This paper presents a study on the robust stability analysis of linear time-invariant systems with parameter uncertainties and norm-bounded uncertainties. By utilizing the structured singular value, necessary and suff...
This paper presents a study on the robust stability analysis of linear time-invariant systems with parameter uncertainties and norm-bounded uncertainties. By utilizing the structured singular value, necessary and sufficient conditions for robust stability are derived. Based on the stability condition, the stability margin of the uncertain system is obtained from the skewed structured singular value. Additionally, numerical simulation results are provided to validate the effectiveness of the proposed methods.
In the realm of linear regression, the concept of utilizing relaxed regression targets for classification has shown considerable success. However, it suffers from a lack of strong discriminative ability. In this study...
In the realm of linear regression, the concept of utilizing relaxed regression targets for classification has shown considerable success. However, it suffers from a lack of strong discriminative ability. In this study, we build upon the theory of relaxed regression targets and propose a more concise and discriminative model for multi-class classification. Our approach introduces the £ 2,1 -norm regularized term to enhance the efficacy and compactness of the learned projection matrix. This regularization term not only generates a sparse row structure but also facilitates feature selection during the training phase. Consequently, both the accuracy of classification and the convergence speed of the model are improved. Furthermore, when compared to various variants of the linear regression model, our designed method exhibits superior performance in image classification. Through extensive experiments conducted on image databases, we provide evidence that our proposed approach excels in recognition capability, surpassing the performance of state-of-the-art methods in terms of classification accuracy.
This paper utilizes the weak approximation method to analyze differential games that involve mixed strategies. Mixed strategies have the potential to produce unique strategic behaviors, whereas traditional models and ...
This paper utilizes the weak approximation method to analyze differential games that involve mixed strategies. Mixed strategies have the potential to produce unique strategic behaviors, whereas traditional models and tools in pure strategy games cannot be directly applied. Based on the stochastic processes with independent increments, we define the mixed strategy without assuming the knowledge of the opponents' strategy and system state. However, this general mixed strategy poses challenges in evaluating game payoff and game value. To overcome these challenges, we utilize the weak approximation method to employ a stochastic differential game to characterize the dynamics of the mixed strategy game. We demonstrate that the game's payoff function can be precisely approximated with an error of the same scale as the step size. Furthermore, we estimate the upper and lower value functions of the weak approximated game to analyze the existence of game value. Finally, we provide numerical examples to illustrate and elaborate on our findings.
In image fusion,the desirable fused image is to obtain advantage information from different images of the same *** for the fusion of the infrared image and the visible image that have distinct features,this paper prop...
In image fusion,the desirable fused image is to obtain advantage information from different images of the same *** for the fusion of the infrared image and the visible image that have distinct features,this paper proposes an adaptive multiweight fusion based on multi-scale *** method designs different weight matrices according to the characteristics of the infrared image and the visible *** can also adaptively adjusts the weight size according to the *** on the difference of information entropy between infrared images and visible images,the method of this paper can keep the important information as much as *** results prove the method of this paper is fast and *** also has certain superiority compared with other methods.
The new generation of industrial cyber-physical systems (ICPS) supported by the edge computing technology enables efficient distributed sensing under massive data volumes and frequent transmissions. Observability is e...
The new generation of industrial cyber-physical systems (ICPS) supported by the edge computing technology enables efficient distributed sensing under massive data volumes and frequent transmissions. Observability is essential to obtain good sensing performance, and most of existing sensing works directly assume that the system is observable. However, it is difficult to satisfy the assumption with the increasingly expanded network scale and dynamic scheduling of devices. To solve this problem, we propose an observability guaranteed distributed method (OGDM) for edge sensing with the cooperation of sensors and edge computing units (ECUs). We analyze the relationship between sensor scheduling and observability based on the network topology and graph signal processing (GSP) technology. In addition, we transform the observability condition into a convex form and take into account sensing error and energy consumption for optimization. Finally, our algorithm is applied to estimate the slab temperature in the hot rolling process. The effectiveness is verified by simulation results.
Aiming at track association before and after multi-target track interruption, a track associating method on the basis of transitive closure fuzzy clustering is proposed. Firstly, the method converts the traditional tr...
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This paper is based on the research background of block relocation in the automated warehouse and proposes a solution utilizing AGV to relocate goods within the warehouse. Building upon the automated warehouse layout ...
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Modular batteries can be aggregated to deliver frequency regulation services for power grids. Although utilizing the idle capacity of battery modules is financially attractive, it remains challenging to consider the h...
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Offset-free model predictive control (MPC) provides a useful means for controlling systems with uncertainties and constraints, but suffers from the heavy computational burden of repeatedly solving an optimization prob...
Offset-free model predictive control (MPC) provides a useful means for controlling systems with uncertainties and constraints, but suffers from the heavy computational burden of repeatedly solving an optimization problem in real time. Such computational issue precludes the possibility of its application in systems requiring high realtime requirements, such as autonomous driving system. To address this problem, we develop a provably safe deep learning-based offset-free MPC framework. Based on the nominal offset-free MPC, the proposed MPC not only reserves the ability of disturbance rejection, but also leverages deep neural networks for approximating the explicit MPC solution to greatly reduce online computational time. Furthermore, a gauge map is used to guarantee the satisfaction of safe constraints. The proposed MPC is used in trajectory tracking control for smart autonomous driving. The simulation results show that the proposed MPC is an order of magnitude faster than the nominal offset-free MPC in safety-critical systems.
Point clouds are naturally sparse, while image pixels are dense. The inconsistency limits feature fusion from both modalities for point-wise scene flow estimation. Previous methods rarely predict scene flow from the e...
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