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Bi-level Optimal Planning of Voltage Regulator in Distribution systems Considering Maximization of Incentive-based Photovoltaic Energy Integration

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CSEE Journal of Power and Energy systems 2023年第6期9卷 2008-2017页

作者： Xu Xu Youwei Jia Chun Sing Lai Minghao Wang Zhao Xu Department of Electrical and Electronic Engineering Southern University of Science and TechnologyShenzhenChina Department of Electrical Engineering The Hong Kong Polytechnic UniversityHung HomHong Kong SARChina Department of Electrical and Electronic Engineering University Key Laboratory of Advanced Wireless Communications of Guangdong ProvinceSouthern University of Science and TechnologyShenzhen 515100China Department of Electronic and Computer Engineering Brunel Institute of Power SystemsBrunel University LondonLondonUB83PH United Kingdom Department of Electrical Engineering Shenzhen Research InstituteThe Hong Kong Polytechnic UniversityHung HomHong Kong SARChina

This paper focuses on optimal voltage regulator(VR)planning to maximize the photovoltaic(PV)energy integration in distribution *** describe the amount of dynamic PV energy that can be integrated into the power system,the concept of PV accommodation capability(PVAC)is introduced and modeled with *** proposed planning model is formulated as a Benders decomposition based bi-level stochastic optimization *** the upper-level problem,VR planning decisions and PVAC are determined via mixed integer linear programming(MILP)before considering *** in the lower-level problem,the feasibility of first-level results is checked by critical network constraints(*** magnitude constraints and line capacity constraints)under uncertainties considered by time-varying loads and PV *** this paper,these uncertainties are represented in the form of operational scenarios,which are generated by the Gaussian copula theory and reduced by a well-studied backward-reduction *** modified IEEE 33-node distribution grid is utilized to verify the effectiveness of the proposed *** results demonstrate that a PV energy integration can be significantly enhanced after optimal voltage regulator planning.

关键词： Bi-level stochastic optimization problem critical network constraints photovoltaic energy integration uncertainties voltage regulator planning

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Data Mining Approach Based on Hierarchical Gaussian Mixture Representation Model

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Intelligent Automation & Soft Computing 2023年第3期35卷 3727-3741页

作者： Hanan A.Hosni Mahmoud Alaaeldin M.Hafez Fahd Althukair Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia Department of Electrical Engineering and Computer Sciences College of EngineeringUniversity of CaliforniaBerkeleyUSA

Inﬁnite Gaussian mixture process is a model that computes the Gaus-sian mixture parameters with *** process is a probability density distribu-tion with adequate training data that can converge to the input density *** this paper,we propose a data mining model namely Beta hierarchical distribution that can solve axial data modeling.A novel hierarchical Two-Hyper-Parameter Poisson stochastic process is developed to solve grouped data *** solution uses data mining techniques to link datum in groups by linking their *** learning techniques are novel presentations of Gaussian model-ling that use prior knowledge of the representation hyper-parameters and approx-imate them in a closed *** are performed on axial data modeling of Arabic Script classiﬁcation and depict the effectiveness of the proposed method using a hand written benchmark dataset which contains complex handwritten Ara-bic *** are also performed on the application of facial expres-sion recognition and prove the accuracy of the proposed method using a benchmark dataset which contains eight different facial expressions.

关键词： Data classiﬁcation handwritten Arabic classiﬁcation facial expressions

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An Intelligent Data Analytics Approach for Crisis Management in Healthcare systems

An Intelligent Data Analytics Approach for Crisis Management...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Madian, Fatma A. Ebrahim, Gamal A. Salah, Mostafa M. Abdelaal, Marwa A. Ain Shams University Faculty of Engineering Computer and Systems Engineering Department Cairo Egypt Future University in Egypt Electrical Engineering Department Cairo Egypt Faculty of Computers and Information Sadat Academy for Management Sciences Computer Science Department Cairo Egypt

ISBN: (纸本)9798350367560

Crisis management is preparing for and managing possible crises that may impact organizations and individuals at different levels. It involves effective communication, quick decision-making, and strategic planning to minimize the negative impact of a crisis and ensure swift recovery. It plays a vital role in healthcare systems, especially in virus outbreaks. Its role is to monitor and manage the spread of viruses. Moreover, it can use Machine Learning (ML) techniques to achieve this goal. This paper discusses how to apply ML approaches to daily reports of infection cases. The adopted ML approaches are Auto Regressive Integrated Moving Average (ARIMA) model and the Long Short-Term Memory (LSTM) model. Furthermore, it uses Root Mean Square Error (RMSE) as a performance measure to evaluate the applied models. Simulation results show that ARIMA model performs better as compared to other models, which can provide a prediction accuracy of more than 99%. © 2024 IEEE.

