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arXiv 2024年

作者： Weigand, Jonas Beintema, Gerben I. Ulmen, Jonas Görges, Daniel Tóth, Roland Schoukens, Maarten Ruskowski, Martin Chair of Machine Tools and Control Systems RPTU Kaiserslautern The German Research Center for Artificial Intelligence Kaiserslautern Germany Group The Department of Electrical Engineering Eindhoven University of Technology Netherlands The Systems and Control Laboratory The Institute for Computer Science and Control Budapest Hungary Institute for Electromobility RPTU Kaiserslautern Germany

The importance of proper data normalization for deep neural networks is well known. However, in continuous-time state-space model estimation, it has been observed that improper normalization of either the hidden state or hidden state derivative of the model estimate, or even of the time interval can lead to numerical and optimization challenges with deep learning based methods. This results in a reduced model quality. In this contribution, we show that these three normalization tasks are inherently coupled. Due to the existence of this coupling, we propose a solution to all three normalization challenges by introducing a normalization constant at the state derivative level. We show that the appropriate choice of the normalization constant is related to the dynamics of the to-be-identified system and we derive multiple methods of obtaining an effective normalization constant. We compare and discuss all the normalization strategies on a benchmark problem based on experimental data from a cascaded tanks system and compare our results with other methods of the identification literature. © 2024, CC BY-SA.

关键词： Ordinary differential equations

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Passivity of nonlinear time-delay systems

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IFAC-PapersOnLine 2023年第2期56卷 8568-8573页

作者： Yu Kawano Saeed Ahmed Michele Cucuzzella Jacquelien M.A. Scherpen Graduate School of Advanced Science and Engineering Hiroshima University Higashi-hiroshima 739-8527 Japan Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen 9747 AG Groningen The Netherlands Department of Electrical Computer and Biomedical Engineering University of Pavia 27100 Pavia Italy

This paper presents passivity-based control of nonlinear systems with retarded delays in the state. To this end, we first show that the standard passivity concept can naturally be generalized to time-delay systems, which readily implies that a feedback interconnection (with or without communication delays) of passive time-delay systems is also passive. Then, we propose a storage functional for passivity analysis and further use it for stability analysis of controlled-passive time-delay systems. In particular, invoking an invariance principle for retarded functional differential equations, we show that a passive time-delay system can always be stabilized by a static output feedback controller under a delayed version of the zero-state detectability assumption.

关键词： Passivity nonlinear systems time-delay systems retarded functional differential equations stabilizing control

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Online Cooperative Optimal Power Scheduling of Multiple Microgrids via Hierarchical Imitation Learning

Online Cooperative Optimal Power Scheduling of Multiple Micr...

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International Conference on control and Automation (ICCA)

作者： Yunyi Zhao Shuhua Gao Jing Wang Congcong Li Ming Yu Cheng Xiang Department of Electrical and Computer Engineering National University of Singapore Singapore School of Control Science and Engineering Shandong University Jinan China State Grid Shandong Electric Power Company Marketing Service Center (Metering Center) Jinan China Power Automation Pte Ltd Singapore

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

This paper looks into the issue of optimal power scheduling of multiple microgrids using hierarchical imitation learning. The system is designed to be a hierarchical learning model towards a two-level microgrid community (MGC) structure. The upper-level MGC agent uses an imitation learning algorithm to schedule exchange power between different microgrids, while lower-level microgrid agents are controlled by individual energy management systems using mixed-integer linear programming (MILP). In this paper, we focus on achieving economic dispatch in a large microgrid community while maintaining the privacy of the local microgrids. A simulation study of hierarchical imitation learning is provided based on an MGC system. Our results show the outstanding performance of the designed algorithm with a cost close to the centralized optimal results, about 10% improvement compared to the offline method, and very fast execution, which would be suitable for online power scheduling.

关键词： Training Schedules Privacy Costs Job shop scheduling Imitation learning Microgrids

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Recent Development, Trends and Challenges in IoT Security 7th

Recent Development, Trends and Challenges in IoT Security

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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Talezari, Morteza Tu, Shanshan Rehman, Sadaqat ur Chuangbai, Xiao Ahmad, Basharat Waqas, Muhammad Rehman, Obaid ur Research Center of Intelligent Perception and Autonomous Control Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Computer Science Namal Institute Mianwali42250 Pakistan Department of Electronic Engineering Tsinghua University Beijing China Institute of Engineering Sciences and Technology Topi23460 Pakistan Department of EE Sarhad University of Science and IT Peshawar Pakistan

ISBN: (纸本)9783030786113

A large amount of data that IoT applications deal, is users’ Private data, and their privacy is a significant issue. However, if users are not sure about the security of their data, they will not desire to use such applications. The goal of this study is to improve the security and privacy of users in the Internet of Things (IoTs) setting. Since, the application of IoT technology is growing day by day. Also, the importance of users’ data security is always top of the list and difficult to achieve. In this study, we studied latest development in the security of Internet of Things (IoTs) so that we can use it to investigate its challenges and advantages. © 2021, Springer Nature Switzerland AG.

