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Sample records for based motion-sensorless vector

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

    DEFF Research Database (Denmark)

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

    2009-01-01

    This paper proposes a novel, active-flux based, motion-sensorless vector control structure for biaxial excitation generator for automobiles (BEGA) for wide speed range operation. BEGA is a hybrid excited synchronous machine having permanent magnets on q-axis and a dc excitation on daxis. Using...... electrical degrees in less than 2 ms test time....

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

    DEFF Research Database (Denmark)

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

    2008-01-01

    This paper proposes a novel hybrid motion- sensorless control system for permanent magnet synchronous motors (PMSM) using a new robust start-up method called I-f control, and a smooth transition to emf-based vector control. The I-f method is based on separate control of id, iq currents with the r......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......-adaptive compensator to eliminate dc-offset and phase-delay. Digital simulations for PMSM start-up with full load torque are presented for different initial rotor-positions. The transitions from I-f to emf motion-sensorless vector control and back as well, at very low-speeds, are fully validated by experimental...

  3. IPMSM Motion-Sensorless Direct Torque and Flux Control

    DEFF Research Database (Denmark)

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

    2005-01-01

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

  4. 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...... speeds to estimate the rotor position and speed, to be used in the stator flux observer and, respectively, as speed feedback. A combined voltage-current model flux observer suits the whole speed range, while the current model fades away as speed increases. PWM and inverter nonlinearities compensation...... provides for a smooth current waveform even at 1 rpm. The paper demonstrates through ample experiments a 1750 rpm 1 1 rpm speed range full-loaded with sensorless DTFC-SVM....

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

    DEFF Research Database (Denmark)

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

    2010-01-01

    A motion sensorless control for single phase permanent magnet brushless d.c. (PM-BLDC) motor drives, based on flux integration and prior knowledge of the PM flux/position characteristic is proposed here and an adequate correction algorithm is adopted, in order to increase the robustness to noise...

  6. 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

    . 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...... with experimental results for single-, two-, and three-phase voltage source sags. Smooth transition through asymmetric voltage sags is demonstrated by all experiments.......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...

  7. 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...... and automatic seamless transfer method from grid connected to stand alone and vice versa. In stand-alone mode, a voltage control scheme with selective harmonic compensation is employed. The PMSG motion-sensorless control system uses an active power controller and a PLL-based observer to estimate the rotor...

  8. 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...... to improve performance at zero and very low speeds. Experimental results validate the proposed control strategies....

  9. 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...... to improve performance at zero and very low speeds. Experimental results validate the proposed control strategies....

  10. 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...

  11. 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...... identification. Digital simulations and experimental results are shown, demonstrating the reliability of the FEM assisted position and speed observer for BLDC PM motor sensorless control operation....

  12. 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...

  13. 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)....

  14. TRICOM vector based cancer vaccines.

    Science.gov (United States)

    Garnett, Charlie T; Greiner, John W; Tsang, Kwong-Yok; Kudo-Saito, Chie; Grosenbach, Douglas W; Chakraborty, Mala; Gulley, James L; Arlen, Philip M; Schlom, Jeffrey; Hodge, James W

    2006-01-01

    For the immune system to mount an effective antitumor T-cell response, an adequate number of T-cells specific for the antigens expressed by the malignancy must be activated [1]. Since most antigens expressed by tumors are "self"-antigens, tumor antigens often lack endogenous immunogenicity and thus do not sufficiently activate T-cells to levels that can mediate tumor eradication. In addition, virtually all solid tumor cells lack the costimulatory molecules necessary to activate tumor-specific T-cells. Approaches that stimulate immune responses to these tumor antigens have the potential to alter this poor responsiveness. This theory has promoted the use of active immunotherapy to generate immune responses against tumor-associated antigens (TAAs) for the treatment of cancer. As one such vaccine strategy, we have utilized poxviruses as delivery vehicles for TAAs in combination with T-cell costimulatory molecules. Initial studies have demonstrated that the insertion of costimulatory molecule trangenes into viral vectors, along with a TAA transgene, greatly enhances the immune response to the antigen. Using this approach, a TRIad of COstimulatory Molecules (TRICOM; B7-1, ICAM-1 and LFA-3) has been shown to enhance T-cell responses to TAAs to levels far greater than any one or two of the costimulatory molecules in combination. In this article, preclinical findings and recent clinical applications of TRICOM-based vaccines as a cancer immunotherapy are reviewed.

  15. 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...

  16. Risk based surveillance for vector borne diseases

    DEFF Research Database (Denmark)

    Bødker, Rene

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

  17. Plant viral vectors based on tobamoviruses.

    Science.gov (United States)

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

    1999-01-01

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

  18. A Support Vector Machine-Based Voice Activity Detection Employing Effective Feature Vectors

    Science.gov (United States)

    Jo, Q.-Haing; Park, Yun-Sik; Lee, Kye-Hwan; Chang, Joon-Hyuk

    In this letter, we propose effective feature vectors to improve the performance of voice activity detection (VAD) employing a support vector machine (SVM), which is known to incorporate an optimized nonlinear decision over two different classes. To extract the effective feature vectors, we present a novel scheme that combines the a posteriori SNR, a priori SNR, and predicted SNR, widely adopted in conventional statistical model-based VAD.

  19. Vectors

    DEFF Research Database (Denmark)

    Boeriis, Morten; van Leeuwen, Theo

    2017-01-01

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

  20. Vectors

    DEFF Research Database (Denmark)

    Boeriis, Morten; van Leeuwen, Theo

    2017-01-01

    This article revisits the concept of vectors, which, in Kress and van Leeuwen’s Reading Images (2006), plays a crucial role in distinguishing between ‘narrative’, action-oriented processes and ‘conceptual’, state-oriented processes. The use of this concept in image analysis has usually focused...... should be taken into account in discussing ‘reactions’, which Kress and van Leeuwen link only to eyeline vectors. Finally, the question can be raised as to whether actions are always realized by vectors. Drawing on a re-reading of Rudolf Arnheim’s account of vectors, these issues are outlined...

  1. Viral vector-based influenza vaccines

    Science.gov (United States)

    de Vries, Rory D.; Rimmelzwaan, Guus F.

    2016-01-01

    ABSTRACT Antigenic drift of seasonal influenza viruses and the occasional introduction of influenza viruses of novel subtypes into the human population complicate the timely production of effective vaccines that antigenically match the virus strains that cause epidemic or pandemic outbreaks. The development of game-changing vaccines that induce broadly protective immunity against a wide variety of influenza viruses is an unmet need, in which recombinant viral vectors may provide. Use of viral vectors allows the delivery of any influenza virus antigen, or derivative thereof, to the immune system, resulting in the optimal induction of virus-specific B- and T-cell responses against this antigen of choice. This systematic review discusses results obtained with vectored influenza virus vaccines and advantages and disadvantages of the currently available viral vectors. PMID:27455345

  2. Viral vector-based influenza vaccines.

    Science.gov (United States)

    de Vries, Rory D; Rimmelzwaan, Guus F

    2016-11-01

    Antigenic drift of seasonal influenza viruses and the occasional introduction of influenza viruses of novel subtypes into the human population complicate the timely production of effective vaccines that antigenically match the virus strains that cause epidemic or pandemic outbreaks. The development of game-changing vaccines that induce broadly protective immunity against a wide variety of influenza viruses is an unmet need, in which recombinant viral vectors may provide. Use of viral vectors allows the delivery of any influenza virus antigen, or derivative thereof, to the immune system, resulting in the optimal induction of virus-specific B- and T-cell responses against this antigen of choice. This systematic review discusses results obtained with vectored influenza virus vaccines and advantages and disadvantages of the currently available viral vectors.

  3. A Subdivision-Based Representation for Vector Image Editing.

    Science.gov (United States)

    Liao, Zicheng; Hoppe, Hugues; Forsyth, David; Yu, Yizhou

    2012-11-01

    Vector graphics has been employed in a wide variety of applications due to its scalability and editability. Editability is a high priority for artists and designers who wish to produce vector-based graphical content with user interaction. In this paper, we introduce a new vector image representation based on piecewise smooth subdivision surfaces, which is a simple, unified and flexible framework that supports a variety of operations, including shape editing, color editing, image stylization, and vector image processing. These operations effectively create novel vector graphics by reusing and altering existing image vectorization results. Because image vectorization yields an abstraction of the original raster image, controlling the level of detail of this abstraction is highly desirable. To this end, we design a feature-oriented vector image pyramid that offers multiple levels of abstraction simultaneously. Our new vector image representation can be rasterized efficiently using GPU-accelerated subdivision. Experiments indicate that our vector image representation achieves high visual quality and better supports editing operations than existing representations.

  4. 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

  5. Comparison of four support-vector based function approximators

    NARCIS (Netherlands)

    de Kruif, B.J.; de Vries, Theodorus J.A.

    2004-01-01

    One of the uses of the support vector machine (SVM), as introduced in V.N. Vapnik (2000), is as a function approximator. The SVM and approximators based on it, approximate a relation in data by applying interpolation between so-called support vectors, being a limited number of samples that have been

  6. Density Based Support Vector Machines for Classification

    OpenAIRE

    Zahra Nazari; Dongshik Kang

    2015-01-01

    Support Vector Machines (SVM) is the most successful algorithm for classification problems. SVM learns the decision boundary from two classes (for Binary Classification) of training points. However, sometimes there are some less meaningful samples amongst training points, which are corrupted by noises or misplaced in wrong side, called outliers. These outliers are affecting on margin and classification performance, and machine should better to discard them. SVM as a popular and widely used cl...

  7. A vector-based, multidimensional scanpath similarity measure

    NARCIS (Netherlands)

    Jarodzka, Halszka; Holmqvist, Kenneth; Nyström, Marcus

    2011-01-01

    Jarodzka, H., Holmqvist, K., & Nyström, M. (2010, March). A vector-based, multidimensional scanpath similarity measure. Presentation at the Eye Tracking Research & Application Symposium (ETRA), Austin, Texas, USA.

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

    Data.gov (United States)

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

  9. A stable RNA virus-based vector for citrus trees

    International Nuclear Information System (INIS)

    Folimonov, Alexey S.; Folimonova, Svetlana Y.; Bar-Joseph, Moshe; Dawson, William O.

    2007-01-01

    Virus-based vectors are important tools in plant molecular biology and plant genomics. A number of vectors based on viruses that infect herbaceous plants are in use for expression or silencing of genes in plants as well as screening unknown sequences for function. Yet there is a need for useful virus-based vectors for woody plants, which demand much greater stability because of the longer time required for systemic infection and analysis. We examined several strategies to develop a Citrus tristeza virus (CTV)-based vector for transient expression of foreign genes in citrus trees using a green fluorescent protein (GFP) as a reporter. These strategies included substitution of the p13 open reading frame (ORF) by the ORF of GFP, construction of a self-processing fusion of GFP in-frame with the major coat protein (CP), or expression of the GFP ORF as an extra gene from a subgenomic (sg) mRNA controlled either by a duplicated CTV CP sgRNA controller element (CE) or an introduced heterologous CE of Beet yellows virus. Engineered vector constructs were examined for replication, encapsidation, GFP expression during multiple passages in protoplasts, and for their ability to infect, move, express GFP, and be maintained in citrus plants. The most successful vectors based on the 'add-a-gene' strategy have been unusually stable, continuing to produce GFP fluorescence after more than 4 years in citrus trees

  10. Coal demand prediction based on a support vector machine model

    Energy Technology Data Exchange (ETDEWEB)

    Jia, Cun-liang; Wu, Hai-shan; Gong, Dun-wei [China University of Mining & Technology, Xuzhou (China). School of Information and Electronic Engineering

    2007-01-15

    A forecasting model for coal demand of China using a support vector regression was constructed. With the selected embedding dimension, the output vectors and input vectors were constructed based on the coal demand of China from 1980 to 2002. After compared with lineal kernel and Sigmoid kernel, a radial basis function(RBF) was adopted as the kernel function. By analyzing the relationship between the error margin of prediction and the model parameters, the proper parameters were chosen. The support vector machines (SVM) model with multi-input and single output was proposed. Compared the predictor based on RBF neural networks with test datasets, the results show that the SVM predictor has higher precision and greater generalization ability. In the end, the coal demand from 2003 to 2006 is accurately forecasted. l0 refs., 2 figs., 4 tabs.

  11. Space vector-based analysis of overmodulation in triangle ...

    Indian Academy of Sciences (India)

    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 ...

  12. Distributed Vector Quantization Based on Kullback-Leibler Divergence

    Directory of Open Access Journals (Sweden)

    Pengcheng Shen

    2015-11-01

    Full Text Available The goal of vector quantization is to use a few reproduction vectors to represent original vectors/data while maintaining the necessary fidelity of the data. Distributed signal processing has received much attention in recent years, since in many applications data are dispersedly collected/stored in distributed nodes over networks, but centralizing all these data to one processing center is sometimes impractical. In this paper, we develop a distributed vector quantization (VQ algorithm based on Kullback-Leibler (K-L divergence. We start from the centralized case and propose to minimize the K-L divergence between the distribution of global original data and the distribution of global reproduction vectors, and then obtain an online iterative solution to this optimization problem based on the Robbins-Monro stochastic approximation. Afterwards, we extend the solution to apply to distributed cases by introducing diffusion cooperation among nodes. Numerical simulations show that the performances of the distributed K-L–based VQ algorithm are very close to the corresponding centralized algorithm. Besides, both the centralized and distributed K-L–based VQ show more robustness to outliers than the (centralized Linde-Buzo-Gray (LBG algorithm and the (centralized self-organization map (SOM algorithm.

  13. Compositional Model Based Fisher Vector Coding for Image Classification.

    Science.gov (United States)

    Liu, Lingqiao; Wang, Peng; Shen, Chunhua; Wang, Lei; Hengel, Anton van den; Wang, Chao; Shen, Heng Tao

    2017-12-01

    Deriving from the gradient vector of a generative model of local features, Fisher vector coding (FVC) has been identified as an effective coding method for image classification. Most, if not all, FVC implementations employ the Gaussian mixture model (GMM) as the generative model for local features. However, the representative power of a GMM can be limited because it essentially assumes that local features can be characterized by a fixed number of feature prototypes, and the number of prototypes is usually small in FVC. To alleviate this limitation, in this work, we break the convention which assumes that a local feature is drawn from one of a few Gaussian distributions. Instead, we adopt a compositional mechanism which assumes that a local feature is drawn from a Gaussian distribution whose mean vector is composed as a linear combination of multiple key components, and the combination weight is a latent random variable. In doing so we greatly enhance the representative power of the generative model underlying FVC. To implement our idea, we design two particular generative models following this compositional approach. In our first model, the mean vector is sampled from the subspace spanned by a set of bases and the combination weight is drawn from a Laplace distribution. In our second model, we further assume that a local feature is composed of a discriminative part and a residual part. As a result, a local feature is generated by the linear combination of discriminative part bases and residual part bases. The decomposition of the discriminative and residual parts is achieved via the guidance of a pre-trained supervised coding method. By calculating the gradient vector of the proposed models, we derive two new Fisher vector coding strategies. The first is termed Sparse Coding-based Fisher Vector Coding (SCFVC) and can be used as the substitute of traditional GMM based FVC. The second is termed Hybrid Sparse Coding-based Fisher vector coding (HSCFVC) since it

  14. Product Quality Modelling Based on Incremental Support Vector Machine

    International Nuclear Information System (INIS)

    Wang, J; Zhang, W; Qin, B; Shi, W

    2012-01-01

    Incremental Support vector machine (ISVM) is a new learning method developed in recent years based on the foundations of statistical learning theory. It is suitable for the problem of sequentially arriving field data and has been widely used for product quality prediction and production process optimization. However, the traditional ISVM learning does not consider the quality of the incremental data which may contain noise and redundant data; it will affect the learning speed and accuracy to a great extent. In order to improve SVM training speed and accuracy, a modified incremental support vector machine (MISVM) is proposed in this paper. Firstly, the margin vectors are extracted according to the Karush-Kuhn-Tucker (KKT) condition; then the distance from the margin vectors to the final decision hyperplane is calculated to evaluate the importance of margin vectors, where the margin vectors are removed while their distance exceed the specified value; finally, the original SVs and remaining margin vectors are used to update the SVM. The proposed MISVM can not only eliminate the unimportant samples such as noise samples, but also can preserve the important samples. The MISVM has been experimented on two public data and one field data of zinc coating weight in strip hot-dip galvanizing, and the results shows that the proposed method can improve the prediction accuracy and the training speed effectively. Furthermore, it can provide the necessary decision supports and analysis tools for auto control of product quality, and also can extend to other process industries, such as chemical process and manufacturing process.

  15. Product Quality Modelling Based on Incremental Support Vector Machine

    Science.gov (United States)

    Wang, J.; Zhang, W.; Qin, B.; Shi, W.

    2012-05-01

    Incremental Support vector machine (ISVM) is a new learning method developed in recent years based on the foundations of statistical learning theory. It is suitable for the problem of sequentially arriving field data and has been widely used for product quality prediction and production process optimization. However, the traditional ISVM learning does not consider the quality of the incremental data which may contain noise and redundant data; it will affect the learning speed and accuracy to a great extent. In order to improve SVM training speed and accuracy, a modified incremental support vector machine (MISVM) is proposed in this paper. Firstly, the margin vectors are extracted according to the Karush-Kuhn-Tucker (KKT) condition; then the distance from the margin vectors to the final decision hyperplane is calculated to evaluate the importance of margin vectors, where the margin vectors are removed while their distance exceed the specified value; finally, the original SVs and remaining margin vectors are used to update the SVM. The proposed MISVM can not only eliminate the unimportant samples such as noise samples, but also can preserve the important samples. The MISVM has been experimented on two public data and one field data of zinc coating weight in strip hot-dip galvanizing, and the results shows that the proposed method can improve the prediction accuracy and the training speed effectively. Furthermore, it can provide the necessary decision supports and analysis tools for auto control of product quality, and also can extend to other process industries, such as chemical process and manufacturing process.

  16. [Model transfer method based on support vector machine].

    Science.gov (United States)

    Xiong, Yu-hong; Wen, Zhi-yu; Liang, Yu-qian; Chen, Qin; Zhang, Bo; Liu, Yu; Xiang, Xian-yi

    2007-01-01

    The model transfer is a basic method to build up universal and comparable performance of spectrometer data by seeking a mathematical transformation relation among different spectrometers. Because of nonlinear effect and small calibration sample set in fact, it is important to solve the problem of model transfer under the condition of nonlinear effect in evidence and small sample set. This paper summarizes support vector machines theory, puts forward the method of model transfer based on support vector machine and piecewise direct standardization, and makes use of computer simulation method, giving a example to explain the method and compare it with artificial neural network in the end.

  17. 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.

  18. 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

    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

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

    African Journals Online (AJOL)

    ajl yemi

    2011-11-23

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

  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...... stable region (the real part of eigenvalue is equal to zero). The vector-based DPC combines with a space vector modulation technique to achieve a constant switching frequency. The simulation and experimental results clearly validate the effectiveness and feasibility of the proposed vector-based DPC...

  1. TMV-Gate vectors: Gateway compatible tobacco mosaic virus based expression vectors for functional analysis of proteins

    Science.gov (United States)

    Kagale, Sateesh; Uzuhashi, Shihomi; Wigness, Merek; Bender, Tricia; Yang, Wen; Borhan, M. Hossein; Rozwadowski, Kevin

    2012-01-01

    Plant viral expression vectors are advantageous for high-throughput functional characterization studies of genes due to their capability for rapid, high-level transient expression of proteins. We have constructed a series of tobacco mosaic virus (TMV) based vectors that are compatible with Gateway technology to enable rapid assembly of expression constructs and exploitation of ORFeome collections. In addition to the potential of producing recombinant protein at grams per kilogram FW of leaf tissue, these vectors facilitate either N- or C-terminal fusions to a broad series of epitope tag(s) and fluorescent proteins. We demonstrate the utility of these vectors in affinity purification, immunodetection and subcellular localisation studies. We also apply the vectors to characterize protein-protein interactions and demonstrate their utility in screening plant pathogen effectors. Given its broad utility in defining protein properties, this vector series will serve as a useful resource to expedite gene characterization efforts. PMID:23166857

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

    Science.gov (United States)

    Deng, Yu; Wu, Yunjie; Zhou, Linna

    2012-07-10

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

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

    Directory of Open Access Journals (Sweden)

    Giorgio Delzanno

    2014-07-01

    Full Text Available 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 transformation as an alternative modelling technique. We model Paxos in a rich declarative transformation language, featuring (among other things nested quantifiers, and we validate our model using the GROOVE model checker, a graph-based tool that exploits isomorphism as a natural way to prune the state space via symmetry reductions. We compare the results with those obtained by the standard model checker Spin on the basis of a vector-based encoding of the algorithm.

  4. Link-Based Similarity Measures Using Reachability Vectors

    Directory of Open Access Journals (Sweden)

    Seok-Ho Yoon

    2014-01-01

    Full Text Available We present a novel approach for computing link-based similarities among objects accurately by utilizing the link information pertaining to the objects involved. We discuss the problems with previous link-based similarity measures and propose a novel approach for computing link based similarities that does not suffer from these problems. In the proposed approach each target object is represented by a vector. Each element of the vector corresponds to all the objects in the given data, and the value of each element denotes the weight for the corresponding object. As for this weight value, we propose to utilize the probability of reaching from the target object to the specific object, computed using the “Random Walk with Restart” strategy. Then, we define the similarity between two objects as the cosine similarity of the two vectors. In this paper, we provide examples to show that our approach does not suffer from the aforementioned problems. We also evaluate the performance of the proposed methods in comparison with existing link-based measures, qualitatively and quantitatively, with respect to two kinds of data sets, scientific papers and Web documents. Our experimental results indicate that the proposed methods significantly outperform the existing measures.

  5. Biosensor method and system based on feature vector extraction

    Science.gov (United States)

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

    2012-04-17

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

  6. Dynamic Model Based Vector Control of Linear Induction Motor

    Science.gov (United States)

    2012-05-01

    sensorless control is critical for LIM control in some special case. Reference [13] introduces a direct torque and flux control based on space...Industry Applications, IEEE Transactions on, vol. 28, no. 5, pp. 1054–1061, 1992. [4] J. Nash, “ Direct torque control , induction motor vector ...13] C. Lascu, I. Boldea, and F. Blaabjerg, “A modified direct torque control for induction motor sensorless drive,” Industry Applications,

  7. Chikungunya Virus Vaccines: Viral Vector-Based Approaches.

    Science.gov (United States)

    Ramsauer, Katrin; Tangy, Frédéric

    2016-12-15

    In 2013, a major chikungunya virus (CHIKV) epidemic reached the Americas. In the past 2 years, >1.7 million people have been infected. In light of the current epidemic, with millions of people in North and South America at risk, efforts to rapidly develop effective vaccines have increased. Here, we focus on CHIKV vaccines that use viral-vector technologies. This group of vaccine candidates shares an ability to potently induce humoral and cellular immune responses by use of highly attenuated and safe vaccine backbones. So far, well-described vectors such as modified vaccinia virus Ankara, complex adenovirus, vesicular stomatitis virus, alphavirus-based chimeras, and measles vaccine Schwarz strain (MV/Schw) have been described as potential vaccines. We summarize here the recent data on these experimental vaccines, with a focus on the preclinical and clinical activities on the MV/Schw-based candidate, which is the first CHIKV-vectored vaccine that has completed a clinical trial. © The Author 2016. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail journals.permissions@oup.com.

  8. [Bases for control of arthropod vectors: I--Definitions, bioecology of vectors (author's transl)].

    Science.gov (United States)

    Picq, J J; Discamps, G; Albert, J P

    1978-01-01

    Arthropoda form the most diversified and multitudinous phyllum of the animal kingdom. In this "arthropod world", the authors give the respective position of the arthropoda: a) detrimental to crops, b) venomous and noxious for human being, but mainly those who are vectors of human diseases, say about a hundred species. Biological, ecological and environmental main features of the most important arthropod vectors of human tropical diseases are reviewed. Various factors acting on the relation between pathological agent and vector and between vector and man are considered. Importance and complexity of entomological surveys are emphasized with, as a consequence, the necessity of specialized medical entomologists to manage them.

  9. Virus-based transient expression vectors for woody crops: a new frontier for vector design and use.

    Science.gov (United States)

    Dawson, William O; Folimonova, Svetlana Y

    2013-01-01

    Virus-based expression vectors are commonplace tools for the production of proteins or the induction of RNA silencing in herbaceous plants. This review considers a completely different set of uses for viral vectors in perennial fruit and nut crops, which can be productive for periods of up to 100 years. Viral vectors could be used in the field to modify existing plants. Furthermore, with continually emerging pathogens and pests, viral vectors could express genes to protect the plants or even to treat plants after they become infected. As technologies develop during the life span of these crops, viral vectors can be used for adding new genes as an alternative to pushing up the crop and replanting with transgenic plants. Another value of virus-based vectors is that they add nothing permanently to the environment. This requires that effective and stable viral vectors be developed for specific crops from endemic viruses. Studies using viruses from perennial hosts suggest that these objectives could be accomplished.

  10. An Incremental Support Vector Machine based Speech Activity Detection Algorithm.

    Science.gov (United States)

    Xianbo, Xiao; Guangshu, Hu

    2005-01-01

    Traditional voice activity detection algorithms are mostly threshold-based or statistical model-based. All those methods are absent of the ability to react quickly to variations of environments. This paper describes an incremental SVM (Support Vector Machine) method for speech activity detection. The proposed incremental procedure makes it adaptive to variation of environments and the special construction of incremental training data set decreases computing consumption effectively. Experiments results demonstrated its higher end point detection accuracy. Further work will be focused on decreasing computing consumption and importing multi-class SVM classifiers.

  11. 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.

  12. Quantum blind signature based on Two-State Vector Formalism

    Science.gov (United States)

    Qi, Su; Zheng, Huang; Qiaoyan, Wen; Wenmin, Li

    2010-11-01

    Two-State Vector Formalism (TSVF) including pre- and postselected states is a complete description of a system between two measurements. Consequently TSVF gives a perfect solution to the Mean King problem. In this paper, utilizing the dramatic correlation in the verification, we propose a quantum blind signature scheme based on TSVF. Compared with Wen's scheme, our scheme has 100% efficiency. Our scheme guarantees the unconditional security. Moreover, the proposed scheme, which is easy to implement, can be applied to E-payment system.

  13. 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.

  14. Study on fault diagnosis technology based on support vector machine

    International Nuclear Information System (INIS)

    Xia Hong; Zhang Nan; Du Xingfu

    2010-01-01

    In this paper, two fault diagnostic casts were constructed using SVM theory for model fault such as cracks of steam generator heat transfer tubes and small break loss of coolant accident in nuclear power plant. One fault diagnostic cast was constructed based on least squares support vector machines using C++ programming language. The other was constructed based on traditionary support vector machines using Matlab7.0 program. The results in the Simulation Test have shown that the performance of the two models based on two kinds of SVMs both depends on the choice of nuclear function model and the parameters. In this study, after the suitable choice, the same diagnostic performance was obtained using two kinds of SVMs. The fault can be diagnosed exactly during the period between the third second until shutdown. In the first three seconds, the fault data were not yet shown or the data were fluctuant. The simulation results demonstrated that the methods could diagnose the fault phenomenon accurately under the circumstances of small example sizes, and the precision was very high. (authors)

  15. Optical vector network analyzer based on double-sideband modulation.

    Science.gov (United States)

    Jun, Wen; Wang, Ling; Yang, Chengwu; Li, Ming; Zhu, Ning Hua; Guo, Jinjin; Xiong, Liangming; Li, Wei

    2017-11-01

    We report an optical vector network analyzer (OVNA) based on double-sideband (DSB) modulation using a dual-parallel Mach-Zehnder modulator. The device under test (DUT) is measured twice with different modulation schemes. By post-processing the measurement results, the response of the DUT can be obtained accurately. Since DSB modulation is used in our approach, the measurement range is doubled compared with conventional single-sideband (SSB) modulation-based OVNA. Moreover, the measurement accuracy is improved by eliminating the even-order sidebands. The key advantage of the proposed scheme is that the measurement of a DUT with bandpass response can also be simply realized, which is a big challenge for the SSB-based OVNA. The proposed method is theoretically and experimentally demonstrated.

  16. 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.

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

    Directory of Open Access Journals (Sweden)

    Y. He

    2008-05-01

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

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

    DEFF Research Database (Denmark)

    Teodorescu, Remus; Dal, Mehmet

    2008-01-01

    In order to increase the accuracy of the current control loop, usually, well known parameter compensation and/or cross decoupling techniques are employed for advanced ac drives. In this paper, instead of using these techniques an observer-based current controller is proposed for vector controlled...... induction motor (IM) drives. The control design, based on synchronously rotating d-q frame model of the machine, has a simple structure that combines the proportional portion of a conventional PI control and output of the observer. The observer is predicted to estimate the disturbances caused by parameters...... change in current control loop and, also to remove undesired cross coupling existing between components of the stator current. The observer uses the measured stator currents and estimated PWM voltages, and produces a disturbance signal with a low pass filter. The proposed control scheme reduces cross...

  19. Gradient Evolution-based Support Vector Machine Algorithm for Classification

    Science.gov (United States)

    Zulvia, Ferani E.; Kuo, R. J.

    2018-03-01

    This paper proposes a classification algorithm based on a support vector machine (SVM) and gradient evolution (GE) algorithms. SVM algorithm has been widely used in classification. However, its result is significantly influenced by the parameters. Therefore, this paper aims to propose an improvement of SVM algorithm which can find the best SVMs’ parameters automatically. The proposed algorithm employs a GE algorithm to automatically determine the SVMs’ parameters. The GE algorithm takes a role as a global optimizer in finding the best parameter which will be used by SVM algorithm. The proposed GE-SVM algorithm is verified using some benchmark datasets and compared with other metaheuristic-based SVM algorithms. The experimental results show that the proposed GE-SVM algorithm obtains better results than other algorithms tested in this paper.

  20. Vector-based excitation amplitude imaging condition for elastic RTM

    Science.gov (United States)

    Zhou, Jinju; Wang, Deli

    2017-12-01

    In recent years, many studies have focused on elastic reverse time migration (RTM). In response to the problems associated with elastic RTM, we propose a new procedure for 2D elastic multicomponent RTM. In this new method, decomposed P- and S-wave components are obtained from the decoupled propagation of the source and receiver wavefields, which allows the expedient calculation of the Poynting vectors and the incident and reflection angles of the P- and S-waves. In addition, we deduce the vector-based excitation amplitude imaging condition. This process automatically accounts for the particle vibration directions when determining the angle-dependent signed reflection coefficients, and does not require the sign to be determined apart from the value of the reflection coefficients. This concept was further extended to the source-normalized crosscorrelation imaging condition. The reflection coefficient of the layered model test was in agreement with the Zoeppritz theory, the PP and PS wave images of the Marmousi II model were clear, and the PS wave images had higher resolution and richer details. In addition, since the calculated reflection coefficients are angle-dependent, they can be easily used for the extraction of angle-domain common-image gathers. Moreover, the imaging condition avoids the polarization reversal in PS wave images and does not require all of the source wavefield data. Consequently, the computation and storage requirements are significantly reduced, which will facilitate the use of the elastic RTM in practice.

  1. Image Jacobian Matrix Estimation Based on Online Support Vector Regression

    Directory of Open Access Journals (Sweden)

    Shangqin Mao

    2012-10-01

    Full Text Available Research into robotics visual servoing is an important area in the field of robotics. It has proven difficult to achieve successful results for machine vision and robotics in unstructured environments without using any a priori camera or kinematic models. In uncalibrated visual servoing, image Jacobian matrix estimation methods can be divided into two groups: the online method and the offline method. The offline method is not appropriate for most natural environments. The online method is robust but rough. Moreover, if the images feature configuration changes, it needs to restart the approximating procedure. A novel approach based on an online support vector regression (OL-SVR algorithm is proposed which overcomes the drawbacks and combines the virtues just mentioned.

  2. Support vector machine based battery model for electric vehicles

    International Nuclear Information System (INIS)

    Wang Junping; Chen Quanshi; Cao Binggang

    2006-01-01

    The support vector machine (SVM) is a novel type of learning machine based on statistical learning theory that can map a nonlinear function successfully. As a battery is a nonlinear system, it is difficult to establish the relationship between the load voltage and the current under different temperatures and state of charge (SOC). The SVM is used to model the battery nonlinear dynamics in this paper. Tests are performed on an 80Ah Ni/MH battery pack with the Federal Urban Driving Schedule (FUDS) cycle to set up the SVM model. Compared with the Nernst and Shepherd combined model, the SVM model can simulate the battery dynamics better with small amounts of experimental data. The maximum relative error is 3.61%

  3. 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.

  4. Traditional and robust vector selection methods for use with similarity based models

    International Nuclear Information System (INIS)

    Hines, J. W.; Garvey, D. R.

    2006-01-01

    Vector selection, or instance selection as it is often called in the data mining literature, performs a critical task in the development of nonparametric, similarity based models. Nonparametric, similarity based modeling (SBM) is a form of 'lazy learning' which constructs a local model 'on the fly' by comparing a query vector to historical, training vectors. For large training sets the creation of local models may become cumbersome, since each training vector must be compared to the query vector. To alleviate this computational burden, varying forms of training vector sampling may be employed with the goal of selecting a subset of the training data such that the samples are representative of the underlying process. This paper describes one such SBM, namely auto-associative kernel regression (AAKR), and presents five traditional vector selection methods and one robust vector selection method that may be used to select prototype vectors from a larger data set in model training. The five traditional vector selection methods considered are min-max, vector ordering, combination min-max and vector ordering, fuzzy c-means clustering, and Adeli-Hung clustering. Each method is described in detail and compared using artificially generated data and data collected from the steam system of an operating nuclear power plant. (authors)

  5. Construction of an expression vector for Lactococcus lactis based on ...

    African Journals Online (AJOL)

    To construct an expression vector for Lactococcus lactis, the EmPMT fragment which contained the erythromycin resistance gene, P32 promoter, multiple cloning site (MCS) and terminator (T) was subcloned into the small cryptic plasmid pAR141. The resulting vector, designated as pAR1411, was found to be stably ...

  6. Environmental noise forecasting based on support vector machine

    Science.gov (United States)

    Fu, Yumei; Zan, Xinwu; Chen, Tianyi; Xiang, Shihan

    2018-01-01

    As an important pollution source, the noise pollution is always the researcher's focus. Especially in recent years, the noise pollution is seriously harmful to the human beings' environment, so the research about the noise pollution is a very hot spot. Some noise monitoring technologies and monitoring systems are applied in the environmental noise test, measurement and evaluation. But, the research about the environmental noise forecasting is weak. In this paper, a real-time environmental noise monitoring system is introduced briefly. This monitoring system is working in Mianyang City, Sichuan Province. It is monitoring and collecting the environmental noise about more than 20 enterprises in this district. Based on the large amount of noise data, the noise forecasting by the Support Vector Machine (SVM) is studied in detail. Compared with the time series forecasting model and the artificial neural network forecasting model, the SVM forecasting model has some advantages such as the smaller data size, the higher precision and stability. The noise forecasting results based on the SVM can provide the important and accuracy reference to the prevention and control of the environmental noise.

  7. Inelastic Vector Soliton Collisions: A Lattice-Based Quantum Representation

    National Research Council Canada - National Science Library

    Vahala, George; Vahala, Linda; Yepez, Jeffrey

    2004-01-01

    .... Under appropriate conditions the exact 2-soliton vector solutions yield in elastic soliton collisions, in agreement with the theoretical predictions of Radhakrishnan et al. (1997 Phys. Rev. E56, 2213...

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

    Directory of Open Access Journals (Sweden)

    Fang Su

    2013-01-01

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

  9. EVE: Explainable Vector Based Embedding Technique Using Wikipedia

    OpenAIRE

    Qureshi, M. Atif; Greene, Derek

    2017-01-01

    We present an unsupervised explainable word embedding technique, called EVE, which is built upon the structure of Wikipedia. The proposed model defines the dimensions of a semantic vector representing a word using human-readable labels, thereby it readily interpretable. Specifically, each vector is constructed using the Wikipedia category graph structure together with the Wikipedia article link structure. To test the effectiveness of the proposed word embedding model, we consider its usefulne...

  10. DNS Tunneling Detection Method Based on Multilabel Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Ahmed Almusawi

    2018-01-01

    Full Text Available DNS tunneling is a method used by malicious users who intend to bypass the firewall to send or receive commands and data. This has a significant impact on revealing or releasing classified information. Several researchers have examined the use of machine learning in terms of detecting DNS tunneling. However, these studies have treated the problem of DNS tunneling as a binary classification where the class label is either legitimate or tunnel. In fact, there are different types of DNS tunneling such as FTP-DNS tunneling, HTTP-DNS tunneling, HTTPS-DNS tunneling, and POP3-DNS tunneling. Therefore, there is a vital demand to not only detect the DNS tunneling but rather classify such tunnel. This study aims to propose a multilabel support vector machine in order to detect and classify the DNS tunneling. The proposed method has been evaluated using a benchmark dataset that contains numerous DNS queries and is compared with a multilabel Bayesian classifier based on the number of corrected classified DNS tunneling instances. Experimental results demonstrate the efficacy of the proposed SVM classification method by obtaining an f-measure of 0.80.

  11. Noninvasive extraction of fetal electrocardiogram based on Support Vector Machine

    Science.gov (United States)

    Fu, Yumei; Xiang, Shihan; Chen, Tianyi; Zhou, Ping; Huang, Weiyan

    2015-10-01

    The fetal electrocardiogram (FECG) signal has important clinical value for diagnosing the fetal heart diseases and choosing suitable therapeutics schemes to doctors. So, the noninvasive extraction of FECG from electrocardiogram (ECG) signals becomes a hot research point. A new method, the Support Vector Machine (SVM) is utilized for the extraction of FECG with limited size of data. Firstly, the theory of the SVM and the principle of the extraction based on the SVM are studied. Secondly, the transformation of maternal electrocardiogram (MECG) component in abdominal composite signal is verified to be nonlinear and fitted with the SVM. Then, the SVM is trained, and the training results are compared with the real data to ensure the effect of the training. Meanwhile, the parameters of the SVM are optimized to achieve the best performance so that the learning machine can be utilized to fit the unknown samples. Finally, the FECG is extracted by removing the optimal estimation of MECG component from the abdominal composite signal. In order to evaluate the performance of FECG extraction based on the SVM, the Signal-to-Noise Ratio (SNR) and the visual test are used. The experimental results show that the FECG with good quality can be extracted, its SNR ratio is significantly increased as high as 9.2349 dB and the time cost is significantly decreased as short as 0.802 seconds. Compared with the traditional method, the noninvasive extraction method based on the SVM has a simple realization, the shorter treatment time and the better extraction quality under the same conditions.

  12. Explaining Support Vector Machines: A Color Based Nomogram.

    Directory of Open Access Journals (Sweden)

    Vanya Van Belle

    Full Text Available 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.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.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.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.

  13. Entropy-Based Video Steganalysis of Motion Vectors

    Directory of Open Access Journals (Sweden)

    Elaheh Sadat Sadat

    2018-04-01

    Full Text Available In this paper, a new method is proposed for motion vector steganalysis using the entropy value and its combination with the features of the optimized motion vector. In this method, the entropy of blocks is calculated to determine their texture and the precision of their motion vectors. Then, by using a fuzzy cluster, the blocks are clustered into the blocks with high and low texture, while the membership function of each block to a high texture class indicates the texture of that block. These membership functions are used to weight the effective features that are extracted by reconstructing the motion estimation equations. Characteristics of the results indicate that the use of entropy and the irregularity of each block increases the precision of the final video classification into cover and stego classes.

  14. 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

    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...... of choice into the vector backbone with time- and cost-effective. The selectable marker cassette is flanked by loxP recognition sites for the CreA recombinase to allow reutilization of the same selectable marker. Furthermore, our USER vector set provides a choice of different selectable markers both...

  15. Skin injury model classification based on shape vector analysis.

    Science.gov (United States)

    Röhrich, Emil; Thali, Michael; Schweitzer, Wolf

    2012-11-06

    Skin injuries can be crucial in judicial decision making. Forensic experts base their classification on subjective opinions. This study investigates whether known classes of simulated skin injuries are correctly classified statistically based on 3D surface models and derived numerical shape descriptors. Skin injury surface characteristics are simulated with plasticine. Six injury classes - abrasions, incised wounds, gunshot entry wounds, smooth and textured strangulation marks as well as patterned injuries - with 18 instances each are used for a k-fold cross validation with six partitions. Deformed plasticine models are captured with a 3D surface scanner. Mean curvature is estimated for each polygon surface vertex. Subsequently, distance distributions and derived aspect ratios, convex hulls, concentric spheres, hyperbolic points and Fourier transforms are used to generate 1284-dimensional shape vectors. Subsequent descriptor reduction maximizing SNR (signal-to-noise ratio) result in an average of 41 descriptors (varying across k-folds). With non-normal multivariate distribution of heteroskedastic data, requirements for LDA (linear discriminant analysis) are not met. Thus, shrinkage parameters of RDA (regularized discriminant analysis) are optimized yielding a best performance with λ = 0.99 and γ = 0.001. Receiver Operating Characteristic of a descriptive RDA yields an ideal Area Under the Curve of 1.0 for all six categories. Predictive RDA results in an average CRR (correct recognition rate) of 97,22% under a 6 partition k-fold. Adding uniform noise within the range of one standard deviation degrades the average CRR to 71,3%. Digitized 3D surface shape data can be used to automatically classify idealized shape models of simulated skin injuries. Deriving some well established descriptors such as histograms, saddle shape of hyperbolic points or convex hulls with subsequent reduction of dimensionality while maximizing SNR seem to work well for the data at hand, as

  16. 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

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

    Science.gov (United States)

    Tasdemir, Kasim; Kurugollu, Fatih; Sezer, Sakir

    2016-05-11

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

  18. 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)...

  19. 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.

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

    DEFF Research Database (Denmark)

    Krenk, Steen; Nielsen, Martin Bjerre

    2014-01-01

    A conservative time integration formulation is developed for rigid bodies based on a convected set of orthonormal base vectors. The base vectors are represented in terms of their absolute coordinates, and thus the formulation makes use of three translation components, plus nine components...... of the base vectors. Orthogonality and unit length of the base vectors are imposed by constraining the equivalent Green strain components, and the kinetic energy is represented corresponding to rigid body motion. The equations of motion are obtained via Hamilton’s equations including the zero...... are eliminated explicitly within each integration interval leaving a projection operator expressed in terms of displacement component mean values. Hereby the number of variables is reduced by six for each rigid body in the problem, and the difference equations lead to conservation of the orthonormality...

  1. Vector mode conversion based on tilted fiber Bragg grating in ring-core fibers

    Science.gov (United States)

    Mi, Yuean; Ren, Guobin; Gao, Yixiao; Li, Haisu; Zhu, Bofeng; Liu, Yu

    2018-03-01

    We propose a vector mode conversion approach based on tilted fiber Bragg grating (TFBG) written in ring-core fiber with effective separation of eigenmodes. The mode coupling properties of TFBG are numerically investigated. It is shown that under the constraint of phase matching, the conversion of high-order vector modes could be achieved at specific wavelengths. Moreover, the polarization of incident light and tilt angle of TFBG play critical roles in mode coupling process. The proposed TFBG provides an efficient method to realize high-order vector mode conversion, and it shows great potential for fibers based OAM beam generation and fiber lasers with vortex beams output.

  2. Nonreciprocal Pseudotyping: Murine Leukemia Virus Proteins Cannot Efficiently Package Spleen Necrosis Virus-Based Vector RNA

    OpenAIRE

    Certo, Jeanine L.; Shook, Betsy F.; Yin, Philip D.; Snider, John T.; Hu, Wei-Shau

    1998-01-01

    It has been documented that spleen necrosis virus (SNV) can package murine leukemia virus (MLV) RNA efficiently and propagate MLV vectors to the same titers as it propagates SNV-based vectors. Although the SNV packaging signal (E) and MLV packaging signal (Ψ) have little sequence homology, similar double-hairpin RNA structures were predicted and supported by experimental evidence. To test whether SNV RNA can be packaged by MLV proteins, we modified an SNV vector to be expressed in an MLV-base...

  3. Multiscale Distance Coherence Vector Algorithm for Content-Based Image Retrieval

    Science.gov (United States)

    Jiexian, Zeng; Xiupeng, Liu

    2014-01-01

    Multiscale distance coherence vector algorithm for content-based image retrieval (CBIR) is proposed due to the same descriptor with different shapes and the shortcomings of antinoise performance of the distance coherence vector algorithm. By this algorithm, the image contour curve is evolved by Gaussian function first, and then the distance coherence vector is, respectively, extracted from the contour of the original image and evolved images. Multiscale distance coherence vector was obtained by reasonable weight distribution of the distance coherence vectors of evolved images contour. This algorithm not only is invariable to translation, rotation, and scaling transformation but also has good performance of antinoise. The experiment results show us that the algorithm has a higher recall rate and precision rate for the retrieval of images polluted by noise. PMID:24883416

  4. Parameter Selection Method for Support Vector Regression Based on Adaptive Fusion of the Mixed Kernel Function

    Directory of Open Access Journals (Sweden)

    Hailun Wang

    2017-01-01

    Full Text Available Support vector regression algorithm is widely used in fault diagnosis of rolling bearing. A new model parameter selection method for support vector regression based on adaptive fusion of the mixed kernel function is proposed in this paper. We choose the mixed kernel function as the kernel function of support vector regression. The mixed kernel function of the fusion coefficients, kernel function parameters, and regression parameters are combined together as the parameters of the state vector. Thus, the model selection problem is transformed into a nonlinear system state estimation problem. We use a 5th-degree cubature Kalman filter to estimate the parameters. In this way, we realize the adaptive selection of mixed kernel function weighted coefficients and the kernel parameters, the regression parameters. Compared with a single kernel function, unscented Kalman filter (UKF support vector regression algorithms, and genetic algorithms, the decision regression function obtained by the proposed method has better generalization ability and higher prediction accuracy.

  5. Fault trend prediction of device based on support vector regression

    International Nuclear Information System (INIS)

    Song Meicun; Cai Qi

    2011-01-01

    The research condition of fault trend prediction and the basic theory of support vector regression (SVR) were introduced. SVR was applied to the fault trend prediction of roller bearing, and compared with other methods (BP neural network, gray model, and gray-AR model). The results show that BP network tends to overlearn and gets into local minimum so that the predictive result is unstable. It also shows that the predictive result of SVR is stabilization, and SVR is superior to BP neural network, gray model and gray-AR model in predictive precision. SVR is a kind of effective method of fault trend prediction. (authors)

  6. 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

  7. Kernel method for clustering based on optimal target vector

    International Nuclear Information System (INIS)

    Angelini, Leonardo; Marinazzo, Daniele; Pellicoro, Mario; Stramaglia, Sebastiano

    2006-01-01

    We introduce Ising models, suitable for dichotomic clustering, with couplings that are (i) both ferro- and anti-ferromagnetic (ii) depending on the whole data-set and not only on pairs of samples. Couplings are determined exploiting the notion of optimal target vector, here introduced, a link between kernel supervised and unsupervised learning. The effectiveness of the method is shown in the case of the well-known iris data-set and in benchmarks of gene expression levels, where it works better than existing methods for dichotomic clustering

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

    Science.gov (United States)

    Huang, Zhuojie; Das, Anirrudha; Qiu, Youliang; Tatem, Andrew J

    2012-08-14

    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. 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. 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. 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 on the air travel network. The framework built provides a flexible

  9. 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

  10. Algevir: An Expression System for Microalgae Based on Viral Vectors

    Science.gov (United States)

    Bañuelos-Hernández, Bernardo; Monreal-Escalante, Elizabeth; González-Ortega, Omar; Angulo, Carlos; Rosales-Mendoza, Sergio

    2017-01-01

    The use of recombinant algae for the production of valuable compounds is opening promising biotechnological applications. However, the development of efficient expression approaches is still needed to expand the exploitation of microalgae in biotechnology. Herein, the concept of using viral expression vectors in microalgae was explored for the first time. An inducible geminiviral vector leading to Rep-mediated replication of the expression cassette allowed the production of antigenic proteins at high levels. This system, called Algevir, allows the production of complex viral proteins (GP1 from Zaire ebolavirus) and bacterial toxin subunits (B subunit of the heat-labile Escherichia coli enterotoxin), which retained their antigenic activity. The highest achieved yield was 1.25 mg/g fresh biomass (6 mg/L of culture), which was attained 3 days after transformation. The Algevir system allows for a fast and efficient production of recombinant proteins, overcoming the difficulties imposed by the low yields and unstable expression patterns frequently observed in stably transformed microalgae at the nuclear level; as well as the toxicity of some target proteins. PMID:28713333

  11. Parallelogram Based Method for Space Vector Pulse Width Modulation

    Directory of Open Access Journals (Sweden)

    José Restrepo

    2010-01-01

    Full Text Available Este artículo presenta un método para la determinación del ciclo de trabajo de la modulación de vectores espaciales utilizando circuitos convencionales de modulación por ancho de pulso. Este método utiliza tres paralelogramos que cubren el espacio hexagonal de la salida del inversor, reduciendo de esta forma el esfuerzo de cálculo computacional. La metodología propuesta es aplicable directamente a la descripción de las componentes α, β del vector espacial en controladores modernos, con altos requerimientos dinámicos, donde la demanda de cambios se realiza a una frecuencia comparable con la de la señal portadora. Combinando las estrategias de modulación DPWMMIN y DPWMMAX propuestas en este trabajo, se obtiene fácilmente el ciclo de trabajo para otros métodos de modulación, utilizados en aplicaciones escalares y ampliamente estudiadas en la literatura. El método propuesto ha sido comprobado mediante simulaciones y ensayos experimentales.

