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Extinction Chains Reveal Intermediate Phases Between the Safety and Collapse in Mutualistic Ecosystems

Engineering 2024年第12期43卷 89-98页

作者： Guangwei Wang Xueming Liu Ying Xiao Ye Yuan Linqiang Pan Xiaohong Guan Jianxi Gao Hai-Tao Zhang The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Digital Manufacturing Equipment and TechnologySchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Guangdong HUST Industrial Technology Research Institute Huazhong University of Science and TechnologyDongguan 523808China Guangdong Provincial Engineering Technology Center of Autonomous Unmanned Vessels Dongguan 523808China MOE Key Laboratory of Intelligent Networks and Network Security&State Key Laboratory of Manufacturing Systems Xi’an Jiaotong UniversityXi’an 710049China Tsinghua National Laboratory of Information Science and Technology Department of AutomationTsinghua UniversityBeijing 100084China Department of Computer Science Rensselaer Polytechnic InstituteTroyNY 12180USA

Ecosystems are undergoing unprecedented persistent deterioration due to unsustainable anthropogenic human activities,such as overfishing and deforestation,and the effects of such damage on ecological stability are *** recent advances in experimental and theoretical studies on regime shifts and tipping points,theoretical tools for understanding the extinction chain,which is the sequence of species extinctions resulting from overexploitation,are still lacking,especially for large-scale nonlinear networked *** this study,we developed a mathematical tool to predict regime shifts and extinction chains in ecosystems under multiple exploitation situations and verified it in 26 real-world mutualistic networks of various sizes and *** discovered five phases during the exploitation process:safe,partial extinction,bistable,tristable,and collapse,which enabled the optimal design of restoration strategies for degraded or collapsed *** validated our approach using a 20-year dataset from an eelgrass restoration ***,we also found a specific region in the diagram spanning exploitation rates and competition intensities,where exploiting more species helps increase *** computational tool provides insights into harvesting,fishing,exploitation,or deforestation plans while conserving or restoring the biodiversity of mutualistic ecosystems.

关键词： Complex system Network science Overexploitation Regime shift Metastability

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On optimal charging scheduling for electric vehicles with wind power generation

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Fundamental Research 2024年第4期4卷 951-960页

作者： Junjie Wu Qing-Shan Jia Center for Intelligent and Networked Systems(CFINS) Department of AutomationBeijing National Research Center for Information Science and Technology(BNRist)TsinghuaUniversityBeijing100084China Huawei Technologies Co.Ltd Shenzhen 518129China

We consider the scheduling of battery charging of electric vehicles(EVs)integrated with renewable power *** increasing adoption of EVs and the development of renewable energies contribute importance to this *** optimization of charging scheduling is challenging because of the large action space,the multi-stage decision making,and the high *** solve this problem is time-consuming when the scale of the system is *** is urgent to develop a practical and efficient method to properly schedule the charging of *** contribution of this work is ***,we provide a sufficient condition on which the charging of EVs can be completely self-sustained by distributed *** algorithm is proposed to obtain the optimal charging policy when the sufficient condition ***,the scenario when the supply of the renewable power generation is deficient is *** prove that when the renewable generation is deterministic there exists an optimal policy which follows the modified least laxity and longer remaining processing time first(mLLLP)***,we provide an adaptive rule-based algorithm which obtains a near-optimal charging policy efficiently in general *** test the proposed algorithm by numerical *** results show that it performs better than the other existing rule-based methods.

