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

    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......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...

  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

    -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......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...... with the reference currents id* = 0 and iq* constant, and the reference frequency having ramp variation. This solution allows ultra low- speed sensorless control without initial rotor-position estimation, and without machine parameters identification. A first-order lag compensator is employed to ensure a smooth...

  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. 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...

  5. 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

    This paper describes a variable-speed motion-sensorless permanent magnet synchronous generator (PMSG) control system for wind energy generation. The proposed system contains a PMSG connected to the grid by a back-to-back PWM inverter with bidirectional power flow, a line filter, and a transformer....... The control system employs PI current controllers with crosscoupling decoupling for both inverters, an active power controller, and a DC link voltage controller. The PMSG rotor speed without using emf integration, and the line voltage frequency are estimated by two PLL based observers. A Dmodule filter...... 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...

  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. 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

    This paper presents a novel, hybrid, motion sensorless control of an axially laminated anisotropic (ALA) reluctance synchronous machine (RSM). By separately controlling Id and Iq currents with the reference currents Id*, Iq* being held constant, and ramping the reference frequency, the motor starts...... flux 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....... with a robust start-up method called I-f control. This kind of control strategy also allows the motor to experience low speeds without initial position estimation or machine parameters identification. The control uses first order lag compensators to ensure smooth transitions from I-f control to active...

  8. "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...... ldquoactive fluxrdquo (or ldquotorque producing fluxrdquo), which ldquoturns all the salient-pole rotor ac machines into nonsalient-pole onesrdquo. As well as giving a detailed explanation of the concept, the paper demonstrates, through a wide range of experimental results, the effectiveness of the active...

  9. 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...

  10. 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,...

  11. High frequency injection assisted “active flux” based sensorless vector control of reluctance synchronous motors, with experiments from zero speed

    DEFF Research Database (Denmark)

    Agarlita, Sorin-Cristian; Boldea, Ion; Blaabjerg, Frede

    2012-01-01

    This paper presents a hybrid, motion sensorless control of an Axially Laminated Anisotropic (ALA) Reluctance Synchronous Machine (RSM). The zero and low speed sensorless method is a saliency based High Frequency Signal Injection technique (HFSI) that uses the motor itself as a resolver. The second...... method is based on a state observer incorporating the “active flux” concept used to deliver RSM rotor position and speed information for medium and high speed range. Even if both methods perform successfully in separate speed regions, estimation of the two algorithms is combined as a sensor fusion to...... improve performance at zero and very low speeds. Experimental results validate the proposed control strategies....

  12. High frequency injection assisted “active flux” based sensorless vector control of reluctance synchronous motors, with experiments from zero speed

    DEFF Research Database (Denmark)

    Agarliţă, Sorin-Cristian; Boldea, I.; Blaabjerg, Frede

    2011-01-01

    This paper presents a hybrid, motion sensorless control of an Axially Laminated Anisotropic (ALA) Reluctance Synchronous Machine (RSM). The zero and low speed sensorless method is a saliency based High Frequency Signal Injection technique (HFSI) that uses the motor itself as a resolver. The second...... method is based on a state observer incorporating the “active flux” concept used to deliver RSM rotor position and speed information for medium and high speed range. Even if both methods perform successfully in separate speed regions, estimation of the two algorithms is combined as a sensor fusion to...... improve performance at zero and very low speeds. Experimental results validate the proposed control strategies....

  13. 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-locked-l...

  14. 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......-speed blower-motor (40 W, 10 krpm, 12 Vdc)....

  15. Nonlinear Growth of Singular Vector Based Perturbations

    Science.gov (United States)

    Reynolds, C. A.

    2002-12-01

    The nonlinearity of singular vector-based perturbation growth is examined within the context of a global atmospheric forecast model. The characteristics of these nonlinearities and their impact on the utility of SV-based diagnostics are assessed both qualitatively and quantitatively. Nonlinearities are quantified by examining the symmetry of evolving positive and negative "twin" perturbations. Perturbations initially scaled to be consistent with estimates of analysis uncertainty become significantly nonlinear by 12 hours. However, the relative magnitude of the nonlinearities is a strong function of scale and metric. Small scales become nonlinear very quickly while synoptic scales can remain significantly linear out to three day. Small shifts between positive and negative perturbations can result in significant nonlinearities even when the basic anomaly patterns are quite similar. Thus, singular vectors may be qualitatively useful even when nonlinearities are large. Post-time pseudo-inverse experiments show that despite significant nonlinear perturbation growth, the nonlinear forecast corrections are similar to the expected linear corrections, even at 72 hours. When the nonlinear correction does differ significantly from the expected linear correction, the nonlinear correction is usually better, indicating that in some cases the pseudo-inverse correction effectively suppresses error growth outside the subspace defined by the leading (dry) singular vectors. Because a significant portion of the nonlinear growth occurs outside of the dry singular vector subspace, an a priori nonlinearity index based on the full perturbations is not a good predictor of when pseudo-inverse based corrections will be ineffective. However, one can construct a reasonable predictor of pseudo-inverse ineffectiveness by focusing on nonlinearities in the synoptic scales or in the singular vector subspace only.

  16. Cost-Based Vectorization of Instance-Based Integration Processes

    Science.gov (United States)

    Boehm, Matthias; Habich, Dirk; Preissler, Steffen; Lehner, Wolfgang; Wloka, Uwe

    The inefficiency of integration processes—as an abstraction of workflow-based integration tasks—is often reasoned by low resource utilization and significant waiting times for external systems. With the aim to overcome these problems, we proposed the concept of process vectorization. There, instance-based integration processes are transparently executed with the pipes-and-filters execution model. Here, the term vectorization is used in the sense of processing a sequence (vector) of messages by one standing process. Although it has been shown that process vectorization achieves a significant throughput improvement, this concept has two major drawbacks. First, the theoretical performance of a vectorized integration process mainly depends on the performance of the most cost-intensive operator. Second, the practical performance strongly depends on the number of available threads. In this paper, we present an advanced optimization approach that addresses the mentioned problems. Therefore, we generalize the vectorization problem and explain how to vectorize process plans in a cost-based manner. Due to the exponential complexity, we provide a heuristic computation approach and formally analyze its optimality. In conclusion of our evaluation, the message throughput can be significantly increased compared to both the instance-based execution as well as the rule-based process vectorization.

  17. Topology based methods for vector field comparisons

    Science.gov (United States)

    Batra, Rajesh Kumar

    Vector fields are commonly found in almost all branches of the physical sciences. Aerodynamics, dynamical systems, electromagnetism, and global climate modeling are a few examples. These multivariate data fields are often large, and no general, automated method exists for comparing these fields. Existing methods require either subjective visual judgments, or data interface compatibility, or domain specific knowledge. A topology based method intrinsically eliminates all of the above limitations and has the additional advantage of significantly compressing the vector field by representing only key features of the flow. Therefore, large databases are compactly represented and quickly searched. Topology is a natural framework for the study of many vector fields. It provides rules of an organizing principle, a flow grammar, that can describe and connect together the properties common to flows. Helman and Hesselink first introduced automated methods to extract and visualize this grammar. This work extends their method by introducing automated methods for vector topology comparison. Basic two-dimensional flows are first compared. The theory is extended to compare three-dimensional flow fields and the topology on no-slip surfaces. Concepts from graph theory and linear programming are utilized to solve these problems. Finally, the first automated method for higher order singularity comparisons is introduced using mathematical theories from geometric (Clifford) algebra.

  18. 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.

  19. 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.

  20. 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. PMID:10394716

  1. Robust Video Stabilization Based on Motion Vectors

    Institute of Scientific and Technical Information of China (English)

    宋利; 周源华; 周军

    2005-01-01

    This paper proposes a new robust video stabilization algorithm to remove unwanted vibrations in video sequences. A complete theoretical analysis is first established for video stabilization, providing a basis for new stabilization algorithm. Secondly, a new robust global motion estimation (GME) algorithm is proposed. Different from classic methods, the GME algorithm is based on spatlal-temporal filtered motion vectors computed by block-matching methods. In addition, effective schemes are employed in correction phase to prevent boundary artifacts and error accumulation. Experiments show that the proposed algorithm has satisfactory stabilization effects while maintaining good tradeoff between speed and precision.

  2. 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.

  3. 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.

  4. 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.

  5. Bethe vectors in GL(3)-based quantum integrable models

    CERN Document Server

    Pakuliak, S; Slavnov, N A

    2015-01-01

    We consider a composite generalized quantum integrable model solvable by the nested algebraic Bethe ansatz. Using explicit formulas of the action of the monodromy matrix elements onto Bethe vectors in the GL(3)-based quantum integrable models we prove a formula for the Bethe vectors of composite model. We show that this representation is a particular case of general coproduct property of the weight functions (Bethe vectors) found in the theory of the deformed Knizhnik--Zamolodchokov equation.

  6. 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

  7. One-Dimensional Vector Based Pattern Matching

    Directory of Open Access Journals (Sweden)

    Y.M.Fouda

    2014-08-01

    Full Text Available Template matching is a basic method in image analysis to extract useful information from images. In this paper, we suggest a new method for pattern matching. Our method transform the template image from two dimensional image into one dimensional vector. Also all sub - windows (same size of template in the reference image will transform into one dimensional vectors. The three similarity measures SAD, SSD, and Euclidean are used to c ompute the likeness between template and all sub - windows in the reference image to find the best match. The experimental results show the superior performance of the proposed metho d over the conventional methods on various template of different sizes

  8. 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.

  9. 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.

  10. Image indexing based on vector quantization

    Science.gov (United States)

    Grana Romay, Manuel; Rebollo, Israel

    2000-10-01

    We propose the computation of the color palette of each image in isolation, using Vector Quantization methods. The image features are, then, the color palette and the histogram of the color quantization of the image with this color palette. We propose as a measure of similitude the weighted sum of the differences between the color palettes and the corresponding histograms. This approach allows the increase of the database without the recomputation of the image features and without substantial loss of discriminative power.

  11. 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.

  12. 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...

  13. 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.

  14. 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.

  15. Risk based surveillance for vector borne diseases

    DEFF Research Database (Denmark)

    Bødker, Rene

    in Northern Europe. This model approach may be used as a basis for risk based surveillance. In risk based surveillance limited resources for surveillance are targeted at geographical areas most at risk and only when the risk is high. This makes risk based surveillance a cost effective alternative...... to the present surveillance strategies based on random samples. We still don’t understand the mechanisms underlying the recent outbreaks of bluetongue, Schmallenberg, Usutu virus, tick borne encephalitis or dirofilarial worms in the Baltic See Region. It is therefore not possible to use mathematical models...... sample to a diagnostic laboratory. Risk based surveillance models may reduce this delay. An important feature of risk based surveillance models is their ability to continuously communicate the level of risk to veterinarians and hence increase awareness when risk is high. This is essential for submission...

  16. The integration profile of EIAV-based vectors.

    Science.gov (United States)

    Hacker, Caroline V; Vink, Conrad A; Wardell, Theresa W; Lee, Sheena; Treasure, Peter; Kingsman, Susan M; Mitrophanous, Kyriacos A; Miskin, James E

    2006-10-01

    Lentiviral vectors based on equine infectious anemia virus (EIAV) stably integrate into dividing and nondividing cells such as neurons, conferring long-term expression of their transgene. The integration profile of an EIAV vector was analyzed in dividing HEK293T cells, alongside an HIV-1 vector as a control, and compared to a random dataset generated in silico. A multivariate regression model was generated and the influence of the following parameters on integration site selection determined: (a) within/not within a gene, (b) GC content within 20 kb, (c) within 10 kb of a CpG island, (d) gene density within a 2-Mb window, and (e) chromosome number. The majority of the EIAV integration sites (68%; n = 458) and HIV-1 integration sites (72%; n = 162) were within a gene, and both vectors favored AT-rich regions. Sites within genes were examined using a second model to determine the influence of the gene-specific parameters, gene region, and transcriptional activity. Both EIAV and HIV-1 vectors preferentially integrated within active genes. Unlike the gammaretrovirus MLV, EIAV and HIV-1 vectors do not integrate preferentially into the promoter region or the 5' end of the transcription unit. PMID:16950499

  17. 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.

  18. 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.

  19. 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

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. Versatile Supramolecular Gene Vector Based on Host-Guest Interaction.

    Science.gov (United States)

    Liu, Jia; Hennink, Wim E; van Steenbergen, Mies J; Zhuo, Renxi; Jiang, Xulin

    2016-04-20

    It is a great challenge to arrange multiple functional components into one gene vector system to overcome the extra- and intracellular obstacles for gene therapy. In this study, we developed a supramolecular approach for constructing a versatile gene delivery system composed of adamantyl-terminated functional polymers and a β-cyclodextrin based polymer. Adamantyl-functionalized low molecular weight PEIs (PEI-Ad) and PEG (Ad-PEG) as well as poly(β-cyclodextrin) (PCD) were synthesized by one-step chemical reactions. The supramolecular inclusion complex formed from PCD to assemble LMW PEI-Ad4 via host-guest interactions can condense plasmid DNA to form nanopolyplexes by electrostatic interactions. The supramolecular polyplexes can be further PEGylated with Ad-PEG to form inclusion complexes, which showed increased salt and serum stability. In vitro experiments revealed that these supramolecular assembly polyplexes had good cytocompatibility and showed high transfection activity close to that of the commercial ExGen 500 at high dose of DNA. Also, the supramolecular vector system exhibited about 60% silencing efficiency as a siRNA vector. Thus, a versatile effective supramolecular gene vector based on host-guest complexes was fabricated with good cytocompatbility and transfection activity. PMID:27019340

  5. 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.

  6. 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.

  7. Biosensor method and system based on feature vector extraction

    Science.gov (United States)

    Greenbaum, Elias; Rodriguez, Jr., Miguel; Qi, Hairong; Wang, Xiaoling

    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.

  8. 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.

  9. Endpoint Prediction of EAF Based on Multiple Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    YUAN Ping; MAO Zhi-zhong; WANG Fu-li

    2007-01-01

    The endpoint parameters are very important to the process of EAF steel-making, but their on-line measurement is difficult. The soft sensor technology is widely used for the prediction of endpoint parameters. Based on the analysis of the smelting process of EAF and the advantages of support vector machines, a soft sensor model for predicting the endpoint parameters was built using multiple support vector machines (MSVM). In this model, the input space was divided by subtractive clustering and a sub-model based on LS-SVM was built in each sub-space. To decrease the correlation among the sub-models and to improve the accuracy and robustness of the model, the sub-models were combined by Principal Components Regression. The accuracy of the soft sensor model is perfectly improved. The simulation result demonstrates the practicability and efficiency of the MSVM model for the endpoint prediction of EAF.

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

    OpenAIRE

    Hongtao Liu; Lianglun Cheng

    2012-01-01

    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 retrans...

  11. A new generation of pPRIG-based retroviral vectors

    Directory of Open Access Journals (Sweden)

    Boulukos Kim E

    2007-11-01

    Full Text Available Abstract Background Retroviral vectors are valuable tools for gene transfer. Particularly convenient are IRES-containing retroviral vectors expressing both the protein of interest and a marker protein from a single bicistronic mRNA. This coupled expression increases the relevance of tracking and/or selection of transduced cells based on the detection of a marker protein. pAP2 is a retroviral vector containing eGFP downstream of a modified IRES element of EMCV origin, and a CMV enhancer-promoter instead of the U3 region of the 5'LTR, which increases its efficiency in transient transfection. However, pAP2 contains a limited multicloning site (MCS and shows weak eGFP expression, which previously led us to engineer an improved version, termed pPRIG, harboring: i the wild-type ECMV IRES sequence, thereby restoring its full activity; ii an optimized MCS flanked by T7 and SP6 sequences; and iii a HA tag encoding sequence 5' of the MCS (pPRIG HAa/b/c. Results The convenience of pPRIG makes it a good basic vector to generate additional derivatives for an extended range of use. Here we present several novel pPRIG-based vectors (collectively referred to as PRIGs in which : i the HA tag sequence was inserted in the three reading frames 3' of the MCS (3'HA PRIGs; ii a functional domain (ER, VP16 or KRAB was inserted either 5' or 3' of the MCS (« modular » PRIGs; iii eGFP was replaced by either eCFP, eYFP, mCherry or puro-R (« single color/resistance » PRIGs; and iv mCherry, eYFP or eGFP was inserted 5' of the MCS of the IRES-eGFP, IRES-eCFP or IRES-Puro-R containing PRIGs, respectively (« dual color/selection » PRIGs. Additionally, some of these PRIGs were also constructed in a pMigR MSCV background which has been widely used in pluripotent cells. Conclusion These novel vectors allow for straightforward detection of any expressed protein (3'HA PRIGs, for functional studies of chimeric proteins (« modular » PRIGs, for multiple transductions and

  12. 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.

  13. 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.

  14. Riesz multiwavelet bases generated by vector refinement equation

    Institute of Scientific and Technical Information of China (English)

    2009-01-01

    In this paper, we investigate compactly supported Riesz multiwavelet sequences and Riesz multiwavelet bases for L2(Rs). Suppose ψ = (ψ1, . . . , ψr)T and ψ = ( ψ1, . . . , ψr)T are two compactly supported vectors of functions in the Sobolev space (Hμ(Rs))r for some μ > 0. We provide a characterization for the sequences {ψjk : = 1, . . . , r, j ∈ Z, k ∈ Zs} and {ψ jk : = 1, . . . , r, j ∈ Z, k ∈ Zs} to form two Riesz sequences for L2(Rs), where ψjk = mj/2ψ (M j ·k) and ψjk = mj/2 ψ (M j ·k), M is an s × s integer matrix such that limn→∞ Mn = 0 and m = |detM|. Furthermore, let = (1, . . . , r)T and = ( 1, . . . , r)T be a pair of compactly supported biorthogonal refinable vectors of functions associated with the refinement masks a, a and M, where a and a are finitely supported sequences of r × r matrices. We obtain a general principle for characterizing vectors of functions ψν = (ψν1, . . . , ψνr)T and ψν = ( ψν1, . . . , ψ?νr)T , ν = 1, . . . , m 1 such that two sequences {ψjνk : ν = 1, . . . , m 1, = 1, . . . , r, j ∈ Z, k ∈ Zs} and {ψ jνk : ν = 1, . . . , m 1, = 1, . . . , r, j ∈ Z, k ∈ Zs} form two Riesz multiwavelet bases for L2(Rs). The bracket product [f, g] of two vectors of functions f, g in (L2(Rs))r is an indispensable tool for our characterization.

  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. 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.

  17. 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.

  18. 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.

  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. 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.

  1. Ship detection in Polarimetric SAR based on Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Xuwu Su

    2012-08-01

    Full Text Available A Support Vector Machine (SVM based method for ship detection in Polarimetric SAR (POLSAR is proposed in this study. Because of similarities of ship and man-made structures on land in scattering mechanisms, land and sea are first segmented by SVM according to polarimetric features and texture features; The SVM-based Recursive Feature Elimination (RFE-SVM approach is adopted to improve the performance of the segmentation algorithm. Then ship targets are extracted from sea by SVM classifier; Threshold-based rules and SVM-based rules are established for discriminating ship from non-ship target at last. The experiments are carried out on POLSAR data from Radarsat-2. For the available SAR images, the average accuracy of ship detection is over 95%.

  2. Terminal Design in Vector Network based on Windows Platform

    Directory of Open Access Journals (Sweden)

    Aqun Zhao

    2013-03-01

    Full Text Available The research work of this study focuses on the design and implementation technology of terminal in Vector Network (VN based indows platform. The VN is a kind of new communication network with vector address as the switching adon Wdress. The premise of successful deployment of VN is its integration with the current IP networks, so it is necessary to study the implementation technology of VN terminal on the base of IP terminal. Firstly, a kind of software implementation method of VN terminal and a kind of integration method of VN and IP networks named “IP over VN” were proposed in this study. Secondly, the VN driver module was designed and implemented based on the NDIS driver interface and the key technique in the implementation was summarized. Finally, the experiment network was built to test the functions of VN terminal. The test results validated the rationality of the design and implementation scheme of VN terminal. The work of this study establishes the foundation for the deployment of VN and provides an example to the development of similar systems.

  3. 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 shown....... Simulation results by using a space vector approach are presented. Special emphasis is given on the total harmonic distortion (THD) by making a comparison with those of the classical NPC topologies....

  4. 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.

  5. Innovation, Vector of the Knowledge-based Society

    Directory of Open Access Journals (Sweden)

    Vladimir-Codrin Ionescu

    2013-12-01

    Full Text Available The innovative potential of a nation is determined by its members’ creative capacity, as well as by the design and implementation of strategies and policies that are meant to support the devise, experimentation and application of new ideas, respectively the transformation thereof both into tangible goods (products and services and intangible ones (knowledge. The present paper approaches innovation as a vector of the new knowledge-based society, which consists of the main actions undertaken by the EU within the context of the “European Year of Creativity and Innovation”, as well as of the actions promoted through the Initiative known as “A Union of Innovation”, comprised by the Europe Strategy 2020. The final part of the paper illustrates the essential role of universities in developing knowledge-based and innovation-based society.

  6. INNOVATION, VECTOR OF THE KNOWLEDGE-BASED SOCIETY

    Directory of Open Access Journals (Sweden)

    VLADIMIR-CODRIN IONESCU

    2013-05-01

    Full Text Available The innovative potential of a nation is determined by its members’ creative capacity, as well as by the design and implementation of strategies and policies that are meant to support the devise, experimentation and application of new ideas, respectively the transformation thereof both into tangible goods (products and services and intangible ones (knowledge. The present paper approaches innovation as a vector of the new knowledge-based society, which consists of the main actions undertaken by the EU within the context of the “European Year of Creativity and Innovation”, as well as of the actions promoted through the Initiative known as “A Union of Innovation”, comprised by the Europe Strategy 2020. The final part of the paper illustrates the essential role of universities in developing knowledge-based and innovation-based society.

  7. 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.

  8. Development and Applications of VSV Vectors Based on Cell Tropism

    OpenAIRE

    HidekiTani; YoshiharuMatsuura

    2012-01-01

    Viral vectors have been available in various fields such as medical and biological research or gene therapy applications. Targeting vectors pseudotyped with distinct viral envelope proteins that influence cell tropism and transfection efficiency is a useful tool not only for examining entry mechanisms or cell tropisms but also for vaccine vector development. Vesicular stomatitis virus (VSV) is an excellent candidate for development as a pseudotype vector. A recombinant VSV lacking its own env...

  9. 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.

  10. The use of chromosome-based vectors for animal transgenesis.

    Science.gov (United States)

    Kuroiwa, Y; Yoshida, H; Ohshima, T; Shinohara, T; Ohguma, A; Kazuki, Y; Oshimura, M; Ishida, I; Tomizuka, K

    2002-06-01

    This article summarizes our efforts to use chromosome-based vectors for animal transgenesis, which may have a benefit for overcoming the size constraints of cloned transgenes in conventional techniques. Since the initial trial for introducing naturally occurring human chromosome fragments (hCFs) with large and complex immunogulobulin (Ig) loci into mice we have obtained several lines of trans-chromosomic (Tc) mice with transmittable hCFs. As expected the normal tissue-specific expression of introduced human genes was reproduced in them by inclusion of essential remote regulatory elements. Recent development of 'chromosome cloning' technique that enable construction of human artificial chromosomes (HACs) containing a defined chromosomal region should prevent the introduction of additional genes other than genes of interest and thus enhance the utility of chromosome vector system. Using this technique a panel of HACs harboring inserts ranging in size from 1.5 to 10 Mb from three human chromosomes (hChr2, 7, 22) has been constructed. Tc animals containing the HACs may be valuable not only as a powerful tool for functional genomics but also as an in vivo model to study therapeutic gene delivery by HACs.

