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Sample records for svm datong chen

  1. Study on the Period of the Use of Datong-li in Korea

    Directory of Open Access Journals (Sweden)

    Ki-Won Lee

    2010-03-01

    Full Text Available It has been generally known that Datong-li (a Chinese calendar in the Ming dynasty was first introduced into Korea in the nineteenth reign of King Gongmin (1370 of the Goryeo dynasty and lasted to the third reign of King Hyeojong (1652 of the Joseon dynasty. This understanding is based on the records of Goryeo-sa (History of the Goryeo dynasty and of Seoungwan-ji (Official book of Seoungwan/ Jeungbomunheon- bigo (Explanatory Notes of Library Document. To verify the period of the use of Datong-li in Korea, we develop a Fortran code to calculate the calendar day by Datong-li and also investigate historical literatures and extant almanacs. As a result, we find the possibility that Datong-li had been in use since 1389 at least. However, we cannot confirm whether Datong-li was first enforced in 1370 or not. On the other hand, we confirm that Datong-li was used until 1653 and reintroduced during the period from 1667 to 1669. Also, we find that previous studies had some errors in the sexagenary cycle of the real first day of a month. We think that this study will contribute to understanding the calendrical history of the Joseon dynasty.

  2. Chen Hengzhe

    DEFF Research Database (Denmark)

    Gimpel, Denise

    Chen Hengzhe has been celebrated as China’s first female professor, first professor of Western history, first person to publish a history of the West that was not a translation into Chinese. She is moreover celebrated as one of the first to write fiction and poetry in the vernacular and to have...... been the first to write children’s literature. In 1914 she was among the first group of women to gain a Boxer Indemnity grant to study in America. The reiteration of these many “firsts” has led to a rather stereotypical portrait of Chen Hengzhe in Chinese sources and, as a result, in most Western...... references to her. To date we have no critical study of her work or activities in Chinese or any other language. Chen Hengzhe’s life and textual production, however, deserve and reward closer scholarly attention. They are not only pertinent to analysis of developments in early twentieth-century China...

  3. Chen Hengzhe

    DEFF Research Database (Denmark)

    Gimpel, Denise

    been the first to write children’s literature. In 1914 she was among the first group of women to gain a Boxer Indemnity grant to study in America. The reiteration of these many “firsts” has led to a rather stereotypical portrait of Chen Hengzhe in Chinese sources and, as a result, in most Western...... references to her. To date we have no critical study of her work or activities in Chinese or any other language. Chen Hengzhe’s life and textual production, however, deserve and reward closer scholarly attention. They are not only pertinent to analysis of developments in early twentieth-century China...

  4. Chen Hengzhe

    DEFF Research Database (Denmark)

    Gimpel, Denise

    Chen Hengzhe has been celebrated as China’s first female professor, first professor of Western history, first person to publish a history of the West that was not a translation into Chinese. She is moreover celebrated as one of the first to write fiction and poetry in the vernacular and to have...

  5. Chen Hengzhe

    DEFF Research Database (Denmark)

    Gimpel, Denise

    Chen Hengzhe has been celebrated as China’s first female professor, first professor of Western history, first person to publish a history of the West that was not a translation into Chinese. She is moreover celebrated as one of the first to write fiction and poetry in the vernacular and to have...... references to her. To date we have no critical study of her work or activities in Chinese or any other language. Chen Hengzhe’s life and textual production, however, deserve and reward closer scholarly attention. They are not only pertinent to analysis of developments in early twentieth-century China......; they speak to important questions in China today. This study, then, is not a biography of a person; it is an attempt to understand the way in which foreign influences (narratives of being, organizing, thinking, writing) seep into a person’s life and work and meld with the “home” influences (narratives...

  6. Chen Hengzhe

    DEFF Research Database (Denmark)

    Gimpel, Denise

    Chen Hengzhe has been celebrated as China’s first female professor, first professor of Western history, first person to publish a history of the West that was not a translation into Chinese. She is moreover celebrated as one of the first to write fiction and poetry in the vernacular and to have......; they speak to important questions in China today. This study, then, is not a biography of a person; it is an attempt to understand the way in which foreign influences (narratives of being, organizing, thinking, writing) seep into a person’s life and work and meld with the “home” influences (narratives...

  7. Chen Li: China's elder psychologist.

    Science.gov (United States)

    Blowers, G H

    1998-01-01

    Chen Li is one of a small group of psychologists in China who trained abroad early in their careers, returned to teach and do research, and continued doing so into later life beyond normal retirement age. His contacts with a number of prominent psychologists in England and Germany in the 1930s, and his inadvertent position at the center of a political row in China in the 1960s, leading to the shutting down of psychology for 10 years, made him historically important. Known for his work in organizational psychology and education, he is a distinguished psychologist and educational leader. Although trained as an experimentalist, he now embraces a broader view of psychology but remains emphatic it should be applied to real-life problems.

  8. Developing a SAR TT-OSL protocol for volcanically-heated aeolian quartz from Datong (China)

    DEFF Research Database (Denmark)

    Liu, Jinfeng; Murray, Andrew S.; Jain, Mayank

    2012-01-01

    The thermally-transferred optically stimulated luminescence (TT-OSL) responses of chemically-purified fine-grained quartz from a lava-baked aeolian sediment from Datong (China) are presented. Our main focus is to examine the suitability of the test dose TT-OSL and OSL response to monitor sensitiv......The thermally-transferred optically stimulated luminescence (TT-OSL) responses of chemically-purified fine-grained quartz from a lava-baked aeolian sediment from Datong (China) are presented. Our main focus is to examine the suitability of the test dose TT-OSL and OSL response to monitor...... with the test dose TT-OSL signal. The revised SAR TT-OSL protocol was tested by dose recovery tests on two very young (quartz OSL De20% of the given dose) in this revised SAR TT-OSL protocol....

  9. Yearning for the Lost Paradise: The "Great Unity" (datong and Its Philosophical Interpretations

    Directory of Open Access Journals (Sweden)

    Bart DESSEIN

    2017-01-01

    Full Text Available In the course of China’s history, the term datong (great unity has been interpreted in multiple ways. This article first discusses the concept as understood in the Liji, and then focuses on the way in which the perceived loss of the “great unity” within “all-under-heaven” (tianxia at the end of the Qing dynasty (1644–1911, and the endeavor to reconstruct the empire as a modern nation-state starting in the early twentieth century, informed the way the term datong was interpreted. After discussing the interpretations by Wang Tao (1828–1897, Hong Xiuquan (1813–1864, Kang Youwei (1858–1927, Liang Qichao (1873–1929, Sun Zhongshan (1866–1925, and Mao Zedong (1893–1976, this work concludes with a discussion on how, against the background of the perceived threat of loss of national unity that characterizes the contemporary People’s Republic of China, a New Confucian interpretation is developed.

  10. Structural and Metamorphic Evolution of the Archaean High-pressure Granulite in Datong-Huaian Area, North China

    NARCIS (Netherlands)

    Zhang, J.

    2001-01-01

    The Archaean granulite terrain in the Datong-Huaian area, north China, comprises a basement complex of fe lsic and mafic granulite (TTG gneiss), overlain by a sedimentary sequence dominated by metapelite and metapsammite (khondalite series). Both lithological associations are separated by

  11. svmPRAT: SVM-based Protein Residue Annotation Toolkit

    Directory of Open Access Journals (Sweden)

    Kauffman Christopher

    2009-12-01

    Full Text Available Abstract Background Over the last decade several prediction methods have been developed for determining the structural and functional properties of individual protein residues using sequence and sequence-derived information. Most of these methods are based on support vector machines as they provide accurate and generalizable prediction models. Results We present a general purpose protein residue annotation toolkit (svmPRAT to allow biologists to formulate residue-wise prediction problems. svmPRAT formulates the annotation problem as a classification or regression problem using support vector machines. One of the key features of svmPRAT is its ease of use in incorporating any user-provided information in the form of feature matrices. For every residue svmPRAT captures local information around the reside to create fixed length feature vectors. svmPRAT implements accurate and fast kernel functions, and also introduces a flexible window-based encoding scheme that accurately captures signals and pattern for training effective predictive models. Conclusions In this work we evaluate svmPRAT on several classification and regression problems including disorder prediction, residue-wise contact order estimation, DNA-binding site prediction, and local structure alphabet prediction. svmPRAT has also been used for the development of state-of-the-art transmembrane helix prediction method called TOPTMH, and secondary structure prediction method called YASSPP. This toolkit developed provides practitioners an efficient and easy-to-use tool for a wide variety of annotation problems. Availability: http://www.cs.gmu.edu/~mlbio/svmprat

  12. An isotope hydrochemical approach to understand fluoride release into groundwaters of the Datong Basin, Northern China.

    Science.gov (United States)

    Su, Chunli; Wang, Yanxin; Xie, Xianjun; Zhu, Yapeng

    2015-04-01

    The hydrogeochemical and isotopic investigations of high fluoride (up to 8.26 mg L(-1)) groundwater in the Datong Basin, Northern China were carried out in order to evaluate the geochemical controls on fluoride enrichment. The groundwater fluoride concentration tends to increase along with the regional groundwater flow path away from the basin margins, towards the central parts of the basin. Groundwater with high F concentrations has a distinctive major ion chemistry, being generally HCO3(-)-rich, Na-rich, Ca-poor, and having weak alkaline pH values (7.2 to 8.2) and Na-HCO3 waters. These data indicate that variations in the groundwater major ion chemistry and possibly pH, which are controlled by water-rock interaction processes in the aquifer, are important in mobilizing F. Positive correlations between fluoride with lithogenic sodium (LNa) and HCO3(-) in groundwater show that the high fluoride content and alkaline sodic characteristics of groundwater result from dissolution of fluorine-bearing minerals. The occurrence and behavior of fluorine in groundwater are mainly controlled by fluorite precipitation as a function of Ca(2+) concentration. A positive correlation between fluoride and δ(18)O, low F(-)/Cl(-) ratios, and the low tritium level in the fluoride-rich groundwater indicate the effects of long-term water-rock interactions and intensive evapotranspiration.

  13. Arsenic release by indigenous bacteria Bacillus cereus from aquifer sediments at Datong Basin, northern China

    Science.gov (United States)

    Xie, Zuoming; Wang, Yanxin; Duan, Mengyu; Xie, Xianjun; Su, Chunli

    2011-03-01

    Endemic arsenic poisoning due to long-term drinking of high arsenic groundwater has been reported in Datong Basin, northern China. To investigate the effects of microbial activities on arsenic mobilization in contaminated aquifers, Bacillus cereus ( B. cereus) isolated from high arsenic aquifer sediments of the basin was used in our microcosm experiments. The arsenic concentration in the treatment with both bacteria and sodium citrate or glucose had a rapid increase in the first 18 d, and then, it declined. Supplemented with bacteria only, the concentration could increase on the second day. By contrast, the arsenic concentration in the treatment supplemented with sodium citrate or glucose was kept very low. These results indicate that bacterial activities promoted the release of arsenic in the sediments. Bacterial activities also influenced other geochemical parameters of the aqueous phase, such as pH, Eh, and the concentrations of dissolved Fe, Mn, and Al that are important controls on arsenic release. The removal of Fe, Mn, and Al from sediment samples was observed with the presence of B. cereus. The effects of microbial activities on Fe, Mn, and Al release were nearly the same as those on As mobilization. The pH values of the treatments inoculated with bacteria were lower than those without bacteria, still at alkaline levels. With the decrease of Eh values in treatments inoculated with bacteria, the microcosms became more reducing and are thus favorable for arsenic release.

  14. Stability analysis of distributed order fractional chen system.

    Science.gov (United States)

    Aminikhah, H; Refahi Sheikhani, A; Rezazadeh, H

    2013-01-01

    We first investigate sufficient and necessary conditions of stability of nonlinear distributed order fractional system and then we generalize the integer-order Chen system into the distributed order fractional domain. Based on the asymptotic stability theory of nonlinear distributed order fractional systems, the stability of distributed order fractional Chen system is discussed. In addition, we have found that chaos exists in the double fractional order Chen system. Numerical solutions are used to verify the analytical results.

  15. A structural SVM approach for reference parsing.

    Science.gov (United States)

    Zhang, Xiaoli; Zou, Jie; Le, Daniel X; Thoma, George R

    2011-06-09

    Automated extraction of bibliographic data, such as article titles, author names, abstracts, and references is essential to the affordable creation of large citation databases. References, typically appearing at the end of journal articles, can also provide valuable information for extracting other bibliographic data. Therefore, parsing individual reference to extract author, title, journal, year, etc. is sometimes a necessary preprocessing step in building citation-indexing systems. The regular structure in references enables us to consider reference parsing a sequence learning problem and to study structural Support Vector Machine (structural SVM), a newly developed structured learning algorithm on parsing references. In this study, we implemented structural SVM and used two types of contextual features to compare structural SVM with conventional SVM. Both methods achieve above 98% token classification accuracy and above 95% overall chunk-level accuracy for reference parsing. We also compared SVM and structural SVM to Conditional Random Field (CRF). The experimental results show that structural SVM and CRF achieve similar accuracies at token- and chunk-levels. When only basic observation features are used for each token, structural SVM achieves higher performance compared to SVM since it utilizes the contextual label features. However, when the contextual observation features from neighboring tokens are combined, SVM performance improves greatly, and is close to that of structural SVM after adding the second order contextual observation features. The comparison of these two methods with CRF using the same set of binary features show that both structural SVM and CRF perform better than SVM, indicating their stronger sequence learning ability in reference parsing.

  16. Antioxidant activities of seed extracts from Dalbergia odorifera T. Chen

    African Journals Online (AJOL)

    The heartwood or root of Dalbergia odorifera T. Chen is an important traditional Chinese medicine. Antioxidant activities of seed extracts from D. odorifera T. Chen were first investigated in this study. Ethanolic extracts were suspended in distilled water and partitioned successively with petroleum ether, ethyl acetate, ...

  17. Consistent Yokoya-Chen Approximation to Beamstrahlung(LCC-0010)

    Energy Technology Data Exchange (ETDEWEB)

    Peskin, M

    2004-04-22

    I reconsider the Yokoya-Chen approximate evolution equation for beamstrahlung and modify it slightly to generate simple, consistent analytical approximations for the electron and photon energy spectra. I compare these approximations to previous ones, and to simulation data.I reconsider the Yokoya-Chen approximate evolution equation for beamstrahlung and modify it slightly to generate simple, consistent analytical approximations for the electron and photon energy spectra. I compare these approximations to previous ones, and to simulation data.

  18. Chen's attractor exists if Lorenz repulsor exists: The Chen system is a special case of the Lorenz system

    Science.gov (United States)

    Algaba, Antonio; Fernández-Sánchez, Fernando; Merino, Manuel; Rodríguez-Luis, Alejandro J.

    2013-09-01

    In this paper, we show, by means of a linear scaling in time and coordinates, that the Chen system, given by ẋ=a(y-x), ẏ=(c-a)x+cy-xz, ż=-bz+xy, is, generically (c ≠0), a special case of the Lorenz system. First, we infer that it is enough to consider two parameters to study its dynamics. Furthermore, we prove that there exists a homothetic transformation between the Chen and the Lorenz systems and, accordingly, all the dynamical behavior exhibited by the Chen system is present in the Lorenz system (since the former is a special case of the second). We illustrate our results relating Hopf bifurcations, periodic orbits, invariant surfaces, and chaotic attractors of both systems. Since there has been a large literature that has ignored this equivalence, the aim of this paper is to review and clarify this field. Unfortunately, a lot of the previous papers on the Chen system are unnecessary or incorrect.

  19. Adaptive SVM for Data Stream Classification

    Directory of Open Access Journals (Sweden)

    Isah A. Lawal

    2017-07-01

    Full Text Available In this paper, we address the problem of learning an adaptive classifier for the classification of continuous streams of data. We present a solution based on incremental extensions of the Support Vector Machine (SVM learning paradigm that updates an existing SVM whenever new training data are acquired. To ensure that the SVM effectiveness is guaranteed while exploiting the newly gathered data, we introduce an on-line model selection approach in the incremental learning process. We evaluated the proposed method on real world applications including on-line spam email filtering and human action classification from videos. Experimental results show the effectiveness and the potential of the proposed approach.

  20. Hydrogeochemistry of co-occurring geogenic arsenic, fluoride and iodine in groundwater at Datong Basin, northern China

    Energy Technology Data Exchange (ETDEWEB)

    Pi, Kunfu; Wang, Yanxin, E-mail: yx.wang@cug.edu.cn; Xie, Xianjun, E-mail: xjxie@cug.edu.cn; Su, Chunli; Ma, Teng; Li, Junxia; Liu, Yaqing

    2015-12-30

    Highlights: • Co-mobilization of As, F and I was identified at Datong Basin. • Both As and I are released via reductive dissolution of Fe minerals. • Some amounts of As and I may be sequestered by FeS precipitates. • Intensive evaporation promotes retention of As but mobilization of F and I. - Abstract: Abnormal levels of co-occurring arsenic (As), fluorine (F) and iodine (I) in groundwater at Datong Basin, northern China are geochemically unique. Hydrochemical, {sup 18}O and {sup 2}H characteristics of groundwater were analyzed to elucidate their mobilization processes. Aqueous As, F and I ranged from 5.6 to 2680 μg/L, 0.40 to 3.32 mg/L and 10.1 to 186 μg/L, respectively. High As, F and I groundwater was characterized by moderately alkaline, high HCO{sub 3}{sup −}, Fe(II), HS{sup −} and DOC concentrations with H{sub 3}AsO{sub 3}, F{sup −} and I{sup −} as the dominant species. The plots of δ{sup 18}O values and Cl/Br ratios versus Cl{sup −} concentration demonstrate build-up of more oxidizing conditions and precipitation of carbonate minerals induced by vertical recharge and intensive evaporation facilitate As retention to Fe (hydr) oxides, but enhance F and I mobilization from host minerals. Under reducing conditions, As and I can be simultaneously released via reductive dissolution of Fe (hydr) oxides and reduction of As(V) and I(V) while F migration may be retarded due to effects of dissolution-precipitation equilibria between carbonate minerals and fluorite. With the prevalence of sulfate-reducing condition and lowering of HCO{sub 3}{sup −} concentration, As and I may be sequestered by Fe(II) sulfides and F is retained to fluorite and on clay mineral surfaces.

  1. Discussion on manufacturing techniques of Chen Zhang Pot

    Directory of Open Access Journals (Sweden)

    Fan Taofeng

    2013-01-01

    Full Text Available The Chen Zhang Pot, made in the late Warring States period is an openwork bronze pot with gold and silver inlay. It was identified as a first-class historical relic of the nation by the State Administration of Cultural Heritage in 2001. To explore its manufacturing techniques, XRF, CT and X-Ray photograph microscopic observation were adopted for the non-destructive examination of the Chen Zhang Pot. The analysis data show that many parts of the Chen Zhang Pot were cast separately, and then assembled. It also shows that the composition proportion of each part of the Chen Zhang Pot is different; and the bronze color, hardness and tensile strength are different for each alloy proportion. A variety of decoration techniques were adopted in the manufacturing process, which make the color more diverse and gorgeous. The openwork decoration is the most complex part of the pot, and the gold and silver decoration technique also represents the high level manufacturing technology in the Warring States period. In summary, the Chen Zhang Pot is a unique art treasure, and as a specimen it allows us to study the bronze casting technology in the Warring States period.

  2. The age grading and the Chen-Ruan cup product

    DEFF Research Database (Denmark)

    A. Hepworth, Richard

    2010-01-01

    We prove that the obstruction bundle used to define the cup-product in Chen-Ruan cohomology is determined by the so-called `age grading' or `degree-shifting numbers'. Indeed, the obstruction bundle can be directly computed using the age grading. We obtain a Kunneth Theorem for Chen-Ruan cohomology...... as a direct consequence of an elementary property of the age grading, and explain how several other results - including associativity of the cup-product - can be proved in a similar way....

  3. Towards understanding the influence of SVM hyperparameters

    CSIR Research Space (South Africa)

    Van Heerden, CJ

    2010-11-01

    Full Text Available -consuming and resource-intensive. On large datasets, 10-fold cross-validation grid searches can become intractable without supercomputers or high performance computing clusters. They present theoretical and empirical arguments as to how SVM hyperparameters scale with N...

  4. Fuzzy Sliding Mode Control for Hyper Chaotic Chen System

    Directory of Open Access Journals (Sweden)

    SARAILOO, M.

    2012-02-01

    Full Text Available In this paper, a fuzzy sliding mode control method is proposed for stabilizing hyper chaotic Chen system. The main objective of the control scheme is to stabilize unstable equilibrium point of the system by controlling the states of the system so that they converge to a pre-defined sliding surface and remain on it. A fuzzy control technique is also utilized in order to overcome the main disadvantage of sliding mode control methods, i.e. chattering problem. It is shown that the equilibrium point of the system is stabilized by using the proposed method. A stability analysis is also performed to prove that the states of the system converge to the sliding surface and remain on it. Simulations show that the control method can be effectively applied to Chen system when it performs hyper chaotic behavior.

  5. Beschichtung hochwertiger Karosserieoberflächen mit Pulver-Slurry

    OpenAIRE

    Weckerle, Gero

    2003-01-01

    Die Beschichtung von Karosserieaußenflächen erfolgt heute vorwiegend durch elektrostatisch unterstützte Hochrotationszerstäuber (ESTA-HR) an Lackierautomaten, die zunehmend durch Lackierroboter ersetzt werden. Dieser technologische Wandel basiert auf den Qualitäts- und Kostenvorteilen, die sich beim Einsatz der Roboter ergeben. So kann beispielsweise durch die höhere Flexibilität der Bewegung die Zahl der erforderlichen Zerstäuber pro Lackierzone deutlich reduziert werden. Um dennoch die gefo...

  6. Data Driven Constraints for the SVM

    DEFF Research Database (Denmark)

    Darkner, Sune; Clemmensen, Line Katrine Harder

    2012-01-01

    . Assuming that two observations of the same subject in different states span a vector, we hypothesise that such structure of the data contains implicit information which can aid the classification, thus the name data driven constraints. We derive a constraint based on the data which allow for the use...... classifier solution, compared to the SVM i.e. reduces variance and improves classification rates. We present a quantitative measure of the information level contained in the pairing and test the method on simulated as well as a high-dimensional paired data set of ear-canal surfaces....

  7. Generalized SMO algorithm for SVM-based multitask learning.

    Science.gov (United States)

    Cai, Feng; Cherkassky, Vladimir

    2012-06-01

    Exploiting additional information to improve traditional inductive learning is an active research area in machine learning. In many supervised-learning applications, training data can be naturally separated into several groups, and incorporating this group information into learning may improve generalization. Recently, Vapnik proposed a general approach to formalizing such problems, known as "learning with structured data" and its support vector machine (SVM) based optimization formulation called SVM+. Liang and Cherkassky showed the connection between SVM+ and multitask learning (MTL) approaches in machine learning, and proposed an SVM-based formulation for MTL called SVM+MTL for classification. Training the SVM+MTL classifier requires the solution of a large quadratic programming optimization problem which scales as O(n(3)) with sample size n. So there is a need to develop computationally efficient algorithms for implementing SVM+MTL. This brief generalizes Platt's sequential minimal optimization (SMO) algorithm to the SVM+MTL setting. Empirical results show that, for typical SVM+MTL problems, the proposed generalized SMO achieves over 100 times speed-up, in comparison with general-purpose optimization routines.

  8. A novel stepwise support vector machine (SVM) method based on ...

    African Journals Online (AJOL)

    ajl yemi

    2011-11-23

    Nov 23, 2011 ... began to use computational approaches, particularly machine learning methods to identify pre-miRNAs (Xue et al., 2005; Huang et al., 2007; Jiang et al., 2007). Xue et al. (2005) presented a support vector machine (SVM)- based classifier called triplet-SVM, which classifies human pre-miRNAs from pseudo ...

  9. Estimating grassland biomass using SVM band shaving of hyperspectral data

    NARCIS (Netherlands)

    Clevers, J.G.P.W.; Heijden, van der G.W.A.M.; Verzakov, S.; Schaepman, M.E.

    2007-01-01

    In this paper, the potential of a band shaving algorithm based on support vector machines (SVM) applied to hyperspectral data for estimating biomass within grasslands is studied. Field spectrometer data and biomass measurements were collected from a homogeneously managed grassland field. The SVM

  10. How Memory Is Tested Influences What Is Measured: Reply to Wyble and Chen (2017)

    Science.gov (United States)

    Swallow, Khena M.; Jiang, Yuhong V.; Tan, Deborah H.

    2017-01-01

    In this response to Wyble and Chen's (2017) commentary on attribute amnesia, we hope to achieve several goals. First, we clarify how our view diverges from that described by Wyble and Chen. We argue that because the surprise memory test is disruptive, it is an insensitive tool for measuring the persistence of recently attended target attributes in…

  11. Parameter optimization using GA in SVM to predict damage level of non-reshaped berm breakwater.

    Digital Repository Service at National Institute of Oceanography (India)

    Harish, N.; Lokesha.; Mandal, S.; Rao, S.; Patil, S.G.

    In the present study, Support Vector Machines (SVM) and hybrid of Genetic Algorithm (GA) with SVM models are developed to predict the damage level of non-reshaped berm breakwaters. Optimal kernel parameters of SVM are determined by using GA...

  12. Research on Classification of Chinese Text Data Based on SVM

    Science.gov (United States)

    Lin, Yuan; Yu, Hongzhi; Wan, Fucheng; Xu, Tao

    2017-09-01

    Data Mining has important application value in today’s industry and academia. Text classification is a very important technology in data mining. At present, there are many mature algorithms for text classification. KNN, NB, AB, SVM, decision tree and other classification methods all show good classification performance. Support Vector Machine’ (SVM) classification method is a good classifier in machine learning research. This paper will study the classification effect based on the SVM method in the Chinese text data, and use the support vector machine method in the chinese text to achieve the classify chinese text, and to able to combination of academia and practical application.

  13. Classifying smoke in laparoscopic videos using SVM

    Directory of Open Access Journals (Sweden)

    Alshirbaji Tamer Abdulbaki

    2017-09-01

    Full Text Available Smoke in laparoscopic videos usually appears due to the use of electrocautery when cutting or coagulating tissues. Therefore, detecting smoke can be used for event-based annotation in laparoscopic surgeries by retrieving the events associated with the electrocauterization. Furthermore, smoke detection can also be used for automatic smoke removal. However, detecting smoke in laparoscopic video is a challenge because of the changeability of smoke patterns, the moving camera and the different lighting conditions. In this paper, we present a video-based smoke detection algorithm to detect smoke of different densities such as fog, low and high density in laparoscopic videos. The proposed method depends on extracting various visual features from the laparoscopic images and providing them to support vector machine (SVM classifier. Features are based on motion, colour and texture patterns of the smoke. We validated our algorithm using experimental evaluation on four laparoscopic cholecystectomy videos. These four videos were manually annotated by defining every frame as smoke or non-smoke frame. The algorithm was applied to the videos by using different feature combinations for classification. Experimental results show that the combination of all proposed features gives the best classification performance. The overall accuracy (i.e. correctly classified frames is around 84%, with the sensitivity (i.e. correctly detected smoke frames and the specificity (i.e. correctly detected non-smoke frames are 89% and 80%, respectively.

  14. Customer and performance rating in QFD using SVM classification

    Science.gov (United States)

    Dzulkifli, Syarizul Amri; Salleh, Mohd Najib Mohd; Leman, A. M.

    2017-09-01

    In a classification problem, where each input is associated to one output. Training data is used to create a model which predicts values to the true function. SVM is a popular method for binary classification due to their theoretical foundation and good generalization performance. However, when trained with noisy data, the decision hyperplane might deviate from optimal position because of the sum of misclassification errors in the objective function. In this paper, we introduce fuzzy in weighted learning approach for improving the accuracy of Support Vector Machine (SVM) classification. The main aim of this work is to determine appropriate weighted for SVM to adjust the parameters of learning method from a given set of noisy input to output data. The performance and customer rating in Quality Function Deployment (QFD) is used as our case study to determine implementing fuzzy SVM is highly scalable for very large data sets and generating high classification accuracy.

  15. Geochemistry of redox-sensitive elements and sulfur isotopes in the high arsenic groundwater system of Datong Basin, China

    Energy Technology Data Exchange (ETDEWEB)

    Xie Xianjun [MOE Key Laboratory of Biogeology and Environmental Geology and School of Environmental Studies, China University of Geosciences, Wuhan 430074 (China); Ellis, Andre [Department of Geological Sciences, University of Texas at El Paso, TX 79968-0555 (United States); Wang Yanxin, E-mail: yx.wang@cug.edu.cn [MOE Key Laboratory of Biogeology and Environmental Geology and School of Environmental Studies, China University of Geosciences, Wuhan 430074 (China); Xie Zuoming; Duan Mengyu; Su Chunli [MOE Key Laboratory of Biogeology and Environmental Geology and School of Environmental Studies, China University of Geosciences, Wuhan 430074 (China)

    2009-06-01

    High arsenic groundwater in the Quaternary aquifers of Datong Basin, northern China contain As up to 1820 {mu}g/L and the high concentration plume is located in the slow flowing central parts of the basin. In this study we used hydrochemical data and sulfur isotope ratios of sulfate to better understand the conditions that are likely to control arsenic mobilization. Groundwater and spring samples were collected along two flow paths from the west and east margins of the basin and a third set along the basin flow path. Arsenic concentrations range from 68 to 670 {mu}g/L in the basin and from 3.1 to 44 {mu}g/L in the western and eastern margins. The margins have relatively oxidized waters with low contents of arsenic, relatively high proportions of As(V) among As species, and high contents of sulfate and uranium. By contrast, the central parts of the basin are reducing with high contents of arsenic in groundwater, commonly with high proportions of As(III) among As species, and low contents of sulfate and uranium. No statistical correlations were observed between arsenic and Eh, sulfate, Fe, Mn, Mo and U. While the mobility of sulfate, uranium and molybdenum is possibly controlled by the change in redox conditions as the groundwater flows towards central parts of the basin, the reducing conditions alone cannot account for the occurrence of high arsenic groundwater in the basin but it does explain the characteristics of arsenic speciation. With one exception, all the groundwaters with As(III) as the major As species have low Eh and those with As(V) have high Eh. Reductive dissolution of Fe-oxyhydroxides or reduction of As(V) are consistent with the observations, however no increase in dissolved Fe concentration was noted. Furthermore, water from the well with the highest arsenic was relatively oxidizing and contained mostly As(V). From previous work Fe-oxyhydroxides are speculated to exist as coatings rather than primary minerals. The wide range of {delta}{sup 34}S

  16. Geochemistry of redox-sensitive elements and sulfur isotopes in the high arsenic groundwater system of Datong Basin, China.

    Science.gov (United States)

    Xie, Xianjun; Ellis, Andre; Wang, Yanxin; Xie, Zuoming; Duan, Mengyu; Su, Chunli

    2009-06-01

    High arsenic groundwater in the Quaternary aquifers of Datong Basin, northern China contain As up to 1820 microg/L and the high concentration plume is located in the slow flowing central parts of the basin. In this study we used hydrochemical data and sulfur isotope ratios of sulfate to better understand the conditions that are likely to control arsenic mobilization. Groundwater and spring samples were collected along two flow paths from the west and east margins of the basin and a third set along the basin flow path. Arsenic concentrations range from 68 to 670 microg/L in the basin and from 3.1 to 44 microg/L in the western and eastern margins. The margins have relatively oxidized waters with low contents of arsenic, relatively high proportions of As(V) among As species, and high contents of sulfate and uranium. By contrast, the central parts of the basin are reducing with high contents of arsenic in groundwater, commonly with high proportions of As(III) among As species, and low contents of sulfate and uranium. No statistical correlations were observed between arsenic and Eh, sulfate, Fe, Mn, Mo and U. While the mobility of sulfate, uranium and molybdenum is possibly controlled by the change in redox conditions as the groundwater flows towards central parts of the basin, the reducing conditions alone cannot account for the occurrence of high arsenic groundwater in the basin but it does explain the characteristics of arsenic speciation. With one exception, all the groundwaters with As(III) as the major As species have low Eh and those with As(V) have high Eh. Reductive dissolution of Fe-oxyhydroxides or reduction of As(V) are consistent with the observations, however no increase in dissolved Fe concentration was noted. Furthermore, water from the well with the highest arsenic was relatively oxidizing and contained mostly As(V). From previous work Fe-oxyhydroxides are speculated to exist as coatings rather than primary minerals. The wide range of delta(34)S([SO4

  17. Estimating grassland biomass using SVM band shaving of hyperspectral data

    OpenAIRE

    Clevers, J G P W; van Der Heijden, G.W.A.M.; Verzakov, S; Schaepman, M. E.

    2007-01-01

    In this paper, the potential of a band shaving algorithm based on support vector machines (SVM) applied to hyperspectral data for estimating biomass within grasslands is studied. Field spectrometer data and biomass measurements were collected from a homogeneously managed grassland field. The SVM band shaving technique was compared with a partial least squares (PLS) and a stepwise forward selection analysis. Using their results, a range of vegetation indices was used as predictors for grasslan...

  18. Review of the genus Neotetricodes Zhang et Chen (Hemiptera: Fulgoromorpha: Issidae) with description of two new species.

    Science.gov (United States)

    Chang, Zhi-Min; Yang, Lin; Zhang, Zheng-Guang; Chen, Xiang-Sheng

    2015-12-11

    Two new species of the issid genus Neotetricodes Zhang et Chen (Hemiptera: Fulgoromorpha: Issidae): Neotetricodes longispinus Chang et Chen sp. nov. (China: Yunnan) and Neotetricodes xiphoideus Chang et Chen sp. nov. (China: Yunnan) are described and illustrated. The generic characteristic is redefined. A checklist and key to the species of the genus are provided. The female genitalia of the genus are firstly described.

  19. SVM2Motif--Reconstructing Overlapping DNA Sequence Motifs by Mimicking an SVM Predictor.

    Directory of Open Access Journals (Sweden)

    Marina M-C Vidovic

    Full Text Available Identifying discriminative motifs underlying the functionality and evolution of organisms is a major challenge in computational biology. Machine learning approaches such as support vector machines (SVMs achieve state-of-the-art performances in genomic discrimination tasks, but--due to its black-box character--motifs underlying its decision function are largely unknown. As a remedy, positional oligomer importance matrices (POIMs allow us to visualize the significance of position-specific subsequences. Although being a major step towards the explanation of trained SVM models, they suffer from the fact that their size grows exponentially in the length of the motif, which renders their manual inspection feasible only for comparably small motif sizes, typically k ≤ 5. In this work, we extend the work on positional oligomer importance matrices, by presenting a new machine-learning methodology, entitled motifPOIM, to extract the truly relevant motifs--regardless of their length and complexity--underlying the predictions of a trained SVM model. Our framework thereby considers the motifs as free parameters in a probabilistic model, a task which can be phrased as a non-convex optimization problem. The exponential dependence of the POIM size on the oligomer length poses a major numerical challenge, which we address by an efficient optimization framework that allows us to find possibly overlapping motifs consisting of up to hundreds of nucleotides. We demonstrate the efficacy of our approach on a synthetic data set as well as a real-world human splice site data set.

  20. SVM2Motif--Reconstructing Overlapping DNA Sequence Motifs by Mimicking an SVM Predictor.

    Science.gov (United States)

    Vidovic, Marina M-C; Görnitz, Nico; Müller, Klaus-Robert; Rätsch, Gunnar; Kloft, Marius

    2015-01-01

    Identifying discriminative motifs underlying the functionality and evolution of organisms is a major challenge in computational biology. Machine learning approaches such as support vector machines (SVMs) achieve state-of-the-art performances in genomic discrimination tasks, but--due to its black-box character--motifs underlying its decision function are largely unknown. As a remedy, positional oligomer importance matrices (POIMs) allow us to visualize the significance of position-specific subsequences. Although being a major step towards the explanation of trained SVM models, they suffer from the fact that their size grows exponentially in the length of the motif, which renders their manual inspection feasible only for comparably small motif sizes, typically k ≤ 5. In this work, we extend the work on positional oligomer importance matrices, by presenting a new machine-learning methodology, entitled motifPOIM, to extract the truly relevant motifs--regardless of their length and complexity--underlying the predictions of a trained SVM model. Our framework thereby considers the motifs as free parameters in a probabilistic model, a task which can be phrased as a non-convex optimization problem. The exponential dependence of the POIM size on the oligomer length poses a major numerical challenge, which we address by an efficient optimization framework that allows us to find possibly overlapping motifs consisting of up to hundreds of nucleotides. We demonstrate the efficacy of our approach on a synthetic data set as well as a real-world human splice site data set.

  1. SVM Method used to Study Gender Differences Based on Microelement

    Science.gov (United States)

    Chun, Yang; Yuan, Liu; Jun, Du; Bin, Tang

    [objective] Intelligent Algorithm of SVM is used for studying gender differences based on microelement data, which provide reference For the application of Microelement in healthy people, such as providing technical support for the investigation of cases.[Method] Our Long-term test results on hair microelement of health people were consolidated. Support vector machine (SVM) is used to classified model of male and female based on microelement data. The radical basis function (RBF) is adopted as a kernel function of SVM, and the model adjusts C and σ to build the optimization classifier, [Result] Healthy population of men and women of manganese, cadmium and nickel are quite different, The classified model of Microelement based on SVM can classifies the male and female, the correct classification ratio set to be 81.71% and 66.47% by SVM based on 7 test date and 3 test data selection. [conclusion] The classified model of microelement data based on SVM can classifies male and female.

  2. A method of neighbor classes based SVM classification for optical printed Chinese character recognition.

    Directory of Open Access Journals (Sweden)

    Jie Zhang

    Full Text Available In optical printed Chinese character recognition (OPCCR, many classifiers have been proposed for the recognition. Among the classifiers, support vector machine (SVM might be the best classifier. However, SVM is a classifier for two classes. When it is used for multi-classes in OPCCR, its computation is time-consuming. Thus, we propose a neighbor classes based SVM (NC-SVM to reduce the computation consumption of SVM. Experiments of NC-SVM classification for OPCCR have been done. The results of the experiments have shown that the NC-SVM we proposed can effectively reduce the computation time in OPCCR.

  3. Multiclass Posterior Probability Twin SVM for Motor Imagery EEG Classification.

    Science.gov (United States)

    She, Qingshan; Ma, Yuliang; Meng, Ming; Luo, Zhizeng

    2015-01-01

    Motor imagery electroencephalography is widely used in the brain-computer interface systems. Due to inherent characteristics of electroencephalography signals, accurate and real-time multiclass classification is always challenging. In order to solve this problem, a multiclass posterior probability solution for twin SVM is proposed by the ranking continuous output and pairwise coupling in this paper. First, two-class posterior probability model is constructed to approximate the posterior probability by the ranking continuous output techniques and Platt's estimating method. Secondly, a solution of multiclass probabilistic outputs for twin SVM is provided by combining every pair of class probabilities according to the method of pairwise coupling. Finally, the proposed method is compared with multiclass SVM and twin SVM via voting, and multiclass posterior probability SVM using different coupling approaches. The efficacy on the classification accuracy and time complexity of the proposed method has been demonstrated by both the UCI benchmark datasets and real world EEG data from BCI Competition IV Dataset 2a, respectively.

  4. Multiclass Posterior Probability Twin SVM for Motor Imagery EEG Classification

    Directory of Open Access Journals (Sweden)

    Qingshan She

    2015-01-01

    Full Text Available Motor imagery electroencephalography is widely used in the brain-computer interface systems. Due to inherent characteristics of electroencephalography signals, accurate and real-time multiclass classification is always challenging. In order to solve this problem, a multiclass posterior probability solution for twin SVM is proposed by the ranking continuous output and pairwise coupling in this paper. First, two-class posterior probability model is constructed to approximate the posterior probability by the ranking continuous output techniques and Platt’s estimating method. Secondly, a solution of multiclass probabilistic outputs for twin SVM is provided by combining every pair of class probabilities according to the method of pairwise coupling. Finally, the proposed method is compared with multiclass SVM and twin SVM via voting, and multiclass posterior probability SVM using different coupling approaches. The efficacy on the classification accuracy and time complexity of the proposed method has been demonstrated by both the UCI benchmark datasets and real world EEG data from BCI Competition IV Dataset 2a, respectively.

  5. COMPARISON OF SVM AND FUZZY CLASSIFIER FOR AN INDIAN SCRIPT

    Directory of Open Access Journals (Sweden)

    M. J. Baheti

    2012-01-01

    Full Text Available With the advent of technological era, conversion of scanned document (handwritten or printed into machine editable format has attracted many researchers. This paper deals with the problem of recognition of Gujarati handwritten numerals. Gujarati numeral recognition requires performing some specific steps as a part of preprocessing. For preprocessing digitization, segmentation, normalization and thinning are done with considering that the image have almost no noise. Further affine invariant moments based model is used for feature extraction and finally Support Vector Machine (SVM and Fuzzy classifiers are used for numeral classification. . The comparison of SVM and Fuzzy classifier is made and it can be seen that SVM procured better results as compared to Fuzzy Classifier.

  6. SA-SVM based automated diagnostic system for skin cancer

    Science.gov (United States)

    Masood, Ammara; Al-Jumaily, Adel

    2015-03-01

    Early diagnosis of skin cancer is one of the greatest challenges due to lack of experience of general practitioners (GPs). This paper presents a clinical decision support system aimed to save time and resources in the diagnostic process. Segmentation, feature extraction, pattern recognition, and lesion classification are the important steps in the proposed decision support system. The system analyses the images to extract the affected area using a novel proposed segmentation method H-FCM-LS. The underlying features which indicate the difference between melanoma and benign lesions are obtained through intensity, spatial/frequency and texture based methods. For classification purpose, self-advising SVM is adapted which showed improved classification rate as compared to standard SVM. The presented work also considers analyzed performance of linear and kernel based SVM on the specific skin lesion diagnostic problem and discussed corresponding findings. The best diagnostic rates obtained through the proposed method are around 90.5 %.

  7. Sociology in Times of Crisis: Chen Da, National Salvation and the Indigenization of Knowledge

    Directory of Open Access Journals (Sweden)

    Ana Maria Candela

    2015-08-01

    Full Text Available Chen Da was one of the foremost sociologists of China from the 1920s to the 1940s. His intellectual habitus took shape from the long crisis that defined Chinese intellectual life from the mid-19th to mid-20th centuries, a period of continuous imperial assault on Chinese sovereignty. As China integrated into the capitalist world-system, neo-Confucian structures of knowledge came into question. Intellectuals took up sociology to guide China’s transition from an empire to a nation-state. Through his studies on labor, migration, and population, Chen Da contributed to the institutionalization of sociology in China. Chen sought to craft a theory of Chinese development that followed universal trajectories of progress but was also attuned to the complexity of Chinese society on the ground. Through his efforts to indigenize sociology, Chen developed a non-Marxist historical materialism, a deterritorialized and pluralistic conceptualization of China as a nation, and a theory of eugenic transformation centered on the concept of “mode of living.” The questions which Chen Da confronted are emblematic of the predicament faced by Chinese social scientists today, who again struggle with the dynamics of a deterritorialzied “Greater China,” rising social fragmentation, and refigured eugenic discourses and policies that aim to craft the Chinese people into ideal national subjects fit for post-socialist development.

  8. Secure Communication Based on Hyperchaotic Chen System with Time-Delay

    Science.gov (United States)

    Ren, Hai-Peng; Bai, Chao; Huang, Zhan-Zhan; Grebogi, Celso

    An experimental secure communication method based on the Chen system with time-delay is being proposed in this paper. The Chen system with time-delay is an infinite-dimensional system having more than one positive Lyapunov exponent. The message to be transmitted is encrypted using an hyperchaotic signal generated by the Chen system with time-delay and multishift cipher function. This encryption makes difficult for an eavesdropper to reconstruct the attractor by using time-delay embedding techniques, return map reconstruction, or spectral analysis, consequently, improving the security. Simulations and experiments on TI TMS320C6713 Digital Signal Processor (DSP) show improved resilience against attack and the feasibility of the proposed scheme.

  9. Chen Ning Yang’s New Contributions After He Returned to Where He Started

    Science.gov (United States)

    Zhu, Bang-Fen

    2018-01-01

    Chen Ning Yang returned to Tsinghua University as a full professor in 2003. Regarding the fact that very few people know what Professor Yang has contributed to science and to China after his return, in this article new contributions of Chen Ning Yang are introduced as far as the author knows, including his leading role in China’s sciences, the research in statistical physics, the role in cultivating gifted students, his research in history of science, and all other aspects relating to China’s developments.

  10. Arrhythmia classification using SVM with selected features | Kohli ...

    African Journals Online (AJOL)

    The various types of arrhythmias in the cardiac arrhythmias ECG database chosen from University of California at Irvine (UCI) to train SVM include ischemic changes (coronary artery disease), old inferior myocardial infarction, sinus bradycardy, right bundle branch block, and others. ECG arrhythmia datasets are of generally ...

  11. Accurate Multisteps Traffic Flow Prediction Based on SVM

    Directory of Open Access Journals (Sweden)

    Zhang Mingheng

    2013-01-01

    Full Text Available Accurate traffic flow prediction is prerequisite and important for realizing intelligent traffic control and guidance, and it is also the objective requirement for intelligent traffic management. Due to the strong nonlinear, stochastic, time-varying characteristics of urban transport system, artificial intelligence methods such as support vector machine (SVM are now receiving more and more attentions in this research field. Compared with the traditional single-step prediction method, the multisteps prediction has the ability that can predict the traffic state trends over a certain period in the future. From the perspective of dynamic decision, it is far important than the current traffic condition obtained. Thus, in this paper, an accurate multi-steps traffic flow prediction model based on SVM was proposed. In which, the input vectors were comprised of actual traffic volume and four different types of input vectors were compared to verify their prediction performance with each other. Finally, the model was verified with actual data in the empirical analysis phase and the test results showed that the proposed SVM model had a good ability for traffic flow prediction and the SVM-HPT model outperformed the other three models for prediction.

  12. Sales Growth Rate Forecasting Using Improved PSO and SVM

    Directory of Open Access Journals (Sweden)

    Xibin Wang

    2014-01-01

    Full Text Available Accurate forecast of the sales growth rate plays a decisive role in determining the amount of advertising investment. In this study, we present a preclassification and later regression based method optimized by improved particle swarm optimization (IPSO for sales growth rate forecasting. We use support vector machine (SVM as a classification model. The nonlinear relationship in sales growth rate forecasting is efficiently represented by SVM, while IPSO is optimizing the training parameters of SVM. IPSO addresses issues of traditional PSO, such as relapsing into local optimum, slow convergence speed, and low convergence precision in the later evolution. We performed two experiments; firstly, three classic benchmark functions are used to verify the validity of the IPSO algorithm against PSO. Having shown IPSO outperform PSO in convergence speed, precision, and escaping local optima, in our second experiment, we apply IPSO to the proposed model. The sales growth rate forecasting cases are used to testify the forecasting performance of proposed model. According to the requirements and industry knowledge, the sample data was first classified to obtain types of the test samples. Next, the values of the test samples were forecast using the SVM regression algorithm. The experimental results demonstrate that the proposed model has good forecasting performance.

  13. Cl/Br ratios and chlorine isotope evidences for groundwater salinization and its impact on groundwater arsenic, fluoride and iodine enrichment in the Datong basin, China.

    Science.gov (United States)

    Li, Junxia; Wang, Yanxin; Xie, Xianjun

    2016-02-15

    In order to identify the salinization processes and its impact on arsenic, fluoride and iodine enrichment in groundwater, hydrogeochemical and environmental isotope studies have been conducted on groundwater from the Datong basin, China. The total dissolved solid (TDS) concentrations in groundwater ranged from 451 to 8250 mg/L, and 41% of all samples were identified as moderately saline groundwater with TDS of 3000-10,000 mg/L. The results of groundwater Cl concentrations, Cl/Br molar ratio and Cl isotope composition suggest that three processes including water-rock interaction, surface saline soil flushing, and evapotranspiration result in the groundwater salinization in the study area. The relatively higher Cl/Br molar ratio in groundwater from multiple screening wells indicates the contribution of halite dissolution from saline soil flushed by vertical infiltration to the groundwater salinization. However, the results of groundwater Cl/Br molar ratio model indicate that the effect of saline soil flushing practice is limited to account for the observed salinity variation in groundwater. The plots of groundwater Cl vs. Cl/Br molar ratio, and Cl vs δ(37)Cl perform the dominant effects of evapotranspiration on groundwater salinization. Inverse geochemical modeling results show that evapotranspiration may cause approximately 66% loss of shallow groundwater to account for the observed hydrochemical pattern. Due to the redox condition fluctuation induced by irrigation activities and evapotranspiration, groundwater salinization processes have negative effects on groundwater arsenic enrichment. For groundwater iodine and fluoride enrichment, evapotranspiration partly accounts for their elevation in slightly saline water. However, too strong evapotranspiration would restrict groundwater fluoride concentration due to the limitation of fluorite solubility. Copyright © 2015. Published by Elsevier B.V.

  14. Mechanism and Prevention of a Chock Support Failure in the Longwall Top-Coal Caving Faces: A Case Study in Datong Coalfield, China

    Directory of Open Access Journals (Sweden)

    Zhu Li

    2018-01-01

    Full Text Available Longwall chock support failures seriously restrain the safety and high-efficiency of mining of extra thick coal seams, as well as causing a great waste of coal resources. During longwall top-coal caving (LTCC, the influential effect of the properties and the movement regulation of top-coal on strata behavior cannot be ignored, since the top-coal is the medium through which the load of the overlying strata is transferred to the chock supports. Taking Datong coalfield as an example, the mechanism of a chock support failure in the LTCC face was investigated. Research findings indicated that the hard top-coal and insufficient chock support capacity were primary reasons for chock support failure accidents. On account of the field-measured results, a new method to determine support capacity was proposed, which fully took the impact of the top-coal strength into consideration. The calculation revealed that the required support capacity had exceeded the existing production maximum, at about 22,000 KN. Since it was unrealistic to simply increase chock support capacity, other approaches, according to the theoretical analysis, were proposed, such as lowering the integrity and strength of the top-coal, and upgrading its crushing effect to weaken the support load effectively during the weighting period, which reduces the likelihood of chock support accidents occurring. Based on this, hydraulic fracturing for hard top-coal and optimization of the caving process (chock supports raised up and down repeatedly by manual operation before moving forward were presented. The proposed solutions were successfully applied in LTCC-west8101 for subsequent mining and achieved substantial benefits. The above research provides valuable references and ideas for the control of strata behavior to ensure safe and highly efficient mining in extremely thick and hard coal seams with the LTCC method.

  15. Cost-Effectiveness of Coal Workers' Pneumoconiosis Prevention Based on Its Predicted Incidence within the Datong Coal Mine Group in China.

    Directory of Open Access Journals (Sweden)

    Fuhai Shen

    Full Text Available We aimed to estimate the economic losses currently caused by coal workers' pneumoconiosis (CWP and, on the basis of these measurements, confirm the economic benefit of preventive measures. Our cohort study included 1,847 patients with CWP and 43,742 coal workers without CWP who were registered in the employment records of the Datong Coal Mine Group. We calculated the cumulative incidence rate of pneumoconiosis using the life-table method. We used the dose-response relationship between cumulative incidence density and cumulative dust exposure to predict the future trend in the incidence of CWP. We calculate the economic loss caused by CWP and economic effectiveness of CWP prevention by a step-wise model. The cumulative incidence rates of CWP in the tunneling, mining, combining, and helping cohorts were 58.7%, 28.1%, 21.7%, and 4.0%, respectively. The cumulative incidence rates increased gradually with increasing cumulative dust exposure (CDE. We predicted 4,300 new CWP cases, assuming the dust concentrations remained at the levels of 2011. If advanced dustproof equipment was adopted, 537 fewer people would be diagnosed with CWP. In all, losses of 1.207 billion Renminbi (RMB, official currency of China would be prevented and 4,698.8 healthy life years would be gained. Investments in advanced dustproof equipment would be total 843 million RMB, according to our study; the ratio of investment to restored economic losses was 1:1.43. Controlling workplace dust concentrations is critical to reduce the onset of pneumoconiosis and to achieve economic benefits.

  16. Cost-Effectiveness of Coal Workers' Pneumoconiosis Prevention Based on Its Predicted Incidence within the Datong Coal Mine Group in China

    Science.gov (United States)

    Yuan, Juxiang; Han, Bing; Cui, Kai; Ding, Yu; Fan, Xueyun; Cao, Hong; Yao, Sanqiao; Suo, Xia; Sun, Zhiqian; Yun, Xiang; Hua, Zhengbing; Chen, Jie

    2015-01-01

    We aimed to estimate the economic losses currently caused by coal workers’ pneumoconiosis (CWP) and, on the basis of these measurements, confirm the economic benefit of preventive measures. Our cohort study included 1,847 patients with CWP and 43,742 coal workers without CWP who were registered in the employment records of the Datong Coal Mine Group. We calculated the cumulative incidence rate of pneumoconiosis using the life-table method. We used the dose-response relationship between cumulative incidence density and cumulative dust exposure to predict the future trend in the incidence of CWP. We calculate the economic loss caused by CWP and economic effectiveness of CWP prevention by a step-wise model. The cumulative incidence rates of CWP in the tunneling, mining, combining, and helping cohorts were 58.7%, 28.1%, 21.7%, and 4.0%, respectively. The cumulative incidence rates increased gradually with increasing cumulative dust exposure (CDE). We predicted 4,300 new CWP cases, assuming the dust concentrations remained at the levels of 2011. If advanced dustproof equipment was adopted, 537 fewer people would be diagnosed with CWP. In all, losses of 1.207 billion Renminbi (RMB, official currency of China) would be prevented and 4,698.8 healthy life years would be gained. Investments in advanced dustproof equipment would be total 843 million RMB, according to our study; the ratio of investment to restored economic losses was 1:1.43. Controlling workplace dust concentrations is critical to reduce the onset of pneumoconiosis and to achieve economic benefits. PMID:26098706

  17. Influence of irrigation practices on arsenic mobilization: Evidence from isotope composition and Cl/Br ratios in groundwater from Datong Basin, northern China

    Science.gov (United States)

    Xie, Xianjun; Wang, Yanxin; Su, Chunli; Li, Junxia; Li, Mengdi

    2012-03-01

    SummaryEnvironment isotopes (δ18O and δ2H) and Cl/Br ratios in groundwater have been used to trace groundwater recharge and geochemical processes for arsenic contamination in Datong Basin. The arsenic concentrations of groundwater samples ranged from 0.4 to 434.9 μg/L with the average of 51.2 μg/L, which exceeded China's drinking water standard (10 μg/L). All the groundwater samples are plotted on or close to the meteoric water line of the δ18O vs. δ2H plot, indicating their meteoric origin. The relationship between δ18O values and Cl/Br ratios and Cl concentrations demonstrate that leaching and mixing are the dominant processes affecting the distribution of high arsenic groundwater in this area. The observed non-linearity in the trend between δ18O and arsenic concentration is due to combined effects of mixing and leaching. The similarity of the trend in Cl/Br ratios and δ18O values for high arsenic groundwater demonstrate that extensive leaching of irrigation return and salt flushing water flow could be the dominant process driving arsenic mobilization in the groundwater system. Moreover, the long term irrigation practice can cause the drastic change of the biogeochemical and redox condition of in the aquifer system, which in turn promotes the mobilization of arsenic. Therefore, groundwater pumping for irrigation in this area of waterborne endemic arsenic poisoning should be under strict control to protect groundwater quality in this area.

  18. Variability of OI 090.4 ShaoMing Hu , Xu Chen & DiFu Guo

    Indian Academy of Sciences (India)

    2009-12-20

    Dec 20, 2009 ... ShaoMing Hu. ∗. , Xu Chen & DiFu Guo. School of Space Science and Physics, Shandong University, Weihai,. 180 Cultural West Road, Shandong 264209, China. ∗ e-mail: husm@sdu.edu.cn. Abstract. OI 090.4 was monitored on 21 nights from 2006 to 2012 for studying the variability. Strong variations ...

  19. TEST OF THE CHEN-ROLL-ROSS MACROECONOMIC FACTOR MODEL: EVIDENCE FROM CROATIAN STOCK MARKET

    Directory of Open Access Journals (Sweden)

    Denis Dolinar

    2015-12-01

    Full Text Available This paper empirically examines the well-known Chen-Roll-Ross model on the Croatian stock market. Modifications of definitions of the Chen-Roll-Ross model variables showed as necessary because of doubtful availability and quality of input data needed. Namely, some macroeconomic and market variables are not available in the originally defined form or do not exist. In that sense this paper gives some alternative definitions for some model variables. Also, in order to improve statistical analysis, in this paper we have modified Fama-MacBeth technique in the way that second-pass regression was substituted with panel regression analysis. Based on the two-pass regression analysis of returns of 34 Croatian stocks on 4 macroeconomic variables during the seven-and-half-year observation period the following conclusion is made. In contrast to the results of Chen, Roll and Ross (1986 for the U.S. stock market, their model is not successful when describing a risk-return relation of Croatian stocks. Nevertheless, one observed version of the Chen-RollRoss model showed certain statistical significance. Namely, two risk factors in that version of the model were statistically significant: default premium, measured as risk premium for the corporate short-term bank loan financing, and term structure premium, measured on short-run basis.

  20. Linear SVM-Based Android Malware Detection for Reliable IoT Services

    National Research Council Canada - National Science Library

    Hyo-Sik Ham; Hwan-Hee Kim; Myung-Sup Kim; Mi-Jung Choi

    2014-01-01

    .... In this paper, we apply a linear support vector machine (SVM) to detect Android malware and compare the malware detection performance of SVM with that of other machine learning classifiers. Through experimental validation, we show that the SVM outperforms other machine learning classifiers.

  1. Effects of recharge and discharge on delta2H and delta18O composition and chloride concentration of high arsenic/fluoride groundwater from the Datong Basin, northern China.

    Science.gov (United States)

    Xie, Xianjun; Wang, Yanxin; Su, Chunli; Duan, Mengyu

    2013-02-01

    To better understand the effects of recharge and discharge on the hydrogeochemistry of high levels of arsenic (As) and fluoride (F) in groundwater, environmental isotopic composition (delta2H and delta18O) and chloride (Cl) concentrations were analyzed in 29 groundwater samples collected from the Datong Basin. High arsenic groundwater samples (As > 50 micog/L) were found to be enriched in lighter isotopic composition that ranged from -92 to -78 per thousand for deuterium (delta2H) and from -12.5 to -9.9 per thousand for oxygen-18 (delta18O). High F-containing groundwater (F > 1 mg/L) was relatively enriched in heavier isotopic composition and varied from -90 to -57 per thousand and from -12.2 to -6.7 per thousand for delta2H and delta18O, respectively. High chloride concentrations and delta18O values were primarily measured in groundwater samples from the northern and southwestern portions of the study area, indicating the effect of evaporation on groundwater. The observation of relatively homogenized and low delta18O values and chloride concentrations in groundwater samples from central part of the Datong Basin might be a result of fast recharge by irrigation returns, which suggests that irrigation using arsenic-contaminated groundwater affected the occurrence of high arsenic-containing groundwater in the basin.

  2. Quality-Oriented Classification of Aircraft Material Based on SVM

    Directory of Open Access Journals (Sweden)

    Hongxia Cai

    2014-01-01

    Full Text Available The existing material classification is proposed to improve the inventory management. However, different materials have the different quality-related attributes, especially in the aircraft industry. In order to reduce the cost without sacrificing the quality, we propose a quality-oriented material classification system considering the material quality character, Quality cost, and Quality influence. Analytic Hierarchy Process helps to make feature selection and classification decision. We use the improved Kraljic Portfolio Matrix to establish the three-dimensional classification model. The aircraft materials can be divided into eight types, including general type, key type, risk type, and leveraged type. Aiming to improve the classification accuracy of various materials, the algorithm of Support Vector Machine is introduced. Finally, we compare the SVM and BP neural network in the application. The results prove that the SVM algorithm is more efficient and accurate and the quality-oriented material classification is valuable.

  3. Research on Bearing Fault Diagnosis Using APSO-SVM Method

    Directory of Open Access Journals (Sweden)

    Guangchun Yang

    2014-07-01

    Full Text Available According to the statistics, over 30 % of rotating equipment faults occurred in bearings. Therefore, the fault diagnosis of bearing has a great significance. To achieve effective bearing faults diagnosis, a diagnosis model based on support vector machine (SVM and accelerated particle swarm optimization (APSO for bearing fault diagnosis is proposed. Firstly, empirical mode decomposition (EMD is adopted to decompose the fault signal into sum of several intrinsic mode function (IMF. Then, the feature vectors for bearing fault diagnosis are obtained from the IMF energy. Finally, the fault mode is identified by SVM model which is optimized by APSO. The experiment results show that the proposed diagnosis method can identify the bearing fault type effectively.

  4. SVM Intrusion Detection Model Based on Compressed Sampling

    Directory of Open Access Journals (Sweden)

    Shanxiong Chen

    2016-01-01

    Full Text Available Intrusion detection needs to deal with a large amount of data; particularly, the technology of network intrusion detection has to detect all of network data. Massive data processing is the bottleneck of network software and hardware equipment in intrusion detection. If we can reduce the data dimension in the stage of data sampling and directly obtain the feature information of network data, efficiency of detection can be improved greatly. In the paper, we present a SVM intrusion detection model based on compressive sampling. We use compressed sampling method in the compressed sensing theory to implement feature compression for network data flow so that we can gain refined sparse representation. After that SVM is used to classify the compression results. This method can realize detection of network anomaly behavior quickly without reducing the classification accuracy.

  5. Fault diagnosis of monoblock centrifugal pump using SVM

    Directory of Open Access Journals (Sweden)

    V. Muralidharan

    2014-09-01

    Full Text Available Monoblock centrifugal pumps are employed in variety of critical engineering applications. Continuous monitoring of such machine component becomes essential in order to reduce the unnecessary break downs. At the outset, vibration based approaches are widely used to carry out the condition monitoring tasks. Particularly fuzzy logic, support vector machine (SVM and artificial neural networks were employed for continuous monitoring and fault diagnosis. In the present study, the application of SVM algorithm in the field of fault diagnosis and condition monitoring is discussed. The continuous wavelet transforms were calculated for different families and at different levels. The computed transformation coefficients form the feature set for the classification of good and faulty conditions of the components of centrifugal pump. The classification accuracies of different continuous wavelet families at different levels were calculated and compared to find the best wavelet for the fault diagnosis of the monoblock centrifugal pump.

  6. A novel transmission line protection using DOST and SVM

    Directory of Open Access Journals (Sweden)

    M. Jaya Bharata Reddy

    2016-06-01

    Full Text Available This paper proposes a smart fault detection, classification and location (SFDCL methodology for transmission systems with multi-generators using discrete orthogonal Stockwell transform (DOST. The methodology is based on synchronized current measurements from remote telemetry units (RTUs installed at both ends of the transmission line. The energy coefficients extracted from the transient current signals due to occurrence of different types of faults using DOST are being utilized for real-time fault detection and classification. Support vector machine (SVM has been deployed for locating the fault distance using the extracted coefficients. A comparative study is performed for establishing the superiority of SVM over other popular computational intelligence methods, such as adaptive neuro-fuzzy inference system (ANFIS and artificial neural network (ANN, for more precise and reliable estimation of fault distance. The results corroborate the effectiveness of the suggested SFDCL algorithm for real-time transmission line fault detection, classification and localization.

  7. The efficacy of support vector machines (SVM) in robust ...

    Indian Academy of Sciences (India)

    (2006) by applying an SVM statistical learning machine on the time-scale wavelet decomposition methods. We used the data of 108 events in central Japan with magnitude ranging from 3 to 7.4 recorded at KiK-net network stations, for a source–receiver distance of up to 150 km during the period 1998–2011. We applied a ...

  8. Power quality events recognition using a SVM-based method

    Energy Technology Data Exchange (ETDEWEB)

    Cerqueira, Augusto Santiago; Ferreira, Danton Diego; Ribeiro, Moises Vidal; Duque, Carlos Augusto [Department of Electrical Circuits, Federal University of Juiz de Fora, Campus Universitario, 36036 900, Juiz de Fora MG (Brazil)

    2008-09-15

    In this paper, a novel SVM-based method for power quality event classification is proposed. A simple approach for feature extraction is introduced, based on the subtraction of the fundamental component from the acquired voltage signal. The resulting signal is presented to a support vector machine for event classification. Results from simulation are presented and compared with two other methods, the OTFR and the LCEC. The proposed method shown an improved performance followed by a reasonable computational cost. (author)

  9. Oil spill detection from SAR image using SVM based classification

    Directory of Open Access Journals (Sweden)

    A. A. Matkan

    2013-09-01

    Full Text Available In this paper, the potential of fully polarimetric L-band SAR data for detecting sea oil spills is investigated using polarimetric decompositions and texture analysis based on SVM classifier. First, power and magnitude measurements of HH and VV polarization modes and, Pauli, Freeman and Krogager decompositions are computed and applied in SVM classifier. Texture analysis is used for identification using SVM method. The texture features i.e. Mean, Variance, Contrast and Dissimilarity from them are then extracted. Experiments are conducted on full polarimetric SAR data acquired from PALSAR sensor of ALOS satellite on August 25, 2006. An accuracy assessment indicated overall accuracy of 78.92% and 96.46% for the power measurement of the VV polarization and the Krogager decomposition respectively in first step. But by use of texture analysis the results are improved to 96.44% and 96.65% quality for mean of power and magnitude measurements of HH and VV polarizations and the Krogager decomposition. Results show that the Krogager polarimetric decomposition method has the satisfying result for detection of sea oil spill on the sea surface and the texture analysis presents the good results.

  10. Automatic Language Identification with Discriminative Language Characterization Based on SVM

    Science.gov (United States)

    Suo, Hongbin; Li, Ming; Lu, Ping; Yan, Yonghong

    Robust automatic language identification (LID) is the task of identifying the language from a short utterance spoken by an unknown speaker. The mainstream approaches include parallel phone recognition language modeling (PPRLM), support vector machine (SVM) and the general Gaussian mixture models (GMMs). These systems map the cepstral features of spoken utterances into high level scores by classifiers. In this paper, in order to increase the dimension of the score vector and alleviate the inter-speaker variability within the same language, multiple data groups based on supervised speaker clustering are employed to generate the discriminative language characterization score vectors (DLCSV). The back-end SVM classifiers are used to model the probability distribution of each target language in the DLCSV space. Finally, the output scores of back-end classifiers are calibrated by a pair-wise posterior probability estimation (PPPE) algorithm. The proposed language identification frameworks are evaluated on 2003 NIST Language Recognition Evaluation (LRE) databases and the experiments show that the system described in this paper produces comparable results to the existing systems. Especially, the SVM framework achieves an equal error rate (EER) of 4.0% in the 30-second task and outperforms the state-of-art systems by more than 30% relative error reduction. Besides, the performances of proposed PPRLM and GMMs algorithms achieve an EER of 5.1% and 5.0% respectively.

