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Adaptive dynamic surface control for quadrotor-slung load transportation system with uncertainties

science China(Technological sciences) 2023年第10期66卷 2917-2929页

作者： DING Feng HUANG Jian SUN Chong AI Yong XU WenXia YANG ChenGuang School of Computer Science South-Central Minzu UniversityWuhan 430074China Key Laboratory of Ministryof EducationforImage Processing and Intelligent ControlHubei Key Laboratoryof Brain-inspired Intelligent Systems School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Engineering Wuhan Institute of TechnologyWuhan 430205China Key Laboratory of Ministry of Education for Autonomous Systems and Networked Control Guangdong Engineering Technology Research Center of Control of Intelligent SystemsCollege of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510640China

Quadrotors play a significant role in our lives and are transforming our *** cable-suspended loads is an unavoidable quadrotor application trend and a hot research topic in the control ***,the load swing and unpredictability pose significant challenges to the quadrotor's *** this paper,an anti-swing controller with an inner-outer control strategy for the quadrotor-slung load transportation system is *** facilitate the controller design,the outer position dynamics are restructured in the form of ***,a virtual controller is created to force the underactuated states to the dynamic surface to ensure the position subsystem's *** improve robustness,an adaptive law is used to eliminate the effects of uncertain cable ***,a dynamic surface controller for the inner attitude subsystem is presented to drive the actual force to the virtual *** is demonstrated that the control strategy can stabilize the quadrotor despite mass and cable length *** results are provided to demonstrate the efficacy and durability of the proposed method.

关键词： quadrotor-slung load transportation system dynamic surface control adaptive law cascade system model uncertainties

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Distributed robust matrix-scaled consensus control of perturbed multi-agent systems

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control Theory and Technology 2024年第4期22卷 623-637页

作者： Bin Cheng Gang Li Kun An Chunhui Yu Department of Control Science and Engineering Tongji UniversityShanghai201804China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210China The Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji UniversityShanghai201804China Shanghai Sunshine Rehabilitation Center Shanghai201613China

Distributed matrix-scaled consensus is a kind of generalized cooperative control problem and has broad applications in the field of social network and *** paper addresses the robust distributed matrix-scaled consensus of perturbed multi-agent systems suffering from unknown *** discontinuous protocols are first proposed to drive agents to achieve cluster consensus and suppress the effect of *** protocols with time-varying gains obeying differential equations are also designed,which are completely distributed and rely on no global *** the boundary layer technique,smooth protocols are proposed to avoid the unexpected chattering effect due to discontinuous *** a cost,under the designed smooth protocols,the defined matrix-scaled consensus error tends to a residual set rather than zero,in which the residual bound is arbitrary small by choosing proper ***,distributed dynamic event-based matrix-scalar consensus controllers are also proposed to avoid continuous *** examples are provided to further verify the designed algorithms.

关键词： Matrix-scaled consensus Perturbed multi-agent systems Adaptive control Distributed control Event-triggered control

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Mining Erasable Itemsets from a Product Database with Quantitative Components 24

Mining Erasable Itemsets from a Product Database with Quanti...

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11th Multidisciplinary International Social Networks Conference, MISNC 2024

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

ISBN: (纸本)9798400717550

Erasable itemset mining is one of the most well-known methods in data mining for optimizing limited materials. After mining erasable itemsets, the manager can rearrange the production plan effectively. However, in real applications, traditional mining methods neglect some critical factors, such as costs or quantities of components forming a product. This thesis thus considers mining erasable itemsets from a product database with quantitative components. An erasable profit-based itemset mining (EPIM) algorithm is proposed to mine erasable itemsets with low profits by analyzing the relationships between products and components. In experiments, a synthetic dataset was used to compare the performance in execution speed and memory usage under various threshold settings. It can also be easily generalized to find erasable itemsets satisfying the other criteria with a quantitative product database. © 2024 ACM.

关键词： Mine safety

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Collaborative Routing and Charging/Discharging Scheduling of Electric autonomous Vehicles in Coupled Power-Traffic Networks: A Multiobjective Approach

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IEEE Internet of Things Journal 2025年第11期12卷 17753-17764页

