Search results for: nn based space vector modulator.

Authors: A. Peveri

Abstract:

The curvature space-time by the presence of material, this deformation must present a pattern of deformation, not random. Space is uniform, elastic and any modification that occurs in one part, causes a change in another.

This deformation exists, must be a constant value and is independent of the observer, and relates the amount of matter, the force caused by the curvature of space and surface space. This unit of space is defined in this study as PIL and represents a constant area of space, deformable in the direction and sense of the center of mass of the body. The PIL is curved and connected to the center of mass of the Earth, to get to that point, through all matter, thus forming part of any place between particles at atomic and subatomic levels. At these levels the space between each particle is flat, unlike the macro where the space curves.

Keywords: Space flat, Space curved, Unit of space, Deformation.

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Authors: R. Krishnamoorthi, N. Kannan

Abstract:

In this paper, a new algorithm for generating codebook is proposed for vector quantization (VQ) in image coding. The significant features of the training image vectors are extracted by using the proposed Orthogonal Polynomials based transformation. We propose to generate the codebook by partitioning these feature vectors into a binary tree. Each feature vector at a non-terminal node of the binary tree is directed to one of the two descendants by comparing a single feature associated with that node to a threshold. The binary tree codebook is used for encoding and decoding the feature vectors. In the decoding process the feature vectors are subjected to inverse transformation with the help of basis functions of the proposed Orthogonal Polynomials based transformation to get back the approximated input image training vectors. The results of the proposed coding are compared with the VQ using Discrete Cosine Transform (DCT) and Pairwise Nearest Neighbor (PNN) algorithm. The new algorithm results in a considerable reduction in computation time and provides better reconstructed picture quality.

Keywords: Orthogonal Polynomials, Image Coding, Vector Quantization, TSVQ, Binary Tree Classifier

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Authors: K. Nirmala Devi, V. Murali Bhaskaran

Abstract:

Traditional document representation for classification follows Bag of Words (BoW) approach to represent the term weights. The conventional method uses the Vector Space Model (VSM) to exploit the statistical information of terms in the documents and they fail to address the semantic information as well as order of the terms present in the documents. Although, the phrase based approach follows the order of the terms present in the documents rather than semantics behind the word. Therefore, a semantic concept based approach is used in this paper for enhancing the semantics by incorporating the ontology information. In this paper a novel method is proposed to forecast the intraday stock market price directional movement based on the sentiments from Twitter and money control news articles. The stock market forecasting is a very difficult and highly complicated task because it is affected by many factors such as economic conditions, political events and investor’s sentiment etc. The stock market series are generally dynamic, nonparametric, noisy and chaotic by nature. The sentiment analysis along with wisdom of crowds can automatically compute the collective intelligence of future performance in many areas like stock market, box office sales and election outcomes. The proposed method utilizes collective sentiments for stock market to predict the stock price directional movements. The collective sentiments in the above social media have powerful prediction on the stock price directional movements as up/down by using Granger Causality test.

Keywords: Bag of Words, Collective Sentiments, Ontology, Semantic relations, Sentiments, Social media, Stock Prediction, Twitter, Vector Space Model and wisdom of crowds.

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Authors: Mona F. M. Mursi, Ghazy M.R. Assassa, Hatim A. Aboalsamh, Khaled Alghathbar

Abstract:

The challenge in the case of image authentication is that in many cases images need to be subjected to non malicious operations like compression, so the authentication techniques need to be compression tolerant. In this paper we propose an image authentication system that is tolerant to JPEG lossy compression operations. A scheme for JPEG grey scale images is proposed based on a data embedding method that is based on a secret key and a secret mapping vector in the frequency domain. An encrypted feature vector extracted from the image DCT coefficients, is embedded redundantly, and invisibly in the marked image. On the receiver side, the feature vector from the received image is derived again and compared against the extracted watermark to verify the image authenticity. The proposed scheme is robust against JPEG compression up to a maximum compression of approximately 80%,, but sensitive to malicious attacks such as cutting and pasting.

Keywords: Authentication, DCT, JPEG, Watermarking.

