scholarly journals An Approach of Load Management and Cost Saving for Industrial Production Line Using Particle Swarm Optimization (Dept.E)

2020 ◽  
Vol 45 (4) ◽  
pp. 37-44
Author(s):  
Esraa Abd- Elsadek ◽  
Hamdy Abd El-Khalek Ashour ◽  
Ragi Hamdy ◽  
Mohamed Moustafa Sedky
Keyword(s):  
Particle Swarm Optimization ◽  
Cost Saving ◽  
Industrial Production ◽  
Production Line ◽  
Particle Swarm ◽  
Load Management ◽  
Swarm Optimization

scholarly journals Energy Cost Saving Control of Water Reuse Pumping System Using Particle Swarm Optimization

2015 ◽  
Vol 16 (1) ◽  
pp. 860-867 ◽  
Author(s):  
Chang-Jin Boo ◽  
Ho-Chan Kim ◽  
Min-Jae Kang
Keyword(s):  
Particle Swarm Optimization ◽  
Cost Saving ◽  
Energy Cost ◽  
Water Reuse ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Pumping System

scholarly journals An Improved Locally Weighted PLS Based on Particle Swarm Optimization for Industrial Soft Sensor Modeling

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4099 ◽  
Author(s):  
Ren ◽  
Song ◽  
Chu
Keyword(s):  
Particle Swarm Optimization ◽  
Industrial Production ◽  
Particle Swarm ◽  
Soft Sensor ◽  
Optimization Strategy ◽  
Weight Changes ◽  
Soft Sensors ◽  
Two Phase ◽  
Swarm Optimization ◽  
Sensor Modeling

In industrial production, soft sensors play very important roles in ensuring product quality and production safety. Traditionally, global modeling methods, which use historical data to construct models offline, are often used to develop soft sensors. However, because of various complex and unknown changes in industrial production processes, the performance of global models deteriorates over time, and frequent model maintenance is difficult. In this study, locally weighted partial least squares (LWPLS) is adopted as a just-in-time learning method for industrial soft sensor modeling. In LWPLS, the bandwidth parameter h has an important impact on the performance of the algorithm, since it decides the range of the neighborhood and affects how the weight changes. Therefore, we propose a two-phase bandwidth optimization strategy that combines particle swarm optimization (PSO) and LWPLS. A numerical simulation example and an industrial application case were studied to estimate the performance of the proposed PSO–LWPLS method. The results show that, compared to the traditional global methods and the LWPLS with a fixed bandwidth, the proposed PSO–LWPLS can achieve a better prediction performance. The results also prove that the proposed method has apparent advantages over other methods in the case of data density changes.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

Particle Swarm Optimization Algorithm for Machine Loading Problem in F.m.s

2011 ◽  
Vol 4 (8) ◽  
pp. 261-263
Author(s):  
A. Somaiah A. Somaiah ◽  
◽  
Dr. M. Indira Rani ◽  
Ch. Varun Kumar
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Machine Loading ◽  
Loading Problem

Adaptive Inertia Weight Particle Swarm Optimization For Linear and Nonlinear Channel Equalization

2012 ◽  
Vol 3 (4) ◽  
pp. 1-4
Author(s):  
Diana D.C Diana D.C ◽  
◽  
Joy Vasantha Rani.S.P Joy Vasantha Rani.S.P ◽  
Nithya.T.R Nithya.T.R ◽  
Srimukhee.B Srimukhee.B
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Channel Equalization ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Linear And Nonlinear
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