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24th International Conference on Digital Signal Processing, DSP 2023

作者： Lykourinas, Antonios Skodras, Athanassios Department of Electrical and Computer Engineering Faculty of Engineering University of Patras Patras Greece

ISBN: (纸本)9798350339598

Due to the significance of the visual information exchanged in Internet of Video Things (IoVT) networks, attackers are constantly launching new attacks and attempt to exploit new vulnerabilities. One of the most common and difficult-to-prevent attacks on the Visual Layer is the Frame Duplication Attack (FDA). Recently, two techniques were proposed for FDA detection at the edge by using the embedded electrical Network Frequency (ENF) signals in an effort to surpass limitations of conventional passive methods. In this paper, a Real-Time ENF signal hiding technique at the edge is proposed. Our motivation is to examine the possibility of authenticating the surveillance feed by hiding the ENF signal. Experiments are conducted, including an extensive performance comparison between the proposed and reference encoder, a feasibility study for the proposed encoder's integration to a Raspberry Pi for video streaming purposes and finally the implementation of a proof-of-concept prototype. According to the findings, the proposed approach provides real-time FDA detection at reduced computational complexity and hardware requirements, thus rendering this method appropriate for applications at the edge. © 2023 IEEE.

关键词： Security systems

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Predicting of Photovoltaic Power Generation Using Similarity-Based Forecasting Models 24

Predicting of Photovoltaic Power Generation Using Similarity...

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24th International Middle East Power System Conference, MEPCON 2023

作者： Aboelmagd, Mohamed R. Selim, Ali Abdel-Akher, Mamdouh Aswan University Electrical Engineering Dept Aswan Egypt Almaaqal University Control and Computer Engineering Dept Basra Iraq Aswan University Faculty of Engineering Department of Electrical Engineering Aswan Egypt

ISBN: (纸本)9798350358469

Accurate forecasting in photovoltaic energy is one of the most essential integrated and influential events for managing the system of new and renewable energy sources in the power grid. The paper presents a practical approach using neural networks to predict photovoltaic energy generation. Applying neural networks in PV power generation forecasting is challenging due to unpredictable environmental changes such as solar radiation, temperature, humidity, wind speed, etc. Neural network methods are distinguished by achieving high performance in prediction compared to traditional mathematical models that have difficulties dealing with complex nonlinear relationships, resulting in substandard prediction. The proposed neural network-based forecasting model offers an effective way to improve the precision of estimating PV generation. It has a great deal of promise to enhance the integration and dependability of solar energy in the current electricity landscape because of its capacity to capture complex linkages in the data and adapt to changing circumstances. © 2023 IEEE.

关键词： Forecasting

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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

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Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye University34010 IstanbulTurkey

Changes in the Atmospheric Electric Field Signal(AEFS)are highly correlated with weather changes,especially with thunderstorm ***,little attention has been paid to the ambiguous weather information implicit in AEFS *** this paper,a Fuzzy C-Means(FCM)clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by ***,a time series dataset is created in the time domain using AEFS *** AEFS-based weather is evaluated according to the time-series Membership Degree(MD)changes obtained by inputting this dataset into the ***,thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF ***,a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space ***,the rationality and reliability of the proposed method are verified by combining radar charts and expert *** results confirm that this method accurately characterizes the weather attributes and changes in the AEFS,and a negative distance-MD correlation is obtained for the first *** detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field(AEF) Thunderstorm Fuzzy C-means(FCM) Attribute

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Contactless monitoring of human behaviors in bed using RSSI signals

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Medical and Biological engineering and Computing 2023年第10期61卷 2561-2579页

作者： Thammachote, Peeradon Intongkum, Chawakorn Sengchuai, Kiattisak Jindapetch, Nattha Phukpattaranont, Pornchai Saito, Hiroshi Booranawong, Apidet Department of Electrical Engineering Faculty of Engineering Prince of Songkla University Songkhla90110 Thailand Division of Computer Engineering The University of Aizu Aizu-Wakamatsu965-8580 Japan

In this paper, contactless monitoring and classification of human activities and sleeping postures in bed using radio signals is presented. The major contribution of this work is the development of a contactless monitoring and classification system with a proposed framework that uses received signal strength indicator (RSSI) signals collected from only one wireless link, where different human activities and sleep postures, including (a) no one in the bed, (b) a man sitting on the bed, (c) sleeping on his back, (d) seizure sleeping, and (e) sleeping on his side, are tested. With our proposed system, there is no need to attach any sensors or medical devices to the human body or the bed. That is the limitation of the sensor-based technology. Additionally, our system does not raise a privacy concern, which is the major limitation of vision-based technology. Experiments using low-cost, low-power 2.4 GHz IEEE802.15.4 wireless networks have been conducted in laboratories. Results demonstrate that the proposed system can automatically monitor and classify human sleeping postures in real time. The average classification accuracy of activities and sleep postures obtained from different subjects, test environments, and hardware platforms is 99.92%, 98.87%, 98.01%, 87.57%, and 95.87% for cases (a) to (e), respectively. Here, the proposed system provides an average accuracy of 96.05%. Furthermore, the system can also monitor and separate the difference between the cases of the man falling from his bed and the man getting out of his bed. This autonomous system and sleep posture information can thus be used to support care people, physicians, and medical staffs in the evaluation and planning of treatment for the benefit of patients and related people. Graphical Abstract: The proposed system for non-invasive monitoring and classification of human activities and sleeping postures in bed using RSSI signals [Figure not available: see fulltext.] © 2023, International Federation for Med

