• Title/Summary/Keyword: support vector machine(SVM)

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Multi-modal Biometrics System Based on Face and Signature by SVM Decision Rule (SVM 결정법칙에 의한 얼굴 및 서명기반 다중생체인식 시스템)

  • Min Jun-Oh;Lee Dae-Jong;Chun Myung-Geun
    • The KIPS Transactions:PartB
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    • v.11B no.7 s.96
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    • pp.885-892
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    • 2004
  • In this paper, we propose a multi-modal biometrics system based on face and signature recognition system. Here, the face recognition system is designed by fuzzy LDA, and the signature recognition system is implemented with the LDA and segment matching methods. To effectively aggregate two systems, we obtain statistical distribution models based on matching values for genuine and impostor, respectively. And then, the final verification is Performed by the support vector machine. From the various experiments, we find that the proposed method shows high recognition rates comparing with the conventional methods.

A Hierarchical Microcalcification Detection Algorithm Using SVM in Korean Digital Mammography (한국형 디지털 마모그래피에서 SVM을 이용한 계층적 미세석회화 검출 방법)

  • Kwon, Ju-Won;Kang, Ho-Kyung;Ro, Yong-Man;Kim, Sung-Min
    • Journal of Biomedical Engineering Research
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    • v.27 no.5
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    • pp.291-299
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    • 2006
  • A Computer-Aided Diagnosis system has been examined to reduce the effort of radiologist. In this paper, we propose the algorithm using Support Vector Machine(SVM) classifier to discriminate whether microcalcifications are malignant or benign tumors. The proposed method to detect microcalcifications is composed of two detection steps each of which uses SVM classifier. The coarse detection step finds out pixels considered high contrasts comparing with neighboring pixels. Then, Region of Interest(ROI) is generated based on microcalcification characteristics. The fine detection step determines whether the found ROIs are microcalcifications or not by merging potential regions using obtained ROIs and SVM classifier. The proposed method is specified on Korean mammogram database. The experimental result of the proposed algorithm presents robustness in detecting microcalcifications than the previous method using Artificial Neural Network as classifier even when using small training data.

Support Vector Machine Based Arrhythmia Classification Using Reduced Features

  • Song, Mi-Hye;Lee, Jeon;Cho, Sung-Pil;Lee, Kyoung-Joung;Yoo, Sun-Kook
    • International Journal of Control, Automation, and Systems
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    • v.3 no.4
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    • pp.571-579
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    • 2005
  • In this paper, we proposed an algorithm for arrhythmia classification, which is associated with the reduction of feature dimensions by linear discriminant analysis (LDA) and a support vector machine (SVM) based classifier. Seventeen original input features were extracted from preprocessed signals by wavelet transform, and attempts were then made to reduce these to 4 features, the linear combination of original features, by LDA. The performance of the SVM classifier with reduced features by LDA showed higher than with that by principal component analysis (PCA) and even with original features. For a cross-validation procedure, this SVM classifier was compared with Multilayer Perceptrons (MLP) and Fuzzy Inference System (FIS) classifiers. When all classifiers used the same reduced features, the overall performance of the SVM classifier was comprehensively superior to all others. Especially, the accuracy of discrimination of normal sinus rhythm (NSR), arterial premature contraction (APC), supraventricular tachycardia (SVT), premature ventricular contraction (PVC), ventricular tachycardia (VT) and ventricular fibrillation (VF) were $99.307\%,\;99.274\%,\;99.854\%,\;98.344\%,\;99.441\%\;and\;99.883\%$, respectively. And, even with smaller learning data, the SVM classifier offered better performance than the MLP classifier.

Video-based Facial Emotion Recognition using Active Shape Models and Statistical Pattern Recognizers (Active Shape Model과 통계적 패턴인식기를 이용한 얼굴 영상 기반 감정인식)

  • Jang, Gil-Jin;Jo, Ahra;Park, Jeong-Sik;Seo, Yong-Ho
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.14 no.3
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    • pp.139-146
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    • 2014
  • This paper proposes an efficient method for automatically distinguishing various facial expressions. To recognize the emotions from facial expressions, the facial images are obtained by digital cameras, and a number of feature points were extracted. The extracted feature points are then transformed to 49-dimensional feature vectors which are robust to scale and translational variations, and the facial emotions are recognized by statistical pattern classifiers such Naive Bayes, MLP (multi-layer perceptron), and SVM (support vector machine). Based on the experimental results with 5-fold cross validation, SVM was the best among the classifiers, whose performance was obtained by 50.8% for 6 emotion classification, and 78.0% for 3 emotions.

