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Found 22 papers, 2 papers with code
Arena: A Patch-of-Interest ViT Inference Acceleration System for Edge-Assisted Video Analytics
In this paper, we find visual foundation models like Vision Transformer (ViT) also have a dedicated acceleration mechanism for video analytics.
Structural Controllability of Switched Continuous and Discrete Time Linear Systems
This paper explores the structural controllability of switched continuous and discrete time linear systems.
Observability Blocking for Functional Privacy of Linear Dynamic Networks
This paper addresses the problem of determining the minimum set of state variables in a network that need to be blocked from direct measurements in order to protect functional privacy with respect to {\emph{any}} output matrices.
On the Reachability and Controllability of Temporal Continuous-Time Linear Networks: A Generic Analysis
This paper investigates the reachability and controllability of temporal continuous-time linear networks from a generic viewpoint, where only the zero-nonzero patterns of subsystem matrices are known.
Encrypted Data-driven Predictive Cloud Control with Disturbance Observer
In this work, a data-driven predictive cloud controller is developed based on homomorphic encryption to protect the cloud data privacy.
Security Defense of Large Scale Networks Under False Data Injection Attacks: An Attack Detection Scheduling Approach
This paper proposes a novel security defense strategy from the perspective of attack detection scheduling to ensure the security of the network.
Workflow-based Fast Data-driven Predictive Control with Disturbance Observer in Cloud-edge Collaborative Architecture
First, a construction method for the DPC workflow is designed, to match the distributed processing environment of cloud computing.
On Polynomially Solvable Constrained Input Selections for Fixed and Switched Linear Structured Systems
In this paper, it is found that, if the input structure satisfies certain `regularizations', which are characterized by the proposed restricted total unimodulairty notion, those problems can be solvable in polynomial time via linear programming (LP) relaxations.
On real structured controllability/stabilizability/stability radius: Complexity and unified rank-relaxation based methods
This paper addresses the real structured controllability, stabilizability, and stability radii (RSCR, RSSZR, and RSSR, respectively) of linear systems, which involve determining the distance (in terms of matrix norms) between a (possibly large-scale) system and its nearest uncontrollable, unstabilizable, and unstable systems, respectively, with a prescribed affine structure.
Design and Implementation of Data-driven Predictive Cloud Control System
Nowadays, the rapid increases of the scale and complexity of the controlled plants bring new challenges such as computing power and storage for conventional control systems.
Fast Subspace Identification Method Based on Containerised Cloud Workflow Processing System
Subspace identification (SID) has been widely used in system identification and control fields since it can estimate system models only relying on the input and output data by reliable numerical operations such as singular value decomposition (SVD).
TFDPM: Attack detection for cyber-physical systems with diffusion probabilistic models
With the development of AIoT, data-driven attack detection methods for cyber-physical systems (CPSs) have attracted lots of attention.
Total Unimodularity and Strongly Polynomial Solvability of Constrained Minimum Input Selections for Structural Controllability: an LP-based Method
Subject to a certain condition on the prescribed input configurations that contains the dedicated input one as a special case, we demonstrate that the constraint matrices of these ILPs are totally unimodular.
AdaL: Adaptive Gradient Transformation Contributes to Convergences and Generalizations
Inspired by this, we propose AdaL, with a transformation on the original gradient.
Partial Strong Structural Controllability
This paper introduces a new controllability notion, termed partial strong structural controllability (PSSC), on a structured system whose entries of system matrices are either fixed zero or indeterminate, which naturally extends the conventional strong structural controllability (SSC) and bridges the gap between structural controllability and SSC.
ScoreGrad: Multivariate Probabilistic Time Series Forecasting with Continuous Energy-based Generative Models
However, many existing works can not be widely used because of the constraints of functional form of generative models or the sensitivity to hyperparameters.
PTSC: a New Definition for Structural Controllability under Numerical Perturbations
This paper proposes a novel notion for structural controllability under structured numerical perturbations, namely the perturbation-tolerant structural controllability (PTSC), on a single-input structured system whose entries can be classified into three categories: fixed zero entries, unknown generic entries whose values are fixed but unknown, and perturbed entries that can take arbitrary complex values.
Fixed-Time Cooperative Behavioral Control for Networked Autonomous Agents with Second-Order Nonlinear Dynamics
In this paper, we investigate the fixed-time behavioral control problem for a team of second-order nonlinear agents, aiming to achieve a desired formation with collision/obstacle~avoidance.
Optimization and Control
Revisiting Graph Convolutional Network on Semi-Supervised Node Classification from an Optimization Perspective
Based on our theoretical and empirical analysis, we establish a universal theoretical framework of GCN from an optimization perspective and derive a novel convolutional kernel named GCN+ which has lower parameter amount while relieving the over-smoothing inherently.
Stochastic Graph Recurrent Neural Network
In addition, to alleviate KL-vanishing problem in SGRNN, a simple and interpretable structure is proposed based on the lower bound of KL-divergence.
A Novel Learning-based Global Path Planning Algorithm for Planetary Rovers
Autonomous path planning algorithms are significant to planetary exploration rovers, since relying on commands from Earth will heavily reduce their efficiency of executing exploration missions.
A Novel Deep Neural Network Architecture for Mars Visual Navigation
In this paper, emerging deep learning techniques are leveraged to deal with Mars visual navigation problem.
