检索结果-内蒙古大学图书馆

TechRxiv 2021年

作者： Hu, Liangchen Dai, Zhenlei Tian, Lei Zhang, Wensheng The School of Computer and Information Anhui Normal University Wuhu241003 China The School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China The Research Center of Precision Sensing and Control Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing101408 China

As one of the ways to acquire efficient image compact representation, graph embedding (GE) based manifold learning has been widely developed over the last two decades. Good graph embedding depends on the construction of graphs concerning intra-class compactness and inter-class separability, which are crucial indicators of the effectiveness of a model in generating discriminative features. Unsupervised approaches are intended to reveal the data structure information from a local or global perspective, but the resulting compact representation often has poorly inter-class margins due to the lack of label information. Moreover, supervised techniques only consider enhancing the adjacency affinity within the class but excluding the affinity of different classes, which results in the inability to fully capture the marginal structure between distributions of different classes. To overcome these issues, we propose a learning framework that implements Category-Oriented Self-Learning Graph Embedding (COSLGE), in which we achieve a flexible low-dimensional compact representation by imposing an adaptive graph learning process across the entire data while examining the inter-class separability of low-dimensional embedding by jointly learning a linear classifier. Besides, our framework can easily be extended to the semi-supervised situation. Extensive experiments on several widely-used benchmark databases demonstrate the effectiveness of the proposed method comparing with some state-of-the-art approaches. © 2021, CC BY.

关键词： Embeddings

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RACE: Reinforced cooperative autonomous vehicle collision avoidance

arXiv

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arXiv 2020年

作者： Yuan, Yali Tasik, Robert Adhatarao, Sripriya Srikant Yuan, Yachao Liu, Zheli Fu, Xiaoming Computer Networking Group Institute of Computer Science University of Goettingen Goettingen37077 Germany Smart Mobility Research Group Institute of Information Systems University of Goettingen Goettingen37075 Germany college of computer and control engineering Nankai University Tianjin China

—With the rapid development of autonomous driving, collision avoidance has attracted attention from both academia and industry. Many collision avoidance strategies have emerged in recent years, but the dynamic and complex nature of driving environment poses a challenge to develop robust collision avoidance algorithms. Therefore, in this paper, we propose a decentralized framework named RACE: Reinforced Cooperative Autonomous Vehicle Collision AvoidancE. Leveraging a hierarchical architecture we develop an algorithm named Co-DDPG to efficiently train autonomous vehicles. Through a security abiding channel, the autonomous vehicles distribute their driving policies. We use the relative distances obtained by the opponent sensors to build the VANET instead of locations, which ensures the vehicle’s location privacy. With a leader-follower architecture and parameter distribution, RACE accelerates the learning of optimal policies and efficiently utilizes the remaining resources. We implement the RACE framework in the widely used TORCS simulator and conduct various experiments to measure the performance of RACE. Evaluations show that RACE quickly learns optimal driving policies and effectively avoids collisions. Moreover, RACE also scales smoothly with varying number of participating vehicles. We further compared RACE with existing autonomous driving systems and show that RACE outperforms them by experiencing 65% less collisions in the training process and exhibits improved performance under varying vehicle density. Copyright © 2020, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Exact Differentiator with Lipschitz Continuous Output and Optimal Worst-Case Accuracy Under Bounded Noise

Exact Differentiator with Lipschitz Continuous Output and Op...

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IEEE Conference on Decision and control

作者： Rodrigo Aldana-López Richard Seeber Hernan Haimovich David Gómez-Gutiérrez Department of Computer Science and Systems Engineering University of Zaragoza Zaragoza Spain Christian Doppler Laboratory for Model Based Control of Complex Test Bed Systems Institute of Automation and Control Graz University of Technology Graz Austria Centro Internacional Franco-Argentino de Ciencias de la Información y de Sistemas (CIFASIS) CONICET-UNR Rosario Argentina Intel Tecnología de México Intel Labs Intelligent Systems Research Lab Jalisco Mexico Tecnológico Nacional de México Instituto Tecnológico José Mario Molina Pasquel y Henríquez Unidad Académica Zapopan Jalisco Mexico

The online differentiation of a signal contaminated with bounded noise is addressed. A differentiator is developed that generates a Lipschitz continuous output, is exact in the absence of noise, and provides the optimal worst-case accuracy among all possible exact differentiators when noise is present. This combination of features is not shared by any previously existing differentiator. Tuning of the developed differentiator is very simple, requiring only the knowledge of a bound for the second-order derivative of the signal. The approach consists in regularizing the possibly highly noisy output of a recently introduced linear adaptive robust exact differentiator and feeding it to a first-order sliding-mode filter designed to maintain optimal accuracy. The proposed regularization and filtering of this output allows trading the speed with which exactness is obtained for the feature of a Lipschitz continuous, hence less noisy, output. An illustrative example is provided to highlight the features of the developed differentiator.