  11. Normal Vector Based Subdivision Scheme to Generate Fractal Curves

    Directory of Open Access Journals (Sweden)

    Yi Li

    2013-08-01

    Full Text Available In this paper, we firstly devise a new and general p-ary subdivision scheme based on normal vectors with multi-parameters to generate fractals. Rich and colorful fractals including some known fractals and a lot of unknown ones can be generated directly and conveniently by using it uniformly. The method is easy to use and effective in generating fractals since the values of the parameters and the directions of normal vectors can be designed freely to control the shape of generated fractals. Secondly, we illustrate the technique with some design results of fractal generation and the corresponding fractal examples from the point of view of visualization, including the classical Lévy curves, Dragon curves, Sierpiński gasket, Koch curve, Koch-type curves and other fractals. Finally, some fractal properties of the limit of the presented subdivision scheme, including existence, self-similarity, non-rectifiability, and continuity but nowhere differentiability are described from the point of view of theoretical analysis.

  12. 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.

  13. Parallel Kalman filter track fit based on vector classes

    International Nuclear Information System (INIS)

    Modern high energy physics experiments have to process terabytes of input data produced in particle collisions. The core of the data reconstruction in high energy physics is the Kalman filter. Therefore, developing the fast Kalman filter algorithm, which uses maximum available power of modern processors, is important, in particular for initial selection of events interesting for the new physics. One of processors features, which can speed up the algorithm, is a SIMD instruction set, which allows to pack several data items in one register and operate on all of them in one go, thus achieving more operations per clock cycle. Therefore a flexible and useful interface, which uses the SIMD instruction set on different CPU and GPU processors architectures, has been realized as a vector classes library. The Kalman filter based track fitting algorithm has been implemented with use of the vector classes. Fitting quality tests show good results with the residuals equal to 49 μm and 44 μm for x and y track parameters and relative momentum resolution of 0.7%. The fitting time of 0.053 μs per track has been achieved on Intel Xeon X5550 with 8 cores at 2.6 GHz by using in addition Intel Threading Building Blocks.

  14. 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.

  15. 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.

  16. 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.

  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. 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.

  20. 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...

  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

    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...... 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...... 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...

  2. 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].

  3. DBCSVM: Density Based Clustering Using Support VectorMachines

    Directory of Open Access Journals (Sweden)

    Santosh Kumar Rai

    2012-07-01

    Full Text Available Data categorization is challenging job in a current scenario. The growth rate of a multimedia data are increase day to day in an internet technology. For the better retrieval and efficient searching of a data, a process required for grouping the data. However, data mining can find out helpful implicit information in large databases. To detect the implicit useful information from large databases various data mining techniques are use. Data clustering is an important data mining technique for grouping data sets into different clusters and each cluster having same properties of data. In this paper we have taken image data sets and firstly applying the density based clustering to grouped the images, density based clustering grouped the images according to the nearest feature sets but not grouped outliers, then we used an important super hyperplane classifier support vector machine (SVM which classify the all outlier left from density based clustering. This method improves the efficiency of image grouping and gives better results.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  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. 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.

  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

    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......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......-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. Agent-based modeling of malaria vectors: the importance of spatial simulation

    OpenAIRE

    Bomblies, Arne

    2014-01-01

    Background The modeling of malaria vector mosquito populations yields great insight into drivers of malaria transmission at the village scale. Simulation of individual mosquitoes as “agents” in a distributed, dynamic model domain may be greatly beneficial for simulation of spatial relationships of vectors and hosts. Methods In this study, an agent-based model is used to simulate the life cycle and movement of individual malaria vector mosquitoes in a Niger Sahel village, with individual simul...

  16. 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.

  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. Development and applications of VSV vectors based on cell tropism

    Directory of Open Access Journals (Sweden)

    Hideki eTani

    2012-01-01

    Full Text Available Viral vectors have been available in various fields such as medical and biological research or gene therapy applications. Targeting vectors pseudotyped with distinct viral envelope proteins that influence cell tropism and transfection efficiency is a useful tool not only for examining entry mechanisms or cell tropisms but also for vaccine vector development. Vesicular stomatitis virus (VSV is an excellent candidate for development as a pseudotype vector. A recombinant VSV lacking its own envelope (G gene has been used to produce a pseudotype or recombinant VSV possessing the envelope proteins of heterologous viruses. These viruses possess a reporter gene instead of a VSV G gene in their genome, and therefore it is easy to evaluate their infectivity in the study of viral entry, including identification of viral receptors. Furthermore, advantage can be taken of a property of the pseudotype VSV, which is competence for single-round infection, in handling many different viruses that are either difficult to amplify in cultured cells or animals or that require specialized containment facilities. Here we describe procedures for producing pseudotype or recombinant VSVs and a few of the more prominent examples from among envelope viruses, such as hepatitis C virus, Japanese encephalitis virus, baculovirus, and hemorrhagic fever viruses.

  19. ELABORATION OF A VECTOR BASED SEMANTIC CLASSIFICATION OVER THE WORDS AND NOTIONS OF THE NATURAL LANGUAGE

    OpenAIRE

    Safonov, K.; Lichargin, D.

    2009-01-01

    The problem of vector-based semantic classification over the words and notions of the natural language is discussed. A set of generative grammar rules is offered for generating the semantic classification vector. Examples of the classification application and a theorem of optional formal classification incompleteness are presented. The principles of assigning the meaningful phrases functions over the classification word groups are analyzed.

  20. A Novel Coding Method Based on Fuzzy Vector Quantization for Noised Image

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    In this paper a novel coding method based on fuzzy vector quantization for noised image with Gaussian white-noise pollution is presented. By restraining the high frequency subbands of wavelet image the noise is significantly removed and coded with fuzzy vector quantization. The experimental result shows that the method can not only achieve high compression ratio but also remove noise dramatically.

  1. 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.

  2. Classification of Regional Ionospheric Disturbances Based on Support Vector Machines

    Science.gov (United States)

    Begüm Terzi, Merve; Arikan, Feza; Arikan, Orhan; Karatay, Secil

    2016-07-01

    Ionosphere is an anisotropic, inhomogeneous, time varying and spatio-temporally dispersive medium whose parameters can be estimated almost always by using indirect measurements. Geomagnetic, gravitational, solar or seismic activities cause variations of ionosphere at various spatial and temporal scales. This complex spatio-temporal variability is challenging to be identified due to extensive scales in period, duration, amplitude and frequency of disturbances. Since geomagnetic and solar indices such as Disturbance storm time (Dst), F10.7 solar flux, Sun Spot Number (SSN), Auroral Electrojet (AE), Kp and W-index provide information about variability on a global scale, identification and classification of regional disturbances poses a challenge. The main aim of this study is to classify the regional effects of global geomagnetic storms and classify them according to their risk levels. For this purpose, Total Electron Content (TEC) estimated from GPS receivers, which is one of the major parameters of ionosphere, will be used to model the regional and local variability that differs from global activity along with solar and geomagnetic indices. In this work, for the automated classification of the regional disturbances, a classification technique based on a robust machine learning technique that have found wide spread use, Support Vector Machine (SVM) is proposed. SVM is a supervised learning model used for classification with associated learning algorithm that analyze the data and recognize patterns. In addition to performing linear classification, SVM can efficiently perform nonlinear classification by embedding data into higher dimensional feature spaces. Performance of the developed classification technique is demonstrated for midlatitude ionosphere over Anatolia using TEC estimates generated from the GPS data provided by Turkish National Permanent GPS Network (TNPGN-Active) for solar maximum year of 2011. As a result of implementing the developed classification

  3. 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.

  4. 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)...

  5. Research on Matrix Converter Based on Space Vector Modulation

    Directory of Open Access Journals (Sweden)

    Zhanjun Qiao

    2013-09-01

    Full Text Available In this study, we study the control strategy for matrix converter. Through absorbing ideas about virtual rectification put forward by P.D.Ziogas, the common control idea of AC-DC-AC convertor is introduced into control of matrix converter; SPWM modulation strategy and space vector modulation strategy for matrix converter are studied respectively. In terms of SPWM modulation strategy, it has some advantages: for example, main circuit structure is simple; control program is simple; control switch function does not need complex mathematical derivation and calculation. In terms of space vector modulation strategy, it has advantages as follows: the physical conception is clear; input power factor can be adjusted; output voltage and current sine degree are high. In this chapter, an in-depth analysis will be conducted for this control method

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

    OpenAIRE

    Krenk, Steen; Nielsen, Martin Bjerre

    2013-01-01

    A conservative time integration algorithm based on a convected set of orthonormal base vectors is presented. The equations of motion are derived from an extended Hamiltonian formulation, combining the components of the three base vectors with a set of orthonormality constraints. The particular form 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 strai...

  7. 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.

  8. Wireless Localization Based on RSSI Fingerprint Feature Vector

    OpenAIRE

    Aiguo Zhang; Ying Yuan; Qunyong Wu; Shunzhi Zhu; Jian Deng

    2015-01-01

    RSSI wireless signal is a reference information that is widely used in indoor positioning. However, due to the wireless multipath influence, the value of the received RSSI will have large fluctuations and cause large distance error when RSSI is fitted to distance. But experimental data showed that, being affected by the combined factors of the environment, the received RSSI feature vector which is formed by lots of RSSI values from different APs is a certain stability. Therefore, the paper pr...

  9. Research on Matrix Converter Based on Space Vector Modulation

    OpenAIRE

    Zhanjun Qiao; Wei Xu

    2013-01-01

    In this study, we study the control strategy for matrix converter. Through absorbing ideas about virtual rectification put forward by P.D.Ziogas, the common control idea of AC-DC-AC convertor is introduced into control of matrix converter; SPWM modulation strategy and space vector modulation strategy for matrix converter are studied respectively. In terms of SPWM modulation strategy, it has some advantages: for example, main circuit structure is simple; control program is simple; control swit...

  10. Window-Based Example Selection in Learning Vector Quantization

    OpenAIRE

    Witoelar, A. W.; Ghosh, Anarta; De Vries, J.J.G.; Hammer, B; Biehl, M.

    2010-01-01

    A variety of modifications have been employed to learning vector quantization (LVQ) algorithms using either crisp or soft windows for selection of data. Although these schemes have been shown in practice to improve performance, a theoretical study on the influence of windows has so far been limited. Here we rigorously analyze the influence of windows in a controlled environment of gaussian mixtures in high dimensions. Concepts from statistical physics and the theory of online learning allow a...

  11. 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.

  12. A new method for comparing scanpaths based on vectors and dimensions

    OpenAIRE

    Dewhurst, Richard; Jarodzka, Halszka; Holmqvist, Kenneth; Foulsham, Tom; Nyström, Marcus

    2011-01-01

    Dewhurst, R., Jarodzka, H., Holmqvist, K., Foulsham, T., & Nyström, M. (2011, May). A new method for comparing scanpaths based on vectors and dimensions. Vision Sciences Society 2011, Naples, Florida.

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

    DEFF Research Database (Denmark)

    Krenk, Steen; Nielsen, Martin Bjerre

    2013-01-01

    of orthogonality relations between the base vector components and their conjugate momentum components. These orthogonality relations permit explicit elimination of the Lagrange multipliers associated with the constraints, leading to a projected form of the dynamic equation without explicit algebraic constraints...

  14. An evidence-based vector control strategy for military deployments: the British Army experience.

    Science.gov (United States)

    Croft, A M; Baker, D; von Bertele, M J

    2001-01-01

    We describe the British Army's current strategy for controlling arthropod vectors of disease during overseas deployments. Military commanders and medical officers have different, but complementary responsibilities in achieving vector control. In this paper we define a hierarchy of evidence-based vector control guidelines. Field guidelines must be based on the best available research evidence, preferably that derived from pragmatic randomised controlled trials (RCTs), and from systematic reviews of trials. Assessing the effectiveness of different vector control measures involves a trade-off between the relative benefits and harm of different technology options. There is compelling scientific evidence that bed nets and screens treated with a pyrethroid insecticide are highly effective in protecting against nocturnally active, anthropophilic arthropods (including ectoparasites), and will reduce the incidence of malaria, leishmaniasis, lymphatic filariasis and Chagas' disease. Etofenprox and deltamethrin are the safest pyrethroids, and permethrin the least safe. Vector control strategies of probable effectiveness are the use of insecticide-treated clothing, the wearing of protective clothing, and the correct use of DEET-based topical insect repellents. Aerosol insecticides are of debatable effectiveness. Other effective vector control measures, of limited usefulness during deployments, include electric fans, mosquito coils/vaporising mats, and smoke. "Biological" vector control measures, and insect buzzers/electrocuters are ineffective. Practical insect avoidance measures, based on an understanding of vector biology, complete the military vector-control arsenal. We conclude that practical insect avoidance measures, combined with pyrethroid-treated nets and clothing, and DEET-based topical repellents, can achieve almost 100% protection against biting arthropods. PMID:11584666

  15. Bethe vectors for models based on the super-Yangian $Y(\\mathfrak{gl}(m|n))$

    CERN Document Server

    Pakuliak, S Z; Slavnov, N A

    2016-01-01

    We study Bethe vectors of integrable models based on the super-Yangian $Y(\\mathfrak{gl}(m|n))$. Starting from the super-trace formula, we exhibit recursion relations for these vectors in the case of $Y(\\mathfrak{gl}(2|1))$ and $Y(\\mathfrak{gl}(1|2))$. These recursion relations allow to get explicit expressions for the Bethe vectors. Using an antimorphism of the super-Yangian $Y(\\mathfrak{gl}(m|n))$, we also construct a super-trace formula for dual Bethe vectors, and, for $Y(\\mathfrak{gl}(2|1))$ and $Y(\\mathfrak{gl}(1|2))$ super-Yangians, show recursion relations for them. Again, the latter allow us to get explicit expressions for dual Bethe vectors.

  16. 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.

  17. 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.

  18. 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

  19. Emotional Vector Distance Based Sentiment Analysis of Wakamono Kotoba

    Institute of Scientific and Technical Information of China (English)

    2012-01-01

    In this paper, we propose a method for estimating emotion in Wakamono Kotoba that were not registered in the system, by using Wakamono Kotoba example sentences as features. The pro- posed method applies Earth Mover's Distance (EMD) to vector of words. As a result of the evaluation ex- periment using 14 440 sentences, higher estimation accuracy is obtained by considering emotional dis- tance between words - an approach that had not been used in the conventional research - than by using only word importance value.

  20. 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.

  1. Sagnac Interferometer Based Generation of Controllable Cylindrical Vector Beams

    Directory of Open Access Journals (Sweden)

    Cristian Acevedo

    2016-01-01

    Full Text Available We report on a novel experimental geometry to generate cylindrical vector beams in a very robust manner. Continuous control of beams’ properties is obtained using an optically addressable spatial light modulator incorporated into a Sagnac interferometer. Forked computer-generated holograms allow introducing different topological charges while orthogonally polarized beams within the interferometer permit encoding the spatial distribution of polarization. We also demonstrate the generation of complex waveforms obtained by combining two orthogonal beams having both radial modulations and azimuthal dislocations.

  2. Inertial Vector Based Attitude Stabilization of Rigid Body Without Angular Velocity Measurements

    OpenAIRE

    Benziane, L.; Benallegue, A.; Chitour, Y.; Tayebi, A.

    2015-01-01

    We address the problem of attitude stabilization of a rigid body, in which neither the angular velocity nor the instantaneous measurements of the attitude are used in the feedback, only body vector measurements are needed. The design of the controller is based on an angular velocity observer-like system, where a first order linear auxiliary system based directly on vector measurements is introduced. The introduction of gain matrices provides more tuning flexibility and better results compared...

  3. Formulation of 2D Graphene Deformation Based on Chiral-Tube Base Vectors

    Directory of Open Access Journals (Sweden)

    Bohua Sun

    2010-01-01

    Full Text Available The intrinsic feature of graphene honeycomb lattice is defined by its chiral index (n,m, which can be taken into account when using molecular dynamics. However, how to introduce the index into the continuum model of graphene is still an open problem. The present manuscript adopts the continuum shell model with single director to describe the mechanical behaviors of graphene. In order to consider the intrinsic features of the graphene honeycomb lattice—chiral index (n,m, the chiral-tube vectors of graphene in real space have been used for construction of reference unit base vectors of the shell model; therefore, the formulations will contain the chiral index automatically, or in an explicit form in physical components. The results are quite useful for future studies of graphene mechanics.

  4. 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

  5. High-base vector beam encoding/decoding for visible-light communications.

    Science.gov (United States)

    Zhao, Yifan; Wang, Jian

    2015-11-01

    Polarization is a basic property of light. Different from well-known linear, circular, and elliptical polarizations, which are spatially homogeneous, a vector light beam with spatially variant polarization states has received increasing interest for its expanded functionalities. In this Letter, we present a visible-light communication link exploiting high-base vector beam encoding/decoding. Using a single phase-only spatial light modulator, we generate 16 states of vector beams representing hexadecimal numbers. In the visible-light communication link experiment, we transmit a random high-base number sequence with 10,000 hexadecimal numbers and a 64×64 pixel Lena gray image with 8192 hexadecimal numbers. The bit error rate is evaluated, and zero error among all received hexadecimal numbers is achieved, showing favorable link communication performance using the high-base vector beam encoding/decoding. PMID:26512464

  6. 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%.

  7. 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.

  8. An Even Grid Based Lattice Vector Quantization Algorithm for Mobile Audio Coding

    Directory of Open Access Journals (Sweden)

    Bo Hang

    2011-06-01

    Full Text Available This paper proposed an even grid based lattice vector quantization method for audio coding. The method uses energy priority, with basic code book and the ball-type expansion, which is applicable to the low rate of the variable-rate vector quantization coding. The method uses the lattice characteristics to resolve rapid index distribution problem, as well as the compression of the basic code book. The experiment results show that the proposed method is as good as vector quantization method in ITU-T standard G729.1 in quality, with lower storage cost and computational complexity.

  9. 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.

  10. 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.

  11. A local technique based on vectorized surfaces for craniofacial reconstruction.

    Science.gov (United States)

    Tilotta, Françoise M; Glaunès, Joan A; Richard, Frédéric J P; Rozenholc, Yves

    2010-07-15

    In this paper, we focus on the automation of facial reconstruction. Since they consider the whole head as the object of interest, usual reconstruction techniques are global and involve a large number of parameters to be estimated. We present a local technique which aims at reaching a good trade-off between bias and variance following the paradigm of non-parametric statistics. The estimation is localized on patches delimited by surface geodesics between anatomical points of the skull. The technique relies on a continuous representation of the individual surfaces embedded in the vectorial space of extended normal vector fields. This allows to compute deformations and averages of surfaces. It consists in estimating the soft-tissue surface over patches. Using a homogeneous database described in [31], we obtain results on the chin and nasal regions with an average error below 1mm, outperforming the global reconstruction techniques.

  12. Aero-Engine Condition Monitoring Based on Support Vector Machine

    Science.gov (United States)

    Zhang, Chunxiao; Wang, Nan

    The maintenance and management of civil aero-engine require advanced monitor approaches to estimate aero-engine performance and health in order to increase life of aero-engine and reduce maintenance costs. In this paper, we adopted support vector machine (SVM) regression approach to monitor an aero-engine health and condition by building monitoring models of main aero-engine performance parameters(EGT, N1, N2 and FF). The accuracy of nonlinear baseline models of performance parameters is tested and the maximum relative error does not exceed ±0.3%, which meets the engineering requirements. The results show that SVM nonlinear regression is an effective method in aero-engine monitoring.

  13. 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.

  14. The electronic image stabilization technology research based on improved optical-flow motion vector estimation

    Science.gov (United States)

    Wang, Chao; Ji, Ming; Zhang, Ying; Jiang, Wentao; Lu, Xiaoyan; Wang, Jiaoying; Yang, Heng

    2016-01-01

    The electronic image stabilization technology based on improved optical-flow motion vector estimation technique can effectively improve the non normal shift, such as jitter, rotation and so on. Firstly, the ORB features are extracted from the image, a set of regions are built on these features; Secondly, the optical-flow vector is computed in the feature regions, in order to reduce the computational complexity, the multi resolution strategy of Pyramid is used to calculate the motion vector of the frame; Finally, qualitative and quantitative analysis of the effect of the algorithm is carried out. The results show that the proposed algorithm has better stability compared with image stabilization based on the traditional optical-flow motion vector estimation method.

  15. A sight on the current nanoparticle-based gene delivery vectors

    Science.gov (United States)

    Dizaj, Solmaz Maleki; Jafari, Samira; Khosroushahi, Ahmad Yari

    2014-05-01

    Nowadays, gene delivery for therapeutic objects is considered one of the most promising strategies to cure both the genetic and acquired diseases of human. The design of efficient gene delivery vectors possessing the high transfection efficiencies and low cytotoxicity is considered the major challenge for delivering a target gene to specific tissues or cells. On this base, the investigations on non-viral gene vectors with the ability to overcome physiological barriers are increasing. Among the non-viral vectors, nanoparticles showed remarkable properties regarding gene delivery such as the ability to target the specific tissue or cells, protect target gene against nuclease degradation, improve DNA stability, and increase the transformation efficiency or safety. This review attempts to represent a current nanoparticle based on its lipid, polymer, hybrid, and inorganic properties. Among them, hybrids, as efficient vectors, are utilized in gene delivery in terms of materials (synthetic or natural), design, and in vitro/ in vivo transformation efficiency.

  16. 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".