  11. Hardware realization of an SVM algorithm implemented in FPGAs

    Science.gov (United States)

    Wiśniewski, Remigiusz; Bazydło, Grzegorz; Szcześniak, Paweł

    2017-08-01

    The paper proposes a technique of hardware realization of a space vector modulation (SVM) of state function switching in matrix converter (MC), oriented on the implementation in a single field programmable gate array (FPGA). In MC the SVM method is based on the instantaneous space-vector representation of input currents and output voltages. The traditional computation algorithms usually involve digital signal processors (DSPs) which consumes the large number of power transistors (18 transistors and 18 independent PWM outputs) and "non-standard positions of control pulses" during the switching sequence. Recently, hardware implementations become popular since computed operations may be executed much faster and efficient due to nature of the digital devices (especially concurrency). In the paper, we propose a hardware algorithm of SVM computation. In opposite to the existing techniques, the presented solution applies COordinate Rotation DIgital Computer (CORDIC) method to solve the trigonometric operations. Furthermore, adequate arithmetic modules (that is, sub-devices) used for intermediate calculations, such as code converters or proper sectors selectors (for output voltages and input current) are presented in detail. The proposed technique has been implemented as a design described with the use of Verilog hardware description language. The preliminary results of logic implementation oriented on the Xilinx FPGA (particularly, low-cost device from Artix-7 family from Xilinx was used) are also presented.

  12. Static Voltage Stability Analysis by Using SVM and Neural Network

    Directory of Open Access Journals (Sweden)

    Mehdi Hajian

    2013-01-01

    Full Text Available Voltage stability is an important problem in power system networks. In this paper, in terms of static voltage stability, and application of Neural Networks (NN and Supported Vector Machine (SVM for estimating of voltage stability margin (VSM and predicting of voltage collapse has been investigated. This paper considers voltage stability in power system in two parts. The first part calculates static voltage stability margin by Radial Basis Function Neural Network (RBFNN. The advantage of the used method is high accuracy in online detecting the VSM. Whereas the second one, voltage collapse analysis of power system is performed by Probabilistic Neural Network (PNN and SVM. The obtained results in this paper indicate, that time and number of training samples of SVM, are less than NN. In this paper, a new model of training samples for detection system, using the normal distribution load curve at each load feeder, has been used. Voltage stability analysis is estimated by well-know L and VSM indexes. To demonstrate the validity of the proposed methods, IEEE 14 bus grid and the actual network of Yazd Province are used.

  13. Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations

    Directory of Open Access Journals (Sweden)

    Yi Zhang

    2015-01-01

    Full Text Available Maximum likelihood classifier (MLC and support vector machines (SVM are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions.

  14. Self-Representation and Cultural Expectations: Yogi Chen and Religious Practices of Life-Writing

    Directory of Open Access Journals (Sweden)

    Richard K. Payne

    2016-03-01

    Full Text Available Explores the differences in self-representation as found in the autobiographical writings of Yogi Chen, Billy Graham, and the Dalai Lama. While the latter two are widely recognized in American popular religious culture, the former is virtually invisible outside the immigrant Chinese American community. This invisibility is consistent with fact that the religious praxes of immigrant communities remain largely under-studied. However, one additional factor appears to be the mismatch between the expectations of the dominant religious culture and the immigrant culture in terms of the ways in which religious leaders represent themselves. Both Billy Graham and the Dalai Lama present themselves in very humble terms, consistent with the expectations of the Pietist background to American popular religion. Yogi Chen on the contrary tends toward a self-aggrandizing style, which although consistent with the competitive nature of premodern Tibetan religious culture is not congruent with the expectations of American popular religion.

  15. Melancholia EEG classification based on CSSD and SVM

    Science.gov (United States)

    Shi, Jian-Jun; Yuan, Qing-Wu; Zhou, La-Wu

    2011-10-01

    It takes an important role to get the disease information from melancholia electroencephalograph (EEG). Firstly, A common spatial subspace decomposition (CSSD) method was used to extract features from 16-channel EEG of melancholia and normal healthy persons. Then based on support vector machines (SVM), a classifier was designed to train and test its classification capability between Melancholia and healthy persons. The results indicated that the proposed method can reach a higher accuracy as 95% in EEG classification, while the accuracy of the method based on wavelet is only 88%.That is, the proposed method is feasible for the melancholia diagnosis and research.

  16. Prediction of nuclear proteins using SVM and HMM models

    Directory of Open Access Journals (Sweden)

    Raghava Gajendra PS

    2009-01-01

    Full Text Available Abstract Background The nucleus, a highly organized organelle, plays important role in cellular homeostasis. The nuclear proteins are crucial for chromosomal maintenance/segregation, gene expression, RNA processing/export, and many other processes. Several methods have been developed for predicting the nuclear proteins in the past. The aim of the present study is to develop a new method for predicting nuclear proteins with higher accuracy. Results All modules were trained and tested on a non-redundant dataset and evaluated using five-fold cross-validation technique. Firstly, Support Vector Machines (SVM based modules have been developed using amino acid and dipeptide compositions and achieved a Mathews correlation coefficient (MCC of 0.59 and 0.61 respectively. Secondly, we have developed SVM modules using split amino acid compositions (SAAC and achieved the maximum MCC of 0.66. Thirdly, a hidden Markov model (HMM based module/profile was developed for searching exclusively nuclear and non-nuclear domains in a protein. Finally, a hybrid module was developed by combining SVM module and HMM profile and achieved a MCC of 0.87 with an accuracy of 94.61%. This method performs better than the existing methods when evaluated on blind/independent datasets. Our method estimated 31.51%, 21.89%, 26.31%, 25.72% and 24.95% of the proteins as nuclear proteins in Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, mouse and human proteomes respectively. Based on the above modules, we have developed a web server NpPred for predicting nuclear proteins http://www.imtech.res.in/raghava/nppred/. Conclusion This study describes a highly accurate method for predicting nuclear proteins. SVM module has been developed for the first time using SAAC for predicting nuclear proteins, where amino acid composition of N-terminus and the remaining protein were computed separately. In addition, our study is a first documentation where exclusively nuclear

  17. A Linear-RBF Multikernel SVM to Classify Big Text Corpora

    Directory of Open Access Journals (Sweden)

    R. Romero

    2015-01-01

    Full Text Available Support vector machine (SVM is a powerful technique for classification. However, SVM is not suitable for classification of large datasets or text corpora, because the training complexity of SVMs is highly dependent on the input size. Recent developments in the literature on the SVM and other kernel methods emphasize the need to consider multiple kernels or parameterizations of kernels because they provide greater flexibility. This paper shows a multikernel SVM to manage highly dimensional data, providing an automatic parameterization with low computational cost and improving results against SVMs parameterized under a brute-force search. The model consists in spreading the dataset into cohesive term slices (clusters to construct a defined structure (multikernel. The new approach is tested on different text corpora. Experimental results show that the new classifier has good accuracy compared with the classic SVM, while the training is significantly faster than several other SVM classifiers.

  18. Penerapan Support Vector Machine (SVM untuk Pengkategorian Penelitian

    Directory of Open Access Journals (Sweden)

    Fithri Selva Jumeilah

    2017-07-01

    Full Text Available Research every college will continue to grow. Research will be stored in softcopy and hardcopy. The preparation of the research should be categorized in order to facilitate the search for people who need reference. To categorize the research, we need a method for text mining, one of them is with the implementation of Support Vector Machines (SVM. The data used to recognize the characteristics of each category then it takes secondary data which is a collection of abstracts of research. The data will be pre-processed with several stages: case folding converts all the letters into lowercase, stop words removal removal of very common words, tokenizing discard punctuation, and stemming searching for root words by removing the prefix and suffix. Further data that has undergone preprocessing will be converted into a numerical form with for the term weighting stage that is the weighting contribution of each word. From the results of term weighting then obtained data that can be used for data training and test data. The training process is done by providing input in the form of text data that is known to the class or category. Then by using the Support Vector Machines algorithm, the input data is transformed into a rule, function, or knowledge model that can be used in the prediction process. From the results of this study obtained that the categorization of research produced by SVM has been very good. This is proven by the results of the test which resulted in an accuracy of 90%.

  19. Forecasting Dry Bulk Freight Index with Improved SVM

    Directory of Open Access Journals (Sweden)

    Qianqian Han

    2014-01-01

    Full Text Available An improved SVM model is presented to forecast dry bulk freight index (BDI in this paper, which is a powerful tool for operators and investors to manage the market trend and avoid price risking shipping industry. The BDI is influenced by many factors, especially the random incidents in dry bulk market, inducing the difficulty in forecasting of BDI. Therefore, to eliminate the impact of random incidents in dry bulk market, wavelet transform is adopted to denoise the BDI data series. Hence, the combined model of wavelet transform and support vector machine is developed to forecast BDI in this paper. Lastly, the BDI data in 2005 to 2012 are presented to test the proposed model. The 84 prior consecutive monthly BDI data are the inputs of the model, and the last 12 monthly BDI data are the outputs of model. The parameters of the model are optimized by genetic algorithm and the final model is conformed through SVM training. This paper compares the forecasting result of proposed method and three other forecasting methods. The result shows that the proposed method has higher accuracy and could be used to forecast the short-term trend of the BDI.

  20. SVM-based glioma grading. Optimization by feature reduction analysis

    Energy Technology Data Exchange (ETDEWEB)

    Zoellner, Frank G.; Schad, Lothar R. [University Medical Center Mannheim, Heidelberg Univ., Mannheim (Germany). Computer Assisted Clinical Medicine; Emblem, Kyrre E. [Massachusetts General Hospital, Charlestown, A.A. Martinos Center for Biomedical Imaging, Boston MA (United States). Dept. of Radiology; Harvard Medical School, Boston, MA (United States); Oslo Univ. Hospital (Norway). The Intervention Center

    2012-11-01

    We investigated the predictive power of feature reduction analysis approaches in support vector machine (SVM)-based classification of glioma grade. In 101 untreated glioma patients, three analytic approaches were evaluated to derive an optimal reduction in features; (i) Pearson's correlation coefficients (PCC), (ii) principal component analysis (PCA) and (iii) independent component analysis (ICA). Tumor grading was performed using a previously reported SVM approach including whole-tumor cerebral blood volume (CBV) histograms and patient age. Best classification accuracy was found using PCA at 85% (sensitivity = 89%, specificity = 84%) when reducing the feature vector from 101 (100-bins rCBV histogram + age) to 3 principal components. In comparison, classification accuracy by PCC was 82% (89%, 77%, 2 dimensions) and 79% by ICA (87%, 75%, 9 dimensions). For improved speed (up to 30%) and simplicity, feature reduction by all three methods provided similar classification accuracy to literature values ({proportional_to}87%) while reducing the number of features by up to 98%. (orig.)

  1. Chen Jingrun, China's famous mathematician: devastated by brain injuries on the doorstep to solving a fundamental mathematical puzzle.

    Science.gov (United States)

    Lei, Ting; Belykh, Evgenii; Dru, Alexander B; Yagmurlu, Kaan; Elhadi, Ali M; Nakaji, Peter; Preul, Mark C

    2016-07-01

    Chen Jingrun (1933-1996), perhaps the most prodigious mathematician of his time, focused on the field of analytical number theory. His work on Waring's problem, Legendre's conjecture, and Goldbach's conjecture led to progress in analytical number theory in the form of "Chen's Theorem," which he published in 1966 and 1973. His early life was ravaged by the Second Sino-Japanese War and the Chinese Cultural Revolution. On the verge of solving Goldbach's conjecture in 1984, Chen was struck by a bicyclist while also bicycling and suffered severe brain trauma. During his hospitalization, he was also found to have Parkinson's disease. Chen suffered another serious brain concussion after a fall only a few months after recovering from the bicycle crash. With significant deficits, he remained hospitalized for several years without making progress while receiving modern Western medical therapies. In 1988 traditional Chinese medicine experts were called in to assist with his treatment. After a year of acupuncture and oxygen therapy, Chen could control his basic bowel and bladder functions, he could walk slowly, and his swallowing and speech improved. When Chen was unable to produce complex work or finish his final work on Goldbach's conjecture, his mathematical pursuits were taken up vigorously by his dedicated students. He was able to publish Youth Math, a mathematics book that became an inspiration in Chinese education. Although he died in 1996 at the age of 63 after surviving brutal political repression, being deprived of neurological function at the very peak of his genius, and having to be supported by his wife, Chen ironically became a symbol of dedication, perseverance, and motivation to his students and associates, to Chinese youth, to a nation, and to mathematicians and scientists worldwide.

  2. "Active Flux" DTFC-SVM Sensorless Control of IPMSM

    DEFF Research Database (Denmark)

    Boldea, Ion; Codruta Paicu, Mihaela; Gheorghe-Daniel, Andreescu,

    2009-01-01

    This paper proposes an implementation of a motionsensorless control system in wide speed range based on "active flux" observer, and direct torque and flux control with space vector modulation (DTFC-SVM) for the interior permanent magnet synchronous motor (IPMSM), without signal injection...... is obtained, because the active flux position is identical with the rotor position. Extensive experimental results are presented to verify the principles and to demonstrate the effectiveness of the proposed sensorless control system. With the active flux observer, the IPMSM drive system operates from very low....... The concept of "active flux" (or "torque producing flux") turns all the rotor salient-pole ac machines into fully nonsalient-pole ones. A new function for Lq inductance depending on torque is introduced to model the magnetic saturation. Notable simplification in the rotor position and speed estimation...

  3. [Application of SVM and wavelet analysis in EEG classification].

    Science.gov (United States)

    Zhao, Jianlin; Zhou, Weidong; Liu, Kai; Cai, Dongmei

    2011-04-01

    We employed two methods of support vector machines (SVM) combined with two kinds of wavelet analysis to classify these EEG signals, on the basis of the different profiles, energy, and frequency characteristics of the EEG during the seizures. One method was to classify these signals using waveform characteristics of the EEG signal. The other was to classify these signals based on fluctuation index and variation coefficient of the EEG signal. We compared the classification accuracies of these two methods with the intermittent EEG and epileptic EEG. The results of the experiments showed that both the two methods for distinguishing epileptic EEG and interictal EEG can achieve an effective performance. It was also confirmed that the latter, the method based on the fluctuation index and variation coefficient, possesses a better effect of classification.

  4. Optimal parameters of the SVM for temperature prediction

    Directory of Open Access Journals (Sweden)

    X. Shi

    2015-05-01

    Full Text Available This paper established three different optimization models in order to predict the Foping station temperature value. The dimension was reduced to change multivariate climate factors into a few variables by principal component analysis (PCA. And the parameters of support vector machine (SVM were optimized with genetic algorithm (GA, particle swarm optimization (PSO and developed genetic algorithm. The most suitable method was applied for parameter optimization by comparing the results of three different models. The results are as follows: The developed genetic algorithm optimization parameters of the predicted values were closest to the measured value after the analog trend, and it is the most fitting measured value trends, and its homing speed is relatively fast.

  5. Feature selection based on SVM significance maps for classification of dementia

    NARCIS (Netherlands)

    E.E. Bron (Esther); M. Smits (Marion); J.C. van Swieten (John); W.J. Niessen (Wiro); S. Klein (Stefan)

    2014-01-01

    textabstractSupport vector machine significance maps (SVM p-maps) previously showed clusters of significantly different voxels in dementiarelated brain regions. We propose a novel feature selection method for classification of dementia based on these p-maps. In our approach, the SVM p-maps are

  6. Spectral Reconstruction Based on Svm for Cross Calibration

    Science.gov (United States)

    Gao, H.; Ma, Y.; Liu, W.; He, H.

    2017-05-01

    Chinese HY-1C/1D satellites will use a 5nm/10nm-resolutional visible-near infrared(VNIR) hyperspectral sensor with the solar calibrator to cross-calibrate with other sensors. The hyperspectral radiance data are composed of average radiance in the sensor's passbands and bear a spectral smoothing effect, a transform from the hyperspectral radiance data to the 1-nm-resolution apparent spectral radiance by spectral reconstruction need to be implemented. In order to solve the problem of noise cumulation and deterioration after several times of iteration by the iterative algorithm, a novel regression method based on SVM is proposed, which can approach arbitrary complex non-linear relationship closely and provide with better generalization capability by learning. In the opinion of system, the relationship between the apparent radiance and equivalent radiance is nonlinear mapping introduced by spectral response function(SRF), SVM transform the low-dimensional non-linear question into high-dimensional linear question though kernel function, obtaining global optimal solution by virtue of quadratic form. The experiment is performed using 6S-simulated spectrums considering the SRF and SNR of the hyperspectral sensor, measured reflectance spectrums of water body and different atmosphere conditions. The contrastive result shows: firstly, the proposed method is with more reconstructed accuracy especially to the high-frequency signal; secondly, while the spectral resolution of the hyperspectral sensor reduces, the proposed method performs better than the iterative method; finally, the root mean square relative error(RMSRE) which is used to evaluate the difference of the reconstructed spectrum and the real spectrum over the whole spectral range is calculated, it decreses by one time at least by proposed method.

  7. SPECTRAL RECONSTRUCTION BASED ON SVM FOR CROSS CALIBRATION

    Directory of Open Access Journals (Sweden)

    H. Gao

    2017-05-01

    Full Text Available Chinese HY-1C/1D satellites will use a 5nm/10nm-resolutional visible-near infrared(VNIR hyperspectral sensor with the solar calibrator to cross-calibrate with other sensors. The hyperspectral radiance data are composed of average radiance in the sensor’s passbands and bear a spectral smoothing effect, a transform from the hyperspectral radiance data to the 1-nm-resolution apparent spectral radiance by spectral reconstruction need to be implemented. In order to solve the problem of noise cumulation and deterioration after several times of iteration by the iterative algorithm, a novel regression method based on SVM is proposed, which can approach arbitrary complex non-linear relationship closely and provide with better generalization capability by learning. In the opinion of system, the relationship between the apparent radiance and equivalent radiance is nonlinear mapping introduced by spectral response function(SRF, SVM transform the low-dimensional non-linear question into high-dimensional linear question though kernel function, obtaining global optimal solution by virtue of quadratic form. The experiment is performed using 6S-simulated spectrums considering the SRF and SNR of the hyperspectral sensor, measured reflectance spectrums of water body and different atmosphere conditions. The contrastive result shows: firstly, the proposed method is with more reconstructed accuracy especially to the high-frequency signal; secondly, while the spectral resolution of the hyperspectral sensor reduces, the proposed method performs better than the iterative method; finally, the root mean square relative error(RMSRE which is used to evaluate the difference of the reconstructed spectrum and the real spectrum over the whole spectral range is calculated, it decreses by one time at least by proposed method.

  8. Settlement Prediction of Road Soft Foundation Using a Support Vector Machine (SVM Based on Measured Data

    Directory of Open Access Journals (Sweden)

    Yu Huiling

    2016-01-01

    Full Text Available The suppor1t vector machine (SVM is a relatively new artificial intelligence technique which is increasingly being applied to geotechnical problems and is yielding encouraging results. SVM is a new machine learning method based on the statistical learning theory. A case study based on road foundation engineering project shows that the forecast results are in good agreement with the measured data. The SVM model is also compared with BP artificial neural network model and traditional hyperbola method. The prediction results indicate that the SVM model has a better prediction ability than BP neural network model and hyperbola method. Therefore, settlement prediction based on SVM model can reflect actual settlement process more correctly. The results indicate that it is effective and feasible to use this method and the nonlinear mapping relation between foundation settlement and its influence factor can be expressed well. It will provide a new method to predict foundation settlement.

  9. Antifungal Activity of Isoliquiritin and Its Inhibitory Effect against Peronophythora litchi Chen through a Membrane Damage Mechanism

    Directory of Open Access Journals (Sweden)

    Jianjun Luo

    2016-02-01

    Full Text Available This study investigated the antifungal activity and potential antifungal mechanism(s of isoliquiritin against P. litchi Chen, one of the main litchi pathogens. The antifungal activity of isoliquiritin against P. litchi Chen had been proven in a dose-dependent manner through in vitro (mycelial growth and sporangia germination and in vivo (detached leaf tests. Results revealed that isoliquiritin exhibited significant antifungal activity against the tested pathogens, especially, P. litchi Chen, with a minimum inhibitory concentration of 27.33 mg/L. The morphology of P. litchi Chen was apparently changed by isoliquiritin through cytoplasm leakage and distortion of mycelia. The cell membrane permeability of the P. litchi Chen increased with the increasing concentration of isoliquiritin, as evidenced by a rise in relative electric conductivity and a decrease in reducing sugar contents. These results indicated that the antifungal effects of isoliquiritin could be explained by a membrane lesion mechanism causing damage to the cell membrane integrity leading to the death of mycelial cells. Taken together, isoliquiritin may be used as a natural alternative to commercial fungicides or a lead compound to develop new fungicides for the control of litchi downy blight.

  10. DSP Based Direct Torque Control of Permanent Magnet Synchronous Motor (PMSM) using Space Vector Modulation (DTC-SVM)

    DEFF Research Database (Denmark)

    Swierczynski, Dariusz; Kazmierkowski, Marian P.; Blaabjerg, Frede

    2002-01-01

    DSP Based Direct Torque Control of Permanent Magnet Synchronous Motor (PMSM) using Space Vector Modulation (DTC-SVM)......DSP Based Direct Torque Control of Permanent Magnet Synchronous Motor (PMSM) using Space Vector Modulation (DTC-SVM)...

  11. Minimal contact CR submanifolds in S satisfying the δ(2)-Chen equality

    Science.gov (United States)

    Munteanu, Marian Ioan; Vrancken, Luc

    2014-01-01

    In his book on Pseudo-Riemannian geometry, δ-invariants and applications, B.Y. Chen introduced a sequence of curvature invariants. Each of these invariants is used to obtain a lower bound for the length of the mean curvature vector for an immersion in a real space form. A submanifold is called an ideal submanifold, for that curvature invariant, if and only if it realizes equality at every point. The first such introduced invariant is called δ(2). On the other hand, a well known notion for submanifolds of Sasakian space forms, is the notion of a contact CR-submanifold. In this paper we combine both notions and start the study of minimal contact CR-submanifolds which are δ(2) ideal. We relate this to a special class of surfaces and obtain a complete classification in arbitrary dimensions.

  12. Combination model of empirical mode decomposition and SVM for river flow forecasting

    Science.gov (United States)

    Ismail, Shuhaida; Shabri, Ani

    2017-04-01

    A reliable prediction of river flow is always important for sound planning and smooth operation of the water resource system. In this study, a combination models based on Empirical Mode Decomposition (EMD) and Support Vector Machine (SVM) model referred as EMD-SVM is proposed for estimating future value of monthly river flow data. The proposed EMD-SVM has three important stages. The first stage, the data were decomposed into several numbers of Intrinsic Mode Functions (IMF) and a residual using EMD technique. In the second stage, the meaningful signals are identified using a statistical measure and the new dataset are obtained in this stage. The final stage applied SVM as forecasting tool to perform the river flow forecasting. To assess the effectiveness of EMD-SVM model, Selangor and Bernam Rivers were used as case studies. The experiment results stated that the proposed EMD-SVM have outperformed other model based on Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Correlation Coefficient (r). This indicating that EMD-SVM is a useful tool to predict complex time series with non-stationary and nonlinearity issues as well as a promising new method for river flow forecasting.

  13. An Improved Grey Wolf Optimization Strategy Enhanced SVM and Its Application in Predicting the Second Major

    Directory of Open Access Journals (Sweden)

    Yan Wei

    2017-01-01

    Full Text Available In order to develop a new and effective prediction system, the full potential of support vector machine (SVM was explored by using an improved grey wolf optimization (GWO strategy in this study. An improved GWO, IGWO, was first proposed to identify the most discriminative features for major prediction. In the proposed approach, particle swarm optimization (PSO was firstly adopted to generate the diversified initial positions, and then GWO was used to update the current positions of population in the discrete searching space, thus getting the optimal feature subset for the better classification purpose based on SVM. The resultant methodology, IGWO-SVM, is rigorously examined based on the real-life data which includes a series of factors that influence the students’ final decision to choose the specific major. To validate the proposed method, other metaheuristic based SVM methods including GWO based SVM, genetic algorithm based SVM, and particle swarm optimization-based SVM were used for comparison in terms of classification accuracy, AUC (the area under the receiver operating characteristic (ROC curve, sensitivity, and specificity. The experimental results demonstrate that the proposed approach can be regarded as a promising success with the excellent classification accuracy, AUC, sensitivity, and specificity of 87.36%, 0.8735, 85.37%, and 89.33%, respectively. Promisingly, the proposed methodology might serve as a new candidate of powerful tools for second major selection.

  14. sw-SVM: sensor weighting support vector machines for EEG-based brain-computer interfaces

    Science.gov (United States)

    Jrad, N.; Congedo, M.; Phlypo, R.; Rousseau, S.; Flamary, R.; Yger, F.; Rakotomamonjy, A.

    2011-10-01

    In many machine learning applications, like brain-computer interfaces (BCI), high-dimensional sensor array data are available. Sensor measurements are often highly correlated and signal-to-noise ratio is not homogeneously spread across sensors. Thus, collected data are highly variable and discrimination tasks are challenging. In this work, we focus on sensor weighting as an efficient tool to improve the classification procedure. We present an approach integrating sensor weighting in the classification framework. Sensor weights are considered as hyper-parameters to be learned by a support vector machine (SVM). The resulting sensor weighting SVM (sw-SVM) is designed to satisfy a margin criterion, that is, the generalization error. Experimental studies on two data sets are presented, a P300 data set and an error-related potential (ErrP) data set. For the P300 data set (BCI competition III), for which a large number of trials is available, the sw-SVM proves to perform equivalently with respect to the ensemble SVM strategy that won the competition. For the ErrP data set, for which a small number of trials are available, the sw-SVM shows superior performances as compared to three state-of-the art approaches. Results suggest that the sw-SVM promises to be useful in event-related potentials classification, even with a small number of training trials.

  15. Online Fault Diagnosis for Biochemical Process Based on FCM and SVM.

    Science.gov (United States)

    Wang, Xianfang; Du, Haoze; Tan, Jinglu

    2016-12-01

    Fault diagnosis is becoming an important issue in biochemical process, and a novel online fault detection and diagnosis approach is designed by combining fuzzy c-means (FCM) and support vector machine (SVM). The samples are preprocessed via FCM algorithm to enhance the ability of classification firstly. Then, those samples are input to the SVM classifier to realize the biochemical process fault diagnosis. In this study, a glutamic acid fermentation process is chosen as an example to diagnose the fault by this method, the result shows that the diagnosis time is largely shortened, and the accuracy is extremely improved by comparing to a single SVM method.

  16. An improved conjugate gradient scheme to the solution of least squares SVM.

    Science.gov (United States)

    Chu, Wei; Ong, Chong Jin; Keerthi, S Sathiya

    2005-03-01

    The least square support vector machines (LS-SVM) formulation corresponds to the solution of a linear system of equations. Several approaches to its numerical solutions have been proposed in the literature. In this letter, we propose an improved method to the numerical solution of LS-SVM and show that the problem can be solved using one reduced system of linear equations. Compared with the existing algorithm for LS-SVM, the approach used in this letter is about twice as efficient. Numerical results using the proposed method are provided for comparisons with other existing algorithms.

  17. SVM-based automatic diagnosis method for keratoconus

    Science.gov (United States)

    Gao, Yuhong; Wu, Qiang; Li, Jing; Sun, Jiande; Wan, Wenbo

    2017-06-01

    Keratoconus is a progressive cornea disease that can lead to serious myopia and astigmatism, or even to corneal transplantation, if it becomes worse. The early detection of keratoconus is extremely important to know and control its condition. In this paper, we propose an automatic diagnosis algorithm for keratoconus to discriminate the normal eyes and keratoconus ones. We select the parameters obtained by Oculyzer as the feature of cornea, which characterize the cornea both directly and indirectly. In our experiment, 289 normal cases and 128 keratoconus cases are divided into training and test sets respectively. Far better than other kernels, the linear kernel of SVM has sensitivity of 94.94% and specificity of 97.87% with all the parameters training in the model. In single parameter experiment of linear kernel, elevation with 92.03% sensitivity and 98.61% specificity and thickness with 97.28% sensitivity and 97.82% specificity showed their good classification abilities. Combining elevation and thickness of the cornea, the proposed method can reach 97.43% sensitivity and 99.19% specificity. The experiments demonstrate that the proposed automatic diagnosis method is feasible and reliable.

  18. SVM-Based Control System for a Robot Manipulator

    Directory of Open Access Journals (Sweden)

    Foudil Abdessemed

    2012-12-01

    Full Text Available Real systems are usually non-linear, ill-defined, have variable parameters and are subject to external disturbances. Modelling these systems is often an approximation of the physical phenomena involved. However, it is from this approximate system of representation that we propose - in this paper - to build a robust control, in the sense that it must ensure low sensitivity towards parameters, uncertainties, variations and external disturbances. The computed torque method is a well-established robot control technique which takes account of the dynamic coupling between the robot links. However, its main disadvantage lies on the assumption of an exactly known dynamic model which is not realizable in practice. To overcome this issue, we propose the estimation of the dynamics model of the nonlinear system with a machine learning regression method. The output of this regressor is used in conjunction with a PD controller to achieve the tracking trajectory task of a robot manipulator. In cases where some of the parameters of the plant undergo a change in their values, poor performance may result. To cope with this drawback, a fuzzy precompensator is inserted to reinforce the SVM computed torque-based controller and avoid any deterioration. The theory is developed and the simulation results are carried out on a two-degree of freedom robot manipulator to demonstrate the validity of the proposed approach.

  19. Multitask SVM learning for remote sensing data classification

    Science.gov (United States)

    Leiva-Murillo, Jose M.; Gómez-Chova, Luis; Camps-Valls, Gustavo

    2010-10-01

    Many remote sensing data processing problems are inherently constituted by several tasks that can be solved either individually or jointly. For instance, each image in a multitemporal classification setting could be taken as an individual task but relation to previous acquisitions should be properly considered. In such problems, different modalities of the data (temporal, spatial, angular) gives rise to changes between the training and test distributions, which constitutes a difficult learning problem known as covariate shift. Multitask learning methods aim at jointly solving a set of prediction problems in an efficient way by sharing information across tasks. This paper presents a novel kernel method for multitask learning in remote sensing data classification. The proposed method alleviates the dataset shift problem by imposing cross-information in the classifiers through matrix regularization. We consider the support vector machine (SVM) as core learner and two regularization schemes are introduced: 1) the Euclidean distance of the predictors in the Hilbert space; and 2) the inclusion of relational operators between tasks. Experiments are conducted in the challenging remote sensing problems of cloud screening from multispectral MERIS images and for landmine detection.

  20. A Realistic Seizure Prediction Study Based on Multiclass SVM.

    Science.gov (United States)

    Direito, Bruno; Teixeira, César A; Sales, Francisco; Castelo-Branco, Miguel; Dourado, António

    2017-05-01

    A patient-specific algorithm, for epileptic seizure prediction, based on multiclass support-vector machines (SVM) and using multi-channel high-dimensional feature sets, is presented. The feature sets, combined with multiclass classification and post-processing schemes aim at the generation of alarms and reduced influence of false positives. This study considers 216 patients from the European Epilepsy Database, and includes 185 patients with scalp EEG recordings and 31 with intracranial data. The strategy was tested over a total of 16,729.80[Formula: see text]h of inter-ictal data, including 1206 seizures. We found an overall sensitivity of 38.47% and a false positive rate per hour of 0.20. The performance of the method achieved statistical significance in 24 patients (11% of the patients). Despite the encouraging results previously reported in specific datasets, the prospective demonstration on long-term EEG recording has been limited. Our study presents a prospective analysis of a large heterogeneous, multicentric dataset. The statistical framework based on conservative assumptions, reflects a realistic approach compared to constrained datasets, and/or in-sample evaluations. The improvement of these results, with the definition of an appropriate set of features able to improve the distinction between the pre-ictal and nonpre-ictal states, hence minimizing the effect of confounding variables, remains a key aspect.

  1. A modular spectrum sensing system based on PSO-SVM.

    Science.gov (United States)

    Cai, Zhuoran; Zhao, Honglin; Yang, Zhutian; Mo, Yun

    2012-11-08

    In the cognitive radio system, spectrum sensing for detecting the presence of primary users in a licensed spectrum is a fundamental problem. Energy detection is the most popular spectrum sensing scheme used to differentiate the case where the primary user’s signal is present from the case where there is only noise. In fact, the nature of spectrum sensing can be taken as a binary classification problem, and energy detection is a linear classifier. If the signal-to-noise ratio (SNR) of the received signal is low, and the number of received signal samples for sensing is small, the binary classification problem is linearly inseparable. In this situation the performance of energy detection will decrease seriously. In this paper, a novel approach for obtaining a nonlinear threshold based on support vector machine with particle swarm optimization (PSO-SVM) to replace the linear threshold used in traditional energy detection is proposed. Simulations demonstrate that the performance of the proposed algorithm is much better than that of traditional energy detection.

  2. Towards Transformation of Knowledge and Subjectivity in Curriculum Inquiry: Insights from Chen Kuan-Hsing's "Asia as Method"

    Science.gov (United States)

    Lin, Angel M. Y.

    2012-01-01

    Chen's book, "Asia as Method" (Duke University Press, 2010), and his theorization on topics of de-imperialization, de-colonization, de-cold war, as well as on foregrounding epistemologies and frames of reference situated in the diverse contexts in Asia have contributed to empowering scholars and researchers situated not only in Taiwan,…

  3. Control of the Fractional-Order Chen Chaotic System via Fractional-Order Scalar Controller and Its Circuit Implementation

    OpenAIRE

    Qiong Huang; Chunyang Dong; Qianbin Chen

    2014-01-01

    A fractional-order scalar controller which involves only one state variable is proposed. By this fractional-order scalar controller, the unstable equilibrium points in the fractional-order Chen chaotic system can be asymptotically stable. The present control strategy is theoretically rigorous. Some circuits are designed to realize these control schemes. The outputs of circuit agree with the results of theoretical results.

  4. On the automatic link between affect and tendencies to approach and avoid: Chen and Bargh (1999 revisited.

    Directory of Open Access Journals (Sweden)

    Mark eRotteveel

    2015-04-01

    Full Text Available Within the literature on emotion and behavioral action, studies on approach-avoidance take up a prominent place. Several experimental paradigms feature successful conceptual replications but many original studies have not yet been replicated directly. We present such a direct replication attempt of two seminal experiments originally conducted by Chen and Bargh (1999. In their first experiment, participants affectively evaluated attitude objects by pulling or pushing a lever. Participants who had to pull the lever with positively valenced attitude objects and push the lever with negatively valenced attitude objects (i.e., congruent instruction did so faster than participants who had to follow the reverse (i.e., incongruent instruction. In Chen and Bargh's second experiment, the explicit evaluative instructions were absent and participants merely responded to the attitude objects by either always pushing or always pulling the lever. Similar results were obtained as in Experiment 1. Based on these findings, Chen and Bargh concluded that (1 attitude objects are evaluated automatically; and (2 attitude objects automatically trigger a behavioral tendency to approach or avoid. We attempted to replicate both experiments and failed to find the effects reported by Chen and Bargh as indicated by our pre-registered Bayesian data analyses; nevertheless, the evidence in favor of the null hypotheses was only anecdotal, and definitive conclusions await further study.

  5. Detecting microcalcifications in mammograms by using SVM method for the diagnostics of breast cancer

    Science.gov (United States)

    Wan, Baikun; Wang, Ruiping; Qi, Hongzhi; Cao, Xuchen

    2005-01-01

    Support vector machine (SVM) is a new statistical learning method. Compared with the classical machine learning methods, SVM learning discipline is to minimize the structural risk instead of the empirical risk of the classical methods, and it gives better generative performance. Because SVM algorithm is a convex quadratic optimization problem, the local optimal solution is certainly the global optimal one. In this paper a SVM algorithm is applied to detect the micro-calcifications (MCCs) in mammograms for the diagnostics of breast cancer that has not been reported yet. It had been tested with 10 mammograms and the results show that the algorithm can achieve a higher true positive in comparison with artificial neural network (ANN) based on the empirical risk minimization, and is valuable for further study and application in the clinical engineering.

  6. Damage level prediction of non-reshaped berm breakwater using ANN, SVM and ANFIS models

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; SubbaRao; Harish, N.; Lokesha

    Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, NITK, Surathkal, India. Soft computing techniques like Artificial Neural Network (ANN), Support Vector Machine (SVM) and Adaptive Neuro Fuzzy Inference system (ANFIS) models...

  7. SVM ensemble based transfer learning for large-scale membrane proteins discrimination.

    Science.gov (United States)

    Mei, Suyu

    2014-01-07

    Membrane proteins play important roles in molecular trans-membrane transport, ligand-receptor recognition, cell-cell interaction, enzyme catalysis, host immune defense response and infectious disease pathways. Up to present, discriminating membrane proteins remains a challenging problem from the viewpoints of biological experimental determination and computational modeling. This work presents SVM ensemble based transfer learning model for membrane proteins discrimination (SVM-TLM). To reduce the data constraints on computational modeling, this method investigates the effectiveness of transferring the homolog knowledge to the target membrane proteins under the framework of probability weighted ensemble learning. As compared to multiple kernel learning based transfer learning model, the method takes the advantages of sparseness based SVM optimization on large data, thus more computationally efficient for large protein data analysis. The experiments on large membrane protein benchmark dataset show that SVM-TLM achieves significantly better cross validation performance than the baseline model. © 2013 Elsevier Ltd. All rights reserved.

  8. Human Walking Pattern Recognition Based on KPCA and SVM with Ground Reflex Pressure Signal

    Directory of Open Access Journals (Sweden)

    Zhaoqin Peng

    2013-01-01

    Full Text Available Algorithms based on the ground reflex pressure (GRF signal obtained from a pair of sensing shoes for human walking pattern recognition were investigated. The dimensionality reduction algorithms based on principal component analysis (PCA and kernel principal component analysis (KPCA for walking pattern data compression were studied in order to obtain higher recognition speed. Classifiers based on support vector machine (SVM, SVM-PCA, and SVM-KPCA were designed, and the classification performances of these three kinds of algorithms were compared using data collected from a person who was wearing the sensing shoes. Experimental results showed that the algorithm fusing SVM and KPCA had better recognition performance than the other two methods. Experimental outcomes also confirmed that the sensing shoes developed in this paper can be employed for automatically recognizing human walking pattern in unlimited environments which demonstrated the potential application in the control of exoskeleton robots.

  9. The impact of systematic landscape conservation planning on ecosystem: Chen Youlan river watershed

    Science.gov (United States)

    Chen, Chi-ju

    2017-04-01

    Heraclitus said that "no man ever steps in the same river twice." Everything continues to change. Land use change will keep redefine itself and subject the Earth and humankind to collateral changes. Humankind benefits from ecosystem in many ways. The ecosystem provides people with nutrients, enriches soil with sediment, and sustains all living organisms with water; these benefits are known as ecosystem services. In Taiwan, land use change has impacted ecosystem and biodiversity on various levels. Thus, we took six land use scenarios from 1999 to 2005 in Chen Youlan river watershed as our case study, intending to observe the course of ecosystem and biodiversity changes and the cause of it. Systematic Landscape conservation planning (SLCP) framework can be adopted when designing land use policy to safeguard human interests and ecosystem. This study use SLCP to develop ecosystem services and biodiversity protection strategies. Several strategies were designed by using 1999 to 2005 data as provision to protect the intactness of future ecosystem services and biodiversity. This research explores the potential and possible impacts of different land use protection strategies in the future. It is possible to identify the conservation priority of a certain region by using the Zonation meta-algorithm. This study selects the zonation critical protection area (Joint set of Yushan National Park) as strategy A, B and C. Strategy D takes Yushan National Park as a protected area; unstable hot spots in 1999/03 (Joint set of Yushan National Park) are selected as strategy E. Next, we used Kappa statistical method to find the minimal ecosystem services change and biodiversity hotspots change of the five aforementioned strategies and compared with those from 1999/03. By the Kappa statistical method, we further prioritized the important conservation areas by strategy A, B, C, E in the future. The results can not only serve as management reference for government agencies, but also develop

  10. Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification

    Directory of Open Access Journals (Sweden)

    C. Fernandez-Lozano

    2013-01-01

    Full Text Available Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM. Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA, the most representative variables for a specific classification problem can be selected.

  11. Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification

    Science.gov (United States)

    Fernandez-Lozano, C.; Canto, C.; Gestal, M.; Andrade-Garda, J. M.; Rabuñal, J. R.; Dorado, J.; Pazos, A.

    2013-01-01

    Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA), the most representative variables for a specific classification problem can be selected. PMID:24453933

  12. Comparison of sensorless FOC and SVM-DTFC of PMSM for low-speed applications

    DEFF Research Database (Denmark)

    Basar, Mehmet Sertug

    2013-01-01

    This article presents the performance analysis of Field Oriented Control (FOC) and Space Vector Modulation (SVM) Direct Torque and Flux Control (DTFC) of a Non-Salient Permanent Magnet Synchronous Machine (PMSM) under sensorless control within low speed region. The high-frequency alternating...... with a commercially available PMSM machine. Both controllers show satisfactory sensorless performance. FOC provides smoother and more accurate response while SVM-DTFC has the advantage of faster control....

  13. Accurate Fluid Level Measurement in Dynamic Environment Using Ultrasonic Sensor and ν-SVM

    Directory of Open Access Journals (Sweden)

    Jenny TERZIC

    2009-10-01

    Full Text Available A fluid level measurement system based on a single Ultrasonic Sensor and Support Vector Machines (SVM based signal processing and classification system has been developed to determine the fluid level in automotive fuel tanks. The novel approach based on the ν-SVM classification method uses the Radial Basis Function (RBF to compensate for the measurement error induced by the sloshing effects in the tank caused by vehicle motion. A broad investigation on selected pre-processing filters, namely, Moving Mean, Moving Median, and Wavelet filter, has also been presented. Field drive trials were performed under normal driving conditions at various fuel volumes ranging from 5 L to 50 L to acquire sample data from the ultrasonic sensor for the training of SVM model. Further drive trials were conducted to obtain data to verify the SVM results. A comparison of the accuracy of the predicted fluid level obtained using SVM and the pre-processing filters is provided. It is demonstrated that the ν-SVM model using the RBF kernel function and the Moving Median filter has produced the most accurate outcome compared with the other signal filtration methods in terms of fluid level measurement.

  14. A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images.

    Science.gov (United States)

    Xu, Yongzheng; Yu, Guizhen; Wang, Yunpeng; Wu, Xinkai; Ma, Yalong

    2016-08-19

    A new hybrid vehicle detection scheme which integrates the Viola-Jones (V-J) and linear SVM classifier with HOG feature (HOG + SVM) methods is proposed for vehicle detection from low-altitude unmanned aerial vehicle (UAV) images. As both V-J and HOG + SVM are sensitive to on-road vehicles' in-plane rotation, the proposed scheme first adopts a roadway orientation adjustment method, which rotates each UAV image to align the roads with the horizontal direction so the original V-J or HOG + SVM method can be directly applied to achieve fast detection and high accuracy. To address the issue of descending detection speed for V-J and HOG + SVM, the proposed scheme further develops an adaptive switching strategy which sophistically integrates V-J and HOG + SVM methods based on their different descending trends of detection speed to improve detection efficiency. A comprehensive evaluation shows that the switching strategy, combined with the road orientation adjustment method, can significantly improve the efficiency and effectiveness of the vehicle detection from UAV images. The results also show that the proposed vehicle detection method is competitive compared with other existing vehicle detection methods. Furthermore, since the proposed vehicle detection method can be performed on videos captured from moving UAV platforms without the need of image registration or additional road database, it has great potentials of field applications. Future research will be focusing on expanding the current method for detecting other transportation modes such as buses, trucks, motors, bicycles, and pedestrians.

  15. Selecting Feature Subsets Based on SVM-RFE and the Overlapping Ratio with Applications in Bioinformatics

    Directory of Open Access Journals (Sweden)

    Xiaohui Lin

    2017-12-01

    Full Text Available Feature selection is an important topic in bioinformatics. Defining informative features from complex high dimensional biological data is critical in disease study, drug development, etc. Support vector machine-recursive feature elimination (SVM-RFE is an efficient feature selection technique that has shown its power in many applications. It ranks the features according to the recursive feature deletion sequence based on SVM. In this study, we propose a method, SVM-RFE-OA, which combines the classification accuracy rate and the average overlapping ratio of the samples to determine the number of features to be selected from the feature rank of SVM-RFE. Meanwhile, to measure the feature weights more accurately, we propose a modified SVM-RFE-OA (M-SVM-RFE-OA algorithm that temporally screens out the samples lying in a heavy overlapping area in each iteration. The experiments on the eight public biological datasets show that the discriminative ability of the feature subset could be measured more accurately by combining the classification accuracy rate with the average overlapping degree of the samples compared with using the classification accuracy rate alone, and shielding the samples in the overlapping area made the calculation of the feature weights more stable and accurate. The methods proposed in this study can also be used with other RFE techniques to define potential biomarkers from big biological data.

  16. Wormholes supported by phantom energy from Shan-Chen cosmological fluids

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Deng [Nankai University, Theoretical Physics Division, Chern Institute of Mathematics, Tianjin (China); Meng, Xin-He [Nankai University, Department of Physics, Tianjin (China); Institute of Theoretical Physics, CAS, State key Lab of Theoretical Physics, Beijing (China)

    2016-03-15

    In the present paper, the exact solutions of spherically symmetrical Einstein field equations describing wormholes supported by phantom energy that violates the null energy condition from Shan-Chen background fluid are obtained. We have considered the important case of the model parameter ψ ∼ 1, which corresponds to the ''saturation effect'', and this regime corresponds to an effective form of ''asymptotic freedom'' for the fluids, but occurring at cosmological rather than subnuclear scales. Then we investigate the allowed range for the values of the model parameters g and ω when the spacetime metrics describe wormholes and discuss the possible singularities of the solutions, finding that the obtained spacetimes are geodesically complete. Furthermore, we construct two traversable wormholes through matching our obtained interior solutions to the exterior Schwarzschild solutions and analyze the traversabilities of the wormholes. Finally, we consider the case of anisotropic pressure and discover that the transverse pressure also crosses the phantom divide -1 with the growth of the wormhole dimension, and it tends to be the same as the radial pressure with the growth of the wormhole radius. (orig.)

  17. Psychometric properties of the Revised Chen Internet Addiction Scale (CIAS-R) in Chinese adolescents.

    Science.gov (United States)

    Mak, Kwok-Kei; Lai, Ching-Man; Ko, Chih-Hung; Chou, Chien; Kim, Dong-Il; Watanabe, Hiroko; Ho, Roger C M

    2014-10-01

    The Revised Chen Internet Addiction Scale (CIAS-R) was developed to assess Internet addiction in Chinese populations, but its psychometric properties in adolescents have not been examined. This study aimed to evaluate the factor structure and psychometric properties of CIAS-R in Hong Kong Chinese adolescents. 860 Grade 7 to 13 students (38 % boys) completed the CIAS-R, the Young's Internet Addiction Test (IAT), and the Health of the Nation Outcome Scales for Children and Adolescents (HoNOSCA) in a survey. The prevalence of Internet addiction as assessed by CIAS-R was 18 %. High internal consistency and inter-item correlations were reported for the CIAS-R. Results from the confirmatory factor analysis suggested a four-factor structure of Compulsive Use and Withdrawal, Tolerance, Interpersonal and Health-related Problems, and Time Management Problems. Moreover, results of hierarchical multiple regression supported the incremental validity of the CIAS-R to predict mental health outcomes beyond the effects of demographic differences and self-reported time spent online. The CIAS is a reliable and valid measure of internet addiction problems in Hong Kong adolescents. Future study is warranted to validate the cutoffs of the CIAS-R for identification of adolescents with Internet use problems who may have mental health needs.

  18. Parallel SVM for the analysis of hyperspectral data

    Science.gov (United States)

    Cavallaro, Gabriele; Atli Benediktsson, Jón; Riedel, Morris

    2014-05-01

    .e., borders, edges, discontinuities, surfaces, shapes) by performing a detailed physical analysis of the structures. Mathematical morphology provides very useful tools which allow enriching the image analysis when dealing with very high resolution (VHR) images. One of the most promising of the recent developments in the field of pattern recognition are Support Vector Machines (SVMs). These are supervised learning methods which are widely used for classification and regression. In such a context, our work aims to explore some issues regarding the SVMs. In particular, SVMs require a significant computational and storage capacity due to the large number of training vectors used for the analysis of very high spatial and spectral resolution remote sensing data. Specifically, we will adopt a parallel SVM based on the iterative MapReduce in order to analyze large scale classification problems by improving the computation speed and preserving the classification accuracies.

  19. Comparative Analysis of ANN and SVM Models Combined with Wavelet Preprocess for Groundwater Depth Prediction

    Directory of Open Access Journals (Sweden)

    Ting Zhou

    2017-10-01

    Full Text Available Reliable prediction of groundwater depth fluctuations has been an important component in sustainable water resources management. In this study, a data-driven prediction model combining discrete wavelet transform (DWT preprocess and support vector machine (SVM was proposed for groundwater depth forecasting. Regular artificial neural networks (ANN, regular SVM, and wavelet preprocessed artificial neural networks (WANN models were also developed for comparison. These methods were applied to the monthly groundwater depth records over a period of 37 years from ten wells in the Mengcheng County, China. Relative absolute error (RAE, Pearson correlation coefficient (r, root mean square error (RMSE, and Nash-Sutcliffe efficiency (NSE were adopted for model evaluation. The results indicate that wavelet preprocess extremely improved the training and test performance of ANN and SVM models. The WSVM model provided the most precise and reliable groundwater depth prediction compared with ANN, SVM, and WSVM models. The criterion of RAE, r, RMSE, and NSE values for proposed WSVM model are 0.20, 0.97, 0.18 and 0.94, respectively. Comprehensive comparisons and discussion revealed that wavelet preprocess extremely improves the prediction precision and reliability for both SVM and ANN models. The prediction result of SVM model is superior to ANN model in generalization ability and precision. Nevertheless, the performance of WANN is superior to SVM model, which further validates the power of data preprocess in data-driven prediction models. Finally, the optimal model, WSVM, is discussed by comparing its subseries performances as well as model performance stability, revealing the efficiency and universality of WSVM model in data driven prediction field.

  20. Politics in the Western Maya Region (I: Ajawil/Ajawlel and Ch'e'n

    Directory of Open Access Journals (Sweden)

    Péter Bíró

    2011-01-01

    Full Text Available In a series of articles I reflect on the use of various expressions which are connected to what we call the political in the inscriptions of the Classic Maya Western Region. These words express concepts which help to understand the intricate details of the interactions between the political entities and their internal organisations in the Classic Maya Lowlands. Words such as 7ajawil, 7ajawlel, the kennings built on the base of ch'e7n (cave, pond, the emblem glyphs and titles will be examined in light of what they tell us about the functioning of the political organisation of the Classic Period in a constrained region.En una serie de artículos como éste investigo el uso de varias palabras en las inscripciones mayas de la época Clásica de la Región Occidental vinculadas con lo que nosotros llamamos "política". Estas palabras expresan conceptos que ayudan a entender los matices de las relaciones entre las entidades políticas de las Tierras Bajas Mayas y su organización interna. Términos como ajaw'ü I ajawlel, los difrasismos con base ch'e'n (cueva, pozo, los glifos emblemas y los títulos serán examinados tomando en cuenta la información que nos proporcionan sobre el funcionamiento de la organización política de la época Clásica en una región restringida.

  1. Writing Beyond the Wall: Translation, Cross-cultural Exchange, and Chen Ran's 'A Private Life'

    Directory of Open Access Journals (Sweden)

    Kay Schaffer

    2006-09-01

    Full Text Available The past decade has witnessed an unprecedented rise in the global flow of knowledge, nowhere more apparent in the exchange of ideas between China and modern western democracies. Our interest concerns one aspect of this global flow— the translation of Chinese women’s autobiographical writing into English. Taking Chen Ran’s A Private Life (English edition, 2004 as a point of departure, the paper explores issues of translingual practice and cross-cultural exchange. It considers what escapes or is lost in translation as well as the additive potential of the host text. It is sometimes the case that the translation can deliberately make certain ambiguities visible—whether from pragmatic, market-driven motivations or from more complex political, historical and cultural considerations. These negotiations of meaning that occur in the translation process can reverberate on the critical reception of texts in both the ‘guest’ and ‘host’ languages (Liu 1995, with open-ended, incomplete and indeterminate effects. The paper examines the effects of the omission of a brief parenthetical section of three paragraphs from one chapter of the Chinese edition of A Private Life. Yet, even that small emendation changes the original text as a cultural object and alters potential modes of its reception. In this case, the translation results in a loss of ambiguity, irony, philosophic and rhetorical sophistication while also offering additive potentials that enhance the generation of new meanings in the translingual exchange, here with reference Tiananmen and contemporary feminism in China. The translation process provides new channels for readers, writers and theorists to dialogue and communicate across gaps of difference, despite inhibiting factors like the imposition of local restraints, the universalising pressures of western modernity, and asymmetrical relations of power between guest and host language contexts.

  2. Aktivitätsmessung auf nukleinsäuremodifizierten Oberflächen

    Science.gov (United States)

    Schmidt, Peter Michael

    2003-06-01

    Im Bereich der medizinischen Diagnostik spielen DNA-Chips eine immer wichtigere Rolle. Dabei werden Glas- oder Silikon-Oberflächen mit Tausenden von einzelsträngigen DNA-Fragmenten, sog. Sonden, bestückt, die mit den passenden DNA-Fragmenten in der zugefügten Patientenprobe verschmelzen. Die Auswertung solcher Messungen liefert die Diagnose für Krankheiten wie z.B. Krebs, Alzheimer oder für den Nachweis pathogener Erreger. Durch fortschreitende Miniaturisierung dieser Meßsysteme können bis zu 40.000 Genfragmente des Menschen in einer einzigen Messung analysiert werden. Neben den DNA-Fragmenten können Bio-Chips auch für andere biologische Komponenten wie Antikörper und Proteine eingesetzt werden, wobei bei letzteren neben der Bindung auch die Aktivität ein wichtiger Diagnoseparamter ist. Am Fraunhofer-Institut für medizinische Technik und am Lehrstuhl für Analytische Biochemie der Universität Potsdam wurden im Rahmen einer Doktorarbeit Methoden entwickelt, die es ermöglichen auf nukleinsäuremodifizierten Sensoroberflächen die Aktivität von Proteinen zu messen. Es wurden Nukleinsäuren auf Oberflächen optischer Sensoren verankert. Diese fungierten als Rezeptor für die Proteine sowie auch als Substrat für Restriktionsenzyme, die Nukleinsäuren schneiden und Polymerasen, die Nukleinsäuren synthetisieren und verlängern können. Seine Anwendung fand diese Messmethode in der Messung der Aktivität des Proteins Telomerase, das in 90% aller Tumore erhöhte Aktivität gegenüber gesunden Zellen aufweist. Die Vorteile dieses neuen Assays gegenüber älteren Methoden liegt im Verzicht auf radioaktiv-markierten Komponenten und einer deutlich verkürzten Analysezeit. Die Arbeit schliesst mit einem funktionsfähigen Nachweis der Telomeraseaktivität im Zellextrakt von gesunden und kranken Zellen. Der direkte Einfluß von Hemmstoffen auf die Aktivität konnte sichtbar gemacht werden, und steht daher bei der Entwicklung neuer Tumor-Diagnostika und

  3. Control of the Fractional-Order Chen Chaotic System via Fractional-Order Scalar Controller and Its Circuit Implementation

    Directory of Open Access Journals (Sweden)

    Qiong Huang

    2014-01-01

    Full Text Available A fractional-order scalar controller which involves only one state variable is proposed. By this fractional-order scalar controller, the unstable equilibrium points in the fractional-order Chen chaotic system can be asymptotically stable. The present control strategy is theoretically rigorous. Some circuits are designed to realize these control schemes. The outputs of circuit agree with the results of theoretical results.

  4. Linear regression-based efficient SVM learning for large-scale classification.

    Science.gov (United States)

    Wu, Jianxin; Yang, Hao

    2015-10-01

    For large-scale classification tasks, especially in the classification of images, additive kernels have shown a state-of-the-art accuracy. However, even with the recent development of fast algorithms, learning speed and the ability to handle large-scale tasks are still open problems. This paper proposes algorithms for large-scale support vector machines (SVM) classification and other tasks using additive kernels. First, a linear regression SVM framework for general nonlinear kernel is proposed using linear regression to approximate gradient computations in the learning process. Second, we propose a power mean SVM (PmSVM) algorithm for all additive kernels using nonsymmetric explanatory variable functions. This nonsymmetric kernel approximation has advantages over the existing methods: 1) it does not require closed-form Fourier transforms and 2) it does not require extra training for the approximation either. Compared on benchmark large-scale classification data sets with millions of examples or millions of dense feature dimensions, PmSVM has achieved the highest learning speed and highest accuracy among recent algorithms in most cases.

  5. A Method for Aileron Actuator Fault Diagnosis Based on PCA and PGC-SVM

    Directory of Open Access Journals (Sweden)

    Wei-Li Qin

    2016-01-01

    Full Text Available Aileron actuators are pivotal components for aircraft flight control system. Thus, the fault diagnosis of aileron actuators is vital in the enhancement of the reliability and fault tolerant capability. This paper presents an aileron actuator fault diagnosis approach combining principal component analysis (PCA, grid search (GS, 10-fold cross validation (CV, and one-versus-one support vector machine (SVM. This method is referred to as PGC-SVM and utilizes the direct drive valve input, force motor current, and displacement feedback signal to realize fault detection and location. First, several common faults of aileron actuators, which include force motor coil break, sensor coil break, cylinder leakage, and amplifier gain reduction, are extracted from the fault quadrantal diagram; the corresponding fault mechanisms are analyzed. Second, the data feature extraction is performed with dimension reduction using PCA. Finally, the GS and CV algorithms are employed to train a one-versus-one SVM for fault classification, thus obtaining the optimal model parameters and assuring the generalization of the trained SVM, respectively. To verify the effectiveness of the proposed approach, four types of faults are introduced into the simulation model established by AMESim and Simulink. The results demonstrate its desirable diagnostic performance which outperforms that of the traditional SVM by comparison.

  6. Parallelization of multicategory support vector machines (PMC-SVM for classifying microarray data

    Directory of Open Access Journals (Sweden)

    Deng Youping

    2006-12-01

    Full Text Available Abstract Background Multicategory Support Vector Machines (MC-SVM are powerful classification systems with excellent performance in a variety of data classification problems. Since the process of generating models in traditional multicategory support vector machines for large datasets is very computationally intensive, there is a need to improve the performance using high performance computing techniques. Results In this paper, Parallel Multicategory Support Vector Machines (PMC-SVM have been developed based on the sequential minimum optimization-type decomposition method for support vector machines (SMO-SVM. It was implemented in parallel using MPI and C++ libraries and executed on both shared memory supercomputer and Linux cluster for multicategory classification of microarray data. PMC-SVM has been analyzed and evaluated using four microarray datasets with multiple diagnostic categories, such as different cancer types and normal tissue types. Conclusion The experiments show that the PMC-SVM can significantly improve the performance of classification of microarray data without loss of accuracy, compared with previous work.

  7. Intelligent gearbox diagnosis methods based on SVM, wavelet lifting and RBR.

    Science.gov (United States)

    Gao, Lixin; Ren, Zhiqiang; Tang, Wenliang; Wang, Huaqing; Chen, Peng

    2010-01-01

    Given the problems in intelligent gearbox diagnosis methods, it is difficult to obtain the desired information and a large enough sample size to study; therefore, we propose the application of various methods for gearbox fault diagnosis, including wavelet lifting, a support vector machine (SVM) and rule-based reasoning (RBR). In a complex field environment, it is less likely for machines to have the same fault; moreover, the fault features can also vary. Therefore, a SVM could be used for the initial diagnosis. First, gearbox vibration signals were processed with wavelet packet decomposition, and the signal energy coefficients of each frequency band were extracted and used as input feature vectors in SVM for normal and faulty pattern recognition. Second, precision analysis using wavelet lifting could successfully filter out the noisy signals while maintaining the impulse characteristics of the fault; thus effectively extracting the fault frequency of the machine. Lastly, the knowledge base was built based on the field rules summarized by experts to identify the detailed fault type. Results have shown that SVM is a powerful tool to accomplish gearbox fault pattern recognition when the sample size is small, whereas the wavelet lifting scheme can effectively extract fault features, and rule-based reasoning can be used to identify the detailed fault type. Therefore, a method that combines SVM, wavelet lifting and rule-based reasoning ensures effective gearbox fault diagnosis.

  8. [Academic thoughts on Practice of acupuncture and moxibustion written by CHEN Jingwen, the acupuncture master in the Republic of China].

    Science.gov (United States)

    Huang, Weiping; Li, Naiqi

    2015-03-01

    Through the collection of Practice of acupuncture and moxibustion written by CHEN Jingwen, the acupuncture master in the Republic of China, the academic characteristics on acupuncture and moxibusiton were analyzed. The literature comparison method was adopted to compare the works of LUO Zhaoju, ZENG Tianzhi and LI Wenxian, etc. at the same period. It was discovered that CHEN Jingwen was the medical master who systematicly brought up the theory of acupoint properties earlier in the modern times. Classifying drugs based on acupoints was his academic feature. Additionally, the compatibility therapy of Chinese medicine was introduced to explain the essential ideas on the acupoints combination. The treatment was determined on the basis of zangxiang theory and the reinforcing and reducing therapy of acupuncture was emphasized in the determination of treatment and prescription. CHEN Jingwen's theory of acupoint property had been stressed and spread among the medical scholars in the Republic of China and he had made the beneficial exploration for the development of modern acupuncture and moxibustion therapy.

  9. Cryptanalysis and improvement of Chen-Hsiang- Shih’s remote user authentication scheme using smart cards

    Directory of Open Access Journals (Sweden)

    Rafael Martínez-Peláez

    2013-01-01

    Full Text Available Recientemente, Chen-Hsiang-Shih propusieron un nuevo esquema de autenticación de usuario remoto basado en un identificador dinámico. Los autores afirman que su esquema es más seguro que los trabajos previos. Sin embargo, se demuestra que su esquema continúa siendo inseguro contra diferentes tipos de ataques. Con el fin de mejorar la seguridad del esquema propuesto por Chen-Hsiang-Shih, se propone un esquema que consigue los siguientes objetivos de seguridad: el esquema no requiere de una tabla de verificación, cada usuario elige y cambia su contraseña libremente, cada usuario mantiene su contraseña en secreto, el esquema requiere autenticación mutua, el esquema establece una clave de sesión después de una autenticación correcta, y el esquema mantiene el anonimato del usuario. El análisis de seguridad y la comparación demuestran que nuestro esquema es más seguro que el esquema propuesto por Das-Saxena-Gulati, Wang-Liu-Xiao-Dan, y Chen-Hsiang-Shih.

  10. Modeling of SVM Diode Clamping Three-Level Inverter Connected to Grid

    DEFF Research Database (Denmark)

    Guo, Yougui; Zeng, Ping; Zhu, Jieqiong

    2011-01-01

    PLECS is used to model the diode clamping three-level inverter connected to grid and good results are obtained. First the output voltage SVM is described for diode clamping three-level inverter with loads connected to Y. Then the output voltage SVM of diode clamping three-level inverter is simply...... analyzed with loads connected to △. But it will be further researched in the future. Third, PLECS is briefly introduced. Fourth, the modeling of diode clamping three-level inverter is briefly presented with PLECS. Finally, a series of simulations are carried out. The simulation results tell us PLECS...... is very powerful tool to real power circuits and it is very easy to simulate them. They have also verified that SVM control strategy is feasible to control the diode clamping three-level inverter....

  11. A RLS-SVM Aided Fusion Methodology for INS during GPS Outages

    Science.gov (United States)

    Yao, Yiqing; Xu, Xiaosu

    2017-01-01

    In order to maintain a relatively high accuracy of navigation performance during global positioning system (GPS) outages, a novel robust least squares support vector machine (LS-SVM)-aided fusion methodology is explored to provide the pseudo-GPS position information for the inertial navigation system (INS). The relationship between the yaw, specific force, velocity, and the position increment is modeled. Rather than share the same weight in the traditional LS-SVM, the proposed algorithm allocates various weights for different data, which makes the system immune to the outliers. Field test data was collected to evaluate the proposed algorithm. The comparison results indicate that the proposed algorithm can effectively provide position corrections for standalone INS during the 300 s GPS outage, which outperforms the traditional LS-SVM method. Historical information is also involved to better represent the vehicle dynamics. PMID:28245549

  12. Linear SVM-Based Android Malware Detection for Reliable IoT Services

    Directory of Open Access Journals (Sweden)

    Hyo-Sik Ham

    2014-01-01

    Full Text Available Current many Internet of Things (IoT services are monitored and controlled through smartphone applications. By combining IoT with smartphones, many convenient IoT services have been provided to users. However, there are adverse underlying effects in such services including invasion of privacy and information leakage. In most cases, mobile devices have become cluttered with important personal user information as various services and contents are provided through them. Accordingly, attackers are expanding the scope of their attacks beyond the existing PC and Internet environment into mobile devices. In this paper, we apply a linear support vector machine (SVM to detect Android malware and compare the malware detection performance of SVM with that of other machine learning classifiers. Through experimental validation, we show that the SVM outperforms other machine learning classifiers.

