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A New Approach to Evidence Combination and Its Application to Targets Recognition in Image Sequence 被引量:1
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作者 陈良洲 施文康 杜峰 《Journal of Shanghai Jiaotong university(Science)》 EI 2007年第2期143-147,共5页
The classical Dempster's combination rule is the most popular rule of combinations,but it is a poor solution for the management of the evidence conflict at the normalization step.When deal with high conflict infor... The classical Dempster's combination rule is the most popular rule of combinations,but it is a poor solution for the management of the evidence conflict at the normalization step.When deal with high conflict information it can even involve counter-intuitive results.Based on evidence distance,some inherent characters of evidences are extracted,and discount method to combine conflicting evidence was proposed.The discount method can be also used to fuse image sequences to recognize targets.Examples show that the proposed method can provide reasonable results with good convergence efficiency. 展开更多
关键词 evidence theory conflict evidence discount coefficient target recognition
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Ship recognition based on HRRP via multi-scale sparse preserving method
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作者 YANG Xueling ZHANG Gong SONG Hu 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2024年第3期599-608,共10页
In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) ba... In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) based on the maximum margin criterion(MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance. 展开更多
关键词 ship target recognition high-resolution range profile(HRRP) multi-scale fusion kernel sparse preserving projection(MSFKSPP) feature extraction dimensionality reduction
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YOLOv5-Based Seabed Sediment Recognition Method for Side-Scan Sonar Imagery 被引量:1
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作者 WANG Ziwei HU Yi +1 位作者 DING Jianxiang SHI Peng 《Journal of Ocean University of China》 SCIE CAS CSCD 2023年第6期1529-1540,共12页
Seabed sediment recognition is vital for the exploitation of marine resources.Side-scan sonar(SSS)is an excellent tool for acquiring the imagery of seafloor topography.Combined with ocean surface sampling,it provides ... Seabed sediment recognition is vital for the exploitation of marine resources.Side-scan sonar(SSS)is an excellent tool for acquiring the imagery of seafloor topography.Combined with ocean surface sampling,it provides detailed and accurate images of marine substrate features.Most of the processing of SSS imagery works around limited sampling stations and requires manual interpretation to complete the classification of seabed sediment imagery.In complex sea areas,with manual interpretation,small targets are often lost due to a large amount of information.To date,studies related to the automatic recognition of seabed sediments are still few.This paper proposes a seabed sediment recognition method based on You Only Look Once version 5 and SSS imagery to perform real-time sedi-ment classification and localization for accuracy,particularly on small targets and faster speeds.We used methods such as changing the dataset size,epoch,and optimizer and adding multiscale training to overcome the challenges of having a small sample and a low accuracy.With these methods,we improved the results on mean average precision by 8.98%and F1 score by 11.12%compared with the original method.In addition,the detection speed was approximately 100 frames per second,which is faster than that of previous methods.This speed enabled us to achieve real-time seabed sediment recognition from SSS imagery. 展开更多
关键词 seabed sediment real-time target recognition YOLOv5 model side-scan sonar imagery transfer learning
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Underwater Noise Target Recognition Based on Sparse Adversarial Co-Training Model with Vertical Line Array
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作者 ZHOU Xingyue YANG Kunde +2 位作者 YAN Yonghong LI Zipeng DUAN Shunli 《Journal of Ocean University of China》 SCIE CAS CSCD 2023年第5期1201-1215,共15页
