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59th International Universities Power engineering Conference, UPEC 2024

作者： Anwar, Arshad Syed Farrag, Mohamed Emad Baird, Jim Glasgow Caledonian University School of Computing Engineering and Built Environment Glasgow United Kingdom Scotland Helwan University Cairo Egypt

ISBN: (纸本)9798350379730

In response to escalating electrical demands from Electric Vehicles and Heat Pumps, the impact on transformer aging has been comprehensively investigated. By incorporating extensive demand data analysis, this study enhances the Network Output Measures methodology to predict transformer aging more accurately. The penetration of Electric Vehicles and adoption of Heat Pumps, which are expected to double the typical household electricity consumption, are quantified and simulated. This simulation uses detailed data from a lead asset database, applying comprehensive equations to predict the aging and degradation of transformers under increased load conditions. This study presents a critical advancement in the understanding and management of transformer aging under the increased stress imposed by modern energy demands. The results highlight the accelerated aging process, quantified as a rate substantially higher than under normal conditions. A revised approach to the Network Output Measures methodology is proposed, emphasizing the need for integrating real-time monitoring and advanced analytics to better manage the life cycle of electrical network assets. © 2024 IEEE.

关键词： Demand response

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Using STEAM Principles and Experiences to Critique and Enhance a Transdisciplinary Collaboration Framework

Using STEAM Principles and Experiences to Critique and Enhan...

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2023 IEEE IFEES World engineering Education Forum and Global engineering Deans Council, WEEF-GEDC 2023

作者： Rodier, Chantal Carter, Claudia University of Ottawa School of Engineering Design & Teaching Innovation Faculty of Engineering Ottawa Canada Birmingham City University College of the Built Environment Faculty of Computing Engineering and Built Environment United Kingdom

ISBN: (纸本)9798350316025

STEAM interdisciplinary collaborations are essential to address complex 21st-century challenges, as they can offer new ways of learning, knowing, and collaborating with more holistic perspectives on the world. Transdisciplinary problem framing and knowledge creation can furthermore benefit deep and applied learning and guide projects. However, setting up teams across academic disciplines and practices to achieve meaningful and effective outcomes is challenging, and a coherent framework and repeatable approach could aid in their success. Today, no framework for STEAM collaborations addresses the entire collaboration cycle between project partners from diverse academic disciplines and non-academic backgrounds to systematically achieve transdisciplinary knowledge creation. This study presents a STEAM evaluation of a general framework for conducting transdisciplinary research (TDR) in three phases: TDR initiation, TDR management, and transdisciplinary knowledge exchange. This STEAM evaluation was based on the researchers' experience and network of STEAM research and recent projects. A particular focus in this paper is to share insights into the challenges faced by diverse disciplinary collaborations with engineering students and engineers in achieving transdisciplinarity and how a STEAM lens to a TDR framework may help to prepare for or overcome these challenges. The study is expected to contribute to the development of a comprehensive framework for STEAM collaborations that could be used by researchers and practitioners to achieve transdisciplinary knowledge creation, be that in their research or teaching. © 2023 IEEE.

关键词： Steam

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Study of Energy Saving Potential of Solar Shading Devices in Various Climates 5th

Study of Energy Saving Potential of Solar Shading Devices in...

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5th International Conference on Building Energy and environment, COBEE 2022

作者： Turner, Lewis Yang, Siliang White, Michael School of Built Environment Engineering and Computing Leeds Beckett University West Yorkshire Leeds United Kingdom

ISBN: (纸本)9789811998218

Research studies previously performed using a singular climate had shown that certain solar shading devices have the potential for energy savings for buildings. In this paper, a commercial building was studied using the DesignBuilder programme to identify the solar performance of the building. The model represented a typical commercial building with standard properties. This study allowed for comparison for horizontal louvre, overhang and vertical shading types in various climates (Florida, Cairo, Leeds and Reykjavik) for a year presented in terms of monthly energy demand. Comparative analysis identified that the horizontal louvre type, which consisted of 5-louvre with 400 mm spacing between the adjacent louvres, performed best to reduce the annual energy usage for Florida (4191 kWh), Cairo (5194 kWh), Leeds (1882 kWh) and Reykjavik (957 kWh). In general, all environments showed increases through heating and lighting, but proportionally cooling reduction was higher annually. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Energy conservation

