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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 213  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
ICM 2024-03-22
15:25
Okinawa Okinawa Prefectural Museum and Art Museum
(Primary: On-site, Secondary: Online)
Study of AI Model Training Method Using Network Digital Twin in Scale-out 5GC Environment
Daiki Koyama, Minato Sakuraba, Junichi Kawasaki, Takuya Miyasaka (KDDI Research) ICM2023-62
This technical report proposes a method for building AI models for network operations by utilizing the network digital t... [more] ICM2023-62
pp.89-94
WIT, IPSJ-AAC 2024-03-19
15:10
Ibaraki Tsukuba University of Technology
(Primary: On-site, Secondary: Online)
Anomaly Detection Method of Elderly using Human Body Surface Temperature
Tasuku Hanato, Shin Morishima, Akira Urashima (TPU), Hiroshi Minematsu, Takashi Yamamoto (Shikino High-Tech Co., Ltd.), Tomoji Toriyama (TPU) WIT2023-55
Detecting Anomalies in bedridden elderly humans is an important issue. However, it is difficult for caregivers to consta... [more] WIT2023-55
pp.69-74
IE, MVE, CQ, IMQ
(Joint) [detail]
2024-03-14
16:20
Okinawa Okinawa Sangyo Shien Center
(Primary: On-site, Secondary: Online)
Detection and Tracking of Abnormal Objects in Forests from Dynamic Time-sequential RGBD Images Based on Deep Learning Segment Anything
Yuta Suzuki (Waseda Univ.), Junji Yamato (Kougakuin Univ.), Jun Ohya (Waseda Univ.) IMQ2023-59 IE2023-114 MVE2023-88
This paper proposes a method for distinguishing changes caused by abnormal objects such as illegal dumping and fallen tr... [more] IMQ2023-59 IE2023-114 MVE2023-88
pp.252-257
IE, MVE, CQ, IMQ
(Joint) [detail]
2024-03-15
15:30
Okinawa Okinawa Sangyo Shien Center
(Primary: On-site, Secondary: Online)
High Precision Anomaly Detection using PaDiM based on Pre-training with Normality Constraint of Normal Features
Hiroki Kobayashi, Manabu Hashimoto (Chukyo Univ.) IMQ2023-89 IE2023-144 MVE2023-118
In recent years, automatic visual inspection is expected with machine learning. Among them, PaDiM is attracting attentio... [more] IMQ2023-89 IE2023-144 MVE2023-118
pp.408-413
IA, SITE, IPSJ-IOT [detail] 2024-03-12
15:35
Okinawa Miyakojima City Future Creation Center
(Primary: On-site, Secondary: Online)
Data Analysis and Visualization Method for Equipment Anomaly Detection by Using Acoustic Data
Koichiro Kawano, Nobayashi Daiki, Tsukamo Kazuya, Mizumachi Mitsunori, Ikenaga Takeshi (KIT) SITE2023-82 IA2023-88
Manual inspections of general equipment present many challenges in terms of manpower, time, and labor costs. In addition... [more] SITE2023-82 IA2023-88
pp.86-91
MI 2024-03-03
11:40
Okinawa OKINAWAKEN SEINENKAIKAN
(Primary: On-site, Secondary: Online)
[Short Paper] Weak supervised chest lesion detection on FDG-PET/CT Images using Pix2Pix image modality translation
Kazuki Otani, Kohei Yoshida, Mitsutaka Nemoto (Kindai Univ.), Hayato Kaida (KU Hosp), Yuichi Kimura, Takashi Nagaoka, Katsuhiro Mikami (Kindai Univ.), Takahiro Yamada, Kohei Hanaoka (KU Hosp), Tsuchitani Tatsuya, Kazuhiro Kitajima (HMU Hosp), Kazunari Ishii (KU Hosp) MI2023-42
In this study, we propose a method for detecting thoracic lesions on PET/CT images using 3D Pix2Pix, which learns a tran... [more] MI2023-42
pp.36-38
MI 2024-03-04
14:12
Okinawa OKINAWAKEN SEINENKAIKAN
(Primary: On-site, Secondary: Online)
Anomaly Detection for Lung CT Images Using VQ-VAE with SVDD
Zhihui Gao, Ryohei Nakayama, Aikiyoshi Hizukuri (Ritsumeikan Univ.), Shoji Kido (Oosaka Univ.) MI2023-80
In this study, VQ-VAE with SVDD for anomaly detection in CT images was constructed by introducing a support vector data ... [more] MI2023-80
pp.158-159
NS, IN
(Joint)
2024-02-29
11:10
Okinawa Okinawa Convention Center An unsupervised online learning-based traffic classification and anomaly detection method for 5G-IIoT systems
Yuxuan Shi, Qianqian Pan, Akihiro Nakao (U Tokyo) NS2023-188
In the context of Society 5.0, the evolution of the Internet of Things (IoT) and its ever growing demands of massive Mac... [more] NS2023-188
pp.96-102
ITS, IE, ITE-MMS, ITE-ME, ITE-AIT [detail] 2024-02-20
13:15
Hokkaido Hokkaido Univ. DoG-PaDiM:Anomaly Detection with Defect Size Selectivity based on Bandpass Filtering
Naoto Hiramatsu, Naoki Murakami, Hiroki Kobayashi, Shuichi Akizuki, Manabu Hashimoto (Chukyo Univ.) ITS2023-69 IE2023-58
In the background of the increasing precision of visual inspections, recently, anomaly detection methods using machine l... [more] ITS2023-69 IE2023-58
pp.124-129
NS, NWS
(Joint)
2024-01-25
13:00
Hiroshima Higashisenda Campus, HiroshimaUniversity + Online
(Primary: On-site, Secondary: Online)
[Encouragement Talk] Performance evaluation of network anomaly detection and failure scale estimation method.