  12. Texture Classification Using Local Pattern Based on Vector Quantization.

    Science.gov (United States)

    Pan, Zhibin; Fan, Hongcheng; Zhang, Li

    2015-12-01

    Local binary pattern (LBP) is a simple and effective descriptor for texture classification. However, it has two main disadvantages: (1) different structural patterns sometimes have the same binary code and (2) it is sensitive to noise. In order to overcome these disadvantages, we propose a new local descriptor named local vector quantization pattern (LVQP). In LVQP, different kinds of texture images are chosen to train a local pattern codebook, where each different structural pattern is described by a unique codeword index. Contrarily to the original LBP and its many variants, LVQP does not quantize each neighborhood pixel separately to 0/1, but aims at quantizing the whole difference vector between the central pixel and its neighborhood pixels. Since LVQP deals with the structural pattern as a whole, it has a high discriminability and is less sensitive to noise. Our experimental results, achieved by using four representative texture databases of Outex, UIUC, CUReT, and Brodatz, show that the proposed LVQP method can improve classification accuracy significantly and is more robust to noise.

  13. Algevir: An Expression System for Microalgae Based on Viral Vectors

    Directory of Open Access Journals (Sweden)

    Bernardo Bañuelos-Hernández

    2017-06-01

    Full Text Available The use of recombinant algae for the production of valuable compounds is opening promising biotechnological applications. However, the development of efficient expression approaches is still needed to expand the exploitation of microalgae in biotechnology. Herein, the concept of using viral expression vectors in microalgae was explored for the first time. An inducible geminiviral vector leading to Rep-mediated replication of the expression cassette allowed the production of antigenic proteins at high levels. This system, called Algevir, allows the production of complex viral proteins (GP1 from Zaire ebolavirus and bacterial toxin subunits (B subunit of the heat-labile Escherichia coli enterotoxin, which retained their antigenic activity. The highest achieved yield was 1.25 mg/g fresh biomass (6 mg/L of culture, which was attained 3 days after transformation. The Algevir system allows for a fast and efficient production of recombinant proteins, overcoming the difficulties imposed by the low yields and unstable expression patterns frequently observed in stably transformed microalgae at the nuclear level; as well as the toxicity of some target proteins.

  14. Margin based ontology sparse vector learning algorithm and applied in biology science.

    Science.gov (United States)

    Gao, Wei; Qudair Baig, Abdul; Ali, Haidar; Sajjad, Wasim; Reza Farahani, Mohammad

    2017-01-01

    In biology field, the ontology application relates to a large amount of genetic information and chemical information of molecular structure, which makes knowledge of ontology concepts convey much information. Therefore, in mathematical notation, the dimension of vector which corresponds to the ontology concept is often very large, and thus improves the higher requirements of ontology algorithm. Under this background, we consider the designing of ontology sparse vector algorithm and application in biology. In this paper, using knowledge of marginal likelihood and marginal distribution, the optimized strategy of marginal based ontology sparse vector learning algorithm is presented. Finally, the new algorithm is applied to gene ontology and plant ontology to verify its efficiency.

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

    CERN Document Server

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

    2014-01-01

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

  16. An Adenoviral Vector Based Vaccine for Rhodococcus equi.

    Directory of Open Access Journals (Sweden)

    Carla Giles

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

  17. Classification of e-government documents based on cooperative expression of word vectors

    Science.gov (United States)

    Fu, Qianqian; Liu, Hao; Wei, Zhiqiang

    2017-03-01

    The effective document classification is a powerful technique to deal with the huge amount of e-government documents automatically instead of accomplishing them manually. The word-to-vector (word2vec) model, which converts semantic word into low-dimensional vectors, could be successfully employed to classify the e-government documents. In this paper, we propose the cooperative expressions of word vector (Co-word-vector), whose multi-granularity of integration explores the possibility of modeling documents in the semantic space. Meanwhile, we also aim to improve the weighted continuous bag of words model based on word2vec model and distributed representation of topic-words based on LDA model. Furthermore, combining the two levels of word representation, performance result shows that our proposed method on the e-government document classification outperform than the traditional method.

  18. 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.

  19. An Autonomous Star Identification Algorithm Based on One-Dimensional Vector Pattern for Star Sensors.

    Science.gov (United States)

    Luo, Liyan; Xu, Luping; Zhang, Hua

    2015-07-07

    In order to enhance the robustness and accelerate the recognition speed of star identification, an autonomous star identification algorithm for star sensors is proposed based on the one-dimensional vector pattern (one_DVP). In the proposed algorithm, the space geometry information of the observed stars is used to form the one-dimensional vector pattern of the observed star. The one-dimensional vector pattern of the same observed star remains unchanged when the stellar image rotates, so the problem of star identification is simplified as the comparison of the two feature vectors. The one-dimensional vector pattern is adopted to build the feature vector of the star pattern, which makes it possible to identify the observed stars robustly. The characteristics of the feature vector and the proposed search strategy for the matching pattern make it possible to achieve the recognition result as quickly as possible. The simulation results demonstrate that the proposed algorithm can effectively accelerate the star identification. Moreover, the recognition accuracy and robustness by the proposed algorithm are better than those by the pyramid algorithm, the modified grid algorithm, and the LPT algorithm. The theoretical analysis and experimental results show that the proposed algorithm outperforms the other three star identification algorithms.

  20. An Autonomous Star Identification Algorithm Based on One-Dimensional Vector Pattern for Star Sensors

    Science.gov (United States)

    Luo, Liyan; Xu, Luping; Zhang, Hua

    2015-01-01

    In order to enhance the robustness and accelerate the recognition speed of star identification, an autonomous star identification algorithm for star sensors is proposed based on the one-dimensional vector pattern (one_DVP). In the proposed algorithm, the space geometry information of the observed stars is used to form the one-dimensional vector pattern of the observed star. The one-dimensional vector pattern of the same observed star remains unchanged when the stellar image rotates, so the problem of star identification is simplified as the comparison of the two feature vectors. The one-dimensional vector pattern is adopted to build the feature vector of the star pattern, which makes it possible to identify the observed stars robustly. The characteristics of the feature vector and the proposed search strategy for the matching pattern make it possible to achieve the recognition result as quickly as possible. The simulation results demonstrate that the proposed algorithm can effectively accelerate the star identification. Moreover, the recognition accuracy and robustness by the proposed algorithm are better than those by the pyramid algorithm, the modified grid algorithm, and the LPT algorithm. The theoretical analysis and experimental results show that the proposed algorithm outperforms the other three star identification algorithms. PMID:26198233

  1. Segmentation Based Video Steganalysis to Detect Motion Vector Modification

    Directory of Open Access Journals (Sweden)

    Peipei Wang

    2017-01-01

    Full Text Available This paper presents a steganalytic approach against video steganography which modifies motion vector (MV in content adaptive manner. Current video steganalytic schemes extract features from fixed-length frames of the whole video and do not take advantage of the content diversity. Consequently, the effectiveness of the steganalytic feature is influenced by video content and the problem of cover source mismatch also affects the steganalytic performance. The goal of this paper is to propose a steganalytic method which can suppress the differences of statistical characteristics caused by video content. The given video is segmented to subsequences according to block’s motion in every frame. The steganalytic features extracted from each category of subsequences with close motion intensity are used to build one classifier. The final steganalytic result can be obtained by fusing the results of weighted classifiers. The experimental results have demonstrated that our method can effectively improve the performance of video steganalysis, especially for videos of low bitrate and low embedding ratio.

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

    International Nuclear Information System (INIS)

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

    2014-01-01

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

  3. 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.

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

    Science.gov (United States)

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

    2011-01-01

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

  5. Track Circuit Fault Diagnosis Method based on Least Squares Support Vector

    Science.gov (United States)

    Cao, Yan; Sun, Fengru

    2018-01-01

    In order to improve the troubleshooting efficiency and accuracy of the track circuit, track circuit fault diagnosis method was researched. Firstly, the least squares support vector machine was applied to design the multi-fault classifier of the track circuit, and then the measured track data as training samples was used to verify the feasibility of the methods. Finally, the results based on BP neural network fault diagnosis methods and the methods used in this paper were compared. Results shows that the track fault classifier based on least squares support vector machine can effectively achieve the five track circuit fault diagnosis with less computing time.

  6. Multiple image encryption scheme based on pixel exchange operation and vector decomposition

    Science.gov (United States)

    Xiong, Y.; Quan, C.; Tay, C. J.

    2018-02-01

    We propose a new multiple image encryption scheme based on a pixel exchange operation and a basic vector decomposition in Fourier domain. In this algorithm, original images are imported via a pixel exchange operator, from which scrambled images and pixel position matrices are obtained. Scrambled images encrypted into phase information are imported using the proposed algorithm and phase keys are obtained from the difference between scrambled images and synthesized vectors in a charge-coupled device (CCD) plane. The final synthesized vector is used as an input in a random phase encoding (DRPE) scheme. In the proposed encryption scheme, pixel position matrices and phase keys serve as additional private keys to enhance the security of the cryptosystem which is based on a 4-f system. Numerical simulations are presented to demonstrate the feasibility and robustness of the proposed encryption scheme.

  7. A Coarse Alignment Method Based on Digital Filters and Reconstructed Observation Vectors

    Directory of Open Access Journals (Sweden)

    Xiang Xu

    2017-03-01

    Full Text Available In this paper, a coarse alignment method based on apparent gravitational motion is proposed. Due to the interference of the complex situations, the true observation vectors, which are calculated by the apparent gravity, are contaminated. The sources of the interference are analyzed in detail, and then a low-pass digital filter is designed in this paper for eliminating the high-frequency noise of the measurement observation vectors. To extract the effective observation vectors from the inertial sensors’ outputs, a parameter recognition and vector reconstruction method are designed, where an adaptive Kalman filter is employed to estimate the unknown parameters. Furthermore, a robust filter, which is based on Huber’s M-estimation theory, is developed for addressing the outliers of the measurement observation vectors due to the maneuver of the vehicle. A comprehensive experiment, which contains a simulation test and physical test, is designed to verify the performance of the proposed method, and the results show that the proposed method is equivalent to the popular apparent velocity method in swaying mode, but it is superior to the current methods while in moving mode when the strapdown inertial navigation system (SINS is under entirely self-contained conditions.

  8. A Coarse Alignment Method Based on Digital Filters and Reconstructed Observation Vectors.

    Science.gov (United States)

    Xu, Xiang; Xu, Xiaosu; Zhang, Tao; Li, Yao; Wang, Zhicheng

    2017-03-29

    In this paper, a coarse alignment method based on apparent gravitational motion is proposed. Due to the interference of the complex situations, the true observation vectors, which are calculated by the apparent gravity, are contaminated. The sources of the interference are analyzed in detail, and then a low-pass digital filter is designed in this paper for eliminating the high-frequency noise of the measurement observation vectors. To extract the effective observation vectors from the inertial sensors' outputs, a parameter recognition and vector reconstruction method are designed, where an adaptive Kalman filter is employed to estimate the unknown parameters. Furthermore, a robust filter, which is based on Huber's M-estimation theory, is developed for addressing the outliers of the measurement observation vectors due to the maneuver of the vehicle. A comprehensive experiment, which contains a simulation test and physical test, is designed to verify the performance of the proposed method, and the results show that the proposed method is equivalent to the popular apparent velocity method in swaying mode, but it is superior to the current methods while in moving mode when the strapdown inertial navigation system (SINS) is under entirely self-contained conditions.

  9. Fuzzy support vector machine: an efficient rule-based classification technique for microarrays.

    Science.gov (United States)

    Hajiloo, Mohsen; Rabiee, Hamid R; Anooshahpour, Mahdi

    2013-01-01

    The abundance of gene expression microarray data has led to the development of machine learning algorithms applicable for tackling disease diagnosis, disease prognosis, and treatment selection problems. However, these algorithms often produce classifiers with weaknesses in terms of accuracy, robustness, and interpretability. This paper introduces fuzzy support vector machine which is a learning algorithm based on combination of fuzzy classifiers and kernel machines for microarray classification. Experimental results on public leukemia, prostate, and colon cancer datasets show that fuzzy support vector machine applied in combination with filter or wrapper feature selection methods develops a robust model with higher accuracy than the conventional microarray classification models such as support vector machine, artificial neural network, decision trees, k nearest neighbors, and diagonal linear discriminant analysis. Furthermore, the interpretable rule-base inferred from fuzzy support vector machine helps extracting biological knowledge from microarray data. Fuzzy support vector machine as a new classification model with high generalization power, robustness, and good interpretability seems to be a promising tool for gene expression microarray classification.

  10. Polarity classification using structure-based vector representations of text

    NARCIS (Netherlands)

    Hogenboom, Alexander; Frasincar, Flavius; de Jong, Franciska M.G.; Kaymak, Uzay

    The exploitation of structural aspects of content is becoming increasingly popular in rule-based polarity classification systems. Such systems typically weight the sentiment conveyed by text segments in accordance with these segments' roles in the structure of a text, as identified by deep

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

    Science.gov (United States)

    Abdulameer, Mohammed Hasan; 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

  12. 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.

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

    Directory of Open Access Journals (Sweden)

    Jian-Jiun Ding

    2012-07-01

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

  14. Antigen delivery systems for veterinary vaccine development. Viral-vector based delivery systems.

    Science.gov (United States)

    Brun, Alejandro; Albina, Emmanuel; Barret, Tom; Chapman, David A G; Czub, Markus; Dixon, Linda K; Keil, Günther M; Klonjkowski, Bernard; Le Potier, Marie-Frédérique; Libeau, Geneviève; Ortego, Javier; Richardson, Jennifer; Takamatsu, Haru-H

    2008-12-02

    The recent advances in molecular genetics, pathogenesis and immunology have provided an optimal framework for developing novel approaches in the rational design of vaccines effective against viral epizootic diseases. This paper reviews most of the viral-vector based antigen delivery systems (ADSs) recently developed for vaccine testing in veterinary species, including attenuated virus and DNA and RNA viral vectors. Besides their usefulness in vaccinology, these ADSs constitute invaluable tools to researchers for understanding the nature of protective responses in different species, opening the possibility of modulating or potentiating relevant immune mechanisms involved in protection.

  15. 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.

  16. Energy Based Clutter Filtering for Vector Flow Imaging

    DEFF Research Database (Denmark)

    Villagómez Hoyos, Carlos Armando; Jensen, Jonas; Ewertsen, Caroline

    2017-01-01

    To obtain accurate blood flow velocity estimates it is important to remove the clutter signal originating from tissue. Conventionally, the clutter signal has been separated from the blood signal based on the difference of their spectral frequencies. However, this approach is not enough...... spectrum function to a predetermined threshold. The effect of the clutter filtering is evaluated on a plane wave (PW) scan sequence in combination with transverse oscillation (TO) and directional beamforming (DB) for velocity estimation. The performance of the filter is assessed by comparison...... of the velocity estimates of the proposed filter against a conventional moving average clutter filter. The effect of tissue motion is investigated using a Field II simulation of a straight vessel with moving wall, while the direct effect of the filter on the velocity estimates is evaluated on a CFD model...

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

    DEFF Research Database (Denmark)

    Krenk, Steen; Nielsen, Martin Bjerre

    2013-01-01

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

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

    Science.gov (United States)

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

    2017-01-29

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

  19. Fuzzy-based multi-kernel spherical support vector machine for ...

    Indian Academy of Sciences (India)

    In the proposed classifier, we design a new multi-kernel function based on the fuzzy triangular membership function. Finally, a newly developed multi-kernel function is incorporated into the spherical support vector machine to enhance the performance significantly. The experimental results are evaluated and performance is ...

  20. Fuzzy-based multi-kernel spherical support vector machine for ...

    Indian Academy of Sciences (India)

    A K Sampath

    2017-08-08

    Aug 8, 2017 ... performance is compared with existing systems based on the percentage of training data samples. Thus, the outcome of our proposed ... be correlated with the training data samples since the character is varied in style and size; ...... port vector machine (SVM) for English handwritten char- acter recognition.

  1. Transient expression of heterologous model gene in plants using Potato virusX-based vector

    Czech Academy of Sciences Publication Activity Database

    Čeřovská, Noemi; Pečenková, Tamara; Moravec, Tomáš; Velemínský, Jiří

    2004-01-01

    Roč. 79, č. 2 (2004), s. 147-152 ISSN 0167-6857 R&D Projects: GA ČR GA310/00/0381 Institutional research plan: CEZ:AV0Z5038910 Keywords : plant virus * based vector * transient expression Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 1.028, year: 2004

  2. A support vector density-based importance sampling for reliability assessment

    International Nuclear Information System (INIS)

    Dai, Hongzhe; Zhang, Hao; Wang, Wei

    2012-01-01

    An importance sampling method based on the adaptive Markov chain simulation and support vector density estimation is developed in this paper for efficient structural reliability assessment. The methodology involves the generation of samples that can adaptively populate the important region by the adaptive Metropolis algorithm, and the construction of importance sampling density by support vector density. The use of the adaptive Metropolis algorithm may effectively improve the convergence and stability of the classical Markov chain simulation. The support vector density can approximate the sampling density with fewer samples in comparison to the conventional kernel density estimation. The proposed importance sampling method can effectively reduce the number of structural analysis required for achieving a given accuracy. Examples involving both numerical and practical structural problems are given to illustrate the application and efficiency of the proposed methodology.

  3. VGSC: A Web-Based Vector Graph Toolkit of Genome Synteny and Collinearity

    Directory of Open Access Journals (Sweden)

    Yiqing Xu

    2016-01-01

    Full Text Available Background. In order to understand the colocalization of genetic loci amongst species, synteny and collinearity analysis is a frequent task in comparative genomics research. However many analysis software packages are not effective in visualizing results. Problems include lack of graphic visualization, simple representation, or inextensible format of outputs. Moreover, higher throughput sequencing technology requires higher resolution image output. Implementation. To fill this gap, this paper publishes VGSC, the Vector Graph toolkit of genome Synteny and Collinearity, and its online service, to visualize the synteny and collinearity in the common graphical format, including both raster (JPEG, Bitmap, and PNG and vector graphic (SVG, EPS, and PDF. Result. Users can upload sequence alignments from blast and collinearity relationship from the synteny analysis tools. The website can generate the vector or raster graphical results automatically. We also provide a java-based bytecode binary to enable the command-line execution.

  4. Margin based ontology sparse vector learning algorithm and applied in biology science

    Directory of Open Access Journals (Sweden)

    Wei Gao

    2017-01-01

    Full Text Available In biology field, the ontology application relates to a large amount of genetic information and chemical information of molecular structure, which makes knowledge of ontology concepts convey much information. Therefore, in mathematical notation, the dimension of vector which corresponds to the ontology concept is often very large, and thus improves the higher requirements of ontology algorithm. Under this background, we consider the designing of ontology sparse vector algorithm and application in biology. In this paper, using knowledge of marginal likelihood and marginal distribution, the optimized strategy of marginal based ontology sparse vector learning algorithm is presented. Finally, the new algorithm is applied to gene ontology and plant ontology to verify its efficiency.

  5. 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.

  6. Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations

    KAUST Repository

    Smaili, Fatima Zohra

    2018-01-31

    We propose the Onto2Vec method, an approach to learn feature vectors for biological entities based on their annotations to biomedical ontologies. Our method can be applied to a wide range of bioinformatics research problems such as similarity-based prediction of interactions between proteins, classification of interaction types using supervised learning, or clustering.

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

    International Nuclear Information System (INIS)

    Yang, Ning; Yang, Ming; Huo, Ju

    2015-01-01

    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)

  8. Medical image compression based on vector quantization with variable block sizes in wavelet domain.

    Science.gov (United States)

    Jiang, Huiyan; Ma, Zhiyuan; Hu, Yang; Yang, Benqiang; Zhang, Libo

    2012-01-01

    An optimized medical image compression algorithm based on wavelet transform and improved vector quantization is introduced. The goal of the proposed method is to maintain the diagnostic-related information of the medical image at a high compression ratio. Wavelet transformation was first applied to the image. For the lowest-frequency subband of wavelet coefficients, a lossless compression method was exploited; for each of the high-frequency subbands, an optimized vector quantization with variable block size was implemented. In the novel vector quantization method, local fractal dimension (LFD) was used to analyze the local complexity of each wavelet coefficients, subband. Then an optimal quadtree method was employed to partition each wavelet coefficients, subband into several sizes of subblocks. After that, a modified K-means approach which is based on energy function was used in the codebook training phase. At last, vector quantization coding was implemented in different types of sub-blocks. In order to verify the effectiveness of the proposed algorithm, JPEG, JPEG2000, and fractal coding approach were chosen as contrast algorithms. Experimental results show that the proposed method can improve the compression performance and can achieve a balance between the compression ratio and the image visual quality.

  9. Medical Image Compression Based on Vector Quantization with Variable Block Sizes in Wavelet Domain

    Directory of Open Access Journals (Sweden)

    Huiyan Jiang

    2012-01-01

    Full Text Available An optimized medical image compression algorithm based on wavelet transform and improved vector quantization is introduced. The goal of the proposed method is to maintain the diagnostic-related information of the medical image at a high compression ratio. Wavelet transformation was first applied to the image. For the lowest-frequency subband of wavelet coefficients, a lossless compression method was exploited; for each of the high-frequency subbands, an optimized vector quantization with variable block size was implemented. In the novel vector quantization method, local fractal dimension (LFD was used to analyze the local complexity of each wavelet coefficients, subband. Then an optimal quadtree method was employed to partition each wavelet coefficients, subband into several sizes of subblocks. After that, a modified K-means approach which is based on energy function was used in the codebook training phase. At last, vector quantization coding was implemented in different types of sub-blocks. In order to verify the effectiveness of the proposed algorithm, JPEG, JPEG2000, and fractal coding approach were chosen as contrast algorithms. Experimental results show that the proposed method can improve the compression performance and can achieve a balance between the compression ratio and the image visual quality.

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

    Directory of Open Access Journals (Sweden)

    Lin Wan

    2016-11-01

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

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

    International Nuclear Information System (INIS)

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

    2011-01-01

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

  12. Viral vector-mediated and cell-based therapies for treatment of cystic fibrosis.

    Science.gov (United States)

    Flotte, Terence R; Ng, Philip; Dylla, Doug E; McCray, Paul B; Wang, Guoshun; Kolls, Jay K; Hu, Jim

    2007-02-01

    Gene and cell-based therapies are considered to be potentially powerful new approaches for the management of cystic fibrosis (CF) lung disease. Despite tremendous efforts that have been made, especially in studies to understand the obstacles to gene delivery, major challenges to the application of these approaches remain to be solved. This article will review the advancements made and challenges remaining in the development of viral vector-mediated and cell-based approaches to treat patients with CF.

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

    Science.gov (United States)

    González, Alvaro J; Liao, Li

    2010-10-29

    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. 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. 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 the web at http

  14. 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

  15. VIP Barcoding: composition vector-based software for rapid species identification based on DNA barcoding.

    Science.gov (United States)

    Fan, Long; Hui, Jerome H L; Yu, Zu Guo; Chu, Ka Hou

    2014-07-01

    Species identification based on short sequences of DNA markers, that is, DNA barcoding, has emerged as an integral part of modern taxonomy. However, software for the analysis of large and multilocus barcoding data sets is scarce. The Basic Local Alignment Search Tool (BLAST) is currently the fastest tool capable of handling large databases (e.g. >5000 sequences), but its accuracy is a concern and has been criticized for its local optimization. However, current more accurate software requires sequence alignment or complex calculations, which are time-consuming when dealing with large data sets during data preprocessing or during the search stage. Therefore, it is imperative to develop a practical program for both accurate and scalable species identification for DNA barcoding. In this context, we present VIP Barcoding: a user-friendly software in graphical user interface for rapid DNA barcoding. It adopts a hybrid, two-stage algorithm. First, an alignment-free composition vector (CV) method is utilized to reduce searching space by screening a reference database. The alignment-based K2P distance nearest-neighbour method is then employed to analyse the smaller data set generated in the first stage. In comparison with other software, we demonstrate that VIP Barcoding has (i) higher accuracy than Blastn and several alignment-free methods and (ii) higher scalability than alignment-based distance methods and character-based methods. These results suggest that this platform is able to deal with both large-scale and multilocus barcoding data with accuracy and can contribute to DNA barcoding for modern taxonomy. VIP Barcoding is free and available at http://msl.sls.cuhk.edu.hk/vipbarcoding/. © 2014 John Wiley & Sons Ltd.

  16. 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.

  17. Frequency-agile vector signal generation based on optical frequency comb and pre-coding

    Science.gov (United States)

    Qu, Kun; Zhao, ShangHong; Tan, QingGui; Liang, DanYa

    2017-06-01

    In this paper, we experimentally demonstrate the generation of frequency-agile vector signals based on an optical frequency comb (OFC) and unbalanced pre-coding technology by employing a dual-driven Mach-Zehnder Modulator (DD-MZM) and an intensity modulator (IM). The OFC is generated by the DD-MZM and sent to the IM as a carrier. The IM is driven by a 5 GHz 2 Gbaud quadrature phase-shift keying (QPSK) vector signal with unbalanced pre-coding. The -1st order sideband of one OFC line and the +1st order sideband of another OFC line are selected by a programmable pulse shaper (PPS), after square-low photodiode detection, the frequency-agile vector signal can be obtained. The results show that the 2 Gbaud QPSK vector signals at 30 GHz, 50 GHz, 70 GHz and 90 GHz can be generated by only pre-coding once. It is possible to achieve a bit-error-rate (BER) below 1e-3 for wireless transmissions over 0.5 m using this method.

  18. Predictions of malaria vector distribution in Belize based on multispectral satellite data.

    Science.gov (United States)

    Roberts, D R; Paris, J F; Manguin, S; Harbach, R E; Woodruff, R; Rejmankova, E; Polanco, J; Wullschleger, B; Legters, L J

    1996-03-01

    Use of multispectral satellite data to predict arthropod-borne disease trouble spots is dependent on clear understandings of environmental factors that determine the presence of disease vectors. A blind test of remote sensing-based predictions for the spatial distribution of a malaria vector, Anopheles pseudopunctipennis, was conducted as a follow-up to two years of studies on vector-environmental relationships in Belize. Four of eight sites that were predicted to be high probability locations for presence of An. pseudopunctipennis were positive and all low probability sites (0 of 12) were negative. The absence of An. pseudopunctipennis at four high probability locations probably reflects the low densities that seem to characterize field populations of this species, i.e., the population densities were below the threshold of our sampling effort. Another important malaria vector, An. darlingi, was also present at all high probability sites and absent at all low probability sites. Anopheles darlingi, like An. pseudopunctipennis, is a riverine species. Prior to these collections at ecologically defined locations, this species was last detected in Belize in 1946.

  19. Multimodal biometric authentication based on score level fusion using support vector machine

    Science.gov (United States)

    Wang, F.; Han, J.

    2009-03-01

    Fusion of multiple biometrics for human authentication performance improvement has received considerable attention. This paper presents a novel multimodal biometric authentication method integrating face and iris based on score level fusion. For score level fusion, support vector machine (SVM) based fusion rule is applied to combine two matching scores, respectively from Laplacianface based face verifier and phase information based iris verifier, to generate a single scalar score which is used to make the final decision. Experimental results show that the performance of the proposed method can bring obvious improvement comparing to the unimodal biometric identification methods and the previous fused face-iris methods.

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

    Science.gov (United States)

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

    2011-05-01

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

  1. Comparative Visualization of Vector Field Ensembles Based on Longest Common Subsequence

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Richen; Guo, Hanqi; Zhang, Jiang; Yuan, Xiaoru

    2016-04-19

    We propose a longest common subsequence (LCS) based approach to compute the distance among vector field ensembles. By measuring how many common blocks the ensemble pathlines passing through, the LCS distance defines the similarity among vector field ensembles by counting the number of sharing domain data blocks. Compared to the traditional methods (e.g. point-wise Euclidean distance or dynamic time warping distance), the proposed approach is robust to outlier, data missing, and sampling rate of pathline timestep. Taking the advantages of smaller and reusable intermediate output, visualization based on the proposed LCS approach revealing temporal trends in the data at low storage cost, and avoiding tracing pathlines repeatedly. Finally, we evaluate our method on both synthetic data and simulation data, which demonstrate the robustness of the proposed approach.

  2. An alternative clinical routine for subjective refraction based on power vectors with trial frames.

    Science.gov (United States)

    María Revert, Antonia; Conversa, Maria Amparo; Albarrán Diego, César; Micó, Vicente

    2017-01-01

    Subjective refraction determines the final point of refractive error assessment in most clinical environments and its foundations have remained unchanged for decades. The purpose of this paper is to compare the results obtained when monocular subjective refraction is assessed in trial frames by a new clinical procedure based on a pure power vector interpretation with conventional clinical refraction procedures. An alternative clinical routine is described that uses power vector interpretation with implementation in trial frames. Refractive error is determined in terms of: (i) the spherical equivalent (M component), and (ii) a pair of Jackson Crossed Cylinder lenses oriented at 0°/90° (J 0 component) and 45°/135° (J 45 component) for determination of astigmatism. This vector subjective refraction result (VR) is compared separately for right and left eyes of 25 subjects (mean age, 35 ± 4 years) against conventional sphero-cylindrical subjective refraction (RX) using a phoropter. The VR procedure was applied with both conventional tumbling E optotypes (VR1) and modified optotypes with oblique orientation (VR2). Bland-Altman plots and intra-class correlation coefficient showed good agreement between VR, and RX (with coefficient values above 0.82) and anova showed no significant differences in any of the power vector components between RX and VR. VR1 and VR2 procedure results were similar (p ≥ 0.77). The proposed routine determines the three components of refractive error in power vector notation [M, J 0 , J 45 ], with a refraction time similar to the one used in conventional subjective procedures. The proposed routine could be helpful for inexperienced clinicians and for experienced clinicians in those cases where it is difficult to get a valid starting point for conventional RX (irregular corneas, media opacities, etc.) and for refractive situations/places with inadequate refractive facilities/equipment. © 2016 The Authors Ophthalmic & Physiological

  3. Extending the trend vector: The trend matrix and sample-based partial least squares

    Science.gov (United States)

    Sheridan, Robert P.; Nachbar, Robert B.; Bush, Bruce L.

    1994-06-01

    Trend vector analysis [Carhart, R.E. et al., J. Chem. Inf. Comput. Sci., 25 (1985) 64], in combination with topological descriptors such as atom pairs, has proved useful in drug discovery for ranking large collections of chemical compounds in order of predicted biological activity. The compounds with the highest predicted activities, upon being tested, often show a several-fold increase in the fraction of active compounds relative to a randomly selected set. A trend vector is simply the one-dimensional array of correlations between the biological activity of interest and a set of properties or `descriptors' of compounds in a training set. This paper examines two methods for generalizing the trend vector to improve the predicted rank order. The trend matrix method finds the correlations between the residuals and the simultaneous occurrence of descriptors, which are stored in a two-dimensional analog of the trend vector. The SAMPLS method derives a linear model by partial least squares (PLS), using the `sample-based' formulation of PLS [Bush, B.L. and Nachbar, R.B., J. Comput.-Aided Mol. Design, 7 (1993) 587] for efficiency in treating the large number of descriptors. PLS accumulates a predictive model as a sum of linear components. Expressed as a vector of prediction coefficients on properties, the first PLS component is proportional to the trend vector. Subsequent components adjust the model toward full least squares. For both methods the residuals decrease, while the risk of overfitting the training set increases. We therefore also describe statistical checks to prevent overfitting. These methods are applied to two data sets, a small homologous series of disubstituted piperidines, tested on the dopamine receptor, and a large set of diverse chemical structures, some of which are active at the muscarinic receptor. Each data set is split into a training set and a test set, and the activities in the test set are predicted from a fit on the training set. Both the trend

  4. Quantifying Similarity and Distance Measures for Vector-Based Datasets: Histograms, Signals, and Probability Distribution Functions

    Science.gov (United States)

    2017-02-01

    note, a number of different measures implemented in both MATLAB and Python as functions are used to quantify similarity/distance between 2 vector-based...datasets. The scripts are attached as appendixes as is a description of their execution. Python, MATLAB , similarity, distance, X-ray diffraction 40...iv List of Tables v 1. Introduction 1 2. Function Description 2 3. Implementation and Usage 11 4. Examples 11 5. Summary 18 6. References 19 Appendix

  5. An Open-Loop Vector Receiver Architecture for GNSS-Based Scintillation Monitoring

    OpenAIRE

    CURRAN JAMES THOMAS; BAVARO MICHELE; FORTUNY GUASCH Joaquim

    2014-01-01

    GNSS-based studies of the ionosphere are typically conducted using navigation receivers which track both the carrier and code phase either on a satellite-by-satellite basis, or collectively via a vector structure [3]. Information relating to phase and amplitude scintillation is gathered from the receiver’s estimate of the carrier phase and the receiver correlators values, respectively. The quality of these parameters, however, is directly influenced by how well the receiver can track the GNSS...

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2004-07-01

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

  7. Vector magnetometer based on synchronous manipulation of nitrogen-vacancy centers in all crystal directions

    Science.gov (United States)

    Zhang, Chen; Yuan, Heng; Zhang, Ning; Xu, Lixia; Zhang, Jixing; Li, Bo; Fang, Jiancheng

    2018-04-01

    Negatively charged nitrogen vacancy (NV‑) centers in diamond have been extensively studied as high-sensitivity magnetometers, showcasing a wide range of applications. This study experimentally demonstrates a vector magnetometry scheme based on synchronous manipulation of NV‑ center ensembles in all crystal directions using double frequency microwaves (MWs) and multi-coupled-strip-lines (mCSL) waveguide. The application of the mCSL waveguide ensures a high degree of synchrony (99%) for manipulating NV‑ centers in multiple orientations in a large volume. Manipulation with double frequency MWs makes NV‑ centers of all four crystal directions involved, and additionally leads to an enhancement of the manipulation field. In this work, by monitoring the changes in the slope of the resonance line consisting of multi-axes NV‑ centers, measurement of the direction of the external field vector was demonstrated with a sensitivity of {{10}\\prime}/\\sqrt{Hz} . Based on the scheme, the fluorescence signal contrast was improved by four times higher and the sensitivity to the magnetic field strength was improved by two times. The method provides a more practical way of achieving vector sensors based on NV‑ center ensembles in diamond.

  8. PGMA-Based Cationic Nanoparticles with Polyhydric Iodine Units for Advanced Gene Vectors.

    Science.gov (United States)

    Sun, Yue; Hu, Hao; Yu, Bingran; Xu, Fu-Jian

    2016-11-16

    It is crucial for successful gene delivery to develop safe, effective, and multifunctional polycations. Iodine-based small molecules are widely used as contrast agents for CT imaging. Herein, a series of star-like poly(glycidyl methacrylate) (PGMA)-based cationic vectors (II-PGEA/II) with abundant flanking polyhydric iodine units are prepared for multifunctional gene delivery systems. The proposed II-PGEA/II star vector is composed of one iohexol intermediate (II) core and five ethanolamine (EA) and II-difunctionalized PGMA arms. The amphipathic II-PGEA/II vectors readily self-assemble into well-defined cationic nanoparticles, where massive hydroxyl groups can establish a hydration shell to stabilize the nanoparticles. The II introduction improves cell viabilities of polycations. Moreover, by controlling the suitable amount of introduced II units, the resultant II-PGEA/II nanoparticles can produce fairly good transfection performances in different cell lines. Particularly, the II-PGEA/II nanoparticles induce much better in vitro CT imaging abilities in tumor cells than iohexol (one commonly used commercial CT contrast agent). The present design of amphipathic PGMA-based nanoparticles with CT contrast agents would provide useful information for the development of new multifunctional gene delivery systems.

  9. 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.

  10. Improvement of Carrier Phase Tracking Based on a Joint Vector Architecture

    Directory of Open Access Journals (Sweden)

    Shaohua Chen

    2017-01-01

    Full Text Available Carrier phase measurements are essential to high precision positioning. Usually, the carrier phase measurements are generated from the phase lock loop in a conventional Global Navigation Satellite System (GNSS receiver. However there is a dilemma problem to the design of the loop parameters in a conventional tracking loop. To address this problem and improve the carrier phase tracking sensitivity, a carrier phase tracking method based on a joint vector architecture is proposed. The joint vector architecture contains a common loop based on extended Kalman filter to track the common dynamics of the different channels and the individual loops for each channel to track the satellite specific dynamics. The transfer function model of the proposed architecture is derived. The proposed method and the conventional scalar carrier phase tracking are tested with a high quality simulator. The test results indicate that carrier phase measurements of satellites start to show cycle slips using the proposed method when carrier noise ratio is equal to and below 15 dB-Hz instead of 21 dB-Hz with using the conventional phase tracking loop. Since the joint vector based tracking loops jointly process the signals of all available satellites, the potential interchannel influence between different satellites is also investigated.

  11. Potato virus X and Tobacco mosaic virus-based vectors compatible with the Gateway-TM cloning system

    NARCIS (Netherlands)

    Lacorte, C.C.; Ribeiro, S.G.; Lohuis, H.; Goldbach, R.W.; Prins, M.W.

    2010-01-01

    Virus-based expression vectors are important tools for high-level production of foreign proteins and for gene function analysis through virus induced gene silencing. To exploit further their advantages as fast, high yield replicons, a set of vectors was produced by converting and adapting Potato

  12. A novel license plate recognition method based on least squares support vector machine

    Science.gov (United States)

    Gui, Lin

    2011-12-01

    Support vector machine (SVM) is a new type of machine learning method based on statistical learning. It avoids the neural network of some inherent disadvantages, such as the local minimum in the training process, structure, selection, slow convergence speed problem, have very strong nonlinear system identification and generalization ability and small sample. In this paper, we established multi-classifier based on binary tree model. Category rules are based on experience. But this method cans classifier to come to less than optimal results. Our future work focused on how to find a category to get the optimal rules of multi-classifier method, experience risk analysis, cluster analysis will consider further.

  13. 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 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.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 (2-4-fold and booster (2-fold

  14. 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.

  15. Implementation of algorithms based on support vector machine (SVM for electric systems: topic review

    Directory of Open Access Journals (Sweden)

    Jefferson Jara Estupiñan

    2016-06-01

    Full Text Available Objective: To perform a review of implementation of algorithms based on support vectore machine applied to electric systems. Method: A paper search is done mainly on Biblio­graphic Indexes (BI and Bibliographic Bases with Selection Committee (BBSC about support vector machine. This work shows a qualitative and/or quan­titative description about advances and applications in the electrical environment, approaching topics such as: electrical market prediction, demand predic­tion, non-technical losses (theft, alternative energy source and transformers, among others, in each work the respective citation is done in order to guarantee the copy right and allow to the reader a dynamic mo­vement between the reading and the cited works. Results: A detailed review is done, focused on the searching of implemented algorithms in electric sys­tems and innovating application areas. Conclusion: Support vector machines have a lot of applications due to their multiple benefits, however in the electric energy area; they have not been tota­lly applied, this allow to identify a promising area of researching.

  16. Vector magnetometry based on electromagnetically induced transparency in linearly polarized light

    International Nuclear Information System (INIS)

    Yudin, V. I.; Taichenachev, A. V.; Dudin, Y. O.; Velichansky, V. L.; Zibrov, A. S.; Zibrov, S. A.

    2010-01-01

    We develop a generalized principle of electromagnetically induced transparency (EIT) vector magnetometry based on high-contrast EIT resonances and the symmetry of atom-light interaction in the linearly polarized bichromatic fields. Operation of such vector magnetometer on the D 1 line of 87 Rb has been demonstrated. The proposed compass-magnetometer has an increased immunity to shifts produced by quadratic Zeeman and ac-Stark effects, as well as by atom-buffer gas and atom-atom collisions. In our proof-of-principle experiment the detected angular sensitivity to magnetic field orientation is 10 -3 deg/Hz 1/2 , which is limited by laser intensity fluctuations, light polarization quality, and magnitude of the magnetic field.

  17. Thai Language Sentence Similarity Computation Based on Syntactic Structure and Semantic Vector

    Science.gov (United States)

    Wang, Hongbin; Feng, Yinhan; Cheng, Liang

    2018-03-01

    Sentence similarity computation plays an increasingly important role in text mining, Web page retrieval, machine translation, speech recognition and question answering systems. Thai language as a kind of resources scarce language, it is not like Chinese language with HowNet and CiLin resources. So the Thai sentence similarity research faces some challenges. In order to solve this problem of the Thai language sentence similarity computation. This paper proposes a novel method to compute the similarity of Thai language sentence based on syntactic structure and semantic vector. This method firstly uses the Part-of-Speech (POS) dependency to calculate two sentences syntactic structure similarity, and then through the word vector to calculate two sentences semantic similarity. Finally, we combine the two methods to calculate two Thai language sentences similarity. The proposed method not only considers semantic, but also considers the sentence syntactic structure. The experiment result shows that this method in Thai language sentence similarity computation is feasible.

  18. Microwave vector signal transmission over an optical fiber based on IQ modulation and coherent detection.

    Science.gov (United States)

    Chen, Yang; Shao, Tong; Wen, Aijun; Yao, Jianping

    2014-03-15

    A novel approach to transmitting two vector signals using a single optical carrier based on IQ modulation and coherent detection is proposed and demonstrated. In the proposed system, two quadrature phase-shift keying (QPSK) signals are IQ modulated on an optical carrier with one polarization state using a dual-parallel Mach-Zehnder modulator (DP-MZM). The optical carrier with an orthogonal polarization state is not modulated but transmitted with the modulated optical wave. At the receiver, the two orthogonally polarized light waves are separated and sent to a coherent detector, where the two QPSK signals are separated and demodulated. An experiment is performed. The transmission of two QPSK signals at 2 GHz with a data rate of 1 Gbps is implemented over a 25 km single-mode fiber. The performance of the transmission in terms of error vector magnitude is evaluated.

  19. Deformation vector measurement by means of ground based interferometric radar system

    Science.gov (United States)

    Michelini, Alberto; Coppi, Francesco

    2017-10-01

    Ground Based Interferometric Radar (GBInRad) is a class of terrestrial remote sensing imaging system, based on microwave interferometric techniques. The principal application of GBInRad system is deformation monitoring, since respect to other techniques they can provide remote sensing, high sensitivity to small deformations, long range of measurements, imaging capability and fast scan time. The main limitation of standard GBInRad system is their capability of detecting movements only along the Line of Sight (LoS) of the sensor, although actual targets may show deformations in any direction of space; this represents an important limitation with respect to other techniques able to estimate the full 3D deformation vector. If the displacement direction is not known a priori, combining together LoS displacement measured from different spatial positions, it is possible to reconstruct the actual 3D displacement vector of monitored targets. In this paper are introduced and analysed the various aspect of the displacement vector measurement with multiple GBInRad system that work both in a monostatic and in a bistatic configuration. In the monostatic configuration every system transmits and receives the signal independently from the others; this approach requires multiple GBInRad system deployed to monitoring the same scenario and therefore its main limitations lie in the costs, power consumption and maintenance. A possible cost-effective evolution of the monostatic configuration is to exploit GBInRad system in a multiple bistatic configuration; a multiple bistatic Radar is a system in which a transmitter operates together with multiple receivers located in different positions in space. In this paper, the deformation vector measurement by means of bistatic GBInRad is proposed.

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

    Directory of Open Access Journals (Sweden)

    Anne Louise Askou

    Full Text Available 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 from a single vector have certain limitations that affect transgene expression levels and/or vector titers. In this study, we describe a novel vector design that facilitates combined expression of therapeutic RNA- and protein-based antiangiogenic factors as well as a fluorescent reporter from back-to-back RNApolII-driven expression cassettes. This configuration allows effective production of intron-embedded miRNAs that are released upon transduction of target cells. Exploiting such multigenic lentiviral vectors, we demonstrate robust miRNA-directed downregulation of vascular endothelial growth factor (VEGF expression, leading to reduced angiogenesis, and parallel impairment of angiogenic pathways by codelivering the gene encoding pigment epithelium-derived factor (PEDF. Notably, subretinal injections of lentiviral vectors reveal efficient retinal pigment epithelium-specific gene expression driven 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 combination therapies for amelioration of age-related macular degeneration.

  1. Bidding strategy with forecast technology based on support vector machine in the electricity market

    Science.gov (United States)

    Gao, Ciwei; Bompard, Ettore; Napoli, Roberto; Wan, Qiulan; Zhou, Jian

    2008-06-01

    The participants in the electricity market are concerned very much with the market price evolution. Various technologies have been developed for price forecasting. The SVM (Support Vector Machine) has shown its good performance in market price forecasting. Two approaches for forming the market bidding strategies based on SVM are proposed. One is based on the price forecasting accuracy, with which the rejection 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 parameter settings. The proposed approaches have been tested on a numerical example.

  2. A novel retinal vessel extraction algorithm based on matched filtering and gradient vector flow

    Science.gov (United States)

    Yu, Lei; Xia, Mingliang; Xuan, Li

    2013-10-01

    The microvasculature network of retina plays an important role in the study and diagnosis of retinal diseases (age-related macular degeneration and diabetic retinopathy for example). Although it is possible to noninvasively acquire high-resolution retinal images with modern retinal imaging technologies, non-uniform illumination, the low contrast of thin vessels and the background noises all make it difficult for diagnosis. In this paper, we introduce a novel retinal vessel extraction algorithm based on gradient vector flow and matched filtering to segment retinal vessels with different likelihood. Firstly, we use isotropic Gaussian kernel and adaptive histogram equalization to smooth and enhance the retinal images respectively. Secondly, a multi-scale matched filtering method is adopted to extract the retinal vessels. Then, the gradient vector flow algorithm is introduced to locate the edge of the retinal vessels. Finally, we combine the results of matched filtering method and gradient vector flow algorithm to extract the vessels at different likelihood levels. The experiments demonstrate that our algorithm is efficient and the intensities of vessel images exactly represent the likelihood of the vessels.