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Partial Safety Factors Determination to Model Uncertainty on Persian Historic Masonry Elements

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Iranian Journal of Science and Technology - Transactions of Civil engineering 2025年 1-24页

作者： Ghamari, Mehrdad Karimi, MohammadSaeed School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh EH10 5DT United Kingdom Faculty of Civil Engineering Semnan University Semnan Iran

This research explores the determination of partial safety factors for Persian historical masonry walls, following Eurocode-6 principles, which account for material and geometric uncertainties. The study explores the impact of lateral constraints or boundary conditions (B.C.), Height-Length Proportion (α), the thickness of wall (t), and pre-compression levels as a gravity loading (P) on the structural behavior of these walls when subjected to uncertain conditions. Nonlinear pushover analyses were carried out on 200 masonry wall specimens, considering four α, ten variations in B.C. (including the impact of vertical (lateral walls) and horizontal (upper slabs) components), and three standard t (0.20, 0.35, and 0.50 m), all subjected to three different P (0.05, 0.1, and 0.2 MPa). The research also evaluates how variations in the modulus of elasticity and t affect the results under nonlinear analysis. Findings show that the Maximum Shear Capacity (F_u), maximum drift limit (δ_u/H), and effective stiffness (K_eff) are highly responsive to these variables. Higher F_u and K_eff were observed with increased t, P, and B.C., while reducing α had the opposite effect. As B.C., t, and P increased, δ_u/H showed a reduction, while a decrease in α led to an increase. The estimated F_u ranged 114.8–1476.5 kN, δu/H varied 1.56–3.43, and Keff spanned 7.72–250.80 kN/mm. The proposed partial safety factors are 1.17–1.96 for material strength (γ_M), 1.16–1.77 for displacement capacity (γ_du), and 1.15–1.96 for effective stiffness (γ_k). The results emphasize that increasing B.C., t, and reducing α result in higher γ_M and γ_k values, while γ_du values decrease. Additionally, all safety factors rise with increased P. These findings offer important insights for enhancing the preservation of Persian historical masonry structures. © The Author(s), under exclusive

关键词： Eurocode-6 Masonry walls Nonlinear analysis Partial safety factors Persian historical structures Structural behavior

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Optimal DC link voltage stabilisation technique for a grid-connected PV system

Optimal DC link voltage stabilisation technique for a grid-c...

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2024 Energy Conversion Congress and Expo Europe, ECCE Europe 2024

作者： Khan, Taimoor Ahmad Kahwash, Fadi Ahmed, Jubaer Goh, Keng Edinburgh Napier University School of Computing Engineering & Built Environment EdinburghEH10 5DT United Kingdom

ISBN: (纸本)9798350364446

The utilisation of renewable energy (RES) powered microgrids has seen significant growth due to environmental concerns. These RES resources are often integrated with the grid through a DC link. Maintaining the DC link voltage at a specified level is of prime importance to ensure smooth and efficient supply in the presence of fluctuating RES. Therefore, this work presents a fractional order super twisting sliding mode control technique (FOSTSMC) to stabilise the voltage, reduce response time, and overshoot at the DC link of a grid-connected microgrid. The microgrid under study is composed of a photovoltaic (PV) system with an incremental conductance and integral regulator-based maximum power point tracking (IC+IR MPPT) algorithm. Firstly, a conventional proportional-integral (PI) control-based voltage control technique is implemented, which is considered a benchmark technique for comparison and validation purposes. Secondly, the proposed control technique is implemented to control the DC link voltage to a reference voltage in constant and varying solar irradiance. Additionally, an Ant colony optimisation algorithm is used to optimally tune the coefficients of the FOSTSMC controller. The simulation results demonstrated the effectiveness of the ACO-FOSTSMC which exhibits 72% lower settling time, and no overshoot when compared with the benchmark technique. The results of the PI controller exhibit a longer settling time with a 23.6% overshoot, and 26% undershoot. The results indicated that the proposed control strategy can be effective in maintaining the DC link voltage to ensure a smooth power supply. © 2024 IEEE.