  13. A RLS-SVM Aided Fusion Methodology for INS during GPS Outages

    Directory of Open Access Journals (Sweden)

    Yiqing Yao

    2017-02-01

    Full Text Available In order to maintain a relatively high accuracy of navigation performance during global positioning system (GPS outages, a novel robust least squares support vector machine (LS-SVM-aided fusion methodology is explored to provide the pseudo-GPS position information for the inertial navigation system (INS. The relationship between the yaw, specific force, velocity, and the position increment is modeled. Rather than share the same weight in the traditional LS-SVM, the proposed algorithm allocates various weights for different data, which makes the system immune to the outliers. Field test data was collected to evaluate the proposed algorithm. The comparison results indicate that the proposed algorithm can effectively provide position corrections for standalone INS during the 300 s GPS outage, which outperforms the traditional LS-SVM method. Historical information is also involved to better represent the vehicle dynamics.

  14. Laos Organization Name Using Cascaded Model Based on SVM and CRF

    Directory of Open Access Journals (Sweden)

    Duan Shaopeng

    2017-01-01

    Full Text Available According to the characteristics of Laos organization name, this paper proposes a two layer model based on conditional random field (CRF and support vector machine (SVM for Laos organization name recognition. A layer of model uses CRF to recognition simple organization name, and the result is used to support the decision of the second level. Based on the driving method, the second layer uses SVM and CRF to recognition the complicated organization name. Finally, the results of the two levels are combined, And by a subsequent treatment to correct results of low confidence recognition. The results show that this approach based on SVM and CRF is efficient in recognizing organization name through open test for real linguistics, and the recalling rate achieve 80. 83%and the precision rate achieves 82. 75%.

  15. Damage level prediction of non-reshaped berm breakwater using ANN, SVM and ANFIS models

    Directory of Open Access Journals (Sweden)

    Sukomal Mandal

    2012-06-01

    Full Text Available The damage analysis of coastal structure is very important as it involves many design parameters to be considered for the better and safe design of structure. In the present study experimental data for non-reshaped berm breakwater are collected from Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, NITK, Surathkal, India. Soft computing techniques like Artificial Neural Network (ANN, Support Vector Machine (SVM and Adaptive Neuro Fuzzy Inference system (ANFIS models are constructed using experimental data sets to predict the damage level of non-reshaped berm breakwater. The experimental data are used to train ANN, SVM and ANFIS models and results are determined in terms of statistical measures like mean square error, root mean square error, correlation coefficient and scatter index. The result shows that soft computing techniques i.e., ANN, SVM and ANFIS can be efficient tools in predicting damage levels of non reshaped berm breakwater.

  16. Extraction of prostatic lumina and automated recognition for prostatic calculus image using PCA-SVM.

    Science.gov (United States)

    Wang, Zhuocai; Xu, Xiangmin; Ding, Xiaojun; Xiao, Hui; Huang, Yusheng; Liu, Jian; Xing, Xiaofen; Wang, Hua; Liao, D Joshua

    2011-01-01

    Identification of prostatic calculi is an important basis for determining the tissue origin. Computation-assistant diagnosis of prostatic calculi may have promising potential but is currently still less studied. We studied the extraction of prostatic lumina and automated recognition for calculus images. Extraction of lumina from prostate histology images was based on local entropy and Otsu threshold recognition using PCA-SVM and based on the texture features of prostatic calculus. The SVM classifier showed an average time 0.1432 second, an average training accuracy of 100%, an average test accuracy of 93.12%, a sensitivity of 87.74%, and a specificity of 94.82%. We concluded that the algorithm, based on texture features and PCA-SVM, can recognize the concentric structure and visualized features easily. Therefore, this method is effective for the automated recognition of prostatic calculi.

  17. SVM-based spectrum mobility prediction scheme in mobile cognitive radio networks.

    Science.gov (United States)

    Wang, Yao; Zhang, Zhongzhao; Ma, Lin; Chen, Jiamei

    2014-01-01

    Spectrum mobility as an essential issue has not been fully investigated in mobile cognitive radio networks (CRNs). In this paper, a novel support vector machine based spectrum mobility prediction (SVM-SMP) scheme is presented considering time-varying and space-varying characteristics simultaneously in mobile CRNs. The mobility of cognitive users (CUs) and the working activities of primary users (PUs) are analyzed in theory. And a joint feature vector extraction (JFVE) method is proposed based on the theoretical analysis. Then spectrum mobility prediction is executed through the classification of SVM with a fast convergence speed. Numerical results validate that SVM-SMP gains better short-time prediction accuracy rate and miss prediction rate performance than the two algorithms just depending on the location and speed information. Additionally, a rational parameter design can remedy the prediction performance degradation caused by high speed SUs with strong randomness movements.

  18. Energy-efficient SVM learning control system for biped walking robots.

    Science.gov (United States)

    Wang, Liyang; Liu, Zhi; Chen, Chun Lung Philip; Zhang, Yun; Lee, Sukhan; Chen, Xin

    2013-05-01

    An energy-efficient support vector machine (EE-SVM) learning control system considering the energy cost of each training sample of biped dynamic is proposed to realize energy-efficient biped walking. Energy costs of the biped walking samples are calculated. Then the samples are weighed with the inverses of the energy costs. An EE-SVM objective function with energy-related slack variables is proposed, which follows the principle that the sample with the lowest energy consumption is treated as the most important one in the training. That means the samples with lower energy consumption contribute more to the EE-SVM regression function learning, which highly increases the energy efficiency of the biped walking. Simulation results demonstrate the effectiveness of the proposed method.

  19. Combined SVM-CRFs for biological named entity recognition with maximal bidirectional squeezing.

    Science.gov (United States)

    Zhu, Fei; Shen, Bairong

    2012-01-01

    Biological named entity recognition, the identification of biological terms in text, is essential for biomedical information extraction. Machine learning-based approaches have been widely applied in this area. However, the recognition performance of current approaches could still be improved. Our novel approach is to combine support vector machines (SVMs) and conditional random fields (CRFs), which can complement and facilitate each other. During the hybrid process, we use SVM to separate biological terms from non-biological terms, before we use CRFs to determine the types of biological terms, which makes full use of the power of SVM as a binary-class classifier and the data-labeling capacity of CRFs. We then merge the results of SVM and CRFs. To remove any inconsistencies that might result from the merging, we develop a useful algorithm and apply two rules. To ensure biological terms with a maximum length are identified, we propose a maximal bidirectional squeezing approach that finds the longest term. We also add a positive gain to rare events to reinforce their probability and avoid bias. Our approach will also gradually extend the context so more contextual information can be included. We examined the performance of four approaches with GENIA corpus and JNLPBA04 data. The combination of SVM and CRFs improved performance. The macro-precision, macro-recall, and macro-F(1) of the SVM-CRFs hybrid approach surpassed conventional SVM and CRFs. After applying the new algorithms, the macro-F1 reached 91.67% with the GENIA corpus and 84.04% with the JNLPBA04 data.

  20. PSO-SVM-Based Online Locomotion Mode Identification for Rehabilitation Robotic Exoskeletons

    Directory of Open Access Journals (Sweden)

    Yi Long

    2016-09-01

    Full Text Available Locomotion mode identification is essential for the control of a robotic rehabilitation exoskeletons. This paper proposes an online support vector machine (SVM optimized by particle swarm optimization (PSO to identify different locomotion modes to realize a smooth and automatic locomotion transition. A PSO algorithm is used to obtain the optimal parameters of SVM for a better overall performance. Signals measured by the foot pressure sensors integrated in the insoles of wearable shoes and the MEMS-based attitude and heading reference systems (AHRS attached on the shoes and shanks of leg segments are fused together as the input information of SVM. Based on the chosen window whose size is 200 ms (with sampling frequency of 40 Hz, a three-layer wavelet packet analysis (WPA is used for feature extraction, after which, the kernel principal component analysis (kPCA is utilized to reduce the dimension of the feature set to reduce computation cost of the SVM. Since the signals are from two types of different sensors, the normalization is conducted to scale the input into the interval of [0, 1]. Five-fold cross validation is adapted to train the classifier, which prevents the classifier over-fitting. Based on the SVM model obtained offline in MATLAB, an online SVM algorithm is constructed for locomotion mode identification. Experiments are performed for different locomotion modes and experimental results show the effectiveness of the proposed algorithm with an accuracy of 96.00% ± 2.45%. To improve its accuracy, majority vote algorithm (MVA is used for post-processing, with which the identification accuracy is better than 98.35% ± 1.65%. The proposed algorithm can be extended and employed in the field of robotic rehabilitation and assistance.

  1. A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images

    Directory of Open Access Journals (Sweden)

    Yongzheng Xu

    2016-08-01

    Full Text Available A new hybrid vehicle detection scheme which integrates the Viola-Jones (V-J and linear SVM classifier with HOG feature (HOG + SVM methods is proposed for vehicle detection from low-altitude unmanned aerial vehicle (UAV images. As both V-J and HOG + SVM are sensitive to on-road vehicles’ in-plane rotation, the proposed scheme first adopts a roadway orientation adjustment method, which rotates each UAV image to align the roads with the horizontal direction so the original V-J or HOG + SVM method can be directly applied to achieve fast detection and high accuracy. To address the issue of descending detection speed for V-J and HOG + SVM, the proposed scheme further develops an adaptive switching strategy which sophistically integrates V-J and HOG + SVM methods based on their different descending trends of detection speed to improve detection efficiency. A comprehensive evaluation shows that the switching strategy, combined with the road orientation adjustment method, can significantly improve the efficiency and effectiveness of the vehicle detection from UAV images. The results also show that the proposed vehicle detection method is competitive compared with other existing vehicle detection methods. Furthermore, since the proposed vehicle detection method can be performed on videos captured from moving UAV platforms without the need of image registration or additional road database, it has great potentials of field applications. Future research will be focusing on expanding the current method for detecting other transportation modes such as buses, trucks, motors, bicycles, and pedestrians.

  2. Novel Hybrid of LS-SVM and Kalman Filter for GPS/INS Integration

    Science.gov (United States)

    Xu, Zhenkai; Li, Yong; Rizos, Chris; Xu, Xiaosu

    Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) technologies can overcome the drawbacks of the individual systems. One of the advantages is that the integrated solution can provide continuous navigation capability even during GPS outages. However, bridging the GPS outages is still a challenge when Micro-Electro-Mechanical System (MEMS) inertial sensors are used. Methods being currently explored by the research community include applying vehicle motion constraints, optimal smoother, and artificial intelligence (AI) techniques. In the research area of AI, the neural network (NN) approach has been extensively utilised up to the present. In an NN-based integrated system, a Kalman filter (KF) estimates position, velocity and attitude errors, as well as the inertial sensor errors, to output navigation solutions while GPS signals are available. At the same time, an NN is trained to map the vehicle dynamics with corresponding KF states, and to correct INS measurements when GPS measurements are unavailable. To achieve good performance it is critical to select suitable quality and an optimal number of samples for the NN. This is sometimes too rigorous a requirement which limits real world application of NN-based methods.The support vector machine (SVM) approach is based on the structural risk minimisation principle, instead of the minimised empirical error principle that is commonly implemented in an NN. The SVM can avoid local minimisation and over-fitting problems in an NN, and therefore potentially can achieve a higher level of global performance. This paper focuses on the least squares support vector machine (LS-SVM), which can solve highly nonlinear and noisy black-box modelling problems. This paper explores the application of the LS-SVM to aid the GPS/INS integrated system, especially during GPS outages. The paper describes the principles of the LS-SVM and of the KF hybrid method, and introduces the LS-SVM regression algorithm. Field

  3. Combined SVM-CRFs for biological named entity recognition with maximal bidirectional squeezing.

    Directory of Open Access Journals (Sweden)

    Fei Zhu

    Full Text Available Biological named entity recognition, the identification of biological terms in text, is essential for biomedical information extraction. Machine learning-based approaches have been widely applied in this area. However, the recognition performance of current approaches could still be improved. Our novel approach is to combine support vector machines (SVMs and conditional random fields (CRFs, which can complement and facilitate each other. During the hybrid process, we use SVM to separate biological terms from non-biological terms, before we use CRFs to determine the types of biological terms, which makes full use of the power of SVM as a binary-class classifier and the data-labeling capacity of CRFs. We then merge the results of SVM and CRFs. To remove any inconsistencies that might result from the merging, we develop a useful algorithm and apply two rules. To ensure biological terms with a maximum length are identified, we propose a maximal bidirectional squeezing approach that finds the longest term. We also add a positive gain to rare events to reinforce their probability and avoid bias. Our approach will also gradually extend the context so more contextual information can be included. We examined the performance of four approaches with GENIA corpus and JNLPBA04 data. The combination of SVM and CRFs improved performance. The macro-precision, macro-recall, and macro-F(1 of the SVM-CRFs hybrid approach surpassed conventional SVM and CRFs. After applying the new algorithms, the macro-F1 reached 91.67% with the GENIA corpus and 84.04% with the JNLPBA04 data.

  4. A comparative QSAR study on the estrogenic activities of persistent organic pollutants by PLS and SVM

    Directory of Open Access Journals (Sweden)

    Fei Li

    2015-11-01

    Full Text Available Quantitative structure-activity relationships (QSARs were determined using partial least square (PLS and support vector machine (SVM. The predicted values by the final QSAR models were in good agreement with the corresponding experimental values. Chemical estrogenic activities are related to atomic properties (atomic Sanderson electronegativities, van der Waals volumes and polarizabilities. Comparison of the results obtained from two models, the SVM method exhibited better overall performances. Besides, three PLS models were constructed for some specific families based on their chemical structures. These predictive models should be useful to rapidly identify potential estrogenic endocrine disrupting chemicals.

  5. OPTIMALISASI SUPPORT VEKTOR MACHINE (SVM UNTUK KLASIFIKASI TEMA TUGAS AKHIR BERBASIS K-MEANS

    Directory of Open Access Journals (Sweden)

    Oman Somantri

    2017-01-01

    Full Text Available The difficulty in determining the classification of students final project theme often experienced by each college. The purpose of this study is to provide a decision support for policy makers in the study program so that each student can be achieved in accordance with their own competence. From the research that has been done text mining algorithms using Support Vector Machine ( SVM and K -Means as the technology used was produced a better accuracy rate with an accuracy rate of 86.21 % when compared to the SVM without K -Means is 85 , 38 %

  6. SVM and ANFIS Models for precipitaton Modeling (Case Study: GonbadKavouse

    Directory of Open Access Journals (Sweden)

    N. Zabet Pishkhani

    2016-10-01

    Full Text Available Introduction: In recent years, according to the intelligent models increased as new techniques and tools in hydrological processes such as precipitation forecasting. ANFIS model has good ability in train, construction and classification, and also has the advantage that allows the extraction of fuzzy rules from numerical information or knowledge. Another intelligent technique in recent years has been used in various areas is support vector machine (SVM. In this paper the ability of artificial intelligence methods including support vector machine (SVM and adaptive neuro fuzzy inference system (ANFIS were analyzed in monthly precipitation prediction. Materials and Methods: The study area was the city of Gonbad in Golestan Province. The city has a temperate climate in the southern highlands and southern plains, mountains and temperate humid, semi-arid and semi-arid in the north of Gorganroud river. In total, the city's climate is temperate and humid. In the present study, monthly precipitation was modeled in Gonbad using ANFIS and SVM and two different database structures were designed. The first structure: input layer consisted of mean temperature, relative humidity, pressure and wind speed at Gonbad station. The second structure: According to Pearson coefficient, the monthly precipitation data were used from four stations: Arazkoose, Bahalke, Tamar and Aqqala which had a higher correlation with Gonbad station precipitation. In this study precipitation data was used from 1995 to 2012. 80% data were used for model training and the remaining 20% of data for validation. SVM was developed from support vector machines in the 1990s by Vapnik. SVM has been widely recognized as a powerful tool to deal with function fitting problems. An Adaptive Neuro-Fuzzy Inference System (ANFIS refers, in general, to an adaptive network which performs the function of a fuzzy inference system. The most commonly used fuzzy system in ANFIS architectures is the Sugeno model

  7. Biofunktionalisierung von Implantatoberflächen mit einem synthetisch hergestellten Peptid (P15) bei diabetischen Versuchstieren gegenüber gesunden Versuchstieren

    OpenAIRE

    abu-Nasir, Mohammed

    2014-01-01

    1. Zusammenfassung 1.1 Hintergrund und Ziele Die zunehmende Prävalenz von Diabetes mellitus in Deutschland rückt dieses Patientenklientel näher in den Fokus der Implantologie . In dieser Studie wurde der Einfluss von sandgestrahlten und geätzten Implantatoberflächen mit einer Peptidsequenz (P15) beschichteten Oberflächen auf die Implantateinheilung bei diabetischen Versuchstieren gegenüber gesunden Versuchstieren untersucht. 1.2 Material und Methoden Insgesamt wurden...

  8. The spatial and temporal expression of Ch-en, the engrailed gene in the polychaete Chaetopterus, does not support a role in body axis segmentation

    Science.gov (United States)

    Seaver, E. C.; Paulson, D. A.; Irvine, S. Q.; Martindale, M. Q.

    2001-01-01

    We are interested in understanding whether the annelids and arthropods shared a common segmented ancestor and have approached this question by characterizing the expression pattern of the segment polarity gene engrailed (en) in a basal annelid, the polychaete Chaetopterus. We have isolated an en gene, Ch-en, from a Chaetopterus cDNA library. Genomic Southern blotting suggests that this is the only en class gene in this animal. The predicted protein sequence of the 1.2-kb cDNA clone contains all five domains characteristic of en proteins in other taxa, including the en class homeobox. Whole-mount in situ hybridization reveals that Ch-en is expressed throughout larval life in a complex spatial and temporal pattern. The Ch-en transcript is initially detected in a small number of neurons associated with the apical organ and in the posterior portion of the prototrochophore. At later stages, Ch-en is expressed in distinct patterns in the three segmented body regions (A, B, and C) of Chaetopterus. In all segments, Ch-en is expressed in a small set of segmentally iterated cells in the CNS. In the A region, Ch-en is also expressed in a small group of mesodermal cells at the base of the chaetal sacs. In the B region, Ch-en is initially expressed broadly in the mesoderm that then resolves into one band/segment coincident with morphological segmentation. The mesodermal expression in the B region is located in the anterior region of each segment, as defined by the position of ganglia in the ventral nerve cord, and is involved in the morphogenesis of segment-specific feeding structures late in larval life. We observe banded mesodermal and ectodermal staining in an anterior-posterior sequence in the C region. We do not observe a segment polarity pattern of expression of Ch-en in the ectoderm, as is observed in arthropods. Copyright 2001 Academic Press.

  9. Estimation of hydraulic jump characteristics of channels with sudden diverging side walls via SVM.

    Science.gov (United States)

    Roushangar, Kiyoumars; Valizadeh, Reyhaneh; Ghasempour, Roghayeh

    2017-10-01

    Sudden diverging channels are one of the energy dissipaters which can dissipate most of the kinetic energy of the flow through a hydraulic jump. An accurate prediction of hydraulic jump characteristics is an important step in designing hydraulic structures. This paper focuses on the capability of the support vector machine (SVM) as a meta-model approach for predicting hydraulic jump characteristics in different sudden diverging stilling basins (i.e. basins with and without appurtenances). In this regard, different models were developed and tested using 1,018 experimental data. The obtained results proved the capability of the SVM technique in predicting hydraulic jump characteristics and it was found that the developed models for a channel with a central block performed more successfully than models for channels without appurtenances or with a negative step. The superior performance for the length of hydraulic jump was obtained for the model with parameters F 1 (Froude number) and (h 2- h 1 )/h 1 (h 1 and h 2 are sequent depth of upstream and downstream respectively). Concerning the relative energy dissipation and sequent depth ratio, the model with parameters F 1 and h 1 /B (B is expansion ratio) led to the best results. According to the outcome of sensitivity analysis, Froude number had the most significant effect on the modeling. Also comparison between SVM and empirical equations indicated the great performance of the SVM.

  10. Positioning Errors Predicting Method of Strapdown Inertial Navigation Systems Based on PSO-SVM

    Directory of Open Access Journals (Sweden)

    Xunyuan Yin

    2013-01-01

    Full Text Available The strapdown inertial navigation systems (SINS have been widely used for many vehicles, such as commercial airplanes, Unmanned Aerial Vehicles (UAVs, and other types of aircrafts. In order to evaluate the navigation errors precisely and efficiently, a prediction method based on support vector machine (SVM is proposed for positioning error assessment. Firstly, SINS error models that are used for error calculation are established considering several error resources with respect to inertial units. Secondly, flight paths for simulation are designed. Thirdly, the -SVR based prediction method is proposed to predict the positioning errors of navigation systems, and particle swarm optimization (PSO is used for the SVM parameters optimization. Finally, 600 sets of error parameters of SINS are utilized to train the SVM model, which is used for the performance prediction of new navigation systems. By comparing the predicting results with the real errors, the latitudinal predicting accuracy is 92.73%, while the longitudinal predicting accuracy is 91.64%, and PSO is effective to increase the prediction accuracy compared with traditional SVM with fixed parameters. This method is also demonstrated to be effective for error prediction for an entire flight process. Moreover, the prediction method can save 75% of calculation time compared with analyses based on error models.

  11. Pressure Model of Control Valve Based on LS-SVM with the Fruit Fly Algorithm

    Directory of Open Access Journals (Sweden)

    Huang Aiqin

    2014-07-01

    Full Text Available Control valve is a kind of essential terminal control component which is hard to model by traditional methodologies because of its complexity and nonlinearity. This paper proposes a new modeling method for the upstream pressure of control valve using the least squares support vector machine (LS-SVM, which has been successfully used to identify nonlinear system. In order to improve the modeling performance, the fruit fly optimization algorithm (FOA is used to optimize two critical parameters of LS-SVM. As an example, a set of actual production data from a controlling system of chlorine in a salt chemistry industry is applied. The validity of LS-SVM modeling method using FOA is verified by comparing the predicted results with the actual data with a value of MSE 2.474 × 10−3. Moreover, it is demonstrated that the initial position of FOA does not affect its optimal ability. By comparison, simulation experiments based on PSO algorithm and the grid search method are also carried out. The results show that LS-SVM based on FOA has equal performance in prediction accuracy. However, from the respect of calculation time, FOA has a significant advantage and is more suitable for the online prediction.

  12. Using evolutionary computation to optimize an SVM used in detecting buried objects in FLIR imagery

    Science.gov (United States)

    Paino, Alex; Popescu, Mihail; Keller, James M.; Stone, Kevin

    2013-06-01

    In this paper we describe an approach for optimizing the parameters of a Support Vector Machine (SVM) as part of an algorithm used to detect buried objects in forward looking infrared (FLIR) imagery captured by a camera installed on a moving vehicle. The overall algorithm consists of a spot-finding procedure (to look for potential targets) followed by the extraction of several features from the neighborhood of each spot. The features include local binary pattern (LBP) and histogram of oriented gradients (HOG) as these are good at detecting texture classes. Finally, we project and sum each hit into UTM space along with its confidence value (obtained from the SVM), producing a confidence map for ROC analysis. In this work, we use an Evolutionary Computation Algorithm (ECA) to optimize various parameters involved in the system, such as the combination of features used, parameters on the Canny edge detector, the SVM kernel, and various HOG and LBP parameters. To validate our approach, we compare results obtained from an SVM using parameters obtained through our ECA technique with those previously selected by hand through several iterations of "guess and check".

  13. SVM versus MAP on accelerometer data to distinguish among locomotor activities executed at different speeds.

    Science.gov (United States)

    Schmid, Maurizio; Riganti-Fulginei, Francesco; Bernabucci, Ivan; Laudani, Antonino; Bibbo, Daniele; Muscillo, Rossana; Salvini, Alessandro; Conforto, Silvia

    2013-01-01

    Two approaches to the classification of different locomotor activities performed at various speeds are here presented and evaluated: a maximum a posteriori (MAP) Bayes' classification scheme and a Support Vector Machine (SVM) are applied on a 2D projection of 16 features extracted from accelerometer data. The locomotor activities (level walking, stair climbing, and stair descending) were recorded by an inertial sensor placed on the shank (preferred leg), performed in a natural indoor-outdoor scenario by 10 healthy young adults (age 25-35 yrs.). From each segmented activity epoch, sixteen features were chosen in the frequency and time domain. Dimension reduction was then performed through 2D Sammon's mapping. An Artificial Neural Network (ANN) was trained to mimic Sammon's mapping on the whole dataset. In the Bayes' approach, the two features were then fed to a Bayes' classifier that incorporates an update rule, while, in the SVM scheme, the ANN was considered as the kernel function of the classifier. Bayes' approach performed slightly better than SVM on both the training set (91.4% versus 90.7%) and the testing set (84.2% versus 76.0%), favoring the proposed Bayes' scheme as more suitable than the proposed SVM in distinguishing among the different monitored activities.

  14. A hybrid particle swarm optimization-SVM classification for automatic cardiac auscultation

    Directory of Open Access Journals (Sweden)

    Prasertsak Charoen

    2017-04-01

    Full Text Available Cardiac auscultation is a method for a doctor to listen to heart sounds, using a stethoscope, for examining the condition of the heart. Automatic cardiac auscultation with machine learning is a promising technique to classify heart conditions without need of doctors or expertise. In this paper, we develop a classification model based on support vector machine (SVM and particle swarm optimization (PSO for an automatic cardiac auscultation system. The model consists of two parts: heart sound signal processing part and a proposed PSO for weighted SVM (WSVM classifier part. In this method, the PSO takes into account the degree of importance for each feature extracted from wavelet packet (WP decomposition. Then, by using principle component analysis (PCA, the features can be selected. The PSO technique is used to assign diverse weights to different features for the WSVM classifier. Experimental results show that both continuous and binary PSO-WSVM models achieve better classification accuracy on the heart sound samples, by reducing system false negatives (FNs, compared to traditional SVM and genetic algorithm (GA based SVM.

  15. Research on gesture recognition of augmented reality maintenance guiding system based on improved SVM

    Science.gov (United States)

    Zhao, Shouwei; Zhang, Yong; Zhou, Bin; Ma, Dongxi

    2014-09-01

    Interaction is one of the key techniques of augmented reality (AR) maintenance guiding system. Because of the complexity of the maintenance guiding system's image background and the high dimensionality of gesture characteristics, the whole process of gesture recognition can be divided into three stages which are gesture segmentation, gesture characteristic feature modeling and trick recognition. In segmentation stage, for solving the misrecognition of skin-like region, a segmentation algorithm combing background mode and skin color to preclude some skin-like regions is adopted. In gesture characteristic feature modeling of image attributes stage, plenty of characteristic features are analyzed and acquired, such as structure characteristics, Hu invariant moments features and Fourier descriptor. In trick recognition stage, a classifier based on Support Vector Machine (SVM) is introduced into the augmented reality maintenance guiding process. SVM is a novel learning method based on statistical learning theory, processing academic foundation and excellent learning ability, having a lot of issues in machine learning area and special advantages in dealing with small samples, non-linear pattern recognition at high dimension. The gesture recognition of augmented reality maintenance guiding system is realized by SVM after the granulation of all the characteristic features. The experimental results of the simulation of number gesture recognition and its application in augmented reality maintenance guiding system show that the real-time performance and robustness of gesture recognition of AR maintenance guiding system can be greatly enhanced by improved SVM.

  16. Hyperspectral recognition of processing tomato early blight based on GA and SVM

    Science.gov (United States)

    Yin, Xiaojun; Zhao, SiFeng

    2013-03-01

    Processing tomato early blight seriously affect the yield and quality of its.Determine the leaves spectrum of different disease severity level of processing tomato early blight.We take the sensitive bands of processing tomato early blight as support vector machine input vector.Through the genetic algorithm(GA) to optimize the parameters of SVM, We could recognize different disease severity level of processing tomato early blight.The result show:the sensitive bands of different disease severity levels of processing tomato early blight is 628-643nm and 689-692nm.The sensitive bands are as the GA and SVM input vector.We get the best penalty parameters is 0.129 and kernel function parameters is 3.479.We make classification training and testing by polynomial nuclear,radial basis function nuclear,Sigmoid nuclear.The best classification model is the radial basis function nuclear of SVM. Training accuracy is 84.615%,Testing accuracy is 80.681%.It is combined GA and SVM to achieve multi-classification of processing tomato early blight.It is provided the technical support of prediction processing tomato early blight occurrence, development and diffusion rule in large areas.

  17. Traditional uses, botany, phytochemistry, pharmacology and toxicology of Panax notoginseng (Burk.) F.H. Chen: A review.

    Science.gov (United States)

    Wang, Ting; Guo, Rixin; Zhou, Guohong; Zhou, Xidan; Kou, Zhenzhen; Sui, Feng; Li, Chun; Tang, Liying; Wang, Zhuju

    2016-07-21

    Panax notoginseng (Burk.) F.H. Chen is a widely used traditional Chinese medicine known as Sanqi or Tianqi in China. This plant, which is distributed primarily in the southwest of China, has wide-ranging pharmacological effects and can be used to treat cardiovascular diseases, pain, inflammation and trauma as well as internal and external bleeding due to injury. This paper provides up-to-date information on investigations of this plant, including its botany, ethnopharmacology, phytochemistry, pharmacology and toxicology. The possible uses and perspectives for future investigation of this plant are also discussed. The relevant information on Panax notoginseng (Burk.) F.H. Chen was collected from numerous resources, including classic books about Chinese herbal medicine, and scientific databases, including Pubmed, SciFinder, ACS, Ebsco, Elsevier, Taylor, Wiley and CNKI. More than 200 chemical compounds have been isolated from Panax notoginseng (Burk.) F.H. Chen, including saponins, flavonoids and cyclopeptides. The plant has pharmacological effects on the cardiovascular system, immune system as well as anti-inflammatory, anti-atherosclerotic, haemostatic and anti-tumour activities, etc. Panax notoginseng is a valuable traditional Chinese medical herb with multiple pharmacological effects. This review summarizes the botany, ethnopharmacology, phytochemistry, pharmacology and toxicology of P. notoginseng, and presents the constituents and their corresponding chemical structures found in P. notoginseng comprehensively for the first time. Future research into its phytochemistry of bio-active components should be performed by using bioactivity-guided isolation strategies. Further work on elucidation of the structure-function relationship among saponins, understanding of multi-target network pharmacology of P. notoginseng, as well as developing its new clinical usage and comprehensive utilize will enhance the therapeutic potentials of P. notoginseng. Copyright © 2016

  18. Pauropods (Myriapoda: Pauropoda) from Eastern China, descriptions of three new species and revision of Pauropus bifurcus Zhang & Chen, 1988.

    Science.gov (United States)

    Qian, Changyuan; Dong, Yan; Guo, Hua; Chu, Kelin; Sun, Hongying

    2013-01-17

    Pauropods from Zhejiang and Jiangsu in eastern China were collected from the field. Among 23 specimens, four species assigned to two genera are identified. Three of these species are new and from the genus Decapauropus Remy, 1931: Decapauropus bifurcodicoccus Qian & Dong sp. nov., D. duomamillatus Qian & Dong sp. nov. and D. bidrepanoides Qian sp. nov. The status of Pauropus bifurcus Zhang & Chen, 1988 is reexamined and reallocated to the genus Stylopauropus Cook, 1896. A key to Decapauropus species of eastern China (including Jiangsu, Anhui and Zhejiang province) is provided.

  19. Equity Valuation and Accounting Numbers: Applying Zhang (2000 and Zhang and Chen (2007 models to Brazilian Market

    Directory of Open Access Journals (Sweden)

    Fernando Caio Galdi

    2011-03-01

    Full Text Available This paper investigates how accounting variables explain cross-sectional stocks returns in Brazilian capital markets. The analysis is based on Zhang (2000 and Zhang and Chen (2007 models. These models predict that stock returns are a function of net income, change in profitability, invested capital, changes in opportunity growths and discount rate. Generally, the empirical results for the Brazilian capital market are consistent with the theoretical relations that models describe, similarly to the results found in the US. Using different empirical tests (pooled regressions, Fama-Macbeth and panel data the results and coefficients remain similar, what support the robustness of our findings.

  20. Automatic epileptic seizure detection in EEGs using MF-DFA, SVM based on cloud computing.

    Science.gov (United States)

    Zhang, Zhongnan; Wen, Tingxi; Huang, Wei; Wang, Meihong; Li, Chunfeng

    2017-01-01

    Epilepsy is a chronic disease with transient brain dysfunction that results from the sudden abnormal discharge of neurons in the brain. Since electroencephalogram (EEG) is a harmless and noninvasive detection method, it plays an important role in the detection of neurological diseases. However, the process of analyzing EEG to detect neurological diseases is often difficult because the brain electrical signals are random, non-stationary and nonlinear. In order to overcome such difficulty, this study aims to develop a new computer-aided scheme for automatic epileptic seizure detection in EEGs based on multi-fractal detrended fluctuation analysis (MF-DFA) and support vector machine (SVM). New scheme first extracts features from EEG by MF-DFA during the first stage. Then, the scheme applies a genetic algorithm (GA) to calculate parameters used in SVM and classify the training data according to the selected features using SVM. Finally, the trained SVM classifier is exploited to detect neurological diseases. The algorithm utilizes MLlib from library of SPARK and runs on cloud platform. Applying to a public dataset for experiment, the study results show that the new feature extraction method and scheme can detect signals with less features and the accuracy of the classification reached up to 99%. MF-DFA is a promising approach to extract features for analyzing EEG, because of its simple algorithm procedure and less parameters. The features obtained by MF-DFA can represent samples as well as traditional wavelet transform and Lyapunov exponents. GA can always find useful parameters for SVM with enough execution time. The results illustrate that the classification model can achieve comparable accuracy, which means that it is effective in epileptic seizure detection.

  1. Feasibility and effectiveness of a Chen-style Tai Chi programme for stress reduction in junior secondary school students.

    Science.gov (United States)

    Lee, Linda Y K; Chong, Yeuk Lan; Li, Ngai Yin; Li, Man Chung; Lin, Lai Na; Wong, Lee Yi; Wong, Brian Kit; Yip, Wing Ping; Hon, Cho Hang; Chung, Pui Kuen; Man, Shuk Yee

    2013-04-01

    Stress is common in junior secondary school students (JSSS). This study aimed to determine the feasibility and effectiveness of a Chen-style Tai Chi programme for stress reduction in JSSS. A non-equivalent pre-test/post-test control group design was adopted, and a convenience sample of 69 JSSS was recruited. The experimental group (n = 32) joined a Chen-style Tai Chi programme, which included 10 sessions of 80-minute Tai Chi training (one session per week). The control group (n = 37) proceeded with self-study. Participants' stress levels were assessed using the Perceived Stress Scale. Feasibility was determined as the percentage of participants completing and attending the programme. Effectiveness was measured as the significant difference in changes in stress levels before and after the intervention between the two groups. Results preliminarily supported that the programme was feasible for JSSS. Completion rate was 100%, and attendance rate was 90%. However, no significant difference was noted in changes in stress levels before and after the intervention between the two groups. The potential health benefits of Tai Chi could not be detected owing to the restrictions imposed by the research setting and study limitations. The present study represents initial efforts in this direction and serves as reference for future study. Copyright © 2012 John Wiley & Sons, Ltd.

  2. A DWT and SVM based method for rolling element bearing fault diagnosis and its comparison with Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Sunil Tyagi

    2017-04-01

    Full Text Available A classification technique using Support Vector Machine (SVM classifier for detection of rolling element bearing fault is presented here.  The SVM was fed from features that were extracted from of vibration signals obtained from experimental setup consisting of rotating driveline that was mounted on rolling element bearings which were run in normal and with artificially faults induced conditions. The time-domain vibration signals were divided into 40 segments and simple features such as peaks in time domain and spectrum along with statistical features such as standard deviation, skewness, kurtosis etc. were extracted. Effectiveness of SVM classifier was compared with the performance of Artificial Neural Network (ANN classifier and it was found that the performance of SVM classifier is superior to that of ANN. The effect of pre-processing of the vibration signal by Discreet Wavelet Transform (DWT prior to feature extraction is also studied and it is shown that pre-processing of vibration signal with DWT enhances the effectiveness of both ANN and SVM classifiers. It has been demonstrated from experiment results that performance of SVM classifier is better than ANN in detection of bearing condition and pre-processing the vibration signal with DWT improves the performance of SVM classifier.

  3. LS-SVM: uma nova ferramenta quimiométrica para regressão multivariada. Comparação de modelos de regressão LS-SVM e PLS na quantificação de adulterantes em leite em pó empregando NIR LS-SVM: a new chemometric tool for multivariate regression. Comparison of LS-SVM and pls regression for determination of common adulterants in powdered milk by nir spectroscopy

    Directory of Open Access Journals (Sweden)

    Marco F. Ferrão

    2007-08-01

    Full Text Available Least-squares support vector machines (LS-SVM were used as an alternative multivariate calibration method for the simultaneous quantification of some common adulterants found in powdered milk samples, using near-infrared spectroscopy. Excellent models were built using LS-SVM for determining R², RMSECV and RMSEP values. LS-SVMs show superior performance for quantifying starch, whey and sucrose in powdered milk samples in relation to PLSR. This study shows that it is possible to determine precisely the amount of one and two common adulterants simultaneously in powdered milk samples using LS-SVM and NIR spectra.

  4. Three new anti-HBV active constituents from the traditional Chinese herb of Yin-Chen (Artemisia scoparia).

    Science.gov (United States)

    Geng, Chang-An; Huang, Xiao-Yan; Chen, Xing-Long; Ma, Yun-Bao; Rong, Guang-Qing; Zhao, Yong; Zhang, Xue-Mei; Chen, Ji-Jun

    2015-12-24

    Yin-Chen is a famous traditional Chinese medicine (TCM) in China for the treatment of acute and chronic hepatitis. Two species, namely Artemisia scoparia and Artemisia capillaris, are documented in Chinese Pharmacopoeia as the authentic resources for Yin-Chen. Previous investigation has proved that chlorogenic acid analogs and phenolic acids are two main types of the anti-HBV active constituents of A. capillaris. However, there is no investigation concerned with the anti-HBV components of A. scoparia. The aim of the present study is to recognize the new anti-HBV constituents of A. scoparia by detailed LCMS analyses. LCMS and bioassay-guided fractionation on the active part of A. scoparia led to the isolation of three new compounds. Their structures were determined by detailed spectroscopic analyses. Anti-HBV assay involving inhibition on HBsAg and HBeAg secretions and HBV DNA replication were performed in virto on HepG 2.2.15 cell line. The 90% ethanol extract of A. scoparia was revealed with anti-HBV activity for the first time, which was further separated into several fractions by column chromatography. Fr. D-4 was revealed with the highest anti-HBV activity, from which three new compounds including one unusual 4-pyridone glucoside (1) and two polyacetylene glucosides (2-3) were isolated under the guidance of LCMS analyses. Compounds 1-3 exhibited activity against the secretions of HBsAg and HBeAg, and HBV DNA replication. In particular, compounds 2 and 3 inhibited HBV DNA replication with IC50 values of 0.07 ± 0.04 and 0.012 ± 0.05 mM, with SI values of 23.6 and 17.1, respectively. Based on the MS/MS experiment, the fragmentation pathways of 1 in both positive and negative modes, and 2 and 3 in negative mode were proposed. The ion pairs of 388-208 (positive) and 432-206 (negative) for 1, 503-341 (negative) for 2, and 503-203 (negative) for 3, could be recognized as their respective diagnostic ions. The first time investigation on the anti-HBV constituents of A

  5. A PSO-SVM-based 24 Hours Power Load Forecasting Model

    Directory of Open Access Journals (Sweden)

    Yu Xiaoxu

    2015-01-01

    Full Text Available In order to improve the drawbacks of over-fitting and easily get stuck into local extremes of BACK propagation Neural Network, a new method of combination of wavelet transform and PSO-SVM (Particle Swarm Optimization- Support Vector Machine power load forecasting model is proposed. By employing wave-let transform, the authors decompose the time sequences of power load into high-frequency and low-frequency parts, namely the low-frequency part forecast with this model and the high-frequency part forecast with weighted average method. With PSO, which is a heuristic bionic optimization algorithm, the authors figure out the prefer-able parameters of SVM, and the model proposed in this paper is tested to be more accurately to forecast the 24h power load than BP model.

  6. Fault Diagnosis of Complex Industrial Process Using KICA and Sparse SVM

    Directory of Open Access Journals (Sweden)

    Jie Xu

    2013-01-01

    Full Text Available New approaches are proposed for complex industrial process monitoring and fault diagnosis based on kernel independent component analysis (KICA and sparse support vector machine (SVM. The KICA method is a two-phase algorithm: whitened kernel principal component analysis (KPCA. The data are firstly mapped into high-dimensional feature subspace. Then, the ICA algorithm seeks the projection directions in the KPCA whitened space. Performance monitoring is implemented through constructing the statistical index and control limit in the feature space. If the statistical indexes exceed the predefined control limit, a fault may have occurred. Then, the nonlinear score vectors are calculated and fed into the sparse SVM to identify the faults. The proposed method is applied to the simulation of Tennessee Eastman (TE chemical process. The simulation results show that the proposed method can identify various types of faults accurately and rapidly.

  7. Classification of surface defects on bridge cable based on PSO-SVM

    Science.gov (United States)

    Li, Xinke; Gao, Chao; Guo, Yongcai; Shao, Yanhua; He, Fuliang

    2014-07-01

    Distributed machine vision system was applied for the detection on the cable surface defect of the cable-stayed bridge, and access to surface defects including longitudinal cracking, transverse cracking, surface erosion and scarring pit holes and other scars. In order to achieve the automatic classification of surface defects, firstly, part of the texture features, gray features and shape features on the defect image were selected as the target classification feature quantities; then the particle swarm optimization (PSO) was introduced to optimize the punitive coefficient and kernel function parameter of the support vector machine (SVM) model; and finally the objective of defects was identified with the help of the PSOSVM classifier. Recognition experiments were performed on cable surface defects, presenting a recognition rate of 96.25 percent. The results showed that PSO-SVM has high recognition rate for classification of surface defects on bridge cable.

  8. SVM-based learning control of space robots in capturing operation.

    Science.gov (United States)

    Huang, Panfeng; Xu, Yangsheng

    2007-12-01

    In this paper, we presents a novel approach for tracking and catching operation of space robots using learning and transferring human control strategies (HCS). We firstly use an efficient support vector machine (SVM) to parametrize the model of HCS. Then we develop a new SVM-based learning structure to better implement human control strategy learning in tracking and capturing control. The approach is fundamentally valuable in dealing with some problems such as small sample data and local minima, and so on. Therefore this approach is efficient in modeling, understanding and transferring its learning process. The simulation results attest that this approach is useful and feasible in generating tracking trajectory and catching objects autonomously.

  9. Activity Recognition in Egocentric video using SVM, kNN and Combined SVMkNN Classifiers

    Science.gov (United States)

    Sanal Kumar, K. P.; Bhavani, R., Dr.

    2017-08-01

    Egocentric vision is a unique perspective in computer vision which is human centric. The recognition of egocentric actions is a challenging task which helps in assisting elderly people, disabled patients and so on. In this work, life logging activity videos are taken as input. There are 2 categories, first one is the top level and second one is second level. Here, the recognition is done using the features like Histogram of Oriented Gradients (HOG), Motion Boundary Histogram (MBH) and Trajectory. The features are fused together and it acts as a single feature. The extracted features are reduced using Principal Component Analysis (PCA). The features that are reduced are provided as input to the classifiers like Support Vector Machine (SVM), k nearest neighbor (kNN) and combined Support Vector Machine (SVM) and k Nearest Neighbor (kNN) (combined SVMkNN). These classifiers are evaluated and the combined SVMkNN provided better results than other classifiers in the literature.

  10. An IPSO-SVM algorithm for security state prediction of mine production logistics system

    Science.gov (United States)

    Zhang, Yanliang; Lei, Junhui; Ma, Qiuli; Chen, Xin; Bi, Runfang

    2017-06-01

    A theoretical basis for the regulation of corporate security warning and resources was provided in order to reveal the laws behind the security state in mine production logistics. Considering complex mine production logistics system and the variable is difficult to acquire, a superior security status predicting model of mine production logistics system based on the improved particle swarm optimization and support vector machine (IPSO-SVM) is proposed in this paper. Firstly, through the linear adjustments of inertia weight and learning weights, the convergence speed and search accuracy are enhanced with the aim to deal with situations associated with the changeable complexity and the data acquisition difficulty. The improved particle swarm optimization (IPSO) is then introduced to resolve the problem of parameter settings in traditional support vector machines (SVM). At the same time, security status index system is built to determine the classification standards of safety status. The feasibility and effectiveness of this method is finally verified using the experimental results.

  11. Power line identification of millimeter wave radar based on PCA-GS-SVM

    Science.gov (United States)

    Fang, Fang; Zhang, Guifeng; Cheng, Yansheng

    2017-12-01

    Aiming at the problem that the existing detection method can not effectively solve the security of UAV's ultra low altitude flight caused by power line, a power line recognition method based on grid search (GS) and the principal component analysis and support vector machine (PCA-SVM) is proposed. Firstly, the candidate line of Hough transform is reduced by PCA, and the main feature of candidate line is extracted. Then, upport vector machine (SVM is) optimized by grid search method (GS). Finally, using support vector machine classifier optimized parameters to classify the candidate line. MATLAB simulation results show that this method can effectively identify the power line and noise, and has high recognition accuracy and algorithm efficiency.

  12. Comparison of sensorless FOC and SVM-DTFC of PMSM for low-speed applications

    DEFF Research Database (Denmark)

    Basar, M. Sertug; Bech, Michael Møller; Andersen, Torben Ole

    2013-01-01

    This article presents the performance analysis of Field Oriented Control (FOC) and Space Vector Modulation (SVM) Direct Torque and Flux Control (DTFC) of a Non-Salient Permanent Magnet Synchronous Machine (PMSM) under sensorless control within low speed region. The high-frequency alternating...... voltage signal injection method has been chosen for sensorless control design. PMSM is modelled at high frequencies, and a rotor speed and position estimation algorithm is proposed. The proposed estimator is designed and implemented using MATLAB/Simulink® and is tested under several operating conditions...... with a commercially available PMSM machine. Both controllers show satisfactory sensorless performance. FOC provides smoother and more accurate response while SVM-DTFC has the advantage of faster control....

  13. A Multi-Classification Method of Improved SVM-based Information Fusion for Traffic Parameters Forecasting

    Directory of Open Access Journals (Sweden)

    Hongzhuan Zhao

    2016-04-01

    Full Text Available With the enrichment of perception methods, modern transportation system has many physical objects whose states are influenced by many information factors so that it is a typical Cyber-Physical System (CPS. Thus, the traffic information is generally multi-sourced, heterogeneous and hierarchical. Existing research results show that the multisourced traffic information through accurate classification in the process of information fusion can achieve better parameters forecasting performance. For solving the problem of traffic information accurate classification, via analysing the characteristics of the multi-sourced traffic information and using redefined binary tree to overcome the shortcomings of the original Support Vector Machine (SVM classification in information fusion, a multi-classification method using improved SVM in information fusion for traffic parameters forecasting is proposed. The experiment was conducted to examine the performance of the proposed scheme, and the results reveal that the method can get more accurate and practical outcomes.

  14. A SVM-based method for sentiment analysis in Persian language

    Science.gov (United States)

    Hajmohammadi, Mohammad Sadegh; Ibrahim, Roliana

    2013-03-01

    Persian language is the official language of Iran, Tajikistan and Afghanistan. Local online users often represent their opinions and experiences on the web with written Persian. Although the information in those reviews is valuable to potential consumers and sellers, the huge amount of web reviews make it difficult to give an unbiased evaluation to a product. In this paper, standard machine learning techniques SVM and naive Bayes are incorporated into the domain of online Persian Movie reviews to automatically classify user reviews as positive or negative and performance of these two classifiers is compared with each other in this language. The effects of feature presentations on classification performance are discussed. We find that accuracy is influenced by interaction between the classification models and the feature options. The SVM classifier achieves as well as or better accuracy than naive Bayes in Persian movie. Unigrams are proved better features than bigrams and trigrams in capturing Persian sentiment orientation.

  15. SVM classification model in depression recognition based on mutation PSO parameter optimization

    Directory of Open Access Journals (Sweden)

    Zhang Ming

    2017-01-01

    Full Text Available At present, the clinical diagnosis of depression is mainly through structured interviews by psychiatrists, which is lack of objective diagnostic methods, so it causes the higher rate of misdiagnosis. In this paper, a method of depression recognition based on SVM and particle swarm optimization algorithm mutation is proposed. To address on the problem that particle swarm optimization (PSO algorithm easily trap in local optima, we propose a feedback mutation PSO algorithm (FBPSO to balance the local search and global exploration ability, so that the parameters of the classification model is optimal. We compared different PSO mutation algorithms about classification accuracy for depression, and found the classification accuracy of support vector machine (SVM classifier based on feedback mutation PSO algorithm is the highest. Our study promotes important reference value for establishing auxiliary diagnostic used in depression recognition of clinical diagnosis.

  16. Detection of Alzheimer's disease using group lasso SVM-based region selection

    Science.gov (United States)

    Sun, Zhuo; Fan, Yong; Lelieveldt, Boudewijn P. F.; van de Giessen, Martijn

    2015-03-01

    Alzheimer's disease (AD) is one of the most frequent forms of dementia and an increasing challenging public health problem. In the last two decades, structural magnetic resonance imaging (MRI) has shown potential in distinguishing patients with Alzheimer's disease and elderly controls (CN). To obtain AD-specific biomarkers, previous research used either statistical testing to find statistically significant different regions between the two clinical groups, or l1 sparse learning to select isolated features in the image domain. In this paper, we propose a new framework that uses structural MRI to simultaneously distinguish the two clinical groups and find the bio-markers of AD, using a group lasso support vector machine (SVM). The group lasso term (mixed l1- l2 norm) introduces anatomical information from the image domain into the feature domain, such that the resulting set of selected voxels are more meaningful than the l1 sparse SVM. Because of large inter-structure size variation, we introduce a group specific normalization factor to deal with the structure size bias. Experiments have been performed on a well-designed AD vs. CN dataset1 to validate our method. Comparing to the l1 sparse SVM approach, our method achieved better classification performance and a more meaningful biomarker selection. When we vary the training set, the selected regions by our method were more stable than the l1 sparse SVM. Classification experiments showed that our group normalization lead to higher classification accuracy with fewer selected regions than the non-normalized method. Comparing to the state-of-art AD vs. CN classification methods, our approach not only obtains a high accuracy with the same dataset, but more importantly, we simultaneously find the brain anatomies that are closely related to the disease.

  17. Prediction of protein-protein interactions between viruses and human by an SVM model

    Directory of Open Access Journals (Sweden)

    Cui Guangyu

    2012-05-01

    Full Text Available Abstract Background Several computational methods have been developed to predict protein-protein interactions from amino acid sequences, but most of those methods are intended for the interactions within a species rather than for interactions across different species. Methods for predicting interactions between homogeneous proteins are not appropriate for finding those between heterogeneous proteins since they do not distinguish the interactions between proteins of the same species from those of different species. Results We developed a new method for representing a protein sequence of variable length in a frequency vector of fixed length, which encodes the relative frequency of three consecutive amino acids of a sequence. We built a support vector machine (SVM model to predict human proteins that interact with virus proteins. In two types of viruses, human papillomaviruses (HPV and hepatitis C virus (HCV, our SVM model achieved an average accuracy above 80%, which is higher than that of another SVM model with a different representation scheme. Using the SVM model and Gene Ontology (GO annotations of proteins, we predicted new interactions between virus proteins and human proteins. Conclusions Encoding the relative frequency of amino acid triplets of a protein sequence is a simple yet powerful representation method for predicting protein-protein interactions across different species. The representation method has several advantages: (1 it enables a prediction model to achieve a better performance than other representations, (2 it generates feature vectors of fixed length regardless of the sequence length, and (3 the same representation is applicable to different types of proteins.

  18. Penilaian Esai Jawaban Bahasa Indonesia Menggunakan Metode Svm - Lsa Dengan Fitur Generik

    OpenAIRE

    Adhitia, Rama; Purwarianti, Ayu

    2009-01-01

    Paper ini mengkaji sebuah solusi untuk permasalahan penilaian jawaban esai secara otomatis dengan menggabungkan support vector machine (SVM) sebagai teknik klasifikasi teks otomatis dengan LSA sebagai USAha untuk menangani sinonim dan polisemi antar index term. Berbeda dengan sistem penilaian esai yang biasa yakni fitur yang digunakan berupa index term, fitur yang digunakan proses penilaian jawaban esai adalah berupa fitur generic yang memungkinkan pengujian model penilaian esai untuk berbaga...

  19. SVM-Based Classification of Segmented Airborne LiDAR Point Clouds in Urban Areas

    OpenAIRE

    Xiaogang Ning; Xiangguo Lin; Jixian Zhang

    2013-01-01

    Object-based point cloud analysis (OBPA) is useful for information extraction from airborne LiDAR point clouds. An object-based classification method is proposed for classifying the airborne LiDAR point clouds in urban areas herein. In the process of classification, the surface growing algorithm is employed to make clustering of the point clouds without outliers, thirteen features of the geometry, radiometry, topology and echo characteristics are calculated, a support vector machine (SVM) is ...

  20. Grouped fuzzy SVM with EM-based partition of sample space for clustered microcalcification detection.

    Science.gov (United States)

    Wang, Huiya; Feng, Jun; Wang, Hongyu

    2017-07-20

    Detection of clustered microcalcification (MC) from mammograms plays essential roles in computer-aided diagnosis for early stage breast cancer. To tackle problems associated with the diversity of data structures of MC lesions and the variability of normal breast tissues, multi-pattern sample space learning is required. In this paper, a novel grouped fuzzy Support Vector Machine (SVM) algorithm with sample space partition based on Expectation-Maximization (EM) (called G-FSVM) is proposed for clustered MC detection. The diversified pattern of training data is partitioned into several groups based on EM algorithm. Then a series of fuzzy SVM are integrated for classification with each group of samples from the MC lesions and normal breast tissues. From DDSM database, a total of 1,064 suspicious regions are selected from 239 mammography, and the measurement of Accuracy, True Positive Rate (TPR), False Positive Rate (FPR) and EVL = TPR* 1-FPR are 0.82, 0.78, 0.14 and 0.72, respectively. The proposed method incorporates the merits of fuzzy SVM and multi-pattern sample space learning, decomposing the MC detection problem into serial simple two-class classification. Experimental results from synthetic data and DDSM database demonstrate that our integrated classification framework reduces the false positive rate significantly while maintaining the true positive rate.

  1. Combination of minimum enclosing balls classifier with SVM in coal-rock recognition.

    Directory of Open Access Journals (Sweden)

    QingJun Song

    Full Text Available Top-coal caving technology is a productive and efficient method in modern mechanized coal mining, the study of coal-rock recognition is key to realizing automation in comprehensive mechanized coal mining. In this paper we propose a new discriminant analysis framework for coal-rock recognition. In the framework, a data acquisition model with vibration and acoustic signals is designed and the caving dataset with 10 feature variables and three classes is got. And the perfect combination of feature variables can be automatically decided by using the multi-class F-score (MF-Score feature selection. In terms of nonlinear mapping in real-world optimization problem, an effective minimum enclosing ball (MEB algorithm plus Support vector machine (SVM is proposed for rapid detection of coal-rock in the caving process. In particular, we illustrate how to construct MEB-SVM classifier in coal-rock recognition which exhibit inherently complex distribution data. The proposed method is examined on UCI data sets and the caving dataset, and compared with some new excellent SVM classifiers. We conduct experiments with accuracy and Friedman test for comparison of more classifiers over multiple on the UCI data sets. Experimental results demonstrate that the proposed algorithm has good robustness and generalization ability. The results of experiments on the caving dataset show the better performance which leads to a promising feature selection and multi-class recognition in coal-rock recognition.

  2. Fault diagnosis method based on FFT-RPCA-SVM for Cascaded-Multilevel Inverter.

    Science.gov (United States)

    Wang, Tianzhen; Qi, Jie; Xu, Hao; Wang, Yide; Liu, Lei; Gao, Diju

    2016-01-01

    Thanks to reduced switch stress, high quality of load wave, easy packaging and good extensibility, the cascaded H-bridge multilevel inverter is widely used in wind power system. To guarantee stable operation of system, a new fault diagnosis method, based on Fast Fourier Transform (FFT), Relative Principle Component Analysis (RPCA) and Support Vector Machine (SVM), is proposed for H-bridge multilevel inverter. To avoid the influence of load variation on fault diagnosis, the output voltages of the inverter is chosen as the fault characteristic signals. To shorten the time of diagnosis and improve the diagnostic accuracy, the main features of the fault characteristic signals are extracted by FFT. To further reduce the training time of SVM, the feature vector is reduced based on RPCA that can get a lower dimensional feature space. The fault classifier is constructed via SVM. An experimental prototype of the inverter is built to test the proposed method. Compared to other fault diagnosis methods, the experimental results demonstrate the high accuracy and efficiency of the proposed method. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  3. Automated Classification and Removal of EEG Artifacts with SVM and Wavelet-ICA.

    Science.gov (United States)

    Sai, Chong Yeh; Mokhtar, Norrima; Arof, Hamzah; Cumming, Paul; Iwahashi, Masahiro

    2017-07-04

    Brain electrical activity recordings by electroencephalography (EEG) are often contaminated with signal artifacts. Procedures for automated removal of EEG artifacts are frequently sought for clinical diagnostics and brain computer interface (BCI) applications. In recent years, a combination of independent component analysis (ICA) and discrete wavelet transform (DWT) has been introduced as standard technique for EEG artifact removal. However, in performing the wavelet-ICA procedure, visual inspection or arbitrary thresholding may be required for identifying artifactual components in the EEG signal. We now propose a novel approach for identifying artifactual components separated by wavelet-ICA using a pre-trained support vector machine (SVM). Our method presents a robust and extendable system that enables fully automated identification and removal of artifacts from EEG signals, without applying any arbitrary thresholding. Using test data contaminated by eye blink artifacts, we show that our method performed better in identifying artifactual components than did existing thresholding methods. Furthermore, wavelet-ICA in conjunction with SVM successfully removed target artifacts, while largely retaining the EEG source signals of interest. We propose a set of features including kurtosis, variance, Shannon's entropy and range of amplitude as training and test data of SVM to identify eye blink artifacts in EEG signals. This combinatorial method is also extendable to accommodate multiple types of artifacts present in multi-channel EEG. We envision future research to explore other descriptive features corresponding to other types of artifactual components.

  4. 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.

  5. Wind Power Prediction Based on LS-SVM Model with Error Correction

    Directory of Open Access Journals (Sweden)

    ZHANG, Y.

    2017-02-01

    Full Text Available As conventional energy sources are non-renewable, the world's major countries are investing heavily in renewable energy research. Wind power represents the development trend of future energy, but the intermittent and volatility of wind energy are the main reasons that leads to the poor accuracy of wind power prediction. However, by analyzing the error level at different time points, it can be found that the errors of adjacent time are often approximately the same, the least square support vector machine (LS-SVM model with error correction is used to predict the wind power in this paper. According to the simulation of wind power data of two wind farms, the proposed method can effectively improve the prediction accuracy of wind power, and the error distribution is concentrated almost without deviation. The improved method proposed in this paper takes into account the error correction process of the model, which improved the prediction accuracy of the traditional model (RBF, Elman, LS-SVM. Compared with the single LS-SVM prediction model in this paper, the mean absolute error of the proposed method had decreased by 52 percent. The research work in this paper will be helpful to the reasonable arrangement of dispatching operation plan, the normal operation of the wind farm and the large-scale development as well as fully utilization of renewable energy resources.

  6. Applications of PCA and SVM-PSO Based Real-Time Face Recognition System

    Directory of Open Access Journals (Sweden)

    Ming-Yuan Shieh

    2014-01-01

    Full Text Available This paper incorporates principal component analysis (PCA with support vector machine-particle swarm optimization (SVM-PSO for developing real-time face recognition systems. The integrated scheme aims to adopt the SVM-PSO method to improve the validity of PCA based image recognition systems on dynamically visual perception. The face recognition for most human-robot interaction applications is accomplished by PCA based method because of its dimensionality reduction. However, PCA based systems are only suitable for processing the faces with the same face expressions and/or under the same view directions. Since the facial feature selection process can be considered as a problem of global combinatorial optimization in machine learning, the SVM-PSO is usually used as an optimal classifier of the system. In this paper, the PSO is used to implement a feature selection, and the SVMs serve as fitness functions of the PSO for classification problems. Experimental results demonstrate that the proposed method simplifies features effectively and obtains higher classification accuracy.

  7. A SVM framework for fault detection of the braking system in a high speed train

    Science.gov (United States)

    Liu, Jie; Li, Yan-Fu; Zio, Enrico

    2017-03-01

    In April 2015, the number of operating High Speed Trains (HSTs) in the world has reached 3603. An efficient, effective and very reliable braking system is evidently very critical for trains running at a speed around 300 km/h. Failure of a highly reliable braking system is a rare event and, consequently, informative recorded data on fault conditions are scarce. This renders the fault detection problem a classification problem with highly unbalanced data. In this paper, a Support Vector Machine (SVM) framework, including feature selection, feature vector selection, model construction and decision boundary optimization, is proposed for tackling this problem. Feature vector selection can largely reduce the data size and, thus, the computational burden. The constructed model is a modified version of the least square SVM, in which a higher cost is assigned to the error of classification of faulty conditions than the error of classification of normal conditions. The proposed framework is successfully validated on a number of public unbalanced datasets. Then, it is applied for the fault detection of braking systems in HST: in comparison with several SVM approaches for unbalanced datasets, the proposed framework gives better results.

  8. Research on Intersession Variability Compensation for MLLR-SVM Speaker Recognition

    Science.gov (United States)

    Zhong, Shan; Shan, Yuxiang; He, Liang; Liu, Jia

    One of the most important challenges in speaker recognition is intersession variability (ISV), primarily cross-channel effects. Recent NIST speaker recognition evaluations (SRE) include a multilingual scenario with training conversations involving multilingual speakers collected in a number of other languages, leading to further performance decline. One important reason for this is that more and more researchers are using phonetic clustering to introduce high level information to improve speaker recognition. But such language dependent methods do not work well in multilingual conditions. In this paper, we study both language and channel mismatch using a support vector machine (SVM) speaker recognition system. Maximum likelihood linear regression (MLLR) transforms adapting a universal background model (UBM) are adopted as features. We first introduce a novel language independent statistical binary-decision tree to reduce multi-language effects, and compare this data-driven approach with a traditional knowledge based one. We also construct a framework for channel compensation using feature-domain latent factor analysis (LFA) and MLLR supervector kernel-based nuisance attribute projection (NAP) in the model-domain. Results on the NIST SRE 2006 1conv4w-1conv4w/mic corpus show significant improvement. We also compare our compensated MLLR-SVM system with state-of-the-art cepstral Gaussian mixture and SVM systems, and combine them for a further improvement.

  9. A self-trained semisupervised SVM approach to the remote sensing land cover classification

    Science.gov (United States)

    Liu, Ying; Zhang, Bai; Wang, Li-min; Wang, Nan

    2013-09-01

    Support vector machines (SVM) are nowadays receiving increasing attention in remote sensing applications although this technique is very sensitive to the parameters setting and training set definition. Self-training is an effective semisupervised method, which can reduce the effort needed to prepare the training set by training the model with a small number of labeled examples and an additional set of unlabeled examples. In this study, a novel semisupervised SVM model that uses self-training approach is proposed to address the problem of remote sensing land cover classification. The key characteristics of this approach are that (1) the self-adaptive mutation particle swarm optimization algorithm is introduced to get the optimum parameters that improve the generalization performance of the SVM classifier, and (2) the Gustafson-Kessel fuzzy clustering algorithm is proposed for the selection of unlabeled points to reduce the impact of ineffective labels. The effectiveness of the proposed technique is evaluated firstly with samples from remote sensing data and then by identifying different land cover regions in the remote sensing imagery. Experimental results show that accuracy level is increased by applying this learning scheme, which results in the smallest generalization error compared with the other schemes.

  10. Using Multidimensional ADTPE and SVM for Optical Modulation Real-Time Recognition

    Directory of Open Access Journals (Sweden)

    Junyu Wei

    2016-01-01

    Full Text Available Based on the feature extraction of multidimensional asynchronous delay-tap plot entropy (ADTPE and multiclass classification of support vector machine (SVM, we propose a method for recognition of multiple optical modulation formats and various data rates. We firstly present the algorithm of multidimensional ADTPE, which is extracted from asynchronous delay sampling pairs of modulated optical signal. Then, a multiclass SVM is utilized for fast and accurate classification of several widely-used optical modulation formats. In addition, a simple real-time recognition scheme is designed to reduce the computation time. Compared to the existing method based on asynchronous delay-tap plot (ADTP, the theoretical analysis and simulation results show that our recognition method can effectively enhance the tolerance of transmission impairments, obtaining relatively high accuracy. Finally, it is further demonstrated that the proposed method can be integrated in an optical transport network (OTN with flexible expansion. Through simply adding the corresponding sub-SVM module in the digital signal processer (DSP, arbitrary new modulation formats can be recognized with high recognition accuracy in a short response time.

  11. A Fault Diagnosis Approach for Gears Based on IMF AR Model and SVM

    Directory of Open Access Journals (Sweden)

    Yu Yang

    2008-05-01

    Full Text Available An accurate autoregressive (AR model can reflect the characteristics of a dynamic system based on which the fault feature of gear vibration signal can be extracted without constructing mathematical model and studying the fault mechanism of gear vibration system, which are experienced by the time-frequency analysis methods. However, AR model can only be applied to stationary signals, while the gear fault vibration signals usually present nonstationary characteristics. Therefore, empirical mode decomposition (EMD, which can decompose the vibration signal into a finite number of intrinsic mode functions (IMFs, is introduced into feature extraction of gear vibration signals as a preprocessor before AR models are generated. On the other hand, by targeting the difficulties of obtaining sufficient fault samples in practice, support vector machine (SVM is introduced into gear fault pattern recognition. In the proposed method in this paper, firstly, vibration signals are decomposed into a finite number of intrinsic mode functions, then the AR model of each IMF component is established; finally, the corresponding autoregressive parameters and the variance of remnant are regarded as the fault characteristic vectors and used as input parameters of SVM classifier to classify the working condition of gears. The experimental analysis results show that the proposed approach, in which IMF AR model and SVM are combined, can identify working condition of gears with a success rate of 100% even in the case of smaller number of samples.