The automatic identification of underwater noncooperative targets without label records remains an arduous task considering the marine noise interference and the shortage of labeled samples.In particular,the data-driv... The automatic identification of underwater noncooperative targets without label records remains an arduous task considering the marine noise interference and the shortage of labeled samples.In particular,the data-driven mechanism of deep learning cannot identify false samples,aggravating the difficulty in noncooperative underwater target recognition.A semi-supervised ensemble framework based on vertical line array fusion and the sparse adversarial co-training algorithm is proposed to identify noncooperative targets effectively.The sound field cross-correlation compression(SCC)feature is developed to reduce noise and computational redundancy.Starting from an incomplete dataset,a joint adversarial autoencoder is constructed to extract the sparse features with source depth sensitivity,aiming to discover the unknown underwater targets.The adversarial prediction label is converted to initialize the joint co-forest,whose evaluation function is optimized by introducing adaptive confidence.The experiments prove the strong denoising performance,low mean square error,and high separability of SCC features.Compared with several state-of-the-art approaches,the numerical results illustrate the superiorities of the proposed method due to feature compression,secondary recognition,and decision fusion. 展开更多
关键词 underwater acoustic target recognition marine acoustic signal processing sound field feature extraction sparse adversarial network
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Air target recognition method against ISRJ for radio frequency proximity sensors using chaotic stream encryption
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作者 Jian-feng Li Jian Dai +2 位作者 Xin-hong Hao Xiao-peng Yan Xin-wei Wang 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2023年第10期267-279,共13页
The interrupted-sampling repeater jamming(ISRJ)can cause false targets to the radio-frequency proximity sensors(RFPSs),resulting in a serious decline in the target detection capability of the RFPS.This article propose... The interrupted-sampling repeater jamming(ISRJ)can cause false targets to the radio-frequency proximity sensors(RFPSs),resulting in a serious decline in the target detection capability of the RFPS.This article proposes a recognition method for RFPSs to identify the false targets caused by ISRJ.The proposed method is realized by assigning a unique identity(ID)to each RFPS,and each ID is a periodically and chaotically encrypted in every pulse period.The processing technique of the received signal is divided into ranging and ID decryption.In the ranging part,a high-resolution range profile(HRRP)can be obtained by performing pulse compression with the binary chaotic sequences.To suppress the noise,the singular value decomposition(SVD)is applied in the preprocessing.Regarding ID decryption,targets and ISRJ can be recognized through the encryption and decryption processes,which are controlled by random keys.An adaptability analysis conducted in terms of the peak-to-side lobe ratio(PSLR)and bit error rate(BER)indicates that the proposed method performs well within a 70-k Hz Doppler shift.A simulation and experimental results show that the proposed method achieves extremely stable target and ISRJ recognition accuracies at different signal-to-noise ratios(SNRs)and jamming-to-signal ratios(JSRs). 展开更多
关键词 Interrupted-sampling repeater jamming(ISRJ) Radio frequency proximity sensors(RFPS) Chaotic stream encryption Air target recognition Identity(ID)decryption
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New Algorithm for Image Target Recognition Based on Fractal Feature Fusion 被引量:2
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作者 潘秀琴 侯朝桢 苏利敏 《Journal of Beijing Institute of Technology》 EI CAS 2002年第4期342-345,共4页
By combining fractal theory with D-S evidence theory, an algorithm based on the fusion of multi-fractal features is presented. Fractal features are extracted, and basic probability assignment function is designed. Com... By combining fractal theory with D-S evidence theory, an algorithm based on the fusion of multi-fractal features is presented. Fractal features are extracted, and basic probability assignment function is designed. Comparison and simulation are performed on the new algorithm, the old algorithm based on single feature and the algorithm based on neural network. Results of the comparison and simulation illustrate that the new algorithm is feasible and valid. 展开更多
关键词 FRACTAL feature fusion target recognition
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OPTIMIZATION OF WEIGHTED HIGH-RESOLUTION RANGE PROFILE FOR RADAR TARGET RECOGNITION 被引量:1
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作者 朱劼昊 周建江 吴杰 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2011年第2期157-162,共6页
For the recognition of high-resolution range profile (HRRP) in radar, the weighted HRRP can reduce the instability of range cells caused by the attitude change of targets. A novel approach is proposed to optimize th... For the recognition of high-resolution range profile (HRRP) in radar, the weighted HRRP can reduce the instability of range cells caused by the attitude change of targets. A novel approach is proposed to optimize the weighted HRRP. In the approach, the separability of weighted HRRPs in different targets is measured by de- signing an objective function, and the weighted coefficients are computed by using the gradient descent method, thus enhancing the influence of stable range cells. Simulation results based on five aircraft models show that the approach can effectively optimize the weighted HRRP and improve the recognition accuracy. 展开更多
