-score, Youden’s index, discriminant power, and the receiver operating characteristic (ROC curve with its criterion values including area under curve (AUC and 95% confidential interval (CI. This represents an improvement in diagnostic decision support system, while reducing computational complexity.

  7. Multispectral Palmprint Recognition Using a Hybrid Feature

    CERN Document Server

    Mistani, Sina Akbari; Fatemizadeh, Emad

    2011-01-01

    Personal identification problem has been a major field of research in recent years. Biometrics-based technologies that exploit fingerprints, iris, face, voice and palmprints, have been in the center of attention to solve this problem. Palmprints can be used instead of fingerprints that have been of the earliest of these biometrics technologies. A palm is covered with the same skin as the fingertips but has a larger surface, giving us more information that the fingertips. The major features of the palm are palm-lines, including principal lines, wrinkles and ridges. Using these lines is one of the most popular approaches towards solving palmprint recognition problem. Another robust feature is the wavelet energy of palms. In this paper, we used a hybrid of these two features. Moreover, multispectral analysis is applied to improve the performance of the system. Main steps of our approach are: extracting principal lines and computing wavelet transform of the palm, computing block-based power of the resulting image...

  8. Spoken Language Identification Using Hybrid Feature Extraction Methods

    CERN Document Server

    Kumar, Pawan; Mishra, A N; Chandra, Mahesh

    2010-01-01

    This paper introduces and motivates the use of hybrid robust feature extraction technique for spoken language identification (LID) system. The speech recognizers use a parametric form of a signal to get the most important distinguishable features of speech signal for recognition task. In this paper Mel-frequency cepstral coefficients (MFCC), Perceptual linear prediction coefficients (PLP) along with two hybrid features are used for language Identification. Two hybrid features, Bark Frequency Cepstral Coefficients (BFCC) and Revised Perceptual Linear Prediction Coefficients (RPLP) were obtained from combination of MFCC and PLP. Two different classifiers, Vector Quantization (VQ) with Dynamic Time Warping (DTW) and Gaussian Mixture Model (GMM) were used for classification. The experiment shows better identification rate using hybrid feature extraction techniques compared to conventional feature extraction methods.BFCC has shown better performance than MFCC with both classifiers. RPLP along with GMM has shown be...

  9. Training Faculty to Teach in Hybrid Settings

    Science.gov (United States)

    Linder, Kathryn E.

    2017-01-01

    Based on the author's experiences developing and implementing a multi-week hybrid course design institute, this chapter outlines the components of training--both andragogical and technological--most helpful for faculty who are planning to teach a hybrid course.

  10. Generalized rough sets hybrid structure and applications

    CERN Document Server

    Mukherjee, Anjan

    2015-01-01

    The book introduces the concept of “generalized interval valued intuitionistic fuzzy soft sets”. It presents the basic properties of these sets and also, investigates an application of generalized interval valued intuitionistic fuzzy soft sets in decision making with respect to interval of degree of preference. The concept of “interval valued intuitionistic fuzzy soft rough sets” is discussed and interval valued intuitionistic fuzzy soft rough set based multi criteria group decision making scheme is presented, which refines the primary evaluation of the whole expert group and enables us to select the optimal object in a most reliable manner. The book also details concept of interval valued intuitionistic fuzzy sets of type 2. It presents the basic properties of these sets. The book also introduces the concept of “interval valued intuitionistic fuzzy soft topological space (IVIFS topological space)” together with intuitionistic fuzzy soft open sets (IVIFS open sets) and intuitionistic fuzzy soft cl...

  11. Discovering highly informative feature set over high dimensions

    KAUST Repository

    Zhang, Chongsheng

    2012-11-01

    For many textual collections, the number of features is often overly large. These features can be very redundant, it is therefore desirable to have a small, succinct, yet highly informative collection of features that describes the key characteristics of a dataset. Information theory is one such tool for us to obtain this feature collection. With this paper, we mainly contribute to the improvement of efficiency for the process of selecting the most informative feature set over high-dimensional unlabeled data. We propose a heuristic theory for informative feature set selection from high dimensional data. Moreover, we design data structures that enable us to compute the entropies of the candidate feature sets efficiently. We also develop a simple pruning strategy that eliminates the hopeless candidates at each forward selection step. We test our method through experiments on real-world data sets, showing that our proposal is very efficient. © 2012 IEEE.

  12. Whispered speaker identification based on feature and model hybrid compensation

    Institute of Scientific and Technical Information of China (English)

    GU Xiaojiang; ZHAO Heming; Lu Gang

    2012-01-01

    In order to increase short time whispered speaker recognition rate in variable chan- nel conditions, the hybrid compensation in model and feature domains was proposed. This method is based on joint factor analysis in training model stage. It extracts speaker factor and eliminates channel factor by estimating training speech speaker and channel spaces. Then in the test stage, the test speech channel factor is projected into feature space to engage in feature compensation, so it can remove channel information both in model and feature domains in order to improve recognition rate. The experiment result shows that the hybrid compensation can obtain the similar recognition rate in the three different training channel conditions and this method is more effective than joint factor analysis in the test of short whispered speech.

  13. Level Sets and Voronoi based Feature Extraction from any Imagery

    DEFF Research Database (Denmark)

    Sharma, O.; Anton, François; Mioc, Darka

    2012-01-01

    Polygon features are of interest in many GEOProcessing applications like shoreline mapping, boundary delineation, change detection, etc. This paper presents a unique new GPU-based methodology to automate feature extraction combining level sets, or mean shift based segmentation together with Voronoi...

  14. HybridGO-Loc: mining hybrid features on gene ontology for predicting subcellular localization of multi-location proteins.

    Directory of Open Access Journals (Sweden)

    Shibiao Wan

    Full Text Available Protein subcellular localization prediction, as an essential step to elucidate the functions in vivo of proteins and identify drugs targets, has been extensively studied in previous decades. Instead of only determining subcellular localization of single-label proteins, recent studies have focused on predicting both single- and multi-location proteins. Computational methods based on Gene Ontology (GO have been demonstrated to be superior to methods based on other features. However, existing GO-based methods focus on the occurrences of GO terms and disregard their relationships. This paper proposes a multi-label subcellular-localization predictor, namely HybridGO-Loc, that leverages not only the GO term occurrences but also the inter-term relationships. This is achieved by hybridizing the GO frequencies of occurrences and the semantic similarity between GO terms. Given a protein, a set of GO terms are retrieved by searching against the gene ontology database, using the accession numbers of homologous proteins obtained via BLAST search as the keys. The frequency of GO occurrences and semantic similarity (SS between GO terms are used to formulate frequency vectors and semantic similarity vectors, respectively, which are subsequently hybridized to construct fusion vectors. An adaptive-decision based multi-label support vector machine (SVM classifier is proposed to classify the fusion vectors. Experimental results based on recent benchmark datasets and a new dataset containing novel proteins show that the proposed hybrid-feature predictor significantly outperforms predictors based on individual GO features as well as other state-of-the-art predictors. For readers' convenience, the HybridGO-Loc server, which is for predicting virus or plant proteins, is available online at http://bioinfo.eie.polyu.edu.hk/HybridGoServer/.

  15. HybridGO-Loc: Mining Hybrid Features on Gene Ontology for Predicting Subcellular Localization of Multi-Location Proteins

    Science.gov (United States)

    Wan, Shibiao; Mak, Man-Wai; Kung, Sun-Yuan

    2014-01-01

    Protein subcellular localization prediction, as an essential step to elucidate the functions in vivo of proteins and identify drugs targets, has been extensively studied in previous decades. Instead of only determining subcellular localization of single-label proteins, recent studies have focused on predicting both single- and multi-location proteins. Computational methods based on Gene Ontology (GO) have been demonstrated to be superior to methods based on other features. However, existing GO-based methods focus on the occurrences of GO terms and disregard their relationships. This paper proposes a multi-label subcellular-localization predictor, namely HybridGO-Loc, that leverages not only the GO term occurrences but also the inter-term relationships. This is achieved by hybridizing the GO frequencies of occurrences and the semantic similarity between GO terms. Given a protein, a set of GO terms are retrieved by searching against the gene ontology database, using the accession numbers of homologous proteins obtained via BLAST search as the keys. The frequency of GO occurrences and semantic similarity (SS) between GO terms are used to formulate frequency vectors and semantic similarity vectors, respectively, which are subsequently hybridized to construct fusion vectors. An adaptive-decision based multi-label support vector machine (SVM) classifier is proposed to classify the fusion vectors. Experimental results based on recent benchmark datasets and a new dataset containing novel proteins show that the proposed hybrid-feature predictor significantly outperforms predictors based on individual GO features as well as other state-of-the-art predictors. For readers' convenience, the HybridGO-Loc server, which is for predicting virus or plant proteins, is available online at http://bioinfo.eie.polyu.edu.hk/HybridGoServer/. PMID:24647341

  16. Efficient feature selection using a hybrid algorithm for the task of epileptic seizure detection

    Science.gov (United States)

    Lai, Kee Huong; Zainuddin, Zarita; Ong, Pauline

    2014-07-01

    Feature selection is a very important aspect in the field of machine learning. It entails the search of an optimal subset from a very large data set with high dimensional feature space. Apart from eliminating redundant features and reducing computational cost, a good selection of feature also leads to higher prediction and classification accuracy. In this paper, an efficient feature selection technique is introduced in the task of epileptic seizure detection. The raw data are electroencephalography (EEG) signals. Using discrete wavelet transform, the biomedical signals were decomposed into several sets of wavelet coefficients. To reduce the dimension of these wavelet coefficients, a feature selection method that combines the strength of both filter and wrapper methods is proposed. Principal component analysis (PCA) is used as part of the filter method. As for wrapper method, the evolutionary harmony search (HS) algorithm is employed. This metaheuristic method aims at finding the best discriminating set of features from the original data. The obtained features were then used as input for an automated classifier, namely wavelet neural networks (WNNs). The WNNs model was trained to perform a binary classification task, that is, to determine whether a given EEG signal was normal or epileptic. For comparison purposes, different sets of features were also used as input. Simulation results showed that the WNNs that used the features chosen by the hybrid algorithm achieved the highest overall classification accuracy.

  17. Design and Implementation of a Hybrid SET-CMOS Based Sequential Circuits

    Directory of Open Access Journals (Sweden)

    Anindya Jana

    2012-05-01

    Full Text Available Single Electron Transistor is a hot cake in the present research area of VLSI design and Microelectron-ics technology. It operates through one-by-one tunneling of electrons through the channel, utilizing the Coulomb blockade Phenomenon. Due to nanoscale feature size, ultralow power dissipation, and unique Coulomb blockade oscillation characteristics it may replace Field Effect Transistor FET. SET is very much advantageous than CMOS in few points. And in few points CMOS is advantageous than SET. So it has been seen that Combination of SET and CMOS is very much effective in the nanoscale, low power VLSI circuits. This paper has given a idea to make different sequential circuits using the Hybrid SET-CMOS. The MIB model for SET and BSIM4 model for CMOS are used. The operations of the proposed circuits are verified in Tanner environment. The performances of CMOS and Hybrid SET-CMOS based circuits are compared. The hybrid SET-CMOS circuit is found to consume lesser power than the CMOS based circuit. Further it is established that hybrid SET-CMOS based circuit is much faster compared to CMOS based circuit.

  18. Ensemble classification of colon biopsy images based on information rich hybrid features.

    Science.gov (United States)

    Rathore, Saima; Hussain, Mutawarra; Aksam Iftikhar, Muhammad; Jalil, Abdul

    2014-04-01

    In recent years, classification of colon biopsy images has become an active research area. Traditionally, colon cancer is diagnosed using microscopic analysis. However, the process is subjective and leads to considerable inter/intra observer variation. Therefore, reliable computer-aided colon cancer detection techniques are in high demand. In this paper, we propose a colon biopsy image classification system, called CBIC, which benefits from discriminatory capabilities of information rich hybrid feature spaces, and performance enhancement based on ensemble classification methodology. Normal and malignant colon biopsy images differ with each other in terms of the color distribution of different biological constituents. The colors of different constituents are sharp in normal images, whereas the colors diffuse with each other in malignant images. In order to exploit this variation, two feature types, namely color components based statistical moments (CCSM) and Haralick features have been proposed, which are color components based variants of their traditional counterparts. Moreover, in normal colon biopsy images, epithelial cells possess sharp and well-defined edges. Histogram of oriented gradients (HOG) based features have been employed to exploit this information. Different combinations of hybrid features have been constructed from HOG, CCSM, and Haralick features. The minimum Redundancy Maximum Relevance (mRMR) feature selection method has been employed to select meaningful features from individual and hybrid feature sets. Finally, an ensemble classifier based on majority voting has been proposed, which classifies colon biopsy images using the selected features. Linear, RBF, and sigmoid SVM have been employed as base classifiers. The proposed system has been tested on 174 colon biopsy images, and improved performance (=98.85%) has been observed compared to previously reported studies. Additionally, the use of mRMR method has been justified by comparing the

  19. A Hybrid On-line Verification Method of Relay Setting

    Science.gov (United States)

    Gao, Wangyuan; Chen, Qing; Si, Ji; Huang, Xin

    2017-05-01

    Along with the rapid development of the power industry, grid structure gets more sophisticated. The validity and rationality of protective relaying are vital to the security of power systems. To increase the security of power systems, it is essential to verify the setting values of relays online. Traditional verification methods mainly include the comparison of protection range and the comparison of calculated setting value. To realize on-line verification, the verifying speed is the key. The verifying result of comparing protection range is accurate, but the computation burden is heavy, and the verifying speed is slow. Comparing calculated setting value is much faster, but the verifying result is conservative and inaccurate. Taking the overcurrent protection as example, this paper analyses the advantages and disadvantages of the two traditional methods above, and proposes a hybrid method of on-line verification which synthesizes the advantages of the two traditional methods. This hybrid method can meet the requirements of accurate on-line verification.

  20. Using Fuzzy Hybrid Features to Classify Strokes in Interactive Sketches

    Directory of Open Access Journals (Sweden)

    Shuxia Wang

    2013-01-01

    Full Text Available A novel method is presented based on fuzzy hybrid-based features to classify strokes into 2D line drawings, and a human computer interactive system is developed for assisting designers in conceptual design stage. Fuzzy classifiers are built based on some geometric features and speed features. The prototype system can support rapid classification based on fuzzy classifiers, and the classified stroke is then fitted with a 2D geometry primitive which could be a line segment, polyline, circle, circular arc, ellipse, elliptical arc, hyperbola, and parabola. The human computer interaction can determine the ambiguous results and then revise the misrecognitions. The test results showed that the proposed method can support online freehand sketching based on conceptual design with no limitation on drawing sequence and direction while achieving a satisfactory interpretation rate.

  1. Hybrid Feature Based War Scene Classification using ANN and SVM: A Comparative Study

    Directory of Open Access Journals (Sweden)

    Shanmugam A

    2011-05-01

    Full Text Available In this paper we are proposing a hybrid feature extraction method for classifying the war scene from the natural scene. For this purpose two set of image categories are taken viz., opencountry & war tank. Byusing the hybrid method, features are extracted from the images/scenes. The extracted features are trained and tested with (i Artificial Neural Networks (ANN using feed forward back propagationalgorithm and (ii Support Vector Machines (SVM using Radial basis kernel functions with p=5. The results are also compared with the commonly used feature extraction methods such as haar wavelet,daubechies(db4 wavelet, Zernike moments, Invariant moments, co-occurrence features and statistical moments. The comparative results are proving efficiency of the proposed hybrid feature extraction method (i.e., the combination of GLCM & Statistical moments in war scene classification problems. It can be concluded that the proposed work significantly and directly contributes to scene classification and its new applications. The complete work is experimented in Matlab 7.6.0 using real world dataset.

  2. A linear-time algorithm for Euclidean feature transform sets

    NARCIS (Netherlands)

    Hesselink, Wim H.

    2007-01-01

    The Euclidean distance transform of a binary image is the function that assigns to every pixel the Euclidean distance to the background. The Euclidean feature transform is the function that assigns to every pixel the set of background pixels with this distance. We present an algorithm to compute the

  3. Monkey hybrid stem cells develop cellular features of Huntington's disease

    Directory of Open Access Journals (Sweden)

    Lorthongpanich Chanchao

    2010-02-01

    Full Text Available Abstract Background Pluripotent stem cells that are capable of differentiating into different cell types and develop robust hallmark cellular features are useful tools for clarifying the impact of developmental events on neurodegenerative diseases such as Huntington's disease. Additionally, a Huntington's cell model that develops robust pathological features of Huntington's disease would be valuable for drug discovery research. Results To test this hypothesis, a pluripotent Huntington's disease monkey hybrid cell line (TrES1 was established from a tetraploid Huntington's disease monkey blastocyst generated by the fusion of transgenic Huntington's monkey skin fibroblast and a wild-type non-transgenic monkey oocyte. The TrES1 developed key Huntington's disease cellular pathological features that paralleled neural development. It expressed mutant huntingtin and stem cell markers, was capable of differentiating to neural cells, and developed teratoma in severely compromised immune deficient (SCID mice. Interestingly, the expression of mutant htt, the accumulation of oligomeric mutant htt and the formation of intranuclear inclusions paralleled neural development in vitro , and even mutant htt was ubiquitously expressed. This suggests the development of Huntington's disease cellular features is influenced by neural developmental events. Conclusions Huntington's disease cellular features is influenced by neural developmental events. These results are the first to demonstrate that a pluripotent stem cell line is able to mimic Huntington's disease progression that parallels neural development, which could be a useful cell model for investigating the developmental impact on Huntington's disease pathogenesis.

  4. Feature Selection Strategies for Classifying High Dimensional Astronomical Data Sets

    CERN Document Server

    Donalek, Ciro; Djorgovski, S G; Mahabal, Ashish A; Graham, Matthew J; Fuchs, Thomas J; Turmon, Michael J; Philip, N Sajeeth; Yang, Michael Ting-Chang; Longo, Giuseppe

    2013-01-01

    The amount of collected data in many scientific fields is increasing, all of them requiring a common task: extract knowledge from massive, multi parametric data sets, as rapidly and efficiently possible. This is especially true in astronomy where synoptic sky surveys are enabling new research frontiers in the time domain astronomy and posing several new object classification challenges in multi dimensional spaces; given the high number of parameters available for each object, feature selection is quickly becoming a crucial task in analyzing astronomical data sets. Using data sets extracted from the ongoing Catalina Real-Time Transient Surveys (CRTS) and the Kepler Mission we illustrate a variety of feature selection strategies used to identify the subsets that give the most information and the results achieved applying these techniques to three major astronomical problems.

  5. Reliable Steganalysis Using a Minimum Set of Samples and Features

    Directory of Open Access Journals (Sweden)

    Bas Patrick

    2009-01-01

    Full Text Available This paper proposes to determine a sufficient number of images for reliable classification and to use feature selection to select most relevant features for achieving reliable steganalysis. First dimensionality issues in the context of classification are outlined, and the impact of the different parameters of a steganalysis scheme (the number of samples, the number of features, the steganography method, and the embedding rate is studied. On one hand, it is shown that, using Bootstrap simulations, the standard deviation of the classification results can be very important if too small training sets are used; moreover a minimum of 5000 images is needed in order to perform reliable steganalysis. On the other hand, we show how the feature selection process using the OP-ELM classifier enables both to reduce the dimensionality of the data and to highlight weaknesses and advantages of the six most popular steganographic algorithms.

  6. A Set of SCAR Markers Efficiently Differentiating Hybrid Rice

    Institute of Scientific and Technical Information of China (English)

    LI Shu-jing; XIE Hong-wei; QIAN Ming-juan; CHEN Guang-hui; LI Shao-qing; ZHU Ying-guo

    2012-01-01

    Molecular markers have been widely used in crop genetic improvement,seed test and genetic mapping.Of which,sequence characterized amplified region (SCAR) markers are particularly popular for its diversity,stable reproducibility,and suitability for analyzing large number of samples.In this study,500 random amplified polymorphic DNA (RAPD) primers were tested,and a set of SCAR markers comprising 37 pairs of loci-specific primers were developed from the DNA fragments ranging from 300 to 1000 bp which correspond to the stable,distinctive RAPD banding patterns.Using these SCAR markers,59 hybrid rice combinations were assessed and distinguished into 58 subgroups at the similarity coefficient of 0.97 in a genetic clustering tree based on the allele diversities of the SCAR markers.Furthermore,13 hybrid rice combinations were reassayed with 40 randomly selected simple sequence repeat (SSR) markers to evaluate the effectiveness of these SCAR markers.SSR markers produced similar results to SCAR markers as the 13 hybrid rice combinations were completely separated at the similarity coefficient of 0.91 in the clustering tree established from SSR patterns.Taken together,SCAR markers prove to be effective tools for identifying and differentiating hybrid rice combinations.

  7. A Set of SCAR Markers Efficiently Differentiating Hybrid Rice

    Directory of Open Access Journals (Sweden)

    Shu-jing LI

    2012-03-01

    Full Text Available Molecular markers have been widely used in crop genetic improvement, seed test and genetic mapping. Of which, sequence characterized amplified region (SCAR markers are particularly popular for its diversity, stable reproducibility, and suitability for analyzing large number of samples. In this study, 500 random amplified polymorphic DNA (RAPD primers were tested, and a set of SCAR markers comprising 37 pairs of loci-specific primers were developed from the DNA fragments ranging from 300 to 1000 bp which correspond to the stable, distinctive RAPD banding patterns. Using these SCAR markers, 59 hybrid rice combinations were assessed and distinguished into 58 subgroups at the similarity coefficient of 0.97 in a genetic clustering tree based on the allele diversities of the SCAR markers. Furthermore, 13 hybrid rice combinations were reassayed with 40 randomly selected simple sequence repeat (SSR markers to evaluate the effectiveness of these SCAR markers. SSR markers produced similar results to SCAR markers as the 13 hybrid rice combinations were completely separated at the similarity coefficient of 0.91 in the clustering tree established from SSR patterns. Taken together, SCAR markers prove to be effective tools for identifying and differentiating hybrid rice combinations.

  8. A Hybrid Feature Subset Selection using Metrics and Forward Selection

    Directory of Open Access Journals (Sweden)

    K. Fathima Bibi

    2015-04-01

    Full Text Available The aim of this study is to design a Feature Subset Selection Technique that speeds up the Feature Selection (FS process in high dimensional datasets with reduced computational cost and great efficiency. FS has become the focus of much research on decision support system areas for which data with tremendous number of variables are analyzed. Filters and wrappers are proposed techniques for the feature subset selection process. Filters make use of association based approach but wrappers adopt classification algorithms to identify important features. Filter method lacks the ability of minimization of simplification error while wrapper method burden weighty computational resource. To pull through these difficulties, a hybrid approach is proposed combining both filters and wrappers. Filter approach uses a permutation of ranker search methods and a wrapper which improves the learning accurateness and obtains a lessening in the memory requirements and finishing time. The UCI machine learning repository was chosen to experiment the approach. The classification accuracy resulted from our approach proves to be higher.

  9. IMPROVED HYBRID SEGMENTATION OF BRAIN MRI TISSUE AND TUMOR USING STATISTICAL FEATURES

    Directory of Open Access Journals (Sweden)

    S. Allin Christe

    2010-08-01

    Full Text Available Medical image segmentation is the most essential and crucial process in order to facilitate the characterization and visualization of the structure of interest in medical images. Relevant application in neuroradiology is the segmentation of MRI data sets of the human brain into the structure classes gray matter, white matter and cerebrospinal fluid (CSF and tumor. In this paper, brain image segmentation algorithms such as Fuzzy C means (FCM segmentation and Kohonen means(K means segmentation were implemented. In addition to this, new hybrid segmentation technique, namely, Fuzzy Kohonen means of image segmentation based on statistical feature clustering is proposed and implemented along with standard pixel value clustering method. The clustered segmented tissue images are compared with the Ground truth and its performance metric is also found. It is found that the feature based hybrid segmentation gives improved performance metric and improved classification accuracy rather than pixel based segmentation.

  10. Improved Framework for Breast Cancer Detection using Hybrid Feature Extraction Technique and FFNN

    Directory of Open Access Journals (Sweden)

    Ibrahim Mohamed Jaber Alamin

    2016-10-01

    Full Text Available Breast Cancer early detection using terminologies of image processing is suffered from the less accuracy performance in different automated medical tools. To improve the accuracy, still there are many research studies going on different phases such as segmentation, feature extraction, detection, and classification. The proposed framework is consisting of four main steps such as image preprocessing, image segmentation, feature extraction and finally classification. This paper presenting the hybrid and automated image processing based framework for breast cancer detection. For image preprocessing, both Laplacian and average filtering approach is used for smoothing and noise reduction if any. These operations are performed on 256 x 256 sized gray scale image. Output of preprocessing phase is used at efficient segmentation phase. Algorithm is separately designed for preprocessing step with goal of improving the accuracy. Segmentation method contributed for segmentation is nothing but the improved version of region growing technique. Thus breast image segmentation is done by using proposed modified region growing technique. The modified region growing technique overcoming the limitations of orientation as well as intensity. The next step we proposed is feature extraction, for this framework we have proposed to use combination of different types of features such as texture features, gradient features, 2D-DWT features with higher order statistics (HOS. Such hybrid feature set helps to improve the detection accuracy. For last phase, we proposed to use efficient feed forward neural network (FFNN. The comparative study between existing 2D-DWT feature extraction and proposed HOS-2D-DWT based feature extraction methods is proposed.

  11. The Hybrid KICA-GDA-LSSVM Method Research on Rolling Bearing Fault Feature Extraction and Classification

    Directory of Open Access Journals (Sweden)

    Jiyong Li

    2015-01-01

    Full Text Available Rolling element bearings are widely used in high-speed rotating machinery; thus proper monitoring and fault diagnosis procedure to avoid major machine failures is necessary. As feature extraction and classification based on vibration signals are important in condition monitoring technique, and superfluous features may degrade the classification performance, it is needed to extract independent features, so LSSVM (least square support vector machine based on hybrid KICA-GDA (kernel independent component analysis-generalized discriminate analysis is presented in this study. A new method named sensitive subband feature set design (SSFD based on wavelet packet is also presented; using proposed variance differential spectrum method, the sensitive subbands are selected. Firstly, independent features are obtained by KICA; the feature redundancy is reduced. Secondly, feature dimension is reduced by GDA. Finally, the projected feature is classified by LSSVM. The whole paper aims to classify the feature vectors extracted from the time series and magnitude of spectral analysis and to discriminate the state of the rolling element bearings by virtue of multiclass LSSVM. Experimental results from two different fault-seeded bearing tests show good performance of the proposed method.

  12. Genome display tool: visualizing features in complex data sets

    Directory of Open Access Journals (Sweden)

    Lu Yue

    2007-02-01

    Full Text Available Abstract Background The enormity of the information contained in large data sets makes it difficult to develop intuitive understanding. It would be useful to have software that allows visualization of possible correlations between properties that can be associated with a core data set. In the case of bacterial genomes, existing visualization tools focus on either global properties such as variations in composition or detailed local displays of the features that comprise the annotation. It is not easy to visualize other information in the context of this core information. Results A Java based software known as the Genome Display Tool (GDT, allows the user to simultaneously view the distribution of multiple attributes pertaining to genes and intragenic regions in a single bacterial genome using different colours and shapes on a single screen. The display represents each gene by small boxes that correlate with physical position in the genome. The size of the boxes is dynamically allocated based on the number of genes and a zoom feature allows close-up inspection of regions of interest. The display is interfaced with a MS-Access relational database and can display any feature in the database that can be represented by discrete values. Data is readily added to the database from an MS-Excel spread sheet. The functionality of GDT is demonstrated by comparing the results of two predictions of recent horizontal transfer events in the genome of Synechocystis PCC-6803. The resulting display allows the user to immediately see how much agreement exists between the two methods and also visualize how genes in various categories (e.g. predicted in both methods, one method etc are distributed in the genome. Conclusion The GDT software provides the user with a powerful tool that allows development of an intuitive understanding of the relative distribution of features in a large data set. As additional features are added to the data set, the number of possible

  13. Evaluation of systems and components for hybrid optical firing sets

    Energy Technology Data Exchange (ETDEWEB)

    Landry, M.J.; Rupert, J.W.; Mittas, A.

    1989-06-01

    High-energy density light appears to be a unique energy form that may be used to enhance the nuclear safety of weapon systems. Hybrid optical firing sets (HOFS) utilize the weak-link/strong-link exclusion region concept for nuclear safety; this method is similar to present systems, but uses light to transmit power across the exclusion region barrier. This report describes the assembling, operating, and testing of fourteen HOFS. These firing sets were required to charge a capacitor-discharge unit to 2.0 and 2.5 kV (100 mJ) in less than 1 s. First, we describe the components, the measurement techniques used to evaluate the components, and the different characteristics of the measured components. Second, we describe the HOFS studied, the setups used for evaluating them, and the resulting characteristics. Third, we make recommendations for improving the overall performance and suggest the best HOFS for packaging. 36 refs., 145 figs., 14 tabs.

  14. Comparing sociocultural features of cholera in three endemic African settings

    Science.gov (United States)

    2013-01-01

    Background Cholera mainly affects developing countries where safe water supply and sanitation infrastructure are often rudimentary. Sub-Saharan Africa is a cholera hotspot. Effective cholera control requires not only a professional assessment, but also consideration of community-based priorities. The present work compares local sociocultural features of endemic cholera in urban and rural sites from three field studies in southeastern Democratic Republic of Congo (SE-DRC), western Kenya and Zanzibar. Methods A vignette-based semistructured interview was used in 2008 in Zanzibar to study sociocultural features of cholera-related illness among 356 men and women from urban and rural communities. Similar cross-sectional surveys were performed in western Kenya (n = 379) and in SE-DRC (n = 360) in 2010. Systematic comparison across all settings considered the following domains: illness identification; perceived seriousness, potential fatality and past household episodes; illness-related experience; meaning; knowledge of prevention; help-seeking behavior; and perceived vulnerability. Results Cholera is well known in all three settings and is understood to have a significant impact on people’s lives. Its social impact was mainly characterized by financial concerns. Problems with unsafe water, sanitation and dirty environments were the most common perceived causes across settings; nonetheless, non-biomedical explanations were widespread in rural areas of SE-DRC and Zanzibar. Safe food and water and vaccines were prioritized for prevention in SE-DRC. Safe water was prioritized in western Kenya along with sanitation and health education. The latter two were also prioritized in Zanzibar. Use of oral rehydration solutions and rehydration was a top priority everywhere; healthcare facilities were universally reported as a primary source of help. Respondents in SE-DRC and Zanzibar reported cholera as affecting almost everybody without differentiating much for gender, age

  15. Time course of programmed cell death, which included autophagic features, in hybrid tobacco cells expressing hybrid lethality.

    Science.gov (United States)

    Ueno, Naoya; Nihei, Saori; Miyakawa, Naoto; Hirasawa, Tadashi; Kanekatsu, Motoki; Marubashi, Wataru; van Doorn, Wouter G; Yamada, Tetsuya

    2016-12-01

    PCD with features of vacuolar cell death including autophagy-related features were detected in hybrid tobacco cells, and detailed time course of features of vacuolar cell death were established. A type of interspecific Nicotiana hybrid, Nicotiana suaveolens × N. tabacum exhibits temperature-sensitive lethality. This lethality results from programmed cell death (PCD) in hybrid seedlings, but this PCD occurs only in seedlings and suspension-cultured cells grown at 28 °C, not those grown at 36 °C. Plant PCD can be classified as vacuolar cell death or necrotic cell death. Induction of autophagy, vacuolar membrane collapse and actin disorganization are each known features of vacuolar cell death, but observed cases of PCD showing all these features simultaneously are rare. In this study, these features of vacuolar cell death were evident in hybrid tobacco cells expressing hybrid lethality. Ion leakage, plasma membrane disruption, increased activity of vacuolar processing enzyme, vacuolar membrane collapse, and formation of punctate F-actin foci were each evident in these cells. Transmission electron microscopy revealed that macroautophagic structures formed and tonoplasts ruptured in these cells. The number of cells that contained monodansylcadaverine (MDC)-stained structures and the abundance of nine autophagy-related gene transcripts increased just before cell death at 28 °C; these features were not evident at 36 °C. We assessed whether an autophagic inhibitor, wortmannin (WM), influenced lethality in hybrid cells. After the hybrid cell began to die, WM suppressed increases in ion leakage and cell deaths, and it decreased the number of cells containing MDC-stained structures. These results showed that several features indicative of autophagy and vacuolar cell death were evident in the hybrid tobacco cells subject to lethality. In addition, we documented a detailed time course of these vacuolar cell death features.

  16. Rough Set Model for Discovering Hybrid Association Rules

    CERN Document Server

    Pandey, Anjana

    2009-01-01

    In this paper, the mining of hybrid association rules with rough set approach is investigated as the algorithm RSHAR.The RSHAR algorithm is constituted of two steps mainly. At first, to join the participant tables into a general table to generate the rules which is expressing the relationship between two or more domains that belong to several different tables in a database. Then we apply the mapping code on selected dimension, which can be added directly into the information system as one certain attribute. To find the association rules, frequent itemsets are generated in second step where candidate itemsets are generated through equivalence classes and also transforming the mapping code in to real dimensions. The searching method for candidate itemset is similar to apriori algorithm. The analysis of the performance of algorithm has been carried out.

  17. Set of Frequent Word Item sets as Feature Representation for Text with Indonesian Slang

    Science.gov (United States)

    Sa’adillah Maylawati, Dian; Putri Saptawati, G. A.

    2017-01-01

    Indonesian slang are commonly used in social media. Due to their unstructured syntax, it is difficult to extract their features based on Indonesian grammar for text mining. To do so, we propose Set of Frequent Word Item sets (SFWI) as text representation which is considered match for Indonesian slang. Besides, SFWI is able to keep the meaning of Indonesian slang with regard to the order of appearance sentence. We use FP-Growth algorithm with adding separation sentence function into the algorithm to extract the feature of SFWI. The experiments is done with text data from social media such as Facebook, Twitter, and personal website. The result of experiments shows that Indonesian slang were more correctly interpreted based on SFWI.

  18. Performance Evaluation of Conventional and Hybrid Feature Extractions Using Multivariate HMM Classifier

    Directory of Open Access Journals (Sweden)

    Veton Z. Këpuska

    2015-04-01

    Full Text Available Speech feature extraction and likelihood evaluation are considered the main issues in speech recognition system. Although both techniques were developed and improved, but they remain the most active area of research. This paper investigates the performance of conventional and hybrid speech feature extraction algorithm of Mel Frequency Cepstrum Coefficient (MFCC, Linear Prediction Cepstrum Coefficient (LPCC, perceptual linear production (PLP and RASTA-PLP through using multivariate Hidden Markov Model (HMM classifier. The performance of the speech recognition system is evaluated based on word error rate (WER, which is given for different data set of human voice using isolated speech TIDIGIT corpora sampled by 8 Khz. This data includes the pronunciation of eleven words (zero to nine plus oh are recorded from 208 different adult speakers (men & women each person uttered each word 2 times.

  19. Features Management and Middleware of Hybrid Cloud Infrastructures

    OpenAIRE

    Evgeny Nikulchev; Oleg Lukyanchikov; Evgeniy Pluzhnik; Dmitry Biryukov

    2016-01-01

    The wide spread of cloud computing has identified the need to develop specialized approaches to the design, management and programming for cloud infrastructures. In the article were reviewed the peculiarities of the hybrid cloud and middleware software development, adaptive to implementing the principles of governance and change in the structure of storing data in clouds. The examples and results of experimental research are presented.

  20. A Dynamic Feature-Based Method for Hybrid Blurred/Multiple Object Detection in Manufacturing Processes

    Directory of Open Access Journals (Sweden)

    Tsun-Kuo Lin

    2016-01-01

    Full Text Available Vision-based inspection has been applied for quality control and product sorting in manufacturing processes. Blurred or multiple objects are common causes of poor performance in conventional vision-based inspection systems. Detecting hybrid blurred/multiple objects has long been a challenge in manufacturing. For example, single-feature-based algorithms might fail to exactly extract features when concurrently detecting hybrid blurred/multiple objects. Therefore, to resolve this problem, this study proposes a novel vision-based inspection algorithm that entails selecting a dynamic feature-based method on the basis of a multiclassifier of support vector machines (SVMs for inspecting hybrid blurred/multiple object images. The proposed algorithm dynamically selects suitable inspection schemes for classifying the hybrid images. The inspection schemes include discrete wavelet transform, spherical wavelet transform, moment invariants, and edge-feature-descriptor-based classification methods. The classification methods for single and multiple objects are adaptive region growing- (ARG- based and local adaptive region growing- (LARG- based learning approaches, respectively. The experimental results demonstrate that the proposed algorithm can dynamically select suitable inspection schemes by applying a selection algorithm, which uses SVMs for classifying hybrid blurred/multiple object samples. Moreover, the method applies suitable feature-based schemes on the basis of the classification results for employing the ARG/LARG-based method to inspect the hybrid objects. The method improves conventional methods for inspecting hybrid blurred/multiple objects and achieves high recognition rates for that in manufacturing processes.

  1. CURE-SMOTE algorithm and hybrid algorithm for feature selection and parameter optimization based on random forests.

    Science.gov (United States)

    Ma, Li; Fan, Suohai

    2017-03-14

    The random forests algorithm is a type of classifier with prominent universality, a wide application range, and robustness for avoiding overfitting. But there are still some drawbacks to random forests. Therefore, to improve the performance of random forests, this paper seeks to improve imbalanced data processing, feature selection and parameter optimization. We propose the CURE-SMOTE algorithm for the imbalanced data classification problem. Experiments on imbalanced UCI data reveal that the combination of Clustering Using Representatives (CURE) enhances the original synthetic minority oversampling technique (SMOTE) algorithms effectively compared with the classification results on the original data using random sampling, Borderline-SMOTE1, safe-level SMOTE, C-SMOTE, and k-means-SMOTE. Additionally, the hybrid RF (random forests) algorithm has been proposed for feature selection and parameter optimization, which uses the minimum out of bag (OOB) data error as its objective function. Simulation results on binary and higher-dimensional data indicate that the proposed hybrid RF algorithms, hybrid genetic-random forests algorithm, hybrid particle swarm-random forests algorithm and hybrid fish swarm-random forests algorithm can achieve the minimum OOB error and show the best generalization ability. The training set produced from the proposed CURE-SMOTE algorithm is closer to the original data distribution because it contains minimal noise. Thus, better classification results are produced from this feasible and effective algorithm. Moreover, the hybrid algorithm's F-value, G-mean, AUC and OOB scores demonstrate that they surpass the performance of the original RF algorithm. Hence, this hybrid algorithm provides a new way to perform feature selection and parameter optimization.

  2. Features Management and Middleware of Hybrid Cloud Infrastructures

    Directory of Open Access Journals (Sweden)

    Evgeny Nikulchev

    2016-01-01

    Full Text Available The wide spread of cloud computing has identified the need to develop specialized approaches to the design, management and programming for cloud infrastructures. In the article were reviewed the peculiarities of the hybrid cloud and middleware software development, adaptive to implementing the principles of governance and change in the structure of storing data in clouds. The examples and results of experimental research are presented.

  3. Syntactic and Sentence Feature Based Hybrid Approach for Text Summarization

    Directory of Open Access Journals (Sweden)

    D.Y. Sakhare

    2014-02-01

    Full Text Available Recently, there has been a significant research in automatic text summarization using feature-based techniques in which most of them utilized any one of the soft computing techniques. But, making use of syntactic structure of the sentences for text summarization has not widely applied due to its difficulty of handling it in summarization process. On the other hand, feature-based technique available in the literature showed efficient results in most of the techniques. So, combining syntactic structure into the feature-based techniques is surely smooth the summarization process in a way that the efficiency can be achieved. With the intention of combining two different techniques, we have presented an approach of text summarization that combines feature and syntactic structure of the sentences. Here, two neural networks are trained based on the feature score and the syntactic structure of sentences. Finally, the two neural networks are combined with weighted average to find the sentence score of the sentences. The experimentation is carried out using DUC 2002 dataset for various compression ratios. The results showed that the proposed approach achieved F-measure of 80% for the compression ratio 50 % that proved the better results compared with the existing techniques.

  4. Hybrid edge and feature-based single-image superresolution

    Science.gov (United States)

    Islam, Mohammad Moinul; Islam, Mohammed Nazrul; Asari, Vijayan K.; Karim, Mohammad A.

    2016-07-01

    A neighborhood-dependent component feature learning method for regression analysis in single-image superresolution is presented. Given a low-resolution input, the method uses a directional Fourier phase feature component to adaptively learn the regression kernel based on local covariance to estimate the high-resolution image. The unique feature of the proposed method is that it uses image features to learn about the local covariance from geometric similarity between the low-resolution image and its high-resolution counterpart. For each patch in the neighborhood, we estimate four directional variances to adapt the interpolated pixels. This gives us edge information and Fourier phase gives features, which are combined to interpolate using kernel regression. In order to compare quantitatively with other state-of-the-art techniques, root-mean-square error and measure mean-square similarity are computed for the example images, and experimental results show that the proposed algorithm outperforms similar techniques available in the literature, especially at higher resolution scales.

  5. HYBRID FEATURE SELECTION ALGORITHM FOR INTRUSION DETECTION SYSTEM

    Directory of Open Access Journals (Sweden)

    Seyed Reza Hasani

    2014-01-01

    Full Text Available Network security is a serious global concern. Usefulness Intrusion Detection Systems (IDS are increasing incredibly in Information Security research using Soft computing techniques. In the previous researches having irrelevant and redundant features are recognized causes of increasing the processing speed of evaluating the known intrusive patterns. In addition, an efficient feature selection method eliminates dimension of data and reduce redundancy and ambiguity caused by none important attributes. Therefore, feature selection methods are well-known methods to overcome this problem. There are various approaches being utilized in intrusion detections, they are able to perform their method and relatively they are achieved with some improvements. This work is based on the enhancement of the highest Detection Rate (DR algorithm which is Linear Genetic Programming (LGP reducing the False Alarm Rate (FAR incorporates with Bees Algorithm. Finally, Support Vector Machine (SVM is one of the best candidate solutions to settle IDSs problems. In this study four sample dataset containing 4000 random records are excluded randomly from this dataset for training and testing purposes. Experimental results show that the LGP_BA method improves the accuracy and efficiency compared with the previous related research and the feature subcategory offered by LGP_BA gives a superior representation of data.

  6. A Rough Set GA-based Hybrid Method for Robot Path Planning

    Institute of Scientific and Technical Information of China (English)

    Cheng-Dong Wu; Ying Zhang; Meng-Xin Li; Yong Yue

    2006-01-01

    In this paper, a hybrid method based on rough sets and genetic algorithms, is proposed to improve the speed of robot path planning. Decision rules are obtained using rough set theory. A series of available paths are produced by training obtained minimal decision rules. Path populations are optimised by using genetic algorithms until the best path is obtained. Experiment results show that this hybrid method is capable of improving robot path planning speed.

  7. Classification of melanoma using wavelet-transform-based optimal feature set

    Science.gov (United States)

    Walvick, Ronn P.; Patel, Ketan; Patwardhan, Sachin V.; Dhawan, Atam P.

    2004-05-01

    The features used in the ABCD rule for characterization of skin lesions suggest that the spatial and frequency information in the nevi changes at various stages of melanoma development. To analyze these changes wavelet transform based features have been reported. The classification of melanoma using these features has produced varying results. In this work, all the reported wavelet transform based features are combined to form a single feature set. This feature set is then optimized by removing redundancies using principal component analysis. A feed forward neural network trained with the back propagation algorithm is then used in the classification process to obtain better classification results.

  8. Detection of tuberculosis using hybrid features from chest radiographs

    Science.gov (United States)

    Fatima, Ayesha; Akram, M. Usman; Akhtar, Mahmood; Shafique, Irrum

    2017-02-01

    Tuberculosis is an infectious disease and becomes a major threat all over the world but still diagnosis of tuberculosis is a challenging task. In literature, chest radiographs are considered as most commonly used medical images in under developed countries for the diagnosis of TB. Different methods have been proposed but they are not helpful for radiologists due to cost and accuracy issues. Our paper presents a methodology in which different combinations of features are extracted based on intensities, shape and texture of chest radiograph and given to classifier for the detection of TB. The performance of our methodology is evaluated using publically available standard dataset Montgomery Country (MC) which contains 138 CXRs among which 80 CXRs are normal and 58 CXRs are abnormal including effusion and miliary patterns etc. The accuracy of 81.16% was achieved and the results show that proposed method have outperformed existing state of the art methods on MC dataset.

  9. Influence of Familiar Features on Diagnosis: Instantiated Features in an Applied Setting

    Science.gov (United States)

    Dore, Kelly L.; Brooks, Lee R.; Weaver, Bruce; Norman, Geoffrey R.

    2012-01-01

    Medical diagnosis can be viewed as a categorization task. There are two mechanisms whereby humans make categorical judgments: "analytical reasoning," based on explicit consideration of features and "nonanalytical reasoning," an unconscious holistic process of matching against prior exemplars. However, there is evidence that prior experience can…

  10. Influence of Familiar Features on Diagnosis: Instantiated Features in an Applied Setting

    Science.gov (United States)

    Dore, Kelly L.; Brooks, Lee R.; Weaver, Bruce; Norman, Geoffrey R.

    2012-01-01

    Medical diagnosis can be viewed as a categorization task. There are two mechanisms whereby humans make categorical judgments: "analytical reasoning," based on explicit consideration of features and "nonanalytical reasoning," an unconscious holistic process of matching against prior exemplars. However, there is evidence that prior experience can…

  11. A hybrid feature selection method using multiclass SVM for diagnosis of erythemato-squamous disease

    Science.gov (United States)

    Maryam, Setiawan, Noor Akhmad; Wahyunggoro, Oyas

    2017-08-01

    The diagnosis of erythemato-squamous disease is a complex problem and difficult to detect in dermatology. Besides that, it is a major cause of skin cancer. Data mining implementation in the medical field helps expert to diagnose precisely, accurately, and inexpensively. In this research, we use data mining technique to developed a diagnosis model based on multiclass SVM with a novel hybrid feature selection method to diagnose erythemato-squamous disease. Our hybrid feature selection method, named ChiGA (Chi Square and Genetic Algorithm), uses the advantages from filter and wrapper methods to select the optimal feature subset from original feature. Chi square used as filter method to remove redundant features and GA as wrapper method to select the ideal feature subset with SVM used as classifier. Experiment performed with 10 fold cross validation on erythemato-squamous diseases dataset taken from University of California Irvine (UCI) machine learning database. The experimental result shows that the proposed model based multiclass SVM with Chi Square and GA can give an optimum feature subset. There are 18 optimum features with 99.18% accuracy.

  12. Hardwood species classification with DWT based hybrid texture feature extraction techniques

    Indian Academy of Sciences (India)

    Arvind R Yadav; R S Anand; M L Dewal; Sangeeta Gupta

    2015-12-01

    In this work, discrete wavelet transform (DWT) based hybrid texture feature extraction techniques have been used to categorize the microscopic images of hardwood species into 75 different classes. Initially, the DWT has been employed to decompose the image up to 7 levels using Daubechies (db3) wavelet as decomposition filter. Further, first-order statistics (FOS) and four variants of local binary pattern (LBP) descriptors are used to acquire distinct features of these images at various levels. The linear support vector machine (SVM), radial basis function (RBF) kernel SVM and random forest classifiers have been employed for classification. The classification accuracy obtained with state-of-the-art and DWT based hybrid texture features using various classifiers are compared. The DWT based FOS-uniform local binary pattern (DWTFOSLBPu2) texture features at the 4th level of image decomposition have produced best classification accuracy of 97.67 ± 0.79% and 98.40 ± 064% for grayscale and RGB images, respectively, using linear SVM classifier. Reduction in feature dataset by minimal redundancy maximal relevance (mRMR) feature selection method is achieved and the best classification accuracy of 99.00 ± 0.79% and 99.20 ± 0.42% have been obtained for DWT based FOS-LBP histogram Fourier features (DWTFOSLBP-HF) technique at the 5th and 6th levels of image decomposition for grayscale and RGB images, respectively, using linear SVM classifier. The DWTFOSLBP-HF features selected with mRMR method has also established superiority amongst the DWT based hybrid texture feature extraction techniques for randomly divided database into different proportions of training and test datasets.

  13. [Oil atomic spectrometric feature selection by Parzen window based vague sets theory].

    Science.gov (United States)

    Xu, Chao; Zhang, Pei-Lin; Ren, Guo-Quan; Zhang, Xiao-Dong; Yang, Yu-Dong

    2011-02-01

    Large quantity and ambiguity of oil atomic spectrometric information greatly affects the applicable efficiency and accuracy in fault diagnosis. A novel method for choosing less and effective spectrometric features is presented. Based on gearbox test bed, we simulated the normal wear state and two typical faults to acquire the lubricant samples. The three wear states are regarded as three vague sets, and spectrometric feature values are vague values on vague sets. Based on similarity between vague values, mean vague sensibility (MVS) is defined to describe the sensitive degree of spectrometric feature to wear state. Besides, the membership degrees of vague sets greatly depend on human experience. The probability density distribution of spectrometric data of three wear states was estimated with Parzen window. Combined with Bayesian formula, the range of vague sets membership was calculated. Experimental results verify that the proposed method is of efficient help in choosing high fault-sensitive features from so many spectrometric features.

  14. Design of Bioactive Organic-inorganic Hybrid Materials with Self-setting Ability

    Energy Technology Data Exchange (ETDEWEB)

    Miyazaki, T; Machida, S; Morita, Y [Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology (Japan); Ishida, E, E-mail: tmiya@life.kyutech.ac.jp [Faculty of Engineering, Kyushu Institute of Technology (Japan)

    2011-10-29

    Paste-like materials with ability of self-setting are attractive for bone substitutes, since they can be injected from the small hole with minimized invasion to the patient. Although bone cements which set as apatite are clinically used, there is limitation on clinical applications due to their mechanical properties such as high brittleness and low fracture toughness. To overcome this problem, organic-inorganic hybrids based on a flexible polymer are attractive. We have obtained an idea for design of self-setting hybrids using polyion complex fabricated by ionic interaction of anionic and cationic polymers. We aimed at preparation of organic-inorganic hybrids exhibiting self-setting ability and bioactivity. The liquid component was prepared from cationic chitosan aqueous solution. The powder component was prepared by mixing various carrageenans with {alpha}-tricalcium phosphate ({alpha}-TCP). The obtained cements set within 1 day. Compressive strength showed tendency to increase with increase in {alpha}-TCP content in the powder component. The prepared cements formed the apatite in simulated body fluid within 3 days. Novel self-setting materials based on organic-inorganic hybrid can be designed utilizing ionic interaction of polysaccharide.

  15. Reduction of Feature Vectors Using Rough Set Theory for Human Face Recognition

    CERN Document Server

    Bhattacharjee, Debotosh; Nasipuri, Mita; Kundu, M

    2010-01-01

    In this paper we describe a procedure to reduce the size of the input feature vector. A complex pattern recognition problem like face recognition involves huge dimension of input feature vector. To reduce that dimension here we have used eigenspace projection (also called as Principal Component Analysis), which is basically transformation of space. To reduce further we have applied feature selection method to select indispensable features, which will remain in the final feature vectors. Features those are not selected are removed from the final feature vector considering them as redundant or superfluous. For selection of features we have used the concept of reduct and core from rough set theory. This method has shown very good performance. It is worth to mention that in some cases the recognition rate increases with the decrease in the feature vector dimension.

  16. Feature Extraction for Facial Expression Recognition based on Hybrid Face Regions

    Directory of Open Access Journals (Sweden)

    LAJEVARDI, S.M.

    2009-10-01

    Full Text Available Facial expression recognition has numerous applications, including psychological research, improved human computer interaction, and sign language translation. A novel facial expression recognition system based on hybrid face regions (HFR is investigated. The expression recognition system is fully automatic, and consists of the following modules: face detection, facial detection, feature extraction, optimal features selection, and classification. The features are extracted from both whole face image and face regions (eyes and mouth using log Gabor filters. Then, the most discriminate features are selected based on mutual information criteria. The system can automatically recognize six expressions: anger, disgust, fear, happiness, sadness and surprise. The selected features are classified using the Naive Bayesian (NB classifier. The proposed method has been extensively assessed using Cohn-Kanade database and JAFFE database. The experiments have highlighted the efficiency of the proposed HFR method in enhancing the classification rate.

  17. A hybrid BCI speller paradigm combining P300 potential and the SSVEP blocking feature

    Science.gov (United States)

    Xu, Minpeng; Qi, Hongzhi; Wan, Baikun; Yin, Tao; Liu, Zhipeng; Ming, Dong

    2013-04-01

    Objective. Hybrid brain-computer interfaces (BCIs) have been proved to be more effective in mental control by combining another channel of physiologic control signals. Among those studies, little attention has been paid to the combined use of a steady-state visual evoked potential (SSVEP) and P300 potential, both providing the fastest and the most reliable EEG based BCIs. In this paper, a novel hybrid BCI speller is developed to elicit P300 potential and SSVEP blocking (SSVEP-B) distinctly and simultaneously with the same target stimulus. Approach. Twelve subjects were involved in the study and every one performed offline spelling twice in succession with two different speller paradigms for comparison: hybrid speller and control P300-speller. Feature analysis was adopted from the view of time domain, frequency domain and spatial distribution; the performances were evaluated by character accuracy and information transfer rate (ITR). Main results. Signal analysis of the hybrid paradigm shows that SSVEPs are an evident EEG component during the nontarget phase but are dismissed and replaced by P300 potentials after target stimuli. The absence of an SSVEP, called SSVEP-B, mostly appearing in channel Oz, presents a sharp distinction between target responses and nontarget responses. The r2 value of SSVEP-B in channel Oz is comparable to that of P300 in channel Cz. Compared with the control P300-speller, the hybrid speller achieves significantly higher accuracy and ITR with combined features. Significance. The results indicate that the combination of P300 with an SSVEP-B improves target discrimination greatly; the proposed hybrid paradigm is superior to the control paradigm in spelling performance. Thus, our findings provide a new approach to improve BCI performances.

  18. On the selection of optimal feature region set for robust digital image watermarking.

    Science.gov (United States)

    Tsai, Jen-Sheng; Huang, Win-Bin; Kuo, Yau-Hwang

    2011-03-01

    A novel feature region selection method for robust digital image watermarking is proposed in this paper. This method aims to select a nonoverlapping feature region set, which has the greatest robustness against various attacks and can preserve image quality as much as possible after watermarked. It first performs a simulated attacking procedure using some predefined attacks to evaluate the robustness of every candidate feature region. According to the evaluation results, it then adopts a track-with-pruning procedure to search a minimal primary feature set which can resist the most predefined attacks. In order to enhance its resistance to undefined attacks under the constraint of preserving image quality, the primary feature set is then extended by adding into some auxiliary feature regions. This work is formulated as a multidimensional knapsack problem and solved by a genetic algorithm based approach. The experimental results for StirMark attacks on some benchmark images support our expectation that the primary feature set can resist all the predefined attacks and its extension can enhance the robustness against undefined attacks. Comparing with some well-known feature-based methods, the proposed method exhibits better performance in robust digital watermarking.

  19. Prediction of hot spots in protein interfaces using a random forest model with hybrid features.

    Science.gov (United States)

    Wang, Lin; Liu, Zhi-Ping; Zhang, Xiang-Sun; Chen, Luonan

    2012-03-01

    Prediction of hot spots in protein interfaces provides crucial information for the research on protein-protein interaction and drug design. Existing machine learning methods generally judge whether a given residue is likely to be a hot spot by extracting features only from the target residue. However, hot spots usually form a small cluster of residues which are tightly packed together at the center of protein interface. With this in mind, we present a novel method to extract hybrid features which incorporate a wide range of information of the target residue and its spatially neighboring residues, i.e. the nearest contact residue in the other face (mirror-contact residue) and the nearest contact residue in the same face (intra-contact residue). We provide a novel random forest (RF) model to effectively integrate these hybrid features for predicting hot spots in protein interfaces. Our method can achieve accuracy (ACC) of 82.4% and Matthew's correlation coefficient (MCC) of 0.482 in Alanine Scanning Energetics Database, and ACC of 77.6% and MCC of 0.429 in Binding Interface Database. In a comparison study, performance of our RF model exceeds other existing methods, such as Robetta, FOLDEF, KFC, KFC2, MINERVA and HotPoint. Of our hybrid features, three physicochemical features of target residues (mass, polarizability and isoelectric point), the relative side-chain accessible surface area and the average depth index of mirror-contact residues are found to be the main discriminative features in hot spots prediction. We also confirm that hot spots tend to form large contact surface areas between two interacting proteins. Source data and code are available at: http://www.aporc.org/doc/wiki/HotSpot.

  20. Feature-based Analysis of Large-scale Spatio-Temporal Sensor Data on Hybrid Architectures.

    Science.gov (United States)

    Saltz, Joel; Teodoro, George; Pan, Tony; Cooper, Lee; Kong, Jun; Klasky, Scott; Kurc, Tahsin

    2013-08-01

    Analysis of large sensor datasets for structural and functional features has applications in many domains, including weather and climate modeling, characterization of subsurface reservoirs, and biomedicine. The vast amount of data obtained from state-of-the-art sensors and the computational cost of analysis operations create a barrier to such analyses. In this paper, we describe middleware system support to take advantage of large clusters of hybrid CPU-GPU nodes to address the data and compute-intensive requirements of feature-based analyses in large spatio-temporal datasets.

  1. Conceptual Design of Hybrid Safety Features for NPP by Utilizing Solar Updraft Tower

    Energy Technology Data Exchange (ETDEWEB)

    Song, Sub Lee [Handong Global University, Pohang (Korea, Republic of); Choi, Young Jae; Kim, Yong Jin [KAIST, Daejeon (Korea, Republic of); Park, Hyo Chan; Park, Youn Won [BEES, Daejeon (Korea, Republic of)

    2016-05-15

    In this study, hybrid safety features for NPP with solar updraft tower (SUT) is conceptually suggested to cope with loss of ultimate heat sink accident. The hybrid safety features utilizing SUT target NPPs in seashore of Arabian Gulf. Usually NPPs are constructed near seashore to utilize sea water as an ultimate heat sink. Residual heat or decay heat of nuclear reactor will diffuse into the ocean through the condenser. NPPs in Middle East are expected to be placed in seashore of Arabian Gulf. The NPP site of Barakah is an actual example. For NPPs in seashore of Arabian Gulf, an additional safety concern should be considered. Arabian Gulf is the largest oil transporting route in the world. The oil spill risk in Arabian Gulf will be the largest simultaneously. Unfortunately, not like other oceans, Arabian Gulf is a kind of closed ocean which does not have strong ocean currents connected to out of the gulf. If once oil spill is occurred, its influence can be propagated more than our expectation. The spilled oil also can affect to NPPs in seashore by covering surfaces of condenser. It will directly cause loss of ultimate heat sink. The hybrid safety features of SUT system are expected to aid normal operation of safety system and mitigate consequence of severe accident. Detail analysis and technology development is ongoing now.

  2. A HYBRID APPROACH BASED MEDICAL IMAGE RETRIEVAL SYSTEM USING FEATURE OPTIMIZED CLASSIFICATION SIMILARITY FRAMEWORK

    Directory of Open Access Journals (Sweden)

    Yogapriya Jaganathan

    2013-01-01

    Full Text Available For the past few years, massive upgradation is obtained in the pasture of Content Based Medical Image Retrieval (CBMIR for effective utilization of medical images based on visual feature analysis for the purpose of diagnosis and educational research. The existing medical image retrieval systems are still not optimal to solve the feature dimensionality reduction problem which increases the computational complexity and decreases the speed of a retrieval process. The proposed CBMIR is used a hybrid approach based on Feature Extraction, Optimization of Feature Vectors, Classification of Features and Similarity Measurements. This type of CBMIR is called Feature Optimized Classification Similarity (FOCS framework. The selected features are Textures using Gray level Co-occurrence Matrix Features (GLCM and Tamura Features (TF in which extracted features are formed as feature vector database. The Fuzzy based Particle Swarm Optimization (FPSO technique is used to reduce the feature vector dimensionality and classification is performed using Fuzzy based Relevance Vector Machine (FRVM to form groups of relevant image features that provide a natural way to classify dimensionally reduced feature vectors of images. The Euclidean Distance (ED is used as similarity measurement to measure the significance between the query image and the target images. This FOCS approach can get the query from the user and has retrieved the needed images from the databases. The retrieval algorithm performances are estimated in terms of precision and recall. This FOCS framework comprises several benefits when compared to existing CBMIR. GLCM and TF are used to extract texture features and form a feature vector database. Fuzzy-PSO is used to reduce the feature vector dimensionality issues while selecting the important features in the feature vector database in which computational complexity is decreased. Fuzzy based RVM is used for feature classification in which it increases the

  3. Feature subset selection based on mahalanobis distance: a statistical rough set method

    Institute of Scientific and Technical Information of China (English)

    Sun Liang; Han Chongzhao

    2008-01-01

    In order to select effective feature subsets for pattern classification, a novel statistics rough set method is presented based on generalized attribute reduction. Unlike classical reduction approaches, the objects in universe of discourse are signs of training sample sets and values of attributes are taken as statistical parameters. The binary relation and discernibility matrix for the reduction are induced by distance function. Furthermore, based on the monotony of the distance function defined by Mahalanobis distance, the effective feature subsets are obtained as generalized attribute reducts. Experiment result shows that the classification performance can be improved by using the selected feature subsets.

  4. Entropy based unsupervised Feature Selection in digital mammogram image using rough set theory.

    Science.gov (United States)

    Velayutham, C; Thangavel, K

    2012-01-01

    Feature Selection (FS) is a process, which attempts to select features, which are more informative. In the supervised FS methods various feature subsets are evaluated using an evaluation function or metric to select only those features, which are related to the decision classes of the data under consideration. However, for many data mining applications, decision class labels are often unknown or incomplete, thus indicating the significance of unsupervised FS. However, in unsupervised learning, decision class labels are not provided. The problem is that not all features are important. Some of the features may be redundant, and others may be irrelevant and noisy. In this paper, a novel unsupervised FS in mammogram image, using rough set-based entropy measures, is proposed. A typical mammogram image processing system generally consists of mammogram image acquisition, pre-processing of image, segmentation, features extracted from the segmented mammogram image. The proposed method is used to select features from data set, the method is compared with the existing rough set-based supervised FS methods and classification performance of both methods are recorded and demonstrates the efficiency of the method.

