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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| QIT (2nd) |
2023-12-17 17:30 |
Okinawa | OIST (Primary: On-site, Secondary: Online) |
[Poster Presentation]
Liouvillian spectral analysis for dynamics of open quantum systems via dynamic mode decomposition Yuzuru Kato (FUN), Hiroya Nakao (Tokyo Tech) |
Dynamic mode decomposition (DMD) is a data-driven method for the estimation, prediction, and control of complex dynamica... [more] | |
| QIT (2nd) |
2023-12-17 17:30 |
Okinawa | OIST (Primary: On-site, Secondary: Online) |
[Poster Presentation]
Sparse identification of quantum dynamics via quantum circuit learning Yusei Tateyama, Yuzuru Kato (FUN) |
Sparse Identification of Nonlinear Dynamics (SINDy) is a data-driven method for estimation and prediction of nonlinear d... [more] | |
| 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 |
| NLP | 2023-11-28 13:00 |
Okinawa | Nago city commerce and industry association | Recurrence quantification analysis on electroencephalographic (EEG) potentials of epileptic seizure Makoto Sekiguchi, Tohru Ikeguchi (TUS) NLP2023-63 |
In this report, we analyze the characteristics of electroencephalographic (EEG) potentials of healthy individuals and ep... [more] | NLP2023-63 pp.21-26 |
| NLP | 2022-11-25 15:40 |
Shiga | (Primary: On-site, Secondary: Online) |
A study on Participation factor for nonlinear dynamical systems based on Koopman mode decomposition Kenji Takamichi (Osaka Prefecture Univ.), Yoshihiko Susuki (Kyoto Univ.), Marcos Netto (NREL), Atsushi Ishigame (Osaka Prefecture Univ.) NLP2022-79 |
The so-called participation factor analysis has been used to evaluate the relative contribution between state vari- able... [more] | NLP2022-79 pp.103-108 |
| IN, CCS (Joint) |
2022-08-05 10:30 |
Hokkaido | Hokkaido University(Centennial Hall) (Primary: On-site, Secondary: Online) |
Detecting causality for spike trains based on reconstructing dynamical system from inter-spike intervals Kazuya Sawada (TUS), Yutaka Shimada (Saitama Univ.), Tohru Ikeguchi (TUS) CCS2022-36 |
In this report, by modifying a nonlinear method of detecting causality, we propose a method of detecting causality for p... [more] | CCS2022-36 pp.48-53 |
| 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 |
| MBE, NC (Joint) |
2021-10-29 11:40 |
Online | Online | A numerical study on the relationship between complexity and accuracy of neural networks based on ordinary differential equations Kaoru Esashika, Jun Ohkubo (Saitama Univ.) NC2021-26 |
In recent years, many reports have been published on deep neural networks. The residual networks have contributed to rem... [more] | NC2021-26 pp.46-50 |
| 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 |
| CCS | 2019-03-27 10:30 |
Tokyo | NICT, Koganei, Tokyo | [Invited Talk]
Analysis and Control of Nonlinear Dynamical Systems via the Koopman Operator Yoshihiko Susuki (Osaka Pref. Univ.) CCS2018-56 |
The Koopman operator is a composition operator defined for a large class of nonlinear dynamical systems. Even if a ta... [more] |
CCS2018-56 p.55 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2018-09-21 11:10 |
Fukuoka | [Invited Talk]
Reservoir Computing: Theory, Physical Implementations, and Applications Kohei Nakajima (Univ. Tokyo) PRMU2018-60 IBISML2018-37 |
Reservoir Computing (RC) has been proposed as a framework for training recurrent neural networks. In this framework, low... [more] | PRMU2018-60 IBISML2018-37 pp.149-154 |
|
| CCS, NLP | 2016-06-13 11:30 |
Tokyo | Kikai-Shinko-Kaikan Bldg. | Optimal parameter selection for kernel dynamic mode decomposition Wataru Kurebayashi (Aomori Univ.), Sho Shirasaka, Hiroya Nakao (Tokyo Tech.) NLP2016-21 CCS2016-4 |
A novel mode decomposition method based on the dynamical systems theory, called the dynamic mode decomposition (DMD), ha... [more] | NLP2016-21 CCS2016-4 pp.13-14 |
| NLP | 2014-11-06 15:00 |
Niigata | TsubameSanjo Regional Industries Promotion Center | Path-wise definition of integral input-to-state stability and Lyapunov functions Hiroshi Ito (Kyushu Inst. of Tech.), Yuki Nishimura (Kagoshima Univ.) NLP2014-84 |
For stochastic nonlinear dynamical systems, path-wise probability is employed to define integral input-to-state stabilit... [more] | NLP2014-84 pp.17-22 |
| NLP | 2014-11-07 10:20 |
Niigata | TsubameSanjo Regional Industries Promotion Center | On global analysis of nonlinear systems and topology Ryuji Enomoto (NIT, Kochi College) NLP2014-92 |
We discuss a global analysis of nonlinear systems based on gradient-like Morse-Smale dynamical systems. In our method, n... [more] | NLP2014-92 pp.63-66 |
| NLP | 2014-01-22 16:00 |
Hokkaido | Niseko Park Hotel | Phase-reduction analysis of coupled limit-cycle oscillators in hybrid dynamical systems Sho Shirasaka, Wataru Kurebayashi, Hiroya Nakao (Tokyo Inst. of Tech.) NLP2013-161 |
Hybrid dynamical systems, possessing both characteristics of discrete and continuous dynamical systems, is often used to... [more] | NLP2013-161 pp.167-171 |
| NLP | 2012-03-27 15:15 |
Nagasaki | Fukue Cultural Hall | Electronic Circuit Representing Large-Scale Augmented Lorenz Oscillator and its Dynamical Properties Koki Yoshimoto, Mitsuhiro Aono, Takaya Miyano (Ritsumeikan Univ.) NLP2011-146 |
The nondimensionalized expression for the equations of motion of a chaotic gas turbine that randomly reverses the direct... [more] | NLP2011-146 pp.29-33 |
| NLP | 2008-03-27 15:00 |
Hyogo | Analysis method of chaotic time series using measures of networks. Yutaka Shimada, Tohru Ikeguchi (Saitama Univ.) NLP2007-161 |
Complex phenomena are ubiquitous in the real world, for example, fluctuation of financial indices in a stock market, po... [more] |
NLP2007-161 pp.43-48 |
|
| NLP | 2007-12-19 14:25 |
Fukui | Global perturbation method for Hamilton-Jacobi equations of nonlinear control systems Kazuyuki Yagasaki (Gifu Univ.), Noboru Sakamoto (Nagoya Univ.), Yoshihiro Hirata (Ishii Lab.) NLP2007-111 |
We develop a global perturbation technique for obtaining approximate but analytical expressions of stabilizing solutions... [more] | NLP2007-111 pp.17-21 |
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