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arXiv 2024年

作者： Tang, Xiaoyu Chen, Xingming Cheng, Jintao Wu, Jin Fan, Rui Zhang, Chengxi Zhou, Zebo The School of Electronic and Information Engineering Faculty of Engineering South China Normal University Guangdong Foshan528225 China The School of Physics South China Normal University Guangdong Guangzhou510000 China The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong 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 School of Internet of Things Engineering Jiangnan University Wuxi214122 China The School of Aeronautics Astronautics University of Electronic Science and Technology of China and Aircraft swarm intelligent sensing and cooperative control Key Laboratory of Sichuan Province Chengdu610097 China The National Laboratory on Adaptive Optics Chengdu610209 China

In the era of 5G communication, removing interference sources that affect communication is a resource-intensive task. The rapid development of computer vision has enabled unmanned aerial vehicles to perform various high-altitude detection tasks. Because the field of object detection for antenna interference sources has not been fully explored, this industry lacks dedicated learning samples and detection models for this specific task. In this article, an antenna dataset is created to address important antenna interference source detection issues and serves as the basis for subsequent research. We introduce YOLO-Ant, a lightweight CNN and transformer hybrid detector specifically designed for antenna interference source detection. Specifically, we initially formulated a lightweight design for the network depth and width, ensuring that subsequent investigations were conducted within a lightweight framework. Then, we propose a DSLK-Block module based on depthwise separable convolution and large convolution kernels to enhance the network’s feature extraction ability, effectively improving small object detection. To address challenges such as complex backgrounds and large interclass differences in antenna detection, we construct DSLKVit-Block, a powerful feature extraction module that combines DSLK-Block and transformer structures. Considering both its lightweight design and accuracy, our method not only achieves optimal performance on the antenna dataset but also yields competitive results on public datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Antennas

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Safe-visor architecture for sandboxing (AI-BASED) unverified controllers in stochastic cyber-physical systems

arXiv

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arXiv 2021年

作者： Zhong, Bingzhuo Lavaei, Abolfazl Cao, Hongpeng Zamani, Majid Caccamo, Marco Department of Mechanical Engineering Technical University of Munich Germany Institute for Dynamic Systems and Control ETH Zurich Switzerland Department of Computer Science University of Colorado Boulder United States Department of Computer Science LMU Munich Germany

High performance but unverified controllers, e.g., artificial intelligence-based (a.k.a. AI-based) controllers, are widely employed in cyber-physical systems (CPSs) to accomplish complex control missions. However, guaranteeing the safety and reliability of CPSs with this kind of controllers is currently very challenging, which is of vital importance in many real-life safety-critical applications. To cope with this difficulty, we propose in this work a Safe-visor architecture for sandboxing unverified controllers in CPSs operating in noisy environments (a.k.a. stochastic CPSs). The proposed architecture contains a history-based supervisor, which checks inputs from the unverified controller and makes a compromise between functionality and safety of the system, and a safety advisor that provides fallback when the unverified controller endangers the safety of the system. Both the history-based supervisor and the safety advisor are designed based on an approximate probabilistic relation between the original system and its finite abstraction. By employing this architecture, we provide formal probabilistic guarantees on preserving the safety specifications expressed by accepting languages of deterministic finite automata (DFA). Meanwhile, the unverified controllers can still be employed in the control loop even though they are not reliable. We demonstrate the effectiveness of our proposed results by applying them to two (physical) case studies. Copyright © 2021, The Authors. All rights reserved.

