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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| SeMI, IPSJ-UBI, IPSJ-MBL | 2024-02-29 15:50 |
Fukuoka | Development and Evaluation of the Water Flow Prediction Model for Remote Control of Sluice Gates in the Onga River Takahiro Ueno (Fukuoka Univ.), Koki Ozono (AJP), Masayoshi Ohashi (Fukuoka Univ.)  SeMI2023-77 |
Our laboratory is engaged in the research and development of a system for the remote control and monitoring of sluice ga... [more] | SeMI2023-77 pp.37-41 |
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| NLP, CAS | 2023-10-06 10:30 |
Gifu | Work plaza Gifu | Prediction and detection for extreme events of semiconductor laser using echo state network Shoma Ohara (Tokyo Univ. of Tech.), Kazutaka Kanno, Atsushi Uchida (Saitama Univ.), Hiroaki Kurokawa (Tokyo Univ. of Tech.) CAS2023-33 NLP2023-32 |
Intermittent chaos is a one of nonlinear dynamics of a semiconductor laser with optical-feedback. Intermittent chaos con... [more] | CAS2023-33 NLP2023-32 p.13 |
| WIT | 2023-06-16 16:15 |
Okinawa | Okinawa Industry Support Center (Primary: On-site, Secondary: Online) |
Proposing a Feature-Analysis Method of Finger-Movement Data for Predicting Cognitive Function of Elderly People Hayato Seiichi, Sinan Chen, Atsuko Hayashi, Masahide Nakamura (Kobe Univ.) WIT2023-6 |
In recent years, a growing body of research has suggested a relationship between cognitive function and manual dexterity... [more] | WIT2023-6 pp.30-35 |
| MBE | 2022-07-23 13:50 |
Online | Online | Pilot Study on Early Prediction of Sleep Apnea Based on Heart Rate Variability Time-Series Forecasting Muhammad Shaufil Adha, Tomohiko Igasaki (Kumamoto Univ.) MBE2022-14 |
A widely accepted alternative to address the continuous positive airway pressure adherence problem is the use of its aut... [more] | MBE2022-14 pp.9-12 |
| KBSE, SWIM | 2022-05-20 15:00 |
Tokyo | (Primary: On-site, Secondary: Online) |
Practical Application of Self-Adaptive Anomaly Detection Method Using XAI Shimon Sumita, Hiroyuki Nakagawa, Tatsuhiro Tsuchiya (Osaka Univ.) KBSE2022-3 SWIM2022-3 |
In this study, we examine the use of XAI to improve the performance of a self-adaptive anomaly detection method. As a sp... [more] | KBSE2022-3 SWIM2022-3 pp.13-18 |
| RCS, SIP, IT | 2022-01-20 13:40 |
Online | Online | Received Power Prediction of 60 GHz Millimeter-Wave Propagation in Indoor Environment from Time-Series Images Using Neural Networks Khanh Nam Nguyen, Kenichi Takizawa (NICT) IT2021-55 SIP2021-63 RCS2021-223 |
A millimeter-wave (mmWave) indoor propagation environment with obstacles in 60 GHz frequency band is set up where receiv... [more] | IT2021-55 SIP2021-63 RCS2021-223 pp.149-154 |
| IN | 2022-01-18 11:35 |
Online | Online | Evaluation on Prediction Method for Missing Probability of Sensor Value based on Hierarchical Structure of Missing Value Norifumi Hirata, Osamu Maeshima, Kiyohito Yoshihara (KDDI Research) IN2021-25 |
Collecting sensor data via networks is important for IoT (Internet of Things) services.However, sensors sometimes have m... [more] | IN2021-25 pp.7-12 |
| NLP, CCS | 2021-06-11 10:50 |
Online | Online | A Study on Prediction of Synchrophasor Time-Series Data of In-Campus Distribution Voltage Using Gaussian Process Regression Munetaka Noguchi (Osaka Pref Univ.), Yoshihiko Susuki (Osaka Pref Univ./JST), Atsushi Ishigame (Osaka Pref Univ.) NLP2021-3 CCS2021-3 |
Due to recent penetration of distributed energy resources, dynamics of power distribution systems have been complicated ... [more] | NLP2021-3 CCS2021-3 pp.10-13 |
| EE, IEE-HCA | 2021-05-27 09:25 |
Online | Online | LSTM-based Neural Network Model for Predicting Solar Power Generation Kundjanasith Thonglek, Kohei Ichikawa (NAIST), Kazufumi Yuasa, Tadatoshi Babasaki (NTT-F) EE2021-2 |
Currently, the most popular renewable energy is solar power which reduces pollution consequences from using conventional... [more] | EE2021-2 pp.7-12 |
| IBISML | 2021-03-03 11:15 |
Online | Online | IBISML2020-46 |
