Gait and Posture Analysis Method Based on Genetic Algorithm and Support Vector Machines with Acceleration Data

2016 ◽  
Vol 28 (3) ◽  
pp. 418-424 ◽  
Author(s):  
Huan Gou ◽  
◽  
Tengda Shi ◽  
Lei Yan ◽  
Jiang Xiao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Parameters Optimization ◽  
Support Vector ◽  
Analysis Method ◽  
Posture Analysis ◽  
Acceleration Data ◽  
Vector Machines ◽  
Posture Recognition ◽  
Simulation Results

[abstFig src='/00280003/18.jpg' width=""300"" text='The result of parameters optimization by GA' ] The support vector machine (SVM) we propose for automated gait and posture recognition is based on acceleration. Acceleration data are obtained from four accelerators attached to the human thigh and lower leg. In the experiment, volunteers take part in four gaits and postures, i.e., sitting, standing, walking and ascending stairs. Acceleration data that are preprocessed include normalization, a wavelet filter and dimension reduction. We used the SVM and a neural network to analyze the data processed. Simulation results indicate that SVM parametersCandgselected by a genetic algorithm (GA) are more effective for gait and posture analysis when compared to the parameterCandgselected by a grid search. The overall classification precision of the four gaits and postures exceeds 90.0%, and neural network simulation results indicate that the SVM using the GA is preferable for use in analysis.

Modeling Knowledge Employee’s Turnover Based on P-SVM

2010 ◽  
Vol 121-122 ◽  
pp. 825-831
Author(s):  
Yong Zhao ◽  
Ye Zheng Liu
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Model Development ◽  
Forecast Model ◽  
Support Vector ◽  
Multi Criteria Decision Making ◽  
Parameters Selection ◽  
Vector Machines ◽  
Decision Making Problem ◽  
Simulation Results

Knowledge employee’s turnover forecast is a multi-criteria decision-making problem involving various factors. In order to forecast accurately turnover of knowledge employees, the potential support vector machines(P-SVM) is introduced to develop a turnover forecast model. In the model development, a chaos algorithm and a genetic algorithm (GA) are employed to optimize P-SVM parameters selection. The simulation results show that the model based on potential support vector machine with chaos not only has much stronger generalization ability but also has the ability of feature selection.

A compact neural network for training support vector machines

2012 ◽  
Vol 86 ◽  
pp. 193-198 ◽  
Author(s):  
Yun Yang ◽  
Qiaochu He ◽  
Xiaolin Hu
Keyword(s):  
Neural Network ◽  
Support Vector Machines ◽  
Support Vector ◽  
Training Support ◽  
Vector Machines

A novel identification approach for shearer running status through integration of rough sets and improved wavelet neural network

2016 ◽  
Vol 230 (16) ◽  
pp. 2792-2805 ◽  
Author(s):  
Lei Si ◽  
Zhongbin Wang ◽  
Xinhua Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Rough Sets ◽  
Particle Swarm Optimization Algorithm ◽  
Wavelet Neural Network ◽  
Decision Table ◽  
Swarm Optimization ◽  
Novel Approach ◽  
Identification Approach ◽  
Simulation Results

In order to accurately and conveniently identify the shearer running status, a novel approach based on the integration of rough sets (RS) and improved wavelet neural network (WNN) was proposed. The decision table of RS was discretized through genetic algorithm and the attribution reduction was realized by MIBARK algorithm to simply the samples of WNN. Furthermore, an improved particle swarm optimization algorithm was proposed to optimize the parameters of WNN and the flowchart of proposed approach was designed. Then, a simulation example was provided and some comparisons with other methods were carried out. The simulation results indicated that the proposed approach was feasible and outperforming others. Finally, an industrial application example of mining automation production was demonstrated to verify the effect of proposed system.

scholarly journals Identifying Travel Mode with GPS Data Using Support Vector Machines and Genetic Algorithm

