Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
SANE |
2024-01-19 13:25 |
Miyagi |
(Primary: On-site, Secondary: Online) |
Development of Riemannian Quaternion Self-Organizing Map and Its Application in Full-Polarimetric GPR Landmine Detection Yicheng Song, Ryo Natsuaki, Akira Hirose (UTokyo) SANE2023-97 |
Ground penetrating radar (GPR) based landmine detection has advantages such as high safety and high efficiency. There ar... [more] |
SANE2023-97 pp.41-46 |
RISING (3rd) |
2022-10-31 15:00 |
Kyoto |
Kyoto Terrsa (Day 1), and Online (Day 2, 3) |
[Poster Presentation]
Cache Scheme Using Different Initial Placement Multiple Self-organizing Maps in Information-centric Networking Kei Yamashiro, Minami Kotake, Takashi Nishitsuji, Takuya Asaka (TMU) |
Information Centric Networking (ICN) has been proposed to revolutionize the traditional Internet architecture. In ICN, c... [more] |
|
NC, MBE (Joint) |
2021-03-05 13:50 |
Online |
Online |
DCSOM with Ensemble Learning Classifier Akito Takahashi, Yukari Yamauchi (Nihon Univ) NC2020-71 |
Deep Convolutional Self-Organizing Map (DCSOM) which extracts visual features from images by using self-organizing maps ... [more] |
NC2020-71 pp.163-168 |
SIS |
2020-12-01 14:25 |
Online |
Online |
Interpretability of deep neural networks with self-organizing map modules. Takahiro Sono, Keiichi Horio (KIT) SIS2020-32 |
In recent years, the technology of neural networks has made great progress due to the improvement of computational power... [more] |
SIS2020-32 pp.27-30 |
MBE, NC, NLP, CAS (Joint) [detail] |
2020-10-30 15:45 |
Online |
Online |
A Proposal of Self-Organizing Map Based on Attribute Information with Attenuate Rate Tetsuya Sato, Yukari Yamauti (Nihon Univ.) NC2020-23 |
Self-organizing Maps(SOM) is a simple algorithm, has excellent clustering capabilities, and can create a nonlinear model... [more] |
NC2020-23 pp.77-82 |
NC, MBE (Joint) |
2020-03-05 13:50 |
Tokyo |
University of Electro Communications (Cancelled but technical report was issued) |
A Proposal of Self-Organizing Maps Based on Learning with Attribute Information Tetsuya Sato, Yukari Yamauti (Nihon Univ.) NC2019-96 |
Self-organizing maps(SOM) is a simple algorithm, and has excellent clustering capabilities. However, since SOM performs ... [more] |
NC2019-96 pp.119-124 |
MBE, NC |
2019-10-12 10:50 |
Miyagi |
|
An Optimization for Classification by Self-Organizing Maps Based on Attribute Information Tetsuya Sato (Nihon Univ.), Kazuma Tsuchida (STUDIO ONE OR EIGHT), Yukari Yamauti (Nihon Univ.) MBE2019-41 NC2019-32 |
Self-Organizing Map (SOM) is a simple algorithm that has excellent clustering capabilities and adapts continuous changes... [more] |
MBE2019-41 NC2019-32 pp.59-63 |
IA, ICSS |
2019-06-07 11:20 |
Miyagi |
Research Institute for Electrical Communication, Tohoku University |
Development of System to Analyze Aggressive Communication Using Self-Organizing Map and Convolutional Neural Network Akifumi Iwasa, Hikofumi Suzuki, Takumi Uchiyama (Shinshu Univ.), Tetsuya ui (NEC) IA2019-8 ICSS2019-8 |
In recent years, the importance of the Internet is increasing. However, DoS / DDoS attacks is increasing. It is difficul... [more] |
IA2019-8 ICSS2019-8 pp.37-41 |
SIS |
2018-12-06 14:50 |
Yamaguchi |
Hagi Civic Center |
Multi-View Analysis for Conditions of Players in Team Sports Haruka Kondo (Kyushu Inst. of Tech.), Masaki Iwasaaki (BraTech Co., Ltd.), Hirohisa Isogai (BAS Lab.), Tetsuo Furukawa, Keiichi Horio (Kyushu Inst. of Tech.) SIS2018-26 |
In this study, an estimation of an optimal psychological state for each player is achieved to improve the performances o... [more] |
SIS2018-26 pp.25-29 |
CQ, ICM, NS, NV (Joint) |
2018-11-16 09:15 |
Ishikawa |
|
Development of System to Analyze Advanced Attacks Using Self-Organizing Map Akifumi Iwasa, Hikohmi Suzuki (Shinshu Univ.), Tetsuya Ui (NEC) NS2018-140 |
In recent years, the importance of the Internet is increasing. However, DoS / DDoS attacks is increasing. It is difficul... [more] |
NS2018-140 pp.57-61 |
NC, IPSJ-BIO, IBISML, IPSJ-MPS [detail] |
2017-06-23 17:40 |
Okinawa |
