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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Allard, Julien Vallée, François De Grève, Zacharie Stegen, Thomas Glavic, Mevludin Cornélusse, Bertrand University of Mons Power Systems and Markets Research Group Electrical Power Engineering Unit Mons Belgium University of Liège Department of Electrical Engineering and Computer Science Liège Belgium

ISBN: (纸本)9789531842976

Demand-side flexibility from renewable energy community members increases the benefits of local production and exchanges. To effectively harness this flexibility, end-users must be rewarded for their efforts regarding the shifted energy volumes and the associated discomfort. This work quantifies these last two quantities for three types of loads (electric vehicles, thermal loads and wet appliances). Then, a two-step model is proposed which first optimally activates the offered flexibility and then shares the benefits of this activation among the renewable energy community members through a rule-based or an optimization-based approach. Results show that the community framework can further increase the benefits brought by flexible demand. © 2024 IEEE.

关键词： Demand response

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Fault Secured JPEG-Codec Hardware Accelerator with Piracy Detective Control using Secure Fingerprint Template 36

Fault Secured JPEG-Codec Hardware Accelerator with Piracy De...

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36th IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems, DFT 2023

作者： Chaurasia, Rahul Reddy Asireddy, Abhinav Sengupta, Anirban Indian Institute of Technology Computer Science and Engineering Indore India Indian Institute of Technology Department of Computer Science and Engineering Indore India Indian Statistical Institute Advanced Computing and Microelectronics Unit Kolkata India

ISBN: (纸本)9798350315004

This paper presents a novel methodology to design fault secured JPEG-compression-decompression (codec) hardware accelerator with piracy detective control using secure fingerprint template of intellectual property (IP) vendor. In the proposed approach, firstly, the fault secured schedule for JPEG-codec design is generated from its behavioral description. Subsequently, in order to enable detective control against piracy, secure digital template corresponding to fingerprint biometric of IP vendor is generated and implanted into the design. In order to do so, firstly, fingerprint features are transformed based on IP vendor specified key-set. Subsequently, the generated secure fingerprint template in the form of encoded hardware security constraints is implanted into the design during behavioral/ high-level synthesis phase of integrated circuit (IC) design. The implanted hardware security constraints thereby ensure the seamless detection and isolation of fake/pirated design versions during piracy detection process, before their conjunction into system on chip (SoC) of electronic systems. The proposed approach achieves the following: i) fault secured hardware accelerator for JPEG-codec with integrated piracy detective control at negligible design cost overhead ii) higher resiliency against tampering attack and iii) lower probability of coincidence (indicating definitive proof of ownership for genuine IP vendor). © 2023 IEEE.

关键词： Fault detection

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"Fiction to Function"Shaping Renewable Energy Education with MATLAB and ChatGPT-Driven Environments 21

"Fiction to Function"Shaping Renewable Energy Education with...

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21st International Learning and Technology Conference, L and T 2024

作者： Mohamed, Baher Babiker, Safia Aldybous, Alaa Kabbaj, Narjisse Brahimi, Tayeb Effat University Effat College of Engineering Electrical and Computer Engineering Department Jeddah Saudi Arabia Effat University Effat College of Engineering Energy and Technology Research Center Electrical and Computer Engineering Department Jeddah Saudi Arabia Effat University Natural Science Math and Tech Unit Effat College of Engineering Energy and Technology Research Center Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

Addressing the gap in effective and engaging renewable energy education, this study, 'Fiction to Function,' transitions imaginative frameworks into practical educational tools, focusing on solar and wind energy in line with Sustainable Development Goals (SDGs). The goal is to empower users with deep insights into renewable energy through the novel integration of MATLAB and ChatGPT-driven environments, enhancing the analysis and exploration of these energy sources. The study employs a structured approach using MATLAB App Designer to develop a user-friendly GUI, aligning instructional content with the SDGs and integrating ChatGPT via the OpenAI API for dynamic, interactive learning experiences. This integration provides a multifaceted platform that not only educates but also engages users in practical scenarios, bridging the gap between theoretical knowledge and real-world application. The results demonstrate an enhanced understanding of renewable energy concepts among users, showcasing the efficacy of combining MATLAB's analytical capabilities with ChatGPT's AI-driven guidance. Ultimately, the study highlights the transformative potential of innovative technology in sustainable energy education, setting a precedent for future educational tools in this domain. © 2024 IEEE.

