Interdisciplinary Evolution of the Machine Brain

Wenfeng Wang is a full professor in Shanghai Institute of Technology and the director of International Academy of Visual Art and Engineering. A key tallent in Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences (2018- ). A reviewer of many SCI journals,including some top journals - Water Research, Science China-Information Sciences, Science of the Total Environment, Environmental Pollution, IEEE Transactions on Automation Science and Engineering and etc. A keynote speaker of AMICR2019, IACICE2020, OAES2020, AICS2021, 3DIT-MSP&DL2020 and etc. A scientist in chief of RealMax, Shanghai Lingang Artificial Intelligence Laboratory and etc.Hengjin Cai is currently a Full Professor and Ph.D. advisor at the School of Computer Science, Wuhan University, Hubei, China. His major research interests include economics, sociology, management and artificial intelligence. He also serves as the Executive Director of Zall Institute of Research, a Vice-Presidentof China Communications Industry Association Professional Committee of Blockchain (CCIAPCB), a visiting researcher at the Center for Multimedia Technologies of Shenzhen Institutes of Advanced Technology, etc. He is a recipient of the 2017 Wu Wenjun Artificial Intelligence Science and Technology Award. Xiangyang Deng is currently a Full Professor (assistant) at the Institute of Information Fusion, Naval Aeronautical University, Yantai, China. His current research interests include video big data, deep learning and computational intelligence. Prof. Deng has extensive experience in R&D management. He has won three First Class Prizes and two Third Class Prizes for the Military Scientific and Technological Progress Award. He has published nine papers on related topics, five of which were indexed by SCI and EI. Chenguang Lu is a retired teacher and a guest professor at the Institute of Intelligence Engineering and Mathematics, Liaoning Technical University, China. He used to be an associate professor at the department of information and computer science of Changsha University, China. He studied as a visiting scholar at the Department of Mathematics of Beijing Normal University from Sept. 1990 to Sept. 1991 and was trained on computer applications at Niagara College, Canada, from March of 1987 to Mach of 1988. He received a bachalor’s degree in Engineering from Nanjing University of Aeronautics and Astronautics in 1982. He was a worker of a steel plant before Dec. of 1977 and a peasant in a forest farm before Dec. of 1976. His main research interests are semantic information theory, color vision mechanism, portfolio, and statistical learning. His current research is focused on mixture models, maximum mutual information classifications, confirmation measures, and the probability framework. Limin Zhang is currently a Full Professor and Tutor for Doctor of Naval Aeronautical University, Yantai, Shangdong, China. In 2005, he graduated from Tianjin University with a doctorate degree in signal and information processing. He was a senior visiting scholar at the University of London (UCL) Modern Space Analysis and Research Center (CASA) from 2006 to 2007. His current research interests include signal processing, Complex system simulation and computational intelligence. More than 180 papers are published and 80 papers are indexed by SCI, EI. 2 monographs are published and 20 patents are applied and 6 were authorized. Professor Zhang enjoys the State Council special allowance, and has been rated as outstanding national science and technology workers, millions of talent engineering national candidates, Taishan scholars experts, provincial leaders, outstanding innovation team in Shandong Province. Professor Zhang won two Second Class Prizes of National Scientific and Technological Progress Award, five First Class Prizes and four Second Class Prizes of Military Scientific and Technological Progress Award, two First ClassPrizes of the Military Teaching Achievement, two Second Class Prizes of Provincial Teaching Achievement.

• Data-driven Approach in Machine Learning.- Environments Monitoring and Understanding.- Process-based Perception to the Environments.- Intelligent Manufacturing for the Implementation.- Reconciled Interpretation of Vison, Touch & Minds.- Expanded Insights into the Evolution of Machine Brain.