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Revisiting the thermal ageing on the metallised polypropylene film capacitor:from device to dielectric film

High Voltage 2023年第2期8卷 305-314页

作者： Yushuang He Feipeng Wang Guoqiang Du Lei Pan Kaizheng Wang Reimund Gerhard Ronald Plath Pawel Rozga Pavel Trnka State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing UniversityChongqingChina Faculty of Electrical Engineering Kunming University of Science and TechnologyKunmingChina Soft Transducer Materials and Systems Universität PotsdamPotsdamGermany Faculty IV Electrical Engineering and Computer Science Technische Universität BerlinBerlinGermany Institute of Electrical Power Engineering Lodz University of TechnologyLodzPoland Faculty of Electrical Engineering University of West BohemiaPilsenCzech Republic

Metallised polypropylene film capacitors(MPPFCs)are widely used in power electronics and are generally degraded by elevated *** work aims to determine the relationships between the structural changes of MPPFC and the microstructural variations of the PP film during the thermal ageing of MPPFC at 100℃ for 38 *** capacitance of MPPFC has a slight decrease during thermal ***,the breakdown voltage of the MPPFC decreases by 39.4%by the *** partial discharge(PD)number of MPPFC increases linearly with ageing *** tear-down analysis of the MPPFC reveals that the molecular structure of the PP film has not been altered but has led to molecular chain scission and the generation of some polar fragments/***,the relative permittivity of the PP films rises as the ageing time ***,thermal ageing causes the conversion of aluminum to alumina in the metallised electrode,which is hydrophilic for polar groups and leads to an adhesion effect between the metallised electrodes and the PP *** angle measurements prove that the surface hydrophilicity of the PP sample increased after thermal ***,the PD/breakdown voltage in the MPPFC increases/decreases due to the uneven adhesion of the metallised PP film.

关键词： film thermal ageing

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Enhancing Telemarketing Success Using Ensemble-Based Online Machine Learning

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Big Data Mining and Analytics 2024年第2期7卷 294-314页

作者： Shahriar Kaisar Md Mamunur Rashid Abdullahi Chowdhury Sakib Shahriar Shafin Joarder Kamruzzaman Abebe Diro Department of Information Systems and Business Analytics RMIT UniversityMelbourne 3000Australia School of Engineering and Technology Central Queensland UniversityRockhampton 4700Australia Faculty of Engineering Computer and Mathematical ScienceUniversity of AdelaideAdelaide 5005Australia Centre for Smart Analytics Federation University AustraliaBallarat 3350Australia School of Accounting Information Systems and Supply ChainRMIT UniversityMelbourne 3000Australia

Telemarketing is a well-established marketing approach to offering products and services to prospective *** effectiveness of such an approach,however,is highly dependent on the selection of the appropriate consumer base,as reaching uninterested customers will induce annoyance and consume costly enterprise resources in vain while missing interested *** introduction of business intelligence and machine learning models can positively influence the decision-making process by predicting the potential customer base,and the existing literature in this direction shows promising ***,the selection of influential features and the construction of effective learning models for improved performance remain a ***,from the modelling perspective,the class imbalance nature of the training data,where samples with unsuccessful outcomes highly outnumber successful ones,further compounds the problem by creating biased and inaccurate ***,customer preferences are likely to change over time due to various reasons,and/or a fresh group of customers may be targeted for a new product or service,necessitating model retraining which is not addressed at all in existing works.A major challenge in model retraining is maintaining a balance between stability(retaining older knowledge)and plasticity(being receptive to new information).To address the above issues,this paper proposes an ensemble machine learning model with feature selection and oversampling techniques to identify potential customers more accurately.A novel online learning method is proposed for model retraining when new samples are available over *** newly introduced method equips the proposed approach to deal with dynamic data,leading to improved readiness of the proposed model for practical adoption,and is a highly useful addition to the *** experiments with real-world data show that the proposed approach achieves excellent results in all cases(e.g.,98.6

关键词： machine learning online learning oversampling telemarketing imbalanced dataset ensemble model

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Quantum Annealing for the Mechanics of Robot Inspectors on Electrical Transmission Lines' Conductors

Quantum Annealing for the Mechanics of Robot Inspectors on E...

