检索结果-内蒙古大学图书馆

Bayesian hypergame approach to equilibrium stability and robustness in moving target defense

学校读者我要写书评

暂无评论

Bayesian hypergame approach to equilibrium stability and rob...

IEEE Conference on Decision and control

作者： Hanzheng Zhang Zhaoyang Cheng Guanpu Chen Karl Henrik Johansson Shanghai Research Institute for Autonomous Intelligent Systems Tongji University Shanghai China Academy of Mathematics and System Science Chinese Academy of Science University of Chinese Academy of Science Beijing China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology and the Digital Futures Stockholm Sweden

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We investigate the equilibrium stability and robustness in a class of moving target defense problems, in which players have both incomplete information and asymmetric cognition. We first establish a Bayesian Stackelberg game model for incomplete information and then employ a hypergame reformulation to address asymmetric cognition. With the core concept of the hyper Bayesian Nash equilibrium (HBNE), a condition for achieving both the strategic and cognitive stability in equilibria can be realized by solving linear equations. Moreover, to deal with players’ underlying perturbed knowledge, we study the equilibrium robustness by presenting a condition of robust HBNE under the given configuration. Experiments evaluate our theoretical results.

关键词： Games Nash equilibrium Stability analysis Robustness Mathematical models Cognition Bayes methods

Bayesian hypergame approach to equilibrium stability and robustness in moving target defense

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Zhang, Hanzheng Cheng, Zhaoyang Chen, Guanpu Johansson, Karl Henrik Shanghai Research Institute for Autonomous Intelligent Systems Tongji University Shanghai China Academy of Mathematics and System Science Chinese Academy of Science University of Chinese Academy of Science Beijing China The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology The Digital Futures Stockholm Sweden

关键词： Nash equilibrium

Social Force Embedded Mixed Graph Convolutional Network for Multi-class Trajectory Prediction

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Du, Quancheng Wang, Xiao Yin, Shouguo Li, Lingxi Ning, Huansheng The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China The School of Artificial Intelligence Anhui University Hefei230031 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Electrical and Computer Engineering Indiana University Purdue University IndianapolisIN46202 United States

Accurate prediction of agent motion trajectories is crucial for autonomous driving, contributing to the reduction of collision risks in human-vehicle interactions and ensuring ample response time for other traffic participants. Current research predominantly focuses on traditional deep learning methods, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). These methods leverage relative distances to forecast the motion trajectories of a single class of agents. However, in complex traffic scenarios, the motion patterns of various types of traffic participants exhibit inherent randomness and uncertainty. Relying solely on relative distances may not adequately capture the nuanced interaction patterns between different classes of road users. In this paper, we propose a novel multi-class trajectory prediction method named the social force embedded mixed graph convolutional network (SFEM-GCN). The primary goal is to extract social interactions among agents more accurately. SFEM-GCN comprises three graph topologies: the semantic graph (SG), position graph (PG), and velocity graph (VG). These graphs encode various of social force relationships among different classes of agents in complex scenes. Specifically, SG utilizes one-hot encoding of agent-class information to guide the construction of graph adjacency matrices based on semantic information. PG and VG create adjacency matrices to capture motion interaction relationships between different classes agents. These graph structures are then integrated into a mixed graph, where learning is conducted using a spatio-temporal graph convolutional neural network (ST-GCNN). To further enhance prediction performance, we adopt temporal convolutional networks (TCNs) to generate the predicted trajectory with fewer parameters. Experimental results on publicly available datasets demonstrate that SFEM-GCN surpasses state-of-the-art methods in terms of accuracy and robustness. Copyright © 2024, The Authors.