关键词： Patient treatment

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ECG Signal Classification of Cardiovascular Disorder using CWT and DCNN

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Journal of Biomedical Physics and engineering 2025年第1期15卷 77-92页

作者： Rahman, Tawfikur Ahommed, Rasel Deb, Nibedita Das, Utpal Kanti Moniruzzaman, Md. Bhuiyan, Md. Alamgir Sultana, Farzana Kausar, Md. Kamruzzaman Department of Electrical and Electronic Engineering Faculty of Engineering International University of Business Agriculture and Technology Uttara Dhaka 1230 Bangladesh Department of Agriculture International University of Business Agriculture and Technology Uttara Dhaka 1230 Bangladesh Department of Computer Science and Engineering Faculty of Engineering International University of Business Agriculture and Technology Uttara Dhaka 1230 Bangladesh

Background: Cardiovascular Diseases (CVD) requires precise and efficient diagnostic tools. The manual analysis of Electrocardiograms (ECGs) is labor-intensive, necessitating the development of automated methods to enhance diagnostic accuracy and efficiency. Objective: This research aimed to develop an automated ECG classification using Continuous Wavelet Transform (CWT) and Deep Convolutional Neural Network (DCNN), and transform 1D ECG signals into 2D spectrograms using CWT and train a DCNN to accurately detect abnormalities associated with CVD. The DCNN is trained on datasets from PhysioNet and the MIT-BIH arrhythmia dataset. The integrated CWT and DCNN enable simultaneous classification of multiple ECG abnormalities along-side normal signals. Material and Methods: This analytical observational research employed CWT to generate spectrograms from 1D ECG signals, as input to a DCNN trained on di-verse datasets. The model is evaluated using performance metrics, such as precision, specificity, recall, overall accuracy, and F1-score. Results: The proposed algorithm demonstrates remarkable performance metrics with a precision of 100% for normal signals, an average specificity of 100%, an average recall of 97.65%, an average overall accuracy of 98.67%, and an average F1-score of 98.81%. This model achieves an approximate average overall accuracy of 98.67%, highlighting its effectiveness in detecting ***: The integration of CWT and DCNN in ECG classification improves accuracy and classification capabilities, addressing the challenges with manual analy-sis. This algorithm can reduce misdiagnoses in primary care and enhance efficiency in larger medical institutions. By contributing to automated diagnostic tools for cardiovascular disorders, it can significantly improve healthcare practices in the field of CVD detection. © Journal of Biomedical Physics and engineering.

关键词： Wavelet transforms

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Improving Transient Stability in Power Systems Through Integration of Large-Scale Photovoltaic Power Plants 9

Improving Transient Stability in Power Systems Through Integ...

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9th International Conference on Technology and Energy Management, ICTEM 2024

作者： Abbaspour, Hadi Yari, Siavash Khoshkhoo, Hamid Kamwa, Innocent Sahand University of Technology Sahand New Town Faculty of Electrical Engineering Tabriz Iran Laval University Department of Electrical and Computer Engineering Quebec Canada

ISBN: (纸本)9798350329797

This paper investigates the impact of integrating large-scale photovoltaic (PV) power plants (LSPVPPs) on the transient stability of power systems. As renewable energy sources, including PV and wind generation systems, become more prevalent, power systems are experiencing significant operational and control changes. The decrease in system inertia in new power grids with high renewable energy penetration raises concerns regarding transient stability during major disturbances. Using simulation studies on the IEEE 39-Bus test system, this paper focuses on assessing the effectiveness of the WECC-large-scale photovoltaic power plant in improving transient stability. Specifically, it analyzes the ability of inverter-based generators to adjust their power output and examines the influence of this capability on transient stability. Additionally, the fault ride-through capability of the WECCLSPVPP and its impact on maintaining the power supply after a short circuit event is assessed. © 2024 IEEE.

