研究者詳細 - 田中　悠一朗

論文 - 田中　悠一朗

分割表示55 件中 41 - 55 件目／全件表示 >>

Open Set Recognition Using the Feature Space of Deep Neural Networks 査読有り国際誌

Kanaoka D., Tanaka Y., Tamukoh H.

ISPACS 2021 - International Symposium on Intelligent Signal Processing and Communication Systems: 5G Dream to Reality, Proceeding 2021年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Image classification assumes that all classes used in testing are known. Therefore, when an unknown class data is input, it cannot be recognized correctly. A method that enables unknown classes to be identified is called open set recognition. In this paper, we propose a method of open set recognition focusing on the feature space of the classifier and a Mahalanobis-based threshold. The experimental results show that the proposed method surpasses state-of-the-art methods on some datasets, demonstrating the potential of a method focusing on the feature space.

DOI： 10.1109/ISPACS51563.2021.9650985

Kyutacar

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85124169792&origin=inward

In-Materio Reservoir Computing in a Sulfonated Polyaniline Network 査読有り

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年01月

記述言語：英語掲載種別：研究論文（学術雑誌）

A sulfonated polyaniline (SPAN) organic electrochemical network device (OEND) is fabricated using a simple drop-casting method on multiple Au electrodes for use in reservoir computing (RC). The SPAN network has humidity-dependent electrical properties. Under high humidity, the SPAN OEND exhibits mainly ionic conduction, including charging of an electric double layer and ionic diffusion. The nonlinearity and hysteresis of the current–voltage characteristics progressively increase with increasing humidity. The rich dynamic output behavior indicates wide variations for each electrode, which improves the RC performance because of the disordered network. For RC, waveform generation and short-term memory tasks are realized by a linear combination of outputs. The waveform task accuracy and memory capacity calculated from a short-term memory task reach 90% and 33.9, respectively. Improved spoken-digit classification is realized with 60% accuracy by only 12 outputs, demonstrating that the SPAN OEND can manage time series dynamic data operation in RC owing to a combination of rich dynamic and nonlinear electronic properties. The results suggest that SPAN-based electrochemical systems can be applied for material-based computing, by exploiting their intrinsic physicochemical behavior.

DOI： 10.1002/adma.202102688

Scopus

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A Brain-Inspired Artificial Intelligence Model of Hippocampus, Amygdala, and Prefrontal Cortex on Home Service Robots 査読有り国際誌

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年11月

担当区分：筆頭著者,　責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

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

その他リンク： https://kyutech.repo.nii.ac.jp/records/2001270

A Reservoir Based Q-Learning Model for Autonomous Mobile Robots 査読有り国際誌

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年11月

記述言語：英語掲載種別：研究論文（学術雑誌）

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

その他リンク： https://kyutech.repo.nii.ac.jp/records/2001273

An Amygdala-Inspired Classical Conditioning Model Implemented on an FPGA for Home Service Robots 査読有り

Tanaka Y., Morie T., Tamukoh H.

IEEE Access 8 212066 - 212078 2020年11月

担当区分：筆頭著者記述言語：英語掲載種別：研究論文（学術雑誌）

This study develops an intelligent system for home service robots mimicking human brain function that can manage common knowledge applicable to any environment and local knowledge reflecting its specific environment. Deep learning is effective for acquiring common knowledge because the performance of deep learning relies on the amounts of training and big training data that can be accessed for such knowledge; however, deep learning is ineffective for acquiring local knowledge because no big training data for such knowledge exist. Thus, we propose a brain-inspired learning model and system for acquiring local knowledge using small training data. We focus on the amygdala because its classical fear conditioning is effective for training using small training data. We propose an amygdala-inspired classical conditioning model comprising multiple self-organizing maps (lateral nucleus) and a fully connected neural network (central nucleus), imitating the function and structure of the amygdala. The proposed model is applied to a task of a waiter robot in a restaurant, and the model can learn customers' preferences after only a few human-robot interactions. We accelerate the computation of the model and reduce its power consumption by proposing a hardware-oriented algorithm for the model and its digital hardware design and implement it in an XCZU9EG field programmable gate array. The hardware-oriented algorithm reduces the multiplication operations and exponential functions requiring huge hardware resources. The performance of the hardware operated at 150 MHz is 1,273 times faster than the software implementation on Arm Cortex-A53, and the power consumption of the chip is 5.009 W.

DOI： 10.1109/ACCESS.2020.3038161

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85097712435&origin=inward

Object Recognition Using Flexible Tactile Sensor 査読有り

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

Asia Pacific Conference on Robot IoT System Development and Platform 2020 2020年11月

Prediction of Sensory Information and Generation of Motor Commands for Autonomous Mobile Robots using Reservoir Computing 査読有り

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

2019 International Symposium on Nonlinear Theory and Its Applications 333 - 336 2019年12月

社会実装を目指したホームサービスロボットの研究開発

小野智寛, 坂田拓馬, 吉井拓巳, 上村大地, 金丸和樹, 中村健太郎, 西村雄太, 森江隆, 田向権, 堀三晟, 石田裕太郎, 田中悠一朗, 吉元裕真, 阿部佑志, 武藤冬樹, 椛島康平, 福宿将士

ロボティクス・メカトロニクス講演会講演概要集 ( 一般社団法人日本機械学会 ) 2019 ( 0 ) 1P2 - I02 2019年06月

記述言語：日本語掲載種別：研究論文（研究会，シンポジウム資料等）

<p>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.</p>

DOI： 10.1299/jsmermd.2019.1P2-I02

CiNii Article

CiNii Research

その他リンク： https://ci.nii.ac.jp/naid/130007774525

Hardware implementation of brain-inspired amygdala model 査読有り国際誌

Tanaka Y., Tamukoh H.

