Search Results - (( parameter estimation path algorithm ) OR ( variable designing learning algorithm ))
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All-pass filtered x least mean square algorithm for narrowband active noise control
Published 2018“…Most available ANC uses the secondary path modelling including filtered x least mean square (FxLMS) algorithm. …”
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Real-Time Optimal Trajectory Correction (ROTC) for autonomous quadrotor / Noorfadzli Abdul Razak
Published 2018“…The algorithm works if the quadrotor undergoes a deviation, an admissible trajectory path is generated for the quadrotor rapidly return on the straight-line route. …”
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Estimation of Transformers Health Index Based on Condition Parameter Factor and Hidden Markov Model
Published 2023Conference Paper -
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Development of robust control scheme for wheeled mobile robot in restricted environment
Published 2021“…A novel algorithm so called laser simulator logic (LSL) has been develo ped to estimate the inertia moment when the environment is noisy and cannot use fuzzy logic algorithm. …”
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Estimation of transformers health index based on condition parameter factor and hidden Markov model
Published 2018“…Subsequently, the future states probability distribution was computed based on the HMM prediction model and viterbi algorithm was applied to find the best optimal path sequence of HI for the respective observable condition. …”
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A new hybrid multiaxial fatigue life model based on critical plane continuum damage mechanics and genetic algorithm
Published 2015“…The proposed model serves as path-independent fatigue life estimating tool hence can be used with any type of loading conditions. …”
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Autonomous road roundabout detection and navigation system for smart vehicles and cities using laser simulator–fuzzy logic algorithms and sensor fusion
Published 2020“…The path generation algorithm was fully derived within the local maps and tested with a WMR platform in real-time.…”
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Dynamic Bayesian networks and variable length genetic algorithm for designing cue-based model for dialogue act recognition
Published 2010“…The model is, essentially, a dynamic Bayesian network induced from manually annotated dialogue corpus via dynamic Bayesian machine learning algorithms. Furthermore, the dynamic Bayesian network's random variables are constituted from sets of lexical cues selected automatically by means of a variable length genetic algorithm, developed specifically for this purpose. …”
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Reliability performance evaluation and integration of routing algorithm in shuffle exchange with minus one stage
Published 2012“…These routing algorithms are Zero X, Zero Y, ZeroYbit, ZeroXbit, Sequential Increasing and Sequential Decreasing Algorithm. …”
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Genetic algorithm for control and optimisation of exothermic batch process
Published 2013“…Although the proposed genetic algorithm controller (GAC) is able to regulate the process temperature to the desired path, it does not limit the waste production effectively. …”
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Feasibility analysis for predicting the compressive and tensile strength of concrete using machine learning algorithms
Published 2024Subjects:Article -
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Compressed channel estimation for massive MIMO-OFDM systems over doubly selective channels
Published 2019“…Simulation results demonstrate that the proposed scheme can effectively estimate the DS channel for massive MIMO-OFDM systems compared with other existing algorithms. …”
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Multi-stage thermal-economical optimization of compact heat exchangers: A new evolutionary-based design approach for real-world problems
Published 2015“…Learning automata based particle swarm optimization (LAPSO), is employed for handling nine design variables while satisfying various equality and inequality constraints. …”
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Dynamic Bayesian Networks and Variable Length Genetic Algorithm for Dialogue Act Recognition
Published 2007“…In the selection phase, a new variable length genetic algorithm is applied to select the lexical cues. …”
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Effect of LiDAR mounting parameters and speed on HDL graph SLAM-Based 3D mapping for autonomous vehicles
Published 2025“…Results showed that a 0° angle at 30 km/h produced the most accurate 3D map, achieving a Root Mean Square Error (RMSE) of 0.0812 for straight paths and 0.1345 for curved paths. These findings demonstrate the significance of physical mounting parameters and speed on mapping performance. …”
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Machine Learning Based Optimal Design of On-Road Charging Lane for Smart Cities Applications
Published 2025“…The learning algorithms consider variables such as core structure, cross-coupling effect, and coil flux pipe length. …”
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