研究者詳細 - 田向　権

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

Live Demonstration: Hardware-Oriented Dual Stream Object Recognition System using Binarized Neural Networks 査読有り国際誌

Yuma Yoshimoto, Hakaru Tamukoh

IEEE International Symposium on Circuits and Systems (ISCAS) 2020年10月

ホームサービスロボットのための高精度な特定人物追跡システムの開発査読有り

小野智寛，阿部佑志，石田裕太郎，柴田智広，田向権

日本ロボット学会 38 2020年10月

A hardware intelligent processing accelerator for domestic service robots 査読有り

Ishida Y., Morie T., Tamukoh H.

Advanced Robotics 34 ( 14 ) 947 - 957 2020年07月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

We present a method for implementing hardware intelligent processing accelerator on domestic service robots. These domestic service robots support human life; therefore, they are required to recognize environments using intelligent processing. Moreover, the intelligent processing requires large computational resources. Therefore, standard personal computers (PCs) with robot middleware on the robots do not have enough resources for this intelligent processing. We propose a ‘connective object for middleware to an accelerator (COMTA),’ which is a system that integrates hardware intelligent processing accelerators and robot middleware. Herein, by constructing dedicated architecture digital circuits, field-programmable gate arrays (FPGAs) accelerate intelligent processing. In addition, the system can configure and access applications on hardware accelerators via a robot middleware space; consequently, robotic engineers do not require the knowledge of FPGAs. We conducted an experiment on the proposed system by utilizing a human-following application with image processing, which is commonly applied in the robots. Experimental results demonstrated that the proposed system can be automatically constructed from a single-configuration file on the robot middleware and can execute the application 5.2 times more efficiently than an ordinary PC.

DOI： 10.1080/01691864.2020.1769726

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85087072938&origin=inward

「自動運転のための知的処理」特集にあたって査読有り

我妻広明, 田向権, 大屋勝敬

知能と情報 ( 日本知能情報ファジィ学会 ) 32 ( 3 ) 54 - 55 2020年06月

Training autoencoder using three different reversed color models for anomaly detection 査読有り

Al aama O., Tamukoh H.

Journal of Robotics, Networking and Artificial Life 7 ( 1 ) 35 - 40 2020年06月

記述言語：英語掲載種別：研究論文（学術雑誌）

© 2020 The Authors. Published by Atlantis Press SARL. This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/). Autoencoders (AEs) have been applied in several applications such as anomaly detectors and object recognition systems. However, although the recent neural networks have relatively high accuracy but sometimes false detection may occur. This paper introduces AE as an anomaly detector. The proposed AE is trained using both normal and anomalous data based on convolutional neural network with three different color models Hue Saturation Value (HSV), Red Green Blue (RGB), and our own model (TUV). As a result, the trained AE reconstruct the normal images without change, whereas the anomalous image would be reconstructed reversely. The training and testing of the AE in case of RGB, HSV, and TUV color models were demonstrated and Cifar-10 dataset had been used for the evaluation process. It can be noticed that HSV color model has been more effective and achievable as an anomaly detector rather than other color models based on Z- and F-test analyses.

DOI： 10.2991/jrnal.k.200512.008

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A hardware-oriented echo state network and its FPGA implementation 査読有り

Honda K., Tamukoh H.

Journal of Robotics, Networking and Artificial Life 7 ( 1 ) 58 - 62 2020年06月

記述言語：英語掲載種別：研究論文（学術雑誌）

© 2020 The Authors. Published by Atlantis Press SARL. This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/). This paper proposes implementation of an Echo State Network (ESN) to Field Programmable Gate Array (FPGA). The proposed method is able to reduce hardware resources by using fixed-point operation, quantization of weights, which includes accumulate operations and efficient dataflow modules. The performance of the designed circuit is verified via experiments including prediction of sine and cosine waves. Experimental result shows that the proposed circuit supports to 200 MHz of operation frequency and facilitates faster computing of the ESN algorithm compared with a central processing unit.

DOI： 10.2991/jrnal.k.200512.012

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85097052837&origin=inward

組込みソフトウェア開発とオントロジー査読有り

渡辺政彦, 市瀬龍太郎, 我妻広明, 田向権, 穴田啓樹

人工知能 ( 一般社団法人人工知能学会 ) 35 ( 2 ) 155 - 162 2020年03月

組込みソフトウェア開発とオントロジー査読有り

渡辺政彦，市瀬龍太郎，我妻広明，田向権，穴田啓樹

人工知能学会誌 35 ( 2 ) 155 - 162 2020年03月

Network with sub-networks 査読有り

Fuengfusin N., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 2020 191 - 194 2020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）

We introduce network with sub-networks, a neural network which it’s weight layers can be detached into sub-neural networks during inference phase. To develop trainable parameters which can be inserted into both base-and sub-models, firstly, the parameters of sub-models are duplicated to base-model. Each model is forward-propagated separately. All models are grouped into pairs. Gradients from selected pairs of networks are averaged and updated both networks. With MNIST dataset, our base-model achieves the identical test-accuracy to the regularly trained models. In other hand, the sub-models are suffered an extend of loss in test-accuracy, nevertheless the sub-models provide alternative approaches to be deployed with less parameters compare to the regular model.

