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

作者： Mohamed, Baher S., Babiker A., Aldybous N., Kabbaj T., Brahimi 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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Advanced Frequency Control for Renewable and Electric Vehicle Integrated Systems

Advanced Frequency Control for Renewable and Electric Vehicl...

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2023 IEEE IAS Global Conference on Emerging Technologies, GlobConET 2023

作者： Pandit, Deepak Saket, Ram K. Cai, Niannian Nguyen, Nga University of Wyoming Electrical Engineering and Computer Science LaramieWY United States Department of Electrical Engineering Uttar Pradesh Varanasi India Google AustinTX United States

ISBN: (纸本)9798350331790

Modern power systems are experiencing a rapid movement from fossil-based generations to renewable energy resources (RERs) due to concerns about the environment and the dependence on fossil fuel sources. However, the replacement of conventional generation with intermittent and low inertia RERs in the power grid can negatively affect system regulation capability. Also, the increasing integration of electric vehicles (EVs) presents concerns over power system stability and reliability. To maintain the frequency of the system within stable limits, load frequency control (LFC) regulates the power output of the generating units in response to the changes in load demand. Therefore, appropriate control strategies and optimization algorithms are required for effective LFC while RER/EV penetration increases. In this paper, an advanced LFC model of a multi-area interconnected system with EV and RER integration is developed which considers the change in system inertia that arises from generator outages or the replacement of conventional units with RERs. A non-sequential Monte Carlo simulation (MCS) is implemented to calculate the system inertia and regulation capacity of each area considering the generator failure rates and load changes. The contribution of inertial response and primary frequency regulation from EVs to the system is assessed using a simulation-based approach. A particle swarm optimization (PSO) algorithm is used to tune the parameters of the PID controllers. The efficacy of the developed algorithm is demonstrated on a two-area model of the IEEE RTS-96 system with varying RER penetration levels. © 2023 IEEE.

关键词： Renewable energy resources

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Deep Moore-Penrose Inverse Network with Refinement Strategy for One-class Classification

Deep Moore-Penrose Inverse Network with Refinement Strategy ...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Gao, Junna Kong, Dehui Yin, Baocai Lin, Weisi Zhang, Wandong Beijing Institute of Artificial Intelligence Beijing University of Technology Beijing China School of Computer Science and Engineering Nanyang Technological University Singapore Western University Department of Electrical and Computer Engineering London Canada

ISBN: (纸本)9798350337020

Multilayer least-square (LS)-based one-class classification networks (MLS-OCNs) have gained great attention for the purpose of identifying anomalies and outliers. However, many MLS-OCNs encounter the issue of loosely connected feature coding because they use two separate mechanisms for feature encoding and final pattern recognition. This paper proposes a solution to this problem by introducing a multilayer algorithm called deep Moore-Penrose inverse network with refinement (DMPINR). In particular, DMPINR employs an end-to-end learning process based on the Moore-Penrose inverse (MPI) to identify optimal latent space and classify objects simultaneously. To enhance the robustness of representations, the DMPINR technique pulls back the residual error from the output layer to the hidden layers sequentially, recalculating the parameters of these hidden layers using MPI. The experimental results on ten popular OCC datasets demonstrate that the proposed approach outperforms many existing MLS-OCNs in G-Mean and F1 scores. © 2023 IEEE.

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Water-harvesting polymer coatings for plant leaves

RSC Applied Polymers

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RSC Applied Polymers 2024年第1期3卷 173-180页

作者： Milatz, Roland Reink, Carmen van den Berg, Tomas E. Duvigneau, Joost Vancso, G. Julius Wurm, Frederik R. Sustainable Polymer Chemistry Department of Molecules and Materials MESA+ Institute for Nanotechnology Faculty of Science and Technology University of Twente Enschede Netherlands Department of Integrated Devices and Systems Faculty of Electrical Engineering Mathematics & Computer Science University of Twente Enschede 7522 NB Netherlands

