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arXiv 2023年

作者： Bunker, Rory Duy, Vo Nguyen Le Tabei, Yasuo Takeuchi, Ichiro Fujii, Keisuke Graduate School of Informatics Nagoya University Japan Data-Driven Biomedical Science Team RIKEN Center for Advanced Intelligence Project Japan RIKEN Center for Advanced Intelligence Project Japan Graduate School of Engineering Mechanical Systems Engineering Nagoya University Japan PRESTO Japan Science and Technology Agency Japan

Improvements in tracking technology through optical and computer vision systems have enabled a greater understanding of the movement-based behaviour of multiple agents, including in team sports. In this study, a Multi-Agent Statistically Discriminative Sub-Trajectory Mining (MA-Stat-DSM) method is proposed that takes a set of binary-labelled agent trajectory matrices as input and incorporates Hausdorff distance to identify sub-matrices that statistically significantly discriminate between the two groups of labelled trajectory matrices. Utilizing 2015/16 SportVU NBA tracking data, agent trajectory matrices representing attacks consisting of the trajectories of five agents (the ball, shooter, last passer, shooter defender, and last passer defender), were truncated to correspond to the time interval following the receipt of the ball by the last passer, and labelled as effective or ineffective based on a definition of attack effectiveness that we devise in the current study. After identifying appropriate parameters for MA-Stat-DSM by iteratively applying it to all matches involving the two top- and two bottom-placed teams from the 2015/16 NBA season, the method was then applied to selected matches and could identify and visualize the portions of plays, e.g., involving passing, on-, and/or off-the-ball movements, which were most relevant in rendering attacks effective or ineffective. © 2023, CC BY.

关键词： Geographic information systems

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An X-band Spatial Power Combining Using Rectangular Waveguide with Dielectric Lens

An X-band Spatial Power Combining Using Rectangular Waveguid...

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IEEE Radio and Wireless Symposium (RWS formerly RAWCON)

作者： Takuma Kinoshita Yuki Shinhama Masaru Sato Futoshi Kuroki Electrical Engineering and Imformation Science NIT Kure College Hiroshima Japan Devices & Materials Research Center Advanced Sensor Devices Project Fujitsu Ltd Atsugi Japan

Because the performance of the output power and the bandwidth of the monolithic microwave integrated circuit (MMIC) amplifier is being improved in recent years, the development of a power combining technique with high efficiency becomes the next research target to enhance the output power. In this paper, an X-band 3-way power combiner for MMIC chips using rectangular waveguides is fabricated and tested for its S-parameters with microstrip thru-lines. To improve the combining efficiency, phase imbalance on MMIC inputs is minimized with a new type low-loss dielectric lens. This dielectric lens is very suitable for power combining system because this lens can be fabricated easily and withstand high power output compared to the conventional dielectric lens. The measured combining efficiency is higher than 75% in 8.5-11.5 GHz.

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EpilepsyLLM: Domain-Specific Large Language Model Fine-tuned with Epilepsy Medical Knowledge

arXiv

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arXiv 2024年

作者： Zhao, Xuyang Zhao, Qibin Tanaka, Toshihisa Department of Electrical Engineering and Computer Science Tokyo University of Agriculture and Technology Tokyo Japan Tensor Learning Team RIKEN Center for Advanced Intelligence Project Tokyo Japan

With large training datasets and massive amounts of computing sources, large language models (LLMs) achieve remarkable performance in comprehensive and generative ability. Based on those powerful LLMs, the model fine-tuned with domain-specific datasets posseses more specialized knowledge and thus is more practical like medical LLMs. However, the existing fine-tuned medical LLMs are limited to general medical knowledge with English language. For disease-specific problems, the model’s response is inaccurate and sometimes even completely irrelevant, especially when using a language other than English. In this work, we focus on the particular disease of Epilepsy with Japanese language and introduce a customized LLM termed as EpilepsyLLM. Our model is trained from the pre-trained LLM by fine-tuning technique using datasets from the epilepsy domain. The datasets contain knowledge of basic information about disease, common treatment methods and drugs, and important notes in life and work. The experimental results demonstrate that EpilepsyLLM can provide more reliable and specialized medical knowledge responses. Copyright © 2024, The Authors. All rights reserved.