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Authors: Nemat Abazari, Ilgin Sager

Abstract:

In this paper smooth trajectories are computed in the Lie group SO(2, 1) as a motion planning problem by assigning a Frenet frame to the rigid body system to optimize the cost function of the elastic energy which is spent to track a timelike curve in Minkowski space. A method is proposed to solve a motion planning problem that minimizes the integral of the Lorentz inner product of Darboux vector of a timelike curve. This method uses the coordinate free Maximum Principle of Optimal control and results in the theory of integrable Hamiltonian systems. The presence of several conversed quantities inherent in these Hamiltonian systems aids in the explicit computation of the rigid body motions.

Keywords: Optimal control, Hamiltonian vector field, Darboux vector, maximum principle, lie group, rigid body motion, Lorentz metric.

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Authors: M. K. Bhuyan, P. K. Bora, D. Ghosh

Abstract:

One very interesting field of research in Pattern Recognition that has gained much attention in recent times is Gesture Recognition. In this paper, we consider a form of dynamic hand gestures that are characterized by total movement of the hand (arm) in space. For these types of gestures, the shape of the hand (palm) during gesturing does not bear any significance. In our work, we propose a model-based method for tracking hand motion in space, thereby estimating the hand motion trajectory. We employ the dynamic time warping (DTW) algorithm for time alignment and normalization of spatio-temporal variations that exist among samples belonging to the same gesture class. During training, one template trajectory and one prototype feature vector are generated for every gesture class. Features used in our work include some static and dynamic motion trajectory features. Recognition is accomplished in two stages. In the first stage, all unlikely gesture classes are eliminated by comparing the input gesture trajectory to all the template trajectories. In the next stage, feature vector extracted from the input gesture is compared to all the class prototype feature vectors using a distance classifier. Experimental results demonstrate that our proposed trajectory estimator and classifier is suitable for Human Computer Interaction (HCI) platform.

Keywords: Hand gesture, human computer interaction, key video object plane, dynamic time warping.

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Authors: Durga Prasad Sapkota, Madhu Sudan Kayastha, Koichi Wakita

Abstract:

In this paper, we have compared and analyzed the electroabsorption properties between with and without excitonic effect bulk in high purity GaAs spatial light modulator for optical fiber communication network. The eletroabsorption properties such as absorption spectra, change in absorption spectra, change in refractive index and extinction ration has been calculated. We have also compared the result of absorption spectra and change in absorption spectra with the experimental results and found close agreement with experimental results.

Keywords: Exciton, Refractive index change, Extinction ratio.

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Authors: Jin-Woong Park, Sun-Kyung Kang, Sung-Tae Jung

Abstract:

In this study, we propose a tongue diagnosis method which detects the tongue from face image and divides the tongue area into six areas, and finally generates tongue coating ratio of each area. To detect the tongue area from face image, we use ASM as one of the active shape models. Detected tongue area is divided into six areas widely used in the Korean traditional medicine and the distribution of tongue coating of the six areas is examined by SVM(Support Vector Machine). For SVM, we use a 3-dimensional vector calculated by PCA(Principal Component Analysis) from a 12-dimentional vector consisting of RGB, HIS, Lab, and Luv. As a result, we detected the tongue area stably using ASM and found that PCA and SVM helped raise the ratio of tongue coating detection.

Keywords: Active Shape Model, Principal Component Analysis, Support Vector Machine, Tongue diagnosis

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Authors: Tamal Bose, Zhurun Zhang, Miloje Radenkovic, Ojas Chauhan

Abstract:

In this paper, a novel approach is presented for designing multiplier-free state-space digital filters. The multiplier-free design is obtained by finding power-of-2 coefficients and also quantizing the state variables to power-of-2 numbers. Expressions for the noise variance are derived for the quantized state vector and the output of the filter. A “structuretransformation matrix" is incorporated in these expressions. It is shown that quantization effects can be minimized by properly designing the structure-transformation matrix. Simulation results are very promising and illustrate the design algorithm.

Keywords: Digital filters, minimum noise, multiplier-free, quantization, state-space.