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2024 International Conference on Control, Robotics and Informatics, ICCRI 2024

作者： Salloum, Hadi Bahrami, Mohammad Reza Matevosian, Artem Gamershmidt, Sofia Jnadi, Ali Mazzara, Manuel Innopolis University Faculty of Computer Science and Engineering Innopolis Russia Innopolis University Cyber-physical systems lab Innopolis Russia Institute of Robotics and Computer Vision Innopolis University Innopolis Russia

ISBN: (纸本)9798350380309

This paper explores the integration of quantum computing, specifically quantum annealing, into robotics for inspecting electrical transmission lines. By using quantum annealing's computational power, we address the dynamics of robot inspectors on transmission line conductors, enhancing the computational efficiency of managing the movements of robot inspectors on transmission line conductors. The use of these robots is crucial for performing efficient and safe inspections. We discuss the mathematical foundations of quantum annealing, transforming differential equations governing robot dynamics into Ising model Hamiltonians suitable for quantum annealing devices. Through experiments using D-Wave's 'Advantage' quantum annealer, we demonstrate the viability of quantum annealing in solving complex robotics problems, paving the way for advanced applications in the field. © 2024 IEEE.

关键词： Ising model

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Harnessing the Power of AI for Effective Cybersecurity Defense

Harnessing the Power of AI for Effective Cybersecurity Defen...

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Computing and Informatics (ICCI), International Conference on

作者： Samar M.Nour Samar A.Said Department of Computer and Systems Engineering Faculty of Engineering Badr University Cairo Egypt Department of Computer and Systems Engineering Faculty of Engineering Helwan University Cairo Egypt

Due to the extreme growth in digital information and data, cybersecurity has become one of the major concerns addressed by recent research, organizations, and governments. However, Traditional security methods are finding it more and more difficult to keep up with the volume and complexity of cybersecurity threats. To mitigate this problem, artificial intelligence (AI) can be a promising candidate that helps with cybersecurity defenses in the face of sophisticated and rising cyber threats. As a result, Artificial intelligence based systems can take advantage of machine learning, natural language processing, and other methods to enhance threat identification, response, and mitigation. This paper provides a blueprint for the state of the art of AI's potential to enhance cyber defense strategies in the field of cybersecurity. In addition, it highlights a variety of AI-based cybersecurity strategies that include anomaly detection, behavior analysis, and predictive modeling. Additionally, it explores the drawbacks and limitations of AI in cybersecurity, including data privacy issues and adversarial attacks, and offers suggestions for how to resolve these problems. As a whole, it highlights the significance of using AI to strengthen cybersecurity defenses and offers recommendations for further research and development in this field.

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Pneumonia Detection from X-ray Images Using Convolutional Neural Networks 24

Pneumonia Detection from X-ray Images Using Convolutional Ne...

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24th International Conference on Control systems and computer Science, CSCS 2023

作者： Dediu, Viorel Udrea, Andreea Faculty of Automatic Control and Computer Science Univesity Politehnica of Bucharest Department of Automatic Control and Systems Engineering Bucharest Romania

ISBN: (纸本)9798350313390

Pneumonia is one of the top causes of death in Romania and early detection of this disease improves the recovery chances and shortens the length of hospitalization. In this work, we develop a solution for automatic pneumonia detection based on convolutional neural networks. Four network models are investigated. They are trained on 4.163 images from a public dataset and tested on 530 images. The best results are obtained by one of the proposed models conducting to a sensitivity of 98.72%, an accuracy of 89.81%, and ROC 93.46%. Thus, this research proposes a lightweight screening tool that can help triaging the patients with pneumonia. © 2023 IEEE.