  12. Diesel Engine Valve Clearance Fault Diagnosis Based on Features Extraction Techniques and FastICA-SVM

    Science.gov (United States)

    Jing, Ya-Bing; Liu, Chang-Wen; Bi, Feng-Rong; Bi, Xiao-Yang; Wang, Xia; Shao, Kang

    2017-07-01

    Numerous vibration-based techniques are rarely used in diesel engines fault diagnosis in a direct way, due to the surface vibration signals of diesel engines with the complex non-stationary and nonlinear time-varying features. To investigate the fault diagnosis of diesel engines, fractal correlation dimension, wavelet energy and entropy as features reflecting the diesel engine fault fractal and energy characteristics are extracted from the decomposed signals through analyzing vibration acceleration signals derived from the cylinder head in seven different states of valve train. An intelligent fault detector FastICA-SVM is applied for diesel engine fault diagnosis and classification. The results demonstrate that FastICA-SVM achieves higher classification accuracy and makes better generalization performance in small samples recognition. Besides, the fractal correlation dimension and wavelet energy and entropy as the special features of diesel engine vibration signal are considered as input vectors of classifier FastICA-SVM and could produce the excellent classification results. The proposed methodology improves the accuracy of feature extraction and the fault diagnosis of diesel engines.

  13. Detection of Cross Site Scripting Attack in Wireless Networks Using n-Gram and SVM

    Directory of Open Access Journals (Sweden)

    Jun-Ho Choi

    2012-01-01

    Full Text Available Large parts of attacks targeting the web are aiming at the weak point of web application. Even though SQL injection, which is the form of XSS (Cross Site Scripting attacks, is not a threat to the system to operate the web site, it is very critical to the places that deal with the important information because sensitive information can be obtained and falsified. In this paper, the method to detect themalicious SQL injection script code which is the typical XSS attack using n-Gram indexing and SVM (Support Vector Machine is proposed. In order to test the proposed method, the test was conducted after classifying each data set as normal code and malicious code, and the malicious script code was detected by applying index term generated by n-Gram and data set generated by code dictionary to SVM classifier. As a result, when the malicious script code detection was conducted using n-Gram index term and SVM, the superior performance could be identified in detecting malicious script and the more improved results than existing methods could be seen in the malicious script code detection recall.

  14. SVM-Based Spectral Analysis for Heart Rate from Multi-Channel WPPG Sensor Signals

    Directory of Open Access Journals (Sweden)

    Jiping Xiong

    2017-03-01

    Full Text Available Although wrist-type photoplethysmographic (hereafter referred to as WPPG sensor signals can measure heart rate quite conveniently, the subjects’ hand movements can cause strong motion artifacts, and then the motion artifacts will heavily contaminate WPPG signals. Hence, it is challenging for us to accurately estimate heart rate from WPPG signals during intense physical activities. The WWPG method has attracted more attention thanks to the popularity of wrist-worn wearable devices. In this paper, a mixed approach called Mix-SVM is proposed, it can use multi-channel WPPG sensor signals and simultaneous acceleration signals to measurement heart rate. Firstly, we combine the principle component analysis and adaptive filter to remove a part of the motion artifacts. Due to the strong relativity between motion artifacts and acceleration signals, the further denoising problem is regarded as a sparse signals reconstruction problem. Then, we use a spectrum subtraction method to eliminate motion artifacts effectively. Finally, the spectral peak corresponding to heart rate is sought by an SVM-based spectral analysis method. Through the public PPG database in the 2015 IEEE Signal Processing Cup, we acquire the experimental results, i.e., the average absolute error was 1.01 beat per minute, and the Pearson correlation was 0.9972. These results also confirm that the proposed Mix-SVM approach has potential for multi-channel WPPG-based heart rate estimation in the presence of intense physical exercise.

  15. Combination of minimum enclosing balls classifier with SVM in coal-rock recognition.

    Science.gov (United States)

    Song, QingJun; Jiang, HaiYan; Song, Qinghui; Zhao, XieGuang; Wu, Xiaoxuan

    2017-01-01

    Top-coal caving technology is a productive and efficient method in modern mechanized coal mining, the study of coal-rock recognition is key to realizing automation in comprehensive mechanized coal mining. In this paper we propose a new discriminant analysis framework for coal-rock recognition. In the framework, a data acquisition model with vibration and acoustic signals is designed and the caving dataset with 10 feature variables and three classes is got. And the perfect combination of feature variables can be automatically decided by using the multi-class F-score (MF-Score) feature selection. In terms of nonlinear mapping in real-world optimization problem, an effective minimum enclosing ball (MEB) algorithm plus Support vector machine (SVM) is proposed for rapid detection of coal-rock in the caving process. In particular, we illustrate how to construct MEB-SVM classifier in coal-rock recognition which exhibit inherently complex distribution data. The proposed method is examined on UCI data sets and the caving dataset, and compared with some new excellent SVM classifiers. We conduct experiments with accuracy and Friedman test for comparison of more classifiers over multiple on the UCI data sets. Experimental results demonstrate that the proposed algorithm has good robustness and generalization ability. The results of experiments on the caving dataset show the better performance which leads to a promising feature selection and multi-class recognition in coal-rock recognition.

  16. Hybrid SVM-HMM based recognition algorithm for pen-based tutoring system

    Science.gov (United States)

    Yuan, Zhenming; Pan, Hong

    2007-11-01

    Pen-based computing takes advantage of human skill with the pen, which is more than a substitute for the mouse. A hybrid SVM-HMM based recognition algorithm is presented for pen-based single stroke diagram. The algorithm includes five steps: sampling and pre-processing, segmentation, formal feature computing, SVM based feature classification, and HMM based symbol recognition. The formal feature of a stroke is composed of five static features and one dynamic feature. A group of one-to-one combinations of binary SVMs are used as feature classifiers to produce fixed length feature vectors, each of which is produced by the probability output with Sigmoid function and act as the posterior probability of observation of HMM. Finally HMMs are employed as final recognizer to recognize the unknown stroke. Based on this algorithm, a tutoring system is designed to identify the sketches of the flowchart diagrams. Experiment results show the hybrid algorithm has a good learning and recognition ability, which is benefited from combining the SVM's classification ability of static properties with the HMM's recognition ability of dynamic properties.

  17. Development of the Fray-Farthing-Chen Cambridge Process: Towards the Sustainable Production of Titanium and Its Alloys

    Science.gov (United States)

    Hu, Di; Dolganov, Aleksei; Ma, Mingchan; Bhattacharya, Biyash; Bishop, Matthew T.; Chen, George Z.

    2018-02-01

    The Kroll process has been employed for titanium extraction since the 1950s. It is a labour and energy intensive multi-step semi-batch process. The post-extraction processes for making the raw titanium into alloys and products are also excessive, including multiple remelting steps. Invented in the late 1990s, the Fray-Farthing-Chen (FFC) Cambridge process extracts titanium from solid oxides at lower energy consumption via electrochemical reduction in molten salts. Its ability to produce alloys and powders, while retaining the cathode shape also promises energy and material efficient manufacturing. Focusing on titanium and its alloys, this article reviews the recent development of the FFC-Cambridge process in two aspects, (1) resource and process sustainability and (2) advanced post-extraction processing.

  18. Landscape change and its effects on the wintering range of a lesser snow goose Chen caerulescens caerulescens population: A review

    Science.gov (United States)

    Robertson, Donna G.; Slack, R. Douglas

    1995-01-01

    The Texas coast has experienced considerable urban, industrial, and agricultural growth during the 20th Century. The region provides important wintering habitat to many avian species, including lesser snow geese Chen caerulescens caerulescens. This paper draws the biological and ecological fields into an historical perspective by examining available literature on the development of the upper Texas coast and range changes of lesser snow geese. Historically, lesser snow geese wintered in the coastal marshes, but expanded their range into the adjacent prairies in the mid-1900s. Winter range expansion was negatively affected by urban and industrial encroachment in the coastal marshes and positively influenced by agricultural development in the prairies, which increased dramatically during World War II. The lesser snow goose population flourished alongside some human-induced landscape alterations. However, projected declines in agriculture and increased urbanization of prairie and coastal marsh habitats may result in significant negative effects on the lesser snow goose population.

  19. Fault Diagnosis for Constant Deceleration Braking System of Mine Hoist based on Principal Component Analysis and SVM

    Directory of Open Access Journals (Sweden)

    Li Juan-Juan

    2017-01-01

    Full Text Available Based on AMESim simulation platform, the pressure-time curve of constant deceleration braking system is obtained in this paper firstly, by simulating three typical faults of brake, the spring stiffness decrease, the brake shoe friction coefficient decrease and brake leaking. Then pressure data on the curve for each time are seen as a variable and the curve is chosen as the fault sample, analysed by the method of Principal Component Analysis (PCA. Last, principal components or sum of variance contribution rates more than 95% are selected as sample eigenvalues and Support Vector Machine (SVM is used for fault diagnosis. Diagnosis results show that all testing faults can be identified accurately, which indicates SVM model has an extremely excellent ability to identify faults. To further verify the performance of SVM for fault identification, BP neural network is established to compare. The result shows that SVM model is more accurate than BP neural network in fault recognition.

  20. Performance Analysis of DTC-SVM Sliding Mode Controllers-Based Parameters Estimator of Electric Motor Speed Drive

    Directory of Open Access Journals (Sweden)

    Fatma Ben Salem

    2014-01-01

    Full Text Available This paper is concerned with a framework which unifies direct torque control space vector modulation (DTC-SVM and variable structure control (VSC. The result is a hybrid VSC-DTC-SVM controller design which eliminates several major limitations of the two individual controls and retains merits of both controllers. It has been shown that obtained control laws are very sensitive to variations of the stator resistance, the rotor resistance, and the mutual inductance. This paper discusses the performances of adaptive controllers of VSC-DTC-SVM monitored induction motor drive in a wide speed range and even in the presence of parameters uncertainties and mismatching disturbances. Better estimations of the stator resistance, the rotor resistance, and the mutual inductance yield improvements of induction motor performances using VSC-DTC-SVM, thereby facilitating torque ripple minimization. Simulation results verified the performances of the proposed approach.

  1. Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems

    Directory of Open Access Journals (Sweden)

    Ming-Yuan Cho

    2017-01-01

    Full Text Available Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO based support vector machine (SVM classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR method with a pseudorandom binary sequence (PRBS stimulus has been used to generate a dataset for purposes of classification. The proposed technique has been tested on a typical radial distribution network to identify ten different types of faults considering 12 given input features generated by using Simulink software and MATLAB Toolbox. The success rate of the SVM classifier is over 97%, which demonstrates the effectiveness and high efficiency of the developed method.

  2. Performance Analysis of DTC-SVM Sliding Mode Controllers-Based Parameters Estimator of Electric Motor Speed Drive

    OpenAIRE

    Ben Salem, Fatma; Derbel, Nabil

    2014-01-01

    This paper is concerned with a framework which unifies direct torque control space vector modulation (DTC-SVM) and variable structure control (VSC). The result is a hybrid VSC-DTC-SVM controller design which eliminates several major limitations of the two individual controls and retains merits of both controllers. It has been shown that obtained control laws are very sensitive to variations of the stator resistance, the rotor resistance, and the mutual inductance. This paper discusses the per...

  3. Classification of different kinds of pesticide residues on lettuce based on fluorescence spectra and WT-BCC-SVM algorithm

    Science.gov (United States)

    Zhou, Xin; Jun, Sun; Zhang, Bing; Jun, Wu

    2017-07-01

    In order to improve the reliability of the spectrum feature extracted by wavelet transform, a method combining wavelet transform (WT) with bacterial colony chemotaxis algorithm and support vector machine (BCC-SVM) algorithm (WT-BCC-SVM) was proposed in this paper. Besides, we aimed to identify different kinds of pesticide residues on lettuce leaves in a novel and rapid non-destructive way by using fluorescence spectra technology. The fluorescence spectral data of 150 lettuce leaf samples of five different kinds of pesticide residues on the surface of lettuce were obtained using Cary Eclipse fluorescence spectrometer. Standard normalized variable detrending (SNV detrending), Savitzky-Golay coupled with Standard normalized variable detrending (SG-SNV detrending) were used to preprocess the raw spectra, respectively. Bacterial colony chemotaxis combined with support vector machine (BCC-SVM) and support vector machine (SVM) classification models were established based on full spectra (FS) and wavelet transform characteristics (WTC), respectively. Moreover, WTC were selected by WT. The results showed that the accuracy of training set, calibration set and the prediction set of the best optimal classification model (SG-SNV detrending-WT-BCC-SVM) were 100%, 98% and 93.33%, respectively. In addition, the results indicated that it was feasible to use WT-BCC-SVM to establish diagnostic model of different kinds of pesticide residues on lettuce leaves.

  4. Abnormal Gait Behavior Detection for Elderly Based on Enhanced Wigner-Ville Analysis and Cloud Incremental SVM Learning

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    Jian Luo

    2016-01-01

    Full Text Available A cloud based health care system is proposed in this paper for the elderly by providing abnormal gait behavior detection, classification, online diagnosis, and remote aid service. Intelligent mobile terminals with triaxial acceleration sensor embedded are used to capture the movement and ambulation information of elderly. The collected signals are first enhanced by a Kalman filter. And the magnitude of signal vector features is then extracted and decomposed into a linear combination of enhanced Gabor atoms. The Wigner-Ville analysis method is introduced and the problem is studied by joint time-frequency analysis. In order to solve the large-scale abnormal behavior data lacking problem in training process, a cloud based incremental SVM (CI-SVM learning method is proposed. The original abnormal behavior data are first used to get the initial SVM classifier. And the larger abnormal behavior data of elderly collected by mobile devices are then gathered in cloud platform to conduct incremental training and get the new SVM classifier. By the CI-SVM learning method, the knowledge of SVM classifier could be accumulated due to the dynamic incremental learning. Experimental results demonstrate that the proposed method is feasible and can be applied to aged care, emergency aid, and related fields.

  5. Disorder recognition in clinical texts using multi-label structured SVM.

    Science.gov (United States)

    Lin, Wutao; Ji, Donghong; Lu, Yanan

    2017-01-31

    Information extraction in clinical texts enables medical workers to find out problems of patients faster as well as makes intelligent diagnosis possible in the future. There has been a lot of work about disorder mention recognition in clinical narratives. But recognition of some more complicated disorder mentions like overlapping ones is still an open issue. This paper proposes a multi-label structured Support Vector Machine (SVM) based method for disorder mention recognition. We present a multi-label scheme which could be used in complicated entity recognition tasks. We performed three sets of experiments to evaluate our model. Our best F1-Score on the 2013 Conference and Labs of the Evaluation Forum data set is 0.7343. There are six types of labels in our multi-label scheme, all of which are represented by 24-bit binary numbers. The binary digits of each label contain information about different disorder mentions. Our multi-label method can recognize not only disorder mentions in the form of contiguous or discontiguous words but also mentions whose spans overlap with each other. The experiments indicate that our multi-label structured SVM model outperforms the condition random field (CRF) model for this disorder mention recognition task. The experiments show that our multi-label scheme surpasses the baseline. Especially for overlapping disorder mentions, the F1-Score of our multi-label scheme is 0.1428 higher than the baseline BIOHD1234 scheme. This multi-label structured SVM based approach is demonstrated to work well with this disorder recognition task. The novel multi-label scheme we presented is superior to the baseline and it can be used in other models to solve various types of complicated entity recognition tasks as well.

  6. Intrusion detection model using fusion of chi-square feature selection and multi class SVM

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    Ikram Sumaiya Thaseen

    2017-10-01

    Full Text Available Intrusion detection is a promising area of research in the domain of security with the rapid development of internet in everyday life. Many intrusion detection systems (IDS employ a sole classifier algorithm for classifying network traffic as normal or abnormal. Due to the large amount of data, these sole classifier models fail to achieve a high attack detection rate with reduced false alarm rate. However by applying dimensionality reduction, data can be efficiently reduced to an optimal set of attributes without loss of information and then classified accurately using a multi class modeling technique for identifying the different network attacks. In this paper, we propose an intrusion detection model using chi-square feature selection and multi class support vector machine (SVM. A parameter tuning technique is adopted for optimization of Radial Basis Function kernel parameter namely gamma represented by ‘ϒ’ and over fitting constant ‘C’. These are the two important parameters required for the SVM model. The main idea behind this model is to construct a multi class SVM which has not been adopted for IDS so far to decrease the training and testing time and increase the individual classification accuracy of the network attacks. The investigational results on NSL-KDD dataset which is an enhanced version of KDDCup 1999 dataset shows that our proposed approach results in a better detection rate and reduced false alarm rate. An experimentation on the computational time required for training and testing is also carried out for usage in time critical applications.

  7. [SVM-based qualitative analysis of Muscat Hamburg wine produced in Tianjin region].

    Science.gov (United States)

    Zhang, Jun; Wang, Fang; Wei, Ji-Ping; Li, Chang-Wen; Yang, Hua; Shao, Chun-Fu; Zhang, Fu-Qing; Yin, Ji-Tai; Xiao, Dong-Guang

    2011-01-01

    The purpose was to achieve the identification of Muscat Hamburg wines produced in Tianjin region through scanning and analyzing dry white wine samples of different grape varieties and regions by infrared spectroscopy technology. A support vector machine (SVM) based method was introduced to analyze infrared spectra of dry white wines. The pretreatment processes of the IR spectra were also elaborated, including baseline adjustment, noise Elimination, standard normalization and eliminating the main component of abnormal sample points. The authors selected great quantity of dry white wine samples of different grape regions including 511 Muscat Hamburg wine samples, 438 Italian Riesling wine samples, 307 Chardonnay wine samples, 29 Ugni Blanc wine samples, 44 Rkatsiteli wine samples, 31 longan wine samples and 79 ZeHong wine samples. According to different classification problems, 80% of IR spectra of the wine samples were used to establish discrimination models with SVM-based method, and the remaining 20% of IR spectra were used for the validation of the discrimination models. Experimental results showed that the proposed method is effective, since high classification accuracy, identification rate and rejecting rate were achieved: over 97% for the white wine samples of different grape varieties, meanwhile over 98% for the Muscat Hamburg wine samples produced in different regions. So the method developed in this paper played a good role in the qualitative classification and discrimination of Muscat Hamburg wines produced in Tianjin region. This novel method has a considerable potential and a rosy application future due to the expeditiousness, stability and easy-operation of FTIR method, as well as the veracity and credibility of SVM method.

  8. A Research of Speech Emotion Recognition Based on Deep Belief Network and SVM

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    Chenchen Huang

    2014-01-01

    Full Text Available Feature extraction is a very important part in speech emotion recognition, and in allusion to feature extraction in speech emotion recognition problems, this paper proposed a new method of feature extraction, using DBNs in DNN to extract emotional features in speech signal automatically. By training a 5 layers depth DBNs, to extract speech emotion feature and incorporate multiple consecutive frames to form a high dimensional feature. The features after training in DBNs were the input of nonlinear SVM classifier, and finally speech emotion recognition multiple classifier system was achieved. The speech emotion recognition rate of the system reached 86.5%, which was 7% higher than the original method.

  9. Prediction of carcinogenicity for diverse chemicals based on substructure grouping and SVM modeling.

    Science.gov (United States)

    Tanabe, Kazutoshi; Lučić, Bono; Amić, Dragan; Kurita, Takio; Kaihara, Mikio; Onodera, Natsuo; Suzuki, Takahiro

    2010-11-01

    The Carcinogenicity Reliability Database (CRDB) was constructed by collecting experimental carcinogenicity data on about 1,500 chemicals from six sources, including IARC, and NTP databases, and then by ranking their reliabilities into six unified categories. A wide variety of 911 organic chemicals were selected from the database for QSAR modeling, and 1,504 kinds of different molecular descriptors were calculated, based on their 3D molecular structures as modeled by the Dragon software. Positive (carcinogenic) and negative (non-carcinogenic) chemicals containing various substructures were counted using atom and functional group count descriptors, and the statistical significance of ratios of positives to negatives was tested for those substructures. Very few were judged to be strongly related to carcinogenicity, among substructures known to be responsible for carcinogens as revealed from biomedical studies. In order to develop QSAR models for the prediction of the carcinogenicities of a wide variety of chemicals with a satisfactory performance level, the relationship between the carcinogenicity data with improved reliability and a subset of significant descriptors selected from 1,504 Dragon descriptors was analyzed with a support vector machine (SVM) method: the classification function (SVC) for weighted data in LIBSVM program was used to classify chemicals into two carcinogenic categories (positive or negative), where weights were set depending on the reliabilities of the carcinogenicity data. The quality and stability of the models presented were tested by performing a dual cross-validation procedure. A single SVM model as the first step was developed for all the 911 chemicals using 250 selected descriptors, achieving an overall accuracy level, i.e., positive and negative correct estimate, of about 70%. In order to improve the accuracy of the final model, the 911 chemicals were classified into 20 mutually overlapping subgroups according to contained substructures

  10. Time Reversal Reconstruction Algorithm Based on PSO Optimized SVM Interpolation for Photoacoustic Imaging

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    Mingjian Sun

    2015-01-01

    Full Text Available Photoacoustic imaging is an innovative imaging technique to image biomedical tissues. The time reversal reconstruction algorithm in which a numerical model of the acoustic forward problem is run backwards in time is widely used. In the paper, a time reversal reconstruction algorithm based on particle swarm optimization (PSO optimized support vector machine (SVM interpolation method is proposed for photoacoustics imaging. Numerical results show that the reconstructed images of the proposed algorithm are more accurate than those of the nearest neighbor interpolation, linear interpolation, and cubic convolution interpolation based time reversal algorithm, which can provide higher imaging quality by using significantly fewer measurement positions or scanning times.

  11. SVM-BALSA: Remote Homology Detection based on Bayesian Sequence Alignment

    Energy Technology Data Exchange (ETDEWEB)

    Webb-Robertson, Bobbie-Jo M.; Oehmen, Chris S.; Matzke, Melissa M.

    2005-11-10

    Using biopolymer sequence comparison methods to identify evolutionarily related proteins is one of the most common tasks in bioinformatics. Recently, support vector machines (SVMs) utilizing statistical learning theory have been employed in the problem of remote homology detection and shown to outperform iterative profile methods such as PSI-BLAST. In this study we demonstrate the utilization of a Bayesian alignment score, which accounts for the uncertainty of all possible alignments, in the SVM construction improves sensitivity compared to the traditional dynamic programming implementation.

  12. Screening for Internet Addiction: An Empirical Study on Cut-off Points for the Chen Internet Addiction Scale

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    Chih-Hung Ko

    2005-12-01

    Full Text Available The aim of this study was to establish the optimal cut-off points of the Chen Internet Addiction Scale (CIAS, to screen for and diagnose Internet addiction among adolescents in the community by using the well- established diagnostic criteria of Internet addiction. This survey of 454 adolescents used screening (57/58 and diagnostic (63/64 cut-off points of the CIAS, a self-reported instrument, based on the results of systematic diagnostic interviews by psychiatrists. The area under the curve of the receiver operating characteristic curve revealed that CIAS has good diagnostic accuracy (89.6%. The screening cut-off point had high sensitivity (85.6% and the diagnostic cut-off point had the highest diagnostic accuracy, classifying 87.6% of participants correctly. Accordingly, the screening point of the CIAS could provide a screening function in two-stage diagnosis, and the diagnostic point could serve as a diagnostic criterion in one-stage massive epidemiologic research.

  13. Satellite-derived land covers for runoff estimation using SCS-CN method in Chen-You-Lan Watershed, Taiwan

    Science.gov (United States)

    Zhang, Wen-Yan; Lin, Chao-Yuan

    2017-04-01

    The Soil Conservation Service Curve Number (SCS-CN) method, which was originally developed by the USDA Natural Resources Conservation Service, is widely used to estimate direct runoff volume from rainfall. The runoff Curve Number (CN) parameter is based on the hydrologic soil group and land use factors. In Taiwan, the national land use maps were interpreted from aerial photos in 1995 and 2008. Rapid updating of post-disaster land use map is limited due to the high cost of production, so the classification of satellite images is the alternative method to obtain the land use map. In this study, Normalized Difference Vegetation Index (NDVI) in Chen-You-Lan Watershed was derived from dry and wet season of Landsat imageries during 2003 - 2008. Land covers were interpreted from mean value and standard deviation of NDVI and were categorized into 4 groups i.e. forest, grassland, agriculture and bare land. Then, the runoff volume of typhoon events during 2005 - 2009 were estimated using SCS-CN method and verified with the measured runoff data. The result showed that the model efficiency coefficient is 90.77%. Therefore, estimating runoff by using the land cover map classified from satellite images is practicable.

  14. A variational numerical method based on finite elements for the nonlinear solution characteristics of the periodically forced Chen system

    Science.gov (United States)

    Khan, Sabeel M.; Sunny, D. A.; Aqeel, M.

    2017-09-01

    Nonlinear dynamical systems and their solutions are very sensitive to initial conditions and therefore need to be approximated carefully. In this article, we present and analyze nonlinear solution characteristics of the periodically forced Chen system with the application of a variational method based on the concept of finite time-elements. Our approach is based on the discretization of physical time space into finite elements where each time-element is mapped to a natural time space. The solution of the system is then determined in natural time space using a set of suitable basis functions. The numerical algorithm is presented and implemented to compute and analyze nonlinear behavior at different time-step sizes. The obtained results show an excellent agreement with the classical RK-4 and RK-5 methods. The accuracy and convergence of the method is shown by comparing numerically computed results with the exact solution for a test problem. The presented method has shown a great potential in dealing with the solutions of nonlinear dynamical systems and thus can be utilized in delineating different features and characteristics of their solutions.

  15. Plasma biochemistry values in emperor geese (Chen canagica) in Alaska: comparisons among age, sex, incubation, and molt.

    Science.gov (United States)

    Franson, J. Christian; Hoffman, D.J.; Schmutz, J.A.

    2009-01-01

    Reduced populations of emperor geese (Chen canagica), a Bering Sea endemic, provided the need to assess plasma biochemistry values as indicators of population health. A precursory step to such an investigation was to evaluate patterns of variability in plasma biochemistry values among age, sex, and reproductive period. Plasma from 63 emperor geese was collected on their breeding grounds on the Yukon-Kuskokwim Delta in western Alaska, USA. The geese sampled included 18 incubating adult females captured, in mid June, on their nests by using bow nets, and 30 adults and 15 goslings captured in corral traps in late July and early August, when the adults were molting their wing feathers and the goslings were 5-6 weeks old. Plasma was evaluated for 15 biochemical parameters, by comparing results among age, sex, and sampling period (incubation versus wing-feather molt). Ten of the 15 biochemical parameters assayed differed among adults during incubation, the adults during molt, and the goslings at molt, whereas sex differences were noted in few parameters.

  16. Comment on “Chen et al., Fabrication and photovoltaic conversion enhancement…”, Electrochimica Acta, 2014

    Science.gov (United States)

    Valentic, Lara; Gorji, Nima E.

    2015-09-01

    In a recent article, Chen et al. [Electrochimica Acta, 2014, 130: 279] presented their fabrication and characterization results on a graphene/n-Si solar cell where the Au nanoparticles were inserted in graphene to increase its optical and electrical properties. The higher efficiency of the device was attributed to increased conductivity of graphene after doping with Au nanoparticles. However, the knowledge in the field of Schottky diode solar cells relates this to increased band bending at the junction. Also, to explain the instability behaviour, they concluded that the growth of silicon oxide on the Si surface or oxygen adsorption on the window layer resulted in the device performance increasing initially and decreasing in the end. However, this instability seems to be due to variation in series resistance reduced at the beginning because of slightly lowered Fermi level and increased at the end by the self-compensation by deep in-diffusion of Au nanoparticles into n-Si layer. We also propose that inserting a very thin p-type layer at the junction will enhance the carrier collection and performance of this device.

  17. Debris flow run off simulation and verification ‒ case study of Chen-You-Lan Watershed, Taiwan

    Directory of Open Access Journals (Sweden)

    M.-L. Lin

    2005-01-01

    Full Text Available In 1996 typhoon Herb struck the central Taiwan area, causing severe debris flow in many subwatersheds of the Chen-You-Lan river watershed. More severe cases of debris flow occurred following Chi-Chi earthquake, 1999. In order to identify the potentially affected area and its severity, the ability to simulate the flow route of debris is desirable. In this research numerical simulation of debris flow deposition process had been carried out using FLO-2D adopting Chui-Sue river watershed as the study area. Sensitivity study of parameters used in the numerical model was conducted and adjustments were made empirically. The micro-geomorphic database of Chui-Sue river watershed was generated and analyzed to understand the terrain variations caused by the debris flow. Based on the micro-geomorphic analysis, the debris deposition in the Chui-Sue river watershed in the downstream area, and the position and volume of debris deposition were determined. The simulated results appeared to agree fairly well with the results of micro-geomorphic study of the area when not affected by other inflow rivers, and the trends of debris distribution in the study area appeared to be fairly consistent.

  18. Classification of Multiple Chinese Liquors by Means of a QCM-based E-Nose and MDS-SVM Classifier.

    Science.gov (United States)

    Li, Qiang; Gu, Yu; Jia, Jing

    2017-01-30

    Chinese liquors are internationally well-known fermentative alcoholic beverages. They have unique flavors attributable to the use of various bacteria and fungi, raw materials, and production processes. Developing a novel, rapid, and reliable method to identify multiple Chinese liquors is of positive significance. This paper presents a pattern recognition system for classifying ten brands of Chinese liquors based on multidimensional scaling (MDS) and support vector machine (SVM) algorithms in a quartz crystal microbalance (QCM)-based electronic nose (e-nose) we designed. We evaluated the comprehensive performance of the MDS-SVM classifier that predicted all ten brands of Chinese liquors individually. The prediction accuracy (98.3%) showed superior performance of the MDS-SVM classifier over the back-propagation artificial neural network (BP-ANN) classifier (93.3%) and moving average-linear discriminant analysis (MA-LDA) classifier (87.6%). The MDS-SVM classifier has reasonable reliability, good fitting and prediction (generalization) performance in classification of the Chinese liquors. Taking both application of the e-nose and validation of the MDS-SVM classifier into account, we have thus created a useful method for the classification of multiple Chinese liquors.

  19. Classification of Multiple Chinese Liquors by Means of a QCM-based E-Nose and MDS-SVM Classifier

    Directory of Open Access Journals (Sweden)

    Qiang Li

    2017-01-01

    Full Text Available Chinese liquors are internationally well-known fermentative alcoholic beverages. They have unique flavors attributable to the use of various bacteria and fungi, raw materials, and production processes. Developing a novel, rapid, and reliable method to identify multiple Chinese liquors is of positive significance. This paper presents a pattern recognition system for classifying ten brands of Chinese liquors based on multidimensional scaling (MDS and support vector machine (SVM algorithms in a quartz crystal microbalance (QCM-based electronic nose (e-nose we designed. We evaluated the comprehensive performance of the MDS-SVM classifier that predicted all ten brands of Chinese liquors individually. The prediction accuracy (98.3% showed superior performance of the MDS-SVM classifier over the back-propagation artificial neural network (BP-ANN classifier (93.3% and moving average-linear discriminant analysis (MA-LDA classifier (87.6%. The MDS-SVM classifier has reasonable reliability, good fitting and prediction (generalization performance in classification of the Chinese liquors. Taking both application of the e-nose and validation of the MDS-SVM classifier into account, we have thus created a useful method for the classification of multiple Chinese liquors.

  20. Bearing Fault Diagnosis Based on Improved Locality-Constrained Linear Coding and Adaptive PSO-Optimized SVM

    Directory of Open Access Journals (Sweden)

    Haodong Yuan

    2017-01-01

    Full Text Available A novel bearing fault diagnosis method based on improved locality-constrained linear coding (LLC and adaptive PSO-optimized support vector machine (SVM is proposed. In traditional LLC, each feature is encoded by using a fixed number of bases without considering the distribution of the features and the weight of the bases. To address these problems, an improved LLC algorithm based on adaptive and weighted bases is proposed. Firstly, preliminary features are obtained by wavelet packet node energy. Then, dictionary learning with class-wise K-SVD algorithm is implemented. Subsequently, based on the learned dictionary the LLC codes can be solved using the improved LLC algorithm. Finally, SVM optimized by adaptive particle swarm optimization (PSO is utilized to classify the discriminative LLC codes and thus bearing fault diagnosis is realized. In the dictionary leaning stage, other methods such as selecting the samples themselves as dictionary and K-means are also conducted for comparison. The experiment results show that the LLC codes can effectively extract the bearing fault characteristics and the improved LLC outperforms traditional LLC. The dictionary learned by class-wise K-SVD achieves the best performance. Additionally, adaptive PSO-optimized SVM can greatly enhance the classification accuracy comparing with SVM using default parameters and linear SVM.

  1. Discrimination between Alzheimer's Disease and Mild Cognitive Impairment Using SOM and PSO-SVM

    Directory of Open Access Journals (Sweden)

    Shih-Ting Yang

    2013-01-01

    Full Text Available In this study, an MRI-based classification framework was proposed to distinguish the patients with AD and MCI from normal participants by using multiple features and different classifiers. First, we extracted features (volume and shape from MRI data by using a series of image processing steps. Subsequently, we applied principal component analysis (PCA to convert a set of features of possibly correlated variables into a smaller set of values of linearly uncorrelated variables, decreasing the dimensions of feature space. Finally, we developed a novel data mining framework in combination with support vector machine (SVM and particle swarm optimization (PSO for the AD/MCI classification. In order to compare the hybrid method with traditional classifier, two kinds of classifiers, that is, SVM and a self-organizing map (SOM, were trained for patient classification. With the proposed framework, the classification accuracy is improved up to 82.35% and 77.78% in patients with AD and MCI. The result achieved up to 94.12% and 88.89% in AD and MCI by combining the volumetric features and shape features and using PCA. The present results suggest that novel multivariate methods of pattern matching reach a clinically relevant accuracy for the a priori prediction of the progression from MCI to AD.

  2. Efficient and Privacy-Preserving Online Medical Prediagnosis Framework Using Nonlinear SVM.

    Science.gov (United States)

    Zhu, Hui; Liu, Xiaoxia; Lu, Rongxing; Li, Hui

    2017-05-01

    With the advances of machine learning algorithms and the pervasiveness of network terminals, the online medical prediagnosis system, which can provide the diagnosis of healthcare provider anywhere anytime, has attracted considerable interest recently. However, the flourish of online medical prediagnosis system still faces many challenges including information security and privacy preservation. In this paper, we propose an e fficient and privacy-preserving online medical prediagnosis framework, called eDiag, by using nonlinear kernel support vector machine (SVM). With eDiag, the sensitive personal health information can be processed without privacy disclosure during online prediagnosis service. Specifically, based on an improved expression for the nonlinear SVM, an efficient and privacy-preserving classification scheme is introduced with lightweight multiparty random masking and polynomial aggregation techniques. The encrypted user query is directly operated at the service provider without decryption, and the diagnosis result can only be decrypted by user. Through extensive analysis, we show that eDiag can ensure that users' health information and healthcare provider's prediction model are kept confidential, and has significantly less computation and communication overhead than existing schemes. In addition, performance evaluations via implementing eDiag on smartphone and computer demonstrate eDiag's effectiveness in term of real online environment.

  3. Realization of SVM Algorithm for Indirect Matrix Converter and Its Application in Power Factor Control

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    Gang Li

    2015-01-01

    Full Text Available Compared with AC-DC-AC converter, matrix converter (MC has several advantages for its bidirectional power flow, controllable power factor, and the absence of large energy storage in dc-link. The topology of MC includes direct matrix converter (DMC and indirect matrix converter (IMC. IMC has received great attention worldwide because of its easy implementation and safe commutation. Space vector PWM (SVM algorithm for indirect matrix converter is realized on DSP and CPLD platform in this paper. The control of the rectifier and inverter in IMC can be decoupled because of the intermediate dc-link. The space vector modulation scheme for IMC is discussed and the PWM sequences for the rectifier and inverter are generated. And a two-step commutation of zero current switching (ZCS in the rectifier is achieved. Input power factor of IMC can be changed by adjusting the angle of the reference current vector. Experimental tests have been conducted on a RB-IGBT based indirect matrix converter prototype. The results verify the performance of the SVM algorithm and the ability of power factor correction.

  4. APPLICATION OF FUSION WITH SAR AND OPTICAL IMAGES IN LAND USE CLASSIFICATION BASED ON SVM

    Directory of Open Access Journals (Sweden)

    C. Bao

    2012-07-01

    Full Text Available As the increment of remote sensing data with multi-space resolution, multi-spectral resolution and multi-source, data fusion technologies have been widely used in geological fields. Synthetic Aperture Radar (SAR and optical camera are two most common sensors presently. The multi-spectral optical images express spectral features of ground objects, while SAR images express backscatter information. Accuracy of the image classification could be effectively improved fusing the two kinds of images. In this paper, Terra SAR-X images and ALOS multi-spectral images were fused for land use classification. After preprocess such as geometric rectification, radiometric rectification noise suppression and so on, the two kind images were fused, and then SVM model identification method was used for land use classification. Two different fusion methods were used, one is joining SAR image into multi-spectral images as one band, and the other is direct fusing the two kind images. The former one can raise the resolution and reserve the texture information, and the latter can reserve spectral feature information and improve capability of identifying different features. The experiment results showed that accuracy of classification using fused images is better than only using multi-spectral images. Accuracy of classification about roads, habitation and water bodies was significantly improved. Compared to traditional classification method, the method of this paper for fused images with SVM classifier could achieve better results in identifying complicated land use classes, especially for small pieces ground features.

  5. Damage Detection of Structures for Ambient Loading Based on Cross Correlation Function Amplitude and SVM

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    Lin-sheng Huo

    2016-01-01

    Full Text Available An effective method for the damage detection of skeletal structures which combines the cross correlation function amplitude (CCFA with the support vector machine (SVM is presented in this paper. The proposed method consists of two stages. Firstly, the data features are extracted from the CCFA, which, calculated from dynamic responses and as a representation of the modal shapes of the structure, changes when damage occurs on the structure. The data features are then input into the SVM with the one-against-one (OAO algorithm to classify the damage status of the structure. The simulation data of IASC-ASCE benchmark model and a vibration experiment of truss structure are adopted to verify the feasibility of proposed method. The results show that the proposed method is suitable for the damage identification of skeletal structures with the limited sensors subjected to ambient excitation. As the CCFA based data features are sensitive to damage, the proposed method demonstrates its reliability in the diagnosis of structures with damage, especially for those with minor damage. In addition, the proposed method shows better noise robustness and is more suitable for noisy environments.

  6. IMPROVED LS-SVM USING ACO TO ESTIMATE FLASHOVER VOLTAGE OF POLLUTED INSULATORS

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    SID AHMED BESSEDIK

    2017-01-01

    Full Text Available The reliability of insulators under polluted environment is one of the guiding factors in the insulation coordination of high voltage transmission lines. In order to improve understanding of the flashover phenomenon in polluted insulators, several experimental studies and mathematical approaches have been made‎ in‎ last‎ year’s.‎ In‎ this‎ paper,‎ the‎ critical flashover voltage behavior of polluted insulators has been calculated and a hybrid model between machine Learning (ML and optimization technique has been proposed. For this purpose, firstly the ant colony optimization (ACO technique is utilized to optimize the hyper-parameters needed in least squares support vector machines (LS-SVM. Then, a LS-SVM-ACO model is designed to establish a nonlinear model between the characteristics of the insulator and the critical flashover voltage. The data used to train the model and test its performance is derived from experimental measurements and a mathematical model. The results obtained from the proposed model are in good accord with other mathematical and experimental results of previous researchers.

  7. Multi-Sectional Views Textural Based SVM for MS Lesion Segmentation in Multi-Channels MRIs.

    Science.gov (United States)

    Abdullah, Bassem A; Younis, Akmal A; John, Nigel M

    2012-01-01

    In this paper, a new technique is proposed for automatic segmentation of multiple sclerosis (MS) lesions from brain magnetic resonance imaging (MRI) data. The technique uses a trained support vector machine (SVM) to discriminate between the blocks in regions of MS lesions and the blocks in non-MS lesion regions mainly based on the textural features with aid of the other features. The classification is done on each of the axial, sagittal and coronal sectional brain view independently and the resultant segmentations are aggregated to provide more accurate output segmentation. The main contribution of the proposed technique described in this paper is the use of textural features to detect MS lesions in a fully automated approach that does not rely on manually delineating the MS lesions. In addition, the technique introduces the concept of the multi-sectional view segmentation to produce verified segmentation. The proposed textural-based SVM technique was evaluated using three simulated datasets and more than fifty real MRI datasets. The results were compared with state of the art methods. The obtained results indicate that the proposed method would be viable for use in clinical practice for the detection of MS lesions in MRI.

  8. SVM and ANN Based Classification of Plant Diseases Using Feature Reduction Technique

    Directory of Open Access Journals (Sweden)

    Jagadeesh D.Pujari

    2016-06-01

    Full Text Available Computers have been used for mechanization and automation in different applications of agriculture/horticulture. The critical decision on the agricultural yield and plant protection is done with the development of expert system (decision support system using computer vision techniques. One of the areas considered in the present work is the processing of images of plant diseases affecting agriculture/horticulture crops. The first symptoms of plant disease have to be correctly detected, identified, and quantified in the initial stages. The color and texture features have been used in order to work with the sample images of plant diseases. Algorithms for extraction of color and texture features have been developed, which are in turn used to train support vector machine (SVM and artificial neural network (ANN classifiers. The study has presented a reduced feature set based approach for recognition and classification of images of plant diseases. The results reveal that SVM classifier is more suitable for identification and classification of plant diseases affecting agriculture/horticulture crops.

  9. Improving Accuracy of Intrusion Detection Model Using PCA and optimized SVM

    Directory of Open Access Journals (Sweden)

    Sumaiya Thaseen Ikram

    2016-06-01

    Full Text Available Intrusion detection is very essential for providing security to different network domains and is mostly used for locating and tracing the intruders. There are many problems with traditional intrusion detection models (IDS such as low detection capability against unknown network attack, high false alarm rate and insufficient analysis capability. Hence the major scope of the research in this domain is to develop an intrusion detection model with improved accuracy and reduced training time. This paper proposes a hybrid intrusiondetection model by integrating the principal component analysis (PCA and support vector machine (SVM. The novelty of the paper is the optimization of kernel parameters of the SVM classifier using automatic parameter selection technique. This technique optimizes the punishment factor (C and kernel parameter gamma (γ, thereby improving the accuracy of the classifier and reducing the training and testing time. The experimental results obtained on the NSL KDD and gurekddcup dataset show that the proposed technique performs better with higher accuracy, faster convergence speed and better generalization. Minimum resources are consumed as the classifier input requires reduced feature set for optimum classification. A comparative analysis of hybrid models with the proposed model is also performed.

  10. Effect of feeding and fasting on excess post-exercise oxygen consumption in juvenile southern catfish (Silurus meridionalis Chen).

    Science.gov (United States)

    Fu, Shi-Jian; Cao, Zhen-Dong; Peng, Jiang-Lan

    2007-03-01

    The impact of feeding (fed to satiation, 13.85% body mass) on excess post-exercise oxygen consumption (EPOC, chasing for 2.5 min) was investigated in juvenile southern catfish (Silurus meridionalis Chen) (38.62-57.55 g) at 25. Cutlets of freshly killed loach species without viscera, head and tail were used as the test meal, and oxygen consumption (VO(2)) was adjusted to a standard body mass of 1 kg using a mass exponent of 0.75. Resting VO(2) increased significantly above fasting levels (49.89 versus 148.25 mg O(2) h(-)(1)) in 12 h postprandial catfish. VO(2) and ventilation frequency (V(f)) both increased immediately after exhaustive exercise and slowly returned to pre-exercise values in all experimental groups. The times taken for post-exercise VO(2) to return to the pre-exercise value were 20, 25 and 30 min in 12 h, 60 h and 120 h postprandial catfish, respectively. Peak VO(2) levels were 257.36+/-6.06, 219.32+/-6.32 and 200.91+/-5.50 mg O(2) h(-1) in 12 h, 60 h and 120 h postprandial catfish and EPOC values were 13.85+/-4.50, 27.24+/-3.15 and 41.91+/-3.02 mg O(2) in 12 h, 60 h and 120 h postprandial southern catfish, respectively. There were significant differences in both EPOC and peak VO(2) during the post-exercise recovery process among three experimental groups (pcatfish, (2) both the digestive process and exercise (also the post-exercise recovery process) were curtailed under postprandial exercise, (3) the change of V(f) was smaller than that of VO(2) during the exhaustive exercise recovery process, (4) for a similar increment in VO(2), the change in V(f) was larger during the post-exercise process than during the digestive process.

  11. Development of microsatellite loci exhibiting reverse ascertainment bias and a sexing marker for use in Emperor Geese (Chen canagica)

    Science.gov (United States)

    Gravley, Meg C.; Sage, George K.; Schmutz, Joel A.; Talbot, Sandra

    2017-01-01

    The Alaskan population of Emperor Geese (Chen canagica) nests on the Yukon–Kuskokwim Delta in western Alaska. Numbers of Emperor Geese in Alaska declined from the 1960s to the mid-1980s and since then, their numbers have slowly increased. Low statistical power of microsatellite loci developed in other waterfowl species and used in previous studies of Emperor Geese are unable to confidently assign individual identity. Microsatellite loci for Emperor Goose were therefore developed using shotgun amplification and next-generation sequencing technology. Forty-one microsatellite loci were screened and 14 were found to be polymorphic in Emperor Geese. Only six markers – a combination of four novel loci and two loci developed in other waterfowl species – are needed to identify an individual from among the Alaskan Emperor Goose population. Genetic markers for identifying sex in Emperor Geese were also developed. The 14 novel variable loci and 15 monomorphic loci were screened for polymorphism in four other Arctic-nesting goose species, Black Brant (Branta bernicla nigricans), Greater White-fronted (Anser albifrons), Canada (B. canadensis) and Cackling (B. hutchinsii) Goose. Emperor Goose exhibited the smallest average number of alleles (3.3) and the lowest expected heterozygosity (0.467). Greater White-fronted Geese exhibited the highest average number of alleles (4.7) and Cackling Geese the highest expected heterozygosity (0.599). Six of the monomorphic loci were variable and able to be characterised in the other goose species assayed, a predicted outcome of reverse ascertainment bias. These findings fail to support the hypothesis of ascertainment bias due to selection of microsatellite markers.

  12. 3d-Übergangsmetalloxide: Ultradünne Schichten und Grenzflächen von MnO und NiO

    OpenAIRE

    Nagel, Mathias

    2009-01-01

    Die vorliegende Arbeit untersucht binäre Übergangsmetalloxide. Im Speziellen werden sowohl ultradünne und epitaktische Manganoxid-Schichten (MnO) auf einkristallinen Silbersubstraten als auch Grenzflächen zwischen Übergangsmetallen und Oxiden betrachtet. Strukturelle, elektronische und magnetische Eigenschaften von Übergangsmetalloxiden in dünnen Schichtsystemen sind von grundlegendem Interesse. Für Anwendungen im Bereich der magnetischen Datenspeicherung oder in der Entwicklung von Spintr...

  13. Data on Support Vector Machines (SVM model to forecast photovoltaic power

    Directory of Open Access Journals (Sweden)

    M. Malvoni

    2016-12-01

    Full Text Available The data concern the photovoltaic (PV power, forecasted by a hybrid model that considers weather variations and applies a technique to reduce the input data size, as presented in the paper entitled “Photovoltaic forecast based on hybrid pca-lssvm using dimensionality reducted data” (M. Malvoni, M.G. De Giorgi, P.M. Congedo, 2015 [1]. The quadratic Renyi entropy criteria together with the principal component analysis (PCA are applied to the Least Squares Support Vector Machines (LS-SVM to predict the PV power in the day-ahead time frame. The data here shared represent the proposed approach results. Hourly PV power predictions for 1,3,6,12, 24 ahead hours and for different data reduction sizes are provided in Supplementary material.

  14. Determination Of Gas Mixture Components Using Fluctuation Enhanced Sensing And The LS-SVM Regression Algorithm

    Directory of Open Access Journals (Sweden)

    Lentka Łukasz

    2015-09-01

    Full Text Available This paper analyses the effectiveness of determining gas concentrations by using a prototype WO3 resistive gas sensor together with fluctuation enhanced sensing. We have earlier demonstrated that this method can determine the composition of a gas mixture by using only a single sensor. In the present study, we apply Least-Squares Support-Vector-Machine-based (LS-SVM-based nonlinear regression to determine the gas concentration of each constituent in a mixture. We confirmed that the accuracy of the estimated gas concentration could be significantly improved by applying temperature change and ultraviolet irradiation of the WO3 layer. Fluctuation-enhanced sensing allowed us to predict the concentration of both component gases.

  15. An SVM Framework for Malignant Melanoma Detection Based on Optimized HOG Features

    Directory of Open Access Journals (Sweden)

    Samy Bakheet

    2017-01-01

    Full Text Available Early detection of skin cancer through improved techniques and innovative technologies has the greatest potential for significantly reducing both morbidity and mortality associated with this disease. In this paper, an effective framework of a CAD (Computer-Aided Diagnosis system for melanoma skin cancer is developed mainly by application of an SVM (Support Vector Machine model on an optimized set of HOG (Histogram of Oriented Gradient based descriptors of skin lesions. Experimental results obtained by applying the presented methodology on a large, publicly accessible dataset of dermoscopy images demonstrate that the proposed framework is a strong contender for the state-of-the-art alternatives by achieving high levels of sensitivity, specificity, and accuracy (98.21%, 96.43% and 97.32%, respectively, without sacrificing computational soundness.

  16. Deeper understanding about the genetic structure of dengue virus using SVM

    Directory of Open Access Journals (Sweden)

    Choi Subin

    2016-01-01

    Full Text Available Dengue fever, mainly found in the tropical and subtropical regions, is carried by mosquitoes. With the help of greenhouse effect, places considered to be a Dengue safe-zone are becoming more and more dangerous. Dengue fever shows similar aspects to MERS, which caused heavy casualties in South Korea; Dengue virus does not have clear treatments nor vaccines like MERS. Development of Dengue vaccine is actively investigated lately. However, it is not easy to succeed; the fact that Dengue’s 4 serotypes have different properties and that repeated infections worsen the symptoms. This research aims to analyze the 4 serotypes (DENV1, DENV2, DENV3, DENV4 using SVM and ANN algorithms to investigate the constraints in the development of Dengue’s vaccines and treatments.

  17. Comparison of CIV, SIV and AIV using Decision Tree and SVM

    Directory of Open Access Journals (Sweden)

    Park Hyorin

    2016-01-01

    Full Text Available The H3N2, the canine influenza virus has numerous types of animal hosts that can live and reproduce on. They mostly settle on pigs and birds. However, some concerned voices are rising that there is high possibility that humans could be an additional victim for the canine flu. Consequently, our project group expect that the information about the H3N2’s DNA are valuable, since the information could attribute to development of vaccine and medicine. In the experiments of analysing the properties of CIV, Canine Influenza Virus with the comparison of SIV, Swine Influenza Virus and AIV, Avian Influenza Virus with the decision tree and SVM, Support Vector Machine. The result came out that CIV, SIV and AIV are alike but also different in some aspects.

  18. Semi-supervised Learning for Classification of Polarimetric SAR Images Based on SVM-Wishart

    Directory of Open Access Journals (Sweden)

    Hua Wen-qiang

    2015-02-01

    Full Text Available In this study, we propose a new semi-supervised classification method for Polarimetric SAR (PolSAR images, aiming at handling the issue that the number of train set is small. First, considering the scattering characters of PolSAR data, this method extracts multiple scattering features using target decomposition approach. Then, a semi-supervised learning model is established based on a co-training framework and Support Vector Machine (SVM. Both labeled and unlabeled data are utilized in this model to obtain high classification accuracy. Third, a recovery scheme based on the Wishart classifier is proposed to improve the classification performance. From the experiments conducted in this study, it is evident that the proposed method performs more effectively compared with other traditional methods when the number of train set is small.

  19. Using LS-SVM based motion recognition for smartphone indoor wireless positioning.

    Science.gov (United States)

    Pei, Ling; Liu, Jingbin; Guinness, Robert; Chen, Yuwei; Kuusniemi, Heidi; Chen, Ruizhi

    2012-01-01

    The paper presents an indoor navigation solution by combining physical motion recognition with wireless positioning. Twenty-seven simple features are extracted from the built-in accelerometers and magnetometers in a smartphone. Eight common motion states used during indoor navigation are detected by a Least Square-Support Vector Machines (LS-SVM) classification algorithm, e.g., static, standing with hand swinging, normal walking while holding the phone in hand, normal walking with hand swinging, fast walking, U-turning, going up stairs, and going down stairs. The results indicate that the motion states are recognized with an accuracy of up to 95.53% for the test cases employed in this study. A motion recognition assisted wireless positioning approach is applied to determine the position of a mobile user. Field tests show a 1.22 m mean error in "Static Tests" and a 3.53 m in "Stop-Go Tests".

  20. Using LS-SVM Based Motion Recognition for Smartphone Indoor Wireless Positioning

    Directory of Open Access Journals (Sweden)

    Ruizhi Chen

    2012-05-01

    Full Text Available The paper presents an indoor navigation solution by combining physical motion recognition with wireless positioning. Twenty-seven simple features are extracted from the built-in accelerometers and magnetometers in a smartphone. Eight common motion states used during indoor navigation are detected by a Least Square-Support Vector Machines (LS-SVM classification algorithm, e.g., static, standing with hand swinging, normal walking while holding the phone in hand, normal walking with hand swinging, fast walking, U-turning, going up stairs, and going down stairs. The results indicate that the motion states are recognized with an accuracy of up to 95.53% for the test cases employed in this study. A motion recognition assisted wireless positioning approach is applied to determine the position of a mobile user. Field tests show a 1.22 m mean error in “Static Tests” and a 3.53 m in “Stop-Go Tests”.

  1. AN IMPLEMENTATION OF EIS-SVM CLASSIFIER USING RESEARCH ARTICLES FOR TEXT CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    B Ramesh

    2016-04-01

    Full Text Available Automatic text classification is a prominent research topic in text mining. The text pre-processing is a major role in text classifier. The efficiency of pre-processing techniques is increasing the performance of text classifier. In this paper, we are implementing ECAS stemmer, Efficient Instance Selection and Pre-computed Kernel Support Vector Machine for text classification using recent research articles. We are using better pre-processing techniques such as ECAS stemmer to find root word, Efficient Instance Selection for dimensionality reduction of text data and Pre-computed Kernel Support Vector Machine for classification of selected instances. In this experiments were performed on 750 research articles with three classes such as engineering article, medical articles and educational articles. The EIS-SVM classifier provides better performance in real-time research articles classification.

  2. Text independent writer identification based on Gabor filter and SVM classifier

    Science.gov (United States)

    Feng, Jun; Zhu, Yanhai

    2006-11-01

    Writer identification has become a hot topic in pattern recognition and machine learning research area. This paper studies on the technology of text independent writer identification based on texture analysis. At first in the preprocessing stage the uniform texture images are created from the input document. An approach for improved characters segmentation is presented based on analysis for the character elements and their topological relations. Then the 32-channel Gabor filter is utilized to extract 64 texture features of writing image by calculating the mean values and the standard deviations of filtering output images. Finally, multi-class support vector machines (SVM) classifier is adopted to fulfill the identification task. The experiment result shows that the scheme is effective and promising.

  3. A novel robust adaptive control algorithm and application to DTC-SVM of AC drives

    Directory of Open Access Journals (Sweden)

    Belkacem Sebti

    2010-01-01

    Full Text Available In this paper a new robust adaptive control algorithm for AC machine is presented. The main feature of this algorithm is that minimum synthesis is required to implement the strategy. The MCS algorithm is a significant development of MRAC and is similary based on the hyper stability theory of Popov. The hyperstability theory guarantees the global asymptotic stability of the error vector (i.e. the difference between the reference model and system states. Finally, a new approach has been successfully implemented to DTC-SVM. Discussion on theoretical aspects, such as, selection of a reference model, stability analysis, gain adaptive and steady state error are included. Results of simulations are also presented.

  4. A fast image retrieval method based on SVM and imbalanced samples in filtering multimedia message spam

    Science.gov (United States)

    Chen, Zhang; Peng, Zhenming; Peng, Lingbing; Liao, Dongyi; He, Xin

    2011-11-01

    With the swift and violent development of the Multimedia Messaging Service (MMS), it becomes an urgent task to filter the Multimedia Message (MM) spam effectively in real-time. For the fact that most MMs contain images or videos, a method based on retrieving images is given in this paper for filtering MM spam. The detection method used in this paper is a combination of skin-color detection, texture detection, and face detection, and the classifier for this imbalanced problem is a very fast multi-classification combining Support vector machine (SVM) with unilateral binary decision tree. The experiments on 3 test sets show that the proposed method is effective, with the interception rate up to 60% and the average detection time for each image less than 1 second.

  5. Research on Chinese web page SVM classifer based on information gain

    Directory of Open Access Journals (Sweden)

    PAN Zhengcai

    2013-06-01

    Full Text Available In order to improve the efficiency and accuracy of text classification,optimization and improvement are made for defects and deficiencies of the feature dimensionality reduction method and traditional information gain method in text classification of Chinese web pages.At first,part-of-speech filtering and synonyms merging processes are taken for the first feature dimension reduction of feature items.Then,an improved information gain method is proposed for feature weighting computation of feature items.Finally,the classification algorithm of Support Vector Machine (SVM is used for text classification of Chinese web pages.Both theoretical analysis and experimental results show that this method has better performance and classification results than traditional method.

  6. Protein-protein interaction site prediction in Homo sapiens and E. coli using an interaction-affinity based membership function in fuzzy SVM.

    Science.gov (United States)

    Sriwastava, Brijesh Kumar; Basu, Subhadip; Maulik, Ujjwal

    2015-10-01

    Protein-protein interaction (PPI) site prediction aids to ascertain the interface residues that participate in interaction processes. Fuzzy support vector machine (F-SVM) is proposed as an effective method to solve this problem, and we have shown that the performance of the classical SVM can be enhanced with the help of an interaction-affinity based fuzzy membership function. The performances of both SVM and F-SVM on the PPI databases of the Homo sapiens and E. coli organisms are evaluated and estimated the statistical significance of the developed method over classical SVM and other fuzzy membership-based SVM methods available in the literature. Our membership function uses the residue-level interaction affinity scores for each pair of positive and negative sequence fragments. The average AUC scores in the 10-fold cross-validation experiments are measured as 79.94% and 80.48% for the Homo sapiens and E. coli organisms respectively. On the independent test datasets, AUC scores are obtained as 76.59% and 80.17% respectively for the two organisms. In almost all cases, the developed F-SVM method improves the performances obtained by the corresponding classical SVM and the other classifiers, available in the literature.

  7. [Application of optimized parameters SVM based on photoacoustic spectroscopy method in fault diagnosis of power transformer].

    Science.gov (United States)

    Zhang, Yu-xin; Cheng, Zhi-feng; Xu, Zheng-ping; Bai, Jing

    2015-01-01

    In order to solve the problems such as complex operation, consumption for the carrier gas and long test period in traditional power transformer fault diagnosis approach based on dissolved gas analysis (DGA), this paper proposes a new method which is detecting 5 types of characteristic gas content in transformer oil such as CH4, C2H2, C2H4, C2H6 and H2 based on photoacoustic Spectroscopy and C2H2/C2H4, CH4/H2, C2H4/C2H6 three-ratios data are calculated. The support vector machine model was constructed using cross validation method under five support vector machine functions and four kernel functions, heuristic algorithms were used in parameter optimization for penalty factor c and g, which to establish the best SVM model for the highest fault diagnosis accuracy and the fast computing speed. Particles swarm optimization and genetic algorithm two types of heuristic algorithms were comparative studied in this paper for accuracy and speed in optimization. The simulation result shows that SVM model composed of C-SVC, RBF kernel functions and genetic algorithm obtain 97. 5% accuracy in test sample set and 98. 333 3% accuracy in train sample set, and genetic algorithm was about two times faster than particles swarm optimization in computing speed. The methods described in this paper has many advantages such as simple operation, non-contact measurement, no consumption for the carrier gas, long test period, high stability and sensitivity, the result shows that the methods described in this paper can instead of the traditional transformer fault diagnosis by gas chromatography and meets the actual project needs in transformer fault diagnosis.

  8. Forecasting Seizures Using Intracranial EEG Measures and SVM in Naturally Occurring Canine Epilepsy.

    Science.gov (United States)

    Brinkmann, Benjamin H; Patterson, Edward E; Vite, Charles; Vasoli, Vincent M; Crepeau, Daniel; Stead, Matt; Howbert, J Jeffry; Cherkassky, Vladimir; Wagenaar, Joost B; Litt, Brian; Worrell, Gregory A

    2015-01-01

    Management of drug resistant focal epilepsy would be greatly assisted by a reliable warning system capable of alerting patients prior to seizures to allow the patient to adjust activities or medication. Such a system requires successful identification of a preictal, or seizure-prone state. Identification of preictal states in continuous long- duration intracranial electroencephalographic (iEEG) recordings of dogs with naturally occurring epilepsy was investigated using a support vector machine (SVM) algorithm. The dogs studied were implanted with a 16-channel ambulatory iEEG recording device with average channel reference for a mean (st. dev.) of 380.4 (+87.5) days producing 220.2 (+104.1) days of intracranial EEG recorded at 400 Hz for analysis. The iEEG records had 51.6 (+52.8) seizures identified, of which 35.8 (+30.4) seizures were preceded by more than 4 hours of seizure-free data. Recorded iEEG data were stratified into 11 contiguous, non-overlapping frequency bands and binned into one-minute synchrony features for analysis. Performance of the SVM classifier was assessed using a 5-fold cross validation approach, where preictal training data were taken from 90 minute windows with a 5 minute pre-seizure offset. Analysis of the optimal preictal training time was performed by repeating the cross validation over a range of preictal windows and comparing results. We show that the optimization of feature selection varies for each subject, i.e. algorithms are subject specific, but achieve prediction performance significantly better than a time-matched Poisson random predictor (pdogs analyzed.

  9. Multimodal analysis of functional and structural disconnection in Alzheimer's disease using multiple kernel SVM.

    Science.gov (United States)

    Dyrba, Martin; Grothe, Michel; Kirste, Thomas; Teipel, Stefan J

    2015-06-01

    Alzheimer's disease (AD) patients exhibit alterations in the functional connectivity between spatially segregated brain regions which may be related to both local gray matter (GM) atrophy as well as a decline in the fiber integrity of the underlying white matter tracts. Machine learning algorithms are able to automatically detect the patterns of the disease in image data, and therefore, constitute a suitable basis for automated image diagnostic systems. The question of which magnetic resonance imaging (MRI) modalities are most useful in a clinical context is as yet unresolved. We examined multimodal MRI data acquired from 28 subjects with clinically probable AD and 25 healthy controls. Specifically, we used fiber tract integrity as measured by diffusion tensor imaging (DTI), GM volume derived from structural MRI, and the graph-theoretical measures 'local clustering coefficient' and 'shortest path length' derived from resting-state functional MRI (rs-fMRI) to evaluate the utility of the three imaging methods in automated multimodal image diagnostics, to assess their individual performance, and the level of concordance between them. We ran the support vector machine (SVM) algorithm and validated the results using leave-one-out cross-validation. For the single imaging modalities, we obtained an area under the curve (AUC) of 80% for rs-fMRI, 87% for DTI, and 86% for GM volume. When it came to the multimodal SVM, we obtained an AUC of 82% using all three modalities, and 89% using only DTI measures and GM volume. Combined multimodal imaging data did not significantly improve classification accuracy compared to the best single measures alone. © 2015 Wiley Periodicals, Inc.

  10. Elucidation of Metallic Plume and Spatter Characteristics Based on SVM During High-Power Disk Laser Welding

    Science.gov (United States)

    Gao, Xiangdong; Liu, Guiqian

    2015-01-01

    During deep penetration laser welding, there exist plume (weak plasma) and spatters, which are the results of weld material ejection due to strong laser heating. The characteristics of plume and spatters are related to welding stability and quality. Characteristics of metallic plume and spatters were investigated during high-power disk laser bead-on-plate welding of Type 304 austenitic stainless steel plates at a continuous wave laser power of 10 kW. An ultraviolet and visible sensitive high-speed camera was used to capture the metallic plume and spatter images. Plume area, laser beam path through the plume, swing angle, distance between laser beam focus and plume image centroid, abscissa of plume centroid and spatter numbers are defined as eigenvalues, and the weld bead width was used as a characteristic parameter that reflected welding stability. Welding status was distinguished by SVM (support vector machine) after data normalization and characteristic analysis. Also, PCA (principal components analysis) feature extraction was used to reduce the dimensions of feature space, and PSO (particle swarm optimization) was used to optimize the parameters of SVM. Finally a classification model based on SVM was established to estimate the weld bead width and welding stability. Experimental results show that the established algorithm based on SVM could effectively distinguish the variation of weld bead width, thus providing an experimental example of monitoring high-power disk laser welding quality.

  11. Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

    Directory of Open Access Journals (Sweden)

    Fei Wang

    2017-12-01

    Full Text Available Accurate solar photovoltaic (PV power forecasting is an essential tool for mitigating the negative effects caused by the uncertainty of PV output power in systems with high penetration levels of solar PV generation. Weather classification based modeling is an effective way to increase the accuracy of day-ahead short-term (DAST solar PV power forecasting because PV output power is strongly dependent on the specific weather conditions in a given time period. However, the accuracy of daily weather classification relies on both the applied classifiers and the training data. This paper aims to reveal how these two factors impact the classification performance and to delineate the relation between classification accuracy and sample dataset scale. Two commonly used classification methods, K-nearest neighbors (KNN and support vector machines (SVM are applied to classify the daily local weather types for DAST solar PV power forecasting using the operation data from a grid-connected PV plant in Hohhot, Inner Mongolia, China. We assessed the performance of SVM and KNN approaches, and then investigated the influences of sample scale, the number of categories, and the data distribution in different categories on the daily weather classification results. The simulation results illustrate that SVM performs well with small sample scale, while KNN is more sensitive to the length of the training dataset and can achieve higher accuracy than SVM with sufficient samples.

  12. Effect of feeding on the function and structure of the digestive system in juvenile southern catfish (Silurus meridionalis Chen).

