scholarly journals Combining Biometric Fractal Pattern and Particle Swarm Optimization-Based Classifier for Fingerprint Recognition

2010 ◽  
Vol 2010 ◽  
pp. 1-14 ◽  
Author(s):  
Chia-Hung Lin ◽  
Jian-Liung Chen ◽  
Zwe-Lee Gaing
Keyword(s):  
Particle Swarm Optimization ◽  
Probabilistic Neural Network ◽  
Particle Swarm ◽  
Fingerprint Recognition ◽  
Small Scale ◽  
Identification System ◽  
Swarm Optimization ◽  
Binary Images ◽  
Fractal Pattern ◽  
Fractal Patterns

This paper proposes combining the biometric fractal pattern and particle swarm optimization (PSO)-based classifier for fingerprint recognition. Fingerprints have arch, loop, whorl, and accidental morphologies, and embed singular points, resulting in the establishment of fingerprint individuality. An automatic fingerprint identification system consists of two stages: digital image processing (DIP) and pattern recognition. DIP is used to convert to binary images, refine out noise, and locate the reference point. For binary images, Katz's algorithm is employed to estimate the fractal dimension (FD) from a two-dimensional (2D) image. Biometric features are extracted as fractal patterns using different FDs. Probabilistic neural network (PNN) as a classifier performs to compare the fractal patterns among the small-scale database. A PSO algorithm is used to tune the optimal parameters and heighten the accuracy. For 30 subjects in the laboratory, the proposed classifier demonstrates greater efficiency and higher accuracy in fingerprint recognition.

scholarly journals Atmospheric refractivity estimation from AIS signal power using the quantum-behaved particle swarm optimization algorithm

2019 ◽  
Vol 11 (1) ◽  
pp. 542-548
Author(s):  
Wenlong Tang ◽  
Hao Cha ◽  
Min Wei ◽  
Bin Tian ◽  
Xichuang Ren
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Communication System ◽  
Particle Swarm Optimization Algorithm ◽  
Estimation Method ◽  
Particle Swarm ◽  
Automatic Identification ◽  
Identification System ◽  
Signal Power ◽  
Swarm Optimization

Abstract This paper proposes a new refractivity profile estimation method based on the use of AIS signal power and quantum-behaved particle swarm optimization (QPSO) algorithm to solve the inverse problem. Automatic identification system (AIS) is a maritime navigation safety communication system that operates in the very high frequency mobile band and was developed primarily for collision avoidance. Since AIS is a one-way communication system which does not need to consider the target echo signal, it can estimate the atmospheric refractivity profile more accurately. Estimating atmospheric refractivity profiles from AIS signal power is a complex nonlinear optimization problem, the QPSO algorithm is adopted to search for the optimal solution from various refractivity parameters, and the inversion results are compared with those of the particle swarm optimization algorithm to validate the superiority of the QPSO algorithm. In order to test the anti-noise ability of the QPSO algorithm, the synthetic AIS signal power with different Gaussian noise levels is utilized to invert the surface-based duct. Simulation results indicate that the QPSO algorithm can invert the surface-based duct using AIS signal power accurately, which verify the feasibility of the new atmospheric refractivity estimation method based on the automatic identification system.

Complex lithology prediction using mean impact value, particle swarm optimization, and probabilistic neural network techniques

2020 ◽  
Vol 68 (6) ◽  
pp. 1727-1752
Author(s):  
Yufeng Gu ◽  
Zhongmin Zhang ◽  
Demin Zhang ◽  
Yixuan Zhu ◽  
Zhidong Bao ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Probabilistic Neural Network ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Lithology Prediction

Marine Asynchronous Propulsion Motor Parameter Identification Using Dynamic Particle Swarm Optimization

2013 ◽  
Vol 860-863 ◽  
pp. 2211-2217
Author(s):  
Si Yuan Liu ◽  
Yan Cheng Liu ◽  
Chuan Wang ◽  
Jun Jie Ren
Keyword(s):  
Particle Swarm Optimization ◽  
Parameter Identification ◽  
Electric Propulsion ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Identification System ◽  
Solution Quality ◽  
Swarm Optimization ◽  
Motor Parameter ◽  
Weight Learning

This paper proposes a new application of dynamic particle swarm optimization (PSO) algorithm for parameter identification of vector controlled asynchronous propulsion motor (APM) in electric propulsion ship. The dynamic PSO modifies the inertia weight, learning coefficients and two independent random sequences which affect the convergence capability and solution quality, in order to improve the performance of the standard PSO algorithm. The standard PSO and dynamic PSO algorithms use measurements of the mt-axis currents, voltages of APM as the inputs to parameter identification system. The experimental results obtained compare the identified parameters with the actual parameters. There is also a comparison of the solution quality between standard PSO and dynamic PSO algorithms. The results demonstrate that the dynamic PSO algorithm is better than standard PSO algorithm for APM parameter identification. Dynamic PSO algorithm can improve the performance of ship propulsion motor under abrupt load variation.

