Search Results - (( parallel optimization sensor algorithm ) OR ( _ distribution function algorithm ))
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1
Estimation of core size distribution of magnetic nanoparticles using high-Tc SQUID magnetometer and particle swarm optimizer-based inversion technique
Published 2023“…The combination of the high-Tc SQUID magnetometer and the PSO-based reconstruction technique offers a powerful approach for characterizing the MNP core size distribution, and further improvements can be expected from the recent state-of-the-art optimization algorithm to optimize further the computation time and the best objective function value.…”
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2
Simulated kalman filter (SKF) based image template matching for distance measurement by using stereo vision system
Published 2018“…Stereo vision sensor consists of two stereo cameras, mounted parallel in stationary position. …”
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3
Single-objective and multi-objective optimization algorithms based on sperm fertilization procedure / Hisham Ahmad Theeb Shehadeh
Published 2018“…The obtained results are compared with the results of four algorithms. These algorithms are Genetic Algorithms (GA), Parallel Genetic Algorithm (PGA), Particle Swarm Optimization (PSO) and Accelerated Particle Swarm Optimization (APSO). …”
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4
Performance monitoring algorithm for optimizing electrical power generated by using photovoltaic system
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An empirical study of density and distribution functions for ant swarm optimized rough reducts
Published 2011“…The density and distribution functions are two common types of Gaussian distribution representation. …”
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Embedded parallel distributed artificial intelligent processors for adaptive beam forming in WCDMA system
Published 2023Subjects:Conference paper -
7
Firefly analytical hierarchy algorithm for optimal allocation and sizing of distributed generation in radial distribution network
Published 2022“…The simulations of single and multi-objective function optimisations have been carried out using MATLAB for meta-heuristic techniques on the IEEE 33-bus and 69-bus radial distribution networks. …”
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8
Levy tunicate swarm algorithm for solving numerical and real-world optimization problems
Published 2022“…The proposed Levy Tunicate Swarm Algorithm (LTSA) is a novel metaheuristic algorithm that integrates the Levy distribution into a new metaheuristic algorithm called Tunicate Swarm Algorithm (TSA) to solve numerical and real-world optimization problems. …”
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9
A review of the optimal allocation of distributed generation: Objectives, constraints, methods, and algorithms
Published 2023“…Circuit theory; Energy efficiency; Optimization; Dg siting; Dg sizings; Distributed generators; Distributed resources; Objective functions; Optimal allocation; Optimal DG allocation; Optimization algorithms; Distributed power generation…”
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Online teleoperation of writing manipulator through graphics processing unit based accelerated stereo vision
Published 2021“…These algorithms are then parallelized using Compute Unified Device Architecture CUDA C language to run on Graphics Processing Unit GPU for hardware acceleration. …”
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11
Levy slime mould algorithm for solving numerical and engineering optimization problems
Published 2022“…The proposed Levy Slime Mould Algorithm (LSMA) is a novel metaheuristic algorithm that integrates the Levy distribution into a new metaheuristic called Slime Mould Algorithm (SMA) for solving numerical and engineering problems. …”
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12
Distributed generation allocation to reduce losses and improve voltage profile genetic algorithm: article
Published 2010“…Genetic Algorithm (GA) was used as the solving tool, which referring two determined aim the problem is defined and objective function is introduced. …”
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13
Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets
Published 2019“…Thereafter, a multi-objective hybrid algorithm (MOHA), an extension of the self-adaptive hybrid algorithm is proposed and tested on the established multi-objective (MO) test functions. …”
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14
Offline study on loss optimization in distribution networks using Artificial Bee Colony (ABC) algorithm: article / Deezex Noor Ainizaa Abdullah
Published 2009“…This paper present offline study on loss optimization in distribution networks using Artificial Bee Colony (ABC) algorithm. …”
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Offline study on loss optimization in distribution networks using Artificial Bee Colony (ABC) algorithm / Deezex Noor Ainizaa Abdullah
Published 2009“…This paper present offline study on loss optimization in distribution networks using Artificial Bee Colony (ABC) algorithm. …”
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16
Slice sampler algorithm for generalized pareto distribution
Published 2018“…In this paper, we developed the slice sampler algorithm for the generalized Pareto distribution (GPD) model. …”
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17
Optimal location and size estimation of distributed generators by employing grouping particle swarm optimization and grouping genetic algorithm
Published 2017“…Therefore, there are various algorithms that could be applied in order to integrate distribution generation units into the distribution network. …”
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18
Reliability Analysis and Prediction of Time to Failure Distribution of an Automobile Crankshaft
Published 2015“…The developed stochastic algorithm has the capability to measure the parametric distribution function and validate the predict the reliability rate, mean time to failure and hazard rate. …”
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Optimal network reconfiguration and intelligent service restoration prediction technique based on Cuckoo search spring algorithm / Mohamad Izwan Zainal
Published 2022“…In addition, objective function using the same CSSA algorithm were applied i.e., Vmin and Ploss as the objective function, and multi-objective involves Vmin and Ploss as the objective function. …”
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20
A Comparative Performance Analysis of Gaussian Distribution Functions in Ant Swarm Optimized Rough Reducts
Published 2011“…Coexistence, cooperation, and individual contribution to food searching by a particle (ant) as a swarm (ant) survival behavior, depict the common characteristics of both algorithms. Solution vector of ACO is presented by implementing density and distribution function to search for a better solution and to specify a probability functions for every particle (ant). …”
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