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Showing 1 - 15 results of 15 for search 'error reduction algorithm', query time: 0.05s Refine Results
  1. 1

    Erratum to: Efficient experimental design for uncertainty reduction in gene regulatory networks by Roozbeh Dehghannasiri (5725805)

    Published 2015
    “…</p><p dir="ltr">During the production of this article [1], errors occurred in equations and algorithms. The Editorial Department of BMC Bioinformatics would like to apologise and inform its readers that an updated version is now available on the BMC Bioinformatics website.…”
  2. 2

    Intelligent route to design efficient CO<sub>2</sub> reduction electrocatalysts using ANFIS optimized by GA and PSO by Majedeh Gheytanzadeh (17541927)

    Published 2022
    “…<p dir="ltr">Recently, electrochemical reduction of CO<sub>2</sub> into value-added fuels has been noticed as a promising process to decrease CO<sub>2</sub> emissions. …”
  3. 3

    High speed multi-stage code search algorithm in CELP by Elshafei, M.

    Published 1991
    “…The algorithm requires binary clustering of the codebook into a fixed number of cells and inverse filtering of the incoming speech by the format and pitch filters to produce a residual error sequence (RES). …”
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  4. 4

    Enhancement of SAR Speckle Denoising Using the Improved Iterative Filter by Yahia, Mohamed

    Published 2020
    “…The recent advancement in synthetic aperture radar (SAR) technology has enabled high-resolution imaging capability that calls for efficient speckle filtering algorithms to preprocess radar imagery. Since the introduction of the Lee sigma filter in 1980, the various versions of the minimum mean square error (MMSE) filter were developed, focusing essentially on how to estimate the processed pixels. …”
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  5. 5

    Novel Peak Detection Algorithms for Pileup Minimization in Gamma Ray Spectroscopy by Raad, M.W.

    Published 2006
    “…A number of parameter estimation and digital online peak localisation algorithms are being developed, including a pulse classification technique which uses a simple peak search routine based on the smoothed first derivative method, which gave a percentage error of peak amplitude of less than 1%. …”
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  6. 6

    A Hybrid Fault Detection and Diagnosis of Grid-Tied PV Systems: Enhanced Random Forest Classifier Using Data Reduction and Interval-Valued Representation by Khaled Dhibi (16891524)

    Published 2021
    “…The proposed approach deals with system uncertainties (current/voltage variability, noise, measurement errors, ⋯) by using an interval-valued data representation, and with large-scale systems by using a dataset size-reduction framework. …”
  7. 7

    Nonlinear Control of Brushless Dual-Fed Induction Generator With a Flywheel Energy Storage System for Improved System Performance by Mohammed Hamidat (3722086)

    Published 2025
    “…After optimization, the SMC settling time was significantly reduced from 0.7 seconds to 19.97 milliseconds, achieving a 96.9% improvement in response speed, while its steady-state error decreased from 0.48 to 0.06, marking an 87.5% reduction in tracking error. …”
  8. 8

    Mixed precision iterative refinement with adaptive precision sparse approximate inverse preconditioning by Noaman Khan (19810050)

    Published 2025
    “…In this work, we develop an adaptive precision sparse approximate inverse preconditioner and demonstrate its use within a five-precision GMRES-based iterative refinement method. We call this algorithm variant BSPAI-GMRES-IR. We then analyze the conditions for the convergence of BSPAI-GMRES-IR, and determine settings under which BSPAI-GMRES-IR will produce similar backward and forward errors as the existing SPAI-GMRES-IR method, the latter of which does not use adaptive precision in preconditioning. …”
  9. 9

    An Optimal Approach for Assessing Weibull Parameters and Wind Power Potential for Six Coastal Cities in Pakistan by Ghulam Abbas (764241)

    Published 2024
    “…An enormous reduction in wind power density-based percentage error (for example, 15.3531% than 51.7205% for Gwadar at 10 m height) was observed in NEPFM-SSA compared to NEPFM. …”
  10. 10

    General Inspection Plan For Critical Multicharacteristic Components by Duffuaa, S. O.

    Published 2020
    “…The expected total cost consists of the cost of false acceptance (cost of type II error), cost of false rejection (cost of type I error), and the cost of inspection. …”
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  11. 11

    Multi-Model Investigation and Adaptive Estimation of the Acoustic Release of a Model Drug From Liposomes by Wadi, Ali

    Published 2019
    “…In comparison with the KF, the AKF approach exhibited as low as a 69% reduction in the level of error in estimating the drug release state. …”
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  13. 13

    Multi-Modal Emotion Aware System Based on Fusion of Speech and Brain Information by M. Ghoniem, Rania

    Published 2019
    “…For classifying unimodal data of either speech or EEG, a hybrid fuzzy c-means-genetic algorithm-neural network model is proposed, where its fitness function finds the optimal fuzzy cluster number reducing the classification error. …”
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  14. 14

    MLMRS-Net: Electroencephalography (EEG) motion artifacts removal using a multi-layer multi-resolution spatially pooled 1D signal reconstruction network by Sakib Mahmud (15302404)

    Published 2022
    “…The performance of the deep learning models is measured using three well-known performance matrices viz. mean absolute error (MAE)-based construction error, the difference in the signal-to-noise ratio (ΔSNR), and percentage reduction in motion artifacts (<i>η</i>). …”
  15. 15

    Integrated Energy Optimization and Stability Control Using Deep Reinforcement Learning for an All-Wheel-Drive Electric Vehicle by Reza Jafari (3494018)

    Published 2025
    “…In addition to the deployment without requiring an explicit model of the plant, the simulation results demonstrate that the proposed solution modifies vehicle dynamics and maneuverability in most cases compared to the model-based conventional controller. Furthermore, the reduction in sideslip angle, excellent traction through minimizing tire slip ratio, avoiding oversteering and understeering, and maintaining an acceptable range of energy optimization are demonstrated for DRL controllers, especially for the TD3 and CL TD3 algorithms.…”