WorldWideScience

Sample records for based motion-sensorless vector

  1. Active-flux based motion sensorless vector control of biaxial excitation generator/motor for automobiles (BEGA)

    DEFF Research Database (Denmark)

    Coroban-Schramel, Vasile; Boldea, Ion; Andreescu, Gheorghe-Daniel

    2009-01-01

    This paper proposes a novel, active-flux based, motion-sensorless vector control structure for biaxial excitation generator for automobiles (BEGA) for wide speed range operation. BEGA is a hybrid excited synchronous machine having permanent magnets on q-axis and a dc excitation on daxis. Using...... the active-flux concept the estimated rotor position is given by the sum of the active flux angle and torque angle. The active flux is calculated by subtracting the term Lq i s from the estimated stator flux vector. The experimental results validate the active flux-principle and show good performance...

  2. I-F starting method with smooth transition to EMF based motion-sensorless vector control of PM synchronous motor/generator

    DEFF Research Database (Denmark)

    Blaabjerg, Frede; Teodorescu, Remus; Fatu, M.;

    2008-01-01

    This paper proposes a novel hybrid motion- sensorless control system for permanent magnet synchronous motors (PMSM) using a new robust start-up method called I-f control, and a smooth transition to emf-based vector control. The I-f method is based on separate control of id, iq currents...... transition from I-f to emf sensorless vector control when the frequency reaches a certain level, and back. The PMSM rotor position and speed are extracted by using a PLL state-observer from the estimated rotor-flux, which is based on an equivalent integrator in close- loop with a PI speed......-adaptive compensator to eliminate dc-offset and phase-delay. Digital simulations for PMSM start-up with full load torque are presented for different initial rotor-positions. The transitions from I-f to emf motion-sensorless vector control and back as well, at very low-speeds, are fully validated by experimental...

  3. IPMSM Motion-Sensorless Direct Torque and Flux Control

    DEFF Research Database (Denmark)

    Pitict, Christian Ilie; Andreescu, Gheorghe-Daniel; Blaabjerg, Frede

    2005-01-01

    The paper presents a rather comprehensive implementation of a wide speed motion-sensorless control of IPMSM drives via direct torque and flux control (DTFC) with space vector modulation (SVM). Signal injection with only one D-module vector filter and phase-locked loop (PLL) observer is used at low...

  4. "Active flux" orientation vector sensorless control of IPMSM

    DEFF Research Database (Denmark)

    Blaabjerg, Frede; Boldea, I.; Paicu, M.C.

    2008-01-01

    This paper presents a novel strategy for the vector control of IPMSM, without signal injection. The overall performance of the motion-sensorless control depends strongly on the accuracy of the rotor position and speed estimation. The proposed state observer is based on the concept of the ldquoact......This paper presents a novel strategy for the vector control of IPMSM, without signal injection. The overall performance of the motion-sensorless control depends strongly on the accuracy of the rotor position and speed estimation. The proposed state observer is based on the concept...... flux observer under half full rated torque operating conditions in 2 rpm-1000 rpm speed range....

  5. Novel Motion Sensorless Control of Single Phase Brushless D.C. PM Motor Drive, with experiments

    DEFF Research Database (Denmark)

    Lepure, Liviu Ioan; Boldea, Ion; Andreescu, Gheorghe Daniel;

    2010-01-01

    A motion sensorless control for single phase permanent magnet brushless d.c. (PM-BLDC) motor drives, based on flux integration and prior knowledge of the PM flux/position characteristic is proposed here and an adequate correction algorithm is adopted, in order to increase the robustness to noise...... and to reduce the sensitivity to accuracy of flux linkage estimation. An I-f strategy is used for starting, by prescribing a ramped reference frequency and a constant current, and then the seamless transition to close loop sensorless control takes place. The proposed control system is detailed and validated...

  6. Grid to Standalone Transition Motion-Sensorless Dual-Inverter Control of PMSG With Asymmetrical Grid Voltage Sags and Harmonics Filtering

    DEFF Research Database (Denmark)

    Fatu, M.; Blaabjerg, Frede; Boldea, I.

    2014-01-01

    -coupling decoupling and line-voltage feedforward disturbance compensation. Also, a D-module filter is used to robustly extract the line voltage positive sequence followed by a phase-locked-loop (PLL) based observer to estimate the positive-sequence angle for control, including the case of asymmetric voltages......This paper describes a variable-speed motion-sensorless control system for permanent-magnet synchronous generator (PMSG) connected to grid via back-to-back inverters for wind energy generation. The grid-side inverter control system employs proportional-integral (PI) current controllers with cross...

  7. VectorBase

    Data.gov (United States)

    U.S. Department of Health & Human Services — VectorBase is a Bioinformatics Resource Center for invertebrate vectors. It is one of four Bioinformatics Resource Centers funded by NIAID to provide web-based...

  8. Vehicle Based Vector Sensor

    Science.gov (United States)

    2015-09-28

    300001 1 of 16 VEHICLE-BASED VECTOR SENSOR STATEMENT OF GOVERNMENT INTEREST [0001] The invention described herein may be manufactured and...unmanned underwater vehicle that can function as an acoustic vector sensor . (2) Description of the Prior Art [0004] It is known that a propagating...mechanics. An acoustic vector sensor measures the particle motion via an accelerometer and combines Attorney Docket No. 300001 2 of 16 the

  9. Motion Sensorless Control of BLDC PM Motor with Offline FEM Info Assisted State Observer

    DEFF Research Database (Denmark)

    Stirban, Alin; Boldea, Ion; Andreescu, Gheorghe-Daniel;

    2010-01-01

    This paper describes a new offline FEM assisted position and speed observer, for brushless dc (BLDC) PM motor drive sensorless control, based on the line-to-line PM flux linkage estimation. The zero-crossing of the line-to-line PM flux linkage occurs right in the middle of two commutation points...... on the fundamental model of the machine, a safe starting strategy under heavy load torque, called I-f control, is used, with seamless transition to the proposed sensorless control. The I-f starting method allows lowspeed sensorless control, without knowing the initial position, and without machine parameters...

  10. Motion Sensorless Bidirectional PWM Converter Control with Seamless Switching from Power Grid to Stand Alone and Back

    DEFF Research Database (Denmark)

    Fatu, Marius; Tutelea, Lucian; Teodorescu, Remus;

    2007-01-01

    This paper presents concepts and tests results on a flexible sensorless control strategy for a PMSG driven by a small wind turbine with back-to-back power converters capable to function in both stand alone and grid connection mode. A new automatic seamless transfer method, based on phase...

  11. Hybrid I-f starting and observer-based Ssnsorless control of single-phase BLDC-PM motor drives

    DEFF Research Database (Denmark)

    Iepure, Liviu Ioan; Boldea, Ion; Blaabjerg, Frede

    2012-01-01

    A motion sensorless control for single-phase permanent magnet brushless dc motor based on an I-f starting sequence and a real-time permanent magnet flux estimation is proposed here. The special calculation for extracting the position and speed used here implies the generating of an orthogonal flux...

  12. Image Coding Based on Address Vector Quantization.

    Science.gov (United States)

    Feng, Yushu

    Image coding is finding increased application in teleconferencing, archiving, and remote sensing. This thesis investigates the potential of Vector Quantization (VQ), a relatively new source coding technique, for compression of monochromatic and color images. Extensions of the Vector Quantization technique to the Address Vector Quantization method have been investigated. In Vector Quantization, the image data to be encoded are first processed to yield a set of vectors. A codeword from the codebook which best matches the input image vector is then selected. Compression is achieved by replacing the image vector with the index of the code-word which produced the best match, the index is sent to the channel. Reconstruction of the image is done by using a table lookup technique, where the label is simply used as an address for a table containing the representative vectors. A code-book of representative vectors (codewords) is generated using an iterative clustering algorithm such as K-means, or the generalized Lloyd algorithm. A review of different Vector Quantization techniques are given in chapter 1. Chapter 2 gives an overview of codebook design methods including the Kohonen neural network to design codebook. During the encoding process, the correlation of the address is considered and Address Vector Quantization is developed for color image and monochrome image coding. Address VQ which includes static and dynamic processes is introduced in chapter 3. In order to overcome the problems in Hierarchical VQ, Multi-layer Address Vector Quantization is proposed in chapter 4. This approach gives the same performance as that of the normal VQ scheme but the bit rate is about 1/2 to 1/3 as that of the normal VQ method. In chapter 5, a Dynamic Finite State VQ based on a probability transition matrix to select the best subcodebook to encode the image is developed. In chapter 6, a new adaptive vector quantization scheme, suitable for color video coding, called "A Self -Organizing

  13. Generalized Derivative Based Kernelized Learning Vector Quantization

    NARCIS (Netherlands)

    Schleif, Frank-Michael; Villmann, Thomas; Hammer, Barbara; Schneider, Petra; Biehl, Michael; Fyfe, Colin; Tino, Peter; Charles, Darryl; Garcia-Osoro, Cesar; Yin, Hujun

    2010-01-01

    We derive a novel derivative based version of kernelized Generalized Learning Vector Quantization (KGLVQ) as an effective, easy to interpret, prototype based and kernelized classifier. It is called D-KGLVQ and we provide generalization error bounds, experimental results on real world data, showing t

  14. Risk based surveillance for vector borne diseases

    DEFF Research Database (Denmark)

    Bødker, Rene

    of samples and hence early detection of outbreaks. Models for vector borne diseases in Denmark have demonstrated dramatic variation in outbreak risk during the season and between years. The Danish VetMap project aims to make these risk based surveillance estimates available on the veterinarians smart phones...

  15. Vectores

    OpenAIRE

    2016-01-01

    Documento que contiene la explicación sobre las temáticas de Sistemas coordenados, Cantidades vectoriales y escalares, Algunas propiedades de los vectores, Componentes de un vector y vectores unitarios

  16. Vector Control Based on SVPWM for ACIM

    Directory of Open Access Journals (Sweden)

    Zhu Jun

    2013-05-01

    Full Text Available To solve the large torque ripple and current harmonics, low DC bus voltage problems, a new control strategy is proposed for AC induction motor by using space vector pulse width modulation, so that the static and dynamic performance are improved. The system simulation experiment mode was established based on SVPWM to verify the effectiveness of the system control mode. It is showed that it can reduce the current ripple and torque ripple, improve the utilization of DC bus voltage. It means that the control strategy based SVPWM can improve dynamic and static performance effectively for the ACIM servo system.

  17. Plant viral vectors based on tobamoviruses.

    Science.gov (United States)

    Yusibov, V; Shivprasad, S; Turpen, T H; Dawson, W; Koprowski, H

    1999-01-01

    The potential of plant virus-based transient expression vectors is substantial. One objective is the production of large quantities of foreign peptides or proteins. At least one commercial group (Biosource Technologies) is producing large quantities of product in the field, has built factories to process truck-loads of material and soon expects to market virus-generated products. In the laboratory, large amounts of protein have been produced for structural or biochemical analyses. An important aspect of producing large amounts of a protein or peptide is to make the product easily purifiable. This has been done by attaching peptides or proteins to easily purified units such as virion particles or by exporting proteins to the apoplast so that purification begins with a highly enriched product. For plant molecular biology, virus-based vectors have been useful in identifying previously unknown genes by overexpression or silencing or by expression in different genotypes. Also, foreign peptides fused to virions are being used as immunogens for development of antisera for experimental use or as injected or edible vaccines for medical use. As with liposomes and microcapsules, plant cells and plant viruses are also expected to provide natural protection for the passage of antigen through the gastrointestinal tract. Perhaps the greatest advantage of plant virus-based transient expression vectors is their host, plants. For the production of large amounts of commercial products, plants are one of the most economical and productive sources of biomass. They also present the advantages of lack of contamination with animal pathogens, relative ease of genetic manipulation and the presence eukaryotic protein modification machinery.

  18. DBSC-Based Grayscale Line Image Vectorization

    Institute of Scientific and Technical Information of China (English)

    Konstantin Melikhov; Feng Tian; Jie Qiu; Quan Chen; Hock Soon Seah

    2006-01-01

    Vector graphics plays an important role in computer animation and imaging technologies. However present techniques and tools cannot fully replace traditional pencil and paper. Additionally, vector representation of an image is not always available. There is not yet a good solution for vectorizing a picture drawn on a paper. This work attempts to solve the problem of vectorizing grayscale line drawings. The solution proposed uses Disk B-Spline curves to represent strokes of an image in vector form. The algorithm builds a vector representation from a grayscale raster image, which can be a scanned picture for instance. The proposed method uses a Gaussian sliding window to calculate skeleton and perceptive width of a stroke. As a result of vectorization, the given image is represented by a set of Disk B-Spline curves.

  19. A COMPREHENSIVE ANALYSIS OF SPACE VECTOR PWM TECHNIQUE BASED ON PLACEMENT OF ZERO-SPACE VECTOR

    Directory of Open Access Journals (Sweden)

    G.SAMBASIVA RAO,

    2011-04-01

    Full Text Available In this paper, the effect of placement of zero-space vector for the implementation of space vector based Pulse Width Modulation techniques for 3-phase Voltage Source Inverter is presented. Several pulse width modulation (PWM control strategies have been proposed for 3-phase voltage source inverter (VSI in the past. It is known that space vector modulation (SVM offers a degree of freedom in its implementation with regard to the placement of the zero-space vector. Apart from constructing a consistent theoretical framework, simulation results with conventional continuous SVM and various discontinuous SVM techniques are presented and all the cases are compared in this paper.

  20. Vectors

    DEFF Research Database (Denmark)

    Boeriis, Morten; van Leeuwen, Theo

    2017-01-01

    This article revisits the concept of vectors, which, in Kress and van Leeuwen’s Reading Images (2006), plays a crucial role in distinguishing between ‘narrative’, action-oriented processes and ‘conceptual’, state-oriented processes. The use of this concept in image analysis has usually focused...... on the most salient vectors, and this works well, but many images contain a plethora of vectors, which makes their structure quite different from the linguistic transitivity structures with which Kress and van Leeuwen have compared ‘narrative’ images. It can also be asked whether facial expression vectors...... should be taken into account in discussing ‘reactions’, which Kress and van Leeuwen link only to eyeline vectors. Finally, the question can be raised as to whether actions are always realized by vectors. Drawing on a re-reading of Rudolf Arnheim’s account of vectors, these issues are outlined...

  1. Density Based Support Vector Machines for Classification

    Directory of Open Access Journals (Sweden)

    Zahra Nazari

    2015-04-01

    Full Text Available Support Vector Machines (SVM is the most successful algorithm for classification problems. SVM learns the decision boundary from two classes (for Binary Classification of training points. However, sometimes there are some less meaningful samples amongst training points, which are corrupted by noises or misplaced in wrong side, called outliers. These outliers are affecting on margin and classification performance, and machine should better to discard them. SVM as a popular and widely used classification algorithm is very sensitive to these outliers and lacks the ability to discard them. Many research results prove this sensitivity which is a weak point for SVM. Different approaches are proposed to reduce the effect of outliers but no method is suitable for all types of data sets. In this paper, the new method of Density Based SVM (DBSVM is introduced. Population Density is the basic concept which is used in this method for both linear and non-linear SVM to detect outliers. Experiments on artificial data sets, real high-dimensional benchmark data sets of Liver disorder and Heart disease, and data sets of new and fatigued banknotes’ acoustic signals can prove the efficiency of this method on noisy data classification and the better generalization that it can provide compared to the standard SVM.

  2. Support vector machines optimization based theory, algorithms, and extensions

    CERN Document Server

    Deng, Naiyang; Zhang, Chunhua

    2013-01-01

    Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents an accessible treatment of the two main components of support vector machines (SVMs)-classification problems and regression problems. The book emphasizes the close connection between optimization theory and SVMs since optimization is one of the pillars on which SVMs are built.The authors share insight on many of their research achievements. They give a precise interpretation of statistical leaning theory for C-support vector classification. They also discuss regularized twi

  3. Recent progress in polymer-based gene delivery vectors

    Institute of Scientific and Technical Information of China (English)

    HUANG Shiwen; ZHUO Renxi

    2003-01-01

    The gene delivery system is one of the three components of a gene medicine, which is the bottle neck of current gene therapy. Nonviral vectors offer advantages over the viral system of safety, ease of manufacturing, etc. As important nonviral vectors, polymer gene delivery systems have gained increasing attention and have begun to show increasing promising. In this review, the fundamental and recent progress of polymer-based gene delivery vectors is reviewed.

  4. Research of DOA Estimation Based on Single MEMS Vector Hydrophone

    Directory of Open Access Journals (Sweden)

    Wen Dong Zhang

    2009-08-01

    Full Text Available The MEMS vector hydrophone is a novel acoustic sensor with a “four-beamcilia” structure. Based on the MEMS vector hydrophone with this structure, the paper studies the method of estimated direction of arrival (DOA. According to various research papers, many algorithms can be applied to vector hydrophones. The beam-forming approach and bar graph approach are described in detail. Laboratory tests by means of the a standing-wave tube are performed to validate the theoretical results. Both the theoretical analysis and the results of tests prove that the proposed MEMS vector hydrophone possesses the desired directional function.

  5. Research of DOA Estimation Based on Single MEMS Vector Hydrophone.

    Science.gov (United States)

    Zhang, Wen Dong; Guan, Ling Gang; Zhang, Guo Jun; Xue, Chen Yang; Zhang, Kai Rui; Wang, Jian Ping

    2009-01-01

    The MEMS vector hydrophone is a novel acoustic sensor with a "four-beam-cilia" structure. Based on the MEMS vector hydrophone with this structure, the paper studies the method of estimated direction of arrival (DOA). According to various research papers, many algorithms can be applied to vector hydrophones. The beam-forming approach and bar graph approach are described in detail. Laboratory tests by means of the a standing-wave tube are performed to validate the theoretical results. Both the theoretical analysis and the results of tests prove that the proposed MEMS vector hydrophone possesses the desired directional function.

  6. Great Ellipse Route Planning Based on Space Vector

    Directory of Open Access Journals (Sweden)

    LIU Wenchao

    2015-07-01

    Full Text Available Aiming at the problem of navigation error caused by unified earth model in great circle route planning using sphere model and modern navigation equipment using ellipsoid mode, a method of great ellipse route planning based on space vector is studied. By using space vector algebra method, the vertex of great ellipse is solved directly, and description of great ellipse based on major-axis vector and minor-axis vector is presented. Then calculation formulas of great ellipse azimuth and distance are deduced using two basic vectors. Finally, algorithms of great ellipse route planning are studied, especially equal distance route planning algorithm based on Newton-Raphson(N-R method. Comparative examples show that the difference of route planning between great circle and great ellipse is significant, using algorithms of great ellipse route planning can eliminate the navigation error caused by the great circle route planning, and effectively improve the accuracy of navigation calculation.

  7. A Fingerprint Minutiae Matching Method Based on Line Segment Vector

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Minutiae-based fingerprint matching is the most commonly used in an automatic fingerprint identification system. In this paper, we propose a minutia matching method based on line segment vector. This method uses all the detected minutiae (the ridge ending and the ridge bifurcation) in a fingerprint image to create a set of new vectors (line segment vector). Using these vectors, we can determine a truer reference point more efficiently. In addition, this new minutiae vector can also increase the accuracy of the minutiae matching. By experiment on the public domain collections of fingerprint images fvc2004 DB3 set A and DB4 set A, the result shows that our algorithm can obtain an improved verification performance.

  8. Image Segmentation Based on Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    XU Hai-xiang; ZHU Guang-xi; TIAN Jin-wen; ZHANG Xiang; PENG Fu-yuan

    2005-01-01

    Image segmentation is a necessary step in image analysis. Support vector machine (SVM) approach is proposed to segment images and its segmentation performance is evaluated.Experimental results show that: the effects of kernel function and model parameters on the segmentation performance are significant; SVM approach is less sensitive to noise in image segmentation; The segmentation performance of SVM approach is better than that of back-propagation multi-layer perceptron (BP-MLP) approach and fuzzy c-means (FCM) approach.

  9. LandSat-Based Land Use-Land Cover (Vector)

    Data.gov (United States)

    Minnesota Department of Natural Resources — Vector-based land cover data set derived from classified 30 meter resolution Thematic Mapper satellite imagery. Classification is divided into 16 classes with source...

  10. Improved NYVAC-based vaccine vectors.

    Directory of Open Access Journals (Sweden)

    Karen V Kibler

    Full Text Available While as yet there is no vaccine against HIV/AIDS, the results of the phase III Thai trial (RV144 have been encouraging and suggest that further improvements of the prime/boost vaccine combination of a poxvirus and protein are needed. With this aim, in this investigation we have generated derivatives of the candidate vaccinia virus vaccine vector NYVAC with potentially improved functions. This has been achieved by the re-incorporation into the virus genome of two host range genes, K1L and C7L, in conjunction with the removal of the immunomodulatory viral molecule B19, an antagonist of type I interferon action. These novel virus vectors, referred to as NYVAC-C-KC and NYVAC-C-KC-ΔB19R, have acquired relevant biological characteristics, giving higher levels of antigen expression in infected cells, replication-competency in human keratinocytes and dermal fibroblasts, activation of selective host cell signal transduction pathways, and limited virus spread in tissues. Importantly, these replication-competent viruses have been demonstrated to maintain a highly attenuated phenotype.

  11. Fuzzy rule-based support vector regression system

    Institute of Scientific and Technical Information of China (English)

    Ling WANG; Zhichun MU; Hui GUO

    2005-01-01

    In this paper,we design a fuzzy rule-based support vector regression system.The proposed system utilizes the advantages of fuzzy model and support vector regression to extract support vectors to generate fuzzy if-then rules from the training data set.Based on the first-order linear Tagaki-Sugeno (TS) model,the structure of rules is identified by the support vector regression and then the consequent parameters of rules are tuned by the global least squares method.Our model is applied to the real world regression task.The simulation results gives promising performances in terms of a set of fuzzy rules,which can be easily interpreted by humans.

  12. Support Vector Machine-Based Nonlinear System Modeling and Control

    Institute of Scientific and Technical Information of China (English)

    张浩然; 韩正之; 冯瑞; 于志强

    2003-01-01

    This paper provides an introduction to a support vector machine, a new kernel-based technique introduced in statistical learning theory and structural risk minimization, then presents a modeling-control framework based on SVM.At last a numerical experiment is taken to demonstrate the proposed approach's correctness and effectiveness.

  13. VectorBase: improvements to a bioinformatics resource for invertebrate vector genomics.

    Science.gov (United States)

    Megy, Karine; Emrich, Scott J; Lawson, Daniel; Campbell, David; Dialynas, Emmanuel; Hughes, Daniel S T; Koscielny, Gautier; Louis, Christos; Maccallum, Robert M; Redmond, Seth N; Sheehan, Andrew; Topalis, Pantelis; Wilson, Derek

    2012-01-01

    VectorBase (http://www.vectorbase.org) is a NIAID-supported bioinformatics resource for invertebrate vectors of human pathogens. It hosts data for nine genomes: mosquitoes (three Anopheles gambiae genomes, Aedes aegypti and Culex quinquefasciatus), tick (Ixodes scapularis), body louse (Pediculus humanus), kissing bug (Rhodnius prolixus) and tsetse fly (Glossina morsitans). Hosted data range from genomic features and expression data to population genetics and ontologies. We describe improvements and integration of new data that expand our taxonomic coverage. Releases are bi-monthly and include the delivery of preliminary data for emerging genomes. Frequent updates of the genome browser provide VectorBase users with increasing options for visualizing their own high-throughput data. One major development is a new population biology resource for storing genomic variations, insecticide resistance data and their associated metadata. It takes advantage of improved ontologies and controlled vocabularies. Combined, these new features ensure timely release of multiple types of data in the public domain while helping overcome the bottlenecks of bioinformatics and annotation by engaging with our user community.

  14. Graph- versus Vector-Based Analysis of a Consensus Protocol

    NARCIS (Netherlands)

    Delzanno, Giorgio; Rensink, Arend; Traverso, Riccardo; Bošnački, Dragan; Edelkamp, Stefan; Lluch Lafuente, Alberto; Wijs, Anton

    2014-01-01

    The Paxos distributed consensus algorithm is a challenging case-study for standard, vector-based model checking techniques. Due to asynchronous communication, exhaustive analysis may generate very large state spaces already for small model instances. In this paper, we show the advantages of graph tr

  15. The Mathematics of Divergence Based Online Learning in Vector Quantization

    NARCIS (Netherlands)

    Villmann, Thomas; Haase, Sven; Schleif, Frank-Michael; Hammer, Barbara; Biehl, Michael

    2010-01-01

    We propose the utilization of divergences in gradient descent learning of supervised and unsupervised vector quantization as an alternative for the squared Euclidean distance. The approach is based on the determination of the Fréchet-derivatives for the divergences, wich can be immediately plugged i

  16. Geoacoustic Inversion Based on a Vector Hydrophone Array

    Institute of Scientific and Technical Information of China (English)

    PENG Han-Shu; LI Feng-Hua

    2007-01-01

    We propose a geoacoustic inversion scheme employing a vector hydrophone array based on the fact that vector hydrophone can provide more acoustic field information than traditional pressure hydrophones. Firstly, the transmission loss of particle velocities is discussed. Secondly, the sediment sound speed is acquired by a matchedfield processing (MFP) procedure, which is the optimization in combination of the pressure field and vertical particle velocity field. Finally, the bottom attenuation is estimated from the transmission loss difference between the vertical particle velocity and the pressure. The inversion method based on the vector hydrophone array mainly has two advantages: One is that the MFP method based on vector field can decrease the uncertain estimation of the sediment sound speed. The other is that the objective function based on the transmission loss difference has good sensitivity to the sediment attenuation and the inverted sediment attenuation is independent of source level.The wlidity of the inverted parameters is examined by comparison of the numerical results with the experimentaldata.

  17. Dynamic Vector Space Secret Sharing Based on Certificates

    Institute of Scientific and Technical Information of China (English)

    XU Chunxiang; LI Jiajia; LIU Dongsu

    2006-01-01

    A vector space secret sharing scheme based on certificates is proposed in this paper. The difficulties of solving discrete logarithm assure confidential information's security, and the use of each participant's certificate makes the dealer have no need to transfer secret information to the participants. The proposed scheme is dynamic. It can effectively check cheaters and does not have secure channel requirements.

  18. Prediction of Banking Systemic Risk Based on Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Shouwei Li

    2013-01-01

    Full Text Available Banking systemic risk is a complex nonlinear phenomenon and has shed light on the importance of safeguarding financial stability by recent financial crisis. According to the complex nonlinear characteristics of banking systemic risk, in this paper we apply support vector machine (SVM to the prediction of banking systemic risk in an attempt to suggest a new model with better explanatory power and stability. We conduct a case study of an SVM-based prediction model for Chinese banking systemic risk and find the experiment results showing that support vector machine is an efficient method in such case.

  19. High stability vector-based direct power control for DFIG-based wind turbine

    DEFF Research Database (Denmark)

    Zhu, Rongwu; Chen, Zhe; Wu, Xiaojie

    2015-01-01

    This paper proposes an improved vector-based direct power control (DPC) strategy for the doubly-fed induction generator (DFIG)-based wind energy conversion system. Based on the small signal model, the proposed DPC improves the stability of the DFIG, and avoids the DFIG operating in the marginal...... stable region (the real part of eigenvalue is equal to zero). The vector-based DPC combines with a space vector modulation technique to achieve a constant switching frequency. The simulation and experimental results clearly validate the effectiveness and feasibility of the proposed vector-based DPC...

  20. Estimation of sand liquefaction based on support vector machines

    Institute of Scientific and Technical Information of China (English)

    苏永华; 马宁; 胡检; 杨小礼

    2008-01-01

    The origin and influence factors of sand liquefaction were analyzed, and the relation between liquefaction and its influence factors was founded. A model based on support vector machines (SVM) was established whose input parameters were selected as following influence factors of sand liquefaction: magnitude (M), the value of SPT, effective pressure of superstratum, the content of clay and the average of grain diameter. Sand was divided into two classes: liquefaction and non-liquefaction, and the class label was treated as output parameter of the model. Then the model was used to estimate sand samples, 20 support vectors and 17 borderline support vectors were gotten, then the parameters were optimized, 14 support vectors and 6 borderline support vectors were gotten, and the prediction precision reaches 100%. In order to verify the generalization of the SVM method, two other practical samples’ data from two cities, Tangshan of Hebei province and Sanshui of Guangdong province, were dealt with by another more intricate model for polytomies, which also considered some influence factors of sand liquefaction as the input parameters and divided sand into four liquefaction grades: serious liquefaction, medium liquefaction, slight liquefaction and non-liquefaction as the output parameters. The simulation results show that the latter model has a very high precision, and using SVM model to estimate sand liquefaction is completely feasible.

  1. Efficient Vector-Based Forwarding for Underwater Sensor Networks

    Directory of Open Access Journals (Sweden)

    Peng Xie

    2010-01-01

    Full Text Available Underwater Sensor Networks (UWSNs are significantly different from terrestrial sensor networks in the following aspects: low bandwidth, high latency, node mobility, high error probability, and 3-dimensional space. These new features bring many challenges to the network protocol design of UWSNs. In this paper, we tackle one fundamental problem in UWSNs: robust, scalable, and energy efficient routing. We propose vector-based forwarding (VBF, a geographic routing protocol. In VBF, the forwarding path is guided by a vector from the source to the target, no state information is required on the sensor nodes, and only a small fraction of the nodes is involved in routing. To improve the robustness, packets are forwarded in redundant and interleaved paths. Further, a localized and distributed self-adaptation algorithm allows the nodes to reduce energy consumption by discarding redundant packets. VBF performs well in dense networks. For sparse networks, we propose a hop-by-hop vector-based forwarding (HH-VBF protocol, which adapts the vector-based approach at every hop. We evaluate the performance of VBF and HH-VBF through extensive simulations. The simulation results show that VBF achieves high packet delivery ratio and energy efficiency in dense networks and HH-VBF has high packet delivery ratio even in sparse networks.

  2. Digital video steganalysis using motion vector recovery-based features.

    Science.gov (United States)

    Deng, Yu; Wu, Yunjie; Zhou, Linna

    2012-07-10

    As a novel digital video steganography, the motion vector (MV)-based steganographic algorithm leverages the MVs as the information carriers to hide the secret messages. The existing steganalyzers based on the statistical characteristics of the spatial/frequency coefficients of the video frames cannot attack the MV-based steganography. In order to detect the presence of information hidden in the MVs of video streams, we design a novel MV recovery algorithm and propose the calibration distance histogram-based statistical features for steganalysis. The support vector machine (SVM) is trained with the proposed features and used as the steganalyzer. Experimental results demonstrate that the proposed steganalyzer can effectively detect the presence of hidden messages and outperform others by the significant improvements in detection accuracy even with low embedding rates.

  3. Vaxvec: The first web-based recombinant vaccine vector database and its data analysis.

    Science.gov (United States)

    Deng, Shunzhou; Martin, Carly; Patil, Rasika; Zhu, Felix; Zhao, Bin; Xiang, Zuoshuang; He, Yongqun

    2015-11-27

    A recombinant vector vaccine uses an attenuated virus, bacterium, or parasite as the carrier to express a heterologous antigen(s). Many recombinant vaccine vectors and related vaccines have been developed and extensively investigated. To compare and better understand recombinant vectors and vaccines, we have generated Vaxvec (http://www.violinet.org/vaxvec), the first web-based database that stores various recombinant vaccine vectors and those experimentally verified vaccines that use these vectors. Vaxvec has now included 59 vaccine vectors that have been used in 196 recombinant vector vaccines against 66 pathogens and cancers. These vectors are classified to 41 viral vectors, 15 bacterial vectors, 1 parasitic vector, and 1 fungal vector. The most commonly used viral vaccine vectors are double-stranded DNA viruses, including herpesviruses, adenoviruses, and poxviruses. For example, Vaxvec includes 63 poxvirus-based recombinant vaccines for over 20 pathogens and cancers. Vaxvec collects 30 recombinant vector influenza vaccines that use 17 recombinant vectors and were experimentally tested in 7 animal models. In addition, over 60 protective antigens used in recombinant vector vaccines are annotated and analyzed. User-friendly web-interfaces are available for querying various data in Vaxvec. To support data exchange, the information of vaccine vectors, vaccines, and related information is stored in the Vaccine Ontology (VO). Vaxvec is a timely and vital source of vaccine vector database and facilitates efficient vaccine vector research and development.

  4. Convex Decomposition Based Cluster Labeling Method for Support Vector Clustering

    Institute of Scientific and Technical Information of China (English)

    Yuan Ping; Ying-Jie Tian; Ya-Jian Zhou; Yi-Xian Yang

    2012-01-01

    Support vector clustering (SVC) is an important boundary-based clustering algorithm in multiple applications for its capability of handling arbitrary cluster shapes. However,SVC's popularity is degraded by its highly intensive time complexity and poor label performance.To overcome such problems,we present a novel efficient and robust convex decomposition based cluster labeling (CDCL) method based on the topological property of dataset.The CDCL decomposes the implicit cluster into convex hulls and each one is comprised by a subset of support vectors (SVs).According to a robust algorithm applied in the nearest neighboring convex hulls,the adjacency matrix of convex hulls is built up for finding the connected components; and the remaining data points would be assigned the label of the nearest convex hull appropriately.The approach's validation is guaranteed by geometric proofs.Time complexity analysis and comparative experiments suggest that CDCL improves both the efficiency and clustering quality significantly.

  5. A new support vector machine based multiuser detection scheme

    Institute of Scientific and Technical Information of China (English)

    WANG Yong-jian; ZHAO Hong-lin

    2008-01-01

    In order to suppress the multiple access interference(MAI)in 3G,which limits the capacity of a CDMA communication system,a fast relevance vector machine(FRVM)is employed in the muhinser detection (MUD)scheme.This method aims to overcome the shortcomings of many ordinary support vector machine (SVM)based MUD schemes,such as the long training time and the inaccuracy of the decision data,and enhance the performance of a CDMA communication system.Computer simulation results demonstrate that the proposed FRVM based muhiuser detection has lower bit error rate,costs short training time,needs fewer kernel functions and possesses better near-far resistance.

  6. Study on Support Vector Machine Based on 1-Norm

    Institute of Scientific and Technical Information of China (English)

    PAN Mei-qin; HE Guo-ping; HAN Cong-ying; XUE Xin; SHI You-qun

    2006-01-01

    The model of optimization problem for Support Vector Machine(SVM) is provided, which based on the definitions of the dual norm and the distance between a point and its projection onto a given plane. The model of improved Support Vector Machine based on 1-norm (1 - SVM) is provided from the optimization problem, yet it is a discrete programming. With the smoothing technique and optimality knowledge, the discrete programming is changed into a continuous programming. Experimental results show that the algorithm is easy to implement and this method can select and suppress the problem features more efficiently.Illustrative examples show that the 1 - SVM deal with the linear or nonlinear classification well.

  7. Support vector machine-based multi-model predictive control

    Institute of Scientific and Technical Information of China (English)

    Zhejing BA; Youxian SUN

    2008-01-01

    In this paper,a support vector machine-based multi-model predictive control is proposed,in which SVM classification combines well with SVM regression.At first,each working environment is modeled by SVM regression and the support vector machine network-based model predictive control(SVMN-MPC)algorithm corresponding to each environment is developed,and then a multi-class SVM model is established to recognize multiple operating conditions.As for control,the current environment is identified by the multi-class SVM model and then the corresponding SVMN.MPCcontroller is activated at each sampling instant.The proposed modeling,switching and controller design is demonstrated in simulation results.

  8. Biosensor method and system based on feature vector extraction

    Science.gov (United States)

    Greenbaum, Elias [Knoxville, TN; Rodriguez, Jr., Miguel; Qi, Hairong [Knoxville, TN; Wang, Xiaoling [San Jose, CA

    2012-04-17

    A method of biosensor-based detection of toxins comprises the steps of providing at least one time-dependent control signal generated by a biosensor in a gas or liquid medium, and obtaining a time-dependent biosensor signal from the biosensor in the gas or liquid medium to be monitored or analyzed for the presence of one or more toxins selected from chemical, biological or radiological agents. The time-dependent biosensor signal is processed to obtain a plurality of feature vectors using at least one of amplitude statistics and a time-frequency analysis. At least one parameter relating to toxicity of the gas or liquid medium is then determined from the feature vectors based on reference to the control signal.

  9. Disturbance observer based current controller for vector controlled IM drives

    DEFF Research Database (Denmark)

    Teodorescu, Remus; Dal, Mehmet

    2008-01-01

    In order to increase the accuracy of the current control loop, usually, well known parameter compensation and/or cross decoupling techniques are employed for advanced ac drives. In this paper, instead of using these techniques an observer-based current controller is proposed for vector controlled...... coupling effects and increase robustness against parameters change without requiring any other compensation strategies. The experimental implementation results are provided to demonstrate validity and performance of the proposed control scheme.......In order to increase the accuracy of the current control loop, usually, well known parameter compensation and/or cross decoupling techniques are employed for advanced ac drives. In this paper, instead of using these techniques an observer-based current controller is proposed for vector controlled...

  10. Saudi License Plate Recognition Algorithm Based on Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    Khaled Suwais; Rana Al-Otaibi; Ali Alshahrani

    2013-01-01

    License plate recognition (LPR) is an image processing technology that is used to identify vehicles by their license plates. This paper presents a license plate recognition algorithm for Saudi car plates based on the support vector machine (SVM) algorithm. The new algorithm is efficient in recognizing the vehicles from the Arabic part of the plate. The performance of the system has been investigated and analyzed. The recognition accuracy of the algorithm is about 93.3%.

  11. Efficient Satellite Scheduling Based on Improved Vector Evaluated Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Tengyue Mao

    2012-03-01

    Full Text Available Satellite scheduling is a typical multi-peak, many-valley, nonlinear multi-objective optimization problem. How to effectively implement the satellite scheduling is a crucial research in space areas.This paper mainly discusses the performance of VEGA (Vector Evaluated Genetic Algorithm based on the study of basic principles of VEGA algorithm, algorithm realization and test function, and then improves VEGA algorithm through introducing vector coding, new crossover and mutation operators, new methods to assign fitness and hold good individuals. As a result, the diversity and convergence of improved VEGA algorithm of improved VEGA algorithm have been significantly enhanced and will be applied to Earth-Mars orbit optimization. At the same time, this paper analyzes the results of the improved VEGA, whose results of performance analysis and evaluation show that although VEGA has a profound impact upon multi-objective evolutionary research,  multi-objective evolutionary algorithm on the basis of Pareto seems to be a more effective method to get the non-dominated solutions from the perspective of diversity and convergence of experimental result. Finally, based on Visual C + + integrated development environment, we have implemented improved vector evaluation algorithm in the satellite scheduling.

  12. Available Bandwidth Estimation Strategy Based on the Network Allocation Vector

    Directory of Open Access Journals (Sweden)

    Hongtao Liu

    2012-12-01

    Full Text Available Available bandwidth is of great importance to network Quality of Service assurance, network load balancing, streaming media rate control, routing, and congestion control, etc.. In this paper, the available bandwidth estimation strategy based on the Network Allocation Vector for Wireless Sensor Networks is proposed. According to the size of the average contention window, network nodes predict the probability of collision in process of frame transmission, and then estimate the number of retransmission. Through the collection of Hello packets periodically sent by neighbors, nodes obtain their Network Allocation Vector, and then estimate the available bandwidth. The simulation results show that the strategy is simple and effective, can accurately estimate the collision of data frames as well as the available bandwidth of Wireless Sensor Networks.

  13. Virtual-vector-based space vector pulse width modulation of the DC-AC multilevel-clamped multilevel converter (MLC2)

    DEFF Research Database (Denmark)

    Rodriguez, Pedro; Busquets-Monge, Sergio; Blaabjerg, Frede;

    2011-01-01

    This work presents the development of the space vector pulse width modulation (SVPWM) of a new multi-level converter topology. First, the proposed converter and its natural space vector diagram are presented. Secondly, a modified space vector diagram based on the virtual-vectors technique is show...

  14. Study of solar active regions based on BOAO vector magnetograms

    CERN Document Server

    Moon, Y J; Yun, H S; Cho, E A

    1999-01-01

    In this study we present the study of solar active regions based on BOAO vector magnetograms and $H\\alpha$ filtergrams. With the new calibration method we analyzed BOAO vector magnetograms taken from the SOFT observational system to compare with those of other observing systems. In this study it has been demonstrated that (1) our longitudinal magnetogram matches very well the corresponding Mitaka's magnetogram to the extent that the maximum correlation yields r=0.962 between our re-scaled longitudinal magnetogram and the Mitaka's magnetogram; (2) according to a comparison of our magnetograms of AR 8422 with those taken at Mitaka solar observatory their longitudinal fields are very similar to each other while transverse fields are a little different possibly due to large noise level; (3) main features seen by our longitudinal magnetograms of AR 8422 and AR 8419 and the corresponding Kitt Peak magnetograms are very similar to each other; (4) time series of our vector magnetograms and H-alpha observations of AR ...

  15. Support vector based battery state of charge estimator

    Science.gov (United States)

    Hansen, Terry; Wang, Chia-Jiu

    This paper investigates the use of a support vector machine (SVM) to estimate the state-of-charge (SOC) of a large-scale lithium-ion-polymer (LiP) battery pack. The SOC of a battery cannot be measured directly and must be estimated from measurable battery parameters such as current and voltage. The coulomb counting SOC estimator has been used in many applications but it has many drawbacks [S. Piller, M. Perrin, Methods for state-of-charge determination and their application, J. Power Sources 96 (2001) 113-120]. The proposed SVM based solution not only removes the drawbacks of the coulomb counting SOC estimator but also produces accurate SOC estimates, using industry standard US06 [V.H. Johnson, A.A. Pesaran, T. Sack, Temperature-dependent battery models for high-power lithium-ion batteries, in: Presented at the 17th Annual Electric Vehicle Symposium Montreal, Canada, October 15-18, 2000. The paper is downloadable at website http://www.nrel.gov/docs/fy01osti/28716.pdf] aggressive driving cycle test procedures. The proposed SOC estimator extracts support vectors from a battery operation history then uses only these support vectors to estimate SOC, resulting in minimal computation load and suitable for real-time embedded system applications.

  16. 2D Vector Field Simplification Based on Robustness

    KAUST Repository

    Skraba, Primoz

    2014-03-01

    Vector field simplification aims to reduce the complexity of the flow by removing features in order of their relevance and importance, to reveal prominent behavior and obtain a compact representation for interpretation. Most existing simplification techniques based on the topological skeleton successively remove pairs of critical points connected by separatrices, using distance or area-based relevance measures. These methods rely on the stable extraction of the topological skeleton, which can be difficult due to instability in numerical integration, especially when processing highly rotational flows. These geometric metrics do not consider the flow magnitude, an important physical property of the flow. In this paper, we propose a novel simplification scheme derived from the recently introduced topological notion of robustness, which provides a complementary view on flow structure compared to the traditional topological-skeleton-based approaches. Robustness enables the pruning of sets of critical points according to a quantitative measure of their stability, that is, the minimum amount of vector field perturbation required to remove them. This leads to a hierarchical simplification scheme that encodes flow magnitude in its perturbation metric. Our novel simplification algorithm is based on degree theory, has fewer boundary restrictions, and so can handle more general cases. Finally, we provide an implementation under the piecewise-linear setting and apply it to both synthetic and real-world datasets. © 2014 IEEE.

  17. Interframe hierarchical vector quantization using hashing-based reorganized codebook

    Science.gov (United States)

    Choo, Chang Y.; Cheng, Che H.; Nasrabadi, Nasser M.

    1995-12-01

    Real-time multimedia communication over PSTN (Public Switched Telephone Network) or wireless channel requires video signals to be encoded at the bit rate well below 64 kbits/second. Most of the current works on such very low bit rate video coding are based on H.261 or H.263 scheme. The H.263 encoding scheme, for example, consists mainly of motion estimation and compensation, discrete cosine transform, and run and variable/fixed length coding. Vector quantization (VQ) is an efficient and alternative scheme for coding at very low bit rate. One such VQ code applied to video coding is interframe hierarchical vector quantization (IHVQ). One problem of IHVQ, and VQ in general, is the computational complexity due to codebook search. A number of techniques have been proposed to reduce the search time which include tree-structured VQ, finite-state VQ, cache VQ, and hashing based codebook reorganization. In this paper, we present an IHVQ code with a hashing based scheme to reorganize the codebook so that codebook search time, and thus encoding time, can be significantly reduced. We applied the algorithm to the same test environment as in H.263 and evaluated coding performance. It turned out that the performance of the proposed scheme is significantly better than that of IHVQ without hashed codebook. Also, the performance of the proposed scheme was comparable to and often better than that of the H.263, due mainly to hashing based reorganized codebook.

  18. Estimation of underdetermined mixing matrix based on support vector machine

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    In underdetermined blind source separation (BSS), a novel algorithm based on extended support vector machine(SVM) is proposed to estimate the mixing matrix in this paper, including the number of the active sources. Instead of traditional clustering algorithms, it mainly takes the modulus of observations and the number in each direction of arrival, without any prior knowledge about the sources except for sparsity, and it is not sensitive to the initial values. Simulations are given to illustrate availability and robustness of our algorithm.

  19. Support vector classification algorithm based on variable parameter linear programming

    Institute of Scientific and Technical Information of China (English)

    Xiao Jianhua; Lin Jian

    2007-01-01

    To solve the problems of SVM in dealing with large sample size and asymmetric distributed samples, a support vector classification algorithm based on variable parameter linear programming is proposed.In the proposed algorithm, linear programming is employed to solve the optimization problem of classification to decrease the computation time and to reduce its complexity when compared with the original model.The adjusted punishment parameter greatly reduced the classification error resulting from asymmetric distributed samples and the detailed procedure of the proposed algorithm is given.An experiment is conducted to verify whether the proposed algorithm is suitable for asymmetric distributed samples.

  20. Stokes vector formalism based second harmonic generation microscopy

    Science.gov (United States)

    Qiu, Jianjun; Mazumder, Nirmal; Tsai, Han-Ruei; Hu, Chih-Wei; Kao, Fu-Jen

    2012-02-01

    In this study, we have developed a four-channel Stokes vector formalism based second harmonic generation (SHG) microscopy to map and analyze SHG signal. A four-channel Stokesmeter setup is calibrated and integrated into a laser scanning microscope to measure and characterize the SH's corresponding Stokes parameters. We are demonstrating the use of SH and its Stokes parameters to visualize the birefringence and crystalline orientation of KDP and collagen. We believe the developed method can reveal unprecedented information for biomedical and biomaterial studies.

  1. Debris Flow Hazard Assessment Based on Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    YUAN Lifeng; ZHANG Youshui

    2006-01-01

    Seven factors, including the maximum volume of once flow , occurrence frequency of debris flow , watershed area , main channel length , watershed relative height difference , valley incision density and the length ratio of sediment supplement are chosen as evaluation factors of debris flow hazard degree. Using support vector machine (SVM) theory, we selected 259 basic data of 37 debris flow channels in Yunnan Province as learning samples in this study. We create a debris flow hazard assessment model based on SVM. The model was validated though instance applications and showed encouraging results.

  2. Slope Deformation Prediction Based on Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Lei JIA

    2013-07-01

    Full Text Available This paper principally studies the prediction of slope deformation based on Support Vector Machine (SVM. In the prediction process,explore how to reconstruct the phase space. The geological body’s displacement data obtained from chaotic time series are used as SVM’s training samples. Slope displacement caused by multivariable coupling is predicted by means of single variable. Results show that this model is of high fitting accuracy and generalization, and provides reference for deformation prediction in slope engineering.

  3. Threat Assessment of Targets Based on Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    CAI Huai-ping; LIU Jing-xu; CHEN Ying-wu

    2006-01-01

    In the context of cooperative engagement of armored vehicles, the threat factors of offensive targets are analyzed, and a threat assessment (TA) model is built based on a support v.ector machine (SVM) method. The SVM-based model has some advantages over the traditional method-based models: the complex factors of threat are considered in the cooperative engagement; the shortcomings of neural networks, such as local minimum and "over fitting", are overcome to improve the generalization ability; its operation speed is high and meets the needs of real time C2 of cooperative engagement; the assessment results could be more reasonable because of its self-learning capability. The analysis and simulation indicate that the SVM method is an effective method to resolve the TA problems.

  4. Main features of DNA-based immunization vectors

    Directory of Open Access Journals (Sweden)

    V. Azevedo

    1999-02-01

    Full Text Available DNA-based immunization has initiated a new era of vaccine research. One of the main goals of gene vaccine development is the control of the levels of expression in vivo for efficient immunization. Modifying the vector to modulate expression or immunogenicity is of critical importance for the improvement of DNA vaccines. The most frequently used vectors for genetic immunization are plasmids. In this article, we review some of the main elements relevant to their design such as strong promoter/enhancer region, introns, genes encoding antigens of interest from the pathogen (how to choose and modify them, polyadenylation termination sequence, origin of replication for plasmid production in Escherichia coli, antibiotic resistance gene as selectable marker, convenient cloning sites, and the presence of immunostimulatory sequences (ISS that can be added to the plasmid to enhance adjuvanticity and to activate the immune system. In this review, the specific modifications that can increase overall expression as well as the potential of DNA-based vaccination are also discussed.

  5. An Efficient Audio Classification Approach Based on Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Lhoucine Bahatti

    2016-05-01

    Full Text Available In order to achieve an audio classification aimed to identify the composer, the use of adequate and relevant features is important to improve performance especially when the classification algorithm is based on support vector machines. As opposed to conventional approaches that often use timbral features based on a time-frequency representation of the musical signal using constant window, this paper deals with a new audio classification method which improves the features extraction according the Constant Q Transform (CQT approach and includes original audio features related to the musical context in which the notes appear. The enhancement done by this work is also lay on the proposal of an optimal features selection procedure which combines filter and wrapper strategies. Experimental results show the accuracy and efficiency of the adopted approach in the binary classification as well as in the multi-class classification.

  6. Reinforced Angle-based Multicategory Support Vector Machines

    Science.gov (United States)

    Zhang, Chong; Liu, Yufeng; Wang, Junhui; Zhu, Hongtu

    2015-01-01

    The Support Vector Machine (SVM) is a very popular classification tool with many successful applications. It was originally designed for binary problems with desirable theoretical properties. Although there exist various Multicategory SVM (MSVM) extensions in the literature, some challenges remain. In particular, most existing MSVMs make use of k classification functions for a k-class problem, and the corresponding optimization problems are typically handled by existing quadratic programming solvers. In this paper, we propose a new group of MSVMs, namely the Reinforced Angle-based MSVMs (RAMSVMs), using an angle-based prediction rule with k − 1 functions directly. We prove that RAMSVMs can enjoy Fisher consistency. Moreover, we show that the RAMSVM can be implemented using the very efficient coordinate descent algorithm on its dual problem. Numerical experiments demonstrate that our method is highly competitive in terms of computational speed, as well as classification prediction performance. Supplemental materials for the article are available online. PMID:27891045

  7. Support vector classifier based on principal component analysis

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Support vector classifier (SVC) has the superior advantages for small sample learning problems with high dimensions,with especially better generalization ability.However there is some redundancy among the high dimensions of the original samples and the main features of the samples may be picked up first to improve the performance of SVC.A principal component analysis (PCA) is employed to reduce the feature dimensions of the original samples and the pre-selected main features efficiently,and an SVC is constructed in the selected feature space to improve the learning speed and identification rate of SVC.Furthermore,a heuristic genetic algorithm-based automatic model selection is proposed to determine the hyperparameters of SVC to evaluate the performance of the learning machines.Experiments performed on the Heart and Adult benchmark data sets demonstrate that the proposed PCA-based SVC not only reduces the test time drastically,but also improves the identify rates effectively.

  8. Matrix-Vector Based Fast Fourier Transformations on SDR Architectures

    Directory of Open Access Journals (Sweden)

    Y. He

    2008-05-01

    Full Text Available Today Discrete Fourier Transforms (DFTs are applied in various radio standards based on OFDM (Orthogonal Frequency Division Multiplex. It is important to gain a fast computational speed for the DFT, which is usually achieved by using specialized Fast Fourier Transform (FFT engines. However, in face of the Software Defined Radio (SDR development, more general (parallel processor architectures are often desirable, which are not tailored to FFT computations. Therefore, alternative approaches are required to reduce the complexity of the DFT. Starting from a matrix-vector based description of the FFT idea, we will present different factorizations of the DFT matrix, which allow a reduction of the complexity that lies between the original DFT and the minimum FFT complexity. The computational complexities of these factorizations and their suitability for implementation on different processor architectures are investigated.

  9. Robust Source Localization in Shallow Water Based on Vector Optimization

    Institute of Scientific and Technical Information of China (English)

    SONG Hai-yan; SHI Jie; LIU Bo-sheng

    2013-01-01

    Owing to the multipath effect,the source localization in shallow water has been an area of active interest.However,most methods for source localization in shallow water are sensitive to the assumed model of the underwater environment and have poor robustness against the underwater channel uncertainty,which limit their further application in practical engineering.In this paper,a new method of source localization in shallow water,based on vector optimization concept,is described,which is highly robust against environmental factors affecting the localization,such as the channel depth,the bottom reflection coefficients,and so on.Through constructing the uncertainty set of the source vector errors and extracting the multi-path sound rays from the sea surface and bottom,the proposed method can accurately localize one or more sources in shallow water dominated by multipath propagation.It turns out that the natural formulation of our approach involves minimization of two quadratic functions subject to infinitely many nonconvex quadratic constraints.It shows that this problem (originally intractable) can be reformulated in a convex form as the so-called second-order cone program (SOCP) and solved efficiently by using the well-established interior point method,such as the software tool,SeDuMi.Computer simulations show better performance of the proposed method as compared with existing algorithms and establish a theoretical foundation for the practical engineering application.

  10. Robust source localization in shallow water based on vector optimization

    Science.gov (United States)

    Song, Hai-yan; Shi, Jie; Liu, Bo-sheng

    2013-06-01

    Owing to the multipath effect, the source localization in shallow water has been an area of active interest. However, most methods for source localization in shallow water are sensitive to the assumed model of the underwater environment and have poor robustness against the underwater channel uncertainty, which limit their further application in practical engineering. In this paper, a new method of source localization in shallow water, based on vector optimization concept, is described, which is highly robust against environmental factors affecting the localization, such as the channel depth, the bottom reflection coefficients, and so on. Through constructing the uncertainty set of the source vector errors and extracting the multi-path sound rays from the sea surface and bottom, the proposed method can accurately localize one or more sources in shallow water dominated by multipath propagation. It turns out that the natural formulation of our approach involves minimization of two quadratic functions subject to infinitely many nonconvex quadratic constraints. It shows that this problem (originally intractable) can be reformulated in a convex form as the so-called second-order cone program (SOCP) and solved efficiently by using the well-established interior point method, such as the software tool, SeDuMi. Computer simulations show better performance of the proposed method as compared with existing algorithms and establish a theoretical foundation for the practical engineering application.

  11. Facial biometrics based on 2D vector geometry

    Science.gov (United States)

    Malek, Obaidul; Venetsanopoulos, Anastasios; Androutsos, Dimitrios

    2014-05-01

    The main challenge of facial biometrics is its robustness and ability to adapt to changes in position orientation, facial expression, and illumination effects. This research addresses the predominant deficiencies in this regard and systematically investigates a facial authentication system in the Euclidean domain. In the proposed method, Euclidean geometry in 2D vector space is being constructed for features extraction and the authentication method. In particular, each assigned point of the candidates' biometric features is considered to be a 2D geometrical coordinate in the Euclidean vector space. Algebraic shapes of the extracted candidate features are also computed and compared. The proposed authentication method is being tested on images from the public "Put Face Database". The performance of the proposed method is evaluated based on Correct Recognition (CRR), False Acceptance (FAR), and False Rejection (FRR) rates. The theoretical foundation of the proposed method along with the experimental results are also presented in this paper. The experimental results demonstrate the effectiveness of the proposed method.

  12. SENSITIVITY ANALYSIS FOR ROLLING PROCESS BASED ON SUPPORT VECTOR MACHINE

    Institute of Scientific and Technical Information of China (English)

    Huang Yanwei; Wu Tihua; Zhao Jingyi; Wang Yiqun

    2005-01-01

    A method for the calculation of the sensitivity factors of the rolling process has been obtained by differentiating the roll force model based on support vector machine. It can eliminate the algebraic loop of the analytical model of the rolling process. The simulations in the first stand of five stand cold tandem rolling mill indicate that the calculation for sensitivities by this proposed method can obtain a good accuracy, and an appropriate adjustment on the control variables determined directly by the sensitivity has an excellent compensation accuracy. Moreover, the roll gap has larger effect on the exit thickness than both front tension and back tension, and it is more efficient to select the roll gap as the controlvariable of the thickness control system in the first stand.

  13. BEHAVIOR BASED CREDIT CARD FRAUD DETECTION USING SUPPORT VECTOR MACHINES

    Directory of Open Access Journals (Sweden)

    V. Dheepa

    2012-07-01

    Full Text Available Along with the great increase of internet and e-commerce, the use of credit card is an unavoidable one. Due to the increase of credit card usage, the frauds associated with this have also increased. There are a lot of approaches used to detect the frauds. In this paper, behavior based classification approach using Support Vector Machines are employed and efficient feature extraction method also adopted. If any discrepancies occur in the behaviors transaction pattern then it is predicted as suspicious and taken for further consideration to find the frauds. Generally credit card fraud detection problem suffers from a large amount of data, which is rectified by the proposed method. Achieving finest accuracy, high fraud catching rate and low false alarms are the main tasks of this approach.

  14. A three-axis SQUID-based absolute vector magnetometer

    Energy Technology Data Exchange (ETDEWEB)

    Schönau, T.; Schmelz, M.; Stolz, R.; Anders, S.; Linzen, S.; Meyer, H.-G. [Department of Quantum Detection, Leibniz Institute of Photonic Technology, Jena 07745 (Germany); Zakosarenko, V.; Meyer, M. [Supracon AG, An der Lehmgrube 11, Jena 07751 (Germany)

    2015-10-15

    We report on the development of a three-axis absolute vector magnetometer suited for mobile operation in the Earth’s magnetic field. It is based on low critical temperature dc superconducting quantum interference devices (LTS dc SQUIDs) with sub-micrometer sized cross-type Josephson junctions and exhibits a white noise level of about 10 fT/Hz{sup 1/2}. The width of superconducting strip lines is restricted to less than 6 μm in order to avoid flux trapping during cool-down in magnetically unshielded environment. The long-term stability of the flux-to-voltage transfer coefficients of the SQUID electronics is investigated in detail and a method is presented to significantly increase their reproducibility. We further demonstrate the long-term operation of the setup in a magnetic field varying by about 200 μT amplitude without the need for recalibration.

  15. A three-axis SQUID-based absolute vector magnetometer.

    Science.gov (United States)

    Schönau, T; Zakosarenko, V; Schmelz, M; Stolz, R; Anders, S; Linzen, S; Meyer, M; Meyer, H-G

    2015-10-01

    We report on the development of a three-axis absolute vector magnetometer suited for mobile operation in the Earth's magnetic field. It is based on low critical temperature dc superconducting quantum interference devices (LTS dc SQUIDs) with sub-micrometer sized cross-type Josephson junctions and exhibits a white noise level of about 10 fT/Hz(1/2). The width of superconducting strip lines is restricted to less than 6 μm in order to avoid flux trapping during cool-down in magnetically unshielded environment. The long-term stability of the flux-to-voltage transfer coefficients of the SQUID electronics is investigated in detail and a method is presented to significantly increase their reproducibility. We further demonstrate the long-term operation of the setup in a magnetic field varying by about 200 μT amplitude without the need for recalibration.

  16. Three—Dimensional Vector Field Visualization Based on Tensor Decomposition

    Institute of Scientific and Technical Information of China (English)

    梁训东; 李斌; 等

    1996-01-01

    This paper presents a visualization method called the deformed cube for visualizing 3D velocity vector field.Based on the decomposition of the tensor which describes the changes of the velocity,it provides a technique for visualizing local flow.A deformed cube,a cube transformed by a tensor in a local coordinate frame,shows the local stretch,shear and rigid body rotation of the local flow corresponding to the decomposed component of the tensor.Users can interactively view the local deformation or any component of the changes.The animation of the deformed cube moving along a streamline achieves a more global impression of the flow field.This method is intended as a complement to global visualization methods.

  17. Temperature prediction control based on least squares support vector machines

    Institute of Scientific and Technical Information of China (English)

    Bin LIU; Hongye SU; Weihua HUANG; Jian CHU

    2004-01-01

    A prediction control algorithm is presented based on least squares support vector machines (LS-SVM) model for a class of complex systems with strong nonlinearity.The nonlinear off-line model of the controlled plant is built by LS-SVM with radial basis function (RBF) kernel.In the process of system running,the off-line model is linearized at each sampling instant,and the generalized prediction control (GPC) algorithm is employed to implement the prediction control for the controlled plant.The obtained algorithm is applied to a boiler temperature control system with complicated nonlinearity and large time delay.The results of the experiment verify the effectiveness and merit of the algorithm.

  18. TYRE DYNAMICS MODELLING OF VEHICLE BASED ON SUPPORT VECTOR MACHINES

    Institute of Scientific and Technical Information of China (English)

    ZHENG Shuibo; TANG Houjun; HAN Zhengzhi; ZHANG Yong

    2006-01-01

    Various methods of tyre modelling are implemented from pure theoretical to empirical or semi-empirical models based on experimental results. A new way of representing tyre data obtained from measurements is presented via support vector machines (SVMs). The feasibility of applying SVMs to steady-state tyre modelling is investigated by comparison with three-layer backpropagation(BP) neural network at pure slip and combined slip. The results indicate SVMs outperform the BP neural network in modelling the tyre characteristics with better generalization performance. The SVMs-tyre is implemented in 8-DOF vehicle model for vehicle dynamics simulation by means of the PAC 2002 Magic Formula as reference. The SVMs-tyre can be a competitive and accurate method to model a tyre for vehicle dynamics simulation.

