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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NLP, CCS | 2024-06-06 09:30 |
Fukuoka | West Japan General Exhibition Center AIM | Tug-of-war algorithm for collective decision making with a laser network Shun Kotoku, Takatomo Mihana, Andre Roehm, Ryoichi Horisaki (UTokyo) |
(To be available after the conference date) [more] | |
| IN, RCS, NV (Joint) |
2024-05-31 14:15 |
Fukuoka | Fukuoka University | A Multi-Agent Reinforcement Learning Deployment Method for Multiple UAVs Jin Nakazato (UTOKYO), Gia Khanh Tran (TokyoTech), Katsuya Suto (UEC)  RCS2024-25 |
(To be available after the conference date) [more] | RCS2024-25 pp.45-50 |
| EA | 2024-05-22 14:15 |
Online | Online | 未定
-- 未定 -- Tsubasa Ochiai (NTT), Kazuma Iwamoto (Doshisha Univ.), Marc Delcroix, Rintaro Ikeshita, Hiroshi Sato, Shoko Araki (NTT), Shigeru Katagiri (Doshisha Univ.) EA2024-4 |
Deep learning techniques have dramatically improved the speech enhancement (SE) performance of single-channel SE. Howeve... [more] | EA2024-4 pp.20-21 |
| EA | 2024-05-22 16:50 |
Online | Online | [Invited Talk]
Fundamentals of Diffusion-based Generative Models and their Application to Speech Enhancement and Separation Scheibler Robin (LY Corp.) EA2024-9 |
Diffusion models are a class of generative models that operate in an iterative manner, progressively transforming noise ... [more] | EA2024-9 p.38 |
| CQ, CS (Joint) |
2024-05-16 14:55 |
Aichi | (Primary: On-site, Secondary: Online) |
Force Adjustment Control in Cooperative Work between Remote Robot Systems with Force Feedback
-- Application of Reinforcement Learning -- Hitoshi Ohnishi (OUJ), Hiroya Kato, Yutaka Ishibashi (Nagoya Institute of Technology), Pingguo Huang (Gifu Shotoku Gakuen Univ.) CQ2024-7 |
In this study, two systems each of which remotely controls an industrial robot arm using a haptic interface device that... [more] | CQ2024-7 pp.24-29 |
| NS | 2024-05-09 14:15 |
Mie | Sinfonia Technology Hibiki Hall Ise (Primary: On-site, Secondary: Online) |
[Encouragement Talk]
Improvement on the Dueling Networks Architecture for GNN in the Deep Reinforcement Learning for the Automated ICT System Design Tianchen Zhou (Sophia Univ.), Yutaka Yakuwa (NEC), Natsuki Okamura, Hiroyuki Hochigai (Sophia Univ.), Takayuki Kuroda (NEC), Ikuko E. Yairi (Sophia Univ.) NS2024-16 |
We have been addressing the challenge of low learning efficiency in automated ICT system design with reinforcement learn... [more] | NS2024-16 pp.17-22 |
| SIS | 2024-03-14 13:00 |
Kanagawa | Kanagawa Institute of Technology (Primary: On-site, Secondary: Online) |
On Time-Position Detection of Signals under Noise Considering Threshold
-- Applications of Fractal Dimension Filters -- Hideo Shibayama (Shibaura Institute of Technology), Yoshiaki Makabe (Kanagawa Institute of Technology), Kenji Muto (Shibaura Institute of Technology), Tomoaki Kimura (Kanagawa Institute of Technology) SIS2023-45 |
Conflicts due to neighborhood noise can occur even when the sound pressure level is low. In such cases, the sound pressu... [more] | SIS2023-45 pp.1-6 |
| CAS, CS | 2024-03-14 16:20 |
Okinawa | An Approximate Solution Using K-Shortest Path and Reinforcement Learning for a Load Balancing Problem in Communication Networks Himeno Takahashi, Norihiko Shinomiya (Soka Univ.) CAS2023-123 CS2023-116 |
In recent years, the amount of data traffic in information and communication networks has been increasing and the risk o... [more] | CAS2023-123 CS2023-116 pp.70-73 |
|
| KBSE | 2024-03-14 15:40 |
Okinawa | Okinawa Prefectual General Welfare Center (Primary: On-site, Secondary: Online) |
An approach for improving perceived safety in autonomous driving using personalized shielding Ryotaro Abe, Jialong Li, Jinyu Cai (Waseda Univ.), Shinichi Honiden (NII), Kenji Tei (Tokyo Tech) KBSE2023-76 |
This research introduces an innovative Reinforcement Learning (RL) approach tailored for autonomous driving systems, ter... [more] | KBSE2023-76 pp.67-72 |
| SIS | 2024-03-15 10:20 |
Kanagawa | Kanagawa Institute of Technology (Primary: On-site, Secondary: Online) |
Enhancement of Hazy Image Using Convex Combination Coefficients of White, Black, and Pure Color Mashiho Mukaida, Haruhito Suzuki (Yamaguchi Univ.), Takanori Koga (Kindai Univ.), Noriaki Suetake (Yamaguchi Univ.) SIS2023-56 |