  17. Direct power control of DFIG based on discrete space vector modulation

    Energy Technology Data Exchange (ETDEWEB)

    Verij Kazemi, Mohammad; Sadeghi Yazdankhah, Ahmad; Madadi Kojabadi, Hossein [Electrical Engineering Department, Sahand University of Technology, Tabriz (Iran)

    2010-05-15

    This paper presents a new direct power control (DPC) strategy for a double fed induction generator (DFIG) based wind energy generation system. Switching vectors for rotor side converter were selected from the optimal switching table using the estimated stator flux position and the errors of the active and reactive power. A few number of voltage vectors may cause undesired power and stator current ripple. In this paper the increased number of voltage vectors with application of the Discrete Space Vector Modulation (DSVM) will be presented. Then a new switching table in supersynchronous and subsynchronous frames will be proposed. Simulation results of a 2 MW DFIG system demonstrate the effectiveness and robustness of the proposed control strategy during variations of active and reactive power, machine parameters, and wind speed. (author)

  18. A versatile bacterial expression vector based on the synthetic biology plasmid pSB1.

    Science.gov (United States)

    Skrlj, Nives; Erculj, Nina; Dolinar, Marko

    2009-04-01

    We have developed an Escherichia coli expression vector that is particularly useful for construction and production of fusion proteins. Based on the synthetic biology pSB1C3 platform, the resulting vector offers a combination of useful features: the strong T7 promoter combined with lac operator, OmpA signal sequence, a selection of cloning sites located at convenient positions and a 3'-terminal His-10 tag. Each of these regions is flanked by a restriction site that allows for easy vector modification, including removal of the signal sequence without perturbation of the reading frame. All the elements were assembled by stepwise addition of three cassettes for which the design was made de novo. To prove the efficiency of the new vector, named pMD204, we successfully produced a cysteine proteinase inhibitor variant in the periplasm and in the cytoplasm of E. coli, in both cases as a soluble and active protein. PMID:19027858

  19. A replication competent lentivirus (RCL) assay for equine infectious anaemia virus (EIAV)-based lentiviral vectors.

    Science.gov (United States)

    Miskin, J; Chipchase, D; Rohll, J; Beard, G; Wardell, T; Angell, D; Roehl, H; Jolly, D; Kingsman, S; Mitrophanous, K

    2006-02-01

    Lentiviral vectors are being developed to satisfy a wide range of currently unmet medical needs. Vectors destined for clinical evaluation have been rendered multiply defective by deletion of all viral coding sequences and nonessential cis-acting sequences from the transfer genome. The viral envelope and accessory proteins are excluded from the production system. The vectors are produced from separate expression plasmids that are designed to minimize the potential for homologous recombination. These features ensure that the regeneration of the starting virus is impossible. It is a regulatory requirement to confirm the absence of any replication competent virus, so we describe here the development and validation of a replication competent lentivirus (RCL) assay for equine infectious anaemia virus (EIAV)-based vectors. The assay is based on the guidelines developed for testing retroviral vectors, and uses the F-PERT (fluorescent-product enhanced reverse transcriptase) assay to test for the presence of a transmissible reverse transcriptase. We have empirically modelled the replication kinetics of an EIAV-like entity in human cells and devised an amplification protocol by comparison with a replication competent MLV. The RCL assay has been validated at the 20 litre manufacturing scale, during which no RCL was detected. The assay is theoretically applicable to any lentiviral vector and pseudotype combination. PMID:16208418

  20. Vector perturbation based adaptive distributed precoding scheme with limited feedback for CoMP systems

    Directory of Open Access Journals (Sweden)

    Cuthbert Laurie

    2011-01-01

    Full Text Available Abstract A downlink adaptive distributed precoding scheme is proposed for coordinated multi-point (CoMP transmission systems. The serving base station (BS obtains the optimal precoding vector via user feedback. Meanwhile, the precoding vector of each coordinated BS is determined by adaptive gradient iteration according to the perturbation vector and the adjustment factor based on the vector perturbation method. In each transmission frame, the CoMP user feeds the precoding matrix index back to the serving BS, and feeds back the adjustment factor index to the coordinated BSs, which can reduce the uplink feedback overhead. The selected adjustment factor for each coordinated BS is obtained via the precoding vector of the coordinated BS used in the previous frame and the preferred precoding vector of the serving BS in this frame. The proposed scheme takes advantage of the spatial non-correlation and temporal correlation of the distributed MIMO channel. The design of the adjustment factor set is given and the channel feedback delay is considered. The system performance of the proposed scheme is verified with and without feedback delay respectively and the system feedback overhead is analyzed. Simulation results show that the proposed scheme has a good trade-off between system performance and the system control information overhead on feedback.

  1. 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.

  2. Cloning and stable maintenance of DNA fragments over 300 kb in Escherichia coli with conventional plasmid-based vectors.

    OpenAIRE

    Tao, Q.; Zhang, H. B.

    1998-01-01

    Bacterial artificial chromosome (BAC) and P1-derived artificial chromosome (PAC) systems were previously developed for cloning of very large eukaryotic DNA fragments in bacteria. We report the feasibility of cloning very large fragments of eukaryotic DNA in bacteria using conventional plasmid-based vectors. One conventional plasmid vector (pGEM11), one conventional binary plasmid vector (pSLJ1711) and one conventional binary cosmid vector (pCLD04541) were investigated using the widely used BA...

  3. Opportunities for Improved Chagas Disease Vector Control Based on Knowledge, Attitudes and Practices of Communities in the Yucatan Peninsula, Mexico

    OpenAIRE

    Kathryn Rosecrans; Gabriela Cruz-Martin; Ashley King; Eric Dumonteil

    2014-01-01

    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...

  4. 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.

  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. 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.

  7. A Novel CSR-Based Sparse Matrix-Vector Multiplication on GPUs

    OpenAIRE

    Guixia He; Jiaquan Gao

    2016-01-01

    Sparse matrix-vector multiplication (SpMV) is an important operation in scientific computations. Compressed sparse row (CSR) is the most frequently used format to store sparse matrices. However, CSR-based SpMVs on graphic processing units (GPUs), for example, CSR-scalar and CSR-vector, usually have poor performance due to irregular memory access patterns. This motivates us to propose a perfect CSR-based SpMV on the GPU that is called PCSR. PCSR involves two kernels and accesses CSR arrays in ...

  8. 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.

  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. Model based wind vector field reconstruction from lidar data

    OpenAIRE

    Schlipf, David; Rettenmeier, Andreas; Haizmann, Florian; Hofsäß, Martin; Courtney, Mike; Cheng, Po Wen

    2012-01-01

    In recent years lidar technology found its way into wind energy for resource assessment and control. For both fields of application it is crucial to reconstruct the wind field from the limited information provided by a lidar system. For lidar assisted wind turbine control model based wind field reconstruction is used to obtain signals from wind characteristics such as wind speed, direction and shears in a high temporal resolution. This work shows how these methods can be used for lidar based ...

  11. 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.

  12. Production, purification and titration of a lentivirus-based vector for gene delivery purposes.

    Science.gov (United States)

    Nasri, Masoud; Karimi, Ali; Allahbakhshian Farsani, Mehdi

    2014-12-01

    Viral vectors are valuable tools to deliver genetic materials into cells. Vectors derived from human immunodeficiency virus type 1 are being widely used for gene delivery, mainly because they are able to transduce both dividing and non-dividing cells which leads to stable and long term gene expression. In addition, these types of vectors are safe, with low toxicity, high stability and cell type specificity. Therefore, this work was aimed to produce lentivirus-based vector using a three-plasmid system. To produce this system, the eGFP marker gene was cloned into the plasmid pWPXLd. Subsequently, this vector plasmid, along with packaging plasmids, psPAX2 and envelope plasmid, pMD2.G, was co-transfected into packaging cell line (293T) using calcium phosphate method. 48 h post transfection, the constructed viral vector was harvested, purified and concentrated and stored at -80 °C for next experiments. The titration of the vector was carried out, using ELISA, flowcytometry, and fluorescent microscopy. Finally, transduction of HEK-293T, CHO, HepG2, MCF-7, MEFs and Jurkat cell lines was carried out with indicated cell numbers and multiplicities of infections of the vector in the presence of polybrene. Using this system, high titer lentivirus at titers of up to 2 × 10(8) transducing units/ml (TU/ml) was successfully generated and its transduction efficacy was improved by seven to over 20-fold in various cell types. We demonstrate the applicability of this vector for the efficient transduction of dividing and non-dividing cells, including HEK-293T, CHO, HepG2, MCF-7, MEFs and Jurkat cell line. Transduction efficiency yielded titers of (6.3 ± 1.2) 10(5) TU/ml. Furthermore, lentivirus transferred transgene was expressed at high level in the target cells and expression was followed until 90 days after transduction. Thus, the vector generated in this work, might be able to deliver the transgene into a wide range of mammalian cells.

  13. Acoustic Event Detection Based on MRMR Selected Feature Vectors

    OpenAIRE

    VOZARIKOVA Eva; Juhar, Jozef; CIZMAR Anton

    2012-01-01

    This paper is focused on the detection of potentially dangerous acoustic events such as gun shots and breaking glass in the urban environment. Various feature extraction methods can be used forrepresenting the sound in the detection system based on Hidden Markov Models of acoustic events. Mel – frequency cepstral coefficients, low - level descriptors defined in MPEG-7 standard and another time andspectral features were considered in the system. For the selection of final subset of features Mi...

  14. Image Replica Detection based on Binary Support Vector Classifier

    OpenAIRE

    Maret, Y.; Dufaux, F.; Ebrahimi, T.

    2005-01-01

    In this paper, we present a system for image replica detection. More specifically, the technique is based on the extraction of 162 features corresponding to texture, color and gray-level characteristics. These features are then weighted and statistically normalized. To improve training and performances, the features space dimensionality is reduced. Lastly, a decision function is generated to classify the test image as replica or non-replica of a given reference image. Experimental results sho...

  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. Support vector machine based on adaptive acceleration particle swarm optimization.

    Science.gov (United States)

    Abdulameer, Mohammed Hasan; Sheikh Abdullah, Siti Norul Huda; Othman, Zulaiha Ali

    2014-01-01

    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. PMID:24790584

  17. 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.

  18. Yeast recombination-based cloning as an efficient way of constructing vectors for Zymoseptoria tritici

    OpenAIRE

    Kilaru, S.; Steinberg, G.

    2015-01-01

    Highlights • Yeast recombination-based cloning (YRBC) is a reliable and inexpensive way of generating plasmids. • We provide 4 vectors for YRBC that a cover different resistance genes. • Using this technique promises rapid generation of molecular tools to study Z. tritici.

  19. Scale-Invariance of Support Vector Machines based on the Triangular Kernel

    OpenAIRE

    Sahbi, Hichem; Fleuret, François

    2002-01-01

    This report focuses on the scale-invariance and the good performances of Support Vector Machines based on the triangular kernel. After a mathematica- l analysis of the scale-invariance of learning with that kernel, we illustrate its behavior with a simple 2D classification problem and compare its performances to those of a Gaussian kernel on face detection and handwritten character recognition

  20. 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

  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.

    2016-01-01

    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…

  2. Ultrasensitive vector bending sensor based on multicore optical fiber.

    Science.gov (United States)

    Villatoro, Joel; Van Newkirk, Amy; Antonio-Lopez, Enrique; Zubia, Joseba; Schülzgen, Axel; Amezcua-Correa, Rodrigo

    2016-02-15

    In this Letter, we demonstrate a compellingly simple directional bending sensor based on multicore optical fibers (MCF). The device operates in reflection mode and consists of a short segment of a three-core MCF that is fusion spliced at the distal end of a standard single mode optical fiber. The asymmetry of our MCF along with the high sensitivity of the supermodes of the MCF make the small bending on the MCF induce drastic changes in the supermodes, their excitation, and, consequently, on the reflected spectrum. Our MCF bending sensor was found to be highly sensitive (4094  pm/deg) to small bending angles. Moreover, it is capable of distinguishing multiple bending orientations. PMID:26872200

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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

    Saccharomyces cerevisiae is one of the most widely used cell factory in industrial biotechnology and it is used for the production of fuels, chemicals, food ingredients, food and beverages, and pharmaceuticals. Such bioprocesses frequently require multiple rounds of metabolic engineering to obtain...... of shuttle vectors for convenience of use for high-throughput cloning and selectable marker recycling. The new USER-based cloning vectors consist of a unique USER site and a CRE-loxP-mediated marker recycling system. The USER site allows insertion of genes of interest along with a bidirectional promoter...

  8. Energy-saving technology of vector controlled induction motor based on the adaptive neuro-controller

    Science.gov (United States)

    Engel, E.; Kovalev, I. V.; Karandeev, D.

    2015-10-01

    The ongoing evolution of the power system towards a Smart Grid implies an important role of intelligent technologies, but poses strict requirements on their control schemes to preserve stability and controllability. This paper presents the adaptive neuro-controller for the vector control of induction motor within Smart Gird. The validity and effectiveness of the proposed energy-saving technology of vector controlled induction motor based on adaptive neuro-controller are verified by simulation results at different operating conditions over a wide speed range of induction motor.

  9. Production of Retrovirus-Based Vectors in Mildly Acidic pH Conditions.

    Science.gov (United States)

    Holic, Nathalie; Fenard, David

    2016-01-01

    Gene transfer vectors based on retroviridae are increasingly becoming a tool of choice for biomedical research and for the development of biotherapies in rare diseases or cancers. To meet the challenges of preclinical and clinical production, different steps of the production process of self-inactivating γ-retroviral (RVs) and lentiviral vectors (LVs) have been improved (e.g., transfection, media optimization, cell culture conditions). However, the increasing need for mass production of such vectors is still a challenge and could hamper their availability for therapeutic use. Recently, we observed that the use of a neutral pH during vector production is not optimal. The use of mildly acidic pH conditions (pH 6) can increase by two- to threefold the production of RVs and LVs pseudotyped with the vesicular stomatitis virus G (VSV-G) or gibbon ape leukemia virus (GALV) glycoproteins. Here, we describe the production protocol in mildly acidic pH conditions of GALVTR- and VSV-G-pseudotyped LVs using the transient transfection of HEK293T cells and the production protocol of GALV-pseudotyped RVs produced from a murine producer cell line. These protocols should help to achieve higher titers of vectors, thereby facilitating experimental research and therapeutic applications. PMID:27317171

  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. Research on Amplifier Performance Evaluation Based on δ-Support Vector Regression

    Directory of Open Access Journals (Sweden)

    Xing Huo

    2014-01-01

    Full Text Available Focusing on the amplifier performance evaluation demand, a novel evaluation strategy based on δ-support vector regression (δ-SVR is proposed in this paper. Lower computer calculation demand is considered firstly. And this is dealt with by the superiority of δ-SVR which can be significantly improved on the number of support vectors. Moreover, the function of δ-SVR employs the modified RBF kernel function which is constructed from an original kernel by removing the last coordinate and adding the linear term with the last coordinate. Experiment adopted the typical circuit Sallen-Key low pass filter to prove the proposed evaluation strategy via the eight performance indexes. Simulation results reveal that the need of the number of δ-SVR support vectors is the lowest among the other two methods LSSVR and ε-SVR under obtaining nearly the same evaluation result. And this is also suitable for promotion computational speed.

  12. Alternative-splicing-based bicistronic vectors for ratio-controlled protein expression and application to recombinant antibody production.

    OpenAIRE

    Fallot, Stéphanie; Ben Naya, Raouia; Hieblot, Corinne; Mondon, Philippe; Lacazette, Eric; Bouayadi, Khalil; Kharrat, Abdelhakim; Touriol, Christian; Prats, Hervé

    2009-01-01

    In the last decade polycistronic vectors have become essential tools for both basic science and gene therapy applications. In order to co-express heterologous polypeptides, different systems have been developed from Internal Ribosome Entry Site (IRES) based vectors to the use of the 2A peptide. Unfortunately, these methods are not fully suitable for the efficient and reproducible modulation of the ratio between the proteins of interest. Here we describe a novel bicistronic vector type based o...

  13. 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...

  14. 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

  15. A Novel CSR-Based Sparse Matrix-Vector Multiplication on GPUs

    Directory of Open Access Journals (Sweden)

    Guixia He

    2016-01-01

    Full Text Available Sparse matrix-vector multiplication (SpMV is an important operation in scientific computations. Compressed sparse row (CSR is the most frequently used format to store sparse matrices. However, CSR-based SpMVs on graphic processing units (GPUs, for example, CSR-scalar and CSR-vector, usually have poor performance due to irregular memory access patterns. This motivates us to propose a perfect CSR-based SpMV on the GPU that is called PCSR. PCSR involves two kernels and accesses CSR arrays in a fully coalesced manner by introducing a middle array, which greatly alleviates the deficiencies of CSR-scalar (rare coalescing and CSR-vector (partial coalescing. Test results on a single C2050 GPU show that PCSR fully outperforms CSR-scalar, CSR-vector, and CSRMV and HYBMV in the vendor-tuned CUSPARSE library and is comparable with a most recently proposed CSR-based algorithm, CSR-Adaptive. Furthermore, we extend PCSR on a single GPU to multiple GPUs. Experimental results on four C2050 GPUs show that no matter whether the communication between GPUs is considered or not PCSR on multiple GPUs achieves good performance and has high parallel efficiency.

  16. 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.

  17. 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.

  18. Bluetongue disease risk assessment based on observed and projected Culicoides obsoletus spp. vector densities.

    Directory of Open Access Journals (Sweden)

    Katharina Brugger

    Full Text Available Bluetongue is an arboviral disease of ruminants causing significant economic losses. Our risk assessment is based on the epidemiological key parameter, the basic reproduction number. It is defined as the number of secondary cases caused by one primary case in a fully susceptible host population, in which values greater than one indicate the possibility, i.e., the risk, for a major disease outbreak. In the course of the Bluetongue virus serotype 8 (BTV-8 outbreak in Europe in 2006 we developed such a risk assessment for the University of Veterinary Medicine Vienna, Austria. Basic reproduction numbers were calculated using a well-known formula for vector-borne diseases considering the population densities of hosts (cattle and small ruminants and vectors (biting midges of the Culicoides obsoletus spp. as well as temperature dependent rates. The latter comprise the biting and mortality rate of midges as well as the reciprocal of the extrinsic incubation period. Most important, but generally unknown, is the spatio-temporal distribution of the vector density. Therefore, we established a continuously operating daily monitoring to quantify the seasonal cycle of the vector population by a statistical model. We used cross-correlation maps and Poisson regression to describe vector densities by environmental temperature and precipitation. Our results comprise time series of observed and simulated Culicoides obsoletus spp. counts as well as basic reproduction numbers for the period 2009-2011. For a spatio-temporal risk assessment we projected our results from the location of Vienna to the entire region of Austria. We compiled both daily maps of vector densities and the basic reproduction numbers, respectively. Basic reproduction numbers above one were generally found between June and August except in the mountainous regions of the Alps. The highest values coincide with the locations of confirmed BTV cases.

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

    OpenAIRE

    Mushtaq, M. T.; Khan, I.; M. S. Khan; Koudelka, O.

    2015-01-01

    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 success...

  20. 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.

  1. Can community-based integrated vector control hasten the process of LF elimination?

    Science.gov (United States)

    Sunish, I P; Kalimuthu, M; Kumar, V Ashok; Munirathinam, A; Nagaraj, J; Tyagi, B K; White, Graham B; Arunachalam, N

    2016-06-01

    Community-based integrated vector control (IVC) using polystyrene beads (EPS) and pyrethroid impregnated curtains (PIC) as an adjunct to mass drug administration (MDA) was implemented for lymphatic filariasis elimination, in the filaria endemic villages of Tirukoilur, south India. In all the villages, MDA was carried out by the state health machinery, as part of the national filariasis elimination programme. Thirty-six difficult-to-control villages were grouped as, viz, MDA alone, MDA + EPS and MDA + EPS + PIC arms. Implementation and monitoring of IVC was carried out by the community. After 3 years of IVC, higher reductions in filariometric indices were observed in both the community and vector population. Decline in antigenaemia prevalence was higher in MDA + IVC as compared to MDA alone arm. Vector density dropped significantly (P < 0.05) in both the IVC arms, and nil transmission was observed during post-IVC period. Almost 53.8 and 75.8 % of the cesspits in MDA + EPS and MDA + EPS + PIC arms were closed by the householders, due to the enhanced awareness on vector breeding. The paper presents the key elements of IVC implementation through social mobilization in a LF prevalent area. Thus, community-based IVC strategy can hasten LF elimination, as it reduced the transmission and filariometric indices significantly. Indices were maintained at low level with nil transmission, by the community through IVC tools. PMID:26969179

  2. Yeast recombination-based cloning as an efficient way of constructing vectors for Zymoseptoria tritici.

    Science.gov (United States)

    Kilaru, S; Steinberg, G

    2015-06-01

    Many pathogenic fungi are genetically tractable. Analysis of their cellular organization and invasion mechanisms underpinning virulence determinants profits from exploiting such molecular tools as fluorescent fusion proteins or conditional mutant protein alleles. Generation of these tools requires efficient cloning methods, as vector construction is often a rate-limiting step. Here, we introduce an efficient yeast recombination-based cloning (YRBC) method to construct vectors for the fungus Zymoseptoria tritici. This method is of low cost and avoids dependency on the availability of restriction enzyme sites in the DNA sequence, as needed in more conventional restriction/ligation-based cloning procedures. Furthermore, YRBC avoids modification of the DNA of interest, indeed this potential risk limits the use of site-specific recombination systems, such as Gateway cloning. Instead, in YRBC, multiple DNA fragments, with 30bp overlap sequences, are transformed into Saccharomyces cerevisiae, whereupon homologous recombination generates the vector in a single step. Here, we provide a detailed experimental protocol and four vectors, each encoding a different dominant selectable marker cassette, that enable YRBC of constructs to be used in the wheat pathogen Z. tritici. PMID:26092792

  3. Identification and verification of a Preisach-based vector model for ferromagnetic materials

    Science.gov (United States)

    Sutor, Alexander; Bi, Shasha; Lerch, Reinhard

    2015-03-01

    In many applications of ferromagnetic materials concerning sensors and actuators, magnetic fields are rotating. In order to precisely describe the behavior of ferromagnetic materials in rotating magnetic fields, vector hysteresis models are necessary. Therefore, much effort is being put into the development of efficient vector models. For the reason of computational efficiency, models have been developed that differ from the Preisach approach and are for example based on rotationally coupled step functions. We have proposed a very efficient Preisach-based model before, which we called the rotational vector Preisach model. In this paper, we propose an extension of the rotational switching function, which improves the model characteristics for arbitrary H-field trajectories. We also introduce a set of special vectorial minor loops for the general validation and comparison of vector models. We apply those H-field trajectories to isotropic materials such as sputtered FeCo thin films as used in micromechanical systems. The vectorial minor loops can readily be utilized to evaluate the model output, and the results agree well with vectorial measurements.

  4. A method for attitude measurement of a test vehicle based on the tracking of vectors

    International Nuclear Information System (INIS)

    In the vehicle simulation test, in order to improve the measuring precision for the attitude of a test vehicle, a measuring method based on the vectors of light beams is presented, in which light beams are mounted on the test vehicle as the cooperation target, and the attitude of the test vehicle is calculated with the light beams’ vectors in the test vehicle’s coordinate system and the world coordinate system. Meanwhile, in order to expand the measuring range of the attitude parameters, cooperation targets and light beams in each cooperation target are increased. On this basis, the concept of an attitude calculation container is defined, and the selection method for the attitude calculation container that participates in the calculation is given. Simultaneously, the vectors of light beams are tracked so as to ensure the normal calculation of the attitude parameters. The experiments results show that this measuring method based on the tracking of vectors can achieve the high precision and wide range of measurement for the attitude of the test vehicle. (paper)

  5. 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.

  6. 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

  7. 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.

  8. 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.

  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. 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 <2.5 cm of variability in components of displacement, and are eclipsed by the 10–60 cm epistemic errors introduced by reconstructing the field sites to their pre-erosion geometries. Although the higher resolution TLS data sets enabled visualization and data interactivity critical for reconstructing the 3D slip vector and for assessing uncertainties, dense topographic constraints alone were not sufficient to significantly narrow the wide (<26°) range of allowable slip vector orientations that resulted from accounting for epistemic uncertainties.

  11. 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.

  12. 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.

  13. 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.

  14. 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].

  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. Support vector machine based nonlinear model multi-step-ahead optimizing predictive control

    Institute of Scientific and Technical Information of China (English)

    ZHONG Wei-min; PI Dao-ying; SUN You-xian

    2005-01-01

    A support vector machine with guadratic polynomial kernel function based nonlinear model multi-step-ahead optimizing predictive controller was presented. A support vector machine based predictive model was established by black-box identification. And a quadratic objective function with receding horizon was selected to obtain the controller output. By solving a nonlinear optimization problem with equality constraint of model output and boundary constraint of controller output using Nelder-Mead simplex direct search method, a sub-optimal control law was achieved in feature space. The effect of the controller was demonstrated on a recognized benchmark problem and a continuous-stirred tank reactor. The simulation results show that the multi-step-ahead predictive controller can be well applied to nonlinear system, with better performance in following reference trajectory and disturbance-rejection.

