Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
RCS, SIP, IT |
2022-01-20 17:45 |
Online |
Online |
Anomaly detection based on the unity, duality and dependence of entropy
-- Usefulness of Entropy, Kullback-Leibler divergence and Mutual Information -- Akira Imori (HIT), Shunji Maeda (HIT/*), Takashi Komatsu, Tetsuji Taniguchi (HIT/*), Osamu Toda IT2021-53 SIP2021-61 RCS2021-221 |
Monitoring with multiple sensors is important for system diagnosis, and active researches are being conducted using mach... [more] |
IT2021-53 SIP2021-61 RCS2021-221 pp.137-142 |
NLP |
2015-07-21 16:20 |
Hokkaido |
Bibai Onsen Yu-rinkan |
Analysis of Wave-Motion on 9 or 10 Van Der Pol Oscillators Coupled by Inductors as A Ring Ryouhei Takano, Shouhei Fujimoto, Masayuki Yamauchi, Shunji Maeda, Takeshi Tanaka (Hiroshima I.T.) NLP2015-73 |
We can observed various synchronization phenomena on coupled oscillators.
Changing synchronization states can be observ... [more] |
NLP2015-73 pp.31-36 |
NLP |
2015-07-21 16:45 |
Hokkaido |
Bibai Onsen Yu-rinkan |
Analysis of Phase-Inversion Waves on 4 Ladders Including 4 or 8 Van Der Pol Oscillators Coupled by Inductors as A Cross Mikiya Tanaka, Shouhei Fujimoto, Yoshihito Todani, Masayuki Yamauchi (HIT), Yoshifumi Nishio (Tokushima), Shunji Maeda, Takeshi Tanaka (HIT) NLP2015-74 |
Synchronization phenomena are investigated in various fields. We analyzed wave phenomena on a ladder which van der Pol o... [more] |
NLP2015-74 pp.37-42 |
PRMU, MVE, IPSJ-CVIM (Joint) [detail] |
2013-01-24 14:45 |
Kyoto |
|
Vector output estimation by Gaussian Process Regression using dynamic Active Set Yuki Matsumura, Toshikazu Wada (Wakayama Univ.), Shunji Maeda, Hisae Shibuya (Hitachi) PRMU2012-120 MVE2012-85 |
This report presents a method to estimate vector outputs in the framework of Gaussian Process Regression (GPR) . GPR is ... [more] |
PRMU2012-120 MVE2012-85 pp.317-322 |
PRMU, SP |
2012-02-09 13:00 |
Miyagi |
|
Fault Classification Based on Likelihood Profile Feature Takuya Mikami, Masashi Ando, Jun Koyama, Seiji Hotta (TUAT), Hisae Shibuya, Shunji Maeda (HITACHI) PRMU2011-192 SP2011-107 |
In this paper, we propose a feature called likelihood profile for classifying fault events of a precision machine. This ... [more] |
PRMU2011-192 SP2011-107 pp.37-40 |
PRMU, MVE, CQ, IPSJ-CVIM [detail] |
2012-01-19 16:35 |
Osaka |
|
Gaussian Processes based Pre-fault detection with multi-resolutional temporal analysis Shinsaku Ozaki (Wakayama Univ.), Hisae Shibuya, Shunji Maeda (Hitachi, Ltd.), Toshikazu Wada (Wakayama Univ.) PRMU2011-159 MVE2011-68 |
We have been proposed a pre-fault detection system based on Gaussian Processes (GP). The advantage of GP based pre-fault... [more] |
PRMU2011-159 MVE2011-68 pp.109-114 |
PRMU, MI, IE |
2011-05-20 13:30 |
Aichi |
|
Connection between Gaussian Processes and Similarity Based Modeling for Anomaly Detection Shinsaku Ozaki, Toshikazu Wada (Wakayama Univ.), Shunji Maeda, Hisae Shibuya (Hitachi) IE2011-32 PRMU2011-24 MI2011-24 |
Anomaly detection can be applied to health monitoring of industrial plants, human medical conditions, vehicle conditions... [more] |
