Faculty Profiles - TANAKA Yuichiro

Papers - TANAKA Yuichiro

Division display21 - 40 of about 71／ All the affair displays >>

Simple open-set recognition combining metric learning and anomaly detection Reviewed International journal

Kanaoka D., Tanaka Y., Tamukoh H.

Advanced Robotics 39 ( 7 ) 368 - 382 2025.01

Embedded Brain-like Computer Systems for Home Service Robots Invited Reviewed

Tamukoh Hakaru, Tanaka Yuichiro

Journal of the Robotics Society of Japan ( The Robotics Society of Japan ) 43 ( 7 ) 656 - 660 2025.01

Hardware-Oriented and Precisely Approximated Online Softmax for Deep Learning Models Reviewed International journal

Hirayae S., Yoshioka K., Tanaka Y., Tamuko H.

2025 32nd IEEE International Conference on Electronics Circuits and Systems Icecs 2025 2025.01

Object recognition and grasping point detection using carbon nanotube - polydimethylsiloxane nanocomposite sensor Reviewed International journal

Tokuno Shoshi, Kimizuka Kouki, Tanaka Yuichiro, Usami Yuki, Tanaka Hirofumi, Tamukoh Hakaru

Nonlinear Theory and Its Applications, IEICE ( The Institute of Electronics, Information and Communication Engineers ) 15 ( 4 ) 883 - 898 2024.10

Language：English Publishing type：Research paper (scientific journal)

This paper presents a system for processing tactile information using a carbon nanotube (CNT)-polydimethylsiloxane (PDMS) nanocomposite sensor designed for robotic applications. This study introduces an approach for recognizing objects and detecting optimal grasping points using tactile data from a sensor-equipped robotic hand. The sensor is expected to be more efficient than a computerized implementation using sensor dynamics directly in the computation. The experiments demonstrated the system's ability to classify nine types of objects with an accuracy of 83.56% and to discern two grasping points on four object types, achieving a 71.7% success rate.

DOI： 10.1587/nolta.15.883

Scopus

CiNii Research

Other Link： https://www.jstage.jst.go.jp/article/nolta/15/4/15_883/_pdf

Physical Reservoir Computing Devices Utilizing Nanomaterial Random Network and Its Sensing Application Invited Reviewed

TANAKA Hirofumi, AZHARI Saman, BANERJEE Deep, KIMIZUKA Kouki, TANAKA Yuichiro, USAMI Yuki

Vacuum and Surface Science ( The Japan Society of Vacuum and Surface Science ) 67 ( 9 ) 428 - 432 2024.09

Language：Japanese Publishing type：Research paper (scientific journal)

The rapid advancement of software-based deep learning has led to a surge in AI applications, yet hardware limitations in silicon CMOS technology hinder performance. Consequently, interest is growing in hardware technologies and new materials for artificial neural networks (ANNs) and neuromorphic systems. Exploiting nanomaterials nonlinearity caused by spontaneous physical phenomena holds promise of reducing power consumption in AI hardware. Reservoir computing devices, derived from recurrent neural networks, play a crucial role, with material reservoir devices showcasing “material intelligence.” Tailored nanomaterials for reservoir devices hold potential for revolutionizing AI, especially in robotics. As research progresses, focusing on device functionalization and applications, recent findings underline the significance of integrating nanomaterials into AI hardware for enhanced computational capabilities and energy efficiency.

DOI： 10.1380/vss.67.428

CiNii Research

Other Link： https://www.jstage.jst.go.jp/article/vss/67/9/67_20181334/_pdf

A Hippocampus-Inspired Environment-Specific Knowledge Acquisition System Utilizing Common Knowledge with Contextual Information Reviewed International journal

Mizutani A., Tanaka Y., Tamukoh H., Nomura O., Tateno K., Morie T.

Proceedings of the International Joint Conference on Neural Networks 2024.07

Reservoir-based 1D convolution: low-training-cost AI Reviewed International journal

TANAKA Yuichiro, TAMUKOH Hakaru

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences ( The Institute of Electronics, Information and Communication Engineers ) advpub ( 0 ) 941 - 944 2024.06

Authorship：Lead author,　Corresponding author Language：English Publishing type：Research paper (scientific journal)

In this study, we introduce a reservoir-based one-dimensional (1D) convolutional neural network that processes time-series data at a low computational cost, and investigate its performance and training time. Experimental results show that the proposed network consumes lower training computational costs and that it outperforms the conventional reservoir computing in a sound-classification task.