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

    OpenAIRE

    Xiaoyong Liu; Hui Fu

    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 ...

  18. 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.

  19. Vector magnetic measurement based on directional scattering between polarized plasmon wave and arrayed nanoparticles

    Science.gov (United States)

    Zhang, Zhaochuan; Guo, Tuan; Liu, Fu; Wu, Qiang; Li, Jie; Cheng, Linghao; Guan, Bai-Ou

    2015-09-01

    A vector magnetic field sensor based on surface plasmon resonance (SPR) of a 15° tilted fiber Bragg grating (TFBG) and magnetic fluid is proposed and experimentally demonstrated. Both the orientation and the amplitude of the magnetic fields can be determined unambiguously via the wavelength and intensity monitoring of the SPR, which is essentially dominated by the arrayed Fe3O4 nanoparticles over the nanometric-film of fiber surface.

  20. An adaptation of the vector-space model for ontology based information retrieval

    OpenAIRE

    Castells, Pablo; Fernández Sánchez, Miriam; Vallet Weadon, David Jordi

    2007-01-01

    Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. P. Castells, M. Fernández, D. Vallet. "An Adaptation of the Vector-Space Model for Ontology-Based Information Retrieval", IEEE...

  1. 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.

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

    OpenAIRE

    Gao, Lei; Zhu, Tao; 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...

  3. 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.

  4. A Novel Approach to Asynchronous MVP Data Interpretation Based on Elliptical-Vectors

    Science.gov (United States)

    Kruglyakov, M.; Trofimov, I.; Korotaev, S.; Shneyer, V.; Popova, I.; Orekhova, D.; Scshors, Y.; Zhdanov, M. S.

    2014-12-01

    We suggest a novel approach to asynchronous magnetic-variation profiling (MVP) data interpretation. Standard method in MVP is based on the interpretation of the coefficients of linear relation between vertical and horizontal components of the measured magnetic field.From mathematical point of view this pair of linear coefficients is not a vector which leads to significant difficulties in asynchronous data interpretation. Our approach allows us to actually treat such a pair of complex numbers as a special vector called an ellipse-vector (EV). By choosing the particular definitions of complex length and direction, the basic relation of MVP can be considered as the dot product. This considerably simplifies the interpretation of asynchronous data. The EV is described by four real numbers: the values of major and minor semiaxes, the angular direction of the major semiaxis and the phase. The notation choice is motivated by historical reasons. It is important that different EV's components have different sensitivity with respect to the field sources and the local heterogeneities. Namely, the value of major semiaxis and the angular direction are mostly determined by the field source and the normal cross-section. On the other hand, the value of minor semiaxis and the phase are responsive to local heterogeneities. Since the EV is the general form of complex vector, the traditional Schmucker vectors can be explicitly expressed through its components.The proposed approach was successfully applied to interpretation the results of asynchronous measurements that had been obtained in the Arctic Ocean at the drift stations "North Pole" in 1962-1976.

  5. 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.

  6. 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.

  7. Vector quantizer based on brightness maps for image compression with the polynomial transform

    Science.gov (United States)

    Escalante-Ramirez, Boris; Moreno-Gutierrez, Mauricio; Silvan-Cardenas, Jose L.

    2002-11-01

    We present a vector quantization scheme acting on brightness fields based on distance/distortion criteria correspondent with psycho-visual aspects. These criteria quantify sensorial distortion between vectors that represent either portions of a digital image or alternatively, coefficients of a transform-based coding system. In the latter case, we use an image representation model, namely the Hermite transform, that is based on some of the main perceptual characteristics of the human vision system (HVS) and in their response to light stimulus. Energy coding in the brightness domain, determination of local structure, code-book training and local orientation analysis are all obtained by means of the Hermite transform. This paper, for thematic reasons, is divided in four sections. The first one will shortly highlight the importance of having newer and better compression algorithms. This section will also serve to explain briefly the most relevant characteristics of the HVS, advantages and disadvantages related with the behavior of our vision in front of ocular stimulus. The second section shall go through a quick review of vector quantization techniques, focusing their performance on image treatment, as a preview for the image vector quantizer compressor actually constructed in section 5. Third chapter was chosen to concentrate the most important data gathered on brightness models. The building of this so-called brightness maps (quantification of the human perception on the visible objects reflectance), in a bi-dimensional model, will be addressed here. The Hermite transform, a special case of polynomial transforms, and its usefulness, will be treated, in an applicable discrete form, in the fourth chapter. As we have learned from previous works 1, Hermite transform has showed to be a useful and practical solution to efficiently code the energy within an image block, deciding which kind of quantization is to be used upon them (whether scalar or vector). It will also be

  8. 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.

  9. Expanding the repertoire of Modified Vaccinia Ankara-based vaccine vectors via genetic complementation strategies.

    Directory of Open Access Journals (Sweden)

    David A Garber

    Full Text Available BACKGROUND: Modified Vaccinia virus Ankara (MVA is a safe, highly attenuated orthopoxvirus that is being developed as a recombinant vaccine vector for immunization against a number of infectious diseases and cancers. However, the expression by MVA vectors of large numbers of poxvirus antigens, which display immunodominance over vectored antigens-of-interest for the priming of T cell responses, and the induction of vector-neutralizing antibodies, which curtail the efficacy of subsequent booster immunizations, remain as significant impediments to the overall utility of such vaccines. Thus, genetic approaches that enable the derivation of MVA vectors that are antigenically less complex may allow for rational improvement of MVA-based vaccines. PRINCIPAL FINDINGS: We have developed a genetic complementation system that enables the deletion of essential viral genes from the MVA genome, thereby allowing us to generate MVA vaccine vectors that are antigenically less complex. Using this system, we deleted the essential uracil-DNA-glycosylase (udg gene from MVA and propagated this otherwise replication-defective variant on a complementing cell line that constitutively expresses the poxvirus udg gene and that was derived from a newly identified continuous cell line that is permissive for growth of wild type MVA. The resulting virus, MVADeltaudg, does not replicate its DNA genome or express late viral gene products during infection of non-complementing cells in culture. As proof-of-concept for immunological 'focusing', we demonstrate that immunization of mice with MVADeltaudg elicits CD8+ T cell responses that are directed against a restricted repertoire of vector antigens, as compared to immunization with parental MVA. Immunization of rhesus macaques with MVADeltaudg-gag, a udg(- recombinant virus that expresses an HIV subtype-B consensus gag transgene, elicited significantly higher frequencies of Gag-specific CD8 and CD4 T cells following both primary (2

  10. 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.

  11. Support vector regression model based predictive control of water level of U-tube steam generators

    International Nuclear Information System (INIS)

    Highlights: • Water level of U-tube steam generators was controlled in a model predictive fashion. • Models for steam generator water level were built using support vector regression. • Cost function minimization for future optimal controls was performed by using the steepest descent method. • The results indicated the feasibility of the proposed method. - Abstract: A predictive control algorithm using support vector regression based models was proposed for controlling the water level of U-tube steam generators of pressurized water reactors. Steam generator data were obtained using a transfer function model of U-tube steam generators. Support vector regression based models were built using a time series type model structure for five different operating powers. Feedwater flow controls were calculated by minimizing a cost function that includes the level error, the feedwater change and the mismatch between feedwater and steam flow rates. Proposed algorithm was applied for a scenario consisting of a level setpoint change and a steam flow disturbance. The results showed that steam generator level can be controlled at all powers effectively by the proposed method

  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. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. Privacy Preserving Fall Detection Based on Simple Human Silhouette Extraction and a Linear Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Velislava Spasova

    2016-06-01

    Full Text Available The paper presents a novel fast, real-time and privacy protecting algorithm for fall detection based on geometric properties of the human silhouette and a linear support vector machine. The algorithm uses infrared and visible light imagery in order to detect the human. A simple real-time human silhouette extraction algorithm has been developed and used to extract features for training of the support vector machine. The achieved sensitivity and specificity of the proposed approach are over 97% which match state of the art research in the area of fall detection. The developed solution uses low-cost hardware components and open source software library and is suitable for usage in assistive systems for the home or nursing homes.

  1. 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.

  2. 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.

  3. Polarization imaging of multiply-scattered radiation based on integral-vector Monte Carlo method

    International Nuclear Information System (INIS)

    A new integral-vector Monte Carlo method (IVMCM) is developed to analyze the transfer of polarized radiation in 3D multiple scattering particle-laden media. The method is based on a 'successive order of scattering series' expression of the integral formulation of the vector radiative transfer equation (VRTE) for application of efficient statistical tools to improve convergence of Monte Carlo calculations of integrals. After validation against reference results in plane-parallel layer backscattering configurations, the model is applied to a cubic container filled with uniformly distributed monodispersed particles and irradiated by a monochromatic narrow collimated beam. 2D lateral images of effective Mueller matrix elements are calculated in the case of spherical and fractal aggregate particles. Detailed analysis of multiple scattering regimes, which are very similar for unpolarized radiation transfer, allows identifying the sensitivity of polarization imaging to size and morphology.

  4. Recursions in Calogero-Sutherland Model Based on Virasoro Singular Vectors

    International Nuclear Information System (INIS)

    The present work is much motivated by finding an explicit way in the construction of the Jack symmetric function, which is the spectrum generating function for the Calogero-Sutherland (CS) model. To accomplish this work, the hidden Virasoro structure in the CS model is much explored. In particular, we found that the Virasoro singular vectors form a skew hierarchy in the CS model. Literally, skew is analogous to coset, but here specifically refer to the operation on the Young tableaux. In fact, based on the construction of the Virasoro singular vectors, this hierarchical structure can be used to give a complete construction of the CS states, i.e. the Jack symmetric functions, recursively. The construction is given both in operator formalism as well as in integral representation. This new integral representation for the Jack symmetric functions may shed some insights on the spectrum constructions for the other integrable systems. (general)

  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. 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.

  8. Bidding Strategy with Forecast Technology Based on Support Vector Machine in Electrcity Market

    CERN Document Server

    Gao, C; Napoli, R; Wan, Q

    2007-01-01

    The participants of the electricity market concern very much the market price evolution. Various technologies have been developed for price forecast. SVM (Support Vector Machine) has shown its good performance in market price forecast. Two approaches for forming the market bidding strategies based on SVM are proposed. One is based on the price forecast accuracy, with which the being rejected risk is defined. The other takes into account the impact of the producer's own bid. The risks associated with the bidding are controlled by the parameters setting. The proposed approaches have been tested on a numerical example.

  9. Reference Function Based Spatiotemporal Fuzzy Logic Control Design Using Support Vector Regression Learning

    OpenAIRE

    Xian-Xia Zhang; Ye Jiang; Shiwei Ma; Bing Wang

    2013-01-01

    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 ...

  10. 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.

  11. 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 ...

  12. 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.

  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. A Parallel Genetic Algorithm Based Feature Selection and Parameter Optimization for Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Zhi Chen

    2016-01-01

    Full Text Available The extensive applications of support vector machines (SVMs require efficient method of constructing a SVM classifier with high classification ability. The performance of SVM crucially depends on whether optimal feature subset and parameter of SVM can be efficiently obtained. In this paper, a coarse-grained parallel genetic algorithm (CGPGA is used to simultaneously optimize the feature subset and parameters for SVM. The distributed topology and migration policy of CGPGA can help find optimal feature subset and parameters for SVM in significantly shorter time, so as to increase the quality of solution found. In addition, a new fitness function, which combines the classification accuracy obtained from bootstrap method, the number of chosen features, and the number of support vectors, is proposed to lead the search of CGPGA to the direction of optimal generalization error. Experiment results on 12 benchmark datasets show that our proposed approach outperforms genetic algorithm (GA based method and grid search method in terms of classification accuracy, number of chosen features, number of support vectors, and running time.

  15. A series of TA-based and zero-background vectors for plant functional genomics.

    Directory of Open Access Journals (Sweden)

    Chuntao Wang

    Full Text Available With the sequencing of genomes from many organisms now complete and the development of high-throughput sequencing, life science research has entered the functional post-genome era. Therefore, deciphering the function of genes and how they interact is in greater demand. To study an unknown gene, the basic methods are either overexpression or gene knockout by creating transgenic plants, and gene construction is usually the first step. Although traditional cloning techniques using restriction enzymes or a site-specific recombination system (Gateway or Clontech cloning technology are highly useful for efficiently transferring DNA fragments into destination plasmids, the process is time consuming and expensive. To facilitate the procedure of gene construction, we designed a TA-based cloning system in which only one step was needed to subclone a DNA fragment into vectors. Such a cloning system was developed from the pGreen binary vector, which has a minimal size and facilitates construction manipulation, combined with the negative selection marker gene ccdB, which has the advantages of eliminating the self-ligation background and directly enabling high-efficiency TA cloning technology. We previously developed a set of transient and stable transformation vectors for constitutive gene expression, gene silencing, protein tagging, subcellular localization analysis and promoter activity detection. Our results show that such a system is highly efficient and serves as a high-throughput platform for transient or stable transformation in plants for functional genome research.

  16. Triple-image encryption based on phase-truncated Fresnel transform and basic vector operation.

    Science.gov (United States)

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

    2015-10-01

    A triple-image encryption method is proposed that is based on phase-truncated Fresnel transform (PTFT), basic vector composition, and XOR operation. In the encryption process, two random phase masks, with one each placed at the input plane and the transform plane, are generated by basic vector resolution operations over the first and the second plaintext images, and then a ciphered image in the input plane is fabricated by XOR encoding for the third plaintext image. When the cryptosystem is illuminated by an on-axis plane, assisted by PTFT, the ciphered image is finally encrypted into an amplitude-only noise-like image in the output plane. During decryption, possessing the correct private key, decryption keys, and the assistant geometrical parameter keys, and placing them at the corresponding correct positions, the original three plaintext images can be successfully decrypted by inverse PTFT, basic vector composition, and XOR decoding. Theoretical analysis and numerical simulations both verify the feasibility of the proposed method. PMID:26479627

  17. 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.

  18. 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.

  19. PDM-16QAM vector signal generation and detection based on intensity modulation and direct detection

    Science.gov (United States)

    Chen, Long; Yu, Jianjun; Li, Xinying

    2016-07-01

    We experimentally demonstrate a novel and simple method to generate and detect high speed polarization-division-multiplexing 16-ary quadrature-amplitude-modulation (PDM-16QAM) vector signal enabled by Mach-Zehnder modulator-based (MZM-based) optical-carrier-suppression (OCS) intensity modulation and direct detection. Due to the adoption of OCS intensity modulation, carrier beating can be avoided at the receiver, and thus polarization de-multiplexing can be implemented by digital-signal-processing-based (DSP-based) cascaded multi-modulus algorithm (CMMA) equalization instead of a polarization tracking system. The change of both amplitude and phase information due to the adoption of OCS modulation can be equalized by DSP-based amplitude and phase precoding at the transmitter. Up to 64-Gb/s PDM-16QAM vector signal is generated and detected after 2-km single-mode fiber-28 (SMF-28) or 20-km large-effective-area fiber (LEAF) transmission with a bit-error-ratio (BER) less than the hard-decision forward-error-correction (HD-FEC) threshold of 3.8×10-3.

  20. 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.

  1. 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. PMID:26844553

  2. 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.

  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. 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

  5. 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.

  6. 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.

  7. 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.

  8. Hydroxyl PAMAM dendrimer-based gene vectors for transgene delivery to human retinal pigment epithelial cells

    Science.gov (United States)

    Mastorakos, Panagiotis; Kambhampati, Siva P.; Mishra, Manoj K.; Wu, Tony; Song, Eric; Hanes, Justin; Kannan, Rangaramanujam M.

    2015-02-01

    Ocular gene therapy holds promise for the treatment of numerous blinding disorders. Despite the significant progress in the field of viral and non-viral gene delivery to the eye, significant obstacles remain in the way of achieving high-level transgene expression without adverse effects. The retinal pigment epithelium (RPE) is involved in the pathogenesis of retinal diseases and is a key target for a number of gene-based therapeutics. In this study, we addressed the inherent drawbacks of non-viral gene vectors and combined different approaches to design an efficient and safe dendrimer-based gene-delivery platform for delivery to human RPE cells. We used hydroxyl-terminated polyamidoamine (PAMAM) dendrimers functionalized with various amounts of amine groups to achieve effective plasmid compaction. We further used triamcinolone acetonide (TA) as a nuclear localization enhancer for the dendrimer-gene complex and achieved significant improvement in cell uptake and transfection of hard-to-transfect human RPE cells. To improve colloidal stability, we further shielded the gene vector surface through incorporation of PEGylated dendrimer along with dendrimer-TA for DNA complexation. The resultant complexes showed improved stability while minimally affecting transgene delivery, thus improving the translational relevance of this platform.Ocular gene therapy holds promise for the treatment of numerous blinding disorders. Despite the significant progress in the field of viral and non-viral gene delivery to the eye, significant obstacles remain in the way of achieving high-level transgene expression without adverse effects. The retinal pigment epithelium (RPE) is involved in the pathogenesis of retinal diseases and is a key target for a number of gene-based therapeutics. In this study, we addressed the inherent drawbacks of non-viral gene vectors and combined different approaches to design an efficient and safe dendrimer-based gene-delivery platform for delivery to human RPE

  9. STATE SPACE POINT DISTRIBUTION PARAMETER FOR SUPPORT VECTOR MACHINE BASED CV UNIT CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    N K Narayanan

    2012-01-01

    Full Text Available In this paper we extend Support Vector Machines (SVM for speaker independent Consonant – Vowel (CV unit classification. Here we adopt the technique known as Decision Directed Acyclic Graph (DDAG , which is used to combine many two class classifiers into multiclass classifier. Using Reconstructed State Space (RSS based State Space Point Distribution (SSPD parameters, we obtain an average speaker independent phoneme recognition accuracy of 90% on the Malayalam V/CV speech unit database. The recognition results indicate that this method is efficient and can be adopted for developing a complete speech recognition system for Malayalam language.

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

    Science.gov (United States)

    Gao, Lei; Zhu, Tao; Huang, Wei; Zeng, Jing

    2014-10-01

    A vector dual-wavelength rectangular-shape laser (RSL) based on a long fiber taper deposited with reduced graphene oxide is proposed, where nonlinearity is enhanced due to a large evanescent-field-interacting length and strong field confinement of an 8 mm fiber taper with a waist diameter of 4 μm. 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 shows fast polarization switching in two orthogonal polarization directions, and temporal and spectral characteristics are investigated. PMID:25322232

  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. 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.

  14. Design optical antenna and fiber coupling system based on the vector theory of reflection and refraction.

    Science.gov (United States)

    Jiang, Ping; Yang, Huajun; Mao, Shengqian

    2015-10-01

    A Cassegrain antenna system and an optical fiber coupling system which consists of a plano-concave lens and a plano-convex lens are designed based on the vector theory of reflection and refraction, so as to improve the transmission performance of the optical antenna and fiber coupling system. Three-dimensional ray tracing simulation are performed and results of the optical aberrations calculation and the experimental test show that the aberrations caused by on-axial defocusing, off-axial defocusing and deflection of receiving antenna can be well corrected by the optical fiber coupling system. PMID:26480125

  15. Application of frequency estimation algorithm based on a single vector sensor in underwater acoustic communication

    Institute of Scientific and Technical Information of China (English)

    QIAO Gang; ZHANG Xiao-ping; ZHAO Xin

    2006-01-01

    A method of high resolution frequency estimation based on a single vector sensor using ESPRIT (Estimating Signal Parameters via Rotational Invariance Techniques) algorithm is proposed and applied to the underwater acoustic (UWA) communication system of frequency modulation. Higher resolution frequency estimation can be obtained by this algorithm using fewer snapshots comparing with the sound intensity frequency estimation. Results of simulation and lake experiment show that the proposed algorithm can improve the communication data rate and reduce the bandwidth of the system.Because higher signal-to-noise ratio (SNR) is demanded, this algorithm can be used in high speed short range UWA communication at present.

  16. 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.

  17. 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.

  18. Local Sequence Information-based Support Vector Machine to Classify Voltage-gated Potassium Channels

    Institute of Scientific and Technical Information of China (English)

    Li-Xia LIU; Meng-Long LI; Fu-Yuan TAN; Min-Chun LU; Ke-Long WANG; Yan-Zhi GUO; Zhi-Ning WEN; Lin JIANG

    2006-01-01

    In our previous work, we developed a computational tool, PreK-ClassK-ClassKv, to predict and classify potassium (K+) channels. For K+ channel prediction (PreK) and classification at family level (ClassK), this method performs well. However, it does not perform so well in classifying voltage-gated potassium (Kv) channels (ClassKv). In this paper, a new method based on the local sequence information of Kv channels is introduced to classify Kv channels. Six transmembrane domains of a Kv channel protein are used to define a protein, and the dipeptide composition technique is used to transform an amino acid sequence to a numerical sequence. A Kv channel protein is represented by a vector with 2000 elements, and a support vector machine algorithm is applied to classify Kv channels. This method shows good performance with averages of total accuracy (Acc), sensitivity (SE), specificity (SP); reliability (R) and Matthews correlation coefficient (MCC) of 98.0%, 89.9%, 100%, 0.95 and 0.94 respectively. The results indicate that the local sequence information-based method is better than the global sequence information-based method to classify Kv channels.

  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. 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...... 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...

  3. The behaviour of conjugate gradient based algorithms on a multi-vector processor with a memory hierarchy

    Energy Technology Data Exchange (ETDEWEB)

    Jalby, W.; Meier, U.; Sameh, A.

    1986-11-23

    The conjugate gradient algorithm is one of the most efficient solvers for elliptic partial differential equations and has been successfully implemented on various vector computers. In this paper, the behaviour of conjugate gradient based algorithms on a multi-vector processor with a two-level memory hierarchy is investigated. Several domain decomposition techniques are considered as well as preconditioners that are suitable to be parallelized such as polynomial preconditioners or a vectorized version of the ICCG. Experimental results for the Poisson equation performed on an Alliant FX/8 are presented. 16 refs., 17 figs., 13 tabs.

  4. 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。

  5. 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.

  6. 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.

  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

    2015-06-17

    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.

  8. 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).

  9. 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

  10. 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.

  11. Experimental Study on MUSIC-Based DOA Estimation by Using Universal Steering Vector

    Science.gov (United States)

    Yuan, Qiaowei; Chen, Qiang; Sawaya, Kunio

    MUSIC-based estimation of direction of arrival (DOA) using universal steering vector (USV) is experimentally studied. A four-element array antenna and a four-channel receiver are employed for the experiment. In order to improve the accuracy of DOA estimation, USV which has already included the effect of mutual coupling between array elements and effect of array elements themselves is compensated to further include the electric delay and loss of four channels in the receiver. The compensated USV (C-USV) approach proposed in this paper does not need the time-consuming measurement of array element pattern because the compensating matrix for USV is obtained by measuring the S parameters between RF input ports of the feeding cables and IF output ports of the receiver. The experimental results of MUSIC-based DOA estimation show that C-USV approach is an accurate, effective and practical method for the MUSIC-based DOA estimation.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. Hybrid-parallel sparse matrix-vector multiplication with explicit communication overlap on current multicore-based systems

    OpenAIRE

    Schubert, Gerald; Fehske, Holger; Hager, Georg; Wellein, Gerhard

    2011-01-01

    We evaluate optimized parallel sparse matrix-vector operations for several representative application areas on widespread multicore-based cluster configurations. First the single-socket baseline performance is analyzed and modeled with respect to basic architectural properties of standard multicore chips. Beyond the single node, the performance of parallel sparse matrix-vector operations is often limited by communication overhead. Starting from the observation that nonblocking MPI is not able...

