Search Results - (( using optimization model algorithm ) OR ( basic optimization matching algorithm ))

Block based motion vector estimation using fuhs16 uhds16 and uhds8 algorithms for video sequence by S. S. S. , Ranjit

“…In addition, the mentioned block-matching algorithms are the baseline techniques that have been used to further develop all the enhanced or improved algorithms. …”
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A fuzzy case-based reasoning model for software requirements specifications quality assessment by Mostafa S.A., Gunasekaran S.S., Khaleefah S.H., Mustapha A., Jubair M.A., Hassan M.H.

“…Additionally, for efficient cases retrieval in the CBR, relevant cases selection and nearest cases selection heuristic search algorithms are used in the system. Basically, the input to the relevant cases algorithm is the available cases in the system case base and the output is the relevant cases. …”

Gravitational energy harvesting system based on multistage braking technique for multilevel elevated car parking building by Al Kubaisi, Yasir Mahmood

“…Thirdly, modeling of electrical and mechanical parameters for the presented system such that the system performance matching the model, this process is performed by using a parameter optimization algorithm. …”
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Development Of Double Stage Filter (DSF) On Stereo Matching Algorithm For 3D Computer Vision Applications by Teo, Chee Huat

“…DSF algorithm is a hybrid stereo matching algorithm which divided into two phases. …”
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Hexagon pattern particle swarm optimization based block matching algorithm for motion estimation / Siti Eshah Che Osman by Che Osman, Siti Eshah

“…Recently, intelligent searching methods were proposed to enhance the computational optimization issues in motion estimation but still lack in obtaining the best solution of block matching. …”
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Stereo matching algorithm using census transform and segment tree for depth estimation by Hamzah, Rostam Affendi, Zainal Azali, Muhammad Nazmi, Mohd Noh, Zarina, Tengku Wook, Tg Mohd Faisal, Zainal Abidin, Izwan

“…This article proposes an algorithm for stereo matching corresponding process that will be used in many applications such as augmented reality, autonomous vehicle navigation and surface reconstruction. …”
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Comparison between Lamarckian Evolution and Baldwin Evolution of neural network by Taha, Imad, Inazy, Qabas

“…Genetic Algorithms are very efficient at exploring the entire search space; however, they are relatively poor at finding the precise local optimal solution in the region at which the algorithm converges. …”
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Enhanced Image View Synthesis Using Multistage Hybrid Median Filter For Stereo Images by Ali Hussein Aboali, Maged Yahya

“…Disparity depth map estimation of stereo matching algorithm is one of the most active research topics in computer vision.In the field of image processing,many existing stereo matching algorithms to obtain disparity depth map are developed and designed with low accuracy.To improve the accuracy of disparity depth map is quite challenging and difficult especially with uncontrolled dynamic environment.The accuracy is affected by many unwanted aspects including random noises,horizontal streaks,low texture,depth map non-edge preserving, occlusion,and depth discontinuities.Thus,this research proposed a new robust method of hybrid stereo matching algorithm with significant accuracy of computation.The thesis will present in detail the development,design, and analysis of performance on Multistage Hybrid Median Filter (MHMF).There are two main parts involved in our developed method which combined in two main stages.Stage 1 consists of the Sum of Absolute Differences (SAD) from Basic Block Matching (BBM) algorithm and the part of Scanline Optimization (SO) from Dynamic Programming (DP) algorithm.While,Stage 2 is the main core of our MHMF as a post-processing step which included segmentation,merging, and hybrid median filtering.The significant feature of the post-processing step is on its ability to handle efficiently the unwanted aspects obtained from the raw disparity depth map on the step of optimization.In order to remove and overcome the challenges unwanted aspects, the proposed MHMF has three stages of filtering process along with the developed approaches in Stage 2 of MHMF algorithm.There are two categories of evaluation performed on the obtained disparity depth map: subjective evaluation and objective evaluation.The objective evaluation involves the evaluation on Middlebury Stereo Vision system and evaluation using traditional methods such as Mean Square Errors (MSE),Peak to Signal Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM).Based on the results of the standard benchmarking datasets from Middlebury,the proposed algorithm is able to reduce errors of non-occluded and all errors respectively.While,the subjective evaluation is done for datasets captured from MV BLUE FOX camera using human's eyes perception.Based on the results,the proposed MHMF is able to obtain accurate results, specifically 69% and 71% of non-occluded and all errors for disparity depth map, and it outperformed some of the existing methods in the literature such as BBM and DP algorithms.…”
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Integrated optimal control and parameter estimation algorithms for discrete-time nonlinear stochastic dynamical systems by Kek, Sie Long

