| IEICE Technical Committee Submission System Conference Schedule |
Online Proceedings [Sign in] Tech. Rep. Archives |
All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NLP | 2023-11-28 11:15 |
Okinawa | Nago city commerce and industry association | Dynamics of Reservoir in Echo State Network Shion Yoshida, Tohru Ikeguchi (TUS)  NLP2023-62 |
Reservoir computing is one of the frameworks for machine learning for fast and highly accurate analysis of time series a... [more] | NLP2023-62 pp.15-20 |
| NC, MBE (Joint) |
2023-10-27 14:45 |
Miyagi | Tohoku Univ. (Primary: On-site, Secondary: Online) |
Adaptive motion generation for a redundant robot arm using an echo state network Hiroshi Atsuta, Yuji Kawai (Osaka Univ.), Minoru Asada (IPUT/Osaka Univ./Chubu Univ./NICT) NC2023-28 |
Teaching playback is a convenient method to instruct robots how to move. However, this method has an issue of excessive ... [more] | NC2023-28 pp.17-22 |
| CCS, NLP | 2023-06-09 13:55 |
Tokyo | Tokyo City Univ. | Analysis of Vocal and Ventricular Folds Data Using Machine Learning Takumi Inoue, Kota Shiozawa, Isao Tokuda (Rits Univ) NLP2023-24 CCS2023-12 |
Vocal fold vibration is a nonlinear phenomenon in the real world. In humans, vocal folds can produce complex sounds by i... [more] | NLP2023-24 CCS2023-12 pp.49-52 |
| CCS, NLP | 2023-06-09 15:00 |
Tokyo | Tokyo City Univ. | Bifurcation diagram reconstruction using trace norm regularization Kota Shiozawa, Isao Tokuda (Ritsumeikan Univ.) NLP2023-26 CCS2023-14 |
A method for reconstructing the bifurcation diagram using time series is introduced. In this method, we employ two machi... [more] | NLP2023-26 CCS2023-14 pp.57-60 |
| NLP, MSS | 2023-03-17 16:45 |
Nagasaki | (Primary: On-site, Secondary: Online) |
Enhancement of functionality in Deep Echo State Network by optimizing leak rate Shuichi Inoue, Sou Nobukawa (CIT), Haruhiko Nishimura (UOH), Eiji Watanabe (NIBB), Teijiro Isokawa (UOH) MSS2022-110 NLP2022-155 |
Deep echo state network (Deep-ESN) model consists of multiple reservoir layers, which can respond on layer-specific diff... [more] | MSS2022-110 NLP2022-155 pp.231-236 |
| NC, NLP | 2023-01-29 15:55 |
Hokkaido | Future University Hakodate (Primary: On-site, Secondary: Online) |
Indoor air quality prediction using multi-reservoir echo state network with attention mechanism Wenrui Qiu, Gouhei Tanaka (UTokyo) NLP2022-106 NC2022-90 |
Indoor air quality (IAQ) is a critical matter of concern in terms of its impact on public health and well-being. Researc... [more] | NLP2022-106 NC2022-90 pp.135-140 |
| NLP | 2022-11-25 11:10 |
Shiga | (Primary: On-site, Secondary: Online) |
[Invited Talk]
Chaotic time series and Ueda's theory of chaos Takaya Miyano (Ritsumeikan Univ.) NLP2022-72 |
In terms of Ueda’s theory of chaos, i.e., the concept of randomly transitional oscillations, we discuss the implications... [more] | NLP2022-72 pp.71-72 |
| MBE, NC (Joint) |
2022-03-02 09:55 |
Online | Online | NC2021-47 |
In this paper, we propose reconstructive reservoir computing (RRC), which can detect anomaly in time-series signals. In ... [more] | NC2021-47 pp.5-10 |
| NLP | 2021-11-19 15:20 |
Hiroshima | Feature extraction from speech data using leaky echo state network Naoto Kikukawa, Koshiro Onuki, Takaya Miyano (Ritsumeikan Univ.) NLP2021-40 |
(To be available after the conference date) [more] | NLP2021-40 pp.23-26 |
|
| CCS | 2021-11-19 13:00 |
Osaka | Osaka Univ. (Primary: On-site, Secondary: Online) |
Physical reservoir computing on analog-digital hybrid circuit systems consisting of discrete semiconductor devices Yuki Abe, Kose Yoshida (Hokkaido Univ), Megumi Akai-Kasaya (Hokkaido Univ/Osaka Univ), Tetsuya Asai (Hokkaido Univ) CCS2021-30 |
This report describes machine learning process & benchmarks by using physical reservoir computing device. We design phys... [more] | CCS2021-30 pp.73-78 |
| MBE, NC (Joint) |
2021-10-28 14:20 |
