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Backstepping Integral Super Twisting Sliding Mode Control Algorithm For Autonomous Underwater Glider
Published 2019“…The BISTSMC was tested for external disturbance and parameter variations. The BISTSMC has been benchmarked its performances with other sliding mode control (SMC) strategies to evaluate the chattering suppression of the controllers. …”
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Thesis -
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The effect of key parameters on the design of an optimized CAES power plant
Published 2017“…In order to obtain a more tangible realization, it is necessary to verify the results against the variation of key parameters. In this study, the sensitivity analysis is performed based on main parameters including plant loading and ambient condition and the resultant trends of each case are presented. …”
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Development of controller for an underactuated autonomous underwater vehicle (AUV)
Published 2019“…The simulation results have shown that the proposed controller provides the smallest chattering about more than 1000 times smaller than STSMC, more than 100 times smaller than back-stepping SMC in nominal, disturbance and parameter variation cases respectively. The steady error of the proposed controller also gives the smallest steady state error of four times smaller than STSMC and back-stepping SMC in all cases for pitching angle and 100 times smaller than STSMC and back-stepping for excess mass. …”
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Scheduling dynamic cellular manufacturing systems in the presence of cost uncertainty using heuristic method
Published 2016“…It is observed that in 67% of studied cases, inflation rate can strengthen cell load variation. …”
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Using algorithmic taxonomy to evaluate lecturer workload
Published 2006“…Results of the study highlight the contributions of this algorithmic method in better evaluation of teaching workload for lecture.…”
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Evaluate the performance of university course timetabling problem with different combinations of genetic algorithm
Published 2025“…University course timetabling problem (UCTP) is a scheduling problem that requires courses to be assigned to the limited time slots, classrooms, and lecturers, while adhering to a set of predefined constraints. …”
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Variational Bayesian inference for exponentiated Weibull right censored survival data
Published 2023“…The results from the experiments reveal that the Variational Bayesian (VB) approach is better than the competing Metropolis-Hasting Algorithm and the reference maximum likelihood estimates.…”
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Using algorithmic taxonomy to evaluate lecture workload: a case study of services application prototype in the UPM KM Portal
Published 2006“…Results of the study highlight the contributions of this algorithmic method in better evaluation of teaching workload for lecture.…”
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Optimisation of fed-batch fermentation process using deep reinforcement learning
Published 2023“…When dealing with model mismatch (±15% parameter variation in critical growth and maximum glucose uptake rate) and process disturbance (±20% deviation in substrate feeding concentration), the proposed algorithm was able to handle the changes with a minor effect on the yeast yield up to 13.78% and 2.52%, respectively, across all different initial condition cases. …”
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Using algorithmic taxonomy to evaluate lecture workload: A case study of services application prototype in the UPM KM portal
Published 2006“…Lecturer workload at universities includes three major categories: teaching, research and services.Teaching workload is influence by various factors such as level taught courses, number of student, credit and contact hour and off campus or on campus course design.The UPM has a KM Portal that contains sets of metadata on lecturer profile and knowledge assets.The Lecturer profile contains information lecturer teaching, research, publication and many more.We constructed an algorithmic taxonomy based at the lecturer profile data to measure lecturer teaching workload.This method measures the lecturer teaching workload.The taxonomy is a dynamic hierarchy that extracts validated parameters from the dataset.Results of the study highlight the contributions of this algorithmic method in better evaluation of teaching workload for lecture.…”
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Evaluate the performance of university timetabling problem with various artificial intelligence techniques
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Automated study plan generator using rule-based and knapsack problem
Published 2022“…A quantitative methodology through expert’s reviews and questionnaire survey was conducted to evaluate the accuracy of the proposed algorithm. …”
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A decomposed streamflow non-gradientbased artificial intelligence forecasting algorithm with factoring in aleatoric and epistemic variables / Wei Yaxing
Published 2024“…Given the multitude of components to manage, streamflow forecasting is preferable to employ an algorithm with low sensitivity to parameter variations. …”
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Using web questionnaire for web-based course evaluation: advantages and disadvantages
Published 2017“…It explores current features and issues associated with Web questionnaire through the implementation of a Web questionnaire for evaluating the effectiveness of computer courses at Iranian university E-learning centers. …”
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A PSO inspired asynchronous cooperative distributed hyper-heuristic for course timetabling problems
Published 2017“…The performances of the proposed cooperative hyper-heuristics are evaluated using the standard course timetabling benchmark problem. …”
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