关键词： Integration

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Optihybrid: a modified firebug swarm optimization algorithm for optimal sizing of hybrid renewable power system

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Neural Computing and Applications 2024年第34期36卷 21517-21543页

作者： Abd El-Sattar, Hoda Kamel, Salah Hashim, Fatma A. Sabbeh, Sahar F. Luxor Higher Institute of Engineering and Technology Luxor85834 Egypt Department of Electrical Engineering Faculty of Engineering Aswan University Aswan81542 Egypt Faculty of Engineering Helwan University Cairo Egypt MEU Research Unit Middle East University Amman1183 Jordan College of Computer Science and Engineering University of Jeddah Jeddah21493 Saudi Arabia Faculty of Computers and Artificial Intelligence Benha University Benha13518 Egypt

In areas where conventional energy sources are unavailable, alternative energy technologies play a crucial role in generating electricity. These technologies offer various benefits, such as reliable energy supply, environmental sustainability, and employment opportunities in rural regions. This study focuses on the development of a novel optimization algorithm called the modified firebug swarm algorithm (mFSO). Its objective is to determine the optimal size of an integrated renewable power system for supplying electricity to a specific remote site in Dehiba town, located in the eastern province of Tataouine, Tunisia. The proposed configuration for the standalone hybrid system involves PV/biomass/battery, and three objective functions are considered: minimizing the total energy cost (COE), reducing the loss of power supply probability (LPSP), and managing excess energy (EXC). The effectiveness of the modified algorithm is evaluated using various tests, including the Wilcoxon test, boxplot analysis, and the ten benchmark functions of the CEC2020 benchmark. Comparative analysis between the mFSO and widely used algorithms like the original Firebug Swarm Optimization (FSO), Slime Mold Algorithm (SMA), and Seagull Optimization Algorithm (SOA) demonstrates that the proposed mFSO technique is efficient and effective in solving the design problem, surpassing other optimization algorithms. © The Author(s) 2024.

关键词： Hybrid power

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Text analytics of materials and scanning electron microscope image classification of cadmium sulphide

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International Journal of Business Intelligence and Data Mining 2023年第3期22卷 333-358页

作者： Jayaram, Kavitha Prakash, G. Jayaram, V. Department of Computer Science and Engineering BNM Institute of Technology Bangalore India VIT Tamil Nadu Vellore India Solid State and Structural Chemistry Unit Indian Institute of Science Bengaluru India

A framework is proposed using machine learning techniques to extract information (text analytics and SEM image classification) from material research documents. This paper provides complete information on material [cadmium sulphide (CdS)] research about the ongoing research problem, knowing the country and funding agencies, different new joint research collaborations, and characterising techniques used to understand material’s phase transformation. The proposed system includes a new citation network connecting author, material, phase change, and funding organisation related to material and instruments. The abstract section is extracted to build a summary of material research. SEM images of CdS using a deep CNN method are classified into the (cubic, hexagon, crystalline and amorphous) crystal structure. Based on the observation with extensive literature studies, it is found that no such work has been done with materials, characterisation techniques, and acknowledgment section in the material research domain using deep learning and machine learning techniques. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Cadmium sulfide

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Multi-gradient-direction based deep learning model for arecanut disease identification

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CAAI Transactions on Intelligence Technology 2022年第2期7卷 156-166页

