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2024 IEEE PES/IAS PowerAfrica, PowerAfrica 2024

作者： Lafleni, Sipho P Sumbwanyambe, Mbuyu Hlalele, Tlotlollo S University of South Africa Department of Electrical Engineering and Smart Systems Engineering Johannesburg South Africa

ISBN: (纸本)9798350389388

Intelligent fault diagnosis is a method that involves the utilization of advanced computational tools, which are frequently based on artificial intelligence (AI) and machine learning, in order to diagnose defects or malfunctions in systems or processes with the purpose of resolving issues. As a means of achieving the goal of preventing catastrophic failures by the early detection and resolution of defects, the optimization of hyper-parameters in machine learning models is an essential stage in the process of achieving higher performance and generalization. An intelligent fault detection technique for micro-grids that is based on Artificial Neural Network and is driven by data is presented in this study. The technique makes use of a model that utilizes hyper-parametrical tuning to optimize Machine learning (ML) performance. Within the framework of the suggested method, three-phase currents and voltages are utilized as variable features that are fed into classifier layers, using hyper-parameters that have been optimized. The Bayesian optimization strategy is utilized during the model learning phase in order to maximize the hyper- parameters. Last but not least, multi-class classification of the five distinct types of faults can be accomplished through the utilization of a ML classifier model whose hyper-parameters has been optimized. © 2024 IEEE.

关键词： Bayesian Diagnosis hyper parameter Machine learning Micro-grid optimization

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Developing a Help Desk Service for Enhanced Coordination in Health Informatics Projects: A Sharepoint and Power Automate Approach

Developing a Help Desk Service for Enhanced Coordination in ...

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34th Medical Informatics Europe Conference, MIE 2024

作者： Pournik, Omid Ahmad, Bilal Gour, Shramika Peake, Ashley Tong, Chao Churm, James Arvanitis, Theodoros N. Department of Electronic Electrical and Systems Engineering School of Engineering University of Birmingham Birmingham United Kingdom

ISBN: (纸本)9781643685335

Healthcare projects necessitate effective collaboration between clinical and technical partners, particularly during pivotal phases like lab testing and piloting. However, challenges in coordination often impede seamless collaboration, leading to inefficiencies and delays. This paper presents a comprehensive approach to developing a help desk service tailored for CAREPATH projects, leveraging SharePoint services and Power Automate. The solution aims to bridge communication gaps, foster collaboration, and enhance coordination among clinical and technical partners. Through iterative development and testing, we refined the system based on stakeholder feedback, resulting in streamlined workflows and improved document management. During the lab testing phase, the help desk system demonstrated significant improvements in resolution duration, communication efficiency, and success solution rates. Stakeholder feedback highlighted enhanced collaboration and improved access to project documentation. With successful testing, the help desk is poised for implementation in subsequent phases, promising further enhancements in patient engagement, technology integration, and scalability. These findings underscore the critical role of help desks in healthcare ICT projects, offering a transformative approach to project management and stakeholder collaboration. Future directions include enhancing patient engagement, leveraging advanced technologies, and conducting longitudinal studies to evaluate long-term impact. Embracing these directions will drive positive change, delivering better outcomes for patients and caregivers in healthcare ICT projects. © 2024 The Authors.

关键词： Project management

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Reliability-aware swarm based multi-objective optimization for controller placement in distributed SDN architecture

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Digital Communications and Networks 2024年第5期10卷 1245-1257页

作者： Abeer A.Z.Ibrahim Fazirulhisyam Hashim Aduwati Sali Nor K.Noordin Keivan Navaie Saber M.E.Fadul Department of Computer and Communication Systems Engineering Faculty of EngineeringUniversiti Putra MalaysiaSerdang43400Malaysia Wireless and Photonics Networks Research Centre(WiPNET) Faculty of EngineeringUniversiti Putra MalaysiaSerdang43400Malaysia Department of Communication and Computer Engineering College of Engineering and Medical SciencesKhartoum11111Sudan School of Computing and Communications Lancaster UniversityLancasterLA14YWUnited Kingdom Department of Electrical and Electronics Engineering Faculty of EngineeringUniversiti Putra MalaysiaSerdang43400Malaysia

The deployment of distributed multi-controllers for Software-Defined Networking(SDN)architecture is an emerging solution to improve network scalability and ***,the network control failure affects the dynamic resource allocation in distributed networks resulting in network disruption and low ***,we consider the control plane fault tolerance for cost-effective and accurate controller location models during control plane *** fault-tolerance strategy has been applied to distributed SDN control architecture,which allows each switch to migrate to next controller to enhance network *** this paper,the Reliable and Dynamic Mapping-based Controller Placement(RDMCP)problem in distributed architecture is framed as an optimization problem to improve the system reliability,quality,and *** considering the bound constraints,a heuristic state-of-the-art Controller Placement Problem(CPP)algorithm is used to address the optimal assignment and reassignment of switches to nearby controllers other than their regular *** algorithm identifies the optimal controller location,minimum number of controllers,and the expected assignment costs after failure at the lowest effective cost.A metaheuristic Particle Swarm Optimization(PSO)algorithm was combined with RDMCP to form a hybrid approach that improves objective function optimization in terms of reliability and *** effectiveness of our hybrid RDMCP-PSO was then evaluated using extensive experiments and compared with other baseline *** findings demonstrate that the proposed hybrid technique significantly increases the network performance regarding the controller number and load balancing of the standalone heuristic CPP algorithm.