  19. Concept Vector for Similarity Measurement Based on Hierarchical Domain Structure

    OpenAIRE

    Hong Zhe Liu; Hong Bao; Xu

    2012-01-01

    The concept vector model generalizes standard representations of similarity concept in terms of tree-like structure. In the model, each concept node in the hierarchical tree has ancestor and descendent concept nodes composing its relevancy nodes, thus a concept node is represented as a concept vector according to its relevancy nodes' density and the similarity of the two concepts is obtained by computing cosine similarity between their vectors. In addition, the model is adjusted in terms of l...

  20. GA SPEED AND DQ CURRNETS CONTROL OF PMSM WITH VECTOR CONTROL BASED SPACE VECTOR MODULATION USING MATLAB/SIMULINK

    Directory of Open Access Journals (Sweden)

    A. El Janati El Idrissi

    2011-08-01

    Full Text Available In recent years, permanent magnet synchronous motors (PMSM have gained variety industrial applications, because of simple structures, high efficiency and ease of maintenance. But these motors have a nonlinear mathematical model. To resolve this problem several studies have suggested the application of soft-computing technique. This paper presents vector control of PMSM fed by space vector modulation inverter using genetic algorithm (GA controllers to improve speed, currents and the electric torque by significantly reducing their ripples, which offer an extra advantage of this study. The proposed method effectiveness has been verified by computer simulations using Matlab/Simulink®. These results are compared with the ones obtained with a vector control using PI controllers for speed and current [1] and the second obtained with adaptive controller based speed estimation technique [2].

  1. Support Vector Machine Ensemble Based on Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    LI Ye; YIN Ru-po; CAI Yun-ze; XU Xiao-ming

    2006-01-01

    Support vector machines (SVMs) have been introduced as effective methods for solving classification problems.However, due to some limitations in practical applications,their generalization performance is sometimes far from the expected level. Therefore, it is meaningful to study SVM ensemble learning. In this paper, a novel genetic algorithm based ensemble learning method, namely Direct Genetic Ensemble (DGE), is proposed. DGE adopts the predictive accuracy of ensemble as the fitness function and searches a good ensemble from the ensemble space. In essence, DGE is also a selective ensemble learning method because the base classifiers of the ensemble are selected according to the solution of genetic algorithm. In comparison with other ensemble learning methods, DGE works on a higher level and is more direct. Different strategies of constructing diverse base classifiers can be utilized in DGE.Experimental results show that SVM ensembles constructed by DGE can achieve better performance than single SVMs,bagged and boosted SVM ensembles. In addition, some valuable conclusions are obtained.

  2. Magnetic vector sensors based on the Hall effect

    Science.gov (United States)

    Roumenin, Ch. S.

    Integrated two- and three-dimensional vector versions of the parallel-field Hall microsensor proposed by Roumenin (1987) are presented. The characteristics of Roumenin's microsensor, which is activated by the external magnetic field parallel to the IC plane, are reviewed. The configurations of the magnetic two- and three-dimensional vector microsensors are illustrated and the operation of the microsensors is discussed.

  3. Feline Foamy Virus-Based Vectors: Advantages of an Authentic Animal Model

    Directory of Open Access Journals (Sweden)

    Martin Löchelt

    2013-07-01

    Full Text Available New-generation retroviral vectors have potential applications in vaccination and gene therapy. Foamy viruses are particularly interesting as vectors, because they are not associated to any disease. Vector research is mainly based on primate foamy viruses (PFV, but cats are an alternative animal model, due to their smaller size and the existence of a cognate feline foamy virus (FFV. The potential of replication-competent (RC FFV vectors for vaccination and replication-deficient (RD FFV-based vectors for gene delivery purposes has been studied over the past years. In this review, the key achievements and functional evaluation of the existing vectors from in vitro cell culture systems to out-bred cats will be described. The data presented here demonstrate the broad application spectrum of FFV-based vectors, especially in pathogen-specific prophylactic and therapeutic vaccination using RD vectors in cats and in classical gene delivery. In the cat-based system, FFV-based vectors provide an advantageous platform to evaluate and optimize the applicability, efficacy and safety of foamy virus (FV vectors, especially the understudied aspect of FV cell and organ tropism.

  4. Hybrid Support Vector Machines-Based Multi-fault Classification

    Institute of Scientific and Technical Information of China (English)

    GAO Guo-hua; ZHANG Yong-zhong; ZHU Yu; DUAN Guang-huang

    2007-01-01

    Support Vector Machines (SVM) is a new general machine-learning tool based on structural risk minimization principle. This characteristic is very signific ant for the fault diagnostics when the number of fault samples is limited. Considering that SVM theory is originally designed for a two-class classification, a hybrid SVM scheme is proposed for multi-fault classification of rotating machinery in our paper. Two SVM strategies, 1-v-1 (one versus one) and 1-v-r (one versus rest), are respectively adopted at different classification levels. At the parallel classification level, using 1-v-1 strategy, the fault features extracted by various signal analysis methods are transferred into the multiple parallel SVM and the local classification results are obtained. At the serial classification level, these local results values are fused by one serial SVM based on 1-v-r strategy. The hybrid SVM scheme introduced in our paper not only generalizes the performance of signal binary SVMs but improves the precision and reliability of the fault classification results. The actually testing results show the availability suitability of this new method.

  5. Improved Support Vector Machine Approach Based on Determining Thresholds Automatically

    Institute of Scientific and Technical Information of China (English)

    WANG Xiao-hua; YAN Xue-mei; WANG Xiao-guang

    2007-01-01

    To improve the training speed of support vector machine (SVM), a method called improved center distance ratio method (ICDRM) with determining thresholds automatically is presented here without reduce the identification rate. In this method border vectors are chosen from the given samples by comparing sample vectors with center distance ratio in advance. The number of training samples is reduced greatly and the training speed is improved. This method is used to the identification for license plate characters. Experimental results show that the improved SVM method-ICDRM does well at identification rate and training speed.

  6. Polarization tailored novel vector beams based on conical refraction

    CERN Document Server

    Turpin, A; Peinado, A; Lizana, A; Campos, J; Kalkandjiev, T K; Mompart, J

    2014-01-01

    Coherent vector beams with involved states of polarization (SOP) are widespread in the literature, having applications in laser processing, super-resolution imaging and particle trapping. We report novel vector beams obtained by transforming a Gaussian beam passing through a biaxial crystal, by means of the conical refraction phenomenon. We analyze both experimentally and theoretically the SOP of the different vector beams generated and demonstrate that the SOP of the input beam can be used to control both the shape and the SOP of the transformed beam. We also identify polarization singularities of such beams for the first time and demonstrate their control by the SOP of an input beam.

  7. Progress in Chimeric Vector and Chimeric Gene Based Cardiovascular Gene Therapy

    Institute of Scientific and Technical Information of China (English)

    HU Chun-Song; YOON Young-sup; ISNER Jeffrey M.; LOSORDO Douglas W.

    2003-01-01

    Gene therapy for cardiovascular diseases has developed from preliminary animal experiments to clinical trials. However, vectors and target genes used currently in gene therapy are mainly focused on viral, nonviral vector and single target gene or monogene. Each vector system has a series of advantages and limitations. Chimeric vectors which combine the advantages of viral and nonviral vector,chimeric target genes which combine two or more target genes and novel gene delivery modes are being developed. In this article, we summarized the progress in chimeric vectors and chimeric genes based cardiovascular gene therapy, which including proliferative or occlusive vascular diseases such as atheroslerosis and restenosis, hypertonic vascular disease such as hypertension and cardiac diseases such as myocardium ischemia, dilated cardiomyopathy and heart failure, even heart transplantation. The development of chimeric vector, chimeric gene and their cardiovascular gene therapy is promising.

  8. A Wavelet Kernel-Based Primal Twin Support Vector Machine for Economic Development Prediction

    Directory of Open Access Journals (Sweden)

    Fang Su

    2013-01-01

    Full Text Available Economic development forecasting allows planners to choose the right strategies for the future. This study is to propose economic development prediction method based on the wavelet kernel-based primal twin support vector machine algorithm. As gross domestic product (GDP is an important indicator to measure economic development, economic development prediction means GDP prediction in this study. The wavelet kernel-based primal twin support vector machine algorithm can solve two smaller sized quadratic programming problems instead of solving a large one as in the traditional support vector machine algorithm. Economic development data of Anhui province from 1992 to 2009 are used to study the prediction performance of the wavelet kernel-based primal twin support vector machine algorithm. The comparison of mean error of economic development prediction between wavelet kernel-based primal twin support vector machine and traditional support vector machine models trained by the training samples with the 3–5 dimensional input vectors, respectively, is given in this paper. The testing results show that the economic development prediction accuracy of the wavelet kernel-based primal twin support vector machine model is better than that of traditional support vector machine.

  9. Generating Fuzzy Rule-based Systems from Examples Based on Robust Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    JIA Jiong; ZHANG Hao-ran

    2006-01-01

    This paper firstly proposes a new support vector machine regression (SVR) with a robust loss function, and designs a gradient based algorithm for implementation of the SVR,then uses the SVR to extract fuzzy rules and designs fuzzy rule-based system. Simulations show that fuzzy rule-based system technique based on robust SVR achieves superior performance to the conventional fuzzy inference method, the proposed method provides satisfactory performance with excellent approximation and generalization property than the existing algorithm.

  10. Fuzzy support vector machines based on linear clustering

    Science.gov (United States)

    Xiong, Shengwu; Liu, Hongbing; Niu, Xiaoxiao

    2005-10-01

    A new Fuzzy Support Vector Machines (FSVMs) based on linear clustering is proposed in this paper. Its concept comes from the idea of linear clustering, selecting the data points near to the preformed hyperplane, which is formed on the training set including one positive and one negative training samples respectively. The more important samples near to the preformed hyperplane are selected by linear clustering technique, and the new FSVMs are formed on the more important data set. It integrates the merit of two kinds of FSVMs. The membership functions are defined using the relative distance between the data points and the preformed hyperplane during the training process. The fuzzy membership decision functions of multi-class FSVMs adopt the minimal value of all the decision functions of two-class FSVMs. To demonstrate the superiority of our methods, the benchmark data sets of machines learning databases are selected to verify the proposed FSVMs. The experimental results indicate that the proposed FSVMs can reduce the training data and running time, and its recognition rate is greater than or equal to that of FSVMs through selecting a suitable linear clustering parameter.

  11. Priori Information Based Support Vector Regression and Its Applications

    Directory of Open Access Journals (Sweden)

    Litao Ma

    2015-01-01

    Full Text Available In order to extract the priori information (PI provided by real monitored values of peak particle velocity (PPV and increase the prediction accuracy of PPV, PI based support vector regression (SVR is established. Firstly, to extract the PI provided by monitored data from the aspect of mathematics, the probability density of PPV is estimated with ε-SVR. Secondly, in order to make full use of the PI about fluctuation of PPV between the maximal value and the minimal value in a certain period of time, probability density estimated with ε-SVR is incorporated into training data, and then the dimensionality of training data is increased. Thirdly, using the training data with a higher dimension, a method of predicting PPV called PI-ε-SVR is proposed. Finally, with the collected values of PPV induced by underwater blasting at Dajin Island in Taishan nuclear power station in China, contrastive experiments are made to show the effectiveness of the proposed method.

  12. Explaining Support Vector Machines: A Color Based Nomogram

    Science.gov (United States)

    Van Belle, Vanya; Van Calster, Ben; Van Huffel, Sabine; Suykens, Johan A. K.; Lisboa, Paulo

    2016-01-01

    Problem setting Support vector machines (SVMs) are very popular tools for classification, regression and other problems. Due to the large choice of kernels they can be applied with, a large variety of data can be analysed using these tools. Machine learning thanks its popularity to the good performance of the resulting models. However, interpreting the models is far from obvious, especially when non-linear kernels are used. Hence, the methods are used as black boxes. As a consequence, the use of SVMs is less supported in areas where interpretability is important and where people are held responsible for the decisions made by models. Objective In this work, we investigate whether SVMs using linear, polynomial and RBF kernels can be explained such that interpretations for model-based decisions can be provided. We further indicate when SVMs can be explained and in which situations interpretation of SVMs is (hitherto) not possible. Here, explainability is defined as the ability to produce the final decision based on a sum of contributions which depend on one single or at most two input variables. Results Our experiments on simulated and real-life data show that explainability of an SVM depends on the chosen parameter values (degree of polynomial kernel, width of RBF kernel and regularization constant). When several combinations of parameter values yield the same cross-validation performance, combinations with a lower polynomial degree or a larger kernel width have a higher chance of being explainable. Conclusions This work summarizes SVM classifiers obtained with linear, polynomial and RBF kernels in a single plot. Linear and polynomial kernels up to the second degree are represented exactly. For other kernels an indication of the reliability of the approximation is presented. The complete methodology is available as an R package and two apps and a movie are provided to illustrate the possibilities offered by the method. PMID:27723811

  13. Using a geographical-information-system-based decision support to enhance malaria vector control in zambia.

    Science.gov (United States)

    Chanda, Emmanuel; Mukonka, Victor Munyongwe; Mthembu, David; Kamuliwo, Mulakwa; Coetzer, Sarel; Shinondo, Cecilia Jill

    2012-01-01

    Geographic information systems (GISs) with emerging technologies are being harnessed for studying spatial patterns in vector-borne diseases to reduce transmission. To implement effective vector control, increased knowledge on interactions of epidemiological and entomological malaria transmission determinants in the assessment of impact of interventions is critical. This requires availability of relevant spatial and attribute data to support malaria surveillance, monitoring, and evaluation. Monitoring the impact of vector control through a GIS-based decision support (DSS) has revealed spatial relative change in prevalence of infection and vector susceptibility to insecticides and has enabled measurement of spatial heterogeneity of trend or impact. The revealed trends and interrelationships have allowed the identification of areas with reduced parasitaemia and increased insecticide resistance thus demonstrating the impact of resistance on vector control. The GIS-based DSS provides opportunity for rational policy formulation and cost-effective utilization of limited resources for enhanced malaria vector control.

  14. Conservative rigid body dynamics by convected base vectors with implicit constraints

    DEFF Research Database (Denmark)

    Krenk, Steen; Nielsen, Martin Bjerre

    2014-01-01

    A conservative time integration formulation is developed for rigid bodies based on a convected set of orthonormal base vectors. The base vectors are represented in terms of their absolute coordinates, and thus the formulation makes use of three translation components, plus nine components...... of the base vectors. Orthogonality and unit length of the base vectors are imposed by constraining the equivalent Green strain components, and the kinetic energy is represented corresponding to rigid body motion. The equations of motion are obtained via Hamilton’s equations including the zero......-strain conditions as well as external constraints via Lagrange multipliers. Subsequently, the Lagrange multipliers associated with the internal zero-strain constraints are eliminated by use of a set of orthogonality conditions between the generalized displacements and the momentum vector, leaving a set...

  15. Model Based Defect Detection in Multi-Dimensional Vector Spaces

    Science.gov (United States)

    Honda, Toshifumi; Obara, Kenji; Harada, Minoru; Igarashi, Hajime

    A highly sensitive inspection algorithm is proposed that extracts defects in multidimensional vector spaces from multiple images. The proposed algorithm projects subtraction vectors calculated from test and reference images to control the noise by reducing the dimensionality of vector spaces. The linear projection vectors are optimized using a physical defect model, and the noise distribution is calculated from the images. Because the noise distribution varies with the intensity or texture of the pixels, the target image is divided into small regions and the noise distribution of the subtraction images are calculated for each divided region. The bidirectional local perturbation pattern matching (BD-LPPM) which is an enhanced version of the LPPM, is proposed to increase the sensitivity when calculating the subtraction vectors, especially when the reference image contains more high-frequency components than the test image. The proposed algorithm is evaluated using defect samples for three different scanning electron microscopy images. The results reveal that the proposed algorithm increases the signal-to-noise ratio by a factor of 1.32 relative to that obtained using the Mahalanobis distance algorithm.

  16. Novel adenovirus vaccine vectors based on the enteric-tropic serotype 41.

    Science.gov (United States)

    Lemiale, Franck; Haddada, Hedi; Nabel, Gary J; Brough, Douglas E; King, C Richter; Gall, Jason G D

    2007-03-01

    Replication-defective adenovirus vectors, primarily developed from serotype 5 (Ad5) viruses, have been widely used for gene transfer and vaccination approaches. Vectors based on other serotypes of adenovirus could be used in conjunction with, or in place of, Ad5 vectors. In this study, Ad41, an enteric adenovirus usually described as 'non-cultivable' or 'fastidious,' has been successfully cloned, rescued and propagated on 293-ORF6 cells. The complementation capabilities of this cell line allow generation of Ad41 vectors at titers comparable to those obtained for Ad5 vectors. Mice immunized with an Ad41 vector containing an HIV envelope (Env) gene mounted anti-Env cellular and humoral immune responses. Ad41-Env vectors appear to be particularly attractive when used in heterologous prime-boost regimens, where they induce significantly higher cellular immune responses to HIV-Env than Ad5-based regimens. Ad41-based constructs are attractive vaccine vectors alone or in combination with Ad5 adenovectors, since each vector type can provide circumvention of pre-existing immunity to the other.

  17. A Vector-based Cellular Automata Model for Simulating Urban Land Use Change

    Institute of Scientific and Technical Information of China (English)

    LU Yi; CAO Min; ZHANG Lei

    2015-01-01

    Cellular Automata (CA) is widely used for the simulation of land use changes.This study applied a vector-based CA model to simulate land use change in order to minimize or eliminate the scale sensitivity in traditional raster-based CA model.The cells of vector-based CA model are presented according to the shapes and attributes of geographic entities,and the transition rules of vector-based CA model are improved by taking spatial variables of the study area into consideration.The vector-based CA model is applied to simulate land use changes in downtown of Qidong City,Jiangsu Province,China and its validation is confirmed by the methods of visual assessment and spatial accuracy.The simulation result of vector-based CA model reveals that nearly 75% of newly increased urban cells are located in the northwest and southwest parts of the study area from 2002 to 2007,which is in consistent with real land use map.In addition,the simulation results of the vector-based and raster-based CA models are compared to real land use data and their spatial accuracies are found to be 84.0% and 81.9%,respectively.In conclusion,results from this study indicate that the vector-based CA model is a practical and applicable method for the simulation of urbanization processes.

  18. New Iterative Learning Control Algorithms Based on Vector Plots Analysis1)

    Institute of Scientific and Technical Information of China (English)

    XIESheng-Li; TIANSen-Ping; XIEZhen-Dong

    2004-01-01

    Based on vector plots analysis, this paper researches the geometric frame of iterativelearning control method. New structure of iterative learning algorithms is obtained by analyzingthe vector plots of some general algorithms. The structure of the new algorithm is different fromthose of the present algorithms. It is of faster convergence speed and higher accuracy. Simulationspresented here illustrate the effectiveness and advantage of the new algorithm.

  19. Likelihood-Based Cointegration Analysis in Panels of Vector Error Correction Models

    NARCIS (Netherlands)

    J.J.J. Groen (Jan); F.R. Kleibergen (Frank)

    1999-01-01

    textabstractWe propose in this paper a likelihood-based framework for cointegration analysis in panels of a fixed number of vector error correction models. Maximum likelihood estimators of the cointegrating vectors are constructed using iterated Generalized Method of Moments estimators. Using these

  20. A versatile system for USER cloning-based assembly of expression vectors for mammalian cell engineering.

    Directory of Open Access Journals (Sweden)

    Anne Mathilde Lund

    Full Text Available A new versatile mammalian vector system for protein production, cell biology analyses, and cell factory engineering was developed. The vector system applies the ligation-free uracil-excision based technique--USER cloning--to rapidly construct mammalian expression vectors of multiple DNA fragments and with maximum flexibility, both for choice of vector backbone and cargo. The vector system includes a set of basic vectors and a toolbox containing a multitude of DNA building blocks including promoters, terminators, selectable marker- and reporter genes, and sequences encoding an internal ribosome entry site, cellular localization signals and epitope- and purification tags. Building blocks in the toolbox can be easily combined as they contain defined and tested Flexible Assembly Sequence Tags, FASTs. USER cloning with FASTs allows rapid swaps of gene, promoter or selection marker in existing plasmids and simple construction of vectors encoding proteins, which are fused to fluorescence-, purification-, localization-, or epitope tags. The mammalian expression vector assembly platform currently allows for the assembly of up to seven fragments in a single cloning step with correct directionality and with a cloning efficiency above 90%. The functionality of basic vectors for FAST assembly was tested and validated by transient expression of fluorescent model proteins in CHO, U-2-OS and HEK293 cell lines. In this test, we included many of the most common vector elements for heterologous gene expression in mammalian cells, in addition the system is fully extendable by other users. The vector system is designed to facilitate high-throughput genome-scale studies of mammalian cells, such as the newly sequenced CHO cell lines, through the ability to rapidly generate high-fidelity assembly of customizable gene expression vectors.

  1. Spatio-temporal Rich Model Based Video Steganalysis on Cross Sections of Motion Vector Planes.

    Science.gov (United States)

    Tasdemir, Kasim; Kurugollu, Fatih; Sezer, Sakir

    2016-05-11

    A rich model based motion vector steganalysis benefiting from both temporal and spatial correlations of motion vectors is proposed in this work. The proposed steganalysis method has a substantially superior detection accuracy than the previous methods, even the targeted ones. The improvement in detection accuracy lies in several novel approaches introduced in this work. Firstly, it is shown that there is a strong correlation, not only spatially but also temporally, among neighbouring motion vectors for longer distances. Therefore, temporal motion vector dependency along side the spatial dependency is utilized for rigorous motion vector steganalysis. Secondly, unlike the filters previously used, which were heuristically designed against a specific motion vector steganography, a diverse set of many filters which can capture aberrations introduced by various motion vector steganography methods is used. The variety and also the number of the filter kernels are substantially more than that of used in previous ones. Besides that, filters up to fifth order are employed whereas the previous methods use at most second order filters. As a result of these, the proposed system captures various decorrelations in a wide spatio-temporal range and provides a better cover model. The proposed method is tested against the most prominent motion vector steganalysis and steganography methods. To the best knowledge of the authors, the experiments section has the most comprehensive tests in motion vector steganalysis field including five stego and seven steganalysis methods. Test results show that the proposed method yields around 20% detection accuracy increase in low payloads and 5% in higher payloads.

  2. 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)...

  3. Support vector regression-based internal model control

    Institute of Scientific and Technical Information of China (English)

    HUANG Yan-wei; PENG Tie-gen

    2007-01-01

    This paper proposes a design of internal model control systems for process with delay by using support vector regression (SVR). The proposed system fully uses the excellent nonlinear estimation performance of SVR with the structural risk minimization principle. Closed-system stability and steady error are analyzed for the existence of modeling errors. The simulations show that the proposed control systems have the better control performance than that by neural networks in the cases of the training samples with small size and noises.

  4. Chord Recognition Based on Temporal Correlation Support Vector Machine

    OpenAIRE

    Zhongyang Rao; Xin Guan; Jianfu Teng

    2016-01-01

    In this paper, we propose a method called temporal correlation support vector machine (TCSVM) for automatic major-minor chord recognition in audio music. We first use robust principal component analysis to separate the singing voice from the music to reduce the influence of the singing voice and consider the temporal correlations of the chord features. Using robust principal component analysis, we expect the low-rank component of the spectrogram matrix to contain the musical accompaniment and...

  5. Chord Recognition Based on Temporal Correlation Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Zhongyang Rao

    2016-05-01

    Full Text Available In this paper, we propose a method called temporal correlation support vector machine (TCSVM for automatic major-minor chord recognition in audio music. We first use robust principal component analysis to separate the singing voice from the music to reduce the influence of the singing voice and consider the temporal correlations of the chord features. Using robust principal component analysis, we expect the low-rank component of the spectrogram matrix to contain the musical accompaniment and the sparse component to contain the vocal signals. Then, we extract a new logarithmic pitch class profile (LPCP feature called enhanced LPCP from the low-rank part. To exploit the temporal correlation among the LPCP features of chords, we propose an improved support vector machine algorithm called TCSVM. We perform this study using the MIREX’09 (Music Information Retrieval Evaluation eXchange Audio Chord Estimation dataset. Furthermore, we conduct comprehensive experiments using different pitch class profile feature vectors to examine the performance of TCSVM. The results of our method are comparable to the state-of-the-art methods that entered the MIREX in 2013 and 2014 for the MIREX’09 Audio Chord Estimation task dataset.

  6. Fast encoding algorithm for vector quantization based on subvector L2-norm

    Institute of Scientific and Technical Information of China (English)

    Chen Shanxue; Li Fangwei; Zhu Weile

    2008-01-01

    A fast encoding algorithm based on the mean square error (MSE) distortion for vector quantization is introduced. The vector, which is effectively constructed with wavelet transform (WT) coefficients of images, can simplify the realization of the non-linear interpolated vector quantization (NLIVQ) technique and make the partial distance search (PDS) algorithm more efficient. Utilizing the relationship of vector L2-norm and its Euclidean distance, some conditions of eliminating unnecessary codewords are obtained. Further, using inequality constructed by the subvector L2-norm, more unnecessary codewords are eliminated. During the search process for code, mostly unlikely codewords can be rejected by the proposed algorithm combined with the non-linear interpolated vector quantization technique and the partial distance search technique. The experimental results show that the reduction of computation is outstanding in the encoding time and complexity against the full search method.

  7. Real-time traffic information extraction based on compressed video with interframe motion vector

    Institute of Scientific and Technical Information of China (English)

    黄庆明; 王聪

    2003-01-01

    Extraction of traffic information from image or video sequence is a hot research topic in intelligenttransportation system and computer vision. A real-time traffic information extraction method based on com-pressed video with interframe motion vectors for speed, density and flow detection, has been proposed for ex-traction of traffic information under fixed camera setting and well-defined environment. The motion vectors arefirst separated from the compressed video streams, and then filtered to eliminate incorrect and noisy vectors u-sing the well-defined environmental knowledge. By applying the projective transform and using the filtered mo-tion vectors, speed can be calculated from motion vector statistics, density can be estimated using the motionvector occupancy, and flow can be detected using the combination of speed and density. The embodiment of aprototype system for sky camera traffic monitoring using the MPEG video has been implemented, and experi-mental results proved the effectiveness of the method proposed.

  8. Digital Simulation of Space Vector Modulation Based Induction Motor Drive

    Directory of Open Access Journals (Sweden)

    G.V. Siva Krishna Rao and T.S. Surendra

    2011-04-01

    Full Text Available This study deals with simulation of Space vector modulated inverter fed induction motor drive. The drive system is modeled using matlab simulink and the results are presented. This drive has advantages like reduced harmonics and heating. Fixed AC is converted into DC and this DC is converted into variable voltage and variable frequency AC using SVM inverter. The output of SVM is applied to the stator of induction motor. The simulation results are compared with the analytical results. The FFT analysis shows that the current spectrum has reduced harmonics compared to the conventional system.

  9. Automatic SIMD vectorization of SSA-based control flow graphs

    CERN Document Server

    Karrenberg, Ralf

    2015-01-01

    Ralf Karrenberg presents Whole-Function Vectorization (WFV), an approach that allows a compiler to automatically create code that exploits data-parallelism using SIMD instructions. Data-parallel applications such as particle simulations, stock option price estimation or video decoding require the same computations to be performed on huge amounts of data. Without WFV, one processor core executes a single instance of a data-parallel function. WFV transforms the function to execute multiple instances at once using SIMD instructions. The author describes an advanced WFV algorithm that includes a v

  10. Web-based GIS: the vector-borne disease airline importation risk (VBD-AIR tool

    Directory of Open Access Journals (Sweden)

    Huang Zhuojie

    2012-08-01

    Full Text Available Abstract Background Over the past century, the size and complexity of the air travel network has increased dramatically. Nowadays, there are 29.6 million scheduled flights per year and around 2.7 billion passengers are transported annually. The rapid expansion of the network increasingly connects regions of endemic vector-borne disease with the rest of the world, resulting in challenges to health systems worldwide in terms of vector-borne pathogen importation and disease vector invasion events. Here we describe the development of a user-friendly Web-based GIS tool: the Vector-Borne Disease Airline Importation Risk Tool (VBD-AIR, to help better define the roles of airports and airlines in the transmission and spread of vector-borne diseases. Methods Spatial datasets on modeled global disease and vector distributions, as well as climatic and air network traffic data were assembled. These were combined to derive relative risk metrics via air travel for imported infections, imported vectors and onward transmission, and incorporated into a three-tier server architecture in a Model-View-Controller framework with distributed GIS components. A user-friendly web-portal was built that enables dynamic querying of the spatial databases to provide relevant information. Results The VBD-AIR tool constructed enables the user to explore the interrelationships among modeled global distributions of vector-borne infectious diseases (malaria. dengue, yellow fever and chikungunya and international air service routes to quantify seasonally changing risks of vector and vector-borne disease importation and spread by air travel, forming an evidence base to help plan mitigation strategies. The VBD-AIR tool is available at http://www.vbd-air.com. Conclusions VBD-AIR supports a data flow that generates analytical results from disparate but complementary datasets into an organized cartographical presentation on a web map for the assessment of vector-borne disease movements

  11. Vector Shift Method for Islanding Detection Based on Simulation Test

    Institute of Scientific and Technical Information of China (English)

    HOU Meiyi; GAO Houlei; LIU Bingxu; ZOU Guibin

    2008-01-01

    Vector shift (VS) is one of the typical methods used for islanding detection in distributed generations. This paper investigates the impact of both the active power imbalance and load variation on VS method. The investigation was conducted via simulation test in the power system dynamic simulation laboratory of Shandong University. The results show that it will take longer time for the VS relay to detect islanding state with the decrease of active power imbalance. In some cases, the vector shift angle is smaller than the setting and VS method would not be able to detect islanding state. In addition, the performance of VS method is impacted by the load variation in normal operation in which the distributed generator is operated in parallel with the main grid. The simulation results show that VS method would cause nuisance tripping if the load changes sharply. It can be summarized that VS method would be unable to reliably discriminate islanding state and normal system disturbances in some cases.

  12. Mass detection algorithm based on support vector machine and relevance feedback

    Institute of Scientific and Technical Information of China (English)

    Ying WANG; Xinbo GAO

    2008-01-01

    To improve the detection of mass with appearance that borders on the similarity between mass and density tissues in the breast,an support vector machine classifier based on typical features iS designed to classify the region of interest(ROI).Furthermore,relevance feedback is introduced to improve the performance of support vector machines.A new mass detection scheme based on the support vector machine and the relevance feedback is proposed.Simulation experiments on mammograms illustrate that the novel support vector machine classifier based on typical features can improve the detection performance of the featureless classifier by 5%,while the introduction of relevance feedback can further improve the detection performance to about 90%.

  13. Development of novel AAV serotype 6 based vectors with selective tropism for human cancer cells.

    Science.gov (United States)

    Sayroo, R; Nolasco, D; Yin, Z; Colon-Cortes, Y; Pandya, M; Ling, C; Aslanidi, G

    2016-01-01

    Viral vectors-based gene therapy is an attractive alternative to common anti-cancer treatments. In the present studies, AAV serotype 6 vectors were identified to be particularly effective in the transduction of human prostate (PC3), breast (T47D) and liver (Huh7) cancer cells. Next, we developed chimeric AAV vectors with Arg-Gly-Asp (RGD) peptide incorporated into the viral capsid to enable specific targeting of integrin-overexpressing malignant cells. These AAV6-RGD vectors improved transduction efficiency approximately 3-fold compared with wild-type AAV6 vectors by enhancing the viral entry into the cells. We also observed that transduction efficiency significantly improved, up to approximately 5-fold, by the mutagenesis of surface-exposed tyrosine and threonine residues involved in the intracellular trafficking of AAV vectors. Therefore, in our study, the AAV6-Y705-731F+T492V vector was identified as the most efficient. The combination of RGD peptide, tyrosine and threonine mutations on the same AAV6 capsid further increased the transduction efficiency, approximately 8-fold in vitro. In addition, we mutated lysine (K531E) to impair the affinity of AAV6 vectors to heparan sulfate proteoglycan. Finally, we showed a significant increase in both specificity and efficiency of AAV6-RGD-Y705-731F+T492V+K531E vectors in a xenograft animal model in vivo. In summary, the approach described here can lead to the development of AAV vectors with selective tropism to human cancer cells.

  14. Properties and use of novel replication-competent vectors based on Semliki Forest virus

    Directory of Open Access Journals (Sweden)

    Lulla Valeria

    2009-03-01

    Full Text Available Abstract Background Semliki Forest virus (SFV has a positive strand RNA genome and infects different cells of vertebrates and invertebrates. The 5' two-thirds of the genome encodes non-structural proteins that are required for virus replication and synthesis of subgenomic (SG mRNA for structural proteins. SG-mRNA is generated by internal initiation at the SG-promoter that is located at the complementary minus-strand template. Different types of expression systems including replication-competent vectors, which represent alphavirus genomes with inserted expression units, have been developed. The replication-competent vectors represent useful tools for studying alphaviruses and have potential therapeutic applications. In both cases, the properties of the vector, such as its genetic stability and expression level of the protein of interest, are important. Results We analysed 14 candidates of replication-competent vectors based on the genome of an SFV4 isolate that contained a duplicated SG promoter or an internal ribosomal entry site (IRES-element controlled marker gene. It was found that the IRES elements and the minimal -21 to +5 SG promoter were non-functional in the context of these vectors. The efficient SG promoters contained at least 26 residues upstream of the start site of SG mRNA. The insertion site of the SG promoter and its length affected the genetic stability of the vectors, which was always higher when the SG promoter was inserted downstream of the coding region for structural proteins. The stability also depended on the conditions used for vector propagation. A procedure based on the in vitro transcription of ligation products was used for generation of replication-competent vector-based expression libraries that contained hundreds of thousands of different genomes, and maintained genetic diversity and the ability to express inserted genes over five passages in cell culture. Conclusion The properties of replication-competent vectors

  15. A concurrent vector-based steering framework for particle transport

    CERN Document Server

    Apostolakis, John; Carminati, Federico; Gheata, Andrei; Wenzel, Sandro

    2014-01-01

    High Energy Physics has traditionally been a technology - limited science that has pushed the boundaries of both the detectors collecting the information about the particles and the computing infrastructure processing this information. However, since a few years the increase in computing power comes in the form of increased parallelism at all levels, and High Energy Physics has now to optimise its code to take advantage of the new architectures, including GPUs and hybrid systems. One of the primary targets for optimisation is the particle transport code used to simulate the detector response, as it is largely experiment independent and one of the most demanding applications in terms of CPU resources . The Geant Vector Prototype project aims to explore innovative designs in particle transport aimed at obtaining maximal performance on the new architectures. This paper describes the current status of the project and its future perspectives. In particular we describe how the present design tries to expose the par...

  16. An Adenoviral Vector Based Vaccine for Rhodococcus equi.

    Directory of Open Access Journals (Sweden)

    Carla Giles

    Full Text Available Rhodococcus equi is a respiratory pathogen which primarily infects foals and is endemic on farms around the world with 50% mortality and 80% morbidity in affected foals. Unless detected early and treated appropriately the disease can be fatal. Currently, there is no vaccine available to prevent this disease. For decades researchers have endeavoured to develop an effective vaccine to no avail. In this study a novel human adenoviral vector vaccine for R. equi was developed and tested in the mouse model. This vaccine generated a strong antibody and cytokine response and clearance of R. equi was demonstrated following challenge. These results show that this vaccine could potentially be developed further for use as a vaccine to prevent R. equi disease in foals.

  17. The seam offset identification based on support vector regression machines

    Institute of Scientific and Technical Information of China (English)

    Zeng Songsheng; Shi Yonghua; Wang Guorong; Huang Guoxing

    2009-01-01

    The principle of the support vector regression machine(SVR) is first analysed. Then the new data-dependent kernel function is constructed from information geometry perspective. The current waveforms change regularly in accordance with the different horizontal offset when the rotational frequency of the high speed rotational arc sensor is in the range from 15 Hz to 30 Hz. The welding current data is pretreated by wavelet filtering, mean filtering and normalization treatment. The SVR model is constructed by making use of the evolvement laws, the decision function can be achieved by training the SVR and the seam offset can be identified. The experimental results show that the precision of the offset identification can be greatly improved by modifying the SVR and applying mean filtering from the longitudinal direction.

  18. Support vector machine based on chaos particle swarm optimization for fault diagnosis of rotating machine

    Institute of Scientific and Technical Information of China (English)

    TANG Xian-lun; ZHUANG Ling; QIU Guo-qing; CAI Jun

    2009-01-01

    The performance of the support vector machine models depends on a proper setting of its parameters to a great extent. A novel method of searching the optimal parameters of support vector machine based on chaos particle swarm optimization is proposed. A multi-fault classification model based on SVM optimized by chaos particle swarm optimization is established and applied to the fault diagnosis of rotating machines. The results show that the proposed fault classification model outperforms the neural network trained by chaos particle swarm optimization and least squares support vector machine, and the precision and reliability of the fault classification results can meet the requirement of practical application. It indicates that chaos particle swarm optimization is a suitable method for searching the optimal parameters of support vector machine.

  19. A Highly Parallelized MIMO Detector for Vector-Based Reconfigurable Architectures

    OpenAIRE

    Zhang, Chenxin; Liu, Liang; Wang, Yian; Zhu, Meifang; Edfors, Ove; Öwall, Viktor

    2013-01-01

    This paper presents a highly parallelized MIMO signal detection algorithm targeting vector-based reconfigurable architectures. The detector achieves high data-level parallelism and near-ML performance by adopting a vector-architecture-friendly technique - parallel node perturbation. To further reduce the computational complexity, imbalanced node and successive partial node expansion schemes in conjunction with sorted QR decomposition are applied. The effectiveness of the proposed algorithm is...

  20. Classification of a set of vectors using self-organizing map- and rule-based technique

    Science.gov (United States)

    Ae, Tadashi; Okaniwa, Kaishirou; Nosaka, Kenzaburou

    2005-02-01

    There exist various objects, such as pictures, music, texts, etc., around our environment. We have a view for these objects by looking, reading or listening. Our view is concerned with our behaviors deeply, and is very important to understand our behaviors. We have a view for an object, and decide the next action (data selection, etc.) with our view. Such a series of actions constructs a sequence. Therefore, we propose a method which acquires a view as a vector from several words for a view, and apply the vector to sequence generation. We focus on sequences of the data of which a user selects from a multimedia database containing pictures, music, movie, etc... These data cannot be stereotyped because user's view for them changes by each user. Therefore, we represent the structure of the multimedia database as the vector representing user's view and the stereotyped vector, and acquire sequences containing the structure as elements. Such a vector can be classified by SOM (Self-Organizing Map). Hidden Markov Model (HMM) is a method to generate sequences. Therefore, we use HMM of which a state corresponds to the representative vector of user's view, and acquire sequences containing the change of user's view. We call it Vector-state Markov Model (VMM). We introduce the rough set theory as a rule-base technique, which plays a role of classifying the sets of data such as the sets of "Tour".

  1. Wavelet-based texture image classification using vector quantization

    Science.gov (United States)

    Lam, Eric P.

    2007-02-01

    Classification of image segments on textures can be helpful for target recognition. Sometimes target cueing is performed before target recognition. Textures are sometimes used to cue an image processor of a potential region of interest. In certain imaging sensors, such as those used in synthetic aperture radar, textures may be abundant. The textures may be caused by the object material or speckle noise. Even speckle noise can create the illusion of texture, which must be compensated in image pre-processing. In this paper, we will discuss how to perform texture classification but constrain the number of wavelet packet node decomposition. The new approach performs a twochannel wavelet decomposition. Comparing the strength of each new subband with others at the same level of the wavelet packet determines when to stop further decomposition. This type of decomposition is performed recursively. Once the decompositions stop, the structure of the packet is stored in a data structure. Using the information from the data structure, dominating channels are extracted. These are defined as paths from the root of the packet to the leaf with the highest strengths. The list of dominating channels are used to train a learning vector quantization neural network.

  2. A time-dependent vector field topology based on streak surfaces.

    Science.gov (United States)

    Uffinger, Markus; Sadlo, Filip; Ertl, Thomas

    2013-03-01

    It was shown recently how the 2D vector field topology concept, directly applicable to stationary vector fields only, can be generalized to time-dependent vector fields by replacing the role of stream lines by streak lines. The present paper extends this concept to 3D vector fields. In traditional 3D vector field topology separatrices can be obtained by integrating stream lines from 0D seeds corresponding to critical points. We show that in our new concept, in contrast, 1D seeding constructs are required for computing streak-based separatrices. In analogy to the 2D generalization we show that invariant manifolds can be obtained by seeding streak surfaces along distinguished path surfaces emanating from intersection curves between codimension-1 ridges in the forward and reverse finite-time Lyapunov exponent (FTLE) fields. These path surfaces represent a time-dependent generalization of critical points and convey further structure in time-dependent topology of vector fields. Compared to the traditional approach based on FTLE ridges, the resulting streak manifolds ease the analysis of Lagrangian coherent structures (LCS) with respect to visual quality and computational cost, especially when time series of LCS are computed. We exemplify validity and utility of the new approach using both synthetic examples and computational fluid dynamics results.

  3. Development and use of an efficient DNA-based viral gene silencing vector for soybean.

    Science.gov (United States)

    Zhang, Chunquan; Yang, Chunling; Whitham, Steven A; Hill, John H

    2009-02-01

    Virus-induced gene silencing (VIGS) is increasingly being used as a reverse genetics tool to study functions of specific plant genes. It is especially useful for plants, such as soybean, that are recalcitrant to transformation. Previously, Bean pod mottle virus (BPMV) was shown to be an effective VIGS vector for soybean. However, the reported BPMV vector requires in vitro RNA transcription and inoculation, which is not reliable or amenable to high-throughput applications. To increase the efficiency of the BPMV vector for soybean functional genomics, a DNA-based version was developed. Reported here is the construction of a Cauliflower mosaic virus 35S promoter-driven BPMV vector that is efficient for the study of soybean gene function. The selection of a mild rather than a severe BPMV strain greatly reduced the symptom interference caused by virus infection. The DNA-based BPMV vector was used to silence soybean homologues of genes involved in plant defense, translation, and the cytoskeleton in shoots and in roots. VIGS of the Actin gene resulted in reduced numbers of Soybean mosaic virus infection foci. The results demonstrate the utility of this new vector as an efficient tool for a wide range of applications for soybean functional genomics.

  4. Space vector-based analysis of overmodulation in triangle-comparison based PWM for voltage source inverter

    Indian Academy of Sciences (India)

    Manoj Kumar Modi; S Venugopal; G Narayanan

    2013-06-01

    The equivalence of triangle-comparison-based pulse width modulation (TCPWM) and space vector based PWM (SVPWM) during linear modulation is well-known. This paper analyses triangle-comparison based PWM techniques (TCPWM) such as sine-triangle PWM (SPWM) and common-mode voltage injection PWM during overmodulation from a space vector point of view. The average voltage vector produced by TCPWM during overmodulation is studied in the stationary (a–b) reference frame. This is compared and contrasted with the average voltage vector corresponding to the well-known standard two-zone algorithm for space vector modulated inverters. It is shown that the two-zone overmodulation algorithm itself can be derived from the variation of average voltage vector with TCPWM. The average voltage vector is further studied in a synchronously revolving (d-q) reference frame. The RMS value of low-order voltage ripple can be estimated, and can be used to compare harmonic distortion due to different PWM methods during overmodulation. The measured values of the total harmonic distortion (THD) in the line currents are presented at various fundamental frequencies. The relative values of measured current THD pertaining to different PWM methods tally with those of analytically evaluated RMS voltage ripple.

  5. Small-time scale network traffic prediction based on a local support vector machine regression model

    Institute of Scientific and Technical Information of China (English)

    Meng Qing-Fang; Chen Yue-Hui; Peng Yu-Hua

    2009-01-01

    In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.

  6. Development of avian sarcoma and leukosis virus-based vector-packaging cell lines

    Energy Technology Data Exchange (ETDEWEB)

    Stoker, A.W.; Bissell, M.J. (Univ. of California, Berkeley (USA))

    1988-03-01

    The authors have constructed an avian leukosis virus derivative with a 5{prime} deletion extending from within the tRNA primer binding site to a SacI site in the leader region. The aim was to remove cis-acting replicative and/or encapsidation sequences and to use this derivative, RAV-1{Psi}{sup {minus}}, to develop vector-packaging cell lines. They show that RAV-1{Psi}{sup {minus}} can be stably expressed in the quail cell line QT6 and chicken embryo fibroblasts and that it is completely replication deficient in both cell types. Moreover, they have demonstrated that QT6-derived lines expressing RAV-1{Psi}{sup {minus}} can efficiently package four structurally different replication-defective v-src expression vectors into infectious virus, with very low or undetectable helper virus release. These RAV-{Psi}{sup {minus}}-expressing cell lines comprise the first prototype avian sarcoma and leukosis virus-based vector-packaging system. The construction of our vectors has also shown us that a sequence present within gag, thought to facilitate virus packaging, is not necessary for efficient vector expression and high virus production. They show that quantitation and characterization of replication-defective viruses can be achieved with a sensitive immunocytochemical procedure, presenting an alternative to internal selectable vector markers.

  7. The generation of arbitrary vector beams using a division of a wavefront-based setup

    Science.gov (United States)

    Kalita, Ranjan; Gaffar, Md; Boruah, Bosanta R.

    2016-07-01

    In this paper, we introduce an arbitrary vector-beam-forming scheme using a simple arrangement involving only one liquid crystal spatial light modulator. An arbitrary vector beam can be obtained by overlapping two orthogonally polarized beams. In most of the existing vector-beam-forming schemes the two orthogonally polarized beams are essentially copies of a single incident wavefront. However, in the proposed scheme the two orthogonally polarized beams correspond to two separated parts of a single incident wavefront. Taking a cue from the two-beam interference phenomenon, the present scheme can be referred to as a division of a wavefront-based scheme. The proposed setup offers certain important advantages and is more suitable for the generation of higher average-power vector beams. We demonstrate the working of the vector-beam-forming scheme by generating various vector beams such as radially polarized, azimuthally polarized, and Bessel-Gauss beams and also a boat-shaped beam in the focal volume of a low-numerical-aperture focusing lens. The boat-shaped beam comprises a dark center surrounded by intense light from all but one direction. The beam is realized at the focus of an azimuthally polarized beam in the presence of a moderate amount of coma in the beam. The experimental results obtained using the proposed setup are verified by comparing them with the theoretical results.

  8. Gene Therapy Vectors with Enhanced Transfection Based on Hydrogels Modified with Affinity Peptides

    Science.gov (United States)

    Shepard, Jaclyn A.; Wesson, Paul J.; Wang, Christine E.; Stevans, Alyson C.; Holland, Samantha J.; Shikanov, Ariella; Grzybowski, Bartosz A.; Shea, Lonnie D.

    2011-01-01

    Regenerative strategies for damaged tissue aim to present biochemical cues that recruit and direct progenitor cell migration and differentiation. Hydrogels capable of localized gene delivery are being developed to provide a support for tissue growth, and as a versatile method to induce the expression of inductive proteins; however, the duration, level, and localization of expression isoften insufficient for regeneration. We thus investigated the modification of hydrogels with affinity peptides to enhance vector retention and increase transfection within the matrix. PEG hydrogels were modified with lysine-based repeats (K4, K8), which retained approximately 25% more vector than control peptides. Transfection increased 5- to 15-fold with K8 and K4 respectively, over the RDG control peptide. K8- and K4-modified hydrogels bound similar quantities of vector, yet the vector dissociation rate was reduced for K8, suggesting excessive binding that limited transfection. These hydrogels were subsequently applied to an in vitro co-culture model to induce NGF expression and promote neurite outgrowth. K4-modified hydrogels promoted maximal neurite outgrowth, likely due to retention of both the vector and the NGF. Thus, hydrogels modified with affinity peptides enhanced vector retention and increased gene delivery, and these hydrogels may provide a versatile scaffold for numerous regenerative medicine applications. PMID:21514659

  9. Gear Fault Diagnosis Based on Rough Set and Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    TIAN Huifang; SUN Shanxia

    2006-01-01

    By introducing Rough Set Theory and the principle of Support vector machine, a gear fault diagnosis method based on them is proposed. Firstly, diagnostic decision-making is reduced based on rough set theory, and the noise and redundancy in the sample are removed, then, according to the chosen reduction, a support vector machine multi-classifier is designed for gear fault diagnosis. Therefore, SVM' training data can be reduced and running speed can quicken. Test shows its accuracy and efficiency of gear fault diagnosis.

  10. Construction and Application of Newcastle Disease Virus-Based Vector Vaccines.

    Science.gov (United States)

    Wichgers Schreur, Paul J

    2016-01-01

    Paramyxoviruses are able to stably express a wide-variety of heterologous antigens at relatively high levels in various species and are consequently considered as potent gene delivery vehicles. A single vaccination is frequently sufficient for the induction of robust humoral and cellular immune responses. Here we provide detailed methods for the construction and application of Newcastle disease virus (NDV)-based vector vaccines. The in silico design and in vitro rescue as well as the in vivo evaluation of NDV based vectors are described in this chapter.

  11. Development of a Simulink/RTW-Based Realtime Control System for an Induction Motor Vector Control

    Energy Technology Data Exchange (ETDEWEB)

    Kang, M. H. [Sunmoon University, Chonan (Korea)

    2001-03-01

    In this research a Simulink/RTW-based realtime control system was developed for an induction motor vector control. On the Simulink window, the control system is designed in the form of block diagrams, program codes are produced automatically with the RTW(Real Time Workshop), then an DSP c compiles the program codes. With this automatic program producing method rapid prototyping is realized with the least time-consuming manual programming procedures. To show effectiveness of the proposed system designing scheme a DSP-based induction motor vector controller was constructed and implemented. (author). 10 refs., 15 figs., 2 tabs.

  12. Hybrid Asymmetric Space Vector Modulation for inverter based direct torque control induction motor drive

    Directory of Open Access Journals (Sweden)

    Nandakumar Sundararaju

    2014-05-01

    Full Text Available This paper proposes novel hybrid asymmetric space vector modulation technique for inverter operated direct torque control induction motor drive. The hybridization process is performed by the combination of continuous asymmetric space vector modulation pulse width technique (ASVPWM and fuzzy operated discontinuous ASVPWM technique. Combination process is based on pulse mismatching technique. Pulse mismatching technique helps to reduce the active region of the switch. Finally, optimal pulses are applied to control the inverter. The optimal hybrid pulse condense switching losses of the inverter and also improves the operating performance of the direct torque control (DTC based drive system like smooth dynamic response in speed reversal, minimum torque error, settling time of speed. Simulation results of proposed hybrid asymmetric space vector pulse width modulation technique to direct torque control (HASVPWM-DTC approach has been carried out by using Matlab-Simulink environment.

  13. Support Vector Machine for Behavior-Based Driver Identification System

    OpenAIRE

    Huihuan Qian; Yongsheng Ou; Xinyu Wu; Xiaoning Meng; Yangsheng Xu

    2010-01-01

    We present an intelligent driver identification system to handle vehicle theft based on modeling dynamic human behaviors. We propose to recognize illegitimate drivers through their driving behaviors. Since human driving behaviors belong to a dynamic biometrical feature which is complex and difficult to imitate compared with static features such as passwords and fingerprints, we find that this novel idea of utilizing human dynamic features for enhanced security applicat...

  14. Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Mohammed Hasan Abdulameer

    2014-01-01

    Full Text Available Existing face recognition methods utilize particle swarm optimizer (PSO and opposition based particle swarm optimizer (OPSO to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM. In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented.

  15. A Vector Network Analyzer Based on Pulse Generators

    Directory of Open Access Journals (Sweden)

    B. Schulte

    2005-01-01

    Full Text Available A fast four channel network analyzer is introduced to measure S-parameters in a frequency range from 10MHz to 3GHz. The signal generation for this kind of analyzer is based on pulse generators, which are realized with bipolar transistors. The output signal of the transistor is differentiated and two short pulses, a slow and a fast one, with opposite polarities are generated. The slow pulse is suppressed with a clipping network. Thus the generation of very short electrical pulses with a duration of about 100ps is possible. The structure of the following network analyzer is similar to the structure of a conventional four channel network analyzer. All four pulses, which contain the high frequency information of the device under test, are evaluated after the digitalization of intermediate frequencies. These intermediate frequencies are generated with sampling mixers. The recorded data is evaluated with a special analysis technique, which is based on a Fourier transformation. The calibration techniques used are the same as for conventional four channel network analyzers, no new calibration techniques need to be developed.

  16. A Kalman Filter for SINS Self-Alignment Based on Vector Observation.

    Science.gov (United States)

    Xu, Xiang; Xu, Xiaosu; Zhang, Tao; Li, Yao; Tong, Jinwu

    2017-01-29

    In this paper, a self-alignment method for strapdown inertial navigation systems based on the q-method is studied. In addition, an improved method based on integrating gravitational apparent motion to form apparent velocity is designed, which can reduce the random noises of the observation vectors. For further analysis, a novel self-alignment method using a Kalman filter based on adaptive filter technology is proposed, which transforms the self-alignment procedure into an attitude estimation using the observation vectors. In the proposed method, a linear psuedo-measurement equation is adopted by employing the transfer method between the quaternion and the observation vectors. Analysis and simulation indicate that the accuracy of the self-alignment is improved. Meanwhile, to improve the convergence rate of the proposed method, a new method based on parameter recognition and a reconstruction algorithm for apparent gravitation is devised, which can reduce the influence of the random noises of the observation vectors. Simulations and turntable tests are carried out, and the results indicate that the proposed method can acquire sound alignment results with lower standard variances, and can obtain higher alignment accuracy and a faster convergence rate.

  17. Enhanced transduction of mouse salivary glands with AAV5-based vectors

    NARCIS (Netherlands)

    H. Katano; M.R. Kok; A.P. Cotrim; S. Yamano; M. Schmidt; S. Afione; B.J. Baum; J.A. Chiorini

    2006-01-01

    We previously demonstrated that recombinant adeno-associated virus vectors based on serotype 2 (rAAV2) can direct transgene expression in salivary gland cells in vitro and in vivo. However, it is not known how other rAAV serotypes perform when infused into salivary glands. The capsids of serotypes 4

  18. Factors Affecting Learning of Vector Math from Computer-Based Practice: Feedback Complexity and Prior Knowledge

    Science.gov (United States)

    Heckler, Andrew F.; Mikula, Brendon D.

    2016-01-01

    In experiments including over 450 university-level students, we studied the effectiveness and time efficiency of several levels of feedback complexity in simple, computer-based training utilizing static question sequences. The learning domain was simple vector math, an essential skill in introductory physics. In a unique full factorial design, we…

  19. Rigid Body Time Integration by Convected Base Vectors with Implicit Constraints

    DEFF Research Database (Denmark)

    Krenk, Steen; Nielsen, Martin Bjerre

    2013-01-01

    of the kinetic energy used in the present formulation is deliberately chosen to correspond to a rigid body rotation, and the orthonormality constraints are introduced via the equivalent Green strain components of the base vectors. The particular form of the extended inertia tensor used here implies a set...

  20. A NEW METHOD OF CHANNEL FRICTION INVERSION BASED ON KALMAN FILTER WITH UNKNOWN PARAMETER VECTOR

    Institute of Scientific and Technical Information of China (English)

    CHENG Wei-ping; MAO Gen-hai; LIU Guo-hua

    2005-01-01

    Channel friction is an important parameter in hydraulic analysis.A channel friction parameter inversion method based on Kalman Filter with unknown parameter vector is proposed.Numerical simulations indicate that when the number of monitoring stations exceeds a critical value, the solution is hardly affected.In addition, Kalman Filter with unknown parameter vector is effective only at unsteady state.For the nonlinear equations, computations of sensitivity matrices are time-costly.Two simplified measures can reduce computing time, but not influence the results.One is to reduce sensitivity matrix analysis time, the other is to substitute for sensitivity matrix.

  1. Bearing Fault Diagnosis Based on Multiscale Permutation Entropy and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Jian-Jiun Ding

    2012-07-01

    Full Text Available Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, multiscale permutation entropy (MPE was introduced for feature extraction from faulty bearing vibration signals. After extracting feature vectors by MPE, the support vector machine (SVM was applied to automate the fault diagnosis procedure. Simulation results demonstrated that the proposed method is a very powerful algorithm for bearing fault diagnosis and has much better performance than the methods based on single scale permutation entropy (PE and multiscale entropy (MSE.

  2. Design and simulation of MEMS vector hydrophone with reduced cross section based meander beams

    Science.gov (United States)

    Kumar, Manoj; Dutta, S.; Pal, Ramjay; Jain, K. K.; Gupta, Sudha; Bhan, R. K.

    2016-04-01

    MEMS based vector hydrophone is being one of the key device in the underwater communications. In this paper, we presented a bio-inspired MEMS vector hydrophone. The hydrophone structure consists of a proof mass suspended by four meander type beams with reduced cross-section. Modal patterns of the structure were studied. First three modal frequencies of the hydrophone structure were found to be 420 Hz, 420 Hz and 1646 Hz respectively. The deflection and stress of the hydrophone is found have linear behavior in the 1 µPa - 1Pa pressure range.

  3. Block-Based Adaptive Vector Lifting Schemes for Multichannel Image Coding

    Directory of Open Access Journals (Sweden)

    Amel Benazza-Benyahia

    2007-04-01

    Full Text Available We are interested in lossless and progressive coding of multispectral images. To this respect, nonseparable vector lifting schemes are used in order to exploit simultaneously the spatial and the interchannel similarities. The involved operators are adapted to the image contents thanks to block-based procedures grounded on an entropy optimization criterion. A vector encoding technique derived from EZW allows us to further improve the efficiency of the proposed approach. Simulation tests performed on remote sensing images show that a significant gain in terms of bit rate is achieved by the resulting adaptive coding method with respect to the non-adaptive one.

  4. Block-Based Adaptive Vector Lifting Schemes for Multichannel Image Coding

    Directory of Open Access Journals (Sweden)

    Pesquet Jean-Christophe

    2007-01-01

    Full Text Available We are interested in lossless and progressive coding of multispectral images. To this respect, nonseparable vector lifting schemes are used in order to exploit simultaneously the spatial and the interchannel similarities. The involved operators are adapted to the image contents thanks to block-based procedures grounded on an entropy optimization criterion. A vector encoding technique derived from EZW allows us to further improve the efficiency of the proposed approach. Simulation tests performed on remote sensing images show that a significant gain in terms of bit rate is achieved by the resulting adaptive coding method with respect to the non-adaptive one.

  5. Estructuras mentales para modelar el aprendizaje del teorema de cambio base de vectores

    OpenAIRE

    2016-01-01

    Basados en la teoría APOE como marco teórico y metodológico, investigamos, desde una postura cognitiva, las estructuras mentales necesarias para construir el teorema para el cambio de base de vectores (TCBV). Con el propósito de analizar la forma en que estudiantes universitarios lo aprenden, se diseñó una descomposición genética (DG) para el teorema. Mediante tres casos de estudio se muestra cómo estudiantes de esos casos construyen el concepto de coordenadas de un vector, pero tienen dificu...

  6. Space Vector Based Generalized Dpwm Algorithms for Vsi Fed Induction Motor Drive

    Directory of Open Access Journals (Sweden)

    N. Praveena

    2013-09-01

    Full Text Available This paper presents Space Vector based Generalized DiscontinuousPulse width modulation (GDPWM algorithms for VSI fed Induction motor drive. To avoid the complexity due to angle calculation and sector identification involved in Conventional space vector pulse width modulation (CSVPWM. The Proposed algorithms use the concept of Imaginary Switching times and a constant variable µ and modulation phase angle δ are used to generate modulating waveforms.The proposed algorithms results in reduced current ripple over CSVPWM. To validate the proposed methods, simulation is carried on V/f controlled Induction Motor drive in MATLAB/SIMULINK environment and the results are discussed.

  7. Prediction and Classification of Human G-protein Coupled Receptors Based on Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    Yun-Fei Wang; Huan Chen; Yan-Hong Zhou

    2005-01-01

    A computational system for the prediction and classification of human G-protein coupled receptors (GPCRs) has been developed based on the support vector machine (SVM) method and protein sequence information. The feature vectors used to develop the SVM prediction models consist of statistically significant features selected from single amino acid, dipeptide, and tripeptide compositions of protein sequences. Furthermore, the length distribution difference between GPCRsand non-GPCRs has also been exploited to improve the prediction performance.The testing results with annotated human protein sequences demonstrate that this system can get good performance for both prediction and classification of human GPCRs.

  8. Cost-Effective Implementation of Order-Statistics Based Vector Filters Using Minimax Approximations

    CERN Document Server

    Celebi, M Emre; Lukac, Rastislav; Celiker, Fatih; 10.1364/JOSAA.26.001518

    2010-01-01

    Vector operators based on robust order statistics have proved successful in digital multichannel imaging applications, particularly color image filtering and enhancement, in dealing with impulsive noise while preserving edges and fine image details. These operators often have very high computational requirements which limits their use in time-critical applications. This paper introduces techniques to speed up vector filters using the minimax approximation theory. Extensive experiments on a large and diverse set of color images show that proposed approximations achieve an excellent balance among ease of implementation, accuracy, and computational speed.

  9. Modified sensorless vector control system of IM based on flux observer

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    This paper presents a speed sensorless vector control system for induction machine (IM), which is based on a flux observer. According to vector control theory of IM, the q-axis rotor flux converging on zero is utilized for speed estimation. Additionally this system solved the online identification of stator resistance by d-axis flux error.The advantages of the proposed system are simplicity and avoidance of the problems caused by only using a voltage model. The effectiveness of the proposed system has been verified by simulation and experimentation.

