no code implementations • 30 Mar 2024 • Runze Lin, Junghui Chen, Lei Xie, Hongye Su, Biao Huang
This paper provides insights into deep reinforcement learning (DRL) for process control from the perspective of transfer learning.
no code implementations • 24 Jan 2024 • Nathan P. Lawrence, Seshu Kumar Damarla, Jong Woo Kim, Aditya Tulsyan, Faraz Amjad, Kai Wang, Benoit Chachuat, Jong Min Lee, Biao Huang, R. Bhushan Gopaluni
Soft sensing contains a wealth of industrial applications of statistical and machine learning methods.
no code implementations • 5 Aug 2023 • Runze Lin, Yangyang Luo, Xialai Wu, Junghui Chen, Biao Huang, Lei Xie, Hongye Su
The Organic Rankine Cycle (ORC) is widely used in industrial waste heat recovery due to its simple structure and easy maintenance.
no code implementations • 19 May 2023 • Zhiyinan Huang, Jinfeng Liu, Biao Huang
We propose a robust nonlinear model predictive control design with generalized zone tracking (ZMPC) in this work.
no code implementations • 15 Apr 2023 • Xiuli Zhu, Seshu Kumar Damarla, Biao Huang
Most of the predictive models developed in this study exhibited superior performance over correlation and predictive models reported in literature.
no code implementations • 3 Apr 2023 • Biao Huang, Jipeng Yan, Megan Morris, Victoria Sinnett, Navita Somaiah, Meng-Xing Tang
The simulation results show that the acceleration-based method outperformed the non-acceleration-based method at different levels of acceleration and acquisition frame rates and achieved significant improvement in true positive rate (up to 10. 03%), false negative rate (up to 28. 61%) and correctly pairing fraction (up to 170. 14%).
1 code implementation • 24 Mar 2023 • Jipeng Yan, Biao Huang, Johanna Tonko, Matthieu Toulemonde, Joseph Hansen-Shearer, Qingyuan Tan, Kai Riemer, Konstantinos Ntagiantas, Rasheda A Chowdhury, Pier Lambiase, Roxy Senior, Meng-Xing Tang
Micro-vascular flow in the myocardium is of significant importance clinically but remains poorly understood.
no code implementations • 22 Sep 2022 • R. Bhushan Gopaluni, Aditya Tulsyan, Benoit Chachuat, Biao Huang, Jong Min Lee, Faraz Amjad, Seshu Kumar Damarla, Jong Woo Kim, Nathan P. Lawrence
Over the last ten years, we have seen a significant increase in industrial data, tremendous improvement in computational power, and major theoretical advances in machine learning.
1 code implementation • 1 Sep 2022 • Yan Qin, Chau Yuen, Xunyuan Yin, Biao Huang
A transferable multi-stage SOH estimation model is proposed to perform TL across batteries in the same stage, consisting of four steps.
no code implementations • 15 Jul 2022 • Jianwei Lin, Jiatai Lin, Cheng Lu, Hao Chen, Huan Lin, Bingchao Zhao, Zhenwei Shi, Bingjiang Qiu, Xipeng Pan, Zeyan Xu, Biao Huang, Changhong Liang, Guoqiang Han, Zaiyi Liu, Chu Han
To bridge the gap between Transformer and CNN features, we propose a Trans&CNN Feature Calibration block (TCFC) in the decoder.
no code implementations • 27 Jun 2022 • Wanke Yu, Min Wu, Biao Huang, Chengda Lu
Many multivariate statistical analysis methods and their corresponding probabilistic counterparts have been adopted to develop process monitoring models in recent decades.
no code implementations • 27 Apr 2022 • Zhiyinan Huang, Jinfeng Liu, Biao Huang
To handle the tracking offset caused by the plant-model-mismatch of the proposed NN framework, a shrinking target zone is proposed for the ZMPC.
no code implementations • 21 Jun 2021 • Zhiyinan Huang, Qinyao Liu, Jinfeng Liu, Biao Huang
Economic model predictive control (EMPC) has attracted significant attention in recent years and is recognized as a promising advanced process control method for the next generation smart manufacturing.
no code implementations • 7 Sep 2018 • Wenqing Li, Chunhui Zhao, Biao Huang
Based on the distributed monitoring system, a two-level monitoring strategy is proposed to check different influences on process characteristics resulting from changes of the operating conditions and control action, and thus the two changes can be well distinguished from each other.
no code implementations • 12 Jul 2013 • Aditya Tulsyan, Biao Huang, R. Bhushan Gopaluni, J. Fraser Forbes
The simultaneous estimation is performed by filtering an extended vector of states and parameters using an adaptive sequential-importance-resampling (SIR) filter with a kernel density estimation method.