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Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

computers, Materials & Continua 2023年第11期77卷 2579-2594页

作者： Abdennasser Dahmani Yamina Ammi Nadjem Bailek Alban Kuriqi Nadhir Al-Ansari Salah Hanini Ilhami Colak Laith Abualigah El-Sayed M.El-kenawy Department of Mechanical Engineering Faculty of Science and TechnologyGIDD Industrial Engineering and Sustainable Development LaboratoryUniversity of RelizaneBourmadiaRelizane48000Algeria Laboratory of Biomaterials and Transport Phenomena(LBMPT) University of MedeaUrban PoleMedea26000Algeria Energies and Materials Research Laboratory Faculty of Sciences and TechnologyUniversity of TamanghassetTamanrasset10034Algeria Sustainable Development and Computer Science Laboratory Faculty of Sciences and TechnologyAhmed Draia University of AdrarAdrarAlgeria CERIS Instituto Superior TécnicoUniversidade de LisboaLisbon1049-001Portugal Civil Engineering Department University for Business and TechnologyPristina10000Kosovo Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Engineering and Architecture Faculty Nisantasi UniversityIstanbulTurkey Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains MalaysiaPulau Pinang11800Malaysia Department of Computing and Information Systems School of Engineering and TechnologySunway University MalaysiaPetaling Jaya27500Malaysia Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Increasing global energy consumption has become an urgent problem as natural energy sources such as oil,gas,and uranium are rapidly running *** into renewable energy sources such as solar energy is being pursued to counter *** energy is one of the most promising renewable energy sources,as it has the potential to meet the world’s energy needs *** study aims to develop and evaluate artificial intelligence(AI)models for predicting hourly global *** hyperparameters were optimized using the Broyden-FletcherGoldfarb-Shanno(BFGS)quasi-Newton training algorithm and STATISTICA *** from two stations in Algeria with different climatic zones were used to develop the *** error measurements were used to determine the accuracy of the prediction models,including the correlation coefficient,the mean absolute error,and the root mean square error(RMSE).The optimal support vector machine(SVM)model showed exceptional efficiency during the training phase,with a high correlation coefficient(R=0.99)and a low mean absolute error(MAE=26.5741 Wh/m^(2)),as well as an RMSE of 38.7045 Wh/m^(2) across all ***,this study highlights the importance of accurate prediction models in the renewable energy,which can contribute to better energy management and planning.

关键词： Renewable energy energy prediction global irradiation artificial intelligence BFGS quasi-Newton training algorithm

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L3DMC: Lifelong Learning using Distillation via Mixed-Curvature Space

arXiv

引用

arXiv 2023年

作者： Roy, Kaushik Moghadam, Peyman Harandi, Mehrtash Department of Electrical and Computer Systems Engineering Faculty of Engineering Monash University Melbourne Australia Data61 CSIRO BrisbaneQLD Australia

The performance of a lifelong learning (L3) model degrades when it is trained on a series of tasks, as the geometrical formation of the embedding space changes while learning novel concepts sequentially. The majority of existing L3 approaches operate on a fixed-curvature (e.g., zero-curvature Euclidean) space that is not necessarily suitable for modeling the complex geometric structure of data. Furthermore, the distillation strategies apply constraints directly on low-dimensional embeddings, discouraging the L3 model from learning new concepts by making the model highly stable. To address the problem, we propose a distillation strategy named L3DMC that operates on mixed-curvature spaces to preserve the already-learned knowledge by modeling and maintaining complex geometrical structures. We propose to embed the projected low dimensional embedding of fixed-curvature spaces (Euclidean and hyperbolic) to higher-dimensional Reproducing Kernel Hilbert Space (RKHS) using a positive-definite kernel function to attain rich representation. Afterward, we optimize the L3 model by minimizing the discrepancies between the new sample representation and the subspace constructed using the old representation in RKHS. L3DMC is capable of adapting new knowledge better without forgetting old knowledge as it combines the representation power of multiple fixed-curvature spaces and is performed on higher-dimensional RKHS. Thorough experiments on three benchmarks demonstrate the effectiveness of our proposed distillation strategy for medical image classification in L3 settings. Our code implementation is publicly available at https://***/csiro-robotics/L3DMC. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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Assessment of Demand Response Impact on the Frequency Stability of Low-Inertia Power systems

Assessment of Demand Response Impact on the Frequency Stabil...