关键词： Software defined networking Dynamic mapping Particle swarm optimization Reliability Multi-objective optimization Evolutionary

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Power and Voltage Control Based on DC Offset Injection for Bipolar Low-voltage DC Distribution System

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Journal of Modern Power systems and Clean Energy 2023年第5期11卷 1529-1539页

作者： Xinyi Kong Jianwen Zhang Jianqiao Zhou Jiajie Zang Jiacheng Wang Gang Shi Xu Cai key Laboratory of Control of Power Transmission and Conversion of Ministry of Education Department of Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China School of Electronic and Electrical Engineering Shanghai University of Engineering ScienceShanghaiChina School of Mechatronic Systems Engineering Simon Fraser UniversitySurreyBCCanada

The bipolar low-voltage DC(LVDC) distribution system has become a prospective solution to better integration of renewables and improvement of system efficiency and reliability. However, it also faces the challenge of power and voltage imbalance between two poles. To solve this problem, an interface converter with bipolar asymmetrical operating capabilities is applied in this paper. The steady-state models of the bipolar LVDC distribution system equipped with this interface converter in the gridconnected mode and off-grid mode are analyzed. A control scheme based on DC offset injection at the secondary side of the interface converter is proposed, enabling the bipolar LVDC distribution system to realize the unbalanced power transfer between two poles in the grid-connected mode and maintain the inherentpole voltage balance in the off-grid mode. Furthermore, this paper also proposes a primary-side DC offset injection control scheme according to the analysis of the magnetic circuit model, which can eliminate the DC bias flux caused by the secondaryside DC offset. Thereby, the potential core magnetic saturation and overcurrent issues can be prevented, ensuring the safety of the interface converter and distribution system. Detailed simulations based on the proposed control scheme are conducted to validate the function of power and voltage balance under the operation conditions of different DC loads.

关键词： Bipolar low-voltage DC distribution system interface converter DC offset injection unbalanced power voltage balance

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Reinforcement Learning-based Receding Horizon Control using Adaptive Control Barrier Functions for Safety-Critical systems 63

Reinforcement Learning-based Receding Horizon Control using ...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Sabouni, Ehsan Sabbir Ahmad, H.M. Giammarino, Vittorio Cassandras, Christos G. Paschalidis, Ioannis Ch. Li, Wenchao Boston University Division of Systems Engineering BostonMA02215 United States Boston University Department of Electrical and Computer Engineering The Division of Systems Engineering BostonMA02215 United States Boston University Department of Electrical and Computer Engineering Division of Systems Engineering Department of Biomedical Engineering BostonMA02215 United States

ISBN: (纸本)9798350316339

Optimal control methods provide solutions to safety-critical problems but easily become intractable. Control Barrier Functions (CBFs) have emerged as a popular technique that facilitates their solution by provably guaranteeing safety, through their forward invariance property, at the expense of some performance loss. This approach involves defining a performance objective alongside CBF-based safety constraints that must always be enforced with both performance and solution feasibility significantly impacted by two key factors: (i) the selection of the cost function and associated parameters, and (ii) the calibration of parameters within the CBF-based constraints, which capture the trade-off between performance and conservativeness. To address these challenges, we propose a Reinforcement Learning (RL)-based Receding Horizon Control (RHC) approach leveraging Model Predictive Control (MPC) with CBFs (MPC-CBF). In particular, we parameterize our controller and use bilevel optimization, where RL is used to learn the optimal parameters while MPC computes the optimal control input. We validate our method by applying it to the challenging automated merging control problem for Connected and Automated Vehicles (CAVs) at conflicting roadways. Results demonstrate improved performance and a significant reduction in the number of infeasible cases compared to traditional heuristic approaches used for tuning CBF-based controllers, showcasing the effectiveness of the proposed method. In order to guarantee reproducibility, our code is provided here: ***/EhsanSabouni/CDC2024_RL_adpative_MPC_CBF/tree/main © 2024 IEEE.

关键词： Adaptive control systems

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A Hierarchical Index Evaluation System based on Analytic Hierarchy Process (AHP) for Photovoltaic Collection System 20

A Hierarchical Index Evaluation System based on Analytic Hie...

