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Optimal Coordinated Scheduling of Electric Vehicles and Battery Energy Storage Systems

IEEE Transactions on Consumer Electronics 2025年

作者： Shang, Xiao-Ju Mishra, Yateendra Yang, Yin Liu, Jin-Long Yu, Zu-Guo Tian, Yu-Chu School of Mathematics and Computational Science Xiangtan University Hunan411105 China National Center for Applied Mathematics in Hunan Xiangtan University Hunan411105 China School of Electrical Engineering and Robotics Queensland University of Technology GPO Box 2434 BrisbaneQLD4001 Australia School of Computer Science Queensland University of Technology GPO Box 2434 BrisbaneQLD4001 Australia

Electric vehicles (EVs) and battery energy storage systems (BESS) are rapidly gaining adoption worldwide as emerging consumer electronics products, playing an important role in the transition to sustainable energy. While their integration into power grids offers significant benefits when managed appropriately, it also introduces challenges related to grid stability and efficiency, such as increased peak demand and voltage fluctuations. Effective management of these challenges demands coordinated scheduling of EVs and BESS for both charging from the grid and discharging back into it. Various optimization approaches, including mixed-integer nonlinear programming (MINLP), have been proposed to tackle this problem. However, these approaches often involve numerous binary decision variables, which result in increased computational complexity or even make the computation practically infeasible in large-scale scenarios. To address this issue, this paper formulates the coordinated scheduling of EVs and BESS as a nonconvex constrained optimization problem with significantly fewer decision variables. A Modified Jacobi Proximal Alternating Direction Method of Multipliers (M-ProxJADMM) is proposed to efficiently solve the problem, with guaranteed convergence. Case studies are conducted to validate the effectiveness of the M-ProxJADMM algorithm in optimizing the coordinated scheduling of EVs and BESS. © 1975-2011 IEEE.

关键词： Battery storage

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Multi-Stage Gaussian Noise Reduction with Recurrent Neural Networks 55

Multi-Stage Gaussian Noise Reduction with Recurrent Neural N...

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55th Asilomar Conference on Signals, Systems and computers, ACSSC 2021

作者： Ranganath, Aditya Deguchy, Omar Singhal, Mukesh Marcia, Roummel F. University of California Merced Electrical Engineering and Computer Science MercedCA United States University of California Merced Applied Mathematics MercedCA United States

ISBN: (纸本)9781665458283

In this paper, we implement multi-stage deep learning methods to recover signals from noisy observations. We seek an alternative to the traditional iterative optimization-based methods by exploiting the denoising properties of autoencoders. The novelty of our method is in the reformulation of the denoising problem as a multi-stage process and in the use of recurrent neural networks to gradually recover the intended signal. Numerical experiments on a modified version of the CIFAR-10 dataset show that signal recovery in multiple stages produces sharper images than a standard autoencoder approach. We present quantitative evidence of improvement using both the mean squared error and the structural similarity index. © 2021 IEEE.

关键词： Recurrent neural networks

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Analysis and investigation of fuzzy expert system for predicting the child anaemia

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Materials Today: Proceedings 2022年 56卷 231-236页

作者： Boadh, Rahul Chaudhary, Kabir Dahiya, Mamta Dogra, Namrata Rathee, Sarita Kumar, Ajay Rajoria, Yogendra Kumar Department of the Mathematics School of Basic and Applied Science K.R. Mangalam University Sohna Road Haryana Gurugram122103 India Department of Computer Science and Engineering Faculty of Engineering and Technology Shree Guru Govind Singh Tricentenary University Haryana Gurugram122505 India Department of Orthodontics and Dentofacial Orthopaedic Shree Guru Gobind Tricentenary University Haryana Gurugram122505 India Department of Electrical Engineering DPG Institute of Technology and Management Haryana Gurgaon122001 India Department of Mechanical Engineering Faculty of Engineering and Technology Shree Guru Govind Singh Tricentenary University Haryana Gurugram122505 India

In this article, the fuzzy expert system has been dealing with and is involved in the analysis of childhood anaemia (erythrocytopenia). Recent studies on fuzzy set theory and fuzzy logic are highly applicable and therefore related sources for the development of knowledge-based structures in accountability drugs as an explanation of a set of medical positions and its results, the diagnosis of disease, coronary artery disease, blood pressure and childhood anaemia. In the present study, childhood anaemia was diagnosed using haemoglobin and haematocrit as the input parameters. The results were compared with previous studies and were found to be in total agreement. That FIS can also be used by physicians or in pathology labs for the diagnosis of other disease. © 2022

关键词： Fuzzy logic

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Clinically adaptable machine learning model to identify early appreciable features of diabetes

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Intelligent Medicine 2024年第1期4卷 22-32页