  18. Stable S/MAR-based episomal vectors are regulated at the chromatin level

    NARCIS (Netherlands)

    F. Tessadori; K. Zeng; E. Manders; M. Riool; D. Jackson; R. van Driel

    2010-01-01

    Episomal vectors assembled from defined genetic components are a promising alternative to traditional gene therapy vectors that integrate in the host genome and may cause insertional mutations. The vector pEPI-eGFP is stably retained in the episomal state in cultured mammalian cells at low copy numb

  19. 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.

  20. In vivo image analysis of BoHV-4-based vector in mice.

    Directory of Open Access Journals (Sweden)

    Valentina Franceschi

    Full Text Available Due to its biological characteristics bovine herpesvirus 4 (BoHV-4 has been considered as an appropriate gene delivery vector. Its genomic clone, modified as a bacterial artificial chromosome (BAC, is better genetically manipulable and can be used as an efficient gene delivery and vaccine vector. Although a large amount of data have been accumulated in vitro on this specific aspect, the same cannot be asserted for the in vivo condition. Therefore, here we investigated the fate of a recombinant BoHV-4 strain expressing luciferase (BoHV-4-A-CMVlucΔTK after intraperitoneal or intravenous inoculation in mice, by generating a novel recombinant BoHV-4 expressing luciferase (BoHV-4-A-CMVlucΔTK and by following the virus replication through in vivo imaging analysis. BoHV-4-A-CMVlucΔTK was first characterized in vitro where it was shown, on one hand that its replication properties are identical to those of the parental virus, and on the other that the transduced/infected cells strongly express luciferase. When BoHV-4-A-CMVlucΔTK was inoculated in mice, either intraperitoneally or intravenously, BoHV-4-A-CMVlucΔTK infection/transduction was exclusively localized to the liver, as detected by in vivo image analysis, and in particular almost exclusively in the hepatocytes, as determined by immuno-histochemistry. These data, that add a new insight on the biology of BoHV-4 in vivo, provide the first indication for the potential use of a BoHV-4-based vector in gene-transfer in the liver.

  1. 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.

  2. 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.

  3. 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%.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. Robustly stable model predictive control based on parallel support vector machines with linear kernel

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Robustly stable multi-step-ahead model predictive control (MPC) based on parallel support vector machines (SVMs) with linear kernel was proposed. First, an analytical solution of optimal control laws of parallel SVMs based MPC was derived, and then the necessary and sufficient stability condition for MPC closed loop was given according to SVM model, and finally a method of judging the discrepancy between SVM model and the actual plant was presented, and consequently the constraint sets, which can guarantee that the stability condition is still robust for model/plant mismatch within some given bounds, were obtained by applying small-gain theorem. Simulation experiments show the proposed stability condition and robust constraint sets can provide a convenient way of adjusting controller parameters to ensure a closed-loop with larger stable margin.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. Support vector machine based classification of fast Fourier transform spectroscopy of proteins

    Science.gov (United States)

    Lazarevic, Aleksandar; Pokrajac, Dragoljub; Marcano, Aristides; Melikechi, Noureddine

    2009-02-01

    Fast Fourier transform spectroscopy has proved to be a powerful method for study of the secondary structure of proteins since peak positions and their relative amplitude are affected by the number of hydrogen bridges that sustain this secondary structure. However, to our best knowledge, the method has not been used yet for identification of proteins within a complex matrix like a blood sample. The principal reason is the apparent similarity of protein infrared spectra with actual differences usually masked by the solvent contribution and other interactions. In this paper, we propose a novel machine learning based method that uses protein spectra for classification and identification of such proteins within a given sample. The proposed method uses principal component analysis (PCA) to identify most important linear combinations of original spectral components and then employs support vector machine (SVM) classification model applied on such identified combinations to categorize proteins into one of given groups. Our experiments have been performed on the set of four different proteins, namely: Bovine Serum Albumin, Leptin, Insulin-like Growth Factor 2 and Osteopontin. Our proposed method of applying principal component analysis along with support vector machines exhibits excellent classification accuracy when identifying proteins using their infrared spectra.

  1. 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

    2014-01-01

    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.

  2. 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.

  3. 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.

  4. PCR-based detection of gene transfer vectors: application to gene doping surveillance.

    Science.gov (United States)

    Perez, Irene C; Le Guiner, Caroline; Ni, Weiyi; Lyles, Jennifer; Moullier, Philippe; Snyder, Richard O

    2013-12-01

    Athletes who illicitly use drugs to enhance their athletic performance are at risk of being banned from sports competitions. Consequently, some athletes may seek new doping methods that they expect to be capable of circumventing detection. With advances in gene transfer vector design and therapeutic gene transfer, and demonstrations of safety and therapeutic benefit in humans, there is an increased probability of the pursuit of gene doping by athletes. In anticipation of the potential for gene doping, assays have been established to directly detect complementary DNA of genes that are top candidates for use in doping, as well as vector control elements. The development of molecular assays that are capable of exposing gene doping in sports can serve as a deterrent and may also identify athletes who have illicitly used gene transfer for performance enhancement. PCR-based methods to detect foreign DNA with high reliability, sensitivity, and specificity include TaqMan real-time PCR, nested PCR, and internal threshold control PCR. PMID:23912835

  5. 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.

  6. 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.

  7. A Discriminant Distance Based Composite Vector Selection Method for Odor Classification

    Directory of Open Access Journals (Sweden)

    Sang-Il Choi

    2014-04-01

    Full Text Available We present a composite vector selection method for an effective electronic nose system that performs well even in noisy environments. Each composite vector generated from a electronic nose data sample is evaluated by computing the discriminant distance. By quantitatively measuring the amount of discriminative information in each composite vector, composite vectors containing informative variables can be distinguished and the final composite features for odor classification are extracted using the selected composite vectors. Using the only informative composite vectors can be also helpful to extract better composite features instead of using all the generated composite vectors. Experimental results with different volatile organic compound data show that the proposed system has good classification performance even in a noisy environment compared to other methods.

  8. 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 (ppredicate-based (80%) than for the keyword-based (71%) approach. Relevance was almost doubled with the predicate-based approach-2.1 versus 1.6 without rank order adjustment (ppredicate--versus keyword-based approach respectively. Predicates can support more precise searching than keywords, laying the foundation for rich and sophisticated information search. PMID:23892296

  9. 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

  10. 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

  11. Murine leukemia virus-based Tat-inducible long terminal repeat replacement vectors: a new system for anti-human immunodeficiency virus gene therapy.

    Science.gov (United States)

    Cannon, P M; Kim, N; Kingsman, S M; Kingsman, A J

    1996-11-01

    We have constructed new murine leukemia virus (MLV)-based vectors (TIN vectors) which, following integration, contain human immunodeficiency virus (HIV) type 1 U3 and R sequences in place of the MLV U3 and R regions. This provides, for the first time, single transcriptional unit retroviral vectors under the control of Tat. TIN vectors have several advantages for anti-HIV gene therapy applications. PMID:8892960

  12. Murine leukemia virus-based Tat-inducible long terminal repeat replacement vectors: a new system for anti-human immunodeficiency virus gene therapy.

    OpenAIRE

    Cannon, P M; Kim, N.; Kingsman, S M; Kingsman, A J

    1996-01-01

    We have constructed new murine leukemia virus (MLV)-based vectors (TIN vectors) which, following integration, contain human immunodeficiency virus (HIV) type 1 U3 and R sequences in place of the MLV U3 and R regions. This provides, for the first time, single transcriptional unit retroviral vectors under the control of Tat. TIN vectors have several advantages for anti-HIV gene therapy applications.

  13. AN INTEGRATED FRAMEWORK BASED ON TEXTURE FEATURES, CUCKOO SEARCH AND RELEVANCE VECTOR MACHINE FOR MEDICAL IMAGE RETRIEVAL SYSTEM

    Directory of Open Access Journals (Sweden)

    Yogapriya Jaganathan

    2013-01-01

    Full Text Available As medical images are widely used in healthcare applications, Content Based Medical Image Retrieval (CBMIR system is needed for physicians to convey effective decisions to patients and for medical research students to learn imaging characteristics for their extensive research based on visual features. However the performance of the retrieval is restricted due to high feature dimensionality of visual features. To reduce the high feature dimension, an integrated approach is proposed such as Visual feature extraction, Feature selection, Feature Classification and Similarity measurements. The selected feature is texture features by using Local Binary Patterns (LBP in which extracted texture features are designed as feature vector database. Fuzzy based Cuckoo Search (FCKS techniques are applied for feature selection to reduce the high feature vector dimensionality and addresses the difficulty of feature vectors being surrounded in local feature optima also the global optimum feature position to be special for all feature cuckoo hosts. Fuzzy based Relevance Vector Machine (FRVM classification is an proficient method to customize the collections of relevant image features that would classify dimensionally determined optimized feature vectors of images. The Euclidean Distance (ED is a standard technique for similarity measurement between the query image and the image database. The proposed system is implemented on thousands of medical images and achieved a high retrieval precision and recall compared with other two methods as validated through experiments.

  14. 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. PMID:24971382

  15. 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.

  16. 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. PMID:27375941

  17. A newborn screening system based on service-oriented architecture embedded support vector machine.

    Science.gov (United States)

    Hsu, Kai-Ping; Hsieh, Sung-Huai; Hsieh, Sheau-Ling; Cheng, Po-Hsun; Weng, Yung-Ching; Wu, Jang-Hung; Lai, Feipei

    2010-10-01

    The clinical symptoms of metabolic disorders are rarely apparent during the neonatal period, and if they are not treated earlier, irreversible damages, such as mental retardation or even death, may occur. Therefore, the practice of newborn screening is essential to prevent permanent disabilities in newborns. In the paper, we design, implement a newborn screening system using Support Vector Machine (SVM) classifications. By evaluating metabolic substances data collected from tandem mass spectrometry (MS/MS), we can interpret and determine whether a newborn has a metabolic disorder. In addition, National Taiwan University Hospital Information System (NTUHIS) has been developed and implemented to integrate heterogeneous platforms, protocols, databases as well as applications. To expedite adapting the diversities, we deploy Service-Oriented Architecture (SOA) concepts to the newborn screening system based on web services. The system can be embedded seamlessly into NTUHIS. PMID:20703618

  18. 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.

  19. 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.

  20. 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.

  1. 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”.

  2. 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.

  3. A relevance vector machine-based approach with application to oil sand pump prognostics.

    Science.gov (United States)

    Hu, Jinfei; Tse, Peter W

    2013-01-01

    Oil sand pumps are widely used in the mining industry for the delivery of mixtures of abrasive solids and liquids. Because they operate under highly adverse conditions, these pumps usually experience significant wear. Consequently, equipment owners are quite often forced to invest substantially in system maintenance to avoid unscheduled downtime. In this study, an approach combining relevance vector machines (RVMs) with a sum of two exponential functions was developed to predict the remaining useful life (RUL) of field pump impellers. To handle field vibration data, a novel feature extracting process was proposed to arrive at a feature varying with the development of damage in the pump impellers. A case study involving two field datasets demonstrated the effectiveness of the developed method. Compared with standalone exponential fitting, the proposed RVM-based model was much better able to predict the remaining useful life of pump impellers.

  4. Sensorless tension control of shuttleless loom system based on support vector regression

    Science.gov (United States)

    Han, Dong Chang; Back, Woon Jae; Lee, Yoon Chul; Lee, Sang Hwa; Lee, Hyuk Jin; Noh, Seok Hong; Kim, Han Kil; Park, Jae Yong; Lee, Suk Gyu; Chun, Du Hwan

    2005-12-01

    Tension control of loom system are usually achieved by using loadcell sensor and powder clutch, which require additional mounting space, reduce the reliability in harsh environments and increase the cost of a loom system. Moreover, the physical properties of textile fabrics are very sensitive to several factors(temperature, humidity, radius change of warp beam etc.) which result in tension change. In this paper, a novel sensorless tension control of a shuttleless loom system based on SVR(Support Vector Regression) is presented. The sensorless tension algorithm of shuttleless loom system driven by servo motor which is robust to disturbance and tension variation. First, the modeling and dynamic behaviors of a shuttleless loom system is described. Then, different tension control strategies are analyzed and discussed. And finally, the validity and the usefulness of proposed algorithm are thoroughly verified through numerical simulation.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. Compression of 3D meshes based on a decomposition into singular vectors

    Directory of Open Access Journals (Sweden)

    El Mostafa RAJAALLAH

    2016-06-01

    Full Text Available Compression of 3D meshes is an important operation for many applications involving transfer and storage of 3D objects data in their process, such as research by content and navigation in 3D objects databases. Compression is to reduce the space needed to store and/or display the 3D mesh. To do that, we usually try to project it in a frequency space where information is less correlated. The approach suggested in this work is based on a decomposition into singular vectors of the laplacian transform of the adjacency matrix of the vertices of the 3D mesh. The obtained results show the invariance of this approach to a normalization and very encouraging performance semantically.

  10. 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

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. Minimal requirement for a lentivirus vector based on human immunodeficiency virus type 1.

    Science.gov (United States)

    Kim, V N; Mitrophanous, K; Kingsman, S M; Kingsman, A J

    1998-01-01

    The use of human immunodeficiency virus vectors for gene therapy is hampered by concern over their safety. This concern might be ameliorated, in part, if the viral accessory genes and proteins could be eliminated from the vector genomes and particles. Here we describe a minimal vector system that is capable of transducing nondividing cells and which does not contain tat, vif, vpr, vpu, and nef. PMID:9420292

  16. Minimal Requirement for a Lentivirus Vector Based on Human Immunodeficiency Virus Type 1

    OpenAIRE

    Kim, V. Narry; Mitrophanous, Kyriacos; Kingsman, Susan M.; Kingsman, Alan J.

    1998-01-01

    The use of human immunodeficiency virus vectors for gene therapy is hampered by concern over their safety. This concern might be ameliorated, in part, if the viral accessory genes and proteins could be eliminated from the vector genomes and particles. Here we describe a minimal vector system that is capable of transducing nondividing cells and which does not contain tat, vif, vpr, vpu, and nef.

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

    OpenAIRE

    Deb J K; Walia Rupali; Mukherjee K J

    2007-01-01

    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% st...

  18. Bacillus subtilis genome vector-based complete manipulation and reconstruction of genomic DNA for mouse transgenesis

    OpenAIRE

    Iwata, Tetsuo; Kaneko, Shinya; Shiwa, Yuh; Enomoto, Takayuki; Yoshikawa, Hirofumi; Hirota, Junji

    2013-01-01

    Background The Bacillus subtilis genome (BGM) vector is a novel cloning system for large DNA fragments, in which the entire 4.2 Mb genome of B. subtilis functions as a vector. The BGM vector system has several attractive properties, such as a large cloning capacity of over 3 Mb, stable propagation of cloned DNA and various modification strategies using RecA-mediated homologous recombination. However, genetic modifications using the BGM vector system have not been fully established, and this s...

  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. 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.

  1. 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.

  2. Insulated Foamy Viral Vectors.

    Science.gov (United States)

    Browning, Diana L; Collins, Casey P; Hocum, Jonah D; Leap, David J; Rae, Dustin T; Trobridge, Grant D

    2016-03-01

    Retroviral vector-mediated gene therapy is promising, but genotoxicity has limited its use in the clinic. Genotoxicity is highly dependent on the retroviral vector used, and foamy viral (FV) vectors appear relatively safe. However, internal promoters may still potentially activate nearby genes. We developed insulated FV vectors, using four previously described insulators: a version of the well-studied chicken hypersensitivity site 4 insulator (650cHS4), two synthetic CCCTC-binding factor (CTCF)-based insulators, and an insulator based on the CCAAT box-binding transcription factor/nuclear factor I (7xCTF/NF1). We directly compared these insulators for enhancer-blocking activity, effect on FV vector titer, and fidelity of transfer to both proviral long terminal repeats. The synthetic CTCF-based insulators had the strongest insulating activity, but reduced titers significantly. The 7xCTF/NF1 insulator did not reduce titers but had weak insulating activity. The 650cHS4-insulated FV vector was identified as the overall most promising vector. Uninsulated and 650cHS4-insulated FV vectors were both significantly less genotoxic than gammaretroviral vectors. Integration sites were evaluated in cord blood CD34(+) cells and the 650cHS4-insulated FV vector had fewer hotspots compared with an uninsulated FV vector. These data suggest that insulated FV vectors are promising for hematopoietic stem cell gene therapy. PMID:26715244

  3. Vector-based dynamic modeling and control of the quattro parallel robot by means of leg orientations.

    OpenAIRE

    Ozgür, Erol; Andreff, Nicolas; Martinet, Philippe

    2010-01-01

    One of the key steps in high-speed control of a parallel robot is to define an efficient dynamic model. It is usually not easy to have such a model for parallel robots, since many of them have complex structures. Here, we propose a vector-based approach, which employs the robot leg orientations, to obtain a simplified inverse dynamic model. At the least, this vector-based methodology is pioneering, when combined with the observation of orientations by a calibrated camera, in the sense of solv...

  4. Determinants of Health Service Responsiveness in Community-Based Vector Surveillance for Chagas Disease in Guatemala, El Salvador, and Honduras.

    Directory of Open Access Journals (Sweden)

    Ken Hashimoto

    Full Text Available Central American countries face a major challenge in the control of Triatoma dimidiata, a widespread vector of Chagas disease that cannot be eliminated. The key to maintaining the risk of transmission of Trypanosoma cruzi at lowest levels is to sustain surveillance throughout endemic areas. Guatemala, El Salvador, and Honduras integrated community-based vector surveillance into local health systems. Community participation was effective in detection of the vector, but some health services had difficulty sustaining their response to reports of vectors from the population. To date, no research has investigated how best to maintain and reinforce health service responsiveness, especially in resource-limited settings.We reviewed surveillance and response records of 12 health centers in Guatemala, El Salvador, and Honduras from 2008 to 2012 and analyzed the data in relation to the volume of reports of vector infestation, local geography, demography, human resources, managerial approach, and results of interviews with health workers. Health service responsiveness was defined as the percentage of households that reported vector infestation for which the local health service provided indoor residual spraying of insecticide or educational advice. Eight potential determinants of responsiveness were evaluated by linear and mixed-effects multi-linear regression. Health service responsiveness (overall 77.4% was significantly associated with quarterly monitoring by departmental health offices. Other potential determinants of responsiveness were not found to be significant, partly because of short- and long-term strategies, such as temporary adjustments in manpower and redistribution of tasks among local participants in the effort.Consistent monitoring within the local health system contributes to sustainability of health service responsiveness in community-based vector surveillance of Chagas disease. Even with limited resources, countries can improve health

  5. 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.

  6. 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.

  7. Nondestructive detection of pork comprehensive quality based on spectroscopy and support vector machine

    Science.gov (United States)

    Liu, Yuanyuan; Peng, Yankun; Zhang, Leilei; Dhakal, Sagar; Wang, Caiping

    2014-05-01

    Pork is one of the highly consumed meat item in the world. With growing improvement of living standard, concerned stakeholders including consumers and regulatory body pay more attention to comprehensive quality of fresh pork. Different analytical-laboratory based technologies exist to determine quality attributes of pork. However, none of the technologies are able to meet industrial desire of rapid and non-destructive technological development. Current study used optical instrument as a rapid and non-destructive tool to classify 24 h-aged pork longissimus dorsi samples into three kinds of meat (PSE, Normal and DFD), on the basis of color L* and pH24. Total of 66 samples were used in the experiment. Optical system based on Vis/NIR spectral acquisition system (300-1100 nm) was self- developed in laboratory to acquire spectral signal of pork samples. Median smoothing filter (M-filter) and multiplication scatter correction (MSC) was used to remove spectral noise and signal drift. Support vector machine (SVM) prediction model was developed to classify the samples based on their comprehensive qualities. The results showed that the classification model is highly correlated with the actual quality parameters with classification accuracy more than 85%. The system developed in this study being simple and easy to use, results being promising, the system can be used in meat processing industry for real time, non-destructive and rapid detection of pork qualities in future.

  8. 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.

  9. Support vector machine-based classification of Alzheimer's disease from whole-brain anatomical MRI

    International Nuclear Information System (INIS)

    We present and evaluate a new automated method based on support vector machine (SVM) classification of whole-brain anatomical magnetic resonance imaging to discriminate between patients with Alzheimer's disease (AD) and elderly control subjects. We studied 16 patients with AD [mean age ± standard deviation (SD)=74.1 ±5.2 years, mini-mental score examination (MMSE) = 23.1 ± 2.9] and 22 elderly controls (72.3±5.0 years, MMSE=28.5± 1.3). Three-dimensional T1-weighted MR images of each subject were automatically parcellated into regions of interest (ROIs). Based upon the characteristics of gray matter extracted from each ROI, we used an SVM algorithm to classify the subjects and statistical procedures based on bootstrap resampling to ensure the robustness of the results. We obtained 94.5% mean correct classification for AD and control subjects (mean specificity, 96.6%; mean sensitivity, 91.5%). Our method has the potential in distinguishing patients with AD from elderly controls and therefore may help in the early diagnosis of AD. (orig.)

  10. 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.

  11. Geographic Distribution of Chagas Disease Vectors in Brazil Based on Ecological Niche Modeling

    Directory of Open Access Journals (Sweden)

    Rodrigo Gurgel-Gonçalves

    2012-01-01

    Full Text Available Although Brazil was declared free from Chagas disease transmission by the domestic vector Triatoma infestans, human acute cases are still being registered based on transmission by native triatomine species. For a better understanding of transmission risk, the geographic distribution of Brazilian triatomines was analyzed. Sixteen out of 62 Brazilian species that both occur in >20 municipalities and present synanthropic tendencies were modeled based on their ecological niches. Panstrongylus geniculatus and P. megistus showed broad ecological ranges, but most of the species sort out by the biome in which they are distributed: Rhodnius pictipes and R. robustus in the Amazon; R. neglectus, Triatoma sordida, and T. costalimai in the Cerrado; R. nasutus, P. lutzi, T. brasiliensis, T. pseudomaculata, T. melanocephala, and T. petrocchiae in the Caatinga; T. rubrovaria in the southern pampas; T. tibiamaculata and T. vitticeps in the Atlantic Forest. Although most occurrences were recorded in open areas (Cerrado and Caatinga, our results show that all environmental conditions in the country are favorable to one or more of the species analyzed, such that almost nowhere is Chagas transmission risk negligible.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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. PMID:25122746

  20. Pointing-Vector and Velocity Based Frequency Predicts for Deep-Space Uplink Array Applications

    Science.gov (United States)

    Tsao, P.; Vilnrotter, Victor A.; Jamnejad, V.

    2008-01-01

    Uplink array technology is currently being developed for NASA's Deep Space Network (DSN) to provide greater range and data throughput for future NASA missions, including manned missions to Mars and exploratory missions to the outer planets, the Kuiper belt, and beyond. Here we describe a novel technique for generating the frequency predicts that are used to compensate for relative Doppler, derived from interpolated earth position and spacecraft ephemerides. The method described here guarantees velocity and range estimates that are consistent with each other, hence one can always be recovered from the other. Experimental results have recently proven that these frequency predicts are accurate enough to maintain the phase of a three element array at the EPOXI spacecraft for three hours. Previous methods derive frequency predicts directly from interpolated relative velocities. However, these velocities were found to be inconsistent with the corresponding spacecraft range, meaning that range could not always be recovered accurately from the velocity predicts, and vice versa. Nevertheless, velocity-based predicts are also capable of maintaining uplink array phase calibration for extended periods, as demonstrated with the EPOXI spacecraft, however with these predicts important range and phase information may be lost. A comparison of the steering-vector method with velocity-based techniques for generating precise frequency predicts specifically for uplink array applications is provided in the following sections.