IE2011-32 PRMU2011-24 MI2011-24 pp.133-138 |
PRMU |
2011-03-10 16:00 |
Ibaraki |
|
Fault Detection Based on Multi-Dimensional Representation of Multisensor and Linear Subspace Classifier Masaki Nakajima, Seiji Hotta (TUAT), Hisae Shibuya, Shunji Maeda (Hitachi, Ltd.) PRMU2010-258 |
[more] |
PRMU2010-258 pp.123-126 |
PRMU |
2011-03-10 16:30 |
Ibaraki |
|
Likelihood Histogram for Fault Detection Masashi Ando, Seiji Hotta (TUAT), Hisae Shibuya, Shunji Maeda (Hitachi, Ltd.) PRMU2010-259 |
[more] |
PRMU2010-259 pp.127-130 |
PRMU, MVE, IPSJ-CVIM [detail] |
2011-01-21 09:25 |
Shiga |
|
Fault Detection and Prediction of Industrial Plants Based on Gaussian Processes Shinsaku Ozaki, Toshikazu Wada (Wakayama Univ.), Shunji Maeda, Hisae Shibuya (Hitachi) PRMU2010-175 MVE2010-100 |
This report proposes a fault and pre-fault detection method for industrial plants based on Gaussian process. Industrial ... [more] |
PRMU2010-175 MVE2010-100 pp.211-216 |
PRMU, HIP |
2010-03-16 08:55 |
Kagoshima |
Kagoshima Univ. |
Fault Detection of Human-Operated Systems Based on Whitening and Linear Prediction Analysis Shinsaku Ozaki, Toshikazu Wada (Wakayama Univ.), Shunji Maeda, Hisae Shibuya (Hitachi) PRMU2009-280 HIP2009-165 |
This report presents a method for fault detection of human-operated systems. Industrial plants and other systems can be ... [more] |
PRMU2009-280 HIP2009-165 pp.275-279 |
PRMU, HIP |
2010-03-16 13:35 |
Kagoshima |
Kagoshima Univ. |
Feature Transformation Reflecting User's Relevance Takahiro Takamiya, Toshikazu Wada (Wakayama Univ.), Shunji Maeda, Hisae Shibuya (Hitachi,Ltd.) PRMU2009-305 HIP2009-190 |
It is often pointed out by researchers working on similar image search that human and computer similarity measures are q... [more] |
PRMU2009-305 HIP2009-190 pp.425-430 |
PRMU |
2009-03-14 11:45 |
Miyagi |
Tohoku Institute of Technology |
Linear discriminant feature transform Masayoshi Ogura, Takahiro Takamiya, Toshikazu Wada (Wakayama Univ.), Shunji Maeda, Kaoru Sakai (Hitachi) PRMU2008-269 |
[more] |
PRMU2008-269 pp.197-204 |
PRMU |
2009-03-14 16:00 |
Miyagi |
Tohoku Institute of Technology |
Defect detection based on self reference for wafer inspection Tetsuya Asami, Toshikazu Wada (Wakayama Univ.), Kaoru Sakai, Shunji Maeda (Hitachi, LTD.) PRMU2008-283 |
[more] |
PRMU2008-283 pp.287-292 |
PRMU |
2006-09-08 13:20 |
Fukuoka |
|
LSI wafer inspection method using recursive splitting of feature space Kaoru Sakai, Shunji Maeda (HPERL) |
[more] |
PRMU2006-69 pp.65-72 |
PRMU |
2006-03-16 09:00 |
Fukuoka |
Kyushu Univ. |
Recognition Method of Minute Defect Based on Statistical Outlier Detection using Plural Pattern Images Kaoru Sakai, Shunji Maeda (Hitachi) |
[more] |
PRMU2005-233 pp.1-6 |
PRMU, NLC |
2005-09-21 10:00 |
Tokyo |
|
Recognition Method of Minute Defect Based on Comparison to Statistical Pattern and Outlier Detection on Feature Space Kaoru Sakai, Shunji Maeda (Hitachi) |
Reductions of the noise caused by the pattern shape difference and the sampling errors are essential to recognize a minu... [more] |
NLC2005-26 PRMU2005-53 pp.11-16 |