Prof. Toshio Fukuda ( Keynote Speaker)

Member of Science Council of Japan

IEEE Robotics and Automation Technical Field Award

IEEE Fellow (1995- ). SICE Fellow (1995- ), JSME Fellow (2002- ), RSJ Fellow (2004- ), VRSJ Fellow (2011-), Member of Science Council of Japan (2008- ), Honorary Doctor of Aalto University School of Science and Technology (2010)

Meijo University,Japan / Beijing Institute of Technology, China

Speech Title: TBA

Abstract: TBA

 

 

 

 

 

 

 

 

 

 

 

 

 

Prof. Dan Zhang ( Keynote Speaker)

Fellow of the Engineering Institute of Canada (EIC)

Fellow of American Society of Mechanical Engineers (ASME)

Fellow of Canadian Society for Mechanical Engineering (CSME)

York University, Canada

Speech Title: Performance Improvement of Robotic Systems for Manufacturing

Abstract: There has been increasing in developing enviromentally-benign manufacturing technologies, robots, etc. This is considered a significant step in achieving sustainable development. Sustainability of a manufacturing system becomes critical technology that enables manufacturing companies to reduce production costs and improve their global competitiveness. System sustainability can be achieved by reconfiguration and decentralization, whose system configurations are evolved with the changes of design requirements and dynamic environment. The modular construction of parallel robotic machines allows them to be used as a class of reconfigurable machine tools. Nevertheless, parallel robotic machines as contemporary manufacturing robotic systems often have difficulty meeting the highly increased workplace demands on (1) operational accuracy, (2) operational load capacity, (3) task adaptability, and (4) reliability. For example, according to some large robot/robotic machine tool manufacturers and manufacturing robot user, i.e., ABB Robotics, Ingersoll Machine Tools Inc. and ATS Automation Tooling Systems Inc., the current robotic systems for high speed machining often fail due to thermal effects, which fatally distort the accuracy of the systems. According to the International Federation of Robotics (IFR), more than 60% of industry robots operating in the manufacturing industry are articulated robots (i.e., serial robots), or robots that can only allow material handling, but not material fabrication.

In this talk, the rational of using parallel robotic machines for green and sustainable manufacturing is discussed and explained. A comparative study is carried out on some successful parallel robotic machines and conventional machine tools. Meanwhile, the latest research activities of parallel manipulators in the Laboratory of Robotics and Automation of UOIT are introduced, they are: parallel robotic machines, reconfigurable/green robotic manipulators, web-based remote manipulation as well as the applications of parallel manipulators in micro-motion device, MEMS (parallel robot based sensors), wearable power assist hip exoskeleton, and rescue robot.

Prof. Wei Gao ( Keynote Speaker)

University of New South Wales (Sydney), Australia

Speech Title: TBA

Abstract: TBA

 

 

 

 

Prof. Kenji Suzuki (Keynote Speaker)

Senior Member, Institute of Electrical and Electronics Engineers (IEEE)

Member, American Association for the Advancement of Science (AAAS)

Member, American Association of Physicists in Medicine (AAPM)

Tokyo Institute of Technology, Japan

Speech Title: AI Doctor and Smart Medical Imaging with Deep Learning

Abstract: It is said that artificial intelligence driven by deep learning would make the 4th Industrial Revolution. Deep leaning becomes one of the most active areas of research in computer vision, pattern recognition, and imaging fields, because “learning from examples or data” is crucial to handling a large amount of data (“big data”) coming from informatics and imaging systems. Deep learning is a versatile, powerful framework that can acquire image-processing and analysis functions through training with image examples; and it is an end-to-end machine-learning model that enables a direct mapping from raw input data to desired outputs, eliminating the need for handcrafted features in conventional feature-based machine learning. I invented ones of the earliest deep-learning models for image processing, semantic segmentation, object enhancement, and classification of patterns in medical imaging. I have been actively studying on deep learning in medical imaging in the past 23 years. In this talk, AI-aided diagnosis and smart medical imaging with deep learning are introduced, including 1) computer-aided diagnosis for lung cancer in CT, 2) distinction between benign and malignant lung nodules in CT, 3) polyp detection and classification in CT colonography, 4) separation of bones from soft tissue in chest radiographs, and 5) radiation dose reduction in CT and mammography.

 

 

Prof. Jinsong Bao ( Invited Speaker)

Donghua University, China

Speech Title: TBA

Abstract: TBA

 

 

 

 

Prof. Lingfei Kong( Invited Speaker)

Xi'an University of Technology, China

Speech Title:Control energy optimality for multi-input planar cable-driven parallel robot

Bio: Prof. Lingfei Kong, was born in October 1977, and now is the faculty of mechanical engineering, Xi'an University of Technology. He received his Ph.D. degree in mechanical engineering from Xi’an University of Technology in 2010. From 2014 to 2015, he worked as a visiting scholar at the Smart and Sustainable Automation Research Lab at University of Michigan, Ann Arbor. His research interests are in the areas of nano-positioning stage control, low-cost ultra-precision machining and intelligent 3D Printers for next generation manufacturing. In recent years, he hosted two projects of National Natural Science Foundation and a Major Project of National Science and Technology of China. Based on the above research work, he published more than 20 papers in journals or international academic conference and applied 5 patents.

 

 

 

 

 

 

 

AIACT Past Speakers

Prof. Dan Zhang
Prof. Songyi Dian
Prof. Makoto Kaneko
Prof. ZhiDong Wang
Prof. Wei Dong
York University, Canada
Sichuan University, China
Osaka University, Japan
Chiba Institute of Technology, Japan
Harbin Institute of Technology, China