关键词： Convolution

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Deep Learning Performance Evaluation Model for Enhancing Network Intrusion Detection systems

Deep Learning Performance Evaluation Model for Enhancing Net...

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Novel Intelligent and Leading Emerging Sciences Conference (NILES)

作者： Samar M. Nour Samar A. Said Department of Computer and Systems Engineering Faculty of Engineering Badr University Cairo Egypt Department of Computer and Systems Engineering Faculty of Engineering Helwan University Cairo Egypt

ISBN: (数字)9798350378511

ISBN: (纸本)9798350378528

Network intrusion detection systems (NIDSs) play an important role in protecting network infrastructure from cyber threats. Traditional NIDS often rely on signature-based or rule-based methods, which can contention to detect incoming strong attacks. Deep learning techniques have emerged as a promising method for improving NIDS due to their ability to learn complex structures and features from raw network traffic data. In this paper, a performance evaluation model for enhancing network intrusion detection is proposed. Deep learning methods including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Attention Mechanisms, and Deep Neural Networks are investigated in the proposed model. Furthermore, the proposed model discusses the challenges and opportunities associated with the implementation of deep learning-based NIDS. These challenges include scalability, interpretability, and adversarial attacks. The performance of the proposed model is examined across numerous datasets that are real-world. The experimental results prove the efficiency to deep learning techniques and their capabilities with NIDS for enhancing the detection capability. Finally, the empirical results show that CNN outperforms the other competitive models for network intrusion detection in the face of cyber threats and in front of the ongoing.

关键词： Deep learning Performance evaluation Recurrent neural networks Accuracy Network intrusion detection Telecommunication traffic Self-supervised learning Data models Threat assessment Convolutional neural networks

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Automatic Diagnosis of Knee Osteoarthritis Using Deep Learning Approaches

Automatic Diagnosis of Knee Osteoarthritis Using Deep Learni...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Mohamedesmaeil Nabil, Lamia Ali, Dalia Khalifa, Ayman Faculty of Engineering Biomedical Engineering Department Helwan Egypt Higher Technological Institute Biomedical Engineering Department 10th of Ramadan City Egypt Zagazig University Computer and Systems Engineering Department Zagazig Egypt

ISBN: (纸本)9798350367560

Knee osteoarthritis is a common degenerative joint disease that affects millions of people worldwide, especially those aged over 50 years. It is characterized by gradual breakdown of the cartilage in the knee joint, leading to pain, stiffness, and reduced mobility. Knee osteoarthritis can develop because of a variety of factors, including genetics, old injuries, obesity, and repetitive stress on the knee joint. It is a chronic condition that often worsens over time if not treated properly. Various treatment options are available for managing knee osteoarthritis symptoms, including medication, physical therapy, and surgery. Early diagnosis and intervention can help individuals with knee osteoarthritis to manage their symptoms and maintain their quality of life. In this research, a range of deep learning models are utilized to aid clinicians in the diagnosis of knee osteoarthritis, reduce the workload on primary radiologists, and facilitate early detection and treatment. The deep learning model EfficientNetB7 implemented and compared with DenseNet169 and Inception ResNet V2. The EfficientNetB7 model achieved the highest results, with Accuracy 93.90%. © 2024 IEEE.

关键词： Joints (anatomy)

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Intelligent optimisation in smart and sustainable compressed air systems: Towards support for decision-making under faulty conditions 31

Intelligent optimisation in smart and sustainable compressed...

