A novel platform system for gait analysis was developed, with its two main parts: force platform and pressure platform, it can offer complete components of pressures in walking, including vertical pressure, shear forc...
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A novel platform system for gait analysis was developed, with its two main parts: force platform and pressure platform, it can offer complete components of pressures in walking, including vertical pressure, shear forces, pressure distribution and COP (centre of pressure) with reliable result. System configuration was introduced as an important part, and gait model was established. Some examples of application were also reported.
Inspired by the transmission of seeds in nature, an evolutionary algorithm, seed optimization algorithm (SOA), is proposed. The algorithm is designed by simulating the self-adaptive phenomena of plant and it can be us...
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Inspired by the transmission of seeds in nature, an evolutionary algorithm, seed optimization algorithm (SOA), is proposed. The algorithm is designed by simulating the self-adaptive phenomena of plant and it can be used to resolve complex optimization problems with the evolution of plant. The global convergence analysis of SOA is made by using the Solis and Wets'research results. Finally, SOA is applied to three function optimization problems and compared with particle swarm optimization (PSO) algorithm. The experimental results show that SOA has stable and robust behaviour and it can be used as a promising alternative to existing optimization methods for engineering design.
This paper proposes an effective method for constructing and pruning support vector machine ensembles for improved classification performance. Firstly we propose a novel method for constructing SVM ensembles. Traditio...
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This paper proposes an effective method for constructing and pruning support vector machine ensembles for improved classification performance. Firstly we propose a novel method for constructing SVM ensembles. Traditionally an SVM ensemble is constructed by the data sampling method; In our method, however,each individual SVM classifier is trained by using the same original training set, but with different kernel *** to traditional SVM ensemble methods, our method need not to tune the kernel parameters for each individual SVM, thus the training of the SVM ensemble can be simplified considerably. Furthermore, we also propose several efficient method for pruning the constructed SVM ensembles. The proposed pruning methods cannot only simplify the SVM ensemble, but also improve its performance. A set of experiments were conducted to prove the efficiency and affectivity of our proposed approaches.
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