• 제목/요약/키워드: Support Vector Machine

검색결과 1,816건 처리시간 0.029초

Fault Location and Classification of Combined Transmission System: Economical and Accurate Statistic Programming Framework

  • Tavalaei, Jalal;Habibuddin, Mohd Hafiz;Khairuddin, Azhar;Mohd Zin, Abdullah Asuhaimi
    • Journal of Electrical Engineering and Technology
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    • 제12권6호
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    • pp.2106-2117
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    • 2017
  • An effective statistical feature extraction approach of data sampling of fault in the combined transmission system is presented in this paper. The proposed algorithm leads to high accuracy at minimum cost to predict fault location and fault type classification. This algorithm requires impedance measurement data from one end of the transmission line. Modal decomposition is used to extract positive sequence impedance. Then, the fault signal is decomposed by using discrete wavelet transform. Statistical sampling is used to extract appropriate fault features as benchmark of decomposed signal to train classifier. Support Vector Machine (SVM) is used to illustrate the performance of statistical sampling performance. The overall time of sampling is not exceeding 1 1/4 cycles, taking into account the interval time. The proposed method takes two steps of sampling. The first step takes 3/4 cycle of during-fault and the second step takes 1/4 cycle of post fault impedance. The interval time between the two steps is assumed to be 1/4 cycle. Extensive studies using MATLAB software show accurate fault location estimation and fault type classification of the proposed method. The classifier result is presented and compared with well-established travelling wave methods and the performance of the algorithms are analyzed and discussed.

Statistical Analysis for Feature Subset Selection Procedures.

  • Kim, In-Young;Lee, Sun-Ho;Kim, Sang-Cheol;Rha, Sun-Young;Chung, Hyun-Cheol;Kim, Byung-Soo
    • 한국생물정보학회:학술대회논문집
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    • 한국생물정보시스템생물학회 2003년도 제2차 연례학술대회 발표논문집
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    • pp.101-106
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    • 2003
  • In this paper, we propose using Hotelling's T2 statistic for the detection of a set of a set of differentially expressed (DE) genes in colorectal cancer based on its gene expression level in tumor tissues compared with those in normal tissues and to evaluate its predictivity which let us rank genes for the development of biomarkers for population screening of colorectal cancer. We compared the prediction rate based on the DE genes selected by Hotelling's T2 statistic and univariate t statistic using various prediction methods, a regulized discrimination analysis and a support vector machine. The result shows that the prediction rate based on T2 is better than that of univatiate t. This implies that it may not be sufficient to look at each gene in a separate universe and that evaluating combinations of genes reveals interesting information that will not be discovered otherwise.

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IoT 환경에서 최적 R파 검출 및 최소 특징점 추출을 통한 향상된 PVC 분류방법 (Optimal R Wave Detection and Advanced PVC Classification Method through Extracting Minimal Feature in IoT Environments)

  • 조익성;우동식
    • 디지털산업정보학회논문지
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    • 제13권4호
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    • pp.91-98
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    • 2017
  • Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require higher computational cost and larger processing time. Therefore it is necessary to design efficient algorithm that classifies PVC(premature ventricular contraction) and decreases computational cost by accurately detecting minimal feature point based on only R peak through optimal R wave. We propose an optimal R wave detection and PVC classification method through extracting minimal feature point in IoT environment. For this purpose, we detected R wave through optimal threshold value and extracted RR interval and R peak pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through RR interval and R peak pattern. The performance of R wave detection and PVC classification is evaluated by using record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.758% in R wave detection and the rate of 93.94% in PVC classification.