关键词： Electronic health record

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Worst-Case Integrity Attacks and Resilient State Estimation With Partially Secured Measurements

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IEEE Transactions on Control of Network systems 2025年第2期12卷 1769-1779页

作者： Zhou, Jing Shang, Jun Chen, Tongwen University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China

This paper examines the problem of optimal deception attacks against state estimation with partially secured measurements, where smart sensors transmit innovation sequences to the remote end for information fusion. Due to resource limitations or defensive countermeasures, the adversary can only modify data packets transmitted through unreliable channels. Meanwhile, the attack should be synthesized with sophistication to deceive an anomaly detector. To investigate the vulnerabilities of such estimation systems without feedback links and enhance security performance, the optimal attack policy is derived by formulating and explicitly solving a convex optimization problem, with the goal of maximizing the sum of estimation errors. Subsequently, a novel attack detection and resilient state estimation algorithm is proposed to ensure an acceptable level of estimation accuracy. The theoretical performance metrics, including false alarm rates for the proposed detector, are provided. Finally, the effectiveness of the results is confirmed through numerical examples. © 2014 IEEE.

关键词： Optimal detection

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Detection of Left Ventricular Cavity from Cardiac MRI Images Using Faster R-CNN

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computers, Materials & Continua 2023年第1期74卷 1819-1835页

作者： Zakarya Farea Shaaf Muhammad Mahadi Abdul Jamil Radzi Ambar Ahmed Abdu Alattab Anwar Ali Yahya Yousef Asiri Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn MalaysiaParit RajaBatu Pahat86400JohorMalaysia Department of Computer Science College of Science and ArtsSharurahNajran UniversityNajran61441Saudi Arabia Department of Computer Science Faculty of Computer Science and Info.SystemsThamar UniversityDhamar87246Yemen Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

The automatic localization of the left ventricle(LV)in short-axis magnetic resonance(MR)images is a required step to process cardiac images using convolutional neural networks for the extraction of a region of interest(ROI).The precise extraction of the LV’s ROI from cardiac MRI images is crucial for detecting heart disorders via cardiac segmentation or ***,this task appears to be intricate due to the diversities in the size and shape of the LV and the scattering of surrounding tissues across different ***,this study proposed a region-based convolutional network(Faster R-CNN)for the LV localization from short-axis cardiac MRI images using a region proposal network(RPN)integrated with deep feature classification and *** was trained using images with corresponding bounding boxes(labels)around the LV,and various experiments were applied to select the appropriate layers and set the suitable *** experimental findings showthat the proposed modelwas adequate,with accuracy,precision,recall,and F1 score values of 0.91,0.94,0.95,and 0.95,*** model also allows the cropping of the detected area of LV,which is vital in reducing the computational cost and time during segmentation and classification ***,itwould be an ideal model and clinically applicable for diagnosing cardiac diseases.

关键词： Cardiac short-axis MRI images automatic left ventricle localization deep learning models faster R-CNN

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Detecting Structural Shifts in Multivariate Hawkes Processes with Fréchet Statistic 63

Detecting Structural Shifts in Multivariate Hawkes Processes...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Luo, Rui Krishnamurthy, Vikram City University of Hong Kong Department of Systems Engineering Hong Kong Cornell University School of Electrical and Computer Engineering IthacaNY14850 United States

ISBN: (纸本)9798350316339

This paper proposes a new approach for change point detection in multivariate Hawkes processes using Fréchet statistic of a network. The method splits the point process into overlapping windows, estimates kernel matrices in each window, and reconstructs the signed Laplacians by treating the kernel matrices as the adjacency matrices of the causal network. We demonstrate the effectiveness of our method through experiments on both simulated and cryptocurrency datasets. Our results show that our method is capable of accurately detecting and characterizing changes in the causal structure of multivariate Hawkes processes, and may have potential applications in fields such as finance and neuroscience. The proposed method is an extension of previous work on Fréchet statistics in point process settings and represents an important contribution to the field of change point detection in multivariate point processes. © 2024 IEEE.

关键词： Matrix algebra

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MIMO-Aided Index Modulation-Based Orthogonal Frequency Division Multiple Access 7

MIMO-Aided Index Modulation-Based Orthogonal Frequency Divis...

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7th International Conference on Advanced Communication Technologies and Networking, CommNet 2024

作者： Belaoura, Widad Althunibat, Saud Hasna, Mazen Qaraqe, Khalid University of m'Hamed Bougara Boumerdes Electrical Systems Engineering Department Boumerdes35000 Algeria Texas A and M University at Qatar Department of Electrical and Computer Engineering Doha Qatar Qatar University Department of Electrical Engineering Doha Qatar Hamad Bin Khalifa University College of Science and Engineering Doha Qatar

ISBN: (纸本)9798350367027

Index modulation-Aided orthogonal frequency division multiple access (IM-OFDMA) is envisioned as an advanced solution for the upcoming wireless communication systems. This paper proposes a highly efficient scheme by combining multiple-input multiple-output (MIMO) and IM-OFDMA to leverage both techniques collectively, offering a new possibility to boost spectrum efficiency compared with the IM-OFDMA and MIMO-OFDMA schemes. The basic principle of the resulting MIMO-IM-OFDMA scheme is to independently perform the IMOFDMA process on each transmit antenna, thereby achieving higher diversity. Considering different system configurations, Monte Carlo simulations demonstrate that the MIMO-IMOFDMA scheme not only enhances the communication error's performance but also improves the spectral efficiency compared to the conventional IM-OFDMA and MIMO-OFDMA schemes. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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A Review of Genome to Chaos: Exploring DNA Dynamics in Security