关键词： Large dataset

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Proposal an Automated Management Service for Hybrid Meeting Spaces Using Uni-Messe and IoT

Proposal an Automated Management Service for Hybrid Meeting ...

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IEEE International Conference on Artificial Intelligence in engineering and Technology (IICAIET)

作者： Takeshi Yoshida Sinan Chen Masahide Nakamura Sachio Saiki Faculty of Engineering Graduate School of Engineering Kobe University Japan RIKEN Center for Advanced Intelligence Project Tokyo Japan School of Data and Innovation Kochi University of Technology Kochi Japan

In recent years, Japan has been actively promoting the concept of a super-smart society, which aims to leverage various scientific technologies to address societal challenges spanning environmental issues, energy management, and disaster preparedness. Within this context, our focus lies on harnessing the potential of Internet of Things (IoT) devices to playa pivotal role in advancing these objectives. The purpose of this paper is to propose an innovative service that streamlines the automatic management of hybrid conferences. To achieve this, we introduce a service coordination platform that leverages the Unified Rule- Based Message Delivery Service (Uni-messe). Our proposed approach involves the initial implementation of a simplified service version, followed by a case study specifically tailored to the conference setting. Throughout our study, we identified two key areas that require further improvement. Firstly, delays arising from communication between devices during the implementation process need to be addressed. Secondly, there is a limitation in operating devices that do not support infrared functionality. In conclusion, our research contributes to advancing super-smart society initiatives by presenting a novel and promising avenue for automating hybrid conferences using IoT and the Uni-messe service coordination platform.

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Capturing Contact Surfaces by a Frustrated Total Internal Reflection System Using a Curved Plate for Comparison of the Beginning of Touching Motions by Humanitude Experts and Novices

Capturing Contact Surfaces by a Frustrated Total Internal Re...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Yuguchi, Akishige Toyoda, Mayuki Cho, Sung-Gwi Nakazawa, Atsushi Takamatsu, Jun Yoshino, Koichiro Ogasawara, Tsukasa Tokyo University of Science Faculty of Advanced Engineering Katsushika-ku Tokyo125-8585 Japan Guardian Robot Project Riken Seika-cho Sorakugun Kyoto619-0288 Japan Nara Institute of Science and Technology Nara Ikoma630-0192 Japan School of Science and Engineering Tokyo Denki University Hatoyama-machi Saitama Hiki-gun350-0394 Japan Graduate School of Interdisciplinary Science and Engineering in Health Systems Okayama University Okayama Kita-ku700-8530 Japan Applied Robotics Research Microsoft Corporation RedmondWA98052 United States

ISBN: (纸本)9781665410205

Analyzing time-series changes of the contact surface by touching is important to elucidate the touching skills of Humanitude as one of the pervasive multimodal comprehensive care methodologies. For the analysis, there is a frustrated total internal reflection (FTIR) method to capture contact surfaces on a transparent flat plate by a camera. However, this conventional flat plate is far from the actual surfaces of care receivers because the surfaces of humans consist of curved shapes. In this paper, we propose an FTIR sensing system using a transparent curved plate to capture more ideal contact states with a surface shape more similar to the human body. We collect the contact surface data of the beginning of touching motions by Humanitude experts and novices using the FTIR sensing system with the curved and flat plates. Then, we compare the data by the experts and novices in terms of time-series contact areas and the quantitative indices and subjective evaluation and discuss the analysis results. Through these experiments, we confirm that the proposed system has the potential for the novices to perform more correctly the beginning of Humanitude's touching motions. © 2024 IEEE.