引用

Power Tech Conference

作者： Rodrigo da Cunha Afonso João Abel Peças Lopes Department of Electrical and Computer Engineering Faculty of Engineering University of Porto Porto Portugal Center for Power and Energy Systems INESC-TEC Porto Portugal

This paper describes a study that sought to analyse the impact of an active demand response on the frequency stability of the Iberian Peninsula for operation scenarios extending to 2040. For that purpose, one developed dynamic models for primary and secondary frequency control provision from demand-side resources, namely Electric Vehicles (EV), thermostatically controlled loads (TCL), and electrolysers. Those models were developed under a Matlab/Simulink environment, and added to a two-area control model representative of the Iberian Peninsula interconnected to the CESA area. Then, one ran simulations of reference disturbances (loss of a large generator or distributed generation) in the developed platform, once it was fully implemented.

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Image Recognition systems in Augmented Reality systems

Image Recognition Systems in Augmented Reality Systems

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2022 Conference of Russian Young Researchers in electrical and Electronic engineering, ElConRus 2022

作者： Kozlova, Olga A. Kozlova, Liudmila P. Bonch-Bruevich St. Petersburg State University of Communication Faculty of Information Systems and Technologies Saint Petersburg Russia Saint Petersburg Electrotechnical University Faculty of Industrial Automation and Electrical Engineering Leti Saint Petersburg Russia

ISBN: (纸本)9781665409933

Augmented reality systems have become widespread not only in the entertainment sector, but also in the industrial industry. And for such systems, along with the choice of hardware, it is very important to correctly integrate the task of pattern recognition. The article examines the peculiarities of building such systems, the advantages, complexities and prospects for development. © 2022 IEEE.

关键词： Augmented reality

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Robust Sliding Manifold Design for Uncertain Linear systems

Robust Sliding Manifold Design for Uncertain Linear Systems

引用

European Control Conference (ECC)

作者： Edoardo Vacchini Nikolas Sacchi Michele Cucuzzella Antonella Ferrara Department of Electrical Computer Science and Biomedical Engineering University of Pavia Pavia Italy Center for Systems and Control ENTEG Faculty of Science and Engineering (FSE) University of Groningen (UG) Groningen Netherlands

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

This paper proposes a novel approach for the design of stabilizing sliding manifolds for linear systems affected by model uncertainties and external disturbances. In classical sliding mode control approaches, rejecting model uncertainties and external disturbances often relies on designing a dis-continuous control law with a suitable gain. Specifically, the greater the uncertainty, the larger the control gain. However, this approach might be detrimental to the plant. Instead, the proposed technique deals with this problem by focusing on the design of a suitable sliding manifold, where stability is guaranteed despite model uncertainties. This approach exhibits several benefits such as not needing any further identification process and designing a smaller control gain.

关键词： Manifolds Linear systems Uncertainty RLC circuits Stability analysis Mathematical models Robustness

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Electric Car Motion Control System

Electric Car Motion Control System

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2022 Conference of Russian Young Researchers in electrical and Electronic engineering, ElConRus 2022

作者： Kozlova, Liudmila P. Kozlova, Olga A. Saint Petersburg Electrotechnical University Faculty of Industrial Automation and Electrical Engineering Leti Saint Petersburg Russia Bonch-Bruevich St. Petersburg State University of Communication Faculty of Information Systems and Technologies Saint Petersburg Russia

ISBN: (纸本)9781665409933

关键词： Control systems

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The Deep Learning Approach For American Sign Language Detection

The Deep Learning Approach For American Sign Language Detect...

引用

2022 IEEE Creative Communication and Innovative Technology, ICCIT 2022

作者： Pangestu, Gusti Heryadi, Yaya Lukas Suparta, Wayan Arifin, Yulyani Bina Nusantara University School of Computer Science Computer Science Department Binus Graduate Program Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9781665468763

Sign language has importance rule to deal with communication process especially with impairments hearing people. Sign language detection also attract lot of researchers to join the challenge of research to detect and recognize the sign language in the field of computer Science. Hence, there is still no any standard approach and method to recognize the meaning in every pose of sign language. This research proposed a mechanism to detect Alphabet American Sign Language by utilizing Convolutional Neural Network (CNN) process. The CNN approach was chosen based on the ability and capability to recognize image. In this research, MNIST dataset is used for traning and testing process. The proposed CNN architecture produced 97% of accuracy that outperform the previous research using the same dataset which made this architecture promising. © 2022 IEEE.

