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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NS, IN (Joint) |
2024-03-01 11:10 |
Okinawa | Okinawa Convention Center | Blockchain-based malicious node detection and defense method for potential-based routing Kanato Otsu (Osaka Univ.), Naomi Kuze (Wakayama Univ.)  NS2023-200 |
In recent years, the scale and complexity in networks have grown such as the Internet of Things (IoT). For controlling... [more] |
NS2023-200 pp.166-171 |
| 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 |
| ICM, NS, CQ, NV (Joint) |
2023-11-21 09:30 |
Ehime | Ehime Prefecture Gender Equality Center (Primary: On-site, Secondary: Online) |
Fallback control based false injection attack defense mechanism for managed potential-based routing Tasuku Nagata, Naomi Kuze (Osaka Univ.) NS2023-109 |
Due to the rapid growth of information networks, self-organization is a promising approach for controlling network syst... [more] |
NS2023-109 pp.1-6 |
| NC, MBE (Joint) |
2023-03-15 10:55 |
Tokyo | The Univ. of Electro-Communications (Primary: On-site, Secondary: Online) |
Proposal for Mini-Batch Learning in Clustering V-SOINN Tetsuya Komura, Rintaro Funada, Yukari Yamauchi (Nihon Univ.) NC2022-111 |
Yamazaki et al. proposed a learning method called Self-Organizing Incremental Neural Network (SOINN). This method is an ... [more] | NC2022-111 pp.109-112 |
| NC, MBE (Joint) |
2023-03-15 11:20 |
Tokyo | The Univ. of Electro-Communications (Primary: On-site, Secondary: Online) |
Optimizing SOINN Space for High-Dimensional Data Robustness Yu Takahagi, Yusuke Tsuchida, Yukari Yamauchi (Nihon Univ.) NC2022-112 |
Yamazaki et al. proposed a learning method called Self-Organizing Incremental Neural Network (SOINN). This method is an ... [more] | NC2022-112 pp.113-118 |
| 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] | |
| MBE, NC (Joint) |
2022-03-02 09:30 |
Online | Online | A Study on Improvement of Recognition Accuracy and Speed-up of SOM-based Classification System Shun Tasaka, Hiroomi Hikawa (Kansai Univ.) NC2021-46 |
This paper discusses a new type of image classifier called class-SOM, which is based on self-organizing map (SOM). The... [more] |
NC2021-46 pp.1-4 |
| MBE, NC (Joint) |
2022-03-02 15:45 |
Online | Online | NC2021-57 |
We propose a polarimetric remote sensing system to classify daily movements of humans such as walking and standing. We e... [more] | NC2021-57 pp.56-61 |
| IBISML | 2022-01-18 15:20 |
Online | Online | Determining the number of clusters using the shrinking maximum likelihood self-organizing map Ryosuke Motegi, Yoichi Seki (Gunma Univ.) IBISML2021-29 |
Determining the number of clusters is one of the major challenges in clustering. The conventional method, such as the Ex... [more] | IBISML2021-29 pp.81-87 |
| SIS | 2021-03-05 10:50 |
Online | Online | A trial of quantitative evaluation focused on area change in self-organizing map Yuto Nakashima, Hiroshi Wakuya (Saga Univ.), Fukuko Moriya (Kurume Univ.), Kaoru Araki, Hideaki Itoh (Saga Univ.) SIS2020-56 |
A self-organizing map (SOM) is one of the AI techniques to visualize an applied multi-dimensional data set onto the two-... [more] | SIS2020-56 pp.114-119 |
| NC, MBE (Joint) |
2021-03-04 16:25 |
Online | Online | Hierarchical Feature Extraction for Dynamic Q-Network Taishi Komatsu, Yukari Yamauchi (Nihon Univ.) NC2020-62 |
Recently, Convolutional Neural Networks (CNN), which have been successful in the field of image recognition, use a hiera... [more] | NC2020-62 pp.112-116 |
| 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 |
| NLP | 2020-05-15 11:25 |
Online | Online | Facial Expression Recognition by a Neural Network Inspired from Processing between the Visual Cortex and Amygdala Daiki Yoshihara, Toshikazu Samura (Yamaguchi Univ.) NLP2020-2 |
Facial expressions are important to communication. The visual cortex and amygdala are involved in the recognition of fac... [more] | NLP2020-2 pp.7-10 |
| 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 |
| NC, MBE (Joint) |
2020-03-06 10:20 |
Tokyo | University of Electro Communications (Cancelled but technical report was issued) |
Feature Extraction by Competitive Learning for Dynamic Q-Network Taishi Komatsu, Yukari Yamauchi (NU) NC2019-106 |
Deep Q-Network is a reinforcement learning algorithm that performs feature extraction by convolution from state space in... [more] | NC2019-106 pp.175-179 |
| NC, MBE (Joint) |
2020-03-06 14:55 |
Tokyo | University of Electro Communications (Cancelled but technical report was issued) |
Efficient cluster mapping for conditions of weather based on combination of self-organizing map and hierarchical clustering Kazuki Osawa, Keiji Kamei (NIT), Masumi Ishikawa (KIT) NC2019-113 |
Recently, applications of Deep Learning(AI) for solving social problems have been frequently proposed. However, there ar... [more] | NC2019-113 pp.213-218 |
| EMT, IEE-EMT | 2019-11-07 15:15 |
Saga | Hotel Syunkeiya | Land classification using unsupervised quaternion neural network with neighbor pixel information Jungmin Song, Ryo Natusaki, Akira Hirose (The Univ. of Tokyo) EMT2019-57 |
(To be available after the conference date) [more] | EMT2019-57 pp.117-122 |
| 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 |
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