Artificial brain-vision computer for creating seamless interactive applications between real and virtual worlds

Prof. Takeshi Ikenaga

Waseda University,Japan

Abstract

In a situation where all human activities are frequently carried out on a global scale, there is a limit to what can be done only in real space. So, an environment that can seamlessly merge the real and virtual worlds is highly expected. To achieve this, the following three are important: 1) Ultra-high-definition videos that exceed the resolution of the human retina, 2) Ultra-realistic content where the difference from the real cannot be recognized, and 3) Ultra-low delay that humans do not perceive. As for the ultra-high-definition video, advances in video compression technology have made it possible to easily use 8K or 360-degree video. As for the ultra-realistic content, technologies such as deep-learning based generated AI images that are indistinguishable from the real are also being developed. On the other hand, research efforts focused on the ultra-low delay processing are still limited, and many breakthroughs are expected. This presentation introduces our proposed “Artificial Brain-vision Computer”. The human brain consists of the cerebrum which processes complex tasks and deep judgments, and the cerebellum which is capable of rather simple but instantaneous processing. The development of deep learning has expanded the possibilities of the cerebrum, but in order to create a whole brain-vision computer, it is essential to devise new algorithms and architecture that play the role of the cerebellum. While introducing our various examples of both rule-based and learning-based 1ms delay vision processing systems, the key technologies and future direction are explained.

Biographical Sketch

Takeshi Ikenaga received the B.E. and M.E. degrees in electrical engineering and the Ph.D. degree in information computer science from Waseda University, Tokyo, Japan, in 1988, 1990, and 2002, respectively. He joined the LSI Laboratories, Nippon Telegraph and Telephone Corporation (NTT), in 1990, where he had been undertaking research on the design and test methodologies for high performance ASICs, a real-time MPEG2 encoder chip set, and a highly parallel LSI design for image understanding processing. He is currently a professor in the integrated system field with the Graduate School of Information, Production and Systems, Waseda University. His current interests are image and video processing systems, which covers video compression (e.g., VVC and SCC), video filter (e.g., super resolution and high-dynamic range imaging), and video recognition (e.g., sport analysis, human / object pose estimation, ultra-low delay vision systems). 

Keynote Speech 02——Dr. Chunsheng Yang

AI-enabled PHM Solution for Complex Systems
复杂系统的智能化预测维护与健康管理

Dr.Chunsheng Yang

Abstract

Maintenance costs for complex systems like aircraft can easily exceed 65% of total life-cycle cost. Operators such as military, government and related industries needed sophisticated AI-enabled breakthroughs to phase out classical time-based methods and further reduce maintenance costs while increasing reliability and safety. Over the past two decades we have developed an AI-enabled solution for Prognostic and Health Management (PHM) to address these needs. The developed AI-enabled PHM technology has been proved as a useful and efficient solution for smart maintenance of the complex systems and energy management systems. In this talk, I will introduce the challenges and roadmap in developing AI-enabled PHM solution, discuss the developed core technologies briefly, and present some application in production. 

Biographical Sketch 

Dr. Chunsheng Yang is a Fellow of the Canadian Academy of Engineering (FCAE) and Fellow of the Asia-Pacific Artificial Intelligence Association (FAAIA). He is a Principal Scientist with the National Research Council Canada and a Director of Institute of Artificial Intelligence at Guangzhou University.  Dr. Yang is an Adjunct Professor with Carleton University (Canada) and East Jiao Tong University. He is interested in data science, machine learning, hybrid reasoning, intelligent systems, digital twins and Prognostic and Health Management (PHM). He received an Hons. B.Sc. in Electronic Engineering from Harbin Engineering University, China, an M.Sc. in computer science from Shanghai Jiao Tong University, China, and a Ph.D. from the National Hiroshima University, Japan. He worked with Fujitsu Inc., Japan, as a Senior Engineer and engaged on the development of intelligent traffic management for ATM backbone telecommunication networks.  Yang has been the author for 226 technical papers (book chapters) and reporters published in the referred journals and conference proceedings. Dr. Yang is a Program Co-Chair of the 20th IEEE International Conference on Computer Supported Cooperative Work in Design (CSCWD 2016) and a Program Co-Chair for the 17^th International Conference on Industry and Engineering Applications of Artificial Intelligence and Expert Systems in 2004. He is also a guest editor for the International Journal of Applied Intelligence and the Journal of Clustering Computing. He severs scientific advisor for the institutions such as NSERC, Irish Research Council, etc. 

To Be Continued ......