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20th International Conference on AC and DC Power Transmission 2024, ACDC 2024

作者： Xiang, Miaoyi Guo, Peiqian Yuan, Zhichang Tian, Zhongbei Jia, Zixuan Jiang, Zhilin He, Ningyi Department of Electrical Engineering Tsinghua University Beijing China Department of Electronic Electrical and Systems Engineering University of Birmingham Birmingham United Kingdom

ISBN: (纸本)9781837242672

With the development of power electronics technologies, photovoltaic (PV) DC convergence can now effectively solve the problems caused by PV power stations connected to microgrids. In order to explore the advantages and disadvantages of AC and DC PV collection systems, this paper proposes a hierarchical index evaluation system of PV collection systems based on analytic hierarchy process (AHP). Under three different PV power levels, microgrids including PV, energy storage (ES) and electric vehicles (EV) are constructed, and the AC and DC PV convergence topologies are evaluated by considering five indicators: the comprehensive loss rate of the converter, the comprehensive line loss rate, the net exchange of electricity with the grid, the average power supply radius and the investment cost. The results show that the PV DC collection system has obvious advantages compared with the traditional AC collection system for the above five indexes. In addition, at each power level, the differences between different pooling schemes are evaluated, and the results show that the scheme with the photovoltaic capacity matching the charging demand of the electric vehicle connected to the low-voltage bus, and the remaining photovoltaic connected to the medium-voltage bus has the highest performance. © The Institution of engineering & Technology 2024.

关键词： DC distribution systems

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Enhanced quantum resources via two distant atom-cavity systems under the influence of atomic dissipation

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Communications in Theoretical Physics 2024年第2期76卷 62-73页

作者： M Setodeh Kheirabady M K Tavassoly M Rafeie E Ghasemian Laser and Optics Group Faculty of PhysicsYazd UniversityYazd 89195-741Iran Department of Electrical Engineering Faculty of Intelligent Systems Engineering and Data SciencePersian Gulf UniversityBushehrIran

Quantum resources such as entanglement and coherence are the holy grail for modern quantum technologies. Although the unwanted environmental effects tackle quantum information processing tasks, suprisingly these key quantum resources may be protected and even enhanced by the implementation of some special hybrid open quantum systems. Here, we aim to show how a dissipative atom-cavity-system can be accomplished to generate enhanced quantum *** do so, we consider a couple of dissipative cavities, where each one contains two effective two-level atoms interacting with a single-mode cavity field. In practical applications, a classical laser field may be applied to drive each atomic subsystem. After driving the system, a Bell-state measurement is performed on the output of the system to quantify the entanglement and coherence. The obtained results reveal that the remote entanglement and coherence between the atoms existing inside the two distant cavities are not only enhanced, but can be stabilized, even under the action of dissipation. In contrast, the local entanglement between two atoms inside each dissipative cavity attenuates due to the presence of unwanted environmental ***, the local coherence may show the same behavior as the remote ***, the system provides the steady state entanglement in various interaction regimes,particularly in the strong atom-cavity coupling and with relatively large detuning. More interestingly, our numerical analyses demonstrate that the system may show a memory effect due to the fact that the death and revival of the entanglement take place during the interaction. Our proposed model may find potential applications for the implementation of long distance quantum networks. In particular, it facilitates the distribution of quantum resources between the nodes of large-scale quantum networks for secure communication.

关键词： remote entanglement and coherence atomic dissipation steady state entanglement Bell-statemeasurement

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Advancing Healthcare Through Interoperability: Implementing Scalable Solutions for Patient Data Integration

Advancing Healthcare Through Interoperability: Implementing ...

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34th Medical Informatics Europe Conference, MIE 2024

作者： Gour, Shramika Peake, Ashley Tong, Chao Churm, James Ahmad, Bilal Pournik, Omid Arvanitis, Theodoros N. Department of Electronic Electrical and Systems Engineering School of Engineering University of Birmingham Birmingham United Kingdom

ISBN: (纸本)9781643685335

Healthcare faces significant challenges in exchanging and utilizing health information across diverse providers, necessitating innovative solutions for improved interoperability. This study presents a comprehensive exploration of scalable technical and semantic solutions for patient care integration, emphasizing the implementation of these solutions within the framework of the Fast Healthcare Interoperability Resources (FHIR) standard. Our approach revolves around the development and deployment of Technical Interoperability Suite (TIS) and Semantic Interoperability Suite (SIS) technology solutions to disparate health information systems, predominantly Electronic Health Records (EHRs) into a unified Patient Care Platform, fostering comprehensive data exchange and utilization. The integration process involves importing data from various EHR systems and transforming imported patient data into FHIR-standardized formats. The provided solution supports various functionalities, including automatic and manual importation of patient data, through standard computer-readable templates. The integration of TIS and SIS solutions is underpinned by a robust technological framework, incorporating technologies such as Typescript, Deno, and document-oriented databases such as MongoDB. The effectiveness of our interoperability solutions was validated through deployment in multinational EU projects: ADLIFE and CAREPATH. The scalability and generalizability of our approach underscore its potential for diverse healthcare settings. © 2024 The Authors.

