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  • International Journal Advanced Network Monitoring Controls

 

Article

An Analog Circuit Fault Diagnosis Method Based on SVM Optimized with BQPSO

In order to improve the correct rate of analog circuit fault diagnosis, a method based on SVM optimized with BQPSO is put forward. Firstly, BQPSO algorithm and its steps are presented; Then the performance impact factors of SVM are analyzed, and the steps of SVM parameters optimized with BQPSO are given; Finally, a filter circuit is taken as an example to simulate. The result shows that this method is effective.

Wang Zhongsheng, Yang Sen, Huang Shujuan

International Journal of Advanced Network, Monitoring and Controls , ISSUE 2, 53–58

Research paper

STUDY ON FEATURE SELECTION AND IDENTIFICATION METHOD OF TOOL WEAR STATES BASED ON SVM

extracted features, i.e. high computational cost and inefficient complexity of the model, which leads to overfitting. It is crucial to extract a smaller feature set by an effective feature selection algorithm. In this paper, an approach based on one-versus-one multi-class Support Vector Machine Recursive Feature Elimination (SVM-RFE) is proposed to solve the feature selection problem in tool wear condition monitoring. Moreover, in order to analyze a performance degradation process on the machine tool

Weilin Li, Pan Fu, Weiqing Cao

International Journal on Smart Sensing and Intelligent Systems , ISSUE 2, 448–465

Research Article

PERFORMANCE EVALUATION OF SVM KERNELS ON MULTISPECTRAL LISS III DATA FOR OBJECT CLASSIFICATION

the LIBSVM package. In this paper, Support Vector Machine (SVM) is evaluated as classifier with four different kernels namely linear kernel, polynomial kernel, radial basis function kernel and sigmoid kernel. Several datasets are being experimented to find out the performance of various kernels of SVM .By changing the value of ‘C’ and γ varying results are observed. Among these RBF kernel with a value of C = 1000 and gamma=0.75 got an excellent accuracy of 99.1509%.The SVM-RBF

S.V.S. Prasad, T. Satya Savithri, Iyyanki V. Murali Krishna

International Journal on Smart Sensing and Intelligent Systems , ISSUE 4, 829–844

Research Article

PERFORMANCE EVALUATION OF SVM KERNELS ON MULTISPECTRAL LISS III DATA FOR OBJECT CLASSIFICATION

the LIBSVM package. In this paper, Support Vector Machine (SVM) is evaluated as classifier with four different kernels namely linear kernel, polynomial kernel, radial basis function kernel and sigmoid kernel. Several datasets are being experimented to find out the performance of various kernels of SVM .By changing the value of ‘C’ and γ varying results are observed. Among these RBF kernel with a value of C = 1000 and gamma=0.75 got an excellent accuracy of 99.1509%.The SVM-RBF kernel gave an edge

S.V.S. Prasad, T. Satya Savithri, Iyyanki V. Murali Krishna

International Journal on Smart Sensing and Intelligent Systems , ISSUE 4, 863–878

Article

NOVEL MULTI-CLASS SVM ALGORITHM FOR MULTIPLE OBJECT RECOGNITION

Object recognition is a fundamental task in applications of computer vision, which aims at detecting and locating the interested objects out of the backgrounds in images or videos, and can be originally formulated as a binary classification problem that can be effectively handled by binary SVM. Although the binary technique can be naturally extended to solve the multiple object recognition, which are known as one-vs.-one and one-vs.-all techniques, but the scalability of traditional methods

Yongqing Wang, Yanzhou Zhang

International Journal on Smart Sensing and Intelligent Systems , ISSUE 2, 1203–1224

research-article

Indonesian traffic sign detection based on Haar-PHOG features and SVM classification

. Each area is extracted for its PHOG features and results in four PHOG feature vectors. This means that the number of features produced in Haar–PHOG feature extraction is four times those of PHOG features. Afterward, each ROI feature candidate is classified using binary SVM to determine whether the ROI is a traffic sign or not. This research contributes to the extraction of Haar–PHOG features, which emphasize frequency and resolution. Haar–PHOG combines four regions of different frequencies from

