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.
Virus Database and Online Inquiry System Based on Natural Vectors.
Dong, Rui; Zheng, Hui; Tian, Kun; Yau, Shek-Chung; Mao, Weiguang; Yu, Wenping; Yin, Changchuan; Yu, Chenglong; He, Rong Lucy; Yang, Jie; Yau, Stephen St
2017-01-01
We construct a virus database called VirusDB (http://yaulab.math.tsinghua.edu.cn/VirusDB/) and an online inquiry system to serve people who are interested in viral classification and prediction. The database stores all viral genomes, their corresponding natural vectors, and the classification information of the single/multiple-segmented viral reference sequences downloaded from National Center for Biotechnology Information. The online inquiry system serves the purpose of computing natural vectors and their distances based on submitted genomes, providing an online interface for accessing and using the database for viral classification and prediction, and back-end processes for automatic and manual updating of database content to synchronize with GenBank. Submitted genomes data in FASTA format will be carried out and the prediction results with 5 closest neighbors and their classifications will be returned by email. Considering the one-to-one correspondence between sequence and natural vector, time efficiency, and high accuracy, natural vector is a significant advance compared with alignment methods, which makes VirusDB a useful database in further research.
Biosensor method and system based on feature vector extraction
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.
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.
Algevir: An Expression System for Microalgae Based on Viral Vectors
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
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...
Raster images vectorization system
Genytė, Jurgita
2006-01-01
The problem of raster images vectorization was analyzed and researched in this work. Existing vectorization systems are quite expensive, the results are inaccurate, and the manual vectorization of a large number of drafts is impossible. That‘s why our goal was to design and develop a new raster images vectorization system using our suggested automatic vectorization algorithm and the way to record results in a new universal vectorial file format. The work consists of these main parts: analysis...
Directory of Open Access Journals (Sweden)
Anne Mathilde Lund
Full Text Available A new versatile mammalian vector system for protein production, cell biology analyses, and cell factory engineering was developed. The vector system applies the ligation-free uracil-excision based technique--USER cloning--to rapidly construct mammalian expression vectors of multiple DNA fragments and with maximum flexibility, both for choice of vector backbone and cargo. The vector system includes a set of basic vectors and a toolbox containing a multitude of DNA building blocks including promoters, terminators, selectable marker- and reporter genes, and sequences encoding an internal ribosome entry site, cellular localization signals and epitope- and purification tags. Building blocks in the toolbox can be easily combined as they contain defined and tested Flexible Assembly Sequence Tags, FASTs. USER cloning with FASTs allows rapid swaps of gene, promoter or selection marker in existing plasmids and simple construction of vectors encoding proteins, which are fused to fluorescence-, purification-, localization-, or epitope tags. The mammalian expression vector assembly platform currently allows for the assembly of up to seven fragments in a single cloning step with correct directionality and with a cloning efficiency above 90%. The functionality of basic vectors for FAST assembly was tested and validated by transient expression of fluorescent model proteins in CHO, U-2-OS and HEK293 cell lines. In this test, we included many of the most common vector elements for heterologous gene expression in mammalian cells, in addition the system is fully extendable by other users. The vector system is designed to facilitate high-throughput genome-scale studies of mammalian cells, such as the newly sequenced CHO cell lines, through the ability to rapidly generate high-fidelity assembly of customizable gene expression vectors.
International Nuclear Information System (INIS)
Cai Qi; Shang Yanlong; Chen Lisheng; Zhao Yuguang
2013-01-01
Vector-universal generating function was presented to analyze the availability of thermodynamic system with multiple performance parameters. Vector-universal generating function of component's performance was defined, the arithmetic model based on vector-universal generating function was derived for the thermodynamic system, and the calculation method was given for state probability of multi-state component. With the stochastic simulation of the degeneration trend of the multiple factors, the system availability with multiple performance parameters was obtained under composite factors. It is shown by an example that the results of the availability obtained by the binary availability analysis method are somewhat conservative, and the results considering parameter failure based on vector-universal generating function reflect the operation characteristics of the thermodynamic system better. (authors)
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
A simple and robust vector-based shRNA expression system used for RNA interference.
Wang, Xue-jun; Li, Ying; Huang, Hai; Zhang, Xiu-juan; Xie, Pei-wen; Hu, Wei; Li, Dan-dan; Wang, Sheng-qi
2013-01-01
RNA interference (RNAi) mediated by small interfering RNAs (siRNAs) or short hairpin RNAs (shRNAs) has become a powerful genetic tool for conducting functional studies. Previously, vector-based shRNA-expression strategies capable of inducing RNAi in viable cells have been developed, however, these vector systems have some disadvantages, either because they were error-prone or cost prohibitive. In this report we described the development of a simple, robust shRNA expression system utilizing 1 long oligonucleotide or 2 short oligonucleotides for half the cost of conventional shRNA construction methods and with a >95% cloning success rate. The shRNA loop sequence and stem structure were also compared and carefully selected for better RNAi efficiency. Furthermore, an easier strategy was developed based on isocaudomers which permit rapid combination of the most efficient promoter-shRNA cassettes. Finally, using this method, the conservative target sites for hepatitis B virus (HBV) knockdown were systemically screened and HBV antigen expression shown to be successfully suppressed in the presence of connected multiple shRNAs both in vitro and in vivo. This novel design describes an inexpensive and effective way to clone and express single or multiple shRNAs from the same vector with the capacity for potent and effective silencing of target genes.
A simple and robust vector-based shRNA expression system used for RNA interference.
Directory of Open Access Journals (Sweden)
Xue-jun Wang
Full Text Available BACKGROUND: RNA interference (RNAi mediated by small interfering RNAs (siRNAs or short hairpin RNAs (shRNAs has become a powerful genetic tool for conducting functional studies. Previously, vector-based shRNA-expression strategies capable of inducing RNAi in viable cells have been developed, however, these vector systems have some disadvantages, either because they were error-prone or cost prohibitive. RESULTS: In this report we described the development of a simple, robust shRNA expression system utilizing 1 long oligonucleotide or 2 short oligonucleotides for half the cost of conventional shRNA construction methods and with a >95% cloning success rate. The shRNA loop sequence and stem structure were also compared and carefully selected for better RNAi efficiency. Furthermore, an easier strategy was developed based on isocaudomers which permit rapid combination of the most efficient promoter-shRNA cassettes. Finally, using this method, the conservative target sites for hepatitis B virus (HBV knockdown were systemically screened and HBV antigen expression shown to be successfully suppressed in the presence of connected multiple shRNAs both in vitro and in vivo. CONCLUSION: This novel design describes an inexpensive and effective way to clone and express single or multiple shRNAs from the same vector with the capacity for potent and effective silencing of target genes.
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.
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.
Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines.
del Val, Lara; Izquierdo-Fuente, Alberto; Villacorta, Juan J; Raboso, Mariano
2015-06-17
Drawing on the results of an acoustic biometric system based on a MSE classifier, a new biometric system has been implemented. This new system preprocesses acoustic images, extracts several parameters and finally classifies them, based on Support Vector Machine (SVM). The preprocessing techniques used are spatial filtering, segmentation-based on a Gaussian Mixture Model (GMM) to separate the person from the background, masking-to reduce the dimensions of images-and binarization-to reduce the size of each image. An analysis of classification error and a study of the sensitivity of the error versus the computational burden of each implemented algorithm are presented. This allows the selection of the most relevant algorithms, according to the benefits required by the system. A significant improvement of the biometric system has been achieved by reducing the classification error, the computational burden and the storage requirements.
DEFF Research Database (Denmark)
Lund, Anne Mathilde; Kildegaard, Helene Faustrup; Petersen, Maja Borup Kjær
2014-01-01
, in addition the system is fully extendable by other users. The vector system is designed to facilitate high-throughput genome-scale studies of mammalian cells, such as the newly sequenced CHO cell lines, through the ability to rapidly generate high-fidelity assembly of customizable gene expression vectors....
Directory of Open Access Journals (Sweden)
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.
Forecasting systems reliability based on support vector regression with genetic algorithms
International Nuclear Information System (INIS)
Chen, K.-Y.
2007-01-01
This study applies a novel neural-network technique, support vector regression (SVR), to forecast reliability in engine systems. The aim of this study is to examine the feasibility of SVR in systems reliability prediction by comparing it with the existing neural-network approaches and the autoregressive integrated moving average (ARIMA) model. To build an effective SVR model, SVR's parameters must be set carefully. This study proposes a novel approach, known as GA-SVR, which searches for SVR's optimal parameters using real-value genetic algorithms, and then adopts the optimal parameters to construct the SVR models. A real reliability data for 40 suits of turbochargers were employed as the data set. The experimental results demonstrate that SVR outperforms the existing neural-network approaches and the traditional ARIMA models based on the normalized root mean square error and mean absolute percentage error
Implementation of a vector-based tracking loop receiver in a pseudolite navigation system.
So, Hyoungmin; Lee, Taikjin; Jeon, Sanghoon; Kim, Chongwon; Kee, Changdon; Kim, Taehee; Lee, Sanguk
2010-01-01
We propose a vector tracking loop (VTL) algorithm for an asynchronous pseudolite navigation system. It was implemented in a software receiver and experiments in an indoor navigation system were conducted. Test results show that the VTL successfully tracks signals against the near-far problem, one of the major limitations in pseudolite navigation systems, and could improve positioning availability by extending pseudolite navigation coverage.
Wang, Xibin; Luo, Fengji; Qian, Ying; Ranzi, Gianluca
2016-01-01
With the rapid development of ICT and Web technologies, a large an amount of information is becoming available and this is producing, in some instances, a condition of information overload. Under these conditions, it is difficult for a person to locate and access useful information for making decisions. To address this problem, there are information filtering systems, such as the personalized recommendation system (PRS) considered in this paper, that assist a person in identifying possible products or services of interest based on his/her preferences. Among available approaches, collaborative Filtering (CF) is one of the most widely used recommendation techniques. However, CF has some limitations, e.g., the relatively simple similarity calculation, cold start problem, etc. In this context, this paper presents a new regression model based on the support vector machine (SVM) classification and an improved PSO (IPSO) for the development of an electronic movie PRS. In its implementation, a SVM classification model is first established to obtain a preliminary movie recommendation list based on which a SVM regression model is applied to predict movies' ratings. The proposed PRS not only considers the movie's content information but also integrates the users' demographic and behavioral information to better capture the users' interests and preferences. The efficiency of the proposed method is verified by a series of experiments based on the MovieLens benchmark data set.
Support vector machine based diagnostic system for breast cancer using swarm intelligence.
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
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.
Liu, Yuan; Wang, Renxin; Zhang, Guojun; Du, Jin; Zhao, Long; Xue, Chenyang; Zhang, Wendong; Liu, Jun
2015-07-01
This paper presents methods of promoting the sensitivity of Microelectromechanical Systems (MEMS) vector hydrophone by increasing the sensing area of cilium and perfect insulative Parylene membrane. First, a low-density sphere is integrated with the cilium to compose a "lollipop shape," which can considerably increase the sensing area. A mathematic model on the sensitivity of the "lollipop-shaped" MEMS vector hydrophone is presented, and the influences of different structural parameters on the sensitivity are analyzed via simulation. Second, the MEMS vector hydrophone is encapsulated through the conformal deposition of insulative Parylene membrane, which enables underwater acoustic monitoring without any typed sound-transparent encapsulation. Finally, the characterization results demonstrate that the sensitivity reaches up to -183 dB (500 Hz 0dB at 1 V/ μPa ), which is increased by more than 10 dB, comparing with the previous cilium-shaped MEMS vector hydrophone. Besides, the frequency response takes on a sensitivity increment of 6 dB per octave. The working frequency band is 20-500 Hz and the concave point depth of 8-shaped directivity is beyond 30 dB, indicating that the hydrophone is promising in underwater acoustic application.
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.
Performance monitoring for coherent DP-QPSK systems based on stokes vectors analysis
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.
Saybani, Mahmoud Reza; Shamshirband, Shahaboddin; Golzari Hormozi, Shahram; Wah, Teh Ying; Aghabozorgi, Saeed; Pourhoseingholi, Mohamad Amin; Olariu, Teodora
2015-04-01
Tuberculosis (TB) is a major global health problem, which has been ranked as the second leading cause of death from an infectious disease worldwide. Diagnosis based on cultured specimens is the reference standard, however results take weeks to process. Scientists are looking for early detection strategies, which remain the cornerstone of tuberculosis control. Consequently there is a need to develop an expert system that helps medical professionals to accurately and quickly diagnose the disease. Artificial Immune Recognition System (AIRS) has been used successfully for diagnosing various diseases. However, little effort has been undertaken to improve its classification accuracy. In order to increase the classification accuracy of AIRS, this study introduces a new hybrid system that incorporates a support vector machine into AIRS for diagnosing tuberculosis. Patient epacris reports obtained from the Pasteur laboratory of Iran were used as the benchmark data set, with the sample size of 175 (114 positive samples for TB and 60 samples in the negative group). The strategy of this study was to ensure representativeness, thus it was important to have an adequate number of instances for both TB and non-TB cases. The classification performance was measured through 10-fold cross-validation, Root Mean Squared Error (RMSE), sensitivity and specificity, Youden's Index, and Area Under the Curve (AUC). Statistical analysis was done using the Waikato Environment for Knowledge Analysis (WEKA), a machine learning program for windows. With an accuracy of 100%, sensitivity of 100%, specificity of 100%, Youden's Index of 1, Area Under the Curve of 1, and RMSE of 0, the proposed method was able to successfully classify tuberculosis patients. There have been many researches that aimed at diagnosing tuberculosis faster and more accurately. Our results described a model for diagnosing tuberculosis with 100% sensitivity and 100% specificity. This model can be used as an additional tool for
2015-09-28
buoyant underwater vehicle with an interior space in which a length of said underwater vehicle is equal to one tenth of the acoustic wavelength...underwater vehicle with an interior space in which a length of said underwater vehicle is equal to one tenth of the acoustic wavelength; an...unmanned underwater vehicle that can function as an acoustic vector sensor. (2) Description of the Prior Art [0004] It is known that a propagating
Dheeba, J.; Jaya, T.; Singh, N. Albert
2017-09-01
Classification of cancerous masses is a challenging task in many computerised detection systems. Cancerous masses are difficult to detect because these masses are obscured and subtle in mammograms. This paper investigates an intelligent classifier - fuzzy support vector machine (FSVM) applied to classify the tissues containing masses on mammograms for breast cancer diagnosis. The algorithm utilises texture features extracted using Laws texture energy measures and a FSVM to classify the suspicious masses. The new FSVM treats every feature as both normal and abnormal samples, but with different membership. By this way, the new FSVM have more generalisation ability to classify the masses in mammograms. The classifier analysed 219 clinical mammograms collected from breast cancer screening laboratory. The tests made on the real clinical mammograms shows that the proposed detection system has better discriminating power than the conventional support vector machine. With the best combination of FSVM and Laws texture features, the area under the Receiver operating characteristic curve reached .95, which corresponds to a sensitivity of 93.27% with a specificity of 87.17%. The results suggest that detecting masses using FSVM contribute to computer-aided detection of breast cancer and as a decision support system for radiologists.
Xie, Yifang; Wang, Daqi; Lan, Feng; Wei, Gang; Ni, Ting; Chai, Renjie; Liu, Dong; Hu, Shijun; Li, Mingqing; Li, Dajin; Wang, Hongyan; Wang, Yongming
2017-05-24
Human pluripotent stem cells (hPSCs) represent a unique opportunity for understanding the molecular mechanisms underlying complex traits and diseases. CRISPR/Cas9 is a powerful tool to introduce genetic mutations into the hPSCs for loss-of-function studies. Here, we developed an episomal vector-based CRISPR/Cas9 system, which we called epiCRISPR, for highly efficient gene knockout in hPSCs. The epiCRISPR system enables generation of up to 100% Insertion/Deletion (indel) rates. In addition, the epiCRISPR system enables efficient double-gene knockout and genomic deletion. To minimize off-target cleavage, we combined the episomal vector technology with double-nicking strategy and recent developed high fidelity Cas9. Thus the epiCRISPR system offers a highly efficient platform for genetic analysis in hPSCs.
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.
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.
Dual delivery systems based on polyamine analog BENSpm as prodrug and gene delivery vectors
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
CW-THz vector spectroscopy and imaging system based on 1.55-µm fiber-optics.
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.
Directory of Open Access Journals (Sweden)
Gang Chen
2012-01-01
Full Text Available It is not easy for the system identification-based reduced-order model (ROM and even eigenmode based reduced-order model to predict the limit cycle oscillation generated by the nonlinear unsteady aerodynamics. Most of these traditional ROMs are sensitive to the flow parameter variation. In order to deal with this problem, a support vector machine- (SVM- based ROM was investigated and the general construction framework was proposed. The two-DOF aeroelastic system for the NACA 64A010 airfoil in transonic flow was then demonstrated for the new SVM-based ROM. The simulation results show that the new ROM can capture the LCO behavior of the nonlinear aeroelastic system with good accuracy and high efficiency. The robustness and computational efficiency of the SVM-based ROM would provide a promising tool for real-time flight simulation including nonlinear aeroelastic effects.
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.
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.
Madero Orozco, Hiram; Vergara Villegas, Osslan Osiris; Cruz Sánchez, Vianey Guadalupe; Ochoa Domínguez, Humberto de Jesús; Nandayapa Alfaro, Manuel de Jesús
2015-02-12
Lung cancer is a leading cause of death worldwide; it refers to the uncontrolled growth of abnormal cells in the lung. A computed tomography (CT) scan of the thorax is the most sensitive method for detecting cancerous lung nodules. A lung nodule is a round lesion which can be either non-cancerous or cancerous. In the CT, the lung cancer is observed as round white shadow nodules. The possibility to obtain a manually accurate interpretation from CT scans demands a big effort by the radiologist and might be a fatiguing process. Therefore, the design of a computer-aided diagnosis (CADx) system would be helpful as a second opinion tool. The stages of the proposed CADx are: a supervised extraction of the region of interest to eliminate the shape differences among CT images. The Daubechies db1, db2, and db4 wavelet transforms are computed with one and two levels of decomposition. After that, 19 features are computed from each wavelet sub-band. Then, the sub-band and attribute selection is performed. As a result, 11 features are selected and combined in pairs as inputs to the support vector machine (SVM), which is used to distinguish CT images containing cancerous nodules from those not containing nodules. The clinical data set used for experiments consists of 45 CT scans from ELCAP and LIDC. For the training stage 61 CT images were used (36 with cancerous lung nodules and 25 without lung nodules). The system performance was tested with 45 CT scans (23 CT scans with lung nodules and 22 without nodules), different from that used for training. The results obtained show that the methodology successfully classifies cancerous nodules with a diameter from 2 mm to 30 mm. The total preciseness obtained was 82%; the sensitivity was 90.90%, whereas the specificity was 73.91%. The CADx system presented is competitive with other literature systems in terms of sensitivity. The system reduces the complexity of classification by not performing the typical segmentation stage of most CADx
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.
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.
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.
High performance computing on vector systems
Roller, Sabine
2008-01-01
Presents the developments in high-performance computing and simulation on modern supercomputer architectures. This book covers trends in hardware and software development in general and specifically the vector-based systems and heterogeneous architectures. It presents innovative fields like coupled multi-physics or multi-scale simulations.
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.
Directory of Open Access Journals (Sweden)
Anbang Zhao
2017-02-01
Full Text Available 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.
Cell Phone-Based System (Chaak) for Surveillance of Immatures of Dengue Virus Mosquito Vectors
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
Automated Vectorization of Decision-Based Algorithms
James, Mark
2006-01-01
Virtually all existing vectorization algorithms are designed to only analyze the numeric properties of an algorithm and distribute those elements across multiple processors. This advances the state of the practice because it is the only known system, at the time of this reporting, that takes high-level statements and analyzes them for their decision properties and converts them to a form that allows them to automatically be executed in parallel. The software takes a high-level source program that describes a complex decision- based condition and rewrites it as a disjunctive set of component Boolean relations that can then be executed in parallel. This is important because parallel architectures are becoming more commonplace in conventional systems and they have always been present in NASA flight systems. This technology allows one to take existing condition-based code and automatically vectorize it so it naturally decomposes across parallel architectures.
Reversible Vector Ratchet Effect in Skyrmion Systems
Ma, Xiaoyu; Reichhardt, Charles; Reichhardt, Cynthia
Magnetic skyrmions are topological non-trivial spin textures found in several magnetic materials. Since their motion can be controlled using ultralow current densities, skyrmions are appealing for potential applications in spintronics as information carriers and processing devices. In this work, we studied the collective transport properties of driven skyrmions based on a particle-like model with molecular dynamics (MD) simulation. Our results show that ac driven skyrmions interacting with an asymmetric substrate provide a realization of a new class of ratchet system, which we call a vector ratchet, that arises due to the effect of the Magnus term on the skyrmion dynamics. In a vector ratchet, the dc motion induced by the ac drive can be described as a vector that can be rotated up to 360 degrees relative to the substrate asymmetry direction. This could represent a new method for controlling skyrmion motion for spintronic applications.
Directory of Open Access Journals (Sweden)
Zhanjiang Li
2018-01-01
Full Text Available The micro enterprises’ credit indicators with credit identification ability are selected by the two classification models of Support Vector Machine for the first round of indicator selection and then for the second round of indicator selection, deleting credit indicators with redundant information by clustering variables through the principle of minimum sum of deviation squares. This paper provides a screening model for credit evaluation indicators of micro enterprises and uses credit data of 860 micro enterprises samples in Inner Mongolia in western China for application analysis. The test results show that, first, the constructed final micro enterprises’ credit indicator system is in line with the 5C model; second, the validity test based on the ROC (Receiver Operating Characteristic curve reveals that each of the screened credit evaluation indicators is valid.
Development of oral CTL vaccine using a CTP-integrated Sabin 1 poliovirus-based vector system.
Han, Seung-Soo; Lee, Jinjoo; Jung, Yideul; Kang, Myeong-Ho; Hong, Jung-Hyub; Cha, Min-Suk; Park, Yu-Jin; Lee, Ezra; Yoon, Cheol-Hee; Bae, Yong-Soo
2015-09-11
We developed a CTL vaccine vector by modification of the RPS-Vax system, a mucosal vaccine vector derived from a poliovirus Sabin 1 strain, and generated an oral CTL vaccine against HIV-1. A DNA fragment encoding a cytoplasmic transduction peptide (CTP) was integrated into the RPS-Vax system to generate RPS-CTP, a CTL vaccine vector. An HIV-1 p24 cDNA fragment was introduced into the RPS-CTP vector system and a recombinant poliovirus (rec-PV) named vRPS-CTP/p24 was produced. vRPS-CTP/p24 was genetically stable and efficiently induced Th1 immunity and p24-specific CTLs in immunized poliovirus receptor-transgenic (PVR-Tg) mice. In challenge experiments, PVR-Tg mice that were pre-immunized orally with vRPS-CTP/p24 were resistant to challenge with a lethal dose of p24-expressing recombinant vaccinia virus (rMVA-p24). These results suggested that the RPS-CTP vector system had potential for developing oral CTL vaccines against infectious diseases. Copyright © 2015 Elsevier Ltd. All rights reserved.
International Nuclear Information System (INIS)
Asayama, Tai
2003-03-01
For the commercialization of fast breeder reactors, 'System Based Code', a completely new scheme of a code on structural integrity, is being developed. One of the distinguished features of the System Based Code is that it is able to determine a reasonable total margin on a structural of system, by allowing the exchanges of margins between various technical items. Detailed estimation of failure probability of a given combination of technical items and its comparison with a target value is one way to achieve this. However, simpler and easier methods that allow margin exchange without detailed calculation of failure probability are desirable in design. The authors have developed a simplified method such as a 'design factor method' from this viewpoint. This report describes a 'Vector Method', which was been newly developed. Following points are reported: 1) The Vector Method allows margin exchange evaluation on an 'equi-quality assurance plane' using vector calculation. Evaluation is easy and sufficient accuracy is achieved. The equi-quality assurance plane is obtained by a projection of an 'equi-failure probability surface in a n-dimensional space, which is calculated beforehand for typical combinations of design variables. 2) The Vector Method is considered to give the 'Quality Assurance Index Method' a probabilistic interpretation. 3) An algebraic method was proposed for the calculation of failure probabilities, which is necessary to obtain a equi-failure probability surface. This method calculates failure probabilities without using numerical methods such as Monte Carlo simulation or numerical integration. Under limited conditions, this method is quite effective compared to numerical methods. 4) An illustration of the procedure of margin exchange evaluation is given. It may be possible to use this method to optimize ISI plans; even it is not fully implemented in the System Based Code. (author)
Directory of Open Access Journals (Sweden)
Chuanfu Hu
2017-12-01
Full Text Available This paper aims at improving and utilizing the ontology information in ontology design of FOAF and vCard in real time, and the application of open relational data technology, SPARQL query information results and sending RDF/JSON data format. In addition, improve the effectiveness and efficiency of patient information extraction from the medical information website. This article includes two web search engines that are used to inform patients about medical care information. The experiment uses Drupal as the main software tool, and the Drupal RDF extension module provides some meaningful mapping. In the evaluation part, the structure of the experimental test platform is established and the system function test is carried out. The evaluation results include consumers or patients retrieving the latest doctor information and comparing search capabilities and techniques, between our system and existing systems.
Efficient transformation system for Propionibacterium freudenreichii based on a novel vector
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
Glushkova, Yu O.; Gordashnukova, O. Yu; Pahomova, A. V.; Shatohina, S. P.; Filippov, D. V.
2018-05-01
The modern markets are characterized by fierce competition, constantly changing demand, increasing demands of consumers, shortening of the life cycle of goods and services in connection with scientific and technological progress. Therefore, for survival, modern logistic systems of industrial enterprises must be constantly improved. Modern economic literature is represented by a large volume of publications on various aspects of the studied issues. They consider the issues of project management in the logistics system that inevitably encounter with triple Limited. It initially describes the balance between project content, cost, and time. Later it was suggested to either replace the content with quality or add a fourth criterion. Therefore it is possible to name such limitation as triple or four-criteria limitation.
Well-defined polypeptide-based systems as non-viral vectors for cytosolic delivery
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...
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)
International Nuclear Information System (INIS)
Gatos, I; Tsantis, S; Kagadis, G; Karamesini, M; Skouroliakou, A
2015-01-01
Purpose: The design and implementation of a computer-based image analysis system employing the support vector machine (SVM) classifier system for the classification of Focal Liver Lesions (FLLs) on routine non-enhanced, T2-weighted Magnetic Resonance (MR) images. Materials and Methods: The study comprised 92 patients; each one of them has undergone MRI performed on a Magnetom Concerto (Siemens). Typical signs on dynamic contrast-enhanced MRI and biopsies were employed towards a three class categorization of the 92 cases: 40-benign FLLs, 25-Hepatocellular Carcinomas (HCC) within Cirrhotic liver parenchyma and 27-liver metastases from Non-Cirrhotic liver. Prior to FLLs classification an automated lesion segmentation algorithm based on Marcov Random Fields was employed in order to acquire each FLL Region of Interest. 42 texture features derived from the gray-level histogram, co-occurrence and run-length matrices and 12 morphological features were obtained from each lesion. Stepwise multi-linear regression analysis was utilized to avoid feature redundancy leading to a feature subset that fed the multiclass SVM classifier designed for lesion classification. SVM System evaluation was performed by means of leave-one-out method and ROC analysis. Results: Maximum accuracy for all three classes (90.0%) was obtained by means of the Radial Basis Kernel Function and three textural features (Inverse- Different-Moment, Sum-Variance and Long-Run-Emphasis) that describe lesion's contrast, variability and shape complexity. Sensitivity values for the three classes were 92.5%, 81.5% and 96.2% respectively, whereas specificity values were 94.2%, 95.3% and 95.5%. The AUC value achieved for the selected subset was 0.89 with 0.81 - 0.94 confidence interval. Conclusion: The proposed SVM system exhibit promising results that could be utilized as a second opinion tool to the radiologist in order to decrease the time/cost of diagnosis and the need for patients to undergo invasive
An efficient rHSV-based complementation system for the production of multiple rAAV vector serotypes.
Kang, W; Wang, L; Harrell, H; Liu, J; Thomas, D L; Mayfield, T L; Scotti, M M; Ye, G J; Veres, G; Knop, D R
2009-02-01
Recombinant herpes simplex virus type 1 (rHSV)-assisted recombinant adeno-associated virus (rAAV) vector production provides a highly efficient and scalable method for manufacture of clinical grade rAAV vectors. Here, we present an rHSV co-infection system for rAAV production, which uses two ICP27-deficient rHSV constructs, one bearing the rep2 and cap (1, 2 or 9) genes of rAAV, and the second bearing an AAV2 ITR-gene of interest (GOI) cassette. The optimum rAAV production parameters were defined by producing rAAV2/GFP in HEK293 cells, yielding greater than 9000 infectious particles per cell with a 14:1 DNase resistance particle to infectious particle (DRP/ip) ratio. The optimized co-infection parameters were then used to generate large-scale stocks of rAAV1/AAT, which encode the human alpha-1-antitrypsin (hAAT) protein, and purified by column chromatography. The purified vector was extensively characterized by rAAV- and rHSV-specific assays and compared to transfection-made vector for in vivo efficacy in mice through intramuscular injection. The co-infection method was also used to produce rAAV9/AAT for comparison to rAAV1/AAT in vivo. Intramuscular administration of 1 x 10(11) DRP per animal of rHSV-produced rAAV1/AAT and rAAV9/AAT resulted in hAAT protein expression of 5.4 x 10(4) and 9.4 x 10(5) ng ml(-1) serum respectively, the latter being clinically relevant.
Directory of Open Access Journals (Sweden)
A. Yu. Bykov
2015-01-01
Full Text Available Modern practical task-solving techniques for designing information security systems in different purpose automated systems assume the solution of optimization tasks when choosing different elements of a security system. Formulations of mathematical programming tasks are rather often used, but in practical tasks it is not always analytically possible to set target function and (or restrictions in an explicit form. Sometimes, calculation of the target function value or checking of restrictions for the possible decision can be reduced to carrying out experiments on a simulation model of system. Similar tasks are considered within optimization-simulation approach and require the ad hoc methods of optimization considering the possible high computational effort of simulation.The article offers a modified recession vector method, which is used in tasks of discrete optimization to solve the similar problems. The method is applied when the task to be solved is to minimize the cost of selected information security tools in case of restriction on the maximum possible damage. The cost index is the linear function of the Boolean variables, which specify the selected security tools, with the restriction set as an "example simulator". Restrictions can be actually set implicitly. A validity of the possible solution is checked using a simulation model of the system.The offered algorithm of a method considers features of an objective. The main advantage of algorithm is that it requires a maximum of m+1 of steps where m is a dimensionality of the required vector of Boolean variables. The algorithm provides finding a local minimum by using the Hamming metrics in the discrete space; the radius of neighborhood is equal to 1. These statements are proved.The paper presents solution results of choosing security tools with the specified basic data.
Gren, Tetiana; Ortseifen, Vera; Wibberg, Daniel; Schneiker-Bekel, Susanne; Bednarz, Hanna; Niehaus, Karsten; Zemke, Till; Persicke, Marcus; Pühler, Alfred; Kalinowski, Jörn
2016-08-20
The α-glucosidase inhibitor acarbose is used for treatment of diabetes mellitus type II, and is manufactured industrially with overproducing derivatives of Actinoplanes sp. SE50/110, reportedly obtained by conventional mutagenesis. Despite of high industrial significance, only limited information exists regarding acarbose metabolism, function and regulation of these processes, due to the absence of proper genetic engineering methods and tools developed for this strain. Here, a basic toolkit for genetic engineering of Actinoplanes sp. SE50/110 was developed, comprising a standardized protocol for a DNA transfer through Escherichia coli-Actinoplanes intergeneric conjugation and applied for the transfer of ϕC31, ϕBT1 and VWB actinophage-based integrative vectors. Integration sites, occurring once per genome for all vectors, were sequenced and characterized for the first time in Actinoplanes sp. SE50/110. Notably, in case of ϕC31 based vector pSET152, the integration site is highly conserved, while for ϕBT1 and the VWB based vectors pRT801 and pSOK804, respectively, no sequence similarities to those in other bacteria were detected. The studied plasmids were proven to be stable and neutral with respect to strain morphology and acarbose production, enabling future use for genetic manipulations of Actinoplanes sp. SE50/110. To further broaden the spectrum of available tools, a GUS reporter system, based on the pSET152 derived vector, was also established in Actinoplanes sp. SE50/110. Copyright © 2016 Elsevier B.V. All rights reserved.
Xu, Danfeng; Gu, Bing; Rui, Guanghao; Zhan, Qiwen; Cui, Yiping
2016-02-22
We present an arbitrary vector field with hybrid polarization based on the combination of a pair of orthogonal elliptically polarized base vectors on the Poincaré sphere. It is shown that the created vector field is only dependent on the latitude angle 2χ but is independent on the longitude angle 2ψ on the Poincaré sphere. By adjusting the latitude angle 2χ, which is related to two identical waveplates in a common path interferometric arrangement, one could obtain arbitrary type of vector fields. Experimentally, we demonstrate the generation of such kind of vector fields and confirm the distribution of state of polarization by the measurement of Stokes parameters. Besides, we investigate the tight focusing properties of these vector fields. It is found that the additional degree of freedom 2χ provided by arbitrary vector field with hybrid polarization allows one to control the spatial structure of polarization and to engineer the focusing field.
Image Coding Based on Address Vector Quantization.
Feng, Yushu
Image coding is finding increased application in teleconferencing, archiving, and remote sensing. This thesis investigates the potential of Vector Quantization (VQ), a relatively new source coding technique, for compression of monochromatic and color images. Extensions of the Vector Quantization technique to the Address Vector Quantization method have been investigated. In Vector Quantization, the image data to be encoded are first processed to yield a set of vectors. A codeword from the codebook which best matches the input image vector is then selected. Compression is achieved by replacing the image vector with the index of the code-word which produced the best match, the index is sent to the channel. Reconstruction of the image is done by using a table lookup technique, where the label is simply used as an address for a table containing the representative vectors. A code-book of representative vectors (codewords) is generated using an iterative clustering algorithm such as K-means, or the generalized Lloyd algorithm. A review of different Vector Quantization techniques are given in chapter 1. Chapter 2 gives an overview of codebook design methods including the Kohonen neural network to design codebook. During the encoding process, the correlation of the address is considered and Address Vector Quantization is developed for color image and monochrome image coding. Address VQ which includes static and dynamic processes is introduced in chapter 3. In order to overcome the problems in Hierarchical VQ, Multi-layer Address Vector Quantization is proposed in chapter 4. This approach gives the same performance as that of the normal VQ scheme but the bit rate is about 1/2 to 1/3 as that of the normal VQ method. In chapter 5, a Dynamic Finite State VQ based on a probability transition matrix to select the best subcodebook to encode the image is developed. In chapter 6, a new adaptive vector quantization scheme, suitable for color video coding, called "A Self -Organizing
Viral vector-based influenza vaccines
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
The evaporative vector: Homogeneous systems
International Nuclear Information System (INIS)
Klots, C.E.
1987-05-01
Molecular beams of van der Waals molecules are the subject of much current research. Among the methods used to form these beams, three-sputtering, laser ablation, and the sonic nozzle expansion of neat gases - yield what are now recognized to be ''warm clusters.'' They contain enough internal energy to undergo a number of first-order processes, in particular that of evaporation. Because of this evaporation and its attendant cooling, the properties of such clusters are time-dependent. The states of matter which can be arrived at via an evaporative vector on a typical laboratory time-scale are discussed. Topics include the (1) temperatures, (2) metastability, (3) phase transitions, (4) kinetic energies of fragmentation, and (5) the expression of magical properties, all for evaporating homogeneous clusters
Chen, Wei; Zhang, Junfeng; Gao, Mingyi; Shen, Gangxiang
2018-03-01
High-order modulation signals are suited for high-capacity communication systems because of their high spectral efficiency, but they are more vulnerable to various impairments. For the signals that experience degradation, when symbol points overlap on the constellation diagram, the original linear decision boundary cannot be used to distinguish the classification of symbol. Therefore, it is advantageous to create an optimum symbol decision boundary for the degraded signals. In this work, we experimentally demonstrated the 64-quadrature-amplitude modulation (64-QAM) coherent optical communication system using support-vector machine (SVM) decision boundary algorithm to create the optimum symbol decision boundary for improving the system performance. We investigated the influence of various impairments on the 64-QAM coherent optical communication systems, such as the impairments caused by modulator nonlinearity, phase skew between in-phase (I) arm and quadrature-phase (Q) arm of the modulator, fiber Kerr nonlinearity and amplified spontaneous emission (ASE) noise. We measured the bit-error-ratio (BER) performance of 75-Gb/s 64-QAM signals in the back-to-back and 50-km transmission. By using SVM to optimize symbol decision boundary, the impairments caused by I/Q phase skew of the modulator, fiber Kerr nonlinearity and ASE noise are greatly mitigated.
Cluster Based Vector Attribute Filtering
Kiwanuka, Fred N.; Wilkinson, Michael H.F.
2016-01-01
Morphological attribute filters operate on images based on properties or attributes of connected components. Until recently, attribute filtering was based on a single global threshold on a scalar property to remove or retain objects. A single threshold struggles in case no single property or
Directory of Open Access Journals (Sweden)
Wei Li
2013-01-01
Full Text Available Belt conveyors are the equipment widely used in coal mines and other manufacturing factories, whose main components are a number of idlers. The faults of belt conveyors can directly influence the daily production. In this paper, a fault diagnosis method combining wavelet packet decomposition (WPD and support vector machine (SVM is proposed for monitoring belt conveyors with the focus on the detection of idler faults. Since the number of the idlers could be large, one acceleration sensor is applied to gather the vibration signals of several idlers in order to reduce the number of sensors. The vibration signals are decomposed with WPD, and the energy of each frequency band is extracted as the feature. Then, the features are employed to train an SVM to realize the detection of idler faults. The proposed fault diagnosis method is firstly tested on a testbed, and then an online monitoring and fault diagnosis system is designed for belt conveyors. An experiment is also carried out on a belt conveyor in service, and it is verified that the proposed system can locate the position of the faulty idlers with a limited number of sensors, which is important for operating belt conveyors in practices.