  3. Immunogenicity of ORFV-based vectors expressing the rabies virus glycoprotein in livestock species.

    Science.gov (United States)

    Martins, Mathias; Joshi, Lok R; Rodrigues, Fernando S; Anziliero, Deniz; Frandoloso, Rafael; Kutish, Gerald F; Rock, Daniel L; Weiblen, Rudi; Flores, Eduardo F; Diel, Diego G

    2017-11-01

    The parapoxvirus Orf virus (ORFV) encodes several immunomodulatory proteins (IMPs) that modulate host-innate and pro-inflammatory responses and has been proposed as a vaccine delivery vector for use in animal species. Here we describe the construction and characterization of two recombinant ORFV vectors expressing the rabies virus (RABV) glycoprotein (G). The RABV-G gene was inserted in the ORFV024 or ORFV121 gene loci, which encode for IMPs that are unique to parapoxviruses and inhibit activation of the NF-κB signaling pathway. The immunogenicity of the resultant recombinant viruses (ORFV ∆024 RABV-G or ORFV ∆121 RABV-G, respectively) was evaluated in pigs and cattle. Immunization of the target species with ORFV ∆024 RABV-G and ORFV ∆121 RABV-G elicited robust neutralizing antibody responses against RABV. Notably, neutralizing antibody titers induced in ORFV ∆121 RABV-G-immunized pigs and cattle were significantly higher than those detected in ORFV ∆024 RABV-G-immunized animals, indicating a higher immunogenicity of ORFV Δ121 -based vectors in these animal species. Copyright © 2017 Elsevier Inc. All rights reserved.

  4. On the angular error of intensity vector based direction of arrival estimation in reverberant sound fields.

    Science.gov (United States)

    Levin, Dovid; Habets, Emanuël A P; Gannot, Sharon

    2010-10-01

    An acoustic vector sensor provides measurements of both the pressure and particle velocity of a sound field in which it is placed. These measurements are vectorial in nature and can be used for the purpose of source localization. A straightforward approach towards determining the direction of arrival (DOA) utilizes the acoustic intensity vector, which is the product of pressure and particle velocity. The accuracy of an intensity vector based DOA estimator in the presence of noise has been analyzed previously. In this paper, the effects of reverberation upon the accuracy of such a DOA estimator are examined. It is shown that particular realizations of reverberation differ from an ideal isotropically diffuse field, and induce an estimation bias which is dependent upon the room impulse responses (RIRs). The limited knowledge available pertaining the RIRs is expressed statistically by employing the diffuse qualities of reverberation to extend Polack's statistical RIR model. Expressions for evaluating the typical bias magnitude as well as its probability distribution are derived.

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

    Directory of Open Access Journals (Sweden)

    Papia Ray

    2016-09-01

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

  6. A New Suite of Plasmid Vectors for Fluorescence-Based Imaging of Root Colonizing Pseudomonads

    Directory of Open Access Journals (Sweden)

    Rosemarie Wilton

    2018-02-01

    Full Text Available In the terrestrial ecosystem, plant–microbe symbiotic associations are ecologically and economically important processes. To better understand these associations at structural and functional levels, different molecular and biochemical tools are applied. In this study, we have constructed a suite of vectors that incorporates several new elements into the rhizosphere stable, broad-host vector pME6031. The new vectors are useful for studies requiring multi-color tagging and visualization of plant-associated, Gram-negative bacterial strains such as Pseudomonas plant growth promotion and biocontrol strains. A number of genetic elements, including constitutive promoters and signal peptides that target secretion to the periplasm, have been evaluated. Several next generation fluorescent proteins, namely mTurquoise2, mNeonGreen, mRuby2, DsRed-Express2 and E2-Crimson have been incorporated into the vectors for whole cell labeling or protein tagging. Secretion of mTurquoise2 and mNeonGreen into the periplasm of Pseudomonas fluorescens SBW25 has also been demonstrated, providing a vehicle for tagging proteins in the periplasmic compartment. A higher copy number version of select plasmids has been produced by introduction of a previously described repA mutation, affording an increase in protein expression levels. The utility of these plasmids for fluorescence-based imaging is demonstrated by root colonization of Solanum lycopersicum seedlings by P. fluorescens SBW25 in a hydroponic growth system. The plasmids are stably maintained during root colonization in the absence of selective pressure for more than 2 weeks.

  7. 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.

  8. 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.

  9. Vision-Based Perception and Classification of Mosquitoes Using Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Masataka Fuchida

    2017-01-01

    Full Text Available The need for a novel automated mosquito perception and classification method is becoming increasingly essential in recent years, with steeply increasing number of mosquito-borne diseases and associated casualties. There exist remote sensing and GIS-based methods for mapping potential mosquito inhabitants and locations that are prone to mosquito-borne diseases, but these methods generally do not account for species-wise identification of mosquitoes in closed-perimeter regions. Traditional methods for mosquito classification involve highly manual processes requiring tedious sample collection and supervised laboratory analysis. In this research work, we present the design and experimental validation of an automated vision-based mosquito classification module that can deploy in closed-perimeter mosquito inhabitants. The module is capable of identifying mosquitoes from other bugs such as bees and flies by extracting the morphological features, followed by support vector machine-based classification. In addition, this paper presents the results of three variants of support vector machine classifier in the context of mosquito classification problem. This vision-based approach to the mosquito classification problem presents an efficient alternative to the conventional methods for mosquito surveillance, mapping and sample image collection. Experimental results involving classification between mosquitoes and a predefined set of other bugs using multiple classification strategies demonstrate the efficacy and validity of the proposed approach with a maximum recall of 98%.

  10. 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.

  11. A Shellcode Detection Method Based on Full Native API Sequence and Support Vector Machine

    Science.gov (United States)

    Cheng, Yixuan; Fan, Wenqing; Huang, Wei; An, Jing

    2017-09-01

    Dynamic monitoring the behavior of a program is widely used to discriminate between benign program and malware. It is usually based on the dynamic characteristics of a program, such as API call sequence or API call frequency to judge. The key innovation of this paper is to consider the full Native API sequence and use the support vector machine to detect the shellcode. We also use the Markov chain to extract and digitize Native API sequence features. Our experimental results show that the method proposed in this paper has high accuracy and low detection rate.

  12. Automatic content extraction of filled-form images based on clustering component block projection vectors

    Science.gov (United States)

    Peng, Hanchuan; He, Xiaofeng; Long, Fuhui

    2003-12-01

    Automatic understanding of document images is a hard problem. Here we consider a sub-problem, automatically extracting content from filled form images. Without pre-selected templates or sophisticated structural/semantic analysis, we propose a novel approach based on clustering the component-block-projection-vectors. By combining spectral clustering and minimal spanning tree clustering, we generate highly accurate clusters, from which the adaptive templates are constructed to extract the filled-in content. Our experiments show this approach is effective for a set of 1040 US IRS tax form images belonging to 208 types.

  13. Parameter identification based synchronization for a class of chaotic systems with offset vectors

    International Nuclear Information System (INIS)

    Chen Cailian; Feng Gang; Guan Xinping

    2004-01-01

    Based on a parameter identification scheme, a novel synchronization method is presented for a class of chaotic systems with offset vectors which can be represented by the so-called T-S fuzzy model. It is shown that the slave system can synchronize the master system and the unknown parameters of the master system can be identified simultaneously. The delayed feedback technique is also developed in order to reduce the energy and time required for the identification and synchronization. Numerical simulations demonstrate the effectiveness of the proposed method

  14. Optical vector network analyzer with improved accuracy based on polarization modulation and polarization pulling.

    Science.gov (United States)

    Li, Wei; Liu, Jian Guo; Zhu, Ning Hua

    2015-04-15

    We report a novel optical vector network analyzer (OVNA) with improved accuracy based on polarization modulation and stimulated Brillouin scattering (SBS) assisted polarization pulling. The beating between adjacent higher-order optical sidebands which are generated because of the nonlinearity of an electro-optic modulator (EOM) introduces considerable error to the OVNA. In our scheme, the measurement error is significantly reduced by removing the even-order optical sidebands using polarization discrimination. The proposed approach is theoretically analyzed and experimentally verified. The experimental results show that the accuracy of the OVNA is greatly improved compared to a conventional OVNA.

  15. 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.

  16. Beam Structure Damage Identification Based on BP Neural Network and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Bo Yan

    2014-01-01

    Full Text Available It is not easy to find marine cracks of structures by directly manual testing. When the cracks of important components are extended under extreme offshore environment, the whole structure would lose efficacy, endanger the staff’s safety, and course a significant economic loss and marine environment pollution. Thus, early discovery of structure cracks is very important. In this paper, a beam structure damage identification model based on intelligent algorithm is firstly proposed to identify partial cracks in supported beams on ocean platform. In order to obtain the replacement mode and strain mode of the beams, the paper takes simple supported beam with single crack and double cracks as an example. The results show that the difference curves of strain mode change drastically only on the injured part and different degrees of injury would result in different mutation degrees of difference curve more or less. While the model based on support vector machine (SVM and BP neural network can identify cracks of supported beam intelligently, the methods can discern injured degrees of sound condition, single crack, and double cracks. Furthermore, the two methods are compared. The results show that the two methods presented in the paper have a preferable identification precision and adaptation. And damage identification based on support vector machine (SVM has smaller error results.

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

    Directory of Open Access Journals (Sweden)

    Andrew F. Heckler

    2016-06-01

    Full Text Available 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

  18. Research on bearing life prediction based on support vector machine and its application

    International Nuclear Information System (INIS)

    Sun Chuang; Zhang Zhousuo; He Zhengjia

    2011-01-01

    Life prediction of rolling element bearing is the urgent demand in engineering practice, and the effective life prediction technique is beneficial to predictive maintenance. Support vector machine (SVM) is a novel machine learning method based on statistical learning theory, and is of advantage in prediction. This paper develops SVM-based model for bearing life prediction. The inputs of the model are features of bearing vibration signal and the output is the bearing running time-bearing failure time ratio. The model is built base on a few failed bearing data, and it can fuse information of the predicted bearing. So it is of advantage to bearing life prediction in practice. The model is applied to life prediction of a bearing, and the result shows the proposed model is of high precision.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. Framework and implementation for improving physics essential skills via computer-based practice: Vector math

    Science.gov (United States)

    Mikula, Brendon D.; Heckler, Andrew F.

    2017-06-01

    We propose a framework for improving accuracy, fluency, and retention of basic skills essential for solving problems relevant to STEM introductory courses, and implement the framework for the case of basic vector math skills over several semesters in an introductory physics course. Using an iterative development process, the framework begins with a careful identification of target skills and the study of specific student difficulties with these skills. It then employs computer-based instruction, immediate feedback, mastery grading, and well-researched principles from cognitive psychology such as interleaved training sequences and distributed practice. We implemented this with more than 1500 students over 2 semesters. Students completed the mastery practice for an average of about 13 min /week , for a total of about 2-3 h for the whole semester. Results reveal large (>1 SD ) pretest to post-test gains in accuracy in vector skills, even compared to a control group, and these gains were retained at least 2 months after practice. We also find evidence of improved fluency, student satisfaction, and that awarding regular course credit results in higher participation and higher learning gains than awarding extra credit. In all, we find that simple computer-based mastery practice is an effective and efficient way to improve a set of basic and essential skills for introductory physics.

  5. Nanotechnologies in delivery of mRNA therapeutics using nonviral vector-based delivery systems.

    Science.gov (United States)

    Guan, S; Rosenecker, J

    2017-03-01

    Because of its safe and effective protein expression profile, in vitro transcribed messenger RNA (IVT-mRNA) represents a promising candidate in the development of novel therapeutics for genetic diseases, vaccines or gene editing strategies, especially when its inherent shortcomings (for example, instability and immunogenicity) have been partially addressed via structural modifications. However, numerous unsolved technical difficulties in successful in vivo delivery of IVT-mRNA have greatly hindered the applications of IVT-mRNA in clinical development. Recent advances in nanotechnology and material science have yielded many promising nonviral delivery systems, some of which were able to efficiently facilitate targeted in vivo delivery of IVT-mRNA in safe and noninvasive manners. The diversity and flexibility of these delivery systems highlight the recent progress of IVT-mRNA-based therapy using nonviral vectors. In this review, we summarize recent advances of existing and emerging nonviral vector-based nanotechnologies for IVT-mRNA delivery and briefly summarize the interesting but rarely discussed applications on simultaneous delivery of IVT-mRNA with DNA.

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

    International Nuclear Information System (INIS)

    Xiao, Xiao; Gang, Yi; Wang, Honghong; Wang, Jiayin; Zhao, Lina; Xu, Li; Liu, Zhiguo

    2015-01-01

    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

  7. Engineered XcmI cassette-containing vector for PCR-based ...

    Indian Academy of Sciences (India)

    Unknown

    A simple and general method is described to construct a new vector bearing a synthetic XcmI cassette for direct cloning of PCR-amplified genes of interest. Cleavage of the vector with XcmI generates a linearized molecule with a single thymidine (T) overhang at the 3′ ends (T-vector) that facilitates TA cloning of PCR ...

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

    Science.gov (United States)

    Zhu, Yu

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

  9. Kochen-Specker vectors

    International Nuclear Information System (INIS)

    Pavicic, Mladen; Merlet, Jean-Pierre; McKay, Brendan; Megill, Norman D

    2005-01-01

    We give a constructive and exhaustive definition of Kochen-Specker (KS) vectors in a Hilbert space of any dimension as well as of all the remaining vectors of the space. KS vectors are elements of any set of orthonormal states, i.e., vectors in an n-dimensional Hilbert space, H n , n≥3, to which it is impossible to assign 1s and 0s in such a way that no two mutually orthogonal vectors from the set are both assigned 1 and that not all mutually orthogonal vectors are assigned 0. Our constructive definition of such KS vectors is based on algorithms that generate MMP diagrams corresponding to blocks of orthogonal vectors in R n , on algorithms that single out those diagrams on which algebraic (0)-(1) states cannot be defined, and on algorithms that solve nonlinear equations describing the orthogonalities of the vectors by means of statistically polynomially complex interval analysis and self-teaching programs. The algorithms are limited neither by the number of dimensions nor by the number of vectors. To demonstrate the power of the algorithms, all four-dimensional KS vector systems containing up to 24 vectors were generated and described, all three-dimensional vector systems containing up to 30 vectors were scanned, and several general properties of KS vectors were found

  10. Generation of a Replication-Competent, Propagation-Deficient Virus Vector Based on the Transmissible Gastroenteritis Coronavirus Genome

    OpenAIRE

    Ortego, Javier; Escors, David; Laude, Hubert; Enjuanes, Luis

    2002-01-01

    Replication-competent propagation-deficient virus vectors based on the transmissible gastroenteritis coronavirus (TGEV) genome that are deficient in the essential E gene have been developed by complementation within E+ packaging cell lines. Cell lines expressing the TGEV E protein were established using the noncytopathic Sindbis virus replicon pSINrep21. In addition, cell lines stably expressing the E gene under the CMV promoter have been developed. The Sindbis replicon vector and the ectopic...

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

    Science.gov (United States)

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

    2011-05-01

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

  12. 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 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.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.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.

  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. Towards artificial intelligence based diesel engine performance control under varying operating conditions using support vector regression

    Directory of Open Access Journals (Sweden)

    Naradasu Kumar Ravi

    2013-01-01

    Full Text Available Diesel engine designers are constantly on the look-out for performance enhancement through efficient control of operating parameters. In this paper, the concept of an intelligent engine control system is proposed that seeks to ensure optimized performance under varying operating conditions. The concept is based on arriving at the optimum engine operating parameters to ensure the desired output in terms of efficiency. In addition, a Support Vector Machines based prediction model has been developed to predict the engine performance under varying operating conditions. Experiments were carried out at varying loads, compression ratios and amounts of exhaust gas recirculation using a variable compression ratio diesel engine for data acquisition. It was observed that the SVM model was able to predict the engine performance accurately.

  15. Aging Detection of Electrical Point Machines Based on Support Vector Data Description

    Directory of Open Access Journals (Sweden)

    Jaewon Sa

    2017-11-01

    Full Text Available Electrical point machines (EPM must be replaced at an appropriate time to prevent the occurrence of operational safety or stability problems in trains resulting from aging or budget constraints. However, it is difficult to replace EPMs effectively because the aging conditions of EPMs depend on the operating environments, and thus, a guideline is typically not be suitable for replacing EPMs at the most timely moment. In this study, we propose a method of classification for the detection of an aging effect to facilitate the timely replacement of EPMs. We employ support vector data description to segregate data of “aged” and “not-yet-aged” equipment by analyzing the subtle differences in normalized electrical signals resulting from aging. Based on the before and after-replacement data that was obtained from experimental studies that were conducted on EPMs, we confirmed that the proposed method was capable of classifying machines based on exhibited aging effects with adequate accuracy.

  16. 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.

  17. Automatic road sign detecion and classification based on support vector machines and HOG descriptos

    Science.gov (United States)

    Adam, A.; Ioannidis, C.

    2014-05-01

    This paper examines the detection and classification of road signs in color-images acquired by a low cost camera mounted on a moving vehicle. A new method for the detection and classification of road signs is proposed based on color based detection, in order to locate regions of interest. Then, a circular Hough transform is applied to complete detection taking advantage of the shape properties of the road signs. The regions of interest are finally represented using HOG descriptors and are fed into trained Support Vector Machines (SVMs) in order to be recognized. For the training procedure, a database with several training examples depicting Greek road sings has been developed. Many experiments have been conducted and are presented, to measure the efficiency of the proposed methodology especially under adverse weather conditions and poor illumination. For the experiments training datasets consisting of different number of examples were used and the results are presented, along with some possible extensions of this work.

  18. Accuracy improvement of optical vector network analyzer based on single-sideband modulation.

    Science.gov (United States)

    Xue, Min; Pan, Shilong; Zhao, Yongjiu

    2014-06-15

    An approach to suppress the measurement errors induced by the high-order sidebands of the optical single-sideband (OSSB) signal in the OSSB-based optical vector network analyzer (OVNA) is proposed and experimentally demonstrated. An analytical model for studying the measurement errors of the OSSB-based OVNA is established. Results show that the measurement errors introduced by the high-order sidebands can be obtained by suppressing the optical carrier in the OSSB signal. By subtracting these errors from the ordinary frequency responses measured by the OVNA, accurate frequency responses can be achieved. A proof-of-concept experiment is performed. The magnitude and phase responses of a fiber Bragg grating are measured with good coincidence by the OSSB signals with different modulation indices.

  19. Dynamic analysis of suspension cable based on vector form intrinsic finite element method

    Science.gov (United States)

    Qin, Jian; Qiao, Liang; Wan, Jiancheng; Jiang, Ming; Xia, Yongjun

    2017-10-01

    A vector finite element method is presented for the dynamic analysis of cable structures based on the vector form intrinsic finite element (VFIFE) and mechanical properties of suspension cable. Firstly, the suspension cable is discretized into different elements by space points, the mass and external forces of suspension cable are transformed into space points. The structural form of cable is described by the space points at different time. The equations of motion for the space points are established according to the Newton’s second law. Then, the element internal forces between the space points are derived from the flexible truss structure. Finally, the motion equations of space points are solved by the central difference method with reasonable time integration step. The tangential tension of the bearing rope in a test ropeway with the moving concentrated loads is calculated and compared with the experimental data. The results show that the tangential tension of suspension cable with moving loads is consistent with the experimental data. This method has high calculated precision and meets the requirements of engineering application.

  20. Parameter Improved Particle Swarm Optimization Based Direct-Current Vector Control Strategy for Solar PV System

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    NAMMALVAR, P.

    2018-02-01

    Full Text Available This paper projects Parameter Improved Particle Swarm Optimization (PIPSO based direct current vector control technology for the integration of photovoltaic array in an AC micro-grid to enhance the system performance and stability. A photovoltaic system incorporated with AC micro-grid is taken as the pursuit of research study. The test system features two power converters namely, PV side converter which consists of DC-DC boost converter with Perturbation and Observe (P&O MPPT control to reap most extreme power from the PV array, and grid side converter which consists of Grid Side-Voltage Source Converter (GS-VSC with proposed direct current vector control strategy. The gain of the proposed controller is chosen from a set of three values obtained using apriori test and tuned through the PIPSO algorithm so that the Integral of Time multiplied Absolute Error (ITAE between the actual and the desired DC link capacitor voltage reaches a minimum and allows the system to extract maximum power from PV system, whereas the existing d-q control strategy is found to perform slowly to control the DC link voltage under varying solar insolation and load fluctuations. From simulation results, it is evident that the proposed optimal control technique provides robust control and improved efficiency.

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

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    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.

  2. 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.

  3. Normal mammogram detection based on local probability difference transforms and support vector machines

    International Nuclear Information System (INIS)

    Chiracharit, W.; Kumhom, P.; Chamnongthai, K.; Sun, Y.; Delp, E.J.; Babbs, C.F

    2007-01-01

    Automatic detection of normal mammograms, as a ''first look'' for breast cancer, is a new approach to computer-aided diagnosis. This approach may be limited, however, by two main causes. The first problem is the presence of poorly separable ''crossed-distributions'' in which the correct classification depends upon the value of each feature. The second problem is overlap of the feature distributions that are extracted from digitized mammograms of normal and abnormal patients. Here we introduce a new Support Vector Machine (SVM) based method utilizing with the proposed uncrossing mapping and Local Probability Difference (LPD). Crossed-distribution feature pairs are identified and mapped into a new features that can be separated by a zero-hyperplane of the new axis. The probability density functions of the features of normal and abnormal mammograms are then sampled and the local probability difference functions are estimated to enhance the features. From 1,000 ground-truth-known mammograms, 250 normal and 250 abnormal cases, including spiculated lesions, circumscribed masses or microcalcifications, are used for training a support vector machine. The classification results tested with another 250 normal and 250 abnormal sets show improved testing performances with 90% sensitivity and 89% specificity. (author)

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

    International Nuclear Information System (INIS)

    Hall, Joanne L; Rao, Asha

    2010-01-01

    Mutually unbiased bases (MUBs) are important in quantum information theory. While constructions of complete sets of d + 1 MUBs in C 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 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.

  5. Fault Diagnosis of a Reconfigurable Crawling–Rolling Robot Based on Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Karthikeyan Elangovan

    2017-10-01

    Full Text Available As robots begin to perform jobs autonomously, with minimal or no human intervention, a new challenge arises: robots also need to autonomously detect errors and recover from faults. In this paper, we present a Support Vector Machine (SVM-based fault diagnosis system for a bio-inspired reconfigurable robot named Scorpio. The diagnosis system needs to detect and classify faults while Scorpio uses its crawling and rolling locomotion modes. Specifically, we classify between faulty and non-faulty conditions by analyzing onboard Inertial Measurement Unit (IMU sensor data. The data capture nine different locomotion gaits, which include rolling and crawling modes, at three different speeds. Statistical methods are applied to extract features and to reduce the dimensionality of original IMU sensor data features. These statistical features were given as inputs for training and testing. Additionally, the c-Support Vector Classification (c-SVC and nu-SVC models of SVM, and their fault classification accuracies, were compared. The results show that the proposed SVM approach can be used to autonomously diagnose locomotion gait faults while the reconfigurable robot is in operation.

  6. 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. PMID:25078058

  7. Design for Motor Controller in Hybrid Electric Vehicle Based on Vector Frequency Conversion Technology

    Directory of Open Access Journals (Sweden)

    Jing Lian

    2010-01-01

    Full Text Available Motor and its control technology are one of the main components of Hybrid Electric Vehicle (HEV. To meet HEV's fast torque response, vector control algorithm based on rotor flux-oriented and simulation model is concerned and modular designs for controller's hardware and software are presented in the paper in order to build a platform to achieve the vector control of asynchronous induction motor. Analyze the controller's electromagnetic compatibility, introduce the corresponding antijamming measures to assure the normal operation of the electromagnetic sensitive devices such as CAN bus; experiment proves that the measure is practical and feasible. On the basis of the control logic correct, such as improving CAN bus communication reliability, assuring power-on sequence and fault treatment, carry on the motor bench experiment, test its static properties, and adjust the controller parameters. The experimental results show that the designed driving system has the performance of low speed and high torque, a wide range of variable speed and high comprehensive efficiency.

  8. Automated valve fault detection based on acoustic emission parameters and support vector machine

    Directory of Open Access Journals (Sweden)

    Salah M. Ali

    2018-03-01

    Full Text Available Reciprocating compressors are one of the most used types of compressors with wide applications in industry. The most common failure in reciprocating compressors is always related to the valves. Therefore, a reliable condition monitoring method is required to avoid the unplanned shutdown in this category of machines. Acoustic emission (AE technique is one of the effective recent methods in the field of valve condition monitoring. However, a major challenge is related to the analysis of AE signal which perhaps only depends on the experience and knowledge of technicians. This paper proposes automated fault detection method using support vector machine (SVM and AE parameters in an attempt to reduce human intervention in the process. Experiments were conducted on a single stage reciprocating air compressor by combining healthy and faulty valve conditions to acquire the AE signals. Valve functioning was identified through AE waveform analysis. SVM faults detection model was subsequently devised and validated based on training and testing samples respectively. The results demonstrated automatic valve fault detection model with accuracy exceeding 98%. It is believed that valve faults can be detected efficiently without human intervention by employing the proposed model for a single stage reciprocating compressor. Keywords: Condition monitoring, Faults detection, Signal analysis, Acoustic emission, Support vector machine

  9. Light axial vector mesons

    Science.gov (United States)

    Chen, Kan; Pang, Cheng-Qun; Liu, Xiang; Matsuki, Takayuki

    2015-04-01

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

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

    Science.gov (United States)

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

    2012-09-01

    In this work we suggest an original fault signature based on an improved combination of Hilbert and Park transforms. Starting from this combination we can create two fault signatures: Hilbert modulus current space vector (HMCSV) and Hilbert phase current space vector (HPCSV). These two fault signatures are subsequently analysed using the classical fast Fourier transform (FFT). The effects of mechanical faults on the HMCSV and HPCSV spectrums are described, and the related frequencies are determined. The magnitudes of spectral components, relative to the studied faults (air-gap eccentricity and outer raceway ball bearing defect), are extracted in order to develop the input vector necessary for learning and testing the support vector machine with an aim of classifying automatically the various states of the induction motor. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

  11. Genomic Footprints of Selective Sweeps from Metabolic Resistance to Pyrethroids in African Malaria Vectors Are Driven by Scale up of Insecticide-Based Vector Control.

    Science.gov (United States)

    Barnes, Kayla G; Weedall, Gareth D; Ndula, Miranda; Irving, Helen; Mzihalowa, Themba; Hemingway, Janet; Wondji, Charles S

    2017-02-01

    Insecticide resistance in mosquito populations threatens recent successes in malaria prevention. Elucidating patterns of genetic structure in malaria vectors to predict the speed and direction of the spread of resistance is essential to get ahead of the 'resistance curve' and to avert a public health catastrophe. Here, applying a combination of microsatellite analysis, whole genome sequencing and targeted sequencing of a resistance locus, we elucidated the continent-wide population structure of a major African malaria vector, Anopheles funestus. We identified a major selective sweep in a genomic region controlling cytochrome P450-based metabolic resistance conferring high resistance to pyrethroids. This selective sweep occurred since 2002, likely as a direct consequence of scaled up vector control as revealed by whole genome and fine-scale sequencing of pre- and post-intervention populations. Fine-scaled analysis of the pyrethroid resistance locus revealed that a resistance-associated allele of the cytochrome P450 monooxygenase CYP6P9a has swept through southern Africa to near fixation, in contrast to high polymorphism levels before interventions, conferring high levels of pyrethroid resistance linked to control failure. Population structure analysis revealed a barrier to gene flow between southern Africa and other areas, which may prevent or slow the spread of the southern mechanism of pyrethroid resistance to other regions. By identifying a genetic signature of pyrethroid-based interventions, we have demonstrated the intense selective pressure that control interventions exert on mosquito populations. If this level of selection and spread of resistance continues unabated, our ability to control malaria with current interventions will be compromised.

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

    Science.gov (United States)

    Gürtler, Ricardo E; Yadon, Zaida E

    2015-02-01

    This article provides an overview of three research projects which designed and implemented innovative interventions for Chagas disease vector control in Bolivia, Guatemala and Mexico. The research initiative was based on sound principles of community-based ecosystem management (ecohealth), integrated vector management, and interdisciplinary analysis. The initial situational analysis achieved a better understanding of ecological, biological and social determinants of domestic infestation. The key factors identified included: housing quality; type of peridomestic habitats; presence and abundance of domestic dogs, chickens and synanthropic rodents; proximity to public lights; location in the periphery of the village. In Bolivia, plastering of mud walls with appropriate local materials and regular cleaning of beds and of clothes next to the walls, substantially decreased domestic infestation and abundance of the insect vector Triatoma infestans. The Guatemalan project revealed close links between house infestation by rodents and Triatoma dimidiata, and vector infection with Trypanosoma cruzi. A novel community-operated rodent control program significantly reduced rodent infestation and bug infection. In Mexico, large-scale implementation of window screens translated into promising reductions in domestic infestation. A multi-pronged approach including community mobilisation and empowerment, intersectoral cooperation and adhesion to integrated vector management principles may be the key to sustainable vector and disease control in the affected regions. © World Health Organization 2015. The World Health Organization has granted Oxford University Press permission for the reproduction of this article.

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

    Science.gov (United States)

    Gürtler, Ricardo E.; Yadon, Zaida E.

    2015-01-01

    This article provides an overview of three research projects which designed and implemented innovative interventions for Chagas disease vector control in Bolivia, Guatemala and Mexico. The research initiative was based on sound principles of community-based ecosystem management (ecohealth), integrated vector management, and interdisciplinary analysis. The initial situational analysis achieved a better understanding of ecological, biological and social determinants of domestic infestation. The key factors identified included: housing quality; type of peridomestic habitats; presence and abundance of domestic dogs, chickens and synanthropic rodents; proximity to public lights; location in the periphery of the village. In Bolivia, plastering of mud walls with appropriate local materials and regular cleaning of beds and of clothes next to the walls, substantially decreased domestic infestation and abundance of the insect vector Triatoma infestans. The Guatemalan project revealed close links between house infestation by rodents and Triatoma dimidiata, and vector infection with Trypanosoma cruzi. A novel community-operated rodent control program significantly reduced rodent infestation and bug infection. In Mexico, large-scale implementation of window screens translated into promising reductions in domestic infestation. A multi-pronged approach including community mobilisation and empowerment, intersectoral cooperation and adhesion to integrated vector management principles may be the key to sustainable vector and disease control in the affected regions. PMID:25604759

  14. SVMQA: support-vector-machine-based protein single-model quality assessment.

    Science.gov (United States)

    Manavalan, Balachandran; Lee, Jooyoung

    2017-08-15

    The accurate ranking of predicted structural models and selecting the best model from a given candidate pool remain as open problems in the field of structural bioinformatics. The quality assessment (QA) methods used to address these problems can be grouped into two categories: consensus methods and single-model methods. Consensus methods in general perform better and attain higher correlation between predicted and true quality measures. However, these methods frequently fail to generate proper quality scores for native-like structures which are distinct from the rest of the pool. Conversely, single-model methods do not suffer from this drawback and are better suited for real-life applications where many models from various sources may not be readily available. In this study, we developed a support-vector-machine-based single-model global quality assessment (SVMQA) method. For a given protein model, the SVMQA method predicts TM-score and GDT_TS score based on a feature vector containing statistical potential energy terms and consistency-based terms between the actual structural features (extracted from the three-dimensional coordinates) and predicted values (from primary sequence). We trained SVMQA using CASP8, CASP9 and CASP10 targets and determined the machine parameters by 10-fold cross-validation. We evaluated the performance of our SVMQA method on various benchmarking datasets. Results show that SVMQA outperformed the existing best single-model QA methods both in ranking provided protein models and in selecting the best model from the pool. According to the CASP12 assessment, SVMQA was the best method in selecting good-quality models from decoys in terms of GDTloss. SVMQA method can be freely downloaded from http://lee.kias.re.kr/SVMQA/SVMQA_eval.tar.gz. jlee@kias.re.kr. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  15. Output-only modal parameter estimator of linear time-varying structural systems based on vector TAR model and least squares support vector machine

    Science.gov (United States)

    Zhou, Si-Da; Ma, Yuan-Chen; Liu, Li; Kang, Jie; Ma, Zhi-Sai; Yu, Lei

    2018-01-01

    Identification of time-varying modal parameters contributes to the structural health monitoring, fault detection, vibration control, etc. of the operational time-varying structural systems. However, it is a challenging task because there is not more information for the identification of the time-varying systems than that of the time-invariant systems. This paper presents a vector time-dependent autoregressive model and least squares support vector machine based modal parameter estimator for linear time-varying structural systems in case of output-only measurements. To reduce the computational cost, a Wendland's compactly supported radial basis function is used to achieve the sparsity of the Gram matrix. A Gamma-test-based non-parametric approach of selecting the regularization factor is adapted for the proposed estimator to replace the time-consuming n-fold cross validation. A series of numerical examples have illustrated the advantages of the proposed modal parameter estimator on the suppression of the overestimate and the short data. A laboratory experiment has further validated the proposed estimator.

  16. Feature Extraction of Underwater Target Signal Using Mel Frequency Cepstrum Coefficients Based on Acoustic Vector Sensor

    Directory of Open Access Journals (Sweden)

    Lanyue Zhang

    2016-01-01

    Full Text Available Feature extraction method using Mel frequency cepstrum coefficients (MFCC based on acoustic vector sensor is researched in the paper. Signals of pressure are simulated as well as particle velocity of underwater target, and the features of underwater target using MFCC are extracted to verify the feasibility of the method. The experiment of feature extraction of two kinds of underwater targets is carried out, and these underwater targets are classified and recognized by Backpropagation (BP neural network using fusion of multi-information. Results of the research show that MFCC, first-order differential MFCC, and second-order differential MFCC features could be used as effective features to recognize those underwater targets and the recognition rate, which using the particle velocity signal is higher than that using the pressure signal, could be improved by using fusion features.

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

    Directory of Open Access Journals (Sweden)

    Kai Hu

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

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

    Science.gov (United States)

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

    2016-01-01

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

  19. Modulation transfer function (MTF) measurement method based on support vector machine (SVM)

    Science.gov (United States)

    Zhang, Zheng; Chen, Yueting; Feng, Huajun; Xu, Zhihai; Li, Qi

    2016-03-01

    An imaging system's spatial quality can be expressed by the system's modulation spread function (MTF) as a function of spatial frequency in terms of the linear response theory. Methods have been proposed to assess the MTF of an imaging system using point, slit or edge techniques. The edge method is widely used for the low requirement of targets. However, the traditional edge methods are limited by the edge angle. Besides, image noise will impair the measurement accuracy, making the measurement result unstable. In this paper, a novel measurement method based on the support vector machine (SVM) is proposed. Image patches with different edge angles and MTF levels are generated as the training set. Parameters related with MTF and image structure are extracted from the edge images. Trained with image parameters and the corresponding MTF, the SVM classifier can assess the MTF of any edge image. The result shows that the proposed method has an excellent performance on measuring accuracy and stability.

  20. 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

  1. A DNA vector-based RNAi technology to suppress gene expression in mammalian cells.

    Science.gov (United States)

    Sui, Guangchao; Soohoo, Christina; Affar, El Bachir; Gay, Frédérique; Shi, Yujiang; Forrester, William C; Shi, Yang

    2002-04-16

    Double-stranded RNA-mediated interference (RNAi) has recently emerged as a powerful reverse genetic tool to silence gene expression in multiple organisms including plants, Caenorhabditis elegans, and Drosophila. The discovery that synthetic double-stranded, 21-nt small interfering RNA triggers gene-specific silencing in mammalian cells has further expanded the utility of RNAi into mammalian systems. Here we report a technology that allows synthesis of small interfering RNAs from DNA templates in vivo to efficiently inhibit endogenous gene expression. Significantly, we were able to use this approach to demonstrate, in multiple cell lines, robust inhibition of several endogenous genes of diverse functions. These findings highlight the general utility of this DNA vector-based RNAi technology in suppressing gene expression in mammalian cells.

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

    Science.gov (United States)

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

    2016-11-20

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

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

    Science.gov (United States)

    Hu, Kai; Gui, Zhipeng; Cheng, Xiaoqiang; Qi, Kunlun; Zheng, Jie; You, Lan; Wu, Huayi

    2016-01-01

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

  4. Towards human behavior recognition based on spatio temporal features and support vector machines

    Science.gov (United States)

    Ghabri, Sawsen; Ouarda, Wael; Alimi, Adel M.

    2017-03-01

    Security and surveillance are vital issues in today's world. The recent acts of terrorism have highlighted the urgent need for efficient surveillance. There is indeed a need for an automated system for video surveillance which can detect identity and activity of person. In this article, we propose a new paradigm to recognize an aggressive human behavior such as boxing action. Our proposed system for human activity detection includes the use of a fusion between Spatio Temporal Interest Point (STIP) and Histogram of Oriented Gradient (HoG) features. The novel feature called Spatio Temporal Histogram Oriented Gradient (STHOG). To evaluate the robustness of our proposed paradigm with a local application of HoG technique on STIP points, we made experiments on KTH human action dataset based on Multi Class Support Vector Machines classification. The proposed scheme outperforms basic descriptors like HoG and STIP to achieve 82.26% us an accuracy value of classification rate.

  5. Casing Vibration Fault Diagnosis Based on Variational Mode Decomposition, Local Linear Embedding, and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Yizhou Yang

    2017-01-01

    Full Text Available To diagnose mechanical faults of rotor-bearing-casing system by analyzing its casing vibration signal, this paper proposes a training procedure of a fault classifier based on variational mode decomposition (VMD, local linear embedding (LLE, and support vector machine (SVM. VMD is used first to decompose the casing signal into several modes, which are subsignals usually modulated by fault frequencies. Vibrational features are extracted from both VMD subsignals and the original one. LLE is employed here to reduce the dimensionality of these extracted features and make the samples more separable. Then low-dimensional data sets are used to train the multiclass SVM whose accuracy is tested by classifying the test samples. When the parameters of LLE and SVM are well optimized, this proposed method performs well on experimental data, showing its capacity of diagnosing casing vibration faults.

  6. 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.

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

    Science.gov (United States)

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

    2010-01-01

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

  8. [Study on predicting model for acute hypotensive episodes in ICU based on support vector machine].

    Science.gov (United States)

    Lai, Lijuan; Wang, Zhigang; Wu, Xiaoming; Xiong, Dongsheng

    2011-06-01

    The occurrence of acute hypotensive episodes (AHE) in intensive care units (ICU) seriously endangers the lives of patients, and the treatment is mainly depended on the expert experience of doctors. In this paper, a model for predicting the occurrence of AHE in ICU has been developed using the theory of medical Informatics. We analyzed the trend and characteristics of the mean arterial blood pressure (MAP) between the patients who were suffering AHE and those who were not, and extracted the median, mean and other statistical parameters for learning and training based on support vector machine (SVM), then developed a predicting model. On this basis, we also compared different models consisted of different kernel functions. Experiments demonstrated that this approach performed well on classification and prediction, which would contribute to forecast the occurrence of AHE.

  9. Expression of Separate Proteins in the Same Plant Leaves and Cells Using Two Independent Virus-Based Gene Vectors

    Directory of Open Access Journals (Sweden)

    Maria R. Mendoza

    2017-11-01

    Full Text Available Plant viral vectors enable the expression of proteins at high levels in a relatively short time. For many purposes (e.g., cell biological interaction studies it may be desirable to express more than one protein in a single cell but that is often not feasible when using a single virus vector. Such a co-expression strategy requires the simultaneous delivery by two compatible and non-competitive viruses that can co-exist to each express a separate protein. Here, we report on the use of two agro-launchable coat-protein gene substitution GFP-expressing virus vector systems based on Tomato bushy stunt virus (TBSV referred to as TG, and Tobacco mosaic virus (TMV annotated as TRBO-G. TG expressed GFP in Nicotiana benthamiana, tomato, lettuce and cowpea, whereas expression from TRBO-G was detected only in the first two species. Upon co-infiltration of the two vectors co-expression was monitored by: molecular detection of the two slightly differently sized GFPs, suppressor-complementation assays, and using TG in combination with TRBO-RFP. All the results revealed that in N. benthamiana and tomato the TBSV and TMV vectors accumulated and expressed proteins in the same plants, the same leaves, and in the same cells. Therefore, co-expression by these two vectors provides a platform for fast and high level expression of proteins to study their cell biology or other properties.

  10. Chimpanzee adenovirus vector-based avian influenza vaccine completely protects mice against lethal challenge of H5N1.

    Science.gov (United States)

    Cheng, Tao; Wang, Xiang; Song, Yufeng; Tang, Xinying; Zhang, Chao; Zhang, Hongbo; Jin, Xia; Zhou, Dongming

    2016-09-22

    Highly pathogenic avian H5N1 viruses may give rise to the next influenza pandemic due to their reassortment and mutation of the genome. Vaccine against this virus is important for coping with its potential threat. Chimpanzee adenovirus (Ad) vectors are a novel type of vaccine vectors that share the advantages of human serotype Ad vectors but without being affected by pre-existing human neutralizing antibody to the vaccine vector. Based on a replication-deficient chimpanzee Ad vector, AdC7, we generated a novel H5N1 vaccine candidate AdC7-H5HA that expresses H5N1 Hemagglutinin(HA). When tested in mice, the vaccine significantly reduced the virus load and pathological lesions in the lung tissues, and conferred complete protection against lethal challenge by a homologous virus. Mechanistically, the AdC7-H5HA vaccine can induce both HA-specific humoral and cell-mediated immune responses in mice. Also, sera transfer experiments demonstrated that neutralizing antibodies alone could provide protection. In conclusion, our results show that chimpanzee Ad vector expressing influenza virus HA may represent a promising vaccine candidate for H5N1 viruses and other influenza virus subtypes. Copyright © 2016 Elsevier Ltd. All rights reserved.

  11. Simplified production and concentration of HIV-1-based lentiviral vectors using HYPERFlask vessels and anion exchange membrane chromatography

    Directory of Open Access Journals (Sweden)

    Marino Michael P

    2009-02-01

    Full Text Available Abstract Background During the past twelve years, lentiviral (LV vectors have emerged as valuable tools for transgene delivery because of their ability to transduce nondividing cells and their capacity to sustain long-term transgene expression in target cells in vitro and in vivo. However, despite significant progress, the production and concentration of high-titer, high-quality LV vector stocks is still cumbersome and costly. Methods Here we present a simplified protocol for LV vector production on a laboratory scale using HYPERFlask vessels. HYPERFlask vessels are high-yield, high-performance flasks that utilize a multilayered gas permeable growth surface for efficient gas exchange, allowing convenient production of high-titer LV vectors. For subsequent concentration of LV vector stocks produced in this way, we describe a facile protocol involving Mustang Q anion exchange membrane chromatography. Results Our results show that unconcentrated LV vector stocks with titers in excess of 108 transduction units (TU per ml were obtained using HYPERFlasks and that these titers were higher than those produced in parallel using regular 150-cm2 tissue culture dishes. We also show that up to 500 ml of an unconcentrated LV vector stock prepared using a HYPERFlask vessel could be concentrated using a single Mustang Q Acrodisc with a membrane volume of 0.18 ml. Up to 5.3 × 1010 TU were recovered from a single HYPERFlask vessel. Conclusion The protocol described here is easy to implement and should facilitate high-titer LV vector production for preclinical studies in animal models without the need for multiple tissue culture dishes and ultracentrifugation-based concentration protocols.

  12. Simplified production and concentration of HIV-1-based lentiviral vectors using HYPERFlask vessels and anion exchange membrane chromatography

    Science.gov (United States)

    Kutner, Robert H; Puthli, Sharon; Marino, Michael P; Reiser, Jakob

    2009-01-01

    Background During the past twelve years, lentiviral (LV) vectors have emerged as valuable tools for transgene delivery because of their ability to transduce nondividing cells and their capacity to sustain long-term transgene expression in target cells in vitro and in vivo. However, despite significant progress, the production and concentration of high-titer, high-quality LV vector stocks is still cumbersome and costly. Methods Here we present a simplified protocol for LV vector production on a laboratory scale using HYPERFlask vessels. HYPERFlask vessels are high-yield, high-performance flasks that utilize a multilayered gas permeable growth surface for efficient gas exchange, allowing convenient production of high-titer LV vectors. For subsequent concentration of LV vector stocks produced in this way, we describe a facile protocol involving Mustang Q anion exchange membrane chromatography. Results Our results show that unconcentrated LV vector stocks with titers in excess of 108 transduction units (TU) per ml were obtained using HYPERFlasks and that these titers were higher than those produced in parallel using regular 150-cm2 tissue culture dishes. We also show that up to 500 ml of an unconcentrated LV vector stock prepared using a HYPERFlask vessel could be concentrated using a single Mustang Q Acrodisc with a membrane volume of 0.18 ml. Up to 5.3 × 1010 TU were recovered from a single HYPERFlask vessel. Conclusion The protocol described here is easy to implement and should facilitate high-titer LV vector production for preclinical studies in animal models without the need for multiple tissue culture dishes and ultracentrifugation-based concentration protocols. PMID:19220915

  13. 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.

  14. Novel Approach for Automatic Detection of Atrial Fibrillation Based on Inter Beat Intervals and Support Vector Machine

    DEFF Research Database (Denmark)

    Andersen, Rasmus S.; Poulsen, Erik S.; Puthusserypady, Sadasivan

    2017-01-01

    for AF detection based on Inter Beat Intervals (IBI) extracted from long term electrocardiogram (ECG) recordings. Five time-domain features are extracted from the IBIs and a Support Vector Machine (SVM) is used for classification. The results are compared to a state of the art algorithm based on raw ECG...

  15. Structural analysis of online handwritten mathematical symbols based on support vector machines

    Science.gov (United States)

    Simistira, Foteini; Papavassiliou, Vassilis; Katsouros, Vassilis; Carayannis, George

    2013-01-01

    Mathematical expression recognition is still a very challenging task for the research community mainly because of the two-dimensional (2d) structure of mathematical expressions (MEs). In this paper, we present a novel approach for the structural analysis between two on-line handwritten mathematical symbols of a ME, based on spatial features of the symbols. We introduce six features to represent the spatial affinity of the symbols and compare two multi-class classification methods that employ support vector machines (SVMs): one based on the "one-against-one" technique and one based on the "one-against-all", in identifying the relation between a pair of symbols (i.e. subscript, numerator, etc). A dataset containing 1906 spatial relations derived from the Competition on Recognition of Online Handwritten Mathematical Expressions (CROHME) 2012 training dataset is constructed to evaluate the classifiers and compare them with the rule-based classifier of the ILSP-1 system participated in the contest. The experimental results give an overall mean error rate of 2.61% for the "one-against-one" SVM approach, 6.57% for the "one-against-all" SVM technique and 12.31% error rate for the ILSP-1 classifier.

  16. 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.

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

    Science.gov (United States)

    Hashimoto, Ken; Zúniga, Concepción; Romero, Eduardo; Morales, Zoraida; Maguire, James H.