  5. Decision forests for machine learning classification of large, noisy seafloor feature sets

    Science.gov (United States)

    Lawson, Ed; Smith, Denson; Sofge, Donald; Elmore, Paul; Petry, Frederick

    2017-02-01

    Extremely randomized trees (ET) classifiers, an extension of random forests (RF) are applied to classification of features such as seamounts derived from bathymetry data. This data is characterized by sparse training data from by large noisy features sets such as often found in other geospatial data. A variety of feature metrics may be useful for this task and we use a large number of metrics relevant to the task of finding seamounts. The major significant results to be described include: an outstanding seamount classification accuracy of 97%; an automated process to produce the most useful classification features that are relevant to geophysical scientists (as represented by the feature metrics); demonstration that topography provides the most important data representation for classification. As well as achieving good accuracy in classification, the human-understandable set of metrics generated by the classifier that are most relevant for the results are discussed.

  6. Selecting Optimal Feature Set in High-Dimensional Data by Swarm Search

    Directory of Open Access Journals (Sweden)

    Simon Fong

    2013-01-01

    Full Text Available Selecting the right set of features from data of high dimensionality for inducing an accurate classification model is a tough computational challenge. It is almost a NP-hard problem as the combinations of features escalate exponentially as the number of features increases. Unfortunately in data mining, as well as other engineering applications and bioinformatics, some data are described by a long array of features. Many feature subset selection algorithms have been proposed in the past, but not all of them are effective. Since it takes seemingly forever to use brute force in exhaustively trying every possible combination of features, stochastic optimization may be a solution. In this paper, we propose a new feature selection scheme called Swarm Search to find an optimal feature set by using metaheuristics. The advantage of Swarm Search is its flexibility in integrating any classifier into its fitness function and plugging in any metaheuristic algorithm to facilitate heuristic search. Simulation experiments are carried out by testing the Swarm Search over some high-dimensional datasets, with different classification algorithms and various metaheuristic algorithms. The comparative experiment results show that Swarm Search is able to attain relatively low error rates in classification without shrinking the size of the feature subset to its minimum.

  7. Fast detection of covert visuospatial attention using hybrid N2pc and SSVEP features

    Science.gov (United States)

    Xu, Minpeng; Wang, Yijun; Nakanishi, Masaki; Wang, Yu-Te; Qi, Hongzhi; Jung, Tzyy-Ping; Ming, Dong

    2016-12-01

    Objective. Detecting the shift of covert visuospatial attention (CVSA) is vital for gaze-independent brain-computer interfaces (BCIs), which might be the only communication approach for severely disabled patients who cannot move their eyes. Although previous studies had demonstrated that it is feasible to use CVSA-related electroencephalography (EEG) features to control a BCI system, the communication speed remains very low. This study aims to improve the speed and accuracy of CVSA detection by fusing EEG features of N2pc and steady-state visual evoked potential (SSVEP). Approach. A new paradigm was designed to code the left and right CVSA with the N2pc and SSVEP features, which were then decoded by a classification strategy based on canonical correlation analysis. Eleven subjects were recruited to perform an offline experiment in this study. Temporal waves, amplitudes, and topographies for brain responses related to N2pc and SSVEP were analyzed. The classification accuracy derived from the hybrid EEG features (SSVEP and N2pc) was compared with those using the single EEG features (SSVEP or N2pc). Main results. The N2pc could be significantly enhanced under certain conditions of SSVEP modulations. The hybrid EEG features achieved significantly higher accuracy than the single features. It obtained an average accuracy of 72.9% by using a data length of 400 ms after the attention shift. Moreover, the average accuracy reached ˜80% (peak values above 90%) when using 2 s long data. Significance. The results indicate that the combination of N2pc and SSVEP is effective for fast detection of CVSA. The proposed method could be a promising approach for implementing a gaze-independent BCI.

  8. Daily reservoir inflow forecasting using multiscale deep feature learning with hybrid models

    Science.gov (United States)

    Bai, Yun; Chen, Zhiqiang; Xie, Jingjing; Li, Chuan

    2016-01-01

    Inflow forecasting applies data supports for the operations and managements of reservoirs. A multiscale deep feature learning (MDFL) method with hybrid models is proposed in this paper to deal with the daily reservoir inflow forecasting. Ensemble empirical mode decomposition and Fourier spectrum are first employed to extract multiscale (trend, period and random) features, which are then represented by three deep belief networks (DBNs), respectively. The weights of each DBN are subsequently applied to initialize a neural network (D-NN). The outputs of the three-scale D-NNs are finally reconstructed using a sum-up strategy toward the forecasting results. A historical daily inflow series (from 1/1/2000 to 31/12/2012) of the Three Gorges reservoir, China, is investigated by the proposed MDFL with hybrid models. For comparison, four peer models are adopted for the same task. The results show that, the present model overwhelms all the peer models in terms of mean absolute percentage error (MAPE = 11.2896%), normalized root-mean-square error (NRMSE = 0.2292), determination coefficient criteria (R2 = 0.8905), and peak percent threshold statistics (PPTS(5) = 10.0229%). The addressed method integrates the deep framework with multiscale and hybrid observations, and therefore being good at exploring sophisticated natures in the reservoir inflow forecasting.

  9. Initial and final fruit set in some plum (Prunus domestica L. hybrids under different pollination types

    Directory of Open Access Journals (Sweden)

    Glišić Ivana

    2012-01-01

    Full Text Available The paper presents results of two-year study (2009-2010 of initial and final fruit set in promising plum (Prunus domestica L. hybrids developed at Fruit Research Institute - Čačak, under different pollination conditions. The following hybrids were studied: 38/62/70 (‘Hall’ x ‘California Blue’, 32/21/87 (‘Stanley’ x ‘Scoldus’, IV/63/81 (‘Large Sugar Prune’ x ‘Scoldus’, 22/17/87 (‘Čačanska Najbolja’ x ‘Zh'lta Butilcovidna’, 29/29/87 (‘Stanley’ x ‘Scoldus’ and 34/41/87 (‘Valjevka’ x ‘Čačanska Lepotica’. Each of the hybrids was studied both under self- pollination and open pollination. In vitro pollen germination was also performed as well as characteristics of flowering phenophase and flowering abundance. Generally, the results suggest lower flowering abundance in the second year of the study. Pollen germination ranged from averagely 25.31% (29/29/87 to 40.01% (38/62/70. With averagely 31.59% final fruit set under self-pollination and 29.38% under open pollination variants, respectively, hybrid 34/41/87 gave the best results. The lowest performance was observed in hybrid 32/21/87 with 1.61% and 7.69% final fruit set under self- and open pollination variants, respectively. [Projekat Ministarstva nauke Republike Srbije, br. TR-31064

  10. A hybrid feature selection approach for the early diagnosis of Alzheimer’s disease

    Science.gov (United States)

    Gallego-Jutglà, Esteve; Solé-Casals, Jordi; Vialatte, François-Benoît; Elgendi, Mohamed; Cichocki, Andrzej; Dauwels, Justin

    2015-02-01

    Objective. Recently, significant advances have been made in the early diagnosis of Alzheimer’s disease (AD) from electroencephalography (EEG). However, choosing suitable measures is a challenging task. Among other measures, frequency relative power (RP) and loss of complexity have been used with promising results. In the present study we investigate the early diagnosis of AD using synchrony measures and frequency RP on EEG signals, examining the changes found in different frequency ranges. Approach. We first explore the use of a single feature for computing the classification rate (CR), looking for the best frequency range. Then, we present a multiple feature classification system that outperforms all previous results using a feature selection strategy. These two approaches are tested in two different databases, one containing mild cognitive impairment (MCI) and healthy subjects (patients age: 71.9 ± 10.2, healthy subjects age: 71.7 ± 8.3), and the other containing Mild AD and healthy subjects (patients age: 77.6 ± 10.0 healthy subjects age: 69.4 ± 11.5). Main results. Using a single feature to compute CRs we achieve a performance of 78.33% for the MCI data set and of 97.56% for Mild AD. Results are clearly improved using the multiple feature classification, where a CR of 95% is found for the MCI data set using 11 features, and 100% for the Mild AD data set using four features. Significance. The new features selection method described in this work may be a reliable tool that could help to design a realistic system that does not require prior knowledge of a patient's status. With that aim, we explore the standardization of features for MCI and Mild AD data sets with promising results.

  11. Quantum Statistical Mechanical Derivation of the Second Law of Thermodynamics: A Hybrid Setting Approach.

    Science.gov (United States)

    Tasaki, Hal

    2016-04-29

    Based on quantum statistical mechanics and microscopic quantum dynamics, we prove Planck's and Kelvin's principles for macroscopic systems in a general and realistic setting. We consider a hybrid quantum system that consists of the thermodynamic system, which is initially in thermal equilibrium, and the "apparatus" which operates on the former, and assume that the whole system evolves autonomously. This provides a satisfactory derivation of the second law for macroscopic systems.

  12. Feature subset selection based on mahalanobis distance: a statistical rough set method

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    In order to select effective feature subsets for pattern classification, a novel statistics rough set method is presented based on generalized attribute reduction. Unlike classical reduction approaches, the objects in universe of discourse are signs of training sample sets and values of attributes are taken as statistical parameters. The binary relation and discernibility matrix for the reduction are induced by distance function. Furthermore, based on the monotony of the distance function defined by Mahalan...

  13. Zone Based Hybrid Feature Extraction Algorithm for Handwritten Numeral Recognition of South Indian Scripts

    Science.gov (United States)

    Rajashekararadhya, S. V.; Ranjan, P. Vanaja

    India is a multi-lingual multi script country, where eighteen official scripts are accepted and have over hundred regional languages. In this paper we propose a zone based hybrid feature extraction algorithm scheme towards the recognition of off-line handwritten numerals of south Indian scripts. The character centroid is computed and the image (character/numeral) is further divided in to n equal zones. Average distance and Average angle from the character centroid to the pixels present in the zone are computed (two features). Similarly zone centroid is computed (two features). This procedure is repeated sequentially for all the zones/grids/boxes present in the numeral image. There could be some zones that are empty, and then the value of that particular zone image value in the feature vector is zero. Finally 4*n such features are extracted. Nearest neighbor classifier is used for subsequent classification and recognition purpose. We obtained 97.55 %, 94 %, 92.5% and 95.2 % recognition rate for Kannada, Telugu, Tamil and Malayalam numerals respectively.

  14. Pose-Independent Face Recognition Using Biologically Inspired Feature Set and Mixture of Experts

    Directory of Open Access Journals (Sweden)

    Reza Azad

    2014-08-01

    Full Text Available Automatic face recognition system has received significant attention during the last decades due to its wide range of applications, such as security, human-computer interaction, visual surveillance, and so on. In this paper, a new and efficient face recognition method, based on features inspired by the human’s visual cortex and applying mixture of experts’ architecture on the extracted feature set is proposed. A feature set is extracted by means of a feed-forward model, which contains a view and illumination invariant C2 features from all images in the data set. Then, these C2 feature vector which derived from a cortex-like mechanism passed to a mixture of multilayer perceptron neural networks. In the result part, the proposed approach is applied on ORL and Yale databases and the accuracy rate achieved 99.75% and 100% respectively. In addition, experimental results have demonstrated our method robust in successful recognition of human faces even with variant lighting and poses.

  15. A Multistage Feature Selection Model for Document Classification Using Information Gain and Rough Set

    Directory of Open Access Journals (Sweden)

    Mrs. Leena. H. Patil

    2014-11-01

    Full Text Available Huge number of documents are increasing rapidly, therefore, to organize it in digitized form text categorization becomes an challenging issue. A major issue for text categorization is its large number of features. Most of the features are noisy, irrelevant and redundant, which may mislead the classifier. Hence, it is most important to reduce dimensionality of data to get smaller subset and provide the most gain in information. Feature selection techniques reduce the dimensionality of feature space. It also improves the overall accuracy and performance. Hence, to overcome the issues of text categorization feature selection is considered as an efficient technique . Therefore, we, proposed a multistage feature selection model to improve the overall accuracy and performance of classification. In the first stage document preprocessing part is performed. Secondly, each term within the documents are ranked according to their importance for classification using the information gain. Thirdly rough set technique is applied to the terms which are ranked importantly and feature reduction is carried out. Finally a document classification is performed on the core features using Naive Bayes and KNN classifier. Experiments are carried out on three UCI datasets, Reuters 21578, Classic 04 and Newsgroup 20. Results show the better accuracy and performance of the proposed model.

  16. Rough set-based hybrid fuzzy-neural controller design for industrial wastewater treatment.

    Science.gov (United States)

    Chen, W C; Chang, Ni-Bin; Chen, Jeng-Chung

    2003-01-01

    Recent advances in control engineering suggest that hybrid control strategies, integrating some ideas and paradigms existing in different soft computing techniques, such as fuzzy logic, genetic algorithms, rough set theory, and neural networks, may provide improved control performance in wastewater treatment processes. This paper presents an innovative hybrid control algorithm leading to integrate the distinct aspects of indiscernibility capability of rough set theory and search capability of genetic algorithms with conventional neural-fuzzy controller design. The methodology proposed in this study employs a three-stage analysis that is designed in series for generating a representative state function, searching for a set of multi-objective control strategies, and performing a rough set-based autotuning for the neural-fuzzy logic controller to make it applicable for controlling an industrial wastewater treatment process. Research findings in the case study clearly indicate that the use of rough set theory to aid in the neural-fuzzy logic controller design can produce relatively better plant performance in terms of operating cost, control stability, and response time simultaneously, which is effective at least in the selected industrial wastewater treatment plant. Such a methodology is anticipated to be capable of dealing with many other types of process control problems in waste treatment processes by making only minor modifications.

  17. SOME FEATURES OF HYDROLYSIS OF THE HYBRID B-Z-FORM DNA BY SERRATIA MARCESCENS NUCLEASE

    Directory of Open Access Journals (Sweden)

    Maria Filimonova

    2014-01-01

    Full Text Available Highly polymerized herring testis DNA of the random nucleotide sequence was used as a model of natural substrate to study some features of hydrolysis of the hybrid B-Z form with Serratia marcescens nuclease. The hybrid B-Z-form was formed upon addition of 1.15 M MgSO4 and 0.421 mM Co(NH36Cl3. The DNA transition from the right handed B-form to the hybrid B-Z-form caused a decrease in Vmax of DNA cleavage with the nuclease. The diminishing Vmax was consistent with diminishing values of Km and Kcat. The binding of Mg2+ or Co(NH363+ to highly polymerized DNA caused correspondingly about 80-or 7-fold decrease in Km and more than 1600 or 600 decrease in Kcat compared with that of Mg-DNA complex of B-form.

  18. Management and performance features of cancer centers in Europe: A fuzzy-set analysis

    NARCIS (Netherlands)

    Wind, Anke; Lobo, Mariana Fernandes; van Dijk, Joris; Lepage-Nefkens, Isabelle; Laranja-Pontes, Jose; da Conceicao Goncalves, Vitor; van Harten, Willem H.; Rocha-Goncalves, Francisco Nuno

    2016-01-01

    The specific aim of this study is to identify the performance features of cancer centers in the European Union by using a fuzzy-set qualitative comparative analysis (fsQCA). The fsQCA method represents cases (cancer centers) as a combination of explanatory and outcome conditions. This study uses

  19. Fuzzy rough sets, and a granular neural network for unsupervised feature selection.

    Science.gov (United States)

    Ganivada, Avatharam; Ray, Shubhra Sankar; Pal, Sankar K

    2013-12-01

    A granular neural network for identifying salient features of data, based on the concepts of fuzzy set and a newly defined fuzzy rough set, is proposed. The formation of the network mainly involves an input vector, initial connection weights and a target value. Each feature of the data is normalized between 0 and 1 and used to develop granulation structures by a user defined α-value. The input vector and the target value of the network are defined using granulation structures, based on the concept of fuzzy sets. The same granulation structures are also presented to a decision system. The decision system helps in extracting the domain knowledge about data in the form of dependency factors, using the notion of new fuzzy rough set. These dependency factors are assigned as the initial connection weights of the proposed network. It is then trained using minimization of a novel feature evaluation index in an unsupervised manner. The effectiveness of the proposed network, in evaluating selected features, is demonstrated on several real-life datasets. The results of FRGNN are found to be statistically more significant than related methods in 28 instances of 40 instances, i.e., 70% of instances, using the paired t-test.

  20. Supervised feature evaluation by consistency analysis: application to measure sets used to characterise geographic objects

    CERN Document Server

    Taillandier, Patrick

    2012-01-01

    Nowadays, supervised learning is commonly used in many domains. Indeed, many works propose to learn new knowledge from examples that translate the expected behaviour of the considered system. A key issue of supervised learning concerns the description language used to represent the examples. In this paper, we propose a method to evaluate the feature set used to describe them. Our method is based on the computation of the consistency of the example base. We carried out a case study in the domain of geomatic in order to evaluate the sets of measures used to characterise geographic objects. The case study shows that our method allows to give relevant evaluations of measure sets.

  1. Hybrid Feature Extraction-based Approach for Facial Parts Representation and Recognition

    Science.gov (United States)

    Rouabhia, C.; Tebbikh, H.

    2008-06-01

    Face recognition is a specialized image processing which has attracted a considerable attention in computer vision. In this article, we develop a new facial recognition system from video sequences images dedicated to person identification whose face is partly occulted. This system is based on a hybrid image feature extraction technique called ACPDL2D (Rouabhia et al. 2007), it combines two-dimensional principal component analysis and two-dimensional linear discriminant analysis with neural network. We performed the feature extraction task on the eyes and the nose images separately then a Multi-Layers Perceptron classifier is used. Compared to the whole face, the results of simulation are in favor of the facial parts in terms of memory capacity and recognition (99.41% for the eyes part, 98.16% for the nose part and 97.25 % for the whole face).

  2. An ensemble method with hybrid features to identify extracellular matrix proteins.

    Science.gov (United States)

    Yang, Runtao; Zhang, Chengjin; Gao, Rui; Zhang, Lina

    2015-01-01

    The extracellular matrix (ECM) is a dynamic composite of secreted proteins that play important roles in numerous biological processes such as tissue morphogenesis, differentiation and homeostasis. Furthermore, various diseases are caused by the dysfunction of ECM proteins. Therefore, identifying these important ECM proteins may assist in understanding related biological processes and drug development. In view of the serious imbalance in the training dataset, a Random Forest-based ensemble method with hybrid features is developed in this paper to identify ECM proteins. Hybrid features are employed by incorporating sequence composition, physicochemical properties, evolutionary and structural information. The Information Gain Ratio and Incremental Feature Selection (IGR-IFS) methods are adopted to select the optimal features. Finally, the resulting predictor termed IECMP (Identify ECM Proteins) achieves an balanced accuracy of 86.4% using the 10-fold cross-validation on the training dataset, which is much higher than results obtained by other methods (ECMPRED: 71.0%, ECMPP: 77.8%). Moreover, when tested on a common independent dataset, our method also achieves significantly improved performance over ECMPP and ECMPRED. These results indicate that IECMP is an effective method for ECM protein prediction, which has a more balanced prediction capability for positive and negative samples. It is anticipated that the proposed method will provide significant information to fully decipher the molecular mechanisms of ECM-related biological processes and discover candidate drug targets. For public access, we develop a user-friendly web server for ECM protein identification that is freely accessible at http://iecmp.weka.cc.

  3. Prediction of protein secondary structure using probability based features and a hybrid system.

    Science.gov (United States)

    Ghanty, Pradip; Pal, Nikhil R; Mudi, Rajani K

    2013-10-01

    In this paper, we propose some co-occurrence probability-based features for prediction of protein secondary structure. The features are extracted using occurrence/nonoccurrence of secondary structures in the protein sequences. We explore two types of features: position-specific (based on position of amino acid on fragments of protein sequences) as well as position-independent (independent of amino acid position on fragments of protein sequences). We use a hybrid system, NEUROSVM, consisting of neural networks and support vector machines for classification of secondary structures. We propose two schemes NSVMps and NSVM for protein secondary structure prediction. The NSVMps uses position-specific probability-based features and NEUROSVM classifier whereas NSVM uses the same classifier with position-independent probability-based features. The proposed method falls in the single-sequence category of methods because it does not use any sequence profile information such as position specific scoring matrices (PSSM) derived from PSI-BLAST. Two widely used datasets RS126 and CB513 are used in the experiments. The results obtained using the proposed features and NEUROSVM classifier are better than most of the existing single-sequence prediction methods. Most importantly, the results using NSVMps that are obtained using lower dimensional features, are comparable to those by other existing methods. The NSVMps and NSVM are finally tested on target proteins of the critical assessment of protein structure prediction experiment-9 (CASP9). A larger dataset is used to compare the performance of the proposed methods with that of two recent single-sequence prediction methods. We also investigate the impact of presence of different amino acid residues (in protein sequences) that are responsible for the formation of different secondary structures.

  4. Mental sets in conduct problem youth with psychopathic features: entity versus incremental theories of intelligence.

    Science.gov (United States)

    Salekin, Randall T; Lester, Whitney S; Sellers, Mary-Kate

    2012-08-01

    The purpose of the current study was to examine the effect of a motivational intervention on conduct problem youth with psychopathic features. Specifically, the current study examined conduct problem youths' mental set (or theory) regarding intelligence (entity vs. incremental) upon task performance. We assessed 36 juvenile offenders with psychopathic features and tested whether providing them with two different messages regarding intelligence would affect their functioning on a task related to academic performance. The study employed a MANOVA design with two motivational conditions and three outcomes including fluency, flexibility, and originality. Results showed that youth with psychopathic features who were given a message that intelligence grows over time, were more fluent and flexible than youth who were informed that intelligence is static. There were no significant differences between the groups in terms of originality. The implications of these findings are discussed including the possible benefits of interventions for adolescent offenders with conduct problems and psychopathic features.

  5. Generalizations of the subject-independent feature set for music-induced emotion recognition.

    Science.gov (United States)

    Lin, Yuan-Pin; Chen, Jyh-Horng; Duann, Jeng-Ren; Lin, Chin-Teng; Jung, Tzyy-Ping

    2011-01-01

    Electroencephalogram (EEG)-based emotion recognition has been an intensely growing field. Yet, how to achieve acceptable accuracy on a practical system with as fewer electrodes as possible is less concerned. This study evaluates a set of subject-independent features, based on differential power asymmetry of symmetric electrode pairs [1], with emphasis on its applicability to subject variability in music-induced emotion classification problem. Results of this study have evidently validated the feasibility of using subject-independent EEG features to classify four emotional states with acceptable accuracy in second-scale temporal resolution. These features could be generalized across subjects to detect emotion induced by music excerpts not limited to the music database that was used to derive the emotion-specific features.

  6. Studying non-linearity of Mercury magnetosphere and solar wind interaction features using the combined hybrid model

    Science.gov (United States)

    Parunakian, David; Dyadechkin, Sergey; Alexeev, Igor; Belenkaya, Elena; Khodachenko, Maxim; Kallio, Esa; Alho, Markku

    2017-04-01

    The main focus of this work is to investigate non-linearity of Mercury's magnetospheric features. We use the paraboloid magnetospheric model (PMM) to calculate the initial magnetospheric field which we then use in hybrid simulations. We show that the initial total magnetospheric field can be considered a linear combination of the planetary dipole field, magnetospheric current system fields, and a penetrating portion of the interplanetary magnetic field (IMF). We use two sets of modelling runs with IMF values of identical magnitudes and anti-parallel directions. We then compute semi-sums and semi-differences of final magnetic field maps generated by hybrid plasma simulations, and use semi-sums to cancel out IMF contributions and semi-differences to cancel out PMM contributions. The remnant fields outside and inside the magnetosphere (for semi-sum and semi-difference fields, accordingly) are used to improve our ability to determine the position of the bow shock and magnetopause, as well as calculate the IMF penetration coefficient that results into best matches of this model to observational MESSENGER data. We compare Mercury's magnetosheath magnetic field predicted by our model with MESSENGER data in the appropriate orbit sections.

  7. Real-Time Hand Gesture Recognition based on Modified Contour Chain Code Feature Set

    Directory of Open Access Journals (Sweden)

    Reza Azad

    2014-07-01

    Full Text Available Hand gesture recognition and pattern recognition are the growing fields of research. Gestures are the motion of the body or physical action form by the user in order to convey some meaningful information. In this paper we propose a robust and efficient method for real-time hand gesture recognition system. In the suggested method, first, the hand gesture is extracted from the main image by edge detection and morphological operation and then is sent to feature extraction stage. In feature extraction stage modified contour chain code feature set is extracted. Finally in classification stage, we employ multiclass support vector machine (SVM as classifier. In the result part, the proposed approach is applied on American Sign Language (ASL database and the accuracy rate obtained 99.40%. Further, we obtained 99.80% accuracy using five-fold cross validation technique on ASL database.

  8. Coastal karren features in temperate microtidal settings: spatial organization and temporal evolution

    Directory of Open Access Journals (Sweden)

    Lluís Gómez-Pujol

    2010-04-01

    Full Text Available Basin pools are the diagnostic feature of Coastal Karren landscape at temperate settings. According to the size and connectivity parameters four morphological zones are identified along limestone coastal profiles. Each zone reflects the balance between the effects of physical and chemical weathering-erosion agents. Broadly, marine abrasion, bioerosion and biological driven solution show a larger influence seaward, whereas non-biological driven solution enhances its participation landward

  9. Complex extreme learning machine applications in terahertz pulsed signals feature sets.

    Science.gov (United States)

    Yin, X-X; Hadjiloucas, S; Zhang, Y

    2014-11-01

    This paper presents a novel approach to the automatic classification of very large data sets composed of terahertz pulse transient signals, highlighting their potential use in biochemical, biomedical, pharmaceutical and security applications. Two different types of THz spectra are considered in the classification process. Firstly a binary classification study of poly-A and poly-C ribonucleic acid samples is performed. This is then contrasted with a difficult multi-class classification problem of spectra from six different powder samples that although have fairly indistinguishable features in the optical spectrum, they also possess a few discernable spectral features in the terahertz part of the spectrum. Classification is performed using a complex-valued extreme learning machine algorithm that takes into account features in both the amplitude as well as the phase of the recorded spectra. Classification speed and accuracy are contrasted with that achieved using a support vector machine classifier. The study systematically compares the classifier performance achieved after adopting different Gaussian kernels when separating amplitude and phase signatures. The two signatures are presented as feature vectors for both training and testing purposes. The study confirms the utility of complex-valued extreme learning machine algorithms for classification of the very large data sets generated with current terahertz imaging spectrometers. The classifier can take into consideration heterogeneous layers within an object as would be required within a tomographic setting and is sufficiently robust to detect patterns hidden inside noisy terahertz data sets. The proposed study opens up the opportunity for the establishment of complex-valued extreme learning machine algorithms as new chemometric tools that will assist the wider proliferation of terahertz sensing technology for chemical sensing, quality control, security screening and clinic diagnosis. Furthermore, the proposed

  10. A Discrete Wavelet Based Feature Extraction and Hybrid Classification Technique for Microarray Data Analysis

    Directory of Open Access Journals (Sweden)

    Jaison Bennet

    2014-01-01

    Full Text Available Cancer classification by doctors and radiologists was based on morphological and clinical features and had limited diagnostic ability in olden days. The recent arrival of DNA microarray technology has led to the concurrent monitoring of thousands of gene expressions in a single chip which stimulates the progress in cancer classification. In this paper, we have proposed a hybrid approach for microarray data classification based on nearest neighbor (KNN, naive Bayes, and support vector machine (SVM. Feature selection prior to classification plays a vital role and a feature selection technique which combines discrete wavelet transform (DWT and moving window technique (MWT is used. The performance of the proposed method is compared with the conventional classifiers like support vector machine, nearest neighbor, and naive Bayes. Experiments have been conducted on both real and benchmark datasets and the results indicate that the ensemble approach produces higher classification accuracy than conventional classifiers. This paper serves as an automated system for the classification of cancer and can be applied by doctors in real cases which serve as a boon to the medical community. This work further reduces the misclassification of cancers which is highly not allowed in cancer detection.

  11. Partial imputation to improve predictive modelling in insurance risk classification using a hybrid positive selection algorithm and correlation-based feature selection

    CSIR Research Space (South Africa)

    Duma, M

    2013-09-01

    Full Text Available We propose a hybrid missing data imputation technique using positive selection and correlation-based feature selection for insurance data. The hybrid is used to help supervised learning methods improve their classification accuracy and resilience...

  12. A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features

    Directory of Open Access Journals (Sweden)

    P. Amudha

    2015-01-01

    Full Text Available Intrusion detection has become a main part of network security due to the huge number of attacks which affects the computers. This is due to the extensive growth of internet connectivity and accessibility to information systems worldwide. To deal with this problem, in this paper a hybrid algorithm is proposed to integrate Modified Artificial Bee Colony (MABC with Enhanced Particle Swarm Optimization (EPSO to predict the intrusion detection problem. The algorithms are combined together to find out better optimization results and the classification accuracies are obtained by 10-fold cross-validation method. The purpose of this paper is to select the most relevant features that can represent the pattern of the network traffic and test its effect on the success of the proposed hybrid classification algorithm. To investigate the performance of the proposed method, intrusion detection KDDCup’99 benchmark dataset from the UCI Machine Learning repository is used. The performance of the proposed method is compared with the other machine learning algorithms and found to be significantly different.

  13. Topological Properties and Transition Features Generated by a New Hybrid Preferential Model

    Institute of Scientific and Technical Information of China (English)

    FANG Jin-Qing; LIANG Yong

    2005-01-01

    @@ A new hybrid preferential model (HPM) is proposed for generating both scale-free and small world properties.The topological transition features in the HPM from random preferential attachment to deterministic preferential attachment are investigated. It is found that the exponents γ of the power law are very sensitive to the hybrid ratio (d/r) of determination to random attachment, and γincreases as the ratio d/r increases. It is also found that there exists a threshold at d/r = 1/1, beyond which γ increases rapidly and can tend to infinity if there is no random preferential attachment (r = 0), which implies that the power law scaling disappears completely.Moreover, it is also found that when the ratio d/r increases, the average path length L is decreased, while the average clustering coefficient C is increased. Compared to the BA model and random graph, the new HPM has both the smallest L and the biggest C, which is consistent with most real-world growing networks.

  14. Hybridization of Evolutionary Mechanisms for Feature Subset Selection in Unsupervised Learning

    Science.gov (United States)

    Torres, Dolores; Ponce-de-León, Eunice; Torres, Aurora; Ochoa, Alberto; Díaz, Elva

    Feature subset selection for unsupervised learning, is a very important topic in artificial intelligence because it is the base for saving computational resources. In this implementation we use a typical testor’s methodology in order to incorporate an importance index for each variable. This paper presents the general framework and the way two hybridized meta-heuristics work in this NP-complete problem. The evolutionary mechanisms are based on the Univariate Marginal Distribution Algorithm (UMDA) and the Genetic Algorithm (GA). GA and UMDA - Estimation of Distribution Algorithm (EDA) use a very useful rapid operator implemented for finding typical testors on a very large dataset and also, both algorithms, have a local search mechanism for improving time and fitness. Experiments show that EDA is faster than GA because it has a better exploitation performance; nevertheless, GA’ solutions are more consistent.

  15. Multilevel Wavelet Feature Statistics for Efficient Retrieval, Transmission, and Display of Medical Images by Hybrid Encoding

    Science.gov (United States)

    Yang, Shuyu; Mitra, Sunanda; Corona, Enrique; Nutter, Brian; Lee, DJ

    2003-12-01

    Many common modalities of medical images acquire high-resolution and multispectral images, which are subsequently processed, visualized, and transmitted by subsampling. These subsampled images compromise resolution for processing ability, thus risking loss of significant diagnostic information. A hybrid multiresolution vector quantizer (HMVQ) has been developed exploiting the statistical characteristics of the features in a multiresolution wavelet-transformed domain. The global codebook generated by HMVQ, using a combination of multiresolution vector quantization and residual scalar encoding, retains edge information better and avoids significant blurring observed in reconstructed medical images by other well-known encoding schemes at low bit rates. Two specific image modalities, namely, X-ray radiographic and magnetic resonance imaging (MRI), have been considered as examples. The ability of HMVQ in reconstructing high-fidelity images at low bit rates makes it particularly desirable for medical image encoding and fast transmission of 3D medical images generated from multiview stereo pairs for visual communications.

  16. Credit scoring using ensemble of various classifiers on reduced feature set

    Directory of Open Access Journals (Sweden)

    Dahiya Shashi

    2015-01-01

    Full Text Available Credit scoring methods are widely used for evaluating loan applications in financial and banking institutions. Credit score identifies if applicant customers belong to good risk applicant group or a bad risk applicant group. These decisions are based on the demographic data of the customers, overall business by the customer with bank, and loan payment history of the loan applicants. The advantages of using credit scoring models include reducing the cost of credit analysis, enabling faster credit decisions and diminishing possible risk. Many statistical and machine learning techniques such as Logistic Regression, Support Vector Machines, Neural Networks and Decision tree algorithms have been used independently and as hybrid credit scoring models. This paper proposes an ensemble based technique combining seven individual models to increase the classification accuracy. Feature selection has also been used for selecting important attributes for classification. Cross classification was conducted using three data partitions. German credit dataset having 1000 instances and 21 attributes is used in the present study. The results of the experiments revealed that the ensemble model yielded a very good accuracy when compared to individual models. In all three different partitions, the ensemble model was able to classify more than 80% of the loan customers as good creditors correctly. Also, for 70:30 partition there was a good impact of feature selection on the accuracy of classifiers. The results were improved for almost all individual models including the ensemble model.

  17. Modeling the Formation Process of Grouping Stimuli Sets through Cortical Columns and Microcircuits to Feature Neurons

    Directory of Open Access Journals (Sweden)

    Frank Klefenz

    2013-01-01

    Full Text Available A computational model of a self-structuring neuronal net is presented in which repetitively applied pattern sets induce the formation of cortical columns and microcircuits which decode distinct patterns after a learning phase. In a case study, it is demonstrated how specific neurons in a feature classifier layer become orientation selective if they receive bar patterns of different slopes from an input layer. The input layer is mapped and intertwined by self-evolving neuronal microcircuits to the feature classifier layer. In this topical overview, several models are discussed which indicate that the net formation converges in its functionality to a mathematical transform which maps the input pattern space to a feature representing output space. The self-learning of the mathematical transform is discussed and its implications are interpreted. Model assumptions are deduced which serve as a guide to apply model derived repetitive stimuli pattern sets to in vitro cultures of neuron ensembles to condition them to learn and execute a mathematical transform.

  18. A Hybrid method of face detection based on Feature Extraction using PIFR and Feature Optimization using TLBO

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    Kapil Verma

    2016-01-01

    Full Text Available In this paper we proposed a face detection method based on feature selection and feature optimization. Now in current research trend of biometric security used the process of feature optimization for better improvement of face detection technique. Basically our face consists of three types of feature such as skin color, texture and shape and size of face. The most important feature of face is skin color and texture of face. In this detection technique used texture feature of face image. For the texture extraction of image face used partial feature extraction function, these function is most promising shape feature analysis. For the selection of feature and optimization of feature used multi-objective TLBO. TLBO algorithm is population based searching technique and defines two constraints function for the process of selection and optimization. The proposed algorithm of face detection based on feature selection and feature optimization process. Initially used face image data base and passes through partial feature extractor function and these transform function gives a texture feature of face image. For the evaluation of performance our proposed algorithm implemented in MATLAB 7.8.0 software and face image used provided by Google face image database. For numerical analysis of result used hit and miss ratio. Our empirical evaluation of result shows better prediction result in compression of PIFR method of face detection.

  19. Hybrid Binary Imperialist Competition Algorithm and Tabu Search Approach for Feature Selection Using Gene Expression Data

    Science.gov (United States)

    Aorigele; Zeng, Weiming; Hong, Xiaomin

    2016-01-01

    Gene expression data composed of thousands of genes play an important role in classification platforms and disease diagnosis. Hence, it is vital to select a small subset of salient features over a large number of gene expression data. Lately, many researchers devote themselves to feature selection using diverse computational intelligence methods. However, in the progress of selecting informative genes, many computational methods face difficulties in selecting small subsets for cancer classification due to the huge number of genes (high dimension) compared to the small number of samples, noisy genes, and irrelevant genes. In this paper, we propose a new hybrid algorithm HICATS incorporating imperialist competition algorithm (ICA) which performs global search and tabu search (TS) that conducts fine-tuned search. In order to verify the performance of the proposed algorithm HICATS, we have tested it on 10 well-known benchmark gene expression classification datasets with dimensions varying from 2308 to 12600. The performance of our proposed method proved to be superior to other related works including the conventional version of binary optimization algorithm in terms of classification accuracy and the number of selected genes. PMID:27579323

  20. Feature Reduction Based on Genetic Algorithm and Hybrid Model for Opinion Mining

    Directory of Open Access Journals (Sweden)

    P. Kalaivani

    2015-01-01

    Full Text Available With the rapid growth of websites and web form the number of product reviews is available on the sites. An opinion mining system is needed to help the people to evaluate emotions, opinions, attitude, and behavior of others, which is used to make decisions based on the user preference. In this paper, we proposed an optimized feature reduction that incorporates an ensemble method of machine learning approaches that uses information gain and genetic algorithm as feature reduction techniques. We conducted comparative study experiments on multidomain review dataset and movie review dataset in opinion mining. The effectiveness of single classifiers Naïve Bayes, logistic regression, support vector machine, and ensemble technique for opinion mining are compared on five datasets. The proposed hybrid method is evaluated and experimental results using information gain and genetic algorithm with ensemble technique perform better in terms of various measures for multidomain review and movie reviews. Classification algorithms are evaluated using McNemar’s test to compare the level of significance of the classifiers.

  1. Real-Time Illumination Invariant Face Detection Using Biologically Inspired Feature Set and BP Neural Network

    Directory of Open Access Journals (Sweden)

    Reza Azad

    2014-06-01

    Full Text Available In recent years, face detection has been thoroughly studied due to its wide potential applications, including face recognition, human-computer interaction, video surveillance, etc.In this paper, a new and illumination invariant face detection method, based on features inspired by the human's visual cortexand applying BP neural network on the extracted featureset is proposed.A feature set is extracted from face and non-face images, by means of a feed-forward model, which contains a view and illumination invariant C2 features from all images in the dataset. Then, these C2 feature vector which derived from a cortex-like mechanism passed to a BP neural network. In the result part, the proposed approach is applied on FEI and Wild face detection databases and high accuracy rate is achieved. In addition, experimental results have demonstrated our proposed face detector outperforms the most of the successful face detection algorithms in the literature and gives the first best result on all tested challenging face detection databases.

  2. Feature Geo Analytics and Big Data Processing: Hybrid Approaches for Earth Science and Real-Time Decision Support

    Science.gov (United States)

    Wright, D. J.; Raad, M.; Hoel, E.; Park, M.; Mollenkopf, A.; Trujillo, R.

    2016-12-01

    Introduced is a new approach for processing spatiotemporal big data by leveraging distributed analytics and storage. A suite of temporally-aware analysis tools summarizes data nearby or within variable windows, aggregates points (e.g., for various sensor observations or vessel positions), reconstructs time-enabled points into tracks (e.g., for mapping and visualizing storm tracks), joins features (e.g., to find associations between features based on attributes, spatial relationships, temporal relationships or all three simultaneously), calculates point densities, finds hot spots (e.g., in species distributions), and creates space-time slices and cubes (e.g., in microweather applications with temperature, humidity, and pressure, or within human mobility studies). These "feature geo analytics" tools run in both batch and streaming spatial analysis mode as distributed computations across a cluster of servers on typical "big" data sets, where static data exist in traditional geospatial formats (e.g., shapefile) locally on a disk or file share, attached as static spatiotemporal big data stores, or streamed in near-real-time. In other words, the approach registers large datasets or data stores with ArcGIS Server, then distributes analysis across a cluster of machines for parallel processing. Several brief use cases will be highlighted based on a 16-node server cluster at 14 Gb RAM per node, allowing, for example, the buffering of over 8 million points or thousands of polygons in 1 minute. The approach is "hybrid" in that ArcGIS Server integrates open-source big data frameworks such as Apache Hadoop and Apache Spark on the cluster in order to run the analytics. In addition, the user may devise and connect custom open-source interfaces and tools developed in Python or Python Notebooks; the common denominator being the familiar REST API.

  3. Research on Heuristic Feature Extraction and Classification of EEG Signal Based on BCI Data Set

    Directory of Open Access Journals (Sweden)

    Lijuan Duan

    2013-01-01

    Full Text Available In this study, an EEG signal classification framework was proposed. The framework contained three feature extraction methods refer to optimization strategy. Firstly, we selected optimal electrodes based on the single electrode classification performance and combined all the optimal electrodes’ data as the feature. Then, we discussed the contribution of each time span of EEG signals for each electrode and joined all the optimal time spans’ data together to be used for classifying. In addition, we further selected useful information from original data based on genetic algorithm. Finally, the performances were evaluated by Bayes and SVM classifiers on BCI 2003 Competition data set Ia. And the accuracy of genetic algorithm has reached 91.81%. The experimental results show that our methods offer the better performance for reliable classification of the EEG signal.

  4. Detection of Small-Scaled Features Using Landsat and Sentinel-2 Data Sets

    Science.gov (United States)

    Steensen, Torge; Muller, Sonke; Dresen, Boris; Buscher, Olaf

    2016-08-01

    In advanced times of renewable energies, our attention has to be on secondary features that can be utilised to enhance our independence from fossil fuels. In terms of biomass, this focus lies on small-scaled features like vegetation units alongside roads or hedges between agricultural fields. Currently, there is no easily- accessible inventory, if at all, outlining the growth and re-growth patterns of such vegetation. Since they are trimmed at least annually to allow the passing of traffic, we can, theoretically, harvest the cut and convert it into energy. This, however, requires a map outlining the vegetation growth and the potential energy amount at different locations as well as adequate transport routes and potential processing plant locations. With the help of Landsat and Sentinel-2 data sets, we explore the possibilities to create such a map. Additional data is provided in the form of regularly acquired, airborne orthophotos and GIS-based infrastructure data.

  5. An intelligent hybrid scheme for optimizing parking space: A Tabu metaphor and rough set based approach

    Directory of Open Access Journals (Sweden)

    Soumya Banerjee

    2011-03-01

    Full Text Available Congested roads, high traffic, and parking problems are major concerns for any modern city planning. Congestion of on-street spaces in official neighborhoods may give rise to inappropriate parking areas in office and shopping mall complex during the peak time of official transactions. This paper proposes an intelligent and optimized scheme to solve parking space problem for a small city (e.g., Mauritius using a reactive search technique (named as Tabu Search assisted by rough set. Rough set is being used for the extraction of uncertain rules that exist in the databases of parking situations. The inclusion of rough set theory depicts the accuracy and roughness, which are used to characterize uncertainty of the parking lot. Approximation accuracy is employed to depict accuracy of a rough classification [1] according to different dynamic parking scenarios. And as such, the hybrid metaphor proposed comprising of Tabu Search and rough set could provide substantial research directions for other similar hard optimization problems.

  6. Person-specific named entity recognition using SVM with rich feature sets

    Institute of Scientific and Technical Information of China (English)

    Hui; NIE

    2012-01-01

    Purpose:The purpose of the study is to explore the potential use of nature language process(NLP)and machine learning(ML)techniques and intents to find a feasible strategy and effective approach to fulfill the NER task for Web oriented person-specific information extraction.Design/methodology/approach:An SVM-based multi-classification approach combined with a set of rich NLP features derived from state-of-the-art NLP techniques has been proposed to fulfill the NER task.A group of experiments has been designed to investigate the influence of various NLP-based features to the performance of the system,especially the semantic features.Optimal parameter settings regarding with SVM models,including kernel functions,margin parameter of SVM model and the context window size,have been explored through experiments as well.Findings:The SVM-based multi-classification approach has been proved to be effective for the NER task.This work shows that NLP-based features are of great importance in datadriven NE recognition,particularly the semantic features.The study indicates that higher order kernel function may not be desirable for the specific classification problem in practical application.The simple linear-kernel SVM model performed better in this case.Moreover,the modified SVM models with uneven margin parameter are more common and flexible,which have been proved to solve the imbalanced data problem better.Research limitations/implications:The SVM-based approach for NER problem is only proved to be effective on limited experiment data.Further research need to be conducted on the large batch of real Web data.In addition,the performance of the NER system need be tested when incorporated into a complete IE framework.Originality/value:The specially designed experiments make it feasible to fully explore the characters of the data and obtain the optimal parameter settings for the NER task,leading to a preferable rate in recall,precision and F1measures.The overall system performance

  7. A set-theoretic approach to linguistic feature structures and unification algorithms (I

    Directory of Open Access Journals (Sweden)

    N. Curteanu

    2000-10-01

    Full Text Available The paper proposes formal inductive definitions for linguistic feature structures (FSs taking values within a class of value types or sorts: single, disjunctive, (ordered lists, multisets (or bags, po-multisets (multisets embedded into a partially ordered set, and indexed (re-entrance values. The linguistic realization (semantics of the considered sorts is proposed. The FSs having these multi-sort values are organized as (rooted directed acyclic graphs. The concrete model of the FSs we had in mind for our set-theoretic definitions are the FSs used within the well-known HPSG linguistic theory. Set-theoretic general definitions for the proposed multi-sort FSs are defined. These constructive definitions start from atomic values and build recurrently multi-sorted values and structures, providing naturally a fixed-point semantics of the obtained FSs as a counterpart to the large class of logical semantics models on FSs. The linguistic unification algorithm based on tableau-subsumption is outlined. The Prolog code of the unification algorithm is provided and results of running it on some of the main multi-sort FSs is enclosed in the appendices. We consider the proposed formal approach to FS definitions and unification as necessary steps to set-theoretical implementations of natural language processing systems.

  8. A set-theoretic approach to linguistic feature structures and unification algorithms (II

    Directory of Open Access Journals (Sweden)

    N.Curteanu

    2001-02-01

    Full Text Available The paper proposes formal inductive definitions for linguistic feature structures (FSs taking values within a class of value types or sorts: single, disjunctive, (ordered lists, multisets (or bags, po-multisets (multisets embedded into a partially ordered set, and indexed (re-entrance values. The linguistic realization (semantics of the considered sorts is proposed. The FSs having these multi-sort values are organized as (rooted directed acyclic graphs. The concrete model of the FSs we had in mind for our set-theoretic definitions are the FSs used within the well-known HPSG linguistic theory. Set-theoretic general definitions for the proposed multi-sort FSs are defined. These constructive definitions start from atomic values and build recurrent multi-sorted values and structures, providing naturally a fixed-point semantics of the obtained FSs as a counterpart to the large class of logical semantics models on FSs. The linguistic unification algorithm based on tableau-subsumption is outlined. The Prolog code of the unification algorithm is provided and results of running it on some of the main multi-sort FSs is enclosed in the appendices. We consider the proposed formal approach to FSs definitions and unification as necessary steps to set-theoretical implementations of natural language processing systems.

  9. Improved residue contact prediction using support vector machines and a large feature set

    Directory of Open Access Journals (Sweden)

    Baldi Pierre

    2007-04-01

    Full Text Available Abstract Background Predicting protein residue-residue contacts is an important 2D prediction task. It is useful for ab initio structure prediction and understanding protein folding. In spite of steady progress over the past decade, contact prediction remains still largely unsolved. Results Here we develop a new contact map predictor (SVMcon that uses support vector machines to predict medium- and long-range contacts. SVMcon integrates profiles, secondary structure, relative solvent accessibility, contact potentials, and other useful features. On the same test data set, SVMcon's accuracy is 4% higher than the latest version of the CMAPpro contact map predictor. SVMcon recently participated in the seventh edition of the Critical Assessment of Techniques for Protein Structure Prediction (CASP7 experiment and was evaluated along with seven other contact map predictors. SVMcon was ranked as one of the top predictors, yielding the second best coverage and accuracy for contacts with sequence separation >= 12 on 13 de novo domains. Conclusion We describe SVMcon, a new contact map predictor that uses SVMs and a large set of informative features. SVMcon yields good performance on medium- to long-range contact predictions and can be modularly incorporated into a structure prediction pipeline.

  10. Using hybrid associative classifier with translation (HACT for studying imbalanced data sets

    Directory of Open Access Journals (Sweden)

    Laura Cleofas Sánchez

    2012-04-01

    Full Text Available Class imbalance may reduce the classifier performance in several recognition pattern problems. Such negative effect is more notable with least represented class (minority class Patterns. A strategy for handling this problem consisted of treating the classes included in this problem separately (majority and minority classes to balance the data sets (DS. This paper has studied high sensitivity to class imbalance shown by an associative model of classification: hybrid associative classifier with translation (HACT; imbalanced DS impact on associative model performance was studied. The convenience of using sub-sampling methods for decreasing imbalanced negative effects on associative memories was analysed. This proposal’s feasibility was based on experimental results obtained from eleven real-world datasets.

  11. The prescribing clinical health psychologist: a hybrid skill set in the new era of integrated healthcare.

    Science.gov (United States)

    McGuinness, Kevin M

    2012-12-01

    The prescribing clinical health psychologist brings together in one individual a combination of skills to create a hybrid profession that can add value to any healthcare organization. This article addresses the high demand for mental health services and the inequitable distribution of mental health practitioners across the nation. The close link between physical and mental health and evidence that individuals in psychological distress often enter the mental health system via primary care medical clinics is offered as background to a discussion of the author's work as a commissioned officer of the U.S. Public Health Service assigned to the Chaparral Medical Center of La Clinica de Familia, Inc. near the U.S.-Mexico border. The prescribing clinical health psychologist in primary care medical settings is described as a valuable asset to the future of professional psychology.

  12. Application of Microarray-Based Comparative Genomic Hybridization in Prenatal and Postnatal Settings: Three Case Reports

    Directory of Open Access Journals (Sweden)

    Jing Liu

    2011-01-01

    Full Text Available Microarray-based comparative genomic hybridization (array CGH is a newly emerged molecular cytogenetic technique for rapid evaluation of the entire genome with sub-megabase resolution. It allows for the comprehensive investigation of thousands and millions of genomic loci at once and therefore enables the efficient detection of DNA copy number variations (a.k.a, cryptic genomic imbalances. The development and the clinical application of array CGH have revolutionized the diagnostic process in patients and has provided a clue to many unidentified or unexplained diseases which are suspected to have a genetic cause. In this paper, we present three clinical cases in both prenatal and postnatal settings. Among all, array CGH played a major discovery role to reveal the cryptic and/or complex nature of chromosome arrangements. By identifying the genetic causes responsible for the clinical observation in patients, array CGH has provided accurate diagnosis and appropriate clinical management in a timely and efficient manner.

  13. Types of tree growth and fruit setting in F1 apple hybrids

    Directory of Open Access Journals (Sweden)

    Radu E. SESTRAS

    1998-08-01

    Full Text Available 1656 F1 hybrid apple seedlings, belonging to 127 combinations, have been screened according to their growing and fruit setting types, as it was phenotypically expressed. LESPINASSE (1977; 1992 amalgamated these two traits into a single one which was named ";ideotype";. The screened F1 individuals have been considered as resembling one of the following four architectural ideotypes of the trees indicated by Lespinasse: columnar, spur, standard and weeping. Different ratios of spur, standard and columnar F1 individuals were obtained depending on genitors and on the fact that a certain genitor had been used as a maternal or paternal partner in direct/reciprocal crosses. The monogenic inheritance of the columnar ideotype, proposed by Kelsey and Brown (1992; Lane (1992, does not seem to be the only genetic mechanism involved in the inheritance of this trait. Our experimental results suggest the polygenic determination of this ideotype as more probable than the monogenic one.

  14. Study of Machine-Learning Classifier and Feature Set Selection for Intent Classification of Korean Tweets about Food Safety

    Directory of Open Access Journals (Sweden)

    Yeom, Ha-Neul

    2014-09-01

    Full Text Available In recent years, several studies have proposed making use of the Twitter micro-blogging service to track various trends in online media and discussion. In this study, we specifically examine the use of Twitter to track discussions of food safety in the Korean language. Given the irregularity of keyword use in most tweets, we focus on optimistic machine-learning and feature set selection to classify collected tweets. We build the classifier model using Naive Bayes & Naive Bayes Multinomial, Support Vector Machine, and Decision Tree Algorithms, all of which show good performance. To select an optimum feature set, we construct a basic feature set as a standard for performance comparison, so that further test feature sets can be evaluated. Experiments show that precision and F-measure performance are best when using a Naive Bayes Multinomial classifier model with a test feature set defined by extracting Substantive, Predicate, Modifier, and Interjection parts of speech.

  15. Indirect Effects of Field Management on Pollination Service and Seed Set in Hybrid Onion Seed Production.

    Science.gov (United States)

    Gillespie, Sandra; Long, Rachael; Williams, Neal

    2015-12-01

    Pollination in crops, as in native ecosystems, is a stepwise process that can be disrupted at any stage. Healthy pollinator populations are critical for adequate visitation, but pollination still might fail if crop management interferes with the attraction and retention of pollinators. Farmers must balance the direct benefits of applying insecticide and managing irrigation rates against their potential to indirectly interfere with the pollination process. We investigated these issues in hybrid onion seed production, where previous research has shown that high insecticide use reduces pollinator attraction. We conducted field surveys of soil moisture, nectar production, pollinator visitation, pollen-stigma interactions, and seed set at multiple commercial fields across 2 yr. We then examined how management actions, such as irrigation rate (approximated by soil moisture), or insecticide use could affect the pollination process. Onions produced maximum nectar at intermediate soil moisture, and high nectar production attracted more pollinators. Insecticide use weakly affected pollinator visitation, but when applied close to bloom reduced pollen germination and pollen tube growth. Ultimately, neither soil moisture nor insecticide use directly affected seed set, but the high correlation between pollinator visitation and seed set suggests that crop management will ultimately affect yields via indirect effects on the pollination process. © The Authors 2015. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  16. Generation of New Genotypic and Phenotypic Features in Artificial and Natural Yeast Hybrids

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    Walter P. Pfliegler

    2014-01-01

    Full Text Available Evolution and genome stabilization have mostly been studied on the Saccharomyces hybrids isolated from natural and alcoholic fermentation environments. Genetic and phenotypic properties have usually been compared to the laboratory and reference strains, as the true ancestors of the natural hybrid yeasts are unknown. In this way the exact impact of different parental fractions on the genome organization or metabolic activity of the hybrid yeasts is difficult to resolve completely. In the present work the evolution of geno- and phenotypic properties is studied in the interspecies hybrids created by the cross-breeding of S. cerevisiae with S. uvarum or S. kudriavzevii auxotrophic mutants. We hypothesized that the extent of genomic alterations in S. cerevisiae × S. uvarum and S. cerevisiae × S. kudriavzevii should affect the physiology of their F1 offspring in different ways. Our results, obtained by amplified fragment length polymorphism (AFLP genotyping and karyotyping analyses, showed that both subgenomes of the S. cerevisiae x S. uvarum and of S. cerevisiae × S. kudriavzevii hybrids experienced various modifications. However, the S. cerevisiae × S. kudriavzevii F1 hybrids underwent more severe genomic alterations than the S. cerevisiae × S. uvarum ones. Generation of the new genotypes also influenced the physiological performances of the hybrids and the occurrence of novel phenotypes. Significant differences in carbohydrate utilization and distinct growth dynamics at increasing concentrations of sodium chloride, urea and miconazole were observed within and between the S. cerevisiae × S. uvarum and S. cerevisiae × S. kudriavzevii hybrids. Parental strains also demonstrated different contributions to the final metabolic outcomes of the hybrid yeasts. A comparison of the genotypic properties of the artificial hybrids with several hybrid isolates from the wine-related environments and wastewater demonstrated a greater genetic variability of

  17. A Comparative Performance Study of Hybrid SET-CMOS Based Logic Circuits for the Estimation of Robustness

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    Biswabandhu Jana

    2013-10-01

    Full Text Available The urge of inventing a new low power consuming device for the post CMOS future technology has drawn the attention of the researchers on Single Electron Transistor [SET]. The two main virtues, ultra low power consumption [1] and ultra small dimension of SET [12, 13] have stimulated the researchers to consider it as a possible alternative. In our past paper [1] we have designed and simulated some basic gates. In this paper we have designed and simulated hybrid SET-CMOS based counter circuits, shift register to show that the hybrid SET-MOS based circuits consumes the lesser power than MOS based circuits. All the simulation were done and verified in Tanner environment using the MIB model for SET and the BSIM4.6.1 model for MOSFET.

  18. Hybrid Deep Network and Polar Transformation Features for Static Hand Gesture Recognition in Depth Data

    Directory of Open Access Journals (Sweden)

    Vo Hoai Viet

    2016-05-01

    Full Text Available Static hand gesture recognition is an interesting and challenging problem in computer vision. It is considered a significant component of Human Computer Interaction and it has attracted many research efforts from the computer vision community in recent decades for its high potential applications, such as game interaction and sign language recognition. With the recent advent of the cost-effective Kinect, depth cameras have received a great deal of attention from researchers. It promoted interest within the vision and robotics community for its broad applications. In this paper, we propose the effective hand segmentation from the full depth image that is important step before extracting the features to represent for hand gesture. We also represent the novel hand descriptor explicitly encodes the shape and appearance information from depth maps that are significant characteristics for static hand gestures. We propose hand descriptor based on Polar Transformation coordinate is called Histogram of Polar Transformation (HPT in order to capture both shape and appearance. Beside a robust hand descriptor, a robust classification model also plays a very important role in the hand recognition model. In order to have a high performance in recognition rate, we propose hybrid model for classification based on Sparse Auto-encoder and Deep Neural Network. We demonstrate large improvements over the state-of-the-art methods on two challenging benchmark datasets are NTU Hand Digits and ASL Finger Spelling and achieve the overall accuracy as 97.7% and 84.58%, respectively. Our experiments show that the proposed method significantly outperforms state-of-the-art techniques.

  19. 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.

  20. Combination of Biorthogonal Wavelet Hybrid Kernel OCSVM with Feature Weighted Approach Based on EVA and GRA in Financial Distress Prediction

    Directory of Open Access Journals (Sweden)

    Chao Huang

    2014-01-01

    Full Text Available Financial distress prediction plays an important role in the survival of companies. In this paper, a novel biorthogonal wavelet hybrid kernel function is constructed by combining linear kernel function with biorthogonal wavelet kernel function. Besides, a new feature weighted approach is presented based on economic value added (EVA and grey relational analysis (GRA. Considering the imbalance between financially distressed companies and normal ones, the feature weighted one-class support vector machine based on biorthogonal wavelet hybrid kernel (BWH-FWOCSVM is further put forward for financial distress prediction. The empirical study with real data from the listed companies on Growth Enterprise Market (GEM in China shows that the proposed approach has good performance.

  1. A Novel Evaluation Model for Hybrid Power System Based on Vague Set and Dempster-Shafer Evidence Theory

    Directory of Open Access Journals (Sweden)

    Dongxiao Niu

    2012-01-01

    Full Text Available Because clean energy and traditional energy have different advantages and disadvantages, it is of great significance to evaluate comprehensive benefits for hybrid power systems. Based on thorough analysis of important characters on hybrid power systems, an index system including security, economic benefit, environmental benefit, and social benefit is established in this paper. Due to advantages of processing abundant uncertain and fuzzy information, vague set is used to determine the decision matrix. Convert vague decision matrix to real one by vague combination ruleand determine uncertain degrees of different indexes by grey incidence analysis, then the mass functions of different comment set in different indexes are obtained. Information can be fused in accordance with Dempster-Shafer (D-S combination rule and the evaluation result is got by vague set and D-S evidence theory. A simulation of hybrid power system including thermal power, wind power, and photovoltaic power in China is provided to demonstrate the effectiveness and potential of the proposed design scheme. It can be clearly seen that the uncertainties in decision making can be dramatically decreased compared with existing methods in the literature. The actual implementation results illustrate that the proposed index system and evaluation model based on vague set and D-S evidence theory are effective and practical to evaluate comprehensive benefit of hybrid power system.

  2. ClusTrack: feature extraction and similarity measures for clustering of genome-wide data sets.

    Directory of Open Access Journals (Sweden)

    Halfdan Rydbeck

    Full Text Available Clustering is a popular technique for explorative analysis of data, as it can reveal subgroupings and similarities between data in an unsupervised manner. While clustering is routinely applied to gene expression data, there is a lack of appropriate general methodology for clustering of sequence-level genomic and epigenomic data, e.g. ChIP-based data. We here introduce a general methodology for clustering data sets of coordinates relative to a genome assembly, i.e. genomic tracks. By defining appropriate feature extraction approaches and similarity measures, we allow biologically meaningful clustering to be performed for genomic tracks using standard clustering algorithms. An implementation of the methodology is provided through a tool, ClusTrack, which allows fine-tuned clustering analyses to be specified through a web-based interface. We apply our methods to the clustering of occupancy of the H3K4me1 histone modification in samples from a range of different cell types. The majority of samples form meaningful subclusters, confirming that the definitions of features and similarity capture biological, rather than technical, variation between the genomic tracks. Input data and results are available, and can be reproduced, through a Galaxy Pages document at http://hyperbrowser.uio.no/hb/u/hb-superuser/p/clustrack. The clustering functionality is available as a Galaxy tool, under the menu option "Specialized analyzis of tracks", and the submenu option "Cluster tracks based on genome level similarity", at the Genomic HyperBrowser server: http://hyperbrowser.uio.no/hb/.