    Science.gov (United States)

    Zeng, Ling-Qing; Li, Feng-Jie; Fu, Shi-Jian; Cao, Zhen-Dong; Zhang, Yao-Guang

    2012-10-01

    Postprandial physiological and morphological responses to feeding were examined in juvenile southern catfish (Silurus meridionalis Chen) that had consumed a loach (Misgurnus anguillicaudatus Cantor) meal equivalent to 6 % of the body mass of the catfish. The gastric evacuation rate (GER) peaked at 4 h postfeeding, averaging 0.36 g food weight h(-1), at which time 14 % of the ingested meal had passed into the intestine. Less than 10 % of the ingested meal remained in the stomach at 24 h postfeeding. Pepsin activity peaked at 8 h postfeeding, reaching a level approximately twofold higher than the prefeeding level. Pancreatic trypsin activity peaked at 16 h postfeeding, reaching a level 4.5-fold higher than the prefeeding level. Peaks in lipase activity in both the proximal and middle intestinal segments occurred at 16 h, reaching 2.8- and 2.4-fold higher levels than the prefeeding level, respectively, while the activity in the distal intestine segment reached a level 2.9-fold higher than the prefeeding level at 24 h postfeeding. With respect to amylase activity, only the middle intestinal segment exhibited a change, first an increase and then a decrease, after feeding. Feeding also triggered an approximately 200 % increase in the metabolic rate and resulted in 44.6 kJ kg(-1) being expended on specific dynamic action, equivalent to 16.1 % of the meal's energy. In terms of organ size, the wet mass of the liver increased by 11 % at 24 h postfeeding, whereas the wet mass of the pancreas did not change. Except for a decrease in the thickness of the submucosa in the middle intestinal segment, the thickness of the intestinal fold, mucosa, submucosa, muscularis and serosa of each intestinal segment did not change significantly with feeding. These results suggest that the continuum of physiological responses observed with respect to metabolic increases, GER, regulation of pancreatic and intestinal digestive enzyme activities and liver wet mass to feeding

  13. Microcalcification detection in full-field digital mammograms with PFCM clustering and weighted SVM-based method

    Science.gov (United States)

    Liu, Xiaoming; Mei, Ming; Liu, Jun; Hu, Wei

    2015-12-01

    Clustered microcalcifications (MCs) in mammograms are an important early sign of breast cancer in women. Their accurate detection is important in computer-aided detection (CADe). In this paper, we integrated the possibilistic fuzzy c-means (PFCM) clustering algorithm and weighted support vector machine (WSVM) for the detection of MC clusters in full-field digital mammograms (FFDM). For each image, suspicious MC regions are extracted with region growing and active contour segmentation. Then geometry and texture features are extracted for each suspicious MC, a mutual information-based supervised criterion is used to select important features, and PFCM is applied to cluster the samples into two clusters. Weights of the samples are calculated based on possibilities and typicality values from the PFCM, and the ground truth labels. A weighted nonlinear SVM is trained. During the test process, when an unknown image is presented, suspicious regions are located with the segmentation step, selected features are extracted, and the suspicious MC regions are classified as containing MC or not by the trained weighted nonlinear SVM. Finally, the MC regions are analyzed with spatial information to locate MC clusters. The proposed method is evaluated using a database of 410 clinical mammograms and compared with a standard unweighted support vector machine (SVM) classifier. The detection performance is evaluated using response receiver operating (ROC) curves and free-response receiver operating characteristic (FROC) curves. The proposed method obtained an area under the ROC curve of 0.8676, while the standard SVM obtained an area of 0.8268 for MC detection. For MC cluster detection, the proposed method obtained a high sensitivity of 92 % with a false-positive rate of 2.3 clusters/image, and it is also better than standard SVM with 4.7 false-positive clusters/image at the same sensitivity.

  14. Chinese Commission of Science Technology and Industry for National Defense Senior Vice Minister CHEN Qiufa exchanging gifts at luncheon and signing the Guest Book on 1st November 2007 with CERN Director-General R. Aymar.

    CERN Multimedia

    Maximilien Brice

    2007-01-01

    Chinese Commission of Science Technology and Industry for National Defense Senior Vice Minister CHEN Qiufa exchanging gifts at luncheon and signing the Guest Book on 1st November 2007 with CERN Director-General R. Aymar.

  15. 6 June 2012 - Chinese Nanjing University President J.Chen in the ATLAS visitor centre with Member of the ATLAS Collaboration I. Wingerter and International Relations Office Adviser E. Tsesmelis. M. Qi, Nanjing University and ATLAS Collaboration, accompanies the delegation.

    CERN Multimedia

    Maximilien Brice

    2012-01-01

    6 June 2012 - Chinese Nanjing University President J.Chen in the ATLAS visitor centre with Member of the ATLAS Collaboration I. Wingerter and International Relations Office Adviser E. Tsesmelis. M. Qi, Nanjing University and ATLAS Collaboration, accompanies the delegation.

  16. Effects of hardware heterogeneity on the performance of SVM Alzheimer's disease classifier.

    Science.gov (United States)

    Abdulkadir, Ahmed; Mortamet, Bénédicte; Vemuri, Prashanthi; Jack, Clifford R; Krueger, Gunnar; Klöppel, Stefan

    2011-10-01

    Fully automated machine learning methods based on structural magnetic resonance imaging (MRI) data can assist radiologists in the diagnosis of Alzheimer's disease (AD). These algorithms require large data sets to learn the separation of subjects with and without AD. Training and test data may come from heterogeneous hardware settings, which can potentially affect the performance of disease classification. A total of 518 MRI sessions from 226 healthy controls and 191 individuals with probable AD from the multicenter Alzheimer's Disease Neuroimaging Initiative (ADNI) were used to investigate whether grouping data by acquisition hardware (i.e. vendor, field strength, coil system) is beneficial for the performance of a support vector machine (SVM) classifier, compared to the case where data from different hardware is mixed. We compared the change of the SVM decision value resulting from (a) changes in hardware against the effect of disease and (b) changes resulting simply from rescanning the same subject on the same machine. Maximum accuracy of 87% was obtained with a training set of all 417 subjects. Classifiers trained with 95 subjects in each diagnostic group and acquired with heterogeneous scanner settings had an empirical detection accuracy of 84.2±2.4% when tested on an independent set of the same size. These results mirror the accuracy reported in recent studies. Encouragingly, classifiers trained on images acquired with homogenous and heterogeneous hardware settings had equivalent cross-validation performances. Two scans of the same subject acquired on the same machine had very similar decision values and were generally classified into the same group. Higher variation was introduced when two acquisitions of the same subject were performed on two scanners with different field strengths. The variation was unbiased and similar for both diagnostic groups. The findings of the study encourage the pooling of data from different sites to increase the number of

  17. An Automatic Traffic Sign Detection and Recognition System Based on Colour Segmentation, Shape Matching, and SVM

    Directory of Open Access Journals (Sweden)

    Safat B. Wali

    2015-01-01

    Full Text Available The main objective of this study is to develop an efficient TSDR system which contains an enriched dataset of Malaysian traffic signs. The developed technique is invariant in variable lighting, rotation, translation, and viewing angle and has a low computational time with low false positive rate. The development of the system has three working stages: image preprocessing, detection, and recognition. The system demonstration using a RGB colour segmentation and shape matching followed by support vector machine (SVM classifier led to promising results with respect to the accuracy of 95.71%, false positive rate (0.9%, and processing time (0.43 s. The area under the receiver operating characteristic (ROC curves was introduced to statistically evaluate the recognition performance. The accuracy of the developed system is relatively high and the computational time is relatively low which will be helpful for classifying traffic signs especially on high ways around Malaysia. The low false positive rate will increase the system stability and reliability on real-time application.

  18. Identification of potential ACAT-2 selective inhibitors using pharmacophore, SVM and SVR from Chinese herbs.

    Science.gov (United States)

    Qiao, Lian-Sheng; Zhang, Xian-Bao; Jiang, Lu-di; Zhang, Yan-Ling; Li, Gong-Yu

    2016-11-01

    Acyl-coenzyme A cholesterol acyltransferase (ACAT) plays an important role in maintaining cellular and organismal cholesterol homeostasis. Two types of ACAT isozymes with different functions exist in mammals, named ACAT-1 and ACAT-2. Numerous studies showed that ACAT-2 selective inhibitors are effective for the treatment of hypercholesterolemia and atherosclerosis. However, as a typical endoplasmic reticulum protein, ACAT-2 protein has not been purified and revealed, so combinatorial ligand-based methods might be the optimal strategy for discovering the ACAT-2 selective inhibitors. In this study, selective pharmacophore models of ACAT-1 inhibitors and ACAT-2 inhibitors were built, respectively. The optimal pharmacophore model for each subtype was identified and utilized as queries for the Traditional Chinese Medicine Database screening. A total of 180 potential ACAT-2 selective inhibitors were obtained, which were identified using an ACAT-2 pharmacophore and not by our ACAT-1 model. Selective SVM model and bioactive SVR model were generated for further identification of the obtained ACAT-2 inhibitors. Ten compounds were finally obtained with predicted inhibitory activities toward ACAT-2. Hydrogen bond acceptor, 2D autocorrelations, GETAWAY descriptors, and BCUT descriptors were identified as key structural features for selectivity and activity of ACAT-2 inhibitors. This study provides a reasonable ligand-based approach to discover potential ACAT-2 selective inhibitors from Chinese herbs, which could help in further screening and development of ACAT-2 selective inhibitors.

  19. FUSION OF NON-THERMAL AND THERMAL SATELLITE IMAGES BY BOOSTED SVM CLASSIFIERS FOR CLOUD DETECTION

    Directory of Open Access Journals (Sweden)

    N. Ghasemian

    2017-09-01

    Full Text Available The goal of ensemble learning methods like Bagging and Boosting is to improve the classification results of some weak classifiers gradually. Usually, Boosting algorithms show better results than Bagging. In this article, we have examined the possibility of fusion of non-thermal and thermal bands of Landsat 8 satellite images for cloud detection by using the boosting method. We used SVM as a base learner and the performance of two kinds of Boosting methods including AdaBoost.M1 and σ Boost was compared on remote sensing images of Landsat 8 satellite. We first extracted the co-occurrence matrix features of non-thermal and thermal bands separately and then used PCA method for feature selection. In the next step AdaBoost.M1 and σ Boost algorithms were applied on non-thermal and thermal bands and finally, the classifiers were fused using majority voting. Also, we showed that by changing the regularization parameter (C the result of σ Boost algorithm can significantly change and achieve overall accuracy and cloud producer accuracy of 74%, and 0.53 kappa coefficient that shows better results in comparison to AdaBoost.M1.

  20. Iterative Reweighted Noninteger Norm Regularizing SVM for Gene Expression Data Classification

    Directory of Open Access Journals (Sweden)

    Jianwei Liu

    2013-01-01

    Full Text Available Support vector machine is an effective classification and regression method that uses machine learning theory to maximize the predictive accuracy while avoiding overfitting of data. L2 regularization has been commonly used. If the training dataset contains many noise variables, L1 regularization SVM will provide a better performance. However, both L1 and L2 are not the optimal regularization method when handing a large number of redundant values and only a small amount of data points is useful for machine learning. We have therefore proposed an adaptive learning algorithm using the iterative reweighted p-norm regularization support vector machine for 0 < p ≤ 2. A simulated data set was created to evaluate the algorithm. It was shown that a p value of 0.8 was able to produce better feature selection rate with high accuracy. Four cancer data sets from public data banks were used also for the evaluation. All four evaluations show that the new adaptive algorithm was able to achieve the optimal prediction error using a p value less than L1 norm. Moreover, we observe that the proposed Lp penalty is more robust to noise variables than the L1 and L2 penalties.

  1. Iterative reweighted noninteger norm regularizing SVM for gene expression data classification.

    Science.gov (United States)

    Liu, Jianwei; Li, Shuang Cheng; Luo, Xionglin

    2013-01-01

    Support vector machine is an effective classification and regression method that uses machine learning theory to maximize the predictive accuracy while avoiding overfitting of data. L2 regularization has been commonly used. If the training dataset contains many noise variables, L1 regularization SVM will provide a better performance. However, both L1 and L2 are not the optimal regularization method when handing a large number of redundant values and only a small amount of data points is useful for machine learning. We have therefore proposed an adaptive learning algorithm using the iterative reweighted p-norm regularization support vector machine for 0 < p ≤ 2. A simulated data set was created to evaluate the algorithm. It was shown that a p value of 0.8 was able to produce better feature selection rate with high accuracy. Four cancer data sets from public data banks were used also for the evaluation. All four evaluations show that the new adaptive algorithm was able to achieve the optimal prediction error using a p value less than L1 norm. Moreover, we observe that the proposed Lp penalty is more robust to noise variables than the L1 and L2 penalties.

  2. Fusion of Non-Thermal and Thermal Satellite Images by Boosted Svm Classifiers for Cloud Detection

    Science.gov (United States)

    Ghasemian, N.; Akhoondzadeh, M.

    2017-09-01

    The goal of ensemble learning methods like Bagging and Boosting is to improve the classification results of some weak classifiers gradually. Usually, Boosting algorithms show better results than Bagging. In this article, we have examined the possibility of fusion of non-thermal and thermal bands of Landsat 8 satellite images for cloud detection by using the boosting method. We used SVM as a base learner and the performance of two kinds of Boosting methods including AdaBoost.M1 and σ Boost was compared on remote sensing images of Landsat 8 satellite. We first extracted the co-occurrence matrix features of non-thermal and thermal bands separately and then used PCA method for feature selection. In the next step AdaBoost.M1 and σ Boost algorithms were applied on non-thermal and thermal bands and finally, the classifiers were fused using majority voting. Also, we showed that by changing the regularization parameter (C) the result of σ Boost algorithm can significantly change and achieve overall accuracy and cloud producer accuracy of 74%, and 0.53 kappa coefficient that shows better results in comparison to AdaBoost.M1.

  3. Simultaneous localization of lumbar vertebrae and intervertebral discs with SVM-based MRF.

    Science.gov (United States)

    Oktay, Ayse Betul; Akgul, Yusuf Sinan

    2013-09-01

    This paper presents a method for localizing and labeling the lumbar vertebrae and intervertebral discs in mid-sagittal MR image slices. The approach is based on a Markov-chain-like graphical model of the ordered discs and vertebrae in the lumbar spine. The graphical model is formulated by combining local image features and semiglobal geometrical information. The local image features are extracted from the image by employing pyramidal histogram of oriented gradients (PHOG) and a novel descriptor that we call image projection descriptor (IPD). These features are trained with support vector machines (SVM) and each pixel in the target image is locally assigned a score. These local scores are combined with the semiglobal geometrical information like the distance ratio and angle between the neighboring structures under the Markov random field (MRF) framework. An exact localization of discs and vertebrae is inferred from the MRF by finding a maximum a posteriori solution efficiently using dynamic programming. As a result of the novel features introduced, our system can scale-invariantly localize discs and vertebra at the same time even in the existence of missing structures. The proposed system is tested and validated on a clinical lumbar spine MR image dataset containing 80 subjects of which 64 have disc- and vertebra-related diseases and abnormalities. The experiments show that our system is successful even in abnormal cases and our results are comparable to the state of the art.

  4. SVM to detect the presence of visitors in a smart home environment.

    Science.gov (United States)

    Petersen, Johanna; Larimer, Nicole; Kaye, Jeffrey A; Pavel, Misha; Hayes, Tamara L

    2012-01-01

    With the rising age of the population, there is increased need to help elderly maintain their independence. Smart homes, employing passive sensor networks and pervasive computing techniques, enable the unobtrusive assessment of activities and behaviors of the elderly which can be useful for health state assessment and intervention. Due to the multiple health benefits associated with socializing, accurately tracking whether an individual has visitors to their home is one of the more important aspects of elders' behaviors that could be assessed with smart home technology. With this goal, we have developed a preliminary SVM model to identify periods where untagged visitors are present in the home. Using the dwell time, number of sensor firings, and number of transitions between major living spaces (living room, dining room, kitchen and bathroom) as features in the model, and self report from two subjects as ground truth, we were able to accurately detect the presence of visitors in the home with a sensitivity and specificity of 0.90 and 0.89 for subject 1, and of 0.67 and 0.78 for subject 2, respectively. These preliminary data demonstrate the feasibility of detecting visitors with in-home sensor data, but highlight the need for more advanced modeling techniques so the model performs well for all subjects and all types of visitors.

  5. A Novel Ensemble Method for Imbalanced Data Learning: Bagging of Extrapolation-SMOTE SVM.

    Science.gov (United States)

    Wang, Qi; Luo, ZhiHao; Huang, JinCai; Feng, YangHe; Liu, Zhong

    2017-01-01

    Class imbalance ubiquitously exists in real life, which has attracted much interest from various domains. Direct learning from imbalanced dataset may pose unsatisfying results overfocusing on the accuracy of identification and deriving a suboptimal model. Various methodologies have been developed in tackling this problem including sampling, cost-sensitive, and other hybrid ones. However, the samples near the decision boundary which contain more discriminative information should be valued and the skew of the boundary would be corrected by constructing synthetic samples. Inspired by the truth and sense of geometry, we designed a new synthetic minority oversampling technique to incorporate the borderline information. What is more, ensemble model always tends to capture more complicated and robust decision boundary in practice. Taking these factors into considerations, a novel ensemble method, called Bagging of Extrapolation Borderline-SMOTE SVM (BEBS), has been proposed in dealing with imbalanced data learning (IDL) problems. Experiments on open access datasets showed significant superior performance using our model and a persuasive and intuitive explanation behind the method was illustrated. As far as we know, this is the first model combining ensemble of SVMs with borderline information for solving such condition.

  6. STUDY COMPARISON OF SVM-, K-NN- AND BACKPROPAGATION-BASED CLASSIFIER FOR IMAGE RETRIEVAL

    Directory of Open Access Journals (Sweden)

    Muhammad Athoillah

    2015-03-01

    Full Text Available Classification is a method for compiling data systematically according to the rules that have been set previously. In recent years classification method has been proven to help many people’s work, such as image classification, medical biology, traffic light, text classification etc. There are many methods to solve classification problem. This variation method makes the researchers find it difficult to determine which method is best for a problem. This framework is aimed to compare the ability of classification methods, such as Support Vector Machine (SVM, K-Nearest Neighbor (K-NN, and Backpropagation, especially in study cases of image retrieval with five category of image dataset. The result shows that K-NN has the best average result in accuracy with 82%. It is also the fastest in average computation time with 17,99 second during retrieve session for all categories class. The Backpropagation, however, is the slowest among three of them. In average it needed 883 second for training session and 41,7 second for retrieve session.

  7. Towards multilevel mental stress assessment using SVM with ECOC: an EEG approach.

    Science.gov (United States)

    Al-Shargie, Fares; Tang, Tong Boon; Badruddin, Nasreen; Kiguchi, Masashi

    2017-10-18

    Mental stress has been identified as one of the major contributing factors that leads to various diseases such as heart attack, depression, and stroke. To avoid this, stress quantification is important for clinical intervention and disease prevention. This study aims to investigate the feasibility of exploiting electroencephalography (EEG) signals to discriminate between different stress levels. We propose a new assessment protocol whereby the stress level is represented by the complexity of mental arithmetic (MA) task for example, at three levels of difficulty, and the stressors are time pressure and negative feedback. Using 18-male subjects, the experimental results showed that there were significant differences in EEG response between the control and stress conditions at different levels of MA task with p values < 0.001. Furthermore, we found a significant reduction in alpha rhythm power from one stress level to another level, p values < 0.05. In comparison, results from self-reporting questionnaire NASA-TLX approach showed no significant differences between stress levels. In addition, we developed a discriminant analysis method based on multiclass support vector machine (SVM) with error-correcting output code (ECOC). Different stress levels were detected with an average classification accuracy of 94.79%. The lateral index (LI) results further showed dominant right prefrontal cortex (PFC) to mental stress (reduced alpha rhythm). The study demonstrated the feasibility of using EEG in classifying multilevel mental stress and reported alpha rhythm power at right prefrontal cortex as a suitable index.

  8. Intelligent Agent-Based Intrusion Detection System Using Enhanced Multiclass SVM

    Directory of Open Access Journals (Sweden)

    S. Ganapathy

    2012-01-01

    Full Text Available Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set.

  9. Intelligent agent-based intrusion detection system using enhanced multiclass SVM.

    Science.gov (United States)

    Ganapathy, S; Yogesh, P; Kannan, A

    2012-01-01

    Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set.

  10. Intelligent Agent-Based Intrusion Detection System Using Enhanced Multiclass SVM

    Science.gov (United States)

    Ganapathy, S.; Yogesh, P.; Kannan, A.

    2012-01-01

    Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set. PMID:23056036

  11. DisArticle: a web server for SVM-based discrimination of articles on traditional medicine.

    Science.gov (United States)

    Kim, Sang-Kyun; Nam, SeJin; Kim, SangHyun

    2017-01-28

    Much research has been done in Northeast Asia to show the efficacy of traditional medicine. While MEDLINE contains many biomedical articles including those on traditional medicine, it does not categorize those articles by specific research area. The aim of this study was to provide a method that searches for articles only on traditional medicine in Northeast Asia, including traditional Chinese medicine, from among the articles in MEDLINE. This research established an SVM-based classifier model to identify articles on traditional medicine. The TAK + HM classifier, trained with the features of title, abstract, keywords, herbal data, and MeSH, has a precision of 0.954 and a recall of 0.902. In particular, the feature of herbal data significantly increased the performance of the classifier. By using the TAK + HM classifier, a total of about 108,000 articles were discriminated as articles on traditional medicine from among all articles in MEDLINE. We also built a web server called DisArticle ( http://informatics.kiom.re.kr/disarticle ), in which users can search for the articles and obtain statistical data. Because much evidence-based research on traditional medicine has been published in recent years, it has become necessary to search for articles on traditional medicine exclusively in literature databases. DisArticle can help users to search for and analyze the research trends in traditional medicine.

  12. Comparison between SARS CoV and MERS CoV Using Apriori Algorithm, Decision Tree, SVM

    Directory of Open Access Journals (Sweden)

    Jang Seongpil

    2016-01-01

    Full Text Available MERS (Middle East Respiratory Syndrome is a worldwide disease these days. The number of infected people is 1038(08/03/2015 in Saudi Arabia and 186(08/03/2015 in South Korea. MERS is all over the world including Europe and the fatality rate is 38.8%, East Asia and the Middle East. The MERS is also known as a cousin of SARS (Severe Acute Respiratory Syndrome because both diseases show similar symptoms such as high fever and difficulty in breathing. This is why we compared MERS with SARS. We used data of the spike glycoprotein from NCBI. As a way of analyzing the protein, apriori algorithm, decision tree, SVM were used, and particularly SVM was iterated by normal, polynomial, and sigmoid. The result came out that the MERS and the SARS are alike but also different in some way.

  13. A Novel Feature Extraction Approach Using Window Function Capturing and QPSO-SVM for Enhancing Electronic Nose Performance

    Directory of Open Access Journals (Sweden)

    Xiuzhen Guo

    2015-06-01

    Full Text Available In this paper, a novel feature extraction approach which can be referred to as moving window function capturing (MWFC has been proposed to analyze signals of an electronic nose (E-nose used for detecting types of infectious pathogens in rat wounds. Meanwhile, a quantum-behaved particle swarm optimization (QPSO algorithm is implemented in conjunction with support vector machine (SVM for realizing a synchronization optimization of the sensor array and SVM model parameters. The results prove the efficacy of the proposed method for E-nose feature extraction, which can lead to a higher classification accuracy rate compared to other established techniques. Meanwhile it is interesting to note that different classification results can be obtained by changing the types, widths or positions of windows. By selecting the optimum window function for the sensor response, the performance of an E-nose can be enhanced.

  14. Energy Management in Wireless Sensor Networks Based on Naive Bayes, MLP, and SVM Classifications: A Comparative Study

    Directory of Open Access Journals (Sweden)

    Abdulaziz Y. Barnawi

    2016-01-01

    Full Text Available Maximizing wireless sensor networks (WSNs lifetime is a primary objective in the design of these networks. Intelligent energy management models can assist designers to achieve this objective. These models aim to reduce the number of selected sensors to report environmental measurements and, hence, achieve higher energy efficiency while maintaining the desired level of accuracy in the reported measurement. In this paper, we present a comparative study of three intelligent models based on Naive Bayes, Multilayer Perceptrons (MLP, and Support Vector Machine (SVM classifiers. Simulation results show that Linear-SVM selects sensors that produce higher energy efficiency compared to those selected by MLP and Naive Bayes for the same WSNs Lifetime Extension Factor.

  15. DISEÑO Y EVALUACIÓN DE UN CLASIFICADOR DE TEXTURAS BASADO EN LS-SVM

    Directory of Open Access Journals (Sweden)

    Beitmantt Cárdenas Quintero

    2013-07-01

    Full Text Available Evaluar el desempeño y el costo computacional de diferentes arquitecturas y metodologías Least Square Support Vector Machine (LS-SVM ante la segmentación de imágenes por textura y a partir de dichos resultados postular un modelo de un clasificador de texturas LS-SVM.  Metodología: Ante un problema de clasificación binaria representado por la segmentación  de 32 imágenes, organizadas en 4 grupos y formadas por pares de texturas típicas (granito/corteza, ladrillo/tapicería, madera/mármol, tejido/pelaje, se mide y compara el desempeño y el costo computacional de dos tipos de núcleo (Radial / Polinomial, dos funciones de optimización (mínimo local / búsqueda exhaustiva y dos funciones de costo (validación cruzada aleatoria / Validación cruzada dejando al menos uno en una LS-SVM que toma como entrada los pixeles que conforman la vecindad cruz del pixel a evaluar (no se hace extracción de características. Resultados: LS-SVM como clasificador de texturas, presenta mejor desempeño y exige menor costo computacional cuando utiliza un kernel de base radial y una función de optimización basada en un algoritmo de búsqueda de mínimos locales acompañado de una función de costo que use validación cruzada aleatoria.

  16. An Efficient Normalized Rank Based SVM for Room Level Indoor WiFi Localization with Diverse Devices

    Directory of Open Access Journals (Sweden)

    Yasmine Rezgui

    2017-01-01

    Full Text Available This paper proposes an efficient and effective WiFi fingerprinting-based indoor localization algorithm, which uses the Received Signal Strength Indicator (RSSI of WiFi signals. In practical harsh indoor environments, RSSI variation and hardware variance can significantly degrade the performance of fingerprinting-based localization methods. To address the problem of hardware variance and signal fluctuation in WiFi fingerprinting-based localization, we propose a novel normalized rank based Support Vector Machine classifier (NR-SVM. Moving from RSSI value based analysis to the normalized rank transformation based analysis, the principal features are prioritized and the dimensionalities of signature vectors are taken into account. The proposed method has been tested using sixteen different devices in a shopping mall with 88 shops. The experimental results demonstrate its robustness with no less than 98.75% correct estimation in 93.75% of the tested cases and 100% correct rate in 56.25% of cases. In the experiments, the new method shows better performance over the KNN, Naïve Bayes, Random Forest, and Neural Network algorithms. Furthermore, we have compared the proposed approach with three popular calibration-free transformation based methods, including difference method (DIFF, Signal Strength Difference (SSD, and the Hyperbolic Location Fingerprinting (HLF based SVM. The results show that the NR-SVM outperforms these popular methods.

  17. Comparison of SVM, RF and ELM on an Electronic Nose for the Intelligent Evaluation of Paraffin Samples

    Directory of Open Access Journals (Sweden)

    Hong Men

    2018-01-01

    Full Text Available Paraffin odor intensity is an important quality indicator when a paraffin inspection is performed. Currently, paraffin odor level assessment is mainly dependent on an artificial sensory evaluation. In this paper, we developed a paraffin odor analysis system to classify and grade four kinds of paraffin samples. The original feature set was optimized using Principal Component Analysis (PCA and Partial Least Squares (PLS. Support Vector Machine (SVM, Random Forest (RF, and Extreme Learning Machine (ELM were applied to three different feature data sets for classification and level assessment of paraffin. For classification, the model based on SVM, with an accuracy rate of 100%, was superior to that based on RF, with an accuracy rate of 98.33–100%, and ELM, with an accuracy rate of 98.01–100%. For level assessment, the R2 related to the training set was above 0.97 and the R2 related to the test set was above 0.87. Through comprehensive comparison, the generalization of the model based on ELM was superior to those based on SVM and RF. The scoring errors for the three models were 0.0016–0.3494, lower than the error of 0.5–1.0 measured by industry standard experts, meaning these methods have a higher prediction accuracy for scoring paraffin level.

  18. A Hybrid ICA-SVM Approach for Determining the Quality Variables at Fault in a Multivariate Process

    Directory of Open Access Journals (Sweden)

    Yuehjen E. Shao

    2012-01-01

    Full Text Available The monitoring of a multivariate process with the use of multivariate statistical process control (MSPC charts has received considerable attention. However, in practice, the use of MSPC chart typically encounters a difficulty. This difficult involves which quality variable or which set of the quality variables is responsible for the generation of the signal. This study proposes a hybrid scheme which is composed of independent component analysis (ICA and support vector machine (SVM to determine the fault quality variables when a step-change disturbance existed in a multivariate process. The proposed hybrid ICA-SVM scheme initially applies ICA to the Hotelling T2 MSPC chart to generate independent components (ICs. The hidden information of the fault quality variables can be identified in these ICs. The ICs are then served as the input variables of the classifier SVM for performing the classification process. The performance of various process designs is investigated and compared with the typical classification method. Using the proposed approach, the fault quality variables for a multivariate process can be accurately and reliably determined.

  19. Computer-Aided Lung Nodule Recognition by SVM Classifier Based on Combination of Random Undersampling and SMOTE

    Directory of Open Access Journals (Sweden)

    Yuan Sui

    2015-01-01

    Full Text Available In lung cancer computer-aided detection/diagnosis (CAD systems, classification of regions of interest (ROI is often used to detect/diagnose lung nodule accurately. However, problems of unbalanced datasets often have detrimental effects on the performance of classification. In this paper, both minority and majority classes are resampled to increase the generalization ability. We propose a novel SVM classifier combined with random undersampling (RU and SMOTE for lung nodule recognition. The combinations of the two resampling methods not only achieve a balanced training samples but also remove noise and duplicate information in the training sample and retain useful information to improve the effective data utilization, hence improving performance of SVM algorithm for pulmonary nodules classification under the unbalanced data. Eight features including 2D and 3D features are extracted for training and classification. Experimental results show that for different sizes of training datasets our RU-SMOTE-SVM classifier gets the highest classification accuracy among the four kinds of classifiers, and the average classification accuracy is more than 92.94%.

  20. Comparison of SVM, RF and ELM on an Electronic Nose for the Intelligent Evaluation of Paraffin Samples

    Science.gov (United States)

    Men, Hong; Fu, Songlin; Yang, Jialin; Cheng, Meiqi; Shi, Yan

    2018-01-01

    Paraffin odor intensity is an important quality indicator when a paraffin inspection is performed. Currently, paraffin odor level assessment is mainly dependent on an artificial sensory evaluation. In this paper, we developed a paraffin odor analysis system to classify and grade four kinds of paraffin samples. The original feature set was optimized using Principal Component Analysis (PCA) and Partial Least Squares (PLS). Support Vector Machine (SVM), Random Forest (RF), and Extreme Learning Machine (ELM) were applied to three different feature data sets for classification and level assessment of paraffin. For classification, the model based on SVM, with an accuracy rate of 100%, was superior to that based on RF, with an accuracy rate of 98.33–100%, and ELM, with an accuracy rate of 98.01–100%. For level assessment, the R2 related to the training set was above 0.97 and the R2 related to the test set was above 0.87. Through comprehensive comparison, the generalization of the model based on ELM was superior to those based on SVM and RF. The scoring errors for the three models were 0.0016–0.3494, lower than the error of 0.5–1.0 measured by industry standard experts, meaning these methods have a higher prediction accuracy for scoring paraffin level. PMID:29346328

  1. Approche de sélection d’attributs pour la classification basée sur l’algorithme RFE-SVM

    OpenAIRE

    Slimani, yahya; Essegir, Mohamed Amir; Samb, Mouhamadou Lamine; Camara, Fodé; Ndiaye, Samba

    2014-01-01

    International audience; The feature selection for classification is a very active research field in data mining and optimization. Its combinatorial nature requires the development of specific techniques (such as filters, wrappers, genetic algorithms, and so on) or hybrid approaches combining several optimization methods. In this context, the support vector machine recursive feature elimination (SVM-RFE), is distinguished as one of the most effective methods. However, the RFE-SVM algorithm is ...

  2. Segmentasi Citra menggunakan Support Vector Machine (SVM dan Ellipsoid Region Search Strategy (ERSS Arimoto Entropy berdasarkan Ciri Warna dan Tekstur

    Directory of Open Access Journals (Sweden)

    Lukman Hakim

    2016-02-01

    Full Text Available Abstrak Segmentasi citra merupakan suatu metode penting dalam pengolahan citra digital yang bertujuan membagi citra menjadi beberapa region yang homogen berdasarkan kriteria kemiripan tertentu. Salah satu syarat utama yang harus dimiliki suatu metode segmentasi citra yaitu menghasilkan citra boundary yang optimal.Untuk memenuhi syarat tersebut suatu metode segmentasi membutuhkan suatu klasifikasi piksel citra yang dapat memisahkan piksel secara linier dan non-linear. Pada penelitian ini, penulis mengusulkan metode segmentasi citra menggunakan SVM dan entropi Arimoto berbasis ERSS sehingga tahan terhadap derau dan mempunyai kompleksitas yang rendah untuk menghasilkan citra boundary yang optimal. Pertama, ekstraksi ciri warna dengan local homogeneity dan ciri tekstur dengan menggunakan Gray Level Co-occurrence Matrix (GLCM yang menghasilkan beberapa fitur. Kedua, pelabelan dengan Arimoto berbasis ERSS yang digunakan sebagai kelas dalam klasifikasi. Ketiga, hasil ekstraksi fitur dan training kemudian diklasifikasi berdasarkan label dengan SVM yang telah di-training. Dari percobaan yang dilakukan menunjukkan hasil segmentasi kurang optimal dengan akurasi 69 %. Reduksi fitur perlu dilakukan untuk menghasilkan citra yang tersegmentasi dengan baik. Kata kunci: segmentasi citra, support vector machine, ERSS Arimoto Entropy, ekstraksi ciri. Abstract Image segmentation is an important tool in image processing that divides an image into homogeneous regions based on certain similarity criteria, which ideally should be meaning-full for a certain purpose. Optimal boundary is one of the main criteria that an image segmentation method should has. A classification method that can partitions pixel linearly or non-linearly is needed by an image segmentation method. We propose a color image segmentation using Support Vector Machine (SVM classification and ERSS Arimoto entropy thresholding to get optimal boundary of segmented image that noise-free and low complexity

  3. SVM-based classification of LV wall motion in cardiac MRI with the assessment of STE

    Science.gov (United States)

    Mantilla, Juan; Garreau, Mireille; Bellanger, Jean-Jacques; Paredes, José Luis

    2015-01-01

    In this paper, we propose an automated method to classify normal/abnormal wall motion in Left Ventricle (LV) function in cardiac cine-Magnetic Resonance Imaging (MRI), taking as reference, strain information obtained from 2D Speckle Tracking Echocardiography (STE). Without the need of pre-processing and by exploiting all the images acquired during a cardiac cycle, spatio-temporal profiles are extracted from a subset of radial lines from the ventricle centroid to points outside the epicardial border. Classical Support Vector Machines (SVM) are used to classify features extracted from gray levels of the spatio-temporal profile as well as their representations in the Wavelet domain under the assumption that the data may be sparse in that domain. Based on information obtained from radial strain curves in 2D-STE studies, we label all the spatio-temporal profiles that belong to a particular segment as normal if the peak systolic radial strain curve of this segment presents normal kinesis, or abnormal if the peak systolic radial strain curve presents hypokinesis or akinesis. For this study, short-axis cine- MR images are collected from 9 patients with cardiac dyssynchrony for which we have the radial strain tracings at the mid-papilary muscle obtained by 2D STE; and from one control group formed by 9 healthy subjects. The best classification performance is obtained with the gray level information of the spatio-temporal profiles using a RBF kernel with 91.88% of accuracy, 92.75% of sensitivity and 91.52% of specificity.

  4. Automatic schizophrenic discrimination on fNIRS by using complex brain network analysis and SVM.

    Science.gov (United States)

    Song, Hong; Chen, Lei; Gao, RuiQi; Bogdan, Iordachescu Ilie Mihaita; Yang, Jian; Wang, Shuliang; Dong, Wentian; Quan, Wenxiang; Dang, Weimin; Yu, Xin

    2017-12-20

    Schizophrenia is a kind of serious mental illness. Due to the lack of an objective physiological data supporting and a unified data analysis method, doctors can only rely on the subjective experience of the data to distinguish normal people and patients, which easily lead to misdiagnosis. In recent years, functional Near-Infrared Spectroscopy (fNIRS) has been widely used in clinical diagnosis, it can get the hemoglobin concentration through the variation of optical intensity. Firstly, the prefrontal brain networks were constructed based on oxy-Hb signals from 52-channel fNIRS data of schizophrenia and healthy controls. Then, Complex Brain Network Analysis (CBNA) was used to extract features from the prefrontal brain networks. Finally, a classier based on Support Vector Machine (SVM) is designed and trained to discriminate schizophrenia from healthy controls. We recruited a sample which contains 34 healthy controls and 42 schizophrenia patients to do the one-back memory task. The hemoglobin response was measured in the prefrontal cortex during the task using a 52-channel fNIRS system. The experimental results indicate that the proposed method can achieve a satisfactory classification with the accuracy of 85.5%, 92.8% for schizophrenia samples and 76.5% for healthy controls. Also, our results suggested that fNIRS has the potential capacity to be an effective objective biomarker for the diagnosis of schizophrenia. Our results suggested that, using the appropriate classification method, fNIRS has the potential capacity to be an effective objective biomarker for the diagnosis of schizophrenia.

  5. SVM-Based CAC System for B-Mode Kidney Ultrasound Images.

    Science.gov (United States)

    Subramanya, M B; Kumar, Vinod; Mukherjee, Shaktidev; Saini, Manju

    2015-08-01

    The present study proposes a computer-aided classification (CAC) system for three kidney classes, viz. normal, medical renal disease (MRD) and cyst using B-mode ultrasound images. Thirty-five B-mode kidney ultrasound images consisting of 11 normal images, 8 MRD images and 16 cyst images have been used. Regions of interest (ROIs) have been marked by the radiologist from the parenchyma region of the kidney in case of normal and MRD cases and from regions inside lesions for cyst cases. To evaluate the contribution of texture features extracted from de-speckled images for the classification task, original images have been pre-processed by eight de-speckling methods. Six categories of texture features are extracted. One-against-one multi-class support vector machine (SVM) classifier has been used for the present work. Based on overall classification accuracy (OCA), features from ROIs of original images are concatenated with the features from ROIs of pre-processed images. On the basis of OCA, few feature sets are considered for feature selection. Differential evolution feature selection (DEFS) has been used to select optimal features for the classification task. DEFS process is repeated 30 times to obtain 30 subsets. Run-length matrix features from ROIs of images pre-processed by Lee's sigma concatenated with that of enhanced Lee method have resulted in an average accuracy (in %) and standard deviation of 86.3 ± 1.6. The results obtained in the study indicate that the performance of the proposed CAC system is promising, and it can be used by the radiologists in routine clinical practice for the classification of renal diseases.

  6. AI-based (ANN and SVM) statistical downscaling methods for precipitation estimation under climate change scenarios

    Science.gov (United States)

    Mehrvand, Masoud; Baghanam, Aida Hosseini; Razzaghzadeh, Zahra; Nourani, Vahid

    2017-04-01

    Since statistical downscaling methods are the most largely used models to study hydrologic impact studies under climate change scenarios, nonlinear regression models known as Artificial Intelligence (AI)-based models such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been used to spatially downscale the precipitation outputs of Global Climate Models (GCMs). The study has been carried out using GCM and station data over GCM grid points located around the Peace-Tampa Bay watershed weather stations. Before downscaling with AI-based model, correlation coefficient values have been computed between a few selected large-scale predictor variables and local scale predictands to select the most effective predictors. The selected predictors are then assessed considering grid location for the site in question. In order to increase AI-based downscaling model accuracy pre-processing has been developed on precipitation time series. In this way, the precipitation data derived from various GCM data analyzed thoroughly to find the highest value of correlation coefficient between GCM-based historical data and station precipitation data. Both GCM and station precipitation time series have been assessed by comparing mean and variances over specific intervals. Results indicated that there is similar trend between GCM and station precipitation data; however station data has non-stationary time series while GCM data does not. Finally AI-based downscaling model have been applied to several GCMs with selected predictors by targeting local precipitation time series as predictand. The consequences of recent step have been used to produce multiple ensembles of downscaled AI-based models.

  7. Application of ANFIS and SVM Systems in Order to Estimate Monthly Reference Crop Evapotranspiration in the Northwest of Iran

    Directory of Open Access Journals (Sweden)

    F. Ahmadi

    2016-10-01

    Full Text Available Introduction Crop evapotranspiration modeling process mainly performs with empirical methods, aerodynamic and energy balance. In these methods, the evapotranspiration is calculated based on the average values of meteorological parameters at different time steps. The linear models didn’t have a good performance in this field due to high variability of evapotranspiration and the researchers have turned to the use of nonlinear and intelligent models. For accurate estimation of this hydrologic variable, it should be spending much time and money to measure many data (19. Materials and Methods Recently the new hybrid methods have been developed by combining some of methods such as artificial neural networks, fuzzy logic and evolutionary computation, that called Soft Computing and Intelligent Systems. These soft techniques are used in various fields of engineering. A fuzzy neurosis is a hybrid system that incorporates the decision ability of fuzzy logic with the computational ability of neural network, which provides a high capability for modeling and estimating. Basically, the Fuzzy part is used to classify the input data set and determines the degree of membership (that each number can be laying between 0 and 1 and decisions for the next activity made based on a set of rules and move to the next stage. Adaptive Neuro-Fuzzy Inference Systems (ANFIS includes some parts of a typical fuzzy expert system which the calculations at each step is performed by the hidden layer neurons and the learning ability of the neural network has been created to increase the system information (9. SVM is a one of supervised learning methods which used for classification and regression affairs. This method was developed by Vapink (15 based on statistical learning theory. The SVM is a method for binary classification in an arbitrary characteristic space, so it is suitable for prediction problems (12. The SVM is originally a two-class Classifier that separates the classes

  8. Geographical traceability of wild Boletus edulis based on data fusion of FT-MIR and ICP-AES coupled with data mining methods (SVM)

    Science.gov (United States)

    Li, Yun; Zhang, Ji; Li, Tao; Liu, Honggao; Li, Jieqing; Wang, Yuanzhong

    2017-04-01

    In this work, the data fusion strategy of Fourier transform mid infrared (FT-MIR) spectroscopy and inductively coupled plasma-atomic emission spectrometry (ICP-AES) was used in combination with Support Vector Machine (SVM) to determine the geographic origin of Boletus edulis collected from nine regions of Yunnan Province in China. Firstly, competitive adaptive reweighted sampling (CARS) was used for selecting an optimal combination of key wavenumbers of second derivative FT-MIR spectra, and thirteen elements were sorted with variable importance in projection (VIP) scores. Secondly, thirteen subsets of multi-elements with the best VIP score were generated and each subset was used to fuse with FT-MIR. Finally, the classification models were established by SVM, and the combination of parameter C and γ (gamma) of SVM models was calculated by the approaches of grid search (GS) and genetic algorithm (GA). The results showed that both GS-SVM and GA-SVM models achieved good performances based on the #9 subset and the prediction accuracy in calibration and validation sets of the two models were 81.40% and 90.91%, correspondingly. In conclusion, it indicated that the data fusion strategy of FT-MIR and ICP-AES coupled with the algorithm of SVM can be used as a reliable tool for accurate identification of B. edulis, and it can provide a useful way of thinking for the quality control of edible mushrooms.

  9. Comment on "Current separation and upwelling over the southeast shelf of Vietnam in the South China Sea" by Chen et al.

    Science.gov (United States)

    Dippner, Joachim W.; Bombar, Deniz; Loick-Wilde, Natalie; Voss, Maren; Subramaniam, Ajit

    2013-03-01

    In a recent paper, Chen et al. (2012) showed that the offshore current in front of the Vietnamese upwelling area in the South China Sea (SCS) is caused by an encounter of southward buoyancy-driven coastal current and tidal rectified currents from the southwest. These findings seem not in agreement with in-situ observations. The mechanism for the formation of the offshore current has its origin in the inter-annual variability of atmospheric forcing. El Niño Southern Oscillation events modulate the northern position of Inter-Tropical Convergence Zone and the intensity of both, SW monsoon and upwelling. Strong upwelling influences the spatial distribution of characteristic water masses which results in a blocking of the near coastal northward propagation of the plume of River Mekong and the formation of an offshore current.

  10. Polsar Land Cover Classification Based on Hidden Polarimetric Features in Rotation Domain and Svm Classifier

    Science.gov (United States)

    Tao, C.-S.; Chen, S.-W.; Li, Y.-Z.; Xiao, S.-P.

    2017-09-01

    Land cover classification is an important application for polarimetric synthetic aperture radar (PolSAR) data utilization. Rollinvariant polarimetric features such as H / Ani / α / Span are commonly adopted in PolSAR land cover classification. However, target orientation diversity effect makes PolSAR images understanding and interpretation difficult. Only using the roll-invariant polarimetric features may introduce ambiguity in the interpretation of targets' scattering mechanisms and limit the followed classification accuracy. To address this problem, this work firstly focuses on hidden polarimetric feature mining in the rotation domain along the radar line of sight using the recently reported uniform polarimetric matrix rotation theory and the visualization and characterization tool of polarimetric coherence pattern. The former rotates the acquired polarimetric matrix along the radar line of sight and fully describes the rotation characteristics of each entry of the matrix. Sets of new polarimetric features are derived to describe the hidden scattering information of the target in the rotation domain. The latter extends the traditional polarimetric coherence at a given rotation angle to the rotation domain for complete interpretation. A visualization and characterization tool is established to derive new polarimetric features for hidden information exploration. Then, a classification scheme is developed combing both the selected new hidden polarimetric features in rotation domain and the commonly used roll-invariant polarimetric features with a support vector machine (SVM) classifier. Comparison experiments based on AIRSAR and multi-temporal UAVSAR data demonstrate that compared with the conventional classification scheme which only uses the roll-invariant polarimetric features, the proposed classification scheme achieves both higher classification accuracy and better robustness. For AIRSAR data, the overall classification accuracy with the proposed

  11. POLSAR LAND COVER CLASSIFICATION BASED ON HIDDEN POLARIMETRIC FEATURES IN ROTATION DOMAIN AND SVM CLASSIFIER

    Directory of Open Access Journals (Sweden)

    C.-S. Tao

    2017-09-01

    Full Text Available Land cover classification is an important application for polarimetric synthetic aperture radar (PolSAR data utilization. Rollinvariant polarimetric features such as H / Ani / α / Span are commonly adopted in PolSAR land cover classification. However, target orientation diversity effect makes PolSAR images understanding and interpretation difficult. Only using the roll-invariant polarimetric features may introduce ambiguity in the interpretation of targets’ scattering mechanisms and limit the followed classification accuracy. To address this problem, this work firstly focuses on hidden polarimetric feature mining in the rotation domain along the radar line of sight using the recently reported uniform polarimetric matrix rotation theory and the visualization and characterization tool of polarimetric coherence pattern. The former rotates the acquired polarimetric matrix along the radar line of sight and fully describes the rotation characteristics of each entry of the matrix. Sets of new polarimetric features are derived to describe the hidden scattering information of the target in the rotation domain. The latter extends the traditional polarimetric coherence at a given rotation angle to the rotation domain for complete interpretation. A visualization and characterization tool is established to derive new polarimetric features for hidden information exploration. Then, a classification scheme is developed combing both the selected new hidden polarimetric features in rotation domain and the commonly used roll-invariant polarimetric features with a support vector machine (SVM classifier. Comparison experiments based on AIRSAR and multi-temporal UAVSAR data demonstrate that compared with the conventional classification scheme which only uses the roll-invariant polarimetric features, the proposed classification scheme achieves both higher classification accuracy and better robustness. For AIRSAR data, the overall classification accuracy

  12. Ab-initio conformational epitope structure prediction using genetic algorithm and SVM for vaccine design.

    Science.gov (United States)

    Moghram, Basem Ameen; Nabil, Emad; Badr, Amr

    2018-01-01

    T-cell epitope structure identification is a significant challenging immunoinformatic problem within epitope-based vaccine design. Epitopes or antigenic peptides are a set of amino acids that bind with the Major Histocompatibility Complex (MHC) molecules. The aim of this process is presented by Antigen Presenting Cells to be inspected by T-cells. MHC-molecule-binding epitopes are responsible for triggering the immune response to antigens. The epitope's three-dimensional (3D) molecular structure (i.e., tertiary structure) reflects its proper function. Therefore, the identification of MHC class-II epitopes structure is a significant step towards epitope-based vaccine design and understanding of the immune system. In this paper, we propose a new technique using a Genetic Algorithm for Predicting the Epitope Structure (GAPES), to predict the structure of MHC class-II epitopes based on their sequence. The proposed Elitist-based genetic algorithm for predicting the epitope's tertiary structure is based on Ab-Initio Empirical Conformational Energy Program for Peptides (ECEPP) Force Field Model. The developed secondary structure prediction technique relies on Ramachandran Plot. We used two alignment algorithms: the ROSS alignment and TM-Score alignment. We applied four different alignment approaches to calculate the similarity scores of the dataset under test. We utilized the support vector machine (SVM) classifier as an evaluation of the prediction performance. The prediction accuracy and the Area Under Receiver Operating Characteristic (ROC) Curve (AUC) were calculated as measures of performance. The calculations are performed on twelve similarity-reduced datasets of the Immune Epitope Data Base (IEDB) and a large dataset of peptide-binding affinities to HLA-DRB1*0101. The results showed that GAPES was reliable and very accurate. We achieved an average prediction accuracy of 93.50% and an average AUC of 0.974 in the IEDB dataset. Also, we achieved an accuracy of 95

  13. Novel SVM-based technique to improve rainfall estimation over the Mediterranean region (north of Algeria) using the multispectral MSG SEVIRI imagery

    Science.gov (United States)

    Sehad, Mounir; Lazri, Mourad; Ameur, Soltane

    2017-03-01

    In this work, a new rainfall estimation technique based on the high spatial and temporal resolution of the Spinning Enhanced Visible and Infra Red Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) is presented. This work proposes efficient scheme rainfall estimation based on two multiclass support vector machine (SVM) algorithms: SVM_D for daytime and SVM_N for night time rainfall estimations. Both SVM models are trained using relevant rainfall parameters based on optical, microphysical and textural cloud proprieties. The cloud parameters are derived from the Spectral channels of the SEVIRI MSG radiometer. The 3-hourly and daily accumulated rainfall are derived from the 15 min-rainfall estimation given by the SVM classifiers for each MSG observation image pixel. The SVMs were trained with ground meteorological radar precipitation scenes recorded from November 2006 to March 2007 over the north of Algeria located in the Mediterranean region. Further, the SVM_D and SVM_N models were used to estimate 3-hourly and daily rainfall using data set gathered from November 2010 to March 2011 over north Algeria. The results were validated against collocated rainfall observed by rain gauge network. Indeed, the statistical scores given by correlation coefficient, bias, root mean square error and mean absolute error, showed good accuracy of rainfall estimates by the present technique. Moreover, rainfall estimates of our technique were compared with two high accuracy rainfall estimates methods based on MSG SEVIRI imagery namely: random forests (RF) based approach and an artificial neural network (ANN) based technique. The findings of the present technique indicate higher correlation coefficient (3-hourly: 0.78; daily: 0.94), and lower mean absolute error and root mean square error values. The results show that the new technique assign 3-hourly and daily rainfall with good and better accuracy than ANN technique and (RF) model.

  14. [Selection of Characteristic Wavelengths Using SPA and Qualitative Discrimination of Mildew Degree of Corn Kernels Based on SVM].

    Science.gov (United States)

    Yuan, Ying; Wang, Wei; Chu, Xuan; Xi, Ming-jie

    2016-01-01

    The feasibility of Fourier transform near infrared (FT-NIR) spectroscopy with spectral range between 833 and 2 500 nm to detect the moldy corn kernels with different levels of mildew was verified in this paper. Firstly, to avoid the influence of noise, moving average smoothing was used for spectral data preprocessing after four common pretreatment methods were compared. Then to improve the prediction performance of the model, SPXY (sample set partitioning based on joint x-y distance) was selected and used for sample set partition. Furthermore, in order to reduce the dimensions of the original spectral data, successive projection algorithm (SPA) was adopted and ultimately 7 characteristic wavelengths were extracted, the characteristic wave-lengths were 833, 927, 1 208, 1 337, 1 454, 1 861, 2 280 nm. The experimental results showed when the spectrum data of the 7 characteristic wavelengths were taken as the input of SVM, the radial basic function (RBF) used as the kernel function, and kernel parameter C = 7 760 469, γ = 0.017 003, the classification accuracies of the established SVM model were 97.78% and 93.33% for the training and testing sets respectively. In addition, the independent validation set was selected in the same standard, and used to verify the model. At last, the classification accuracy of 91.11% for the independent validation set was achieved. The result indicated that it is feasible to identify and classify different degree of moldy corn grain kernels using SPA and SVM, and characteristic wavelengths selected by SPA in this paper also lay a foundation for the online NIR detection of mildew corn kernels.

  15. Klasifikasi Topik Keluhan Pelanggan Berdasarkan Tweet dengan Menggunakan Penggabungan Feature Hasil Ekstraksi pada Metode Support Vector Machine (SVM

    Directory of Open Access Journals (Sweden)

    Enda Esyudha Pratama

    2015-12-01

    Full Text Available Pemanfaatan twitter sebagai layanan customer serevice perusahaan sudah mulai banyak digunakan, tak terkecuali Speedy. Mekanisme yang ada saat ini untuk proses klasifikasi bentuk dan jenis keluhan serta informasi tentang jumlah keluhan lewat twitter masih dilakukan secara manual. Belum lagi data twitter yang bersifat tidak terstruktur tentunya akan menyulitkan untuk dilakukan analisa dan penggalian informasi dari data tersebut. Berdasarkan permasalahan tersebut, penelitian ini bertujuan untuk memproses data teks dari tweet pengguna twitteryang masuk ke akun @TelkomSpeedy untuk diolah menjadi informasi. Informasi tersebut nantinya digunakan untuk klasifikasi bentuk dan jenis keluhan. Merujuk pada beberapa penelitian terkait, salah satu metode klasifikasi yang paling baik untuk digunakan adalah metode Support Vector Machine (SVM. Konsep dari SVM dapat dijelaskan secara sederhana sebagai usaha mencari hyperplane yang dapat memisahkan dataset sesuai dengan kelasnya. Kelas yang digunakan dalam penelitian kali ini berdasarkan topik keluhan pelanggan yaitu billing, pemasangan/instalasi, putus (disconnect, dan lambat. Faktor penting lainnya dalam hal klasifikasi adalah penentuan feature atau atribut kata yang akan digunakan. Metode feature selection yang digunakan pada penlitian ini adalah term frequency (TF, document frequency (DF, information gain, dan chi-square. Pada penelitian ini juga dilakukan metode penggabungan feature yang telah dihasilkan dari beberapa metode feature selection sebelumnya. Dari hasil penelitian menunjukan bahwa SVM mampu melakukan klasifikasi keluhan dengan baik, hal ini dibuktikan dengan akurasi 82,50% untuk klasifikasi bentuk keluhan dan 86,67% untuk klasifikasi jenis keluhan. Sedangkan untuk kombinasi penggunaan feature dapat meningkatkan akurasi menjadi 83,33% untuk bentuk keluhan dan 89,17% untuk jenis keluhan.   Kata Kunci—customer service, klasifikasi topik keluhan, penggabungan feature, support vector machine

  16. KOMPARASI MODEL SUPPORT VECTOR MACHINES (SVM DAN NEURAL NETWORK UNTUK MENGETAHUI TINGKAT AKURASI PREDIKSI TERTINGGI HARGA SAHAM

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    R. Hadapiningradja Kusumodestoni

    2017-09-01

    Full Text Available There are many types of investments to make money, one of which is in the form of shares. Shares is a trading company dealing with securities in the global capital markets. Stock Exchange or also called stock market is actually the activities of private companies in the form of buying and selling investments. To avoid losses in investing, we need a model of predictive analysis with high accuracy and supported by data - lots of data and accurately. The correct techniques in the analysis will be able to reduce the risk for investors in investing. There are many models used in the analysis of stock price movement prediction, in this study the researchers used models of neural networks (NN and a model of support vector machine (SVM. Based on the background of the problems that have been mentioned in the previous description it can be formulated the problem as follows: need an algorithm that can predict stock prices, and need a high accuracy rate by adding a data set on the prediction, two algorithms will be investigated expected results last researchers can deduce where the algorithm accuracy rate predictions are the highest or accurate, then the purpose of this study was to mengkomparasi or compare between the two algorithms are algorithms Neural Network algorithm and Support Vector Machine which later on the end result has an accuracy rate forecast stock prices highest to see the error value RMSEnya. After doing research using the model of neural network and model of support vector machine (SVM to predict the stock using the data value of the shares on the stock index hongkong dated July 20, 2016 at 16:26 pm until the date of 15 September 2016 at 17:40 pm as many as 729 data sets within an interval of 5 minute through a process of training, learning, and then continue the process of testing so the result is that by using a neural network model of the prediction accuracy of 0.503 +/- 0.009 (micro 503 while using the model of support vector machine

  17. Using Generalized Entropies and OC-SVM with Mahalanobis Kernel for Detection and Classification of Anomalies in Network Traffic

    Directory of Open Access Journals (Sweden)

    Jayro Santiago-Paz

    2015-09-01

    Full Text Available Network anomaly detection and classification is an important open issue in network security. Several approaches and systems based on different mathematical tools have been studied and developed, among them, the Anomaly-Network Intrusion Detection System (A-NIDS, which monitors network traffic and compares it against an established baseline of a “normal” traffic profile. Then, it is necessary to characterize the “normal” Internet traffic. This paper presents an approach for anomaly detection and classification based on Shannon, Rényi and Tsallis entropies of selected features, and the construction of regions from entropy data employing the Mahalanobis distance (MD, and One Class Support Vector Machine (OC-SVM with different kernels (Radial Basis Function (RBF and Mahalanobis Kernel (MK for “normal” and abnormal traffic. Regular and non-regular regions built from “normal” traffic profiles allow anomaly detection, while the classification is performed under the assumption that regions corresponding to the attack classes have been previously characterized. Although this approach allows the use of as many features as required, only four well-known significant features were selected in our case. In order to evaluate our approach, two different data sets were used: one set of real traffic obtained from an Academic Local Area Network (LAN, and the other a subset of the 1998 MIT-DARPA set. For these data sets, a True positive rate up to 99.35%, a True negative rate up to 99.83% and a False negative rate at about 0.16% were yielded. Experimental results show that certain q-values of the generalized entropies and the use of OC-SVM with RBF kernel improve the detection rate in the detection stage, while the novel inclusion of MK kernel in OC-SVM and k-temporal nearest neighbors improve accuracy in classification. In addition, the results show that using the Box-Cox transformation, the Mahalanobis distance yielded high detection rates with

  18. Identifying 1 Method of Meat Containing Excessive Moisture Based on hyperspectral and SVM Multi-Information Fusion

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    Guo Peiyuan

    2016-01-01

    Full Text Available In this paper, a quick and accurate detection method which can identify whether the meat contain excessive moisture is mentioned. By using near-infrared spectroscopy measurement and SVM Multi-Information Fusion, the meat moisture content model has been established. In order to improve the accuracy of NIR measurement predicted model and to reduce the measurement sensitivity, utilizing image information and the PH value data as the parameters of the meat moisture content model. The study concluded that the theory and method can be further extended to the detection of other related meat agricultural products.

  19. Comments on “The boundary point method for the calculation of exterior acoustic radiation problem” [S.Y. Zhang, X.Z. Chen, Journal of Sound and Vibration 228(4) (1999) 761 772

    Science.gov (United States)

    Chen, J. T.; Chen, I. L.; Lee, Y. T.

    2008-03-01

    Zhang and Chen [The boundary point method for the calculation of exterior acoustic radiation problem, Journal of Sound and Vibration 228 (1999) 761-772] proposed a boundary point method (BPM) for exterior acoustic problems. The idea is similar to the CHUNKY CHIEF by Wu [A weighted residual formulation for the CHIEF method in acoustic, Journal of Acoustical Society of America 90 (1991) 1608-1614], but Chunky CHIEF provides constraints using null-field equations while the BPM used the CHUNKY BLOCK singularity outside the domain. The mathematical structure is similar to Trefftz method and method of fundamental solutions [J.T. Chen et al., On the equivalence of the Trefftz method and method of fundamental solutions for Laplace and biharmonic equations, Computers & Mathematics with Applications 53 (2007) 851-879], since the interpolation function satisfies the governing equation. Later, Wu commented twice [Sean F. Wu, Comments on "The boundary point method for the calculation of exterior acoustic radiation" (by S.Y. Zhang, X.Z. Chen, Journal of Sound and Vibration 228(4) (1999) 761-772), Journal of Sound and Vibration, 298 (2006) 1173]; Sean F. Wu, Comments on "Reply to the comments on 'The boundary point method for the calculation of exterior acoustic radiation' (by S.Y. Zhang, X.Z. Chen, Journal of Sound and Vibration 228(4) (1999) 761-772)", Journal of Sound and Vibration, 298 (2006) 1176-1177] that the formulation of BPM is wrong and the authors replied also twice [X.Z. Chen, C.X. Bi, Reply to the comments on "The boundary point method for the calculation of exterior acoustic radiation" (by S.Y. Zhang, X.Z. Chen, Journal of Sound and Vibration 228(4) (1999) 761-772), Journal of Sound and Vibration, 298 (2006) 1174-1175; [X.Z. Chen, C.X. Bi, Reply to the comments on "Reply to the comments on 'The boundary point method for the calculation of exterior acoustic radiation' (by S.Y. Zhang, X.Z. Chen, Journal of Sound and Vibration 228(4) (1999) 761-772)", Journal of Sound

  20. An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications.

    Directory of Open Access Journals (Sweden)

    Fei Ye

    Full Text Available This paper proposes a new support vector machine (SVM optimization scheme based on an improved chaotic fly optimization algorithm (FOA with a mutation strategy to simultaneously perform parameter setting turning for the SVM and feature selection. In the improved FOA, the chaotic particle initializes the fruit fly swarm location and replaces the expression of distance for the fruit fly to find the food source. However, the proposed mutation strategy uses two distinct generative mechanisms for new food sources at the osphresis phase, allowing the algorithm procedure to search for the optimal solution in both the whole solution space and within the local solution space containing the fruit fly swarm location. In an evaluation based on a group of ten benchmark problems, the proposed algorithm's performance is compared with that of other well-known algorithms, and the results support the superiority of the proposed algorithm. Moreover, this algorithm is successfully applied in a SVM to perform both parameter setting turning for the SVM and feature selection to solve real-world classification problems. This method is called chaotic fruit fly optimization algorithm (CIFOA-SVM and has been shown to be a more robust and effective optimization method than other well-known methods, particularly in terms of solving the medical diagnosis problem and the credit card problem.

  1. An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications

    Science.gov (United States)

    Lou, Xin Yuan; Sun, Lin Fu

    2017-01-01

    This paper proposes a new support vector machine (SVM) optimization scheme based on an improved chaotic fly optimization algorithm (FOA) with a mutation strategy to simultaneously perform parameter setting turning for the SVM and feature selection. In the improved FOA, the chaotic particle initializes the fruit fly swarm location and replaces the expression of distance for the fruit fly to find the food source. However, the proposed mutation strategy uses two distinct generative mechanisms for new food sources at the osphresis phase, allowing the algorithm procedure to search for the optimal solution in both the whole solution space and within the local solution space containing the fruit fly swarm location. In an evaluation based on a group of ten benchmark problems, the proposed algorithm’s performance is compared with that of other well-known algorithms, and the results support the superiority of the proposed algorithm. Moreover, this algorithm is successfully applied in a SVM to perform both parameter setting turning for the SVM and feature selection to solve real-world classification problems. This method is called chaotic fruit fly optimization algorithm (CIFOA)-SVM and has been shown to be a more robust and effective optimization method than other well-known methods, particularly in terms of solving the medical diagnosis problem and the credit card problem. PMID:28369096

  2. Intelligent Optimized Combined Model Based on GARCH and SVM for Forecasting Electricity Price of New South Wales, Australia

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    Yi Yang

    2014-01-01

    Full Text Available Daily electricity price forecasting plays an essential role in electrical power system operation and planning. The accuracy of forecasting electricity price can ensure that consumers minimize their electricity costs and make producers maximize their profits and avoid volatility. However, the fluctuation of electricity price depends on other commodities and there is a very complicated randomization in its evolution process. Therefore, in recent years, although large number of forecasting methods have been proposed and researched in this domain, it is very difficult to forecast electricity price with only one traditional model for different behaviors of electricity price. In this paper, we propose an optimized combined forecasting model by ant colony optimization algorithm (ACO based on the generalized autoregressive conditional heteroskedasticity (GARCH model and support vector machine (SVM to improve the forecasting accuracy. First, both GARCH model and SVM are developed to forecast short-term electricity price of New South Wales in Australia. Then, ACO algorithm is applied to determine the weight coefficients. Finally, the forecasting errors by three models are analyzed and compared. The experiment results demonstrate that the combined model makes accuracy higher than the single models.

  3. a Comparison Study of Different Kernel Functions for Svm-Based Classification of Multi-Temporal Polarimetry SAR Data

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    Yekkehkhany, B.; Safari, A.; Homayouni, S.; Hasanlou, M.

    2014-10-01

    In this paper, a framework is developed based on Support Vector Machines (SVM) for crop classification using polarimetric features extracted from multi-temporal Synthetic Aperture Radar (SAR) imageries. The multi-temporal integration of data not only improves the overall retrieval accuracy but also provides more reliable estimates with respect to single-date data. Several kernel functions are employed and compared in this study for mapping the input space to higher Hilbert dimension space. These kernel functions include linear, polynomials and Radial Based Function (RBF). The method is applied to several UAVSAR L-band SAR images acquired over an agricultural area near Winnipeg, Manitoba, Canada. In this research, the temporal alpha features of H/A/α decomposition method are used in classification. The experimental tests show an SVM classifier with RBF kernel for three dates of data increases the Overall Accuracy (OA) to up to 3% in comparison to using linear kernel function, and up to 1% in comparison to a 3rd degree polynomial kernel function.

