A main challenge in deploying wireless sensor networks (WSNs) is determining the minimum quantity of sensor nodes required to fully cover the region of interest while avoiding coverage holes. This study proposes a met...
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A main challenge in deploying wireless sensor networks (WSNs) is determining the minimum quantity of sensor nodes required to fully cover the region of interest while avoiding coverage holes. This study proposes a method to compute the number of nodes needed to monitor a circular region and a distributed control strategy based on circular formations to move dynamic agents to their desired positions. The method addresses the coverage problem, ensuring that each point in the monitored region is detected without losing connectivity. In addition, the study compares this approach with a sensor node allocation method based on Voronoi diagrams, highlighting the need for an algorithm that computes the desired positions of the agents to provide guaranteed flawless coverage; the proposed method achieves this by obtaining the desired final positions. The hybrid WSN architecture, together with the proposed method, achieves full coverage efficiently and better utilizes the detection circumference of sensors compared to traditional rectangular monitoring regions.
Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of various sleep disorders. In this research, electrocardiography (ECG...
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In this paper, a method to design online optimal policies that encompasses Hamilton-Jacobi-Bellman (HJB) equation solution approximation and heuristic dynamic programming (HDP) approach is proposed. Recursive least sq...
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In this paper, a method to design online optimal policies that encompasses Hamilton-Jacobi-Bellman (HJB) equation solution approximation and heuristic dynamic programming (HDP) approach is proposed. Recursive least squares (RLS) algorithms are developed to approximate the HJB equation solution that is supported by a sequence of greedy policies. The proposal investigates the convergence properties of a family of RLS algorithms and its numerical complexity in the context of reinforcement learning and optimal control. The algorithms are computationally evaluated in an electric circuit model that represents an MIMO dynamic system. The results presented herein emphasize the convergence behaviour of the RLS, projection and Kaczmarz algorithms that are developed for online applications.
The proposed methodology is based on development of online algorithms for approximate solutions of the Hamilton-Jacobi-Bellman (HJB) equation through a family of non-squares approximators for critic adaptive solution ...
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The proposed methodology is based on development of online algorithms for approximate solutions of the Hamilton-Jacobi-Bellman (HJB) equation through a family of non-squares approximators for critic adaptive solution of the Discrete Algebraic Riccati Equation (DARE), associated with the problem of Discrete Linear Quadratic Regulator (DLQR). The proposed method is evaluated in a multivariable dynamic system of 4th order with two inputs and it is compared with standard recursive least square algorithm.
This paper is concerned with the development of online algorithms for approximate solutions of the Hamilton-Jacobi-Bellman (HJB) equation. In the discrete linear quadratic regulator (DLQR) control system design, the H...
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This paper is concerned with the development of online algorithms for approximate solutions of the Hamilton-Jacobi-Bellman (HJB) equation. In the discrete linear quadratic regulator (DLQR) control system design, the HJB equation is the discrete algebraic Riccati (DARE) equation. Due to the problem of dimensionality curse, this equation is approximated via heuristic dynamic programming (HDP). The proposed methodology is based on a familiy of non-squares approximators for critic adaptive solution of the DARE associated to the DLQR problem, referred to in this work as HJB-Riccati equation, which is characterized as a parameterization of the HJB equation. The proposed method is evaluated in a multivariable dynamic system of 4th order with two inputs and it is compared with standard recursive least square algorithm.
Mission planning was thoroughly studied in the areas of multiple intelligent agent systems,such as multiple unmanned air vehicles,and multiple processor ***,it still faces challenges due to the system complexity,the e...
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Mission planning was thoroughly studied in the areas of multiple intelligent agent systems,such as multiple unmanned air vehicles,and multiple processor ***,it still faces challenges due to the system complexity,the execution order constraints,and the dynamic environment *** address it,a coordinated dynamic mission planning scheme is proposed utilizing the method of the weighted AND/OR tree and the *** the scheme,the mission is decomposed into a time-constraint weighted AND/OR tree,which is converted into an AOE-Network for mission ***,a dynamic planning algorithm is designed which uses task subcontracting and dynamic re-decomposition to coordinate *** scheme can reduce the task complexity and its execution time by implementing real-time dynamic *** simulation proves the effectiveness of this approach.
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