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1st EAI International Conference on Computer Science and engineering, COMPSE 2016

作者： Thomas, J. Joshua Lokanathan, Raaj Jothi, Justtina Anantha Department of Computing School of Engineering Computing and Built Environment KDU Penang University College Penang Malaysia

ISBN: (纸本)9781631901362

The paper describes based on techniques on the air pollution data visualization. This approach involves graph plotting algorithms with parallel coordinates that explore the raw data with interactive filtering that facilitates the insight of the materials that mixed and harm the population in northern Malaysia. By presenting the parallel coordinates method to visualize the parameter space that influence and visually identify the hazardous, moderate, unhealthy gaseous content in the air. The paper discusses the performance of the chosen visualization method and tested with northern region datasets.

关键词： Data visualization

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Model Predictive Current Control for Low-Cost Shunt Active Power Filter

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CSEE Journal of Power and Energy Systems 2024年第4期10卷 1589-1598页

作者： Ali A.Abdel-Aziz Mohamed A.Elgenedy Barry W.Williams the Electronic and Electrical Engineering Department Strathclyde UniversityGlasgowG11XQUK School of Computing Engineering and the Built Environment(SCEBE)Glasgow Caledonian UniversityGlasgowG40BAUK

Performance of a three-phase shunt active power filter(SAPF)relies on the capability of the controller to track the reference ***,designing an accurate current controller is crucial to guarantee satisfactory SAPF *** paper presents a model predictive current controller(MPCC)for a low-cost,four-switch,shunt active power filter for power quality improvement.A four-switch,B4,converter topology is adopted as an SAPF,hence offering a simple,robust,and low-cost *** addition,to further reduce overall cost,only two interfacing filter inductors,instead of three,are used to eliminate switching current *** proposed SAPF model MPCC is detailed for implementation,where simulation and experimental results validate effectiveness of the proposed control algorithm showing a 20%improvement in total harmonic distortion compared with a conventional hysteresis band current controller.

关键词： Four-switch converter harmonic distortion model predictive current control power quality shunt active power filter

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Vacuum Insulation Panels for Domestic New Builds and Refurbishment in Scotland 15th

Vacuum Insulation Panels for Domestic New Builds and Refurbi...

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15th KES International Conference on Sustainability and Energy in Buildings, SEB 2023

作者： Mitchell, Nicolle Sajjadian, Seyed Masoud School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom

ISBN: (纸本)9789819985005

Insulations have fundamental role in buildings’ thermal performance by assisting in reducing heat loss and excessive heat gain through the thermal envelope, which subsequently reduces the demand on heating and cooling systems. Increased energy performance requirements within regulations and government targets for reduced carbon emissions have driven an expansion in the types of insulation available within the industry. High demand for housing has increased the number of new build developments and conversions of existing properties, which must comply with current building regulations. This paper reviews the suitability of vacuum insulation panels (VIPs) for domestic properties in the UK and concludes that the panels are not recommended for new properties with a proposed lengthy lifespan, and further research and standardising of tests are required to establish more accurate lifespans of the panels. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Thermal insulation

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Instils Trust in Random Forest Predictions 10

Instils Trust in Random Forest Predictions

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10th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2023

作者： Jamnal, Gopal Singh Leeds Beckett University School of Built Environment Engineering & Computing Leeds United Kingdom

ISBN: (纸本)9798350345032

This paper addresses the interpretability and transparency behind the random forest model predictions. Random forest is an ensemble of bootstrapped independent decision trees that are trained on subsets of input data to make predictions. Although random forest is a robust model that can overcome bias, its inherent complexity, and poor interpretability can make it challenging to apply in many application domains that require transparency and explainability in the model's predictions. This lack of transparency in the decision-making process can prevent users from analyzing what makes the model arrive at a specific prediction. The paper presents a visual analytic application to overcome transparency challenges by providing a clear structure of individual decision trees and hierarchical relationships between features. This allows users to analyze latent information and instils trust in the random forest model. Additionally, statistical analysis of feature ranking agreement and prediction popularity reduces mental burden of the user. The paper includes two case studies to evaluate model's uncertainty, bias, and variances in predictions with explainability at local and global scales of decision paths. The visual analytics application provides a coordinated multiple-view system to instil trust in random forest models. © 2023 IEEE.

