Faculty Profiles - TANAKA Yuichiro

Papers - TANAKA Yuichiro

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Inner Speech Recognition Using EEG Signals with Reservoir Computing Reviewed International journal

Syulistyo Arie Rachmad, Tanaka Yuichiro, Tamukoh Hakaru

International Journal of Activity and Behavior Computing ( Care XDX Center, Kyushu Institute of Technology ) 2025 ( 2 ) 1 - 19 2025.06

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

Inner speech recognition using electroencephalography (EEG) signals holds significant potential for advancing brain-computer interface (BCI), particularly for individuals with speech impairments. However, decoding inner speech from EEG data remains a challenging task due to the nonlinear, high-dimensional and temporal dynamic nature of neural signals. In order to address the challenges, this study explores the application of reservoir computing (RC), with a particular focus on bidirectional RC, for the purpose of classifying inner speech from EEG signals. Contrary to unidirectional RC, which processes data in a single time direction, bidirectional RC captures both past and future temporal dependencies. This enhancement of the extraction of meaningful EEG features for classification is a significant contribution of this study. We evaluate the performance of both unidirectional and bidirectional RC architectures across a range of reservoir sizes (400, 500, and 600 units) to identify the most effective configuration for this task. Our results demonstrate that the bidirectional RC consistently outperforms the unidirectional RC in terms of accuracy and F1-score across all reservoir sizes, highlighting its superior ability to extract comprehensive temporal features from EEG data. The optimal performance is achieved by bidirectional RC with 600 reservoir units, yielding an accuracy of 18.94% and an F1-score of 19.02%, which surpasses all other configurations. In contrast, unidirectional RC with 600 units exhibits a lower accuracy of 17.22%. These findings underscore the potential of bidirectional RC in EEG signals classification for inner speech recognition, offering a promising direction for developing efficient BCI with low-cost computation.

DOI： 10.60401/ijabc.117

CiNii Research

Prediction of Timing and Amount of Houseplants Watering by an Echo State Network on Jetson Reviewed International journal

Yoshimura W., Arimura K., Kobayashi R., Mizutani A., Fujino T., Tanaka Y., Sudo T., Ishizuka N., Ito K., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 529 - 534 2025.01

Lightweight Hand-Waving Action Recognition Using Reservoir Computing in a Cafeteria Environment Reviewed

Isomoto K., Fumoto S., Kobayashi R., Tanaka Y., Tamukoh H.

2025 IEEE/SICE International Symposium on System Integration, SII 2025 1034 - 1040 2025.01

Classification of Human Activity by Spiking Neural Networks using Event-based Vision Sensors Reviewed

Rakheja A., Saini R., Toyoda R., Tanaka Y., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 484 - 486 2025.01

Classification of Human Activity by Event-based Vision Sensors using Echo State Networks Reviewed International journal

Saini R., Rakheja A., Toyoda R., Tanaka Y., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 476 - 478 2025.01

Application of AI Robot Technology for Biophilic Design Reviewed International journal

Manabe K., Miyazono R., Ito K., Sudo T., Ishizuka N., Mizutani A., Tamukoh H., Tanaka Y., Ueda E., Anamizu Y., Furuya L., Tamai H., Nakano S., Tanaka H.

Proceedings of International Conference on Artificial Life and Robotics 513 - 518 2025.01

An Exhibition Environment with 2D Markers for Guide Robot Reviewed International journal

Mizutani A., Hattori Y., Tanaka Y., Ishizuka N., Tanaka H., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 524 - 528 2025.01

Hibikino-Musashi@Home RoboCup@Home DSPL Champion 2024 International journal

Mizutani A., Isomoto K., Yamao K., Kobayashi R., Fumoto S., Arimura K., Yamaguchi N., Shiba T., Kimizuka K., Ohno Y., Terashima R., Yamaguchi H., Fujino T., Maruno R., Yoshimura W., Mine K., Phu Thien Nhan T., Yano Y., Tanaka Y., Nishida T., Morie T., Tamukoh H.

Lecture Notes in Computer Science 15570 LNAI 461 - 472 2025.01

Creating a Future Space Where Humans and Robots Live Together

Mizutani Akinobu, Ito Keitaro, Ishizuka Naoto, Sudo Tomomi, Tanaka Hirofumi, Tanaka Yuichiro, Tamukoh Hakaru

Journal of the Robotics Society of Japan ( The Robotics Society of Japan ) 43 ( 4 ) 367 - 370 2025.01

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

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

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

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

In-material reservoir implementation of reservoir-based convolution Reviewed International journal

Tanaka Y., Usami Y., Tanaka H., Tamukoh H.