  3. H2RM: A Hybrid Rough Set Reasoning Model for Prediction and Management of Diabetes Mellitus.

    Science.gov (United States)

    Ali, Rahman; Hussain, Jamil; Siddiqi, Muhammad Hameed; Hussain, Maqbool; Lee, Sungyoung

    2015-07-03

    Diabetes is a chronic disease characterized by high blood glucose level that results either from a deficiency of insulin produced by the body, or the body's resistance to the effects of insulin. Accurate and precise reasoning and prediction models greatly help physicians to improve diagnosis, prognosis and treatment procedures of different diseases. Though numerous models have been proposed to solve issues of diagnosis and management of diabetes, they have the following drawbacks: (1) restricted one type of diabetes; (2) lack understandability and explanatory power of the techniques and decision; (3) limited either to prediction purpose or management over the structured contents; and (4) lack competence for dimensionality and vagueness of patient's data. To overcome these issues, this paper proposes a novel hybrid rough set reasoning model (H2RM) that resolves problems of inaccurate prediction and management of type-1 diabetes mellitus (T1DM) and type-2 diabetes mellitus (T2DM). For verification of the proposed model, experimental data from fifty patients, acquired from a local hospital in semi-structured format, is used. First, the data is transformed into structured format and then used for mining prediction rules. Rough set theory (RST) based techniques and algorithms are used to mine the prediction rules. During the online execution phase of the model, these rules are used to predict T1DM and T2DM for new patients. Furthermore, the proposed model assists physicians to manage diabetes using knowledge extracted from online diabetes guidelines. Correlation-based trend analysis techniques are used to manage diabetic observations. Experimental results demonstrate that the proposed model outperforms the existing methods with 95.9% average and balanced accuracies.

  4. Optimal setting of FACTS devices for voltage stability improvement using PSO adaptive GSA hybrid algorithm

    Directory of Open Access Journals (Sweden)

    Sai Ram Inkollu

    2016-09-01

    Full Text Available This paper presents a novel technique for optimizing the FACTS devices, so as to maintain the voltage stability in the power transmission systems. Here, the particle swarm optimization algorithm (PSO and the adaptive gravitational search algorithm (GSA technique are proposed for improving the voltage stability of the power transmission systems. In the proposed approach, the PSO algorithm is used for optimizing the gravitational constant and to improve the searching performance of the GSA. Using the proposed technique, the optimal settings of the FACTS devices are determined. The proposed algorithm is an effective method for finding out the optimal location and the sizing of the FACTS controllers. The optimal locations and the power ratings of the FACTS devices are determined based on the voltage collapse rating as well as the power loss of the system. Here, two FACTS devices are used to evaluate the performance of the proposed algorithm, namely, the unified power flow controller (UPFC and the interline power flow controller (IPFC. The Newton–Raphson load flow study is used for analyzing the power flow in the transmission system. From the power flow analysis, bus voltages, active power, reactive power, and power loss of the transmission systems are determined. Then, the voltage stability is enhanced while satisfying a given set of operating and physical constraints. The proposed technique is implemented in the MATLAB platform and consequently, its performance is evaluated and compared with the existing GA based GSA hybrid technique. The performance of the proposed technique is tested with the benchmark system of IEEE 30 bus using two FACTS devices such as, the UPFC and the IPFC.

  5. Sedimentary features of tsunami backwash deposits in a shallow marine Miocene setting, Mejillones Peninsula, northern Chile

    Science.gov (United States)

    Cantalamessa, Gino; Di Celma, Claudio

    2005-07-01

    Miocene shoreface sandstones in the Caleta Herradura half-graben, northern Chile, contain an exceptionally coarse deposit that, based on sedimentologic and stratigraphic features, is regarded as having been laid down during a tsunami event by non-cohesive and sediment-laden subaqueous density flows. Interpretations of the principal sediment-depositing mechanisms effective in the tsunami surges rely largely on field observations of deposit geometry and internal sedimentary characteristics. This example comprises two erosively based sedimentation units that were probably deposited by successive waves in the tsunami wave train. The Lower Unit consists of a clast-supported, polymodal, boulder-bearing breccia composed mostly of angular clasts and fewer well-rounded clasts. Framework components are mostly chaotic but may also exhibit either inverse-to-normal grading or crude normal grading. Laterally, changes in characters of depositional facies are common and abrupt. The sand-sized, bioclastic-rich matrix is poorly sorted and very similar to the underlying lower shoreface bioclastic sandstone, implying that soft sediments eroded at the lower erosional surface contributed to the tsunami deposit. The bulk of the Upper Unit is a poorly sorted, breccia-bearing sandstone. Pebbles and cobbles are scattered, massive or normally graded. Sporadic outsized boulders, emplaced as debris fall deposits, may occur along the erosional base. An array of signatures, such as unusually coarse grain size in comparison to the surrounding deposits, erosional bases, the mixed sources of sediments, multiple erosional and depositional events, normal size grading or massive texture, are all considered distinctive features of tsunamigenic deposits. Backwash deposition is indicated by the incorporation within the tsunami deposits of sediments derived from mixed sources, such as angular clasts from nearby subaerial settings, rounded clasts reworked from beach gravels, and bioclastic sand eroded from

  6. Pulmonary Nodule Detection Model Based on SVM and CT Image Feature-Level Fusion with Rough Sets

    Science.gov (United States)

    Lu, Huiling; Zhang, Junjie; Shi, Hongbin

    2016-01-01

    In order to improve the detection accuracy of pulmonary nodules in CT image, considering two problems of pulmonary nodules detection model, including unreasonable feature structure and nontightness of feature representation, a pulmonary nodules detection algorithm is proposed based on SVM and CT image feature-level fusion with rough sets. Firstly, CT images of pulmonary nodule are analyzed, and 42-dimensional feature components are extracted, including six new 3-dimensional features proposed by this paper and others 2-dimensional and 3-dimensional features. Secondly, these features are reduced for five times with rough set based on feature-level fusion. Thirdly, a grid optimization model is used to optimize the kernel function of support vector machine (SVM), which is used as a classifier to identify pulmonary nodules. Finally, lung CT images of 70 patients with pulmonary nodules are collected as the original samples, which are used to verify the effectiveness and stability of the proposed model by four groups' comparative experiments. The experimental results show that the effectiveness and stability of the proposed model based on rough set feature-level fusion are improved in some degrees.

  7. A hybrid semi-automatic method for liver segmentation based on level-set methods using multiple seed points.

    Science.gov (United States)

    Yang, Xiaopeng; Yu, Hee Chul; Choi, Younggeun; Lee, Wonsup; Wang, Baojian; Yang, Jaedo; Hwang, Hongpil; Kim, Ji Hyun; Song, Jisoo; Cho, Baik Hwan; You, Heecheon

    2014-01-01

    The present study developed a hybrid semi-automatic method to extract the liver from abdominal computerized tomography (CT) images. The proposed hybrid method consists of a customized fast-marching level-set method for detection of an optimal initial liver region from multiple seed points selected by the user and a threshold-based level-set method for extraction of the actual liver region based on the initial liver region. The performance of the hybrid method was compared with those of the 2D region growing method implemented in OsiriX using abdominal CT datasets of 15 patients. The hybrid method showed a significantly higher accuracy in liver extraction (similarity index, SI=97.6 ± 0.5%; false positive error, FPE = 2.2 ± 0.7%; false negative error, FNE=2.5 ± 0.8%; average symmetric surface distance, ASD=1.4 ± 0.5mm) than the 2D (SI=94.0 ± 1.9%; FPE = 5.3 ± 1.1%; FNE=6.5 ± 3.7%; ASD=6.7 ± 3.8mm) region growing method. The total liver extraction time per CT dataset of the hybrid method (77 ± 10 s) is significantly less than the 2D region growing method (575 ± 136 s). The interaction time per CT dataset between the user and a computer of the hybrid method (28 ± 4 s) is significantly shorter than the 2D region growing method (484 ± 126 s). The proposed hybrid method was found preferred for liver segmentation in preoperative virtual liver surgery planning.

  8. A hybrid feature selection algorithm integrating an extreme learning machine for landslide susceptibility modeling of Mt. Woomyeon, South Korea

    Science.gov (United States)

    Vasu, Nikhil N.; Lee, Seung-Rae

    2016-06-01

    An ever-increasing trend of extreme rainfall events in South Korea owing to climate change is causing shallow landslides and debris flows in mountains that cover 70% of the total land area of the nation. These catastrophic, gravity-driven processes cost the government several billion KRW (South Korean Won) in losses in addition to fatalities every year. The most common type of landslide observed is the shallow landslide, which occurs at 1-3 m depth, and may mobilize into more catastrophic flow-type landslides. Hence, to predict potential landslide areas, susceptibility maps are developed in a geographical information system (GIS) environment utilizing available morphological, hydrological, geotechnical, and geological data. Landslide susceptibility models were developed using 163 landslide points and an equal number of nonlandslide points in Mt. Woomyeon, Seoul, and 23 landslide conditioning factors. However, because not all of the factors contribute to the determination of the spatial probability for landslide initiation, and a simple filter or wrapper-based approach is not efficient in identifying all of the relevant features, a feedback-loop-based hybrid algorithm was implemented in conjunction with a learning scheme called an extreme learning machine, which is based on a single-layer, feed-forward network. Validation of the constructed susceptibility model was conducted using a testing set of landslide inventory data through a prediction rate curve. The model selected 13 relevant conditioning factors out of the initial 23; and the resulting susceptibility map shows a success rate of 85% and a prediction rate of 89.45%, indicating a good performance, in contrast to the low success and prediction rate of 69.19% and 56.19%, respectively, as obtained using a wrapper technique.

  9. Synthesis and optical features of an europium organic-inorganic silicate hybrid

    Energy Technology Data Exchange (ETDEWEB)

    Franville, A.C.; Zambon, D.; Mahiou, R.; Chou, S.; Cousseins, J.C. [Universite Blaise Pascal, Aubiere (France). Lab. des Materiaux Inorganiques; Troin, Y. [Laboratoire de Chimie des Heterocycles et des Glucides, EA 987, Universite Blaise-Pascal and ENSCCF, F-63177 Aubiere Cedex (France)

    1998-07-24

    A europium organic-inorganic silicate hybrid was synthesized by grafting a coordinative group (dipicolinic acid) to a silicate network precursor (3-aminopropyltriethoxysilane) via a covalent bonding. Sol-gel process and complexation were performed using different experimental conditions. The hybrid materials, in particular the Eu{sup 3+} coordination mode, were characterized by infrared and luminescence spectroscopies. Morphology of the materials and TG analysis showed that grafted silica enhanced thermal and mechanical resistances of the organic part. (orig.) 7 refs.

  10. 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.

  11. A Hybrid FPGA/Coarse Parallel Processing Architecture for Multi-modal Visual Feature Descriptors

    DEFF Research Database (Denmark)

    Jensen, Lars Baunegaard With; Kjær-Nielsen, Anders; Alonso, Javier Díaz

    2008-01-01

    This paper describes the hybrid architecture developed for speeding up the processing of so-called multi-modal visual primitives which are sparse image descriptors extracted along contours. In the system, the first stages of visual processing are implemented on FPGAs due to their highly parallel...

  12. Hybrid Control of a Two-Wheeled Automatic-Balancing Robot with Backlash Feature

    DEFF Research Database (Denmark)

    Løhndorf, Petar Durdevic; Yang, Zhenyu

    2013-01-01

    This paper investigates the application of hybrid control for an automatic balancing robot system subject to backlash effect. The developed controller is a type of sliding mode controller, refereed to as a switching controller, with respect to different situations i.e., whether the backlash...

  13. Obscene Video Recognition Using Fuzzy SVM and New Sets of Features

    Directory of Open Access Journals (Sweden)

    Alireza Behrad

    2013-02-01

    Full Text Available In this paper, a novel approach for identifying normal and obscene videos is proposed. In order to classify different episodes of a video independently and discard the need to process all frames, first, key frames are extracted and skin regions are detected for groups of video frames starting with key frames. In the second step, three different features including 1- structural features based on single frame information, 2- features based on spatiotemporal volume and 3-motion-based features, are extracted for each episode of video. The PCA-LDA method is then applied to reduce the size of structural features and select more distinctive features. For the final step, we use fuzzy or a Weighted Support Vector Machine (WSVM classifier to identify video episodes. We also employ a multilayer Kohonen network as an initial clustering algorithm to increase the ability to discriminate between the extracted features into two classes of videos. Features based on motion and periodicity characteristics increase the efficiency of the proposed algorithm in videos with bad illumination and skin colour variation. The proposed method is evaluated using 1100 videos in different environmental and illumination conditions. The experimental results show a correct recognition rate of 94.2% for the proposed algorithm.

  14. Investigating small scale transient deformation features in convergent settings- Insights from analogue modeling

    Science.gov (United States)

    Santimano, T. N.; Rosenau, M.; Oncken, O.

    2013-12-01

    The evolution of a convergent orogenic belt can be dissected into a combination of small scale events. Deformation in the orogenic belts can range in a timescale from earthquake cycle to millions of years. Moreover, long term deformation trends are a composition of the smaller events that together create the final geometry of an orogen. Therefore, it is important to understand the complexity of these events in order to further understand large scale mechanics of deformation. In this study, we employ analogue models of sand wedges representing the brittle upper crust to visualize temporal and spatial deformation in a convergent setting. The time-series evolution of these convergent sand wedges is monitored by Particle Image Velocimetry (PIV). In addition, the stress change within the wedge, especially at the localization of strain i.e. faulting events and between fault events is monitored by a force sensor. The sensor is attached to the back wall, in the experimental setup and against which the sand wedge grows. In these experiments the effect of basal friction on the final geometry of the wedge is tested. This parameter is varied twice. Results show that displacement data from the PIV system, analyzed in the form of strain correlates well with data from the force sensor. On a larger scale, force increases (indicating a linear trend) until strain is localized and a fault is formed causing a sudden drop in force and a release of stress. The magnitude of the force drop after a fault has occurred is related mainly to the horizontal length of the fault. However between fault events, recordings of the force measurements show a cyclic pattern with a decreasing frequency towards a fault event. Over time and as the wedge grows and matures, this intra fault frequency decreases as well. Varying basal friction demonstrates a cutoff in the maximum stress a wedge can handle due to the strength of its base. Time-series image analysis of strain combined with stress analysis

  15. A Novel Hybrid Dimension Reduction Technique for Undersized High Dimensional Gene Expression Data Sets Using Information Complexity Criterion for Cancer Classification

    Directory of Open Access Journals (Sweden)

    Esra Pamukçu

    2015-01-01

    Full Text Available Gene expression data typically are large, complex, and highly noisy. Their dimension is high with several thousand genes (i.e., features but with only a limited number of observations (i.e., samples. Although the classical principal component analysis (PCA method is widely used as a first standard step in dimension reduction and in supervised and unsupervised classification, it suffers from several shortcomings in the case of data sets involving undersized samples, since the sample covariance matrix degenerates and becomes singular. In this paper we address these limitations within the context of probabilistic PCA (PPCA by introducing and developing a new and novel approach using maximum entropy covariance matrix and its hybridized smoothed covariance estimators. To reduce the dimensionality of the data and to choose the number of probabilistic PCs (PPCs to be retained, we further introduce and develop celebrated Akaike’s information criterion (AIC, consistent Akaike’s information criterion (CAIC, and the information theoretic measure of complexity (ICOMP criterion of Bozdogan. Six publicly available undersized benchmark data sets were analyzed to show the utility, flexibility, and versatility of our approach with hybridized smoothed covariance matrix estimators, which do not degenerate to perform the PPCA to reduce the dimension and to carry out supervised classification of cancer groups in high dimensions.

  16. Earthquake-induced liquefaction features in the coastal setting of South Carolina and in the fluvial setting of the New Madrid seismic zone

    Science.gov (United States)

    Obermeier, S.F.; Jacobson, R.B.; Smoot, J.P.; Weems, R.E.; Gohn, G.S.; Monroe, J.E.; Powars, D.S.

    1990-01-01

    Many types of liquefaction-related features (sand blows, fissures, lateral spreads, dikes, and sills) have been induced by earthquakes in coastal South Carolina and in the New Madrid seismic zone in the Central United States. In addition, abundant features of unknown and nonseismic origin are present. Geologic criteria for interpreting an earthquake origin in these areas are illustrated in practical applications; these criteria can be used to determine the origin of liquefaction features in many other geographic and geologic settings. In both coastal South Carolina and the New Madrid seismic zone, the earthquake-induced liquefaction features generally originated in clean sand deposits that contain no or few intercalated silt or clay-rich strata. The local geologic setting is a major influence on both development and surface expression of sand blows. Major factors controlling sand-blow formation include the thickness and physical properties of the deposits above the source sands, and these relationships are illustrated by comparing sand blows found in coastal South Carolina (in marine deposits) with sand blows found in the New Madrid seismic zone (in fluvial deposits). In coastal South Carolina, the surface stratum is typically a thin (about 1 m) soil that is weakly cemented with humate, and the sand blows are expressed as craters surrounded by a thin sheet of sand; in the New Madrid seismic zone the surface stratum generally is a clay-rich deposit ranging in thickness from 2 to 10 m, in which case sand blows characteristically are expressed as sand mounded above the original ground surface. Recognition of the various features described in this paper, and identification of the most probable origin for each, provides a set of important tools for understanding paleoseismicity in areas such as the Central and Eastern United States where faults are not exposed for study and strong seismic activity is infrequent.

  17. Toward accurate tooth segmentation from computed tomography images using a hybrid level set model

    Energy Technology Data Exchange (ETDEWEB)

    Gan, Yangzhou; Zhao, Qunfei [Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240 (China); Xia, Zeyang, E-mail: zy.xia@siat.ac.cn, E-mail: jing.xiong@siat.ac.cn; Hu, Ying [Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, and The Chinese University of Hong Kong, Shenzhen 518055 (China); Xiong, Jing, E-mail: zy.xia@siat.ac.cn, E-mail: jing.xiong@siat.ac.cn [Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 510855 (China); Zhang, Jianwei [TAMS, Department of Informatics, University of Hamburg, Hamburg 22527 (Germany)

    2015-01-15

    Purpose: A three-dimensional (3D) model of the teeth provides important information for orthodontic diagnosis and treatment planning. Tooth segmentation is an essential step in generating the 3D digital model from computed tomography (CT) images. The aim of this study is to develop an accurate and efficient tooth segmentation method from CT images. Methods: The 3D dental CT volumetric images are segmented slice by slice in a two-dimensional (2D) transverse plane. The 2D segmentation is composed of a manual initialization step and an automatic slice by slice segmentation step. In the manual initialization step, the user manually picks a starting slice and selects a seed point for each tooth in this slice. In the automatic slice segmentation step, a developed hybrid level set model is applied to segment tooth contours from each slice. Tooth contour propagation strategy is employed to initialize the level set function automatically. Cone beam CT (CBCT) images of two subjects were used to tune the parameters. Images of 16 additional subjects were used to validate the performance of the method. Volume overlap metrics and surface distance metrics were adopted to assess the segmentation accuracy quantitatively. The volume overlap metrics were volume difference (VD, mm{sup 3}) and Dice similarity coefficient (DSC, %). The surface distance metrics were average symmetric surface distance (ASSD, mm), RMS (root mean square) symmetric surface distance (RMSSSD, mm), and maximum symmetric surface distance (MSSD, mm). Computation time was recorded to assess the efficiency. The performance of the proposed method has been compared with two state-of-the-art methods. Results: For the tested CBCT images, the VD, DSC, ASSD, RMSSSD, and MSSD for the incisor were 38.16 ± 12.94 mm{sup 3}, 88.82 ± 2.14%, 0.29 ± 0.03 mm, 0.32 ± 0.08 mm, and 1.25 ± 0.58 mm, respectively; the VD, DSC, ASSD, RMSSSD, and MSSD for the canine were 49.12 ± 9.33 mm{sup 3}, 91.57 ± 0.82%, 0.27 ± 0.02 mm, 0

  18. Active vibration control of hybrid smart structures featuring piezoelectric films and electrorheological fluids

    Science.gov (United States)

    Choi, Seung-Bok; Park, Yong-Kun; Cheong, ChaeCheon

    1996-05-01

    This paper presents a proof-of-concept investigation on an active vibration control of a hybrid smart structure (HSS) consisting of a piezoelectric film actuator (PFA) and an electro- rheological fluid actuator (ERFA). Firstly, an HSS beam is constructed by inserting a starch- based electro-rheological fluid into a hollow composite beam and perfectly bonding two piezoelectric films on the upper and lower surfaces of the structure as an actuator and as a sensor, respectively. As for the PFA, a neuro-sliding mode controller (NSC) incorporating neural networks with the concept of sliding mode control is formulated. On the other hand, the control scheme for the ERFA is developed as a function of excitation frequencies on the basis of field-dependent frequency responses. An experimental implementation for the PFA and ERFA is then established to perform an active vibration control of the HSS in the transient and forced vibrations. Both the increment of damping ratios and the suppression of tip deflections are evaluated in order to demonstrate control effectiveness of the PFA, the ERFA, and the hybrid actuation. The experimental results exhibit a superior ability of the hybrid actuation system to tailor elastodynamic responses of the HSS rather than a single class of actuation system alone.

  19. A HYBRID FILTER AND WRAPPER FEATURE SELECTION APPROACH FOR DETECTING CONTAMINATION IN DRINKING WATER MANAGEMENT SYSTEM

    Directory of Open Access Journals (Sweden)

    S. VISALAKSHI

    2017-07-01

    Full Text Available Feature selection is an important task in predictive models which helps to identify the irrelevant features in the high - dimensional dataset. In this case of water contamination detection dataset, the standard wrapper algorithm alone cannot be applied because of the complexity. To overcome this computational complexity problem and making it lighter, filter-wrapper based algorithm has been proposed. In this work, reducing the feature space is a significant component of water contamination. The main findings are as follows: (1 The main goal is speeding up the feature selection process, so the proposed filter - based feature pre-selection is applied and guarantees that useful data are improbable to be detached in the initial stage which discussed briefly in this paper. (2 The resulting features are again filtered by using the Genetic Algorithm coded with Support Vector Machine method, where it facilitates to nutshell the subset of features with high accuracy and decreases the expense. Experimental results show that the proposed methods trim down redundant features effectively and achieved better classification accuracy.

  20. Hybrid Feature Selection Based Weighted Least Squares Twin Support Vector Machine Approach for Diagnosing Breast Cancer, Hepatitis, and Diabetes

    Directory of Open Access Journals (Sweden)

    Divya Tomar

    2015-01-01

    Full Text Available There is a necessity for analysis of a large amount of data in many fields such as healthcare, business, industries, and agriculture. Therefore, the need of the feature selection (FS technique for the researchers is quite evident in many fields of science, especially in computer science. Furthermore, an effective FS technique that is best suited to a particular learning algorithm is of great help for the researchers. Hence, this paper proposes a hybrid feature selection (HFS based efficient disease diagnostic model for Breast Cancer, Hepatitis, and Diabetes. A HFS is an efficient method that combines the positive aspects of both Filter and Wrapper FS approaches. The proposed model adopts weighted least squares twin support vector machine (WLSTSVM as a classification approach, sequential forward selection (SFS as a search strategy, and correlation feature selection (CFS to evaluate the importance of each feature. This model not only selects relevant feature subset but also efficiently deals with the data imbalance problem. The effectiveness of the HFS based WLSTSVM approach is examined on three well-known disease datasets taken from UCI repository with the help of predictive accuracy, sensitivity, specificity, and geometric mean. The experiment confirms that our proposed HFS based WLSTSVM disease diagnostic model can result in positive outcomes.

  1. Homogeneity analysis with k sets of variables: An alternating least squares method with optimal scaling features

    NARCIS (Netherlands)

    van der Burg, Eeke; de Leeuw, Jan; Verdegaal, R.

    1986-01-01

    Homogeneity analysis, or multiple correspondence analysis, is usually applied to k separate variables. In this paper, it is applied to sets of variables by using sums within sets. The resulting technique is referred to as OVERALS. It uses the notion of optimal scaling, with transformations that can

  2. A Set of Handwriting Features for Use in Automated Writer Identification().

    Science.gov (United States)

    Miller, John J; Patterson, Robert Bradley; Gantz, Donald T; Saunders, Christopher P; Walch, Mark A; Buscaglia, JoAnn

    2017-05-01

    A writer's biometric identity can be characterized through the distribution of physical feature measurements ("writer's profile"); a graph-based system that facilitates the quantification of these features is described. To accomplish this quantification, handwriting is segmented into basic graphical forms ("graphemes"), which are "skeletonized" to yield the graphical topology of the handwritten segment. The graph-based matching algorithm compares the graphemes first by their graphical topology and then by their geometric features. Graphs derived from known writers can be compared against graphs extracted from unknown writings. The process is computationally intensive and relies heavily upon statistical pattern recognition algorithms. This article focuses on the quantification of these physical features and the construction of the associated pattern recognition methods for using the features to discriminate among writers. The graph-based system described in this article has been implemented in a highly accurate and approximately language-independent biometric recognition system of writers of cursive documents. © 2017 American Academy of Forensic Sciences.

  3. Reduced isothermal feature set for long wave infrared (LWIR) face recognition

    Science.gov (United States)

    Donoso, Ramiro; San Martín, Cesar; Hermosilla, Gabriel

    2017-06-01

    In this paper, we introduce a new concept in the thermal face recognition area: isothermal features. This consists of a feature vector built from a thermal signature that depends on the emission of the skin of the person and its temperature. A thermal signature is the appearance of the face to infrared sensors and is unique to each person. The infrared face is decomposed into isothermal regions that present the thermal features of the face. Each isothermal region is modeled as circles within a center representing the pixel of the image, and the feature vector is composed of a maximum radius of the circles at the isothermal region. This feature vector corresponds to the thermal signature of a person. The face recognition process is built using a modification of the Expectation Maximization (EM) algorithm in conjunction with a proposed probabilistic index to the classification process. Results obtained using an infrared database are compared with typical state-of-the-art techniques showing better performance, especially in uncontrolled acquisition conditions scenarios.

  4. Configural and featural processing during face perception: A new stimulus set

    National Research Council Canada - National Science Library

    Van Belle, Goedele; De Smet, Michael; De Graef, Peter; Van Gool, Luc; Verfaillie, Karl

    2009-01-01

    .... In all faces, extrafacial cues have been eliminated or standardized. The stimulus set also contains a color-coded division of each face in areas of interest, which is useful for eye movement research on face scanning strategies...

  5. Simultaneous and Sequential Feature Negative Discriminations: Elemental Learning and Occasion Setting in Human Pavlovian Conditioning

    Science.gov (United States)

    Baeyens, Frank; Vervliet, Bram; Vansteenwegen, Debora; Beckers, Tom; Hermans, Dirk; Eelen, Paul

    2004-01-01

    Using a conditioned suppression task, we investigated simultaneous (XA-/A+) vs. sequential (X [right arrow] A-/A+) Feature Negative (FN) discrimination learning in humans. We expected the simultaneous discrimination to result in X (or alternatively the XA configuration) becoming an inhibitor acting directly on the US, and the sequential…

  6. Single-channel EEG sleep stage classification based on a streamlined set of statistical features in wavelet domain.

    Science.gov (United States)

    da Silveira, Thiago L T; Kozakevicius, Alice J; Rodrigues, Cesar R

    2017-02-01

    The main objective of this study was to enhance the performance of sleep stage classification using single-channel electroencephalograms (EEGs), which are highly desirable for many emerging technologies, such as telemedicine and home care. The proposed method consists of decomposing EEGs by a discrete wavelet transform and computing the kurtosis, skewness and variance of its coefficients at selected levels. A random forest predictor is trained to classify each epoch into one of the Rechtschaffen and Kales' stages. By performing a comprehensive set of tests on 106,376 epochs available from the Physionet public database, it is demonstrated that the use of these three statistical moments has enhanced performance when compared to their application in the time domain. Furthermore, the chosen set of features has the advantage of exhibiting a stable classification performance for all scoring systems, i.e., from 2- to 6-state sleep stages. The stability of the feature set is confirmed with ReliefF tests which show a performance reduction when any individual feature is removed, suggesting that this group of feature cannot be further reduced. The accuracies and kappa coefficients yield higher than 90 % and 0.8, respectively, for all of the 2- to 6-state sleep stage classification cases.

  7. Classifying human voices by using hybrid SFX time-series preprocessing and ensemble feature selection.

    Science.gov (United States)

    Fong, Simon; Lan, Kun; Wong, Raymond

    2013-01-01

    Voice biometrics is one kind of physiological characteristics whose voice is different for each individual person. Due to this uniqueness, voice classification has found useful applications in classifying speakers' gender, mother tongue or ethnicity (accent), emotion states, identity verification, verbal command control, and so forth. In this paper, we adopt a new preprocessing method named Statistical Feature Extraction (SFX) for extracting important features in training a classification model, based on piecewise transformation treating an audio waveform as a time-series. Using SFX we can faithfully remodel statistical characteristics of the time-series; together with spectral analysis, a substantial amount of features are extracted in combination. An ensemble is utilized in selecting only the influential features to be used in classification model induction. We focus on the comparison of effects of various popular data mining algorithms on multiple datasets. Our experiment consists of classification tests over four typical categories of human voice data, namely, Female and Male, Emotional Speech, Speaker Identification, and Language Recognition. The experiments yield encouraging results supporting the fact that heuristically choosing significant features from both time and frequency domains indeed produces better performance in voice classification than traditional signal processing techniques alone, like wavelets and LPC-to-CC.

  8. Deformable Registration of Feature-Endowed Point Sets Based on Tensor Fields

    Science.gov (United States)

    Wassermann, Demian; Ross, James; Washko, George; Wells, William M.; San Jose-Estepar, Raul

    2014-01-01

    The main contribution of this work is a framework to register anatomical structures characterized as a point set where each point has an associated symmetric matrix. These matrices can represent problem-dependent characteristics of the registered structure. For example, in airways, matrices can represent the orientation and thickness of the structure. Our framework relies on a dense tensor field representation which we implement sparsely as a kernel mixture of tensor fields. We equip the space of tensor fields with a norm that serves as a similarity measure. To calculate the optimal transformation between two structures we minimize this measure using an analytical gradient for the similarity measure and the deformation field, which we restrict to be a diffeomorphism. We illustrate the value of our tensor field model by comparing our results with scalar and vector field based models. Finally, we evaluate our registration algorithm on synthetic data sets and validate our approach on manually annotated airway trees. PMID:25473253

  9. On the transferability of rule sets for mapping cirques using Object-based feature extraction

    OpenAIRE

    Seijmonsbergen, A. C.; Anders, N.S.; Gabriner, R.; Bouten, W.

    2014-01-01

    Cirques are complex landforms resulting from glacial erosion and occur in the mountains of western Austria at various topographic levels. After deglaciation they may potentially hold climate proxies, are showcases of vegetation regrowth and play an important role in the regulation of mountain hydrology. Our objective is to develop a workflow to test an object‐based rule‐set that decomposes LiDAR DEMs into the main cirque components: divide, cirque headwall, cirque floor and into the sub‐compo...

  10. Vapor intrusion in urban settings: effect of foundation features and source location

    OpenAIRE

    Yao, Yijun; Pennell, Kelly G.; Suuberg, Eric

    2011-01-01

    In many urban settings, groundwater contains volatile organic compounds, such as tricholoroethene, tetrachloroethene, benzene, etc., at concentrations that are at or slightly below non-potable groundwater standards. Some non-potable groundwater standards do not protect against human health risks that might result from vapor intrusion. Vapor intrusion is a process by which vapor phase contaminants present in the subsurface migrate through the soil and ultimately enter a building through founda...

  11. Tech-Assisted Language Learning Tasks in an EFL Setting: Use of Hand phone Recording Feature

    Directory of Open Access Journals (Sweden)

    Alireza Shakarami

    2014-09-01

    Full Text Available Technology with its speedy great leaps forward has undeniable impact on every aspect of our life in the new millennium. It has supplied us with different affordances almost daily or more precisely in a matter of hours. Technology and Computer seems to be a break through as for their roles in the Twenty-First century educational system. Examples are numerous, among which CALL, CMC, and Virtual learning spaces come to mind instantly. Amongst the newly developed gadgets of today are the sophisticated smart Hand phones which are far more ahead of a communication tool once designed for. Development of Hand phone as a wide-spread multi-tasking gadget has urged researchers to investigate its effect on every aspect of learning process including language learning. This study attempts to explore the effects of using cell phone audio recording feature, by Iranian EFL learners, on the development of their speaking skills. Thirty-five sophomore students were enrolled in a pre-posttest designed study. Data on their English speaking experience using audio–recording features of their Hand phones were collected. At the end of the semester, the performance of both groups, treatment and control, were observed, evaluated, and analyzed; thereafter procured qualitatively at the next phase. The quantitative outcome lent support to integrating Hand phones as part of the language learning curriculum. Keywords:

  12. Robust PLS Prediction Model for Saikosaponin A in Bupleurum chinense DC. Coupled with Granularity-Hybrid Calibration Set

    Directory of Open Access Journals (Sweden)

    Zhisheng Wu

    2015-01-01

    Full Text Available This study demonstrated particle size effect on the measurement of saikosaponin A in Bupleurum chinense DC. by near infrared reflectance (NIR spectroscopy. Four types of granularity were prepared including powder samples passed through 40-mesh, 65-mesh, 80-mesh, and 100-mesh sieve. Effects of granularity on NIR spectra were investigated, which showed to be wavelength dependent. NIR intensity was proportional to particle size in the first combination-overtone and combination region. Local partial least squares model was constructed separately for every kind of samples, and data-preprocessing techniques were performed to optimize calibration model. The 65-mesh model exhibited the best prediction ability with root mean of square error of prediction (RMSEP = 0.492 mg·g−1, correlation coefficient RP=0.9221, and relative predictive determinant (RPD = 2.58. Furthermore, a granularity-hybrid calibration model was developed by incorporating granularity variation. Granularity-hybrid model showed better performance than local model. The model performance with 65-mesh samples was still the most accurate with RMSEP = 0.481 mg·g−1, RP=0.9279, and RPD = 2.64. All the results presented the guidance for construction of a robust model coupled with granularity-hybrid calibration set.

  13. Clinical and microbiologic features of Shigella and enteroinvasive Escherichia coli infections detected by DNA hybridization.

    OpenAIRE

    Taylor, D N; Echeverria, P.; Sethabutr, O.; Pitarangsi, C; Leksomboon, U; Blacklow, N R; Rowe, B.; R. Gross; Cross, J.

    1988-01-01

    To determine the clinical and microbiologic features of Shigella and enteroinvasive Escherichia coli (EIEC) infections, we investigated 410 children with diarrhea and 410 control children without diarrhea who were seen at Children's Hospital, Bangkok, Thailand, from January to June 1985. Shigella spp. were isolated from 96 (23%) and EIEC were isolated from 17 (4%) of 410 children with diarrhea and from 12 (3%) and 6 (1%) of 410 control children, respectively. The isolation rates of both patho...

  14. Representing hybrid compensatory non-compensatory choice set formation in semi-compensatory models

    DEFF Research Database (Denmark)

    Kaplan, Sigal; Bekhor, Shlomo; Shigtan, Yoram

    2012-01-01

    Semi-compensatory models represent a choice process consisting of an elimination-based choice set formation upon satisfying criteria thresholds and a utility-based choice. Current semi-compensatory models assume a purely non-compensatory choice set formation and hence do not support multinomial c...

  15. Synthesis and Catalytic Features of Hybrid Metal Nanoparticles Supported on Cellulose Nanofibers

    Directory of Open Access Journals (Sweden)

    Hirotaka Koga

    2011-11-01

    Full Text Available The structural and functional design of metal nanoparticles has recently allowed remarkable progress in the development of high-performance catalysts. Gold nanoparticles (AuNPs are among the most innovative catalysts, despite bulk Au metal being regarded as stable and inactive. The hybridization of metal NPs has attracted major interest in the field of advanced nanocatalysts, due to electro-mediated ligand effects. In practical terms, metal NPs need to be supported on a suitable matrix to avoid any undesirable aggregation; many researchers have reported the potential of polymer-supported AuNPs. However, the use of conventional polymer matrices make it difficult to take full advantage of the inherent properties of the metal NPs, since most of active NPs are imbedded inside the polymer support. This results in poor accessibility for the reactants. Herein, we report the topochemical synthesis of Au and palladium (Pd bimetallic NPs over the surfaces of 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO-oxidized cellulose nanofibers (TOCNs, and their exceptional catalytic performance. Highly-dispersed AuPdNPs were successfully synthesized in situ on the crystal surfaces of TOCNs with a very high density of carboxylate groups. The AuPdNPs@TOCN nanocomposites exhibit excellent catalytic efficiencies in the aqueous reduction of 4-nitrophenol to 4-aminophenol, depending on the molar ratios of Au and Pd.

  16. ACOPLAMIENTO E HIBRIDACION EN EL CLIMA CULTURAL DE POSMODERNIDAD Coupling and Hybridization in the Cultural Setting of Post-modernity

    Directory of Open Access Journals (Sweden)

    Pablo Martínez Fernández

    2004-12-01

    Full Text Available El siguiente texto habla de la posibilidad de múltiples afinidades en lo real-existente. Para ello es que se presenta una perspectiva socio-analítica del acoplamiento y la hibridación, lo que permite visualizar con mayor claridad la forma en que se produce dicha afinidad por el flujo semiótico-material contemporáneo. Se plantea que el acoplamiento y la hibridación son dos manifestaciones posibles de lo posmoderno, en la medida que dan cuenta de un clima cultural específico que afecta a lo social-contemporáneo. Para revisar una instalación socio-cultural se expondrá el ejemplo del “pololeo” como una posible manifestación del acoplamiento que se produce en los lenguajes que hablan del modo de vida de las sociedades contemporáneas. A partir del acoplamiento se llegará a la hibridización como la condición social de las sociedades que se constituyen en los movimientos de estructuración y desestructuración en un capitalismo cuya dinámica opera de manera global y fluida.This paper discusses the possibility of multiple affinities related to the real-existing. A social analysis on coupling and hybridization is set forth. This allows to clearly see how this affinity is achieved through contemporary semiotic material. It is stated that coupling and hybridization are two possible manifestations out of post-modernism. To review this socio-cultural setting, an instance of pololeo will be presented as a feasible manifestation about coupling that is found in a discourse related to contemporary society. From coupling, hybridization comes up as a social conditioning, which is made up by structure and de-structure movements within capitalism, which dynamics operates in a functional and global fashion.

  17. Independence-oriented VMD to identify fault feature for wheel set bearing fault diagnosis of high speed locomotive

    Science.gov (United States)

    Li, Zipeng; Chen, Jinglong; Zi, Yanyang; Pan, Jun

    2017-02-01

    As one of most critical component of high-speed locomotive, wheel set bearing fault identification has attracted an increasing attention in recent years. However, non-stationary vibration signal with modulation phenomenon and heavy background noise make it difficult to excavate the hidden weak fault feature. Variational Mode Decomposition (VMD), which can decompose the non-stationary signal into couple Intrinsic Mode Functions adaptively and non-recursively, brings a feasible tool. However, heavy background noise seriously affects setting of mode number, which may lead to information loss or over decomposition problem. In this paper, an independence-oriented VMD method via correlation analysis is proposed to adaptively extract weak and compound fault feature of wheel set bearing. To overcome the information loss problem, the appropriate mode number is determined by the criterion of approximate complete reconstruction. Then the similar modes are combined according to the similarity of their envelopes to solve the over decomposition problem. Finally, three applications to wheel set bearing fault of high speed locomotive verify the effectiveness of the proposed method compared with original VMD, EMD and EEMD methods.

  18. Automated oral cancer identification using histopathological images: a hybrid feature extraction paradigm.

    Science.gov (United States)

    Krishnan, M Muthu Rama; Venkatraghavan, Vikram; Acharya, U Rajendra; Pal, Mousumi; Paul, Ranjan Rashmi; Min, Lim Choo; Ray, Ajoy Kumar; Chatterjee, Jyotirmoy; Chakraborty, Chandan

    2012-02-01

    Oral cancer (OC) is the sixth most common cancer in the world. In India it is the most common malignant neoplasm. Histopathological images have widely been used in the differential diagnosis of normal, oral precancerous (oral sub-mucous fibrosis (OSF)) and cancer lesions. However, this technique is limited by subjective interpretations and less accurate diagnosis. The objective of this work is to improve the classification accuracy based on textural features in the development of a computer assisted screening of OSF. The approach introduced here is to grade the histopathological tissue sections into normal, OSF without Dysplasia (OSFWD) and OSF with Dysplasia (OSFD), which would help the oral onco-pathologists to screen the subjects rapidly. The biopsy sections are stained with H&E. The optical density of the pixels in the light microscopic images is recorded and represented as matrix quantized as integers from 0 to 255 for each fundamental color (Red, Green, Blue), resulting in a M×N×3 matrix of integers. Depending on either normal or OSF condition, the image has various granular structures which are self similar patterns at different scales termed "texture". We have extracted these textural changes using Higher Order Spectra (HOS), Local Binary Pattern (LBP), and Laws Texture Energy (LTE) from the histopathological images (normal, OSFWD and OSFD). These feature vectors were fed to five different classifiers: Decision Tree (DT), Sugeno Fuzzy, Gaussian Mixture Model (GMM), K-Nearest Neighbor (K-NN), Radial Basis Probabilistic Neural Network (RBPNN) to select the best classifier. Our results show that combination of texture and HOS features coupled with Fuzzy classifier resulted in 95.7% accuracy, sensitivity and specificity of 94.5% and 98.8% respectively. Finally, we have proposed a novel integrated index called Oral Malignancy Index (OMI) using the HOS, LBP, LTE features, to diagnose benign or malignant tissues using just one number. We hope that this OMI can

  19. A hybrid structure for the storage and manipulation of very large spatial data sets

    Science.gov (United States)

    Peuquet, Donna J.

    1982-01-01

    The map data input and output problem for geographic information systems is rapidly diminishing with the increasing availability of mass digitizing, direct spatial data capture and graphics hardware based on raster technology. Although a large number of efficient raster-based algorithms exist for performing a wide variety of common tasks on these data, there are a number of procedures which are more efficiently performed in vector mode or for which raster mode equivalents of current vector-based techniques have not yet been developed. This paper presents a hybrid spatial data structure, named the ?vaster' structure, which can utilize the advantages of both raster and vector structures while potentially eliminating, or greatly reducing, the need for raster-to-vector and vector-to-raster conversion. Other advantages of the vaster structure are also discussed.

  20. Hybrid approach to limb salvage in the setting of an infected femoral-femoral bypass graft.

    Science.gov (United States)

    Jones, Douglas W; Meltzer, Andrew J; Schneider, Darren B

    2014-08-01

    Prosthetic vascular graft infection in patients with advanced peripheral arterial disease can lead to multiple additional procedures, including extra-anatomic bypass or even amputation. We report the case of an 88-year-old woman with critical limb ischemia and an infected prosthetic femoral-femoral bypass graft. Using a planned hybrid 2-stage approach, we performed endovascular recanalization of the native left iliac arterial system using remote access via the superficial femoral artery to avoid infected groin wounds. Recanalization of the patient's Trans-Atlantic Inter-Society Consensus II D chronic iliac occlusion allowed for removal of the infected graft and placement of a profunda femoris artery to proximal posterior tibial artery bypass, thereby restoring inflow and avoiding the infected left groin. Newer endovascular techniques coupled with open surgical options may lead to limb salvage in patients with previously unreconstructable peripheral arterial disease.

  1. A hybrid particle swarm optimization approach with neural network and set pair analysis for transmission network planning

    Institute of Scientific and Technical Information of China (English)

    刘吉成; 颜苏莉; 乞建勋

    2008-01-01

    Transmission network planning (TNP) is a large-scale, complex, with more non-linear discrete variables and the multi-objective constrained optimization problem. In the optimization process, the line investment, network reliability and the network loss are the main objective of transmission network planning. Combined with set pair analysis (SPA), particle swarm optimization (PSO), neural network (NN), a hybrid particle swarm optimization model was established with neural network and set pair analysis for transmission network planning (HPNS). Firstly, the contact degree of set pair analysis was introduced, the traditional goal set was converted into the collection of the three indicators including the identity degree, difference agree and contrary degree. On this bases, using shi(H), the three objective optimization problem was converted into single objective optimization problem. Secondly, using the fast and efficient search capabilities of PSO, the transmission network planning model based on set pair analysis was optimized. In the process of optimization, by improving the BP neural network constantly training so that the value of the fitness function of PSO becomes smaller in order to obtain the optimization program fitting the three objectives better. Finally, compared HPNS with PSO algorithm and the classic genetic algorithm, HPNS increased about 23% efficiency than THA, raised about 3.7% than PSO and improved about 2.96% than GA.

  2. Structural characterization and physicochemical features of new hybrid compound containing chlorate anions of cadmate (II)

    Science.gov (United States)

    Lassoued, Mohamed Saber; Abdelbaky, Mohammed S. M.; Lassoued, Abdelmajid; Gadri, Abdellatif; Ammar, Salah; Ben Salah, Abdelhamid; García-Granda, Santiago

    2017-08-01

    The present paper reports the synthesis of a single crystal of a new organic-inorganic hybrid compound, with the formula (C6H14N2) CdCl4·H2O, by slow evaporation method at room temperature. It was characterized by single crystal X-ray diffraction (SCXRD), powder X-ray diffraction (PXRD), Hirshfeld surface, spectroscopy measurement, thermal study and photoluminescence (PL) properties. A preliminary SCXRD structural analysis revealed that it crystallized in the monoclinic system (space group P21/c) with the following unit cell parameters: a = 12.95823(16) Å, b = 14.92449(16) Å, c = 7.13838(9) Å and β = 103.2108(12)° with Z = 4. The refinement converged to R = 0.0164 and ωR = 0.0393. Its atomic arrangement can be described as an alternation of organic and inorganic layers along the a-axis. The crystal packing was governed by the N-H⋯Cl and O-H⋯Cl hydrogen bonding interaction between the 1.2-diammoniumcyclohexane cations, the [CdCl42n-]n anions and water molecule. The Hirshfeld surface analysis was conducted to investigate intermolecular interactions and associated 2D fingerprint plots, revealing the relative contribution of these interactions in the crystal structure quantitatively. Furthermore, the room temperature infrared (IR) spectrum of the title compound was recorded and analyzed on the basis of data found in the literature. Besides, the thermal analysis studies were performed, but no phase transition was found in the temperature range between 30 and 450 °C. The optical and PL properties of the compound were investigated in the solid state at room temperature and exhibited three bands at 225, 268 and 315 nm and a strong fluorescence at 443 nm.

  3. IMPROVED HYBRID SEGMENTATION OF BRAIN MRI TISSUE AND TUMOR USING STATISTICAL FEATURES

    OpenAIRE

    S. Allin Christe; K. Malathy; A.Kandaswamy

    2010-01-01

    Medical image segmentation is the most essential and crucial process in order to facilitate the characterization and visualization of the structure of interest in medical images. Relevant application in neuroradiology is the segmentation of MRI data sets of the human brain into the structure classes gray matter, white matter and cerebrospinal fluid (CSF) and tumor. In this paper, brain image segmentation algorithms such as Fuzzy C means (FCM) segmentation and Kohonen means(K means) segmentati...

  4. The Role of Interaction Patterns with Hybrid Electric Vehicle Eco-Features for Drivers' Eco-Driving Performance.

    Science.gov (United States)

    Arend, Matthias G; Franke, Thomas

    2017-03-01

    The objective of the present research was to understand drivers' interaction patterns with hybrid electric vehicles' (HEV) eco-features (electric propulsion, regenerative braking, neutral mode) and their relationship to fuel efficiency and driver characteristics (technical system knowledge, eco-driving motivation). Eco-driving (driving behaviors performed to achieve higher fuel efficiency) has the potential to reduce CO2 emissions caused by road vehicles. Eco-driving in HEVs is particularly challenging due to the systems' dynamic energy flows. As a result, drivers are likely to show diverse eco-driving behaviors, depending on factors like knowledge and motivation. The eco-features represent an interface for the control of the systems' energy flows. A sample of 121 HEV drivers who had constantly logged their fuel consumption prior to the study participated in an online questionnaire. Drivers' interaction patterns with the eco-features were related to fuel efficiency. A common factor was identified in an exploratory factor analysis, characterizing the intensity of actively dealing with electric energy, which was also related to fuel efficiency. Driver characteristics were not related to this factor, yet they were significant predictors of fuel efficiency. From the perspective of user-energy interaction, the relationship of the aggregated factor to fuel efficiency emphasizes the central role of drivers' perception of and interaction with energy conversions in determining HEV eco-driving success. To arrive at an in-depth understanding of drivers' eco-driving behaviors that can guide interface design, authors of future research should be concerned with the psychological processes that underlie drivers' interaction patterns with eco-features.

  5. Correlation of a set of gene variants, life events and personality features on adult ADHD severity.

    Science.gov (United States)

    Müller, Daniel J; Chiesa, Alberto; Mandelli, Laura; De Luca, Vincenzo; De Ronchi, Diana; Jain, Umesh; Serretti, Alessandro; Kennedy, James L

    2010-07-01

    Increasing evidence suggests that symptoms of attention deficit hyperactivity disorder (ADHD) could persist into adult life in a substantial proportion of cases. The aim of the present study was to investigate the impact of (1) adverse events, (2) personality traits and (3) genetic variants chosen on the basis of previous findings and (4) their possible interactions on adult ADHD severity. One hundred and ten individuals diagnosed with adult ADHD were evaluated for occurrence of adverse events in childhood and adulthood, and personality traits by the Temperament and Character Inventory (TCI). Common polymorphisms within a set of nine important candidate genes (SLC6A3, DBH, DRD4, DRD5, HTR2A, CHRNA7, BDNF, PRKG1 and TAAR9) were genotyped for each subject. Life events, personality traits and genetic variations were analyzed in relationship to severity of current symptoms, according to the Brown Attention Deficit Disorder Scale (BADDS). Genetic variations were not significantly associated with severity of ADHD symptoms. Life stressors displayed only a minor effect as compared to personality traits. Indeed, symptoms' severity was significantly correlated with the temperamental trait of Harm avoidance and the character trait of Self directedness. The results of the present work are in line with previous evidence of a significant correlation between some personality traits and adult ADHD. However, several limitations such as the small sample size and the exclusion of patients with other severe comorbid psychiatric disorders could have influenced the significance of present findings.

  6. Vapor intrusion in urban settings: effect of foundation features and source location.

    Science.gov (United States)

    Yao, Yijun; Pennell, Kelly G; Suuberg, Eric

    2011-01-01

    In many urban settings, groundwater contains volatile organic compounds, such as tricholoroethene, tetrachloroethene, benzene, etc., at concentrations that are at or slightly below non-potable groundwater standards. Some non-potable groundwater standards do not protect against human health risks that might result from vapor intrusion. Vapor intrusion is a process by which vapor phase contaminants present in the subsurface migrate through the soil and ultimately enter a building through foundation cracks. The end result is a decrease in air quality within the building. Predicting whether or not vapor intrusion will occur at rates sufficient to cause health risks is extremely difficult and depends on many factors. In many cities, a wide-range of property uses take place over a relatively small area. For instance, schools, commercial buildings and residential buildings may all reside within a few city blocks. Most conceptual site models assume the ground surface is open to the atmosphere (i.e. green space); however the effect that an impervious surface (e.g. paving) may have on vapor transport rates is not routinely considered. Using a 3-D computational fluid dynamics model, we are investigating how the presence of impervious surfaces affects vapor intrusion rates. To complement our modeling efforts, we are in the initial stages of conducting a field study in a neighborhood where vapor intrusion is occurring.

  7. A hybrid method for pancreas extraction from CT image based on level set methods.

    Science.gov (United States)

    Jiang, Huiyan; Tan, Hanqing; Fujita, Hiroshi

    2013-01-01

    This paper proposes a novel semiautomatic method to extract the pancreas from abdominal CT images. Traditional level set and region growing methods that request locating initial contour near the final boundary of object have problem of leakage to nearby tissues of pancreas region. The proposed method consists of a customized fast-marching level set method which generates an optimal initial pancreas region to solve the problem that the level set method is sensitive to the initial contour location and a modified distance regularized level set method which extracts accurate pancreas. The novelty in our method is the proper selection and combination of level set methods, furthermore an energy-decrement algorithm and an energy-tune algorithm are proposed to reduce the negative impact of bonding force caused by connected tissue whose intensity is similar with pancreas. As a result, our method overcomes the shortages of oversegmentation at weak boundary and can accurately extract pancreas from CT images. The proposed method is compared to other five state-of-the-art medical image segmentation methods based on a CT image dataset which contains abdominal images from 10 patients. The evaluated results demonstrate that our method outperforms other methods by achieving higher accuracy and making less false segmentation in pancreas extraction.

  8. The Diversity of Martian Volcanic features as Seen in the MOC, THEMIS, and MOM Data Sets

    Science.gov (United States)

    Mouginis-Mark, Peter J.

    2005-01-01

    This one-year project (which included one-year no-cost tension) focused on the evolution of the summit areas of Martian volcanoes. It extended the studies conducted under an earlier MDAP project (Grant NAG5-9576, Principal Investigator P. Mouginis- Mark). By using data collected from the Mars Orbiter Camera (MOC), Thermal Emission Imaging System (THEMIS), and the Mars Orbiter Laser Altimeter (MOLA) instruments, we tried to better understand the diversity of constructional volcanism on Mars, and hence further understand the eruption processes. By inspecting THEMIS and MOC data, we explored the following four questions: (1) Where might near-surface volatiles have been released at the summits of the Tharsis volcanoes? Is the trapping and subsequent remobilization of degassed volatiles [Scott and Wilson, 19991 adequate to produce eruptions responsible for extensive deposits such as the ones identified on Arsia Mons [Mouginis-Mark, 2002]? To answer this question, we investigated the diversity of eruption styles by studying the summit areas of Arsia, Pavonis and Ascraeus Montes. (2) What are the geomorphic characteristics of the valley system on Hecates Tholus, a volcano that we have previously proposed experienced explosive activity [Mouginis-Murk et al., 1982]? Our inspection of THEMIS data suggests that water release on the volcano took place over an extended period of time, suggesting that hydrothermal activity may have taken place here. (3) How similar are the collapse processes observed at Martian and terrestrial calderas? New THEMIS data provide a more complete view of the entire Olympus Mons caldera, thereby enabling the comparison with the collapse features at Masaya volcano, Nicaragua, to be investigated. (4) What can we learn about the emplacement of long lava flows in the lava plains of Eastern Tharsis? The result of this work provided a greater understanding of the temporal and spatial variations in the eruptive history of volcanoes on Mars, and the

  9. New adders using hybrid circuit consisting of three-gate single-electron transistors (TG-SETs) and MOSFETs.

    Science.gov (United States)

    Yu, YunSeop; Choi, JungBum

    2007-11-01

    A half-adder (HA) and a full-adder (FA) using hybrid circuits combining three-gate single-electron transistors (TG-SETs) with metal-oxide-semiconductor field-effect-transistors (MOSFETs) are proposed. The proposed HA consists of three TG-SETs, two enhanced-mode NMOSFETs, and two depletion-mode NMOSFETs, and the proposed FA consists of eight TG-SETs, two enhanced-mode NMOSFETs, and two depletion-mode NMOSFETs. The complexities in the HA and the FA are 7 and 12, respectively, and the worst-case delays in the HA and the FA are 1.48 ns and 2.25 ns, respectively. Compared with the conventional CMOS FA with 0.35 microm technology, the proposed FA can be constructed with 0.43 of devices, and can operate with 3.5 of worst-case delay, 1/534 of average power consumption, and 1/152 of power-delay-product (PDP). The proposed HA and FA can be operated as a half-subtractor (HS) and a full-subtractor (FS) in the case when the levels of the control gates in the HA and the FA are fitly determined. The basic operations of the proposed HA and the proposed FA have been successfully confirmed through SPICE circuit simulation based on the physical device model of TG-SETs.

  10. A hybrid method based on fuzzy clustering and local region-based level set for segmentation of inhomogeneous medical images.

    Science.gov (United States)

    Rastgarpour, Maryam; Shanbehzadeh, Jamshid; Soltanian-Zadeh, Hamid

    2014-08-01

    medical images are more affected by intensity inhomogeneity rather than noise and outliers. This has a great impact on the efficiency of region-based image segmentation methods, because they rely on homogeneity of intensities in the regions of interest. Meanwhile, initialization and configuration of controlling parameters affect the performance of level set segmentation. To address these problems, this paper proposes a new hybrid method that integrates a local region-based level set method with a variation of fuzzy clustering. Specifically it takes an information fusion approach based on a coarse-to-fine framework that seamlessly fuses local spatial information and gray level information with the information of the local region-based level set method. Also, the controlling parameters of level set are directly computed from fuzzy clustering result. This approach has valuable benefits such as automation, no need to prior knowledge about the region of interest (ROI), robustness on intensity inhomogeneity, automatic adjustment of controlling parameters, insensitivity to initialization, and satisfactory accuracy. So, the contribution of this paper is to provide these advantages together which have not been proposed yet for inhomogeneous medical images. Proposed method was tested on several medical images from different modalities for performance evaluation. Experimental results approve its effectiveness in segmenting medical images in comparison with similar methods.

  11. Using Frequent Item Set Mining and Feature Selection Methods to Identify Interacted Risk Factors - The Atrial Fibrillation Case Study.

    Science.gov (United States)

    Li, Xiang; Liu, Haifeng; Du, Xin; Hu, Gang; Xie, Guotong; Zhang, Ping

    2016-01-01

    Disease risk prediction is highly important for early intervention and treatment, and identification of predictive risk factors is the key point to achieve accurate prediction. In addition to original independent features in a dataset, some interacted features, such as comorbidities and combination therapies, may have non-additive influence on the disease outcome and can also be used in risk prediction to improve the prediction performance. However, it is usually difficult to manually identify the possible interacted risk factors due to the combination explosion of features. In this paper, we propose an automatic approach to identify predictive risk factors with interactions using frequent item set mining and feature selection methods. The proposed approach was applied in the real world case study of predicting ischemic stroke and thromboembolism for atrial fibrillation patients on the Chinese atrial fibrillation registry dataset, and the results show that our approach can not only improve the prediction performance, but also identify the comorbidities and combination therapies that have potential influences on TE occurrence for AF.

  12. THE USE OF DISCOURSE MARKERS AS AN INTERACTIVE FEATURE IN SCIENCE LECTURE DISCOURSE IN L2 SETTING

    Directory of Open Access Journals (Sweden)

    Akhyar Rido

    2010-02-01

    Full Text Available The objective of this research is to investigate the function of discourse markers as an interpersonal-interactive feature in a science lecture in second language (L2 setting in Malaysia. This research employs qualitative method while the data are gathered through non-participant observation and video-recording. From the findings, there are various discourse markers found. Macro markers signal the transition of the moves and indicate a shift of one topic/sub-topic to another topic/sub-topic. Meanwhile, micro markers signal the internal or ideational relations within sentences. In conclusion, the use of discourse markers will help students to comprehend a lecture.

  13. Development of a novel adenovirus-alphavirus hybrid vector with RNA replicon features for malignant hematopoietic cell transduction.

    Science.gov (United States)

    Yang, Y; Xiao, F; Lu, Z; Li, Z; Zuo, H; Zhang, Q; Li, Q; Wang, H; Wang, L-S

    2013-08-01

    To improve the expression levels of transgenes in malignant hematopoietic cells, we developed a novel adenoviral-alphavirus hybrid vector Ad5/F11p-SFV-GFP that contains a Semliki Forest Virus (SFV) replicon and chimeric fibers of Ad5 and Ad11p. Ad5/F11p-SFV-GFP infected >95% of K562, U937 or Jurkat cells and 23.65% of HL-60 cells, and led to moderate Enhanced Green Fluorescent Protein (EGFP) transgene expression intensity. The infection efficiency of Ad5/F11p-SFV-GFP in primary human leukemia cells ranged from 9.34-89.63% (median, 28.58%) at a multiplicity of infection (MOI) of 100, compared with only 3.37-44.54% (median, 10.42%) in cells infected by Ad5/F11p-GFP. Importantly, Ad5/F11p-SFV-GFP led to a significantly higher transgene expression level in primary leukemia cells, as indicated by the relative fluorescence intensity, compared to cells infected with Ad5/F11p-GFP. The increased expression of EGFP in Ad5/F11p-SFV-GFP-infected cells was associated with the accumulation of abundant subgenomic mRNA. Additionally, infection of K562, U937 or Jurkat cells by Ad5/F11p-SFV-GFP was significantly inhibited by blocking CD46 receptor; however, other factors may affect the gene-transfer efficiency of Ad5/F11p-SFV-GFP in primary leukemia cells. In conclusion, we successfully developed a novel adenoviral-alphavirus hybrid vector with RNA replicon features, which represents a promising vector for gene modifications during the production of cell-based vaccines for leukemia patients.

  14. Segmentation of Lungs via Hybridization of CA and Level Set Algorithm

    National Research Council Canada - National Science Library

    A. Anbu Megelin Star; P. Subburaj

    2014-01-01

    ...) and then the level set algorithm is applied for acquiring the acute tumor tissues. As a result of the mentioned process, the tumor sector is segmented and the results are depicted. Studies on lung tumor datasets demonstrate 80-85% overlap performance of the proposed algorithm with less sensitivity to seed initialization, robustness with respect to heterogeneous tumor types and its efficiency in terms of computation time.

  15. On a distinctive feature of problems of calculating time-average characteristics of nuclear reactor optimal control sets

    Science.gov (United States)

    Trifonenkov, A. V.; Trifonenkov, V. P.

    2017-01-01

    This article deals with a feature of problems of calculating time-average characteristics of nuclear reactor optimal control sets. The operation of a nuclear reactor during threatened period is considered. The optimal control search problem is analysed. The xenon poisoning causes limitations on the variety of statements of the problem of calculating time-average characteristics of a set of optimal reactor power off controls. The level of xenon poisoning is limited. There is a problem of choosing an appropriate segment of the time axis to ensure that optimal control problem is consistent. Two procedures of estimation of the duration of this segment are considered. Two estimations as functions of the xenon limitation were plot. Boundaries of the interval of averaging are defined more precisely.