关键词： Transparency

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Identification and Control of Transportation Units with Sequenced Learning Models Using Neural Networks

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Recent Patents on engineering 2025年

作者： Selvarajan, Shitharth Manoharan, Hariprasath Saleem, Farrukh Ramesh, S. Manikandan, T. Cyber Security & Digital Forensics School of Built Environment Engineering and Computing Leeds Beckett University Leeds LS1 3HE United Kingdom Department of Electronics and Communication Engineering Panimalar Engineering College Poonamallee Chennai India School of Built Environment Engineering and Computing Leeds Beckett University Leeds LS1 3HE United Kingdom Department of Mechatronics Engineering Rajalakshmi Engineering College Rajalakshmi Nagar Thandalam Chennai 602105 India Department of CSBS Rajalakshmi Engineering College Rajalakshmi Nagar Thandalam Chennai 602105 India

Aim: This study illustrates the significance of transport units in monitoring diverse paths using a critical system model. The suggested method identifies proficiency and framework patterns that evolve across different time intervals, utilising machine learning optimisation that incorporates sequence learning with interconnected neural networks. Background: As an increasing number of cars are interconnected for data communication to illustrate available routes, it is essential to have suitable connectivity for transportation units. This study may facilitate intelligent connectivity across transportation units by employing essential shifts without compromising the efficiency of connected units. Objective: This study aimed to integrate the parametric design representations with neural networks to address the primary goal of min-max functions, hence enhancing the efficiency of transportation units. Method: The method presented here has employed sequenced learning patterns to select the shortest path while rapidly altering pathway representations. Results: The alterations in pathways influenced by emissions have been noted and excluded from connectivity units to enhance the overall lifetime of transportation units in the projected model. Conclusion: The results have been examined through a simulation framework encompassing four scenarios, wherein potential connectedness has enhanced both the proficiency rate and the structure while minimising the shifts. Subsequently, a comparison of the proposed method with the existing methodology, where total efficiency has been assessed, has revealed the proposed method to maximise the efficiency to 95%. In contrast, the existing strategy has yielded a reduced efficiency of 86%. © 2024 The Author(s). Published by Bentham Science Publishers.

关键词： adeptness rate machine learning shortest path Transportation units

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Investigating Markers and Drivers of Gender Bias in Machine Translations 31

Investigating Markers and Drivers of Gender Bias in Machine ...

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31st IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2024

作者： Barclay, Peter J Sami, Ashkan School of Computing Engineering and Built Environment Edinburgh Napier University Edinburgh United Kingdom

ISBN: (纸本)9798350330663

Implicit gender bias in Large Language Models (LLMs) is a well-documented problem that needs to be better understood in order to be addressed effectively. Implications of gender introduced into automatic translations can perpetuate real-world biases in Software engineering and other domains. However, some LLMs use heuristics or post-processing to mask such bias, which makes investigation more difficult. Here, we examine bias in language models via back-translation, using the DeepL online translation service to investigate the bias evinced when repeatedly translating a set of 56 Software engineering tasks used in a previous study. Each statement starts with 'she', and is translated first into a 'genderless' intermediate language then back into English;we then examine pronoun-choice in the back-translated texts. We believe this approach provides a useful alternative to large-scale surveys in mapping biases. We expand prior research in the following ways: (1) by comparing results across five intermediate languages, namely Finnish, Indonesian, Estonian, Turkish and Hungarian;(2) by proposing a novel metric for assessing the variation in gender implied in repeated translations of the same phrase, avoiding the over-interpretation of individual pronouns, apparent in earlier work;(3) by investigating sentence features that drive bias;(4) and by comparing results from three time-lapsed datasets to establish the reproducibility of the approach. We found that some languages display similar patterns of pronoun use, falling into three loose groups, but that patterns vary between groups;this underlines the need to work with multiple languages. We also identify the main verb appearing in a sentence as a likely significant driver of implied gender in the translations. Moreover, we see a good level of replicability in the results, and establish that our variation metric proves robust despite an obvious change in the behaviour of the DeepL translation API during the course of the stu

关键词： Machine translation

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The Threat of Adversarial Attacks Against Machine Learning-based Anomaly Detection Approach in a Clean Water Treatment System 19

The Threat of Adversarial Attacks Against Machine Learning-b...

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19th Annual International Conference on Distributed computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023

作者： Moradpoor, Naghmeh Maglaras, Leandros Abah, Ezra Robles-Durazno, Andres School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom

ISBN: (纸本)9798350346497

The protection of Critical National Infrastructure is extremely important due to nations being dependent on their operation and steadiness. Any disturbance to this infrastructure could have a devastating consequence on physical security, economic wellbeing, and public health and safety. To deal with the growing number of attacks, with differing degrees of impact against such systems, various machine learning-based Intrusion Detection Systems have been employed given their success in the automated detection of known and unknown cyberattacks with high degrees of accuracy. However, since machine learning models are susceptible to attacks, also known as Adversarial Machine Learning, employing such Intrusion Detection Systems has also created an additional attack vector which could potentially help hackers to evade detection. This paper explores the robustness of both traditional and non-traditional supervised machine learning algorithms by studying their classification behaviour under adversarial attacks. This includes machine learning algorithms such as Support Vector Machine, Logistic Regression, and Deep Learning models, such as Artificial Neural Network. Additionally, this contains adversarial machine learning attacks such as random & targeted label flipping, Fast Gradient Sign Method, and Jacobian Saliency Map Attack. A genuine dataset captured from a model of a clean water treatment system was used to support the experiments presented in this paper. Overall, the adversarial attacks were successful to decrease the classification performance of the machine learning algorithms but with varying degrees of influence. For example, the targeted label flipping has a stronger impact on the classification performance reduction compared with the random label flipping attacks. Additionally, Deep Learning model and Support Vector Machine both show longer fight against the adversarial attacks compared with Logistic Regression. © 2023 IEEE.