关键词： controllers

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Nonlinear Tracking and Rejection using Linear Parameter-Varying control

arXiv

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arXiv 2021年

作者： Koelewijn, Patrick J.W. Tóth, Roland Nijmeijer, Henk Weiland, Siep Control Systems Group Department of Electrical Engineering Eindhoven University of Technology P.O. Box 513 Eindhoven5600 MB Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest1111 Hungary Dynamics and Control Group Department of Mechanical Engineering Eindhoven University of Technology P.O. Box 513 Eindhoven5600 MB Netherlands

The Linear Parameter-Varying (LPV) framework has been introduced with the intention to provide stability and performance guarantees for analysis and controller synthesis for Nonlinear (NL) systems via convex methods. By extending results of the Linear Time-Invariant framework, mainly based on quadratic stability and performance using dissipativity theory, it has been assumed that they generalize tracking and disturbance rejection guarantees for NL systems. However, as has been shown in literature, stability and performance through standard dissipativity is not sufficient in order to satisfy the desired guarantees in case of reference tracking and disturbance rejection for nonlinear systems. We propose to solve this problem by the application of incremental dissipativity, which does ensure these specifications. A novel approach is proposed to synthesize and realize an NL controller which is able to guarantee incremental stability and performance for NL systems via convex optimization using methods from the LPV framework. Through simulations and experiments, the presented method is compared to standard LPV controller designs, showing significant performance improvements. Copyright © 2021, The Authors. All rights reserved.

关键词： Nonlinear systems

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Frequency Measurement Research with Weight Averaging of Pulse Output Signal of Voltage-to-Frequency Converter

SSRN

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SSRN 2022年

作者： Dorozhovets, Mykhaylo Pawlowski, Eligiusz Świsulski, Dariusz Department of Metrology and Diagnostic Systems Faculty of Electrical and Computer Engineering Rzeszow University of Technology ul. Wincentego Pola 2A Rzeszów35-959 Poland Department of Information and Measuring Technology National University Lviv Polytechnic Lviv Ukraine Faculty of Electrical Engineering and Computer Science Lublin University of Technology Lublin Poland Faculty of Electrical and Control Engineering Gdańsk University of Technology Gdańsk Poland

The paper presents investigation of the efficiency of weight averaging of a pulse frequency modulated signal. The sampling effects and the interference influence in the input signal of the voltage-to frequency converter on the result of output frequency weight averaging are analyzed. A general form of the averaging result dependence on the used weighting function, signal value, interference level and modulation parameters is derived. In the case of high interferences whose frequencies are unstable or may change over a wide range, the specified level of their suppression, together with the reduction of the sampling effect, can be achieved using weighting functions, such as trigonometric, with an appropriate level of side lobes. The interference suppression dependently on frequency ranges using selected weighing functions are tested using both the simulation and experimental methods. The obtained results show very good convergence with the values calculated from theoretical formulas. © 2022, The Authors. All rights reserved.

关键词： Weighing

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Nearest-Better Network for Fitness Landscape Analysis of Continuous Optimization Problems

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IEEE Transactions on Evolutionary Computation 2024年

作者： Diao, Yiya Li, Changhe Zeng, Sanyou Yang, Shengxiang Coello, Carlos A. Coello China University of Geosciences School of Automation Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China Anhui University of Sciences and Technology School of Artificial Intelligence Hefei China China University of Geosciences School of Mechanical Engineering and Electronic Information Wuhan China De Montfort University School of Computer Science and Informatics LeicesterLE1 9BH United Kingdom Department of Computer Science CINVESTAV-IPN 07360 Mexico Tecnologico de Monterrey Faculty of Excellence The School of Engineering and Sciences N.L. Monterrey Mexico

Fitness landscape analysis (FLA) is quite important in evolutionary computation. In this paper, we propose a novel FLA method, the nearest-better network (NBN), which uses the nearest-better relationship to simplify the original fitness landscape of continuous optimization problems. We introduce an efficient algorithm to calculate NBN for continuous problems. We also propose four numerical measurements and a 3D visualization method based on NBN. Experiments show that compared to the other main FLA methods, the four numerical measurements proposed here can effectively measure the four intended features: neutrality, ruggedness, modality, and basin of attraction, respectively, and common features of the fitness landscape can be maintained in 3D NBN visualization, regardless of the scale of the problem. NBN also provides a view of how algorithms search in high-dimensional problems with the help of the 3D NBN visualization. © 1997-2012 IEEE.

