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

We propose an auxiliary data stream structure as a robust classification model. The model treats one modal as the main input and the other modals as supports. The model chooses how much of the sub-modal is used for classification. We experimented with two and three modal inputs. Moreover, we added pseudo-shadows to the visual information for the experiment with three modal inputs. In all experiments, our proposed model improves the accuracy and robustness to environmental disturbances by using multiple modals.

DOI： 10.5954/icarob.2023.os17-3

CiNii Research

Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)

This paper proposes a method for recognizing the poses of a flexible-indefinite object. Some flexible-indefinite objects, such as fried shrimp, differ between individuals. Therefore, it is difficult to estimate the pose of these foods by point cloud fitting or other methods. We propose "pseudo-skeleton" data for these objects. Pseudo-skeleton data consists of "key-points," which are joints, and "bones," which connect between key-points. For example, fried shrimp are given 3 key-points; "head," "belly," and "tail." In addition, the bones that connect them are given. In the experiment, objects pose recognition model based on the human pose recognition model trains pseudo-skeleton data of fried shrimp. We confirmed that the model estimates the poses in the images.

DOI： 10.5954/icarob.2023.os17-1

CiNii Research

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

Home service robots have pick-and-place tasks to grasp and carry objects. When performing pick-andplace tasks, robots need to process to search for objects in the case of missing a target object. We propose approaches for the robot to search for a target object from a shelf and a method for selecting empty spaces to move off-target objects. The proposed method is based on two recognition models to select a space and plan motions. The object is to be moved and the space is selected based on the size and position of empty spaces. In the experiments, the robot planned actions to search for a target object from a shelf and to place an object near a group of similar objects. We used these in the RoboCup@Home competition to evaluate the effectiveness of the proposed method.

DOI： 10.5954/icarob.2023.os17-2

CiNii Research

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

A field-programmable gate array (FPGA) device which has a Zynq architecture becomes popular these days. It is featured by integration of processing system (PS) and programmable logic (PL) into a single chip. While we tend to focus on the performance of PL, we can not ignore PS load completely. In this paper, using a Zynq FPGA board, we explore how our object detection system performance changes with PS load and report our experiment results.

DOI： 10.5954/icarob.2023.os17-5

CiNii Research

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

We propose an efficient semantic map to realize flexible human-robot interaction (HRI) in domestic environments. Our proposed map is created from an output of Simultaneous Localization and Mapping and already-known environmental information such as furniture and room. In this study, we evaluated the effectiveness of our proposed method on two benchmark tests for HRI in RoboCup@Home held in Bangkok in 2022. In the RoboCup@Home, we employ 3D human recognition to apply our proposed map to HRI, such as "find and offer an empty seat." Our proposed method had the best score of all teams on both tests. The results of our experiments are available at https://youtube.com/playlist?list=PLfbN50Mwh2DG3OPDeCHo4TNuyrU4qYCrJ.

DOI： 10.5954/icarob.2023.os17-4

CiNii Research

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

Manual annotation is common, but problems occur, such as oversight and mislabeling via human error. These problems are known to affect the quality of datasets significantly. To resolve these problems, we propose a method to automatically generate high-quality and large datasets in a short time using a simulator. Our proposed method uses domain randomization to minimize domain gaps without faithfully reproducing real scenes. The generated dataset achieved more than 80% recognition accuracy against the real image dataset.

DOI： 10.5954/icarob.2023.os17-6

CiNii Research

Language：English   Publishing type：Research paper (international conference proceedings)

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

任意視点画像生成において Neural Radiance Fields は非常に強力な手法ではあるが，学習のためには多くの画像が必要である．本発表では，パラメトリック固有空間法のアナロジーとして，教師データが存在しない未知視点において，近傍視点の画像から擬似的に生成した特徴ベクトルを教師信号として学習に用いることで，少数の画像でも学習できる Manifold NeRF を提案する．実験の結果，疑似教師信号を用いることで，データセットが少量でも精度良く任意視点画像が生成できることを確認した．

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

Kyutacar

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

Kyutacar

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

Kyutacar

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

Kyutacar

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

Kyutacar

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

Kyutacar

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

Lisbon, Portugal  

DOI： 10.5220/0011698400003417

Scopus

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Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.35848/1882-0786/acae6a

Kyutacar

Scopus

Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85146494177&origin=inward

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

California, United States of America  

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

Scopus

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Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

Scopus

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Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

Scopus

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Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

Scopus

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Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

Scopus

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Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

Scopus

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Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)

<p>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.</p>

DOI： 10.1587/transfun.2023eal2050

CiNii Research

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

Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1109/ISCAS46773.2023.10181436

Scopus

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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1109/IJCNN54540.2023.10191342

Scopus

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1109/IJCNN54540.2023.10191433

Scopus

Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85169554268&origin=inward

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1109/IV55152.2023.10186556

Scopus

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Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1109/ICFPT59805.2023.00038

Scopus

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Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)

DOI： 10.1109/ACCESS.2023.3340311

Scopus

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Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)

A field-programmable gate array (FPGA) device with a Zynq architecture integrates a processing
system (PS) and programmable logic (PL) into a single chip. Although the PL performance is
typically considered, the PS load cannot be completely ignored. In this study, using an FPGA
board with a Zynq architecture, the conditions under which an object detection system performs
the best, while considering the PS and PL load balance, are explored.

