Search results for: Park's vector approach

Authors: Daniel I. Morariu, Lucian N. Vintan, Volker Tresp

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Text categorization is the problem of classifying text documents into a set of predefined classes. After a preprocessing step, the documents are typically represented as large sparse vectors. When training classifiers on large collections of documents, both the time and memory restrictions can be quite prohibitive. This justifies the application of feature selection methods to reduce the dimensionality of the document-representation vector. In this paper, we present three feature selection methods: Information Gain, Support Vector Machine feature selection called (SVM_FS) and Genetic Algorithm with SVM (called GA_SVM). We show that the best results were obtained with GA_SVM method for a relatively small dimension of the feature vector.

Keywords: Feature Selection, Learning with Kernels, Support Vector Machine, Genetic Algorithm, and Classification.

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Authors: Chutarat Boontho

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The purposes of this study were as follows to evaluate the economic value of Phu Kradueng National Park by the travel cost method (TCM) and the contingent valuation method (CVM) and to estimate the demand for traveling and the willingness to pay. The data for this study were collected by conducting two large scale surveys on users and non-users. A total of 1,016 users and 1,034 non-users were interviewed. The data were analyzed using multiple linear regression analysis, logistic regression model and the consumer surplus (CS) was the integral of demand function for trips. The survey found, were as follows: 1)Using the travel cost method which provides an estimate of direct benefits to park users, we found that visitors- total willingness to pay per visit was 2,284.57 bath, of which 958.29 bath was travel cost, 1,129.82 bath was expenditure for accommodation, food, and services, and 166.66 bath was consumer surplus or the visitors -net gain or satisfaction from the visit (the integral of demand function for trips). 2) Thai visitors to Phu Kradueng National Park were further willing to pay an average of 646.84 bath per head per year to ensure the continued existence of Phu Kradueng National Park and to preserve their option to use it in the future. 3) Thai non-visitors, on the other hand, are willing to pay an average of 212.61 bath per head per year for the option and existence value provided by the Park. 4) The total economic value of Phu Kradueng National Park to Thai visitors and non-visitors taken together stands today at 9,249.55 million bath per year. 5) The users- average willingness to pay for access to Phu Kradueng National Park rises from 40 bath to 84.66 bath per head per trip for improved services such as road improvement, increased cleanliness, and upgraded information. This paper was needed to investigate of the potential market demand for bio prospecting in Phu Kradueng national Park and to investigate how a larger share of the economic benefits of tourism could be distributed income to the local residents.

Keywords: Contingent Valuation Method, Travel Cost Method, Consumer surplus.

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Authors: Gehad S. Kaseb, Mona F. Ahmed

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Arabic Sentiment Analysis (SA) is one of the most common research fields with many open areas. This paper proposes different pre-processing steps and a modified methodology to improve the accuracy using normal Support Vector Machine (SVM) classification. The paper works on two datasets, Arabic Sentiment Tweets Dataset (ASTD) and Extended Arabic Tweets Sentiment Dataset (Extended-ATSD), which are publicly available for academic use. The results show that the classification accuracy approaches 86%.

Keywords: Arabic, hybrid classification, sentiment analysis, tweets.

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Authors: Daningsih Sulaeman, Achmad Sjarmidi, Djoko T. Iskandar

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Komodo National Park can be associated with the implementation of ecotourism program. The result of Principal Components Analysis is synthesized, tested, and compared to the basic concept of ecotourism with some field adjustments. Principal aspects of professional management should involve ecotourism and wildlife welfare. The awareness should be focused on the future of the Natural Park as 7^th Wonder Natural Heritage and its wildlife components, free from human wastes and beneficial to wildlife and local people. According to perceptions and expectations of visitors from various results of tourism programs, the visitor’s perceptions showed that the tourism management in Komodo National Park should pay more attention to visitor's satisfaction and expectation and gives positive impact directly to the ecosystem sustainability, local community and transparency to the conservation program.

Keywords: 7th Wonders of Nature, Ecotourism, Komodo dragon, visitor’s perceptions, wildlife management.