关键词 radar target recognition high-resolution range profile scattering center model gradient descentmethod
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Color-polarization synergistic target detection method considering shadow interference
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作者 Bin Feng Jinpei Xiao +3 位作者 Junchao Zhang Lin Li Yunlong Wu Qing Ye 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2024年第7期50-61,共12页
Aiming at the problem of shadow interference in UAV's ground reconnaissance,a color and polarization synergistic target detection method is proposed for anti-shadow interference,based on the influence of two physi... Aiming at the problem of shadow interference in UAV's ground reconnaissance,a color and polarization synergistic target detection method is proposed for anti-shadow interference,based on the influence of two physical characteristics(wavelength and polarization)under different illuminations.A valid fusion strategy is proposed via integrating two separate detection results on color and polarization images.Moreover,a local enhancement and recognition module(LER)is introduced to boost the performance.Based on our built dataset,experimental results show that our method achieves mAPof 87.76%,and12.37%higher than that of color image and 14.68%higher than that of polarization image. 展开更多
关键词 Electro-optical countermeasure Ground reconnaissance Color-polarization Shadow interference Target recognition
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Depolarization Degree to Determine Dihedral Attribute of Radar Target
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作者 Faisal Aldhubaib 《Journal of Electromagnetic Analysis and Applications》 2024年第6期85-101,共17页
This paper investigates the ability of the depolarization degree, derived from the characteristic polarization states at the resonant frequency set, to identify corner or swept, i.e. dihedral, changes in same-class ta... This paper investigates the ability of the depolarization degree, derived from the characteristic polarization states at the resonant frequency set, to identify corner or swept, i.e. dihedral, changes in same-class targets by a metallic wire example. A well-estimated depolarization degree requires a robust extraction of the fundamental target resonance set in two orthogonal sets of fully co-polarized and cross-polarized polarization channels, then finding the null polarization states using the Lagrangian method. Such depolarization degree per resonance mode has the potential to form a robust feature set because it is relatively less sensitive to onset ambiguity, invariant to rotation, and could create a compact, recognizable, and separable distribution in the proposed feature space. The study was limited to two targets with two swept changes of fifteen degrees within normal incidence;under a supervised learning approach, the results showed that the identification rate converging to upper-bound (100%) for a signal-to-noise ratio above 20 dB and lower-bound around (50%) below −10 dB. 展开更多
关键词 POLARIMETRY Radar Target recognition Time-Domain Analysis Remote Sensing
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Target recognition based on modified combination rule 被引量:16
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作者 Chen Tianlu Que Peiwen 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2006年第2期279-283,共5页
Evidence theory is widely used in the field of target recognition. The invalidation problem of this theory when dealing with highly conflict evidences is a research hotspot. Several alternatives of the combination rul... Evidence theory is widely used in the field of target recognition. The invalidation problem of this theory when dealing with highly conflict evidences is a research hotspot. Several alternatives of the combination rule are analyzed and compared. A new combination approach is proposed. Calculate the reliabilities of evidence sources using existing evidences. Construct reliabilities judge matrixes and get the weights of each evidence source. Weight average all inputted evidences. Combine processed evidences with D-S combination rule repeatedly to identify a target. The application in multi-sensor target reeognition as well as the comparison with typical alternatives all validated that this approach can dispose highly conflict evidences efficiently and get reasonable reeognition results rapidly. 展开更多
关键词 evidence theory combination rule conflict evidences target recognition data fusion.
