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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 | 2024-03-01 10:30 |
Fukuoka | A Preliminary Study on Parameter Optimization Using a Backpropagation Algorithm for a Neonatal Thermal Model Natsumi Sakamoto, Hiroki Kudo, Akira Uchiyama (Osaka Univ.), Keisuke Hamada (Nagasaki Harbor Medical Center), Eiji Hirakawa (Kagoshima City Hospital)  SeMI2023-81 |
Neonates need temperature management in incubators due to their underdeveloped thermoregulatory functions. Traditional m... [more] | SeMI2023-81 pp.60-65 |
|
| RISING (3rd) |
2023-10-31 14:00 |
Hokkaido | Kaderu 2・7 (Sapporo) | [Poster Presentation]
Optimal Compression Rate for Multiple Data Compression Techniques in Data Parallel Distributed Deep Learning Ryudai Fukuda, Takuji Tachibana (Univ. Fukui) |
In distributed deep learning, where multiple processors are used, the learning time can be significantly reduced by exec... [more] | |
| NS | 2023-10-05 10:10 |
Hokkaido | Hokkaidou University + Online (Primary: On-site, Secondary: Online) |
[Encouragement Talk]
Communication Scheduling Based on Heuristic Algorithm in Distributed Deep Learning Ryudai Fukuda, Takuji Tachibana (Univ. Fukui) NS2023-86 |
In distributed deep learning, where multiple processors are used, the learning time can be significantly reduced by exec... [more] | NS2023-86 pp.83-88 |
| CS | 2023-07-28 14:50 |
Tokyo | Hachijo-machi Chamber of Commerce and Industry | Improving position estimation accuracy method by reducing RSSI fluctuations in BLE fingerprinting-based indoor positioning Jingshi Qian, Nobuyoshi Komuro (Chiba Univ.) CS2023-58 |
The complex indoor environment will reflect and absorb the RSSI (Received Signal Strength Indicator) from the sensor. Be... [more] | CS2023-58 pp.163-168 |
| NS, ICM, CQ, NV (Joint) |
2022-11-24 13:00 |
Fukuoka | Humanities and Social Sciences Center, Fukuoka Univ. + Online (Primary: On-site, Secondary: Online) |
Optimal Data Communication Scheduling Considering Multiple Data Compression Techniques in Distributed Deep Learning Fukuda Ryudai, Takuji Tachibana (Univ. Fukui) NS2022-105 |
In distributed deep learning, which uses multiple processors, the training time can be greatly reduced by executing the ... [more] | NS2022-105 pp.29-34 |
| SIS, IPSJ-AVM | 2022-06-10 11:20 |
Fukuoka | KIT(Wakamatsu Campus) (Primary: On-site, Secondary: Online) |
Sugar content detection using wireless LAN system for sucrose aqueous solution Souta Yakura, Naoto Sasaoka, Tadao Nakagawa, Yoshihiro Takemura (Tottori Univ.) SIS2022-8 |
Currently, Japan’s agricultural industry is facing various problems such as farming population. To solve such problems ,... [more] | SIS2022-8 pp.36-40 |
| SeMI | 2022-01-20 15:20 |
Nagano | (Primary: On-site, Secondary: Online) |
[Short Paper]
A Study of Beamforming Feedback-based Model-driven Angle of Departure Estimation Sohei Itahara (Kyoto Univ.), Takayuki Nishio (Kyoto Univ./Tokyo Tech.), Koji Yamamoto (Kyoto Univ.) SeMI2021-68 |
This paper introduces the angle of departure (AoD) estimation method [1] using the multiple signal classification (MUSIC... [more] | SeMI2021-68 pp.59-61 |
| MBE, NC (Joint) |
2021-10-28 15:55 |
Online | Online | A Study on Improvement Learning Performance with Chaos Neurons Renshi Nagasawa, Masahiro Nakagawa (NUT) NC2021-23 |
In the backpropagation method in neural networks, the problem is that the energy converges to the local minimum. On the... [more] | NC2021-23 pp.28-33 |
| IN, CCS (Joint) |
2021-08-05 14:25 |
Online | Online | Digital Implement of 3-layered Neural Networks with Stochastic Activation, Shunting Inhibition, and a Dual-rail Backpropagation Yoshiaki Sasaki, Seiya Muramatsu, Kohei Nishida, Megumi Akai-Kasaya, Tetsuya Asai (Hokkaido Univ.) CCS2021-16 |
Stochastic computing (SC) is an arithmetic technique that enables various operations to be performed with a small number... [more] | CCS2021-16 pp.7-13 |
| NC, MBE (Joint) |
2021-03-05 13:25 |
Online | Online | Applying Ensemble Learning in Relay BP Keisuke Toyama, Yukari Yamauchi (Nihon Univ.) NC2020-70 |
Convolutional Neural Network (CNN) is one of the network models that can produce highly accurate output even though it u... [more] | NC2020-70 pp.157-162 |
