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Articles 1 - 15 of 15
Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Fatigue Detection Method Based On Facial Features And Head Posture, Rongxiu Lu, Bihao Zhang, Zhenlong Mo
Fatigue Detection Method Based On Facial Features And Head Posture, Rongxiu Lu, Bihao Zhang, Zhenlong Mo
Journal of System Simulation
Abstract: Aiming at the of the single fatigue characteristics, low robustness and inability to customize fatigue thresholds for different drivers of fatigue detection methods, a method based on facial features and head posture is proposed. In face detection and face key point positioning HOG feature operator and regression tree algorithm are used. In head posture estimation, head posture Euler angle is estimated by combining the face key points with the coordinate system transformation. In fatigue feature extraction, a deep residual neural network model is established to extract the eye fatigue features, which the eye, mouth aspect ratio and head posture …
Research On Intelligent Gait Recognition Method Based On Plantar Pressure Perception, Xueqin Liu, Liu Ning, Su Zhong, Jingxiao Wang, Chaojie Yuan
Research On Intelligent Gait Recognition Method Based On Plantar Pressure Perception, Xueqin Liu, Liu Ning, Su Zhong, Jingxiao Wang, Chaojie Yuan
Journal of System Simulation
Abstract: In view of the complexity and low accuracy of gait recognition in the past, an intelligent gait recognition method based on plantar pressure perception is proposed. The pressure data of the gait of plantar periodic motion is collected and the obtained gait data is classified by the vector machines,the intelligent gait recognition of plantar pressure perception is realized, and the accuracy of gait feature analysis is improved. Through experiment verification, the overall classification accuracy of the classifier is more than 90%, which verifies the rationality of the feature extraction. By evaluating the real state and the results of …
Fault Diagnosis Of Mechanical Equipment Based On Ga-Svr With Missing Data In Small Samples, Jingjing Wei, Qinming Liu, Chunming Ye, Guanlin Li
Fault Diagnosis Of Mechanical Equipment Based On Ga-Svr With Missing Data In Small Samples, Jingjing Wei, Qinming Liu, Chunming Ye, Guanlin Li
Journal of System Simulation
Abstract: In view of the equipment fault diagnosis with small and missing sample data, a method of missing data filling based on support vector regression optimized by genetic algorithm is proposed to improve the accuracy of equipment fault diagnosis. The support vector regression optimized by genetic algorithm was trained by other data values of missing data, and univariate prediction results were obtained. The training set was reconstructed through correlation analysis, so as to obtain the multivariate prediction results. Dynamic weights were established to combine univariate prediction results and multivariate prediction results to fill in the missing data. The …
Safety Evaluation And Risk Level Prediction Of Driving Behavior Considering Multi-Factors Influence, An Yu, Pengpeng Jiao, Zixiu Bai
Safety Evaluation And Risk Level Prediction Of Driving Behavior Considering Multi-Factors Influence, An Yu, Pengpeng Jiao, Zixiu Bai
Journal of System Simulation
Abstract: In order to study the influence of multi-factors of human-vehicle-road on driving behavior and vehicle safety status in road traffic system, a simulated driving comparison test of six scenarios combined by multiple factors is designed. Driving simulator, physiography and eye tracker are used to collect 19 indicators related to driving behavior respectively. The differences of sample data are compared by variance analysis. The nonlinear SVM(support vector machine) is used to classify and predict the sample data. The driving behavior risk level prediction model is established, and the validity of the model is verified by the experimental data. The …
Fault Diagnosis Of Reverse Osmosis Water Desalination Based On Optimized Support Vector Machine, Zhang Biao, Jianfeng Xing, Zhicheng Ji
Fault Diagnosis Of Reverse Osmosis Water Desalination Based On Optimized Support Vector Machine, Zhang Biao, Jianfeng Xing, Zhicheng Ji
Journal of System Simulation
Abstract: According to the reverse osmosis membrane fault problems in reverse osmosis water desalination system, a fault diagnosis method based on support vector machine (SVM) was introduced for fault diagnoses. To solve the problem of parameter optimization in SVM, an improved chaos particle swarm algorithm was proposed. The introduction of Chaos theory to particle swarm optimization algorithm may not only enhance the diversity of the population and particle global search ability, but also improve the convergence speed and accuracy of the particle swarm algorithm. The optimized SVM model was applied to the fault diagnosis of reverse osmosis water desalination system. …
Study On Hand Gesture Recognition And Portfolio Optimization Model Based On Svm, Zhiwei Cai, Shuyan Wu, Junfeng Song
Study On Hand Gesture Recognition And Portfolio Optimization Model Based On Svm, Zhiwei Cai, Shuyan Wu, Junfeng Song
Journal of System Simulation
Abstract: Hand gesture recognition was researched. The idea of extracting related features was proposed by using SVM algorithm in machine learning domain, and combination optimization method was used, which consists of ANN, HMM and DTW, to do hand gesture recognition. The experimental results show that portfolio optimization model based gesture recognition method has high accuracy and is very effective.