  16. Description and validation of a new set of object-based temporal geostatistical features for land-use/land-cover change detection

    Science.gov (United States)

    Gil-Yepes, Jose L.; Ruiz, Luis A.; Recio, Jorge A.; Balaguer-Beser, Ángel; Hermosilla, Txomin

    2016-11-01

    A new set of temporal features derived from codispersion and cross-semivariogram geostatistical functions is proposed, described, extracted, and evaluated for object-based land-use/land-cover change detection using high resolution images. Five features were extracted from the codispersion function and another six from the cross-semivariogram. The set of features describes the temporal behaviour of the internal structure of the image objects defined in a cadastral database. The set of extracted features was combined with spectral information and a feature selection study was performed using forward stepwise discriminant analysis, principal component analysis, as well as correlation and feature interpretation analysis. The temporal feature set was validated using high resolution aerial images from an agricultural area located in south-east Spain, in order to solve a tree crop change detection problem. Direct classification using decision tree classifier was used as change detection method. Different classifications were performed comparing various feature group combinations in order to obtain the most suitable features for this study. Results showed that the new sets of cross-semivariogram and codispersion features provided high global accuracy classification results (83.55% and 85.71% respectively), showing high potential for detecting changes related to the internal structure of agricultural tree crop parcels. A significant increase in accuracy value was obtained when combining features from both groups with spectral information (94.59%).

  17. Obtaining Good Performance With Triple-ζ-Type Basis Sets in Double-Hybrid Density Functional Theory Procedures.

    Science.gov (United States)

    Chan, Bun; Radom, Leo

    2011-09-13

    A variety of combinations of B-LYP-based double-hybrid density functional theory (DHDFT) procedures and basis sets have been examined. A general observation is that the optimal combination of exchange contributions is in the proximity of 30% Becke 1988 (B88) exchange and 70% Hartree-Fock (HF) exchange, while for the correlation contributions, the use of independently optimized spin-component-scaled Møller-Plesset second-order perturbation theory (SCS-MP2) parameters (MP2OS and MP2SS) is beneficial. The triple-ζ Dunning aug'-cc-pVTZ+d and Pople 6-311+G(3df,2p)+d basis sets are found to be cost-effective for DHDFT methods. As a result, we have formulated the DuT-D3 DHDFT procedure, which employs the aug'-cc-pVTZ+d basis set and includes 30% B88 and 70% HF exchange energies, 59% LYP, 47% MP2OS, and 36% MP2SS correlation energies, and a D3 dispersion correction with the parameters s6 = 0.5, sr,6 = 1.569, and s8 = 0.35. Likewise, the PoT-D3 DHDFT procedure was formulated with the 6-311+G(3df,2p)+d basis set and has 32% B88 and 68% HF exchange energies, 63% LYP, 46% MP2OS, and 27% MP2SS correlation energies, and the D3 parameters s6 = 0.5, sr,6 = 1.569, and s8 = 0.30. Testing using the large E3 set of 740 energies demonstrates the robustness of these methods. Further comparisons show that the performance of these methods, particularly DuT-D3, compares favorably with the previously reported DSD-B-LYP and DSD-B-LYP-D3 methods used in conjunction with quadruple-ζ aug'-pc3+d and aug'-def2-QZVP basis sets but at lower computational expense. The previously reported ωB97X-(LP)/6-311++G(3df,3pd) procedure also performs very well. Our findings highlight the cost-effectiveness of appropriate- and moderate-sized triple-ζ basis sets in the application of DHDFT procedures.

  18. New Hybrid Multiple Attribute Decision-Making Model for Improving Competence Sets: Enhancing a Company’s Core Competitiveness

    Directory of Open Access Journals (Sweden)

    Kuan-Wei Huang

    2016-02-01

    Full Text Available A company’s core competitiveness depends on the strategic allocation of its human resources in alignment with employee capabilities. Competency models can identify the range of capabilities at a company’s disposal, and this information can be used to develop internal or external education training policies for sustainable development. Such models can ensure the importation of a strategic orientation reflecting the growth of its employee competence set and enhancing human resource sustainably. This approach ensures that the most appropriate people are assigned to the most appropriate positions. In this study, we proposed a new hybrid multiple attributed decision-making model by using the Decision-making trial and Evaluation Laboratory Technique (DEMATEL to construct an influential network relation map (INRM and determined the influential weights by using the basic concept of the analytic network process (called DEMATEL-based ANP, DANP; the influential weights were then adopted with a modified Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR method. A simple forecasting technique as an iteration function was also proposed. The proposed model was effective. We expect that the proposed model can facilitate making timely revisions, reflecting the growth of employee competence sets, reducing the performance gap toward the aspiration level, and ensuring the sustainability of a company.

  19. Partial least squares regression can aid in detecting differential abundance of multiple features in sets of metagenomic samples

    Directory of Open Access Journals (Sweden)

    Ondrej eLibiger

    2015-12-01

    Full Text Available It is now feasible to examine the composition and diversity of microbial communities (i.e., `microbiomes‘ that populate different human organs and orifices using DNA sequencing and related technologies. To explore the potential links between changes in microbial communities and various diseases in the human body, it is essential to test associations involving different species within and across microbiomes, environmental settings and disease states. Although a number of statistical techniques exist for carrying out relevant analyses, it is unclear which of these techniques exhibit the greatest statistical power to detect associations given the complexity of most microbiome datasets. We compared the statistical power of principal component regression, partial least squares regression, regularized regression, distance-based regression, Hill's diversity measures, and a modified test implemented in the popular and widely used microbiome analysis methodology 'Metastats‘ across a wide range of simulated scenarios involving changes in feature abundance between two sets of metagenomic samples. For this purpose, simulation studies were used to change the abundance of microbial species in a real dataset from a published study examining human hands. Each technique was applied to the same data, and its ability to detect the simulated change in abundance was assessed. We hypothesized that a small subset of methods would outperform the rest in terms of the statistical power. Indeed, we found that the Metastats technique modified to accommodate multivariate analysis and partial least squares regression yielded high power under the models and data sets we studied. The statistical power of diversity measure-based tests, distance-based regression and regularized regression was significantly lower. Our results provide insight into powerful analysis strategies that utilize information on species counts from large microbiome data sets exhibiting skewed frequency

  20. Prenatal detection of aneuploidies using fluorescence in situ hybridization: A preliminary experience in an Indian set up

    Indian Academy of Sciences (India)

    Vaidehi Jobanputra; Kalol Kumar Roy; Kiran Kucheria

    2002-03-01

    Fluorescence in situ hybridization (FISH) is a powerful molecular cytogenetic technique which allows rapid detection of aneuploidies on interphase cells and metaphase spreads. The aim of the present study was to evaluate FISH as a tool in prenatal diagnosis of aneuploidies in high risk pregnancies in an Indian set up. Prenatal diagnosis was carried out in 88 high-risk pregnancies using FISH and cytogenetic analysis. Multicolour commercially available FISH probes specific for chromosomes 13, 18, 21, X and Y were used. Interphase FISH was done on uncultured cells from chorionic villus and amniotic fluid samples. FISH on metaphase spreads was done from cord blood samples. The results of FISH were in conformity with the results of cytogenetic analysis in all the normal and aneuploid cases except in one case of structural chromosomal abnormality. The hybridization efficiency of the 5 probes used for the detection of aneuploidies was 100%. Using these probes FISH assay yielded discrete differences in the signal profiles between cytogenetically normal and abnormal samples. The overall mean interphase disomic signal patterns of chromosomes 13, 18, 21, X and Y were 94.45%; for interphase trisomic signal pattern of chromosome 21 was 97.3%. Interphase FISH is very useful in urgent high risk cases. The use of FISH overcomes the difficulties of conventional banding on metaphase spreads and reduces the time of reporting. However, with the limited number of probes used, the conventional cytogenetic analysis serves as a gold standard at present. It should be employed as an adjunctive tool to conventional cytogenetics.

  1. Patterns of Limnohabitans microdiversity across a large set of freshwater habitats as revealed by Reverse Line Blot Hybridization.

    Directory of Open Access Journals (Sweden)

    Jan Jezbera

    Full Text Available Among abundant freshwater Betaproteobacteria, only few groups are considered to be of central ecological importance. One of them is the well-studied genus Limnohabitans and mainly its R-BT subcluster, investigated previously mainly by fluorescence in situ hybridization methods. We designed, based on sequences from a large Limnohabitans culture collection, 18 RLBH (Reverse Line Blot Hybridization probes specific for different groups within the genus Limnohabitans by targeting diagnostic sequences on their 16 S-23 S rRNA ITS regions. The developed probes covered in sum 92% of the available isolates. This set of probes was applied to environmental DNA originating from 161 different European standing freshwater habitats to reveal the microdiversity (intra-genus patterns of the Limnohabitans genus along a pH gradient. Investigated habitats differed in various physicochemical parameters, and represented a very broad range of standing freshwater habitats. The Limnohabitans microdiversity, assessed as number of RLBH-defined groups detected, increased significantly along the gradient of rising pH of habitats. 14 out of 18 probes returned detection signals that allowed predictions on the distribution of distinct Limnohabitans groups. Most probe-defined Limnohabitans groups showed preferences for alkaline habitats, one for acidic, and some seemed to lack preferences. Complete niche-separation was indicated for some of the probe-targeted groups. Moreover, bimodal distributions observed for some groups of Limnohabitans, suggested further niche separation between genotypes within the same probe-defined group. Statistical analyses suggested that different environmental parameters such as pH, conductivity, oxygen and altitude influenced the distribution of distinct groups. The results of our study do not support the hypothesis that the wide ecological distribution of Limnohabitans bacteria in standing freshwater habitats results from generalist adaptations of

  2. Feature- and category-specific attentional control settings are differently affected by attentional engagement in contingent attentional capture.

    Science.gov (United States)

    Wu, Xia; Liu, Xiaoyue; Fu, Shimin

    2016-07-01

    A distractor can capture attention and impair target processing when it shares a target-defining property and matches specific attentional control settings (ACS). We studied how feature-specific ACS (fACS) and category-specific ACS (cACS) operate in a conjunction search task and how they are influenced by attentional engagement. The feature- and category-matching level and temporal lags between the distractor and target were manipulated in a rapid serial visual presentation (RSVP) task. The N2pc component and impairment of target identification, which are associated with attentional allocation at an earlier stage and response selection at a later stage, respectively, were measured as markers of attentional capture. The interaction of two ACSs was observed in behavioral data, but disappeared in N2pc data, suggesting two-stage processing of multiple ACSs during a conjunction search, including an early independent and a late integrated stage. Moreover, a reliable N2pc was observed for fACS regardless of the sufficiency of attentional engagement, whereas the N2pc for cACS was only observed with sufficient attentional engagement, but disappeared when the attentional engagement was insufficient. This suggests that cACS demands sufficient attentional engagement, while fACS does not. In conclusion, fACS and cACS can be activated independently at an earlier stage, but they are integrated at a later stage during a conjunction search task and are differently influenced by attentional engagement.

  3. A novel SVM-ID3 hybrid feature selection method to build a disease model for melanoma using integrated genotyping and phenotype data from dbGaP.

    Science.gov (United States)

    Son, Yeşim Aydın; Yücebaş, Sait Can

    2014-01-01

    The relations between Single Nucleotide Polymorphism (SNP) and complex diseases are likely to be non-linear and require analysis of the high dimensional data. Previous studies in the field mostly focus on genotyping and effects of various phenotypes are not considered. To fill this gap a hybrid feature selection model of support vector machine and decision tree has been designed. The designed method is tested on melanoma. We were able to select phenotypic features such as moles and dysplastic nevi, and SNPs those maps to specific genes such as CAMK1D. The performance results of the proposed hybrid model, on melanoma dataset are 79.07% of sensitivity and 0.81 of area under ROC curve.

  4. Fluorescence in situ hybridization (FISH screening for the 22q11.2 deletion in patients with clinical features of velocardiofacial syndrome but without cardiac anomalies

    Directory of Open Access Journals (Sweden)

    Paula Sandrin-Garcia

    2007-01-01

    Full Text Available The velocardiofacial syndrome (VCFS, a condition associated with 22q11.2 deletions, is characterized by a typical facies, palatal anomalies, learning disabilities, behavioral disturbances and cardiac defects. We investigated the frequency of these chromosomal deletions in 16 individuals with VCFS features who presented no cardiac anomalies, one of the main characteristics of VCFS. Fluorescent in situ hybridization (FISH with the N25 (D22S75; 22q11.2 probe revealed deletions in ten individuals (62%. Therefore, even in the absence of cardiac anomalies testing for the 22q11.2 microdeletions in individuals showing other clinical features of this syndrome is recommended.

  5. Comparative analysis of RISAT-1 and simulated RADARSAT-2 hybrid polarimetric SAR data for different land features

    OpenAIRE

    Kumar, V; S Rao, Y.

    2014-01-01

    The purpose of this study is to compare the performance of first hybrid polarimetric spaceborne satellite RISAT-1 data and simulated hybrid polarimetric data from quad-pol RADARSAT-2 data for different land use land cover (LULC) classes. The present study compares Stokes (g0, g1, g2 and g3) and its decomposed parameters (m, chi, delta and CPR) for satellite data acquired from RISAT- 1 and RADARSAT-2 over Vijayawada, Andhra Pradesh, India. Further, backscattering coefficients are also...

  6. Predicting future trends in stock market by decision tree rough-set based hybrid system with HHMM

    Directory of Open Access Journals (Sweden)

    Shweta Tiwari

    2012-06-01

    Full Text Available Around the world, trading in the stock market has gained huge attractiveness as a means through which, one can obtain vast profits. Attempting to profitably and precisely predict the financial market has long engrossed the interests and attention of bankers, economists and scientists alike. Stock market prediction is the act of trying, to determine the future value of a company’s stock or other financial instrument traded on a financial exchange. Accurate stock market predictions are important for many reasons. Chief among all is the need for investors, to hedge against potential market risks and the opportunities for arbitrators and speculators, to make profits by trading indexes. Stock Market is a place, where shares are issued and traded. These shares are either traded through Stock exchanges or Overthe-Counter in physical or electronic form. Data mining, as a process of discovering useful patterns, correlations has its own role in financial modeling. Data mining is a discipline in computational intelligence that deals with knowledge discovery, data analysis and full and semi-autonomous decision making. Prediction of stock market by data mining techniques has been receiving a lot of attention recently. This paper presents a hybrid system based on decision tree- rough set, for predicting the trends in the Bombay Stock Exchange (BSESENSEX with the combination of Hierarchical Hidden Markov Model. In this paper we present future trends on the bases of price earnings and dividend. The data on accounting earnings when averaged over many years help to predict the present value of future dividends.

  7. 基于图的混合加工特征识别方法%Hybrid Recognition of Machining Features Based on Graph

    Institute of Scientific and Technical Information of China (English)

    李大磊; 陈广飞; 尹跃峰

    2013-01-01

    Since traditional feature recognition method based on graph is difficult to recognize intersecting features and features with variable topology,this paper presents a method of hybrid feature recognition based on graph.Firstly,merged faces are split by creating split lines in order to separate intersecting feature.Secondly,the method establishes the extended attribute adjacency graph again,which is decomposed into several minimal condition sub-graphs (MCSG).Finally,according to boundary pattern of quadric features and features consist of planes,constructs separately feature recognition knowledge bases,and recognizes features by using the knowledge tree and reasoning.The results show the method can separate reasonably intersecting feature and recognize effectively machining features.%针对传统的基于图的特征识别方法难以识别相交特征和拓扑不固定特征的问题,提出了一种基于图的混合加工特征识别方法.该方法首先利用插入分割线分割贴合的面的方法拆分相交特征,然后重构扩展属性邻接图,从中分解出最小条件子图,最后根据二次曲面特征、由平面组成的特征的边界模式,分别建立相应的特征识别知识库,并应用知识树通过推理识别特征.验证结果表明该方法能合理地拆分相交特征,有效地识别常见的加工特征.

  8. Employing injection-locked FP LDs to set up a hybrid CATV/MW/MMW WDM light wave transmission system.

    Science.gov (United States)

    Lin, Chun-Yu; Lu, Hai-Han; Li, Chung-Yi; Wu, Po-Yi; Peng, Peng-Chun; Jhang, Tai-Wei; Lin, Che-Yu

    2014-07-01

    A hybrid cable television (CATV)/microwave (MW)/millimeter-wave (MMW) wavelength-division-multiplexing (WDM) light wave transmission system based on injection-locked Fabry-Perot laser diodes (FP LDs) is proposed and demonstrated. Different from conventional hybrid WDM light wave transmission systems, which need wavelength-selected distributed feedback laser diodes to support various services, the proposed systems employ injection-locked FP LDs to provide multiple applications. Over a 40 km single-mode fiber transport, impressive performances of carrier-to-noise ratio/composite second-order/composite triple-beat/bit error rate are obtained for 550 MHz CATV/20 GHz MW/40 GHz MMW/60 GHz MMW signal transmissions. Such a hybrid WDM light wave transmission system would be attractive for fiber links to provide broadband integrated services.

  9. 1-Mb resolution array-based comparative genomic hybridization using a BAC clone set optimized for cancer gene analysis

    NARCIS (Netherlands)

    Greshock, J; Naylor, TL; Margolin, A; Diskin, S; Cleaver, SH; Futreal, PA; deJong, PJ; Zhao, SY; Liebman, M; Weber, BL

    2004-01-01

    Array-based comparative genomic hybridization (aCGH) is a recently developed tool for genome-wide determination of DNA copy number alterations. This technology has tremendous potential for disease-gene discovery in cancer and developmental disorders as well as numerous other applications. However, w

  10. Technology of building an expert system based on a set of quantitative features of tumor cell nuclei for diagnosing breast cancer.

    Science.gov (United States)

    Kirillov, Vladimir

    2013-06-01

    The technology of building an expert system for diagnosing malignant nature of invasive tumors of the mammary gland based on a set of quantitative features of the cell nuclei has been developed. Its peculiarity was the presence of weighting coefficients in all the features. Quantitative features were obtained by transforming the initial morphometric data with the help of simple (evaluation of mean values and building of histograms) and complex (regression analysis) mathematical operations. The expert system consisted of one-dimensional X-matrix used for investigations and two-dimensional standard S-matrix. The X-matrix elements were assigned for filling with the quantitative features of the studied sample with a nonestablished diagnosis. The S-matrix elements contained threshold values of quantitative features from the system of diagnostic decision criteria for malignant forms of diseases and their weighting coefficients. Threshold values of nuclear features (larger or smaller) were determined taking into account the range of their values in the groups of malignant and benign pathology. Significance of quantitative features in diagnosing diseases has been assessed. The presence of weighting coefficients allowed diagnosing malignant and benign pathology in a quantitative form by the diagnostic index value. Diagnostic index was calculated by the sum of weighting coefficients of features of the studied sample, which fell within the range of system of the S-matrix diagnostic decision criteria. Clinical trials revealed high efficiency of the developed approach while diagnosis of breast cancer invasive forms at a preoperative stage. Copyright © 2012 Wiley Periodicals, Inc.

  11. Text feature selection method based on hybrid clone quantum genetic strategy%基于混合克隆量子遗传策略的文本特征选择方法

    Institute of Scientific and Technical Information of China (English)

    符保龙

    2012-01-01

    The metrics of vector reduction rate and classification accuracy, and to use of the qubits encoded on the genetic algorithm, combined with the cloning operator, this paper proposed a strategy based on hybrid genetic quantum cloning text feature selection method. Experimental results show that the method can effectively reduce the dimension of feature vector text, set of extracted features can improve the quantum accuracy of text classification.%引入向量约简率和分类准确率的度量标准,采用量子比特对遗传算法进行编码,结合克隆算子,提出一种基于混合克隆量子遗传策略的文本特征选择方法.实验结果显示,该方法能有效地降低文本特征向量的维度,所提取的特征向量子集能有效提高文本分类的精度.

  12. Cycle killer... qu'est-ce que c'est? On the comparative approximability of hybridization number and directed feedback vertex set

    CERN Document Server

    Kelk, Steven; Lekic, Nela; Linz, Simone; Scornavacca, Celine; Stougie, Leen

    2011-01-01

    We show that the problem of computing the hybridization number of two rooted binary phylogenetic trees on the same set of taxa X has a constant factor polynomial-time approximation if and only if the problem of computing a minimum-size feedback vertex set in a directed graph (DFVS) has a constant factor polynomial-time approximation. The latter problem, which asks for a minimum number of vertices to be removed from a directed graph to transform it into a directed acyclic graph, is one of the problems in Karp's seminal 1972 list of 21 NP-complete problems. However, despite considerable attention from the combinatorial optimization community it remains to this day unknown whether a constant factor polynomial-time approximation exists for DFVS. Our result thus places the (in)approximability of hybridization number in a much broader complexity context, and as a consequence we obtain that hybridization number inherits inapproximability results from the problem Vertex Cover. On the positive side, we use results fro...

  13. Multicriteria analysis of the hybrid systems with biogas: fuzzy set and rules; Analise multicriterio de sistemas hibridos com biogas: conjuntos e regras fuzzy

    Energy Technology Data Exchange (ETDEWEB)

    Barin, A.; Canha, L.; Abaide, A.; Magnago, K. [Federal University of Santa Maria (UFSM), RS (Brazil)], E-mail: chbarin@gmail.com; Machado, R. [Universidade de Sao Paulo (EESC/USP), Sao Carlos, SP (Brazil). Escola de Engenharia], E-mail: rquadros@sel.eesc.usp.br

    2009-07-01

    A multicriteria analysis to manage de renewable sources of energy is presented, identifying the most appropriate hybrid system to be used as distributed generation of electric energy using biogas. In this methodology, fuzzy sets and rules are defined simulated in the software MATLAB, where the main characteristics of the operation and application of hybrid systems of electric power generation are considered. The main generation system, that can use the biogas, as micro turbines and fuel cells, are evaluated. Afterwards, the systems of energy storage are analyzed: flywheel, H{sub 2} storage and conventional and redox batteries. For the development of the proposed methodology, it was considered the following criteria: efficiency, costs, technological maturity, environmental impacts, the amplitude of the system action (power range), useful life, co-generation possibility and operation temperature. A classification, by priority order, for the use of the sources and storages associated to the environment and cost scenarios is also presented.

  14. Evaluation of two-center Coulomb and hybrid integrals over complete orthonormal sets of Ψα-ETO using auxiliary functions.

    Science.gov (United States)

    Guseinov, Israfil I; Sahin, Ercan

    2011-04-01

    By the use of ellipsoidal coordinates, the two-center Coulomb and hybrid integrals over complete orthonormal sets of Ψα-ETO exponential type orbitals arising in ab initio calculations of molecules are evaluated, where α = 1,0, -1, -2, ...,. These integrals are expressed through the auxiliary functions Q(ns)(q) and G(-ns)(q). The comparison is made with some values of integrals for Slater type orbitals the computation results of which are in good agreement with those obtained in the literature. The relationships obtained are valid for the arbitrary quantum numbers, screening constants and location of orbitals. Closed form expressions for two-center Coulomb and hybrid integrals for 1s and 2s orbitals with α = 1 are also presented. As an example of application, the Hartree-Fock-Roothaan calculations for the ground state of H(2) molecule are carried out with α = 1 and α = 0.

  15. Note: Easy-to-maintain electron cyclotron resonance (ECR) plasma sputtering apparatus featuring hybrid waveguide and coaxial cables for microwave delivery.

    Science.gov (United States)

    Akazawa, Housei

    2016-06-01

    The branched-waveguide electron cyclotron resonance plasma sputtering apparatus places quartz windows for transmitting microwaves into the plasma source not in the line of sight of the target. However, the quartz windows must be replaced after some time of operation. For maintenance, the loop waveguide branching from the T-junction must be dismounted and re-assembled accurately, which is a time-consuming job. We investigated substituting the waveguide branches with two sets of coaxial cables and waveguide/coaxial cable converters to simplify assembly as far as connection and disconnection go. The resulting hybrid system worked well for the purposes of plasma generation and film deposition.

  16. Flavonol-carbon nanostructure hybrid systems: a DFT study on the interaction mechanism and UV/Vis features.

    Science.gov (United States)

    García, Gregorio; Atilhan, Mert; Aparicio, Santiago

    2016-02-14

    Flavonols are a class of natural compounds with potential biological and pharmacological applications. They are also natural pigments responsible for the diversity of colors in plants. Flavonols offer the possibility of tuning their features through chemical functionalization as well as the presence of an aromatic backbone, which could lead to non-covalent interactions with different nanostructures or aromatic molecules. In this work, a protocol based on ONIOM (QM/QM) calculations to investigate the structural features (binding energies, intermolecular interactions) of flavonols interacting with the surface of several carbon nanostructures (such as graphene, fullerene C60 and carbon nanotubes) is developed. The confinement of flavonols inside carbon nanotubes has also been studied. Three flavonols, galangin, quercetin and myricetin, as well as pristine flavone were selected. Special attention has also been paid to the changes in UV/Vis features of flavonols due to the interaction with carbon nanostructures. Our results point out that π-stacking interactions are the driving force for the adsorption onto carbon nanostructures as well as for the confinement inside carbon nanotubes. Likewise, UV/Vis features of flavonols could be fine-tuned through the interaction with suitable carbon nanostructures.

  17. Rough sets and near sets in medical imaging: a review.

    Science.gov (United States)

    Hassanien, Aboul Ella; Abraham, Ajith; Peters, James F; Schaefer, Gerald; Henry, Christopher

    2009-11-01

    This paper presents a review of the current literature on rough-set- and near-set-based approaches to solving various problems in medical imaging such as medical image segmentation, object extraction, and image classification. Rough set frameworks hybridized with other computational intelligence technologies that include neural networks, particle swarm optimization, support vector machines, and fuzzy sets are also presented. In addition, a brief introduction to near sets and near images with an application to MRI images is given. Near sets offer a generalization of traditional rough set theory and a promising approach to solving the medical image correspondence problem as well as an approach to classifying perceptual objects by means of features in solving medical imaging problems. Other generalizations of rough sets such as neighborhood systems, shadowed sets, and tolerance spaces are also briefly considered in solving a variety of medical imaging problems. Challenges to be addressed and future directions of research are identified and an extensive bibliography is also included.

  18. Clinical application of modified bag-of-features coupled with hybrid neural-based classifier in dengue fever classification using gene expression data.

    Science.gov (United States)

    Chatterjee, Sankhadeep; Dey, Nilanjan; Shi, Fuqian; Ashour, Amira S; Fong, Simon James; Sen, Soumya

    2017-09-11

    Dengue fever detection and classification have a vital role due to the recent outbreaks of different kinds of dengue fever. Recently, the advancement in the microarray technology can be employed for such classification process. Several studies have established that the gene selection phase takes a significant role in the classifier performance. Subsequently, the current study focused on detecting two different variations, namely, dengue fever (DF) and dengue hemorrhagic fever (DHF). A modified bag-of-features method has been proposed to select the most promising genes in the classification process. Afterward, a modified cuckoo search optimization algorithm has been engaged to support the artificial neural (ANN-MCS) to classify the unknown subjects into three different classes namely, DF, DHF, and another class containing convalescent and normal cases. The proposed method has been compared with other three well-known classifiers, namely, multilayer perceptron feed-forward network (MLP-FFN), artificial neural network (ANN) trained with cuckoo search (ANN-CS), and ANN trained with PSO (ANN-PSO). Experiments have been carried out with different number of clusters for the initial bag-of-features-based feature selection phase. After obtaining the reduced dataset, the hybrid ANN-MCS model has been employed for the classification process. The results have been compared in terms of the confusion matrix-based performance measuring metrics. The experimental results indicated a highly statistically significant improvement with the proposed classifier over the traditional ANN-CS model.

  19. SET-MOS混合结构的触发器设计及应用%Design and Application of Hybrid SET-MOS Flip-flop Circuit

    Institute of Scientific and Technical Information of China (English)

    李芹; 蔡理; 李明

    2009-01-01

    基于SET-MOS混合结构的或非门构建了基本RS触发器和主从式D触发器,对所设计的新型触发器电路进行了分析研究,并将其应用到寄存器和移位寄存器电路.利用SPICE对所设计的触发器电路进行仿真验证,仿真结果表明电路运行良好.该新型触发器电路与SET实现的电路相比,具有更高的驱动能力;与传统CMOS电路相比,电路的功耗仅为10-10 W的数量级.

  20. Data mining framework for identification of myocardial infarction stages in ultrasound: A hybrid feature extraction paradigm (PART 2).

    Science.gov (United States)

    Sudarshan, Vidya K; Acharya, U Rajendra; Ng, E Y K; Tan, Ru San; Chou, Siaw Meng; Ghista, Dhanjoo N

    2016-04-01

    Early expansion of infarcted zone after Acute Myocardial Infarction (AMI) has serious short and long-term consequences and contributes to increased mortality. Thus, identification of moderate and severe phases of AMI before leading to other catastrophic post-MI medical condition is most important for aggressive treatment and management. Advanced image processing techniques together with robust classifier using two-dimensional (2D) echocardiograms may aid for automated classification of the extent of infarcted myocardium. Therefore, this paper proposes novel algorithms namely Curvelet Transform (CT) and Local Configuration Pattern (LCP) for an automated detection of normal, moderately infarcted and severely infarcted myocardium using 2D echocardiograms. The methodology extracts the LCP features from CT coefficients of echocardiograms. The obtained features are subjected to Marginal Fisher Analysis (MFA) dimensionality reduction technique followed by fuzzy entropy based ranking method. Different classifiers are used to differentiate ranked features into three classes normal, moderate and severely infarcted based on the extent of damage to myocardium. The developed algorithm has achieved an accuracy of 98.99%, sensitivity of 98.48% and specificity of 100% for Support Vector Machine (SVM) classifier using only six features. Furthermore, we have developed an integrated index called Myocardial Infarction Risk Index (MIRI) to detect the normal, moderately and severely infarcted myocardium using a single number. The proposed system may aid the clinicians in faster identification and quantification of the extent of infarcted myocardium using 2D echocardiogram. This system may also aid in identifying the person at risk of developing heart failure based on the extent of infarcted myocardium.

  1. 中西文化背景下的“关系”研究%Feature of Hybridity in Gu Hongming' s Version of The Analects of Confucius

    Institute of Scientific and Technical Information of China (English)

    陈勇; 胡步芬

    2011-01-01

    With the notion of hybridity of post-colonial theories as the theoretic perspective, a systemauc analysis is carried out on Gu Hongming' s version of The Analects of Confucius. It is argued that Gu Hongming' s version entails the clear feature of hybridity which indicates his endeavor to resist western hegemony of culture in special context of history and his intention to promote the equal dialogue between Chinese and western cultures.%中西方学者对关系的研究,从关系的定义、对关系的认知、研究的意义等方面充分体现了双方社会与文化的差异。这种差异背后是强大的中国传统社会与文化的持续影响力。正确引导和利用好关系是构建和谐社会必不可少的一部分。

  2. The features of neutronic calculations for fast reactors with hybrid cores on the basis of BFS-62-3A critical assembly experiments

    Energy Technology Data Exchange (ETDEWEB)

    Mitenkova, E. F.; Novikov, N. V. [Nuclear Safety Inst. of Russian Academy of Sciences, B. Tulskaya 52, Moscow, 115119 (Russian Federation); Blokhin, A. I. [State Scientific Center of Russian Federation, Inst. of Physics and Power Engineering Named after A.I. Leypunsky, Bondarenko Square 1, Obninsk, Kaluga Region, 249030 (Russian Federation)

    2012-07-01

    The different (U-Pu) fuel compositions are considered for next generation of sodium fast breeder reactors. The considerable discrepancies in axial and radial neutron spectra for hybrid reactor systems compared to the cores with UO{sub 2} fuel cause increasing uncertainty of generating the group nuclear constants in those reactor systems. The calculation results of BFS-62-3A critical assembly which is considered as full-scale model of BN-600 hybrid core with steel reflector specify quite different spectra in local areas. For those systems the MCNP 5 calculations demonstrate significant sensitivity of effective multiplication factor K{sub eff} and spectral indices to nuclear data libraries. For {sup 235}U, {sup 238}U, {sup 239}Pu the results of calculated radial fission rate distributions against the reconstructed ones are analyzed. Comparative analysis of spectral indices, neutron spectra and radial fission rate distributions are performed using the different versions of ENDF/B, JENDL-3.3, JENDL-4, JEFF-3.1.1 libraries and BROND-3 for Fe, Cr isotopes. For analyzing the fission rate sensitivity to the plutonium presence in the fuel {sup 239}Pu is substituted for {sup 235}U (enrichment 90%) in the FA areas containing the plutonium. For {sup 235}U, {sup 238}U, {sup 239}Pu radial fission rate distributions the explanation of pick values discrepancies is based on the group fission constants analyses and possible underestimation of some features at the experimental data recovery method (Westcott factors, temperature dependence). (authors)

  3. Chromosome deletion of 14q32.33 detected by array comparative genomic hybridization in a patient with features of dubowitz syndrome.

    Science.gov (United States)

    Darcy, Diana C; Rosenthal, Scott; Wallerstein, Robert J

    2011-01-01

    We report a 4-year-old girl of Mexican origins with a clinical diagnosis of Dubowitz syndrome who carries a de novo terminal deletion at the 14q32.33 locus identified by array comparative genomic hybridization (aCGH). Dubowitz syndrome is a rare condition characterized by a constellation of features including growth retardation, short stature, microcephaly, micrognathia, eczema, telecanthus, blepharophimosis, ptosis, epicanthal folds, broad nasal bridge, round-tipped nose, mild to moderate developmental delay, and high-pitched hoarse voice. This syndrome is thought to be autosomal recessive; however, the etiology has not been determined. This is the first report of this deletion in association with this phenotype; it is possible that this deletion may be causal for a Dubowitz phenocopy.

  4. Chromosome Deletion of 14q32.33 Detected by Array Comparative Genomic Hybridization in a Patient with Features of Dubowitz Syndrome

    Directory of Open Access Journals (Sweden)

    Diana C. Darcy

    2011-01-01

    Full Text Available We report a 4-year-old girl of Mexican origins with a clinical diagnosis of Dubowitz syndrome who carries a de novo terminal deletion at the 14q32.33 locus identified by array comparative genomic hybridization (aCGH. Dubowitz syndrome is a rare condition characterized by a constellation of features including growth retardation, short stature, microcephaly, micrognathia, eczema, telecanthus, blepharophimosis, ptosis, epicanthal folds, broad nasal bridge, round-tipped nose, mild to moderate developmental delay, and high-pitched hoarse voice. This syndrome is thought to be autosomal recessive; however, the etiology has not been determined. This is the first report of this deletion in association with this phenotype; it is possible that this deletion may be causal for a Dubowitz phenocopy.

  5. Unsupervised Feature Subset Selection

    DEFF Research Database (Denmark)

    Søndberg-Madsen, Nicolaj; Thomsen, C.; Pena, Jose

    2003-01-01

    This paper studies filter and hybrid filter-wrapper feature subset selection for unsupervised learning (data clustering). We constrain the search for the best feature subset by scoring the dependence of every feature on the rest of the features, conjecturing that these scores discriminate some...... irrelevant features. We report experimental results on artificial and real data for unsupervised learning of naive Bayes models. Both the filter and hybrid approaches perform satisfactorily....

  6. Predicting High or Low Transfer Efficiency of Photovoltaic Systems Using a Novel Hybrid Methodology Combining Rough Set Theory, Data Envelopment Analysis and Genetic Programming

    Directory of Open Access Journals (Sweden)

    Lee-Ing Tong

    2012-02-01

    Full Text Available Solar energy has become an important energy source in recent years as it generates less pollution than other energies. A photovoltaic (PV system, which typically has many components, converts solar energy into electrical energy. With the development of advanced engineering technologies, the transfer efficiency of a PV system has been increased from low to high. The combination of components in a PV system influences its transfer efficiency. Therefore, when predicting the transfer efficiency of a PV system, one must consider the relationship among system components. This work accurately predicts whether transfer efficiency of a PV system is high or low using a novel hybrid model that combines rough set theory (RST, data envelopment analysis (DEA, and genetic programming (GP. Finally, real data-set are utilized to demonstrate the accuracy of the proposed method.

  7. The role of the cold sector of extratropical cyclones in setting atmospheric mean state features of the Gulf Stream basin.

    Science.gov (United States)

    Vannière, Benoît; Czaja, Arnaud; Dacre, Helen; Woollings, Tim

    2016-04-01

    The mechanism by which the Gulf Stream SST front anchors a band of precipitation on its warm edge is still a matter of debate and little is known about how synoptic activity contributes to shape precipitation mean state pattern. In this talk, we introduce a new indicator for the cold sector of extratropical storms based on low-level PV. This indicator is used in ERA interim data to separate the cold sector contribution to precipitation and vertical wind from the contribution of the rest of the storm. We find that cold sector precipitation forms a band following the SST front closely. In contrast, the enhanced ascent on the warm edge of the front is set primarily by the warm sector and cannot be directly related to the precipitation band as proposed by previous studies. Numerical sensitivity experiments of an extratropical cyclone passing over different sets of SST further confirms that the anchoring effect of the SST front on precipitation comes exclusively from the cold sector. These results lead us to revisit the atmospheric boundary layer model proposed to describe air-sea interactions over the Gulf-Stream SST gradient. Finally, we explore the role of the cold sector convection in restoring baroclinicity in the wake of an extratropical cyclone.

  8. Feature and duration of metre-scale sequences in a storm-dominated carbonate ramp setting (Kimmeridgian, northeastern Spain)

    Science.gov (United States)

    Colombié, C.; Bádenas, B.; Aurell, M.; Götz, A. E.; Bertholon, S.; Boussaha, M.

    2014-10-01

    Metre-scale sequences may result from the combined effects of allocyclic and autocyclic processes which are closely inter-related. The carbonate ramp that developed northwest of the Iberian Basin during the late Kimmeridgian was affected by northwestward migrating cyclones. Marl-limestone alternations that settled in mid-ramp environments contain abundant mm to cm thick coarse-grained accumulations that have been related to these events. The aim of this paper is to determine the impact of storm-induced processes on the metre-scale sequence features. Four sections (R3, R4, R6, and R7), which are 5 to 7 m in thickness, were studied bed-by-bed along a 4 km-long outcrop, which shows the transition between the shallow and the relatively deep realms of the middle ramp. Metre-scale sequences were defined and correlated along this outcrop according to the detailed microfacies analysis of host, fine-grained deposits, palynofacies and sequence-stratigraphic analyses, and carbon- and oxygen-isotope measurements. The evolution through time of sedimentary features such as the size of quartz grains and the relative abundance of grains other than quartz (i.e., muscovite, bivalve, ooid, and intraclast) does not correlate from one section to the other, suggesting that the finest as well as the coarsest sediment was reworked in these storm-dominated environments. Small- and medium-scale sequences are defined according to changes in alternation, marly interbed, and limestone bed thickness, and correlated from one section to the other. Because of the effects of storms on sediment distribution and preservation, sequence boundaries coincide with thin alternations and marly interbeds in the most proximal sections (i.e., R3, R4), while they correspond to thin alternations and limestone beds in the most distal sections (i.e., R6, R7). Field observations and palynofacies analyses confirm this sequence-stratigraphic analysis. The excursions in carbon- and oxygen-isotope values are consistent

  9. The critical shortage of speech-language pathologists in the public school setting: features of the work environment that affect recruitment and retention.

    Science.gov (United States)

    Edgar, Debra L; Rosa-Lugo, Linda I

    2007-01-01

    The primary focus of this study was to elicit the perspectives of speech-language pathologists (SLPs) regarding features of the work environment that contribute to and/or hinder recruitment and retention in the public school setting. A questionnaire was distributed to SLPs employed in 10 school districts in Central Florida representing small, medium, and large school districts. The primary goal of the questionnaire was to elicit the perspectives of school-based SLPs regarding (a) factors in the work environment that contribute to retention, (b) factors in the work environment that hinder retention, and (c) issues that may contribute to the recruitment and retention of SLPs in the school setting. A total of 382 questionnaires was obtained, yielding a 64.5% response rate. The participants ranked working with children, school schedule, and educational setting as primary reasons for their satisfaction with working in the public school setting. The participants ranked workload, role ambiguity, salary, and caseload as primary reasons for their dissatisfaction with working in the public school setting. Themes emerged from the data that provide insight into several factors that serve as powerful influences in understanding issues of recruitment and retention of SLPs in the public school setting.

  10. Research and Application of Hybrid Forecasting Model Based on an Optimal Feature Selection System—A Case Study on Electrical Load Forecasting

    Directory of Open Access Journals (Sweden)

    Yunxuan Dong

    2017-04-01

    Full Text Available The process of modernizing smart grid prominently increases the complexity and uncertainty in scheduling and operation of power systems, and, in order to develop a more reliable, flexible, efficient and resilient grid, electrical load forecasting is not only an important key but is still a difficult and challenging task as well. In this paper, a short-term electrical load forecasting model, with a unit for feature learning named Pyramid System and recurrent neural networks, has been developed and it can effectively promote the stability and security of the power grid. Nine types of methods for feature learning are compared in this work to select the best one for learning target, and two criteria have been employed to evaluate the accuracy of the prediction intervals. Furthermore, an electrical load forecasting method based on recurrent neural networks has been formed to achieve the relational diagram of historical data, and, to be specific, the proposed techniques are applied to electrical load forecasting using the data collected from New South Wales, Australia. The simulation results show that the proposed hybrid models can not only satisfactorily approximate the actual value but they are also able to be effective tools in the planning of smart grids.

  11. Clinical and cytogenetic features of a patient with partial trisomy 8q and partial monosomy 13q delineated by array comparative genomic hybridization.

    Science.gov (United States)

    Sohn, Young Bae; Yun, Jun No; Park, Sang-Jin; Park, Moon Sung; Kim, Sung Hwan; Lee, Jang Hoon

    2013-01-01

    Partial trisomy 8q is rare and has distinctive clinical features, including severe mental retardation, growth impairment, dysmorphic facial appearances, cleft palate, congenital heart disease, and urogenital anomalies. Partial monosomy 13q is a rare genetic disorder displaying a variety of phenotypic characteristics including mental retardation, dysmorphic facial features, and congenital anomalies. Here, we describe for the first time clinical observations and cytogenetic analysis of a patient with a concomitant occurrence of partial trisomy of 8q (8q21.3→qter) and partial monosomy 13q(13q34→qter). The patient was a female neonate with facial dysmorphia, agenesis of the corpus callosum, cleft palate, and congenital heart disease. G-band standard karyotype was 46,XX,add(13)(q34). To determine the origin of additional genomic gain in chromosome 13, array comparative genomic hybridization (CGH) was performed. Array CGH showed a 56.8 Mb sized gain on chromosome 8q and a 0.28 Mb sized loss on chromosome 13q. Therefore, the final karyotype of the patient was defined as 46,XX, der(13)t(8;13)(q21.3;q34). In conclusion, we described the clinical and cytogenetic analysis of the patient with concomitant occurrence of partial trisomy 8q and partial monosomy 13q delineated by array CGH. This report suggests that the array CGH would be a valuable diagnostic tool for identifying the origin of small additional genetic materials.

  12. Specific features of modelling rules of monetary policy on the basis of hybrid regression models with a neural component

    Directory of Open Access Journals (Sweden)

    Lukianenko Iryna H.

    2014-01-01

    Full Text Available The article considers possibilities and specific features of modelling economic phenomena with the help of the category of models that unite elements of econometric regressions and artificial neural networks. This category of models contains auto-regression neural networks (AR-NN, regressions of smooth transition (STR/STAR, multi-mode regressions of smooth transition (MRSTR/MRSTAR and smooth transition regressions with neural coefficients (NCSTR/NCSTAR. Availability of the neural network component allows models of this category achievement of a high empirical authenticity, including reproduction of complex non-linear interrelations. On the other hand, the regression mechanism expands possibilities of interpretation of the obtained results. An example of multi-mode monetary rule is used to show one of the cases of specification and interpretation of this model. In particular, the article models and interprets principles of management of the UAH exchange rate that come into force when economy passes from a relatively stable into a crisis state.

  13. A Computer-Aided Diagnosis System for Dynamic Contrast-Enhanced MR Images Based on Level Set Segmentation and ReliefF Feature Selection

    Directory of Open Access Journals (Sweden)

    Zhiyong Pang

    2015-01-01

    Full Text Available This study established a fully automated computer-aided diagnosis (CAD system for the classification of malignant and benign masses via breast magnetic resonance imaging (BMRI. A breast segmentation method consisting of a preprocessing step to identify the air-breast interfacing boundary and curve fitting for chest wall line (CWL segmentation was included in the proposed CAD system. The Chan-Vese (CV model level set (LS segmentation method was adopted to segment breast mass and demonstrated sufficiently good segmentation performance. The support vector machine (SVM classifier with ReliefF feature selection was used to merge the extracted morphological and texture features into a classification score. The accuracy, sensitivity, and specificity measurements for the leave-half-case-out resampling method were 92.3%, 98.2%, and 76.2%, respectively. For the leave-one-case-out resampling method, the measurements were 90.0%, 98.7%, and 73.8%, respectively.

  14. Transcriptome comparison of global distinctive features between pollination and parthenocarpic fruit set reveals transcriptional phytohormone cross-talk in cucumber (Cucumis sativus L.).

    Science.gov (United States)

    Li, Ji; Wu, Zhe; Cui, Li; Zhang, Tinglin; Guo, Qinwei; Xu, Jian; Jia, Li; Lou, Qunfeng; Huang, Sanwen; Li, Zhengguo; Chen, Jinfeng

    2014-07-01

    Parthenocarpy is an important trait determining yield and quality of fruit crops. However, the understanding of the mechanisms underlying parthenocarpy induction is limited. Cucumber (Cucumis sativus L.) is abundant in parthenocarpic germplasm resources and is an excellent model organism for parthenocarpy studies. In this study, the transcriptome of cucumber fruits was studied using RNA sequencing (RNA-Seq). Differentially expressed genes (DEGs) of set fruits were compared against aborted fruits. Distinctive features of parthenocarpic and pollinated fruits were revealed by combining the analysis of the transcriptome together with cytomorphological and physiological analysis. Cell division and the transcription of cell division genes were found to be more active in parthenocarpic fruit. The study also indicated that parthenocarpic fruit set is a high sugar-consuming process which is achieved via enhanced carbohydrate degradation through transcription of genes that lead to the breakdown of carbohydrates. Furthermore, the evidence provided by this work supports a hypothesis that parthenocarpic fruit set is induced by mimicking the processes of pollination/fertilization at the transcriptional level, i.e. by performing the same transcriptional patterns of genes inducing pollination and gametophyte development as in pollinated fruit. Based on the RNA-Seq and ovary transient expression results, 14 genes were predicted as putative parthenocarpic genes. The transcription analysis of these candidate genes revealed auxin, cytokinin and gibberellin cross-talk at the transcriptional level during parthenocarpic fruit set.

  15. Robot vision environmental perception method based on hybrid features%机器人的混合特征视觉环境感知方法

    Institute of Scientific and Technical Information of China (English)

    杨俊友; 马乐; 白殿春; 东俊光

    2012-01-01

    提出一种基于颜色直方图和SIFT混合特征的机器人视觉环境感知方法.该方法将颜色直方图的“色”与SIFT算法的“形”有机结合,有效提高了感知精度和实时性.对图像进行平均亮度调整并对颜色直方图特征加入主颜色直方图,使之对环境光照和动态变化具有更好的鲁棒性;通过控制特征点数和加入局部颜色统计信息方式改进SIFT算法,提高了特征生成速度和匹配准确度.利用分级匹配方法加速了特征检索过程,并采用本文提出的基于数据知识的推理方法进一步提高了感知精度.仿真与实验结果表明,随着数据库规模扩大,本文方法在感知精度和实时性上的性能优势越发明显.%An image-matching method for robot environmental perception based on hybrid features from color histograms baaed on the scale-invariant feature transform (SIFT) is proposed. The SIFT is combined with color histograms to make a compromise between high perception accuracy and real-time processing needs. First, images are processed by making an average of the lightness, then the extracted features are added to the main color histogram, which is more robust against lightness and dynamics in the environment. The number of SIFT values is controlled and local color statistical information is added to the SIFT,which is more accurate and faster for real-time matching. After wards,the process of features-retrieval is accelerated by hierarchical matching. Finally, the scheme is optimized using the proposed reasoning method based on previous knowledge from databases,to further improve the accuracy of perception the simulation and experiment results show that when the scale of the database is growing,the advantage of the proposea method proposed is prominent.

  16. Development of Feature Set, Classification Implementation and Applications for Vowel Migration/Modification in Sung Filipino (Tagalog Texts and Perceived Intelligibility

    Directory of Open Access Journals (Sweden)

    Virginia B. Bustos

    2009-12-01

    Full Text Available With the emergence of research on real-time visual feedback to supplement vocal pedagogy, the utilization of technology in the world of music is now seen to accelerate skills learning and enhance cognitive development. The researchers of this project aim to further analyze vowel intelligibility and develop software applications intended to be used not only by professional singers but also by individuals who wish to improve their singing capability. Data in the form of sung vowels and song pieces were obtained from 46 singers. A Listening Test was then conducted on these samples to obtain the ground truth for vowel classification based on human perception. Simulation of the human auditory perception of sung Filipino vowels was performed using formant frequencies and Mel-frequency cepstral coefficients as feature vector inputs to a two-stage Discriminant Analysis classifier. The setup resulted in an over-all Training Set accuracy of 89.4% and an over-all Test Set accuracy of 90.9%. The accuracy of the classifier, measured in terms of the correspondence of vowel classifications obtained from the classifier with the results of the Listening Test, reached 92.3%. Using information obtained from the classifier, offline and online/real-time software applications were developed. The main application features include the display of the spectral envelope and spectrogram, pitch and vibrato analysis and direct feedback on the classification of the sung vowel. These features were recommended by singers who were surveyed and were incorporated in the applications to aid singers to adjust formant locations, directly determine listener’s perception of sung vowels, perform modeling effectively and carry out vowel migration.

  17. A level set methodology for predicting the effect of mask wear on surface evolution of features in abrasive jet micro-machining

    Science.gov (United States)

    Burzynski, T.; Papini, M.

    2012-07-01

    A previous implementation of narrow-band level set methodology developed by the authors was extended to allow for the modelling of mask erosive wear in abrasive jet micro-machining (AJM). The model permits the prediction of the surface evolution of both the mask and the target simultaneously, by representing them as a hybrid and continuous mask-target surface. The model also accounts for the change in abrasive mass flux incident to both the target surface and, for the first time, the eroding mask edge, that is brought about by the presence of the mask edge itself. The predictions of the channel surface and eroded mask profiles were compared with measurements on channels machined in both glass and poly-methyl-methacrylate (PMMA) targets at both normal and oblique incidence, using tempered steel and elastomeric masks. A much better agreement between the predicted and measured profiles was found when mask wear was taken into account. Mask wear generally resulted in wider and deeper glass target profiles and wider PMMA target profiles, respectively, when compared to cases where no mask wear was present. This work has important implications for the AJM of complex MEMS and microfluidic devices that require longer machining times.

  18. An enhanced feature set for pattern recognition based contrast enhancement of contact-less captured latent fingerprints in digitized crime scene forensics

    Science.gov (United States)

    Hildebrandt, Mario; Kiltz, Stefan; Dittmann, Jana; Vielhauer, Claus

    2014-02-01

    In crime scene forensics latent fingerprints are found on various substrates. Nowadays primarily physical or chemical preprocessing techniques are applied for enhancing the visibility of the fingerprint trace. In order to avoid altering the trace it has been shown that contact-less sensors offer a non-destructive acquisition approach. Here, the exploitation of fingerprint or substrate properties and the utilization of signal processing techniques are an essential requirement to enhance the fingerprint visibility. However, especially the optimal sensory is often substrate-dependent. An enhanced generic pattern recognition based contrast enhancement approach for scans of a chromatic white light sensor is introduced in Hildebrandt et al.1 using statistical, structural and Benford's law2 features for blocks of 50 micron. This approach achieves very good results for latent fingerprints on cooperative, non-textured, smooth substrates. However, on textured and structured substrates the error rates are very high and the approach thus unsuitable for forensic use cases. We propose the extension of the feature set with semantic features derived from known Gabor filter based exemplar fingerprint enhancement techniques by suggesting an Epsilon-neighborhood of each block in order to achieve an improved accuracy (called fingerprint ridge orientation semantics). Furthermore, we use rotation invariant Hu moments as an extension of the structural features and two additional preprocessing methods (separate X- and Y Sobel operators). This results in a 408-dimensional feature space. In our experiments we investigate and report the recognition accuracy for eight substrates, each with ten latent fingerprints: white furniture surface, veneered plywood, brushed stainless steel, aluminum foil, "Golden-Oak" veneer, non-metallic matte car body finish, metallic car body finish and blued metal. In comparison to Hildebrandt et al.,1 our evaluation shows a significant reduction of the error rates

  19. A hybrid multi-objective evolutionary algorithm approach for handling sequence- and machine-dependent set-up times in unrelated parallel machine scheduling problem

    Indian Academy of Sciences (India)

    V K MANUPATI; G RAJYALAKSHMI; FELIX T S CHAN; J J THAKKAR

    2017-03-01

    This paper addresses a fuzzy mixed-integer non-linear programming (FMINLP) model by considering machine-dependent and job-sequence-dependent set-up times that minimize the total completion time,the number of tardy jobs, the total flow time and the machine load variation in the context of unrelated parallel machine scheduling (UPMS) problem. The above-mentioned multi-objectives were considered based on nonzero ready times, machine- and sequence-dependent set-up times and secondary resource constraints for jobs.The proposed approach considers unrelated parallel machines with inherent uncertainty in processing times and due dates. Since the problem is shown to be NP-hard in nature, it is a challenging task to find the optimal/nearoptimal solutions for conflicting objectives simultaneously in a reasonable time. Therefore, we introduced a new multi-objective-based evolutionary artificial immune non-dominated sorting genetic algorithm (AI-NSGA-II) to resolve the above-mentioned complex problem. The performance of the proposed multi-objective AI-NSGA-II algorithm has been compared to that of multi-objective particle swarm optimization (MOPSO) and conventionalnon-dominated sorting genetic algorithm (CNSGA-II), and it is found that the proposed multi-objective-based hybrid meta-heuristic produces high-quality solutions. Finally, the results obtained from benchmark instances and randomly generated instances as test problems evince the robust performance of the proposed multiobjective algorithm.

  20. Hybrid Fuzzy Wavelet Neural Networks Architecture Based on Polynomial Neural Networks and Fuzzy Set/Relation Inference-Based Wavelet Neurons.

    Science.gov (United States)

    Huang, Wei; Oh, Sung-Kwun; Pedrycz, Witold

    2017-08-11

    This paper presents a hybrid fuzzy wavelet neural network (HFWNN) realized with the aid of polynomial neural networks (PNNs) and fuzzy inference-based wavelet neurons (FIWNs). Two types of FIWNs including fuzzy set inference-based wavelet neurons (FSIWNs) and fuzzy relation inference-based wavelet neurons (FRIWNs) are proposed. In particular, a FIWN without any fuzzy set component (viz., a premise part of fuzzy rule) becomes a wavelet neuron (WN). To alleviate the limitations of the conventional wavelet neural networks or fuzzy wavelet neural networks whose parameters are determined based on a purely random basis, the parameters of wavelet functions standing in FIWNs or WNs are initialized by using the C-Means clustering method. The overall architecture of the HFWNN is similar to the one of the typical PNNs. The main strategies in the design of HFWNN are developed as follows. First, the first layer of the network consists of FIWNs (e.g., FSIWN or FRIWN) that are used to reflect the uncertainty of data, while the second and higher layers consist of WNs, which exhibit a high level of flexibility and realize a linear combination of wavelet functions. Second, the parameters used in the design of the HFWNN are adjusted through genetic optimization. To evaluate the performance of the proposed HFWNN, several publicly available data are considered. Furthermore a thorough comparative analysis is covered.

  1. Characterization of mature maize (Zea mays L.) root system architecture and complexity in a diverse set of Ex-PVP inbreds and hybrids.

    Science.gov (United States)

    Hauck, Andrew L; Novais, Joana; Grift, Tony E; Bohn, Martin O

    2015-01-01

    The mature root system is a vital plant organ, which is critical to plant performance. Commercial maize (Zea mays L.) breeding has resulted in a steady increase in plant performance over time, along with noticeable changes in above ground vegetative traits, but the corresponding changes in the root system are not presently known. In this study, roughly 2500 core root systems from field trials of a set of 10 diverse elite inbreds formerly protected by Plant Variety Protection plus B73 and Mo17 and the 66 diallel intercrosses among them were evaluated for root traits using high throughput image-based phenotyping. Overall root architecture was modeled by root angle (RA) and stem diameter (SD), while root complexity, the amount of root branching, was quantified using fractal analysis to obtain values for fractal dimension (FD) and fractal abundance (FA). For each trait, per se line effects were highly significant and the most important contributor to trait performance. Mid-parent heterosis and specific combining ability was also highly significant for FD, FA, and RA, while none of the traits showed significant general combining ability. The interaction between the environment and the additive line effect was also significant for all traits. Within the inbred and hybrid generations, FD and FA were highly correlated (rp ≥ 0.74), SD was moderately correlated to FD and FA (0.69 ≥ rp ≥ 0.48), while the correlation between RA and other traits was low (0.13 ≥ rp ≥ -0.40). Inbreds with contrasting effects on complexity and architecture traits were observed, suggesting that root complexity and architecture traits are inherited independently. A more comprehensive understanding of the maize root system and the way it interacts with the environment will be useful for defining adaptation to nutrient acquisition and tolerance to stress from drought and high plant densities, critical factors in the yield gains of modern hybrids.

  2. Structural aspects and porosity features of nano-size high surface area alumina-silica mixed oxide catalyst generated through hybrid sol-gel route

    Energy Technology Data Exchange (ETDEWEB)

    Padmaja, P. [Ceramic Technology Division, Regional Research Laboratory (CSIR), Trivandrum 695019, Kerala (India)]. E-mail: padmavasudev@yahoo.com; Warrier, K.G.K. [Ceramic Technology Division, Regional Research Laboratory (CSIR), Trivandrum 695019, Kerala (India)]. E-mail: kgk_warrier@yahoo.com; Padmanabhan, M. [School of Chemical Sciences, Mahatma Gandhi University, Kottayam 686560, Kerala (India); Wunderlich, W. [Graduate School of Engineering, Nagoya Institute of Technology, 466-8555 Nagoya (Japan); Berry, F.J. [Department of Chemistry, Open University, Walton Hall, Milton Keynes MK7 6AA (United Kingdom); Mortimer, M. [Department of Chemistry, Open University, Walton Hall, Milton Keynes MK7 6AA (United Kingdom); Creamer, N.J. [Department of Chemistry, Open University, Walton Hall, Milton Keynes MK7 6AA (United Kingdom)

    2006-01-10

    Alumina-silica mixed oxide nano-catalyst materials with compositions 83.6 wt.% Al{sub 2}O{sub 3}-16.4 wt.% SiO{sub 2} (3Al{sub 2}O{sub 3}.1SiO{sub 2}), 71.82 wt.% Al{sub 2}O{sub 3}-28.18 wt.% SiO{sub 2} (3Al{sub 2}O{sub 3}.2SiO{sub 2}), 62.84 wt.% Al{sub 2}O{sub 3}-37.16 wt.% SiO{sub 2} (3Al{sub 2}O{sub 3}.3SiO{sub 2}) and 56.03 wt.% Al{sub 2}O{sub 3}-43.97 wt.% SiO{sub 2} (3Al{sub 2}O{sub 3}.4SiO{sub 2}) have been prepared by a hybrid sol-gel technique using boehmite as the precursor for alumina and tetraethoxysilane as that for silica. The bonding characteristics and coordination features around Al and Si in the mixed oxide catalysts have been studied using FTIR and {sup 27}Al MAS NMR after calcination at 400 deg. C which is the temperature region where cross-condensation is seen to take place. A high BET specific surface area of 287 m{sup 2} g{sup -1} is obtained for 3Al{sub 2}O{sub 3}.1SiO{sub 2} mixed oxide composition. The porosity features are further established by BET adsorption isotherms and pore size distribution analysis. The temperature-programmed desorption studies showed more surface active sites for the silica-rich composition, suggesting enhanced catalytic potential. The TEM features of the mixed oxides showed a homogeneous distribution of alumina and silica phases with particle sizes in the nano-range. The low silica-containing mixed oxide showed a needle-like morphology with a high aspect ratio of 1:50 and {approx}10 nm particle size while the silica-rich composition had particle size in a wide range ({approx}20-75 nm)

  3. Model-Based Comparison of Deep Brain Stimulation Array Functionality with Varying Number of Radial Electrodes and Machine Learning Feature Sets.

    Science.gov (United States)

    Teplitzky, Benjamin A; Zitella, Laura M; Xiao, YiZi; Johnson, Matthew D

    2016-01-01

    Deep brain stimulation (DBS) leads with radially distributed electrodes have potential to improve clinical outcomes through more selective targeting of pathways and networks within the brain. However, increasing the number of electrodes on clinical DBS leads by replacing conventional cylindrical shell electrodes with radially distributed electrodes raises practical design and stimulation programming challenges. We used computational modeling to investigate: (1) how the number of radial electrodes impact the ability to steer, shift, and sculpt a region of neural activation (RoA), and (2) which RoA features are best used in combination with machine learning classifiers to predict programming settings to target a particular area near the lead. Stimulation configurations were modeled using 27 lead designs with one to nine radially distributed electrodes. The computational modeling framework consisted of a three-dimensional finite element tissue conductance model in combination with a multi-compartment biophysical axon model. For each lead design, two-dimensional threshold-dependent RoAs were calculated from the computational modeling results. The models showed more radial electrodes enabled finer resolution RoA steering; however, stimulation amplitude, and therefore spatial extent of the RoA, was limited by charge injection and charge storage capacity constraints due to the small electrode surface area for leads with more than four radially distributed electrodes. RoA shifting resolution was improved by the addition of radial electrodes when using uniform multi-cathode stimulation, but non-uniform multi-cathode stimulation produced equivalent or better resolution shifting without increasing the number of radial electrodes. Robust machine learning classification of 15 monopolar stimulation configurations was achieved using as few as three geometric features describing a RoA. The results of this study indicate that, for a clinical-scale DBS lead, more than four radial

  4. Major difference in visible-light photocatalytic features between perfect and self-defective Ta3N5 materials: A screened coulomb hybrid dft investigation

    KAUST Repository

    Harb, Moussab

    2014-09-11

    Relevant properties to visible-light overall water splitting reactions of perfect and self-defective bulk Ta3N5 semiconductor photocatalysts are investigated using accurate first-principles quantum calculations on the basis of density functional theory (DFT, including the perturbation theory DFPT) within the screened coulomb hybrid (HSE06) exchange-correlation formalism. Among the various explored self-defective structures, a strong stabilization is obtained for the configuration displaying a direct interaction between the created N- and Ta-vacancies. In the lowest-energy structure, each of the three created Ta-vacancies and the five created N-vacancies is found to be in aggregated disposition, leading to the formation of cages into the lattice. Although the calculated structural, electronic, and optical properties of the two materials are found to be very similar and in good agreement with available experimental works, their photocatalytic features for visible-light overall water splitting reactions show completely different behaviors. On the basis of calculated band edge positions relative to water redox potentials, the perfect Ta3N5 (calculated band gap of 2.2 eV) is predicted by HSE06 to be a good candidate only for H+ reduction while the self-defective Ta3N5 (calculated band gap of 2.0 eV) reveals suitable band positions for both water oxidation and H+ reduction similar to the experimental data reported on Ta3N5 powders. Its ability to reduce H+ is predicted to be lower than the perfect one. However, the strongly localized electronic characters of the valence band (VB) and conduction band (CB) edge states of the self-defective material only on the N 2p and Ta 5d orbitals surrounding the aggregated N- and Ta-vacancies are expected to strongly limit the probability of photogenerated carrier mobility through its crystal structure.