作者： S.B.Mallikarjuna Palaiahnakote Shivakumara Vijeta Khare M.Basavanna Umapada Pal B.Poornima Department of Computer Science and Engineering Bapuji Institute of Engineering and TechnologyDavanagereAffiliated to Visvesvaraya Theological UniversityBelagaviKarnatakaIndia Faculty of Computer Science and Information Technology University of MalayaKuala LumpurMalaysia Adani Institute of Infrastructure Engineering AhmedabadIndia Department of Computer Science Davanagere UniversityDavanagereKarnatakaIndia Computer Vision and Pattern Recognition Unit Indian Statistical InstituteKolkataWest BengalIndia

Arecanut disease identification is a challenging problem in the field of image *** this work,we present a new combination of multi-gradient-direction and deep con-volutional neural networks for arecanut disease identification,namely,rot,split and *** to the effect of the disease,there are chances of losing vital details in the *** enhance the fine details in the images affected by diseases,we explore multi-Sobel directional masks for convolving with the input image,which results in enhanced *** proposed method extracts arecanut as foreground from the enhanced images using Otsu ***,the features are extracted for foreground information for disease identification by exploring the ResNet *** advantage of the proposed approach is that it identifies the diseased images from the healthy arecanut *** results on the dataset of four classes(healthy,rot,split and rot-split)show that the proposed model is superior in terms of classification rate.

关键词： deep learning image analysis pattern recognition

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Load-profile based sizing method for distributed energy resources in industrial microgrids 33

Load-profile based sizing method for distributed energy reso...

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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Jansen, Phillip Schoor, George Van Uren, Kenneth R. North-West University School of Electrical Electronic and Computer Engineering Potchefstroom South Africa North-West University Unit for Energy and Technology Systems Potchefstroom South Africa

ISBN: (纸本)9798331535162

The demand for microgrids is increasing. Many countries such as South Africa are experiencing difficulties with power reliability, whereas other countries are on a drive to minimise carbon footprint in the power generation industry. In addressing the difficulties mentioned above, optimal sizing of renewable energy resources within a microgrid is essential. Studies show that a variety of sizing approaches have proven viable for analysing and optimising the sizes of distributed energy resources (DERs) within a microgrid. This paper gives an overview of existing sizing approaches from the literature. This overview of existing methods will contextualize the developed method presented in this paper. This study proposes a DER sizing approach called the load-profile based sizing method (LPBSM). The method simplifies factors such as the cost saving, the meteorological area, the peak demands and the average consumption of the site being tested and then calculates the sizes of the DERs using real hourly load data. The LPBSM follows the same fundamentals as that of the intuitive sizing approach from literature but now uses real-time load data as well as the specific electricity tariff of the end user to verify the reliability and availability of power. In this paper, a case study is presented on a site in Cape Town to verify how the LPBSM simulates real results based on a constructed site. © 2025 IEEE.

关键词： Distributed energy

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Can end-user feedback in social media be trusted for software evolution: Exploring and analyzing fake reviews

Can end-user feedback in social media be trusted for softwar...

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作者： Khan, Javed Ali Ullah, Tahir Khan, Arif Ali Yasin, Affan Akbar, Muhammad Azeem Aurangzeb, Khursheed Department of Computer Science Faculty of Physics Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom School of Computer & Technology Beijing Institute of Technology Beijing China M3S Empirical Software Engineering Research Unit University of Oulu Oulu Finland School of Software Northwestern Polytechnical University Shaanxi Xian China Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta Finland Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

