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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 |
| 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 |
| 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 |
| 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 |
| 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 |
|
| SP, SIP, EA | 2017-03-01 12:40 |
Okinawa | Okinawa Industry Support Center | [Poster Presentation]
Application and Evaluation of CIP Methods to Acoustic Wave Propagation Analysis with Multi-dimensional Advection Akihiro Fukuda, Kan Okubo (Tokyo Met. Univ.), Takuya Oshima (Niigata Univ.), Takao Tsuchiya (Doshisha Univ.), Masashi Kanamori (JAXA) EA2016-90 SIP2016-145 SP2016-85 |
The constrained interpolation profile (CIP) method is a calculation method with less numerical dispersion error, which u... [more] | EA2016-90 SIP2016-145 SP2016-85 pp.49-54 |
| SC | 2016-08-26 11:00 |
Tokyo | Kikai-Shinko-Kaikan Bldg. B2F Room No.2 | Improvement of Trust Value Prediction Using Text Mining for Recommender System Incheon Paik, Tomoya Maemori (UoA) SC2016-12 |
Recommender system will help to provide a list of items information that users are interested in. And with growth of soc... [more] | SC2016-12 pp.7-11 |
| EMD, OPE, LQE, CPM | 2010-08-26 17:15 |
Hokkaido | Chitose Arcadia Plaza | Loss characteristics of optical sensitive polymer waveguide using UV laser drawing Seigi Oki, Atsushi Fujisawa (Chitose Inst. of Sci and Tech.), Toshihiro Suda (Photonic Sci Tech.), Soichi Kobayashi (Chitose Inst. of Sci and Tech.) EMD2010-40 CPM2010-56 OPE2010-65 LQE2010-38 |
In this report the fabrications and measurements of the polymer multimode optical waveguides with the hybrid silicone ar... [more] | EMD2010-40 CPM2010-56 OPE2010-65 LQE2010-38 pp.67-70 |
| PRMU | 2008-02-22 10:45 |
Ibaraki | Univ. of Tsukuba | Handwritten Chinese Character Recognition by Background Propagation Method Takafumi Fukui, Michio Umeda (Osaka Electro Communication Univ.) PRMU2007-233 |
In the field of character recognition, many feature extraction methods are proposed. Those are the methods extract feat... [more] |
PRMU2007-233 pp.111-116 |
| NC | 2007-05-21 10:25 |
Kanagawa | Tokyo Inst. Tech.(Suzukakedai Campus) | Unbiased Likelihood Backpropagation Learning Masashi Sekino, Katsumi Nitta (Tokyo Inst. of Tech.) NC2007-1 |
The error backpropagation is one of the popular methods for training an artificial neural network.When the error backpro... [more] | NC2007-1 pp.1-6 |
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