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
System for Automated Calibration of Vector Modulators
Lux, James; Boas, Amy; Li, Samuel
2009-01-01
Vector modulators are used to impose baseband modulation on RF signals, but non-ideal behavior limits the overall performance. The non-ideal behavior of the vector modulator is compensated using data collected with the use of an automated test system driven by a LabVIEW program that systematically applies thousands of control-signal values to the device under test and collects RF measurement data. The technology innovation automates several steps in the process. First, an automated test system, using computer controlled digital-to-analog converters (DACs) and a computer-controlled vector network analyzer (VNA) systematically can apply different I and Q signals (which represent the complex number by which the RF signal is multiplied) to the vector modulator under test (VMUT), while measuring the RF performance specifically, gain and phase. The automated test system uses the LabVIEW software to control the test equipment, collect the data, and write it to a file. The input to the Lab - VIEW program is either user-input for systematic variation, or is provided in a file containing specific test values that should be fed to the VMUT. The output file contains both the control signals and the measured data. The second step is to post-process the file to determine the correction functions as needed. The result of the entire process is a tabular representation, which allows translation of a desired I/Q value to the required analog control signals to produce a particular RF behavior. In some applications, corrected performance is needed only for a limited range. If the vector modulator is being used as a phase shifter, there is only a need to correct I and Q values that represent points on a circle, not the entire plane. This innovation has been used to calibrate 2-GHz MMIC (monolithic microwave integrated circuit) vector modulators in the High EIRP Cluster Array project (EIRP is high effective isotropic radiated power). These calibrations were then used to create
A vector-based system for the differentiation of mouse embryonic stem cells toward germ-line cells
Directory of Open Access Journals (Sweden)
Reza Ebrahimzadeh-Vesal
2014-08-01
Conclusion: In this study, we demonstrated the in vitro generation of mouse embryonic stem cells to germ cells by using a backbone vector containing the fusion gene Stra8-EGFP. The Stra8 gene is a retinoic acid-responsive protein and is able to regulate meiotic initiation.
DEFF Research Database (Denmark)
Vlachogiannis, Ioannis (John); Lee, K Y
2009-01-01
In this paper the state-of-the-art extended particle swarm optimization (PSO) methods for solving multi-objective optimization problems are represented. We emphasize in those, the co-evolution technique of the parallel vector evaluated PSO (VEPSO), analysed and applied in a multi-objective problem...
Takala, T M; Saris, P E J; Tynkkynen, S S H
2003-01-01
A new food-grade host/vector system for Lactobacillus casei based on lactose selection was constructed. The wild-type non-starter host Lb. casei strain E utilizes lactose via a plasmid-encoded phosphotransferase system. For food-grade cloning, a stable lactose-deficient mutant was constructed by deleting a 141-bp fragment from the phospho-beta-galactosidase gene lacG via gene replacement. The deletion resulted in an inactive phospho-beta-galactosidase enzyme with an internal in-frame deletion of 47 amino acids. A complementation plasmid was constructed containing a replicon from Lactococcus lactis, the lacG gene from Lb. casei, and the constitutive promoter of pepR for lacG expression from Lb. rhamnosus. The expression of the lacG gene from the resulting food-grade plasmid pLEB600 restored the ability of the lactose-negative mutant strain to grow on lactose to the wild-type level. The vector pLEB600 was used for expression of the proline iminopeptidase gene pepI from Lb. helveticus in Lb. casei. The results show that the food-grade expression system reported in this paper can be used for expression of foreign genes in Lb. casei.
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...
Command vector memory systems: high performance at low cost
Corbal San Adrián, Jesús; Espasa Sans, Roger; Valero Cortés, Mateo
1998-01-01
The focus of this paper is on designing both a low cost and high performance, high bandwidth vector memory system that takes advantage of modern commodity SDRAM memory chips. To successfully extract the full bandwidth from SDRAM parts, we propose a new memory system organization based on sending commands to the memory system as opposed to sending individual addresses. A command specifies, in a few bytes, a request for multiple independent memory words. A command is similar to a burst found in...
Vector disparity sensor with vergence control for active vision systems.
Barranco, Francisco; Diaz, Javier; Gibaldi, Agostino; Sabatini, Silvio P; Ros, Eduardo
2012-01-01
This paper presents an architecture for computing vector disparity for active vision systems as used on robotics applications. The control of the vergence angle of a binocular system allows us to efficiently explore dynamic environments, but requires a generalization of the disparity computation with respect to a static camera setup, where the disparity is strictly 1-D after the image rectification. The interaction between vision and motor control allows us to develop an active sensor that achieves high accuracy of the disparity computation around the fixation point, and fast reaction time for the vergence control. In this contribution, we address the development of a real-time architecture for vector disparity computation using an FPGA device. We implement the disparity unit and the control module for vergence, version, and tilt to determine the fixation point. In addition, two on-chip different alternatives for the vector disparity engines are discussed based on the luminance (gradient-based) and phase information of the binocular images. The multiscale versions of these engines are able to estimate the vector disparity up to 32 fps on VGA resolution images with very good accuracy as shown using benchmark sequences with known ground-truth. The performances in terms of frame-rate, resource utilization, and accuracy of the presented approaches are discussed. On the basis of these results, our study indicates that the gradient-based approach leads to the best trade-off choice for the integration with the active vision system.
Efficient modeling of vector hysteresis using fuzzy inference systems
International Nuclear Information System (INIS)
Adly, A.A.; Abd-El-Hafiz, S.K.
2008-01-01
Vector hysteresis models have always been regarded as important tools to determine which multi-dimensional magnetic field-media interactions may be predicted. In the past, considerable efforts have been focused on mathematical modeling methodologies of vector hysteresis. This paper presents an efficient approach based upon fuzzy inference systems for modeling vector hysteresis. Computational efficiency of the proposed approach stems from the fact that the basic non-local memory Preisach-type hysteresis model is approximated by a local memory model. The proposed computational low-cost methodology can be easily integrated in field calculation packages involving massive multi-dimensional discretizations. Details of the modeling methodology and its experimental testing are presented
Directory of Open Access Journals (Sweden)
Xiang-ming Gao
2017-01-01
Full Text Available Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD and support vector machine (SVM optimized with an artificial bee colony (ABC algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.
Gao, Xiang-Ming; Yang, Shi-Feng; Pan, San-Bo
2017-01-01
Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.
International Nuclear Information System (INIS)
Chen, Xia; Hu, Hong-li; Liu, Fei; Gao, Xiang Xiang
2011-01-01
The task of image reconstruction for an electrical capacitance tomography (ECT) system is to determine the permittivity distribution and hence the phase distribution in a pipeline by measuring the electrical capacitances between sets of electrodes placed around its periphery. In view of the nonlinear relationship between the permittivity distribution and capacitances and the limited number of independent capacitance measurements, image reconstruction for ECT is a nonlinear and ill-posed inverse problem. To solve this problem, a new image reconstruction method for ECT based on a least-squares support vector machine (LS-SVM) combined with a self-adaptive particle swarm optimization (PSO) algorithm is presented. Regarded as a special small sample theory, the SVM avoids the issues appearing in artificial neural network methods such as difficult determination of a network structure, over-learning and under-learning. However, the SVM performs differently with different parameters. As a relatively new population-based evolutionary optimization technique, PSO is adopted to realize parameters' effective selection with the advantages of global optimization and rapid convergence. This paper builds up a 12-electrode ECT system and a pneumatic conveying platform to verify this image reconstruction algorithm. Experimental results indicate that the algorithm has good generalization ability and high-image reconstruction quality
Gloger, Oliver; Tönnies, Klaus; Mensel, Birger; Völzke, Henry
2015-11-01
In epidemiological studies as well as in clinical practice the amount of produced medical image data strongly increased in the last decade. In this context organ segmentation in MR volume data gained increasing attention for medical applications. Especially in large-scale population-based studies organ volumetry is highly relevant requiring exact organ segmentation. Since manual segmentation is time-consuming and prone to reader variability, large-scale studies need automatized methods to perform organ segmentation. Fully automatic organ segmentation in native MR image data has proven to be a very challenging task. Imaging artifacts as well as inter- and intrasubject MR-intensity differences complicate the application of supervised learning strategies. Thus, we propose a modularized framework of a two-stepped probabilistic approach that generates subject-specific probability maps for renal parenchyma tissue, which are refined subsequently by using several, extended segmentation strategies. We present a three class-based support vector machine recognition system that incorporates Fourier descriptors as shape features to recognize and segment characteristic parenchyma parts. Probabilistic methods use the segmented characteristic parenchyma parts to generate high quality subject-specific parenchyma probability maps. Several refinement strategies including a final shape-based 3D level set segmentation technique are used in subsequent processing modules to segment renal parenchyma. Furthermore, our framework recognizes and excludes renal cysts from parenchymal volume, which is important to analyze renal functions. Volume errors and Dice coefficients show that our presented framework outperforms existing approaches.
International Nuclear Information System (INIS)
Gloger, Oliver; Völzke, Henry; Tönnies, Klaus; Mensel, Birger
2015-01-01
In epidemiological studies as well as in clinical practice the amount of produced medical image data strongly increased in the last decade. In this context organ segmentation in MR volume data gained increasing attention for medical applications. Especially in large-scale population-based studies organ volumetry is highly relevant requiring exact organ segmentation. Since manual segmentation is time-consuming and prone to reader variability, large-scale studies need automatized methods to perform organ segmentation. Fully automatic organ segmentation in native MR image data has proven to be a very challenging task. Imaging artifacts as well as inter- and intrasubject MR-intensity differences complicate the application of supervised learning strategies. Thus, we propose a modularized framework of a two-stepped probabilistic approach that generates subject-specific probability maps for renal parenchyma tissue, which are refined subsequently by using several, extended segmentation strategies. We present a three class-based support vector machine recognition system that incorporates Fourier descriptors as shape features to recognize and segment characteristic parenchyma parts. Probabilistic methods use the segmented characteristic parenchyma parts to generate high quality subject-specific parenchyma probability maps. Several refinement strategies including a final shape-based 3D level set segmentation technique are used in subsequent processing modules to segment renal parenchyma. Furthermore, our framework recognizes and excludes renal cysts from parenchymal volume, which is important to analyze renal functions. Volume errors and Dice coefficients show that our presented framework outperforms existing approaches. (paper)
DEFF Research Database (Denmark)
Boeriis, Morten; van Leeuwen, Theo
2017-01-01
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......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...
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)
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...
A support vector machine (SVM) based voltage stability classifier
Energy Technology Data Exchange (ETDEWEB)
Dosano, R.D.; Song, H. [Kunsan National Univ., Kunsan, Jeonbuk (Korea, Republic of); Lee, B. [Korea Univ., Seoul (Korea, Republic of)
2007-07-01
Power system stability has become even more complex and critical with the advent of deregulated energy markets and the growing desire to completely employ existing transmission and infrastructure. The economic pressure on electricity markets forces the operation of power systems and components to their limit of capacity and performance. System conditions can be more exposed to instability due to greater uncertainty in day to day system operations and increase in the number of potential components for system disturbances potentially resulting in voltage stability. This paper proposed a support vector machine (SVM) based power system voltage stability classifier using local measurements of voltage and active power of load. It described the procedure for fast classification of long-term voltage stability using the SVM algorithm. The application of the SVM based voltage stability classifier was presented with reference to the choice of input parameters; input data preconditioning; moving window for feature vector; determination of learning samples; and other considerations in SVM applications. The paper presented a case study with numerical examples of an 11-bus test system. The test results for the feasibility study demonstrated that the classifier could offer an excellent performance in classification with time-series measurements in terms of long-term voltage stability. 9 refs., 14 figs.
A Subdivision-Based Representation for Vector Image Editing.
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.
Support vector machines optimization based theory, algorithms, and extensions
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
An improved ternary vector system for Agrobacterium-mediated rapid maize transformation.
Anand, Ajith; Bass, Steven H; Wu, Emily; Wang, Ning; McBride, Kevin E; Annaluru, Narayana; Miller, Michael; Hua, Mo; Jones, Todd J
2018-05-01
A simple and versatile ternary vector system that utilizes improved accessory plasmids for rapid maize transformation is described. This system facilitates high-throughput vector construction and plant transformation. The super binary plasmid pSB1 is a mainstay of maize transformation. However, the large size of the base vector makes it challenging to clone, the process of co-integration is cumbersome and inefficient, and some Agrobacterium strains are known to give rise to spontaneous mutants resistant to tetracycline. These limitations present substantial barriers to high throughput vector construction. Here we describe a smaller, simpler and versatile ternary vector system for maize transformation that utilizes improved accessory plasmids requiring no co-integration step. In addition, the newly described accessory plasmids have restored virulence genes found to be defective in pSB1, as well as added virulence genes. Testing of different configurations of the accessory plasmids in combination with T-DNA binary vector as ternary vectors nearly doubles both the raw transformation frequency and the number of transformation events of usable quality in difficult-to-transform maize inbreds. The newly described ternary vectors enabled the development of a rapid maize transformation method for elite inbreds. This vector system facilitated screening different origins of replication on the accessory plasmid and T-DNA vector, and four combinations were identified that have high (86-103%) raw transformation frequency in an elite maize inbred.
International Nuclear Information System (INIS)
Norwood, Adrienne; Kalnay, Eugenia; Ide, Kayo; Yang, Shu-Chih; Wolfe, Christopher
2013-01-01
We compute and compare the three types of vectors frequently used to explore the instability properties of dynamical models, namely Lyapunov vectors (LVs), singular vectors (SVs) and bred vectors (BVs) in two systems, using the Wolfe–Samelson (2007 Tellus A 59 355–66) algorithm to compute all of the Lyapunov vectors. The first system is the Lorenz (1963 J. Atmos. Sci. 20 130–41) three-variable model. Although the leading Lyapunov vector, LV1, grows fastest globally, the second Lyapunov vector, LV2, which has zero growth globally, often grows faster than LV1 locally. Whenever this happens, BVs grow closer to LV2, suggesting that in larger atmospheric or oceanic models where several instabilities can grow in different areas of the world, BVs will grow toward the fastest growing local unstable mode. A comparison of their growth rates at different times shows that all three types of dynamical vectors have the ability to predict regime changes and the duration of the new regime based on their growth rates in the last orbit of the old regime, as shown for BVs by Evans et al (2004 Bull. Am. Meteorol. Soc. 520–4). LV1 and BVs have similar predictive skill, LV2 has a tendency to produce false alarms, and even LV3 shows that maximum decay is also associated with regime change. Initial and final SVs grow much faster and are the most accurate predictors of regime change, although the characteristics of the initial SVs are strongly dependent on the length of the optimization window. The second system is the toy ‘ocean-atmosphere’ model developed by Peña and Kalnay (2004 Nonlinear Process. Geophys. 11 319–27) coupling three Lorenz (1963 J. Atmos. Sci. 20 130–41) systems with different time scales, in order to test the effects of fast and slow modes of growth on the dynamical vectors. A fast ‘extratropical atmosphere’ is weakly coupled to a fast ‘tropical atmosphere’ which is, in turn, strongly coupled to a slow ‘ocean’ system, the latter coupling
Progress in developing cationic vectors for non-viral systemic gene therapy against cancer.
Morille, Marie; Passirani, Catherine; Vonarbourg, Arnaud; Clavreul, Anne; Benoit, Jean-Pierre
2008-01-01
Initially, gene therapy was viewed as an approach for treating hereditary diseases, but its potential role in the treatment of acquired diseases such as cancer is now widely recognized. The understanding of the molecular mechanisms involved in cancer and the development of nucleic acid delivery systems are two concepts that have led to this development. Systemic gene delivery systems are needed for therapeutic application to cells inaccessible by percutaneous injection and for multi-located tumor sites, i.e. metastases. Non-viral vectors based on the use of cationic lipids or polymers appear to have promising potential, given the problems of safety encountered with viral vectors. Using these non-viral vectors, the current challenge is to obtain a similarly effective transfection to viral ones. Based on the advantages and disadvantages of existing vectors and on the hurdles encountered with these carriers, the aim of this review is to describe the "perfect vector" for systemic gene therapy against cancer.
Great Ellipse Route Planning Based on Space Vector
Directory of Open Access Journals (Sweden)
LIU Wenchao
2015-07-01
Full Text Available Aiming at the problem of navigation error caused by unified earth model in great circle route planning using sphere model and modern navigation equipment using ellipsoid mode, a method of great ellipse route planning based on space vector is studied. By using space vector algebra method, the vertex of great ellipse is solved directly, and description of great ellipse based on major-axis vector and minor-axis vector is presented. Then calculation formulas of great ellipse azimuth and distance are deduced using two basic vectors. Finally, algorithms of great ellipse route planning are studied, especially equal distance route planning algorithm based on Newton-Raphson(N-R method. Comparative examples show that the difference of route planning between great circle and great ellipse is significant, using algorithms of great ellipse route planning can eliminate the navigation error caused by the great circle route planning, and effectively improve the accuracy of navigation calculation.
Comparison of four support-vector based function approximators
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
Vector and Raster Data Storage Based on Morton Code
Zhou, G.; Pan, Q.; Yue, T.; Wang, Q.; Sha, H.; Huang, S.; Liu, X.
2018-05-01
Even though geomatique is so developed nowadays, the integration of spatial data in vector and raster formats is still a very tricky problem in geographic information system environment. And there is still not a proper way to solve the problem. This article proposes a method to interpret vector data and raster data. In this paper, we saved the image data and building vector data of Guilin University of Technology to Oracle database. Then we use ADO interface to connect database to Visual C++ and convert row and column numbers of raster data and X Y of vector data to Morton code in Visual C++ environment. This method stores vector and raster data to Oracle Database and uses Morton code instead of row and column and X Y to mark the position information of vector and raster data. Using Morton code to mark geographic information enables storage of data make full use of storage space, simultaneous analysis of vector and raster data more efficient and visualization of vector and raster more intuitive. This method is very helpful for some situations that need to analyse or display vector data and raster data at the same time.
Kinoshita, Yusuke; Kamitani, Hideki; Mamun, Mahabub Hasan; Wasita, Brian; Kazuki, Yasuhiro; Hiratsuka, Masaharu; Oshimura, Mitsuo; Watanabe, Takashi
2010-05-01
Mesenchymal stem cells (MSCs) have been expected to become useful gene delivery vehicles against human malignant gliomas when coupled with an appropriate vector system, because they migrate towards the lesion. Human artificial chromosomes (HACs) are non-integrating vectors with several advantages for gene therapy, namely, no limitations on the size and number of genes that can be inserted. We investigated the migration of human immortalized MSCs bearing a HAC vector containing the herpes simplex virus thymidine kinase gene (HAC-tk-hiMSCs) towards malignant gliomas in vivo. Red fluorescence protein-labeled human glioblastoma HTB14 cells were implanted into a subcortical region in nude mice. Four days later, green fluorescence protein-labeled HAC-tk-hiMSCs were injected into a contralateral subcortical region (the HTB14/HAC-tk-hiMSC injection model). Tropism to the glioma mass and the route of migration were visualized by fluorescence microscopy and immunohistochemical staining. HAC-tk-hiMSCs began to migrate toward the HTB14 glioma area via the corpus callosum on day 4, and gathered around the HTB14 glioma mass on day 7. To test whether the delivered gene could effectively treat glioblastoma in vivo, HTB14/HAC-tk-hiMSC injected mice were treated with ganciclovir (GCV) or PBS. The HTB14 glioma mass was significantly reduced by GCV treatment in mice injected with HAC-tk-hiMSCs. It was confirmed that gene delivery by our HAC-hiMSC system was effective after migration of MSCs to the glioma mass in vivo. Therefore, MSCs containing HACs carrying an anticancer gene or genes may provide a new tool for the treatment of malignant gliomas and possibly of other tumor types.
Density Based Support Vector Machines for Classification
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...
LandSat-Based Land Use-Land Cover (Vector)
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...
Directory of Open Access Journals (Sweden)
Huanxiong Xia
2014-03-01
Full Text Available Optimization of a vector showerhead in a vertical reactor involves thousands of holes on the showerhead face plate and the spatial distribution of physical fields, so parameterizing the geometry configuration of the holes in high resolution is very difficult, which makes the conventional optimization methods hard to deal with. To solve this problem, a profile error feedback (PEF optimization solution was proposed to optimize a vector showerhead gas delivery system for the control of mass transport. The gas velocity profile in the reactor and the continuous-feature impedance distribution profile on the showerhead face plate are defined as design objective and variables, respectively. A cyclic iterative approximation idea was implemented in this solution. The algorithm was started from a guessed initial design model and then cyclically adjusted the design variables by the constructed PEF iterative formula to generate a better model and to make the gas velocity profile in the critical domain of the new model continually approximate to the expected profile, until it could be accepted. Finally, the optimized impedance profile was mapped to the holes geometry configuration through the established equivalent impedance model for the showerhead face plate.
DEFF Research Database (Denmark)
Kildegaard, Kanchana Rueksomtawin; Jensen, Niels Bjerg; Maury, Jerome
2013-01-01
auxotrophic and dominant markers for convenience of use. Our vector set also contains both integrating and multicopy vectors for stability of protein expression and high expression level. We will make the new vector system available to the yeast community and provide a comprehensive protocol for cloning...... the production strain with the proper phenotype and product yield. However, the sequential number of metabolic engineering is time-consuming. Furthermore, the number of available selectable markers is also limiting the number of genetic modifications. To overcome these limitations, we have developed a new set...... 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...
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.
Spatio-temporal Rich Model Based Video Steganalysis on Cross Sections of Motion Vector Planes.
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.
Ebolavirus Classification Based on Natural Vectors
Zheng, Hui; Yin, Changchuan; Hoang, Tung; He, Rong Lucy; Yang, Jie
2015-01-01
According to the WHO, ebolaviruses have resulted in 8818 human deaths in West Africa as of January 2015. To better understand the evolutionary relationship of the ebolaviruses and infer virulence from the relationship, we applied the alignment-free natural vector method to classify the newest ebolaviruses. The dataset includes three new Guinea viruses as well as 99 viruses from Sierra Leone. For the viruses of the family of Filoviridae, both genus label classification and species label classification achieve an accuracy rate of 100%. We represented the relationships among Filoviridae viruses by Unweighted Pair Group Method with Arithmetic Mean (UPGMA) phylogenetic trees and found that the filoviruses can be separated well by three genera. We performed the phylogenetic analysis on the relationship among different species of Ebolavirus by their coding-complete genomes and seven viral protein genes (glycoprotein [GP], nucleoprotein [NP], VP24, VP30, VP35, VP40, and RNA polymerase [L]). The topology of the phylogenetic tree by the viral protein VP24 shows consistency with the variations of virulence of ebolaviruses. The result suggests that VP24 be a pharmaceutical target for treating or preventing ebolaviruses. PMID:25803489
Generalized decompositions of dynamic systems and vector Lyapunov functions
Ikeda, M.; Siljak, D. D.
1981-10-01
The notion of decomposition is generalized to provide more freedom in constructing vector Lyapunov functions for stability analysis of nonlinear dynamic systems. A generalized decomposition is defined as a disjoint decomposition of a system which is obtained by expanding the state-space of a given system. An inclusion principle is formulated for the solutions of the expansion to include the solutions of the original system, so that stability of the expansion implies stability of the original system. Stability of the expansion can then be established by standard disjoint decompositions and vector Lyapunov functions. The applicability of the new approach is demonstrated using the Lotka-Volterra equations.
Testing resonating vector strength: Auditory system, electric fish, and noise
Leo van Hemmen, J.; Longtin, André; Vollmayr, Andreas N.
2011-12-01
Quite often a response to some input with a specific frequency ν○ can be described through a sequence of discrete events. Here, we study the synchrony vector, whose length stands for the vector strength, and in doing so focus on neuronal response in terms of spike times. The latter are supposed to be given by experiment. Instead of singling out the stimulus frequency ν○ we study the synchrony vector as a function of the real frequency variable ν. Its length turns out to be a resonating vector strength in that it shows clear maxima in the neighborhood of ν○ and multiples thereof, hence, allowing an easy way of determining response frequencies. We study this "resonating" vector strength for two concrete but rather different cases, viz., a specific midbrain neuron in the auditory system of cat and a primary detector neuron belonging to the electric sense of the wave-type electric fish Apteronotus leptorhynchus. We show that the resonating vector strength always performs a clear resonance correlated with the phase locking that it quantifies. We analyze the influence of noise and demonstrate how well the resonance associated with maximal vector strength indicates the dominant stimulus frequency. Furthermore, we exhibit how one can obtain a specific phase associated with, for instance, a delay in auditory analysis.
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.
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%
Upport vector machines for nonlinear kernel ARMA system identification.
Martínez-Ramón, Manel; Rojo-Alvarez, José Luis; Camps-Valls, Gustavo; Muñioz-Marí, Jordi; Navia-Vázquez, Angel; Soria-Olivas, Emilio; Figueiras-Vidal, Aníbal R
2006-11-01
Nonlinear system identification based on support vector machines (SVM) has been usually addressed by means of the standard SVM regression (SVR), which can be seen as an implicit nonlinear autoregressive and moving average (ARMA) model in some reproducing kernel Hilbert space (RKHS). The proposal of this letter is twofold. First, the explicit consideration of an ARMA model in an RKHS (SVM-ARMA2K) is proposed. We show that stating the ARMA equations in an RKHS leads to solving the regularized normal equations in that RKHS, in terms of the autocorrelation and cross correlation of the (nonlinearly) transformed input and output discrete time processes. Second, a general class of SVM-based system identification nonlinear models is presented, based on the use of composite Mercer's kernels. This general class can improve model flexibility by emphasizing the input-output cross information (SVM-ARMA4K), which leads to straightforward and natural combinations of implicit and explicit ARMA models (SVR-ARMA2K and SVR-ARMA4K). Capabilities of these different SVM-based system identification schemes are illustrated with two benchmark problems.
Directory of Open Access Journals (Sweden)
Kamadjeu Raoul
2006-06-01
Full Text Available Abstract Background Geographic Information Systems (GIS are powerful communication tools for public health. However, using GIS requires considerable skill and, for this reason, is sometimes limited to experts. Web-based GIS has emerged as a solution to allow a wider audience to have access to geospatial information. Unfortunately the cost of implementing proprietary solutions may be a limiting factor in the adoption of a public health GIS in a resource-constrained environment. Scalable Vector Graphics (SVG is used to define vector-based graphics for the internet using XML (eXtensible Markup Language; it is an open, platform-independent standard maintained by the World Wide Web Consortium (W3C since 2003. In this paper, we summarize our methodology and demonstrate the potential of this free and open standard to contribute to the dissemination of Expanded Program on Immunization (EPI information by providing interactive maps to a wider audience through the Internet. Results We used SVG to develop a database driven web-based GIS applied to EPI data from three countries of WHO AFRO (World Health Organization – African Region. The system generates interactive district-level country immunization coverage maps and graphs. The approach we describe can be expanded to cover other public health GIS demanding activities, including the design of disease atlases in a resources-constrained environment. Conclusion Our system contributes to accumulating evidence demonstrating the potential of SVG technology to develop web-based public health GIS in resources-constrained settings.
Kamadjeu, Raoul; Tolentino, Herman
2006-06-03
Geographic Information Systems (GIS) are powerful communication tools for public health. However, using GIS requires considerable skill and, for this reason, is sometimes limited to experts. Web-based GIS has emerged as a solution to allow a wider audience to have access to geospatial information. Unfortunately the cost of implementing proprietary solutions may be a limiting factor in the adoption of a public health GIS in a resource-constrained environment. Scalable Vector Graphics (SVG) is used to define vector-based graphics for the internet using XML (eXtensible Markup Language); it is an open, platform-independent standard maintained by the World Wide Web Consortium (W3C) since 2003. In this paper, we summarize our methodology and demonstrate the potential of this free and open standard to contribute to the dissemination of Expanded Program on Immunization (EPI) information by providing interactive maps to a wider audience through the Internet. We used SVG to develop a database driven web-based GIS applied to EPI data from three countries of WHO AFRO (World Health Organization - African Region). The system generates interactive district-level country immunization coverage maps and graphs. The approach we describe can be expanded to cover other public health GIS demanding activities, including the design of disease atlases in a resources-constrained environment. Our system contributes to accumulating evidence demonstrating the potential of SVG technology to develop web-based public health GIS in resources-constrained settings.
Directory of Open Access Journals (Sweden)
Prokofjeva Maria M
2013-01-01
Full Text Available Abstract Background Despite progress in the development of combined antiretroviral therapies (cART, HIV infection remains a significant challenge for human health. Current problems of cART include multi-drug-resistant virus variants, long-term toxicity and enormous treatment costs. Therefore, the identification of novel effective drugs is urgently needed. Methods We developed a straightforward screening approach for simultaneously evaluating the sensitivity of multiple HIV gag-pol mutants to antiviral drugs in one assay. Our technique is based on multi-colour lentiviral self-inactivating (SIN LeGO vector technology. Results We demonstrated the successful use of this approach for screening compounds against up to four HIV gag-pol variants (wild-type and three mutants simultaneously. Importantly, the technique was adapted to Biosafety Level 1 conditions by utilising ecotropic pseudotypes. This allowed upscaling to a large-scale screening protocol exploited by pharmaceutical companies in a successful proof-of-concept experiment. Conclusions The technology developed here facilitates fast screening for anti-HIV activity of individual agents from large compound libraries. Although drugs targeting gag-pol variants were used here, our approach permits screening compounds that target several different, key cellular and viral functions of the HIV life-cycle. The modular principle of the method also allows the easy exchange of various mutations in HIV sequences. In conclusion, the methodology presented here provides a valuable new approach for the identification of novel anti-HIV drugs.
Environmental noise forecasting based on support vector machine
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.
IUSThrust Vector Control (TVC) servo system
Conner, G. E.
1979-01-01
The IUS TVC SERVO SYSTEM which consists of four electrically redundant electromechanical actuators, four potentiometer assemblies, and two controllers to provide movable nozzle control on both IUS solid rocket motors is developed. An overview of the more severe IUS TVC servo system design requirements, the system and component designs, and test data acquired on a preliminary development unit is presented. Attention is focused on the unique methods of sensing movable nozzle position and providing for redundant position locks.
Web-based GIS: the vector-borne disease airline importation risk (VBD-AIR) tool.
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
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.
Efficient Vector-Based Forwarding for Underwater Sensor Networks
Directory of Open Access Journals (Sweden)
Peng Xie
2010-01-01
Full Text Available Underwater Sensor Networks (UWSNs are significantly different from terrestrial sensor networks in the following aspects: low bandwidth, high latency, node mobility, high error probability, and 3-dimensional space. These new features bring many challenges to the network protocol design of UWSNs. In this paper, we tackle one fundamental problem in UWSNs: robust, scalable, and energy efficient routing. We propose vector-based forwarding (VBF, a geographic routing protocol. In VBF, the forwarding path is guided by a vector from the source to the target, no state information is required on the sensor nodes, and only a small fraction of the nodes is involved in routing. To improve the robustness, packets are forwarded in redundant and interleaved paths. Further, a localized and distributed self-adaptation algorithm allows the nodes to reduce energy consumption by discarding redundant packets. VBF performs well in dense networks. For sparse networks, we propose a hop-by-hop vector-based forwarding (HH-VBF protocol, which adapts the vector-based approach at every hop. We evaluate the performance of VBF and HH-VBF through extensive simulations. The simulation results show that VBF achieves high packet delivery ratio and energy efficiency in dense networks and HH-VBF has high packet delivery ratio even in sparse networks.
Vaxvec: The first web-based recombinant vaccine vector database and its data analysis
Deng, Shunzhou; Martin, Carly; Patil, Rasika; Zhu, Felix; Zhao, Bin; Xiang, Zuoshuang; He, Yongqun
2015-01-01
A recombinant vector vaccine uses an attenuated virus, bacterium, or parasite as the carrier to express a heterologous antigen(s). Many recombinant vaccine vectors and related vaccines have been developed and extensively investigated. To compare and better understand recombinant vectors and vaccines, we have generated Vaxvec (http://www.violinet.org/vaxvec), the first web-based database that stores various recombinant vaccine vectors and those experimentally verified vaccines that use these vectors. Vaxvec has now included 59 vaccine vectors that have been used in 196 recombinant vector vaccines against 66 pathogens and cancers. These vectors are classified to 41 viral vectors, 15 bacterial vectors, 1 parasitic vector, and 1 fungal vector. The most commonly used viral vaccine vectors are double-stranded DNA viruses, including herpesviruses, adenoviruses, and poxviruses. For example, Vaxvec includes 63 poxvirus-based recombinant vaccines for over 20 pathogens and cancers. Vaxvec collects 30 recombinant vector influenza vaccines that use 17 recombinant vectors and were experimentally tested in 7 animal models. In addition, over 60 protective antigens used in recombinant vector vaccines are annotated and analyzed. User-friendly web-interfaces are available for querying various data in Vaxvec. To support data exchange, the information of vaccine vectors, vaccines, and related information is stored in the Vaccine Ontology (VO). Vaxvec is a timely and vital source of vaccine vector database and facilitates efficient vaccine vector research and development. PMID:26403370
Baculovirus expression vector system: An efficient tool for the ...
African Journals Online (AJOL)
Baculovirus expression vector system is considered one of the most successful and widely acceptable means for the production of recombinant proteins in extremely large quantities. Proper posttranslational modifications of the expressed proteins in insect cells, the usual host of baculoviruses, get them soluble, correctly ...
Digital video steganalysis using motion vector recovery-based features.
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.
Diagnosis of nutrient imbalances with vector analysis in agroforestry systems.
Isaac, Marney E; Kimaro, Anthony A
2011-01-01
Agricultural intensification has had unintended environmental consequences, including increased nutrient leaching and surface runoff and other agrarian-derived pollutants. Improved diagnosis of on-farm nutrient dynamics will have the advantage of increasing yields and will diminish financial and environmental costs. To achieve this, a management support system that allows for site-specific rapid evaluation of nutrient production imbalances and subsequent management prescriptions is needed for agroecological design. Vector diagnosis, a bivariate model to depict changes in yield and nutritional response simultaneously in a single graph, facilitates identification of nutritional status such as growth dilution, deficiency, sufficiency, luxury uptake, and toxicity. Quantitative data from cocoa agroforestry systems and pigeonpea intercropping trials in Ghana and Tanzania, respectively, were re-evaluated with vector analysis. Relative to monoculture, biomass increase in cocoa ( L.) under shade (35-80%) was accompanied by a 17 to 25% decline in P concentration, the most limiting nutrient on this site. Similarly, increasing biomass with declining P concentrations was noted for pigeonpea [ (L). Millsp.] in response to soil moisture availability under intercropping. Although vector analysis depicted nutrient responses, the current vector model does not consider non-nutrient resource effects on growth, such as ameliorated light and soil moisture, which were particularly active in these systems. We revisit and develop vector analysis into a framework for diagnosing nutrient and non-nutrient interactions in agroforestry systems. Such a diagnostic technique advances management decision-making by increasing nutrient precision and reducing environmental issues associated with agrarian-derived soil contamination. American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America.
Wang, Jianzhou; Niu, Tong; Wang, Rui
2017-01-01
The worsening atmospheric pollution increases the necessity of air quality early warning systems (EWSs). Despite the fact that a massive amount of investigation about EWS in theory and practicality has been conducted by numerous researchers, studies concerning the quantification of uncertain information and comprehensive evaluation are still lacking, which impedes further development in the area. In this paper, firstly a comprehensive warning system is proposed, which consists of two vital indispensable modules, namely effective forecasting and scientific evaluation, respectively. For the forecasting module, a novel hybrid model combining the theory of data preprocessing and numerical optimization is first developed to implement effective forecasting for air pollutant concentration. Especially, in order to further enhance the accuracy and robustness of the warning system, interval forecasting is implemented to quantify the uncertainties generated by forecasts, which can provide significant risk signals by using point forecasting for decision-makers. For the evaluation module, a cloud model, based on probability and fuzzy set theory, is developed to perform comprehensive evaluations of air quality, which can realize the transformation between qualitative concept and quantitative data. To verify the effectiveness and efficiency of the warning system, extensive simulations based on air pollutants data from Dalian in China were effectively implemented, which illustrate that the warning system is not only remarkably high-performance, but also widely applicable. PMID:28257122
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.
Development of Novel Adenoviral Vectors to Overcome Challenges Observed With HAdV-5–based Constructs
Alonso-Padilla, Julio; Papp, Tibor; Kaján, Győző L; Benkő, Mária; Havenga, Menzo; Lemckert, Angelique; Harrach, Balázs; Baker, Andrew H
2016-01-01
Recombinant vectors based on human adenovirus serotype 5 (HAdV-5) have been extensively studied in preclinical models and clinical trials over the past two decades. However, the thorough understanding of the HAdV-5 interaction with human subjects has uncovered major concerns about its product applicability. High vector-associated toxicity and widespread preexisting immunity have been shown to significantly impede the effectiveness of HAdV-5–mediated gene transfer. It is therefore that the in-depth knowledge attained working on HAdV-5 is currently being used to develop alternative vectors. Here, we provide a comprehensive overview of data obtained in recent years disqualifying the HAdV-5 vector for systemic gene delivery as well as novel strategies being pursued to overcome the limitations observed with particular emphasis on the ongoing vectorization efforts to obtain vectors based on alternative serotypes. PMID:26478249
Learning word vector representations based on acoustic counts
Ribeiro, Sam; Watts, Oliver; Yamagishi, Junichi
2017-01-01
This paper presents a simple count-based approach to learning word vector representations by leveraging statistics of cooccurrences between text and speech. This type of representation requires two discrete sequences of units defined across modalities. Two possible methods for the discretization of an acoustic signal are presented, which are then applied to fundamental frequency and energy contours of a transcribed corpus of speech, yielding a sequence of textual objects (e.g. words, syllable...
Ces-VP: consultation expert system for vector programming of nuclear codes
International Nuclear Information System (INIS)
Fujisaki, Masahide; Makino, Mitsuhiro; Ishiguro, Misako
1988-08-01
Ces-VP is a prototype rule-based expert system for consulting the vector programming, based on the knowledge of vectorization of nuclear codes at JAERI during these 10 years. Experts in vectorization can restructure nuclear codes with high performance on vector processors, since they have know-how for choosing the best technique among a lot of techniques that were acquired from the experience of vectorization in the past. Frequency in trial and error will be reduced if a beginner can easily use the know-how of experts. In this report, at first the contents of Ces-VP and its intention are shown. Then, the method for acquiring the know-how of vectorization and the method for making rules from the know-how are described. The outline of Ces-VP implemented on Fujitsu expert tool ESHELL is described. Finally, the availability of Ces-VP is evaluated from the data gathered from practical use and its present problems are discussed. (author)
Sequential Bethe vectors and the quantum Ernst system
International Nuclear Information System (INIS)
Niedermaier, M.; Samtleben, H.