关键词： Electric vehicle Charging scheduling Wind power Optimal policy Renewable energy

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A Non-Parametric Scheme for Identifying Data Characteristic Based on Curve Similarity Matching

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1424-1437页

作者： Quanbo Ge Yang Cheng Hong Li Ziyi Ye Yi Zhu Gang Yao School of Automation Nanjing University of Information Science&TechnologyJiangsu Provincial University Key Laboratory of Big Data Analysis and Intelligent SystemsNanjing University of Information Science&Technologyand the Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)Nanjing University of Information Science&TechnologyNanjing 210044China School of Department of Logistics Engineering Shanghai Maritime UniversityShanghai 201306China China Classification Society Nantong Office Nantong 226006China Chinese Flight Test Establishment Xi’an 710089China School of Mathematics Tsinghua UniversityBeijing 100084China School of Automation Nanjing University of Information Science&TechnologyNanjing 210044China Department of Logistics Engineering Shanghai Maritime UniversityShanghai 201306China IEEE

For accurately identifying the distribution charac-teristic of Gaussian-like noises in unmanned aerial vehicle(UAV)state estimation,this paper proposes a non-parametric scheme based on curve similarity *** the framework of the pro-posed scheme,a Parzen window(kernel density estimation,KDE)method on sliding window technology is applied for roughly esti-mating the sample probability density,a precise data probability density function(PDF)model is constructed with the least square method on K-fold cross validation,and the testing result based on evaluation method is obtained based on some data characteristic analyses of curve shape,abruptness and *** com-parison simulations with classical methods and UAV flight exper-iment shows that the proposed scheme has higher recognition accuracy than classical methods for some kinds of Gaussian-like data,which provides better reference for the design of Kalman filter(KF)in complex water environment.

关键词： Curve similarity matching Gaussian-like noise non-parametric scheme parzen window.

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Design of Composite Two-Channel Disturbance Estimation Adaptive Controller for Backlash-Like Hysteretic Nonlinear systems

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Journal of Shanghai Jiaotong University (Science) 2025年 1-11页

作者： Liu, Qunpo Li, Jiakun Fei, Shumin Bu, Xuhui Naohiko, Hanajima School of Electrical Engineering and Automation Henan Polytechnic University Henan Jiaozuo454003 China Henan International Joint Laboratory of Direct Drive and Control of Intelligent Equipment Henan Jiaozuo454003 China School of Automation Southeast University Nanjing210096 China College of Information and Systems Muroran Institute of Technology Hokkaido Muroran050-8585 Japan

For nonlinear systems with backlash-like hysteresis characteristics and external disturbance, a composite two-channel disturbance estimation adaptive controller is proposed to improve the trajectory tracking accuracy of the system. The unmodeled hysteresis and external disturbances are treated as lumped uncertainties, which are approximated by radial basis neural network and disturbance estimator respectively. These approximations are then linearly fused to form the compensation term for the lumped uncertainty. The second order linear filter is employed to estimate multiple differential terms, which are integrated into the controller design and dynamic system state updates, thereby reducing computational complexity. A weighted fusion mechanism is implemented for the two channels, and the adaptive update rate for each channel is determined based on the deviation between the lumped uncertainty reference value and the output of each channel. To address the challenges posed by the discontinuity of deviation and maintain system stability, the first-order low-pass filter is applied to smooth the deviation, enhancing system robustness. A trajectory tracking simulation of a single-input single-output nonlinear system is conducted to compare the performance of the proposed controller with baseline controllers, demonstrating the effectiveness of the composite two-channel disturbance estimation adaptive controller. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： Self tuning control systems

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A novel deep domain-adversarial method based on DRN-DATL for rolling bearing 43

A novel deep domain-adversarial method based on DRN-DATL for...

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43rd Chinese Control Conference, CCC 2024

作者： Li, Zhuorui Wang, Xiaodong Ma, Bao Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Kunming650500 China