    2015-01-01

    Background 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. Methodology/Principal Findings 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. Conclusions/Significance Consistent monitoring within the local health system contributes to sustainability of health service responsiveness in community-based vector surveillance of Chagas disease. Even with

  18. 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

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

    Science.gov (United States)

    Hashimoto, Ken; Zúniga, Concepción; Romero, Eduardo; Morales, Zoraida; Maguire, James H

    2015-01-01

    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 service

  20. Support vector machine based diagnostic system for breast cancer using swarm intelligence.

    Science.gov (United States)

    Chen, Hui-Ling; Yang, Bo; Wang, Gang; Wang, Su-Jing; Liu, Jie; Liu, Da-You

    2012-08-01

    Breast cancer is becoming a leading cause of death among women in the whole world, meanwhile, it is confirmed that the early detection and accurate diagnosis of this disease can ensure a long survival of the patients. In this paper, a swarm intelligence technique based support vector machine classifier (PSO_SVM) is proposed for breast cancer diagnosis. In the proposed PSO-SVM, the issue of model selection and feature selection in SVM is simultaneously solved under particle swarm (PSO optimization) framework. A weighted function is adopted to design the objective function of PSO, which takes into account the average accuracy rates of SVM (ACC), the number of support vectors (SVs) and the selected features simultaneously. Furthermore, time varying acceleration coefficients (TVAC) and inertia weight (TVIW) are employed to efficiently control the local and global search in PSO algorithm. The effectiveness of PSO-SVM has been rigorously evaluated against the Wisconsin Breast Cancer Dataset (WBCD), which is commonly used among researchers who use machine learning methods for breast cancer diagnosis. The proposed system is compared with the grid search method with feature selection by F-score. The experimental results demonstrate that the proposed approach not only obtains much more appropriate model parameters and discriminative feature subset, but also needs smaller set of SVs for training, giving high predictive accuracy. In addition, Compared to the existing methods in previous studies, the proposed system can also be regarded as a promising success with the excellent classification accuracy of 99.3% via 10-fold cross validation (CV) analysis. Moreover, a combination of five informative features is identified, which might provide important insights to the nature of the breast cancer disease and give an important clue for the physicians to take a closer attention. We believe the promising result can ensure that the physicians make very accurate diagnostic decision in

  1. Characteristics of a class of vector-valued non-separable higher-dimensional wavelet packet bases

    International Nuclear Information System (INIS)

    Chen Qingjiang; Qu Xiaogang

    2009-01-01

    In this paper, we introduce vector-valued non-separable higher-dimensional wavelet packets with an arbitrary integer dilation factor. An approach for constructing vector-valued higher-dimensional wavelet packet bases is proposed. Their characteristics are investigated by means of harmonic analysis method, matrix theory and operator theory, and three orthogonality formulas concerning the wavelet packets are presented. Orthogonal decomposition relation formulas of the space L 2 (R n ) p are derived by designing a series of subspaces of the vector-valued wavelet packets. Moreover, several orthonormal wavelet packet bases of L 2 (R n ) p are constructed from the wavelet packets. Relation to some physical theories such as E-infinity theory and multifractal theory is also discussed.

  2. 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.

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

    International Nuclear Information System (INIS)

    Huang, Hong Zhong; Wang, Hai Kun; Li, Yan Feng; Zhang, Longlong; Liu, Zhiliang

    2015-01-01

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

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

    Science.gov (United States)

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

    2013-10-01

    Although biomedical information available in articles and patents is increasing exponentially, we continue to rely on the same information retrieval methods and use very few keywords to search millions of documents. We are developing a fundamentally different approach for finding much more precise and complete information with a single query using predicates instead of keywords for both query and document representation. Predicates are triples that are more complex datastructures than keywords and contain more structured information. To make optimal use of them, we developed a new predicate-based vector space model and query-document similarity function with adjusted tf-idf and boost function. Using a test bed of 107,367 PubMed abstracts, we evaluated the first essential function: retrieving information. Cancer researchers provided 20 realistic queries, for which the top 15 abstracts were retrieved using a predicate-based (new) and keyword-based (baseline) approach. Each abstract was evaluated, double-blind, by cancer researchers on a 0-5 point scale to calculate precision (0 versus higher) and relevance (0-5 score). Precision was significantly higher (psearching than keywords, laying the foundation for rich and sophisticated information search. Copyright © 2013 Elsevier Inc. All rights reserved.

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

    International Nuclear Information System (INIS)

    Magnin, Benoit; Mesrob, Lilia; Kinkingnehun, Serge; Pelegrini-Issac, Melanie; Colliot, Olivier; Sarazin, Marie; Dubois, Bruno; Lehericy, Stephane; Benali, Habib

    2009-01-01

    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.)

  6. A Novel Gradient Vector Flow Snake Model Based on Convex Function for Infrared Image Segmentation.

    Science.gov (United States)

    Zhang, Rui; Zhu, Shiping; Zhou, Qin

    2016-10-21

    Infrared image segmentation is a challenging topic because infrared images are characterized by high noise, low contrast, and weak edges. Active contour models, especially gradient vector flow, have several advantages in terms of infrared image segmentation. However, the GVF (Gradient Vector Flow) model also has some drawbacks including a dilemma between noise smoothing and weak edge protection, which decrease the effect of infrared image segmentation significantly. In order to solve this problem, we propose a novel generalized gradient vector flow snakes model combining GGVF (Generic Gradient Vector Flow) and NBGVF (Normally Biased Gradient Vector Flow) models. We also adopt a new type of coefficients setting in the form of convex function to improve the ability of protecting weak edges while smoothing noises. Experimental results and comparisons against other methods indicate that our proposed snakes model owns better ability in terms of infrared image segmentation than other snakes models.

  7. Is outdoor vector control needed for malaria elimination? An individual-based modelling study.

    Science.gov (United States)

    Zhu, Lin; Müller, Günter C; Marshall, John M; Arheart, Kristopher L; Qualls, Whitney A; Hlaing, WayWay M; Schlein, Yosef; Traore, Sekou F; Doumbia, Seydou; Beier, John C

    2017-07-03

    Residual malaria transmission has been reported in many areas even with adequate indoor vector control coverage, such as long-lasting insecticidal nets (LLINs). The increased insecticide resistance in Anopheles mosquitoes has resulted in reduced efficacy of the widely used indoor tools and has been linked with an increase in outdoor malaria transmission. There are considerations of incorporating outdoor interventions into integrated vector management (IVM) to achieve malaria elimination; however, more information on the combination of tools for effective control is needed to determine their utilization. A spatial individual-based model was modified to simulate the environment and malaria transmission activities in a hypothetical, isolated African village setting. LLINs and outdoor attractive toxic sugar bait (ATSB) stations were used as examples of indoor and outdoor interventions, respectively. Different interventions and lengths of efficacy periods were tested. Simulations continued for 420 days, and each simulation scenario was repeated 50 times. Mosquito populations, entomologic inoculation rates (EIRs), probabilities of local mosquito extinction, and proportion of time when the annual EIR was reduced below one were compared between different intervention types and efficacy periods. In the village setting with clustered houses, the combinational intervention of 50% LLINs plus outdoor ATSBs significantly reduced mosquito population and EIR in short term, increased the probability of local mosquito extinction, and increased the time when annual EIR is less than one per person compared to 50% LLINs alone; outdoor ATSBs alone significantly reduced mosquito population in short term, increased the probability of mosquito extinction, and increased the time when annual EIR is less than one compared to 50% LLINs alone, but there was no significant difference in EIR in short term between 50% LLINs and outdoor ATSBs. In the village setting with dispersed houses, the

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

    Science.gov (United States)

    Gurgel-Gonçalves, Rodrigo; Galvão, Cléber; Costa, Jane; Peterson, A. Townsend

    2012-01-01

    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. PMID:22523500

  9. 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.

  10. A bead-based suspension array for the multiplexed detection of begomoviruses and their whitefly vectors.

    Science.gov (United States)

    van Brunschot, S L; Bergervoet, J H W; Pagendam, D E; de Weerdt, M; Geering, A D W; Drenth, A; van der Vlugt, R A A

    2014-03-01

    Bead-based suspension array systems enable simultaneous fluorescence-based identification of multiple nucleic acid targets in a single reaction. This study describes the development of a novel approach to plant virus and vector diagnostics, a multiplexed 7-plex array that comprises a hierarchical set of assays for the simultaneous detection of begomoviruses and Bemisia tabaci, from both plant and whitefly samples. The multiplexed array incorporates genus, species and strain-specific assays, offering a unique approach for identifying both known and unknown viruses and B. tabaci species. When tested against a large panel of sequence-characterized begomovirus and whitefly samples, the array was shown to be 100% specific to the homologous target. Additionally, the multiplexed array was highly sensitive, efficiently and concurrently determining both virus and whitefly identity from single viruliferous whitefly samples. The detection limit for one assay within the multiplexed array that specifically detects Tomato yellow leaf curl virus-Israel (TYLCV-IL) was quantified as 200fg of TYLCV-IL DNA, directly equivalent to that of TYLCV-specific qPCR. Highly reproducible results were obtained over multiple tests. The flexible multiplexed array described in this study has great potential for use in plant quarantine, biosecurity and disease management programs worldwide. Copyright © 2014 Elsevier B.V. All rights reserved.

  11. Smart DNA vectors based on cyclodextrin polymers: compaction and endosomal release.

    Science.gov (United States)

    Wintgens, Véronique; Leborgne, Christian; Baconnais, Sonia; Burckbuchler, Virginie; Le Cam, Eric; Scherman, Daniel; Kichler, Antoine; Amiel, Catherine

    2012-02-01

    Neutral β-cyclodextrin polymers (polyβCD) associated with cationic adamantyl derivatives (Ada) can be used to deliver plasmid DNA into cells. In absence of an endosomolytic agent, transfection efficiency remains low because most complexes are trapped in the endosomal compartment. We asked whether addition of an imidazole-modified Ada can increase efficiency of polyβCD/cationic Ada-based delivery system. We synthesized two adamantyl derivatives: Ada5, which has a spacer arm between the Ada moiety and a bi-cationic polar head group, and Ada6, which presents an imidazole group. Strength of association between polyβCD and Ada derivatives was evaluated by fluorimetric titration. Gel mobility shift assay, zeta potential, and dark field transmission electron microscopy experiments demonstrated the system allowed for efficient DNA compaction. In vitro transfection experiments performed on HepG2 and HEK293 cells revealed the quaternary system polyβCD/Ada5/Ada6/DNA has efficiency comparable to cationic lipid DOTAP. We successfully designed fine-tuned DNA vectors based on cyclodextrin polymers combined with two new adamantyl derivatives, leading to significant transfection associated with low toxicity.

  12. Predictive based monitoring of nuclear plant component degradation using support vector regression

    International Nuclear Information System (INIS)

    Agarwal, Vivek; Alamaniotis, Miltiadis; Tsoukalas, Lefteri H.

    2015-01-01

    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.

  13. Support vector machine-based open crop model (SBOCM: Case of rice production in China

    Directory of Open Access Journals (Sweden)

    Ying-xue Su

    2017-03-01

    Full Text Available Existing crop models produce unsatisfactory simulation results and are operationally complicated. The present study, however, demonstrated the unique advantages of statistical crop models for large-scale simulation. Using rice as the research crop, a support vector machine-based open crop model (SBOCM was developed by integrating developmental stage and yield prediction models. Basic geographical information obtained by surface weather observation stations in China and the 1:1000000 soil database published by the Chinese Academy of Sciences were used. Based on the principle of scale compatibility of modeling data, an open reading frame was designed for the dynamic daily input of meteorological data and output of rice development and yield records. This was used to generate rice developmental stage and yield prediction models, which were integrated into the SBOCM system. The parameters, methods, error resources, and other factors were analyzed. Although not a crop physiology simulation model, the proposed SBOCM can be used for perennial simulation and one-year rice predictions within certain scale ranges. It is convenient for data acquisition, regionally applicable, parametrically simple, and effective for multi-scale factor integration. It has the potential for future integration with extensive social and economic factors to improve the prediction accuracy and practicability.

  14. 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.

  15. 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.

  16. Zn(II)-dipicolylamine-based metallo-lipids as novel non-viral gene vectors.

    Science.gov (United States)

    Su, Rong-Chuan; Liu, Qiang; Yi, Wen-Jing; Zhao, Zhi-Gang

    2017-08-01

    In this study, a series of Zn(II)-dipicolylamine (Zn-DPA) based cationic lipids bearing different hydrophobic tails (long chains, α-tocopherol, cholesterol or diosgenin) were synthesized. Structure-activity relationship (SAR) of these lipids was studied in detail by investigating the effects of several structural aspects including the type of hydrophobic tails, the chain length and saturation degree. In addition, several assays were used to study their interactions with plasmid DNA, and results reveal that these lipids could condense DNA into nanosized particles with appropriate size and zeta-potentials. MTT-based cell viability assays showed that lipoplexes 5 had low cytotoxicity. The in vitro gene transfection studies showed the hydrophobic tails clearly affected the TE, and hexadecanol-containing lipid 5b gives the best TE, which was 2.2 times higher than bPEI 25k in the presence of 10% serum. The results not only demonstrate that these lipids might be promising non-viral gene vectors, but also afford us clues for further optimization of lipidic gene delivery materials.

  17. 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.

  18. Knowledge-based analysis of microarray gene expression data by using support vector machines

    Energy Technology Data Exchange (ETDEWEB)

    William Grundy; Manuel Ares, Jr.; David Haussler

    2001-06-18

    The authors introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods, such as hierarchical clustering and self-organizing maps. SVMs have many mathematical features that make them attractive for gene expression analysis, including their flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. They test several SVMs that use different similarity metrics, as well as some other supervised learning methods, and find that the SVMs best identify sets of genes with a common function using expression data. Finally, they use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data.

  19. Accurate optical vector network analyzer based on optical single-sideband modulation and balanced photodetection.

    Science.gov (United States)

    Xue, Min; Pan, Shilong; Zhao, Yongjiu

    2015-02-15

    A novel optical vector network analyzer (OVNA) based on optical single-sideband (OSSB) modulation and balanced photodetection is proposed and experimentally demonstrated, which can eliminate the measurement error induced by the high-order sidebands in the OSSB signal. According to the analytical model of the conventional OSSB-based OVNA, if the optical carrier in the OSSB signal is fully suppressed, the measurement result is exactly the high-order-sideband-induced measurement error. By splitting the OSSB signal after the optical device-under-test (ODUT) into two paths, removing the optical carrier in one path, and then detecting the two signals in the two paths using a balanced photodetector (BPD), high-order-sideband-induced measurement error can be ideally eliminated. As a result, accurate responses of the ODUT can be achieved without complex post-signal processing. A proof-of-concept experiment is carried out. The magnitude and phase responses of a fiber Bragg grating (FBG) measured by the proposed OVNA with different modulation indices are superimposed, showing that the high-order-sideband-induced measurement error is effectively removed.

  20. Wideband optical vector network analyzer based on optical single-sideband modulation and optical frequency comb.

    Science.gov (United States)

    Xue, Min; Pan, Shilong; He, Chao; Guo, Ronghui; Zhao, Yongjiu

    2013-11-15

    A novel approach to increase the measurement range of the optical vector network analyzer (OVNA) based on optical single-sideband (OSSB) modulation is proposed and experimentally demonstrated. In the proposed system, each comb line in an optical frequency comb (OFC) is selected by an optical filter and used as the optical carrier for the OSSB-based OVNA. The frequency responses of an optical device-under-test (ODUT) are thus measured channel by channel. Because the comb lines in the OFC have fixed frequency spacing, by fitting the responses measured in all channels together, the magnitude and phase responses of the ODUT can be accurately achieved in a large range. A proof-of-concept experiment is performed. A measurement range of 105 GHz and a resolution of 1 MHz is achieved when a five-comb-line OFC with a frequency spacing of 20 GHz is applied to measure the magnitude and phase responses of a fiber Bragg grating.

  1. [Study on Sleep Staging Based on Support Vector Machines and Feature Selection in Single Channel Electroencephalogram].

    Science.gov (United States)

    Lin, Xiujing; Xia, Yongming; Qian, Songrong

    2015-06-01

    Sleep electroencephalogram (EEG) is an important index in diagnosing sleep disorders and related diseases. Manual sleep staging is time-consuming and often influenced by subjective factors. Existing automatic sleep staging methods have high complexity and a low accuracy rate. A sleep staging method based on support vector machines (SVM) and feature selection using single channel EEG single is proposed in this paper. Thirty-eight features were extracted from the single channel EEG signal. Then based on the feature selection method F-Score's definition, it was extended to multiclass with an added eliminate factor in order to find proper features, which were used as SVM classifier inputs. The eliminate factor was adopted to reduce the negative interaction of features to the result. Research on the F-Score with an added eliminate factor was further accomplished with the data from a standard open source database and the results were compared with none feature selection and standard F-Score feature selection. The results showed that the present method could effectively improve the sleep staging accuracy and reduce the computation time.

  2. Viral vector-based tools advance knowledge of basal ganglia anatomy and physiology.

    Science.gov (United States)

    Sizemore, Rachel J; Seeger-Armbruster, Sonja; Hughes, Stephanie M; Parr-Brownlie, Louise C

    2016-04-01

    Viral vectors were originally developed to deliver genes into host cells for therapeutic potential. However, viral vector use in neuroscience research has increased because they enhance interpretation of the anatomy and physiology of brain circuits compared with conventional tract tracing or electrical stimulation techniques. Viral vectors enable neuronal or glial subpopulations to be labeled or stimulated, which can be spatially restricted to a single target nucleus or pathway. Here we review the use of viral vectors to examine the structure and function of motor and limbic basal ganglia (BG) networks in normal and pathological states. We outline the use of viral vectors, particularly lentivirus and adeno-associated virus, in circuit tracing, optogenetic stimulation, and designer drug stimulation experiments. Key studies that have used viral vectors to trace and image pathways and connectivity at gross or ultrastructural levels are reviewed. We explain how optogenetic stimulation and designer drugs used to modulate a distinct pathway and neuronal subpopulation have enhanced our mechanistic understanding of BG function in health and pathophysiology in disease. Finally, we outline how viral vector technology may be applied to neurological and psychiatric conditions to offer new treatments with enhanced outcomes for patients. Copyright © 2016 the American Physiological Society.

  3. Use of a simian virus 40-based shuttle vector to analyze enhanced mutagenesis in mitomycin C-treated monkey cells

    International Nuclear Information System (INIS)

    Roilides, E.; Munson, P.J.; Levine, A.S.; Dixon, K.

    1988-01-01

    When monkey cells were treated with mitomycin C 24 h before transfection with UV-irradiated pZ189 (a simian virus 40-based shuttle vector), there was a twofold increase in the frequency of mutations in the supF gene of the vector. These results suggest the existence of an enhancible mutagenesis pathway in mammalian cells. However, DNA sequence analysis of the SupF- mutants suggested no dramatic changes in the mechanisms of mutagenesis due to mitomycin C treatment of the cells

  4. Stokes vector based interpolation method to improve the efficiency of bio-inspired polarization-difference imaging in turbid media

    Science.gov (United States)

    Guan, Jinge; Ren, Wei; Cheng, Yaoyu

    2018-04-01

    We demonstrate an efficient polarization-difference imaging system in turbid conditions by using the Stokes vector of light. The interaction of scattered light with the polarizer is analyzed by the Stokes-Mueller formalism. An interpolation method is proposed to replace the mechanical rotation of the polarization axis of the analyzer theoretically, and its performance is verified by the experiment at different turbidity levels. We show that compared with direct imaging, the Stokes vector based imaging method can effectively reduce the effect of light scattering and enhance the image contrast.

  5. 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.

  6. Development and assessment of plant-based synthetic odor baits for surveillance and control of Malaria vectors

    Science.gov (United States)

    Recent malaria vector control measures have considerably reduced indoor biting mosquito populations. However, reducing the outdoor biting populations remains a challenge because of the unavailability of appropriate lures to achieve this. This study sought to test the efficacy of plant-based syntheti...

  7. 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

  8. Margin based ontology sparse vector learning algorithm and applied in biology science

    OpenAIRE

    Gao, Wei; Qudair Baig, Abdul; Ali, Haidar; Sajjad, Wasim; Reza Farahani, Mohammad

    2016-01-01

    In biology field, the ontology application relates to a large amount of genetic information and chemical information of molecular structure, which makes knowledge of ontology concepts convey much information. Therefore, in mathematical notation, the dimension of vector which corresponds to the ontology concept is often very large, and thus improves the higher requirements of ontology algorithm. Under this background, we consider the designing of ontology sparse vector algorithm and applicatio...

  9. Gauge conditions for binary black hole puncture data based on an approximate helical Killing vector

    OpenAIRE

    Tichy, Wolfgang; Bruegmann, Bernd; Laguna, Pablo

    2003-01-01

    We show that puncture data for quasicircular binary black hole orbits allow a special gauge choice that realizes some of the necessary conditions for the existence of an approximate helical Killing vector field. Introducing free parameters for the lapse at the punctures we can satisfy the condition that the Komar and ADM mass agree at spatial infinity. Several other conditions for an approximate Killing vector are then automatically satisfied, and the 3-metric evolves on a timescale smaller t...

  10. IntelliGO: a new vector-based semantic similarity measure including annotation origin

    Directory of Open Access Journals (Sweden)

    Devignes Marie-Dominique

    2010-12-01

    Full Text Available Abstract Background The Gene Ontology (GO is a well known controlled vocabulary describing the biological process, molecular function and cellular component aspects of gene annotation. It has become a widely used knowledge source in bioinformatics for annotating genes and measuring their semantic similarity. These measures generally involve the GO graph structure, the information content of GO aspects, or a combination of both. However, only a few of the semantic similarity measures described so far can handle GO annotations differently according to their origin (i.e. their evidence codes. Results We present here a new semantic similarity measure called IntelliGO which integrates several complementary properties in a novel vector space model. The coefficients associated with each GO term that annotates a given gene or protein include its information content as well as a customized value for each type of GO evidence code. The generalized cosine similarity measure, used for calculating the dot product between two vectors, has been rigorously adapted to the context of the GO graph. The IntelliGO similarity measure is tested on two benchmark datasets consisting of KEGG pathways and Pfam domains grouped as clans, considering the GO biological process and molecular function terms, respectively, for a total of 683 yeast and human genes and involving more than 67,900 pair-wise comparisons. The ability of the IntelliGO similarity measure to express the biological cohesion of sets of genes compares favourably to four existing similarity measures. For inter-set comparison, it consistently discriminates between distinct sets of genes. Furthermore, the IntelliGO similarity measure allows the influence of weights assigned to evidence codes to be checked. Finally, the results obtained with a complementary reference technique give intermediate but correct correlation values with the sequence similarity, Pfam, and Enzyme classifications when compared to

  11. A novel support vector machine-based approach for rare variant detection.

    Directory of Open Access Journals (Sweden)

    Yao-Hwei Fang

    Full Text Available Advances in next-generation sequencing technologies have enabled the identification of multiple rare single nucleotide polymorphisms involved in diseases or traits. Several strategies for identifying rare variants that contribute to disease susceptibility have recently been proposed. An important feature of many of these statistical methods is the pooling or collapsing of multiple rare single nucleotide variants to achieve a reasonably high frequency and effect. However, if the pooled rare variants are associated with the trait in different directions, then the pooling may weaken the signal, thereby reducing its statistical power. In the present paper, we propose a backward support vector machine (BSVM-based variant selection procedure to identify informative disease-associated rare variants. In the selection procedure, the rare variants are weighted and collapsed according to their positive or negative associations with the disease, which may be associated with common variants and rare variants with protective, deleterious, or neutral effects. This nonparametric variant selection procedure is able to account for confounding factors and can also be adopted in other regression frameworks. The results of a simulation study and a data example show that the proposed BSVM approach is more powerful than four other approaches under the considered scenarios, while maintaining valid type I errors.

  12. 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.

  13. Transformer fault diagnosis based on chemical reaction optimization algorithm and relevance vector machine

    Directory of Open Access Journals (Sweden)

    Luo Wei

    2017-01-01

    Full Text Available Power transformer is one of the most important equipment in power system. In order to predict the potential fault of power transformer and identify the fault types correctly, we proposed a transformer fault intelligent diagnosis model based on chemical reaction optimization (CRO algorithm and relevance vector machine(RVM. RVM is a powerful machine learning method, which can solve nonlinear, high-dimensional classification problems with a limited number of samples. CRO algorithm has well global optimization and simple calculation, so it is suitable to solve parameter optimization problems. In this paper, firstly, a multi-layer RVM classification model was built by binary tree recognition strategy. Secondly, CRO algorithm was adopted to optimize the kernel function parameters which could enhance the performance of RVM classifiers. Compared with IEC three-ratio method and the RVM model, the CRO-RVM model not only overcomes the coding defect problem of IEC three-ratio method, but also has higher classification accuracy than the RVM model. Finally, the new method was applied to analyze a transformer fault case, Its predicted result accord well with the real situation. The research provides a practical method for transformer fault intelligent diagnosis and prediction.

  14. Video Waterscrambling: Towards a Video Protection Scheme Based on the Disturbance of Motion Vectors

    Science.gov (United States)

    Bodo, Yann; Laurent, Nathalie; Laurent, Christophe; Dugelay, Jean-Luc

    2004-12-01

    With the popularity of high-bandwidth modems and peer-to-peer networks, the contents of videos must be highly protected from piracy. Traditionally, the models utilized to protect this kind of content are scrambling and watermarking. While the former protects the content against eavesdropping (a priori protection), the latter aims at providing a protection against illegal mass distribution (a posteriori protection). Today, researchers agree that both models must be used conjointly to reach a sufficient level of security. However, scrambling works generally by encryption resulting in an unintelligible content for the end-user. At the moment, some applications (such as e-commerce) may require a slight degradation of content so that the user has an idea of the content before buying it. In this paper, we propose a new video protection model, called waterscrambling, whose aim is to give such a quality degradation-based security model. This model works in the compressed domain and disturbs the motion vectors, degrading the video quality. It also allows embedding of a classical invisible watermark enabling protection against mass distribution. In fact, our model can be seen as an intermediary solution to scrambling and watermarking.

  15. Video Waterscrambling: Towards a Video Protection Scheme Based on the Disturbance of Motion Vectors

    Directory of Open Access Journals (Sweden)

    Yann Bodo

    2004-10-01

    Full Text Available With the popularity of high-bandwidth modems and peer-to-peer networks, the contents of videos must be highly protected from piracy. Traditionally, the models utilized to protect this kind of content are scrambling and watermarking. While the former protects the content against eavesdropping (a priori protection, the latter aims at providing a protection against illegal mass distribution (a posteriori protection. Today, researchers agree that both models must be used conjointly to reach a sufficient level of security. However, scrambling works generally by encryption resulting in an unintelligible content for the end-user. At the moment, some applications (such as e-commerce may require a slight degradation of content so that the user has an idea of the content before buying it. In this paper, we propose a new video protection model, called waterscrambling, whose aim is to give such a quality degradation-based security model. This model works in the compressed domain and disturbs the motion vectors, degrading the video quality. It also allows embedding of a classical invisible watermark enabling protection against mass distribution. In fact, our model can be seen as an intermediary solution to scrambling and watermarking.

  16. Support vector machine based fault detection approach for RFT-30 cyclotron

    Energy Technology Data Exchange (ETDEWEB)

    Kong, Young Bae, E-mail: ybkong@kaeri.re.kr; Lee, Eun Je; Hur, Min Goo; Park, Jeong Hoon; Park, Yong Dae; Yang, Seung Dae

    2016-10-21

    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.

  17. Research on the factors influencing the price of commercial housing based on support vector machine (SVM)

    Science.gov (United States)

    Xiaoyang, Zhong; Hong, Ren; Jingxin, Gao

    2018-03-01

    With the gradual maturity of the real estate market in China, urban housing prices are also better able to reflect changes in market demand and the commodity property of commercial housing has become more and more obvious. Many scholars in our country have made a lot of research on the factors that affect the price of commercial housing in the city and the number of related research papers increased rapidly. These scholars’ research results provide valuable wealth to solve the problem of urban housing price changes in our country. However, due to the huge amount of literature, the vast amount of information is submerged in the library and cannot be fully utilized. Text mining technology has been widely concerned and developed in the field of Humanities and Social Sciences in recent years. But through the text mining technology to obtain the influence factors on the price of urban commercial housing is still relatively rare. In this paper, the research results of the existing scholars were excavated by text mining algorithm based on support vector machine in order to further make full use of the current research results and to provide a reference for stabilizing housing prices.

  18. Gas detonation cell width prediction model based on support vector regression

    Directory of Open Access Journals (Sweden)

    Jiyang Yu

    2017-10-01

    Full Text Available Detonation cell width is an important parameter in hydrogen explosion assessments. The experimental data on gas detonation are statistically analyzed to establish a universal method to numerically predict detonation cell widths. It is commonly understood that detonation cell width, λ, is highly correlated with the characteristic reaction zone width, δ. Classical parametric regression methods were widely applied in earlier research to build an explicit semiempirical correlation for the ratio of λ/δ. The obtained correlations formulate the dependency of the ratio λ/δ on a dimensionless effective chemical activation energy and a dimensionless temperature of the gas mixture. In this paper, support vector regression (SVR, which is based on nonparametric machine learning, is applied to achieve functions with better fitness to experimental data and more accurate predictions. Furthermore, a third parameter, dimensionless pressure, is considered as an additional independent variable. It is found that three-parameter SVR can significantly improve the performance of the fitting function. Meanwhile, SVR also provides better adaptability and the model functions can be easily renewed when experimental database is updated or new regression parameters are considered.

  19. Delineating Individual Trees from Lidar Data: A Comparison of Vector- and Raster-based Segmentation Approaches

    Directory of Open Access Journals (Sweden)

    Maggi Kelly

    2013-08-01

    Full Text Available Light detection and ranging (lidar data is increasingly being used for ecosystem monitoring across geographic scales. This work concentrates on delineating individual trees in topographically-complex, mixed conifer forest across the California’s Sierra Nevada. We delineated individual trees using vector data and a 3D lidar point cloud segmentation algorithm, and using raster data with an object-based image analysis (OBIA of a canopy height model (CHM. The two approaches are compared to each other and to ground reference data. We used high density (9 pulses/m2, discreet lidar data and WorldView-2 imagery to delineate individual trees, and to classify them by species or species types. We also identified a new method to correct artifacts in a high-resolution CHM. Our main focus was to determine the difference between the two types of approaches and to identify the one that produces more realistic results. We compared the delineations via tree detection, tree heights, and the shape of the generated polygons. The tree height agreement was high between the two approaches and the ground data (r2: 0.93–0.96. Tree detection rates increased for more dominant trees (8–100 percent. The two approaches delineated tree boundaries that differed in shape: the lidar-approach produced fewer, more complex, and larger polygons that more closely resembled real forest structure.

  20. Targeting Sindbis virus-based vectors to Fc receptor-positive cell types

    International Nuclear Information System (INIS)

    Klimstra, William B.; Williams, Jacqueline C.; Ryman, Kate D.; Heidner, Hans W.

    2005-01-01

    Some viruses display enhanced infection for Fc receptor (FcR)-positive cell types when complexed with virus-specific immunoglobulin (Ig). This process has been termed antibody-dependent enhancement of viral infection (ADE). We reasoned that the mechanism of ADE could be exploited and adapted to target alphavirus-based vectors to FcR-positive cell types. Towards this goal, recombinant Sindbis viruses were constructed that express 1 to 4 immunoglobulin-binding domains of protein L (PpL) as N-terminal extensions of the E2 glycoprotein. PpL is a bacterial protein that binds the variable region of antibody kappa light chains from a range of mammalian species. The recombinant viruses incorporated PpL/E2 fusion proteins into the virion structure and recapitulated the species-specific Ig-binding phenotypes of native PpL. Virions reacted with non-immune serum or purified IgG displayed enhanced binding and ADE for several species-matched FcR-positive murine and human cell lines. ADE required virus expression of a functional PpL Ig-binding domain, and appeared to be FcγR-mediated. Specifically, ADE did not occur with FcγR-negative cells, did not require active complement proteins, and did not occur on FcγR-positive murine cell lines when virions were bound by murine IgG-derived F(ab') 2 fragments

  1. New method for numerical approximations of vector derivatives based on digital signal processing techniques

    Science.gov (United States)

    Brice, Henry; Ahmed, Mohammed Z.

    2012-05-01

    Accurate propagation models are required for predicting the propagation of electromagnetic waves within complex environments. This paper proposes the use of a new method to accurately compute the divergence and curl of electromagnetic fields. The computation of the derivatives of vector fields is normally approximated using numerical methods such as the Finite-Dierence Time-Domain Method (FDTD), the Finite Integration Technique and the Multi-Resolution Time-Domain Method. These methods are all limited in terms of their accuracy, resolution, computational efficiency and numerical stability. This paper introduces a new method for computing derivatives based on Two-Dimensional (2D) Digital Signal Processing (DSP) techniques. The method involves computing a numerical approximation of the derivative of a function by considering the frequency domain definition of the derivative and designing a 2Dfinite impulse response (FIR) filter that implements the differentiation. Appropriate windowing functions are used to ensure that the FIR response is as close to the ideal 2D differentiator response as possible. This paper provides an example where the curl of a vectorfield is determined using this method and accuracy within a few percent is achieved. The proposed innovative method can be extended to three dimensions and used to find numerical solutions of Maxwells Equations, thus allowing it to be applied to the design of accurate propagation models.

  2. Analysis of Vector Quantizers Using Transformed Codebooks with Application to Feedback-Based Multiple Antenna Systems

    Directory of Open Access Journals (Sweden)

    Bhaskar D. Rao

    2008-07-01

    Full Text Available Transformed codebooks are obtained by a transformation of a given codebook to best match the statistical environment at hand. The procedure, though suboptimal, has recently been suggested for feedback of channel state information (CSI in multiple antenna systems with correlated channels because of their simplicity and effectiveness. In this paper, we first consider the general distortion analysis of vector quantizers with transformed codebooks. Bounds on the average system distortion of this class of quantizers are provided. It exposes the effects of two kinds of suboptimality introduced by the transformed codebook, namely, the loss caused by suboptimal point density and the loss caused by mismatched Voronoi shape. We then focus our attention on the application of the proposed general framework to providing capacity analysis of a feedback-based MISO system over spatially correlated fading channels. In particular, with capacity loss as an objective function, upper and lower bounds on the average distortion of MISO systems with transformed codebooks are provided and compared to that of the optimal channel quantizers. The expressions are examined to provide interesting insights in the high and low SNR regime. Numerical and simulation results are presented which confirm the tightness of the distortion bounds.

  3. A downstream algorithm based on extended gradient vector flow field for object segmentation.

    Science.gov (United States)

    Chuang, Cheng-Hung; Lie, Wen-Nung

    2004-10-01

    For object segmentation, traditional snake algorithms often require human interaction; region growing methods are considerably dependent on the selected homogeneity criterion and initial seeds; watershed algorithms, however, have the drawback of over segmentation. A new downstream algorithm based on a proposed extended gradient vector flow (E-GVF) field model is presented in this paper for multiobject segmentation. The proposed flow field, on one hand, diffuses and propagates gradients near object boundaries to provide an effective guiding force and, on the other hand, presents a higher resolution of direction than traditional GVF field. The downstream process starts with a set of seeds scored and selected by considering local gradient direction information around each pixel. This step is automatic and requires no human interaction, making our algorithm more suitable for practical applications. Experiments show that our algorithm is noise resistant and has the advantage of segmenting objects that are separated from the background, while ignoring the internal structures of them. We have tested the proposed algorithm with several realistic images (e.g., medical and complex background images) and gained good results.

  4. Rotating Machinery Fault Diagnosis for Imbalanced Data Based on Fast Clustering Algorithm and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Xiaochen Zhang

    2017-01-01

    Full Text Available To diagnose rotating machinery fault for imbalanced data, a method based on fast clustering algorithm (FCA and support vector machine (SVM was proposed. Combined with variational mode decomposition (VMD and principal component analysis (PCA, sensitive features of the rotating machinery fault were obtained and constituted the imbalanced fault sample set. Next, a fast clustering algorithm was adopted to reduce the number of the majority data from the imbalanced fault sample set. Consequently, the balanced fault sample set consisted of the clustered data and the minority data from the imbalanced fault sample set. After that, SVM was trained with the balanced fault sample set and tested with the imbalanced fault sample set so the fault diagnosis model of the rotating machinery could be obtained. Finally, the gearbox fault data set and the rolling bearing fault data set were adopted to test the fault diagnosis model. The experimental results showed that the fault diagnosis model could effectively diagnose the rotating machinery fault for imbalanced data.

  5. 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.

  6. Position Sensorless Vector Control for Permanent Magnet Synchronous Motors Based on Maximum Torque Control Frame

    Science.gov (United States)

    Hida, Hajime; Tomigashi, Yoshio; Kishimoto, Keiji

    High efficiency drive can be achieved by the maximum torque-per-ampere (MTPA) control which used reluctance torque effectively. However, the calculations for estimating rotor position and for controlling the d-axis current are required. The motor parameters of inductance etc. that are easily affected by magnetic saturation are included in those calculations. This paper proposes a new MTPA control method, which is robust against changes of motor parameters caused by magnetic saturation. In addition, complex calculation for d-axis current or reference to the table is not necessary. In this method, we define a novel coordinate frame, which has one axis aligned with the current vector of the MTPA control, and estimate the frame directly. Because the parameter Lqm for estimating the frame is less affected by the magnetic saturation than the conventional Lq, the effect of magnetic saturation on the position estimation can be greatly suppressed. First, an extended electromotive force model based on the proposed frame and a parameter Lqm for an estimation of the frame are derived. Next, the effectiveness of this proposed method is confirmed by simulations and experiments.

  7. 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.

  8. Chaos characteristics and least squares support vector machines based online pipeline small leakages detection

    International Nuclear Information System (INIS)

    Liu, Jinhai; Su, Hanguang; Ma, Yanjuan; Wang, Gang; Wang, Yuan; Zhang, Kun

    2016-01-01

    Small leakages are severe threats to the long distance pipeline transportation. An online small leakage detection method based on chaos characteristics and Least Squares Support Vector Machines (LS-SVMs) is proposed in this paper. For the first time, the relationship between the chaos characteristics of pipeline inner pressures and the small leakages is investigated and applied in the pipeline detection method. Firstly, chaos in the pipeline inner pressure is found. Relevant chaos characteristics are estimated by the nonlinear time series analysis package (TISEAN). Then LS-SVM with a hybrid kernel is built and named as hybrid kernel LS-SVM (HKLS-SVM). It is applied to analyze the chaos characteristics and distinguish the negative pressure waves (NPWs) caused by small leaks. A new leak location method is also expounded. Finally, data of the chaotic Logistic-Map system is used in the simulation. A comparison between HKLS-SVM and other methods, in terms of the identification accuracy and computing efficiency, is made. The simulation result shows that HKLS-SVM gets the best performance and is effective in error analysis of chaotic systems. When real pipeline data is used in the test, the ultimate identification accuracy of HKLS-SVM reaches 97.38% and the position accuracy is 99.28%, indicating that the method proposed in this paper has good performance in detecting and locating small pipeline leaks.

  9. 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

  10. Analysis of Vector Quantizers Using Transformed Codebooks with Application to Feedback-Based Multiple Antenna Systems

    Directory of Open Access Journals (Sweden)

    Zheng Jun

    2008-01-01

    Full Text Available Transformed codebooks are obtained by a transformation of a given codebook to best match the statistical environment at hand. The procedure, though suboptimal, has recently been suggested for feedback of channel state information (CSI in multiple antenna systems with correlated channels because of their simplicity and effectiveness. In this paper, we first consider the general distortion analysis of vector quantizers with transformed codebooks. Bounds on the average system distortion of this class of quantizers are provided. It exposes the effects of two kinds of suboptimality introduced by the transformed codebook, namely, the loss caused by suboptimal point density and the loss caused by mismatched Voronoi shape. We then focus our attention on the application of the proposed general framework to providing capacity analysis of a feedback-based MISO system over spatially correlated fading channels. In particular, with capacity loss as an objective function, upper and lower bounds on the average distortion of MISO systems with transformed codebooks are provided and compared to that of the optimal channel quantizers. The expressions are examined to provide interesting insights in the high and low SNR regime. Numerical and simulation results are presented which confirm the tightness of the distortion bounds.

  11. 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

    2016-04-28

    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.

  12. GPR identification of voids inside concrete based on the support vector machine algorithm

    International Nuclear Information System (INIS)

    Xie, Xiongyao; Li, Pan; Qin, Hui; Liu, Lanbo; Nobes, David C

    2013-01-01

    Voids inside reinforced concrete, which affect structural safety, are identified from ground penetrating radar (GPR) images using a completely automatic method based on the support vector machine (SVM) algorithm. The entire process can be characterized into four steps: (1) the original SVM model is built by training synthetic GPR data generated by finite difference time domain simulation and after data preprocessing, segmentation and feature extraction. (2) The classification accuracy of different kernel functions is compared with the cross-validation method and the penalty factor (c) of the SVM and the coefficient (σ2) of kernel functions are optimized by using the grid algorithm and the genetic algorithm. (3) To test the success of classification, this model is then verified and validated by applying it to another set of synthetic GPR data. The result shows a high success rate for classification. (4) This original classifier model is finally applied to a set of real GPR data to identify and classify voids. The result is less than ideal when compared with its application to synthetic data before the original model is improved. In general, this study shows that the SVM exhibits promising performance in the GPR identification of voids inside reinforced concrete. Nevertheless, the recognition of shape and distribution of voids may need further improvement. (paper)

  13. 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.

  14. Diagnostic Method of Diabetes Based on Support Vector Machine and Tongue Images

    Directory of Open Access Journals (Sweden)

    Jianfeng Zhang

    2017-01-01

    Full Text Available Objective. The purpose of this research is to develop a diagnostic method of diabetes based on standardized tongue image using support vector machine (SVM. Methods. Tongue images of 296 diabetic subjects and 531 nondiabetic subjects were collected by the TDA-1 digital tongue instrument. Tongue body and tongue coating were separated by the division-merging method and chrominance-threshold method. With extracted color and texture features of the tongue image as input variables, the diagnostic model of diabetes with SVM was trained. After optimizing the combination of SVM kernel parameters and input variables, the influences of the combinations on the model were analyzed. Results. After normalizing parameters of tongue images, the accuracy rate of diabetes predication was increased from 77.83% to 78.77%. The accuracy rate and area under curve (AUC were not reduced after reducing the dimensions of tongue features with principal component analysis (PCA, while substantially saving the training time. During the training for selecting SVM parameters by genetic algorithm (GA, the accuracy rate of cross-validation was grown from 72% or so to 83.06%. Finally, we compare with several state-of-the-art algorithms, and experimental results show that our algorithm has the best predictive accuracy. Conclusions. The diagnostic method of diabetes on the basis of tongue images in Traditional Chinese Medicine (TCM is of great value, indicating the feasibility of digitalized tongue diagnosis.

  15. 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.

  16. Fault diagnosis of direct-drive wind turbine based on support vector machine

    International Nuclear Information System (INIS)

    An, X L; Jiang, D X; Li, S H; Chen, J

    2011-01-01

    A fault diagnosis method of direct-drive wind turbine based on support vector machine (SVM) and feature selection is presented. The time-domain feature parameters of main shaft vibration signal in the horizontal and vertical directions are considered in the method. Firstly, in laboratory scale five experiments of direct-drive wind turbine with normal condition, wind wheel mass imbalance fault, wind wheel aerodynamic imbalance fault, yaw fault and blade airfoil change fault are carried out. The features of five experiments are analyzed. Secondly, the sensitive time-domain feature parameters in the horizontal and vertical directions of vibration signal in the five conditions are selected and used as feature samples. By training, the mapping relation between feature parameters and fault types are established in SVM model. Finally, the performance of the proposed method is verified through experimental data. The results show that the proposed method is effective in identifying the fault of wind turbine. It has good classification ability and robustness to diagnose the fault of direct-drive wind turbine.

  17. LVQ-SMOTE - Learning Vector Quantization based Synthetic Minority Over-sampling Technique for biomedical data.

    Science.gov (United States)

    Nakamura, Munehiro; Kajiwara, Yusuke; Otsuka, Atsushi; Kimura, Haruhiko

    2013-10-02

    Over-sampling methods based on Synthetic Minority Over-sampling Technique (SMOTE) have been proposed for classification problems of imbalanced biomedical data. However, the existing over-sampling methods achieve slightly better or sometimes worse result than the simplest SMOTE. In order to improve the effectiveness of SMOTE, this paper presents a novel over-sampling method using codebooks obtained by the learning vector quantization. In general, even when an existing SMOTE applied to a biomedical dataset, its empty feature space is still so huge that most classification algorithms would not perform well on estimating borderlines between classes. To tackle this problem, our over-sampling method generates synthetic samples which occupy more feature space than the other SMOTE algorithms. Briefly saying, our over-sampling method enables to generate useful synthetic samples by referring to actual samples taken from real-world datasets. Experiments on eight real-world imbalanced datasets demonstrate that our proposed over-sampling method performs better than the simplest SMOTE on four of five standard classification algorithms. Moreover, it is seen that the performance of our method increases if the latest SMOTE called MWMOTE is used in our algorithm. Experiments on datasets for β-turn types prediction show some important patterns that have not been seen in previous analyses. The proposed over-sampling method generates useful synthetic samples for the classification of imbalanced biomedical data. Besides, the proposed over-sampling method is basically compatible with basic classification algorithms and the existing over-sampling methods.

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

    Science.gov (United States)

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

    2013-05-03

    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 system has not been applied to transgenesis. In this study, we developed important additions to the genetic modification methods of the BGM vector system. To explore the potential of the BGM vector, we focused on the fish-like odorant receptor (class I OR) gene family, which consists of 158 genes and forms a single gene cluster. Although a cis-acting locus control region is expected to regulate transcription, this has not yet been determined experimentally. Using two contiguous bacterial artificial chromosome clones containing several class I OR genes, we constructed two transgenes in the BGM vector by inserting a reporter gene cassette into one class I OR gene. Because they were oriented in opposite directions, we performed an inversion modification to align their orientation and then fused them to enlarge the genomic structure. DNA sequencing revealed that no mutations occurred during gene manipulations with the BGM vector. We further demonstrated that the modified, reconstructed genomic DNA fragments could be used to generate transgenic mice. Transgenic mice carrying the enlarged transgene recapitulated the expression and axonal projection patterns of the target class I OR gene in the main olfactory system. We offer a complete genetic modification method for the BGM vector system, including insertion, deletion, inversion and fusion, to engineer genomic DNA fragments without any trace of modifications. In addition, we demonstrate that this system can be used for mouse transgenesis. Thus

  19. Development of an equine-tropic replication-competent lentivirus assay for equine infectious anemia virus-based lentiviral vectors.

    Science.gov (United States)

    Farley, Daniel C; Bannister, Richard; Leroux-Carlucci, Marie A; Evans, Nerys E; Miskin, James E; Mitrophanous, Kyriacos A

    2012-10-01

    The release of lentiviral vectors for clinical use requires the testing of vector material, production cells, and, if applicable, ex vivo-transduced cells for the presence of replication-competent lentivirus (RCL). Vectors derived from the nonprimate lentivirus equine infectious anemia virus (EIAV) have been directly administered to patients in several clinical trials, with no toxicity observed to date. Because EIAV does not replicate in human cells, and because putative RCLs derived from vector components within human vector production cells would most likely be human cell-tropic, we previously developed an RCL assay using amphotropic murine leukemia virus (MLV) as a surrogate positive control and human cells as RCL amplification/indicator cells. Here we report an additional RCL assay that tests for the presence of theoretical "equine-tropic" RCLs. This approach provides further assurance of safety by detecting putative RCLs with an equine cell-specific tropism that might not be efficiently amplified by the human cell-based RCL assay. We tested the ability of accessory gene-deficient EIAV mutant viruses to replicate in a highly permissive equine cell line to direct our choice of a suitable EIAV-derived positive control. In addition, we report for the first time the mathematical rationale for use of the Poisson distribution to calculate minimal infectious dose of positive control virus and for use in monitoring assay positive/spike control failures in accumulating data sets. No RCLs have been detected in Good Manufacturing Practice (GMP)-compliant RCL assays to date, further demonstrating that RCL formation is highly unlikely in contemporary minimal lentiviral vector systems.

  20. 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

  1. 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

  2. Polarimetric signatures of a canopy of dielectric cylinders based on first and second order vector radiative transfer theory

    Science.gov (United States)

    Tsang, Leung; Chan, Chi Hou; Kong, Jin AU; Joseph, James

    1992-01-01

    Complete polarimetric signatures of a canopy of dielectric cylinders overlying a homogeneous half space are studied with the first and second order solutions of the vector radiative transfer theory. The vector radiative transfer equations contain a general nondiagonal extinction matrix and a phase matrix. The energy conservation issue is addressed by calculating the elements of the extinction matrix and the elements of the phase matrix in a manner that is consistent with energy conservation. Two methods are used. In the first method, the surface fields and the internal fields of the dielectric cylinder are calculated by using the fields of an infinite cylinder. The phase matrix is calculated and the extinction matrix is calculated by summing the absorption and scattering to ensure energy conservation. In the second method, the method of moments is used to calculate the elements of the extinction and phase matrices. The Mueller matrix based on the first order and second order multiple scattering solutions of the vector radiative transfer equation are calculated. Results from the two methods are compared. The vector radiative transfer equations, combined with the solution based on method of moments, obey both energy conservation and reciprocity. The polarimetric signatures, copolarized and depolarized return, degree of polarization, and phase differences are studied as a function of the orientation, sizes, and dielectric properties of the cylinders. It is shown that second order scattering is generally important for vegetation canopy at C band and can be important at L band for some cases.