  5. OPTESIM, a versatile toolbox for numerical simulation of electron spin echo envelope modulation (ESEEM) that features hybrid optimization and statistical assessment of parameters.

    Science.gov (United States)

    Sun, Li; Hernandez-Guzman, Jessica; Warncke, Kurt

    2009-09-01

    Electron spin echo envelope modulation (ESEEM) is a technique of pulsed-electron paramagnetic resonance (EPR) spectroscopy. The analyis of ESEEM data to extract information about the nuclear and electronic structure of a disordered (powder) paramagnetic system requires accurate and efficient numerical simulations. A single coupled nucleus of known nuclear g value (g(N)) and spin I=1 can have up to eight adjustable parameters in the nuclear part of the spin Hamiltonian. We have developed OPTESIM, an ESEEM simulation toolbox, for automated numerical simulation of powder two- and three-pulse one-dimensional ESEEM for arbitrary number (N) and type (I, g(N)) of coupled nuclei, and arbitrary mutual orientations of the hyperfine tensor principal axis systems for N>1. OPTESIM is based in the Matlab environment, and includes the following features: (1) a fast algorithm for translation of the spin Hamiltonian into simulated ESEEM, (2) different optimization methods that can be hybridized to achieve an efficient coarse-to-fine grained search of the parameter space and convergence to a global minimum, (3) statistical analysis of the simulation parameters, which allows the identification of simultaneous confidence regions at specific confidence levels. OPTESIM also includes a geometry-preserving spherical averaging algorithm as default for N>1, and global optimization over multiple experimental conditions, such as the dephasing time (tau) for three-pulse ESEEM, and external magnetic field values. Application examples for simulation of (14)N coupling (N=1, N=2) in biological and chemical model paramagnets are included. Automated, optimized simulations by using OPTESIM lead to a convergence on dramatically shorter time scales, relative to manual simulations.

  6. DESIGNING A HYBRID INTELLIGENT MINING SYSTEM FOR CREDIT RISK EVALUATION

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    In this study,a novel hybrid intelligent mining system integrating rough sets theory and support vector machines is developed to extract efficiently association rules from original information table for credit risk evaluation and analysis.In the proposed hybrid intelligent system,support vector machines are used as a tool to extract typical features and filter its noise,which are different from the previous studies where rough sets were only used as a preprocessor for support vector machines.Such an approach could reduce the information table and generate the final knowledge from the reduced information table by rough sets.Therefore,the proposed hybrid intelligent system overcomes the diffi-culty of extracting rules from a trained support vector machine classifier and possesses the robustness which is lacking for rough-set-based approaches.In addition,the effectiveness of the proposed hybrid intelligent system is illustrated with two real-world credit datasets.

  7. Hydrography, HydroBndy-The data set is a line feature containing representing the outline ponds and small reservoirs. It consists of more than 150 lines representing natural and engineered surface water bodies., Published in 2005, Davis County Government.

    Data.gov (United States)

    NSGIC Local Govt | GIS Inventory — Hydrography dataset current as of 2005. HydroBndy-The data set is a line feature containing representing the outline ponds and small reservoirs. It consists of more...

  8. Fair Grounds, Feature data set including roads, buildings, multi-use areas, and area of the Rock County Fairgrounds in the City of Janesville, Wisconsin., Published in 2004, Rock County Planning, Economic, and Community Development Agency.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Fair Grounds dataset, was produced all or in part from Other information as of 2004. It is described as 'Feature data set including roads, buildings, multi-use...

  9. Hybrid quantum information processing

    Energy Technology Data Exchange (ETDEWEB)

    Furusawa, Akira [Department of Applied Physics, School of Engineering, The University of Tokyo (Japan)

    2014-12-04

    I will briefly explain the definition and advantage of hybrid quantum information processing, which is hybridization of qubit and continuous-variable technologies. The final goal would be realization of universal gate sets both for qubit and continuous-variable quantum information processing with the hybrid technologies. For that purpose, qubit teleportation with a continuousvariable teleporter is one of the most important ingredients.

  10. Early Triassic stromatolites in a siliciclastic nearshore setting in northern Perth Basin, Western Australia: Geobiologic features and implications for post-extinction microbial proliferation

    Science.gov (United States)

    Chen, Zhong-Qiang; Wang, Yongbiao; Kershaw, Stephen; Luo, Mao; Yang, Hao; Zhao, Laishi; Feng, Yuheng; Chen, Jianbo; Yang, Li; Zhang, Lei

    2014-10-01

    inhabited not only carbonate settings, but also siliciclastic nearshore settings. All features of these Early Triassic stromatolites indicate a microbial bloom in the aftermath of the P-Tr mass extinction.

  11. 基于特征点集搜索的三维序列livewire分割方法%3D livewire segmentation based on feature point set searching

    Institute of Scientific and Technical Information of China (English)

    金勇; 蒋建国; 郝世杰; 鲁清凯; 李鸿; 杨青青

    2011-01-01

    On account of the large amount of the three-dimensional(3D) medical image data sets such as computed tomography images(CT) and magnetic resonance image(MRI), the manual image segmentation is time consuming and operator-dependent. Considering the similarity of shape and texture of the segmentation targets between adjacent slices, a 3D livewire segmentation method based on feature point set searching is proposed in this paper. With minimal human interaction, the effective segmentation of objectives in 3D medical image data is achieved. The experiments on the lung CT and cancer MRI show that the temporal cost of the segmentation dramatically falls while its accuracy is close to the manual one.%三维计算机断层图像(CT)或核磁共振图像(MRI)数据量较大,仅仅依靠人工分割整个数据集相当耗时,且分割结果因操作者不同而带有主观性.三维序列图像数据相邻切面间的分割目标形状和纹理通常具有一定的相关性,文章充分利用了这样的先验知识,提出了基于特征点集搜索的三维序列Live Wire 分割方法,旨在尽可能少的人工交互下,完成整个三维医学图像数据中目标的有效分割.实验中,对肺部CT图像和肿瘤MRI图像进行了三维分割,在分割精度与人工分割相当的前提下,分割速度大大提高.

  12. Population Analysis and Epidemiological Features of Inhibitor-Resistant-TEM-β-Lactamase-Producing Escherichia coli Isolates from both Community and Hospital Settings in Madrid, Spain▿

    Science.gov (United States)

    Martín, Oihane; Valverde, Aránzazu; Morosini, María I.; Rodríguez-Domínguez, Mario; Rodríguez-Baños, Mercedes; Coque, Teresa M.; Cantón, Rafael; del Campo, Rosa

    2010-01-01

    Punctual mutations in the TEM-1 or TEM-2 gene may lead to inhibitor-resistant-TEM (IRT) β-lactamases with resistance to β-lactam-β-lactamase inhibitor combinations and susceptibility to cephalosporins. The aim of this work was to analyze the current epidemiology of IRT β-lactamases in contemporary clinical Escherichia coli. Isolates were prospectively collected in our hospital (2007 and 2008) from both outpatients (59.8%) and hospitalized patients (40.2%). The genetic relationships of the isolates were determined by XbaI pulsed-field gel electrophoresis, multilocus sequence typing, and phylogenetic group analysis. IRT genes were sequenced and located by hybridization, and the incompatibility group of the plasmids was determined. From a total of 3,556 E. coli isolates recovered during the study period, 152 (4.3%) showed reduced susceptibility to amoxicillin-clavulanate, with 18 of them producing IRT enzymes (0.5%). These were mostly recovered from urine (77.8%). A high degree of IRT diversity was detected (TEM-30, -32, -33, -34, -36, -37, -40, and -54), and the isolates were clonally unrelated but were mostly associated with phylogenetic group B2 (55.5%). In 12 out of 16 (75%) isolates, the blaIRT gene was plasmid located and transferred by conjugation in 9 of them, whereas chromosomal localization was demonstrated in 4 isolates (25%). The sizes of the plasmids ranged from 40 kb (IncN) to 100 kb (IncFII, IncFI/FIIA), and they showed different restriction patterns by restriction fragment length polymorphism analysis. Unlike extended-spectrum β-lactamase producers, the frequency of IRT producers remains low in both community and hospital settings, with most of them causing urinary tract infections. Although blaIRT genes are mainly associated with plasmids, they can be also located in the chromosome. Despite this situation, clonal expansion and/or gene dispersion was not observed, denoting the independent emergence of these enzymes. PMID:20444963

  13. Modélisation du procédé de soudage hybride Arc / Laser par une approche level set application aux toles d'aciers de fortes épaisseurs A level-set approach for the modelling of hybrid arc/laser welding process application for high thickness steel sheets joining

    Directory of Open Access Journals (Sweden)

    Desmaison Olivier

    2013-11-01

    Full Text Available Le procédé de soudage hybride Arc/Laser est une solution aux assemblages difficiles de tôles de fortes épaisseurs. Ce procédé innovant associe deux sources de chaleur : un arc électrique produit par une torche MIG et une source laser placée en amont. Ce couplage améliore le rendement du procédé, la qualité du cordon et les déformations finales. La modélisation de ce procédé par une approche Level Set permet une prédiction du développement du cordon et du champ de température associé. La simulation du soudage multi-passes d'une nuance d'acier 18MnNiMo5 est présentée ici et les résultats sont comparés aux observations expérimentales. The hybrid arc/laser welding process has been developed in order to overcome the difficulties encountered for joining high thickness steel sheets. This innovative process gathers two heat sources: an arc source developed by a MIG torch and a pre-located laser source. This coupling improves the efficiency of the process, the weld bead quality and the final deformations. The Level-Set approach for the modelling of this process enables the prediction of the weld bead development and the temperature field evolution. The simulation of the multi-passes welding of a 18MnNiMo5 steel grade is detailed and the results are compared to the experimental observations.

  14. Medical Image Feature, Extraction, Selection And Classification

    Directory of Open Access Journals (Sweden)

    M.VASANTHA,

    2010-06-01

    Full Text Available Breast cancer is the most common type of cancer found in women. It is the most frequent form of cancer and one in 22 women in India is likely to suffer from breast cancer. This paper proposes a image classifier to classify the mammogram images. Mammogram image is classified into normal image, benign image and malignant image. Totally 26 features including histogram intensity features and GLCM features are extracted from mammogram image. A hybrid approach of feature selection is proposed in this paper which reduces 75% of the features. Decision tree algorithms are applied to mammography lassification by using these reduced features. Experimental results have been obtained for a data set of 113 images taken from MIAS of different types. This technique of classification has not been attempted before and it reveals the potential of Data mining in medical treatment.

  15. [Features of interaction bacterial strains Micrococcus luteus LBK1 from plants varieties/hybrids cucumber and sweet pepper and with fungus Fusarium oxysporum Scelecht].

    Science.gov (United States)

    Parfeniuk, A; Sterlikova, O; Beznosko, I; Krut', V

    2014-01-01

    The article presents the results of studying the impact of bacterial strain M. luteus LBK1, stimulating the growth and development of plant varieties/hybrids of cucumber and sweet pepper on the intensity of sporulation of the fungus F. oxysporum Scelecht--fusariose rot pathogen.

  16. Facial feature descriptor using hybrid projection entropy in multi-scale transform domain.%多尺度变换域内混合投影熵的人脸特征描述

    Institute of Scientific and Technical Information of China (English)

    黄源源; 李建平

    2011-01-01

    提出一种新的人脸特征描述方法.使用 Contourlet 变换提取人脸图像低频子带,并对子带图像适当分块从而减少图像局部扭曲对识别的影响,利用混合投影函数和图像熵提取特征从而构建混合投影特征矩阵.在 ORL、Yale、CMU PIE 人脸数据库的实验表明该方法具有一定的优势.%This paper proposes a new facial image feature description method.This method uses the Contourlet transform to get the low frequency sub-band and divides the sub-band image into several appropriate non-overlapping blocks so that the local image distortion will less affect the recognition result. It uses the hybrid projection function and image entropy to extract the features and construct the hybrid projection feature vector. The experimental results on ORL, Yale and CMU PIE face databases demonstrate that the new method is competitive.

  17. A comparison of hybridization efficiency between flat glass and channel glass solid supports.

    Science.gov (United States)

    Betanzos-Cabrera, Gabriel; Harker, Brent W; Doktycz, Mitchel J; Weber, James L; Beattie, Kenneth L

    2008-01-01

    Two different solid supports, channel glass and flat glass, were compared for their affect on the sensitivity and efficiency of DNA hybridization reactions. Both solid supports were tested using a set of arrayed, synthetic oligonucleotides that are designed to detect short insertion/deletion polymorphisms (SIDPs). A total of 13 different human SIDPs were chosen for analysis. Capture probes, designed for this test set, were covalently immobilized on substrates. Hybridization efficiency was assessed using fluorescently labeled stacking probes which were preannealed to the target and then hybridized to the support-bound oligonucleotide array; the hybridization pattern was detected by fluorescence imaging. It was found that structural features of nucleic acid capture probes tethered to a solid support and the molecular basis of their interaction with targets in solution have direct implications on the hybridization process. Our results demonstrate that channel glass has a number of practical advantages over flat glass including higher sensitivity and a faster hybridization rate.

  18. Feature-Based Classification of Networks

    CERN Document Server

    Barnett, Ian; Kuijjer, Marieke L; Mucha, Peter J; Onnela, Jukka-Pekka

    2016-01-01

    Network representations of systems from various scientific and societal domains are neither completely random nor fully regular, but instead appear to contain recurring structural building blocks. These features tend to be shared by networks belonging to the same broad class, such as the class of social networks or the class of biological networks. At a finer scale of classification within each such class, networks describing more similar systems tend to have more similar features. This occurs presumably because networks representing similar purposes or constructions would be expected to be generated by a shared set of domain specific mechanisms, and it should therefore be possible to classify these networks into categories based on their features at various structural levels. Here we describe and demonstrate a new, hybrid approach that combines manual selection of features of potential interest with existing automated classification methods. In particular, selecting well-known and well-studied features that ...

  19. Hybride textuelle Strukturen und hybride textuelle Einheiten. Ein Beitrag zur Theorie der Wörterbuchform

    Directory of Open Access Journals (Sweden)

    Herbert Ernst Wiegand

    2011-10-01

    Textsegmentklassen aufweisen (vgl. die Stichwörter.

    Stichwörter: ANGABERELATION, ELEMENTENHETEROGENE TRÄGERMENGE, FUNKTIONALER ANGABEZUSATZ, FUNKTIONAL-POSITIONALE SEGMENTATION, HIERARCHISCHE ARCHITEKTONISCH ANGEREICHERTE ARTIKELMIKROSTRUKTUR, HIERARCHISCHE HYBRIDE ANGABENKONSTITUENTENSTRUKTUR MIT GLOSSATBEDINGTER TEILSTRUKTUR, HIERARCHISCHE HYBRIDE ANGABENSTRUKTUR, HIERARCHISCHE HYBRIDE ARTIKELKONSTITUENTENSTRUKTUR, HIERARCHISCHE HYBRIDE ARTIKELMIKROSTRUKTUR, HIERARCHISCHE HYBRIDE EXHAUSTIVE ANGABENSTRUKTUR, HIERARCHISCHE HYBRIDE GLOSSATBEDINGTE ANGABESTRUKTUR, HIERARCHISCHE HYBRIDE FLACHE DOPPELGLOSSATBEDINGTE ANGABESTRUKTUR, HIERARCHISCHE HYBRIDE MINIMIERTE GLOSSATBEDINGTE ANGABESTRUKTUR, HIERARCHISCHE HYBRIDE TEXTKONSTITUENTENSTRUKTUR, HIERARCHISCHE HYBRIDE TIEFE DOPPELGLOSSATBEDINGTE ANGABESTRUKTUR, HIERARCHISCHE REINE TEXTKONSTITUENTENSTRUKTUR, HYBRIDE VERWEISKENNZEICHNUNG, NICHTFUNKTIONALE-POSITIONALE SEGMENTATION, ORDNUNGSRELATION, SEGMENTATIVE ISOLIERUNG, VERTIKALE ANGABEARCHITEKTUR

     

    ABSTRACT: Hybrid textual structures and hybrid textual units. A contribution to the theory of dictionary structures. In this contribution, the formation, presentation and performance of hybrid textual structures that display accessible entries are discussed by using examples from dictionary articles. The features of hybrid textual units are also explained. A dictionary article in a printed dictionary always displays both a hierarchical pure and a hierarchical hybrid text constituent structure, when it contains at least one functional item addition, e.g. an upward- or downward- or an internally-expanded one. Because functional item additions are text segments with an item function but without text constituent status, they are enabled by means of non-functional segmentation, so that both functional and non-functional text segments prevail. During the formation of structures they then enter the structure-carrying set so that the structurecarrying set of all hybrid

  20. Coupling physical chemical techniques with hydrotalcite-like compounds to exploit their structural features and new multifunctional hybrids with luminescent properties.

    Science.gov (United States)

    Costantino, Umberto; Costantino, Ferdinando; Elisei, Fausto; Latterini, Loredana; Nocchetti, Morena

    2013-08-28

    Hydrotalcite-like compounds (HTlc), belonging to the large class of Layered Double Hydroxides (LDH), have excited wide interest owing to the incredible number of their potential and achieved applications in physical, chemical and bio-chemical fields. This perspective review deals with recent advances in the application of physical-chemical techniques for the study of HTlc structure and for the design and synthesis, using intercalation chemistry routes, of new hybrid materials. Firstly, a rapid survey on the most common synthetic strategies for the attainment of HTlc with different crystallinity degree and crystal size and for their modification to obtain hybrids has been made, and the use of coupled techniques (XRPD, luminescence, Solid State MAS NMR and Molecular Dynamics) to gain structural information is reported. Then, the design, synthesis and photophysical characterization of azoic dyes-intercalated and co-intercalated HTlc hybrid materials are described. Hybrids constituted of ZnAl-HTlc, co-intercalated with stearate anions and methyl orange or methyl yellow dyes, have been used as nanofillers of hydrophobic polymers. The polymeric nano-composites obtained have been characterized by means of XRPD patterns, Thermo-Gravimetric Analysis and Confocal Fluorescence Microscopy. This latter technique has been found to be an excellent, complementary and non-invasive tool to probe the dispersion degree of the fluorescent fillers into the polymeric matrices and their stability in the compounding process. Finally, the synthesis and spectroscopic characterization of nanoparticle (NP) decorated HTlc for advanced antimicrobial and photo-catalytic applications are also reported. The review terminates with a concluding short note and future trends.

  1. 基于Relief F和PSO混合特征选择的面向对象土地利用分类%Object basedland-use classification based on hybrid feature selection method of combining Relief F and PSO

    Institute of Scientific and Technical Information of China (English)

    肖艳; 姜琦刚; 王斌; 李远华; 刘舒; 崔璨

    2016-01-01

    针对面向对象土地利用分类存在特征维数过高的问题,提出了一种结合Relief F和粒子群优化算法(particle swarm optimization,PSO)的混合特征选择方法,即首先利用Relief F作为特征预选器滤除相关性小的特征,然后以PSO作为搜索算法,以支持向量机(support vector machine,SVM)的分类精度作为评估函数在剩余特征中选择出最优特征子集。该文以吉林省长春市部分区域为研究区,采用Landsat8遥感影像为数据源,首先对其进行多尺度分割,然后提取影像对象的光谱、纹理、形状和空间关系特征,利用提出的混合特征选择方法选取最优特征子集,最后使用SVM分类器对研究区进行土地利用分类,总体分类精度和Kappa系数分别为85.88%和0.8036,与基于4种其他特征选择方法的土地利用分类结果进行比较,基于Relief F和PSO的混合特征选择方法利用最少的特征获得最高的分类精度,能够有效地用于面向对象土地利用分类。%In recent years, object-based methods have been increasingly used for the land-use classification of remote sensing data. However, the availability of numerous features with object-based image analysis renders the selection of optimal features. In this study, a hybrid feature selection method that combined filter approach and wrapper approach was proposed. In the filter approach, the Relief F algorithm was employed to select features that had the higher relevance with land-use classes. The wrapper approach used the particle swarm optimization (PSO) algorithm as a search method and the classification accuracy of support vector machine (SVM) as an evaluator to search for an optimal feature subset from the selected features. The objective of this research was to examine the effectiveness of the proposed feature selection method on object-based classification. The study site was located in the southeastern part of Changchun City

  2. PDMS-SiO{sub 2}-TiO{sub 2}-CaO hybrid materials – Cytocompatibility and nanoscale surface features

    Energy Technology Data Exchange (ETDEWEB)

    Almeida, J. Carlos [CICECO - Aveiro Institute of Materials, Department of Materials and Ceramic Engineering, University of Aveiro, 3810-193 Aveiro (Portugal); Wacha, András [Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar Tudósok körútja 2, Budapest 1117 (Hungary); Gomes, Pedro S.; Fernandes, M. Helena R. [Laboratory for Bone Metabolism and Regeneration, Faculdade de Medicina Dentária, Universidade do Porto (Portugal); Fernandes, M. Helena Vaz [CICECO - Aveiro Institute of Materials, Department of Materials and Ceramic Engineering, University of Aveiro, 3810-193 Aveiro (Portugal); Salvado, Isabel M. Miranda, E-mail: isabelmsalvado@ua.pt [CICECO - Aveiro Institute of Materials, Department of Materials and Ceramic Engineering, University of Aveiro, 3810-193 Aveiro (Portugal)

    2016-07-01

    Two PDMS-SiO{sub 2}-TiO{sub 2}-CaO porous hybrid materials were prepared using the same base composition, precursors, and solvents, but following two different sol-gel procedures, based on the authors' previous works where for the first time, in this hybrid system, calcium acetate was used as calcium source. The two different procedures resulted in monolithic materials with different structures, microstructures, and surface wettability. Even though both are highly hydrophobic (contact angles of 127.2° and 150.6°), and present different filling regimes due to different surface topographies, they have demonstrated to be cytocompatible when tested with human osteoblastic cells, against the accepted idea that high-hydrophobic surfaces are not suitable to cell adhesion and proliferation. At the nanoscale, the existence of hydrophilic silica domains containing calcium, where water molecules are physisorbed, is assumed to support this capability, as discussed. - Highlights: • Two hybrid materials were prepared following two different sol-gel procedures. • Both are highly hydrophobic but demonstrated to be cytocompatible. • Different filling regimes were observed.

  3. Efficient design and simulation of an expandable hybrid (wind-photovoltaic) power system with MPPT and inverter input voltage regulation features in compliance with electric grid requirements

    Energy Technology Data Exchange (ETDEWEB)

    Skretas, Sotirios B.; Papadopoulos, Demetrios P. [Electrical Machines Laboratory, Department of Electrical and Computer Engineering, Democritos University of Thrace (DUTH), 12 V. Sofias, 67100 Xanthi (Greece)

    2009-09-15

    In this paper an efficient design along with modeling and simulation of a transformer-less small-scale centralized DC - bus Grid Connected Hybrid (Wind-PV) power system for supplying electric power to a single phase of a three phase low voltage (LV) strong distribution grid are proposed and presented. The main components of the hybrid system are: a PV generator (PVG); and an array of horizontal-axis, fixed-pitch, small-size, variable-speed wind turbines (WTs) with direct-driven permanent magnet synchronous generator (PMSG) having an embedded uncontrolled bridge rectifier. An overview of the basic theory of such systems along with their modeling and simulation via Simulink/MATLAB software package are presented. An intelligent control method is applied to the proposed configuration to simultaneously achieve three desired goals: to extract maximum power from each hybrid power system component (PVG and WTs); to guarantee DC voltage regulation/stabilization at the input of the inverter; to transfer the total produced electric power to the electric grid, while fulfilling all necessary interconnection requirements. Finally, a practical case study is conducted for the purpose of fully evaluating a possible installation in a city site of Xanthi/Greece, and the practical results of the simulations are presented. (author)

  4. Structure and weights optimisation of a modified Elman network emotion classifier using hybrid computational intelligence algorithms: a comparative study

    Science.gov (United States)

    Sheikhan, Mansour; Abbasnezhad Arabi, Mahdi; Gharavian, Davood

    2015-10-01

    Artificial neural networks are efficient models in pattern recognition applications, but their performance is dependent on employing suitable structure and connection weights. This study used a hybrid method for obtaining the optimal weight set and architecture of a recurrent neural emotion classifier based on gravitational search algorithm (GSA) and its binary version (BGSA), respectively. By considering the features of speech signal that were related to prosody, voice quality, and spectrum, a rich feature set was constructed. To select more efficient features, a fast feature selection method was employed. The performance of the proposed hybrid GSA-BGSA method was compared with similar hybrid methods based on particle swarm optimisation (PSO) algorithm and its binary version, PSO and discrete firefly algorithm, and hybrid of error back-propagation and genetic algorithm that were used for optimisation. Experimental tests on Berlin emotional database demonstrated the superior performance of the proposed method using a lighter network structure.

  5. Irrigated Lands and Features, hydrology data set attributes;ditches, Published in 2006, 1:1200 (1in=100ft) scale, Washoe County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Irrigated Lands and Features dataset, published at 1:1200 (1in=100ft) scale, was produced all or in part from Published Reports/Deeds information as of 2006....

  6. Irrigated Lands and Features, hydrology data set attributes;ditches, Published in 2006, 1:1200 (1in=100ft) scale, Washoe County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Irrigated Lands and Features dataset, published at 1:1200 (1in=100ft) scale, was produced all or in part from Published Reports/Deeds information as of 2006. It...

  7. FEATURES OF THE DEVELOPMENT OF CORN HYBRIDS DEPENDING ON THE TERMS OF PLANTING AND TMTD-PLUS DISINFECTANT IN THE ARID ZONE CENTRAL CISCAUCASIA

    Directory of Open Access Journals (Sweden)

    Kravchenko R. V.

    2015-11-01

    Full Text Available There was given the review of the results of the study in the arid zone of Central Ciscaucasia, the influence of planting terms and presowing seed treatment by the drug called "TMTD-plus", containing the growth stimulator Krezatsin in its composition, on the development of corn hybrids of different maturity groups of the selection of Krasnodar Research Institute of Agriculture named after P.P. Lukyanenko (Ross 199, Ross 299, Krasnodar 382 and Krasnodar 410 and the All-Russian Research Institute of Corn (Mashuk 170, Newton, RIC 345 and Eric, as well as middlematurity population Rossiyskaya 1. The studies were conducted in accordance with the thematic plan of scientific researches of the chair of crop and forage production of the Stavropol State Agrarian University. The soil surface was presented as southern black earth. The technology of growing of maize on the experimental plot corresponds to the standard one for the present area and cultivar. The predecessor is winter wheat. Sowing was performed in three terms. The first (early sowing term was carried out at t = + 7 ... +8 ° C. The second (recommended - when t = + 10 ... + 12 ° C. The third (later sowing time was carried out at t = +15 ° C. The plant density: early-maturing hybrids – 70 thousand pieces/ha, is mid-maturing ones – 60 thousand pieces/ha, middle-ripe – 50 thousand piece/ha, middle-later ones – 45 thousand pieces/ha. The scheme is single-row, with spacing of 70 cm. The application of the studied drug TMTD-plus helped to reduce the growing season of maize plants for one - two days. Thus, changing the sowing terms of maize hybrids and populations, we can largely control the development of plants changing the length of the growing season to two weeks and form a harvesting conveyor, thereby reducing the intensity of field work

  8. Training set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery

    Science.gov (United States)

    Ma, Lei; Cheng, Liang; Li, Manchun; Liu, Yongxue; Ma, Xiaoxue

    2015-04-01

    Unmanned Aerial Vehicle (UAV) has been used increasingly for natural resource applications in recent years due to their greater availability and the miniaturization of sensors. In addition, Geographic Object-Based Image Analysis (GEOBIA) has received more attention as a novel paradigm for remote sensing earth observation data. However, GEOBIA generates some new problems compared with pixel-based methods. In this study, we developed a strategy for the semi-automatic optimization of object-based classification, which involves an area-based accuracy assessment that analyzes the relationship between scale and the training set size. We found that the Overall Accuracy (OA) increased as the training set ratio (proportion of the segmented objects used for training) increased when the Segmentation Scale Parameter (SSP) was fixed. The OA increased more slowly as the training set ratio became larger and a similar rule was obtained according to the pixel-based image analysis. The OA decreased as the SSP increased when the training set ratio was fixed. Consequently, the SSP should not be too large during classification using a small training set ratio. By contrast, a large training set ratio is required if classification is performed using a high SSP. In addition, we suggest that the optimal SSP for each class has a high positive correlation with the mean area obtained by manual interpretation, which can be summarized by a linear correlation equation. We expect that these results will be applicable to UAV imagery classification to determine the optimal SSP for each class.

  9. Rough Neutrosophic Sets

    OpenAIRE

    Said Broumi; Florentin Smarandache; Mamoni Dhar

    2013-01-01

     Both neutrosophic sets theory and rough sets theory are emerging as powerful tool for managing uncertainty, indeterminate, incomplete and imprecise information. In this paper we develop an hybrid structure called rough neutrosophic sets and studied their properties. 

  10. Hybrid trajectory spaces

    NARCIS (Netherlands)

    Collins, P.J.

    2005-01-01

    In this paper, we present a general framework for describing and studying hybrid systems. We represent the trajectories of the system as functions on a hybrid time domain, and the system itself by its trajectory space, which is the set of all possible trajectories. The trajectory space is given a na

  11. Facial expression feature selection method based on neighborhood rough set theory and quantum genetic algorithm%基于邻域粗糙集与量子遗传算法的人脸表情特征选择方法

    Institute of Scientific and Technical Information of China (English)

    冯林; 李聪; 沈莉

    2013-01-01

    人脸表情特征选择是人脸表情识别研究领域关注的一个热点.基于量子遗传算法与邻域粗糙集理论,文章提出一种新的人脸表情特征选择方法(Feature Selection based on Neighborhood Rough Set Theory and Quantum Genetic Algorithm,简称FSNRSTQGA),以邻域粗糙集理论为基础,定义了最优特征集的适应度函数来评价表情特征子集的选择效果;并结合量子遗传算法进化策略,提出了一种表情特征选择方法.Cohn-Kanade表情数据集上的仿真实验结果表明了该方法的有效性.%Facial expression feature selection is one of the hot issues in the field of facial expression recognition. A novel facial expression feature selection method named feature selection based on neighborhood rough set theory and quantum genetic algorithm (FSNRSTQGA) is proposed. First, an evaluation criterion of the optimization expression feature subset is established based on neighborhood rough set theory and used as the fitness function. Then, by quantum genetic algorithm evolutionary strategy, an approach of facial expression feature selection is proposed. The results of the simulation on Cohn-Kanade expression dataset illustrate that the FSNRSTQGA method is effective.

  12. Design Features of a Planar Hybrid/Permanent Magnet Strong Focusing Undulator for Free Electron Laser (FEL) And Synchrotron Radiation (SR) Applications

    Energy Technology Data Exchange (ETDEWEB)

    Tatchyn, Roman; /SLAC

    2011-09-09

    Insertion devices for Angstrom-wavelength Free Electron Laser (FEL) amplifiers driven by multi-GeV electron beams generally require distributed focusing substantially stronger than their own natural focusing fields. Over the last several years a wide variety of focusing schemes and configurations have been proposed for undulators of this class, ranging from conventional current-driven quadrupoles external to the undulator magnets to permanent magnet (PM) lattices inserted into the insertion device gap. In this paper we present design studies of a flexible high-field hybrid/PM undulator with strong superimposed planar PM focusing proposed for a 1.5 Angstrom Linac Coherent Light Source (LCLS) driven by an electron beam with a 1 mm-mr normalized emittance. Attainable field parameters, tuning modes, and potential applications of the proposed structure are discussed.

  13. Successful Wide Hybridization and Introgression Breeding in a Diverse Set of Common Peppers (Capsicum annuum) Using Different Cultivated Ají (C. baccatum) Accessions as Donor Parents.

    Science.gov (United States)

    Manzur, Juan Pablo; Fita, Ana; Prohens, Jaime; Rodríguez-Burruezo, Adrián

    2015-01-01

    Capsicum baccatum, commonly known as ají, has been reported as a source of variation for many different traits to improve common pepper (C. annuum), one of the most important vegetables in the world. However, strong interspecific hybridization barriers exist between them. A comparative study of two wide hybridization approaches for introgressing C. baccatum genes into C. annuum was performed: i) genetic bridge (GB) using C. chinense and C. frutescens as bridge species; and, ii) direct cross between C. annuum and C. baccatum combined with in vitro embryo rescue (ER). A diverse and representative collection of 18 accessions from four cultivated species of Capsicum was used, including C. annuum (12), C. baccatum (3), C. chinense (2), and C. frutescens (1). More than 5000 crosses were made and over 1000 embryos were rescued in the present study. C. chinense performed as a good bridge species between C. annuum and C. baccatum, with the best results being obtained with the cross combination [C. baccatum (♀) × C. chinense (♂)] (♀) × C. annuum (♂), while C. frutescens gave poor results as bridge species due to strong prezygotic and postzygotic barriers. Virus-like-syndrome or dwarfism was observed in F1 hybrids when both C. chinense and C. frutescens were used as female parents. Regarding the ER strategy, the best response was found in C. annuum (♀) × C. baccatum (♂) crosses. First backcrosses to C. annuum (BC1s) were obtained according to the crossing scheme [C. annuum (♀) × C. baccatum (♂)] (♀) × C. annuum (♂) using ER. Advantages and disadvantages of each strategy are discussed in relation to their application to breeding programmes. These results provide breeders with useful practical information for the regular utilization of the C. baccatum gene pool in C. annuum breeding.

  14. Successful Wide Hybridization and Introgression Breeding in a Diverse Set of Common Peppers (Capsicum annuum Using Different Cultivated Aji (C. baccatum Accessions as Donor Parents.

    Directory of Open Access Journals (Sweden)

    Juan Pablo Manzur

    Full Text Available Capsicum baccatum, commonly known as ají, has been reported as a source of variation for many different traits to improve common pepper (C. annuum, one of the most important vegetables in the world. However, strong interspecific hybridization barriers exist between them. A comparative study of two wide hybridization approaches for introgressing C. baccatum genes into C. annuum was performed: i genetic bridge (GB using C. chinense and C. frutescens as bridge species; and, ii direct cross between C. annuum and C. baccatum combined with in vitro embryo rescue (ER. A diverse and representative collection of 18 accessions from four cultivated species of Capsicum was used, including C. annuum (12, C. baccatum (3, C. chinense (2, and C. frutescens (1. More than 5000 crosses were made and over 1000 embryos were rescued in the present study. C. chinense performed as a good bridge species between C. annuum and C. baccatum, with the best results being obtained with the cross combination [C. baccatum (♀ × C. chinense (♂] (♀ × C. annuum (♂, while C. frutescens gave poor results as bridge species due to strong prezygotic and postzygotic barriers. Virus-like-syndrome or dwarfism was observed in F1 hybrids when both C. chinense and C. frutescens were used as female parents. Regarding the ER strategy, the best response was found in C. annuum (♀ × C. baccatum (♂ crosses. First backcrosses to C. annuum (BC1s were obtained according to the crossing scheme [C. annuum (♀ × C. baccatum (♂] (♀ × C. annuum (♂ using ER. Advantages and disadvantages of each strategy are discussed in relation to their application to breeding programmes. These results provide breeders with useful practical information for the regular utilization of the C. baccatum gene pool in C. annuum breeding.

  15. Determination of the correlation relationship of pedagogical tests of general physical training with a set of parameters describing the morphological features and canoeists.

    Directory of Open Access Journals (Sweden)

    Flerchuk Viktor Viktorovich

    2011-09-01

    Full Text Available Correlation connections of tests are certain to on general physical preparation with indexes morphological possibilities of sportsmen. 15 sportsmen took part in research. Propensity of sportsmen is set to certain distances in competition activity. Directions of selection and orientation of sportsmen are recommended to work of different orientation.

  16. A set of host proteins interacting with papaya ringspot virus NIa-Pro protein identified in a yeast two-hybrid system.

    Science.gov (United States)

    Gao, L; Shen, W T; Yan, P; Tuo, D C; Li, X Y; Zhou, P

    2012-01-01

    The protein-protein interactions between viral and host proteins play an essential role in plant virus infection and host defense. The potyviral nuclear inclusion protein a protease (NIa-Pro) is involved in various steps of viral infection. In this study, the host proteins interacting with papaya ringspot virus (PRSV) NIa-Pro were screened in a Carica papaya L. plant cDNA library using a Sos recruitment two-hybrid system (SRS). We confirmed that the full-length EIF3G, FBPA1, FK506BP, GTPBP, MSRB1, and MTL from papaya can interact specifically with PRSV NIa-Pro in yeast, respectively. These proteins fufill important functions in plant protein translation, biotic and abiotic stress, energy metabolism and signal transduction. In this paper, we discuss possible functions of interactions between these host proteins and NIa-Pro in PRSV infection and their role in host defense. Sos recruitment two-hybrid system; papaya ringspot virus; NIa-Pro; protein-protein interaction.

  17. The Hybrid Automobile and the Atkinson Cycle

    Science.gov (United States)

    Feldman, Bernard J.

    2008-01-01

    The hybrid automobile is a strikingly new automobile technology with a number of new technological features that dramatically improve energy efficiency. This paper will briefly describe how hybrid automobiles work; what are these new technological features; why the Toyota Prius hybrid internal combustion engine operates on the Atkinson cycle…

  18. The Hybrid Automobile and the Atkinson Cycle

    Science.gov (United States)

    Feldman, Bernard J.

    2008-01-01

    The hybrid automobile is a strikingly new automobile technology with a number of new technological features that dramatically improve energy efficiency. This paper will briefly describe how hybrid automobiles work; what are these new technological features; why the Toyota Prius hybrid internal combustion engine operates on the Atkinson cycle…

  19. 辜鸿铭《论语》译本“杂合”特征解读%Feature of Hybridity in Gu Hongming' s Version of The Analects of Confucius

    Institute of Scientific and Technical Information of China (English)

    边立红; 吴鹏

    2011-01-01

    With the notion of hybridity of post-colonial theories as the theoretic perspective, a systematic analysis is carried out on Gu Hong:ning' s version of The Analects of Confucius. It is argued that Gu Hongrning' s version entails the clear feature of hybridity which indicates his endeavor to resist western hegemony of culture in special context of history and his intention to promote the equal dialogue between Chinese and western cultures.%以后殖民主义理论中的“杂合”概念解读辜鸿铭的《论语》译本,指出辜鸿铭的译本体现了一种杂合特征,展示了他在特定历史语境中抵制西方文化中心主义,构建中国文化与西方文化间平等对话的良苦用心。

  20. Integrating Concurrency and Object-Oriented Programming: An Evaluation of Hybrid

    OpenAIRE

    Konstantas, Dimitri; Papathomas, Michael

    1990-01-01

    In this paper we address the effective use of the object-oriented programming approach for concurrent programming from a language design viewpoint. We present a set of requirements for the design of concurrent object-oriented languages. We then use a particular language, Hybrid, as a concrete example and examine to what extent its features meet these requirements. We identify the solutions offered by Hybrid and its shortcomings and we underline both the difficulties and promising directions f...

  1. Features of Creation and Operation of Electric and Hybrid Vehicles in Countries with Difficult Climatic Conditions, for Example, in the Russian Federation

    Science.gov (United States)

    Karpukhin, K.; Terenchenko, A.

    2016-11-01

    The trend of increasing fleet of electric or hybrid vehicles and determines the extension of the geographical areas of operation, including the Northern areas with cold winter weather. Practically in all territory of Russia the average winter temperature is negative. With the winter temperatures can be below in Moscow -30°C, in Krasnoyarsk -50°C. Battery system can operate in a wide temperature range, but there are extremes that should be remembered all the time, especially in cold climates like Russia. In the operating instructions of the electric car Tesla Model S indicate that to save the battery don't use at temperatures below -15°C. The paper presents the dependence of the cooling time and heating of the battery cell at different ambient temperatures and provides guidance on allowable cooling time while using and not thermally insulated thermally containers Suggests using the temperature control on the basis of thermoelectric converters Peltier connection from the onboard electrical network of the electric vehicle.

  2. Characterization of mature maize (Zea mays L.) root system architecture and complexity in a diverse set of Ex-PVP inbreds and hybrids

    National Research Council Canada - National Science Library

    Hauck, Andrew L; Novais, Joana; Grift, Tony E; Bohn, Martin O

    2015-01-01

    .... In this study, roughly 2500 core root systems from field trials of a set of 10 diverse elite inbreds formerly protected by Plant Variety Protection plus B73 and Mo17 and the 66 diallel intercrosses...

  3. Synthesis of hybrid metal-organic frameworks of {FexMyM'1-x-y}-MIL-88B and the use of anions to control their structural features.

    Science.gov (United States)

    Choi, Sora; Cha, Wonhee; Ji, Hoyeon; Kim, Dooyoung; Lee, Hee Jung; Oh, Moonhyun

    2016-09-22

    The controlled formation of metal-organic frameworks (MOFs) or coordination polymers (CPs) with suitable components and structural features is one of the most important themes in MOF research. In particular, the reliable preparation of hybrid MOFs containing more than two different kinds of metal ions or organic linkers and a comprehensive understanding of the structural flexibility of MOFs are the central issues for the production of MOFs with the desired properties. We report the synthesis of micro-sized hybrid MOF particles [also known as coordination polymer particles (CPPs)] containing two or three kinds of metal ions in each particle: {FexMyM'1-x-y}-MIL-88B (MIL stands for Materials of Institut Lavoisier, M and M' = Ga, Co, or Mn). Scanning electron microscopy images revealed the formation of well-defined uniform micro-sized hexagonal rods, and energy-dispersive X-ray spectroscopy and elemental mapping images verified the simultaneous incorporation of two or three kinds of metal ions within the CPPs. Interestingly, the structural features of CPPs made from MIL-88B were controlled by altering the anions involved in the structure. Incorporating large acetylacetonate anions within the structure resulted in the closed MIL-88B structure with a small cell volume. However, the open MIL-88B structure with a large cell volume was obtained when small chloride anions were incorporated. The intermediate semi-open MIL-88B structure was also prepared using nitrate anions. Three different structural forms of MIL-88B were verified by powder X-ray diffraction, whole pattern fitting, and thermogravimetric analysis.

  4. An Efficient Framework for EEG Analysis with Application to Hybrid Brain Computer Interfaces Based on Motor Imagery and P300

    Directory of Open Access Journals (Sweden)

    Jinyi Long

    2017-01-01

    Full Text Available The hybrid brain computer interface (BCI based on motor imagery (MI and P300 has been a preferred strategy aiming to improve the detection performance through combining the features of each. However, current methods used for combining these two modalities optimize them separately, which does not result in optimal performance. Here, we present an efficient framework to optimize them together by concatenating the features of MI and P300 in a block diagonal form. Then a linear classifier under a dual spectral norm regularizer is applied to the combined features. Under this framework, the hybrid features of MI and P300 can be learned, selected, and combined together directly. Experimental results on the data set of hybrid BCI based on MI and P300 are provided to illustrate competitive performance of the proposed method against other conventional methods. This provides an evidence that the method used here contributes to the discrimination performance of the brain state in hybrid BCI.

  5. An Efficient Framework for EEG Analysis with Application to Hybrid Brain Computer Interfaces Based on Motor Imagery and P300

    Science.gov (United States)

    Wang, Jue; Yu, Tianyou

    2017-01-01

    The hybrid brain computer interface (BCI) based on motor imagery (MI) and P300 has been a preferred strategy aiming to improve the detection performance through combining the features of each. However, current methods used for combining these two modalities optimize them separately, which does not result in optimal performance. Here, we present an efficient framework to optimize them together by concatenating the features of MI and P300 in a block diagonal form. Then a linear classifier under a dual spectral norm regularizer is applied to the combined features. Under this framework, the hybrid features of MI and P300 can be learned, selected, and combined together directly. Experimental results on the data set of hybrid BCI based on MI and P300 are provided to illustrate competitive performance of the proposed method against other conventional methods. This provides an evidence that the method used here contributes to the discrimination performance of the brain state in hybrid BCI. PMID:28316617

  6. 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

  7. Gene Set-Based Functionome Analysis of Pathogenesis in Epithelial Ovarian Serous Carcinoma and the Molecular Features in Different FIGO Stages

    Directory of Open Access Journals (Sweden)

    Chia-Ming Chang

    2016-06-01

    Full Text Available Serous carcinoma (SC is the most common subtype of epithelial ovarian carcinoma and is divided into four stages by the Federation of Gynecologists and Obstetrics (FIGO staging system. Currently, the molecular functions and biological processes of SC at different FIGO stages have not been quantified. Here, we conducted a whole-genome integrative analysis to investigate the functions of SC at different stages. The function, as defined by the GO term or canonical pathway gene set, was quantified by measuring the changes in the gene expressional order between cancerous and normal control states. The quantified function, i.e., the gene set regularity (GSR index, was utilized to investigate the pathogenesis and functional regulation of SC at different FIGO stages. We showed that the informativeness of the GSR indices was sufficient for accurate pattern recognition and classification for machine learning. The function regularity presented by the GSR indices showed stepwise deterioration during SC progression from FIGO stage I to stage IV. The pathogenesis of SC was centered on cell cycle deregulation and accompanied with multiple functional aberrations as well as their interactions.

  8. 基于混合特征提取和WNN的齿轮箱故障诊断%Gearbox fault diagnosis based on hybrid feature extraction and wavelet neural network

    Institute of Scientific and Technical Information of China (English)

    鲁艳军; 陈汉新; 贺文杰; 尚云飞; 陈绪兵

    2011-01-01

    A new method of fault diagnosis for gearbox based on hybrid feature extraction and wavelet neural network (WNN) was proposed in this paper.The time domain analysis, wavelet packet decomposition and wavelet decomposition were applied to extract the fault feature information of vibration signals collected from gearbox.The extracted feature values were regarded as the feature input vector of WNN.The scale parameters, translation parameters, weight values and threshold values in WNN structure were optimized by traditional back- propagation (BP) algorithm.Three gear fault modes were simulated with different crack sizes in the experiment.The effectiveness and reliability of the presented fault diagnosis method were demonstrated through identification and classification for several fault modes.%提出了一种基于混合特征提取和小波神经网络(WNN)的齿轮箱故障诊断方法,运用时域分析法、小波分解和小波包分解相结合的方法对齿轮箱振动信号进行故障特征提取,将所提取的特征值作为WNN分类器的特征输入参数,采用反向传播(BP)算法对WNN结构中的平移参数、尺度参数、连接权值和阈值进行调整和优化.在实验中采用不同裂纹尺寸的齿轮来模拟三种故障模式,通过对三种故障齿轮进行诊断和分类,能证明本文所提议的故障诊断方法是有效且可靠的.

  9. Dynamical systems revisited : Hybrid systems with Zeno executions

    OpenAIRE

    ZHANG, JUN; Johansson, Karl Henrik; Lygeros, John; Sastry, Shankar

    2000-01-01

    Results from classical dynamical systems are generalized to hybrid dynamical systems. The concept of omega limit set is introduced for hybrid systems and is used to prove new results on invariant sets and stability, where Zeno and non-Zeno hybrid systems can be treated within the same framework. As an example, LaSalle's Invariance Principle is extended to hybrid systems. Zeno hybrid systems are discussed in detail. The omega limit set of a Zeno execution is characterized for classes of hybrid...

  10. Hybrid reactors. [Fuel cycle

    Energy Technology Data Exchange (ETDEWEB)

    Moir, R.W.

    1980-09-09

    The rationale for hybrid fusion-fission reactors is the production of fissile fuel for fission reactors. A new class of reactor, the fission-suppressed hybrid promises unusually good safety features as well as the ability to support 25 light-water reactors of the same nuclear power rating, or even more high-conversion-ratio reactors such as the heavy-water type. One 4000-MW nuclear hybrid can produce 7200 kg of /sup 233/U per year. To obtain good economics, injector efficiency times plasma gain (eta/sub i/Q) should be greater than 2, the wall load should be greater than 1 MW.m/sup -2/, and the hybrid should cost less than 6 times the cost of a light-water reactor. Introduction rates for the fission-suppressed hybrid are usually rapid.

  11. 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

  12. A Hybrid Data Association Approach for SLAM in Dynamic Environments

    Directory of Open Access Journals (Sweden)

    Baifan Chen

    2013-02-01

    Full Text Available Data association is critical for Simultaneous Localization and Mapping (SLAM. In a real environment, dynamic obstacles will lead to false data associations which compromise SLAM results. This paper presents a simple and effective data association method for SLAM in dynamic environments. A hybrid approach of data association based on local maps by combining ICNN and JCBB algorithms is used initially. Secondly, we set a judging condition of outlier features in association assumptions and then the static and dynamic features are detected according to spatial and temporal difference. Finally, association assumptions are updated by filtering out the dynamic features. Simulations and experimental results show that this method is feasible.

  13. Facial Animation Based on Feature Points

    Directory of Open Access Journals (Sweden)

    Beibei Li

    2013-01-01

    Full Text Available This paper presents a hybrid method for synthesizing natural animation of facial expression with data from motion capture. The captured expression was transferred from the space of source performance to that of a 3D target face using an accurate mapping process in order to realize the reuse of motion data. The transferred animation was then applied to synthesize the expression of the target model through a framework of two-stage deformation. A local deformation technique preliminarily considered a set of neighbor feature points for every vertex and their impact on the vertex. Furthermore, the global deformation was exploited to ensure the smoothness of the whole facial mesh. The experimental results show our hybrid mesh deformation strategy was effective, which could animate different target face without complicated manual efforts required by most of facial animation approaches.

  14. Comparing the Performance of Hybrid Capture II and Polymerase Chain Reaction (PCR) for the Identification of Cervical Dysplasia in the Screening and Diagnostic Settings.

    Science.gov (United States)

    Luu, Hung N; Adler-Storthz, Karen; Dillon, Laura M; Follen, Michele; Scheurer, Michael E

    2013-01-01

    Both PCR and Hybrid Capture II (HCII) have been used for identifying cervical dysplasia; however, comparisons on the performance between these two tests show inconsistent results. We evaluated the performance of HCII and PCR MY09/11 in both screening and diagnostic populations in sub-sample of 1,675 non-pregnant women from a cohort in three clinical centers in the United States and Canada. Sensitivity, specificity, positive predictive value, negative predictive value, and concordance between the two tests were calculated. Specificity of HCII in detecting low-grade squamous intraepithelial lesion (LSIL) was higher in the screening group (88.7%; 95% CI: 86.2%-90.8%) compared to the diagnostic group (46.3%; 95% CI: 42.1%-50.6%); however, specificity of PCR was low in both the screening (32.8%; 95% CI: 29.6%-36.2%) and diagnostic (14.4%; 95% CI: 11.6%-17.6%) groups. There was comparable sensitivity by both tests in both groups to detect high-grade squamous intraepithelial lesion (HSIL); however, HCII was more specific (89.1%; 95% CI: 86.8%-91.0%; 66.2%; 95% CI: 62.0%-70.1%) than PCR (33.3%; 95% CI: 30.2%-36.5%; 17.9%; 95% CI: 14.8%-21.6%) in the screening and diagnostic groups, respectively. Overall agreement for HPV positivity was approximately 50% between HCII and PCR MY09/11; with more positive results coming from the PCR MY09/11. In the current study, PCR MY09/11 was more sensitive but less specific than HCII in detecting LSIL, and HCII was more sensitive and specific in detecting HSIL than PCR in both screening and diagnostic groups.

  15. Intrusion detection using rough set classification

    Institute of Scientific and Technical Information of China (English)

    张连华; 张冠华; 郁郎; 张洁; 白英彩

    2004-01-01

    Recently machine learning-based intrusion detection approaches have been subjected to extensive researches because they can detect both misuse and anomaly. In this paper, rough set classification (RSC), a modern learning algorithm, is used to rank the features extracted for detecting intrusions and generate intrusion detection models. Feature ranking is a very critical step when building the model. RSC performs feature ranking before generating rules, and converts the feature ranking to minimal hitting set problem addressed by using genetic algorithm (GA). This is done in classical approaches using Support Vector Machine (SVM) by executing many iterations, each of which removes one useless feature. Compared with those methods, our method can avoid many iterations. In addition, a hybrid genetic algorithm is proposed to increase the convergence speed and decrease the training time of RSC. The models generated by RSC take the form of"IF-THEN" rules, which have the advantage of explication. Tests and comparison of RSC with SVM on DARPA benchmark data showed that for Probe and DoS attacks both RSC and SVM yielded highly accurate results (greater than 99% accuracy on testing set).

  16. Intrusion detection using rough set classification

    Institute of Scientific and Technical Information of China (English)

    张连华; 张冠华; 郁郎; 张洁; 白英彩

    2004-01-01

    Recently machine learning-based intrusion detection approaches have been subjected to extensive researches because they can detect both misuse and anomaly. In this paper, rough set classification (RSC), a modem learning algorithm,is used to rank the features extracted for detecting intrusions and generate intrusion detection models. Feature ranking is a very critical step when building the model. RSC performs feature ranking before generating rules, and converts the feature ranking to minimal hitting set problem addressed by using genetic algorithm (GA). This is done in classical approaches using Support Vector Machine (SVM) by executing many iterations, each of which removes one useless feature. Compared with those methods, our method can avoid many iterations. In addition, a hybrid genetic algorithm is proposed to increase the convergence speed and decrease the training time of RSC. The models generated by RSC take the form of"IF-THEN" rules,which have the advantage of explication. Tests and comparison of RSC with SVM on DARPA benchmark data showed that for Probe and DoS attacks both RSC and SVM yielded highly accurate results (greater than 99% accuracy on testing set).

  17. Online feature selection with streaming features.

    Science.gov (United States)

    Wu, Xindong; Yu, Kui; Ding, Wei; Wang, Hao; Zhu, Xingquan

    2013-05-01

    We propose a new online feature selection framework for applications with streaming features where the knowledge of the full feature space is unknown in advance. We define streaming features as features that flow in one by one over time whereas the number of training examples remains fixed. This is in contrast with traditional online learning methods that only deal with sequentially added observations, with little attention being paid to streaming features. The critical challenges for Online Streaming Feature Selection (OSFS) include 1) the continuous growth of feature volumes over time, 2) a large feature space, possibly of unknown or infinite size, and 3) the unavailability of the entire feature set before learning starts. In the paper, we present a novel Online Streaming Feature Selection method to select strongly relevant and nonredundant features on the fly. An efficient Fast-OSFS algorithm is proposed to improve feature selection performance. The proposed algorithms are evaluated extensively on high-dimensional datasets and also with a real-world case study on impact crater detection. Experimental results demonstrate that the algorithms achieve better compactness and higher prediction accuracy than existing streaming feature selection algorithms.

  18. Hybrid Baryons

    CERN Document Server

    Page, P R

    2003-01-01

    We review the status of hybrid baryons. The only known way to study hybrids rigorously is via excited adiabatic potentials. Hybrids can be modelled by both the bag and flux-tube models. The low-lying hybrid baryon is N 1/2^+ with a mass of 1.5-1.8 GeV. Hybrid baryons can be produced in the glue-rich processes of diffractive gamma N and pi N production, Psi decays and p pbar annihilation.

  19. 基于粗糙集与信息增益的情感特征选择方法%A Sentiment Feature Selection Method Based on Rough Set and Information Gain

    Institute of Scientific and Technical Information of China (English)

    蒲国林

    2016-01-01

    为了提高情感特征提取的准确率 ,为高性能情感分析打下坚实的基础 ,提出了一种融合粗糙集与信息增益的情感特征选择方法 .该方法借助信息增益判据选出高相关性的特征子集 ,再通过粗糙集剔除高冗余性的特征 ,从而得到最优的特征子集 .在多个数据集上的测试表明 ,该方法可将若干经典方法的准确率提高4~9个百分点 ,是一种优秀的特征选择方法 ,对提升情感分析的整体性能有明显意义 .%A Rough Set and Information Gain based on sentiment feature selection method is proposed for building a solid foundation in sentiment analysis .The novel method firstly uses Information Gain to select a feature subset which has high relativity with the class attribute .Secondly ,the features which have high redundancy will be eliminated by Rough Set .Experimental results on several datasets reveal the method makes accuracy increase 4-9 percentages than other methods .It is an outstanding feature selection method and has significance in sentiment analysis .

  20. Feature subset selection algorithms for incomplete decision systems based on neighborhood rough sets%基于邻域粗糙集的不完整决策系统特征选择算法

    Institute of Scientific and Technical Information of China (English)

    谢娟英; 李楠; 乔子芮

    2011-01-01

    针对不完整决策系统属性约简算法时间复杂度较高问题,基于正域不变条件下,决策系统分类能力保持不变原则,提出不完整决策系统前向顺序特征选择算法.该算法从约简集为空集开始,根据在约简集合中加入各属性后对正域影响程度大小将属性降序排列,采用顺序前向搜索,选择当前最佳特征加入特征约简集合,确定最佳特征子集.将该算法扩展到基于邻域粗糙集的实值和混合型不完整决策系统,得到基于邻域粗糙集的不完整决策系统前向顺序特征选择算法.同时,将基于相容关系的不完整决策系统快速属性约简算法推广到实值和混合属性的不完整决策系统,得到适用于实值、混合属性的不完整决策系统后向特征选择算法.理论分析和University of California Irvine机器学习数据库数据集的实验共同表明,本文提出的基于邻域粗糙集的不完整决策系统前向特征选择算法有效降低了不完整决策系统特征选择算法的时间复杂度,在保持系统识别能力的情况下,用更少的时间得到决策系统的属性约简子集,即特征子集.然而,本文前向特征选择算法的缺陷是有可能因为无法选择到第一个最重要的特征(属性)而使特征选择过程不能进行下去,从而不能完成特征选择过程.%New feature subset selection algorithms are presented in this paper to reduce the heavy computational load of available algorithms to feature subset selection for incomplete decision systems.We firstly propose the forward sequential feature selection algorithm for incomplete decision systems based on the fact that that the discernibility of an incomplete decision system will not change with its unchangeable positive region;then we generalize the algorithm to heterogeneous incomplete decision systems based on neighborhood rough sets theory;finally we extend the fast approach to attribute reduction in incomplete

  1. Feature-Aware Verification

    CERN Document Server

    Apel, Sven; Wendler, Philipp; von Rhein, Alexander; Beyer, Dirk

    2011-01-01

    A software product line is a set of software products that are distinguished in terms of features (i.e., end-user--visible units of behavior). Feature interactions ---situations in which the combination of features leads to emergent and possibly critical behavior--- are a major source of failures in software product lines. We explore how feature-aware verification can improve the automatic detection of feature interactions in software product lines. Feature-aware verification uses product-line verification techniques and supports the specification of feature properties along with the features in separate and composable units. It integrates the technique of variability encoding to verify a product line without generating and checking a possibly exponential number of feature combinations. We developed the tool suite SPLverifier for feature-aware verification, which is based on standard model-checking technology. We applied it to an e-mail system that incorporates domain knowledge of AT&T. We found that feat...

  2. Design Principles for Hybrid Ventilation

    DEFF Research Database (Denmark)

    Heiselberg, Per

    For many years mechanical and natural ventilation systems have developed separately. Naturally, the next step in this development is the development of ventilation concepts that utilize and combine the best features from each system to create a new type of ventilation system -Hybrid Ventilation....... The hybrid ventilation concepts, design challenges and - principles are discussed and illustrated by four building examples....

  3. 俄汉语五官词固定词组结构对比研究%Comparative St udy on the Structure of Facial Feature Words of Set Phrases in Chinese and Russian Language

    Institute of Scientific and Technical Information of China (English)

    祁国江

    2015-01-01

    研究语言中固定词组的特点是现代词汇学和熟语学最重要的任务之一。应用分类法,实例分析俄汉语五官词固定词组的结构异同点。俄汉五官固定词组一般由两个及以上成素组成,但俄语有伴随词,汉语没有伴随词;在变体方面,俄语变体较多,汉语较少;俄语任意成素较多,汉语一般比较固定。%Study on the features of set phrases in language is one of the most important tasks of modern lexicology and idioms.There are instances of the same and different structure of facial features words of set phrases in Chinese and Russian language through classification.The research of the thesis is of certain theoret-ical value that it can enrich and complement the lexicology in Chinese and Russian language;provide instruc-tion for the Russian newspaper reading teaching and translation practice and a reference for the compilation of dictionaries.The result of the study can be applied to the teaching of Russian vocabulary.

  4. A Recognition Algorithm for Radar PRI Modulation Mode Based on Extremum Sequence Features Set%基于极值序列特征集的雷达PRI调制模式识别算法

    Institute of Scientific and Technical Information of China (English)

    周一鹏; 王星; 田元荣; 周东青; 程嗣怡

    2016-01-01

    识别雷达信号的脉冲重复间隔( PRI)调制模式是分析雷达工作状态和工作任务的重要手段。针对复杂体制雷达的PRI调制模式可实时切换并改变调制参数因而难于识别的问题,提出一种基于极值序列特征集的雷达PRI调制模式识别算法。该算法首先提取PRI序列的极值特征,构建极值序列特征集;然后,基于PRI序列及其特征集建立恒参、类正弦、正弦和抖动判定准则,实现雷达PRI调制模式的分层识别。仿真分析表明:该算法对复杂体制雷达PRI调制模式的识别正确率达95.3%,同时具有较高的实时性,在电子对抗应用领域具有良好的前景。%The recognition of pulse repetition interval ( PRI) modulation mode is meaningful for analyzing the condition and task of radar.In order to recognize advanced radar which could change its PRI modulation mode and parameters quickly , a recognition al-gorithm based on extremum sequence features set is proposed .Firstly, by extracting the extremum sequence features from PRI se-quence , the PRI extremum features set is constructed .Then the judge criterions of five PRI modulation modes based on PRI se-quence and its extremum features set is proposed .Finally, a multi-layer recognition algorithm is presented .The result shows that the recognition algorithm has preferable recognition correct rate (95.3%), and could classify the PRI modulation modes quickly , so the resae rch has a good application prospect in electronic support measures.