关键词： Anomaly detection

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A Metal Selection and Structural Optimisation Methodology for Large-Scale Direct-Drive Wind Turbine Electrical Generators 12

A Metal Selection and Structural Optimisation Methodology fo...

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12th International Conference on Power Electronics, Machines and Drives, PEMD 2023

作者： Bichan, Magnus Jack, Andrew Jaen-Sola, Pablo School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom

With an ever-growing market share for offshore wind, direct-drive generators are touted as a low maintenance alternative to conventional drivetrains. However, to contend with higher torque requirements essential for low-speed operation, direct-drive machines are vastly heavier than conventional geared systems. As material use and sustainable engineering remains a key theme in the current climate, establishing methods for best practice in design stages is critical. This report therefore details a proposed methodology for the efficient, sustainable design of large-scale, direct-drive wind turbine electrical generator structures, considering mechanical, and thermal performances, whilst meeting sustainability metrics. This investigation finds that Coefficient of Thermal Expansion has a considerable influence on the maximum Equivalent Von-Mises’ stress, for an optimised rotor structure under the expected loading scenario. Material choice was also found to have a significant impact on the rotor’s carbon footprint and the energy use during manufacture and transport. © The Institution of engineering & Technology 2023.

关键词： Carbon footprint

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Virtual power plants and net zero targets: A case study of the United Kingdom 10

Virtual power plants and net zero targets: A case study of t...

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10th IEEE International Conference on Power Systems, ICPS 2023

作者： Ullah, Zahid Arshad, Arshad Nekahi, Azam Glasgow Caledonian University School of Computing Engineering and Built Environment Glasgow United Kingdom

ISBN: (纸本)9798350318739

With increasing international climate concerns, getting to net zero emissions has become a top priority for many countries. The energy sector is crucial to the shift to a future powered by sustainable energy. To this end, virtual power plants (VPPs), have emerged as a powerful tool with the ability to transform energy systems by integrating renewable energy sources (RES), energy storage technologies, enabling demand response programs, empowering consumer participation, and improving grid management. This study presents a detailed analysis of VPPs as a way of achieving net-zero targets, focusing on the case study of the United Kingdom (UK). By assessing the UK's energy environment, its energy policies and legislative framework, the advancement of renewable energy technologies, increasing flexibility, and the transition towards sustainable energy systems. The approach presented shows the viability of VPPs to lower carbon footprint and emphasizes on accelerating the transition away from fossil fuels to further reduce carbon emissions associated with traditional power generation. © 2023 IEEE.

关键词： Virtual Power Plants

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SRSS: A New Chaos-Based Single-Round Single S-Box Image Encryption Scheme for Highly Auto-Correlated Data 9

SRSS: A New Chaos-Based Single-Round Single S-Box Image Encr...

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9th International Conference on engineering and Emerging Technology, ICEET 2023

作者： Khan, Muhammad Shahbaz Ahmad, Jawad Ali, Hisham Pitropakis, Nikolaos Al-Dubai, Ahmed Ghaleb, Baraq Buchanan, William J. School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom

ISBN: (纸本)9798350316926

With the advent of digital communication, securing digital images during transmission and storage has become a critical concern. The traditional s-box substitution methods often fail to effectively conceal the information within highly auto-correlated regions of an image. This paper addresses the security issues presented by three prevalent S-box substitution methods, i.e., single S-box, multiple S-boxes, and multiple rounds with multiple S-boxes, especially when handling images with highly auto-correlated pixels. To resolve the addressed security issues, this paper proposes a new scheme SRSS-the Single Round Single S-Box encryption scheme. SRSS uses a single S-box for substitution in just one round to break the pixel correlations and encrypt the plaintext image effectively. Additionally, this paper introduces a new Chaos-based Random Operation Selection System-CROSS, which nullifies the requirement for multiple S-boxes, thus reducing the encryption scheme's complexity. By randomly selecting the operation to be performed on each pixel, driven by a chaotic sequence, the proposed scheme effectively scrambles even high auto-correlation areas. When compared to the substitution methods mentioned above, the proposed encryption scheme exhibited exceptionally well in just a single round with a single S-box. The close-to-ideal statistical security analysis results, i.e., an entropy of 7.89 and a correlation coefficient of 0.007, validate the effectiveness of the proposed scheme. This research offers an innovative path forward for securing images in applications requiring low computational complexity and fast encryption and decryption speeds. © 2023 IEEE.

关键词： Pixels

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