DOI： 10.5954/ICAROB.2020.OS20-2

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85108824140&origin=inward

Anomaly detection using autoencoder trained with reversed color models 査読有り

Al Aama O., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 2020 201 - 205 2020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）

Autoencoders (AEs) have been applied in several applications such as anomaly detectors and object recognition systems. However, although the recent neural networks have relatively high accuracy but sometimes false detection may occur. This paper introduces AE as an anomaly detector. The proposed AE is trained using both normal and anomalous data based on convolutional neural network (CNN) with three different color models HSV, RGB and TUV (own model). As a result, the trained AE reconstruct the normal images without change, whereas the anomalous image would be reconstructed reversely. The training and testing of the autoencoder in case of RGB, HSV, and TUV color models were demonstrated and Cifar-10 dataset had been used for the evaluation process. It can be noticed that HSV color model has been more effective and achievable as an anomaly detector rather than other color models based on Z-test and F-test analyses.

DOI： 10.5954/ICAROB.2020.OS20-5

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85108814747&origin=inward

A fast pick-and-place method for home service robots using 3d point clouds 査読有り

Ono T., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 2020 195 - 196 2020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）

Home service robots have begun attracting attention due to decreasing birthrate and increasing aging population. The basic functions of home service robots are object recognition, picking and placing, recognition of people and environment, and interaction with peoples. In this paper, we focus on object picking and placing in the domestic environment. The pick-and-place task is a very important technique used for arrangement shelves and tidying up rooms. In order for the robot to operate smoothly, the movement to pick-and-place the object must be fast. Therefore, we develop a fast pick-and-place method using 3D point clouds. Regarding the picking, we describe a grasping-point estimation method. Regarding the placing, we describe a placeable position estimation method. These methods are used at RoboCup@Home, an international competition aimed at the practical application of home service robots, and their effectiveness and validity are verified.

DOI： 10.5954/ICAROB.2020.OS20-3

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超立方体上の疑似ビリヤードダイナミクスに基づくレザバー計算のFPGA実装査読有り

山本大智, 川島一郎, 田向権, 森江隆

人工知能学会全国大会論文集 ( 一般社団法人人工知能学会 ) JSAI2020 ( 0 ) 3Rin422 - 3Rin422 2020年01月

記述言語：日本語掲載種別：研究論文（学術雑誌）

<p>レザバー計算(RC)はリカレントニューラルネットワーク(RNN)の枠組みの1つで，時系列データの処理を目的に応用されている．機械学習モデルに対して，ディジタル回路実装を行うことで高速な演算速度を省電力で実現することが期待できる．香取らはディジタル回路との親和性が高いレザバー計算モデルを提案した．しかし，香取らのモデルをField Programmable Gate Array(FPGA)のようなディジタル回路に実装する場合，ニューロン数の変更に大きな制限が発生する．小規模な回路実装を行う手法として川嶋らがカオスボルツマンマシンのディジタル回路実装に用いたハードウェア指向アルゴリズムを提案している．本研究では川嶋らの提案したハードウェア指向アルゴリズムを基に対象のレザバー計算モデルを改良することで，ディジタル回路実装における回路リソースの消費を軽減可能なモデルを構築した．また，本研究のハードウェア指向アルゴリズムにより、レザバー計算の高速化の実現、人工知能構築のための新しいプラットフォームの確立が期待される．</p>

DOI： 10.11517/pjsai.jsai2020.0_3rin422

CiNii Article

その他リンク： https://kyutech.repo.nii.ac.jp/records/2001366

Semi-automatic dataset generation for object detection and recognition and its evaluation on domestic service robots 査読有り

Ishida Y., Tamukoh H.

Journal of Robotics and Mechatronics ( 富士技術出版株式会社 ) 32 ( 1 ) 245 - 253 2020年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

This paper proposes a method for the semi-automatic generation of a dataset for deep neural networks to perform end-to-end object detection and classification from images, which is expected to be applied to domestic service robots. In the proposed method, the background image of the floor or furniture is first captured. Subsequently, objects are captured from various viewpoints. Then, the background image and the object images are composited by the system (software) to generate images of the virtual scenes expected to be encountered by the robot. At this point, the annotation files, which will be used as teaching signals by the deep neural network, are automatically generated, as the region and category of the object composited with the background image are known. This reduces the human workload for dataset generation. Experiment results showed that the proposed method reduced the time taken to generate a data unit from 167 s, when performed manually, to 0.58 s, i.e., by a factor of approximately 1/287. The dataset generated using the proposed method was used to train a deep neural network, which was then applied to a domestic service robot for evaluation. The robot was entered into the World Robot Challenge, in which, out of ten trials, it succeeded in touching the target object eight times and grasping it four times.

