| IEICE Technical Committee Submission System Conference Schedule |
Online Proceedings [Sign in] Tech. Rep. Archives |
All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| CCS, NLP | 2023-06-08 15:35 |
Tokyo | Tokyo City Univ. | A place and route method in AQFP circuits using multi-objective optimization Syota Kasai, Hidehiro Nakano (Tokyo City Univ.)  NLP2023-18 CCS2023-6 |
In recent years, research has been conducted on AQFP circuits, which are superconducting logic circuits that consume les... [more] | NLP2023-18 CCS2023-6 pp.21-24 |
| CS, CQ (Joint) |
2023-05-19 09:00 |
Kagawa | Rexxam Hall (Kagawa Kenmin Hall) (Primary: On-site, Secondary: Online) |
[Invited Lecture]
D2EcoSys for realization of Web3-based digital twins Kenji Kanai (U Tokyo/Waseda Univ.), Hidenori Nakazato (Waseda Univ.), Taku Yamazaki, Sumiko Miyata (Shibaura Institute of Technology), Hidehiro Kanemitsu (Tokyo University of Technology), Aramu Mine (Gaiax), Shintaro Mori (Fukuoka University), Hironobu Imamura (HAFT) CS2023-6 |
(To be available after the conference date) [more] | CS2023-6 p.26 |
| CS, CQ (Joint) |
2023-05-19 09:50 |
Kagawa | Rexxam Hall (Kagawa Kenmin Hall) (Primary: On-site, Secondary: Online) |
[Invited Lecture]
Range Queries with Content Versioning on IPFS Hidehiro Kanemitsu (TUT), Kenji Kanai, Hidenori Nakazato (WAS) CS2023-8 |
[more] | CS2023-8 p.28 |
| NLP | 2023-05-13 11:15 |
Fukushima | Kenshin Koriyama Cultural Center (Koriyama, Fukushima) | Parameter adjustment methods of ACO based on moving costs in time-dependent TSP Teppei Yamauchi, Hidehiro Nakano (Tokyo City Univ.) NLP2023-4 |
The Time Dependent Traveling Salesman Problem (TDTSP) is a combinatorial optimization problem with dynamically changing ... [more] | NLP2023-4 pp.16-19 |
| ICD | 2023-04-11 09:55 |
Kanagawa | (Primary: On-site, Secondary: Online) |
[Invited Talk]
A 4-nm 6163-TOPS/W/b 4790-TOPS/mm2/b SRAM based Digital-Computing-in-Memory Macro Supporting Bit-Width Flexibility and Simultaneous MAC and Weight Update Haruki Mori, Wei-Chang Zhao, Cheng-En Lee, Chia-Fu Lee, Yu-Hao Hsu, Chao-Kai Chuang, Takeshi Hashizume, Hao-Chun Tung, Yao-Yi Liu, Shin-Rung Wu, Kerem Akarvardar, Tan-Li Chou, Hidehiro Fujiwara, Yih Wang, Yu-Der Chih, Yen-Huei Chen, Hung-Jen Liao, Tsung-Yung Jonathan Chang (TSMC) |
[more] | |
| CCS | 2023-03-26 13:35 |
Hokkaido | RUSUTSU RESORT | Analysis of learning performance in CycleGAN by applying data augmentation to few data Syuhei Kanzaki, Hidehiro Nakano (Tokyo City Univ.) CCS2022-72 |
In machine learning and deep learning, a huge amount of data is required for training. The image generation model GAN ex... [more] | CCS2022-72 pp.54-58 |
| NLP, MSS | 2023-03-17 15:45 |
Nagasaki | (Primary: On-site, Secondary: Online) |
Improving Recognition Accuracy in Contrastive Learning by Weighted Similarity Based on Data Source Ryotaro Sei, Hidehiro Nakano (Tokyo City Univ.) MSS2022-107 NLP2022-152 |
[more] | MSS2022-107 NLP2022-152 pp.214-219 |
| NLP, MSS | 2023-03-17 16:05 |
Nagasaki | (Primary: On-site, Secondary: Online) |
Lightweighting Noisy Student Semi-Supervised Learning by Applying MobileNet Yuga Morishima, Hidehiro Nakano (Tokyo City Univ.) MSS2022-108 NLP2022-153 |
Recently, Convolutional Neural Networks (CNNs) have attracted much attention in various fields such as image classificat... [more] | MSS2022-108 NLP2022-153 pp.220-224 |
| NLP, MSS | 2023-03-17 16:25 |
Nagasaki | (Primary: On-site, Secondary: Online) |
Investigation on improving diversity of options in option-critic reinforcement learning Aya Nakagawa, Hidehiro Nakano (Tokyo City Univ.) MSS2022-109 NLP2022-154 |
Recently, reinforcement learning has been attracting attention in various fields such as automatic control and game AI. ... [more] | MSS2022-109 NLP2022-154 pp.225-230 |
| IN, NS (Joint) |
2023-03-03 10:30 |
Okinawa | Okinawa Convention Centre + Online (Primary: On-site, Secondary: Online) |
A Routing Method Considering Content Server Mobility in a Tree-Structured Mobile Network over CCN Yuto Nakagawa, Masaki Hanada (Tokyo Univ. of Information Sciences), Hidehiro Kanemitsu (Tokyo Univ. of Technology) IN2022-106 |