  5. A hybrid method using the widely-used WIEN2k and VASP codes to calculate the complete set of XAS/EELS edges in a hundred-atoms system.

    Science.gov (United States)

    Donval, Gaël; Moreau, Philippe; Danet, Julien; Larbi, Séverine Jouanneau-Si; Bayle-Guillemaud, Pascale; Boucher, Florent

    2017-01-04

    Most of the recent developments in EELS modelling has been focused on getting a better agreement with measurements. Less work however has been dedicated to bringing EELS calculations to larger structures that can more realistically describe actual systems. The purpose of this paper is to present a hybrid approach well adapted to calculating the whole set of localised EELS core-loss edges (at the XAS level of theory) on larger systems using only standard tools, namely the WIEN2k and VASP codes. We illustrate the usefulness of this method by applying it to a set of amorphous silicon structures in order to explain the flattening of the silicon L2,3 EELS edge peak at the onset. We show that the peak flattening is actually caused by the collective contribution of each of the atoms to the average spectrum, as opposed to a flattening occurring on each individual spectrum. This method allowed us to reduce the execution time by a factor of 3 compared to a usual-carefully optimised-WIEN2k calculation. It provided even greater speed-ups on more complex systems (interfaces, ∼300 atoms) that will be presented in a future paper. This method is suited to calculate all the localized edges of all the atoms of a structure in a single calculation for light atoms as long as the core-hole effects can be neglected.

  6. Hybrid Logical Analyses of the Ambient Calculus

    DEFF Research Database (Denmark)

    Bolander, Thomas; Hansen, Rene Rydhof

    2010-01-01

    In this paper, hybrid logic is used to formulate three control flow analyses for Mobile Ambients, a process calculus designed for modelling mobility. We show that hybrid logic is very well-suited to express the semantic structure of the ambient calculus and how features of hybrid logic can...

  7. STRICT STABILITY OF IMPULSIVE SET VALUED DIFFERENTIAL EQUATIONS

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    In this paper, we develop strict stability concepts of ODE to impulsive hybrid set valued differential equations. By Lyapunov’s original method, we get some basic strict stability criteria of impulsive hybrid set valued equations.

  8. In silico method for systematic analysis of feature importance in microRNA-mRNA interactions

    Directory of Open Access Journals (Sweden)

    Wen Zhining

    2009-12-01

    Full Text Available Abstract Background MicroRNA (miRNA, which is short non-coding RNA, plays a pivotal role in the regulation of many biological processes and affects the stability and/or translation of mRNA. Recently, machine learning algorithms were developed to predict potential miRNA targets. Most of these methods are robust but are not sensitive to redundant or irrelevant features. Despite their good performance, the relative importance of each feature is still unclear. With increasing experimental data becoming available, research interest has shifted from higher prediction performance to uncovering the mechanism of microRNA-mRNA interactions. Results Systematic analysis of sequence, structural and positional features was carried out for two different data sets. The dominant functional features were distinguished from uninformative features in single and hybrid feature sets. Models were developed using only statistically significant sequence, structural and positional features, resulting in area under the receiver operating curves (AUC values of 0.919, 0.927 and 0.969 for one data set and of 0.926, 0.874 and 0.954 for another data set, respectively. Hybrid models were developed by combining various features and achieved AUC of 0.978 and 0.970 for two different data sets. Functional miRNA information is well reflected in these features, which are expected to be valuable in understanding the mechanism of microRNA-mRNA interactions and in designing experiments. Conclusions Differing from previous approaches, this study focused on systematic analysis of all types of features. Statistically significant features were identified and used to construct models that yield similar accuracy to previous studies in a shorter computation time.

  9. Hybrid vehicles

    Energy Technology Data Exchange (ETDEWEB)

    West, J.G.W. [Electrical Machines (United Kingdom)

    1997-07-01

    The reasons for adopting hybrid vehicles result mainly from the lack of adequate range from electric vehicles at an acceptable cost. Hybrids can offer significant improvements in emissions and fuel economy. Series and parallel hybrids are compared. A combination of series and parallel operation would be the ideal. This can be obtained using a planetary gearbox as a power split device allowing a small generator to transfer power to the propulsion motor giving the effect of a CVT. It allows the engine to run at semi-constant speed giving better fuel economy and reduced emissions. Hybrid car developments are described that show the wide range of possible hybrid systems. (author)

  10. Hybrid2 - The hybrid power system simulation model

    Energy Technology Data Exchange (ETDEWEB)

    Baring-Gould, E.I.; Green, H.J.; Dijk, V.A.P. van [National Renewable Energy Lab., Golden, CO (United States); Manwell, J.F. [Univ. of Massachusetts, Amherst, MA (United States)

    1996-12-31

    There is a large-scale need and desire for energy in remote communities, especially in the developing world; however the lack of a user friendly, flexible performance prediction model for hybrid power systems incorporating renewables hindered the analysis of hybrids as options to conventional solutions. A user friendly model was needed with the versatility to simulate the many system locations, widely varying hardware configurations, and differing control options for potential hybrid power systems. To meet these ends, researchers from the National Renewable Energy Laboratory (NREL) and the University of Massachusetts (UMass) developed the Hybrid2 software. This paper provides an overview of the capabilities, features, and functionality of the Hybrid2 code, discusses its validation and future plans. Model availability and technical support provided to Hybrid2 users are also discussed. 12 refs., 3 figs., 4 tabs.

  11. 基于灰朦胧集动态演化的线段特征提取%Line feature extraction based on dynamic evolution of the grey hazy set

    Institute of Scientific and Technical Information of China (English)

    屈薇薇; 陈宗海

    2015-01-01

    针对未知环境知识表达问题,模拟人类思维处理空间知识,提出一种基于灰朦胧集动态演化的线段特征提取方法.该方法在具有几何约束的环境中,通过灰朦胧集动态演化形成不同认知阶段表达,逐步消除信息中的不确定性,采用自组织映射和基于灰关联度的主方向提取实现线段特征的信息显化. 通过创建室内走廊环境地图检验了所提出方法模拟人类智能对未知环境信息进行表达和推理的有效性.%Based on the dynamic evolution of the grey hazy set, a line feature extraction algorithm is proposed for the knowledge expression of uncertain information by simulating the gradually cognitive process of human. The algorithm eliminates the uncertainty of the information in unknown environment with the geometric constraint gradually by producing expression of different cognitive stage through dynamic evolution of the grey hazy set. As the manifestation of environmental information, the line-segment feature is detected by self-organizing mapping with main direction exacting based on the grey correlation degree. The example in the corridor environment is provided to verify the effectiveness of the proposed algorithm in simulating human's intelligence of environmental information expression and reasoning.

  12. Evaluation of performance of three different hybrid mesoporous solids based on silica for preconcentration purposes in analytical chemistry: From the study of sorption features to the determination of elements of group IB.

    Science.gov (United States)

    Kim, Manuela Leticia; Tudino, Mabel Beatríz

    2010-08-15

    Several studies involving the physicochemical interaction of three silica based hybrid mesoporous materials with metal ions of the group IB have been performed in order to employ them for preconcentration purposes in the determination of traces of Cu(II), Ag(I) and Au(III). The three solids were obtained from mesoporous silica functionalized with 3-aminopropyl (APS), 3-mercaptopropyl (MPS) and N-[2-aminoethyl]-3-aminopropyl (NN) groups, respectively. Adsorption capacities for Au, Cu and Ag were calculated using Langmuir's isotherm model and then, the optimal values for the retention of each element onto each one of the solids were found. Physicochemical data obtained under thermodynamic equilibrium and under kinetic conditions - imposed by flow through experiments - allowed the design of simple analytical methodologies where the solids were employed as fillings of microcolumns held in continuous systems coupled on-line to an atomic absorption spectrometry. In order to control the interaction between the filling and the analyte at short times (flow through conditions) and thus, its effect on the analytical signal and the presence of interferences, the initial adsorption velocities were calculated using the pseudo second order model. All these experiments allowed the comparison of the solids in terms of their analytical behaviour at the moment of facing the determination of the three elements. Under optimized conditions mainly given by the features of the filling, the analytical methodologies developed in this work showed excellent performances with limits of detection of 0.14, 0.02 and 0.025 microg L(-1) and RSD % values of 3.4, 2.7 and 3.1 for Au, Cu and Ag, respectively. A full discussion of the main findings on the interaction metal ions/fillings will be provided. The analytical results for the determination of the three metals will be also presented.

  13. Soft sets combined with interval valued intuitionistic fuzzy sets of type-2 and rough sets

    OpenAIRE

    Anjan Mukherjee; Abhijit Saha

    2015-01-01

    Fuzzy set theory, rough set theory and soft set theory are all mathematical tools dealing with uncertainties. The concept of type-2 fuzzy sets was introduced by Zadeh in 1975 which was extended to interval valued intuitionistic fuzzy sets of type-2 by the authors.This paper is devoted to the discussions of the combinations of interval valued intuitionistic sets of type-2, soft sets and rough sets.Three different types of new hybrid models, namely-interval valued intuitionistic fuzzy soft sets...

  14. Deepening teaching reform,emphasizing the features of specialty——Practical exploration of the features setting of Touris%深化教学改革 彰显专业特色——镇江高等专科学校旅游英语特色专业建设实践探索

    Institute of Scientific and Technical Information of China (English)

    华双林; 周勤亚; 蒋肖华; 鲍旦旦

    2011-01-01

    分析高职高专旅游英语专业特色建设存在的问题,提出旅游英语专业特色建设思路,阐述加强高职高专旅游英语专业特色建设的举措:优化课程设置与人才培养模式;深化教学改革,革新教学手段;加强教学管理,完善教学效果评价体系;加强课程与教材建设;强化实践性教学环节;加强师资队伍建设,以适应旅游业不断发展的需要。%Starting from the existing problems,this paper puts forward the outline concerning features setting of tourism English specialty in vocational colleges and focuses on how to set the features of vocational tourism English specialty: strengthening curriculu

  15. 数据驱动的锑粗选泡沫图像特征优化设定%Data-driven optimal setting for froth image features of stibium rougher flotation

    Institute of Scientific and Technical Information of China (English)

    吴佳; 谢永芳; 阳春华; 桂卫华

    2016-01-01

    针对锑浮选过程中精、尾矿品位难以在线检测,浮选性能不稳定的问题,提出一种数据驱动的泡沫图像特征优化设定方法。该方法根据入矿品位类型对泡沫图像特征进行优化设定,并针对不同入矿品位类型的样本分布特点,先尝试采用案例推理的方法从历史数据中寻找浮选性能优良的泡沫状态。若经验知识不足,则采用基于多中心模糊C均值聚类与概率支持向量回归的区间II型模糊系统建模方法建立精、尾矿品位指标模型,并在此基础上利用智能优化方法寻优泡沫图像特征值。某锑浮选工业实验结果表明了所提出方法的有效性。%Due to the difficulties of measuring concentrate and tailing grade online and unstability of the flotation performance, a data-driven optimal setting for froth image features is proposed. The froth image features are optimal set according to the type of feed ore grade. Considering the distribution nature of samples of each feed grade type, it is tried to use case-based reasoning method for obtaining the froth status with optimal flotation performance from history data. When lack of enough experiential knowledge, the interval type II fuzzy system modeling method based on the multi-centers fuzzy C-means clustering and probabilistic support vector regress method is adopted to build the concentrate and tailing grade model. Then intelligent optimization algorithm is applied to search the optimal values for the froth image features. The application to the stibium flotation process shows the effectiveness of the proposed method.

  16. 0.6-1.0 V operation set/reset voltage (3 V) generator for three-dimensional integrated resistive random access memory and NAND flash hybrid solid-state drive

    Science.gov (United States)

    Tanaka, Masahiro; Hachiya, Shogo; Ishii, Tomoya; Ning, Sheyang; Tsurumi, Kota; Takeuchi, Ken

    2016-04-01

    A 0.6-1.0 V, 25.9 mm2 boost converter is proposed to generate resistive random access memory (ReRAM) write (set/reset) voltage for three-dimensional (3D) integrated ReRAM and NAND flash hybrid solid-state drive (SSD). The proposed boost converter uses an integrated area-efficient V BUF generation circuit to obtain short ReRAM sector write time, small circuit size, and small energy consumption simultaneously. In specific, the proposed boost converter reduces ReRAM sector write time by 65% compared with a conventional one-stage boost converter (Conventional 1) which uses 1.0 V operating voltage. On the other hand, by using the same ReRAM sector write time, the proposed boost converter reduces 49% circuit area and 46% energy consumption compared with a conventional two-stage boost converter (Conventional 2). In addition, by using the proposed boost converter, the operating voltage, V DD, can be reduced to 0.6 V. The lowest 159 nJ energy consumption can be obtained when V DD is 0.7 V.

  17. Energy Efficiency Comparison between Hydraulic Hybrid and Hybrid Electric Vehicles

    Directory of Open Access Journals (Sweden)

    Jia-Shiun Chen

    2015-05-01

    Full Text Available Conventional vehicles tend to consume considerable amounts of fuel, which generates exhaust gases and environmental pollution during intermittent driving cycles. Therefore, prospective vehicle designs favor improved exhaust emissions and energy consumption without compromising vehicle performance. Although pure electric vehicles feature high performance and low pollution characteristics, their limitations are their short driving range and high battery costs. Hybrid electric vehicles (HEVs are comparatively environmentally friendly and energy efficient, but cost substantially more compared with conventional vehicles. Hydraulic hybrid vehicles (HHVs are mainly operated using engines, or using alternate combinations of engine and hydraulic power sources while vehicles accelerate. When the hydraulic system accumulator is depleted, the conventional engine reengages; concurrently, brake-regenerated power is recycled and reused by employing hydraulic motor–pump modules in circulation patterns to conserve fuel and recycle brake energy. This study adopted MATLAB Simulink to construct complete HHV and HEV models for backward simulations. New European Driving Cycles were used to determine the changes in fuel economy. The output of power components and the state-of-charge of energy could be retrieved. Varying power component models, energy storage component models, and series or parallel configurations were combined into seven different vehicle configurations: the conventional manual transmission vehicle, series hybrid electric vehicle, series hydraulic hybrid vehicle, parallel hybrid electric vehicle, parallel hydraulic hybrid vehicle, purely electric vehicle, and hydraulic-electric hybrid vehicle. The simulation results show that fuel consumption was 21.80% lower in the series hydraulic hybrid vehicle compared to the series hybrid electric vehicle; additionally, fuel consumption was 3.80% lower in the parallel hybrid electric vehicle compared to the

  18. Hybrid Platforms, Tools, and Resources

    Science.gov (United States)

    Linder, Kathryn E.; Bruenjes, Linda S.; Smith, Sarah A.

    2017-01-01

    This chapter discusses common tools and resources for building a hybrid course in a higher education setting and provides recommendations for best practices in Learning Management Systems and Open Educational Resources.

  19. Hybrid-secure MPC 

    DEFF Research Database (Denmark)

    Lucas, Christoph; Raub, Dominik; Maurer, Ueli

    2010-01-01

    Most protocols for distributed, fault-tolerant computation, or multi-party computation (MPC), provide security guarantees in an all-or-nothing fashion. In contrast, a hybrid-secure protocol provides different security guarantees depending on the set of corrupted parties and the computational powe...

  20. Hybrid undulator numerical optimization

    Energy Technology Data Exchange (ETDEWEB)

    Hairetdinov, A.H. [Kurchatov Institute, Moscow (Russian Federation); Zukov, A.A. [Solid State Physics Institute, Chernogolovka (Russian Federation)

    1995-12-31

    3D properties of the hybrid undulator scheme arc studied numerically using PANDIRA code. It is shown that there exist two well defined sets of undulator parameters which provide either maximum on-axis field amplitude or minimal higher harmonics amplitude of the basic undulator field. Thus the alternative between higher field amplitude or pure sinusoidal field exists. The behavior of the undulator field amplitude and harmonics structure for a large set of (undulator gap)/(undulator wavelength) values is demonstrated.

  1. Hybrid materials science: a promised land for the integrative design of multifunctional materials.

    Science.gov (United States)

    Nicole, Lionel; Laberty-Robert, Christel; Rozes, Laurence; Sanchez, Clément

    2014-06-21

    For more than 5000 years, organic-inorganic composite materials created by men via skill and serendipity have been part of human culture and customs. The concept of "hybrid organic-inorganic" nanocomposites exploded in the second half of the 20th century with the expansion of the so-called "chimie douce" which led to many collaborations between a large set of chemists, physicists and biologists. Consequently, the scientific melting pot of these very different scientific communities created a new pluridisciplinary school of thought. Today, the tremendous effort of basic research performed in the last twenty years allows tailor-made multifunctional hybrid materials with perfect control over composition, structure and shape. Some of these hybrid materials have already entered the industrial market. Many tailor-made multiscale hybrids are increasingly impacting numerous fields of applications: optics, catalysis, energy, environment, nanomedicine, etc. In the present feature article, we emphasize several fundamental and applied aspects of the hybrid materials field: bioreplication, mesostructured thin films, Lego-like chemistry designed hybrid nanocomposites, and advanced hybrid materials for energy. Finally, a few commercial applications of hybrid materials will be presented.

  2. Hybrid materials science: a promised land for the integrative design of multifunctional materials

    Science.gov (United States)

    Nicole, Lionel; Laberty-Robert, Christel; Rozes, Laurence; Sanchez, Clément

    2014-05-01

    For more than 5000 years, organic-inorganic composite materials created by men via skill and serendipity have been part of human culture and customs. The concept of ``hybrid organic-inorganic'' nanocomposites exploded in the second half of the 20th century with the expansion of the so-called ``chimie douce'' which led to many collaborations between a large set of chemists, physicists and biologists. Consequently, the scientific melting pot of these very different scientific communities created a new pluridisciplinary school of thought. Today, the tremendous effort of basic research performed in the last twenty years allows tailor-made multifunctional hybrid materials with perfect control over composition, structure and shape. Some of these hybrid materials have already entered the industrial market. Many tailor-made multiscale hybrids are increasingly impacting numerous fields of applications: optics, catalysis, energy, environment, nanomedicine, etc. In the present feature article, we emphasize several fundamental and applied aspects of the hybrid materials field: bioreplication, mesostructured thin films, Lego-like chemistry designed hybrid nanocomposites, and advanced hybrid materials for energy. Finally, a few commercial applications of hybrid materials will be presented.

  3. 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

  4. A Feature Subset Selection Algorithm Based on Neighborhood Rough Set for Incremental Updating Datasets%基于邻域粗糙集的增量特征选择

    Institute of Scientific and Technical Information of China (English)

    李楠; 谢娟英

    2011-01-01

    A feature subset selection algorithm is presented based on neighborhood rough set theory for die datasets which are updated by the increment in their samples. It is well known that the increment in samples can cause the changeable in the reduction of attributes of the dataset. Did a through-paced analysis to the variety on positive region brought by the new added sample to the dataset, and discussed the selective updating to the feature subset (attribute reduction) according to all the cases. The selective updating to the original reduction of attributes of the dataset can avoid the unwanted operations, and reduce the complexity of the feature subset selection algorithm. Finally, gave a real example and demonstrated the algorithm.%针对连续型属性的数据集,当有新样本加入时,可能引起最佳属性约简子集变化的问题,提出了基于邻域粗糙集的特征子集增量式更新方法.根据新增样本对正域的影响,分情况对原数据集的属性约简子集进行动态更新,以便得到增加样本后的新数据的最佳属性约简子集.这种对原约简集合进行的有选择的动态更新可以有效地避免重复操作,降低算法复杂度,只有在最坏的情况下才需要对整个数据集进行重新约简.并以一个实例进行分析说明.实例分析表明,先对新增样本进行分析,然后选择性对新数据集进行约简可以有效地避免重复操作,得到新数据集的最佳属性约简子集.

  5. Genetic characterization of early maturing maize hybrids (Zea mays L. obtained by protein and RAPD markers

    Directory of Open Access Journals (Sweden)

    Bauer Iva

    2005-01-01

    Full Text Available Knowledge of maize germplasm genetic diversity is important for planning breeding programmes, germplasm conservation per se etc. Genetic variability of maize hybrids grown in the fields is also very important because genetic uniformity implies risks of genetic vulnerability to stress factors and can cause great losts in yield. Early maturing maize hybrids are characterized by shorter vegetation period and they are grown in areas with shorter vegetation season. Because of different climatic conditions in these areas lines and hybrids are developed with different features in respect to drought resistance and disease resistance. The objective of our study was to characterize set of early maturing maize hybrids with protein and RAPD markers and to compare this clasification with their pedigree information. RAPD markers gave significantly higher rate of polymorphism than protein markers. Better corelation was found among pedigree information and protein markers.

  6. Clarifying differences in definitions between ASHA policy documents and the authors in "the critical shortage of speech-language pathologists in the public school setting: features of the work environment that affect recruitment and retention".

    Science.gov (United States)

    Cirrin, Frank M

    2007-10-01

    The January 2007 issue of Language, Speech, and Hearing Services in Schools includes an article by D. Edgar and L. Rosa-Lugo entitled "The Critical Shortage of Speech-Language Pathologists in the Public School Setting: Features of the Work Environment That Affect Recruitment and Retention." In their article, the authors cite the American Speech-Language-Hearing Association (ASHA) policy document "A Workload Analysis Approach for Establishing Speech-Language Caseload Standards in Schools: Guidelines" (ASHA, 2002) and include the term workload in their survey instrument and study results. However, their definition of workload differs from the definition in ASHA policy documents. In addition, they provide some information from the ASHA Technical Report on workload (ASHA, 2002) that may be misconstrued out of context. The purpose of this letter is to reduce the possibility of any misunderstanding on the part of readers by highlighting the differences in definitions between the authors' work and ASHA policy documents and by providing context for information from the policy document. D. Edgar and L. Rosa-Lugo should be commended for conducting research related to the successful recruitment and retention of qualified speech-language pathologists in schools. Hopefully, these comments will contribute to those discussions by clarifying important points.

  7. The Hybrids of Postmodernism

    Directory of Open Access Journals (Sweden)

    Dana BĂDULESCU

    2014-09-01

    Full Text Available Hybridization is a fundamental characteristic of postmodernism, included by Ihab Hassan in his “catena” of features. This paper looks into the hybrids of postmodernism, which are the result of migration, displacement and uprooting, the re-visitation of myths, folklore and legends, or projections of their author’s imagination. The hybrids used as examples here are drawn from several novels written by Salman Rushdie, especially The Satanic Verses, two short stories, one by Márquez and the other by Donald Barthelme, Borges’s Book of Imaginary Beings, Cărtărescu’s Encyclopaedia of Dragons and Michelle Cliff’s No Telephone to Heaven. Diverse as they may be, these hybrids emphasize a defining characteristic of postmodernism, which is its pluralism. I conclude that the hybrids of postmodernism are aesthetically or politically subversive. Besides, what makes them difficult to grasp is their unfixed and protean nature. They ask for high leaps of the imagination, a total suspension of disbelief and a complete surrender to the powerful seduction of imagination on the reader’s part.

  8. Hybrid models for complex fluids

    CERN Document Server

    Tronci, Cesare

    2010-01-01

    This paper formulates a new approach to complex fluid dynamics, which accounts for microscopic statistical effects in the micromotion. While the ordinary fluid variables (mass density and momentum) undergo usual dynamics, the order parameter field is replaced by a statistical distribution on the order parameter space. This distribution depends also on the point in physical space and its dynamics retains the usual fluid transport features while containing the statistical information on the order parameter space. This approach is based on a hybrid moment closure for Yang-Mills Vlasov plasmas, which replaces the usual cold-plasma assumption. After presenting the basic properties of the hybrid closure, such as momentum map features, singular solutions and Casimir invariants, the effect of Yang-Mills fields is considered and a direct application to ferromagnetic fluids is presented. Hybrid models are also formulated for complex fluids with symmetry breaking. For the special case of liquid crystals, a hybrid formul...

  9. Hybrid Dynamical Systems Modeling, Stability, and Robustness

    CERN Document Server

    Goebel, Rafal; Teel, Andrew R

    2012-01-01

    Hybrid dynamical systems exhibit continuous and instantaneous changes, having features of continuous-time and discrete-time dynamical systems. Filled with a wealth of examples to illustrate concepts, this book presents a complete theory of robust asymptotic stability for hybrid dynamical systems that is applicable to the design of hybrid control algorithms--algorithms that feature logic, timers, or combinations of digital and analog components. With the tools of modern mathematical analysis, Hybrid Dynamical Systems unifies and generalizes earlier developments in continuous-time and discret

  10. Parallel Feature Extraction System

    Institute of Scientific and Technical Information of China (English)

    MAHuimin; WANGYan

    2003-01-01

    Very high speed image processing is needed in some application specially for weapon. In this paper, a high speed image feature extraction system with parallel structure was implemented by Complex programmable logic device (CPLD), and it can realize image feature extraction in several microseconds almost with no delay. This system design is presented by an application instance of flying plane, whose infrared image includes two kinds of feature: geometric shape feature in the binary image and temperature-feature in the gray image. Accordingly the feature extraction is taken on the two kind features. Edge and area are two most important features of the image. Angle often exists in the connection of the different parts of the target's image, which indicates that one area ends and the other area begins. The three key features can form the whole presentation of an image. So this parallel feature extraction system includes three processing modules: edge extraction, angle extraction and area extraction. The parallel structure is realized by a group of processors, every detector is followed by one route of processor, every route has the same circuit form, and works together at the same time controlled by a set of clock to realize feature extraction. The extraction system has simple structure, small volume, high speed, and better stability against noise. It can be used in the war field recognition system.

  11. Polarimetric Synthetic Aperture Radar Image Classification by a Hybrid Method

    Institute of Scientific and Technical Information of China (English)

    Kamran Ullah Khan; YANG Jian

    2007-01-01

    Different methods proposed so far for accurate classification of land cover types in polarimetric synthetic aperture radar (SAR) image are data specific and no general method is available. A novel hybrid framework for this classification was developed in this work. A set of effective features derived from the coherence matrix of polarimetric SARdata was proposed.Constituents of the feature set are wavelet,texture,and nonlinear features.The proposed feature set has a strong discrimination power. A neural network was used as the classification engine in a unique way. By exploiting the speed of the conjugate gradient method and the convergence rate of the Levenberg-Marquardt method (near the optimal point), an overall speed up of the classification procedure was achieved. Principal component analysis(PCA)was used to shrink the dimension of the feature vector without sacrificing much of the classification accuracy. The proposed approach is compared with the maximum likelihood estimator (MLE)based on the complex Wishart distribution and the results show the superiority of the proposed method,with the average classification accuracy by the proposed method(95.4%)higher than that of the MLE(93.77%). Use of PCA to reduce the dimensionality of the feature vector helps reduce the memory requirements and computational cost, thereby enhancing the speed of the process.

  12. A feature selection method based on multiple kernel learning with expression profiles of different types.

    Science.gov (United States)

    Du, Wei; Cao, Zhongbo; Song, Tianci; Li, Ying; Liang, Yanchun

    2017-01-01

    With the development of high-throughput technology, the researchers can acquire large number of expression data with different types from several public databases. Because most of these data have small number of samples and hundreds or thousands features, how to extract informative features from expression data effectively and robustly using feature selection technique is challenging and crucial. So far, a mass of many feature selection approaches have been proposed and applied to analyse expression data of different types. However, most of these methods only are limited to measure the performances on one single type of expression data by accuracy or error rate of classification. In this article, we propose a hybrid feature selection method based on Multiple Kernel Learning (MKL) and evaluate the performance on expression datasets of different types. Firstly, the relevance between features and classifying samples is measured by using the optimizing function of MKL. In this step, an iterative gradient descent process is used to perform the optimization both on the parameters of Support Vector Machine (SVM) and kernel confidence. Then, a set of relevant features is selected by sorting the optimizing function of each feature. Furthermore, we apply an embedded scheme of forward selection to detect the compact feature subsets from the relevant feature set. We not only compare the classification accuracy with other methods, but also compare the stability, similarity and consistency of different algorithms. The proposed method has a satisfactory capability of feature selection for analysing expression datasets of different types using different performance measurements.

  13. 从媒介议程设置到受众自我设置:网络媒体议程设置理论的新特点%From the Media Agenda Setting to the Audience Self-setting:New Features of the Network Media Agenda Setting Theory

    Institute of Scientific and Technical Information of China (English)

    苗海洋

    2011-01-01

    网络媒体的发展大大改变了媒介环境,凭借数字化、多媒体、超文本的技术特征和多元化、个性化、交互性、快捷性、开放性和传播内容的丰富性等传播特征,网络媒体下的议程设置功能发生了显著变化,受众的匿名心理和角色扮演心理决定其对传播内容的选择不会完全受到传播者的影响,反而会根据自己的喜好、文化背景、价值观等形成"自我设置"。此外,本文认为,传统媒体中的某些新闻事件往往来源于网络,即网络媒体具有给传统媒体设置"议事日程"的功能。%The development of network media has dramatically changed the media environment.The network media has some new features different from traditional media,such as digitalization,multimedia,hypertext technical features and diversity,personalization,interactivity,promptness,openness and abundant content of communication.These features make the agenda setting theory undergo significant changes.Decided by the anonymous psychology and role-playing psychology,the audiences will not be completely affected by the disseminators when choosing the information they need,but will form the "self-setting" according to their own preferences,cultural background and values.In addition,some events reported by the traditional media firstly come from the network media,which indicates that the network media sets the agenda for the traditional media.

  14. Feature Driven and Point Process Approaches for Popularity Prediction

    CERN Document Server

    Mishra, Swapnil; Xie, Lexing

    2016-01-01

    Predicting popularity, or the total volume of information outbreaks, is an important subproblem for understanding collective behavior in networks. Each of the two main types of recent approaches to the problem, feature-driven and generative models, have desired qualities and clear limitations. This paper bridges the gap between these solutions with a new hybrid approach and a new performance benchmark. We model each social cascade with a marked Hawkes self-exciting point process, and estimate the content virality, memory decay, and user influence. We then learn a predictive layer for popularity prediction using a collection of cascade history. To our surprise, Hawkes process with a predictive overlay outperform recent feature-driven and generative approaches on existing tweet data [43] and a new public benchmark on news tweets. We also found that a basic set of user features and event time summary statistics performs competitively in both classification and regression tasks, and that adding point process info...

  15. Hybrid Metaheuristics

    CERN Document Server

    2013-01-01

    The main goal of this book is to provide a state of the art of hybrid metaheuristics. The book provides a complete background that enables readers to design and implement hybrid metaheuristics to solve complex optimization problems (continuous/discrete, mono-objective/multi-objective, optimization under uncertainty) in a diverse range of application domains. Readers learn to solve large scale problems quickly and efficiently combining metaheuristics with complementary metaheuristics, mathematical programming, constraint programming and machine learning. Numerous real-world examples of problems and solutions demonstrate how hybrid metaheuristics are applied in such fields as networks, logistics and transportation, bio-medical, engineering design, scheduling.

  16. Hybrid Batch Bayesian Optimization

    CERN Document Server

    Azimi, Javad; Fern, Xiaoli

    2012-01-01

    Bayesian Optimization aims at optimizing an unknown non-convex/concave function that is costly to evaluate. We are interested in application scenarios where concurrent function evaluations are possible. Under such a setting, BO could choose to either sequentially evaluate the function, one input at a time and wait for the output of the function before making the next selection, or evaluate the function at a batch of multiple inputs at once. These two different settings are commonly referred to as the sequential and batch settings of Bayesian Optimization. In general, the sequential setting leads to better optimization performance as each function evaluation is selected with more information, whereas the batch setting has an advantage in terms of the total experimental time (the number of iterations). In this work, our goal is to combine the strength of both settings. Specifically, we systematically analyze Bayesian optimization using Gaussian process as the posterior estimator and provide a hybrid algorithm t...

  17. New inroads on the Physics of Upper Hybrid Turbulence

    Science.gov (United States)

    Papadopoulos, Konstantinos Dennis; Najmi, Amir; Eliasson, Bengt

    2016-07-01

    The physics associated with excitation of Upper Hybrid (UH) turbulence, including the observed high and low frequency wave spectra and the resultant plasma heating and acceleration is still covered with many puzzles that have yet to be understood within the context of traditional theories. A set of new computer simulations using a Vlasov code revealed several critical plasma wave features associated with driving waves in the upper hybrid resonance. In addition to the well-known excitation of a lower hybrid wave and an upper hybrid sideband shifted by the Lower Hybrid (LH) frequency usually seen in Stimulated Electromagnetic Emission (SEE) observations in ionospheric HF heating experiments the following major features were apparent: 1. Broadening of the wavenumber spectral region at the at the UH frequency 2. Excitation of all Bernstein modes associated with cyclotron frequency harmonics both below and above the UH frequency 3. The electron heating, in the form of bulk heating (close to a Dryvestein distribution) is due to a part of the wave-number spectrum associated with the first Bernstein mode, although its wave intensity is more than 20 dB lower than the intensity of the UH branch 4. An exception to the previous feature occurs when the UH frequency is close to an harmonic of the electron cyclotron frequency (ω_{UH} ≈ n Ω_e), when the first UH lower sideband equals a multiple of the cyclotron frequency (ω_{UH} - ω_{LH} ≈ n Ω_e). In this case the electron heating is due the downshifted UH waves, it is much stronger than in the non-resonant case and it is in the form of the high-energy tail. The implications of the new theory of the UH turbulence in the interpretation of observations in space plasmas and wave particle interactions will be discussed. Acknowledgment: The authors acknowledge discussions with their colleagues G. Milikh, S. Surma, Xi Shao and R. Sagdeev. Work supported by AFOSR MURI grant FA95501410019.

  18. Conformational behaviors of trans-2,3- and trans-2,5-dihalo-1,4-diselenanes. A complete basis set, hybrid-density functional theory study and natural bond orbital interpretations.

    Science.gov (United States)

    Nori-Shargh, Davood; Mousavi, Seiedeh Negar; Kayi, Hakan

    2014-05-01

    Complete basis set CBS-4, hybrid-density functional theory (hybrid-DFT: B3LYP/6-311+G**) based methods and natural bond orbital (NBO) interpretations have been used to examine the contributions of the hyperconjugative, electrostatic, and steric effects on the conformational behaviors of trans-2,3-dihalo-1,4-diselenane [halo = F (1), Cl (2), Br (3)] and trans-2,5-dihalo-1,4-diselenane [halo = F (4), Cl (5), Br (6)]. Both levels of theory showed that the axial conformation stability, compared to its corresponding equatorial conformation, decreases from compounds 1 → 3 and 4 → 6. Based on the results obtained from the NBO analysis, there are significant anomeric effects for compounds 1-6. The anomeric effect associated with the electron delocalization is in favor of the axial conformation and increases from compounds 1 → 3 and 4 → 6. On the other hand, dipole moment differences between the axial and equatorial conformations [Δ(μ(eq)-μ(ax)] decrease from compounds 1 → 3. Although Δ(μ(eq)-μ(ax)) parameter decreases from compound 1 to compound 3, the dipole moment values of the axial conformations are smaller than those of their corresponding equatorial conformations. Therefore, the anomeric effect associated with the electron delocalizations (for halogen-C-Se segments) and the electrostatic model associated with the dipole-dipole interactions fail to account for the increase of the equatorial conformations stability on going from compound 1 to compound 3. Since there is no dipole moment for the axial and equatorial conformations of compounds 4-6, consequently, the conformational preferences in compounds 1-6 is in general dictated by the steric hindrance factor associated with the 1,3-syn-axial repulsions. Importantly, the CBS-4 results show that the entropy difference (∆S) between the equatorial axial conformations increases from compounds 1 → 3 and 4 → 6. This fact can be explained by the anomeric effect associated

  19. Model of the variational level set image segmentation based on visual attention features%视觉注意特征的变分水平集图像分割模型

    Institute of Scientific and Technical Information of China (English)

    王徐民; 张晓光

    2013-01-01

    针对传统主动轮廓模型较低的鲁棒性能和对先验知识融合能力的不足,基于视觉注意机制的先验知识和曲线演化的理论框架,首先建立图像底层视觉显著性特征的数学模型,在此基础上提出新的曲线演化能量泛函模型,然后对该能量泛函采用变分水平集方法进行推导,得到曲线演化的偏微分方程,数值实验表明该模型相对于经典主动轮廓模型具有更强的抗噪性与分割效率.该模型的提出为进一步在主动轮廓模型中引入更高层次视觉显著性特征、得到更优越的分割模型打下了基础.%The robust and fusion capacity of the traditional active contour models is poor. The mathematical model of rock-bottom visual attention characteristics in image was first established based on a priori knowledge of mechanism of visual attention and theoretical framework of curve evolution, a new curve evolution energy functional model was put forward, then partial differential equations to guide the curve evolution were established according to variational level set to this energy functional. The numerical experiments showed that the model was more robust and had higher segmentation efficiency than classical active contour model. The model laid the foundation for higher level visual significant features and getting better segmentation.

  20. Hybrid intermediaries

    OpenAIRE

    Cetorelli, Nicola

    2014-01-01

    I introduce the concept of hybrid intermediaries: financial conglomerates that control a multiplicity of entity types active in the "assembly line" process of modern financial intermediation, a system that has become known as shadow banking. The complex bank holding companies of today are the best example of hybrid intermediaries, but I argue that financial firms from the "nonbank" space can just as easily evolve into conglomerates with similar organizational structure, thus acquiring the cap...

  1. Hybrid composites

    CSIR Research Space (South Africa)

    Jacob John, Maya

    2009-04-01

    Full Text Available effect was observed for the elongation at break of the hybrid composites. The impact strength of the hybrid composites increased with the addition of glass fibres. The tensile and impact properties of thermoplastic natural rubber reinforced short... panels made from conventional structural materials. Figure 3 illustrates the performance of cellular biocomposite panels against conventional systems used for building and residential construction, namely a pre- cast pre-stressed hollow core concrete...

  2. Integration of micro milling highspeed spindle on a microEDM-milling machine set-up

    DEFF Research Database (Denmark)

    De Grave, Arnaud; Hansen, Hans Nørgaard; Andolfatto, Loic

    2009-01-01

    In order to cope with repositioning errors and to combine the fast removal rate of micro milling with the precision and small feature size achievable with micro EDM milling, a hybrid micro-milling and micro-EDM milling centre was built and tested. The aim was to build an affordable set-up, easy...... by micro milling. Examples of test parts are shown and used as an experimental validation....

  3. Effective Feature Selection for 5G IM Applications Traffic Classification

    Directory of Open Access Journals (Sweden)

    Muhammad Shafiq

    2017-01-01

    Full Text Available Recently, machine learning (ML algorithms have widely been applied in Internet traffic classification. However, due to the inappropriate features selection, ML-based classifiers are prone to misclassify Internet flows as that traffic occupies majority of traffic flows. To address this problem, a novel feature selection metric named weighted mutual information (WMI is proposed. We develop a hybrid feature selection algorithm named WMI_ACC, which filters most of the features with WMI metric. It further uses a wrapper method to select features for ML classifiers with accuracy (ACC metric. We evaluate our approach using five ML classifiers on the two different network environment traces captured. Furthermore, we also apply Wilcoxon pairwise statistical test on the results of our proposed algorithm to find out the robust features from the selected set of features. Experimental results show that our algorithm gives promising results in terms of classification accuracy, recall, and precision. Our proposed algorithm can achieve 99% flow accuracy results, which is very promising.

  4. Understanding Legacy Features with Featureous

    DEFF Research Database (Denmark)

    Olszak, Andrzej; Jørgensen, Bo Nørregaard

    2011-01-01

    Feature-centric comprehension of source code is essential during software evolution. However, such comprehension is oftentimes difficult to achieve due the discrepancies between structural and functional units of object-oriented programs. We present a tool for feature-centric analysis of legacy...

  5. Soft sets combined with interval valued intuitionistic fuzzy sets of type-2 and rough sets

    Directory of Open Access Journals (Sweden)

    Anjan Mukherjee

    2015-03-01

    Full Text Available Fuzzy set theory, rough set theory and soft set theory are all mathematical tools dealing with uncertainties. The concept of type-2 fuzzy sets was introduced by Zadeh in 1975 which was extended to interval valued intuitionistic fuzzy sets of type-2 by the authors.This paper is devoted to the discussions of the combinations of interval valued intuitionistic sets of type-2, soft sets and rough sets.Three different types of new hybrid models, namely-interval valued intuitionistic fuzzy soft sets of type-2, soft rough interval valued intuitionistic fuzzy sets of type-2 and soft interval valued intuitionistic fuzzy rough sets of type-2 are proposed and their properties are derived.

  6. Features of resilience

    Energy Technology Data Exchange (ETDEWEB)

    Connelly, Elizabeth B.; Allen, Craig R.; Hatfield, Kirk; Palma-Oliveira, José M.; Woods, David D.; Linkov, Igor

    2017-02-20

    The National Academy of Sciences (NAS) definition of resilience is used here to organize common concepts and synthesize a set of key features of resilience that can be used across diverse application domains. The features in common include critical functions (services), thresholds, cross-scale (both space and time) interactions, and memory and adaptive management. We propose a framework for linking these features to the planning, absorbing, recovering, and adapting phases identified in the NAS definition. The proposed delineation of resilience can be important in understanding and communicating resilience concepts.

  7. Dendroidal sets

    NARCIS (Netherlands)

    Weiss, I.

    2007-01-01

    The thesis introduces the new concept of dendroidal set. Dendroidal sets are a generalization of simplicial sets that are particularly suited to the study of operads in the context of homotopy theory. The relation between operads and dendroidal sets is established via the dendroidal nerve functor wh

  8. Site Features

    Data.gov (United States)

    U.S. Environmental Protection Agency — This dataset consists of various site features from multiple Superfund sites in U.S. EPA Region 8. These data were acquired from multiple sources at different times...

  9. Feature Extraction

    CERN Document Server

    CERN. Geneva

    2015-01-01

    Feature selection and reduction are key to robust multivariate analyses. In this talk I will focus on pros and cons of various variable selection methods and focus on those that are most relevant in the context of HEP.

  10. Solar Features

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Collection includes a variety of solar feature datasets contributed by a number of national and private solar observatories located worldwide.

  11. Computer code for intraply hybrid composite design

    Science.gov (United States)

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

    1981-01-01

    A computer program has been developed and is described herein for intraply hybrid composite design (INHYD). The program includes several composite micromechanics theories, intraply hybrid composite theories and a hygrothermomechanical theory. These theories provide INHYD with considerable flexibility and capability which the user can exercise through several available options. Key features and capabilities of INHYD are illustrated through selected samples.

  12. Design Principles for Natural and Hybrid Ventilation

    DEFF Research Database (Denmark)

    Heiselberg, Per

    For many years mechanical and natural ventilation systems have developed separately. Naturally, the next step in this development is the development of ventilation concepts that utilize and combine the best features from each system to create a new type of ventilation system- Hybrid Ventilation....... The hybrid ventilation concepts, design challenges and principles are discussed and illustrated by four building examples....

  13. Fabrication of a Homogeneous, Integrated, and Compact Film of Organic-Inorganic Hybrid Ni(en)3Ag2I4 with Near-Infrared Absorbance and Semiconducting Features.

    Science.gov (United States)

    Chen, Tian-Yu; Shi, Lei; Yang, Hao; Ren, Xiao-Ming; Xiao, Chen; Jin, Wanqin

    2016-02-01

    The organic-inorganic hybrid crystal Ni(en)3Ag2I4 (where en represents 1,2-ethylenediamine) crystallizes in hexagonal space group P63, in which the AgI4(3-) tetrahedra connect into a diamondlike inorganic framework via sharing of the vertex and the Ni(en)3(2+) octahedra fill in the pores of the framework. UV-vis-near-IR (NIR) spectroscopy disclosed that this hybrid shows intense NIR absorbance centered at ca. 870 nm, and the variable-temperature conductivity measurement revealed that the hybrid is a semiconductor with Ea = 0.46 eV. The electronic band structure of Ni(en)3Ag2I4 was calculated using the density functional theory method, indicating that the NIR absorbance arises from d-d transition within the Ni(2+) cation of Ni(en)3(2+). The homogeneous, compact, and transparent crystalline film of Ni(en)3Ag2I4 was fabricated via a secondary seed growth strategy, which has promising application in NIR devices.

  14. Energy Efficiency Comparison between Hydraulic Hybrid and Hybrid Electric Vehicles

    OpenAIRE

    Jia-Shiun Chen

    2015-01-01

    Conventional vehicles tend to consume considerable amounts of fuel, which generates exhaust gases and environmental pollution during intermittent driving cycles. Therefore, prospective vehicle designs favor improved exhaust emissions and energy consumption without compromising vehicle performance. Although pure electric vehicles feature high performance and low pollution characteristics, their limitations are their short driving range and high battery costs. Hybrid electric vehicles (HEVs) ar...

  15. Design Procedure for Hybrid Ventilation

    DEFF Research Database (Denmark)

    Heiselberg, Per; Tjelflaat, Per Olaf

    Mechanical and natural ventilation systems have developed separately during many years. The natural next step in this development is development of ventilation concepts that utilises and combines the best features from each system into a new type of ventilation system - Hybrid Ventilation....... Buildings with hybrid ventilation often include other sustainable technologies and an energy optimisation requires an integrated approach in the design of the building and its mechanical systems. Therefore, the hybrid ventilation design procedure differs from the design procedure for conventional HVAC....... The first ideas on a design procedure for hybrid ventilation is presented and the different types of design methods, that is needed in different phases of the design process, is discussed....

  16. Hybrid Model of Content Extraction

    DEFF Research Database (Denmark)

    Qureshi, Pir Abdul Rasool; Memon, Nasrullah

    2012-01-01

    We present a hybrid model for content extraction from HTML documents. The model operates on Document Object Model (DOM) tree of the corresponding HTML document. It evaluates each tree node and associated statistical features like link density and text distribution across the node to predict signi...

  17. Hybrid machine learning technique for forecasting Dhaka stock market timing decisions.

    Science.gov (United States)

    Banik, Shipra; Khodadad Khan, A F M; Anwer, Mohammad

    2014-01-01

    Forecasting stock market has been a difficult job for applied researchers owing to nature of facts which is very noisy and time varying. However, this hypothesis has been featured by several empirical experiential studies and a number of researchers have efficiently applied machine learning techniques to forecast stock market. This paper studied stock prediction for the use of investors. It is always true that investors typically obtain loss because of uncertain investment purposes and unsighted assets. This paper proposes a rough set model, a neural network model, and a hybrid neural network and rough set model to find optimal buy and sell of a share on Dhaka stock exchange. Investigational findings demonstrate that our proposed hybrid model has higher precision than the single rough set model and the neural network model. We believe this paper findings will help stock investors to decide about optimal buy and/or sell time on Dhaka stock exchange.

  18. Classification of Medical Datasets Using SVMs with Hybrid Evolutionary Algorithms Based on Endocrine-Based Particle Swarm Optimization and Artificial Bee Colony Algorithms.

    Science.gov (United States)

    Lin, Kuan-Cheng; Hsieh, Yi-Hsiu

    2015-10-01

    The classification and analysis of data is an important issue in today's research. Selecting a suitable set of features makes it possible to classify an enormous quantity of data quickly and efficiently. Feature selection is generally viewed as a problem of feature subset selection, such as combination optimization problems. Evolutionary algorithms using random search methods have proven highly effective in obtaining solutions to problems of optimization in a diversity of applications. In this study, we developed a hybrid evolutionary algorithm based on endocrine-based particle swarm optimization (EPSO) and artificial bee colony (ABC) algorithms in conjunction with a support vector machine (SVM) for the selection of optimal feature subsets for the classification of datasets. The results of experiments using specific UCI medical datasets demonstrate that the accuracy of the proposed hybrid evolutionary algorithm is superior to that of basic PSO, EPSO and ABC algorithms, with regard to classification accuracy using subsets with a reduced number of features.

  19. Coherent branching feature bisimulation

    Directory of Open Access Journals (Sweden)

    Tessa Belder

    2015-04-01

    Full Text Available Progress in the behavioral analysis of software product lines at the family level benefits from further development of the underlying semantical theory. Here, we propose a behavioral equivalence for feature transition systems (FTS generalizing branching bisimulation for labeled transition systems (LTS. We prove that branching feature bisimulation for an FTS of a family of products coincides with branching bisimulation for the LTS projection of each the individual products. For a restricted notion of coherent branching feature bisimulation we furthermore present a minimization algorithm and show its correctness. Although the minimization problem for coherent branching feature bisimulation is shown to be intractable, application of the algorithm in the setting of a small case study results in a significant speed-up of model checking of behavioral properties.

  20. Inheritance of economic traits of Microcerasus tomentosa thunb. intervarietal hybrids

    Directory of Open Access Journals (Sweden)

    Buchenkov Igor Eduardovich

    2014-01-01

    Full Text Available A hybrid fund of Microcerasus tomentosa comprising 6 families with a total of 287 plants has been created. The features of the inheritance of important economic traits in hybrid offspring intervarietal hybrids Microcerasus tomentosa are defined. The hybrid family and cross combinations with high features of macrocarpa, small fruit size, dry berry separation, vitamin C, immunity and precocity are defined. During the study period of controlled hybrid offspring of crosses a number of elite seedlings was identified - Natali x Jubilejnaja, Natali x Smugljanka vostočnaja, and Natali x Rozovaja urožajnaja that combine high rates of fruit weight with other economic traits.

  1. Towards Modelling of Hybrid Systems

    DEFF Research Database (Denmark)

    Wisniewski, Rafal

    2006-01-01

    The article is an attempt to use methods of category theory and topology for analysis of hybrid systems. We use the notion of a directed topological space; it is a topological space together with a set of privileged paths. Dynamical systems are examples of directed topological spaces. A hybrid...... system consists of a number of dynamical systems that are glued together according to information encoded in the discrete part of the system. We develop a definition of a hybrid system as a functor from the category generated by a transition system to the category of directed topological spaces. Its...... directed homotopy colimit (geometric realization) is a single directed topological space. The behavior of hybrid systems can be then understood in terms of the behavior of dynamical systems through the directed homotopy colimit....

  2. 基于自由度分配和方位特征集的混联机器人机型设计方法及应用%Type Design Method and the Application for Hybrid Robot Based on Freedom Distribution and Position and Orientation Characteristic Set

    Institute of Scientific and Technical Information of China (English)

    沈惠平; 赵海彬; 邓嘉鸣; 孟庆梅; 朱伟; 杨廷力

    2011-01-01

    Hybrid robot mechanisms has both better performances and advantages which combines the better stiffness and precise position that parallel mechanism owns and the bigger workspace and better control decoupling that series mechanisms owns. The key of design hybrid robot is the number of DOFs in series or in parallel, the order distribution of DOFs, the combination method of DOFs based on position and orientation characteristic (POC) set of manipulator, and the module structure and performance evaluation of novel parallel mechanisms with less DOF. The design method for hybrid robot based on freedom distribution and POC set is proposed, including the definition, symbol denotation method and the advantage and disadvantage analysis of both hybrid robot with single-point operation and hybrid robot with mult-point collaborative operation. Accordingly, all the possible structure combinations, design principles and design procedure of hybrid robots with 3~5 DOFs are given. A lot of novel topological structures of hybrid robot were designed. Meanwhile, both 5-axis hybrid robots with single airbrush and with mult-airbrush for spray painting are developed respectively according to the design method. Furthermore, the performance for the two spray robots with different structures is compared. The research provides a general method for hybrid robot mechanism design and provides mechanism design theory basis for development of complex advanced equipment.%混联机器人机构兼有并联机构刚度好、定位精确及串联机构工作空间大、控制解耦性好等两方面的综合性能及优点,其设计的关键是基于操作器方位特征集的串(并)联自由度数目和顺序分配、组合方式及新型少自由度并联机构模块的构造及其性能评价.提出基于自由度分配和方位特征集的混联机器人机型设计方法,提出单点作业、多点协同作业二类混联机器人的定义、符号表示方法并分析其优缺点;给出

  3. Hybrid Learning at the Community College

    Science.gov (United States)

    Snart, Jason

    2017-01-01

    This chapter discusses how the community college represents a potentially ideal educational setting for hybrid learning to thrive. The multimodal nature of hybrids, combining both online and face-to-face learning, affords the opportunity to engage students in a variety of ways. Further, many community college students can benefit from the…

  4. Design comparison of single phase outer and inner-rotor hybrid excitation flux switching motor for hybrid electric vehicles

    Science.gov (United States)

    Mazlan, Mohamed Mubin Aizat; Sulaiman, Erwan; Husin, Zhafir Aizat; Othman, Syed Muhammad Naufal Syed; Khan, Faisal

    2015-05-01

    In hybrid excitation machines (HEMs), there are two main flux sources which are permanent magnet (PM) and field excitation coil (FEC). These HEMs have better features when compared with the interior permanent magnet synchronous machines (IPMSM) used in conventional hybrid electric vehicles (HEVs). Since all flux sources including PM, FEC and armature coils are located on the stator core, the rotor becomes a single piece structure similar with switch reluctance machine (SRM). The combined flux generated by PM and FEC established more excitation fluxes that are required to produce much higher torque of the motor. In addition, variable DC FEC can control the flux capabilities of the motor, thus the machine can be applied for high-speed motor drive system. In this paper, the comparisons of single-phase 8S-4P outer and inner rotor hybrid excitation flux switching machine (HEFSM) are presented. Initially, design procedures of the HEFSM including parts drawing, materials and conditions setting, and properties setting are explained. Flux comparisons analysis is performed to investigate the flux capabilities at various current densities. Then the flux linkages of PM with DC FEC of various DC FEC current densities are examined. Finally torque performances are analyzed at various armature and FEC current densities for both designs. As a result, the outer-rotor HEFSM has higher flux linkage of PM with DC FEC and higher average torque of approximately 10% when compared with inner-rotor HEFSM.

  5. Hybrid microelectronic technology

    Science.gov (United States)

    Moran, P.

    Various areas of hybrid microelectronic technology are discussed. The topics addressed include: basic thick film processing, thick film pastes and substrates, add-on components and attachment methods, thin film processing, and design of thick film hybrid circuits. Also considered are: packaging hybrid circuits, automating the production of hybrid circuits, application of hybrid techniques, customer's view of hybrid technology, and quality control and assurance in hybrid circuit production.

  6. Patch layout generation by detecting feature networks

    KAUST Repository

    Cao, Yuanhao

    2015-02-01

    The patch layout of 3D surfaces reveals the high-level geometric and topological structures. In this paper, we study the patch layout computation by detecting and enclosing feature loops on surfaces. We present a hybrid framework which combines several key ingredients, including feature detection, feature filtering, feature curve extension, patch subdivision and boundary smoothing. Our framework is able to compute patch layouts through concave features as previous approaches, but also able to generate nice layouts through smoothing regions. We demonstrate the effectiveness of our framework by comparing with the state-of-the-art methods.

  7. Complex Topographic Feature Ontology Patterns

    Science.gov (United States)

    Varanka, Dalia E.; Jerris, Thomas J.

    2015-01-01

    Semantic ontologies are examined as effective data models for the representation of complex topographic feature types. Complex feature types are viewed as integrated relations between basic features for a basic purpose. In the context of topographic science, such component assemblages are supported by resource systems and found on the local landscape. Ontologies are organized within six thematic modules of a domain ontology called Topography that includes within its sphere basic feature types, resource systems, and landscape types. Context is constructed not only as a spatial and temporal setting, but a setting also based on environmental processes. Types of spatial relations that exist between components include location, generative processes, and description. An example is offered in a complex feature type ‘mine.’ The identification and extraction of complex feature types are an area for future research.

  8. Featuring animacy

    Directory of Open Access Journals (Sweden)

    Elizabeth Ritter

    2015-01-01

    Full Text Available Algonquian languages are famous for their animacy-based grammatical properties—an animacy based noun classification system and direct/inverse system which gives rise to animacy hierarchy effects in the determination of verb agreement. In this paper I provide new evidence for the proposal that the distinctive properties of these languages is due to the use of participant-based features, rather than spatio-temporal ones, for both nominal and verbal functional categories (Ritter & Wiltschko 2009, 2014. Building on Wiltschko (2012, I develop a formal treatment of the Blackfoot aspectual system that assumes a category Inner Aspect (cf. MacDonald 2008, Travis 1991, 2010. Focusing on lexical aspect in Blackfoot, I demonstrate that the classification of both nouns (Seinsarten and verbs (Aktionsarten is based on animacy, rather than boundedness, resulting in a strikingly different aspectual system for both categories. 

  9. Hybrid Method for 3D Segmentation of Magnetic Resonance Images

    Institute of Scientific and Technical Information of China (English)

    ZHANGXiang; ZHANGDazhi; TIANJinwen; LIUJian

    2003-01-01

    Segmentation of some complex images, especially in magnetic resonance brain images, is often difficult to perform satisfactory results using only single approach of image segmentation. An approach towards the integration of several techniques seems to be the best solution. In this paper a new hybrid method for 3-dimension segmentation of the whole brain is introduced, based on fuzzy region growing, edge detection and mathematical morphology, The gray-level threshold, controlling the process of region growing, is determined by fuzzy technique. The image gradient feature is obtained by the 3-dimension sobel operator considering a 3×3×3 data block with the voxel to be evaluated at the center, while the gradient magnitude threshold is defined by the gradient magnitude histogram of brain magnetic resonance volume. By the combined methods of edge detection and region growing, the white matter volume of human brain is segmented perfectly. By the post-processing using mathematical morphological techniques, the whole brain region is obtained. In order to investigate the validity of the hybrid method, two comparative experiments, the region growing method using only gray-level feature and the thresholding method by combining gray-level and gradient features, are carried out. Experimental results indicate that the proposed method provides much better results than the traditional method using a single technique in the 3-dimension segmentation of human brain magnetic resonance data sets.

  10. Flight Testing of Hybrid Powered Vehicles

    Science.gov (United States)

    Story, George; Arves, Joe

    2006-01-01

    Hybrid Rocket powered vehicles have had a limited number of flights. Most recently in 2004, Scaled Composites had a successful orbital trajectory that put a private vehicle twice to over 62 miles high, the edge of space to win the X-Prize. This endeavor man rates a hybrid system. Hybrids have also been used in a number of one time launch attempts - SET-1, HYSR, HPDP. Hybrids have also been developed for use and flown in target drones. This chapter discusses various flight-test programs that have been conducted, hybrid vehicles that are in development, other hybrid vehicles that have been proposed and some strap-on applications have also been examined.

  11. Pattern Recognition in Collective Cognitive Systems: Hybrid Human-Machine Learning (HHML) By Heterogeneous Ensembles

    CERN Document Server

    Dashti, Hesam T; Siahpirani, Alireza F; Tonejc, Jernej; Uilecan, Ioan V; Simas, Tiago; Miranda, Bruno; Ribeiro, Rita; Wang, Liya; Assadi, Amir H

    2010-01-01

    The ubiquitous role of the cyber-infrastructures, such as the WWW, provides myriad opportunities for machine learning and its broad spectrum of application domains taking advantage of digital communication. Pattern classification and feature extraction are among the first applications of machine learning that have received extensive attention. The most remarkable achievements have addressed data sets of moderate-to-large size. The 'data deluge' in the last decade or two has posed new challenges for AI researchers to design new, effective and accurate algorithms for similar tasks using ultra-massive data sets and complex (natural or synthetic) dynamical systems. We propose a novel principled approach to feature extraction in hybrid architectures comprised of humans and machines in networked communication, who collaborate to solve a pre-assigned pattern recognition (feature extraction) task. There are two practical considerations addressed below: (1) Human experts, such as plant biologists or astronomers, often...

  12. Hybrid Gear

    Science.gov (United States)

    Handschuh, Robert F. (Inventor); Roberts, Gary D. (Inventor)

    2016-01-01

    A hybrid gear consisting of metallic outer rim with gear teeth and metallic hub in combination with a composite lay up between the shaft interface (hub) and gear tooth rim is described. The composite lay-up lightens the gear member while having similar torque carrying capability and it attenuates the impact loading driven noise/vibration that is typical in gear systems. The gear has the same operational capability with respect to shaft speed, torque, and temperature as an all-metallic gear as used in aerospace gear design.

  13. Hybrid Qualifications

    DEFF Research Database (Denmark)

    has turned out as a major focus of European education and training policies and certainly is a crucial principle underlying the European Qualifications Framework (EQF). In this context, «hybrid qualifications» (HQ) may be seen as an interesting approach to tackle these challenges as they serve «two...... masters», i.e. by producing skills for the labour market and enabling individuals to progress more or less directly to higher education. The specific focus of this book is placed on conditions, structures and processes which help to combine VET with qualifications leading into higher education...

  14. DDoS attack detection method based on conditional random field with feature set%融合规则的条件随机场DDoS攻击检测方法

    Institute of Scientific and Technical Information of China (English)

    陈世文; 邬江兴; 黄万伟

    2013-01-01

    The traditional detection methods for DDoS attacks have low accuracy and high false alarms rate because those means are only based on one of such flow features as burst feature, dispersed source IP address, asymmetry flow and etc. This paper uses conditional random field to integrate many pattern match rules for DDoS attack detection. The feature vector includes one way connection density, source IP entropy, destination IP entropy, destination port entropy and protocol entropy. The simulation results show that the proposed method outperforms other well-known methods such as naïve Bayes and SVM. The detection accuracy rate reaches 99.82%and the false alarm rate is less than 0.6%.The method is robustness under strong interference traffic noise.%  基于流量突发性、源IP地址的分散性、流非对称性等单一手段进行DDoS攻击检测,存在准确率低,虚警率高等问题。利用条件随机场不要求严格独立性假设与综合多特征能力的优点,提出了基于CRF模型融合特征规则集实现对DDoS攻击的检测方法,采用单边连接密度OWCD、IP包五元组熵IPE组成多维特征向量,仿真结果表明,在DARPA2000数据集下,检测准确率达99.82%、虚警率低于0.6%,且在强背景噪声干扰下无明显恶化。

  15. Hybrid2: The hybrid system simulation model, Version 1.0, user manual

    Energy Technology Data Exchange (ETDEWEB)

    Baring-Gould, E.I.