ISBN: (纸本)9789887581581

In recent years, a variety of rolling bearing fault diagnosis methods based on deep learning has become an emerging research orientation. However, there is still a gap between the existing diagnostic model and the practical application, i.e., 1) due to the variability of working conditions, the domain shift problem between source domain and the target domain have led to the obvious degradation of the diagnostic performance. 2) It is time-consuming and expensive to label a mass of monitoring data in practical situation. To address this problem, a domain-adversarial transfer learning method based on deep residual network (DRN-DATL) for rolling bearing fault diagnosis is proposed. Firstly, DRN is designed to extract features from raw vibration signal, which can avoid gradient problems. Then, the APReLU activation function is adopted to improved diagnostic accuracy under different working conditions. Meanwhile, domain-adversarial training is utilized to minimize distribution differences between source domain and target domain. Finally, seven comparison methods are selected for systematic analysis, and the results show that the proposed method can effectively improve the overall performance of the diagnosis model, which has achieved higher classification accuracy. © 2024 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Adversarial machine learning

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Time-delay effects on the dynamical behavior of switched nonlinear time-delay systems

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Science China(information Sciences) 2025年

作者： Zhichuang WANG Wei HE Jian SUN Gang WANG School of Intelligence Science and Technology Institute of Artificial IntelligenceUniversity of Science and Technology Beijing Key Laboratory of Intelligent Bionic Unmanned Systems of Ministry of Education University of Science and Technology Beijing School of Automation Institute of Artificial IntelligenceBeijing Information Science and Technology University School of Automation Beijing Institute of Technology

The switched system is a standard hybrid system comprising a finite number of subsystems, a switching signal and their interactions[1]. Over the past few decades, interest in switched systems has grown significantly owing to two primary reasons. First, many practical systems operate with multiple systems or modes, such as energy systems, multiunmanned vehicle systems, and robotics[2].

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When LoRa meets distributed machine learning to optimize the network connectivity for green and intelligent transportation system

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Green Energy and intelligent Transportation 2024年第3期3卷 24-41页

作者： Malak Abid Ali Khan Hongbin Ma Arshad Farhad Asad Mujeeb Imran Khan Mirani Muhammad Hamza National Key Lab of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Computer Science Namal UniversityPakistan Department of Electrical Engineering Tsinghua UniversityBeijing 100084China Faculty of Information Technology Beijing University of TechnologyBeijing 100124China School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China

LoRa technology contributes to green energy by enabling efficient,long-range communication for the Internet of Things(IoT).This paper addresses the challenges related to coverage range in outdoor monitoring systems utilizing LoRa,where the network performance is affected by the density of gateways(GWs)and end devices(EDs),as well as environmental *** mitigate interference,data throughput losses,and high-power consumption,the proposed spreading factor(SF)and hybrid(data rate|SF)models dynamically adjust the transmission *** orchestration of concurrent data modifications within the network server(NS)is crucial for uninterrupted communication between GWs and EDs,especially in monitoring electric vehicle(EV)stations to reduce traffic congestion and *** K-means and density-based spatial clustering of applications with noise(DBSCAN)algorithms optimizes ED allocation,averts data congestion,and improves the signal-tointerference noise ratio(SINR).These methods ensure seamless information reception by meticulously allocated EDs across various GW *** estimate the free-space losses(FSL),a log-distance path loss model(log-PL)is *** various bandwidths(BWs),bidirectional communications,and duty cycles(DCs)helps to prevent saturation,thus prolonging the operational lifespan of *** findings reveal a notable packet rejection rate(PRR)of 0%for the DBSCAN(hybrid model).In contrast,the K-means exhibits a PRR ranging from 5%(hybrid model)to 35.29%(SF model)for the ten GWs ***,the network saturation is reduced to 10.185%and 9.503%,respectively,highlighting an improvement in the average efficiency of slotted ALOHA(91.1%)and pure ALOHA(90.7%).These enhancements increase the lifespan of EDs to 15,465.27 days.

关键词： DBSCAN Green transportation Hybrid model intelligent transportation K-means

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Collaborative Pushing and Grasping in Complex Scenarios via a Generator-Evaluator Network with Multiple Action Primitives 17th

Collaborative Pushing and Grasping in Complex Scenarios vi...