  1. 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.

  2. Short-Term Traffic Flow Local Prediction Based on Combined Kernel Function Relevance Vector Machine Model

    Directory of Open Access Journals (Sweden)

    Qichun Bing

    2015-01-01

    Full Text Available Short-term traffic flow prediction is one of the most important issues in the field of adaptive traffic control system and dynamic traffic guidance system. In order to improve the accuracy of short-term traffic flow prediction, a short-term traffic flow local prediction method based on combined kernel function relevance vector machine (CKF-RVM model is put forward. The C-C method is used to calculate delay time and embedding dimension. The number of neighboring points is determined by use of Hannan-Quinn criteria, and the CKF-RVM model is built based on genetic algorithm. Finally, case validation is carried out using inductive loop data measured from the north–south viaduct in Shanghai. The experimental results demonstrate that the CKF-RVM model is 31.1% and 52.7% higher than GKF-RVM model and GKF-SVM model in the aspect of MAPE. Moreover, it is also superior to the other two models in the aspect of EC.

  3. Open Source Scalable Vector Graphics Components for Enabling GIS in Web-based Public Health Surveillance Systems

    OpenAIRE

    Kamadjeu, Raoul; Tolentino, Herman

    2006-01-01

    Geographic Information Systems (GIS) are useful for visual analysis and sharing of spatial data. Open standards like Scalable Vector Graphics (SVG) provide viable alternatives to overcome traditional GIS limitations in resource-constrained settings (low-bandwidth, cost and manpower barriers). This project describes the design and implementation of reusable SVG components for managing geographic information for web-based public health surveillance systems.

  4. 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.

  5. Development of a novel Gateway-based vector system for efficient, multiparallel protein expression in Escherichia coli.

    Science.gov (United States)

    Freuler, Felix; Stettler, Thomas; Meyerhofer, Marco; Leder, Lukas; Mayr, Lorenz M

    2008-06-01

    We describe a cloning and expression system which is based on the Escherichia coli T7 expression system and Gateway recombination technology. We have produced numerous destination vectors with selected fusion tags and an additional set of entry vectors containing the gene of interest and optional labeling tags. This powerful system enables us to transfer a cDNA to several expression vectors in parallel and combine them with various labeling tags. To remove the attached amino terminal tags along with the unwanted attB1 site, we inserted PreScission protease cleavage sites. In contrast to the commercially available destination vectors, our plasmids provide kanamycin resistance, which can be an advantage when expressing toxic proteins in E. coli. Some small-scale protein expression experiments are shown to demonstrate the usefulness of these novel Gateway vectors. In summary, this system has some benefits over the widely used and commercially available Gateway standard system, and it enables many different combinations for expression constructs from a single gene of interest. PMID:18375142

  6. Hybrid-parallel sparse matrix-vector multiplication with explicit communication overlap on current multicore-based systems

    CERN Document Server

    Schubert, Gerald; Hager, Georg; Wellein, Gerhard

    2011-01-01

    We evaluate optimized parallel sparse matrix-vector operations for several representative application areas on widespread multicore-based cluster configurations. First the single-socket baseline performance is analyzed and modeled with respect to basic architectural properties of standard multicore chips. Beyond the single node, the performance of parallel sparse matrix-vector operations is often limited by communication overhead. Starting from the observation that nonblocking MPI is not able to hide communication cost using standard MPI implementations, we demonstrate that explicit overlap of communication and computation can be achieved by using a dedicated communication thread, which may run on a virtual core. Moreover we identify performance benefits of hybrid MPI/OpenMP programming due to improved load balancing even without explicit communication overlap. We compare performance results for pure MPI, the widely used "vector-like" hybrid programming strategies, and explicit overlap on a modern multicore-b...

  7. 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.

  8. Flood damage assessment performed based on Support Vector Machines combined with Landsat TM imagery and GIS

    Science.gov (United States)

    Alouene, Y.; Petropoulos, G. P.; Kalogrias, A.; Papanikolaou, F.

    2012-04-01

    Floods are a water-related natural disaster affecting and often threatening different aspects of human life, such as property damage, economic degradation, and in some instances even loss of precious human lives. Being able to provide accurately and cost-effectively assessment of damage from floods is essential to both scientists and policy makers in many aspects ranging from mitigating to assessing damage extent as well as in rehabilitation of affected areas. Remote Sensing often combined with Geographical Information Systems (GIS) has generally shown a very promising potential in performing rapidly and cost-effectively flooding damage assessment, particularly so in remote, otherwise inaccessible locations. The progress in remote sensing during the last twenty years or so has resulted to the development of a large number of image processing techniques suitable for use with a range of remote sensing data in performing flooding damage assessment. Supervised image classification is regarded as one of the most widely used approaches employed for this purpose. Yet, the use of recently developed image classification algorithms such as of machine learning-based Support Vector Machines (SVMs) classifier has not been adequately investigated for this purpose. The objective of our work had been to quantitatively evaluate the ability of SVMs combined with Landsat TM multispectral imagery in performing a damage assessment of a flood occurred in a Mediterranean region. A further objective has been to examine if the inclusion of additional spectral information apart from the original TM bands in SVMs can improve flooded area extraction accuracy. As a case study is used the case of a river Evros flooding of 2010 located in the north of Greece, in which TM imagery before and shortly after the flooding was available. Assessment of the flooded area is performed in a GIS environment on the basis of classification accuracy assessment metrics as well as comparisons versus a vector

  9. 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...

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. A Novel Classification Algorithm Based on Incremental Semi-Supervised Support Vector Machine.

    Directory of Open Access Journals (Sweden)

    Fei Gao

    Full Text Available For current computational intelligence techniques, a major challenge is how to learn new concepts in changing environment. Traditional learning schemes could not adequately address this problem due to a lack of dynamic data selection mechanism. In this paper, inspired by human learning process, a novel classification algorithm based on incremental semi-supervised support vector machine (SVM is proposed. Through the analysis of prediction confidence of samples and data distribution in a changing environment, a "soft-start" approach, a data selection mechanism and a data cleaning mechanism are designed, which complete the construction of our incremental semi-supervised learning system. Noticeably, with the ingenious design procedure of our proposed algorithm, the computation complexity is reduced effectively. In addition, for the possible appearance of some new labeled samples in the learning process, a detailed analysis is also carried out. The results show that our algorithm does not rely on the model of sample distribution, has an extremely low rate of introducing wrong semi-labeled samples and can effectively make use of the unlabeled samples to enrich the knowledge system of classifier and improve the accuracy rate. Moreover, our method also has outstanding generalization performance and the ability to overcome the concept drift in a changing environment.

  15. 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.

  16. 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.

  17. Large-scale ligand-based predictive modelling using support vector machines.

    Science.gov (United States)

    Alvarsson, Jonathan; Lampa, Samuel; Schaal, Wesley; Andersson, Claes; Wikberg, Jarl E S; Spjuth, Ola

    2016-01-01

    The increasing size of datasets in drug discovery makes it challenging to build robust and accurate predictive models within a reasonable amount of time. In order to investigate the effect of dataset sizes on predictive performance and modelling time, ligand-based regression models were trained on open datasets of varying sizes of up to 1.2 million chemical structures. For modelling, two implementations of support vector machines (SVM) were used. Chemical structures were described by the signatures molecular descriptor. Results showed that for the larger datasets, the LIBLINEAR SVM implementation performed on par with the well-established libsvm with a radial basis function kernel, but with dramatically less time for model building even on modest computer resources. Using a non-linear kernel proved to be infeasible for large data sizes, even with substantial computational resources on a computer cluster. To deploy the resulting models, we extended the Bioclipse decision support framework to support models from LIBLINEAR and made our models of logD and solubility available from within Bioclipse. PMID:27516811

  18. Power Load Event Detection and Classification Based on Edge Symbol Analysis and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Lei Jiang

    2012-01-01

    Full Text Available Energy signature analysis of power appliance is the core of nonintrusive load monitoring (NILM where the detailed data of the appliances used in houses are obtained by analyzing changes in the voltage and current. This paper focuses on developing an automatic power load event detection and appliance classification based on machine learning. In power load event detection, the paper presents a new transient detection algorithm. By turn-on and turn-off transient waveforms analysis, it can accurately detect the edge point when a device is switched on or switched off. The proposed load classification technique can identify different power appliances with improved recognition accuracy and computational speed. The load classification method is composed of two processes including frequency feature analysis and support vector machine. The experimental results indicated that the incorporation of the new edge detection and turn-on and turn-off transient signature analysis into NILM revealed more information than traditional NILM methods. The load classification method has achieved more than ninety percent recognition rate.

  19. Advances in Viral Vector-Based TRAIL Gene Therapy for Cancer

    International Nuclear Information System (INIS)

    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

  20. 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)

  1. An incremental learning algorithm based on Support Vector Machine for pattern recognition

    Science.gov (United States)

    Zou, Lamei; Zhang, Tianxu; Cao, Zhiguo

    2009-10-01

    With the advent of information age, especially with the rapid development of network, "information explosion" problem has emerged. How to improve the classifier's training precision steadily with accumulation of the samples is the original idea of the incremental learning. Support Vector Machine (SVM) has been successfully applied in many pattern recognition fields. While its complex computation is the bottle-neck to deal with large-scale data. It's important to do researches on the SVM's incremental learning. This article proposes a SVM's incremental learning algorithm based on the filtering fixed partition of the data set. This article firstly presents "Two-class problem"s algorithm and then generalizes it to the "Multiclass problem" algorithm by the One-vs-One method. The experimental results on three types of data sets' classification show that the proposed incremental learning technique can greatly improve the efficiency of SVM learning. SVM Incremental learning can not only ensure the correct identification rate but also speedup the training process.

  2. 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.

  3. A replicating plasmid-based vector for GFP expression in Mycoplasma hyopneumoniae.

    Science.gov (United States)

    Ishag, H Z A; Liu, M J; Yang, R S; Xiong, Q Y; Feng, Z X; Shao, G Q

    2016-01-01

    Mycoplasma hyopneumoniae (M. hyopneumoniae) causes porcine enzootic pneumonia (PEP) that significantly affects the pig industry worldwide. Despite the availability of the whole genome sequence, studies on the pathogenesis of this organism have been limited due to the lack of a genetic manipulation system. Therefore, the aim of the current study was to generate a general GFP reporter vector based on a replicating plasmid. Here, we describe the feasibility of GFP reporter expression in M. hyopneumoniae (strain 168L) controlled by the p97 gene promoter of this mycoplasma. An expression plasmid (pMD18-TOgfp) containing the p97 gene promoter, and origin of replication (oriC) of M. hyopneumoniae, tetracycline resistant marker (tetM), and GFP was constructed and used to transform competent M. hyopneumoniae cells. We observed green fluorescence in M. hyopneumoniae transformants under fluorescence microscopy, which indicates that there was expression of the GFP reporter that was driven by the p97 gene promoter. Additionally, an electroporation method for M. hyopneumoniae with an efficiency of approximately 1 x 10(-6) transformants/μg plasmid DNA was optimized and is described herein. In conclusion, our data demonstrate the susceptibility of M. hyopneumoniae to genetic manipulation whereby foreign genes are expressed. This work may encourage the development of genetic tools to manipulate the genome of M. hyopneumoniae for functional genomic analyses. PMID:27173288

  4. Bioreducible cross-linked polymers based on G1 peptide dendrimer as potential gene delivery vectors.

    Science.gov (United States)

    Li, Chun-Yan; Wang, Hai-Jiao; Cao, Jing-Ming; Zhang, Ji; Yu, Xiao-Qi

    2014-11-24

    A series of cationic polymers based on low generation (G1) peptide dendrimer were synthesized with disulfide-containing linkages. The DNA binding abilities of the target polymers were studied by gel electrophoresis and fluorescence quenching assay. The bioreducible property of the disulfide-containing polymers P2 and P3 was also investigated in the presence of dithiothreitol (DTT). Results from dynamic light scattering (DLS) and transmission electron microscopy (TEM) assays reveal that these materials may condense DNA into nanoparticles with proper sizes and zeta-potentials. In vitro cell experiments show that compared to branched 25 KDa PEI, P2 and P3 may exhibit much higher gene transfection efficiency and lower cytotoxicity in both HEK293 and U-2OS cells. Additionally, polymer prepared from Michael addition gives better gene transfection ability, while polymer prepared from ring-opening reaction has better serum tolerance. Results indicate that these polymers might be promising non-viral gene vectors for their easy preparation, very low cytotoxicity, and good transfection efficiency. PMID:25282264

  5. A Novel Classification Algorithm Based on Incremental Semi-Supervised Support Vector Machine

    Science.gov (United States)

    Gao, Fei; Mei, Jingyuan; Sun, Jinping; Wang, Jun; Yang, Erfu; Hussain, Amir

    2015-01-01

    For current computational intelligence techniques, a major challenge is how to learn new concepts in changing environment. Traditional learning schemes could not adequately address this problem due to a lack of dynamic data selection mechanism. In this paper, inspired by human learning process, a novel classification algorithm based on incremental semi-supervised support vector machine (SVM) is proposed. Through the analysis of prediction confidence of samples and data distribution in a changing environment, a “soft-start” approach, a data selection mechanism and a data cleaning mechanism are designed, which complete the construction of our incremental semi-supervised learning system. Noticeably, with the ingenious design procedure of our proposed algorithm, the computation complexity is reduced effectively. In addition, for the possible appearance of some new labeled samples in the learning process, a detailed analysis is also carried out. The results show that our algorithm does not rely on the model of sample distribution, has an extremely low rate of introducing wrong semi-labeled samples and can effectively make use of the unlabeled samples to enrich the knowledge system of classifier and improve the accuracy rate. Moreover, our method also has outstanding generalization performance and the ability to overcome the concept drift in a changing environment. PMID:26275294

  6. 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.

  7. 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.

  8. Knodle: A Support Vector Machines-Based Automatic Perception of Organic Molecules from 3D Coordinates.

    Science.gov (United States)

    Kadukova, Maria; Grudinin, Sergei

    2016-08-22

    Here we address the problem of the assignment of atom types and bond orders in low molecular weight compounds. For this purpose, we have developed a prediction model based on nonlinear Support Vector Machines (SVM), implemented in a KNOwledge-Driven Ligand Extractor called Knodle, a software library for the recognition of atomic types, hybridization states, and bond orders in the structures of small molecules. We trained the model using an excessive amount of structural data collected from the PDBbindCN database. Accuracy of the results and the running time of our method is comparable with other popular methods, such as NAOMI, fconv, and I-interpret. On the popular Labute's benchmark set consisting of 179 protein-ligand complexes, Knodle makes five to six perception errors, NAOMI makes seven errors, I-interpret makes nine errors, and fconv makes 13 errors. On a larger set of 3,000 protein-ligand structures collected from the PDBBindCN general data set (v2014), Knodle and NAOMI have a comparable accuracy of approximately 3.9% and 4.7% of errors, I-interpret made 6.0% of errors, while fconv produced approximately 12.8% of errors. On a more general set of 332,974 entries collected from the Ligand Expo database, Knodle made 4.5% of errors. Overall, our study demonstrates the efficiency and robustness of nonlinear SVM in structure perception tasks. Knodle is available at https://team.inria.fr/nano-d/software/Knodle . PMID:27405533

  9. Nighttime Fire/Smoke Detection System Based on a Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Chao-Ching Ho

    2013-01-01

    Full Text Available Currently, video surveillance-based early fire smoke detection is crucial to the prevention of large fires and the protection of life and goods. To overcome the nighttime limitations of video smoke detection methods, a laser light can be projected into the monitored field of view, and the returning projected light section image can be analyzed to detect fire and/or smoke. If smoke appears within the monitoring zone created from the diffusion or scattering of light in the projected path, the camera sensor receives a corresponding signal. The successive processing steps of the proposed real-time algorithm use the spectral, diffusing, and scattering characteristics of the smoke-filled regions in the image sequences to register the position of possible smoke in a video. Characterization of smoke is carried out by a nonlinear classification method using a support vector machine, and this is applied to identify the potential fire/smoke location. Experimental results in a variety of nighttime conditions demonstrate that the proposed fire/smoke detection method can successfully and reliably detect fires by identifying the location of smoke.

  10. 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.

  11. Considering polarization in MODIS-based cloud property retrievals by using a vector radiative transfer code

    International Nuclear Information System (INIS)

    In this study, a full-vector, adding–doubling radiative transfer model is used to investigate the influence of the polarization state on cloud property retrievals from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations. Two sets of lookup tables (LUTs) are developed for the retrieval purposes, both of which provide water cloud and ice cloud reflectivity functions at two wavelengths in various sun-satellite viewing geometries. However, only one of the LUTs considers polarization. The MODIS reflectivity observations at 0.65 μm (band 1) and 2.13 μm (band 7) are used to infer the cloud optical thickness and particle effective diameter, respectively. Results indicate that the retrievals for both water cloud and ice cloud show considerable sensitivity to polarization. The retrieved water and ice cloud effective diameter and optical thickness differences can vary by as much as ±15% due to polarization state considerations. In particular, the polarization state has more influence on completely smooth ice particles than on severely roughened ice particles. - Highlights: • Impact of polarization on satellite-based retrieval of water/ice cloud properties is studied. • Inclusion of polarization can change water/ice optical thickness and effective diameter values by up to ±15%. • Influence of polarization on cloud property retrievals depends on sun-satellite viewing geometries

  12. An Artificial Intelligence Approach for Groutability Estimation Based on Autotuning Support Vector Machine

    OpenAIRE

    Hong-Hai Tran; Nhat-Duc Hoang

    2014-01-01

    Permeation grouting is a commonly used approach for soil improvement in construction engineering. Thus, predicting the results of grouting activities is a crucial task that needs to be carried out in the planning phase of any grouting project. In this research, a novel artificial intelligence approach—autotuning support vector machine—is proposed to forecast the result of grouting activities that employ microfine cement grouts. In the new model, the support vector machine (SVM) algorithm is u...

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

    OpenAIRE

    Jian-Jiun Ding; Chun-Chieh Wang; Chiu-Wen Wu; Po-Hung Wu; Shuen-De Wu

    2012-01-01

    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 demonstr...

  14. 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 ...

  15. Quantitative evaluation of first, second, and third generation hairpin systems reveals the limit of mammalian vector-based RNAi

    OpenAIRE

    Watanabe, Colin; Cuellar, Trinna L.; Haley, Benjamin

    2016-01-01

    ABSTRACT Incorporating miRNA-like features into vector-based hairpin scaffolds has been shown to augment small RNA processing and RNAi efficiency. Therefore, defining an optimal, native hairpin context may obviate a need for hairpin-specific targeting design schemes, which confound the movement of functional siRNAs into shRNA/artificial miRNA backbones, or large-scale screens to identify efficacious sequences. Thus, we used quantitative cell-based assays to compare separate third generation a...

  16. Physical Characterization of Gemini Surfactant-Based Synthetic Vectors for the Delivery of Linear Covalently Closed (LCC) DNA Ministrings.

    Science.gov (United States)

    Sum, Chi Hong; Nafissi, Nafiseh; Slavcev, Roderick A; Wettig, Shawn

    2015-01-01

    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(16-3-16)gemini-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. PMID:26561857

  17. 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.

  18. 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.

  19. Advanced signal processing based on support vector regression for lidar applications

    Science.gov (United States)

    Gelfusa, M.; Murari, A.; Malizia, A.; Lungaroni, M.; Peluso, E.; Parracino, S.; Talebzadeh, S.; Vega, J.; Gaudio, P.

    2015-10-01

    The LIDAR technique has recently found many applications in atmospheric physics and remote sensing. One of the main issues, in the deployment of systems based on LIDAR, is the filtering of the backscattered signal to alleviate the problems generated by noise. Improvement in the signal to noise ratio is typically achieved by averaging a quite large number (of the order of hundreds) of successive laser pulses. This approach can be effective but presents significant limitations. First of all, it implies a great stress on the laser source, particularly in the case of systems for automatic monitoring of large areas for long periods. Secondly, this solution can become difficult to implement in applications characterised by rapid variations of the atmosphere, for example in the case of pollutant emissions, or by abrupt changes in the noise. In this contribution, a new method for the software filtering and denoising of LIDAR signals is presented. The technique is based on support vector regression. The proposed new method is insensitive to the statistics of the noise and is therefore fully general and quite robust. The developed numerical tool has been systematically compared with the most powerful techniques available, using both synthetic and experimental data. Its performances have been tested for various statistical distributions of the noise and also for other disturbances of the acquired signal such as outliers. The competitive advantages of the proposed method are fully documented. The potential of the proposed approach to widen the capability of the LIDAR technique, particularly in the detection of widespread smoke, is discussed in detail.

  20. 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.

  1. 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

  2. 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

  3. 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

  4. 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

  5. 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.

  6. Quantitative evaluation of first, second, and third generation hairpin systems reveals the limit of mammalian vector-based RNAi.

    Science.gov (United States)

    Watanabe, Colin; Cuellar, Trinna L; Haley, Benjamin

    2016-01-01

    Incorporating miRNA-like features into vector-based hairpin scaffolds has been shown to augment small RNA processing and RNAi efficiency. Therefore, defining an optimal, native hairpin context may obviate a need for hairpin-specific targeting design schemes, which confound the movement of functional siRNAs into shRNA/artificial miRNA backbones, or large-scale screens to identify efficacious sequences. Thus, we used quantitative cell-based assays to compare separate third generation artificial miRNA systems, miR-E (based on miR-30a) and miR-3G (based on miR-16-2 and first described in this study) to widely-adopted, first and second generation formats in both Pol-II and Pol-III expression vector contexts. Despite their unique structures and strandedness, and in contrast to first and second-generation RNAi triggers, the third generation formats operated with remarkable similarity to one another, and strong silencing was observed with a significant fraction of the evaluated target sequences within either promoter context. By pairing an established siRNA design algorithm with the third generation vectors we could readily identify targeting sequences that matched or exceeded the potency of those discovered through large-scale sensor-based assays. We find that third generation hairpin systems enable the maximal level of siRNA function, likely through enhanced processing and accumulation of precisely-defined guide RNAs. Therefore, we predict future gains in RNAi potency will come from improved hairpin expression and identification of optimal siRNA-intrinsic silencing properties rather than further modification of these scaffolds. Consequently, third generation systems should be the primary format for vector-based RNAi studies; miR-3G is advantageous due to its small expression cassette and simplified, cost-efficient cloning scheme. PMID:26786363

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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%.

