研究者詳細 - 枝廣　正人

論文 - 枝廣　正人

分割表示 217 件中 81 - 100 件目／全件表示 >>

Special Section on Discrete Mathematics and Its Applications

CHAO Jinhui, EDAHIRO Masato, FUJITO Toshihiro, HAGIHARA Kenichi, HANDA Keiichi, HIRAISHI Kunihiko, ITO Hiro, MAMBO Masahiro, MATSUI Tomomi, MIYANO Eiji, OKAMOTO Eiji, OSAWA Shingo, OKABE Yasuo, IMAI Keiko

IEICE transactions on fundamentals of electronics, communications and computer sciences 84 巻 ( 5 ) 頁： 1093 2001年5月

Parallel Metaheuristic Algorithms: for Task Scheduling Problems 査読有り

Mao Sirui, M. Edahiro

International Journal of Computer & Technology 25 巻頁： 1-13 2025年3月

Quality Evaluation for 3D Point Cloud Maps Using Entropy Focused on Road Surfaces 査読有り

91 巻 ( 3 ) 頁： 390 - 396 2025年3月

LLVM Instruction Latency Measurement for Software-Hardware Interface for Multi-many-core 査読有り

H. Mikami, K. Torigoe, M. Inokawa, and M. Edahiro

COOL CHIPS 25 2022年4月

Mapping Method Usable with Clustered Many-core Platforms for Simulink Model 査読有り

Yutaro Kobayashi, Kentaro Honda, Sasuga Kojima, Hiroshi Fujimoto, Masato Edahiro, and Takuya Azumi

63 巻 ( 2 ) 2022年2月

Robustness Evaluation of Vehicle Localization in 3D Map Using Convergence of Scan Matching

Kitsukawa Yuki, Minami Tatsuya, Yamazaki Yudai, Meguro Junich, Takeuchi Eijiro, Ninomiya Yoshiki, Kato Shinpei, Edahiro Masato

International Journal of Automotive Engineering 13 巻 ( 4 ) 頁： 206 - 213 2022年

記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：Society of Automotive Engineers of Japan, INC

ABSTRACT: Ego-vehicle localization is a critical technology in autonomous driving systems, and one of the widely used methods for localization is scan matching between a 3D map and real-time LiDAR scan. This method is known to fail due to factors such as an incorrect initial position and orientation for scan matching. In this paper, we propose a simulator-based localization evaluation framework to verify the robustness of localization. By using a simulator, localization can be evaluated without driving a real vehicle, and can be evaluated by creating disturbances such as traffic jams. Our framework also allows to evaluate the robustness of localization by using multiple particles with random errors of the initial position and orientation for scan matching to simulate dead reckoning errors caused by multiple factors such as road surface conditions and tire diameter. In the evaluation experiments, we confirmed that the robustness of localization can be evaluated by applying this method to factors such as sensor setup, disturbances in the traffic environment, and the amount of 3D features in the environment.

DOI： 10.20485/jsaeijae.13.4_206

CiNii Research

A Study on the Implementation of Vector Control System Using the Model Base Parallelization Tool on Multi-Core Processor

Jinsoo Kim, Seiya Kato, Shinya Honda, Masato Edahiro, Shinji Doki

23rd International Conference on Electrical Machines and Systems, ICEMS 2020 頁： 1951 - 1956 2020年11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）出版者・発行元：Institute of Electrical and Electronics Engineers Inc.

The scope of embedded systems used by multi-core processors is widely applied to various products that require control, such as home appliances, industrial devices, transportation devices, and communication terminals. Model-Base Parallelization technology was proposed towards efficient parallelization to implement a control system in the introduction of multi-core processors. By applying MBP, it is possible to parallelization even details that people are not aware of, but the control system with numerous causal relationships still does not sufficiently utilize multi-core. Therefore, a method of inserting a time-delay is being studied as a method for improving the parallelization of multi-cores.In this paper, We describe the insertion location that improves parallelization and does not affect the control performance by inserting time-delay to implement the vector control system of PMSM on multi-core efficiently.

DOI： 10.23919/ICEMS50442.2020.9290965

Scopus

Switching Hybrid Method Based on User Similarity and Global Statistics for Collaborative Filtering 査読有り

Patricia Ortal and Masato Edahiro

IEEE Access 8 巻頁： 213401 - 213415 2020年9月

Similarity Measure for Product Attribute Estimation 査読有り

Patricia Ortal and Masato Edahiro

IEEE Access 8 巻頁： 179073 - 179082 2020年9月

FPGA混在のHM-SoCに対するモデルベース並列化設計開発環境の検討査読有り

山本椋太, 小川真彩高, 生沼正博, 近藤真己, 本田晋也, 枝廣正人

DAシンポジウム論文集頁： 81 - 88 2020年9月

モデルベース並列化ツールを用いたマルチコアシステム開発フローの提案査読有り

生沼正博, 山本椋太, 竹内成樹, 権藤正樹, 本田晋也, 近藤真己, 枝廣正人

DAシンポジウム論文集頁： 73 - 80 2020年9月

Fast Euclidean Cluster Extraction Using GPUs 査読有り

Anh Nguyen, Abraham Monrroy Cano, Masato Edahiro, and Shinpei Kato

Journal of Robotics and Mechatronics 32 巻 ( 3 ) 頁： 548 - 560 2020年6月

Fast Euclidean Cluster Extraction Using GPUs

Nguyen Anh, Cano Abraham Monrroy, Edahiro Masato, Kato Shinpei

Journal of Robotics and Mechatronics 32 巻 ( 3 ) 頁： 548 - 560 2020年6月

記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：富士技術出版株式会社

<p>Clustering is the task of dividing an input dataset into groups of objects based on their similarity. This process is frequently required in many applications. However, it is computationally expensive when running on traditional CPUs due to the large number of connections and objects the system needs to inspect. In this paper, we investigate the use of NVIDIA graphics processing units and their programming platform CUDA in the acceleration of the Euclidean clustering (EC) process in autonomous driving systems. We propose GPU-accelerated algorithms for the EC problem on point cloud datasets, optimization strategies, and discuss implementation issues of each method. Our experiments show that our solution outperforms the CPU algorithm with speedup rates up to 87X on real-world datasets.</p>

DOI： 10.20965/jrm.2020.p0548

CiNii Research

An Open Multi-Sensor Fusion Toolbox for Autonomous Vehicles 査読有り

A. C. Monrroy, E. Takeuchi, M. Edahiro and S. Kato

IEICE Transastions on Fundamentals E103-A 巻 ( 1 ) 頁： 252-264 2020年1月