    1996-06-01

    In light of the large scale desire for energy in remote communities, especially in the developing world, the need for a detailed long term performance prediction model for hybrid power systems was seen. To meet these ends, engineers from the National Renewable Energy Laboratory (NREL) and the University of Massachusetts (UMass) have spent the last three years developing the Hybrid2 software. The Hybrid2 code provides a means to conduct long term, detailed simulations of the performance of a large array of hybrid power systems. This work acts as an introduction and users manual to the Hybrid2 software. The manual describes the Hybrid2 code, what is included with the software and instructs the user on the structure of the code. The manual also describes some of the major features of the Hybrid2 code as well as how to create projects and run hybrid system simulations. The Hybrid2 code test program is also discussed. Although every attempt has been made to make the Hybrid2 code easy to understand and use, this manual will allow many organizations to consider the long term advantages of using hybrid power systems instead of conventional petroleum based systems for remote power generation.

  16. Intuitionistic hybrid logic

    DEFF Research Database (Denmark)

    Braüner, Torben

    2011-01-01

    Intuitionistic hybrid logic is hybrid modal logic over an intuitionistic logic basis instead of a classical logical basis. In this short paper we introduce intuitionistic hybrid logic and we give a survey of work in the area.......Intuitionistic hybrid logic is hybrid modal logic over an intuitionistic logic basis instead of a classical logical basis. In this short paper we introduce intuitionistic hybrid logic and we give a survey of work in the area....

  17. Continuity Controlled Hybrid Automata

    OpenAIRE

    Bergstra, J. A.; Middelburg, C.A.

    2004-01-01

    We investigate the connections between the process algebra for hybrid systems of Bergstra and Middelburg and the formalism of hybrid automata of Henzinger et al. We give interpretations of hybrid automata in the process algebra for hybrid systems and compare them with the standard interpretation of hybrid automata as timed transition systems. We also relate the synchronized product operator on hybrid automata to the parallel composition operator of the process algebra. It turns out that the f...

  18. 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.

  19. Towards an expansive hybrid psychology

    DEFF Research Database (Denmark)

    Brinkmann, Svend

    2011-01-01

    This article develops an integrative theory of the mind by examining how the mind, understood as a set of skills and dispositions, depends upon four sources of mediators. Harré’s hybrid psychology is taken as a meta-theoretical starting point, but is expanded significantly by including the four...... sources of mediators that are the brain, the body, social practices and technological artefacts. It is argued that the mind is normative in the sense that mental processes do not simply happen, but can be done more or less well, and thus are subject to normative appraisal. The expanded hybrid psychology...

  20. Asymmetric synthesis and in vitro and in vivo activity of tetrahydroquinolines featuring a diverse set of polar substitutions at the 6 position as mixed-efficacy μ opioid receptor/δ opioid receptor ligands.

    Science.gov (United States)

    Bender, Aaron M; Griggs, Nicholas W; Anand, Jessica P; Traynor, John R; Jutkiewicz, Emily M; Mosberg, Henry I

    2015-08-19

    We previously reported a small series of mixed-efficacy μ opioid receptor (MOR) agonist/δ opioid receptor (DOR) antagonist peptidomimetics featuring a tetrahydroquinoline scaffold and showed the promise of this series as effective analgesics after intraperitoneal administration in mice. We report here an expanded structure-activity relationship study of the pendant region of these compounds and focus in particular on the incorporation of heteroatoms into this side chain. These analogues provide new insight into the binding requirements for this scaffold at MOR, DOR, and the κ opioid receptor (KOR), and several of them (10j, 10k, 10m, and 10n) significantly improve upon the overall MOR agonist/DOR antagonist profile of our previous compounds. In vivo data for 10j, 10k, 10m, and 10n are also reported and show the antinociceptive potency and duration of action of compounds 10j and 10m to be comparable to those of morphine.

  1. On the Dynamic Life Features of the Construction and the Setting of Literary Text%论文学文本建构的动态性生命特征

    Institute of Scientific and Technical Information of China (English)

    李家富

    2012-01-01

    Based on the existing forms between the subject and the object,the content and the form;the time and the space;the emotion and the scenery in the construction of literary text,this paper is aimed at pointing out the construction of literary text also has the dynamic life features.%通过主体与客体、内容与形式、时间与空间、情感与景物在文学文本建构中的存在形态,阐明了文学文本的建构具有动态性的生命形态特征。

  2. Correlative Feature Analysis for Multimodality Breast CAD

    Science.gov (United States)

    2009-09-01

    developed two sets of “large-scale” features. Firstly, we extracted a set of texture features based on a gray-level co-occurrence matrix ( GLCM ). For...each region, four GLCMs were constructed along four different directions of 0°, 45°, 90° and 135°. Assuming that there is no directional texture...features in mammograms, a non-directional GLCM was obtained by summing all the directional GLCMs . Texture features were then computed from each non

  3. Safety Verification for Probabilistic Hybrid Systems

    DEFF Research Database (Denmark)

    Zhang, Lijun; She, Zhikun; Ratschan, Stefan;

    2010-01-01

    The interplay of random phenomena and continuous real-time control deserves increased attention for instance in wireless sensing and control applications. Safety verification for such systems thus needs to consider probabilistic variations of systems with hybrid dynamics. In safety verification...... hybrid systems and develop a general abstraction technique for verifying probabilistic safety problems. This gives rise to the first mechanisable technique that can, in practice, formally verify safety properties of non-trivial continuous-time stochastic hybrid systems-without resorting to point...... of classical hybrid systems we are interested in whether a certain set of unsafe system states can be reached from a set of initial states. In the probabilistic setting, we may ask instead whether the probability of reaching unsafe states is below some given threshold. In this paper, we consider probabilistic...

  4. Safety Verification for Probabilistic Hybrid Systems

    DEFF Research Database (Denmark)

    Zhang, Lijun; She, Zhikun; Ratschan, Stefan;

    2012-01-01

    The interplay of random phenomena and continuous dynamics deserves increased attention, especially in the context of wireless sensing and control applications. Safety verification for such systems thus needs to consider probabilistic variants of systems with hybrid dynamics. In safety verification...... hybrid systems and develop a general abstraction technique for verifying probabilistic safety problems. This gives rise to the first mechanisable technique that can, in practice, formally verify safety properties of non-trivial continuous-time stochastic hybrid systems. Moreover, being based...... of classical hybrid systems, we are interested in whether a certain set of unsafe system states can be reached from a set of initial states. In the probabilistic setting, we may ask instead whether the probability of reaching unsafe states is below some given threshold. In this paper, we consider probabilistic...

  5. Parcels and Land Ownership, This data set consists of digital map files containing parcel-level cadastral information obtained from property descriptions. Cadastral features contained in the data set include real property boundary lines, rights-of-way boundaries, property dimensions, Published in Not Provided, 1:2400 (1in=200ft) scale, Racine County Government.

    Data.gov (United States)

    NSGIC Local Govt | GIS Inventory — Parcels and Land Ownership dataset current as of unknown. This data set consists of digital map files containing parcel-level cadastral information obtained from...

  6. Hybridized tetraquarks

    Directory of Open Access Journals (Sweden)

    A. Esposito

    2016-07-01

    Full Text Available We propose a new interpretation of the neutral and charged X,Z exotic hadron resonances. Hybridized-tetraquarks are neither purely compact tetraquark states nor bound or loosely bound molecules but rather a manifestation of the interplay between the two. While meson molecules need a negative or zero binding energy, its counterpart for h-tetraquarks is required to be positive. The formation mechanism of this new class of hadrons is inspired by that of Feshbach metastable states in atomic physics. The recent claim of an exotic resonance in the Bs0π± channel by the D0 Collaboration and the negative result presented subsequently by the LHCb Collaboration are understood in this scheme, together with a considerable portion of available data on X,Z particles. Considerations on a state with the same quantum numbers as the X(5568 are also made.

  7. Hybridized Tetraquarks

    CERN Document Server

    Esposito, A.; Polosa, A.D.

    2016-01-01

    We propose a new interpretation of the neutral and charged X, Z exotic hadron resonances. Hybridized-tetraquarks are neither purely compact tetraquark states nor bound or loosely bound molecules. The latter would require a negative or zero binding energy whose counterpart in h-tetraquarks is a positive quantity. The formation mechanism of this new class of hadrons is inspired by that of Feshbach metastable states in atomic physics. The recent claim of an exotic resonance in the Bs pi+- channel by the D0 collaboration and the negative result presented subsequently by the LHCb collaboration are understood in this scheme, together with a considerable portion of available data on X, Z particles. Considerations on a state with the same quantum numbers as the X(5568) are also made.

  8. Hybrid percolation transition in complex networks

    Science.gov (United States)

    Kahng, Byungnam

    Percolation has been one of the most applied statistical models. Percolation transition is one of the most robust continuous transitions known thus far. However, recent extensive researches reveal that it exhibits diverse types of phase transitions such as discontinuous and hybrid phase transitions. Here hybrid phase transition means the phase transition exhibiting natures of both continuous and discontinuous phase transitions simultaneously. Examples include k-core percolation, cascading failures in interdependent networks, synchronization, etc. Thus far, it is not manifest if the critical behavior of hybrid percolation transitions conforms to the conventional scaling laws of second-order phase transition. Here, we investigate the critical behaviors of hybrid percolation transitions in the cascading failure model in inter-dependent networks and the restricted Erdos-Renyi model. We find that the critical behaviors of the hybrid percolation transitions contain some features that cannot be described by the conventional theory of second-order percolation transitions.

  9. Constructive Sets in Computable Sets

    Institute of Scientific and Technical Information of China (English)

    傅育熙

    1997-01-01

    The original interpretation of the constructive set theory CZF in Martin-Loef's type theory uses the‘extensional identity types’.It is generally believed that these‘types’do not belong to type theory.In this paper it will be shown that the interpretation goes through without identity types.This paper will also show that the interpretation can be given in an intensional type theory.This reflects the computational nature of the interpretation.This computational aspect is reinforced by an ω-Set moel of CZF.

  10. Staggered Chromosomal Hybrid Zones in the House Mouse: Relevance to Reticulate Evolution and Speciation

    Directory of Open Access Journals (Sweden)

    İslam Gündüz

    2010-07-01

    Full Text Available In the house mouse there are numerous chromosomal races distinguished by different combinations of metacentric chromosomes. These may come into contact with each other and with the ancestral all-acrocentric race, and form hybrid zones. The chromosomal clines that make up these hybrid zones may be coincident or separated from each other (staggered. Such staggered hybrid zones are interesting because they may include populations of individuals homozygous for a mix of features of the hybridising races. We review the characteristics of four staggered hybrid zones in the house mouse and discuss whether they are examples of primary or secondary contact and whether they represent reticulate evolution or not. However, the most important aspect of staggered hybrid zones is that the homozygous populations within the zones have the potential to expand their distributions and become new races (a process termed ‘zonal raciation’. In this way they can add to the total ‘stock’ of chromosomal races in the species concerned. Speciation is an infrequent phenomenon that may involve an unusual set of circumstances. Each one of the products of zonal raciation has the potential to become a new species and by having more races increases the chance of a speciation event.

  11. Power Quality Application of Hybrid Drivetrain

    OpenAIRE

    Rassõlkin, Anton; Hõimoja, Hardi

    2012-01-01

    This paper presents a study on the power conditioning features of hybrid powertrain, especially regarding diesel-electric locomotives. Equipped with an embarked energy buffer for diesel generator support and utility grid interface, such a locomotive can be considered as a plug-in series hybrid vehicle. The driveline inductive components, like generator and motor windings, and capacitive components like dc link capacitors can be used to provide STATCOM functions, and the energy buffer can be u...

  12. Hybrid Odontogenic Lesion: A Rare Entity

    Directory of Open Access Journals (Sweden)

    Reza Imani

    2017-03-01

    Full Text Available Hybrid tumors are very rare tumors composed of two different tumor entities, each of which conforms to an exactly defined tumor category. A 14-year-old boy was referred for an intraosseous painless lesion with a histopathological feature of multiple odontogenic lesions including calcifying odontogenic cyst, complex odontoma and ameloblastic fibro-odontoma. The final diagnosis considered to be a hybrid odontogenic lesion.

  13. Improved Evolutionary Hybrids for Flexible Ligand Docking in Autodock

    Energy Technology Data Exchange (ETDEWEB)

    Belew, R.K.; Hart, W.E.; Morris, G.M.; Rosin, C.

    1999-01-27

    In this paper we evaluate the design of the hybrid evolutionary algorithms (EAs) that are currently used to perform flexible ligand binding in the Autodock docking software. Hybrid EAs incorporate specialized operators that exploit domain-specific features to accelerate an EA's search. We consider hybrid EAs that use an integrated local search operator to reline individuals within each iteration of the search. We evaluate several factors that impact the efficacy of a hybrid EA, and we propose new hybrid EAs that provide more robust convergence to low-energy docking configurations than the methods currently available in Autodock.

  14. Optimization of the BLASTN substitution matrix for prediction of non-specific DNA microarray hybridization

    DEFF Research Database (Denmark)

    Eklund, Aron Charles; Friis, Pia; Wernersson, Rasmus;

    2010-01-01

    DNA microarray measurements are susceptible to error caused by non-specific hybridization between a probe and a target (cross-hybridization), or between two targets (bulk-hybridization). Search algorithms such as BLASTN can quickly identify potentially hybridizing sequences. We set out to improve...

  15. SEEDLING DISCRIMINATION USING SHAPE FEATURES DERIVED FROM A DISTANCE TRANSFORM

    DEFF Research Database (Denmark)

    Mosgaard Giselsson, Thomas; Jørgensen, Rasmus Nyholm; Midtiby, Henrik

    ). The method have been tested through a discrimination task where two similar plant species were to be divided into their respective classes. Since the LPFS feature set is meant to be used with a classification algorithm, the performance assessment is the classification accuracy of 4 different classifiers (k...... achieved discrimination accuracy with the LPFS feature set was 98.75 % and contained 10 numerical features. The SFS feature set achieved an accuracy of 87.1 % using 22 features. The results show the LPFS feature set can compete with the SFS feature set. Further testing is needed to reveal the true value...

  16. Optimizing Hybrid Spreading in Metapopulations

    CERN Document Server

    Zhang, Changwang; Cox, Ingemar J; Chain, Benjamin M

    2014-01-01

    Epidemic spreading phenomena are ubiquitous in nature and society. Examples include the spreading of diseases, information, and computer viruses. Epidemics can spread by \\textit{local spreading}, where infected nodes can only infect a limited set of direct target nodes and \\textit{global spreading}, where an infected node can infect every other node. In reality, many epidemics spread using a hybrid mixture of both types of spreading. In this study we develop a theoretical framework for studying hybrid epidemics, and examine the optimum balance between spreading mechanisms in terms of achieving the maximum outbreak size. In a metapopulation, made up of many weakly connected subpopulations, we show that one can calculate an optimal tradeoff between local and global spreading which will maximise the extent of the epidemic. As an example we analyse the 2008 outbreak of the Internet worm Conficker, which uses hybrid spreading to propagate through the internet. Our results suggests that the worm would have been eve...

  17. 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.

  18. Confidence-Based Feature Acquisition

    Science.gov (United States)

    Wagstaff, Kiri L.; desJardins, Marie; MacGlashan, James

    2010-01-01

    Confidence-based Feature Acquisition (CFA) is a novel, supervised learning method for acquiring missing feature values when there is missing data at both training (learning) and test (deployment) time. To train a machine learning classifier, data is encoded with a series of input features describing each item. In some applications, the training data may have missing values for some of the features, which can be acquired at a given cost. A relevant JPL example is that of the Mars rover exploration in which the features are obtained from a variety of different instruments, with different power consumption and integration time costs. The challenge is to decide which features will lead to increased classification performance and are therefore worth acquiring (paying the cost). To solve this problem, CFA, which is made up of two algorithms (CFA-train and CFA-predict), has been designed to greedily minimize total acquisition cost (during training and testing) while aiming for a specific accuracy level (specified as a confidence threshold). With this method, it is assumed that there is a nonempty subset of features that are free; that is, every instance in the data set includes these features initially for zero cost. It is also assumed that the feature acquisition (FA) cost associated with each feature is known in advance, and that the FA cost for a given feature is the same for all instances. Finally, CFA requires that the base-level classifiers produce not only a classification, but also a confidence (or posterior probability).

  19. Hybrid Ceramic Matrix Fibrous Composites: an Overview

    Energy Technology Data Exchange (ETDEWEB)

    Naslain, R, E-mail: naslain@lcts.u-bordeaux1.fr [University of Bordeaux 3, Allee de La Boetie, 33600 Pessac (France)

    2011-10-29

    Ceramic-Matrix Composites (CMCs) consist of a ceramic fiber architecture in a ceramic matrix, bonded together through a thin interphase. The present contribution is limited to non-oxide CMCs. Their constituents being oxidation-prone, they are protected by external coatings. We state here that CMCs display a hybrid feature, when at least one of their components is not homogeneous from a chemical or microstructural standpoint. Hybrid fiber architectures are used to tailor the mechanical or thermal CMC-properties whereas hybrid interphases, matrices and coatings to improve CMC resistance to aggressive environments.

  20. Hybrid Logical Analyses of the Ambient Calculus

    DEFF Research Database (Denmark)

    Bolander, Thomas; Hansen, René Rydhof

    2007-01-01

    In this paper, hybrid logic is used to formulate a rational reconstruction of a previously published control flow analysis for the mobile ambients calculus and we further show how a more precise flow-sensitive analysis, that takes the ordering of action sequences into account, can be formulated...... in a natural way. We show that hybrid logic is very well suited to express the semantic structure of the ambient calculus and how features of hybrid logic can be exploited to reduce the "administrative overhead" of the analysis specification and thus simplify it. Finally, we use HyLoTab, a fully automated...

  1. Identifying the distinct features of geometric structures for hole trapping to generate radicals on rutile TiO₂(110) in photooxidation using density functional theory calculations with hybrid functional.

    Science.gov (United States)

    Wang, Dong; Wang, Haifeng; Hu, P

    2015-01-21

    Using density functional theory calculations with HSE 06 functional, we obtained the structures of spin-polarized radicals on rutile TiO2(110), which is crucial to understand the photooxidation at the atomic level, and further calculate the thermodynamic stabilities of these radicals. By analyzing the results, we identify the structural features for hole trapping in the system, and reveal the mutual effects among the geometric structures, the energy levels of trapped hole states and their hole trapping capacities. Furthermore, the results from HSE 06 functional are compared to those from DFT + U and the stability trend of radicals against the number of slabs is tested. The effect of trapped holes on two important steps of the oxygen evolution reaction, i.e. water dissociation and the oxygen removal, is investigated and discussed.

  2. Hierarchal Variable Switching Sets of Interacting Multiple Model for Tracking Maneuvering Targets in Sensor Network

    Directory of Open Access Journals (Sweden)

    Seham Moawoud Ay Ebrahim

    2013-01-01

    Full Text Available Tracking maneuvering targets introduce two major directions to improve the Multiple Model (MM approach: Develop a better MM algorithm and design a better model set. The Interacting Multiple Model (IMM estimator is a suboptimal hybrid filter that has been shown to be one of the most cost-effective hybrid state estimation schemes. The main feature of this algorithm is the ability to estimate the state of a dynamic system with several behavior modes which can "switch" from one to another. In particular, the use of too many models is performance-wise as bad as that of too few models. In this paper we show that the use of too many models is performance-wise as bad as that of too few models. To overcome this we divide the models into a small number of sets, tuning these sets during operation at the right operating set. We proposed Hierarchal Switching sets of IMM (HSIMM. The state space of the nonlinear variable is divided into sets each set has its own IMM. The connection between them is the switching algorithm which manages the activation and termination of sets. Also the re-initialization process overcomes the error accumulation due to the targets changes from one model to another. This switching can introduce a number of different models while no restriction on their number. The activation of sets depends on the threshold value of set likely hood. As the likely hood of the set is higher than threshold it is active otherwise it is minimized. The result is the weighted sum of the output of active sets. The computational time is minimum than introduced by IMM and VIMM. HSIMM introduces less error as the noise increase and there is no need for re adjustment to the Covariance as the noise increase so it is more robust against noise and introduces minimum computational time.

  3. Temporal feature integration for music genre classification

    DEFF Research Database (Denmark)

    Meng, Anders; Ahrendt, Peter; Larsen, Jan

    2007-01-01

    Temporal feature integration is the process of combining all the feature vectors in a time window into a single feature vector in order to capture the relevant temporal information in the window. The mean and variance along the temporal dimension are often used for temporal feature integration......, but they capture neither the temporal dynamics nor dependencies among the individual feature dimensions. Here, a multivariate autoregressive feature model is proposed to solve this problem for music genre classification. This model gives two different feature sets, the diagonal autoregressive (DAR......) and multivariate autoregressive (MAR) features which are compared against the baseline mean-variance as well as two other temporal feature integration techniques. Reproducibility in performance ranking of temporal feature integration methods were demonstrated using two data sets with five and eleven music genres...

  4. 结合Gabor纹理特征的局域化多通道水平集分割方法%Localized Multi-Channel Level Set Segmentation Combined with Gabor Texture Feature

    Institute of Scientific and Technical Information of China (English)

    张立和; 朱莉莉; 米晓莉

    2011-01-01

    本文提出了一种局域化多通道主动轮廓模型的图像分割算法.针对纹理特征比较明显的图像,通过Gabor滤波提取纹理特征,与图像灰度信息构成多通道.考虑到演化过程中曲线内部和外部特征属性不均匀,引入局域化思想,通过计算各像素在局部区域的最小能量得到图像分割结果.最后算法结合先验形状对有遮挡目标进行分割,并能得到理想结果.大量实验验证了该方法具有良好的分割性能,优于同类算法.%An new algorithm based on localized Multi-Channel active contour model is proposed for image segmentation. For the images with obvious texture, Gabor texture features ate constituted multiple channels of active contour model together with image intensity information. Considering that intensity and texture characteristics are inconsistent in the interior and exterior of the evolution curve, localized energy idea is introduced, the minimum energy is calculated in the specific local area around each pixel on the evolution curve. Our model combined with the shape prior is used to segment the shadowed objects. The proposed algorithm is exemplified on various images objects and its superiority over state of the art variations] segmentation techniques is demonstrated.

  5. Feature dimensionality reduction for myoelectric pattern recognition: a comparison study of feature selection and feature projection methods.

    Science.gov (United States)

    Liu, Jie

    2014-12-01

    This study investigates the effect of the feature dimensionality reduction strategies on the classification of surface electromyography (EMG) signals toward developing a practical myoelectric control system. Two dimensionality reduction strategies, feature selection and feature projection, were tested on both EMG feature sets, respectively. A feature selection based myoelectric pattern recognition system was introduced to select the features by eliminating the redundant features of EMG recordings instead of directly choosing a subset of EMG channels. The Markov random field (MRF) method and a forward orthogonal search algorithm were employed to evaluate the contribution of each individual feature to the classification, respectively. Our results from 15 healthy subjects indicate that, with a feature selection analysis, independent of the type of feature set, across all subjects high overall accuracies can be achieved in classification of seven different forearm motions with a small number of top ranked original EMG features obtained from the forearm muscles (average overall classification accuracy >95% with 12 selected EMG features). Compared to various feature dimensionality reduction techniques in myoelectric pattern recognition, the proposed filter-based feature selection approach is independent of the type of classification algorithms and features, which can effectively reduce the redundant information not only across different channels, but also cross different features in the same channel. This may enable robust EMG feature dimensionality reduction without needing to change ongoing, practical use of classification algorithms, an important step toward clinical utility.

  6. Continuity Controlled Hybrid Automata

    NARCIS (Netherlands)

    Bergstra, J.A.; Middelburg, C.A.

    2004-01-01

    We investigate the connections between the process algebra for hybrid systems of Bergstra and Middelburg and the formalism of hybrid automata of Henzinger et al. We give interpretations of hybrid automata in the process algebra for hybrid systems and compare them with the standard interpretation of

  7. Continuity controlled Hybrid Automata

    NARCIS (Netherlands)

    Bergstra, J.A.; Middelburg, C.A.

    2008-01-01

    We investigate the connections between the process algebra for hybrid systems of Bergstra and Middelburg and the formalism of hybrid automata of Henzinger et al. We give interpretations of hybrid automata in the process algebra for hybrid systems and compare them with the standard interpretation of

  8. A single-layer network unsupervised feature learning method for white matter hyperintensity segmentation

    Science.gov (United States)

    Vijverberg, Koen; Ghafoorian, Mohsen; van Uden, Inge W. M.; de Leeuw, Frank-Erik; Platel, Bram; Heskes, Tom

    2016-03-01

    Cerebral small vessel disease (SVD) is a disorder frequently found among the old people and is associated with deterioration in cognitive performance, parkinsonism, motor and mood impairments. White matter hyperintensities (WMH) as well as lacunes, microbleeds and subcortical brain atrophy are part of the spectrum of image findings, related to SVD. Accurate segmentation of WMHs is important for prognosis and diagnosis of multiple neurological disorders such as MS and SVD. Almost all of the published (semi-)automated WMH detection models employ multiple complex hand-crafted features, which require in-depth domain knowledge. In this paper we propose to apply a single-layer network unsupervised feature learning (USFL) method to avoid hand-crafted features, but rather to automatically learn a more efficient set of features. Experimental results show that a computer aided detection system with a USFL system outperforms a hand-crafted approach. Moreover, since the two feature sets have complementary properties, a hybrid system that makes use of both hand-crafted and unsupervised learned features, shows a significant performance boost compared to each system separately, getting close to the performance of an independent human expert.

  9. Genome evolution in alpine oat-like grasses through homoploid hybridization and polyploidy.

    Science.gov (United States)

    Winterfeld, Grit; Wölk, Alexandra; Röser, Martin

    2016-01-01

    Hybridization and polyploidization can radically impact genome organization from sequence level to chromosome structure. As a result, often in response to environmental change and species isolation, the development of novel traits can arise and will tend to result in the formation of homoploid or polyploid hybrid species. In this study we focus on evidence of hybridization and polyploidization by ascertaining the species parentage of the endemic alpine Helictotrichon parlatorei group. This group comprises five taxa; the diploids H. parlatorei, Helictotrichon setaceum subsp. setaceum and subsp. petzense, their putative hybrid Helictotrichon ×krischae and the hexaploid Helictotrichon sempervirens. For molecular analyses, cloned nuclear Topoisomerase VI genes of H. sempervirens and H. ×krischae were sequenced and compared with sequences of the diploids to estimate the evolutionary history in this group. In addition, detailed chromosome studies were carried out including fluorescence in situ hybridization (FISH) with 5S and 45S ribosomal and satellite DNA probes, and fluorochrome staining with chromomycin and DAPI. Two distinct types of Topoisomerase VI sequences were identified. One of them (SET) occurs in both subspecies of H. setaceum, the other (PAR) in H. parlatorei. Both types were found in H. ×krischae and H. sempervirens Karyotypes of H. parlatorei and H. setaceum could be distinguished by chromosomes with a clearly differentiated banding pattern of ribosomal DNAs. Both patterns occurred in the hybrid H. ×krischae Hexaploid H. sempervirens shares karyotype features with diploid H. parlatorei, but lacks the expected chromosome characteristics of H. setaceum, possibly an example of beginning diploidization after polyploidization. The geographic origin of the putative parental species and their hybrids and the possible biogeographical spread through the Alps are discussed.

  10. 基于Gabor特征分解的高斯混合非线性滤波算法%Gauss Hybrid Nonlinear Filter Design Based on Gabor Feature Decomposition

    Institute of Scientific and Technical Information of China (English)

    高菲菲

    2015-01-01

    传统的窄带信号检测滤波器采用IIR自适应线谱增强滤波算法,对信号特征分解的阶数要求高,导致非线性失真,提出一种基于Gabor特征分解的高斯混合非线性滤波器设计算法,在IIR滤波器设计的基础上,对信号进行尺度和时延估计,构建自适应高阶累积量滤波设计方法,采用高阶累积量对窄带信号进行均方一致估计,对Gabor特征函数Taylor级数展开,求得高斯混合非线性滤波器的带宽参数,最后实现高斯混合非线性滤波器设计改进,提高对窄带信号的检测性能.仿真结果表明,该算法具有较好的滤波性能,可以明显地抑制色噪声的影响,提高信号增益达到20 dB.%Narrow band signal detection filter is used in the traditional IIR adaptive line enhancement algorithm, order de-composition on signal feature requirements, resulting in nonlinear distortion, this paper puts forward a Gabor feature decom-position algorithm based on Gauss mixture nonlinear filter design, based on IIR filter design, scale and time delay estima-tion of signal, to construct an adaptive high order cumulants filter design method, using high order cumulant of mean square consistent estimation of narrowband signals, the characteristic function expansion on the Gabor Taylor series, the band-width parameter obtained Gauss mixed nonlinear filter, finally realize the Gauss improvement of mixed nonlinear filter de-sign, improve the detection performance of the narrowband signal. The simulation results show that the proposed algorithm has good filtering performance and can obviously suppress the color noise and improve the signal gain of 20 dB.

  11. Hybrid reactors: Nuclear breeding or energy production?

    Energy Technology Data Exchange (ETDEWEB)

    Piera, Mireia [UNED, ETSII-Dp Ingenieria Energetica, c/Juan del Rosal 12, 28040 Madrid (Spain); Lafuente, Antonio; Abanades, Alberto; Martinez-Val, J.M. [ETSII-UPM, c/Jose Gutierrez Abascal 2, 28006 Madrid (Spain)

    2010-09-15

    After reviewing the long-standing tradition on hybrid research, an assessment model is presented in order to characterize the hybrid performance under different objectives. In hybrids, neutron multiplication in the subcritical blanket plays a major role, not only for energy production and nuclear breeding, but also for tritium breeding, which is fundamental requirement in fusion-fission hybrids. All three objectives are better achieved with high values of the neutron multiplication factor (k-eff) with the obvious and fundamental limitation that it cannot reach criticality under any event, particularly, in the case of a loss of coolant accident. This limitation will be very important in the selection of the coolant. Some general considerations will be proposed, as guidelines for assessing the hybrid potential in a given scenario. Those guidelines point out that hybrids can be of great interest for the future of nuclear energy in a framework of Sustainable Development, because they can contribute to the efficient exploitation of nuclear fuels, with very high safety features. Additionally, a proposal is presented on a blanket specially suited for fusion-fission hybrids, although this reactor concept is still under review, and new work is needed for identifying the most suitable blanket composition, which can vary depending on the main objective of the hybrid. (author)

  12. Organization and Variation Analysis of 5S rDNA in Different Ploidy-level Hybrids of Red Crucian Carp × Topmouth Culter

    OpenAIRE

    Weiguo He; Qinbo Qin; Shaojun Liu; Tangluo Li; Jing Wang; Jun Xiao; Lihua Xie; Chun Zhang; Yun Liu

    2012-01-01

    Through distant crossing, diploid, triploid and tetraploid hybrids of red crucian carp (Carassius auratus red var., RCC♀, Cyprininae, 2n = 100) × topmouth culter (Erythroculter ilishaeformis Bleeker, TC♂, Cultrinae, 2n = 48) were successfully produced. Diploid hybrids possessed 74 chromosomes with one set from RCC and one set from TC; triploid hybrids harbored 124 chromosomes with two sets from RCC and one set from TC; tetraploid hybrids had 148 chromosomes with two sets from RCC and two sets...

  13. Double-hybrid density-functional theory made rigorous

    CERN Document Server

    Sharkas, Kamal; Savin, Andreas

    2010-01-01

    We provide a rigorous derivation of a class of double-hybrid approximations, combining Hartree-Fock exchange and second-order Moller-Plesset correlation with a semilocal exchange-correlation density functional. These double-hybrid approximations contain only one empirical parameter and use a density-scaled correlation energy functional. Neglecting density scaling leads to an one-parameter version of the standard double-hybrid approximations. We assess the performance of these double-hybrid schemes on representative test sets of atomization energies and reaction barrier heights, and we compare to other hybrid approximations, including range-separated hybrids. Our best one-parameter double-hybrid approximation, called 1DH-BLYP, roughly reproduces the two parameters of the standard B2-PLYP or B2GP-PLYP double-hybrid approximations, which shows that these methods are not only empirically close to an optimum for general chemical applications but are also theoretically supported.

  14. Hybrid optofluidic biosensors

    Science.gov (United States)

    Parks, Joshua W.

    Optofluidics, born of the desire to create a system containing microfluidic environments with integrated optical elements, has seen dramatic increases in popularity over the last 10 years. In particular, the application of this technology towards chip based molecular sensors has undergone significant development. The most sensitive of these biosensors interface liquid- and solid-core antiresonant reflecting optical waveguides (ARROWs). These sensor chips are created using conventional silicon microfabrication. As such, ARROW technology has previously been unable to utilize state-of-the-art microfluidic developments because the technology used--soft polydimethyl siloxane (PDMS) micromolded chips--is unamenable to the silicon microfabrication workflows implemented in the creation of ARROW detection chips. The original goal of this thesis was to employ hybrid integration, or the connection of independently designed and fabricated optofluidic and microfluidic chips, to create enhanced biosensors with the capability of processing and detecting biological samples on a single hybrid system. After successful demonstration of this paradigm, this work expanded into a new direction--direct integration of sensing and detection technologies on a new platform with dynamic, multi-dimensional photonic re-configurability. This thesis reports a number of firsts, including: • 1,000 fold optical transmission enhancement of ARROW optofluidic detection chips through thermal annealing, • Detection of single nucleic acids on a silicon-based ARROW chip, • Hybrid optofluidic integration of ARROW detection chips and passive PDMS microfluidic chips, • Hybrid optofluidic integration of ARROW detection chips and actively controllable PDMS microfluidic chips with integrated microvalves, • On-chip concentration and detection of clinical Ebola nucleic acids, • Multimode interference (MMI) waveguide based wavelength division multiplexing for detection of single influenza virions,

  15. Genomic networks of hybrid sterility.

    Directory of Open Access Journals (Sweden)

    Leslie M Turner

    2014-02-01

    Full Text Available Hybrid dysfunction, a common feature of reproductive barriers between species, is often caused by negative epistasis between loci ("Dobzhansky-Muller incompatibilities". The nature and complexity of hybrid incompatibilities remain poorly understood because identifying interacting loci that affect complex phenotypes is difficult. With subspecies in the early stages of speciation, an array of genetic tools, and detailed knowledge of reproductive biology, house mice (Mus musculus provide a model system for dissecting hybrid incompatibilities. Male hybrids between M. musculus subspecies often show reduced fertility. Previous studies identified loci and several X chromosome-autosome interactions that contribute to sterility. To characterize the genetic basis of hybrid sterility in detail, we used a systems genetics approach, integrating mapping of gene expression traits with sterility phenotypes and QTL. We measured genome-wide testis expression in 305 male F2s from a cross between wild-derived inbred strains of M. musculus musculus and M. m. domesticus. We identified several thousand cis- and trans-acting QTL contributing to expression variation (eQTL. Many trans eQTL cluster into eleven 'hotspots,' seven of which co-localize with QTL for sterility phenotypes identified in the cross. The number and clustering of trans eQTL-but not cis eQTL-were substantially lower when mapping was restricted to a 'fertile' subset of mice, providing evidence that trans eQTL hotspots are related to sterility. Functional annotation of transcripts with eQTL provides insights into the biological processes disrupted by sterility loci and guides prioritization of candidate genes. Using a conditional mapping approach, we identified eQTL dependent on interactions between loci, revealing a complex system of epistasis. Our results illuminate established patterns, including the role of the X chromosome in hybrid sterility. The integrated mapping approach we employed is

  16. Genomic networks of hybrid sterility.

    Science.gov (United States)

    Turner, Leslie M; White, Michael A; Tautz, Diethard; Payseur, Bret A

    2014-02-01

    Hybrid dysfunction, a common feature of reproductive barriers between species, is often caused by negative epistasis between loci ("Dobzhansky-Muller incompatibilities"). The nature and complexity of hybrid incompatibilities remain poorly understood because identifying interacting loci that affect complex phenotypes is difficult. With subspecies in the early stages of speciation, an array of genetic tools, and detailed knowledge of reproductive biology, house mice (Mus musculus) provide a model system for dissecting hybrid incompatibilities. Male hybrids between M. musculus subspecies often show reduced fertility. Previous studies identified loci and several X chromosome-autosome interactions that contribute to sterility. To characterize the genetic basis of hybrid sterility in detail, we used a systems genetics approach, integrating mapping of gene expression traits with sterility phenotypes and QTL. We measured genome-wide testis expression in 305 male F2s from a cross between wild-derived inbred strains of M. musculus musculus and M. m. domesticus. We identified several thousand cis- and trans-acting QTL contributing to expression variation (eQTL). Many trans eQTL cluster into eleven 'hotspots,' seven of which co-localize with QTL for sterility phenotypes identified in the cross. The number and clustering of trans eQTL-but not cis eQTL-were substantially lower when mapping was restricted to a 'fertile' subset of mice, providing evidence that trans eQTL hotspots are related to sterility. Functional annotation of transcripts with eQTL provides insights into the biological processes disrupted by sterility loci and guides prioritization of candidate genes. Using a conditional mapping approach, we identified eQTL dependent on interactions between loci, revealing a complex system of epistasis. Our results illuminate established patterns, including the role of the X chromosome in hybrid sterility. The integrated mapping approach we employed is applicable in a broad

  17. Landmarks in Hybrid Planning

    Directory of Open Access Journals (Sweden)

    Mohamed Elkawkagy

    2013-11-01

    Full Text Available Although planning techniques achieved a significant progress during recent years, solving many planning problem still difficult even for modern planners. In this paper, we will adopt landmark concept to hybrid planning setting - a method that combines reasoning about procedural knowledge and causalities. Land-marks are a well-known concept in the realm of classical planning. Recently, they have been adapted to hierarchical approaches. Such landmarks can be extracted in a pre-processing step from a declarative hierarchical planning domain and problem description. It was shown how this technique allows for a considerable reduction of the search space by eliminating futile plan development options before the actual planning. Therefore, we will present a new approach to in¬tegrate landmark pre-processing technique in the context of hierarchical planning with landmark technique in the classical planning. This integration allows to incorporate the ability of using extracted landmark tasks from hierarchical domain knowledge in the form of HTN and using landmark literals from classical planning. To this end, we will construct a transformation technique to transform the hybrid planning domain into a classical domain model. The method¬ologies in this paper have been implemented successfully, and we will present some experimental results that give evidence for the consid-erable performance increase gained through planning system.

  18. A New Hybrid Method for Improving the Performance of Myocardial Infarction Prediction

    Directory of Open Access Journals (Sweden)

    Hojatollah Hamidi

    2016-06-01

    Full Text Available Abstract Introduction: Myocardial Infarction, also known as heart attack, normally occurs due to such causes as smoking, family history, diabetes, and so on. It is recognized as one of the leading causes of death in the world. Therefore, the present study aimed to evaluate the performance of classification models in order to predict Myocardial Infarction, using a feature selection method that includes Forward Selection and Genetic Algorithm. Materials & Methods: The Myocardial Infarction data set used in this study contains the information related to 519 visitors to Shahid Madani Specialized Hospital of Khorramabad, Iran. This data set includes 33 features. The proposed method includes a hybrid feature selection method in order to enhance the performance of classification algorithms. The first step of this method selects the features using Forward Selection. At the second step, the selected features were given to a genetic algorithm, in order to select the best features. Classification algorithms entail Ada Boost, Naïve Bayes, J48 decision tree and simpleCART are applied to the data set with selected features, for predicting Myocardial Infarction. Results: The best results have been achieved after applying the proposed feature selection method, which were obtained via simpleCART and J48 algorithms with the accuracies of 96.53% and 96.34%, respectively. Conclusion: Based on the results, the performances of classification algorithms are improved. So, applying the proposed feature selection method, along with classification algorithms seem to be considered as a confident method with respect to predicting the Myocardial Infarction.

  19. Statistical Model Checking for Stochastic Hybrid Systems

    DEFF Research Database (Denmark)

    David, Alexandre; Du, Dehui; Larsen, Kim Guldstrand

    2012-01-01

    This paper presents novel extensions and applications of the UPPAAL-SMC model checker. The extensions allow for statistical model checking of stochastic hybrid systems. We show how our race-based stochastic semantics extends to networks of hybrid systems, and indicate the integration technique...... applied for implementing this semantics in the UPPAAL-SMC simulation engine. We report on two applications of the resulting tool-set coming from systems biology and energy aware buildings....

  20. Fuzzy Hybrid Deliberative/Reactive Paradigm (FHDRP)

    Science.gov (United States)

    Sarmadi, Hengameth

    2004-01-01

    This work aims to introduce a new concept for incorporating fuzzy sets in hybrid deliberative/reactive paradigm. After a brief review on basic issues of hybrid paradigm the definition of agent-based fuzzy hybrid paradigm, which enables the agents to proceed and extract their behavior through quantitative numerical and qualitative knowledge and to impose their decision making procedure via fuzzy rule bank, is discussed. Next an example performs a more applied platform for the developed approach and finally an overview of the corresponding agents architecture enhances agents logical framework.

  1. An aggregation method of Markov graphs for the reliability analysis of hybrid systems

    Energy Technology Data Exchange (ETDEWEB)

    Schoenig, Raphael [Centre de Recherche en Automatique de Nancy (CRAN), 2, avenue de la Foret de Haye, 54516 Vandoeuvre-les-Nancy (France) and GFI Consulting, 12, rue Rouget de Lisle, 92442 Issy les Moulineaux (France)]. E-mail: raphael.schoenig@mpsa.com; Aubry, Jean-Francois [Centre de Recherche en Automatique de Nancy (CRAN), 2, avenue de la Foret de Haye, 54516 Vandoeuvre-les-Nancy (France)]. E-mail: jean-francois.aubry@isi.u-nancy.fr; Cambois, Thierry [PSA Peugeot Citroen, 18, rue des Fauvelles, 92256 La Garenne Colombes (France)]. E-mail: thierry.cambois@mpsa.com; Hutinet, Tony [GFI Consulting, 12, rue Rouget de Lisle, 92442 Issy les Moulineaux (France)]. E-mail: thutinet@gfi.fr

    2006-02-01

    To meet always increasing safety requirements in car industry, design and safety assessment methods are developed in order to fit the complexity of new embedded mecatronic systems. Hybrid (discrete/continuous) and dynamic features, specific to these systems, require choosing a suitable formalism. These features should also be considered in safety studies made all through the system design. The aim of this paper is to propose a quantitative analysis method based on the construction of an aggregated Markov graph, which allows a limitation of the combinatorial expansion. This graph is directly deducted from the Petri net modelling of the system. It is composed by a set of functional modes and a set of transitions to which statistical information regarding the system dynamics has been added.

  2. Using Leaf-level Hyperspectral Reflectance Data to Analyze Genetic Gain in CIMMYT Maize Hybrids

    Data.gov (United States)

    US Agency for International Development — A set of recent CIMMYT era hybrids - spanning from the early 1990s to the late 2000s - was analyzed. The hybrids were grown in four different environments in two...

  3. Supermarket Refrigeration System - Benchmark for Hybrid System Control

    DEFF Research Database (Denmark)

    Sloth, Lars Finn; Izadi-Zamanabadi, Roozbeh; Wisniewski, Rafal

    2007-01-01

    This paper presents a supermarket refrigeration system as a benchmark for development of new ideas and a comparison of methods for hybrid systems' modeling and control. The benchmark features switch dynamics and discrete valued input making it a hybrid system, furthermore the outputs are subjected...

  4. A Sparse-Feature-Based Face Detector

    Institute of Scientific and Technical Information of China (English)

    LUXiaofeng; ZHENGNanning; ZHENGSongfeng

    2003-01-01

    Local features and global features are two kinds of important statistical features used to distinguish faces from nonfaces. They are both special cases of sparse features. A final classifier can be considered as a combination of a set of selected weak classiflers, and each weak classifier uses a sparse feature to classify samples. Motivated by this thought, we construct an over complete set of weak classifiers using LPSVM (Linear proximal support vector machine) algorithm, and then we select part of them using AdaBoost algorithm and combine the selected weak classifiers to form a strong classifier. And duringthe course of feature extraction and selection, our method can minimize the classification error directly, whereas most previous works cannot do this. The main difference from other methods is that the local features are learned from the training set instead of being arbitrarily defined. We applied our method to face detection; the test result shows that this method performs well.

  5. Hybridization and Selective Release of DNA Microarrays

    Energy Technology Data Exchange (ETDEWEB)

    Beer, N R; Baker, B; Piggott, T; Maberry, S; Hara, C M; DeOtte, J; Benett, W; Mukerjee, E; Dzenitis, J; Wheeler, E K

    2011-11-29

    DNA microarrays contain sequence specific probes arrayed in distinct spots numbering from 10,000 to over 1,000,000, depending on the platform. This tremendous degree of multiplexing gives microarrays great potential for environmental background sampling, broad-spectrum clinical monitoring, and continuous biological threat detection. In practice, their use in these applications is not common due to limited information content, long processing times, and high cost. The work focused on characterizing the phenomena of microarray hybridization and selective release that will allow these limitations to be addressed. This will revolutionize the ways that microarrays can be used for LLNL's Global Security missions. The goals of this project were two-fold: automated faster hybridizations and selective release of hybridized features. The first study area involves hybridization kinetics and mass-transfer effects. the standard hybridization protocol uses an overnight incubation to achieve the best possible signal for any sample type, as well as for convenience in manual processing. There is potential to significantly shorten this time based on better understanding and control of the rate-limiting processes and knowledge of the progress of the hybridization. In the hybridization work, a custom microarray flow cell was used to manipulate the chemical and thermal environment of the array and autonomously image the changes over time during hybridization. The second study area is selective release. Microarrays easily generate hybridization patterns and signatures, but there is still an unmet need for methodologies enabling rapid and selective analysis of these patterns and signatures. Detailed analysis of individual spots by subsequent sequencing could potentially yield significant information for rapidly mutating and emerging (or deliberately engineered) pathogens. In the selective release work, optical energy deposition with coherent light quickly provides the thermal energy

  6. Hybrid Computational Model for High-Altitude Aeroassist Vehicles Project

    Data.gov (United States)

    National Aeronautics and Space Administration — A hybrid continuum/noncontinuum computational model will be developed for analyzing the aerodynamics and heating on aeroassist vehicles. Unique features of this...

  7. Hybrid metric-Palatini gravity

    CERN Document Server

    Capozziello, Salvatore; Koivisto, Tomi S; Lobo, Francisco S N; Olmo, Gonzalo J

    2015-01-01

    Recently, the phenomenology of f(R) gravity has been scrutinized motivated by the possibility to account for the self-accelerated cosmic expansion without invoking dark energy sources. Besides, this kind of modified gravity is capable of addressing the dynamics of several self-gravitating systems alternatively to the presence of dark matter. It has been established that both metric and Palatini versions of these theories have interesting features but also manifest severe and different downsides. A hybrid combination of theories, containing elements from both these two formalisms, turns out to be also very successful accounting for the observed phenomenology and is able to avoid some drawbacks of the original approaches. This article reviews the formulation of this hybrid metric-Palatini approach and its main achievements in passing the local tests and in applications to astrophysical and cosmological scenarios, where it provides a unified approach to the problems of dark energy and dark matter.

  8. Importance of local exact exchange potential in hybrid functionals for accurate excited states

    CERN Document Server

    Kim, Jaewook; Hwang, Sang-Yeon; Ryu, Seongok; Choi, Sunghwan; Kim, Woo Youn

    2016-01-01

    Density functional theory has been an essential analysis tool for both theoretical and experimental chemists since accurate hybrid functionals were developed. Here we propose a local hybrid method derived from the optimized effective potential (OEP) method and compare its distinct features with conventional nonlocal ones from the Hartree-Fock (HF) exchange operator. Both are formally exact for ground states and thus show similar accuracy for atomization energies and reaction barrier heights. For excited states, the local version yields virtual orbitals with N-electron character, while those of the nonlocal version have mixed characters between N- and (N+1)-electron orbitals. As a result, the orbital energy gaps from the former well approximate excitation energies with a small mean absolute error (MAE = 0.40 eV) for the Caricato benchmark set. The correction from time-dependent density functional theory with a simple local density approximation kernel further improves its accuracy by incorporating multi-config...

  9. From hybrid swarms to swarms of hybrids

    Science.gov (United States)

    The introgression of modern humans (Homo sapiens) with Neanderthals 40,000 YBP after a half-million years of separation, may have led to the best example of a hybrid swarm on earth. Modern trade and transportation in support of the human hybrids has continued to introduce additional species, genotyp...

  10. The Hybrid Museum: Hybrid Economies of Meaning

    DEFF Research Database (Denmark)

    Vestergaard, Vitus

    2013-01-01

    this article shows that there are two different museum mindsets where the second mindset leans towards participatory practices. It is shown how a museum can support a hybrid economy of meaning that builds on both a user generated economy of meaning and an institutional economy of meaning and adds value to both....... Such a museum is referred to as a hybrid museum....

  11. Hydraulic Hybrid Vehicles

    Science.gov (United States)

    EPA and the United Parcel Service (UPS) have developed a hydraulic hybrid delivery vehicle to explore and demonstrate the environmental benefits of the hydraulic hybrid for urban pick-up and delivery fleets.

  12. Hybrid Management in Hospitals

    DEFF Research Database (Denmark)

    Byrkjeflot, Haldor; Jespersen, Peter Kragh

    2010-01-01

    Artiklen indeholder et litteraturbaseret studium af ledelsesformer i sygehuse, hvor sundhedsfaglig ledelse og generel ledelse mikses til hybride ledelsesformer......Artiklen indeholder et litteraturbaseret studium af ledelsesformer i sygehuse, hvor sundhedsfaglig ledelse og generel ledelse mikses til hybride ledelsesformer...

  13. 全矢谱-粗集理论在旋转机械故障频谱特征提取中的应用研究%Research on spplication of full vector spectrum-rough set in extracting fault spectrum feature of rotating machinery

    Institute of Scientific and Technical Information of China (English)

    王宏超; 韩捷; 陈宏; 巩晓赟

    2011-01-01

    随着旋转机械的大型化、高速化、高精度化,全面、及时、有效的对其进行故障特征提取的重要性愈来愈明显.传统的单通道信息采集方式有着信息量不全面易造成误判的弊端;传统的信息处理方式存在着效率低等弊端.基于同源信息融合和故障特征提取的思想,将全矢谱技术和粗集理论结合,提出了全矢-粗集理论在旋转机械故障频谱特征提取中的应用方法,给出了相关的定义和算法.并通过典型故障的实验验证,此方法在旋转机械故障频谱特征提取中有着更为准确、全面的优势,是一种有效的故障频谱特征提取方法.为旋转机械故障的在线监测提供参考.%With the rotating machinery developign more larger in scale,more faster in speed and more higher in accuracy,it is becoming more important to extract its faults feature comprehensively,timely and effectively.White the traditional extraction is characterized with its low efficiency,incomplete that may lead to wrong judgement Basing on the ideology of same source information fusion and fault feature extraction,it the vector spectrum shall be combined with rough set and propose the method on application of full vector spectrum- rough set in extracting fault spectrum feature of rotating machinery,which definition and algorithm are given.And with the experimental test for typical rotating machinery fault,this method is proven with the advantages of accuracy and comprehensiveness in fault spectrum feature extraction.therefore it is not only an effective way in fault spectrum feature extruction but also an reference for online monitoring and diagnosis.

  14. Hybrid Filter Membrane

    Science.gov (United States)

    Laicer, Castro; Rasimick, Brian; Green, Zachary

    2012-01-01

    Cabin environmental control is an important issue for a successful Moon mission. Due to the unique environment of the Moon, lunar dust control is one of the main problems that significantly diminishes the air quality inside spacecraft cabins. Therefore, this innovation was motivated by NASA s need to minimize the negative health impact that air-suspended lunar dust particles have on astronauts in spacecraft cabins. It is based on fabrication of a hybrid filter comprising nanofiber nonwoven layers coated on porous polymer membranes with uniform cylindrical pores. This design results in a high-efficiency gas particulate filter with low pressure drop and the ability to be easily regenerated to restore filtration performance. A hybrid filter was developed consisting of a porous membrane with uniform, micron-sized, cylindrical pore channels coated with a thin nanofiber layer. Compared to conventional filter media such as a high-efficiency particulate air (HEPA) filter, this filter is designed to provide high particle efficiency, low pressure drop, and the ability to be regenerated. These membranes have well-defined micron-sized pores and can be used independently as air filters with discreet particle size cut-off, or coated with nanofiber layers for filtration of ultrafine nanoscale particles. The filter consists of a thin design intended to facilitate filter regeneration by localized air pulsing. The two main features of this invention are the concept of combining a micro-engineered straight-pore membrane with nanofibers. The micro-engineered straight pore membrane can be prepared with extremely high precision. Because the resulting membrane pores are straight and not tortuous like those found in conventional filters, the pressure drop across the filter is significantly reduced. The nanofiber layer is applied as a very thin coating to enhance filtration efficiency for fine nanoscale particles. Additionally, the thin nanofiber coating is designed to promote capture of

  15. Multifunctional hybrids for electromagnetic absorption

    Energy Technology Data Exchange (ETDEWEB)

    Huynen, I. [Research Center in Micro and Nanoscopic Materials and Electronic Devices, CeRMiN, Universite catholique de Louvain, B-1348 Louvain-la-Neuve (Belgium); Information and Communications Technologies, Electronics and Applied Mathematics (ICTEAM), Microwave Laboratory, Universite catholique de Louvain, B-1348 Louvain-la-Neuve (Belgium); Quievy, N. [Institute of Condensed Matter and Nanosciences (IMCN), Universite catholique de Louvain, B-1348 Louvain-la-Neuve (Belgium); Bailly, C. [Research Center in Micro and Nanoscopic Materials and Electronic Devices, CeRMiN, Universite catholique de Louvain, B-1348 Louvain-la-Neuve (Belgium); Institute of Condensed Matter and Nanosciences (IMCN), Universite catholique de Louvain, B-1348 Louvain-la-Neuve (Belgium); Institute of Mechanics, Materials and Civil Engineering (iMMC), Universite catholique de Louvain, B-1348 Louvain-la-Neuve (Belgium); Bollen, P. [Information and Communications Technologies, Electronics and Applied Mathematics (ICTEAM), Microwave Laboratory, Universite catholique de Louvain, B-1348 Louvain-la-Neuve (Belgium); Institute of Condensed Matter and Nanosciences (IMCN), Universite catholique de Louvain, B-1348 Louvain-la-Neuve (Belgium); Institute of Mechanics, Materials and Civil Engineering (iMMC), Universite catholique de Louvain, B-1348 Louvain-la-Neuve (Belgium); Detrembleur, C. [Center for Education and Research on Macromolecules (CERM), University of Liege, Sart-Tilman B6a, 4000 Liege (Belgium); Eggermont, S.; Molenberg, I. [Information and Communications Technologies, Electronics and Applied Mathematics (ICTEAM), Microwave Laboratory, Universite catholique de Louvain, B-1348 Louvain-la-Neuve (Belgium); Thomassin, J.M.; Urbanczyk, L. [Center for Education and Research on Macromolecules (CERM), University of Liege, Sart-Tilman B6a, 4000 Liege (Belgium)

    2011-05-15

    Highlights: > EM absorption requires low dielectric constant and {approx}1 S/m electrical conductivity. > New hybrids were processed with CNT-filled polymer foam inserted in Al honeycomb. > The EM absorption in the GHz range is superior to any known material. > A closed form model is used to guide the design of the hybrid. > The architectured material is light with potential for thermal management. - Abstract: Electromagnetic (EM) interferences are ubiquitous in modern technologies and impact on the reliability of electronic devices and on living cells. Shielding by EM absorption, which is preferable over reflection in certain instances, requires combining a low dielectric constant with high electrical conductivity, which are antagonist properties in the world of materials. A novel class of hybrid materials for EM absorption in the gigahertz range has been developed based on a hierarchical architecture involving a metallic honeycomb filled with a carbon nanotube-reinforced polymer foam. The waveguide characteristics of the honeycomb combined with the performance of the foam lead to unexpectedly large EM power absorption over a wide frequency range, superior to any known material. The peak absorption frequency can be tuned by varying the shape of the honeycomb unit cell. A closed form model of the EM reflection and absorption provides a tool for the optimization of the hybrid. This designed material sets the stage for a new class of sandwich panels combining high EM absorption with mass efficiency, stiffness and thermal management.

  16. Expanding discourse repertoires with hybridity

    Science.gov (United States)

    Kelly, Gregory J.

    2012-09-01

    In "Hybrid discourse practice and science learning" Kamberelis and Wehunt present a theoretically rich argument about the potential of hybrid discourses for science learning. These discourses draw from different forms of "talk, social practice, and material practices" to create interactions that are "intertextually complex" and "interactionally dynamic." The hybrid discourse practices are described as involving the dynamic interplay of at least three key elements: "the lamination of multiple cultural frames, the shifting relations between people and their discourse, and the shifting power relations between and among people." Each of these elements requires a respective unit of analysis and are often mutually reinforcing. The authors present a theoretically cogent argument for the study of hybrid discourse practices and identify the potential such discourses may have for science education. This theoretical development leads to an analysis of spoken and written discourse around a set of educational events concerning the investigation of owl pellets by two fifth grade students, their classmates, and teacher. Two discourse segments are presented and analyzed by the authors in detail. The first is a discourse analysis of the dissection of the owl pellet by two students, Kyle and Max. The second analysis examines the science report of these same two students. In this article, I pose a number of questions about the study with the hope that by doing so I expand the conversation around the insightful analysis presented.

  17. Rule set transferability for object-based feature extraction

    NARCIS (Netherlands)

    Anders, N.S.; Seijmonsbergen, Arie C.; Bouten, Willem

    2015-01-01

    Cirques are complex landforms resulting from glacial erosion and can be used to estimate Equilibrium Line Altitudes and infer climate history. Automated extraction of cirques may help research on glacial geomorphology and climate change. Our objective was to test the transferability of an object-

  18. Rule set transferability for object-based feature extraction

    NARCIS (Netherlands)

    Anders, N.S.; Seijmonsbergen, Arie C.; Bouten, Willem

    2015-01-01

    Cirques are complex landforms resulting from glacial erosion and can be used to estimate Equilibrium Line Altitudes and infer climate history. Automated extraction of cirques may help research on glacial geomorphology and climate change. Our objective was to test the transferability of an

  19. Resin Catalyst Hybrids

    Institute of Scientific and Technical Information of China (English)

    S. Asaoka

    2005-01-01

    @@ 1Introduction: What are resin catalyst hybrids? There are typically two types of resin catalyst. One is acidic resin which representative is polystyrene sulfonic acid. The other is basic resin which is availed as metal complex support. The objective items of this study on resin catalyst are consisting of pellet hybrid, equilibrium hybrid and function hybrid of acid and base,as shown in Fig. 1[1-5].

  20. Mesoscale hybrid calibration artifact

    Science.gov (United States)

    Tran, Hy D.; Claudet, Andre A.; Oliver, Andrew D.

    2010-09-07

    A mesoscale calibration artifact, also called a hybrid artifact, suitable for hybrid dimensional measurement and the method for make the artifact. The hybrid artifact has structural characteristics that make it suitable for dimensional measurement in both vision-based systems and touch-probe-based systems. The hybrid artifact employs the intersection of bulk-micromachined planes to fabricate edges that are sharp to the nanometer level and intersecting planes with crystal-lattice-defined angles.

  1. Analysis of Hybrid-Electric Propulsion System Designs for Small Unmanned Aircraft Systems

    Science.gov (United States)

    2010-03-01

    arrival of the Insight, nearly every major automotive manufacturer has released its own hybrid model. The Toyota Prius , released to the US in 2001, has...dominated the hybrid marketplace with US sales topping 1,000,000 in March 2009.17 The Prius features a power-split hybrid system enabling use of an

  2. The Publishing Industry as a Hybrid.

    Science.gov (United States)

    Coser, Lewis A.

    1984-01-01

    Characterizes publishing industry as a hybrid because it has some features usually found in bureaucratic enterprises and others characteristic of industries based on craftlike enterprises. It is concluded that, although this makes for a great deal of inefficiency, it does permit the coexistence of concern for commerce and culture. (EJS)

  3. Fitting PAC spectra with a hybrid algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Alves, M. A., E-mail: mauro@sepn.org [Instituto de Aeronautica e Espaco (Brazil); Carbonari, A. W., E-mail: carbonar@ipen.br [Instituto de Pesquisas Energeticas e Nucleares (Brazil)

    2008-01-15

    A hybrid algorithm (HA) that blends features of genetic algorithms (GA) and simulated annealing (SA) was implemented for simultaneous fits of perturbed angular correlation (PAC) spectra. The main characteristic of the HA is the incorporation of a selection criterion based on SA into the basic structure of GA. The results obtained with the HA compare favorably with fits performed with conventional methods.

  4. Feature-level domain adaptation

    DEFF Research Database (Denmark)

    Kouw, Wouter M.; Van Der Maaten, Laurens J P; Krijthe, Jesse H.

    2016-01-01

    Domain adaptation is the supervised learning setting in which the training and test data are sampled from different distributions: training data is sampled from a source domain, whilst test data is sampled from a target domain. This paper proposes and studies an approach, called feature...

  5. Feature Selection and Effective Classifiers.

    Science.gov (United States)

    Deogun, Jitender S.; Choubey, Suresh K.; Raghavan, Vijay V.; Sever, Hayri

    1998-01-01

    Develops and analyzes four algorithms for feature selection in the context of rough set methodology. Experimental results confirm the expected relationship between the time complexity of these algorithms and the classification accuracy of the resulting upper classifiers. When compared, results of upper classifiers perform better than lower…

  6. Correlated Non-Parametric Latent Feature Models

    CERN Document Server

    Doshi-Velez, Finale

    2012-01-01

    We are often interested in explaining data through a set of hidden factors or features. When the number of hidden features is unknown, the Indian Buffet Process (IBP) is a nonparametric latent feature model that does not bound the number of active features in dataset. However, the IBP assumes that all latent features are uncorrelated, making it inadequate for many realworld problems. We introduce a framework for correlated nonparametric feature models, generalising the IBP. We use this framework to generate several specific models and demonstrate applications on realworld datasets.

