Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
EST |
2024-01-26 14:50 |
Kyoto |
Kyoto University ROHM Plaza (Primary: On-site, Secondary: Online) |
Magnetic field separation of DC signal source in geomagnetic environment by using tensor decomposition Yuji Ogata, Tomonori Yanagida, Bunichi Kakinuma, Masayuki Kimishima (Advantest Lab) EST2023-120 |
In non-destructive testing and tracking using magnetic fields, it is necessary to estimate the position of the signal so... [more] |
EST2023-120 pp.117-122 |
EST |
2024-01-26 15:15 |
Kyoto |
Kyoto University ROHM Plaza (Primary: On-site, Secondary: Online) |
Estimation of DC signal source position in geomagnetic environment Tomonori Yanagida, Yuji Ogata, Bunichi Kakinuma, Masayuki Kimishima (Advantest Lab) EST2023-121 |
Magnetic fields have attracted attention as applications for non-contact, non-destructive measurement of objects, and in... [more] |
EST2023-121 pp.123-126 |
RCS, SR, NS, SeMI, RCC (Joint) |
2021-07-15 10:30 |
Online |
Online |
Extraction of User Communication Behavior from DNS Query Logs by Non-Negative Tensor Factorization Approach Kotaro Hatanaka, Tatsuaki Kimura, Tetsuya Takine (Osaka Univ.) NS2021-43 |
Understanding user communication behavior by analyzing network logs has been playing an important role in security monit... [more] |
NS2021-43 pp.57-62 |
RCS |
2021-06-25 13:35 |
Online |
Online |
Tensor-aided Beamforming Design for Full Duplex Cell-Free MIMO Kengo Ando (UEC), Hiroki Iimori (JUB), Koji Ishibashi (UEC), Giuseppe Abreu (JUB) RCS2021-72 |
In this paper, we investigate a transmit (TX) and receive (RX) beamforming (BF) design for full-duplex cell-free multipl... [more] |
RCS2021-72 pp.249-254 |
RCS, SR, SRW (Joint) |
2021-03-05 11:20 |
Online |
Online |
Tensor-aided Beamforming Design for Cell-Free MIMO Kengo Ando (UEC), Hiroki Iimori, Giuseppe Abreu (JUB), Koji Ishibashi (UEC) RCS2020-257 |
In this paper, we investigate a transmit (TX) and receive (RX) beamforming (BF) design for cell free multiple input mult... [more] |
RCS2020-257 pp.252-257 |
IBISML |
2021-03-02 10:50 |
Online |
Online |
Kernel tensor decomposition based unsupervised feature extraction
-- Applications to bioinformatics -- Y-h. Taguchi (Chuo Univ.) IBISML2020-36 |
A lot of research has been done on the so-called textit{large p small n} problem, where the number of samples is small c... [more] |
IBISML2020-36 pp.16-23 |
PRMU |
2020-12-17 11:00 |
Online |
Online |
Fast algorithm for low-rank tensor completion in multi-way delay embedded space Ryuki Yamamoto, Tatsuya Yokota (Nagoya Institute of Tech.), Akira Imakura (Univ. of Tsukuba), Hidekata Hontani (Nagoya Institute of Tech.) PRMU2020-42 |
In recent years, low-rank tensor completion using delay embedding has been an important technique. In order to capture s... [more] |
PRMU2020-42 pp.24-29 |
MBE, NC, NLP, CAS (Joint) [detail] |
2020-10-29 17:35 |
Online |
Online |
Effort-dependent emergence of uniform and diverse muscle activity features in skilled pitching Tsubasa Hashimoto, Ken Takiyama (TUAT), Takeshi Miki, Hirohumi Kobayashi (UTokyo), Daiki Nasu (NTT), Tetsuya Ijiri, Masumi Kuwata (UTokyo), Makio Kashino (NTT), Kimitaka Nakazawa (UTokyo) NC2020-17 |
Skilled pitchers show common and individually different motion features. In contrast, common and individually different ... [more] |
NC2020-17 pp.44-49 |
IBISML |
2020-01-09 16:45 |
Tokyo |
ISM |
Application of tensor decomposition based unsupervised feature extraction to single cell RNA-seq analysis Y-h. Taguchi (Chuo Univ.) IBISML2019-26 |
Cannonical correlation analysis (CCA) is known to integrate two matrices, each of which have elements, $x_{ij} in mathbb... [more] |
IBISML2019-26 pp.55-59 |
SITE |
2019-12-06 13:25 |
Kanagawa |
|
Interpretation of Multi-Label Learning by combining two probability models
-- An approach that interprets evaluate texts by regarding labels as teacher data -- Kurebayashi Kosuke, Morizumi Tetsuya, Kinoshita Hirotsugu (Kanagawa Univ.) SITE2019-81 |
We have already proposed a security model with a three-layer structure of AI architecture. However, there is a certain l... [more] |
SITE2019-81 pp.7-12 |
PRMU, IPSJ-CVIM |
2019-05-31 09:40 |
Tokyo |