Aris Sugiharto, Agus Harjoko, Suharto Suharto

International Journal on Smart Sensing and Intelligent Systems , ISSUE 1, 1–15

Article

FACE DETECTION IN PROFILE VIEWS USING FAST DISCRETE CURVELET TRANSFORM (FDCT) AND SUPPORT VECTOR MACHINE (SVM)

YCgCr (luminance - green chrominance - red chrominance) color models is used to extract skin blocks. The segmentation scheme utilizes only the S and CgCr components, and is therefore luminance independent. Features extracted from three frequency bands from curvelet decomposition are used to detect face in each block. A support vector machine (SVM) classifier is trained for the classification task. In the performance test, the results showed that the proposed algorithm can detect profile faces in

Bashir Muhammad, Syed Abd Rahman Abu-Bakar

International Journal on Smart Sensing and Intelligent Systems , ISSUE 1, 108–123

Research Article

LEARNING TO RANK AND CLASSIFICATION OF BUG REPORTS USING SVM AND FEATURE EVALUATION

descriptions of library components, the bug-fixing history, the code change history, and the file dependency graph. Given a bug report, the ranking score of each source file is computed as a weighted combination of an array of features, where the weights are trained automatically on previously solved bug reports using a learning-to-rank technique. I applied SVM (Support Virtual Machine) to classify the bug reports to identify, which category the bug belongs to. It helps to fix the critical defects early

S. Rajeswari, S. Sharavanan, R. Vijai, RM. Balajee

International Journal on Smart Sensing and Intelligent Systems , ISSUE 5, 311–329

Research Article

INDEX FINGER MOTION RECOGNITION USING SELF-ADVISE SUPPORT VECTOR MACHINE

and well-known classifier, Support Vector Machine (SVM). An improvement of SVM, Self-advise SVM (SA-SVM), is tested to evaluate and compare its performance with the original one. The experimental result shows that SA-SVM improves the classification performance by on average 0.63 %.

Khairul Anam, Adel Al Jumaily, Yashar Maali

International Journal on Smart Sensing and Intelligent Systems , ISSUE 2, 644–657

Article

A New Method of Improving the Traditional Traffic Identification and Accuracy

traffic traces, used machine learning techniques(SVM model[17]) to improve system performance and enable real-time traffic identification for high-speed networks. Zhao X proposed a P2P network traffic classification method based on support vector machine [19], using a statistical principle to divide the network traffic of four different types of P2P traffic applications (file sharing BitTorrent, media streaming PPLive, Internet phone Skype, instant messaging MSN), and studied network traffic

Wang Zhongsheng, Gao Jiaqiong

International Journal of Advanced Network, Monitoring and Controls , ISSUE 3, 53–60

Article

AN INTELLIGENT FEATURE SELECTION AND CLASSIFICATION METHOD BASED ON HYBRID ABC–SVM

by the SVM classifier are both considered to update the food source. Finally, the most superior features and optimal model parameter are fed into SVM to identify different class. The testing results verify the effectiveness of the method in extracting feature subset and pattern classification

Jie Li, Qiuwen Zhang, Zhang Yongzhi, Li Chang, Xiao Jian

International Journal on Smart Sensing and Intelligent Systems , ISSUE 4, 1876–

Research Article

MACHINE VISION BASED MISSING FASTENER DETECTION IN RAIL TRACK IMAGES USING SVM CLASSIFIER

Missing fastener detection is a critical task due to its similar characteristics with surrounding environments. In this paper, a machine vision based fully automatic detection and classification of missing fastener detection system is proposed using Support Vector Machine (SVM) classifier. This proposed system consists of preprocessing, transformation, feature extraction and classifications. Image resizing is performed as preprocessing step and Gabor transform is used as transformation

R. Manikandan, M. Balasubramanian, S. Palanivel

International Journal on Smart Sensing and Intelligent Systems , ISSUE 5, 574–589