关键词： Microgrids

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Enhancing Security and Privacy in Federated Learning for Connected Autonomous Vehicles With Lightweight Blockchain and Binius ZeroKnowledge Proofs 17

Enhancing Security and Privacy in Federated Learning for Con...

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17th International Conference on Security of Information and Networks, SIN 2024

作者： Andriambelo, Ny Hasina Moradpoor, Naghmeh University of Antananarivo Cognitive Sciences and Applications Madagascar School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom

ISBN: (纸本)9798331509736

The rise of Autonomous Vehicles (AVs) brings with it the need for secure and privacy-preserving machine learning models. Federated Learning (FL) allows AVs to collaboratively train models while keeping raw data localized. However, traditional FL systems are vulnerable to security threats, including adversarial attacks, data breaches, and dependency on a central aggregator, which can be a single point of failure. To address these concerns, this paper introduces a peer-to-peer decentralized federated learning system that integrates lightweight blockchain technology and Binius Zero- Knowledge Proofs (ZKPs) to enhance security and privacy. In this system, Binius ZKPs ensure that model updates are cryptographically verified without exposing sensitive information, guaranteeing data confidentiality and integrity during the learning process. The lightweight blockchain framework secures the network by creating an immutable, decentralized record of all model updates, thus preventing tampering, fraud, or unauthorized modifications. This decentralized approach eliminates the need for a central aggregator, significantly enhancing system resilience to attacks and making it suitable for dynamic environments like AV networks. Additionally, the system's design includes Byzantine resilience, providing protection against adversarial nodes and ensuring that the global model aggregation process remains robust even in the presence of malicious actors. Extensive performance evaluations demonstrate that the system achieves low-latency, scalability, and efficient resource usage while maintaining strong security and privacy guarantees, making it an ideal solution for real-time federated learning in autonomous vehicle networks. The proposed framework not only ensures privacy but also fosters trust among participants in a fully decentralized environment. © 2024 IEEE.

关键词： Federated learning

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Scalable Machine Learning Architectures for IPA-Driven Maintenance Task Allocation in Large-Scale Building Portfolios 24

Scalable Machine Learning Architectures for IPA-Driven Maint...

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7th International Conference on Information Science and Systems, ICISS 2024

作者： Huang, Zhimeng Liu, Xiaodong Romdhani, Imed Shih, Chi-Sheng School of Computing Engineering and Built Environment Edinburgh Napier University Edinburgh United Kingdom College of Electrical Engineering and Computer Science National Taiwan University Taipei Taiwan

ISBN: (纸本)9798400717567

This research presents a groundbreaking approach to Building Maintenance Management (BMM) by introducing an Intelligent Process Automation (IPA)-Driven Building Maintenance Management (IBMM) model. This innovative model harnesses the synergies between Artificial Intelligence (AI), Machine Learning (ML), and Internet of Things (IoT) technologies to transition from reactive to proactive and predictive building maintenance strategies. The study highlights the critical gap in current BMM practices—the absence of intelligent systems for anticipating and addressing maintenance issues before they escalate. Through an extensive literature review, the transformative potential of AI and IoT for enhancing building maintenance management within smart cities is explored, establishing a foundation for the IBMM model’s application. The core of this research lies in its novel application of scalable machine learning architectures to automate and optimize maintenance task allocation in large-scale building portfolios. The practicality of the IBMM model is demonstrated via a proof of concept (POC) in an industrial setting, evidencing its capacity to improve efficiency, reduce costs, and bolster sustainability in building maintenance operations. The model epitomizes a paradigm shift in BMM by integrating IPA, which combines AI and ML, facilitating automated, intelligent decision-making and task allocation. Among its advancements, the IBMM model introduces enhanced predictive maintenance through real-time data analysis, adaptive learning and optimization, automated decision-making, and human-machine collaboration, contributing to energy efficiency and alignment with smart city objectives. The paper delineates the methodology, design, and implementation of a machine learning model for engineer task assignments, culminating in a case study that validates the model’s efficacy. This research not only signifies a significant advancement in BMM by leveraging IPA technologies for autonomous p

关键词： Predictive maintenance

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Quality-Diversity Optimisation on a Physical Robot Throu Dynamics-Aware and Reset-Free Learning

Quality-Diversity Optimisation on a Physical Robot Throu Dyn...