关键词： Transient stability

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control and Stability of Large-scale Power System with Highly Distributed Renewable Energy Generation:Viewpoints from Six Aspects

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CSEE Journal of Power and Energy Systems 2023年第1期9卷 8-14页

作者： Qing-Hua Wu Anjan Bose Chanan Singh Joe H.Chow Gang Mu Yuanzhang Sun Zhaoxi Liu Zhigang Li Yang Liu School of Electric Power Engineering South China University of TechnologyGuangzhou 510640China School of Electrical Engineering&Computer Science Washington State UniversityPullmanWA 99164USA Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTX 77843USA Department of Electrical Computer and Systems EngineeringRensselaer Polytechnic Institute(RPI)TroyNYUSA Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)Jilin 132012China School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China

Power systems are moving toward a low-carbon or carbon-neutral future where high penetration of renewables is *** conventional fossil-fueled synchronous generators in the transmission network being replaced by renewable energy generation which is highly distributed across the entire grid,new challenges are emerging to the control and stability of large-scale power *** analysis and control methods are needed for power systems to cope with the ongoing *** the CSEE JPES forum,six leading experts were invited to deliver keynote speeches,and the participating researchers and professionals had extensive exchanges and discussions on the control and stability of power ***,potential changes and challenges of power systems with high penetration of renewable energy generation were introduced and explained,and advanced control methods were proposed and analyzed for the transient stability enhancement of power grids.

关键词： Distributed energy resources high converter penetration power system control renewable energy generation stability control transient stability

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Sum-Difference Analysis Based Design for Third-Order Sparse Arrays 2

Sum-Difference Analysis Based Design for Third-Order Sparse ...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Guo, Haodong Chen, Hua Liu, Wei Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong

ISBN: (纸本)9798331515669

Recently, third-order cumulants (TOCs) as a non-conventional odd-order statistics, has been introduced to direction-of-arrival (DOA) estimation with the third-order nested array (TONA), which significantly increases the number of uniform degrees-of-freedom (uDOFs) and can even be comparable to the fourth-order cumulant approach. In this paper, through analysis of the second-order sum-difference co-array, a third-order sparse linear array (SLA) design scheme suitable for TOCs is proposed, which can extend the commonly used second-order SLAs to the third-order. By selecting an appropriate generator array, the derived third-order SLA has far more uDOFs than the existing TONA, greatly improving its DOA estimation accuracy. © 2024 IEEE.

关键词： Direction of arrival

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A Deep Learning Approach for Detecting Diabetic Retinopathy

A Deep Learning Approach for Detecting Diabetic Retinopathy

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2024 International Conference on computer and Applications, ICCA 2024

作者： Nabil, Mohamed Hossam Hassan Hammam, Laila Bastawrous, Hany Ayad Ebrahim, Gamal A. The British University in Egypt Electrical Engineering Department Cairo Egypt Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft Netherlands Ain Shams University Faculty of Engineering Computer and Systems Engineerig Department Cairo Egypt

ISBN: (纸本)9798350367560

The World Health Organization (WHO) reports that diabetic retinopathy affects one-third of diabetics, regardless of their stage of the disease. Several research efforts are focused on its automated detection and diagnosis. Identifying diabetic retinopathy is crucial due to the damage that occurs to the blood vessels of the eye retina, leading to vision blur or even complete blindness. Thus, an annual checkup is needed for people with diabetes. Moreover, uncontrolled sugar levels for diabetes patients could worsen the current stage of diabetic retinopathy. Consequently, automated detection can greatly contribute to the treatment of disease. This can be carried out through several algorithms, including deep learning models and support vector machines, in addition to transfer learning. This contribution proposes a new approach for diabetic retinopathy automated detection based on convolutional neural network (CNN) models. The proposed model provides both binary and multi-class detection. Both scenarios have shown promising results, where the training accuracies of both the binary classification and the multi-class are 92% and 94%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Optimal Management in Island Microgrids with V2G Devices Using Differential Evolution Algorithm 16

Optimal Management in Island Microgrids with V2G Devices Usi...

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16th Annual Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2025

作者： Rahimi, Mohammad Reza Noroozian, Reza Kamali, Sadegh University of Zanjan University Blvd Department of Electrical Engineering Zanjan*** Iran K. N Toosi University of Technology Faculty of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798331533946

The advancement of power systems to meet energy demands and expand supply through renewable energy sources (RESs) is an effective approach. Additionally, replacing conventional automobiles with Electric Vehicles (EVs), which emit fewer pollutants and have charge-discharge capabilities, is highly effective in addressing load demand. This study introduces a microgrid (MG) operating in island mode to optimize energy management and minimize the costs associated with fossil fuel energy production and environmental pollution. Demand-side management (DSM) is a critical strategy for optimizing energy usage in future networks. It helps reduce peak load by reshaping the supplier load profile and adjusting consumers' energy consumption. The charge-discharge capability of EVs can further enhance energy management. Their flexibility and high mobility allow them to be positioned anywhere on the grid. This study employed the Differential Evolution Algorithm to analyze the impact of energy sources and vehicle-to-grid (V2G) technology on peak load reduction and the significant decrease in the objective function value. © 2025 IEEE.

关键词： Demand side management

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