作者： Nurjahan Nipa Mahmudul Hasan Riyad Shahriare Satu Walliullah Koushik Chandra Howlader Mohammad Ali Moni Department of Information and Communication Technology Bangabandhu Sheikh Mujibur Rahman Digital UniversityBangladeshKaliakairGazipur1750 Bangladesh Department of Applied Mathematics Noakhali Science and Technology UniversityNoakhali3814Bangladesh Department of Management Information Systems Noakhali Science and Technology UniversityNoakhali3814Bangladesh Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology UniversityNoakhali3814Bangladesh Department of Computer Science North Dakota State UniversityFargo58105NDUnited States School of Health and Rehabilitation Sciences Faculty of Health and Behavioural SciencesThe University of QueenslandSt LuciaQLD 4072Australia

Objective Diabetes mellitus is a serious disease where the body of affected patients are failed to produce enough insulin that causes an abnormality of blood *** disease happens for a number of reasons including modern lifestyle,lethargic attitude,unhealthy food consumption,family history,age,overweight,*** aim of this study was to propose a machine learning based prediction model that detected diabetes at the *** In this work,we collected 520 patients records from the University of California,Irvine(UCI)machine learning repository of Sylhet Diabetes Hospital,***,a similar questionnaire of that hospital was followed and assembled 558 patients records from all over Bangladesh through this ***,we accumulated patient records of these two *** the next step,these datasets were cleaned and applied thirty five state-of-arts classifiers such as logistic regression(LR),K nearest neighbors(KNN),support vector classifier(SVC),Nave Byes(NB),decision tree(DT),random forest(RF),stochastic gradient descent(SGD),Perceptron,AdaBoost,XGBoost,passive aggressive classifier(PAC),ridge classifier(RC),Nu-support vector classifier(NuSVC),linear support vector classifier(LSVC),calibrated classifier CV(CCCV),nearest centroid(NC),Gaussian process classifier(GPC),multinomial NB(MNB),complement NB,Bernoulli NB(BNB),categorical NB,Bagging,extra tree(ET),gradiant boosting classifier(GBC),Hist gradiant boosting classifier(HGBC),one vs rest classifier(OVsRC),multi-layer perceptron(MLP),label propagation(LP),label spreading(LS),stacking,ridge classifier CV(RCCV),logistic regression CV(LRCV),linear discriminant analysis(LDA),quadratic discriminant analysis(QDA),and light gradient boosting machine(LGBM)to explore best stable predictive *** performance of the classifiers has been measured using five metrics such as accuracy,precision,recall,F1-score,and area under the receiver operating ***,these outcomes were interpret

关键词： Diabetes Early features Machine learning Classification

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Comparative study of data preparation methods in quantum clustering algorithms 2021

Comparative study of data preparation methods in quantum clu...

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4th International Conference on Networking, Information Systems and Security, NISS 2021

作者： Ouedrhiri, Oumayma Banouar, Oumayma Raghay, Said Elhadaj, Salah Laboratory of Computer Science Engineering and Systems FST Marrakesh Morocco Laboratory of Innovating Technologies USMBA Fes Morocco Laboratory of Applied Mathematics FST Marrakesh Morocco

ISBN: (纸本)9781450388719

More powerful and better performing, quantum algorithms offer a noticeable speedup in comparison with classical algorithms. This is due to the superposition property of quantum information. It helps to obtain algorithms that are better in terms of speed and performance. Machine learning benefits from quantum computing advantages to help time consuming algorithms run faster in the best conditions (without having to lose information). In the clustering algorithms case, the distance calculation between data points is the most resource consuming step. Thus, the use of a quantum distance is very helpful. In this paper, we test a number of quantum clustering algorithms with different data preparation methods. Experiments were done for different datasets (Iris, Wine, Breast cancer) and the comparison is based on the Clustering quality and the accuracy score. © 2021 ACM.

关键词： Quantum computing Data preparation K-means clustering Machine learning Clustering algorithms

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Occluded Face Recovery by Image Retrieval

Occluded Face Recovery by Image Retrieval

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2021 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2021

作者： Tu, Ching-Ting Lee, Kuan-Hsin National Chung Hsing University Department of Applied Mathematics Taichung Taiwan Tamkang University Dept. of Computer Science and Information Engineering New Taipei Taiwan

ISBN: (纸本)9781665419512

In this paper, we propose an occluded face recovery framework to improve the face recognition rate for the occlusion case. The occluded facial image of an unseen testing subject is automatically recovered, the generated results are diverse and fit the personal characteristics of the observable regions, and thus can be used to increase the face recognition performance for occlusion cases. © 2021 IEEE.

关键词： Generative adversarial networks

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Tripartite entanglement measure under local operations and classical communication

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Physical Review A 2023年第3期107卷 032405-032405页

作者： Xiaozhen Ge Lijun Liu Shuming Cheng Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong China College of Mathematics and Computer Science Shanxi Normal University Linfen 041000 China The Department of Control Science and Engineering Tongji University Shanghai 201804 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai 201804 China Institute for Advanced Study Tongji University Shanghai 200092 China

Multipartite entanglement is an indispensable resource in quantum communication and computation; however, it is a challenging task to faithfully quantify this global property of multipartite quantum systems. In this work we study the concurrence fill, which admits a geometric interpretation to measure genuine tripartite entanglement for the three-qubit system [Xie and Eberly, Phys. Rev. Lett. 127, 040403 (2021)]. First, we use the well-known three-tangle and bipartite concurrence to reformulate this quantifier for all pure states. We then construct an explicit example to conclusively show that the concurrence fill can be increased under local operations and classical communication (LOCC) on average, implying it is not an entanglement monotone. Moreover, we give a simple proof of the LOCC monotonicity of the three-tangle and find that the bipartite concurrence and the squared concurrence can have distinct performances under the same LOCC. Finally, we propose a reliable monotone to quantify genuine tripartite entanglement, which can also be easily generalized to the multipartite system. Our results shed light on the study of genuine entanglement and also reveal the complex structure of multipartite systems.

