研究者詳細 - 田向　権

論文 - 田向　権

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Robust Classification Model with Multimodal Learning for Home Service Robots 査読有り

Matsumoto I., Kanaoka D., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 407 - 412 2023年01月

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

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.

Scopus

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Object Search and Empty Space Detection System for Home Service Robot 査読有り

Shiba T., Tomohiro O., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 402 - 406 2023年01月

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

Home service robots have pick-and-place tasks to grasp and carry objects. When performing pick-and-place 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.

Scopus

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Pose Detection for Flexible-Indefinite Objects using Pseudo-Bone Data 査読有り

Yoshimoto Y., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 399 - 401 2023年01月

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

This paper proposes 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.

Scopus

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Random network device fabricated using Ag<inf>2</inf>Se nanowires for data augmentation with binarized convolutional neural network 査読有り

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

Applied Physics Express 16 ( 1 ) 2023年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

An Ag2Se nanowire random network was fabricated for application as a data augmentation device and combined with a binary convolutional neural network (BCNN) to achieve high accuracy in voice classification tasks. Due to the nonlinear high-dimensional characteristics resulting from the formation of the conductive filament at the cross junction, the Ag2Se device could transform input data into higher-order multiple signals, thereby enhancing the accuracy of the classification task by augmenting input signals. The results indicate that materials can realize data augmentation with the same performance as software, suggesting that material-based hardware can be used as an elemental technology for information processing.

DOI： 10.35848/1882-0786/acae6a

Scopus

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ホームサービスロボットのための高精度な特定人物追跡システムの開発査読有り

小野智寛, 阿部佑志, 石田裕太郎, 柴田智広, 田向権

日本ロボット学会誌 41 ( 2 ) 198 - 201 2023年01月

An Effective Method for Minimizing Domain Gap in Sim2Real Object Recognition Using Domain Randomization 査読有り

Ono T., Suzuki A., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 424 - 428 2023年01月

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

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.

Scopus

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FPGA Implementation of a Chaotic Boltzmann Machine Annealer 査読有り

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

Proceedings of the International Joint Conference on Neural Networks 2023-June 2023年01月

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

Ising machines are attracting attention for their ability to solve large-scale combinatorial optimization problems because these problems are difficult to solve. To accelerate the computing of Ising machines, implementation of Ising machines with digital circuits such as simulated annealing (SA) machines is in progress. However, these Ising machines on digital circuits require random number generators, which are implemented with large circuit resources. This work focuses on chaotic Boltzmann machines (CBMs), which imitate the stochastic behavior of Boltzmann machines (BMs) with deterministic chaotic dynamics. CBMs are one of the models that work as chaotic simulated annealing (CSA) machines within Ising machines. Therefore, we can implement the Ising machines without random number generators by using CBMs. In conventional work, CSA machines using CBMs (CBM-CSAs) are implemented with some hardware-oriented algorithms, but the CBM-CSA circuit is not optimized for these hardware-oriented algorithms. In the conventional CBM-CSA circuit, memory circuits are implemented separately, which prevents making the CBM-CSA from larger, and neuron circuits require the reset of accumulated values, which causes the increase in the calculation time. To solve these problems, we implement only one large memory circuit to make the CBM-CSA larger and improve the neuron circuits to allow dynamic changes of inputs to arithmetic circuits to inhibit the increase in the calculation time. As a result, we implement a CBM-CSA with 4096 nodes on an FPGA (Alveo U250), and the CBM-CSA can control 16-bit width weights and run at 100MHz. We evaluate the implemented CBM-CSA by solving K4000, max-cut problem, which is one of the combinatorial optimization problems. The best solution of CBM-CSA is comparable to that of the SA on the central processing unit (CPU). Moreover, the CBM-CSA is approximately 600 times as fast as the SA on the CPU and approximately twice as fast as the conventional Ising machine on an FPGA based on the improvements in this work. Furthermore, this work implements one of the highest-performance Ising machines on a single FPGA.