DOI： 10.1587/transfun.2023eal2050

Scopus

CiNii Research

Other Link： https://www.jstage.jst.go.jp/article/transfun/advpub/0/advpub_2023EAL2050/_pdf

Recognizing Nursing Activities in Endotracheal Suction: Utilizing Multiple Readouts Reservoir Computing and Large Language Models Reviewed International journal

Arie Rachmad Syulistyo, Yuichiro Tanaka, Hakaru Tamukoh

6th International Conference on Activity and Behavior Computing 2024.05

Enhancing Memory Capacity of Reservoir Computing with Delayed Input and Efficient Hardware Implementation with Shift Registers Reviewed International journal

Hirayae S., Yoshioka K., Yokota A., Kawashima I., Tanaka Y., Katori Y., Nomura O., Morie T., Tamukoh H.

Proceedings - IEEE International Symposium on Circuits and Systems 2024.05

Development of A SayCan-based Task Planning System Capable of Handling Abstract Nouns Reviewed International journal

Yamao K., Kanaoka D., Isomoto K., Mizutani A., Tanaka Y., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 430 - 434 2024.02

A Low Computational Cost Hand Waving Action Recognition System with Echo State Network for Home Service Robots Reviewed International journal

Yamaguchi H., Mizutani A., Syulistyo A.R., Tanaka Y., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 421 - 424 2024.02

Object recognition using CNT-PDMS nanocomposite sensor on a robot hand Reviewed

Shoshi Tokuno, Kouki Kimizuka, Yuichiro Tanaka, Hirofumi Tanaka, Hakaru Tamukoh

The 22nd International Symposium on Eco-materials Processing and Design 2024.01

Thermally Stable Ag<inf>2</inf>Se Nanowire Network as an Effective In-Materio Physical Reservoir Computing Device Reviewed International journal

Kotooka T., Lilak S., Stieg A.Z., Gimzewski J.K., Sugiyama N., Tanaka Y., Kawabata T., Karacali A., Tamukoh H., Usami Y., Tanaka H.

Advanced Electronic Materials 10 ( 12 ) 2024.01

Self-Organizing Multiple Readouts for Reservoir Computing Reviewed International journal

Tanaka Y., Tamukoh H.

IEEE Access 11 138839 - 138849 2023.12

Traffic Flow Optimization using a Chaotic Boltzmann Machine Annealer on an FPGA Reviewed International journal

Yoshioka K., Tanaka Y., Tamukoh H.

Proceedings - International Conference on Field-Programmable Technology, ICFPT 266 - 267 2023.12

LUTNet-RC: Look-Up Tables Networks for Reservoir Computing on an FPGA Reviewed International journal

Yoshioka K., Tanaka Y., Tamukoh H.

Proceedings - International Conference on Field-Programmable Technology, ICFPT 170 - 178 2023.12

Ensemble Learning of Multiple Readouts for Reservoir Computing Reviewed International journal

Tanaka Yuichiro, Tamukoh Hakaru

IEICE Proceeding Series ( The Institute of Electronics, Information and Communication Engineers ) 76 509 - 512 2023.09

Authorship：Lead author,　Corresponding author Language：English Publishing type：Research paper (scientific journal)

This study proposes a reservoir computing model with multiple readouts and an associated training method to enhance the training capability of reservoir computing. This study conducts a speaker classification task and a word classification task using an audio dataset consisting of digits pronounced by six persons. The experimental results reveal that the proposed model with multiple readouts outperforms a conventional model with a single readout.

DOI： 10.34385/proc.76.c3l-11

Kyutacar

CiNii Research

Other Link： https://kyutech.repo.nii.ac.jp/records/2000241

Robust Trash Can Lid Opening System Reviewed International journal

Kosei Isomoto, Yuga Yano, Yuichiro Tanaka, Hakaru Tamukoh

2023 International Workshop on Smart Info-Media Systems in Asia (SISA 2023) 30 - 33 2023.08

Efficient Repetition Coding for Deep Learning Towards Implementation Using Emerging Non-Volatile Memory with Write-Errors Reviewed International journal

Fuengfusin N., Tamukoh H., Tanaka Y., Nomura O., Morie T.

Proceedings of the International Joint Conference on Neural Networks 2023-June 2023.06

FPGA Implementation of a Chaotic Boltzmann Machine Annealer Reviewed International journal

Yoshioka K., Katori Y., Tanaka Y., Nomura O., Morie T., Tamukoh H.

Proceedings of the International Joint Conference on Neural Networks 2023-June 2023.06

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Papers - TANAKA Yuichiro