  12. Viral Vector-Based Dissection of Marmoset GFAP Promoter in Mouse and Marmoset Brains

    Science.gov (United States)

    Takahashi, Nobutaka; Matsuzaki, Yasunori; Kishi, Shoji; Hirai, Hirokazu

    2016-01-01

    Adeno-associated virus (AAV) vectors are small in diameter, diffuse easily in the brain, and represent a highly efficient means by which to transfer a transgene to the brain of a large animal. A major demerit of AAV vectors is their limited accommodation capacity for transgenes. Thus, a compact promoter is useful when delivering large transgenes via AAV vectors. In the present study, we aimed to identify the shortest astrocyte-specific GFAP promoter region that could be used for AAV-vector-mediated transgene expression in the marmoset brain. The 2.0-kb promoter region upstream of the GFAP gene was cloned from the marmoset genome, and short promoters (1.6 kb, 1.4 kb, 0.6 kb, 0.3 kb and 0.2 kb) were obtained by progressively deleting the original 2.0-kb promoter from the 5’ end. The short promoters were screened in the mouse cerebellum in terms of their strength and astrocyte specificity. We found that the 0.3-kb promoter maintained 40% of the strength of the original 2.0-kb promoter, and approximately 90% of its astrocyte specificity. These properties were superior to those of the 1.4-kb, 0.6-kb (20% promoter strength) and 0.2-kb (70% astrocyte specificity) promoters. Then, we verified whether the 0.3-kb GFAP promoter retained astrocyte specificity in the marmoset cerebral cortex. Injection of viral vectors carrying the 0.3-kb marmoset GFAP promoter specifically transduced astrocytes in both the cerebral cortex and cerebellar cortex of the marmoset. These results suggest that the compact 0.3-kb promoter region serves as an astrocyte-specific promoter in the marmoset brain, which permits us to express a large gene by AAV vectors that have a limited accommodation capacity. PMID:27571575

  13. 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

  14. 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.

  15. Structure-Activity Relationships of JMV4463, a Vectorized Cathepsin D Inhibitor with Antiproliferative Properties: The Unique Role of the AMPA-Based Vector.

    Science.gov (United States)

    Vezenkov, Lubomir L; Sanchez, Clément A; Bellet, Virginie; Martin, Vincent; Maynadier, Marie; Bettache, Nadir; Lisowski, Vincent; Martinez, Jean; Garcia, Marcel; Amblard, Muriel; Hernandez, Jean-François

    2016-02-01

    Cathepsin D (CathD) is overexpressed and secreted by several solid tumors and stimulates their growth, the mechanism of which is still not understood. In this context, the pepstatin bioconjugate JMV4463 [Ac-arg-O2 Oc-(Val)3-Sta-Ala-Sta-(AMPA)4-NH2; O2 Oc=8-amino-3,6-dioxaoctanoyl, Sta=statine, AMPA=ortho-aminomethylphenylacetyl], containing a new kind of cell-penetrating vector, was previously shown to exhibit potent antiproliferative effects in vitro and to delay the onset of tumors in vivo. In this study, we performed a structure-activity relationship analysis to evaluate the significance of the inhibitor and vector moieties of JMV4463. By modifying both statine residues of pepstatin we found that the antiproliferative activity is correlated with CathD inhibition, supporting a major role of the catalytic activity of intracellular CathD in cancer cell proliferation. Replacing the vector composed of four AMPA units with other vectors was found to abolish cytotoxicity, although all of the conjugates enabled pepstatin transport into cells. In addition, the AMPA4 vector must be localized at the C terminus of the bioconjugate. The unexpected importance of the vector structure and position for cytotoxic action suggests that AMPA4 enables pepstatin to inhibit the proteolysis of critical CathD substrates involved in cell proliferation via a unique mechanism of action. PMID:26639308

  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. 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.

  18. Dynamic artificial bee colony algorithm for multi-parameters optimization of support vector machine-based soft-margin classifier

    Science.gov (United States)

    Yan, Yiming; Zhang, Ye; Gao, Fengjiao

    2012-12-01

    This article proposes a `dynamic' artificial bee colony (D-ABC) algorithm for solving optimizing problems. It overcomes the poor performance of artificial bee colony (ABC) algorithm, when applied to multi-parameters optimization. A dynamic `activity' factor is introduced to D-ABC algorithm to speed up convergence and improve the quality of solution. This D-ABC algorithm is employed for multi-parameters optimization of support vector machine (SVM)-based soft-margin classifier. Parameter optimization is significant to improve classification performance of SVM-based classifier. Classification accuracy is defined as the objection function, and the many parameters, including `kernel parameter', `cost factor', etc., form a solution vector to be optimized. Experiments demonstrate that D-ABC algorithm has better performance than traditional methods for this optimizing problem, and better parameters of SVM are obtained which lead to higher classification accuracy.

  19. 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.

  20. 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.

  1. Flight test results of a vector-based failure detection and isolation algorithm for a redundant strapdown inertial measurement unit

    Science.gov (United States)

    Morrell, F. R.; Bailey, M. L.; Motyka, P. R.

    1988-01-01

    Flight test results of a vector-based fault-tolerant algorithm for a redundant strapdown inertial measurement unit are presented. Because the inertial sensors provide flight-critical information for flight control and navigation, failure detection and isolation is developed in terms of a multi-level structure. Threshold compensation techniques for gyros and accelerometers, developed to enhance the sensitivity of the failure detection process to low-level failures, are presented. Four flight tests, conducted in a commercial transport type environment, were used to determine the ability of the failure detection and isolation algorithm to detect failure signals, such a hard-over, null, or bias shifts. The algorithm provided timely detection and correct isolation of flight control- and low-level failures. The flight tests of the vector-based algorithm demonstrated its capability to provide false alarm free dual fail-operational performance for the skewed array of inertial sensors.

  2. 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.

  3. 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

    2016-12-01

    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. PMID:26932761

  4. 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.

  5. 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...

  6. A cache-oblivious sparse matrix-vector multiplication scheme based on the Hilbert curve

    NARCIS (Netherlands)

    Yzelman, A.N.; Bisseling, R.H.

    2012-01-01

    The sparse matrix–vector (SpMV) multiplication is an important kernel in many applications. When the sparse matrix used is unstructured, however, standard SpMV multiplication implementations typically are inefficient in terms of cache usage, sometimes working at only a fraction of peak performance.

  7. Gene vectors based on DOEPC/DOPE mixed cationic liposomes : a physicochemical study

    NARCIS (Netherlands)

    Munoz-Ubeda, Monica; Rodriguez-Pulido, Alberto; Nogales, Aurora; Llorca, Oscar; Quesada-Perez, Manuel; Martin-Molina, Alberto; Aicart, Emilio; Junquera, Elena

    2011-01-01

    A double approach, experimental and theoretical, has been followed to characterize from a physicochemical standpoint the compaction process of DNA by means of cationic colloidal aggregates. The colloidal vectors are cationic liposomes constituted by a mixture of a novel cationic lipid, 1,2-dioleoyl-

  8. 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.

  9. 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

  10. Global rotational motion and displacement estimation of digital image stabilization based on the oblique vectors matching algorithm

    Science.gov (United States)

    Yu, Fei; Hui, Mei; Zhao, Yue-jin

    2009-08-01

    The image block matching algorithm based on motion vectors of correlative pixels in oblique direction is presented for digital image stabilization. The digital image stabilization is a new generation of image stabilization technique which can obtains the information of relative motion among frames of dynamic image sequences by the method of digital image processing. In this method the matching parameters are calculated from the vectors projected in the oblique direction. The matching parameters based on the vectors contain the information of vectors in transverse and vertical direction in the image blocks at the same time. So the better matching information can be obtained after making correlative operation in the oblique direction. And an iterative weighted least square method is used to eliminate the error of block matching. The weights are related with the pixels' rotational angle. The center of rotation and the global emotion estimation of the shaking image can be obtained by the weighted least square from the estimation of each block chosen evenly from the image. Then, the shaking image can be stabilized with the center of rotation and the global emotion estimation. Also, the algorithm can run at real time by the method of simulated annealing in searching method of block matching. An image processing system based on DSP was used to exam this algorithm. The core processor in the DSP system is TMS320C6416 of TI, and the CCD camera with definition of 720×576 pixels was chosen as the input video signal. Experimental results show that the algorithm can be performed at the real time processing system and have an accurate matching precision.

  11. 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.

  12. 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.

  13. New Evaluation Vector through the Stanford Mobile Inquiry-Based Learning Environment (SMILE) for Participatory Action Research

    Science.gov (United States)

    An, Ji-Young

    2016-01-01

    Objectives This article reviews an evaluation vector model driven from a participatory action research leveraging a collective inquiry system named SMILE (Stanford Mobile Inquiry-based Learning Environment). Methods SMILE has been implemented in a diverse set of collective inquiry generation and analysis scenarios including community health care-specific professional development sessions and community-based participatory action research projects. In each scenario, participants are given opportunities to construct inquiries around physical and emotional health-related phenomena in their own community. Results Participants formulated inquiries as well as potential clinical treatments and hypothetical scenarios to address health concerns or clarify misunderstandings or misdiagnoses often found in their community practices. From medical universities to rural village health promotion organizations, all participatory inquiries and potential solutions can be collected and analyzed. The inquiry and solution sets represent an evaluation vector which helps educators better understand community health issues at a much deeper level. Conclusions SMILE helps collect problems that are most important and central to their community health concerns. The evaluation vector, consisting participatory and collective inquiries and potential solutions, helps the researchers assess the participants' level of understanding on issues around health concerns and practices while helping the community adequately formulate follow-up action plans. The method used in SMILE requires much further enhancement with machine learning and advanced data visualization. PMID:27525157

  14. 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.

  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. 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.

  17. 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.

  18. 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.

  19. 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.

  20. AAV Vectors for FRET-Based Analysis of Protein-Protein Interactions in Photoreceptor Outer Segments

    Science.gov (United States)

    Becirovic, Elvir; Böhm, Sybille; Nguyen, Ong N. P.; Riedmayr, Lisa M.; Hammelmann, Verena; Schön, Christian; Butz, Elisabeth S.; Wahl-Schott, Christian; Biel, Martin; Michalakis, Stylianos

    2016-01-01

    Fluorescence resonance energy transfer (FRET) is a powerful method for the detection and quantification of stationary and dynamic protein-protein interactions. Technical limitations have hampered systematic in vivo FRET experiments to study protein-protein interactions in their native environment. Here, we describe a rapid and robust protocol that combines adeno-associated virus (AAV) vector-mediated in vivo delivery of genetically encoded FRET partners with ex vivo FRET measurements. The method was established on acutely isolated outer segments of murine rod and cone photoreceptors and relies on the high co-transduction efficiency of retinal photoreceptors by co-delivered AAV vectors. The procedure can be used for the systematic analysis of protein-protein interactions of wild type or mutant outer segment proteins in their native environment. Conclusively, our protocol can help to characterize the physiological and pathophysiological relevance of photoreceptor specific proteins and, in principle, should also be transferable to other cell types. PMID:27516733

  1. Neutron–gamma discrimination based on the support vector machine method

    International Nuclear Information System (INIS)

    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

  2. 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.

  3. Exploring the limits of vector construction based on Citrus tristeza virus.

    Science.gov (United States)

    El-Mohtar, Choaa; Dawson, William O

    2014-01-01

    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.

  4. Bethe vectors of quantum integrable models based on Uq( gl-hat N)

    International Nuclear Information System (INIS)

    We study quantum Uq( gl-hat N) integrable models solvable by the nested algebraic Bethe ansatz. Different formulas are given for the right and left universal off-shell nested Bethe vectors. It is shown that these formulas can be related by certain morphisms of the positive Borel subalgebra in Uq( gl-hat N) into analogous subalgebra in Uq−1( gl-hat N). (paper)

  5. An Improved MRAS Based Sensorless Vector Control Method for Wind Power Generator

    OpenAIRE

    M. Delimar; MILICEVIC, D.; VASIC, V.; KATIC, V.; DUMNIC, B.

    2012-01-01

    This paper describes an improved sensorless vector control strategy for a squirrel cage induction generator used invariable speed wind energy conversion systems (WECS). The main goal is to design a robust control algorithmimmune to generator parameter variations. In order to estimate the rotational speed of the induction generator, amodel reference adaptive system (MRAS observer) is used. It is shown that a generator parameter mismatch has agreat influence on the rotor speed estimation. In or...

  6. Facial expression recognition based on local region specific features and support vector machines

    OpenAIRE

    Ghimire, Deepak; Jeong, Sunghwan; Lee, Joonwhoan; Park, Sang Hyun

    2016-01-01

    Facial expressions are one of the most powerful, natural and immediate means for human being to communicate their emotions and intensions. Recognition of facial expression has many applications including human-computer interaction, cognitive science, human emotion analysis, personality development etc. In this paper, we propose a new method for the recognition of facial expressions from single image frame that uses combination of appearance and geometric features with support vector machines ...

  7. MEL FREQUENCY CEPSTRAL COEFFICIENTS (MFCC) BASED SPEAKER IDENTIFICATION IN NOISY ENVIRONMENT USING LBG VECTOR QUANTIZATION

    OpenAIRE

    Arun Kumar Choudhary; Jitendra Kumar Mishra

    2016-01-01

    Recognizing A speaker can simplify task of translating speech in systems that have been trained on specific person's voices or it can be used to the authenticate or verify the identity of a speaker as part of a security process. This work discusses Implementation of an Enhanced Speaker Recognition system using MFCC and the LBG Algorithm. The MFCC has been used the extensively for purposes of Speaker Recognition. This work has augmented the existing work by using Vector Quantization and Cl...

  8. Cometabolic oxidation of polychlorinated biphenyls in soil with a surfactant-based field application vector.

    OpenAIRE

    Lajoie, C. A.; Layton, A C; Sayler, G. S.

    1994-01-01

    Polychlorinated biphenyl (PCB)-degradative genes, under the control of a constitutive promoter, were cloned into a broad-host-range plasmid and a transposon. These constructs were inserted into a surfactant-utilizing strain, Pseudomonas putida IPL5, to create a field application vector (FAV) in which a surfactant-degrading organism cometabolizes PCB. By utilizing a surfactant not readily available to indigenous populations and a constitutive promoter, selective growth and PCB-degradative gene...

  9. 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.

  10. 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

  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. 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...

  15. 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

  16. 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%.

  17. 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.

  18. 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.

  19. 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.

  20. A new vector for recombination-based cloning of large DNA fragments from yeast artificial chromosomes.

    OpenAIRE

    Bradshaw, M S; Bollekens, J A; Ruddle, F H

    1995-01-01

    The functional analysis of genes frequently requires manipulation of large genomic regions embedded in yeast artificial chromosomes (YACs). We have designed a yeast-bacteria shuttle vector, pClasper, that can be used to clone specific regions of interest from YACs by homologous recombination. The important feature of pClasper is the presence of the mini-F factor replicon. This leads to a significant increase in the size of the plasmid inserts that can be maintained in bacteria after cloning b...

  1. Converter of laser beams with circular polarization to cylindrical vector beams based on anisotropic crystals

    Science.gov (United States)

    Paranin, Vyacheslav D.; Karpeev, Sergey V.; Kazanskiy, Nikolay L.; Krasnov, Andrey P.

    2016-03-01

    The optical system for converting laser beams with circular polarization to cylindrical vector beams on the basis of anisotropic crystals has been developed. The experimental research of beam formation quality has been carried out on the both polarization and structural characteristics. The research showed differences in the formation of the azimuthal and radial polarizations for Gaussian modes and Bessel beams. The boundaries of changes of the optical system parameters to form different types of polarizations with different amplitude and phase distributions have been identified.

  2. Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection

    OpenAIRE

    Tian Wang; Jie Chen; Yi Zhou; Hichem Snoussi

    2013-01-01

    The abnormal event detection problem is an important subject in real-time video surveillance. In this paper, we propose a novel online one-class classification algorithm, online least squares one-class support vector machine (online LS-OC-SVM), combined with its sparsified version (sparse online LS-OC-SVM). LS-OC-SVM extracts a hyperplane as an optimal description of training objects in a regularized least squares sense. The online LS-OC-SVM learns a training set with a limited number of samp...

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. Multi-Sensor Data Fusion Using a Relevance Vector Machine Based on an Ant Colony for Gearbox Fault Detection.

    Science.gov (United States)

    Liu, Zhiwen; Guo, Wei; Tang, Zhangchun; Chen, Yongqiang

    2015-01-01

    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). PMID:26334280

  9. 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.

  10. 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.

  11. TALEN-based gene disruption in the dengue vector Aedes aegypti.

    Directory of Open Access Journals (Sweden)

    Azadeh Aryan

    Full Text Available In addition to its role as the primary vector for dengue viruses, Aedes aegypti has a long history as a genetic model organism for other bloodfeeding mosquitoes, due to its ease of colonization, maintenance and reproductive productivity. Though its genome has been sequenced, functional characterization of many Ae. aegypti genes, pathways and behaviors has been slow. TALE nucleases (TALENs have been used with great success in a number of organisms to generate site-specific DNA lesions. We evaluated the ability of a TALEN pair to target the Ae. aegypti kmo gene, whose protein product is essential in the production of eye pigmentation. Following injection into pre-blastoderm embryos, 20-40% of fertile survivors produced kmo alleles that failed to complement an existing kh(w mutation. Most of these individuals produced more than 20% white-eyed progeny, with some producing up to 75%. Mutant alleles were associated with lesions of 1-7 bp specifically at the selected target site. White-eyed individuals could also be recovered following a blind intercross of G1 progeny, yielding several new white-eyed strains in the genetic background of the sequenced Liverpool strain. We conclude that TALENs are highly active in the Ae. aegypti germline, and have the potential to transform how reverse genetic experiments are performed in this important disease vector.

  12. A Cognitive Skill Classification Based On Multi Objective Optimization Using Learning Vector Quantization for Serious Games

    Directory of Open Access Journals (Sweden)

    Moh. Aries Syufagi

    2011-12-01

    Full Text Available Nowadays, serious games and game technology are poised to transform the way of educating and training students at all levels. However, pedagogical value in games do not help novice students learn, too many memorizing and reduce learning process due to no information of player’s ability. To asses the cognitive level of player ability, we propose a Cognitive Skill Game (CSG. CSG improves this cognitive concept to monitor how players interact with the game. This game employs Learning Vector Quantization (LVQ for optimizing the cognitive skill input classification of the player. CSG is using teacher’s data to obtain the neuron vector of cognitive skill pattern supervise. Three clusters multi objective target will be classified as; trial and error, carefully and, expert cognitive skill. In the game play experiments using 33 respondent players demonstrates that 61% of players have high trial and error cognitive skill, 21% have high carefully cognitive skill, and 18% have high expert cognitive skill. CSG may provide information to game engine when a player needs help or when wanting a formidable challenge. The game engine will provide the appropriate tasks according to players’ ability. CSG will help balance the emotions of players, so players do not get bored and frustrated. Players have a high interest to finish the game if the player is emotionally stable. Interests in the players strongly support the procedural learning in a serious game.

  13. Cloud removal of remote sensing image based on multi-output suppor t vector regression

    Institute of Scientific and Technical Information of China (English)

    Gensheng Hu; Xiaoqi Sun; Dong Liang; Yingying Sun

    2014-01-01

    Removal of cloud cover on the satel ite remote sens-ing image can effectively improve the availability of remote sensing images. For thin cloud cover, support vector value contourlet trans-form is used to achieve multi-scale decomposition of the area of thin cloud cover on remote sensing images. Through enhancing coefficients of high frequency and suppressing coefficients of low frequency, the thin cloud is removed. For thick cloud cover, if the areas of thick cloud cover on multi-source or multi-temporal remote sensing images do not overlap, the multi-output support vector regression learning method is used to remove this kind of thick clouds. If the thick cloud cover areas overlap, by using the multi-output learning of the surrounding areas to predict the sur-face features of the overlapped thick cloud cover areas, this kind of thick cloud is removed. Experimental results show that the pro-posed cloud removal method can effectively solve the problems of the cloud overlapping and radiation difference among multi-source images. The cloud removal image is clear and smooth.

  14. 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.

  15. An Artificial Intelligence Approach for Groutability Estimation Based on Autotuning Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Hong-Hai Tran

    2014-01-01

    Full Text Available Permeation grouting is a commonly used approach for soil improvement in construction engineering. Thus, predicting the results of grouting activities is a crucial task that needs to be carried out in the planning phase of any grouting project. In this research, a novel artificial intelligence approach—autotuning support vector machine—is proposed to forecast the result of grouting activities that employ microfine cement grouts. In the new model, the support vector machine (SVM algorithm is utilized to classify grouting activities into two classes: success and  failure. Meanwhile, the differential evolution (DE optimization algorithm is employed to identify the optimal tuning parameters of the SVM algorithm, namely, the penalty parameter and the kernel function parameter. The integration of the SVM and DE algorithms allows the newly established method to operate automatically without human prior knowledge or tedious processes for parameter setting. An experiment using a set of in situ data samples demonstrates that the newly established method can produce an outstanding prediction performance.

  16. Dorsolateral Striatal Lesions Impair Navigation Based on Landmark-Goal Vectors but Facilitate Spatial Learning Based on a "Cognitive Map"

    Science.gov (United States)

    Kosaki, Yutaka; Poulter, Steven L.; Austen, Joe M.; McGregor, Anthony

    2015-01-01

    In three experiments, the nature of the interaction between multiple memory systems in rats solving a variation of a spatial task in the water maze was investigated. Throughout training rats were able to find a submerged platform at a fixed distance and direction from an intramaze landmark by learning a landmark-goal vector. Extramaze cues were…

  17. Development and assessment of plant-based synthetic odor baits for surveillance and control of malaria vectors.

    Directory of Open Access Journals (Sweden)

    Vincent O Nyasembe

    Full Text Available BACKGROUND: 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 synthetic odor baits in trapping outdoor populations of malaria vectors. METHODOLOGY AND PRINCIPAL FINDING: Three plant-based lures ((E-linalool oxide [LO], (E-linalool oxide and (E-β-ocimene [LO + OC], and a six-component blend comprising (E-linalool oxide, (E-β-ocimene, hexanal, β-pinene, limonene, and (E-β-farnesene [Blend C], were tested alongside an animal/human-based synthetic lure (comprising heptanal, octanal, nonanal, and decanal [Blend F] and worn socks in a malaria endemic zone in the western part of Kenya. Mosquito Magnet-X (MM-X and lightless Centre for Disease Control (CDC light traps were used. Odor-baited traps were compared with traps baited with either solvent alone or solvent + carbon dioxide (controls for 18 days in a series of randomized incomplete-block designs of days × sites × treatments. The interactive effect of plant and animal/human odor was also tested by combining LO with either Blend F or worn socks. Our results show that irrespective of trap type, traps baited with synthetic plant odors compared favorably to the same traps baited with synthetic animal odors and worn socks in trapping malaria vectors, relative to the controls. Combining LO and worn socks enhanced trap captures of Anopheles species while LO + Blend F recorded reduced trap capture. Carbon dioxide enhanced total trap capture of both plant- and animal/human-derived odors. However, significantly higher proportions of male and engorged female Anopheles gambiae s.l. were caught when the odor treatments did not include carbon dioxide. CONCLUSION AND SIGNIFICANCE: The results highlight the potential of plant-based odors and specifically linalool oxide

  18. A Comparison of Hourly Typhoon Rainfall Forecasting Models Based on Support Vector Machines and Random Forests with Different Predictor Sets

    Science.gov (United States)

    Lin, Kun-Hsiang; Tseng, Hung-Wei; Kuo, Chen-Min; Yang, Tao-Chang; Yu, Pao-Shan

    2016-04-01

    Typhoons with heavy rainfall and strong wind often cause severe floods and losses in Taiwan, which motivates the development of rainfall forecasting models as part of an early warning system. Thus, this study aims to develop rainfall forecasting models based on two machine learning methods, support vector machines (SVMs) and random forests (RFs), and investigate the performances of the models with different predictor sets for searching the optimal predictor set in forecasting. Four predictor sets were used: (1) antecedent rainfalls, (2) antecedent rainfalls and typhoon characteristics, (3) antecedent rainfalls and meteorological factors, and (4) antecedent rainfalls, typhoon characteristics and meteorological factors to construct for 1- to 6-hour ahead rainfall forecasting. An application to three rainfall stations in Yilan River basin, northeastern Taiwan, was conducted. Firstly, the performance of the SVMs-based forecasting model with predictor set #1 was analyzed. The results show that the accuracy of the models for 2- to 6-hour ahead forecasting decrease rapidly as compared to the accuracy of the model for 1-hour ahead forecasting which is acceptable. For improving the model performance, each predictor set was further examined in the SVMs-based forecasting model. The results reveal that the SVMs-based model using predictor set #4 as input variables performs better than the other sets and a significant improvement of model performance is found especially for the long lead time forecasting. Lastly, the performance of the SVMs-based model using predictor set #4 as input variables was compared with the performance of the RFs-based model using predictor set #4 as input variables. It is found that the RFs-based model is superior to the SVMs-based model in hourly typhoon rainfall forecasting. Keywords: hourly typhoon rainfall forecasting, predictor selection, support vector machines, random forests

  19. Multiple mental tasks classification based on nonlinear parameter of mean period using support vector machines

    Institute of Scientific and Technical Information of China (English)

    Liu Hailong; Wang Jue; Zheng Chongxun

    2007-01-01

    Mental task classification is one of the most important problems in Brain-computer interface. This paper studies the classification of five-class mental tasks. The nonlinear parameter of mean period obtained from frequency domain information was used as features for classification implemented by using the method of SVM (support vector machines). The averaged classification accuracy of 85.6% over 7 subjects was achieved for 2-second EEG segments. And the results for EEG segments of 0.5s and 5.0s compared favorably to those of Garrett's. The results indicate that the parameter of mean period represents mental tasks well for classification. Furthermore, the method of mean period is less computationally demanding, which indicates its potential use for online BCI systems.

