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Your search keyword '"Yan, Zhongzhen"' showing total 34 results
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34 results on '"Yan, Zhongzhen"'

4. DC-Gossip: An Enhanced Broadcast Protocol in Hyperledger Fabric Based on Density Clustering

Author

Xu, Zhigang, Ye, Kangze, Dong, Xinhua, Han, Hongmu, Yan, Zhongzhen, Chen, Xingxing, Liao, Duoyue, Wang, Haitao, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wang, Lei, editor, Segal, Michael, editor, Chen, Jenhui, editor, and Qiu, Tie, editor

Published
2022
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12. Deep Learning-Driven Robot Arm Control Fusing Convolutional Visual Perception and Predictive Modeling for Motion Planning

Author

Yan, Zhongzhen, Chang, Yiming, Yuan, Lukang, Wei, Feifei, Wang, Xianglong, Dong, Xinhua, and Han, Hongmu

Abstract

The wide application of robotic technology in various industries from industrial automation to medical assistance is gradually changing our production and lifestyle, and has attracted widespread attention in many fields. However, existing robotic control systems often grapple with limited flexibility and poor adaptability to complex environments, particularly in highly dynamic operational contexts. Against this backdrop, the integration of deep learning technologies offers new possibilities in enhancing robotic perception and decision-making, especially in visual perception and motion planning. To address these challenges, we have introduced a novel robotic arm control network model, MPC-WGAN-Faster R-CNN, which combines Model Predictive Control concepts with Wasserstein Generative Adversarial Networks and Faster R-CNN visual recognition technology. This integration aims to improve the precision and adaptability of robotic arm operations in complex environments.

Published
2024
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24. A multi-energy load prediction of a building using the multi-layer perceptron neural network method with different optimization algorithms.

Author

Yan, Zhongzhen, Zhu, Xinyuan, Wang, Xianglong, Ye, Zhiwei, Guo, Feng, Xie, Lei, and Zhang, Guiju

Abstract

Since cooling and heating loads are recognized as key characteristics for evaluating the energy efficiency of buildings, it appears indisputable that they must be predicted and analyzed for residential structures. Accordingly, the multi-layer perceptron neural network is applied for predicting the heating and cooling loads using the experimental dataset. The used dataset is obtained by monitoring the impact of the building's dimensions on energy consumption. To optimize the training process of the multi-layer perceptron neural network, several optimizers are employed. Besides, different statistical performance indicators are considered to see which selected optimizer outperforms the rest in terms of accuracy and authenticity. The obtained results emphasize the remarkable performance of adaptive chaotic grey wolf optimization, which can be used to train the multi-layer perceptron neural network and forecast the buildings' energy consumption with the highest accuracy. According to the obtained results, the hybrid multi-layer perceptron neural network- adaptive chaotic grey wolf optimization method demonstrates the best performance. The optimum number of neurons in the hidden layer is obtained to be 15. Also, based on the statistical performance indicators, the selected method reveals an R2 of 0.9123 and 0.9419 for cooling and heating loads, respectively. [ABSTRACT FROM AUTHOR]

Published
2023
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