Developing a profitable trading strategy is a central problem in the financial industry. In this presentation, we develo... [more] | IBISML2020-46 p.38 |
| HCGSYMPO (2nd) |
2020-12-15 - 2020-12-17 |
Online | Online | Development and evaluation of time series labeling tool based on work occurrence prediction for restaurant service Karimu Kato, Takahiro Miura, Ryosuke Ichikari, Takashi Okuma, Takeshi Kurata (AIST) |
The cost to create training data for supervised learning has been a problem. Particularly, it takes a long time to label... [more] | |
| DE, IPSJ-DBS | 2019-12-24 16:55 |
Tokyo | National Institute of Informatics | Yuichiro Sakazaki, Rin Adachi, Jun Rokui (univ. of Shizuoka) DE2019-32 |
We proposed a model that integrates several types of data by multiple regression analysis and performs future prediction... [more] | DE2019-32 pp.93-98 |
| NC, MBE | 2019-12-06 11:00 |
Aichi | Toyohashi Tech | Prediction of EEG Time Series with Reservoir Computing Takayuki Koga, Yuta Takahashi, Rieko Osu (Waseda Univ) MBE2019-48 NC2019-39 |
We applied Reservoir Computing (RC) to predict and generate EEG time-series. In the prediction, 10sec EEG was used for t... [more] | MBE2019-48 NC2019-39 pp.19-24 |
| NLP, NC (Joint) |
2019-01-24 12:00 |
Hokkaido | The Centennial Hall, Hokkaido Univ. | Model selection using reinforcement learning in laser-based reservoir computing Kazutaka Kanno (Saitama Univ.), Makoto Naruse (NICT), Atsushi Uchida (Saitama Univ.) NLP2018-116 |
Reservoir computing is machine learning based on artificial neural network and it is a main feature that only output wei... [more] | NLP2018-116 pp.107-112 |
| MBE, NC (Joint) |
2018-05-19 15:45 |
Toyama | Univ. of Toyama | On Simple Growing Reservoir Computing Systems Naoki Sakamoto, Toshimichi Saito (HU) NC2018-4 |
This paper studies basic function of a simple reservoir computing system. The system is based on a ring-type recurrent n... [more] | NC2018-4 pp.15-18 |
| ET | 2018-03-03 16:05 |
Kochi | Kochi University of Technology (Eikokuji Campus) | Modeling the temporal change of student proficiency using records in e-learning Midori Kodama, Takahiro Hata, Ippei Shake (NTT) ET2017-132 |
Estimating student’s proficiency from the records of learning system is the key technology to provide adaptive learning ... [more] | ET2017-132 pp.249-252 |
| SP, SIP, EA | 2017-03-01 15:55 |
Okinawa | Okinawa Industry Support Center | [Invited Talk]
Multikernel Adaptive Filtering: Signal Processing and Machine Learning Masahiro Yukawa (Keio Univ.) EA2016-113 SIP2016-168 SP2016-108 |
We present the multikernel adaptive filtering and introduce its recent advances. Multikernel adaptive filtering is a rec... [more] | EA2016-113 SIP2016-168 SP2016-108 pp.177-182 |
| ASN | 2014-05-29 14:30 |
Tokyo | Convention Hall, RCAST, The University of Tokyo | [Poster Presentation]
A proposal for an agricultural environmental control system based on wireless sensor networks and machine learning Yukimasa Kaneda, Hirofumi Ibayashi, Yuya Suzuki (Shizuoka Univ.), Naoki Oishi (Research Institute of Agric.), Hiroshi Mineno (Shizuoka Univ.) ASN2014-22 |
In recent years , agricultural support using ICT has been actively conducted.An example of such system is monitoring env... [more] | ASN2014-22 pp.75-76 |
| NLP | 2014-01-21 11:00 |
Hokkaido | Niseko Park Hotel | Automated Forex Trading System Using the Nonlinear Portfolio Model Hirotake Wachi, Vu Tat Thanh, Satoshi Inose (Ibaraki Univ.), Atsushi Kannari (MS&AD), Tomoya Suzuki (Ibaraki Univ.) NLP2013-132 |
In our previous studies (S.Inose, 2013) the nonlinear portfolio model was proposed and its usefulness was confirmed in s... [more] | NLP2013-132 pp.19-24 |
| NC, NLP | 2013-01-24 14:10 |
Hokkaido | Hokkaido University Centennial Memory Hall | Characterizing financial crisis by means of the Three states random field Ising model Mitsuaki Murota, Jun-ichi Inoue (Hokkaido Univ.) NLP2012-115 NC2012-105 |
We extend the formulation of time-series prediction using Ising model given by Kaizouji (2001) or Higano et.al. (2012) b... [more] | NLP2012-115 NC2012-105 pp.67-72 |
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