scholarly journals OPTIMIZED PCA BASED FACE RECOGNITION

2021 ◽  
pp. 21577-215715
Author(s):  
Rayaan Grewal
Keyword(s):  
Particle Swarm Optimization ◽  
Face Recognition ◽  
Fitness Function ◽  
Particle Swarm ◽  
Principal Component ◽  
Recognition System ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Comparison Of The Results ◽  
Human Faces

In this paper an algorithm to solve the problem of automatic face recognition is presented. The novelty of the algorithm is the ability to combine the principal component analysis (PCA) with Modified Particle Swarm Optimization (MPSO) to improve the execution time and to obtain better face recognition results. The efficiency of face recognition system is imp measure the similarity of an input face compared with a database of faces. The use of the fitness function helps to obtain more accurate results in a faster way. The results obtained are excellent even when the system w A comparison of the results obtained with the algorithm without MPSO versus the algorithm using MPSO is also presented. The algorithm is also implemented on the colored images of the human faces. Keywords: Particle Swarm Optimization, Face Recognition, Eigenfaces, Evolutionary Computer Vision.

scholarly journals Condition monitoring of Induction motors through Simulation of Bearing Fault and Air Gap Eccentricity Fault

2019 ◽  
Vol 8 (3) ◽  
pp. 176-193
Keyword(s):  
Particle Swarm Optimization ◽  
Induction Motors ◽  
Fitness Function ◽  
Particle Swarm ◽  
Air Gap ◽  
Percentage Error ◽  
Swarm Optimization ◽  
Bearing Fault ◽  
Modified Particle Swarm Optimization ◽  
Comparison Results

Induction motors have an important role in the industry on account of their advantages over other electrical motors. Consequently, there is a huge demand for their safe and sound operation. But it is not free from failures, which result in unnecessary downtimes and create great losses with regards to both revenue and maintenance. For that reason, early fault detection is considered necessary for the safety maintenance of the motor. In the present circumstances, the health monitoring of the induction motors are progressively increasing due to its potential to enhance operating costs, increase the reliability of function and so does the current paper emerge. Also, this paper deals with a novel effective technique for detecting the bearing fault and air gap eccentricity fault of the induction motor. Summarization and analysis of the findings are done based on percentage error and fitness function Value. Comparison results of bad bearing faults and air gap eccentricity are given separately in the paper. The findings of the study concluded that particle swarm optimization (PSO) can be considered as better optimization for bad bearing fault whereas modified particle swarm optimization is concluded as better optimization for air gap eccentricity fault.

scholarly journals Estimation of Fuzzy Parameters in the Linear Muskingum Model with the Aid of Particle Swarm Optimization

2021 ◽  
Vol 13 (13) ◽  
pp. 7152
Author(s):  
Mike Spiliotis ◽  
Alvaro Sordo-Ward ◽  
Luis Garrote
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Flood Routing ◽  
Performance Criteria ◽  
Extension Principle ◽  
Swarm Optimization ◽  
Muskingum Model ◽  
Muskingum Method ◽  
Fuzzy Parameters

The Muskingum method is one of the widely used methods for lumped flood routing in natural rivers. Calibration of its parameters remains an active challenge for the researchers. The task has been mostly addressed by using crisp numbers, but fuzzy seems a reasonable alternative to account for parameter uncertainty. In this work, a fuzzy Muskingum model is proposed where the assessment of the outflow as a fuzzy quantity is based on the crisp linear Muskingum method but with fuzzy parameters as inputs. This calculation can be achieved based on the extension principle of the fuzzy sets and logic. The critical point is the calibration of the proposed fuzzy extension of the Muskingum method. Due to complexity of the model, the particle swarm optimization (PSO) method is used to enable the use of a simulation process for each possible solution that composes the swarm. A weighted sum of several performance criteria is used as the fitness function of the PSO. The function accounts for the inclusive constraints (the property that the data must be included within the produced fuzzy band) and for the magnitude of the fuzzy band, since large uncertainty may render the model non-functional. Four case studies from the references are used to benchmark the proposed method, including smooth, double, and non-smooth data and a complex, real case study that shows the advantages of the approach. The use of fuzzy parameters is closer to the uncertain nature of the problem. The new methodology increases the reliability of the prediction. Furthermore, the produced fuzzy band can include, to a significant degree, the observed data and the output of the existent crisp methodologies even if they include more complex assumptions.

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