DOI： 10.57417/jrnal.10.2_105

CiNii Research

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1109/ICFPT59805.2023.00024

Scopus

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Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

Language：English   Publishing type：Research paper (international conference proceedings)

Language：English   Publishing type：Research paper (international conference proceedings)

Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Last author   Language：Japanese   Publishing type：Research paper (scientific journal)

DOI： 10.11517/jsaisigtwo.2022.challenge-060_07

CiNii Research

Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)

DOI： https://doi.org/10.1587/nolta.13.397

Language：English   Publishing type：Research paper (international conference proceedings)

Language：English   Publishing type：Research paper (international conference proceedings)

Language：English   Publishing type：Research paper (international conference proceedings)

Language：English   Publishing type：Research paper (international conference proceedings)

Language：English   Publishing type：Research paper (international conference proceedings)

Language：English   Publishing type：Research paper (international conference proceedings)

Language：English   Publishing type：Research paper (international conference proceedings)

Language：English   Publishing type：Research paper (international conference proceedings)

Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.5220/0010816600003124

Scopus

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Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.5220/0010848100003124

Scopus

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Language：English   Publishing type：Research paper (international conference proceedings)

Language：English   Publishing type：Research paper (international conference proceedings)

Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)

This paper proposes a method to estimate appropriate primitive shapes by automatically using a point cloud of objects. The process is as follows. First is to estimate a rotation angle of the object and, then place a primitive shape (i.e., cylinder or sphere) in the center of the object. The primitive shape is either stretched or compressed to fit the object. The distance between all the point cloud of the object and the primitive shape are measured. We apply these methods with various primitive shapes and find the most suitable primitive shape. We show that objects like apples and chikuwa can be recognized with primitive objects, such as spheres and cylinders.

DOI： 10.5954/icarob.2022.os15-5

CiNii Research

Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)

We propose binary or ternary weights with 8-bit integer activation convolutional neural networks. This model is designed as a middle ground between 8-bit integer and 1-bit or 2-bit quantized models. We discover that conventional 1-bit or 2-bit only-weight quantization methods (i.e., BinaryConnect and Ternary weights network) can be utilized jointly with 8-bit integer activation quantization without significant fractions. Based on these two methods, we evaluate our model with a VGG16-like model and CIFAR10 dataset. Our model provides competitive results to a conventional floating-point model.

DOI： 10.5954/icarob.2022.os15-1

CiNii Research

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

Hardware-based machine intelligence with the network architecture of reservoir computing (RC) is gaining interest because of its biological computational resemblance along with an easy and efficient neural network training approach. Herein, such a physical RC (in-materio RC) platform consisting of a recurrent network formed by the single-walled carbon nanotube (SWNT)–porphyrin polyoxometalate (Por–POM) complex is demonstrated. The network architecture executes the fundamental reservoir properties of nonlinearity, higher harmonic generation, and 1/fγ power law information processing ability. Based on these functionalities, an RC benchmark task of waveform generation is performed where the device achieves maximum fitting accuracy of 99.4%. Furthermore, a supervised object classification task based on a one-hot vector target is also executed using Toyota Human Support Robot tactile inputs. The successful classification of objects of different hardness is enhanced when the device output response follows the 1/fγ power law of maximized information processing.

DOI： 10.1002/aisy.202100145

Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

Scopus

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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

Scopus

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Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)

Deep learning technology has made remarkable progress in recent years and has been adopted for a variety of applications such as smartphones and cloud servers. These systems employ dedicated processors to reduce power consumption and process massive amounts of data. In this paper, we propose a field-programmable gate array (FPGA) infrastructure for easy verification of user logic. The infrastructure makes it easy to communicate and control a host PC and user logic. We implemented two example logics, which were simple image processing and a restricted Boltzmann machine, into the infrastructure to confirm these features

DOI： 10.57417/jrnal.9.1_37

Scopus

CiNii Research

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

DOI： 10.1080/01691864.2022.2115315

Scopus

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Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)

This paper proposes ternary or binary weights with 8-bit integer activation convolutional neural networks. Our proposed model serves as the middle ground between 8-bit integer and lower than 8-bit precision quantized models. Our empirical experiments established that the conventional 1-bit or 2-bit only-weight quantization methods (i.e., BinaryConnect and ternary weights network) can be used jointly with the 8-bit integer activation quantization. We evaluate our model with the VGG16-like model to operate with the CIFAR10 and CIFAR100 datasets. Our models showcompetitive results to the general 32-bit floating point model.

DOI： 10.57417/jrnal.9.2_171

Scopus

CiNii Research

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Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)

<p>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.</p>

DOI： 10.1587/nolta.13.397

CiNii Research

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

Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1109/ICFPT56656.2022.9974433

Scopus

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Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)

DOI： 10.1109/ACCESS.2022.3168026

Scopus

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Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1109/ISCAS48785.2022.9937768

Scopus

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Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1109/ISPACS51563.2021.9651058

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1109/ISPACS51563.2021.9650985

Language：English   Publishing type：Research paper (international conference proceedings)

In recent years, many high-precision color image quantization (CIQ) methods have been proposed. However, they do not consider human visual impression, exposing the image to the possibility of altering its visual impression. In this paper, we propose a novel CIQ method that considers human visual impression. An impression is calculated by using a saliency map of an original image and the frequency of each of its colors to prevents CIQ from changing the impression of the image. Quantitative and qualitative evaluations of the experimental results demonstrate that the proposed method preserves the impressionistic colors better than the general CIQ methods.

DOI： 10.1109/ISCIT52804.2021.9590610

Scopus

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

This study presents novel ReLU functions-based on stochastic computing (SC-based ReLU). The conventional SC-based ReLU for an addition method using approximate parallel counter (APC), which outputs the sum of multiple inputs every clock cycle works as a ReLU using accumulator. However, this conventional method loses the high fault tolerance, which is one of the advantages of stochastic computing (SC). Therefore, we propose an SC-based ReLU using a finite state machine (FSM) instead of an accumulator for an addition method using a multiplexer (MUX) which determines the outputs stochastically. For example, if an error occurs in the value of the accumulator in the conventional method, all subsequent operations will be wrong. However, in the case of the proposed SC-based ReLU using FSM, even if an error occurs in the current state of the FSM, it quickly returns the correct state of the FSM. Thus, the proposed SC-based ReLU using FSM for MUX is more fault tolerant than the conventional SC-based ReLU using the accumulator. However, the accuracy of the addition methods using MUX is poor because the output is determined stochastically, and a large length of bitstream (BSL) is required for accurate calculation. As the BSL becomes larger, the latency for the calculation increases, decreasing the performance. Therefore, we propose a SC-based ReLU using FSM for APC. The addition method using APC doesn’t require BSL as large as the addition method using MUX to obtain correct calculation results. Compared with the conventional SC-based ReLU using an accumulator for APC, the proposed SC-based ReLU using FSM for APC shows sufficient accuracy even with a small BSL. The proposed SC-based ReLU using FSM has also a high fault tolerance,

