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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| CQ, CBE (Joint) |
2024-01-25 16:50 |
Kumamoto | Kurokawa-Onsen (Primary: On-site, Secondary: Online) |
[Invited Talk]
Analysis of global state fluctuations in functional brain networks Makoto Fukushima (Hiroshima Univ.)  CQ2023-59 |
The degree of synchronization of regional brain activities (i.e., functional connectivity) always fluctuates over time, ... [more] | CQ2023-59 p.43 |
| NC, MBE, NLP, MICT (Joint) [detail] |
2024-01-25 14:30 |
Tokushima | Naruto University of Education | Verification of Conditions for Conducting Virtual Reality Live Concerts Attended by 50 People via Electroencephalography Signals by Dynamical System Modeling Yukino Komoda, Ryota Horie (SIT) NLP2023-112 MICT2023-67 MBE2023-58 |
The authors have hypothesized that collective behavior occurs among the EEG features of multiple viewers in a multiple-p... [more] | NLP2023-112 MICT2023-67 MBE2023-58 pp.133-138 |
| CCS, NLP | 2023-06-09 11:10 |
Tokyo | Tokyo City Univ. | A study on learning Koopman operators from synchrophasor data on distribution voltage amplitudes Tadahiro Yano, Yoshihiko Susuki (Kyoto Univ.) NLP2023-21 CCS2023-9 |
In recent years, the so-called micro-Phasor Measurement Unit ($mu$PMU) has become a promising new option for managing AC... [more] | NLP2023-21 CCS2023-9 pp.35-38 |
| NLP, MSS | 2023-03-15 10:40 |
Nagasaki | (Primary: On-site, Secondary: Online) |
Spectral analysis of synchropahsor data in a campus distribution grid: Comparison of numerical methods Munetaka Noguchi (Osaka Prefecture Univ.), Yoshihiko Susuki (Kyoto Univ.), Atsushi Ishigame (Osaka Metropolitan Univ.) MSS2022-64 NLP2022-109 |
Recently, the so-called micro-Phasor Measurement Unit (μPMU) with high-resolution capability has been expected as a new ... [more] | MSS2022-64 NLP2022-109 pp.11-16 |
| NLP, MSS | 2023-03-16 10:00 |
Nagasaki | (Primary: On-site, Secondary: Online) |
Detecting causality for marked point processes Kazuya Sawada (TUS), Yutaka Shimada (Saitama Univ.), Tohru Ikeguchi (TUS) MSS2022-80 NLP2022-125 |
In this report, by modifying conventional causality detection method for nonlinear dynamical systems, we propose a causa... [more] | MSS2022-80 NLP2022-125 pp.89-94 |
| NLP, MSS | 2023-03-16 11:40 |
Nagasaki | (Primary: On-site, Secondary: Online) |
A Study on Properties of Koopman Eigenfunctions for a Planar Singularly-Perturbed Dynamical System Natsuki Katayama, Yoshihiko Susuki (Kyoto Univ.) MSS2022-85 NLP2022-130 |
The Koopman operator is a composition operator for nonlinear dynamical systems. The eigenfunctions of the Koopman operat... [more] | MSS2022-85 NLP2022-130 pp.114-119 |
| NLP, MSS | 2023-03-17 13:10 |
Nagasaki | (Primary: On-site, Secondary: Online) |
Periodic Memory and Learning Chaotic Dynamical Systems in Hysteresis Reservoir Computing Tsukasa Saito, Kenya Jin'no (Tokyo City Univ.) MSS2022-100 NLP2022-145 |
Hysteresis Reservoir Computing, which applies a simple hysteresis network to the reservoir layer of reservoir computing,... [more] | MSS2022-100 NLP2022-145 pp.178-181 |
| NLP | 2022-08-02 11:15 |
Online | Online | [Invited Talk]
Dimensionality reduction of dynamical systems via Koopman operator theory and applications to nonlinear rhythms Hiroya Nakao (Tokyo Tech.) NLP2022-31 |
A method of dimensionality reduction for nonlinear dynamical systems via the Koopman operator theory and its application... [more] | NLP2022-31 pp.24-26 |
| NLP | 2022-08-02 15:30 |
Online | Online | The energy-accuracy trade-off in thresholding Nobumasa Ishida, Yoshihiko Hasegawa (Univ. Tokyo) NLP2022-36 |
In the last decade, stochastic thermodynamics revealed that the accuracy of various information processing, from biologi... [more] | NLP2022-36 pp.39-42 |
| IBISML | 2022-03-08 13:05 |
Online | Online | [Invited Talk]
--- Takashi Matsubara (Osaka Univ.) IBISML2021-34 |