Online | Online | A Study on Affective BCI Using Reservoir Computing and Fractal Analysis Yuuma Matsuda, Masahiro Nakagawa (NUT) MBE2021-22 |
Today, the Brain Computer Interface (BCI) using EEG for quantification of sensitivity have been studying, and the deep l... [more] | MBE2021-22 pp.26-31 |
| SDM, ICD, ITE-IST [detail] | 2021-08-17 11:45 |
Online | Online | Approximation of Non-Linear Function for Hardware Implementation of Echo-State-Network Amartuvshin Bayasgalan, Makoto Ikeda (UTokyo) SDM2021-32 ICD2021-3 |
Reservoir computing (RC) is a machine-learning algorithm that can learn complex temporal signals while presenting a fast... [more] | SDM2021-32 ICD2021-3 pp.12-17 |
| CCS | 2020-03-26 11:25 |
Tokyo | Hosei Univ. Ichigaya Campus (Cancelled but technical report was issued) |
Reservoir computing using fluid motion Keita Kohashi, Masanobu Inubushi, Susumu Goto (Osaka Univ.) CCS2019-40 |
Reservoir computing (RC) is a machine learning method using nonlinear dynamical systems, which is effective for time-ser... [more] | CCS2019-40 pp.25-27 |
| HWS, VLD [detail] | 2020-03-05 14:55 |
Okinawa | Okinawa Ken Seinen Kaikan (Cancelled but technical report was issued) |
[Memorial Lecture]
A Tuning-Free Hardware Reservoir Based on MOSFET Crossbar Array for Practical Echo State Network Implementation Yuki Kume, Song Bian, Takashi Sato (Kyoto Univ.) VLD2019-118 HWS2019-91 |
Echo state network (ESN) is a class of recurrent neural network, and is known for drastically reducing the training time... [more] | VLD2019-118 HWS2019-91 pp.139-144 |
| HWS, VLD [detail] | 2020-03-06 16:50 |
Okinawa | Okinawa Ken Seinen Kaikan (Cancelled but technical report was issued) |
Performance Evaluation of Echo State Networks with Hardware Reservoirs Yuki Kume, Song Bian, Kenta Nagura, Takashi Sato (Kyoto Univ.) VLD2019-136 HWS2019-109 |
Echo state Network (ESN), a class of recurrent neural network, is characteristic in its use of a reservoir having random... [more] | VLD2019-136 HWS2019-109 pp.245-250 |
| CCS, IN (Joint) |
2019-08-02 14:20 |
Hokkaido | KIKI SHIRETOKO NATURAL RESORT | Effect of shapes of activation functions on predictability in the echo state network Hanten Chang (Univ. of Tsukuba), Shinji Nakaoka (Hokkaido Univ.), Hiroyasu Ando (Univ. of Tsukuba) CCS2019-23 |
We investigate prediction accuracy for time series of Echo state networks with respect to several kinds of activation fu... [more] | CCS2019-23 pp.27-30 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Nonlinear Time Series Prediction using Multi-Step Learning Echo State Networks Takanori Akiyama, Gouhei Tanaka (Tokyo Univ.) IBISML2018-83 |
Reservoir Computing (RC) has recently attracted much attention as brain-like information processing for high-speed learn... [more] | IBISML2018-83 pp.293-299 |
| MBE, NC (Joint) |
2017-10-07 10:55 |
Osaka | Osaka Electro-Communication University | Robust memory capacity in echo state networks with a small-world topology Yuji Kawai, Jihoon Park, Minoru Asada (Osaka Univ.) NC2017-20 |
A small-world (SW) topology was found in the cortical neural connectivity. However, the role of the topology in neural i... [more] | NC2017-20 pp.1-6 |
| MI, MICT | 2016-09-16 16:20 |
Tokyo | Koganei Campus, Tokyo University of Agriculture and Technology | Preliminary study for measuring the state of upper arm muscle by ultrasonic echo and electromyogram Marie Tabaru, Takahiro Aoyagi (Tokyo Tech) MICT2016-46 MI2016-60 |
Body area network (BAN) requires increasing kinds of bio-information to be treated and development of sensors to detect... [more] |
MICT2016-46 MI2016-60 pp.57-61 |
| NC, MBE (Joint) |
2014-03-18 15:40 |
Tokyo | Tamagawa University | A Time-series Processing Neural Network for Natural Language Yukinori Homma, Masafumi Hagiwara (Keio Univ.) NC2013-114 |
This paper proposes a novel time-series processing neural network to treat natural language.The proposed network is comp... [more] | NC2013-114 pp.151-156 |
[Return to Top Page]