Article

Fast Aerial UAV Detection Based on Image Segmentation and HOG-FLD Feature Fusion

In order to detect non-cooperative target UAV quickly and accurately, a novel method of UAV detection method based on graph theory and HOG-FLD feature fusion is presented in this paper. In order to avoid the time-consuming full search, the candidate areas of the UAV are obtained through the selective search of the image segmentation and the similarity, and the features are extracted through the method of gradient orientation histogram fusion FLD linear to train the SVM classifier with

Li Xiaoping, Lei Songze, Wang Yanhong, Xiao Feng, Penghui Tian

International Journal of Advanced Network, Monitoring and Controls , ISSUE 1, 106–114

Article

PREDICTION OF SEWAGE QUALITY BASED ON FUSION OF BPNETWORKS

Sewage treatment system is a complicated nonlinear system with multi-variables, chemical reaction, biological process and altered loads, hard to describe mathematically. Thus prediction of the effluent quality parameters of sewage treatment plant has being a challenge. In this paper we adopt fusion of two BP networks to predict sewage quality parameters with a popular process Cyclic Activated Sludge System (CASS). We take use of SVM (support vector machine) to classify the input data into two

Lijuan Wang

International Journal on Smart Sensing and Intelligent Systems , ISSUE 2, 909–926

Article

OBJECT TRACKING BASED ON MACHINE VISION AND IMPROVED SVDD ALGORITHM

Object tracking is an important research topic in the applications of machine vision, and has made great progress in the past decades, among which the technique based on classification is a very efficient way to solve the tracking problem. The classifier classifies the objects and background into two different classes, where the tracking drift caused by noisy background can be effectively handled by one-class SVM. But the time and space complexities of traditional one-class SVM methods tend to

Yongqing Wang, Yanzhou Zhang

International Journal on Smart Sensing and Intelligent Systems , ISSUE 1, 677–696

Article

ROBUST VISUAL TRACKING BASED ON SUPPORT VECTOR MACHINE AND WEIGHTED SAMPLING METHOD

Visual tracking algorithm based on binary classification has become the research hot issue. The tracking algorithm firstly constructs a binary classifier between object and background, then to determine the object’s location by the probability of the classifier. However, such binary classification may not fully handle the outliers, which may cause drifting. To improve the robustness of these tracking methods, a novel object tracking algorithm is proposed based on support vector machine (SVM

Gao Xiaoxing, Liu Feng

International Journal on Smart Sensing and Intelligent Systems , ISSUE 1, 255–271

Article

PREDICTION OF PCCP FAILURE BASED ON HYDROPHNE DETECTING

Prestressed Concrete Cylinder Pipe (PCCP) is a widely used water pipe all over the world. A major cause of PCCP failure is the internal wire break, which will emit acoustic signal. In this paper, a hydrophone-based PCCP real-time monitoring and failure-prediction system was proposed. By applying wavelet energy normalization analysis to signal feature extraction and Support Vector Machine (SVM) to signal recognition, a high prediction accuracy of 98.33% was achieved. The result showed that the

Yuan Zhang, Yibo Li

International Journal on Smart Sensing and Intelligent Systems , ISSUE 4, 1582–1598

Article

Electrocardiogram for Biometrics by using Adaptive Multilayer Generalized Learning Vector Quantization (AMGLVQ): Integrating Feature Extraction and Classification

than SVM or back-propagation NN and also able to handle some problems of heartbeat classification: imbalanced data set, inconsistency between feature extraction and classification and detecting unknown data on testing phase.

Elly Matul Imah, Wisnu Jatmiko, T. Basaruddin

International Journal on Smart Sensing and Intelligent Systems , ISSUE 5, 1891–1917

Article

AN APPROPRIATE PROCEDURE FOR DETECTION OF JOURNAL-BEARING FAULT USING POWER SPECTRAL DENSITY, K-NEAREST NEIGHBOR AND SUPPORT VECTOR MACHINE

Journal-bearings play a significant role in industrial applications and the necessity of condition monitoring with nondestructive tests is increasing. This paper deals a proper fault detection technique based on power spectral density (PSD) of vibration signals in combination with K-Nearest Neighbor and Support Vector Machine (SVM). The frequency domain vibration signals of an internal combustion engine with three journal-bearing conditions were gained, corresponding to, (i) normal, (ii