DOI： 10.34385/proc.66.RS1-5

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

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

DOI： 10.2991/jrnal.k.210521.012

Scopus

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Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

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

DOI： 10.1371/journal.pone.0251329

Kyutacar

Scopus

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

<p>In this study, with the aim of installing an object recognition algorithm on the hardware device of a service robot, we propose a Binarized Dual Stream VGG-16 (BDS-VGG16) network model to realize high-speed computations and low power consumption. The BDS-VGG16 model has improved in terms of the object recognition accuracy by using not only RGB images but also depth images. It achieved a 99.3% accuracy in tests using an RGB-D Object Dataset. We have also confirmed that the proposed model can be installed in a field-programmable gate array (FPGA). We have further installed BDS-VGG16 Tiny, a small BDS-VGG16 model in XCZU9EG, a system on a chip with a CPU and a middle-scale FPGA on a single chip that can be installed in robots. We have also integrated the BDS-VGG16 Tiny with a robot operating system. As a result, the BDS-VGG16 Tiny installed in the XCZU9EG FPGA realizes approximately 1.9-times more computations than the one installed in the graphics processing unit (GPU) with a power efficiency approximately 8-times higher than that installed in the GPU.</p>

DOI： 10.20965/jrm.2021.p0386

Kyutacar

Scopus

CiNii Article

CiNii Research

Other Link： https://ci.nii.ac.jp/naid/130008027729

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

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

In this paper, we introduce a network with sub-networks: a neural network whose layers can be detached into sub-neural networks during the inference phase. To develop trainable parameters that can be inserted into both base-model and sub-models, first, the parameters of sub-models are duplicated in the base-model. Each model is separately forward-propagated, and all models are grouped into pairs. Gradients from selected pairs of networks are averaged and used to update both networks. With the Modified National Institute of Standards and Technology (MNIST) and Fashion-MNIST datasets, our base-model achieves identical test-accuracy to that of regularly trained models. However, the sub-models result in lower test-accuracy. Nevertheless, the sub-models serve as alternative approaches with fewer parameters than those of regular models.

DOI： 10.2991/jrnal.k.201215.006

Scopus

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

DOI： 10.1016/j.neucom.2020.11.055

Kyutacar

Scopus

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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

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

<p>本研究では，自動運転技術における自車位置推定のために，You Only Look Once（YOLO）を処理に組み込むことで道路標示検出システムの性能向上を行う．従来手法では，輝度値に基づくテンプレートマッチングで道路標示を検出する．しかし，この手法では日光による白飛びや標示の掠れにより検出できないものが存在する．これに対し，提案手法ではYOLOで道路標示の存在する領域を探索し，テンプレートマッチングの適用範囲を制限する．これにより，誤検出の発生を抑制でき，テンプレートマッチングの検出閾値を下げ，これまで未検出であった道路標示の数を削減できる．また，テンプレートマッチングの探索領域も制限され，処理速度も向上できる．実験の結果，提案手法は誤検出数は0のまま，従来手法よりも未検出数を低減できた．また，従来手法と比較してシステムのAccuracyは0.013，処理速度は4.6 FPS上がった．</p>

DOI： 10.3156/jsoft.33.1_566

Kyutacar

CiNii Article

Other Link： https://ci.nii.ac.jp/naid/130007986500

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

Constructing a large food dataset is time and effort consuming due to the need to cover the feature variations of food items. Hence, a huge data is needed for training neural networks. This paper aims to advocate the Cycle-GAN to build up large food dataset based on large number of simulated images and relatively few real captured images thus obtaining more realistic images effortlessly compared with traditional capturing. Real RGB and depth images of variant food samples allocated over turntable were captured in three different angles using real-sense depth camera with different backgrounds. Furthermore, for simulated images, the Autodesk 3D_Maya software was employed using the same parameters of captured real images. Results showed that generally, realistic style transfer on simulated food objects was obtained as a result of employing Cycle-GAN. GAN proved to be an efficient tool that could minimize imaging efforts resulting in realistic images.

DOI： 10.5954/icarob.2021.os19-4

CiNii Research

Language：English   Publishing type：Research paper (international conference proceedings)

Constructing a large food dataset is time and effort consuming due to the need to cover the feature variations of food items. Hence, a huge data is needed for training neural networks. This paper aims to advocate the Cycle-GAN to build up large food dataset based on large number of simulated images and relatively few real captured images thus obtaining more realistic images effortlessly compared with traditional capturing. Real RGB and depth images of variant food samples allocated over turntable were captured in three different angles using real-sense depth camera with different backgrounds. Furthermore, for simulated images, the Autodesk 3D_Maya software was employed using the same parameters of captured real images. Results showed that generally, realistic style transfer on simulated food objects was obtained as a result of employing Cycle-GAN. GAN proved to be an efficient tool that could minimize imaging efforts resulting in realistic images.

Scopus

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Language：English   Publishing type：Research paper (international conference proceedings)

We explain about how the difference of implementation methods written in C++ in High-Level Synthesis (HLS) influences on latency for tiny You only look once (YOLO) v2, a real-time object detection system to infer on an FPGA. To utilize features of FPGA, we need to implement hardware-oriented algorithms such as the weight binalization. We primarily focus on convolution in tiny YOLO v2 network and we report execution results on the Xilinx SDSoC development environment to know whether methods are appropriate or not.