|
Study on feature extraction from leaf-scale plant images Kuniaki Uto (Tokyo Tech), Mauro Dalla Mura, Jocelyn Chanussot (Grenoble INP), Koichi Shinoda (Tokyo Tech) PRMU2019-7 |
With the advent of an unmanned aerial vehicle (UAV) and sensing technologies, it is possible to acquire leaf-scale aeria... [more] |
PRMU2019-7 pp.259-264 |
IBISML |
2018-11-05 15:10 |
Hokkaido |
Hokkaido Citizens Activites Center (Kaderu 2.7) |
[Poster Presentation]
Tensor decomposition based unsupervised feature extraction applied to bioinformatics Y-h. Taguchi (Chuo Univ.) IBISML2018-90 |
Although supervised and reinforcement learning including deap learning performs excellent achievements, it is not applic... [more] |
IBISML2018-90 pp.345-352 |
HWS, ISEC, SITE, ICSS, EMM, IPSJ-CSEC, IPSJ-SPT [detail] |
2018-07-26 11:45 |
Hokkaido |
Sapporo Convention Center |
Real-time Botnet Detection Using Nonnegative Tucker Decomposition Hideaki Kanehara, Yuma Murakami (Waseda Univ.), Jumpei Shimamura (Clwit), Takeshi Takahashi (NICT), Noboru Murata (Waseda Univ.), Daisuke Inoue (NICT) ISEC2018-38 SITE2018-30 HWS2018-35 ICSS2018-41 EMM2018-37 |
This study focuses on darknet traffic analysis and applies tensor factorization in order to detect coordinated group act... [more] |
ISEC2018-38 SITE2018-30 HWS2018-35 ICSS2018-41 EMM2018-37 pp.297-304 |
NLP, CCS |
2018-06-08 09:45 |
Kyoto |
Kyoto Terrsa |
Application of tensor decomposition to chaotic itinerancy time series Takahiro Arai, Toshio Aoyagi (Kyoto Univ.) NLP2018-29 CCS2018-2 |
Tensor decomposition is a typical method for analyzing resting-state BOLD signals. This method can decompose the observe... [more] |
NLP2018-29 CCS2018-2 pp.7-12 |
HCS, HIP, HI-SIGCOASTER [detail] |
2018-05-22 09:30 |
Okinawa |
Okinawa Industry Support Center |
Clustering of Children Based on Behavior Analysis and Consideration of Individuality Analysis Keiichi Horio, Yuji Watanabe, Tetsuo Furukawa (Kyushu Inst. of Tech.), Takashi Omori (Tamagawa Univ.) HCS2018-13 HIP2018-13 |
In this study, features such as speech, line of sight, response, posture, etc. were extracted from moving images taken b... [more] |
HCS2018-13 HIP2018-13 pp.101-106 |
PRMU |
2017-10-12 09:30 |
Kumamoto |
|
Accelerating Convolutional Neural Networks Using Low-Rank Tensor Decomposition Kazuki Osawa, Akira Sekiya, Hiroki Naganuma, Rio Yokota (Tokyo Inst. of Tech.) PRMU2017-63 |
In the image recognition using convolution neural networks (CNN), convolution operations occupies the majority of the co... [more] |
PRMU2017-63 pp.1-6 |
SP |
2017-08-30 11:00 |
Kyoto |
Kyoto Univ. |
[Poster Presentation]
Discrimination and Feature Estimation of Brain Magnetic Field Data Associated with Japanese Speech Sound Imagery Shihomi Uzawa (Kobe Univ./AIST), Tetsuya Takiguchi, Yasuo Ariki (Kobe Univ.), Seiji Nakagawa (Chiba Univ./AIST) SP2017-28 |
Brain computer interface (BCI) technologies, which enable direct communication between the brain and external devices, h... [more] |
SP2017-28 pp.39-43 |
PRMU, IE, MI, SIP |
2017-05-26 10:20 |
Aichi |
|
Deep Subspace Methods
-- Pattern Recognition using Hierarchical Structure of Linear Subspaces -- Hayato Itoh, Atsushi Imiya (Chiba Univ.), Tomoya Sakai (Ngasaki Univ.) SIP2017-18 IE2017-18 PRMU2017-18 MI2017-18 |
We introduce an new geodesic distance between images. This distance is defined as the Wasserstein distance between contr... [more] |
SIP2017-18 IE2017-18 PRMU2017-18 MI2017-18 pp.93-98 |
IBISML |
2016-11-16 15:00 |
Kyoto |
Kyoto Univ. |
[Poster Presentation]
Principal Component Analysis based unsupervised Feature Extraction applied to Bioinformatics Y-h. Taguchi (Chuo Univ.) IBISML2016-47 |
Recently, numerous researches were performed for the machine/statisitical learning. Among those, deep learning is especi... [more] |
IBISML2016-47 pp.17-24 |
MI, MICT |
2016-09-16 14:25 |
Tokyo |
Koganei Campus, Tokyo University of Agriculture and Technology |
[Invited Talk]
Tensor Completion based on Low-Rank and Smooth Structures Tatsuya Yokota (NITECH) MICT2016-42 MI2016-56 |
Completion is a procedure that facilitates the estimation of the values of missing elements of array data, using only th... [more] |
MICT2016-42 MI2016-56 pp.35-40 |