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RISS 인기검색어
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- 저자 : Boyan Jia (검색결과 3 건)
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Nolinear Multi-component Spectroscopy Analysis Based on Evolutionary Construction Optimazation
Boyan Cai,Hui Cao,Yanbin Zhang,Lixin Jia,Gangquan Si,Zhongjian Li 제어로봇시스템학회 2015 제어로봇시스템학회 국제학술대회 논문집 Vol.2015 No.10
Spectroscopy has been widely used to evaluate product quality or to predict components. To deal with the nonlinearity of spectral data, artificial neural networks (ANN) are widely used. One weakness of ANN is we have no accurate analytical method to design a optimal network structure. A multivariate component prediction method based on optimized neural network combined with evolutionary algorithm (EA) for spectral analysis is proposed in the paper. For the proposed method, ANNs are combined with nonlinear adaptive evolutionary programming algorithm (NAEP) to evolve ANNs architecture including the number of hidden nodes and the number of hidden layers. And the root-meansquares error of cross-validation (RMSECV) is the fitness function of NAEP. In order to present the effectiveness of this method, back propagation neural network (BP) and ANN with genetic algorithm (ANN-GA) methods were also used for component predicting models. An application research has been demonstrated with spectral data which is recorded in an experiment of meat content determination. Results indicate that our method has the ability to design the best ANN structure to predict more accurate and robust as a practical spectral analysis tool.
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Hydrophobicity test of silicone rubber based on thermogravimetric analysis
Jie Liu,Boyan Jia,Jianghai Geng 대한전기학회 2019 Journal of Electrical Engineering & Technology Vol.14 No.5
To study the hydrophobicity and the transfer of hydrophobicity of silicone rubber, experiments were conducted under three conditions (low-temperature ashing, room-temperature ashing, and low-temperature salting) on new silicone rubber products. Then the surface hydrophobicity of the material after cleaning the contaminants was measured using the classification of water spray, and the change laws of low molecular-weight substances in the core layer and the surface of silicone rubber samples were studied using the thermogravimetric analysis. The results showed that the low molecular-weight substances, namely the hydrophobic substances, were a typical type of siloxane. After pollutants accumulated on the surface of the silicone rubber, the surface hydrophobicity of the material declined continuously, and the contents of hydrophobic substances in the surface and the core layers reduced with fluctuations at a rate being directly proportional to the ambient temperature. The content and the decrease rate of hydrophobic substances in the surface were slightly higher than those in the core layer. The ashes in the contaminants propelled the transfer of the hydrophobic substances while salts inhibited the transfer. However, the presence of salts accelerated the hydrolysis of silicone rubber molecules in the material surface. The experimental results provide reference for the daily maintenance of composite insulators of electric power enterprises.
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Wang Jianping,Chen Boyan,Wang Youchao,Pu Dexi,Jia Xiaoyue 한국CDE학회 2024 Journal of computational design and engineering Vol.11 No.2
The current study on the synthesis problems of four-bar mechanism trajectories primarily relies on establishing a numerical atlas based on trajectory characteristics and employing neural networks to synthesize mechanism parameters. However, this approach has several shortcomings, including a vast database, inefficient retrieval, and challenges in maintaining accuracy. This paper presents a method for synthesizing a trajectory-generation mechanism that combines the extreme gradient boosting (XGBoost) algorithm with a genetic algorithm (GA). The purpose is to synthesize, based on a particular trajectory, the dimensions and installation position parameters of a four-bar mechanism. The paper classifies the trajectories according to their shape features and geometric center placements, thereby improving the accuracy of the XGBoost model for synthesizing mechanisms. The XGBoost algorithm is employed to synthesize the basic dimensional parameters for the mechanism, with the relative slopes of trajectories as input features. The synthesized basic dimensional parameters are turned into parameters for the actual mechanism by researching the scaling, translation, and rotation relationships between mechanisms and the trajectories they generate. The accuracy of the generated trajectories from the synthesized mechanism can be improved by applying GA to optimize the mechanism parameters. Five comparative examples are provided in this research for the different scenarios of given trajectory curves and trajectory points. The effectiveness and accuracy of the proposed approach in this study are validated in comparison to existing research methods by comparing errors between the generated trajectories and the given trajectories.
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