DOI： 10.5954/ICAROB.2021.OS19-2

Scopus

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Language：English   Publishing type：Research paper (international conference proceedings)

In the field of neuroscience, reservoir computing (RC) has been viewed as a model of the neuron computational system. RC is a framework for constructing recurrent neural networks, which is used for modeling the parts of the brain to solve the temporal problem. We construct the network inside the reservoir using the Pulse-Coupled Phase Oscillator (PCPO) with neighbor topology connections on field-programmable-gate-array (FPGA). Winfree model is used for PCPO spiking-based. We investigate the stability of the edge phenomenon using the zero one test (Z1-Test) methodology. We evaluate the proposed model on the time series generation tasks. We have successfully implemented and verified using FPGA that the 3x3 and 10x10 PCPO working as a Reservoir on the network.

DOI： 10.1109/ISCAS51556.2021.9401215

Scopus

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Language：English   Publishing type：Research paper (international conference proceedings)

We propose a convolutional network with sub-networks (CNSN), a convolutional neural network (CNN) that can be detached into sub-models on fly. Due to a conventional design of CNN, shapes of feature map are varied throughout the model. Therefore, the hidden layer within CNN may not directly process the input image without any modifications. To address this problem, we propose a step-down convolutional layer, a convolutional layer which acts as an input layer for the sub-model. The step-down convolutional layer reshapes the input image to a preferred representation to the sub-model. To train CNSN, we treat the base-model and sub-models as different models. We separately forward-and back-propagate each model. By using multi-model loss, a linear combination of losses from base-and sub-models, we can update weights that can be utilized in both base-and sub-models.

DOI： 10.5954/ICAROB.2021.OS19-1

Scopus

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Language：English   Publishing type：Research paper (international conference proceedings)

Deep learning technology has made remarkable progress in recent years and has been applied to a variety of applications such as smartphones and cloud servers. These systems employ dedicated processors to save power consumptions and process massive data. In this paper, we introduce a hardware-oriented restricted Boltzmann machine and propose a field-programmable gate array (FPGA) infrastructure for easy verification of user circuits. The infrastructure makes it easy to communicate and control between the host PC and the user circuit.

DOI： 10.5954/ICAROB.2021.OS19-3

Scopus

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Language：English   Publishing type：Research paper (international conference proceedings)

In this study, we propose a hardware-oriented Gaussian mixture model - multiresolution co-occurrence histograms of oriented gradients (GMM-MRCoHOG) algorithm for efficient human detection by a field-programmable gate array (FPGA). GMM-MRCoHOG is a HOG-based human detection method in which the computation of angles is quantized to 36 directions and 2D Gaussian distribution computation causes a decrease in processing speed and an increase in hardware resource usage. We propose a hardware-oriented algorithm to solve these problems. First, we propose a rough angle computation method of comparison with a tangent table. Second, we propose a bit-shifting-based Gaussian distribution computation method. Experimental results show that the proposed hardware-oriented algorithm does not significantly reduce the detection accuracy of GMM-MRCoHOG. High-level synthesis results of the FPGA implementation show that fast, low-resource processing is possible.

DOI： 10.1117/12.2591024

Scopus

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

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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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1109/ICFPT52863.2021.9609809

Scopus

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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1109/ICFPT52863.2021.9609880

Scopus

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Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)

Constructing a food dataset is time and effort consuming due to the requirement for covering the feature variations of food samples. Additionally, a large dataset is needed for training neural networks. Generative adversarial networks (GANs) are a recently developed technique to learn deep representations without extensively annotated training data. They can be used in several applications, including generating food datasets. This paper advocates the use of Cycle-GAN to generate a large pseudo-realistic food dataset based on a large number of simulated images and a small number of real images in comparison to traditional techniques. A single depth camera in three different angles and a turntable are arranged to capture real RGB-D images of food samples. 3D modeling software is used to generate simulated images using the same configuration of captured real images. Results showed that Cycle-GAN realistic style transfer on simulated food objects is achievable, and that it can be an efficient tool to minimize real image capturing efforts.

DOI： 10.57417/jaalr.2.3_128

CiNii Research

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

DOI： 10.1109/ACCESS.2020.3047619

Scopus

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1007/978-981-15-8269-1_6

Language：English   Publishing type：Research paper (international conference proceedings)

© 2020, Springer Nature Switzerland AG. Reservoir computing (RC) has been viewed as a model of a neurological system. The RC framework constructs a recurrent neural network, which mimics parts of the brain, to solve temporal problems. To construct a neural network inside a reservoir, we adopt the pulse-coupled phase oscillator (PCPO) with neighbor topology connections on a field-programmable gate array (FPGA). Neural spikes for the PCPO are generated by the Winfree model. The low resource consumption of the proposed model in time-series generation tasks was confirmed in an evaluation study. We also demonstrate that on the FPGA, we can expand a 3 &#x0024;&#x0024;\times &#x0024;&#x0024; 3 PCPO into a 10 &#x0024;&#x0024;\times &#x0024;&#x0024; 10 PCPO, generate spike behavior, and predict the target signal with a maximum frequency of 418.796 MHz.

DOI： 10.1007/978-3-030-63823-8_39

Scopus

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

DOI： 10.1109/ACCESS.2020.3038161

Kyutacar

Scopus

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Language：English   Publishing type：Research paper (international conference proceedings)

Language：English   Publishing type：Research paper (international conference proceedings)

Language：English   Publishing type：Research paper (international conference proceedings)

Language：English   Publishing type：Research paper (international conference proceedings)

Language：English   Publishing type：Research paper (international conference proceedings)

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

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

Scopus

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Authorship：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

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

We proposed reservoir models using chaotic Boltzmann machines (CBMs) based on pseudo-billiard dynamics in hypercube. In order to utilize CBMs as a reservoir, we must control the chaotic behavior and satisfy the echo state property. We added an external reference clock signal to control the CBM. In this paper, we demonstrate the effect of an external reference clock signal and show that our model works as a reservoir by using NARMA task.

