For the complex batch process with characteristics of unequal batch data length,a novel data-driven batch process monitoring method is proposed based on mixed data features analysis and multi-way kernel entropy compon...For the complex batch process with characteristics of unequal batch data length,a novel data-driven batch process monitoring method is proposed based on mixed data features analysis and multi-way kernel entropy component analysis(MDFA-MKECA)in this paper.Combining the mechanistic knowledge,different mixed data features of each batch including statistical and thermodynamics entropy features,are extracted to finish data pre-processing.After that,MKECA is applied to reduce data dimensionality and finally establish a monitoring model.The proposed method is applied to a reheating furnace industry process,and the experimental results demonstrate that the MDFA-MKECA method can reduce the calculated amount and effectively provide on-line monitoring of the batch process.展开更多
Research has demonstrated a significant overlap between sleep issues and other medical conditions.In this paper,we consider mild difficulty in falling asleep(MDFA).Recognition of MDFA has the potential to assist in th...Research has demonstrated a significant overlap between sleep issues and other medical conditions.In this paper,we consider mild difficulty in falling asleep(MDFA).Recognition of MDFA has the potential to assist in the provision of appropriate treatment plans for both sleep issues and related medical conditions.An issue in the diagnosis of MDFA lies in subjectivity.To address this issue,a decision support tool based on dual-modal physiological feature fusion which is able to automatically identify MDFA is proposed in this study.Special attention is given to the problem of how to extract candidate features and fuse dual-modal features.Following the identification of the optimal feature set,this study considers the correlations between each feature and class and evaluates correlations between the inter-modality features.Finally,the recognition accuracy was measured using 10-fold cross validation.The experimental results for our method demonstrate improved performance.The highest recognition rate of MDFA using the optimal feature set can reach 96.22%.Based on the results of current study,the authors will,in projected future research,develop a real-time MDFA recognition system.展开更多
基金supported by National Key R&D Program of China(Smart process control technology for aluminum&copper strip based on industrial big data)(2017YFB0306405)。
文摘For the complex batch process with characteristics of unequal batch data length,a novel data-driven batch process monitoring method is proposed based on mixed data features analysis and multi-way kernel entropy component analysis(MDFA-MKECA)in this paper.Combining the mechanistic knowledge,different mixed data features of each batch including statistical and thermodynamics entropy features,are extracted to finish data pre-processing.After that,MKECA is applied to reduce data dimensionality and finally establish a monitoring model.The proposed method is applied to a reheating furnace industry process,and the experimental results demonstrate that the MDFA-MKECA method can reduce the calculated amount and effectively provide on-line monitoring of the batch process.
基金supported by National Natural Science Foundation of China (Nos. 61761027 and 61461025)the Yong Scholar Fund of Lanzhou Jiaotong University (No. 2016004)the Teaching Reform Project of Lanzhou Jiaotong University (No. JGY201841)。
文摘Research has demonstrated a significant overlap between sleep issues and other medical conditions.In this paper,we consider mild difficulty in falling asleep(MDFA).Recognition of MDFA has the potential to assist in the provision of appropriate treatment plans for both sleep issues and related medical conditions.An issue in the diagnosis of MDFA lies in subjectivity.To address this issue,a decision support tool based on dual-modal physiological feature fusion which is able to automatically identify MDFA is proposed in this study.Special attention is given to the problem of how to extract candidate features and fuse dual-modal features.Following the identification of the optimal feature set,this study considers the correlations between each feature and class and evaluates correlations between the inter-modality features.Finally,the recognition accuracy was measured using 10-fold cross validation.The experimental results for our method demonstrate improved performance.The highest recognition rate of MDFA using the optimal feature set can reach 96.22%.Based on the results of current study,the authors will,in projected future research,develop a real-time MDFA recognition system.