Texture Classification Based On Multi-Scale Wavelet, Liao Ning, Lisha Xu, Xiaoshan Qian
Texture Classification Based On Multi-Scale Wavelet, Liao Ning, Lisha Xu, Xiaoshan Qian
Journal of System Simulation
Abstract: Texture analysis is quite sensitive to rotations. An efficient approach, called Invariant Contourlet-Fourier Descriptor, was proposed to achieve rotation invariance in texture analysis by extracting a set of Shannon entropy in contourlet domain. Discrete Fourier Transform analysis was applied to entropy vectors of each scale to form rotation invariant feature vectors, the dimensionality of which was reduced further due to the symmetry of DFT magnitude spectrum. Two classifiers, including the well-known Euclidean distance and Support Vector Machine, were studied to measure the distance between the known and unknown features. Experimental results on 1500 texture images show that …
Simulation And Application Of Dkipso-Svc Combined Model For Credit Risk Assessment, Zhenhai Wan, Tieying Liu, Zhang Yang, Jishuang Li
Simulation And Application Of Dkipso-Svc Combined Model For Credit Risk Assessment, Zhenhai Wan, Tieying Liu, Zhang Yang, Jishuang Li
Journal of System Simulation
Abstract: In order to improve the problem of inefficient parameter selection of the GDS-SVC model and DIPSO-SVC model, and utilize the generalization ability and robustness of support vector classification (SVC), the reduction factor of location updating was introduced based on the dynamic improvement Particle Swarm Optimization (DIPSO), and then the DKIPSO-SVC of parameters selecting in SVC was established based on DKIPSO. The method was applied to credit scoring of commercial banks. The simulation results demonstrate that the robustness of the DKIPSO-SVC model is better than DIPSO-SVC. But beyond that, the accuracy of DKIPSO-SVC model achieves 96.6049%, higher than that of …
Study Of Adaptive Dynamic Search Pso Based Svm Parameter Optimization, Chunneng Gao, Zhang Biao, Zhicheng Ji
Study Of Adaptive Dynamic Search Pso Based Svm Parameter Optimization, Chunneng Gao, Zhang Biao, Zhicheng Ji
Journal of System Simulation
Abstract: According to critical control points (CCPs) selection problem in wheat processing HACCP (hazard analysis and critical control point), an automatic identification method based on SVM model was introduced. In order to improve the model’s recognition stability and accuracy, an adaptive dynamic search particle swarm optimization (ADS-PSO) for the optimization of kernel function parameters in SVM was proposed. ADS-PSO introduced an evolutionary factor and threshold (ET) to estimate the evolutionary state and adjusted the search strategy adaptively. Besides, an inertia parameter for the velocity was defined in ADS-PSO. The simulation results show that the improved SVM model can identify …
Fault Diagnosis Method Of Pmsm Based On Adaptive Dynamic Cat Swarm Optimization Of Svm, Wang Yan, Wang Xin, Zhicheng Ji, Dahu Yan
Fault Diagnosis Method Of Pmsm Based On Adaptive Dynamic Cat Swarm Optimization Of Svm, Wang Yan, Wang Xin, Zhicheng Ji, Dahu Yan
Journal of System Simulation
Abstract: In order to solve the problems of common inter-turn short circuit faults of permanent magnet synchronous motor (PMSM), a corresponding motor fault model based on the existing basis of PMSM is established. The eigenvector is extracted by energy spectrum analysis. The penalty factor and RBF-kernel parameter of SVM are optimized by adaptive dynamic cat swarm optimization (ADACSO) algorithm. The optimized SVM is adopted to motor fault diagnosis. The eigenvector obtained by energy spectrum analysis is taken as sample data to conduct simulation experiment. The experiment results indicate that, compared with other optimization algorithms, using ADACSO to optimize …
Radar Emitter Signal Identification Based On Slide+Svm, Yingkun Huang, Weidong Jin
Radar Emitter Signal Identification Based On Slide+Svm, Yingkun Huang, Weidong Jin
Journal of System Simulation