Naoya Ogawa, Ryoichi Kawahara (Toyo Univ.) NS2023-159
In this paper, we evaluate the performance of the method proposed in "Network anomaly detection and failure scale estima... [more] NS2023-159
pp.1-6
LOIS, ICM 2024-01-26
10:10
Nagasaki Nagasaki Prefectural Art Museum
(Primary: On-site, Secondary: Online)
Service Failure Detection Focusing on Simultaneous Increase in Various Types of Connection Retries in User Traffic
Naoki Hayashi, Fumio Katayama, Naoki Tateishi, Osamu Okino, Mitsuho Tahara (NTT) ICM2023-34 LOIS2023-38
Many communication services are provided in carrier networks, and these communication services are realized by various e... [more] ICM2023-34 LOIS2023-38
pp.33-38
IBISML 2023-12-20
14:55
Tokyo National Institute of Informatics
(Primary: On-site, Secondary: Online)
Anomaly Detection by One-class Convolution Extreme Learning Machine Using Multiple Kernel
Yuta Okami, Takuya Kitamura (NIT, Toyama College) IBISML2023-31
In this paper, we propose a one-class convolutional extreme learning machine using multiple kernel. In this method, for ... [more] IBISML2023-31
pp.7-12
IBISML 2023-12-20
16:25
Tokyo National Institute of Informatics
(Primary: On-site, Secondary: Online)
Anomaly detection by deep support data descriptions with pseudo-anomaly data
Shuta Tsuchio, Takuya Kitamura (NIT, Toyama college) IBISML2023-34
This paper presents deep support vector data description (DSVDD) with pseudo-anomaly data that generated by generative m... [more] IBISML2023-34
pp.25-30
NS, RCS
(Joint)
2023-12-14
16:00
Fukuoka Kyushu Institute of Technology Tobata campus, and Online
(Primary: On-site, Secondary: Online)
Evaluation of HttpRequest anomaly detection model using fastText and convolutional autoencoder
Haruta Yamada, Ryoichi Kawahara (Toyo Univ.) NS2023-133
With the advent of the Internet and its close connection to people's lives, web applications are becoming increasingly i... [more] NS2023-133
pp.42-47
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
IA 2023-11-22
16:25
Aomori Aomori Prefecture Tourist Center ASPM (Aomori)
(Primary: On-site, Secondary: Online)
Improving the accuracy of flow prediction and anomaly detection in GAMPAL, a general-purpose anomaly detection mechanism for Internet traffic
Taku Wakui (Keio Univ./Hitachi), Fumio Teraoka (Keio Univ.), Takao Kondo (Hokkaido Univ./Keio Univ.) IA2023-41
The authors propose a general-purpose anomaly detection mechanism using Prefix Aggregate without Labeled data (GAMPAL) f... [more] IA2023-41
pp.33-40
ICM, NS, CQ, NV
(Joint)
2023-11-22
09:25
Ehime Ehime Prefecture Gender Equality Center
(Primary: On-site, Secondary: Online)
A Study on Transfer of Decision Tree for Operation of Future Managed Networks
Takaaki Moriya, Takashi Mukai, Manabu Nishio, Ai Tsunoda, Ken Kanishima (NTT) ICM2023-26
When we build a new managed network, we need knowledge to solve various failures that will be occurred in the network. H... [more] ICM2023-26
pp.20-25
KBSE, SC 2023-11-18
13:55
Miyagi Sento Kaikan Improving the accuracy of machine learning based HDD failure prediction in on-premises storage
Taku Wakui, Mineyoshi Masuda, Tomoya Oota (Hitachi) KBSE2023-48 SC2023-31
The HDD (Hard Disk Drive) failure predictive detection technology is one of the functions for managing on-premises stora... [more] KBSE2023-48 SC2023-31
pp.81-86
MIKA
(3rd)
2023-10-10
15:35
Okinawa Okinawa Jichikaikan
(Primary: On-site, Secondary: Online)
[Poster Presentation] Anomaly Detection using HDBSCAN and Deep SVDD
Yusuke Noji, Tomotaka Kimura, Jun Cheng (Doshisha Univ.)
In this presentation, we examine an anomaly detection method using Deep SVDD (Support Vector Data Description), a type o... [more]
IA 2023-09-22
14:00
Hokkaido Hokkaido Univeristy
(Primary: On-site, Secondary: Online)
Investigation of Acoustic Feature Estimation Based on Periodicity of Mechanical Equipment for Remote Anomaly Detection System
Rio Shigyo, Daiki Nobayashi, Kazuya Tsukamoto, Mitsunori Mizumachi, Takeshi Ikenaga (KIT) IA2023-28
Status checks of mechanical equipment are typically being conducted by workers (manpower). However, since the check timi... [more] IA2023-28
pp.105-108
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