DOI： 10.34385/proc.74.a4l-c-1

CiNii Research

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

Reservoir computing (RC) is a framework that constructs recurrent neural networks that can be utilized for generation, prediction, and discrimination of complex time-series. The recurrent connection on the RC is sparse and fixed, and thus, the computational cost of learning is small. The pseudo-billiard dynamics in a hypercube is known to be applicable for various types of computation. This hypercube based computing is very efficient for hardware implementation. In this research, we implement the hypercube based RC model in a digital circuit with hardware-oriented algorithms and verify its performance and effectiveness as a new artificial intelligence platform.

DOI： 10.34385/proc.74.b4l-b-4

CiNii Research

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

Reservoir computing (RC) has received increasing attention as a computational framework for recurrent neural networks. As an implementation of reservoir, using a chaotic Boltzmann machine (CBM) was proposed, and we prototyped a CMOS CBM LSI chip controlled by an external reference clock (CBM-crc LSI) for working as a reservoir. We have developed a system using a System-on-Chip Field programmable gate array (SoC FPGA). In this paper, we propose a reservoir system that consists of a CBM-crc LSI controlled by an SoC FPGA and show the results of the system operations.

DOI： 10.34385/proc.74.a4l-c-3

CiNii Research

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

Language：English   Publishing type：Research paper (international conference proceedings)

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.

DOI： 10.1109/ISCAS45731.2020.9180921

Scopus

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Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1109/ISCAS45731.2020.9180685

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

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

DOI： 10.3156/jsoft.32.3_54

CiNii Article

CiNii Research

Other Link： https://www.jstage.jst.go.jp/article/jsoft/32/3/32_54/_pdf

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

DOI： 10.1080/01691864.2020.1769726

Kyutacar

Scopus

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

© 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

Kyutacar

Scopus

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

© 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

Kyutacar

Scopus

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

DOI： 10.11517/jjsai.35.2_155

CiNii Article

CiNii Research

Other Link： http://id.ndl.go.jp/bib/030297626

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

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

<p>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.</p>

DOI： 10.20965/jrm.2020.p0245

Kyutacar

Scopus

CiNii Article

CiNii Research

Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85079900477&origin=inward

Language：English   Publishing type：Research paper (international conference proceedings)

Japan   Oita  

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

Scopus

CiNii Research

Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85108782809&origin=inward

Language：English   Publishing type：Research paper (international conference proceedings)

Japan   Oita  

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

Scopus

CiNii Research

Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85108817228&origin=inward

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

<p>We propose a You Only Look Once (YOLO) based road marking detection method for self- position estimation in autonomous cars. In the proposed method, YOLO pre-processing is added to a conventional detection method, which searches road marking from a whole input image including various background, to restrict the searching area by removing background areas. In the conventional method, it is necessary to set a high detection threshold to prevent false detections. On the other hand, the proposed method can set a lower threshold than the conventional method because the searching area is restricted to road marking areas only. Therefore, several road markings that were previously undetected can be detected. The experimental results show that the proposed method reduces the number of negative detection while keeping the number of false detections at zero. Also, the overall accuracy was improved by 1.3 points.</p>

DOI： 10.14864/fss.36.0_149

CiNii Article

Other Link： https://ci.nii.ac.jp/naid/130007957941

Language：English   Publishing type：Research paper (international conference proceedings)

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

Scopus

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Language：English   Publishing type：Research paper (international conference proceedings)

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

Scopus

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Language：English   Publishing type：Research paper (international conference proceedings)

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

Scopus

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

<p>Reservoir computing (RC) is one of the frameworks of the recurrent neural network (RNN) and is applied to processing of time series data. The implementation of machine learning and neural networks generally demands large computational resources (or circuit resource) and power. Katori et al. proposed a reservoir computing model based on pseudo-billiard dynamics on a hypercube. This hypercube-based reservoir computing (HRC) can be implemented with less circuit resource and with low power consumption. In this study, we improve the HRC model based on the hardware-oriented algorithm that reduces the circuit resource consumption in digital circuit implementation with Field Programmable Gate Array (FPGA). We evaluate the proposed model with time series generation tasks and confirm that the accuracy of the time-series generation is comparable with the previously proposed model. This research may enhance the ability of RC and contribute to establish a new platform for artificial intelligence.</p>

DOI： 10.11517/pjsai.jsai2020.0_3rin422

CiNii Article

CiNii Research

Language：English   Publishing type：Research paper (international conference proceedings)

Australia   Sydney  

DOI： 10.1007/978-3-030-36802-9_34

Scopus

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Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1109/ISPACS.2018.8923117

Scopus

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Language：English   Publishing type：Research paper (international conference proceedings)

Malaysia   Kuala Lumpur  

Language：English   Publishing type：Research paper (international conference proceedings)

Thailand   Pattaya  

Language：English   Publishing type：Research paper (international conference proceedings)

Thailand   Pattaya  

Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1109/ICSyS47076.2019.8982469

Scopus

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Language：English   Publishing type：Research paper (international conference proceedings)

Japan   Tokyo  

Language：English   Publishing type：Research paper (international conference proceedings)

Hungary   Budapest  

DOI： 10.1109/IJCNN.2019.8852329

Scopus

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Language：English   Publishing type：Research paper (international conference proceedings)

Hungary   Budapest  

DOI： 10.1109/IJCNN.2019.8852325

Scopus

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Language：English   Publishing type：Research paper (international conference proceedings)

Singapore  

Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1109/ISCAS.2019.8702213

Scopus

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Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1109/ISCAS.2019.8702222

Scopus

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Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1109/ISCAS.2019.8702430

Scopus

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Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1117/12.2521580

Scopus

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Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1117/12.2521607

Scopus

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Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1117/12.2521356

Scopus

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Authorship：Last author,　Corresponding author   Language：Japanese   Publishing type：Research paper (scientific journal)