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17th International Conference on intelligent Robotics and Applications, ICIRA 2024

作者： Zuo, Guoyu Wang, Zihao Luo, Yongkang Yu, Shuangyue Zhao, Min School of Information Science and Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9789819607761

As robotic technologies advance, robotic arm manipulation tasks in complex environments become increasingly important. This paper presents a new collaborative pushing and grasping strategy to address two major challenges robotic arms encounter in complex environments: invisibility and tight enclosure of target objects. We propose a generator-evaluator network architecture based on multiple action primitives, incorporating hierarchical reinforcement learning and Double DQN techniques to optimize the operational efficiency of the robotic arm. By introducing a new action primitive to prioritize pushing operations around the topmost objects in space, our approach effectively reveals and locates obscured target objects. Additionally, utilizing the Spatial Correlation Test (SCT), our method explores all possible collision-free operational positions in the action space, selecting the optimal position from among them. Experimental results demonstrate that our approach achieves high efficiency and success rates in scenarios with randomly distributed objects, objects tightly enclosed by others, and invisible objects. This strategy enhances the speed and accuracy of tasks and improves the robot’s adaptability to complex environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Robotic arms

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AMA-Net: Adaptive Masking Attention Network for Agricultural Crop Classification From UAV Images

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2025年第1期3卷 246-253页

作者： Wang, Xu Wang, Deyi He, Zhaoshui Lin, Zhijie Xie, Shengli Guangdong Mechanical and Electrical Polytechnic School of Electronics and Communication Guangzhou510550 China Guangdong University of Technology School of Automation Guangzhou510006 China Key Laboratory for IoT Intelligent Information Processing and System Integration of Ministry of Education Guangzhou510006 China Guangdong Provincial Key Laboratory of Intelligent Systems and Optimization Integration Guangzhou510006 China

Agriculture crop classification is helpful for agricultural production. However, it is challenging to classify crops from the agriculture image suffering from these problems: 1) Crops are often masked in complex backgrounds;2) There is high similarity between crop categories. To address these problems, an adaptive masking attention network (AMA-Net) is proposed for agriculture crop identification from natural images, where the adaptive masking (AM) module is developed to distinguish the crop from the complex background by selectively eliminating redundant information of feature maps, and the fair attention module is devised to identify similar crops between categories by modeling the fine-grained features. Experiments conducted on the benchmark show the effectiveness and superiority of the proposed AMA-Net, achieving the performance of 96.65%, 96.65%, 97.13%, and 96.72% on the accuracy, precision, recall, and F1-score, respectively, which is better than other state-of-the-art methods. © 2023 IEEE.

关键词： Crops Agriculture Accuracy Visualization Training Production Autonomous aerial vehicles Attention mechanisms Adaptive systems Support vector machines

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A Novel Parameter-dependent Input Normalization-based Direct MRAC with Unknown Control Direction 63

A Novel Parameter-dependent Input Normalization-based Direct...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Gong, Yizhou Pin, Gilberto Zhang, Yanjun Wang, Yang ShanghaiTech University School of Information Science and Technology Shanghai China Digital Technology Org. of Electrolux Italia S.p.A. Italy Beijing Institute of Technology School of Automation Beijing100081 China Beijing Institute of Technology State Key Lab of Automation Intelligent Unmanned Systems Beijing100081 China

ISBN: (纸本)9798350316339

In this paper, we endow the model reference adaptive control (MRAC) with a novel parameter-dependent input normalization (PIN) to completely eliminate the conventional assumption of the high-frequency gain. Specifically, neither the sign nor the prior knowledge of the upper or lower bounds is required. To this end, we resort to an error augmentation together with a smart design of an adaptive law with a dead zone operation. Global stability in the mean square sense is established with the conventional proof concepts of the augmented error approach. In this way, no persistent excitation requirement is required. Although the system in question is assumed to be unity-relative-degree, the proposed technique can be easily extended to systems of arbitrary relative degrees. Finally, compared with the Nussbaum function-based methods in a numerical experiment, we show that transient behavior in our method is significantly improved. © 2024 IEEE.

关键词： Dead zones

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