DOI： 10.1109/IJCNN54540.2023.10191342

Kyutacar

Scopus

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Flexible Human-Robot Interaction in Domestic Environment Using Semantic Map 査読有り

Yano Y., Fukuda Y., Ono T., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 413 - 418 2023年01月

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

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.

Scopus

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Efficient Repetition Coding for Deep Learning Towards Implementation Using Emerging Non-Volatile Memory with Write-Errors 査読有り

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

Proceedings of the International Joint Conference on Neural Networks 2023-June 2023年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Emerging non-volatile memory devices, such as resistive random access memory (ReRAM) and voltage-controlled magnetoresistive random access memory (VC-MRAM), promise low energy consumption for artificial intelligence applications. However, when implementing deep neural networks (DNNs) using such memory devices, write-error may cause millions of bit-flipping to DNN. This easily degrades the DNN performance. To address this problem, we propose a novel repetition coding for deep-learning (RC-DL), which is a repetition coding designed to protect IEEE 32-bit floating-point (FP32) DNN models. Compared to conventional repetition coding, the proposed RC-DL exploits FP32 non-uniform magnitude encoding by increasing the repeat rates to protect sensitive bit positions and reduce the repeat rates to insensitive bit positions. Hence, RC-DL uses a number of bits equivalent to a 3-bit repetition code while delivering the performance close to 11-bit repetition code. We perform extensive Monte Carlo simulations to simulate the write-error property with ImageNet 2012 pretrained models. The DNN models with RC-DL are shown to be operable in the extremely imperfect environment while delivering with only minor reductions in DNN performance.

DOI： 10.1109/IJCNN54540.2023.10191433

Kyutacar

Scopus

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Dense Traversability Estimation System for Extreme Environments 査読有り

Fukuda Y., Mii Y., Yano Y., Iwai H., Inoue S., Tamukoh H.

IEEE Intelligent Vehicles Symposium, Proceedings 2023-June 2023年01月

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

Traversability estimation is essential for safe path planning in robotics and autonomous driving. In this study, we propose an accurate and dense traversability estimation system for extreme environments such as disaster areas. Traversability estimations often occur undefined regions due to the shielding and sparsity of sensor data. These regions may lead to the selecting of dangerous paths. The proposed system uses point cloud accumulation and the Bayesian generalized kernel (BGK) elevation estimation to create a dense Digital elevation map (DEM) with no undefined regions from point clouds obtained from light detection and ranging (LiDAR). Subsequently, traversability is estimated from road surface roughness, slope and vehicle performance using fuzzy logic in our system. In our experiments, our system is installed in an experimental vehicle, and the experiments conducted on rocky terrain, slopes, and craters to verify its effectivity in extreme environments. Results show that our system can create a dense DEM with few undefined regions and estimate valid traversability for all obstacles.

DOI： 10.1109/IV55152.2023.10186556

Scopus

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Memory-Efficient Implementation of GMM-MRCoHOG for HumaRecognition Hardware 査読有り

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

Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications 4 648 - 655 2023年01月

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

High-speed and accurate human recognition is necessary to realize safe autonomous mobile robots. Recently, human recognition methods based on deep learning have been studied extensively. However, these methods consume large amounts of power. Therefore, this study focuses on the Gaussian mixture model of multiresolution co-occurrence histograms of oriented gradients (GMM-MRCoHOG), which is a feature extraction method for human recognition that entails lower computational costs compared to deep learning-based methods, and aims to implement its hardware for high-speed, high-accuracy, and low-power human recognition. A digital hardware implementation method of GMM-MRCoHOG has been proposed. However, the method requires numerous look-up tables (LUTs) to store state spaces of GMM-MRCoHOG, thereby impeding the realization of human recognition systems. This study proposes a LUT reduction method to overcome this drawback by standardizing basis function arrangements of Gaussian mixture distributions in GMM-MRCoHOG. Experimental results show that the proposed method is as accurate as the previous method, and the memory required for state spaces consuming LUTs can be reduced to 1/504th of that required in the previous method.