<p>In recent years, automated driving and AGVs are actively researched and developed. The important technique is Simultaneous Localization and Mapping (SLAM) which creates maps and self-location estimation at simultaneously. There are several kinds of SLAM depending on the sensor and algorithm used, ORB-SLAM uses only monocular camera. Since it is cheaper than other sensors, it is easy to board it into AGV etc. However, the calculation amount is large. Although high performance PCs is required, it is not able to board it into AGV etc., so we focused on FPGA. The FPGA development environment is also actively developed, and the development with high-level synthesis has become realistic. Therefore, in this research, in order to improve FPGA development efficiency, we implemented ORB-SLAM from open source software to FPGA.</p>

DOI： 10.1299/jsmermd.2019.1a1-f06

CiNii Article

CiNii Research

Other Link： https://www.jstage.jst.go.jp/article/jsmermd/2019/0/2019_1A1-F06/_pdf

Authorship：Last author,　Corresponding author   Language：Japanese   Publishing type：Research paper (scientific journal)

<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

Other Link： https://www.jstage.jst.go.jp/article/jsmermd/2019/0/2019_1P2-I02/_pdf

Authorship：Last author,　Corresponding author   Language：Japanese   Publishing type：Research paper (scientific journal)

<p>We propose a decision-making system for autonomous cars based on a decision table. By using the decision table, the proposed system offers faster reasoning speed than a conventional system using ontologies and reasoning rules. Furthermore, we propose an automatic conversion system from a set of ontologies and reasoning rules to a decision table, thus, the result of reasoning is equivalent to the conventional one. Experimental results show that the proposed system realizes approximately 40 times faster processing time than the conventional system.</p>

DOI： 10.14864/fss.35.0_722

CiNii Article

CiNii Research

Other Link： http://id.ndl.go.jp/bib/029976316

Authorship：Last author,　Corresponding author   Language：Japanese   Publishing type：Research paper (scientific journal)

<p>The purpose of this study is to reduce calculation cost of Gaussian Mixture Model - Mul- tiResolution Co-occurrence Histogram of Oriented Gradients (GMM-MRCoHOG) feature for hardware implementation. This feature shows high accuracy for human detection but has problems at hardware implementation: it requires inverse trigonometric function for gradient computation and large amount of memory resources for storing parameters. So, We proposed two methods for solving each problem. First, use conditional branch instead of inverse trigonometric function. Second, use fuzzy inference for saving memory resources. The experimental results show that these methods maintain high accuracy with reduce in calculation cost and memory resources.</p>

DOI： 10.14864/fss.35.0_715

CiNii Article

CiNii Research

Other Link： http://id.ndl.go.jp/bib/029976312

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

Kyutacar

Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (other)

DOI： 10.1587/transfun.E101.A.1735

Scopus

CiNii Article

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Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1109/ISPACS.2018.8923533

Scopus

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

DOI： 10.1038/s41467-018-04886-2

Scopus

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

Italy   Florence   2018.05.27  -  2018.05.30

Kyutacar

Language：English   Publishing type：Research paper (international conference proceedings)

Italy   Florence   2018.05.27  -  2018.05.30

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

Kyutacar

Authorship：Last author,　Corresponding author   Language：Japanese   Publishing type：Research paper (scientific journal)

<p>Danger forecast and its avoidance are highly important for the design of automated driving system and the function is expected to be solved by artificial intelligences (AIs). However the reasoning process is unclear in the conventional scheme such as machine learning and as deep neural network models. In this paper, we focus on the ability of the logical reasoning based on the Semantic Web techniques, which is called knowledge-based AI, and demonstrate successfully its implementation into a module of Robot Operating System (ROS) to control a real vehicle. The processing speed of proposed system is evaluated using the real vehicle in the situation of the pedestrian avoidance in the crossroad.</p>

DOI： 10.5687/iscie.31.191

CiNii Article

CiNii Research

Other Link： http://hdl.handle.net/10228/00008368

Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1109/ISCAS.2018.8351722

Scopus

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Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1109/ISCAS.2018.8351837

Scopus

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

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

Kyutacar

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

DOI： 10.1371/journal.pone.0194049

Scopus

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Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1007/978-3-030-04179-3_54

Scopus

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Authorship：Last author,　Corresponding author   Language：Japanese   Publishing type：Research paper (scientific journal)

<p>Autonomous driving is performed by artificial intelligence (AI) on a computer. If an accident is occurred, the autonomous driving system should show the evidence of decision making to analyze the reasons of accident. In this report, we propose an integrated simulator for a knowledge-based AI that shows the evidence of decision making. To construct the simulator, we combine Autoware, Gazebo and the knowledge-based AI. Autoware is an open source software for autonomous driving, and Gazebo can simulate arbitarary road environments. By using the proposed simulator, we can verify the practicality of knowledge-based AI for autonomous driving many times. Experimental results show that the proposed simulator successfully presents the evidence of decision making.</p>

DOI： 10.14864/fss.34.0_450

CiNii Article

CiNii Research

Other Link： http://id.ndl.go.jp/bib/029269004

Authorship：Last author,　Corresponding author   Language：Japanese   Publishing type：Research paper (scientific journal)

<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

Other Link： https://www.jstage.jst.go.jp/article/jsmermd/2018/0/2018_2P2-E02/_pdf

Authorship：Last author,　Corresponding author   Language：Japanese   Publishing type：Research paper (scientific journal)

<p>Service robots must assemble and execute its own action based on user’s voice instructions. Additionally, in order to assist human daily life, the service robots execute not a single action like an industrial robot but plurality of actions must be combined according to the situation. As the number of actions the robots can perform increases, phrase of the voice instructions increases to an enormous pattern. Therefore, the robots must understand the instructions by natural language processing. In this paper, we focus on general purpose service robot (GPSR) task of RoboCup@Home, which is a competition for performance of the service robots, and identify behavior of the robots from natural language by using word vectorization and machine learning.</p>