  10. Development of cup-shaped micro-electromechanical systems-based vector hydrophone

    Science.gov (United States)

    Xu, Wei; Liu, Yuan; Zhang, Guojun; Wang, Renxin; Xue, Chenyang; Zhang, Wendong; Liu, Jun

    2016-09-01

    Similar to the vital performance factors, the receiving sensitivity and the bandwidth exist interactively in the micro-electromechanical systems (MEMS)-based vector hydrophones. Some existing methods can improve the sensitivity of the hydrophone, but these improvements are usually gained at a cost of the bandwidth. However, the cup-shaped MEMS vector hydrophone that is presented in this paper can improve its sensitivity while retaining a sufficient bandwidth. The cup-shaped structure acts as a new sensing unit in the MEMS vector hydrophone, replacing the bionic columnar hair that was previously used for sensing. The relationships between the parameters of the cup-shaped structure and the sensitivity of the vector hydrophone were determined by a theoretical deduction. In addition, simulation analyses were performed, and optimized structural parameters were obtained in this work. ANSYS 15.0 simulation was used to derive the optimum characteristics for the cup-shaped structure. The results of the calibration experiments showed that the sensitivity reached up to -188.5 dB (gain of 40 dB, 1 kHz, 0 dB@1 V/μPa), and the bandwidth was in the 20 Hz-1 kHz range, which is sufficient for an underwater acoustic detection at low frequencies. This work has, thus, proved that the cup-shaped vector hydrophone has superior properties for the engineering applications.

  11. A very low bit rate video coder based on vector quantization.

    Science.gov (United States)

    Corte-Real, L; Alves, A P

    1996-01-01

    Describes a video coder based on a hybrid DPCM-vector quantization algorithm that is suited for bit rates ranging from 8-16 kb/s. The proposed approach involves segmenting difference images into variable-size and variable-shape blocks and performing segmentation and motion compensation simultaneously. The purpose of obtaining motion vectors for variable-size and variable-shape blocks is to improve the quality of motion estimation, particularly in those areas where the edges of moving objects are situated. For the larger blocks, decimation takes place in order to simplify vector quantization. For very active blocks, which are always of small dimension, a specific vector quantizer has been applied, the fuzzy classified vector quantizer (FCVQ). The coding algorithm described displays good performance in the compression of test sequences at the rates of 8 and 16 kb/s; the signal-to-noise ratios obtained are good in both cases. The complexity of the coder implementation is comparable to that of conventional hybrid coders, while the decoder is much simpler in this proposal.

  12. Highly stable atomic vector magnetometer based on free spin precession.

    Science.gov (United States)

    Afach, S; Ban, G; Bison, G; Bodek, K; Chowdhuri, Z; Grujić, Z D; Hayen, L; Hélaine, V; Kasprzak, M; Kirch, K; Knowles, P; Koch, H-C; Komposch, S; Kozela, A; Krempel, J; Lauss, B; Lefort, T; Lemière, Y; Mtchedlishvili, A; Naviliat-Cuncic, O; Piegsa, F M; Prashanth, P N; Quéméner, G; Rawlik, M; Ries, D; Roccia, S; Rozpedzik, D; Schmidt-Wellenburg, P; Severjins, N; Weis, A; Wursten, E; Wyszynski, G; Zejma, J; Zsigmond, G

    2015-08-24

    We present a magnetometer based on optically pumped Cs atoms that measures the magnitude and direction of a 1 μT magnetic field. Multiple circularly polarized laser beams were used to probe the free spin precession of the Cs atoms. The design was optimized for long-time stability and achieves a scalar resolution better than 300 fT for integration times ranging from 80 ms to 1000 s. The best scalar resolution of less than 80 fT was reached with integration times of 1.6 to 6 s. We were able to measure the magnetic field direction with a resolution better than 10 μrad for integration times from 10 s up to 2000 s.

  13. Screw Remaining Life Prediction Based on Quantum Genetic Algorithm and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Xiaochen Zhang

    2017-01-01

    Full Text Available To predict the remaining life of ball screw, a screw remaining life prediction method based on quantum genetic algorithm (QGA and support vector machine (SVM is proposed. A screw accelerated test bench is introduced. Accelerometers are installed to monitor the performance degradation of ball screw. Combined with wavelet packet decomposition and isometric mapping (Isomap, the sensitive feature vectors are obtained and stored in database. Meanwhile, the sensitive feature vectors are randomly chosen from the database and constitute training samples and testing samples. Then the optimal kernel function parameter and penalty factor of SVM are searched with the method of QGA. Finally, the training samples are used to train optimized SVM while testing samples are adopted to test the prediction accuracy of the trained SVM so the screw remaining life prediction model can be got. The experiment results show that the screw remaining life prediction model could effectively predict screw remaining life.

  14. Super-resolution microscopy based on fluorescence emission difference of cylindrical vector beams

    Science.gov (United States)

    Rong, Zihao; Kuang, Cuifang; Fang, Yue; Zhao, Guangyuan; Xu, Yingke; Liu, Xu

    2015-11-01

    We propose a novel fluorescence emission difference microscopy (FED) system based on focusing cylindrical vector beams. In conventional FED, a Gaussian beam and a 0-2π vortex phase plate are used to generate solid and hollow spots. We focus radially polarized and azimuthally polarized cylindrical vector beams to obtain an expanded solid spot and a shrunken hollow spot, taking advantage of the optical properties of cylindrical vector beams to improve the conventional FED performance. Our novel method enhances FED performance because the hollow spot size determines the FED resolution and an expanded solid spot effectively reduces negative side-lobe emergence during image processing. We demonstrate improved performance theoretically and experimentally using an in-house built FED. Our FED achieved resolution of less than λ/4 in test images of 100 nm nanoparticles, better than the confocal image resolution by a factor of approximately 1/3. We also discuss detailed simulation analyses and FED imaging of biological cells.

  15. Content Based Image Retrieval Using Exact Legendre Moments and Support Vector Machine

    CERN Document Server

    Rao, Ch Srinivasa; Mohan, B Chandra; 10.5121/ijma.2010.2206

    2010-01-01

    Content Based Image Retrieval (CBIR) systems based on shape using invariant image moments, viz., Moment Invariants (MI) and Zernike Moments (ZM) are available in the literature. MI and ZM are good at representing the shape features of an image. However, non-orthogonality of MI and poor reconstruction of ZM restrict their application in CBIR. Therefore, an efficient and orthogonal moment based CBIR system is needed. Legendre Moments (LM) are orthogonal, computationally faster, and can represent image shape features compactly. CBIR system using Exact Legendre Moments (ELM) for gray scale images is proposed in this work. Superiority of the proposed CBIR system is observed over other moment based methods, viz., MI and ZM in terms of retrieval efficiency and retrieval time. Further, the classification efficiency is improved by employing Support Vector Machine (SVM) classifier. Improved retrieval results are obtained over existing CBIR algorithm based on Stacked Euler Vector (SERVE) combined with Modified Moment In...

  16. A comparative study on change vector analysis based change detection techniques

    Indian Academy of Sciences (India)

    Sartajvir Singh; Rajneesh Talwar

    2014-12-01

    Detection of Earth surface changes are essential to monitor regional climatic, snow avalanche hazard analysis and energy balance studies that occur due to air temperature irregularities. Geographic Information System (GIS) enables such research activities to be carried out through change detection analysis. From this viewpoint, different change detection algorithms have been developed for land-use land-cover (LULC) region. Among the different change detection algorithms, change vector analysis (CVA) has level headed capability of extracting maximuminformation in terms of overall magnitude of change and the direction of change between multispectral bands from multi-temporal satellite data sets. Since past two–three decades, many effective CVA based change detection techniques e.g., improved change vector analysis (ICVA), modified change vector analysis (MCVA) and change vector analysis posterior-probability space (CVAPS), have been developed to overcome the difficulty that exists in traditional change vector analysis (CVA). Moreover, many integrated techniques such as cross correlogram spectral matching (CCSM) based CVA. CVA uses enhanced principal component analysis (PCA) and inverse triangular (IT) function, hyper-spherical direction cosine (HSDC), and median CVA (m-CVA), as an effective LULC change detection tools. This paper comprises a comparative analysis on CVA based change detection techniques such as CVA, MCVA, ICVA and CVAPS. This paper also summarizes the necessary integrated CVA techniques along with their characteristics, features and shortcomings. Based on experiment outcomes, it has been evaluated that CVAPS technique has greater potential than other CVA techniques to evaluate the overall transformed information over three differentMODerate resolution Imaging Spectroradiometer (MODIS) satellite data sets of different regions. Results of this study are expected to be potentially useful for more accurate analysis of LULC changes which will, in turn

  17. Atmospheric correction of remote sensing imagery based on the surface spectrum's vector space

    Institute of Scientific and Technical Information of China (English)

    CHEN Chun; LIU ChengYu; ZHANG ShuQing

    2012-01-01

    Due to the atmosphere effect,the qualities of images decrease conspicuously,practically in the visible bands,in the processing of earth observation by the satellite-borne sensors.Thus,removing the atmosphere effects has become a key step to improve the qualities of images and to retrieve the actual reflectivity of surface features.An atmospheric correction approach,called ACVSS (Atmospheric Correction based Vector Space of Spectrum),is proposed here based on the vector space of the features'spectrum.The reflectance image of each band is retrieved first according to the radiative transfer equation,then the spectrum's vector space is constructed using the infrared bands,and finally the residual errors of the reflectance images in the visible bands are corrected based on the pixel position in the spectrum's vector space.The proposed methodology is verified through atmospheric correction on Landsat-7 ETM+ imagery.The experimental results show that our method is more accurate and the corrected image is more distinct,compared with those offered by current popular atmospheric correction software.

  18. Hadoop-Based Distributed System for Online Prediction of Air Pollution Based on Support Vector Machine

    Science.gov (United States)

    Ghaemi, Z.; Farnaghi, M.; Alimohammadi, A.

    2015-12-01

    The critical impact of air pollution on human health and environment in one hand and the complexity of pollutant concentration behavior in the other hand lead the scientists to look for advance techniques for monitoring and predicting the urban air quality. Additionally, recent developments in data measurement techniques have led to collection of various types of data about air quality. Such data is extremely voluminous and to be useful it must be processed at high velocity. Due to the complexity of big data analysis especially for dynamic applications, online forecasting of pollutant concentration trends within a reasonable processing time is still an open problem. The purpose of this paper is to present an online forecasting approach based on Support Vector Machine (SVM) to predict the air quality one day in advance. In order to overcome the computational requirements for large-scale data analysis, distributed computing based on the Hadoop platform has been employed to leverage the processing power of multiple processing units. The MapReduce programming model is adopted for massive parallel processing in this study. Based on the online algorithm and Hadoop framework, an online forecasting system is designed to predict the air pollution of Tehran for the next 24 hours. The results have been assessed on the basis of Processing Time and Efficiency. Quite accurate predictions of air pollutant indicator levels within an acceptable processing time prove that the presented approach is very suitable to tackle large scale air pollution prediction problems.

  19. An Adaptive Integration Model of Vector Polyline to DEM Data Based on Spherical Degeneration Quadtree Grids

    Science.gov (United States)

    Zhao, X. S.; Wang, J. J.; Yuan, Z. Y.; Gao, Y.

    2013-10-01

    Traditional geometry-based approach can maintain the characteristics of vector data. However, complex interpolation calculations limit its applications in high resolution and multi-source spatial data integration at spherical scale in digital earth systems. To overcome this deficiency, an adaptive integration model of vector polyline and spherical DEM is presented. Firstly, Degenerate Quadtree Grid (DQG) which is one of the partition models for global discrete grids, is selected as a basic framework for the adaptive integration model. Secondly, a novel shift algorithm is put forward based on DQG proximity search. The main idea of shift algorithm is that the vector node in a DQG cell moves to the cell corner-point when the displayed area of the cell is smaller or equal to a pixel of screen in order to find a new vector polyline approximate to the original one, which avoids lots of interpolation calculations and achieves seamless integration. Detailed operation steps are elaborated and the complexity of algorithm is analyzed. Thirdly, a prototype system has been developed by using VC++ language and OpenGL 3D API. ASTER GDEM data and DCW roads data sets of Jiangxi province in China are selected to evaluate the performance. The result shows that time consumption of shift algorithm decreased about 76% than that of geometry-based approach. Analysis on the mean shift error from different dimensions has been implemented. In the end, the conclusions and future works in the integration of vector data and DEM based on discrete global grids are also given.

  20. A 'Drift' algorithm for integrating vector polyline and DEM based on the spherical DQG

    Science.gov (United States)

    Wang, Jiaojiao; Wang, Lei; Cao, Wenmin; Zhao, Xuesheng

    2014-03-01

    The efficient integration method of vector and DEM data on a global scale is one of the important issues in the community of Digital Earth. Among the existing methods, geometry-based approach maintains the characteristics of vector data necessary for inquiry and analysis. However, the complexity of geometry-based approach, which needs lots of interpolation calculation, limits its applications greatly in the multi-source spatial data integration on a global scale. To overcome this serious deficiency, a novel 'drift' algorithm is developed based on the spherical Degenerate Quadtree Grid (DQG) on which the global DEMs data is represented. The main principle of this algorithm is that the vector node in a DQG cell can be moved to the cell corner-point without changing the visualization effects if the cell is smaller or equal to a pixel of screen. A detailed algorithm and the multi-scale operation steps are also presented. By the 'drift' algorithm, the vector polylines and DEM grids are integrated seamlessly, avoiding lots of interpolation calculating. Based on the approach described above, we have developed a computer program in platform OpenGL 3D API with VC++ language. In this experiment, USGS GTOPO30 DEM data and 1:1,000,000 DCW roads data sets in China area are selected. Tests have shown that time consumption of the 'drift' algorithm is only about 25% of that of the traditional ones, moreover, the mean error of drift operation on vector nodes can be controlled within about half a DQG cell. In the end, the conclusions and future works are also given.

  1. Novel Method of Predicting Network Bandwidth Based on Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    沈伟; 冯瑞; 邵惠鹤

    2004-01-01

    In order to solve the problems of small sample over-fitting and local minima when neural networks learn online, a novel method of predicting network bandwidth based on support vector machines(SVM) is proposed. The prediction and learning online will be completed by the proposed moving window learning algorithm(MWLA). The simulation research is done to validate the proposed method, which is compared with the method based on neural networks.

  2. A method of recovering the initial vectors of globally coupled map lattices based on symbolic dynamics

    Institute of Scientific and Technical Information of China (English)

    Sun Li-Sha; Kang Xiao-Yun; Zhang Qiong; Lin Lan-Xin

    2011-01-01

    Based on symbolic dynamics,a novel computationally efficient algorithm is proposed to estimate the unknown initial vectors of globally coupled map lattices (CMLs).It is proved that not all inverse chaotic mapping functions are satisfied for contraction mapping.It is found that the values in phase space do not always converge on their initial values with respect to sufficient backward iteration of the symbolic vectors in terms of global convergence or divergence (CD).Both CD property and the coupling strength are directly related to the mapping function of the existing CML.Furthermore,the CD properties of Logistic,Bernoulli,and Tent chaotic mapping functions are investigated and compared.Various simulation results and the performances of the initial vector estimation with different signal-tonoise ratios (SNRs) are also provided to confirm the proposed algorithm.Finally,based on the spatiotemporal chaotic characteristics of the CML,the conditions of estimating the initial vectors using symbolic dynamics are discussed.The presented method provides both theoretical and experimental results for better understanding and characterizing the behaviours of spatiotemporal chaotic systems.

  3. An Effective NoSQL-Based Vector Map Tile Management Approach

    Directory of Open Access Journals (Sweden)

    Lin Wan

    2016-11-01

    Full Text Available Within a digital map service environment, the rapid growth of Spatial Big-Data is driving new requirements for effective mechanisms for massive online vector map tile processing. The emergence of Not Only SQL (NoSQL databases has resulted in a new data storage and management model for scalable spatial data deployments and fast tracking. They better suit the scenario of high-volume, low-latency network map services than traditional standalone high-performance computer (HPC or relational databases. In this paper, we propose a flexible storage framework that provides feasible methods for tiled map data parallel clipping and retrieval operations within a distributed NoSQL database environment. We illustrate the parallel vector tile generation and querying algorithms with the MapReduce programming model. Three different processing approaches, including local caching, distributed file storage, and the NoSQL-based method, are compared by analyzing the concurrent load and calculation time. An online geological vector tile map service prototype was developed to embed our processing framework in the China Geological Survey Information Grid. Experimental results show that our NoSQL-based parallel tile management framework can support applications that process huge volumes of vector tile data and improve performance of the tiled map service.

  4. Greedy Algorithm Computing Minkowski Reduced Lattice Bases with Quadratic Bit Complexity of Input Vectors

    Institute of Scientific and Technical Information of China (English)

    Hao CHEN; Liqing XU

    2011-01-01

    The authors present an algorithm which is a modification of the Nguyen-Stehle greedy reduction algorithm due to Nguyen and Stehle in 2009.This algorithm can be used to compute the Minkowski reduced lattice bases for arbitrary rank lattices with quadratic bit complexity on the size of the input vectors.The total bit complexity of the algorithm is O(n2·(4n!)n·(n!/2n)n/2·(4/3)n(n-1)/2·log2A),wherenistherankofthe lattice and A is maximal norm of the input base vectors.This is an O(log2 A) algorithm which can be used to compute Minkowski reduced bases for the fixed rank lattices.A time complexity n! · 3n(log A)O(1) algorithm which can be used to compute the successive minima with the help of the dual Hermite-Korkin-Zolotarev base was given by Blomer in 2000 and improved to the time complexity n! · (log A)O(1) by Micciancio in 2008.The algorithm in this paper is more suitable for computing the Minkowski reduced bases of low rank lattices with very large base vector sizes.

  5. Silencing Status Epilepticus-Induced BDNF Expression with Herpes Simplex Virus Type-1 Based Amplicon Vectors.

    Science.gov (United States)

    Falcicchia, Chiara; Trempat, Pascal; Binaschi, Anna; Perrier-Biollay, Coline; Roncon, Paolo; Soukupova, Marie; Berthommé, Hervé; Simonato, Michele

    2016-01-01

    Brain-derived neurotrophic factor (BDNF) has been found to produce pro- but also anti-epileptic effects. Thus, its validity as a therapeutic target must be verified using advanced tools designed to block or to enhance its signal. The aim of this study was to develop tools to silence the BDNF signal. We generated Herpes simplex virus type 1 (HSV-1) derived amplicon vectors, i.e. viral particles containing a genome of 152 kb constituted of concatameric repetitions of an expression cassette, enabling the expression of the gene of interest in multiple copies. HSV-1 based amplicon vectors are non-pathogenic and have been successfully employed in the past for gene delivery into the brain of living animals. Therefore, amplicon vectors should represent a logical choice for expressing a silencing cassette, which, in multiple copies, is expected to lead to an efficient knock-down of the target gene expression. Here, we employed two amplicon-based BDNF silencing strategies. The first, antisense, has been chosen to target and degrade the cytoplasmic mRNA pool of BDNF, whereas the second, based on the convergent transcription technology, has been chosen to repress transcription at the BDNF gene. Both these amplicon vectors proved to be effective in down-regulating BDNF expression in vitro, in BDNF-expressing mesoangioblast cells. However, only the antisense strategy was effective in vivo, after inoculation in the hippocampus in a model of status epilepticus in which BDNF mRNA levels are strongly increased. Interestingly, the knocking down of BDNF levels induced with BDNF-antisense was sufficient to produce significant behavioral effects, in spite of the fact that it was produced only in a part of a single hippocampus. In conclusion, this study demonstrates a reliable effect of amplicon vectors in knocking down gene expression in vitro and in vivo. Therefore, this approach may find broad applications in neurobiological studies.

  6. Silencing Status Epilepticus-Induced BDNF Expression with Herpes Simplex Virus Type-1 Based Amplicon Vectors.

    Directory of Open Access Journals (Sweden)

    Chiara Falcicchia

    Full Text Available Brain-derived neurotrophic factor (BDNF has been found to produce pro- but also anti-epileptic effects. Thus, its validity as a therapeutic target must be verified using advanced tools designed to block or to enhance its signal. The aim of this study was to develop tools to silence the BDNF signal. We generated Herpes simplex virus type 1 (HSV-1 derived amplicon vectors, i.e. viral particles containing a genome of 152 kb constituted of concatameric repetitions of an expression cassette, enabling the expression of the gene of interest in multiple copies. HSV-1 based amplicon vectors are non-pathogenic and have been successfully employed in the past for gene delivery into the brain of living animals. Therefore, amplicon vectors should represent a logical choice for expressing a silencing cassette, which, in multiple copies, is expected to lead to an efficient knock-down of the target gene expression. Here, we employed two amplicon-based BDNF silencing strategies. The first, antisense, has been chosen to target and degrade the cytoplasmic mRNA pool of BDNF, whereas the second, based on the convergent transcription technology, has been chosen to repress transcription at the BDNF gene. Both these amplicon vectors proved to be effective in down-regulating BDNF expression in vitro, in BDNF-expressing mesoangioblast cells. However, only the antisense strategy was effective in vivo, after inoculation in the hippocampus in a model of status epilepticus in which BDNF mRNA levels are strongly increased. Interestingly, the knocking down of BDNF levels induced with BDNF-antisense was sufficient to produce significant behavioral effects, in spite of the fact that it was produced only in a part of a single hippocampus. In conclusion, this study demonstrates a reliable effect of amplicon vectors in knocking down gene expression in vitro and in vivo. Therefore, this approach may find broad applications in neurobiological studies.

  7. Predicting domain-domain interaction based on domain profiles with feature selection and support vector machines

    Directory of Open Access Journals (Sweden)

    Liao Li

    2010-10-01

    Full Text Available Abstract Background Protein-protein interaction (PPI plays essential roles in cellular functions. The cost, time and other limitations associated with the current experimental methods have motivated the development of computational methods for predicting PPIs. As protein interactions generally occur via domains instead of the whole molecules, predicting domain-domain interaction (DDI is an important step toward PPI prediction. Computational methods developed so far have utilized information from various sources at different levels, from primary sequences, to molecular structures, to evolutionary profiles. Results In this paper, we propose a computational method to predict DDI using support vector machines (SVMs, based on domains represented as interaction profile hidden Markov models (ipHMM where interacting residues in domains are explicitly modeled according to the three dimensional structural information available at the Protein Data Bank (PDB. Features about the domains are extracted first as the Fisher scores derived from the ipHMM and then selected using singular value decomposition (SVD. Domain pairs are represented by concatenating their selected feature vectors, and classified by a support vector machine trained on these feature vectors. The method is tested by leave-one-out cross validation experiments with a set of interacting protein pairs adopted from the 3DID database. The prediction accuracy has shown significant improvement as compared to InterPreTS (Interaction Prediction through Tertiary Structure, an existing method for PPI prediction that also uses the sequences and complexes of known 3D structure. Conclusions We show that domain-domain interaction prediction can be significantly enhanced by exploiting information inherent in the domain profiles via feature selection based on Fisher scores, singular value decomposition and supervised learning based on support vector machines. Datasets and source code are freely available on

  8. Earth-Based Radar Speckle Displacement Interferometry to Study the Spin-Vector of Venus

    Science.gov (United States)

    Holin, I. V.

    2002-01-01

    The spin-vector of Venus was investigated by various Earth- and spacecraft-based techniques but until now no experimental data have been obtained on variations of both in magnitude and direction because of insufficient accuracy and too long measuring interval (much longer than the period of variations). In this work a new on principle ground-based radar interferometric technique named Radar Speckle Displacement Interferometry (RSDI) is proposed to measure precisely instantaneous vector components of and their variations with time. The technique is based on a so called far coherence (speckle displacement) effect for speckled radar fields scattered from randomly rough surfaces of moving objects and aims at precise measurement of their instantaneous rotational- progressive motion parameters.

  9. Video segmentation and classification for content-based storage and retrieval using motion vectors

    Science.gov (United States)

    Fernando, W. A. C.; Canagarajah, Cedric N.; Bull, David R.

    1998-12-01

    Video parsing is an important step in content-based indexing techniques, where the input video is decomposed into segments with uniform content. In video parsing detection of scene changes is one of the approaches widely used for extracting key frames from the video sequence. In this paper, an algorithm, based on motion vectors, is proposed to detect sudden scene changes and gradual scene changes (camera movements such as panning, tilting and zooming). Unlike some of the existing schemes, the proposed scheme is capable of detecting both sudden and gradual changes in uncompressed, as well as, compressed domain video. It is shown that the resultant motion vector can be used to identify and classify gradual changes due to camera movements. Results show that algorithm performed as well as the histogram-based schemes, with uncompressed video. The performance of the algorithm was also investigated with H.263 compressed video. The detection and classification of both sudden and gradual scene changes was successfully demonstrated.

  10. Dynamic Voltage Restorer Based on Space Vector Pulse Width Modulation Technique

    Directory of Open Access Journals (Sweden)

    B.N S P Venkatesh

    2011-07-01

    Full Text Available Power Quality problems encompass a wide range of disturbances such as voltage sags, swells, flicker,harmonics distortion and interruptions. The strategic deployment of custom power devices has been proposed asone of the means to protect sensitive loads from power quality problems such as voltage sags and swells. The Dynamic Voltage Restorer (DVR is a power electronic device that is used to inject 3-phase voltage in series and in synchronism with the distribution feeder voltages in order to compensate voltage sag and similarly itreacts quickly to inject the appropriate voltage component (negative voltage magnitude in order to compensate voltage swell. The principal component of the DVR is a voltage source inverter that generates three phase voltages and provides the voltage support to a sensitive load during voltage sags and swells. Pulse Width Modulation Technique is very critical for proper control of DVR. Sinusoidal Pulse Width Modulation (SPWM and Space Vector Pulse Width Modulation (SVPWM control techniques are used for controlling the DVR. Inthis work, the operation of DVR is presented and the control technique used for voltage source inverter is Space Vector PWM technique. Space vector PWM can utilize the better dc voltage and generates the fewer harmonic in inverter output voltage than Sinusoidal PWM technique. This work describes the DVR based on Space Vector PWM which provides voltage support to sensitive loads and is simulated by using MATLAB/SIMULINK. Simulation results show that the control approach is able to compensate for any type of voltage sags and swells.

  11. A Novel Soft Sensor Modeling Approach Based on Least Squares Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    Feng Rui(冯瑞); Song Chunlin; Zhang Yanzhu; Shao Huihe

    2004-01-01

    Artificial Neural Networks (ANNs) such as radial basis function neural networks (RBFNNs) have been successfully used in soft sensor modeling. However, the generalization ability of conventional ANNs is not very well. For this reason, we present a novel soft sensor modeling approach based on Support Vector Machines (SVMs). Since standard SVMs have the limitation of speed and size in training large data set, we hereby propose Least Squares Support Vector Machines (LS_SVMs) and apply it to soft sensor modeling. Systematic analysis is performed and the result indicates that the proposed method provides satisfactory performance with excellent approximation and generalization property. Monte Carlo simulations show that our soft sensor modeling approach achieves performance superior to the conventional method based on RBFNNs.

  12. Climate-based models for West Nile Culex mosquito vectors in the Northeastern US

    Science.gov (United States)

    Gong, Hongfei; Degaetano, Arthur T.; Harrington, Laura C.

    2011-05-01

    Climate-based models simulating Culex mosquito population abundance in the Northeastern US were developed. Two West Nile vector species, Culex pipiens and Culex restuans, were included in model simulations. The model was optimized by a parameter-space search within biological bounds. Mosquito population dynamics were driven by major environmental factors including temperature, rainfall, evaporation rate and photoperiod. The results show a strong correlation between the timing of early population increases (as early warning of West Nile virus risk) and decreases in late summer. Simulated abundance was highly correlated with actual mosquito capture in New Jersey light traps and validated with field data. This climate-based model simulates the population dynamics of both the adult and immature mosquito life stage of Culex arbovirus vectors in the Northeastern US. It is expected to have direct and practical application for mosquito control and West Nile prevention programs.

  13. Efficient production of transgenic chickens using self-inactive HIV-based lentiviral vectors

    Institute of Scientific and Technical Information of China (English)

    Shiyong XU; Yan SUN; Hongmei DING; Meng WANG; Yafei CAI; Jie CHEN; Honglin LIU

    2009-01-01

    We demonstrated the simple and effective production of transgenic chickens, in which the enhanced green fluorescence protein (EGFP) was expressed by using third-generation self-inactive HIV-based lentiviral vectors. In our experiments, lentiviruses were injected into 204 fertilized eggs, from which 30 ( 15% ) chickens were hatched. The exogenous gene was detected in the genomes of 16 out of 30 (53%) chickens. The green fluorescence signal was observed directly in various body parts, and was particularly significant in the testes. The transgenes were also found in the offspring of these chickens. The results indicate that HIV-based lentivirul vectors can be used to generate transgenic birds economically and effectively [Current Zoology 55 (5): 383 - 387,2009].

  14. AIG Based Nonlinear Anisotropic Smoothing Strategy for Vector-Valued Images

    Institute of Scientific and Technical Information of China (English)

    ZHANG Xiang-fen; TIAN Wei-feng; CHEN Wu-fan; YE Hong

    2009-01-01

    The effects of the Rician noise on the calculated tensors are analyzed and an affine invariant gradient (AIG) based nonlinear anisotropic smoothing strategy is presented. The AIG based smoothing strategy is a development of the affine invariant nonlinear anisotropic diffusion (AINAD) restoration model, introduced by Guillermo Sapiro, and adopted to restore vector-valued data. To evaluate the efficiency of the presented AINAD model in accounting for the Rician noise introduced into the vector-valued data, the peak-to-peak signal-to-noise ratio (PSNR), signal-to-mean squared error ratio (SMSE) and Beta(parameter that stands for edge preservation) metrics are used. The experiment results acquired from the synthetic and real data prove the good performance of the presented filter.

  15. Minimum Distortion Direction Prediction-based Fast Half-pixel Motion Vector Search Algorithm

    Institute of Scientific and Technical Information of China (English)

    DONG Hai-yan; ZHANG Qi-shan

    2005-01-01

    A minimum distortion direction prediction-based novel fast half-pixel motion vector search algorithm is proposed, which can reduce considerably the computation load of half-pixel search. Based on the single valley characteristic of half-pixel error matching function inside search grid, the minimum distortion direction is predicted with the help of comparative results of sum of absolute difference(SAD) values of four integer-pixel points around integer-pixel motion vector. The experimental results reveal that, to all kinds of video sequences, the proposed algorithm can obtain almost the same video quality as that of the half-pixel full search algorithm with a decrease of computation cost by more than 66%.

  16. Novel Sensorless Vector Control System of Induction Machine Based on Flux Observer in Field Weakening

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    A speed-sensorless vector control system for induction machines (IMs) is presented. According to the vector control theory of IMs, the rotor flux is estimated based on a flux observer,and the speed is estimated through the method of q-axis rotor flux converging on zero with proportional integral regulator. A 0.75 kW,50 Hz,two-pole induction machine was used in the simulation and experimental verification. The simulation model was constructed in Matlab. A series of tests were performed in the field weakening region, for both no-load and loaded operation. The estimated speed tracks the actual speed well in the based speed region and field weakening region (1 per unit value to 4 per unit value). The small estimation error of residual speed is due to the existence of slip.

  17. A Parallel Decision Model Based on Support Vector Machines and Its Application to Fault Diagnosis

    Institute of Scientific and Technical Information of China (English)

    Yan Weiwu(阎威武); Shao Huihe

    2004-01-01

    Many industrial process systems are becoming more and more complex and are characterized by distributed features. To ensure such a system to operate under working order, distributed parameter values are often inspected from subsystems or different points in order to judge working conditions of the system and make global decisions. In this paper, a parallel decision model based on Support Vector Machine (PDMSVM) is introduced and applied to the distributed fault diagnosis in industrial process. PDMSVM is convenient for information fusion of distributed system and it performs well in fault diagnosis with distributed features. PDMSVM makes decision based on synthetic information of subsystems and takes the advantage of Support Vector Machine. Therefore decisions made by PDMSVM are highly reliable and accurate.

  18. A Multiple Model Approach to Modeling Based on Fuzzy Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    冯瑞; 张艳珠; 宋春林; 邵惠鹤

    2003-01-01

    A new multiple models(MM) approach was proposed to model complex industrial process by using Fuzzy Support Vector Machines (F SVMs). By applying the proposed approach to a pH neutralization titration experi-ment, F_SVMs MM not only provides satisfactory approximation and generalization property, but also achieves superior performance to USOCPN multiple modeling method and single modeling method based on standard SVMs.

  19. Drifting model approach to modeling based on weighted support vector machines

    Institute of Scientific and Technical Information of China (English)

    冯瑞; 宋春林; 邵惠鹤

    2004-01-01

    This paper proposes a novel drifting modeling (DM) method. Briefly, we first employ an improved SVMs algorithm named weighted support vector machines (W_SVMs), which is suitable for locally learning, and then the DM method using the algorithm is proposed. By applying the proposed modeling method to Fluidized Catalytic Cracking Unit (FCCU), the simulation results show that the property of this proposed approach is superior to global modeling method based on standard SVMs.

  20. A Support Vector Machine-based Evaluation Model of Customer Satisfaction Degree in Logistics

    Institute of Scientific and Technical Information of China (English)

    SUN Hua-li; XIE Jian-ying

    2007-01-01

    This paper pressnts a novel evaluation model of the customer satisfaction degree (CSD) in logistics based on support vector machine (SVM). Firstly, the relation between the suppliers and the customers is analyzed. Secondly, the evaluation index system and fuzzy quantitative methods are provided. Thirdly, the CSD evaluation system including eight indexes and three ranks rinsed on one-against-one mode of SVM is built. Last simulation experiment is presented to illustrate the theoretical results.

  1. Fast Training of Support Vector Machines Using Error-Center-Based Optimization

    Institute of Scientific and Technical Information of China (English)

    L. Meng; Q. H. Wu

    2005-01-01

    This paper presents a new algorithm for Support Vector Machine (SVM) training, which trains a machine based on the cluster centers of errors caused by the current machine. Experiments withvarious training sets show that the computation time of this new algorithm scales almost linear with training set size and thus may be applied to much larger training sets, in comparison to standard quadratic programming (QP) techniques.

  2. A DSP-based discrete space vector modulation direct torque control of sensorless induction machines

    Energy Technology Data Exchange (ETDEWEB)

    Khoucha, F.; Marouani, K.; Kheloui, A.; Aliouane, K.

    2004-07-01

    In this paper, we present a Direct Torque Control scheme of an induction motor operating without speed sensor. The estimation of the stator flux and the rotor speed is performed by an adaptive observer. In order to reduce the torque, flux, current and speed ripple a Discrete Space Vector Modulation (DSVM-DTC) strategy is implemented using a DSP-based hardware. To illustrate the performances of this control scheme, experimental results are presented. (author)

  3. DSP-based fuzzy implementation of indirect vector controlled induction motor

    Energy Technology Data Exchange (ETDEWEB)

    Radwan, T.S.; Uddin, M.N.; Rahman, M.A. [Memorial University of Newfoundland, Faculty of Engineering and Applied Science, St John' s, NF (Canada)

    2000-08-01

    In this paper, the fuzzy logic speed controller for high performance induction motor drive is proposed. The controller is based on the indirect vector control. The fuzzy logic speed controller is employed as an outer loop. The results of applying the developed fuzzy logic controllers are compared to those obtained by the application of a conventional PI controller. The results indicate superior performance and robustness of fuzzy logic controllers over the PI controller at any working conditions. (orig.)

  4. Simplified polarization demultiplexing based on Stokes vector analysis for intensity-modulation direct-detection systems

    Science.gov (United States)

    Zhou, Xinyu; Yan, Lianshan; Chen, Zhiyu; Yi, Anlin; Pan, Yan; Jiang, Lin; Pan, Wei; Luo, Bin

    2016-10-01

    A simple and effective polarization demultiplexing method is proposed based on the improved Stokes vector analysis and digital signal processor algorithm for the intensity-modulation direct-detection optical communication systems. Such a scheme could significantly simplify optical receivers with low system cost. The experimental results demonstrate the feasibility of our proposed method and show that only 1- and 1.7-dB power penalties are measured for 10- and 25-km transmissions compared to back-to-back case.

  5. A Single Loop Vectorization Method Based on Assemble Code%一种基于汇编代码的单重循环向量化方法

    Institute of Scientific and Technical Information of China (English)

    陆洪毅; 戴葵; 王志英

    2003-01-01

    Through loops vectorization in instruction sequence, the vector power provided by hardware can be fully utilized. This paper analyzes the RISC instructton set, and presents a single loop vectorization method that is based on assemble code, it can efficiently detect single loops in instruct sequence and vectorize them.

  6. Similarity measure of spectral vectors based on set theory and its application in hyperspectral RS image retrieval

    Institute of Scientific and Technical Information of China (English)

    Peijun Du (杜培军); Tao Fang (方涛); Hong Tang (唐宏); Pengfei Shi (施鹏飞)

    2003-01-01

    In this paper, two new similarity measure methods based on set theory were proposed. Firstly, similarity measure of two sets based on set theory and set operation was discussed. This principle was used to spectral vectors, and two approaches were proposed. The first method was to create a spectral polygon corresponding to spectral curve, and similarity of two spectral vectors can be replaced by that of two polygons. Area of spectral polygon was used as quantification function and some effective indexes for similarity and dissimilarity were computed. The second method was to transform the original spectral vector to encoding vector according to absorption or reflectance feature bands, and similarity measure was conducted to encoding vectors. It proved that the spectral polygon-based approach was effective and can be used to hyperspectral RS image retrieval.

  7. Robustness-Based Simplification of 2D Steady and Unsteady Vector Fields

    KAUST Repository

    Skraba, Primoz

    2015-08-01

    © 2015 IEEE. Vector field simplification aims to reduce the complexity of the flow by removing features in order of their relevance and importance, to reveal prominent behavior and obtain a compact representation for interpretation. Most existing simplification techniques based on the topological skeleton successively remove pairs of critical points connected by separatrices, using distance or area-based relevance measures. These methods rely on the stable extraction of the topological skeleton, which can be difficult due to instability in numerical integration, especially when processing highly rotational flows. In this paper, we propose a novel simplification scheme derived from the recently introduced topological notion of robustness which enables the pruning of sets of critical points according to a quantitative measure of their stability, that is, the minimum amount of vector field perturbation required to remove them. This leads to a hierarchical simplification scheme that encodes flow magnitude in its perturbation metric. Our novel simplification algorithm is based on degree theory and has minimal boundary restrictions. Finally, we provide an implementation under the piecewise-linear setting and apply it to both synthetic and real-world datasets. We show local and complete hierarchical simplifications for steady as well as unsteady vector fields.

  8. A terrestrial lidar-based workflow for determining three-dimensional slip vectors and associated uncertainties

    Science.gov (United States)

    Gold, Peter O.; Cowgill, Eric; Kreylos, Oliver; Gold, Ryan D.

    2012-01-01

    Three-dimensional (3D) slip vectors recorded by displaced landforms are difficult to constrain across complex fault zones, and the uncertainties associated with such measurements become increasingly challenging to assess as landforms degrade over time. We approach this problem from a remote sensing perspective by using terrestrial laser scanning (TLS) and 3D structural analysis. We have developed an integrated TLS data collection and point-based analysis workflow that incorporates accurate assessments of aleatoric and epistemic uncertainties using experimental surveys, Monte Carlo simulations, and iterative site reconstructions. Our scanning workflow and equipment requirements are optimized for single-operator surveying, and our data analysis process is largely completed using new point-based computing tools in an immersive 3D virtual reality environment. In a case study, we measured slip vector orientations at two sites along the rupture trace of the 1954 Dixie Valley earthquake (central Nevada, United States), yielding measurements that are the first direct constraints on the 3D slip vector for this event. These observations are consistent with a previous approximation of net extension direction for this event. We find that errors introduced by variables in our survey method result in accounting for epistemic uncertainties.

  9. 2D Gaze Estimation Based on Pupil-Glint Vector Using an Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Jianzhong Wang

    2016-06-01

    Full Text Available Gaze estimation methods play an important role in a gaze tracking system. A novel 2D gaze estimation method based on the pupil-glint vector is proposed in this paper. First, the circular ring rays location (CRRL method and Gaussian fitting are utilized for pupil and glint detection, respectively. Then the pupil-glint vector is calculated through subtraction of pupil and glint center fitting. Second, a mapping function is established according to the corresponding relationship between pupil-glint vectors and actual gaze calibration points. In order to solve the mapping function, an improved artificial neural network (DLSR-ANN based on direct least squares regression is proposed. When the mapping function is determined, gaze estimation can be actualized through calculating gaze point coordinates. Finally, error compensation is implemented to further enhance accuracy of gaze estimation. The proposed method can achieve a corresponding accuracy of 1.29°, 0.89°, 0.52°, and 0.39° when a model with four, six, nine, or 16 calibration markers is utilized for calibration, respectively. Considering error compensation, gaze estimation accuracy can reach 0.36°. The experimental results show that gaze estimation accuracy of the proposed method in this paper is better than that of linear regression (direct least squares regression and nonlinear regression (generic artificial neural network. The proposed method contributes to enhancing the total accuracy of a gaze tracking system.

  10. A simple and robust vector-based shRNA expression system used for RNA interference.

    Directory of Open Access Journals (Sweden)

    Xue-jun Wang

    Full Text Available BACKGROUND: RNA interference (RNAi mediated by small interfering RNAs (siRNAs or short hairpin RNAs (shRNAs has become a powerful genetic tool for conducting functional studies. Previously, vector-based shRNA-expression strategies capable of inducing RNAi in viable cells have been developed, however, these vector systems have some disadvantages, either because they were error-prone or cost prohibitive. RESULTS: In this report we described the development of a simple, robust shRNA expression system utilizing 1 long oligonucleotide or 2 short oligonucleotides for half the cost of conventional shRNA construction methods and with a >95% cloning success rate. The shRNA loop sequence and stem structure were also compared and carefully selected for better RNAi efficiency. Furthermore, an easier strategy was developed based on isocaudomers which permit rapid combination of the most efficient promoter-shRNA cassettes. Finally, using this method, the conservative target sites for hepatitis B virus (HBV knockdown were systemically screened and HBV antigen expression shown to be successfully suppressed in the presence of connected multiple shRNAs both in vitro and in vivo. CONCLUSION: This novel design describes an inexpensive and effective way to clone and express single or multiple shRNAs from the same vector with the capacity for potent and effective silencing of target genes.

  11. Applying two channels to vector space secret sharing based multi-signature scheme

    Institute of Scientific and Technical Information of China (English)

    XIAO Qing-hua; PING Ling-di; CHEN Xiao-ping; PAN Xue-zeng

    2005-01-01

    Secret sharing and digital signature is an important research area in information security and has wide applications in such fields as safeguarding and legal use of confidential information, secure multiparty computation and electronic commerce. But up to now, study of signature based on general vector space secret sharing is very weak. Aiming at this drawback, the authors did some research on vector space secret sharing against cheaters, and proposed an efficient but secure vector space secret sharing based multi-signature scheme, which is implemented in two channels. In this scheme, the group signature can be easily produced if an authorized subset of participants pool their secret shadows and it is impossible for them to generate a group signature if an unauthorized subset of participants pool their secret shadows. The validity of the group signature can be verified by means of verification equations. A group signature of authorized subset of participants cannot be impersonated by any other set of participants. Moreover, the suspected forgery can be traced, and the malicious participants can be detected in the scheme. None of several possible attacks can successfully break this scheme.

  12. Advances in SVM-Based System Using GMM Super Vectors for Text-Independent Speaker Verification

    Institute of Scientific and Technical Information of China (English)

    ZHAO Jian; DONG Yuan; ZHAO Xianyu; YANG Hao; LU Liang; WANG Haila

    2008-01-01

    For text-independent speaker verification,the Gaussian mixture model (GMM) using a universal background model strategy and the GMM using support vector machines are the two most commonly used methodologies.Recently,a new SVM-based speaker verification method using GMM super vectors has been proposed.This paper describes the construction of a new speaker verification system and investigates the use of nuisance attribute projection and test normalization to further enhance performance.Experiments were conducted on the core test of the 2006 NIST speaker recognition evaluation corpus.The experimental results indicate that an SVM-based speaker verification system using GMM super vectors can achieve ap-pealing performance.With the use of nuisance attribute projection and test normalization,the system per-formance can be significantly improved,with improvements in the equal error rate from 7.78% to 4.92% and detection cost function from 0.0376 to 0.0251.

  13. An Improved Endmember Selection Method Based on Vector Length for MODIS Reflectance Channels

    Directory of Open Access Journals (Sweden)

    Yuanliu Xu

    2015-05-01

    Full Text Available Endmember selection is the basis for sub-pixel land cover classifications using multiple endmember spectral mixture analysis (MESMA that adopts variant endmember matrices for each pixel to mitigate errors caused by endmember variability in SMA. A spectral library covering a large number of endmembers can account for endmember variability, but it also lowers the computational efficiency. Therefore, an efficient endmember selection scheme to optimize the library is crucial to implement MESMA. In this study, we present an endmember selection method based on vector length. The spectra of a land cover class were divided into subsets using vector length intervals of the spectra, and the representative endmembers were derived from these subsets. Compared with the available endmember average RMSE (EAR method, our approach improved the computational efficiency in endmember selection. The method accuracy was further evaluated using spectral libraries derived from the ground reference polygon and Moderate Resolution Imaging Spectroradiometer (MODIS imagery respectively. Results using the different spectral libraries indicated that MESMA combined with the new approach performed slightly better than EAR method, with Kappa coefficient improved from 0.75 to 0.78. A MODIS image was used to test the mapping fraction, and the representative spectra based on vector length successfully modeled more than 90% spectra of the MODIS pixels by 2-endmember models.

  14. Modelling of chaotic systems based on modified weighted recurrent least squares support vector machines

    Institute of Scientific and Technical Information of China (English)

    Sun Jian-Cheng; Zhang Tai-Yi; Liu Feng

    2004-01-01

    Positive Lyapunov exponents cause the errors in modelling of the chaotic time series to grow exponentially. In this paper, we propose the modified version of the support vector machines (SVM) to deal with this problem. Based on recurrent least squares support vector machines (RLS-SVM), we introduce a weighted term to the cost function to compensate the prediction errors resulting from the positive global Lyapunov exponents. To demonstrate the effectiveness of our algorithm, we use the power spectrum and dynamic invariants involving the Lyapunov exponents and the correlation dimension as criterions, and then apply our method to the Santa Fe competition time series. The simulation results shows that the proposed method can capture the dynamics of the chaotic time series effectively.

  15. Classification of power quality combined disturbances based on phase space reconstruction and support vector machines

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Power Quality (PQ) combined disturbances become common along with ubiquity of voltage flickers and harmonics. This paper presents a novel approach to classify the different patterns of PQ combined disturbances. The classification system consists of two parts, namely the feature extraction and the automatic recognition. In the feature extraction stage, Phase Space Reconstruction (PSR), a time series analysis tool, is utilized to construct disturbance signal trajectories. For these trajectories, several indices are proposed to form the feature vectors. Support Vector Machines (SVMs) are then implemented to recognize the different patterns and to evaluate the efficiencies. The types of disturbances discussed include a combination of short-term disturbances (voltage sags, swells) and long-term disturbances (flickers, harmonics), as well as their homologous single ones. The feasibilities of the proposed approach are verified by simulation with thousands of PQ events. Comparison studies based on Wavelet Transform (WT) and Artificial Neural Network (ANN) are also reported to show its advantages.

  16. The study of underwater acoustic communication technology based-on the acoustic vector sensor

    Institute of Scientific and Technical Information of China (English)

    QIAO Gang; SANG Enfang

    2007-01-01

    Underwater acoustic (UWA) communication based on an acoustic vector sensor is studied. The method of joint weighted sound pressure and velocity processing is used in phase modulation high-speed UWA communication system combined with coherent demodulation and adaptive equalization algorithm to demodulate and decode. Whereas the sound intensity could be used instead of pressure for frequency decoding in frequency modulation UWA communication system. The results of theory analysis, simulation calculations and lake trials have shown that either in phase modulation or in frequency modulation UWA communication system, the processing gain can be evidently increased, so that the BER (bit error rate) can be effectively reduced and the telemetry distance can be enlarged by using the acoustic vector sensor.

  17. Spatial aliasing for efficient direction-of-arrival estimation based on steering vector reconstruction

    Science.gov (United States)

    Yan, Feng-Gang; Cao, Bin; Rong, Jia-Jia; Shen, Yi; Jin, Ming

    2016-12-01

    A new technique is proposed to reduce the computational complexity of the multiple signal classification (MUSIC) algorithm for direction-of-arrival (DOA) estimate using a uniform linear array (ULA). The steering vector of the ULA is reconstructed as the Kronecker product of two other steering vectors, and a new cost function with spatial aliasing at hand is derived. Thanks to the estimation ambiguity of this spatial aliasing, mirror angles mathematically relating to the true DOAs are generated, based on which the full spectral search involved in the MUSIC algorithm is highly compressed into a limited angular sector accordingly. Further complexity analysis and performance studies are conducted by computer simulations, which demonstrate that the proposed estimator requires an extremely reduced computational burden while it shows a similar accuracy to the standard MUSIC.

  18. Particle Swarm Optimization Based Support Vector Regression for Blind Image Restoration

    Institute of Scientific and Technical Information of China (English)

    Ratnakar Dash; Pankaj Kumar Sa; Banshidhar Majhi

    2012-01-01

    This paper presents a swarm intelligence based parameter optimization of the support vector machine (SVM)for blind image restoration.In this work,SVM is used to solve a regression problem.Support vector regression (SVR)has been utilized to obtain a true mapping of images from the observed noisy blurred images.The parameters of SVR are optimized through particle swarm optimization (PSO) technique.The restoration error function has been utilized as the fitness function for PSO.The suggested scheme tries to adapt the SVM parameters depending on the type of blur and noise strength and the experimental results validate its effectiveness.The results show that the parameter optimization of the SVR model gives better performance than conventional SVR model as well as other competent schemes for blind image restoration.

  19. Vibration reliability analysis for aeroengine compressor blade based on support vector machine response surface method

    Institute of Scientific and Technical Information of China (English)

    GAO Hai-feng; BAI Guang-chen

    2015-01-01

    To ameliorate reliability analysis efficiency for aeroengine components, such as compressor blade, support vector machine response surface method (SRSM) is proposed. SRSM integrates the advantages of support vector machine (SVM) and traditional response surface method (RSM), and utilizes experimental samples to construct a suitable response surface function (RSF) to replace the complicated and abstract finite element model. Moreover, the randomness of material parameters, structural dimension and operating condition are considered during extracting data so that the response surface function is more agreeable to the practical model. The results indicate that based on the same experimental data, SRSM has come closer than RSM reliability to approximating Monte Carlo method (MCM); while SRSM (17.296 s) needs far less running time than MCM (10958 s) and RSM (9840 s). Therefore, under the same simulation conditions, SRSM has the largest analysis efficiency, and can be considered a feasible and valid method to analyze structural reliability.

  20. Compensation method for temperature error of fiber optical gyroscope based on relevance vector machine.

    Science.gov (United States)

    Wang, Guochen; Wang, Qiuying; Zhao, Bo; Wang, Zhenpeng

    2016-02-10

    Aiming to improve the bias stability of the fiber optical gyroscope (FOG) in an ambient temperature-change environment, a temperature-compensation method based on the relevance vector machine (RVM) under Bayesian framework is proposed and applied. Compared with other temperature models such as quadratic polynomial regression, neural network, and the support vector machine, the proposed RVM method possesses higher accuracy to explain the temperature dependence of the FOG gyro bias. Experimental results indicate that, with the proposed RVM method, the bias stability of an FOG can be apparently reduced in the whole temperature ranging from -40°C to 60°C. Therefore, the proposed method can effectively improve the adaptability of the FOG in a changing temperature environment.

  1. Simulation of Electromagnetic Wave Logging Response in Deviated Wells Based on Vector Finite Element Method

    Institute of Scientific and Technical Information of China (English)

    LV Wei-Guo; CHU Zhao-Tan; ZHAO Xiao-Qing; FAN Yu-Xiu; SONG Ruo-Long; HAN Wei

    2009-01-01

    The vector finite element method of tetrahedral elements is used to model 3D electromagnetic wave logging response. The tangential component of the vector field at the mesh edges is used as a degree of freedom to overcome the shortcomings of node-based finite element methods. The algorithm can simulate inhomogeneous media with arbitrary distribution of conductivity and magnetic permeability. The electromagnetic response of well logging tools are studied in dipping bed layers with the borehole and invasion included. In order to simulate realistic logging tools, we take the transmitter antennas consisting of circular wire loops instead of magnetic dipoles. We also investigate the apparent resistivity of inhomogeneous formation for different dip angles.

  2. SAR Images Unsupervised Change Detection Based on Combination of Texture Feature Vector with Maximum Entropy Principle

    Directory of Open Access Journals (Sweden)

    ZHUANG Huifu

    2016-03-01

    Full Text Available Generally, spatial-contextual information would be used in change detection because there is significant speckle noise in synthetic aperture radar(SAR images. In this paper, using the rich texture information of SAR images, an unsupervised change detection approach to high-resolution SAR images based on texture feature vector and maximum entropy principle is proposed. The difference image is generated by using the 32-dimensional texture feature vector of gray-level co-occurrence matrix(GLCM. And the automatic threshold is obtained by maximum entropy principle. In this method, the appropriate window size to change detection is 11×11 according to the regression analysis of window size and precision index. The experimental results show that the proposed approach is better could both reduce the influence of speckle noise and improve the detection accuracy of high-resolution SAR image effectively; and it is better than Markov random field.

  3. A NEW FUZZY LOGIC BASED SPACE VECTOR MODULATION APPROACH ON DIRECT TORQUE CONTROLLED INDUCTION MOTORS

    Directory of Open Access Journals (Sweden)

    Fatih Korkmaz

    2013-11-01

    Full Text Available The induction motors are indispensable motor types for industrial applications due to its wellknown advantages. Therefore, many kind of control scheme are proposed for induction motors over the past years and direct torque control has gained great importance inside of them due to fast dynamic torque response behavior and simple control structure. This paper suggests a new approach on the direct torque controlled induction motors, Fuzzy logic based space vector modulation, to overcome disadvantages of conventional direct torque control like high torque ripple. In the proposed approach, optimum switching states are calculated by fuzzy logic controller and applied by space vector pulse width modulator to voltage source inverter. In order to test and compare the proposed DTC scheme with conventional DTC scheme simulations, in Matlab/Simulink, have been carried out in different speed and load conditions. The simulation results showed that a significant improvement in the dynamic torque and speed responses when compared to the conventional DTC scheme.

  4. Multipurpose speech watermarking based on multistage vector quantization of linear prediction coefficients

    Institute of Scientific and Technical Information of China (English)

    CHEN Ning; ZHU Jie

    2007-01-01

    To make speech watermarking achieve both copyright protection and integrity verification, a novel multipurpose speech watermarking algorithm based on the multistage vector quantization (MSVQ) of linear prediction coefficients (LPCs) is presented in this article. The property of natural speech that the vector quantization (VQ) indices of the LPCs amongst neigh- boring frames tend to be very similar is utilized to embed the robust watermark in the indices of the first-stage VQ (VQ1). Then, the semi-fragile watermark is embedded in the indices of the second-stage VQ (VQ2) with index constrained VQ encoding scheme. Both the robust watermark and the semi-fragile water- mark can be extracted without host speech. Simulation results verify the effectiveness of the proposed algorithm in terms of robustness and semi-fragility.

  5. Multiple Measurement Vectors ISAR Imaging Algorithm Based on a Class of Linearized Bregman Iteration

    Directory of Open Access Journals (Sweden)

    Chen Wenfeng

    2016-08-01

    Full Text Available This study aims to enable steady and speedy acquisition of Inverse Synthetic Aperture Radar (ISAR images using sparse echo data. To this end, a Multiple Measurement Vectors (MMV ISAR echo model is studied. This model is then combined with the Compressive Sensing (CS theory to realize a class of MMV fast ISAR imaging algorithms based on the Linearized Bregman Iteration (LBI. The algorithms involve four methods, and the iterative framework, application conditions, and relationship between the four methods are given. The reconstructed performance of the methods, convergence, anti-noise, and selection of regularization parameters are then compared and analyzed comprehensively. Finally, the experimental results are compared with the traditional Single Measurement Vector (SMV ISAR imaging algorithm; this comparison shows that the proposed algorithm delivers an improved imaging quality with a low Signal-to-Noise Ratio (SNR.

  6. Design and Development of a Vector Control System of Induction Motor Based on Dual CPU for Electric Vehicle

    Institute of Scientific and Technical Information of China (English)

    孙逢春; 翟丽; 张承宁; 彭连云

    2003-01-01

    A vector control system for electric vehicle (EV) induction motor drive system is designed and developed. Its hardware system based on dual CPU(microcomputer 80C196KC and DSP TMS320F2407) is implemented. The fundamental mathematics equations of induction motor in the general synchronously rotating reference frame (M-T frame) used for vector control are achieved by coordinate transformation. Rotor flux equation and torque equation are deduced. According to these equations, an induction motor mathematical model and rotor flux observer model are built separately. The rotor flux field-oriented vector control method is implemented based on these models in system software, some of the simulation results with Matab/Simulink are given. The simulation results show that the vector control system for EV induction motor drive system has better static and dynamic performance, and the rotor flux field-oriented vector control method was practically verified.

  7. Hepatic CT image retrieval based on the combination of Gabor filters and support vector machine

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Content-based image retrieval has been an active area of research for more than ten years.Gabor schemes and support vector machine (SVM) method have been proven effective in image representation and classification. In this paper,we propose a retrieval scheme based on Gabor filters and SVMs for hepatic computed tomography (CT) images query.In our experiments,a batch of hepatic CT images containing several types of CT findings are used for the retrieval test.Precision comparison between our scheme and existing methods is presented.

  8. Thrust estimator design based on least squares support vector regression machine

    Institute of Scientific and Technical Information of China (English)

    ZHAO Yong-ping; SUN Jian-guo

    2010-01-01

    In order to realize direct thrust control instead of traditional sensor-based control for nero-engines,it is indispensable to design a thrust estimator with high accuracy,so a scheme for thrust estimator design based on the least square support vector regression machine is proposed to solve this problem.Furthermore,numerical simulations confirm the effectiveness of our presented scheme.During the process of estimator design,a wrap per criterion that can not only reduce the computational complexity but also enhance the generalization performance is proposed to select variables as input variables for estimator.

  9. Accurate performance estimators for information retrieval based on span bound of support vector machines

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Support vector machines have met with significant success in the information retrieval field, especially in handling text classification tasks. Although various performance estimators for SVMs have been proposed,these only focus on accuracy which is based on the leave-one-out cross validation procedure. Information-retrieval-related performance measures are always neglected in a kernel learning methodology. In this paper, we have proposed a set of information-retrieval-oriented performance estimators for SVMs, which are based on the span bound of the leave-one-out procedure. Experiments have proven that our proposed estimators are both effective and stable.

  10. Glycoprotein Exchange Vectors Based on Vesicular Stomatitis Virus Allow Effective Boosting and Generation of Neutralizing Antibodies to a Primary Isolate of Human Immunodeficiency Virus Type 1

    OpenAIRE

    Rose, Nina F.; Roberts, Anjeanette; Buonocore, Linda; Rose, John K.

    2000-01-01

    Live recombinant vesicular stomatitis viruses (VSVs) expressing foreign antigens are highly effective vaccine vectors. However, these vectors induce high-titer neutralizing antibody directed at the single VSV glycoprotein (G), and this antibody alone can prevent reinfection and boosting with the same vector. To determine if efficient boosting could be achieved by changing the G protein of the vector, we have developed two new recombinant VSV vectors based on the VSV Indiana serotype but with ...

  11. Feature Selection Based on the SVM Weight Vector for Classification of Dementia.

    Science.gov (United States)

    Bron, Esther E; Smits, Marion; Niessen, Wiro J; Klein, Stefan

    2015-09-01

    Computer-aided diagnosis of dementia using a support vector machine (SVM) can be improved with feature selection. The relevance of individual features can be quantified from the SVM weights as a significance map (p-map). Although these p-maps previously showed clusters of relevant voxels in dementia-related brain regions, they have not yet been used for feature selection. Therefore, we introduce two novel feature selection methods based on p-maps using a direct approach (filter) and an iterative approach (wrapper). To evaluate these p-map feature selection methods, we compared them with methods based on the SVM weight vector directly, t-statistics, and expert knowledge. We used MRI data from the Alzheimer's disease neuroimaging initiative classifying Alzheimer's disease (AD) patients, mild cognitive impairment (MCI) patients who converted to AD (MCIc), MCI patients who did not convert to AD (MCInc), and cognitively normal controls (CN). Features for each voxel were derived from gray matter morphometry. Feature selection based on the SVM weights gave better results than t-statistics and expert knowledge. The p-map methods performed slightly better than those using the weight vector. The wrapper method scored better than the filter method. Recursive feature elimination based on the p-map improved most for AD-CN: the area under the receiver-operating-characteristic curve (AUC) significantly increased from 90.3% without feature selection to 92.0% when selecting 1.5%-3% of the features. This feature selection method also improved the other classifications: AD-MCI 0.1% improvement in AUC (not significant), MCI-CN 0.7%, and MCIc-MCInc 0.1% (not significant). Although the performance improvement due to feature selection was limited, the methods based on the p-map generally had the best performance, and were therefore better in estimating the relevance of individual features.

  12. Regulation of U6 Promoter Activity by Transcriptional Interference in Viral Vector-Based RNAi

    Institute of Scientific and Technical Information of China (English)

    Linghu Nie; Meghna Das Thakur; Yumei Wang; Qin Su; Yongliang Zhao; Yunfeng Feng

    2010-01-01

    The direct negative impact of the transcriptional activity of one component on the second one in c/s is referred to as transcriptional interference (TI).U6 is a type Ⅲ RNA polymerase Ⅲ promoter commonly used for driving small hairpin RNA (shRNA) expression in vector-based RNAi.In the design and construction of viral vectors,multiple transcription units may be arranged in close proximity in a space-limited vector.Determining if U6 promoter activity can be affected by TI is critical for the expression of target shRNA in gene therapy or loss-of-function studies.In this research,we designed and implemented a modified retroviral system where shRNA and exogenous gene expressions were driven by two independent transcriptional units.We arranged U6 promoter driving.shRNA expression and UbiC promoter in two promoter arrangements.In primary macrophages,we found U6 promoter activity was inhibited by UbiC promoter when in the divergent arrangement but not in tandem.In contrast,PKG promoter had no such negative impact.Instead of enhancing U6 promoter activity,CMV enhancer had significant negative impact on U6 promoter activity in the presence of UbiC promoter.Our results indicate that U6 promoter activity can be affected by TI in a proximal promoter-specific and arrangement-dependent manner.