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Radar high resolution range profile recognition via multi-SV method 被引量:6
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作者 Long Li Zheng Liu Tao Li 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2017年第5期879-889,共11页
For radar high resolution range profile (HRRP) recognition, three aspects are of great importance to improve the performance, i.e. discrimination for outlier, classification for inner and an accurate description for f... For radar high resolution range profile (HRRP) recognition, three aspects are of great importance to improve the performance, i.e. discrimination for outlier, classification for inner and an accurate description for feature space. To tackle these issues, a novel target recognition method is designed, denoted by the multiple support vectors (multi-SV) method. With the proposed method, a special framework is constructed by a treble correlate support vector model to segment the feature space to two regions with the distribution of density, and then the description and classification hyperplane for each region are achieved. Based on the support vector framework, this method needs less memory and computation complexity to fit practical radar HRRP recognition. Finally, the experiment based on the measured data verifies the excellent performance of this method. 展开更多
关键词 radar target recognition high resolution range profile support vector DISCRIMINATION CLASSIFICATION
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Radar group target recognition based on HRRPs and weighted mean shift clustering 被引量:7
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作者 GUO Pengcheng LIU Zheng WANG Jingjing 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2020年第6期1152-1159,共8页
When range high-resolution radar is applied to target recognition,it is quite possible for the high-resolution range profiles(HRRPs)of group targets in a beam to overlap,which reduces the target recognition performanc... When range high-resolution radar is applied to target recognition,it is quite possible for the high-resolution range profiles(HRRPs)of group targets in a beam to overlap,which reduces the target recognition performance of the radar.In this paper,we propose a group target recognition method based on a weighted mean shift(weighted-MS)clustering method.During the training phase,subtarget features are extracted based on the template database,which is established through simulation or data acquisition,and the features are fed to the support vector machine(SVM)classifier to obtain the classifier parameters.In the test phase,the weighted-MS algorithm is exploited to extract the HRRP of each subtarget.Then,the features of the subtarget HRRP are extracted and used as input in the SVM classifier to be recognized.Compared to the traditional group target recognition method,the proposed method has the advantages of requiring only a small amount of computation,setting parameters automatically,and having no requirement for target motion.The experimental results based on the measured data show that the method proposed in this paper has better recognition performance and is more robust against noise than other recognition methods. 展开更多
关键词 CLUSTERING group target recognition high resolution range profile(HRRP) mean shift(MS)
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Research on PCA and KPCA Self-Fusion Based MSTAR SAR Automatic Target Recognition Algorithm 被引量:6
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作者 Chuang Lin Fei Peng +2 位作者 Bing-Hui Wang Wei-Feng Sun Xiang-Jie Kong 《Journal of Electronic Science and Technology》 CAS 2012年第4期352-357,共6页
This paper proposes a PCA and KPCA self-fusion based MSTAR SAR automatic target recognition algorithm. This algorithm combines the linear feature extracted from principal component analysis (PCA) and nonlinear featu... This paper proposes a PCA and KPCA self-fusion based MSTAR SAR automatic target recognition algorithm. This algorithm combines the linear feature extracted from principal component analysis (PCA) and nonlinear feature extracted from kernel principal component analysis (KPCA) respectively, and then utilizes the adaptive feature fusion algorithm which is based on the weighted maximum margin criterion (WMMC) to fuse the features in order to achieve better performance. The linear regression classifier is used in the experiments. The experimental results indicate that the proposed self-fusion algorithm achieves higher recognition rate compared with the traditional PCA and KPCA feature fusion algorithms. 展开更多
关键词 Automatic target recognition principal component analysis self-fusion syntheticaperture radar.