Proceedings - IEEE International Symposium on Circuits and Systems 2019-May 2019年01月

担当区分：筆頭著者,　責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Deep neural networks (DNNs) have achieved state-of-the-art results in several computing tasks. However, the performance of these DNNs is reliant on the availability of large amounts of training data, which is not always present. We approached this problem by developing a brain-inspired amygdala model to achieve computer learning based on limited training data. The amygdala is an area of the brain associated with classical fear conditioning. The proposed amygdala model is composed of a single layer of deep self-organizing map network (deep SOM network) and a fully-connected neural network (FCNN), which imitates the function and structure of an amygdala. We applied the proposed amygdala model to a robot waiter task in a restaurant. The experimental results show that the model learned a customer's preferences after only a few human robot interactions. To develop the digital hardware of the amygdala model, we designed hardware for the deep SOM network and the FCNN and implemented them in an XCZU9EG field programmable gate array (FPGA). Our FPGA implementation of a deep SOM network with 272 neurons and an FCNN with three output neurons outperformed a software implementation on an Intel Core i5-3470 CPU by over 600 times.

DOI： 10.1109/ISCAS.2019.8702430

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85066804926&origin=inward

Live demonstration: Hardware implementation of brain-inspired amygdala model 査読有り国際誌

Tanaka Y., Tamukoh H.

Proceedings - IEEE International Symposium on Circuits and Systems 2019-May 2019年01月

担当区分：筆頭著者,　責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

This live demonstration presents a brain-inspired amygdala model. Amygdala is an area of the brain that is associated with fear conditioning, which is a type of classical conditioning. The model can learn preferences through human-robot interactions by application of classical conditioning to the model. Additionally, to develop a high speed and low power system, we design a hardware of the amygdala model, and implemented the hardware into field programmable gate array.

DOI： 10.1109/ISCAS.2019.8702213

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85066814574&origin=inward

Live demonstration: A VLSI implementation of time-domain analog weighted-sum calculation model for intelligent processing on robots 査読有り国際誌

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年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

This live demonstration presents a VLSI chip based on “Time-domain Analog Computing with Transient states (TACT)” approach for intelligent processing on robots. This TACT chip, fabricated using 250-nm CMOS technology, implements a time-domain analog weighted-sum calculation model with very high energy efficiency. We integrate the TACT chip into a robot via Robot Operating System (ROS) interfaces. A human tracking robot demonstration is performed by the TACT chip with energy efficiency of 300 TOPS/W.

DOI： 10.1109/ISCAS.2019.8702222

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85066803614&origin=inward

Application of digital hardware of deep self-organizing map network 査読有り

Yuichiro Tanaka, Hakaru Tamukoh

Asia Pacific Conference on Robot IoT System Development and Platform 2018 2018年10月

競技会活動を通じたホームサービスロボットの研究開発

吉元裕真, 岩元剛毅, 橋本康平, 森江隆, 田向権, 堀三晟, 石田裕太郎, 木山雄太, 黒田裕貴, 田中悠一朗, 久野昌隆, 藤田啓斗, 新谷嘉也

ロボティクス・メカトロニクス講演会講演概要集 ( 一般社団法人日本機械学会 ) 2018 ( 0 ) 2P2 - E02 2018年06月

記述言語：日本語掲載種別：研究論文（研究会，シンポジウム資料等）

<p>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.</p>

DOI： 10.1299/jsmermd.2018.2P2-E02

CiNii Article

CiNii Research

その他リンク： https://ci.nii.ac.jp/naid/130007552042

Hardware implementation of deep self-organizing map networks 査読有り

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年09月

担当区分：筆頭著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

We aim to develop a recognition system of high accuracy and low power consumption by designing a digital circuit for deep neural networks (DNNs), and by implementing the circuit on field programmable gate arrays (FPGAs). DNNs include numerous multiply operations, whereas FPGAs include a limited number of multipliers. We aim to reduce the number of multiply operations generated by the algorithms within DNNs. Deep self-organizing map networks (DSNs) [1] are DNNs comprising self-organizing maps (SOMs) [2] as shown in Fig. 1. A hardware-oriented algorithm for SOMs has been proposed herein [3]. The algorithm represents SOMs by replacing multiply operations with bitshift operations. DSNs that include only a few multiply operations can then be represented by employing the algorithm. In this paper, we propose a hardware-oriented algorithm and a hardware architecture for DSNs. The hardware-oriented algorithm reduces multiply operations and exponential functions in a computation of SOM Module as shown in Fig. 1. In addition, we confirm that the algorithm does not worsen performance of DSN by a software simulation. Figure 2 shows error rates of DSN during learning MNIST Dataset [4]. The performance of the proposed algorithm achieve comparable results to the conventional algorithm. We also describe a DSN comprising three layers by Verilog-HDL as shown in Fig. 1, and implement it on a Xilinx Vertex-6 XC6VLX240T FPGA. Experimental results showed that the proposed DSN circuit estimates a label of an input image in 2 µs while the software implemented using an Intel Core i5-3470 (3.20 GHz) CPU estimates it in about 1 ms. Thus the hardware is 500 times faster than the software. Its logic utilization is shown in Table 1.

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85034254406&origin=inward

Hardware-Oriented Algorithm for Deep Neural Networks Composed of Self-Organizing Maps 査読有り

Yuichiro Tanaka, Hakaru Tamukoh

Proceedings of International Workshop on Smart Info-Media Systems in Asia 116 - 121 2016年09月

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