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Developing a Framework for Rapid Collapsed Building Mapping Using Satellite Imagery and Deep Learning Models

Developing a Framework for Rapid Collapsed Building Mapping ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Bruno Adriano Hiroyuki Miura Wen Liu Masashi Matsuoka Shunichi Koshimura International Research Institute of Disaster Science Tohoku University Japan RIKEN Center for Advanced Intelligence Project Japan School of Advanced Science and Engineering Hiroshima University Japan Graduate School of Engineering Chiba University Japan Department of Architecture and Building Engineering Tokyo Institute of Technology Japan

After a major disaster, a rapid assessment of building damage is highly required for emergency response and prompt recovery. Remote sensing technologies have been widely applied for building damage mapping. Combining machine-learning algorithms (e.g., deep learning) and satellite images has recently demonstrated success in boosting damage recognition methods. Although previous techniques have shown great success, they primarily adopt supervised settings, often requiring a minimum number of training samples to achieve acceptable accuracy. Moreover, previous methods also are developed for specific target areas, which makes it challenging to apply them to other regions in case of future disasters. This paper presents a novel unsupervised approach for building damage mapping, focusing on collapsed structures, using modern convolutional neural network (CNN) models and high-resolution remote sensing imagery. We apply our mapping framework to revise the building damage following the 2007 Peru-Pisco Earthquake and the recent 2023 Turkey and Syria Earthquakes.

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Covariance Matrix Adaptation Evolutionary Strategy with Worst-Case Ranking Approximation for Min–Max Optimization and its Application to Berthing Control Tasks

arXiv

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arXiv 2023年

作者： Miyagi, Atsuhiro Miyauchi, Yoshiki Maki, Atsuo Fukuchi, Kazuto Sakuma, Jun Akimoto, Youhei Taisei corporation University of Tsukuba Japan Department of Naval Architecture and Ocean Engineering Graduate School of Engineering Osaka University Japan Faculty of Engineering Information and Systems University of Tsukuba RIKEN Center for Advanced Intelligence Project Japan

In this study, we consider a continuous min–max optimization problem minx∈X maxy∈Y f (x, y) whose objective function is a black-box. We propose a novel approach to minimize the worst-case objective function f (x) = maxy f (x, y) directly using a covariance matrix adaptation evolution strategy (CMA-ES) in which the rankings of solution candidates are approximated by our proposed worst-case ranking approximation (WRA) mechanism. We develop two variants of WRA combined with CMA-ES and approximate gradient ascent as numerical solvers for the inner maximization problem. Numerical experiments show that our proposed approach outperforms several existing approaches when the objective function is a smooth strongly convex–concave function and the interaction between x and y is strong. We investigate the advantages of the proposed approach for problems where the objective function is not limited to smooth strongly convex–concave functions. The effectiveness of the proposed approach is demonstrated in the robust berthing control problem with uncertainty. © 2023, CC BY-NC-ND.

关键词： Optimization

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Active Learning for Level Set Estimation Using Randomized Straddle Algorithms

arXiv

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arXiv 2024年

作者： Inatsu, Yu Takeno, Shion Kutsukake, Kentaro Takeuchi, Ichiro Department of Computer Science Nagoya Institute of Technology Japan Department of Mechanical Systems Engineering Graduate School of Engineering Nagoya University Japan RIKEN Center for Advanced Intelligence Project Japan Institute of Materials and Systems for Sustainability Nagoya University Japan Department of Materials Process Engineering Graduate School of Engineering Nagoya University Japan

Level set estimation (LSE), the problem of identifying the set of input points where a function takes value above (or below) a given threshold, is important in practical applications. When the function is expensive-to-evaluate and black-box, the straddle algorithm, which is a representative heuristic for LSE based on Gaussian process models, and its extensions having theoretical guarantees have been developed. However, many of existing methods include a confidence parameter βt1/2 that must be specified by the user, and methods that choose βt1/2 heuristically do not provide theoretical guarantees. In contrast, theoretically guaranteed values of βt1/2 need to be increased depending on the number of iterations and candidate points, and are conservative and not good for practical performance. In this study, we propose a novel method, the randomized straddle algorithm, in which βt in the straddle algorithm is replaced by a random sample from the chi-squared distribution with two degrees of freedom. The confidence parameter in the proposed method has the advantages of not needing adjustment, not depending on the number of iterations and candidate points, and not being conservative. Furthermore, we show that the proposed method has theoretical guarantees that depend on the sample complexity and the number of iterations. Finally, we confirm the usefulness of the proposed method through numerical experiments using synthetic and real data. Copyright © 2024, The Authors. All rights reserved.