Deep learning is being considered as the most promising approach to building an artificial intelligence (AI) system; it ... [more] | IBISML2021-34 p.27 |
| NLP, MICT, MBE, NC (Joint) [detail] |
2022-01-21 11:45 |
Online | Online | Physical deep learning based on optimal control of dynamical systems Satoshi Sunada, Genki Furuhata, Tomoaki Niiyama (Kanazawa Univ.) NLP2021-79 MICT2021-54 MBE2021-40 |
An underlying key factor of deep neural networks is the information propagation through the layers. This suggests a conn... [more] | NLP2021-79 MICT2021-54 MBE2021-40 p.36 |
| NLP | 2021-12-17 14:55 |
Oita | J:COM Horuto Hall OITA | PSO-based Bifurcation Point Detection of Discrete-Time Dynamical System with Numerical Differentiation Takashi Kawashita, Haruna Matsushita (kagawa Univ.), Hiroaki Kurokawa (Tokyo Univ. of Technology), Takuji Kousaka (Chukyo Univ.) NLP2021-52 |
In dynamical systems, it is important to analyze bifurcation phenomena, and Particle Swarm Optimization(PSO)-based bifur... [more] | NLP2021-52 pp.44-47 |
| NLP | 2021-12-17 15:20 |
Oita | J:COM Horuto Hall OITA | Application of bifurcation point detection method by improved nested-layer particle swarm optimization to two-dimensional discrete dynamical systems Takaya Hirayama, Haruna Matsushita (Kagawa Univ.), Hiroaki Kurokawa (Tokyo Univ. of Techology), Takuji Kousaka (Chukyo Univ.) NLP2021-53 |
Nested-layer particle swarm optimization (NLPSO) fails to detect a target bifurcation point when various types of bifurc... [more] | NLP2021-53 pp.48-51 |
| NLP | 2021-12-18 14:15 |
Oita | J:COM Horuto Hall OITA | A study on estimating appropriate parameters of state space reconstruction Kazuya Sawada (TUS), Yutaka Shimada (Saitama Univ.), Tohru Ikeguchi (TUS) NLP2021-64 |
In this report, we investigated appropriate parameter values for reconstructing a dynamical system using delay-coordinat... [more] | NLP2021-64 pp.96-99 |
| NLP, CCS | 2021-06-11 13:30 |
Online | Online | Performance Investigation of Bifurcation Point Search Method Using Differential Evolution in Discrete Dynamical Systems Ryo Adachi, Haruna Matsushita (Kagawa Univ.), Hiroaki Kurokawa (Tokyo Univ. of Technology), Takuji Kousaka (Cyukyo Univ.) NLP2021-6 CCS2021-6 |
Nested particle swarm optimization (NLPSO) has been proposed as a bifurcation point derivation method that doesnot use g... [more] | NLP2021-6 CCS2021-6 pp.24-27 |
| HCS | 2021-01-23 16:50 |
Online | Online | [Invited Talk]
Embodiment as the basis of human communication
-- An approach from movement science -- Kentaro Kodama (TMU) |
This lecture provides a brief review of a communication study from the perspective of the dynamical systems approach, wh... [more] | |
| CCS | 2020-11-26 15:00 |
Online | Online | Analysis and learning of digital spike maps Yuya Sawano, Toshimichi Saito (HU) CCS2020-17 |
A digital spike map is a simple digital dynamical system defined on a set of points. Depending on parameters and initi... [more] |
CCS2020-17 pp.1-5 |
| IBISML | 2020-10-21 14:40 |
Online | Online | IBISML2020-23 |
Continuous deep learning architectures have recently re-emerged as variants of Neural Ordinary Differential Equations (N... [more] | IBISML2020-23 p.41 |
| IBISML | 2020-10-21 16:15 |
Online | Online | IBISML2020-25 |
Analysis and prediction of dynamic processes using data are fundamental in a variety of scientific and industrial fields... [more] | IBISML2020-25 pp.43-44 |
| MSS, NLP (Joint) |
2020-03-10 10:20 |
Aichi | (Cancelled but technical report was issued) |
Detecting unobserved nodes in networks of dynamical systems estimated only from time series data. Yuuki Sakakibara, Yutaka Shimada, Takaomi Shigehara (Saitama Univ.) NLP2019-125 |
Real complex phenomena can be described as coupled dynamical systems, where many dynamical systems are coupled with each... [more] | NLP2019-125 pp.71-76 |
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