Climate change-induced water scarcity threatens global plant life and agricultural productivity. Here, we present a novel atmospheric water harvesting (AWH) coating designed to alleviate heat and dry stress potentially. This polymer coating utilizes block copolymers carrying catechol-anchoring groups, specifically poly(dopamine methacrylamide) (PDOMA), to adhere to plant leaves. As a hydrophilic block, either poly((oligoethylene glycol) methacrylate) (POEGMA) or the thermoresponsive block poly(N-isopropylacrylamide) (PNIPAM) was used, which can adsorb water from the air during cooler periods in its hydrophilic state. As the temperature increases above the lower critical solution temperature (LCST) of PNIPAM, the polymer transitions to a hydrophobic state, releasing the captured water to the leaf surface. We synthesized PNIPAM-b-PDOMA copolymers via RAFT polymerization and confirmed their composition (IR, ¹H NMR and ¹H DOSY NMR spectroscopy) with a cloud point temperature of 33 ± 1 °C. The coatings were applied to model substrates (SiO₂, polyethylene) and corn leaves. Compared to uncoated controls, coated substrates demonstrated a substantial increase in water uptake from humid air, absorbing up to 50 wt% of the coating's weight. The coating's adherence and thermoresponsive behavior were confirmed on corn leaves through contact angle measurements, showing a shift from hydrophilic (29 ± 3°) below the LCST to hydrophobic (80 ± 2°) above the LCST, closer to the native, hydrophobic leaf (110 ± 10°). Crucially, photosynthesis induction experiments revealed that the coating did not negatively impact the plant's natural photosynthetic processes. This study establishes a promising copolymer platform for developing AWH coatings to support plants in the face of increasing drought conditions. © 2025 RSC.

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Efficient Premature Ventricular Contraction Detection Based on Network Dynamics Features

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IEEE Transactions on Instrumentation and Measurement 2024年 73卷 1-15页

作者： Shen, Yumin Cai, Zhipeng Zhang, Li Lin, Bor-Shyh Li, Jianqing Liu, Chengyu Southeast University State Key Laboratory of Digital Medical Engineering School of Instrument Science and Engineering Nanjing210096 China University of London Royal Holloway Department of Computer Science SurreyTW20 0EX United Kingdom Institute of Imaging and Biomedical Photonics National Chiao Tung University Hsinchu30010 Taiwan

Automatic detection of premature ventricular contractions (PVCs) is essential for early identification of cardiovascular abnormalities and reduction of clinical workload. As the most prevalent arrhythmia, PVCs can cause cardiac failure or sudden death. The difficulty resides in extracting features that effectively reflect the electrocardiogram (ECG) signals. Transition networks (TNs), which represent the transition relationships between various phases of a time series, are advantageous for capturing temporal dynamics. Therefore, in order to recognize PVCs, each heartbeat was first split into serval segments;then, their statistical properties were calculated for the sequence construction;and finally, network topology-related features were extracted from TN constructed by these sequences of statistical properties and input into decision tree-based Gentleboost for PVC recognition. The algorithm was trained on the MIT-BIH arrhythmia (MIT-BIH-AR) database and tested on the St. Petersburg Institute of Cardiological Technics (INCART) 12-lead arrhythmia database, wearable ECG (WECG) database, and noise stress test database by four evaluation metrics: sensitivity, positive predictivity, F1 -score (F1 ), and area under the curve (AUC). The proposed algorithm achieved an average F1 of 0.9784 and AUC of 0.9975 on MIT-BIH-AR and proved good generalization ability on INCART and WECG with F1 = 0.9633 and 0.9467 and AUC = 0.9887 and 0.9755, respectively. The algorithm also exhibited robustness and noise immunity as evidenced by tests on sensitivity of R-wave peak offset and noise, and real-world daily life conditions. Overall, the proposed PVC detection algorithm based on TN theory offered high classification accuracy, strong robustness, and good generalization ability, with great potential for wearable mobile applications. © 1963-2012 IEEE.