  13. Robust Vision-Based Pose Estimation Algorithm for AN Uav with Known Gravity Vector

    Science.gov (United States)

    Kniaz, V. V.

    2016-06-01

    Accurate estimation of camera external orientation with respect to a known object is one of the central problems in photogrammetry and computer vision. In recent years this problem is gaining an increasing attention in the field of UAV autonomous flight. Such application requires a real-time performance and robustness of the external orientation estimation algorithm. The accuracy of the solution is strongly dependent on the number of reference points visible on the given image. The problem only has an analytical solution if 3 or more reference points are visible. However, in limited visibility conditions it is often needed to perform external orientation with only 2 visible reference points. In such case the solution could be found if the gravity vector direction in the camera coordinate system is known. A number of algorithms for external orientation estimation for the case of 2 known reference points and a gravity vector were developed to date. Most of these algorithms provide analytical solution in the form of polynomial equation that is subject to large errors in the case of complex reference points configurations. This paper is focused on the development of a new computationally effective and robust algorithm for external orientation based on positions of 2 known reference points and a gravity vector. The algorithm implementation for guidance of a Parrot AR.Drone 2.0 micro-UAV is discussed. The experimental evaluation of the algorithm proved its computational efficiency and robustness against errors in reference points positions and complex configurations.

  14. Support vector machine based fault classification and location of a long transmission line

    Directory of Open Access Journals (Sweden)

    Papia Ray

    2016-09-01

    Full Text Available This paper investigates support vector machine based fault type and distance estimation scheme in a long transmission line. The planned technique uses post fault single cycle current waveform and pre-processing of the samples is done by wavelet packet transform. Energy and entropy are obtained from the decomposed coefficients and feature matrix is prepared. Then the redundant features from the matrix are taken out by the forward feature selection method and normalized. Test and train data are developed by taking into consideration variables of a simulation situation like fault type, resistance path, inception angle, and distance. In this paper 10 different types of short circuit fault are analyzed. The test data are examined by support vector machine whose parameters are optimized by particle swarm optimization method. The anticipated method is checked on a 400 kV, 300 km long transmission line with voltage source at both the ends. Two cases were examined with the proposed method. The first one is fault very near to both the source end (front and rear and the second one is support vector machine with and without optimized parameter. Simulation result indicates that the anticipated method for fault classification gives high accuracy (99.21% and least fault distance estimation error (0.29%.

  15. System identification modelling of ship manoeuvring motion based onε- support vector regression

    Institute of Scientific and Technical Information of China (English)

    王雪刚; 邹早建; 侯先瑞; 徐锋

    2015-01-01

    Based on theε-support vector regression, three modelling methods for the ship manoeuvring motion, i.e., the white-box modelling, the grey-box modelling and the black-box modelling, are investigated. Theoo10/10,oo20/20 zigzag tests and the o35 turning circle manoeuvre are simulated. Part of the simulation data for theoo20/20 zigzag test are used to train the support vectors, and the trained support vector machine is used to predict the wholeoo20/20 zigzag test. Comparison between the simula- ted and predictedoo20/20 zigzag test shows a good predictive ability of the three modelling methods. Then all mathematical models obtained by the modelling methods are used to predict theoo10/10 zigzag test ando35 turning circle manoeuvre, and the predicted results are compared with those of simulation tests to demonstrate the good generalization performance of the mathematical models. Finally, the modelling methods are analyzed and compared with each other in terms of the application conditions, the prediction accuracy and the computation speed. An appropriate modelling method can be chosen according to the intended use of the mathematical models and the available data for the system identification.

  16. A Content based CT Lung Image Retrieval by DCT Matrix and Feature Vector Technique

    Directory of Open Access Journals (Sweden)

    J.Bridget Nirmala

    2012-03-01

    Full Text Available Most of the image retrieval systems are still incapable of providing retrieval result with high retrieval accuracy and less computational complexity. Image Retrieval technique to retrieve similar and relevant Computed Tomography (CT images of lung from a large database of images. During the process of retrieval, a query image which contains the affected area / abnormal region is given as an input to retrieve similar images which contain affected area/abnormal region from the database. DCT Matrix (DCTM is a kind of commonly used color feature representation in image retrieval. This paper describes a content based image retrieval (CBIR that represent each image in database by a vector of feature values called DCT vector matrix(8x8. Using this DCTM row and column feature vector values considered as a query image which is compared with existing database to cull out more similar and relevant images. The experimental result shows that 97% of images can be retrieved correctly using this technique

  17. A VECTOR QUANTIZATION BASED APPROACH FOR CFA DATA COMPRESSION IN WIRELESS ENDOSCOPY CAPSULE

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    A novel approach for near-lossless compression of Color Filtering Array(CFA)data in wireless endoscopy capsule is proposed in this paper.The compression method is based on pre-processing and vector quantization.First,the CFA raw data are low pass filtered and rearranged during pre-processing.Then,pairs of pixels are vector quantized into macros of 9 bits by applying block par-tition and index mapping in succession.These macros are entropy compressed by Joint Photographic Experts Group-Lossless Standard(JPEG-LS)finally.The complex step of codeword searching in Vector Quantization(VQ)is avoided by a predefined partition rule,which is suitable for hardware imple- mentation.By control of the pre-processor and VQ scheme,either high quality compression under an- filtered case or high ratio compression under filtered case can be realized,with the average Peak Sig- nal-to-Noise Ratio(PSNR)more than 43dB and 37dB respectively.Compared with the state-of-the-art method and the previously proposed method,our compression approach outperforms in compression performance as well as in flexibility.

  18. Construction of permanently inducible miRNA-based expression vectors using site-specific recombinases

    Directory of Open Access Journals (Sweden)

    Garwick-Coppens Sara E

    2011-11-01

    Full Text Available Abstract Background RNA interference (RNAi is a conserved gene silencing mechanism mediated by small inhibitory microRNAs (miRNAs. Promoter-driven miRNA expression vectors have emerged as important tools for delivering natural or artificially designed miRNAs to eukaryotic cells and organisms. Such systems can be used to query the normal or pathogenic functions of natural miRNAs or messenger RNAs, or to therapeutically silence disease genes. Results As with any molecular cloning procedure, building miRNA-based expression constructs requires a time investment and some molecular biology skills. To improve efficiency and accelerate the construction process, we developed a method to rapidly generate miRNA expression vectors using recombinases instead of more traditional cut-and-paste molecular cloning techniques. In addition to streamlining the construction process, our cloning strategy provides vectors with added versatility. In our system, miRNAs can be constitutively expressed from the U6 promoter, or inducibly expressed by Cre recombinase. We also engineered a built-in mechanism to destroy the vector with Flp recombinase, if desired. Finally, to further simplify the construction process, we developed a software package that automates the prediction and design of optimal miRNA sequences using our system. Conclusions We designed and tested a modular system to rapidly clone miRNA expression cassettes. Our strategy reduces the hands-on time required to successfully generate effective constructs, and can be implemented in labs with minimal molecular cloning expertise. This versatile system provides options that permit constitutive or inducible miRNA expression, depending upon the needs of the end user. As such, it has utility for basic or translational applications.

  19. Development of expression vectors for Escherichia coli based on the pCR2 replicon

    Directory of Open Access Journals (Sweden)

    Deb J K

    2007-05-01

    Full Text Available Abstract Background Recent developments in metabolic engineering and the need for expanded compatibility required for co-expression studies, underscore the importance of developing new plasmid vectors with properties such as stability and compatibility. Results We utilized the pCR2 replicon of Corynebacterium renale, which harbours multiple plasmids, for constructing a range of expression vectors. Different antibiotic-resistance markers were introduced and the vectors were found to be 100% stable over a large number of generations in the absence of selection pressure. Compatibility of this plasmid was studied with different Escherichia coli plasmid replicons viz. pMB1 and p15A. It was observed that pCR2 was able to coexist with these E.coli plasmids for 60 generations in the absence of selection pressure. Soluble intracellular production was checked by expressing GFP under the lac promoter in an expression plasmid pCR2GFP. Also high level production of human IFNγ was obtained by cloning the h-IFNγ under a T7 promoter in the expression plasmid pCR2-IFNγ and using a dual plasmid heat shock system for expression. Repeated sub-culturing in the absence of selection pressure for six days did not lead to any fall in the production levels post induction, for both GFP and h-IFNγ, demonstrating that pCR2 is a useful plasmid in terms of stability and compatibility. Conclusion We have constructed a series of expression vectors based on the pCR2 replicon and demonstrated its high stability and sustained expression capacity, in the absence of selection pressure which will make it an efficient tool for metabolic engineering and co-expression studies, as well as for scale up of expression.

  20. Particulate matter characterization by gray level co-occurrence matrix based support vector machines.

    Science.gov (United States)

    Manivannan, K; Aggarwal, P; Devabhaktuni, V; Kumar, A; Nims, D; Bhattacharya, P

    2012-07-15

    An efficient and highly reliable automatic selection of optimal segmentation algorithm for characterizing particulate matter is presented in this paper. Support vector machines (SVMs) are used as a new self-regulating classifier trained by gray level co-occurrence matrix (GLCM) of the image. This matrix is calculated at various angles and the texture features are evaluated for classifying the images. Results show that the performance of GLCM-based SVMs is drastically improved over the previous histogram-based SVMs. Our proposed GLCM-based approach of training SVM predicts a robust and more accurate segmentation algorithm than the standard histogram technique, as additional information based on the spatial relationship between pixels is incorporated for image classification. Further, the GLCM-based SVM classifiers were more accurate and required less training data when compared to the artificial neural network (ANN) classifiers.

  1. Comparison of strapdown inertial navigation algorithm based on rotation vector and dual quaternion

    Institute of Scientific and Technical Information of China (English)

    Wang Zhenhuan; Chen Xijun; Zeng Qingshuang

    2013-01-01

    For the navigation algorithm of the strapdown inertial navigation system,by comparing to the equations of the dual quaternion and quaternion,the superiority of the attitude algorithm based on dual quaternion over the ones based on rotation vector in accuracy is analyzed in the case of the rotation of navigation frame.By comparing the update algorithm of the gravitational velocity in dual quaternion solution with the compensation algorithm of the harmful acceleration in traditional velocity solution,the accuracy advantage of the gravitational velocity based on dual quaternion is addressed.In view of the idea of the attitude and velocity algorithm based on dual quaternion,an improved navigation algorithm is proposed,which is as much as the rotation vector algorithm in computational complexity.According to this method,the attitude quaternion does not require compensating as the navigation frame rotates.In order to verify the correctness of the theoretical analysis,simulations are carried out utilizing the software,and the simulation results show that the accuracy of the improved algorithm is approximately equal to the dual quaternion algorithm.

  2. Ecotope-Based Entomological Surveillance and Molecular Xenomonitoring of Multidrug Resistant Malaria Parasites in Anopheles Vectors

    Directory of Open Access Journals (Sweden)

    Prapa Sorosjinda-Nunthawarasilp

    2014-01-01

    Full Text Available The emergence and spread of multidrug resistant (MDR malaria caused by Plasmodium falciparum or Plasmodium vivax have become increasingly important in the Greater Mekong Subregion (GMS. MDR malaria is the heritable and hypermutable property of human malarial parasite populations that can decrease in vitro and in vivo susceptibility to proven antimalarial drugs as they exhibit dose-dependent drug resistance and delayed parasite clearance time in treated patients. MDR malaria risk situations reflect consequences of the national policy and strategy as this influences the ongoing national-level or subnational-level implementation of malaria control strategies in endemic GMS countries. Based on our experience along with current literature review, the design of ecotope-based entomological surveillance (EES and molecular xenomonitoring of MDR falciparum and vivax malaria parasites in Anopheles vectors is proposed to monitor infection pockets in transmission control areas of forest and forest fringe-related malaria, so as to bridge malaria landscape ecology (ecotope and ecotone and epidemiology. Malaria ecotope and ecotone are confined to a malaria transmission area geographically associated with the infestation of Anopheles vectors and particular environments to which human activities are related. This enables the EES to encompass mosquito collection and identification, salivary gland DNA extraction, Plasmodium- and species-specific identification, molecular marker-based PCR detection methods for putative drug resistance genes, and data management. The EES establishes strong evidence of Anopheles vectors carrying MDR P. vivax in infection pockets epidemiologically linked with other data obtained during which a course of follow-up treatment of the notified P. vivax patients receiving the first-line treatment was conducted. For regional and global perspectives, the EES would augment the epidemiological surveillance and monitoring of MDR falciparum and

  3. Flagellin Encoded in Gene-Based Vector Vaccines Is a Route-Dependent Immune Adjuvant.

    Science.gov (United States)

    Rady, Hamada F; Dai, Guixiang; Huang, Weitao; Shellito, Judd E; Ramsay, Alistair J

    2016-01-01

    Flagellin has been tested as a protein-based vaccine adjuvant, with the majority of studies focused on antibody responses. Here, we evaluated the adjuvant activity of flagellin for both cellular and humoral immune responses in BALB/c mice in the setting of gene-based immunization, and have made several novel observations. DNA vaccines and adenovirus (Ad) vectors were engineered to encode mycobacterial protein Ag85B, with or without flagellin of Salmonella typhimurium (FliC). DNA-encoded flagellin given IM enhanced splenic CD4+ and CD8+ T cell responses to co-expressed vaccine antigen, including memory responses. Boosting either IM or intranasally with Ad vectors expressing Ag85B without flagellin led to durable enhancement of Ag85B-specific antibody and CD4+ and CD8+ T cell responses in both spleen and pulmonary tissues, correlating with significantly improved protection against challenge with pathogenic aerosolized M. tuberculosis. However, inclusion of flagellin in both DNA prime and Ad booster vaccines induced localized pulmonary inflammation and transient weight loss, with route-dependent effects on vaccine-induced T cell immunity. The latter included marked reductions in levels of mucosal CD4+ and CD8+ T cell responses following IM DNA/IN Ad mucosal prime-boosting, although antibody responses were not diminished. These findings indicate that flagellin has differential and route-dependent adjuvant activity when included as a component of systemic or mucosally-delivered gene-based prime-boost immunization. Clear adjuvant activity for both T and B cell responses was observed when flagellin was included in the DNA priming vaccine, but side effects occurred when given in an Ad boosting vector, particularly via the pulmonary route.

  4. Flagellin Encoded in Gene-Based Vector Vaccines Is a Route-Dependent Immune Adjuvant.

    Directory of Open Access Journals (Sweden)

    Hamada F Rady

    Full Text Available Flagellin has been tested as a protein-based vaccine adjuvant, with the majority of studies focused on antibody responses. Here, we evaluated the adjuvant activity of flagellin for both cellular and humoral immune responses in BALB/c mice in the setting of gene-based immunization, and have made several novel observations. DNA vaccines and adenovirus (Ad vectors were engineered to encode mycobacterial protein Ag85B, with or without flagellin of Salmonella typhimurium (FliC. DNA-encoded flagellin given IM enhanced splenic CD4+ and CD8+ T cell responses to co-expressed vaccine antigen, including memory responses. Boosting either IM or intranasally with Ad vectors expressing Ag85B without flagellin led to durable enhancement of Ag85B-specific antibody and CD4+ and CD8+ T cell responses in both spleen and pulmonary tissues, correlating with significantly improved protection against challenge with pathogenic aerosolized M. tuberculosis. However, inclusion of flagellin in both DNA prime and Ad booster vaccines induced localized pulmonary inflammation and transient weight loss, with route-dependent effects on vaccine-induced T cell immunity. The latter included marked reductions in levels of mucosal CD4+ and CD8+ T cell responses following IM DNA/IN Ad mucosal prime-boosting, although antibody responses were not diminished. These findings indicate that flagellin has differential and route-dependent adjuvant activity when included as a component of systemic or mucosally-delivered gene-based prime-boost immunization. Clear adjuvant activity for both T and B cell responses was observed when flagellin was included in the DNA priming vaccine, but side effects occurred when given in an Ad boosting vector, particularly via the pulmonary route.

  5. A multi-label learning based kernel automatic recommendation method for support vector machine.

    Science.gov (United States)

    Zhang, Xueying; Song, Qinbao

    2015-01-01

    Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance.

  6. Fast Matrix Computation Algorithms Based on Rough Attribute Vector Tree Method in RDSS

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    The concepts of Rough Decision Support System (RDSS)and equivalence matrix are introduced in this paper. Based on a rough attribute vector tree (RAVT) method, two kinds of matrix computation algorithms - Recursive Matrix Computation (RMC) and Parallel Matrix Computation (PMC) are proposed for rules extraction, attributes reduction and data cleaning finished synchronously. The algorithms emphasize the practicability and efficiency of rules generation. A case study of PMC is analyzed, and a comparison experiment of RMC algorithm shows that it is feasible and efficient for data mining and knowledge-discovery in RDSS.

  7. Face Recognition Based on Support Vector Machine and Nearest Neighbor Classifier

    Institute of Scientific and Technical Information of China (English)

    张燕昆; 刘重庆

    2003-01-01

    Support vector machine (SVM), as a novel approach in pattern recognition, has demonstrated a success in face detection and face recognition. In this paper, a face recognition approach based on the SVM classifier with the nearest neighbor classifier (NNC) is proposed. The principal component analysis (PCA) is used to reduce the dimension and extract features. Then one-against-all stratedy is used to train the SVM classifiers. At the testing stage, we propose an algorithm by combining SVM classifier with NNC to improve the correct recognition rate. We conduct the experiment on the Cambridge ORL face database. The result shows that our approach outperforms the standard eigenface approach and some other approaches.

  8. Ultrasonic image restoration based on support vector machine for surfacing interface testing

    Institute of Scientific and Technical Information of China (English)

    Gao Shuangsheng; Gang Tie; Chi Dazhao

    2007-01-01

    In order to restore the degraded ultrasonic C-scan image for testing surfacing interface, a method based on support vector regression (SVR) network is proposed. By using the image of a simulating defect, the network is trained and a mapping relationship between the degraded and restored image is founded. The degraded C-scan image of Cu-Steel surfacing interface is processed by the trained network and improved image is obtained. The result shows that the method can effectively suppress the noise and deblur the defect edge in the image, and provide technique support for quality and reliability evaluation of the surfacing weld.

  9. Mixture gas component concentration analysis based on support vector machine and infrared spectrum

    Institute of Scientific and Technical Information of China (English)

    Peng Bai; Junhua Liu

    2006-01-01

    @@ A novel quantitative analysis method of multi-component mixture gas concentration based on support vector machine (SVM) and spectroscopy is proposed. Through transformation of the kernel function, the seriously overlapped and nonlinear spectrum data are transformed in high-dimensional space, but the highdimensional data can be processed in the original space. Some factors, such as kernel function, range of the wavelength, and penalty coefficient, are discussed. This method is applied to the quantitative analysis of natural gas components concentration, and the component concentration maximal deviation is 2.28%.

  10. Vector-lifting schemes based on sorting techniques for lossless compression of multispectral images

    Science.gov (United States)

    Benazza-Benyahia, Amel; Pesquet, Jean-Christophe

    2003-01-01

    In this paper, we introduce vector-lifting schemes which allow to generate very compact multiresolution representations, suitable for lossless and progressive coding of multispectral images. These new decomposition schemes exploit simultaneously the spatial and the spectral redundancies contained in multispectral images. When the spectral bands have different dynamic ranges, we improve dramatically the performances of the proposed schemes by a reversible histogram modification based on sorting permutations. Simulation tests carried out on real images allow to evaluate the performances of this new compression method. They indicate that the achieved compression ratios are higher than those obtained with currently used lossless coders.

  11. Material grain size characterization method based on energy attenuation coefficient spectrum and support vector regression.

    Science.gov (United States)

    Li, Min; Zhou, Tong; Song, Yanan

    2016-07-01

    A grain size characterization method based on energy attenuation coefficient spectrum and support vector regression (SVR) is proposed. First, the spectra of the first and second back-wall echoes are cut into several frequency bands to calculate the energy attenuation coefficient spectrum. Second, the frequency band that is sensitive to grain size variation is determined. Finally, a statistical model between the energy attenuation coefficient in the sensitive frequency band and average grain size is established through SVR. Experimental verification is conducted on austenitic stainless steel. The average relative error of the predicted grain size is 5.65%, which is better than that of conventional methods.

  12. Fast direction of arrival algorithm based on vector-sensor arrays using wideband sources

    Institute of Scientific and Technical Information of China (English)

    SUN Guo-cang; HUI Jun-ying; CHEN Yang

    2008-01-01

    An acoustic vector sensor (AVS) can capture more information than a conventional acoustic pressure sensor (APS). As a result, more output channels are required when multiple AVS are formed into arrays, making processing the data stream computationally intense. This paper proposes a new algorithm based on the propagator method for wideband coherent sources that eliminates eigen-decomposition in order to reduce the computational burden. Data from simulations and lake trials showed that the new algorithm is valid: it resolves coherent sources, breaks left/right ambiguity, and allows inter element spacing to exceed a half-wavelength.

  13. Speech/Music Classification Enhancement for 3GPP2 SMV Codec Based on Support Vector Machine

    Science.gov (United States)

    Kim, Sang-Kyun; Chang, Joon-Hyuk

    In this letter, we propose a novel approach to speech/music classification based on the support vector machine (SVM) to improve the performance of the 3GPP2 selectable mode vocoder (SMV) codec. We first analyze the features and the classification method used in real time speech/music classification algorithm in SMV, and then apply the SVM for enhanced speech/music classification. For evaluation of performance, we compare the proposed algorithm and the traditional algorithm of the SMV. The performance of the proposed system is evaluated under the various environments and shows better performance compared to the original method in the SMV.

  14. Control Strategy for Three Phase Voltage Source PWM Rectifier Based on the Space Vector Modulation

    Directory of Open Access Journals (Sweden)

    MILOUD, Y.

    2010-08-01

    Full Text Available This paper proposes the space vector pulse width modulation (SVPWM control scheme for three-phase voltage source PWM rectifier. The control system based on SVPWM includes two PI controllers which are used to regulate the AC currents and DC-link voltage. The proposed control can stabilize the minimum of the systems storage function at the desired equilibrium point determined by unity power factor and sinusoidal current on the AC side, and constant output voltage on the DC side. So the stable state performance and robustness against the load�s disturbance of PWM rectifiers are both improved. The simulation result shows feasibility of this strategy.

  15. FAULT DIAGNOSIS APPROACH BASED ON HIDDEN MARKOV MODEL AND SUPPORT VECTOR MACHINE

    Institute of Scientific and Technical Information of China (English)

    LIU Guanjun; LIU Xinmin; QIU Jing; HU Niaoqing

    2007-01-01

    Aiming at solving the problems of machine-learning in fault diagnosis, a diagnosis approach is proposed based on hidden Markov model (HMM) and support vector machine (SVM). HMM usually describes intra-class measure well and is good at dealing with continuous dynamic signals. SVM expresses inter-class difference effectively and has perfect classify ability. This approach is built on the merit of HMM and SVM. Then, the experiment is made in the transmission system of a helicopter. With the features extracted from vibration signals in gearbox, this HMM-SVM based diagnostic approach is trained and used to monitor and diagnose the gearbox's faults. The result shows that this method is better than HMM-based and SVM-based diagnosing methods in higher diagnostic accuracy with small training samples.

  16. Fuzzy Support Vector Machine-based Multi-agent Optimal Path

    Directory of Open Access Journals (Sweden)

    Gireesh Kumar T

    2010-07-01

    Full Text Available A mobile robot to navigate purposefully from a start location to a target location, needs three basic requirements: sensing, learning, and reasoning. In the existing system, the mobile robot navigates in a known environment on a predefined path. However, the pervasive presence of uncertainty in sensing and learning, makes the choice of a suitable tool of reasoning and decision-making that can deal with incomplete information, vital to ensure a robust control system. This problem can be overcome by the proposed navigation method using fuzzy support vector machine (FSVM. It proposes a fuzzy logic-based support vector machine (SVM approach to secure a collision-free path avoiding multiple dynamic obstacles. The navigator consists of an FSVM-based collision avoidance. The decisions are taken at each step for the mobile robot to attain the goal position without collision. Fuzzy-SVM rule bases are built, which require simple evaluation data rather than thousands of input-output training data. The effectiveness of the proposed method is verified by a series of simulations and implemented with a microcontroller for navigation.Defence Science Journal, 2010, 60(4, pp.387-391, DOI:http://dx.doi.org/10.14429/dsj.60.496

  17. Cross-Layer Optimization of MIMO-Based Mesh Networks with Gaussian Vector Broadcast Channels

    CERN Document Server

    Liu, Jia

    2007-01-01

    MIMO technology is one of the most significant advances in the past decade to increase channel capacity and has a great potential to improve network capacity for mesh networks. In a MIMO-based mesh network, the links outgoing from each node sharing the common communication spectrum can be modeled as a Gaussian vector broadcast channel. Recently, researchers showed that ``dirty paper coding'' (DPC) is the optimal transmission strategy for Gaussian vector broadcast channels. So far, there has been little study on how this fundamental result will impact the cross-layer design for MIMO-based mesh networks. To fill this gap, we consider the problem of jointly optimizing DPC power allocation in the link layer at each node and multihop/multipath routing in a MIMO-based mesh networks. It turns out that this optimization problem is a very challenging non-convex problem. To address this difficulty, we transform the original problem to an equivalent problem by exploiting the channel duality. For the transformed problem,...

  18. Modelling and Simulation of SVPWM Based Vector Controlled HVDC Light Systems

    Directory of Open Access Journals (Sweden)

    Ajay Kumar MOODADLA

    2012-11-01

    Full Text Available Recent upgrades in power electronics technology have lead to the improvements of insulated gate bipolar transistor (IGBT based Voltage source converter High voltage direct current (VSC HVDC transmission systems. These are also commercially known as HVDC Light systems, which are popular in renewable, micro grid, and electric power systems. Out of different pulse width modulation (PWM schemes, Space vector PWM (SVPWM control scheme finds growing importance in power system applications because of its better dc bus utilization. In this paper, modelling of the converter is described, and SVPWM scheme is utilized to control the HVDC Light system in order to achieve better DC bus utilization, harmonic reduction, and for reduced power fluctuations. The simulations are carried out in the MATLAB/SIMULINK environment and the results are provided for steady state and dynamic conditions. Finally, the performance of SVPWM based vector controlled HVDC Light transmission system is compared with sinusoidal pulse width modulation (SPWM based HVDC Light system in terms of output voltage and total harmonic distortion (THD.

  19. 基于支持向量机的分段线性学习方法%A Subsection Learning Algorithm Based on Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    杨强; 吴中福; 王茜

    2003-01-01

    In this paper, we discuss drawback of traditional subsection learning algorithm in pattern recognition and exiting support vector machines (including kernel functions), the necessity of using subsection learning algorithm based on support vector machines as well as. In turn, a subsection learning algorithm based on support vector machines, is proposed in this paper.

  20. An efficient strategy for cell-based antibody library selection using an integrated vector system

    Directory of Open Access Journals (Sweden)

    Yoon Hyerim

    2012-09-01

    Full Text Available Abstract Background Cell panning of phage-displayed antibody library is a powerful tool for the development of therapeutic and imaging agents since disease-related cell surface proteins in native complex conformation can be directly targeted. Here, we employed a strategy taking advantage of an integrated vector system which allows rapid conversion of scFv-displaying phage into scFv-Fc format for efficient cell-based scFv library selection on a tetraspanin protein, CD9. Results A mouse scFv library constructed by using a phagemid vector, pDR-D1 was subjected to cell panning against stable CD9 transfectant, and the scFv repertoire from the enriched phage pool was directly transferred to a mammalian cassette vector, pDR-OriP-Fc1. The resulting constructs enabled transient expression of enough amounts of scFv-Fcs in HEK293E cells, and flow cytometric screening of binders for CD9 transfectant could be performed simply by using the culture supernatants. All three clones selected from the screening showed correct CD9-specificity. They could immunoprecipitate CD9 molecules out of the transfectant cell lysate and correctly stain endogenous CD9 expression on cancer cell membrane. Furthermore, competition assay with a known anti-CD9 monoclonal antibody (mAb suggested that the binding epitopes of some of them overlap with that of the mAb which resides within the large extracellular loop of CD9. Conclusions This study demonstrates that scFv-Fc from mammalian transient expression can be chosen as a reliable format for rapid screening and validation in cell-based scFv library selection, and the strategy described here will be applicable to efficient discovery of antibodies to diverse cell-surface targets.

  1. Factors affecting learning of vector math from computer-based practice: Feedback complexity and prior knowledge

    Science.gov (United States)

    Heckler, Andrew F.; Mikula, Brendon D.

    2016-06-01

    In experiments including over 450 university-level students, we studied the effectiveness and time efficiency of several levels of feedback complexity in simple, computer-based training utilizing static question sequences. The learning domain was simple vector math, an essential skill in introductory physics. In a unique full factorial design, we studied the relative effects of "knowledge of correct response" feedback and "elaborated feedback" (i.e., a general explanation) both separately and together. A number of other factors were analyzed, including training time, physics course grade, prior knowledge of vector math, and student beliefs about both their proficiency in and the importance of vector math. We hypothesize a simple model predicting how the effectiveness of feedback depends on prior knowledge, and the results confirm this knowledge-by-treatment interaction. Most notably, elaborated feedback is the most effective feedback, especially for students with low prior knowledge and low course grade. In contrast, knowledge of correct response feedback was less effective for low-performing students, and including both kinds of feedback did not significantly improve performance compared to elaborated feedback alone. Further, while elaborated feedback resulted in higher scores, the learning rate was at best only marginally higher because the training time was slightly longer. Training time data revealed that students spent significantly more time on the elaborated feedback after answering a training question incorrectly. Finally, we found that training improved student self-reported proficiency and that belief in the importance of the learned domain improved the effectiveness of training. Overall, we found that computer based training with static question sequences and immediate elaborated feedback in the form of simple and general explanations can be an effective way to improve student performance on a physics essential skill, especially for less prepared and low

  2. Introducing Vectors.

    Science.gov (United States)

    Roche, John

    1997-01-01

    Suggests an approach to teaching vectors that promotes active learning through challenging questions addressed to the class, as opposed to subtle explanations. Promotes introducing vector graphics with concrete examples, beginning with an explanation of the displacement vector. Also discusses artificial vectors, vector algebra, and unit vectors.…

  3. 甲病毒载体的研究进展%Review on Alphavirus-based Vectors

    Institute of Scientific and Technical Information of China (English)

    龚文芝; 刘灿; 张东东; 宁宜宝

    2015-01-01

    Alphaviruses belong to the family of Togaviridae, which are encapsulated by a capsid protein and possess single strand plus RNA. In recent years, the domestic and foreign research on alphavirus-based vectors is active and the alphavirus-based vectors have been used for vaccine research, gene therapy and molecular biology research. Based on the structural principle, the alphavirus vectors are classified into three main types:replication-competent vector, replication-deficient vectors and DNA-layered vector. In this review, the transformation, characteristics and application of three different alphavirus-based vectors are discussed in order to provide references for the development of safer, more effective alphavirus-based vectors.%甲病毒属于披膜病毒科,是一类有包膜的单股正链RNA病毒。近些年来,国内外有关甲病毒载体的研究非常活跃,其已被用于疫苗研究、基因治疗以及分子生物学等研究领域。基于甲病毒载体的构造原理,将其分为三类,分别为复制完全型载体、复制缺陷型载体和DNA/RNA载体。就这三类载体的改造过程进行了探讨,并对这三类载体的特点及其应用研究进展等方面进行了阐述,以期为开发出更安全、更有效的甲病毒载体提供参考。

  4. 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.

  5. SOFT SENSING MODEL BASED ON SUPPORT VECTOR MACHINE AND ITS APPLICATION

    Institute of Scientific and Technical Information of China (English)

    Yan Weiwu; Shao Huihe; Wang Xiaofan

    2004-01-01

    Soft sensor is widely used in industrial process control.It plays an important role to improve the quality of product and assure safety in production.The core of soft sensor is to construct soft sensing model.A new soft sensing modeling method based on support vector machine (SVM) is proposed.SVM is a new machine learning method based on statistical learning theory and is powerful for the problem characterized by small sample, nonlinearity, high dimension and local minima.The proposed methods are applied to the estimation of frozen point of light diesel oil in distillation column.The estimated outputs of soft sensing model based on SVM match the real values of frozen point and follow varying trend of frozen point very well.Experiment results show that SVM provides a new effective method for soft sensing modeling and has promising application in industrial process applications.

  6. Robust image hashing based on random Gabor filtering and dithered lattice vector quantization.

    Science.gov (United States)

    Li, Yuenan; Lu, Zheming; Zhu, Ce; Niu, Xiamu

    2012-04-01

    In this paper, we propose a robust-hash function based on random Gabor filtering and dithered lattice vector quantization (LVQ). In order to enhance the robustness against rotation manipulations, the conventional Gabor filter is adapted to be rotation invariant, and the rotation-invariant filter is randomized to facilitate secure feature extraction. Particularly, a novel dithered-LVQ-based quantization scheme is proposed for robust hashing. The dithered-LVQ-based quantization scheme is well suited for robust hashing with several desirable features, including better tradeoff between robustness and discrimination, higher randomness, and secrecy, which are validated by analytical and experimental results. The performance of the proposed hashing algorithm is evaluated over a test image database under various content-preserving manipulations. The proposed hashing algorithm shows superior robustness and discrimination performance compared with other state-of-the-art algorithms, particularly in the robustness against rotations (of large degrees).

  7. Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines

    Science.gov (United States)

    del Val, Lara; Izquierdo-Fuente, Alberto; Villacorta, Juan J.; Raboso, Mariano

    2015-01-01

    Drawing on the results of an acoustic biometric system based on a MSE classifier, a new biometric system has been implemented. This new system preprocesses acoustic images, extracts several parameters and finally classifies them, based on Support Vector Machine (SVM). The preprocessing techniques used are spatial filtering, segmentation—based on a Gaussian Mixture Model (GMM) to separate the person from the background, masking—to reduce the dimensions of images—and binarization—to reduce the size of each image. An analysis of classification error and a study of the sensitivity of the error versus the computational burden of each implemented algorithm are presented. This allows the selection of the most relevant algorithms, according to the benefits required by the system. A significant improvement of the biometric system has been achieved by reducing the classification error, the computational burden and the storage requirements. PMID:26091392

  8. Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines.

    Science.gov (United States)

    del Val, Lara; Izquierdo-Fuente, Alberto; Villacorta, Juan J; Raboso, Mariano

    2015-06-17

    Drawing on the results of an acoustic biometric system based on a MSE classifier, a new biometric system has been implemented. This new system preprocesses acoustic images, extracts several parameters and finally classifies them, based on Support Vector Machine (SVM). The preprocessing techniques used are spatial filtering, segmentation-based on a Gaussian Mixture Model (GMM) to separate the person from the background, masking-to reduce the dimensions of images-and binarization-to reduce the size of each image. An analysis of classification error and a study of the sensitivity of the error versus the computational burden of each implemented algorithm are presented. This allows the selection of the most relevant algorithms, according to the benefits required by the system. A significant improvement of the biometric system has been achieved by reducing the classification error, the computational burden and the storage requirements.

  9. Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Lara del Val

    2015-06-01

    Full Text Available Drawing on the results of an acoustic biometric system based on a MSE classifier, a new biometric system has been implemented. This new system preprocesses acoustic images, extracts several parameters and finally classifies them, based on Support Vector Machine (SVM. The preprocessing techniques used are spatial filtering, segmentation—based on a Gaussian Mixture Model (GMM to separate the person from the background, masking—to reduce the dimensions of images—and binarization—to reduce the size of each image. An analysis of classification error and a study of the sensitivity of the error versus the computational burden of each implemented algorithm are presented. This allows the selection of the most relevant algorithms, according to the benefits required by the system. A significant improvement of the biometric system has been achieved by reducing the classification error, the computational burden and the storage requirements.

  10. Signal Detection for QPSK Based Cognitive Radio Systems using Support Vector Machines

    Directory of Open Access Journals (Sweden)

    M. T. Mushtaq

    2015-04-01

    Full Text Available Cognitive radio based network enables opportunistic dynamic spectrum access by sensing, adopting and utilizing the unused portion of licensed spectrum bands. Cognitive radio is intelligent enough to adapt the communication parameters of the unused licensed spectrum. Spectrum sensing is one of the most important tasks of the cognitive radio cycle. In this paper, the auto-correlation function kernel based Support Vector Machine (SVM classifier along with Welch's Periodogram detector is successfully implemented for the detection of four QPSK (Quadrature Phase Shift Keying based signals propagating through an AWGN (Additive White Gaussian Noise channel. It is shown that the combination of statistical signal processing and machine learning concepts improve the spectrum sensing process and spectrum sensing is possible even at low Signal to Noise Ratio (SNR values up to -50 dB.

  11. MULTI-VIEW FACE DETECTION BASED ON KERNEL PRINCIPAL COMPONENT ANALYSIS AND KERNEL SUPPORT VECTOR TECHNIQUES

    Directory of Open Access Journals (Sweden)

    Muzhir Shaban Al-Ani

    2011-05-01

    Full Text Available Detecting faces across multiple views is more challenging than in a frontal view. To address this problem,an efficient approach is presented in this paper using a kernel machine based approach for learning suchnonlinear mappings to provide effective view-based representation for multi-view face detection. In thispaper Kernel Principal Component Analysis (KPCA is used to project data into the view-subspaces thencomputed as view-based features. Multi-view face detection is performed by classifying each input imageinto face or non-face class, by using a two class Kernel Support Vector Classifier (KSVC. Experimentalresults demonstrate successful face detection over a wide range of facial variation in color, illuminationconditions, position, scale, orientation, 3D pose, and expression in images from several photo collections.

  12. A Stock Market Prediction Method Based on Support Vector Machines (SVM and Independent Component Analysis (ICA

    Directory of Open Access Journals (Sweden)

    Hakob GRIGORYAN

    2016-08-01

    Full Text Available The research presented in this work focuses on financial time series prediction problem. The integrated prediction model based on support vector machines (SVM with independent component analysis (ICA (called SVM-ICA is proposed for stock market prediction. The presented approach first uses ICA technique to extract important features from the research data, and then applies SVM technique to perform time series prediction. The results obtained from the SVM-ICA technique are compared with the results of SVM-based model without using any pre-processing step. In order to show the effectiveness of the proposed methodology, two different research data are used as illustrative examples. In experiments, the root mean square error (RMSE measure is used to evaluate the performance of proposed models. The comparative analysis leads to the conclusion that the proposed SVM-ICA model outperforms the simple SVM-based model in forecasting task of nonstationary time series.

  13. A Systematic Approach for Solving the Great Circle Track Problems based on Vector Algebra

    Directory of Open Access Journals (Sweden)

    Chen Chih-Li

    2016-04-01

    Full Text Available A systematic approach, based on multiple products of the vector algebra (S-VA, is proposed to derive the spherical triangle formulae for solving the great circle track (GCT problems. Because the mathematical properties of the geometry and algebra are both embedded in the S-VA approach, derivations of the spherical triangle formulae become more understandable and more straightforward as compared with those approaches which use the complex linear combination of a vector basis. In addition, the S-VA approach can handle all given initial conditions for solving the GCT problems simpler, clearer and avoid redundant formulae existing in the conventional approaches. With the technique of transforming the Earth coordinates system of latitudes and longitudes into the Cartesian one and adopting the relative longitude concept, the concise governing equations of the S-VA approach can be easily and directly derived. Owing to the advantage of the S-VA approach, it makes the practical navigator quickly adjust to solve the GCT problems. Based on the S-VA approach, a program namely GCTPro_VA is developed for friendly use of the navigator. Several validation examples are provided to show the S-VA approach is simple and versatile to solve the GCT problems.

  14. Dependency-based algorithms for vector processing of sparse matrix forward/backward substitutions

    Energy Technology Data Exchange (ETDEWEB)

    Vuong, G.T.; Chahine, R. [Hydro-Quebec, Montreal, Quebec (Canada); Granelli, G.P.; Montagna, M. [Univ. di Pavia (Italy)

    1996-02-01

    In this paper two algorithms for forward/backward substitutions and their implementation on vector computers are considered. A dependency-based substitution algorithm (DBSA) is proposed and compared with the well known W-matrix method. According to DBSA, the non-zero entries of the factor matrices are rearranged in groups of elements (slices) leading to independent operations. In the implementation of the W-matrix method, the non-zero elements of the inverse factors are grouped in sets (pseudocolumns) to overcome the problem of dependency between addition operations. Test cases, performed on a CRAY X-MP2/216 and a CRAY Y-MP8/464 vector computer, are taken from real life power system problems and consist in the solution of linear systems with up to 12,000 equations. The maximum speed-ups achieved (with respect to a code based on standard sparsity programming) are near to 7 for complex arithmetic and to 11 for real arithmetic.

  15. Physiological levels of HBB transgene expression from S/MAR element-based replicating episomal vectors.

    Science.gov (United States)

    Sgourou, Argyro; Routledge, Samantha; Spathas, Dionysios; Athanassiadou, Aglaia; Antoniou, Michael N

    2009-08-20

    Replicating episomal vectors (REV) are in principle able to provide long-term transgene expression in the absence of integration into the target cell genome. The scaffold/matrix attachment region (S/MAR) located 5' of the human beta-interferon gene (IFNB1) has been shown to confer a stable episomal replication and retention function within plasmid vectors when stably transfected and selected in mammalian cells. The minimal requirement for the IFNB1 S/MAR to function in DNA replication and episomal retention is transcription through this element. We used the erythroid beta-globin locus control region-beta-globin gene (betaLCR-HBB) microlocus cassette as a model to assess tissue-specific expression from within an IFNB1 S/MAR-based plasmid REV. The betaLCR-HBB plus S/MAR combination constructs provided either high or low levels of transcription through the S/MAR element. Our results show that the betaLCR-HBB microlocus is able to reproducibly and stably express at full physiological levels on an episome copy number basis. In addition, our data show that even low levels of transcription from betaLCR-HBB through the S/MAR element are sufficient to allow efficient episomal replication and retention. These data provide the principles upon which generic and flexible expression cassette-S/MAR-based REVs can be designed for a wide range of applications.

  16. Hierarchical multilevel authentication system for multiple-image based on phase retrieval and basic vector operations

    Science.gov (United States)

    Li, Xianye; Meng, Xiangfeng; Yin, Yongkai; Yang, Xiulun; Wang, Yurong; Peng, Xiang; He, Wenqi; Pan, Xuemei; Dong, Guoyan; Chen, Hongyi

    2017-02-01

    A hierarchical multilevel authentication system for multiple-image based on phase retrieval and basic vector operations in the Fresnel domain is proposed, by which more certification images are iteratively encoded into multiple cascaded phase masks according to different hierarchical levels. Based on the secret sharing algorithm by basic vector decomposition and composition operations, the iterated phase distributions are split into n pairs of shadow images keys (SIKs), and then distributed to n different participants (the authenticators). During each level in the high authentication process, any 2 or more participants can be gathered to reconstruct the original meaningful certification images. While in the case of each level in the low authentication process, only one authenticator who possesses a correct pair of SIKs, will gain no significant information of certification image; however, it can result in a remarkable peak output in the nonlinear correlation coefficient of the recovered image and the standard certification image, which can successfully provide an additional authentication layer for the high-level authentication. Theoretical analysis and numerical simulations both verify the feasibility of the proposed method.

  17. [NIR spectroscopy based on least square support vector machines for quality prediction of tomato juice].

    Science.gov (United States)

    Huang, Kang; Wang, Hui-jun; Xu, Hui-rong; Wang, Jian-ping; Ying, Yi-bin

    2009-04-01

    The application of least square support vector machines (LS-SVM) regression method based on statistics study theory to the analysis with near infrared (NIR) spectra of tomato juice was introduced in the present paper. In this method, LS-SVM was used for establishing model of spectral analysis, and was applied to predict the sugar contents (SC) and available acid (VA) in tomato juice samples. NIR transmission spectra of tomato juice were measured in the spectral range of 800-2,500 nm using InGaAs detector. The radial basis function (RBF) was adopted as a kernel function of LS-SVM. Sixty seven tomato juice samples were used as calibration set, and thirty three samples were used as validation set. The results of the method for sugar contents (SC) and available acid (VA) prediction were: a high correlation coefficient of 0.9903 and 0.9675, and a low root mean square error of prediction (RMSEP) of 0.0056 degree Brix and 0.0245, respectively. And compared to PLS and PCR methods, the performance of the LSSVM method was better. The results indicated that it was possible to built statistic models to quantify some common components in tomato juice using near-infrared (NIR) spectroscopy and least square support vector machines (LS-SVM) regression method as a nonlinear multivariate calibration procedure, and LS-SVM could be a rapid and accurate method for juice components determination based on NIR spectra.

  18. Double-stranded RNA transcribed from vector-based oligodeoxynucleotide acts as transcription factor decoy

    Energy Technology Data Exchange (ETDEWEB)

    Xiao, Xiao [State Key Laboratory of Cancer Biology and Xijing Hospital of Digestive Diseases, Xijing Hospital, Fourth Military Medical University, Xi’an 710032, Shaanxi Province (China); Gang, Yi [State Key Laboratory of Cancer Biology and Xijing Hospital of Digestive Diseases, Xijing Hospital, Fourth Military Medical University, Xi’an 710032, Shaanxi Province (China); Department of Infectious Diseases, Tangdu Hospital, Fourth Military Medical University, Xi’an 710038, Shaanxi Province (China); Wang, Honghong [No. 518 Hospital of Chinese People’s Liberation Army, Xi’an 710043, Shaanxi Province (China); Wang, Jiayin [The Genome Institute, Washington University in St. Louis, St. Louis, MO 63108 (United States); Zhao, Lina [Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, Xi’an 710032, Shaanxi Province (China); Xu, Li, E-mail: lxuhelen@163.com [State Key Laboratory of Cancer Biology and Xijing Hospital of Digestive Diseases, Xijing Hospital, Fourth Military Medical University, Xi’an 710032, Shaanxi Province (China); Liu, Zhiguo, E-mail: liuzhiguo@fmmu.edu.cn [State Key Laboratory of Cancer Biology and Xijing Hospital of Digestive Diseases, Xijing Hospital, Fourth Military Medical University, Xi’an 710032, Shaanxi Province (China)

    2015-02-06

    Highlights: • A shRNA vector based transcription factor decoy, VB-ODN, was designed. • VB-ODN for NF-κB inhibited cell viability in HEK293 cells. • VB-ODN inhibited expression of downstream genes of target transcription factors. • VB-ODN may enhance nuclear entry ratio for its feasibility of virus production. - Abstract: In this study, we designed a short hairpin RNA vector-based oligodeoxynucleotide (VB-ODN) carrying transcription factor (TF) consensus sequence which could function as a decoy to block TF activity. Specifically, VB-ODN for Nuclear factor-κB (NF-κB) could inhibit cell viability and decrease downstream gene expression in HEK293 cells without affecting expression of NF-κB itself. The specific binding between VB-ODN produced double-stranded RNA and NF-κB was evidenced by electrophoretic mobility shift assay. Moreover, similar VB-ODNs designed for three other TFs also inhibit their downstream gene expression but not that of themselves. Our study provides a new design of decoy for blocking TF activity.

  19. Anticipatory Monitoring and Control of Complex Systems using a Fuzzy based Fusion of Support Vector Regressors

    Energy Technology Data Exchange (ETDEWEB)

    Miltiadis Alamaniotis; Vivek Agarwal

    2014-10-01

    This paper places itself in the realm of anticipatory systems and envisions monitoring and control methods being capable of making predictions over system critical parameters. Anticipatory systems allow intelligent control of complex systems by predicting their future state. In the current work, an intelligent model aimed at implementing anticipatory monitoring and control in energy industry is presented and tested. More particularly, a set of support vector regressors (SVRs) are trained using both historical and observed data. The trained SVRs are used to predict the future value of the system based on current operational system parameter. The predicted values are then inputted to a fuzzy logic based module where the values are fused to obtain a single value, i.e., final system output prediction. The methodology is tested on real turbine degradation datasets. The outcome of the approach presented in this paper highlights the superiority over single support vector regressors. In addition, it is shown that appropriate selection of fuzzy sets and fuzzy rules plays an important role in improving system performance.

  20. Prognostics of Lithium-Ion Batteries Based on Battery Performance Analysis and Flexible Support Vector Regression

    Directory of Open Access Journals (Sweden)

    Shuai Wang

    2014-10-01

    Full Text Available Accurate prediction of the remaining useful life (RUL of lithium-ion batteries is important for battery management systems. Traditional empirical data-driven approaches for RUL prediction usually require multidimensional physical characteristics including the current, voltage, usage duration, battery temperature, and ambient temperature. From a capacity fading analysis of lithium-ion batteries, it is found that the energy efficiency and battery working temperature are closely related to the capacity degradation, which account for all performance metrics of lithium-ion batteries with regard to the RUL and the relationships between some performance metrics. Thus, we devise a non-iterative prediction model based on flexible support vector regression (F-SVR and an iterative multi-step prediction model based on support vector regression (SVR using the energy efficiency and battery working temperature as input physical characteristics. The experimental results show that the proposed prognostic models have high prediction accuracy by using fewer dimensions for the input data than the traditional empirical models.

  1. Dual delivery systems based on polyamine analog BENSpm as prodrug and gene delivery vectors

    Science.gov (United States)

    Zhu, Yu

    Combination drug and gene therapy shows promise in cancer treatment. However, the success of such strategy requires careful selection of the therapeutic agents, as well as development of efficient delivery vectors. BENSpm (N 1, N11-bisethylnorspermine), a polyamine analogue targeting the intracellular polyamine pathway, draws our special attention because of the following reasons: (1) polyamine pathway is frequently dysregulated in cancer; (2) BENSpm exhibits multiple functions to interfere with the polyamine pathway, such as to up-regulate polyamine metabolism enzymes and down-regulate polyamine biosynthesis enzymes. Therefore BENSpm depletes all natural polyamines and leads to apoptosis and cell growth inhibition in a wide range of cancers; (3) preclinical studies proved that BENSpm can act synergistically with various chemotherapy agents, making it a promising candidate in combination therapy; (4) multiple positive charges in BENSpm enable it as a suitable building block for cationic polymers, which can be further applied to gene delivery. In this dissertation, our goal was to design dual-function delivery vector based on BENSpm that can function as a gene delivery vector and, after intracellular degradation, as an active anticancer agent targeting dysregulated polyamine metabolism. We first demonstrated strong synergism between BENSpm and a potential therapeutic gene product TRAIL. Strong synergism was obtained in both estrogen-dependent MCF-7 breast cancer cells and triple-negative MDA-MB-231 breast cancer cells. Significant dose reduction of TRAIL in combination with BENSpm in MDA-MB-231 cells, together with the fact that BENSpm rendered MCF-7 cells more sensitive to TRAIL treatment verified our rationale of designing BENSpm-based delivery platform. This was expected to be beneficial for overcoming drug resistance in chemotherapy, as well as boosting the therapeutic effect of therapeutic genes. We first designed a lipid-based BENSpm dual vector (Lipo

  2. Development of new USER-based cloning vectors for multiple genes expression in Saccharomyces cerevisiae

    DEFF Research Database (Denmark)

    Kildegaard, Kanchana Rueksomtawin; Jensen, Niels Bjerg; Maury, Jerome;

    2013-01-01

    auxotrophic and dominant markers for convenience of use. Our vector set also contains both integrating and multicopy vectors for stability of protein expression and high expression level. We will make the new vector system available to the yeast community and provide a comprehensive protocol for cloning...

  3. Multiple insecticide resistance: an impediment to insecticide-based malaria vector control program.

    Directory of Open Access Journals (Sweden)

    Delenasaw Yewhalaw

    Full Text Available BACKGROUND: Indoor Residual Spraying (IRS, insecticide-treated nets (ITNs and long-lasting insecticidal nets (LLINs are key components in malaria prevention and control strategy. However, the development of resistance by mosquitoes to insecticides recommended for IRS and/or ITNs/LLINs would affect insecticide-based malaria vector control. We assessed the susceptibility levels of Anopheles arabiensis to insecticides used in malaria control, characterized basic mechanisms underlying resistance, and evaluated the role of public health use of insecticides in resistance selection. METHODOLOGY/PRINCIPAL FINDINGS: Susceptibility status of An. arabiensis was assessed using WHO bioassay tests to DDT, permethrin, deltamethrin, malathion and propoxur in Ethiopia from August to September 2009. Mosquito specimens were screened for knockdown resistance (kdr and insensitive acetylcholinesterase (ace-1(R mutations using AS-PCR and PCR-RFLP, respectively. DDT residues level in soil from human dwellings and the surrounding environment were determined by Gas Chromatography with Electron Capture Detector. An. arabiensis was resistant to DDT, permethrin, deltamethrin and malathion, but susceptible to propoxur. The West African kdr allele was found in 280 specimens out of 284 with a frequency ranged from 95% to 100%. Ace-1(R mutation was not detected in all specimens scored for the allele. Moreover, DDT residues were found in soil samples from human dwellings but not in the surrounding environment. CONCLUSION: The observed multiple-resistance coupled with the occurrence of high kdr frequency in populations of An. arabiensis could profoundly affect the malaria vector control programme in Ethiopia. This needs an urgent call for implementing rational resistance management strategies and integrated vector control intervention.

  4. Clustering technique-based least square support vector machine for EEG signal classification.

    Science.gov (United States)

    Siuly; Li, Yan; Wen, Peng Paul

    2011-12-01

    This paper presents a new approach called clustering technique-based least square support vector machine (CT-LS-SVM) for the classification of EEG signals. Decision making is performed in two stages. In the first stage, clustering technique (CT) has been used to extract representative features of EEG data. In the second stage, least square support vector machine (LS-SVM) is applied to the extracted features to classify two-class EEG signals. To demonstrate the effectiveness of the proposed method, several experiments have been conducted on three publicly available benchmark databases, one for epileptic EEG data, one for mental imagery tasks EEG data and another one for motor imagery EEG data. Our proposed approach achieves an average sensitivity, specificity and classification accuracy of 94.92%, 93.44% and 94.18%, respectively, for the epileptic EEG data; 83.98%, 84.37% and 84.17% respectively, for the motor imagery EEG data; and 64.61%, 58.77% and 61.69%, respectively, for the mental imagery tasks EEG data. The performance of the CT-LS-SVM algorithm is compared in terms of classification accuracy and execution (running) time with our previous study where simple random sampling with a least square support vector machine (SRS-LS-SVM) was employed for EEG signal classification. We also compare the proposed method with other existing methods in the literature for the three databases. The experimental results show that the proposed algorithm can produce a better classification rate than the previous reported methods and takes much less execution time compared to the SRS-LS-SVM technique. The research findings in this paper indicate that the proposed approach is very efficient for classification of two-class EEG signals.

  5. NVR-BIP: Nuclear Vector Replacement using Binary Integer Programming for NMR Structure-Based Assignments.

    Science.gov (United States)

    Apaydin, Mehmet Serkan; Çatay, Bülent; Patrick, Nicholas; Donald, Bruce R

    2011-05-01

    Nuclear magnetic resonance (NMR) spectroscopy is an important experimental technique that allows one to study protein structure and dynamics in solution. An important bottleneck in NMR protein structure determination is the assignment of NMR peaks to the corresponding nuclei. Structure-based assignment (SBA) aims to solve this problem with the help of a template protein which is homologous to the target and has applications in the study of structure-activity relationship, protein-protein and protein-ligand interactions. We formulate SBA as a linear assignment problem with additional nuclear overhauser effect constraints, which can be solved within nuclear vector replacement's (NVR) framework (Langmead, C., Yan, A., Lilien, R., Wang, L. and Donald, B. (2003) A Polynomial-Time Nuclear Vector Replacement Algorithm for Automated NMR Resonance Assignments. Proc. the 7th Annual Int. Conf. Research in Computational Molecular Biology (RECOMB), Berlin, Germany, April 10-13, pp. 176-187. ACM Press, New York, NY. J. Comp. Bio., (2004), 11, pp. 277-298; Langmead, C. and Donald, B. (2004) An expectation/maximization nuclear vector replacement algorithm for automated NMR resonance assignments. J. Biomol. NMR, 29, 111-138). Our approach uses NVR's scoring function and data types and also gives the option of using CH and NH residual dipolar coupling (RDCs), instead of NH RDCs which NVR requires. We test our technique on NVR's data set as well as on four new proteins. Our results are comparable to NVR's assignment accuracy on NVR's test set, but higher on novel proteins. Our approach allows partial assignments. It is also complete and can return the optimum as well as near-optimum assignments. Furthermore, it allows us to analyze the information content of each data type and is easily extendable to accept new forms of input data, such as additional RDCs.

  6. Opportunities for improved chagas disease vector control based on knowledge, attitudes and practices of communities in the yucatan peninsula, Mexico.

    Directory of Open Access Journals (Sweden)

    Kathryn Rosecrans

    2014-03-01

    Full Text Available BACKGROUND: Chagas disease is a vector-borne parasitic disease of major public health importance. Current prevention efforts are based on triatomine vector control to reduce transmission to humans. Success of vector control interventions depends on their acceptability and value to affected communities. We aimed to identify opportunities for and barriers to improved vector control strategies in the Yucatan peninsula, Mexico. METHODOLOGY/PRINCIPAL FINDINGS: We employed a sequence of qualitative and quantitative research methods to investigate knowledge, attitudes and practices surrounding Chagas disease, triatomines and vector control in three rural communities. Our combined data show that community members are well aware of triatomines and are knowledgeable about their habits. However, most have a limited understanding of the transmission dynamics and clinical manifestations of Chagas disease. While triatomine control is not a priority for community members, they frequently use domestic insecticide products including insecticide spray, mosquito coils and plug-in repellents. Families spend about $32 US per year on these products. Alternative methods such as yard cleaning and window screens are perceived as desirable and potentially more effective. Screens are nonetheless described as unaffordable, in spite of a cost comparable to the average annual spending on insecticide products. CONCLUSION/SIGNIFICANCE: Further education campaigns and possibly financing schemes may lead families to redirect their current vector control spending from insecticide products to window screens. Also, synergism with mosquito control efforts should be further explored to motivate community involvement and ensure sustainability of Chagas disease vector control.

  7. High expression of hepatitis B virus based vector with reporter gene in hepatitis B virus infection system

    Institute of Scientific and Technical Information of China (English)

    Shi-Hong Li; Wen-Ge Huang; Bing Huang; Xi-Gu Chen

    2007-01-01

    AIM: To construct a hepatitis B virus (HBV)-based vector with a reporter gene and to establish an HBV infection system to evaluate the availability of the vector.METHODS: The HBV-based vectors with green fluorescence protein (GFP) were packaged into the liver of immunodeficient mice through transfer and helper plasmid using hydrodynamic technology. Wild type HBV (wt HBV) was provided by plasmid MC2009. Primary human hepatocytes (PHH) were isolated and infected with recombinant HBV (rHBV) or wt HBV. GFP expression was monitored by confocal and flow cytometry. HBV DNA and HBV surface antigen (HBSAg) were analyzed by PCR and ELISA.RESULTS: 3 × 107 wt HBV copies/mL and 5 × 106 rHBV copies/mL were collected from mice serum. In the wt HBV infected group, HBV progeny was 2 × 107 copies/mL and HBSAg was 770 ng/mL. In the rHBV infected group, GFP fluorescence was detected on d 3 post-infection and over 85% of the parenchymal cells expressed green fluorescence on d 12 post-infection. Compared with wt HBV in the PHH infection system, no rHBV DNA or HBSAg were detected in PHH culture media.CONCLUSION: An effective HBV based vector was developed, which proved to be a useful HBV infection system. This vector and infection system can be applied to develop a therapeutic vector and study the HBV life cycle and viral pathogenesis.

  8. A LIMITED RESOURCE VECTOR LOAD-BALANCING ALGORITHM FOR SOFTSWITCH-BASED HETEROGENEOUS CLUSTERED MEDIA SERVER

    Institute of Scientific and Technical Information of China (English)

    Wu Naixing; Liao Jianxin; Zhu Xiaomin

    2006-01-01

    Based on the system feature of softswitch-based heterogeneous clustered media server, this paper proposed a limited resource vector load-balancing algorithm. The purpose of the algorithm was to balance the load of clusters by utilizing all system resources effectively and to avoid violent shaking of the system performance. A lot of simulations on the Petri net model of load balance system are conducted and the algorithm is compared with some traditional algorithms on balancing ability for heterogeneity, system throughput, request response time and performance stability. The results of simulations show that the algorithm achieves system higher performance and it has excellent ability to deal with the heterogeneity of clustered media server.

  9. Combining Self-organizing Feature Map with Support Vector Regression Based on Expert System

    Institute of Scientific and Technical Information of China (English)

    WANGLing; MUZhi-Chun; GUOHui

    2005-01-01

    A new approach is proposed to model nonlinear dynamic systems by combining SOM(self-organizing feature map) with support vector regression (SVR) based on expert system. The whole system has a two-stage neural network architecture. In the first stage SOM is used as a clustering algorithm to partition the whole input space into several disjointed regions. A hierarchical architecture is adopted in the partition to avoid the problem of predetermining the number of partitioned regions. Then, in the second stage, multiple SVR, also called SVR experts, that best fit each partitioned region by the combination of different kernel function of SVR and promote the configuration and tuning of SVR. Finally, to apply this new approach to time-series prediction problems based on the Mackey-Glass differential equation and Santa Fe data, the results show that SVR experts has effective improvement in the generalization performance in comparison with the single SVR model.

  10. A reliability assessment method based on support vector machines for CNC equipment

    Institute of Scientific and Technical Information of China (English)

    WU Jun; DENG Chao; SHAO XinYu; XIE S Q

    2009-01-01

    With the applications of high technology, a catastrophic failure of CNC equipment rarely occurs at normal operation conditions. So it is difficult for traditional reliability assessment methods based on time-to-failure distributions to deduce the reliability level. This paper presents a novel reliability assessment methodology to estimate the reliability level of equipment with machining performance degradation data when only a few samples are available. The least squares support vector machines(LS-SVM) are introduced to analyze the performance degradation process on the equipment. A two-stage parameter optimization and searching method is proposed to improve the LS-SVM regression performance and a reliability assessment model based on the LS-SVM is built. A machining performance degradation experiment has been carried out on an OTM650 machine tool to validate the effectiveness of the proposed reliability assessment methodology.