关键词： Internet of things

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Fundus Image Quality Assessment and Enhancement: a Systematic Review

arXiv

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arXiv 2025年

作者： Li, Heng Li, Haojin Ou, Mingyang Yu, Xiangyang Zhang, Xiaoqing Niu, Ke Fu, Huazhu Liu, Jiang Research Institute of Trustworthy Autonomous Systems SUSTech Shenzhen China Department of Computer Science and Engineering SUSTech Shenzhen China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Computer School Beijing Information Science and Technology University Beijing China Singapore

As an affordable and convenient eye scan, fundus photography holds the potential for preventing vision impairment, especially in resource-limited regions. However, fundus image degradation is common under intricate imaging environments, impacting following diagnosis and treatment. Consequently, image quality assessment (IQA) and enhancement (IQE) are essential for ensuring the clinical value and reliability of fundus images. While existing reviews offer some overview of this field, a comprehensive analysis of the interplay between IQA and IQE, along with their clinical deployment challenges, is lacking. This paper addresses this gap by providing a thorough review of fundus IQA and IQE algorithms, research advancements, and practical applications. We outline the fundamentals of the fundus photography imaging system and the associated interferences, and then systematically summarize the paradigms in fundus IQA and IQE. Furthermore, we discuss the practical challenges and solutions in deploying IQA and IQE, as well as offer insights into potential future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Photography

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MULTIMODAL TRANSFORMER NETWORKS WITH LATENT INTERACTION FOR AUDIO-VISUAL EVENT LOCALIZATION

MULTIMODAL TRANSFORMER NETWORKS WITH LATENT INTERACTION FOR ...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： He, Yixuan Xu, Xing Liu, Xin Ou, Weihua Lu, Huimin Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China China Glasgow College University of Electronic Science and Technology of China China Department of Computer Science Huaqiao University China School of Big Data and Computer Science Guizhou Normal University China Department of Mechanical and Control Engineering Kyushu Institute of Technology Japan

ISBN: (纸本)9781665438643

The task of audio-visual event localization (AVEL) aims to localize a visible and audible event in a video. Previous methods first divide a video into segments and then fuse visual and acoustic features at the segment level via a co-attention mechanism. However, existing methods mostly model relations between individual visual and audio segments in a limitedly short period, which may not cover a longer video duration for better high-level event information modeling. In this paper, we proposed a novel model termed Multimodal Transformer Network with Latent Interaction (MTNLI) to tackle this problem. The proposed MTNLI model employs a multimodal Transformer structure to learn the cross-modality relationships between latent visual and audio summarizations in long segment sequences, which summarize the visual and audio segments into a small number of latent representations to avoid modeling uninformative individual visual-audio relations. The cross-modality information between the latent summarizations is propagated to fuse valuable information from both modalities, which can effectively handle large temporal inconsistent between vision and audio. Our MTNLI method achieves state-of-the-art performance on the benchmark AVE (Audio-Visual Event) dataset for the event localization task. © 2021 IEEE

关键词： Benchmarking

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Using the Pre-Large Concept for Maintaining High Fuzzy Utility Itemsets

Using the Pre-Large Concept for Maintaining High Fuzzy Utili...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Tzung-Pei Hong Wei-Teng Hung Yu-Chuan Tsai Wei-Ming Huang Department of Computer Science and Information Engineering National University of Kaohsiung Kaohsiung Taiwan Department of Computer Science and Engineering National Sun Yat-Sen University Kaohsiung Taiwan Library and Information Center National University of Kaohsiung Kaohsiung Taiwan Department of Electrical and Control China Steel Inc. Kaohsiung Taiwan

Utility mining is a popular research field in data mining. It uses item utilities and quantities to fit more into real applications. Fuzzy utility mining has also been proposed to reflect the linguistics of human perceptron for item association. In the past, we proposed a fast-up date-based (FUP-based) approach to maintain high fuzzy utility itemsets for continuously coming transaction data. This paper proposes an algorithm that applies the pre-large strategy on fuzzy utility mining to raise the maintenance performance. Nine cases are considered to maintain the current high fuzzy utility itemsets based on the fuzzy upper-bound utility. The results of the numerical experiments show that our proposed algorithm has better efficiency than the batch and the FUP-based approach in the execution time.

