A Lattice Discrete Model for Intensive RFID Reader Deployment

In an intensive RFID reader environment, multiple RFID reader are deployed together to cover a pointed area. In such intensive RFID reader application, it needs to determine how many readers are enough to cover the expect area and calculate the position of readers. However, the coverage of multiple readers is a NP problem. Therefore, it needs an approximate approach to optimize the coverage. In this paper, we proposed a lattice decentralized approach to model the coverage problem of intensive RFID reader deployment. In our novel model, both the deployment area and the reader reading region are discretized to a lattice and described by a matrix. Then, the coverage is easily calculated by matrix operation. In order to test our discrete method, we propose a heuristic algorithm to deploy readers based on the PSO (particle swarm optimization) algorithm. The simulations show that the proposed algorithm can cover an irregular or regular area with a high coverage rate and a low overlapping rate.

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Modelling of surface roughness and studying of optimal machining position in side milling

10.21203/rs.3.rs-405052/v1 ◽

2021 ◽

Author(s):

Jinfeng Bai ◽

Huiying Zhao ◽

Lingyu Zhao ◽

Mingchen Cao ◽

Duanzhi Duan

Keyword(s):

Surface Roughness ◽

Particle Swarm Optimization Algorithm ◽

Surface Generation ◽

The Novel ◽

Dynamic Effects ◽

Computational System ◽

Swarm Optimization ◽

Side Milling ◽

Milling Operations ◽

Novel Model

Abstract In this work, a theoretical analysis of surface generation numerical model is presented to predict the surface roughness achieved by side milling operations with cylindrical tools. This work is focused on the trajectory of tools with two teeth by influencing of tool errors such as radial runouts, as well as straightness with dynamic effects. A computational system was developed to simulate roughness topography in contour milling with cylindrical tool. Finally, the PSO (particle swarm optimization) algorithm is employed to find the optimal machining position for the best surface roughness. Experimental data is satisfied with the the novel pretiction model for the tooth’s trajectory, and the the final prediction accuracy is high enough, i.e. that the prediction surface roughness. Low prediction surface roughness error (1.37 ~ 15.04%) and position error (0.95 ~ 1.25 mm) indicate effectiveness of the model built in this work. The novel model may be used to determine the variation in surface roughness.

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Solving RFID Networks Scheduling Problems Using Hybird Binary Particle Swarm Optimization Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.29-32.966 ◽

2010 ◽

Vol 29-32 ◽

pp. 966-972 ◽

Cited By ~ 3

Author(s):

Jun Tang

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Binary Particle Swarm Optimization ◽

Scheduling Problems ◽

Swarm Optimization ◽

Rfid Reader ◽

Rfid Networks ◽

Binary Pso

This paper presents an hybrid binary particle swarm optimization algorithm integration of genetic algorithms (HBPSO) to solve the RFID networks scheduling problems and get the optimal scheduling result in the problem. HBPSO is combined with advantages of binary PSO and GA. We use HBPSO to solve three problems of the RFID reader network and we attempt to minimize the total transaction time. By the results of the three problems, we can conclude that HBPSO is an effective algorithm which can find optimal solutions in the problem of the RFID reader network.

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