2000-01-01
We give a brief review on the use of Bethe Ansatz techniques to construct solutions of recursive functional equations which emerged in a bootstrap approach to the quantum Ernst system. The construction involves two particular limits of a rational Bethe Ansatz system with complex inhomogeneities. First, we pinch two insertions to the critical value. This links Bethe systems with different number of insertions and leads to the concept of sequential Bethe vectors. Second, we study the semiclassical limit of the system in which the scale parameter of the insertions tends to infinity. (author)
A new generation of pPRIG-based retroviral vectors
Directory of Open Access Journals (Sweden)
Boulukos Kim E
2007-11-01
Full Text Available Abstract Background Retroviral vectors are valuable tools for gene transfer. Particularly convenient are IRES-containing retroviral vectors expressing both the protein of interest and a marker protein from a single bicistronic mRNA. This coupled expression increases the relevance of tracking and/or selection of transduced cells based on the detection of a marker protein. pAP2 is a retroviral vector containing eGFP downstream of a modified IRES element of EMCV origin, and a CMV enhancer-promoter instead of the U3 region of the 5'LTR, which increases its efficiency in transient transfection. However, pAP2 contains a limited multicloning site (MCS and shows weak eGFP expression, which previously led us to engineer an improved version, termed pPRIG, harboring: i the wild-type ECMV IRES sequence, thereby restoring its full activity; ii an optimized MCS flanked by T7 and SP6 sequences; and iii a HA tag encoding sequence 5' of the MCS (pPRIG HAa/b/c. Results The convenience of pPRIG makes it a good basic vector to generate additional derivatives for an extended range of use. Here we present several novel pPRIG-based vectors (collectively referred to as PRIGs in which : i the HA tag sequence was inserted in the three reading frames 3' of the MCS (3'HA PRIGs; ii a functional domain (ER, VP16 or KRAB was inserted either 5' or 3' of the MCS (« modular » PRIGs; iii eGFP was replaced by either eCFP, eYFP, mCherry or puro-R (« single color/resistance » PRIGs; and iv mCherry, eYFP or eGFP was inserted 5' of the MCS of the IRES-eGFP, IRES-eCFP or IRES-Puro-R containing PRIGs, respectively (« dual color/selection » PRIGs. Additionally, some of these PRIGs were also constructed in a pMigR MSCV background which has been widely used in pluripotent cells. Conclusion These novel vectors allow for straightforward detection of any expressed protein (3'HA PRIGs, for functional studies of chimeric proteins (« modular » PRIGs, for multiple transductions and
Chikungunya Virus Vaccines: Viral Vector-Based Approaches.
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.
SAM: Support Vector Machine Based Active Queue Management
International Nuclear Information System (INIS)
Shah, M.S.
2014-01-01
Recent years have seen an increasing interest in the design of AQM (Active Queue Management) controllers. The purpose of these controllers is to manage the network congestion under varying loads, link delays and bandwidth. In this paper, a new AQM controller is proposed which is trained by using the SVM (Support Vector Machine) with the RBF (Radial Basis Function) kernal. The proposed controller is called the support vector based AQM (SAM) controller. The performance of the proposed controller has been compared with three conventional AQM controllers, namely the Random Early Detection, Blue and Proportional Plus Integral Controller. The preliminary simulation studies show that the performance of the proposed controller is comparable to the conventional controllers. However, the proposed controller is more efficient in controlling the queue size than the conventional controllers. (author)
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
Multiple image encryption scheme based on pixel exchange operation and vector decomposition
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.
DEFF Research Database (Denmark)
Rodriguez, Pedro; Busquets-Monge, Sergio; Blaabjerg, Frede
2011-01-01
This work presents the development of the space vector pulse width modulation (SVPWM) of a new multi-level converter topology. First, the proposed converter and its natural space vector diagram are presented. Secondly, a modified space vector diagram based on the virtual-vectors technique is show...
2D Vector Field Simplification Based on Robustness
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.
Energy Based Clutter Filtering for Vector Flow Imaging
DEFF Research Database (Denmark)
Villagómez Hoyos, Carlos Armando; Jensen, Jonas; Ewertsen, Caroline
2017-01-01
for obtaining vector flow measurements, since the spectra overlaps at high beam-to-flow angles. In this work a distinct approach is proposed, where the energy of the velocity spectrum is used to differentiate among the two signals. The energy based method is applied by limiting the amplitude of the velocity...... 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...
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.
MODELING OF DYNAMIC SYSTEMS WITH MODULATION BY MEANS OF KRONECKER VECTOR-MATRIX REPRESENTATION
Directory of Open Access Journals (Sweden)
A. S. Vasilyev
2015-09-01
Full Text Available The paper deals with modeling of dynamic systems with modulation by the possibilities of state-space method. This method, being the basis of modern control theory, is based on the possibilities of vector-matrix formalism of linear algebra and helps to solve various problems of technical control of continuous and discrete nature invariant with respect to the dimension of their “input-output” objects. Unfortunately, it turned its back on the wide group of control systems, which hardware environment modulates signals. The marked system deficiency is partially offset by this paper, which proposes Kronecker vector-matrix representations for purposes of system representation of processes with signal modulation. The main result is vector-matrix representation of processes with modulation with no formal difference from continuous systems. It has been found that abilities of these representations could be effectively used in research of systems with modulation. Obtained model representations of processes with modulation are best adapted to the state-space method. These approaches for counting eigenvalues of Kronecker matrix summaries, that are matrix basis of model representations of processes described by Kronecker vector products, give the possibility to use modal direction in research of dynamics for systems with modulation. It is shown that the use of controllability for eigenvalues of general matrixes applied to Kronecker structures enabled to divide successfully eigenvalue spectrum into directed and not directed components. Obtained findings including design problems for models of dynamic processes with modulation based on the features of Kronecker vector and matrix structures, invariant with respect to the dimension of input-output relations, are applicable in the development of alternate current servo drives.
Optical vector network analyzer based on double-sideband modulation.
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.
Stability and Control of Large-Scale Dynamical Systems A Vector Dissipative Systems Approach
Haddad, Wassim M
2011-01-01
Modern complex large-scale dynamical systems exist in virtually every aspect of science and engineering, and are associated with a wide variety of physical, technological, environmental, and social phenomena, including aerospace, power, communications, and network systems, to name just a few. This book develops a general stability analysis and control design framework for nonlinear large-scale interconnected dynamical systems, and presents the most complete treatment on vector Lyapunov function methods, vector dissipativity theory, and decentralized control architectures. Large-scale dynami
A Kalman Filter for SINS Self-Alignment Based on Vector Observation.
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.
International Nuclear Information System (INIS)
Jin Jing; Wei Biao; Feng Peng; Tang Yuelin; Zhou Mi
2010-01-01
Based on the interdependent relationship between fission neutrons ( 252 Cf) and fission chain ( 235 U system), the paper presents the time-frequency feature analysis and recognition in fission neutron signal based on support vector machine (SVM) through the analysis on signal characteristics and the measuring principle of the 252 Cf fission neutron signal. The time-frequency characteristics and energy features of the fission neutron signal are extracted by using wavelet decomposition and de-noising wavelet packet decomposition, and then applied to training and classification by means of support vector machine based on statistical learning theory. The results show that, it is effective to obtain features of nuclear signal via wavelet decomposition and de-noising wavelet packet decomposition, and the latter can reflect the internal characteristics of the fission neutron system better. With the training accomplished, the SVM classifier achieves an accuracy rate above 70%, overcoming the lack of training samples, and verifying the effectiveness of the algorithm. (authors)
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)
Relativistic gravitation from massless systems of scalar and vector fields
International Nuclear Information System (INIS)
Fonseca Teixeira, A.F. da.
1979-01-01
Under the laws of Einstein's gravitational theory, a massless system consisting of the diffuse sources of two fields is discussed. One fields is scalar, of long range, the other is a vector field of short range. A proportionality between the sources is assumed. Both fields are minimally coupled to gravitation, and contribute positive definitely to the time component of the energy momentum tensor. A class of static, spherically symmetric solutions of the equations is obtained, in the weak field limit. The solutions are regular everywhere, stable, and can represent large or small physical systems. The gravitational field presents a Schwarzschild-type asymptotic behavior. The dependence of the energy on the various parameters characterizing the system is discussed in some detail. (Author) [pt
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.
Gradient Evolution-based Support Vector Machine Algorithm for Classification
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.
Disturbance observer based current controller for vector controlled IM drives
DEFF Research Database (Denmark)
Teodorescu, Remus; Dal, Mehmet
2008-01-01
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...... coupling effects and increase robustness against parameters change without requiring any other compensation strategies. The experimental implementation results are provided to demonstrate validity and performance of the proposed control scheme.......In order to increase the accuracy of the current control loop, usually, well known parameter compensation and/or cross decoupling techniques are employed for advanced ac drives. In this paper, instead of using these techniques an observer-based current controller is proposed for vector controlled...
Trial watch: Naked and vectored DNA-based anticancer vaccines.
Bloy, Norma; Buqué, Aitziber; Aranda, Fernando; Castoldi, Francesca; Eggermont, Alexander; Cremer, Isabelle; Sautès-Fridman, Catherine; Fucikova, Jitka; Galon, Jérôme; Spisek, Radek; Tartour, Eric; Zitvogel, Laurence; Kroemer, Guido; Galluzzi, Lorenzo
2015-05-01
One type of anticancer vaccine relies on the administration of DNA constructs encoding one or multiple tumor-associated antigens (TAAs). The ultimate objective of these preparations, which can be naked or vectored by non-pathogenic viruses, bacteria or yeast cells, is to drive the synthesis of TAAs in the context of an immunostimulatory milieu, resulting in the (re-)elicitation of a tumor-targeting immune response. In spite of encouraging preclinical results, the clinical efficacy of DNA-based vaccines employed as standalone immunotherapeutic interventions in cancer patients appears to be limited. Thus, efforts are currently being devoted to the development of combinatorial regimens that allow DNA-based anticancer vaccines to elicit clinically relevant immune responses. Here, we discuss recent advances in the preclinical and clinical development of this therapeutic paradigm.
Parallel Kalman filter track fit based on vector classes
Energy Technology Data Exchange (ETDEWEB)
Kisel, Ivan [GSI Helmholtzzentrum fuer Schwerionenforschung GmbH (Germany); Kretz, Matthias [Kirchhoff-Institut fuer Physik, Ruprecht-Karls Universitaet, Heidelberg (Germany); Kulakov, Igor [Goethe-Universitaet, Frankfurt am Main (Germany); National Taras Shevchenko University, Kyiv (Ukraine)
2010-07-01
Modern high energy physics experiments have to process terabytes of input data produced in particle collisions. The core of the data reconstruction in high energy physics is the Kalman filter. Therefore, developing the fast Kalman filter algorithm, which uses maximum available power of modern processors, is important, in particular for initial selection of events interesting for the new physics. One of processors features, which can speed up the algorithm, is a SIMD instruction set, which allows to pack several data items in one register and operate on all of them in one go, thus achieving more operations per clock cycle. Therefore a flexible and useful interface, which uses the SIMD instruction set on different CPU and GPU processors architectures, has been realized as a vector classes library. The Kalman filter based track fitting algorithm has been implemented with use of the vector classes. Fitting quality tests show good results with the residuals equal to 49 {mu}m and 44 {mu}m for x and y track parameters and relative momentum resolution of 0.7%. The fitting time of 0.053 {mu}s per track has been achieved on Intel Xeon X5550 with 8 cores at 2.6 GHz by using in addition Intel Threading Building Blocks.
Effective data compaction algorithm for vector scan EB writing system
Ueki, Shinichi; Ashida, Isao; Kawahira, Hiroichi
2001-01-01
We have developed a new mask data compaction algorithm dedicated to vector scan electron beam (EB) writing systems for 0.13 μm device generation. Large mask data size has become a significant problem at mask data processing for which data compaction is an important technique. In our new mask data compaction, 'array' representation and 'cell' representation are used. The mask data format for the EB writing system with vector scan supports these representations. The array representation has a pitch and a number of repetitions in both X and Y direction. The cell representation has a definition of figure group and its reference. The new data compaction method has the following three steps. (1) Search arrays of figures by selecting pitches of array so that a number of figures are included. (2) Find out same arrays that have same repetitive pitch and number of figures. (3) Search cells of figures, where the figures in each cell take identical positional relationship. By this new method for the mask data of a 4M-DRAM block gate layer with peripheral circuits, 202 Mbytes without compaction was highly compacted to 6.7 Mbytes in 20 minutes on a 500 MHz PC.
International Nuclear Information System (INIS)
Kawasaki, Nobuo; Ogasawara, Shinobu; Adachi, Masaaki; Kume, Etsuo; Ishizuki, Shigeru; Tanabe, Hidenobu; Nemoto, Toshiyuki; Kawai, Wataru; Watanabe, Hideo
1999-05-01
Several computer codes in the nuclear field have been vectorized, parallelized and transported on the FUJITSU VPP500 system and/or the AP3000 system at Center for Promotion of Computational Science and Engineering in Japan Atomic Energy Research Institute. We dealt with 14 codes in fiscal 1997. These results are reported in 3 parts, i.e., the vectorization part, the parallelization part and the porting part. In this report, we describe the vectorization. In this vectorization part, the vectorization of multidimensional two-fluid model code ACE-3D for evaluation of constitutive equations, statistical decay code SD and three-dimensional thermal analysis code for in-core test section (T2) of HENDEL SSPHEAT are described. In the parallelization part, the parallelization of cylindrical direct numerical simulation code CYLDNS44N, worldwide version of system for prediction of environmental emergency dose information code WSPEEDI, extension of quantum molecular dynamics code EQMD and three-dimensional non-steady compressible fluid dynamics code STREAM are described. In the porting part, the porting of transient reactor analysis code TRAC-BF1 and Monte Carlo radiation transport code MCNP4A on the AP3000 are described. In addition, a modification of program libraries for command-driven interactive data analysis plotting program IPLOT is described. (author)
Chui, Siu Lit; Lu, Ya Yan
2004-03-01
Wide-angle full-vector beam propagation methods (BPMs) for three-dimensional wave-guiding structures can be derived on the basis of rational approximants of a square root operator or its exponential (i.e., the one-way propagator). While the less accurate BPM based on the slowly varying envelope approximation can be efficiently solved by the alternating direction implicit (ADI) method, the wide-angle variants involve linear systems that are more difficult to handle. We present an efficient solver for these linear systems that is based on a Krylov subspace method with an ADI preconditioner. The resulting wide-angle full-vector BPM is used to simulate the propagation of wave fields in a Y branch and a taper.
Research on intrusion detection based on Kohonen network and support vector machine
Shuai, Chunyan; Yang, Hengcheng; Gong, Zeweiyi
2018-05-01
In view of the problem of low detection accuracy and the long detection time of support vector machine, which directly applied to the network intrusion detection system. Optimization of SVM parameters can greatly improve the detection accuracy, but it can not be applied to high-speed network because of the long detection time. a method based on Kohonen neural network feature selection is proposed to reduce the optimization time of support vector machine parameters. Firstly, this paper is to calculate the weights of the KDD99 network intrusion data by Kohonen network and select feature by weight. Then, after the feature selection is completed, genetic algorithm (GA) and grid search method are used for parameter optimization to find the appropriate parameters and classify them by support vector machines. By comparing experiments, it is concluded that feature selection can reduce the time of parameter optimization, which has little influence on the accuracy of classification. The experiments suggest that the support vector machine can be used in the network intrusion detection system and reduce the missing rate.
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.
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.
A three-axis SQUID-based absolute vector magnetometer
Energy Technology Data Exchange (ETDEWEB)
Schönau, T.; Schmelz, M.; Stolz, R.; Anders, S.; Linzen, S.; Meyer, H.-G. [Department of Quantum Detection, Leibniz Institute of Photonic Technology, Jena 07745 (Germany); Zakosarenko, V.; Meyer, M. [Supracon AG, An der Lehmgrube 11, Jena 07751 (Germany)
2015-10-15
We report on the development of a three-axis absolute vector magnetometer suited for mobile operation in the Earth’s magnetic field. It is based on low critical temperature dc superconducting quantum interference devices (LTS dc SQUIDs) with sub-micrometer sized cross-type Josephson junctions and exhibits a white noise level of about 10 fT/Hz{sup 1/2}. The width of superconducting strip lines is restricted to less than 6 μm in order to avoid flux trapping during cool-down in magnetically unshielded environment. The long-term stability of the flux-to-voltage transfer coefficients of the SQUID electronics is investigated in detail and a method is presented to significantly increase their reproducibility. We further demonstrate the long-term operation of the setup in a magnetic field varying by about 200 μT amplitude without the need for recalibration.
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)
Sun, Li-Sha; Kang, Xiao-Yun; Zhang, Qiong; Lin, Lan-Xin
2011-12-01
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.
Energy Technology Data Exchange (ETDEWEB)
Nemoto, Toshiyuki; Kawai, Wataru [Fujitsu Ltd., Tokyo (Japan); Kawasaki, Nobuo [and others
1997-12-01
Several computer codes in the nuclear field have been vectorized, parallelized and transported on the FUJITSU VPP500 system at Center for Promotion of Computational Science and Engineering in Japan Atomic Energy Research Institute. These results are reported in 3 parts, i.e., the vectorization part, the parallelization part and the porting part. In this report, we describe the vectorization. In this vectorization part, the vectorization of two and three dimensional discrete ordinates simulation code DORT-TORT, gas dynamics analysis code FLOWGR and relativistic Boltzmann-Uehling-Uhlenbeck simulation code RBUU are described. In the parallelization part, the parallelization of 2-Dimensional relativistic electromagnetic particle code EM2D, Cylindrical Direct Numerical Simulation code CYLDNS and molecular dynamics code for simulating radiation damages in diamond crystals DGR are described. And then, in the porting part, the porting of reactor safety analysis code RELAP5/MOD3.2 and RELAP5/MOD3.2.1.2, nuclear data processing system NJOY and 2-D multigroup discrete ordinate transport code TWOTRAN-II are described. And also, a survey for the porting of command-driven interactive data analysis plotting program IPLOT are described. (author)
Design and Modeling of a Novel Torque Vectoring Differential System
Directory of Open Access Journals (Sweden)
Chen Yu-Fan
2017-01-01
Full Text Available In this paper, a new concept torque vectoring differential (TVD system is presented. It is shown that the structure and the mechanism of the system, the operating methods, and the parameters design by a simulation program, i.e. SimulationX. First of all, the structure of the new TVD system is introduced, as well as the relevant mechanic equations. Second, we attempt to verify the feasibility and accuracy of SimulationX through establishing a simple mechanical model by MATLAB, so that the further modeling and simulation results of the new TVD system will be credible. Then, the simulation results at the setting conditions are presented. Finally, the sensitivity of the design parameters is analyzed, including adjusting the braking torque and the dimensions of the gear sets in the differential. According to these results, the characteristics of the new TVD system can be derived in order to develop the whole system with vehicle dynamic model in the next stage.
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 ...
Space vector-based analysis of overmodulation in triangle ...
Indian Academy of Sciences (India)
methods such as vector control or field oriented control are used for fast dynamic response .... This average voltage vector falls in sector-I as shown in figure 5 for .... The dwell times T1, T2 and Tz can be derived using volt-second balance.
Energy Technology Data Exchange (ETDEWEB)
Paulsson, Bjorn N.P. [Paulsson, Inc., Van Nuys, CA (United States); Thornburg, Jon A. [Paulsson, Inc., Van Nuys, CA (United States); He, Ruiqing [Paulsson, Inc., Van Nuys, CA (United States)
2015-04-21
Seismic techniques are the dominant geophysical techniques for the characterization of subsurface structures and stratigraphy. The seismic techniques also dominate the monitoring and mapping of reservoir injection and production processes. Borehole seismology, of all the seismic techniques, despite its current shortcomings, has been shown to provide the highest resolution characterization and most precise monitoring results because it generates higher signal to noise ratio and higher frequency data than surface seismic techniques. The operational environments for borehole seismic instruments are however much more demanding than for surface seismic instruments making both the instruments and the installation much more expensive. The current state-of-the-art borehole seismic instruments have not been robust enough for long term monitoring compounding the problems with expensive instruments and installations. Furthermore, they have also not been able to record the large bandwidth data available in boreholes or having the sensitivity allowing them to record small high frequency micro seismic events with high vector fidelity. To reliably achieve high resolution characterization and long term monitoring of Enhanced Geothermal Systems (EGS) sites a new generation of borehole seismic instruments must therefore be developed and deployed. To address the critical site characterization and monitoring needs for EGS programs, US Department of Energy (DOE) funded Paulsson, Inc. in 2010 to develop a fiber optic based ultra-large bandwidth clamped borehole seismic vector array capable of deploying up to one thousand 3C sensor pods suitable for deployment into ultra-high temperature and high pressure boreholes. Tests of the fiber optic seismic vector sensors developed on the DOE funding have shown that the new borehole seismic sensor technology is capable of generating outstanding high vector fidelity data with extremely large bandwidth: 0.01 – 6,000 Hz. Field tests have shown
Large-scale production of lentiviral vector in a closed system hollow fiber bioreactor
Directory of Open Access Journals (Sweden)
Jonathan Sheu
Full Text Available Lentiviral vectors are widely used in the field of gene therapy as an effective method for permanent gene delivery. While current methods of producing small scale vector batches for research purposes depend largely on culture flasks, the emergence and popularity of lentiviral vectors in translational, preclinical and clinical research has demanded their production on a much larger scale, a task that can be difficult to manage with the numbers of producer cell culture flasks required for large volumes of vector. To generate a large scale, partially closed system method for the manufacturing of clinical grade lentiviral vector suitable for the generation of induced pluripotent stem cells (iPSCs, we developed a method employing a hollow fiber bioreactor traditionally used for cell expansion. We have demonstrated the growth, transfection, and vector-producing capability of 293T producer cells in this system. Vector particle RNA titers after subsequent vector concentration yielded values comparable to lentiviral iPSC induction vector batches produced using traditional culture methods in 225 cm2 flasks (T225s and in 10-layer cell factories (CF10s, while yielding a volume nearly 145 times larger than the yield from a T225 flask and nearly three times larger than the yield from a CF10. Employing a closed system hollow fiber bioreactor for vector production offers the possibility of manufacturing large quantities of gene therapy vector while minimizing reagent usage, equipment footprint, and open system manipulation.
Directory of Open Access Journals (Sweden)
O. A. Dunaeva
2013-01-01
Full Text Available In this work, we describe a prototype of an automatic segmentation system and annotation of endoscopy images. The used algorithm is based on the classification of vectors of the topological features of the original image. We use the image processing scheme which includes image preprocessing, calculation of vector descriptors defined for every point of the source image and the subsequent classification of descriptors. Image preprocessing includes finding and selecting artifacts and equalizating the image brightness. In this work, we give the detailed algorithm of the construction of topological descriptors and the classifier creating procedure based on mutual sharing the AdaBoost scheme and a naive Bayes classifier. In the final section, we show the results of the classification of real endoscopic images.
PGMA-Based Cationic Nanoparticles with Polyhydric Iodine Units for Advanced Gene Vectors.
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.
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.
Directory of Open Access Journals (Sweden)
A. Teoh
2017-12-01
Full Text Available This paw presents fusion detection technique comparisons based on support vector machine and its variations for a bimodal biometric verification system that makes use of face images and speech utterances. The system is essentially constructed by a face expert, a speech expert and a fusion decision module. Each individual expert has been optimized to operate in automatic mode and designed for security access application. Fusion decision schemes considered are linear, weighted Support Vector Machine (SVM and linear SVM with quadratic transformation. The conditions tested include the balanced and unbalanced conditions between the two experts in order to obtain the optimum fusion module from these techniques best suited to the target application.
Wu, Qi
2010-03-01
Demand forecasts play a crucial role in supply chain management. The future demand for a certain product is the basis for the respective replenishment systems. Aiming at demand series with small samples, seasonal character, nonlinearity, randomicity and fuzziness, the existing support vector kernel does not approach the random curve of the sales time series in the space (quadratic continuous integral space). In this paper, we present a hybrid intelligent system combining the wavelet kernel support vector machine and particle swarm optimization for demand forecasting. The results of application in car sale series forecasting show that the forecasting approach based on the hybrid PSOWv-SVM model is effective and feasible, the comparison between the method proposed in this paper and other ones is also given, which proves that this method is, for the discussed example, better than hybrid PSOv-SVM and other traditional methods.
Perinatal systemic gene delivery using adeno-associated viral vectors
Directory of Open Access Journals (Sweden)
Rajvinder eKarda
2014-11-01
Full Text Available Neurodegenerative monogenic diseases can also affect a broad range of tissues and organs throughout the body. An effective treatment would require a systemic approach. The intravenous administration of novel therapies is ideal but is hampered by the inability of such drugs to cross the blood-brain barrier and precludes efficacy in the central nervous system. A number of these early lethal intractable diseases also present devastating irreversible pathology at birth or soon after. Therefore, any therapy would ideally be administered during the perinatal period to prevent, stop or ameliorate disease progression. The concept of perinatal gene therapy has moved a step further towards being a feasible approach to treating such disorders. This has primarily been driven by the recent discoveries that particular serotypes of adeno-associated virus (AAV gene delivery vectors have the ability to cross the blood-brain barrier following intravenous administration. Furthermore, this has been safely demonstrated in perinatal mice and non-human primates. This review focuses on the progress made in using AAV to achieve systemic transduction and what this means for developing perinatal gene therapy for early lethal neurodegenerative diseases.
Using Vector Projection Method to evaluate maintainability of mechanical system in design review
International Nuclear Information System (INIS)
Chen Lu; Cai Jianguo
2003-01-01
Maintainability of a mechanical system is one of the system design parameters that has a great impact in terms of ease of maintenance. In this article, based on the definition of the terms of maintenance and maintainability, an important tool of Design for Maintenance is developed as a way to improve maintainability through design. A set of standard and organized guidelines is provided and maintainability factors in terms of physical design, logistics support and ergonomics are identified. As a specific application of design review, a methodology so called Vector Projection Method is developed to evaluate the maintainability of the mechanical system. Lastly, an example is discussed
Fachrurrozi, Muhammad; Saparudin; Erwin
2017-04-01
Real-time Monitoring and early detection system which measures the quality standard of waste in Musi River, Palembang, Indonesia is a system for determining air and water pollution level. This system was designed in order to create an integrated monitoring system and provide real time information that can be read. It is designed to measure acidity and water turbidity polluted by industrial waste, as well as to show and provide conditional data integrated in one system. This system consists of inputting and processing the data, and giving output based on processed data. Turbidity, substances, and pH sensor is used as a detector that produce analog electrical direct current voltage (DC). Early detection system works by determining the value of the ammonia threshold, acidity, and turbidity level of water in Musi River. The results is then presented based on the level group pollution by the Support Vector Machine classification method.
2D Vector Field Simplification Based on Robustness
Skraba, Primoz; Wang, Bei; Chen, Guoning; Rosen, Paul
2014-01-01
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
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.
EVE: Explainable Vector Based Embedding Technique Using Wikipedia
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...
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.
A Semisupervised Support Vector Machines Algorithm for BCI Systems
Qin, Jianzhao; Li, Yuanqing; Sun, Wei
2007-01-01
As an emerging technology, brain-computer interfaces (BCIs) bring us new communication interfaces which translate brain activities into control signals for devices like computers, robots, and so forth. In this study, we propose a semisupervised support vector machine (SVM) algorithm for brain-computer interface (BCI) systems, aiming at reducing the time-consuming training process. In this algorithm, we apply a semisupervised SVM for translating the features extracted from the electrical recordings of brain into control signals. This SVM classifier is built from a small labeled data set and a large unlabeled data set. Meanwhile, to reduce the time for training semisupervised SVM, we propose a batch-mode incremental learning method, which can also be easily applied to the online BCI systems. Additionally, it is suggested in many studies that common spatial pattern (CSP) is very effective in discriminating two different brain states. However, CSP needs a sufficient labeled data set. In order to overcome the drawback of CSP, we suggest a two-stage feature extraction method for the semisupervised learning algorithm. We apply our algorithm to two BCI experimental data sets. The offline data analysis results demonstrate the effectiveness of our algorithm. PMID:18368141
Solution of single linear tridiagonal systems and vectorization of the ICCG algorithm on the Cray 1
International Nuclear Information System (INIS)
Kershaw, D.S.
1981-01-01
The numerical algorithms used to solve the physics equation in codes which model laser fusion are examined, it is found that a large number of subroutines require the solution of tridiagonal linear systems of equations. One dimensional radiation transport, thermal and suprathermal electron transport, ion thermal conduction, charged particle and neutron transport, all require the solution of tridiagonal systems of equations. The standard algorithm that has been used in the past on CDC 7600's will not vectorize and so cannot take advantage of the large speed increases possible on the Cray-1 through vectorization. There is however, an alternate algorithm for solving tridiagonal systems, called cyclic reduction, which allows for vectorization, and which is optimal for the Cray-1. Software based on this algorithm is now being used in LASNEX to solve tridiagonal linear systems in the subroutines mentioned above. The new algorithm runs as much as five times faster than the standard algorithm on the Cray-1. The ICCG method is being used to solve the diffusion equation with a nine-point coupling scheme on the CDC 7600. In going from the CDC 7600 to the Cray-1, a large part of the algorithm consists of solving tridiagonal linear systems on each L line of the Lagrangian mesh in a manner which is not vectorizable. An alternate ICCG algorithm for the Cray-1 was developed which utilizes a block form of the cyclic reduction algorithm. This new algorithm allows full vectorization and runs as much as five times faster than the old algorithm on the Cray-1. It is now being used in Cray LASNEX to solve the two-dimensional diffusion equation in all the physics subroutines mentioned above
High-speed vector-processing system of the MELCOM-COSMO 900II
Energy Technology Data Exchange (ETDEWEB)
Masuda, K; Mori, H; Fujikake, J; Sasaki, Y
1983-01-01
Progress in scientific and technical calculations has lead to a growing demand for high-speed vector calculations. Mitsubishi electric has developed an integrated array processor and automatic-vectorizing fortran compiler as an option for the MELCOM-COSMO 900II computer system. This facilitates the performance of vector calculations and matrix calculations, achieving significant gains in cost-effectiveness. The article outlines the high-speed vector system, includes discussion of compiler structuring, and cites examples of effective system application. 1 reference.
Noninvasive extraction of fetal electrocardiogram based on Support Vector Machine
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.
Thai Language Sentence Similarity Computation Based on Syntactic Structure and Semantic Vector
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.
Roux, Emmanuel; de Fátima Venâncio, Annamaria; Girres, Jean-François; Romaña, Christine A
2011-11-01
While the former part of this back-to-back paper dealt with the identification of multi-scale spatial patterns associated with the presence, abundance and dispersion of the insect vectors (Triatominae) of Chagas disease, this latter part examines the need for pattern characterisation by means of detailed data on environmental, residential, peri-domiciliary and human behaviour. The study site was, in both cases, a single village situated in Bahia, Brazil, wherefrom the data were collected through field observation and a standardised questionnaire, while the environmental characteristics were derived from satellite images and landscape characterisation. Following this, factorial analysis of mixed group (FAMG), an exploratory data analysis method, was applied to "mine" the huge dataset in a hierarchical way and to evaluate the relative impact of different factors such as the surrounding environment, the domiciliary/peri-domiciliary space properties and the presence of domestic animals. In the study village, five principal "districts" associated with different possible causes of infestation were identified. The results favour the role of depressions of the ground surface due to collapse of karstic subsoil (dolines) and open rock faces as infestation sources, vector attraction by outdoor lighting, risk of insect domiciliation in dwellings constructed without finishing materials and associated with apparent disorder. Ultimately, this study not only provides the basic information needed for decision-making and specification of vector control in the study village, but offers also a knowledge-base for more general control strategies in the region.
Ultrasound Vector Flow Imaging: Part I: Sequential Systems
DEFF Research Database (Denmark)
Jensen, Jørgen Arendt; Nikolov, Svetoslav Ivanov; Yu, Alfred C. H.
2016-01-01
, and variants of these. The review covers both 2-D and 3-D velocity estimation and gives a historical perspective on the development along with a summary of various vector flow visualization algorithms. The current state-of-the-art is explained along with an overview of clinical studies conducted and methods......The paper gives a review of the most important methods for blood velocity vector flow imaging (VFI) for conventional, sequential data acquisition. This includes multibeam methods, speckle tracking, transverse oscillation, color flow mapping derived vector flow imaging, directional beamforming...
Wang, Tiantian; Sun, Hui; Zhang, Jie; Liu, Qing; Wang, Longjiang; Chen, Peipei; Wang, Fangkun; Li, Hongmei; Xiao, Yihong; Zhao, Xiaomin
2014-03-01
In the present study, an a-agglutinin-based Saccharomyces boulardii surface display system was successfully established using a single expression vector. Based on the two protein co-expression vector pSP-G1 built by Partow et al., a S. boulardii surface display vector-pSDSb containing all the display elements was constructed. The display results of heterologous proteins were confirmed by successfully displaying enhanced green fluorescent protein (EGFP) and chicken Eimeria tenella Microneme-2 proteins (EtMic2) on the S. boulardii cell surface. The DNA sequence of AGA1 gene from S. boulardii (SbAGA1) was determined and used as the cell wall anchor partner. This is the first time heterologous proteins have been displayed on the cell surface of S. boulardii. Because S. boulardii is probiotic and eukaryotic, its surface display system would be very valuable, particularly in the development of a live vaccine against various pathogenic organisms especially eukaryotic pathogens such as protistan parasites. Copyright © 2013 Elsevier Inc. All rights reserved.
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.
3D Model Retrieval Based on Vector Quantisation Index Histograms
International Nuclear Information System (INIS)
Lu, Z M; Luo, H; Pan, J S
2006-01-01
This paper proposes a novel technique to retrieval 3D mesh models using vector quantisation index histograms. Firstly, points are sampled uniformly on mesh surface. Secondly, to a point five features representing global and local properties are extracted. Thus feature vectors of points are obtained. Third, we select several models from each class, and employ their feature vectors as a training set. After training using LBG algorithm, a public codebook is constructed. Next, codeword index histograms of the query model and those in database are computed. The last step is to compute the distance between histograms of the query and those of the models in database. Experimental results show the effectiveness of our method
Genetic modification of lymphocytes by retrovirus-based vectors.
Suerth, Julia D; Schambach, Axel; Baum, Christopher
2012-10-01
The genetic modification of lymphocytes is an important topic in the emerging field of gene therapy. Many clinical trials targeting immunodeficiency syndromes or cancer have shown therapeutic benefit; further applications address inflammatory and infectious disorders. Retroviral vector development requires a detailed understanding of the interactions with the host. Most researchers have used simple gammaretroviral vectors to modify lymphocytes, either directly or via hematopoietic stem and progenitor cells. Lentiviral, spumaviral (foamyviral) and alpharetroviral vectors were designed to reduce the necessity for cell stimulation and to utilize potentially safer integration properties. Novel surface modifications (pseudotyping) and transgenes, built using synthetic components, expand the retroviral toolbox, altogether promising increased specificity and potency. Product consistency will be an important criterion for routine clinical use. Copyright © 2012. Published by Elsevier Ltd.
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.
Orecchini, Fabio
2012-01-01
What lies beyond the era of fossil fuels? While most answers focus on different primary energy resources, Energy Systems in the Era of Energy Vectors provides a completely new approach. Instead of providing a traditional consumption analysis of classical primary energy resources such as oil, coal, nuclear power and gas, Energy Systems in the Era of Energy Vectors describes and assesses energy technologies, markets and future strategies, focusing on their capacity to produce, exchange, and use energy vectors. Special attention is given to the renewable energy resources available in different areas of the world and made exploitable by the integration of energy vectors in the global energy system. Clear definitions of energy vectors and energy systems are used as the basis for a complete explanation and assessment of up-to-date, available technologies for energy resources, transport and storage systems, conversion and use. The energy vectors scheme allows the potential realisation of a worldwide sustainable ener...
Development and applications of VSV vectors based on cell tropism
Directory of Open Access Journals (Sweden)
Hideki eTani
2012-01-01
Full Text Available Viral vectors have been available in various fields such as medical and biological research or gene therapy applications. Targeting vectors pseudotyped with distinct viral envelope proteins that influence cell tropism and transfection efficiency is a useful tool not only for examining entry mechanisms or cell tropisms but also for vaccine vector development. Vesicular stomatitis virus (VSV is an excellent candidate for development as a pseudotype vector. A recombinant VSV lacking its own envelope (G gene has been used to produce a pseudotype or recombinant VSV possessing the envelope proteins of heterologous viruses. These viruses possess a reporter gene instead of a VSV G gene in their genome, and therefore it is easy to evaluate their infectivity in the study of viral entry, including identification of viral receptors. Furthermore, advantage can be taken of a property of the pseudotype VSV, which is competence for single-round infection, in handling many different viruses that are either difficult to amplify in cultured cells or animals or that require specialized containment facilities. Here we describe procedures for producing pseudotype or recombinant VSVs and a few of the more prominent examples from among envelope viruses, such as hepatitis C virus, Japanese encephalitis virus, baculovirus, and hemorrhagic fever viruses.
Community based vector control in Malindi, Kenya | Kibe | African ...
African Journals Online (AJOL)
Results: Nineteen of 34 community groups (56%) registered at social services reported intended malaria vector control activities such as treating ditches, making and selling insecticide-treated mosquito nets, draining stagnant water, organizing clean-ups, making and selling neem soap, and the organization of campaigns ...
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
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
Directory of Open Access Journals (Sweden)
Abdul Mannan Dadu
2017-12-01
Full Text Available A high computational burden is required in conventional model predictive control, as all of the voltage vectors of a power inverter are used to predict the future behavior of the system. Apart from that, the common mode voltage (CMV of a three-phase four-leg inverter utilizes up to half of the DC-link voltage due to the use of all of the available voltage vectors. Thus, this paper proposes a near state vector selection-based model predictive control (NSV-MPC scheme to mitigate the CMV and reduce computational burden. In the proposed technique, only six active voltage vectors are used in the predictive model, and the vectors are selected based on the position of the future reference vector. In every sampling period, the position of the reference current is used to detect the voltage vectors surrounding the reference voltage vector. Besides the six active vectors, one of the zero vectors is also used. The proposed technique is compared with the conventional control scheme in terms of execution time, CMV variation, and load current ripple in both simulation and an experimental setup. The LabVIEW Field programmable gate array rapid prototyping controller is used to validate the proposed control scheme experimentally, and demonstrate that the CMV can be bounded within one-fourth of the DC-link voltage.