Many enhancement methods for the visibility improvement of hazy image have been proposed. However, the conventional meth... [more] | SIS2023-56 pp.61-66 |
| SIS | 2024-03-15 12:20 |
Kanagawa | Kanagawa Institute of Technology (Primary: On-site, Secondary: Online) |
Extension of decision transformer model for controlling future reward and motion control Taisei Inada, Shigeru Kubota (Yamagata Univ.) SIS2023-58 |
In the field of control, reinforcement learning is sometimes required to dynamically adjust the speed of automatic drivi... [more] | SIS2023-58 pp.73-76 |
| IE, MVE, CQ, IMQ (Joint) [detail] |
2024-03-14 10:30 |
Okinawa | Okinawa Sangyo Shien Center (Primary: On-site, Secondary: Online) |
Simulation of the electric field in the oral cavity during modulation of taste intensity by extraoral electrical stimulation So Tanaka, Takuji Narumi, Tomohiro Amemiya, Hideaki Kuzuoka (UTokyo), Kazuma Aoyama (GU) IMQ2023-46 IE2023-101 MVE2023-75 |
It is known that the enhancement and suppression of the sense of taste can be caused by electrical stimulation of the or... [more] | IMQ2023-46 IE2023-101 MVE2023-75 pp.182-187 |
| RCS, SR, SRW (Joint) |
2024-03-15 16:15 |
Tokyo | The University of Tokyo (Hongo Campus), and online (Primary: On-site, Secondary: Online) |
Study on Small Cell ON/OFF Control Using Different Frequency Cell Information Takaharu Kobayashi, Takashi Dateki (Fujitsu) RCS2023-292 |
In this paper, we propose small cell ON/OFF control without using UE position information and information on the proximi... [more] | RCS2023-292 pp.176-181 |
| IE, MVE, CQ, IMQ (Joint) [detail] |
2024-03-15 13:10 |
Okinawa | Okinawa Sangyo Shien Center (Primary: On-site, Secondary: Online) |
A Voting-based Solution to Base Station Placement Problem Ryo Nakamura, Ryotaro Matsuo (Fukuoka Univ.), Toshiro Nakahira, Daisuke Murayama, Tomoaki Ogawa (NTT) CQ2023-85 |
With the rapid increase in wireless devices and the diversification of frequency band, the wireless network has become m... [more] | CQ2023-85 pp.75-80 |
| IE, MVE, CQ, IMQ (Joint) [detail] |
2024-03-15 13:50 |
Okinawa | Okinawa Sangyo Shien Center (Primary: On-site, Secondary: Online) |
IMQ2023-87 IE2023-142 MVE2023-116 |
This paper introduces physics-inspired synthesized underwater image dataset (PHISWID). Deep learning approaches to unde... [more] |
IMQ2023-87 IE2023-142 MVE2023-116 pp.396-401 |
| PRMU, IBISML, IPSJ-CVIM | 2024-03-03 17:00 |
Hiroshima | Hiroshima Univ. Higashi-Hiroshima campus (Primary: On-site, Secondary: Online) |
Multi-agent reinforcement learning based control method for large-scale crowd movement on Mojiko Fireworks Festival dataset Kazuya Miyazaki, Masato Kiyama, Motoki Amagasaki, Toshiaki Okamoto (Kumamoto Univ.) IBISML2023-45 |
The importance of human flow guidance is increasing in response to accidents at events. In recent years, some research h... [more] | IBISML2023-45 pp.36-43 |
| AI | 2024-03-01 13:40 |
Aichi | Room0221, Bldg.2-C, Nagoya Institute of Technology | Applying Graph Neural Networks and Reinforcement Learning to the Multiple Depot-Multiple Traveling Salesman Problem Dongyeop Kim, Toshihiro Matsui (NITech) AI2023-39 |
In this study, we introduce a method combining Graph Neural Networks (GNN) and reinforcement learning for the Multiple D... [more] | AI2023-39 pp.13-18 |
| AI | 2024-03-01 15:00 |
Aichi | Room0221, Bldg.2-C, Nagoya Institute of Technology | Performance Improvement for Mobile Edge Computing with Multi-Agent Deep Reinforcement Learning Kohei Suzuki, Toshiharu Sugawara (Waseda Univ.) AI2023-42 |
In this paper, we propose a method for mobile edge computing using unmanned aerial vehicles (UAVs) to improve both the n... [more] | AI2023-42 pp.31-36 |
| SIP, SP, EA, IPSJ-SLP [detail] | 2024-02-29 16:20 |
Okinawa | (Primary: On-site, Secondary: Online) |
EA2023-77 SIP2023-124 SP2023-59 |
In this paper, we consider a dynamic sensor placement problem where sensors can move within a network over time. Sensor ... [more] | EA2023-77 SIP2023-124 SP2023-59 pp.97-102 |
| SIP, SP, EA, IPSJ-SLP [detail] | 2024-03-01 09:30 |
Okinawa | (Primary: On-site, Secondary: Online) |
Improving training recipe of Remixed2Remixed for speech enhancement Li Li, Shogo Seki (CyberAgent) EA2023-95 SIP2023-142 SP2023-77 |
In the use of deep learning for speech enhancement, supervised learning models that use pairs of clean speech and artifi... [more] | EA2023-95 SIP2023-142 SP2023-77 pp.202-207 |
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