robotics

January 28, 2022

Scientific Program

Keynote Session:

Meetings International - Conference Keynote Speaker Hirohisa Sakai photo

Hirohisa Sakai

Kawasaki Heavy Industries, Ltd., Japan

Title: Development of Advanced TPS for Global Production Strategy: Proposal and Demonstration of V-MICS-VM for Intelligence Operators

Biography:

Dr. Sakai had received a bachelor’s degree in Electrical Engineering from Nagoya Institute of Technology in 1986 and joined Toyota Motor Corporation. He has been responsible for Research & Development and Application of Robots in Body Production Engineering Division, specifically Fully Automated Assembly Processes. He has a Doctoral Degree (PhD.) in Industrial Engineering from Meiji University in 2010. From 2016 to 2018 he was engaged in Vice President of Toyota Motor Manufacturing, Texas, Inc. From the beginning of this year, he is currently being sent on loan to Kawasaki Heavy Industries, Ltd., Senior Staff Officer of Robot Business Division

Abstract:

To achieve simultaneous, worldwide high-quality assurance and other global production developments, today’s task is to maintain high reliability in production facilities.  In response to the increasing expansion of overseas plants, it is necessary to improve and maintain highly accurate production equipment through the development of intelligence operators. The authors have clarified Advanced TPS as a global production technology and management model designed to realize high quality assurance in global production. Furthermore, the authors propose V-MICS-VM (Virtual - Maintenance Innovated Computer System – utilizing Visual Manual) as a new people-centered principle that contributes to Advanced TPS utilizing a visual manual that consists of three elements, (i) fundamental skill acquisition (-FSA), (ii) equipment knowledge acquisition (-EKA) and (iii) preventive maintenance acquisition (-PMA). Specifically, the authors have developed a visual manual that can be simultaneously distributed and used throughout the world.  The effectiveness of this system has been verified at the domestic and overseas Toyota plants. 

Meetings International - Conference Keynote Speaker Farah Jemili photo

Farah Jemili

University of Sousse, Tunisia

Title: Artificial Intelligence for Cyber Security Applications

Biography:

Farah JEMILI had the Engineer degree in Computer Science in 2002 and the Ph.D degree in 2010. She is currently Assistant Professor at Higher Institute of Computer Science and Telecom of Hammam Sousse (ISITCOM), University of Sousse, Tunisia. She is a senior Researcher at MARS Laboratory (ISITCOM –Tunisia). Her research interests include Artificial Intelligence, Cyber Security, Big Data Analysis, Cloud Computing and Distributed Systems. She served as reviewer for many international conferences and journals and has many publications; book chapters, journal publications and conference papers. 

Abstract:

	The recent White House report on artificial intelligence (AI) highlights the importance of AI and the need for a clear roadmap and strategic investment in this area. As AI emerges from science fiction to become the frontier of world-changing technologies, there is an urgent need to systematically develop and implement AI to see its real impact in diverse fields of study. 
	This paper offers a contribution to the deployment of AI in cybersecurity context. Intrusion detection has been the subject of numerous studies in industry and academia, but cybersecurity analysts still want a greater accuracy and comprehensive threat analysis to secure their systems in cyberspace. Improvements to intrusion detection could be achieved by adopting a more comprehensive approach in monitoring security events from many heterogeneous sources. Merging security events from heterogeneous sources and learning from data can offer a more holistic view and a better knowledge of the cyber threat situation. A problem with this approach is that at present even a single event source (for example, network traffic) can encounter big data challenges when it is considered alone. Attempts to use more heterogeneous data sources poses far greater challenges. Artificial Intelligence and Big Data Technologies can help solve these heterogeneous data Problems. 
	The proposed approach includes the pre-processing of data and learning. The experimental results show effectiveness of the approach in terms of accuracy and detection rate and prove that Artificial Intelligence can help achieve better results in Cyber Security context.
	Key words: Artificial Intelligence, Cyber Security