scholarly journals DAMAGE DETECTION FOR UHPFRC COMMUNICATION TOWER BASED ON FREQUENCY DATA AND PARTICLE SWARM OPTIMIZATION

2019 ◽  
Vol 25 (6) ◽  
pp. 495-517
Author(s):  
Sarah Jabbar ◽  
Farzad Hejazi ◽  
Ammar N. Hanoon ◽  
Rizal S. M. Rashid
Keyword(s):  
Particle Swarm Optimization ◽  
High Performance ◽  
Particle Swarm ◽  
Correlation Factor ◽  
Fiber Reinforced Concrete ◽  
Swarm Optimization ◽  
Computational Procedures ◽  
The Stability ◽  
Reliability Performance ◽  
Communication Towers

Advances in the telecommunication and broadcasting sectors have increased the need for networking equipment of communication towers. Slender structures, such as towers, are sensitive to dynamic loads, such as vibration forces. Therefore, the stability and reliability performance of towers can be maintained effectively through the prompt detection, localization, and quantification of structural damages by obtaining the dynamic frequency response of towers. However, frequency analysis for damaged structures requires long computational procedures and is difficult to perform because of the damages in real structures, particularly in towers. Therefore, this study proposed a correlation factor that can identify the relationship between frequenciesunderhealthy and damaged conditions of ultra high performance fiber-reinforced concrete (UHPFRC) communication towers using particle swarm optimization. The finite element method was implemented to simulate three UHPFRC communication towers, and an experimental test was conducted to validate and verify the developed correlation factor

scholarly journals Design of median-type filters with an impulse noise detector using decision tree and particle swarm optimization for image restoration

2010 ◽  
Vol 7 (4) ◽  
pp. 859-882 ◽  
Author(s):  
Bae-Muu Chang ◽  
Hung-Hsu Tsai ◽  
Xuan-Ping Lin ◽  
Pao-Ta Yu
Keyword(s):  
Particle Swarm Optimization ◽  
Decision Tree ◽  
High Performance ◽  
Impulse Noise ◽  
Median Filter ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Ordered Median ◽  
Mean Filter ◽  
Hybrid Detector

This paper proposes the median-type filters with an impulse noise detector using the decision tree and the particle swarm optimization, for the recovery of the corrupted gray-level images by impulse noises. It first utilizes an impulse noise detector to determine whether a pixel is corrupted or not. If yes, the filtering component in this method is triggered to filter it. Otherwise, the pixel is kept unchanged. In this work, the impulse noise detector is an adaptive hybrid detector which is constructed by integrating 10 impulse noise detectors based on the decision tree and the particle swarm optimization. Subsequently, the restoring process in this method respectively utilizes the median filter, the rank ordered mean filter, and the progressive noise-free ordered median filter to restore the corrupted pixel. Experimental results demonstrate that this method achieves high performance for detecting and restoring impulse noises, and outperforms the existing well-known methods.

Job Scheduling Algorithm for Cloud Computing Based on Particle Swarm Optimization

2013 ◽  
Vol 662 ◽  
pp. 957-960 ◽  
Author(s):  
Jing Liu ◽  
Xing Guo Luo ◽  
Xing Ming Zhang ◽  
Fan Zhang
Keyword(s):  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
High Performance ◽  
Large Scale ◽  
Job Scheduling ◽  
Scheduling Algorithm ◽  
Autonomous Systems ◽  
Particle Swarm ◽  
Cost Benefit ◽  
Swarm Optimization

Cloud computing is an emerging high performance computing environment with a large scale, heterogeneous collection of autonomous systems and flexible computational architecture. The performance of the scheduling system influences the cost benefit of this computing paradigm. To reduce the energy consumption and improve the profit, a job scheduling model based on the particle swarm optimization(PSO) algorithm is established for cloud computing. Based on open source cloud computing simulation platform CloudSim, compared to GA and random scheduling algorithms, the results show that the proposed algorithm can obtain a better solution concerning the energy cost and profit.

scholarly journals A Novel Simple Particle Swarm Optimization Algorithm for Global Optimization

Mathematics ◽  
2018 ◽  
Vol 6 (12) ◽  
pp. 287 ◽  
Author(s):  
Xin Zhang ◽  
Dexuan Zou ◽  
Xin Shen
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Search Time ◽  
Success Rates ◽  
Swarm Optimization ◽  
Efficiency Of Algorithms ◽  
Fair Comparison ◽  
Random Weight ◽  
The Stability ◽  
Searching Ability

In order to overcome the several shortcomings of Particle Swarm Optimization (PSO) e.g., premature convergence, low accuracy and poor global searching ability, a novel Simple Particle Swarm Optimization based on Random weight and Confidence term (SPSORC) is proposed in this paper. The original two improvements of the algorithm are called Simple Particle Swarm Optimization (SPSO) and Simple Particle Swarm Optimization with Confidence term (SPSOC), respectively. The former has the characteristics of more simple structure and faster convergence speed, and the latter increases particle diversity. SPSORC takes into account the advantages of both and enhances exploitation capability of algorithm. Twenty-two benchmark functions and four state-of-the-art improvement strategies are introduced so as to facilitate more fair comparison. In addition, a t-test is used to analyze the differences in large amounts of data. The stability and the search efficiency of algorithms are evaluated by comparing the success rates and the average iteration times obtained from 50-dimensional benchmark functions. The results show that the SPSO and its improved algorithms perform well comparing with several kinds of improved PSO algorithms according to both search time and computing accuracy. SPSORC, in particular, is more competent for the optimization of complex problems. In all, it has more desirable convergence, stronger stability and higher accuracy.

