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Optimized Gaussian Pulse Design for UWB System Using Particle Swarm Optimization Based-on Generalized Bessel Polynomials
(2022-01-01)
Chutchavong, Vanvisa
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Anuwongpinit, Thanavit
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Purahong, Boonchana
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Archevapanich, Tuanjai
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Janchitrapongvej, Kanok
The ultrawideband system operates a very short pulse with enormous bandwidth to provide high data rates for data transmission. To design the UWB pulse, considering the pulse shape is very necessary, and a spectral emission mask of the designed pulse should meet the FCC spectral mask requirement between frequency range 3.1 GHz to 10.6 GHz. The traditional UWB pulse design is based on the Gaussian derivative. However, the frequency spectrum is not satisfied the FCC spectral mask requirement. In this study, the Gaussian pulse can be designed from the mathematical characteristic of the generalized Bessel polynomial. The spectral efficiency of the proposed pulse can be improved by the combination of the derivative of Gaussian pulse with a weight coefficient optimization with particle swarm optimization (PSO). PSO is a population-based optimization algorithm inspired by animal behavior. PSO is applied with generalized Bessel polynomial transfer function to gain the best weight coefficient, we proposed to optimize its weight vector to design a pulse that exceeds to FCC spectral mask. The results were found in MATLAB software show that generalized Bessel polynomials can approximate the proposed pulse with combination method and PSO. The spectral efficiency is improved to 89.30% and the spectrum is greater close to the FCC spectral mask requirement. To confirm an improved spectral efficiency compared to the previous works.
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Remote sensing to minimize energy consumption of six-axis robot arm using particle swarm optimization and artificial neural network to control changes in real time
(2020-01-01)
Kaitwanidvilai, Somyot
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Chanarungruengkij, Veerasak
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Konghuayrob, Poom
We propose a new method for the analysis and design of a robotic system that minimizes the energy consumption of a six-axis robot arm by controlling the velocity and acceleration of each arm of the robot to achieve the specified trajectory of the robot determined from a lean manufacturing method. A dynamic model of the PUMA 560 robot has been simulated on MATLAB, while the Robotics Toolbox and particle swarm optimization (PSO) are utilized to search for optimal paths and the optimal velocity and acceleration of the robot arms. The optimal velocity and acceleration are described as those giving minimum overall energy consumption constrained by a specified cycle time of the entire robotic system. Typically, the picking and placing of materials are carried out by humans, causing a variation in production rate, whereas our system using a robot arm ensures a stable production rate. Moreover, the optimal results obtained from PSO are adopted to train an artificial neural network (ANN) to extend the design system from discrete optimal values to a continuous and near-optimal value. In other words, the ANN is used to obtain an approximate optimal value between those obtained from PSO to make the system applicable to a real-world system. As shown by the simulation results, this method reduces the energy consumption of 12.3% from the initial energy and reduces the time for optimization by 99.8% compared with that for the PSO technique.
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Improved Rerun Particle Swarm Optimization Algorithm with Harmony Search
(2019-04-10)
Phuchan, Wikrom
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Kruatrachue, Boontee
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Siriboon, Kritawan
One of the most crucial problem of the particle swarm optimization is that it can easily trap in local optima. There are some studies tried to resolve the drawback using the reset and mutation mechanism. Although it unraveled the problem well when the optimal position of benchmark function is at zeros in all dimensions. It cannot locate optima when the exact answer randomly shifted over the possible region. This study integrates Harmony Search (HS) to the rerun and reset mechanism to PSO. It performs well with selected benchmark functions, when the optimal position is randomly shifted in most of the test functions. Since it does not favor optimal point at zeroes when the optimal position is not shifted, the number of calls of the evaluation function is more than the other algorithms in some cases.
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Music genre classification of audio signals using particle swarm optimization and stacking ensemble
(2019-03-01)
Leartpantulak, Krittika
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Kitjaidure, Yuttana
Genre classification is a process of grouping similarities, such as patterns, styles, or objectives with management data as already in the music (e.g. pop and rock). It is used along with the classification of topics. This paper will classify songs from audio signal to a hierarchy of musical genre by using feature extraction. Trimbral texture, rhythmic content and pitch content are used as the main feature sets. Feature selection is selected by using Particle Swarm Optimization (PSO) and sent selected feature to classification. The result in classification has low accuracy. Thus, using stacking ensemble method is to improve the prediction. In this paper, the purpose is to improve the prediction by using stacking ensemble method. Stacking ensemble that have the second level is base classifier and meta-classifier. In base classifier consists of 5 classification; K-Nearest Neighbors (k-NN), Decision Tree (DT), Random Forest, Support Vector Machines (SVM); and Naïve Bayes. This process is generated to build multiple classifier predictors and sent it to meta-classifier. In the process of meta-classifier will create new model to predict test data. The new model has been created from neural network which train data is the output of base classifier.
