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Domain adaptation in reinforcement learning:a comprehensive and systematic study

Frontiers of Information Technology & Electronic engineering 2024年第11期25卷 1446-1465页

作者： Amirfarhad FARHADI Mitra MIRZAREZAEE Arash SHARIFI Mohammad TESHNEHLAB Department of Computer Engineering Science and Research BranchIslamic Azad UniversityTehran ***Iran Department of Control Engineering K.N.Toosi University of TechnologyTehran ***Iran

Reinforcement learning(RL)has shown significant potential for dealing with complex decision-making ***,its performance relies heavily on the availability of a large amount of high-quality *** many real-world situations,data distribution in the target domain may differ significantly from that in the source domain,leading to a significant drop in the performance of RL *** adaptation(DA)strategies have been proposed to address this issue by transferring knowledge from a source domain to a target ***,there have been no comprehensive and in-depth studies to evaluate these *** this paper we present a comprehensive and systematic study of DA in *** first introduce the basic concepts and formulations of DA in RL and then review the existing DA methods used in *** main objective is to fill the existing literature gap regarding DA in *** achieve this,we conduct a rigorous evaluation of state-of-the-art DA *** aim to provide comprehensive insights into DA in RL and contribute to advancing knowledge in this *** existing DA approaches are divided into seven categories based on application *** approaches in each category are discussed based on the important data adaptation metrics,and then their key characteristics are ***,challenging issues and future research trends are highlighted to assist researchers in developing innovative improvements.

关键词： Reinforcement learning Domain adaptation Machine learning

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Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

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computers, Materials & Continua 2023年第2期74卷 4531-4545页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Mostafa Abotaleb Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine *** feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical *** a result,a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization(DTO-GW)algorithms has been developed in this *** can result when the selection of features is subject to metaheuristics,which can lead to a wide range of ***,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more *** propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of *** the proposed method,the number of features selected is minimized,while classification accuracy is *** test the proposed method’s performance against eleven other state-of-theart approaches,eight datasets from the UCI repository were used,such as binary grey wolf search(bGWO),binary hybrid grey wolf,and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hysteresis optimization(bHy),and binary hysteresis optimization(bHWO).The suggested method is superior 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments.

关键词： Metaheuristics dipper throated optimization grey wolf optimization binary optimizer feature selection

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Some Remarks on Fault-Tolerant Dynamic control Allocation for Weakly Redundant Systems 32

Some Remarks on Fault-Tolerant Dynamic Control Allocation fo...

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32nd Mediterranean Conference on control and Automation, MED 2024

作者： Cristofaro, Andrea Faleschini, Michelangelo Sapienza University of Rome Department of Computer Control and Management Engineering Italy School of Science and Technology Mathematics Division University of Camerino Italy

ISBN: (纸本)9798350395440

We consider the problem of control allocation for weakly redundant systems subject to actuator faults. In particular, the design of a suitable allocator will be devised with the aim of compensating for the fault effects while, at the same time, keeping the control burden as low as possible. To this goal, two approaches can be followed for the synthesis of the allocation servomechanism: direct allocation using orthogonal projection and optimization-based allocation. Some numerical examples illustrate and highlight advantages, disadvantages and limitations of both strategies. © 2024 IEEE.

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Wind Power Prediction Based on Machine Learning and Deep Learning Models

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computers, Materials & Continua 2023年第1期74卷 715-732页

作者： Zahraa Tarek Mahmoud Y.Shams Ahmed M.Elshewey El-Sayed M.El-kenawy Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Mohamed A.El-dosuky Faculty of Computers and Information Computer Science DepartmentMansoura UniversityMansoura35561Egypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt Faculty of Computers and Information Computer Science DepartmentSuez UniversitySuezEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Arab East CollegesRiyadh13544Saudi Arabia

Wind power is one of the sustainable ways to generate renewable *** recent years,some countries have set renewables to meet future energy needs,with the primary goal of reducing emissions and promoting sustainable growth,primarily the use of wind and solar *** achieve the prediction of wind power generation,several deep and machine learning models are constructed in this article as base *** regression models are Deep neural network(DNN),k-nearest neighbor(KNN)regressor,long short-term memory(LSTM),averaging model,random forest(RF)regressor,bagging regressor,and gradient boosting(GB)*** addition,data cleaning and data preprocessing were performed to the *** dataset used in this study includes 4 features and 50530 *** accurately predict the wind power values,we propose in this paper a new optimization technique based on stochastic fractal search and particle swarm optimization(SFSPSO)to optimize the parameters of LSTM *** evaluation criteria were utilized to estimate the efficiency of the regression models,namely,mean absolute error(MAE),Nash Sutcliffe Efficiency(NSE),mean square error(MSE),coefficient of determination(R2),root mean squared error(RMSE).The experimental results illustrated that the proposed optimization of LSTM using SFS-PSO model achieved the best results with R2 equals 99.99%in predicting the wind power values.