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Trade-off Between Execution Time and Memory Consumption in Fuzzy Average-Utility Mining

Trade-off Between Execution Time and Memory Consumption in F...

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International Conference on Awareness science and Technology (iCAST)

作者： Tzung-Pei Hong Meng-Ping Ku Yu-Chuan Tsai Wei-Ming Huang Shu-Min Li Department of Computer Science and Information Engineering National University of Kaohsiung Kaohsiung Taiwan Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung Taiwan Library and Information Center National University of Kaohsiung Kaohsiung Taiwan Department of Electrical and Control China Steel Inc. Kaohsiung Taiwan

Fuzzy utility mining considers high-utility fuzzy itemsets as valuable knowledge by integrating quantities of items, their profits, and meaningful fuzzy terms derived by quantities according to membership functions. In fuzzy utility mining, the utility value of a fuzzy itemset in a transaction will always be greater than or equal to those of its subsets, so the measurement of fuzzy utility is an unfair evaluation method. Therefore, the fuzzy average-utility mining problem was issued in 2020, and three solutions were proposed to solve fuzzy average-utility itemsets as two-phase fuzzy average-utility algorithm (TPFAU), two-phase method with tree-based structure (HFAUIM) and one-phase approach with tree-based structure (FHFAUIM), respectively. The second and third methods decrease the candidates generated compared to the first. However, the sorting strategy for mining steps for the last two approaches is based on the frequencies of items in a database and then inserting items of a transaction into a tree in descending order of their frequencies, thus spending more computing time on deriving the actual fuzzy utility value of itemsets. To overcome the above-mentioned problem, this paper adopts a different sorting strategy with a tree-based structure and then by using it to design an algorithm named IFHFAUIM to mine high fuzzy average-utility itemsets. It reduces the storage of required fuzzy utility values in tree nodes and recovers them through tree traversal. Computational experiments show that the proposed method could make a good trade-off between execution time and memory usage.

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UDTIRI: An Online Open-Source Intelligent Road Inspection Benchmark Suite

arXiv

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arXiv 2023年

作者： Guo, Sicen Li, Jiahang Feng, Yi Zhou, Dacheng Zhang, Denghuang Chen, Chen Su, Shuai Zhu, Xingyi Chen, Qijun Fan, Rui Group The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The Department of Control Science & Engineering Tongji University Shanghai201804 China The College of Electronics & Information Engineering Tongji University Shanghai201804 China The Department of Road & Airport Engineering The Key Laboratory of Road & Traffic Engineering of Ministry of Education Tongji University Shanghai200092 China

In the nascent domain of urban digital twins (UDT), the prospects for leveraging cutting-edge deep learning techniques are vast and compelling. Particularly within the specialized area of intelligent road inspection (IRI), a noticeable gap exists, underscored by the current dearth of dedicated research efforts and the lack of large-scale well-annotated datasets. To foster advancements in this burgeoning field, we have launched an online open-source benchmark suite, referred to as UDTIRI. Along with this article, we introduce the road pothole detection task, the first online competition published within this benchmark suite. This task provides a well-annotated dataset, comprising 1,000 RGB images and their pixel/instance-level ground-truth annotations, captured in diverse real-world scenarios under different illumination and weather conditions. Our benchmark provides a systematic and thorough evaluation of state-of-the-art object detection, semantic segmentation, and instance segmentation networks, developed based on either convolutional neural networks or Transformers. We anticipate that our benchmark will serve as a catalyst for the integration of advanced UDT techniques into IRI. By providing algorithms with a more comprehensive understanding of diverse road conditions, we seek to unlock their untapped potential and foster innovation in this critical domain. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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Distributed Event-Triggered Bandit Convex Optimization With Time-Varying Constraints

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IEEE Transactions on control of Network Systems 2025年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Ministry of Education Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China Southeast University School of Automation Nanjing210096 China University of Groningen Engineering and Technology Institute Groningen Faculty of Science and Engineering GroningenAG 9747 Netherlands KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden KTH Royal Institute of Technology Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms solving this problem require that each agent broadcasts its decision to its neighbors at each iteration. However, communication resources are often limited. To better utilize communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. © 2014 IEEE.

关键词： Optimization algorithms

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