  11. Anomaly Detection System Based on Principal Component Analysis and Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    LI Zhanchun; LI Zhitang; LIU Bin

    2006-01-01

    This article presents an anomaly detection system based on principal component analysis (PCA) and support vector machine (SVM). The system first creates a profile defining a normal behavior by frequency-based scheme, and then compares the similarity of a current behavior with the created profile to decide whether the input instance is normal or anomaly. In order to avoid overfitting and reduce the computational burden, normal behavior principal features are extracted by the PCA method. SVM is used to distinguish normal or anomaly for user behavior after training procedure has been completed by learning. In the experiments for performance evaluation the system achieved a correct detection rate equal to 92.2% and a false detection rate equal to 2.8%.

  12. Aero-Engine Fault Diagnosis Using Improved Local Discriminant Bases and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Jianwei Cui

    2014-01-01

    Full Text Available This paper presents an effective approach for aero-engine fault diagnosis with focus on rub-impact, through combination of improved local discriminant bases (LDB with support vector machine (SVM. The improved LDB algorithm, using both the normalized energy difference and the relative entropy as quantification measures, is applied to choose the optimal set of orthogonal subspaces for wavelet packet transform- (WPT- based signal decomposition. Then two optimal sets of orthogonal subspaces have been obtained and the energy features extracted from those subspaces appearing in both sets will be selected as input to a SVM classifier to diagnose aero-engine faults. Experiment studies conducted on an aero-engine rub-impact test system have verified the effectiveness of the proposed approach for classifying working conditions of aero-engines.

  13. FEATURE RANKING BASED NESTED SUPPORT VECTOR MACHINE ENSEMBLE FOR MEDICAL IMAGE CLASSIFICATION.

    Science.gov (United States)

    Varol, Erdem; Gaonkar, Bilwaj; Erus, Guray; Schultz, Robert; Davatzikos, Christos

    2012-01-01

    This paper presents a method for classification of structural magnetic resonance images (MRI) of the brain. An ensemble of linear support vector machine classifiers (SVMs) is used for classifying a subject as either patient or normal control. Image voxels are first ranked based on the voxel wise t-statistics between the voxel intensity values and class labels. Then voxel subsets are selected based on the rank value using a forward feature selection scheme. Finally, an SVM classifier is trained on each subset of image voxels. The class label of a test subject is calculated by combining individual decisions of the SVM classifiers using a voting mechanism. The method is applied for classifying patients with neurological diseases such as Alzheimer's disease (AD) and autism spectrum disorder (ASD). The results on both datasets demonstrate superior performance as compared to two state of the art methods for medical image classification.

  14. Vector rectangular-shape laser based on reduced graphene oxide interacting with long fiber taper

    CERN Document Server

    Gao, Lei; Zeng, Jing; Huang, Wei; Liu, Min

    2014-01-01

    A vector dual-wavelength rectangular-shape laser (RSL) based on a long fiber taper deposited with reduced graphene oxide is proposed, where the nonlinearity is enhanced due to large evanescent-field-interacting length and strong field confinement of a 8 mm fiber taper with a waist diameter of 4 micronmeters. Graphene flakes are deposited uniformly on the taper waist with light pressure effect, so this structure guarantees both excellent saturable absorption and high nonlinearity. The RSL with a repetition rate of 7.9 MHz exhibits fast polarization switching in two orthogonal polarization directions, and the temporal and spectral characteristics are investigated. The results suggest that the long taper-based graphene structure is an efficient choice for nonlinear devices.

  15. A reliability assessment method based on support vector machines for CNC equipment

    Institute of Scientific and Technical Information of China (English)

    2009-01-01

    With the applications of high technology,a catastrophic failure of CNC equipment rarely occurs at normal operation conditions.So it is difficult for traditional reliability assessment methods based on time-to-failure distributions to deduce the reliability level.This paper presents a novel reliability assessment methodology to estimate the reliability level of equipment with machining performance degradation data when only a few samples are available.The least squares support vector machines(LS-SVM) are introduced to analyze the performance degradation process on the equipment.A two-stage parameter optimization and searching method is proposed to improve the LS-SVM regression performance and a reliability assessment model based on the LS-SVM is built.A machining performance degradation experiment has been carried out on an OTM650 machine tool to validate the effectiveness of the proposed reliability assessment methodology.

  16. Relevant XML Documents - Approach Based on Vectors and Weight Calculation of Terms

    Directory of Open Access Journals (Sweden)

    Abdeslem DENNAI

    2016-10-01

    Full Text Available Three classes of documents, based on their data, circulate in the web: Unstructured documents (.Doc, .html, .pdf ..., semi-structured documents (.xml, .Owl ... and structured documents (Tables database for example. A semi-structured document is organized around predefined tags or defined by its author. However, many studies use a document classification by taking into account their textual content and underestimate their structure. We attempt in this paper to propose a representation of these semi-structured web documents based on weighted vectors allowing exploiting their content for a possible treatment. The weight of terms is calculated using: The normal frequency for a document, TF-IDF (Term Frequency - Inverse Document Frequency and logic (Boolean frequency for a set of documents. To assess and demonstrate the relevance of our proposed approach, we will realize several experiments on different corpus.

  17. Support-Vector-Machine-Based False Alarm Filter of Mechatronic Built-in Test

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    Diagnosing intermittent fault is an important approach to reduce built-in test (BIT) false alarms. Aiming at solving the shortcoming of the present diagnostic method of intermittent fault, and according to the merit of support vector machines ( SVM) which can be trained with a small-sample, an SVM-based diagnostic model of 3 states that include OK state, intermittent state and faulty state is presented. With the features based on the reflection coefficients of an alarm rate(AR) model extracted from small vibration samples, these models are trained to diagnose intermittent faults. The experimental results show that this method can diagnose multiple intermittent faults accurately with small training samples and BIT false alarms are reduced.

  18. Support-vector-based emergent self-organising approach for emotional understanding

    Science.gov (United States)

    Nguwi, Yok-Yen; Cho, Siu-Yeung

    2010-12-01

    This study discusses the computational analysis of general emotion understanding from questionnaires methodology. The questionnaires method approaches the subject by investigating the real experience that accompanied the emotions, whereas the other laboratory approaches are generally associated with exaggerated elements. We adopted a connectionist model called support-vector-based emergent self-organising map (SVESOM) to analyse the emotion profiling from the questionnaires method. The SVESOM first identifies the important variables by giving discriminative features with high ranking. The classifier then performs the classification based on the selected features. Experimental results show that the top rank features are in line with the work of Scherer and Wallbott [(1994), 'Evidence for Universality and Cultural Variation of Differential Emotion Response Patterning', Journal of Personality and Social Psychology, 66, 310-328], which approached the emotions physiologically. While the performance measures show that using the full features for classifications can degrade the performance, the selected features provide superior results in terms of accuracy and generalisation.

  19. Operator functional state classification using least-square support vector machine based recursive feature elimination technique.

    Science.gov (United States)

    Yin, Zhong; Zhang, Jianhua

    2014-01-01

    This paper proposed two psychophysiological-data-driven classification frameworks for operator functional states (OFS) assessment in safety-critical human-machine systems with stable generalization ability. The recursive feature elimination (RFE) and least square support vector machine (LSSVM) are combined and used for binary and multiclass feature selection. Besides typical binary LSSVM classifiers for two-class OFS assessment, two multiclass classifiers based on multiclass LSSVM-RFE and decision directed acyclic graph (DDAG) scheme are developed, one used for recognizing the high mental workload and fatigued state while the other for differentiating overloaded and base-line states from the normal states. Feature selection results have revealed that different dimensions of OFS can be characterized by specific set of psychophysiological features. Performance comparison studies show that reasonable high and stable classification accuracy of both classification frameworks can be achieved if the RFE procedure is properly implemented and utilized.

  20. Relevance Vector Machines for Enhanced BER Probability in DMT-Based Systems

    Directory of Open Access Journals (Sweden)

    Ashraf A. Tahat

    2010-01-01

    Full Text Available A new channel estimation method for discrete multitone (DMT communication system based on sparse Bayesian learning relevance vector machine (RVM method is presented. The Bayesian frame work is used to obtain sparse solutions for regression tasks with linear models. By exploiting a probabilistic Bayesian learning framework, sparse Bayesian learning provides accurate models for estimation and consequently equalization. We consider frequency domain equalization (FEQ using the proposed channel estimate at both the transmitter (preequalization and receiver (postequalization and compare the resulting bit error rate (BER performance curves for both approaches and various channel estimation techniques. Simulation results show that the proposed RVM-based method is superior to the traditional least squares technique.

  1. Novel Discrete Compactness-Based Training for Vector Quantization Networks: Enhancing Automatic Brain Tissue Classification

    Directory of Open Access Journals (Sweden)

    Ricardo Pérez-Aguila

    2013-01-01

    Full Text Available An approach for nonsupervised segmentation of Computed Tomography (CT brain slices which is based on the use of Vector Quantization Networks (VQNs is described. Images are segmented via a VQN in such way that tissue is characterized according to its geometrical and topological neighborhood. The main contribution rises from the proposal of a similarity metric which is based on the application of Discrete Compactness (DC which is a factor that provides information about the shape of an object. One of its main strengths lies in the sense of its low sensitivity to variations, due to noise or capture defects, in the shape of an object. We will present, compare, and discuss some examples of segmentation networks trained under Kohonen’s original algorithm and also under our similarity metric. Some experiments are established in order to measure the effectiveness and robustness, under our application of interest, of the proposed networks and similarity metric.

  2. Fault diagnosis based on support vector machines with parameter optimisation by artificial immunisation algorithm

    Science.gov (United States)

    Yuan, Shengfa; Chu, Fulei

    2007-04-01

    Support vector machines (SVM) is a new general machine-learning tool based on the structural risk minimisation principle that exhibits good generalisation when fault samples are few, it is especially fit for classification, forecasting and estimation in small-sample cases such as fault diagnosis, but some parameters in SVM are selected by man's experience, this has hampered its efficiency in practical application. Artificial immunisation algorithm (AIA) is used to optimise the parameters in SVM in this paper. The AIA is a new optimisation method based on the biologic immune principle of human being and other living beings. It can effectively avoid the premature convergence and guarantees the variety of solution. With the parameters optimised by AIA, the total capability of the SVM classifier is improved. The fault diagnosis of turbo pump rotor shows that the SVM optimised by AIA can give higher recognition accuracy than the normal SVM.

  3. Pipeline leakage recognition based on the projection singular value features and support vector machine

    Energy Technology Data Exchange (ETDEWEB)

    Liang, Wei; Zhang, Laibin; Mingda, Wang; Jinqiu, Hu [College of Mechanical and Transportation Engineering, China University of Petroleum, Beijing, (China)

    2010-07-01

    The negative wave pressure method is one of the processes used to detect leaks on oil pipelines. The development of new leakage recognition processes is difficult because it is practically impossible to collect leakage pressure samples. The method of leakage feature extraction and the selection of the recognition model are also important in pipeline leakage detection. This study investigated a new feature extraction approach Singular Value Projection (SVP). It projects the singular value to a standard basis. A new pipeline recognition model based on the multi-class Support Vector Machines was also developed. It was found that SVP is a clear and concise recognition feature of the negative pressure wave. Field experiments proved that the model provided a high recognition accuracy rate. This approach to pipeline leakage detection based on the SVP and SVM has a high application value.

  4. Bearing Degradation Process Prediction Based on the Support Vector Machine and Markov Model

    Directory of Open Access Journals (Sweden)

    Shaojiang Dong

    2014-01-01

    Full Text Available Predicting the degradation process of bearings before they reach the failure threshold is extremely important in industry. This paper proposed a novel method based on the support vector machine (SVM and the Markov model to achieve this goal. Firstly, the features are extracted by time and time-frequency domain methods. However, the extracted original features are still with high dimensional and include superfluous information, and the nonlinear multifeatures fusion technique LTSA is used to merge the features and reduces the dimension. Then, based on the extracted features, the SVM model is used to predict the bearings degradation process, and the CAO method is used to determine the embedding dimension of the SVM model. After the bearing degradation process is predicted by SVM model, the Markov model is used to improve the prediction accuracy. The proposed method was validated by two bearing run-to-failure experiments, and the results proved the effectiveness of the methodology.

  5. New Metric Based Algorithm for Test Vector Generation in VLSI Testing

    Directory of Open Access Journals (Sweden)

    M. V. Atre

    1995-07-01

    Full Text Available A new algorithm for test-vector-generation (TVG for combinational circuits has been presented for testing VLSI chips. This is done by defining a suitable metric or distance, in the space of all input vectors, between a vector and a set of vectors. The test vectors are generated by suitably maximising the above distance. Two different methods of maximising the distance are suggested. Performances of the two methods for different circuits are presented and compared with the random method of TVG. It was observed that method B is superior to the other two methods. Also, method A is slightly better than method R.

  6. Support Vector Regression-Based Adaptive Divided Difference Filter for Nonlinear State Estimation Problems

    Directory of Open Access Journals (Sweden)

    Hongjian Wang

    2014-01-01

    Full Text Available We present a support vector regression-based adaptive divided difference filter (SVRADDF algorithm for improving the low state estimation accuracy of nonlinear systems, which are typically affected by large initial estimation errors and imprecise prior knowledge of process and measurement noises. The derivative-free SVRADDF algorithm is significantly simpler to compute than other methods and is implemented using only functional evaluations. The SVRADDF algorithm involves the use of the theoretical and actual covariance of the innovation sequence. Support vector regression (SVR is employed to generate the adaptive factor to tune the noise covariance at each sampling instant when the measurement update step executes, which improves the algorithm’s robustness. The performance of the proposed algorithm is evaluated by estimating states for (i an underwater nonmaneuvering target bearing-only tracking system and (ii maneuvering target bearing-only tracking in an air-traffic control system. The simulation results show that the proposed SVRADDF algorithm exhibits better performance when compared with a traditional DDF algorithm.

  7. The Study of an Improved Relevant Vector Machine MUD Based on Perfect Sampling

    Institute of Scientific and Technical Information of China (English)

    YANGTao; XIEJianying

    2004-01-01

    In this paper, we investigate the recently introduced new learning technique-rrelevant vector machine (RVM) to construct a Multi-user detection (MUD) scheme through perfect sampling~ which forms a new a posteriori parameter distribution estimation scheme based on Markov chain Monte Carlo (MCMC) algorithm. First set a prior on the parameter to be estimated, then have the parameter as a state variable. As the parameter in MUD is concerned, the number of w is prefixed, then make use of the given prior and perfect sampling to acquire a posteriori estimation ofw. Once Bit error rate (BER) representing a kernel function output increases, a timely updating of mean and variance of prior distribution is implemented to acquire the ‘needed sample'. Thus the evaluation of w forms an adaptive close loop process, which generates predictive distribution rather than makes point prediction, meanwhile the sparseness is acquired through a Jeffreys prior. Compared with other Bayesian detection scheme, this algorithm is especially efficient in solving high dimension and global optimization problems. Digital result shows that the detection performance is better than that of conventional Support vector machine (SVM) as well as Minimum mean square error (MMSE) scheme especially under massive user environment.

  8. Fuzzy nonlinear proximal support vector machine for land extraction based on remote sensing image.

    Directory of Open Access Journals (Sweden)

    Xiaomei Zhong

    Full Text Available Currently, remote sensing technologies were widely employed in the dynamic monitoring of the land. This paper presented an algorithm named fuzzy nonlinear proximal support vector machine (FNPSVM by basing on ETM(+ remote sensing image. This algorithm is applied to extract various types of lands of the city Da'an in northern China. Two multi-category strategies, namely "one-against-one" and "one-against-rest" for this algorithm were described in detail and then compared. A fuzzy membership function was presented to reduce the effects of noises or outliers on the data samples. The approaches of feature extraction, feature selection, and several key parameter settings were also given. Numerous experiments were carried out to evaluate its performances including various accuracies (overall accuracies and kappa coefficient, stability, training speed, and classification speed. The FNPSVM classifier was compared to the other three classifiers including the maximum likelihood classifier (MLC, back propagation neural network (BPN, and the proximal support vector machine (PSVM under different training conditions. The impacts of the selection of training samples, testing samples and features on the four classifiers were also evaluated in these experiments.

  9. Carbohydrate-Based Ice Recrystallization Inhibitors Increase Infectivity and Thermostability of Viral Vectors

    Science.gov (United States)

    Ghobadloo, Shahrokh M.; Balcerzak, Anna K.; Gargaun, Ana; Muharemagic, Darija; Mironov, Gleb G.; Capicciotti, Chantelle J.; Briard, Jennie G.; Ben, Robert N.; Berezovski, Maxim V.

    2014-07-01

    The inability of vaccines to retain sufficient thermostability has been an obstacle to global vaccination programs. To address this major limitation, we utilized carbohydrate-based ice recrystallization inhibitors (IRIs) to eliminate the cold chain and stabilize the potency of Vaccinia virus (VV), Vesicular Stomatitis virus (VSV) and Herpes virus-1 (HSV-1). The impact of these IRIs was tested on the potency of the viral vectors using a plaque forming unit assay following room temperature storage, cryopreservation with successive freeze-thaw cycles and lyophilization. Viral potency after storage with all three conditions demonstrated that N-octyl-gluconamide (NOGlc) recovered the infectivity of shelf stored VV, 5.6 Log10 PFU mL-1 during 40 days, and HSV-1, 2.7 Log10 PFU mL-1 during 9 days. Carbon-linked antifreeze glycoprotein analogue ornithine-glycine-glycine-galactose (OGG-Gal) increases the recovery of VV and VSV more than 1 Log10 PFU mL-1 after 10 freeze-thaw cycles. In VSV, cryostorage with OGG-Gal maintains high infectivity and reduces temperature-induced aggregation of viral particles by 2 times that of the control. In total, OGG-Gal and NOGlc preserve virus potency during cryostorage. Remarkably, NOGlc has potential to eliminate the cold chain and permit room temperature storage of viral vectors.

  10. A high-sensitive static vector magnetometer based on two vibrating coils.

    Science.gov (United States)

    Yin, Jing; Pan, Cheng Liang; Wang, Hong Bo; Feng, Zhi Hua

    2011-12-01

    A static vector magnetometer based on two-dimensional (2D) vibrating coils actuated by a piezoelectric cantilever is presented. Two individual sensing coils are orthogonally fastened at the tip of cantilever and piezoelectric sheets are used to excite the cantilever bending. Due to off-axis coupler on the tip, the cantilever generates bending and twisting vibrations simultaneously on their corresponding resonant frequencies, realizing the 2D rotating vibrations of the coils. According to Faraday-Lenz Law, output voltages are induced from the coils. They are amplified by a pre-amplifier circuit, decoupled by a phase-sensitive detector, and finally used to calculate the vector of magnetic field at the coil location. The coil head of a prototype magnetometer possesses a dc sensitivity of around 10 μV/Gs with a good linearity in the measuring range from 0 to 16 μT. The corresponding noise level is about 13.1 nT in the bandwidth from 0.01 Hz to 1 Hz.

  11. Mutually orthogonal Latin squares from the inner products of vectors in mutually unbiased bases

    Energy Technology Data Exchange (ETDEWEB)

    Hall, Joanne L; Rao, Asha [School of Mathematical and Geospatial Sciences, RMIT University, Melbourne 3001 (Australia)], E-mail: joanne.hall@rmit.edu.au, E-mail: asha@rmit.edu.au

    2010-04-02

    Mutually unbiased bases (MUBs) are important in quantum information theory. While constructions of complete sets of d + 1 MUBs in C{sup d} are known when d is a prime power, it is unknown if such complete sets exist in non-prime power dimensions. It has been conjectured that complete sets of MUBs only exist in C{sup d} if a maximal set of mutually orthogonal Latin squares (MOLS) of side length d also exists. There are several constructions (Roy and Scott 2007 J. Math. Phys. 48 072110; Paterek, Dakic and Brukner 2009 Phys. Rev. A 79 012109) of complete sets of MUBs from specific types of MOLS, which use Galois fields to construct the vectors of the MUBs. In this paper, two known constructions of MUBs (Alltop 1980 IEEE Trans. Inf. Theory 26 350-354; Wootters and Fields 1989 Ann. Phys. 191 363-381), both of which use polynomials over a Galois field, are used to construct complete sets of MOLS in the odd prime case. The MOLS come from the inner products of pairs of vectors in the MUBs.

  12. A high excision potential of TALENs for integrated DNA of HIV-based lentiviral vector.

    Science.gov (United States)

    Ebina, Hirotaka; Kanemura, Yuka; Misawa, Naoko; Sakuma, Tetsushi; Kobayashi, Tomoko; Yamamoto, Takashi; Koyanagi, Yoshio

    2015-01-01

    DNA-editing technology has made it possible to rewrite genetic information in living cells. Human immunodeficiency virus (HIV) provirus, an integrated form of viral complementary DNA in host chromosomes, could be a potential target for this technology. We recently reported that HIV proviral DNA could be excised from the chromosomal DNA of HIV-based lentiviral DNA-transduced T cells after multiple introductions of a clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9 endonuclease system targeting HIV long terminal repeats (LTR). Here, we generated a more efficient strategy that enables the excision of HIV proviral DNA using customized transcription activator-like effector nucleases (TALENs) targeting the same HIV LTR site. A single transfection of TALEN-encoding mRNA, prepared from in vitro transcription, resulted in more than 80% of lentiviral vector DNA being successfully removed from the T cell lines. Furthermore, we developed a lentiviral vector system that takes advantage of the efficient proviral excision with TALENs and permits the simple selection of gene-transduced and excised cells in T cell lines.

  13. A high excision potential of TALENs for integrated DNA of HIV-based lentiviral vector.

    Directory of Open Access Journals (Sweden)

    Hirotaka Ebina

    Full Text Available DNA-editing technology has made it possible to rewrite genetic information in living cells. Human immunodeficiency virus (HIV provirus, an integrated form of viral complementary DNA in host chromosomes, could be a potential target for this technology. We recently reported that HIV proviral DNA could be excised from the chromosomal DNA of HIV-based lentiviral DNA-transduced T cells after multiple introductions of a clustered regularly interspaced short palindromic repeat (CRISPR/Cas9 endonuclease system targeting HIV long terminal repeats (LTR. Here, we generated a more efficient strategy that enables the excision of HIV proviral DNA using customized transcription activator-like effector nucleases (TALENs targeting the same HIV LTR site. A single transfection of TALEN-encoding mRNA, prepared from in vitro transcription, resulted in more than 80% of lentiviral vector DNA being successfully removed from the T cell lines. Furthermore, we developed a lentiviral vector system that takes advantage of the efficient proviral excision with TALENs and permits the simple selection of gene-transduced and excised cells in T cell lines.

  14. Twin Support Vector Machine for Multiple Instance Learning Based on Bag Dissimilarities

    Directory of Open Access Journals (Sweden)

    Divya Tomar

    2016-01-01

    Full Text Available In multiple instance learning (MIL framework, an object is represented by a set of instances referred to as bag. A positive class label is assigned to a bag if it contains at least one positive instance; otherwise a bag is labeled with negative class label. Therefore, the task of MIL is to learn a classifier at bag level rather than at instance level. Traditional supervised learning approaches cannot be applied directly in such kind of situation. In this study, we represent each bag by a vector of its dissimilarities to the other existing bags in the training dataset and propose a multiple instance learning based Twin Support Vector Machine (MIL-TWSVM classifier. We have used different ways to represent the dissimilarity between two bags and performed a comparative analysis of them. The experimental results on ten benchmark MIL datasets demonstrate that the proposed MIL-TWSVM classifier is computationally inexpensive and competitive with state-of-the-art approaches. The significance of the experimental results has been tested by using Friedman statistic and Nemenyi post hoc tests.

  15. Virtual Velocity Vector-based Offline Collision-free Path Planning of Industrial Robotic Manipulator

    Directory of Open Access Journals (Sweden)

    Fan Ouyang

    2015-09-01

    Full Text Available Currently, industrial robotic manipulators are applied in many manufacturing applications. In most cases, an industrial environment is a cluttered and complex one where moving obstacles may exist and hinder the movement of robotic manipulators. Therefore, a robotic manipulator not only has to avoid moving obstacles, but also needs to fulfill the manufacturing requirements of smooth movement in fixed tact time. Thus, this paper proposes a virtual velocity vector-based algorithm of offline collision-free path planning for manipulator arms in a controlled industrial environment. The minimum distance between a manipulator and a moving obstacle can be maintained at an expected value by utilizing our proposed algorithm with established offline collision-free path-planning and trajectory generating systems. Furthermore, both joint space velocity and Cartesian space velocity of generated time-efficient trajectory are continuous and smooth. In addition, the vector of detour velocity in a 3D environment is determined and depicted. Simulation results indicate that detour velocity can shorten the total task time as well as escaping the local minimal effectively. In summary, our approach can fulfill both safety requirements of collision avoidance of moving obstacles and manufacturing requirements of smooth movement within fixed tact time in an industrial environment.

  16. Carbohydrate-based ice recrystallization inhibitors increase infectivity and thermostability of viral vectors.

    Science.gov (United States)

    Ghobadloo, Shahrokh M; Balcerzak, Anna K; Gargaun, Ana; Muharemagic, Darija; Mironov, Gleb G; Capicciotti, Chantelle J; Briard, Jennie G; Ben, Robert N; Berezovski, Maxim V

    2014-07-31

    The inability of vaccines to retain sufficient thermostability has been an obstacle to global vaccination programs. To address this major limitation, we utilized carbohydrate-based ice recrystallization inhibitors (IRIs) to eliminate the cold chain and stabilize the potency of Vaccinia virus (VV), Vesicular Stomatitis virus (VSV) and Herpes virus-1 (HSV-1). The impact of these IRIs was tested on the potency of the viral vectors using a plaque forming unit assay following room temperature storage, cryopreservation with successive freeze-thaw cycles and lyophilization. Viral potency after storage with all three conditions demonstrated that N-octyl-gluconamide (NOGlc) recovered the infectivity of shelf stored VV, 5.6 Log₁₀ PFU mL(-1) during 40 days, and HSV-1, 2.7 Log₁₀ PFU mL(-1) during 9 days. Carbon-linked antifreeze glycoprotein analogue ornithine-glycine-glycine-galactose (OGG-Gal) increases the recovery of VV and VSV more than 1 Log₁₀ PFU mL(-1) after 10 freeze-thaw cycles. In VSV, cryostorage with OGG-Gal maintains high infectivity and reduces temperature-induced aggregation of viral particles by 2 times that of the control. In total, OGG-Gal and NOGlc preserve virus potency during cryostorage. Remarkably, NOGlc has potential to eliminate the cold chain and permit room temperature storage of viral vectors.

  17. Face Recognition Performance Improvement using a Similarity Score of Feature Vectors based on Probabilistic Histograms

    Directory of Open Access Journals (Sweden)

    SRIKOTE, G.

    2016-08-01

    Full Text Available This paper proposes an improved performance algorithm of face recognition to identify two face mismatch pairs in cases of incorrect decisions. The primary feature of this method is to deploy the similarity score with respect to Gaussian components between two previously unseen faces. Unlike the conventional classical vector distance measurement, our algorithms also consider the plot of summation of the similarity index versus face feature vector distance. A mixture of Gaussian models of labeled faces is also widely applicable to different biometric system parameters. By comparative evaluations, it has been shown that the efficiency of the proposed algorithm is superior to that of the conventional algorithm by an average accuracy of up to 1.15% and 16.87% when compared with 3x3 Multi-Region Histogram (MRH direct-bag-of-features and Principal Component Analysis (PCA-based face recognition systems, respectively. The experimental results show that similarity score consideration is more discriminative for face recognition compared to feature distance. Experimental results of Labeled Face in the Wild (LFW data set demonstrate that our algorithms are suitable for real applications probe-to-gallery identification of face recognition systems. Moreover, this proposed method can also be applied to other recognition systems and therefore additionally improves recognition scores.

  18. Concise vector analysis

    CERN Document Server

    Eliezer, C J; Maxwell, E A; Sneddon, I N

    1963-01-01

    Concise Vector Analysis is a five-chapter introductory account of the methods and techniques of vector analysis. These methods are indispensable tools in mathematics, physics, and engineering. The book is based on lectures given by the author in the University of Ceylon.The first two chapters deal with vector algebra. These chapters particularly present the addition, representation, and resolution of vectors. The next two chapters examine the various aspects and specificities of vector calculus. The last chapter looks into some standard applications of vector algebra and calculus.This book wil

  19. Light axial vector mesons

    CERN Document Server

    Chen, Kan; Liu, Xiang; Matsuki, Takayuki

    2015-01-01

    Inspired by the abundant experimental observation of axial vector states, we study whether the observed axial vector states can be categorized into the conventional axial vector meson family. In this paper we carry out analysis based on the mass spectra and two-body Okubo-Zweig-Iizuka-allowed decays. Besides testing the possible axial vector meson assignments, we also predict abundant information for their decays and the properties of some missing axial vector mesons, which are valuable to further experimental exploration of the observed and predicted axial vector mesons.

  20. Robust Adaptive Beamforming Based on Steering Vector Estimation via Semidefinite Programming Relaxation

    CERN Document Server

    Khabbazibasmenj, Arash; Hassanien, Aboulnasr

    2010-01-01

    We develop a new approach to robust adaptive beamforming in the presence of signal steering vector errors. Since the signal steering vector is known imprecisely, its presumed (prior) value is used to find a more accurate estimate of the actual steering vector, which then is used for obtaining the optimal beamforming weight vector. The objective for finding such an estimate of the actual signal steering vector is the maximization of the beamformer output power, while the constraints are the normalization condition and the requirement that the estimate of the steering vector does not converge to an interference steering vector. Our objective and constraints are free of any design parameters of non-unique choice. The resulting optimization problem is a non-convex quadratically constrained quadratic program, which is NP hard in general. However, for our problem we show that an efficient solution can be found using the semi-definite relaxation technique. Moreover, the strong duality holds for the proposed problem ...

  1. Support vector machine based decision for mechanical fault condition monitoring in induction motor using an advanced Hilbert-Park transform.

    Science.gov (United States)

    Ben Salem, Samira; Bacha, Khmais; Chaari, Abdelkader

    2012-09-01

    In this work we suggest an original fault signature based on an improved combination of Hilbert and Park transforms. Starting from this combination we can create two fault signatures: Hilbert modulus current space vector (HMCSV) and Hilbert phase current space vector (HPCSV). These two fault signatures are subsequently analysed using the classical fast Fourier transform (FFT). The effects of mechanical faults on the HMCSV and HPCSV spectrums are described, and the related frequencies are determined. The magnitudes of spectral components, relative to the studied faults (air-gap eccentricity and outer raceway ball bearing defect), are extracted in order to develop the input vector necessary for learning and testing the support vector machine with an aim of classifying automatically the various states of the induction motor.

  2. On-line forecasting model for zinc output based on self-tuning support vector regression and its application

    Institute of Scientific and Technical Information of China (English)

    胡志坤; 桂卫华; 彭小奇

    2004-01-01

    An on-line forecasting model based on self-tuning support vectors regression for zinc output was put forward to maximize zinc output by adjusting operational parameters in the process of imperial smelting furnace. In this model, the mathematical model of support vector regression was converted into the same format as support vector machine for classification. Then a simplified sequential minimal optimization for classification was applied to train the regression coefficient vector α- α* and threshold b. Sequentially penalty parameter C was tuned dynamically through forecasting result during the training process. Finally, an on-line forecasting algorithm for zinc output was proposed. The simulation result shows that in spite of a relatively small industrial data set, the effective error is less than 10% with a remarkable performance of real time. The model was applied to the optimization operation and fault diagnosis system for imperial smelting furnace.

  3. Multigenic lentiviral vectors for combined and tissue-specific expression of miRNA- and protein-based antiangiogenic factors

    DEFF Research Database (Denmark)

    Askou, Anne Louise; Aagaard, Lars; Kostic, Corinne

    2015-01-01

    Lentivirus-based gene delivery vectors carrying multiple gene cassettes are powerful tools in gene transfer studies and gene therapy, allowing coexpression of multiple therapeutic factors and, if desired, fluorescent reporters. Current strategies to express transgenes and microRNA (miRNA) clusters......-to-back RNApolII-driven expression cassettes. This configuration allows effective production of intron-embedded miRNAs that are released upon transduction of target cells. Exploiting such multigenic lentiviral vectors, we demonstrate robust miRNA-directed downregulation of vascular endothelial growth factor...... by the VMD2 promoter, verifying that multigenic lentiviral vectors can be produced with high titers sufficient for in vivo applications. Altogether, our results suggest the potential applicability of combined miRNA- and protein-encoding lentiviral vectors in antiangiogenic gene therapy, including new...

  4. Eco-bio-social research on community-based approaches for Chagas disease vector control in Latin America.

    Science.gov (United States)

    Gürtler, Ricardo E; Yadon, Zaida E

    2015-02-01

    This article provides an overview of three research projects which designed and implemented innovative interventions for Chagas disease vector control in Bolivia, Guatemala and Mexico. The research initiative was based on sound principles of community-based ecosystem management (ecohealth), integrated vector management, and interdisciplinary analysis. The initial situational analysis achieved a better understanding of ecological, biological and social determinants of domestic infestation. The key factors identified included: housing quality; type of peridomestic habitats; presence and abundance of domestic dogs, chickens and synanthropic rodents; proximity to public lights; location in the periphery of the village. In Bolivia, plastering of mud walls with appropriate local materials and regular cleaning of beds and of clothes next to the walls, substantially decreased domestic infestation and abundance of the insect vector Triatoma infestans. The Guatemalan project revealed close links between house infestation by rodents and Triatoma dimidiata, and vector infection with Trypanosoma cruzi. A novel community-operated rodent control program significantly reduced rodent infestation and bug infection. In Mexico, large-scale implementation of window screens translated into promising reductions in domestic infestation. A multi-pronged approach including community mobilisation and empowerment, intersectoral cooperation and adhesion to integrated vector management principles may be the key to sustainable vector and disease control in the affected regions.

  5. Sensitivity Analysis of a Spatio-Temporal Avalanche Forecasting Model Based on Support Vector Machines

    Science.gov (United States)

    Matasci, G.; Pozdnoukhov, A.; Kanevski, M.

    2009-04-01

    The recent progress in environmental monitoring technologies allows capturing extensive amount of data that can be used to assist in avalanche forecasting. While it is not straightforward to directly obtain the stability factors with the available technologies, the snow-pack profiles and especially meteorological parameters are becoming more and more available at finer spatial and temporal scales. Being very useful for improving physical modelling, these data are also of particular interest regarding their use involving the contemporary data-driven techniques of machine learning. Such, the use of support vector machine classifier opens ways to discriminate the ``safe'' and ``dangerous'' conditions in the feature space of factors related to avalanche activity based on historical observations. The input space of factors is constructed from the number of direct and indirect snowpack and weather observations pre-processed with heuristic and physical models into a high-dimensional spatially varying vector of input parameters. The particular system presented in this work is implemented for the avalanche-prone site of Ben Nevis, Lochaber region in Scotland. A data-driven model for spatio-temporal avalanche danger forecasting provides an avalanche danger map for this local (5x5 km) region at the resolution of 10m based on weather and avalanche observations made by forecasters on a daily basis at the site. We present the further work aimed at overcoming the ``black-box'' type modelling, a disadvantage the machine learning methods are often criticized for. It explores what the data-driven method of support vector machine has to offer to improve the interpretability of the forecast, uncovers the properties of the developed system with respect to highlighting which are the important features that led to the particular prediction (both in time and space), and presents the analysis of sensitivity of the prediction with respect to the varying input parameters. The purpose of the

  6. Engineering web maps with gradual content zoom based on streaming vector data

    Science.gov (United States)

    Huang, Lina; Meijers, Martijn; Šuba, Radan; van Oosterom, Peter

    2016-04-01

    Vario-scale data structures have been designed to support gradual content zoom and the progressive transfer of vector data, for use with arbitrary map scales. The focus to date has been on the server side, especially on how to convert geographic data into the proposed vario-scale structures by means of automated generalisation. This paper contributes to the ongoing vario-scale research by focusing on the client side and communication, particularly on how this works in a web-services setting. It is claimed that these functionalities are urgently needed, as many web-based applications, both desktop and mobile, require gradual content zoom, progressive transfer and a high performance level. The web-client prototypes developed in this paper make it possible to assess the behaviour of vario-scale data and to determine how users will actually see the interactions. Several different options of web-services communication architectures are possible in a vario-scale setting. These options are analysed and tested with various web-client prototypes, with respect to functionality, ease of implementation and performance (amount of transmitted data and response times). We show that the vario-scale data structure can fit in with current web-based architectures and efforts to standardise map distribution on the internet. However, to maximise the benefits of vario-scale data, a client needs to be aware of this structure. When a client needs a map to be refined (by means of a gradual content zoom operation), only the 'missing' data will be requested. This data will be sent incrementally to the client from a server. In this way, the amount of data transferred at one time is reduced, shortening the transmission time. In addition to these conceptual architecture aspects, there are many implementation and tooling design decisions at play. These will also be elaborated on in this paper. Based on the experiments conducted, we conclude that the vario-scale approach indeed supports gradual

  7. Development of new plasmid DNA vaccine vectors with R1-based replicons

    Directory of Open Access Journals (Sweden)

    Bower Diana M

    2012-08-01

    Full Text Available Abstract Background There has been renewed interest in biopharmaceuticals based on plasmid DNA (pDNA in recent years due to the approval of several veterinary DNA vaccines, on-going clinical trials of human pDNA-based therapies, and significant advances in adjuvants and delivery vehicles that have helped overcome earlier efficacy deficits. With this interest comes the need for high-yield, cost-effective manufacturing processes. To this end, vector engineering is one promising strategy to improve plasmid production. Results In this work, we have constructed a new DNA vaccine vector, pDMB02-GFP, containing the runaway R1 origin of replication. The runaway replication phenotype should result in plasmid copy number amplification after a temperature shift from 30°C to 42°C. However, using Escherichia coli DH5α as a host, we observed that the highest yields of pDMB02-GFP were achieved during constant-temperature culture at 30°C, with a maximum yield of approximately 19 mg pDNA/g DCW being observed. By measuring mRNA and protein levels of the R1 replication initiator protein, RepA, we determined that RepA may be limiting pDMB02-GFP yield at 42°C. A mutant plasmid, pDMB-ATG, was constructed by changing the repA start codon from the sub-optimal GTG to ATG. In cultures of DH5α[pDMB-ATG], temperature-induced plasmid amplification was more dramatic than that observed with pDMB02-GFP, and RepA protein was detectable for several hours longer than in cultures of pDMB02-GFP at 42°C. Conclusions Overall, we have demonstrated that R1-based plasmids can produce high yields of high-quality pDNA without the need for a temperature shift, and have laid the groundwork for further investigation of this class of vectors in the context of plasmid DNA production.

  8. Control Method of Three-level Neutral-point-clamped Inverter Based on Voltage Vector Diagram Partition

    Institute of Scientific and Technical Information of China (English)

    SONG Wen-xiang; YAO Gang; CHEN Chen; CHEN Guo-cheng

    2008-01-01

    A new modulation approach was presented for the control of neutral-point (NP) voltage variation in the three-level NP-clamped voltage source inverter, and the average NP current model was established based on vector diagram partition. Thus, theory base was built for balancing control of NP potential. Theoretical analysis and experimental results indicate that the proposed method for NP balancing control vector synthesizing concept based can make the average NP current zero, and do not influence NP potential within every sample period. The effectiveness of proposed research approach was verified by simulative and experimental results.

  9. Feature-matching pattern-based support vector machines for robust peptide mass fingerprinting.

    Science.gov (United States)

    Li, Youyuan; Hao, Pei; Zhang, Siliang; Li, Yixue

    2011-12-01

    Peptide mass fingerprinting, regardless of becoming complementary to tandem mass spectrometry for protein identification, is still the subject of in-depth study because of its higher sample throughput, higher level of specificity for single peptides and lower level of sensitivity to unexpected post-translational modifications compared with tandem mass spectrometry. In this study, we propose, implement and evaluate a uniform approach using support vector machines to incorporate individual concepts and conclusions for accurate PMF. We focus on the inherent attributes and critical issues of the theoretical spectrum (peptides), the experimental spectrum (peaks) and spectrum (masses) alignment. Eighty-one feature-matching patterns derived from cleavage type, uniqueness and variable masses of theoretical peptides together with the intensity rank of experimental peaks were proposed to characterize the matching profile of the peptide mass fingerprinting procedure. We developed a new strategy including the participation of matched peak intensity redistribution to handle shared peak intensities and 440 parameters were generated to digitalize each feature-matching pattern. A high performance for an evaluation data set of 137 items was finally achieved by the optimal multi-criteria support vector machines approach, with 491 final features out of a feature vector of 35,640 normalized features through cross training and validating a publicly available "gold standard" peptide mass fingerprinting data set of 1733 items. Compared with the Mascot, MS-Fit, ProFound and Aldente algorithms commonly used for MS-based protein identification, the feature-matching patterns algorithm has a greater ability to clearly separate correct identifications and random matches with the highest values for sensitivity (82%), precision (97%) and F1-measure (89%) of protein identification. Several conclusions reached via this research make general contributions to MS-based protein identification. Firstly

  10. Constructing Support Vector Machine Ensembles for Cancer Classification Based on Proteomic Profiling

    Institute of Scientific and Technical Information of China (English)

    Yong Mao; Xiao-Bo Zhou; Dao-Ying Pi; You-Xian Sun

    2005-01-01

    In this study, we present a constructive algorithm for training cooperative support vector machine ensembles (CSVMEs). CSVME combines ensemble architecture design with cooperative training for individual SVMs in ensembles. Unlike most previous studies on training ensembles, CSVME puts emphasis on both accuracy and collaboration among individual SVMs in an ensemble. A group of SVMs selected on the basis of recursive classifier elimination is used in CSVME, and the number of the individual SVMs selected to construct CSVME is determined by 10-fold cross-validation. This kind of SVME has been tested on two ovarian cancer datasets previously obtained by proteomic mass spectrometry. By combining several individual SVMs, the proposed method achieves better performance than the SVME of all base SVMs.

  11. A Stress Vector-Based Constitutive Model for Cohesionless Soil( Ⅱ )-Application

    Institute of Scientific and Technical Information of China (English)

    史宏彦; 谢定义; 白琳

    2002-01-01

    The stress vector-based constitutive model for cohesionless soil, proposed by SHI Hong-yan et al., was applied to analyze the deformation behaviors of materials subjected to various stress paths. The result of analysis shows that the constitutive model can capture well the main deformation behavior of cohesionless soil, such as stress-strain nonlinearity,hardening property, dilatancy , stress path dependency, non- coaxiality between the principal stress and the principal strain increment directions, and the coupling of mean effective and deviatoric stress with deformation. In addition, the model can also take into account the rotation of principal stress axes and the influence of intermediate principal stress on deformation and strength of soil simultaneously. The excellent agreement between the predicted and measured behavior indicates the comprehensive applicability of the model.

  12. Tungsten disulphide based all fiber Q-switching cylindrical-vector beam generation

    Energy Technology Data Exchange (ETDEWEB)

    Lin, J.; Yan, K.; Zhou, Y. [Department of Optics and Optical Engineering, University of Science and Technology of China, Hefei 230026 (China); Xu, L. X., E-mail: xulixin@ustc.edu.cn; Gu, C. [Department of Optics and Optical Engineering, University of Science and Technology of China, Hefei 230026 (China); Haixi Collaborative Innovation Center for New Display Devices and Systems Integration, Fuzhou University, Fuzhou 350002 (China); Zhan, Q. W. [Electro-Optics Program, University of Dayton, Dayton, Ohio 45469 (United States)

    2015-11-09

    We proposed and demonstrated an all fiber passively Q-switching laser to generate cylindrical-vector beam, a two dimensional material, tungsten disulphide (WS{sub 2}), was adopted as a saturable absorber inside the laser cavity, while a few-mode fiber Bragg grating was used as a transverse mode-selective output coupler. The repetition rate of the Q-switching output pulses can be varied from 80 kHz to 120 kHz with a shortest duration of 958 ns. Attributed to the high damage threshold and polarization insensitivity of the WS{sub 2} based saturable absorber, the radially polarized beam and azimuthally polarized beam can be easily generated in the Q-switching fiber laser.

  13. PSO-based support vector machine with cuckoo search technique for clinical disease diagnoses.

    Science.gov (United States)

    Liu, Xiaoyong; Fu, Hui

    2014-01-01

    Disease diagnosis is conducted with a machine learning method. We have proposed a novel machine learning method that hybridizes support vector machine (SVM), particle swarm optimization (PSO), and cuckoo search (CS). The new method consists of two stages: firstly, a CS based approach for parameter optimization of SVM is developed to find the better initial parameters of kernel function, and then PSO is applied to continue SVM training and find the best parameters of SVM. Experimental results indicate that the proposed CS-PSO-SVM model achieves better classification accuracy and F-measure than PSO-SVM and GA-SVM. Therefore, we can conclude that our proposed method is very efficient compared to the previously reported algorithms.

  14. PSO-Based Support Vector Machine with Cuckoo Search Technique for Clinical Disease Diagnoses

    Directory of Open Access Journals (Sweden)

    Xiaoyong Liu

    2014-01-01

    Full Text Available Disease diagnosis is conducted with a machine learning method. We have proposed a novel machine learning method that hybridizes support vector machine (SVM, particle swarm optimization (PSO, and cuckoo search (CS. The new method consists of two stages: firstly, a CS based approach for parameter optimization of SVM is developed to find the better initial parameters of kernel function, and then PSO is applied to continue SVM training and find the best parameters of SVM. Experimental results indicate that the proposed CS-PSO-SVM model achieves better classification accuracy and F-measure than PSO-SVM and GA-SVM. Therefore, we can conclude that our proposed method is very efficient compared to the previously reported algorithms.

  15. Blind multiuser detector for chaos-based CDMA using support vector machine.

    Science.gov (United States)

    Kao, Johnny Wei-Hsun; Berber, Stevan Mirko; Kecman, Vojislav

    2010-08-01

    The algorithm and the results of a blind multiuser detector using a machine learning technique called support vector machine (SVM) on a chaos-based code division multiple access system is presented in this paper. Simulation results showed that the performance achieved by using SVM is comparable to existing minimum mean square error (MMSE) detector under both additive white Gaussian noise (AWGN) and Rayleigh fading conditions. However, unlike the MMSE detector, the SVM detector does not require the knowledge of spreading codes of other users in the system or the estimate of the channel noise variance. The optimization of this algorithm is considered in this paper and its complexity is compared with the MMSE detector. This detector is much more suitable to work in the forward link than MMSE. In addition, original theoretical bit-error rate expressions for the SVM detector under both AWGN and Rayleigh fading are derived to verify the simulation results.

  16. Large dynamic range optical vector analyzer based on optical single-sideband modulation and Hilbert transform

    Science.gov (United States)

    Xue, Min; Pan, Shilong; Zhao, Yongjiu

    2016-07-01

    A large dynamic range optical vector analyzer (OVA) based on optical single-sideband modulation is proposed and demonstrated. By dividing the optical signal after optical device under test into two paths, reversing the phase of one swept sideband using a Hilbert transformer in one path, and detecting the two signals from the two paths with a balanced photodetector, the measurement errors induced by the residual -1st-order sideband and the high-order sidebands can be eliminated and the dynamic range of the measurement is increased. In a proof-of-concept experiment, the stimulated Brillouin scattering and a fiber Bragg grating are measured by OVAs with and without the Hilbert transform and balanced photodetection. Results show that about 40-dB improvement in the measurement dynamic range is realized by the proposed OVA.

  17. Acousto-optic gyrotropic-crystal-based modulator with a rotating polarisation vector

    Science.gov (United States)

    Kotov, V. M.; Averin, S. V.; Kotov, E. V.; Voronko, A. I.; Tikhomirov, S. A.

    2017-02-01

    We study the influence of ellipticity of gyrotropic-crystal eigenwaves on the output characteristics of an acousto-optic (AO) modulator based on the interferometer scheme. The schemes of AO modulators are considered, which provide the polarisation vector rotation frequency fn/2, where f is the frequency of the acoustic wave, and n is the integer. Preference is given to the scheme combining cascade and polarisation-independent diffraction. An experimental layout of the AO modulator operating at f = 44.5 MHz is described, the modulation frequency of the output laser light intensity being 89 MHz. The frequency of the electrical signal from the photodetector is equal to 179.5 MHz.

  18. Support Vector Machine Based Pades Approximant for Diabetic Retinal Eye Detection

    Directory of Open Access Journals (Sweden)

    S. Vijayalakshmi

    2014-05-01

    Full Text Available Diabetic Retina (DR, a problem of formation of blood clot must be diagnosed at an early stage for laser therapy. A number of automated diagnosis methods based on image segmentation of fundus image is present which can diagnose DR at late mild proliferative stage. Proposed work is aimed to detect DR at early mild proliferative stage. Method uses feature extraction of fundus image using 2D Gabor filtering and pre-classification for feature vector extraction using Pades approximation. The Padesvector are then again classified using SVM by forming a dual of convex quadratic type minimization problem for linearly separable hyper plane. The performance of the proposed work is tested with set of images taken from fundus camera.

  19. Bio-signal analysis system design with support vector machines based on cloud computing service architecture.

    Science.gov (United States)

    Shen, Chia-Ping; Chen, Wei-Hsin; Chen, Jia-Ming; Hsu, Kai-Ping; Lin, Jeng-Wei; Chiu, Ming-Jang; Chen, Chi-Huang; Lai, Feipei

    2010-01-01

    Today, many bio-signals such as Electroencephalography (EEG) are recorded in digital format. It is an emerging research area of analyzing these digital bio-signals to extract useful health information in biomedical engineering. In this paper, a bio-signal analyzing cloud computing architecture, called BACCA, is proposed. The system has been designed with the purpose of seamless integration into the National Taiwan University Health Information System. Based on the concept of. NET Service Oriented Architecture, the system integrates heterogeneous platforms, protocols, as well as applications. In this system, we add modern analytic functions such as approximated entropy and adaptive support vector machine (SVM). It is shown that the overall accuracy of EEG bio-signal analysis has increased to nearly 98% for different data sets, including open-source and clinical data sets.

  20. Correlation technique and least square support vector machine combine for frequency domain based ECG beat classification.

    Science.gov (United States)

    Dutta, Saibal; Chatterjee, Amitava; Munshi, Sugata

    2010-12-01

    The present work proposes the development of an automated medical diagnostic tool that can classify ECG beats. This is considered an important problem as accurate, timely detection of cardiac arrhythmia can help to provide proper medical attention to cure/reduce the ailment. The proposed scheme utilizes a cross-correlation based approach where the cross-spectral density information in frequency domain is used to extract suitable features. A least square support vector machine (LS-SVM) classifier is developed utilizing the features so that the ECG beats are classified into three categories: normal beats, PVC beats and other beats. This three-class classification scheme is developed utilizing a small training dataset and tested with an enormous testing dataset to show the generalization capability of the scheme. The scheme, when employed for 40 files in the MIT/BIH arrhythmia database, could produce high classification accuracy in the range 95.51-96.12% and could outperform several competing algorithms.

  1. Soft sensor design for hydrodesulfurization process using support vector regression based on WT and PCA

    Institute of Scientific and Technical Information of China (English)

    Saeid Shokri; Mohammad Taghi Sadeghi; Mahdi Ahmadi Marvast; Shankar Narasimhan

    2015-01-01

    A novel method for developing a reliable data driven soft sensor to improve the prediction accuracy of sulfur content in hydrodesulfurization (HDS) process was proposed. Therefore, an integrated approach using support vector regression (SVR) based on wavelet transform (WT) and principal component analysis (PCA) was used. Experimental data from the HDS setup were employed to validate the proposed model. The results reveal that the integrated WT-PCA with SVR model was able to increase the prediction accuracy of SVR model. Implementation of the proposed model delivers the best satisfactory predicting performance (EAARE=0.058 andR2=0.97) in comparison with SVR. The obtained results indicate that the proposed model is more reliable and more precise than the multiple linear regression (MLR), SVR and PCA-SVR.

  2. Fast Fourier Transform-based Support Vector Machine for Subcellular Localization Prediction Using Different Substitution Models

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    There are approximately 109 proteins in a cell. A hotspot in bioinformatics is how to identify a protein's subcellular localization, if its sequence is known. In this paper, a method using fast Fourier transform-based support vector machine is developed to predict the subcellular localization of proteins from their physicochemical properties and structural parameters. The prediction accuracies reached 83% in prokaryotic organisms and 84% in eukaryotic organisms with the substitution model of the c-p-v matrix (c, composition; p, polarity; and v, molecular volume). The overall prediction accuracy was also evaluated using the "leave-one-out" jackknife procedure. The influence of the substitution model on prediction accuracy has also been discussed in the work. The source code of the new program is available on request from the authors.

  3. A Bayesian least squares support vector machines based framework for fault diagnosis and failure prognosis

    Science.gov (United States)

    Khawaja, Taimoor Saleem

    A high-belief low-overhead Prognostics and Health Management (PHM) system is desired for online real-time monitoring of complex non-linear systems operating in a complex (possibly non-Gaussian) noise environment. This thesis presents a Bayesian Least Squares Support Vector Machine (LS-SVM) based framework for fault diagnosis and failure prognosis in nonlinear non-Gaussian systems. The methodology assumes the availability of real-time process measurements, definition of a set of fault indicators and the existence of empirical knowledge (or historical data) to characterize both nominal and abnormal operating conditions. An efficient yet powerful Least Squares Support Vector Machine (LS-SVM) algorithm, set within a Bayesian Inference framework, not only allows for the development of real-time algorithms for diagnosis and prognosis but also provides a solid theoretical framework to address key concepts related to classification for diagnosis and regression modeling for prognosis. SVM machines are founded on the principle of Structural Risk Minimization (SRM) which tends to find a good trade-off between low empirical risk and small capacity. The key features in SVM are the use of non-linear kernels, the absence of local minima, the sparseness of the solution and the capacity control obtained by optimizing the margin. The Bayesian Inference framework linked with LS-SVMs allows a probabilistic interpretation of the results for diagnosis and prognosis. Additional levels of inference provide the much coveted features of adaptability and tunability of the modeling parameters. The two main modules considered in this research are fault diagnosis and failure prognosis. With the goal of designing an efficient and reliable fault diagnosis scheme, a novel Anomaly Detector is suggested based on the LS-SVM machines. The proposed scheme uses only baseline data to construct a 1-class LS-SVM machine which, when presented with online data is able to distinguish between normal behavior

  4. Fault Detection and Recovery for Full Range of Hydrogen Sensor Based on Relevance Vector Machine

    Institute of Scientific and Technical Information of China (English)

    Kai Song; Bing Wang; Ming Diao; Hongquan Zhang; Zhenyu Zhang

    2015-01-01

    In order to improve the reliability of hydrogen sensor, a novel strategy for full range of hydrogen sensor fault detection and recovery is proposed in this paper. Three kinds of sensors are integrated to realize the measurement for full range of hydrogen concentration based on relevance vector machine ( RVM ) . Failure detection of hydrogen sensor is carried out by using the variance detection method. When a sensor fault is detected, the other fault⁃free sensors can recover the fault data in real⁃time by using RVM predictor accounting for the relevance of sensor data. Analysis, together with both simulated and experimental results, a full⁃range hydrogen detection and hydrogen sensor self⁃validating experiment is presented to demonstrate that the proposed strategy is superior at accuracy and runtime compared with the conventional methods. Results show that the proposed methodology provides a better solution to the full range of hydrogen detection and the reliability improvement of hydrogen sensor.

  5. Nonlinear decoupling controller design based on least squares support vector regression

    Institute of Scientific and Technical Information of China (English)

    WEN Xiang-jun; ZHANG Yu-nong; YAN Wei-wu; XU Xiao-ming

    2006-01-01

    Support Vector Machines (SVMs) have been widely used in pattern recognition and have also drawn considerable interest in control areas. Based on a method of least squares SVM (LS-SVM) for multivariate function estimation, a generalized inverse system is developed for the linearization and decoupling control ora general nonlinear continuous system. The approach of inverse modelling via LS-SVM and parameters optimization using the Bayesian evidence framework is discussed in detail. In this paper, complex high-order nonlinear system is decoupled into a number of pseudo-linear Single Input Single Output (SISO) subsystems with linear dynamic components. The poles of pseudo-linear subsystems can be configured to desired positions. The proposed method provides an effective alternative to the controller design of plants whose accurate mathematical model is unknown or state variables are difficult or impossible to measure. Simulation results showed the efficacy of the method.

  6. Optical image encryption based on multi-beam interference and common vector decomposition

    Science.gov (United States)

    Chen, Linfei; He, Bingyu; Chen, Xudong; Gao, Xiong; Liu, Jingyu

    2016-02-01

    Based on multi-beam interference and common vector decomposition, we propose a new method for optical image encryption. In encryption process, the information of an original image is encoded into n amplitude masks and n phase masks which are regarded as a ciphertext and many keys. In decryption process, parallel light irradiates the amplitude masks and phase masks, then passes through lens that takes place Fourier transform, and finally we obtain the original image at the output plane after interference. The security of the encryption system is also discussed in the paper, and we find that only when all the keys are correct, can the information of the original image be recovered. Computer simulation results are presented to verify the validity and the security of the proposed method.

  7. Analysis of dengue infection based on Raman spectroscopy and support vector machine (SVM).

    Science.gov (United States)

    Khan, Saranjam; Ullah, Rahat; Khan, Asifullah; Wahab, Noorul; Bilal, Muhammad; Ahmed, Mushtaq

    2016-06-01

    The current study presents the use of Raman spectroscopy combined with support vector machine (SVM) for the classification of dengue suspected human blood sera. Raman spectra for 84 clinically dengue suspected patients acquired from Holy Family Hospital, Rawalpindi, Pakistan, have been used in this study.The spectral differences between dengue positive and normal sera have been exploited by using effective machine learning techniques. In this regard, SVM models built on the basis of three different kernel functions including Gaussian radial basis function (RBF), polynomial function and linear functionhave been employed to classify the human blood sera based on features obtained from Raman Spectra.The classification model have been evaluated with the 10-fold cross validation method. In the present study, the best performance has been achieved for the polynomial kernel of order 1. A diagnostic accuracy of about 85% with the precision of 90%, sensitivity of 73% and specificity of 93% has been achieved under these conditions.

  8. Content-Based Discovery for Web Map Service using Support Vector Machine and User Relevance Feedback

    Science.gov (United States)

    Cheng, Xiaoqiang; Qi, Kunlun; Zheng, Jie; You, Lan; Wu, Huayi

    2016-01-01

    Many discovery methods for geographic information services have been proposed. There are approaches for finding and matching geographic information services, methods for constructing geographic information service classification schemes, and automatic geographic information discovery. Overall, the efficiency of the geographic information discovery keeps improving., There are however, still two problems in Web Map Service (WMS) discovery that must be solved. Mismatches between the graphic contents of a WMS and the semantic descriptions in the metadata make discovery difficult for human users. End-users and computers comprehend WMSs differently creating semantic gaps in human-computer interactions. To address these problems, we propose an improved query process for WMSs based on the graphic contents of WMS layers, combining Support Vector Machine (SVM) and user relevance feedback. Our experiments demonstrate that the proposed method can improve the accuracy and efficiency of WMS discovery. PMID:27861505

  9. FINGERPRINT CLASSIFICATION BASED ON RECURSIVE NEURAL NETWORK WITH SUPPORT VECTOR MACHINE

    Directory of Open Access Journals (Sweden)

    T. Chakravarthy

    2011-01-01

    Full Text Available Fingerprint classification based on statistical and structural (RNN and SVM approach. RNNs are trained on a structured representation of the fingerprint image. They are also used to extract a set of distributed features of the fingerprint which can be integrated in this support vector machine. SVMs are combined with a new error correcting codes scheme. This approach has two main advantages. (a It can tolerate the presence of ambiguous fingerprint images in the training set and (b It can effectively identify the most difficult fingerprint images in the test set. In this experiment on the fingerprint database NIST-4 (National Institute of Science and Technology, our best classification accuracy of 94.7% is obtained by training SVM on both fingerCode and RNN –extracted futures of segmentation algorithm which has used very sophisticated “region growing process”.

  10. Multi-Objective Optimization Algorithms Design based on Support Vector Regression Metamodeling

    Directory of Open Access Journals (Sweden)

    Qi Zhang

    2013-11-01

    Full Text Available In order to solve the multi-objective optimization problem in the complex engineering, in this paper a NSGA-II multi-objective optimization algorithms based on Support Vector Regression Metamodeling is presented. Appropriate design parameter samples are selected by experimental design theories, and the response samples are obtained from the experiments or numerical simulations, used the SVM method to establish the metamodels of the objective performance functions and constraints, and reconstructed the original optimal problem. The reconstructed metamodels was solved by NSGA-II algorithm and took the structure optimization of the microwave power divider as an example to illustrate the proposed methodology and solve themulti-objective optimization problem. The results show that this methodology is feasible and highly effective, and thus it can be used in the optimum design of engineering fields.

  11. Angular velocity estimation based on star vector with improved current statistical model Kalman filter.

    Science.gov (United States)

    Zhang, Hao; Niu, Yanxiong; Lu, Jiazhen; Zhang, He

    2016-11-20

    Angular velocity information is a requisite for a spacecraft guidance, navigation, and control system. In this paper, an approach for angular velocity estimation based merely on star vector measurement with an improved current statistical model Kalman filter is proposed. High-precision angular velocity estimation can be achieved under dynamic conditions. The amount of calculation is also reduced compared to a Kalman filter. Different trajectories are simulated to test this approach, and experiments with real starry sky observation are implemented for further confirmation. The estimation accuracy is proved to be better than 10-4  rad/s under various conditions. Both the simulation and the experiment demonstrate that the described approach is effective and shows an excellent performance under both static and dynamic conditions.

