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A review on applications of holomorphic embedding methods

iEnergy 2023年第4期2卷 264-274页

作者： Kaiyang Huang Kai Sun Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTN 37996USA

The holomorphic embedding method(HEM)stands as a mathematical technique renowned for its favorable convergence properties when resolving algebraic systems involving complex *** key idea behind the HEM is to convert the task of solving complex algebraic equations into a series expansion involving one or multiple embedded complex *** transformation empowers the utilization of complex analysis tools to tackle the original problem *** the 2010s,the HEM has been applied to steady-state and dynamic problems in power systems and has shown superior convergence and robustness compared to traditional numerical *** paper provides a comprehensive review on the diverse applications of the HEM and its variants reported by the literature in the past *** paper discusses both the strengths and limitations of these HEMs and provides guidelines for practical *** also outlines the challenges and potential directions for future research in this field.

关键词： Holomorphic embedding method power flow polynomial solutions nonlinear algebraic equations differential equations

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Digital Twin Enabled Intelligent Network Orchestration for 6G: A Dual-Layered Approach

Digital Twin Enabled Intelligent Network Orchestration for 6...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Jia, Pengyi Wang, Xianbin Shen, Xuemin Sherman Western University Department of Electrical and Computer Engineering Canada University of Waterloo Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9781665494274

Meeting diverse service requirements concurrently in the future 6th-generation (6G) networks is becoming extremely challenging due to the dramatically increased network and service complexities. Overcoming this challenge relies on accurate and timely understanding of network dynamics and distributed service requirements. However, the excessive delay and resource consumption associated with network-wide information gathering and centralized processing will inevitably deteriorate the 6G network operation effectiveness. To address this challenge, a dual-layered digital twin paradigm is proposed in this paper to enable intelligent network orchestration for fast real-time optimization of 6G networks. Rapid identification of problematic network situations and the corresponding fast decision-making in tackling the related issues are successively achieved by the two layers of the proposed digital twin. The new dual-layered digital twin is further adopted for traffic engineering in 6G networks with maximized quality of service provisioning. Simulation results demonstrate that the dual-layered digital twin paradigm can intelligently achieve accurate and situation-aware digital twin construction and network optimization with enhanced efficiency. © 2023 IEEE.

关键词： Quality of service

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A Review on Self-healing in Modern Power Distribution Systems

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Journal of Modern Power Systems and Clean Energy 2023年第6期11卷 1719-1733页

作者： Seyed Ali Arefifar Md Shahin Alam Abdullah Hamadi IEEE Department of Electrical and Computer Engineering Oakland UniversityRochesterUSA Department of Energy and Technology Odessa CollegeOdessaUSA School of Engineering and Technology Western Illinois UniversityMacombUSA

The ever-increasing dependence on electrical power has posed more challenges to power system engineers to deliver secure, stable, and sustained energy to electricity consumers. Due to the increasing occurrence of short-and long-term power interruptions in the power system, the need for a systematic approach to mitigate the negative impacts of such events is further manifested. Self-healing and its control strategies are generally accepted as a solution for this concern. Due to the importance of self-healing subject in power distribution systems, this paper conducts a comprehensive literature review on self-healing from existing published papers. The concept of self-healing is briefly described, and the published papers in this area are categorized based on key factors such as self-healing optimization goals, available control actions, and solution methods. Some proficient techniques adopted for self-healing improvements are also classified to have a better comparison and selection of methods for new investigators. Moreover, future research directions that need to be explored to improve self-healing operations in modern power distribution systems are investigated and described at the end of this paper.

关键词： Self-healing distributed generator renewable energy source microgrid optimization reconfiguration service restoration automation system

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Agricultural supply chain management using hyperledger and AIOT

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Journal of Ambient Intelligence and Humanized Computing 2025年第4期16卷 471-485页

作者： Jha, Anurag Kumar Raj, Aparna Jha, Ashish Kumar Shetty, Sujala D. Department of Computer Science Bits-Pilani Dubai Campus Dubai United Arab Emirates Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA United States