Proceedings - IEEE International Symposium on Circuits and Systems 2023-May 2023.05

Memory-Efficient Implementation of GMM-MRCoHOG for HumaRecognition Hardware Reviewed

Takemoto R., Nagamine Y., Yoshihiro K., Shibata M., Yamada H., Tanaka Y., Enokida S., Tamukoh H.

Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications 4 648 - 655 2023.02

Random network device fabricated using Ag<inf>2</inf>Se nanowires for data augmentation with binarized convolutional neural network Reviewed International journal

Kotooka T., Tanaka Y., Tamukoh H., Usami Y., Tanaka H.

Applied Physics Express 16 ( 1 ) 2023.01

Material computing based on electrochemical dynamics Reviewed

Yuki Usami, Yuichiro Tanaka, Takumi Kotooka, Hakaru Tamukoh, Hirofumi Tanaka

The 4th International Symposium on Neuromorphic AI Hardware 6 - 6 2022.12

Robust Trash Can Lid Opening System Reviewed

Kosei Isomoto, Yuga Yano, Yuichiro Tanaka, Hakaru Tamukoh

The 4th International Symposium on Neuromorphic AI Hardware 46 - 46 2022.12

Hardware-oriented Brain-inspired Model with Memory Accumulation and Recall Functions to Generate Actions of Home Service Robots Reviewed

Akinobu Mizutani, Ichiro Kawashima, Yuichiro Tanaka, Hakaru Tamukoh, Katsumi Tateno, Osamu Nomura, Takashi Morie

The 4th International Symposium on Neuromorphic AI Hardware 44 - 44 2022.12

Resorvoir-based neuron model to emulate cellular neural activity responding to odor in C. elegans Reviewed

Ryosuke Ishibashi, Takumi Nakamura, Noriko Sato, Takeshi Ishihara, Yuichiro Tanaka, Hakaru Tamukoh, Terumasa Tokunaga

The 4th International Symposium on Neuromorphic AI Hardware 42 - 42 2022.12

Deep Learning Method on Word Level American Sign Language Recognition from Video Reviewed

Arie Rachmad Syulistyo, Yuichiro Tanaka, Hakaru Tamukoh

The 4th International Symposium on Neuromorphic AI Hardware 28 - 28 2022.12

Reservoir-based 1D convolution Reviewed

Yuichiro Tanaka, Hakaru Tamukoh,

The 4th International Symposium on Neuromorphic AI Hardware 12 - 12 2022.12

Reservoir computing in the matter based on electrochemical dynamics Reviewed International journal

Usami Y, Tanaka Y, Tamukoh H, Matsumoto T, van der Wiel W, Tanaka H

16th International Conference on Nanostructured Materials (NANO2022) 2022.06

Reservoir-based convolution Reviewed International journal

Tanaka Yuichiro, Tamukoh Hakaru

Nonlinear Theory and Its Applications, IEICE ( The Institute of Electronics, Information and Communication Engineers ) 13 ( 2 ) 397 - 402 2022.04

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

Reservoir computing (RC) has attracted attention and has been used in many applications because of its low training cost. Multiple studies using RC for image recognition have been proposed, and some have achieved accuracy rates of greater than 99% on the MNIST dataset. For the Fashion-MNIST and CIFAR-10 datasets, however, they have not yet achieved high accuracy. This study proposes a novel convolutional neural network based on RC that can be optimized by ridge regression rather than back-propagation. The reservoir-based network has multiple reservoirs with various leak rates to extract features with various spatial frequencies from the inputs. The experimental results show that the performance of the proposed model achieves higher accuracy rates in the mentioned datasets compared with those of other reservoir-based image recognition approaches.

DOI： 10.1587/nolta.13.397

Kyutacar

Scopus

CiNii Research

Other Link： http://hdl.handle.net/10228/00009117

Hardware-oriented Algorithm for Human Detection using GMM-MRCoHOG Features Reviewed International journal

Takemoto R., Nagamine Y., Yoshihiro K., Shibata M., Yamada H., Tanaka Y., Enokida S., Tamukoh H.

Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications 4 749 - 757 2022.02

Applying Center Loss to Multidimensional Feature Space in Deep Neural Networks for Open-set Recognition Reviewed International journal

Daiju Kanaoka, Yuichiro Tanaka, Hakaru Tamukoh

17th International Conference on Computer Vision Theory and Applications (VISAPP2022) 5 359 - 365 2022.02

Applying Center Loss to Multidimensional Feature Space in Deep Neural Networks for Open-set Recognition Reviewed

Kanaoka D., Tanaka Y., Tamukoh H.

Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications 5 359 - 365 2022.01

Reservoir-based Convolutional Neural Network Reviewed International journal

Yuichiro Tanaka, Hakaru Tamukoh

The 2021 NonLinear Science Workshop NLSW-34 2021.12

Brain-inspired neural network navigation system with hippocampus, prefrontal cortex, and amygdala functions Reviewed

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

ISPACS 2021 - International Symposium on Intelligent Signal Processing and Communication Systems: 5G Dream to Reality, Proceeding 2021.01

Open Set Recognition Using the Feature Space of Deep Neural Networks Reviewed International journal

Kanaoka D., Tanaka Y., Tamukoh H.

ISPACS 2021 - International Symposium on Intelligent Signal Processing and Communication Systems: 5G Dream to Reality, Proceeding 2021.01

In-Materio Reservoir Computing in a Sulfonated Polyaniline Network Reviewed

Usami Y., van de Ven B., Mathew D.G., Chen T., Kotooka T., Kawashima Y., Tanaka Y., Otsuka Y., Ohoyama H., Tamukoh H., Tanaka H., van der Wiel W.G., Matsumoto T.

Advanced Materials 33 ( 48 ) 2021.01

A Brain-Inspired Artificial Intelligence Model of Hippocampus, Amygdala, and Prefrontal Cortex on Home Service Robots Reviewed International journal

Tanaka Yuichiro, Tamukoh Hakaru, Tateno Katsumi, Katori Yuichi, Morie Takashi

IEICE Proceeding Series ( The Institute of Electronics, Information and Communication Engineers ) 74 138 - 141 2020.11

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

We propose a brain-inspired artificial intelligence model that acquires local knowledge from a small number of human-robot interactions, and uses this knowledge for future prediction. We focus on areas of the brain: the hippocampus, the amygdala, and the prefrontal cortex, thereby providing an internal representation of place and time, a value judgment, and a prediction. By integrating these functions, our model achieves local knowledge acquisition and future prediction. The proposed model is evaluated in a task for home service robots. In the experiment, the model learns local time-series events from a small number of interactions, and predicts the future events.

DOI： 10.34385/proc.74.a4l-c-4

CiNii Research

Other Link： https://kyutech.repo.nii.ac.jp/records/2001270

A Reservoir Based Q-Learning Model for Autonomous Mobile Robots Reviewed International journal

Inada Masafumi, Tanaka Yuichiro, Tamukoh Hakaru, Tateno Katsumi, Morie Takashi, Katori Yuichi

IEICE Proceeding Series ( The Institute of Electronics, Information and Communication Engineers ) 74 213 - 216 2020.11

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

We propose a robot navigation system using reinforcement learning and reservoir computing. Reservoir computing is a framework for the processing of complex time series based on a recurrent neural network (RNN). The proposed system is trained by reinforcement learning, which is a framework that performs trial and error based on the rewards given by the environment. The trained network generates motor commands reflecting on action values obtained by the reinforcement learning and operates the autonomous mobile robot.

DOI： 10.34385/proc.74.b3l-b-5

CiNii Research

Other Link： https://kyutech.repo.nii.ac.jp/records/2001273

An Amygdala-Inspired Classical Conditioning Model Implemented on an FPGA for Home Service Robots Reviewed

Tanaka Y., Morie T., Tamukoh H.

IEEE Access 8 212066 - 212078 2020.11

Object Recognition Using Flexible Tactile Sensor Reviewed

Shoshi Tokuno, Yuichiro Tanaka, Takumi Kawasetsu, Koh Hosoda, Hakaru Tamukoh

Asia Pacific Conference on Robot IoT System Development and Platform 2020 2020.11

Prediction of Sensory Information and Generation of Motor Commands for Autonomous Mobile Robots using Reservoir Computing Reviewed

Masafumi Inada, Yuichiro Tanaka, Hakaru Tamukoh, Katsumi Tateno, Takashi Morie, Yuichi Katori