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Authors: Khin May Win, Nan Sai Moon Kham

Abstract:

Identification of cancer genes that might anticipate the clinical behaviors from different types of cancer disease is challenging due to the huge number of genes and small number of patients samples. The new method is being proposed based on supervised learning of classification like support vector machines (SVMs).A new solution is described by the introduction of the Maximized Margin (MM) in the subset criterion, which permits to get near the least generalization error rate. In class prediction problem, gene selection is essential to improve the accuracy and to identify genes for cancer disease. The performance of the new method was evaluated with real-world data experiment. It can give the better accuracy for classification.

Keywords: Microarray data, feature selection, recursive featureelimination, support vector machines.

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Authors: Tomoya Okubo, Kuninori Nakamura, Shin-ichi Mayekawa

Abstract:

In this research, a latent class vector model for pairwise data is formulated. As compared to the basic vector model, this model yields consistent estimates of the parameters since the number of parameters to be estimated does not increase with the number of subjects. The result of the analysis reveals that the model was stable and could classify each subject to the latent classes representing the typical scales used by these subjects.

Keywords: finite mixture models, latent class analysis, Thrustone's paired comparison method, vector model

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Authors: Jatinderkumar R. Saini, Apurva A. Desai

Abstract:

e-mail has become an important means of electronic communication but the viability of its usage is marred by Unsolicited Bulk e-mail (UBE) messages. UBE consists of many types like pornographic, virus infected and 'cry-for-help' messages as well as fake and fraudulent offers for jobs, winnings and medicines. UBE poses technical and socio-economic challenges to usage of e-mails. To meet this challenge and combat this menace, we need to understand UBE. Towards this end, the current paper presents a content-based textual analysis of more than 2700 body enhancement medicinal UBE. Technically, this is an application of Text Parsing and Tokenization for an un-structured textual document and we approach it using Bag Of Words (BOW) and Vector Space Document Model techniques. We have attempted to identify the most frequently occurring lexis in the UBE documents that advertise various products for body enhancement. The analysis of such top 100 lexis is also presented. We exhibit the relationship between occurrence of a word from the identified lexis-set in the given UBE and the probability that the given UBE will be the one advertising for fake medicinal product. To the best of our knowledge and survey of related literature, this is the first formal attempt for identification of most frequently occurring lexis in such UBE by its textual analysis. Finally, this is a sincere attempt to bring about alertness against and mitigate the threat of such luring but fake UBE.

Keywords: Body Enhancement, Lexis, Medicinal, Unsolicited Bulk e-mail (UBE), Vector Space Document Model, Viagra

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Authors: Shenpu Liu

Abstract:

Space syntax, a paradigm of the urban research, which manifests people’s intuitive and abstract perception of a material space with a solid mathematical way, explores how space represents its social characteristics. Taking Xiazhuang village and Shijia Village in Huzhou as an example and focusing on inward structure and street space, this article recognizes the connotative significance of the settlement with the aid of space syntax theory and quantitative analysis method from the perspective of spatial configuration to present relevant suggestions for its future planning and provides references for traditional rural settlement protection.

Keywords: Shijia village, space configuration, space syntax, traditional rural settlement, Xiazhuang village.

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Authors: G. R. Arab Markadeh, J. Soltani, N. R. Abjadi, M. Hajian

Abstract:

In this paper, a direct torque control - space vector modulation (DTC-SVM) scheme is presented for a six-phase speed and voltage sensorless induction motor (IM) drive. The decoupled torque and stator flux control is achieved based on IM stator flux field orientation. The rotor speed is detected by on-line estimating of the rotor angular slip speed and stator vector flux speed. In addition, a simple method is introduced to estimate the stator resistance. Moreover in this control scheme the voltage sensors are eliminated and actual motor phase voltages are approximated by using PWM inverter switching times and the dc link voltage. Finally, some simulation and experimental results are presented to verify the effectiveness and capability of the proposed control scheme.

Keywords: Stator FOC, Multiphase motors, sensorless.

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Authors: Tatjana Eitrich, Bruno Lang

Abstract:

This work deals with aspects of support vector learning for large-scale data mining tasks. Based on a decomposition algorithm that can be run in serial and parallel mode we introduce a data transformation that allows for the usage of an expensive generalized kernel without additional costs. In order to speed up the decomposition algorithm we analyze the problem of working set selection for large data sets and analyze the influence of the working set sizes onto the scalability of the parallel decomposition scheme. Our modifications and settings lead to improvement of support vector learning performance and thus allow using extensive parameter search methods to optimize classification accuracy.