关键词： Prediction of wind power data preprocessing performance evaluation

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A Generalized Current Balancing control for Series-Capacitor Buck Converter with Interleaved Phase Angle 24

A Generalized Current Balancing Control for Series-Capacitor...

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24th IEEE Workshop on control and Modeling for Power Electronics, COMPEL 2023

作者： Li, Chung-Yi Nien, Chin-Fu Lin, Li Chen, Hung-Chi Chang Gung University Department of Electronics Engineering Taiwan Chang Gung University Department of Computer Science and Information Engineering Taiwan National Yang Ming Chiao Tung University Institution of Electrical Control Engineering Department of Electrical and Computer Engineering Taiwan

ISBN: (纸本)9798350316186

In addition to increasing the output current, an interleaved buck converter can significantly reduce the current ripple at the output. However, the bottleneck of the interleaved buck converter application is the unbalanced inductor current. In order to achieve the current balance in the entire section of the duty cycle, this paper proposes a control architecture with interleaved alignment, using the adjustment of the duty cycle and phase offset, to analyze and realize a high output current and current balanced in three-arm series capacitor interleaved buck converters (SCIBCs) in this work. By analyzing and modeling the SCIBC and using a single-loop control architecture that feedbacks the output voltage, the proposed current-balancing method balances all inductors' current within the full duty cycle without any current sensor. In addition, the small-signal transfer function of the control signal to the output voltage and design of the controller parameters are derived according to the specification, and the SCIBC with the proposed current-balancing control with PI controller of the alignment of interleaved phase angle is constructed. The high output current and current-balancing characteristics of the proposed circuit control architecture are validated in the simulation tool SIMPLIS. © 2023 IEEE.

关键词： controllers

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Hybrid Sine Cosine and Stochastic Fractal Search for Hemoglobin Estimation

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computers, Materials & Continua 2022年第8期72卷 2467-2482页

作者： Marwa M.Eid Fawaz Alassery Abdelhameed Ibrahim Bandar Abdullah Aloyaydi Hesham Arafat Ali Shady Y.El-Mashad Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Mechanical Engineering Department Qassim UniversityBuraidah51452Saudi Arabia Department of Computer Systems Engineering Faculty of Engineering at ShoubraBenha UniversityEgypt

The sample’s hemoglobin and glucose levels can be determined by obtaining a blood sample from the human body using a needle and analyzing ***(HGB)is a critical component of the human body because it transports oxygen from the lungs to the body’s tissues and returns carbon dioxide from the tissues to the *** the HGB level is a critical step in any blood analysis *** often indicate whether a person is anemic or polycythemia *** ensemble models by combining two or more base machine learning(ML)models can help create a more improved *** purpose of this work is to present a weighted average ensemble model for predicting hemoglobin *** optimization method is utilized to get the ensemble’s optimum *** optimum weight for this work is determined using a sine cosine algorithm based on stochastic fractal search(SCSFS).The proposed SCSFS ensemble is compared toDecision Tree,Multilayer perceptron(MLP),Support Vector Regression(SVR)and Random Forest Regressors as model-based approaches and the average ensemble *** SCSFS results indicate that the proposed model outperforms existing models and provides an almost accurate hemoglobin estimate.

关键词： Sine cosine optimization metaheuristics optimization hemoglobin estimation weight average ensemble

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Hamiltonian quantum generative adversarial networks

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Physical Review Research 2024年第3期6卷 033019-033019页

作者： Leeseok Kim Seth Lloyd Milad Marvian Department of Electrical & Computer Engineering and Center for Quantum Information and Control Department of Mechanical Engineering

We propose Hamiltonian quantum generative adversarial networks (HQuGANs) to learn to generate unknown input quantum states using two competing quantum optimal controls. The game-theoretic framework of the algorithm is inspired by the success of classical generative adversarial networks in learning high-dimensional distributions. The quantum optimal control approach not only makes the algorithm naturally adaptable to the experimental constraints of near-term hardware, but also offers a more natural characterization of overparameterization compared to the circuit model. We numerically demonstrate the capabilities of the proposed framework to learn various highly entangled many-body quantum states, using simple two-body Hamiltonians and under experimentally relevant constraints such as low-bandwidth controls. We analyze the computational cost of implementing HQuGANs on quantum computers and show how the framework can be extended to learn quantum dynamics. Furthermore, we introduce a cost function that circumvents the problem of mode collapse that prevents convergence of HQuGANs and demonstrate how to accelerate the convergence of them when generating a pure state.