DOI： 10.5220/0011698400003417

Scopus

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LUTNet-RC: Look-Up Tables Networks for Reservoir Computing on an FPGA 査読有り

Yoshioka K., Tanaka Y., Tamukoh H.

Proceedings - International Conference on Field-Programmable Technology, ICFPT 170 - 178 2023年01月

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

We propose look-up tables networks-based reservoir computing (LUTNet-RC). This work is the first trial of applying LUTNets to RC. LUTNet-RC consists of a LUT-based reservoir layer and a non-LUT-based output layer. LUTNets have disadvantages such as limited sparse connectivity and weights cannot be changed after implementation. However, when applied to a reservoir layer of RC (LUT-based reservoir layer), these disadvantages are eliminated, because this layer works with sparse connectivity and the weights are fixed, so only the advantage of small circuit resources is obtained. For the LUT-based reservoir layer, we propose and model a multi-bit weight reservoir, modifying the conventional binarized reservoir to improve calculation accuracy. In the case of LUTNets, the proposed multi-bit weight reservoir can be implemented without the increase in utilized circuit resources because LUTNets focus only on the input-output relationship on neurons. Additionally, we propose a speed-up method in the output layer with time division calculation, which compares the current network state with previous states and then calculates only status-changed neurons. As a result, we implement a LUTNet-RC with 1500 reservoir neurons on a field-programmable gate array (KR260) running at 100MHz. The utilized circuit resources are dominated by LUTs, which use approximately 26% of the total amount of LUTs. The LUTNet-RC can infer more than 106 data per second. We also verify the LUTNet-RC performance using nonlinear auto-regressive moving average 10 (NARMA10) and the performance is comparable to conventional works. We conclude that the LUTNet-RC is one of the highest-performance RC on an FPGA.

DOI： 10.1109/ICFPT59805.2023.00024

Kyutacar

Scopus

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Impact of PS Load on FPGA Object Detection System Performance 査読有り

Watanabe Y., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 419 - 423 2023年01月

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

A 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.

Scopus

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Proposal of Robust Multimodal Learning Model for Object Classification 査読有り

Ikuya Matsumoto, Daiju Kanaoka and Hakaru Tamukoh

The 4th International Symposium on Neuromorphic AI Hardware P1-13 2022年12月

Binarized Dual Stream Convolutional Neural Network Implemented in an FPGA 査読有り

Ninnart Fuengfusin, Dinda Pramanda and Hakaru Tamukoh

10th International Symposium on Applied Engineering and Sciences (SAES2022) 2022年12月

Reservoir-based 1D convolution 査読有り

Yuichiro Tanaka and Hakaru Tamukoh

The 4th International Symposium on Neuromorphic AI Hardware 2022年12月

Toward Low-Power Intelligent Embedded Systems for Home Service Robots: FPGA Implementation of Binarized Dual-Stream CNN 査読有り

Yuma Yoshimoto, and Hakaru Tamukoh

The 4th International Symposium on Neuromorphic AI Hardware 2022年12月

Embedding Binary Neural Network to Floating-Point Neural Network 査読有り

Ninnart Fuengfusin, Dinda Pramanda and Hakaru Tamukoh

The 4th International Symposium on Neuromorphic AI Hardware 2022年12月

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

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

The 4th International Symposium on Neuromorphic AI Hardware P2-6 2022年12月

Multi Object Tracking using SORT with Simple Re-Identification, 査読有り

Wei-Ding Wang, Ninnart Fuengfusin, Danny Wee-Kiat Ng, Ban-Hoe Kwan and Hakaru Tamukoh

The 4th International Symposium on Neuromorphic AI Hardware P1-16 2022年12月

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