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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IBISML | 2023-12-20 14:55 |
Tokyo | National Institute of Informatics (Primary: On-site, Secondary: Online) |
Anomaly Detection by One-class Convolution Extreme Learning Machine Using Multiple Kernel Yuta Okami, Takuya Kitamura (NIT, Toyama College)  IBISML2023-31 |
In this paper, we propose a one-class convolutional extreme learning machine using multiple kernel. In this method, for ... [more] | IBISML2023-31 pp.7-12 |
| PRMU, IPSJ-CVIM, IPSJ-DCC, IPSJ-CGVI | 2023-11-17 09:20 |
Tottori | (Primary: On-site, Secondary: Online) |
Co-speech Gesture Generation with Variational Auto Encoder Shihichi Ka, Koichi Shinoda (Tokyo Tech) PRMU2023-29 |
Co-speech gesture generation is the study of generating gestures from speech. In prior works, deterministic methods lear... [more] | PRMU2023-29 pp.74-79 |
| SIS | 2023-03-02 14:40 |
Chiba | Chiba Institute of Technology (Primary: On-site, Secondary: Online) |
QR code image dnoising netwroks based on decodability assessment Kazumitsu Takahashi, Makoto Nakashizuka (CIT) SIS2022-46 |
In this paper, an image denoising method for QR code images is proposed. The image recovery from the degraded QR code im... [more] | SIS2022-46 pp.33-36 |
| KBSE | 2023-01-20 13:00 |
Ishikawa | (Primary: On-site, Secondary: Online) |
A study for using deep learning inference results of program defects in code review checklists Kazuhiko Ogawa, Takako Nakatani (OUJ) KBSE2022-51 |
In system development, various efforts are made to improve the quality of programs. One of these efforts is code review... [more] |
KBSE2022-51 pp.46-51 |
| MBE, MICT, IEE-MBE [detail] | 2023-01-17 09:50 |
Saga | Oral Cytology Based on Representation Learning of Visually Salient Cells Kazuki Matsuo, Eiji Mitate, Tomoya Sakai (Nagasaki Univ.) MICT2022-44 MBE2022-44 |
We classify microscopically photographed cells for screening tests to find oral cancer in its early stages. Oral cancer ... [more] | MICT2022-44 MBE2022-44 pp.7-12 |
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| SIP | 2022-08-25 14:15 |
Okinawa | Nobumoto Ohama Memorial Hall (Ishigaki Island) (Primary: On-site, Secondary: Online) |
Multiresolution Convolutional-Sparse-Coded Dynamic Mode Decomposition and Its Application to Riverbed State Estimation Eisuke Kobayashi, Shogo Muramatsu, Hiroyasu Yasuda, Kiyoshi Hayasaka (Niigata Univ.) SIP2022-54 |
In this report, we propose a method that incorporates multi-resolution representation into Convolutional-Sparse-Coded Dy... [more] | SIP2022-54 pp.25-30 |
| KBSE | 2022-03-09 16:20 |
Online | Online (Zoom) | Code review support and verification of effectiveness using deep learning with images of programs Kazuhiko Ogawa, Takako Nakatani (OUJ) KBSE2021-49 |
Code review is one of the ways to improve the quality of programs. Code reviews cannot point out all faults, but if rev... [more] |
KBSE2021-49 pp.48-53 |
| IBISML | 2022-03-09 09:05 |
Online | Online | [Invited Talk]
--- Koji Fukagata (Keio Univ.) IBISML2021-39 |
In recent years, the application of machine learning to various problems of fluid mechanics has been actively studied. I... [more] | IBISML2021-39 p.32 |
| IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] | 2022-02-22 15:20 |
Online | Online | A Note on Perceived Visual Content Estimation Based on Compressed Reconstruction Network Using Brain Signals While Gazing on Images Takaaki Higashi, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama (Hokkaido University) |
In this paper, we propose a method to reconstruct a perceived image using brain signals obtained during gazing images. S... [more] | |
| NLP, MICT, MBE, NC (Joint) [detail] |
2022-01-22 15:45 |
Online | Online | A Study on IEEE 802.15.6 UWB PHY Utilizing Super Orthogonal Convolutional Codes Kento Takabayashi (Okayama Prefectural Univ.), Hirokazu Tanaka (Hiroshima City Univ), Katsumi Sakakibara (Okayama Prefectural Univ.) NLP2021-112 MICT2021-87 MBE2021-73 |