Information ◽  
2015 ◽  
Vol 6 (2) ◽  
pp. 212-227 ◽  
Author(s):  
Fang Zong ◽  
Yu Bai ◽  
Xiao Wang ◽  
Yixin Yuan ◽  
Yanan He
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machines ◽  
Support Vector ◽  
Gps Data ◽  
Travel Mode ◽  
Vector Machines

Patent Innovation Factors Evolution Based on P-SVM and GCCA

2010 ◽  
Vol 39 ◽  
pp. 247-252
Author(s):  
Sheng Xu ◽  
Zhi Juan Wang ◽  
Hui Fang Zhao
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machines ◽  
Correlation Coefficient ◽  
Network Architecture ◽  
Innovation Management ◽  
Model Development ◽  
Real Data ◽  
Support Vector ◽  
Vector Machines ◽  
Gray Correlation

A two-stage neural network architecture constructed by combining potential support vector machines (P-SVM) with genetic algorithm (GA) and gray correlation coefficient analysis (GCCA) is proposed for patent innovation factors evolution. The enterprises patent innovation is complex to conduct due to its nonlinearity of influenced factors. It is necessary to make a trade off among these factors when some of them conflict firstly. A novel way about nonlinear regression model with the potential support vector machines (P-SVM) is presented in this paper. In the model development, the genetic algorithm is employed to optimize P-SVM parameters selection. After the selected key factors by the PSVM with GA model, the main factors that affect patent innovation generation have been quantitatively studied using the method of gray correlation coefficient analysis. Using a set of real data in China, the results show that the methods developed in this paper can provide valuable information for patent innovation management and related municipal planning projects.

scholarly journals A selection of AKARI FIS BSC extragalactic objects

2015 ◽  
Vol 11 (S319) ◽  
pp. 101-101
Author(s):  
G. Marton ◽  
L.V. Tóth ◽  
L. G. Balázs ◽  
S. Zahorecz ◽  
Z. Bagoly ◽  
...  
Keyword(s):  
Far Infrared ◽  
Point Sources ◽  
Support Vector ◽  
Multidimensional Space ◽  
Quadratic Discriminant Analysis ◽  
Analysis Method ◽  
Vector Machines ◽  
Discriminant Analysis Method ◽  
Bright Source ◽  
Selection Of

AbstractThe point sources in the Bright Source Catalogue (BSC) of the AKARI Far–Infrared Surveyor (FIS) were classified based on their far–IR and mid–IR fluxes and colours using Quadratic Discriminant Analysis method (QDA) and Support Vector Machines (SVM). The reliability of our results show that we can successfully separate galactic and extragalactic AKARI point sources in the multidimensional space of fluxes and colours. However, differentiating among the extragalactic sub–types needs further information.

scholarly journals Obscure Image Classification and Restoration using Support Vector Machines

2019 ◽  
Vol 8 (4) ◽  
pp. 8231-8236
Keyword(s):  
Kernel Estimation ◽  
Classification Algorithm ◽  
Support Vector ◽  
Svm Classification ◽  
Vector Machines ◽  
Blurred Image ◽  
Simulation Results ◽  
Blurred Images ◽  
Identification And Characterization ◽  
Restoration Model

A restoration and classification computation for blurred image which depends on obscure identification and characterization is proposed in this paper. Initially, new obscure location calculation is proposed to recognize the Gaussian, Motion and Defocus based blurred locales in the image. The degradation-restoration model referred with pre-processing followed by binarization and features extraction/classification algorithm applied on obscure images. At this point, support vector machine (SVM) classification algorithm is proposed to cluster the blurred images. Once the obscure class of the locales is affirmed, the structure of the obscure kernels of the blurred images are affirmed. At that point, the obscure kernel estimation techniques are embraced to appraise the obscure kernels. At last, the blurred locales are re-established utilizing nonblind image deblurring calculation and supplant the blurred images with the restored images. The simulation results demonstrate that the proposed calculation performs well

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