비강압력신호를 이용한 수면호흡장애 환자의 수면/각성 분류 (Classification of Sleep/Wakefulness using Nasal Pressure for Patients with Sleep-disordered Breathing)

  • 박종욱;정필수;강규민;이경중
    • 대한의용생체공학회:의공학회지
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    • 제37권4호
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    • pp.127-133
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    • 2016
  • This study proposes the feasibility for automatic classification of sleep/wakefulness using nasal pressure in patients with sleep-disordered breathing (SDB). First, SDB events were detected using the methods developed in our previous studies. In epochs for normal breathing, we extracted the features for classifying sleep/wakefulness based on time-domain, frequency-domain and non-linear analysis. And then, we conducted the independent two-sample t-test and calculated Mahalanobis distance (MD) between the two categories. As a results, $SD_{LEN}$ (MD = 0.84, p < 0.01), $P_{HF}$ (MD = 0.81, p < 0.01), $SD_{AMP}$ (MD = 0.76, p = 0.031) and $MEAN_{AMP}$ (MD = 0.75, p = 0.027) were selected as optimal feature. We classified sleep/wakefulness based on support vector machine (SVM). The classification results showed mean of sensitivity (Sen.), specificity (Spc.) and accuracy (Acc.) of 60.5%, 89.0% and 84.8% respectively. This method showed the possibilities to automatically classify sleep/wakefulness only using nasal pressure.

Multiscale self-coordination of bidimensional empirical mode decomposition in image fusion

  • An, Feng-Ping;Zhou, Xian-Wei;Lin, Da-Chao
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제9권4호
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    • pp.1441-1456
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    • 2015
  • The bidimensional empirical mode decomposition (BEMD) algorithm with high adaptability is more suitable to process multiple image fusion than traditional image fusion. However, the advantages of this algorithm are limited by the end effects problem, multiscale integration problem and number difference of intrinsic mode functions in multiple images decomposition. This study proposes the multiscale self-coordination BEMD algorithm to solve this problem. This algorithm outside extending the feather information with the support vector machine which has a high degree of generalization, then it also overcomes the BEMD end effects problem with conventional mirror extension methods of data processing,. The coordination of the extreme value point of the source image helps solve the problem of multiscale information fusion. Results show that the proposed method is better than the wavelet and NSCT method in retaining the characteristics of the source image information and the details of the mutation information inherited from the source image and in significantly improving the signal-to-noise ratio.

Evaluation of soil-concrete interface shear strength based on LS-SVM

  • Zhang, Chunshun;Ji, Jian;Gui, Yilin;Kodikara, Jayantha;Yang, Sheng-Qi;He, Lei
    • Geomechanics and Engineering
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    • 제11권3호
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    • pp.361-372
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    • 2016
  • The soil-concrete interface shear strength, although has been extensively studied, is still difficult to predict as a result of the dependence on many factors such as normal stresses, surface roughness, particle sizes, moisture contents, dilation angles of soils, etc. In this study, a well-known rigorous statistical learning approach, namely the least squares support vector machine (LS-SVM) realized in a ubiquitous spreadsheet platform is firstly used in estimating the soil-structure interface shear strength. Instead of studying the complicated mechanism, LS-SVM enables to explore the possible link between the fundamental factors and the interface shear strengths, via a sophisticated statistic approach. As a preliminary investigation, the authors study the expansive soils that are found extensively in most countries. To reduce the complexity, three major influential factors, e.g., initial moisture contents, initial dry densities and normal stresses of soils are taken into account in developing the LS-SVM models for the soil-concrete interface shear strengths. The predicted results by LS-SVM show reasonably good agreement with experimental data from direct shear tests.

디지털 마모그램 반자동 종괴검출 방법 (Semi-automatic System for Mass Detection in Digital Mammogram)

  • 조선일;권주원;노용만
    • 대한의용생체공학회:의공학회지
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    • 제30권2호
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    • pp.153-161
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    • 2009
  • Mammogram is one of the important techniques for mass detection, which is the early diagnosis stage of a breast cancer. Especially, the CAD(Computer Aided Diagnosis) using mammogram improves the working performance of radiologists as it offers an effective mass detection. There are two types of CAD systems using mammogram; automatic and semi-automatic CAD systems. However, the automatic segmentation is limited in performance due to the difficulty of obtaining an accurate segmentation since mass occurs in the dense areas of the breast tissue and has smoother boundaries. Semi-automatic CAD systems overcome these limitations, however, they also have problems including high FP (False Positive) rate and a large amount of training data required for training a classifier. The proposed system which overcomes the aforementioned problems to detect mass is composed of the suspected area selection, the level set segmentation and SVM (Support Vector Machine) classification. To assess the efficacy of the system, 60 test images from the FFDM (Full-Field Digital Mammography) are analyzed and compared with the previous semi-automatic system, which uses the ANN classifier. The experimental results of the proposed system indicate higher accuracy of detecting mass in comparison to the previous systems.

