2021 SICE Annual Conference Awardの贈呈
Mr. Akio ITO
He graduated from the University of Tokyo and jointed Yokogawa Electric Corporation in 1983. His responsibility in Yokogawa had been industrial robot research and development, LSI test system development, field device development and business planning. He is now responsible for research of industrial network as an adjunc researcher in Waseda Research Institute for Science and Engineering, Waseda University. He is serving as a FDT Group Asia Pacific Marketing Chair. He was the Head of SICE Technical Division of Industrial Applications. He received the SICE Young Researcher's Award in 1983, SICE Technology Award in 2018, and SICE Outstanding Paper Award in 2020. His interests are in sensors and industrial communication.
Mr. Jason Chan Sin Wai
He graduated from Nanyang Technological University of Singpore in 2008 and received a Bachelor Degree in Computer Engineering before joining Yokogawa Engineering Asia. He also received the master’s degree in Knowledge Engineering from National University of Singapore in 2013. He has been working with FDT technology since 2008 and is currently a member of the FDT Architecture and Specification Working Group. He received the SICE Technology Award in 2018, and SICE Outstanding Paper Award in 2020. His interests are in sensors and industrial communication.
Mr. Tetsuo TAKEUCHI
He graduated from Osaka University in 1979 and received B.S. degree in Mechanical Engineering before joining Yokogawa Electric Corporation. His experience in Yokogawa is mainly product development of recorders and field instruments. He has been being engaged in international standardization with consortia and IEC past 10 years. He had served as FDT Group Technology Chair for 9 years until 2018. He received the International Standardization Award: Achievement Prize form SICE in 2014, and International Standardization Contribtution Award from METI in 2018. His interests include device integration and industrial communications.
Prof. Yoshiharu AMANO
He received Bachelor in mechanical engineering from Waseda University in 1991. He also received a master in control engineering and a doctoral degree in Engineering from Waseda University in 1998. He has been a professor at the department of Applied Mechanics and Aerospace Engineering, Waseda University from 2008. He was also a visiting professor in EPFL, Swiss Fedetal Institute for Technology in Lausanne, Switzerland in 2008. He is the director of the Industrial Open-Network Laboratory (IONL) in Waseda Research Institute for Science and Engineering, Waseda University. His interests are in sensors and industrial communication for energy management.
受賞論文「Device Data Utilization Use Case Analysis for FDT Technology in Industrial Control System」
To achieve increased stability in industrial control systems (ICS) and reduction in maintenance cost, the asset management application in addition to control application is evaluated important. In that situation, device data must be integrated to upper layer in a standardized way under various field networks and field devices environment. FDT (IEC62453) is a standard technology which allows integration of devices and networks to engineering tools and can integrate device data to upper layer in a standardized way regardless of process automation or factory automation. Recently FDT technology is upgraded to FDT3.0 (FDT version 3) which enables the FDT IIoT Server? (FITS?) concept. This paper focuses the device data utilization use case analysis for FDT technology in ICS. We categorized the device data into three categories (1) Network Variables, (2) Device data exposed on EDD, and (3) Device data exposed beyond EDD. After then, we made amalysis between Device data and DTM software module which represents the device. Also we made analysis combined with OPC UA server and Web server which FITS concept support. Through these analysis, we showed concrete image of use cases, e.g. any device data can be processed with AI library and passed to upper layer by OPC UA server and/or Web server which will greatly increase the values to the device data. We showed benefits in which FDT3.0 realizes attractive role in IIoT for device integration in ICS.
Prof. Katsuhiro HONDA
He received the B.E., M.E. and D.Eng. degrees in industrial engineering from Osaka Prefecture University, Osaka, Japan, in 1997, 1999 and 2004, respectively. From 1999 to 2013, he was a Research Associate, Assistant Professor and Associate Professor at Osaka Prefecture University, where he is a Professor in the Department of Computer Sciences and Intelligent Systems. His research interests include hybrid techniques of fuzzy clustering and multivariate analysis, data mining with fuzzy data analysis and neural networks.
Mr. Kosuke HAYASHI
He received the B.E. degree in engineering from Osaka Prefecture University, Osaka, Japan, in 2021. He has been in the Master's course at Graduate School of Engineering, Osaka Prefecture University. His research interests include fuzzy clustering and its application to intelligent data analysis.
Dr. Seiki UBUKATA
He received the B.E., M.I.S., and Ph.D. degrees in Information Science from Hokkaido University, Sapporo, Japan, in 2007, 2009, and 2014, respectively. From 2014 to 2020, he held Assistant Professor positions at Osaka University and Osaka Prefecture University. Since 2020, he has been an Associate Professor at Graduate School of Engineering, Osaka Prefecture University, Sakai, Japan. His research interests include soft computing and cluster analysis.
Prof. Akira NOTSU
He received the B.E., M.E., and Ph.D. degrees in Informatics from Kyoto University, Kyoto, Japan, in 2000, 2002, and 2005, respectively. From 2005 to 2020, he held Assistant Professor and Associate Professor at Osaka Prefecture University, Sakai, Japan. Since 2020, he has been a Professor at Graduate School of Humanities and Sustainable System Sciences, Osaka Prefecture University. His research interests include reinforcement learning, clustering, evolutionary computation, neural networks and cognitive models.
受賞論文「Fuzzy-Possibilistic Clustering for Categorical Multivariate Data」
Fuzzy co-clustering is a technique for extracting co-cluster structures from cooccurrence data among objects and items, where fuzzy partition of objects is associated with possibilistic partition of items. In this paper, with the goal of improving the noise robustness of object fuzzy partition, fuzzy-possibilistic partition is introduced into fuzzy clustering for categorical multivariate data (FCCM). Not only the conventional probabilistic fuzzy memberships but also possibilistic typicality memberships are jointly utilized in estimation of object partition while item partition is still remained to be possibilistic. The characteristics of the proposed algorithm are demonstrated through numerical experiments.
○Young Author's Award
Ms. Yuri MIKAWA
She received the B.E. and M.E. degrees in information science and technology from the University of Tokyo, Japan, in 2018 and 2020, respectively. She is currently a doctoral student at the Graduate School of Information Science and Technology, The University of Tokyo. Her research interests include augmented reality display, dynamic projection mapping, aerial imaging, and high-speed computer vision.
受賞論文「Far-Field Aerial Image Presentation of One Point by a Laser Source Using Beam Scanning by Two-Axis Galvanometer Mirror」
This paper proposes far-field aerial image presentation using a laser and two-axis galvanometer mirror. A high-speed camera tracks the human's pupil subsequently, and thus, the angle of galvanometer mirror is controlled subsequently so that the laser light is incident into the pupil. This method can extend the distance between the user and the system, allowing far-field aerial image presentation. This is a basic research in the point that only the aerial image of one point can be presented, but the problems of the system is clarified and solved by the proposed algorithms, and the elaborate evaluations are conducted.