关键词： Electrocardiograms

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Optimizing Software Defect Classification with Locally Linear Embedding Techniques 71

Optimizing Software Defect Classification with Locally Linea...

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71st Annual Reliability and Maintainability Symposium, RAMS 2025

作者： Salboukh, Fatemeh Saki, Hesam Fiondella, Lance Department of Applied Science and Engineering University of Massachusetts - Dartmouth 285 Old Westport Road North DartmouthMA02747 United States Department of Computer Engineering University of Tehran Tehran Iran Department of Electrical and Computer Engineering University of Massachusetts - Dartmouth 285 Old Westport Road North DartmouthMA02747 United States

ISBN: (纸本)9798350367744

Software defect classification is crucial for enhancing the quality and reliability of software. This research explores the integration of Locally Linear Embedding (LLE) into the preprocessing stages of classification models to improve accuracy. LLE is particularly effective as it transforms complex, nonlinear data into a simpler, linear format. This transformation is instrumental in reducing the complexity of the data, facilitating a more straightforward analysis and potentially improving the predictive accuracy of classifiers. For this study, we used the KC2 dataset from the PROMISE repository and applied several feature selection techniques, including Correlation-Based Feature Selection, Gain Ratio, Sequential Forward Selection, and K-nearest neighbors. After selecting the most informative features, LLE was employed to linearize this data, aiming to enhance the classifiers’ performance by presenting them with data that is easier to interpret and analyze. The impact of LLE was evaluated by comparing the outcomes of five different classifiers, such as Random Forest, Gradient Boosting, Support Vector Machines, Non-Linear Support Vector Machines, and Naive Bayes, on both the original and the LLE-processed datasets. Our results demonstrated an average improvement of 4.5% in accuracy and 4% in F1-Score across the classifiers with the LLE-processed data, confirming the effectiveness of transforming nonlinear data into a linear space for enhancing software defect classification. © 2025 IEEE.

关键词： Software quality

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Long range electromagnetic field nature of nerve signal propagation in myelinated axons

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Chinese Physics B 2022年第3期31卷 619-623页

作者： Qing-Wei Zhai Kelvin J A Ooi Sheng-Yong Xu C K Ong School of Electrical and Computer Engineering Xiamen University MalaysiaSelangor Darul Ehsan 43900Malaysia Department of Physics Xiamen University MalaysiaSelangor Darul Ehsan 43900Malaysia Department of Electronics School of Electronics Engineering and Computer SciencePeking UniversiyBeijing 100871China Department of Physics National University of Singapore2 Science Drive 3117551 Singapore

The nature of saltatory conduction in myelinated axon described by equivalent circuit and circuit theory is still contentious. Recent experimental observations of action potentials transmitting through disjointed nerve fibers strongly suggest an electromagnetic wave propagation mechanism of the nerve signals. In this paper, we employ the electromagnetic wave model of the myelinated axon to describe action potential signal propagation. We use the experimental frequency-dependent conductivity and permittivity values of the nerve tissues in order to reliably calculate the electromagnetic modes by using electromagnetic mode solvers. We find that the electromagnetic waves above 10 kHz can be well confined in extracellular fluid–myelin sheath–intracellular fluid waveguide and propagate a distance of 7 mm without much attenuation. Our study may serve as one of the fundamental researches for the better understanding of the nervous system.

