This paper investigates the stability of discrete-time Lur'e systems with a time-varying delay. Firstly, an extended-matrix-separated-based summation inequality is established to estimate the summation term contai...
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This paper investigates the stability of discrete-time Lur'e systems with a time-varying delay. Firstly, an extended-matrix-separated-based summation inequality is established to estimate the summation term containing the system state and the forward difference of the state. Then, an improved delay-product-type Lyapunov-Krasovskii functional (LKF) is proposed, which involves the characteristics of matrices refined and the information of delay squared. By using a quadratic function negative transformation lemma based on matrix-injection, a delay-dependent stability criterion is obtained. Finally, a numerical example demonstrates the advantages of the proposed criterion.
This paper investigates the recursive filtering (RF) problem for stochastic multi-rate (MR) systems, where the information transmission is regulated by an improved weighted try-once-discard protocol (IWTODP). In order...
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Improving the production efficiency of Microwave Filters (MFs) is of great practical significance for constructing modern communication systems. The characteristics of MFs are with various degrees of individual differ...
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Improving the production efficiency of Microwave Filters (MFs) is of great practical significance for constructing modern communication systems. The characteristics of MFs are with various degrees of individual differences caused by materials. The most tuning techniques have to start from scratch for tuning different MFs, which is time-consuming. The knowledge from previous tuning processes can be used in the current tuning due to the relevance of MFs. Motivated by this, a knowledge transfer method for tuning MFs with unknown individual differences is proposed. The main contributions are threefold: 1) An Knowledge Transfer technique for Optimization Tuning (KTOT) is created to tune various MFs efficiently; 2) an Evaluation mechanism of Difference Degree (EDD) is proposed to guide transfer; 3) a Guiding strategy of Knowledge Transfer Intensity (KTIG) in accordance with the difference degree is presented to reuse knowledge reasonably. The high-efficiency of KTOT and the effectiveness of KTIG based on EDD are demonstrated.
This paper studies the mobile robots with multiple constraints based on path planning of A-star algorithm. A hierarchical adaptive control method is presented to handle multiple constaints. On the upper layer, a Astar...
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Coordination between unmanned aerial vehicles(UAVs)and unmanned ground vehicles(UGVs)has received increasing attention in recent *** list of successful applications of UAV-UGV coordination systems is growing and demon...
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Coordination between unmanned aerial vehicles(UAVs)and unmanned ground vehicles(UGVs)has received increasing attention in recent *** list of successful applications of UAV-UGV coordination systems is growing and demonstrates that UAV-UGV coordination can provide real-world solutions that other types of coordination cannot *** paper systematically reviews the advances in UAV-UGV coordination systems during the period of 2015-2020 and offers a comprehensive investigation and analysis of the recent ***,the essential elements in the UAV-UGV coordination systems are analyzed,and four key functional roles are *** close collaboration among functional roles can achieve the UAV-UGV coordination on perception,task,and *** the perspective of functional roles,UAV-UGV coordination systems can be further classified into eight *** functional-role-based category pro-vides novel insights into analyzing various patterns of UAV-UGV *** paper also discusses the challenges related to UAV-UGV coordination.
The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of n local cost functions by using local information exchange is *** problem is an important component of many machin...
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The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of n local cost functions by using local information exchange is *** problem is an important component of many machine learning techniques with data parallelism,such as deep learning and federated *** propose a distributed primal-dual stochastic gradient descent(SGD)algorithm,suitable for arbitrarily connected communication networks and any smooth(possibly nonconvex)cost *** show that the proposed algorithm achieves the linear speedup convergence rate O(1/(√nT))for general nonconvex cost functions and the linear speedup convergence rate O(1/(nT)) when the global cost function satisfies the Polyak-Lojasiewicz(P-L)condition,where T is the total number of *** also show that the output of the proposed algorithm with constant parameters linearly converges to a neighborhood of a global *** demonstrate through numerical experiments the efficiency of our algorithm in comparison with the baseline centralized SGD and recently proposed distributed SGD algorithms.
DURING our discussion at workshops for writing“What Does ChatGPT Say:The DAO from Algorithmic Intelligence to Linguistic Intelligence”[1],we had expected the next milestone for Artificial Intelligence(AI)would be in...
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DURING our discussion at workshops for writing“What Does ChatGPT Say:The DAO from Algorithmic Intelligence to Linguistic Intelligence”[1],we had expected the next milestone for Artificial Intelligence(AI)would be in the direction of Imaginative Intelligence(II),i.e.,something similar to automatic wordsto-videos generation or intelligent digital movies/theater technology that could be used for conducting new“Artificiofactual Experiments”[2]to replace conventional“Counterfactual Experiments”in scientific research and technical development for both natural and social studies[2]-[6].Now we have OpenAI’s Sora,so soon,but this is not the final,actually far away,and it is just the beginning.
Landslide is a common geological disaster. Landslide sensitivity mapping (LSM) is the key technology for landslide monitoring, early warning and risk assessment. Deep learning shows good performance in feature extract...
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