作者： Chu, Kai-Fung Chen, Tianlun Xie, Yue Lam, Albert Y. S. Song, Yue Iida, Fumiya University of Cambridge Department of Engineering CambridgeCB2 1PZ United Kingdom The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong The University of Hong Kong Department of Electrical and Electronic Engineering Department of Geography Hong Kong Fano Labs Hong Kong Tongji University Department of Control Science and Engineering Shanghai201804 China Tongji University National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai201210 China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai200120 China

autonomous vehicles (AVs) are vehicles that traverse on the road without active human intervention. With a coordinator, AVs can be connected to provide high-efficiency transport services, such as AV-based public transport networks. The controller can manage the network by coordinating the transport request assignment, traveling, and charging/discharging schedule. On the other hand, AVs are likely to be electric and benefit the smart grid via vehicle-to-grid technology. A well-designed mobility network connecting electric AVs (EAVs) and smart grid can substantially reduce unnecessary travel and energy costs. In this article, we aim to maximize utilities in the AV-based public transport network and the power distribution network for the vehicle network containing EAVs, charging stations, and distributed power generations. We formulate the assignment and scheduling problem as a multiobjective mixed-integer program (MIP). To solve the optimization problem, we develop a hybrid heuristic approach based on nondominated sorting genetic algorithm II (NSGA-II) and branch-and-bound (BnB) algorithms. Experiments are conducted on a modified 15-bus distribution system and a simulated traffic network. The results show that the proposed strategy effectively minimizes the total travel and energy purchase cost by 21%. This study provides valuable insights on vehicle coordination for multiple tasks, offering visionary guidance for stakeholders engaged in multifaceted transportation endeavors. © 2014 IEEE.

关键词： Vehicle-to-grid

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Modelling information warfare dynamics to counter propaganda using a nonlinear differential equation with a PINN-based learning approach

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International Journal of Information Technology (Singapore) 2024年第3期16卷 1527-1538页

作者： Pandey, Rashmikiran Pandey, Mrinal Nazarov, Alexey Nikolaevich Department of Radio Engineering and Cybernetics Moscow Institute of Physics and Technology Moscow Russian Federation Federal Research Center Computer Science and Control Russian Academy of Science Moscow Russian Federation

The widespread dissemination of misinformation and propaganda has become a crucial issue in societal conversations. This study presents an innovative framework to counter propaganda within information warfare using a modified logistic differential equation integrated with a Physics-Informed Neural Network (PINN). Employing the LIAR dataset, which reflects real-world political discourse, our model captures the intricate dynamics of propaganda. The novel approach effectively estimates underlying dynamics and unknown parameters, yielding superior accuracy (97.32%) compared to benchmark models. A parameter sensitivity analysis reveals the significant impact of the spread rate parameter, shedding light on aggressive propaganda dissemination. This research paves the way for robust methods in combating propaganda, demonstrating the effectiveness and superiority of the proposed PINN-based model over established benchmark models. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Information warfare Nonlinear differential equations PINN Propaganda Random forest classifier TF-IDF

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control of 2-D Semi-Markov Jump Systems:A View from Mode Generation Mechanism

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 258-260页

作者： Yunzhe Men Jian Sun Jie Chen the National Key Laboratory of Autonomous Intelligent Unmanned Systems the School of AutomationBeijing Institute of TechnologyBeijing 100081 the Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China the Department of Control Science and Engineering Tongji UniversityShanghai 201804 the National Key Laboratory of Autonomous Intelligent Unmanned Systems Beijing Institute of TechnologyBeijing 100081China

Dear Editor,Two-dimensional(2-D) systems have wide applications in image data processing,gas absorption and fluid dynamics analysis [1]-[3].When there exist abrupt changes in 2-D systems,they are usually modeled by 2-D Markov jump systems(MJSs) or 2-D semi-Markov jump systems(SMJSs).This letter investigates the control of 2-D SMJSs based on a novel mode generation mechanism,which could avoid mode ambiguousness phenomenon caused by the evolution of system mode in two different *** criterion that guarantees the almost surely exponential stability of the system is obtained.A thermal process is studied to demonstrate the availability of the proposed method.

关键词： Markov mode system

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Enhanced Nature Inspired-Support Vector Machine for Glaucoma Detection

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computers, Materials & Continua 2023年第7期76卷 1151-1172页

作者： Jahanzaib Latif Shanshan Tu Chuangbai Xiao Anas Bilal Sadaqat Ur Rehman Zohaib Ahmad Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of Technology100124BeijingChina College of Information Science Technology Hainan Normal UniversityHainan ProvinceHaikou571158China Department of Computer Science University of SalfordManchesterUK Faculty of Information Technology Beijing University of Technology Chaoyang District BeijingChina

Glaucoma is a progressive eye disease that can lead to blindness if left *** detection is crucial to prevent vision loss,but current manual scanning methods are expensive,time-consuming,and require specialized *** study presents a novel approach to Glaucoma detection using the Enhanced Grey Wolf Optimized Support Vector Machine(EGWO-SVM)*** proposed method involves preprocessing steps such as removing image noise using the adaptive median filter(AMF)and feature extraction using the previously processed speeded-up robust feature(SURF),histogram of oriented gradients(HOG),and Global *** enhanced Grey Wolf Optimization(GWO)technique is then employed with SVM for *** evaluate the proposed method,we used the online retinal images for glaucoma analysis(ORIGA)database,and it achieved high accuracy,sensitivity,and specificity rates of 94%,92%,and 92%,*** results demonstrate that the proposed method outperforms other current algorithms in detecting the presence or absence of *** study provides a novel and effective approach to Glaucoma detection that can potentially improve the detection process and outcomes.