외부조명 변화에 강인한 운전자 졸음 감지 시스템 (System for Detecting Driver's Drowsiness Robust Variations of External Illumination)

  • 최원웅;반성범;신주현
    • 한국멀티미디어학회논문지
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    • 제19권6호
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    • pp.1024-1033
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    • 2016
  • In this study, a system is proposed for analyzing whether driver's eyes are open or closed on the basis of images to determine driver's drowsiness. The proposed system converts eye areas detected by a camera to a color space area to effectively detect eyes in a dark situation, for example, tunnels, and a bright situation due to a backlight. In addition, the system used a thickness distribution of a detected eye area as a feature value to analyze whether eyes are open or closed through the Support Vector Machine(SVM), representing 90.09% of accuracy. In the experiment for the images of driver wearing glasses, 83.83% of accuracy was obtained. In addition, in a comparative experiment with the existing PCA method by using Eigen-eye and Pupil Measuring System the detection rate is shown improved. After the experiment, driver's drowsiness was identified accurately by using the method of summing up the state of driver's eyes open and closes over time and the method of detecting driver's eyes that continue to be closed to examine drowsy driving.

An Optimization Algorithm with Novel Flexible Grid: Applications to Parameter Decision in LS-SVM

  • Gao, Weishang;Shao, Cheng;Gao, Qin
    • Journal of Computing Science and Engineering
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    • 제9권2호
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    • pp.39-50
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    • 2015
  • Genetic algorithm (GA) and particle swarm optimization (PSO) are two excellent approaches to multimodal optimization problems. However, slow convergence or premature convergence readily occurs because of inappropriate and inflexible evolution. In this paper, a novel optimization algorithm with a flexible grid optimization (FGO) is suggested to provide adaptive trade-off between exploration and exploitation according to the specific objective function. Meanwhile, a uniform agents array with adaptive scale is distributed on the gird to speed up the calculation. In addition, a dominance centroid and a fitness center are proposed to efficiently determine the potential guides when the population size varies dynamically. Two types of subregion division strategies are designed to enhance evolutionary diversity and convergence, respectively. By examining the performance on four benchmark functions, FGO is found to be competitive with or even superior to several other popular algorithms in terms of both effectiveness and efficiency, tending to reach the global optimum earlier. Moreover, FGO is evaluated by applying it to a parameter decision in a least squares support vector machine (LS-SVM) to verify its practical competence.

쉼표의 자동분류에 따른 중국에 장문분할 (Segmentation of Long Chinese Sentences using Comma Classification)

  • 김미훈;김미영;이종혁
    • 한국정보과학회논문지:소프트웨어및응용
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    • 제33권5호
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    • pp.470-480
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    • 2006
  • 입력문장이 길어질수록 구문분석의 정확률은 크게 낮아진다. 따라서 긴 문장의 구문분석 정확률을 높이기 위해 장문분할 방법들이 많이 연구되었다. 중국어는 고립어로서 자연언어처리에 도움을 줄 수 있는 굴절이나 어미정보가 없는 대신 쉼표를 비교적 많이, 또 정확히 사용하고 있어서 이러한 쉼표사용이 장문분할에 도움을 줄 수 있다. 본 논문에서는 중국어 문장에서 쉼표 주변의 문맥을 파악하여 해당 쉼표위치에 문장분할이 가능한지 Support Vector Machine을 이용해 판단하고자 한다. 쉼표의 분류의 정확률이 87.1%에 이르고, 이 분할모델을 적용한 후 구문분석한 결과, 의존트리의 정확률이 5.6% 증가했다.