Keywords: Support Vector Machines, Shared Memory Parallel Computing, Large Data

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Authors: S. Deswal, M. Pal

Abstract:

Presently various computational techniques are used in modeling and analyzing environmental engineering data. In the present study, an intra-comparison of polynomial and radial basis kernel functions based on Support Vector Regression and, in turn, an inter-comparison with Multi Linear Regression has been attempted in modeling mass transfer capacity of vertical (θ = 90O) and inclined (θ multiple plunging jets (varying from 1 to 16 numbers). The data set used in this study consists of four input parameters with a total of eighty eight cases, forty four each for vertical and inclined multiple plunging jets. For testing, tenfold cross validation was used. Correlation coefficient values of 0.971 and 0.981 along with corresponding root mean square error values of 0.0025 and 0.0020 were achieved by using polynomial and radial basis kernel functions based Support Vector Regression respectively. An intra-comparison suggests improved performance by radial basis function in comparison to polynomial kernel based Support Vector Regression. Further, an inter-comparison with Multi Linear Regression (correlation coefficient = 0.973 and root mean square error = 0.0024) reveals that radial basis kernel functions based Support Vector Regression performs better in modeling and estimating mass transfer by multiple plunging jets.

Keywords: Mass transfer, multiple plunging jets, polynomial and radial basis kernel functions, Support Vector Regression.

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Authors: Ren-Jieh Kuo, Chun-Shou Huang

Abstract:

The research of juice flavor forecasting has become more important in China. Due to the fast economic growth in China, many different kinds of juices have been introduced to the market. If a beverage company can understand their customers’ preference well, the juice can be served more attractive. Thus, this study intends to introducing the basic theory and computing process of grapes juice flavor forecasting based on support vector regression (SVR). Applying SVR, BPN, and LR to forecast the flavor of grapes juice in real data shows that SVR is more suitable and effective at predicting performance.

Keywords: Flavor forecasting, artificial neural networks, support vector regression, grape juice flavor.

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Authors: Abdullah E. Al-Mazrooei

Abstract:

In this paper, we present a new kind of the bilinear systems in the form of state space model. The evolution of this system depends on the product of state vector by its self. The well known Lotak Volterra and Lorenz models are special cases of this new model. We also present here a generalization of Kalman filter which is suitable to work with the new bilinear model. An application to real measurements is introduced to illustrate the efficiency of the proposed algorithm.

Keywords: Bilinear systems, state space model, Kalman filter.

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Authors: Kheldoun Aissa, Khodja Djalal Eddine

Abstract:

The iron loss is a source of detuning in vector controlled induction motor drives if the classical rotor vector controller is used for decoupling. In fact, the field orientation will not be satisfied and the output torque will not truck the reference torque mostly used by Loss Model Controllers (LMCs). In addition, this component of loss, among others, may be excessive if the vector controlled induction motor is driving light loads. In this paper, the series iron loss model is used to develop a vector controller immune to iron loss effect and then an LMC to minimize the total power loss using the torque generated by the speed controller.

Keywords: Field Oriented Controller, Induction Motor, Loss ModelController, Series Iron Loss.

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Authors: Hani M. Aburas

Abstract:

“Dengue" is an African word meaning “bone breaking" because it causes severe joint and muscle pain that feels like bones are breaking. It is an infectious disease mainly transmitted by female mosquito, Aedes aegypti, and causes four serotypes of dengue viruses. In recent years, a dramatic increase in the dengue fever confirmed cases around the equator-s belt has been reported. Several conventional indices have been designed so far to monitor the transmitting vector populations known as House Index (HI), Container Index (CI), Breteau Index (BI). However, none of them describes the adult mosquito population size which is important to direct and guide comprehensive control strategy operations since number of infected people has a direct relationship with the vector density. Therefore, it is crucial to know the population size of the transmitting vector in order to design a suitable and effective control program. In this context, a study is carried out to report a new statistical index, ABURAS Index, using Poisson distribution based on the collection of vector population in Jeddah Governorate, Saudi Arabia.

Keywords: Poisson distribution, statistical index, prediction, Aedes aegypti.

