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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 178  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
NS 2022-10-05
15:10
Hokkaido Hokkaidou University + Online
(Primary: On-site, Secondary: Online)
An Anomaly Detection System of IoT Traffic for Smart Home Environments
Naoto Watanabe, Taku Yamazaki, Takumi Miyoshi (Shibaura Inst. Tech.), Ryo Yamamoto (UEC) NS2022-83
(To be available after the conference date) [more] NS2022-83
pp.8-11
IA, CQ, MIKA
(Joint)
2022-09-16
15:45
Hokkaido Hokkaido Citizens Actives Center
(Primary: On-site, Secondary: Online)
Study of Data Collection System for Ultrasonic Waves Based Anomaly Detection of Mechanical Equipment
Rio Shigyo, Daiki Nobayashi, Kazuya Tsukamoto, Mitsunori Mizumachi, Takeshi Ikenaga (KIT) IA2022-32
Status checks of mechanical equipment are typically being conducted by workers (manpower). However, since the check timi... [more] IA2022-32
pp.97-102
ITE-ME, EMM, IE, LOIS, IEE-CMN, IPSJ-AVM [detail] 2022-09-13
13:40
Online Keio Univ. Yagami Campus (Hybrid)
(Primary: Online, Secondary: On-site)
Video Anomaly Detection Method using Deep Learning Models and Crowd Workers
Ryuya Itano, Tomoya Nohara, Takahiro Koita (Doshisha Univ.) LOIS2022-10 IE2022-32 EMM2022-38
In recent years, the number of surveillance cameras installed has been increasing due to the spread of IoT. However, thi... [more] LOIS2022-10 IE2022-32 EMM2022-38
pp.1-6
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
MI 2022-07-08
17:00
Hokkaido
(Primary: On-site, Secondary: Online)
[Short Paper] Weakly-Supervised Focal Liver Lesion Detection in CT Images
He Li, Yutaro Iwamoto (Ritsumeikan Univ.), Xianhua Han (Yamaguchi Univ.), Lanfen Lin, Ruofeng Tong, Hongjie Hu (Zhejiang Univ.), Akira Furukawa (Tokyo Metropolitan Univ.), Shuzo Kanasaki (Koseikai Takeda Hospital), Yen-Wei Chen (Ritsumeikan Univ.) MI2022-40
Convolutional neural networks have been widely used for anomaly detection and one of their most common methods is autoen... [more] MI2022-40
pp.30-33
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
KBSE, SWIM 2022-05-20
15:00
Tokyo
(Primary: On-site, Secondary: Online)
Practical Application of Self-Adaptive Anomaly Detection Method Using XAI
Shimon Sumita, Hiroyuki Nakagawa, Tatsuhiro Tsuchiya (Osaka Univ.) KBSE2022-3 SWIM2022-3
In this study, we examine the use of XAI to improve the performance of a self-adaptive anomaly detection method. As a sp... [more] KBSE2022-3 SWIM2022-3
pp.13-18
ICM, IPSJ-CSEC, IPSJ-IOT 2022-05-19
10:25
Nagano
(Primary: On-site, Secondary: Online)
BGP Hijacking Detection Using Deep Learning
Kenta Nakashima, Mitiko Harayama (Gifu City Univ) ICM2022-2
Failures of large autonomous systems (ASs) and network attacks can affect Border Gateway Protocol (BGP) messages, causin... [more] ICM2022-2
pp.6-11
EA 2022-05-13
14:10
Online Online Acoustic diagnosis and monitoring for high voltage switchgear
Takashi Endo, Purohit Harsh, Yohei Kawaguchi (Hitachi) EA2022-7
This paper proposes a method for estimating stroke speed from sounds made during the opening and closing of high-voltage... [more] EA2022-7
pp.30-34
MSS, NLP 2022-03-29
15:20
Online Online Process Division Learning Method for Input-Output Signals of Production Equipment
Daiki Nakahara, Masahiko Shibata, Tsuyoshi Kobayashi (Mitsubishi Electric) MSS2021-79 NLP2021-150
When a problem occurs at a production line, the cause should be found out immediately. The authors have proposed a predi... [more] MSS2021-79 NLP2021-150
pp.127-132
CCS 2022-03-27
15:40
Hokkaido RUSUTSU RESORT HOTEL & CONVENTION
(Primary: On-site, Secondary: Online)
Evaluation of Industrial Anomaly Detection using Diffusion Model
Yu Kashihara, Takashi Matsubara (Osaka Univ.) CCS2021-48
Anomaly detection by generative models is achieved by comparing the reconstruction and the original image. However, exis... [more] CCS2021-48
pp.72-77
NS, IN
(Joint)
2022-03-10
10:40
Online Online Edge cloud cooperation system for anomaly detection using image data
Yuki Tanaka, Ryoichi Kawahara (Toyo Univ.) IN2021-32
In the field of IoT, which is becoming more widespread, it is expected that useful information extraction from image dat... [more] IN2021-32
pp.7-12
MBE, NC
(Joint)
2022-03-02
09:55
Online Online NC2021-47 In this paper, we propose reconstructive reservoir computing (RRC), which can detect anomaly in time-series signals. In ... [more] NC2021-47
pp.5-10
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
EST 2022-01-27
11:00
Online Online Research on anomaly detection using machine learning in external images of Optical Lens
Tatsuki Ono, Takuto Ito, Tomoatsu Hasegawa, Hideaki Kimura (Chubu Univ.) EST2021-60
Appearance inspection is an important task for maintaining quality and is performed at various places in the manufacturi... [more] EST2021-60
pp.19-22
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
MI 2022-01-26
10:13
Online Online [Short Paper] Abnormality Detection for Covid-19 Chest CT Images by Dimensionality Reduction Based on Contrastive Learning
Hiroki Tobise, Kugler Mauricio, Tatsuya Yokota (NITech), Masahiro Hashimoto (Keio Univ.), Yoshito Otake (NAIST), Toshiaki Akashi (Juntendo Univ.), Akinobu Shimizu (TUAT), Hidekata Hontani (NITech) MI2021-53
In this article, we propose a method that detects anomaly regions in chest CT images for the aid of Covid-19 diagnosis. ... [more] MI2021-53
pp.41-42
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
PRMU 2021-12-16
11:00
Online Online Anomaly Detection using PatchCore with Self-attention module
Yuki Takena (Shizuoka Univ.), Yoshiki Nota, Rinpei Mochizuki (Meidensya Corp.), Itaru Matsumura (Railway Technical Research Inst.), Gosuke Ohashi (Shizuoka Univ.) PRMU2021-29
In recent years, in visual inspection of industrial products using deep learning, There are many models that achieve exc... [more] PRMU2021-29
pp.31-36
PRMU 2021-12-16
11:15
Online Online A Dataset for Work Instruction Deviation Detection from Field Work Videos
Ryosuke Furuta (UTokyo), Motohiro Takagi (NTT), Yusuke Sugano, Yoichi Sato (UTokyo) PRMU2021-30
It is important to follow the work instruction manuals for field workers in order to protect them from accidents. In thi... [more] PRMU2021-30
pp.37-42
 Results 1 - 20 of 178  /  [Next]  
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