Real Time Traffic Light Detection Algorithm Based on Color Map and Multilayer HOG-SVM (색상지도와 멀티 레이어 HOG-SVM 기반의 실시간 신호등 검출 알고리즘)

  • Kim, Sanggi;Han, Dong Seog
    • Journal of Broadcast Engineering
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    • v.22 no.1
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    • pp.62-69
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    • 2017
  • Accurate detection of traffic lights is very important for the advanced driver assistance system (ADAS). There have been many research developments in this area. However, conventional of image processing methods are usually sensitive to varying illumination conditions. This paper proposes a traffic light detection algorithm to overcome this situation. The proposed algorithm first detects the candidates of traffic light using the proposed color map and hue-saturation-value (HSV) Traffic lights are then detected using the conventional histogram of oriented gradients (HOG) descriptor and support vector machine (SVM). Finally, the proposed Multilayer HOG descriptor is used to determine the direction information indicated by traffic lights. The proposed algorithm shows a high detection rate in real-time.

Application of support vector machine with firefly algorithm for investigation of the factors affecting the shear strength of angle shear connectors

  • Chahnasir, E. Sadeghipour;Zandi, Y.;Shariati, M.;Dehghani, E.;Toghroli, A.;Mohamad, E. Tonnizam;Shariati, A.;Safa, M.;Wakil, K.;Khorami, M.
    • Smart Structures and Systems
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    • v.22 no.4
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    • pp.413-424
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    • 2018
  • The factors affecting the shear strength of the angle shear connectors in the steel-concrete composite beams can play an important role to estimate the efficacy of a composite beam. Therefore, the current study has aimed to verify the output of shear capacity of angle shear connector according to the input provided by Support Vector Machine (SVM) coupled with Firefly Algorithm (FFA). SVM parameters have been optimized through the use of FFA, while genetic programming (GP) and artificial neural networks (ANN) have been applied to estimate and predict the SVM-FFA models' results. Following these results, GP and ANN have been applied to develop the prediction accuracy and generalization capability of SVM-FFA. Therefore, SVM-FFA could be performed as a novel model with predictive strategy in the shear capacity estimation of angle shear connectors. According to the results, the Firefly algorithm has produced a generalized performance and be learnt faster than the conventional learning algorithms.

Prediction of time dependent local scour around bridge piers in non-cohesive and cohesive beds using machine learning technique (기계학습을 이용한 비점성토 및 점성토 지반에서 시간의존 교각주위 국부세굴의 예측)

  • Choi, Sung-Uk;Choi, Seongwook;Choi, Byungwoong
    • Journal of Korea Water Resources Association
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    • v.54 no.12
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    • pp.1275-1284
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    • 2021
  • This paper presents a machine learning technique applied to prediction of time-dependent local scour around bridge piers in both non-cohesive and cohesive beds. The support vector machines (SVM), which is known to be free from overfitting, is used. The time-dependent scour depths are expressed by 7 and 9 variables for the non-cohesive and cohesive beds, respectively. The SVM models are trained and validated with time series data from different sources of experiments. Resulting Mean Absolute Percentage Error (MAPE) indicates that the models are trained and validated properly. Comparisons are made with the results from Choi and Choi's formula and Scour Rate in Cohesive Soils (SRICOS) method by Briaud et al., as well as measured data. This study reveals that the SVM is capable of predicting time-dependent local scour in both non-cohesive and cohesive beds under the condition that sufficient data of good quality are provided.

A multi-layer approach to DN 50 electric valve fault diagnosis using shallow-deep intelligent models

  • Liu, Yong-kuo;Zhou, Wen;Ayodeji, Abiodun;Zhou, Xin-qiu;Peng, Min-jun;Chao, Nan
    • Nuclear Engineering and Technology
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    • v.53 no.1
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    • pp.148-163
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    • 2021
  • Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.

PCA-SVM Based Vehicle Color Recognition (PCA-SVM 기법을 이용한 차량의 색상 인식)

  • Park, Sun-Mi;Kim, Ku-Jin
    • The KIPS Transactions:PartB
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    • v.15B no.4
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    • pp.285-292
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    • 2008
  • Color histograms have been used as feature vectors to characterize the color features of given images, but they have a limitation in efficiency by generating high-dimensional feature vectors. In this paper, we present a method to reduce the dimension of the feature vectors by applying PCA (principal components analysis) to the color histogram of a given vehicle image. With SVM (support vector machine) method, the dimension-reduced feature vectors are used to recognize the colors of vehicles. After reducing the dimension of the feature vector by a factor of 32, the successful recognition rate is reduced only 1.42% compared to the case when we use original feature vectors. Moreover, the computation time for the color recognition is reduced by a factor of 31, so we could recognize the colors efficiently.

Bayesian Model Selection for Support Vector Regression using the Evidence Framework

  • Hwang, Chang-Ha;Seok, Kyung-Ha
    • Communications for Statistical Applications and Methods
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    • v.6 no.3
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    • pp.813-820
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    • 1999
  • Supprot vector machine(SVM) is a new and very promising regression and classification technique developed by Vapnik and his group at AT&T Bell Laboratories. in this paper we provide a brief overview of SVM for regression. Furthermore we describe Bayesian model selection based on macKay's evidence framework for SVM regression.

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