  3. Space Vector Pulse Width Modulation Technique Based Design and Simulation of a Three-Phase Voltage Source Converter Systems

    OpenAIRE

    Farhan Beg

    2014-01-01

    A Space Vector based Pulse Width Modulation control technique for the three-phase PWM converter is proposed in this paper. The proposed control scheme is based on a synchronous reference frame model. High performance and efficiency is obtained with regards to the DC bus voltage and the power factor considerations of the PWM rectifier thus leading to low losses. MATLAB/SIMULINK are used as a platform for the simulations and a SIMULINK model is presented in the paper. The r...

  4. Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

    Science.gov (United States)

    Yeganeh, B.; Motlagh, M. Shafie Pour; Rashidi, Y.; Kamalan, H.

    2012-08-01

    Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations. The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS-SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS-SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65-85% for hybrid PLS-SVM model respectively. Also it was found that the hybrid PLS-SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS-SVM model.

  5. Post hoc support vector machine learning for impedimetric biosensors based on weak protein-ligand interactions.

    Science.gov (United States)

    Rong, Y; Padron, A V; Hagerty, K J; Nelson, N; Chi, S; Keyhani, N O; Katz, J; Datta, S P A; Gomes, C; McLamore, E S

    2018-04-09

    Impedimetric biosensors for measuring small molecules based on weak/transient interactions between bioreceptors and target analytes are a challenge for detection electronics, particularly in field studies or in the analysis of complex matrices. Protein-ligand binding sensors have enormous potential for biosensing, but achieving accuracy in complex solutions is a major challenge. There is a need for simple post hoc analytical tools that are not computationally expensive, yet provide near real time feedback on data derived from impedance spectra. Here, we show the use of a simple, open source support vector machine learning algorithm for analyzing impedimetric data in lieu of using equivalent circuit analysis. We demonstrate two different protein-based biosensors to show that the tool can be used for various applications. We conclude with a mobile phone-based demonstration focused on the measurement of acetone, an important biomarker related to the onset of diabetic ketoacidosis. In all conditions tested, the open source classifier was capable of performing as well as, or better, than the equivalent circuit analysis for characterizing weak/transient interactions between a model ligand (acetone) and a small chemosensory protein derived from the tsetse fly. In addition, the tool has a low computational requirement, facilitating use for mobile acquisition systems such as mobile phones. The protocol is deployed through Jupyter notebook (an open source computing environment available for mobile phone, tablet or computer use) and the code was written in Python. For each of the applications, we provide step-by-step instructions in English, Spanish, Mandarin and Portuguese to facilitate widespread use. All codes were based on scikit-learn, an open source software machine learning library in the Python language, and were processed in Jupyter notebook, an open-source web application for Python. The tool can easily be integrated with the mobile biosensor equipment for rapid

  6. A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification

    Directory of Open Access Journals (Sweden)

    Wang Lily

    2008-07-01

    Full Text Available Abstract Background Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray technology with several molecular signatures on their way toward clinical deployment. Use of the most accurate classification algorithms available for microarray gene expression data is a critical ingredient in order to develop the best possible molecular signatures for patient care. As suggested by a large body of literature to date, support vector machines can be considered "best of class" algorithms for classification of such data. Recent work, however, suggests that random forest classifiers may outperform support vector machines in this domain. Results In the present paper we identify methodological biases of prior work comparing random forests and support vector machines and conduct a new rigorous evaluation of the two algorithms that corrects these limitations. Our experiments use 22 diagnostic and prognostic datasets and show that support vector machines outperform random forests, often by a large margin. Our data also underlines the importance of sound research design in benchmarking and comparison of bioinformatics algorithms. Conclusion We found that both on average and in the majority of microarray datasets, random forests are outperformed by support vector machines both in the settings when no gene selection is performed and when several popular gene selection methods are used.

  7. Internet-based biosurveillance methods for vector-borne diseases: Are they novel public health tools or just novelties?

    Science.gov (United States)

    Pollett, Simon; Althouse, Benjamin M; Forshey, Brett; Rutherford, George W; Jarman, Richard G

    2017-11-01

    Internet-based surveillance methods for vector-borne diseases (VBDs) using "big data" sources such as Google, Twitter, and internet newswire scraping have recently been developed, yet reviews on such "digital disease detection" methods have focused on respiratory pathogens, particularly in high-income regions. Here, we present a narrative review of the literature that has examined the performance of internet-based biosurveillance for diseases caused by vector-borne viruses, parasites, and other pathogens, including Zika, dengue, other arthropod-borne viruses, malaria, leishmaniasis, and Lyme disease across a range of settings, including low- and middle-income countries. The fundamental features, advantages, and drawbacks of each internet big data source are presented for those with varying familiarity of "digital epidemiology." We conclude with some of the challenges and future directions in using internet-based biosurveillance for the surveillance and control of VBD.

  8. Internet-based biosurveillance methods for vector-borne diseases: Are they novel public health tools or just novelties?

    Directory of Open Access Journals (Sweden)

    Simon Pollett

    2017-11-01

    Full Text Available Internet-based surveillance methods for vector-borne diseases (VBDs using "big data" sources such as Google, Twitter, and internet newswire scraping have recently been developed, yet reviews on such "digital disease detection" methods have focused on respiratory pathogens, particularly in high-income regions. Here, we present a narrative review of the literature that has examined the performance of internet-based biosurveillance for diseases caused by vector-borne viruses, parasites, and other pathogens, including Zika, dengue, other arthropod-borne viruses, malaria, leishmaniasis, and Lyme disease across a range of settings, including low- and middle-income countries. The fundamental features, advantages, and drawbacks of each internet big data source are presented for those with varying familiarity of "digital epidemiology." We conclude with some of the challenges and future directions in using internet-based biosurveillance for the surveillance and control of VBD.

  9. A novel selection method of seismic attributes based on gray relational degree and support vector machine.

    Science.gov (United States)

    Huang, Yaping; Yang, Haijun; Qi, Xuemei; Malekian, Reza; Pfeiffer, Olivia; Li, Zhixiong

    2018-01-01

    The selection of seismic attributes is a key process in reservoir prediction because the prediction accuracy relies on the reliability and credibility of the seismic attributes. However, effective selection method for useful seismic attributes is still a challenge. This paper presents a novel selection method of seismic attributes for reservoir prediction based on the gray relational degree (GRD) and support vector machine (SVM). The proposed method has a two-hierarchical structure. In the first hierarchy, the primary selection of seismic attributes is achieved by calculating the GRD between seismic attributes and reservoir parameters, and the GRD between the seismic attributes. The principle of the primary selection is that these seismic attributes with higher GRD to the reservoir parameters will have smaller GRD between themselves as compared to those with lower GRD to the reservoir parameters. Then the SVM is employed in the second hierarchy to perform an interactive error verification using training samples for the purpose of determining the final seismic attributes. A real-world case study was conducted to evaluate the proposed GRD-SVM method. Reliable seismic attributes were selected to predict the coalbed methane (CBM) content in southern Qinshui basin, China. In the analysis, the instantaneous amplitude, instantaneous bandwidth, instantaneous frequency, and minimum negative curvature were selected, and the predicted CBM content was fundamentally consistent with the measured CBM content. This real-world case study demonstrates that the proposed method is able to effectively select seismic attributes, and improve the prediction accuracy. Thus, the proposed GRD-SVM method can be used for the selection of seismic attributes in practice.

  10. A novel balanced-lethal host-vector system based on glmS.

    Directory of Open Access Journals (Sweden)

    Kwangsoo Kim

    Full Text Available During the last decade, an increasing number of papers have described the use of various genera of bacteria, including E. coli and S. typhimurium, in the treatment of cancer. This is primarily due to the facts that not only are these bacteria capable of accumulating in the tumor mass, but they can also be engineered to deliver specific therapeutic proteins directly to the tumor site. However, a major obstacle exists in that bacteria because the plasmid carrying the therapeutic gene is not needed for bacterial survival, these plasmids are often lost from the bacteria. Here, we report the development of a balanced-lethal host-vector system based on deletion of the glmS gene in E. coli and S. typhimurium. This system takes advantage of the phenotype of the GlmS(- mutant, which undergoes lysis in animal systems that lack the nutrients required for proliferation of the mutant bacteria, D-glucosamine (GlcN or N-acetyl-D-glucosamine (GlcNAc, components necessary for peptidoglycan synthesis. We demonstrate that plasmids carrying a glmS gene (GlmS(+p complemented the phenotype of the GlmS(- mutant, and that GlmS(+ p was maintained faithfully both in vitro and in an animal system in the absence of selection pressure. This was further verified by bioluminescent signals from GlmS (+pLux carried in bacteria that accumulated in grafted tumor tissue in a mouse model. The signal was up to several hundred-fold stronger than that from the control plasmid, pLux, due to faithful maintenance of the plasmid. We believe this system will allow to package a therapeutic gene onto an expression plasmid for bacterial delivery to the tumor site without subsequent loss of plasmid expression as well as to quantify bioluminescent bacteria using in vivo imaging by providing a direct correlation between photon flux and bacterial number.

  11. Mining protein function from text using term-based support vector machines

    Science.gov (United States)

    Rice, Simon B; Nenadic, Goran; Stapley, Benjamin J

    2005-01-01

    Background Text mining has spurred huge interest in the domain of biology. The goal of the BioCreAtIvE exercise was to evaluate the performance of current text mining systems. We participated in Task 2, which addressed assigning Gene Ontology terms to human proteins and selecting relevant evidence from full-text documents. We approached it as a modified form of the document classification task. We used a supervised machine-learning approach (based on support vector machines) to assign protein function and select passages that support the assignments. As classification features, we used a protein's co-occurring terms that were automatically extracted from documents. Results The results evaluated by curators were modest, and quite variable for different problems: in many cases we have relatively good assignment of GO terms to proteins, but the selected supporting text was typically non-relevant (precision spanning from 3% to 50%). The method appears to work best when a substantial set of relevant documents is obtained, while it works poorly on single documents and/or short passages. The initial results suggest that our approach can also mine annotations from text even when an explicit statement relating a protein to a GO term is absent. Conclusion A machine learning approach to mining protein function predictions from text can yield good performance only if sufficient training data is available, and significant amount of supporting data is used for prediction. The most promising results are for combined document retrieval and GO term assignment, which calls for the integration of methods developed in BioCreAtIvE Task 1 and Task 2. PMID:15960835

  12. Robot Training With Vector Fields Based on Stroke Survivors' Individual Movement Statistics.

    Science.gov (United States)

    Wright, Zachary A; Lazzaro, Emily; Thielbar, Kelly O; Patton, James L; Huang, Felix C

    2018-02-01

    The wide variation in upper extremity motor impairments among stroke survivors necessitates more intelligent methods of customized therapy. However, current strategies for characterizing individual motor impairments are limited by the use of traditional clinical assessments (e.g., Fugl-Meyer) and simple engineering metrics (e.g., goal-directed performance). Our overall approach is to statistically identify the range of volitional movement capabilities, and then apply a robot-applied force vector field intervention that encourages under-expressed movements. We investigated whether explorative training with such customized force fields would improve stroke survivors' (n = 11) movement patterns in comparison to a control group that trained without forces (n = 11). Force and control groups increased Fugl-Meyer UE scores (average of 1.0 and 1.1, respectively), which is not considered clinically meaningful. Interestingly, participants from both groups demonstrated dramatic increases in their range of velocity during exploration following only six days of training (average increase of 166.4% and 153.7% for the Force and Control group, respectively). While both groups showed evidence of improvement, we also found evidence that customized forces affected learning in a systematic way. When customized forces were active, we observed broader distributions of velocity that were not present in the controls. Second, we found that these changes led to specific changes in unassisted motion. In addition, while the shape of movement distributions changed significantly for both groups, detailed analysis of the velocity distributions revealed that customized forces promoted a greater proportion of favorable changes. Taken together, these results provide encouraging evidence that patient-specific force fields based on individuals' movement statistics can be used to create new movement patterns and shape them in a customized manner. To the best of our knowledge, this paper is the first

  13. AUTOMATIC LUNG NODULE DETECTION BASED ON STATISTICAL REGION MERGING AND SUPPORT VECTOR MACHINES

    Directory of Open Access Journals (Sweden)

    Elaheh Aghabalaei Khordehchi

    2017-06-01

    Full Text Available Lung cancer is one of the most common diseases in the world that can be treated if the lung nodules are detected in their early stages of growth. This study develops a new framework for computer-aided detection of pulmonary nodules thorough a fully-automatic analysis of Computed Tomography (CT images. In the present work, the multi-layer CT data is fed into a pre-processing step that exploits an adaptive diffusion-based smoothing algorithm in which the parameters are automatically tuned using an adaptation technique. After multiple levels of morphological filtering, the Regions of Interest (ROIs are extracted from the smoothed images. The Statistical Region Merging (SRM algorithm is applied to the ROIs in order to segment each layer of the CT data. Extracted segments in consecutive layers are then analyzed in such a way that if they intersect at more than a predefined number of pixels, they are labeled with a similar index. The boundaries of the segments in adjacent layers which have the same indices are then connected together to form three-dimensional objects as the nodule candidates. After extracting four spectral, one morphological, and one textural feature from all candidates, they are finally classified into nodules and non-nodules using the Support Vector Machine (SVM classifier. The proposed framework has been applied to two sets of lung CT images and its performance has been compared to that of nine other competing state-of-the-art methods. The considerable efficiency of the proposed approach has been proved quantitatively and validated by clinical experts as well.

  14. A temporal subtraction method for thoracic CT images based on generalized gradient vector flow

    International Nuclear Information System (INIS)

    Miyake, Noriaki; Kim, H.; Maeda, Shinya; Itai, Yoshinori; Tan, J.K.; Ishikawa, Seiji; Katsuragawa, Shigehiko

    2010-01-01

    A temporal subtraction image, which is obtained by subtraction of a previous image from a current one, can be used for enhancing interval changes (such as formation of new lesions and changes in existing abnormalities) on medical images by removing most of the normal structures. If image registration is incorrect, not only the interval changes but also the normal structures would be appeared as some artifacts on the temporal subtraction image. In a temporal subtraction technique for 2-D X-ray image, the effectiveness is shown through a lot of clinical evaluation experiments, and practical use is advancing. Moreover, the MDCT (Multi-Detector row Computed Tomography) can easily introduced on medical field, the development of a temporal subtraction for thoracic CT Images is expected. In our study, a temporal subtraction technique for thoracic CT Images is developed. As the technique, the vector fields are described by use of GGVF (Generalized Gradient Vector Flow) from the previous and current CT images. Afterwards, VOI (Volume of Interest) are set up on the previous and current CT image pairs. The shift vectors are calculated by using nearest neighbor matching of the vector fields in these VOIs. The search kernel on previous CT image is set up from the obtained shift vector. The previous CT voxel which resemble standard the current voxel is detected by voxel value and vector of the GGVF in the kernel. And, the previous CT image is transformed to the same coordinate of standard voxel. Finally, temporal subtraction image is made by subtraction of a warping image from a current one. To verify the proposal method, the result of application to 7 cases and the effectiveness are described. (author)

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

    Directory of Open Access Journals (Sweden)

    Yoichiro Shinohara

    Full Text Available 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.

  16. 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%.

  17. Use of mapping and statistical modelling for the prediction of bluetongue occurrence in Switzerland based on vector biology.

    Science.gov (United States)

    Racloz, Vanessa; Presi, Patrick; Vounatsou, Penelope; Schwermer, Heinzpeter; Casati, Simona; Vanzetti, Tullio; Griot, Christian; Stärk, Katarina D C

    2007-01-01

    Due to the spread of bluetongue (BT) in Europe in the last decade, a sentinel surveillance programme was initiated for Switzerland in 2003, consisting of serological sampling of sentinel cattle tested for BT virus antibodies, as well as entomological trapping of Culicoides midges from June until October. The aim of this study was to create a 'suitability map' of Switzerland, indicating areas of potential disease occurrence based on the biological parameters of Obsoletus Complex habitat. Data on Culicoides catches from insect traps together with various environmental parameters were recorded and analysed. A multiple regression analysis was performed to determine correlation between the environmental conditions and vector abundance. Meteorological data were collected from 50 geo-referenced weather stations across Switzerland and maps of temperature, precipitation and altitude were created. A range of values of temperature, precipitation and altitude influencing vector biology were obtained from the literature. The final combined map highlighted areas in Switzerland which are most suitable for vector presence, hence implying a higher probability of disease occurrence given the presence of susceptible animals. The results confirmed the need for an early warning system for the surveillance of BT disease and its vectors in Switzerland.

  18. Construction of Expression Shuttle Vectors for the Haloarchaeon Natrinema sp. J7 Based on Its Chromosomal Origins of Replication

    Directory of Open Access Journals (Sweden)

    Yuchen Wang

    2017-01-01

    Full Text Available Haloarchaeon Natrinema sp. J7, the first reported archaeon harboring both plasmid and chromosome-based temperate viruses, is a useful model for investigating archaeal virus-host and virus-virus interactions. However, the lack of genetic tools has limited such studies. On the basis of the automatically replicating sequences of the J7 chromosome and the pyrF marker, we constructed seven vectors, six of which were confirmed to possess replication ability in a pyrF-deletion derivative of J7 (J7-F. Among these vectors, pFJ1, pFJ4, and pFJ6 could be transformed into the host strain with relatively high efficiency (approximately 103 colony-forming units/μg DNA and were present at about one copy per chromosome. These three vectors could be stably maintained in J7-F without selection and were used for heterologous protein expression. Only pFJ6 was found to be present in the transformed cells in an exclusively episomal, nonintegrated state (one copy per chromosome. In contrast, some pFJ1 and pFJ4 DNA was probably integrated into the J7-F chromosome. In addition, pFJ6 was found to be compatible with pYCJ in J7 cells, suggesting that these two vectors could be used for further studies of virus-virus and virus-host interactions.

  19. 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

  20. 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

  1. Real time implementation of adaptive sliding mode observer based speed sensor less vector control of induction motor

    Directory of Open Access Journals (Sweden)

    Negadi Karim

    2010-01-01

    Full Text Available Sensor less induction motor drives are widely used in industry for their reliability and flexibility. However, rotor flux and speed sensors are required for vector control of induction motor. These sensors are sources of trouble, mainly in hostile environments, and their application reduces the drive robustness. The cost of the sensors is not also negligible. All the reasons lead to development of different sensor less methods for rotor flux and mechanical speed estimation in electrical drives. The paper deals with the speed estimators for applications in sensor less induction motor drive with vector control, which are based on application of model adaptive, based sliding mode observer methods. This paper presents the development and DSP implementation of the speed estimators for applications in sensor less drives with induction motor.

  2. 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

  3. Permanent Magnet Flux Online Estimation Based on Zero-Voltage Vector Injection Method

    DEFF Research Database (Denmark)

    Xie, Ge; Lu, Kaiyuan; Kumar, Dwivedi Sanjeet

    2015-01-01

    In this paper, a simple signal injection method is proposed for sensorless control of PMSM at low speed, which ideally requires one voltage vector only for position estimation. The proposed method is easy to implement resulting in low computation burden. No filters are needed for extracting the h...

  4. The effectiveness of problem-based learning on students’ problem solving ability in vector analysis course

    Science.gov (United States)

    Mushlihuddin, R.; Nurafifah; Irvan

    2018-01-01

    The student’s low ability in mathematics problem solving proved to the less effective of a learning process in the classroom. Effective learning was a learning that affects student’s math skills, one of which is problem-solving abilities. Problem-solving capability consisted of several stages: understanding the problem, planning the settlement, solving the problem as planned, re-examining the procedure and the outcome. The purpose of this research was to know: (1) was there any influence of PBL model in improving ability Problem solving of student math in a subject of vector analysis?; (2) was the PBL model effective in improving students’ mathematical problem-solving skills in vector analysis courses? This research was a quasi-experiment research. The data analysis techniques performed from the test stages of data description, a prerequisite test is the normality test, and hypothesis test using the ANCOVA test and Gain test. The results showed that: (1) there was an influence of PBL model in improving students’ math problem-solving abilities in vector analysis courses; (2) the PBL model was effective in improving students’ problem-solving skills in vector analysis courses with a medium category.

  5. 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,

  6. 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...

  7. DSP-based vector controlled IM drive system with identification of secondary time constant

    Energy Technology Data Exchange (ETDEWEB)

    Iwasaki, Makoto; Matsui, Nobuyuki

    1988-09-20

    The development of the variable-speed driving technology for AC motors have put into practice the vector control of induction motors having the same high-speed torque response as that of DC motors. Several control systems are available but each of them has its own problems. A secondary time constant identification function vector control system has been developed in this background. It consists of processing all vectors by using DSP (digital signal processor) on the basis of non-interacting vector control conditions derived from a voltage model of an induction motor. The prototype system consists mainly of TMS 32010 to realize a softwere version of a full control system composed of a current control unit, speed control unit, and secondary time constant control unit. Experiments using a 2.2 kW induction motor have revealed a control precision of less than 3% at any torque up to rated torque. The operation time is 135 microseconds. (14 figs, 1 tab, 12 refs)

  8. Engineered XcmI cassette-containing vector for PCR-based ...

    Indian Academy of Sciences (India)

    Unknown

    and pT7Blue (Novagen), are expensive and cannot be re- generated in the laboratory for further use. We describe here the development of a simple and general method for constructing T-vectors bearing an oligonucleotide cassette that produces complementary 3′ thymidine overhangs by restriction enzyme digestion.

  9. Engineered XcmI cassette-containing vector for PCR-based ...

    Indian Academy of Sciences (India)

    T-vector; direct cloning; XcmI cassette; sequencing; PCR; marine population genetics. Author Affiliations. Futoshi Aranishi1 2 Takane Okimoto1 3. Molecular Biology Division, National Institute of Fisheries Science, Yokohama 236-8648, Japan; Department of Biological and Environmental Sciences, Mirjazaki University, ...

  10. Neighboring block based disparity vector derivation for multiview compatible 3D-AVC

    Science.gov (United States)

    Kang, Jewon; Chen, Ying; Zhang, Li; Zhao, Xin; Karczewicz, Marta

    2013-09-01

    3D-AVC being developed under Joint Collaborative Team on 3D Video Coding (JCT-3V) significantly outperforms the Multiview Video Coding plus Depth (MVC+D) which simultaneously encodes texture views and depth views with the multiview extension of H.264/AVC (MVC). However, when the 3D-AVC is configured to support multiview compatibility in which texture views are decoded without depth information, the coding performance becomes significantly degraded. The reason is that advanced coding tools incorporated into the 3D-AVC do not perform well due to the lack of a disparity vector converted from the depth information. In this paper, we propose a disparity vector derivation method utilizing only the information of texture views. Motion information of neighboring blocks is used to determine a disparity vector for a macroblock, so that the derived disparity vector is efficiently used for the coding tools in 3D-AVC. The proposed method significantly improves a coding gain of the 3D-AVC in the multiview compatible mode about 20% BD-rate saving in the coded views and 26% BD-rate saving in the synthesized views on average.

  11. Automated beam placement for breast radiotherapy using a support vector machine based algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Zhao Xuan; Kong, Dewen; Jozsef, Gabor; Chang, Jenghwa; Wong, Edward K.; Formenti, Silvia C.; Wang Yao [Department of Electrical and Computer Engineering, Polytechnic Institute of New York University, Brooklyn, New York 11201 (United States); Department of Radiation Oncology, School of Medicine, Langone Medical Center, New York University, New York, New York 10016 (United States); Department of Computer Science and Engineering, Polytechnic Institute of New York University, Brooklyn, New York 11201 (United States); Department of Radiation Oncology, School of Medicine, Langone Medical Center, New York University, New York, New York 10016 (United States); Department of Electrical and Computer Engineering, Polytechnic Institute of New York University, Brooklyn, New York 11201 (United States)

    2012-05-15

    Purpose: To develop an automated beam placement technique for whole breast radiotherapy using tangential beams. We seek to find optimal parameters for tangential beams to cover the whole ipsilateral breast (WB) and minimize the dose to the organs at risk (OARs). Methods: A support vector machine (SVM) based method is proposed to determine the optimal posterior plane of the tangential beams. Relative significances of including/avoiding the volumes of interests are incorporated into the cost function of the SVM. After finding the optimal 3-D plane that separates the whole breast (WB) and the included clinical target volumes (CTVs) from the OARs, the gantry angle, collimator angle, and posterior jaw size of the tangential beams are derived from the separating plane equation. Dosimetric measures of the treatment plans determined by the automated method are compared with those obtained by applying manual beam placement by the physicians. The method can be further extended to use multileaf collimator (MLC) blocking by optimizing posterior MLC positions. Results: The plans for 36 patients (23 prone- and 13 supine-treated) with left breast cancer were analyzed. Our algorithm reduced the volume of the heart that receives >500 cGy dose (V5) from 2.7 to 1.7 cm{sup 3} (p = 0.058) on average and the volume of the ipsilateral lung that receives >1000 cGy dose (V10) from 55.2 to 40.7 cm{sup 3} (p = 0.0013). The dose coverage as measured by volume receiving >95% of the prescription dose (V95%) of the WB without a 5 mm superficial layer decreases by only 0.74% (p = 0.0002) and the V95% for the tumor bed with 1.5 cm margin remains unchanged. Conclusions: This study has demonstrated the feasibility of using a SVM-based algorithm to determine optimal beam placement without a physician's intervention. The proposed method reduced the dose to OARs, especially for supine treated patients, without any relevant degradation of dose homogeneity and coverage in general.

  12. SEBAL-based Daily Actual Evapotranspiration Forecasting using Wavelets Decomposition Analysis and Multivariate Relevance Vector Machines

    Science.gov (United States)

    Torres, A. F.

    2011-12-01

    Agricultural lands are sources of food and energy for population around the globe. These lands are vulnerable to the impacts of climate change including variations in rainfall regimes, weather patterns, and decreased availability of water for irrigation. In addition, it is not unusual that irrigated agriculture is forced to divert less water in order to make it available for other uses, e.g. human consumption and others. As part of implementation of better policies for water control and management, irrigation companies and water user associations have been implemented water conveyance and distribution monitoring systems along with soil moisture sensors networks in the last decades. These systems allow them to manage and distribute water among the users based on their requirements and water availability while collecting information about actual soil moisture conditions in representative crop fields. In spite of this, requested water deliveries by farmers/water users is based typically on total water share, traditions and past experience on irrigation, which in most cases do not correspond to the actual crop evapotranspiration, already affected by climate change. Therefore it is necessary to provide actual information about the crop water requirements to water users/managers, so they can better quantify the required vs. available water for the irrigation events along the irrigation season. To estimate the actual evapotranspiration in a spatial extent the Sensitivity Analysis of the Surface Energy Balance Algorithm for Land (SEBAL) algorithm has demonstrated its effectiveness using satellite or airborne data. Nonetheless the estimation is restricted to the day when the geospatial information was obtained. Without information of precise future daily water crop demand there is a continuous challenge for the implementation of better water distribution and management policies in the irrigation system. The purpose of this study is to investigate the plausibility of using

  13. Speed Sensorless Direct Torque Control of a PMSM Drive using Space Vector Modulation Based MRAS and Stator Resistance Estimator

    OpenAIRE

    A. Ameur; B. Mokhtari; N. Essounbouli; L. Mokrani

    2012-01-01

    This paper presents a speed sensorless direct torque control scheme using space vector modulation (DTC-SVM) for permanent magnet synchronous motor (PMSM) drive based a Model Reference Adaptive System (MRAS) algorithm and stator resistance estimator. The MRAS is utilized to estimate speed and stator resistance and compensate the effects of parameter variation on stator resistance, which makes flux and torque estimation more accurate and insensitive to parameter variation. ...

  14. Unit cell determination of epitaxial thin films based on reciprocal space vectors by high-resolution X-ray diffractometry

    OpenAIRE

    Yang, Ping; Liu, Huajun; Chen, Zuhuang; Chen, Lang; Wang, John

    2013-01-01

    A new approach, based on reciprocal space vectors (RSVs), is developed to determine Bravais lattice types and accurate lattice parameters of epitaxial thin films by high-resolution X-ray diffractometry (HR-XRD). The lattice parameters of single crystal substrates are employed as references to correct the systematic experimental errors of RSVs of thin films. The general procedure is summarized, involving correction of RSVs, derivation of raw unit cell, subsequent conversion to the Niggli unit ...

  15. Addressing vulnerability, building resilience: community-based adaptation to vector-borne diseases in the context of global change.

    Science.gov (United States)

    Bardosh, Kevin Louis; Ryan, Sadie J; Ebi, Kris; Welburn, Susan; Singer, Burton

    2017-12-11

    The threat of a rapidly changing planet - of coupled social, environmental and climatic change - pose new conceptual and practical challenges in responding to vector-borne diseases. These include non-linear and uncertain spatial-temporal change dynamics associated with climate, animals, land, water, food, settlement, conflict, ecology and human socio-cultural, economic and political-institutional systems. To date, research efforts have been dominated by disease modeling, which has provided limited practical advice to policymakers and practitioners in developing policies and programmes on the ground. In this paper, we provide an alternative biosocial perspective grounded in social science insights, drawing upon concepts of vulnerability, resilience, participation and community-based adaptation. Our analysis was informed by a realist review (provided in the Additional file 2) focused on seven major climate-sensitive vector-borne diseases: malaria, schistosomiasis, dengue, leishmaniasis, sleeping sickness, chagas disease, and rift valley fever. Here, we situate our analysis of existing community-based interventions within the context of global change processes and the wider social science literature. We identify and discuss best practices and conceptual principles that should guide future community-based efforts to mitigate human vulnerability to vector-borne diseases. We argue that more focused attention and investments are needed in meaningful public participation, appropriate technologies, the strengthening of health systems, sustainable development, wider institutional changes and attention to the social determinants of health, including the drivers of co-infection. In order to respond effectively to uncertain future scenarios for vector-borne disease in a changing world, more attention needs to be given to building resilient and equitable systems in the present.

  16. An Energy Scaled and Expanded Vector-Based Forwarding Scheme for Industrial Underwater Acoustic Sensor Networks with Sink Mobility.

    Science.gov (United States)

    Wadud, Zahid; Hussain, Sajjad; Javaid, Nadeem; Bouk, Safdar Hussain; Alrajeh, Nabil; Alabed, Mohamad Souheil; Guizani, Nadra

    2017-09-30

    Industrial Underwater Acoustic Sensor Networks (IUASNs) come with intrinsic challenges like long propagation delay, small bandwidth, large energy consumption, three-dimensional deployment, and high deployment and battery replacement cost. Any routing strategy proposed for IUASN must take into account these constraints. The vector based forwarding schemes in literature forward data packets to sink using holding time and location information of the sender, forwarder, and sink nodes. Holding time suppresses data broadcasts; however, it fails to keep energy and delay fairness in the network. To achieve this, we propose an Energy Scaled and Expanded Vector-Based Forwarding (ESEVBF) scheme. ESEVBF uses the residual energy of the node to scale and vector pipeline distance ratio to expand the holding time. Resulting scaled and expanded holding time of all forwarding nodes has a significant difference to avoid multiple forwarding, which reduces energy consumption and energy balancing in the network. If a node has a minimum holding time among its neighbors, it shrinks the holding time and quickly forwards the data packets upstream. The performance of ESEVBF is analyzed through in network scenario with and without node mobility to ensure its effectiveness. Simulation results show that ESEVBF has low energy consumption, reduces forwarded data copies, and less end-to-end delay.

  17. 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.

  18. A Novel Method for Mechanical Fault Diagnosis Based on Variational Mode Decomposition and Multikernel Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Zhongliang Lv

    2016-01-01

    Full Text Available A novel fault diagnosis method based on variational mode decomposition (VMD and multikernel support vector machine (MKSVM optimized by Immune Genetic Algorithm (IGA is proposed to accurately and adaptively diagnose mechanical faults. First, mechanical fault vibration signals are decomposed into multiple Intrinsic Mode Functions (IMFs by VMD. Then the features in time-frequency domain are extracted from IMFs to construct the feature sets of mixed domain. Next, Semisupervised Locally Linear Embedding (SS-LLE is adopted for fusion and dimension reduction. The feature sets with reduced dimension are inputted to the IGA optimized MKSVM for failure mode identification. Theoretical analysis demonstrates that MKSVM can approximate any multivariable function. The global optimal parameter vector of MKSVM can be rapidly identified by IGA parameter optimization. The experiments of mechanical faults show that, compared to traditional fault diagnosis models, the proposed method significantly increases the diagnosis accuracy of mechanical faults and enhances the generalization of its application.

  19. 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.

  20. 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.

  1. 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.

  2. Potential Distribution of Chagas Disease Vectors (Hemiptera, Reduviidae, Triatominae) in Colombia, Based on Ecological Niche Modeling.

    Science.gov (United States)

    Parra-Henao, Gabriel; Suárez-Escudero, Laura C; González-Caro, Sebastián

    2016-01-01

    Ecological niche modeling of Triatominae bugs allow us to establish the local risk of transmission of the parasite Trypanosoma cruzi, which causes Chagas disease. This information could help to guide health authority recommendations on infection monitoring, prevention, and control. In this study, we estimated the geographic distribution of triatomine species in Colombia and identified the relationship between landscape structure and climatic factors influencing their occurrence. A total of 2451 records of 4 triatomine species ( Panstrongylus geniculatus , Rhodnius pallescens , R. prolixus , and Triatoma maculata ) were analyzed. The variables that provided more information to explain the ecologic niche of these vectors were related to precipitation, altitude, and temperature. We found that the species with the broadest potential geographic distribution were P. geniculatus , R. pallescens , and R. prolixus . In general, the models predicted the highest occurrence probability of these vectors in the eastern slope of the Eastern Cordillera, the southern region of the Magdalena valley, and the Sierra Nevada of Santa Marta.

  3. Multiwavelength mode-locked cylindrical vector beam fiber laser based on mode selective coupler

    Science.gov (United States)

    Huang, Ping; Cai, Yu; Zhang, Zuxing

    2017-10-01

    We propose and demonstrate a multiwavelength mode-locked fiber laser with cylindrical vector beam generation for the first time, to the best of our knowledge. The mode-locking mechanism is nonlinear polarization rotation, and the multiwavelength operation is contributed to the in-line birefringence fiber filter with periodic multiple passbands formed by incorporating a section of polarization maintaining fiber into the laser cavity with a polarizer. Furthermore, using the mode selective coupler, which acts as mode converter from fundamental mode to higher-order mode, multiwavelength mode-locked cylindrical vector beams have been obtained, which may have potential applications in mode-division multiplexing optical fiber communication and material processing.

  4. Potential Distribution of Chagas Disease Vectors (Hemiptera, Reduviidae, Triatominae) in Colombia, Based on Ecological Niche Modeling

    Science.gov (United States)

    Suárez-Escudero, Laura C.; González-Caro, Sebastián

    2016-01-01

    Ecological niche modeling of Triatominae bugs allow us to establish the local risk of transmission of the parasite Trypanosoma cruzi, which causes Chagas disease. This information could help to guide health authority recommendations on infection monitoring, prevention, and control. In this study, we estimated the geographic distribution of triatomine species in Colombia and identified the relationship between landscape structure and climatic factors influencing their occurrence. A total of 2451 records of 4 triatomine species (Panstrongylus geniculatus, Rhodnius pallescens, R. prolixus, and Triatoma maculata) were analyzed. The variables that provided more information to explain the ecologic niche of these vectors were related to precipitation, altitude, and temperature. We found that the species with the broadest potential geographic distribution were P. geniculatus, R. pallescens, and R. prolixus. In general, the models predicted the highest occurrence probability of these vectors in the eastern slope of the Eastern Cordillera, the southern region of the Magdalena valley, and the Sierra Nevada of Santa Marta. PMID:28115946

  5. Topology Optimization of 3-DOF Peristaltic Structure Robot Based on Vector Continuous Mapping Matrix

    Directory of Open Access Journals (Sweden)

    Gao Wang

    2016-01-01

    Full Text Available A mechanism for topology optimization of 3-DOF parallel peristaltic structure robot with vector continuous mapping matrix using Solid Isotropic Material with Penalization (SIMP method is presented in this paper. We focus on how to prevent the differential motion consistency between parallel prototype mechanisms with peristaltic structure. As the conventional parallel robot joints/hinges are no longer needed after topology optimization, therefore, we renamed this kind of 3-DOF robot structures as peristaltic structure. In the proposed method, the vector continuous mapping matrix is built as stress/strain transfer direction conditions for topology optimization of peristaltic structure, and SIMP method is used for multi-inputs and multioutputs decided by parallel prototype mechanisms. Some numerical examples are presented to illustrate the validity of the proposed method.

  6. Walking Planning Based on Artificial Vector Field with Prediction Simulation for Biped Robot

    Science.gov (United States)

    Yamaguchi, Takashi; Shibata, Masaaki

    This paper proposes a way of gait trajectory generation with artificial vector field for stable walking of a biped robot. The tip of the robot on walking can often deviate from the desired trajectory by the disturbances forced by unexpected outside factors. In our approach, though no prepared trajectory is specified a priori, the tip follows the artificial vectors designed in the workspace. Moreover, the prediction simulation is performed on-line. The simulator judges the stability under comparison with the present state and the prediction results, and then the gait parameters are adaptively improved in feedforward for the stable walk. The numerical and physical experimental results show the validity of the proposed method in the continuous walk.

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

    International Nuclear Information System (INIS)

    Yu, Xunzhen; Zhu, Jingjun; Lin, ShinTed; Wang, Li; Xing, Haoyang; Zhang, Caixun; Xia, Yuxi; Liu, Shukui; Yue, Qian; Wei, Weiwei; Du, Qiang; Tang, Changjian

    2015-01-01

    In this study, the combination of the support vector machine (SVM) method with the moment analysis method (MAM) is proposed and utilized to perform neutron/gamma (n/γ) discrimination of the pulses from an organic liquid scintillator (OLS). Neutron and gamma events, which can be firmly separated on the scatter plot drawn by the charge comparison method (CCM), are detected to form the training data set and the test data set for the SVM, and the MAM is used to create the feature vectors for individual events in the data sets. Compared to the traditional methods, such as CCM, the proposed method can not only discriminate the neutron and gamma signals, even at lower energy levels, but also provide the corresponding classification accuracy for each event, which is useful in validating the discrimination. Meanwhile, the proposed method can also offer a predication of the classification for the under-energy-limit events

  8. Assessing the tobacco-rattle-virus-based vectors system as an efficient gene silencing technique in Datura stramonium (Solanaceae).

    Science.gov (United States)

    Eftekhariyan Ghamsari, Mohammad Reza; Karimi, Farah; Mousavi Gargari, Seyed Latif; Hosseini Tafreshi, Seyed Ali; Salami, Seyed Alireza

    2014-12-01

    Datura stramonium is a well-known medicinal plant, which is important for its alkaloids. There are intrinsic limitations for the natural production of alkaloids in plants; metabolic engineering methods can be effectively used to conquer these limitations. In order for this the genes involved in corresponding pathways need to be studied. Virus-Induced Gene Silencing is known as a functional genomics technique to knock-down expression of endogenous genes. In this study, we silenced phytoene desaturase as a marker gene in D. stramonium in a heterologous and homologous manner by tobacco-rattle-virus-based VIGS vectors. Recombinant TRV vector containing pds gene from D. stramonium (pTRV2-Dspds) was constructed and injected into seedlings. The plants injected with pTRV2-Dspds showed photobleaching 2 weeks after infiltration. Spectrophotometric analysis demonstrated that the amount of chlorophylls and carotenoids in leaves of the bleached plants decreased considerably compared to that of the control plants. Semi-Quantitative RT-PCR results also confirmed that the expression of pds gene in the silenced plants was significantly reduced in comparison with the control plants. The results showed that the viral vector was able to influence the levels of total alkaloid content in D. stramonium. Our results illustrated that TRV-based VIGS vectors are able to induce effective and reliable functional gene silencing in D. stramonium as an alternative tool for studying the genes of interest in this plant, such as the targeted genes in tropane alkaloid biosynthetic pathway. The present work is the first report of establishing VIGS as an efficient method for transient silencing of any gene of interest in D. stramonium.

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

    OpenAIRE

    Lin Wan; Zhou Huang; Xia Peng

    2016-01-01

    Within a digital map service environment, the rapid growth of Spatial Big-Data is driving new requirements for effective mechanisms for massive online vector map tile processing. The emergence of Not Only SQL (NoSQL) databases has resulted in a new data storage and management model for scalable spatial data deployments and fast tracking. They better suit the scenario of high-volume, low-latency network map services than traditional standalone high-performance computer (HPC) or relational data...

  10. Performance monitoring for coherent DP-QPSK systems based on stokes vectors analysis

    Science.gov (United States)

    Louchet, Hadrien; Koltchanov, Igor; Richter, André

    2010-12-01

    We show how to estimate accurately the Jones matrix of the transmission line by analyzing the Stokes vectors of DP-QPSK signals. This method can be used to perform in-situ PMD measurement in dual-polarization QPSK systems, and in addition to the constant modulus algorithm (CMA) to mitigate polarization-induced impairments. The applicability of this method to other modulation formats is discussed.

  11. A Fast Classification Method of Faults in Power Electronic Circuits Based on Support Vector Machines

    OpenAIRE

    Cui Jiang; Shi Ge; Gong Chunying

    2017-01-01

    Fault detection and location are important and front-end tasks in assuring the reliability of power electronic circuits. In essence, both tasks can be considered as the classification problem. This paper presents a fast fault classification method for power electronic circuits by using the support vector machine (SVM) as a classifier and the wavelet transform as a feature extraction technique. Using one-against-rest SVM and one-against-one SVM are two general approaches to fault classificatio...

  12. A Symbol Spotting Approach Based on the Vector Model and a Visual Vocabulary

    OpenAIRE

    Nguyen , Thi Oanh; Tabbone , Salvatore; Boucher , Alain

    2009-01-01

    International audience; This paper addresses the difficult problem of symbol spotting for graphic documents. We propose an approach where each graphic document is indexed as a text document by using the vector model and an inverted file structure. The method relies on a visual vocabulary built from a shape descriptor adapted to the document level and invariant under classical geometric transforms (rotation, scaling and translation). Regions of interest selected with high degree of confidence ...

  13. Swinepox virus vector-based vaccines: attenuation and biosafety assessments following subcutaneous prick inoculation.

    Science.gov (United States)

    Yuan, Xiaomin; Lin, Huixing; Li, Bin; He, Kongwang; Fan, Hongjie

    2018-02-07

    Swinepox virus (SPV) has several advantages as a potential clinical vector for a live vector vaccine. In this study, to obtain a safer and more efficient SPV vector, three SPV mutants, Δ003, Δ010, and ΔTK were successfully constructed. A virus replication experiment showed that these SPV mutants had lower replication abilities compared to wtSPV in 10 different host-derived cell lines. Animal experiments with mouse and rabbit models demonstrate that these three mutants and wtSPV did not cause any clinical signs of dermatitis. No fatalities were observed during a peritoneal challenge assay with these mutants and wtSPV in a mouse model. Additionally, the three mutants and wtSPV were not infectious at 60 h after vaccination in rabbit models. Furthermore, we evaluated biosafety, immunogenicity and effectiveness of the three mutants in 65 1-month-old piglets. The results show that there were no clinical signs of dermatitis in the Δ003 and ΔTK vaccination groups. However, mild signs were observed in the Δ010 vaccination groups when virus titres were high, and apparent clinical signs were observed at the sites of inoculation. Samples from all experimental pig groups were assessed by qPCR, and no SPV genomic DNA was found in five organs, faeces or blood. This suggests that the infectious abilities of wtSPV and the SPV mutants were poor and limited. In summary, this study indicates that two mutants of SPV, Δ003 and ΔTK, may be promising candidates for an attenuated viral vector in veterinary medicine.

  14. Operational Mosquito and Vector Borne Diseases Surveillance at Incirlik Air Base, Turkey

    Science.gov (United States)

    2017-01-01

    Leishmania spp., Plasmodium spp., and Rickettsia spp.5-10 Many of these for both threat assessments and as part of standard pest management operations...Long-term data on both vectors and associated patho- gens are maintained for later review and determination the Middle East, analysis of trends in...by Lanciotti et al.19 The PCR products were sequenced and compared to sequences in GenBank us- ing the BLAST† program. There were 6 pools of Culex

  15. A histogram-based technique for rapid vector extraction from PIV photographs

    Science.gov (United States)

    Humphreys, William M., Jr.

    1991-01-01

    A new analysis technique, performed totally in the image plane, is proposed which rapidly extracts all available vectors from individual interrogation regions on PIV photographs. The technique avoids the need for using Fourier transforms with the associated computational burden. The data acquisition and analysis procedure is described, and results of a preliminary simulation study to evaluate the accuracy of the technique are presented. Recently obtained PIV photographs are analyzed.

  16. 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.

  17. Implementing cargo movement into climate based risk assessment of vector-borne diseases.

    Science.gov (United States)

    Thomas, Stephanie Margarete; Tjaden, Nils Benjamin; van den Bos, Sanne; Beierkuhnlein, Carl

    2014-03-20

    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.

  18. Dendritic cell based PSMA immunotherapy for prostate cancer using a CD40-targeted adenovirus vector.

    Directory of Open Access Journals (Sweden)

    Briana Jill Williams

    Full Text Available Human prostate tumor vaccine and gene therapy trials using ex vivo methods to prime dendritic cells (DCs with prostate specific membrane antigen (PSMA have been somewhat successful, but to date the lengthy ex vivo manipulation of DCs has limited the widespread clinical utility of this approach. Our goal was to improve upon cancer vaccination with tumor antigens by delivering PSMA via a CD40-targeted adenovirus vector directly to DCs as an efficient means for activation and antigen presentation to T-cells. To test this approach, we developed a mouse model of prostate cancer by generating clonal derivatives of the mouse RM-1 prostate cancer cell line expressing human PSMA (RM-1-PSMA cells. To maximize antigen presentation in target cells, both MHC class I and TAP protein expression was induced in RM-1 cells by transduction with an Ad vector expressing interferon-gamma (Ad5-IFNγ. Administering DCs infected ex vivo with CD40-targeted Ad5-huPSMA, as well as direct intraperitoneal injection of the vector, resulted in high levels of tumor-specific CTL responses against RM-1-PSMA cells pretreated with Ad5-IFNγ as target cells. CD40 targeting significantly improved the therapeutic antitumor efficacy of Ad5-huPSMA encoding PSMA when combined with Ad5-IFNγ in the RM-1-PSMA model. These results suggest that a CD-targeted adenovirus delivering PSMA may be effective clinically for prostate cancer immunotherapy.

  19. 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.

  20. 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.