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.
International Nuclear Information System (INIS)
Pang, Hongfeng; Zhu, XueJun; Pan, Mengchun; Zhang, Qi; Wan, Chengbiao; Luo, Shitu; Chen, Dixiang; Chen, Jinfei; Li, Ji; Lv, Yunxiao
2016-01-01
Misalignment error is one key factor influencing the measurement accuracy of geomagnetic vector measurement system, which should be calibrated with the difficulties that sensors measure different physical information and coordinates are invisible. A new misalignment calibration method by rotating a parallelepiped frame is proposed. Simulation and experiment result show the effectiveness of calibration method. The experimental system mainly contains DM-050 three-axis fluxgate magnetometer, INS (inertia navigation system), aluminium parallelepiped frame, aluminium plane base. Misalignment angles are calculated by measured data of magnetometer and INS after rotating the aluminium parallelepiped frame on aluminium plane base. After calibration, RMS error of geomagnetic north, vertical and east are reduced from 349.441 nT, 392.530 nT and 562.316 nT to 40.130 nT, 91.586 nT and 141.989 nT respectively. - Highlights: • A new misalignment calibration method by rotating a parallelepiped frame is proposed. • It does not need to know sensor attitude information or local dip angle. • The calibration system attitude change angle is not strictly required. • It can be widely used when sensors measure different physical information. • Geomagnetic vector measurement error is reduced evidently.
Energy Technology Data Exchange (ETDEWEB)
Pang, Hongfeng [Academy of Equipment, Beijing 101416 (China); College of Mechatronics Engineering and Automation, National University of Defense Technology, Changsha 410073 (China); Zhu, XueJun, E-mail: zhuxuejun1990@126.com [College of Mechatronics Engineering and Automation, National University of Defense Technology, Changsha 410073 (China); Pan, Mengchun; Zhang, Qi; Wan, Chengbiao; Luo, Shitu; Chen, Dixiang; Chen, Jinfei; Li, Ji; Lv, Yunxiao [College of Mechatronics Engineering and Automation, National University of Defense Technology, Changsha 410073 (China)
2016-12-01
Misalignment error is one key factor influencing the measurement accuracy of geomagnetic vector measurement system, which should be calibrated with the difficulties that sensors measure different physical information and coordinates are invisible. A new misalignment calibration method by rotating a parallelepiped frame is proposed. Simulation and experiment result show the effectiveness of calibration method. The experimental system mainly contains DM-050 three-axis fluxgate magnetometer, INS (inertia navigation system), aluminium parallelepiped frame, aluminium plane base. Misalignment angles are calculated by measured data of magnetometer and INS after rotating the aluminium parallelepiped frame on aluminium plane base. After calibration, RMS error of geomagnetic north, vertical and east are reduced from 349.441 nT, 392.530 nT and 562.316 nT to 40.130 nT, 91.586 nT and 141.989 nT respectively. - Highlights: • A new misalignment calibration method by rotating a parallelepiped frame is proposed. • It does not need to know sensor attitude information or local dip angle. • The calibration system attitude change angle is not strictly required. • It can be widely used when sensors measure different physical information. • Geomagnetic vector measurement error is reduced evidently.
Mapping of courses on vector biology and vector-borne diseases systems: time for a worldwide effort
Casas, Jérôme; Lazzari, Claudio; Insausti, Teresita; Launois, Pascal; Fouque, Florence
2016-01-01
Major emergency efforts are being mounted for each vector-borne disease epidemiological crisis anew, while knowledge about the biology of arthropods vectors is dwindling slowly but continuously, as is the number of field entomologists. The discrepancy between the rates of production of knowledge and its use and need for solving crises is widening, in particular due to the highly differing time spans of the two concurrent processes. A worldwide web based search using multiple key words and search engines of onsite and online courses in English, Spanish, Portuguese, French, Italian and German concerned with the biology of vectors identified over 140 courses. They are geographically and thematically scattered, the vast majority of them are on-site, with very few courses using the latest massive open online course (MOOC) powerfulness. Over two third of them is given in English and Western Africa is particularity poorly represented. The taxonomic groups covered are highly unbalanced towards mosquitoes. A worldwide unique portal to guide students of all grades and levels of expertise, in particular those in remote locations, is badly needed. This is the objective a new activity supported by the Special Programme for Research and Training in Tropical Diseases (TDR). PMID:27759770
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.
International Nuclear Information System (INIS)
Xunjing, L.
1981-12-01
The vector-valued measure defined by the well-posed linear boundary value problems is discussed. The maximum principle of the optimal control problem with non-convex constraint is proved by using the vector-valued measure. Especially, the necessary conditions of the optimal control of elliptic systems is derived without the convexity of the control domain and the cost function. (author)
Vector control of three-phase AC machines system development in the practice
Quang, Nguyen Phung; Dittrich, J
2015-01-01
This book addresses the vector control of three-phase AC machines, in particular induction motors with squirrel-cage rotors (IM), permanent magnet synchronous motors (PMSM) and doubly-fed induction machines (DFIM), from a practical design and development perspective. The main focus is on the application of IM and PMSM in electrical drive systems, where field-orientated control has been successfully established in practice. It also discusses the use of grid-voltage oriented control of DFIMs in wind power plants. This second, enlarged edition includes new insights into flatness-based nonlinear
Support Vector Machine Based Tool for Plant Species Taxonomic Classification
Manimekalai .K; Vijaya.MS
2014-01-01
Plant species are living things and are generally categorized in terms of Domain, Kingdom, Phylum, Class, Order, Family, Genus and name of Species in a hierarchical fashion. This paper formulates the taxonomic leaf categorization problem as the hierarchical classification task and provides a suitable solution using a supervised learning technique namely support vector machine. Features are extracted from scanned images of plant leaves and trained using SVM. Only class, order, family of plants...
Directory of Open Access Journals (Sweden)
SRIKOTE, G.
2016-08-01
Full Text Available This paper proposes an improved performance algorithm of face recognition to identify two face mismatch pairs in cases of incorrect decisions. The primary feature of this method is to deploy the similarity score with respect to Gaussian components between two previously unseen faces. Unlike the conventional classical vector distance measurement, our algorithms also consider the plot of summation of the similarity index versus face feature vector distance. A mixture of Gaussian models of labeled faces is also widely applicable to different biometric system parameters. By comparative evaluations, it has been shown that the efficiency of the proposed algorithm is superior to that of the conventional algorithm by an average accuracy of up to 1.15% and 16.87% when compared with 3x3 Multi-Region Histogram (MRH direct-bag-of-features and Principal Component Analysis (PCA-based face recognition systems, respectively. The experimental results show that similarity score consideration is more discriminative for face recognition compared to feature distance. Experimental results of Labeled Face in the Wild (LFW data set demonstrate that our algorithms are suitable for real applications probe-to-gallery identification of face recognition systems. Moreover, this proposed method can also be applied to other recognition systems and therefore additionally improves recognition scores.
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)...
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.
Patel, R.M.; Kan, van J.A.L.; Bailey, A.M.; Foster, G.D.
2010-01-01
For several years, researchers working on the plant pathogen Botrytis cinerea and a number of other related fungi have routinely used the pLOB1 vector system, based on hygromycin resistance, under the control of the Aspergillus nidulans oliC promoter and what was reported to be the ß-tubulin (tubA)
Directory of Open Access Journals (Sweden)
Hongjian Wang
2014-01-01
Full Text Available We present a support vector regression-based adaptive divided difference filter (SVRADDF algorithm for improving the low state estimation accuracy of nonlinear systems, which are typically affected by large initial estimation errors and imprecise prior knowledge of process and measurement noises. The derivative-free SVRADDF algorithm is significantly simpler to compute than other methods and is implemented using only functional evaluations. The SVRADDF algorithm involves the use of the theoretical and actual covariance of the innovation sequence. Support vector regression (SVR is employed to generate the adaptive factor to tune the noise covariance at each sampling instant when the measurement update step executes, which improves the algorithm’s robustness. The performance of the proposed algorithm is evaluated by estimating states for (i an underwater nonmaneuvering target bearing-only tracking system and (ii maneuvering target bearing-only tracking in an air-traffic control system. The simulation results show that the proposed SVRADDF algorithm exhibits better performance when compared with a traditional DDF algorithm.
Linear Matrix Inequalities for Analysis and Control of Linear Vector Second-Order Systems
DEFF Research Database (Denmark)
Adegas, Fabiano Daher; Stoustrup, Jakob
2015-01-01
the Lyapunov matrix and the system matrices by introducing matrix multipliers, which potentially reduce conservativeness in hard control problems. Multipliers facilitate the usage of parameter-dependent Lyapunov functions as certificates of stability of uncertain and time-varying vector second-order systems......SUMMARY Many dynamical systems are modeled as vector second-order differential equations. This paper presents analysis and synthesis conditions in terms of LMI with explicit dependence in the coefficient matrices of vector second-order systems. These conditions benefit from the separation between....... The conditions introduced in this work have the potential to increase the practice of analyzing and controlling systems directly in vector second-order form. Copyright © 2014 John Wiley & Sons, Ltd....
Multiscale Distance Coherence Vector Algorithm for Content-Based Image Retrieval
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
Modulation transfer function (MTF) measurement method based on support vector machine (SVM)
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.
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.
Arthropod Innate Immune Systems and Vector-Borne Diseases
Baxter, Richard H. G.; Contet, Alicia; Krueger, Kathryn
2017-01-01
Arthropods, especially ticks and mosquitoes, are the vectors for a number of parasitic and viral human diseases, including malaria, sleeping sickness, Dengue, and Zika, yet arthropods show tremendous individual variation in their capacity to transmit disease. A key factor in this capacity is the group of genetically encoded immune factors that counteract infection by the pathogen. Arthropod-specific pattern recognition receptors and protease cascades detect and respond to infection. Proteins ...
National Research Council Canada - National Science Library
Qi, Yuan
2000-01-01
In this thesis, we propose two new machine learning schemes, a subband-based Independent Component Analysis scheme and a hybrid Independent Component Analysis/Support Vector Machine scheme, and apply...
Automatic SIMD vectorization of SSA-based control flow graphs
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
Sagnac Interferometer Based Generation of Controllable Cylindrical Vector Beams
Directory of Open Access Journals (Sweden)
Cristian Acevedo
2016-01-01
Full Text Available We report on a novel experimental geometry to generate cylindrical vector beams in a very robust manner. Continuous control of beams’ properties is obtained using an optically addressable spatial light modulator incorporated into a Sagnac interferometer. Forked computer-generated holograms allow introducing different topological charges while orthogonally polarized beams within the interferometer permit encoding the spatial distribution of polarization. We also demonstrate the generation of complex waveforms obtained by combining two orthogonal beams having both radial modulations and azimuthal dislocations.
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)
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
Huang, Ai-Mei; Nguyen, Truong
2009-04-01
In this paper, we address the problems of unreliable motion vectors that cause visual artifacts but cannot be detected by high residual energy or bidirectional prediction difference in motion-compensated frame interpolation. A correlation-based motion vector processing method is proposed to detect and correct those unreliable motion vectors by explicitly considering motion vector correlation in the motion vector reliability classification, motion vector correction, and frame interpolation stages. Since our method gradually corrects unreliable motion vectors based on their reliability, we can effectively discover the areas where no motion is reliable to be used, such as occlusions and deformed structures. We also propose an adaptive frame interpolation scheme for the occlusion areas based on the analysis of their surrounding motion distribution. As a result, the interpolated frames using the proposed scheme have clearer structure edges and ghost artifacts are also greatly reduced. Experimental results show that our interpolated results have better visual quality than other methods. In addition, the proposed scheme is robust even for those video sequences that contain multiple and fast motions.
A Core Set Based Large Vector-Angular Region and Margin Approach for Novelty Detection
Directory of Open Access Journals (Sweden)
Jiusheng Chen
2016-01-01
Full Text Available A large vector-angular region and margin (LARM approach is presented for novelty detection based on imbalanced data. The key idea is to construct the largest vector-angular region in the feature space to separate normal training patterns; meanwhile, maximize the vector-angular margin between the surface of this optimal vector-angular region and abnormal training patterns. In order to improve the generalization performance of LARM, the vector-angular distribution is optimized by maximizing the vector-angular mean and minimizing the vector-angular variance, which separates the normal and abnormal examples well. However, the inherent computation of quadratic programming (QP solver takes O(n3 training time and at least O(n2 space, which might be computational prohibitive for large scale problems. By (1+ε and (1-ε-approximation algorithm, the core set based LARM algorithm is proposed for fast training LARM problem. Experimental results based on imbalanced datasets have validated the favorable efficiency of the proposed approach in novelty detection.
Efficient gene transfer into nondividing cells by adeno-associated virus-based vectors.
Podsakoff, G; Wong, K K; Chatterjee, S
1994-09-01
Gene transfer vectors based on adeno-associated virus (AAV) are emerging as highly promising for use in human gene therapy by virtue of their characteristics of wide host range, high transduction efficiencies, and lack of cytopathogenicity. To better define the biology of AAV-mediated gene transfer, we tested the ability of an AAV vector to efficiently introduce transgenes into nonproliferating cell populations. Cells were induced into a nonproliferative state by treatment with the DNA synthesis inhibitors fluorodeoxyuridine and aphidicolin or by contact inhibition induced by confluence and serum starvation. Cells in logarithmic growth or DNA synthesis arrest were transduced with vCWR:beta gal, an AAV-based vector encoding beta-galactosidase under Rous sarcoma virus long terminal repeat promoter control. Under each condition tested, vCWR:beta Gal expression in nondividing cells was at least equivalent to that in actively proliferating cells, suggesting that mechanisms for virus attachment, nuclear transport, virion uncoating, and perhaps some limited second-strand synthesis of AAV vectors were present in nondividing cells. Southern hybridization analysis of vector sequences from cells transduced while in DNA synthetic arrest and expanded after release of the block confirmed ultimate integration of the vector genome into cellular chromosomal DNA. These findings may provide the basis for the use of AAV-based vectors for gene transfer into quiescent cell populations such as totipotent hematopoietic stem cells.
Global Positioning Systems (GPS) Technology to Study Vector-Pathogen-Host Interactions
2016-12-01
Award Number: W81XWH-11-2-0175 TITLE: Global Positioning Systems (GPS) Technology to Study Vector-Pathogen-Host Interactions PRINCIPAL...Positioning Systems (GPS) Technology to Study Vector-Pathogen-Host Interactions 5b. GRANT NUMBER W81XWH-11-2-0175 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S...genetic diversity in the population, in hospitalized children with severe dengue illness and cluster investigation of their neighborhoods, and by using
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.
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.
Heading-vector navigation based on head-direction cells and path integration.
Kubie, John L; Fenton, André A
2009-05-01
Insect navigation is guided by heading vectors that are computed by path integration. Mammalian navigation models, on the other hand, are typically based on map-like place representations provided by hippocampal place cells. Such models compute optimal routes as a continuous series of locations that connect the current location to a goal. We propose a "heading-vector" model in which head-direction cells or their derivatives serve both as key elements in constructing the optimal route and as the straight-line guidance during route execution. The model is based on a memory structure termed the "shortcut matrix," which is constructed during the initial exploration of an environment when a set of shortcut vectors between sequential pairs of visited waypoint locations is stored. A mechanism is proposed for calculating and storing these vectors that relies on a hypothesized cell type termed an "accumulating head-direction cell." Following exploration, shortcut vectors connecting all pairs of waypoint locations are computed by vector arithmetic and stored in the shortcut matrix. On re-entry, when local view or place representations query the shortcut matrix with a current waypoint and goal, a shortcut trajectory is retrieved. Since the trajectory direction is in head-direction compass coordinates, navigation is accomplished by tracking the firing of head-direction cells that are tuned to the heading angle. Section 1 of the manuscript describes the properties of accumulating head-direction cells. It then shows how accumulating head-direction cells can store local vectors and perform vector arithmetic to perform path-integration-based homing. Section 2 describes the construction and use of the shortcut matrix for computing direct paths between any pair of locations that have been registered in the shortcut matrix. In the discussion, we analyze the advantages of heading-based navigation over map-based navigation. Finally, we survey behavioral evidence that nonhippocampal
Managing the resilience space of the German energy system - A vector analysis.
Schlör, Holger; Venghaus, Sandra; Märker, Carolin; Hake, Jürgen-Friedrich
2018-07-15
The UN Sustainable Development Goals formulated in 2016 confirmed the sustainability concept of the Earth Summit of 1992 and supported UNEP's green economy transition concept. The transformation of the energy system (Energiewende) is the keystone of Germany's sustainability strategy and of the German green economy concept. We use ten updated energy-related indicators of the German sustainability strategy to analyse the German energy system. The development of the sustainable indicators is examined in the monitoring process by a vector analysis performed in two-dimensional Euclidean space (Euclidean plane). The aim of the novel vector analysis is to measure the current status of the Energiewende in Germany and thereby provide decision makers with information about the strains for the specific remaining pathway of the single indicators and of the total system in order to meet the sustainability targets of the Energiewende. Within this vector model, three vectors (the normative sustainable development vector, the real development vector, and the green economy vector) define the resilience space of our analysis. The resilience space encloses a number of vectors representing different pathways with different technological and socio-economic strains to achieve a sustainable development of the green economy. In this space, the decision will be made as to whether the government measures will lead to a resilient energy system or whether a readjustment of indicator targets or political measures is necessary. The vector analysis enables us to analyse both the government's ambitiousness, which is expressed in the sustainability target for the indicators at the start of the sustainability strategy representing the starting preference order of the German government (SPO) and, secondly, the current preference order of German society in order to bridge the remaining distance to reach the specific sustainability goals of the strategy summarized in the current preference order (CPO
A New Curve Tracing Algorithm Based on Local Feature in the Vectorization of Paper Seismograms
Directory of Open Access Journals (Sweden)
Maofa Wang
2014-02-01
Full Text Available History paper seismograms are very important information for earthquake monitoring and prediction. The vectorization of paper seismograms is an import problem to be resolved. Auto tracing of waveform curves is a key technology for the vectorization of paper seismograms. It can transform an original scanning image into digital waveform data. Accurately tracing out all the key points of each curve in seismograms is the foundation for vectorization of paper seismograms. In the paper, we present a new curve tracing algorithm based on local feature, applying to auto extraction of earthquake waveform in paper seismograms.
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.
Towards human behavior recognition based on spatio temporal features and support vector machines
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.
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.
Arthropod Innate Immune Systems and Vector-Borne Diseases.
Baxter, Richard H G; Contet, Alicia; Krueger, Kathryn
2017-02-21
Arthropods, especially ticks and mosquitoes, are the vectors for a number of parasitic and viral human diseases, including malaria, sleeping sickness, Dengue, and Zika, yet arthropods show tremendous individual variation in their capacity to transmit disease. A key factor in this capacity is the group of genetically encoded immune factors that counteract infection by the pathogen. Arthropod-specific pattern recognition receptors and protease cascades detect and respond to infection. Proteins such as antimicrobial peptides, thioester-containing proteins, and transglutaminases effect responses such as lysis, phagocytosis, melanization, and agglutination. Effector responses are initiated by damage signals such as reactive oxygen species signaling from epithelial cells and recognized by cell surface receptors on hemocytes. Antiviral immunity is primarily mediated by siRNA pathways but coupled with interferon-like signaling, antimicrobial peptides, and thioester-containing proteins. Molecular mechanisms of immunity are closely linked to related traits of longevity and fertility, and arthropods have the capacity for innate immunological memory. Advances in understanding vector immunity can be leveraged to develop novel control strategies for reducing the rate of transmission of both ancient and emerging threats to global health.
Classification of e-government documents based on cooperative expression of word vectors
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.
A sight on the current nanoparticle-based gene delivery vectors
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.
Modeling a ground-coupled heat pump system by a support vector machine
Energy Technology Data Exchange (ETDEWEB)
Esen, Hikmet; Esen, Mehmet [Department of Mechanical Education, Faculty of Technical Education, Firat University, 23119 Elazig (Turkey); Inalli, Mustafa [Department of Mechanical Engineering, Faculty of Engineering, Firat University, 23279 Elazig (Turkey); Sengur, Abdulkadir [Department of Electronic and Computer Science, Faculty of Technical Education, Firat University, 23119 Elazig (Turkey)
2008-08-15
This paper reports on a modeling study of ground coupled heat pump (GCHP) system performance (COP) by using a support vector machine (SVM) method. A GCHP system is a multi-variable system that is hard to model by conventional methods. As regards the SVM, it has a superior capability for generalization, and this capability is independent of the dimensionality of the input data. In this study, a SVM based method was intended to adopt GCHP system for efficient modeling. The Lin-kernel SVM method was quite efficient in modeling purposes and did not require a pre-knowledge about the system. The performance of the proposed methodology was evaluated by using several statistical validation parameters. It is found that the root-mean squared (RMS) value is 0.002722, the coefficient of multiple determinations (R{sup 2}) value is 0.999999, coefficient of variation (cov) value is 0.077295, and mean error function (MEF) value is 0.507437 for the proposed Lin-kernel SVM method. The optimum parameters of the SVM method were determined by using a greedy search algorithm. This search algorithm was effective for obtaining the optimum parameters. The simulation results show that the SVM is a good method for prediction of the COP of the GCHP system. The computation of SVM model is faster compared with other machine learning techniques (artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS)); because there are fewer free parameters and only support vectors (only a fraction of all data) are used in the generalization process. (author)
Learn the Lagrangian: A Vector-Valued RKHS Approach to Identifying Lagrangian Systems.
Cheng, Ching-An; Huang, Han-Pang
2016-12-01
We study the modeling of Lagrangian systems with multiple degrees of freedom. Based on system dynamics, canonical parametric models require ad hoc derivations and sometimes simplification for a computable solution; on the other hand, due to the lack of prior knowledge in the system's structure, modern nonparametric models in machine learning face the curse of dimensionality, especially in learning large systems. In this paper, we bridge this gap by unifying the theories of Lagrangian systems and vector-valued reproducing kernel Hilbert space. We reformulate Lagrangian systems with kernels that embed the governing Euler-Lagrange equation-the Lagrangian kernels-and show that these kernels span a subspace capturing the Lagrangian's projection as inverse dynamics. By such property, our model uses only inputs and outputs as in machine learning and inherits the structured form as in system dynamics, thereby removing the need for the mundane derivations for new systems as well as the generalization problem in learning from scratches. In effect, it learns the system's Lagrangian, a simpler task than directly learning the dynamics. To demonstrate, we applied the proposed kernel to identify the robot inverse dynamics in simulations and experiments. Our results present a competitive novel approach to identifying Lagrangian systems, despite using only inputs and outputs.
Xie, Pingping; Joyce, Robert; Wu, Shaorong
2015-04-01
As reported at the EGU General Assembly of 2014, a prototype system was developed for the second generation CMORPH to produce global analyses of 30-min precipitation on a 0.05olat/lon grid over the entire globe from pole to pole through integration of information from satellite observations as well as numerical model simulations. The second generation CMORPH is built upon the Kalman Filter based CMORPH algorithm of Joyce and Xie (2011). Inputs to the system include rainfall and snowfall rate retrievals from passive microwave (PMW) measurements aboard all available low earth orbit (LEO) satellites, precipitation estimates derived from infrared (IR) observations of geostationary (GEO) as well as LEO platforms, and precipitation simulations from numerical global models. Key to the success of the 2nd generation CMORPH, among a couple of other elements, are the development of a LEO-IR based precipitation estimation to fill in the polar gaps and objectively analyzed cloud motion vectors to capture the cloud movements of various spatial scales over the entire globe. In this presentation, we report our recent work on the refinement for these two important algorithm components. The prototype algorithm for the LEO IR precipitation estimation is refined to achieve improved quantitative accuracy and consistency with PMW retrievals. AVHRR IR TBB data from all LEO satellites are first remapped to a 0.05olat/lon grid over the entire globe and in a 30-min interval. Temporally and spatially co-located data pairs of the LEO TBB and inter-calibrated combined satellite PMW retrievals (MWCOMB) are then collected to construct tables. Precipitation at a grid box is derived from the TBB through matching the PDF tables for the TBB and the MWCOMB. This procedure is implemented for different season, latitude band and underlying surface types to account for the variations in the cloud - precipitation relationship. At the meantime, a sub-system is developed to construct analyzed fields of
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.
Medical Image Compression Based on Vector Quantization with Variable Block Sizes in Wavelet Domain
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 vari...
Efficient gene transfer into nondividing cells by adeno-associated virus-based vectors.
Podsakoff, G; Wong, K K; Chatterjee, S
1994-01-01
Gene transfer vectors based on adeno-associated virus (AAV) are emerging as highly promising for use in human gene therapy by virtue of their characteristics of wide host range, high transduction efficiencies, and lack of cytopathogenicity. To better define the biology of AAV-mediated gene transfer, we tested the ability of an AAV vector to efficiently introduce transgenes into nonproliferating cell populations. Cells were induced into a nonproliferative state by treatment with the DNA synthe...
Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization
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
Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
Mohammed Hasan Abdulameer
2014-01-01
Full Text Available Existing face recognition methods utilize particle swarm optimizer (PSO and opposition based particle swarm optimizer (OPSO to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM. In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented.
Energy Technology Data Exchange (ETDEWEB)
Hong Junjie, E-mail: hongjjie@mail.sysu.edu.cn [School of Engineering, Sun Yat-Sen University, Guangzhou 510006 (China); Li Liyi, E-mail: liliyi@hit.edu.cn [Dept. Electrical Engineering, Harbin Institute of Technology, Harbin 150000 (China); Zong Zhijian; Liu Zhongtu [School of Engineering, Sun Yat-Sen University, Guangzhou 510006 (China)
2011-10-15
Highlights: {yields} The structure of the permanent magnet linear synchronous motor (SW-PMLSM) is new. {yields} A new current control method CEVPC is employed in this motor. {yields} The sectional power supply method is different to the others and effective. {yields} The performance gets worse with voltage and current limitations. - Abstract: To include features such as greater thrust density, higher efficiency without reducing the thrust stability, this paper proposes a section winding permanent magnet linear synchronous motor (SW-PMLSM), whose iron core is continuous, whereas winding is divided. The discrete system model of the motor is derived. With the definition of the current error vector and selection of the value function, the theory of the current error vector based prediction control (CEVPC) for the motor currents is explained clearly. According to the winding section feature, the motion region of the mover is divided into five zones, in which the implementation of the current predictive control method is proposed. Finally, the experimental platform is constructed and experiments are carried out. The results show: the current control effect has good dynamic response, and the thrust on the mover remains constant basically.
Burbank, Lindsey P; Stenger, Drake C
2016-08-01
The phytopathogen Xylella fastidiosa causes disease in a variety of important crop and landscape plants. Functional genetic studies have led to a broader understanding of virulence mechanisms used by this pathogen in the grapevine host. Plasmid shuttle vectors are important tools in studies of bacterial genetics but there are only a limited number of plasmid vectors available that replicate in X. fastidiosa, and even fewer that are retained without antibiotic selection. Two plasmids are described here that show stable replication in X. fastidiosa and are effective for gene complementation both in vitro and in planta. Plasmid maintenance is facilitated by incorporation of the PemI/PemK plasmid addiction system, consisting of PemK, an endoribonuclease toxin, and its cognate antitoxin, PemI. Vector pXf20pemIK utilizes a native X. fastidiosa replication origin as well as a high-copy-number pUC origin for propagation in Escherichia coli cloning strains. Broad-host-range vector pBBR5pemIK is a medium- to low-copy-number plasmid based on the pBBR1 backbone. Both plasmids are maintained for extended periods of time in the absence of antibiotic selection, as well as up to 14 weeks in grapevine, without affecting bacterial fitness. These plasmids present an alternative to traditional complementation and expression vectors which rely on antibiotic selection for plasmid retention.
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.
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
A program system for ab initio MO calculations on vector and parallel processing machines. Pt. 1
International Nuclear Information System (INIS)
Ernenwein, R.; Rohmer, M.M.; Benard, M.
1990-01-01
We present a program system for ab initio molecular orbital calculations on vector and parallel computers. The present article is devoted to the computation of one- and two-electron integrals over contracted Gaussian basis sets involving s-, p-, d- and f-type functions. The McMurchie and Davidson (MMD) algorithm has been implemented and parallelized by distributing over a limited number of logical tasks the calculation of the 55 relevant classes of integrals. All sections of the MMD algorithm have been efficiently vectorized, leading to a scalar/vector ratio of 5.8. Different algorithms are proposed and compared for an optimal vectorization of the contraction of the 'intermediate integrals' generated by the MMD formalism. Advantage is taken of the dynamic storage allocation for tuning the length of the vector loops (i.e. the size of the vectorization buffer) as a function of (i) the total memory available for the job, (ii) the number of logical tasks defined by the user (≤13), and (iii) the storage requested by each specific class of integrals. Test calculations carried out on a CRAY-2 computer show that the average number of finite integrals computed over a (s, p, d, f) CGTO basis set is about 1180000 per second and per processor. The combination of vectorization and parallelism on this 4-processor machine reduces the CPU time by a factor larger than 20 with respect to the scalar and sequential performance. (orig.)
An introduction to vectors, vector operators and vector analysis
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.
Lu, Zhao; Sun, Jing; Butts, Kenneth
2014-05-01
Support vector regression for approximating nonlinear dynamic systems is more delicate than the approximation of indicator functions in support vector classification, particularly for systems that involve multitudes of time scales in their sampled data. The kernel used for support vector learning determines the class of functions from which a support vector machine can draw its solution, and the choice of kernel significantly influences the performance of a support vector machine. In this paper, to bridge the gap between wavelet multiresolution analysis and kernel learning, the closed-form orthogonal wavelet is exploited to construct new multiscale asymmetric orthogonal wavelet kernels for linear programming support vector learning. The closed-form multiscale orthogonal wavelet kernel provides a systematic framework to implement multiscale kernel learning via dyadic dilations and also enables us to represent complex nonlinear dynamics effectively. To demonstrate the superiority of the proposed multiscale wavelet kernel in identifying complex nonlinear dynamic systems, two case studies are presented that aim at building parallel models on benchmark datasets. The development of parallel models that address the long-term/mid-term prediction issue is more intricate and challenging than the identification of series-parallel models where only one-step ahead prediction is required. Simulation results illustrate the effectiveness of the proposed multiscale kernel learning.
On-line transient stability assessment of large-scale power systems by using ball vector machines
International Nuclear Information System (INIS)
Mohammadi, M.; Gharehpetian, G.B.
2010-01-01
In this paper ball vector machine (BVM) has been used for on-line transient stability assessment of large-scale power systems. To classify the system transient security status, a BVM has been trained for all contingencies. The proposed BVM based security assessment algorithm has very small training time and space in comparison with artificial neural networks (ANN), support vector machines (SVM) and other machine learning based algorithms. In addition, the proposed algorithm has less support vectors (SV) and therefore is faster than existing algorithms for on-line applications. One of the main points, to apply a machine learning method is feature selection. In this paper, a new Decision Tree (DT) based feature selection technique has been presented. The proposed BVM based algorithm has been applied to New England 39-bus power system. The simulation results show the effectiveness and the stability of the proposed method for on-line transient stability assessment procedure of large-scale power system. The proposed feature selection algorithm has been compared with different feature selection algorithms. The simulation results demonstrate the effectiveness of the proposed feature algorithm.
Selection vector filter framework
Lukac, Rastislav; Plataniotis, Konstantinos N.; Smolka, Bogdan; Venetsanopoulos, Anastasios N.
2003-10-01
We provide a unified framework of nonlinear vector techniques outputting the lowest ranked vector. The proposed framework constitutes a generalized filter class for multichannel signal processing. A new class of nonlinear selection filters are based on the robust order-statistic theory and the minimization of the weighted distance function to other input samples. The proposed method can be designed to perform a variety of filtering operations including previously developed filtering techniques such as vector median, basic vector directional filter, directional distance filter, weighted vector median filters and weighted directional filters. A wide range of filtering operations is guaranteed by the filter structure with two independent weight vectors for angular and distance domains of the vector space. In order to adapt the filter parameters to varying signal and noise statistics, we provide also the generalized optimization algorithms taking the advantage of the weighted median filters and the relationship between standard median filter and vector median filter. Thus, we can deal with both statistical and deterministic aspects of the filter design process. It will be shown that the proposed method holds the required properties such as the capability of modelling the underlying system in the application at hand, the robustness with respect to errors in the model of underlying system, the availability of the training procedure and finally, the simplicity of filter representation, analysis, design and implementation. Simulation studies also indicate that the new filters are computationally attractive and have excellent performance in environments corrupted by bit errors and impulsive noise.
International Nuclear Information System (INIS)
Seo, In Yong; Ha, Bok Nam; Lee, Sung Woo; Shin, Chang Hoon; Kim, Seong Jun
2010-01-01
In nuclear power plants (NPPs), periodic sensor calibrations are required to assure that sensors are operating correctly. By checking the sensor's operating status at every fuel outage, faulty sensors may remain undetected for periods of up to 24 months. Moreover, typically, only a few faulty sensors are found to be calibrated. For the safe operation of NPP and the reduction of unnecessary calibration, on-line instrument calibration monitoring is needed. In this study, principal component based auto-associative support vector regression (PCSVR) using response surface methodology (RSM) is proposed for the sensor signal validation of NPPs. This paper describes the design of a PCSVR-based sensor validation system for a power generation system. RSM is employed to determine the optimal values of SVR hyperparameters and is compared to the genetic algorithm (GA). The proposed PCSVR model is confirmed with the actual plant data of Kori Nuclear Power Plant Unit 3 and is compared with the Auto-Associative support vector regression (AASVR) and the auto-associative neural network (AANN) model. The auto-sensitivity of AASVR is improved by around six times by using a PCA, resulting in good detection of sensor drift. Compared to AANN, accuracy and cross-sensitivity are better while the auto-sensitivity is almost the same. Meanwhile, the proposed RSM for the optimization of the PCSVR algorithm performs even better in terms of accuracy, auto-sensitivity, and averaged maximum error, except in averaged RMS error, and this method is much more time efficient compared to the conventional GA method
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.
Ruitenberg, Marc J; Eggers, Ruben; Boer, Gerard J; Verhaagen, J.
2002-01-01
The use of viral vectors as agents for gene delivery provides a direct approach to manipulate gene expression in the mammalian central nervous system (CNS). The present article describes in detail the methodology for the injection of viral vectors, in particular adeno-associated virus (AAV) vectors,
Gene Therapy Vectors with Enhanced Transfection Based on Hydrogels Modified with Affinity Peptides
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
Duffy, Margaret R; Alonso-Padilla, Julio; John, Lijo; Chandra, Naresh; Khan, Selina; Ballmann, Monika Z; Lipiec, Agnieszka; Heemskerk, Evert; Custers, Jerome; Arnberg, Niklas; Havenga, Menzo; Baker, Andrew H; Lemckert, Angelique
2018-01-01
The vectorization of rare human adenovirus (HAdV) types will widen our knowledge of this family and their interaction with cells, tissues and organs. In this study we focus on HAdV-56, a member of human Ad species D, and create ease-of-use cloning systems to generate recombinant HAdV-56 vectors carrying foreign genes. We present in vitro transduction profiles for HAdV-56 in direct comparison to the most commonly used HAdV-5-based vector. In vivo characterizations demonstrate that when it is delivered intravenously (i.v.) HAdV-56 mainly targets the spleen and, to a lesser extent, the lungs, whilst largely bypassing liver transduction in mice. HAdV-56 triggered robust inflammatory and cellular immune responses, with higher induction of IFNγ, TNFα, IL5, IL6, IP10, MCP1 and MIG1 compared to HAdV-5 following i.v. administration. We also investigated its potential as a vaccine vector candidate by performing prime immunizations in mice with HAdV-56 encoding luciferase (HAdV-56-Luc). Direct comparisons were made to HAdV-26, a highly potent human vaccine vector currently in phase II clinical trials. HAdV-56-Luc induced luciferase 'antigen'-specific IFNγ-producing cells and anti-HAdV-56 neutralizing antibodies in Balb/c mice, demonstrating a near identical profile to that of HAdV-26. Taken together, the data presented provides further insight into human Ad receptor/co-receptor usage, and the first report on HAdV-56 vectors and their potential for gene therapy and vaccine applications.
Conservative rigid body dynamics by convected base vectors with implicit constraints
DEFF Research Database (Denmark)
Krenk, Steen; Nielsen, Martin Bjerre
2014-01-01
of differential equations without additional algebraic constraints on the base vectors. A discretized form of the equations of motion is obtained by starting from a finite time increment of the Hamiltonian, and retracing the steps of the continuous formulation in discrete form in terms of increments and mean...... 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...... values over each integration time increment. In this discrete form the Lagrange multipliers are given in terms of a representative value within the integration time interval, and the equations of motion are recast into a conservative mean-value and finite difference format. The Lagrange multipliers...
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...
Smart DNA vectors based on cyclodextrin polymers: compaction and endosomal release.
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.
Track Circuit Fault Diagnosis Method based on Least Squares Support Vector
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.
Strategic vectors of transformational shifts in the national tourism system of Ukraine
Directory of Open Access Journals (Sweden)
Alla OKHRIMENKO
2017-10-01
Full Text Available The article determines transformational factors, which influence a national tourism system (NTS of Ukraine and proposes strategical vectors of its development. Research of the NTS as an economic system is a pre-condition for formation of strategic vectors of development. Transformational driving forces principally change scales, components, and proportions between external and internal factors of development of the NTS. Correspondingly, the mentioned processes objectively encourage modernization of the national tourism system and application of innovative managerial methods. The following Strategical vectors of transformational shifts in the NTS were grounded: 1 Safety of tourists and investors; 2 The normative and legislative framework of the NTS development; 3 Development of infrastructure of the NTS component; 4 Human resources development; 5 A marketing policy of the NTS promotion; 6 Ecological and cultural policies. Their implementation will improve efficiency and competitiveness of the NTS and the national economy.