2019 International Symposium on Nonlinear Theory and Its Applications 333 - 336 2019.12

Research and Development of Home Service Robots Aiming for Social Implementation

ONO Tomohiro, SAKATA Takuma, YOSHII Takumi, KAMIMURA Daichi, KANAMARU Kazuki, NAKAMURA Kentaro, NISHIMURA Yuta, MORIE Takashi, TAMUKOH Hakaru, HORI Sansei, ISHIDA Yutaro, TANAKA Yuichiro, YOSHIMOTO Yuma, ABE Yushi, MUTO Fuyuki, KABASHIMA Kohei, FUKUYADO Masashi

The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) ( The Japan Society of Mechanical Engineers ) 2019 ( 0 ) 1P2 - I02 2019.06

Language：Japanese Publishing type：Research paper (conference, symposium, etc.)

Home service robots have begun attracting attention due to decreasing birthrate and increasing aging population. We,"Hibikino-Musashi@Home"team, develop home service robots, and participate international competitions; RoboCup@Home and World Robot Challenge (WRC), which are established to realize that robots will work and live with people in domestic environments. In this paper, we introduce our robots and technologies, especially an object recognition and manipulation system. We use You Only Look Once (YOLO) for object recognition. To train YOLO, a big dataset which is difficult to prepare in a short term is required. For this, we develop an automatic annotation system for YOLO which can generate 1.7 million training data, and we can complete training of YOLO in a day. To grasp objects, estimation of object orientations is required. Our system can detect the orientations by processing point clouds of objects. By using the system, we obtained about 30% of scores in Tidy Up Task in the WRC.

DOI： 10.1299/jsmermd.2019.1P2-I02

CiNii Article

CiNii Research

Other Link： https://ci.nii.ac.jp/naid/130007774525

Hardware implementation of brain-inspired amygdala model Reviewed International journal

Tanaka Y., Tamukoh H.

Proceedings - IEEE International Symposium on Circuits and Systems 2019-May 2019.01

Live demonstration: Hardware implementation of brain-inspired amygdala model Reviewed International journal

Tanaka Y., Tamukoh H.

Proceedings - IEEE International Symposium on Circuits and Systems 2019-May 2019.01

Live demonstration: A VLSI implementation of time-domain analog weighted-sum calculation model for intelligent processing on robots Reviewed International journal

Yamaguchi M., Iwamoto G., Abe Y., Tanaka Y., Ishida Y., Tamukoh H., Morie T.

Proceedings - IEEE International Symposium on Circuits and Systems 2019-May 2019.01

Application of digital hardware of deep self-organizing map network Reviewed

Yuichiro Tanaka, Hakaru Tamukoh

Asia Pacific Conference on Robot IoT System Development and Platform 2018 2018.10

Research and development of service robots by competition activities

YOSHIMOTO Yuma, IWAMOTO Gouki, HASHIMOTO Kouhei, MORIE Takashi, TAMUKOH Hakaru, HORI Sansei, ISHIDA Yutaro, KIYAMA Yuta, KURODA Yuki, TANAKA Yuichiro, HISANO Masataka, FUJITA Hiroto, ARATANI Yoshiya

The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) ( The Japan Society of Mechanical Engineers ) 2018 ( 0 ) 2P2 - E02 2018.06

Language：Japanese Publishing type：Research paper (conference, symposium, etc.)

In real world applications of home service robots, they are required to be able to perform housekeeping or other tasks. "RoboCup@Home" is a competition for benchmarking home service robots. It includes tests to service robots for real world implementation. These tests require the combination of intelligent functions such as object detection and voice recognition. We have developed a home service robot "Exi@" and evaluated its performance by participating in RoboCup@Home. This paper describes real world implementation of robots and RoboCup@Home. The functions embedded in Exi@ and a task "Restaurant" are also explained.

DOI： 10.1299/jsmermd.2018.2P2-E02

CiNii Article

CiNii Research

Other Link： https://ci.nii.ac.jp/naid/130007552042

Hardware implementation of deep self-organizing map networks Reviewed

Tanaka Y., Tamukoh H.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 10613 LNCS 439 - 441 2017.09

Hardware-Oriented Algorithm for Deep Neural Networks Composed of Self-Organizing Maps Reviewed

Yuichiro Tanaka, Hakaru Tamukoh

Proceedings of International Workshop on Smart Info-Media Systems in Asia 116 - 121 2016.09

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Papers - TANAKA Yuichiro