  1. 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

  2. Combined prediction model for supply risk in nuclear power equipment manufacturing industry based on support vector machine and decision tree

    International Nuclear Information System (INIS)

    Shi Chunsheng; Meng Dapeng

    2011-01-01

    The prediction index for supply risk is developed based on the factor identifying of nuclear equipment manufacturing industry. The supply risk prediction model is established with the method of support vector machine and decision tree, based on the investigation on 3 important nuclear power equipment manufacturing enterprises and 60 suppliers. Final case study demonstrates that the combination model is better than the single prediction model, and demonstrates the feasibility and reliability of this model, which provides a method to evaluate the suppliers and measure the supply risk. (authors)

  3. Image Retrieval Based on Local Mesh Vector Co-occurrence Pattern for Medical Diagnosis from MRI Brain Images.

    Science.gov (United States)

    Jenitta, A; Samson Ravindran, R

    2017-08-31

    In modern health-care, for evidence-based diagnosis, there is a requirement for an efficient image retrieval approach to retrieve the cases of interest that have similar characteristics from the large image databases. This paper presents a feature extraction approach that aims at extracting texture features present in the medical images using Local Pattern Descriptor (LPD) and Gray-level Co-occurrence Matrix (GLCM). As a main contribution, a novel local pattern named Local Mesh Vector Co-occurrence Pattern (LMVCoP) has been proposed by concatenating the Local Mesh Co-occurrence Pattern (LMCoP) and the Local Vector Co-occurrence Pattern (LVCoP). The fusion of GLCM with the Local Mesh Pattern (LMeP) and the Local Vector Pattern (LVP) produces LMCoP and LVCoP respectively. The LMVCoP method has been investigated on the Open Access Series of Imaging Studies (OASIS): a Magnetic Resonance Imaging (MRI) brain image database. LMVCoP descriptor achieves 87.57% of ARP and 53.21% of ARR which are higher than the existing methods of LTCoP, PVEP, LBDP, LMeP and LVP. The LMVCoP method enhances the retrieval results of LMeP/LVP from 81.36%/83.52% to 87.57% in terms of ARP on OASIS MRI brain database.

  4. Herpes simplex virus type 1-based amplicon vectors for fundamental research in neurosciences and gene therapy of neurological diseases.

    Science.gov (United States)

    Jerusalinsky, Diana; Baez, María Verónica; Epstein, Alberto Luis

    2012-01-01

    Somatic manipulation of the nervous system without the involvement of the germinal line appears as a powerful counterpart of the transgenic strategy. The use of viral vectors to produce specific, transient and localized knockout, knockdown, ectopic expression or overexpression of a gene, leads to the possibility of analyzing both in vitro and in vivo molecular basis of neural function. In this approach, viral particles engineered to carry transgenic sequences are delivered into discrete brain regions, to transduce cells that will express the transgenic products. Amplicons are replication-incompetent helper-dependent vectors derived from herpes simplex virus type 1 (HSV-1), with several advantages that potentiate their use in neurosciences: (1) minimal toxicity: amplicons do not encode any virus proteins, are neither toxic for the infected cells nor pathogenic for the inoculated animals and elicit low levels of adaptive immune responses; (2) extensive transgene capacity to carry up to 150-kb of foreign DNA; i.e., entire genes with regulatory sequences could be delivered; (3) widespread cellular tropism: amplicons can experimentally infect several cell types including glial cells, though naturally the virus infects mainly neurons and epithelial cells; (4) since the viral genome does not integrate into cellular chromosomes there is low probability to induce insertional mutagenesis. Recent investigations on gene transfer into the brain using these vectors, have focused on gene therapy of inherited genetic diseases affecting the nervous system, such as ataxias, or on neurodegenerative disorders using experimental models of Parkinson's or Alzheimer's disease. Another group of studies used amplicons to investigate complex neural functions such as neuroplasticity, anxiety, learning and memory. In this short review, we summarize recent data supporting the potential of HSV-1 based amplicon vector model for gene delivery and modulation of gene expression in primary cultures

  5. 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.

  6. 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.

  7. Vector velocimeter

    DEFF Research Database (Denmark)

    2012-01-01

    The present invention relates to a compact, reliable and low-cost vector velocimeter for example for determining velocities of particles suspended in a gas or fluid flow, or for determining velocity, displacement, rotation, or vibration of a solid surface, the vector velocimeter comprising a laser...

  8. Quantifying external and internal collagen organization from Stokes-vector-based second harmonic generation imaging polarimetry

    Science.gov (United States)

    Ávila, Francisco J.; del Barco, Oscar; Bueno, Juan M.

    2017-10-01

    Collagen organization has been analyzed at both external and internal scales by combining Stokes-vector polarimetry and second harmonic generation microscopy. A significant linear relationship between the diattenuation and the external collagen organization was found. The dominant orientation of the collagen fibers was found to run parallel to the axis of diattenuation. Information on the collagen chirality was obtained from the circular dichroism, which showed also a strong dependence with the internal collagen organization. The results show that certain polarimetric parameters might be useful to extract quantitative information and characterize collagen arrangement.

  9. 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.

  10. 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.

  11. Generation of a Vero-Based Packaging Cell Line to Produce SV40 Gene Delivery Vectors for Use in Clinical Gene Therapy Studies

    Directory of Open Access Journals (Sweden)

    Miguel G. Toscano

    2017-09-01

    Full Text Available Replication-defective (RD recombinant simian virus 40 (SV40-based gene delivery vectors hold a great potential for clinical applications because of their presumed non-immunogenicity and capacity to induce immune tolerance to the transgene products in humans. However, the clinical use of SV40 vectors has been hampered by the lack of a packaging cell line that produces replication-competent (RC free SV40 particles in the vector production process. To solve this problem, we have adapted the current SV40 vector genome used for the production of vector particles and generated a novel Vero-based packaging cell line named SuperVero that exclusively expresses the SV40 large T antigen. SuperVero cells produce similar numbers of SV40 vector particles compared to the currently used packaging cell lines, albeit in the absence of contaminating RC SV40 particles. Our unique SV40 vector platform named SVac paves the way to clinically test a whole new generation of SV40-based therapeutics for a broad range of important diseases.

  12. Multi-class parkinsonian disorders classification with quantitative MR markers and graph-based features using support vector machines.

    Science.gov (United States)

    Morisi, Rita; Manners, David Neil; Gnecco, Giorgio; Lanconelli, Nico; Testa, Claudia; Evangelisti, Stefania; Talozzi, Lia; Gramegna, Laura Ludovica; Bianchini, Claudio; Calandra-Buonaura, Giovanna; Sambati, Luisa; Giannini, Giulia; Cortelli, Pietro; Tonon, Caterina; Lodi, Raffaele

    2018-02-01

    In this study we attempt to automatically classify individual patients with different parkinsonian disorders, making use of pattern recognition techniques to distinguish among several forms of parkinsonisms (multi-class classification), based on a set of binary classifiers that discriminate each disorder from all others. We combine diffusion tensor imaging, proton spectroscopy and morphometric-volumetric data to obtain MR quantitative markers, which are provided to support vector machines with the aim of recognizing the different parkinsonian disorders. Feature selection is used to find the most important features for classification. We also exploit a graph-based technique on the set of quantitative markers to extract additional features from the dataset, and increase classification accuracy. When graph-based features are not used, the MR markers that are most frequently automatically extracted by the feature selection procedure reflect alterations in brain regions that are also usually considered to discriminate parkinsonisms in routine clinical practice. Graph-derived features typically increase the diagnostic accuracy, and reduce the number of features required. The results obtained in the work demonstrate that support vector machines applied to multimodal brain MR imaging and using graph-based features represent a novel and highly accurate approach to discriminate parkinsonisms, and a useful tool to assist the diagnosis. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. A Multi-Sensor Data Fusion Approach for Atrial Hypertrophy Disease Diagnosis Based on Characterized Support Vector Hyperspheres.

    Science.gov (United States)

    Zhu, Yungang; Liu, Dayou; Grosu, Radu; Wang, Xinhua; Duan, Hongying; Wang, Guodong

    2017-09-07

    Disease diagnosis can be performed based on fusing the data acquired by multiple medical sensors from patients, and it is a crucial task in sensor-based e-healthcare systems. However, it is a challenging problem that there are few effective diagnosis methods based on sensor data fusion for atrial hypertrophy disease. In this article, we propose a novel multi-sensor data fusion method for atrial hypertrophy diagnosis, namely, characterized support vector hyperspheres (CSVH). Instead of constructing a hyperplane, as a traditional support vector machine does, the proposed method generates "hyperspheres" to collect the discriminative medical information, since a hypersphere is more powerful for data description than a hyperplane. In detail, CSVH constructs two characterized hyperspheres for the classes of patient and healthy subject, respectively. The hypersphere for the patient class is developed in a weighted version so as to take the diversity of patient instances into consideration. The hypersphere for the class of healthy people keeps furthest away from the patient class in order to achieve maximum separation from the patient class. A query is labelled by membership functions defined based on the two hyperspheres. If the query is rejected by the two classes, the angle information of the query to outliers and overlapping-region data is investigated to provide the final decision. The experimental results illustrate that the proposed method achieves the highest diagnosis accuracy among the state-of-the-art methods.

  14. 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.

  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. 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

    2013-09-01

    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 XE "multi objective"  target will be classified as; trial and error, carefully and, expert cognitive skill. In the game play experiments employ 33 respondent players demonstrates that 61% of players have high trial and error, 21% have high carefully, 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. 

  17. 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.

  18. Predicting sumoylation sites using support vector machines based on various sequence features, conformational flexibility and disorder.

    Science.gov (United States)

    Yavuz, Ahmet Sinan; Sezerman, Osman Ugur

    2014-01-01

    Sumoylation, which is a reversible and dynamic post-translational modification, is one of the vital processes in a cell. Before a protein matures to perform its function, sumoylation may alter its localization, interactions, and possibly structural conformation. Abberations in protein sumoylation has been linked with a variety of disorders and developmental anomalies. Experimental approaches to identification of sumoylation sites may not be effective due to the dynamic nature of sumoylation, laborsome experiments and their cost. Therefore, computational approaches may guide experimental identification of sumoylation sites and provide insights for further understanding sumoylation mechanism. In this paper, the effectiveness of using various sequence properties in predicting sumoylation sites was investigated with statistical analyses and machine learning approach employing support vector machines. These sequence properties were derived from windows of size 7 including position-specific amino acid composition, hydrophobicity, estimated sub-window volumes, predicted disorder, and conformational flexibility. 5-fold cross-validation results on experimentally identified sumoylation sites revealed that our method successfully predicts sumoylation sites with a Matthew's correlation coefficient, sensitivity, specificity, and accuracy equal to 0.66, 73%, 98%, and 97%, respectively. Additionally, we have showed that our method compares favorably to the existing prediction methods and basic regular expressions scanner. By using support vector machines, a new, robust method for sumoylation site prediction was introduced. Besides, the possible effects of predicted conformational flexibility and disorder on sumoylation site recognition were explored computationally for the first time to our knowledge as an additional parameter that could aid in sumoylation site prediction.

  19. Adaptive image denoising based on support vector machine and wavelet description

    Science.gov (United States)

    An, Feng-Ping; Zhou, Xian-Wei

    2017-12-01

    Adaptive image denoising method decomposes the original image into a series of basic pattern feature images on the basis of wavelet description and constructs the support vector machine regression function to realize the wavelet description of the original image. The support vector machine method allows the linear expansion of the signal to be expressed as a nonlinear function of the parameters associated with the SVM. Using the radial basis kernel function of SVM, the original image can be extended into a MEXICAN function and a residual trend. This MEXICAN represents a basic image feature pattern. If the residual does not fluctuate, it can also be represented as a characteristic pattern. If the residuals fluctuate significantly, it is treated as a new image and the same decomposition process is repeated until the residuals obtained by the decomposition do not significantly fluctuate. Experimental results show that the proposed method in this paper performs well; especially, it satisfactorily solves the problem of image noise removal. It may provide a new tool and method for image denoising.

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

    Science.gov (United States)

    Aryan, Azadeh; Anderson, Michelle A E; Myles, Kevin M; Adelman, Zach N

    2013-01-01

    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.

  1. 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.

  2. Q-Axis Flux-Based Sensorless Vector Control of Induction Motor Taking into Account Iron Loss

    Science.gov (United States)

    Tsuji, Mineo; Chen, Shuo; Kai, Toshihiro; Hamasaki, Shin-Ichi

    This paper presents a sensorless vector control system for induction motors by taking into account iron loss, in which a flux-observer-based method is applied. Since the flux observer is constructed in a synchronously rotating reference frame with respect to the rotor flux of a current model and the iron loss resistance of parallel exiting circuit is used, the proposed system is very simple and the compensation of iron loss related to the operating frequency is directly realized while calculating rotor fluxes and slip frequency. The accuracies of estimated torque and speed are improved. The effectiveness of the proposed system has been verified by digital simulation and experimentation.

  3. 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 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.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.The results highlight the potential of plant-based odors and specifically linalool oxide, with or without carbon dioxide, for surveillance and mass trapping of malaria

  4. A novel minicircle vector based system for inhibting the replication and gene expression of enterovirus 71 and coxsackievirus A16.

    Science.gov (United States)

    Yang, Zhuo; Li, Guodong; Zhang, Yingqiu; Liu, Xiaoman; Tien, Po

    2012-11-01

    Enterovirus 71 (EV 71) and Coxsackievirus A16 (CA 16) are two major causative agents of hand, foot and mouth disease (HFMD). They have been associated with severe neurological and cardiological complications worldwide, and have caused significant mortalities during large-scale outbreaks in China. Currently, there are no effective treatments against EV 71 and CA 16 infections. We now describe the development of a novel minicircle vector based RNA interference (RNAi) system as a therapeutic approach to inhibiting EV 71 and CA 16 replication. Small interfering RNA (siRNA) molecules targeting the conserved regions of the 3C(pro) and 3D(pol) function gene of the EV 71 and CA 16 China strains were designed based on their nucleotide sequences available in GenBank. This RNAi system was found to effectively block the replication and gene expression of these viruses in rhabdomyosarcoma (RD) cells and virus-infected mice model. The inhibitory effects were confirmed by a corresponding decrease in viral RNA, viral protein, and progeny virus production. In addition, no significant adverse off-target silencing or cytotoxic effects were observed. These results demonstrated the potential and feasibility of this novel minicircle vector based RNAi system for antiviral therapy against EV 71 and CA 16 infection. Copyright © 2012 Elsevier B.V. All rights reserved.

  5. Deep vector-based convolutional neural network approach for automatic recognition of colonies of induced pluripotent stem cells.

    Directory of Open Access Journals (Sweden)

    Muthu Subash Kavitha

    Full Text Available Pluripotent stem cells can potentially be used in clinical applications as a model for studying disease progress. This tracking of disease-causing events in cells requires constant assessment of the quality of stem cells. Existing approaches are inadequate for robust and automated differentiation of stem cell colonies. In this study, we developed a new model of vector-based convolutional neural network (V-CNN with respect to extracted features of the induced pluripotent stem cell (iPSC colony for distinguishing colony characteristics. A transfer function from the feature vectors to the virtual image was generated at the front of the CNN in order for classification of feature vectors of healthy and unhealthy colonies. The robustness of the proposed V-CNN model in distinguishing colonies was compared with that of the competitive support vector machine (SVM classifier based on morphological, textural, and combined features. Additionally, five-fold cross-validation was used to investigate the performance of the V-CNN model. The precision, recall, and F-measure values of the V-CNN model were comparatively higher than those of the SVM classifier, with a range of 87-93%, indicating fewer false positives and false negative rates. Furthermore, for determining the quality of colonies, the V-CNN model showed higher accuracy values based on morphological (95.5%, textural (91.0%, and combined (93.2% features than those estimated with the SVM classifier (86.7, 83.3, and 83.4%, respectively. Similarly, the accuracy of the feature sets using five-fold cross-validation was above 90% for the V-CNN model, whereas that yielded by the SVM model was in the range of 75-77%. We thus concluded that the proposed V-CNN model outperforms the conventional SVM classifier, which strongly suggests that it as a reliable framework for robust colony classification of iPSCs. It can also serve as a cost-effective quality recognition tool during culture and other experimental

  6. Cloning vector

    Science.gov (United States)

    Guilfoyle, R.A.; Smith, L.M.

    1994-12-27

    A vector comprising a filamentous phage sequence containing a first copy of filamentous phage gene X and other sequences necessary for the phage to propagate is disclosed. The vector also contains a second copy of filamentous phage gene X downstream from a promoter capable of promoting transcription in a bacterial host. In a preferred form of the present invention, the filamentous phage is M13 and the vector additionally includes a restriction endonuclease site located in such a manner as to substantially inactivate the second gene X when a DNA sequence is inserted into the restriction site. 2 figures.

  7. Cloning vector

    Science.gov (United States)

    Guilfoyle, Richard A.; Smith, Lloyd M.

    1994-01-01

    A vector comprising a filamentous phage sequence containing a first copy of filamentous phage gene X and other sequences necessary for the phage to propagate is disclosed. The vector also contains a second copy of filamentous phage gene X downstream from a promoter capable of promoting transcription in a bacterial host. In a preferred form of the present invention, the filamentous phage is M13 and the vector additionally includes a restriction endonuclease site located in such a manner as to substantially inactivate the second gene X when a DNA sequence is inserted into the restriction site.

  8. Very low speed performance of active flux based sensorless control: interior permanent magnet synchronous motor vector control versus direct torque and flux control

    DEFF Research Database (Denmark)

    Paicu, M. C.; Boldea, I.; Andreescu, G. D.

    2009-01-01

    This study is focused on very low speed performance comparison between two sensorless control systems based on the novel ‘active flux' concept, that is, the current/voltage vector control versus direct torque and flux control (DTFC) for interior permanent magnet synchronous motor (IPMSM) drives...... with space vector modulation (SVM), without signal injection. The active flux, defined as the flux that multiplies iq current in the dq-model torque expression of all ac machines, is easily obtained from the stator-flux vector and has the rotor position orientation. Therefore notable simplification...

  9. Hybrid Radar Emitter Recognition Based on Rough k-Means Classifier and Relevance Vector Machine

    Science.gov (United States)

    Yang, Zhutian; Wu, Zhilu; Yin, Zhendong; Quan, Taifan; Sun, Hongjian

    2013-01-01

    Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for recognizing radar emitter signals. In this paper, a hybrid recognition approach is presented that classifies radar emitter signals by exploiting the different separability of samples. The proposed approach comprises two steps, namely the primary signal recognition and the advanced signal recognition. In the former step, a novel rough k-means classifier, which comprises three regions, i.e., certain area, rough area and uncertain area, is proposed to cluster the samples of radar emitter signals. In the latter step, the samples within the rough boundary are used to train the relevance vector machine (RVM). Then RVM is used to recognize the samples in the uncertain area; therefore, the classification accuracy is improved. Simulation results show that, for recognizing radar emitter signals, the proposed hybrid recognition approach is more accurate, and presents lower computational complexity than traditional approaches. PMID:23344380

  10. Support vector machine-based feature extractor for L/H transitions in JET

    Energy Technology Data Exchange (ETDEWEB)

    Gonzalez, S.; Vega, J.; Pereira, A. [Asociacion EURATOM/CIEMAT para Fusion, Madrid 28040 (Spain); Murari, A. [Consorzio RFX, Associazione EURATOM ENEA per la Fusione, Padova 4-35127 (Italy); Ramirez, J. M.; Dormido-Canto, S. [Departamento de Informatica y Automatica, UNED, Madrid 28040 (Spain); Collaboration: JET-EFDA Contributors

    2010-10-15

    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.

  11. Support-vector-machine tree-based domain knowledge learning toward automated sports video classification

    Science.gov (United States)

    Xiao, Guoqiang; Jiang, Yang; Song, Gang; Jiang, Jianmin

    2010-12-01

    We propose a support-vector-machine (SVM) tree to hierarchically learn from domain knowledge represented by low-level features toward automatic classification of sports videos. The proposed SVM tree adopts a binary tree structure to exploit the nature of SVM's binary classification, where each internal node is a single SVM learning unit, and each external node represents the classified output type. Such a SVM tree presents a number of advantages, which include: 1. low computing cost; 2. integrated learning and classification while preserving individual SVM's learning strength; and 3. flexibility in both structure and learning modules, where different numbers of nodes and features can be added to address specific learning requirements, and various learning models can be added as individual nodes, such as neural networks, AdaBoost, hidden Markov models, dynamic Bayesian networks, etc. Experiments support that the proposed SVM tree achieves good performances in sports video classifications.

  12. Linear and support vector regressions based on geometrical correlation of data

    Directory of Open Access Journals (Sweden)

    Kaijun Wang

    2007-10-01

    Full Text Available Linear regression (LR and support vector regression (SVR are widely used in data analysis. Geometrical correlation learning (GcLearn was proposed recently to improve the predictive ability of LR and SVR through mining and using correlations between data of a variable (inner correlation. This paper theoretically analyzes prediction performance of the GcLearn method and proves that GcLearn LR and SVR will have better prediction performance than traditional LR and SVR for prediction tasks when good inner correlations are obtained and predictions by traditional LR and SVR are far away from their neighbor training data under inner correlation. This gives the applicable condition of GcLearn method.

  13. 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.

  14. Quantitative Assessment of Pap Smear Cells by PC-Based Cytopathologic Image Analysis System and Support Vector Machine

    Science.gov (United States)

    Huang, Po-Chi; Chan, Yung-Kuan; Chan, Po-Chou; Chen, Yung-Fu; Chen, Rung-Ching; Huang, Yu-Ruei

    Cytologic screening has been widely used for controlling the prevalence of cervical cancer. Errors from sampling, screening and interpretation, still concealed some unpleasant results. This study aims at designing a cellular image analysis system based on feasible and available software and hardware for a routine cytologic laboratory. Totally 1814 cellular images from the liquid-based cervical smears with Papanicolaou stain in 100x, 200x, and 400x magnification were captured by a digital camera. Cell images were reviewed by pathologic experts with peer agreement and only 503 images were selected for further study. The images were divided into 4 diagnostic categories. A PC-based cellular image analysis system (PCCIA) was developed for computing morphometric parameters. Then support vector machine (SVM) was used to classify signature patterns. The results show that the selected 13 morphometric parameters can be used to correctly differentiate the dysplastic cells from the normal cells (pgynecologic cytologic specimens.

  15. Ontology-based vector space model and fuzzy query expansion to retrieve knowledge on medical computational problem solutions.

    Science.gov (United States)

    Bratsas, Charalampos; Koutkias, Vassilis; Kaimakamis, Evangelos; Bamidis, Panagiotis; Maglaveras, Nicos

    2007-01-01

    Medical Computational Problem (MCP) solving is related to medical problems and their computerized algorithmic solutions. In this paper, an extension of an ontology-based model to fuzzy logic is presented, as a means to enhance the information retrieval (IR) procedure in semantic management of MCPs. We present herein the methodology followed for the fuzzy expansion of the ontology model, the fuzzy query expansion procedure, as well as an appropriate ontology-based Vector Space Model (VSM) that was constructed for efficient mapping of user-defined MCP search criteria and MCP acquired knowledge. The relevant fuzzy thesaurus is constructed by calculating the simultaneous occurrences of terms and the term-to-term similarities derived from the ontology that utilizes UMLS (Unified Medical Language System) concepts by using Concept Unique Identifiers (CUI), synonyms, semantic types, and broader-narrower relationships for fuzzy query expansion. The current approach constitutes a sophisticated advance for effective, semantics-based MCP-related IR.

  16. Near-field and high-resolution cylindrical noise source location method based on vector sound pressure array

    Directory of Open Access Journals (Sweden)

    ZUO Xiang

    2017-08-01

    Full Text Available The existing underwater noise source near-field location method usually assumes that the measurement plane is flat, which increases the difficulty of applying the underwater noise target test for cylindrical distribution. Simultaneously, the conventional near-field focused beam has a lower spatial resolution when used to locate an underwater noise source with cylindrical distribution. Moreover, the near-field underwater noise source location method based on the sound pressure array has a left and right side fuzzy problem. In order to solve these problems, by establishing the near-field measurement model of the noise source with cylindrical distribution as the measurement surface, and combining the unilateral directivity of the vector hydrophone and the high resolution characteristics of the MUSIC algorithm, a near-field and high resolution location method is proposed for cylindrical distribution based on vector sound pressure, and a computer simulation is carried out. The results show that the method can use a smaller array aperture to locate the underwater noise source, enabling it to be used to locate and recognize the noise sources of complex and large-scale cylindrical systems.

  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. Frequent dual initiation of reverse transcription in murine leukemia virus-based vectors containing two primer-binding sites

    International Nuclear Information System (INIS)

    Voronin, Yegor A.; Pathak, Vinay K.

    2003-01-01

    Retroviruses package two copies of viral RNA into each virion. Although each RNA contains a primer-binding site for initiation of DNA synthesis, it is unknown whether reverse transcription is initiated on both RNAs. To determine whether a single virion is capable of initiating reverse transcription more than once, we constructed a murine leukemia virus-based vector containing a second primer-binding site (PBS) derived from spleen necrosis virus and inserted the green fluorescent protein gene (GFP) between the two PBSs. Initiation of reverse transcription at either PBS results in a provirus that expresses GFP. However, initiation at both PBSs can result in the deletion of GFP, which can be detected by flow cytometry and Southern blotting analysis. Approximately 22-29% of the proviruses formed deleted the GFP in a single replication cycle, indicating the minimum proportion of virions that initiated reverse transcription on both PBSs. These results show that a significant proportion of MLV-based vectors containing two PBSs have the capacity to initiate reverse transcription more than once

  19. 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.

  20. Dynamic Price Vector Formation Model-Based Automatic Demand Response Strategy for PV-Assisted EV Charging Stations

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Qifang; Wang, Fei; Hodge, Bri-Mathias; Zhang, Jianhua; Li, Zhigang; Shafie-Khah, Miadreza; Catalao, Joao P. S.

    2017-11-01

    A real-time price (RTP)-based automatic demand response (ADR) strategy for PV-assisted electric vehicle (EV) Charging Station (PVCS) without vehicle to grid is proposed. The charging process is modeled as a dynamic linear program instead of the normal day-ahead and real-time regulation strategy, to capture the advantages of both global and real-time optimization. Different from conventional price forecasting algorithms, a dynamic price vector formation model is proposed based on a clustering algorithm to form an RTP vector for a particular day. A dynamic feasible energy demand region (DFEDR) model considering grid voltage profiles is designed to calculate the lower and upper bounds. A deduction method is proposed to deal with the unknown information of future intervals, such as the actual stochastic arrival and departure times of EVs, which make the DFEDR model suitable for global optimization. Finally, both the comparative cases articulate the advantages of the developed methods and the validity in reducing electricity costs, mitigating peak charging demand, and improving PV self-consumption of the proposed strategy are verified through simulation scenarios.

  1. A Support Vector Learning-Based Particle Filter Scheme for Target Localization in Communication-Constrained Underwater Acoustic Sensor Networks.

    Science.gov (United States)

    Li, Xinbin; Zhang, Chenglin; Yan, Lei; Han, Song; Guan, Xinping

    2017-12-21

    Target localization, which aims to estimate the location of an unknown target, is one of the key issues in applications of underwater acoustic sensor networks (UASNs). However, the constrained property of an underwater environment, such as restricted communication capacity of sensor nodes and sensing noises, makes target localization a challenging problem. This paper relies on fractional sensor nodes to formulate a support vector learning-based particle filter algorithm for the localization problem in communication-constrained underwater acoustic sensor networks. A node-selection strategy is exploited to pick fractional sensor nodes with short-distance pattern to participate in the sensing process at each time frame. Subsequently, we propose a least-square support vector regression (LSSVR)-based observation function, through which an iterative regression strategy is used to deal with the distorted data caused by sensing noises, to improve the observation accuracy. At the same time, we integrate the observation to formulate the likelihood function, which effectively update the weights of particles. Thus, the particle effectiveness is enhanced to avoid "particle degeneracy" problem and improve localization accuracy. In order to validate the performance of the proposed localization algorithm, two different noise scenarios are investigated. The simulation results show that the proposed localization algorithm can efficiently improve the localization accuracy. In addition, the node-selection strategy can effectively select the subset of sensor nodes to improve the communication efficiency of the sensor network.

  2. 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.

  3. Assimilation of PFISR Data Using Support Vector Regression and Ground Based Camera Constraints

    Science.gov (United States)

    Clayton, R.; Lynch, K. A.; Nicolls, M. J.; Hampton, D. L.; Michell, R.; Samara, M.; Guinther, J.

    2013-12-01

    In order to best interpret the information gained from multipoint in situ measurements, a Support Vector Regression algorithm is being developed to interpret the data collected from the instruments in the context of ground observations (such as those from camera or radar array). The idea behind SVR is to construct the simplest function that models the data with the least squared error, subject to constraints given by the user. Constraints can be brought into the algorithm from other data sources or from models. As is often the case with data, a perfect solution to such a problem may be impossible, thus 'slack' may be introduced to control how closely the model adheres to the data. The algorithm employs kernels, and chooses radial basis functions as an appropriate kernel. The current SVR code can take input data as one to three dimensional scalars or vectors, and may also include time. External data can be incorporated and assimilated into a model of the environment. Regions of minimal and maximal values are allowed to relax to the sample average (or a user-supplied model) on size and time scales determined by user input, known as feature sizes. These feature sizes can vary for each degree of freedom if the user desires. The user may also select weights for each data point, if it is desirable to weight parts of the data differently. In order to test the algorithm, Poker Flat Incoherent Scatter Radar (PFISR) and MICA sounding rocket data are being used as sample data. The PFISR data consists of many beams, each with multiple ranges. In addition to analyzing the radar data as it stands, the algorithm is being used to simulate data from a localized ionospheric swarm of Cubesats using existing PFISR data. The sample points of the radar at one altitude slice can serve as surrogates for satellites in a cubeswarm. The number of beams of the PFISR radar can then be used to see what the algorithm would output for a swarm of similar size. By using PFISR data in the 15-beam to

  4. Cloud Monitoring for Solar Plants with Support Vector Machine Based Fault Detection System

    Directory of Open Access Journals (Sweden)

    Hong-Chan Chang

    2014-01-01

    Full Text Available This study endeavors to develop a cloud monitoring system for solar plants. This system incorporates numerous subsystems, such as a geographic information system, an instantaneous power-consumption information system, a reporting system, and a failure diagnosis system. Visual C# was integrated with ASP.NET and SQL technologies for the proposed monitoring system. A user interface for database management system was developed to enable users to access solar power information and management systems. In addition, by using peer-to-peer (P2P streaming technology and audio/video encoding/decoding technology, real-time video data can be transmitted to the client end, providing instantaneous and direct information. Regarding smart failure diagnosis, the proposed system employs the support vector machine (SVM theory to train failure mathematical models. The solar power data are provided to the SVM for analysis in order to determine the failure types and subsequently eliminate failures at an early stage. The cloud energy-management platform developed in this study not only enhances the management and maintenance efficiency of solar power plants but also increases the market competitiveness of solar power generation and renewable energy.

  5. A Fast Classification Method of Faults in Power Electronic Circuits Based on Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Cui Jiang

    2017-12-01

    Full Text Available Fault detection and location are important and front-end tasks in assuring the reliability of power electronic circuits. In essence, both tasks can be considered as the classification problem. This paper presents a fast fault classification method for power electronic circuits by using the support vector machine (SVM as a classifier and the wavelet transform as a feature extraction technique. Using one-against-rest SVM and one-against-one SVM are two general approaches to fault classification in power electronic circuits. However, these methods have a high computational complexity, therefore in this design we employ a directed acyclic graph (DAG SVM to implement the fault classification. The DAG SVM is close to the one-against-one SVM regarding its classification performance, but it is much faster. Moreover, in the presented approach, the DAG SVM is improved by introducing the method of Knearest neighbours to reduce some computations, so that the classification time can be further reduced. A rectifier and an inverter are demonstrated to prove effectiveness of the presented design.

  6. Searches for the Higgs Boson at the LHC based on its couplings to Vector Bosons

    CERN Document Server

    Hackstein, C

    One of the primary goals of the Large Hadron Collider (LHC) is the sea rch for the Higgs Boson. All Higgs searches rely heavily on Monte Carlo predic tions of both the signal and background processes. These simulations n ecessarily include models and assumptions not derived from first principles. Esp ecially the process of hadronization and the underlying event are only par tially un- derstood and differ strongly between different generators. As a r esult, the predictions can be wrong for special regions of phase space. Ther efore, pre- dictions by several programs should be compared to gain an estimat e of the uncertainty of the observables considered. In this work, two different Monte Carlo generators were compared in their pre- dictions for a Higgs search in the Vector Boson Fusion (VBF) Higgs pr oduction channel with subsequent decay into W bosons that decay leptonica lly in turn. A significant difference in the description of both signal and backgro und was found between the two generators. As the Monte...

  7. Automatic Geometric Processing for Very High Resolution Optical Satellite Data Based on Vector Roads and Orthophotos

    Directory of Open Access Journals (Sweden)

    Peter Pehani

    2016-04-01

    Full Text Available In response to the increasing need for fast satellite image processing SPACE-SI developed STORM—a fully automatic image processing chain that performs all processing steps from the input optical images to web-delivered map-ready products for various sensors. This paper focuses on the automatic geometric corrections module and its adaptation to very high resolution (VHR multispectral images. In the automatic ground control points (GCPs extraction sub-module a two-step algorithm that utilizes vector roads as a reference layer and delivers GCPs for high resolution RapidEye images with near pixel accuracy was initially implemented. Super-fine positioning of individual GCPs onto an aerial orthophoto was introduced for VHR images. The enhanced algorithm is capable of achieving accuracy of approximately 1.5 pixels on WorldView-2 data. In the case of RapidEye images the accuracies of the physical sensor model reach sub-pixel values at independent check points. When compared to the reference national aerial orthophoto the accuracies of WorldView-2 orthoimages automatically produced with the rational function model reach near-pixel values. On a heterogeneous set of 41 RapidEye images the rate of automatic processing reached 97.6%. Image processing times remained under one hour for standard-size images of both sensor types.

  8. Support vector machines classification of hERG liabilities based on atom types.

    Science.gov (United States)

    Jia, Lei; Sun, Hongmao

    2008-06-01

    Drug-induced long QT syndrome (LQTS) can cause critical cardiovascular side effects and has accounted for the withdrawal of several drugs from the market. Blockade of the potassium ion channel encoded by the human ether-a-go-go-related gene (hERG) has been identified as a major contributor to drug-induced LQTS. Experimental measurement of hERG activity for each compound in development is costly and time-consuming, thus it is beneficial to develop a predictive hERG model. Here, we present a hERG classification model formulated using support vector machines (SVM) as machine learning method and using atom types as molecular descriptors. The training set used in this study was composed of 977 corporate compounds with hERG activities measured under the same conditions. The impact of soft margin and kernel function on the performance of the SVM models was examined. The robustness of SVM was evaluated by comparing the predictive power of the models built with 90%, 50%, and 10% of the training set data. The final SVM model was able to correctly classify 94% of an external testing set containing 66 drug molecules. The most important atom types with respect to discriminative power were extracted and analyzed.

  9. Virtual-view PSNR prediction based on a depth distortion tolerance model and support vector machine.

    Science.gov (United States)

    Chen, Fen; Chen, Jiali; Peng, Zongju; Jiang, Gangyi; Yu, Mei; Chen, Hua; Jiao, Renzhi

    2017-10-20

    Quality prediction of virtual-views is important for free viewpoint video systems, and can be used as feedback to improve the performance of depth video coding and virtual-view rendering. In this paper, an efficient virtual-view peak signal to noise ratio (PSNR) prediction method is proposed. First, the effect of depth distortion on virtual-view quality is analyzed in detail, and a depth distortion tolerance (DDT) model that determines the DDT range is presented. Next, the DDT model is used to predict the virtual-view quality. Finally, a support vector machine (SVM) is utilized to train and obtain the virtual-view quality prediction model. Experimental results show that the Spearman's rank correlation coefficient and root mean square error between the actual PSNR and the predicted PSNR by DDT model are 0.8750 and 0.6137 on average, and by the SVM prediction model are 0.9109 and 0.5831. The computational complexity of the SVM method is lower than the DDT model and the state-of-the-art methods.

  10. Sensor network based solar forecasting using a local vector autoregressive ridge framework

    Energy Technology Data Exchange (ETDEWEB)

    Xu, J. [Stony Brook Univ., NY (United States); Yoo, S. [Brookhaven National Lab. (BNL), Upton, NY (United States); Heiser, J. [Brookhaven National Lab. (BNL), Upton, NY (United States); Kalb, P. [Brookhaven National Lab. (BNL), Upton, NY (United States)

    2016-04-04

    The significant improvements and falling costs of photovoltaic (PV) technology make solar energy a promising resource, yet the cloud induced variability of surface solar irradiance inhibits its effective use in grid-tied PV generation. Short-term irradiance forecasting, especially on the minute scale, is critically important for grid system stability and auxiliary power source management. Compared to the trending sky imaging devices, irradiance sensors are inexpensive and easy to deploy but related forecasting methods have not been well researched. The prominent challenge of applying classic time series models on a network of irradiance sensors is to address their varying spatio-temporal correlations due to local changes in cloud conditions. We propose a local vector autoregressive framework with ridge regularization to forecast irradiance without explicitly determining the wind field or cloud movement. By using local training data, our learned forecast model is adaptive to local cloud conditions and by using regularization, we overcome the risk of overfitting from the limited training data. Our systematic experimental results showed an average of 19.7% RMSE and 20.2% MAE improvement over the benchmark Persistent Model for 1-5 minute forecasts on a comprehensive 25-day dataset.

  11. Adaptive Digital Watermarking Scheme Based on Support Vector Machines and Optimized Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Xiaoyi Zhou

    2018-01-01

    Full Text Available Digital watermarking is an effective solution to the problem of copyright protection, thus maintaining the security of digital products in the network. An improved scheme to increase the robustness of embedded information on the basis of discrete cosine transform (DCT domain is proposed in this study. The embedding process consisted of two main procedures. Firstly, the embedding intensity with support vector machines (SVMs was adaptively strengthened by training 1600 image blocks which are of different texture and luminance. Secondly, the embedding position with the optimized genetic algorithm (GA was selected. To optimize GA, the best individual in the first place of each generation directly went into the next generation, and the best individual in the second position participated in the crossover and the mutation process. The transparency reaches 40.5 when GA’s generation number is 200. A case study was conducted on a 256 × 256 standard Lena image with the proposed method. After various attacks (such as cropping, JPEG compression, Gaussian low-pass filtering (3,0.5, histogram equalization, and contrast increasing (0.5,0.6 on the watermarked image, the extracted watermark was compared with the original one. Results demonstrate that the watermark can be effectively recovered after these attacks. Even though the algorithm is weak against rotation attacks, it provides high quality in imperceptibility and robustness and hence it is a successful candidate for implementing novel image watermarking scheme meeting real timelines.

  12. BLProt: Prediction of bioluminescent proteins based on support vector machine and relieff feature selection

    KAUST Repository

    Kandaswamy, Krishna Kumar

    2011-08-17

    Background: Bioluminescence is a process in which light is emitted by a living organism. Most creatures that emit light are sea creatures, but some insects, plants, fungi etc, also emit light. The biotechnological application of bioluminescence has become routine and is considered essential for many medical and general technological advances. Identification of bioluminescent proteins is more challenging due to their poor similarity in sequence. So far, no specific method has been reported to identify bioluminescent proteins from primary sequence.Results: In this paper, we propose a novel predictive method that uses a Support Vector Machine (SVM) and physicochemical properties to predict bioluminescent proteins. BLProt was trained using a dataset consisting of 300 bioluminescent proteins and 300 non-bioluminescent proteins, and evaluated by an independent set of 141 bioluminescent proteins and 18202 non-bioluminescent proteins. To identify the most prominent features, we carried out feature selection with three different filter approaches, ReliefF, infogain, and mRMR. We selected five different feature subsets by decreasing the number of features, and the performance of each feature subset was evaluated.Conclusion: BLProt achieves 80% accuracy from training (5 fold cross-validations) and 80.06% accuracy from testing. The performance of BLProt was compared with BLAST and HMM. High prediction accuracy and successful prediction of hypothetical proteins suggests that BLProt can be a useful approach to identify bioluminescent proteins from sequence information, irrespective of their sequence similarity. 2011 Kandaswamy et al; licensee BioMed Central Ltd.

  13. Intelligent Fault Diagnosis of Delta 3D Printers Using Attitude Sensors Based on Support Vector Machines.

    Science.gov (United States)

    He, Kun; Yang, Zhijun; Bai, Yun; Long, Jianyu; Li, Chuan

    2018-04-23

    Health condition is a vital factor affecting printing quality for a 3D printer. In this work, an attitude monitoring approach is proposed to diagnose the fault of the delta 3D printer using support vector machines (SVM). An attitude sensor was mounted on the moving platform of the printer to monitor its 3-axial attitude angle, angular velocity, vibratory acceleration and magnetic field intensity. The attitude data of the working printer were collected under different conditions involving 12 fault types and a normal condition. The collected data were analyzed for diagnosing the health condition. To this end, the combination of binary classification, one-against-one with least-square SVM, was adopted for fault diagnosis modelling by using all channels of attitude monitoring data in the experiment. For comparison, each one channel of the attitude monitoring data was employed for model training and testing. On the other hand, a back propagation neural network (BPNN) was also applied to diagnose fault using the same data. The best fault diagnosis accuracy (94.44%) was obtained when all channels of the attitude monitoring data were used with SVM modelling. The results indicate that the attitude monitoring with SVM is an effective method for the fault diagnosis of delta 3D printers.

  14. An Improved Azimuth Angle Estimation Method with a Single Acoustic Vector Sensor Based on an Active Sonar Detection System

    Science.gov (United States)

    Zhao, Anbang; Ma, Lin; Ma, Xuefei; Hui, Juan

    2017-01-01

    In this paper, an improved azimuth angle estimation method with a single acoustic vector sensor (AVS) is proposed based on matched filtering theory. The proposed method is mainly applied in an active sonar detection system. According to the conventional passive method based on complex acoustic intensity measurement, the mathematical and physical model of this proposed method is described in detail. The computer simulation and lake experiments results indicate that this method can realize the azimuth angle estimation with high precision by using only a single AVS. Compared with the conventional method, the proposed method achieves better estimation performance. Moreover, the proposed method does not require complex operations in frequency-domain and achieves computational complexity reduction. PMID:28230763

  15. Robust anti-synchronization of uncertain chaotic systems based on multiple-kernel least squares support vector machine modeling

    International Nuclear Information System (INIS)

    Chen Qiang; Ren Xuemei; Na Jing

    2011-01-01

    Highlights: Model uncertainty of the system is approximated by multiple-kernel LSSVM. Approximation errors and disturbances are compensated in the controller design. Asymptotical anti-synchronization is achieved with model uncertainty and disturbances. Abstract: In this paper, we propose a robust anti-synchronization scheme based on multiple-kernel least squares support vector machine (MK-LSSVM) modeling for two uncertain chaotic systems. The multiple-kernel regression, which is a linear combination of basic kernels, is designed to approximate system uncertainties by constructing a multiple-kernel Lagrangian function and computing the corresponding regression parameters. Then, a robust feedback control based on MK-LSSVM modeling is presented and an improved update law is employed to estimate the unknown bound of the approximation error. The proposed control scheme can guarantee the asymptotic convergence of the anti-synchronization errors in the presence of system uncertainties and external disturbances. Numerical examples are provided to show the effectiveness of the proposed method.

  16. An Improved Azimuth Angle Estimation Method with a Single Acoustic Vector Sensor Based on an Active Sonar Detection System.

    Science.gov (United States)

    Zhao, Anbang; Ma, Lin; Ma, Xuefei; Hui, Juan

    2017-02-20

    In this paper, an improved azimuth angle estimation method with a single acoustic vector sensor (AVS) is proposed based on matched filtering theory. The proposed method is mainly applied in an active sonar detection system. According to the conventional passive method based on complex acoustic intensity measurement, the mathematical and physical model of this proposed method is described in detail. The computer simulation and lake experiments results indicate that this method can realize the azimuth angle estimation with high precision by using only a single AVS. Compared with the conventional method, the proposed method achieves better estimation performance. Moreover, the proposed method does not require complex operations in frequencydomain and achieves computational complexity reduction.

  17. Wavelength tunable L Band polarization-locked vector soliton fiber laser based on SWCNT-SA and CFBG

    Science.gov (United States)

    Yan, Yaxi; Wang, Jiaqi; Wang, Liang; Cheng, Zhenzhou

    2018-04-01

    Wavelength tunable L-Band polarization-locked vector soliton fiber laser based on single-walled carbon nanotube saturable absorber (SWCNT-SA) and chirped fiber Bragg grating (CFBG) is presented for the first time. By inserting the SWCNT-SA into an all-fiber laser cavity, polarization-locked vector solitons (PLVS) are obtained. The CFBG glued on a plastic cantilever is used for wavelength tuning. By mechanically bending the cantilever, the center wavelength of the PLVS pulses can be continuously tuned from 1606.8 nm to 1614 nm, while the polarization-locked state is kept stable. The properties and dynamics of PLVSs are experimentally investigated and stable PLVS operation including high-order PLVSs is demonstrated. The pulse width and repetition rate are 7.06 ps and 11.9 MHz at a wavelength of 1611 nm, respectively. This work demonstrates the feasibility of using polarization-insensitive CFBG to realize wavelength tuning in PLVS fiber laser.

  18. Performance optimization of Sparse Matrix-Vector Multiplication for multi-component PDE-based applications using GPUs

    KAUST Repository

    Abdelfattah, Ahmad

    2016-05-23

    Simulations of many multi-component PDE-based applications, such as petroleum reservoirs or reacting flows, are dominated by the solution, on each time step and within each Newton step, of large sparse linear systems. The standard solver is a preconditioned Krylov method. Along with application of the preconditioner, memory-bound Sparse Matrix-Vector Multiplication (SpMV) is the most time-consuming operation in such solvers. Multi-species models produce Jacobians with a dense block structure, where the block size can be as large as a few dozen. Failing to exploit this dense block structure vastly underutilizes hardware capable of delivering high performance on dense BLAS operations. This paper presents a GPU-accelerated SpMV kernel for block-sparse matrices. Dense matrix-vector multiplications within the sparse-block structure leverage optimization techniques from the KBLAS library, a high performance library for dense BLAS kernels. The design ideas of KBLAS can be applied to block-sparse matrices. Furthermore, a technique is proposed to balance the workload among thread blocks when there are large variations in the lengths of nonzero rows. Multi-GPU performance is highlighted. The proposed SpMV kernel outperforms existing state-of-the-art implementations using matrices with real structures from different applications. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  19. A New Power-Factor-Based Vector Control Method for Sensorless Drive of Permanent-Magnet Synchronous Motors

    Science.gov (United States)

    Shinnaka, Shinji

    As a simple vector control method for sensorless drives of permanent-magnet synchronous motors, the so-called “Power-Factor-Based (PFB) Vector Control Method” has been proposed. The conventional PFB method directly estimates the phase of the quasi-optimal stator current through a control of the power factor phase, instead of the estimation of the rotor phase. The stator current is controlled in the current reference frame whose secondary axis phase is the same as the stator current phase. This paper proposes a new PEB method where the stator current is controlled in the voltage reference frame whose secondary axis phase is the same as the voltage phase rather than the current phase. It is shown that the similar quasi-optimal stator current control can be attained through the current control with appropriate current commands taking the power factor phase into account. This paper also shows a practical method for generating the current commands and a practical guideline for the design parameters of the new PFB method.

  20. X-ray standing wave simulations based on Fourier vector analysis as a method to retrieve complex molecular adsorption geometries

    Directory of Open Access Journals (Sweden)

    Giuseppe eMercurio

    2014-01-01

    Full Text Available We present an analysis method of normal incidence x-ray standing wave (NIXSW data that allows detailed adsorption geometries of complex molecules to be retrieved. This method (Fourier vector analysis is based on the comparison of both the coherence and phase of NIXSW data to NIXSW simulations of different molecular geometries as the relevant internal degrees of freedom are tuned. We introduce this analysis method using the prototypical molecular switch azobenzene (AB adsorbed on the Ag(111 surface as a model system. The application of the Fourier vector analysis to AB/Ag(111 provides, on the one hand, detailed adsorption geometries including dihedral angles, and on the other hand, insights into the dynamics of molecules and their bonding to the metal substrate. This analysis scheme is generally applicable to any adsorbate, it is necessary for molecules with potentially large distortions, and will be particularly valuable for molecules whose distortion on adsorption can be mapped on a limited number of internal degrees of freedom.

  1. Generation of retroviral particles for the spleen necrosis virus (SNV)-based vector system and their use in transduction of various cell types.

