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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NS, RCS (Joint) |
2023-12-15 15:15 |
Fukuoka | Kyushu Institute of Technology Tobata campus, and Online (Primary: On-site, Secondary: Online) |
Investigation on Shortening the Time to Fix Transmission Timing in Wireless Sensor Networks Using Reinforcement Learning Kureha Ikeda, Yasushi Fuwa, David Asano (Shinshu Univ.)  NS2023-149 |
This study proposes a novel approach to optimizing transmission timing in Wireless Sensor Networks (WSNs) by applying re... [more] | NS2023-149 pp.132-137 |
| HCGSYMPO (2nd) |
2023-12-11 - 2023-12-13 |
Fukuoka | Asia pacific Import Mart (Kitakyushu) (Primary: On-site, Secondary: Online) |
Transition and analysis by mutual learning within a group in incomplete information game in " Hol's der Geier" Shintaro Abe, Kazuki Takahashi, Takashi Takekawa (Kogakuin Univ) |
In perfect information games, AI learned through self-play and achieved high performance. In incomplete information game... [more] | |
| NC, MBE (Joint) |
2023-11-27 10:30 |
Osaka | Kindai Univ. (Primary: On-site, Secondary: Online) |
Improving the reproduction of animal intelligence using reinforcement learning with World Model Takumi Fukaya, Hirokazu Tanaka (Tokyo City Univ.) NC2023-34 |
One way to evaluate artificial intelligence models that reproduce animal intelligence is to have reinforcement learning ... [more] | NC2023-34 pp.6-9 |
| PN | 2023-11-16 11:00 |
Ehime | (Primary: On-site, Secondary: Online) |
A Reinforcement Learning-Based Multilayer Path Planning Method that Adapts Various Path Requirements Takafumi Tanaka (NTT) PN2023-38 |
There has been renewed interest in multilayer networks that integrate layers to realize high-capacity communications dir... [more] | PN2023-38 pp.9-14 |
| MSS, CAS, IPSJ-AL [detail] | 2023-11-16 16:30 |
Okinawa | Deep Reinforcement Learning for Multi-Agent Systems with Temporal Logic Specifications Keita Terashima, Koichi Kobayashi, Yuh Yamashita (Hokkaido Univ.) CAS2023-70 MSS2023-40 |
In multi-agent systems, the challenge is how a group of agents collaborate to achieve a common goal. In our previous wor... [more] | CAS2023-70 MSS2023-40 pp.54-58 |
|
| RISING (3rd) |
2023-10-31 13:00 |
Hokkaido | Kaderu 2・7 (Sapporo) | [Poster Presentation]
Wireless MAC Protocol Adaptation Method Considering Application Layer Koshiro Aruga, Takeo Fujii (UEC) |
In recent years, with the development of the Internet of Things (IoT), the number of devices performing wireless communi... [more] | |
| RISING (3rd) |
2023-10-31 13:00 |
Hokkaido | Kaderu 2・7 (Sapporo) | [Poster Presentation]
Blind center frequency estimation using deep reinforcement learning for modulation scheme identification. Shunsuke Uehashi, Yasutaka Yamashita, Mari Ochiai (Mitsubishi Electric Corp.) |
Identification of modulation schemes in wireless signals is a crucial technology for analyzing the status of wireless co... [more] | |
| NC, MBE (Joint) |
2023-10-27 13:30 |
Miyagi | Tohoku Univ. (Primary: On-site, Secondary: Online) |
Significance of single cell recording
-- Reverse engineering from supplementary motor cortex neuronal activity to reinforcement learning model -- Nao Matsumoto, Naoki M. Tamura, Hajime Mushiake (Tohoku Univ. Sch. Med.), Kazuhiro Sakamoto (TMPU) NC2023-25 |
Elucidating the regions of the brain that are active in a given cognitive activity is an important mission in neuroscien... [more] | NC2023-25 pp.1-5 |
| AI | 2023-09-12 15:15 |
Hokkaido | A Study on the Implementation of Cooperative CAVs by Sharing the Observation Information Using Simulations and Considerations Based on Qualitative Evaluation Ken Matsuda (Graduate School of FUN), Ei-Ichi Osawa (FUN) AI2023-4 |
This study focuses on cooperative connected autonomous vehicles (cooperative CAVs). This research aims to propose a simu... [more] | AI2023-4 pp.19-24 |
|
| AI | 2023-09-12 14:55 |
Hokkaido | Event-Driven Reinforcement Learning with Semi Markov Models for Stable Air-Conditioning Control Hayato Chujo, Arai Sachiyo (Chiba Univ) AI2023-16 |
This study deals with air conditioning control that optimizes room temperature by switching heaters on/off. The control ... [more] | AI2023-16 pp.83-86 |
|
| AP | 2023-08-31 13:25 |
Tokyo | KOZO KEIKAKU ENGINEERING Inc. (Primary: On-site, Secondary: Online) |