DOI： 10.1299/jsmermd.2018.2p1-b18

CiNii Article

CiNii Research

Other Link： https://www.jstage.jst.go.jp/article/jsmermd/2018/0/2018_2P1-B18/_pdf

Authorship：Last author,　Corresponding author   Language：Japanese   Publishing type：Research paper (scientific journal)

<p>In recent years, robots has come to have useful, but high load functions, e.g. navigating automotive movement, image recognition by deep learning. Usually, these functions are executed by high-spec CPU or GPUs. However, in practical use these devices cannot be implemented into robot, because of the size or power consumption. So in this research we implemented image filter function on FPGA included in AP SoC as a sample of high load function. And we have also implemented RT-Middleware and some RT-Components as a sample robot system which includes a component handling FPGA on CPU included in AP SoC. We show the effectiveness of the collaboration with FPGA and RT-Middleware to develop robot system.</p>

DOI： 10.1299/jsmermd.2018.2a1-g10

CiNii Article

CiNii Research

Other Link： https://www.jstage.jst.go.jp/article/jsmermd/2018/0/2018_2A1-G10/_pdf

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

<p>We propose a coupled-map-lattice complementary-metal-oxide-semiconductor very-large-scale-integration (CMOS VLSI) circuit based on the threshold-coupled map (TCM) that has been proposed previously as a unidirectional connected network model exhibiting different spatiotemporal patterns according to its underlying nonlinear map and update scheme. We introduce mutual connections and arbitrarily valued connection weights into the TCM to realize cellular automata. In this study, we design, fabricate, and evaluate a CMOS integrated circuit with which to implement this extended TCM (ETCM). The ETCM is a universal Turing machine as confirmed in circuit experiments using the fabricated circuit, which can achieve Rule110 of a one-dimensional cellular automaton.</p>

DOI： 10.1587/nolta.9.268

CiNii Article

CiNii Research

Other Link： https://www.jstage.jst.go.jp/article/nolta/9/2/9_268/_pdf

Language：English   Publishing type：Research paper (international conference proceedings)

Japan   Dazaifu   2017.09.06  -  2017.09.08

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

Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1109/IJCNN.2017.7965991

Scopus

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

Kyutacar

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

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

This paper proposes an implementation of multi-Field Programmable Gate Array (FPGA) communication using pulse-coupled phase oscillators. At first, we construct a digital phase oscillator circuit with adjustable in-phase parameters. While performing the synchronization task, the oscillators are connected into the first input and first output (FIFO) interface. The communication in-between of FPGAs will occur by enabling the following inputs and outputs on the FIFO through the Gigabit Transceiver (GTX) clock domain. Pulse-coupled phase oscillators of Winfree's model are utilized as a spike generator and we expect the ideal of model circuit will synchronize. We employ two FPGA boards of Virtex6 ML605 and implement four oscillators on a hardware level. Experimental results show that first spike synchronizing over two FPGAs takes 12.47 μsec with data bit speed stream 3.2Gbps.

DOI： 10.5954/icarob.2017.os9-1

CiNii Research

Language：English   Publishing type：Research paper (international conference proceedings)

Scopus

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Language：English   Publishing type：Research paper (international conference proceedings)

Scopus

Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85047884344&origin=inward

Language：English   Publishing type：Research paper (international conference proceedings)

Scopus

Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85034266960&origin=inward

Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1007/978-3-319-70136-3_87

Scopus

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

The Joint Graduate School Intelligent Car and Robotics Course, called “Car-Robo Joint Graduate School” is an educational program that started in 2012 mainly at The Kitakyushu Science and Research Park (KSRP). Graduate schools of engineering in Kyushu Institute of Technology, the University of Kitakyushu, and Waseda University at KSRP participate in this program. The purpose of this program is to foster highly skilled personnel in technical fields on intelligent car and robot technology. They will gain practical ability as leaders, mastering their special fields, understanding the peripheral technologies, and leading research and development teams. The special-feature program of this course is seven comprehensive practicums as team learning. Internship students of national colleges of technology and other universities as well as master and PhD course students of these graduate schools participate in the practicums.

DOI： 10.4307/jsee.65.4_51

CiNii Article

CiNii Research

Other Link： https://www.jstage.jst.go.jp/article/jsee/65/4/65_4_51/_pdf

Authorship：Last author,　Corresponding author   Language：Japanese   Publishing type：Research paper (scientific journal)

<p> </p>

DOI： 10.14864/fss.33.0_681

CiNii Article

CiNii Research

Other Link： http://id.ndl.go.jp/bib/029133349

Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1109/SCIS-ISIS.2016.0116

Scopus

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

DOI： 10.1587/transfun.E99.A.2008

Scopus

CiNii Article

Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84994577827&origin=inward

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

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

DOI： 10.11470/jsapmeeting.2016.1.0_2286

CiNii Research

Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1007/978-3-319-46687-3_26

Scopus

Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84992665665&origin=inward

Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1007/978-3-319-44778-0_46

Scopus

Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84987949104&origin=inward

Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1007/978-3-319-46687-3_25

Scopus

Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84992561479&origin=inward

Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1007/978-3-319-46687-3_27

Scopus

Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84992609437&origin=inward

Authorship：Last author,　Corresponding author   Language：Japanese   Publishing type：Research paper (scientific journal)

<p> </p>

DOI： 10.14864/fss.32.0_49

CiNii Article

CiNii Research

Authorship：Last author,　Corresponding author   Language：Japanese   Publishing type：Research paper (scientific journal)

<p> </p>

DOI： 10.14864/fss.32.0_53

CiNii Article

CiNii Research

Other Link： http://id.ndl.go.jp/bib/027632005

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

United States of America   New York City  

Language：English   Publishing type：Research paper (international conference proceedings)

China   Hong Kong  

Language：English   Publishing type：Research paper (international conference proceedings)