  20. Multivariate TVaR-Based Risk Decomposition for Vector-Valued Portfolios

    Directory of Open Access Journals (Sweden)

    Mélina Mailhot

    2016-09-01

    Full Text Available In order to protect stakeholders of insurance companies and financial institutions against adverse outcomes of risky businesses, regulators and senior management use capital allocation techniques. For enterprise-wide risk management, it has become important to calculate the contribution of each risk within a portfolio. For that purpose, bivariate lower and upper orthant tail value-at-risk can be used for capital allocation. In this paper, we present multivariate value-at-risk and tail-value-at-risk for d ≥ 2 , and we focus on three different methods to calculate optimal values for the contribution of each risk within the sums of random vectors to the overall portfolio, which could particularly apply to insurance and financial portfolios.

  1. A Solution-Based Analysis of Attack Vectors on Smart Home Systems

    Institute of Scientific and Technical Information of China (English)

    Andreas Brauchli; Depeng Li

    2015-01-01

    The development and wider adoption of smart home technology also created an increased requirement for safe and secure smart home environments with guaranteed privacy constraints. In this paper, a short survey of privacy and security in the more broad smart⁃world context is first presented. The main contribution is then to analyze and rank attack vectors or entry points into a smart home system and propose solutions to remedy or diminish the risk of compromised security or privacy. Further, the usability impacts resulting from the proposed solutions are evaluated. The smart home system used for the analysis in this paper is a digital⁃STROM installation, a home⁃automation solution that is quickly gaining popularity in central Europe, the findings, however, aim to be as solution independent as possible.

  2. Space Vector Modulation Based Direct Matrix Converter for Stand-Alone system

    Directory of Open Access Journals (Sweden)

    Chandra Sekhar Ajin Sekhar

    2014-02-01

    Full Text Available In this paper Permanent Magnet Synchronous Generator (PMSG is used for wind power generation in standalone system due to their feature of high efficiency and low maintenance cost, which was fed with smart direct matrix converter for direct AC-AC conversion, It provides sinusoidal output waveforms with minimal higher order harmonics and no sub harmonics and also it eliminate the usage of dc-link and other passive elements. Space vector modulation (SVM controlled technique is used for matrix converter switching which can eliminate the switching loses by selected switching states.Proposed work are often seen as a future concept for variable speed drives technology.The  proposed model for RL load was analysed and verified by varying the resistor and inductance value and analysed using MATLAB simulation.

  3. Support vector machine-based feature extractor for L/H transitions in JETa)

    Science.gov (United States)

    González, S.; Vega, J.; Murari, A.; Pereira, A.; Ramírez, J. M.; Dormido-Canto, S.; Jet-Efda Contributors

    2010-10-01

    Support vector machines (SVM) are machine learning tools originally developed in the field of artificial intelligence to perform both classification and regression. In this paper, we show how SVM can be used to determine the most relevant quantities to characterize the confinement transition from low to high confinement regimes in tokamak plasmas. A set of 27 signals is used as starting point. The signals are discarded one by one until an optimal number of relevant waveforms is reached, which is the best tradeoff between keeping a limited number of quantities and not loosing essential information. The method has been applied to a database of 749 JET discharges and an additional database of 150 JET discharges has been used to test the results obtained.

  4. A Vector-based Method for the Extraction of Catchment from Grid DEMs

    Institute of Scientific and Technical Information of China (English)

    ZHU Qing; TIAN Yixiang

    2005-01-01

    The methodology of catchment extraction especially from regular grid digital elevation models (DEMs) is briefly reviewed.Then an efficient algorithm, which combines vector process and traditional neighbourhood raster process, is designed for extracting the catchments and subcatchments from depressionless DEMs.The catchment area of each river in the grid DEM data is identified and delineated, then is divided into subcatchments as required.Compared to traditional processes, this method for identifying catchments focuses on the boundaries instead of the area inside the catchments and avoids the boundary intersection phenomena.Last, the algorithm is tested with a set of DEMs of different sizes, and the result proves that the computation efficiency and accuracy are better than existent methods.

  5. Predicting and Classifying User Identification Code System Based on Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    In digital fingerprinting, preventing piracy of images by colluders is an important and tedious issue. Each image will be embedded with a unique User IDentification (U ID) code that is the fingerprint for tracking the authorized user. The proposed hiding scheme makes use of a random number generator to scramble two copies of a UID,which will then be hidden in the randomly selected medium frequency coefficients of the host image. The linear support vector machine (SVM) will be used to train classifications by calculating the normalized correlation (NC) for the 2-class UID codes. The trained classifications will be the models used for identifying unreadable UID codes.Experimental results showed that the success of predicting the unreadable UID codes can be increased by applying SVM. The proposed scheme can be used to provide protections to intellectual property rights of digital images and to keep track of users to prevent collaborative piracies.

  6. Electrocardiogram Pattern Recognition and Analysis Based on Artificial Neural Networks and Support Vector Machines: A Review

    Directory of Open Access Journals (Sweden)

    Mario Sansone

    2013-01-01

    Full Text Available Computer systems for Electrocardiogram (ECG analysis support the clinician in tedious tasks (e.g., Holter ECG monitored in Intensive Care Units or in prompt detection of dangerous events (e.g., ventricular fibrillation. Together with clinical applications (arrhythmia detection and heart rate variability analysis, ECG is currently being investigated in biometrics (human identification, an emerging area receiving increasing attention. Methodologies for clinical applications can have both differences and similarities with respect to biometrics. This paper reviews methods of ECG processing from a pattern recognition perspective. In particular, we focus on features commonly used for heartbeat classification. Considering the vast literature in the field and the limited space of this review, we dedicated a detailed discussion only to a few classifiers (Artificial Neural Networks and Support Vector Machines because of their popularity; however, other techniques such as Hidden Markov Models and Kalman Filtering will be also mentioned.

  7. Vector-based model of elastic bonds for DEM simulation of solids

    CERN Document Server

    Kuzkin, Vitaly A

    2012-01-01

    A new model for computer simulation of solids, composed of bonded particles, is proposed. Vectors rigidly connected with particles are used for description of deformation of a single bond. The expression for potential energy of the bond and corresponding expressions for forces and moments are proposed. Formulas, connecting parameters of the model with longitudinal, shear, bending and torsional stiffnesses of the bond, are derived. It is shown that the model allows to describe any values of the bond stiffnesses exactly. Two different calibration procedures depending on bond length/thickness ratio are proposed. It is shown that parameters of model can be chosen so that under small deformations the bond is equivalent to either Bernoulli-Euler or Timoshenko rod or short cylinder connecting particles. Simple expressions, connecting parameters of V-model with geometrical and mechanical characteristics of the bond, are derived. Computer simulation of dynamical buckling of the straight discrete rod and discrete half-...

  8. An Improved MRAS Based Sensorless Vector Control Method for Wind Power Generator

    Directory of Open Access Journals (Sweden)

    M. Delimar

    2012-10-01

    Full Text Available This paper describes an improved sensorless vector control strategy for a squirrel cage induction generator used invariable speed wind energy conversion systems (WECS. The main goal is to design a robust control algorithmimmune to generator parameter variations. In order to estimate the rotational speed of the induction generator, amodel reference adaptive system (MRAS observer is used. It is shown that a generator parameter mismatch has agreat influence on the rotor speed estimation. In order to estimate the speed accurately, the generator statorresistance must be identified at the same time to correct the mismatched resistance value used in the observer. Theproposed rotor speed estimator with parallel stator resistance identification is first verified by computer simulation.Finally, the experiment is conducted in order to verify the obtained simulation results. It is proved that this controlscheme can enhance the efficiency of a variable speed WECS.

  9. Process Optimization of Ultrasonic Extraction of Puerarin Based on Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    Juan Chen; Xiaoyi Huang; Yanlei Qi; Xin Qi; Qing Guo

    2014-01-01

    In ultrasonic extraction technology, optimization of technical parameters often considers extraction medium only, without including ultrasonic parameters. This paper focuses on controlling the ultrasonic extraction process of puerarin, investigating the influence of ultrasonic parameters on extraction rate, and empirical y analyzing the main components of Pueraria, i.e., isoflavone compounds. A method is presented combining orthogonal experi-mental design with a support vector machine and a predictive model is established for optimization of technical parameters. From the analysis with the predictive model, appropriate process parameters are achieved for higher extraction rate. With these parameters in the ultrasonic extraction of puerarin, the experimental result is satisfactory. This method is of significance to the study of extracting root-stock plant medicines.

  10. An online estimator for rotor resistance in vector drives of induction machines based on Walsh functions

    Institute of Scientific and Technical Information of China (English)

    Hamidreza SHIRAZI; Jalal NAZARZADEH

    2014-01-01

    In a modern electrical driver, rotor field oriented control (RFOC) method has been used to achieve a good performance and an appropriate transient response. In this method, the space vector of the rotor flux comes handy by the rotor resistance value. The rotor resistance is one of the important parameters which varies according to motor speed and room temperature alteration. In this paper, a new on-line estimation method is utilized to obtain the rotor resistance by using Walsh functions domain. The Walsh functions are one of the most applicable functions in piecewise constant basis functions (PCBF) to solve dynamic equations. On the other hand, an integral operational matrix is used to simplify the process and speed of the computation algorithm. The simulations results show that the proposed method is capable of solving the dynamic equations in an electrical machine on a time interval which robustly estimates the rotor resistance in contrast with injection noises.

  11. Parameter selection of support vector machine for function approximation based on chaos optimization

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    The support vector machine (SVM) is a novel machine learning method,which has the ability to approximate nonlinear functions with arbitrary accuracy.Setting parameters well is very crucial for SVM learning results and generalization ability,and now there is no systematic,general method for parameter selection.In this article,the SVM parameter selection for function approximation is regarded as a compound optimization problem and a mutative scale chaos optimization algorithm is employed to search for optimal parameter values.The chaos optimization algorithm is an effective way for global optimal and the mutative scale chaos algorithm could improve the search efficiency and accuracy.Several simulation examples show the sensitivity of the SVM parameters and demonstrate the superiority of this proposed method for nonlinear function approximation.

  12. Structure-activity relationship study of oxindole-based inhibitors of cyclin-dependent kinases based on least-squares support vector machines

    Energy Technology Data Exchange (ETDEWEB)

    Li Jiazhong [Department of Chemistry, Lanzhou University, Lanzhou 730000 (China); Liu Huanxiang [Department of Chemistry, Lanzhou University, Lanzhou 730000 (China); Yao Xiaojun [Department of Chemistry, Lanzhou University, Lanzhou 730000 (China)]. E-mail: xjyao@lzu.edu.cn; Liu Mancang [Department of Chemistry, Lanzhou University, Lanzhou 730000 (China); Hu Zhide [Department of Chemistry, Lanzhou University, Lanzhou 730000 (China); Fan Botao [Universite Paris 7-Denis Diderot, ITODYS 1, rue Guy de la Brosse, 75005 Paris (France)

    2007-01-09

    The least-squares support vector machines (LS-SVMs), as an effective modified algorithm of support vector machine, was used to build structure-activity relationship (SAR) models to classify the oxindole-based inhibitors of cyclin-dependent kinases (CDKs) based on their activity. Each compound was depicted by the structural descriptors that encode constitutional, topological, geometrical, electrostatic and quantum-chemical features. The forward-step-wise linear discriminate analysis method was used to search the descriptor space and select the structural descriptors responsible for activity. The linear discriminant analysis (LDA) and nonlinear LS-SVMs method were employed to build classification models, and the best results were obtained by the LS-SVMs method with prediction accuracy of 100% on the test set and 90.91% for CDK1 and CDK2, respectively, as well as that of LDA models 95.45% and 86.36%. This paper provides an effective method to screen CDKs inhibitors.

  13. A support vector regression-firefly algorithm-based model for limiting velocity prediction in sewer pipes.

    Science.gov (United States)

    Ebtehaj, Isa; Bonakdari, Hossein

    2016-01-01

    Sediment transport without deposition is an essential consideration in the optimum design of sewer pipes. In this study, a novel method based on a combination of support vector regression (SVR) and the firefly algorithm (FFA) is proposed to predict the minimum velocity required to avoid sediment settling in pipe channels, which is expressed as the densimetric Froude number (Fr). The efficiency of support vector machine (SVM) models depends on the suitable selection of SVM parameters. In this particular study, FFA is used by determining these SVM parameters. The actual effective parameters on Fr calculation are generally identified by employing dimensional analysis. The different dimensionless variables along with the models are introduced. The best performance is attributed to the model that employs the sediment volumetric concentration (C(V)), ratio of relative median diameter of particles to hydraulic radius (d/R), dimensionless particle number (D(gr)) and overall sediment friction factor (λ(s)) parameters to estimate Fr. The performance of the SVR-FFA model is compared with genetic programming, artificial neural network and existing regression-based equations. The results indicate the superior performance of SVR-FFA (mean absolute percentage error = 2.123%; root mean square error =0.116) compared with other methods.

  14. A Real-Time Interference Monitoring Technique for GNSS Based on a Twin Support Vector Machine Method.

    Science.gov (United States)

    Li, Wutao; Huang, Zhigang; Lang, Rongling; Qin, Honglei; Zhou, Kai; Cao, Yongbin

    2016-03-04

    Interferences can severely degrade the performance of Global Navigation Satellite System (GNSS) receivers. As the first step of GNSS any anti-interference measures, interference monitoring for GNSS is extremely essential and necessary. Since interference monitoring can be considered as a classification problem, a real-time interference monitoring technique based on Twin Support Vector Machine (TWSVM) is proposed in this paper. A TWSVM model is established, and TWSVM is solved by the Least Squares Twin Support Vector Machine (LSTWSVM) algorithm. The interference monitoring indicators are analyzed to extract features from the interfered GNSS signals. The experimental results show that the chosen observations can be used as the interference monitoring indicators. The interference monitoring performance of the proposed method is verified by using GPS L1 C/A code signal and being compared with that of standard SVM. The experimental results indicate that the TWSVM-based interference monitoring is much faster than the conventional SVM. Furthermore, the training time of TWSVM is on millisecond (ms) level and the monitoring time is on microsecond (μs) level, which make the proposed approach usable in practical interference monitoring applications.

  15. Development of a rapid, robust, and universal picogreen-based method to titer adeno-associated vectors.

    Science.gov (United States)

    Piedra, Jose; Ontiveros, Maria; Miravet, Susana; Penalva, Cristina; Monfar, Mercè; Chillon, Miguel

    2015-02-01

    Recombinant adeno-associated viruses (rAAVs) are promising vectors in preclinical and clinical assays for the treatment of diseases with gene therapy strategies. Recent technological advances in amplification and purification have allowed the production of highly purified rAAV vector preparations. Although quantitative polymerase chain reaction (qPCR) is the current method of choice for titrating rAAV genomes, it shows high variability. In this work, we report a rapid and robust rAAV titration method based on the quantitation of encapsidated DNA with the fluorescent dye PicoGreen®. This method allows detection from 3×10(10) viral genome/ml up to 2.4×10(13) viral genome/ml in a linear range. Contrasted with dot blot or qPCR, the PicoGreen-based assay has less intra- and interassay variability. Moreover, quantitation is rapid, does not require specific primers or probes, and is independent of the rAAV pseudotype analyzed. In summary, development of this universal rAAV-titering method may have substantive implications in rAAV technology.

  16. Retrieval algorithm of sea surface wind vectors for WindSat based on a simple forward model

    Institute of Scientific and Technical Information of China (English)

    ZHAO Yili

    2013-01-01

    WindSat/Coriolis is the first satellite-borne polarimetric microwave radiometer,which aims to improve the potential of polarimetric microwave radiometry for measuring sea surface wind vectors from space.In this paper,a wind vector retrieval algorithm based on a novel and simple forward model was developed for WindSat.The retrieval algorithm of sea surface wind speed was developed using multiple linear regression based on the simulation dataset of the novel forward model.Sea surface wind directions that minimize the difference between simulated and measured values of the third and fourth Stokes parameters were found using maximum likelihood estimation,by which a group of ambiguous wind directions was obtained.A median filter was then used to remove ambiguity of wind direction.Evaluated with sea surface wind speed and direction data from the U.S.National Data Buoy Center (NDBC),root mean square errors are 1.2 m/s and 30° for retrieved wind speed and wind direction,respectively.The evaluation results suggest that the simple forward model and the retrieval algorithm are practicable for near-real time applications,without reducing accuracy.

  17. A Real-Time Interference Monitoring Technique for GNSS Based on a Twin Support Vector Machine Method

    Directory of Open Access Journals (Sweden)

    Wutao Li

    2016-03-01

    Full Text Available Interferences can severely degrade the performance of Global Navigation Satellite System (GNSS receivers. As the first step of GNSS any anti-interference measures, interference monitoring for GNSS is extremely essential and necessary. Since interference monitoring can be considered as a classification problem, a real-time interference monitoring technique based on Twin Support Vector Machine (TWSVM is proposed in this paper. A TWSVM model is established, and TWSVM is solved by the Least Squares Twin Support Vector Machine (LSTWSVM algorithm. The interference monitoring indicators are analyzed to extract features from the interfered GNSS signals. The experimental results show that the chosen observations can be used as the interference monitoring indicators. The interference monitoring performance of the proposed method is verified by using GPS L1 C/A code signal and being compared with that of standard SVM. The experimental results indicate that the TWSVM-based interference monitoring is much faster than the conventional SVM. Furthermore, the training time of TWSVM is on millisecond (ms level and the monitoring time is on microsecond (μs level, which make the proposed approach usable in practical interference monitoring applications.

  18. A Real-Time Interference Monitoring Technique for GNSS Based on a Twin Support Vector Machine Method.

    Science.gov (United States)

    Li, Wutao; Huang, Zhigang; Lang, Rongling; Qin, Honglei; Zhou, Kai; Cao, Yongbin

    2016-01-01

    Interferences can severely degrade the performance of Global Navigation Satellite System (GNSS) receivers. As the first step of GNSS any anti-interference measures, interference monitoring for GNSS is extremely essential and necessary. Since interference monitoring can be considered as a classification problem, a real-time interference monitoring technique based on Twin Support Vector Machine (TWSVM) is proposed in this paper. A TWSVM model is established, and TWSVM is solved by the Least Squares Twin Support Vector Machine (LSTWSVM) algorithm. The interference monitoring indicators are analyzed to extract features from the interfered GNSS signals. The experimental results show that the chosen observations can be used as the interference monitoring indicators. The interference monitoring performance of the proposed method is verified by using GPS L1 C/A code signal and being compared with that of standard SVM. The experimental results indicate that the TWSVM-based interference monitoring is much faster than the conventional SVM. Furthermore, the training time of TWSVM is on millisecond (ms) level and the monitoring time is on microsecond (μs) level, which make the proposed approach usable in practical interference monitoring applications. PMID:26959020

  19. A support vector regression-firefly algorithm-based model for limiting velocity prediction in sewer pipes.

    Science.gov (United States)

    Ebtehaj, Isa; Bonakdari, Hossein

    2016-01-01

    Sediment transport without deposition is an essential consideration in the optimum design of sewer pipes. In this study, a novel method based on a combination of support vector regression (SVR) and the firefly algorithm (FFA) is proposed to predict the minimum velocity required to avoid sediment settling in pipe channels, which is expressed as the densimetric Froude number (Fr). The efficiency of support vector machine (SVM) models depends on the suitable selection of SVM parameters. In this particular study, FFA is used by determining these SVM parameters. The actual effective parameters on Fr calculation are generally identified by employing dimensional analysis. The different dimensionless variables along with the models are introduced. The best performance is attributed to the model that employs the sediment volumetric concentration (C(V)), ratio of relative median diameter of particles to hydraulic radius (d/R), dimensionless particle number (D(gr)) and overall sediment friction factor (λ(s)) parameters to estimate Fr. The performance of the SVR-FFA model is compared with genetic programming, artificial neural network and existing regression-based equations. The results indicate the superior performance of SVR-FFA (mean absolute percentage error = 2.123%; root mean square error =0.116) compared with other methods. PMID:27148727

  20. A role for the histone deacetylase HDAC4 in the life-cycle of HIV-1-based vectors

    Directory of Open Access Journals (Sweden)

    Kao Gary D

    2010-09-01

    Full Text Available Abstract HIV-1 integration is mediated by the HIV-1 integrase protein, which joins 3'-ends of viral DNA to host cell DNA. To complete the integration process, HIV-1 DNA has to be joined to host cell DNA also at the 5'-ends. This process is called post-integration repair (PIR. Integration and PIR involve a number of cellular co-factors. These proteins exhibit different degrees of involvement in integration and/or PIR. Some are required for efficient integration or PIR. On the other hand, some reduce the efficiency of integration. Finally, some are involved in integration site selection. We have studied the role of the histone deacetylase HDAC4 in these processes. HDAC4 was demonstrated to play a role in both cellular double-strand DNA break repair and transcriptional regulation. We observed that HDAC4 associates with viral DNA in an integrase-dependent manner. Moreover, infection with HIV-1-based vectors induces foci of the HDAC4 protein. The related histone deacetylases, HDAC2 and HDAC6, failed to associate with viral DNA after infection. These data suggest that HDAC4 accumulates at integration sites. Finally, overexpression studies with HDAC4 mutants suggest that HDAC4 may be required for efficient transduction by HIV-1-based vectors in cells that are deficient in other DNA repair proteins. We conclude that HDAC4 is likely involved in PIR.