Computational intelligence (CI) can perform jobs human-likely by learning from human experience. In recent years, there are many successful applications of CI technologies, for example, using deep learning to train computers to accomplish specific complicated tasks like Alpha Go. CI technologies such as machine learning, reasoning, computer vision, speech recognition and autonomous operations would have significant impacts for industries. They may make the industrial operations much more efficient and improve resource (including human and material resources) and energy utility, even help economic, environmental, and social sustainability
Topics
It will focus on the following topics in two areas:
A. General CI theory and methods for industrial process
This area consists of general CI theory and methods that can be used to benefit industry on towards improving production efficiency, product quality, resource utility, energy saving, environmental protection, etc.
- Symbolic methods for industrial process. The topic aims at on modeling and control of industrial processes with symbolic CI technologies such as decision trees, random forests fuzzy logic , and fuzzy systems logic, etc.
- Connection structures for industrial process. The topic consists of studies on supervised, unsupervised, and semi-supervised machine learning methods for industry processes, modifications of deep learning, reinforcement learning, meta-learning and transfer learning for industry.
- Probabilistic methods for industrial process. The topic covers Bayesian method and Bayesian networks in process control, Markov chains, stochastic neural networks for process modeling and control.
B. Particular CI technologies for industrial processes
This area concentrates on particular CI technologies developed for specific industrial processes.
- Process control
- Performance monitoring
- Robotics
- Manufacturing
- Human-computer interactions and system
Symposium Chairs
- Wen Yu
yuw@ctrl.cinvestav.mx
CINVESTAV-IPN, Mexico - Jinliang Ding
jlding@mail.neu.edu.cn
Northeastern University, China
Programme Committee
- Xuemin Chen, Texas Southern University, USA, email:xuemin.chen@tsu.edu
- Wenxin Liu, Lehigh University, USA, email:wliu@lehigh.edu
- Xianta Jiang, Memorial University of Newfoundland, Canada, email:xiantaj@mun.ca
- Oleg Sergiyenko, Universidad Autónoma de Baja California, Mexico, email: srgnk@uabc.edu.mx
- Suresh Thenozhi, Universidad Autónoma de Querétaro, Mexico, email: suresht@ctrl.cinvestav.mx
- Xiangjie Liu, North China Electric Power University, China, liuxj@ncepu.edu.cn
- Shoulie Xie, Institute for Infocomm Research, Singapore, email: slxie@i2r.a-star.edu.sg
- Kang Li, University of Leeds, UK, email: K.LI1@leeds.ac.uk
- Zhao Liang, University of Sao Paulo, Brazil, email: zhao@usp.br
- Erick Dasaev de la Rosa, GE Aviation, Mexico, email: edelarosa@ge.com.mx