Search Results - (( variable integration a algorithm ) OR ( variable estimation using algorithm ))
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1
Railway wheelset parameter estimation using signals from lateral velocity sensor
Published 2008“…A type of parameter estimation technique based on the linear integral filter (LIF) method, the least-absolute error with variable forgetting factor (LAE+VFF) estimation method, is proposed in this paper to estimate the railway wheelset parameters modelled as a time-varying continuous-time (C-T) system. …”
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Article -
2
Variable Speed Control Of Two-Mass Wind Turbine System Via State Feedback With Adaptation Law
Published 2018“…Wind turbine convert kinetic energy from the wind to rotational energy and then to electrical energy.In a wind energy conversion system (WECS),its electrical power control (EPC) side demanded a maximum mechanical power from the mechanical power control (MPC) side despite any intermittent wind and seasonal interference.Therefore,it is necessary to develop a variable speed algorithm for a modern WECS.For a two-mass horizontal axis wind turbine, the rotor and generator stiffness is commonly being neglected in the system dynamic.The inclusion of stiffness in system dynamic introduces integral term in the system expression and hence,incur mathematical complexity in the controller design phase.Contrary,this study consider stiffness as unknown parameter in the wind turbine dynamic.In order to obtain the maximum output power,the design of an algorithm with adaptation law for the speed control of a two-mass wind turbine system with an unknown stiffness is proposed in this research.The algorithm is formulated using a full-state feedback.In pursuance of solving the tracking control as a regulation case,the speed of the turbine is bijective mapped into the error dynamic.The stability of the proposed algorithm is guaranteed by Lyapunov.The adaptation law used in the variable speed algorithm is to successfully acquire the adaptability of the algorithm towards an unknown stiffness.Therein,the estimated stiffness is augmented in the Lyapunov function.The Lie derivative of the function is made into a negative semi-definite via the non-negative control parameters.In order to control the rotor speed to sustain the optimum tip-speed ratio (TSR),as well as obtaining the maximum power output from the turbine,the proposed algorithm is constructed.A MATLAB with Simulink® toolbox is used to validate the effectiveness of the proposed control speed.The simulation result showed that the rotor speed achieved an asymptotic tracking towards the demanded rotor speed irrespective of the stiffness value.The error is proved to be minimized as the integral of absolute error (IAE) obtained for wind turbine with stiffness ranging from 134550 Nmrad-1,269100 Nmrad-1,and 403650 Nmrad-1 are recorded as 0.003088,0.003063 and 0.003088 respectively. …”
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Thesis -
3
Sure (EM)-Autometrics: An Automated Model Selection Procedure with Expectation Maximization Algorithm Estimation Method (S/O 14925)
Published 2021“…Hence, this study concentrates on an automated model selection procedure for the SURE model by integrating the expectation-maximization (EM) algorithm estimation method, named SURE(EM)-Autometrics. …”
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Monograph -
4
Hybrid fuzzy-sliding mode observer design for estimation and advanced control of an ethylene polymerization process / Jarinah Mohd Ali
Published 2017“…Observers are computational algorithms designed to estimate unmeasured state variables due to the lack of appropriate estimating devices or to replace the high-priced sensors in a plant. …”
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5
Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli
Published 2018“…In the developed feature selection approach, multi-objective binary-valued backtracking search algorithm (MOBBSA) is used as an efficient evolutionary search algorithm to search within different combinations of input variables and selects the non-dominated feature subsets, which minimize simultaneously both the estimation error and the number of features. …”
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6
Enhanced Adaptive Confidence-Based Q Routing Algorithms For Network Traffic
Published 2004“…ECQ Routing Algorithm is integrates the Variable of Decay Constant and Update All Q Value approaches for updating the C values of non-selected Q values. …”
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7
New Algorithm of Location Model based on Robust Estimators and Smoothing Approach
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Extended multiple models selection algorithms based on iterative feasible generalized least squares (IFGLS) and expectation-maximization (EM) algorithm
Published 2019“…This extension of Autometrics for model selection was also developed for multiple equations by integrating it with seemingly unrelated regressions equations (SURE) and estimated using feasible generalized least squares (FGLS), known as SURE-Autometrics algorithm. …”
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Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm
Published 2025“…In order to validate the proposed algorithm, a number of experiments using various datasets were conducted and compared the outcomes with different�state-of-the-art algorithms. …”
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Robust Data Fusion Techniques Integrated Machine Learning Models For Estimating Reference Evapotranspiration
Published 2022“…As for the NNE, a novel meta-learner based on the stochastic-enabled extreme learning machine integrated with whale optimisation algorithm (WOA-ELM) was developed and used in such an application for the first time. …”
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Final Year Project / Dissertation / Thesis -
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A case study on quality of sleep and health using Bayesian networks
Published 2012“…The network scores computation is implemented to estimate the fitting of the resulting network of each structural learning algorithm in order to choose the best-fitted network. …”
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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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14
Improving forest above-ground biomass estimation by integrating individual machine learning models
Published 2024“…Machine learning algorithms have been proven to have great potential in forest AGB estimation with remote sensing data. …”
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Properties of selected garma models and their estimation procedures
Published 2012“…Time series is an ordered sequence of random variables. In other words, a time series is a set of observations fxtg, each one being recorded at a speci¯c time t. …”
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DeMI interface tool for profit estimation and waste conversion technology recommendations in enhancing municipal solid waste management
Published 2024“…The true strength of the DeMI tool lies in its integration with WEKA, where it uses the feasible structures derived from the P-graph as a comprehensive database. …”
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Efficient Quadrature Rules For Numerical Integration Based On Linear Legendre Multi-wavelets
Published 2024journal::journal article -
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Estimating Forest Aboveground Biomass Density Using Remote Sensing and Machine Learning : A RSME Approach
Published 2025“…This study employs remote sensing datasets and collaborative efforts with ArcGIS to model AGB density across the Terengganu region. Integrated with the random forest algorithm in the Google Earth Engine for AGB density modeling at a spatial resolution of 1 km, the methodology incorporates GEDI Level 4, Sentinel-1 radar, Sentinel-2 optical imagery, and elevation/slope maps. …”
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Measurement Of Power Flow And State Of Charge Of Li-Ion Battery Pack For Hybrid/Electrical Vehicle Application
Published 2014“…A circuit will be build consist a shunt resistor to measure the voltage and current, and this variable will ultimately use to provide a measure of SOC. …”
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Final Year Project -
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Input Information in the Approximate Calculation of Two-Dimensional Integral from Highly Oscillating Functions (Irregular Case)
Published 2019“…The proposed formula is based on the algorithm, which is also effective for a class of discontinuous functions.…”
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