  4. A COMPARISON STUDY OF DIFFERENT KERNEL FUNCTIONS FOR SVM-BASED CLASSIFICATION OF MULTI-TEMPORAL POLARIMETRY SAR DATA

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    B. Yekkehkhany

    2014-10-01

    Full Text Available In this paper, a framework is developed based on Support Vector Machines (SVM for crop classification using polarimetric features extracted from multi-temporal Synthetic Aperture Radar (SAR imageries. The multi-temporal integration of data not only improves the overall retrieval accuracy but also provides more reliable estimates with respect to single-date data. Several kernel functions are employed and compared in this study for mapping the input space to higher Hilbert dimension space. These kernel functions include linear, polynomials and Radial Based Function (RBF. The method is applied to several UAVSAR L-band SAR images acquired over an agricultural area near Winnipeg, Manitoba, Canada. In this research, the temporal alpha features of H/A/α decomposition method are used in classification. The experimental tests show an SVM classifier with RBF kernel for three dates of data increases the Overall Accuracy (OA to up to 3% in comparison to using linear kernel function, and up to 1% in comparison to a 3rd degree polynomial kernel function.

  5. A Hybrid Prediction Method of Thermal Extension Error for Boring Machine Based on PCA and LS-SVM

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    Cheng Qiang

    2017-01-01

    Full Text Available Thermal extension error of boring bar in z-axis is one of the key factors that have a bad influence on the machining accuracy of boring machine, so how to exactly establish the relationship between the thermal extension length and temperature and predict the changing rule of thermal error are the premise of thermal extension error compensation. In this paper, a prediction method of thermal extension length of boring bar in boring machine is proposed based on principal component analysis (PCA and least squares support vector machine (LS-SVM model. In order to avoid the multiple correlation and coupling among the great amount temperature input variables, firstly, PCA is introduced to extract the principal components of temperature data samples. Then, LS-SVM is used to predict the changing tendency of the thermally induced thermal extension error of boring bar. Finally, experiments are conducted on a boring machine, the application results show that Boring bar axial thermal elongation error residual value dropped below 5 μm and minimum residual error is only 0.5 μm. This method not only effectively improve the efficiency of the temperature data acquisition and analysis, and improve the modeling accuracy and robustness.

  6. A Study on SVM Based on the Weighted Elitist Teaching-Learning-Based Optimization and Application in the Fault Diagnosis of Chemical Process

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    Cao Junxiang

    2015-01-01

    Full Text Available Teaching-Learning-Based Optimization (TLBO is a new swarm intelligence optimization algorithm that simulates the class learning process. According to such problems of the traditional TLBO as low optimizing efficiency and poor stability, this paper proposes an improved TLBO algorithm mainly by introducing the elite thought in TLBO and adopting different inertia weight decreasing strategies for elite and ordinary individuals of the teacher stage and the student stage. In this paper, the validity of the improved TLBO is verified by the optimizations of several typical test functions and the SVM optimized by the weighted elitist TLBO is used in the diagnosis and classification of common failure data of the TE chemical process. Compared with the SVM combining other traditional optimizing methods, the SVM optimized by the weighted elitist TLBO has a certain improvement in the accuracy of fault diagnosis and classification.

  7. QSAR study of anthranilic acid sulfonamides as inhibitors of methionine aminopeptidase-2 using LS-SVM and GRNN based on principal components.

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    Shahlaei, Mohsen; Sabet, Razieh; Ziari, Maryam Bahman; Moeinifard, Behzad; Fassihi, Afshin; Karbakhsh, Reza

    2010-10-01

    Quantitative relationships between molecular structure and methionine aminopeptidase-2 inhibitory activity of a series of cytotoxic anthranilic acid sulfonamide derivatives were discovered. We have demonstrated the detailed application of two efficient nonlinear methods for evaluation of quantitative structure-activity relationships of the studied compounds. Components produced by principal component analysis as input of developed nonlinear models were used. The performance of the developed models namely PC-GRNN and PC-LS-SVM were tested by several validation methods. The resulted PC-LS-SVM model had a high statistical quality (R(2)=0.91 and R(CV)(2)=0.81) for predicting the cytotoxic activity of the compounds. Comparison between predictability of PC-GRNN and PC-LS-SVM indicates that later method has higher ability to predict the activity of the studied molecules. Copyright (c) 2010 Elsevier Masson SAS. All rights reserved.

  8. [Reiner Nürnberg, Ekkehard Höxtermann, Martina Voigt. Elisabeth Schiemann 1881-1972. Vom AußBruch der Genetik und der Frauen in den UmBrüchen des 20. Jahrhunderts] / Monika von Hirschheydt

    Index Scriptorium Estoniae

    Hirschheydt, Monika von

    2015-01-01

    Arvustus: Nürnberg, Reiner, Höxtermann, Ekkehard, Voigt, Martina. Elisabeth Schiemann 1881-1972. Vom AußBruch der Genetik und der Frauen in den UmBrüchen des 20. Jahrhunderts. Beiträge eines Symposiums zum 200. Gründungsjubiläum der Humboldt-Universität Berlin. Rangsdorf: Basilisken-Presse 2014

  9. Héritages mortifères. Rupture dans/de la filiation chez Ying Chen et Jane Sautière

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    Anne-Marie Parent

    2012-01-01

    Full Text Available Les récits Nullipare (2008 de Jane Sautière et Un enfant à ma porte (2008 de Ying Chen mettent tous deux en scène des femmes qui se révèlent incapables d’être mères. Le présent article montre que cette incapacité à avoir un enfant vient du fait que pour les deux narratrices, la filiation est liée à la mort, à la perte et à l’absence ; dans les deux textes, un héritage mortifère fait rupture et empêche la filiation de se poursuivre. Jane Sautière’s Nullipare (2008 and Ying Chen’s Un enfant à ma porte (2008 both portray a woman unable to become a mother. This article shows that the inability to have a child stems from the fact that, for the narrators of each work, filiation is linked to death, to loss, and to absence; in the two texts, a deadly legacy breaks the line of filiation and prevents its continuation.

  10. "Is political behavior a viable coping strategy to perceived organizational politics? Unveiling the underlying resource dynamics": Correction to Sun and Chen (2017).

    Science.gov (United States)

    2017-10-01

    Reports an error in "Is Political Behavior a Viable Coping Strategy to Perceived Organizational Politics? Unveiling the Underlying Resource Dynamics" by Shuhua Sun and Huaizhong Chen (Journal of Applied Psychology, Advanced Online Publication, May 22, 2017, np). In the article, Table 1 contained a formatting error. Correlation coefficient values in the last four cells of column 6 were misplaced with correlation coefficient values in the last four cells of column 7. All versions of this article have been corrected. (The following abstract of the original article appeared in record 2017-22542-001.) We conduct a theory-driven empirical investigation on whether political behavior, as a coping strategy to perceived organizational politics, creates resource trade-offs in moderating the relationship between perceived organizational politics and task performance. Drawing on conservation of resources theory, we hypothesize that political behavior mitigates the adverse effect of perceived organizational politics on task performance via psychological empowerment, yet exacerbates its adverse effect on task performance via emotional exhaustion. Three-wave multisource data from a sample of 222 employees and their 75 supervisors were collected for hypothesis testing. Findings supported our hypotheses. Our study enhances understandings of the complex resource dynamics of using political behavior to cope with perceived organizational politics and highlights the need to move stress-coping research from a focus on the stress-buffering effect of coping on outcomes to a focus on the underlying competing resource dynamics. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  11. Paragonimus heterotremus Chen and Hsia (1964), in Vietnam: a molecular identification and relationships of isolates from different hosts and geographical origins.

    Science.gov (United States)

    Le, Thanh H; Van De, Nguyen; Blair, David; McManus, Donald P; Kino, Hideto; Agatsuma, Takeshi

    2006-04-01

    Paragonimus heterotremus Chen and Hsia (1964), and paragonimiasis caused by this species is a newly detected disease in Vietnam. Twelve samples of Paragonimus (Platyhelminthes: Trematoda: Digenea: Paragonimidae) from different life-stages (eggs, miracidia, metacercariae, adults from natural and experimental hosts) and host species (crab, dog, cat and human) were collected in different geographical locations in Vietnam. DNA sequences were obtained from each for partial mitochondrial cytochrome c oxidase subunit 1 (cox1) (387 bp) and the entire second ribosomal internal transcribed spacer (ITS-2) (361 bp). The ITS-2 sequences were identical among all specimens, including those previously reported in GenBank. For cox1, there were sequence differences between specimens from Vietnam (four provinces, different locations) and those from Guangxi (China) and Saraburi (Thailand). Phylogenetic trees inferred from cox1 and ITS-2 sequences using sequence data for 15 P. heterotremus and for other Paragonimus spp. revealed that all P. heterotremus originating from Vietnam, Thailand and China form a distinct group. This information also confirms the identity of the Vietnamese specimens as P. heterotremus.

  12. Using self-organizing map (SOM) and support vector machine (SVM) for classification of selectivity of ACAT inhibitors.

    Science.gov (United States)

    Wang, Ling; Wang, Maolin; Yan, Aixia; Dai, Bin

    2013-02-01

    Using a self-organizing map (SOM) and support vector machine, two classification models were built to predict whether a compound is a selective inhibitor toward the two Acyl-coenzyme A: cholesterol acyltransferase (ACAT) isozymes, ACAT-1 and ACAT-2. A dataset of 97 ACAT inhibitors was collected. For each molecule, the global descriptors, 2D and 3D property autocorrelation descriptors and autocorrelation of surface properties were calculated from the program ADRIANA.Code. The prediction accuracies of the models (based on the training/ test set splitting by SOM method) for the test sets are 88.9 % for SOM1, 92.6 % for SVM1 model. In addition, the extended connectivity fingerprints (ECFP_4) for all the molecules were calculated and the structure-activity relationship of selective ACAT inhibitors was summarized, which may help find important structural features of inhibitors relating to the selectivity of ACAT isozymes.

  13. Comparison of hand-craft feature based SVM and CNN based deep learning framework for automatic polyp classification.

    Science.gov (United States)

    Younghak Shin; Balasingham, Ilangko

    2017-07-01

    Colonoscopy is a standard method for screening polyps by highly trained physicians. Miss-detected polyps in colonoscopy are potential risk factor for colorectal cancer. In this study, we investigate an automatic polyp classification framework. We aim to compare two different approaches named hand-craft feature method and convolutional neural network (CNN) based deep learning method. Combined shape and color features are used for hand craft feature extraction and support vector machine (SVM) method is adopted for classification. For CNN approach, three convolution and pooling based deep learning framework is used for classification purpose. The proposed framework is evaluated using three public polyp databases. From the experimental results, we have shown that the CNN based deep learning framework shows better classification performance than the hand-craft feature based methods. It achieves over 90% of classification accuracy, sensitivity, specificity and precision.

  14. SVM-based prediction of propeptide cleavage sites in spider toxins identifies toxin innovation in an Australian tarantula.

    Directory of Open Access Journals (Sweden)

    Emily S W Wong

    Full Text Available Spider neurotoxins are commonly used as pharmacological tools and are a popular source of novel compounds with therapeutic and agrochemical potential. Since venom peptides are inherently toxic, the host spider must employ strategies to avoid adverse effects prior to venom use. It is partly for this reason that most spider toxins encode a protective proregion that upon enzymatic cleavage is excised from the mature peptide. In order to identify the mature toxin sequence directly from toxin transcripts, without resorting to protein sequencing, the propeptide cleavage site in the toxin precursor must be predicted bioinformatically. We evaluated different machine learning strategies (support vector machines, hidden Markov model and decision tree and developed an algorithm (SpiderP for prediction of propeptide cleavage sites in spider toxins. Our strategy uses a support vector machine (SVM framework that combines both local and global sequence information. Our method is superior or comparable to current tools for prediction of propeptide sequences in spider toxins. Evaluation of the SVM method on an independent test set of known toxin sequences yielded 96% sensitivity and 100% specificity. Furthermore, we sequenced five novel peptides (not used to train the final predictor from the venom of the Australian tarantula Selenotypus plumipes to test the accuracy of the predictor and found 80% sensitivity and 99.6% 8-mer specificity. Finally, we used the predictor together with homology information to predict and characterize seven groups of novel toxins from the deeply sequenced venom gland transcriptome of S. plumipes, which revealed structural complexity and innovations in the evolution of the toxins. The precursor prediction tool (SpiderP is freely available on ArachnoServer (http://www.arachnoserver.org/spiderP.html, a web portal to a comprehensive relational database of spider toxins. All training data, test data, and scripts used are available from

  15. SVM classifier to predict genes important for self-renewal and pluripotency of mouse embryonic stem cells

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    Xu Huilei

    2010-12-01

    Full Text Available Abstract Background Mouse embryonic stem cells (mESCs are derived from the inner cell mass of a developing blastocyst and can be cultured indefinitely in-vitro. Their distinct features are their ability to self-renew and to differentiate to all adult cell types. Genes that maintain mESCs self-renewal and pluripotency identity are of interest to stem cell biologists. Although significant steps have been made toward the identification and characterization of such genes, the list is still incomplete and controversial. For example, the overlap among candidate self-renewal and pluripotency genes across different RNAi screens is surprisingly small. Meanwhile, machine learning approaches have been used to analyze multi-dimensional experimental data and integrate results from many studies, yet they have not been applied to specifically tackle the task of predicting and classifying self-renewal and pluripotency gene membership. Results For this study we developed a classifier, a supervised machine learning framework for predicting self-renewal and pluripotency mESCs stemness membership genes (MSMG using support vector machines (SVM. The data used to train the classifier was derived from mESCs-related studies using mRNA microarrays, measuring gene expression in various stages of early differentiation, as well as ChIP-seq studies applied to mESCs profiling genome-wide binding of key transcription factors, such as Nanog, Oct4, and Sox2, to the regulatory regions of other genes. Comparison to other classification methods using the leave-one-out cross-validation method was employed to evaluate the accuracy and generality of the classification. Finally, two sets of candidate genes from genome-wide RNA interference screens are used to test the generality and potential application of the classifier. Conclusions Our results reveal that an SVM approach can be useful for prioritizing genes for functional validation experiments and complement the analyses of high

  16. Epileptic seizure classifications of single-channel scalp EEG data using wavelet-based features and SVM.

    Science.gov (United States)

    Janjarasjitt, Suparerk

    2017-02-13

    In this study, wavelet-based features of single-channel scalp EEGs recorded from subjects with intractable seizure are examined for epileptic seizure classification. The wavelet-based features extracted from scalp EEGs are simply based on detail and approximation coefficients obtained from the discrete wavelet transform. Support vector machine (SVM), one of the most commonly used classifiers, is applied to classify vectors of wavelet-based features of scalp EEGs into either seizure or non-seizure class. In patient-based epileptic seizure classification, a training data set used to train SVM classifiers is composed of wavelet-based features of scalp EEGs corresponding to the first epileptic seizure event. Overall, the excellent performance on patient-dependent epileptic seizure classification is obtained with the average accuracy, sensitivity, and specificity of, respectively, 0.9687, 0.7299, and 0.9813. The vector composed of two wavelet-based features of scalp EEGs provide the best performance on patient-dependent epileptic seizure classification in most cases, i.e., 19 cases out of 24. The wavelet-based features corresponding to the 32-64, 8-16, and 4-8 Hz subbands of scalp EEGs are the mostly used features providing the best performance on patient-dependent classification. Furthermore, the performance on both patient-dependent and patient-independent epileptic seizure classifications are also validated using tenfold cross-validation. From the patient-independent epileptic seizure classification validated using tenfold cross-validation, it is shown that the best classification performance is achieved using the wavelet-based features corresponding to the 64-128 and 4-8 Hz subbands of scalp EEGs.

  17. Classification of EEG-P300 Signals Extracted from Brain Activities in BCI Systems Using ν-SVM and BLDA Algorithms

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    Ali MOMENNEZHAD

    2014-06-01

    Full Text Available In this paper, a linear predictive coding (LPC model is used to improve classification accuracy, convergent speed to maximum accuracy, and maximum bitrates in brain computer interface (BCI system based on extracting EEG-P300 signals. First, EEG signal is filtered in order to eliminate high frequency noise. Then, the parameters of filtered EEG signal are extracted using LPC model. Finally, the samples are reconstructed by LPC coefficients and two classifiers, a Bayesian Linear discriminant analysis (BLDA, and b the υ-support vector machine (υ-SVM are applied in order to classify. The proposed algorithm performance is compared with fisher linear discriminant analysis (FLDA. Results show that the efficiency of our algorithm in improving classification accuracy and convergent speed to maximum accuracy are much better. As example at the proposed algorithms, respectively BLDA with LPC model and υ-SVM with LPC model with8 electrode configuration for subject S1 the total classification accuracy is improved as 9.4% and 1.7%. And also, subject 7 at BLDA and υ-SVM with LPC model algorithms (LPC+BLDA and LPC+ υ-SVM after block 11th converged to maximum accuracy but Fisher Linear Discriminant Analysis (FLDA algorithm did not converge to maximum accuracy (with the same configuration. So, it can be used as a promising tool in designing BCI systems.

  18. Measuring the impact of a 3D simulation experience on nursing students' cultural empathy using a modified version of the Kiersma-Chen Empathy Scale.

    Science.gov (United States)

    Everson, Naleya; Levett-Jones, Tracy; Lapkin, Samuel; Pitt, Victoria; van der Riet, Pamela; Rossiter, Rachel; Jones, Donovan; Gilligan, Conor; Courtney-Pratt, Helen

    2015-10-01

    To determine the effect of immersive 3D cultural simulation on nursing students' empathy towards culturally and linguistically diverse patients. Accelerated globalisation has seen a significant increase in cultural diversity in most regions of the world over the past forty years. Clinical encounters that do not acknowledge cultural factors contribute to adverse patient outcomes and health care inequities for culturally and linguistically diverse people. Cultural empathy is an antecedent to cultural competence. Thus, appropriate educational strategies are needed to enhance nursing students' cultural empathy and the capacity to deliver culturally competent care. A one-group pretest, post-test design was used for this study. The simulation exposed students to an unfolding scene in a hospital ward of a developing county. A convenience sample of second-year undergraduate nursing students (n = 460) from a semi-metropolitan university in Australia were recruited for the study. Characteristics of the sample were summarised using descriptive statistics. T-tests were performed to analyse the differences between pre- and post simulation empathy scores using an eight item modified version of the Kiersma-Chen Empathy Scale. Students' empathy towards culturally and linguistically diverse patients significantly improved after exposure to the 3D simulation experience. The mean scores for the Perspective Taking and Valuing Affective Empathy subscales also increased significantly postsimulation. The immersive 3D simulation had a positive impact on nursing students' empathy levels in regards to culturally and linguistically diverse groups. Research with other cohorts and in other contexts is required to further explore the impact of this educational approach. Immersive cultural simulation experiences offer opportunities to enhance the cultural empathy of nursing students. This may in turn have a positive impact on their cultural competence and consequently the quality of care they

  19. Automatic SVM classification of sudden cardiac death and pump failure death from autonomic and repolarization ECG markers.

    Science.gov (United States)

    Ramírez, Julia; Monasterio, Violeta; Mincholé, Ana; Llamedo, Mariano; Lenis, Gustavo; Cygankiewicz, Iwona; Bayés de Luna, Antonio; Malik, Marek; Martínez, Juan Pablo; Laguna, Pablo; Pueyo, Esther

    2015-01-01

    Considering the rates of sudden cardiac death (SCD) and pump failure death (PFD) in chronic heart failure (CHF) patients and the cost-effectiveness of their preventing treatments, identification of CHF patients at risk is an important challenge. In this work, we studied the prognostic performance of the combination of an index potentially related to dispersion of repolarization restitution (Δα), an index quantifying T-wave alternans (IAA) and the slope of heart rate turbulence (TS) for classification of SCD and PFD. Holter ECG recordings of 597 CHF patients with sinus rhythm enrolled in the MUSIC study were analyzed and Δα, IAA and TS were obtained. A strategy was implemented using support vector machines (SVM) to classify patients in three groups: SCD victims, PFD victims and other patients (the latter including survivors and victims of non-cardiac causes). Cross-validation was used to evaluate the performance of the implemented classifier. Δα and IAA, dichotomized at 0.035 (dimensionless) and 3.73 μV, respectively, were the ECG markers most strongly associated with SCD, while TS, dichotomized at 2.5 ms/RR, was the index most strongly related to PFD. When separating SCD victims from the rest of patients, the individual marker with best performance was Δα≥0.035, which, for a fixed specificity (Sp) of 90%, showed a sensitivity (Se) value of 10%, while the combination of Δα and IAA increased Se to 18%. For separation of PFD victims from the rest of patients, the best individual marker was TS ≤ 2.5 ms/RR, which, for Sp=90%, showed a Se of 26%, this value being lower than Se=34%, produced by the combination of Δα and TS. Furthermore, when performing SVM classification into the three reported groups, the optimal combination of risk markers led to a maximum Sp of 79% (Se=18%) for SCD and Sp of 81% (Se=14%) for PFD. The results shown in this work suggest that it is possible to efficiently discriminate SCD and PFD in a population of CHF patients using ECG

  20. A multitemporal probabilistic error correction approach to SVM classification of alpine glacier exploiting sentinel-1 images (Conference Presentation)

    Science.gov (United States)

    Callegari, Mattia; Marin, Carlo; Notarnicola, Claudia; Carturan, Luca; Covi, Federico; Galos, Stephan; Seppi, Roberto

    2016-10-01

    In mountain regions and their forelands, glaciers are key source of melt water during the middle and late ablation season, when most of the winter snow has already melted. Furthermore, alpine glaciers are recognized as sensitive indicators of climatic fluctuations. Monitoring glacier extent changes and glacier surface characteristics (i.e. snow, firn and bare ice coverage) is therefore important for both hydrological applications and climate change studies. Satellite remote sensing data have been widely employed for glacier surface classification. Many approaches exploit optical data, such as from Landsat. Despite the intuitive visual interpretation of optical images and the demonstrated capability to discriminate glacial surface thanks to the combination of different bands, one of the main disadvantages of available high-resolution optical sensors is their dependence on cloud conditions and low revisit time frequency. Therefore, operational monitoring strategies relying only on optical data have serious limitations. Since SAR data are insensitive to clouds, they are potentially a valid alternative to optical data for glacier monitoring. Compared to past SAR missions, the new Sentinel-1 mission provides much higher revisit time frequency (two acquisitions each 12 days) over the entire European Alps, and this number will be doubled once the Sentinel1-b will be in orbit (April 2016). In this work we present a method for glacier surface classification by exploiting dual polarimetric Sentinel-1 data. The method consists of a supervised approach based on Support Vector Machine (SVM). In addition to the VV and VH signals, we tested the contribution of local incidence angle, extracted from a digital elevation model and orbital information, as auxiliary input feature in order to account for the topographic effects. By exploiting impossible temporal transition between different classes (e.g. if at a given date one pixel is classified as rock it cannot be classified as

  1. Fault Diagnosis of Rotating Machinery Based on Multisensor Information Fusion Using SVM and Time-Domain Features

    Directory of Open Access Journals (Sweden)

    Ling-li Jiang

    2014-01-01

    Full Text Available Multisensor information fusion, when applied to fault diagnosis, the time-space scope, and the quantity of information are expanded compared to what could be acquired by a single sensor, so the diagnostic object can be described more comprehensively. This paper presents a methodology of fault diagnosis in rotating machinery using multisensor information fusion that all the features are calculated using vibration data in time domain to constitute fusional vector and the support vector machine (SVM is used for classification. The effectiveness of the presented methodology is tested by three case studies: diagnostic of faulty gear, rolling bearing, and identification of rotor crack. For each case study, the sensibilities of the features are analyzed. The results indicate that the peak factor is the most sensitive feature in the twelve time-domain features for identifying gear defect, and the mean, amplitude square, root mean square, root amplitude, and standard deviation are all sensitive for identifying gear, rolling bearing, and rotor crack defect comparatively.

  2. Dimensionality of ICA in resting-state fMRI investigated by feature optimized classification of independent components with SVM

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    Wang, Yanlu; Li, Tie-Qiang

    2015-01-01

    Different machine learning algorithms have recently been used for assisting automated classification of independent component analysis (ICA) results from resting-state fMRI data. The success of this approach relies on identification of artifact components and meaningful functional networks. A limiting factor of ICA is the uncertainty of the number of independent components (NIC). We aim to develop a framework based on support vector machines (SVM) and optimized feature-selection for automated classification of independent components (ICs) and use the framework to investigate the effects of input NIC on the ICA results. Seven different resting-state fMRI datasets were studied. 18 features were devised by mimicking the empirical criteria for manual evaluation. The five most significant (p NIC. Through tracking, we demonstrate that incrementing NIC affects most ICs when NIC NIC is incremented beyond NIC > 40. For a given IC, its changes with increasing NIC are individually specific irrespective whether the component is a potential resting-state functional network or an artifact component. Using FOCIS, we investigated experimentally the ICA dimensionality of resting-state fMRI datasets and found that the input NIC can critically affect the ICA results of resting-state fMRI data. PMID:26005413

  3. Prediction of healthy blood with data mining classification by using Decision Tree, Naive Baysian and SVM approaches

    Science.gov (United States)

    Khalilinezhad, Mahdieh; Minaei, Behrooz; Vernazza, Gianni; Dellepiane, Silvana

    2015-03-01

    Data mining (DM) is the process of discovery knowledge from large databases. Applications of data mining in Blood Transfusion Organizations could be useful for improving the performance of blood donation service. The aim of this research is the prediction of healthiness of blood donors in Blood Transfusion Organization (BTO). For this goal, three famous algorithms such as Decision Tree C4.5, Naïve Bayesian classifier, and Support Vector Machine have been chosen and applied to a real database made of 11006 donors. Seven fields such as sex, age, job, education, marital status, type of donor, results of blood tests (doctors' comments and lab results about healthy or unhealthy blood donors) have been selected as input to these algorithms. The results of the three algorithms have been compared and an error cost analysis has been performed. According to this research and the obtained results, the best algorithm with low error cost and high accuracy is SVM. This research helps BTO to realize a model from blood donors in each area in order to predict the healthy blood or unhealthy blood of donors. This research could be useful if used in parallel with laboratory tests to better separate unhealthy blood.

  4. Real-time human pose estimation and gesture recognition from depth images using superpixels and SVM classifier.

    Science.gov (United States)

    Kim, Hanguen; Lee, Sangwon; Lee, Dongsung; Choi, Soonmin; Ju, Jinsun; Myung, Hyun

    2015-05-26

    In this paper, we present human pose estimation and gesture recognition algorithms that use only depth information. The proposed methods are designed to be operated with only a CPU (central processing unit), so that the algorithm can be operated on a low-cost platform, such as an embedded board. The human pose estimation method is based on an SVM (support vector machine) and superpixels without prior knowledge of a human body model. In the gesture recognition method, gestures are recognized from the pose information of a human body. To recognize gestures regardless of motion speed, the proposed method utilizes the keyframe extraction method. Gesture recognition is performed by comparing input keyframes with keyframes in registered gestures. The gesture yielding the smallest comparison error is chosen as a recognized gesture. To prevent recognition of gestures when a person performs a gesture that is not registered, we derive the maximum allowable comparison errors by comparing each registered gesture with the other gestures. We evaluated our method using a dataset that we generated. The experiment results show that our method performs fairly well and is applicable in real environments.

  5. Real-Time Human Pose Estimation and Gesture Recognition from Depth Images Using Superpixels and SVM Classifier

    Directory of Open Access Journals (Sweden)

    Hanguen Kim

    2015-05-01

    Full Text Available In this paper, we present human pose estimation and gesture recognition algorithms that use only depth information. The proposed methods are designed to be operated with only a CPU (central processing unit, so that the algorithm can be operated on a low-cost platform, such as an embedded board. The human pose estimation method is based on an SVM (support vector machine and superpixels without prior knowledge of a human body model. In the gesture recognition method, gestures are recognized from the pose information of a human body. To recognize gestures regardless of motion speed, the proposed method utilizes the keyframe extraction method. Gesture recognition is performed by comparing input keyframes with keyframes in registered gestures. The gesture yielding the smallest comparison error is chosen as a recognized gesture. To prevent recognition of gestures when a person performs a gesture that is not registered, we derive the maximum allowable comparison errors by comparing each registered gesture with the other gestures. We evaluated our method using a dataset that we generated. The experiment results show that our method performs fairly well and is applicable in real environments.

  6. Dynamic partial reconfiguration implementation of the SVM/KNN multi-classifier on FPGA for bioinformatics application.

    Science.gov (United States)

    Hussain, Hanaa M; Benkrid, Khaled; Seker, Huseyin

    2015-01-01

    Bioinformatics data tend to be highly dimensional in nature thus impose significant computational demands. To resolve limitations of conventional computing methods, several alternative high performance computing solutions have been proposed by scientists such as Graphical Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs). The latter have shown to be efficient and high in performance. In recent years, FPGAs have been benefiting from dynamic partial reconfiguration (DPR) feature for adding flexibility to alter specific regions within the chip. This work proposes combing the use of FPGAs and DPR to build a dynamic multi-classifier architecture that can be used in processing bioinformatics data. In bioinformatics, applying different classification algorithms to the same dataset is desirable in order to obtain comparable, more reliable and consensus decision, but it can consume long time when performed on conventional PC. The DPR implementation of two common classifiers, namely support vector machines (SVMs) and K-nearest neighbor (KNN) are combined together to form a multi-classifier FPGA architecture which can utilize specific region of the FPGA to work as either SVM or KNN classifier. This multi-classifier DPR implementation achieved at least ~8x reduction in reconfiguration time over the single non-DPR classifier implementation, and occupied less space and hardware resources than having both classifiers. The proposed architecture can be extended to work as an ensemble classifier.

  7. SVM-Based Classification of Segmented Airborne LiDAR Point Clouds in Urban Areas

    Directory of Open Access Journals (Sweden)

    Xiaogang Ning

    2013-07-01

    Full Text Available Object-based point cloud analysis (OBPA is useful for information extraction from airborne LiDAR point clouds. An object-based classification method is proposed for classifying the airborne LiDAR point clouds in urban areas herein. In the process of classification, the surface growing algorithm is employed to make clustering of the point clouds without outliers, thirteen features of the geometry, radiometry, topology and echo characteristics are calculated, a support vector machine (SVM is utilized to classify the segments, and connected component analysis for 3D point clouds is proposed to optimize the original classification results. Three datasets with different point densities and complexities are employed to test our method. Experiments suggest that the proposed method is capable of making a classification of the urban point clouds with the overall classification accuracy larger than 92.34% and the Kappa coefficient larger than 0.8638, and the classification accuracy is promoted with the increasing of the point density, which is meaningful for various types of applications.

  8. Robust Non-Linear Direct Torque and Flux Control of Adjustable Speed Sensorless PMSM Drive Based on SVM Using a PI Predictive Controller

    Directory of Open Access Journals (Sweden)

    F. Naceri

    2010-01-01

    Full Text Available This paper presents a new sensorless direct torque control method for voltage inverter – fed PMSM. The control methodis used a modified Direct Torque Control scheme with constant inverter switching frequency using Space Vector Modulation(DTC-SVM. The variation of stator and rotor resistance due to changes in temperature or frequency deteriorates theperformance of DTC-SVM controller by introducing errors in the estimated flux linkage and the electromagnetic torque.As a result, this approach will not be suitable for high power drives such as those used in tractions, as they require goodtorque control performance at considerably lower frequency. A novel stator resistance estimator is proposed. The estimationmethod is implemented using the Extended Kalman Filter. Finally extensive simulation results are presented to validate theproposed technique. The system is tested at different speeds and a very satisfactory performance has been achieved.

  9. Máquinas de soporte vectorial (svm) para la detección de nódulos pulmonares en tomografía axial computarizada (tac)

    OpenAIRE

    Ledezma Garrido, Willmar

    2012-01-01

    El cáncer de pulmón es uno de los mas comunes en el mundo y los nódulos pulmonares son su principal indicador de alerta temprana para su diagnóstico. Se presenta un proyecto para la detección de nódulos pulmonares con el uso de máquinas de soporte vectorial (svm), usando el kernel función de base radial gausiana (RBFG), previo a la aplicación de la svm, se hace un trabajo de procesamiento de imágenes que incluye la extracción de la región de interés y extracción de las características que ide...

  10. Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers

    Energy Technology Data Exchange (ETDEWEB)

    Salahshoor, Karim [Department of Instrumentation and Automation, Petroleum University of Technology, Tehran (Iran, Islamic Republic of); Kordestani, Mojtaba; Khoshro, Majid S. [Department of Control Engineering, Islamic Azad University South Tehran branch (Iran, Islamic Republic of)

    2010-12-15

    The subject of FDD (fault detection and diagnosis) has gained widespread industrial interest in machine condition monitoring applications. This is mainly due to the potential advantage to be achieved from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a new FDD scheme for condition machinery of an industrial steam turbine using a data fusion methodology. Fusion of a SVM (support vector machine) classifier with an ANFIS (adaptive neuro-fuzzy inference system) classifier, integrated into a common framework, is utilized to enhance the fault detection and diagnostic tasks. For this purpose, a multi-attribute data is fused into aggregated values of a single attribute by OWA (ordered weighted averaging) operators. The simulation studies indicate that the resulting fusion-based scheme outperforms the individual SVM and ANFIS systems to detect and diagnose incipient steam turbine faults. (author)

  11. ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment

    Science.gov (United States)

    Quej, Victor H.; Almorox, Javier; Arnaldo, Javier A.; Saito, Laurel

    2017-03-01

    Daily solar radiation is an important variable in many models. In this paper, the accuracy and performance of three soft computing techniques (i.e., adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and support vector machine (SVM) were assessed for predicting daily horizontal global solar radiation from measured meteorological variables in the Yucatán Peninsula, México. Model performance was assessed with statistical indicators such as root mean squared error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). The performance assessment indicates that the SVM technique with requirements of daily maximum and minimum air temperature, extraterrestrial solar radiation and rainfall has better performance than the other techniques and may be a promising alternative to the usual approaches for predicting solar radiation.

  12. Support vector machine regression (SVR/LS-SVM)--an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data.

    Science.gov (United States)

    Balabin, Roman M; Lomakina, Ekaterina I

    2011-04-21

    In this study, we make a general comparison of the accuracy and robustness of five multivariate calibration models: partial least squares (PLS) regression or projection to latent structures, polynomial partial least squares (Poly-PLS) regression, artificial neural networks (ANNs), and two novel techniques based on support vector machines (SVMs) for multivariate data analysis: support vector regression (SVR) and least-squares support vector machines (LS-SVMs). The comparison is based on fourteen (14) different datasets: seven sets of gasoline data (density, benzene content, and fractional composition/boiling points), two sets of ethanol gasoline fuel data (density and ethanol content), one set of diesel fuel data (total sulfur content), three sets of petroleum (crude oil) macromolecules data (weight percentages of asphaltenes, resins, and paraffins), and one set of petroleum resins data (resins content). Vibrational (near-infrared, NIR) spectroscopic data are used to predict the properties and quality coefficients of gasoline, biofuel/biodiesel, diesel fuel, and other samples of interest. The four systems presented here range greatly in composition, properties, strength of intermolecular interactions (e.g., van der Waals forces, H-bonds), colloid structure, and phase behavior. Due to the high diversity of chemical systems studied, general conclusions about SVM regression methods can be made. We try to answer the following question: to what extent can SVM-based techniques replace ANN-based approaches in real-world (industrial/scientific) applications? The results show that both SVR and LS-SVM methods are comparable to ANNs in accuracy. Due to the much higher robustness of the former, the SVM-based approaches are recommended for practical (industrial) application. This has been shown to be especially true for complicated, highly nonlinear objects.

  13. QSAR studies of the bioactivity of hepatitis C virus (HCV) NS3/4A protease inhibitors by multiple linear regression (MLR) and support vector machine (SVM).

    Science.gov (United States)

    Qin, Zijian; Wang, Maolin; Yan, Aixia

    2017-07-01

    In this study, quantitative structure-activity relationship (QSAR) models using various descriptor sets and training/test set selection methods were explored to predict the bioactivity of hepatitis C virus (HCV) NS3/4A protease inhibitors by using a multiple linear regression (MLR) and a support vector machine (SVM) method. 512 HCV NS3/4A protease inhibitors and their IC 50 values which were determined by the same FRET assay were collected from the reported literature to build a dataset. All the inhibitors were represented with selected nine global and 12 2D property-weighted autocorrelation descriptors calculated from the program CORINA Symphony. The dataset was divided into a training set and a test set by a random and a Kohonen's self-organizing map (SOM) method. The correlation coefficients (r 2 ) of training sets and test sets were 0.75 and 0.72 for the best MLR model, 0.87 and 0.85 for the best SVM model, respectively. In addition, a series of sub-dataset models were also developed. The performances of all the best sub-dataset models were better than those of the whole dataset models. We believe that the combination of the best sub- and whole dataset SVM models can be used as reliable lead designing tools for new NS3/4A protease inhibitors scaffolds in a drug discovery pipeline. Copyright © 2017 Elsevier Ltd. All rights reserved.

  14. The effects of dissolved oxygen level on the metabolic interaction between digestion and locomotion in juvenile southern catfish (Silurus meridionalis Chen).

    Science.gov (United States)

    Zhang, Wei; Cao, Zhen-Dong; Peng, Jiang-Lan; Chen, Bo-Jian; Fu, Shi-Jian

    2010-11-01

    To investigate the effect of dissolved oxygen level ([O(2)]) on maintenance metabolism, feeding metabolism, aerobic swimming performance and their metabolic interaction in juvenile southern catfish (Silurus meridionalis Chen), we measured the following: (1) the resting oxygen consumption rate (MO(2rest)) over a range of water [O(2)] and from this we calculated the critical oxygen tension (P(crit)) of fasting fish; (2) the postprandial MO(2) response (10% body mass meal size) at water [O(2)] of 1, 2, 4 and 8mgO(2)L(-1); and (3) the swimming performance of fasting and digesting fish at water [O(2)] of 1, 2, 4 and 8mgO(2)L(-1) at 25 degrees C. The MO(2rest) remained constant over a broad range of water [O(2)] but then dropped markedly upon reaching the P(crit) (16.4% saturation). Hypoxic groups presented lower peak postprandial MO(2) (MO(2peak)) (1mgO(2)L(-1) group), larger energy expenditure and longer digestive process (both 1 and 2mgO(2)L(-1)) than those of normoxic groups. Both critical swimming speed (U(crit)) and the active metabolic rate (MO(2active)) of fasting fish remained unchanged over a decrease in water [O(2)] from 8 to 4mgO(2)L(-1) and then decreased significantly with further decreases in water [O(2)]. These parameters in fed fish showed a pronounced decrease as water [O(2)] decreased from 8 to 1mgO(2)L(-1). Feeding caused a significantly lower U(crit) in the 2mgO(2)L(-1) water [O(2)] group, a significantly higher MO(2active) in both the 2 and 8mgL(-1) water [O(2)] groups and a significantly higher metabolic scope (MO(2active)-MO(2rest)) in both the 2 and 4mgO(2)L(-1) water [O(2)] groups compared to fasting fish. The MO(2) increased greatly with swimming speed in the higher water [O(2)] groups, whereas it leveled off as swimming speeds approached the U(crit) in the lower water [O(2)] groups. Within all water [O(2)] groups, feeding caused a higher MO(2) compared to fasting fish when fish swam at the same speeds, except in the 1mgO(2)L(-1) group. This

  15. Dimensionality of ICA in resting-state fMRI investigated by feature optimized classification of independent components with SVM.

    Science.gov (United States)

    Wang, Yanlu; Li, Tie-Qiang

    2015-01-01

    Different machine learning algorithms have recently been used for assisting automated classification of independent component analysis (ICA) results from resting-state fMRI data. The success of this approach relies on identification of artifact components and meaningful functional networks. A limiting factor of ICA is the uncertainty of the number of independent components (NIC). We aim to develop a framework based on support vector machines (SVM) and optimized feature-selection for automated classification of independent components (ICs) and use the framework to investigate the effects of input NIC on the ICA results. Seven different resting-state fMRI datasets were studied. 18 features were devised by mimicking the empirical criteria for manual evaluation. The five most significant (p ICA results. The classification results obtained using FOCIS and previously published FSL-FIX were compared against manually evaluated results. On average the false negative rate in identifying artifact contaminated ICs for FOCIS and FSL-FIX were 98.27 and 92.34%, respectively. The number of artifact and functional network components increased almost linearly with the input NIC. Through tracking, we demonstrate that incrementing NIC affects most ICs when NIC 40. For a given IC, its changes with increasing NIC are individually specific irrespective whether the component is a potential resting-state functional network or an artifact component. Using FOCIS, we investigated experimentally the ICA dimensionality of resting-state fMRI datasets and found that the input NIC can critically affect the ICA results of resting-state fMRI data.

  16. Multi-class clustering of cancer subtypes through SVM based ensemble of pareto-optimal solutions for gene marker identification.

    Science.gov (United States)

    Mukhopadhyay, Anirban; Bandyopadhyay, Sanghamitra; Maulik, Ujjwal

    2010-11-12

    With the advancement of microarray technology, it is now possible to study the expression profiles of thousands of genes across different experimental conditions or tissue samples simultaneously. Microarray cancer datasets, organized as samples versus genes fashion, are being used for classification of tissue samples into benign and malignant or their subtypes. They are also useful for identifying potential gene markers for each cancer subtype, which helps in successful diagnosis of particular cancer types. In this article, we have presented an unsupervised cancer classification technique based on multiobjective genetic clustering of the tissue samples. In this regard, a real-coded encoding of the cluster centers is used and cluster compactness and separation are simultaneously optimized. The resultant set of near-Pareto-optimal solutions contains a number of non-dominated solutions. A novel approach to combine the clustering information possessed by the non-dominated solutions through Support Vector Machine (SVM) classifier has been proposed. Final clustering is obtained by consensus among the clusterings yielded by different kernel functions. The performance of the proposed multiobjective clustering method has been compared with that of several other microarray clustering algorithms for three publicly available benchmark cancer datasets. Moreover, statistical significance tests have been conducted to establish the statistical superiority of the proposed clustering method. Furthermore, relevant gene markers have been identified using the clustering result produced by the proposed clustering method and demonstrated visually. Biological relationships among the gene markers are also studied based on gene ontology. The results obtained are found to be promising and can possibly have important impact in the area of unsupervised cancer classification as well as gene marker identification for multiple cancer subtypes.

  17. Detecting brain structural changes as biomarker from magnetic resonance images using a local feature based SVM approach.

    Science.gov (United States)

    Chen, Ye; Storrs, Judd; Tan, Lirong; Mazlack, Lawrence J; Lee, Jing-Huei; Lu, Long J

    2014-01-15

    Detecting brain structural changes from magnetic resonance (MR) images can facilitate early diagnosis and treatment of neurological and psychiatric diseases. Many existing methods require an accurate deformation registration, which is difficult to achieve and therefore prevents them from obtaining high accuracy. We develop a novel local feature based support vector machine (SVM) approach to detect brain structural changes as potential biomarkers. This approach does not require deformation registration and thus is less influenced by artifacts such as image distortion. We represent the anatomical structures based on scale invariant feature transform (SIFT). Likelihood scores calculated using feature-based morphometry is used as the criterion to categorize image features into three classes (healthy, patient and noise). Regional SVMs are trained to classify the three types of image features in different brain regions. Only healthy and patient features are used to predict the disease status of new brain images. An ensemble classifier is built from the regional SVMs to obtain better prediction accuracy. We apply this approach to 3D MR images of Alzheimer's disease, Parkinson's disease and bipolar disorder. The classification accuracy ranges between 70% and 87%. The highly predictive disease-related regions, which represent significant anatomical differences between the healthy and diseased, are shown in heat maps. The common and disease-specific brain regions are identified by comparing the highly predictive regions in each disease. All of the top-ranked regions are supported by literature. Thus, this approach will be a promising tool for assisting automatic diagnosis and advancing mechanism studies of neurological and psychiatric diseases. Copyright © 2013 Elsevier B.V. All rights reserved.

  18. Two phases of droplet evaporation during plasma arc spraying: reply to Chen's comment about the 'rocket' effect under conditions of thermal plasma spraying

    Energy Technology Data Exchange (ETDEWEB)

    Nemchinsky, V A [Key College, 225 Dania Beach Blvd, Dania Beach, FL 33040 (United States)

    2007-07-07

    The heating history of a droplet during its flight can be divided into two phases: (a) the initial phase when evaporation, although it occurs, does not change the heat balance of the droplet much (the case considered in our previous paper and (b) the final phase when the cooling due to evaporation balances the heat flux from the plasma. The later phase is considered in Chen's comment. In our reply, a very straightforward consideration demonstrates that even in the final phase of the droplet flight, the 'rocket' effect can be significant. (reply)

  19. Struktur und Funktion von S-Layern acidophiler Bakterien und Archaeen, ihre Rolle bei der Pyrit-Oxidation sowie die Adhäsion an Oberflächen

    OpenAIRE

    Klingl, Andreas

    2011-01-01

    Anhand physiologischer und morphologischer Untersuchungen an den γ-Proteobakterienstämmen SP5/1 und HV2/2 sollten Einsichten über deren Einordnung innerhalb der Gattung Acidithiobacillus erlangt werden. Zusätzlich waren die Charakterisierung der Surface Layer des Acidithiobacillus-Isolates SP5/1 und von M. sedula TH2, sowie deren mögliche Funktionen und ihre Rolle bei der Interaktion mit Oberflächen von besonderem Interesse. Angesichts der Ergebnisse, welche aus der Sequenzierung der 16S ...

  20. PERBANDINGAN TINGKAT PENGENALAN CITRA DIABETIC RETINOPATHY PADA KOMBINASI PRINCIPLE COMPONENT DARI 4 CIRI BERBASIS METODE SVM (SUPPORT VECTOR MACHINE

    Directory of Open Access Journals (Sweden)

    Sari Ayu Wulandari

    2016-06-01

    Full Text Available Perbedaan pigmentasi mempengaruhi me­­­­tode pengenalan pola citra retinopati di­a­betik beserta set­ting poinnya. Di­butuhkan sebuah pe­rangkat lunak, yang mampu menjadi alat bantu pengenalan citra retinopati diabetik. Telah dilakukan penelitian tentang pe­nge­nalan po­la citra retinopati dia­be­tik, dengan meng­gunakan citra kanal ku­ning (Yello­w, dengan menggunakan filter gabor dan ciri yang diambil dari tiap citra ada­lah ciri rerata (Means, variasi Varians, skewness dan entropy, yang dilanjutkan de­ngan ekstraksi ciri  PCA (Principle Com­­ponent Analysis. Pada ekstraksi ci­ri PCA, Matriks hasil PCA meru­pakan ma­triks bujur sangkar, yang jumlah ko­lom­nya, sama dengan jumlah ciri. Pe­ne­li­tian menggunakan 4 ciri, dengan de­mi­­kian, terdapat 4 buah PC (Principle Com­ponent, PC1, PC2, PC3 dan PC4. Pada artikel ini akan dibahas mengenai tingkat akurasi tertinggi dari peng­gunaan pasangan PC. Tingkat aku­ra­si, dihitung dengan meng­gu­­nakan mo­del linear dari SVM. Model de­ngan akurasi tertinggi dan tercepat ada­lah model pasangan PC1 dan PC2, yang mempunyai akurasi citra pem­be­lajaran tertinggi yaitu 100% dan waktu terce­pat, yang secara eksplisit diperli­hat­kan pada jumlah support vektor ter­kecil, yaitu 2. Pasa­ngan yang mempu­nyai ting­kat akurasi terburuk adalah PC3 dan PC4. Pengenalan turun pada citra pengu­jian, yaitu hanya 93,75%, hal ini disebabkan oleh pelebaran daerah ca­ku­pan. Pelebaran daerah cakupan ke­mungkinan disebabkan oleh pemi­lihan nilai rerata pada PCA, sebelum matriks reduksi. Pada penelitian berikutnya, bi­sa dilakukan dengan menggunakan pencarian nilai standart deviasi atau varians, dengan begitu, akan diketahui matriks reduksi yang mewakili sebaran angka pada matriks.

  1. Comparison Algorithm Kernels on Support Vector Machine (SVM To Compare The Trend Curves with Curves Online Forex Trading

    Directory of Open Access Journals (Sweden)

    irfan abbas

    2017-01-01

    Full Text Available At this time, the players Forex Trading generally still use the data exchange in the form of a Forex Trading figures from different sources. Thus they only receive or know the data rate of a Forex Trading prevailing at the time just so difficult to analyze or predict exchange rate movements future. Forex players usually use the indicators to enable them to analyze and memperdiksi future value. Indicator is a decision making tool. Trading forex is trading currency of a country, the other country's currency. Trading took place globally between the financial centers of the world with the involvement of the world's major banks as the major transaction. Trading Forex offers profitable investment type with a small capital and high profit, with relatively small capital can earn profits doubled. This is due to the forex trading systems exist leverage which the invested capital will be doubled if the predicted results of buy / sell is accurate, but Trading Forex having high risk level, but by knowing the right time to trade (buy or sell, the losses can be avoided. Traders who invest in the foreign exchange market is expected to have the ability to analyze the circumstances and situations in predicting the difference in currency exchange rates. Forex price movements that form the pattern (curve up and down greatly assist traders in making decisions. The movement of the curve used as an indicator in the decision to purchase (buy or sell (sell. This study compares (Comparation type algorithm kernel on Support Vector Machine (SVM to predict the movement of the curve in live time trading forex using the data GBPUSD, 1H. Results of research on the study of the results and discussion can be concluded that the Kernel Dot, Kernel Multiquaric, Kernel Neural inappropriately used for data is non-linear in the case of data forex to follow the pattern of trend curves, because curves generated curved linear (straight and then to type of kernel is the closest curve

  2. Combining high-speed SVM learning with CNN feature encoding for real-time target recognition in high-definition video for ISR missions

    Science.gov (United States)

    Kroll, Christine; von der Werth, Monika; Leuck, Holger; Stahl, Christoph; Schertler, Klaus

    2017-05-01

    For Intelligence, Surveillance, Reconnaissance (ISR) missions of manned and unmanned air systems typical electrooptical payloads provide high-definition video data which has to be exploited with respect to relevant ground targets in real-time by automatic/assisted target recognition software. Airbus Defence and Space is developing required technologies for real-time sensor exploitation since years and has combined the latest advances of Deep Convolutional Neural Networks (CNN) with a proprietary high-speed Support Vector Machine (SVM) learning method into a powerful object recognition system with impressive results on relevant high-definition video scenes compared to conventional target recognition approaches. This paper describes the principal requirements for real-time target recognition in high-definition video for ISR missions and the Airbus approach of combining an invariant feature extraction using pre-trained CNNs and the high-speed training and classification ability of a novel frequency-domain SVM training method. The frequency-domain approach allows for a highly optimized implementation for General Purpose Computation on a Graphics Processing Unit (GPGPU) and also an efficient training of large training samples. The selected CNN which is pre-trained only once on domain-extrinsic data reveals a highly invariant feature extraction. This allows for a significantly reduced adaptation and training of the target recognition method for new target classes and mission scenarios. A comprehensive training and test dataset was defined and prepared using relevant high-definition airborne video sequences. The assessment concept is explained and performance results are given using the established precision-recall diagrams, average precision and runtime figures on representative test data. A comparison to legacy target recognition approaches shows the impressive performance increase by the proposed CNN+SVM machine-learning approach and the capability of real-time high

  3. Predicting the Metabolic Sites by Flavin-Containing Monooxygenase on Drug Molecules Using SVM Classification on Computed Quantum Mechanics and Circular Fingerprints Molecular Descriptors.

    Directory of Open Access Journals (Sweden)

    Chien-Wei Fu

    Full Text Available As an important enzyme in Phase I drug metabolism, the flavin-containing monooxygenase (FMO also metabolizes some xenobiotics with soft nucleophiles. The site of metabolism (SOM on a molecule is the site where the metabolic reaction is exerted by an enzyme. Accurate prediction of SOMs on drug molecules will assist the search for drug leads during the optimization process. Here, some quantum mechanics features such as the condensed Fukui function and attributes from circular fingerprints (called Molprint2D are computed and classified using the support vector machine (SVM for predicting some potential SOMs on a series of drugs that can be metabolized by FMO enzymes. The condensed Fukui function fA- representing the nucleophilicity of central atom A and the attributes from circular fingerprints accounting the influence of neighbors on the central atom. The total number of FMO substrates and non-substrates collected in the study is 85 and they are equally divided into the training and test sets with each carrying roughly the same number of potential SOMs. However, only N-oxidation and S-oxidation features were considered in the prediction since the available C-oxidation data was scarce. In the training process, the LibSVM package of WEKA package and the option of 10-fold cross validation are employed. The prediction performance on the test set evaluated by accuracy, Matthews correlation coefficient and area under ROC curve computed are 0.829, 0.659, and 0.877 respectively. This work reveals that the SVM model built can accurately predict the potential SOMs for drug molecules that are metabolizable by the FMO enzymes.

  4. A Comparative Study between SVM and Fuzzy Inference System for the Automatic Prediction of Sleep Stages and the Assessment of Sleep Quality

    Directory of Open Access Journals (Sweden)

    John Gialelis

    2015-11-01

    Full Text Available This paper compares two supervised learning algorithms for predicting the sleep stages based on the human brain activity. The first step of the presented work regards feature extraction from real human electroencephalography (EEG data together with its corresponding sleep stages that are utilized for training a support vector machine (SVM, and a fuzzy inference system (FIS algorithm. Then, the trained algorithms are used to predict the sleep stages of real human patients. Extended comparison results are demonstrated which indicate that both classifiers could be utilized as a basis for an unobtrusive sleep quality assessment.

  5. Performance of svm, k-nn and nbc classifiers for text-independent speaker identification with and without modelling through merging models

    Directory of Open Access Journals (Sweden)

    Yussouf Nahayo

    2016-04-01

    Full Text Available This paper proposes some methods of robust text-independent speaker identification based on Gaussian Mixture Model (GMM. We implemented a combination of GMM model with a set of classifiers such as Support Vector Machine (SVM, K-Nearest Neighbour (K-NN, and Naive Bayes Classifier (NBC. In order to improve the identification rate, we developed a combination of hybrid systems by using validation technique. The experiments were performed on the dialect DR1 of the TIMIT corpus. The results have showed a better performance for the developed technique compared to the individual techniques.

  6. Estudio de un sistema de reconocimiento biométrico mediante firma manuscrita online basado en SVM usando Análisis Formal de Conceptos

    OpenAIRE

    Mendaza Ormaza, Aitor; Miguel Hurtado, Óscar; Sánchez Reillo, Raúl; Valverde Albacete, Francisco José; Peláez Moreno, Carmen

    2010-01-01

    10 pages, 8 figures.-- Contributed to: V Jornadas de Reconocimiento Biométrico de Personas (JRBP 2010, Huesca, Spain, Sep 2-3, 2010). En el presente artículo se pretende estudiar las prestaciones de un sistema de reconocimiento biométrico mediante firma manuscrita usando la teoría de Análisis Formal de Conceptos (FCA). Se usará la modalidad online de la firma manuscrita, con un algoritmo basado en Máquinas de Vectores Soporte (SVM). Para analizar el desempeño del sistema se realizará un es...

  7. Pre-cancer risk assessment in habitual smokers from DIC images of oral exfoliative cells using active contour and SVM analysis.

    Science.gov (United States)

    Dey, Susmita; Sarkar, Ripon; Chatterjee, Kabita; Datta, Pallab; Barui, Ananya; Maity, Santi P

    2017-04-01

    Habitual smokers are known to be at higher risk for developing oral cancer, which is increasing at an alarming rate globally. Conventionally, oral cancer is associated with high mortality rates, although recent reports show the improved survival outcomes by early diagnosis of disease. An effective prediction system which will enable to identify the probability of cancer development amongst the habitual smokers, is thus expected to benefit sizable number of populations. Present work describes a non-invasive, integrated method for early detection of cellular abnormalities based on analysis of different cyto-morphological features of exfoliative oral epithelial cells. Differential interference contrast (DIC) microscopy provides a potential optical tool as this mode provides a pseudo three dimensional (3-D) image with detailed morphological and textural features obtained from noninvasive, label free epithelial cells. For segmentation of DIC images, gradient vector flow snake model active contour process has been adopted. To evaluate cellular abnormalities amongst habitual smokers, the selected morphological and textural features of epithelial cells are compared with the non-smoker (-ve control group) group and clinically diagnosed pre-cancer patients (+ve control group) using support vector machine (SVM) classifier. Accuracy of the developed SVM based classification has been found to be 86% with 80% sensitivity and 89% specificity in classifying the features from the volunteers having smoking habit. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. A Fast SVM-Based Tongue’s Colour Classification Aided by k-Means Clustering Identifiers and Colour Attributes as Computer-Assisted Tool for Tongue Diagnosis

    Directory of Open Access Journals (Sweden)

    Nur Diyana Kamarudin

    2017-01-01

    Full Text Available In tongue diagnosis, colour information of tongue body has kept valuable information regarding the state of disease and its correlation with the internal organs. Qualitatively, practitioners may have difficulty in their judgement due to the instable lighting condition and naked eye’s ability to capture the exact colour distribution on the tongue especially the tongue with multicolour substance. To overcome this ambiguity, this paper presents a two-stage tongue’s multicolour classification based on a support vector machine (SVM whose support vectors are reduced by our proposed k-means clustering identifiers and red colour range for precise tongue colour diagnosis. In the first stage, k-means clustering is used to cluster a tongue image into four clusters of image background (black, deep red region, red/light red region, and transitional region. In the second-stage classification, red/light red tongue images are further classified into red tongue or light red tongue based on the red colour range derived in our work. Overall, true rate classification accuracy of the proposed two-stage classification to diagnose red, light red, and deep red tongue colours is 94%. The number of support vectors in SVM is improved by 41.2%, and the execution time for one image is recorded as 48 seconds.

  9. Effect of Substrate and Culture Conditions on the Production of Amylase and Pullulanase by Thermophilic Clostridium thermosulforegenes SVM17 in Solid State Fermentation

    Directory of Open Access Journals (Sweden)

    Seenayya, G.

    2011-01-01

    Full Text Available The endo acting enzyme with dual specificity towards α-1,4- and α-1,6-glycosidic linkages are named as amylopullulanase. The production of extracellular thermostable amylopullulanase by Clostridium thermosulfurogenes SVM17 was investigated in solid state fermentation (SSF. Coarse type wheat bran was found to be the best substrate among ten easily available complex organic substrates evaluated. The production of enzyme reached a peak in 72 h. A high level of enzyme was produced in wheat bran moistened with PYE medium with a moisture content of 73 %. The optimum temperature and pH for amylopullulanase production was 60 °C and 7.5, respectively. An inoculum size of 20 % resulted in maximum production of amylopullulanase. Under the optimum conditions the strain showed a maximum of 17,227 and 21,526 U of amylase and pullulanase activity, respectively per kilogram of bacterial bran (BB. The enzyme production was high in SSF than that in SmF. The use of SSF for the production of thermostable amylopullulanase by C. thermosulfurogenes SVM17 could, therefore led to reduction in the overall cost of enzyme production.

  10. In Silico Prediction of Gamma-Aminobutyric Acid Type-A Receptors Using Novel Machine-Learning-Based SVM and GBDT Approaches

    Directory of Open Access Journals (Sweden)

    Zhijun Liao

    2016-01-01

    Full Text Available Gamma-aminobutyric acid type-A receptors (GABAARs belong to multisubunit membrane spanning ligand-gated ion channels (LGICs which act as the principal mediators of rapid inhibitory synaptic transmission in the human brain. Therefore, the category prediction of GABAARs just from the protein amino acid sequence would be very helpful for the recognition and research of novel receptors. Based on the proteins’ physicochemical properties, amino acids composition and position, a GABAAR classifier was first constructed using a 188-dimensional (188D algorithm at 90% cd-hit identity and compared with pseudo-amino acid composition (PseAAC and ProtrWeb web-based algorithms for human GABAAR proteins. Then, four classifiers including gradient boosting decision tree (GBDT, random forest (RF, a library for support vector machine (libSVM, and k-nearest neighbor (k-NN were compared on the dataset at cd-hit 40% low identity. This work obtained the highest correctly classified rate at 96.8% and the highest specificity at 99.29%. But the values of sensitivity, accuracy, and Matthew’s correlation coefficient were a little lower than those of PseAAC and ProtrWeb; GBDT and libSVM can make a little better performance than RF and k-NN at the second dataset. In conclusion, a GABAAR classifier was successfully constructed using only the protein sequence information.

  11. Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM and Artificial Neural Network (ANN

    Directory of Open Access Journals (Sweden)

    Maria Grazia De Giorgi

    2014-08-01

    Full Text Available A high penetration of wind energy into the electricity market requires a parallel development of efficient wind power forecasting models. Different hybrid forecasting methods were applied to wind power prediction, using historical data and numerical weather predictions (NWP. A comparative study was carried out for the prediction of the power production of a wind farm located in complex terrain. The performances of Least-Squares Support Vector Machine (LS-SVM with Wavelet Decomposition (WD were evaluated at different time horizons and compared to hybrid Artificial Neural Network (ANN-based methods. It is acknowledged that hybrid methods based on LS-SVM with WD mostly outperform other methods. A decomposition of the commonly known root mean square error was beneficial for a better understanding of the origin of the differences between prediction and measurement and to compare the accuracy of the different models. A sensitivity analysis was also carried out in order to underline the impact that each input had in the network training process for ANN. In the case of ANN with the WD technique, the sensitivity analysis was repeated on each component obtained by the decomposition.

  12. The system evaluation for report writing skills of summary by HGA-SVM with Ontology: Medical case study in problem based learning

    Science.gov (United States)

    Yenaeng, Sasikanchana; Saelee, Somkid; Samai, Wirachai

    2018-01-01

    The system evaluation for report writing skills of summary by Hybrid Genetic Algorithm-Support Vector Machines (HGA-SVM) with Ontology of Medical Case Study in Problem Based Learning (PBL) is a system was developed as a guideline of scoring for the facilitators or medical teacher. The essay answers come from medical student of medical education courses in the nervous system motion and Behavior I and II subject, a third year medical student 20 groups of 9-10 people, the Faculty of Medicine in Prince of Songkla University (PSU). The audit committee have the opinion that the ratings of individual facilitators are inadequate, this system to solve such problems. In this paper proposes a development of the system evaluation for report writing skills of summary by HGA-SVM with Ontology of medical case study in PBL which the mean scores of machine learning score and humans (facilitators) score were not different at the significantly level .05 all 3 essay parts contain problem essay part, hypothesis essay part and learning objective essay part. The result show that, the average score all 3 essay parts that were not significantly different from the rate at the level of significance .05.

  13. Claire Shen Hsiu-chen, L’Encre et l’Ecran. A la recherche de la stylistique cinématographique chinoise, Hou Hsiao-hsien et Zhang Yimou (Ink and the Screen : In Search of Chinese Film Stylistics, Hou Hsiao-hsien and Zhang Yimou)

    OpenAIRE

    Reynaud, Bérénice

    2006-01-01

    Reading Claire Shen Hsiu-chen gave me the impression several times of discovering a kindred spirit, so closely does the first part of her book confirm some of my analyses. Like her, I sought to scrutinise the Chinese pictorial and cosmological tradition in order to find the origins of Hou Hsiao-hsien’s directing, which is both specific to an original author and rooted in Taiwanese history and tradition. We have read the same texts (those of François Chen, Noel Burch, Michel Chion and Yeh Yueh...

  14. Using an Integrated Group Decision Method Based on SVM, TFN-RS-AHP, and TOPSIS-CD for Cloud Service Supplier Selection

    Directory of Open Access Journals (Sweden)

    Lian-hui Li

    2017-01-01

    Full Text Available To solve the cloud service supplier selection problem under the background of cloud computing emergence, an integrated group decision method is proposed. The cloud service supplier selection index framework is built from two perspectives of technology and technology management. Support vector machine- (SVM- based classification model is applied for the preliminary screening to reduce the number of candidate suppliers. A triangular fuzzy number-rough sets-analytic hierarchy process (TFN-RS-AHP method is designed to calculate supplier’s index value by expert’s wisdom and experience. The index weight is determined by criteria importance through intercriteria correlation (CRITIC. The suppliers are evaluated by the improved TOPSIS replacing Euclidean distance with connection distance (TOPSIS-CD. An electric power enterprise’s case is given to illustrate the correctness and feasibility of the proposed method.

  15. Research on big data risk assessment of major transformer defects and faults fusing power grid, equipment and environment based on SVM

    Science.gov (United States)

    Guo, Lijuan; Yan, Haijun; Gao, Wensheng; Chen, Yun; Hao, Yongqi

    2018-01-01

    With the development of power big data, considering the wider power system data, the appropriate large data analysis method can be used to mine the potential law and value of power big data. On the basis of considering all kinds of monitoring data and defects and fault records of main transformer, the paper integrates the power grid, equipment as well as environment data and uses SVM as the main algorithm to evaluate the risk of the main transformer. It gets and compares the evaluation results under different modes, and proves that the risk assessment algorithms and schemes have certain effectiveness. This paper provides a new idea for data fusion of smart grid, and provides a reference for further big data evaluation of power grid equipment.

  16. A two-dimensional matrix image based feature extraction method for classification of sEMG: A comparative analysis based on SVM, KNN and RBF-NN.

    Science.gov (United States)

    Wen, Tingxi; Zhang, Zhongnan; Qiu, Ming; Zeng, Ming; Luo, Weizhen

    2017-01-01

    The computer mouse is an important human-computer interaction device. But patients with physical finger disability are unable to operate this device. Surface EMG (sEMG) can be monitored by electrodes on the skin surface and is a reflection of the neuromuscular activities. Therefore, we can control limbs auxiliary equipment by utilizing sEMG classification in order to help the physically disabled patients to operate the mouse. To develop a new a method to extract sEMG generated by finger motion and apply novel features to classify sEMG. A window-based data acquisition method was presented to extract signal samples from sEMG electordes. Afterwards, a two-dimensional matrix image based feature extraction method, which differs from the classical methods based on time domain or frequency domain, was employed to transform signal samples to feature maps used for classification. In the experiments, sEMG data samples produced by the index and middle fingers at the click of a mouse button were separately acquired. Then, characteristics of the samples were analyzed to generate a feature map for each sample. Finally, the machine learning classification algorithms (SVM, KNN, RBF-NN) were employed to classify these feature maps on a GPU. The study demonstrated that all classifiers can identify and classify sEMG samples effectively. In particular, the accuracy of the SVM classifier reached up to 100%. The signal separation method is a convenient, efficient and quick method, which can effectively extract the sEMG samples produced by fingers. In addition, unlike the classical methods, the new method enables to extract features by enlarging sample signals' energy appropriately. The classical machine learning classifiers all performed well by using these features.