  12. Efficient gray-level digital image watermarking based on vector quantization

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Digital watermarking has been presented as a new method for copyright protection by embedding a se cret signal in a digital image or video sequence. Common digital image watermarking techniques are based on the concept of spread-spectrum communications, which can be classified in two catalogues: spatial-domain and transform-domain based. Most of transform-domain watermarking methods are based on discrete cosine trans forms (DCT) and robust to JPEG lossy compression. Recently, digital image watermarking based on another important lossy compression technique, vector quantization (VQ), has been presented, which carries water mark information by codeword indices. It is secret and efficient, and is robust to VQ compression with the same codebook. However, the embedded information is less and the extraction process requires the original image.This paper presents a more efficient VQ-based image watermarking method, which can embed a large gray-level watermark into the original image with less extra distortion and perform the watermark extraction without the original image. In addition, the proposed watermarking algorithm is very secret because two keys are required for watermark extraction. Experimental results demonstrate the effectiveness of the proposed technique.

  13. Information extraction with object based support vector machines and vegetation indices

    Science.gov (United States)

    Ustuner, Mustafa; Abdikan, Saygin; Balik Sanli, Fusun

    2016-07-01

    Information extraction through remote sensing data is important for policy and decision makers as extracted information provide base layers for many application of real world. Classification of remotely sensed data is the one of the most common methods of extracting information however it is still a challenging issue because several factors are affecting the accuracy of the classification. Resolution of the imagery, number and homogeneity of land cover classes, purity of training data and characteristic of adopted classifiers are just some of these challenging factors. Object based image classification has some superiority than pixel based classification for high resolution images since it uses geometry and structure information besides spectral information. Vegetation indices are also commonly used for the classification process since it provides additional spectral information for vegetation, forestry and agricultural areas. In this study, the impacts of the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE) on the classification accuracy of RapidEye imagery were investigated. Object based Support Vector Machines were implemented for the classification of crop types for the study area located in Aegean region of Turkey. Results demonstrated that the incorporation of NDRE increase the classification accuracy from 79,96% to 86,80% as overall accuracy, however NDVI decrease the classification accuracy from 79,96% to 78,90%. Moreover it is proven than object based classification with RapidEye data give promising results for crop type mapping and analysis.

  14. Texture discrimination of green tea categories based on least squares support vector machine (LSSVM) classifier

    Science.gov (United States)

    Li, Xiaoli; He, Yong; Qiu, Zhengjun; Wu, Di

    2008-03-01

    This research aimed for development multi-spectral imaging technique for green tea categories discrimination based on texture analysis. Three key wavelengths of 550, 650 and 800 nm were implemented in a common-aperture multi-spectral charged coupled device camera, and images were acquired for 190 unique images in a four different kinds of green tea data set. An image data set consisting of 15 texture features for each image was generated based on texture analysis techniques including grey level co-occurrence method (GLCM) and texture filtering. For optimization the texture features, 5 features that weren't correlated with the category of tea were eliminated. Unsupervised cluster analysis was conducted using the optimized texture features based on principal component analysis. The cluster analysis showed that the four kinds of green tea could be separated in the first two principal components space, however there was overlapping phenomenon among the different kinds of green tea. To enhance the performance of discrimination, least squares support vector machine (LSSVM) classifier was developed based on the optimized texture features. The excellent discrimination performance for sample in prediction set was obtained with 100%, 100%, 75% and 100% for four kinds of green tea respectively. It can be concluded that texture discrimination of green tea categories based on multi-spectral image technology is feasible.

  15. Improved Reliability-Based Optimization with Support Vector Machines and Its Application in Aircraft Wing Design

    Directory of Open Access Journals (Sweden)

    Yu Wang

    2015-01-01

    Full Text Available A new reliability-based design optimization (RBDO method based on support vector machines (SVM and the Most Probable Point (MPP is proposed in this work. SVM is used to create a surrogate model of the limit-state function at the MPP with the gradient information in the reliability analysis. This guarantees that the surrogate model not only passes through the MPP but also is tangent to the limit-state function at the MPP. Then, importance sampling (IS is used to calculate the probability of failure based on the surrogate model. This treatment significantly improves the accuracy of reliability analysis. For RBDO, the Sequential Optimization and Reliability Assessment (SORA is employed as well, which decouples deterministic optimization from the reliability analysis. The improved SVM-based reliability analysis is used to amend the error from linear approximation for limit-state function in SORA. A mathematical example and a simplified aircraft wing design demonstrate that the improved SVM-based reliability analysis is more accurate than FORM and needs less training points than the Monte Carlo simulation and that the proposed optimization strategy is efficient.

  16. Overview of gene delivery into cells using HSV-1-based vectors.

    Science.gov (United States)

    Neve, Rachael L

    2012-10-01

    This overview describes the considerations involved in the preparation and use of a herpes simplex virus type 1 (HSV-1) amplicon as a vector for gene transfer into neurons. Strategies for gene delivery into neurons, either to study the molecular biology of brain function or for gene therapy, must utilize vectors that persist stably in postmitotic cells and that can be targeted both spatially and temporally in the nervous system in vivo. This unit describes the biology of HSV-1 along with a discussion covering development of amplicon and genomic HSV-1 vectors. Advantages and disadvantages of current HSV-1 vectors are presented, and HSV-1 vectors are compared with other vectors for gene transfer into neurons.

  17. I-f Starting and Active Flux Based Sensorless Vector Control of Reluctance Synchronous Motors, with Experiments

    DEFF Research Database (Denmark)

    Agarlita, Sorin-Christian; Fatu, M.; Tutelea, L. N.

    2010-01-01

    based sensorless vector control and vice versa when the frequency reaches a certain level. The control also integrates a state observer based on the active “flux concept” used to deliver RSM rotor position and speed information. Experimental results validate the proposed control strategies....

  18. 概念向量文本聚类算法%Text clustering algorithm based on concept vector

    Institute of Scientific and Technical Information of China (English)

    白秋产; 金春霞; 周海岩

    2011-01-01

    为了解决基于传统关键词的文本聚类算法没有考虑特征关键词之间的相关性,而导致文本向量概念表达不够准确,提出基于概念向量的文本聚类算法TCBCV(Text Clustering Based on Concept Vector),采用HowNet的概念属性,并利用语义场密度和义原在概念树的权值选取合适的义原作为关键词的概念,实现关键词到概念的映射,不仅增加了文本之间的语义关系,而且降低了向量维度,将其应用于文本聚类,能够提高文本聚类效果.实验结果表明,该算法在文本聚类的准确率和召回率上都得到了较大的提高.%The text clustering algorithm based on traditional keyword does not take into account the semantic relation between key words, and then causes the concept of the text vector is not accurate enough.The paper proposes the text clustering algorithm based on concept vector.The algorithm adopts HowNet properties and the density of semantic field and the weight of meaning in concept tree to select the appropriate meaning of the original concepts as keywords, the text vector would be transformed from keyword vector to concept vector.lt not only adds the texts semantic,but also reduces vector di-mensions.lt is used to realize text clustering to increase the efforts clustering.Experimental results show that the algorithm improves the accuracy and recall of text clustering.

  19. A STRESS VECTOR-BASED CONSTITUTIVE MODEL FOR COHESIONLESS SOIL (Ⅰ)-THEORY

    Institute of Scientific and Technical Information of China (English)

    史宏彦; 谢定义

    2002-01-01

    On the basis of the sufficient consideration of vectorial characteristics of stress,a new nonlinear constitutive model for cohesionless soil under plane strain and 3-D conditions was presented in a way that the action effects of stress vector are decomposed into the action effect of mean effective stress and that of the stress ratio vector (ratio of deviatoric stress vector to mean effective stress ). The constitutive model can take account of the influence of both numerical and directional changes of stress vector on deformation of soil simultaneously, and is applicable of both static and dynamic loading.

  20. [Comparative Efficiency of Algorithms Based on Support Vector Machines for Regression].

    Science.gov (United States)

    Kadyrova, N O; Pavlova, L V

    2015-01-01

    Methods of construction of support vector machines do not require additional a priori information and can be used to process large scale data set. It is especially important for various problems in computational biology. The main set of algorithms of support vector machines for regression is presented. The comparative efficiency of a number of support-vector-algorithms for regression is investigated. A thorough analysis of the study results found the most efficient support vector algorithms for regression. The description of the presented algorithms, sufficient for their practical implementation is given.

  1. Re-engineering an alphoid(tetO)-HAC-based vector to enable high-throughput analyses of gene function.

    Science.gov (United States)

    Kononenko, Artem V; Lee, Nicholas C O; Earnshaw, William C; Kouprina, Natalay; Larionov, Vladimir

    2013-05-01

    Human artificial chromosome (HAC)-based vectors represent an alternative technology for gene delivery and expression with a potential to overcome the problems caused by the use of viral-based vectors. The recently developed alphoid(tetO)-HAC has an advantage over other HAC vectors because it can be easily eliminated from cells by inactivation of the HAC kinetochore via binding of tTS chromatin modifiers to its centromeric tetO sequences. This provides unique control for phenotypes induced by genes loaded into the alphoid(tetO)-HAC. However, inactivation of the HAC kinetochore requires transfection of cells by a retrovirus vector, a step that is potentially mutagenic. Here, we describe an approach to re-engineering the alphoid(tetO)-HAC that allows verification of phenotypic changes attributed to expression of genes from the HAC without a transfection step. In the new HAC vector, a tTS-EYFP cassette is inserted into a gene-loading site along with a gene of interest. Expression of the tTS generates a self-regulating fluctuating heterochromatin on the alphoid(tetO)-HAC that induces fast silencing of the genes on the HAC without significant effects on HAC segregation. This silencing of the HAC-encoded genes can be readily recovered by adding doxycycline. The newly modified alphoid(tetO)-HAC-based system has multiple applications in gene function studies.

  2. A Wavelet-Based Robust Relevance Vector Machine Based on Sensor Data Scheduling Control for Modeling Mine Gas Gushing Forecasting on Virtual Environment

    OpenAIRE

    Wang Ting; Cai Lin-qin; Fu Yao; Zhu Tingcheng

    2013-01-01

    It is wellknown that mine gas gushing forecasting is very significant to ensure the safety of mining. A wavelet-based robust relevance vector machine based on sensor data scheduling control for modeling mine gas gushing forecasting is presented in the paper. Morlet wavelet function can be used as the kernel function of robust relevance vector machine. Mean percentage error has been used to measure the performance of the proposed method in this study. As the mean prediction error of mine gas g...

  3. Modal macro-strain vector based damage detection methodology with long-gauge FBG sensors

    Science.gov (United States)

    Xu, Bin; Liu, Chongwu W.; Masri, Sami F.

    2009-07-01

    Advances in optic fiber sensing technology provide easy and reliable way for the vibration-based strain measurement of engineering structures. As a typical optic fiber sensing techniques with high accuracy and resolution, long-gauge Fiber Bragg Grating (FBG) sensors have been widely employed in health monitoring of civil engineering structures. Therefore, the development of macro strain-based identification methods is crucial for damage detection and structural condition evaluation. In the previous study by the authors, a damage detection algorithm for a beam structure with the direct use of vibration-based macro-strain measurement time history with neural networks had been proposed and validated with experimental measurements. In this paper, a damage locating and quantifying method was proposed using modal macrostrain vectors (MMSVs) which can be extracted from vibration induced macro-strain response measurement time series from long-gage FBG sensors. The performance of the proposed methodology for damage detection of a beam with different damage scenario was studied with numerical simulation firstly. Then, dynamic tests on a simply-supported steel beam with different damage scenarios were carried out and macro-strain measurements were employed to detect the damage severity. Results show that the proposed MMSV based structural identification and damage detection methodology can locate and identify the structural damage severity with acceptable accuracy.

  4. High-accurate optical vector analysis based on optical single-sideband modulation

    Science.gov (United States)

    Xue, Min; Pan, Shilong

    2016-11-01

    Most of the efforts devoted to the area of optical communications were on the improvement of the optical spectral efficiency. Varies innovative optical devices are thus developed to finely manipulate the optical spectrum. Knowing the spectral responses of these devices, including the magnitude, phase and polarization responses, is of great importance for their fabrication and application. To achieve high-resolution characterization, optical vector analyzers (OVAs) based on optical single-sideband (OSSB) modulation have been proposed and developed. Benefiting from the mature and highresolution microwave technologies, the OSSB-based OVA can potentially achieve a resolution of sub-Hz. However, the accuracy is restricted by the measurement errors induced by the unwanted first-order sideband and the high-order sidebands in the OSSB signal, since electrical-to-optical conversion and optical-to-electrical conversion are essentially required to achieve high-resolution frequency sweeping and extract the magnitude and phase information in the electrical domain. Recently, great efforts have been devoted to improve the accuracy of the OSSB-based OVA. In this paper, the influence of the unwanted-sideband induced measurement errors and techniques for implementing high-accurate OSSB-based OVAs are discussed.

  5. Support vector machine-based facial-expression recognition method combining shape and appearance

    Science.gov (United States)

    Han, Eun Jung; Kang, Byung Jun; Park, Kang Ryoung; Lee, Sangyoun

    2010-11-01

    Facial expression recognition can be widely used for various applications, such as emotion-based human-machine interaction, intelligent robot interfaces, face recognition robust to expression variation, etc. Previous studies have been classified as either shape- or appearance-based recognition. The shape-based method has the disadvantage that the individual variance of facial feature points exists irrespective of similar expressions, which can cause a reduction of the recognition accuracy. The appearance-based method has a limitation in that the textural information of the face is very sensitive to variations in illumination. To overcome these problems, a new facial-expression recognition method is proposed, which combines both shape and appearance information, based on the support vector machine (SVM). This research is novel in the following three ways as compared to previous works. First, the facial feature points are automatically detected by using an active appearance model. From these, the shape-based recognition is performed by using the ratios between the facial feature points based on the facial-action coding system. Second, the SVM, which is trained to recognize the same and different expression classes, is proposed to combine two matching scores obtained from the shape- and appearance-based recognitions. Finally, a single SVM is trained to discriminate four different expressions, such as neutral, a smile, anger, and a scream. By determining the expression of the input facial image whose SVM output is at a minimum, the accuracy of the expression recognition is much enhanced. The experimental results showed that the recognition accuracy of the proposed method was better than previous researches and other fusion methods.

  6. Fruit fly optimization based least square support vector regression for blind image restoration

    Science.gov (United States)

    Zhang, Jiao; Wang, Rui; Li, Junshan; Yang, Yawei

    2014-11-01

    The goal of image restoration is to reconstruct the original scene from a degraded observation. It is a critical and challenging task in image processing. Classical restorations require explicit knowledge of the point spread function and a description of the noise as priors. However, it is not practical for many real image processing. The recovery processing needs to be a blind image restoration scenario. Since blind deconvolution is an ill-posed problem, many blind restoration methods need to make additional assumptions to construct restrictions. Due to the differences of PSF and noise energy, blurring images can be quite different. It is difficult to achieve a good balance between proper assumption and high restoration quality in blind deconvolution. Recently, machine learning techniques have been applied to blind image restoration. The least square support vector regression (LSSVR) has been proven to offer strong potential in estimating and forecasting issues. Therefore, this paper proposes a LSSVR-based image restoration method. However, selecting the optimal parameters for support vector machine is essential to the training result. As a novel meta-heuristic algorithm, the fruit fly optimization algorithm (FOA) can be used to handle optimization problems, and has the advantages of fast convergence to the global optimal solution. In the proposed method, the training samples are created from a neighborhood in the degraded image to the central pixel in the original image. The mapping between the degraded image and the original image is learned by training LSSVR. The two parameters of LSSVR are optimized though FOA. The fitness function of FOA is calculated by the restoration error function. With the acquired mapping, the degraded image can be recovered. Experimental results show the proposed method can obtain satisfactory restoration effect. Compared with BP neural network regression, SVR method and Lucy-Richardson algorithm, it speeds up the restoration rate and

  7. Persistent gene expression in mouse nasal epithelia following feline immunodeficiency virus-based vector gene transfer.

    Science.gov (United States)

    Sinn, Patrick L; Burnight, Erin R; Hickey, Melissa A; Blissard, Gary W; McCray, Paul B

    2005-10-01

    Gene transfer development for treatment or prevention of cystic fibrosis lung disease has been limited by the inability of vectors to efficiently and persistently transduce airway epithelia. Influenza A is an enveloped virus with natural lung tropism; however, pseudotyping feline immunodeficiency virus (FIV)-based lentiviral vector with the hemagglutinin envelope protein proved unsuccessful. Conversely, pseudotyping FIV with the envelope protein from influenza D (Thogoto virus GP75) resulted in titers of 10(6) transducing units (TU)/ml and conferred apical entry into well-differentiated human airway epithelial cells. Baculovirus GP64 envelope glycoproteins share sequence identity with influenza D GP75 envelope glycoproteins. Pseudotyping FIV with GP64 from three species of baculovirus resulted in titers of 10(7) to 10(9) TU/ml. Of note, GP64 from Autographa californica multicapsid nucleopolyhedrovirus resulted in high-titer FIV preparations (approximately 10(9) TU/ml) and conferred apical entry into polarized primary cultures of human airway epithelia. Using a luciferase reporter gene and bioluminescence imaging, we observed persistent gene expression from in vivo gene transfer in the mouse nose with A. californica GP64-pseudotyped FIV (AcGP64-FIV). Longitudinal bioluminescence analysis documented persistent expression in nasal epithelia for approximately 1 year without significant decline. According to histological analysis using a LacZ reporter gene, olfactory and respiratory epithelial cells were transduced. In addition, methylcellulose-formulated AcGP64-FIV transduced mouse nasal epithelia with much greater efficiency than similarly formulated vesicular stomatitis virus glycoprotein-pseudotyped FIV. These data suggest that AcGP64-FIV efficiently transduces and persistently expresses a transgene in nasal epithelia in the absence of agents that disrupt the cellular tight junction integrity.

  8. Content-Based Image Retrieval Using Support Vector Machine in digital image processing techniques

    Directory of Open Access Journals (Sweden)

    G.V.Hari Prasad

    2012-04-01

    Full Text Available The rapid growth of computer technologies and the ad-vent of the World Wide Web have increased the amount and the complexity of multimedia information. A content-based image retrieval (CBIR system has been developed as an ef-ficient image retrieval tool, whereby the user can provide their query to the system to allow it to retrieve the user’s desired image from the image database. However, the tradi-tional relevance feedback of CBIR has some limitations that will decrease the performance of the CBIR system, such as the imbalance oftraining-set problem, classification prob-lem, limited information from user problem, and insuffi-cient trainingset problem. Therefore, in this study, we pro-posed an enhanced relevance-feedback method to support the user query based on the representative image selection and weight ranking of the images retrieved. The support vector machine (SVM has been used to support the learn-ing process to reduce the semantic gap between the user and the CBIR system. From these experiments, the proposed learning method has enabled users to improve their search results based on the performance of CBIR system. In addi-tion, the experiments also proved that by solving the imbal-ance training set issue, the performance of CBIR could be improved.

  9. MULTI—SOURCE REMOTE SENSING IMAGE FUSION BASED ON SUPPORT VECTOR MACHINE

    Institute of Scientific and Technical Information of China (English)

    ZHAOShu-he; FENGXue-zhi; 等

    2002-01-01

    Remote Sensing image fusion is an effective way to use the large volume of data from multi-source images.This paper introduces a new method of remote sensing image fusion based on support vector machine(SVM),using high spatial resolution data SPIN-2 and multi-spectral remote sensing data SPOT-4.Firstly,the new method is established by building a model of remote sensing image fusion based on SVM.Then by using SPIN-2 data and SPOT-4 data ,image classify-cation fusion in tested.Finally,and evaluation of the fusion result is made in two ways.1)From subjectivity assessment,the spatial resolution of the fused image is improved compared to the SPOT-4.And it is clearly that the texture of the fused image is distinctive.2)From quantitative analysis,the effect of classification fusion is better.As a whole ,the re-sult shows that the accuracy of image fusion based on SVM is high and the SVM algorithm can be recommended for applica-tion in remote sensing image fusion processes.

  10. Development and evaluation of a biomedical search engine using a predicate-based vector space model.

    Science.gov (United States)

    Kwak, Myungjae; Leroy, Gondy; Martinez, Jesse D; Harwell, Jeffrey

    2013-10-01

    Although biomedical information available in articles and patents is increasing exponentially, we continue to rely on the same information retrieval methods and use very few keywords to search millions of documents. We are developing a fundamentally different approach for finding much more precise and complete information with a single query using predicates instead of keywords for both query and document representation. Predicates are triples that are more complex datastructures than keywords and contain more structured information. To make optimal use of them, we developed a new predicate-based vector space model and query-document similarity function with adjusted tf-idf and boost function. Using a test bed of 107,367 PubMed abstracts, we evaluated the first essential function: retrieving information. Cancer researchers provided 20 realistic queries, for which the top 15 abstracts were retrieved using a predicate-based (new) and keyword-based (baseline) approach. Each abstract was evaluated, double-blind, by cancer researchers on a 0-5 point scale to calculate precision (0 versus higher) and relevance (0-5 score). Precision was significantly higher (psearching than keywords, laying the foundation for rich and sophisticated information search.

  11. MULTI-SOURCE REMOTE SENSING IMAGE FUSION BASED ON SUPPORT VECTOR MACHINE

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    Remote Sensing image fusion is an effective way to use the large volume of data from multi-source images.This paper introduces a new method of remote sensing image fusion based on support vector machine (SVM), using highspatial resolution data SPIN-2 and multi-spectral remote sensing data SPOT-4. Firstly, the new method is established bybuilding a model of remote sensing image fusion based on SVM. Then by using SPIN-2 data and SPOT-4 data, image classification fusion is tested. Finally, an evaluation of the fusion result is made in two ways. 1 ) From subjectivity assessment,the spatial resolution of the fused image is improved compared to the SPOT-4. And it is clearly that the texture of thefused image is distinctive. 2) From quantitative analysis, the effect of classification fusion is better. As a whole, the result shows that the accuracy of image fusion based on SVM is high and the SVM algorithm can be recommended for application in remote sensing image fusion processes.

  12. MAPPING LOCAL CLIMATE ZONES WITH A VECTOR-BASED GIS METHOD

    Directory of Open Access Journals (Sweden)

    E. Lelovics

    2013-03-01

    Full Text Available In this study we determined Local Climate Zones in a South-Hungarian city, using vector-based and raster-based databases. We calculated seven of the originally proposed ten physical (geometric, surface cover and radiative properties for areas which are based on the mobile temperature measurement campaigns earlier carried out in this city.As input data we applied 3D building database (earlier created with photogrammetric methods, 2D road database, topographic map, aerial photographs, remotely sensed reflectance information from RapidEye satellite image and our local knowledge about the area. The values of the properties were calculated by GIS methods developed for this purpose.We derived for the examined areas and applied for classification sky view factor, mean building height, terrain roughness class, building surface fraction, pervious surface fraction, impervious surface fraction and albedo.Six built and one land cover LCZ classes could be detected with this method on our study area. From each class one circle area was selected, which is representative for that class. Their thermal reactions were examined with the application of mobile temperature measurement dataset. The comparison was made in cases, when the weather was clear and calm and the surface was dry. We found that compact built-in types have more temperature surplus than open ones, and midrise types also have more than lowrise ones. According to our primary results, these categories provide a useful opportunity for intra- and inter-urban comparisons.

  13. A suction detection system for rotary blood pumps based on the Lagrangian support vector machine algorithm.

    Science.gov (United States)

    Wang, Yu; Simaan, Marwan A

    2013-05-01

    The Left Ventricular Assist Device (LVAD) is a rotary mechanical pump that is implanted in patients with congestive heart failure to help the left ventricle in pumping blood in the circulatory system. However, using such a device may result in a very dangerous event, called ventricular suction that can cause ventricular collapse due to overpumping of blood from the left ventricle when the rotational speed of the pump is high. Therefore, a reliable technique for detecting ventricular suction is crucial. This paper presents a new suction detection system that can precisely classify pump flow patterns, based on a Lagrangian Support Vector Machine (LSVM) model that combines six suction indices extracted from the pump flow signal to make a decision about whether the pump is in suction, approaching suction, or not in suction. The proposed method has been tested using in vivo experimental data based on two different pumps. The simulation results show that the system can produce superior performance in terms of classification accuracy, stability, learning speed, and good robustness compared to three other existing suction detection methods and the original SVM-based algorithm. The ability of the proposed algorithm to detect suction provides a reliable platform for the development of a feedback control system to control the speed of the pump while at the same time ensuring that suction is avoided.

  14. Support vector machine based estimation of remaining useful life: current research status and future trends

    Energy Technology Data Exchange (ETDEWEB)

    Huang, Hong Zhong; Wang, Hai Kun; Li, Yan Feng; Zhang, Longlong; Liu, Zhiliang [University of Electronic Science and Technology of China, Chengdu (China)

    2015-01-15

    Estimation of remaining useful life (RUL) is helpful to manage life cycles of machines and to reduce maintenance cost. Support vector machine (SVM) is a promising algorithm for estimation of RUL because it can easily process small training sets and multi-dimensional data. Many SVM based methods have been proposed to predict RUL of some key components. We did a literature review related to SVM based RUL estimation within a decade. The references reviewed are classified into two categories: improved SVM algorithms and their applications to RUL estimation. The latter category can be further divided into two types: one, to predict the condition state in the future and then build a relationship between state and RUL; two, to establish a direct relationship between current state and RUL. However, SVM is seldom used to track the degradation process and build an accurate relationship between the current health condition state and RUL. Based on the above review and summary, this paper points out that the ability to continually improve SVM, and obtain a novel idea for RUL prediction using SVM will be future works.

  15. Expression of multiple artificial microRNAs from a chicken miRNA126-based lentiviral vector.

    Directory of Open Access Journals (Sweden)

    Steve C-Y Chen

    Full Text Available BACKGROUND: The use of RNAi in both basic and translational research often requires expression of multiple siRNAs from the same vector. METHODS/PRINCIPAL FINDINGS: We have developed a novel chicken miR126-based artificial miRNA expression system that can express one, two or three miRNAs from a single cassette in a lentiviral vector. We show that each of the miRNAs expressed from the same lentiviral vector is capable of potent inhibition of reporter gene expression in transient transfection and stable integration assays in chicken fibroblast DF-1 cells. Transduction of Vero cells with lentivirus expressing two or three different anti-influenza miRNAs leads to inhibition of influenza virus production. In addition, the chicken miR126-based expression system effectively inhibits reporter gene expression in human, monkey, dog and mouse cells. These results demonstrate that the flanking regions of a single primary miRNA can support processing of three different stem-loops in a single vector. CONCLUSIONS/SIGNIFICANCE: This novel design expands the means to express multiple miRNAs from the same vector for potent and effective silencing of target genes and influenza virus.

  16. Voltage Sags Ride-Through of Motion Sensorless Controlled PMSG for Wind Turbines

    DEFF Research Database (Denmark)

    Fatu, Marius; Lascu, Cristian; Andreescu, Gheorghe-Daniel;

    2007-01-01

    is used to robustly estimate the grid voltage positivesequence for control in the case of asymmetric voltages. The paper investigates the ride-through performance of this system during asymmetric power grid voltage sags. Design details for various parts of the control system are presented, together...... with experimental results for single-, two-, and three-phase voltage source sags. Smooth transition through asymmetric voltage sags is demonstrated by all experiments....

  17. A Novel Gradient Vector Flow Snake Model Based on Convex Function for Infrared Image Segmentation.

    Science.gov (United States)

    Zhang, Rui; Zhu, Shiping; Zhou, Qin

    2016-10-21

    Infrared image segmentation is a challenging topic because infrared images are characterized by high noise, low contrast, and weak edges. Active contour models, especially gradient vector flow, have several advantages in terms of infrared image segmentation. However, the GVF (Gradient Vector Flow) model also has some drawbacks including a dilemma between noise smoothing and weak edge protection, which decrease the effect of infrared image segmentation significantly. In order to solve this problem, we propose a novel generalized gradient vector flow snakes model combining GGVF (Generic Gradient Vector Flow) and NBGVF (Normally Biased Gradient Vector Flow) models. We also adopt a new type of coefficients setting in the form of convex function to improve the ability of protecting weak edges while smoothing noises. Experimental results and comparisons against other methods indicate that our proposed snakes model owns better ability in terms of infrared image segmentation than other snakes models.

  18. Reference Function Based Spatiotemporal Fuzzy Logic Control Design Using Support Vector Regression Learning

    Directory of Open Access Journals (Sweden)

    Xian-Xia Zhang

    2013-01-01

    Full Text Available This paper presents a reference function based 3D FLC design methodology using support vector regression (SVR learning. The concept of reference function is introduced to 3D FLC for the generation of 3D membership functions (MF, which enhance the capability of the 3D FLC to cope with more kinds of MFs. The nonlinear mathematical expression of the reference function based 3D FLC is derived, and spatial fuzzy basis functions are defined. Via relating spatial fuzzy basis functions of a 3D FLC to kernel functions of an SVR, an equivalence relationship between a 3D FLC and an SVR is established. Therefore, a 3D FLC can be constructed using the learned results of an SVR. Furthermore, the universal approximation capability of the proposed 3D fuzzy system is proven in terms of the finite covering theorem. Finally, the proposed method is applied to a catalytic packed-bed reactor and simulation results have verified its effectiveness.

  19. New predictive control algorithms based on Least Squares Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    LIU Bin; SU Hong-ye; CHU Jian

    2005-01-01

    Used for industrial process with different degree of nonlinearity, the two predictive control algorithms presented in this paper are based on Least Squares Support Vector Machines (LS-SVM) model. For the weakly nonlinear system, the system model is built by using LS-SVM with linear kernel function, and then the obtained linear LS-SVM model is transformed into linear input-output relation of the controlled system. However, for the strongly nonlinear system, the off-line model of the controlled system is built by using LS-SVM with Radial Basis Function (RBF) kernel. The obtained nonlinear LS-SVM model is linearized at each sampling instant of system running, after which the on-line linear input-output model of the system is built. Based on the obtained linear input-output model, the Generalized Predictive Control (GPC) algorithm is employed to implement predictive control for the controlled plant in both algorithms. The simulation results after the presented algorithms were implemented in two different industrial processes model; respectively revealed the effectiveness and merit of both algorithms.

  20. Multiplex CRISPR/Cas9-based genome engineering from a single lentiviral vector.

    Science.gov (United States)

    Kabadi, Ami M; Ousterout, David G; Hilton, Isaac B; Gersbach, Charles A

    2014-10-29

    Engineered DNA-binding proteins that manipulate the human genome and transcriptome have enabled rapid advances in biomedical research. In particular, the RNA-guided CRISPR/Cas9 system has recently been engineered to create site-specific double-strand breaks for genome editing or to direct targeted transcriptional regulation. A unique capability of the CRISPR/Cas9 system is multiplex genome engineering by delivering a single Cas9 enzyme and two or more single guide RNAs (sgRNAs) targeted to distinct genomic sites. This approach can be used to simultaneously create multiple DNA breaks or to target multiple transcriptional activators to a single promoter for synergistic enhancement of gene induction. To address the need for uniform and sustained delivery of multiplex CRISPR/Cas9-based genome engineering tools, we developed a single lentiviral system to express a Cas9 variant, a reporter gene and up to four sgRNAs from independent RNA polymerase III promoters that are incorporated into the vector by a convenient Golden Gate cloning method. Each sgRNA is efficiently expressed and can mediate multiplex gene editing and sustained transcriptional activation in immortalized and primary human cells. This delivery system will be significant to enabling the potential of CRISPR/Cas9-based multiplex genome engineering in diverse cell types.

  1. Land Cover Classification from Full-Waveform LIDAR Data Based on Support Vector Machines

    Science.gov (United States)

    Zhou, M.; Li, C. R.; Ma, L.; Guan, H. C.

    2016-06-01

    In this study, a land cover classification method based on multi-class Support Vector Machines (SVM) is presented to predict the types of land cover in Miyun area. The obtained backscattered full-waveforms were processed following a workflow of waveform pre-processing, waveform decomposition and feature extraction. The extracted features, which consist of distance, intensity, Full Width at Half Maximum (FWHM) and back scattering cross-section, were corrected and used as attributes for training data to generate the SVM prediction model. The SVM prediction model was applied to predict the types of land cover in Miyun area as ground, trees, buildings and farmland. The classification results of these four types of land covers were obtained based on the ground truth information according to the CCD image data of Miyun area. It showed that the proposed classification algorithm achieved an overall classification accuracy of 90.63%. In order to better explain the SVM classification results, the classification results of SVM method were compared with that of Artificial Neural Networks (ANNs) method and it showed that SVM method could achieve better classification results.

  2. LAND COVER CLASSIFICATION FROM FULL-WAVEFORM LIDAR DATA BASED ON SUPPORT VECTOR MACHINES

    Directory of Open Access Journals (Sweden)

    M. Zhou

    2016-06-01

    Full Text Available In this study, a land cover classification method based on multi-class Support Vector Machines (SVM is presented to predict the types of land cover in Miyun area. The obtained backscattered full-waveforms were processed following a workflow of waveform pre-processing, waveform decomposition and feature extraction. The extracted features, which consist of distance, intensity, Full Width at Half Maximum (FWHM and back scattering cross-section, were corrected and used as attributes for training data to generate the SVM prediction model. The SVM prediction model was applied to predict the types of land cover in Miyun area as ground, trees, buildings and farmland. The classification results of these four types of land covers were obtained based on the ground truth information according to the CCD image data of Miyun area. It showed that the proposed classification algorithm achieved an overall classification accuracy of 90.63%. In order to better explain the SVM classification results, the classification results of SVM method were compared with that of Artificial Neural Networks (ANNs method and it showed that SVM method could achieve better classification results.

  3. Support Vector Regression Based Indoor Location in IEEE 802.11 Environments

    Directory of Open Access Journals (Sweden)

    Ke Shi

    2015-01-01

    Full Text Available The wide spread of the 802.11-based wireless technology brings about a good opportunity for the indoor positioning system. In this paper, we present a new 802.11-based indoor positioning method using support vector regression (SVR, which consists of offline training stage and online location stage. The model that describes the relations between the position and the received signal strength (RSS of the mobile device is established at the offline training stage by SVR, and at the online location stage the exact position is determined by this model. Due to the complex indoor environment, RSS is vulnerable and changeable. To address this issue, data filtering rules obtained through statistical analysis are applied at offline training stage to improve the quality of training samples and thus improve the quality of prediction model. At the online location stage, k-times continuous measurement is utilized to obtain the high quality RSS input, which guarantees the consistency with the training samples and improves the position accuracy of mobile devices. Performance evaluation shows that the proposed method has a higher positioning accuracy compared with the probability and neutral network method, and the demand for the storage capacity and computing power is also low at the same time.

  4. Knowledge-based analysis of microarray gene expression data by using support vector machines

    Energy Technology Data Exchange (ETDEWEB)

    William Grundy; Manuel Ares, Jr.; David Haussler

    2001-06-18

    The authors introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods, such as hierarchical clustering and self-organizing maps. SVMs have many mathematical features that make them attractive for gene expression analysis, including their flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. They test several SVMs that use different similarity metrics, as well as some other supervised learning methods, and find that the SVMs best identify sets of genes with a common function using expression data. Finally, they use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data.

  5. [Hyperspectral image classification based on 3-D gabor filter and support vector machines].

    Science.gov (United States)

    Feng, Xiao; Xiao, Peng-feng; Li, Qi; Liu, Xiao-xi; Wu, Xiao-cui

    2014-08-01

    A three-dimensional Gabor filter was developed for classification of hyperspectral remote sensing image. This method is based on the characteristics of hyperspectral image and the principle of texture extraction with 2-D Gabor filters. Three-dimensional Gabor filter is able to filter all the bands of hyperspectral image simultaneously, capturing the specific responses in different scales, orientations, and spectral-dependent properties from enormous image information, which greatly reduces the time consumption in hyperspectral image texture extraction, and solve the overlay difficulties of filtered spectrums. Using the designed three-dimensional Gabor filters in different scales and orientations, Hyperion image which covers the typical area of Qi Lian Mountain was processed with full bands to get 26 Gabor texture features and the spatial differences of Gabor feature textures corresponding to each land types were analyzed. On the basis of automatic subspace separation, the dimensions of the hyperspectral image were reduced by band index (BI) method which provides different band combinations for classification in order to search for the optimal magnitude of dimension reduction. Adding three-dimensional Gabor texture features successively according to its discrimination to the given land types, supervised classification was carried out with the classifier support vector machines (SVM). It is shown that the method using three-dimensional Gabor texture features and BI band selection based on automatic subspace separation for hyperspectral image classification can not only reduce dimensions; but also improve the classification accuracy and efficiency of hyperspectral image.

  6. Support vector machine-based open crop model (SBOCM: Case of rice production in China

    Directory of Open Access Journals (Sweden)

    Ying-xue Su

    2017-03-01

    Full Text Available Existing crop models produce unsatisfactory simulation results and are operationally complicated. The present study, however, demonstrated the unique advantages of statistical crop models for large-scale simulation. Using rice as the research crop, a support vector machine-based open crop model (SBOCM was developed by integrating developmental stage and yield prediction models. Basic geographical information obtained by surface weather observation stations in China and the 1:1000000 soil database published by the Chinese Academy of Sciences were used. Based on the principle of scale compatibility of modeling data, an open reading frame was designed for the dynamic daily input of meteorological data and output of rice development and yield records. This was used to generate rice developmental stage and yield prediction models, which were integrated into the SBOCM system. The parameters, methods, error resources, and other factors were analyzed. Although not a crop physiology simulation model, the proposed SBOCM can be used for perennial simulation and one-year rice predictions within certain scale ranges. It is convenient for data acquisition, regionally applicable, parametrically simple, and effective for multi-scale factor integration. It has the potential for future integration with extensive social and economic factors to improve the prediction accuracy and practicability.

  7. Time series online prediction algorithm based on least squares support vector machine

    Institute of Scientific and Technical Information of China (English)

    WU Qiong; LIU Wen-ying; YANG Yi-han

    2007-01-01

    Deficiencies of applying the traditional least squares support vector machine (LS-SVM) to time series online prediction were specified. According to the kernel function matrix's property and using the recursive calculation of block matrix, a new time series online prediction algorithm based on improved LS-SVM was proposed. The historical training results were fully utilized and the computing speed of LS-SVM was enhanced. Then, the improved algorithm was applied to time series online prediction. Based on the operational data provided by the Northwest Power Grid of China, the method was used in the transient stability prediction of electric power system. The results show that, compared with the calculation time of the traditional LS-SVM(75-1 600 ms), that of the proposed method in different time windows is 40-60 ms, and the prediction accuracy(normalized root mean squared error) of the proposed method is above 0.8. So the improved method is better than the traditional LS-SVM and more suitable for time series online prediction.

  8. Predictive based monitoring of nuclear plant component degradation using support vector regression

    Energy Technology Data Exchange (ETDEWEB)

    Agarwal, Vivek [Idaho National Lab. (INL), Idaho Falls, ID (United States). Dept. of Human Factors, Controls, Statistics; Alamaniotis, Miltiadis [Purdue Univ., West Lafayette, IN (United States). School of Nuclear Engineering; Tsoukalas, Lefteri H. [Purdue Univ., West Lafayette, IN (United States). School of Nuclear Engineering

    2015-02-01

    Nuclear power plants (NPPs) are large installations comprised of many active and passive assets. Degradation monitoring of all these assets is expensive (labor cost) and highly demanding task. In this paper a framework based on Support Vector Regression (SVR) for online surveillance of critical parameter degradation of NPP components is proposed. In this case, on time replacement or maintenance of components will prevent potential plant malfunctions, and reduce the overall operational cost. In the current work, we apply SVR equipped with a Gaussian kernel function to monitor components. Monitoring includes the one-step-ahead prediction of the component’s respective operational quantity using the SVR model, while the SVR model is trained using a set of previous recorded degradation histories of similar components. Predictive capability of the model is evaluated upon arrival of a sensor measurement, which is compared to the component failure threshold. A maintenance decision is based on a fuzzy inference system that utilizes three parameters: (i) prediction evaluation in the previous steps, (ii) predicted value of the current step, (iii) and difference of current predicted value with components failure thresholds. The proposed framework will be tested on turbine blade degradation data.

  9. An Enhanced MEMS Error Modeling Approach Based on Nu-Support Vector Regression

    Directory of Open Access Journals (Sweden)

    Deepak Bhatt

    2012-07-01

    Full Text Available Micro Electro Mechanical System (MEMS-based inertial sensors have made possible the development of a civilian land vehicle navigation system by offering a low-cost solution. However, the accurate modeling of the MEMS sensor errors is one of the most challenging tasks in the design of low-cost navigation systems. These sensors exhibit significant errors like biases, drift, noises; which are negligible for higher grade units. Different conventional techniques utilizing the Gauss Markov model and neural network method have been previously utilized to model the errors. However, Gauss Markov model works unsatisfactorily in the case of MEMS units due to the presence of high inherent sensor errors. On the other hand, modeling the random drift utilizing Neural Network (NN is time consuming, thereby affecting its real-time implementation. We overcome these existing drawbacks by developing an enhanced Support Vector Machine (SVM based error model. Unlike NN, SVMs do not suffer from local minimisation or over-fitting problems and delivers a reliable global solution. Experimental results proved that the proposed SVM approach reduced the noise standard deviation by 10–35% for gyroscopes and 61–76% for accelerometers. Further, positional error drifts under static conditions improved by 41% and 80% in comparison to NN and GM approaches.

  10. A bead-based suspension array for the multiplexed detection of begomoviruses and their whitefly vectors.

    Science.gov (United States)

    van Brunschot, S L; Bergervoet, J H W; Pagendam, D E; de Weerdt, M; Geering, A D W; Drenth, A; van der Vlugt, R A A

    2014-03-01

    Bead-based suspension array systems enable simultaneous fluorescence-based identification of multiple nucleic acid targets in a single reaction. This study describes the development of a novel approach to plant virus and vector diagnostics, a multiplexed 7-plex array that comprises a hierarchical set of assays for the simultaneous detection of begomoviruses and Bemisia tabaci, from both plant and whitefly samples. The multiplexed array incorporates genus, species and strain-specific assays, offering a unique approach for identifying both known and unknown viruses and B. tabaci species. When tested against a large panel of sequence-characterized begomovirus and whitefly samples, the array was shown to be 100% specific to the homologous target. Additionally, the multiplexed array was highly sensitive, efficiently and concurrently determining both virus and whitefly identity from single viruliferous whitefly samples. The detection limit for one assay within the multiplexed array that specifically detects Tomato yellow leaf curl virus-Israel (TYLCV-IL) was quantified as 200fg of TYLCV-IL DNA, directly equivalent to that of TYLCV-specific qPCR. Highly reproducible results were obtained over multiple tests. The flexible multiplexed array described in this study has great potential for use in plant quarantine, biosecurity and disease management programs worldwide.

  11. Development and assessment of plant-based synthetic odor baits for surveillance and control of Malaria vectors

    Science.gov (United States)

    Recent malaria vector control measures have considerably reduced indoor biting mosquito populations. However, reducing the outdoor biting populations remains a challenge because of the unavailability of appropriate lures to achieve this. This study sought to test the efficacy of plant-based syntheti...

  12. Influence of the velocity vector base relocation to the center of mass of the interrogation area on PIV accuracy

    Directory of Open Access Journals (Sweden)

    Kouba Jan

    2014-03-01

    Full Text Available This paper is aimed at modification of calculation algorithm used in data processing from PIV (Particle Image Velocimetry method. The modification of standard Multi-step correlation algorithm is based on imaging the centre of mass of the interrogation area to define the initial point of the respective vector, instead of the geometrical centre. This paper describes the principle of initial point-vector assignment, the corresponding data processing methodology including the test track analysis. Both approaches are compared within the framework of accuracy in the conclusion. The accuracy test is performed using synthetic and real data.

  13. Avian CD154 enhances humoral and cellular immune responses induced by an adenovirus vector-based vaccine in chickens.

    Science.gov (United States)

    Sánchez Ramos, Oliberto; González Pose, Alain; Gómez-Puerta, Silvia; Noda Gomez, Julia; Vega Redondo, Armando; Águila Benites, Julio César; Suárez Amarán, Lester; Parra, Natalie C; Toledo Alonso, Jorge R

    2011-05-01

    Recombinant adenoviral vectors have emerged as an attractive system for veterinary vaccines development. However, for poultry vaccination a very important criterion for an ideal vaccine is its low cost. The objective of this study was to test the ability of chicken CD154 to enhance the immunogenicity of an adenoviral vector-based vaccine against avian influenza virus in order to reduce the amount of antigen required to induce an effective immune response in avian. Chickens were vaccinated with three different doses of adenoviral vectors encoding either HA (AdHA), or HA fused to extracellular domain chicken's CD154 (AdHACD). Hemagglutination inhibition (HI) assay and relative quantification of IFN-γ showed that the adenoviral vector encoding for the chimeric antigen is able to elicit an improved humoral and cellular immune response, which demonstrated that CD154 can be used as a molecular adjuvant allowing to reduce in about 50-fold the amount of adenoviral vector vaccine required to induce an effective immune response.

  14. Dimension Reduction via Unsupervised Learning Yields Significant Computational Improvements for Support Vector Machine Based Protein Family Classification.

    Energy Technology Data Exchange (ETDEWEB)

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

    2009-02-26

    Reducing the dimension of vectors used in training support vector machines (SVMs) results in a proportional speedup in training time. For large-scale problems this can make the difference between tractable and intractable training tasks. However, it is critical that classifiers trained on reduced datasets perform as reliably as their counterparts trained on high-dimensional data. We assessed principal component analysis (PCA) and sequential project pursuit (SPP) as dimension reduction strategies in the biology application of classifying proteins into well-defined functional ‘families’ (SVM-based protein family classification) by their impact on run-time, sensitivity and selectivity. Homology vectors of 4352 elements were reduced to approximately 2% of the original data size without significantly affecting accuracy using PCA and SPP, while leading to approximately a 28-fold speedup in run-time.

  15. Oil spill detection by a support vector machine based on polarization decomposition characteristics

    Institute of Scientific and Technical Information of China (English)

    ZOU Yarong; SHI Lijian; ZHANG Shengli; LIANG Chao; ZENG Tao

    2016-01-01

    Marine oil spills have caused major threats to marine environment over the past few years. The early detection of the oil spill is of great significance for the prevention and control of marine disasters. At present, remote sensing is one of the major approaches for monitoring the oil spill. Full polarization synthetic aperture radarc SAR data are employed to extract polarization decomposition parameters including entropy (H) and reflection entropy (A). The characteristic spectrum of the entropy and reflection entropy combination has analyzed and the polarization characteristic spectrum of the oil spill has developed to support remote sensing of the oil spill. The findings show that the information extracted from (1–A)×(1–H) and (1–H)×A parameters is relatively evident effects. The results of extraction of the oil spill information based onH×A parameter are relatively not good. The combination of the two has something to do withH andA values. In general, whenH>0.7,A value is relatively small. Here, the extraction of the oil spill information using (1–A)×(1–H) and (1–H)×A parameters obtains evident effects. Whichever combined parameter is adopted, oil well data would cause certain false alarm to the extraction of the oil spill information. In particular the false alarm of the extracted oil spill information based on (1–A)×(1–H) is relatively high, while the false alarm based on (1–A)×H and (1–H)×A parameters is relatively small, but an image noise is relatively big. The oil spill detection employing polarization characteristic spectrum support vector machine can effectively identify the oil spill information with more accuracy than that of the detection method based on single polarization feature.

  16. Vector magneto-optical sensor based on transparent magnetic films with cubic crystallographic symmetry

    Science.gov (United States)

    Rogachev, A. E.; Vetoshko, P. M.; Gusev, N. A.; Kozhaev, M. A.; Prokopov, A. R.; Popov, V. V.; Dodonov, D. V.; Shumilov, A. G.; Shaposhnikov, A. N.; Berzhansky, V. N.; Zvezdin, A. K.; Belotelov, V. I.

    2016-10-01

    The concept of vector magneto-optical magnetometry is proposed and experimentally demonstrated. The key element of the vector magnetometer is a transparent high Faraday activity magnetic film with a cubic crystal lattice. Magnetocrystalline anisotropy of the film leads to the three dimensional trajectory of the film magnetization when the magnetization is rotated by the control magnetic field. It makes the magnetization sensitive to all three components of the external magnetic field. This field can be found from the harmonic composition of the Faraday rotation dependence on the azimuth angle of the control magnetic field. The demonstrated vector magnetometer is promising for mapping and visualization of ultra small magnetic fields.

  17. [Comparative efficiency of algorithms based on support vector machines for binary classification].

    Science.gov (United States)

    Kadyrova, N O; Pavlova, L V

    2015-01-01

    Methods of construction of support vector machines require no further a priori infoimation and provide big data processing, what is especially important for various problems in computational biology. The question of the quality of learning algorithms is considered. The main algorithms of support vector machines for binary classification are reviewed and they were comparatively explored for their efficiencies. The critical analysis of the results of this study revealed the most effective support-vector-classifiers. The description of the recommended algorithms, sufficient for their practical implementation, is presented.

  18. RELIABILITY ASSESSMENT OF STRUCTURAL SYSTEMS BASED ON DIRECTIONAL VECTOR SIMULATION TECHNIQUE

    Institute of Scientific and Technical Information of China (English)

    Zhang Liangxin; Hu Yunchang

    2000-01-01

    The new improved directional vector simulation method for analyzing the reliability of struc tural systems failure probability is researched. This paper also points out the defects of general directional vector simulation, and gives rise to a new higher accuracy approximate integral formula of structural systems failure probability. A new geometric meaning of characteristic function is obtained. A new simple method of generating uniformly distributed random vector samples in n-dimensional unit hyper-spherical surface is put forward and strictly proved. This method is easy to put into practice. Numerical examples are given to show the applicability and effectiveness of the suggested approach to structural systems reliability problems.

  19. Global stabilization of nonlinear systems based on vector control lyapunov functions

    CERN Document Server

    Karafyllis, Iasson

    2012-01-01

    This paper studies the use of vector Lyapunov functions for the design of globally stabilizing feedback laws for nonlinear systems. Recent results on vector Lyapunov functions are utilized. The main result of the paper shows that the existence of a vector control Lyapunov function is a necessary and sufficient condition for the existence of a smooth globally stabilizing feedback. Applications to nonlinear systems are provided: simple and easily checkable sufficient conditions are proposed to guarantee the existence of a smooth globally stabilizing feedback law. The obtained results are applied to the problem of the stabilization of an equilibrium point of a reaction network taking place in a continuous stirred tank reactor.

  20. Production of intense attosecond vector beam pulse trains based on harmonics

    Institute of Scientific and Technical Information of China (English)

    韩玉晶; 廖国前; 陈黎明; 李玉同; 王伟民; 张杰

    2015-01-01

    We provide the first report on the harmonics generated by an intense femtosecond vector beam that is normally incident on a solid target. By using 2D particle-in-cell (PIC) codes, we observe the third and the fifth harmonic signals with the same vector structure as the driving beam, and obtain an attosecond vector beam pulse train. We also show that the conversion efficiencies of the third and the fifth harmonics reach their maxima for a plasma density of four times the critical density due to the plasma resonating with the driving force. This method provides a new means of generating intense extreme ultraviolet (XUV) vector beams via ultra-intense laser-driven harmonics.

  1. A Versatile System for USER Cloning-Based Assembly of Expression Vectors for Mammalian Cell Engineering

    DEFF Research Database (Denmark)

    Lund, Anne Mathilde; Kildegaard, Helene Faustrup; Petersen, Maja Borup Kjær;

    2014-01-01

    , in addition the system is fully extendable by other users. The vector system is designed to facilitate high-throughput genome-scale studies of mammalian cells, such as the newly sequenced CHO cell lines, through the ability to rapidly generate high-fidelity assembly of customizable gene expression vectors......., cellular localization signals and epitope- and purification tags. Building blocks in the toolbox can be easily combined as they contain defined and tested Flexible Assembly Sequence Tags, FASTs. USER cloning with FASTs allows rapid swaps of gene, promoter or selection marker in existing plasmids and simple...... construction of vectors encoding proteins, which are fused to fluorescence-, purification-, localization-, or epitope tags. The mammalian expression vector assembly platform currently allows for the assembly of up to seven fragments in a single cloning step with correct directionality and with a cloning...

  2. Metrics for vector quantization-based parametric speech enhancement and separation

    DEFF Research Database (Denmark)

    Christensen, Mads Græsbøll

    2013-01-01

    Speech enhancement and separation algorithms sometimes employ a two-stage processing scheme, wherein the signal is first mapped to an intermediate low-dimensional parametric description after which the parameters are mapped to vectors in codebooks trained on, for exam- ple, individual noise......-free sources using a vector quantizer. To obtain accurate parameters, one must employ a good estimator in finding the parameters of the intermediate representation, like a maximum like- lihood estimator. This leaves some unanswered questions, however, like what metrics to use in the subsequent vector...... quantization process and how to systematically derive them. This paper aims at answering these questions. Metrics for this are presented and derived, and their use is exemplified on a number of different signal models by deriving closed-form expressions. The metrics essentially take into account in the vector...

  3. DOA Estimation based on Vector Analysis%基于矢量分析的DOA估计

    Institute of Scientific and Technical Information of China (English)

    王海江; 刘玟宏; 罗文

    2015-01-01

    To estimating the direction of arrival of a received electronic magnetic wave based on its three dimensional polarization components,in this paper the issue of utilizing polarization parameters to describe the electromagnetic wave and using the known compound electric field to composite its polarization parameters is investigated.An new approach based on vector analysis method is adopted that can closely combine geometric configuration and algebraic operation in the process of polarized electromagnetic wave transmitting and receiving.Basing on this approach,simulation experiments are carried out with simulated electronic magnetic wave.The simulation results of this approach are compared with the results of the traditional spatial spectrum estimation algorithm.It is found that the DOA estimation approach proposed by this paper is more effective.%为了基于接收极化电磁波的三维极化分量对其到达方向进行估计,讨论了通过极化参数来描述极化电磁波及通过已知复合电场求取极化参数的问题,提出利用矢量对几何位形进行描述,通过三维振子天线测量复合电场的电波矢量,从而利用电波矢量的3个分量求取发射电磁波的到达方向(DOA)的一种新算法,基于此算法,利用仿真的极化电磁波进行了DOA估计仿真实验,并与传统的空间谱估计算法进行对比,对比发现,提出的DOA估计算法是更为有效的.

  4. Identification of the Hammerstein model of a PEMFC stack based on least squares support vector machines

    Energy Technology Data Exchange (ETDEWEB)

    Li, Chun-Hua; Zhu, Xin-Jian; Cao, Guang-Yi; Sui, Sheng; Hu, Ming-Ruo [Fuel Cell Research Institute, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240 (China)

    2008-01-03

    This paper reports a Hammerstein modeling study of a proton exchange membrane fuel cell (PEMFC) stack using least squares support vector machines (LS-SVM). PEMFC is a complex nonlinear, multi-input and multi-output (MIMO) system that is hard to model by traditional methodologies. Due to the generalization performance of LS-SVM being independent of the dimensionality of the input data and the particularly simple structure of the Hammerstein model, a MIMO SVM-ARX (linear autoregression model with exogenous input) Hammerstein model is used to represent the PEMFC stack in this paper. The linear model parameters and the static nonlinearity can be obtained simultaneously by solving a set of linear equations followed by the singular value decomposition (SVD). The simulation tests demonstrate the obtained SVM-ARX Hammerstein model can efficiently approximate the dynamic behavior of a PEMFC stack. Furthermore, based on the proposed SVM-ARX Hammerstein model, valid control strategy studies such as predictive control, robust control can be developed. (author)

  5. The design and implementation of stereoscopic 3D scalable vector graphics based on WebKit

    Science.gov (United States)

    Liu, Zhongxin; Wang, Wenmin; Wang, Ronggang

    2014-03-01

    Scalable Vector Graphics (SVG), which is a language designed based on eXtensible Markup Language (XML), is used to describe basic shapes embedded in webpages, such as circles and rectangles. However, it can only depict 2D shapes. As a consequence, web pages using classical SVG can only display 2D shapes on a screen. With the increasing development of stereoscopic 3D (S3D) technology, binocular 3D devices have been widely used. Under this circumstance, we intend to extend the widely used web rendering engine WebKit to support the description and display of S3D webpages. Therefore, the extension of SVG is of necessity. In this paper, we will describe how to design and implement SVG shapes with stereoscopic 3D mode. Two attributes representing the depth and thickness are added to support S3D shapes. The elimination of hidden lines and hidden surfaces, which is an important process in this project, is described as well. The modification of WebKit is also discussed, which is made to support the generation of both left view and right view at the same time. As is shown in the result, in contrast to the 2D shapes generated by the Google Chrome web browser, the shapes got from our modified browser are in S3D mode. With the feeling of depth and thickness, the shapes seem to be real 3D objects away from the screen, rather than simple curves and lines as before.

  6. QoS Requirement Generation and Algorithm Selection for Composite Service Based on Reference Vector

    Institute of Scientific and Technical Information of China (English)

    Bang-Yu Wu; Chi-Hung Chi; Shi-Jie Xu; Ming Gu; Jia-Guang Sun

    2009-01-01

    Under SOA (Service-Oriented Architecture), composite service is formed by aggregating multiple component services together in a given workflow. One key criterion of this research topic is QoS composition. Most work on service composition mainly focuses on the algorithms about how to compose services according to assumed QoS, without considering where the required QoS comes from and the selection of user preferred composition algorithm among those with different computational cost and different selection results. In this paper, we propose to strengthen current service composition mechanism by generation of QoS requirement and its algorithm selection based on the QoS reference vectors which are calculated optimally from the existing individual services' QoS by registry to represent QoS overview about the best QoS,the worst (or most economical) QoS, or the average QoS of all composite services. To implement QoS requirement, which is determined according to QoS overview, this paper introduces two selection algorithms as two kinds of experiment examples,one aiming at the most accurate service selection and the other chasing for trade-off between selection cost and result.Experimental results show our mechanism can help the requester achieve his expected composite service with appropriate QoS requirement and customized selection algorithm.

  7. Classifying Data Sets Using Support Vector Machines Based on Geometric Distance

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Support vector machines (SVMs) are not as favored for large-scale data mining as for pattern recognition and machine learning because the training complexity of SVMs is highly dependent on the size of data set. This paper presents a geometric distance-based SVM (GDB-SVM). It takes the distance between a point and classified hyperplane as classification rule,and is designed on the basis of theoretical analysis and geometric intuition. Experimental code is derived from LibSVM with Microsoft Visual C ++ 6.0 as system of translating and editing. Four predicted results of five of GDB-SVM are better than those of the method of one against all (OAA). Three predicted results of five of GDB-SVM are better than those of the method of one against one (OAO). Experiments on real data sets show that GDB-SVM is not only superior to the methods of OAA and OAO,but highly scalable for large data sets while generating high classification accuracy.

  8. RNAi-based conditional gene knockdown in mice using a U6 promoter driven vector

    Directory of Open Access Journals (Sweden)

    Vivek Shukla, Xavier Coumoul, Chu-Xia Deng

    2007-01-01

    Full Text Available RNA interference (RNAi is a powerful tool widely used for studying gene function in a number of species. We have previously developed an approach that allows conditional expression of a polymerase III promoter based small hairpin RNA (shRNA in mice using the Cre-LoxP system. This approach uses a U6 promoter, which is inactive due to the presence of a ploxPneo cassette in the promoter; this promoter can be activated after excision of the neo gene in transgenic mice that express a Cre recombinase transgene. As a proof of principle, we have previously knocked down over 95% of Fgfr2 transcripts in mouse germlines, leading to embryonic lethality, while restricting the knockdown to the progress zone of the limb results in live animals with malformation of digits of both the forelimbs and hindlimbs. We now provide a detailed protocol, including a simplified single-step cloning procedure for vector construction. This method provides a fast yet efficient way to decipher gene functions in vivo in a tissue specific manner.

  9. A Fault Alarm and Diagnosis Method Based on Sensitive Parameters and Support Vector Machine

    Science.gov (United States)

    Zhang, Jinjie; Yao, Ziyun; Lv, Zhiquan; Zhu, Qunxiong; Xu, Fengtian; Jiang, Zhinong

    2015-08-01

    Study on the extraction of fault feature and the diagnostic technique of reciprocating compressor is one of the hot research topics in the field of reciprocating machinery fault diagnosis at present. A large number of feature extraction and classification methods have been widely applied in the related research, but the practical fault alarm and the accuracy of diagnosis have not been effectively improved. Developing feature extraction and classification methods to meet the requirements of typical fault alarm and automatic diagnosis in practical engineering is urgent task. The typical mechanical faults of reciprocating compressor are presented in the paper, and the existing data of online monitoring system is used to extract fault feature parameters within 15 types in total; the inner sensitive connection between faults and the feature parameters has been made clear by using the distance evaluation technique, also sensitive characteristic parameters of different faults have been obtained. On this basis, a method based on fault feature parameters and support vector machine (SVM) is developed, which will be applied to practical fault diagnosis. A better ability of early fault warning has been proved by the experiment and the practical fault cases. Automatic classification by using the SVM to the data of fault alarm has obtained better diagnostic accuracy.

  10. Position Sensorless Vector Control for Permanent Magnet Synchronous Motors Based on Maximum Torque Control Frame

    Science.gov (United States)

    Hida, Hajime; Tomigashi, Yoshio; Kishimoto, Keiji

    High efficiency drive can be achieved by the maximum torque-per-ampere (MTPA) control which used reluctance torque effectively. However, the calculations for estimating rotor position and for controlling the d-axis current are required. The motor parameters of inductance etc. that are easily affected by magnetic saturation are included in those calculations. This paper proposes a new MTPA control method, which is robust against changes of motor parameters caused by magnetic saturation. In addition, complex calculation for d-axis current or reference to the table is not necessary. In this method, we define a novel coordinate frame, which has one axis aligned with the current vector of the MTPA control, and estimate the frame directly. Because the parameter Lqm for estimating the frame is less affected by the magnetic saturation than the conventional Lq, the effect of magnetic saturation on the position estimation can be greatly suppressed. First, an extended electromotive force model based on the proposed frame and a parameter Lqm for an estimation of the frame are derived. Next, the effectiveness of this proposed method is confirmed by simulations and experiments.