Supply chain management and Hyperledger are two interconnected domains. They leverage blockchain technology to enhance efficiency, transparency, and security in supply chain operations. Together, they provide a decentralized, traceable, and real-time platform for recording and managing transactions. This combination is particularly valuable for industries dealing with sensitive goods, as it provides accurate traceability and real-time information. This paper explores the integration of supply chain management with Hyperledger blockchain technology to enhance efficiency, transparency, and security in supply chain operations. We propose a decentralized Hyperledger Fabric blockchain network to improve traceability, security, and efficiency by monitoring environmental conditions. This approach is particularly beneficial for transporting sensitive goods, such as medical supplies and perishable items, by ensuring optimal conditions and real-time data accessibility. The integration of Artificial Intelligence (AI) further enhances insights, reduces waste, and improves overall efficiency. By utilizing a distributed network free from third-party intermediaries, the system ensures immutability and remote accessibility, addressing challenges related to transporting heat and humidity sensitive products. Our experimental assessment demonstrates the benefits of private blockchain technologies, including enhanced security, regulatory compliance, compatibility, flexibility, and scalability. This study presents a detailed methodology for developing a traceable, efficient, and sustainable agricultural supply chain. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Efficiency

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Content-aware malicious webpage detection using convolutional neural network

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Multimedia Tools and Applications 2024年第3期83卷 8145-8163页

作者： Chang, Yen-Jen Tsai, Kun-Lin Jiang, Wei-Cheng Liu, Meng-Kun Department of Computer Science and Engineering National Chung Hsing University Taichung City Taiwan Department of Electrical Engineering Tunghai University Taichung City Taiwan

Malicious websites often install malware on user devices to gather user information or to disrupt device operations, violate user privacy, or adversely affect company interests. Many commercial tools are available to prevent malicious webpages from accessing devices;however, current versions of these tools may become useless as soon as a new generation of malware is released. In this study, a content-aware malicious webpage detection (CAMD) method was developed;this CAMD method can verify whether a webpage is malicious by applying a novel webpage contextual visualization process, which retrieves the critical codes of webpages, transforms those codes into one-dimensional grayscale images, and applies convolutional neural networks to detect any malicious webpages. To verify the feasibility of proposed CAMD, 50000 normal and 50000 malicious webpages from the VirusTotal website were used. The results indicated that the proposed CAMD achieved an accuracy of > 98%. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Websites

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Energy Management Strategy with a Hybrid Renewable Energy System for a Standalone House 16

Energy Management Strategy with a Hybrid Renewable Energy Sy...

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16th International Conference on Applied and Theoretical Electricity, ICATE 2024

作者： Mustata, Laurentiu-Alex Sorea, Gheorghe-Dan Helerea, Elena Transilvania University Electrical Engineering and Computer Science Faculty Electrical Engineering and Applied Physics Department Brasov Romania

ISBN: (纸本)9798350388107

In the case of standalone houses, ensuring a continuous and regulated power supply from renewable sources is crucial. To address their unpredictable nature, an environmentally conscious hybrid renewable energy system combining photovoltaic panels, a battery, and a Diesel generator is considered. A software application simulates the power flow in a standalone house with common AC loads, analysing summer and winter scenarios over 24 hours. The strategy employed provides an optimal energy management by using the Diesel generator only during peak demand when PV is available and to cover loads and maintain battery life when PV is not in operation. The simulation results provide valuable insights for designing such hybrid energy systems and for enhancing system responsiveness to dynamic energy demands. © 2024 IEEE.

关键词： Hybrid power

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MultiClass Classification of Chest Diseases using CXR Images with DenseNet201+CNN and Grad CAM Visualization 2

MultiClass Classification of Chest Diseases using CXR Images...

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2nd International Conference on Power, electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Ahad, Tanzim Kibria, Hafsa Binte Mehemud, Md. Yusuf Rajshahi University of Engineering & Technology Department of Electrical & Computer Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798331517984

Accurately identifying lung diseases is really important for treating them well, especially now with COVID-19 making respiratory problems worse worldwide. In response to the global impact, this paper has presented a deep learning framework for the multiclass classification of chest diseases, covering COVID-19, pulmonary fibrosis, tuberculosis, lung opacity, and normal cases. A modified DenseNet201 combined with a custom CNN has been introduced in this paper for a more effective classification of lung diseases. The proposed DenseNet201+CNN network surpasses key transfer learning models (ResNet50, VGG16, VGG19, DenseNet201) achieving a testing accuracy of 95.43%. This study employs DenseNet201 due to its superior feature extraction capabilities and efficiency in handling vanishing gradients. Contributions of this study include a 5-class classification system with a sizable combined dataset, addressing class imbalance through augmentation and rigorous model evaluation comparing with four other transfer learning models. The incorporation of Explainable AI (Artificial Intelligence), specifically Gradient Weighted Class Activation Mapping (Grad-CAM), enhances data visualization, ensuring transparency in the model's decision-making process. This study strives to advance automated disease diagnosis, potentially transforming early detection approaches for critical lung conditions. © 2024 IEEE.