Using Vector and Extended Boolean Matching in an Expert System for Selecting Foster Homes.
Fox, Edward A.; Winett, Sheila G.
1990-01-01
Describes FOCES (Foster Care Expert System), a prototype expert system for choosing foster care placements for children which integrates information retrieval techniques with artificial intelligence. The use of prototypes and queries in Prolog routines, extended Boolean matching, and vector correlation are explained, as well as evaluation by…
A formula for the Bloch vector of some Lindblad quantum systems
International Nuclear Information System (INIS)
Salgado, D.; Sanchez-Gomez, J.L.
2004-01-01
Using the Bloch representation of an N-dimensional quantum system and immediate results from quantum stochastic calculus, we establish a closed formula for the Bloch vector, hence also for the density operator, of a quantum system following a Lindblad evolution with selfadjoint Lindblad operators
DODAB:monoolein-based lipoplexes as non-viral vectors for transfection of mammalian cells.
Silva, J P Neves; Oliveira, A C N; Casal, M P P A; Gomes, A C; Coutinho, P J G; Coutinho, O P; Oliveira, M E C D Real
2011-10-01
DNA/Cationic liposome complexes (lipoplexes) have been widely used as non-viral vectors for transfection. Neutral lipids in liposomal formulation are determinant for transfection efficiency using these vectors. In this work, we studied the potential of monoolein (MO) as helper lipid for cellular transfection. Lipoplexes composed of pDNA and dioctadecyldimethylammonium bromide (DODAB)/1-monooleoyl-rac-glycerol (MO) at different molar ratios (4:1, 2:1 and 1:1) and at different cationic lipid/DNA ratios were investigated. The physicochemical properties of the lipoplexes (size, charge and structure), were studied by Dynamic Light Scattering (DLS), Zeta Potential (ζ) and cryo-transmission electron microscopy (cryo-TEM). The effect of MO on pDNA condensation and the effect of heparin and heparan sulphate on the percentage of pDNA release from the lipoplexes were also studied by Ethidium Bromide (EtBr) exclusion assays and electrophoresis. Cytotoxicity and transfection efficiency of these lipoplexes were evaluated using 293T cells and compared with the golden standard helper lipids 1,2-dioleoyl-sn-glycero-3-hosphoethanolamine (DOPE) and cholesterol (Chol) as well as with a commercial transfection agent (Lipofectamine™ LTX). The internalization of transfected fluorescently-labeled pDNA was also visualized using the same cell line. The results demonstrate that the presence of MO not only increases pDNA compactation efficiency, but also affects the physicochemical properties of the lipoplexes, which can interfere with lipoplex-cell interactions. The DODAB:MO formulations tested showed little toxicity and successfully mediated in vitro cell transfection. These results were supported by fluorescence microscopy studies, which illustrated that lipoplexes were able to access the cytosol and deliver pDNA to the nucleus. DODAB:MO-based lipoplexes were thus validated as non-toxic, efficient lipofection vectors for genetic modification of mammalian cells. Understanding the
Energy Technology Data Exchange (ETDEWEB)
Ghennam, Tarak [Laboratoire d' Electronique de Puissance (LEP), UER: Electrotechnique, Ecole Militaire Polytechnique d' Alger, BP 17, Bordj EL Bahri, Alger (Algeria); Berkouk, El-Madjid [Laboratoire de Commande des Processus (LCP), Ecole Nationale Polytechnique d' Alger, BP 182, 10 avenue Hassen Badi, 16200 el Harrach (Algeria)
2010-04-15
In this paper, a novel space-vector hysteresis current control (SVHCC) is proposed for a back-to-back three-level converter which is used as an electronic interface in a wind conversion system. The proposed SVHCC controls the active and reactive powers delivered to the grid by the doubly fed induction machine (DFIM) through the control of its rotor currents. In addition, it controls the neutral point voltage by using the redundant inverter switching states. The three rotor current errors are gathered into a single space-vector quantity. The magnitude of the error vector is limited within boundary areas of a square shape. The control scheme is based firstly on the detection of the area and sector in which the vector tip of the current error can be located. Then, an appropriate voltage vector among the 27 voltage vectors of the three-level voltage source inverter (VSI) is applied to push the error vector towards the hysteresis boundaries. Simple look-up tables are required for the area and sector detection, and also for vector selection. The performance of the proposed control technique has been verified by simulations. (author)
Axial-vector gluons and the fine structure of heavy quark--antiquark systems
International Nuclear Information System (INIS)
Feinberg, G.; Lynn, B.; Sucher, J.
1979-01-01
We point out that two models of the origin of spin-dependent forces in heavy quark systems make very different predictions about the relative size of these forces in c-barc and b-barb. The model in which these forces are relativistic corrections to vector or scalar gluon exchange predicts smaller spin-dependent effects in b-barb than in c-barc while a model in which these forces are due to exchange of axial-vector gluons predicts a similar size for spin-dependent splittings in the two systems
Structural analysis of online handwritten mathematical symbols based on support vector machines
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.
Improved image retrieval based on fuzzy colour feature vector
Ben-Ahmeida, Ahlam M.; Ben Sasi, Ahmed Y.
2013-03-01
One of Image indexing techniques is the Content-Based Image Retrieval which is an efficient way for retrieving images from the image database automatically based on their visual contents such as colour, texture, and shape. In this paper will be discuss how using content-based image retrieval (CBIR) method by colour feature extraction and similarity checking. By dividing the query image and all images in the database into pieces and extract the features of each part separately and comparing the corresponding portions in order to increase the accuracy in the retrieval. The proposed approach is based on the use of fuzzy sets, to overcome the problem of curse of dimensionality. The contribution of colour of each pixel is associated to all the bins in the histogram using fuzzy-set membership functions. As a result, the Fuzzy Colour Histogram (FCH), outperformed the Conventional Colour Histogram (CCH) in image retrieving, due to its speedy results, where were images represented as signatures that took less size of memory, depending on the number of divisions. The results also showed that FCH is less sensitive and more robust to brightness changes than the CCH with better retrieval recall values.
Yu, Fei; Hui, Mei; Zhao, Yue-jin
2009-08-01
The image block matching algorithm based on motion vectors of correlative pixels in oblique direction is presented for digital image stabilization. The digital image stabilization is a new generation of image stabilization technique which can obtains the information of relative motion among frames of dynamic image sequences by the method of digital image processing. In this method the matching parameters are calculated from the vectors projected in the oblique direction. The matching parameters based on the vectors contain the information of vectors in transverse and vertical direction in the image blocks at the same time. So the better matching information can be obtained after making correlative operation in the oblique direction. And an iterative weighted least square method is used to eliminate the error of block matching. The weights are related with the pixels' rotational angle. The center of rotation and the global emotion estimation of the shaking image can be obtained by the weighted least square from the estimation of each block chosen evenly from the image. Then, the shaking image can be stabilized with the center of rotation and the global emotion estimation. Also, the algorithm can run at real time by the method of simulated annealing in searching method of block matching. An image processing system based on DSP was used to exam this algorithm. The core processor in the DSP system is TMS320C6416 of TI, and the CCD camera with definition of 720×576 pixels was chosen as the input video signal. Experimental results show that the algorithm can be performed at the real time processing system and have an accurate matching precision.
A vector radiative transfer model for coupled atmosphere and ocean systems with a rough interface
International Nuclear Information System (INIS)
Zhai Pengwang; Hu Yongxiang; Chowdhary, Jacek; Trepte, Charles R.; Lucker, Patricia L.; Josset, Damien B.
2010-01-01
We report on an exact vector (polarized) radiative transfer (VRT) model for coupled atmosphere and ocean systems. This VRT model is based on the successive order of scattering (SOS) method, which virtually takes all the multiple scattering processes into account, including atmospheric scattering, oceanic scattering, reflection and transmission through the rough ocean surface. The isotropic Cox-Munk wave model is used to derive the ref and transmission matrices for the rough ocean surface. Shadowing effects are included by the shadowing function. We validated the SOS results by comparing them with those calculated by two independent codes based on the doubling/adding and Monte Carlo methods. Two error analyses related to the ocean color remote sensing are performed in the coupled atmosphere and ocean systems. One is the scalar error caused by ignoring the polarization in the whole system. The other is the error introduced by ignoring the polarization of the light transmitted through the ocean interface. Both errors are significant for the cases studied. This code fits for the next generation of ocean color study because it converges fast for absorbing medium as, for instance, ocean.
Object Recognition System-on-Chip Using the Support Vector Machines
Directory of Open Access Journals (Sweden)
Houzet Dominique
2005-01-01
Full Text Available The first aim of this work is to propose the design of a system-on-chip (SoC platform dedicated to digital image and signal processing, which is tuned to implement efficiently multiply-and-accumulate (MAC vector/matrix operations. The second aim of this work is to implement a recent promising neural network method, namely, the support vector machine (SVM used for real-time object recognition, in order to build a vision machine. With such a reconfigurable and programmable SoC platform, it is possible to implement any SVM function dedicated to any object recognition problem. The final aim is to obtain an automatic reconfiguration of the SoC platform, based on the results of the learning phase on an objects' database, which makes it possible to recognize practically any object without manual programming. Recognition can be of any kind that is from image to signal data. Such a system is a general-purpose automatic classifier. Many applications can be considered as a classification problem, but are usually treated specifically in order to optimize the cost of the implemented solution. The cost of our approach is more important than a dedicated one, but in a near future, hundreds of millions of gates will be common and affordable compared to the design cost. What we are proposing here is a general-purpose classification neural network implemented on a reconfigurable SoC platform. The first version presented here is limited in size and thus in object recognition performances, but can be easily upgraded according to technology improvements.
Optimizing structure of complex technical system by heterogeneous vector criterion in interval form
Lysenko, A. V.; Kochegarov, I. I.; Yurkov, N. K.; Grishko, A. K.
2018-05-01
The article examines the methods of development and multi-criteria choice of the preferred structural variant of the complex technical system at the early stages of its life cycle in the absence of sufficient knowledge of parameters and variables for optimizing this structure. The suggested methods takes into consideration the various fuzzy input data connected with the heterogeneous quality criteria of the designed system and the parameters set by their variation range. The suggested approach is based on the complex use of methods of interval analysis, fuzzy sets theory, and the decision-making theory. As a result, the method for normalizing heterogeneous quality criteria has been developed on the basis of establishing preference relations in the interval form. The method of building preferential relations in the interval form on the basis of the vector of heterogeneous quality criteria suggest the use of membership functions instead of the coefficients considering the criteria value. The former show the degree of proximity of the realization of the designed system to the efficient or Pareto optimal variants. The study analyzes the example of choosing the optimal variant for the complex system using heterogeneous quality criteria.
Support vector machine-based facial-expression recognition method combining shape and appearance
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.
Wearable Vector Electrical Bioimpedance System to Assess Knee Joint Health.
Hersek, Sinan; Toreyin, Hakan; Teague, Caitlin N; Millard-Stafford, Mindy L; Jeong, Hyeon-Ki; Bavare, Miheer M; Wolkoff, Paul; Sawka, Michael N; Inan, Omer T
2017-10-01
We designed and validated a portable electrical bioimpedance (EBI) system to quantify knee joint health. Five separate experiments were performed to demonstrate the: 1) ability of the EBI system to assess knee injury and recovery; 2) interday variability of knee EBI measurements; 3) sensitivity of the system to small changes in interstitial fluid volume; 4) reducing the error of EBI measurements using acceleration signals; and 5) use of the system with dry electrodes integrated to a wearable knee wrap. 1) The absolute difference in resistance ( R) and reactance (X) from the left to the right knee was able to distinguish injured and healthy knees (p knee R was 2.5 Ω and for X was 1.2 Ω. 3) Local heating/cooling resulted in a significant decrease/increase in knee R (p knee R and X measured using the wet electrodes and the designed wearable knee wrap were highly correlated ( R 2 = 0.8 and 0.9, respectively). This study demonstrates the use of wearable EBI measurements in monitoring knee joint health. The proposed wearable system has the potential for assessing knee joint health outside the clinic/lab and help guide rehabilitation.
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.
A NEW METHOD OF CHANNEL FRICTION INVERSION BASED ON KALMAN FILTER WITH UNKNOWN PARAMETER VECTOR
Institute of Scientific and Technical Information of China (English)
CHENG Wei-ping; MAO Gen-hai; LIU Guo-hua
2005-01-01
Channel friction is an important parameter in hydraulic analysis.A channel friction parameter inversion method based on Kalman Filter with unknown parameter vector is proposed.Numerical simulations indicate that when the number of monitoring stations exceeds a critical value, the solution is hardly affected.In addition, Kalman Filter with unknown parameter vector is effective only at unsteady state.For the nonlinear equations, computations of sensitivity matrices are time-costly.Two simplified measures can reduce computing time, but not influence the results.One is to reduce sensitivity matrix analysis time, the other is to substitute for sensitivity matrix.
Design and simulation of MEMS vector hydrophone with reduced cross section based meander beams
Energy Technology Data Exchange (ETDEWEB)
Kumar, Manoj; Dutta, S.; Pal, Ramjay; Jain, K. K.; Gupta, Sudha; Bhan, R. K. [Solid State Physics Laboratory, DRDO, Lucknow Road, Timarpur, Delhi, India 110054 (India)
2016-04-13
MEMS based vector hydrophone is being one of the key device in the underwater communications. In this paper, we presented a bio-inspired MEMS vector hydrophone. The hydrophone structure consists of a proof mass suspended by four meander type beams with reduced cross-section. Modal patterns of the structure were studied. First three modal frequencies of the hydrophone structure were found to be 420 Hz, 420 Hz and 1646 Hz respectively. The deflection and stress of the hydrophone is found have linear behavior in the 1 µPa – 1Pa pressure range.
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...
Support-Vector-based Least Squares for learning non-linear dynamics
de Kruif, B.J.; de Vries, Theodorus J.A.
2002-01-01
A function approximator is introduced that is based on least squares support vector machines (LSSVM) and on least squares (LS). The potential indicators for the LS method are chosen as the kernel functions of all the training samples similar to LSSVM. By selecting these as indicator functions the
Analysis of vector wind change with respect to time for Vandenberg Air Force Base, California
Adelfang, S. I.
1978-01-01
A statistical analysis of the temporal variability of wind vectors at 1 km altitude intervals from 0 to 27 km altitude taken from a 10-year data sample of twice-daily rawinsode wind measurements over Vandenberg Air Force Base, California is presented.
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 ...
Ultrasound Vector Flow Imaging: Part II: Parallel Systems
DEFF Research Database (Denmark)
Jensen, Jørgen Arendt; Nikolov, Svetoslav Ivanov; Yu, Alfred C. H.
2016-01-01
The paper gives a review of the current state-of-theart in ultrasound parallel acquisition systems for flow imaging using spherical and plane waves emissions. The imaging methods are explained along with the advantages of using these very fast and sensitive velocity estimators. These experimental...... ultrasound imaging for studying brain function in animals. The paper explains the underlying acquisition and estimation methods for fast 2-D and 3-D velocity imaging and gives a number of examples. Future challenges and the potentials of parallel acquisition systems for flow imaging are also discussed....
Datum Feature Extraction and Deformation Analysis Method Based on Normal Vector of Point Cloud
Sun, W.; Wang, J.; Jin, F.; Liang, Z.; Yang, Y.
2018-04-01
In order to solve the problem lacking applicable analysis method in the application of three-dimensional laser scanning technology to the field of deformation monitoring, an efficient method extracting datum feature and analysing deformation based on normal vector of point cloud was proposed. Firstly, the kd-tree is used to establish the topological relation. Datum points are detected by tracking the normal vector of point cloud determined by the normal vector of local planar. Then, the cubic B-spline curve fitting is performed on the datum points. Finally, datum elevation and the inclination angle of the radial point are calculated according to the fitted curve and then the deformation information was analyzed. The proposed approach was verified on real large-scale tank data set captured with terrestrial laser scanner in a chemical plant. The results show that the method could obtain the entire information of the monitor object quickly and comprehensively, and reflect accurately the datum feature deformation.
Murni, Bustamam, A.; Ernastuti, Handhika, T.; Kerami, D.
2017-07-01
Calculation of the matrix-vector multiplication in the real-world problems often involves large matrix with arbitrary size. Therefore, parallelization is needed to speed up the calculation process that usually takes a long time. Graph partitioning techniques that have been discussed in the previous studies cannot be used to complete the parallelized calculation of matrix-vector multiplication with arbitrary size. This is due to the assumption of graph partitioning techniques that can only solve the square and symmetric matrix. Hypergraph partitioning techniques will overcome the shortcomings of the graph partitioning technique. This paper addresses the efficient parallelization of matrix-vector multiplication through hypergraph partitioning techniques using CUDA GPU-based parallel computing. CUDA (compute unified device architecture) is a parallel computing platform and programming model that was created by NVIDIA and implemented by the GPU (graphics processing unit).
Screw Remaining Life Prediction Based on Quantum Genetic Algorithm and Support Vector Machine
Directory of Open Access Journals (Sweden)
Xiaochen Zhang
2017-01-01
Full Text Available To predict the remaining life of ball screw, a screw remaining life prediction method based on quantum genetic algorithm (QGA and support vector machine (SVM is proposed. A screw accelerated test bench is introduced. Accelerometers are installed to monitor the performance degradation of ball screw. Combined with wavelet packet decomposition and isometric mapping (Isomap, the sensitive feature vectors are obtained and stored in database. Meanwhile, the sensitive feature vectors are randomly chosen from the database and constitute training samples and testing samples. Then the optimal kernel function parameter and penalty factor of SVM are searched with the method of QGA. Finally, the training samples are used to train optimized SVM while testing samples are adopted to test the prediction accuracy of the trained SVM so the screw remaining life prediction model can be got. The experiment results show that the screw remaining life prediction model could effectively predict screw remaining life.
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.
Combinatorial Vector Fields for Piecewise Affine Control Systems
DEFF Research Database (Denmark)
Wisniewski, Rafal; Larsen, Jesper Abildgaard
2008-01-01
This paper is intended to be a continuation of Habets and van Schuppen (2004) and Habets, Collins and van Schuppen (2006), which address the control problem for piecewise-affine systems on an arbitrary polytope or a family of these. Our work deals with the underlying combinatorics of the underlyi...
3D simulation of a MACH 3 Thrust Vector Control system
International Nuclear Information System (INIS)
Rainville, P.A.; DeChamplain, A.; Kretschmer, D.; Farinaccio, R.; Stowe, R.
2002-01-01
The purpose of a Thrust Vector Control (TVC) system is to allow directional control of a flight vehicle through the use of jet vanes acting on the exhaust plume of the motor. The objective of this study was to validate the commercial code Fluent for the simulation of the unsteady flow field within the nozzle of a solid propellant rocket motor equipped with TVC. The experimental data for the validation of Fluent were based on time-dependent test results completed at Defence R and D Canada - Valcartier (DRDC Valcartier). These experimental results include several parameters for the solid propellant motor that establish the operating conditions for the numerical simulation of the TVC system. With the preliminary numerical results from meshes of the original vane geometry (before subsequent essential modifications), the temperature deep inside the vane was generally underestimated. For the temperature predictions closer to the base of the vane where it is attached to the nozzle wall, the results were slightly higher than the experimental values. This would be caused by an oblique shock wave that strikes the vane at a different location with the modified geometry compared to the original geometry, and therefore causes substantial changes to the internal temperature field of the vane. The grid resolution for the vane boundary layer could also be a reason for these discrepancies. Further simulations are therefore underway to resolve these issues with the modified geometry and a more refined boundary layer. (author)
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.
Space vector modulation strategy for neutral-point voltage balancing in three-level inverter systems
DEFF Research Database (Denmark)
Choi, Uimin; Lee, Kyo Beum
2013-01-01
This study proposes a space vector modulation (SVM) strategy to balance the neutral-point voltage of three-level inverter systems. The proposed method is implemented by combining conventional symmetric SVM with nearest three-vector (NTV) modulation. The conventional SVM is converted to NTV...... modulation by properly adding or subtracting a minimum gate-on time. In addition, using this method, the switching frequency is reduced and a decrease of switching loss would be yielded. The neutral-point voltage is balanced by the proposed SVM strategy without additional hardware or complex calculations....... Simulation and experimental results are shown to verify the validity and feasibility of the proposed SVM strategy....
Usability Evaluation of an Augmented Reality System for Teaching Euclidean Vectors
Martin-Gonzalez, Anabel; Chi-Poot, Angel; Uc-Cetina, Victor
2016-01-01
Augmented reality (AR) is one of the emerging technologies that has demonstrated to be an efficient technological tool to enhance learning techniques. In this paper, we describe the development and evaluation of an AR system for teaching Euclidean vectors in physics and mathematics. The goal of this pedagogical tool is to facilitate user's…
Evaluation of systemic insecticides mixed in rodenticide baits for plague vector control
DEFF Research Database (Denmark)
Larsen, Kim Søholt; Lodal, Jens
1997-01-01
Rodenticide baits containing systemic insecticides were evaluated in the laboratory for their palatability to the house rat Rattus rattus and for their toxicity against the oriental rat flea Xenopsylla cheopis - both animals are important Vectors of plague in Africa. The test bait and a non...
A classification system for one Killing vector solutions of Einstein's equations
International Nuclear Information System (INIS)
Hoenselaers, C.
1978-01-01
A double classification system for one Killing vector solutions in terms of the eigenvectors and eigenvalues of the Ricci and Bach tensor of the associated three manifold is proposed. The calculations of the Bach tensor are carried out for special cases. (author)
Directory of Open Access Journals (Sweden)
Masato Ohtsuka
2009-01-01
Full Text Available We developed Cre-loxP-based systems, termed CRTEIL and CETRIZ, which allow gene switching in a noninvasive manner. Single transfection with pCRTEIL resulted in predominant expression of red fluorescence. Cotransfection with pCRTEIL and Cre-expression plasmid (pCAG/NCre caused switching from red to green fluorescence. Similarly, cotransfection with pCETRIZ and pCAG/NCre resulted in change of green to red fluorescence. These noninvasive systems will be useful in cell lineage analysis, since descendants of cells exhibiting newly activated gene expression can be continuously monitored in noninvasive fashion.
Modeling and control of PEMFC based on least squares support vector machines
International Nuclear Information System (INIS)
Li Xi; Cao Guangyi; Zhu Xinjian
2006-01-01
The proton exchange membrane fuel cell (PEMFC) is one of the most important power supplies. The operating temperature of the stack is an important controlled variable, which impacts the performance of the PEMFC. In order to improve the generating performance of the PEMFC, prolong its life and guarantee safety, credibility and low cost of the PEMFC system, it must be controlled efficiently. A nonlinear predictive control algorithm based on a least squares support vector machine (LS-SVM) model is presented for a family of complex systems with severe nonlinearity, such as the PEMFC, in this paper. The nonlinear off line model of the PEMFC is built by a LS-SVM model with radial basis function (RBF) kernel so as to implement nonlinear predictive control of the plant. During PEMFC operation, the off line model is linearized at each sampling instant, and the generalized predictive control (GPC) algorithm is applied to the predictive control of the plant. Experimental results demonstrate the effectiveness and advantages of this approach
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.
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.
Novel scanning procedure enabling the vectorization of entire rhizotron-grown root systems
Directory of Open Access Journals (Sweden)
Lobet Guillaume
2013-01-01
Full Text Available Abstract This paper presents an original spit-and-combine imaging procedure that enables the complete vectorization of complex root systems grown in rhizotrons. The general principle of the method is to (1 separate the root system into a small number of large pieces to reduce root overlap, (2 scan these pieces one by one, (3 analyze separate images with a root tracing software and (4 combine all tracings into a single vectorized root system. This method generates a rich dataset containing morphological, topological and geometrical information of entire root systems grown in rhizotrons. The utility of the method is illustrated with a detailed architectural analysis of a 20-day old maize root system, coupled with a spatial analysis of water uptake patterns.
Novel scanning procedure enabling the vectorization of entire rhizotron-grown root systems.
Lobet, Guillaume; Draye, Xavier
2013-01-04
: This paper presents an original spit-and-combine imaging procedure that enables the complete vectorization of complex root systems grown in rhizotrons. The general principle of the method is to (1) separate the root system into a small number of large pieces to reduce root overlap, (2) scan these pieces one by one, (3) analyze separate images with a root tracing software and (4) combine all tracings into a single vectorized root system. This method generates a rich dataset containing morphological, topological and geometrical information of entire root systems grown in rhizotrons. The utility of the method is illustrated with a detailed architectural analysis of a 20-day old maize root system, coupled with a spatial analysis of water uptake patterns.
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.
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.
BoHV-4-based vector delivering Ebola virus surface glycoprotein
Directory of Open Access Journals (Sweden)
Alfonso Rosamilia
2016-11-01
Full Text Available Abstract Background Ebola virus (EBOV is a Category A pathogen that is a member of Filoviridae family that causes hemorrhagic fever in humans and non-human primates. Unpredictable and devastating outbreaks of disease have recently occurred in Africa and current immunoprophylaxis and therapies are limited. The main limitation of working with pathogens like EBOV is the need for costly containment. To potentiate further and wider opportunity for EBOV prophylactics and therapies development, innovative approaches are necessary. Methods In the present study, an antigen delivery platform based on a recombinant bovine herpesvirus 4 (BoHV-4, delivering a synthetic EBOV glycoprotein (GP gene sequence, BoHV-4-syEBOVgD106ΔTK, was generated. Results EBOV GP was abundantly expressed by BoHV-4-syEBOVgD106ΔTK transduced cells without decreasing viral replication. BoHV-4-syEBOVgD106ΔTK immunized goats produced high titers of anti-EBOV GP antibodies and conferred a long lasting (up to 6 months, detectable antibody response. Furthermore, no evidence of BoHV-4-syEBOVgD106ΔTK viremia and secondary localization was detected in any of the immunized animals. Conclusions The BoHV-4-based vector approach described here, represents: an alternative antigen delivery system for vaccination and a proof of principle study for anti-EBOV antibodies generation in goats for potential immunotherapy applications.
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.
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.
Smaili, Fatima Z.; Gao, Xin; Hoehndorf, Robert
2018-01-01
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.
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.
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.
Medical image compression based on vector quantization with variable block sizes in wavelet domain.
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.
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.
a Method for the Seamlines Network Automatic Selection Based on Building Vector
Li, P.; Dong, Y.; Hu, Y.; Li, X.; Tan, P.
2018-04-01
In order to improve the efficiency of large scale orthophoto production of city, this paper presents a method for automatic selection of seamlines network in large scale orthophoto based on the buildings' vector. Firstly, a simple model of the building is built by combining building's vector, height and DEM, and the imaging area of the building on single DOM is obtained. Then, the initial Voronoi network of the measurement area is automatically generated based on the positions of the bottom of all images. Finally, the final seamlines network is obtained by optimizing all nodes and seamlines in the network automatically based on the imaging areas of the buildings. The experimental results show that the proposed method can not only get around the building seamlines network quickly, but also remain the Voronoi network' characteristics of projection distortion minimum theory, which can solve the problem of automatic selection of orthophoto seamlines network in image mosaicking effectively.
Short term prediction of the horizontal wind vector within a wake vortex warning system
Energy Technology Data Exchange (ETDEWEB)
Frech, M.; Holzaepfel, F.; Gerz, T. [DLR Deutsches Zentrum fuer Luft- und Raumfahrt e.V., Wessling (Germany). Inst. fuer Physik der Atmosphaere; Konopka, J. [Deutsche Flugsicherung (DFS) GmbH, Langen (Germany)
2000-07-14
A wake vortex warning system (WVWS) has been developed for Frankfurt airport. This airport has two parallel runways which are separated by 518 m, a distance too short to operate them independently because wake vortices may be advected to the adjacent runway. The objective of the WVWS is to enable operation with reduced separation between two aircraft approaching the parallel runways at appropriate wind conditions. The WVWS applies a statistical persistence model to predict the crosswind within a 20 minute period. One of the main problems identified in the old WVWS are discontinuities between successive forecasts. These forecast breakdowns were not acceptable to airtraffic controllers. At least part of the problem was related to the fact that the forecast was solely based on the prediction of crosswind. A new method is developed on the basis of 523 days of sonic anemometer measurements at Frankfurt airport. It is demonstrated that the prediction of the horizontal wind vector avoids these difficulties and significantly improves the system's performance. (orig.)
Fagone, Paolo; Wright, J. Fraser; Nathwani, Amit C.; Nienhuis, Arthur W.; Davidoff, Andrew M.
2012-01-01
Abstract Self-complementary AAV (scAAV) vector genomes contain a covalently closed hairpin derived from a mutated inverted terminal repeat that connects the two monomer single-stranded genomes into a head-to-head or tail-to-tail dimer. We found that during quantitative PCR (qPCR) this structure inhibits the amplification of proximal amplicons and causes the systemic underreporting of copy number by as much as 10-fold. We show that cleavage of scAAV vector genomes with restriction endonuclease to liberate amplicons from the covalently closed terminal hairpin restores quantitative amplification, and we implement this procedure in a simple, modified qPCR titration method for scAAV vectors. In addition, we developed and present an AAV genome titration procedure based on gel electrophoresis that requires minimal sample processing and has low interassay variability, and as such is well suited for the rigorous quality control demands of clinical vector production facilities. PMID:22428975
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.
Predictions of malaria vector distribution in Belize based on multispectral satellite data.
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.
A Fault Alarm and Diagnosis Method Based on Sensitive Parameters and Support Vector Machine
Zhang, Jinjie; Yao, Ziyun; Lv, Zhiquan; Zhu, Qunxiong; Xu, Fengtian; Jiang, Zhinong
2015-08-01
Study on the extraction of fault feature and the diagnostic technique of reciprocating compressor is one of the hot research topics in the field of reciprocating machinery fault diagnosis at present. A large number of feature extraction and classification methods have been widely applied in the related research, but the practical fault alarm and the accuracy of diagnosis have not been effectively improved. Developing feature extraction and classification methods to meet the requirements of typical fault alarm and automatic diagnosis in practical engineering is urgent task. The typical mechanical faults of reciprocating compressor are presented in the paper, and the existing data of online monitoring system is used to extract fault feature parameters within 15 types in total; the inner sensitive connection between faults and the feature parameters has been made clear by using the distance evaluation technique, also sensitive characteristic parameters of different faults have been obtained. On this basis, a method based on fault feature parameters and support vector machine (SVM) is developed, which will be applied to practical fault diagnosis. A better ability of early fault warning has been proved by the experiment and the practical fault cases. Automatic classification by using the SVM to the data of fault alarm has obtained better diagnostic accuracy.
A replicating plasmid-based vector for GFP expression in Mycoplasma hyopneumoniae.
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.
Advanced signal processing based on support vector regression for lidar applications
Gelfusa, M.; Murari, A.; Malizia, A.; Lungaroni, M.; Peluso, E.; Parracino, S.; Talebzadeh, S.; Vega, J.; Gaudio, P.
2015-10-01
The LIDAR technique has recently found many applications in atmospheric physics and remote sensing. One of the main issues, in the deployment of systems based on LIDAR, is the filtering of the backscattered signal to alleviate the problems generated by noise. Improvement in the signal to noise ratio is typically achieved by averaging a quite large number (of the order of hundreds) of successive laser pulses. This approach can be effective but presents significant limitations. First of all, it implies a great stress on the laser source, particularly in the case of systems for automatic monitoring of large areas for long periods. Secondly, this solution can become difficult to implement in applications characterised by rapid variations of the atmosphere, for example in the case of pollutant emissions, or by abrupt changes in the noise. In this contribution, a new method for the software filtering and denoising of LIDAR signals is presented. The technique is based on support vector regression. The proposed new method is insensitive to the statistics of the noise and is therefore fully general and quite robust. The developed numerical tool has been systematically compared with the most powerful techniques available, using both synthetic and experimental data. Its performances have been tested for various statistical distributions of the noise and also for other disturbances of the acquired signal such as outliers. The competitive advantages of the proposed method are fully documented. The potential of the proposed approach to widen the capability of the LIDAR technique, particularly in the detection of widespread smoke, is discussed in detail.
ROBUSTNESS OF A FACE-RECOGNITION TECHNIQUE BASED ON SUPPORT VECTOR MACHINES
Prashanth Harshangi; Koshy George
2010-01-01
The ever-increasing requirements of security concerns have placed a greater demand for face recognition surveillance systems. However, most current face recognition techniques are not quite robust with respect to factors such as variable illumination, facial expression and detail, and noise in images. In this paper, we demonstrate that face recognition using support vector machines are sufficiently robust to different kinds of noise, does not require image pre-processing, and can be used with...
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.
A Novel Approach to Asynchronous MVP Data Interpretation Based on Elliptical-Vectors
Kruglyakov, M.; Trofimov, I.; Korotaev, S.; Shneyer, V.; Popova, I.; Orekhova, D.; Scshors, Y.; Zhdanov, M. S.
2014-12-01
We suggest a novel approach to asynchronous magnetic-variation profiling (MVP) data interpretation. Standard method in MVP is based on the interpretation of the coefficients of linear relation between vertical and horizontal components of the measured magnetic field.From mathematical point of view this pair of linear coefficients is not a vector which leads to significant difficulties in asynchronous data interpretation. Our approach allows us to actually treat such a pair of complex numbers as a special vector called an ellipse-vector (EV). By choosing the particular definitions of complex length and direction, the basic relation of MVP can be considered as the dot product. This considerably simplifies the interpretation of asynchronous data. The EV is described by four real numbers: the values of major and minor semiaxes, the angular direction of the major semiaxis and the phase. The notation choice is motivated by historical reasons. It is important that different EV's components have different sensitivity with respect to the field sources and the local heterogeneities. Namely, the value of major semiaxis and the angular direction are mostly determined by the field source and the normal cross-section. On the other hand, the value of minor semiaxis and the phase are responsive to local heterogeneities. Since the EV is the general form of complex vector, the traditional Schmucker vectors can be explicitly expressed through its components.The proposed approach was successfully applied to interpretation the results of asynchronous measurements that had been obtained in the Arctic Ocean at the drift stations "North Pole" in 1962-1976.
Robustness-Based Simplification of 2D Steady and Unsteady Vector Fields.
Skraba, Primoz; Bei Wang; Guoning Chen; Rosen, Paul
2015-08-01
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. 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.
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.
Robustness-Based Simplification of 2D Steady and Unsteady Vector Fields
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.
Robustness-Based Simplification of 2D Steady and Unsteady Vector Fields
Skraba, Primoz; Wang, Bei; Chen, Guoning; Rosen, Paul
2015-01-01
© 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.
A simple vector system to improve performance and utilisation of recombinant antibodies
Directory of Open Access Journals (Sweden)
Vincent Karen J
2006-12-01
Full Text Available Abstract Background Isolation of recombinant antibody fragments from antibody libraries is well established using technologies such as phage display. Phage display vectors are ideal for efficient display of antibody fragments on the surface of bacteriophage particles. However, they are often inefficient for expression of soluble antibody fragments, and sub-cloning of selected antibody populations into dedicated soluble antibody fragment expression vectors can enhance expression. Results We have developed a simple vector system for expression, dimerisation and detection of recombinant antibody fragments in the form of single chain Fvs (scFvs. Expression is driven by the T7 RNA polymerase promoter in conjunction with the inducible lysogen strain BL21 (DE3. The system is compatible with a simple auto-induction culture system for scFv production. As an alternative to periplasmic expression, expression directly in the cytoplasm of a mutant strain with a more oxidising cytoplasmic environment (Origami 2™ (DE3 was investigated and found to be inferior to periplasmic expression in BL21 (DE3 cells. The effect on yield and binding activity of fusing scFvs to the N terminus of maltose binding protein (a solubility enhancing partner, bacterial alkaline phosphatase (a naturally dimeric enzymatic reporter molecule, or the addition of a free C-terminal cysteine was determined. Fusion of scFvs to the N-terminus of maltose binding protein increased scFv yield but binding activity of the scFv was compromised. In contrast, fusion to the N-terminus of bacterial alkaline phosphatase led to an improved performance. Alkaline phosphatase provides a convenient tag allowing direct enzymatic detection of scFv fusions within crude extracts without the need for secondary reagents. Alkaline phosphatase also drives dimerisation of the scFv leading to an improvement in performance compared to monovalent constructs. This is illustrated by ELISA, western blot and
Huang, L.; Ai, Tinghua; van Oosterom, P.J.M.; Yan, Xiongfeng; Yang, Min
2017-01-01
The representation of vector data at variable scales has been widely applied in geographic information systems and map-based services. When the scale changes across a wide range, a complex generalization that involves multiple operations is required to transform the data. To present such complex
A virus vector based on Canine Herpesvirus for vaccine applications in canids.
Strive, T; Hardy, C M; Wright, J; Reubel, G H
2007-01-31
Canine Herpesvirus (CHV) is being developed as a virus vector for the vaccination of European red foxes. However, initial studies using recombinant CHV vaccines in foxes revealed viral attenuation and lack of antibody response to inserted foreign antigens. These findings were attributed both to inactivation of the thymidine kinase (TK) gene and excess foreign genetic material in the recombinant viral genome. In this study, we report an improved CHV-bacterial artificial chromosome (BAC) vector system designed to overcome attenuation in foxes. A non-essential region was identified in the CHV genome as an alternative insertion site for foreign genes. Replacement of a guanine/cytosine (GC)-rich intergenic region between UL21 and UL22 of CHV with a marker gene did not change growth behaviour in vitro, showing that this region is not essential for virus growth in cell culture. We subsequently produced a CHV-BAC vector with an intact TK gene in which the bacterial genes and the antigen expression cassette were inserted into this GC-rich locus. Unlike earlier constructs, the new CHV-BAC allowed self-excision of the bacterial genes via homologous recombination after transfection of BACs into cell culture. The BAC-CHV system was used to produce a recombinant virus that constitutively expressed porcine zona pellucida subunit C protein between the UL21 and UL22 genes of CHV. Complete self-excision of the bacterial genes from CHV was achieved within one round of replication whilst retaining antigen gene expression.
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.
