基于 Jaya 优化标定的高精度数据采集方法

doi:10.12066/j.issn.1007-2861.2372

Journal of Shanghai University（Natural Science Edition） ›› 2022, Vol. 28 ›› Issue (3): 361-371.doi: 10.12066/j.issn.1007-2861.2372

• Data Collection, Database and Data Processing • Previous Articles Next Articles

High-precision data acquisition method based on Jaya optimization and calibration

ZHANG Hesheng¹(), JIAO Peng¹, HU Qirui¹, CAI Jiangqian², HU Shunbo³^,⁴, CAO He⁵, OUYANG Qiubao⁵

1. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
2. School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
3. Center of Materials Informatics and Data Science, Materials Genome Institute, Shanghai University, Shanghai 200444, China
4. Zhejiang Laboratory, Hangzhou 311100, Zhejiang, China
5. State Key Laboratory of Metal Matrix Composites, School of Material Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

Received:2022-03-20 Online:2022-06-30 Published:2022-05-27
Contact: ZHANG Hesheng E-mail:zhs81@shu.edu.cn

Abstract

Abstract:

Materials genome engineering (MGE) integrates high-throughput experiments, high-throughput computations, databases, and artificial intelligence to accelerate the development of advanced materials. However, a reliable and effective method to acquire data from experimental equipment is yet to be identified in MGE. Because the calibration data of high-precision data acquisition systems are not synchronized in terms of time, a linear model is used in this study as a model for data processing parameters, and the value displayed by the device is used as the real value to construct the objective function to optimize the data processing parameters. The Jaya optimization algorithm is used to realize the optimization search of processing parameters. Based on the data acquisition of the equipment temperature as an example, a high-precision data acquisition system is constructed and verified experimentally. The experimental results show that using the optimized model parameters, the average error of data acquisition is only 0.13 $^\circ$C, and the maximum accuracy is 99.89%. Compared with the non-optimized model parameters, the average error reduced by 63.20%, which significantly improves the data acquisition accuracy.

Key words: time out of sync, data acquisition, Jaya algorithm, data calibration

CLC Number:

TP 319

ZHANG Hesheng, JIAO Peng, HU Qirui, CAI Jiangqian, HU Shunbo, CAO He, OUYANG Qiubao. High-precision data acquisition method based on Jaya optimization and calibration[J]. Journal of Shanghai University（Natural Science Edition）, 2022, 28(3): 361-371.

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High-precision data acquisition method based on Jaya optimization and calibration

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