Showing 21 - 40 results of 504 for search '(((( data modeling algorithm ) OR ( based tracking algorithm ))) OR ( element method algorithm ))*', query time: 0.13s Refine Results
  1. 21

    Physical optimization algorithms for mapping data to distributed-memory multiprocessors by Mansour, Nashat

    Published 1992
    “…We present three parallel physical optimization algorithms for mapping data to distributed-memory multiprocessors, concentrating on irregular loosely synchronous problems. …”
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    masterThesis
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    Efficient Dynamic Cost Scheduling Algorithm for Financial Data Supply Chain by Al Sadawi, Alia

    Published 2021
    “…An iterative dynamic scheduling algorithm (DCSDBP) was developed to address the data batching process. …”
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    article
  5. 25

    DG-Means – A Superior Greedy Algorithm for Clustering Distributed Data by Assaf, Ali

    Published 2022
    “…In this work, we present DG-means, which is a greedy algorithm that performs on distributed sets of data. …”
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    masterThesis
  6. 26

    Resources Allocation for Drones Tracking Utilizing Agent-Based Proximity Policy Optimization by De Rochechouart, Maxence

    Published 2023
    “…This paper presents a reinforcement learning agent-based model that works by incorporating the MESA environment with the Stone Soup radar systems simulator. …”
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  7. 27

    A Novel Internal Model Control Scheme for Adaptive Tracking of Nonlinear Dynamic Plants by Khan, T.

    Published 2006
    “…The U-model utilizes only past data for plant modelling and standard root solving algorithm for control law formulation. …”
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    article
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    Newton-Raphson based adaptive inverse control scheme for tracking of nonlinear dynamic plants by Shafiq, M.

    Published 2006
    “…The U-model is utilized to design an adaptive inverse controller by using a simple root-solving algorithm of Newton-Raphson. The synergy of U-model with AIC structure has provided an effective and straight forward method for adaptive tracking of nonlinear plants. …”
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    article
  10. 30

    Optimum Track to Track Fusion Using CMA-ES and LSTM Techniques by Fares, Samar

    Published 2024
    “…The first method uses an offline technique based on a global optimizer called the CMA-ES algorithm and the second one uses LSTM in its different forms to learn the online adjustment of the fusion weights between the two tracks. …”
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  11. 31

    Optimum sensors allocation for drones multi-target tracking under complex environment using improved prairie dog optimization by Abu Zitar, Raed

    Published 2024
    “…This paper presents a novel hybrid optimization method to solve the resource allocation problem for multi-target multi-sensor tracking of drones. This hybrid approach, the Improved Prairie Dog Optimization Algorithm (IPDOA) with the Genetic Algorithm (GA), utilizes the strengths of both algorithms to improve the overall optimization performance. …”
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    Efficiency Optimization of a DSP-Based Standalone PV System Using Fuzzy Logic and Dual-MPPT Control by Al Nabulsi, Ahmad

    Published 2012
    “…The second MPPT algorithm controls the power converter between the PV panel and the load and implements a new fuzzy-logic (FLC)-based perturb and observe (P&O) scheme to keep the system power operating point at its maximum. …”
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    article
  14. 34

    Learn while Tracking by Saab, Samer S.

    Published 2007
    “…A simple non-iterative digital controller is proposed for the uniform output tracking problem of multi-input multi-output discrete-time varying linear systems with arbitrary relative degree. …”
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    conferenceObject
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    Machine Learning-Driven Prediction of Corrosion Inhibitor Efficiency: Emerging Algorithms, Challenges, and Future Outlooks by Najam Us Sahar Riyaz (22927843)

    Published 2025
    “…At the same time, virtual sample augmentation and genetic algorithm feature selection elevate sparse data performance, raising k-nearest neighbor models from R<sup>2</sup> = 0.05 to 0.99 in a representative thiophene set. …”
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    Using genetic algorithms to optimize software quality estimation models by Azar, Danielle

    Published 2004
    “…In the first approach, we assume the existence of several models, and we use a genetic algorithm to combine them, and adapt them to a given data set. …”
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    masterThesis