The use of the Internet of Things (IoT) in the medical and healthcare fields has received much attention. It is called t... [more] | NLP2021-112 MICT2021-87 MBE2021-73 pp.187-192 |
| RCS, SIP, IT | 2022-01-21 10:55 |
Online | Online | A lossless audio codec based on hierarchical residual prediction Taiyo Mineo, Shouno Hayaru (UEC) IT2021-71 SIP2021-79 RCS2021-239 |
In this study, we propose a novel lossless audio codec that has precise predictive performance from the neural network a... [more] | IT2021-71 SIP2021-79 RCS2021-239 pp.239-244 |
| MW | 2021-12-17 11:15 |
Kanagawa | Kawasaki City Industrial Promotion Hall (Primary: On-site, Secondary: Online) |
Building Surrogate Model Using Convolutional Autoencoder for Fast Frequency Response Calculation of Planar BPFs Ren Shibata, Masataka Ohira, Ma Zhewang (Saitama Univ.) MW2021-99 |
Recently, surrogate models using deep learning are introduced to speed up electromagnetic (EM) analysis. For instance, a... [more] | MW2021-99 pp.85-90 |
| RECONF | 2021-09-10 10:20 |
Online | Online | Convolutional neural network implementations using Vitis AI Akihiko Ushiroyama, Nobuya Watanabe, Akira Nagoya, Minoru Watanabe (Okayama Univ.) RECONF2021-19 |
Recently, Xilinx provides an FPGA-based Vitis AI development environment which is one of deep learning frameworks to acc... [more] | RECONF2021-19 pp.13-18 |
| CS | 2021-07-16 09:40 |
Online | Online | Joint Transmit Power and Beamforming Control based on Unsupervised Machine Learning for MIMO Wireless Communication Networks Naoto Tamada, Yuyuan Chang, Kazuhiko Fukawa (Tokyo Tech) CS2021-29 |
In mobile communications, densely deployed cell systems are expected to improve the system capacity drastically. However... [more] | CS2021-29 pp.63-68 |
| KBSE, IPSJ-SE, SS [detail] | 2021-07-08 14:50 |
Online | Online (Zoom) | Research for using image analysis of program fault by deep learning for code review. Kazuhiko Ogawa, Takako Nakatani (OUJ) SS2021-6 KBSE2021-18 |
In order to predict the location of faults in a program, we imaged the source code of the defective program and verified... [more] | SS2021-6 KBSE2021-18 pp.31-36 |
| RECONF | 2021-06-08 16:10 |
Online | Online | Automatic generation of executable code for ReNA Yuta Masuda, Yasuhiro Nakahara, Motoki Amagasaki, Masahiro Iida (Kumamoto Univ.) RECONF2021-6 |
We have been developing ReNA as a CNN accelerator for the edge, which is controlled by directly specifying control signa... [more] | RECONF2021-6 pp.26-31 |
| KBSE | 2021-03-06 13:40 |
Online | Online | Research for finding faults in Programs using object detection algorithm by CNN-BI system Kazuhiko Ogawa, Takako Nakatani (OUJ) KBSE2020-45 |
In order to predict the location of program faults, we generated images the source code of a faulty program and trained ... [more] | KBSE2020-45 pp.65-70 |
| IN, NS (Joint) |
2021-03-05 10:10 |
Online | Online | A Convolutional Autoencoder Based Method for Cyber Intrusion Detection Xinyi She, Yuji Sekiya (Tokyo Univ.) IN2020-77 |
Cyber intrusion detection systems are increasingly crucial due to the monumental growth of internet applications. Howeve... [more] | IN2020-77 pp.138-143 |
| EA, US, SP, SIP, IPSJ-SLP [detail] | 2021-03-03 11:40 |
Online | Online | Estimation of Imagined Rhythm and Its Active Area from Electroencephalogram Using Deep Learning Naoki Yoshimura, Toshihisa Tanaka (TUAT) EA2020-63 SIP2020-94 SP2020-28 |
Rhythm is one element of music, and it is known that rhythm perception and imagery appear in an electroencephalogram (EE... [more] | EA2020-63 SIP2020-94 SP2020-28 pp.21-26 |
| IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] | 2021-02-18 14:50 |
Online | Online | Production and Evaluation of Data Set for Semantic Segmentation of 3D CG Image by H.265/HEVC Norifumi Kawabata (Tokyo Univ. of Science) ITS2020-30 IE2020-44 |
As one of purpose of study on image segmentation, we are able to consider whether between object and background region c... [more] | ITS2020-30 IE2020-44 pp.19-24 |
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