关键词： Electronic health record

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Multi-Model Fusion Framework Using Deep Learning for Visual-Textual Sentiment Classification

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Computers, Materials & Continua 2023年第8期76卷 2145-2177页

作者： Israa K.Salman Al-Tameemi Mohammad-Reza Feizi-Derakhshi Saeed Pashazadeh Mohammad Asadpour Computerized Intelligence Systems Laboratory Department of Computer EngineeringFaculty of Electrical and Computer EngineeringUniversity of TabrizTabriz51368Iran Department of Computer Engineering Faculty of Electrical and Computer EngineeringUniversity of TabrizTabriz51368Iran State Company for Engineering Rehabilitation and Testing Iraqi Ministry of Industry and MineralsBaghdad10011Iraq

Multimodal Sentiment Analysis(SA)is gaining popularity due to its broad application *** existing studies have focused on the SA of single modalities,such as texts or photos,posing challenges in effectively handling social media data with multiple ***,most multimodal research has concentrated on merely combining the two modalities rather than exploring their complex correlations,leading to unsatisfactory sentiment classification *** by this,we propose a new visualtextual sentiment classification model named Multi-Model Fusion(MMF),which uses a mixed fusion framework for SA to effectively capture the essential information and the intrinsic relationship between the visual and textual *** proposed model comprises three deep neural *** different neural networks are proposed to extract the most emotionally relevant aspects of image and text ***,more discriminative features are gathered for accurate sentiment ***,a multichannel joint fusion modelwith a self-attention technique is proposed to exploit the intrinsic correlation between visual and textual characteristics and obtain emotionally rich information for joint sentiment ***,the results of the three classifiers are integrated using a decision fusion scheme to improve the robustness and generalizability of the proposed *** interpretable visual-textual sentiment classification model is further developed using the Local Interpretable Model-agnostic Explanation model(LIME)to ensure the model’s explainability and *** proposed MMF model has been tested on four real-world sentiment datasets,achieving(99.78%)accuracy on Binary_Getty(BG),(99.12%)on Binary_iStock(BIS),(95.70%)on Twitter,and(79.06%)on the Multi-View Sentiment Analysis(MVSA)*** results demonstrate the superior performance of our MMF model compared to single-model approaches and current state-of-the-art techniques based on model evaluation cr

关键词： Sentiment analysis multimodal classification deep learning joint fusion decision fusion interpretability

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Enhancing Renewable Energy Integration:A Gaussian-Bare-Bones Levy Cheetah Optimization Approach to Optimal Power Flow in electrical Networks

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Computer Modeling in engineering & Sciences 2024年第8期140卷 1339-1370页

作者： Ali S.Alghamdi Mohamed A.Zohdy Saad Aldoihi Department of Electrical Engineering College of EngineeringMajmaah UniversityAl-Majmaah11952Saudi Arabia Electrical and Computer Engineering Department Oakland UniversityRochesterMI48309USA Institute of Earth and Space Science King Abdulaziz City for Science and Technology(KACST)Riyadh11442Saudi Arabia Computer Science and Systems Engineering Institute Polytechnique de ParisPalaiseau CedexFrance

In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for network *** study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic *** primary objective is to minimize the total operating costs of RESs,considering four functions:overall operating costs,voltage deviation management,emissions reduction,voltage stability index(VSI)and power loss ***,a carbon tax is included in the objective function to reduce carbon *** scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal *** results demonstrate GBBLCO’s efficacy in six optimization scenarios:total cost with valve point effects,total cost with emission and carbon tax,total cost with prohibited operating zones,active power loss optimization,voltage deviation optimization and enhancing voltage stability index(VSI).GBBLCO outperforms conventional techniques in each scenario,showcasing rapid convergence and superior solution ***,GBBLCO navigates complexities introduced by valve point effects,adapts to environmental constraints,optimizes costs while considering prohibited operating zones,minimizes active power losses,and optimizes voltage deviation by enhancing the voltage stability index(VSI)*** research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local *** emerges as a versatile and robust optimization tool for diverse challenges in power systems,offering a promising solution for the evolving needs of renewable energy-integrated power grids.

关键词： Renewable energy integration optimal power flow stochastic renewable energy sources gaussian-bare-bones levy cheetah optimizer electrical network optimization carbon tax optimization

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