关键词： Convolutional neural networks

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Logarithmically Quantized Distributed Optimization over Dynamic Multi-Agent Networks

arXiv

引用

arXiv 2024年

作者： Doostmohammadian, Mohammadreza Pequito, Sérgio Mechatronics Department Faculty of Mechanical Engineering Semnan University Semnan Iran Department of Electrical and Computer Engineering Institute for Systems and Robotics Instituto Superior Técnico University of Lisbon Portugal

Distributed optimization finds many applications in machine learning, signal processing, and control systems. In these real-world applications, the constraints of communication networks, particularly limited bandwidth, necessitate implementing quantization techniques. In this paper, we propose distributed optimization dynamics over multi-agent networks subject to logarithmically quantized data transmission. Under this condition, data exchange benefits from representing smaller values with more bits and larger values with fewer bits. As compared to uniform quantization, this allows for higher precision in representing near-optimal values and more accuracy of the distributed optimization algorithm. The proposed optimization dynamics comprise a primary state variable converging to the optimizer and an auxiliary variable tracking the objective function's gradient. Our setting accommodates dynamic network topologies, resulting in a hybrid system requiring convergence analysis using matrix perturbation theory and eigenspectrumanalysis. © 2024, CC BY.

关键词： Support vector machines

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Enhanced Distributed Self-Healing System for electrical Distribution Networks Using ADMM

Enhanced Distributed Self-Healing System for Electrical Dist...

引用

IEEE General Meeting Power& Energy Society

作者： Juan Camilo López E. T. Marco Gerards Johann L. Hurink Marcos J. Rider Department of Systems and Energy School of Electrical and Computer Engineering University of Campinas (UNICAMP) Campinas SP Brazil Faculty of Electrical Engineering Mathematics and Computer Science University of Twente (UT) Enschede Overijssel the Netherlands

Unlike centralized versions, a distributed self-healing system (SHS) for electrical distributed systems is less vulnerable to single-point failures (or attacks), requires less information from the agents, and is more scalable. However, optimality is challenging to achieve because binary variables are used in the modelling of the distributed service restoration problem. To deal with this challenge, this paper proposes an enhanced alternating direction method of multipliers (ADMM)based algorithm used to developed a fully distributed SHS in electrical distribution networks. Hereby, three ADMM-based heuristics are executed in parallel to improve the chances of obtaining a feasible solution. However, if none of the heuristics converge within given reasonable time, the proposed distributed SHS uses a basic restoration plan that is feasible in terms of topology and operational constraints. Results using the IEEE 123node system show that the proposed distributed SHS is reliable and it always provides a feasible solution.

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The Privacy Attitudes Questionnaire (PAQ) Turned 20: What’s Next for a Generalized Measure of Privacy Attitudes?

引用

Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2023年第1期67卷 705 - 709页

作者： Wayne C. W. Giang Alyssa Iglar Mark Chignell Reza Samavi Kopiha Nathan Department of Industrial and Systems Engineering University of Florida Gainesville FL USA Department of Mechanical and Industrial Engineering University of Toronto Toronto ON Canada Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto ON CanadaVector Institute Healthcare Insurance Reciprocal of Canada Toronto ON Canada

Attitudes and concerns related to privacy are not homogeneous, but instead differ based on the individual and context at hand. Understanding how these attitudes and concerns vary could inform product, service, and policy design. Questionnaires, such as the Privacy Attitudes Questionnaire (PAQ), can be used to evaluate and derive implications from people’s privacy orientations in numerous domains, including healthcare, social media, and e-commerce. Our objective is to refine the PAQ to reflect areas of privacy concern in today’s landscape. The panel will serve as a forum for conversation about areas of privacy concern, guided by our panelists’ expertise and questions about demographic and user considerations for the revised PAQ, a framework-inspired understanding of privacy, and privacy in healthcare. Insights and perspectives arising from the discussion panel will be considered for subsequent revisions of the PAQ.

关键词： privacy attitudes concerns questionnaire context healthcare

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