Support vector regression model based predictive control of water level of U-tube steam generators
Energy Technology Data Exchange (ETDEWEB)
Kavaklioglu, Kadir, E-mail: kadir.kavaklioglu@pau.edu.tr
2014-10-15
Highlights: • Water level of U-tube steam generators was controlled in a model predictive fashion. • Models for steam generator water level were built using support vector regression. • Cost function minimization for future optimal controls was performed by using the steepest descent method. • The results indicated the feasibility of the proposed method. - Abstract: A predictive control algorithm using support vector regression based models was proposed for controlling the water level of U-tube steam generators of pressurized water reactors. Steam generator data were obtained using a transfer function model of U-tube steam generators. Support vector regression based models were built using a time series type model structure for five different operating powers. Feedwater flow controls were calculated by minimizing a cost function that includes the level error, the feedwater change and the mismatch between feedwater and steam flow rates. Proposed algorithm was applied for a scenario consisting of a level setpoint change and a steam flow disturbance. The results showed that steam generator level can be controlled at all powers effectively by the proposed method.
Optical threshold secret sharing scheme based on basic vector operations and coherence superposition
Deng, Xiaopeng; Wen, Wei; Mi, Xianwu; Long, Xuewen
2015-04-01
We propose, to our knowledge for the first time, a simple optical algorithm for secret image sharing with the (2,n) threshold scheme based on basic vector operations and coherence superposition. The secret image to be shared is firstly divided into n shadow images by use of basic vector operations. In the reconstruction stage, the secret image can be retrieved by recording the intensity of the coherence superposition of any two shadow images. Compared with the published encryption techniques which focus narrowly on information encryption, the proposed method can realize information encryption as well as secret sharing, which further ensures the safety and integrality of the secret information and prevents power from being kept centralized and abused. The feasibility and effectiveness of the proposed method are demonstrated by numerical results.
Space vector-based modeling and control of a modular multilevel converter in HVDC applications
DEFF Research Database (Denmark)
Bonavoglia, M.; Casadei, G.; Zarri, L.
2013-01-01
Modular multilevel converter (MMC) is an emerging multilevel topology for high-voltage applications that has been developed in recent years. In this paper, the modeling and the control of MMCs are restated in terms of space vectors, which may allow a deeper understanding of the converter behavior....... As a result, a control scheme for three-phase MMCs based on the previous theoretical analysis is presented. Numerical simulations are used to test its feasibility.......Modular multilevel converter (MMC) is an emerging multilevel topology for high-voltage applications that has been developed in recent years. In this paper, the modeling and the control of MMCs are restated in terms of space vectors, which may allow a deeper understanding of the converter behavior...
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
Díaz-Rodríguez, P; Rey-Rico, A; Madry, H; Landin, M; Cucchiarini, M
2015-12-30
Viral vectors are common tools in gene therapy to deliver foreign therapeutic sequences in a specific target population via their natural cellular entry mechanisms. Incorporating such vectors in implantable systems may provide strong alternatives to conventional gene transfer procedures. The goal of the present study was to generate different hydrogel structures based on alginate (AlgPH155) and poloxamer PF127 as new systems to encapsulate and release recombinant adeno-associated viral (rAAV) vectors. Inclusion of rAAV in such polymeric capsules revealed an influence of the hydrogel composition and crosslinking temperature upon the vector release profiles, with alginate (AlgPH155) structures showing the fastest release profiles early on while over time vector release was more effective from AlgPH155+PF127 [H] capsules crosslinked at a high temperature (50°C). Systems prepared at room temperature (AlgPH155+PF127 [C]) allowed instead to achieve a more controlled release profile. When tested for their ability to target human mesenchymal stem cells, the different systems led to high transduction efficiencies over time and to gene expression levels in the range of those achieved upon direct vector application, especially when using AlgPH155+PF127 [H]. No detrimental effects were reported on either cell viability or on the potential for chondrogenic differentiation. Inclusion of PF127 in the capsules was also capable of delaying undesirable hypertrophic cell differentiation. These findings are of promising value for the further development of viral vector controlled release strategies. Copyright © 2015 Elsevier B.V. All rights reserved.
International Nuclear Information System (INIS)
Hao Lihua; Chen Qian; Xiao Xilong
2006-01-01
Olaquindox, a quinoxaline 1,4-dioxide derivative from quindoxin, is widely used as an animal growth promoter in China. We tested olaquindox as a mutagen in a SV40-based shuttle vector pSP189 and African green kidney cell (Vero E6 cell line) system to define the safety of olaquindox as a food-additive for animals. When applied at 6.6 μg/ml, olaquindox caused 12 times higher mutation frequency in comparison to untreated controls. More than 70% of base substitutions happened at G:C base pairs featuring G:C to T:A or G:C to A:T conversions. Frequency of point mutations for in vitro modified plasmids was also dramatically increased from the spontaneous background level. Olaquindox-induced mutations did not occur randomly along the supF shuttle vector, but instead, had a hot spot at base pair no. 155 which accounts for 37% of total mutations. Olaquindox-induced mutations also showed sequence-specificity in which most point mutations occurred at site N in a 5'-NNTTNN-3' sequence while most tandem bases deletion and rearrangement were seen at the 5'-ANGGCCNAAA-3' sequence. We conclude that olaquindox induces DNA mutation, therefore, should not be used as an additive to promote animal growth
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.
Directory of Open Access Journals (Sweden)
Shi-Xiong Zhang
2014-02-01
Full Text Available Permanent Magnet Synchronous motor (PMSM has received widespread acceptance in recent years. In this paper, a new rotor and stator Magnetic Fields Direct-Orthogonalized Vector Control (MFDOVC scheme is proposed for PMSM servo system. This method simplified the complex calculation of traditional vector control, a part of the system resource is economized. At the same time, through the simulation illustration validation, the performance of PMSM servo system with the proposed MFDOVC scheme can achieve the same with the complex traditional vector control method, but much simpler calculation is implemented using the proposed method.
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
2018-04-30
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
Gain in computational efficiency by vectorization in the dynamic simulation of multi-body systems
Amirouche, F. M. L.; Shareef, N. H.
1991-01-01
An improved technique for the identification and extraction of the exact quantities associated with the degrees of freedom at the element as well as the flexible body level is presented. It is implemented in the dynamic equations of motions based on the recursive formulation of Kane et al. (1987) and presented in a matrix form, integrating the concepts of strain energy, the finite-element approach, modal analysis, and reduction of equations. This technique eliminates the CPU intensive matrix multiplication operations in the code's hot spots for the dynamic simulation of the interconnected rigid and flexible bodies. A study of a simple robot with flexible links is presented by comparing the execution times on a scalar machine and a vector-processor with and without vector options. Performance figures demonstrating the substantial gains achieved by the technique are plotted.
Hierarchical Modulation with Vector Rotation for E-MBMS Transmission in LTE Systems
Directory of Open Access Journals (Sweden)
Hui Zhao
2010-01-01
Full Text Available Enhanced Multimedia Broadcast and Multicast Service (E-MBMS is considered of key importance for the proliferation of Long-Term Evolution (LTE network in mobile market. Hierarchical modulation (HM, which involves a “base-layer” (BL and an “enhancement-layer” (EL bit streams, is a simple technique for achieving tradeoff between service quality and radio coverage. Therefore, it is appealing for MBMS. Generally, HM suffers from the severe performance degradation of the less protected EL stream. In this paper, HM with vector rotation operation introduced to EL stream is proposed, in order to improve EL's performance. With the proper interleaving in frequency domain, this operation can exploit the inherent diversity gain from the multipath channel. In this way, HM with vector rotation can effectively enhance multimedia broadcasting on quality video and coverage. The simulation results with scalable video coding (SVC as source show the significant benefits in comparison with the conventional HM and alternative schemes.
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%.
Immunogenicity of ORFV-based vectors expressing the rabies virus glycoprotein in livestock species.
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.
Registration of Urban Aerial Image and LiDAR Based on Line Vectors
Directory of Open Access Journals (Sweden)
Qinghong Sheng
2017-09-01
Full Text Available In a traditional registration of a single aerial image with airborne light detection and ranging (LiDAR data using linear features that regard line direction as a control or linear features as constraints in the solution, lacking the constraint of linear position leads to the error propagation of the adjustment model. To solve this problem, this paper presents a line vector-based registration mode (LVR in which image rays and LiDAR lines are expressed by a line vector that integrates the line direction and the line position. A registration equation of line vector is set up by coplanar imaging rays and corresponding control lines. Three types of datasets consisting of synthetic, theInternational Society for Photogrammetry and Remote Sensing (ISPRS test project, and real aerial data are used. A group of progressive experiments is undertaken to evaluate the robustness of the LVR. Experimental results demonstrate that the integrated line direction and the line position contributes a great deal to the theoretical and real accuracies of the unknowns, as well as the stability of the adjustment model. This paper provides a new suggestion that, for a single image and LiDAR data, registration in urban areas can be accomplished by accommodating rich line features.
Towards a resource-based habitat approach for spatial modelling of vector-borne disease risks.
Hartemink, Nienke; Vanwambeke, Sophie O; Purse, Bethan V; Gilbert, Marius; Van Dyck, Hans
2015-11-01
Given the veterinary and public health impact of vector-borne diseases, there is a clear need to assess the suitability of landscapes for the emergence and spread of these diseases. Current approaches for predicting disease risks neglect key features of the landscape as components of the functional habitat of vectors or hosts, and hence of the pathogen. Empirical-statistical methods do not explicitly incorporate biological mechanisms, whereas current mechanistic models are rarely spatially explicit; both methods ignore the way animals use the landscape (i.e. movement ecology). We argue that applying a functional concept for habitat, i.e. the resource-based habitat concept (RBHC), can solve these issues. The RBHC offers a framework to identify systematically the different ecological resources that are necessary for the completion of the transmission cycle and to relate these resources to (combinations of) landscape features and other environmental factors. The potential of the RBHC as a framework for identifying suitable habitats for vector-borne pathogens is explored and illustrated with the case of bluetongue virus, a midge-transmitted virus affecting ruminants. The concept facilitates the study of functional habitats of the interacting species (vectors as well as hosts) and provides new insight into spatial and temporal variation in transmission opportunities and exposure that ultimately determine disease risks. It may help to identify knowledge gaps and control options arising from changes in the spatial configuration of key resources across the landscape. The RBHC framework may act as a bridge between existing mechanistic and statistical modelling approaches. © 2014 The Authors. Biological Reviews published by John Wiley & Sons Ltd on behalf of Cambridge Philosophical Society.
Gagoski, Dejan; Mureev, Sergey; Giles, Nichole; Johnston, Wayne; Dahmer-Heath, Mareike; Škalamera, Dubravka; Gonda, Thomas J; Alexandrov, Kirill
2015-02-10
Although numerous techniques for protein expression and production are available the pace of genome sequencing outstrips our ability to analyze the encoded proteins. To address this bottleneck, we have established a system for parallelized cloning, DNA production and cell-free expression of large numbers of proteins. This system is based on a suite of pCellFree Gateway destination vectors that utilize a Species Independent Translation Initiation Sequence (SITS) that mediates recombinant protein expression in any in vitro translation system. These vectors introduce C or N terminal EGFP and mCherry fluorescent and affinity tags, enabling direct analysis and purification of the expressed proteins. To maximize throughput and minimize the cost of protein production we combined Gateway cloning with Rolling Circle DNA Amplification. We demonstrate that as little as 0.1 ng of plasmid DNA is sufficient for template amplification and production of recombinant human protein in Leishmania tarentolae and Escherichia coli cell-free expression systems. Our experiments indicate that this approach can be applied to large gene libraries as it can be reliably performed in multi-well plates. The resulting protein expression pipeline provides a valuable new tool for applications of the post genomic era. Copyright © 2014 Elsevier B.V. All rights reserved.
Mining protein function from text using term-based support vector machines
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
Lee, Byungjin; Lee, Young Jae; Sung, Sangkyung
2018-05-01
A novel attitude determination method is investigated that is computationally efficient and implementable in low cost sensor and embedded platform. Recent result on attitude reference system design is adapted to further develop a three-dimensional attitude determination algorithm through the relative velocity incremental measurements. For this, velocity incremental vectors, computed respectively from INS and GPS with different update rate, are compared to generate filter measurement for attitude estimation. In the quaternion-based Kalman filter configuration, an Euler-like attitude perturbation angle is uniquely introduced for reducing filter states and simplifying propagation processes. Furthermore, assuming a small angle approximation between attitude update periods, it is shown that the reduced order filter greatly simplifies the propagation processes. For performance verification, both simulation and experimental studies are completed. A low cost MEMS IMU and GPS receiver are employed for system integration, and comparison with the true trajectory or a high-grade navigation system demonstrates the performance of the proposed algorithm.
International Nuclear Information System (INIS)
Chang, H.; Weinberg, W.H.
1977-01-01
A generalized expression is developed that relates the ''reaction product vector'', epsilon exp(-iphi), to the kinetic parameters of a linear system. The formalism is appropriate for the analysis of modulated molecular beam mass spectrometry data and facilitates the correlation of experimental results to (proposed) linear models. A study of stability criteria appropriate for modulated molecular beam mass spectrometry experiments is also presented. This investigation has led to interesting inherent limitations which have not heretofore been emphasized, as well as a delineation of the conditions under which stable chemical oscillations may occur in the reacting system
Design development of the Apollo command and service module thrust vector attitude control systems
Peters, W. H.
1978-01-01
Development of the Apollo thrust vector control digital autopilot (TVC DAP) was summarized. This is the control system that provided pitch and yaw attitude control during velocity change maneuvers using the main rocket engine on the Apollo service module. A list of ten primary functional requirements for this control system are presented, each being subordinate to a more general requirement appearing earlier on the list. Development process functions were then identified and the essential information flow paths were explored. This provided some visibility into the particular NASA/contractor interface, as well as relationships between the many individual activities.
Abiraj, Keelara; Jaccard, Hugues; Kretzschmar, Martin; Helm, Lothar; Maecke, Helmut R
2008-07-28
Dimeric peptidic vectors, obtained by the divalent grafting of bombesin analogues on a newly synthesized DOTA-based prochelator, showed improved qualities as tumor targeted imaging probes in comparison to their monomeric analogues.
Detection of ferromagnetic target based on mobile magnetic gradient tensor system
Energy Technology Data Exchange (ETDEWEB)
Gang, Y.I.N., E-mail: gang.gang88@163.com; Yingtang, Zhang; Zhining, Li; Hongbo, Fan; Guoquan, Ren
2016-03-15
Attitude change of mobile magnetic gradient tensor system critically affects the precision of gradient measurements, thereby increasing ambiguity in target detection. This paper presents a rotational invariant-based method for locating and identifying ferromagnetic targets. Firstly, unit magnetic moment vector was derived based on the geometrical invariant, such that the intermediate eigenvector of the magnetic gradient tensor is perpendicular to the magnetic moment vector and the source–sensor displacement vector. Secondly, unit source–sensor displacement vector was derived based on the characteristic that the angle between magnetic moment vector and source–sensor displacement is a rotational invariant. By introducing a displacement vector between two measurement points, the magnetic moment vector and the source–sensor displacement vector were theoretically derived. To resolve the problem of measurement noises existing in the realistic detection applications, linear equations were formulated using invariants corresponding to several distinct measurement points and least square solution of magnetic moment vector and source–sensor displacement vector were obtained. Results of simulation and principal verification experiment showed the correctness of the analytical method, along with the practicability of the least square method. - Highlights: • Ferromagnetic target detection method is proposed based on rotational invariants • Intermediate eigenvector is perpendicular to magnetic moment and displacement vector • Angle between magnetic moment and displacement vector is a rotational invariant • Magnetic moment and displacement vector are derived based on invariants of two points.
Two Hop Adaptive Vector Based Quality Forwarding for Void Hole Avoidance in Underwater WSNs.
Javaid, Nadeem; Ahmed, Farwa; Wadud, Zahid; Alrajeh, Nabil; Alabed, Mohamad Souheil; Ilahi, Manzoor
2017-08-01
Underwater wireless sensor networks (UWSNs) facilitate a wide range of aquatic applications in various domains. However, the harsh underwater environment poses challenges like low bandwidth, long propagation delay, high bit error rate, high deployment cost, irregular topological structure, etc. Node mobility and the uneven distribution of sensor nodes create void holes in UWSNs. Void hole creation has become a critical issue in UWSNs, as it severely affects the network performance. Avoiding void hole creation benefits better coverage over an area, less energy consumption in the network and high throughput. For this purpose, minimization of void hole probability particularly in local sparse regions is focused on in this paper. The two-hop adaptive hop by hop vector-based forwarding (2hop-AHH-VBF) protocol aims to avoid the void hole with the help of two-hop neighbor node information. The other protocol, quality forwarding adaptive hop by hop vector-based forwarding (QF-AHH-VBF), selects an optimal forwarder based on the composite priority function. QF-AHH-VBF improves network good-put because of optimal forwarder selection. QF-AHH-VBF aims to reduce void hole probability by optimally selecting next hop forwarders. To attain better network performance, mathematical problem formulation based on linear programming is performed. Simulation results show that by opting these mechanisms, significant reduction in end-to-end delay and better throughput are achieved in the network.
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%.
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.
A multi-label learning based kernel automatic recommendation method for support vector machine.
Zhang, Xueying; Song, Qinbao
2015-01-01
Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance.
Multi-disease data management system platform for vector-borne diseases.
Directory of Open Access Journals (Sweden)
Lars Eisen
2011-03-01
Full Text Available Emerging information technologies present new opportunities to reduce the burden of malaria, dengue and other infectious diseases. For example, use of a data management system software package can help disease control programs to better manage and analyze their data, and thus enhances their ability to carry out continuous surveillance, monitor interventions and evaluate control program performance.We describe a novel multi-disease data management system platform (hereinafter referred to as the system with current capacity for dengue and malaria that supports data entry, storage and query. It also allows for production of maps and both standardized and customized reports. The system is comprised exclusively of software components that can be distributed without the user incurring licensing costs. It was designed to maximize the ability of the user to adapt the system to local conditions without involvement of software developers. Key points of system adaptability include 1 customizable functionality content by disease, 2 configurable roles and permissions, 3 customizable user interfaces and display labels and 4 configurable information trees including a geographical entity tree and a term tree. The system includes significant portions of functionality that is entirely or in large part re-used across diseases, which provides an economy of scope as new diseases downstream are added to the system at decreased cost.We have developed a system with great potential for aiding disease control programs in their task to reduce the burden of dengue and malaria, including the implementation of integrated vector management programs. Next steps include evaluations of operational implementations of the current system with capacity for dengue and malaria, and the inclusion in the system platform of other important vector-borne diseases.
Vector rectangular-shape laser based on reduced graphene oxide interacting with a long fiber taper.
Gao, Lei; Zhu, Tao; Huang, Wei; Zeng, Jing
2014-10-01
A vector dual-wavelength rectangular-shape laser (RSL) based on a long fiber taper deposited with reduced graphene oxide is proposed, where nonlinearity is enhanced due to a large evanescent-field-interacting length and strong field confinement of an 8 mm fiber taper with a waist diameter of 4 μm. Graphene flakes are deposited uniformly on the taper waist with light pressure effect, so this structure guarantees both excellent saturable absorption and high nonlinearity. The RSL with a repetition rate of 7.9 MHz shows fast polarization switching in two orthogonal polarization directions, and temporal and spectral characteristics are investigated.
Prediction on sunspot activity based on fuzzy information granulation and support vector machine
Peng, Lingling; Yan, Haisheng; Yang, Zhigang
2018-04-01
In order to analyze the range of sunspots, a combined prediction method of forecasting the fluctuation range of sunspots based on fuzzy information granulation (FIG) and support vector machine (SVM) was put forward. Firstly, employing the FIG to granulate sample data and extract va)alid information of each window, namely the minimum value, the general average value and the maximum value of each window. Secondly, forecasting model is built respectively with SVM and then cross method is used to optimize these parameters. Finally, the fluctuation range of sunspots is forecasted with the optimized SVM model. Case study demonstrates that the model have high accuracy and can effectively predict the fluctuation of sunspots.
A Shellcode Detection Method Based on Full Native API Sequence and Support Vector Machine
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.
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.
A static investigation of the thrust vectoring system of the F/A-18 high-alpha research vehicle
Mason, Mary L.; Capone, Francis J.; Asbury, Scott C.
1992-01-01
A static (wind-off) test was conducted in the static test facility of the Langley 16-foot Transonic Tunnel to evaluate the vectoring capability and isolated nozzle performance of the proposed thrust vectoring system of the F/A-18 high alpha research vehicle (HARV). The thrust vectoring system consisted of three asymmetrically spaced vanes installed externally on a single test nozzle. Two nozzle configurations were tested: A maximum afterburner-power nozzle and a military-power nozzle. Vane size and vane actuation geometry were investigated, and an extensive matrix of vane deflection angles was tested. The nozzle pressure ratios ranged from two to six. The results indicate that the three vane system can successfully generate multiaxis (pitch and yaw) thrust vectoring. However, large resultant vector angles incurred large thrust losses. Resultant vector angles were always lower than the vane deflection angles. The maximum thrust vectoring angles achieved for the military-power nozzle were larger than the angles achieved for the maximum afterburner-power nozzle.
A Support Vector Machine-Based Gender Identification Using Speech Signal
Lee, Kye-Hwan; Kang, Sang-Ick; Kim, Deok-Hwan; Chang, Joon-Hyuk
We propose an effective voice-based gender identification method using a support vector machine (SVM). The SVM is a binary classification algorithm that classifies two groups by finding the voluntary nonlinear boundary in a feature space and is known to yield high classification performance. In the present work, we compare the identification performance of the SVM with that of a Gaussian mixture model (GMM)-based method using the mel frequency cepstral coefficients (MFCC). A novel approach of incorporating a features fusion scheme based on a combination of the MFCC and the fundamental frequency is proposed with the aim of improving the performance of gender identification. Experimental results demonstrate that the gender identification performance using the SVM is significantly better than that of the GMM-based scheme. Moreover, the performance is substantially improved when the proposed features fusion technique is applied.
A multi vector energy analysis for interconnected power and gas systems
International Nuclear Information System (INIS)
Devlin, Joseph; Li, Kang; Higgins, Paraic; Foley, Aoife
2017-01-01
Highlights: • The first multi vector energy system analysis for Britain and Ireland is performed. • Extreme weather driven gas demands were utilised to increase gas system stress. • GB gas system is capable of satisfying demand but restricts gas generator ramping. • Irish gas system congestion causes a 40% increase in gas generator short run cost. • Gas storage in Ireland relieved congestion reduced operational costs by 14%. - Abstract: This paper presents the first multi vector energy analysis for the interconnected energy systems of Great Britain (GB) and Ireland. Both systems share a common high penetration of wind power, but significantly different security of supply outlooks. Ireland is heavily dependent on gas imports from GB, giving significance to the interconnected aspect of the methodology in addition to the gas and power interactions analysed. A fully realistic unit commitment and economic dispatch model coupled to an energy flow model of the gas supply network is developed. Extreme weather events driving increased domestic gas demand and low wind power output were utilised to increase gas supply network stress. Decreased wind profiles had a larger impact on system security than high domestic gas demand. However, the GB energy system was resilient during high demand periods but gas network stress limited the ramping capability of localised generating units. Additionally, gas system entry node congestion in the Irish system was shown to deliver a 40% increase in short run costs for generators. Gas storage was shown to reduce the impact of high demand driven congestion delivering a reduction in total generation costs of 14% in the period studied and reducing electricity imports from GB, significantly contributing to security of supply.
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
International Nuclear Information System (INIS)
Sanghavi, Suniti; Davis, Anthony B.; Eldering, Annmarie
2014-01-01
In this paper, we build up on the scalar model smartMOM to arrive at a formalism for linearized vector radiative transfer based on the matrix operator method (vSmartMOM). Improvements have been made with respect to smartMOM in that a novel method of computing intensities for the exact viewing geometry (direct raytracing) without interpolation between quadrature points has been implemented. Also, the truncation method employed for dealing with highly peaked phase functions has been changed to a vector adaptation of Wiscombe's delta-m method. These changes enable speedier and more accurate radiative transfer computations by eliminating the need for a large number of quadrature points and coefficients for generalized spherical functions. We verify our forward model against the benchmarking results of Kokhanovsky et al. (2010) [22]. All non-zero Stokes vector elements are found to show agreement up to mostly the seventh significant digit for the Rayleigh atmosphere. Intensity computations for aerosol and cloud show an agreement of well below 0.03% and 0.05% at all viewing angles except around the solar zenith angle (60°), where most radiative models demonstrate larger variances due to the strongly forward-peaked phase function. We have for the first time linearized vector radiative transfer based on the matrix operator method with respect to aerosol optical and microphysical parameters. We demonstrate this linearization by computing Jacobian matrices for all Stokes vector elements for a multi-angular and multispectral measurement setup. We use these Jacobians to compare the aerosol information content of measurements using only the total intensity component against those using the idealized measurements of full Stokes vector [I,Q,U,V] as well as the more practical use of only [I,Q,U]. As expected, we find for the considered example that the accuracy of the retrieved parameters improves when the full Stokes vector is used. The information content for the full Stokes
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.
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.
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.
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
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.
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 ...
Directory of Open Access Journals (Sweden)
Supriyanto A.
2017-12-01
Full Text Available The purpose of this study was to determine the effectiveness of Huanglongbing vector control based on Citrus Grower Group recommendation. Studies have been carried out in 2010 in Tebas Sungai village, Sambas district, with 11 tangerine groves owned by growers in the Citrus grower Association of Sambas district. The tangerine grove that been used are, one grower's orchard as a demonstration plot in a particular citrus grower group (orchard I; five other citrus orchards with different ownership at the same citrus grower Group (orchard II, as well as five other citrus orchard with different ownership which each of them spreads over five different citrus grower groups outside the farm demonstration plots (orchard III. The recommendation technology for controlling Huanglongbing vector which applied in this experiment, included bark painting by systemic insecticide of imidacloprid for two each 1.5-month and spray using contact insecticide with dimethoate to the plant crown which application time been alternated after bark painting application. The effectiveness of technology implementation is measured by a decrease psyllid populations found in citrus samples in adult stage, nymphs and eggs that were observed at regular intervals every two weeks during the flushing to the 14th week after the first treatment. The results showed that recommended treatment technology were absolutely proven to reduce Huanglongbing vector population in significant, namely in the orchard I, II, and III respectively at 95.3%, 84.7%, and 72% for stage adult; 97.3 %, 80%, and 100% for stage nymphs; and 98.5%, 100% and 100% for the egg stage.
Directory of Open Access Journals (Sweden)
Weize Li
2018-01-01
Full Text Available Research on stealthiness has become an important topic in the field of data integrity (DI attacks. To construct stealthy DI attacks, a common assumption in most related studies is that attackers have prior model knowledge of physical systems. In this paper, such assumption is relaxed and a covert agent is proposed based on the least squares support vector regression (LSSVR. By estimating a plant model from control and sensory data, the LSSVR-based covert agent can closely imitate the behavior of the physical plant. Then, the covert agent is used to construct a covert loop, which can keep the controller’s input and output both stealthy over a finite time window. Experiments have been carried out to show the effectiveness of the proposed method.
Rorvig, Mark E.
1991-01-01
Vector-product information retrieval (IR) systems produce retrieval results superior to all other searching methods but presently have no commercial implementations beyond the personal computer environment. The NASA Electronic Library Systems (NELS) provides a ranked list of the most likely relevant objects in collections in response to a natural language query. Additionally, the system is constructed using standards and tools (Unix, X-Windows, Notif, and TCP/IP) that permit its operation in organizations that possess many different hosts, workstations, and platforms. There are no known commercial equivalents to this product at this time. The product has applications in all corporate management environments, particularly those that are information intensive, such as finance, manufacturing, biotechnology, and research and development.
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.
Abdelfattah, Ahmad; Ltaief, Hatem; Keyes, David E.; Dongarra, Jack
2016-01-01
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.
Single vector system for efficient N-myristoylation of recombinant proteins in E. coli.
Directory of Open Access Journals (Sweden)
Julian M Glück
Full Text Available BACKGROUND: N-myristoylation is a crucial covalent modification of numerous eukaryotic and viral proteins that is catalyzed by N-myristoyltransferase (NMT. Prokaryotes are lacking endogenous NMT activity. Recombinant production of N-myristoylated proteins in E. coli cells can be achieved by coexpression of heterologous NMT with the target protein. In the past, dual plasmid systems were used for this purpose. METHODOLOGY/PRINCIPAL FINDINGS: Here we describe a single vector system for efficient coexpression of substrate and enzyme suitable for production of co- or posttranslationally modified proteins. The approach was validated using the HIV-1 Nef protein as an example. A simple and efficient protocol for production of highly pure and completely N-myristoylated Nef is presented. The yield is about 20 mg myristoylated Nef per liter growth medium. CONCLUSIONS/SIGNIFICANCE: The single vector strategy allows diverse modifications of target proteins recombinantly coexpressed in E. coli with heterologous enzymes. The method is generally applicable and provides large amounts of quantitatively processed target protein that are sufficient for comprehensive biophysical and structural studies.
Generalized correlation integral vectors: A distance concept for chaotic dynamical systems
Energy Technology Data Exchange (ETDEWEB)
Haario, Heikki, E-mail: heikki.haario@lut.fi [School of Engineering Science, Lappeenranta University of Technology, Lappeenranta (Finland); Kalachev, Leonid, E-mail: KalachevL@mso.umt.edu [Department of Mathematical Sciences, University of Montana, Missoula, Montana 59812-0864 (United States); Hakkarainen, Janne [Earth Observation Unit, Finnish Meteorological Institute, Helsinki (Finland)
2015-06-15
Several concepts of fractal dimension have been developed to characterise properties of attractors of chaotic dynamical systems. Numerical approximations of them must be calculated by finite samples of simulated trajectories. In principle, the quantities should not depend on the choice of the trajectory, as long as it provides properly distributed samples of the underlying attractor. In practice, however, the trajectories are sensitive with respect to varying initial values, small changes of the model parameters, to the choice of a solver, numeric tolerances, etc. The purpose of this paper is to present a statistically sound approach to quantify this variability. We modify the concept of correlation integral to produce a vector that summarises the variability at all selected scales. The distribution of this stochastic vector can be estimated, and it provides a statistical distance concept between trajectories. Here, we demonstrate the use of the distance for the purpose of estimating model parameters of a chaotic dynamic model. The methodology is illustrated using computational examples for the Lorenz 63 and Lorenz 95 systems, together with a framework for Markov chain Monte Carlo sampling to produce posterior distributions of model parameters.
Energy Technology Data Exchange (ETDEWEB)
Campanini, Renato [Department of Physics, University of Bologna, and INFN, Bologna (Italy); Dongiovanni, Danilo [Department of Physics, University of Bologna, and INFN, Bologna (Italy); Iampieri, Emiro [Department of Physics, University of Bologna, and INFN, Bologna (Italy); Lanconelli, Nico [Department of Physics, University of Bologna, and INFN, Bologna (Italy); Masotti, Matteo [Department of Physics, University of Bologna, and INFN, Bologna (Italy); Palermo, Giuseppe [Department of Physics, University of Bologna, and INFN, Bologna (Italy); Riccardi, Alessandro [Department of Physics, University of Bologna, and INFN, Bologna (Italy); Roffilli, Matteo [Department of Computer Science, University of Bologna, Bologna (Italy)
2004-03-21
In this work, we present a novel approach to mass detection in digital mammograms. The great variability of the appearance of masses is the main obstacle to building a mass detection method. It is indeed demanding to characterize all the varieties of masses with a reduced set of features. Hence, in our approach we have chosen not to extract any feature, for the detection of the region of interest; in contrast, we exploit all the information available on the image. A multiresolution overcomplete wavelet representation is performed, in order to codify the image with redundancy of information. The vectors of the very-large space obtained are then provided to a first support vector machine (SVM) classifier. The detection task is considered here as a two-class pattern recognition problem: crops are classified as suspect or not, by using this SVM classifier. False candidates are eliminated with a second cascaded SVM. To further reduce the number of false positives, an ensemble of experts is applied: the final suspect regions are achieved by using a voting strategy. The sensitivity of the presented system is nearly 80% with a false-positive rate of 1.1 marks per image, estimated on images coming from the USF DDSM database.
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.
Support-vector-based emergent self-organising approach for emotional understanding
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.
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.
Gao, Lingyun; Ye, Mingquan; Wu, Changrong
2017-11-29
Intelligent optimization algorithms have advantages in dealing with complex nonlinear problems accompanied by good flexibility and adaptability. In this paper, the FCBF (Fast Correlation-Based Feature selection) method is used to filter irrelevant and redundant features in order to improve the quality of cancer classification. Then, we perform classification based on SVM (Support Vector Machine) optimized by PSO (Particle Swarm Optimization) combined with ABC (Artificial Bee Colony) approaches, which is represented as PA-SVM. The proposed PA-SVM method is applied to nine cancer datasets, including five datasets of outcome prediction and a protein dataset of ovarian cancer. By comparison with other classification methods, the results demonstrate the effectiveness and the robustness of the proposed PA-SVM method in handling various types of data for cancer classification.
Energy Technology Data Exchange (ETDEWEB)
Liang, Wei; Zhang, Laibin; Mingda, Wang; Jinqiu, Hu [College of Mechanical and Transportation Engineering, China University of Petroleum, Beijing, (China)
2010-07-01
The negative wave pressure method is one of the processes used to detect leaks on oil pipelines. The development of new leakage recognition processes is difficult because it is practically impossible to collect leakage pressure samples. The method of leakage feature extraction and the selection of the recognition model are also important in pipeline leakage detection. This study investigated a new feature extraction approach Singular Value Projection (SVP). It projects the singular value to a standard basis. A new pipeline recognition model based on the multi-class Support Vector Machines was also developed. It was found that SVP is a clear and concise recognition feature of the negative pressure wave. Field experiments proved that the model provided a high recognition accuracy rate. This approach to pipeline leakage detection based on the SVP and SVM has a high application value.
Graphic Design Of “Green Mission” Education Game Using Software Based On Vector
Directory of Open Access Journals (Sweden)
Nur Yanti
2018-01-01
Full Text Available Educational game is a digital game in its design using the elements of education and in it support teaching and learning by using technology that is interactive media. Generally an educational game has a fun look, an easy-to-use menu, as well as color combinations that are used that are GUI-based (Graphic User Interface so as to create appeal to users. Because it is undeniable that the human brain tends to more quickly capture learning through visual images rather than writings. Therefore, graphic design of an educational game becomes one of the important points. Software applications become one of the solutions in making game design, one of which is a vector-based software applications. There are various software that can be used in accordance with the function and usefulness of each. But in general the way the software works almost same.
Directory of Open Access Journals (Sweden)
Masayuki Sano
Full Text Available Tissue-specific control of gene expression is an invaluable tool for studying various biological processes and medical applications. Efficient regulatory systems have been utilized to control transgene expression in various types of DNA viral or integrating viral vectors. However, existing regulatory systems are difficult to transfer into negative-strand RNA virus vector platforms because of significant differences in their transcriptional machineries. In this study, we developed a novel strategy for regulating transgene expression mediated by a cytoplasmic RNA vector based on a replication-defective and persistent Sendai virus (SeVdp. Because of the capacity of Sendai virus (SeV nonstructural C proteins to specifically inhibit viral RNA synthesis, overexpression of C protein significantly reduced transgene expression mediated by SeVdp vectors. We found that SeV C overexpression concomitantly reduced SeVdp mRNA levels and genomic RNA synthesis. To control C expression, target sequences for an endogenous microRNA were incorporated into the 3' untranslated region of the C genes. Incorporation of target sequences for miR-21 into the SeVdp vector restored transgene expression in HeLa cells by decreasing C expression. Furthermore, the SeVdp vector containing target sequences for let-7a enabled cell-specific control of transgene expression in human fibroblasts and induced pluripotent stem cells. Our findings demonstrate that SeV C can be used as an effective regulator for controlling transgene expression. This strategy will contribute to efficient and less toxic SeVdp-mediated gene transfer in various biological applications.
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.
Chen, Xiaol; Guo, Bei; Tuo, Jinliang; Zhou, Ruixin; Lu, Yang
2017-08-01
Nowadays, people are paying more and more attention to the noise reduction of household refrigerator compressor. This paper established a sound field bounded by compressor shell and ISO3744 standard field points. The Acoustic Transfer Vector (ATV) in the sound field radiated by a refrigerator compressor shell were calculated which fits the test result preferably. Then the compressor shell surface is divided into several parts. Based on Acoustic Transfer Vector approach, the sound pressure contribution to the field points and the sound power contribution to the sound field of each part were calculated. To obtain the noise radiation in the sound field, the sound pressure cloud charts were analyzed, and the contribution curves in different frequency of each part were acquired. Meanwhile, the sound power contribution of each part in different frequency was analyzed, to ensure those parts where contributes larger sound power. Through the analysis of acoustic contribution, those parts where radiate larger noise on the compressor shell were determined. This paper provides a credible and effective approach on the structure optimal design of refrigerator compressor shell, which is meaningful in the noise and vibration reduction.
The development of vector based 2.5D print methods for a painting machine
Parraman, Carinna
2013-02-01
Through recent trends in the application of digitally printed decorative finishes to products, CAD, 3D additive layer manufacturing and research in material perception, [1, 2] there is a growing interest in the accurate rendering of materials and tangible displays. Although current advances in colour management and inkjet printing has meant that users can take for granted high-quality colour and resolution in their printed images, digital methods for transferring a photographic coloured image from screen to paper is constrained by pixel count, file size, colorimetric conversion between colour spaces and the gamut limits of input and output devices. This paper considers new approaches to applying alternative colour palettes by using a vector-based approach through the application of paint mixtures, towards what could be described as a 2.5D printing method. The objective is to not apply an image to a textured surface, but where texture and colour are integral to the mark, that like a brush, delineates the contours in the image. The paper describes the difference between the way inks and paints are mixed and applied. When transcribing the fluid appearance of a brush stroke, there is a difference between a halftone printed mark and a painted mark. The issue of surface quality is significant to subjective qualities when studying the appearance of ink or paint on paper. The paper provides examples of a range of vector marks that are then transcribed into brush stokes by the painting machine.
PCR-based detection of gene transfer vectors: application to gene doping surveillance.
Perez, Irene C; Le Guiner, Caroline; Ni, Weiyi; Lyles, Jennifer; Moullier, Philippe; Snyder, Richard O
2013-12-01
Athletes who illicitly use drugs to enhance their athletic performance are at risk of being banned from sports competitions. Consequently, some athletes may seek new doping methods that they expect to be capable of circumventing detection. With advances in gene transfer vector design and therapeutic gene transfer, and demonstrations of safety and therapeutic benefit in humans, there is an increased probability of the pursuit of gene doping by athletes. In anticipation of the potential for gene doping, assays have been established to directly detect complementary DNA of genes that are top candidates for use in doping, as well as vector control elements. The development of molecular assays that are capable of exposing gene doping in sports can serve as a deterrent and may also identify athletes who have illicitly used gene transfer for performance enhancement. PCR-based methods to detect foreign DNA with high reliability, sensitivity, and specificity include TaqMan real-time PCR, nested PCR, and internal threshold control PCR.
