Design and experimental investigation on VL-MLI intended for half height (H-H) method to improve power quality using modified particle swarm optimization (MPSO) algorithm

Ramaraju, Satish Kumar; Kaliannan, Thenmalar; Joseph, Sheela Androse; Kumaravel, Umadevi; Albert, Johny Renoald; Natarajan, Arun Vignesh; Chellakutty, Gokul Prasad

doi:10.3233/jifs-212583

Cited by 66 publications

(5 citation statements)

References 29 publications

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“…In dealing with the reoptimiza-tion problem of the metric maximum path, they proposed the optimization algorithm of the insertion point at the 4/5 approximation of the distance matrix to optimize the path planning problem [8]. Ramaraju et al proposed a hybrid particle swarm optimization algorithm to embed the championship selection method in evolutionary computing into PSO [9].…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Improved Particle Swarm Optimization Algorithm in Logistics Energy-Saving Picking Information Network

2022

Wireless Communications and Mobile Computing

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In order to solve the logistics optimization problem, an application method of the improved particle swarm optimization algorithm in logistics energy-saving pickup information network is proposed. Firstly, a mathematical model of logistics cycle picking information scheduling optimization is established, logistics and picking paths are encoded as particles, and the optimal logistics cycle picking optimization scheme is found through the cooperation between particles. Secondly, the deficiencies of the particle swarm optimization algorithm are improved accordingly. In order to test the performance of the IPSO algorithm in solving the logistics circulation picking problem, in the simulation environment of P42 core, 2.6 GHz CPU, 4 GB memory, and Windows XP, the simulation experiment was carried out using VC++6.0 programming operating system. The particle number of the IPSO algorithm is 20, ω max = 5 , ω max = 1 . The experimental results show that the improved particle swarm optimization algorithm can effectively bypass the premature convergence of the traditional particle swarm optimization algorithm and ensure that the optimal solution is searched in the global scope, and the optimal probabilistic solution is obtained, which is better than other scheduling algorithms, with more obvious advantages.

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Section: Literature Reviewmentioning

confidence: 99%

Application of Improved Particle Swarm Optimization Algorithm in Logistics Energy-Saving Picking Information Network

2022

Wireless Communications and Mobile Computing

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“…The citations [30][31][32][33][34][35] are validating the vision sensor and the algorithm used in the proposed vision system. The citations [36][37][38][39][40] are validating the results and the percentage of error.…”

Section: Resultsmentioning

confidence: 99%

Up-Sampled Cross-Correlation Based Object Tracking & Vibration Measurement in Agriculture Tractor System

Ganesan¹,

Sankaranarayanan²,

Kumar³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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This research introduces a challenge in integrating and cleaning the data, which is a crucial task in object matching. While the object is detected and then measured, the vibration at different light intensities may influence the durability and reliability of mechanical systems or structures and cause problems such as damage, abnormal stopping, and disaster. Recent research failed to improve the accuracy rate and the computation time in tracking an object and in the vibration measurement. To solve all these problems, this proposed research simplifies the scaling factor determination by assigning a known real-world dimension to a predetermined portion of the image. A novel white color sticker of the known dimensions marked with a color dot is pasted on the surface of an object for the best result in the template matching using the Improved Up-Sampled Cross-Correlation (UCC) algorithm. The vibration measurement is calculated using the Finite-Difference Algorithm (FDA), a machine vision system fitted with a macro lens sensor that is capable of capturing the image at a closer range, which does not affect the quality of displacement measurement from the video frames. The field test was conducted on the TAFE (Tractors and Farm Equipment Limited) tractor parts, and the percentage of error was recorded between 30% and 50% at very low vibration values close to zero, whereas it was recorded between 5% and 10% error in most high-accelerations, the essential range for vibration analysis. Finally, the suggested system is more suitable for measuring the vibration of stationary machinery having low frequency ranges. The use of a macro lens enables to capture of image frames at very close-ups. A 30% to 50% error percentage has been reported when the vibration amplitude is very small. Therefore, this study is not suitable for Nano vibration analysis.

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“…Figure 2 is the flow chart of the particle swarm optimization algorithm. It can be used to solve some data that is not easy to obtain the complete or partial decomposition property of the solution but can not be expressed clearly, the number of variables is too large or it is difficult to describe with numerical calculation, and it has good operability and other characteristics [17][18]. The failure of engine gas path components can be diagnosed by quantitative performance parameter estimation.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Fusion Particle Swarm Optimization Algorithm in Automobile Engine Fault Diagnosis

2023

KME

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The automobile engine is a high-speed vehicle. Its working principle and process are to generate gas through fuel burning, and then convert the chemical energy contained in the exhaust gas into heat energy. However, in reality, due to various reasons, serious wear and tear inside the engine and sharp reduction of mechanical strength have occurred. This paper mainly introduces a method based on particle swarm optimization algorithm to simplify the complex structure between various systems in the car and outside. The analysis shows that this method has the advantages of accuracy and reliability, and can effectively diagnose and predict it. The advantage of large workload and small space is very prominent. After that, this paper designs a fault diagnosis model of automobile engine based on particle swarm optimization algorithm, and tests the model. The test results show that the fault diagnosis time of this model is short, and the fault diagnosis accuracy is high, which can meet the user needs.

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Design and experimental investigation on VL-MLI intended for half height (H-H) method to improve power quality using modified particle swarm optimization (MPSO) algorithm

Cited by 66 publications

References 29 publications

Application of Improved Particle Swarm Optimization Algorithm in Logistics Energy-Saving Picking Information Network

Application of Improved Particle Swarm Optimization Algorithm in Logistics Energy-Saving Picking Information Network

Up-Sampled Cross-Correlation Based Object Tracking & Vibration Measurement in Agriculture Tractor System

Fusion Particle Swarm Optimization Algorithm in Automobile Engine Fault Diagnosis

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