  17. A Non-Destructive Method for Distinguishing Reindeer Antler (Rangifer tarandus from Red Deer Antler (Cervus elaphus Using X-Ray Micro-Tomography Coupled with SVM Classifiers.

    Directory of Open Access Journals (Sweden)

    Alexandre Lefebvre

    Full Text Available Over the last decade, biomedical 3D-imaging tools have gained widespread use in the analysis of prehistoric bone artefacts. While initial attempts to characterise the major categories used in osseous industry (i.e. bone, antler, and dentine/ivory have been successful, the taxonomic determination of prehistoric artefacts remains to be investigated. The distinction between reindeer and red deer antler can be challenging, particularly in cases of anthropic and/or taphonomic modifications. In addition to the range of destructive physicochemical identification methods available (mass spectrometry, isotopic ratio, and DNA analysis, X-ray micro-tomography (micro-CT provides convincing non-destructive 3D images and analyses. This paper presents the experimental protocol (sample scans, image processing, and statistical analysis we have developed in order to identify modern and archaeological antler collections (from Isturitz, France. This original method is based on bone microstructure analysis combined with advanced statistical support vector machine (SVM classifiers. A combination of six microarchitecture biomarkers (bone volume fraction, trabecular number, trabecular separation, trabecular thickness, trabecular bone pattern factor, and structure model index were screened using micro-CT in order to characterise internal alveolar structure. Overall, reindeer alveoli presented a tighter mesh than red deer alveoli, and statistical analysis allowed us to distinguish archaeological antler by species with an accuracy of 96%, regardless of anatomical location on the antler. In conclusion, micro-CT combined with SVM classifiers proves to be a promising additional non-destructive method for antler identification, suitable for archaeological artefacts whose degree of human modification and cultural heritage or scientific value has previously made it impossible (tools, ornaments, etc..

  18. THE APPLICATION OF SUPPORT VECTOR MACHINE (SVM USING CIELAB COLOR MODEL, COLOR INTENSITY AND COLOR CONSTANCY AS FEATURES FOR ORTHO IMAGE CLASSIFICATION OF BENTHIC HABITATS IN HINATUAN, SURIGAO DEL SUR, PHILIPPINES

    Directory of Open Access Journals (Sweden)

    J. E. Cubillas

    2016-06-01

    Full Text Available This study demonstrates the application of CIELAB, Color intensity, and One Dimensional Scalar Constancy as features for image recognition and classifying benthic habitats in an image with the coastal areas of Hinatuan, Surigao Del Sur, Philippines as the study area. The study area is composed of four datasets, namely: (a Blk66L005, (b Blk66L021, (c Blk66L024, and (d Blk66L0114. SVM optimization was performed in Matlab® software with the help of Parallel Computing Toolbox to hasten the SVM computing speed. The image used for collecting samples for SVM procedure was Blk66L0114 in which a total of 134,516 sample objects of mangrove, possible coral existence with rocks, sand, sea, fish pens and sea grasses were collected and processed. The collected samples were then used as training sets for the supervised learning algorithm and for the creation of class definitions. The learned hyper-planes separating one class from another in the multi-dimensional feature space can be thought of as a super feature which will then be used in developing the C (classifier rule set in eCognition® software. The classification results of the sampling site yielded an accuracy of 98.85% which confirms the reliability of remote sensing techniques and analysis employed to orthophotos like the CIELAB, Color Intensity and One dimensional scalar constancy and the use of SVM classification algorithm in classifying benthic habitats.

  19. Chinese Commission of Science Technology and Industry for National Defense Senior Vice Minister CHEN Qiufa visiting ALICE experiment on 1st November 2007 with CERN Director-General R. Aymar and Adviser J.-P. Revol. Thursday, 1st and Friday, 2nd November 2007

    CERN Multimedia

    Maximilien Brice

    2007-01-01

    Chinese Commission of Science Technology and Industry for National Defense Senior Vice Minister CHEN Qiufa visiting ALICE experiment on 1st November 2007 with CERN Director-General R. Aymar and Adviser J.-P. Revol. Thursday, 1st and Friday, 2nd November 2007

  20. MARGA_Chen et al_2016

    Data.gov (United States)

    U.S. Environmental Protection Agency — These data describe the chromatography characteristics of the MARGA instrument software as compared to an alternative, independent technique for chromatogram...

  1. Immunogenicity of novel sulfadimethoxide conjugates | Chen ...

    African Journals Online (AJOL)

    SDM antibodies are useful for the detection of residual SDM in foods, feeds and biological fluids by ELISA. In this study, we show that SDM is immunogenic in rabbits when it is conjugated with soy 11S globulin or with β- amylase. Rabbit ...

  2. Reinigung superhydrophober Oberflächen

    OpenAIRE

    Dallmann, Silke

    2011-01-01

    The unique surface structure of the lotus leaf in combination with hydrophobic epicuticular wax crystalloids results in extreme water repellency and self-cleaning properties. In recent years biomimetic superhydrophobic surfaces have been fabricated by mimicking the structure of the lotus leaf. The biggest problem of the fine surface roughness is the sensitivity to oily contaminants and mechanical stress which limit the application of technical superhydrophobic sur...

  3. Blazar Sequence in Fermi Era Liang Chen

    Indian Academy of Sciences (India)

    HSP) blazars indicates that this simple analysis should be problematic. So does the discovery of numerous low-power-low-sychrotron-peaked (LP-LSP) blazars. If a blazar has a less beaming jet, its synchrotron bump should peak at lower fre- quency and lower power. Therefore, beaming effect may account for HP-HSP and.

  4. H-DROP: an SVM based helical domain linker predictor trained with features optimized by combining random forest and stepwise selection.

    Science.gov (United States)

    Ebina, Teppei; Suzuki, Ryosuke; Tsuji, Ryotaro; Kuroda, Yutaka

    2014-08-01

    Domain linker prediction is attracting much interest as it can help identifying novel domains suitable for high throughput proteomics analysis. Here, we report H-DROP, an SVM-based Helical Domain linker pRediction using OPtimal features. H-DROP is, to the best of our knowledge, the first predictor for specifically and effectively identifying helical linkers. This was made possible first because a large training dataset became available from IS-Dom, and second because we selected a small number of optimal features from a huge number of potential ones. The training helical linker dataset, which included 261 helical linkers, was constructed by detecting helical residues at the boundary regions of two independent structural domains listed in our previously reported IS-Dom dataset. 45 optimal feature candidates were selected from 3,000 features by random forest, which were further reduced to 26 optimal features by stepwise selection. The prediction sensitivity and precision of H-DROP were 35.2 and 38.8%, respectively. These values were over 10.7% higher than those of control methods including our previously developed DROP, which is a coil linker predictor, and PPRODO, which is trained with un-differentiated domain boundary sequences. Overall, these results indicated that helical linkers can be predicted from sequence information alone by using a strictly curated training data set for helical linkers and carefully selected set of optimal features. H-DROP is available at http://domserv.lab.tuat.ac.jp.

  5. The role of the continuous wavelet transform in mineral identification using hyperspectral imaging in the long-wave infrared by using SVM classifier

    Science.gov (United States)

    Sojasi, Saeed; Yousefi, Bardia; Liaigre, Kévin; Ibarra-Castanedo, Clemente; Beaudoin, Georges; Maldague, Xavier P. V.; Huot, François; Chamberland, Martin

    2017-05-01

    Hyperspectral imaging (HSI) in the long-wave infrared spectrum (LWIR) provides spectral and spatial information concerning the emissivity of the surface of materials, which can be used for mineral identification. For this, an endmember, which is the purest form of a mineral, is used as reference. All pure minerals have specific spectral profiles in the electromagnetic wavelength, which can be thought of as the mineral's fingerprint. The main goal of this paper is the identification of minerals by LWIR hyperspectral imaging using a machine learning scheme. The information of hyperspectral imaging has been recorded from the energy emitted from the mineral's surface. Solar energy is the source of energy in remote sensing, while a heating element is the energy source employed in laboratory experiments. Our work contains three main steps where the first step involves obtaining the spectral signatures of pure (single) minerals with a hyperspectral camera, in the long-wave infrared (7.7 to 11.8 μm), which measures the emitted radiance from the minerals' surface. The second step concerns feature extraction by applying the continuous wavelet transform (CWT) and finally we use support vector machine classifier with radial basis functions (SVM-RBF) for classification/identification of minerals. The overall accuracy of classification in our work is 90.23+/- 2.66%. In conclusion, based on CWT's ability to capture the information of signals can be used as a good marker for classification and identification the minerals substance.

  6. Determining the Relationship between U.S. County-Level Adult Obesity Rate and Multiple Risk Factors by PLS Regression and SVM Modeling Approaches

    Directory of Open Access Journals (Sweden)

    Chau-Kuang Chen

    2015-02-01

    Full Text Available Data from the Center for Disease Control (CDC has shown that the obesity rate doubled among adults within the past two decades. This upsurge was the result of changes in human behavior and environment. Partial least squares (PLS regression and support vector machine (SVM models were conducted to determine the relationship between U.S. county-level adult obesity rate and multiple risk factors. The outcome variable was the adult obesity rate. The 23 risk factors were categorized into four domains of the social ecological model including biological/behavioral factor, socioeconomic status, food environment, and physical environment. Of the 23 risk factors related to adult obesity, the top eight significant risk factors with high normalized importance were identified including physical inactivity, natural amenity, percent of households receiving SNAP benefits, and percent of all restaurants being fast food. The study results were consistent with those in the literature. The study showed that adult obesity rate was influenced by biological/behavioral factor, socioeconomic status, food environment, and physical environment embedded in the social ecological theory. By analyzing multiple risk factors of obesity in the communities, may lead to the proposal of more comprehensive and integrated policies and intervention programs to solve the population-based problem.

  7. 羅振玉日本教育考察與晚清學制制定的關係 Chen-Yu Lo’ Educational Visit to Japan and the Drafting of School Systems in Late Ch’ing

    Directory of Open Access Journals (Sweden)

    周愚文 Yu-Wen Chou

    2015-03-01

    Full Text Available 羅振玉早年關心農業與教育,曾辦東文學社教日語,出版《農報》,譯農書,故受張之洞聘請,掌湖北農務局及農學堂。1901年辛丑詔改各省書院為學堂,獲張之洞與劉坤一共薦率員考察日本教育兩個月。先後訪談重要政教人物,參訪東京、京都、奈良等地學校24所,蒐購中小學教科書及理化器材。因時短、行程緊湊及語言限制,未讀前人考察紀錄,自認收穫有限。返國後,他曾獲張之洞五次接見,並命向鄂官員講說教育。又代張之洞擬教育制度草案欲與劉督會奏,但因劉屬官反對遂寢。他遂出版訪日紀錄及資料,並發表〈學制私議〉等文建議興學,部分被鄂參採。當朝廷擬訂壬寅及癸卯學制時,羅未參與而資料只供參考,但章程規定中小學堂學科八科及小學堂以學國語為主等二項,可能與其建議有關。隨行陳毅曾譯日教育法規並兩度遊日,起草奏定章程時,當不會只憑文獻閉門造車,但主政者乏直接經驗,故教育借用日制時難盡合國情。 Chen-Yu Lo had concern about agriculture and education. He was invited by Governor-General Chih-tung Chang to preside over the Agriculture Department of Hubei and its Agriculture School. In 1901, ordered by Chang and Governor-General Kun-I Liu, he led a group of delegates to visit educational institutions in Japan. Within two months, he discussed with influential politicians and educators, visited 24 schools in Tokyo, Kyoto, and Nara, and purchased elementary and secondary school textbooks and laboratory equipments. When he returned, he had five meetings with Chang, and lectured on education for Hubei officials. On behalf of Chang, Lo had drafted a new educational system, and it was to be presented to the throne by Chang and Liu, however, the proposal was withdrawn due to the objections of Liu’s subordinates. Later, Lo made the collected

  8. An SVM-Based Classifier for Estimating the State of Various Rotating Components in Agro-Industrial Machinery with a Vibration Signal Acquired from a Single Point on the Machine Chassis

    Directory of Open Access Journals (Sweden)

    Ruben Ruiz-Gonzalez

    2014-11-01

    Full Text Available The goal of this article is to assess the feasibility of estimating the state of various rotating components in agro-industrial machinery by employing just one vibration signal acquired from a single point on the machine chassis. To do so, a Support Vector Machine (SVM-based system is employed. Experimental tests evaluated this system by acquiring vibration data from a single point of an agricultural harvester, while varying several of its working conditions. The whole process included two major steps. Initially, the vibration data were preprocessed through twelve feature extraction algorithms, after which the Exhaustive Search method selected the most suitable features. Secondly, the SVM-based system accuracy was evaluated by using Leave-One-Out cross-validation, with the selected features as the input data. The results of this study provide evidence that (i accurate estimation of the status of various rotating components in agro-industrial machinery is possible by processing the vibration signal acquired from a single point on the machine structure; (ii the vibration signal can be acquired with a uniaxial accelerometer, the orientation of which does not significantly affect the classification accuracy; and, (iii when using an SVM classifier, an 85% mean cross-validation accuracy can be reached, which only requires a maximum of seven features as its input, and no significant improvements are noted between the use of either nonlinear or linear kernels.

  9. A Novel and Practical Chromatographic "Fingerprint-ROC-SVM" Strategy Applied to Quality Analysis of Traditional Chinese Medicine Injections: Using KuDieZi Injection as a Case Study.

    Science.gov (United States)

    Yang, Bin; Wang, Yuan; Shan, Lanlan; Zou, Jingtao; Wu, Yuanyuan; Yang, Feifan; Zhang, Yani; Li, Yubo; Zhang, Yanjun

    2017-07-23

    Fingerprinting is widely and commonly used in the quality control of traditional Chinese medicine (TCM) injections. However, current studies informed that the fingerprint similarity evaluation was less sensitive and easily generated false positive results. For this reason, a novel and practical chromatographic "Fingerprint-ROC-SVM" strategy was established by using KuDieZi (KDZ) injection as a case study in the present article. Firstly, the chromatographic fingerprints of KDZ injection were obtained by UPLC and the common characteristic peaks were identified with UPLC/Q-TOF-MS under the same chromatographic conditions. Then, the receiver operating characteristic (ROC) curve was used to optimize common characteristic peaks by the AUCs value greater than 0.7. Finally, a support vector machine (SVM) model, with the accuracy of 97.06%, was established by the optimized characteristic peaks and applied to monitor the quality of KDZ injection. As a result, the established model could sensitively and accurately distinguish the qualified products (QPs) with the unqualified products (UPs), high-temperature processed samples (HTPs) and high-illumination processed samples (HIPs) of KDZ injection, and the prediction accuracy was 100.00%, 93.75% and 100.00%, respectively. Furthermore, through the comparison with other chemometrics methods, the superiority of the novel analytical strategy was more prominent. It indicated that the novel and practical chromatographic "Fingerprint-ROC-SVM" strategy could be further applied to facilitate the development of the quality analysis of TCM injections.

  10. MAPPING OF HIGH VALUE CROPS THROUGH AN OBJECT-BASED SVM MODEL USING LIDAR DATA AND ORTHOPHOTO IN AGUSAN DEL NORTE PHILIPPINES

    Directory of Open Access Journals (Sweden)

    R. J. Candare

    2016-06-01

    Full Text Available This research describes the methods involved in the mapping of different high value crops in Agusan del Norte Philippines using LiDAR. This project is part of the Phil-LiDAR 2 Program which aims to conduct a nationwide resource assessment using LiDAR. Because of the high resolution data involved, the methodology described here utilizes object-based image analysis and the use of optimal features from LiDAR data and Orthophoto. Object-based classification was primarily done by developing rule-sets in eCognition. Several features from the LiDAR data and Orthophotos were used in the development of rule-sets for classification. Generally, classes of objects can't be separated by simple thresholds from different features making it difficult to develop a rule-set. To resolve this problem, the image-objects were subjected to Support Vector Machine learning. SVMs have gained popularity because of their ability to generalize well given a limited number of training samples. However, SVMs also suffer from parameter assignment issues that can significantly affect the classification results. More specifically, the regularization parameter C in linear SVM has to be optimized through cross validation to increase the overall accuracy. After performing the segmentation in eCognition, the optimization procedure as well as the extraction of the equations of the hyper-planes was done in Matlab. The learned hyper-planes separating one class from another in the multi-dimensional feature-space can be thought of as super-features which were then used in developing the classifier rule set in eCognition. In this study, we report an overall classification accuracy of greater than 90% in different areas.

  11. An S-Transform and Support Vector Machine (SVM-Based Online Method for Diagnosing Broken Strands in Transmission Lines

    Directory of Open Access Journals (Sweden)

    Caxin Sun

    2011-08-01

    Full Text Available During their long-term outdoor field service, overhead transmission lines will be exposed to strikes by lightning, corrosion by chemical contaminants, ice-shedding, wind vibration of conductors, line galloping, external destructive forces and so on, which will generally cause a series of latent faults such as aluminum strand fracture. This may lead to broken transmission lines which will have a very strong impact on the safe operation of power grids that if the latent faults cannot be recognized and fixed as soon as possible. The detection of broken strands in transmission lines using inspection robots equipped with suitable detectors is a method with good prospects. In this paper, a method for detecting broken strands in transmission lines using an eddy current transducer (ECT carried by a robot is developed, and an approach for identifying broken strands in transmission lines based on an S-transform is proposed. The proposed approach utilizes the S-transform to extract the module and phase information at each frequency point from detection signals. Through module phase and comparison, the characteristic frequency points are ascertained, and the fault information of the detection signal is constructed. The degree of confidence of broken strand identification is defined by the Shannon fuzzy entropy (SFE-BSICD. The proposed approach combines module information while utilizing phase information, SFE-BSICD, and the energy, so the reliability is greatly improved. These characteristic qualities of broken strands in transmission lines are used as the input of a multi-classification SVM, allowing the number of broken strands to be determined. Through experimental field verification, it can be shown that the proposed approach displays high accuracy and the SFE-BSICD is defined reasonably.

  12. In-situ arsenic remediation by aquifer iron coating: Field trial in the Datong basin, China

    Energy Technology Data Exchange (ETDEWEB)

    Xie, Xianjun; Pi, Kunfu; Liu, Yaqing; Liu, Chongxuan; Li, Junxia; Zhu, Yapeng; Su, Chunli; Ma, Teng; Wang, Yanxin

    2016-01-01

    In situ As removal from groundwater used for water supply has been performed in Daying village of Shanyin County where mild alkaline groundwater contains high dissolved As concentration. The objective of this study was to evaluate in situ As treatment by aquifer Fe coating technology. The groundwater in the studied aquifer contains As dominated by aqueous As(III) and low dissolved Fe(II) concentration, which are unfavorable conditions for forming Fe-oxides/hydroxides for As removal. In addition, high As(III) concentration limits As adsorption onto Fe-oxides/hydroxides. Accordingly, dissolved Fe(II) (5mM) and NaClO (5mM) were injected into the studied aquifer to form Fe-oxides/hydroxides and oxidize As(III) to As(V), creating favorable conditions for As removal via adsorption and/or co-precipitation. During alternatively cycled injection of Fe(II) and NaClO, the As concentration in groundwater from the pumping well significantly decreased to below drinking water standard. The developed approach can be applied similarly in many parts of the world containing high As concentrations.

  13. Aplicación de las Máquinas de Soporte Vectorial (SVM al diagnóstico clínico de la Enfermedad de Párkinson y el Temblor Esencial

    Directory of Open Access Journals (Sweden)

    Roberto González

    2017-10-01

    Full Text Available Resumen: Los enfermos de Párkinson (EP y de temblor esencial (TE suponen un porcentaje importante de la casuística clínica en los trastornos del movimiento, que impiden a los sujetos afectados el llevar una vida normal, produciendo discapacidad física y una no menos importante exclusión social en muchos de los casos. Las vías de tratamiento son dispares, de ahí que sea crítico acertar con precisión en el diagnóstico en las etapas iniciales de la enfermedad. Hasta la actualidad, los profesionales y expertos en medicina, utilizan unas escalas cualitativas para diferenciar la patología y su grado de afectación. Dichas escalas también se utilizan para efectuar un seguimiento clínico y registrar la historia del paciente. En este trabajo se propone la utilización de clasificadores binarios centrados en las Máquinas de Soporte Vectorial (SVM para obtener un diagnóstico diferencial entre las dos patologías de temblor mencionadas. Abstract: Parkinson's Disease (PD and Essential Tremor (ET patients represent a significant percentage of the clinical cases in movement disorders pathologies, which prevents to the affected people leading a normal life. A physical disability results and important social exclusion in many cases are produced. The treatment methods are very differents, so it is critical hitting with the diagnosis in the early stages of these diseases. Until today, professionals and experts in medicine, use qualitative scales to differentiate pathology cases and its level of affectation. These scales are used to follow up clinically and register the patient's history. This work proposes the use of binary classifiers focused on the Vector Support Machines (SVM to obtain a differential diagnosis between the essential tremor and Parkinson's disease. Palabras clave: Ayuda al diagnóstico, clasificadores binarios, clasificación Párkinson-Esencial, medida objetiva del temblor, análisis de patrones, extracción de caracter

  14. Application of PQR Theory for control of a 3-phase 4-wire 4-legs shunt active power filter in the aß?-axes using 3D-SVM technique

    Directory of Open Access Journals (Sweden)

    Ali CHEBABHI

    2015-05-01

    Full Text Available This article discusses and compares two control strategies applied to a 3-phase 4-wire 4-leg shunt active power filter. These two control strategies, including the cross-vector theory called CV theory and the direct method called PQR theory, are based on the instantaneous control of active and reactive power. On one hand, it is shown that, in some cases, cross-vector theory requires elimination of the zero sequence currents in a 3-phase 4-wire 4-leg shunt active power filter, which needs a power storage element, and on the other hand pretreatment system voltage is necessary to obtain compensated sinusoidal current and a degree of freedom. By relying on the cross-vector theory, the PQR theory is used to extract and remove harmonic currents components. In this control technique, there are two internal current control loops and an external voltage control loop, these control loops have been realized by PI controllers when applied 3D-SVM of switching technique. We choose as criteria for comparison the transient and the Total Harmonic Distortion in the line current. A series of simulations in MATLAB/ Simulink environment have been presented and discussed to show the performance of the two control strategies.

  15. Analysis and Modeling for Short- to Medium-Term Load Forecasting Using a Hybrid Manifold Learning Principal Component Model and Comparison with Classical Statistical Models (SARIMAX, Exponential Smoothing and Artificial Intelligence Models (ANN, SVM: The Case of Greek Electricity Market

    Directory of Open Access Journals (Sweden)

    George P. Papaioannou

    2016-08-01

    Full Text Available In this work we propose a new hybrid model, a combination of the manifold learning Principal Components (PC technique and the traditional multiple regression (PC-regression, for short and medium-term forecasting of daily, aggregated, day-ahead, electricity system-wide load in the Greek Electricity Market for the period 2004–2014. PC-regression is shown to effectively capture the intraday, intraweek and annual patterns of load. We compare our model with a number of classical statistical approaches (Holt-Winters exponential smoothing of its generalizations Error-Trend-Seasonal, ETS models, the Seasonal Autoregressive Moving Average with exogenous variables, Seasonal Autoregressive Integrated Moving Average with eXogenous (SARIMAX model as well as with the more sophisticated artificial intelligence models, Artificial Neural Networks (ANN and Support Vector Machines (SVM. Using a number of criteria for measuring the quality of the generated in-and out-of-sample forecasts, we have concluded that the forecasts of our hybrid model outperforms the ones generated by the other model, with the SARMAX model being the next best performing approach, giving comparable results. Our approach contributes to studies aimed at providing more accurate and reliable load forecasting, prerequisites for an efficient management of modern power systems.

  16. Antioxidant activities of seed extracts from Dalbergia odorifera T. Chen

    African Journals Online (AJOL)

    DR. NJ TONUKARI

    2011-09-21

    Sep 21, 2011 ... luxurious furniture and crafts, but also used in the pharmaceutical industry. The dried heartwood or root of the plant is an important traditional Chinese medicine ..... concentrations (Brand et al., 1995). However, the PE and BE did not show a leveling off with increasing concentration. As a result, the radical.

  17. Mitochondrial genome of Taiwan pig ( Sus Scrofa ) | Chen | African ...

    African Journals Online (AJOL)

    The purpose of this study is to investigate the complete nucleotide sequence of the mitochondrial genome of the Taiwan Lanyu pig (Sus scrofa) and its phylogenetic relationships with other pig breeds. Thirty-four forward and reverse primers were designed. Sequencing was performed in both directions. The results showed ...

  18. Food Fortification to Prevent and Control Iron Deficiency | Chen ...

    African Journals Online (AJOL)

    African Journal of Food, Agriculture, Nutrition and Development ... of increasing the level of iron consumption resulting in improved nutritional status. ... followed by a description of ways of protecting and enhancing the absorption of fortification ...

  19. Massive Star Formation: Accreting from Companion X. Chen1 ...

    Indian Academy of Sciences (India)

    system composed of a diffuse object (possible nebulae or UC HII region) and a Massive Young Stellar Object (MYSO) seen in Spitzer IRAC image. The diffuse object and MYSO are connected by the ... Infrared: ISM—stars: formation—ISM: jets and outflows. 1. Introduction. The dynamics processes in Massive Star Formation ...

  20. Food Fortification to Prevent and Control Iron Deficiency | Chen ...

    African Journals Online (AJOL)

    It is essential to prevent the fortification iron from reacting with the absorption inhibitors. To ensure adequate absorption therefore, various factors must be considered before initiating a fortification programme. These include cost effectiveness of fortification in increasing absorbable iron, palatability of the fortified food and the ...

  1. C. Huang , G. Zhao , HW Zhang & YQ Chen

    Indian Academy of Sciences (India)

    to get near to their central stars (Udry et al. 2003). The facts imply that maybe the. [Si/Fe]-high cloud would not be easy to form massive planets. The extrasolar planet systems' discoveries may be influenced by the selection effects. However, most of the. [Si/Fe]-rich planet hosts gather inside 1 AU of semimajor and their ...

  2. Initiation and Propagation of Coronal Mass Ejections P. F. Chen

    Indian Academy of Sciences (India)

    , which is generated at the tachocline layer, emerges throughout the convection zone and the lower atmosphere into the tenuous corona. The coronal field keeps adjusting to a more and more complex magnetic structure in a quasi-steady way.

  3. Dynamics of fractional-ordered Chen system with delay

    Indian Academy of Sciences (India)

    of fractional calculus to various branches of science and engineering have been real- ized only recently. It is becoming more and more clear that derivatives and integrals of non-integer orders are not mere mathematical curiosities but many processes, such as dielectric polarization [1], diffusion [2–5], viscoelastic systems [6] ...

  4. Research of synthetic mechanized mining technical on the conditions of Datong's 'two-hard' and large dig angle coal-bed

    Energy Technology Data Exchange (ETDEWEB)

    Li Ming; Ji Yang-rui [Datong Mine Group Co. Ltd, Datong (China). Technology Centre

    2005-05-15

    The paper introduced the key technical measures such as forced caving the roof, loose coal, roll steering working face, anti-toppling and anti-skid, etc, under the special mining condition - 'two hard' large dig angle, for 8104 working face of Jinghuagong mine and realised safe mining, high production and low consumption. The maximum daily output of this working face can reach to 5300 t, peak efficiency 42.9 t/man, and recovery rate 97%. The efficiency and output of this working face are about 1.54 times and 1.83 times respectively of those of the adjacent one; the direct cost reduced by 48%, compared with the adjacent one. 5 refs., 1 fig.

  5. SVM detection of epileptiform activity in routine EEG.

    LENUS (Irish Health Repository)

    Kelleher, Daniel

    2010-01-01

    Routine electroencephalogram (EEG) is an important test in aiding the diagnosis of patients with suspected epilepsy. These recordings typically last 20-40 minutes, during which signs of abnormal activity (spikes, sharp waves) are looked for in the EEG trace. It is essential that events of short duration are detected during the routine EEG test. The work presented in this paper examines the effect of changing a range of input values to the detection system on its ability to distinguish between normal and abnormal EEG activity. It is shown that the length of analysis window in the range of 0.5s to 1s are well suited to the task. Additionally, it is reported that patient specific systems should be used where possible due to their better performance.

  6. Budget Online Learning Algorithm for Least Squares SVM.

    Science.gov (United States)

    Jian, Ling; Shen, Shuqian; Li, Jundong; Liang, Xijun; Li, Lei

    2017-09-01

    Batch-mode least squares support vector machine (LSSVM) is often associated with unbounded number of support vectors (SVs'), making it unsuitable for applications involving large-scale streaming data. Limited-scale LSSVM, which allows efficient updating, seems to be a good solution to tackle this issue. In this paper, to train the limited-scale LSSVM dynamically, we present a budget online LSSVM (BOLSSVM) algorithm. Methodologically, by setting a fixed budget for SVs', we are able to update the LSSVM model according to the updated SVs' set dynamically without retraining from scratch. In particular, when a new small chunk of SVs' substitute for the old ones, the proposed algorithm employs a low rank correction technology and the Sherman-Morrison-Woodbury formula to compute the inverse of saddle point matrix derived from the LSSVM's Karush-Kuhn-Tucker (KKT) system, which, in turn, updates the LSSVM model efficiently. In this way, the proposed BOLSSVM algorithm is especially useful for online prediction tasks. Another merit of the proposed BOLSSVM is that it can be used for k -fold cross validation. Specifically, compared with batch-mode learning methods, the computational complexity of the proposed BOLSSVM method is significantly reduced from O(n4) to O(n3) for leave-one-out cross validation with n training samples. The experimental results of classification and regression on benchmark data sets and real-world applications show the validity and effectiveness of the proposed BOLSSVM algorithm.

  7. PD-SVM Integrated Controller for Robotic Manipulator Tracking Control

    Directory of Open Access Journals (Sweden)

    Neha Kapoor

    2014-01-01

    Full Text Available Highly precise tracking of a robotic manipulator in presence of uncertainties like noise, disturbances, and friction has been addressed in this particular paper. An integrated proportional derivative and support vector machine (SVMPD controller has been proposed for manipulator tracking. To illustrate the efficiency of the proposed controller, simulations have been done on a 2-DOF manipulator system. Performance of the proposed controller has been checked and verified with respect to to a simple PID controller and the radial bias neural network proportional integral derivative (RBNNPD controller. It has been proved that the proposed controller can achieve better tracking performance as compared to other controllers as the range of errors is less and the time taken by the controller has reduced up to 14 times as compared to RBNN.

  8. The efficacy of support vector machines (SVM) in robust ...

    Indian Academy of Sciences (India)

    determination of earthquake early warning magnitudes in central Japan. Ramakrushna Reddy ... This work deals with a methodology applied to seismic early warning systems which are designed to provide real-time estimation of the ... S–P differential travel time for issuing an alert prior to damaging ground motion (Allen ...

  9. RBPPred: predicting RNA-binding proteins from sequence using SVM.

    Science.gov (United States)

    Zhang, Xiaoli; Liu, Shiyong

    2017-03-15

    Detection of RNA-binding proteins (RBPs) is essential since the RNA-binding proteins play critical roles in post-transcriptional regulation and have diverse roles in various biological processes. Moreover, identifying RBPs by computational prediction is much more efficient than experimental methods and may have guiding significance on the experiment design. In this study, we present the RBPPred (an RNA-binding protein predictor), a new method based on the support vector machine, to predict whether a protein binds RNAs, based on a comprehensive feature representation. By integrating the physicochemical properties with the evolutionary information of protein sequences, the new approach RBPPred performed much better than state-of-the-art methods. The results show that RBPPred correctly predicted 83% of 2780 RBPs and 96% out of 7093 non-RBPs with MCC of 0.808 using the 10-fold cross validation. Furthermore, we achieved a sensitivity of 84%, specificity of 97% and MCC of 0.788 on the testing set of human proteome. In addition we tested the capability of RBPPred to identify new RBPs, which further confirmed the practicability and predictability of the method. RBPPred program can be accessed at: http://rnabinding.com/RBPPred.html . liushiyong@gmail.com. Supplementary data are available at Bioinformatics online.

  10. Quadratic divergence regularized SVM for optic disc segmentation.

    Science.gov (United States)

    Cheng, Jun; Tao, Dacheng; Wong, Damon Wing Kee; Liu, Jiang

    2017-05-01

    Machine learning has been used in many retinal image processing applications such as optic disc segmentation. It assumes that the training and testing data sets have the same feature distribution. However, retinal images are often collected under different conditions and may have different feature distributions. Therefore, the models trained from one data set may not work well for another data set. However, it is often too expensive and time consuming to label the needed training data and rebuild the models for all different data sets. In this paper, we propose a novel quadratic divergence regularized support vector machine (QDSVM) to transfer the knowledge from domains with sufficient training data to domains with limited or even no training data. The proposed method simultaneously minimizes the distribution difference between the source domain and target domain while training the classifier. Experimental results show that the proposed transfer learning based method reduces the classification error in superpixel level from 14.2% without transfer learning to 2.4% with transfer learning. The proposed method is effective to transfer the label knowledge from source to target domain, which enables it to be used for optic disc segmentation in data sets with different feature distributions.

  11. A novel stepwise support vector machine (SVM) method based on ...

    African Journals Online (AJOL)

    MicroRNAs (miRNAs) are a class of non-coding RNAs that are produced from miRNA precursors (premiRNAs) with stem-loop structure. At present, development of computational approach for pre-miRNA identification continues to be a challenging task, in which feature selection is greatly important. Here, we first extracted ...

  12. Gabor filters and SVM classifier for pattern wafer segmentation

    Science.gov (United States)

    Bourgeat, Pierrick T.; Meriaudeau, Fabrice; Gorria, Patrick; Tobin, Kenneth W.

    2004-11-01

    In the last decade, the accessibility of inexpensive and powerful computers has allowed true digital holography to be used for industrial inspection using microscopy. This technique allows capturing a complex image of a scene (i.e. containing magnitude and phase), and reconstructing the phase and magnitude information. Digital holograms give a new dimension to texture analysis since the topology information can be used as an additional way to extract features. This new technique can be used to extend previous work on image segmentation of patterned wafers for defect detection. This paper presents a comparison between the features obtained using Gabor filtering on complex images under illumination and focus variations.

  13. On non-Frattini chief factors and solvability of finite groups

    Indian Academy of Sciences (India)

    School of Mathematics and Statistics, Southwest University, Chongqing 400715, People's Republic of China; Department of Mathematics, Shanghai University, Shanghai 200444, People's Republic of China; Department of Mathematics, Shanxi Datong University, Datong, Shanxi 037009, People's Republic of China ...

  14. Chen et al., Afr J Tradit Complement Altern Med. (2014) 11(1):15-22

    African Journals Online (AJOL)

    cadewumi

    x300mm,Japan ) column. The columns were eluted with 0.1M nitric acid and calibrated with ..... Mushiake, H, Tsunoda, T, Nukatsuka,M, Shimao K, Fukushima M, Tahara H. (2005). Dendritic cells might be one of key factors for eliciting antitumor ...

  15. Chen et al., Afr J Tradit Complement Altern Med. (2014) 11(1):15-22

    African Journals Online (AJOL)

    cadewumi

    1Medical School of Taizhou University, Taizhou, Zhejiang 318000, China, 2College of .... 3 ml of ice-cold staining buffer was then added with the incubation for 5 min at 4℃ followed by 200 ..... Timeline: Chemotherapy and the war on cancer.

  16. Modellierung von Oberflächen mit Diskontinuitäten

    Science.gov (United States)

    Borkowski, Andrzej

    The laser scanning provides very dense information about the surface to be modelled in the form of a irregularly distributed points cloud {x, y, z} from R3. Such dense information makes possible an efficient modelling of characteristic structures of the terrain surface like discontinuities, which are necessary for high-qualitative description of the surface. Simultaneously, the points not belonging to the modelled surface (for example: reflexes from buildings, trees etc.) stand a very important influence on the obtained data. During the modelling process, such data should be effectively filtered from the whole data set. The laser scanning data can be efficiently elaborated by the use of deformable models of curves and surfaces. These models base on the physical principle of energy-minimizing and are presented as the solution of variational problem. The total energy consists both of internal and external energy. The external energy, depending on a context is generated by the data; in most cases it describes a deviation between the data and a model. The algorithms and approaches developed in this work have been tested on real data sets obtained by a laser scanning. Furthermore, a qualitative consideration of a modelling has been given. Finally, some hints for user according the steering and operating of the approaches have been presented.

  17. Reaktionen gesättigter Kohlenwasserstoffe in der Gasphase und an Oberflächen

    OpenAIRE

    Ryll, Thomas

    2015-01-01

    In der vorliegenden Arbeit konnte erstmals gezeigt werden, dass die Konvertierung von Methan zu Acetylen mit Ausbeuten von über 99 % und Selektivitäten größer als 200:1 mit dem Einsatz von „continious wave“ (cw) Mikrowellenplasmen großtechnisch möglich ist. Im Gegensatz zu vorangegangenen Arbeiten[1,3,5,6] konnte der Vorteil von cw Mikrowellenplasmen in Bezug auf Ausbeute und Selektivität herausgestellt werden. Die vielfach beschriebenen Probleme der Rußbildung[7,8] konnten mit dem Einsatz d...

  18. In search of low cost titanium: the fray farthing chen (FFC) cambridge process

    CSIR Research Space (South Africa)

    Oosthuizen, SJ

    2011-03-01

    Full Text Available . Topics explored include the history of the process, attempts at commercial- ization, NASA?s alternative application, and present status of the process. Keywords FFC Cambridge, titanium, molten salt. * Materials Science and Manufacturing, CSIR.... Oxygen production for rocket propulsion promises by far the greatest cost and mass saving of any off-world in situ resource utilization (ISRU). Since the 1960s most work on lunar resource utilization focused on the mineral ilmenite (FeTiO3...

  19. Untersuchung der Nährstoffauswaschung unter Schotterrasenflächen an einem inneralpinen Standort

    Science.gov (United States)

    Slawitsch, Veronika; Birk, Steffen; Pötsch, Erich M.

    2016-09-01

    Surface stabilisation by planted gravel turf is a reliable alternative to conventionally-paved parking lots. At the HBLFA Raumberg-Gumpenstein test site, biennial nutrient concentrations in seepage water and nutrient leachate fluxes were analysed using six gravity lysimeters. Two different materials (limestone with 10 % humus and recycled building material with 10 % compost) were investigated. Seepage water fluxes showed better infiltration for lime gravel materials in comparison to recycled building materials. The nutrient concentrations and leachate flux showed significantly higher values in the recycled building materials in both years of the experiment. Both materials showed a decrease in nutrient concentration and leaching from the first to the second year. The nitrate threshold concentration given by the drinking water directive (Trinkwasserverordnung BGBI. I Nr. 21/2001) was met for lime materials but was exceeded by a factor of three for recycled building materials within the first year. In terms of groundwater protection, the high risk of nitrate leaching in the first year has to be considered if using recycled building material.

  20. On the automatic link between affect and tendencies to approach and avoid : Chen and Bargh revisited

    NARCIS (Netherlands)

    Rotteveel, M.; Gierholz, A.; Koch, G.; van Aalst, C.; Pinto, Y.; Matzke, D.; Steingroever, H.; Verhagen, J.; Beek, T.F.; Selker, R.; Sasiadek, A.; Wagenmakers, E.-J.

    2015-01-01

    Within the literature on emotion and behavioral action, studies on approach-avoidance take up a prominent place. Several experimental paradigms feature successful conceptual replications but many original studies have not yet been replicated directly. We present such a direct replication attempt of

  1. Über Schwächen und Stärken des Völkerrechts

    OpenAIRE

    Kunig, Philip

    2015-01-01

    Zu beiden Fragen für das deutsche Rechtssystem Philip Kunig, Verfassungsrecht und einfaches Recht ― Verfassungsgerichtsbarkeit und Fachgerichtsbarkeit, in: Veröffentlichungen der Vereinigung der Deutschen Staatsrechtslehrer 61 (2001), 34

  2. In search of low cost titanium: the Fray Farthing Chen (FFC) Cambridge process

    CSIR Research Space (South Africa)

    Oosthuizen, SJ

    2010-10-01

    Full Text Available of aggressive/reactive chemicals to deliver “titanium sponge”, which requires capital and labor intensive product recovery, handling and processing. The batches of titanium sponge are produced over several days in steel vessels, delivering around 10 tons... in technology transfer, innovation management and investment rounds from seed to IPO. 2002 – 2003 COMMERCIAL DIRECTOR, HEAD PORTER, CAMBRIDGE, UK • Head Porter is a wireless technology start-up company founded by four students at the University...

  3. Polychlorinated biphenyl (PCB) congeners in Mussel and other mollusc from Da Chen Island, East China Sea

    Energy Technology Data Exchange (ETDEWEB)

    Chu, S.G.; Xi, Z.Q.; Xu, X.B [Research Center for Eco-Environmental Sciences, Academia Sinica, Beijing (China)

    1995-11-01

    Polychlorinated biphenyls (PCBs) are among the most persistent and toxic pollutants in environment. Determination of these contaminants in fish, shellfish and other mollusc is very important, not only because these aquatics are important food for mankind, but also because they can bioconcentrate contaminants preferentially in their adipose tissue, and serve as biomarker of the aquatic pollution. Mussels and oysters have been widely used to monitor the pollution in the coastal environment. The aim of the study was to investigate the concentrations and the main source of PCBs in mussels and other mollusca from the coastal areas of East China Sea. 10 refs., 3 figs., 2 tabs.

  4. Pemberian Suaka Diplomatik Kepada Tahanan Rumah Chen Guang Cheng Oleh Kedutaan Besar Amerika Serikat Di Beijing

    OpenAIRE

    Wulandari, Ayu Indah

    2017-01-01

    Asylum is the grant of protection granted by a State to an individual or more who requests it and the reason why the individual or individuals are protected is based on humanitarian, religious, racial, political, and so forth (refugee grounds; Ride on life) and Intervention is a form of state intervention on the affairs or sovereignty of another State. The purpose of this study is to Know and understand the suitability between the grant of Diplomatic Asylum conducted by the United States E...

  5. Verfahren zur Identifizierung von oberflächen-adhäsiven Peptidsequenzen

    OpenAIRE

    Grunwald, Ingo; Colombi Ciacchi, Lucio; Steckbeck, Sascha

    2014-01-01

    Characterizing surface adhesive properties of a peptide or protein by: providing a sample mixture containing two or more peptide fragments of a peptide or protein; determining the masses of the peptide fragments by a mass spectrometry and creating a reference peptide mass fingerprint; applying the sample mixture in an inorganic or organic substrate surface with respect to the surface adhesive properties; determining the masses of the peptide fragments not bound to the substrate surface and cr...

  6. VizieR Online Data Catalog: Sample of Fermi Blazars (Chen+, 2016)

    Science.gov (United States)

    Chen, Y.-Y.; Zhang, X.; Xiong, D.-R.; Wang, S.-J.; Yu, X.-L.

    2016-04-01

    We tried to select a large number of blazars with reliable redshift, radio core and extended radio luminosity at 1.4GHz. Firstly, we considered the following samples of blazars to get the radio core luminosity and extended luminosity at 1.4GHz: Kharb et al. (2010, J/ApJ/710/764), Antonucci & Ulvestad (1985ApJ...294..158A), Cassaro et al. (1999A&AS..139..601C), Murphy et al. (1993MNRAS.264..298M), Landt & Bignall (2008MNRAS.391..967L), Caccianiga & Marcha (2004, Cat. J/MNRAS/348/973), Giroletti et al. (2004). We cross-correlated these samples with the Fermi LAT Third Source Catalog (3FGL), and we acquired the 3FGL spectral index and energy flux at 0.1-100GeV from clean sources in 3FGL (Fermi-LAT Collaboration 2015, J/ApJS/218/23) Using these catalogs, we compiled 201 Fermi blazars. (1 data file).

  7. Chen's double sieve, Goldbach's conjecture and the twin prime problem, 2

    Science.gov (United States)

    Wu, J.

    For every even integer N, denote by D_{1,2}(N) the number of representations of N as a sum of a prime and an integer having at most two prime factors. In this paper, we give a new lower bound for D_{1,2}(N).

  8. AtCCX1 transports Na + and K + in Pitch pastoris | Chen | African ...

    African Journals Online (AJOL)

    Yeast expressing AtCCX1 grew better in high H+ concentration medium and high salt medium than original strain and increased Na+ accumulation and decreased K+ accumulation. AtCCX1 was located in tonoplast in yeast. Transport essays were indicated by fluorescence SBFI and PBFI, respectively. Results show that ...

  9. Microvariability Detection of Mrk 421 Xu Chen, Shao Ming Hu & Di ...

    Indian Academy of Sciences (India)

    School of Space Science and Physics, Shandong University, 180 Cultural West Road,. Weihai, Shandong 264209, China. ∗ e-mail: husm@sdu.edu.cn. Abstract. BL Lac ... but no significant microvariability was detected during our observations. Key words. AGN—HBL—Mrk 421—microvariability. 1. Introduction. The BL Lac ...

  10. Metabolic and histopathological profile of Rattus norvegicus (Wistar) experimentally infected by Angiostrongylus cantonensis (Chen, 1935).

    Science.gov (United States)

    Garcia, Juberlan Silva; Lúcio, Camila dos Santos; Bonfim, Tatiane Cristina dos Santos; Junior, Arnaldo Maldonado; Tunholi, Victor Menezes; Tunholi-Alves, Vinícius Menezes; Mota, Esther Maria; Simões, Raquel de Oliveira; Santana, André Campos; Hooper, Cleber; Pinheiro, Jairo; Bóia, Marcio Neves

    2014-02-01

    Eosinophilic meningitis is a disease characterized by increased eosinophils in the cerebrospinal fluid (CSF), which is the most commonly caused by invasion of the central nervous system by helminths, as occurs in Angiostrongylus cantonensis infections. The rodent Rattus norvegicus is the definitive natural host and humans act as accidental hosts and can become infected by eating raw or undercooked snails or food contaminated with infective L3 larvae. Recently in Brazil there have been four cases of eosinophilic meningitis due to ingestion of infected Achatina fulica. To evaluate biochemical and histopathological changes caused by this parasite, R. norvegicus were experimentally infected with 100 L3 larvae of A. cantonensis. After the anesthetic procedure, serum from the rodents was collected from the inferior vena cava for evaluation of the levels of aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALKP), gamma-glutamyl transferase (GGT), total protein and its fractions. During the necropsy, the liver was collected and weighed. Then a 1-g fragment was extracted from the major lobe to quantify the hepatic glycogen and fragment remainder was taken from the same lobe and fixed in Milloning's formalin for histopathological examination. Additionally, helminths were collected from the brain and lungs of the rodents. The activities of AST, ALT, ALKP and GGT in the serum and hepatic glycogen increased in response to infection, while the levels of globulin and total protein increased only in the eighth week of infection and there was a reduction in the levels of serum glucose. Albumin and bilirubin concentrations remained stable during the experiment. Infection with A. cantonensis caused metabolic and histopathological changes in the rodents. This study can contribute to a better understanding of the relationship between A. cantonensis and R. norvegicus. Copyright © 2013 Elsevier Inc. All rights reserved.

  11. VizieR Online Data Catalog: Red giant star sample from SDSS (Chen+, 2014)

    Science.gov (United States)

    Chen, Y. Q.; Zhao, G.; Carrell, K.; Zhao, J. K.; Tan, K. F.; Nissen, P. E.; Wei, P.

    2017-05-01

    Based on the SDSS DR9 database, we selected stars with (g-r)0 color in the range of 0.1 to 1.0 mag, and log g less than 3.5 dex for each metallicity bin ranging from [Fe/H]AJ....136.2022L, 2008, J/AJ/136/2050, 2011, J/AJ/141/90; Allende Prieto et al. 2008AJ....136.2070A; Smolinski et al. 2011, J/AJ/141/89). As described in Ahn et al. (2012ApJS..203...21A), the updated SSPP adopts a much-improved color-temperature relation from the InfraRed Flux Method Casagrande et al. (2010, J/A+A/512/A54), and the estimates of surface gravity and metallicity have been thoroughly recalibrated based on results from high-resolution observations. An estimate of the internal uncertainties of the SSPP is ~50 K for Teff, ~0.12 dex for log g, and ~0.10 dex for [Fe/H]. In order to avoid early-type stars, we limit the stars to have SSPP temperatures from 3000 K to 10000 K and a signal-to-noise ratio larger than 10. (1 data file).

  12. VizieR Online Data Catalog: S2CLS: multiwavelength counterparts to SMGs (Chen+, 2016)

    Science.gov (United States)

    Chen, C.-C.; Smail, I.; Ivison, R. J.; Arumugam, V.; Almaini, O.; Conselice, C. J.; Geach, J. E.; Hartley, W. G.; Ma, C.-J.; Mortlock, A.; Simpson, C.; Simpson, J. M.; Swinbank, A. M.; Aretxaga, I.; Blain, A.; Chapman, S. C.; Dunlop, J. S.; Farrah, D.; Halpern, M.; Michalowski, M. J.; van der Werf, P.; Wilkinson, A.; Zavala, J. A.

    2016-05-01

    The SCUBA-2 data at 850um in the UDS field were taken as part of the SCUBA-2 Cosmology Legacy Survey (S2CLS). The full data reduction steps are described fully in J. E. Geach et al. (2016, in preparation). In total we detect 1088 submillimeter sources at >=3.5σ within the region where rms noise is =4.0σ, for which we expect a false detection rate of ~1% based on simulations and source extractions on negative signals. We also define a supplementary sample of 372 submillimeter sources that are detected at 3.5-4.0σ and have a false detection rate of ~10%. In this paper, we provide counterpart candidates for both main and supplementary samples; however, the scientific analyses were performed on the main sample. We have carried out ALMA follow-up observations at 870um on 30 of the brighter SCUBA-2 sources in a Cycle 1 project 2012.1.00090.S (Simpson et al. 2015ApJ...807..128S, 2015ApJ...799...81S). The K-band-based multiwavelength photometry adopted in this paper is based on the UDS data release 8 (DR8) of the UKIRT Infrared Deep Sky Survey (UKIDSS; Lawrence et al. 2007, see II/319). The VLA radio observations at 1.4GHz (20cm) were carried out by the project UDS20 (V. Arumugam et al. 2016, in preparation), which comprises a mosaic of 14 pointings covering a total area of ~1.3deg2 centered on the UDS. The ~1 square degree UDS field contains a rich set of ancillary data (see section 2 for further details). (2 data files).

  13. Peculiar Physical Properties of HST-1 in M87 Y. J. Chen , G.-Y. Zhao ...

    Indian Academy of Sciences (India)

    This work was supported in part by the grants 10625314, 10633010 and 10821302,. KJCX2-YW-T03, 2007CB815405, the CAS/SAFEA International Partnership Pro- gram for Creative Research Teams, and the Science and Technology Commission of. Shanghai Municipality (09ZR1437400). References. Acciari, V. A. et al.

  14. Calibrating the Shan-Chen lattice Boltzmann model for immiscible displacement in porous media

    DEFF Research Database (Denmark)

    Christensen, Britt Stenhøj Baun; Schaap, M.G.; Wildenschild, D.

    2006-01-01

    physical system; in this case observed oil-water displacement experiments. For this purpose, we use simple, well-characterized, two-fluid-phase systems that furthermore function as a test of the code. The calibrated model is shown to produce realistic capillary pressures, within the pressure range...

  15. Effects of different cleaning treatments on heavy metal removal of Panax notoginseng (Burk) F. H. Chen.

    Science.gov (United States)

    Dahui, Liu; Na, Xu; Li, Wang; Xiuming, Cui; Lanping, Guo; Zhihui, Zhang; Jiajin, Wang; Ye, Yang

    2014-01-01

    The quality and safety of Panax notoginseng products has become a focus of concern in recent years. Contamination with heavy metals is one of the important factors as to P. notoginseng safety. Cleaning treatments can remove dust, soil, impurities or even heavy metals and pesticide residues on agricultural products. But effects of cleaning treatments on the heavy metal content of P. notoginseng roots have still not been studied. In order to elucidate this issue, the effects of five different cleaning treatments (CK, no treatment; T1, warm water (50°C) washing; T2, tap water (10°C) washing; T3, drying followed by polishing; and T4, drying followed by tap water (10°C) washing) on P. notoginseng roots' heavy metal (Cu, Pb, Cd, As and Hg) contents were studied. The results showed that heavy metal (all five) content in the three parts all followed the order of hair root > rhizome > root tuber under the same treatment. Heavy metal removals were in the order of Hg > As > Pb > Cu > Cd. Removal efficiencies of the four treatments were in the order of T2 > T1 > T3 > T4. Treatments (T1-T4) could decrease the contents of heavy metal in P. notoginseng root significantly. Compared with the requirements of WM/T2-2004, P. notoginseng roots' heavy metal contents of Cu, Pb, As and Hg were safe under treatments T1 and T2. In conclusion, the cleaning process after production was necessary and could reduce the content of heavy metals significantly. Fresh P. notoginseng root washed with warm water (T2) was the most efficient treatment to remove heavy metal and should be applied in production.

  16. VizieR Online Data Catalog: GTC transit light curves of WASP-52b (Chen+, 2017)

    Science.gov (United States)

    Chen, G.; Palle, E.; Nortmann, L.; Murgas, F.; Parviainen, H.; Nowak, G.

    2017-04-01

    We provide here the transit light curves of the hot Jupiter WASP-52b obtained on the night of 2015/08/28 using the OSIRIS instrument at the 10.4-m GTC telescope. The data was obtained by using OSIRIS through a 40-arcsec broad slit with the R1000R grism. The white-color light curve covers the wavelength range of 515-905nm, with 755-765nm being excluded. The spectroscopic light curves contain 22 channels of 16.5nm, 3 channels of 1.6nm, and 1 channel of 1.8nm. We provide several auxiliary parameters of the observations, some of which we have used to correct the data from correlated noise, including the position drift of the stars on the CCD detector in spatial and dispersion direction, the relative change in the FWHM of absorption line (dispersion FWHM), seeing (spatial FWHM), air mass, and rotator angle. (3 data files).

  17. Die Beifuß-Ambrosie auf Ackerflächen - ein Problem?

    Directory of Open Access Journals (Sweden)

    Verschwele, Arnd

    2014-07-01

    Full Text Available Common ragweed (Ambrosia artemisiifolia is spreading on agricultural fields in Germany, mainly in Brandenburg and Bayern. Competitive crops like winter cereals and oilseed-rape can reduce growth of this plant better than spring crops. In maize or cereals a chemical control is possible, but problems may arise because of the late emergence of ragweed and its high ability to regrowth. In crops like sunflower or lupines herbicide choice is limited and thus preventive control measures are crucial. An integrated approach is needed in order to avoid yield losses and to stop the spreading of ragweed in Germany.

  18. Retraction: "Identification of Novel Biomarkers for Pancreatic Cancer Using Integrated Transcriptomics With Functional Pathways Analysis" by Zhang, X., Tong, P., Chen, J., Pei, Z., Zhang, X., Chen, W., Xu, J. and Wang, J.

    Science.gov (United States)

    2017-11-01

    The above article from the Journal of Cellular Physiology, published online on 10 March 2016 in Wiley Online Library as Early View (http://onlinelibrary.wiley.com/enhanced/doi/10.1002/jcp.25353/), has been retracted by agreement between Gary Stein, the journal's Editor-in-Chief, and Wiley Periodicals, Inc. The retraction has been agreed following an investigation at the University of Texas, MD Anderson Cancer Center, which confirmed that the article was submitted and approved for publication by Dr. Jin Wang without acknowledgement of NIH funding received or the consent and authorship of Dr. Ann Killary and Dr. Subrata Sen, with whom the manuscript was originally drafted. © 2016 Wiley Periodicals, Inc.

  19. Erste Erfahrungen mit RDA an wissenschaftlichen Universalbibliotheken in Deutschland - Ergebnisse aus Fokusgruppengesprächen mit Katalogisierenden

    Directory of Open Access Journals (Sweden)

    Heidrun Wiesenmüller

    2017-04-01

    Full Text Available Einige Monate nach dem Umstieg auf das neue Regelwerk "Resource Description and Access" (RDA wurden an 18 großen deutschen wissenschaftlichen Universalbibliotheken Fokusgruppengespräche mit Katalogisierererinnen und Katalogisierern durchgeführt. Die Katalogisierenden wurden u.a. befragt, wie sicher sie sich bei der Anwendung von RDA fühlen, was sie am neuen Regelwerk gut oder schlecht finden, wie sie den Aufwand im Vergleich zum früheren Regelwerk RAK einschätzen, welche Informations- und Hilfsmittel sie verwenden und wie sie zu den regelmäßigen Änderungen im Standard stehen. Der vorliegende Aufsatz dokumentiert die Ergebnisse der Gespräche.   Several months after the introduction of the new cataloging standard "Resource Description and Access" (RDA, focus-group interviews with catalogers were conducted at 18 large academic and state libraries in Germany. Among other things, the catalogers were asked how confident they feel in applying RDA, which aspects of the new cataloging code they like or do not like, how they estimate the expenditure of time in comparison to the former cataloging code RAK, which sources they use to get help or information, and what they think about the frequent changes to the new standard. The paper presents the results of these interviews.

  20. Helical Magnetic Fields in AGN Jets Y. J. Chen1,2,∗ , G.-Y. Zhao1,2 ...

    Indian Academy of Sciences (India)

    Abstract. We establish a simple model to describe the helical mag- netic fields in AGN jets projected on the sky plane and the line-of-sight. This kind of profile has been detected in the polarimetric VLBI observa- tion of many blazar objects, suggesting the existence of helical magnetic fields in these sources. Key words.

  1. Effect of temperature on excess post-exercise oxygen consumption in juvenile southern catfish (Silurus meridionalis Chen) following exhaustive exercise.

    Science.gov (United States)

    Zeng, Ling-Qing; Zhang, Yao-Guang; Cao, Zhen-Dong; Fu, Shi-Jian

    2010-12-01

    The effects of temperature on resting oxygen consumption rate (MO2rest) and excess post-exercise oxygen consumption (EPOC) after exhaustive exercise (chasing) were measured in juvenile southern catfish (Silurus meridionalis) (8.40±0.30 g, n=40) to test whether temperature has a significant influence on MO2rest, maximum post-exercise oxygen consumption rate (MO2peak) and EPOC and to investigate how metabolic scope (MS: MO2peak - MO2rest) varies with acclimation temperature. The MO2rest increased from 64.7 (10°C) to 160.3 mg O2 h(-1) kg(-1) (25°C) (PEPOC varied from 32.9 min at 10°C to 345 min at 20°C, depending on the acclimation temperatures. The MS values of the lower temperature groups (10 and 15°C) were significantly smaller than those of the higher temperature groups (20, 25 and 30°C) (PEPOC varied ninefold among all of the temperature groups and was the largest for the 20°C temperature group (about 422.4 mg O2 kg(-1)). These results suggested that (1) the acclimation temperature had a significant effect on maintenance metabolism (as indicated by MO2rest) and the post-exercise metabolic recovery process (as indicated by MO2peak, duration and magnitude of EPOC), and (2) the change of the MS as a function of acclimation temperature in juvenile southern catfish might be related to their high degree of physiological flexibility, which allows them to adapt to changes in environmental conditions in their habitat in the Yangtze River and the Jialing River.

  2. J. Chen1, Z.-Q. Shen1, H. Sudou3, S. Iguchi4, Y. Murata5

    Indian Academy of Sciences (India)

    4National Astronomical Observatory of Japan, Tokyo 181-8588, Japan. 5The Institute of Space and Astronautical Science, JAXA, Kanagawa 229-8510, Japan. 6Research Center for Space and Cosmic Evolution, Ehime University,. Matsuyama 790-8577, Japan. ∗ e-mail: gyzhao@shao.ac.cn. Abstract. It was argued that 3C ...

  3. Chem TV: Choices I, v. 1.5.1 (by B. A. Luceigh, P. Ngo, and J. Chen)

    Science.gov (United States)

    Kraig Steffen, L.

    1999-08-01

    CHEM TV: Sunland, CA, 1998. 24.95, students; 59.95, faculty. This CD-ROM presents a series of interactive overviews and drills for students of organic chemistry. The material covered is generally taught in the first semester. This suite is much more than a simple presentation of material and, for students sufficiently motivated to take the time and work with the problems, will provide valuable review. Five interactive spaces are provided: concentration drills that emphasize recall of related structures/names, reagents/reactions, and stereochemistry; a structural review based on epinephrine; interactive synthesis projects; arcade game reagent review; and a set of timed self-tests. The CD-ROM installed and ran without problem on a Power PC Mac and on a Pentium running Windows 95. The program did fail to run when a student reviewing it switched to a very new version of Windows Quick Time. Most of the drills ran without a problem, although at times it was unclear how to respond to queries. I turned off the music, which would be much less annoying if the loops were simply longer. Publishers are flooding the market with add-on computer-based materials for the various levels of chemistry. Many constitute little more than a stack of overheads. This is one that may be of sufficient value to warrant the extra cost. A large number of examples are provided for many of the areas covered. Most of the graphical interfaces are clear and easy to manipulate, with the exception of a couple of mechanistic screens that had hard-to-figure-out arrows. Two sections, or modules, are of special note. The first of these is the synthesis challenges, where students must choose reactants, reagents, and reaction conditions for a particular reaction. These synthesis problems are well thought out and can be challenging. It is unfortunate that there are only five of them. The Self-Tests module is also of great practical value, forcing students to work through a variety of topics (200 problems) with limited time allotted per test. The structures are clear and easy to view and include a large number of three-dimensional molecular models that help make the connection between simple line drawings and actual molecules. More feedback information on incorrect answers would be nice, although it is obvious that this can balloon into an unworkable situation if every possible "wrong" answer is considered. The structure summary, which uses a single complicated molecule to review a large range of structural topics, is a good integrated look at bonding, stereochemistry, and conformational analysis. The concentration game is informative, albeit a bit obtuse on a few of the answers! The selection drills, a shooting gallery game with various chemical targets, seems a bit silly, but then if one can learn to recognize all the major reducing agents by snagging butterflies with faux nets than this could be helpful! With its varying levels of difficulty, this set of modules should be accessible even to weaker students and yet will provide a challenge for the more advanced. A great future addition would be an interactive module builder that allows faculty to modify existing questions and add their own. How can one effectively use this program? The relatively low cost does make it accessible for individual students to purchase, perhaps as a "recommended" item for the class. Another very practical option is to purchase 5-10 copies (assuming a class size of around 30-40) and make them available through a common computer laboratory (or an appropriate network site-license arrangement) or for check-out to small groups of students. One drawback is that only about 1/3 of the major topics that fill the year in organic are covered. I have used some of the drills as in-class interactive examples with a projector and laptop. This type of interactive tutorial-quiz-based program is a welcome change from the simple textbook on a CD-ROM approach. Given easy access to computers and an environment that encourages individual and small-group interactions, I believe this program can be a worthwhile aid for students of organic chemistry.

  4. Methodology of Leaving America for Asia: Reading South Korea's Social Studies Textbooks through Chen Kuan-Hsing's Asia as Method

    Science.gov (United States)

    Rhee, Jeong-eun

    2013-01-01

    This project began as a content analysis of five South Korean high school Social Studies textbooks. Yet, it has evolved into an epistemological experiment to pursue the question of "what does it mean to leave America for Asia, at least methodologically, for the researcher who left Asia for America?" Using the textbooks as a mediating…

  5. Untersuchung ausgewählter Oberflächen-, Grund- und Bodenwasserproben auf Perchlorat in Deutschland: Erste Ergebnisse

    Science.gov (United States)

    Scheytt, Traugott J.; Freywald, Jessika; Ptacek, Carol J.

    2011-03-01

    Perchlorate is a component found in solid rocket fuels, explosives, fireworks, and road flares. In addition, perchlorate is a minor component of natural salt deposits in semiarid and arid regions. Discharge and storage areas as well as accidents are potential sources for perchlorate contamination of aquatic systems. Perchlorate has been detected in surface water and groundwater from several places worldwide. The aim of this study is to evaluate whether perchlorate occurs in surface water, groundwater, or soil leachate at selected sites in Germany. These sites include surface water from a military base in southern Germany, groundwater from a production site, and groundwater and soil leachate from a location where fireworks shows are performed regularly. Results show that perchlorate was detected in all surface water and groundwater samples with values around 1 μg/l. Highest values (up to 15,000 μg/l) were detected in pore waters of the "Maifeld" area in Berlin where soil was sampled immediately after a fireworks show.

  6. X-ray Time Lags in TeV Blazars X. Chen1,∗ , G. Fossati1, E. Liang1 ...

    Indian Academy of Sciences (India)

    #1: Homogeneous, steady rate, injection of high energy particles with power-law distribution. The flare is caused by an increase/decrease of the maximum electron energy γmax, following an exponential (symmetric) time evolution. #2: Homogeneous mild diffusive particle acceleration mechanism is active in the blob for a ...

  7. Trichodina nobilis Chen, 1963 and Trichodina reticulata Hirschmann et Partsch, 1955 from ornamental freshwater fishes in Brazil.

    Science.gov (United States)

    Martins, M L; Marchiori, N; Roumbedakis, K; Lami, F

    2012-05-01

    In the present work Trichodina reticulata and T. nobilis (Ciliophora: Trichodinidae) are morphologically characterised from ornamental freshwater fish culture in the State of Santa Catarina, Brazil. The prevalence of infection and a list of comparative measurements are discussed. We examined "southern platyfish" Xiphophorus maculatus (n = 35), "goldfish" Carassius auratus (n = 31), "guppy" Poecilia reticulata (n = 20), "sailfin molly" Poecilia latipinna (n = 6), "beta" Betta splendens (n = 2) and "spotted headstander" Chilodus punctatus (n = 1). After being anesthetised in a benzocaine solution, fishes were examined for parasitological evaluation. A total of 51.57% fishes were parasitised by Trichodina spp. Carassius auratus was the most parasitised species, followed by X. maculatus and P. reticulata. Beta splendens, C. punctatus and P. latipinna were not parasitised by any trichodinid species. Two species of Trichodina were collected from the skin of fish: T. nobilis was found in C. auratus, P. reticulata and X. maculatus and T. reticulata was only observed in C. auratus. The importance of adequate handling in ornamental fish culture are also discussed.