“…Instead of solving the original optimal control problem, the model-based optimal control problem is solved. …”
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Optimal neural network approach for estimating state of energy of lithium-ion battery using heuristic optimization techniques by Lipu M.S.H., Hussain A., Saad M.H.M., Hannan M.A.

Optimization of modified Bouc–Wen model for magnetorheological damper using modified cuckoo search algorithm by Rosmazi, Rosli, Zamri, Mohamed

“…This article presents a new modified cuckoo search algorithm with dynamic discovery probability and step-size factor for optimizing the Bouc–Wen Model in magnetorheological damper application. …”
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Application of conjugate gradient approach for nonlinear optimal control problem with model-reality differences by Leong, Wah June, Sie, Long Kek, Teo, Kok Lay, Sim, Sy Yi

“…In our approach, the linear quadratic optimal control model, which is adding the adjusted parameters into the model used, is employed. …”
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Optimization of multipurpose reservoir operation using evolutionary algorithms / Mohammed Heydari by Mohammed , Heydari

“…An improved particle swarm algorithm (HPSOGA) is used to solve complex problems of water resources optimization. …”
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Modeling and optimization of multi-holes drilling path using Particle Swarm Optimization by Ab Rashid, Mohd Fadzil Faisae, Nik Mohamed, Nik Mohd Zuki, Romlay, Fadhlur Rahman Mohd, Razali, Akhtar Razul, Asmizam, Mokhtar

“…Later the problem is optimized using Particle Swarm Optimization (PSO) and compared with other algorithms including the new metaheuristics algorithms. …”
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Application of conjugate gradient approach for nonlinear optimal control problem with model-reality difference by Sie, Long Kek, Wah, June Leong, Sy, Yi Sim, Kok, Lay Teo

“…In our approach, the linear quadratic optimal control model, which is adding the adjusted parameters into the model used, is employed. …”
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Nonlinear auto-regressive model structure selection using binary particle swarm optimization algorithm / Ahmad Ihsan Mohd Yassin by Mohd Yassin, Ahmad Ihsan

“…This thesis proposes the application of a stochastic optimization algorithm called Binary Particle Swarm Optimization algorithm for structure selection of polynomial NARX/NARMA/NARMAX models. …”
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Comparison between multi-objective and single-objective optimization for the modeling of dynamic systems by Mohd Zakimi, Zakaria, Hishamuddin, Jamaluddin, Robiah, Ahmad, Sayed Mohammad Reza, Loghmanian

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Optimization of assembly line balancing with energy efficiency by using tiki-taka algorithm by Ariff Nijay, Ramli

“…This research aimed to establish a computational model that represents the ALB-EE, propose a new Tiki-Taka Algorithm (TTA) to solve and optimize the ALB-EE and validate the developed model through a real-life case study. …”
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Predicting longitudinal dispersion coefficient using ensemble models and optimized multi-layer perceptron models by Gholami M., Ghanbari-Adivi E., Ehteram M., Singh V.P., Najah Ahmed A., Mosavi A., El-Shafie A.

“…The honey badger optimization algorithm (HBOA), salp swarm algorithm (SASA), firefly algorithm (FIFA), and particle swarm optimization algorithm (PASOA) are used to adjust the MULP parameters. …”

A Hybrid Adaptive Leadership GWO Optimization with Category Gradient Boosting on Decision Trees Algorithm for Credit Risk Control Classification by Suihai, Chen, Chih How, Bong, Po Chan, Chiu

“…Secondly, an improved CatBoost algorithm (EBGWO-CatBoost) was proposed, which was a combination of improved GWO algorithm (EBGWO) and CatBoost algorithm, and the optimized GWO algorithm was used to offset the defects of CatBoost algorithm in parameter tuning. …”
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