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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| PRMU, IBISML, IPSJ-CVIM | 2024-03-03 15:36 |
Hiroshima | Hiroshima Univ. Higashi-Hiroshima campus (Primary: On-site, Secondary: Online) |
 PRMU2023-63 |
(To be available after the conference date) [more] | PRMU2023-63 pp.70-75 |
| EMCJ | 2023-11-24 13:00 |
Tokyo | Kikai-Shinko-Kaikan (Primary: On-site, Secondary: Online) |
Detection Sensitivity Improvement by Removing Reflected Pulse Influence from a Bus Type Network Branch in Sequence Time Domain Reflectometry Takashi Kakiuchi, Kengo iokibe, Yoshitaka Toyota (Okayama Univ) EMCJ2023-73 |
Using the fact that cross-correlation between transmitted and reflected pulses in Sequence Time Domain Reflectometry can... [more] | EMCJ2023-73 pp.7-12 |
| CQ | 2022-07-22 11:20 |
Osaka | Ritsumeikan Ibaraki Future Plaza Conference Hall (Primary: On-site, Secondary: Online) |
[Invited Talk]
Spectral Graph Theory and Its Application Yusuke Sakumoto (Kwansei Gakuin Univ.) CQ2022-25 |
The spectral graph theory provides an algebraical approach to investigating the characteristics of networks using the ei... [more] | CQ2022-25 p.43 |
| SIS, IPSJ-AVM | 2022-06-09 15:00 |
Fukuoka | KIT(Wakamatsu Campus) (Primary: On-site, Secondary: Online) |
[Invited Talk]
Advanced applications of machine learning techniques towards high-performance and cost-effective visual inspection AI Terumasa Tokunaga (Kyutech) SIS2022-6 |
Visual inspection is an essential step for quality control in manufacturing. Recently, many researchers have shown great... [more] | SIS2022-6 p.30 |
| EA, SIP, SP, IPSJ-SLP [detail] | 2022-03-01 09:20 |
Okinawa | (Primary: On-site, Secondary: Online) |
[Poster Presentation]
Robust Hyperspectral Anomaly Detection via Component Decomposition Based on Convex Optimization Koyo Sato, Shunsuke Ono (Tokyo Tech) EA2021-71 SIP2021-98 SP2021-56 |
Anomaly detection in hyperspectral (HS) images is a technique to identify pixels whose spectral wavelengths differ from ... [more] | EA2021-71 SIP2021-98 SP2021-56 pp.44-49 |
| CQ, CBE (Joint) |
2022-01-28 12:10 |
Ishikawa | Kanazawa（Ishikawa Pref.） (Primary: On-site, Secondary: Online) |
A Study on Detection of Subjects with Unique Brain Structures Using Eigenvalues of Laplacian Matrices Ohisi Yuki, Taniguchi Toyoaki, Segawa Eriko, Sakumoto Yusuke (Kwansei Gakuin Univ) CQ2021-93 |
In order to understand the complex behavior of brains, many works have analyzed macroscale connectomes between the aggre... [more] | CQ2021-93 pp.94-99 |
| PRMU | 2021-12-16 10:30 |
Online | Online | Real-world Anomaly Detection by Integrating Long-Term and Short-Term Classifications Yudai Watanabe, Makoto Okabe (Shizuoka Univ.), Yasunori Harada, Yasuhiko Naoji (Chubu Electric Power Co., Inc.) PRMU2021-27 |
We propose a model to determine whether a video is normal or abnormal by using all the segments obtained from the video ... [more] | PRMU2021-27 pp.19-24 |
| AI | 2021-02-12 15:40 |
Online | Online | A Fundamental Study on the Real-time Anomaly Detection of Traffic States with DSRC-based Data Collection Technology Shunya Tabata, Daisuke Fukuda (UT) AI2020-34 |
(To be available after the conference date) [more] | AI2020-34 pp.60-65 |
| ICM, LOIS | 2021-01-22 11:05 |
Online | Online | A Mesh Shape Recognition Method Using Triangular Filter Tomoya Wada, Nobuyuki Yonaga, Tetsuo Imai, Kenichi Arai, Toru Kobayashi (Nagasaki City Univ.) ICM2020-54 LOIS2020-42 |
During the production of nets used in aquaculture, skilled technicians visually monitor the nets and adjust them if any ... [more] | ICM2020-54 LOIS2020-42 pp.119-124 |
| IN, NS (Joint) |
2019-03-05 11:50 |
Okinawa | Okinawa Convention Center | Proposal of malicious device detection method by DNS query/response log analysis using machine learning Issei Nakasone, Kitaguchi Yoshiaki, Yamaoka Katsunori (Tokyo Tech) IN2018-129 |
One common way for detecting malware devices in a network is to use a blacklist based on signature detection.However, in... [more] | IN2018-129 pp.271-276 |
| IA | 2019-01-25 14:05 |
