| 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 # | |
| IN, CCS (Joint) |
2022-08-05 09:40 |
Hokkaido | Hokkaido University(Centennial Hall) (Primary: On-site, Secondary: Online) |
Machine Learning-Based Network Traffic Prediction with Tunable Parameters Kaito Kuriyama, Kohei Watabe (Nagaoka Univ. of Tech.)  IN2022-20 |
Network evaluation has become increasingly important in recent years. Network evaluation requires large amounts of traf... [more] |
IN2022-20 pp.27-32 |
| 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 |
| LOIS, IPSJ-DC | 2022-07-07 11:00 |
Online | Online | Greenhouse Microclimate Prediction based on Neural Networks Mujawamariya Marie Grace (Univ. of Tsukuba), Toshiyuki Amagasa (CCS, Univ. of Tsukuba), Naoya Fukuda (Univ. of Tsukuba) LOIS2022-4 |
Multivariate time series prediction approaches have been significant in wide range of real-world application. This paper... [more] | LOIS2022-4 pp.1-6 |
| 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 |
| MICT, EMCJ (Joint) |
2022-03-04 16:25 |
Online | Online | A synchronized measurement system for WBAN channel modeling by human motion parameters Akira Saito, Takahiro Aoyagi (Tokyo Tech) MICT2021-111 |
The development of WBAN channel models requires a lot of experiments and simulations. In order to reduce the number of e... [more] | MICT2021-111 pp.53-58 |
| CAS, CS | 2022-03-04 10:30 |
Online | Online | Automatic Path Following Control and Experimental Verification for Mobile Robots Based on Just-In-Time Modeling Shoichi Miyagaitsu, Tatsuya Kai (Tokyo Univ. of Science) CAS2021-89 CS2021-91 |
The purpose of this study is to develop a new data-driven control method based on just-in-time modeling for the automati... [more] | CAS2021-89 CS2021-91 pp.81-86 |
| AI | 2022-02-28 11:00 |
Miyazaki | Youth Hostel Sunflower MIYAZAKI (Primary: On-site, Secondary: Online) |
Behavior Prediction of Cervus nippon Considering Environmental Factors by Time-Series Transformer Kentaro Kazama, Katsuhide Fujita, Shinsuke Koike (TUAT) AI2021-15 |
In order to prevent damage to agriculture and forestry caused by wild animals, new wildlife management methods are requi... [more] | AI2021-15 pp.19-24 |
| NLP, MICT, MBE, NC (Joint) [detail] |
2022-01-22 10:30 |
Online | Online | On the relationship between properties of the hysteresis reservoir layer and the training output sequence Tsukasa Saito, Kenya Jin'no (Tokyo City Univ) NLP2021-99 MICT2021-74 MBE2021-60 |
Reservoir computing is a type of machine learning model that can be trained at low cost and fast. However, conventional ... [more] | NLP2021-99 MICT2021-74 MBE2021-60 pp.121-124 |
| NLP, MICT, MBE, NC (Joint) [detail] |
2022-01-23 10:15 |
Online | Online | A simple method for estimating phase and amplitude functions of limit-cycle oscillators by polynomial regression from time series data Norihisa Namura, Hiroya Nakao (Tokyo Tech.) NLP2021-122 MICT2021-97 MBE2021-83 |
In the real world, there are various nonlinear rhythmic phenomena, many of which can be modeled mathematically as limit-... [more] | NLP2021-122 MICT2021-97 MBE2021-83 pp.237-242 |
| 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 |
| NLP | 2021-12-18 15:40 |
Oita | J:COM Horuto Hall OITA | Performance evaluation on timeseries prediction of multi-layer simple cycle reservoir computing Kentaro Imai, Masaharu Adachi (Tokyo Denki Univ.) NLP2021-67 |
The purpose of this study is to combine Deep Echo State Network with other models. In this study, we propose and impleme... [more] | NLP2021-67 pp.110-113 |
| PRMU | 2021-12-16 15:15 |
Online | Online | Multivariate time series forecasting accuracy improvement method based on LSTNet Hayato Sano, Jun Rokui (Univ of Shizuoka) PRMU2021-37 |
Multivariate time series forecasting is a field to predict future values by analyzing the past of multiple time series d... [more] | PRMU2021-37 pp.71-76 |
| PRMU | 2021-12-17 15:15 |
Online | Online | An LSTM-based prefetcher exploiting delta correlation Hiroki Taniai, Tomoki Nakamura, Toru Koizumi, Yuya Degawa, Hidetsugu Irie, Shuichi Sakai, Ryota Shioya (Tokyo Univ.) PRMU2021-53 |
Prefetching is one of the major hardware techniques to improve the execution performance of programs in modern processor... [more] | PRMU2021-53 pp.160-164 |
| SWIM | 2021-11-27 14:10 |
Online | Online | Studies of maximum electricity forecasting model including electricity market price
-- Time series analysis with extra regressors added -- Hiroyuki Ogura (Nihon Univ.), Shunsuke Managi (Kyushu Univ.) SWIM2021-27 |
As one of the solutions to the difficult problem of achieving both stable electricity supply and decarbonization, improv... [more] | SWIM2021-27 pp.7-14 |
| CQ, ICM, NS, NV (Joint) |
2021-11-26 17:15 |
Fukuoka | JR Hakata Stn. Hakata EkiHigashi Rental Room (Primary: On-site, Secondary: Online) |
Proposal of change detection technology using cluster transition tensor Shoko Takahashi, Kei Takeshita (NTT) CQ2021-75 |
As in all service fields, the AI-based operation automation is progressing in the communication field as well. Once the... [more] |
CQ2021-75 pp.49-54 |
| MBE, NC (Joint) |
2021-10-29 11:15 |
Online | Online | Visualization and quantification of the difficulty of combinatorial optimization problems in Ising formulation Keiichi Soejima (Saitama Univ.), Makiko Konoshima, Hirotaka Tamura (Fujitsu), Jun Ohkubo (Saitama Univ.) NC2021-25 |
With the aim of rapidly solving combinatorial optimization problems, dedicated hardware using the Ising Model is being d... [more] | NC2021-25 pp.40-45 |
| CAS, NLP | 2021-10-14 13:25 |
Online | Online | A simple method for estimating phase functions of limit cycles by polynomial regression from time series data Norihisa Namura, Hiroya Nakao (Tokyo Tech.) CAS2021-23 NLP2021-21 |
Many rhythmic phenomena in the real world are mathematically modeled as limit-cycle oscillators. The phase reduction a... [more] |
CAS2021-23 NLP2021-21 pp.35-38 |
| 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 |
| NC, MBE (Joint) |
2021-03-03 15:35 |
Online | Online | A Study on Feature Extraction of signal arrival order using unsupervised learning of the pulsed neuron model Kaya Teramoto, Susumu Kuroyanagi (NIT) NC2020-51 |
For time series information processing using pulsed neuron models, a supervised learning rule is proposed that enables c... [more] | NC2020-51 pp.47-52 |
| MBE, NC (Joint) |
2020-12-18 15:40 |
Online | Online | Time-delayed LSTM for historical time series Prediction Rin Adachi, Jun Rokui (Univ of Shizuoka) NC2020-30 |
The research which applies machine learning to the social time series prediction is actively carried out. Among them, ma... [more] | NC2020-30 pp.13-18 |
[Return to Top Page]