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2023 Genetic and Evolutionary Computation Conference Companion, GECCO 2023 Companion

作者： Smith, Simón C. Lim, Bryan Janmohamed, Hannah Cully, Antoine School of Computing Engineering and The Built Environment Edinburgh Napier University United Kingdom Department of Computing Imperial College London United Kingdom

ISBN: (纸本)9798400701207

Learning algorithms, like Quality-Diversity (QD), can be used to acquire repertoires of diverse robotics skills. This learning is commonly done via computer simulation due to the large number of evaluations required. However, training in a virtual environment generates a gap between simulation and reality. Here, we build upon the Reset-Free QD (RF-QD) algorithm to learn controllers directly on a physical robot. This method uses a dynamics model, learned from interactions between the robot and the environment, to predict the robot’s behaviour and improve sample efficiency. A behaviour selection policy filters out uninteresting or unsafe policies predicted by the model. RF-QD also includes a recovery policy that returns the robot to a safe zone when it has walked outside of it, allowing continuous learning. We demonstrate that our method enables a physical quadruped robot to learn a repertoire of behaviours in two hours without human supervision. We successfully test the solution repertoire using a maze navigation task. Finally, we compare our approach to the MAP-Elites algorithm. We show that dynamics awareness and a recovery policy are required for training on a physical robot for optimal archive generation. Video available at https://***/BgGNvIsRh7Q © 2023 Copyright held by the owner/author(s).

关键词： Robots

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Green Wave Control Strategy for Optimal Energy Consumption by Model Predictive Control in Electric Vehicles 6

Green Wave Control Strategy for Optimal Energy Consumption b...

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6th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Ozkan, Furkan Arslan, M. Selcuk Mercan, Hatice Dept. of Mechatronics Engineering Yildiz Technical University Istanbul Turkey School of Computing Engineering and the Built Environment Glasgow Caledonian University Glasgow United Kingdom

ISBN: (纸本)9798350394634

Electric vehicles are becoming increasingly popular as a sustainable alternative to traditional combustion engine vehicles. However, to fully realize the potential of EVs in reducing environmental impact and energy consumption, efficient control strategies are essential. This study explores the application of green wave control using model predictive control for electric vehicles, coupled with energy consumption modeling using neural networks. The strategy consists of three components: a fuel consumption model, a powertrain model, and an optimization algorithm. These components work together to generate an optimal vehicle control plan that minimizes energy usage while maintaining desired performance levels. The green wave control strategy utilizes anticipated roadway information to vary the vehicle's speed within a preset speed window, ensuring an energy-saving operation. The results show that the green wave control strategy can lead to significant fuel savings, making electric vehicles even more sustainable and cost-effective. © 2024 IEEE.

关键词： Energy efficiency

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Dementia Deterioration Prediction Using Machine Learning 24

Dementia Deterioration Prediction Using Machine Learning

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8th International Conference on Cloud and Big Data computing, ICCBDC 2024

作者： Almardoud, Layla Dawood Tawfik, Hissam Majzoub, Sohaib Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates School of Built Environment Engineering and Computing Leeds Beckett University Leeds United Kingdom

ISBN: (纸本)9798400717253

Dementia is a disease that imposes medical, social, and economic challenges on medical professionals, caregivers, and the patients themselves. Dementia monitoring and prognosis are critical factors besides dementia diagnosis. However, recent studies on dementia prognosis involve people with diagnosed dementia and not non-demented with a high risk of being demented, like people with cognitive difficulties. The aim of this paper is to use Machine Learning (ML) algorithms to predict patients with the risk of deterioration from medical histories containing clinical, cognitive, and profile data collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The best model was the Random Forest model with a sensitivity of 0.79, accuracy of 0.77, specificity of 0.76, F1-score of 0.78, and an AUROC of 0.83. Moreover, the model was interpreted through permutation importance. Using the permutation importance tool, the study highlighted the strong effect of diagnosis information and specific symptoms like muscle pain for dementia deterioration prediction. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Adversarial machine learning

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