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Authors: T. C. B. N. Assunção, J. L. Silvino, P. Resende

Abstract:

The winding hot-spot temperature is one of the most critical parameters that affect the useful life of the power transformers. The winding hot-spot temperature can be calculated as function of the top-oil temperature that can estimated by using the ambient temperature and transformer loading measured data. This paper proposes the estimation of the top-oil temperature by using a method based on Least Squares Support Vector Machines approach. The estimated top-oil temperature is compared with measured data of a power transformer in operation. The results are also compared with methods based on the IEEE Standard C57.91-1995/2000 and Artificial Neural Networks. It is shown that the Least Squares Support Vector Machines approach presents better performance than the methods based in the IEEE Standard C57.91-1995/2000 and artificial neural networks.

Keywords: Artificial Neural Networks, Hot-spot Temperature, Least Squares Support Vector, Top-oil Temperature.

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Authors: P. Sawatnatee, S. Udomsuk, K-N. Areerak, K-L. Areerak, A. Srikaew

Abstract:

The paper presents how to design the indirect vector control of three-phase induction motor drive systems using the artificial intelligence technique called the adaptive tabu search. The results from the simulation and the experiment show that the drive system with the controller designed from the proposed method can provide the best output speed response compared with those of the conventional method. The controller design using the proposed technique can be used to create the software package for engineers to achieve the optimal controller design of the induction motor speed control based on the indirect vector concept.

 

Keywords: Indirect Vector Control, Induction Motor, Adaptive Tabu Search, Control Design, Artificial Intelligence.

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Authors: H. B. Kekre, Tanuja K. Sarode, Bhakti Raul

Abstract:

In this paper we propose segmentation approach based on Vector Quantization technique. Here we have used Kekre-s fast codebook generation algorithm for segmenting low-altitude aerial image. This is used as a preprocessing step to form segmented homogeneous regions. Further to merge adjacent regions color similarity and volume difference criteria is used. Experiments performed with real aerial images of varied nature demonstrate that this approach does not result in over segmentation or under segmentation. The vector quantization seems to give far better results as compared to conventional on-the-fly watershed algorithm.

Keywords: Image Segmentation, , Codebook, Codevector, data compression, Encoding

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Authors: Tatjana Eitrich, Bruno Lang

Abstract:

This work deals with aspects of support vector machine learning for large-scale data mining tasks. Based on a decomposition algorithm for support vector machine training that can be run in serial as well as shared memory parallel mode we introduce a transformation of the training data that allows for the usage of an expensive generalized kernel without additional costs. We present experiments for the Gaussian kernel, but usage of other kernel functions is possible, too. In order to further speed up the decomposition algorithm we analyze the critical problem of working set selection for large training data sets. In addition, we analyze the influence of the working set sizes onto the scalability of the parallel decomposition scheme. Our tests and conclusions led to several modifications of the algorithm and the improvement of overall support vector machine learning performance. Our method allows for using extensive parameter search methods to optimize classification accuracy.

Keywords: Support Vector Machine Training, Multi-ParameterKernels, Shared Memory Parallel Computing, Large Data

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Authors: Elizabeth B. Varghese, M. Wilscy

Abstract:

A face recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame. A lot of algorithms have been proposed for face recognition. Vector Quantization (VQ) based face recognition is a novel approach for face recognition. Here a new codebook generation for VQ based face recognition using Integrated Adaptive Fuzzy Clustering (IAFC) is proposed. IAFC is a fuzzy neural network which incorporates a fuzzy learning rule into a competitive neural network. The performance of proposed algorithm is demonstrated by using publicly available AT&T database, Yale database, Indian Face database and a small face database, DCSKU database created in our lab. In all the databases the proposed approach got a higher recognition rate than most of the existing methods. In terms of Equal Error Rate (ERR) also the proposed codebook is better than the existing methods.

Keywords: Face Recognition, Vector Quantization, Integrated Adaptive Fuzzy Clustering, Self Organization Map.

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Authors: N. Subramanian, C. Murugesan

Abstract:

The double difference sequence space I2 (M, of fuzzy numbers for both 1 < p < oo and 0 < p < 1, is introduced. Some general properties of this sequence space are studied. Some inclusion relations involving this sequence space are obtained.