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Regret and cumulative constraint violation analysis for online convex optimization with long term constraints

arXiv

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arXiv 2021年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Chai, Tianyou Johansson, Karl H. School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Control Science and Engineering College of Electronics and Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems Institute for Advanced Study Tongji University Shanghai China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore

This paper considers online convex optimization with long term constraints, where constraints can be violated in intermediate rounds, but need to be satisfied in the long run. The cumulative constraint violation is used as the metric to measure constraint violations, which excludes the situation that strictly feasible constraints can compensate the effects of violated constraints. A novel algorithm is first proposed and it achieves an O(Tmax{c,1-c}) bound for static regret and an O(T (1-c)/2) bound for cumulative constraint violation, where c ϵ (0, 1) is a user-defined trade-off parameter, and thus has improved performance compared with existing results. Both static regret and cumulative constraint violation bounds are reduced to O(log(T )) when the loss functions are strongly convex, which also improves existing results. In order to achieve the optimal regret with respect to any comparator sequence, another algorithm is then proposed and it achieves the optimal O( p T (1 + PT )) regret and an O(√T) cumulative constraint violation, where PT is the path-length of the comparator sequence. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results. Copyright © 2021, The Authors. All rights reserved.

关键词： Comparators (optical)

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Parallel Building: A Complex System Approach for Smart Building Energy Management

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IEEE/CAA Journal of Automatica Sinica 2019年第6期6卷 1452-1461页

作者： Abdulaziz Almalaq Jun Hao Jun Jason Zhang Fei-Yue Wang Department of Electrical Engineering Engineering CollegeUniversity of HailHail 55476Saudi Arabia Department of Electrical and Computer Engineering Ritchie School of Engineering and Computer ScienceUniversity of DenverDenverCO 80208 USA State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 and also with the Research Center for Military Computational Experiments and Parallel Systems TechnologyNational University of Defense TechnologyChangsha 410073China

These days' smart buildings have high intensive information and massive operational parameters, not only extensive power consumption. With the development of computation capability and future 5 G, the ACP theory(i.e., artificial systems,computational experiments, and parallel computing) will play a much more crucial role in modeling and control of complex systems like commercial and academic buildings. The necessity of making accurate predictions of energy consumption out of a large number of operational parameters has become a crucial problem in smart buildings. Previous attempts have been made to seek energy consumption predictions based on historical data in buildings. However, there are still questions about parallel building consumption prediction mechanism using a large number of operational parameters. This article proposes a novel hybrid deep learning prediction approach that utilizes long short-term memory as an encoder and gated recurrent unit as a decoder in conjunction with ACP theory. The proposed approach is tested and validated by real-world dataset, and the results outperformed traditional predictive models compared in this paper.

关键词： ACP theory artificial intelligence data acquisition deep learning(DL) energy consumption machine learning parallel energy prediction prediction algorithms smart grid

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Retraction notice to “An intrusion identification and prevention for cloud computing: From the perspective of deep learning” [Optik 270 (2022) 170044]

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Optik 2025年

作者： Anil Kumar Rajabov Sherzod Umurzoqovich Nguyen Duc Duong Pratik Kanani Arulmani Kuppusamy M. Praneesh Minh Ngyen Hieu Data Science Research Group School of Computing DIT University Dehradun India The Head of the Department of Quality Control of Education Tashkent Institute of Finance Tashkent Uzbekistan East Asia University of Technology Hanoi Viet Nam Department of Computer Engineering D. J. Sanghvi College of Engineering Mumbai 400056 India SASTRA Deemed to be University Thanjavur India PG & Research Department of Computer Science Sri Ramakrishna College of Arts & Science Coimbatore Tamil Nadu India Pusa Research Lab Viet Nam

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Intelligent production of the future - first results of a survey 17

Intelligent production of the future - first results of a su...

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17th IMEKO TC 10 and EUROLAB Virtual Conference: Global Trends in Testing, Diagnostics and Inspection for 2030

作者： Nick, Gábor Szaller, Ádám Zsolt János, Viharos Várgedő, Tamás Bányai, Dóra Fries, Christian Fechter, Manuel EPIC InnoLabs Nonprofit Ltd Budapest Hungary Institute for Computer Science and Control Budapest Hungary Budapest University of Technology and Economics Budapest Hungary Széchenyi University Research Center of Vehicle Industry Győr Hungary Fraunhofer-Institute for Manufacturing Engineering and Automation IPA Stuttgart Germany John von Neumann University Kecskemét Hungary University of Stuttgart Institute of Industrial Manufacturing and Management IFF Germany

Witnessing the unprecedented scale and speed of industry transformation due to digitization in general and the emergence of smart solutions in all aspects of manufacturing in particular, it is well justified to deal with the question of what production of the future will look like. How intelligent will it be when the hard challenges and disruptive effects of the changes will be overcome, and what are the chances to meet the critical requirements of collaboration and cooperation, flexibility and competitiveness? What are the technological and management tools to avoid being stuck in the so-called pilot purgatory? These issues are addressed in an international survey based on a questionnaire specifically tailored to conclude to viable options and recommendations for industrial companies. The results that are mostly in conformity with those published in the literature show that there is yet a lot to do to facilitate a definite turn in the mindset of the actors and to make them realize the relevance of simultaneous and continuous cultural and technological development. © Global Trends in Testing, Diagnostics and Inspection for *** rights reserved.

