Robust consensus control of uncertain multi-agent systems with input delay: a model reduction method

This paper addresses the robust consensus control design for input-delayed multi-agent systems subject to parametric uncertainties. To deal with the input delay, the Artstein model reduction method is employed by a state transformation. The input-dependent integral term that remains in the transformed system, due to the model uncertainties, is judiciously analysed. By carefully exploring certain features of the Laplacian matrix, sufficient conditions for the global consensus under directed communication topology are identified using Lyapunov-Krasovskii functionals in the time domain. The proposed control only relies on relative state information of the subsystems via network connections. The effectiveness and robustness of the proposed control design is demonstrated through a numerical simulation example

Design and Simulation Analysis of Bolt Group Connection of BS-Type Flange Cast Steel Right-Angle Sea Valve

BS type flanged cast steel right-angle sea valve is an important valve used to stop the backflow of medium in the ship pipeline system. The valve and the pipeline are connected by a bolt connection. To ensure the reliability of the bolt connection, the theoretical calculation and finite element method are used to verify the reliability of the design of bolt connection. The theoretical result and the result of finite element analysis (using ANSYS) show that the largest stress on the bolt is located in the middle of the bolt. This paper provides solutions for the verifying the design of bolt connection in valves based on comparing the results of theoretical calculation and finite element analysis

Design and Simulation Analysis of Bolt Group Connection of BS-Type Flange Cast Steel Right-Angle Sea Valve

BS type flanged cast steel right-angle sea valve is an important valve used to stop the backflow of medium in the ship pipeline system. The valve and the pipeline are connected by a bolt connection. To ensure the reliability of the bolt connection, the theoretical calculation and finite element method are used to verify the reliability of the design of bolt connection. The theoretical result and the result of finite element analysis (using ANSYS) show that the largest stress on the bolt is located in the middle of the bolt. This paper provides solutions for the verifying the design of bolt connection in valves based on comparing the results of theoretical calculation and finite element analysis

A fixed-time output feedback control scheme for double integrator systems

A continuous output feedback control scheme rendering the closed-loop double integrator system globally stable in finite-time is presented. In particular, the convergence time is independent of initial conditions. The bi-limit homogeneous technique is used for controller and observer designs with fixed-time convergence. Then, a continuous output feedback control law is proposed for nominal double-integrator system and its perturbed version. The homogeneity and Lyapunov techniques are used to ensure the fixed-time stability of the closed-loop system under output feedback control framework. Finally, the efficiency of the proposed algorithms is illustrated by numerical simulations.</p

Entity-Aspect-Opinion-Sentiment Quadruple Extraction for Fine-grained Sentiment Analysis

Product reviews often contain a large number of implicit aspects and object-attribute co-existence cases. Unfortunately, many existing studies in Aspect-Based Sentiment Analysis (ABSA) have overlooked this issue, which can make it difficult to extract opinions comprehensively and fairly. In this paper, we propose a new task called Entity-Aspect-Opinion-Sentiment Quadruple Extraction (EASQE), which aims to hierarchically decompose aspect terms into entities and aspects to avoid information loss, non-exclusive annotations, and opinion misunderstandings in ABSA tasks. To facilitate research in this new task, we have constructed four datasets (Res14-EASQE, Res15-EASQE, Res16-EASQE, and Lap14-EASQE) based on the SemEval Restaurant and Laptop datasets. We have also proposed a novel two-stage sequence-tagging based Trigger-Opinion framework as the baseline for the EASQE task. Empirical evaluations show that our Trigger-Opinion framework can generate satisfactory EASQE results and can also be applied to other ABSA tasks, significantly outperforming state-of-the-art methods. We have made the four datasets and source code of Trigger-Opinion publicly available to facilitate further research in this area

Consensus disturbance rejection for Lipschitz nonlinear multi-agent systems with input delay: a DOBC approach

In this paper, a new predictor-based consensus disturbance rejection method is proposed for high-order multi agent systems with Lipschitz nonlinearity and input delay. First, a distributed disturbance observer for consensus control is developed for each agent to estimate the disturbance under the delay constraint. Based on the conventional predictor feedback approach, a non-ideal predictor based control scheme is constructed for each agent by utilizing the estimate of the disturbance and the prediction of the relative state information. Then, rigorous analysis is carried out to ensure that the extra terms associated with disturbances and nonlinear functions are properly considered. Sufficient conditions for the consensus of the multi-agent systems with disturbance rejection are derived based on the analysis in the framework of Lyapunov-Krasovskii functionals. A simulation example is included to demonstrate the performance of the proposed control scheme. (C) 2016 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.National Natural Science Foundation of China [61673034]SCI(E)ARTICLE1,SI298-31535

《太上灵宝五符序》里的传说

departmental bulletin pape

《黄帝阴符经》十真集解的成书背景

departmental bulletin pape

Robustness-Inspired Defense Against Backdoor Attacks on Graph Neural Networks

Graph Neural Networks (GNNs) have achieved promising results in tasks such as node classification and graph classification. However, recent studies reveal that GNNs are vulnerable to backdoor attacks, posing a significant threat to their real-world adoption. Despite initial efforts to defend against specific graph backdoor attacks, there is no work on defending against various types of backdoor attacks where generated triggers have different properties. Hence, we first empirically verify that prediction variance under edge dropping is a crucial indicator for identifying poisoned nodes. With this observation, we propose using random edge dropping to detect backdoors and theoretically show that it can efficiently distinguish poisoned nodes from clean ones. Furthermore, we introduce a novel robust training strategy to efficiently counteract the impact of the triggers. Extensive experiments on real-world datasets show that our framework can effectively identify poisoned nodes, significantly degrade the attack success rate, and maintain clean accuracy when defending against various types of graph backdoor attacks with different properties