  11. Delineating Individual Trees from Lidar Data: A Comparison of Vector- and Raster-based Segmentation Approaches

    Directory of Open Access Journals (Sweden)

    Maggi Kelly

    2013-08-01

    Full Text Available Light detection and ranging (lidar data is increasingly being used for ecosystem monitoring across geographic scales. This work concentrates on delineating individual trees in topographically-complex, mixed conifer forest across the California’s Sierra Nevada. We delineated individual trees using vector data and a 3D lidar point cloud segmentation algorithm, and using raster data with an object-based image analysis (OBIA of a canopy height model (CHM. The two approaches are compared to each other and to ground reference data. We used high density (9 pulses/m2, discreet lidar data and WorldView-2 imagery to delineate individual trees, and to classify them by species or species types. We also identified a new method to correct artifacts in a high-resolution CHM. Our main focus was to determine the difference between the two types of approaches and to identify the one that produces more realistic results. We compared the delineations via tree detection, tree heights, and the shape of the generated polygons. The tree height agreement was high between the two approaches and the ground data (r2: 0.93–0.96. Tree detection rates increased for more dominant trees (8–100 percent. The two approaches delineated tree boundaries that differed in shape: the lidar-approach produced fewer, more complex, and larger polygons that more closely resembled real forest structure.

  12. Diagnostic Method of Diabetes Based on Support Vector Machine and Tongue Images

    Science.gov (United States)

    Hu, Xiaojuan; Chen, Qingguang; Tu, Liping; Huang, Jingbin; Cui, Ji

    2017-01-01

    Objective. The purpose of this research is to develop a diagnostic method of diabetes based on standardized tongue image using support vector machine (SVM). Methods. Tongue images of 296 diabetic subjects and 531 nondiabetic subjects were collected by the TDA-1 digital tongue instrument. Tongue body and tongue coating were separated by the division-merging method and chrominance-threshold method. With extracted color and texture features of the tongue image as input variables, the diagnostic model of diabetes with SVM was trained. After optimizing the combination of SVM kernel parameters and input variables, the influences of the combinations on the model were analyzed. Results. After normalizing parameters of tongue images, the accuracy rate of diabetes predication was increased from 77.83% to 78.77%. The accuracy rate and area under curve (AUC) were not reduced after reducing the dimensions of tongue features with principal component analysis (PCA), while substantially saving the training time. During the training for selecting SVM parameters by genetic algorithm (GA), the accuracy rate of cross-validation was grown from 72% or so to 83.06%. Finally, we compare with several state-of-the-art algorithms, and experimental results show that our algorithm has the best predictive accuracy. Conclusions. The diagnostic method of diabetes on the basis of tongue images in Traditional Chinese Medicine (TCM) is of great value, indicating the feasibility of digitalized tongue diagnosis. PMID:28133611

  13. Diagnostic Method of Diabetes Based on Support Vector Machine and Tongue Images

    Directory of Open Access Journals (Sweden)

    Jianfeng Zhang

    2017-01-01

    Full Text Available Objective. The purpose of this research is to develop a diagnostic method of diabetes based on standardized tongue image using support vector machine (SVM. Methods. Tongue images of 296 diabetic subjects and 531 nondiabetic subjects were collected by the TDA-1 digital tongue instrument. Tongue body and tongue coating were separated by the division-merging method and chrominance-threshold method. With extracted color and texture features of the tongue image as input variables, the diagnostic model of diabetes with SVM was trained. After optimizing the combination of SVM kernel parameters and input variables, the influences of the combinations on the model were analyzed. Results. After normalizing parameters of tongue images, the accuracy rate of diabetes predication was increased from 77.83% to 78.77%. The accuracy rate and area under curve (AUC were not reduced after reducing the dimensions of tongue features with principal component analysis (PCA, while substantially saving the training time. During the training for selecting SVM parameters by genetic algorithm (GA, the accuracy rate of cross-validation was grown from 72% or so to 83.06%. Finally, we compare with several state-of-the-art algorithms, and experimental results show that our algorithm has the best predictive accuracy. Conclusions. The diagnostic method of diabetes on the basis of tongue images in Traditional Chinese Medicine (TCM is of great value, indicating the feasibility of digitalized tongue diagnosis.

  14. Off-Line Signature Authentication Based on Moment Invariants Using Support Vector Machine

    Directory of Open Access Journals (Sweden)

    k. R. Radhika

    2010-01-01

    Full Text Available Problem statement: The research addressed the computational load reduction in off-line signature verification based on minimal features using bayes classifier, fast Fourier transform, linear discriminant analysis, principal component analysis and support vector machine approaches. Approach: The variation of signature in genuine cases is studied extensively, to predict the set of quad tree components in a genuine sample for one person with minimum variance criteria. Using training samples, with a high degree of certainty the Minimum Variance Quad tree Components (MVQC of a signature for a person are listed to apply on imposter sample. First, Hu moment is applied on the selected subsections. The summation values of the subsections are provided as feature to classifiers. Results: Results showed that the SVM classifier yielded the most promising 8% False Rejection Rate (FRR and 10% False Acceptance Rate (FAR. The signature is a biometric, where variations in a genuine case, is a natural expectation. In the genuine signature, certain parts of signature vary from one instance to another. Conclusion: The proposed system aimed to provide simple, faster robust system using less number of features when compared to state of art works.

  15. Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes

    Directory of Open Access Journals (Sweden)

    Utku Kose

    2016-03-01

    Full Text Available The definition, diagnosis and classification of Diabetes Mellitus and its complications are very important. First of all, the World Health Organization (WHO and other societies, as well as scientists have done lots of studies regarding this subject. One of the most important research interests of this subject is the computer supported decision systems for diagnosing diabetes. In such systems, Artificial Intelligence techniques are often used for several disease diagnostics to streamline the diagnostic process in daily routine and avoid misdiagnosis. In this study, a diabetes diagnosis system, which is formed via both Support Vector Machines (SVM and Cognitive Development Optimization Algorithm (CoDOA has been proposed. Along the training of SVM, CoDOA was used for determining the sigma parameter of the Gauss (RBF kernel function, and eventually, a classification process was made over the diabetes data set, which is related to Pima Indians. The proposed approach offers an alternative solution to the field of Artificial Intelligence-based diabetes diagnosis, and contributes to the related literature on diagnosis processes.

  16. HFVS: An arbitrary high order approach based on flux vector splitting

    Science.gov (United States)

    Chen, Yibing; Jiang, Song; Liu, Na

    2016-10-01

    In this paper, a new scheme of arbitrary high order accuracy in both space and time is proposed to solve hyperbolic conservative laws. The basic idea in the construction is that, based on the idea of the flux vector splitting (FVS), we split all the spatial and time derivatives in the Taylor expansion of the numerical flux into two parts: one part with positive eigenvalues, another with negative eigenvalues. According to a Lax-Wendroff procedure, all the time derivatives are then replaced by spatial derivatives, which are evaluated by using WENO reconstruction polynomials. One of the most significant advantages of the current scheme is very easy to implement. In addition, it is found that the higher spatial and time derivatives produced in the construction of the numerical flux can be regarded as a building block, in the sense that they can be coupled with any extact/approximate Riemann solvers to extend a first-order scheme to very high order accuracy in both space and time. Numerous numerical tests for linear and nonlinear hyperbolic conservative laws are carried out, and the numerical results demonstrate that the proposed scheme is robust and can be of high order accuracy in both space and time.

  17. Potent tetravalent replicon vaccines against botulinum neurotoxins using DNA-based Semliki Forest virus replicon vectors.

    Science.gov (United States)

    Yu, Yun-Zhou; Guo, Jin-Peng; An, Huai-Jie; Zhang, Shu-Ming; Wang, Shuang; Yu, Wei-Yuan; Sun, Zhi-Wei

    2013-05-07

    Human botulism is commonly associated with botulinum neurotoxin (BoNT) serotypes A, B, E and F. This suggests that the greatest need is for a tetravalent vaccine that provides protection against all four of these serotypes. In current study, we investigated the feasibility of generating several tetravalent vaccines that protected mice against the four serotypes. Firstly, monovalent replicon vaccine against BoNT induced better antibody response and protection than that of corresponding conventional DNA vaccine. Secondly, dual-expression DNA replicon pSCARSE/FHc or replicon particle VRP-E/FHc vaccine was well resistant to the challenge of BoNT/E and BoNT/F mixture as a combination vaccine composed of two monovalent replicon vaccines. Finally, the dual-expression DNA replicon or replicon particle tetravalent vaccine could simultaneously and effectively neutralize and protect the four BoNT serotypes. Protection correlated directly with serum ELISA titers and neutralization antibody levels to BoNTs. Therefore, replicon-based DNA or particle might be effective vector to develop BoNT vaccines, which might be more desirable for use in clinical application than the conventional DNA vaccines. Our studies demonstrate the utility of combining dual-expression DNA replicon or replicon particle vaccines into multi-agent formulations as potent tetravalent vaccines for eliciting protective responses to four serotypes of BoNTs.

  18. Advances in Viral Vector-Based TRAIL Gene Therapy for Cancer

    Energy Technology Data Exchange (ETDEWEB)

    Norian, Lyse A. [Department of Urology, University of Iowa Carver College of Medicine, Iowa City, IA 52242 (United States); James, Britnie R. [Interdisciplinary Graduate Program in Immunology, University of Iowa Carver College of Medicine, Iowa City, IA 52242 (United States); Griffith, Thomas S., E-mail: thomas-griffith@uiowa.edu [Department of Urology, University of Iowa Carver College of Medicine, Iowa City, IA 52242 (United States); Interdisciplinary Graduate Program in Immunology, University of Iowa Carver College of Medicine, Iowa City, IA 52242 (United States)

    2011-02-10

    Numerous biologic approaches are being investigated as anti-cancer therapies in an attempt to induce tumor regression while circumventing the toxic side effects associated with standard chemo- or radiotherapies. Among these, tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) has shown particular promise in pre-clinical and early clinical trials, due to its preferential ability to induce apoptotic cell death in cancer cells and its minimal toxicity. One limitation of TRAIL use is the fact that many tumor types display an inherent resistance to TRAIL-induced apoptosis. To circumvent this problem, researchers have explored a number of strategies to optimize TRAIL delivery and to improve its efficacy via co-administration with other anti-cancer agents. In this review, we will focus on TRAIL-based gene therapy approaches for the treatment of malignancies. We will discuss the main viral vectors that are being used for TRAIL gene therapy and the strategies that are currently being attempted to improve the efficacy of TRAIL as an anti-cancer therapeutic.

  19. A NOVEL ARTIFICIAL HYDROCARBON NETWORKS BASED SPACE VECTOR PULSE WIDTH MODULATION CONTROLLER FOR INDUCTION MOTORS

    Directory of Open Access Journals (Sweden)

    Hiram Ponce

    2014-01-01

    Full Text Available Most of machine-operated industrial processes implement electric machinery as their work sources, implying the necessary improvement of control techniques and power electronics drivers. Many years have passed since the control conflicts related to induction motors have been overcome through torque-flux control techniques so their advantages over direct current motors have made them to be the most common electric actuator found behind industrial automation. In fact, induction motors can be easily operated using a Direct Torque Control (DTC. Since, it is based on a hysteresis control of the torque and flux errors, its performance is characterized by a quick reaching of the set point, but also a high ripple on both torque and flux. In order to enhance that technique, this study introduces a novel hybrid fuzzy controller with artificial hydrocarbon networks (FMC that is used in a Space Vector Pulse Width Modulation (SVPWM technique, so-called FMC-SVPWM-DTC. In fact, this study describes the proposal and its design method. Experimental results over a velocity-torque cascade topology proved that the proposed FMC-SVPWM-DTC responses highly effective almost suppressing rippling in torque and flux. It also performed a faster speed response than in a conventional DTC. In that sense, the proposed FMC-SVPWM-DTC can be used an alternative approach for controlling induction motors.

  20. Rotor Resistance Online Identification of Vector Controlled Induction Motor Based on Neural Network

    Directory of Open Access Journals (Sweden)

    Bo Fan

    2014-01-01

    Full Text Available Rotor resistance identification has been well recognized as one of the most critical factors affecting the theoretical study and applications of AC motor’s control for high performance variable frequency speed adjustment. This paper proposes a novel model for rotor resistance parameters identification based on Elman neural networks. Elman recurrent neural network is capable of performing nonlinear function approximation and possesses the ability of time-variable characteristic adaptation. Those influencing factors of specified parameter are analyzed, respectively, and various work states are covered to ensure the completeness of the training samples. Through signal preprocessing on samples and training dataset, different input parameters identifications with one network are compared and analyzed. The trained Elman neural network, applied in the identification model, is able to efficiently predict the rotor resistance in high accuracy. The simulation and experimental results show that the proposed method owns extensive adaptability and performs very well in its application to vector controlled induction motor. This identification method is able to enhance the performance of induction motor’s variable-frequency speed regulation.

  1. Support vector machine based fault detection approach for RFT-30 cyclotron

    Science.gov (United States)

    Kong, Young Bae; Lee, Eun Je; Hur, Min Goo; Park, Jeong Hoon; Park, Yong Dae; Yang, Seung Dae

    2016-10-01

    An RFT-30 is a 30 MeV cyclotron used for radioisotope applications and radiopharmaceutical researches. The RFT-30 cyclotron is highly complex and includes many signals for control and monitoring of the system. It is quite difficult to detect and monitor the system failure in real time. Moreover, continuous monitoring of the system is hard and time-consuming work for human operators. In this paper, we propose a support vector machine (SVM) based fault detection approach for the RFT-30 cyclotron. The proposed approach performs SVM learning with training samples to construct the classification model. To compensate the system complexity due to the large-scale accelerator, we utilize the principal component analysis (PCA) for transformation of the original data. After training procedure, the proposed approach detects the system faults in real time. We analyzed the performance of the proposed approach utilizing the experimental data of the RFT-30 cyclotron. The performance results show that the proposed SVM approach can provide an efficient way to control the cyclotron system.

  2. Diagnostic Method of Diabetes Based on Support Vector Machine and Tongue Images.

    Science.gov (United States)

    Zhang, Jianfeng; Xu, Jiatuo; Hu, Xiaojuan; Chen, Qingguang; Tu, Liping; Huang, Jingbin; Cui, Ji

    2017-01-01

    Objective. The purpose of this research is to develop a diagnostic method of diabetes based on standardized tongue image using support vector machine (SVM). Methods. Tongue images of 296 diabetic subjects and 531 nondiabetic subjects were collected by the TDA-1 digital tongue instrument. Tongue body and tongue coating were separated by the division-merging method and chrominance-threshold method. With extracted color and texture features of the tongue image as input variables, the diagnostic model of diabetes with SVM was trained. After optimizing the combination of SVM kernel parameters and input variables, the influences of the combinations on the model were analyzed. Results. After normalizing parameters of tongue images, the accuracy rate of diabetes predication was increased from 77.83% to 78.77%. The accuracy rate and area under curve (AUC) were not reduced after reducing the dimensions of tongue features with principal component analysis (PCA), while substantially saving the training time. During the training for selecting SVM parameters by genetic algorithm (GA), the accuracy rate of cross-validation was grown from 72% or so to 83.06%. Finally, we compare with several state-of-the-art algorithms, and experimental results show that our algorithm has the best predictive accuracy. Conclusions. The diagnostic method of diabetes on the basis of tongue images in Traditional Chinese Medicine (TCM) is of great value, indicating the feasibility of digitalized tongue diagnosis.

  3. A pixel-based color image segmentation using support vector machine and fuzzy C-means.

    Science.gov (United States)

    Wang, Xiang-Yang; Zhang, Xian-Jin; Yang, Hong-Ying; Bu, Juan

    2012-09-01

    Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. In this paper, we present a pixel-based color image segmentation using Support Vector Machine (SVM) and Fuzzy C-Means (FCM). Firstly, the pixel-level color feature and texture feature of the image, which is used as input of the SVM model (classifier), are extracted via the local spatial similarity measure model and Steerable filter. Then, the SVM model (classifier) is trained by using FCM with the extracted pixel-level features. Finally, the color image is segmented with the trained SVM model (classifier). This image segmentation can not only take full advantage of the local information of the color image but also the ability of the SVM classifier. Experimental evidence shows that the proposed method has a very effective computational behavior and effectiveness, and decreases the time and increases the quality of color image segmentation in comparison with the state-of-the-art segmentation methods recently proposed in the literature.

  4. Video Waterscrambling: Towards a Video Protection Scheme Based on the Disturbance of Motion Vectors

    Directory of Open Access Journals (Sweden)

    Yann Bodo

    2004-10-01

    Full Text Available With the popularity of high-bandwidth modems and peer-to-peer networks, the contents of videos must be highly protected from piracy. Traditionally, the models utilized to protect this kind of content are scrambling and watermarking. While the former protects the content against eavesdropping (a priori protection, the latter aims at providing a protection against illegal mass distribution (a posteriori protection. Today, researchers agree that both models must be used conjointly to reach a sufficient level of security. However, scrambling works generally by encryption resulting in an unintelligible content for the end-user. At the moment, some applications (such as e-commerce may require a slight degradation of content so that the user has an idea of the content before buying it. In this paper, we propose a new video protection model, called waterscrambling, whose aim is to give such a quality degradation-based security model. This model works in the compressed domain and disturbs the motion vectors, degrading the video quality. It also allows embedding of a classical invisible watermark enabling protection against mass distribution. In fact, our model can be seen as an intermediary solution to scrambling and watermarking.

  5. Inductance and Active Phase Vector Based Torque Control for Switched Reluctance Motor Drives.

    Science.gov (United States)

    Kalpathi, Ramani Raman

    are derived and commutation based on observable phase coil parameters are developed. The commutation methods are based on a composite vector of the observable parameters of the active phase coil. These methods work on a tabular approach which is ideal for implementation using digital computers.

  6. Construction of a cytomegalovirus-based amplicon: a vector with a unique transfer capacity.

    Science.gov (United States)

    Borst, Eva Maria; Messerle, Martin

    2003-07-01

    Cytomegalovirus (CMV) has a number of interesting properties that qualifies it as a vector for gene transfer. Especially appealing is the ability of the CMV genome to persist in hematopoietic progenitor cells and the packaging capacity of the viral capsid that accommodates a DNA genome of 230 kbp. In order to exploit the packaging capacity of the CMV capsid we investigated whether the principles of an amplicon vector can be applied to CMV. Amplicons are herpesviral vectors, which contain only the cis-active sequences required for replication and packaging of the vector genome. For construction of a CMV amplicon the sequences comprising the lytic origin of replication (orilyt) and the cleavage packaging recognition sites (pac) of human CMV were cloned onto a plasmid. A gene encoding the green fluorescent protein was used as a model transgene. The amplicon plasmid replicated in the presence of a CMV helper virus and was packaged into CMV particles, with replication and packaging being dependent on the presence of the orilyt and pac sequences. The packaged amplicon could be transferred to recipient cells and reisolated from the transduced cells. Analysis of the DNA isolated from CMV capsids revealed that the CMV amplicon was packaged as a concatemer with a size of approximately 210 kbp. The CMV amplicon vector has the potential to transfer therapeutic genes with a size of more than 200 kbp and thus provides a unique transfer capacity among viral vectors.

  7. Content-based retrieval based on binary vectors for 2-D medical images

    Institute of Scientific and Technical Information of China (English)

    龚鹏; 邹亚东; 洪海

    2003-01-01

    In medical research and clinical diagnosis, automated or computer-assisted classification and retrieval methods are highly desirable to offset the high cost of manual classification and manipulation by medical experts. To facilitate the decision-making in the health-care and the related areas, in this paper, a two-step content-based medical image retrieval algorithm is proposed. Firstly, in the preprocessing step, the image segmentation is performed to distinguish image objects, and on the basis of the ...

  8. Estimation of Curie temperature of manganite-based materials for magnetic refrigeration application using hybrid gravitational based support vector regression

    Science.gov (United States)

    Owolabi, Taoreed O.; Akande, Kabiru O.; Olatunji, Sunday O.; Alqahtani, Abdullah; Aldhafferi, Nahier

    2016-10-01

    Magnetic refrigeration (MR) technology stands a good chance of replacing the conventional gas compression system (CGCS) of refrigeration due to its unique features such as high efficiency, low cost as well as being environmental friendly. Its operation involves the use of magnetocaloric effect (MCE) of a magnetic material caused by application of magnetic field. Manganite-based material demonstrates maximum MCE at its magnetic ordering temperature known as Curie temperature (TC). Consequently, manganite-based material with TC around room temperature is essentially desired for effective utilization of this technology. The TC of manganite-based materials can be adequately altered to a desired value through doping with appropriate foreign materials. In order to determine a manganite with TC around room temperature and to circumvent experimental challenges therein, this work proposes a model that can effectively estimates the TC of manganite-based material doped with different materials with the aid of support vector regression (SVR) hybridized with gravitational search algorithm (GSA). Implementation of GSA algorithm ensures optimum selection of SVR hyper-parameters for improved performance of the developed model using lattice distortions as the descriptors. The result of the developed model is promising and agrees excellently with the experimental results. The outstanding estimates of the proposed model suggest its potential in promoting room temperature magnetic refrigeration through quick estimation of the effect of dopants on TC so as to obtain manganite that works well around the room temperature.

  9. Estimation of Curie temperature of manganite-based materials for magnetic refrigeration application using hybrid gravitational based support vector regression

    Directory of Open Access Journals (Sweden)

    Taoreed O. Owolabi

    2016-10-01

    Full Text Available Magnetic refrigeration (MR technology stands a good chance of replacing the conventional gas compression system (CGCS of refrigeration due to its unique features such as high efficiency, low cost as well as being environmental friendly. Its operation involves the use of magnetocaloric effect (MCE of a magnetic material caused by application of magnetic field. Manganite-based material demonstrates maximum MCE at its magnetic ordering temperature known as Curie temperature (TC. Consequently, manganite-based material with TC around room temperature is essentially desired for effective utilization of this technology. The TC of manganite-based materials can be adequately altered to a desired value through doping with appropriate foreign materials. In order to determine a manganite with TC around room temperature and to circumvent experimental challenges therein, this work proposes a model that can effectively estimates the TC of manganite-based material doped with different materials with the aid of support vector regression (SVR hybridized with gravitational search algorithm (GSA. Implementation of GSA algorithm ensures optimum selection of SVR hyper-parameters for improved performance of the developed model using lattice distortions as the descriptors. The result of the developed model is promising and agrees excellently with the experimental results. The outstanding estimates of the proposed model suggest its potential in promoting room temperature magnetic refrigeration through quick estimation of the effect of dopants on TC so as to obtain manganite that works well around the room temperature.

  10. About vectors

    CERN Document Server

    Hoffmann, Banesh

    1975-01-01

    From his unusual beginning in ""Defining a vector"" to his final comments on ""What then is a vector?"" author Banesh Hoffmann has written a book that is provocative and unconventional. In his emphasis on the unresolved issue of defining a vector, Hoffmann mixes pure and applied mathematics without using calculus. The result is a treatment that can serve as a supplement and corrective to textbooks, as well as collateral reading in all courses that deal with vectors. Major topics include vectors and the parallelogram law; algebraic notation and basic ideas; vector algebra; scalars and scalar p

  11. Vector analysis

    CERN Document Server

    Newell, Homer E

    2006-01-01

    When employed with skill and understanding, vector analysis can be a practical and powerful tool. This text develops the algebra and calculus of vectors in a manner useful to physicists and engineers. Numerous exercises (with answers) not only provide practice in manipulation but also help establish students' physical and geometric intuition in regard to vectors and vector concepts.Part I, the basic portion of the text, consists of a thorough treatment of vector algebra and the vector calculus. Part II presents the illustrative matter, demonstrating applications to kinematics, mechanics, and e

  12. Automatic event detection in low SNR microseismic signals based on multi-scale permutation entropy and a support vector machine

    Science.gov (United States)

    Jia, Rui-Sheng; Sun, Hong-Mei; Peng, Yan-Jun; Liang, Yong-Quan; Lu, Xin-Ming

    2016-12-01

    Microseismic monitoring is an effective means for providing early warning of rock or coal dynamical disasters, and its first step is microseismic event detection, although low SNR microseismic signals often cannot effectively be detected by routine methods. To solve this problem, this paper presents permutation entropy and a support vector machine to detect low SNR microseismic events. First, an extraction method of signal features based on multi-scale permutation entropy is proposed by studying the influence of the scale factor on the signal permutation entropy. Second, the detection model of low SNR microseismic events based on the least squares support vector machine is built by performing a multi-scale permutation entropy calculation for the collected vibration signals, constructing a feature vector set of signals. Finally, a comparative analysis of the microseismic events and noise signals in the experiment proves that the different characteristics of the two can be fully expressed by using multi-scale permutation entropy. The detection model of microseismic events combined with the support vector machine, which has the features of high classification accuracy and fast real-time algorithms, can meet the requirements of online, real-time extractions of microseismic events.

  13. Physical Characterization of Gemini Surfactant-Based Synthetic Vectors for the Delivery of Linear Covalently Closed (LCC DNA Ministrings.

    Directory of Open Access Journals (Sweden)

    Chi Hong Sum

    Full Text Available In combination with novel linear covalently closed (LCC DNA minivectors, referred to as DNA ministrings, a gemini surfactant-based synthetic vector for gene delivery has been shown to exhibit enhanced delivery and bioavailability while offering a heightened safety profile. Due to topological differences from conventional circular covalently closed (CCC plasmid DNA vectors, the linear topology of LCC DNA ministrings may present differences with regards to DNA interaction and the physicochemical properties influencing DNA-surfactant interactions in the formulation of lipoplexed particles. In this study, N,N-bis(dimethylhexadecyl-α,ω-propanediammonium(16-3-16gemini-based synthetic vectors, incorporating either CCC plasmid or LCC DNA ministrings, were characterized and compared with respect to particle size, zeta potential, DNA encapsulation, DNase sensitivity, and in vitro transgene delivery efficacy. Through comparative analysis, differences between CCC plasmid DNA and LCC DNA ministrings led to variations in the physical properties of the resulting lipoplexes after complexation with 16-3-16 gemini surfactants. Despite the size disparities between the plasmid DNA vectors (CCC and DNA ministrings (LCC, differences in DNA topology resulted in the generation of lipoplexes of comparable particle sizes. The capacity for ministring (LCC derived lipoplexes to undergo complete counterion release during lipoplex formation contributed to improved DNA encapsulation, protection from DNase degradation, and in vitro transgene delivery.

  14. Downscaling of Aircraft-, Landsat-, and MODIS-based Land Surface Temperature Images with Support Vector Machines

    Science.gov (United States)

    Ha, W.; Gowda, P. H.; Oommen, T.; Howell, T. A.; Hernandez, J. E.

    2010-12-01

    High spatial resolution Land Surface Temperature (LST) images are required to estimate evapotranspiration (ET) at a field scale for irrigation scheduling purposes. Satellite sensors such as Landsat 5 Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (MODIS) can offer images at several spectral bandwidths including visible, near-infrared (NIR), shortwave-infrared, and thermal-infrared (TIR). The TIR images usually have coarser spatial resolutions than those from non-thermal infrared bands. Due to this technical constraint of the satellite sensors on these platforms, image downscaling has been proposed in the field of ET remote sensing. This paper explores the potential of the Support Vector Machines (SVM) to perform downscaling of LST images derived from aircraft (4 m spatial resolution), TM (120 m), and MODIS (1000 m) using normalized difference vegetation index images derived from simultaneously acquired high resolution visible and NIR data (1 m for aircraft, 30 m for TM, and 250 m for MODIS). The SVM is a new generation machine learning algorithm that has found a wide application in the field of pattern recognition and time series analysis. The SVM would be ideally suited for downscaling problems due to its generalization ability in capturing non-linear regression relationship between the predictand and the multiple predictors. Remote sensing data acquired over the Texas High Plains during the 2008 summer growing season will be used in this study. Accuracy assessment of the downscaled 1, 30, and 250 m LST images will be made by comparing them with LST data measured with infrared thermometers at a small spatial scale, upscaled 30 m aircraft-based LST images, and upscaled 250 m TM-based LST images, respectively.

  15. Disease prediction based on functional connectomes using a scalable and spatially-informed support vector machine.

    Science.gov (United States)

    Watanabe, Takanori; Kessler, Daniel; Scott, Clayton; Angstadt, Michael; Sripada, Chandra

    2014-08-01

    Substantial evidence indicates that major psychiatric disorders are associated with distributed neural dysconnectivity, leading to a strong interest in using neuroimaging methods to accurately predict disorder status. In this work, we are specifically interested in a multivariate approach that uses features derived from whole-brain resting state functional connectomes. However, functional connectomes reside in a high dimensional space, which complicates model interpretation and introduces numerous statistical and computational challenges. Traditional feature selection techniques are used to reduce data dimensionality, but are blind to the spatial structure of the connectomes. We propose a regularization framework where the 6-D structure of the functional connectome (defined by pairs of points in 3-D space) is explicitly taken into account via the fused Lasso or the GraphNet regularizer. Our method only restricts the loss function to be convex and margin-based, allowing non-differentiable loss functions such as the hinge-loss to be used. Using the fused Lasso or GraphNet regularizer with the hinge-loss leads to a structured sparse support vector machine (SVM) with embedded feature selection. We introduce a novel efficient optimization algorithm based on the augmented Lagrangian and the classical alternating direction method, which can solve both fused Lasso and GraphNet regularized SVM with very little modification. We also demonstrate that the inner subproblems of the algorithm can be solved efficiently in analytic form by coupling the variable splitting strategy with a data augmentation scheme. Experiments on simulated data and resting state scans from a large schizophrenia dataset show that our proposed approach can identify predictive regions that are spatially contiguous in the 6-D "connectome space," offering an additional layer of interpretability that could provide new insights about various disease processes.

  16. Single-electrode-based sliding triboelectric nanogenerator for self-powered displacement vector sensor system.

    Science.gov (United States)

    Yang, Ya; Zhang, Hulin; Chen, Jun; Jing, Qingshen; Zhou, Yu Sheng; Wen, Xiaonan; Wang, Zhong Lin

    2013-08-27

    We report a single-electrode-based sliding-mode triboelectric nanogenerator (TENG) that not only can harvest mechanical energy but also is a self-powered displacement vector sensor system for touching pad technology. By utilizing the relative sliding between an electrodeless polytetrafluoroethylene (PTFE) patch with surface-etched nanoparticles and an Al electrode that is grounded, the fabricated TENG can produce an open-circuit voltage up to 1100 V, a short-circuit current density of 6 mA/m(2), and a maximum power density of 350 mW/m(2) on a load of 100 MΩ, which can be used to instantaneously drive 100 green-light-emitting diodes (LEDs). The working mechanism of the TENG is based on the charge transfer between the Al electrode and the ground by modulating the relative sliding distance between the tribo-charged PTFE patch and the Al plate. Grating of linear rows on the Al electrode enables the detection of the sliding speed of the PTFE patch along one direction. Moreover, we demonstrated that 16 Al electrode channels arranged along four directions were used to monitor the displacement (the direction and the location) of the PTFE patch at the center, where the output voltage signals in the 16 channels were recorded in real-time to form a mapping figure. The advantage of this design is that it only requires the bottom Al electrode to be grounded and the top PTFE patch needs no electrical contact, which is beneficial for energy harvesting in automobile rotation mode and touch pad applications.

  17. Local effects of redundant terrestrial and GPS-based tie vectors in ITRF-like combinations

    Science.gov (United States)

    Abbondanza, Claudio; Altamimi, Zuheir; Sarti, Pierguido; Negusini, Monia; Vittuari, Luca

    2009-11-01

    Tie vectors (TVs) between co-located space geodetic instruments are essential for combining terrestrial reference frames (TRFs) realised using different techniques. They provide relative positioning between instrumental reference points (RPs) which are part of a global geodetic network such as the international terrestrial reference frame (ITRF). This paper gathers the set of very long baseline interferometry (VLBI)-global positioning system (GPS) local ties performed at the observatory of Medicina (Northern Italy) during the years 2001-2006 and discusses some important aspects related to the usage of co-location ties in the combinations of TRFs. Two measurement approaches of local survey are considered here: a GPS-based approach and a classical approach based on terrestrial observations (i.e. angles, distances and height differences). The behaviour of terrestrial local ties, which routinely join combinations of space geodetic solutions, is compared to that of GPS-based local ties. In particular, we have performed and analysed different combinations of satellite laser ranging (SLR), VLBI and GPS long term solutions in order to (i) evaluate the local effects of the insertion of the series of TVs computed at Medicina, (ii) investigate the consistency of GPS-based TVs with respect to space geodetic solutions, (iii) discuss the effects of an imprecise alignment of TVs from a local to a global reference frame. Results of ITRF-like combinations show that terrestrial TVs originate the smallest residuals in all the three components. In most cases, GPS-based TVs fit space geodetic solutions very well, especially in the horizontal components (N, E). On the contrary, the estimation of the VLBI RP Up component through GPS technique appears to be awkward, since the corresponding post fit residuals are considerably larger. Besides, combination tests including multi-temporal TVs display local effects of residual redistribution, when compared to those solutions where Medicina TVs

  18. Brain-derived neurotrophic factor genes transfect rat bone marrow mesenchymal stem cells based on cationic polymer vector

    Institute of Scientific and Technical Information of China (English)

    Zunsheng Zhang; Kun Zan; Yonghai Liu; Xia Shen

    2009-01-01

    BACKGROUND: Gene therapy is an effective expression of genes within target cells after transferring exogenous target genes. Both vector selection and transfection method are important factors for gene transfection. An ideal gene vector is required for a high transfusion of target gene and an exact introduction of target gene into specific target cells so as to express gene products. OBJECTIVE: To study the expression of mRNA and protein after transfecting rat bone marrow mesenchymal stem cells (BMSCs) with brain-derived neurotrophic factor (BDNF) genes based on cationic polymer vector. DESIGN, TIME AND SETTING: A randomized, controlled in vitro study using gene engineering, performed at the Neurobiology Laboratory, Xuzhou Medical College between October 2007 and April 2008. MATERIALS: PcDNA3.1 BDNF was obtained from Youbiai Biotechnological Company, Beijing and cationic polymer vector used was the SofastTM gene transfection reagent that was made by Taiyangma Biotechnological Co., Ltd., Xiamen. METHODS: BMSCs extracted from six Sprague Dawley (SD) rats aged 1 month were isolated and cultured in vitro. Third passage BMSCs were inoculated on a 6-well culture plate at the density of 1×106 cells/L. At about 80% confluence, BMSCs were transfected with PcDNA3.1-BDNF (2 μg) combined with SofastTM gene transfection reagent (6 μg) (BDNF group) or with PcDNA3.1 (2 μg) combined with SofastTM gene transfection reagent (6 μg) (blank vector group). Cells that were not transfected with any reagents but still cultured under primary culture conditions were used as a non-transfection group.MAIN OUTCOME MEASURES: Enzyme linked immunosorbent assay was used to measure time efficiency of BMSC-secreted BDNF protein. Twenty-four hours after gene transfection, RT-PCR was used to detect expression of BDNF mRNA in the BMSCs. Immunohistochemistry was used to determine expression of BDNF protein in the BMSCs.RESULTS: BDNF protein expression was detected at day 1 after gene transfection

  19. A “Salt and Pepper” Noise Reduction Scheme for Digital Images Based on Support Vector Machines Classification and Regression

    Directory of Open Access Journals (Sweden)

    Hilario Gómez-Moreno

    2014-01-01

    Full Text Available We present a new impulse noise removal technique based on Support Vector Machines (SVM. Both classification and regression were used to reduce the “salt and pepper” noise found in digital images. Classification enables identification of noisy pixels, while regression provides a means to determine reconstruction values. The training vectors necessary for the SVM were generated synthetically in order to maintain control over quality and complexity. A modified median filter based on a previous noise detection stage and a regression-based filter are presented and compared to other well-known state-of-the-art noise reduction algorithms. The results show that the filters proposed achieved good results, outperforming other state-of-the-art algorithms for low and medium noise ratios, and were comparable for very highly corrupted images.

  20. Episomal maintenance of S/MAR-containing non-viral vectors for RPE-based diseases.

    Science.gov (United States)

    Koirala, Adarsha; Conley, Shannon M; Naash, Muna I

    2014-01-01

    The efficacy of non-viral genetic therapies has historically been limited by transient gene expression and vector loss. Scaffold matrix attachment regions (S/MARs) have been shown to augment transcription, promote episomal maintenance, and provide insulator-like function to DNA in in vitro and in vivo systems. Here we explore the ability of S/MAR elements to mediate these effects in retinal pigment epithelial (RPE) cells with the eventual goal of improving the persistence of expression of our non-viral gene delivery tools. We engineered an RPE-specific reporter vector with or without an S/MAR immediately downstream of the eGFP expression cassette. We show that the S/MAR vector is maintained as an episome for up to 1 year. Experiments in which rhodamine-labeled DNA was delivered to the subretinal space of mice show better persistence of the S/MAR-containing vector in the RPE than the non-S/MAR vector. These results suggest that inclusion of the S/MAR region promotes episomal maintenance of plasmid DNA in the RPE after subretinal delivery and that inclusion of this DNA element may be beneficial for non-viral ocular gene transfer.

  1. Estimating differential quantities from point cloud based on a linear fitting of normal vectors

    Institute of Scientific and Technical Information of China (English)

    CHENG ZhangLin; ZHANG XiaoPeng

    2009-01-01

    Estimation of differential geometric properties on a discrete surface Is a fundamental work in computer graphics and computer vision.In this paper,we present an accurate and robust method for estimating differential quantities from unorganized point cloud.The principal curvatures and principal directions at each point are computed with the help of partial derivatives of the unit normal vector at that point,where the normal derivatives are estimated by fitting a linear function to each component of the normal vectors in a neighborhood.This method takes into account the normal information of all neighboring points and computes curvatures directly from the varlation of unit normal vectors,which improves the accuracy and robustness of curvature estimation on irregular sampled noisy data.The main advantage of our approach is that the estimation of curvatures at a point does not rely on the accuracy of the normal vector at that point,and the normal vectors can he refined In the process of curvature estimation.Compared with the state of the art methods for estimating curvatures and Darboux frames on both synthetic and real point clouds,the approach is shown to be more accurate and robust for noisy and unorganized point cloud data.

  2. A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification

    Directory of Open Access Journals (Sweden)

    Wang Lily

    2008-07-01

    Full Text Available Abstract Background Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray technology with several molecular signatures on their way toward clinical deployment. Use of the most accurate classification algorithms available for microarray gene expression data is a critical ingredient in order to develop the best possible molecular signatures for patient care. As suggested by a large body of literature to date, support vector machines can be considered "best of class" algorithms for classification of such data. Recent work, however, suggests that random forest classifiers may outperform support vector machines in this domain. Results In the present paper we identify methodological biases of prior work comparing random forests and support vector machines and conduct a new rigorous evaluation of the two algorithms that corrects these limitations. Our experiments use 22 diagnostic and prognostic datasets and show that support vector machines outperform random forests, often by a large margin. Our data also underlines the importance of sound research design in benchmarking and comparison of bioinformatics algorithms. Conclusion We found that both on average and in the majority of microarray datasets, random forests are outperformed by support vector machines both in the settings when no gene selection is performed and when several popular gene selection methods are used.

  3. Support vector machine based classification and mapping of atherosclerotic plaques using fluorescence lifetime imaging (Conference Presentation)

    Science.gov (United States)

    Fatakdawala, Hussain; Gorpas, Dimitris S.; Bec, Julien; Ma, Dinglong M.; Yankelevich, Diego R.; Bishop, John W.; Marcu, Laura

    2016-02-01

    The progression of atherosclerosis in coronary vessels involves distinct pathological changes in the vessel wall. These changes manifest in the formation of a variety of plaque sub-types. The ability to detect and distinguish these plaques, especially thin-cap fibroatheromas (TCFA) may be relevant for guiding percutaneous coronary intervention as well as investigating new therapeutics. In this work we demonstrate the ability of fluorescence lifetime imaging (FLIm) derived parameters (lifetime values from sub-bands 390/40 nm, 452/45 nm and 542/50 nm respectively) for generating classification maps for identifying eight different atherosclerotic plaque sub-types in ex vivo human coronary vessels. The classification was performed using a support vector machine based classifier that was built from data gathered from sixteen coronary vessels in a previous study. This classifier was validated in the current study using an independent set of FLIm data acquired from four additional coronary vessels with a new rotational FLIm system. Classification maps were compared to co-registered histological data. Results show that the classification maps allow identification of the eight different plaque sub-types despite the fact that new data was gathered with a different FLIm system. Regions with diffuse intimal thickening (n=10), fibrotic tissue (n=2) and thick-cap fibroatheroma (n=1) were correctly identified on the classification map. The ability to identify different plaque types using FLIm data alone may serve as a powerful clinical and research tool for studying atherosclerosis in animal models as well as in humans.

  4. Wall parameters estimation based onsupport vector regression for through wall radar sensing

    Science.gov (United States)

    Chen, Xi; Chen, Weidong

    2015-12-01

    In through wall radar sensing, the wall parameters estimation (WPE) problem has been a topic that attracts a lot of attention since the wall parameters, i.e., the permittivity and the thickness, are of crucial importance to locate the targets and to produce a well-focused image, but they are usually unknown in practice. To solve this problem, in this paper, the support vector regression (SVR), a powerful tool for regression analysis, is introduced, and its performance on WPE, provided it is used it in the regular way, is investigated. Unfortunately, it is shown that the regular use of SVR cannot afford satisfactory estimation results since the sample data used in SVR, namely the received echoes from the walls, are seriously interfered with the echoes from the targets which are located near the walls. In view of this limitation, a novel SVR-based WPE approach that consists of three stages is proposed by this paper. In the first stage, three regression functions are trained by SVR, one of which will output the estimate of the permittivity in the second stage, and the others are designed to output two instrumental variables for estimating the thickness. In the third stage, the estimate of thickness will be achieved by minimizing a predefined cost function wherein the estimated permittivity and the outputted instrumental variables are involved. The better robustness and higher estimation accuracy of the proposed approach compared to the regular use of SVR are validated by the numerical experimental results using finite-difference time-domain simulations.

  5. DDoS detection based on wavelet kernel support vector machine

    Institute of Scientific and Technical Information of China (English)

    YANG Ming-hui; WANG Ru-chuan

    2008-01-01

    To enhance the detection accuracy and deduce false positive rate of distributed denial of service (DDoS) attack detection, a new machine learning method was proposed. With the analysis of support vector machine (SVM) and the wavelet kernel function theory, an admissive support vector kernel, which is a wavelet kernel constructed in this article, implements the combination of the wavelet technique with SVM. Then, wavelet support vector machine (WSVM) is applied to DDoS attack detections and as a classifying means to test the validity of the wavelet kernel function. Simulation experiments show that under the same conditions, the predictive ability of WSVM is improved and the computation burden is alleviated. The detection accuracy of WSVM is higher than the traditional SVM by about 4%, while its false positive is lower than the traditional SVM. Thus, for DDoS detections, WSVM shows better detection performance and is more adaptive to the changing network environment.

  6. A Support Vector Machine-Based Dynamic Network for Visual Speech Recognition Applications

    Directory of Open Access Journals (Sweden)

    Mihaela Gordan

    2002-11-01

    Full Text Available Visual speech recognition is an emerging research field. In this paper, we examine the suitability of support vector machines for visual speech recognition. Each word is modeled as a temporal sequence of visemes corresponding to the different phones realized. One support vector machine is trained to recognize each viseme and its output is converted to a posterior probability through a sigmoidal mapping. To model the temporal character of speech, the support vector machines are integrated as nodes into a Viterbi lattice. We test the performance of the proposed approach on a small visual speech recognition task, namely the recognition of the first four digits in English. The word recognition rate obtained is at the level of the previous best reported rates.

  7. A Heading and Flight-Path Angle Control of Aircraft Based on Required Acceleration Vector

    Science.gov (United States)

    Yoshitani, Naoharu

    This paper describes a control of heading and flight-path angles of aircraft to time-varying command angles. The controller first calculates an acceleration command vector (acV), which is vertical to the velocity vector. acV consists of two components; the one is feedforward acceleration obtained from the rates of command angles, and the other is feedback acceleration obtained from angle deviations by using PID control law. A bank angle command around the velocity vector and commands of pitch and yaw rates are then obtained to generate the required acceleration. A roll rate command is calculated from bank angle deviation. Roll, pitch and yaw rate commands are put into the attitude controller, which can be composed of any suitable control laws such as PID control. The control requires neither aerodynamic coefficients nor online calculation of the inverse dynamics of the aircraft. A numerical simulation illustrates the effects of the control.

  8. Implicit Real Vector Automata

    Directory of Open Access Journals (Sweden)

    Jean-François Degbomont

    2010-10-01

    Full Text Available This paper addresses the symbolic representation of non-convex real polyhedra, i.e., sets of real vectors satisfying arbitrary Boolean combinations of linear constraints. We develop an original data structure for representing such sets, based on an implicit and concise encoding of a known structure, the Real Vector Automaton. The resulting formalism provides a canonical representation of polyhedra, is closed under Boolean operators, and admits an efficient decision procedure for testing the membership of a vector.

  9. Lithium-ion battery remaining useful life prediction based on grey support vector machines

    Directory of Open Access Journals (Sweden)

    Xiaogang Li

    2015-12-01

    Full Text Available In this article, an improved grey prediction model is proposed to address low-accuracy prediction issue of grey forecasting model. The first step is using a trigonometric function to transform the original data sequence to smooth the data, which is called smoothness of grey prediction model, and then a grey support vector machine model by integrating the improved grey model with support vector machine is introduced. At the initial stage of the model, trigonometric functions and accumulation generation operation can be used to preprocess the data, which enhances the smoothness of the data and reduces the associated randomness. In addition, support vector machine is implemented to establish a prediction model for the pre-processed data and select the optimal model parameters via genetic algorithms. Finally, the data are restored through the ‘regressive generate’ operation to obtain the forecasting data. To prove that the grey support vector machine model is superior to the other models, the battery life data from the Center for Advanced Life Cycle Engineering are selected, and the presented model is used to predict the remaining useful life of the battery. The predicted result is compared to that of grey model and support vector machines. For a more intuitive comparison of the three models, this article quantifies the root mean square errors for these three different models in the case of different ratio of training samples and prediction samples. The results show that the effect of grey support vector machine model is optimal, and the corresponding root mean square error is only 3.18%.

  10. Sampling strategies based on singular vectors for assimilated models in ocean forecasting systems

    Science.gov (United States)

    Fattorini, Maria; Brandini, Carlo; Ortolani, Alberto

    2016-04-01

    Meteorological and oceanographic models do need observations, not only as a ground truth element to verify the quality of the models, but also to keep model forecast error acceptable: through data assimilation techniques which merge measured and modelled data, natural divergence of numerical solutions from reality can be reduced / controlled and a more reliable solution - called analysis - is computed. Although this concept is valid in general, its application, especially in oceanography, raises many problems due to three main reasons: the difficulties that have ocean models in reaching an acceptable state of equilibrium, the high measurements cost and the difficulties in realizing them. The performances of the data assimilation procedures depend on the particular observation networks in use, well beyond the background quality and the used assimilation method. In this study we will present some results concerning the great impact of the dataset configuration, in particular measurements position, on the evaluation of the overall forecasting reliability of an ocean model. The aim consists in identifying operational criteria to support the design of marine observation networks at regional scale. In order to identify the observation network able to minimize the forecast error, a methodology based on Singular Vectors Decomposition of the tangent linear model is proposed. Such a method can give strong indications on the local error dynamics. In addition, for the purpose of avoiding redundancy of information contained in the data, a minimal distance among data positions has been chosen on the base of a spatial correlation analysis of the hydrodynamic fields under investigation. This methodology has been applied for the choice of data positions starting from simplified models, like an ideal double-gyre model and a quasi-geostrophic one. Model configurations and data assimilation are based on available ROMS routines, where a variational assimilation algorithm (4D-var) is

  11. Protein subcellular localization prediction using multiple kernel learning based support vector machine.

    Science.gov (United States)

    Hasan, Md Al Mehedi; Ahmad, Shamim; Molla, Md Khademul Islam

    2017-03-28

    Predicting the subcellular locations of proteins can provide useful hints that reveal their functions, increase our understanding of the mechanisms of some diseases, and finally aid in the development of novel drugs. As the number of newly discovered proteins has been growing exponentially, which in turns, makes the subcellular localization prediction by purely laboratory tests prohibitively laborious and expensive. In this context, to tackle the challenges, computational methods are being developed as an alternative choice to aid biologists in selecting target proteins and designing related experiments. However, the success of protein subcellular localization prediction is still a complicated and challenging issue, particularly, when query proteins have multi-label characteristics, i.e., if they exist simultaneously in more than one subcellular location or if they move between two or more different subcellular locations. To date, to address this problem, several types of subcellular localization prediction methods with different levels of accuracy have been proposed. The support vector machine (SVM) has been employed to provide potential solutions to the protein subcellular localization prediction problem. However, the practicability of an SVM is affected by the challenges of selecting an appropriate kernel and selecting the parameters of the selected kernel. To address this difficulty, in this study, we aimed to develop an efficient multi-label protein subcellular localization prediction system, named as MKLoc, by introducing multiple kernel learning (MKL) based SVM. We evaluated MKLoc using a combined dataset containing 5447 single-localized proteins (originally published as part of the Höglund dataset) and 3056 multi-localized proteins (originally published as part of the DBMLoc set). Note that this dataset was used by Briesemeister et al. in their extensive comparison of multi-localization prediction systems. Finally, our experimental results indicate that

  12. Modification of a viral satellite DNA-based gene silencing vector and its application to leaf or flower color change in Petunia hybrida

    Institute of Scientific and Technical Information of China (English)

    TAO Xiaorong; QIAN Yajuan; ZHOU Xueping

    2006-01-01

    Virus-induced gene silencing offers a powerful reverse-genetic tool for the study of gene function in plants. We have previously reported effective gene silencing of plant genes using a viral satellite DNA associated with Tomato yellow leaf curl China virus (TYLCCNV). In this study, we further modified the viral satellite DNA-based vector. The modified vector can induce sulfu (Su) gene silencing as effective as the original vector in Nicotiana benthamiana plants, but the new system simplifies procedures for construction of vector derivative. Furthermore, a fragment of petunia Su or chalcone synthase (CHS) endogenous gene was inserted into the modified vector. When petunia plants were agro- inoculated with the modified vector carrying a Su or CHS gene, the Su silenced plants started to appear yellowing in veins of systemically infected upper leaves two weeks after agroinoculation, while the CHS silenced plants started to show flower color change one month after agroinoculation and later single-color flowers became mosaic.

  13. Increased oxygen load in the prefrontal cortex from mouth breathing: a vector-based near-infrared spectroscopy study.

    Science.gov (United States)

    Sano, Masahiro; Sano, Sayaka; Oka, Noriyuki; Yoshino, Kayoko; Kato, Toshinori

    2013-12-01

    Individuals who habitually breathe through the mouth are more likely than nasal breathers to have sleep disorders and attention deficit hyperactive disorder. We hypothesized that brain hemodynamic responses in the prefrontal cortex might be different for mouth and nasal breathing. To test this hypothesis, we measured changes in oxyhemoglobin and deoxyhemoglobin in the prefrontal cortex during mouth breathing and nasal breathing in healthy adults (n=9) using vector-based near-infrared spectroscopy. The angle k, calculated from changes in oxyhemoglobin and deoxyhemoglobin and indicating the degree of oxygen exchange, was significantly higher during mouth breathing (PMouth breathing also caused a significant increase in deoxyhemoglobin, but oxyhemoglobin did not increase. This difference in oxygen load in the brain arising from different breathing routes can be evaluated quantitatively using vector-based near-infrared spectroscopy. Phase responses could help to provide an earlier and more reliable diagnosis of a patient's habitual breathing route than a patient interview.

  14. Two-dimensional direction finding for low altitude target based on intensity measurement using an acoustic vector-sensor

    Institute of Scientific and Technical Information of China (English)

    CHEN Huawei; ZHAO Junwei

    2004-01-01

    A method of two-dimensional direction of arrival (DOA) estimation for low altitude target, which is based on intensity measurement using a three-dimensional differential pressure acoustic vector-sensor, is presented. With the perfect characteristics of acoustic vector sensor in the low frequency band, accurate DOA estimation is achieved under small array size. The validity of the proposed method was assessed by experiments on the noise signals radiated by a helicopter. The influence of acoustic sensor size, integral time and signal to noise ratio to the accuracy of DOA estimation were investigated, respectively. The performance comparisons demonstrated that it outperformed the traditional time-delay measurement based method for a small acoustic array.

  15. Multiple sources and multiple measures based traffic flow prediction using the chaos theory and support vector regression method

    Science.gov (United States)

    Cheng, Anyu; Jiang, Xiao; Li, Yongfu; Zhang, Chao; Zhu, Hao

    2017-01-01

    This study proposes a multiple sources and multiple measures based traffic flow prediction algorithm using the chaos theory and support vector regression method. In particular, first, the chaotic characteristics of traffic flow associated with the speed, occupancy, and flow are identified using the maximum Lyapunov exponent. Then, the phase space of multiple measures chaotic time series are reconstructed based on the phase space reconstruction theory and fused into a same multi-dimensional phase space using the Bayesian estimation theory. In addition, the support vector regression (SVR) model is designed to predict the traffic flow. Numerical experiments are performed using the data from multiple sources. The results show that, compared with the single measure, the proposed method has better performance for the short-term traffic flow prediction in terms of the accuracy and timeliness.

  16. An efficient nonviral gene-delivery vector based on hyperbranched cationic glycogen derivatives

    Directory of Open Access Journals (Sweden)

    Liang X

    2014-01-01

    Full Text Available Xuan Liang,1,* Xianyue Ren,2,* Zhenzhen Liu,1 Yingliang Liu,1 Jue Wang,2 Jingnan Wang,2 Li-Ming Zhang,1 David YB Deng,2 Daping Quan,1 Liqun Yang1 1Institute of Polymer Science, School of Chemistry and Chemical Engineering, Key Laboratory of Designed Synthesis and Application of Polymer Material, Key Laboratory for Polymeric Composite and Functional Materials of Ministry of Education, Sun Yat-Sen University, Guangzhou, People's Republic of China; 2Research Center of Translational Medicine, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China *Both these authors contributed equally to this work Background: The purpose of this study was to synthesize and evaluate hyperbranched cationic glycogen derivatives as an efficient nonviral gene-delivery vector. Methods: A series of hyperbranched cationic glycogen derivatives conjugated with 3-(dimethylamino-1-propylamine (DMAPA-Glyp and 1-(2-aminoethyl piperazine (AEPZ-Glyp residues were synthesized and characterized by Fourier-transform infrared and hydrogen-1 nuclear magnetic resonance spectroscopy. Their buffer capacity was assessed by acid–base titration in aqueous NaCl solution. Plasmid deoxyribonucleic acid (pDNA condensation ability and protection against DNase I degradation of the glycogen derivatives were assessed using agarose gel electrophoresis. The zeta potentials and particle sizes of the glycogen derivative/pDNA complexes were measured, and the images of the complexes were observed using atomic force microscopy. Blood compatibility and cytotoxicity were evaluated by hemolysis assay and MTT (3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide assay, respectively. pDNA transfection efficiency mediated by the cationic glycogen derivatives was evaluated by flow cytometry and fluorescence microscopy in the 293T (human embryonic kidney and the CNE2 (human nasopharyngeal carcinoma cell lines. In vivo delivery of pDNA in model animals (Sprague Dawley

  17. Elementary vectors

    CERN Document Server

    Wolstenholme, E Œ

    1978-01-01

    Elementary Vectors, Third Edition serves as an introductory course in vector analysis and is intended to present the theoretical and application aspects of vectors. The book covers topics that rigorously explain and provide definitions, principles, equations, and methods in vector analysis. Applications of vector methods to simple kinematical and dynamical problems; central forces and orbits; and solutions to geometrical problems are discussed as well. This edition of the text also provides an appendix, intended for students, which the author hopes to bridge the gap between theory and appl

  18. Support vector machine model for diagnosing pneumoconiosis based on wavelet texture features of digital chest radiographs.

    Science.gov (United States)

    Zhu, Biyun; Chen, Hui; Chen, Budong; Xu, Yan; Zhang, Kuan

    2014-02-01

    This study aims to explore the classification ability of decision trees (DTs) and support vector machines (SVMs) to discriminate between the digital chest radiographs (DRs) of pneumoconiosis patients and control subjects. Twenty-eight wavelet-based energy texture features were calculated at the lung fields on DRs of 85 healthy controls and 40 patients with stage I and stage II pneumoconiosis. DTs with algorithm C5.0 and SVMs with four different kernels were trained by samples with two combinations of the texture features to classify a DR as of a healthy subject or of a patient with pneumoconiosis. All of the models were developed with fivefold cross-validation, and the final performances of each model were compared by the area under receiver operating characteristic (ROC) curve. For both SVM (with a radial basis function kernel) and DT (with algorithm C5.0), areas under ROC curves (AUCs) were 0.94 ± 0.02 and 0.86 ± 0.04 (P = 0.02) when using the full feature set and 0.95 ± 0.02 and 0.88 ± 0.04 (P = 0.05) when using the selected feature set, respectively. When built on the selected texture features, the SVM with a polynomial kernel showed a higher diagnostic performance with an AUC value of 0.97 ± 0.02 than SVMs with a linear kernel, a radial basis function kernel and a sigmoid kernel with AUC values of 0.96 ± 0.02 (P = 0.37), 0.95 ± 0.02 (P = 0.24), and 0.90 ± 0.03 (P = 0.01), respectively. The SVM model with a polynomial kernel built on the selected feature set showed the highest diagnostic performance among all tested models when using either all the wavelet texture features or the selected ones. The model has a good potential in diagnosing pneumoconiosis based on digital chest radiographs.

  19. Support Vector Machine Based Red Palm Weevil (Rynchophorus Ferrugineous, Olivier Recognition System

    Directory of Open Access Journals (Sweden)

    Ghulam M. Hassan

    2012-01-01

    Full Text Available Problem statement: Red palm weevil (Rynchophorus Ferrugineous, Oliveir is an insect which threatens the existence of palm trees. The proposed research is to develop a RPW identification system using Support Vector Machine method. The problem is to extract image features from an image and using SVM to find out the existence of RPW in an image. Approach: Images are snapped and image processing techniques of Regional Properties and Zernike Moments are used to extract different features of an image. The obtained features are fed into the SVM based system individually as well as in combination. The database used to train and test the system includes 326 RPW and 93 other insect images. The input data from database is selected randomly and fed into the system in three steps i.e., 25, 50 and 75% while remaining database is used for testing purpose. In SVM, polynomial kernel function and Radial Basis Function are used for training. Each experiment is repeated 10 times and the average results are used for analysis. Results: The optimal results are obtained by using Radial Basis Function in SVM at lower values of sigma σ while Polynomial kernel function is not successful in returning adequate results. Further detailed analysis of results for σ value of 10 and 15 revealed that proposed system works well with large training data and with inputs obtained by Regional Properties. The optimal value of σ for proposed system is found to be 10 when training data ratio is 50%. The training time for proposed system depends on size of database and is found to be 0.025 sec per image while time consumed by proposed system for identification of RPW in an image is found to be 15 milli sec. The proposed systems success in identification of RPW and other insect is found to be 97 and 93% respectively. Conclusion: It is concluded that SVM based system using Radial Basis Function having σ value of 10 is optimal in identifying RPW from an image. The optimal input

  20. Peptide-Mediated Tumor Targeting by a Degradable Nano Gene Delivery Vector Based on Pluronic-Modified Polyethylenimine

    Science.gov (United States)

    Wu, Zhaoyong; Zhan, Shuyu; Fan, Wei; Ding, Xueying; Wu, Xin; Zhang, Wei; Fu, Yinghua; Huang, Yueyan; Huang, Xuan; Chen, Rubing; Li, Mingjuan; Xu, Ningyin; Zheng, Yongxia; Ding, Baoyue

    2016-03-01

    Polyethylenimine (PEI) is considered to be a promising non-viral gene delivery vector. To solve the toxicity versus efficacy and tumor-targeting challenges of PEI used as gene delivery vector, we constructed a novel non-viral vector DR5-TAT-modified Pluronic-PEI (Pluronic-PEI-DR5-TAT), which was based on the attachment of low-molecular-weight polyethylenimine (LMW-PEI) to the amphiphilic polymer Pluronic to prepare Pluronic-modified LMW-PEI (Pluronic-PEI). This was then conjugated to a multifunctional peptide containing a cell-penetrating peptide (TAT) and a synthetic peptide that would bind to DR5—a receptor that is overexpressed in cancer cells. The vector showed controlled degradation, favorable DNA condensation and protection performance. The Pluronic-PEI-DR5-TAT/DNA complexes at an N/P ratio of 15:1 were spherical nanoparticles of 122 ± 11.6 nm and a zeta potential of about 22 ± 2.8 mV. In vitro biological characterization results indicated that Pluronic-PEI-DR5-TAT/DNA complexes had a higher specificity for the DR5 receptor and were taken up more efficiently by tumor cells than normal cells, compared to complexes formed with PEI 25 kDa or Pluronic-PEI. Thus, the novel complexes showed much lower cytotoxicity to normal cells and higher gene transfection efficiency in tumor cells than that exhibited by PEI 25 kDa and Pluronic-PEI. In summary, our novel, degradable non-viral tumor-targeting vector is a promising candidate for use in gene therapy.

  1. Simulation and Prediction of Alkalinity in Sintering Process Based on Grey Least Squares Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    SONG Qiang; WANG Ai-min

    2009-01-01

    The prediction of the alkalinity is difficult during the sintering process. Whether or not the level of the alkalinity of sintering process is successful is directly related to the quality of sinter. There is no very good method for predicting the alkalinity by now owing to the high complexity, high nonlinearity, strong coupling, high time delay, and etc. Therefore, a new technique, the grey squares support machine, was introduced. The grey support vector machine model of the alkalinity enabled the development of new equation and algorithm to predict the alkalinity. During modelling, the fluctuation of data sequence was weakened by the grey theory and the support vector machine was capable of processing nonlinear adaptable information, and the grey support vector machine has a combination of those advantages. The results revealed that the alkalinity of sinter could be accurately predicted using this model by reference to small sample and information. The experimental results showed that the grey support vector machine model was effective and practical owing to the advantages of high precision, less samples required, and simple calculation.

