Search Results - (( sequence optimization sensor algorithm ) OR ( evolution identification learning algorithm ))
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Voltage stability margin identification using evolution programming learning algorithm / Zamzuhairi Darus
Published 2003“…This project proposed on an investigation on the voltage stability margin identification using evolution programming learning algorithm. …”
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An energy efficient reinforcement learning based cooperative channel sensing for cognitive radio sensor networks
Published 2017“…Simulation results show convergence and adaptability of the algorithm to dynamic environment in achieving optimal solutions. …”
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A reinforcement learning-based energy-efficient spectrum-aware clustering algorithm for cognitive radio wireless sensor network
Published 2016“…Simulation results show convergence, learning and adaptability of the RL based algorithms to dynamic environment toward achieving the optimal solutions. …”
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A review on object detection algorithms based deep learning methods / Wan Xing ... [et al.]
Published 2023“…Deep learning-based object detection algorithms can be categorized into three main types: end-to-end algorithms, two-stage algorithms, and one-stage algorithms. …”
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Development of self-learning algorithm for autonomous system utilizing reinforcement learning and unsupervised weightless neural network / Yusman Yusof
Published 2019“…In the simulation the robot is equipped with thirteen distance sensing sensors. From the simulation result, by using these sensors information the AUTOWiSARD algorithm can successfully differentiate and classify states without supervision, while the Q-learning algorithm is able to produce and optimized states-actions policy. …”
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Supervised deep learning algorithms for process fault detection and diagnosis under different temporal subsequence length of process data
Published 2025“…Current FDD technologies mostly rely on data-driven solutions by making full use of abundant process data collected by the state-of-the-art distributed process instruments and sensors. Deep learning algorithms were widely used among all the data-driven algorithms. …”
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Privacy optimization and intrusion detection in modbus/tcp network-based scada in water distribution systems
Published 2021“…Another problematic aspect is related to the intrusion detection solutions that are based on machine learning cluster algorithms to learn systems’ specifications and extract general state-based rules for attacks identification. …”
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Simulated Kalman Filter with modified measurement, substitution mutation and hamming distance calculation for solving traveling salesman problem
Published 2022“…There were also attempts to hybridize SKF with other famous algorithms such as Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Sine Cosine Algorithm (SCA) to improve its performance. …”
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Enhancing Wearable-Based Human Activity Recognition with Binary Nature-Inspired Optimization Algorithms for Feature Selection
Published 2026“…In the experiment, we conducted an evaluation of the effectiveness and efficiency of four nature-inspired binary algorithms for optimization namely Binary Particle Swarm Optimization (BPSO), Binary Grey Wolf Optimization algorithm (BGWO), Binary Differential Evolution algorithm (BDE), and Binary Salp Swarm algorithm (BSS) - in the context of human activity recognition (HAR). …”
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Global Algorithms for Nonlinear Discrete Optimization and Discrete-Valued Optimal Control Problems
Published 2009“…We then consider the task of determining near globally optimal solutions of discrete-valued optimal control problems. …”
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An Evolutionary Stream Clustering Technique for Outlier Detection
Published 2020“…Later, this algorithm will be extended to optimize the model in detecting outlier on data streams. …”
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Direct Adaptive Predictive Control For Wastewater Treatment Plant
Published 2012“…This N4SID plays the role of the software sensor for on-line estimation of prediction matrices and control matrices of the bioprocess, joint together with model predictive control (MPC) in order to obtain the optimal control sequence. …”
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Clarity-optimized wavelet with autoencoder-ReliefF ranking for enhanced UHF PD signal feature extraction
Published 2025“…This article investigates advanced signal processing methodologies, with a focus on wavelet-based techniques, for the analysis of time-domain partial discharge (PD) signals captured using ultra-high frequency (UHF) sensors. The raw signals are systematically processed through a sequence of operations including bandpass filtering, wavelet-based denoising, DC offset removal, and pulse extraction. …”
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