In recent years, mobile network traffic has been rapidly increasing with spread of mobile devices such as smartphones. It ... [more] | IN2022-106 pp.241-246 |
| IN, NS (Joint) |
2023-03-03 12:00 |
Okinawa | Okinawa Convention Centre + Online (Primary: On-site, Secondary: Online) |
Multi-Attribute Queries in IPFS Hidehiro (TUT), Kenji Kanai, Hidenori Nakazato (WAS) IN2022-114 |
IPFS (InterPlanetary File System) is a distributed database middleware, and that is focused on as a Web3.0 key technolog... [more] | IN2022-114 pp.289-292 |
| CS, IE, IPSJ-AVM, ITE-BCT [detail] | 2022-11-24 10:30 |
Aichi | Nagoya Institute of Technology (Primary: On-site, Secondary: Online) |
Research and Development of Co-creating Digital Twins using Web3 Technologies to Accelerate Beyond 5G Kenji Kanai (Waseda Univ.), Taku Yamazaki, Sumiko Miyata (Shibaura Institute of Tech), Hidehiro Kanemitsu (Tokyo University of Tech), Aramu Mine (Gaiax), Shintaro Mori (Fukuoka Univ), Hidenori Nakazato (Waseda Univ.) CS2022-48 IE2022-36 |
This paper introduces a research project about the research and development of co-creating Digital Twins using Web3 tech... [more] | CS2022-48 IE2022-36 pp.1-6 |
| CCS | 2022-11-18 14:30 |
Mie | (Primary: On-site, Secondary: Online) |
Particle swarm optimization considering a positive and negative inertia terms by Levy distribution Sohei Kusaka, Hidehiro Nakano (Tokyo City Univ.) CCS2022-57 |
Particle Swarm Optimization (PSO) is known as a type of swarm intelligence algorithms. The inertia constant of each sear... [more] | CCS2022-57 pp.71-75 |
| CCS | 2022-11-18 14:55 |
Mie | (Primary: On-site, Secondary: Online) |
Particle swarm optimization using bit representation of state variables as random dynamics Masashi Nitanda, Hidehiro Nakano (Tokyo City Univ.) CCS2022-58 |
[more] | CCS2022-58 pp.76-80 |
| CCS | 2022-11-18 15:20 |
Mie | (Primary: On-site, Secondary: Online) |
Multi-domain translation from few data by CycleGAN applying data augmentation Syuhei Kanzaki, Hidehiro Nakano (Tokyo City Univ.) CCS2022-59 |
In machine learning and deep learning, a huge amount of data is required for training. The image generation model GAN ex... [more] | CCS2022-59 pp.81-84 |
| CCS | 2022-11-18 16:00 |
Mie | (Primary: On-site, Secondary: Online) |
Investigation for the coupling interactions in swarm intelligence algorithm based on spiking neural-oscillator networks Tomoyuki Sasaki (SIT), Hidehiro Nakano (TCU) CCS2022-60 |
Optimizer based on Spiking Neural-oscillator Networks (OSNNs) are deterministic swarm intelligence algorithms which intr... [more] | CCS2022-60 pp.85-90 |
| SDM | 2022-11-11 13:00 |
Online | Online | [Invited Talk]
Understanding of Electron Mobility Limiting Factor in Cryo-CMOS Hiroshi Oka, Takumi Inaba, Shota Iizuka, Hidehiro Asai, Kimihiko Kato, Takahiro Mori (AIST) SDM2022-74 |
To realize highly-integrated quantum computers, the Cryo-CMOS circuit has attracted significant attention for control/re... [more] | SDM2022-74 p.49 |
| ICD, SDM, ITE-IST [detail] | 2022-08-09 11:05 |
Online | [Invited Talk]
Effect of Conduction Band Edge States on Coulomb-Limiting Electron Mobility in Cryogenic MOSFET Operation Hiroshi Oka, Takumi Inaba, Shota Iizuka, Hidehiro Asai, Kimihiko Kato, Takahiro Mori (AIST) SDM2022-46 ICD2022-14 |
Cryogenic-CMOS technology has attracted significant attention for control/read-out qubits for realizing a large-scale qu... [more] | SDM2022-46 ICD2022-14 pp.54-59 |
|
| ICD, SDM, ITE-IST [detail] | 2022-08-09 11:50 |
Online | TCAD analysis of threshold voltage increase of short-channel MOSFET in cryogenic operation Hidehiro Asai, Takumi Inaba, Junichi Hattori, Koichi Fukuda, Hiroshi Oka, Takahiro Mori (AIST) SDM2022-47 ICD2022-15 |
[more] | SDM2022-47 ICD2022-15 pp.60-63 |
|
| IN, CCS (Joint) |
2022-08-04 10:00 |
Hokkaido | Hokkaido University(Centennial Hall) (Primary: On-site, Secondary: Online) |
Investigation on Applying Data Augmentation to CycleGAN Syuhei Kanzaki, Hidehiro Nakano (Tokyo City Univ.) CCS2022-26 |
In machine learning and deep learning, a huge amount of data is required for training. The image generation model GAN ex... [more] | CCS2022-26 pp.1-5 |
[Return to Top Page]