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Paper 1: Reducing response delay in multivariate process monitoring by a stacked long short term memory network and real-time contrasts

Publisher:  Computers and Industrial Engineering, 153, pp. 107052. (2021) (SCI) (IF 2019: 4.135, Rank 18/109, Computer Science, Interdisciplinary Applications, 10/48 Engineering, Industrial)

Abstract:  Abstract Response delay is a critical performance measure for detecting the process shift. Reducing response delay in multivariate process monitoring, especially under big streaming data, is one of many data analysis challenges on smart manufacturing. In the literature, researchers introduced the real-time contrast control chart (RTC) to fast detect the process shift. However, most of the RTCs are underperformed in the big data environment. In this research, a new RTC combined with a stacked long short-term memory network named LSTM-RTC was proposed to monitor big streaming data with short response delay. The LSTM-RTC consists of two LSTM layers, a dropout layer between the LSTM layers, and a dense layer to extend the capability to catch the streaming data's hierarchical representation. A variety of synthesized multivariate normal and bivariate gamma datasets were used to evaluate the proposed LSTM-RTC with other benchmark methods. The experiment results show that the proposed LSTM-RTC outperforms the other benchmark RTC methods with the lowest response delay. The evaluation on two real-world cases also highlights the advantage of the proposed LSTM-RTC on real-world process monitoring applications.

Paper 2: A Clustering-Based Symbiotic Organisms Search Algorithm for High-Dimensional Optimization Problems

Publisher:  Applied Soft Computing, 97 Part B, pp 106722-1-16 (2020) (SCI) (IF 2019: 5.472, Rank 20/137, Computer Science, Artificial Intelligence, 9/109 Computer Science, Interdisciplinary Applications).

Abstract:  This paper proposed a fast metaheuristic method for high-dimensional optimizations problem with only one-control parameter in the setting. Essentially, the innovation of the proposed method is to apply automatic k-means clustering on the initial solutions of symbiotic organisms search to create subpopulations. Only the selected elite solutions in each cluster to interact with one another across clusters in the proposed model. This new elite solution searching process can be considered as a combination of local and global searching based on the solution clusters. The proposed method was compared to six representative methods in 28 benchmark problems and 10 composition problems. Also, the proposed method was also compared with four clustering-based metaheuristic methods. The experimental results show that the proposed model is more efficient in its computation and has a better searching quality. For high-dimensional problems, the performances of the proposed method was compared with the original symbiotic organisms search up to 1000 dimensions. The results show that the proposed method can alleviate the dimensionality effect to produce better solution quality with relatively fast computation.