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.
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
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)
Faults Classification Of Power Electronic Circuits Based On A Support Vector Data Description Method
Directory of Open Access Journals (Sweden)
Cui Jiang
2015-06-01
Full Text Available Power electronic circuits (PECs are prone to various failures, whose classification is of paramount importance. This paper presents a data-driven based fault diagnosis technique, which employs a support vector data description (SVDD method to perform fault classification of PECs. In the presented method, fault signals (e.g. currents, voltages, etc. are collected from accessible nodes of circuits, and then signal processing techniques (e.g. Fourier analysis, wavelet transform, etc. are adopted to extract feature samples, which are subsequently used to perform offline machine learning. Finally, the SVDD classifier is used to implement fault classification task. However, in some cases, the conventional SVDD cannot achieve good classification performance, because this classifier may generate some so-called refusal areas (RAs, and in our design these RAs are resolved with the one-against-one support vector machine (SVM classifier. The obtained experiment results from simulated and actual circuits demonstrate that the improved SVDD has a classification performance close to the conventional one-against-one SVM, and can be applied to fault classification of PECs in practice.
Dynamic analysis of suspension cable based on vector form intrinsic finite element method
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.
A novel and highly efficient production system for recombinant adeno-associated virus vector.
Wu, Zhijian; Wu, Xiaobing; Cao, Hui; Dong, Xiaoyan; Wang, Hong; Hou, Yunde
2002-02-01
Recombinant adeno-associated virus (rAAV) has proven to be a promising gene delivery vector for human gene therapy. However, its application has been limited by difficulty in obtaining enough quantities of high-titer vector stocks. In this paper, a novel and highly efficient production system for rAAV is described. A recombinant herpes simplex virus type 1 (rHSV-1) designated HSV1-rc/DeltaUL2, which expressed adeno-associated virus type2 (AAV-2) Rep and Cap proteins, was constructed previously. The data confirmed that its functions were to support rAAV replication and packaging, and the generated rAAV was infectious. Meanwhile, an rAAV proviral cell line designated BHK/SG2, which carried the green fluorescent protein (GFP) gene expression cassette, was established by transfecting BHK-21 cells with rAAV vector plasmid pSNAV-2-GFP. Infecting BHK/SG2 with HSV1-rc/DeltaUL2 at an MOI of 0.1 resulted in the optimal yields of rAAV, reaching 250 transducing unit (TU) or 4.28x10(4) particles per cell. Therefore, compared with the conventional transfection method, the yield of rAAV using this "one proviral cell line, one helper virus" strategy was increased by two orders of magnitude. Large-scale production of rAAV can be easily achieved using this strategy and might meet the demands for clinical trials of rAAV-mediated gene therapy.
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.
Directory of Open Access Journals (Sweden)
Kayla G Barnes
2017-02-01
Full Text Available 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.
Engineering web maps with gradual content zoom based on streaming vector data
Huang, Lina; Meijers, Martijn; Šuba, Radan; van Oosterom, Peter
2016-04-01
Vario-scale data structures have been designed to support gradual content zoom and the progressive transfer of vector data, for use with arbitrary map scales. The focus to date has been on the server side, especially on how to convert geographic data into the proposed vario-scale structures by means of automated generalisation. This paper contributes to the ongoing vario-scale research by focusing on the client side and communication, particularly on how this works in a web-services setting. It is claimed that these functionalities are urgently needed, as many web-based applications, both desktop and mobile, require gradual content zoom, progressive transfer and a high performance level. The web-client prototypes developed in this paper make it possible to assess the behaviour of vario-scale data and to determine how users will actually see the interactions. Several different options of web-services communication architectures are possible in a vario-scale setting. These options are analysed and tested with various web-client prototypes, with respect to functionality, ease of implementation and performance (amount of transmitted data and response times). We show that the vario-scale data structure can fit in with current web-based architectures and efforts to standardise map distribution on the internet. However, to maximise the benefits of vario-scale data, a client needs to be aware of this structure. When a client needs a map to be refined (by means of a gradual content zoom operation), only the 'missing' data will be requested. This data will be sent incrementally to the client from a server. In this way, the amount of data transferred at one time is reduced, shortening the transmission time. In addition to these conceptual architecture aspects, there are many implementation and tooling design decisions at play. These will also be elaborated on in this paper. Based on the experiments conducted, we conclude that the vario-scale approach indeed supports gradual
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.
International Nuclear Information System (INIS)
Rocco S, Claudio M.; Zio, Enrico
2007-01-01
A support vector machine (SVM) approach to the classification of transients in nuclear power plants is presented. SVM is a machine-learning algorithm that has been successfully used in pattern recognition for cluster analysis. In the present work, single- and multiclass SVM are combined into a hierarchical structure for distinguishing among transients in nuclear systems on the basis of measured data. An example of application of the approach is presented with respect to the classification of anomalies and malfunctions occurring in the feedwater system of a boiling water reactor. The data used in the example are provided by the HAMBO simulator of the Halden Reactor Project
Ling, Qing-Hua; Song, Yu-Qing; Han, Fei; Yang, Dan; Huang, De-Shuang
2016-01-01
For ensemble learning, how to select and combine the candidate classifiers are two key issues which influence the performance of the ensemble system dramatically. Random vector functional link networks (RVFL) without direct input-to-output links is one of suitable base-classifiers for ensemble systems because of its fast learning speed, simple structure and good generalization performance. In this paper, to obtain a more compact ensemble system with improved convergence performance, an improved ensemble of RVFL based on attractive and repulsive particle swarm optimization (ARPSO) with double optimization strategy is proposed. In the proposed method, ARPSO is applied to select and combine the candidate RVFL. As for using ARPSO to select the optimal base RVFL, ARPSO considers both the convergence accuracy on the validation data and the diversity of the candidate ensemble system to build the RVFL ensembles. In the process of combining RVFL, the ensemble weights corresponding to the base RVFL are initialized by the minimum norm least-square method and then further optimized by ARPSO. Finally, a few redundant RVFL is pruned, and thus the more compact ensemble of RVFL is obtained. Moreover, in this paper, theoretical analysis and justification on how to prune the base classifiers on classification problem is presented, and a simple and practically feasible strategy for pruning redundant base classifiers on both classification and regression problems is proposed. Since the double optimization is performed on the basis of the single optimization, the ensemble of RVFL built by the proposed method outperforms that built by some single optimization methods. Experiment results on function approximation and classification problems verify that the proposed method could improve its convergence accuracy as well as reduce the complexity of the ensemble system.
International Nuclear Information System (INIS)
Kareim, Ameer A; Mansor, Muhamad Bin
2013-01-01
The aim of this paper is to improve efficiency of maximum power point tracking (MPPT) for PV systems. The Support Vector Machine (SVM) was proposed to achieve the MPPT controller. The theoretical, the perturbation and observation (P and O), and incremental conductance (IC) algorithms were used to compare with proposed SVM algorithm. MATLAB models for PV module, theoretical, SVM, P and O, and IC algorithms are implemented. The improved MPPT uses the SVM method to predict the optimum voltage of the PV system in order to extract the maximum power point (MPP). The SVM technique used two inputs which are solar radiation and ambient temperature of the modeled PV module. The results show that the proposed SVM technique has less Root Mean Square Error (RMSE) and higher efficiency than P and O and IC methods.
Millard, Jon
2014-01-01
The European Space Agency (ESA) has entered into a partnership with the National Aeronautics and Space Administration (NASA) to develop and provide the Service Module (SM) for the Orion Multipurpose Crew Vehicle (MPCV) Program. The European Service Module (ESM) will provide main engine thrust by utilizing the Space Shuttle Program Orbital Maneuvering System Engine (OMS-E). Thrust Vector Control (TVC) of the OMS-E will be provided by the Orbital Maneuvering System (OMS) TVC, also used during the Space Shuttle Program. NASA will be providing the OMS-E and OMS TVC to ESA as Government Furnished Equipment (GFE) to integrate into the ESM. This presentation will describe the OMS-E and OMS TVC and discuss the implementation of the hardware for the ESM.
An efficient transgenic system by TA cloning vectors and RNAi for C. elegans
International Nuclear Information System (INIS)
Gengyo-Ando, Keiko; Yoshina, Sawako; Inoue, Hideshi; Mitani, Shohei
2006-01-01
In the nematode, transgenic analyses have been performed by microinjection of DNA from various sources into the syncytium gonad. To expedite these transgenic analyses, we solved two potential problems in this work. First, we constructed an efficient TA-cloning vector system which is useful for any promoter. By amplifying the genomic DNA fragments which contain regulatory sequences with or without the coding region, we could easily construct plasmids expressing fluorescent protein fusion without considering restriction sites. We could dissect motor neurons with three colors in a single animal. Second, we used feeding RNAi to isolate transgenic strains which express lag-2::venus fusion gene. We found that the fusion protein is toxic when ectopically expressed in embryos but is functional to rescue a loss of function mutant in the lag-2 gene. Thus, the transgenic system described here should be useful to examine the protein function in the nematode
Directory of Open Access Journals (Sweden)
Ulrike Dackermann
2017-12-01
Full Text Available This paper presents a novel non-destructive testing and health monitoring system using a network of tactile transducers and accelerometers for the condition assessment and damage classification of foundation piles and utility poles. While in traditional pile integrity testing an impact hammer with broadband frequency excitation is typically used, the proposed testing system utilizes an innovative excitation system based on a network of tactile transducers to induce controlled narrow-band frequency stress waves. Thereby, the simultaneous excitation of multiple stress wave types and modes is avoided (or at least reduced, and targeted wave forms can be generated. The new testing system enables the testing and monitoring of foundation piles and utility poles where the top is inaccessible, making the new testing system suitable, for example, for the condition assessment of pile structures with obstructed heads and of poles with live wires. For system validation, the new system was experimentally tested on nine timber and concrete poles that were inflicted with several types of damage. The tactile transducers were excited with continuous sine wave signals of 1 kHz frequency. Support vector machines were employed together with advanced signal processing algorithms to distinguish recorded stress wave signals from pole structures with different types of damage. The results show that using fast Fourier transform signals, combined with principal component analysis as the input feature vector for support vector machine (SVM classifiers with different kernel functions, can achieve damage classification with accuracies of 92.5% ± 7.5%.
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.
Generalization of concurrence vectors
International Nuclear Information System (INIS)
Yu Changshui; Song Heshan
2004-01-01
In this Letter, based on the generalization of concurrence vectors for bipartite pure state with respect to employing tensor product of generators of the corresponding rotation groups, we generalize concurrence vectors to the case of mixed states; a new criterion of separability of multipartite pure states is given out, for which we define a concurrence vector; we generalize the vector to the case of multipartite mixed state and give out a good measure of free entanglement
Classification of polynomial integrable systems of mixed scalar and vector evolution equations: I
International Nuclear Information System (INIS)
Tsuchida, Takayuki; Wolf, Thomas
2005-01-01
We perform a classification of integrable systems of mixed scalar and vector evolution equations with respect to higher symmetries. We consider polynomial systems that are homogeneous under a suitable weighting of variables. This paper deals with the KdV weighting, the Burgers (or potential KdV or modified KdV) weighting, the Ibragimov-Shabat weighting and two unfamiliar weightings. The case of other weightings will be studied in a subsequent paper. Making an ansatz for undetermined coefficients and using a computer package for solving bilinear algebraic systems, we give the complete lists of second-order systems with a third-order or a fourth-order symmetry and third-order systems with a fifth-order symmetry. For all but a few systems in the lists, we show that the system (or, at least a subsystem of it) admits either a Lax representation or a linearizing transformation. A thorough comparison with recent work of Foursov and Olver is made
Classification of polynomial integrable systems of mixed scalar and vector evolution equations: I
Energy Technology Data Exchange (ETDEWEB)
Tsuchida, Takayuki [Department of Physics, Kwansei Gakuin University, 2-1 Gakuen, Sanda 669-1337 (Japan); Wolf, Thomas [Department of Mathematics, Brock University, St Catharines, ON L2S 3A1 (Canada)
2005-09-02
We perform a classification of integrable systems of mixed scalar and vector evolution equations with respect to higher symmetries. We consider polynomial systems that are homogeneous under a suitable weighting of variables. This paper deals with the KdV weighting, the Burgers (or potential KdV or modified KdV) weighting, the Ibragimov-Shabat weighting and two unfamiliar weightings. The case of other weightings will be studied in a subsequent paper. Making an ansatz for undetermined coefficients and using a computer package for solving bilinear algebraic systems, we give the complete lists of second-order systems with a third-order or a fourth-order symmetry and third-order systems with a fifth-order symmetry. For all but a few systems in the lists, we show that the system (or, at least a subsystem of it) admits either a Lax representation or a linearizing transformation. A thorough comparison with recent work of Foursov and Olver is made.
Tungsten disulphide based all fiber Q-switching cylindrical-vector beam generation
Energy Technology Data Exchange (ETDEWEB)
Lin, J.; Yan, K.; Zhou, Y. [Department of Optics and Optical Engineering, University of Science and Technology of China, Hefei 230026 (China); Xu, L. X., E-mail: xulixin@ustc.edu.cn; Gu, C. [Department of Optics and Optical Engineering, University of Science and Technology of China, Hefei 230026 (China); Haixi Collaborative Innovation Center for New Display Devices and Systems Integration, Fuzhou University, Fuzhou 350002 (China); Zhan, Q. W. [Electro-Optics Program, University of Dayton, Dayton, Ohio 45469 (United States)
2015-11-09
We proposed and demonstrated an all fiber passively Q-switching laser to generate cylindrical-vector beam, a two dimensional material, tungsten disulphide (WS{sub 2}), was adopted as a saturable absorber inside the laser cavity, while a few-mode fiber Bragg grating was used as a transverse mode-selective output coupler. The repetition rate of the Q-switching output pulses can be varied from 80 kHz to 120 kHz with a shortest duration of 958 ns. Attributed to the high damage threshold and polarization insensitivity of the WS{sub 2} based saturable absorber, the radially polarized beam and azimuthally polarized beam can be easily generated in the Q-switching fiber laser.
Constructing Support Vector Machine Ensembles for Cancer Classification Based on Proteomic Profiling
Institute of Scientific and Technical Information of China (English)
Yong Mao; Xiao-Bo Zhou; Dao-Ying Pi; You-Xian Sun
2005-01-01
In this study, we present a constructive algorithm for training cooperative support vector machine ensembles (CSVMEs). CSVME combines ensemble architecture design with cooperative training for individual SVMs in ensembles. Unlike most previous studies on training ensembles, CSVME puts emphasis on both accuracy and collaboration among individual SVMs in an ensemble. A group of SVMs selected on the basis of recursive classifier elimination is used in CSVME, and the number of the individual SVMs selected to construct CSVME is determined by 10-fold cross-validation. This kind of SVME has been tested on two ovarian cancer datasets previously obtained by proteomic mass spectrometry. By combining several individual SVMs, the proposed method achieves better performance than the SVME of all base SVMs.
Process service quality evaluation based on Dempster-Shafer theory and support vector machine.
Pei, Feng-Que; Li, Dong-Bo; Tong, Yi-Fei; He, Fei
2017-01-01
Human involvement influences traditional service quality evaluations, which triggers an evaluation's low accuracy, poor reliability and less impressive predictability. This paper proposes a method by employing a support vector machine (SVM) and Dempster-Shafer evidence theory to evaluate the service quality of a production process by handling a high number of input features with a low sampling data set, which is called SVMs-DS. Features that can affect production quality are extracted by a large number of sensors. Preprocessing steps such as feature simplification and normalization are reduced. Based on three individual SVM models, the basic probability assignments (BPAs) are constructed, which can help the evaluation in a qualitative and quantitative way. The process service quality evaluation results are validated by the Dempster rules; the decision threshold to resolve conflicting results is generated from three SVM models. A case study is presented to demonstrate the effectiveness of the SVMs-DS method.
Process service quality evaluation based on Dempster-Shafer theory and support vector machine.
Directory of Open Access Journals (Sweden)
Feng-Que Pei
Full Text Available Human involvement influences traditional service quality evaluations, which triggers an evaluation's low accuracy, poor reliability and less impressive predictability. This paper proposes a method by employing a support vector machine (SVM and Dempster-Shafer evidence theory to evaluate the service quality of a production process by handling a high number of input features with a low sampling data set, which is called SVMs-DS. Features that can affect production quality are extracted by a large number of sensors. Preprocessing steps such as feature simplification and normalization are reduced. Based on three individual SVM models, the basic probability assignments (BPAs are constructed, which can help the evaluation in a qualitative and quantitative way. The process service quality evaluation results are validated by the Dempster rules; the decision threshold to resolve conflicting results is generated from three SVM models. A case study is presented to demonstrate the effectiveness of the SVMs-DS method.
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
A novel improved fuzzy support vector machine based stock price trend forecast model
Wang, Shuheng; Li, Guohao; Bao, Yifan
2018-01-01
Application of fuzzy support vector machine in stock price forecast. Support vector machine is a new type of machine learning method proposed in 1990s. It can deal with classification and regression problems very successfully. Due to the excellent learning performance of support vector machine, the technology has become a hot research topic in the field of machine learning, and it has been successfully applied in many fields. However, as a new technology, there are many limitations to support...
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.
International Nuclear Information System (INIS)
Cheng Weixing; Chen Yongzhong; Zhou Weimin; Ye Kairong; Liu Dekang
2002-01-01
EPICS is the most popular developing platform to build control system and beam diagnostic system in modern physics experiment facilities. An EPICS based data acquisition system was built in Redhat 6.2 operation system. The system is successfully used in the beam position monitor mapping, it improves the mapping process a lot
Efficacy and evaluation of environmental management system to control sandfly vector of Kala-azar.
Dinesh, D S; Kumari, S; Hassan, F; Kumar, V; Singh, V P; Das, P
2017-10-01
The established vector for visceral leishmaniasis, Phlebotomus argentipes (Diptera: Psychodidae) breeds inside the human dwellings and cattle shed under crevices at the base of the wall. P. argentipes was controlled by plastering the base of wall (9″height × 9″base). The study was conducted in two phases: (i) Screening of plastering materials (ii) validation of the most suitable material. During the first phase (2014); four intervention materials were evaluated in four different arms: (i) cement (ii) brick chimney fly ash (BCFA i.e. waste material from an oven for backing raw earthen brick in charcoal) mixed with lime (95:5) (iii) wire mesh (25 holes/cm 2 ) and (iv) glazed tiles. Ten houses were selected as test and same as control in four different villages for each arm having similar ecotype and similar density of sandflies. The pre and post intervention density of sandflies were evaluated. Significant reduction in sandfly density was found with cement (46.2%) and BCFA (29.6%) plastering (P < 0.05). In the second phase of the study (2015); the two most effective interventions were validated at village level with one control. A significant reduction in the density of P. argentipes was found with cement; 60.2% (Mean ± S.D. = 2.48 ± 2.78, 95% CI = 1.93-3.02) and BCFA; 48.2% (Mean ± S.D. = 1.98 ± 2.20, 95% CI = 1.55-2.41) (P < 0.05). BCFA was found easily accessible, acceptable and cost effective that can be used in any type of wall materials at own cost. This can be implemented as one of the integrated vector control approach in the programme. Copyright © 2017. Published by Elsevier Ltd.
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...
Tag and Neighbor based Recommender systems for Medical events
DEFF Research Database (Denmark)
Bayyapu, Karunakar Reddy; Dolog, Peter
2010-01-01
This paper presents an extension of a multifactor recommendation approach based on user tagging with term neighbours. Neighbours of words in tag vectors and documents provide for hitting larger set of documents and not only those matching with direct tag vectors or content of the documents. Tag...... in the situations where the quality of tags is lower. We discuss the approach on the examples from the existing Medworm system to indicate the usefulness of the approach....
Fruit fly optimization based least square support vector regression for blind image restoration
Zhang, Jiao; Wang, Rui; Li, Junshan; Yang, Yawei
2014-11-01
The goal of image restoration is to reconstruct the original scene from a degraded observation. It is a critical and challenging task in image processing. Classical restorations require explicit knowledge of the point spread function and a description of the noise as priors. However, it is not practical for many real image processing. The recovery processing needs to be a blind image restoration scenario. Since blind deconvolution is an ill-posed problem, many blind restoration methods need to make additional assumptions to construct restrictions. Due to the differences of PSF and noise energy, blurring images can be quite different. It is difficult to achieve a good balance between proper assumption and high restoration quality in blind deconvolution. Recently, machine learning techniques have been applied to blind image restoration. The least square support vector regression (LSSVR) has been proven to offer strong potential in estimating and forecasting issues. Therefore, this paper proposes a LSSVR-based image restoration method. However, selecting the optimal parameters for support vector machine is essential to the training result. As a novel meta-heuristic algorithm, the fruit fly optimization algorithm (FOA) can be used to handle optimization problems, and has the advantages of fast convergence to the global optimal solution. In the proposed method, the training samples are created from a neighborhood in the degraded image to the central pixel in the original image. The mapping between the degraded image and the original image is learned by training LSSVR. The two parameters of LSSVR are optimized though FOA. The fitness function of FOA is calculated by the restoration error function. With the acquired mapping, the degraded image can be recovered. Experimental results show the proposed method can obtain satisfactory restoration effect. Compared with BP neural network regression, SVR method and Lucy-Richardson algorithm, it speeds up the restoration rate and
Directory of Open Access Journals (Sweden)
Hosein Nouri-Ahmadabadi
2017-12-01
Full Text Available In this study, an intelligent system based on combined machine vision (MV and Support Vector Machine (SVM was developed for sorting of peeled pistachio kernels and shells. The system was composed of conveyor belt, lighting box, camera, processing unit and sorting unit. A color CCD camera was used to capture images. The images were digitalized by a capture card and transferred to a personal computer for further analysis. Initially, images were converted from RGB color space to HSV color ones. For segmentation of the acquired images, H-component in the HSV color space and Otsu thresholding method were applied. A feature vector containing 30 color features was extracted from the captured images. A feature selection method based on sensitivity analysis was carried out to select superior features. The selected features were presented to SVM classifier. Various SVM models having a different kernel function were developed and tested. The SVM model having cubic polynomial kernel function and 38 support vectors achieved the best accuracy (99.17% and then was selected to use in online decision-making unit of the system. By launching the online system, it was found that limiting factors of the system capacity were related to the hardware parts of the system (conveyor belt and pneumatic valves used in the sorting unit. The limiting factors led to a distance of 8 mm between the samples. The overall accuracy and capacity of the sorter were obtained 94.33% and 22.74 kg/h, respectively. Keywords: Pistachio kernel, Sorting, Machine vision, Sensitivity analysis, Support vector machine
Scanning vector Hall probe microscopy
International Nuclear Information System (INIS)
Cambel, V.; Gregusova, D.; Fedor, J.; Kudela, R.; Bending, S.J.
2004-01-01
We have developed a scanning vector Hall probe microscope for mapping magnetic field vector over magnetic samples. The microscope is based on a micromachined Hall sensor and the cryostat with scanning system. The vector Hall sensor active area is ∼5x5 μm 2 . It is realized by patterning three Hall probes on the tilted faces of GaAs pyramids. Data from these 'tilted' Hall probes are used to reconstruct the full magnetic field vector. The scanning area of the microscope is 5x5 mm 2 , space resolution 2.5 μm, field resolution ∼1 μT Hz -1/2 at temperatures 10-300 K
Directory of Open Access Journals (Sweden)
Alfredo Gimelli
2018-04-01
Full Text Available In recent decades, growing concerns about global warming and climate change effects have led to specific directives, especially in Europe, promoting the use of primary energy-saving techniques and renewable energy systems. The increasingly stringent requirements for carbon dioxide reduction have led to a more widespread adoption of distributed energy systems. In particular, besides renewable energy systems for power generation, one of the most effective techniques used to face the energy-saving challenges has been the adoption of polygeneration plants for combined heating, cooling, and electricity generation. This technique offers the possibility to achieve a considerable enhancement in energy and cost savings as well as a simultaneous reduction of greenhouse gas emissions. However, the use of small-scale polygeneration systems does not ensure the achievement of mandatory, but sometimes conflicting, aims without the proper sizing and operation of the plant. This paper is focused on a methodology based on vector optimization algorithms and developed by the authors for the identification of optimal polygeneration plant solutions. To this aim, a specific calculation algorithm for the study of cogeneration systems has also been developed. This paper provides, after a detailed description of the proposed methodology, some specific applications to the study of combined heat and power (CHP and organic Rankine cycle (ORC plants, thus highlighting the potential of the proposed techniques and the main results achieved.
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.
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.
Fuzzy-based multi-kernel spherical support vector machine for ...
Indian Academy of Sciences (India)
A K Sampath
2017-08-08
Aug 8, 2017 ... design a new multi-kernel function based on the fuzzy triangular membership function. Finally .... This paper is structured as follows. Section 2 ..... analysis is compared with some existing systems based on the number of ...
Is outdoor vector control needed for malaria elimination? An individual-based modelling study.
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
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.
Development and evaluation of a biomedical search engine using a predicate-based vector space model.
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.
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.
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.)
Joseph, Joan; Fernández-Lloris, Raquel; Pezzat, Elías; Saubi, Narcís; Cardona, Pere-Joan; Mothe, Beatriz; Gatell, Josep Maria
2010-01-01
Mycobacterium bovis Bacillus Calmette-Guérin (BCG) as a live vector of recombinant bacterial vaccine is a promising system to be used. In this study, we evaluate the disrupted expression of heterologous HIV-1gp120 gene in BCG Pasteur host strain using replicative vectors pMV261 and pJH222. pJH222 carries a lysine complementing gene in BCG lysine auxotrophs. The HIV-1 gp120 gene expression was regulated by BCG hsp60 promoter (in plasmid pMV261) and Mycobacteria spp. α-antigen promoter (in plasmid pJH222). Among 14 rBCG:HIV-1gp120 (pMV261) colonies screened, 12 showed a partial deletion and two showed a complete deletion. However, deletion was not observed in all 10 rBCG:HIV-1gp120 (pJH222) colonies screened. In this study, we demonstrated that E. coli/Mycobacterial expression vectors bearing a weak promoter and lysine complementing gene in a recombinant lysine auxotroph of BCG could prevent genetic rearrangements and disruption of HIV 1gp120 gene expression, a key issue for engineering Mycobacterial based vaccine vectors. PMID:20617151
Directory of Open Access Journals (Sweden)
Joan Joseph
2010-01-01
Full Text Available Mycobacterium bovis Bacillus Calmette-Guérin (BCG as a live vector of recombinant bacterial vaccine is a promising system to be used. In this study, we evaluate the disrupted expression of heterologous HIV-1gp120 gene in BCG Pasteur host strain using replicative vectors pMV261 and pJH222. pJH222 carries a lysine complementing gene in BCG lysine auxotrophs. The HIV-1 gp120 gene expression was regulated by BCG hsp60 promoter (in plasmid pMV261 and Mycobacteria spp. α-antigen promoter (in plasmid pJH222. Among 14 rBCG:HIV-1gp120 (pMV261 colonies screened, 12 showed a partial deletion and two showed a complete deletion. However, deletion was not observed in all 10 rBCG:HIV-1gp120 (pJH222 colonies screened. In this study, we demonstrated that E. coli/Mycobacterial expression vectors bearing a weak promoter and lysine complementing gene in a recombinant lysine auxotroph of BCG could prevent genetic rearrangements and disruption of HIV 1gp120 gene expression, a key issue for engineering Mycobacterial based vaccine vectors.
Combining nonlinear multiresolution system and vector quantization for still image compression
Energy Technology Data Exchange (ETDEWEB)
Wong, Y.
1993-12-17
It is popular to use multiresolution systems for image coding and compression. However, general-purpose techniques such as filter banks and wavelets are linear. While these systems are rigorous, nonlinear features in the signals cannot be utilized in a single entity for compression. Linear filters are known to blur the edges. Thus, the low-resolution images are typically blurred, carrying little information. We propose and demonstrate that edge-preserving filters such as median filters can be used in generating a multiresolution system using the Laplacian pyramid. The signals in the detail images are small and localized to the edge areas. Principal component vector quantization (PCVQ) is used to encode the detail images. PCVQ is a tree-structured VQ which allows fast codebook design and encoding/decoding. In encoding, the quantization error at each level is fed back through the pyramid to the previous level so that ultimately all the error is confined to the first level. With simple coding methods, we demonstrate that images with PSNR 33 dB can be obtained at 0.66 bpp without the use of entropy coding. When the rate is decreased to 0.25 bpp, the PSNR of 30 dB can still be achieved. Combined with an earlier result, our work demonstrate that nonlinear filters can be used for multiresolution systems and image coding.
Energy Technology Data Exchange (ETDEWEB)
Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States)
2017-08-25
This work proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization (PSO) is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system.
A program system for ab initio MO calculations on vector and parallel processing machines. Pt. 3
International Nuclear Information System (INIS)
Wiest, R.; Demuynck, J.; Benard, M.; Rohmer, M.M.; Ernenwein, R.
1991-01-01
This series of three papers presents a program system for ab initio molecular orbital calculations on vector and parallel computers. Part III is devoted to the four-index transformation on a molecular orbital basis of size NMO of the file of two-electorn integrals (pqparallelrs) generated by a contracted Gaussian set of size NATO (number of atomic orbitals). A fast Yoshimine algorithm first sorts the (pqparallelrs) integrals with respect to index pq only. This file of half-sorted integrals labelled by their rs-index can be processed without further modification to generate either the transformed integrals or the supermatrix elements. The large memory available on the CRAY-2 hase made possible to implement the transformation algorithm proposed by Bender in 1972, which requires a core-storage allocation varying as (NATO) 3 . Two versions of Bender's algorithm are included in the present program. The first version is an in-core version, where the complete file of accumulated contributions to transformed integrals in stored and updated in central memory. This version has been parallelized by distributing over a limited number of logical tasks the NATO steps corresponding to the scanning of the most external loop. The second version is an out-of-core version, in which twin files are alternatively used as input and output for the accumulated contributions to transformed integrals. This version is not parallel. The choice of one or another version and (for version 1) the determination of the number of tasks depends upon the balance between the available and the requested amounts of storage. The storage management and the choice of the proper version are carried out automatically using dynamic storage allocation. Both versions are vectorized and take advantage of the molecular symmetry. (orig.)
Chitosan-based DNA delivery vector targeted to gonadotropin-releasing hormone (GnRH) receptor.
Boonthum, Chatwalee; Namdee, Katawut; Boonrungsiman, Suwimon; Chatdarong, Kaywalee; Saengkrit, Nattika; Sajomsang, Warayuth; Ponglowhapan, Suppawiwat; Yata, Teerapong
2017-02-10
The main purpose of this study was to investigate the application of modified chitosan as a potential vector for gene delivery to gonadotropin-releasing hormone receptor (GnRHR)-expressing cells. Such design of gene carrier could be useful in particular for gene therapy for cancers related to the reproductive system, gene disorders of sexual development, and contraception and fertility control. In this study, a decapeptide GnRH was successfully conjugated to chitosan (CS) as confirmed by proton nuclear magnetic resonance spectroscopy ( 1 H NMR) and Attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR). The synthesized GnRH-conjugated chitosan (GnRH-CS) was able to condense DNA to form positively charged nanoparticles and specifically deliver plasmid DNA to targeted cells in both two-dimensional (2D) and three-dimensional (3D) cell cultures systems. Importantly, GnRH-CS exhibited higher transfection activity compared to unmodified CS. In conclusion, GnRH-conjugated chitosan can be a promising carrier for targeted DNA delivery to GnRHR-expressing cells. Copyright © 2016 Elsevier Ltd. All rights reserved.
DEFF Research Database (Denmark)
Lee, Kyo-Beum; Blaabjerg, Frede
2004-01-01
This paper presents a new sensorless vector control system for high performance induction motor drives fed by a matrix converter with non-linearity compensation. The nonlinear voltage distortion that is caused by commutation delay and on-state voltage drop in switching device is corrected by a new...
Directory of Open Access Journals (Sweden)
Il Young Song
2015-01-01
Full Text Available This paper focuses on estimation of a nonlinear function of state vector (NFS in discrete-time linear systems with time-delays and model uncertainties. The NFS represents a multivariate nonlinear function of state variables, which can indicate useful information of a target system for control. The optimal nonlinear estimator of an NFS (in mean square sense represents a function of the receding horizon estimate and its error covariance. The proposed receding horizon filter represents the standard Kalman filter with time-delays and special initial horizon conditions described by the Lyapunov-like equations. In general case to calculate an optimal estimator of an NFS we propose using the unscented transformation. Important class of polynomial NFS is considered in detail. In the case of polynomial NFS an optimal estimator has a closed-form computational procedure. The subsequent application of the proposed receding horizon filter and nonlinear estimator to a linear stochastic system with time-delays and uncertainties demonstrates their effectiveness.
Viral vector-based tools advance knowledge of basal ganglia anatomy and physiology.
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.
Objective Auscultation of TCM Based on Wavelet Packet Fractal Dimension and Support Vector Machine
Yan, Jian-Jun; Wang, Yi-Qin; Liu, Guo-Ping; Yan, Hai-Xia; Xia, Chun-Ming; Shen, Xiaojing
2014-01-01
This study was conducted to illustrate that auscultation features based on the fractal dimension combined with wavelet packet transform (WPT) were conducive to the identification the pattern of syndromes of Traditional Chinese Medicine (TCM). The WPT and the fractal dimension were employed to extract features of auscultation signals of 137 patients with lung Qi-deficient pattern, 49 patients with lung Yin-deficient pattern, and 43 healthy subjects. With these features, the classification model was constructed based on multiclass support vector machine (SVM). When all auscultation signals were trained by SVM to decide the patterns of TCM syndromes, the overall recognition rate of model was 79.49%; when male and female auscultation signals were trained, respectively, to decide the patterns, the overall recognition rate of model reached 86.05%. The results showed that the methods proposed in this paper were effective to analyze auscultation signals, and the performance of model can be greatly improved when the distinction of gender was considered. PMID:24883068
Directory of Open Access Journals (Sweden)
Guan Lian
2018-01-01
Full Text Available Accurate prediction of taxi-out time is significant precondition for improving the operationality of the departure process at an airport, as well as reducing the long taxi-out time, congestion, and excessive emission of greenhouse gases. Unfortunately, several of the traditional methods of predicting taxi-out time perform unsatisfactorily at congested airports. This paper describes and tests three of those conventional methods which include Generalized Linear Model, Softmax Regression Model, and Artificial Neural Network method and two improved Support Vector Regression (SVR approaches based on swarm intelligence algorithm optimization, which include Particle Swarm Optimization (PSO and Firefly Algorithm. In order to improve the global searching ability of Firefly Algorithm, adaptive step factor and Lévy flight are implemented simultaneously when updating the location function. Six factors are analysed, of which delay is identified as one significant factor in congested airports. Through a series of specific dynamic analyses, a case study of Beijing International Airport (PEK is tested with historical data. The performance measures show that the proposed two SVR approaches, especially the Improved Firefly Algorithm (IFA optimization-based SVR method, not only perform as the best modelling measures and accuracy rate compared with the representative forecast models, but also can achieve a better predictive performance when dealing with abnormal taxi-out time states.
Geographic Distribution of Chagas Disease Vectors in Brazil Based on Ecological Niche Modeling
Directory of Open Access Journals (Sweden)
Rodrigo Gurgel-Gonçalves
2012-01-01
Full Text Available Although Brazil was declared free from Chagas disease transmission by the domestic vector Triatoma infestans, human acute cases are still being registered based on transmission by native triatomine species. For a better understanding of transmission risk, the geographic distribution of Brazilian triatomines was analyzed. Sixteen out of 62 Brazilian species that both occur in >20 municipalities and present synanthropic tendencies were modeled based on their ecological niches. Panstrongylus geniculatus and P. megistus showed broad ecological ranges, but most of the species sort out by the biome in which they are distributed: Rhodnius pictipes and R. robustus in the Amazon; R. neglectus, Triatoma sordida, and T. costalimai in the Cerrado; R. nasutus, P. lutzi, T. brasiliensis, T. pseudomaculata, T. melanocephala, and T. petrocchiae in the Caatinga; T. rubrovaria in the southern pampas; T. tibiamaculata and T. vitticeps in the Atlantic Forest. Although most occurrences were recorded in open areas (Cerrado and Caatinga, our results show that all environmental conditions in the country are favorable to one or more of the species analyzed, such that almost nowhere is Chagas transmission risk negligible.
Point-based warping with optimized weighting factors of displacement vectors
Pielot, Ranier; Scholz, Michael; Obermayer, Klaus; Gundelfinger, Eckart D.; Hess, Andreas
2000-06-01
The accurate comparison of inter-individual 3D image brain datasets requires non-affine transformation techniques (warping) to reduce geometric variations. Constrained by the biological prerequisites we use in this study a landmark-based warping method with weighted sums of displacement vectors, which is enhanced by an optimization process. Furthermore, we investigate fast automatic procedures for determining landmarks to improve the practicability of 3D warping. This combined approach was tested on 3D autoradiographs of Gerbil brains. The autoradiographs were obtained after injecting a non-metabolized radioactive glucose derivative into the Gerbil thereby visualizing neuronal activity in the brain. Afterwards the brain was processed with standard autoradiographical methods. The landmark-generator computes corresponding reference points simultaneously within a given number of datasets by Monte-Carlo-techniques. The warping function is a distance weighted exponential function with a landmark- specific weighting factor. These weighting factors are optimized by a computational evolution strategy. The warping quality is quantified by several coefficients (correlation coefficient, overlap-index, and registration error). The described approach combines a highly suitable procedure to automatically detect landmarks in autoradiographical brain images and an enhanced point-based warping technique, optimizing the local weighting factors. This optimization process significantly improves the similarity between the warped and the target dataset.
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.
Zn(II)-dipicolylamine-based metallo-lipids as novel non-viral gene vectors.
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.