关键词： Manufacture

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Defect Detection Method of Railway Fastener Based on SPP-improved ResNet

Defect Detection Method of Railway Fastener Based on SPP-imp...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Dengfei Wang HongSheng Su GuangWu Chen DeJie Xu LiFang Wang Xiaojuan Zhao School of Automation and Electrical Engineering Lanzhou Jiaotong University Lanzhou China School of Traffic and Transportation Lanzhou Jiaotong University Lanzhou China Railway Hohhot Bureau Group Co. Ltd Scientific Research Institute of China Hohhot China Key Laboratory of Plateau Traffic Information Engineering and Control of Gansu Province Lanzhou Jiaotong University Lanzhou China

ISBN: (纸本)9781665401166

Missing and broken states of railway fasteners are directly related to driving safety. In order to realize the accurate detection of fasteners, virtual image technology is used to simulate defect state of fastener to expand feature information of fasteners, so as to overcome the unbalanced problem of fastener samples. Secondly, a deep network image detection method for railway fasteners based on SPP-improved ResNet (SPP-ResNet) is proposed to improve detection performance. Experimental results show that the accuracy is 95.1% when the network is trained on the virtual image extended dataset with Single size input train method, which is 2.4% higher than the accuracy of the unexpanded dataset. It is proved that virtual image can improve the accuracy of fastener detection. After the SPP-ResNet is trained by using multi size input training, the detection accuracy is 96.6%, which is 2.3% higher than the accuracy of ResNet. Therefore, the proposed method can meet the needs of railway-fastener automatic detection under high-speed railway development.

关键词： Rails Training Image recognition Target recognition Fault detection Fasteners Feature extraction

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Current Progress and Challenges in Large-scale 3D Mitochondria Instance Segmentation

TechRxiv

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TechRxiv 2023年

作者： Franco-Barranco, Daniel Lin, Zudi Jang, Won-Dong Wang, Xueying Shen, Qijia Yin, Wenjie Fan, Yutian Li, Mingxing Chen, Chang Xiong, Zhiwei Xin, Rui Liu, Hao Chen, Huai Li, Zhili Zhao, Jie Chen, Xuejin Pape, Constantin Conrad, Ryan De Folter, Jozefus Nightingale, Luke Jones, Martin L. Liu, Yanling Ziaei, Dorsa Huschauer, Stephan Arganda-Carreras, Ignacio Pfister, Hanspeter Wei, Donglai The Department of Computer Science and Artificial Intelligence University of the Basque Country Donostia-San Sebastian Spain San Sebastian Spain Ikerbasque Basque Foundation for Science Bilbao Spain Biofisika Institute CSIC UPV/EHU Bilbao Spain Harvard University All-ston MA United States The Department of Molecular and Cellular Biology Harvard University CambridgeMA United States The Wellcome Centre for Integrative Neuroimaging FMRIB Nuffield Department of Clinical Neurosciences University of Oxford Oxford United Kingdom University of Science and Technology of China Anhui China The Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai China The National Engineering Laboratory for Brain-inspired Intelligence Technology and Application University of Science and Technology of China Anhui China The Georg-August University Goettingen Germany The Center for Molecular Microscopy Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda United States The Cancer Research Technology Program Frederick National Laboratory for Cancer Research Frederick United States The Francis Crick Institute London United Kingdom The Advanced Biomedical Computational Science Group Frederick National Laboratory for Cancer Research FrederickMD United States The Computer Science Department Boston College Chestnut Hill MA United States

In this paper, we present the results of the MitoEM challenge on mitochondria 3D instance segmentation from electron microscopy images, organized in conjunction with the IEEE-ISBI 2021 conference. Our benchmark dataset consists of two large-scale 3D volumes, one from human and one from rat cortex tissue, which are 3,600 times larger than previously used datasets. At the time of paper submission, 257 participants had registered for the challenge, 14 teams had submitted their results, and six teams participated in the challenge workshop. Here, we present eight top-performing approaches from the challenge participants, along with our own baseline strategies. Posterior to the challenge, annotation errors in the ground truth were corrected without altering the final ranking. Additionally, we present a retrospective evaluation of the scoring system which revealed that (1) the challenge metric was permissive with the false positive predictions and (2) the size-based grouping of instances did not correctly categorize mitochondria of interest. Thus, we propose a new scoring system that better reflects the correctness of the segmentation results. Although several of the top methods are compared favorably to our own baselines, substantial errors remain unsolved for mitochondria with challenging morphologies. Thus, the challenge remains open for submission and automatic evaluation, with all volumes available for download. © 2023, CC BY.

关键词： Electron microscopy

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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

arXiv

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

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