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Authors: Chanon Aphirukmatakun, Natasha Dejdumrong

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In image processing and visualization, comparing two bitmapped images needs to be compared from their pixels by matching pixel-by-pixel. Consequently, it takes a lot of computational time while the comparison of two vector-based images is significantly faster. Sometimes these raster graphics images can be approximately converted into the vector-based images by various techniques. After conversion, the problem of comparing two raster graphics images can be reduced to the problem of comparing vector graphics images. Hence, the problem of comparing pixel-by-pixel can be reduced to the problem of polynomial comparisons. In computer aided geometric design (CAGD), the vector graphics images are the composition of curves and surfaces. Curves are defined by a sequence of control points and their polynomials. In this paper, the control points will be considerably used to compare curves. The same curves after relocated or rotated are treated to be equivalent while two curves after different scaled are considered to be similar curves. This paper proposed an algorithm for comparing the polynomial curves by using the control points for equivalence and similarity. In addition, the geometric object-oriented database used to keep the curve information has also been defined in XML format for further used in curve comparisons.

Keywords: Bezier curve, Said-Ball curve, Wang-Ball curve, DP curve, CAGD, comparison, geometric object database.

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Authors: Seungwon Choi, Seungri Jin, Ayoung Heo, Jung-Hyun Park, Dong-Jo Park

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This paper deals with wireless relay communication systems in which multiple sources transmit information to the destination node by the help of multiple relays. We consider a signal forwarding technique based on the minimum mean-square error (MMSE) approach with multiple antennas for each relay. A source-relay-destination joint design strategy is proposed with power constraints at the destination and the source nodes. Simulation results confirm that the proposed joint design method improves the average MSE performance compared with that of conventional MMSE relaying schemes.

Keywords: minimum mean squre error (MMSE), multiple relay, MIMO.

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Authors: Qiuyu Dai, Haochong Zhang, Xiangrong Liu

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Efficient matrix-vector multiplication with diagonal sparse matrices is pivotal in a multitude of computational domains, ranging from scientific simulations to machine learning workloads. When encoded in the conventional Diagonal (DIA) format, these matrices often induce computational overheads due to extensive zero-padding and non-linear memory accesses, which can hamper the computational throughput, and elevate the usage of precious compute and memory resources beyond necessity. The ’DIA-Adaptive’ approach, a methodological enhancement introduced in this paper, confronts these challenges head-on by leveraging the advanced parallel instruction sets embedded within Machine Learning Units (MLUs). This research presents a thorough analysis of the DIA-Adaptive scheme’s efficacy in optimizing Sparse Matrix-Vector Multiplication (SpMV) operations. The scope of the evaluation extends to a variety of hardware architectures, examining the repercussions of distinct thread allocation strategies and cluster configurations across multiple storage formats. A dedicated computational kernel, intrinsic to the DIA-Adaptive approach, has been meticulously developed to synchronize with the nuanced performance characteristics of MLUs. Empirical results, derived from rigorous experimentation, reveal that the DIA-Adaptive methodology not only diminishes the performance bottlenecks associated with the DIA format but also exhibits pronounced enhancements in execution speed and resource utilization. The analysis delineates a marked improvement in parallelism, showcasing the DIA-Adaptive scheme’s ability to adeptly manage the interplay between storage formats, hardware capabilities, and algorithmic design. The findings suggest that this approach could set a precedent for accelerating SpMV tasks, thereby contributing significantly to the broader domain of high-performance computing and data-intensive applications.

Keywords: Adaptive method, DIA, diagonal sparse matrices, MLU, sparse matrix-vector multiplication.

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Authors: Myungraee Cha, Jun Seok Kim, Seung Hwan Park, Jun-Geol Baek

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In manufacturing industries, development of measurement leads to increase the number of monitoring variables and eventually the importance of multivariate control comes to the fore. Statistical process control (SPC) is one of the most widely used as multivariate control chart. Nevertheless, SPC is restricted to apply in processes because its assumption of data as following specific distribution. Unfortunately, process data are composed by the mixture of several processes and it is hard to estimate as one certain distribution. To alternative conventional SPC, therefore, nonparametric control chart come into the picture because of the strength of nonparametric control chart, the absence of parameter estimation. SVDD based control chart is one of the nonparametric control charts having the advantage of flexible control boundary. However,basic concept of SVDD has been an oversight to the important of data characteristic, density distribution. Therefore, we proposed DW-SVDD (Density Weighted SVDD) to cover up the weakness of conventional SVDD. DW-SVDD makes a new attempt to consider dense of data as introducing the notion of density Weight. We extend as control chart using new proposed SVDD and a simulation study of various distributional data is conducted to demonstrate the improvement of performance.