  2. Alcohols' Classification by Infrared Spectra Segment Based on Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    Wei XIE; Fu Sheng NIE; Meng Long LI; Guang Ming LI; Min Chun LU

    2006-01-01

    This paper studies various classifiers to identify primary, secondary or tertiary alcohols by using segmental spectra and their combinations to support vector machines (SVMs). The results showed that the O-H in-plane bending absorption contributed most to identification their substitute. This conclusion disagrees with related known research results.

  3. Towards a resource-based habitat approach for spatial modelling of vector-borne disease risks

    NARCIS (Netherlands)

    Hartemink, N.; Vanwambeke, S.O.; Purse, B.V.; Gilbert, M.; Van Dyck, H.

    2015-01-01

    Given the veterinary and public health impact of vector-borne diseases, there is a clear need to assess the suitability of landscapes for the emergence and spread of these diseases. Current approaches for predicting disease risks neglect key features of the landscape as components of the functional

  4. Neighboring block based disparity vector derivation for multiview compatible 3D-AVC

    Science.gov (United States)

    Kang, Jewon; Chen, Ying; Zhang, Li; Zhao, Xin; Karczewicz, Marta

    2013-09-01

    3D-AVC being developed under Joint Collaborative Team on 3D Video Coding (JCT-3V) significantly outperforms the Multiview Video Coding plus Depth (MVC+D) which simultaneously encodes texture views and depth views with the multiview extension of H.264/AVC (MVC). However, when the 3D-AVC is configured to support multiview compatibility in which texture views are decoded without depth information, the coding performance becomes significantly degraded. The reason is that advanced coding tools incorporated into the 3D-AVC do not perform well due to the lack of a disparity vector converted from the depth information. In this paper, we propose a disparity vector derivation method utilizing only the information of texture views. Motion information of neighboring blocks is used to determine a disparity vector for a macroblock, so that the derived disparity vector is efficiently used for the coding tools in 3D-AVC. The proposed method significantly improves a coding gain of the 3D-AVC in the multiview compatible mode about 20% BD-rate saving in the coded views and 26% BD-rate saving in the synthesized views on average.

  5. Inhibition of avian leukosis virus replication by vector-based RNA interference

    Science.gov (United States)

    RNAi has recently emerged as a promising antiviral technique in vertebrates. To date, most studies have used exogenous short interfering RNAs (siRNAs) to inhibit viral replication, though vectors expressing short hairpin RNAs (shRNA-mirs) in the context of a modified endogenous micro-RNA (miRNA) are...

  6. Permanent Magnet Flux Online Estimation Based on Zero-Voltage Vector Injection Method

    DEFF Research Database (Denmark)

    Xie, Ge; Lu, Kaiyuan; Kumar, Dwivedi Sanjeet;

    2015-01-01

    In this paper, a simple signal injection method is proposed for sensorless control of PMSM at low speed, which ideally requires one voltage vector only for position estimation. The proposed method is easy to implement resulting in low computation burden. No filters are needed for extracting the h...

  7. Space vector-based modeling and control of a modular multilevel converter in HVDC applications

    DEFF Research Database (Denmark)

    Bonavoglia, M.; Casadei, G.; Zarri, L.;

    2013-01-01

    Modular multilevel converter (MMC) is an emerging multilevel topology for high-voltage applications that has been developed in recent years. In this paper, the modeling and the control of MMCs are restated in terms of space vectors, which may allow a deeper understanding of the converter behavior...

  8. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods

    OpenAIRE

    Zhang, Tong

    2001-01-01

    This book is an introduction to support vector machines and related kernel methods in supervised learning, whose task is to estimate an input-output functional relationship from a training set of examples. A learning problem is referred to as classification if its output take discrete values in a set of possible categories and regression if it has continuous real-valued output.

  9. Efficient Transient Expression of Recombinant Proteins in Plants by the Novel pEff Vector Based on the Genome of Potato Virus X

    Science.gov (United States)

    Mardanova, Eugenia S.; Blokhina, Elena A.; Tsybalova, Liudmila M.; Peyret, Hadrien; Lomonossoff, George P.; Ravin, Nikolai V.

    2017-01-01

    Agroinfiltration of plant leaves with binary vectors carrying a gene of interest within a plant viral vector is a rapid and efficient method for protein production in plants. Previously, we constructed a self-replicating vector, pA7248AMV, based on the genetic elements of potato virus X (PVX), and have shown that this vector can be used for the expression of recombinant proteins in Nicotiana benthamiana. However, this vector is almost 18 kb long and therefore not convenient for genetic manipulation. Furthermore, for efficient expression of the target protein it should be co-agroinfiltrated with an additional binary vector expressing a suppressor of post-transcriptional gene silencing. Here, we improved this expression system by creating the novel pEff vector. Its backbone is about 5 kb shorter than the original vector and it contains an expression cassette for the silencing suppressor, P24, from grapevine leafroll-associated virus-2 alongside PVX genetic elements, thus eliminating the need of co-agroinfiltration. The pEff vector provides green fluorescent protein expression levels of up to 30% of total soluble protein. The novel vector was used for expression of the influenza vaccine candidate, M2eHBc, consisting of an extracellular domain of influenza virus M2 protein (M2e) fused to hepatitis B core antigen. Using the pEff system, M2eHBc was expressed to 5–10% of total soluble protein, several times higher than with original pA7248AMV vector. Plant-produced M2eHBc formed virus-like particles in vivo, as required for its use as a vaccine. The new self-replicating pEff vector could be used for fast and efficient production of various recombinant proteins in plants. PMID:28293244

  10. Characterization of the immune responses elicited by baculovirus-based vector vaccines against influenza virus hemagglutinin

    Institute of Scientific and Technical Information of China (English)

    Zhi-peng HU; Juan YIN; Yuan-yuan ZHANG; Shu-ya JIA; Zuo-jia CHEN; Jiang ZHONG

    2012-01-01

    Aim:To compare the specific immune responses elicited by different baculovirus vectors in immunized mice.Methods:We constructed and characterized two recombinant baculoviruses carrying the expression cassette for the H5N1 influenza virus hemagglutinin (HA) gene driven by either an insect cell promoter (vAc-HA) or a dual-promoter active both in insect and mammalian cells (vAc-HA-DUAL).Virus without the HA gene (vAc-EGFP) was used as a control.These viruses were used to immunize mice subcutaneously and intraperitoneally.The production of total and specific antibodies was determined by ELISA and competitive ELISA.Cytokine production by the spleen cells of immunized mice was studied using the ELISPOT assay.Results:Both the vAc-HA and vAc-HA-DUAL vectors expressed HA proteins in insect Sf9 cells,and HA antigen was present in progeny virions.The vAc-HA-DUAL vector also mediated HA expression in virus-transduced mammalian cell lines (BHK and A547).Both vAo-HA and vAc-HA-DUAL exhibited higher transduction efficiencies than vAc-EGFP in mammalian cells,as shown by the expression of the reporter gene egfp.Additionally,both vAc-HA and vAc-HA-DUAL induced high levels of HA-specific antibody production in immunized mice; vAc-HA-DUAL was more efficient in inducing IFN-Y and IL-2 upon stimulation with specific antigen,whereas vAc-HA was more efficient in inducing IL-4 and IL-6.Conclusion:Baculovirus vectors elicited efficient,specific immune responses in immunized mice.The vector displaying the HA antigen on the virion surface (vAc-HA) elicited a Th2-biased immune response,whereas the vector displaying HA and mediating HA expression in the cell (vAc-HA-DUAL) elicited a Th1-biased immune response.

  11. The Weighted Support Vector Machine Based on Hybrid Swarm Intelligence Optimization for Icing Prediction of Transmission Line

    Directory of Open Access Journals (Sweden)

    Xiaomin Xu

    2015-01-01

    Full Text Available Not only can the icing coat on transmission line cause the electrical fault of gap discharge and icing flashover but also it will lead to the mechanical failure of tower, conductor, insulators, and others. It will bring great harm to the people’s daily life and work. Thus, accurate prediction of ice thickness has important significance for power department to control the ice disaster effectively. Based on the analysis of standard support vector machine, this paper presents a weighted support vector machine regression model based on the similarity (WSVR. According to the different importance of samples, this paper introduces the weighted support vector machine and optimizes its parameters by hybrid swarm intelligence optimization algorithm with the particle swarm and ant colony (PSO-ACO, which improves the generalization ability of the model. In the case study, the actual data of ice thickness and climate in a certain area of Hunan province have been used to predict the icing thickness of the area, which verifies the validity and applicability of this proposed method. The predicted results show that the intelligent model proposed in this paper has higher precision and stronger generalization ability.

  12. CW-THz vector spectroscopy and imaging system based on 1.55-µm fiber-optics.

    Science.gov (United States)

    Kim, Jae-Young; Song, Ho-Jin; Yaita, Makoto; Hirata, Akihiko; Ajito, Katsuhiro

    2014-01-27

    We present a continuous-wave terahertz (THz) vector spectroscopy and imaging system based on a 1.5-µm fiber optic uni-traveling-carrier photodiode and InGaAs photo-conductive receiver. Using electro-optic (EO) phase modulators for THz phase control with shortened optical paths, the system achieves fast vector measurement with effective phase stabilization. Dynamic ranges of 100 dB · Hz and 75 dB · Hz at 300 GHz and 1 THz, and phase stability of 1.5° per minute are obtained. With the simultaneous measurement of absorbance and relative permittivity, we demonstrate non-destructive analyses of pharmaceutical cocrystals inside tablets within a few minutes.

  13. A Novel Method for Mechanical Fault Diagnosis Based on Variational Mode Decomposition and Multikernel Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Zhongliang Lv

    2016-01-01

    Full Text Available A novel fault diagnosis method based on variational mode decomposition (VMD and multikernel support vector machine (MKSVM optimized by Immune Genetic Algorithm (IGA is proposed to accurately and adaptively diagnose mechanical faults. First, mechanical fault vibration signals are decomposed into multiple Intrinsic Mode Functions (IMFs by VMD. Then the features in time-frequency domain are extracted from IMFs to construct the feature sets of mixed domain. Next, Semisupervised Locally Linear Embedding (SS-LLE is adopted for fusion and dimension reduction. The feature sets with reduced dimension are inputted to the IGA optimized MKSVM for failure mode identification. Theoretical analysis demonstrates that MKSVM can approximate any multivariable function. The global optimal parameter vector of MKSVM can be rapidly identified by IGA parameter optimization. The experiments of mechanical faults show that, compared to traditional fault diagnosis models, the proposed method significantly increases the diagnosis accuracy of mechanical faults and enhances the generalization of its application.

  14. Mechanical Fault Diagnosis for HV Circuit Breakers Based on Ensemble Empirical Mode Decomposition Energy Entropy and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Jianfeng Zhang

    2015-01-01

    Full Text Available During the operation process of the high voltage circuit breaker, the changes of vibration signals can reflect the machinery states of the circuit breaker. The extraction of the vibration signal feature will directly influence the accuracy and practicability of fault diagnosis. This paper presents an extraction method based on ensemble empirical mode decomposition (EEMD. Firstly, the original vibration signals are decomposed into a finite number of stationary intrinsic mode functions (IMFs. Secondly, calculating the envelope of each IMF and separating the envelope by equal-time segment and then forming equal-time segment energy entropy to reflect the change of vibration signal are performed. At last, the energy entropies could serve as input vectors of support vector machine (SVM to identify the working state and fault pattern of the circuit breaker. Practical examples show that this diagnosis approach can identify effectively fault patterns of HV circuit breaker.

  15. Learning vector quantization neural network–based model reference adaptive control method for intelligent lower-limb prosthesis

    Directory of Open Access Journals (Sweden)

    Jia-Qiang Yang

    2016-04-01

    Full Text Available This article focuses on the design of a control system for intelligent prostheses. Learning vector quantization neural network–based model reference adaptive control method is employed to implement real-time trajectory tracking and damp torque control of intelligent lower-limb prosthesis. The method is then analyzed and proposed. A model reference control system is first built with two learning vector quantization neural networks. One neural network is used for output prediction, and the other is used for input control. The angle information of the prosthetic knee joint is utilized to train these two neural networks with the given learning algorithm. The testing results of different movement patterns verify the effectiveness of the proposed method and its suitability for intelligent lower-limb prostheses.

  16. Nonlinear multifunctional sensor signal reconstruction based on least squares support vector machines and total least squares algorithm

    Institute of Scientific and Technical Information of China (English)

    Xin LIU; Guo WEI; Jin-wei SUN; Dan LIU

    2009-01-01

    Least squares support vector machines (LS-SVMs) are modified support vector machines (SVMs) that involve equality constraints and work with a least squares cost function, which simplifies the optimization procedure. In this paper, a novel training algorithm based on total least squares (TLS) for an LS-SVM is presented and applied to muhifunctional sensor signal reconstruction. For three different nonlinearities of a multi functional sensor model, the reconstruction accuracies of input signals are 0.001 36%, 0.03184% and 0.504 80%, respectively. The experimental results demonstrate the higher reliability and accuracy of the proposed method for multi functional sensor signal reconstruction than the original LS-SVM training algorithm, and verify the feasibility and stability of the proposed method.

  17. Identification of species based on DNA barcode using k-mer feature vector and Random forest classifier.

    Science.gov (United States)

    Meher, Prabina Kumar; Sahu, Tanmaya Kumar; Rao, A R

    2016-11-05

    DNA barcoding is a molecular diagnostic method that allows automated and accurate identification of species based on a short and standardized fragment of DNA. To this end, an attempt has been made in this study to develop a computational approach for identifying the species by comparing its barcode with the barcode sequence of known species present in the reference library. Each barcode sequence was first mapped onto a numeric feature vector based on k-mer frequencies and then Random forest methodology was employed on the transformed dataset for species identification. The proposed approach outperformed similarity-based, tree-based, diagnostic-based approaches and found comparable with existing supervised learning based approaches in terms of species identification success rate, while compared using real and simulated datasets. Based on the proposed approach, an online web interface SPIDBAR has also been developed and made freely available at http://cabgrid.res.in:8080/spidbar/ for species identification by the taxonomists.

  18. Assessing the tobacco-rattle-virus-based vectors system as an efficient gene silencing technique in Datura stramonium (Solanaceae).

    Science.gov (United States)

    Eftekhariyan Ghamsari, Mohammad Reza; Karimi, Farah; Mousavi Gargari, Seyed Latif; Hosseini Tafreshi, Seyed Ali; Salami, Seyed Alireza

    2014-12-01

    Datura stramonium is a well-known medicinal plant, which is important for its alkaloids. There are intrinsic limitations for the natural production of alkaloids in plants; metabolic engineering methods can be effectively used to conquer these limitations. In order for this the genes involved in corresponding pathways need to be studied. Virus-Induced Gene Silencing is known as a functional genomics technique to knock-down expression of endogenous genes. In this study, we silenced phytoene desaturase as a marker gene in D. stramonium in a heterologous and homologous manner by tobacco-rattle-virus-based VIGS vectors. Recombinant TRV vector containing pds gene from D. stramonium (pTRV2-Dspds) was constructed and injected into seedlings. The plants injected with pTRV2-Dspds showed photobleaching 2 weeks after infiltration. Spectrophotometric analysis demonstrated that the amount of chlorophylls and carotenoids in leaves of the bleached plants decreased considerably compared to that of the control plants. Semi-Quantitative RT-PCR results also confirmed that the expression of pds gene in the silenced plants was significantly reduced in comparison with the control plants. The results showed that the viral vector was able to influence the levels of total alkaloid content in D. stramonium. Our results illustrated that TRV-based VIGS vectors are able to induce effective and reliable functional gene silencing in D. stramonium as an alternative tool for studying the genes of interest in this plant, such as the targeted genes in tropane alkaloid biosynthetic pathway. The present work is the first report of establishing VIGS as an efficient method for transient silencing of any gene of interest in D. stramonium.

  19. Curve/surface representation and evolution using vector level sets with application to the shape-based segmentation problem.

    Science.gov (United States)

    Abd El Munim, Hossam E; Farag, Aly A

    2007-06-01

    In this paper, we revisit the implicit front representation and evolution using the vector level set function (VLSF) proposed in [1]. Unlike conventional scalar level sets, this function is designed to have a vector form. The distance from any point to the nearest point on the front has components (projections) in the coordinate directions included in the vector function. This kind of representation is used to evolve closed planar curves and 3D surfaces as well. Maintaining the VLSF property as the distance projections through evolution will be considered together with a detailed derivation of the vector partial differential equation (PDE) for such evolution. A shape-based segmentation framework will be demonstrated as an application of the given implicit representation. The proposed level set function system will be used to represent shapes to give a dissimilarity measure in a variational object registration process. This kind of formulation permits us to better control the process of shape registration, which is an important part in the shape-based segmentation framework. The method depends on a set of training shapes used to build a parametric shape model. The color is taken into consideration besides the shape prior information. The shape model is fitted to the image volume by registration through an energy minimization problem. The approach overcomes the conventional methods problems like point correspondences and weighing coefficients tuning of the evolution (PDEs). It is also suitable for multidimensional data and computationally efficient. Results in 2D and 3D of real and synthetic data will demonstrate the efficiency of the framework.

  20. Support vector machine classification trees based on fuzzy entropy of classification.

    Science.gov (United States)

    de Boves Harrington, Peter

    2017-02-15

    The support vector machine (SVM) is a powerful classifier that has recently been implemented in a classification tree (SVMTreeG). This classifier partitioned the data by finding gaps in the data space. For large and complex datasets, there may be no gaps in the data space confounding this type of classifier. A novel algorithm was devised that uses fuzzy entropy to find optimal partitions for situations when clusters of data are overlapped in the data space. Also, a kernel version of the fuzzy entropy algorithm was devised. A fast support vector machine implementation is used that has no cost C or slack variables to optimize. Statistical comparisons using bootstrapped Latin partitions among the tree classifiers were made using a synthetic XOR data set and validated with ten prediction sets comprised of 50,000 objects and a data set of NMR spectra obtained from 12 tea sample extracts.

  1. Exploring the limits of vector construction based on Citrus tristeza virus.

    Science.gov (United States)

    El-Mohtar, Choaa; Dawson, William O

    2014-01-05

    We examined the limits of manipulation of the Citrus tristeza virus (CTV) genome for expressing foreign genes in plants. We previously created a vector with a foreign gene cassette inserted between the major and minor coat protein genes, which is position 6 from the 3' terminus. Yet, this virus has 10 3'-genes with several other potential locations for expression of foreign genes. Since genes positioned closer to the 3' terminus tend to be expressed in greater amounts, there were opportunities for producing greater amounts of foreign protein. We found that the virus tolerated insertions of an extra gene in most positions within the 3' region of the genome with substantially increased levels of gene product produced throughout citrus trees. CTV was amazingly tolerant to manipulation resulting in a suite of stable transient expression vectors, each with advantages for specific uses and sizes of foreign genes in citrus trees.

  2. Potential Distribution of Chagas Disease Vectors (Hemiptera, Reduviidae, Triatominae) in Colombia, Based on Ecological Niche Modeling

    Science.gov (United States)

    Suárez-Escudero, Laura C.; González-Caro, Sebastián

    2016-01-01

    Ecological niche modeling of Triatominae bugs allow us to establish the local risk of transmission of the parasite Trypanosoma cruzi, which causes Chagas disease. This information could help to guide health authority recommendations on infection monitoring, prevention, and control. In this study, we estimated the geographic distribution of triatomine species in Colombia and identified the relationship between landscape structure and climatic factors influencing their occurrence. A total of 2451 records of 4 triatomine species (Panstrongylus geniculatus, Rhodnius pallescens, R. prolixus, and Triatoma maculata) were analyzed. The variables that provided more information to explain the ecologic niche of these vectors were related to precipitation, altitude, and temperature. We found that the species with the broadest potential geographic distribution were P. geniculatus, R. pallescens, and R. prolixus. In general, the models predicted the highest occurrence probability of these vectors in the eastern slope of the Eastern Cordillera, the southern region of the Magdalena valley, and the Sierra Nevada of Santa Marta. PMID:28115946

  3. A Hashing-Based Search Algorithm for Coding Digital Images by Vector Quantization

    Science.gov (United States)

    Chu, Chen-Chau

    1989-11-01

    This paper describes a fast algorithm to compress digital images by vector quantization. Vector quantization relies heavily on searching to build codebooks and to classify blocks of pixels into code indices. The proposed algorithm uses hashing, localized search, and multi-stage search to accelerate the searching process. The average of pixel values in a block is used as the feature for hashing and intermediate screening. Experimental results using monochrome images are presented. This algorithm compares favorably with other methods with regard to processing time, and has comparable or better mean square error measurements than some of them. The major advantages of the proposed algorithm are its speed, good quality of the reconstructed images, and flexibility.

  4. Constrained Run-to-Run Optimization for Batch Process Based on Support Vector Regression Model

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    An iterative (run-to-run) optimization method was presented for batch processes under input constraints. Generally it is very difficult to acquire an accurate mechanistic model for a batch process. Because support vector machine is powerful for the problems characterized by small samples, nonlinearity, high dimension and local minima, support vector regression models were developed for the end-point optimization of batch processes. Since there is no analytical way to find the optimal trajectory, an iterative method was used to exploit the repetitive nature of batch processes to determine the optimal operating policy. The optimization algorithm is proved convergent. The numerical simulation shows that the method can improve the process performance through iterations.

  5. Direct Vector Control of Induction Motor Based on Sinusoidal PWM Inverter with Fuzzy Logic Controller

    Directory of Open Access Journals (Sweden)

    Nirban Chakraborty

    2014-04-01

    Full Text Available This paper presents the speed control scheme of direct vector control of Induction Motor drive (IM drive. The Fuzzy logic controller is (FLC used as the controller part here for the direct vector control of Induction Motor using Sinusoidal PWM Inverter (SPWM. Fuzzy logic controller has become a very popular controlling scheme in the field of Industrial application. The entire module of this IM is divided into several parts such as IM body module, Inverter module, coordinate transformation module and Sinusoidal pulse width modulation (SPWM production module and so on. With the help of this module we can analyze a variety of different simulation waveforms, which provide an effective means for the analysis and design of the IM control system using FLC technique.

  6. Accelerating Relevance-Vector-Machine-Based Classification of Hyperspectral Image with Parallel Computing

    Directory of Open Access Journals (Sweden)

    Chao Dong

    2012-01-01

    Full Text Available Benefiting from the kernel skill and the sparse property, the relevance vector machine (RVM could acquire a sparse solution, with an equivalent generalization ability compared with the support vector machine. The sparse property requires much less time in the prediction, making RVM potential in classifying the large-scale hyperspectral image. However, RVM is not widespread influenced by its slow training procedure. To solve the problem, the classification of the hyperspectral image using RVM is accelerated by the parallel computing technique in this paper. The parallelization is revealed from the aspects of the multiclass strategy, the ensemble of multiple weak classifiers, and the matrix operations. The parallel RVMs are implemented using the C language plus the parallel functions of the linear algebra packages and the message passing interface library. The proposed methods are evaluated by the AVIRIS Indian Pines data set on the Beowulf cluster and the multicore platforms. It shows that the parallel RVMs accelerate the training procedure obviously.

  7. Neutron–gamma discrimination based on the support vector machine method

    Energy Technology Data Exchange (ETDEWEB)

    Yu, Xunzhen [School of Physical Science and Technology, Sichuan University, Chengdu 610041, Sichuan (China); Key Laboratory of High Energy Density Physics and Technology (Ministry of Education ), Sichuan University, Chengdu 610064 (China); Zhu, Jingjun [School of Physical Science and Technology, Sichuan University, Chengdu 610041, Sichuan (China); Lin, ShinTed [School of Physical Science and Technology, Sichuan University, Chengdu 610041, Sichuan (China); Key Laboratory of High Energy Density Physics and Technology (Ministry of Education ), Sichuan University, Chengdu 610064 (China); Wang, Li [School of Physical Science and Technology, Sichuan University, Chengdu 610041, Sichuan (China); Department of Engineering Physics, Tsinghua University, Beijing 100084 (China); Xing, Haoyang, E-mail: xhy@scu.edu.cn [School of Physical Science and Technology, Sichuan University, Chengdu 610041, Sichuan (China); Key Laboratory of High Energy Density Physics and Technology (Ministry of Education ), Sichuan University, Chengdu 610064 (China); Zhang, Caixun; Xia, Yuxi; Liu, Shukui [School of Physical Science and Technology, Sichuan University, Chengdu 610041, Sichuan (China); Yue, Qian [Department of Engineering Physics, Tsinghua University, Beijing 100084 (China); Wei, Weiwei; Du, Qiang [School of Physical Science and Technology, Sichuan University, Chengdu 610041, Sichuan (China); Tang, Changjian [School of Physical Science and Technology, Sichuan University, Chengdu 610041, Sichuan (China); Key Laboratory of High Energy Density Physics and Technology (Ministry of Education ), Sichuan University, Chengdu 610064 (China)

    2015-03-21

    In this study, the combination of the support vector machine (SVM) method with the moment analysis method (MAM) is proposed and utilized to perform neutron/gamma (n/γ) discrimination of the pulses from an organic liquid scintillator (OLS). Neutron and gamma events, which can be firmly separated on the scatter plot drawn by the charge comparison method (CCM), are detected to form the training data set and the test data set for the SVM, and the MAM is used to create the feature vectors for individual events in the data sets. Compared to the traditional methods, such as CCM, the proposed method can not only discriminate the neutron and gamma signals, even at lower energy levels, but also provide the corresponding classification accuracy for each event, which is useful in validating the discrimination. Meanwhile, the proposed method can also offer a predication of the classification for the under-energy-limit events.

  8. Support Vector Machine Learning-based fMRI Data Group Analysis*

    OpenAIRE

    Wang, Ze; Childress, Anna R.; Wang, Jiongjiong; Detre, John A.

    2007-01-01

    To explore the multivariate nature of fMRI data and to consider the inter-subject brain response discrepancies, a multivariate and brain response model-free method is fundamentally required. Two such methods are presented in this paper by integrating a machine learning algorithm, the support vector machine (SVM), and the random effect model. Without any brain response modeling, SVM was used to extract a whole brain spatial discriminance map (SDM), representing the brain response difference be...

  9. New control algorithm for shunt active filters, based on self-tuned vector filter

    OpenAIRE

    Perales Esteve, Manuel Ángel; Mora Jiménez, José Luis; Carrasco Solís, Juan Manuel; García Franquelo, Leopoldo

    2001-01-01

    A new, improved, method for calculating the reference of a shunt active filter is presented. This method lays on a filter, which is able to extract the main component of a vector signal. This filter acts as a Phase-Locked Loop, capturing a particular frequency. The output of this filter is in phase with the frequency isolated, and has its amplitude. Simulation and experimental results confirms the validity of the proposed algorithm.

  10. Multiple targets vector miss distance measurement accuracy based on 2-D assignment algorithms

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    An extension of 2-D assignment approach is proposed for measurement-to-target association for improving multiple targets vector miss distance measurement accuracy.When the multiple targets move so closely,the measurements can not be fully resolved due to finite resolution.The proposed method adopts an auction algorithm to compute the feasible measurement-to-target assignment with unresolved measurements for solving this 2-D assignment problem.Computer simulation results demonstrate the effectiveness and feasibility of this method.

  11. Mos1 transposon-based transformation of fish cell lines using baculoviral vectors

    Energy Technology Data Exchange (ETDEWEB)

    Yokoo, Masako [Laboratory of Applied Molecular Entomology, Division of Applied Bioscience, Research Faculty of Agriculture, Hokkaido University, Sapporo 060-8589 (Japan); Fujita, Ryosuke [Laboratory of Applied Molecular Entomology, Division of Applied Bioscience, Research Faculty of Agriculture, Hokkaido University, Sapporo 060-8589 (Japan); Innate Immunity Laboratory, Graduate School of Life Science and Creative Research Institution, Hokkaido University, Sapporo 001-0021 (Japan); Nakajima, Yumiko [Functional Genomics Group, COMB, Tropical Biosphere Research Center, University of the Ryukyus, Okinawa 903-0213 (Japan); Yoshimizu, Mamoru; Kasai, Hisae [Faculty of Fisheries Sciences, Hokkaido University, Hakodate 041-8611 (Japan); Asano, Shin-ichiro [Laboratory of Applied Molecular Entomology, Division of Applied Bioscience, Research Faculty of Agriculture, Hokkaido University, Sapporo 060-8589 (Japan); Bando, Hisanori, E-mail: hban@abs.agr.hokudai.ac.jp [Laboratory of Applied Molecular Entomology, Division of Applied Bioscience, Research Faculty of Agriculture, Hokkaido University, Sapporo 060-8589 (Japan)

    2013-09-13

    Highlights: •The baculovirus vector infiltrates the cells of economic important fishes. •Drosophila Mos1 transposase expressed in fish cells maintains its ability to localize to the nucleus. •The baculoviral vector carrying Mos1 is a useful tool to stably transform fish cells. -- Abstract: Drosophila Mos1 belongs to the mariner family of transposons, which are one of the most ubiquitous transposons among eukaryotes. We first determined nuclear transportation of the Drosophila Mos1-EGFP fusion protein in fish cell lines because it is required for a function of transposons. We next constructed recombinant baculoviral vectors harboring the Drosophila Mos1 transposon or marker genes located between Mos1 inverted repeats. The infectivity of the recombinant virus to fish cells was assessed by monitoring the expression of a fluorescent protein encoded in the viral genome. We detected transgene expression in CHSE-214, HINAE, and EPC cells, but not in GF or RTG-2 cells. In the co-infection assay of the Mos1-expressing virus and reporter gene-expressing virus, we successfully transformed CHSE-214 and HINAE cells. These results suggest that the combination of a baculovirus and Mos1 transposable element may be a tool for transgenesis in fish cells.

  12. Mass Spectrometry Based Proteomic Analysis of Salivary Glands of Urban Malaria Vector Anopheles stephensi

    Directory of Open Access Journals (Sweden)

    Sonam Vijay

    2014-01-01

    Full Text Available Salivary gland proteins of Anopheles mosquitoes offer attractive targets to understand interactions with sporozoites, blood feeding behavior, homeostasis, and immunological evaluation of malaria vectors and parasite interactions. To date limited studies have been carried out to elucidate salivary proteins of An. stephensi salivary glands. The aim of the present study was to provide detailed analytical attributives of functional salivary gland proteins of urban malaria vector An. stephensi. A proteomic approach combining one-dimensional electrophoresis (1DE, ion trap liquid chromatography mass spectrometry (LC/MS/MS, and computational bioinformatic analysis was adopted to provide the first direct insight into identification and functional characterization of known salivary proteins and novel salivary proteins of An. stephensi. Computational studies by online servers, namely, MASCOT and OMSSA algorithms, identified a total of 36 known salivary proteins and 123 novel proteins analysed by LC/MS/MS. This first report describes a baseline proteomic catalogue of 159 salivary proteins belonging to various categories of signal transduction, regulation of blood coagulation cascade, and various immune and energy pathways of An. stephensi sialotranscriptome by mass spectrometry. Our results may serve as basis to provide a putative functional role of proteins in concept of blood feeding, biting behavior, and other aspects of vector-parasite host interactions for parasite development in anopheline mosquitoes.

  13. Protein trans-splicing based dual-vector delivery of the coagulation factor Ⅷ gene

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    A dual-vector system was explored for the delivery of the coagulation factor VIII gene,using intein-mediated protein trans-splicing as a means to produce intact functional factor VIII post-translationally.A pair of eukaryotic expression vectors,expressing Ssp DnaB intein-fused heavy and light chain genes of B-domain deleted factor VIII (BDD-FVIII),was constructed.With transient co-transfection of the two vectors into 293 and COS-7 cells,the culture supernatants contained (137±23) and (109±22) ng mL–1 spliced BDD-FVIII antigen with an activity of (1.05±0.16) and (0.79±0.23) IU mL–1 for 293 and COS-7 cells,respectively.The spliced BDD-FVIII was also detected in supernatants from a mixture of cells transfected with inteinfused heavy and light chain genes.The spliced BDD-FVIII protein bands from cell lysates were visualized by Western blotting.The data demonstrated that intein could be used to transfer the split factor VIII gene and provided valuable information on factor VIII gene delivery by dual-adeno-associated virus in hemophilia A gene therapy.

  14. Implementing Cargo Movement into Climate Based Risk Assessment of Vector-Borne Diseases

    Directory of Open Access Journals (Sweden)

    Stephanie Margarete Thomas

    2014-03-01

    Full Text Available During the last decades the disease vector Aedes albopictus (Asian tiger mosquito has rapidly spread around the globe. Global shipment of goods contributes to its permanent introduction. Invaded regions are facing novel and serious public health concerns, especially regarding the transmission of formerly non-endemic arboviruses such as dengue and chikungunya. The further development and potential spread to other regions depends largely on their climatic suitability. Here, we have developed a tool for identifying and prioritizing European areas at risk for the establishment of Aedes albopictus by taking into account, for the first time, the freight imports from this mosquito’s endemic countries and the climate suitability at harbors and their surrounding regions. In a second step we consider the further transport of containers by train and inland waterways because these types of transport can be well controlled. We identify European regions at risk, where a huge amount of transported goods meet climatically suitable conditions for the disease vector. The current and future suitability of the climate for Aedes albopictus was modeled by a correlative niche model approach and the Regional Climate Model COSMO-CLM. This risk assessment combines impacts of globalization and global warming to improve effective and proactive interventions in disease vector surveillance and control actions.

  15. Magnetofection Enhances Adenoviral Vector-based Gene Delivery in Skeletal Muscle Cells

    Science.gov (United States)

    Pereyra, Andrea Soledad; Mykhaylyk, Olga; Lockhart, Eugenia Falomir; Taylor, Jackson Richard; Delbono, Osvaldo; Goya, Rodolfo Gustavo; Plank, Christian; Hereñu, Claudia Beatriz

    2016-01-01

    The goal of magnetic field-assisted gene transfer is to enhance internalization of exogenous nucleic acids by association with magnetic nanoparticles (MNPs). This technique named magnetofection is particularly useful in difficult-to-transfect cells. It is well known that human, mouse, and rat skeletal muscle cells suffer a maturation-dependent loss of susceptibility to Recombinant Adenoviral vector (RAd) uptake. In postnatal, fully differentiated myofibers, the expression of the primary Coxsackie and Adenoviral membrane receptor (CAR) is severely downregulated representing a main hurdle for the use of these vectors in gene transfer/therapy. Here we demonstrate that assembling of Recombinant Adenoviral vectors with suitable iron oxide MNPs into magneto-adenovectors (RAd-MNP) and further exposure to a gradient magnetic field enables to efficiently overcome transduction resistance in skeletal muscle cells. Expression of Green Fluorescent Protein and Insulin-like Growth Factor 1 was significantly enhanced after magnetofection with RAd-MNPs complexes in C2C12 myotubes in vitro and mouse skeletal muscle in vivo when compared to transduction with naked virus. These results provide evidence that magnetofection, mainly due to its membrane-receptor independent mechanism, constitutes a simple and effective alternative to current methods for gene transfer into traditionally hard-to-transfect biological models. PMID:27274908

  16. Dendritic cell based PSMA immunotherapy for prostate cancer using a CD40-targeted adenovirus vector.

    Directory of Open Access Journals (Sweden)

    Briana Jill Williams

    Full Text Available Human prostate tumor vaccine and gene therapy trials using ex vivo methods to prime dendritic cells (DCs with prostate specific membrane antigen (PSMA have been somewhat successful, but to date the lengthy ex vivo manipulation of DCs has limited the widespread clinical utility of this approach. Our goal was to improve upon cancer vaccination with tumor antigens by delivering PSMA via a CD40-targeted adenovirus vector directly to DCs as an efficient means for activation and antigen presentation to T-cells. To test this approach, we developed a mouse model of prostate cancer by generating clonal derivatives of the mouse RM-1 prostate cancer cell line expressing human PSMA (RM-1-PSMA cells. To maximize antigen presentation in target cells, both MHC class I and TAP protein expression was induced in RM-1 cells by transduction with an Ad vector expressing interferon-gamma (Ad5-IFNγ. Administering DCs infected ex vivo with CD40-targeted Ad5-huPSMA, as well as direct intraperitoneal injection of the vector, resulted in high levels of tumor-specific CTL responses against RM-1-PSMA cells pretreated with Ad5-IFNγ as target cells. CD40 targeting significantly improved the therapeutic antitumor efficacy of Ad5-huPSMA encoding PSMA when combined with Ad5-IFNγ in the RM-1-PSMA model. These results suggest that a CD-targeted adenovirus delivering PSMA may be effective clinically for prostate cancer immunotherapy.

  17. Dendritic cell based PSMA immunotherapy for prostate cancer using a CD40-targeted adenovirus vector.

    Science.gov (United States)

    Williams, Briana Jill; Bhatia, Shilpa; Adams, Lisa K; Boling, Susan; Carroll, Jennifer L; Li, Xiao-Lin; Rogers, Donna L; Korokhov, Nikolay; Kovesdi, Imre; Pereboev, Alexander V; Curiel, David T; Mathis, J Michael

    2012-01-01

    Human prostate tumor vaccine and gene therapy trials using ex vivo methods to prime dendritic cells (DCs) with prostate specific membrane antigen (PSMA) have been somewhat successful, but to date the lengthy ex vivo manipulation of DCs has limited the widespread clinical utility of this approach. Our goal was to improve upon cancer vaccination with tumor antigens by delivering PSMA via a CD40-targeted adenovirus vector directly to DCs as an efficient means for activation and antigen presentation to T-cells. To test this approach, we developed a mouse model of prostate cancer by generating clonal derivatives of the mouse RM-1 prostate cancer cell line expressing human PSMA (RM-1-PSMA cells). To maximize antigen presentation in target cells, both MHC class I and TAP protein expression was induced in RM-1 cells by transduction with an Ad vector expressing interferon-gamma (Ad5-IFNγ). Administering DCs infected ex vivo with CD40-targeted Ad5-huPSMA, as well as direct intraperitoneal injection of the vector, resulted in high levels of tumor-specific CTL responses against RM-1-PSMA cells pretreated with Ad5-IFNγ as target cells. CD40 targeting significantly improved the therapeutic antitumor efficacy of Ad5-huPSMA encoding PSMA when combined with Ad5-IFNγ in the RM-1-PSMA model. These results suggest that a CD-targeted adenovirus delivering PSMA may be effective clinically for prostate cancer immunotherapy.

  18. Active-Flux-Based, V/f-with-Stabilizing-Loops Versus Sensorless Vector Control of IPMSM Drives

    DEFF Research Database (Denmark)

    Moldovan, Ana; Blaabjerg, Frede; Boldea, Ion

    2011-01-01

    . By this control strategy, a fast dynamic speed response, without steady state error and without speed or current regulators, for all AC machines is obtained. The second control method is a sensorless vector control strategy which also has been implemented and tested, just for comparison.......This paper proposes two control methods for Interior Permanent Magnet Synchronous Motor (IPMSM) Drives. The first one is a V/f control with two stabilizing loops: one loop based on active flux balance for voltage magnitude correction and a second, based on speed error, with voltage phase correction...

  19. Vector analysis

    CERN Document Server

    Brand, Louis

    2006-01-01

    The use of vectors not only simplifies treatments of differential geometry, mechanics, hydrodynamics, and electrodynamics, but also makes mathematical and physical concepts more tangible and easy to grasp. This text for undergraduates was designed as a short introductory course to give students the tools of vector algebra and calculus, as well as a brief glimpse into these subjects' manifold applications. The applications are developed to the extent that the uses of the potential function, both scalar and vector, are fully illustrated. Moreover, the basic postulates of vector analysis are brou

  20. Viral vector-based prime-boost immunization regimens : a possible involvement of T-cell competition

    NARCIS (Netherlands)

    de Mare, A.; Lambeck, A. J. A.; Regts, J.; van Dam, G. M.; Nijman, H. W.; Snippe, H.; Wilschut, J.; Daemen, T.

    2008-01-01

    Vaccination with recombinant viral vectors may be impeded by preexisting vector-specific immunity or by vector-specific immunity induced during the priming immunization. It is assumed that virus-neutralizing antibodies represent the principal effector mechanism of vector-specific immunity, while kil

  1. Optimized Method for Real-Time Face Recognition System Based on PCA and Multiclass Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Reza Azad

    2013-11-01

    Full Text Available Automatic face recognition system is one of the core technologies in computer vision, machine learning, and biometrics. The present study presents a novel and improved way for face recognition. In the suggested approach, first, the place of face is extracted from the original image and then is sent to feature extraction stage, which is based on Principal Component Analysis (PCA technique. In the previous procedures which were established on PCA technique, the whole picture was taken as a vector feature, then among these features, key features were extracted with use of PCA algorithm, revealing finally some poor efficiency. Thus, in the recommended approach underlying the current investigation, first the areas of face features are extracted; then, the areas are combined and are regarded as vector features. Ultimately, its key features are extracted with use of PCA algorithm. Taken together, after extracting the features, for face recognition and classification, Multiclass Support Vector Machine (SVMs classifiers, which are typical of high efficiency, have been employed. In the result part, the proposed approach is applied on FEI database and the accuracy rate achieved 98.45%.

  2. Herpes simplex virus type 1-based amplicon vectors for fundamental research in neurosciences and gene therapy of neurological diseases.

    Science.gov (United States)

    Jerusalinsky, Diana; Baez, María Verónica; Epstein, Alberto Luis

    2012-01-01

    Somatic manipulation of the nervous system without the involvement of the germinal line appears as a powerful counterpart of the transgenic strategy. The use of viral vectors to produce specific, transient and localized knockout, knockdown, ectopic expression or overexpression of a gene, leads to the possibility of analyzing both in vitro and in vivo molecular basis of neural function. In this approach, viral particles engineered to carry transgenic sequences are delivered into discrete brain regions, to transduce cells that will express the transgenic products. Amplicons are replication-incompetent helper-dependent vectors derived from herpes simplex virus type 1 (HSV-1), with several advantages that potentiate their use in neurosciences: (1) minimal toxicity: amplicons do not encode any virus proteins, are neither toxic for the infected cells nor pathogenic for the inoculated animals and elicit low levels of adaptive immune responses; (2) extensive transgene capacity to carry up to 150-kb of foreign DNA; i.e., entire genes with regulatory sequences could be delivered; (3) widespread cellular tropism: amplicons can experimentally infect several cell types including glial cells, though naturally the virus infects mainly neurons and epithelial cells; (4) since the viral genome does not integrate into cellular chromosomes there is low probability to induce insertional mutagenesis. Recent investigations on gene transfer into the brain using these vectors, have focused on gene therapy of inherited genetic diseases affecting the nervous system, such as ataxias, or on neurodegenerative disorders using experimental models of Parkinson's or Alzheimer's disease. Another group of studies used amplicons to investigate complex neural functions such as neuroplasticity, anxiety, learning and memory. In this short review, we summarize recent data supporting the potential of HSV-1 based amplicon vector model for gene delivery and modulation of gene expression in primary cultures

  3. Fault Diagnosis of Plunger Pump in Truck Crane Based on Relevance Vector Machine with Particle Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Wenliao Du

    2013-01-01

    Full Text Available Promptly and accurately dealing with the equipment breakdown is very important in terms of enhancing reliability and decreasing downtime. A novel fault diagnosis method PSO-RVM based on relevance vector machines (RVM with particle swarm optimization (PSO algorithm for plunger pump in truck crane is proposed. The particle swarm optimization algorithm is utilized to determine the kernel width parameter of the kernel function in RVM, and the five two-class RVMs with binary tree architecture are trained to recognize the condition of mechanism. The proposed method is employed in the diagnosis of plunger pump in truck crane. The six states, including normal state, bearing inner race fault, bearing roller fault, plunger wear fault, thrust plate wear fault, and swash plate wear fault, are used to test the classification performance of the proposed PSO-RVM model, which compared with the classical models, such as back-propagation artificial neural network (BP-ANN, ant colony optimization artificial neural network (ANT-ANN, RVM, and support vectors, machines with particle swarm optimization (PSO-SVM, respectively. The experimental results show that the PSO-RVM is superior to the first three classical models, and has a comparative performance to the PSO-SVM, the corresponding diagnostic accuracy achieving as high as 99.17% and 99.58%, respectively. But the number of relevance vectors is far fewer than that of support vector, and the former is about 1/12–1/3 of the latter, which indicates that the proposed PSO-RVM model is more suitable for applications that require low complexity and real-time monitoring.

  4. Rapid and sensitive lentivirus vector-based conditional gene expression assay to monitor and quantify cell fusion activity.

    Directory of Open Access Journals (Sweden)

    Manuel A F V Gonçalves

    Full Text Available Cell-to-cell fusion is involved in multiple fundamental biological processes. Prominent examples include osteoclast and giant cell formation, fertilization and skeletal myogenesis which involve macrophage, sperm-egg and myoblast fusion, respectively. Indeed, the importance of cell fusion is underscored by the wide range of homeostatic as well as pathologic processes in which it plays a key role. Therefore, rapid and sensitive systems to trace and measure cell fusion events in various experimental systems are in demand. Here, we introduce a bipartite cell fusion monitoring system based on a genetic switch responsive to the site-specific recombinase FLP. To allow flexible deployment in both dividing as well as non-dividing cell populations, inducer and reporter modules were incorporated in lentivirus vector particles. Moreover, the recombinase-inducible transcription units were designed in such a way as to minimize basal activity and chromosomal position effects in the "off" and "on" states, respectively. The lentivirus vector-based conditional gene expression assay was validated in primary human mesenchymal stem cells and in a differentiation model based on muscle progenitor cells from a Duchenne muscular dystrophy patient using reporter genes compatible with live- and single-cell imaging and with whole population measurements. Using the skeletal muscle cell differentiation model, we showed that the new assay displays low background activity, a 2-log dynamic range, high sensitivity and is amenable to the investigation of cell fusion kinetics. The utility of the bipartite cell fusion monitoring system was underscored by a study on the impact of drug- and RNAi-mediated p38 MAPK inhibition on human myocyte differentiation. Finally, building on the capacity of lentivirus vectors to readily generate transgenic animals the present FLP-inducible system should be adaptable, alone or together with Cre/loxP-based assays, to cell lineage tracing and

  5. Developing GIS-based eastern equine encephalitis vector-host models in Tuskegee, Alabama

    Directory of Open Access Journals (Sweden)

    Novak Robert J

    2010-02-01

    Full Text Available Abstract Background A site near Tuskegee, Alabama was examined for vector-host activities of eastern equine encephalomyelitis virus (EEEV. Land cover maps of the study site were created in ArcInfo 9.2® from QuickBird data encompassing visible and near-infrared (NIR band information (0.45 to 0.72 μm acquired July 15, 2008. Georeferenced mosquito and bird sampling sites, and their associated land cover attributes from the study site, were overlaid onto the satellite data. SAS 9.1.4® was used to explore univariate statistics and to generate regression models using the field and remote-sampled mosquito and bird data. Regression models indicated that Culex erracticus and Northern Cardinals were the most abundant mosquito and bird species, respectively. Spatial linear prediction models were then generated in Geostatistical Analyst Extension of ArcGIS 9.2®. Additionally, a model of the study site was generated, based on a Digital Elevation Model (DEM, using ArcScene extension of ArcGIS 9.2®. Results For total mosquito count data, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 5.041 km, nugget of 6.325 km, lag size of 7.076 km, and range of 31.43 km, using 12 lags. For total adult Cx. erracticus count, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 5.764 km, nugget of 6.114 km, lag size of 7.472 km, and range of 32.62 km, using 12 lags. For the total bird count data, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 4.998 km, nugget of 5.413 km, lag size of 7.549 km and range of 35.27 km, using 12 lags. For the Northern Cardinal count data, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 6.387 km, nugget of 5.935 km, lag size of 8.549 km and a range of 41.38 km, using 12 lags. Results of the DEM analyses indicated a statistically significant inverse

  6. Design and Assembly of DNA Sequence Libraries for Chromosomal Insertion in Bacteria Based on a Set of Modified MoClo Vectors.

    Science.gov (United States)

    Schindler, Daniel; Milbredt, Sarah; Sperlea, Theodor; Waldminghaus, Torsten

    2016-12-16

    Efficient assembly of large DNA constructs is a key technology in synthetic biology. One of the most popular assembly systems is the MoClo standard in which restriction and ligation of multiple fragments occurs in a one-pot reaction. The system is based on a smart vector design and type IIs restriction enzymes, which cut outside their recognition site. While the initial MoClo vectors had been developed for the assembly of multiple transcription units of plants, some derivatives of the vectors have been developed over the last years. Here we present a new set of MoClo vectors for the assembly of fragment libraries and insertion of constructs into bacterial chromosomes. The vectors are accompanied by a computer program that generates a degenerate synthetic DNA sequence that excludes "forbidden" DNA motifs. We demonstrate the usability of the new approach by construction of a stable fluorescence repressor operator system (FROS).

  7. Support-Vector-Machine-Based Reduced-Order Model for Limit Cycle Oscillation Prediction of Nonlinear Aeroelastic System

    Directory of Open Access Journals (Sweden)

    Gang Chen

    2012-01-01

    Full Text Available It is not easy for the system identification-based reduced-order model (ROM and even eigenmode based reduced-order model to predict the limit cycle oscillation generated by the nonlinear unsteady aerodynamics. Most of these traditional ROMs are sensitive to the flow parameter variation. In order to deal with this problem, a support vector machine- (SVM- based ROM was investigated and the general construction framework was proposed. The two-DOF aeroelastic system for the NACA 64A010 airfoil in transonic flow was then demonstrated for the new SVM-based ROM. The simulation results show that the new ROM can capture the LCO behavior of the nonlinear aeroelastic system with good accuracy and high efficiency. The robustness and computational efficiency of the SVM-based ROM would provide a promising tool for real-time flight simulation including nonlinear aeroelastic effects.

  8. A nonintegrative lentiviral vector-based vaccine provides long-term sterile protection against malaria.

    Directory of Open Access Journals (Sweden)

    Frédéric Coutant

    Full Text Available Trials testing the RTS,S candidate malaria vaccine and radiation-attenuated sporozoites (RAS have shown that protective immunity against malaria can be induced and that an effective vaccine is not out of reach. However, longer-term protection and higher protection rates are required to eradicate malaria from the endemic regions. It implies that there is still a need to explore new vaccine strategies. Lentiviral vectors are very potent at inducing strong immunological memory. However their integrative status challenges their safety profile. Eliminating the integration step obviates the risk of insertional oncogenesis. Providing they confer sterile immunity, nonintegrative lentiviral vectors (NILV hold promise as mass pediatric vaccine by meeting high safety standards. In this study, we have assessed the protective efficacy of NILV against malaria in a robust pre-clinical model. Mice were immunized with NILV encoding Plasmodium yoelii Circumsporozoite Protein (Py CSP and challenged with sporozoites one month later. In two independent protective efficacy studies, 50% (37.5-62.5 of the animals were fully protected (p = 0.0072 and p = 0.0008 respectively when compared to naive mice. The remaining mice with detectable parasitized red blood cells exhibited a prolonged patency and reduced parasitemia. Moreover, protection was long-lasting with 42.8% sterile protection six months after the last immunization (p = 0.0042. Post-challenge CD8+ T cells to CSP, in contrast to anti-CSP antibodies, were associated with protection (r = -0.6615 and p = 0.0004 between the frequency of IFN-g secreting specific T cells in spleen and parasitemia. However, while NILV and RAS immunizations elicited comparable immunity to CSP, only RAS conferred 100% of sterile protection. Given that a better protection can be anticipated from a multi-antigen vaccine and an optimized vector design, NILV appear as a promising malaria vaccine.

  9. 基于FPGA的空间矢量PWM的实现%A Realization of FPGA-based Space-vector PWM

    Institute of Scientific and Technical Information of China (English)

    孙文焕; 程善美; 秦忆

    2000-01-01

    本文详述了空间矢量SVPWM的算法,并提出用FPGA实现SVPWM的方法,最后分析了使用FPGA的优点。%This paper reviews an arithmetic of the space-vector PWM in detail,and advances a realization of FPGA-based space-vector PWM ,finally analyses the merit of FPGA-based strategy.

  10. PMSVM: An Optimized Support Vector Machine Classification Algorithm Based on PCA and Multilevel Grid Search Methods

    Directory of Open Access Journals (Sweden)

    Yukai Yao

    2015-01-01

    Full Text Available We propose an optimized Support Vector Machine classifier, named PMSVM, in which System Normalization, PCA, and Multilevel Grid Search methods are comprehensively considered for data preprocessing and parameters optimization, respectively. The main goals of this study are to improve the classification efficiency and accuracy of SVM. Sensitivity, Specificity, Precision, and ROC curve, and so forth, are adopted to appraise the performances of PMSVM. Experimental results show that PMSVM has relatively better accuracy and remarkable higher efficiency compared with traditional SVM algorithms.

  11. PENETRATION QUALITY EVALUATION IN ROBOTIZED ARC WELDING BASED ON SUPPORT VECTOR MACHINE

    Institute of Scientific and Technical Information of China (English)

    Ye Feng; Song Yonglun; Li Di; Lai Yizong

    2003-01-01

    A quality monitoring method by means of support vector machines (SVM) for robotized gas metal arc welding (GMAW) is introduced. Through the feature extraction of the welding process signal,a SVM classifier is constructed to establish the relationship between the feature of process parameters and the quality of weld penetration. Under the samples obtained from auto parts welding production line, the learning machine with a radial basis function kernel shows good performance. And this method can be feasible to identify defect online in welding production.

  12. Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting

    Directory of Open Access Journals (Sweden)

    Hong-Juan Li

    2013-04-01

    Full Text Available Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR, this paper presents a SVR model hybridized with the empirical mode decomposition (EMD method and auto regression (AR for electric load forecasting. The electric load data of the New South Wales (Australia market are employed for comparing the forecasting performances of different forecasting models. The results confirm the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.

  13. Study on flaw identification of ultrasonic signal for large shafts based on optimal support vector machine

    Institute of Scientific and Technical Information of China (English)

    Zhao Xiufen; Yin Guofu; Tian Guiyun; Yin Ying

    2008-01-01

    Automatic identification of flaws is very important for ultrasonic nondestructive testing and evaluation of large shaft. A novel automatic defect identification system is presented. Wavelet packet analysis (WPA) was applied to feature extraction of ultrasonic signal, and optimal Support vector machine (SVM) was used to perform the identification task. Meanwhile, comparative study on convergent velocity and classified effect was done among SVM and several improved BP network models. To validate the method, some experiments were performed and the results show that the proposed system has very high identification performance for large shafts and the optimal SVM processes better classification performance and spreading potential than BP manual neural network under small study sample condition.

  14. Direct detection prospects of dark vectors with xenon-based dark matter experiments

    CERN Document Server

    An, Haipeng; Pospelov, Maxim; Pradler, Josef; Ritz, Adam

    2015-01-01

    Dark matter experiments primarily search for the scattering of WIMPs on target nuclei of well shielded underground detectors. The results from liquid scintillator experiments furthermore provide precise probes of very light and very weakly coupled particles that may be absorbed by electrons. In these proceedings we summarize previously obtained constraints on long-lived dark matter vector particles $V$ (dark photons) in the $0.01-100$ keV mass range. In addition, we provide a first projected sensitivity reach for the upcoming XENON1T dark matter search to detect dark photons.

  15. An Auto-flag Method of Radio Visibility Data Based on Support Vector Machine

    Science.gov (United States)

    Hui-mei, Dai; Ying, Mei; Wei, Wang; Hui, Deng; Feng, Wang

    2017-01-01

    The Mingantu Ultrawide Spectral Radioheliograph (MUSER) has entered a test observation stage. After the construction of the data acquisition and storage system, it is urgent to automatically flag and eliminate the abnormal visibility data so as to improve the imaging quality. In this paper, according to the observational records, we create a credible visibility set, and further obtain the corresponding flag model of visibility data by using the support vector machine (SVM) technique. The results show that the SVM is a robust approach to flag the MUSER visibility data, and can attain an accuracy of about 86%. Meanwhile, this method will not be affected by solar activities, such as flare eruptions.

  16. Multi-Sensor Data Fusion Using a Relevance Vector Machine Based on an Ant Colony for Gearbox Fault Detection

    Directory of Open Access Journals (Sweden)

    Zhiwen Liu

    2015-08-01

    Full Text Available Sensors play an important role in the modern manufacturing and industrial processes. Their reliability is vital to ensure reliable and accurate information for condition based maintenance. For the gearbox, the critical machine component in the rotating machinery, the vibration signals collected by sensors are usually noisy. At the same time, the fault detection results based on the vibration signals from a single sensor may be unreliable and unstable. To solve this problem, this paper proposes an intelligent multi-sensor data fusion method using the relevance vector machine (RVM based on an ant colony optimization algorithm (ACO-RVM for gearboxes’ fault detection. RVM is a sparse probability model based on support vector machine (SVM. RVM not only has higher detection accuracy, but also better real-time accuracy compared with SVM. The ACO algorithm is used to determine kernel parameters of RVM. Moreover, the ensemble empirical mode decomposition (EEMD is applied to preprocess the raw vibration signals to eliminate the influence caused by noise and other unrelated signals. The distance evaluation technique (DET is employed to select dominant features as input of the ACO-RVM, so that the redundancy and inference in a large amount of features can be removed. Two gearboxes are used to demonstrate the performance of the proposed method. The experimental results show that the ACO-RVM has higher fault detection accuracy than the RVM with normal the cross-validation (CV.

  17. Diagnosis of Chronic Kidney Disease Based on Support Vector Machine by Feature Selection Methods.

    Science.gov (United States)

    Polat, Huseyin; Danaei Mehr, Homay; Cetin, Aydin

    2017-04-01

    As Chronic Kidney Disease progresses slowly, early detection and effective treatment are the only cure to reduce the mortality rate. Machine learning techniques are gaining significance in medical diagnosis because of their classification ability with high accuracy rates. The accuracy of classification algorithms depend on the use of correct feature selection algorithms to reduce the dimension of datasets. In this study, Support Vector Machine classification algorithm was used to diagnose Chronic Kidney Disease. To diagnose the Chronic Kidney Disease, two essential types of feature selection methods namely, wrapper and filter approaches were chosen to reduce the dimension of Chronic Kidney Disease dataset. In wrapper approach, classifier subset evaluator with greedy stepwise search engine and wrapper subset evaluator with the Best First search engine were used. In filter approach, correlation feature selection subset evaluator with greedy stepwise search engine and filtered subset evaluator with the Best First search engine were used. The results showed that the Support Vector Machine classifier by using filtered subset evaluator with the Best First search engine feature selection method has higher accuracy rate (98.5%) in the diagnosis of Chronic Kidney Disease compared to other selected methods.

  18. Research of Self-Tuning PID for PMSM Vector Control based on Improved KMTOA

    Directory of Open Access Journals (Sweden)

    Lingzhi Yi

    2017-03-01

    Full Text Available The Permanent Magnet Synchronous Motor has been applying widely due to it’s high efficiency, high reliability, relatively low cost and low moment of inertia. However, the PMSM drives are easily affected by the uncertain factors such as the variation of motor parameters and load disturbance. In order to realize the control of the PMSM accurately, a novel adaptive chaotic kinetic molecular theory optimization algorithm was implemented for seeking the best parameters of PID controller. In the PMSM vector control system, the speed loop will be adjusted by a CKMTOA PID controller. In modified kinetic molecular theory optimization algorithm, the adaptive inertia weight factors are used to accelerate the convergence speed, and chaotic searching is conducted within the neighbor set of the solutions to avoid the local minima. The model of PMSM and its` space vector control system are set up in the software of MATLAB/Simulink. The effectiveness of the self-tuning CKMTOA PID controller is verified by comparing with the conventional PID and particle swarm optimization algorithm. The extensive simulations and analysis also show the effectiveness of the proposed approach

  19. Underwater hybrid near-field acoustical holography based on the measurement of vector hydrophone array

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    Hybrid near-field acoustical holography(NAH) is developed for reconstructing acoustic radiation from a cylindrical source in a complex underwater environment. In hybrid NAH,we combine statistically optimized near-field acoustical holography(SONAH) and broadband acoustical holography from intensity measurements(BAHIM) to reconstruct the underwater cylindrical source field. First,the BAHIM is utilized to regenerate as much acoustic pressures on the hologram surface as necessary,and then the acoustic pressures are taken as input to the formulation implemented numerically by SONAH. The main advantages of this technology are that the complex pressure on the hologram surface can be reconstructed without reference signal,and the measurement array can be smaller than the source,thus the practicability and efficiency of this technology are greatly enhanced. Numerical examples of a cylindrical source are demonstrated. Test results show that hybrid NAH can yield a more accurate reconstruction than conventional NAH. Then,an experiment has been carried out with a vector hydrophone array. The experimental results show the advantage of hybrid NAH in the reconstruction of an acoustic field and the feasibility of using a vector hydrophone array in an underwater NAH measurement,as well as the identification and localization of noise sources.

  20. Support Vector Machine-Based Human Behavior Classification in Crowd through Projection and Star Skeletonization

    Directory of Open Access Journals (Sweden)

    Yogameena, B.

    2010-01-01

    Full Text Available Problem statement: Detection of individual’s abnormal human behaviors in the crowd has become a critical problem because in the event of terror strikes. This study presented a real-time video surveillance system which classifies normal and abnormal behaviors in crowds. The aim of this research was to provide a system which can aid in monitoring crowded urban environments. Approach: The proposed behaviour classification was through projection which separated individuals and using star skeletonization the features like body posture and the cyclic motion cues were obtained. Using these cues the Support Vector Machine (SVM classified the normal and abnormal behaviors of human. Results: Experimental results demonstrated the method proposed was robust and efficient in the classification of normal and abnormal human behaviors. A comparative study of classification accuracy between principal component analysis and Support Vector Machine (SVM classification was also presented. Conclusion: The proposed method classified the behavior such as running people in a crowded environment, bending down movement while most are walking or standing, a person carrying a long bar and a person waving hand in the crowd is classified.