关键词： Optimization algorithms

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Prediction of endometrial cancer MRI images based on deep learning and improved Bayesian extreme learning machine ensemble learning

Prediction of endometrial cancer MRI images based on deep le...

引用

Frontiers Technology of Information and computer (ICFTIC), IEEE International Conference on

作者： Ximiao Wang Qinghui Hu Xisheng Dai Peijun Li Yuan Chen Institute of Intelligent Systems and Control Guangxi University of Science and Technology Liuzhou City Guangxi Province China School of Computer Science and Engineering Guilin University of Aerospace Technology Guilin City Guangxi Province China Department of Radiology Jiangmen Central Hospital Jiangmen City Guangdong Province China Department of Gynecology Jiangmen Central Hospital Jiangmen City Guangdong Province China

For the prediction of deep myometrial invasion (DMI) in endometrial cancer (EC), this study proposes an ensemble learning method which combines deep learning (DL) and improved Bayesian extreme learning machine (BELM). Firstly, the MRI images of endometrial cancer are preprocessed to meet the requirements. Secondly, the deep features relevant to the task are extracted from the images by using convolutional kernels. Finally, the bootstrap resampling method is employed to repeatedly sample multiple training subsets from the training set. The improved Bayesian extreme learning machine is used as the base classifier to train multiple independent sub-models. An ensemble learning classifier is constructed using an ensemble strategy to improve the prediction accuracy and stability of the model. Experimental results show that the proposed method achieves an AUC of 0.758 on the internal validation set and 0.740 on the external validation set, which demonstrat good generalization ability and stability of the proposed method.

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An Efficient Spatial-Temporal Trajectory Planner for Autonomous Vehicles in Unstructured Environments

arXiv

引用

arXiv 2022年

作者： Han, Zhichao Wu, Yuwei Li, Tong Zhang, Lu Pei, Liuao Xu, Long Li, Chengyang Ma, Changjia Xu, Chao Shen, Shaojie Gao, Fei State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China Huzhou Institute of Zhejiang University Huzhou313000 China Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA19104 United States Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong

As a core part of autonomous driving systems, motion planning has received extensive attention from academia and industry. However, real-time trajectory planning capable of spatial-temporal joint optimization is challenged by nonholonomic dynamics, particularly in the presence of unstructured environments and dynamic obstacles. To bridge the gap, we propose a real-time trajectory optimization method that can generate a high-quality whole-body trajectory under arbitrary environmental constraints. By leveraging the differential flatness property of car-like robots, we simplify the trajectory representation and analytically formulate the planning problem while maintaining the feasibility of the nonholonomic dynamics. Moreover, we achieve efficient obstacle avoidance with a safe driving corridor for unmodelled obstacles and signed distance approximations for dynamic moving objects. We present comprehensive benchmarks with State-of-the-Art methods, demonstrating the significance of the proposed method in terms of efficiency and trajectory quality. Real-world experiments verify the practicality of our algorithm. We will release our codes for the research community.1 Copyright © 2022, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Multi-Layer SIS Model with an Infrastructure Network

arXiv

引用

arXiv 2021年

作者： Paré, Philip E. Janson, Axel Gracy, Sebin Liu, Ji Sandberg, Henrik Johansson, Karl H. School of Electrical and Computer Engineering Purdue University IN United States Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Digital Futures Stockholm Sweden Department of Electrical and Computer Engineering Stony Brook University United States