Keywords: Density estimation, Multivariate control chart, Oneclass classification, Support vector data description (SVDD)

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Authors: Hiba Basim Alwan, Ku Ruhana Ku-Mahamud

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A direction for ACO is to optimize continuous and mixed (discrete and continuous) variables in solving problems with various types of data. Support Vector Machine (SVM), which originates from the statistical approach, is a present day classification technique. The main problems of SVM are selecting feature subset and tuning the parameters. Discretizing the continuous value of the parameters is the most common approach in tuning SVM parameters. This process will result in loss of information which affects the classification accuracy. This paper presents two algorithms that can simultaneously tune SVM parameters and select the feature subset. The first algorithm, ACO_R-SVM, will tune SVM parameters, while the second IACO_MV-R-SVM algorithm will simultaneously tune SVM parameters and select the feature subset. Three benchmark UCI datasets were used in the experiments to validate the performance of the proposed algorithms. The results show that the proposed algorithms have good performances as compared to other approaches.

Keywords: Continuous ant colony optimization, incremental continuous ant colony, simultaneous optimization, support vector machine.

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Authors: O. S. Eludoyin, A. A. Aiyeloja, O. C. Ndife

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The study investigated the spatial analysis of trees composition, diversity and richness in the built up area of University of Port Harcourt, Nigeria. Four quadrats of 25m x 25m size were laid randomly in each of the three parks and inventories of trees ≥10cm girth at breast height were taken and used to calculate the species composition, diversity and richness. Results showed that species composition and diversity in Abuja Park was the highest with 134 species and 0.866 respectively while the species richness was highest in Choba Park with a value of 2.496. The correlation between the size of park (spatial coverage) and species composition was 0.99 while the correlation between the size of the park and species diversity was 0.78. There was direct relationship between species composition and diversity while the relationship between species composition and species richness was inversely proportional. Rational use of these resources is encouraged.

Keywords: Built up area, composition, diversity, richness, spatial analysis, urban tree.

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Authors: Mona Soliman Habib

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This paper explores the scalability issues associated with solving the Named Entity Recognition (NER) problem using Support Vector Machines (SVM) and high-dimensional features. The performance results of a set of experiments conducted using binary and multi-class SVM with increasing training data sizes are examined. The NER domain chosen for these experiments is the biomedical publications domain, especially selected due to its importance and inherent challenges. A simple machine learning approach is used that eliminates prior language knowledge such as part-of-speech or noun phrase tagging thereby allowing for its applicability across languages. No domain-specific knowledge is included. The accuracy measures achieved are comparable to those obtained using more complex approaches, which constitutes a motivation to investigate ways to improve the scalability of multiclass SVM in order to make the solution more practical and useable. Improving training time of multi-class SVM would make support vector machines a more viable and practical machine learning solution for real-world problems with large datasets. An initial prototype results in great improvement of the training time at the expense of memory requirements.

Keywords: Named entity recognition, support vector machines, language independence, bioinformatics.

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Authors: Heung Seok Chae, Jaegeol Park

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Recently, there have been an increasing interest in RFID system and RFID systems have been applied to various applications. Load balancing is a fundamental technique for providing scalability of systems by moving workload from overloaded nodes to under-loaded nodes. This paper presents an approach to adaptive load balancing for RFID middlewares. Workloads of RFID middlewares can have a considerable variation according to the location of the connected RFID readers and can abruptly change at a particular instance. The proposed approach considers those characteristics of RFID middle- wares to provide an efficient load balancing.

Keywords: RFID middleware, Adaptive load balancing.

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Authors: Tapan Trivedi, Pramod Agarwal, Rajendrasinh Jadeja, Pragnesh Bhatt

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Space Vector Pulse Width Modulation is popular for variable frequency drives. The method has several advantages over carried based PWM and is computation intensive. The implementation of SVPWM for multilevel inverter requires special attention and at the same time consumes considerable resources. Due to faster processing power and reduced over all computational burden, FPGAs are being investigated as an alternative for other controllers. In this paper, a space vector PWM algorithm is implemented using FPGA which requires less computational area and is modular in structure. The algorithm is verified experimentally for Neutral Point Clamped inverter using FPGA development board xc3s5000-4fg900.

Keywords: Modular structure, Multilevel inverter, Space Vector PWM, Switching States.