    Science.gov (United States)

    Parveen, Zahida; Mukhtar, Muhammad; Pomerantz, Roger J

    2010-06-01

    Genetically engineered retroviruses are widely used for gene delivery into human cells. A number of investigators have studied spleen necrosis virus (SNV) as a vehicle for gene delivery. Vectors developed from SNV and its closely associated avian reticuloendotheliosis virus strain A (REV-A) can be used for gene transfer into a variety of cells, including primary hematopoietic cells and human brain and post-mitotic neuronal cells that are difficult to transduce with other vector systems. SNV-based vector systems have the advantage of being quite safe, because wild-type SNV is unable to infect human cells and has less preference for integration into transcriptionally active sites or genes. However, the generation of retroviral vectors requires cotransfection of more than one plasmid into a packaging cell line, which is a tedious process. The development of stable packaging cell lines expressing envelope (Env) proteins and the structural proteins Gag-Pol will enhance mass production of retroviral vectors for future gene therapy experiments both in vitro and in vivo. This protocol describes the generation of retroviral particles for the SNV-based vector system. These particles can then be used for transduction of various cell types; as an example, a technique for transduction of post-mitotic neurons is also presented.

  2. Parameter Identification of Ship Maneuvering Models Using Recursive Least Square Method Based on Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Man Zhu

    2017-03-01

    Full Text Available Determination of ship maneuvering models is a tough task of ship maneuverability prediction. Among several prime approaches of estimating ship maneuvering models, system identification combined with the full-scale or free- running model test is preferred. In this contribution, real-time system identification programs using recursive identification method, such as the recursive least square method (RLS, are exerted for on-line identification of ship maneuvering models. However, this method seriously depends on the objects of study and initial values of identified parameters. To overcome this, an intelligent technology, i.e., support vector machines (SVM, is firstly used to estimate initial values of the identified parameters with finite samples. As real measured motion data of the Mariner class ship always involve noise from sensors and external disturbances, the zigzag simulation test data include a substantial quantity of Gaussian white noise. Wavelet method and empirical mode decomposition (EMD are used to filter the data corrupted by noise, respectively. The choice of the sample number for SVM to decide initial values of identified parameters is extensively discussed and analyzed. With de-noised motion data as input-output training samples, parameters of ship maneuvering models are estimated using RLS and SVM-RLS, respectively. The comparison between identification results and true values of parameters demonstrates that both the identified ship maneuvering models from RLS and SVM-RLS have reasonable agreements with simulated motions of the ship, and the increment of the sample for SVM positively affects the identification results. Furthermore, SVM-RLS using data de-noised by EMD shows the highest accuracy and best convergence.

  3. A novel vector-based method for exclusive overexpression of star-form microRNAs.

    Directory of Open Access Journals (Sweden)

    Bo Qu

    Full Text Available The roles of microRNAs (miRNAs as important regulators of gene expression have been studied intensively. Although most of these investigations have involved the highly expressed form of the two mature miRNA species, increasing evidence points to essential roles for star-form microRNAs (miRNA*, which are usually expressed at much lower levels. Owing to the nature of miRNA biogenesis, it is challenging to use plasmids containing miRNA coding sequences for gain-of-function experiments concerning the roles of microRNA* species. Synthetic microRNA mimics could introduce specific miRNA* species into cells, but this transient overexpression system has many shortcomings. Here, we report that specific miRNA* species can be overexpressed by introducing artificially designed stem-loop sequences into short hairpin RNA (shRNA overexpression vectors. By our prototypic plasmid, designed to overexpress hsa-miR-146b-3p, we successfully expressed high levels of hsa-miR-146b-3p without detectable change of hsa-miR-146b-5p. Functional analysis involving luciferase reporter assays showed that, like natural miRNAs, the overexpressed hsa-miR-146b-3p inhibited target gene expression by 3'UTR seed pairing. Our demonstration that this method could overexpress two other miRNAs suggests that the approach should be broadly applicable. Our novel strategy opens the way for exclusively stable overexpression of miRNA* species and analyzing their unique functions both in vitro and in vivo.

  4. BLProt: prediction of bioluminescent proteins based on support vector machine and relieff feature selection

    Directory of Open Access Journals (Sweden)

    Hazrati Mehrnaz

    2011-08-01

    Full Text Available Abstract Background Bioluminescence is a process in which light is emitted by a living organism. Most creatures that emit light are sea creatures, but some insects, plants, fungi etc, also emit light. The biotechnological application of bioluminescence has become routine and is considered essential for many medical and general technological advances. Identification of bioluminescent proteins is more challenging due to their poor similarity in sequence. So far, no specific method has been reported to identify bioluminescent proteins from primary sequence. Results In this paper, we propose a novel predictive method that uses a Support Vector Machine (SVM and physicochemical properties to predict bioluminescent proteins. BLProt was trained using a dataset consisting of 300 bioluminescent proteins and 300 non-bioluminescent proteins, and evaluated by an independent set of 141 bioluminescent proteins and 18202 non-bioluminescent proteins. To identify the most prominent features, we carried out feature selection with three different filter approaches, ReliefF, infogain, and mRMR. We selected five different feature subsets by decreasing the number of features, and the performance of each feature subset was evaluated. Conclusion BLProt achieves 80% accuracy from training (5 fold cross-validations and 80.06% accuracy from testing. The performance of BLProt was compared with BLAST and HMM. High prediction accuracy and successful prediction of hypothetical proteins suggests that BLProt can be a useful approach to identify bioluminescent proteins from sequence information, irrespective of their sequence similarity. The BLProt software is available at http://www.inb.uni-luebeck.de/tools-demos/bioluminescent%20protein/BLProt

  5. Support vector machine-based differentiation between aggressive and chronic periodontitis using microbial profiles.

    Science.gov (United States)

    Feres, Magda; Louzoun, Yoram; Haber, Simi; Faveri, Marcelo; Figueiredo, Luciene C; Levin, Liran

    2018-02-01

    The existence of specific microbial profiles for different periodontal conditions is still a matter of debate. The aim of this study was to test the hypothesis that 40 bacterial species could be used to classify patients, utilising machine learning, into generalised chronic periodontitis (ChP), generalised aggressive periodontitis (AgP) and periodontal health (PH). Subgingival biofilm samples were collected from patients with AgP, ChP and PH and analysed for their content of 40 bacterial species using checkerboard DNA-DNA hybridisation. Two stages of machine learning were then performed. First of all, we tested whether there was a difference between the composition of bacterial communities in PH and in disease, and then we tested whether a difference existed in the composition of bacterial communities between ChP and AgP. The data were split in each analysis to 70% train and 30% test. A support vector machine (SVM) classifier was used with a linear kernel and a Box constraint of 1. The analysis was divided into two parts. Overall, 435 patients (3,915 samples) were included in the analysis (PH = 53; ChP = 308; AgP = 74). The variance of the healthy samples in all principal component analysis (PCA) directions was smaller than that of the periodontally diseased samples, suggesting that PH is characterised by a uniform bacterial composition and that the bacterial composition of periodontally diseased samples is much more diverse. The relative bacterial load could distinguish between AgP and ChP. An SVC classifier using a panel of 40 bacterial species was able to distinguish between PH, AgP in young individuals and ChP. © 2017 FDI World Dental Federation.

  6. Evaluation of auto-assessment method for C-D analysis based on support vector machine

    International Nuclear Information System (INIS)

    Takei, Takaaki; Ikeda, Mitsuru; Imai, Kuniharu; Kamihira, Hiroaki; Kishimoto, Tomonari; Goto, Hiroya

    2010-01-01

    Contrast-Detail (C-D) analysis is one of the visual quality assessment methods in medical imaging, and many auto-assessment methods for C-D analysis have been developed in recent years. However, for the auto-assessment method for C-D analysis, the effects of nonlinear image processing are not clear. So, we have made an auto-assessment method for C-D analysis using a support vector machine (SVM), and have evaluated its performance for the images processed with a noise reduction method. The feature indexes used in the SVM were the normalized cross correlation (NCC) coefficient on each signal between the noise-free and noised image, the contrast to noise ratio (CNR) on each signal, the radius of each signal, and the Student's t-test statistic for the mean difference between the signal and background pixel values. The results showed that the auto-assessment method for C-D analysis by using Student's t-test statistic agreed well with the visual assessment for the non-processed images, but disagreed for the images processed with the noise reduction method. Our results also showed that the auto-assessment method for C-D analysis by the SVM made of NCC and CNR agreed well with the visual assessment for the non-processed and noise-reduced images. Therefore, the auto-assessment method for C-D analysis by the SVM will be expected to have the robustness for the non-linear image processing. (author)

  7. Agricultural drought prediction using climate indices based on Support Vector Regression in Xiangjiang River basin.

    Science.gov (United States)

    Tian, Ye; Xu, Yue-Ping; Wang, Guoqing

    2018-05-01

    Drought can have a substantial impact on the ecosystem and agriculture of the affected region and does harm to local economy. This study aims to analyze the relation between soil moisture and drought and predict agricultural drought in Xiangjiang River basin. The agriculture droughts are presented with the Precipitation-Evapotranspiration Index (SPEI). The Support Vector Regression (SVR) model incorporating climate indices is developed to predict the agricultural droughts. Analysis of climate forcing including El Niño Southern Oscillation and western Pacific subtropical high (WPSH) are carried out to select climate indices. The results show that SPEI of six months time scales (SPEI-6) represents the soil moisture better than that of three and one month time scale on drought duration, severity and peaks. The key factor that influences the agriculture drought is the Ridge Point of WPSH, which mainly controls regional temperature. The SVR model incorporating climate indices, especially ridge point of WPSH, could improve the prediction accuracy compared to that solely using drought index by 4.4% in training and 5.1% in testing measured by Nash Sutcliffe efficiency coefficient (NSE) for three month lead time. The improvement is more significant for the prediction with one month lead (15.8% in training and 27.0% in testing) than that with three months lead time. However, it needs to be cautious in selection of the input parameters, since adding redundant information could have a counter effect in attaining a better prediction. Copyright © 2017 Elsevier B.V. All rights reserved.

  8. Vector geometry

    CERN Document Server

    Robinson, Gilbert de B

    2011-01-01

    This brief undergraduate-level text by a prominent Cambridge-educated mathematician explores the relationship between algebra and geometry. An elementary course in plane geometry is the sole requirement for Gilbert de B. Robinson's text, which is the result of several years of teaching and learning the most effective methods from discussions with students. Topics include lines and planes, determinants and linear equations, matrices, groups and linear transformations, and vectors and vector spaces. Additional subjects range from conics and quadrics to homogeneous coordinates and projective geom

  9. Classification of Polarimetric SAR Image Based on Support Vector Machine Using Multiple-Component Scattering Model and Texture Features

    Directory of Open Access Journals (Sweden)

    Lamei Zhang

    2010-01-01

    Full Text Available The classification of polarimetric SAR image based on Multiple-Component Scattering Model (MCSM and Support Vector Machine (SVM is presented in this paper. MCSM is a potential decomposition method for a general condition. SVM is a popular tool for machine learning tasks involving classification, recognition, or detection. The scattering powers of single-bounce, double-bounce, volume, helix, and wire scattering components are extracted from fully polarimetric SAR images. Combining with the scattering powers of MCSM and the selected texture features from Gray-level cooccurrence matrix (GCM, SVM is used for the classification of polarimetric SAR image. We generate a validity test for the proposed method using Danish EMISAR L-band fully polarimetric data of Foulum Area (DK, Denmark. The preliminary result indicates that this method can classify most of the areas correctly.

  10. Error vector magnitude based parameter estimation for digital filter back-propagation mitigating SOA distortions in 16-QAM.

    Science.gov (United States)

    Amiralizadeh, Siamak; Nguyen, An T; Rusch, Leslie A

    2013-08-26

    We investigate the performance of digital filter back-propagation (DFBP) using coarse parameter estimation for mitigating SOA nonlinearity in coherent communication systems. We introduce a simple, low overhead method for parameter estimation for DFBP based on error vector magnitude (EVM) as a figure of merit. The bit error rate (BER) penalty achieved with this method has negligible penalty as compared to DFBP with fine parameter estimation. We examine different bias currents for two commercial SOAs used as booster amplifiers in our experiments to find optimum operating points and experimentally validate our method. The coarse parameter DFBP efficiently compensates SOA-induced nonlinearity for both SOA types in 80 km propagation of 16-QAM signal at 22 Gbaud.

  11. A least square support vector machine-based approach for contingency classification and ranking in a large power system

    Directory of Open Access Journals (Sweden)

    Bhanu Pratap Soni

    2016-12-01

    Full Text Available This paper proposes an effective supervised learning approach for static security assessment of a large power system. Supervised learning approach employs least square support vector machine (LS-SVM to rank the contingencies and predict the system severity level. The severity of the contingency is measured by two scalar performance indices (PIs: line MVA performance index (PIMVA and Voltage-reactive power performance index (PIVQ. SVM works in two steps. Step I is the estimation of both standard indices (PIMVA and PIVQ that is carried out under different operating scenarios and Step II contingency ranking is carried out based on the values of PIs. The effectiveness of the proposed methodology is demonstrated on IEEE 39-bus (New England system. The approach can be beneficial tool which is less time consuming and accurate security assessment and contingency analysis at energy management center.

  12. Comparison of confirmed inactive and randomly selected compounds as negative training examples in support vector machine-based virtual screening.

    Science.gov (United States)

    Heikamp, Kathrin; Bajorath, Jürgen

    2013-07-22

    The choice of negative training data for machine learning is a little explored issue in chemoinformatics. In this study, the influence of alternative sets of negative training data and different background databases on support vector machine (SVM) modeling and virtual screening has been investigated. Target-directed SVM models have been derived on the basis of differently composed training sets containing confirmed inactive molecules or randomly selected database compounds as negative training instances. These models were then applied to search background databases consisting of biological screening data or randomly assembled compounds for available hits. Negative training data were found to systematically influence compound recall in virtual screening. In addition, different background databases had a strong influence on the search results. Our findings also indicated that typical benchmark settings lead to an overestimation of SVM-based virtual screening performance compared to search conditions that are more relevant for practical applications.

  13. Data-Based Control for Humanoid Robots Using Support Vector Regression, Fuzzy Logic, and Cubature Kalman Filter

    Directory of Open Access Journals (Sweden)

    Liyang Wang

    2016-01-01

    Full Text Available Time-varying external disturbances cause instability of humanoid robots or even tip robots over. In this work, a trapezoidal fuzzy least squares support vector regression- (TF-LSSVR- based control system is proposed to learn the external disturbances and increase the zero-moment-point (ZMP stability margin of humanoid robots. First, the humanoid states and the corresponding control torques of the joints for training the controller are collected by implementing simulation experiments. Secondly, a TF-LSSVR with a time-related trapezoidal fuzzy membership function (TFMF is proposed to train the controller using the simulated data. Thirdly, the parameters of the proposed TF-LSSVR are updated using a cubature Kalman filter (CKF. Simulation results are provided. The proposed method is shown to be effective in learning and adapting occasional external disturbances and ensuring the stability margin of the robot.

  14. A high resolution optical vector network analyzer based on a wideband and wavelength-tunable optical single-sideband modulator.

    Science.gov (United States)

    Tang, Zhenzhou; Pan, Shilong; Yao, Jianping

    2012-03-12

    A high resolution optical vector network analyzer (OVNA) implemented based on a wideband and wavelength-tunable optical single-sideband (OSSB) modulator is proposed and experimentally demonstrated. The OSSB modulation is achieved using a phase modulator and a tunable optical filter with a passband having two steep edges and a flat top. Wideband and wavelength-tunable OSSB modulation is achieved. The incorporation of the OSSB modulator into the OVNA is experimentally evaluated. The measurement of the magnitude and phase response of an ultra-narrow-band fiber Bragg grating (FBG) and that of the stimulated Brillouin scattering (SBS) in a single-mode fiber is performed. A measurement resolution as high as 78 kHz is achieved.

  15. Classification of dengue fever patients based on gene expression data using support vector machines.

    Directory of Open Access Journals (Sweden)

    Ana Lisa V Gomes

    Full Text Available BACKGROUND: Symptomatic infection by dengue virus (DENV can range from dengue fever (DF to dengue haemorrhagic fever (DHF, however, the determinants of DF or DHF progression are not completely understood. It is hypothesised that host innate immune response factors are involved in modulating the disease outcome and the expression levels of genes involved in this response could be used as early prognostic markers for disease severity. METHODOLOGY/PRINCIPAL FINDINGS: mRNA expression levels of genes involved in DENV innate immune responses were measured using quantitative real time PCR (qPCR. Here, we present a novel application of the support vector machines (SVM algorithm to analyze the expression pattern of 12 genes in peripheral blood mononuclear cells (PBMCs of 28 dengue patients (13 DHF and 15 DF during acute viral infection. The SVM model was trained using gene expression data of these genes and achieved the highest accuracy of approximately 85% with leave-one-out cross-validation. Through selective removal of gene expression data from the SVM model, we have identified seven genes (MYD88, TLR7, TLR3, MDA5, IRF3, IFN-alpha and CLEC5A that may be central in differentiating DF patients from DHF, with MYD88 and TLR7 observed to be the most important. Though the individual removal of expression data of five other genes had no impact on the overall accuracy, a significant combined role was observed when the SVM model of the two main genes (MYD88 and TLR7 was re-trained to include the five genes, increasing the overall accuracy to approximately 96%. CONCLUSIONS/SIGNIFICANCE: Here, we present a novel use of the SVM algorithm to classify DF and DHF patients, as well as to elucidate the significance of the various genes involved. It was observed that seven genes are critical in classifying DF and DHF patients: TLR3, MDA5, IRF3, IFN-alpha, CLEC5A, and the two most important MYD88 and TLR7. While these preliminary results are promising, further

  16. Support Vector Machine Based Monitoring of Cardio-Cerebrovascular Reserve during Simulated Hemorrhage

    Directory of Open Access Journals (Sweden)

    Björn J. P. van der Ster

    2018-01-01

    Full Text Available Introduction: In the initial phase of hypovolemic shock, mean blood pressure (BP is maintained by sympathetically mediated vasoconstriction rendering BP monitoring insensitive to detect blood loss early. Late detection can result in reduced tissue oxygenation and eventually cellular death. We hypothesized that a machine learning algorithm that interprets currently used and new hemodynamic parameters could facilitate in the detection of impending hypovolemic shock.Method: In 42 (27 female young [mean (sd: 24 (4 years], healthy subjects central blood volume (CBV was progressively reduced by application of −50 mmHg lower body negative pressure until the onset of pre-syncope. A support vector machine was trained to classify samples into normovolemia (class 0, initial phase of CBV reduction (class 1 or advanced CBV reduction (class 2. Nine models making use of different features were computed to compare sensitivity and specificity of different non-invasive hemodynamic derived signals. Model features included: volumetric hemodynamic parameters (stroke volume and cardiac output, BP curve dynamics, near-infrared spectroscopy determined cortical brain oxygenation, end-tidal carbon dioxide pressure, thoracic bio-impedance, and middle cerebral artery transcranial Doppler (TCD blood flow velocity. Model performance was tested by quantifying the predictions with three methods: sensitivity and specificity, absolute error, and quantification of the log odds ratio of class 2 vs. class 0 probability estimates.Results: The combination with maximal sensitivity and specificity for classes 1 and 2 was found for the model comprising volumetric features (class 1: 0.73–0.98 and class 2: 0.56–0.96. Overall lowest model error was found for the models comprising TCD curve hemodynamics. Using probability estimates the best combination of sensitivity for class 1 (0.67 and specificity (0.87 was found for the model that contained the TCD cerebral blood flow velocity

  17. Support Vector Machine Based Monitoring of Cardio-Cerebrovascular Reserve during Simulated Hemorrhage.

    Science.gov (United States)

    van der Ster, Björn J P; Bennis, Frank C; Delhaas, Tammo; Westerhof, Berend E; Stok, Wim J; van Lieshout, Johannes J

    2017-01-01

    Introduction: In the initial phase of hypovolemic shock, mean blood pressure (BP) is maintained by sympathetically mediated vasoconstriction rendering BP monitoring insensitive to detect blood loss early. Late detection can result in reduced tissue oxygenation and eventually cellular death. We hypothesized that a machine learning algorithm that interprets currently used and new hemodynamic parameters could facilitate in the detection of impending hypovolemic shock. Method: In 42 (27 female) young [mean (sd): 24 (4) years], healthy subjects central blood volume (CBV) was progressively reduced by application of -50 mmHg lower body negative pressure until the onset of pre-syncope. A support vector machine was trained to classify samples into normovolemia (class 0), initial phase of CBV reduction (class 1) or advanced CBV reduction (class 2). Nine models making use of different features were computed to compare sensitivity and specificity of different non-invasive hemodynamic derived signals. Model features included : volumetric hemodynamic parameters (stroke volume and cardiac output), BP curve dynamics, near-infrared spectroscopy determined cortical brain oxygenation, end-tidal carbon dioxide pressure, thoracic bio-impedance, and middle cerebral artery transcranial Doppler (TCD) blood flow velocity. Model performance was tested by quantifying the predictions with three methods : sensitivity and specificity, absolute error, and quantification of the log odds ratio of class 2 vs. class 0 probability estimates. Results: The combination with maximal sensitivity and specificity for classes 1 and 2 was found for the model comprising volumetric features (class 1: 0.73-0.98 and class 2: 0.56-0.96). Overall lowest model error was found for the models comprising TCD curve hemodynamics. Using probability estimates the best combination of sensitivity for class 1 (0.67) and specificity (0.87) was found for the model that contained the TCD cerebral blood flow velocity derived

  18. Development and Validation of Remote Sensing-Based Surface Inundation Products for Vector-Borne Disease Risk in East Africa

    Science.gov (United States)

    Jensen, K.; McDonald, K. C.; Ceccato, P.; Schroeder, R.; Podest, E.

    2014-12-01

    The potential impact of climate variability and change on the spread of infectious disease is of increasingly critical concern to public health. Newly-available remote sensing datasets may be combined with predictive modeling to develop new capabilities to mitigate risks of vector-borne diseases such as malaria, leishmaniasis, and rift valley fever. We have developed improved remote sensing-based products for monitoring water bodies and inundation dynamics that have potential utility for improving risk forecasts of vector-borne disease epidemics. These products include daily and seasonal surface inundation based on the global mappings of inundated area fraction derived at the 25-km scale from active and passive microwave instruments ERS, QuikSCAT, ASCAT, and SSM/I data - the Satellite Water Microwave Product Series (SWAMPS). Focusing on the East African region, we present validation of this product using multi-temporal classification of inundated areas in this region derived from high resolution PALSAR (100m) and Landsat (30m) observations. We assess historical occurrence of malaria in the east African country of Eritrea with respect to the time series SWAMPS datasets, and we aim to construct a framework for use of these new datasets to improve prediction of future malaria risk in this region. This work is supported through funding from the NASA Applied Sciences Program, the NASA Terrestrial Ecology Program, and the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program. This study is also supported and monitored by National Oceanic and Atmospheric Administration (NOAA) under Grant - CREST Grant # NA11SEC4810004. The statements contained within the manuscript/research article are not the opinions of the funding agency or the U.S. government, but reflect the authors' opinions. This work was conducted in part under the framework of the ALOS Kyoto and Carbon Initiative. ALOS PALSAR data were provided by JAXA EORC.

  19. Alteration of intersubunit acid–base pair interactions at the quasi-threefold axis of symmetry of Cucumber mosaic virus disrupts aphid vector transmission

    Energy Technology Data Exchange (ETDEWEB)

    Bricault, Christine A. [Department of Plant Pathology and Plant-Microbe Biology, 334 Plant Science Building, Cornell University, Ithaca, NY 14850 (United States); Perry, Keith L., E-mail: KLP3@cornell.edu [Department of Plant Pathology and Plant-Microbe Biology, 334 Plant Science Building, Cornell University, Ithaca, NY 14850 (United States)

    2013-06-05

    In the atomic model of Cucumber mosaic virus (CMV), six amino acid residues form stabilizing salt bridges between subunits of the asymmetric unit at the quasi-threefold axis of symmetry. To evaluate the effects of these positions on virion stability and aphid vector transmissibility, six charged amino acid residues were individually mutated to alanine. All of the six engineered viruses were viable and exhibited near wild type levels of virion stability in the presence of urea. Aphid vector transmissibility was nearly or completely eliminated in the case of four of the mutants; two mutants demonstrated intermediate aphid transmissibility. For the majority of the engineered mutants, second-site mutations were observed following aphid transmission and/or mechanical passaging, and one restored transmission rates to that of the wild type. CMV capsids tolerate disruption of acid–base pairing interactions at the quasi-threefold axis of symmetry, but these interactions are essential for maintaining aphid vector transmissibility. - Highlights: ► Amino acids between structural subunits of Cucumber mosaic virus affect vector transmission. ► Mutant structural stability was retained, while aphid vector transmissibility was disrupted. ► Spontaneous, second-site mutations restored aphid vector transmissibility.

  20. Alteration of intersubunit acid–base pair interactions at the quasi-threefold axis of symmetry of Cucumber mosaic virus disrupts aphid vector transmission

    International Nuclear Information System (INIS)

    Bricault, Christine A.; Perry, Keith L.

    2013-01-01

    In the atomic model of Cucumber mosaic virus (CMV), six amino acid residues form stabilizing salt bridges between subunits of the asymmetric unit at the quasi-threefold axis of symmetry. To evaluate the effects of these positions on virion stability and aphid vector transmissibility, six charged amino acid residues were individually mutated to alanine. All of the six engineered viruses were viable and exhibited near wild type levels of virion stability in the presence of urea. Aphid vector transmissibility was nearly or completely eliminated in the case of four of the mutants; two mutants demonstrated intermediate aphid transmissibility. For the majority of the engineered mutants, second-site mutations were observed following aphid transmission and/or mechanical passaging, and one restored transmission rates to that of the wild type. CMV capsids tolerate disruption of acid–base pairing interactions at the quasi-threefold axis of symmetry, but these interactions are essential for maintaining aphid vector transmissibility. - Highlights: ► Amino acids between structural subunits of Cucumber mosaic virus affect vector transmission. ► Mutant structural stability was retained, while aphid vector transmissibility was disrupted. ► Spontaneous, second-site mutations restored aphid vector transmissibility

  1. Short-term traffic flow prediction model using particle swarm optimization–based combined kernel function-least squares support vector machine combined with chaos theory

    Directory of Open Access Journals (Sweden)

    Qiang Shang

    2016-08-01

    Full Text Available Short-term traffic flow prediction is an important part of intelligent transportation systems research and applications. For further improving the accuracy of short-time traffic flow prediction, a novel hybrid prediction model (multivariate phase space reconstruction–combined kernel function-least squares support vector machine based on multivariate phase space reconstruction and combined kernel function-least squares support vector machine is proposed. The C-C method is used to determine the optimal time delay and the optimal embedding dimension of traffic variables’ (flow, speed, and occupancy time series for phase space reconstruction. The G-P method is selected to calculate the correlation dimension of attractor which is an important index for judging chaotic characteristics of the traffic variables’ series. The optimal input form of combined kernel function-least squares support vector machine model is determined by multivariate phase space reconstruction, and the model’s parameters are optimized by particle swarm optimization algorithm. Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. The experimental results suggest that the new proposed model yields better predictions compared with similar models (combined kernel function-least squares support vector machine, multivariate phase space reconstruction–generalized kernel function-least squares support vector machine, and phase space reconstruction–combined kernel function-least squares support vector machine, which indicates that the new proposed model exhibits stronger prediction ability and robustness.

  2. A validated system for ligation-free USER™ -based assembly of expression vectors for mammalian cell engineering

    DEFF Research Database (Denmark)

    Lund, Anne Mathilde; Kildegaard, Helene Faustrup; Hansen, Bjarne Gram

    The development in the field of mammalian cell factories require fast and high-throughput methods, this means a high need for simpler and more efficient cloning techniques. For optimization of protein expression by genetic engineering and for allowing metabolic engineering in mammalian cells, a n...... versatile expression vector system was developed. This vector system applies the ligation-free uracilexcision cloning technique to construct mammalian expression vectors of multiple parts and with maximum flexibility....

  3. Induction of broadly neutralising HCV antibodies in mice by integration-deficient lentiviral vector-based pseudotyped particles.

    Directory of Open Access Journals (Sweden)

    Yao Deng

    Full Text Available INTRODUCTION: Integration-deficient lentiviral vectors (IDLVs are a promising platform for immunisation to elicit both humoral immunity and cellular mediated immunity (CMI. Here, we compared the specific immunity in mice immunised via different regimens (homologous and cocktail with IDLV-based HCV pseudoparticles (HCVpps carrying pseudotyped glycoproteins E1E2 and bearing the HCV NS3 gene. Humoral and cell-mediated immune responses were also evaluated after IDLV-HCVpp immunisation combined with heterologous rAd5-CE1E2 priming protocols. Sera from the mice effectively elicited anti-E1, -E2, and -NS3 antibody responses, and neutralised various HCVpp subtypes (1a, 1b, 2a, 3a and 5a. No significant CMI was detected in the groups immunised with IDLV-based HCVpps. In contrast, the combination of rAd5-CE1E2 priming and IDLV-based HCVpp boosting induced significant CMI against multiple antigens (E1, E2, and NS3. CONCLUSION: IDLV-based HCVpps are a promising vaccination platform and the combination of rAd5-CE1E2 and IDLV-based HCVpp prime-boost strategy should be further explored for the development of a cross-protective HCV vaccine.

  4. VECTOR INTEGRATION

    NARCIS (Netherlands)

    Thomas, E. G. F.

    2012-01-01

    This paper deals with the theory of integration of scalar functions with respect to a measure with values in a, not necessarily locally convex, topological vector space. It focuses on the extension of such integrals from bounded measurable functions to the class of integrable functions, proving

  5. Vectors based on modified vaccinia Ankara expressing influenza H5N1 hemagglutinin induce substantial cross-clade protective immunity.

    Directory of Open Access Journals (Sweden)

    Annett Hessel

    utilizing MVA vector technology, should be based on the VN/1203 hemagglutinin. Furthermore, the recombinant MVA-HA-VN, as characterized in the present study, would be a promising candidate for such a vaccine.

  6. Support Vector Machine and Parametric Wavelet-Based Texture Classification of Stem Cell Images

    National Research Council Canada - National Science Library

    Jeffreys, Christopher

    2004-01-01

    .... Since colony texture is a major discriminating feature in determining quality, we introduce a non-invasive, semi-automated texture-based stem cell colony classification methodology to aid researchers...

  7. A single mutation in the GSTe2 gene allows tracking of metabolically based insecticide resistance in a major malaria vector.

    Science.gov (United States)

    Riveron, Jacob M; Yunta, Cristina; Ibrahim, Sulaiman S; Djouaka, Rousseau; Irving, Helen; Menze, Benjamin D; Ismail, Hanafy M; Hemingway, Janet; Ranson, Hilary; Albert, Armando; Wondji, Charles S

    2014-02-25

    Metabolic resistance to insecticides is the biggest threat to the continued effectiveness of malaria vector control. However, its underlying molecular basis, crucial for successful resistance management, remains poorly characterized. Here, we demonstrate that the single amino acid change L119F in an upregulated glutathione S-transferase gene, GSTe2, confers high levels of metabolic resistance to DDT in the malaria vector Anopheles funestus. Genome-wide transcription analysis revealed that GSTe2 was the most over-expressed detoxification gene in DDT and permethrin-resistant mosquitoes from Benin. Transgenic expression of GSTe2 in Drosophila melanogaster demonstrated that over-transcription of this gene alone confers DDT resistance and cross-resistance to pyrethroids. Analysis of GSTe2 polymorphism established that the point mutation is tightly associated with metabolic resistance to DDT and its geographical distribution strongly correlates with DDT resistance patterns across Africa. Functional characterization of recombinant GSTe2 further supports the role of the L119F mutation, with the resistant allele being more efficient at metabolizing DDT than the susceptible one. Importantly, we also show that GSTe2 directly metabolizes the pyrethroid permethrin. Structural analysis reveals that the mutation confers resistance by enlarging the GSTe2 DDT-binding cavity, leading to increased DDT access and metabolism. Furthermore, we show that GSTe2 is under strong directional selection in resistant populations, and a restriction of gene flow is observed between African regions, enabling the prediction of the future spread of this resistance. This first DNA-based metabolic resistance marker in mosquitoes provides an essential tool to track the evolution of resistance and to design suitable resistance management strategies.

  8. Dengue knowledge, attitudes and practices and their impact on community-based vector control in rural Cambodia.

    Science.gov (United States)

    Kumaran, Emmanuelle; Doum, Dyna; Keo, Vanney; Sokha, Ly; Sam, BunLeng; Chan, Vibol; Alexander, Neal; Bradley, John; Liverani, Marco; Prasetyo, Didot Budi; Rachmat, Agus; Lopes, Sergio; Hii, Jeffrey; Rithea, Leang; Shafique, Muhammad; Hustedt, John

    2018-02-01

    Globally there are an estimated 390 million dengue infections per year, of which 96 million are clinically apparent. In Cambodia, estimates suggest as many as 185,850 cases annually. The World Health Organization global strategy for dengue prevention aims to reduce mortality rates by 50% and morbidity by 25% by 2020. The adoption of integrated vector management approach using community-based methods tailored to the local context is one of the recommended strategies to achieve these objectives. Understanding local knowledge, attitudes and practices is therefore essential to designing suitable strategies to fit each local context. A Knowledge, Attitudes and Practices survey in 600 randomly chosen households was administered in 30 villages in Kampong Cham which is one of the most populated provinces of Cambodia. KAP surveys were administered to a sub-sample of households where an entomology survey was conducted (1200 households), during which Aedes larval/pupae and adult female Aedes mosquito densities were recorded. Participants had high levels of knowledge regarding the transmission of dengue, Aedes breeding, and biting prevention methods; the majority of participants believed they were at risk and that dengue transmission is preventable. However, self-reported vector control practices did not match observed practices recorded in our surveys. No correlation was found between knowledge and observed practices either. An education campaign regarding dengue prevention in this setting with high knowledge levels is unlikely to have any significant effect on practices unless it is incorporated in a more comprehensive strategy for behavioural change, such a COMBI method, which includes behavioural models as well as communication and marketing theory and practice. ISRCTN85307778.

  9. A single mutation in the GSTe2 gene allows tracking of metabolically based insecticide resistance in a major malaria vector

    Science.gov (United States)

    2014-01-01

    Background Metabolic resistance to insecticides is the biggest threat to the continued effectiveness of malaria vector control. However, its underlying molecular basis, crucial for successful resistance management, remains poorly characterized. Results Here, we demonstrate that the single amino acid change L119F in an upregulated glutathione S-transferase gene, GSTe2, confers high levels of metabolic resistance to DDT in the malaria vector Anopheles funestus. Genome-wide transcription analysis revealed that GSTe2 was the most over-expressed detoxification gene in DDT and permethrin-resistant mosquitoes from Benin. Transgenic expression of GSTe2 in Drosophila melanogaster demonstrated that over-transcription of this gene alone confers DDT resistance and cross-resistance to pyrethroids. Analysis of GSTe2 polymorphism established that the point mutation is tightly associated with metabolic resistance to DDT and its geographical distribution strongly correlates with DDT resistance patterns across Africa. Functional characterization of recombinant GSTe2 further supports the role of the L119F mutation, with the resistant allele being more efficient at metabolizing DDT than the susceptible one. Importantly, we also show that GSTe2 directly metabolizes the pyrethroid permethrin. Structural analysis reveals that the mutation confers resistance by enlarging the GSTe2 DDT-binding cavity, leading to increased DDT access and metabolism. Furthermore, we show that GSTe2 is under strong directional selection in resistant populations, and a restriction of gene flow is observed between African regions, enabling the prediction of the future spread of this resistance. Conclusions This first DNA-based metabolic resistance marker in mosquitoes provides an essential tool to track the evolution of resistance and to design suitable resistance management strategies. PMID:24565444

  10. Classification of Alzheimer's disease patients with hippocampal shape wrapper-based feature selection and support vector machine

    Science.gov (United States)

    Young, Jonathan; Ridgway, Gerard; Leung, Kelvin; Ourselin, Sebastien

    2012-02-01

    It is well known that hippocampal atrophy is a marker of the onset of Alzheimer's disease (AD) and as a result hippocampal volumetry has been used in a number of studies to provide early diagnosis of AD and predict conversion of mild cognitive impairment patients to AD. However, rates of atrophy are not uniform across the hippocampus making shape analysis a potentially more accurate biomarker. This study studies the hippocampi from 226 healthy controls, 148 AD patients and 330 MCI patients obtained from T1 weighted structural MRI images from the ADNI database. The hippocampi are anatomically segmented using the MAPS multi-atlas segmentation method, and the resulting binary images are then processed with SPHARM software to decompose their shapes as a weighted sum of spherical harmonic basis functions. The resulting parameterizations are then used as feature vectors in Support Vector Machine (SVM) classification. A wrapper based feature selection method was used as this considers the utility of features in discriminating classes in combination, fully exploiting the multivariate nature of the data and optimizing the selected set of features for the type of classifier that is used. The leave-one-out cross validated accuracy obtained on training data is 88.6% for classifying AD vs controls and 74% for classifying MCI-converters vs MCI-stable with very compact feature sets, showing that this is a highly promising method. There is currently a considerable fall in accuracy on unseen data indicating that the feature selection is sensitive to the data used, however feature ensemble methods may overcome this.

  11. An introduction to vectors, vector operators and vector analysis

    CERN Document Server

    Joag, Pramod S

    2016-01-01

    Ideal for undergraduate and graduate students of science and engineering, this book covers fundamental concepts of vectors and their applications in a single volume. The first unit deals with basic formulation, both conceptual and theoretical. It discusses applications of algebraic operations, Levi-Civita notation, and curvilinear coordinate systems like spherical polar and parabolic systems and structures, and analytical geometry of curves and surfaces. The second unit delves into the algebra of operators and their types and also explains the equivalence between the algebra of vector operators and the algebra of matrices. Formulation of eigen vectors and eigen values of a linear vector operator are elaborated using vector algebra. The third unit deals with vector analysis, discussing vector valued functions of a scalar variable and functions of vector argument (both scalar valued and vector valued), thus covering both the scalar vector fields and vector integration.

  12. Forced recombination of psi-modified murine leukaemia virus-based vectors with murine leukaemia-like and VL30 murine endogenous retroviruses

    DEFF Research Database (Denmark)

    Mikkelsen, J G; Lund, Anders Henrik; Duch, M

    1999-01-01

    -impaired Akv-MLV-derived vectors, we here examine putative genetic interactions between vector RNAs and copackaged endogenous retroviral RNAs of the murine leukaemia virus (MLV) and VL30 retroelement families. We show (i) that MLV recombination is not blocked by nonhomology within the 5' untranslated region....... We note that recombination-based rescue of primer binding site knock-out retroviral vectors may constitute a sensitive assay to register putative genetic interactions involving endogenous retroviral RNAs present in cells of various species.......Co-encapsidation of retroviral RNAs into virus particles allows for the generation of recombinant proviruses through events of template switching during reverse transcription. By use of a forced recombination system based on recombinational rescue of replication- defective primer binding site...

  13. Indirect Vector Control of an Induction Motor with Fuzzy-Logic based Speed Controller

    Directory of Open Access Journals (Sweden)

    BIROU, I.

    2010-02-01

    Full Text Available The aim of this paper is to present a new speed control structure for induction motors (IM by using fuzzy-logic based speed controllers. A fuzzy controller is designed to achieve fast dynamic response and robustness for low and high speeds. Different types of membership functions of the linguistic variables and output/input characteristics are analyzed. A simple but robust structure enables a wide range speed control of the driving system. The rotor flux field oriented control (FOC is realized by using a flux observer based on the IM model with nonlinear parameters. The control is extended to operate also in the field weakening region with an optimal rotor flux regulation. The control structure was implemented on a computer system, based on a fixed point digital signal processor (DSP. To verify the performances of the proposed driving system, simulated and experimental results are presented.

  14. A bovine papillomavirus-1 based vector restores the function of the low-density lipoprotein receptor in the receptor-deficient CHO-ldlA7 cell line

    Directory of Open Access Journals (Sweden)

    Ustav Mart

    2002-04-01

    Full Text Available Abstract Background The rationale of using bovine papillomavirus-1 (BPV-1 derived vectors in gene therapy protocols lies in their episomal maintenance at intermediate to high copy number, and stable, high-level expression of the gene products. We constructed the BPV-1 based vector harbouring the human low-density lipoprotein receptor (LDLR gene cDNA and tested its ability to restore the function of the LDLR in the receptor-deficient cell line CHO-ldlA7. Results The introduced vector p3.7LDL produced functionally active LDL receptors in the receptor-deficient cell line CHO-ldlA7 during the 32-week period of observation as determined by the internalisation assay with the labelled LDL particles. Conclusion Bovine papillomavirus type-1 (BPV-1-derived vectors could be suitable for gene therapy due to their episomal maintenance at intermediate to high copy number and stable, high-level expression of the gene products. The constructed BPV-1 based vector p3.7LDL produced functionally active LDL receptors in the LDLR-deficient cell line CHO-ldlA7 during the 32-week period of observation. In vivo experiments should reveal, whether 1–5% transfection efficiency obtained in the current work is sufficient to bring about detectable and clinically significant lowering of the amount of circulating LDL cholesterol particles.

  15. Fly ash based Bacillus thuringiensis var. israelensis formulation for use against Culex quinquefasciatus, the vector of filariasis in natural ecosystems.

    Science.gov (United States)

    Tamilselvan, S; Jambulingam, P; Manoharan, V; Shanmugasundaram, R; Vivekanandan, G; Manonmani, A M

    2015-09-01

    Fly ash is produced in huge quantities by the various thermal power stations in India. This thermal waste has been employed as a carrier material in the preparation of a biopesticidal water dispersible powder (WDP) formulation for use against mosquitoes. In the present investigation, this newly developed fly ash based WDP formulation was evaluated in natural breeding habitats of mosquito. Fly ash based WDP formulation of Bacillus thuringiensis var. israelensis (VCRC B17) was evaluated for its efficacy and residual activity in aquatic habitats supporting breeding of Culex quinquefasciatus, the vector of lymphatic filariasis in Neyveli Township, Neyveli Lignite Corporation, India for a period of one month. At an application rate of 10 kg/ha, the WDP was effective for five days regardless of the habitat, and provided 80-100% reduction in larval abundance of Cx. quinquefasciatus. The study indicates that for continued control of immature density and prevention of adult emergence, a weekly application of this formulation is necessary. This study also showed that fly ash based formulations can be used for immediate control of mosquitoes in different types of habitats and has also brought out a new avenue for the utilization of coal ash.

  16. Feature selection method based on support vector machine and shape analysis for high-throughput medical data.

    Science.gov (United States)

    Liu, Qiong; Gu, Qiong; Wu, Zhao

    2017-12-01

    Proteomics data analysis based on the mass-spectrometry technique can provide a powerful tool for early diagnosis of tumors and other diseases. It can be used for exploring the features that reflect the difference between samples from high-throughput mass spectrometry data, which are important for the identification of tumor markers. Proteomics mass spectrometry data have the characteristics of too few samples, too many features and noise interference, which pose a great challenge to traditional machine learning methods. Traditional unsupervised dimensionality reduction methods do not utilize the label information effectively, so the subspaces they find may not be the most separable ones of the data. To overcome the shortcomings of traditional methods, in this paper, we present a novel feature selection method based on support vector machine (SVM) and shape analysis. In the process of feature selection, our method considers not only the interaction between features but also the relationship between features and class labels, which improves the classification performance. The experimental results obtained from four groups of proteomics data show that, compared with traditional unsupervised feature extraction methods (i.e., Principal Component Analysis - Procrustes Analysis, PCA-PA), our method not only ensures that fewer features are selected but also ensures a high recognition rate. In addition, compared with the two kinds of multivariate filter methods, i.e., Max-Relevance Min-Redundancy (MRMR) and Fast Correlation-Based Filter (FCBF), our method has a higher recognition rate. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. Efficient transformation system for Propionibacterium freudenreichii based on a novel vector

    NARCIS (Netherlands)

    Jore, J.P.M.; Luijk, N. van; Luiten, R.G.M.; Werf, M.J. van der; Pouwels, P.H.

    2001-01-01

    A 3.6-kb endogenous plasmid was isolated from a Propionibacterium freudenreichii strain and sequenced completely. Based on homologies with plasmids from other bacteria, notably a plasmid from Mycobacterium, a region harboring putative replicative functions was defined. Outside this region two

  18. On POD-based Deflation Vectors for DPCG applied to porous media problems

    NARCIS (Netherlands)

    Diaz Cortes, G.B.; Vuik, C.; Jansen, J.D.

    2018-01-01

    We study fast and robust iterative solvers for large systems of linear equations resulting from simulation of flow trough strongly heterogeneous porous media. We propose the use of preconditioning and deflation techniques, based on information obtained frfrom the system, to reduce the time spent in

  19. Predicting Hepatotoxicity of Drug Metabolites Via an Ensemble Approach Based on Support Vector Machine

    Science.gov (United States)

    Lu, Yin; Liu, Lili; Lu, Dong; Cai, Yudong; Zheng, Mingyue; Luo, Xiaomin; Jiang, Hualiang; Chen, Kaixian

    2017-11-20

    Drug-induced liver injury (DILI) is a major cause of drug withdrawal. The chemical properties of the drug, especially drug metabolites, play key roles in DILI. Our goal is to construct a QSAR model to predict drug hepatotoxicity based on drug metabolites. 64 hepatotoxic drug metabolites and 3,339 non-hepatotoxic drug metabolites were gathered from MDL Metabolite Database. Considering the imbalance of the dataset, we randomly split the negative samples and combined each portion with all the positive samples to construct individually balanced datasets for constructing independent classifiers. Then, we adopted an ensemble approach to make prediction based on the results of all individual classifiers and applied the minimum Redundancy Maximum Relevance (mRMR) feature selection method to select the molecular descriptors. Eventually, for the drugs in the external test set, a Bayesian inference method was used to predict the hepatotoxicity of a drug based on its metabolites. The model showed the average balanced accuracy=78.47%, sensitivity =74.17%, and specificity=82.77%. Five molecular descriptors characterizing molecular polarity, intramolecular bonding strength, and molecular frontier orbital energy were obtained. When predicting the hepatotoxicity of a drug based on all its metabolites, the sensitivity, specificity and balanced accuracy were 60.38%, 70.00%, and 65.19%, respectively, indicating that this method is useful for identifying the hepatotoxicity of drugs. We developed an in silico model to predict hepatotoxicity of drug metabolites. Moreover, Bayesian inference was applied to predict the hepatotoxicity of a drug based on its metabolites which brought out valuable high sensitivity and specificity. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  20. Fish-eye Camera Calibration Model Based on Vector Observations and Its Application

    Directory of Open Access Journals (Sweden)

    ZHAN Yinhu

    2016-03-01

    Full Text Available A fish-eye camera calibration model is presented, basic observations of which consist of both half angle of view and azimuth. Rodrigues matrix is introduced into the model, and three Rodrigues parameters instead of Euler angles are used to represent elements of exterior orientation in order to simplify the expressions and calculations of observation equations.The new model is compared with the existing models based on half angle of view constraint by actual star-map data processing, and the results indicate that the model is superior to control the azimuth error, while slightly inferior to constrain the error of half angle of view. It is advised that radial distortion parameters should be determined by the model based on half angle of view constraint at first, and other camera parameters should be calculated by the new model.

  1. Well-defined polypeptide-based systems as non-viral vectors for cytosolic delivery

    OpenAIRE

    Niño Pariente, Amaya

    2017-01-01

    A convenient cytosolic drug delivery constitutes a very powerful tool for the treatment and/or prevention of several relevant human diseases. Along with recent advances in therapeutic technologies based on biomacromolecules (e.g. oligonucleotides or proteins), we also require the development of technologies which improve the transport of therapeutic molecules to the cell of choice. This has led to the emergence of a variety of promising methods over the last 20 years. Despite significant prog...