关键词： action potential propagation and axons neuronal wave propagation neuroscience electrodynamics in the nervous system

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Das-Alz: Alzheimer's Disease Classification Using Downscaled MRI Scans and a Reinforced Convolutional Neural Network 24

Das-Alz: Alzheimer's Disease Classification Using Downscaled...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Babuc, Diogen Babuc, Gabrijel West University of Timişoara Computer Science Department Timişoara Romania The Secondary School of Electrical Engineering Nikola Tesla Pančevo Pančevo Serbia

ISBN: (纸本)9798331518622

Low-resolution (LR) and high-resolution (HR) images are critical in medical imaging, with HR images often preferred for their detailed information. However, HR MRI scans require significant processing power, posing challenges in computational efficiency. Super-resolution techniques have been developed to enhance LR images, but they often depend on high-quality data and advanced processing capabilities. One type of neurodegenerative illness is Alzheimer's disease (AD), where a precise and timely diagnosis is essential for successful intervention. Despite the importance of HR MRI scans in making the prediagnosis, their processing demands are high. To address these challenges, this paper proposes the DAS-Alz model, a reinforced convolutional neural network designed to classify AD cases using downscaled MRI scans. Even at a lower resolution, DAS-Alz demonstrates the ability of deep learning to extract important information from MRI data, highlighting the potential to overcome computational constraints. The model incorporates a multi-head attention mechanism layer to enable parallel processing, thereby improving efficiency. The DAS-Alz model achieved outstanding accuracy in both training (99.34%) and validation (99.84%), effectively classifying AD from downscaled images. These results suggest that DAS-Alz may play a significant role in AD prediagnosis, potentially leading to improved patient outcomes. © 2024 IEEE.

关键词： Convolutional neural networks

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FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler 41

FedCal: Achieving Local and Global Calibration in Federated ...

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41st International Conference on Machine Learning, ICML 2024

作者： Peng, Hongyi Yu, Han Tang, Xiaoli Li, Xiaoxiao College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering The University of British Columbia VancouverBC Canada Vector Institute Canada

Federated learning (FL) enables collaborative machine learning across distributed data owners. However, this approach poses a significant challenge for model calibration due to data heterogeneity. While prior work focused on improving accuracy for non-iid data, calibration remains under-explored. This study reveals existing FL aggregation approaches lead to sub-optimal calibration, and theoretical analysis shows despite constraining variance in clients' label distributions, global calibration error is still asymptotically lower bounded. To address this, we propose a novel Federated Calibration (FedCal) approach, emphasizing both local and global calibration. It leverages client-specific scalers for local calibration to effectively correct output misalignment without sacrificing prediction accuracy. These scalers are then aggregated via weight averaging to generate a global scaler, minimizing the global calibration error. Extensive experiments demonstrate that FedCal significantly outperforms the best-performing baseline, reducing global calibration error by 47.66% on average. Copyright 2024 by the author(s)

关键词： Federated learning

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3D mmWave MIMO Channel Modeling and Reconstruction for Street Canyon and Highrise Scenarios

3D mmWave MIMO Channel Modeling and Reconstruction for Stree...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Idowu-Bismark, Olabode Oshin, Oluwadamilola Adetiba, Emmanuel Idowu-Bismark, Oluwatobiloba Covenant University Dept of Electrical and Information Engineering Ota Nigeria University of Hertfordshire Department of Computer Science Welwyn HatfieldAL109JD United Kingdom

ISBN: (纸本)9798350358155

The use of millimeter-wave (mmWave) and full-dimensional multiple-input multiple-output (FD-MIMO) antenna systems for 3D wireless communication is being exploited for enhanced network capacity improvement in the ongoing fifth-generation (5G) deployment. For adequate assessment of competing air interface, random access channelization, and beam alignment procedures in mmWave systems, channel models for different use scenarios are necessary. A ray-tracing study was conducted with the use of a Wireless Insite ray tracing algorithm to characterize the mmWave channel in urban areas, using Lagos Island, Nigeria databases to predict measured statistics. These statistics include path loss, rms delay spread, angular spread of arrival, and departure in the azimuth and elevation domain. A 3GPP-style 3D mmWave channel is modeled and reconstructed, emphasizing the use of a ray tracer to determine elevation model parameters. Line of sight (LOS) and non-line of sight (NLOS) 3D models were developed for street canyon and high-rise scenarios © 2024 IEEE.

关键词： 5G mobile communication systems

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