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Authors: Chu-Chu, Liao

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The main purpose of this study is to analyze the relationship of leisure agriculture park visitors on tourist destination image, environmental perception, travel experiences and revisiting willingness. This study used questionnaires to Xinshe leisure agriculture park visitors- targeted convenience sampling manner total of 636 valid questionnaires. Valid questionnaires by descriptive statistics, correlation analysis and multiple regression analysis, the study found that: 1. The agricultural park visitors- correlations exist between the destination image, perception of the environment, tourism experience and revisiting willingness. 2."Excellent facilities and services", "space atmosphere comfortable" and "the spacious paternity outdoor space" imagery, of visitors- "revisiting willingness predict. 3. Visitors- in leisure agriculture park "environmental perception" and "travel experience, future revisiting willingness predict. According to the analysis of the results, the study not only operate on the recommendations of the leisure farm owners also provide follow-up study direction for future researchers.

Keywords: Leisure farms, image, travel experience, revisiting willingness, environmental perception.

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Authors: G. Das, S. Das, P. Purkait, A. Dasgupta, M. Kumar

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Properly sized capacitor banks are connected across induction motors for several reasons including power factor correction, reducing distortions, increasing capacity, etc. Total harmonic distortion (THD) and power factor (PF) are used in such cases to quantify the improvements obtained through connection of the external capacitor banks. On the other hand, one of the methods for assessing the motor internal condition is by the use of Park-s pattern analysis. In spite of taking adequate precautionary measures, the capacitor banks may sometimes malfunction. Such a minor fault in the capacitor bank is often not apparently discernible. This may however, give rise to substantial degradation of power factor correction performance and may also damage the supply profile. The case is more severe with the fact that the Park-s pattern gets distorted due to such external capacitor faults, and can give anomalous results about motor internal fault analyses. The aim of this paper is to present simulation and hardware laboratory test results to have an understanding of the anomalies in harmonic distortion and Park-s pattern analyses in induction motors due to capacitor bank defects.

Keywords: Capacitor bank, harmonic distortion, induction motor, Park's pattern, PSCAD simulation.

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Authors: J. S. Kim, N. P. Hong

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In this paper, a CMOS vector modulator designed for wireless backhaul system based on 802.11ac is presented. A poly phase filter and sign select switches yield two orthogonal signal paths. Two variable gain amplifiers with strongly reduced phase shift of only ±5 ° are used to weight these paths. It has a phase control range of 360 ° and a gain range of -10 dB to 10 dB. The current drawn from a 1.2 V supply amounts 20.4 mA. Using a 0.13 mm technology, the chip die area amounts 1.47x0.75 mm².

Keywords: CMOS, vector modulator, backhaul, 802.11ac.

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Authors: Süha Yılmaz, Melih Turgut

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The curves, of which the square of the distance between the two points equal to zero, are called minimal or isotropic curves [4]. In this work, first, necessary and sufficient conditions to be a Pseudo Helix, which is a special case of such curves, are presented. Thereafter, it is proven that an isotropic curve-s position vector and pseudo curvature satisfy a vector differential equation of fourth order. Additionally, In view of solution of mentioned equation, position vector of pseudo helices is obtained.

Keywords: Classical Differential Geometry, Euclidean space, Minimal Curves, Isotropic Curves, Pseudo Helix.

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Authors: Tatjana Eitrich, Bruno Lang

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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: Hadi Sadoghi Yazdi, Tahereh Royani, Mehri Sadoghi Yazdi, Sohrab Effati

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In this paper, a new version of support vector regression (SVR) is presented namely Fuzzy Cost SVR (FCSVR). Individual property of the FCSVR is operation over fuzzy data whereas fuzzy cost (fuzzy margin and fuzzy penalty) are maximized. This idea admits to have uncertainty in the penalty and margin terms jointly. Robustness against noise is shown in the experimental results as a property of the proposed method and superiority relative conventional SVR.

Keywords: Support vector regression, Fuzzy input, Fuzzy cost.

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Authors: Laila Y. Fannas, Ahmed Y. Ben Sasi

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This research presents a handwritten signature recognition based on angle feature vector using Artificial Neural Network (ANN). Each signature image will be represented by an Angle vector. The feature vector will constitute the input to the ANN. The collection of signature images will be divided into two sets. One set will be used for training the ANN in a supervised fashion. The other set which is never seen by the ANN will be used for testing. After training, the ANN will be tested for recognition of the signature. When the signature is classified correctly, it is considered correct recognition otherwise it is a failure.

Keywords: Signature Recognition, Artificial Neural Network, Angle Features.