  2. Downstream processing and chromatography based analytical methods for production of vaccines, gene therapy vectors, and bacteriophages

    Science.gov (United States)

    Kramberger, Petra; Urbas, Lidija; Štrancar, Aleš

    2015-01-01

    Downstream processing of nanoplexes (viruses, virus-like particles, bacteriophages) is characterized by complexity of the starting material, number of purification methods to choose from, regulations that are setting the frame for the final product and analytical methods for upstream and downstream monitoring. This review gives an overview on the nanoplex downstream challenges and chromatography based analytical methods for efficient monitoring of the nanoplex production. PMID:25751122

  3. Scaling up graph-based semisupervised learning via prototype vector machines.

    Science.gov (United States)

    Zhang, Kai; Lan, Liang; Kwok, James T; Vucetic, Slobodan; Parvin, Bahram

    2015-03-01

    When the amount of labeled data are limited, semisupervised learning can improve the learner's performance by also using the often easily available unlabeled data. In particular, a popular approach requires the learned function to be smooth on the underlying data manifold. By approximating this manifold as a weighted graph, such graph-based techniques can often achieve state-of-the-art performance. However, their high time and space complexities make them less attractive on large data sets. In this paper, we propose to scale up graph-based semisupervised learning using a set of sparse prototypes derived from the data. These prototypes serve as a small set of data representatives, which can be used to approximate the graph-based regularizer and to control model complexity. Consequently, both training and testing become much more efficient. Moreover, when the Gaussian kernel is used to define the graph affinity, a simple and principled method to select the prototypes can be obtained. Experiments on a number of real-world data sets demonstrate encouraging performance and scaling properties of the proposed approach. It also compares favorably with models learned via l1 -regularization at the same level of model sparsity. These results demonstrate the efficacy of the proposed approach in producing highly parsimonious and accurate models for semisupervised learning.

  4. Microneedle-mediated immunization of an adenovirus-based malaria vaccine enhances antigen-specific antibody immunity and reduces anti-vector responses compared to the intradermal route

    Science.gov (United States)

    Carey, John B.; Vrdoljak, Anto; O'Mahony, Conor; Hill, Adrian V. S.; Draper, Simon J.; Moore, Anne C.

    2014-01-01

    Substantial effort has been placed in developing efficacious recombinant attenuated adenovirus-based vaccines. However induction of immunity to the vector is a significant obstacle to its repeated use. Here we demonstrate that skin-based delivery of an adenovirus-based malaria vaccine, HAdV5-PyMSP142, to mice using silicon microneedles induces equivalent or enhanced antibody responses to the encoded antigen, however it results in decreased anti-vector responses, compared to intradermal delivery. Microneedle-mediated vaccine priming and resultant induction of low anti-vector antibody titres permitted repeated use of the same adenovirus vaccine vector. This resulted in significantly increased antigen-specific antibody responses in these mice compared to ID-treated mice. Boosting with a heterologous vaccine; MVA-PyMSP142 also resulted in significantly greater antibody responses in mice primed with HAdV5-PyMSP142 using MN compared to the ID route. The highest protection against blood-stage malaria challenge was observed when a heterologous route of immunization (MN/ID) was used. Therefore, microneedle-mediated immunization has potential to both overcome some of the logistic obstacles surrounding needle-and-syringe-based immunization as well as to facilitate the repeated use of the same adenovirus vaccine thereby potentially reducing manufacturing costs of multiple vaccines. This could have important benefits in the clinical ease of use of adenovirus-based immunization strategies. PMID:25142082

  5. Cell Phone-Based System (Chaak) for Surveillance of Immatures of Dengue Virus Mosquito Vectors

    Science.gov (United States)

    LOZANO–FUENTES, SAUL; WEDYAN, FADI; HERNANDEZ–GARCIA, EDGAR; SADHU, DEVADATTA; GHOSH, SUDIPTO; BIEMAN, JAMES M.; TEP-CHEL, DIANA; GARCÍA–REJÓN, JULIÁN E.; EISEN, LARS

    2014-01-01

    Capture of surveillance data on mobile devices and rapid transfer of such data from these devices into an electronic database or data management and decision support systems promote timely data analyses and public health response during disease outbreaks. Mobile data capture is used increasingly for malaria surveillance and holds great promise for surveillance of other neglected tropical diseases. We focused on mosquito-borne dengue, with the primary aims of: 1) developing and field-testing a cell phone-based system (called Chaak) for capture of data relating to the surveillance of the mosquito immature stages, and 2) assessing, in the dengue endemic setting of Mérida, México, the cost-effectiveness of this new technology versus paper-based data collection. Chaak includes a desktop component, where a manager selects premises to be surveyed for mosquito immatures, and a cell phone component, where the surveyor receives the assigned tasks and captures the data. Data collected on the cell phone can be transferred to a central database through different modes of transmission, including near-real time where data are transferred immediately (e.g., over the Internet) or by first storing data on the cell phone for future transmission. Spatial data are handled in a novel, semantically driven, geographic information system. Compared with a pen-and-paper-based method, use of Chaak improved the accuracy and increased the speed of data transcription into an electronic database. The cost-effectiveness of using the Chaak system will depend largely on the up-front cost of purchasing cell phones and the recurring cost of data transfer over a cellular network. PMID:23926788

  6. Transfection of Infectious RNA and DNA/RNA Layered Vectors of Semliki Forest Virus by the Cell-Penetrating Peptide Based Reagent PepFect6

    Science.gov (United States)

    Pärn, Kalle; Viru, Liane; Lehto, Taavi; Oskolkov, Nikita; Langel, Ülo; Merits, Andres

    2013-01-01

    Viral vectors have a wide variety of applications ranging from fundamental studies of viruses to therapeutics. Recombinant viral vectors are usually constructed using methods of reverse genetics to obtain the genetic material of the viral vector. The physicochemical properties of DNA and RNA make them unable to access cells by themselves, and they require assistance to achieve intracellular delivery. Non-viral delivery vectors can be used for this purpose if they enable efficient intracellular delivery without interfering with the viral life cycle. In this report, we utilize Semliki Forest virus (genus alphavirus) based RNA and DNA vectors to study the transfection efficiency of the non-viral cell-penetrating peptide-based delivery vector PepFect6 in comparison with that of the cationic liposome-based Lipofectamine 2000, and assess their impact on viral replication. The optimal conditions for transfection were determined for both reagents. These results demonstrate, for the first time, the ability of PepFect6 to transport large (13-19 kbp) constructs across the cell membrane. Curiously, DNA molecules delivered using the PepFect6 reagent were found to be transported to the cell nucleus approximately 1.5 hours later than DNA molecules delivered using the Lipofectamine 2000 reagent. Finally, although both PepFect6 and Lipofectamine 2000 reagents can be used for alphavirus research, PepFect6 is preferred because it does not induce changes in the normal cellular phenotype and it does not affect the normal replication-infection cycle of viruses in previously transfected cells. PMID:23861978

  7. The Application of Vector Fitting to Eigenvalue-based Harmonic Stability Analysis

    DEFF Research Database (Denmark)

    Dowlatabadi, Mohammadkazem Bakhshizadeh; Yoon, Changwoo; Hjerrild, Jesper

    2017-01-01

    Participation factor analysis is an interesting feature of the eigenvalue-based stability analysis in a power system, which enables the developers to identify the problematic elements in a multi-vendor project like in an offshore wind power plant. However, this method needs a full state space model...... of the elements that is not always possible to have in a competitive world due to confidentiality. In this paper, by using an identification method, the state space models for power converters are extracted from the provided data by the suppliers. Some uncertainties in the identification process are also...

  8. A Type-2 Block-Component-Decomposition Based 2D AOA Estimation Algorithm for an Electromagnetic Vector Sensor Array

    Directory of Open Access Journals (Sweden)

    Yu-Fei Gao

    2017-04-01

    Full Text Available This paper investigates a two-dimensional angle of arrival (2D AOA estimation algorithm for the electromagnetic vector sensor (EMVS array based on Type-2 block component decomposition (BCD tensor modeling. Such a tensor decomposition method can take full advantage of the multidimensional structural information of electromagnetic signals to accomplish blind estimation for array parameters with higher resolution. However, existing tensor decomposition methods encounter many restrictions in applications of the EMVS array, such as the strict requirement for uniqueness conditions of decomposition, the inability to handle partially-polarized signals, etc. To solve these problems, this paper investigates tensor modeling for partially-polarized signals of an L-shaped EMVS array. The 2D AOA estimation algorithm based on rank- ( L 1 , L 2 , · BCD is developed, and the uniqueness condition of decomposition is analyzed. By means of the estimated steering matrix, the proposed algorithm can automatically achieve angle pair-matching. Numerical experiments demonstrate that the present algorithm has the advantages of both accuracy and robustness of parameter estimation. Even under the conditions of lower SNR, small angular separation and limited snapshots, the proposed algorithm still possesses better performance than subspace methods and the canonical polyadic decomposition (CPD method.

  9. THE LOW BACKSCATTERING OBJECTS CLASSIFICATION IN POLSAR IMAGE BASED ON BAG OF WORDS MODEL USING SUPPORT VECTOR MACHINE

    Directory of Open Access Journals (Sweden)

    L. Yang

    2018-04-01

    Full Text Available Due to the forward scattering and block of radar signal, the water, bare soil, shadow, named low backscattering objects (LBOs, often present low backscattering intensity in polarimetric synthetic aperture radar (PolSAR image. Because the LBOs rise similar backscattering intensity and polarimetric responses, the spectral-based classifiers are inefficient to deal with LBO classification, such as Wishart method. Although some polarimetric features had been exploited to relieve the confusion phenomenon, the backscattering features are still found unstable when the system noise floor varies in the range direction. This paper will introduce a simple but effective scene classification method based on Bag of Words (BoW model using Support Vector Machine (SVM to discriminate the LBOs, without relying on any polarimetric features. In the proposed approach, square windows are firstly opened around the LBOs adaptively to determine the scene images, and then the Scale-Invariant Feature Transform (SIFT points are detected in training and test scenes. The several SIFT features detected are clustered using K-means to obtain certain cluster centers as the visual word lists and scene images are represented using word frequency. At last, the SVM is selected for training and predicting new scenes as some kind of LBOs. The proposed method is executed over two AIRSAR data sets at C band and L band, including water, bare soil and shadow scenes. The experimental results illustrate the effectiveness of the scene method in distinguishing LBOs.

  10. Designing the input vector to ANN-based models for short-term load forecast in electricity distribution systems

    International Nuclear Information System (INIS)

    Santos, P.J.; Martins, A.G.; Pires, A.J.

    2007-01-01

    The present trend to electricity market restructuring increases the need for reliable short-term load forecast (STLF) algorithms, in order to assist electric utilities in activities such as planning, operating and controlling electric energy systems. Methodologies such as artificial neural networks (ANN) have been widely used in the next hour load forecast horizon with satisfactory results. However, this type of approach has had some shortcomings. Usually, the input vector (IV) is defined in a arbitrary way, mainly based on experience, on engineering judgment criteria and on concern about the ANN dimension, always taking into consideration the apparent correlations within the available endogenous and exogenous data. In this paper, a proposal is made of an approach to define the IV composition, with the main focus on reducing the influence of trial-and-error and common sense judgments, which usually are not based on sufficient evidence of comparative advantages over previous alternatives. The proposal includes the assessment of the strictly necessary instances of the endogenous variable, both from the point of view of the contiguous values prior to the forecast to be made, and of the past values representing the trend of consumption at homologous time intervals of the past. It also assesses the influence of exogenous variables, again limiting their presence at the IV to the indispensable minimum. A comparison is made with two alternative IV structures previously proposed in the literature, also applied to the distribution sector. The paper is supported by a real case study at the distribution sector. (author)

  11. Independent Component Analysis-Support Vector Machine-Based Computer-Aided Diagnosis System for Alzheimer's with Visual Support.

    Science.gov (United States)

    Khedher, Laila; Illán, Ignacio A; Górriz, Juan M; Ramírez, Javier; Brahim, Abdelbasset; Meyer-Baese, Anke

    2017-05-01

    Computer-aided diagnosis (CAD) systems constitute a powerful tool for early diagnosis of Alzheimer's disease (AD), but limitations on interpretability and performance exist. In this work, a fully automatic CAD system based on supervised learning methods is proposed to be applied on segmented brain magnetic resonance imaging (MRI) from Alzheimer's disease neuroimaging initiative (ADNI) participants for automatic classification. The proposed CAD system possesses two relevant characteristics: optimal performance and visual support for decision making. The CAD is built in two stages: a first feature extraction based on independent component analysis (ICA) on class mean images and, secondly, a support vector machine (SVM) training and classification. The obtained features for classification offer a full graphical representation of the images, giving an understandable logic in the CAD output, that can increase confidence in the CAD support. The proposed method yields classification results up to 89% of accuracy (with 92% of sensitivity and 86% of specificity) for normal controls (NC) and AD patients, 79% of accuracy (with 82% of sensitivity and 76% of specificity) for NC and mild cognitive impairment (MCI), and 85% of accuracy (with 85% of sensitivity and 86% of specificity) for MCI and AD patients.

  12. Designing the input vector to ANN-based models for short-term load forecast in electricity distribution systems

    Energy Technology Data Exchange (ETDEWEB)

    Santos, P.J. [LabSEI-ESTSetubal-Department of Electrical Engineering at Escola Superior de Tecnologia, Polytechnic Institute of Setubal Rua Vale de Chaves Estefanilha, 2910-761 Setubal (Portugal); Martins, A.G. [Department of Electrical Engineering, FCTUC/INESC, Polo 2 University of Coimbra, Pinhal de Marrocos, 3030 Coimbra (Portugal); Pires, A.J. [LabSEI-ESTSetubal-Department of Electrical Engineering at Escola Superior de Tecnologia, Polytechnic Institute of Setubal Rua Vale de, Chaves Estefanilha, 2910-761 Setubal (Portugal)

    2007-05-15

    The present trend to electricity market restructuring increases the need for reliable short-term load forecast (STLF) algorithms, in order to assist electric utilities in activities such as planning, operating and controlling electric energy systems. Methodologies such as artificial neural networks (ANN) have been widely used in the next hour load forecast horizon with satisfactory results. However, this type of approach has had some shortcomings. Usually, the input vector (IV) is defined in a arbitrary way, mainly based on experience, on engineering judgment criteria and on concern about the ANN dimension, always taking into consideration the apparent correlations within the available endogenous and exogenous data. In this paper, a proposal is made of an approach to define the IV composition, with the main focus on reducing the influence of trial-and-error and common sense judgments, which usually are not based on sufficient evidence of comparative advantages over previous alternatives. The proposal includes the assessment of the strictly necessary instances of the endogenous variable, both from the point of view of the contiguous values prior to the forecast to be made, and of the past values representing the trend of consumption at homologous time intervals of the past. It also assesses the influence of exogenous variables, again limiting their presence at the IV to the indispensable minimum. A comparison is made with two alternative IV structures previously proposed in the literature, also applied to the distribution sector. The paper is supported by a real case study at the distribution sector. (author)

  13. AUTOMATIC ARRHYTHMIAS DETECTION USING VARIOUS TYPES OF ARTIFICIAL NEURAL NETWORK BASED LEARNING VECTOR QUANTIZATION (LVQ

    Directory of Open Access Journals (Sweden)

    Diane Fitria

    2014-08-01

    Full Text Available Abstract An automatic Arrythmias detection system is urgently required due to small number of cardiologits in Indonesia. This paper discusses only about the study and implementation of the system. We use several kinds of signal processing methods to recognize arrythmias from ecg signal. The core of the system is classification. Our LVQ based artificial neural network classifiers based on LVQ, which includes LVQ1, LVQ2, LVQ2.1, FNLVQ, FNLVQ MSA, FNLVQ-PSO, GLVQ and FNGLVQ. Experiment result show that for non round robin dataset, the system could reach accuracy of 94.07%, 92.54%, 88.09% , 86.55% , 83.66%, 82.29 %, 82.25%, and 74.62% respectively for FNGLVQ, FNLVQ-PSO, GLVQ, LVQ2.1, FNLVQ-MSA, LVQ2, FNLVQ and LVQ1. Whereas for round robin dataset, system reached accuracy of 98.12%, 98.04%, 94.31%, 90.43%, 86.75%, 86.12 %, 84.50%, and 74.78% respectively for GLVQ, LVQ2.1, FNGLVQ, FNLVQ-PSO, LVQ2, FNLVQ-MSA, FNLVQ and LVQ1.

  14. Automatic vehicle detection based on automatic histogram-based fuzzy C-means algorithm and perceptual grouping using very high-resolution aerial imagery and road vector data

    Science.gov (United States)

    Ghaffarian, Saman; Gökaşar, Ilgın

    2016-01-01

    This study presents an approach for the automatic detection of vehicles using very high-resolution images and road vector data. Initially, road vector data and aerial images are integrated to extract road regions. Then, the extracted road/street region is clustered using an automatic histogram-based fuzzy C-means algorithm, and edge pixels are detected using the Canny edge detector. In order to automatically detect vehicles, we developed a local perceptual grouping approach based on fusion of edge detection and clustering outputs. To provide the locality, an ellipse is generated using characteristics of the candidate clusters individually. Then, ratio of edge pixels to nonedge pixels in the corresponding ellipse is computed to distinguish the vehicles. Finally, a point-merging rule is conducted to merge the points that satisfy a predefined threshold and are supposed to denote the same vehicles. The experimental validation of the proposed method was carried out on six very high-resolution aerial images that illustrate two highways, two shadowed roads, a crowded narrow street, and a street in a dense urban area with crowded parked vehicles. The evaluation of the results shows that our proposed method performed 86% and 83% in overall correctness and completeness, respectively.

  15. Reliability analysis on resonance for low-pressure compressor rotor blade based on least squares support vector machine with leave-one-out cross-validation

    Directory of Open Access Journals (Sweden)

    Haifeng Gao

    2015-04-01

    Full Text Available This research article analyzes the resonant reliability at the rotating speed of 6150.0 r/min for low-pressure compressor rotor blade. The aim is to improve the computational efficiency of reliability analysis. This study applies least squares support vector machine to predict the natural frequencies of the low-pressure compressor rotor blade considered. To build a more stable and reliable least squares support vector machine model, leave-one-out cross-validation is introduced to search for the optimal parameters of least squares support vector machine. Least squares support vector machine with leave-one-out cross-validation is presented to analyze the resonant reliability. Additionally, the modal analysis at the rotating speed of 6150.0 r/min for the rotor blade is considered as a tandem system to simplify the analysis and design process, and the randomness of influence factors on frequencies, such as material properties, structural dimension, and operating condition, is taken into consideration. Back-propagation neural network is compared to verify the proposed approach based on the same training and testing sets as least squares support vector machine with leave-one-out cross-validation. Finally, the statistical results prove that the proposed approach is considered to be effective and feasible and can be applied to structural reliability analysis.

  16. Numerical simulation of multi-dimensional two-phase flow based on flux vector splitting

    Energy Technology Data Exchange (ETDEWEB)

    Staedtke, H.; Franchello, G.; Worth, B. [Joint Research Centre - Ispra Establishment (Italy)

    1995-09-01

    This paper describes a new approach to the numerical simulation of transient, multidimensional two-phase flow. The development is based on a fully hyperbolic two-fluid model of two-phase flow using separated conservation equations for the two phases. Features of the new model include the existence of real eigenvalues, and a complete set of independent eigenvectors which can be expressed algebraically in terms of the major dependent flow parameters. This facilitates the application of numerical techniques specifically developed for high speed single-phase gas flows which combine signal propagation along characteristic lines with the conservation property with respect to mass, momentum and energy. Advantages of the new model for the numerical simulation of one- and two- dimensional two-phase flow are discussed.

  17. Satellite fault diagnosis using support vector machines based on a hybrid voting mechanism.

    Science.gov (United States)

    Yin, Hong; Yang, Shuqiang; Zhu, Xiaoqian; Jin, Songchang; Wang, Xiang

    2014-01-01

    The satellite fault diagnosis has an important role in enhancing the safety, reliability, and availability of the satellite system. However, the problem of enormous parameters and multiple faults makes a challenge to the satellite fault diagnosis. The interactions between parameters and misclassifications from multiple faults will increase the false alarm rate and the false negative rate. On the other hand, for each satellite fault, there is not enough fault data for training. To most of the classification algorithms, it will degrade the performance of model. In this paper, we proposed an improving SVM based on a hybrid voting mechanism (HVM-SVM) to deal with the problem of enormous parameters, multiple faults, and small samples. Many experimental results show that the accuracy of fault diagnosis using HVM-SVM is improved.

  18. Obfuscated RSUs Vector Based Signature Scheme for Detecting Conspiracy Sybil Attack in VANETs

    Directory of Open Access Journals (Sweden)

    Xia Feng

    2017-01-01

    Full Text Available Given the popularity of vehicular Ad hoc networks (VANETs in traffic management, a new challenging issue comes into traffic safety, that is, security of the networks, especially when the adversary breaks defence. Sybil attack, for example, is a potential security threat through forging several identities to carry out attacks in VANETs. At this point, the paper proposed a solution named DMON that is a Sybil attack detection method with obfuscated neighbor relationship of Road Side Units (RSUs. DMON presents a ring signature based identification scheme and replaces vehicles’ identities with their trajectory for the purpose of anonymity. Furthermore, the neighbor relationship of RSUs is obfuscated to achieve privacy preserving of locations. The proposed scheme has been formally proved in the views of security and performance. Simulation has also been implemented to validate the scheme, in which the findings reveal the lower computational overhead and higher detection rate comparing with other related solutions.

  19. Hybrid dimensionality reduction method based on support vector machine and independent component analysis.

    Science.gov (United States)

    Moon, Sangwoo; Qi, Hairong

    2012-05-01

    This paper presents a new hybrid dimensionality reduction method to seek projection through optimization of both structural risk (supervised criterion) and data independence (unsupervised criterion). Classification accuracy is used as a metric to evaluate the performance of the method. By minimizing the structural risk, projection originated from the decision boundaries directly improves the classification performance from a supervised perspective. From an unsupervised perspective, projection can also be obtained based on maximum independence among features (or attributes) in data to indirectly achieve better classification accuracy over more intrinsic representation of the data. Orthogonality interrelates the two sets of projections such that minimum redundancy exists between the projections, leading to more effective dimensionality reduction. Experimental results show that the proposed hybrid dimensionality reduction method that satisfies both criteria simultaneously provides higher classification performance, especially for noisy data sets, in relatively lower dimensional space than various existing methods.

  20. Satellite Fault Diagnosis Using Support Vector Machines Based on a Hybrid Voting Mechanism

    Science.gov (United States)

    Yang, Shuqiang; Zhu, Xiaoqian; Jin, Songchang; Wang, Xiang

    2014-01-01

    The satellite fault diagnosis has an important role in enhancing the safety, reliability, and availability of the satellite system. However, the problem of enormous parameters and multiple faults makes a challenge to the satellite fault diagnosis. The interactions between parameters and misclassifications from multiple faults will increase the false alarm rate and the false negative rate. On the other hand, for each satellite fault, there is not enough fault data for training. To most of the classification algorithms, it will degrade the performance of model. In this paper, we proposed an improving SVM based on a hybrid voting mechanism (HVM-SVM) to deal with the problem of enormous parameters, multiple faults, and small samples. Many experimental results show that the accuracy of fault diagnosis using HVM-SVM is improved. PMID:25215324

  1. A vector-based, 5-electrode, 12-lead monitoring ECG (EASI) is equivalent to conventional 12-lead ECG for diagnosis of acute coronary syndromes

    NARCIS (Netherlands)

    Wehr, Gabriele; Peters, Ron J.; Khalifé, Khalifé; Banning, Adrian P.; Kuehlkamp, Volker; Rickards, Anthony F.; Sechtem, Udo

    2006-01-01

    AIMS: The conventional 12-lead electrocardiogram (cECG) derived from 10 electrodes using a cardiograph is the gold standard for diagnosing myocardial ischemia. This study tested the hypothesis that a new 5-electrode 12-lead vector-based ECG (EASI; Philips Medical Systems, formerly Hewlett Packard

  2. SUPPORT VECTOR MACHINE CLASSIFICATION OF OBJECT-BASED DATA FOR CROP MAPPING, USING MULTI-TEMPORAL LANDSAT IMAGERY

    Directory of Open Access Journals (Sweden)

    R. Devadas

    2012-07-01

    Full Text Available Crop mapping and time series analysis of agronomic cycles are critical for monitoring land use and land management practices, and analysing the issues of agro-environmental impacts and climate change. Multi-temporal Landsat data can be used to analyse decadal changes in cropping patterns at field level, owing to its medium spatial resolution and historical availability. This study attempts to develop robust remote sensing techniques, applicable across a large geographic extent, for state-wide mapping of cropping history in Queensland, Australia. In this context, traditional pixel-based classification was analysed in comparison with image object-based classification using advanced supervised machine-learning algorithms such as Support Vector Machine (SVM. For the Darling Downs region of southern Queensland we gathered a set of Landsat TM images from the 2010–2011 cropping season. Landsat data, along with the vegetation index images, were subjected to multiresolution segmentation to obtain polygon objects. Object-based methods enabled the analysis of aggregated sets of pixels, and exploited shape-related and textural variation, as well as spectral characteristics. SVM models were chosen after examining three shape-based parameters, twenty-three textural parameters and ten spectral parameters of the objects. We found that the object-based methods were superior to the pixel-based methods for classifying 4 major landuse/land cover classes, considering the complexities of within field spectral heterogeneity and spectral mixing. Comparative analysis clearly revealed that higher overall classification accuracy (95% was observed in the object-based SVM compared with that of traditional pixel-based classification (89% using maximum likelihood classifier (MLC. Object-based classification also resulted speckle-free images. Further, object-based SVM models were used to classify different broadacre crop types for summer and winter seasons. The influence of

  3. A Simple and High Performing Rate Control Initialization Method for H.264 AVC Coding Based on Motion Vector Map and Spatial Complexity at Low Bitrate

    Directory of Open Access Journals (Sweden)

    Yalin Wu

    2014-01-01

    Full Text Available The temporal complexity of video sequences can be characterized by motion vector map which consists of motion vectors of each macroblock (MB. In order to obtain the optimal initial QP (quantization parameter for the various video sequences which have different spatial and temporal complexities, this paper proposes a simple and high performance initial QP determining method based on motion vector map and temporal complexity to decide an initial QP in given target bit rate. The proposed algorithm produces the reconstructed video sequences with outstanding and stable quality. For any video sequences, the initial QP can be easily determined from matrices by target bit rate and mapped spatial complexity using proposed mapping method. Experimental results show that the proposed algorithm can show more outstanding objective and subjective performance than other conventional determining methods.

  4. Recursive cluster elimination based support vector machine for disease state prediction using resting state functional and effective brain connectivity.

    Directory of Open Access Journals (Sweden)

    Gopikrishna Deshpande

    2010-12-01

    Full Text Available Brain state classification has been accomplished using features such as voxel intensities, derived from functional magnetic resonance imaging (fMRI data, as inputs to efficient classifiers such as support vector machines (SVM and is based on the spatial localization model of brain function. With the advent of the connectionist model of brain function, features from brain networks may provide increased discriminatory power for brain state classification.In this study, we introduce a novel framework where in both functional connectivity (FC based on instantaneous temporal correlation and effective connectivity (EC based on causal influence in brain networks are used as features in an SVM classifier. In order to derive those features, we adopt a novel approach recently introduced by us called correlation-purged Granger causality (CPGC in order to obtain both FC and EC from fMRI data simultaneously without the instantaneous correlation contaminating Granger causality. In addition, statistical learning is accelerated and performance accuracy is enhanced by combining recursive cluster elimination (RCE algorithm with the SVM classifier. We demonstrate the efficacy of the CPGC-based RCE-SVM approach using a specific instance of brain state classification exemplified by disease state prediction. Accordingly, we show that this approach is capable of predicting with 90.3% accuracy whether any given human subject was prenatally exposed to cocaine or not, even when no significant behavioral differences were found between exposed and healthy subjects.The framework adopted in this work is quite general in nature with prenatal cocaine exposure being only an illustrative example of the power of this approach. In any brain state classification approach using neuroimaging data, including the directional connectivity information may prove to be a performance enhancer. When brain state classification is used for disease state prediction, our approach may aid the

  5. Ambulatory activity classification with dendogram-based support vector machine: Application in lower-limb active exoskeleton.

    Science.gov (United States)

    Mazumder, Oishee; Kundu, Ananda Sankar; Lenka, Prasanna Kumar; Bhaumik, Subhasis

    2016-10-01

    Ambulatory activity classification is an active area of research for controlling and monitoring state initiation, termination, and transition in mobility assistive devices such as lower-limb exoskeletons. State transition of lower-limb exoskeletons reported thus far are achieved mostly through the use of manual switches or state machine-based logic. In this paper, we propose a postural activity classifier using a 'dendogram-based support vector machine' (DSVM) which can be used to control a lower-limb exoskeleton. A pressure sensor-based wearable insole and two six-axis inertial measurement units (IMU) have been used for recognising two static and seven dynamic postural activities: sit, stand, and sit-to-stand, stand-to-sit, level walk, fast walk, slope walk, stair ascent and stair descent. Most of the ambulatory activities are periodic in nature and have unique patterns of response. The proposed classification algorithm involves the recognition of activity patterns on the basis of the periodic shape of trajectories. Polynomial coefficients extracted from the hip angle trajectory and the centre-of-pressure (CoP) trajectory during an activity cycle are used as features to classify dynamic activities. The novelty of this paper lies in finding suitable instrumentation, developing post-processing techniques, and selecting shape-based features for ambulatory activity classification. The proposed activity classifier is used to identify the activity states of a lower-limb exoskeleton. The DSVM classifier algorithm achieved an overall classification accuracy of 95.2%. Copyright © 2016 Elsevier B.V. All rights reserved.

  6. Biodegradable cyclen-based linear and cross-linked polymers as non-viral gene vectors.

    Science.gov (United States)

    Li, Shuo; Wang, Yu; Wang, Shan; Zhang, Ji; Wu, Shi-Fei; Wang, Bo-Lin; Zhu, Wen; Yu, Xiao-Qi

    2012-02-15

    Several 1,4,7,10-tetraazacyclododecane (cyclen)-based linear (3a-c) and cross-linked (8a-d) polymers containing biodegradable ester or disulfide bonds were described. These polymeric compounds were prepared by ring-opening polymerization from various diol glycidyl ethers. The molecular weights of the title polymers were measured by GPC. Agarose gel retardation assays showed that these compounds have good DNA-binding ability and can completely retard plasmid DNA (pDNA) at weight ratio of 20 for linear polymers and 1.2 for cross-linked polymers. The degradation of these polymers was confirmed by GPC. The formed polyplexes have appropriate sizes around 400 nm and zeta-potential values about 15-40 mV. The cytotoxicities of 8 assayed by MTT are much lower than that of 25 KDa PEI. In vitro transfection toward A549 and 293 cells showed that the transfection efficiency (TE) of 8c-DNA polyplex is close to that of 25 kDa PEI at 8c/DNA weight ratio of 4. Structure-activity relationships (SAR) of these linear and cross-linked polymers were discussed in their DNA-binding, cytotoxicity, and transfection studies. In addition, in the presence of serum, the TE of 8/DNA polyplexes could be improved by introducing chloroquine or Ca(2+) to pretreated cells. Copyright © 2012 Elsevier Ltd. All rights reserved.

  7. Control strategy for Single-phase Transformerless Three-leg Unified Power Quality Conditioner Based on Space Vector Modulation

    DEFF Research Database (Denmark)

    Lu, Yong; Xiao, Guochun; Wang, Xiongfei

    2016-01-01

    The unified power quality conditioner (UPQC) is known as an effective compensation device to improve PQ for sensitive end-users. This paper investigates the operation and control of a single-phase three-leg UPQC (TL-UPQC), where a novel space vector modulation method is proposed for naturally...... solving the coupling problem introduced by the common switching leg. The modulation method is similar to the well-known space vector modulation widely used with three-phase voltage source converters, which thus brings extra flexibility to the TL-UPQC system. Two optimized modulation modes with either...... reduced switching loss or harmonic distortion are derived, evaluated, and discussed, in order to demonstrate the flexibility brought by the space vector modulated TL-UPQC. Simulations and experimental results are presented to verify the feasibility and effectiveness of the proposed space vector modulation...

  8. Field data-based mathematical modeling by Bode equations and vector fitting algorithm for renewable energy applications.

    Directory of Open Access Journals (Sweden)

    A H Sabry

    Full Text Available The power system always has several variations in its profile due to random load changes or environmental effects such as device switching effects when generating further transients. Thus, an accurate mathematical model is important because most system parameters vary with time. Curve modeling of power generation is a significant tool for evaluating system performance, monitoring and forecasting. Several numerical techniques compete to fit the curves of empirical data such as wind, solar, and demand power rates. This paper proposes a new modified methodology presented as a parametric technique to determine the system's modeling equations based on the Bode plot equations and the vector fitting (VF algorithm by fitting the experimental data points. The modification is derived from the familiar VF algorithm as a robust numerical method. This development increases the application range of the VF algorithm for modeling not only in the frequency domain but also for all power curves. Four case studies are addressed and compared with several common methods. From the minimal RMSE, the results show clear improvements in data fitting over other methods. The most powerful features of this method is the ability to model irregular or randomly shaped data and to be applied to any algorithms that estimating models using frequency-domain data to provide state-space or transfer function for the model.

  9. Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation.

    Directory of Open Access Journals (Sweden)

    Hazlee Azil Illias

    Full Text Available Early detection of power transformer fault is important because it can reduce the maintenance cost of the transformer and it can ensure continuous electricity supply in power systems. Dissolved Gas Analysis (DGA technique is commonly used to identify oil-filled power transformer fault type but utilisation of artificial intelligence method with optimisation methods has shown convincing results. In this work, a hybrid support vector machine (SVM with modified evolutionary particle swarm optimisation (EPSO algorithm was proposed to determine the transformer fault type. The superiority of the modified PSO technique with SVM was evaluated by comparing the results with the actual fault diagnosis, unoptimised SVM and previous reported works. Data reduction was also applied using stepwise regression prior to the training process of SVM to reduce the training time. It was found that the proposed hybrid SVM-Modified EPSO (MEPSO-Time Varying Acceleration Coefficient (TVAC technique results in the highest correct identification percentage of faults in a power transformer compared to other PSO algorithms. Thus, the proposed technique can be one of the potential solutions to identify the transformer fault type based on DGA data on site.

  10. Field data-based mathematical modeling by Bode equations and vector fitting algorithm for renewable energy applications

    Science.gov (United States)

    W. Hasan, W. Z.

    2018-01-01

    The power system always has several variations in its profile due to random load changes or environmental effects such as device switching effects when generating further transients. Thus, an accurate mathematical model is important because most system parameters vary with time. Curve modeling of power generation is a significant tool for evaluating system performance, monitoring and forecasting. Several numerical techniques compete to fit the curves of empirical data such as wind, solar, and demand power rates. This paper proposes a new modified methodology presented as a parametric technique to determine the system’s modeling equations based on the Bode plot equations and the vector fitting (VF) algorithm by fitting the experimental data points. The modification is derived from the familiar VF algorithm as a robust numerical method. This development increases the application range of the VF algorithm for modeling not only in the frequency domain but also for all power curves. Four case studies are addressed and compared with several common methods. From the minimal RMSE, the results show clear improvements in data fitting over other methods. The most powerful features of this method is the ability to model irregular or randomly shaped data and to be applied to any algorithms that estimating models using frequency-domain data to provide state-space or transfer function for the model. PMID:29351554

  11. A carbon risk prediction model for Chinese heavy-polluting industrial enterprises based on support vector machine

    International Nuclear Information System (INIS)

    Zhou, Zhifang; Xiao, Tian; Chen, Xiaohong; Wang, Chang

    2016-01-01

    Chinese heavy-polluting industrial enterprises, especially petrochemical or chemical industry, labeled low carbon efficiency and high emission load, are facing the tremendous pressure of emission reduction under the background of global shortage of energy supply and constrain of carbon emission. However, due to the limited amount of theoretic and practical research in this field, problems like lacking prediction indicators or models, and the quantified standard of carbon risk remain unsolved. In this paper, the connotation of carbon risk and an assessment index system for Chinese heavy-polluting industrial enterprises (eg. coal enterprise, petrochemical enterprises, chemical enterprises et al.) based on support vector machine are presented. By using several heavy-polluting industrial enterprises’ related data, SVM model is trained to predict the carbon risk level of a specific enterprise, which allows the enterprise to identify and manage its carbon risks. The result shows that this method can predict enterprise’s carbon risk level in an efficient, accurate way with high practical application and generalization value.

  12. Engineering the transposition-based baculovirus expression vector system for higher efficiency protein production from insect cells

    Science.gov (United States)

    Mehalko, Jennifer L.; Esposito, Dominic

    2016-01-01

    One of the most common methods for producing recombinant baculovirus for insect cell protein production involves a transposition mediated system invented over 2 decades ago. This Tn7-mediated system, commercially sold as Bac-to-Bac, has proven highly useful for construction of high quality baculovirus, but suffers from a number of drawbacks which reduce the efficiency of the process and limit its utility for high throughput protein production processes. We describe here the creation of Bac-2-the-Future, a 2nd generation Tn7-based system for recombinant baculovirus production which uses optimized expression vectors, new E. coli strains, and enhanced protocols to dramatically enhance the efficiency of the baculovirus production process. The new system which we describe eliminates the need for additional screening of positive clones, improves the efficiency of transposition, and reduces the cost and time required for high throughput baculovirus production. The system is compatible with multiple cloning methodologies, and has been demonstrated to produce baculovirus with equal or better titer and protein productivity than the currently available systems. PMID:27616621

  13. Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation.

    Science.gov (United States)

    Illias, Hazlee Azil; Zhao Liang, Wee

    2018-01-01

    Early detection of power transformer fault is important because it can reduce the maintenance cost of the transformer and it can ensure continuous electricity supply in power systems. Dissolved Gas Analysis (DGA) technique is commonly used to identify oil-filled power transformer fault type but utilisation of artificial intelligence method with optimisation methods has shown convincing results. In this work, a hybrid support vector machine (SVM) with modified evolutionary particle swarm optimisation (EPSO) algorithm was proposed to determine the transformer fault type. The superiority of the modified PSO technique with SVM was evaluated by comparing the results with the actual fault diagnosis, unoptimised SVM and previous reported works. Data reduction was also applied using stepwise regression prior to the training process of SVM to reduce the training time. It was found that the proposed hybrid SVM-Modified EPSO (MEPSO)-Time Varying Acceleration Coefficient (TVAC) technique results in the highest correct identification percentage of faults in a power transformer compared to other PSO algorithms. Thus, the proposed technique can be one of the potential solutions to identify the transformer fault type based on DGA data on site.

  14. The identification of high potential archers based on relative psychological coping skills variables: A Support Vector Machine approach

    Science.gov (United States)

    Taha, Zahari; Muazu Musa, Rabiu; Majeed, A. P. P. Abdul; Razali Abdullah, Mohamad; Aizzat Zakaria, Muhammad; Muaz Alim, Muhammad; Arif Mat Jizat, Jessnor; Fauzi Ibrahim, Mohamad

    2018-03-01

    Support Vector Machine (SVM) has been revealed to be a powerful learning algorithm for classification and prediction. However, the use of SVM for prediction and classification in sport is at its inception. The present study classified and predicted high and low potential archers from a collection of psychological coping skills variables trained on different SVMs. 50 youth archers with the average age and standard deviation of (17.0 ±.056) gathered from various archery programmes completed a one end shooting score test. Psychological coping skills inventory which evaluates the archers level of related coping skills were filled out by the archers prior to their shooting tests. k-means cluster analysis was applied to cluster the archers based on their scores on variables assessed. SVM models, i.e. linear and fine radial basis function (RBF) kernel functions, were trained on the psychological variables. The k-means clustered the archers into high psychologically prepared archers (HPPA) and low psychologically prepared archers (LPPA), respectively. It was demonstrated that the linear SVM exhibited good accuracy and precision throughout the exercise with an accuracy of 92% and considerably fewer error rate for the prediction of the HPPA and the LPPA as compared to the fine RBF SVM. The findings of this investigation can be valuable to coaches and sports managers to recognise high potential athletes from the selected psychological coping skills variables examined which would consequently save time and energy during talent identification and development programme.

  15. The employment of Support Vector Machine to classify high and low performance archers based on bio-physiological variables

    Science.gov (United States)

    Taha, Zahari; Muazu Musa, Rabiu; Majeed, Anwar P. P. Abdul; Razali Abdullah, Mohamad; Amirul Abdullah, Muhammad; Hasnun Arif Hassan, Mohd; Khalil, Zubair

    2018-04-01

    The present study employs a machine learning algorithm namely support vector machine (SVM) to classify high and low potential archers from a collection of bio-physiological variables trained on different SVMs. 50 youth archers with the average age and standard deviation of (17.0 ±.056) gathered from various archery programmes completed a one end shooting score test. The bio-physiological variables namely resting heart rate, resting respiratory rate, resting diastolic blood pressure, resting systolic blood pressure, as well as calories intake, were measured prior to their shooting tests. k-means cluster analysis was applied to cluster the archers based on their scores on variables assessed. SVM models i.e. linear, quadratic and cubic kernel functions, were trained on the aforementioned variables. The k-means clustered the archers into high (HPA) and low potential archers (LPA), respectively. It was demonstrated that the linear SVM exhibited good accuracy with a classification accuracy of 94% in comparison the other tested models. The findings of this investigation can be valuable to coaches and sports managers to recognise high potential athletes from the selected bio-physiological variables examined.

  16. An Object-Based Classification of Mangroves Using a Hybrid Decision Tree—Support Vector Machine Approach

    Directory of Open Access Journals (Sweden)

    Benjamin W. Heumann

    2011-11-01

    Full Text Available Mangroves provide valuable ecosystem goods and services such as carbon sequestration, habitat for terrestrial and marine fauna, and coastal hazard mitigation. The use of satellite remote sensing to map mangroves has become widespread as it can provide accurate, efficient, and repeatable assessments. Traditional remote sensing approaches have failed to accurately map fringe mangroves and true mangrove species due to relatively coarse spatial resolution and/or spectral confusion with landward vegetation. This study demonstrates the use of the new Worldview-2 sensor, Object-based image analysis (OBIA, and support vector machine (SVM classification to overcome both of these limitations. An exploratory spectral separability showed that individual mangrove species could not be spectrally separated, but a distinction between true and associate mangrove species could be made. An OBIA classification was used that combined a decision-tree classification with the machine-learning SVM classification. Results showed an overall accuracy greater than 94% (kappa = 0.863 for classifying true mangroves species and other dense coastal vegetation at the object level. There remain serious challenges to accurately mapping fringe mangroves using remote sensing data due to spectral similarity of mangrove and associate species, lack of clear zonation between species, and mixed pixel effects, especially when vegetation is sparse or degraded.

  17. Prediction of Antimicrobial Peptides Based on Sequence Alignment and Support Vector Machine-Pairwise Algorithm Utilizing LZ-Complexity

    Directory of Open Access Journals (Sweden)

    Xin Yi Ng

    2015-01-01

    Full Text Available This study concerns an attempt to establish a new method for predicting antimicrobial peptides (AMPs which are important to the immune system. Recently, researchers are interested in designing alternative drugs based on AMPs because they have found that a large number of bacterial strains have become resistant to available antibiotics. However, researchers have encountered obstacles in the AMPs designing process as experiments to extract AMPs from protein sequences are costly and require a long set-up time. Therefore, a computational tool for AMPs prediction is needed to resolve this problem. In this study, an integrated algorithm is newly introduced to predict AMPs by integrating sequence alignment and support vector machine- (SVM- LZ complexity pairwise algorithm. It was observed that, when all sequences in the training set are used, the sensitivity of the proposed algorithm is 95.28% in jackknife test and 87.59% in independent test, while the sensitivity obtained for jackknife test and independent test is 88.74% and 78.70%, respectively, when only the sequences that has less than 70% similarity are used. Applying the proposed algorithm may allow researchers to effectively predict AMPs from unknown protein peptide sequences with higher sensitivity.

  18. 3D Modelling of a Vectored Water Jet-Based Multi-Propeller Propulsion System for a Spherical Underwater Robot

    Directory of Open Access Journals (Sweden)

    Xichuan Lin

    2013-01-01

    Full Text Available This paper presents an improved modelling method for a water jet-based multi-propeller propulsion system. In our previous work, the modelling experiments were only carried out in 2D planes, whose experimental results had poor agreement when we wanted to control the propulsive forces in 3D space directly. This research extends the 2D modelling described in the authors' previous work into 3D space. By doing this, the model could include 3D space information, which is more useful than that of 2D space. The effective propulsive forces and moments in 3D space can be obtained directly by synthesizing the propulsive vectors of propellers. For this purpose, a novel experimental mechanism was developed to achieve the proposed 3D modelling. This mechanism was designed with the mass distribution centred for the robot. By installing a six-axis load-cell sensor at the equivalent mass centre, we obtained the direct propulsive effect of the system for the robot. Also, in this paper, the orientation surface and propulsive surfaces are developed to provide the 3D information of the propulsive system. Experiments for each propeller were first carried out to establish the models. Then, further experiments were carried out with all of the propellers working together to validate the models. Finally, we compared the various experimental results with the simulation data. The utility of this modelling method is discussed at length.

  19. Field data-based mathematical modeling by Bode equations and vector fitting algorithm for renewable energy applications.

    Science.gov (United States)

    Sabry, A H; W Hasan, W Z; Ab Kadir, M Z A; Radzi, M A M; Shafie, S

    2018-01-01

    The power system always has several variations in its profile due to random load changes or environmental effects such as device switching effects when generating further transients. Thus, an accurate mathematical model is important because most system parameters vary with time. Curve modeling of power generation is a significant tool for evaluating system performance, monitoring and forecasting. Several numerical techniques compete to fit the curves of empirical data such as wind, solar, and demand power rates. This paper proposes a new modified methodology presented as a parametric technique to determine the system's modeling equations based on the Bode plot equations and the vector fitting (VF) algorithm by fitting the experimental data points. The modification is derived from the familiar VF algorithm as a robust numerical method. This development increases the application range of the VF algorithm for modeling not only in the frequency domain but also for all power curves. Four case studies are addressed and compared with several common methods. From the minimal RMSE, the results show clear improvements in data fitting over other methods. The most powerful features of this method is the ability to model irregular or randomly shaped data and to be applied to any algorithms that estimating models using frequency-domain data to provide state-space or transfer function for the model.

  20. Experimental validation of plastic constitutive hardening relationship based upon the direction of the Net Burgers Density Vector

    Science.gov (United States)

    Sarac, Abdulhamit; Kysar, Jeffrey W.

    2018-02-01

    We present a new methodology for experimental validation of single crystal plasticity constitutive relationships based upon spatially resolved measurements of the direction of the Net Burgers Density Vector, which we refer to as the β-field. The β-variable contains information about the active slip systems as well as the ratios of the Geometrically Necessary Dislocation (GND) densities on the active slip systems. We demonstrate the methodology by comparing single crystal plasticity finite element simulations of plane strain wedge indentations into face-centered cubic nickel to detailed experimental measurements of the β-field. We employ the classical Peirce-Asaro-Needleman (PAN) hardening model in this study due to the straightforward physical interpretation of its constitutive parameters that include latent hardening ratio, initial hardening modulus and the saturation stress. The saturation stress and the initial hardening modulus have relatively large influence on the β-variable compared to the latent hardening ratio. A change in the initial hardening modulus leads to a shift in the boundaries of plastic slip sectors with the plastically deforming region. As the saturation strength varies, both the magnitude of the β-variable and the boundaries of the plastic slip sectors change. We thus demonstrate that the β-variable is sensitive to changes in the constitutive parameters making the variable suitable for validation purposes. We identify a set of constitutive parameters that are consistent with the β-field obtained from the experiment.