| 会告：2007 SICE Annual Conference Awardの贈呈
Mr. Takayuki HOSHI (Student Member)
Received the B.E. degree from Dept. of Mathematical Engineering & Information Physics, School of Engineering, and the M.E. degree from Dept. of Information Physics & Computing, Graduate School of Information Science & Technology, the University of Tokyo, in 2003 and 2005, respectively. He is currently candidate for Ph.D. degree in the University of Tokyo.
受賞論文「Gravity-Based 3D Shape Measuring Sheet」
We proposed a novel sensing device named "3-dimensional capture sheet (3DCS)." The cloth-like sheet measures its own 3D configuration. It consists of a lattice structure inside of the sheet, and each link of the structure has a triaxial accelerometer. The link posture is derived from the measured gravity vector. The whole shape of the sheet is reconstructed by combining the postures of all the links.
Prof. Kang-Zhi LIU (Member)
He received the B.E. degree from Northwestern Polytechnical University of China in 1984 and the Ph.D. degree from Chiba University in 1991. Since then, he has been with the Department of Electronics and Mechanical Engineering, Chiba University, and now is an Associate Professor. His interests are in robust control theory, nonlinear control theory and industrial applications. He has authored and co-authored three books.
受賞論文「Beyond The Small-Gain Paradigm: How to Make Use of The Phase Information of Uncertainty」
This paper tries to open a new field for robust control theory research. The celebrated small-gain approach to robust control only makes use of the gain information of uncertainty. This results in a limitation on the achievable control bandwidth in system design. To relax this limitation associated with the small-gain approach, we explore the possibility of utilizing the phase information of uncertainty in robust control.
This paper discusses the modeling of uncertainty accounting for both gain and phase, and the related robust control analysis and synthesis problems. Based on the close examination of numerous practical systems, a finite frequency domain modeling of uncertainty is proposed. The uncertainty is modeled by using its phase information in the low and middle frequency domain and the gain information in the high frequency domain. Then, the robust stability condition is derived in the frequency domain. Finally, to establish a synthesis method the frequency domain condition is converted into an equivalent state space condition based on the separating hyperplane argument and the generalized KYP lemma.
The proposed robust control approach is able to overcome the conservatism of the small-gain approach and to enhance the performance of robust control systems significantly.
○Young Aothor's Award
Mr. Hiroaki MIZUHARA (Member)
(born August 19, 1975) Lecturer at Kyoto University, Japan. He received the B.E. and M.E. degree in Mechanical Engineering from Yamaguchi University in 1998 and 2000, and the Ph.D. degree in Design Engineering from Yamaguchi University in 2002. He continued his research in cognitive neuroscience and functional brain imaging as a research scientist at RIKEN Brain Science Institute from 2002 to 2005, and as a lecturer at Okayama University from 2005 to 2007. From 2007, he is a lecturer at Graduate School of Informatics, Kyoto University.
受賞論文「Parallel Factor Analysis can Decompose Human EEG During a Finger Movement Task」
The non-invasive brain computer interfaces are now a hot topic for providing the communication tool for patients suffering severe impairments of motor and related functions. Previous studies have often used the signal processing tools such as the PCA/ICA for detecting the brain intention form the EEG. However, the technical limitation of these traditional techniques prevents to achieve this end. To overcome this limitation, we have adopted a new powerful technique ‘parallel factor analysis (PARAFAC)’ for decomposing the EEG signals into the meaningful component of the brain intention. The results showed that the PARAFAC can decompose the EEG and identify the intention, which concerns with the finger movement. By applying the core consistency diagnostic to the PARAFAC model, we suggest that the combination of the EEG and other neuroimaging modality should be applied for producing the appropriate number of the components of the PARAFAC for the purpose of the non-invasive brain computer interface. This study is a first paper in the world for applying the PARAFAC decomposition to the EEG for the purpose of achieving the non-invasive brain computer interface.
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