A. Moosavian, H. Ahmadi, A. Tabatabaeefar, B. Sakhaei

International Journal on Smart Sensing and Intelligent Systems , ISSUE 3, 685–700

Research Article

Multi-algorithmic Palmprint Authentication System Based on Score Level Fusion

transform. The match scores obtained from matching modules were normalized using z-score and fused with different score level fusion schemes namely sum-rule,weighted sum-rule, and Support Vector Machine (SVM) fusion, respectively. The experimental results show that SVM score level fusion lead to an increased performance for the proposed multi-algorithmic palmprint authentication system with genuine acceptance rate of 98% for 0.1% false acceptance rate, and equal error rate of 1.5% compared to weighted

C. Murukesh, G. Arul Elango

International Journal on Smart Sensing and Intelligent Systems , ISSUE 1, 1–11

Article

NOVEL SVDD-BASED ALGORITHM FOR MOVING OBJECT DETECTING AND TRACKING UNDER DYNAMIC SCENES

features, where the detecting and tracking drift caused by noisy background can be effectively handled by robust maximum margin classifier, such as one-class SVM. But the time and space complexities of traditional one-class SVM methods tend to be high, which limits its wide applications to various fields. Inspired by the idea proposed by Support Vector Data Description (SVDD), in this paper we present a novel SVDD-based algorithm to efficiently deal with detecting and tracking moving object under

Chunxiang Wang, Dongfang Xu, Yongqing Wang

International Journal on Smart Sensing and Intelligent Systems , ISSUE 2, 1130–1155

Article

A STUDY ON OPTIMIZATION METHODS OF X-RAY MACHINE RECOGNITION FOR AVIATION SECURITY SYSTEM

Traditional X-ray machine image recognition methods for airport security system have difficulties in recognition and are prone to result in recognition errors due to the impact of placing angle, density and volume of detected objects. This paper accurately describes the image features of X-ray machine visual image, carries out SVM classification after a visual dictionary is formed and enhances the accuracy of image discrimination by means of robust acceleration. The experimental results

Ning Zhang

International Journal on Smart Sensing and Intelligent Systems , ISSUE 2, 1313–1332

Article

The Research of Direct Torque Control Based on Space Vector Modulation

In order to solve the conventional direct torque control contradiction between the dynamic and static performance ,a permanent magnet synchronous motor system direct torque control architecture is proposed based on space vector modulation strategy . In this method flux and torque are controlled through stator voltage components in stator fluxlinkage coordinate axes and space vector modulation is used to control inverters.The simulation verifies that SVM-DTCis capable of effectively improving

Su Xiaohui, Chen Guodong, Xu Shuping

International Journal of Advanced Network, Monitoring and Controls , ISSUE 1, 100–107

Article

A Comparative Study of Face Recognition Classification Algorithms

. The methods used involve linear logistic regression, linear discriminant analysis (LDA), K-Nearest Neighbor (KNN), support vector machine (SVM), Naïve Bayes (NB) and other methods. The definition and advantages and disadvantages of the act are briefly explained. Finally, the five methods are compared and analyzed according to the evaluation indicators such as the accuracy rate, recall rate, F1-score, and AUC area commonly used in machine learning. II. RELATED WORK A. Principal component

Changyuan Wang, Guang Li, Pengxiang Xue, Qiyou Wu

International Journal of Advanced Network, Monitoring and Controls , ISSUE 3, 23–29

Article

PEDESTRIAN DETECTION ALGORITHM BASED ON LOCAL COLOR PARALLEL SIMILARITY FEATURES

Xianxian Tian, Hong Bao, Cheng Xu, Bobo Wang

International Journal on Smart Sensing and Intelligent Systems , ISSUE 5, 1869–1890

Article

HYPERSPECTRAL DATA FEATURE EXTRACTION USING DEEP BELIEF NETWORK

belief network, and to extract feature bands of spectral data from low level to high-level gradually. The extracted feature band has a stronger discriminant performance, so that it can better to classify hyperspectral data. Finally, the AVIRIS data is used to extract the feature band, and the SVM classifier is used to classify the data, which verifies the effectiveness of the method.

