10期

最新文章

• Dynamic Information Flow Tracking for Detection of Advanced Persistent Threats: A Stochastic Game Approach AI翻译
  作者：Moothedath, Shana; Sahabandu, Dinuka; Allen, Joey; Clark, Andrew; Bushnell, Linda; Lee, Wenke; Poovendran, Radha
  作者单位：University of Washington; University of Washington Seattle; University System of Georgia; Georgia Institute of Technology; Washington University (WUSTL)
  摘要：Advanced persistent threats (APTs) are stealthy attacks by intelligent adversaries. This article studies the detection of APTs that infiltrate cyber systems and compromise specifically targeted data and/or infrastructures. Dynamic information flow tracking is an information trace-based detection mechanism against APTs that tags suspicious information flows in the system and performs security analysis for unauthorized use of tagged data. In this article, we develop an analytical model for resou...
• Concealability Analysis for Current-State Opacity Enforcement via Editing Functions AI翻译
  作者：Peng, Kun; Chen, Yufeng; Seatzu, Carla; Li, Zhiwu; Giua, Alessandro
  作者单位：Xidian University; University of Cagliari; Macau University of Science & Technology
  摘要：This article focuses on the problem of enforcing current-state opacity of a discrete event system via editing functions. In more detail, the observation exposed to an intruder is modified, either erasing or inserting some observations, so as to guarantee that the intruder is not able to discover the predefined secret. The notion of concealability, which formalizes the possibility of maintaining the secret hidden, is introduced starting from defining some illegal states on a particular structur...
• Intermittent Sampled-Data Stabilization of Highly Nonlinear Delayed Stochastic Networks via Periodic Self-Triggered Strategy AI翻译
  作者：Zhou, Hui; Li, Shufan; Park, Ju H.; Li, Wenxue
  作者单位：Harbin Institute of Technology; Yeungnam University
  摘要：This article considers the stabilization issue of highly nonlinear delayed stochastic networks (HNDSNs) based on periodic self-triggered intermittent control under sampled-data (PICS) for the first time. Therein, the linear growth condition is taken off, and some PICS-based stabilization conditions in previous works are weakened. It is worth pointing out that the existing results are suitable for highly nonlinear networks neither based on PICS nor considering the time-varying delay. Given this...
• Distributed Multiagent Reinforcement Learning Based on Graph-Induced Local Value Functions AI翻译
  作者：Jing, Gangshan; Bai, He; George, Jemin; Chakrabortty, Aranya; Sharma, Piyush K.
  作者单位：Chongqing University; Oklahoma State University System; Oklahoma State University - Stillwater; United States Department of Defense; US Army Research, Development & Engineering Command (RDECOM); US Army Research Laboratory (ARL); North Carolina State University
  摘要：Achieving distributed reinforcement learning (RL) for large-scale cooperative multiagent systems (MASs) is challenging because: 1) each agent has access to only limited information and 2) issues on scalability and sample efficiency emerge due to the curse of dimensionality. In this article, we propose a general distributed framework for sample efficient cooperative multiagent reinforcement learning (MARL) by utilizing the structures of graphs involved in this problem. We introduce three coupli...
• Sampled-Data Feedback Stabilization in Mean Square for Stochastic Homogeneous Systems AI翻译
  作者：Yu, Xin; Lin, Wei
  作者单位：Jiangsu Normal University; University System of Ohio; Case Western Reserve University
  摘要：For stochastic nonlinear systems which are only continuous but not necessarily local Lipschitz nor linear growth, we study the problem of asymptotic stabilization in mean square (AS-in-MS) via sampled-data feedback. We begin by establishing the existence of solutions for a class of hybrid stochastic systems. With the aid of weighted homogeneity, we then prove that for stochastic homogeneous systems of degree zero, asymptotic stabilizability in mean square by homogeneous feedback implies asympt...
• Modification of Infinite and Unstable Invariant Zeros in Linear Systems Using Stability-Preserving State Feedback AI翻译
  作者：Kucera, Vladimir
  作者单位：Czech Technical University Prague
  摘要：The algebra of integral matrices is applied to solve the following problem of control theory: the modification of a specified list of zeros of a linear system with more inputs than outputs using nonregular state feedback while ensuring stability or, equivalently, the modification of a specified list of zeros of a stable system while maintaining stability. The zeros in question are unstable invariant zeros and the zero at infinity. These zeros are the Smith invariant of the transfer matrix of t...
• Continuous-Time Channel Gain Control for Minimum-Information Kalman-Bucy Filtering AI翻译
  作者：Tanaka, Takashi; Zinage, Vrushabh; Ugrinovskii, Valery; Skoglund, Mikael
  作者单位：University of Texas System; University of Texas Austin; University of New South Wales Sydney; Royal Institute of Technology
  摘要：We consider the problem of estimating a continuous-time Gauss-Markov source process observed through a vector Gaussian channel with an adjustable channel gain matrix. Specifically, for various (generally time-varying) choices channel gain matrices, we study the tradeoff relationship between 1) the mean-square estimation error attainable by the classical Kalman-Bucy filter, and 2) the mutual information between the source process and its Kalman-Bucy estimate. We then formulate a novel optimal c...
• Stability Analysis of Trajectories on Manifolds With Applications to Observer and Controller Design AI翻译
  作者：Wu, Dongjun; Yi, Bowen; Rantzer, Anders
  作者单位：Lund University; Universite de Montreal; Polytechnique Montreal
  摘要：This article examines the local exponential stability (LES) of trajectories for nonlinear systems on Riemannian manifolds. We present necessary and sufficient conditions for LES of a trajectory on a Riemannian manifold by analyzing the complete lift of the system along the given trajectory. These conditions are coordinate-free, which reveal fundamental relationships between exponential stability and incremental stability in a local sense. We then apply these results to design tracking controll...
• Learning for Control: L1-Error Bounds for Kernel-Based Regression AI翻译
  作者：Bisiacco, Mauro; Pillonetto, Gianluigi
  作者单位：University of Padua
  摘要：We consider functional regression models with noisy outputs resulting from linear transformations. In the setting of regularization theory in reproducing kernel Hilbert spaces (RKHSs), much work has been devoted to build uncertainty bounds around kernel-based estimates, hence characterizing their convergence rates. Such results are typically formulated using either the average squared loss for the prediction or the RKHS norm. However, in signal processing and in emerging areas, such as learnin...
• Event-Triggered Privacy-Preserving Consensus Control With Edge-Based Additive Noise AI翻译
  作者：Liang, Limei; Ding, Ruiqi; Liu, Shuai; Su, Rong
  作者单位：Shandong University; Shandong University; Nanyang Technological University
  摘要：In this article, we propose a novel event-triggered distributed privacy-preserving consensus scheme for linear continuous-time multiagent systems, which can be divided into three phases. First, for each agent, an event-triggered mechanism is designed to determine whether the current state is transmitted to the corresponding neighbor agents. Then, to protect the privacy of initial states from disclosure, the edge-based mutually independent standard white noise is added to each communication cha...