—This paper deals with the spread of diseases over both a population network and an infrastructure network. We develop a layered networked spread model for a susceptible-infected-susceptible (SIS) pathogen-borne disease spreading over a human contact network and an infrastructure network, and refer to it as a layered networked susceptible-infected-water-susceptible (SIWS) model. The "W" in SIWS represents any infrastructure network contamination, not necessarily restricted to a water distribution network. We say that the SIWS network is in the healthy state (also referred to as the disease-free equilibrium) if none of the individuals in the population are infected nor is the infrastructure network contaminated;otherwise, we say that the network is in the endemic state (also referred to as the endemic equilibrium). First, we establish sufficient conditions for local exponential stability and global asymptotic stability (GAS) of the healthy state. Second, we provide sufficient conditions for existence, uniqueness, and GAS of the endemic state. Building off of these results, we provide a necessary, and sufficient, condition for the healthy state to be the unique equilibrium of our model. Third, we show that the endemic equilibrium of the SIWS model is worse than that of the networked SIS model without any infrastructure network, in the sense that at least one subpopulation has strictly larger infection proportion at the endemic equilibrium in the former model than that in the latter. Fourth, we study an observability problem, and, assuming that the measurements of the sickness-levels of the human contact network are available, provide a necessary and sufficient condition for estimation of the pathogen levels in the infrastructure network. Furthermore, we provide another sufficient, but not necessary, condition for estimation of pathogen levels in the infrastructure network. By leveraging the sufficient condition we finally provide insights in to how the measurement mat

关键词： Asymptotic stability

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Empirical Risk Minimization with Relative Entropy Regularization: Optimality and Sensitivity Analysis

arXiv

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arXiv 2022年

作者： Perlaza, Samir M. Bisson, Gaetan Esnaola, Iñaki Jean-Marie, Alain Rini, Stefano INRIA 2004 Route des Lucioles Sophia Antipolis06902 France The Laboratoire de Mathématiques GAATI Université de la Polynésie Française BP 6570 Faaa98702 French Polynesia The Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom The Department of Electrical and Computer Engineering National Chao Tung University Hsinchu Taiwan The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

The optimality and sensitivity of the empirical risk minimization problem with relative entropy regularization (ERM-RER) are investigated for the case in which the reference is a σ-finite measure instead of a probability measure. This generalization allows for a larger degree of flexibility in the incorporation of prior knowledge over the set of models. In this setting, the interplay of the regularization parameter, the reference measure, the risk function, and the empirical risk induced by the solution of the ERM-RER problem is characterized. This characterization yields necessary and sufficient conditions for the existence of regularization parameters that achieve arbitrarily small empirical risk with arbitrarily high probability. Additionally, the sensitivity of the expected empirical risk to deviations from the solution of the ERM-RER problem is studied. Dataset-dependent and dataset-independent upper bounds on the absolute value of the sensitivity are presented. In a special case, it is shown that the expectation (with respect to the datasets) of the absolute value of the sensitivity is upper bounded, up to a constant factor, by the square root of the lautum information between the models and the datasets. © 2022, CC BY.

关键词： Sensitivity analysis

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Learning-based control for PMSM Using Distributed Gaussian Processes with Optimal Aggregation Strategy

Learning-based Control for PMSM Using Distributed Gaussian P...

引用

Annual Conference of Industrial Electronics Society

作者： Zhenxiao Yin Xiaobing Dai Zewen Yang Yang Shen Georges Hattab Hang Zhao Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Chair of Information-oriented Control Technical University of Munich Munich Germany Center for Artificial Intelligence in Public Health Research (ZKI-PH) Robert Koch Institute Berlin Germany Department of Mathematics and Computer Science Freie Universität Berlin Berlin Germany Department of Electronic & Computer Engineering The Hong Kong University of Science and Technology Hong Kong China

The growing demand for accurate control in varying and unknown environments has sparked a corresponding increase in the requirements for power supply components, including permanent magnet synchronous motors (PMSMs). To infer the unknown part of the system, machine learning techniques are widely employed, especially Gaussian process regression (GPR) due to its flexibility of continuous system modeling and its guaranteed performance. For practical implementation, distributed GPR is adopted to alleviate the high computational complexity. However, the study of distributed GPR from a control perspective remains an open problem. In this paper, a control-aware optimal aggregation strategy of distributed GPR for PMSMs is proposed based on the Lyapunov stability theory. This strategy exclusively leverages the posterior mean, thereby obviating the need for computationally intensive calculations associated with posterior variance in alternative approaches. Moreover, the straightforward calculation process of our proposed strategy lends itself to seamless implementation in high-frequency PMSM control. The effectiveness of the proposed strategy is demonstrated in the simulations.

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