Prof. Hongjun Zhou Profile

Prof. Hongjun Zhou

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Proceedings Article | 7 August 2024 Paper

Wearable device Bluetooth data processing platform based on MATLAB App Designer

Rengyang Liu, Hongjun Zhou, Zedong Nie

Proceedings Volume 13229, 132291Z (2024) https://doi.org/10.1117/12.3038168

KEYWORDS: MATLAB, Data transmission, Data processing, Wearable devices, Human-machine interfaces, Design, Visualization

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This study presents a Bluetooth data processing platform designed using MATLAB App Designer, aimed at addressing the data reception and processing challenges encountered in wearable device algorithm research. This platform provides convenient and fast data analysis and visualization tools, mainly used to solve the problem of cross platform transmission after wearable devices collect data. The platform is structured into modules for data reception, processing, and visualization, with defined protocols for Bluetooth data reception and transmission. Additionally, we designed a real-time dynamic heart rate algorithm based on the collected data and integrated it into the platform to verify its reliability and stability. This research provides essential tools and platform support for algorithmic research in wearable devices and other Bluetooth-enabled devices.

Proceedings Article | 20 October 2022 Paper

Research on prediction model of platform turning speed based on ASSA-ELM

Wangcheng Zhang, Hongjun Zhou

Proceedings Volume 12350, 123501L (2022) https://doi.org/10.1117/12.2652505

KEYWORDS: Evolutionary algorithms, Control systems, Neural networks, Data modeling

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In the automatic unpacking control system, the control accuracy of the flipping speed of the flipping platform is of great significance to the dumping effect and the service life of the equipment. This paper conducts research based on this. Aiming at the fact that the standard extreme learning machine (ELM) is prone to fall into local optimum, this paper proposes a model for extreme learning machine (ASSA-ELM) optimization based on improved salp swarm optimization algorithm is proposed and applied to an example of flipping speed prediction of flipping platform. Based on the salp group optimization algorithm (SSA), a position update strategy combining the adaptive weight method and the proportional weight of the improved step size Euclidean distance is introduced. The weights and hidden layer biases are optimized, which greatly improves the generalization ability of the ELM model and the accuracy of the predicted value. The algorithm models before and after the improvement are compared and analyzed. The results show that the predicted value of the ASSA-ELM model has the highest fitting degree with the actual value collected in the industrial field, and has a high prediction accuracy, which verifies the feasibility and effectiveness of the ASSA-ELM model in the prediction of the turning speed of the turning platform.

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