关键词： Forecasting

Cooperative Global K-exponential Tracking control of Multiple Mobile Robots-Extended Version

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Xu, Liang Su, Youfeng Cai, He College of Computer and Data Science Fuzhou University Fuzhou350116 China Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology Shenzhen518055 China School of Automation Science and Engineering South China University of Technology Guangzhou510641 China

This paper studies the cooperative tracking control problem for multiple mobile robots over a directed communication network. First, it is shown that the closed-loop system is uniformly globally asymptotically stable under the proposed distributed continuous feedback control law, where an explicit strict Lyapunov function is constructed. Then, by investigating the convergence rate, it is further proven that the closed-loop system is globally K-exponentially stable. Moreover, to make the proposed control law more practical, the distributed continuous feedback control law is generalized to a distributed sampled-data feedback control law using the emulation approach, based on the strong integral input-to-state stable Lyapunov function. Numerical simulations are presented to validate the effectiveness of the proposed control methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Lyapunov functions

Global solution to sensor network localization: A non-convex potential game approach and its distributed implementation

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Xu, Gehui Chen, Guanpu Hong, Yiguang Fidan, Baris Parisini, Thomas Johansson, Karl H. Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing100190 China The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures StockholmSE-10044 Sweden Department of Control Science and Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China The Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada The Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom The Department of Engineering and Architecture University of Trieste Trieste34127 Italy

Consider a sensor network consisting of both anchor and non-anchor nodes. We address the following sensor network localization (SNL) problem: given the physical locations of anchor nodes and relative measurements among all nodes, determine the locations of all non-anchor nodes. The solution to the SNL problem is challenging due to its inherent non-convexity. In this paper, the problem takes on the form of a multi-player nonconvex potential game in which canonical duality theory is used to define a complementary dual potential function. After showing the Nash equilibrium (NE) correspondent to the SNL solution, we provide a necessary and sufficient condition for a stationary point to coincide with the NE. An algorithm is proposed_ to reach the NE and shown to have convergence rate O(1/√k). With the aim of reducing the information exchange within a network, a distributed algorithm for NE seeking is implemented and its global convergence analysis is provided. Extensive simulations show the validity and effectiveness of the proposed approach to solve the SNL problem. Copyright © 2024, The Authors. All rights reserved.

关键词： Game theory

Escalator passenger posture anomaly detection based on improved SSD model

学校读者我要写书评

暂无评论

Escalator passenger posture anomaly detection based on impro...

IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Teng Xu Jian Chen Zuoyong Li Yuanzheng Cai School of Electronic Electrical Engineering and Physics Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China

With the widespread use of escalators in daily life, escalator-related injury factors are threatening public health. To secure escalator system, most of existing studies focus on post hoc statistics and trend to fix these deficiencies to reduce escalator-related potential risks. However, they are not designed for early-stage detection of impending escalator unsafe factors. To solve this problem, this paper proposes an improved Single Shot MultiBox Detector (SSD) algorithm for anomaly detection of passenger posture on escalators. Firstly, we replace the widely used VGG16 backbone of SSD network by simpler MobileNetV2 to accelerate model inference. Secondly, the distance intersection over Union (DIoU) loss function is introduced to further improve performance. Based on the characteristics of IoU and considering the shortcomings of GIoU, DIoU directly regresses the Euclidean distance between the center points of the two boxes to accelerate the convergence. The experimental results show that the improved SSD escalator passenger posture anomaly detection algorithm can efficiently identify the dangerous posture of escalator passengers and start relevant safety protection measures. According to inference of model, the escalator system can be controlled in practice to formulate the corresponding safety protection measures, and improve the safety performance of the escalator operation.

关键词： Euclidean distance Market research Inference algorithms Real-time systems Safety Public healthcare Information technology

Short-term wind power forecast with turning weather based on DBSCAN-RFE-LightGBM

学校读者我要写书评

暂无评论

Renewable Energy 2025年 251卷

作者： Wu, Zhenlong Fan, Xinyu Bian, Guibin Liu, Yanhong Zhang, Xiaoke Chen, YangQuan School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China State Key Laboratory of Intelligent Agricultural Power Equipment Zhengzhou University Luoyang471000 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Electric Power Research Institute of State Grid Henan Electric Power Company Zhengzhou450052 China Lab Department of Mechanical Engineering School of Engineering University of California MercedCA95343 United States