| RCS, AP, UWT (Joint) |
2020-11-26 11:20 |
Online | Online | [Invited Lecture]
Adaptive digital down-conversion for underwater acoustic communication Mitsuyasu Deguchi, Yukihiro Kida, Takuya Shimura (JAMSTEC) AP2020-86 RCS2020-125 |
In underwater acoustic communication, effects of the Doppler shift is much larger than that of the radio communication i... [more] | AP2020-86 RCS2020-125 pp.72-77(AP), pp.87-92(RCS) |
| MBE, NC, NLP, CAS (Joint) [detail] |
2020-10-29 16:10 |
Online | Online | Numerical research on effects of quantization in SNN learned by backpropagation Yumi Watanabe, Jun Ohkubo (Saitama Univ.) NC2020-14 |
There are many studies to quantize the parameters of neural networks. For example, while there are methods of quantizing... [more] | NC2020-14 pp.29-33 |
| AP, SANE, SAT (Joint) |
2020-07-17 10:45 |
Online | Online | Computation on Circularly Polarized Electromagnetic Wave Backscattering by A Tree Target using FDTD Method Xiangyu Huang, Mohammad Nasucha, Josaphat T. Sri sumantyo, Cahya E.Santosa (Chiba Univ) SANE2020-18 |
Chiba University is developing Circularly Polarized Synthetic Aperture Radar (CP-SAR). Understanding electromagnetic wav... [more] | SANE2020-18 pp.11-15 |
| NC, MBE (Joint) |
2020-03-05 10:20 |
Tokyo | University of Electro Communications (Cancelled but technical report was issued) |
An extension of the H_infinity learning to deep neural networks Yasuhiro Sugawara, Kiyoshi Nishiyama (Iwate University) NC2019-92 |
In recent years, deep neural networks have achieved remarkable research results. In this study, we propose a method to e... [more] | NC2019-92 pp.95-100 |
| EA, US (Joint) |
2019-01-23 10:50 |
Kyoto | Doshisha Univ. | [Invited Talk]
The study of estimation method for sound exposure level for evaluating the effect of marine organism by radiated noise from ship Toshio Tsuchiya, Yukino Hirai, Etsuro Shimizu (TUMSAT) US2018-103 |
In recent years, investigation of the adverse influence by underwater noise from shipping on marine organisms is activel... [more] | US2018-103 pp.117-122 |
| VLD, DC, CPSY, RECONF, CPM, ICD, IE, IPSJ-SLDM, IPSJ-EMB, IPSJ-ARC (Joint) [detail] |
2018-12-06 10:55 |
Hiroshima | Satellite Campus Hiroshima | On the Generation of Random Capture Safe Test Vectors Using Neural Networks Sayuri Ochi, Kenichirou Misawa, Toshinori Hosokawa, Yukari Yamauchi, Masayuki Arai (Nihon Univ.) VLD2018-51 DC2018-37 |
Excessive capture power consumption at scan testing causes the excessive IR drop and it might cause test-induced yield l... [more] | VLD2018-51 DC2018-37 pp.89-94 |
| MBE, NC (Joint) |
2018-03-14 15:30 |
Tokyo | Kikai-Shinko-Kaikan Bldg. | Gradually Stacking Neural Network Shunya Sasaki, Masafumi Hagiwara (Keio Univ) NC2017-97 |
In this paper, we propose a neural network with multiple layers in a stepwise manner. Neural networks (NNs) become more ... [more] | NC2017-97 pp.175-180 |
| CS, CAS | 2018-03-13 15:55 |
Fukuoka | Nishijin Plaza, Kyushu University | A Channel State Information Feedback Scheme for Multi-user MIMO using Guard Band Frequency Response extrapolated by Adaptive Filter Futoshi Fukuda, Fumio Takahata (Waseda Univ.) CAS2017-159 CS2017-113 |
Several compression schemes of channel state information (CSI) for the downlink multi-user MIMO are proposed, in which a... [more] | CAS2017-159 CS2017-113 pp.145-150 |
| US, EA (Joint) |
2018-01-23 13:00 |
Osaka | [Poster Presentation]
Time Domain Numerical Analysis of Acoustical Doppler Effect Using CIP-MOC Method Takuro Sonobe, Kan OKubo, Norio Tagawa (Tokyo Met. Univ.), Takao Tsuchiya (Doshisha Univ.) US2017-97 |
The constrained interpolation profile (CIP) method is the calculation method to incorporate the spatial differential v... [more] |
US2017-97 pp.77-82 |
|
| MBE, NC (Joint) |
2017-05-26 13:50 |
Toyama | Toyama Prefectural Univ. | A Parallel Forward-Backward Propagation Learning Rule for Auto-Encoder Yoshihiro Ohama, Takayoshi Yoshimura (Toyota CRDL) NC2017-3 |
Auto-encoder is known as a hourglass neural network for acquiring essential representations from multi-dimensional data ... [more] | NC2017-3 pp.13-18 |
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