  8. Embryo development in association with asymbiotic seed germination in vitro of Paphiopedilum armeniacum S. C. Chen et F. Y. Liu.

    Science.gov (United States)

    Zhang, Yan-Yan; Wu, Kun-Lin; Zhang, Jian-Xia; Deng, Ru-Fang; Duan, Jun; Teixeira da Silva, Jaime A; Huang, Wei-Chang; Zeng, Song-Jun

    2015-11-12

    This paper documents the key anatomical features during the development of P. armeniacum zygotic embryos and their ability to germinate asymbiotically in vitro. This study also examines the effect of media and seed pretreatments on seed germination and subsequent seedling growth. Seeds collected from pods 45 days after pollination (DAP) did not germinate while 95 DAP seeds displayed the highest seed germination percentage (96.2%). Most seedlings (50%) developed to stage 5 from 110 DAP seeds whose compact testa had not yet fully formed. Suspensor cells were vacuolated, which enabled the functional uptake of nutrients. The optimum basal medium for seed germination and subsequent protocorm development was eighth-strength Murashige and Skoog (1/8MS) for 95 DAP seeds and ¼MS for 110 DAP seeds. Poor germination was displayed by 140 DAP seeds with a compact testa. Pretreatment of dry mature seeds (180 DAP) with 1.0% sodium hypochlorite solution for 90 min or 40 kHz of ultrasound for 8 min improved germination percentage from 0 to 29.2% or to 19.7%, respectively. Plantlets that were at least 5 cm in height were transplanted to a Zhijing stone substrate for orchids, and 85.3% of plantlets survived 180 days after transplanting.

  9. Implementation of algorithms based on support vector machine (SVM for electric systems: topic review

    Directory of Open Access Journals (Sweden)

    Jefferson Jara Estupiñan

    2016-06-01

    Full Text Available Objective: To perform a review of implementation of algorithms based on support vectore machine applied to electric systems. Method: A paper search is done mainly on Biblio­graphic Indexes (BI and Bibliographic Bases with Selection Committee (BBSC about support vector machine. This work shows a qualitative and/or quan­titative description about advances and applications in the electrical environment, approaching topics such as: electrical market prediction, demand predic­tion, non-technical losses (theft, alternative energy source and transformers, among others, in each work the respective citation is done in order to guarantee the copy right and allow to the reader a dynamic mo­vement between the reading and the cited works. Results: A detailed review is done, focused on the searching of implemented algorithms in electric sys­tems and innovating application areas. Conclusion: Support vector machines have a lot of applications due to their multiple benefits, however in the electric energy area; they have not been tota­lly applied, this allow to identify a promising area of researching.

  10. Robust optimization of SVM hyperparameters in the classification of bioactive compounds.

    Science.gov (United States)

    Czarnecki, Wojciech M; Podlewska, Sabina; Bojarski, Andrzej J

    2015-01-01

    Support Vector Machine has become one of the most popular machine learning tools used in virtual screening campaigns aimed at finding new drug candidates. Although it can be extremely effective in finding new potentially active compounds, its application requires the optimization of the hyperparameters with which the assessment is being run, particularly the C and [Formula: see text] values. The optimization requirement in turn, establishes the need to develop fast and effective approaches to the optimization procedure, providing the best predictive power of the constructed model. In this study, we investigated the Bayesian and random search optimization of Support Vector Machine hyperparameters for classifying bioactive compounds. The effectiveness of these strategies was compared with the most popular optimization procedures-grid search and heuristic choice. We demonstrated that Bayesian optimization not only provides better, more efficient classification but is also much faster-the number of iterations it required for reaching optimal predictive performance was the lowest out of the all tested optimization methods. Moreover, for the Bayesian approach, the choice of parameters in subsequent iterations is directed and justified; therefore, the results obtained by using it are constantly improved and the range of hyperparameters tested provides the best overall performance of Support Vector Machine. Additionally, we showed that a random search optimization of hyperparameters leads to significantly better performance than grid search and heuristic-based approaches. The Bayesian approach to the optimization of Support Vector Machine parameters was demonstrated to outperform other optimization methods for tasks concerned with the bioactivity assessment of chemical compounds. This strategy not only provides a higher accuracy of classification, but is also much faster and more directed than other approaches for optimization. It appears that, despite its simplicity, random search optimization strategy should be used as a second choice if Bayesian approach application is not feasible.Graphical abstractThe improvement of classification accuracy obtained after the application of Bayesian approach to the optimization of Support Vector Machines parameters.

  11. Restoring the Generalizability of SVM Based Decoding in High Dimensional Neuroimage Data

    DEFF Research Database (Denmark)

    Abrahamsen, Trine Julie; Hansen, Lars Kai

    2011-01-01

    for Support Vector Machines. However, good generalization may be recovered in part by a simple renormalization procedure. We show that with proper renormalization, cross-validation based parameter optimization leads to the acceptance of more non-linearity in neuroimage classifiers than would have been...

  12. Methodology for selection of attributes and operating conditions for SVM-Based fault locator's

    Directory of Open Access Journals (Sweden)

    Debbie Johan Arredondo Arteaga

    2017-01-01

    Full Text Available Context: Energy distribution companies must employ strategies to meet their timely and high quality service, and fault-locating techniques represent and agile alternative for restoring the electric service in the power distribution due to the size of distribution services (generally large and the usual interruptions in the service. However, these techniques are not robust enough and present some limitations in both computational cost and the mathematical description of the models they use. Method: This paper performs an analysis based on a Support Vector Machine for the evaluation of the proper conditions to adjust and validate a fault locator for distribution systems; so that it is possible to determine the minimum number of operating conditions that allow to achieve a good performance with a low computational effort. Results: We tested the proposed methodology in a prototypical distribution circuit, located in a rural area of Colombia. This circuit has a voltage of 34.5 KV and is subdivided in 20 zones. Additionally, the characteristics of the circuit allowed us to obtain a database of 630.000 records of single-phase faults and different operating conditions. As a result, we could determine that the locator showed a performance above 98% with 200 suitable selected operating conditions. Conclusions: It is possible to improve the performance of fault locators based on Support Vector Machine. Specifically, these improvements are achieved by properly selecting optimal operating conditions and attributes, since they directly affect the performance in terms of efficiency and the computational cost.

  13. A Fault Diagnosis Method for Rotating Machinery Based on PCA and Morlet Kernel SVM

    Directory of Open Access Journals (Sweden)

    Shaojiang Dong

    2014-01-01

    Full Text Available A novel method to solve the rotating machinery fault diagnosis problem is proposed, which is based on principal components analysis (PCA to extract the characteristic features and the Morlet kernel support vector machine (MSVM to achieve the fault classification. Firstly, the gathered vibration signals were decomposed by the empirical mode decomposition (EMD to obtain the corresponding intrinsic mode function (IMF. The EMD energy entropy that includes dominant fault information is defined as the characteristic features. However, the extracted features remained high-dimensional, and excessive redundant information still existed. So, the PCA is introduced to extract the characteristic features and reduce the dimension. The characteristic features are input into the MSVM to train and construct the running state identification model; the rotating machinery running state identification is realized. The running states of a bearing normal inner race and several inner races with different degree of fault were recognized; the results validate the effectiveness of the proposed algorithm.

  14. A Novel Algorithm for Feature Level Fusion Using SVM Classifier for Multibiometrics-Based Person Identification

    Directory of Open Access Journals (Sweden)

    Ujwalla Gawande

    2013-01-01

    Full Text Available Recent times witnessed many advancements in the field of biometric and ultimodal biometric fields. This is typically observed in the area, of security, privacy, and forensics. Even for the best of unimodal biometric systems, it is often not possible to achieve a higher recognition rate. Multimodal biometric systems overcome various limitations of unimodal biometric systems, such as nonuniversality, lower false acceptance, and higher genuine acceptance rates. More reliable recognition performance is achievable as multiple pieces of evidence of the same identity are available. The work presented in this paper is focused on multimodal biometric system using fingerprint and iris. Distinct textual features of the iris and fingerprint are extracted using the Haar wavelet-based technique. A novel feature level fusion algorithm is developed to combine these unimodal features using the Mahalanobis distance technique. A support-vector-machine-based learning algorithm is used to train the system using the feature extracted. The performance of the proposed algorithms is validated and compared with other algorithms using the CASIA iris database and real fingerprint database. From the simulation results, it is evident that our algorithm has higher recognition rate and very less false rejection rate compared to existing approaches.

  15. Diagnosis of asphaltene stability in crude oil through “two parameters” SVM model

    DEFF Research Database (Denmark)

    Chamkalani, Ali; Mohammadi, Amir H.; Eslamimanesh, Ali

    2012-01-01

    Asphaltene precipitation/deposition and its imposing difficulties are drastic issues in petroleum industry. Monitoring the asphaltene stability conditions in crude oil systems is still a challenge and has been subject of many studies. In this work, the Refractive Index (RI) of several oil samples...

  16. Spatial Pyramids and Two-layer Stacking SVM classifiers for Image Categorization: A Comparative Study

    NARCIS (Netherlands)

    Abdullah, Azizi; Veltkamp, Remco C.; Wiering, Marco

    2009-01-01

    Recent research in image recognition has shown that combining multiple descriptors is a very useful way to improve classification performance. Furthermore, the use of spatial pyramids that compute descriptors at multiple spatial resolution levels generally increases the discriminative power of the

  17. Evaluation of PLS, LS-SVM, and LWR for quantitative spectroscopic analysis of soils

    Science.gov (United States)

    Soil testing requires the analysis of large numbers of samples in laboratory that are often time consuming and expensive. Mid-infrared spectroscopy (mid-IR) and near-infrared spectroscopy (NIRS) are fast, non-destructive, and inexpensive analytical methods that have been used for soil analysis, in l...

  18. Prediction of hERG Liability - Using SVM Classification, Bootstrapping and Jackknifing.

    Science.gov (United States)

    Sun, Hongmao; Huang, Ruili; Xia, Menghang; Shahane, Sampada; Southall, Noel; Wang, Yuhong

    2017-04-01

    Drug-induced QT prolongation leads to life-threatening cardiotoxicity, mostly through blockage of the human ether-à-go-go-related gene (hERG) encoded potassium ion (K+ ) channels. The hERG channel is one of the most important antitargets to be addressed in the early stage of drug discovery process, in order to avoid more costly failures in the development phase. Using a thallium flux assay, 4,323 molecules were screened for hERG channel inhibition in a quantitative high throughput screening (qHTS) format. Here, we present support vector classification (SVC) models of hERG channel inhibition with the averaged area under the receiver operator characteristics curve (AUC-ROC) of 0.93 for the tested compounds. Both Jackknifing and bootstrapping have been employed to rebalance the heavily biased training datasets, and the impact of these two under-sampling rebalance methods on the performance of the predictive models is discussed. Our results indicated that the rebalancing techniques did not enhance the predictive power of the resulting models; instead, adoption of optimal cutoffs could restore the desirable balance of sensitivity and specificity of the binary classifiers. In an external validation set of 66 drug molecules, the SVC model exhibited an AUC-ROC of 0.86, further demonstrating the utility of this modeling approach to predict hERG liabilities. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  19. Performance Improvement of DTC-SVM for Matrix Converter Drives Using an Overmodulation Strategy

    DEFF Research Database (Denmark)

    Lee, Kyo-Beum; Blaabjerg, Frede

    2005-01-01

    In this paper, an improved direct torque control (DTC) method for matrix converter drives is proposed which enables to minimize torque ripple and to obtain unity input power factor, while maintaining constant switching frequency and fast torque dynamics. It is possible to combine the advantages...... of matrix converters with the advantages of the DTC strategy using space vector modulation and a flux deadbeat control. However, drawbacks still exist such as a degrading of dynamic torque response compared to the basic DTC method. In the proposed scheme, an improved DTC strategy for matrix converter drives...

  20. Speed-Sensorless DTC-SVM for Matrix Converter Drives With Simple Non-Linearity Compensation

    DEFF Research Database (Denmark)

    Lee, Kyo-Beum; Blaabjerg, Frede; Yoon, Tae-Woong

    2005-01-01

    This paper presents a new method to improve sensorless performance of matrix converter drives using a parameter estimation scheme. To improve low-speed sensorless performance, the non-Iinearities of a matrix converter drive such as commutation delays, turn-on and turn-off times of switching devices...... method is applied for high performance induction motor drives using a 3 kW matrix converter system without a speed sensor. Experimental results are shown to illustrate the feasibility of the proposed strategy....

  1. Speed-Sensorless DTC-SVM for Matrix Converter Drives With Simple Nonlinearity Compensation

    DEFF Research Database (Denmark)

    Lee, Kyo Beum; Blaabjerg, Frede; Yoon, Tae-Woong

    2007-01-01

    This paper presents a new method to improve the sensorless performance of matrix converter drives using a parameter estimation scheme. To improve low-speed sensorless performance, the nonlinearities of a matrix converter drive such as commutation delays, turn-on and turn-off times of switching...... compensation method is applied for high performance induction motor drives using a 3-kW matrix converter system without a speed sensor. Experimental results are shown to illustrate the feasibility of the proposed strategy....

  2. Kombinasi Sinyal EEG dan Giroskop untuk Kendali Mobil Virtual dengan Menggunakan Modifikasi ICA dan SVM

    OpenAIRE

    Musthafa, Ahmad Reza; Tjandrasa, Handayani

    2016-01-01

    . Penelitian berbasis sinyal Electroencephalogram (EEG) telah banyak diteliti dan dikembangkan pada berbagai bidang ilmu pengetahuan. Sinyal EEG dapat diklasifikasikan ke dalam bentuk informasi untuk pengaplikasian topik Brain Computer Interface (BCI). Pada penelitian ini difokuskan pada topik pengendalian mobil menggunakan perintah sinyal EEG. Terdapat beberapa pendekatan dalam klasifikasi sinyal EEG, tetapi beberapa pendekatan tersebut tidak robust terhadap sinyal EEG yang memiliki banyak a...

  3. SVM-Based CAC System for B-Mode Kidney Ultrasound Images

    National Research Council Canada - National Science Library

    Subramanya, M B; Kumar, Vinod; Mukherjee, Shaktidev; Saini, Manju

    2015-01-01

    .... normal, medical renal disease (MRD) and cyst using B-mode ultrasound images. Thirty-five B-mode kidney ultrasound images consisting of 11 normal images, 8 MRD images and 16 cyst images have been used...

  4. Design and Status of Solar Vector Magnetograph (SVM-I) at Udaipur ...

    Indian Academy of Sciences (India)

    2016-01-27

    Jan 27, 2016 ... The integrated performance of the system on a tracking mount and its control software is being tested. Some test observations of sunspots has been carried out. In this paper we give a technical description of the hardware and software elements of the instrument and present the polarized images obtained ...

  5. Design and Status of Solar Vector Magnetograph (SVM-I) at Udaipur ...

    Indian Academy of Sciences (India)

    Section 3 describes the control software used for acquiring data and controlling the instrument. In the last section, the first-light images of a sunspot and very preliminary analysis is presented. The evaluation of the data is under progress, which will eventually drive the design modifications in second phase of solar vector ...

  6. Computationally efficient SVM multi-class image recognition with confidence measures

    Energy Technology Data Exchange (ETDEWEB)

    Makili, Lazaro [Dpto. Informatica y Automatica - UNED, Madrid (Spain); Vega, Jesus, E-mail: jesus.vega@ciemat.es [Asociacion EURATOM/CIEMAT para Fusion, Madrid (Spain); Dormido-Canto, Sebastian [Dpto. Informatica y Automatica - UNED, Madrid (Spain); Pastor, Ignacio [Asociacion EURATOM/CIEMAT para Fusion, Madrid (Spain); Murari, Andrea [Associazione EURATOM-CIEMAT per la Fusione, Consorzio RFX, Padova (Italy)

    2011-10-15

    Typically, machine learning methods produce non-qualified estimates, i.e. the accuracy and reliability of the predictions are not provided. Transductive predictors are very recent classifiers able to provide, simultaneously with the prediction, a couple of values (confidence and credibility) to reflect the quality of the prediction. Usually, a drawback of the transductive techniques for huge datasets and large dimensionality is the high computational time. To overcome this issue, a more efficient classifier has been used in a multi-class image classification problem in the TJ-II stellarator database. It is based on the creation of a hash function to generate several 'one versus the rest' classifiers for every class. By using Support Vector Machines as the underlying classifier, a comparison between the pure transductive approach and the new method has been performed. In both cases, the success rates are high and the computation time with the new method is up to 0.4 times the old one.

  7. Mesial temporal lobe epilepsy lateralization using SPHARM-based features of hippocampus and SVM

    Science.gov (United States)

    Esmaeilzadeh, Mohammad; Soltanian-Zadeh, Hamid; Jafari-Khouzani, Kourosh

    2012-02-01

    This paper improves the Lateralization (identification of the epileptogenic hippocampus) accuracy in Mesial Temporal Lobe Epilepsy (mTLE). In patients with this kind of epilepsy, usually one of the brain's hippocampi is the focus of the epileptic seizures, and resection of the seizure focus is the ultimate treatment to control or reduce the seizures. Moreover, the epileptogenic hippocampus is prone to shrinkage and deformation; therefore, shape analysis of the hippocampus is advantageous in the preoperative assessment for the Lateralization. The method utilized for shape analysis is the Spherical Harmonics (SPHARM). In this method, the shape of interest is decomposed using a set of bases functions and the obtained coefficients of expansion are the features describing the shape. To perform shape comparison and analysis, some pre- and post-processing steps such as "alignment of different subjects' hippocampi" and the "reduction of feature-space dimension" are required. To this end, first order ellipsoid is used for alignment. For dimension reduction, we propose to keep only the SPHARM coefficients with maximum conformity to the hippocampus shape. Then, using these coefficients of normal and epileptic subjects along with 3D invariants, specific lateralization indices are proposed. Consequently, the 1536 SPHARM coefficients of each subject are summarized into 3 indices, where for each index the negative (positive) value shows that the left (right) hippocampus is deformed (diseased). Employing these indices, the best achieved lateralization accuracy for clustering and classification algorithms are 85% and 92%, respectively. This is a significant improvement compared to the conventional volumetric method.

  8. Accuracy to detection timing for assisting repetitive facilitation exercise system using MRCP and SVM.

    Science.gov (United States)

    Miura, Satoshi; Takazawa, Junichi; Kobayashi, Yo; Fujie, Masakatsu G

    2017-01-01

    This paper presents a feasibility study of a brain-machine interface system to assist repetitive facilitation exercise. Repetitive facilitation exercise is an effective rehabilitation method for patients with hemiplegia. In repetitive facilitation exercise, a therapist stimulates the paralyzed part of the patient while motor commands run along the nerve pathway. However, successful repetitive facilitation exercise is difficult to achieve and even a skilled practitioner cannot detect when a motor command occurs in patient's brain. We proposed a brain-machine interface system for automatically detecting motor commands and stimulating the paralyzed part of a patient. To determine motor commands from patient electroencephalogram (EEG) data, we measured the movement-related cortical potential (MRCP) and constructed a support vector machine system. In this paper, we validated the prediction timing of the system at the highest accuracy by the system using EEG and MRCP. In the experiments, we measured the EEG when the participant bent their elbow when prompted to do so. We analyzed the EEG data using a cross-validation method. We found that the average accuracy was 72.9% and the highest at the prediction timing 280 ms. We conclude that 280 ms is the most suitable to predict the judgment that a patient intends to exercise or not.

  9. MPC-SVM method for Vienna rectifier with PMSG used in Wind Turbine Systems

    DEFF Research Database (Denmark)

    Lee, June-Seok; Bak, Yeongsu; Lee, Kyo-Beum

    2016-01-01

    ) method for the Vienna rectifier used in WTS with a Permanent Magnet Synchronous Generator (PMSG). The proposed MPC method considers the feasible eight-voltage vectors of the Vienna rectifier. In addition, the voltage vectors, which are the center voltage vectors of two feasible adjacent voltage vectors......Using a Vienna rectifier as the machine-side rectifier of back-to-back converter is advantageous in terms of size and cost compared to three-level topologies and for this reason, the Vienna rectifier has been used in Wind Turbine Systems (WTS). This paper proposes a Model Predictive Control (MPC......, are taken into consideration to improve the performance of the MPC method. The optimized voltage vector for the ripple minimization of PMSG currents is determined by cost function. Then, the neutral-point voltage unbalancing problem is considered for selecting the final switching set, which is generated...

  10. Classification of Auditory Evoked Potentials based on the wavelet decomposition and SVM network

    Directory of Open Access Journals (Sweden)

    Michał Suchocki

    2015-12-01

    Full Text Available For electrophysiological hearing assessment and diagnosis of brain stem lesions, the most often used are auditory brainstem evoked potentials of short latency. They are characterized by successively arranged maxima as a function of time, called waves. Morphology of the course, in particular, the timing and amplitude of each wave, allow a neurologist to make diagnose, what is not an easy task. A neurologist should be experienced, concentrated, and should have very good perception. In order to support his diagnostic process, the authors have developed an algorithm implementing the automated classification of auditory evoked potentials to the group of pathological and physiological cases, the sensitivity and specificity determined for an independent test group (of 50 cases of respectively 84% and 88%.[b]Keywords[/b]: biomedical engineering, brainstem auditory evoked potentials, wavelet decomposition, support vector machine

  11. Computer-Aided Diagnosis of Malignant Mammograms using Zernike Moments and SVM

    National Research Council Canada - National Science Library

    Sharma, Shubhi; Khanna, Pritee

    This work is directed toward the development of a computer-aided diagnosis (CAD) system to detect abnormalities or suspicious areas in digital mammograms and classify them as malignant or nonmalignant...

  12. An SVM-based distal lung image classification using texture descriptors.

    Science.gov (United States)

    Désir, Chesner; Petitjean, Caroline; Heutte, Laurent; Thiberville, Luc; Salaün, Mathieu

    2012-06-01

    A novel imaging technique can now provide microscopic images of the distal lung in vivo, for which quantitative analysis tools need to be developed. In this paper, we present an image classification system that is able to discriminate between normal and pathological images. Different feature spaces for discrimination are investigated and evaluated using a support vector machine. Best classification rates reach up to 90% and 95% on non-smoker and smoker groups, respectively. A feature selection process is also implemented, that allows us to gain some insight about these images. Whereas further tests on extended databases are needed, these first results indicate that efficient computer based automated classification of normal vs. pathological images of the distal lung is feasible. Copyright © 2011 Elsevier Ltd. All rights reserved.

  13. The Improved SVM Multi Objects' Identification For the Uncalibrated Visual Servoing

    Directory of Open Access Journals (Sweden)

    Min Wang

    2009-03-01

    Full Text Available For the assembly of multi micro objects in micromanipulation, the first task is to identify multi micro parts. We present an improved support vector machine algorithm, which employs invariant moments based edge extraction to obtain feature attribute and then presents a heuristic attribute reduction algorithm based on rough set's discernibility matrix to obtain attribute reduction, with using support vector machine to identify and classify the targets. The visual servoing is the second task. For avoiding the complicated calibration of intrinsic parameter of camera, We apply an improved broyden's method to estimate the image jacobian matrix online, which employs chebyshev polynomial to construct a cost function to approximate the optimization value, obtaining a fast convergence for online estimation. Last, a two DOF visual controller based fuzzy adaptive PD control law for micro-manipulation is presented. The experiments of micro-assembly of micro parts in microscopes confirm that the proposed methods are effective and feasible.

  14. 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.

  15. Controller Design for Direct Torque Controlled Space Vector Modulated (DTC-SVM) Induction Motor Drives

    DEFF Research Database (Denmark)

    Zelechowski, M.; Kazmierkowski, M.P.; Blaabjerg, Frede

    2005-01-01

    In this paper two different methods of PI controllers for direct torque controlled-space vector modulated induction motor drives have been studied. The first one is simple method based only on symmetric optimum criterion. The second approach takes into account the full model of induction motor...... including rotor voltage equation and uses root locus method. Some simulated and experimental oscillograms that illustrate properties of the presented controller design methods are shown....

  16. Performance prediction of optical image stabilizer using SVM for shaker-free production line

    Science.gov (United States)

    Kim, HyungKwan; Lee, JungHyun; Hyun, JinWook; Lim, Haekeun; Kim, GyuYeol; Moon, HyukSoo

    2016-04-01

    Recent smartphones adapt the camera module with optical image stabilizer(OIS) to enhance imaging quality in handshaking conditions. However, compared to the non-OIS camera module, the cost for implementing the OIS module is still high. One reason is that the production line for the OIS camera module requires a highly precise shaker table in final test process, which increases the unit cost of the production. In this paper, we propose a framework for the OIS quality prediction that is trained with the support vector machine and following module characterizing features : noise spectral density of gyroscope, optically measured linearity and cross-axis movement of hall and actuator. The classifier was tested on an actual production line and resulted in 88% accuracy of recall rate.

  17. A novel unified DTC-SVM for sensorless induction motor drives fed by a matrix converter

    DEFF Research Database (Denmark)

    Lee, Kyo-Beum; Blaabjerg, Frede

    2005-01-01

    In this paper, a simple direct torque control (DTC) method for sensorless matrix converter drives is proposed, which is characterized by a simple structure, minimal torque ripple and unity input power factor. It is possible to combine the advantages of matrix converters with the advantages...... of the DTC strategy using space vector modulation and a deadbeat algorithm in the stator flux reference frame. The flux and torque error are geometrically put together in a new flux leakage vector to make a stator command voltage vector in a deadbeat manner. To overcome the degrading of dynamic torque...... response compared to the basic DTC method, an over modulation strategy is presented in the proposed control scheme. Experimental results are shown to illustrate the feasibility of the proposed strategy....

  18. A modified DTC-SVM for sensorless matrix converter drives using a simple deadbeat scheme

    DEFF Research Database (Denmark)

    Lee, Kyo-Beum; Blaabjerg, Frede

    2005-01-01

    In this paper, a modified direct torque control (DTC) for matrix converter drives is proposed which enables to minimize torque ripple and to obtain unity input power factor, while maintaining constant switching frequency. It is possible to combine the advantages of matrix converters...... with the advantages of the DTC strategy using the basic DTC scheme. However, some drawbacks, such as large torque ripple in the low speed region and switching frequency variation according to the change of the motor speed and the amplitude of hysteresis bands, still exist. In the proposed scheme, a modified DTC...... strategy for matrix converter drives is derived using space vector modulation and flux deadbeat algorithm. Experimental results are shown to illustrate the feasibility of the proposed strategy....

  19. A Simple DTC-SVM method for Matrix Converter Drives Using a Deadbeat Scheme

    DEFF Research Database (Denmark)

    Lee, Kyo-Beum; Blaabjerg, Frede; Lee, Kwang-Won

    2005-01-01

    In this paper, a simple direct torque control (DTC) method for sensorless matrix converter drives is proposed, which is characterized by a simple structure, minimal torque ripple and unity input power factor. Also a good sensorless speed-control performance in the low speed operation is obtained......, while maintaining constant switching frequency and fast torque dynamics. It is possible to combine the advantages of matrix converters with the advantages of the DTC strategy using space vector modulation a deadbeat algorithm in the stator flux reference frame. Experimental results are shown...

  20. An Improved Grey Wolf Optimization Strategy Enhanced SVM and Its Application in Predicting the Second Major

    National Research Council Canada - National Science Library

    Wei, Yan; Ni, Ni; Liu, Dayou; Chen, Huiling; Wang, Mingjing; Li, Qiang; Cui, Xiaojun; Ye, Haipeng

    2017-01-01

    ...) was explored by using an improved grey wolf optimization (GWO) strategy in this study. An improved GWO, IGWO, was first proposed to identify the most discriminative features for major prediction...

  1. Output regularization of SVM seizure predictors: Kalman Filter versus the "Firing Power" method.

    Science.gov (United States)

    Teixeira, Cesar; Direito, Bruno; Bandarabadi, Mojtaba; Dourado, António

    2012-01-01

    Two methods for output regularization of support vector machines (SVMs) classifiers were applied for seizure prediction in 10 patients with long-term annotated data. The output of the classifiers were regularized by two methods: one based on the Kalman Filter (KF) and other based on a measure called the "Firing Power" (FP). The FP is a quantification of the rate of the classification in the preictal class in a past time window. In order to enable the application of the KF, the classification problem was subdivided in a two two-class problem, and the real-valued output of SVMs was considered. The results point that the FP method raise less false alarms than the KF approach. However, the KF approach presents an higher sensitivity, but the high number of false alarms turns their applicability negligible in some situations.

  2. Automatic seizure detection using wavelet transform and SVM in long-term intracranial EEG.

    Science.gov (United States)

    Liu, Yinxia; Zhou, Weidong; Yuan, Qi; Chen, Shuangshuang

    2012-11-01

    Automatic seizure detection is of great significance for epilepsy long-term monitoring, diagnosis, and rehabilitation, and it is the key to closed-loop brain stimulation. This paper presents a novel wavelet-based automatic seizure detection method with high sensitivity. The proposed method first conducts wavelet decomposition of multi-channel intracranial EEG (iEEG) with five scales, and selects three frequency bands of them for subsequent processing. Effective features are extracted, such as relative energy, relative amplitude, coefficient of variation and fluctuation index at the selected scales, and then these features are sent into the support vector machine for training and classification. Afterwards a postprocessing is applied on the raw classification results to obtain more accurate and stable results. Postprocessing includes smoothing, multi-channel decision fusion and collar technique. Its performance is evaluated on a large dataset of 509 h from 21 epileptic patients. Experiments show that the proposed method could achieve a sensitivity of 94.46% and a specificity of 95.26% with a false detection rate of 0.58/h for seizure detection in long-term iEEG.

  3. An automated approach to mapping ecological sites using hyper-temporal remote sensing and SVM classification

    Science.gov (United States)

    The development of ecological sites as management units has emerged as a highly effective land management framework, but its utility has been limited by spatial ambiguity of ecological site locations in the U.S., lack of ecological site concepts in many other parts of the world, and the inability to...

  4. Distributed Anomaly Detection using 1-class SVM for Vertically Partitioned Data

    Data.gov (United States)

    National Aeronautics and Space Administration — There has been a tremendous increase in the volume of sensor data collected over the last decade for different monitoring tasks. For example, petabytes of earth...

  5. USBeSafe: Applying One Class SVM for Effective USB Event Anomaly Detection

    Science.gov (United States)

    2016-04-25

    countless. One study performed in 2011 found that, in only the two year span prior, 50% of orga- nizations, both public and private, had sensitive...Samuel in 1959 as a "field of study that gives com- puters the ability to learn without being explicitly programmed" [14]. Later, Mitchell formalized the... parser , contains built-in capability to parse and interpret USB packets from a trace file. FIGURE 3.2: Sample Wireshark representation of a usbmon

  6. Real Time Monitoring System of Pollution Waste on Musi River Using Support Vector Machine (SVM) Method

    Science.gov (United States)

    Fachrurrozi, Muhammad; Saparudin; Erwin

    2017-04-01

    Real-time Monitoring and early detection system which measures the quality standard of waste in Musi River, Palembang, Indonesia is a system for determining air and water pollution level. This system was designed in order to create an integrated monitoring system and provide real time information that can be read. It is designed to measure acidity and water turbidity polluted by industrial waste, as well as to show and provide conditional data integrated in one system. This system consists of inputting and processing the data, and giving output based on processed data. Turbidity, substances, and pH sensor is used as a detector that produce analog electrical direct current voltage (DC). Early detection system works by determining the value of the ammonia threshold, acidity, and turbidity level of water in Musi River. The results is then presented based on the level group pollution by the Support Vector Machine classification method.

  7. WITHDRAWN: Automatic epileptic seizure detection in EEGs based on MF-DFA and SVM.

    Science.gov (United States)

    Wen, Tingxi; Zhang, Zhongnan; Huang, Wei; Wang, Meihong; Li, Chunfeng

    2016-09-09

    This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy. Copyright © 2016 Elsevier Ltd. All rights reserved.

  8. 75 FR 51754 - Certain Activated Carbon from the People's Republic of China: Notice of Partial Rescission of...

    Science.gov (United States)

    2010-08-23

    ... Activated Carbon from the People's Republic of China: Notice of Partial Rescission of Antidumping Duty... of initiation of an administrative review of the antidumping duty order on certain activated carbon... Activated Carbon Plant; Datong Forward Activated Carbon Co., Ltd.; Datong Guanghua Activated Carbon Co., Ltd...

  9. 77 FR 13284 - Small Diameter Graphite Electrodes From the People's Republic of China: Preliminary Results and...

    Science.gov (United States)

    2012-03-06

    .... 22. Dalian Thrive Metallurgy Imp. & Exp. Co., Ltd. 23. Datong Carbon 24. Datong Carbon Plant 25.... Rt Carbon Co., Ltd. 85. Ruitong Carbon Co., Ltd. 86. Shandong Basan Carbon Plant 87. Shandong Zibo...: Silicon Carbide From the People's Republic of China, 59 FR 22585 (May 2, 1994) (Silicon Carbide). If the...

  10. Ökologisch bewirtschaftete Ackerflächen - eine ökologische Leistung? Ein ergebnisorientierter Ansatz für die Praxis

    OpenAIRE

    Braband, Dorothee; Elsen, Thomas van

    2003-01-01

    Zielorientierte bzw. ergebnisorientierte Ansätze zur Erfassung ökologischer Leistungen der Landwirtschaft könnten künftig eine wesentliche Grundlage für die Bemsessung landwirtschaftlicher Direktzahlungen werden. Als ökologische Leistungen sollte alles abgegolten werden, was über die gute fachliche Praxis im Rahmen der landwirtschaftlichen Fachrechte und des Bundesnaturschutzgestz hinausgeht.

  11. AUTUN, MANÉ-VÉCHEN ET VIEUX : L’APPORT DE TROIS SITES MAJEURS À LA CONNAISSANCE DE L’ARTISANAT DU STUC EN GAULE ROMAINE

    Directory of Open Access Journals (Sweden)

    Julien Boislève

    2011-10-01

    Full Text Available La découverte et l’étude récente d’importants lots de stucs sur les sites de la villa de Mané-Véchenen Plouhinec (Morbihan et du “palais“ urbain du centre hospitalier à Autun (Saône-et-Loire constituent un apport considérable à notre connaissance d’un artisanat relativement méconnu en Gaule: le stuc. Parallèlement, le réexamen de collections plus anciennes sur la domus de Vieux (Calvados a permis la réinterprétation de décors laissant une place considérable et parfois inattendue au relief. Relativement complets, et pour certains restituables, ces décors éclairent un usage du stuc assez différent de celui connu en Italie et permettent d’identifier certaines modes décoratives typiquement provinciales.

  12. Untersuchungen an einer Kolbenexpansionsmaschine mit integrierten Wärmeübertragerflächen (Wärmeübertrager-Expander) zur Realisierung eines neuartigen Neon-Tieftemperatur-Prozesses

    OpenAIRE

    Fredrich, Ole

    2005-01-01

    Viele Anwendungen der Hochtemperatur-Supraleitung arbeiten vorteilhaft im Temperaturbereich zwischen 30 - 50 K. Für diesen Temperaturbereich existieren nur wenige geeignete Kältemaschinen mit kleiner Kälteleistung (1-2 W) u. gutem Wirkungsgrad. Neon ist aufgrund seiner Stoffeigenschaften ein hervorragendes Kältemittel für diesen Temperaturbereich, wie z.B. anhand einer realisierten Joule-Thomson (JT) Demonstrationsanlage deutlich wird. Als Ergebnis einer Prozessanalyse wird ein Kreislauf vorg...

  13. Methodology of Leaving America for Asia: Reading South Korea’s Social Studies Textbooks through Chen Kuan-Hsing’s Asia as Method

    Directory of Open Access Journals (Sweden)

    Joungh-eun Rhee

    2013-10-01

    Full Text Available This project began as a content analysis of five South Korean high school Social Studies textbooks. Yet, it has evolved into an epistemological experiment to pursue the question of “what does it mean to leave America for Asia, at least methodologically, for the researcher who left Asia for America?” Using the textbooks as a mediating site, therefore, I articulate a process that engages with, moves toward, and develops deimperializing methodology. More specifically, I interweave Kuan-Hsing Chen’s Asia as Method: Toward Deimperialization (2010 with my data by analyzing the data through Asia as Method and reading and practicing Asia as Method as methodology. This allows me to move away from fixating on the West as a reference point even through my critique. Rather, I work to produce geo-historically grounded knowledge for specific interventions at this mediating site toward the movements of decolonization, de-cold war, and deimperialization. In the process, I discuss how Asia as Method as methodology provokes political, psychological, and social engagements of everyday, multiplies reference points for knowledge production, and requires a researcher to re-work on one’s subjectivity inevitably constituted by imperialism.

  14. Spinnen ökologischer Ausgleichsflächen in den Schweizer Kantonen Aargau und Schaffhausen (Arachnida: Araneae – mit Anmerkungen zu Phrurolithus nigrinus (Corinnidae

    Directory of Open Access Journals (Sweden)

    Blick, Theo

    2008-07-01

    Full Text Available The spider fauna of open habitats adjacent to arable land was investigated in northern Switzerland. The three habitat types were (1 herbaceous edges of fields (Sa, (2 fallow land sowed with flowers (BB, and (3 grass borders of fields (GS. Four funnel pitfall traps (10 cm diameter were used to catch spiders in three stripe-types in two geographical regions in two years over 5 weeks in May and June: in total 12 sets of data. Spider species typical for open habitats were dominant, mostly lycosids (6 of the 10 most active species. The results were analysed together with environmental factors using a canonical correspondence analysis (CCA and spiders were compared with carabid beetles (Coeloptera: Carabidae. Geographical region, though not very distant, had the largest influence on both spiders and carabids. The age and type of the habitats had a stronger influence on spiders than on carabids. In spiders a larger part of the total variance was explained by the analysed factors. Finally we discuss briefly a remarkable spider species. A review of all known records of Phrurolithus nigrinus in Switzerland and Germany, together with adjacent regions in France, is given. Its phenology is indicated, its habitat discussed and the overall distribution within Europe is listed.

  15. Diskrepanz: Die EU-Frauenpolitik zwischen Deregulierung und Gleichheitsansprüchen Internal Discrepancies: EU Women’s Policies between Deregulation and Claims to Equality

    Directory of Open Access Journals (Sweden)

    Ingrid Biermann

    2003-03-01

    Full Text Available In von neun Autorinnen verfassten Beiträgen gibt der Band Einblicke in zentrale Felder der EU-Politik: die europäische Wirtschafts- und Währungsunion (EWWU, die Strukturpolitik, die Gleichstellungspolitik und das Gender Mainstreaming, die Osterweiterung sowie die Ausarbeitung einer europäischen Verfassung. Damit werden wesentliche Teile des Maastricht-Vertrags (1992, des Amsterdamer Vertrags (1997 und des Nizza-Vertrags (2000 behandelt. Hierdurch bildet dieser Band eine wichtige Grundlage für die Diskussion über die Frauenpolitik der EU.This anthology, consisting of nine essays written by women authors, offers insights into central areas of EU politics: the European Economic and Currency Union, structural policies, policies dealing with gender equality, the expansion of the EU, as well as the creation of a new European constitution. These policies include relevant parts of the 1992 Maastricht Treaty, the 1997 Treaty of Amsterdam, and the 2000 Treaty of Nizza. The information and insights presented here make this book an important basis for any discussions about EU women’s policies.

  16. Elektrochemische Kontrolle von Reibung auf Goldoberflächen in wässrigen Elektrolyten und ionischen Flüssigkeiten

    OpenAIRE

    Hausen, Florian

    2012-01-01

    Die Kontrolle von Reibung auf kleiner Skala ist von fundamentaler Bedeutung, insbesondere im Hinblick auf die fortschreitende Miniaturisierung von mechanischen Bauteilen. Im Rahmen dieser Arbeit wurden hochaufgelöste Experimente zur Reibung in ultrasauberen Flüssigkeiten durchgeführt, um so die Möglichkeiten der Kontrolle von Reibungskräften auf Gold in wässrigen Elektrolyten und ionischen Flüssigkeiten auf atomare Mechanismen zurückführen zu können. Die Kombination der Rasterkraftmikroskopie...

  17. Evaluation des Comet Assays bei neutralem pH zur Detektion von Alpha-Partikel induzierten DNA-Doppelstrangbrüchen

    OpenAIRE

    Hofbauer, Daniela

    2011-01-01

    Das Ziel der Arbeit war die Darstellung von initialen DNA-Schäden in Tumorzellen, verursacht durch Bestrahlung mit Alpha-Partikeln. Mit Hilfe des Comet Assays lassen sich sowohl DNA-Einzelstrangbrüche als auch -Doppelstrangbrüche auf dem Niveau einer einzelnen Zelle darstellen. Als Alpha-Strahler wurde Americium-241 verwendet. Für vergleichende Untersuchungen wurde auch der Gamma-Emitter Caesium-137 eingesetzt. Auf Grund von technischen Problemen bei der Durchführung sowohl des neutralen als ...

  18. How Structural Deficiencies Hamper Estonia’s Catching-up Process. Strukturschwächen als Hemmnis für Estlands Aufholprozess

    Directory of Open Access Journals (Sweden)

    Klaus Schrader

    2016-10-01

    Full Text Available Estonia is widely regarded as a paramount example for a successful transformation of a socialist economic system to a functioning market economy. Against the backdrop of this positive image which contrasts strongly with the crisis scenarios in Southern Europe the remaining problems of Estonia are often ignored. Estonia has hardly succeeded in catching-up economically with the richer countries of the EU. In this paper the authors raise the question why the catching-up process of Estonia is not as successful as it could have been expected from the policy performance during the last decades. It turns out that Estonia faces a serious productivity problem, particularly in the manufacturing sector producing tradable goods which is normally the driving engine behind economic and technological catching-up. The Estonian economy has failed to undergo the necessary structural change towards technologically more advanced employment structures and export patterns. Accordingly, Estonian economic policy needs to create a suitable business environment to support this kind of structural change.

  19. Jasmonic acid and methyl dihydrojasmonate enhance saponin biosynthesis as well as expression of functional genes in adventitious roots of Panax notoginseng F.H. Chen.

    Science.gov (United States)

    Li, Jinxin; Wang, Juan; Wu, Xiaolei; Liu, Dahui; Li, Jing; Li, Jianli; Liu, Shujie; Gao, Wenyuan

    2017-03-01

    Panax notoginseng, an important herbal medicine, has wide uses for its bioactive compounds and health function. In this work, we compared the content of saponin in cultivation and adventitious root. The total content of saponins in adventitious root (8.48 mg⋅g(-1) ) was found lower than in the native one (3-year-old) (34.34 mg⋅g(-1) ). To enhance the content of bioactive compounds, we applied elicitors jasmonic acid (JA) and methyl dihydrojasmonate (MDJ) to the adventitious root culture. It was observed that the highest total content of saponins (71.94 mg⋅g(-1) ) was achieved after treatment with 5 mg⋅L(-1) JA, which was 2.09-fold higher than native roots and 8.45-fold higher than the control group. The findings from high-performance liquid chromatography-electrospray ionization-tandem mass spectrometry analysis showed that six new compounds were present after the treatment with the elicitors. Furthermore, we found that JA and MDJ significantly upregulated the expression of the geranyl diphosphate synthase, farnesyl diphosphate synthase, squalene synthase, squalene epoxidase, dammarenediol synthase, and CYP716A47 and CYP716A53v2 (CYP450 enzyme) genes; downregulated the expression of the cycloartenol synthase gene; and increased superoxide dismutase and peroxidase activities. © 2016 International Union of Biochemistry and Molecular Biology, Inc.

  20. Bya rog prog zhu, The raven crest : the life and teachings of bDe chen 'od gsal rdo rje treasure revealer of contemporary Tibet

    NARCIS (Netherlands)

    Terrone, Antonio

    2010-01-01

    This research starts from the historical assertion that notwithstanding their claim of increased religious tolerance, the dramatic post-Mao political campaigns have continued to weaken the pervasive force of religious faith, traditional monastery-centered religious power, religious leadership, and

  1. Infrared thermography fails to visualize stimulation-induced meridian-like structures: comment by Rixin Chen and Zhimai Lv and reply from Gerhard Litscher

    Science.gov (United States)

    2011-01-01

    A comment on G. Litscher: Infrared thermography fails to visualize stimulation-induced meridian-like structures. Biomed. Eng. OnLine 2005, 4:38 (15 June 2005), with a response by the author. PMID:21906400

  2. The effect of exercise training on the metabolic interaction between feeding and locomotion in the juvenile southern catfish (Silurus meridionalis Chen).

    Science.gov (United States)

    Li, Xiu-Ming; Cao, Zhen-Dong; Fu, Shi-Jian

    2010-11-01

    The southern catfish exhibits the largest decrease in critical swimming speeds (U(crit)) during digestion among the fish species that have been investigated. To test whether the maximum metabolic capacity of the southern catfish was improved after exercise training to alleviate the competitive interaction between digestion and swimming, we measured postprandial metabolic responses, U(crit) and oxygen consumption rates (MO(2)) during swimming in both fasting and digesting fish. Twenty-one days of training (50 min swimming at 60% U(crit) followed by 10 min chasing) did not produce significant differences in resting MO(2) (MO(2rest)) or postprandial peak MO(2) (MO(2peak)). However, it did result in a significant decrease in energy expenditure during digestion. Feeding caused a significant decrease in U(crit) and an increase in active MO(2) (MO(2active)), whereas training caused a significant increase in U(crit) but no significant change in MO(2active). Neither digestion nor training had a significant effect on metabolic scope (MO(2active)-MO(2rest)). Training had no interactive effect on postprandial changes in any measured variable, so we conclude that training did not alleviate the competitive interaction between digestion and swimming. Our results suggest that: (1) the metabolic capacity of nontrained fish cannot support the metabolic demands of both digestion and locomotion simultaneously, and swimming metabolism, therefore, is sacrificed to sustain digestion when feeding and locomotion are combined (digestion-prioritization mode); (2) the metabolic capacity and metabolic mode of competition did not change after training, but trained fish did exhibit improved swimming performance, possibly due to their increased rate of O(2) extraction.

  3. A Tale of Two Utopias: Kang Youwei’s Communism, Mao Zedong’s Classicism and the “Accommodating Look” of the Marxist Li Zehou

    OpenAIRE

    Federico BRUSADELLI

    2017-01-01

    In the Datong Shu the Confucianist philosopher Kang Youwei (1858–1927) attempted to describe in an utopian fashion the end of history, as consisting of the abolition of private property, the institution of a world government, the disruption of marriage and the eradication of social differences. With his book, Kang somehow anticipated Mao’s use of the traditional ideal of datong as a revolutionary concept. In my paper, I will discuss a debate on the Datong Shu from the 1950’s, when a young Li ...

  4. New World species of the genus Calliscelio Ashmead (Hymenoptera, Platygastridae, Scelioninae

    Directory of Open Access Journals (Sweden)

    Hua-yan Chen

    2017-01-01

    Full Text Available The genus Calliscelio Ashmead is presumed to be a diverse group of parasitoids of the eggs of crickets (Orthoptera: Gryllidae. A least one species has been found to be an important factor in depressing cricket pest populations. The New World species of Calliscelio are revised. Forty-two species are recognized, 3 are redescribed: C. bisulcatus (Kieffer, C. laticinctus Ashmead, C. rubriclavus (Ashmead, comb. n.; and 38 are described as new: C. absconditum Chen & Johnson, sp. n., C. absum Chen & Johnson, sp. n., C. alcoa Chen & Masner, sp. n., C. amadoi Chen & Johnson, sp. n., C. armila Chen & Masner, sp. n., C. bidens Chen & Masner, sp. n., C. brachys Chen & Johnson, sp. n., C. brevinotaulus Chen & Johnson, sp. n., C. brevitas Chen & Johnson, sp. n., C. carinigena Chen & Johnson, sp. n., C. crater Chen & Johnson, sp. n., C. crena Chen & Johnson, sp. n., C. eboris Chen & Johnson, sp. n., C. extenuatus Chen & Johnson, sp. n., C. flavicauda Chen & Johnson, sp. n., C. foveolatus Chen & Johnson, sp. n., C. gatineau Chen & Johnson, sp. n., C. glaber Chen & Masner, sp. n., C. granulatus Chen & Masner, sp. n., C. latifrons Chen & Johnson, sp. n., C. levis Chen & Johnson, sp. n., C. longius Chen & Johnson, sp. n., C. magnificus Chen & Masner, sp. n., C. migma Chen & Johnson, sp. n., C. minutia Chen & Johnson, sp. n., C. paraglaber Chen & Johnson, sp. n., C. pararemigio Chen & Masner, sp. n., C. prolixus Chen & Johnson, sp. n., C. punctatifrons Chen & Johnson, sp. n., C. remigio Chen & Masner, sp. n., C. ruga Chen & Johnson, sp. n., C. rugicoxa Chen & Masner, sp. n., C. sfina Chen & Johnson, sp. n., C. storea Chen & Johnson, sp. n., C. suni Chen & Johnson, sp. n., C. telum Chen & Johnson, sp. n., C. torqueo Chen & Johnson, sp. n., C. virga Chen & Johnson, sp. n. Four species are treated as junior synonyms of Calliscelio rubriclavus (Ashmead: Anteris nigriceps Ashmead, syn. n., Caloteleia marlattii Ashmead, syn. n., Caloteleia grenadensis Ashmead, syn. n

  5. List of Participants

    Indian Academy of Sciences (India)

    List of Participants. Margo Aller. Denis Bastieri. Xiongwei Bi. Weihao Bian. Vera Bychkova. Bo Chai. Jianling Chen. Xuhui Chen. Ye Chen. Zhifu Chen. Yongjun Chen. Liang Chen. Zhaoyu Chen. Kwongsang Cheng. Lang Cui. Benzhong Dai. Zhen Ding. Dimitrios Emmanoulopoulos. Xiaohong Fan. Junhui Fan. Longxing Fan.

  6. Research on feature extraction and classification of AE signals of fibers' tensile failure based on HHT and SVM

    Directory of Open Access Journals (Sweden)

    Yanding SHEN

    2016-10-01

    Full Text Available In order to study the feature extraction and recognition method of fibers' tensile failure, AE technology is used to collect AE signals of fiber bundle's tensile fracture of two kinds of fibers of Aramid 1313 and viscose. A transform called wavelet is used to deal with the signals to reduce noise. A method called Hilbert-Huang transform (HHT is used to extract characteristic frequencies of the signals after the noise is reduced. And a classification method called Least Squares support vector machines (LSSVM is used for the classification and recognition of characteristic frequencies of the two kinds of fibers. The results show that wavelet de-noise method can reduce some noise of the signals. Hilbert spectrum can reflect fracture circumstances of the two kinds of fibers in the time dimension to some extent. Characteristic frequencies' extraction can be done from marginal spectrum. The LSSVM can be used for the classification and recognition of characteristic frequencies. The recognition rates of Aramid 1313 and viscose reach 40%, 80% respectively, and the total recognition rate reaches 60%.

  7. Analysis of Human Papillomavirus Using Datamining - Apriori, Decision Tree, and Support Vector Machine (SVM) and its Application Field

    OpenAIRE

    Cho Younghoon; Burm Seungwon; Choi Nayoung; Yoon Taeseon

    2016-01-01

    Human Papillomavirus(HPV) has various types (compared to other viruses) and plays a key role in evoking diverse diseases, especially cervical cancer. In this study, we aim to distinguish the features of HPV of different degree of fatality by analyzing their DNA sequences. We used Decision Tree Algorithm, Apriori Algorithm, and Support Vector Machine in our experiment. By analyzing their DNA sequences, we discovered some relationships between certain types of HPV, especially on the most fatal ...

  8. Comparison Algorithm Kernels on Support Vector Machine (SVM) to Compare the Trend Curves with Curves Online Forex Trading

    OpenAIRE

    Abbas, Irfan

    2016-01-01

    At this time, the players Forex Trading generally still use the data exchange in the form of a Forex Trading figures from different sources. Thus they only receive or know the data rate of a Forex Trading prevailing at the time just so difficult to analyze or predict exchange rate movements future. Forex players usually use the indicators to enable them to analyze and memperdiksi future value. Indicator is a decision making tool. Trading forex is trading currency of a country, the other count...

  9. Intelligent Optimized Combined Model Based on GARCH and SVM for Forecasting Electricity Price of New South Wales, Australia

    OpenAIRE

    Yang, Yi; Dong, Yao; Chen, Yanhua; Li, Caihong

    2014-01-01

    Daily electricity price forecasting plays an essential role in electrical power system operation and planning. The accuracy of forecasting electricity price can ensure that consumers minimize their electricity costs and make producers maximize their profits and avoid volatility. However, the fluctuation of electricity price depends on other commodities and there is a very complicated randomization in its evolution process. Therefore, in recent years, although large number of forecasting metho...

  10. Predicting Alzheimer's disease by classifying 3D-Brain MRI images using SVM and other well-defined classifiers

    Science.gov (United States)

    Matoug, S.; Abdel-Dayem, A.; Passi, K.; Gross, W.; Alqarni, M.

    2012-02-01

    Alzheimer's disease (AD) is the most common form of dementia affecting seniors age 65 and over. When AD is suspected, the diagnosis is usually confirmed with behavioural assessments and cognitive tests, often followed by a brain scan. Advanced medical imaging and pattern recognition techniques are good tools to create a learning database in the first step and to predict the class label of incoming data in order to assess the development of the disease, i.e., the conversion from prodromal stages (mild cognitive impairment) to Alzheimer's disease, which is the most critical brain disease for the senior population. Advanced medical imaging such as the volumetric MRI can detect changes in the size of brain regions due to the loss of the brain tissues. Measuring regions that atrophy during the progress of Alzheimer's disease can help neurologists in detecting and staging the disease. In the present investigation, we present a pseudo-automatic scheme that reads volumetric MRI, extracts the middle slices of the brain region, performs segmentation in order to detect the region of brain's ventricle, generates a feature vector that characterizes this region, creates an SQL database that contains the generated data, and finally classifies the images based on the extracted features. For our results, we have used the MRI data sets from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.

  11. PENGEMBANGAN MODEL SUPPORT VECTOR MACHINES (SVM DENGAN MEMPERBANYAK DATASET UNTUK PREDIKSI BISNIS FOREX MENGGUNAKAN METODE KERNEL TRICK

    Directory of Open Access Journals (Sweden)

    adi sucipto

    2017-09-01

    Full Text Available There are many types of investments that can be used to generate income, such as in the form of land, houses, gold, precious metals etc., there are also in the form of financial assets such as stocks, mutual funds, bonds and money markets or capital markets. One of the investments that attract enough attention today is the capital market investment. The purpose of this study is to predict and improve the accuracy of foreign exchange rates on forex business by using the Support Vector Machine model as a model for predicting and using more data sets compared with previous research that is as many as 1558 dataset. This study uses currency exchange rate data obtained from PT. Best Profit Future Cab. Surabaya is already in the form of data consisting of open, high, low, close attributes by using the current data of Euro currency exchange rate to USA Dollar with period every 1 minutes from May 12, 2016 at 09.51 until 13 May 2016 at 12:30 As much as 1689 dataset, After conducting research using Support Vector Machine model with kernel trick method to predict Forex using current data of Euro exchange rate to USA Dollar with period every 1 minutes from May 12, 2016 at 09.51 until 13 May 2016 at 12:30 as much as 1689 The dataset yielded a considerable prediction accuracy of 97.86%, with this considerable accuracy indicating that the movement of the Euro currency exchange rate to the USA Dollar on May 12 to May 13, 2016 can be predicted precisely.

  12. Analysis of Human Papillomavirus Using Datamining - Apriori, Decision Tree, and Support Vector Machine (SVM and its Application Field

    Directory of Open Access Journals (Sweden)

    Cho Younghoon

    2016-01-01

    Full Text Available Human Papillomavirus(HPV has various types (compared to other viruses and plays a key role in evoking diverse diseases, especially cervical cancer. In this study, we aim to distinguish the features of HPV of different degree of fatality by analyzing their DNA sequences. We used Decision Tree Algorithm, Apriori Algorithm, and Support Vector Machine in our experiment. By analyzing their DNA sequences, we discovered some relationships between certain types of HPV, especially on the most fatal types, 16 and 18. Moreover, we concluded that it would be possible for scientists to develop more potent HPV cures by applying these relationships and features that HPV virus exhibit.

  13. An Improved DTC-SVM Method for Sensorless Matrix Converter Drives Using an Overmodulation Strategy and a Simple Nonlinearity Compensation

    DEFF Research Database (Denmark)

    Lee, Kyo Beum; Blaabjerg, Frede

    2007-01-01

    In this paper, an improved direct torque control (DTC) method for sensorless matrix converter drives is proposed, which is characterized by minimal torque ripple, unity input power factor, and good sensorless speed-control performance in the low-speed operation, while maintaining constant switching...... frequency and fast torque dynamics. It is possible to combine the advantages of matrix converters with the advantages of the DTC strategy using space-vector modulation and two PI controllers. To overcome the degrading of dynamic torque response compared with the basic DTC method and the phase...

  14. Sensorless DTC-SVM for Induction Motor Driven by a Matrix Converter Using a Parameter Estimation Strategy

    DEFF Research Database (Denmark)

    Blaabjerg, Frede; Lee, Kyo-Beum

    2008-01-01

    This paper presents a new direct torque controlled space vector modulated method to improve the sensorless performance of matrix converter drives using a parameter estimation scheme. The flux and torque error are geometrically combined in a new flux leakage vector to make a stator command voltage...... vector in a deadbeat manner. A new sensorless method of estimating the rotor speed, flux, stator resistance, and rotor resistance is derived and verified with experimental results. Common terms in the error dynamics are utilized to find a simpler error model involving some auxiliary variables. Using...

  15. QSBR Study of Bitter Taste of Peptides: Application of GA-PLS in Combination with MLR, SVM, and ANN Approaches

    Directory of Open Access Journals (Sweden)

    Somaieh Soltani

    2013-01-01

    Full Text Available Detailed information about the relationships between structures and properties/activities of peptides as drugs and nutrients is useful in the development of drugs and functional foods containing peptides as active compounds. The bitterness of the peptides is an undesirable property which should be reduced during drug/nutrient production, and quantitative structure bitter taste relationship (QSBR studies can help researchers to design less bitter peptides with higher target efficiency. Calculated structural parameters were used to develop three different QSBR models (i.e., multiple linear regression, support vector machine, and artificial neural network to predict the bitterness of 229 peptides (containing 2–12 amino acids, obtained from the literature. The developed models were validated using internal and external validation methods, and the prediction errors were checked using mean percentage deviation and absolute average error values. All developed models predicted the activities successfully (with prediction errors less than experimental error values, whereas the prediction errors for nonlinear methods were less than those for linear methods. The selected structural descriptors successfully differentiated between bitter and nonbitter peptides.

  16. High-Performance Seizure Detection System Using a Wavelet-Approximate Entropy-fSVM Cascade With Clinical Validation.

    Science.gov (United States)

    Shen, Chia-Ping; Chen, Chih-Chuan; Hsieh, Sheau-Ling; Chen, Wei-Hsin; Chen, Jia-Ming; Chen, Chih-Min; Lai, Feipei; Chiu, Ming-Jang

    2013-10-01

    The classification of electroencephalography (EEG) signals is one of the most important methods for seizure detection. However, verification of an atypical epileptic seizure often can only be done through long-term EEG monitoring for 24 hours or longer. Hence, automatic EEG signal analysis for clinical screening is necessary for the diagnosis of epilepsy. We propose an EEG analysis system of seizure detection, based on a cascade of wavelet-approximate entropy for feature selection, Fisher scores for adaptive feature selection, and support vector machine for feature classification. Performance of the system was tested on open source data, and the overall accuracy reached 99.97%. We further tested the performance of the system on clinical EEG obtained from a clinical EEG laboratory and bedside EEG recordings. The results showed an overall accuracy of 98.73% for routine EEG, and 94.32% for bedside EEG, which verified the high performance and usefulness of such a cascade system for seizure detection. Also, the prediction model, trained by routine EEG, can be successfully generalized to bedside EEG of independent patients.

  17. Computer-Aided Diagnosis of Lung Nodules in Computed Tomography by Using Phylogenetic Diversity, Genetic Algorithm, and SVM.

    Science.gov (United States)

    de Carvalho Filho, Antonio Oseas; Silva, Aristófanes Corrêa; Cardoso de Paiva, Anselmo; Nunes, Rodolfo Acatauassú; Gattass, Marcelo

    2017-12-01

    Lung cancer is pointed as the major cause of death among patients with cancer throughout the world. This work is intended to develop a methodology for diagnosis of lung nodules using images from the Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). The proposed methodology uses image processing and pattern recognition techniques. In order to differentiate between the patterns of malignant and benign nodules, we used phylogenetic diversity by means of particular indexes, that are: intensive quadratic entropy, extensive quadratic entropy, average taxonomic distinctness, total taxonomic distinctness, and pure diversity indexes. After that, we applied the genetic algorithm for selection of the best model. In the tests' stage, we applied the proposed methodology to 1405 (394 malignant and 1011 benign) nodules. The proposed work presents promising results at the classification into malignant and benign, achieving accuracy of 92.52%, sensitivity of 93.1% and specificity of 92.26%. The results demonstrated a good rate of correct detections using texture features. Since a precocious detection allows a faster therapeutic intervention, thus a more favorable prognostic to the patient, we propose herein a methodology that contributes to the area in this aspect.

  18. Computer-aided diagnosis system for lung nodules based on computed tomography using shape analysis, a genetic algorithm, and SVM.

    Science.gov (United States)

    de Carvalho Filho, Antonio Oseas; Silva, Aristófanes Corrêa; de Paiva, Anselmo Cardoso; Nunes, Rodolfo Acatauassú; Gattass, Marcelo

    2017-08-01

    Lung cancer is the major cause of death among patients with cancer worldwide. This work is intended to develop a methodology for the diagnosis of lung nodules using images from the Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). The proposed methodology uses image processing and pattern recognition techniques. To differentiate the patterns of malignant and benign forms, we used a Minkowski functional, distance measures, representation of the vector of points measures, triangulation measures, and Feret diameters. Finally, we applied a genetic algorithm to select the best model and a support vector machine for classification. In the test stage, we applied the proposed methodology to 1405 (394 malignant and 1011 benign) nodules from the LIDC-IDRI database. The proposed methodology shows promising results for diagnosis of malignant and benign forms, achieving accuracy of 93.19 %, sensitivity of 92.75 %, and specificity of 93.33 %. The results are promising and demonstrate a good rate of correct detections using the shape features. Because early detection allows faster therapeutic intervention, and thus a more favorable prognosis for the patient, herein we propose a methodology that contributes to the area.

  19. Ultrasonic evaluation of central retinal artery hemodynamics in patients with hypertensive disorder complicating pregnancy and the correlation with diseaseChen-Xia Liu, Jing-Mian Zhou

    Directory of Open Access Journals (Sweden)

    Chen-Xia Liu

    2017-01-01

    Full Text Available Objective: To study the correlation between central retinal artery hemodynamic characteristics in patients with hypertensive disorder complicating pregnancy and endothelial injury molecules as well as trophoblast cell apoptosis molecules. Methods: 45 healthy pregnant women, 37 patients with gestational hypertension and 24 patients with preeclampsia who gave birth in Obstetrics Department of our hospital between May 2013 and December 2015 were selected and included in the control group, GH group and PE group respectively. Central retinal artery ultrasonography was done to determine peak systolic velocity (PSV, end-diastolic velocity (EDV and resistance index (RI, serum was collected to determine interleukin-6 (IL-6, IL- 17, IL-24, chemokine ligand 10 (CXCL10 and cartilage glycoprotein 40 (YKL40 content, and placenta tissue was collected to determine Fas, FasL, Bax, Caspase-3, Caspase-9, XIAP, Survivin and Livin expression. Results: Central retinal artery PSV and EDV as well as XIAP, Survivin and Livin expression in placenta of GH group and PE group were significantly lower than those of control group (P<0.05 while central retinal artery RI, serum IL-6, IL- 17, IL-24, CXCL10 and YKL40 content as well as Fas, FasL, Bax, Caspase-3 and Caspase-9 expression in placenta were significantly higher than those of control group (P<0.05. Central retinal artery PSV and EDV as well as XIAP, Survivin and Livin expression in placenta of PE group were significantly lower than those of GH group (P<0.05 while central retinal artery RI, serum IL-6, IL-17, IL-24, CXCL10 and YKL40 content as well as Fas, FasL, Bax, Caspase-3 and Caspase-9 expression in placenta were significantly higher than those of GH group (P<0.05. Serum IL-6, IL-17, IL-24, CXCL10 and YKL40 content as well as Fas, FasL, Bax, Caspase-3 and Caspase-9 expression in placenta were negatively correlated with PSV and EDV, and positively correlated with RI; XIAP, Survivin and Livin expression in placenta were positively correlated with PSV and EDV, and negatively correlated with RI. Conclusions: Central retinal artery blood flow characteristics in patients with hypertensive disorder complicating pregnancy are the significantly increased blood flow resistance and the significantly decreased blood flow volume, and the above blood flow characteristics are associated with maternal endothelial injury and trophoblast cell apoptosis.

  20. Monitoring Protein Conformation Changes as an Activating Step for Protein Interactions with Cross-linking/MS Analysis. / Chen, Zhuo; Rasmussen, Morten; Tahir, Salman; Clark, C.A.C; Barlow, Paul; Rappsilber, Juri

    DEFF Research Database (Denmark)

    Rasmussen, Morten

    -linked peptides have been enriched with SCX-StageTips. High resolution MS/MS spectra were acquired on an LTQ-Orbitrap mass spectrometer coupled online to a nanoHPLC. Singly and doubly charged ions were rejected for fragmentation. Peak lists were generated with MaxQuant. The database searches for cross......, the cross-link data and the crystal structure can be harmonized. We suggest that cross-linking can capture aspects of protein dynamics in solution that are not observable in static crystal structures....