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Your search keyword '"Huang, Biao"' showing total 342 results
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342 results on '"Huang, Biao"'

2. Facilitating Reinforcement Learning for Process Control Using Transfer Learning: Perspectives

Author

Lin, Runze, Chen, Junghui, Xie, Lei, Su, Hongye, and Huang, Biao

Subjects
Electrical Engineering and Systems Science - Systems and Control, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

This paper provides insights into deep reinforcement learning (DRL) for process control from the perspective of transfer learning. We analyze the challenges of applying DRL in the field of process industries and the necessity of introducing transfer learning. Furthermore, recommendations and prospects are provided for future research directions on how transfer learning can be integrated with DRL to empower process control., Comment: Final Version of Asian Control Conference (ASCC 2024)

Published
2024

3. Machine learning for industrial sensing and control: A survey and practical perspective

Author

Lawrence, Nathan P., Damarla, Seshu Kumar, Kim, Jong Woo, Tulsyan, Aditya, Amjad, Faraz, Wang, Kai, Chachuat, Benoit, Lee, Jong Min, Huang, Biao, and Gopaluni, R. Bhushan

Subjects
Electrical Engineering and Systems Science - Systems and Control, Computer Science - Machine Learning
Abstract

With the rise of deep learning, there has been renewed interest within the process industries to utilize data on large-scale nonlinear sensing and control problems. We identify key statistical and machine learning techniques that have seen practical success in the process industries. To do so, we start with hybrid modeling to provide a methodological framework underlying core application areas: soft sensing, process optimization, and control. Soft sensing contains a wealth of industrial applications of statistical and machine learning methods. We quantitatively identify research trends, allowing insight into the most successful techniques in practice. We consider two distinct flavors for data-driven optimization and control: hybrid modeling in conjunction with mathematical programming techniques and reinforcement learning. Throughout these application areas, we discuss their respective industrial requirements and challenges. A common challenge is the interpretability and efficiency of purely data-driven methods. This suggests a need to carefully balance deep learning techniques with domain knowledge. As a result, we highlight ways prior knowledge may be integrated into industrial machine learning applications. The treatment of methods, problems, and applications presented here is poised to inform and inspire practitioners and researchers to develop impactful data-driven sensing, optimization, and control solutions in the process industries., Comment: 48 pages

Published
2024
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6. An Investigation on EEG-based Prognosis Prediction of Patients with Disorders of Consciousness

Author

Li, Jingcong, Huang, Biao, Pan, Jiahui, and Wang, Fei

Subjects
Artificial Intelligence, Cognitive Neuroscience, Consciousness, Electroencephalography (EEG), Neural Networks
Abstract

Prognostic assessment of patients with disorders of consciousness (DoC) remains one of the most challenging problems in contemporary medicine. The long treatment cycle and high costs of treatment are heavy burdens to our society. In this paper, we use deep network to investigate potential indicators of consciousness within brain signals of DoC patients. In the experiments, we study P300 and resting-state Electroencephalogram (rs-EEG) signals of 22 DoC patients to investigate neural correlation between brain signals and the improvement of consciousness. Synergistic integration of P300 and rs-EEG signals demonstrated superior predictive proficiency for cross-subject and cross-paradigm prognosis in DoC, achieving an accuracy of 81.1%. Our investigation is the first known to the literature to combine P300 and rs-EEG signals for analyzing DoC. This novel approach leverages advanced neural network models to elucidate the complex neural patterns associated with DoC, setting a precedent for future research in the field.

Published
2024

12. Research Progress on Microbial Diversity, Evaluation and Maintenance of Baijiu Pit Mud

Author

ZOU Fei, YE Li, FENG Liang, ZHENG Xiaowei, HUANG Biao, XIA Bing, ZHANG Wuji, CHEN Xiaoyuan, FANG Zhengguo, SUN Yuting

Subjects
baijiu, pit mud, microbial diversity, functional strains, maintenance and evaluation, Food processing and manufacture, TP368-456
Abstract

Solid-state fermentation in mud pits is a typical brewing process for nongxiangxing baijiu and fuyuxiangxing baijiu. Pit mud is the functional carrier of mud pits, in which the abundance, diversity, metabolic activity and interaction of microorganisms could directly affect the quality of baijiu. This paper reviews the application of traditional culture and non-culture techniques in research on microorganisms in pit mud, the spatiotemporal differences in microbial community structure, the functional microorganisms and their interactions, the development and improvement of the methods for the quality evaluation of pit mud, and pit mud maintenance and aging focusing on the functional strains, and summarizes the development history and application status of different techniques in the field of research on microorganisms related to the production of Chinese baijiu, the growth and decline patterns of prokaryotic and eukaryotic microorganisms during pit mud aging, the spatial difference in the distribution of prokaryotic and eukaryotic microorganisms in the pit, and the functions and interdependence of the core functional microorganisms in pit mud such as caproic acid bacteria, butyric acid bacteria, lactic acid bacteria, acetic acid bacteria and methane bacteria, the reason for pit mud aging, the transformation of the criteria for quality evaluation pit mud from traditional sensory and physicochemical analysis to the comprehensive evaluation system, and studies on pit mud maintenance and aging based on the functional properties of core strains. The purpose of this review is to provide a reference for evaluating the microbial diversity, exploring the metabolic function of microbial community and improving the quality of pit mud.

Published
2024
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13. Evaluation of Left Main Bifurcation Ostial Area by Main Vessel Intravascular Ultrasound

Author

Huang, Biao, Shan, Peiren, Chu, Miao, Lin, Qingcheng, Xiao, Runing, Zhang, Su, Holm, Niel R., Tu, Shengxian, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Su, Ruidan, editor, Zhang, Yu-Dong, editor, and Frangi, Alejandro F., editor

Published
2024
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33. Long-term expandable mouse and human-induced nephron progenitor cells enable kidney organoid maturation and modeling of plasticity and disease

Author

Huang, Biao, Zeng, Zipeng, Kim, Sunghyun, Fausto, Connor C., Koppitch, Kari, Li, Hui, Li, Zexu, Chen, Xi, Guo, Jinjin, Zhang, Chennan C., Ma, Tianyi, Medina, Pedro, Schreiber, Megan E., Xia, Mateo W., Vonk, Ariel C., Xiang, Tianyuan, Patel, Tadrushi, Li, Yidan, Parvez, Riana K., Der, Balint, Chen, Jyun Hao, Liu, Zhenqing, Thornton, Matthew E., Grubbs, Brendan H., Diao, Yarui, Dou, Yali, Gnedeva, Ksenia, Ying, Qilong, Pastor-Soler, Nuria M., Fei, Teng, Hallows, Kenneth R., Lindström, Nils O., McMahon, Andrew P., and Li, Zhongwei

Published
2024
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