DOI： 10.20965/jrm.2020.p0245

CiNii Article

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自動運転車における自車位置推定のためのランドマーク検出の改良査読有り

三井悠也, 福田有輝也, 宮﨑椋瑚, 石田祐太郎, 伊藤太久磨, 通山恭一, 田向権

日本知能情報ファジィ学会ファジィシステムシンポジウム講演論文集 ( 日本知能情報ファジィ学会 ) 36 ( 0 ) 149 - 154 2020年01月

FPGA implementation of hardware-oriented chaotic Boltzmann machines 査読有り

Kawashima I., Morie T., Tamukoh H.

IEEE Access 8 204360 - 204377 2020年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

Boltzmann machines (BMs) are useful in various applications but are limited by their requirement to generate random numbers. In contrast, chaotic Boltzmann machines (CBMs) are neural networks that imitate the stochastic behavior of BMs with the chaotic dynamics and deterministic behavior, without random numbers. CBMs can potentially require fewer hardware resources than the original algorithms due to the unnecessity of random number generators. In this study, hardware-oriented algorithms and a differential multiply-accumulation operation are proposed to overcome the difficulties of implementing CBMs on field-programmable gate arrays (FPGAs). A hardware-oriented algorithm for CBMs, which includes fixed-point operations and shift operations, is proposed to reduce hardware resource utilization in the implemented circuits. In particular, the differential multiply-accumulate operation allows us to implement the multiply-accumulate operation with block random access memory and digital signal processors to reduce the consumption of lookup tables and flip-flops in FPGAs without losing the calculation speed. Our proposed approach was evaluated in numerical simulations, logical synthesis, and FPGA implementation. The calculation speed of FPGA-implemented CBMs was compared with software-implemented CBMs, which resulted in 1 / 6,500 of calculation time reduction in a 300-neuron CBM. Moreover, 2,048 neurons of CBM were realized by the logical synthesis. Therefore, the proposed hardware implementation of CBMs was shown to be feasible. The proposed CBMs can solve combinatorial optimization problems at a larger scale with fewer resources.

DOI： 10.1109/ACCESS.2020.3036882

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85102831540&origin=inward

Hardware-oriented dual stream object recognition system using binarized neural networks 査読有り

Yoshimoto Y., Tamukoh H.

Proceedings - IEEE International Symposium on Circuits and Systems 2020-October 2020年01月

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

Service robots require an object recognition system to ensure their effective functioning in situations where Convolutional Neural Networks (CNN) are the mainstream machine learning technique employed by the system. Particularly, “Dual Stream VGG-16 (DS-VGG16),” which uses RGB and depth images, has been reported to have high accuracy for object recognition. However, it is difficult to implement CNN in robots, because it requires high computation power and consumes a huge amount of power. Although, implementing CNN with Field Programmable Gate Array (FPGA) solves the electric power problem, however it is difficult due to limited resources available. This paper proposes “Binarized Dual Stream VGG-16 (BDSVGG16),” which is Hardware-Oriented DS-VGG16. With the concept of Binarized Neural Networks (BNN), BDS-VGG16 is effective when implemented on FPGA. In results, the accuracy of BDS-VGG16 is 99.2%. It is higher than that of Eitel's model by 5.1 points. Further, we developed an object recognition system based on the Robot Operating System (ROS) which is a well-known middleware for robots, it uses our proposed method for service robot application.

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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01月

担当区分：最終著者記述言語：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

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

Acceleration of training dataset generation by 3d scanning of objects 査読有り

Abe Y., Ishida Y., Ono T., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics ( 株式会社ALife Robotics ) 2020 197 - 200 2020年01月

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

A semi-automatic dataset generation system is effective to prepare a training dataset for object recognition in a personal residence. However, a semi-automatic that method requires significant manual processing to capture images of household objects. Therefore, we apply three-dimensional object scanning to eliminate manual processing and speedup dataset generation. Experimental results demonstrate that the proposed method can generate the dataset 40 minutes faster than a comparable previous method that did not require manual processing.

DOI： 10.5954/ICAROB.2020.OS20-4

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85108817228&origin=inward

A hardware-oriented echo state network for fpga implementation 査読有り

Honda K., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics ( 株式会社ALife Robotics ) 2020 187 - 190 2020年01月

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

This paper proposes implementation of an echo state network (ESN) to field programmable gate array FPGA). The proposed method is able to reduce hardware resources by using fixed-point operation, quantization of weights, which includes accumulate operations and efficient dataflow modules. The performance of the designed circuit is verified via experiments including prediction of sine and cosine waves. Experimental result shows that the proposed circuit supports to 200[MHz] of operation frequency and facilitates faster computing of the ESN algorithm compared with a central processing unit.

DOI： 10.5954/ICAROB.2020.OS20-1