摘要
材料基因组工程融合高通量实验、高通量计算和数据库及人工智能技术,能加速实现新材料的研发.然而,如何快速且可靠地从实验设备中采集数据是材料基因组工程的重要问题.针对高精度数据采集系统标定数据时间不同步的问题,以线性模型作为采集数据处理参数的模型,以设备显示值作为数据采集真实值,构建数据处理参数寻优的目标函数;基于Jaya优化算法实现了模型参数优化搜索;最后以设备温度数据采集为例,构建了高精度数据采集系统并进行实验验证.实验结果表明,采用优化后的模型参数,数据采集平均误差仅为0.13℃,精度可达99.89%,相比于非优化模型参数,平均误差降低了63.20%,显著提高了数据采集精度.
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 experiment ally.The experimental results show that using the optimized model parameters,the average error of data acquisition is only 0.130℃,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.
作者
张合生
焦鹏
胡琪睿
蔡江乾
胡顺波
曹贺
欧阳求保
ZHANG Hesheng;JIAO Peng;HU Qirui;CAI Jiangqian;HU Shunbo;CAO He;OUYANG Qiubao(School of Mechatronic Engineering and Automation,Shanghai University,Shanghai 200444,China;School of Computer Engineering and Science,Shanghai University,Shanghai 200444,China;Center of Materials Informatics and Data Science,Materials Genome Institute,Shanghai University,Shanghai 200444,China;Zhejiang Laboratory,Hangzhou 311100,Zhejiang,China;State Key Laboratory of Metal Matrix Composites,School of Material Science and Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)
出处
《上海大学学报（自然科学版）》
CAS
CSCD
北大核心
2022年第3期361-371,共11页
Journal of Shanghai University:Natural Science Edition
基金
国家重点研发计划资助项目(2018YFB0704400)
云南省重大科技专项资助项目(202002AB080001-2,202102AB080019-3)
之江实验室科研攻关资助项目(2021PE0AC02)
上海张江国家自主创新示范区专项发展资金重大资助项目(ZJ2021-ZD-006)。
