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Titlebook: Recent Developments in Mechatronics and Intelligent Robotics; Proceedings of Inter Kevin Deng,Zhengtao Yu,John Wang Conference proceedings

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發(fā)表于 2025-3-21 18:41:43 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Recent Developments in Mechatronics and Intelligent Robotics
副標題Proceedings of Inter
編輯Kevin Deng,Zhengtao Yu,John Wang
視頻videohttp://file.papertrans.cn/824/823194/823194.mp4
概述Presents the latest research, cutting edge technology of Mechatronics and Intelligent Robotics.Contributed by academicians and trusted experts in the field.Researchers and Academicians and Industry ex
叢書名稱Advances in Intelligent Systems and Computing
圖書封面Titlebook: Recent Developments in Mechatronics and Intelligent Robotics; Proceedings of Inter Kevin Deng,Zhengtao Yu,John Wang Conference proceedings
描述.This book is a collection of proceedings of the International Conference on Mechatronics and Intelligent Robotics (ICMIR2018), held in Kunming, China during May 19–20, 2018. It consists of 155 papers, which have been categorized into 6 different sections: Intelligent Systems, Robotics, Intelligent Sensors & Actuators, Mechatronics, Computational Vision and Machine Learning, and Soft Computing. ..?The volume covers the latest ideas and innovations both from the industrial and academic worlds, as well as shares the best practices in the fields of mechanical engineering, mechatronics, automatic control, IOT and its applications in industry, electrical engineering, finite element analysis and computational engineering. The volume covers key research outputs, which delivers a wealth of new ideas and food for thought to the readers...?.
出版日期Conference proceedings 2019
關鍵詞Mechatronics; Intelligent Robotics; Automation & Control; Sensors; Actuators
版次1
doihttps://doi.org/10.1007/978-3-030-00214-5
isbn_softcover978-3-030-00213-8
isbn_ebook978-3-030-00214-5Series ISSN 2194-5357 Series E-ISSN 2194-5365
issn_series 2194-5357
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

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A New Active Learning Semi-supervised Community Detection Algorithm in Complex Networksis demonstrated with four real-world networks and a artificial network benchmark. Extensive experiments show that it effectively improves community detection results and has lower computational complexity compared with some community detection algorithms.
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2194-5357 ts in the field.Researchers and Academicians and Industry ex.This book is a collection of proceedings of the International Conference on Mechatronics and Intelligent Robotics (ICMIR2018), held in Kunming, China during May 19–20, 2018. It consists of 155 papers, which have been categorized into 6 dif
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A New Active Learning Semi-supervised Community Detection Algorithm in Complex Networksrior information to improve the performance of community detection. But how to obtain these prior information is a challenging problem. In this paper, we present a new active learning semi-supervised community detection algorithm based on node relevance and similarities measures. Firstly, it calcula
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