关键词： Active learning

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Proposing Tap-Track Platform for Facilitating Implementation of Button-Driven Smart Services

Proposing Tap-Track Platform for Facilitating Implementation...

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2024 International of Seminar on Application for Technology of Information and Communication, iSemantic 2024

作者： Murate, Ryota Nakahashi, Tomorou Chen, Sinan Saiki, Sachio Nakamura, Masahide Yasuda, Kiyoshi Tuyuzaki, Yuta Kobe University Faculty of Engineering 1-1 Rokkodai-cho Nada Kobe Japan Kobe University Graduate School of Engineering 1-1 Rokkodai-cho Nada Kobe Japan Kochi University of Technology School of Data and Innovation 185 Miyanokuchi Kochi Japan Kobe University Tokyo103-0027 Japan Riken Center for Advanced Intelligence Project Tokyo103-0027 Japan Oyumino Central Hospital 6-49-9 Oyumino Minami Chiba Japan

ISBN: (纸本)9798350366648

In recent years, the movement toward a super smart society as a future society is being promoted as Society 5.0. In this movement, smart services [1] have become very diverse, and the burden on users has increased. Therefore, in this paper, we focus on button-driven smart services, and propose a platform "Tap Track "that makes it easy to implement button-driven smart services. In Tap Track, users can create services that run when a button [2] is pressed with no code, and users can assign their desired services to the button. The processing that can be executed when the button is pressed is playing media files, sending commands to external services, and so on. The usage history of the button can be looked back later as a life log. In the case study, we created a weather forecast confirmation service and a life log service using Tap Track and verified its operation. © 2024 IEEE.

关键词： Economic and social effects

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Feasibility Analysis of PPE Detection Via YOLOv7 & YOLOv7-Pose to Enhance Workplace Safety

Feasibility Analysis of PPE Detection Via YOLOv7 & YOLOv7-Po...

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International Conference on Smart Sensors and Application (ICSSA)

作者： Nor Awatif Abu Bakar Farid Azhari Norzaidi Latifah Munirah Kamarudin Ammar Zakaria Syed Muhammad Mamduh Syed Zakaria Ahmad Shakaff Ali Yeon Retnam Visvanathan Khairul Hafiz Zolkipli Asrul Sani Abdullah Ahmad Helmi M Amin Muhammad Azri Ahmad Baharom Hafizal Khalid Faculty of Electrical Engineering & Technology University Malaysia Perlis Arau Malaysia Centre of Excellence for Advanced Sensor Technology University Malaysia Perlis Arau Malaysia Engineering Department IDERIA Sdn Bhd Kangar Malaysia Project Delivery and Technology PETRONAS Kuala Lumpur Malaysia Project Excellence PETRONAS Kuala Lumpur Malaysia

ISBN: (数字)9798350354294

ISBN: (纸本)9798350354300

Ensuring workplace safety in Pre-Fabrication environments is important, particularly in adhering to safety compliance protocols through the correct usage of Personal Protective Equipment (PPE). However, existing systems often fall short in detecting whether PPE is correctly worn or not, posing a risk to workplace safety. By creating a specialized dataset for Pre-Fabrication environments, the system can detect four PPE tools which are hard hat, glove, safety shoes and welding mask. The dataset contains 934 training images and 255 images for testing and validation, respectively. After rigorous training, testing and validation process, the experimental result and performance evaluation show that the PPE detection model achieved 96.8% precision, 95% recall and a mean Average Precision (mAP) of 0.879. However, with the current model, there are several false positive errors from the result where the model falsely detects other objects as PPE. Therefore, this study proposes a dual-model system employing YOLOv7 object detection and pose estimation models to further improve the accuracy of the PPE detection model and reduce false positives. This dual-model system, validated through live CCTV footage, provides a comprehensive solution for enhancing safety protocols in Pre-Fabrication workspaces.

关键词： Personal protective equipment Training Analytical models Protocols Accuracy Employment Pose estimation Object detection Safety Testing

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