Dealing with small sample size problems in process industry using virtual sample generation: a Kriging-based approach
Dealing with small sample size problems in process industry using virtual sample generation: a Kriging-based approach
复制标题
使用虚拟样本生成处理流程工业中的小样本量问题：基于克里格的方法
DOI：
10.1007/s00500-019-04326-3
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发表时间：
2020-05
期刊：
影响因子：
4.1
通讯作者：
Chen Yi-Qun
中科院分区：
文献类型：
--
作者：
Zhu Qun-Xiong;Chen Zhong-Sheng;Zhang Xiao-Han;Rajabifard Abbas;Xu Yuan;Chen Yi-Qun
先进工艺系统的运行数据呈爆炸式增长，但其波动很小，提取的代表性样品数量有限，难以反映工艺的性质，也难以建立预测模型。本研究受渔民修网过程的启发，提出了一种基于kriging的虚拟样本生成（VSG）方法——Kriging-VSG，用于在数据稀疏区域生成可行的虚拟样本。然后，利用生成的虚拟样本进一步提高预测模型的准确性。为了合理地寻找数据稀疏区域，在每个维度上施加基于距离的准则，以识别信息缺口较大的重要样本。类似于渔民修网的过程，在不同分位数处初始固定一定的维度。然后在具有大信息间隙的重要样本之间的中心执行Kriging的维度插值过程。为了验证所提出的Kriging-VSG的性能，进行了两个数值模拟和高密度聚乙烯级联反应过程的实际应用。结果表明，Kriging-VSG方法优于其他方法。
The operational data of advanced process systems have met with explosive growth, but its fluctuations are so slight that the number of the extracted representative samples is quite limited, making it difficult to reflect the nature of the process and to establish prediction models. In this study, inspired by the process of fisherman repairing nets, a Kriging-based virtual sample generation (VSG) named Kriging-VSG is proposed to generate feasible virtual samples in data sparse regions. Then, the accuracy of prediction models is further enhanced by applying the generated virtual samples. In order to reasonably find data sparse regions, a distance-based criterion is imposed on each dimension to identify important samples with large information gaps. Similar to the process of fisherman repairing nets, a certain dimension is initially fixed at different quantiles. A dimension-wise interpolation process using Kriging is then performed on the center between important samples with large information gaps. To validate the performance of the proposed Kriging-VSG, two numerical simulations and a real-world application from a cascade reaction process for high-density polyethylene are carried out. The results indicate that the proposed Kriging-VSG outperforms other methods.
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影响因子：
2.7
作者：
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通讯作者：
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影响因子：
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作者：
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影响因子：
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作者：
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DOI：
10.1590/0370-44672015690159
发表时间：
2016-12
期刊：
International Journal of Human–Computer Interaction
影响因子：
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作者：
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通讯作者：
Victor Miguel Silva;J. Costa
DOI：
10.1109/cac.2017.8244101
发表时间：
2017-10
期刊：
2017 Chinese Automation Congress (CAC)
影响因子：
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作者：
Jian Tang;J. Qiao;Ke Gu;Aijun Yan
通讯作者：
Jian Tang;J. Qiao;Ke Gu;Aijun Yan