Jiang Xinhua, Xue Heru, Zhang Lina, Zhou Yanqing

International Journal on Smart Sensing and Intelligent Systems , ISSUE 4, 1991–2009

Research Article

 VIDEO-BASED VEHICLE DETECTION AND CLASSIFICATION IN CHALLENGING SCENARIOS   

Yiling Chen, GuoFeng Qin

International Journal on Smart Sensing and Intelligent Systems , ISSUE 3, 1077–1094

Article

VIBRATION BASED HEALTH MONITORING OF HONEYCOMB CORE SANDWICH PANELS USING SUPPORT VECTOR MACHINE

monitoring of these structures using support vector machine (SVM). The proposed technique is first used on simulated mode shape data of the structure and then the technique is validated using experimental mode shape data. The experimental set up has been developed in laboratory and Laser Doppler Vibrometer (LDV) is used to extract the experimental mode shapes. The results have been obtained using both support vector classification and regression analysis and it is found that that the former is better at

Saurabh Gupta, Satish B Satpal, Sauvik Banerjee, Anirban Guha

International Journal on Smart Sensing and Intelligent Systems , ISSUE 1, 215–232

Article

Multiple Vehicle License Plate Location in Complex Background

the color, dimension, texture and match the similarity to choose, search and merger area in the image, suspicious area of the license plate is obtained. Using visual word package to express rectangular profile after coarse positioning. Using support vector machine (SVM) to classify and identify rectangular area of license plate. Accurate positioning license plate location is positioned accurately. The method of accuracy is 96.4% for 135 pieces of test sample positioning, strong anti-jamming.

Yaxin Zhao, Li Zhao, Ya Li

International Journal of Advanced Network, Monitoring and Controls , ISSUE 1, 62–68

Research Article

Dwipa Ontology III: Implementation of Ontology Method Enrichment on Tourism Domain

This article summarizes some research results related to ontology enrichment specific to tourism domains from 2014 to 2017. Currently, some ontology enrichment approaches can use learning machinery such as support vector machine (SVM), Conditional Random Field (CRF) and kNN. Several studies have also been successful in evaluating ontology enrichment results with several parameters such as precision, recall and F-Measure. In addition, our research can enrich Dwipa Ontology II which has been

Guson Prasamuarso Kuntarto, Irwan Prasetya Gunawan, Fahmi L. Moechtar, Yudhiansyah Ahmadin, Berkah I. Santoso

International Journal on Smart Sensing and Intelligent Systems , ISSUE 4, 903–919

Article

HIGH PRECISION TARGET LOCALIZATION METHOD BASED ON COMPENSATION OF ATTITUDE ANGLE ERRORS

prediction model to compensate each platform and aircraft attitude error is derived by analyzing the error distribution and polynomial regression. Afterwards, because of the limit of the UAV aerial image amount and the similar influence of each attitude angle error on targeting and geometric correction, we use equivalent optical axis angle to represent platform and aircraft attitudes. Furthermore, we also predict and compensate the error of the equivalent angle. In this process, we adopt SVM and

Jialiang LIU, Wenrui Ding, Hongguang Li

International Journal on Smart Sensing and Intelligent Systems , ISSUE 1, 169–190

Article

Research on Combination Forecasting Model of Mine Gas Emission

Liang Rong, Chang Xintan, Jia Pengtao, Dong Dingwen

International Journal of Advanced Network, Monitoring and Controls , ISSUE 3, 194–198

Article

Detection and classification of the behavior of people in an intelligent building by camera

the heating . My goal is to develop a robust and reliable system which is composed of two fixed cameras in every room of intelligent building which are connected to a computer for acquisition of video sequences, with a program using these video sequences as inputs, we use RGB color histograms and textures for LBP represented different images of video sequences, and SVM (support vector machine) Lights as a programming tool for the detection and classification of the behavior of people in this

Henni Sid Ahmed, Belbachir Mohamed Faouzi, Jean Caelen

International Journal on Smart Sensing and Intelligent Systems , ISSUE 4, 1317–1342

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