关键词： Glaucoma detection grey golf optimization support vector machine feature extraction image classification

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Hybrid Visual Inertial Odometry for Robust Underwater Estimation

Hybrid Visual Inertial Odometry for Robust Underwater Estima...

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2023 MTS/IEEE U.S. Gulf Coast, OCEANS 2023

作者： Joshi, Bharat Bandara, Chanaka Poulakakis, Ioannis Tanner, Herbert G. Rekleitis, Ioannis University of South Carolina Department of Computer Science and Engineering United States University of Delaware Center for Autonomous and Robotic Systems United States

ISBN: (纸本)9798218142186

Vision-based state estimation is challenging in underwater environments due to color attenuation, low visibility and floating particulates. All visual-inertial estimators are prone to failure due to degradation in image quality. However, underwater robots are required to keep track of their pose during field deployments. We propose robust estimator fusing the robot's dynamic and kinematic model with proprioceptive sensors to propagate the pose whenever visual-inertial odometry (VIO) fails. To detect the VIO failures, health tracking is used, which enables switching between pose estimates from VIO and a kinematic estimator. Loop closure implemented on weighted posegraph for global trajectory optimization. Experimental results from an Aqua2 autonomous Underwater Vehicle field deployments demonstrates the robustness of our approach over different underwater environments such as over shipwrecks and coral reefs. The proposed hybrid approach is robust to VIO failures producing consistent trajectories even in harsh conditions. © 2023 The Marine Technology Society (MTS).

关键词： autonomous underwater vehicles

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Distributed data-driven consensus control of multi-agent systems under switched uncertainties

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control Theory and Technology 2023年第3期21卷 478-487页

作者： Wenjie Liu Yifei Li Gang Wang Jian Sun Jie Chen National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of TechnologyBeijing 100081China Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China Department of Control Science and Engineering Tongji UniversityShanghai 201804China

Cooperative control of multi-agent systems(MASs),particularly consensus control,has gained significant attention in the last two decades,thanks to the rapid and sustained development of distributed and networked *** this paper,we present some new results focused on consensus control of a set of unknown linear MASs(whose system matrices are unknown)under unknown switched uncertainties,with an emphasis on distributed data-driven *** proposed controller is end-to-end,designed by solving two data-based semi-definite programs(SDPs),which adjust to the changes of the uncertainty *** approach achieves asymptotic consensus of the MAS provided that the switching is slow enough and the uncertainty is *** illustrate the effectiveness of our proposed method through a numerical example.

关键词： Data-driven control Multi-agent systems Consensus-Switched uncertainties

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Advancing Network Anomaly Detection: Comparative Analysis of Machine Learning Models

Advancing Network Anomaly Detection: Comparative Analysis of...

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International Conference on. Cryptology and Network Security with Machine Learning, ICCNSML 2023

作者： Pandey, Rashmikiran Pandey, Mrinal Nazarov, Alexey Department of Radio Engineering and Cybernetics Moscow Institute of Physics and Technology Moscow Russia Federal Research Center Computer Science and Control Russian Academy of Science Moscow Russia

ISBN: (纸本)9789819706402

In the rapidly evolving realm of cyber-security, the detection of network anomalies serves as a pivotal line of defense against a myriad of malicious activities and cyberthreats. This research undertakes the task of enhancing the accuracy and efficacy of network anomaly detection by employing a comparative analysis of various individual machine learning models. The study delves into the performance of distinct models, including Random Forest, Gradient Boosting, AdaBoost, neural networks, and SVM, meticulously scrutinizing their capabilities in detecting network anomalies. The crux of this study lies in its meticulous evaluation of each individual model on the revered NSL-KDD dataset—an established benchmark within the field of network intrusion detection. Through a systematic blend of rigorous mathematical frameworks, precise model implementations, and comprehensive experimental assessments, this research offers a deep understanding of the inner workings of each algorithm. The pivotal aspect of this study revolves around the comprehensive comparative analysis of these standalone models. Going beyond the mere quantifica-tion of accuracy, the exploration delves into aspects of precision, recall, F1-score, and more, shedding light on their diverse facets of performance. With achieved accuracies of 99.2419% for Random Forest, 99.5197% for Gradient Boosting, 86.6044% for AdaBoost, 84.00% for neural networks, and 87.00% for SVM, this research underlines the distinctive attributes and potential of each model in the context of network anomaly detection. As the study unravels the distinct strengths and limitations of each model, it contributes to the broader landscape of cyber-security by providing insights into the efficacy of individual machine learning approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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