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Authors: Mihaela Popescu, Alexandru Bitoleanu, Mircea Dobriceanu

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This paper deals with the current space-vector decomposition in three-phase, three-wire systems on the basis of some case studies. We propose four components of the current spacevector in terms of DC and AC components of the instantaneous active and reactive powers. The term of supplementary useless current vector is also pointed out. The analysis shows that the current decomposition which respects the definition of the instantaneous apparent power vector is useful for compensation reasons only if the supply voltages are sinusoidal. A modified definition of the components of the current is proposed for the operation under nonsinusoidal voltage conditions.

Keywords: Active current, Active filtering, p–q theory, Reactive current.

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Authors: Chan Weng Howe, Mohd Saberi Mohamad

Abstract:

Protein residue contact map is a compact representation of secondary structure of protein. Due to the information hold in the contact map, attentions from researchers in related field were drawn and plenty of works have been done throughout the past decade. Artificial intelligence approaches have been widely adapted in related works such as neural networks, genetic programming, and Hidden Markov model as well as support vector machine. However, the performance of the prediction was not generalized which probably depends on the data used to train and generate the prediction model. This situation shown the importance of the features or information used in affecting the prediction performance. In this research, support vector machine was used to predict protein residue contact map on different combination of features in order to show and analyze the effectiveness of the features.

Keywords: contact map, protein residue contact, support vector machine, protein structure prediction

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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: Mehdi Karbalaye Zadeh, Ehsan M. Siavashi

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The controllable electrical loss which consists of the copper loss and iron loss can be minimized by the optimal control of the armature current vector. The control algorithm of current vector minimizing the electrical loss is proposed and the optimal current vector can be decided according to the operating speed and the load conditions. The proposed control algorithm is applied to the experimental PM motor drive system and this paper presents a modern approach of speed control for permanent magnet synchronous motor (PMSM) applied for Electric Vehicle using a nonlinear control. The regulation algorithms are based on the feedback linearization technique. The direct component of the current is controlled to be zero which insures the maximum torque operation. The near unity power factor operation is also achieved. More over, among EV-s motor electric propulsion features, the energy efficiency is a basic characteristic that is influenced by vehicle dynamics and system architecture. For this reason, the EV dynamics are taken into account.

Keywords: PMSM, Electric Vehicle, Optimal control, Traction.

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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: M. Kidouche, H. Habbi, M. Zelmat

Abstract:

In this paper, the decomposition-aggregation method is used to carry out connective stability criteria for general linear composite system via aggregation. The large scale system is decomposed into a number of subsystems. By associating directed graphs with dynamic systems in an essential way, we define the relation between system structure and stability in the sense of Lyapunov. The stability criteria is then associated with the stability and system matrices of subsystems as well as those interconnected terms among subsystems using the concepts of vector differential inequalities and vector Lyapunov functions. Then, we show that the stability of each subsystem and stability of the aggregate model imply connective stability of the overall system. An example is reported, showing the efficiency of the proposed technique.

Keywords: Composite system, Connective stability, Lyapunovfunctions.

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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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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 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 studies 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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Authors: Veruska C. Dutra, Mary L. G. S. Senna, Felipe S. Spindler

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The State Park Jalapão - PEJ proved to be one of the protected areas that has attracted tourists from all over the world with its unique scenic landscapes. Although the region already has a considerable tourist flow, to our knowledge there is a lack of continuity of studies in the region capable of drawing a plan of activities, such as the profile of the tourist and analysis of their experiences in the region, carried out from 2006-2007. Therefore, this study was proposed to know the profile and experiences of tourists visiting the park today, making a connection with the earlier study, in order to generate subsidies to trace improvement actions. We conducted interviews with tourists in the main tourism season 2015. The results show that after eight years of carrying out the first study, there were no changes, highlighting the lack of a tourism plan for the park.

Keywords: Jalapão, profile tourist, level of satisfaction.

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Authors: Essam Al Daoud

Abstract:

Intrusion detection is a mechanism used to protect a system and analyse and predict the behaviours of system users. An ideal intrusion detection system is hard to achieve due to nonlinearity, and irrelevant or redundant features. This study introduces a new anomaly-based intrusion detection model. The suggested model is based on particle swarm optimisation and nonlinear, multi-class and multi-kernel support vector machines. Particle swarm optimisation is used for feature selection by applying a new formula to update the position and the velocity of a particle; the support vector machine is used as a classifier. The proposed model is tested and compared with the other methods using the KDD CUP 1999 dataset. The results indicate that this new method achieves better accuracy rates than previous methods.

Keywords: Feature selection, Intrusion detection, Support vector machine, Particle swarm.

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