关键词： Machine learning Quantum algorithms Quantum computation Quantum control

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Circularity Reinforcement of Critical Raw Materials in Europe: A Case of Niobium

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Circular Economy and Sustainability 2024年第4期4卷 2449-2472页

作者： von Rennenberg, Theresa Yu, Yifei Fraccascia, Luca Yazan, Devrim Murat Department of Industrial Engineering and Business Information Systems University of Twente Drienerlolaan 5 Enschede 7522 NB Netherlands Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Rome 00185 Italy

Critical Raw Materials attract increasing attention due to their depleting reserves and low recyclability. Niobium, one of the most rare and vital elements, is primarily found in Brazil. This research explores the potential impact of Circular Economy (CE) strategies on mitigating niobium's criticality within Europe. First, a niobium supply chain is designed and analysed by Enterprise Input–Output modelling. Second, the supply risk is calculated based on the criticality matrix proposed by the European Commission under three scenarios associated with resources, technologies, and policies. The results show that urban mining is a potential solution to reduce niobium’s criticality and mitigate its environmental impacts. A higher recycling input rate and/or a mix of recycling and substitution strategies is necessary to offset niobium’s criticality. Aligned with the CE action plan, the research offers a scientific foundation to strategically prevent the risk of niobium supply shortages. © The Author(s) 2024.

关键词： Circular economy Critical raw materials Enterprise input–output Niobium supply chain Scenario analysis

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Correction of Time-varying PMU Phase Angle Deviation with Unknown Transmission Line Parameters

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CSEE Journal of Power and Energy Systems 2023年第1期9卷 315-325页

作者： Ancheng Xue He Kong Feiyang Xu Junbo Zhao Naichao Chang Joe H.Chow Haiyan Hong State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China Department of Electrical and Computer Engineering University of ConnecticutStorrs06268USA National Electric Power Dispatching and Control Center Beijing 100031China Department of Electrical Computerand Systems EngineeringRensselaer Polytechnic Institute(RPI)TroyNY 12180USA

Phasor measurement units(PMUs)provide useful data for real-time monitoring of the smart ***,there may be time-varying deviation in phase angle differences(PADs)between both ends of the transmission line(TL),which may deteriorate application performance based on *** address that,this paper proposes two robust methods of correcting time-varying PAD deviation with unknown parameters of TL(ParTL).First,the phenomena of time-varying PAD deviation observed from field PMU data are *** general formulations for PAD estimation are then *** simplify the formulations,estimation of PADs is converted into the optimal problem with a single ParTL as the variable,yielding a linear estimation of *** latter is used by second-order Taylor series expansion to estimate PADs *** reduce the impact of possible abnormal amplitude data in field data,the IGG(Institute of Geodesy&Geophysics,Chinese Academy of Sciences)weighting function is *** using both simulated and field data verify the effectiveness and robustness of the proposed methods.

关键词： Correction line parameters parameter identification phasor measurement time-varying phase angle difference deviation

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Interpretation of ADRC in Q-Structure 6

Interpretation of ADRC in Q-Structure

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6th International Conference on Industrial Artificial Intelligence, IAI 2024

作者： Xu, Jiapeng Chen, Xiang Tan, Ying Zhou, Kemin University of Windsor Department of Electrical and Computer Engineering Windsor Canada The University of Melbourne Department of Mechanical Engineering Melbourne Australia Center for Advanced Control and Smart Operations Nanjing University Suzhou China

ISBN: (纸本)9798350356618

This paper presents a control structure featuring an operator Q driven by the residual signal, which indicates the difference between the measurement output and the estimated output from an observer. The form of this observer is very general, as long as it can generate the estimated state or relevant information that will be incorporated into this control structure. The operator Q introduces an extra design freedom to address uncertainties, such that linear active disturbance rejection control (LADRC) can be interpreted as a special case in the Q-structure when a linear extended state observer is used. © 2024 IEEE.

关键词： Disturbance rejection

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