Search Results - (( motion optimization model algorithm ) OR ( simulation optimization sensor algorithm ))

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  1. 1

    A hybrid sampling-based path planning algorithm for mobile robot navigation in unknown environments by Khaksar, Weria

    Published 2013
    “…The simulation and comparison results indicate the superiority of the proposed algorithm. …”
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    Thesis
  2. 2

    PID-PSO DC motor position controller design for ankle rehabilitation system by Azizi, Muhammad Azizul Raziq

    Published 2023
    “…Next, the control algorithms are proposed to design and implement the Proportional-Integral-Derivative (PID) with Particle Swarm Optimization (PSO) controller technique for optimal Proportional (Kp), Integral (Ki) and Derivative (Kd) gains. …”
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    Thesis
  3. 3

    Development of optimized damage prediction method for health monitoring of ultra high performance fiber-reinforced concrete communication tower by Gatea, Sarah Jabbar

    Published 2018
    “…In addition, a simple procedure is proposed to determine the optimal solution and predict the correlation factor and the frequency of the damaged communication tower by using the particle swarm optimization (PSO) method. …”
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    Thesis
  4. 4

    Simulated Kalman Filter optimization algorithm for maximization of wireless sensor networks coverage by Nor Azlina, Ab. Aziz, Zuwairie, Ibrahim, Kamarulzaman, Ab Aziz, Nor Hidayati, Abdul Aziz

    Published 2019
    “…Simulated Kalman Filter (SKF) is a population based optimization algorithm inspired by the Kalman filtering method. …”
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    Conference or Workshop Item
  5. 5

    IWDSA: a hybrid Intelligent Water Drops with a Simulated Annealing for the localization improvement in wireless sensor networks by Gumaida, Bassam, Ibrahim, Adamu Abubakar

    Published 2024
    “…Additionally, simulation results confirm that the proposed algorithm IWDSA exhibits outstanding performance compared to other algorithms utilizing optimization techniques, including genetic algorithms, bat algorithms, ant colony optimization, and swarm optimization. …”
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    Article
  6. 6

    The impact of executive function and aerobic exercise recognition in obese children under deep learning by JING, XIN, ABDULLAH, BORHANNUDIN, ABU SAAD, HAZIZI, YANG, XIANGKUN

    Published 2025
    “…Initially, a motion recognition model based on STN and Lucas–Kanade optical flow algorithm optimization was constructed. …”
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    Article
  7. 7

    Multilevel optimization for dense motion estimation by Saaban, Azizan, Kalmoun, El Mostafa, Ibrahim, Haslinda, Ramli, Razamin, Omar, Zurni

    Published 2011
    “…We evaluated the performance of different optimization techniques developed in the context of optical flow computation with different variational models.In particular, based on truncated Newton methods (TN) that have been an effective approach for large-scale unconstrained optimization, we developed the use of efficient multilevel schemes for computing the optical flow.More precisely, we evaluated the performance of a standard unidirectional multilevel algorithm - called multiresolution optimization (MR/Opt), to a bidrectional multilevel algorithm - called full multigrid optimization (FMG/Opt).The FMG/Opt algorithm treats the coarse grid correction as an optimization search direction and eventually scales it using a line search. …”
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    Monograph
  8. 8

    Mobile robot path optimization algorithm using vector calculus and mapping of 2 dimensional space by Zahari, Ammar, Ismail , Amelia Ritahani, Desia, Recky

    Published 2015
    “…The simulated robot is equipped with a sonar sensor and several infrared sensors on its chassis. …”
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    Article
  9. 9

    Hybridization of enhanced ant colony system and Tabu search algorithm for packet routing in wireless sensor network by Husna, Jamal Abdul Nasir

    Published 2020
    “…Better performances were also achieved for success rate, throughput, and latency when compared to other hybrid routing algorithms such as Fish Swarm Ant Colony Optimization (FSACO), Cuckoo Search-based Clustering Algorithm (ICSCA), and BeeSensor-C. …”
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  10. 10

    Deterministic static sensor node placement in wireless sensor network based on territorial predator scent marking behavior by Abidin H.Z., Din N.M., Radzi N.A.M.

    Published 2023
    “…This paper proposes a sensor node placement algorithm that utilizes a new biologically inspired optimization algorithm that imitates the behaviour of territorial predators in marking their territories with their odours known as Territorial Predator Scent Marking Algorithm (TPSMA). …”
    Article
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    Cluster head selection optimization in wireless sensor network via genetic-based evolutionary algorithm by Vincent Chung, Hamzarul Alif Hamzah, Norah Tuah, Kit, Guan Lim, Min, Keng Tan, Kenneth Tze Kin Teo

    Published 2020
    “…Genetic-based evolutionary algorithms such as Genetic Algorithm (GA) and Differential Evolution (DE) have been popularly used to optimize cluster head selection in WSN to improve energy efficiency for the extension of network lifetime. …”
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    Article
  14. 14

    Node placement optimization using extended virtual force and cuckoo search algorithm in wireless sensor network by Puteri Azwa, Ahmad

    Published 2014
    “…This study proposed Extended Virtual Force and Cuckoo Search (EVFCS) algorithm with a combination of EVFA and CS algorithm to find an optimal node placement. …”
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    SLOW DRIFT MOTIONS IDENTIFICATION OF FLOATING STRUCTURES USING TIME-VARYING INPUT -OUTPUT MODELS by YAZID, EDWAR

    Published 2015
    “…This study presents the identification of slow drift motions of floating structures from model test data. …”
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    Wireless sensor network deployment performance based on FOA, PSO and TPSMA / Nurhidayah Kamal Akbar, Husna Zainol Abidin, and Ahmad Ihsan Mohd Yassin by Kamal Akbar, Nurhidayah, Zainol Abidin, Husna, Mohd Yassin, Ahmad Ihsan

    Published 2019
    “…This paper compares the random deployment with other two algorithms known as Fruit Fly Optimization (FOA) and Particles Swarm Optimization (PSO) Territorial Predator Scent Marking Algorithm (TPSMA) to solve the coverage hole problem. …”
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    Article
  20. 20

    An energy-efficient spectrum-aware reinforcement learning-based clustering algorithm for cognitive radio sensor networks by Mustapha, Ibrahim, Mohd Ali, Borhanuddin, A. Rasid, Mohd Fadlee, Sali, Aduwati, Mohamad, Hafizal

    Published 2015
    “…We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. …”
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