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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| 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 |
| EE | 2023-01-19 15:15 |
Fukuoka | Kyushu Institute of Technology (Primary: On-site, Secondary: Online) |
Parameter estimation of component of DC-DC converter with state-space modeling and linear regression Yano Ikuma, Maruta Hidenori (Nagasaki Univ.) EE2022-38 |
This study presents a parameter estimation method of DC-DC converter based on its state-space modelling and linear regre... [more] | EE2022-38 pp.67-71 |
| CS | 2022-07-15 14:20 |
Kagoshima | Yakushima Environmental and Cultural Village Center (Primary: On-site, Secondary: Online) |
[Invited Talk]
Application of state-space models to CNN estimation methods for indoor location estimation Kaishin Hori, Satoru Aikawa, Sinichiro Yamamoto (Univ. of Hyogo) CS2022-36 |
Currently, GNSS is the most accurate outdoor location estimation technique. On the other hand, the accuracy of GNSS loca... [more] | CS2022-36 pp.100-103 |
| KBSE | 2021-03-06 14:25 |
Online | Online | KBSE2020-46 |
In recent years, the use of open source software (OSS) in product software has been increasing in the industrial world, ... [more] | KBSE2020-46 pp.71-76 |
| EA, US, SP, SIP, IPSJ-SLP [detail] | 2021-03-03 13:05 |
Online | Online | [Invited Talk]
* Masahito Togami (LINE) EA2020-64 SIP2020-95 SP2020-29 |
Recently, deep learning based speech source separation has been evolved rapidly. A neural network (NN) is usually learne... [more] | EA2020-64 SIP2020-95 SP2020-29 pp.27-32 |
| SIS | 2019-12-12 16:55 |
Okayama | Okayama University of Science | [Fellow Memorial Lecture]
Adaptive signal processing and image processing, linear or non-linear? Mitsuji Muneyasu (Kansai Univ.) SIS2019-30 |
It is well known that an image is nonstationary, the processing according to its local property should be required. Ther... [more] | SIS2019-30 p.37 |
| PRMU, IPSJ-CVIM | 2019-05-30 11:20 |
Tokyo | Daily Fish Catch Forecasting For Fixed Shore Net Fishing Using State Space Model Describing Probabilistic Behavior of Fish Inside Net Yuya Kokaki, Naohiro Tawara, Tetsunori Kobayashi, Kazuo Hashimoto (Waseda Univ.), Masayoshi Hukushima, Akira Idoue (KDDI Research), Ogawa Tetsuji (Waseda Univ.) PRMU2019-3 |
A state space model that incorporates knowledge on fixed shore net fishing was developed and suc- cessfully applied to d... [more] | PRMU2019-3 pp.13-18 |
|
| AI | 2019-02-23 11:30 |
Tokyo | Evaluation of the similarity between data jackets based on data users' recognition Nao Uehara, Teruaki Hayashi, Yukio Ohsawa (UT) AI2018-41 |
(To be available after the conference date) [more] | AI2018-41 pp.23-28 |
|
| NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] | 2018-06-13 15:25 |
Okinawa | Okinawa Institute of Science and Technology | Current Dipole Localization from EEG with Birth-Death Process Keita Nakamura (Waseda Univ.), Sho Sonoda (RIKEN), Hideitsu Hino (ISM), Masahiro Kawasaki (Univ. of Tsukuba), Shotaro Akaho (AIST), Noboru Murata (Waseda Univ.) IBISML2018-10 |
We explore the EEG source localization problem as the estimation of current dipoles. We formulate the relation between c... [more] | IBISML2018-10 pp.67-74 |
| CQ | 2018-01-19 15:50 |
Tokyo | NII | Time series analysis of failure rates of equipments for telecommunication networks.
-- Improvement of the method of prediction -- Hiroyuki Funakoshi (NTT) CQ2017-101 |
The author has been predicted the failure rate of telecommunication network equipments by the state space model. The met... [more] | CQ2017-101 pp.93-98 |
| CQ | 2017-08-28 10:40 |
Tokyo | Tokyo University of Science | Time series analysis of failure rates of equipments for telecommunication networks.
-- State space model using Kalman filter -- Hiroyuki Funakoshi (NTT) CQ2017-50 |
The author has been predicted the failure rate of telecommunication network equipments by the state space model with Bay... [more] | CQ2017-50 pp.1-6 |
| CQ | 2017-07-27 12:05 |
Hyogo | Kobe University | Time series analysis of failure rates of equipments for telecommunication networks.