End-user feedback in social media platforms, particularly in the app stores, is increasing exponentially with each passing day. Software researchers and vendors started to mine end-user feedback by proposing text analytics methods and tools to extract useful information for software evolution and maintenance. In addition, research shows that positive feedback and high-star app ratings attract more users and increase downloads. However, it emerged in the fake review market, where software vendors started incorporating fake reviews against their corresponding applications to improve overall software ratings. For this purpose, we conducted an exploratory study to understand how end-users register and write fake reviews in the Google Play Store. We curated a research data set containing 68,000 end-user comments from the Google Play Store and a fake review generator, that is, the Testimonial generator (TG). Its purpose is to understand fake reviews on these platforms and identify the common patterns potential end-users and professionals use to report fake reviews by critically analyzing the end-user feedback. We conducted a detailed survey at the University of Science and Technology Bannu, Pakistan, to identify the intelligence and accuracy of crowd-users in manually identifying fake reviews. In addition, we developed a ground truth to be compared with the results obtained from the automated machine and deep learning (M&DL) classifier experiment. In the survey, 512 end-users participated and recorded their responses in identifying fake reviews. Finally, various M&DL classifiers are employed to classify and identify end-user reviews into real and fake to automate the process. Unlike humans, the M&DL classifiers performed well in automatically classifying reviews into real and fake by obtaining much higher accuracy, precision, recall, and f-measures. The accuracy of manually identifying fake reviews by the crowd-users is 44.4%. In contrast, the M&DL classifiers obtained an

关键词： Application programs

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Classification of energy graph-based signatures for FDI using machine learning 33

Classification of energy graph-based signatures for FDI usin...

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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Nel, Luke Uren, Kenneth R. Van Schoor, George North-West University School of Electrical Electronic and Computer Engineering Potchefstroom South Africa North-West University Unit for Energy and Technology Systems Potchefstroom South Africa

ISBN: (纸本)9798331535162

This paper evaluates energy graph-based visualisation (EGBV) data structures for fault classification using machine learning (ML). EGBV transforms process variable (PV) data into node signature matrices (NSMs) and cost matrices. Tests with k-nearest neighbour (KNN), logistic regression, and feedforward neural networks (FNNs) show that transforming data into the NSM format achieves comparable performance to the PV format, indicating that EGBV offers similar classification characteristics. Future work could apply deep learning to the NSMs and cost matrices to leverage their structural features. © 2025 IEEE.

关键词： Contrastive Learning

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CNN-Based Deep Learning Model for Early Identification and Categorization of Melanoma Skin Cancer Using Medical Imaging

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SN computer Science 2024年第7期5卷 911页

作者： Pandimurugan, V. Ahmad, Sultan Prabu, A.V. Rahmani, Mohammad Khalid Imam Abdeljaber, Hikmat A. M. Eswaran, Malathi Nazeer, Jabeen Computer Science and Engineering Acharya Institute of Technology Bangalore India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur 522502 India College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Computer Technology Kongu Engineering College Perundurai Tamil Nadu Erode India

In human life, skin cancer is a curse. If not appropriately diagnosed, it spreads around all body parts in the earlier stage. The melanoma skin cancer death rate is 75% all over the world. There is an urgent need for a cure to be in place. Swarm and evolutionary computation for healthcare deals with real-world healthcare applications. One of the best ways to do this is to find the genes that need to be turned off such that only cancer cells can die. The current therapies used to cure cancer focus on turning off cancer cells but, at times, affect healthy tissues. Using the power of Artificial Intelligence, one can easily mark out the cancer-affected area and provide treatment accordingly. Segmentation techniques can be used to mark cancer lesions, and advanced deep learning algorithms in computer vision can help classify which class of cancer lesions. Automatic detection of melanoma by using a dermoscopic sample is tricky to find the stage and percentage of lesions affected by using deep convolutional neural networks with the help of machine vision tools. To predict skin lesions, we developed a model with three layers, each having output channels of 16, 32, and 64, respectively. For this research, different samples were collected from the international skin imaging collaboration database (ISIC2019, ISIC2020, ISIC2021). The study computed the most important parameters for classifying and identifying the lesion with accuracy, precision, recall, and specificity values. The proposed DCNN model classifier attained an accuracy of 88.82%, 93.45%, and 95.15% with the respective datasets of ISIC2019, ISIC2020, and ISIC2021, indicating high performance compared with the other state-of-the-art networks. The proposed model is a better framework for automating early detection and classification of melanoma skin cancer to protect many lives and avoid mishaps. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification Convolution neural network Deep learning Medical imaging Melanoma Skin cancer Swarm and evolutionary computation

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