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Hybrid adaptive notch filter and fixed-structure pid H ∞ robust loop shaping control based pso for hard disk drive servo actuator
(2019-02-01)
Konghuayrob, Poom
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Kaitwanidvilai, Somyot
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Aoyama, Hisayuki
In order to achieve the high precision head-positioning of the voice coil motor (VCM) actuator with narrow track pitch, the adaptive notch filter based limited-search-range of particle swarm optimization (PSO), as well as the fixed-structure propor-tional-integral-derivative H <inf>∞</inf> robust loop shaping controller using the concept of four closed loop disturbance norms is proposed. Generally the conventional method, fixed-frequency notch filter (FFNF), is combined with the nominal plant to reduce the effect of the mechanical vibration resonance; however, the resonance mode of servo system can be shifted with various factors such as the ambient temperature change, and the unbalanced disk. In addition, mathematical solving in the H <inf>∞</inf> robust control problems and the suitable notch filter design are very complex and the final results of the conventional controller with notch filters are normally complicated structure and high order which is difficult to implement. Thus, the adding of intelligent system in the proposed design with the careful range of the search space is utilized to suppress the vibration caused by resonance mode shifting and also reduce the order of the final robust controller. Simulation results of six scenarios test demonstrate the effectiveness of the proposed design compared with FFNF in the commercial product.
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Improving Multi-Swarm by Slightly Mutation Particle and GBEST of Stuck Swarm Along with Randomly Selecting GBEST of Other Swarm
(2018-08-21)
Chengkhuntod, Kanokporn
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Kruatrachue, Boontee
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Siriboon, Kritawan
This paper proposed another approach in handling trapping in local optimum problem of Particle Swarm Optimization (PSO) using multi-swarm. Since each swarm might trap in different local optimum, the trapped swarm restart with slightly mutation (15% of each particle attributes) along with swaying swarm by randomly use of other swarm GBEST position. In the case of all swarm trapping in the same location, the trap GBEST is also slightly mutate in the same way as particle position. This proposed technique is tested on a set of twenty-four benchmark test functions. The experimental results show that the proposed method is better than other comparing methods.
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Combine multi particle swarm in supporting trapping in local optima
(2018-08-13)
Poempool, Lukkana
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Kruatrachue, Boontee
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Siriboon, Kritawan
This paper proposed using multi swarm to lessen trapping in local optima problem of Particle Swarm Optimization (PSO). The use of multiple swarms can increase wider global search at the cost of decrease narrower local search. Hence, the use of multiple swarms alone can't solve the trapping problem. This paper extends the use of multi swarm by merging all the swarm into single group to enhanced local search when needed. This can increase the local search ability of multi swarm and increase the chance of the trapped swarm to move to other nearby local optima. The proposed method is compared to similarly comparable modified PSO and other similarly multi particle swarm without merging using 26 benchmark functions.
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Hybrid multi-swarm with Harmony Search algorithm
(2017-11-03)
Phuchan, Wikrom
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Kruatrachue, Boontee
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Siriboon, Kritawan
This paper proposed a new metaheuristic algorithm, Hybrid Multi-swarm with Harmony Search algorithm which combines two famous metaheuristics, particle swarm optimization (PSO) and Harmony Search algorithm (HS). The main advantage of PSO is its convergence speed while its main drawback is trapping in local optimum problem. To improve PSO performance, this research use HS to increase PSO diversity and extend its convergence point to a better local optimum. The proposed algorithm was compared with three related algorithms on the optimization benchmark functions. The experiment results show the proposed algorithm yields better fitness value solution.
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Improved particle swarm optimization algorithm using average model on MPPT for partial shading in PV array
(2014-10-15)
Jumpasri, Nattawat
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Pinsuntia, Kittapas
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Woranetsuttikul, Kaweepoj
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Nilsakorn, Taywin
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Khan-Ngern, Werachet
This paper presents the maximum power point tracking (MPPT) technique to improve the system operation during shading in photo voltaic array. This technique was based on the swarm optimization algorithm using average model. The algorithm has the simple topology and accuracy calculation. The PV system composed of the PV array, the synchronous buck-boost converter which can control the output voltage with the same polarity of both source and load. The PV array operating simulation was done by the PowerSim program where the MATLAB program simulates the particle swarm optimization with average model in various partial shading. Finally, the experimental results using average model show a fast response with good agreement of that simulation.
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Comparison of distributed and Centralized control for partial shading in PV parallel based on Particle Swarm Optimization Algorithm
(2014-10-15)
Jumpasri, Nattawat
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Pinsuntia, Kittapas
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Woranetsuttikul, Kaweepoj
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Nilsakorn, Taywin
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Khan-Ngern, Werachet
This paper presents the comparison of Distributed Control and Centralized Control for partial shading in PV parallel based on the Particle Swarm Optimization Algorithm. The distributed control is the connection of PV 1 panel with 1 MPPT and the centralized control is the connection of many PV panels with 1 MPPT. Both methods have difference advantage, efficiency, cost and maintenance. The simulation of both methods made in Powersim and Matlab Simulink. The analysis and conclusion were included.

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