-- State space model using Bayesian inference -- Hiroyuki Funakoshi (NTT) CQ2017-35 |
The author has been analyzed the failure rate of telecommunication network equipments by time series analysis using ARIM... [more] | CQ2017-35 pp.37-42 |
| CQ (2nd) |
2017-01-21 12:30 |
Osaka | Osaka University Nakanoshima Center | [Poster Presentation]
Car Sales Prediction using State Space Model with Search Volume on the Web Taichi Yamaguchi (Univ. of Tsukuba), Takaaki Tsunoda (CyberAgent), Mitsuo Yoshida (Toyohashi Tech), Sho Tsugawa, Mikio Yamamoto (Univ. of Tsukuba) |
Regarding monthly car sales as time series data, we can predict the future car sales using state space models. However, ... [more] | |
| NC, IPSJ-BIO, IBISML, IPSJ-MPS (Joint) [detail] |
2015-06-23 11:35 |
Okinawa | Okinawa Institute of Science and Technology | Multiple Latent Space GTM for Visualization of Tensorial Data Kazushi Higa, Tetsuo Furukawa (Kyutech) IBISML2015-6 |
The generative topographic map (GTM) is a continuous latent space model, which enables to visualize a high-dimensional d... [more] | IBISML2015-6 pp.33-38 |
| WIT, SP, ASJ-H, PRMU | 2015-06-19 10:00 |
Niigata | Study on prediction of quasiperiodic nonlinear phenomena based on Gaussian process state space model Akira Tamamori, Tomoko Matsui (ISM), Masumi Kitazawa (QOL) PRMU2015-49 SP2015-18 WIT2015-18 |
Many non-linear phenomena which exhibit quasiperiodic fluctuations can be widely observed in the world; population of or... [more] | PRMU2015-49 SP2015-18 WIT2015-18 pp.101-106 |
|
| IBISML | 2012-11-07 15:30 |
Tokyo | Bunkyo School Building, Tokyo Campus, Tsukuba Univ. | A Proposal of Adaptive Metric Learning Using Category information for Text Classification Kenta Mikawa, Takashi Ishida, Masayuki Goto, Shigeichi Hirasawa (Waseda Univ.) IBISML2012-45 |
Extended cosine measure has been proposed as one of the method of metric learning which learns metric matrix expressin... [more] |
IBISML2012-45 pp.83-88 |
| PRMU, SP | 2012-02-10 17:00 |
Miyagi | Effects of continuous syllable recognition and query expansion for spoken document retrieval Hiromasa Ohashi (Nagoya Univ.), Satoru Tsuge (Daido Univ.), Norihide Kitaoka, Kazuya Takeda (Nagoya Univ.), Kenji Kita (Tokushima Univ.) PRMU2011-239 SP2011-154 |
In this paper, we propose a spoken document retrieval method which combines query expansion with continuous syllable rec... [more] | PRMU2011-239 SP2011-154 pp.249-254 |
|
| IBISML | 2011-11-09 15:45 |
Nara | Nara Womens Univ. | Inferring the time-varying input signals and the hidden states from the membrane potential of a neuron Ryota Kobayashi (Ritsumeikan Univ.), Petr Lansky (Acad. of Sci. of the Czech), Shigeru Shinomoto (Kyoto Univ.) IBISML2011-52 |
We consider an estimation problem of the time-varying input signals and the ion channel states from a voltage trace of a... [more] | IBISML2011-52 pp.67-70 |
| IBISML | 2011-11-10 15:45 |
Nara | Nara Womens Univ. | Image Segmentation and Restoration using Switching State-Space Model and Variational Bayesian Method Ryota Hasegawa (Kansai Univ.), Ken Takiyama, Masato Okada (Univ. of Tokyo), Seiji Miyoshi (Kansai Univ.) IBISML2011-67 |
We derive a deterministic algorithm that restores and segments image using switching state-space model and variational B... [more] | IBISML2011-67 pp.169-174 |
| NC | 2011-07-25 14:10 |
Hyogo | Graduate School of Engineering, Kobe University | Belief Propagation for Slow Feature Analysis Tomoki Sekiguchi (The Univ. of Tokyo), Toshiaki Omori, Masato Okada (The Univ. of Tokyo/RIKEN) NC2011-26 |
The slow feature analysis (SFA) is a data analysis method that extracts slowly varying features from time series data. ... [more] |
NC2011-26 pp.31-36 |
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