At present, the wind power prediction technology is becoming more and more mature, however, the turning weather brings a decrease in prediction accuracy due to its suddenness and instability. In this paper, a wind power prediction method considering the turning period and non-turning period respectively is proposed to weaken the adverse effects caused by turning weather. Firstly, the prediction effects of multiple models are compared, density-based spatial clustering of applications with noise (DBSCAN) is selected as the outlier processing method, recursive feature elimination (RFE) is used as the feature selection method, and Light gradient boosting machine (LightGBM) is used for prediction. The combined prediction method based on DBSCAN-RFE-LightGBM can reduce the influence of abnormal data and redundant features and improve the prediction effect. Then, the sliding window is set to detect the turning period. Considering that the turning weather is an emergency and does not happen frequently, the amount of data is small, which leads to the inability to train the model well. Generative adversarial networks (GAN) are applied to expand the turning period data. The LightGBM is trained and predicted by using the expended turning period data. Finally, the time-division prediction results are merged. Using the data collected from wind farms for short-term power prediction experiments, the time-segment prediction method proposed in this paper with GAN reduces MAE by 1.913 and RMSE by 3.351 on a single unit compared with the non-differentiated period. © 2025 Elsevier Ltd

关键词： Merging

Transi-Net: An Explainable Deep Learning Model Ensemble For Prostate's Transition Zone Segmentation

学校读者我要写书评

暂无评论

Transi-Net: An Explainable Deep Learning Model Ensemble For ...

IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Dimitrios I. Zaridis Eugenia Mylona Nikolaos S. Tachos Charalampos Kalantzopoulos Kostas Marias Manolis Tsiknakis Dimitrios D. Koutsouris George K. Matsopoulos Dimitrios I. Fotiadis Biomedical Engineering Laboratory School of Electrical & Computer Engineering and Biomedical Research Institute National Technical University of Athens and FORTH Athens & Ioannina Greece Biomedical Research Institute FORTH Ioannina Greece Computational Biomedicine Laboratory FORTH Heraklion Greece Biomedical Engineering Laboratory School of Electrical & Computer Engineering National Technical University of Athens Athens Greece Biomedical Research Institute and Unit of Medical Technology and Intelligent Information Systems FORTH and University of Ioannina Ioannina Greece

The identification of the location of prostate cancer is of paramount importance for improved treatment. This process is strictly bonded with the accurate segmentation of the prostate gland and its zones, on MR images. In the present study, an ensemble of 3 deep learning models along with a Meta-learner module able to refine the outcomes of the models, is proposed (Transi-Net) to segment the prostate's transition zone. A method to quantify the model's uncertainty is introduced to measure the confidence of an architecture with respect to its final decision. The backbone of Transi-Net consist the original U-net, Dense2U-net and Bridged U-net models. The proposed model showcased significant improvement in comparison with its base components as well as an independent model, the USE-Net, while it was proven more confident about its decision. The proposed model resulted in an improvement of 5%, 3%, 3% and 4% for Sensitivity, Balanced Accuracy, Dice Score and Rand Error Index respectively, compared to the second best, USE-Net.

关键词：

Learning to control Linear systems can be Hard

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Tsiamis, Anastasios Ziemann, Ingvar Morari, Manfred Matni, Nikolai Pappas, George J. Automatic Control Laboratory ETH Zurich Switzerland School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Department of Electrical and Systems Engineering University of Pennsylvania United States

In this paper, we study the statistical difficulty of learning to control linear systems. We focus on two standard benchmarks, the sample complexity of stabilization, and the regret of the online learning of the Linear Quadratic Regulator (LQR). Prior results state that the statistical difficulty for both benchmarks scales polynomially with the system state dimension up to system-theoretic quantities. However, this does not reveal the whole picture. By utilizing minimax lower bounds for both benchmarks, we prove that there exist non-trivial classes of systems for which learning complexity scales dramatically, i.e. exponentially, with the system dimension. This situation arises in the case of underactuated systems, i.e. systems with fewer inputs than states. Such systems are structurally difficult to control and their system theoretic quantities can scale exponentially with the system dimension dominating learning complexity. Under some additional structural assumptions (bounding systems away from uncontrollability), we provide qualitatively matching upper bounds. We prove that learning complexity can be at most exponential with the controllability index of the system, that is the degree of underactuation. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear systems