scholarly journals On the Identification of Coupled Pitch and Heave Motions Using Opposition-Based Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Yuntao Dai ◽  
Liqiang Liu ◽  
Shanshan Feng
Keyword(s):  
Particle Swarm Optimization ◽  
Parameter Identification ◽  
Learning Algorithm ◽  
Particle Swarm ◽  
Identification System ◽  
Identification Algorithm ◽  
Swarm Optimization ◽  
Disturbing Force ◽  
Hydrodynamic Parameters ◽  
Opposition Based Learning

A mathematical model must be established to study the motions of ships in order to control them effectively. An assessment of the model depends on the accuracy of hydrodynamic parameters. An algorithm for the parameter identification of the coupled pitch and heave motions in ships is, thus, put forward in this paper. The algorithm proposed is based on particle swarm optimization (PSO) and the opposition-based learning theory known as opposition-based particle swarm optimization (OPSO). A definition of the opposition-based learning algorithm is given first of all, with ideas on how to improve this algorithm and its process being presented next. Secondly, the design of the parameter identification algorithm is put forward, modeling the disturbing force and disturbing moment of the identification system and the output parameters of the identification system. Then, the problem involving the hydrodynamic parameters of motions is identified and the coupled pitch and heave motions of a ship described as an optimization problem with constraints. Finally, the numerical simulations of different sea conditions with unknown parameters are carried out using the PSO and OPSO algorithms. The simulation results show that the OPSO algorithm is relatively stable in terms of the hydrodynamic parameters identification of the coupled pitch and heave motions.

Parameter Identification Method for Turbine Speed Governor System Based on Particle Swarm Optimization

2013 ◽  
Vol 448-453 ◽  
pp. 2511-2515
Author(s):  
Wen Sun ◽  
Xiang Yu Kong ◽  
Qun Yang ◽  
Fang Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Parameter Identification ◽  
Particle Swarm ◽  
Parameters Optimization ◽  
Evolutionary Strategy ◽  
Identification System ◽  
Swarm Optimization ◽  
Identification Method ◽  
Speed Governor ◽  
Speed Governor System

A parameter identification method for generator speed governor system, which combines decoupling parameter identification and overall recognition with measured data, was proposed in the paper. The method bases on particle swarm optimization, and takes parameter identification as a parameters optimization problem under evaluation function. According to an intelligent optimization algorithms evolutionary strategy, the individual's status is continuously adjusted until the identification system and actual system output error is sufficiently small. Case studies show that the proposed method can be applied to the measured parameters and model validation work.

Power Factor Optimization of Distributed Generations in Distribution Networks Based on Improved Particle Swarm Optimization Method

2012 ◽  
Vol 516-517 ◽  
pp. 1408-1413 ◽  
Author(s):  
Cheng Xi Li ◽  
Wen Jun Yan ◽  
Qiang Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Power Factor ◽  
Power Distribution ◽  
Power Loss ◽  
Particle Swarm ◽  
Distribution Networks ◽  
Optimization Method ◽  
Small Scale ◽  
Swarm Optimization ◽  
Power Utilities

The gradually extensive penetration of small-scale distributed renewable generators in existing medium-voltage power distribution networks highlights many technical challenges which call for urgent solutions from power utilities. This paper attempts to optimize the power factor of distributed generators (DGs) integrated in distribution networks and presents a novel algorithmic solution. With the aim of minimizing power loss whilst maintaining the node voltage, the problem is formulated with a mathematical model elaborating the DGs and a set of constraints in distribution networks and addressed through adopting an extended particle swarm optimization (PSO) approach. The suggested algorithm is assessed through numerical simulation experiments with the IEEE 33-bus system and the outcome shows that the optimization algorithm can effectively reduce the power loss and promote the node voltages across the overall distribution network.

The Research of Particle Swarm Algorithm Based on Heuristic Rules for the Layout of Airplane Equipment Cabin

2013 ◽  
Vol 300-301 ◽  
pp. 659-663
Author(s):  
Xiao Jian Han ◽  
Xiang Fang Ding ◽  
Chun Xiao
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Three Dimensions ◽  
Small Scale ◽  
Particle Swarm Algorithm ◽  
Heuristic Rules ◽  
Layout Problem ◽  
Swarm Optimization ◽  
Np Problem

How to get the most optimal solution of equipment layout in the aircraft cabin of the limited space is a completely NP problem. The problem is abstracted as three dimensions （3D） layout problem. A co-evolutionary particle swarm optimization with heuristic rules is presented. The cabin is decomposed into several small-scale layout problems. The co-evolutionary framework is adopted, and particle swarm optimization (PSO) and heuristic roles for layout are integrated to solve this problem. Finally, an example is used to verify the feasibility and effectiveness of the algorithm.

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