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Multimedia Tools and Applications 2024年 1-28页

作者： Aashiq Banu, S. Rao, L. Koteswara Priya, P. Shanmuga Thanikaiselvan Hemalatha, M. Dhivya, R. Rengarajan, Amirtharajan Department of Systems Engineering Military Technological College Muscat Oman Department of ECE Koneru Lakshmaiah Education Foundation Telangana Hyderabad500043 India Department of Electronics and Communication Engineering Rajalakshmi Engineering College ThandalamChennai602105 India Vellore632 014 India Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia Department of Electronics and Communication Engineering Mepco Schlenk Engineering College Tamilnadu Sivakasi626005 India School of Electrical & amp Electronics Engineering SASTRA Deemed University Thanjavur613401 India

One of the largest and fastest-growing industries globally is e-healthcare. Digital medical imaging constantly evolves, advancing from standard computed tomography to powerful multi-dimensional imaging. This medical imaging forms patients’ real-time electronic health records (EHR), making it a prime target for hackers. To counter cipher-attacks, it is necessary to secure medical imaging. This research thoroughly analyses the properties of DNA chaos-based digital medical image encryption and reviews various algorithms. Researchers have performed several statistical and differential metrics, conducting simulation experiments to determine which methodology provides the best security outcome. The paper concludes by analysing significant advances in medical image encryption and emphasising future challenges. This paper discusses the importance of securing medical images, the potential breaches, and the security needs, providing a comprehensive review of DNA-based chaos cryptography techniques. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electronic health record

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D-Band $\times$ 8 Frequency Multiplier Using Complementary Differential Frequency Doubler

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IEEE Microwave and Wireless Components Letters 2022年 1-4页

作者： Park, Jae-Hyun Yang, Dong-Yeol Choi, Kyu-Jin Kim, Byung-Sung Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea Department of Semiconductor Systems Engineering Sungkyunkwan University Suwon South Korea

A D-band $\times$ 8 frequency multiplier is presented in this letter. The $\times$ 8 frequency multiplier consists of two stages of the newly proposed common load complementary push-push doublers (CL-CPPDs) and one stage of NMOS push-push doubler. The proposed CL-CPPD uses a single load to generate a balanced output with high harmonic rejection (HR) and can be easily cascaded using transformers for a high multiplication ratio with low dc power consumption. The chip is fabricated by using a 40-nm CMOS technology, and it achieves a maximum output power of $-$ 2.48 dBm with an input power of $0$ dBm in the frequency range of 131.2–144.8 GHz. All HRs are over 34 dBc in the frequency range of 131.2–144.8 GHz. The total dc power dissipation is 41.4 mW when supplied from a single voltage of 0.9 V and occupies 0.37 ${\mathrm{mm}}^{2}$ of the chip area. IEEE

关键词： Millimeter waves

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Multi-channel CNN to classify Nepali COVID-19 related tweets using hybrid features

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Journal of Ambient Intelligence and Humanized Computing 2024年第3期15卷 2047-2056页

作者： Sitaula, Chiranjibi Shahi, Tej Bahadur Department of Electrical and Computer Systems Engineering Monash University Wellington Rd ClaytonVIC3800 Australia Earth Observation and AI Research Group Department of Infrastructure Engineering The University of Melbourne ParkvilleVIC3010 Australia School of Engineering and Technology Central Queensland University Rockhampton Australia Central Department of Computer Science and Information Technology Tribhuvan University TU Rd Kathmandu Kirtipur44618 Nepal

Because of the current COVID-19 pandemic’s increasing fears among people, it has triggered several health complications such as depression and anxiety. Such complications have not only affected developed countries but also developing countries such as Nepal. These complications can be understood from peoples’ tweets and comments posted online after their felicitous analysis and sentiment classification. Nevertheless, owing to the limited number of tokens and words in each tweet, it is crucial to capture multiple pieces of information associated with them for better understanding. In this study, we, first, represent each tweet by combining both syntactic and semantic information, called hybrid features. The syntactic information is generated from the bag-of-words method, whereas the semantic information is generated from the combination of the fastText-based (ft) and domain-specific (ds) methods. Second, we design a novel multi-channel CNN (MCNN) model, which ensembles the multiple CNNs, to capture multi-scale information for better classification. Last, we evaluate the efficacy of both the proposed feature extraction method and the MCNN model classifying tweets into three sentiment classes (positive, neutral, and negative) on the NepCOV19Tweets dataset, which is the only public COVID-19 tweets dataset in the Nepali language. The evaluation results show that the proposed hybrid features outperform individual feature extraction methods with the highest classification accuracy of 69.7% and the MCNN model outperforms the existing methods with the highest classification accuracy of 71.3% during classification. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： COVID-19

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