  7. Realizing the Hybrid Library.

    Science.gov (United States)

    Pinfield, Stephen; Eaton, Jonathan; Edwards, Catherine; Russell, Rosemary; Wissenburg, Astrid; Wynne, Peter

    1998-01-01

    Outlines five projects currently funded by the United Kingdom's Electronic Libraries Program (eLib): HyLiFe (Hybrid Library of the Future), MALIBU (MAnaging the hybrid Library for the Benefit of Users), HeadLine (Hybrid Electronic Access and Delivery in the Library Networked Environment), ATHENS (authentication scheme), and BUILDER (Birmingham…

  8. Homoploid hybrid expectations

    Science.gov (United States)

    Homoploid hybrid speciation occurs when a stable, fertile, and reproductively isolated lineage results from hybridization between two distinct species without a change in ploidy level. Reproductive isolation between a homoploid hybrid species and its parents is generally attained via chromosomal re...

  9. Hybrid armature projectile

    Science.gov (United States)

    Hawke, Ronald S.; Asay, James R.; Hall, Clint A.; Konrad, Carl H.; Sauve, Gerald L.; Shahinpoor, Mohsen; Susoeff, Allan R.

    1993-01-01

    A projectile for a railgun that uses a hybrid armature and provides a seed block around part of the outer surface of the projectile to seed the hybrid plasma brush. In addition, the hybrid armature is continuously vaporized to replenish plasma in a plasma armature to provide a tandem armature and provides a unique ridge and groove to reduce plasama blowby.

  10. Intraply Hybrid Composite Design

    Science.gov (United States)

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

    1986-01-01

    Several theoretical approaches combined in program. Intraply hybrid composites investigated theoretically and experimentally at Lewis Research Center. Theories developed during investigations and corroborated by attendant experiments used to develop computer program identified as INHYD (Intraply Hybrid Composite Design). INHYD includes several composites micromechanics theories, intraply hybrid composite theories, and integrated hygrothermomechanical theory. Equations from theories used by program as appropriate for user's specific applications.

  11. In vitro and in vivo study of pluripotency in intraspecific hybrid cells obtained by fusion of murine embryonic stem cells with splenocytes.

    Science.gov (United States)

    Matveeva, N M; Shilov, A G; Kaftanovskaya, E M; Maximovsky, L P; Zhelezova, A I; Golubitsa, A N; Bayborodin, S I; Fokina, M M; Serov, O L

    1998-06-01

    Hypoxanthine phosphoribosyltransferase-deficient (HPRT-) mouse embryonic stem (ES) cells, HM-1 cells (genotype XY), were fused with adult female DD/c mouse spleen cells. As a result, a set of HAT-resistant clones was isolated. Four hybrid clones most similar in morphology and growth characteristics to the HM-1 cells were studied in detail with respect to their pluripotency. Of these, three clones contained 41-43 chromosomes, and one clone was nearly tetraploid. All the clones had the XXY set of sex chromosomes and expressed the HPRT of the somatic partner only. The hybrid clones shared features with the HM-1 cells, indicating that they retained their pluripotent properties: (1) embryonic ECMA-7 antigen, not TROMA-1 antigen, was present in most cells; (2) the hybrid cells showed high activity of endogenous alkaline phosphatase (AP); (3) all the hybrid clones were able to form complex embryoid bodies containing derivatives of all the embryonic germinal layers; (4) the hybrid cells contained synchronously replicating X chromosomes, indicating that they were in an active state; and (5) a set of chimeric animals was generated by injecting hybrid cells into BALB/c and C57BL/6J mouse blastocysts. Evidence for chimerism was provided by the spotted coat derived from 129/Ola mice and identification of 129/Ola glucose phosphate isomerase (GPI) in many organs. Thus the results obtained demonstrated that the hybrid cells retain their high pluripotency level despite the close contact of the "pluripotent" HM-1 genome with the "somatic" spleen cell genome during hybrid cell formation and the presence of the "somatic" X chromosome during many cell generations. The presence of HPRT of the somatic partner in many organs and tissues, including the testes in chimeric animals, shows that the "somatic" X chromosome segregates weakly, if at all, during development of the chimeras. There were no individuals with the 129/Ola genotype among the more than 50 offspring from chimeric mice. The

  12. Hybrid cluster identification

    Science.gov (United States)

    Martín-Herrero, J.

    2004-10-01

    I present a hybrid method for the labelling of clusters in two-dimensional lattices, which combines the recursive approach with iterative scanning to reduce the stack size required by the pure recursive technique, while keeping its benefits: single pass and straightforward cluster characterization and percolation detection parallel to the labelling. While the capacity to hold the entire lattice in memory is usually regarded as the major constraint for the applicability of the recursive technique, the required stack size is the real limiting factor. Resorting to recursion only for the transverse direction greatly reduces the recursion depth and therefore the required stack. It also enhances the overall performance of the recursive technique, as is shown by results on a set of uniform random binary lattices and on a set of samples of the Ising model. I also show how this technique may replace the recursive technique in Wolff's cluster algorithm, decreasing the risk of stack overflow and increasing its speed, and the Hoshen-Kopelman algorithm in the Swendsen-Wang cluster algorithm, allowing effortless characterization during generation of the samples and increasing its speed.

  13. Hybrid manifold embedding.

    Science.gov (United States)

    Liu, Yang; Liu, Yan; Chan, Keith C C; Hua, Kien A

    2014-12-01

    In this brief, we present a novel supervised manifold learning framework dubbed hybrid manifold embedding (HyME). Unlike most of the existing supervised manifold learning algorithms that give linear explicit mapping functions, the HyME aims to provide a more general nonlinear explicit mapping function by performing a two-layer learning procedure. In the first layer, a new clustering strategy called geodesic clustering is proposed to divide the original data set into several subsets with minimum nonlinearity. In the second layer, a supervised dimensionality reduction scheme called locally conjugate discriminant projection is performed on each subset for maximizing the discriminant information and minimizing the dimension redundancy simultaneously in the reduced low-dimensional space. By integrating these two layers in a unified mapping function, a supervised manifold embedding framework is established to describe both global and local manifold structure as well as to preserve the discriminative ability in the learned subspace. Experiments on various data sets validate the effectiveness of the proposed method.

  14. Extraction of essential features by quantum density

    Science.gov (United States)

    Wilinski, Artur

    2016-09-01

    In this paper we consider the problem of feature extraction, as an essential and important search of dataset. This problem describe the real ownership of the signals and images. Searches features are often difficult to identify because of data complexity and their redundancy. Here is shown a method of finding an essential features groups, according to the defined issues. To find the hidden attributes we use a special algorithm DQAL with the quantum density for thej-th features from original data, that indicates the important set of attributes. Finally, they have been generated small sets of attributes for subsets with different properties of features. They can be used to the construction of a small set of essential features. All figures were made in Matlab6.

  15. A Generic Hybrid Encryption System (HES

    Directory of Open Access Journals (Sweden)

    Ijaz Ali Shoukat

    2013-03-01

    Full Text Available This study proposes a Generic Hybrid Encryption System (HES under mutual committee of symmetric and asymmetric cryptosystems. Asymmetric (public key Cryptosystems associates several performance issues like computational incompetence, memory wastages, energy consumptions and employment limitations on bulky data sets but they are quite secure and reliable in key exchange over insecure remote communication channels. Symmetric (private key cryptosystems are 100 times out performed, having no such issues but they cannot fulfill non-repudiation, false modifications in secret key, fake modifications in cipher text and origin authentication of both parties while exchanging information. These contradictory issues can be omitted by utilizing hybrid encryption mechanisms (symmetric+asymmetric to get optimal benefits of both schemes. Several hybrid mechanisms are available with different logics but our logic differs in infrastructural design, simplicity, computational efficiency and security as compared to prior hybrid encryption schemes. Some prior schemes are either diversified in performance aspects, customer satisfaction, memory utilization or energy consumptions and some are vulnerable against forgery and password guessing (session key recovery attacks. We have done some functional and design related changes in existing Public Key Infrastructure (PKI to achieve simplicity, optimal privacy and more customer satisfaction by providing Hybrid Encryption System (HES that is able to fulfill all set of standardized security constraints. No such PKI based generic hybrid encryption scheme persists as we have provided in order to manage all these kinds of discussed issues.

  16. The hydrogen hybrid option

    Energy Technology Data Exchange (ETDEWEB)

    Smith, J.R.

    1993-10-15

    The energy efficiency of various piston engine options for series hybrid automobiles are compared with conventional, battery powered electric, and proton exchange membrane (PEM) fuel cell hybrid automobiles. Gasoline, compressed natural gas (CNG), and hydrogen are considered for these hybrids. The engine and fuel comparisons are done on a basis of equal vehicle weight, drag, and rolling resistance. The relative emissions of these various fueled vehicle options are also presented. It is concluded that a highly optimized, hydrogen fueled, piston engine, series electric hybrid automobile will have efficiency comparable to a similar fuel cell hybrid automobile and will have fewer total emissions than the battery powered vehicle, even without a catalyst.

  17. Hybrid Model of Content Extraction

    DEFF Research Database (Denmark)

    Qureshi, Pir Abdul Rasool; Memon, Nasrullah

    2012-01-01

    We present a hybrid model for content extraction from HTML documents. The model operates on Document Object Model (DOM) tree of the corresponding HTML document. It evaluates each tree node and associated statistical features like link density and text distribution across the node to predict...... model outperformed other existing content extraction models. We present a browser based implementation of the proposed model as proof of concept and compare the implementation strategy with various state of art implementations. We also discuss various applications of the proposed model with special...

  18. Hybrid Feature Selection for Myoelectric Signal Classification Using MICA

    Science.gov (United States)

    Naik, Ganesh R.; Kumar, Dinesh K.

    2010-03-01

    This paper presents a novel method to enhance the performance of Independent Component Analysis (ICA) of myoelectric signal by decomposing the signal into components originating from different muscles. First, we use Multi run ICA (MICA) algorithm to separate the muscle activities. Pattern classification of the separated signal is performed in the second step with a back propagation neural network. The focus of this work is to establish a simple, yet robust system that can be used to identify subtle complex hand actions and gestures for control of prosthesis and other computer assisted devices. Testing was conducted using several single shot experiments conducted with five subjects. The results indicate that the system is able to classify four different wrist actions with near 100% accuracy.

  19. Automatic generation of Chinese character using features fusion from calligraphy and font

    Science.gov (United States)

    Shi, Cao; Xiao, Jianguo; Xu, Canhui; Jia, Wenhua

    2014-02-01

    A spatial statistic based contour feature representation is proposed to achieve extraction of local contour feature from Chinese calligraphy character, and a features fusion strategy is designed to automatically generate new hybrid character, making well use of contour feature of calligraphy and structural feature of font. The features fusion strategy employs dilation and erosion operations iteratively to inject the extracted contour feature from Chinese calligraphy into font, which are similar to "pad" and "cut" in a sculpture progress. Experimental results demonstrate that the generated new hybrid character hold both contour feature of calligraphy and structural feature of font. Especially, two kinds of Chinese calligraphy skills called "Fei Bai" and "Zhang Mo" are imitated in the hybrid character. "Fei Bai" depicts a phenomenon that part of a stroke fade out due to the fast movement of hair brush or the lack of ink, and "Zhang Mo" describes a condition that hair brush holds so much ink that strokes overlap.

  20. Hybridization and extinction.

    Science.gov (United States)

    Todesco, Marco; Pascual, Mariana A; Owens, Gregory L; Ostevik, Katherine L; Moyers, Brook T; Hübner, Sariel; Heredia, Sylvia M; Hahn, Min A; Caseys, Celine; Bock, Dan G; Rieseberg, Loren H

    2016-08-01

    Hybridization may drive rare taxa to extinction through genetic swamping, where the rare form is replaced by hybrids, or by demographic swamping, where population growth rates are reduced due to the wasteful production of maladaptive hybrids. Conversely, hybridization may rescue the viability of small, inbred populations. Understanding the factors that contribute to destructive versus constructive outcomes of hybridization is key to managing conservation concerns. Here, we survey the literature for studies of hybridization and extinction to identify the ecological, evolutionary, and genetic factors that critically affect extinction risk through hybridization. We find that while extinction risk is highly situation dependent, genetic swamping is much more frequent than demographic swamping. In addition, human involvement is associated with increased risk and high reproductive isolation with reduced risk. Although climate change is predicted to increase the risk of hybridization-induced extinction, we find little empirical support for this prediction. Similarly, theoretical and experimental studies imply that genetic rescue through hybridization may be equally or more probable than demographic swamping, but our literature survey failed to support this claim. We conclude that halting the introduction of hybridization-prone exotics and restoring mature and diverse habitats that are resistant to hybrid establishment should be management priorities.

  1. The interrelation between plus-hybrid effect on grain yield and genetic distance of studied hybrids

    Directory of Open Access Journals (Sweden)

    Božinović Sofija

    2010-01-01

    Full Text Available The combined effect of cytoplasmic male sterility and xenia is referred to as the Plus-Hybrid effect. A mixture of hybrids, in which one is a sterile female component and the other is a fertile pollinator, was sown. The objective of the present study was to determine whether the increase of a hybrid genetic distance would result in the increased gain from Plus-hybrid effects on grain yield. Two ZP hybrids (ZP 1 and ZP 2, i.e. their sterile and fertile counterparts, as well as, five hybrid pollinators (ZP 1, ZP 2, ZP 3, ZP 4 and ZP 5 were selected for the studies. The three-replicate trail was set up according to the randomized split-plot design at Zemun Polje in 2009. SSR markers were used to determine the genetic distance between hybrids. Ten out of total 12 applied primers gave results. Coefficients of similarity were estimated according to Dice and Jaccard. The greatest (0.37, i.e. smallest genetic distance (0.08, according to Dice, was obtained between hybrids ZP 1 and ZP 5, i.e. ZP 1 and ZP 4, respectively. Values of genetic distance according to Jaccard were between 0.14 (ZP 1 and ZP 4 and 0.54 (ZP 1 i ZP 5 . By using the cluster analysis, four hybrids (ZP 1, ZP 4, ZP 3 and ZP 2 were grouped into one sub-cluster that was loosely linked to ZP 5. The Plus-hybrid effect on grain yield of the hybrid ZP 1 was negative. The greatest gain was detected in the ZP 2st ' ZP 1 combination, between two hybrids that were genetically very similar and belonged to the same sub-cluster, and then in ZP 2st x ZP 3 and ZP 2st x ZP 4 combinations, between hybrids that also belonged to the same sub-cluster. It can be concluded that the Plus-hybrid effect, after all, depends not on the hybrid genetic distance but on the hybrid genotype.

  2. Spoof Plasmon Hybridization

    CERN Document Server

    Zhang, Jingjing; Luo, Yu; Shen, Xiaopeng; Maier, Stefan A; Cui, Tie Jun

    2016-01-01

    Plasmon hybridization between closely spaced nanoparticles yields new hybrid modes not found in individual constituents, allowing for the engineering of resonance properties and field enhancement capabilities of metallic nanostructure. Experimental verifications of plasmon hybridization have been thus far mostly limited to optical frequencies, as metals cannot support surface plasmons at longer wavelengths. Here, we introduce the concept of 'spoof plasmon hybridization' in highly conductive metal structures and investigate experimentally the interaction of localized surface plasmon resonances (LSPR) in adjacent metal disks corrugated with subwavelength spiral patterns. We show that the hybridization results in the splitting of spoof plasmon modes into bonding and antibonding resonances analogous to molecular orbital rule and plasmonic hybridization in optical spectrum. These hybrid modes can be manipulated to produce enormous field enhancements (larger than 5000) by tuning the separation between disks or alte...

  3. Clinical Role of Hybrid Imaging.

    Science.gov (United States)

    Hsiao, Edward M; Ali, Bilal; Dorbala, Sharmila

    2010-10-01

    Recent technological advances have fueled the growth in hybrid radionuclide and CT imaging of the heart. Noninvasive imaging studies are reliable means to diagnose coronary artery disease (CAD), stratify risk, and guide clinical management. Myocardial perfusion scintigraphy is a robust, widely available noninvasive modality for the evaluation of ischemia from known or suspected CAD. Cardiac CT (coronary artery calcium score and coronary CT angiography) has emerged as a clinically robust noninvasive anatomic imaging test, capable of rapidly diagnosing or excluding obstructive CAD. Both anatomic and functional modalities have strengths and weaknesses, and can complement each other by offering integrated structural and physiologic information. As we discuss below, in selected patients, hybrid imaging may facilitate more accurate diagnosis, risk stratification, and management in a "one-stop shop" setting.

  4. A Genetic Algorithm-Based Feature Selection

    Directory of Open Access Journals (Sweden)

    Babatunde Oluleye

    2014-07-01

    Full Text Available This article details the exploration and application of Genetic Algorithm (GA for feature selection. Particularly a binary GA was used for dimensionality reduction to enhance the performance of the concerned classifiers. In this work, hundred (100 features were extracted from set of images found in the Flavia dataset (a publicly available dataset. The extracted features are Zernike Moments (ZM, Fourier Descriptors (FD, Lengendre Moments (LM, Hu 7 Moments (Hu7M, Texture Properties (TP and Geometrical Properties (GP. The main contributions of this article are (1 detailed documentation of the GA Toolbox in MATLAB and (2 the development of a GA-based feature selector using a novel fitness function (kNN-based classification error which enabled the GA to obtain a combinatorial set of feature giving rise to optimal accuracy. The results obtained were compared with various feature selectors from WEKA software and obtained better results in many ways than WEKA feature selectors in terms of classification accuracy

  5. A hybrid system for emotion extraction from suicide notes.

    Science.gov (United States)

    Nikfarjam, Azadeh; Emadzadeh, Ehsan; Gonzalez, Graciela

    2012-01-01

    The reasons that drive someone to commit suicide are complex and their study has attracted the attention of scientists in different domains. Analyzing this phenomenon could significantly improve the preventive efforts. In this paper we present a method for sentiment analysis of suicide notes submitted to the i2b2/VA/Cincinnati Shared Task 2011. In this task the sentences of 900 suicide notes were labeled with the possible emotions that they reflect. In order to label the sentence with emotions, we propose a hybrid approach which utilizes both rule based and machine learning techniques. To solve the multi class problem a rule-based engine and an SVM model is used for each category. A set of syntactic and semantic features are selected for each sentence to build the rules and train the classifier. The rules are generated manually based on a set of lexical and emotional clues. We propose a new approach to extract the sentence's clauses and constitutive grammatical elements and to use them in syntactic and semantic feature generation. The method utilizes a novel method to measure the polarity of the sentence based on the extracted grammatical elements, reaching precision of 41.79 with recall of 55.03 for an f-measure of 47.50. The overall mean f-measure of all submissions was 48.75% with a standard deviation of 7%.

  6. 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

  7. Hybrid recreation by reverse breeding in Arabidopsis thaliana.

    Science.gov (United States)

    Wijnker, Erik; Deurhof, Laurens; van de Belt, Jose; de Snoo, C Bastiaan; Blankestijn, Hetty; Becker, Frank; Ravi, Maruthachalam; Chan, Simon W L; van Dun, Kees; Lelivelt, Cilia L C; de Jong, Hans; Dirks, Rob; Keurentjes, Joost J B

    2014-04-01

    Hybrid crop varieties are traditionally produced by selecting and crossing parental lines to evaluate hybrid performance. Reverse breeding allows doing the opposite: selecting uncharacterized heterozygotes and generating parental lines from them. With these, the selected heterozygotes can be recreated as F1 hybrids, greatly increasing the number of hybrids that can be screened in breeding programs. Key to reverse breeding is the suppression of meiotic crossovers in a hybrid plant to ensure the transmission of nonrecombinant chromosomes to haploid gametes. These gametes are subsequently regenerated as doubled-haploid (DH) offspring. Each DH carries combinations of its parental chromosomes, and complementing pairs can be crossed to reconstitute the initial hybrid. Achiasmatic meiosis and haploid generation result in uncommon phenotypes among offspring owing to chromosome number variation. We describe how these features can be dealt with during a reverse-breeding experiment, which can be completed in six generations (∼1 year).

  8. Application of Gibberellic Acid on Diploid and Tetraploid Cotton Hybridization

    Institute of Scientific and Technical Information of China (English)

    AJAFARI-MOFIDABADI; A; RANJBERAN; F; SOLTANLOO; H

    2008-01-01

    Gibberellic acid growth regulator was used to develop interspecific hybrids between tetraploid and diploid species to increase the genetic variability in cotton.In order to retain bolls and seed set in triploid hybrids,emasculated flowers of two Gossypium hirsutum commercial varieties(Sahel and Sephid)

  9. Efficient Generation and Selection of Combined Features for Improved Classification

    KAUST Repository

    Shono, Ahmad N.

    2014-05-01

    This study contributes a methodology and associated toolkit developed to allow users to experiment with the use of combined features in classification problems. Methods are provided for efficiently generating combined features from an original feature set, for efficiently selecting the most discriminating of these generated combined features, and for efficiently performing a preliminary comparison of the classification results when using the original features exclusively against the results when using the selected combined features. The potential benefit of considering combined features in classification problems is demonstrated by applying the developed methodology and toolkit to three sample data sets where the discovery of combined features containing new discriminating information led to improved classification results.

  10. ADVANCED HYBRID PARTICULATE COLLECTOR

    Energy Technology Data Exchange (ETDEWEB)

    Stanley J. Miller; Grant L. Schelkoph; Grant E. Dunham

    2000-12-01

    A new concept in particulate control, called an advanced hybrid particulate collector (AHPC), is being developed under funding from the US Department of Energy. The AHPC combines the best features of electrostatic precipitators (ESPs) and baghouses in an entirely novel manner. The AHPC concept combines fabric filtration and electrostatic precipitation in the same housing, providing major synergism between the two methods, both in the particulate collection step and in transfer of dust to the hopper. The AHPC provides ultrahigh collection efficiency, overcoming the problem of excessive fine-particle emissions with conventional ESPs, and solves the problem of reentrainment and recollection of dust in conventional baghouses. Phase I of the development effort consisted of design, construction, and testing of a 5.7-m{sup 3}/min (200-acfm) working AHPC model. Results from both 8-hour parametric tests and 100-hour proof-of-concept tests with two different coals demonstrated excellent operability and greater than 99.99% fine-particle collection efficiency.

  11. Vibration Isolation for Parallel Hydraulic Hybrid Vehicles

    Directory of Open Access Journals (Sweden)

    The M. Nguyen

    2008-01-01

    Full Text Available In recent decades, several types of hybrid vehicles have been developed in order to improve the fuel economy and to reduce the pollution. Hybrid electric vehicles (HEV have shown a significant improvement in fuel efficiency for small and medium-sized passenger vehicles and SUVs. HEV has several limitations when applied to heavy vehicles; one is that larger vehicles demand more power, which requires significantly larger battery capacities. As an alternative solution, hydraulic hybrid technology has been found effective for heavy duty vehicle because of its high power density. The mechanical batteries used in hydraulic hybrid vehicles (HHV can be charged and discharged remarkably faster than chemical batteries. This feature is essential for heavy vehicle hybridization. One of the main problems that should be solved for the successful commercialization of HHV is the excessive noise and vibration involving with the hydraulic systems. This study focuses on using magnetorheological (MR technology to reduce the noise and vibration transmissibility from the hydraulic system to the vehicle body. In order to study the noise and vibration of HHV, a hydraulic hybrid subsystem in parallel design is analyzed. This research shows that the MR elements play an important role in reducing the transmitted noise and vibration to the vehicle body. Additionally, locations and orientations of the isolation system also affect the efficiency of the noise and vibration mitigation. In simulations, a skyhook control algorithm is used to achieve the highest possible effectiveness of the MR isolation system.

  12. Formal features and parameter setting: a view from Portuguese past participles and romance future tenses Traços formais e fixação de parâmetro: uma perspectiva a partir dos particípios passados do português e do futuro românico

    Directory of Open Access Journals (Sweden)

    Lucia LOBATO

    2000-01-01

    Full Text Available Este artigo examina a forma morfofonológica dos particípios passados do português, incluindo a mudança na colocação do acento ocorrida na evolução do latim para o português, e argumenta a favor de um conceito de traço formal mais abstrato do que o de traço morfossintático. A fixação paramétrica é tratada como uma questão da localização onde a configuração de traços formais relevante para a interpretação semântica gramatical é visível para o sistema PF. Os estágios no desenvolvimento do futuro românico são analisados como decorrentes de uma mudança na visibilidade dos núcleos funcionais sentenciais.This paper examines the morphophonological shape of Portuguese past participles, including the stress placement change that took place from Latin to Portuguese in these forms, and argues for a concept of formal feature more abstract than the concept of morphosyntactic feature. Parameter setting is treated as relating to the location in which the configuration of formal features relevant to grammatical semantic interpretation is visible to the PF system. The stages in the development of the Romance future tenses are claimed to follow from a shift in the visibility of the sentential functional heads.

  13. The Role of the Basis Set: Assessing Density Functional Theory

    CERN Document Server

    Boese, A D; Handy, N C; Martin, Jan M. L.; Handy, Nicholas C.

    2003-01-01

    When developing and assessing density functional theory methods, a finite basis set is usually employed. In most cases, however, the issue of basis set dependency is neglected. Here, we assess several basis sets and functionals. In addition, the dependency of the semiempirical fits to a given basis set for a generalised gradient approximation and a hybrid functional is investigated. The resulting functionals are then tested for other basis sets, evaluating their errors and transferability.

  14. 紫外线B辐射增强对杂交稻籼型恢复系结实率和千粒重的影响%Effect of Enhanced UV-B Radiation on Seed Setting Rate and 1 000-grain Weight of Indica Hybrid Rice Restorer Lines

    Institute of Scientific and Technical Information of China (English)

    况浩池; 曾祥瑞; 罗俊涛; 曾正明; 杨扬; 陈光珍; 何兴材; 付均

    2013-01-01

    [Objective] This study was to investigate the effect of enhanced UV-B radiation on seed setting rate and 1 000-grain weight of hybrid rice combinations.[Method] The seed setting rate and 1 000-grain weight of 10 new sterile indica restorer lines planted in pots under enhanced UV-B radiation and fluorescent lamps (control) were respectively measured,and the differences were compared.[Result]The enhanced UV-B radiation significantly reduced the seed setting rate of indica restorer lines,and the differences between that UV-B radiation treatment and control all reached extremely significant level.In addition,the enhanced UV-B radiation reduced the 1 000-grain weight of most indica restorer lines,and compared with that of control the difference achieved significant or very significant level.However,the effect of enhanced UV-B radiation on seed setting rate and 1 000-grain weight differed to different indica restorer lines,and the differences among restorer lines tested were significant or very significant,which indicated the possibility to screen antiUV-B radiation rice materials and combinations.Finally,the indica restorer lines 09R-14,Luhui 37 and 10R-7703 which were strongly resistant to UV-B radiation were screened out.[Conclusion] This study laid foundation for breeding hybrid rice varieties resistance to UV-B radiation.%[目的]研究UV-B辐射增强对杂交稻组合及亲本的影响.[方法]通过盆栽和增强UV-B辐射试验,研究了UV-B辐射增强对10个新育籼型恢复系结实率和千粒重的影响.[结果]UV-B辐射增强导致籼型恢复系结实率明显下降,与对照相比差异全部达到极显著水平;UV-B辐射增强导致绝大多数籼型恢复系千粒重下降,与对照相比差异达到显著或极显著水平.但UV-B辐射增强对不同籼型恢复系结实率和千粒重的影响程度差异较大,参试恢复系之间达到显著或极显著差异水平,这一结果预示着筛选抗UV-B辐射的杂交水稻育种材料和

  15. 基于微阵列芯片的比较基因组杂交技术在临床实验室产前诊断中的应用%Prenatal diagnosis by array-based comparative genomic hybridization in the clinical laboratory setting

    Institute of Scientific and Technical Information of China (English)

    Amy M. BREMAN; 毕为民; 张秀慧

    2009-01-01

    Array-based comparative genomic hybridization (array CGH), a method used to detect gains or losses of genetic material, has recently been applied to prenatal diagnosis of genomic imbalance in the clinical laboratory setting. This new and exciting diagnostic tool represents a major technological step forward in cytogenetic testing and addresses many of the limitations of current cytogenetic methods.Conventional chromosome analysis, the current gold standard in prenatal diagnosis, focuses primarily on the detection of common aneuploidies and is limited by its capacity to detect only those copy number changes that are large enough to be microscopically visible (typically 5-6 Mb in size at the 500 band level). In contrast, array CGH analysis simultaneously evaluates regions across the entire genome and al-lows for detection of unbalanced structural and numerical chromosome abnormalities of less than one hun-dred kb. Array CGH analysis also overcomes some of the limitations of chromosome analysis, such as the requirement for cell culture and longer reporting time, by using direct uncultured fetal specimens. With many diagnostic laboratories now embracing this technology, the past year has seen tremendous growth in the use of array CGH analysis for prenatal diagnosis. This review aims to summarize array CGH methodology and its current applications in prenatal diagnosis.

  16. A new MCNP{trademark} test set

    Energy Technology Data Exchange (ETDEWEB)

    Brockhoff, R.C.; Hendricks, J.S.

    1994-09-01

    The MCNP test set is used to test the MCNP code after installation on various computer platforms. For MCNP4 and MCNP4A this test set included 25 test problems designed to test as many features of the MCNP code as possible. A new and better test set has been devised to increase coverage of the code from 85% to 97% with 28 problems. The new test set is as fast as and shorter than the MCNP4A test set. The authors describe the methodology for devising the new test set, the features that were not covered in the MCNP4A test set, and the changes in the MCNP4A test set that have been made for MCNP4B and its developmental versions. Finally, new bugs uncovered by the new test set and a compilation of all known MCNP4A bugs are presented.

  17. Hybrid Algorithms for Solving Variational Inequalities, Variational Inclusions, Mixed Equilibria, and Fixed Point Problems

    Directory of Open Access Journals (Sweden)

    Lu-Chuan Ceng

    2014-01-01

    Full Text Available We present a hybrid iterative algorithm for finding a common element of the set of solutions of a finite family of generalized mixed equilibrium problems, the set of solutions of a finite family of variational inequalities for inverse strong monotone mappings, the set of fixed points of an infinite family of nonexpansive mappings, and the set of solutions of a variational inclusion in a real Hilbert space. Furthermore, we prove that the proposed hybrid iterative algorithm has strong convergence under some mild conditions imposed on algorithm parameters. Here, our hybrid algorithm is based on Korpelevič’s extragradient method, hybrid steepest-descent method, and viscosity approximation method.

  18. Hybrid Compensatory-Noncompensatory Choice Sets in Semicompensatory Models

    DEFF Research Database (Denmark)

    Kaplan, Sigal; Bekhor, Shlomo; Shiftan, Yoram

    2013-01-01

    by a mathematical model that combines multinomial-response and ordered-response thresholds with a utility-based choice. The proposed model is applied to a stated preference experiment of off-campus rental apartment choices by students. Results demonstrate the applicability and feasibility of incorporating...

  19. Rice fertility affected by lower temperature in intersubspecific hybrid

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    @@Intersubspecific hybrids of indica-japonica show strong heterosis on yield,and the partial sterility in F1 hybrids can be mitigated by using the wide-compatibility gene, S5n. In the past decade, such hybrids showed normal fertility and high level of heterosis on grain yield, but some of them showed unstable seed-setting rate at low temperature. The present study was conducted to examine the effect of low temperature on fertility of intersubspecific hybrids and to investigate the male gamete abortion at some markers.

  20. Zebrafish Expression Ontology of Gene Sets (ZEOGS): a tool to analyze enrichment of zebrafish anatomical terms in large gene sets.

    Science.gov (United States)

    Prykhozhij, Sergey V; Marsico, Annalisa; Meijsing, Sebastiaan H

    2013-09-01

    The zebrafish (Danio rerio) is an established model organism for developmental and biomedical research. It is frequently used for high-throughput functional genomics experiments, such as genome-wide gene expression measurements, to systematically analyze molecular mechanisms. However, the use of whole embryos or larvae in such experiments leads to a loss of the spatial information. To address this problem, we have developed a tool called Zebrafish Expression Ontology of Gene Sets (ZEOGS) to assess the enrichment of anatomical terms in large gene sets. ZEOGS uses gene expression pattern data from several sources: first, in situ hybridization experiments from the Zebrafish Model Organism Database (ZFIN); second, it uses the Zebrafish Anatomical Ontology, a controlled vocabulary that describes connected anatomical structures; and third, the available connections between expression patterns and anatomical terms contained in ZFIN. Upon input of a gene set, ZEOGS determines which anatomical structures are overrepresented in the input gene set. ZEOGS allows one for the first time to look at groups of genes and to describe them in terms of shared anatomical structures. To establish ZEOGS, we first tested it on random gene selections and on two public microarray datasets with known tissue-specific gene expression changes. These tests showed that ZEOGS could reliably identify the tissues affected, whereas only very few enriched terms to none were found in the random gene sets. Next we applied ZEOGS to microarray datasets of 24 and 72 h postfertilization zebrafish embryos treated with beclomethasone, a potent glucocorticoid. This analysis resulted in the identification of several anatomical terms related to glucocorticoid-responsive tissues, some of which were stage-specific. Our studies highlight the ability of ZEOGS to extract spatial information from datasets derived from whole embryos, indicating that ZEOGS could be a useful tool to automatically analyze gene expression

  1. Henkin and Hybrid Logic

    DEFF Research Database (Denmark)

    Blackburn, Patrick Rowan; Huertas, Antonia; Manzano, Maria;

    2014-01-01

    Leon Henkin was not a modal logician, but there is a branch of modal logic that has been deeply influenced by his work. That branch is hybrid logic, a family of logics that extend orthodox modal logic with special proposition symbols (called nominals) that name worlds. This paper explains why...... Henkin’s techniques are so important in hybrid logic. We do so by proving a completeness result for a hybrid type theory called HTT, probably the strongest hybrid logic that has yet been explored. Our completeness result builds on earlier work with a system called BHTT, or basic hybrid type theory...... is due to the first-order perspective, which lies at the heart of Henin’s best known work and hybrid logic....

  2. Robust emotion recognition using spectral and prosodic features

    CERN Document Server

    Rao, K Sreenivasa

    2013-01-01

    In this brief, the authors discuss recently explored spectral (sub-segmental and pitch synchronous) and prosodic (global and local features at word and syllable levels in different parts of the utterance) features for discerning emotions in a robust manner. The authors also delve into the complementary evidences obtained from excitation source, vocal tract system and prosodic features for the purpose of enhancing emotion recognition performance. Features based on speaking rate characteristics are explored with the help of multi-stage and hybrid models for further improving emotion recognition performance. Proposed spectral and prosodic features are evaluated on real life emotional speech corpus.

  3. An ensemble approach for feature selection of Cyber Attack Dataset

    CERN Document Server

    Singh, Shailendra

    2009-01-01

    Feature selection is an indispensable preprocessing step when mining huge datasets that can significantly improve the overall system performance. Therefore in this paper we focus on a hybrid approach of feature selection. This method falls into two phases. The filter phase select the features with highest information gain and guides the initialization of search process for wrapper phase whose output the final feature subset. The final feature subsets are passed through the Knearest neighbor classifier for classification of attacks. The effectiveness of this algorithm is demonstrated on DARPA KDDCUP99 cyber attack dataset.

  4. Cultivating Curiosity: Integrating Hybrid Teaching in Courses in Human Behavior in the Social Environment

    Science.gov (United States)

    Rodriguez-Keyes, Elizabeth; Schneider, Dana A.

    2013-01-01

    This study illustrates an experience of implementing a hybrid model for teaching human behavior in the social environment in an urban university setting. Developing a hybrid model in a BSW program arose out of a desire to reach students in a different way. Designed to promote curiosity and active learning, this particular hybrid model has students…

  5. Designing using manufacturing features

    Science.gov (United States)

    Szecsi, T.; Hoque, A. S. M.

    2012-04-01

    This paper presents a design system that enables the composition of a part using manufacturing features. Features are selected from feature libraries. Upon insertion, the system ensures that the feature does not contradict the design-for-manufacture rules. This helps eliminating costly manufacturing problems. The system is developed as an extension to a commercial CAD/CAM system Pro/Engineer.

  6. Features of the Bible

    Institute of Scientific and Technical Information of China (English)

    刘隽

    2008-01-01

    Every literature has its features in some aspects,so is the Bible,one of the greatest literary works in the world that has great impact on western literature.This paper summarizes two features of the Bible,namely,cultural feature and literary feature.

  7. Generalisation of Submarine Features on Nautical Charts

    Science.gov (United States)

    Guilbert, E.; Zhang, X.

    2012-07-01

    On most large scale and middle scale maps, relief is represented by contours and spot heights. In order to adapt the representation to the scale, the terrain is generalised either by smoothing or filtering the terrain model or by simplifying the contours. However this approach is not applicable to nautical chart construction where terrain features are selected according to their importance for navigation. This paper presents an approach for the consideration of feature attributes in the generalisation of a set of contours with respect to nautical chart constraints. Features are defined by sets of contours and a set of generalisation operators applied to features is presented. The definitions are introduced in a multi-agent system in order to perform automatic generalisation of a contour set. Results are discussed on a case study and directions for future work are presented.

  8. Naive Bayes-Guided Bat Algorithm for Feature Selection

    Directory of Open Access Journals (Sweden)

    Ahmed Majid Taha

    2013-01-01

    Full Text Available When the amount of data and information is said to double in every 20 months or so, feature selection has become highly important and beneficial. Further improvements in feature selection will positively affect a wide array of applications in fields such as pattern recognition, machine learning, or signal processing. Bio-inspired method called Bat Algorithm hybridized with a Naive Bayes classifier has been presented in this work. The performance of the proposed feature selection algorithm was investigated using twelve benchmark datasets from different domains and was compared to three other well-known feature selection algorithms. Discussion focused on four perspectives: number of features, classification accuracy, stability, and feature generalization. The results showed that BANB significantly outperformed other algorithms in selecting lower number of features, hence removing irrelevant, redundant, or noisy features while maintaining the classification accuracy. BANB is also proven to be more stable than other methods and is capable of producing more general feature subsets.

  9. BSA Hybrid Synthesized Polymer

    Institute of Scientific and Technical Information of China (English)

    Zong Bin LIU; Xiao Pei DENG; Chang Sheng ZHAO

    2006-01-01

    Bovine serum albumin (BSA), a naturally occurring biopolymer, was regarded as a polymeric material to graft to an acrylic acid (AA)-N-vinyl pyrrolidone (NVP) copolymer to form a biomacromolecular hybrid polymer. The hybrid polymer can be blended with polyethersulfone (PES) to increase the hydrophilicity of the PES membrane, which suggested that the hybrid polymer might have a wide application in the modification of biomaterials.

  10. Hybrid Action Systems

    DEFF Research Database (Denmark)

    Ronkko, Mauno; Ravn, Anders P.

    1997-01-01

    a differential action, which allows differential equations as primitive actions. The extension allows us to model hybrid systems with both continuous and discrete behaviour. The main result of this paper is an extension of such a hybrid action system with parallel composition. The extension does not change...... the original meaning of the parallel composition, and therefore also the ordinary action systems can be composed in parallel with the hybrid action systems....

  11. HYBRID VEHICLE CONTROL SYSTEM

    Directory of Open Access Journals (Sweden)

    V. Dvadnenko

    2016-06-01

    Full Text Available The hybrid vehicle control system includes a start–stop system for an internal combustion engine. The system works in a hybrid mode and normal vehicle operation. To simplify the start–stop system, there were user new possibilities of a hybrid car, which appeared after the conversion. Results of the circuit design of the proposed system of basic blocks are analyzed.

  12. Nanoscale Organic Hybrid Electrolytes

    KAUST Repository

    Nugent, Jennifer L.

    2010-08-20

    Nanoscale organic hybrid electrolytes are composed of organic-inorganic hybrid nanostructures, each with a metal oxide or metallic nanoparticle core densely grafted with an ion-conducting polyethylene glycol corona - doped with lithium salt. These materials form novel solvent-free hybrid electrolytes that are particle-rich, soft glasses at room temperature; yet manifest high ionic conductivity and good electrochemical stability above 5V. © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  13. Hybrid radiator cooling system

    Science.gov (United States)

    France, David M.; Smith, David S.; Yu, Wenhua; Routbort, Jules L.

    2016-03-15

    A method and hybrid radiator-cooling apparatus for implementing enhanced radiator-cooling are provided. The hybrid radiator-cooling apparatus includes an air-side finned surface for air cooling; an elongated vertically extending surface extending outwardly from the air-side finned surface on a downstream air-side of the hybrid radiator; and a water supply for selectively providing evaporative cooling with water flow by gravity on the elongated vertically extending surface.

  14. Continuity in Discrete Sets

    CERN Document Server

    Burgin, Mark

    2010-01-01

    Continuous models used in physics and other areas of mathematics applications become discrete when they are computerized, e.g., utilized for computations. Besides, computers are controlling processes in discrete spaces, such as films and television programs. At the same time, continuous models that are in the background of discrete representations use mathematical technology developed for continuous media. The most important example of such a technology is calculus, which is so useful in physics and other sciences. The main goal of this paper is to synthesize continuous features and powerful technology of the classical calculus with the discrete approach of numerical mathematics and computational physics. To do this, we further develop the theory of fuzzy continuous functions and apply this theory to functions defined on discrete sets. The main interest is the classical Intermediate Value theorem. Although the result of this theorem is completely based on continuity, utilization of a relaxed version of contin...

  15. Hybrid Unifying Variable Supernetwork Model

    Institute of Scientific and Technical Information of China (English)

    LIU; Qiang; FANG; Jin-qing; LI; Yong

    2015-01-01

    In order to compare new phenomenon of topology change,evolution,hybrid ratio and network characteristics of unified hybrid network theoretical model with unified hybrid supernetwork model,this paper constructed unified hybrid variable supernetwork model(HUVSM).The first layer introduces a hybrid ratio dr,the

  16. Large Unifying Hybrid Supernetwork Model

    Institute of Scientific and Technical Information of China (English)

    LIU; Qiang; FANG; Jin-qing; LI; Yong

    2015-01-01

    For depicting multi-hybrid process,large unifying hybrid network model(so called LUHNM)has two sub-hybrid ratios except dr.They are deterministic hybrid ratio(so called fd)and random hybrid ratio(so called gr),respectively.

  17. Hybrid dynamics in a species group of swallowtail butterflies.

    Science.gov (United States)

    Dupuis, J R; Sperling, F A H

    2016-10-01

    Hybrid zones provide unique natural laboratories for studying mechanisms of evolution. But identification and classification of hybrid individuals (F1, F2, backcross, etc.) can be complicated by real population changes over time as well as by use of different marker types, both of which challenge documentation of hybrid dynamics. Here, we use multiple genetic markers (mitochondrial DNA, microsatellites and genomewide single nucleotide polymorphisms) to re-examine population structure in a hybrid zone between two species of swallowtail butterflies in western Canada, Papilio machaon and P. zelicaon. Our aim was to test whether their hybrid dynamics remain the same as found 30 years ago using morphology and allozymes, and we compared different genetic data sets as well as alternative hybrid identification and classification methods. Overall, we found high differentiation between the two parental species, corroborating previous research from the 1980s. We identified fewer hybrid individuals in the main zone of hybridization in recent years, but this finding depended on the genetic markers considered. Comparison of methods with simulated data sets generated from our data showed that single nucleotide polymorphisms were more powerful than microsatellites for both hybrid identification and classification. Moreover, substantial variation among comparisons underlined the value of multiple markers and methods for documenting evolutionarily dynamic systems.

  18. ON THE STABILIZATION OF THE LINEAR HYBRID SYSTEM STRUCTURE

    Directory of Open Access Journals (Sweden)

    Kirillov

    2014-11-01

    Full Text Available The linear control hybrid system, consisting of a fi- nite set of subsystems (modes having different dimensions, is considered. The moments of reset time are determined by some complementary function – evolutionary time. This function satisfies the special complementary ordinary differential equation. The mode stabilization problem is solved for some class of piecewise linear controls. The method of stabilization relies on the set of invariant planes, the existence of which is due to the special form of the hybrid system.

  19. Hybrid Rocket Technology

    National Research Council Canada - National Science Library

    Sankaran Venugopal; K K Rajesh; V Ramanujachari

    2011-01-01

    With their unique operational characteristics, hybrid rockets can potentially provide safer, lower-cost avenues for spacecraft and missiles than the current solid propellant and liquid propellant systems...

  20. Hybrid FOSS Project

    Data.gov (United States)

    National Aeronautics and Space Administration — Armstrong researchers are continuing their efforts to further develop FOSS technologies. A hybrid FOSS technique (HyFOSS) employs conventional continuous grating...

  1. Research into a Feature Selection Method for Hyperspectral Imagery Using PSO and SVM

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Classification and recognition of hyperspectral remote sensing images is not the same as that of conventional multi-spectral remote sensing images.We propose, a novel feature selection and classification method for hyperspectral images by combining the global optimization ability of particle swarm optimization (PSO) algorithm and the superior classification performance of a support vector machine (SVM).Global optimal search performance of PSO is improved by using a chaotic optimization search technique.Granularity based grid search strategy is used to optimize the SVM model parameters.Parameter optimization and classification of the SVM are addressed using the training date corresponding to the feature subset.A false classification rate is adopted as a fitness function.Tests of feature selection and classification are carried out on a hyperspectral data set.Classification performances are also compared among different feature extraction methods commonly used today.Results indicate that this hybrid method has a higher classification accuracy and can effectively extract optimal bands.A feasible approach is provided for feature selection and classification of hyperspectral image data.

  2. Enterprise Projects Set Risk Element Transmission Chaotic Genetic Model

    Directory of Open Access Journals (Sweden)

    Cunbin Li

    2012-08-01

    Full Text Available In order to research projects set risk transfer process and improve risk management efficiency in projects management, combining chaos theory and genetic algorithm, put forward enterprise projects set risk element transmission chaos genetic model. Using logistic chaos mapping and chebyshev chaos mapping mixture, constructed a hybrid chaotic mapping system. The steps of adopting hybrid chaos mapping for genetic operation include projects set initialization, calculation of fitness, selection, crossover and mutation operators, fitness adjustment and condition judgment. The results showed that the model can simulate enterprise projects set risk transmission process very well and it also provides the basis for the enterprise managers to make decisions.

  3. Roles and Responsibilities in Feature Teams

    Science.gov (United States)

    Eckstein, Jutta

    Agile development requires self-organizing teams. The set-up of a (feature) team has to enable self-organization. Special care has to be taken if the project is not only distributed, but also large and more than one feature team is involved. Every feature team needs in such a setting a product owner who ensures the continuous focus on business delivery. The product owners collaborate by working together in a virtual team. Each feature team is supported by a coach who ensures not only the agile process of the individual feature team but also across all feature teams. An architect (or if necessary a team of architects) takes care that the system is technically sound. Contrariwise to small co-located projects, large global projects require a project manager who deals with—among other things—internal and especially external politics.

  4. Reduced-size kernel models for nonlinear hybrid system identification.

    Science.gov (United States)

    Le, Van Luong; Bloch, Grard; Lauer, Fabien

    2011-12-01

    This brief paper focuses on the identification of nonlinear hybrid dynamical systems, i.e., systems switching between multiple nonlinear dynamical behaviors. Thus the aim is to learn an ensemble of submodels from a single set of input-output data in a regression setting with no prior knowledge on the grouping of the data points into similar behaviors. To be able to approximate arbitrary nonlinearities, kernel submodels are considered. However, in order to maintain efficiency when applying the method to large data sets, a preprocessing step is required in order to fix the submodel sizes and limit the number of optimization variables. This brief paper proposes four approaches, respectively inspired by the fixed-size least-squares support vector machines, the feature vector selection method, the kernel principal component regression and a modification of the latter, in order to deal with this issue and build sparse kernel submodels. These are compared in numerical experiments, which show that the proposed approach achieves the simultaneous classification of data points and approximation of the nonlinear behaviors in an efficient and accurate manner.

  5. Fatigue reliability based on residual strength model with hybrid uncertain parameters

    Institute of Scientific and Technical Information of China (English)

    Jun Wang; Zhi-Ping Qiu

    2012-01-01

    The aim of this paper is to evaluate the fatigue reliability with hybrid uncertain parameters based on a residual strength model.By solving the non-probabilistic setbased reliability problem and analyzing the reliability with randomness,the fatigue reliability with hybrid parameters can be obtained.The presented hybrid model can adequately consider all uncertainties affecting the fatigue reliability with hybrid uncertain parameters.A comparison among the presented hybrid model,non-probabilistic set-theoretic model and the conventional random model is made through two typical numerical examples.The results show that the presented hybrid model,which can ensure structural security,is effective and practical.

  6. Hybridization and endangered species protection in the molecular era.

    Science.gov (United States)

    Wayne, Robert K; Shaffer, H Bradley

    2016-06-01

    After decades of discussion, there is little consensus on the extent to which hybrids between endangered and nonendangered species should be protected by US law. As increasingly larger, genome-scale data sets are developed, we can identify individuals and populations with even trace levels of genetic admixture, making the 'hybrid problem' all the more difficult. We developed a decision-tree framework for evaluating hybrid protection, including both the processes that produced hybrids (human-mediated or natural) and the ecological impact of hybrids on natural ecosystems. We then evaluated our decision tree for four case studies drawn from our own work and briefly discuss several other cases from the literature. Throughout, we highlight the management outcomes that our approach provides and the nuances of hybridization as a conservation problem.

  7. Autonomous corrosion detection in gas pipelines: a hybrid-fuzzy classifier approach using ultrasonic nondestructive evaluation protocols.

    Science.gov (United States)

    Qidwai, Uvais A

    2009-12-01

    In this paper, a customized classifier is presented for the industry-practiced nondestructive evaluation (NDE) protocols using a hybrid-fuzzy inference system (FIS) to classify the corrosion and distinguish it from the geometric defects or normal/healthy state of the steel pipes used in the gas/petroleum industry. The presented system is hybrid in the sense that it utilizes both soft computing through fuzzy set theory, as well as conventional parametric modeling through H(infinity) optimization methods. Due to significant uncertainty in the power spectral density of the noise in ultrasonic NDE procedures, the use of optimal H(2) estimators for defect characterization is not so accurate. A more appropriate criterion is the H(infinity) norm of the estimation error spectrum which is based on minimization of the magnitude of this spectrum and hence produces more robust estimates. A hybrid feature set is developed in this work that corresponds to a) geometric features extracted directly from the raw ultrasonic A-scan data (which are the ultrasonic echo pulses in 1-Dtraveling inside the metal perpendicular to its 2 surfaces) and b) mapped features from the impulse response of the estimated model of the defect waveform under study. An experimental strategy is first outlined, through which the necessary data are collected as A-scans. Then, using the H(infinity) estimation approach, a parametric transfer function is obtained for each pulse. In this respect, each A-scan is treated as output from a defining function when a pure/healthy metal's A-scan is used as its input. Three defining states are considered in the paper; healthy, corroded, and defective, where the defective class represents metal with artificial or other defects. The necessary features are then calculated and are then supplied to the fuzzy inference system as input to be used in the classification. The resulting system has shown excellent corrosion classification with very low misclassification and false

  8. Yeast Two-Hybrid: State of the Art

    Directory of Open Access Journals (Sweden)

    Van Criekinge Wim

    1999-01-01

    Full Text Available Genome projects are approaching completion and are saturating sequence databases. This paper discusses the role of the two-hybrid system as a generator of hypotheses. Apart from this rather exhaustive, financially and labour intensive procedure, more refined functional studies can be undertaken. Indeed, by making hybrids of two-hybrid systems, customised approaches can be developed in order to attack specific function-related problems. For example, one could set-up a "differential" screen by combining a forward and a reverse approach in a three-hybrid set-up. Another very interesting project is the use of peptide libraries in two-hybrid approaches. This could enable the identification of peptides with very high specificity comparable to "real" antibodies. With the technology available, the only limitation is imagination.

  9. From hybrid swarms to swarms of hybrids

    Science.gov (United States)

    Stohlgren, Thomas J.; Szalanski, Allen L; Gaskin, John F.; Young, Nicholas E.; West, Amanda; Jarnevich, Catherine S.; Tripodi, Amber

    2015-01-01

    Science has shown that the introgression or hybridization of modern humans (Homo sapiens) with Neanderthals up to 40,000 YBP may have led to the swarm of modern humans on earth. However, there is little doubt that modern trade and transportation in support of the humans has continued to introduce additional species, genotypes, and hybrids to every country on the globe. We assessed the utility of species distributions modeling of genotypes to assess the risk of current and future invaders. We evaluated 93 locations of the genus Tamarix for which genetic data were available. Maxent models of habitat suitability showed that the hybrid, T. ramosissima x T. chinensis, was slightly greater than the parent taxa (AUCs > 0.83). General linear models of Africanized honey bees, a hybrid cross of Tanzanian Apis mellifera scutellata and a variety of European honey bee including A. m. ligustica, showed that the Africanized bees (AUC = 0.81) may be displacing European honey bees (AUC > 0.76) over large areas of the southwestern U.S. More important, Maxent modeling of sub-populations (A1 and A26 mitotypes based on mDNA) could be accurately modeled (AUC > 0.9), and they responded differently to environmental drivers. This suggests that rapid evolutionary change may be underway in the Africanized bees, allowing the bees to spread into new areas and extending their total range. Protecting native species and ecosystems may benefit from risk maps of harmful invasive species, hybrids, and genotypes.

  10. Graph-based features for texture discrimination

    NARCIS (Netherlands)

    Grigorescu, Cosmin; Petkov, Nikolay; Sanfeliu, A; Villanueva, JJ; Vanrell, M; Alquezar, R; Huang, T; Serra, J

    2000-01-01

    Graph-based features, such as the number of connected components, edges of a given orientation and vertices per unit area, and the number of vertices and pixels per connected component, are proposed for the analysis of textures which consist of structural elements. The proposed set of features is

  11. Character feature integration of Chinese calligraphy and font

    Science.gov (United States)

    Shi, Cao; Xiao, Jianguo; Jia, Wenhua; Xu, Canhui

    2013-01-01

    A framework is proposed in this paper to effectively generate a new hybrid character type by means of integrating local contour feature of Chinese calligraphy with structural feature of font in computer system. To explore traditional art manifestation of calligraphy, multi-directional spatial filter is applied for local contour feature extraction. Then the contour of character image is divided into sub-images. The sub-images in the identical position from various characters are estimated by Gaussian distribution. According to its probability distribution, the dilation operator and erosion operator are designed to adjust the boundary of font image. And then new Chinese character images are generated which possess both contour feature of artistical calligraphy and elaborate structural feature of font. Experimental results demonstrate the new characters are visually acceptable, and the proposed framework is an effective and efficient strategy to automatically generate the new hybrid character of calligraphy and font.

  12. Vibron and phonon hybridization in dielectric nanostructures.

    Science.gov (United States)

    Preston, Thomas C; Signorell, Ruth

    2011-04-05

    Plasmon hybridization theory has been an invaluable tool in advancing our understanding of the optical properties of metallic nanostructures. Through the prism of molecular orbital theory, it allows one to interpret complex structures as "plasmonic molecules" and easily predict and engineer their electromagnetic response. However, this formalism is limited to conducting particles. Here, we present a hybridization scheme for the external and internal vibrations of dielectric nanostructures that provides a straightforward understanding of the infrared signatures of these particles through analogy to existing hybridization models of both molecular orbitals and plasmons extending the range of applications far beyond metallic nanostructures. This method not only provides a qualitative understanding, but also allows for the quantitative prediction of vibrational spectra of complex nanoobjects from well-known spectra of their primitive building blocks. The examples of nanoshells illustrate how spectral features can be understood in terms of symmetry, number of nodal planes, and scale parameters.

  13. 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.

  14. Breeding lines with neutral genes to improve fertility of intersubspecific hybrid rice

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    Since the sterility neutral allele S5n has been incorporated into indica or japonica varieties, many intersubspecific hybrids have been released commercially. These hybrids showed high heterosis, but some of them exhibited unstable seed setting rate under low temperature.When the hybrids flowered at low temperature, the fertility of female gamete was normal but the pollen showed sterile. To improve the stability of fertility, the effect of pollen sterility neutral gene was studied for intersubspecific hybrids.

  15. A microarray gene expression data classification using hybrid back propagation neural network

    Directory of Open Access Journals (Sweden)

    Vimaladevi M.

    2014-01-01

    Full Text Available Classification of cancer establishes appropriate treatment and helps to decide the diagnosis. Cancer expands progressively from an alteration in a cell's genetic structure. This change (mutation results in cells with uncontrolled growth patterns. In cancer classification, the approach, Back propagation is sufficient and also it is a universal technique of training artificial neural networks. It is also called supervised learning method. It needs many dataset for input and output for making up the training set. The back propagation method may execute the function of collaborate multiple parties. In existing method, collaborative learning is limited and it considers only two parties. The proposed collaborative function can perform well and problems can be solved by utilizing the power of cloud computing. This technical note applies hybrid models of Back Propagation Neural networks (BPN and fast Genetic Algorithms (GA to estimate the feature selection in gene expression data. The proposed research work examines many feature selection algorithms which are “fragile”; that is, the superiority of their results varies broadly over data sets. By this research, it is suggested that this is due to higherorder interactions between features causing restricted minima in search space in which the algorithm becomes attentive. GAs may escape from such minima by chance, because works are highly stochastic. A neural net classifier with a genetic algorithm, using the GA to select features for classification by the neural net and incorporating the net as part of the objective function of the GA.

  16. Cardiac hybrid imaging

    Energy Technology Data Exchange (ETDEWEB)

    Gaemperli, Oliver [University Hospital Zurich, Cardiac Imaging, Zurich (Switzerland); University Hospital Zurich, Nuclear Cardiology, Cardiovascular Center, Zurich (Switzerland); Kaufmann, Philipp A. [University Hospital Zurich, Cardiac Imaging, Zurich (Switzerland); Alkadhi, Hatem [University Hospital Zurich, Institute of Diagnostic and Interventional Radiology, Zurich (Switzerland)

    2014-05-15

    Hybrid cardiac single photon emission computed tomography (SPECT)/CT imaging allows combined assessment of anatomical and functional aspects of cardiac disease. In coronary artery disease (CAD), hybrid SPECT/CT imaging allows detection of coronary artery stenosis and myocardial perfusion abnormalities. The clinical value of hybrid imaging has been documented in several subsets of patients. In selected groups of patients, hybrid imaging improves the diagnostic accuracy to detect CAD compared to the single imaging techniques. Additionally, this approach facilitates functional interrogation of coronary stenoses and guidance with regard to revascularization procedures. Moreover, the anatomical information obtained from CT coronary angiography or coronary artery calcium scores (CACS) adds prognostic information over perfusion data from SPECT. The use of cardiac hybrid imaging has been favoured by the dissemination of dedicated hybrid systems and the release of dedicated image fusion software, which allow simple patient throughput for hybrid SPECT/CT studies. Further technological improvements such as more efficient detector technology to allow for low-radiation protocols, ultra-fast image acquisition and improved low-noise image reconstruction algorithms will be instrumental to further promote hybrid SPECT/CT in research and clinical practice. (orig.)

  17. Hybrid intelligent engineering systems

    CERN Document Server

    Jain, L C; Adelaide, Australia University of

    1997-01-01

    This book on hybrid intelligent engineering systems is unique, in the sense that it presents the integration of expert systems, neural networks, fuzzy systems, genetic algorithms, and chaos engineering. It shows that these new techniques enhance the capabilities of one another. A number of hybrid systems for solving engineering problems are presented.

  18. A Hybrid Imagination

    DEFF Research Database (Denmark)

    Jamison, Andrew; Christensen, Steen Hyldgaard; Botin, Lars

    contexts, or sites, for mixing scientific knowledge and technical skills from different fields and social domains into new combinations, thus fostering what the authors term a “hybrid imagination”. Such a hybrid imagination is especially important today, as a way to counter the competitive and commercial...

  19. Editorial: Hybrid Systems

    DEFF Research Database (Denmark)

    Olderog, Ernst-Rüdiger; Ravn, Anders Peter

    2007-01-01

    An introduction to three papers in a special issue on Hybrid Systems. These paper were first presented at an IFIP WG 2.2 meeting in Skagen 2005.......An introduction to three papers in a special issue on Hybrid Systems. These paper were first presented at an IFIP WG 2.2 meeting in Skagen 2005....

  20. Portraits of colloidal hybrid nanostructures: controlled synthesis and potential applications.

    Science.gov (United States)

    Nguyen, Thanh-Dinh

    2013-03-01

    Inorganic hybrid nanostructures containing two or more nanocomponents have been emerging in many areas of materials science in recent years. The particle-particle interactions in a hybrid particle system could significantly improve existing local electronic structure and induce tunable physiochemical responses. The current work reviews the diverse inorganic hybrid nanostructures formed by adhesion of the different single components via seed-mediated method. The hybrid nanomaterials have great potentials for real applications in many other fields. The nanohybrids have been used as efficient heterocatalysts for carbon monoxide conversion and photodegradation of organic contaminants. The enhanced catalytic activity of these hybrid nanocatalysts could be attributed the formation of oxygen vacancies and electron transfer across the structural junction in a hybrid system as a result of the interfacial particle-particle interactions. The synergistic combination of up-converting and semiconducting properties in an up-converting semiconducting hybrid particle results in appearance of sub-band-gap photoconductivity. This behavior has a great significance for the design of photovoltaic devices for effective solar energy conversion. The functionalization and subsequent bioconjugation of the hybrid nanostructures to afford the multifunctional nanomedical platforms for simultaneous diagnosis and therapy are reviewed. The conjugated multifunctional hybrid nanostructures exhibit high biocompatibility and highly selective binding with functional groups-fabricated alive organs through delivering them to the tumor sites. The clever combinations of multifunctional features and antibody conjugation within these vehicles make them to generally offer new opportunities for clinical diagnostics and therapeutics.