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Adaptive target and jamming recognition for the pulse doppler radar fuze based on a time-frequency joint feature and an online-updated naive bayesian classifier with minimal risk 被引量:6
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作者 Jian Dai Xin-hong Hao +2 位作者 Ze Li Ping Li Xiao-peng Yan 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2022年第3期457-466,共10页
This paper considers the problem of target and jamming recognition for the pulse Doppler radar fuze(PDRF).To solve the problem,the matched filter outputs of the PDRF under the action of target and jamming are analyzed... This paper considers the problem of target and jamming recognition for the pulse Doppler radar fuze(PDRF).To solve the problem,the matched filter outputs of the PDRF under the action of target and jamming are analyzed.Then,the frequency entropy and peak-to-peak ratio are extracted from the matched filter output of the PDRF,and the time-frequency joint feature is constructed.Based on the time-frequency joint feature,the naive Bayesian classifier(NBC)with minimal risk is established for target and jamming recognition.To improve the adaptability of the proposed method in complex environments,an online update process that adaptively modifies the classifier in the duration of the work of the PDRF is proposed.The experiments show that the PDRF can maintain high recognition accuracy when the signal-to-noise ratio(SNR)decreases and the jamming-to-signal ratio(JSR)increases.Moreover,the applicable analysis shows that he ONBCMR method has low computational complexity and can fully meet the real-time requirements of PDRF. 展开更多
关键词 Pulse Doppler radar fuze(PDRF) Target and jamming recognition Time-frequency joint feature Online-update naive Bayesian classifier minimal risk(ONBCMR)
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Summed volume region selection based three-dimensional automatic target recognition for airborne LIDAR 被引量:2
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作者 Qi-shu Qian Yi-hua Hu +2 位作者 Nan-xiang Zhao Min-le Li Fu-cai Shao 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2020年第3期535-542,共8页
Airborne LIDAR can flexibly obtain point cloud data with three-dimensional structural information,which can improve its effectiveness of automatic target recognition in the complex environment.Compared with 2D informa... Airborne LIDAR can flexibly obtain point cloud data with three-dimensional structural information,which can improve its effectiveness of automatic target recognition in the complex environment.Compared with 2D information,3D information performs better in separating objects and background.However,an aircraft platform can have a negative influence on LIDAR obtained data because of various flight attitudes,flight heights and atmospheric disturbances.A structure of global feature based 3D automatic target recognition method for airborne LIDAR is proposed,which is composed of offline phase and online phase.The performance of four global feature descriptors is compared.Considering the summed volume region(SVR) discrepancy in real objects,SVR selection is added into the pre-processing operations to eliminate mismatching clusters compared with the interested target.Highly reliable simulated data are obtained under various sensor’s altitudes,detection distances and atmospheric disturbances.The final experiments results show that the added step increases the recognition rate by above 2.4% and decreases the execution time by about 33%. 展开更多
关键词 3D automatic target recognition Point cloud LIDAR AIRBORNE Global feature descriptor
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Spin-image surface matching based target recognition in laser radar range imagery 被引量:2
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作者 王丽 孙剑峰 王骐 《Chinese Physics B》 SCIE EI CAS CSCD 2010年第10期281-288,共8页
We explore the problem of in-plane rotation-invariance existing in the vertical detection of laser radar (Ladar) using the algorithm of spin-image surface matching. The method used to recognize the target in the ran... We explore the problem of in-plane rotation-invariance existing in the vertical detection of laser radar (Ladar) using the algorithm of spin-image surface matching. The method used to recognize the target in the range imagery of Ladar is time-consuming, owing to its complicated procedure, which violates the requirement of real-time target recognition in practical applications. To simplify the troublesome procedures, we improve the spin-image algorithm by introducing a statistical correlated coeff^cient into target recognition in range imagery of Ladar. The system performance is demonstrated on sixteen simulated noise range images with targets rotated through an arbitrary angle in plane. A high efficiency and an acceptable recognition rate obtained herein testify the validity of the improved algorithm for practical applications. The proposed algorithm not only solves the problem of in-plane rotation-invariance rationally, but also meets the real-time requirement. This paper ends with a comparison of the proposed method and the previous one. 展开更多
关键词 Ladar automatic target recognition spin-image statistical correlation coefficient
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Tactical intention recognition of aerial target based on XGBoost decision tree 被引量:9
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作者 WANG Lei LI Shi-zhong 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2018年第2期148-152,共5页