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
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
Hano, Mitsuo; Hotta, Masashi
A new multigrid method based on high-order vector finite elements is proposed in this paper. Low level discretizations in this method are obtained by using low-order vector finite elements for the same mesh. Gauss-Seidel method is used as a smoother, and a linear equation of lowest level is solved by ICCG method. But it is often found that multigrid solutions do not converge into ICCG solutions. An elimination algolithm of constant term using a null space of the coefficient matrix is also described. In three dimensional magnetostatic field analysis, convergence time and number of iteration of this multigrid method are discussed with the convectional ICCG method.
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.
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...
DEFF Research Database (Denmark)
Li, Qiyuan; Jorgensen, Flemming Steen; Oprea, Tudor
2008-01-01
and diverse library of 495 compounds. The models combine pharmacophore-based GRIND descriptors with a support vector machine (SVM) classifier in order to discriminate between hERG blockers and nonblockers. Our models were applied at different thresholds from 1 to 40 mu m and achieved an overall accuracy up...
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 th...... electrical degrees in less than 2 ms test time....
Hydrogel based occlusion systems
Stam, F.A.; Jackson, N.; Dubruel, P.; Adesanya, K.; Embrechts, A.; Mendes, E.; Neves, H.P.; Herijgers, P.; Verbrugghe, Y.; Shacham, Y.; Engel, L.; Krylov, V.
2013-01-01
A hydrogel based occlusion system, a method for occluding vessels, appendages or aneurysms, and a method for hydrogel synthesis are disclosed. The hydrogel based occlusion system includes a hydrogel having a shrunken and a swollen state and a delivery tool configured to deliver the hydrogel to a
Wu, Na; Qu, Zhiyu; Si, Weijian; Jiao, Shuhong
2016-12-13
In array signal processing systems, the direction of arrival (DOA) and polarization of signals based on uniform linear or rectangular sensor arrays are generally obtained by rotational invariance techniques (ESPRIT). However, since the ESPRIT algorithm relies on the rotational invariant structure of the received data, it cannot be applied to electromagnetic vector sensor arrays (EVSAs) featuring uniform circular patterns. To overcome this limitation, a fourth-order cumulant-based ESPRIT algorithm is proposed in this paper, for joint estimation of DOA and polarization based on a uniform circular EVSA. The proposed algorithm utilizes the fourth-order cumulant to obtain a virtual extended array of a uniform circular EVSA, from which the pairs of rotation invariant sub-arrays are obtained. The ESPRIT algorithm and parameter pair matching are then utilized to estimate the DOA and polarization of the incident signals. The closed-form parameter estimation algorithm can effectively reduce the computational complexity of the joint estimation, which has been demonstrated by numerical simulations.
Active damage detection method based on support vector machine and impulse response
International Nuclear Information System (INIS)
Taniguchi, Ryuta; Mita, Akira
2004-01-01
An active damage detection method was proposed to characterize damage in bolted joints. The purpose of this study is to propose a damage detection method that can obtain the detailed information of the damage by creating feature vectors for pattern recognition. In the proposed method, the wavelet transform is applied to the sensor signals, and the feature vectors are defined by second power average of the amplitude. The feature vectors generated by experiments were successfully used as the training data for Support Vector Machine (SVM). By applying the wavelet transform to time-frequency analysis, the accuracy of pattern recognition was raised in both correlation coefficient and SVM applications. Moreover, the SVM could identify the damage with very strong discernment capability than others. Applicability of the proposed method was successfully demonstrated. (author)
Directory of Open Access Journals (Sweden)
Jin-peng Liu
2017-07-01
Full Text Available Short-term power load forecasting is an important basis for the operation of integrated energy system, and the accuracy of load forecasting directly affects the economy of system operation. To improve the forecasting accuracy, this paper proposes a load forecasting system based on wavelet least square support vector machine and sperm whale algorithm. Firstly, the methods of discrete wavelet transform and inconsistency rate model (DWT-IR are used to select the optimal features, which aims to reduce the redundancy of input vectors. Secondly, the kernel function of least square support vector machine LSSVM is replaced by wavelet kernel function for improving the nonlinear mapping ability of LSSVM. Lastly, the parameters of W-LSSVM are optimized by sperm whale algorithm, and the short-term load forecasting method of W-LSSVM-SWA is established. Additionally, the example verification results show that the proposed model outperforms other alternative methods and has a strong effectiveness and feasibility in short-term power load forecasting.
Yamashita, Mami; Xu, Jian; Morokuma, Daisuke; Hirata, Kazuma; Hino, Masato; Mon, Hiroaki; Takahashi, Masateru; Hamdan, Samir; Sakashita, Kosuke; Iiyama, Kazuhiro; Banno, Yutaka; Kusakabe, Takahiro; Lee, Jae Man
2017-01-01
The KOD DNA polymerase from Thermococcus kodakarensis (Tkod-Pol) has been preferred for PCR due to its rapid elongation rate, extreme thermostability and outstanding fidelity. Here in this study, we utilized silkworm-baculovirus expression vector system (silkworm-BEVS) to express the recombinant Tkod-Pol (rKOD) with N-terminal (rKOD-N) or C-terminal (rKOD-C) tandem fusion tags. By using BEVS, we produced functional rKODs with satisfactory yields, about 1.1 mg/larva for rKOD-N and 0.25 mg/larva for rKOD-C, respectively. Interestingly, we found that rKOD-C shows higher thermostability at 95 °C than that of rKOD-N, while that rKOD-N is significantly unstable after exposing to long period of heat-shock. We also assessed the polymerase activity as well as the fidelity of purified rKODs under various conditions. Compared with commercially available rKOD, which is expressed in E. coli expression system, rKOD-C exhibited almost the same PCR performance as the commercial rKOD did, while rKOD-N did lower performance. Taken together, our results suggested that silkworm-BEVS can be used to express and purify efficient rKOD in a commercial way.
Performance of Ferrite Vector Modulators in the LLRF System of the Fermilab HINS 6-Cavity Test
Energy Technology Data Exchange (ETDEWEB)
Varghese, Philip [Fermilab; Barnes, Barry [Fermilab; Chase, Brian [Fermilab; Cullerton, Ed [Fermilab; Tan, Cong [Fermilab
2013-04-01
The High Intensity Neutrino Source (HINS) 6-cavity test is a part of the Fermilab HINS Linac R&D program for a low energy, high intensity proton Hsup>- linear accelerator. One of the objectives of the 6-cavity test is to demonstrate the use of high power RF Ferrite Vector Modulators(FVM) for independent control of multiple cavities driven by a single klystron. The beamline includes an RFQ and six cavities. The LLRF system provides a primary feedback loop around the RFQ and the distribution of the regulated klystron output is controlled by secondary learning feed-forward loops on the FVMs for each of the six cavities. The feed-forward loops provide pulse to pulse correction to the current waveform profiles of the FVM power supplies to compensate for beam-loading and other disturbances. The learning feed-forward loops are shown to successfully control the amplitude and phase settings for the cavities well within the 1 % and 1 degree requirements specified for the system.
Yamashita, Mami
2017-05-08
The KOD DNA polymerase from Thermococcus kodakarensis (Tkod-Pol) has been preferred for PCR due to its rapid elongation rate, extreme thermostability and outstanding fidelity. Here in this study, we utilized silkworm-baculovirus expression vector system (silkworm-BEVS) to express the recombinant Tkod-Pol (rKOD) with N-terminal (rKOD-N) or C-terminal (rKOD-C) tandem fusion tags. By using BEVS, we produced functional rKODs with satisfactory yields, about 1.1 mg/larva for rKOD-N and 0.25 mg/larva for rKOD-C, respectively. Interestingly, we found that rKOD-C shows higher thermostability at 95 °C than that of rKOD-N, while that rKOD-N is significantly unstable after exposing to long period of heat-shock. We also assessed the polymerase activity as well as the fidelity of purified rKODs under various conditions. Compared with commercially available rKOD, which is expressed in E. coli expression system, rKOD-C exhibited almost the same PCR performance as the commercial rKOD did, while rKOD-N did lower performance. Taken together, our results suggested that silkworm-BEVS can be used to express and purify efficient rKOD in a commercial way.
Virtual-view PSNR prediction based on a depth distortion tolerance model and support vector machine.
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.
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.
International Nuclear Information System (INIS)
Jiang, B.T.; Zhao, F.Y.
2013-01-01
Highlights: ► CHF data are collected from the published literature. ► Less training data are used to train the LSSVR model. ► PSO is adopted to optimize the key parameters to improve the model precision. ► The reliability of LSSVR is proved through parametric trends analysis. - Abstract: In view of practical importance of critical heat flux (CHF) for design and safety of nuclear reactors, accurate prediction of CHF is of utmost significance. This paper presents a novel approach using least squares support vector regression (LSSVR) and particle swarm optimization (PSO) to predict CHF. Two available published datasets are used to train and test the proposed algorithm, in which PSO is employed to search for the best parameters involved in LSSVR model. The CHF values obtained by the LSSVR model are compared with the corresponding experimental values and those of a previous method, adaptive neuro fuzzy inference system (ANFIS). This comparison is also carried out in the investigation of parametric trends of CHF. It is found that the proposed method can achieve the desired performance and yields a more satisfactory fit with experimental results than ANFIS. Therefore, LSSVR method is likely to be suitable for other parameters processing such as CHF
Subramanya, Sandesh; Kim, Sang-Soo; Manjunath, N; Shankar, Premlata
2010-02-01
Despite the clinical benefits of highly active antiretroviral therapy (HAART), the prospect of life-long antiretroviral treatment poses significant problems, which has spurred interest in developing new drugs and strategies to treat HIV infection and eliminate persistent viral reservoirs. RNAi has emerged as a therapeutic possibility for HIV. We discuss progress in overcoming hurdles to translating transient and stable RNAi enabling technologies to clinical application for HIV; covering the past 2 - 3 years. HIV inhibition can be achieved by transfection of chemically or enzymatically synthesized siRNAs or by DNA-based vector systems expressing short hairpin RNAs (shRNAs) that are processed intracellularly into siRNA. We compare these approaches, focusing on technical and safety issues that will guide the choice of strategy for clinical use. Introduction of synthetic siRNA into cells or its stable endogenous production using vector-driven shRNA have been shown to suppress HIV replication in vitro and, in some instances, in vivo. Each method has advantages and limitations in terms of ease of delivery, duration of silencing, emergence of escape mutants and potential toxicity. Both appear to have potential as future therapeutics for HIV, once the technical and safety issues of each approach are overcome.
Directory of Open Access Journals (Sweden)
Yibing Wang
2011-09-01
Full Text Available Chlamydia trachomatis remains one of the few major human pathogens for which there is no transformation system. C. trachomatis has a unique obligate intracellular developmental cycle. The extracellular infectious elementary body (EB is an infectious, electron-dense structure that, following host cell infection, differentiates into a non-infectious replicative form known as a reticulate body (RB. Host cells infected by C. trachomatis that are treated with penicillin are not lysed because this antibiotic prevents the maturation of RBs into EBs. Instead the RBs fail to divide although DNA replication continues. We have exploited these observations to develop a transformation protocol based on expression of β-lactamase that utilizes rescue from the penicillin-induced phenotype. We constructed a vector which carries both the chlamydial endogenous plasmid and an E.coli plasmid origin of replication so that it can shuttle between these two bacterial recipients. The vector, when introduced into C. trachomatis L2 under selection conditions, cures the endogenous chlamydial plasmid. We have shown that foreign promoters operate in vivo in C. trachomatis and that active β-lactamase and chloramphenicol acetyl transferase are expressed. To demonstrate the technology we have isolated chlamydial transformants that express the green fluorescent protein (GFP. As proof of principle, we have shown that manipulation of chlamydial biochemistry is possible by transformation of a plasmid-free C. trachomatis recipient strain. The acquisition of the plasmid restores the ability of the plasmid-free C. trachomatis to synthesise and accumulate glycogen within inclusions. These findings pave the way for a comprehensive genetic study on chlamydial gene function that has hitherto not been possible. Application of this technology avoids the use of therapeutic antibiotics and therefore the procedures do not require high level containment and will allow the analysis of genome
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
Design of a mixer for the thrust-vectoring system on the high-alpha research vehicle
Pahle, Joseph W.; Bundick, W. Thomas; Yeager, Jessie C.; Beissner, Fred L., Jr.
1996-01-01
One of the advanced control concepts being investigated on the High-Alpha Research Vehicle (HARV) is multi-axis thrust vectoring using an experimental thrust-vectoring (TV) system consisting of three hydraulically actuated vanes per engine. A mixer is used to translate the pitch-, roll-, and yaw-TV commands into the appropriate TV-vane commands for distribution to the vane actuators. A computer-aided optimization process was developed to perform the inversion of the thrust-vectoring effectiveness data for use by the mixer in performing this command translation. Using this process a new mixer was designed for the HARV and evaluated in simulation and flight. An important element of the Mixer is the priority logic, which determines priority among the pitch-, roll-, and yaw-TV commands.
A Peptide-based Vector for Efficient Gene Transfer In Vitro and In Vivo
Lehto, Taavi; Simonson, Oscar E; Mäger, Imre; Ezzat, Kariem; Sork, Helena; Copolovici, Dana-Maria; Viola, Joana R; Zaghloul, Eman M; Lundin, Per; Moreno, Pedro MD; Mäe, Maarja; Oskolkov, Nikita; Suhorutšenko, Julia; Smith, CI Edvard; Andaloussi, Samir EL
2011-01-01
Finding suitable nonviral delivery vehicles for nucleic acid–based therapeutics is a landmark goal in gene therapy. Cell-penetrating peptides (CPPs) are one class of delivery vectors that has been exploited for this purpose. However, since CPPs use endocytosis to enter cells, a large fraction of peptides remain trapped in endosomes. We have previously reported that stearylation of amphipathic CPPs, such as transportan 10 (TP10), dramatically increases transfection of oligonucleotides in vitro partially by promoting endosomal escape. Therefore, we aimed to evaluate whether stearyl-TP10 could be used for the delivery of plasmids as well. Our results demonstrate that stearyl-TP10 forms stable nanoparticles with plasmids that efficiently enter different cell-types in a ubiquitous manner, including primary cells, resulting in significantly higher gene expression levels than when using stearyl-Arg9 or unmodified CPPs. In fact, the transfection efficacy of stearyl-TP10 almost reached the levels of Lipofectamine 2000 (LF2000), however, without any of the observed lipofection-associated toxicities. Most importantly, stearyl-TP10/plasmid nanoparticles are nonimmunogenic, mediate efficient gene delivery in vivo, when administrated intramuscularly (i.m.) or intradermally (i.d.) without any associated toxicity in mice. PMID:21343913
PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine
Directory of Open Access Journals (Sweden)
Balachandran Manavalan
2018-03-01
Full Text Available Accurately identifying bacteriophage virion proteins from uncharacterized sequences is important to understand interactions between the phage and its host bacteria in order to develop new antibacterial drugs. However, identification of such proteins using experimental techniques is expensive and often time consuming; hence, development of an efficient computational algorithm for the prediction of phage virion proteins (PVPs prior to in vitro experimentation is needed. Here, we describe a support vector machine (SVM-based PVP predictor, called PVP-SVM, which was trained with 136 optimal features. A feature selection protocol was employed to identify the optimal features from a large set that included amino acid composition, dipeptide composition, atomic composition, physicochemical properties, and chain-transition-distribution. PVP-SVM achieved an accuracy of 0.870 during leave-one-out cross-validation, which was 6% higher than control SVM predictors trained with all features, indicating the efficiency of the feature selection method. Furthermore, PVP-SVM displayed superior performance compared to the currently available method, PVPred, and two other machine-learning methods developed in this study when objectively evaluated with an independent dataset. For the convenience of the scientific community, a user-friendly and publicly accessible web server has been established at www.thegleelab.org/PVP-SVM/PVP-SVM.html.
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.
International Nuclear Information System (INIS)
Yi, Bingqi; Huang, Xin; Yang, Ping; Baum, Bryan A.; Kattawar, George W.
2014-01-01
In this study, a full-vector, adding–doubling radiative transfer model is used to investigate the influence of the polarization state on cloud property retrievals from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations. Two sets of lookup tables (LUTs) are developed for the retrieval purposes, both of which provide water cloud and ice cloud reflectivity functions at two wavelengths in various sun-satellite viewing geometries. However, only one of the LUTs considers polarization. The MODIS reflectivity observations at 0.65 μm (band 1) and 2.13 μm (band 7) are used to infer the cloud optical thickness and particle effective diameter, respectively. Results indicate that the retrievals for both water cloud and ice cloud show considerable sensitivity to polarization. The retrieved water and ice cloud effective diameter and optical thickness differences can vary by as much as ±15% due to polarization state considerations. In particular, the polarization state has more influence on completely smooth ice particles than on severely roughened ice particles. - Highlights: • Impact of polarization on satellite-based retrieval of water/ice cloud properties is studied. • Inclusion of polarization can change water/ice optical thickness and effective diameter values by up to ±15%. • Influence of polarization on cloud property retrievals depends on sun-satellite viewing geometries
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.
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)
PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine.
Manavalan, Balachandran; Shin, Tae H; Lee, Gwang
2018-01-01
Accurately identifying bacteriophage virion proteins from uncharacterized sequences is important to understand interactions between the phage and its host bacteria in order to develop new antibacterial drugs. However, identification of such proteins using experimental techniques is expensive and often time consuming; hence, development of an efficient computational algorithm for the prediction of phage virion proteins (PVPs) prior to in vitro experimentation is needed. Here, we describe a support vector machine (SVM)-based PVP predictor, called PVP-SVM, which was trained with 136 optimal features. A feature selection protocol was employed to identify the optimal features from a large set that included amino acid composition, dipeptide composition, atomic composition, physicochemical properties, and chain-transition-distribution. PVP-SVM achieved an accuracy of 0.870 during leave-one-out cross-validation, which was 6% higher than control SVM predictors trained with all features, indicating the efficiency of the feature selection method. Furthermore, PVP-SVM displayed superior performance compared to the currently available method, PVPred, and two other machine-learning methods developed in this study when objectively evaluated with an independent dataset. For the convenience of the scientific community, a user-friendly and publicly accessible web server has been established at www.thegleelab.org/PVP-SVM/PVP-SVM.html.
Directory of Open Access Journals (Sweden)
Daqing Zhang
2015-01-01
Full Text Available Blood-brain barrier (BBB is a highly complex physical barrier determining what substances are allowed to enter the brain. Support vector machine (SVM is a kernel-based machine learning method that is widely used in QSAR study. For a successful SVM model, the kernel parameters for SVM and feature subset selection are the most important factors affecting prediction accuracy. In most studies, they are treated as two independent problems, but it has been proven that they could affect each other. We designed and implemented genetic algorithm (GA to optimize kernel parameters and feature subset selection for SVM regression and applied it to the BBB penetration prediction. The results show that our GA/SVM model is more accurate than other currently available log BB models. Therefore, to optimize both SVM parameters and feature subset simultaneously with genetic algorithm is a better approach than other methods that treat the two problems separately. Analysis of our log BB model suggests that carboxylic acid group, polar surface area (PSA/hydrogen-bonding ability, lipophilicity, and molecular charge play important role in BBB penetration. Among those properties relevant to BBB penetration, lipophilicity could enhance the BBB penetration while all the others are negatively correlated with BBB penetration.
Large-scale ligand-based predictive modelling using support vector machines.
Alvarsson, Jonathan; Lampa, Samuel; Schaal, Wesley; Andersson, Claes; Wikberg, Jarl E S; Spjuth, Ola
2016-01-01
The increasing size of datasets in drug discovery makes it challenging to build robust and accurate predictive models within a reasonable amount of time. In order to investigate the effect of dataset sizes on predictive performance and modelling time, ligand-based regression models were trained on open datasets of varying sizes of up to 1.2 million chemical structures. For modelling, two implementations of support vector machines (SVM) were used. Chemical structures were described by the signatures molecular descriptor. Results showed that for the larger datasets, the LIBLINEAR SVM implementation performed on par with the well-established libsvm with a radial basis function kernel, but with dramatically less time for model building even on modest computer resources. Using a non-linear kernel proved to be infeasible for large data sizes, even with substantial computational resources on a computer cluster. To deploy the resulting models, we extended the Bioclipse decision support framework to support models from LIBLINEAR and made our models of logD and solubility available from within Bioclipse.
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.
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.
Video Waterscrambling: Towards a Video Protection Scheme Based on the Disturbance of Motion Vectors
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.
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.
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.
He, Xingyu; Tong, Ningning; Hu, Xiaowei
2018-01-01
Compressive sensing has been successfully applied to inverse synthetic aperture radar (ISAR) imaging of moving targets. By exploiting the block sparse structure of the target image, sparse solution for multiple measurement vectors (MMV) can be applied in ISAR imaging and a substantial performance improvement can be achieved. As an effective sparse recovery method, sparse Bayesian learning (SBL) for MMV involves a matrix inverse at each iteration. Its associated computational complexity grows significantly with the problem size. To address this problem, we develop a fast inverse-free (IF) SBL method for MMV. A relaxed evidence lower bound (ELBO), which is computationally more amiable than the traditional ELBO used by SBL, is obtained by invoking fundamental property for smooth functions. A variational expectation-maximization scheme is then employed to maximize the relaxed ELBO, and a computationally efficient IF-MSBL algorithm is proposed. Numerical results based on simulated and real data show that the proposed method can reconstruct row sparse signal accurately and obtain clear superresolution ISAR images. Moreover, the running time and computational complexity are reduced to a great extent compared with traditional SBL methods.
In-situ, In-Memory Stateful Vector Logic Operations based on Voltage Controlled Magnetic Anisotropy.
Jaiswal, Akhilesh; Agrawal, Amogh; Roy, Kaushik
2018-04-10
Recently, the exponential increase in compute requirements demanded by emerging applications like artificial intelligence, Internet of things, etc. have rendered the state-of-art von-Neumann machines inefficient in terms of energy and throughput owing to the well-known von-Neumann bottleneck. A promising approach to mitigate the bottleneck is to do computations as close to the memory units as possible. One extreme possibility is to do in-situ Boolean logic computations by using stateful devices. Stateful devices are those that can act both as a compute engine and storage device, simultaneously. We propose such stateful, vector, in-memory operations using voltage controlled magnetic anisotropy (VCMA) effect in magnetic tunnel junctions (MTJ). Our proposal is based on the well known manufacturable 1-transistor - 1-MTJ bit-cell and does not require any modifications in the bit-cell circuit or the magnetic device. Instead, we leverage the very physics of the VCMA effect to enable stateful computations. Specifically, we exploit the voltage asymmetry of the VCMA effect to construct stateful IMP (implication) gate and use the precessional switching dynamics of the VCMA devices to propose a massively parallel NOT operation. Further, we show that other gates like AND, OR, NAND, NOR, NIMP (complement of implication) can be implemented using multi-cycle operations.
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.
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.
Advances in Viral Vector-Based TRAIL Gene Therapy for Cancer
International Nuclear Information System (INIS)
Norian, Lyse A.; James, Britnie R.; Griffith, Thomas S.
2011-01-01
Numerous biologic approaches are being investigated as anti-cancer therapies in an attempt to induce tumor regression while circumventing the toxic side effects associated with standard chemo- or radiotherapies. Among these, tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) has shown particular promise in pre-clinical and early clinical trials, due to its preferential ability to induce apoptotic cell death in cancer cells and its minimal toxicity. One limitation of TRAIL use is the fact that many tumor types display an inherent resistance to TRAIL-induced apoptosis. To circumvent this problem, researchers have explored a number of strategies to optimize TRAIL delivery and to improve its efficacy via co-administration with other anti-cancer agents. In this review, we will focus on TRAIL-based gene therapy approaches for the treatment of malignancies. We will discuss the main viral vectors that are being used for TRAIL gene therapy and the strategies that are currently being attempted to improve the efficacy of TRAIL as an anti-cancer therapeutic
Adams, Felix F; Heckl, Dirk; Hoffmann, Thomas; Talbot, Steven R; Kloos, Arnold; Thol, Felicitas; Heuser, Michael; Zuber, Johannes; Schambach, Axel; Schwarzer, Adrian
2017-09-01
RNA interference (RNAi) and CRISPR-Cas9-based screening systems have emerged as powerful and complementary tools to unravel genetic dependencies through systematic gain- and loss-of-function studies. In recent years, a series of technical advances helped to enhance the performance of virally delivered RNAi. For instance, the incorporation of short hairpin RNAs (shRNAs) into endogenous microRNA contexts (shRNAmiRs) allows the use of Tet-regulated promoters for synchronous onset of gene knockdown and precise interrogation of gene dosage effects. However, remaining challenges include lack of efficient cloning strategies, inconsistent knockdown potencies and leaky expression. Here, we present a simple, one-step cloning approach for rapid and efficient cloning of miR-30 shRNAmiR libraries. We combined a human miR-30 backbone retaining native flanking sequences with an optimized all-in-one lentiviral vector system for conditional RNAi to generate a versatile toolbox characterized by higher doxycycline sensitivity, reduced leakiness and enhanced titer. Furthermore, refinement of existing shRNA design rules resulted in substantially improved prediction of powerful shRNAs. Our approach was validated by accurate quantification of the knockdown potency of over 250 single shRNAmiRs. To facilitate access and use by the scientific community, an online tool was developed for the automated design of refined shRNA-coding oligonucleotides ready for cloning into our system. Copyright © 2017 Elsevier Ltd. All rights reserved.
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
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
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.
International Nuclear Information System (INIS)
Ishizuki, Shigeru; Nemoto, Toshiyuki; Kawai, Wataru; Watanabe, Hideo; Tanabe, Hidenobu; Kawasaki, Nobuo; Adachi, Masaaki; Ogasawara, Shinobu; Kume, Etsuo
1999-05-01
Several computer codes in the nuclear field have been vectorized, parallelized and transported on the FUJITSU VPP500 system and/or the AP3000 system at Center for Promotion of Computational Science and Engineering in Japan Atomic Energy Research Institute. We dealt with 14 codes in fiscal 1997. These results are reported in 3 parts, i.e., the vectorization part, the parallelization part and the porting part. In this report, we describe the porting. In this porting part, the porting of transient reactor analysis code TRAC-BF1 and Monte Carlo radiation transport code MCNP4A on the AP3000 are described. In addition, a modification of program libraries for command-driven interactive data analysis plotting program IPLOT is described. In the vectorization part, the vectorization of multidimensional two-fluid model code ACE-3D for evaluation of constitutive equations, statistical decay code SD and three-dimensional thermal analysis code for in-core test section (T2) of HENDEL SSPHEAT are described. In the parallelization part, the parallelization of cylindrical direct numerical simulation code CYLDNS44N, worldwide version of system for prediction of environmental emergency dose information code WSPEEDI, extension of quantum molecular dynamics code EQMD and three-dimensional non-steady compressible fluid dynamics code STREAM are described. (author)
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.
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.
Wang, Yong; Yuan, Fan
2006-01-01
It is a challenge to deliver therapeutic genes to tumor cells using viral vectors because (i) the size of these vectors are close to or larger than the space between fibers in extracellular matrix and (ii) viral proteins are potentially toxic in normal tissues. In general, gene delivery is hindered by various physiological barriers to virus transport from the site of injection to the nucleus of tumor cells and is limited by normal tissue tolerance of toxicity determined by local concentrations of transgene products and viral proteins. To illustrate the obstacles encountered in the delivery and yet limit the scope of discussion, this review focuses only on extracellular transport in solid tumors and distribution of viral vectors in normal organs after they are injected intravenously or intratumorally. This review also discusses current strategies for improving intratumoral transport and specificity of viral vectors.
Symbolic computer vector analysis
Stoutemyer, D. R.
1977-01-01
A MACSYMA program is described which performs symbolic vector algebra and vector calculus. The program can combine and simplify symbolic expressions including dot products and cross products, together with the gradient, divergence, curl, and Laplacian operators. The distribution of these operators over sums or products is under user control, as are various other expansions, including expansion into components in any specific orthogonal coordinate system. There is also a capability for deriving the scalar or vector potential of a vector field. Examples include derivation of the partial differential equations describing fluid flow and magnetohydrodynamics, for 12 different classic orthogonal curvilinear coordinate systems.
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.
Support vector machine learning-based fMRI data group analysis.
Wang, Ze; Childress, Anna R; Wang, Jiongjiong; Detre, John A
2007-07-15
To explore the multivariate nature of fMRI data and to consider the inter-subject brain response discrepancies, a multivariate and brain response model-free method is fundamentally required. Two such methods are presented in this paper by integrating a machine learning algorithm, the support vector machine (SVM), and the random effect model. Without any brain response modeling, SVM was used to extract a whole brain spatial discriminance map (SDM), representing the brain response difference between the contrasted experimental conditions. Population inference was then obtained through the random effect analysis (RFX) or permutation testing (PMU) on the individual subjects' SDMs. Applied to arterial spin labeling (ASL) perfusion fMRI data, SDM RFX yielded lower false-positive rates in the null hypothesis test and higher detection sensitivity for synthetic activations with varying cluster size and activation strengths, compared to the univariate general linear model (GLM)-based RFX. For a sensory-motor ASL fMRI study, both SDM RFX and SDM PMU yielded similar activation patterns to GLM RFX and GLM PMU, respectively, but with higher t values and cluster extensions at the same significance level. Capitalizing on the absence of temporal noise correlation in ASL data, this study also incorporated PMU in the individual-level GLM and SVM analyses accompanied by group-level analysis through RFX or group-level PMU. Providing inferences on the probability of being activated or deactivated at each voxel, these individual-level PMU-based group analysis methods can be used to threshold the analysis results of GLM RFX, SDM RFX or SDM PMU.
Jegadeeshwaran, R.; Sugumaran, V.
2015-02-01
Hydraulic brakes in automobiles are important components for the safety of passengers; therefore, the brakes are a good subject for condition monitoring. The condition of the brake components can be monitored by using the vibration characteristics. On-line condition monitoring by using machine learning approach is proposed in this paper as a possible solution to such problems. The vibration signals for both good as well as faulty conditions of brakes were acquired from a hydraulic brake test setup with the help of a piezoelectric transducer and a data acquisition system. Descriptive statistical features were extracted from the acquired vibration signals and the feature selection was carried out using the C4.5 decision tree algorithm. There is no specific method to find the right number of features required for classification for a given problem. Hence an extensive study is needed to find the optimum number of features. The effect of the number of features was also studied, by using the decision tree as well as Support Vector Machines (SVM). The selected features were classified using the C-SVM and Nu-SVM with different kernel functions. The results are discussed and the conclusion of the study is presented.
Directory of Open Access Journals (Sweden)
Yudong Zhang
2013-01-01
Full Text Available Automated abnormal brain detection is extremely of importance for clinical diagnosis. Over last decades numerous methods had been presented. In this paper, we proposed a novel hybrid system to classify a given MR brain image as either normal or abnormal. The proposed method first employed digital wavelet transform to extract features then used principal component analysis (PCA to reduce the feature space. Afterwards, we constructed a kernel support vector machine (KSVM with RBF kernel, using particle swarm optimization (PSO to optimize the parameters C and σ. Fivefold cross-validation was utilized to avoid overfitting. In the experimental procedure, we created a 90 images dataset brain downloaded from Harvard Medical School website. The abnormal brain MR images consist of the following diseases: glioma, metastatic adenocarcinoma, metastatic bronchogenic carcinoma, meningioma, sarcoma, Alzheimer, Huntington, motor neuron disease, cerebral calcinosis, Pick’s disease, Alzheimer plus visual agnosia, multiple sclerosis, AIDS dementia, Lyme encephalopathy, herpes encephalitis, Creutzfeld-Jakob disease, and cerebral toxoplasmosis. The 5-folded cross-validation classification results showed that our method achieved 97.78% classification accuracy, higher than 86.22% by BP-NN and 91.33% by RBF-NN. For the parameter selection, we compared PSO with those of random selection method. The results showed that the PSO is more effective to build optimal KSVM.
Directory of Open Access Journals (Sweden)
Anak Agung Putri Ratna
2017-06-01
Full Text Available Computerized cross-language plagiarism detection has recently become essential. With the scarcity of scientific publications in Bahasa Indonesia, many Indonesian authors frequently consult publications in English in order to boost the quantity of scientific publications in Bahasa Indonesia (which is currently rising. Due to the syntax disparity between Bahasa Indonesia and English, most of the existing methods for automated cross-language plagiarism detection do not provide satisfactory results. This paper analyses the probability of developing Latent Semantic Analysis (LSA for a computerized cross-language plagiarism detector for two languages with different syntax. To improve performance, various alterations in LSA are suggested. By using a linear vector quantization (LVQ classifier in the LSA and taking into account the Frobenius norm, output has reached up to 65.98% in accuracy. The results of the experiments showed that the best accuracy achieved is 87% with a document size of 6 words, and the document definition size must be kept below 10 words in order to maintain high accuracy. Additionally, based on experimental results, this paper suggests utilizing the frequency occurrence method as opposed to the binary method for the term–document matrix construction.
International Nuclear Information System (INIS)
Takei, Toshifumi; Doi, Shun; Matsumoto, Hideki; Muramatsu, Kazuhiro
2000-01-01
We have developed a concurrent visualization system RVSLIB (Real-time Visual Simulation Library). This paper shows the effectiveness of the system when it is applied to large-scale unsteady simulations, for which the conventional post-processing approach may no longer work, on high-performance parallel vector supercomputers. The system performs almost all of the visualization tasks on a computation server and uses compressed visualized image data for efficient communication between the server and the user terminal. We have introduced several techniques, including vectorization and parallelization, into the system to minimize the computational costs of the visualization tools. The performance of RVSLIB was evaluated by using an actual CFD code on an NEC SX-4. The computational time increase due to the concurrent visualization was at most 3% for a smaller (1.6 million) grid and less than 1% for a larger (6.2 million) one. (author)
On POD-based Deflation Vectors for DPCG applied to porous media problems
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
DEFF Research Database (Denmark)
Mikkelsen, J G; Lund, Anders Henrik; Duch, M
1999-01-01
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-imp....... 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.......-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...... harbouring the supposed RNA dimer-forming cis -elements and (ii) that copackaged retroviral RNAs can recombine despite pronounced sequence dissimilarity at the cross-over site(s) and within parts of the genome involved in RNA dimerization, encapsidation and strand transferring during reverse transcription...
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.
Design of a Two-level Adaptive Multi-Agent System for Malaria Vectors driven by an ontology
Directory of Open Access Journals (Sweden)
Etang Josiane
2007-07-01
Full Text Available Abstract Background The understanding of heterogeneities in disease transmission dynamics as far as malaria vectors are concerned is a big challenge. Many studies while tackling this problem don't find exact models to explain the malaria vectors propagation. Methods To solve the problem we define an Adaptive Multi-Agent System (AMAS which has the property to be elastic and is a two-level system as well. This AMAS is a dynamic system where the two levels are linked by an Ontology which allows it to function as a reduced system and as an extended system. In a primary level, the AMAS comprises organization agents and in a secondary level, it is constituted of analysis agents. Its entry point, a User Interface Agent, can reproduce itself because it is given a minimum of background knowledge and it learns appropriate "behavior" from the user in the presence of ambiguous queries and from other agents of the AMAS in other situations. Results Some of the outputs of our system present a series of tables, diagrams showing some factors like Entomological parameters of malaria transmission, Percentages of malaria transmission per malaria vectors, Entomological inoculation rate. Many others parameters can be produced by the system depending on the inputted data. Conclusion Our approach is an intelligent one which differs from statistical approaches that are sometimes used in the field. This intelligent approach aligns itself with the distributed artificial intelligence. In terms of fight against malaria disease our system offers opportunities of reducing efforts of human resources who are not obliged to cover the entire territory while conducting surveys. Secondly the AMAS can determine the presence or the absence of malaria vectors even when specific data have not been collected in the geographical area. In the difference of a statistical technique, in our case the projection of the results in the field can sometimes appeared to be more general.
Energy Technology Data Exchange (ETDEWEB)
Watanabe, Hideo; Kawai, Wataru; Nemoto, Toshiyuki [Fujitsu Ltd., Tokyo (Japan); and others
1997-12-01
Several computer codes in the nuclear field have been vectorized, parallelized and transported on the FUJITSU VPP500 system at Center for Promotion of Computational Science and Engineering in Japan Atomic Energy Research Institute. These results are reported in 3 parts, i.e., the vectorization part, the parallelization part and the porting part. In this report, we describe the parallelization. In this parallelization part, the parallelization of 2-Dimensional relativistic electromagnetic particle code EM2D, Cylindrical Direct Numerical Simulation code CYLDNS and molecular dynamics code for simulating radiation damages in diamond crystals DGR are described. In the vectorization part, the vectorization of two and three dimensional discrete ordinates simulation code DORT-TORT, gas dynamics analysis code FLOWGR and relativistic Boltzmann-Uehling-Uhlenbeck simulation code RBUU are described. And then, in the porting part, the porting of reactor safety analysis code RELAP5/MOD3.2 and RELAP5/MOD3.2.1.2, nuclear data processing system NJOY and 2-D multigroup discrete ordinate transport code TWOTRAN-II are described. And also, a survey for the porting of command-driven interactive data analysis plotting program IPLOT are described. (author)
Energy Technology Data Exchange (ETDEWEB)
Nemoto, Toshiyuki [Fujitsu Ltd., Tokyo (Japan); Kawasaki, Nobuo; Tanabe, Hidenobu [and others
1998-01-01
Several computer codes in the nuclear field have been vectorized, parallelized and transported on the FUJITSU VPP500 system at Center for Promotion of Computational Science and Engineering in Japan Atomic Energy Research Institute. These results are reported in 3 parts, i.e., the vectorization part, the parallelization part and the porting part. In this report, we describe the porting. In this porting part, the porting of reactor safety analysis code RELAP5/MOD3.2 and RELAP5/MOD3.2.1.2, nuclear data processing system NJOY and 2-D multigroup discrete ordinate transport code TWOTRAN-II are described. And also, a survey for the porting of command-driven interactive data analysis plotting program IPLOT are described. In the parallelization part, the parallelization of 2-Dimensional relativistic electromagnetic particle code EM2D, Cylindrical Direct Numerical Simulation code CYLDNS and molecular dynamics code for simulating radiation damages in diamond crystals DGR are described. And then, in the vectorization part, the vectorization of two and three dimensional discrete ordinates simulation code DORT-TORT, gas dynamics analysis code FLOWGR and relativistic Boltzmann-Uehling-Uhlenbeck simulation code RBUU are described. (author)