NewZealand   Auckland  

DOI： 10.1007/978-3-319-29451-3_27

Scopus

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Language：English   Publishing type：Research paper (international conference proceedings)

Indonesia   Bali  

DOI： 10.1109/ISPACS.2015.7432741

Scopus

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

Japan   Sapporo  

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

DOI： 10.1016/j.neucom.2014.07.084

Scopus

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

DOI： 10.1587/transinf.2014EDP7219

Scopus

CiNii Article

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

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

DOI： 10.1587/nolta.6.570

CiNii Article

Other Link： http://ci.nii.ac.jp/naid/130005102329

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

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

Language：English   Publishing type：Research paper (international conference proceedings)

Language：English   Publishing type：Research paper (international conference proceedings)

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

Language：English   Publishing type：Research paper (international conference proceedings)

Scopus

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Language：English   Publishing type：Research paper (international conference proceedings)

Scopus

Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84906895283&origin=inward

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

Scopus

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Authorship：Corresponding author   Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (other)

Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1109/IECON.2013.6699481

Scopus

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1007/978-3-642-42051-1_3

Scopus

Language：English   Publishing type：Research paper (international conference proceedings)

Language：English   Publishing type：Research paper (international conference proceedings)

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

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Scopus

Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84877792007&origin=inward

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (scientific journal)

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

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

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

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

Indonesia   Jakarta   2012.10.24  -  2012.10.25

DOI： 10.1016/j.proeng.2012.10.030

Scopus

Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1109/ISCAS.2012.6272135

Scopus

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1109/ICNC.2012.6234717

Scopus

Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

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

DOI： 10.1587/elex.8.773

Scopus

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

Language：English   Publishing type：Research paper (international conference proceedings)

Scopus

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1007/978-3-642-17534-3_54

Scopus

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

DOI： 10.1109/PSIVT.2010.56

Scopus

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

DOI： 10.1007/s10043-010-0082-8

Scopus

Authorship：Corresponding author   Language：Japanese   Publishing type：Research paper (scientific journal)

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

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

Language：English   Publishing type：Research paper (international conference proceedings)

CiNii Article

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

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

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

Language：English   Publishing type：Research paper (international conference proceedings)

Language：English   Publishing type：Research paper (international conference proceedings)

Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (trade magazine, newspaper, online media)

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

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

Language：English   Publishing type：Research paper (international conference proceedings)

Language：English   Publishing type：Research paper (international conference proceedings)

Language：English   Publishing type：Research paper (international conference proceedings)

Language：English   Publishing type：Research paper (international conference proceedings)

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

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

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

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

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

Language：English   Publishing type：Research paper (international conference proceedings)

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

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

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

Event date： 2022.09.20 - 2022.09.23   Language：Japanese  

Event date： 2022.09.05 - 2022.09.09   Language：Japanese  

Event date： 2022.08.31 - 2022.09.03   Language：Japanese  

Event date： 2022.06.09 - 2022.06.10   Language：Japanese  

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Event date： 2022.02.18 - 2022.02.19   Language：Japanese  

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Event date： 2022.02.18 - 2022.02.19   Language：Japanese  

Event date： 2021.12.05 - 2021.12.08   Language：English  

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Event date： 2021.10.07 - 2021.10.08   Language：Japanese  

Event date： 2021.09.23   Language：Japanese  

Event date： 2021.09.23   Language：Japanese  

Event date： 2021.09.10 - 2021.09.13   Language：Japanese  

Event date： 2021.09.08 - 2021.09.11   Language：Japanese  

Event date： 2021.03.04 - 2021.03.05   Language：Japanese  

Event date： 2020.12.25 - 2020.12.27   Language：Japanese  

Event date： 2020.12.12 - 2020.12.19   Language：English  

Event date： 2020.12.12 - 2020.12.19   Language：English  

Event date： 2020.12.12 - 2020.12.19   Language：English  

Event date： 2020.12.12 - 2020.12.19   Language：English  

Event date： 2020.12.12 - 2020.12.19   Language：English  

Event date： 2020.12.12 - 2020.12.19   Language：English  

Event date： 2020.12.12 - 2020.12.19   Language：English  

Event date： 2020.12.12 - 2020.12.19   Language：English  

Event date： 2020.12.01   Language：Japanese  

Event date： 2020.10.09 - 2020.10.11   Language：Japanese  

Event date： 2020.10.09 - 2020.10.11   Language：Japanese  

Event date： 2020.10.09 - 2020.10.11   Language：Japanese  

Event date： 2020.10.01 - 2020.10.02   Language：Japanese  

Event date： 2020.06.03 - 2020.06.04   Language：Japanese  

Event date： 2020.05.13 - 2020.05.14   Language：Japanese  

Event date： 2020.03.05 - 2020.03.06   Language：Japanese  

Event date： 2020.03.03 - 2020.03.05   Language：Japanese  

Event date： 2019.12.18 - 2019.12.19   Language：Japanese  

Event date： 2019.12.12 - 2019.12.19   Language：Japanese  

Event date： 2019.11.11 - 2019.11.12   Language：English  

Event date： 2019.11.11 - 2019.11.12   Language：English  

Event date： 2019.11.11 - 2019.11.12   Language：English  

Event date： 2019.11.11 - 2019.11.12   Language：English  

Event date： 2019.09.04 - 2019.09.06   Language：Japanese  

Event date： 2019.09.03 - 2019.09.07   Language：Japanese  

Event date： 2019.09.03 - 2019.09.07   Language：Japanese  

Event date： 2019.09.03 - 2019.09.07   Language：Japanese  

Event date： 2019.08.29 - 2019.08.31   Language：Japanese  

Event date： 2019.08.29 - 2019.08.31   Language：Japanese  

Event date： 2019.08.27   Language：Japanese  

Event date： 2019.08.22 - 2019.08.23   Language：English  

Event date： 2019.08.22 - 2019.08.23   Language：Japanese  

Event date： 2019.08.22 - 2019.08.23   Language：Japanese