Tokyo | Tokyo Metropolitan Univ. Akihabara Satellite Campus | Development of a network anomaly detection system based on packet flow Zheng Lian, Tohru Kondo, Reiji Aibara (Hiroshima Univ.) IA2018-54 |
Detection of network anomalies is becoming more difficult due to network complexity．For example，there are broadcast stor... [more] | IA2018-54 pp.1-6 |
| RCS, SR, SRW (Joint) |
2018-02-28 15:40 |
Kanagawa | YRP | A community-based anomaly detection system by the synergetic use of mobile sensing and delay tolerant networks with cooperative data processing technique Yoshito Watanabe, Yozo Shoji (NICT) SR2017-115 |
This paper proposes a novel cooperative anomaly detection system that uses mobile sensing and delay tolerant network (DT... [more] | SR2017-115 pp.25-30 |
| MBE, NC (Joint) |
2017-03-13 10:50 |
Tokyo | Kikai-Shinko-Kaikan Bldg. | An Impact Force Analysis of the Simple Hammering Mechanism for Concrete Damage Detection Yuki Yoshida (KIT), Okumura Katsuhiro, Koga Fumitaka, Yamada Keiichi, Tanaka Masatoshi (FITC), Hideki Wada (SHK-K), Hiroaki Wagatsuma (KIT/RIKEN/AIST BSI/AIRC, AIST) NC2016-80 |
In order to build an automated system for anomaly detection such as concrete cracks through hammering tests, hammering m... [more] | NC2016-80 pp.97-102 |
| NS, IN (Joint) |
2017-03-02 09:30 |
Okinawa | OKINAWA ZANPAMISAKI ROYAL HOTEL | Detection of Bot-infected PCs based on C&C Session Classification Hinako Moroi, Masatoshi Kawarasaki (Tsukuba Univ) IN2016-99 |
In malware detection by traffic characterization analysis, general-purpose detection and methods specialized to specific... [more] | IN2016-99 pp.13-18 |
| ITS, IE, ITE-AIT, ITE-HI, ITE-ME, ITE-MMS, ITE-CE [detail] | 2016-02-23 12:00 |
Hokkaido | Hokkaido Univ. | Consideration of Frame Image Selection and Size for Detection System of Unusual Situation Sachi Kuranishi, Hitoshi Yamauchi, Hironori Takimoto (Okayama Pref. Univ.) ITS2015-81 IE2015-123 |
We proposed an unusual situation detection system with camera used for elderly living alone. As the system's first step... [more] | ITS2015-81 IE2015-123 pp.269-274 |
| EE | 2016-01-29 13:20 |
Fukuoka | KURUME HOTEL ESPRIT | Anomaly Detection Technique of Duty Ratio in Switching Mode Power Supply Circuit Takahiko Sugawara, Yu Yonezawa, Hiroshi Nakao, Yoshiyasu Nakashima (FUJITSU LAB. LTD.) EE2015-24 |
In this report, we suggest the technique to improve the reliability of the power supply with multiplexing an overcurrent... [more] | EE2015-24 pp.91-96 |
| ICSS | 2015-03-03 13:25 |
Okinawa | Meio Univiersity | Anomaly Detection Based on Clustering of Network Traffic Characteristics Considering Results of Signature Based IDS Evaluation Kohei Imai, Shigeki Aoki, Takao Miyamoto (Osaka Prefecture Univ.) ICSS2014-64 |
There is a lot of attacks over the Internet,intrusion detection systems(IDSs) are an active area of research.IDSs are cl... [more] | ICSS2014-64 pp.7-12 |
| IN | 2015-01-22 13:25 |
Aichi | Nagoya International Center | A Framework for Monitoring Quality of Service and Detecting Bottlenecks of Computer Systems Based on Graph-Structured Log Toshio Ito, Yu Kaneko, Tomonori Maegawa (Toshiba Corp.) IN2014-102 |
This paper proposes a framework for monitoring and improving quality of service of computer systems. The proposed system... [more] | IN2014-102 pp.25-30 |
| CQ, MVE, IE, IMQ, CEA (Joint) [detail] |
2014-03-06 13:30 |
Oita | Beppu International Convention Center | Abnormal Scene Extraction in Machinery Operation using Spatio-temporal Feature and Unsupervised Learning Hiromitsu Kobayashi, Kota Aoki, Hiroshi Nagahashi (Tokyo Tech.) IMQ2013-41 IE2013-150 MVE2013-79 |
This paper proposes a new method for extracting abnormal scene in the video of periodic machinery operations. Firstly, ... [more] | IMQ2013-41 IE2013-150 MVE2013-79 pp.87-92 |
| ASN | 2013-11-29 15:25 |
Tokyo | Kikai-Shinko-Kaikan Bldg. | Current Wave Pattern Analysis for Anomaly Detection of Electrical Devices Yuto Tamura, Takeshi Takai, Takekazu Kato, Takashi Matsuyama (Kyoto Univ.) ASN2013-115 |
This paper proposes a novel method to detect and evaluate anomaly of an electric appliance from variations of current wa... [more] | ASN2013-115 pp.135-140 |
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