关键词： Quantum correlations in quantum information Quantum entanglement Quantum networks

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Artificial Immune Systems Approach for Surface Reconstruction of Shapes with Large Smooth Bumps 23rd

Artificial Immune Systems Approach for Surface Reconstructi...

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23rd International Conference on Computational science, ICCS 2023

作者： Gálvez, Akemi Fister, Iztok You, Lihua Fister, Iztok Iglesias, Andrés Department of Applied Mathematics and Computational Sciences University of Cantabria Santander39005 Spain Faculty of Pharmaceutical Sciences Toho University 2-2-1 Miyama Funabashi274-8510 Japan Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia National Center for Computer Animation Faculty of Media and Communication Bournemouth University PooleBH12 5BB United Kingdom

ISBN: (纸本)9783031360268

Reverse engineering is one of the classical approaches for quailty assessment in industrial manufacturing. A key technology in reverse engineering is surface reconstruction, which aims at obtaining a digital model of a physical object from a cloud of 3D data points obtained by scanning the object. In this paper we address the surface reconstruction problem for surfaces that can exhibit large smooth bumps. To account for this type of features, our approach is based on using exponentials of polynomial functions in two variables as the approximating functions. In particular, we consider three different models, given by bivariate distributions obtained by combining a normal univariate distribution with a normal, Gamma, and Weibull distribution, respectively. The resulting surfaces depend on some parameters whose values have to be optimized. This yields a difficult nonlinear continuous optimization problem solved through an artificial immune systems approach based on the clonal selection theory. The performance of the method is discussed through its application to a benchmark comprised of three examples of point clouds. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Weibull distribution

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Multi-task Scheduling Algorithm Based on Self-adaptive Hybrid ICA–PSO Algorithm in Cloud Environment 8th

Multi-task Scheduling Algorithm Based on Self-adaptive Hybri...

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8th International Workshop on Soft Computing Applications, SOFA 2018

作者： Tabrizchi, Hamed Kuchaki Rafsanjani, Marjan Balas, Valentina Emilia Department of Computer Science Faculty of Mathematics and Computer Shahid Bahonar University of Kerman Kerman Iran Department of Automatics and Applied Software Faculty of Engineering Aurel Vlaicu University of Arad Arad Romania

ISBN: (纸本)9783030521899

Cloud is a new concept in Internet technology and has brought many benefits, particularly, in the field of computing. Cloud has changed how on-demand resources are allocated to the different user requests and has provided more optimal resource utilization. Tasks (user requests) have different kinds, including deadline-based tasks having time-related limitations. This kind of task has various parameters and scheduling them is more challenging than scheduling the individual tasks. In this regard, many papers have been published according to these parameters. The objective of this paper is to propose a novel self-adaptive hybrid ICA–PSO algorithm for dealing with associate multi-task scheduling problem. To improve the exploration, two algorithms, namely, the imperialist competitive algorithm (ICA) and particle swarm optimization (PSO) were combined in this study. The results of the present study outperform those of the existing mechanisms. © 2021, Springer Nature Switzerland AG.

关键词： Scheduling algorithms

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Improving Pareto Front Learning via Multi-Sample Hypernetworks

arXiv

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arXiv 2022年

作者： Hoang, Long P. Le, Dung D. Tuan, Tran Anh Thang, Tran Ngoc College of Engineering and Computer Science VinUniversity Viet Nam School of Applied Mathematics and Informatics Hanoi University of Science and Technology Viet Nam

Pareto Front Learning (PFL) was recently introduced as an effective approach to obtain a mapping function from a given trade-off vector to a solution on the Pareto front, which solves the multi-objective optimization (MOO) problem. Due to the inherent trade-off between conflicting objectives, PFL offers a flexible approach in many scenarios in which the decision makers can not specify the preference of one Pareto solution over another, and must switch between them depending on the situation. However, existing PFL methods ignore the relationship between the solutions during the optimization process, which hinders the quality of the obtained front. To overcome this issue, we propose a novel PFL framework namely PHN-HVI, which employs a hypernetwork to generate multiple solutions from a set of diverse trade-off preferences and enhance the quality of the Pareto front by maximizing the Hypervolume indicator defined by these solutions. The experimental results on several MOO machine learning tasks show that the proposed framework significantly outperforms the baselines in producing the trade-off Pareto front. Copyright © 2022, The Authors. All rights reserved.

关键词： Multiobjective optimization

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