Abstract: For the deficiency of traditional techniques of emitter signal feature extraction which heavily rely on experience, a model of radar emitting signal identification based on feature self-learning was proposed. This model consists of following 2 parts. Firstly, transform radar signal into frequency domain, then reduce signal dimension by using improved Piecewise Aggregate Approximation (PAA) method. Secondly, create the model of multi-layer Liner Denoiser (LIDE) to feature learning by using unsupervised training method. The validity of model was verified by simulating 5 different kinds of emitting signal with the outcome that excellent identification accuracy could be achieved at …
Forecasting Of Short-Term Power Load Of Secrpso-Svm Based On Data-Driven, Hairong Sun, Bixia Xie, Tian Yao, Zhuoqun Li
Forecasting Of Short-Term Power Load Of Secrpso-Svm Based On Data-Driven, Hairong Sun, Bixia Xie, Tian Yao, Zhuoqun Li
Journal of System Simulation
Abstract: For the parameter selection of support vector machine in modeling, a particle swarm optimization algorithm based on second-order oscillation and repulsion factor was proposed to optimize the parameter of SVM. The algorithm employed the nonlinear decreasing weight to balance the global and local search ability. Second-order oscillation factor could maintain the population diversity. The repulsion factor was introduced to make the swarm even distribution in search space, which could avoid local optimum. For the complex characteristics of nonlinearity, time-varying and multifactorial of electric power load, a support vector machine forecasting model based on data was proposed, and the influence …
Image Feature Extraction And Online Grading Method For Weight And Shape Of Strawberry, Zhang Qing, Xiangjun Zou, Guichao Lin, Yanhui Sun
Image Feature Extraction And Online Grading Method For Weight And Shape Of Strawberry, Zhang Qing, Xiangjun Zou, Guichao Lin, Yanhui Sun
Journal of System Simulation
Abstract: To deal with the classification problems of strawberry in production, a machine vision based strawberry weight and shape grading method was proposed. The strawberry image was segmented by thresholding to extract the fruit. The area and perimeter parameters of the fruit were then calculated and used to build the strawberry weight grading model through regression analysis. Elliptic Fourier descriptor was used to extract the shape features of the fruit, and these shape features were applied to train a support vector machine (SVM) which represented the strawberry shape grading model. 200 samples of strawberries were selected to test both …
Evaluation Of Green Smart Cities In China Based On Entropy Weight - Cloud Model, Chen Li, Haixia Zhang
Evaluation Of Green Smart Cities In China Based On Entropy Weight - Cloud Model, Chen Li, Haixia Zhang
Journal of System Simulation
Abstract: Based on the research on green smart city at home and abroad; and aiming at the shortcomings and deficiencies of traditional evaluation methods, this paper proposes an evaluation method of combining entropy and cloud model based on the cloud model which can realize the conversion of qualitative concept and quantitative value. This method synthetically considers the subjective and objective factors; carries on the correlation analysis to the index; determines the set of evaluation indicators; uses the X-conditional cloud generator in cloud model to obtain the different levels of membership matrix corresponding to each evaluation object; and carries on the …
Image Classification Based On Sparse Autoencoder And Support Vector Machine, Liu Fang, Lixia Lu, Hongjuan Wang, Wang Xin
Image Classification Based On Sparse Autoencoder And Support Vector Machine, Liu Fang, Lixia Lu, Hongjuan Wang, Wang Xin
Journal of System Simulation
Abstract: A new algorithm of image classification based on the sparse autoencoder and the support vector machine was proposed in view of the drawbacks that the single layer sparse autoencoder for feature learning is easy to lose the deep abstract feature and the features lack the robustness. The deep sparse autoencoder is constructed to learn each image layer and the feature of each layer is automatically extracted. The each feature weights and the reorganized set of feature are obtained according to the feature weighting method. By combining the strong global search ability of genetic algorithm and the excellent performance of …