Okinawa Institute of Science and Technology |
Kansei analysis of landscape images by Tensor SOM
-- Simultaneous analysis of evaluators, subjects, and evaluation words -- Kyouhei Itonaga (Kyutech), Tohru Iwasaki (Colorcle), Kaori Yoshida, Tetsuo Furukawa (Kyutech) NC2017-12 |
In the field of Kansei evaluation, it is investigated and analyzed by using evaluation words with various subjects and o... [more] |
NC2017-12 pp.45-50 |
SIS |
2017-03-03 11:40 |
Kanagawa |
Kanagawa Inst. Tech. Yokohama Office |
Hardware implementation of deep neural networks composed of self-organizing maps Yuichiro Tanaka, Hakaru Tamukoh (KIT) SIS2016-60 |
In this research, we aim to implement deep neural networks (DNNs) composed of self-organizing maps into field programmab... [more] |
SIS2016-60 pp.101-106 |
NLP |
2016-12-12 15:25 |
Aichi |
Chukyo Univ. |
Visualization and Classification by ElasticSOM Yuto Take, Pitoyo Hartono (Chukyo Univ.) NLP2016-89 |
Due to its simplicity, Self-Organizing Maps(SOM) are often utilized to visualize high dimensional data. While SOM is abl... [more] |
NLP2016-89 pp.27-32 |
MI |
2016-07-26 14:55 |
Hokkaido |
Tomakomai Civic Hall |
A Method for Mapping Organ Volume Model with Internal Structure onto Target Volume Shoko Miyauchi, Ken'ichi Morooka (Kyushu Univ.), Tokuo Tsuji (Kanazawa Univ.), Yasushi Miyagi (Fukuoka Mirai Hosp.), Takaichi Fukuda (Kumamoto Univ.), Ryo Kurazume (Kyushu Univ.) MI2016-45 |
This paper proposes a new method for mapping a volume model of an organ with internal structures to a target volume with... [more] |
MI2016-45 pp.49-54 |
MI |
2016-01-20 13:50 |
Okinawa |
Bunka Tenbusu Kan |
Tissue Volume Model Mapping onto Target Volume Based on modified Self-organizing Deformable Model Shoko Miyauchi, Ken'ichi Morooka, Tokuo Tsuji (Kyushu Univ.), Yasushi Miyagi (Kaizuka hosp.), Takaichi Fukuda (Kumamoto Univ.), Ryo Kurazume (Kyushu Univ.) MI2015-135 |
This paper proposes a new method for mapping volume models of human tissues onto a target volume with simple shape. The ... [more] |
MI2015-135 pp.305-308 |
NC, MBE |
2015-03-16 13:35 |
Tokyo |
Tamagawa University |
Window Size Optimization in a Millimeter-wave Active Imaging System Using Complex-Valued Self-Organizing Map Yuya Arima, Akira Hirose (Univ. Tokyo) MBE2014-164 NC2014-115 |
Millimeter-wave possesses high penetrability and high directivity. In security imaging, we use these features to discove... [more] |
MBE2014-164 NC2014-115 pp.265-270 |
MW, ED |
2015-01-15 14:15 |
Tokyo |
Kikai-Shinko-Kaikan Bldg. |
Millimeter-wave Active Imaging Using Multiple Frequencies with Complex-Valued Self-Organizing Map Image Processing Yuya Arima, Akira Hirose (Univ. Tokyo) ED2014-118 MW2014-182 |
Millimeter-wave possesses high penetrability and high directivity. In security imaging, we use these features to discove... [more] |
ED2014-118 MW2014-182 pp.7-11 |
SIS, IPSJ-AVM |
2014-09-11 14:50 |
Yamagata |
SHONAI INDUSTRIAL PROMOTION CENTER |
Smoothed Data Density Histogram on Self-Organizing Map and Its Application to Cluster Analysis Yuriko Tsunoda, Kouichiro Hayashi, Hideaki Kawano, Hiroshi Maeda (Kyutech) SIS2014-57 |
This paper proposes an automatic cluster analysis method using self-organizing feature maps (SOM). SOM-based cluster ana... [more] |
SIS2014-57 pp.45-50 |
NC, MBE (Joint) |
2014-03-17 15:40 |
Tokyo |
Tamagawa University |
Analysis of the MovieLens dataset using Tensor SOM Yosuke Date, Yasuhiro Wakita, Toru Iwasaki, Tetsuo Furukawa (Kyushu Inst. of Tech.) NC2013-99 |
Tensor SOM is an extension of Self-Organizing Map (SOM) from vectorial data to tensorial ones. The aim of this paper is ... [more] |
NC2013-99 pp.63-68 |
SIP, CAS, MSS, VLD |
2013-07-11 18:00 |
Kumamoto |
Kumamoto Univ. |
SOM Based FPGA Placement Method Considering Wire Segment Length Tetsuro Hamada, Motoki Amagasaki, Masahiro Iida, Morihiro Kuga, Toshinori Sueyoshi (Kumamoto Univ.) CAS2013-16 VLD2013-26 SIP2013-46 MSS2013-16 |
A placement process is one of the heavily computational process in FPGA(Field Programmable Gate Array) design flow.
Al... [more] |
CAS2013-16 VLD2013-26 SIP2013-46 MSS2013-16 pp.83-88 |