In order to improve the accuracy of target intent recognition,a recognition method based on XGBoost(eXtreme Gradient Boosting)decision tree is proposed.This paper adopts relevant data and program of python to calculat... In order to improve the accuracy of target intent recognition,a recognition method based on XGBoost(eXtreme Gradient Boosting)decision tree is proposed.This paper adopts relevant data and program of python to calculate the probability of tactical intention.Then the sequence intention probability is obtained by applying Dempster-Shafer rule of combination.To verify the accuracy of recognition results,we compare the experimental results of this paper with the results in the literatures.The experiment shows that the probability of tactical intention recognition through this method is improved,so this method is feasible. 展开更多
关键词 tactical intention recognition of target XGBoost(eXtreme Gradient Boosting)decision tree Dempster-Shafer combination rule
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New statistical model for radar HRRP target recognition 被引量:2
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作者 Qingyu Hou Feng Chen Hongwei Liu Zheng Bao 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2010年第2期204-210,共7页
The mixture of factor analyzers (MFA) can accurately describe high resolution range profile (HRRP) statistical charac- teristics. But how to determine the proper number of the models is a problem. This paper devel... The mixture of factor analyzers (MFA) can accurately describe high resolution range profile (HRRP) statistical charac- teristics. But how to determine the proper number of the models is a problem. This paper develops a variational Bayesian mixture of factor analyzers (VBMFA) model. This procedure can obtain a lower bound on the Bayesian integral using the Jensen's inequality. An analytical solution of the Bayesian integral could be obtained by a hypothesis that latent variables in the model are indepen- dent. During computing the parameters of the model, birth-death moves are utilized to determine the optimal number of model au- tomatically. Experimental results for measured data show that the VBMFA method has better recognition performance than FA and MFA method. 展开更多
关键词 radar automatic target recognition (RATR) high reso- lution range profile (HRRP) variational Bayesian mixtures of factor analyzers (VBMFA) variational Bayesian(VB) mixtures of factor analyzers (MFA).
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RADAR HRRP RECOGNITION BASED ON THE MINIMUM KULLBACK-LEIBLER DISTANCE CRITERION 被引量:2
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作者 Yuan Li Liu Hongwei Bao Zheng 《Journal of Electronics(China)》 2007年第2期199-203,共5页
To relax the target aspect sensitivity and use more statistical information of the High Range Resolution Profiles (HRRPs), in this paper, the average range profile and the variance range profile are extracted together... To relax the target aspect sensitivity and use more statistical information of the High Range Resolution Profiles (HRRPs), in this paper, the average range profile and the variance range profile are extracted together as the feature vectors for both training data and test data representa-tion. And a decision rule is established for Automatic Target Recognition (ATR) based on the mini-mum Kullback-Leibler Distance (KLD) criterion. The recognition performance of the proposed method is comparable with that of Adaptive Gaussian Classifier (AGC) with multiple test HRRPs, but the proposed method is much more computational efficient. Experimental results based on the measured data show that the minimum KLD classifier is effective. 展开更多
关键词 High Range Resolution Profile (HRRP) Automatic Target recognition (ATR) Kullback-Leibler Distance (KLD) Adaptive Gaussian Classifier (AGC)
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Automatic target recognition of moving target based on empirical mode decomposition and genetic algorithm support vector machine 被引量:4
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作者 张军 欧建平 占荣辉 《Journal of Central South University》 SCIE EI CAS CSCD 2015年第4期1389-1396,共8页
In order to improve measurement accuracy of moving target signals, an automatic target recognition model of moving target signals was established based on empirical mode decomposition(EMD) and support vector machine(S... In order to improve measurement accuracy of moving target signals, an automatic target recognition model of moving target signals was established based on empirical mode decomposition(EMD) and support vector machine(SVM). Automatic target recognition process on the nonlinear and non-stationary of Doppler signals of military target by using automatic target recognition model can be expressed as follows. Firstly, the nonlinearity and non-stationary of Doppler signals were decomposed into a set of intrinsic mode functions(IMFs) using EMD. After the Hilbert transform of IMF, the energy ratio of each IMF to the total IMFs can be extracted as the features of military target. Then, the SVM was trained through using the energy ratio to classify the military targets, and genetic algorithm(GA) was used to optimize SVM parameters in the solution space. The experimental results show that this algorithm can achieve the recognition accuracies of 86.15%, 87.93%, and 82.28% for tank, vehicle and soldier, respectively. 展开更多
关键词 automatic target recognition(ATR) moving target empirical mode decomposition genetic algorithm support vector machine
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