scholarly journals A Decomposition Model for HPLC-DAD Data Set and Its Solution by Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Lizhi Cui ◽  
Zhihao Ling ◽  
Josiah Poon ◽  
Simon K. Poon ◽  
Junbin Gao ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
High Performance ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Physical Principle ◽  
Reference Curve ◽  
Optimal Parameters ◽  
General Reference ◽  
Data Set ◽  
Swarm Optimization

This paper proposes a separation method, based on the model of Generalized Reference Curve Measurement and the algorithm of Particle Swarm Optimization (GRCM-PSO), for the High Performance Liquid Chromatography with Diode Array Detection (HPLC-DAD) data set. Firstly, initial parameters are generated to construct reference curves for the chromatogram peaks of the compounds based on its physical principle. Then, a General Reference Curve Measurement (GRCM) model is designed to transform these parameters to scalar values, which indicate the fitness for all parameters. Thirdly, rough solutions are found by searching individual target for every parameter, and reinitialization only around these rough solutions is executed. Then, the Particle Swarm Optimization (PSO) algorithm is adopted to obtain the optimal parameters by minimizing the fitness of these new parameters given by the GRCM model. Finally, spectra for the compounds are estimated based on the optimal parameters and the HPLC-DAD data set. Through simulations and experiments, following conclusions are drawn: (1) the GRCM-PSO method can separate the chromatogram peaks and spectra from the HPLC-DAD data set without knowing the number of the compounds in advance even when severe overlap and white noise exist; (2) the GRCM-PSO method is able to handle the real HPLC-DAD data set.

Double-Loop PID Parameter Tuning for DC Motor Based on Modified Particle Swarm Optimization

2011 ◽  
Vol 130-134 ◽  
pp. 3139-3142
Author(s):  
Tao Cheng ◽  
Wei Xing Lin
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
High Performance ◽  
Dynamic Performance ◽  
Particle Swarm ◽  
Parameter Tuning ◽  
Parameters Optimization ◽  
Double Loop ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization

This paper proposes a modified particle swarm optimization to solve identification of tuning PID controller parameters. This paper elaborates the process that MPSO algorithm optimizes PID parameters in double-loop speed control system modeled by simulink. Through analyzing the results of the MPSO optimization, and comparing with standard PSO(SPSO) and traditional method, MPSO algorithm has better dynamic performance, provides a high performance methods for PID parameters optimization.

scholarly journals Transient Stability Assessment by Coordinated Control of SVC and TCSC with Particle Swarm Optimization

2019 ◽  
Vol 9 (1) ◽  
pp. 2506-2510
Keyword(s):  
Particle Swarm Optimization ◽  
Transient Stability ◽  
Control Strategies ◽  
Particle Swarm ◽  
Coordinated Control ◽  
Pso Algorithm ◽  
Small Time ◽  
Local Power ◽  
Swarm Optimization ◽  
The Stability

This paper aims on improving the stability of a 9 bus power system under fault condition using coordination of FACTS device. Flexible A.C. transmission system (FACTS) can be regulated reliable with faster output and can improve local power grid status with control with appropriate control strategies in very small time period. Based on that, a particle swarm optimization (PSO) algorithm was executed, to design the coordinated parameter of static VAR compensator (SVC) and Thyristor Controlled Series Capacitor (TCSC). Simulation is performed on WSCC 9-bus system in MATLAB software. When 3 phase fault is applied near to generator, frequency and rotor angle changes accordingly. With coordinated control of FACTS devices with PSO has implemented both, near to its normal condition. PSO performed in this paper was structured on identifying the values of L and C of SVC and TCSC, for superior coordination.

scholarly journals Improve Linear Quadratic Regulator by Particle Swarm Optimization Algorithms for Two Wheeled Self Balancing Mobile robot

2017 ◽  
Vol 13 (2) ◽  
pp. 173-179
Author(s):  
Ekhlas Karam ◽  
Noor Mjeed
Keyword(s):  
Particle Swarm Optimization ◽  
Numerical Method ◽  
Control Method ◽  
Particle Swarm ◽  
Linear Quadratic Regulator ◽  
Linear Quadratic ◽  
Swarm Optimization ◽  
Balancing Robot ◽  
The Stability ◽  
Multivariable Feedback

The aim of this paper is to suggest a methodical smooth control method for improving the stability of two wheeled self-balancing robot under effect disturbance. To promote the stability of the robot, the design of linear quadratic regulator using particle swarm optimization (PSO) method and adaptive particle swarm optimization (APSO). The computation of optimal multivariable feedback control is traditionally by LQR approach by Riccati equation. Regrettably, the method as yet has a trial and error approach when selecting parameters, particularly tuning the Q and R elements of the weight matrices. Therefore, an intelligent numerical method to solve this problem is suggested by depending PSO and APSO algorithm. To appraise the effectiveness of the suggested method, The Simulation result displays that the numerical method makes the system stable and minimizes processing time.

Sign in / Sign up

Export Citation Format

Share Document