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31st CIRP Conference on Life Cycle engineering, LCE 2024

作者： Mallia, Jasmine Francalanza, Emmanuel Xuereb, Peter Baldacchino, Daniel Refalo, Paul Department of Industrial and Manufacturing Engineering Faculty of Engineering University of Malta MsidaMSD2080 Malta Department of Computer Information Systems Faculty of Information & Communication Technology University of Malta MsidaMSD2080 Malta

The monitoring of complex industrial systems through the implementation of smart approaches provides unique opportunities, such as the characterisation of their real-time performance. Within this scope, there exists the need to support decision-making during maintenance processes, due to the presence of a multitude of faults in real-world systems and the difficulty of identifying the appropriate mitigating solutions. The proposed solution uses intelligent optimisation techniques to identify the ideal control solution within these complex systems when faults arise. This paper presents a framework based on an intelligent optimisation approach, which provides a workflow process for the support of decision-making during faulty situations. It is adapted and implemented to the demand-side of a Compressed Air System (CAS), thus providing a holistic approach in automating fault mitigation during real-time system operation. In implementing this framework, multiple intelligent optimisation techniques such as the Genetic Algorithm and the Particle Swarm Optimisation algorithms were adopted and implemented. Both algorithms were successful in providing the ideal control solution under fault conditions. For a typical production case study, the proposed optimisation approach results in a reduction of 40% in its air consumption, which directly improves its environmental performance and energy costs. This result demonstrates that this approach contributes a suitable control strategy for CAS when experiencing a pneumatic fault, which has a direct positive effect on the operational energy performance and costs. © 2024 The Author(s).

关键词： Decision making

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The development of a generic IIOT framework for an industrial pneumatic system 17

The development of a generic IIOT framework for an industria...

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17th CIRP Conference on Intelligent Computation in Manufacturing engineering, CIRP ICME 2023

作者： Mallia, Jasmine Francalanza, Emmanuel Xuereb, Peter Refalo, Paul Department of Industrial and Manufacturing Engineering Faculty of Engineering University of Malta MsidaMSD2080 Malta Department of Computer Information Systems Faculty of Information & Communication Technology University of Malta MsidaMSD2080 Malta

The development of digital technologies, such as Cyber Physical systems (CPS) and Industrial Internet of Things (IIoT), under the umbrella of Industry 4.0, has increased rapidly within the manufacturing industry. Monitoring system behaviour in real time is becoming prevalent with manufacturing stakeholders to minimise downtime, maintain or improve equipment efficiency, and enhance production output. This paper proposes a generic IIoT framework for the online real time monitoring of system parameters. Such an approach has been implemented on the demand-side of a pneumatic setup, and a visualisation technique was developed for evaluation purposes. The developed framework paves the way towards further data analytics with the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques. © 2024 Elsevier B.V.. All rights reserved.

关键词： Compressed Air Industrial Internet of Things Industry 4.0 Online Monitoring Real Time Data

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Renewable Integration for Green Urban Mobility: A Case Study of Public Transport Depot Electrification 14

Renewable Integration for Green Urban Mobility: A Case Study...

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14th International Conference on Power, Energy and Electrical engineering, CPEEE 2024

作者： Colombo, Cristian Giovanni Saldarini, Alessandro Longo, Michela Ota, Ryosuke Politecnico di Milano Department of Energy Milan Italy Tokyo Metropolitan University Department of Electrical Engineering and Computer Science Faculty of Systems Design Japan

ISBN: (纸本)9781643685199

This paper analyses an electrification strategy for a depot for a public transport company, through an optimized model. The case study analyses both the transition of a depot able to host 10 buses towards e-buses and proposes the integration of a micro-grid, powered by PV panels to power the e-buses in a sustainable way. The study addresses key challenges such as spatial constraints, passenger demand, and financial considerations, through a Genetic Algorithm (GA). The analysis encompasses two scenarios: a mixed fleet of electric and diesel buses and an exclusive focus on e-buses. The study highlights the importance of considering both electric and diesel buses in fleet optimization, and combined with renewables emphasize the advantages of operational flexibility and sustainability. Thanks to the implementation of a micro-grid capable of providing 1123.87 kWh/day to power the e-buses as much as possible. © 2024 The Authors.

关键词： Electric buses

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