2-layer Joint Interference Coordination for A Cellular System with Cluster-wise Distributed MU-MIMO Chang Ge, Sijie Xia, Qiang Chen, Fumiyuki Adachi (Tohoku Univ.) AP2023-70 |
In a cellular system with distributed MU-MIMO, virtual small cells (called the user-clusters) are formed to reduce the h... [more] | AP2023-70 pp.21-24 |
| CCS, IN (Joint) |
2023-08-03 11:09 |
Hokkaido | Banya-no-yu | Reinforcement learning-based control of CWmin and Carrier Sense Threshold for IEEE 802.11 WLAN. Yuto Higashiyama, Kosuke Sanada, Hiroyuki Hatano, Kazuo Mori (Mie Univ.) CCS2023-20 |
Distribute Coordination Function (DCF) is a basic channel access protocol in IEEE 802.11 Wireless Local Area Networks (W... [more] | CCS2023-20 pp.19-24 |
| SeMI, RCS, RCC, NS, SR (Joint) |
2023-07-13 16:25 |
Osaka | Osaka University Nakanoshima Center + Online (Primary: On-site, Secondary: Online) |
[Short Paper]
A study of Cross-Layer Adaptation using Learning in Wireless MAC Protocols Koshiro Aruga, Takeo Fujii (UEC) SR2023-40 |
In recent years, with the development of wireless communication technology, networks have become larger and more complex... [more] | SR2023-40 pp.55-57 |
| SRW | 2023-06-12 14:25 |
Tokyo | Kikai-Shinko-Kaikan Bldg. (Primary: On-site, Secondary: Online) |
[Invited Lecture]
An Analog Beamforming Control Method using Deep Reinforcement Learning Daisuke Sasaki, Hang Zhou, Xiaoyan Wang (Ibaraki Univ.), Masahiro Umehira (Nanzan Univ.) SRW2023-8 |
As the development of small cell configurations in B5G networks, the frequency utilization efficiency could be significa... [more] | SRW2023-8 pp.39-44 |
| CS, CQ (Joint) |
2023-05-18 15:10 |
Kagawa | Rexxam Hall (Kagawa Kenmin Hall) (Primary: On-site, Secondary: Online) |
On the performance of sorting out invalid jobs in scheduling using the policy gradient method for deadline-aware jobs Tatusya Sagisaka, Kohei Shiomoto (TCU), Takashi Kurimoto (NII) CQ2023-1 |
When transferring data in the field of communication between data centers, existing methods such as Earliest Deadline Fi... [more] | CQ2023-1 pp.1-6 |
| DC, CPSY, IPSJ-SLDM, IPSJ-EMB, IPSJ-ARC [detail] | 2023-03-24 14:30 |
Kagoshima | Amagi Town Disaster Prevention Center (Tokunoshima) (Primary: On-site, Secondary: Online) |
A study of reinforcemtent learning-based AGV route scheduling using local graph information Hirotada Sugimoto, Shaswot Shresthamali, Masaaki Kondo (Keio Univ.) CPSY2022-49 DC2022-108 |
In this paper, we propose a reinforcement learning-based route planning method for multiple AGVs. The proposed scheduli... [more] | CPSY2022-49 DC2022-108 pp.89-94 |
| ICM | 2023-03-17 09:10 |
Okinawa | Okinawa Prefectural Museum and Art Museum (Primary: On-site, Secondary: Online) |
Automation of human decision making by using reinforcement-learning for office work with PC Misa Fukai, Masashi Tadokoro, Haruo Oishi (NTT) ICM2022-50 |
AI technology is a key which improve diversified and complex business with saving personnel. However, the technologies c... [more] | ICM2022-50 pp.31-36 |
| NLP, MSS | 2023-03-17 16:25 |
Nagasaki | (Primary: On-site, Secondary: Online) |
Investigation on improving diversity of options in option-critic reinforcement learning Aya Nakagawa, Hidehiro Nakano (Tokyo City Univ.) MSS2022-109 NLP2022-154 |
Recently, reinforcement learning has been attracting attention in various fields such as automatic control and game AI. ... [more] | MSS2022-109 NLP2022-154 pp.225-230 |
| IMQ, IE, MVE, CQ (Joint) [detail] |
2023-03-16 16:05 |
Okinawa | Okinawaken Seinenkaikan (Naha-shi) (Primary: On-site, Secondary: Online) |
Automated Driving Methods Using Federated Learning Koki Ono, Celimuge Wu, Tsutomu Yoshinaga (UEC) CQ2022-99 |
When learning autonomous driving behavior using machine learning, a huge amount of driving data is required, and a large... [more] | CQ2022-99 pp.96-101 |
| RCC, ISEC, IT, WBS | 2023-03-14 13:00 |
Yamaguchi | (Primary: On-site, Secondary: Online) |
On Reward Distribution in Reinforcement Learning of Multi-Agent Surveillance Systems with Temporal Logic Specifications Keita Terashima, Koichi Kobayashi, Yuh Yamashita (Hokkaido Univ.) IT2022-81 ISEC2022-60 WBS2022-78 RCC2022-78 |
In multi-agent systems, it is important to design a reward distribution method based on the contribution of agents for e... [more] | IT2022-81 ISEC2022-60 WBS2022-78 RCC2022-78 pp.86-90 |
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