Localization of Cooperative WSN using Distributed PSO with Optimum References

<p>In indoor environment WSN nodes are deployed randomly and do not know the accurate position. Find the node position with the help of anchor nodes is known as localization. CRB algorithm selects the best anchor nodes which gives high accuracy. In this paper distributed PSO algorithm with optimum selection of reference nodes using CRB is proposed to find accurate node position. The proposed method performs better in comparison with other algorithms like PSO, RLS, LMS and GPS in terms of position accuracy, latency and complexity.</p>

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Localization of Cooperative WSN using Distributed PSO with Optimum References

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v6i6.pp3094-3102 ◽

2016 ◽

Vol 6 (6) ◽

pp. 3094

Author(s):

Ravichander Janapati ◽

Ch. Balaswamy ◽

K. Soundararajan

Keyword(s):

Indoor Environment ◽

Pso Algorithm ◽

High Accuracy ◽

Position Accuracy ◽

Optimum Selection ◽

Anchor Nodes ◽

Accurate Position ◽

Node Position ◽

Selection Of

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Localization Technology Based on Quantum-Behaved Particle Swarm Optimization Algorithm for Wireless Sensor Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.220-223.1852 ◽

2012 ◽

Vol 220-223 ◽

pp. 1852-1856 ◽

Cited By ~ 1

Author(s):

Ji Zhao ◽

Yi Fu ◽

Han Bo Wang

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Pso Algorithm ◽

Wireless Sensor ◽

Node Localization ◽

Swarm Optimization ◽

Position Accuracy ◽

Node Position

This paper proposed a distributed iterative localization technology of wireless sensor networks (WSNs) to solve the problem of node localization. In this approch, once the nodes get localized, they act as references for the rest of nodes to localize. The ranging-based localization problem is formulated as a multidimensional optimization issue, and the quantum-behaved particle swarm optimization algorithm (QPSO) is used to exploit their quick convergence to quality solutions. Finally, the simulation results compared with the particle swarm optimization algorithm (PSO) algorithm show that QPSO outperforms the PSO and improve the node position accuracy, which prove the validity of the presented method.

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RTLIO: Real-Time LiDAR-Inertial Odometry and Mapping for UAVs

Sensors ◽

10.3390/s21123955 ◽

2021 ◽

Vol 21 (12) ◽

pp. 3955

Author(s):

Jung-Cheng Yang ◽

Chun-Jung Lin ◽

Bing-Yuan You ◽

Yin-Long Yan ◽

Teng-Hu Cheng

Keyword(s):

Feedback Control ◽

Real Time ◽

High Frequency ◽

Indoor Environment ◽

Indoor Localization ◽

Sampling Rate ◽

Outdoor Environment ◽

Position Accuracy ◽

Graph Optimization ◽

Pose Graph Optimization

Most UAVs rely on GPS for localization in an outdoor environment. However, in GPS-denied environment, other sources of localization are required for UAVs to conduct feedback control and navigation. LiDAR has been used for indoor localization, but the sampling rate is usually too low for feedback control of UAVs. To compensate this drawback, IMU sensors are usually fused to generate high-frequency odometry, with only few extra computation resources. To achieve this goal, a real-time LiDAR inertial odometer system (RTLIO) is developed in this work to generate high-precision and high-frequency odometry for the feedback control of UAVs in an indoor environment, and this is achieved by solving cost functions that consist of the LiDAR and IMU residuals. Compared to the traditional LIO approach, the initialization process of the developed RTLIO can be achieved, even when the device is stationary. To further reduce the accumulated pose errors, loop closure and pose-graph optimization are also developed in RTLIO. To demonstrate the efficacy of the developed RTLIO, experiments with long-range trajectory are conducted, and the results indicate that the RTLIO can outperform LIO with a smaller drift. Experiments with odometry benchmark dataset (i.e., KITTI) are also conducted to compare the performance with other methods, and the results show that the RTLIO can outperform ALOAM and LOAM in terms of exhibiting a smaller time delay and greater position accuracy.

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Optimum Selection of Variable Pitch for Chatter Suppression in Face Milling Operations

Materials ◽

10.3390/ma12010112 ◽

2018 ◽

Vol 12 (1) ◽

pp. 112 ◽

Cited By ~ 9

Author(s):

Alex Iglesias ◽

Zoltan Dombovari ◽

German Gonzalez ◽

Jokin Munoa ◽

Gabor Stepan

Keyword(s):

Removal Rate ◽

Face Milling ◽

Heavy Duty ◽

Variable Pitch ◽

Thin Walls ◽

Chatter Suppression ◽

Milling Operations ◽

Optimum Selection ◽

Cutting Capacity ◽

Selection Of

Cutting capacity can be seriously limited in heavy duty face milling processes due to self-excited structural vibrations. Special geometry tools and, specifically, variable pitch milling tools have been extensively used in aeronautic applications with the purpose of removing these detrimental chatter vibrations, where high frequency chatter related to slender tools or thin walls limits productivity. However, the application of this technique in heavy duty face milling operations has not been thoroughly explored. In this paper, a method for the definition of the optimum angles between inserts is presented, based on the optimum pitch angle and the stabilizability diagrams. These diagrams are obtained through the brute force (BF) iterative method, which basically consists of an iterative maximization of the stability by using the semidiscretization method. From the observed results, hints for the selection of the optimum pitch pattern and the optimum values of the angles between inserts are presented. A practical application is implemented and the cutting performance when using an optimized variable pitch tool is assessed. It is concluded that with an optimum selection of the pitch, the material removal rate can be improved up to three times. Finally, the existence of two more different stability lobe families related to the saddle-node and flip type stability losses is demonstrated.

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