关键词： COVID-19

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A Systolic Neural CPU Processor Combining Deep Learning and General-Purpose Computing with Enhanced Data Locality and End-to-End Performance

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IEEE Journal of Solid-State Circuits 2023年第1期58卷 216-226页

作者： Ju, Yuhao Gu, Jie Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States

While neural network (NN) accelerators are being significantly developed in recent years, CPU is still essential for data management and pre-/post-processing of accelerators in a commonly used heterogeneous architecture, which usually contains an NN accelerator and a processor core with data transfer performed by direct memory access (DMA) engine. This work presents a special neural processor, referred to as a systolic neural CPU processor (SNCPU), which is a unified architecture combining deep learning and general-purpose computing for fifth-generation of reduced instruction set computer (RISC-V) to improve end-to-end performance for machine learning (ML) tasks compared with a common heterogeneous architecture with CPU and accelerator. With 64%-80% processing elements (PEs) logic reuse and 10% area overhead, SNCPU can be configured into ten RISC-V CPU cores. Special bi-directional dataflow and four different working modes are developed to enhance the utilization of deep NN (DNN) accelerator and eliminate the expensive data transfer between CPU and DNN accelerator in existing heterogeneous architecture. A 65-nm test chip was fabricated demonstrating a 39%-64% performance improvement on end-to-end image classification tasks for ImageNet, Cifar10, and MNIST datasets with over 95% PE utilization and up to 1.8TOPs/W power efficiency. © 2022 IEEE.

关键词： Deep neural networks

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Statistical approaches to assess the performance of domestic air-to-water heat pumps

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International Journal of Ambient Energy 2023年第1期44卷 1316-1328页

作者： Tangwe, Stephen Kusakana, Kanzumba Department of Electrical Electronic and Computer Engineering Central University of Technology Bloemfontein South Africa

Air-to-water heat pump (AWHP) is a renewable and efficient technology used in sanitary water heating. This study focused on the employment of statistical methods to compare the coefficient of performance (COP) and electrical energies consumed using 1.2 kW, 150 L split type AWHP and 0.9 kW, 150 L integrated type AWHP, with 0.5 kW as auxiliary electric heater. The COPs and the electrical energies consumed were determined from the controlled volumes of hot water drawn from each tank at different use periods. The average month-day COPs of the specific volumes of hot water from either system were above two. The p-values from the one-way ANOVA (analysis of variance) and the Kruskal–Wallis tests of the groups’ COPs were smaller than 0.01 and showed significant differences at 99% confidence level. The study can assist energy service companies and researchers, to test for statistical differences in the performance among different AWHPs. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Analysis of variance (ANOVA)

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Enhanced Network Intrusion Detection Using a Hybrid CNN-LSTM Approach on the UNSW-NB15 Dataset 21

Enhanced Network Intrusion Detection Using a Hybrid CNN-LSTM...

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21st International Conference on electrical engineering, Computing Science and Automatic Control, CCE 2024

作者： Pear, Zareen Tasnim Kibria, Hafsa Binte Rajshahi University of Engineering and Technology Department of Electrical and Computer Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350377545

In the era of rapid technological advancements, data and information have become significant assets that require continuous protection from unauthorized access and malicious attacks. Network Intrusion Detection Systems (NIDS) can effectively manage the increasing data demands assuring robust security. However, traditional or manual methods are often inadequate to cope with the sophisticated and evolving nature of cyber threats due to their inability to quickly process large volumes of data. Traditional machine learning models are widely used for this purpose. Recently deep learning approaches have become popular in classification tasks showing promising results. This paper introduces a hybrid deep-learning approach for detecting various network intrusions using binary and multi-class (ten classes) classification on the well-known UNSW-NB15 dataset. The proposed hybrid CNN-LSTM model demonstrates significant improvements in intrusion detection, achieving an accuracy of 97.19% for binary classification and 87.70% for multi-class classification, particularly in handling complex attack patterns and reducing false positives. Evaluated using metrics such as precision, recall and F1-score, the model consistently performs robustly across nine distinct attack categories. These results highlight the model's effectiveness in establishing enhanced network security. © 2024 IEEE.

关键词： Network intrusion

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