Keywords: Orlicz function, solid space, metric space, completeness

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Authors: Roozbeh Molavi, Davood A. Khaburi

Abstract:

The permanent magnet synchronous motor (PMSM) is very useful in many applications. Vector control of PMSM is popular kind of its control. In this paper, at first an optimal vector control for PMSM is designed and then results are compared with conventional vector control. Then, it is assumed that the measurements are noisy and linear quadratic Gaussian (LQG) methodology is used to filter the noises. The results of noisy optimal vector control and filtered optimal vector control are compared to each other. Nonlinearity of PMSM and existence of inverter in its control circuit caused that the system is nonlinear and time-variant. With deriving average model, the system is changed to nonlinear time-invariant and then the nonlinear system is converted to linear system by linearization of model around average values. This model is used to optimize vector control then two optimal vector controls are compared to each other. Simulation results show that the performance and robustness to noise of the control system has been highly improved.

Keywords: Kalman filter, Linear quadratic Gaussian (LQG), Linear quadratic regulator (LQR), Permanent-Magnet synchronousmotor (PMSM).

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Authors: Guo Xiuhua, Sun Tao, Wu Haifeng, He Wen, Liang Zhigang, Zhang Mengxia, Guo Aimin, Wang Wei

Abstract:

Purpose: To explore the use of Curvelet transform to extract texture features of pulmonary nodules in CT image and support vector machine to establish prediction model of small solitary pulmonary nodules in order to promote the ratio of detection and diagnosis of early-stage lung cancer. Methods: 2461 benign or malignant small solitary pulmonary nodules in CT image from 129 patients were collected. Fourteen Curvelet transform textural features were as parameters to establish support vector machine prediction model. Results: Compared with other methods, using 252 texture features as parameters to establish prediction model is more proper. And the classification consistency, sensitivity and specificity for the model are 81.5%, 93.8% and 38.0% respectively. Conclusion: Based on texture features extracted from Curvelet transform, support vector machine prediction model is sensitive to lung cancer, which can promote the rate of diagnosis for early-stage lung cancer to some extent.

Keywords: CT image, Curvelet transform, Small pulmonary nodules, Support vector machines, Texture extraction.

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Authors: Zhiyong Li, Zhigang Chen, Chao Fu, Shipeng Zhang

Abstract:

Load forecasting has always been the essential part of an efficient power system operation and planning. A novel approach based on support vector machines is proposed in this paper for annual power load forecasting. Different kernel functions are selected to construct a combinatorial algorithm. The performance of the new model is evaluated with a real-world dataset, and compared with two neural networks and some traditional forecasting techniques. The results show that the proposed method exhibits superior performance.

Keywords: combinatorial algorithm, data mining, load forecasting, support vector machines

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Authors: Wei Zhang, Yao-Yu Li, Yi-Fan Zhu, Qun Li, Wei-Ping Wang

Abstract:

It-s known that incorporating prior knowledge into support vector regression (SVR) can help to improve the approximation performance. Most of researches are concerned with the incorporation of knowledge in the form of numerical relationships. Little work, however, has been done to incorporate the prior knowledge on the structural relationships among the variables (referred as to Structural Prior Knowledge, SPK). This paper explores the incorporation of SPK in SVR by constructing appropriate admissible support vector kernel (SV kernel) based on the properties of reproducing kernel (R.K). Three-levels specifications of SPK are studied with the corresponding sub-levels of prior knowledge that can be considered for the method. These include Hierarchical SPK (HSPK), Interactional SPK (ISPK) consisting of independence, global and local interaction, Functional SPK (FSPK) composed of exterior-FSPK and interior-FSPK. A convenient tool for describing the SPK, namely Description Matrix of SPK is introduced. Subsequently, a new SVR, namely Motivated Support Vector Regression (MSVR) whose structure is motivated in part by SPK, is proposed. Synthetic examples show that it is possible to incorporate a wide variety of SPK and helpful to improve the approximation performance in complex cases. The benefits of MSVR are finally shown on a real-life military application, Air-toground battle simulation, which shows great potential for MSVR to the complex military applications.

Keywords: admissible support vector kernel, reproducing kernel, structural prior knowledge, motivated support vector regression

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