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STAR-RIS Assisted Covert Wireless Communications with Randomly Distributed Blockages

IEEE Transactions on Wireless Communications 2025年第6期24卷 4690-4705页

作者： Li, Xingwang Zhao, Jingwei Chen, Gaojie Hao, Wanming Da Costa, Daniel Benevides Nallanathan, Arumugam Shin, Hyundong Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454099 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Shenzhen518107 China 5GIC 6GIC University of Surrey United Kingdom Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China King Fahd University of Petroleum and Minerals Interdisciplinary Research Center for Communication Systems and Sensing Department of Electrical Engineering Dhahran31261 Saudi Arabia Queen Mary University of London School of Electronic Engineering and Computer Science London United Kingdom Kyung Hee University Department of Electronic Engineering Yongin-si17104 Korea Republic of Kyung Hee University Department of Electronics and Information Convergence Engineering Korea Republic of Nanyang Technological University School of Electrical and Electronics Engineering 639798 Singapore

As one of the promising technologies, reconfigurable intelligent surface (RIS) and simultaneous transmitting and reflecting RIS (STAR-RIS) have attracted great interest. However, the existing RISs offer broadband tuning capability without filtering function due to the absence of radio frequency (RF) units, which easily leads to the unexpected tuning of the RIS undesired signals, especially in large-scale deployments. For the target network, it is difficult to obtain the parameter settings of RISs to serve other networks, which causes the unpredictability of the wireless environment. In this paper, we consider the covert communication in a STAR-RIS assisted random wireless network with randomly distributed blockages. We investigate the impact of STAR-RIS large-scale deployment on covert communication and leverage its inherent unpredictability for improving the covertness. We derive the average detection error probability for warden within the random wireless networks. Furthermore, we optimize the passive beamforming of STAR-RIS to maximize the covert communication rate, considering both direct and indirect line-of-sight (LoS) links. To address this, we employ an alternating optimization (AO) algorithm based on the semi-definite programming (SDP) method. Finally, numerical results demonstrate significant enhancements and increase covert capability achieved through the large-scale deployment of STAR-RIS. 2002-2012 IEEE.

关键词： Numerical methods

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A Transfer Learning Based Approach for COVID-19 Detection Using Inception-v4 Model

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Intelligent Automation & Soft Computing 2023年第2期35卷 1721-1736页

作者： Ali Alqahtani Shumaila Akram Muhammad Ramzan Fouzia Nawaz Hikmat Ullah Khan Essa Alhashlan Samar MAlqhtani Areeba Waris Zain Ali College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia Department of Computer Science and Information Technology University of SargodhaSargodha40100Pakistan School of Systems and Technology University of Management and TechnologyLahore54782Pakistan Department of Computer Science COMSATS University IslamabadWah CampusIslamabad47040Pakistan Department of Medical Imaging King Khaled HospitalNajran61441Saudi Arabia Electrical&Electronics Engineering Department Universiti TeknologiPetronas31750Malaysia Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan

Coronavirus(COVID-19 or SARS-CoV-2)is a novel viral infection that started in December 2019 and has erupted rapidly in more than 150 *** rapid spread of COVID-19 has caused a global health emergency and resulted in governments imposing lock-downs to stop its *** is a signiﬁ-cant increase in the number of patients infected,resulting in a lack of test resources and kits in most *** overcome this panicked state of affairs,researchers are looking forward to some effective solutions to overcome this situa-tion:one of the most common and effective methods is to examine the X-radiation(X-rays)and computed tomography(CT)images for detection of ***-ever,this method burdens the radiologist to examine each ***,to reduce the burden on the radiologist,an effective,robust and reliable detection system has been developed,which may assist the radiologist and medical specia-list in effective detecting of *** proposed a deep learning approach that uses readily available chest radio-graphs(chest X-rays)to diagnose COVID-19 *** proposed approach applied transfer learning to the Deep Convolutional Neural Network(DCNN)model,Inception-v4,for the automatic detection of COVID-19 infection from chest X-rays *** dataset used in this study contains 1504 chest X-ray images,504 images of COVID-19 infection,and 1000 normal images obtained from publicly available medical *** results showed that the proposed approach detected COVID-19 infection with an overall accuracy of 99.63%.

关键词： COVID-19 transfer learning deep learning artiﬁcial intelligence chest X-rays machine learning

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Green Algorithms for Energy-Efficient Distributed Flow-Shop Manufacturing Problem

Green Algorithms for Energy-Efficient Distributed Flow-Shop ...

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2023 International Symposium on Green Technologies and Applications, ISGTA 2023

作者： Sayah, Achraf Aqil, Said Lahby, Mohamed Laboratory of Applied Mathematics and Computer Sciences Higher Normal School Hassan II University of Casablanca Morocco Laboratory of Artificial Intelligence and Complex Systems Engineering National Higher School of Arts And Crafts Hassan II University of Casablanca Morocco

Minimizing energy consumption is one of the most interesting issues in recent industrial production. Energy-efficient scheduling is a promising approach to reducing energy consumption in manufacturing systems. However, energy-efficient scheduling is a problem of great interest, especially for the Distributed permutation Flow-Shop Manufacturing Problem (DPFSMP). This paper addresses the critical challenge of energy efficiency in the industry of DPFSMP optimization, where task processing times depend closely on their position in the production line. The objective is to minimize the total energy consumption (TEC) for all machines in distributed workshops. To solve this problem, we propose two efficient constructive algorithms for energy-efficient scheduling of this type of problem with speed-dependent tasks: the Nawaz Enscore and Ham (NEH) based algorithm and the Greedy Random Adaptive Search Procedure (GRASP) based algorithm. We evaluate the performance of the two proposed algorithms on a set of benchmark problems to identify the one with the best performance. The results show that the (NEH) algorithm outperforms the GRASP algorithm in terms of energy efficiency and solution quality. In fact, the proposed algorithms can be used by manufacturers to reduce energy consumption in their production systems. This can help manufacturers save money, reduce environmental impact and improve overall sustainability. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Energy utilization

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Day-ahead Network-constrained Unit Commitment Considering Distributional Robustness and Intraday Discreteness:A Sparse Solution Approach

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Journal of Modern Power systems and Clean Energy 2023年第2期11卷 489-501页

作者： Xiaodong Zheng Baorong Zhou Xiuli Wang Bo Zeng Jizhong Zhu Haoyong Chen Waisheng Zheng School of Electrical Engineering Xi’an Jiaotong UniversityXi’an 710049China Electric Power Research Institute of China Southern Power Grid Co. Ltd.Guangzhou 510663China Shaanxi Key Laboratory of Smart Grid School of Electrical EngineeringXi’an Jiaotong UniversityXi’an 710049China Department of Industrial Engineering and the Department of Electrical and Computer EngineeringUniversity of PittsburghPittsburghPA 15260USA School of Electric Power Engineering South China University of TechnologyGuangzhou 510641China China Southern Power Grid Co. Ltd.Guangzhou 510663China

Quick-start generation units are critical devices and flexible resources to ensure a high penetration level of renewable energy in power *** considering the wind uncertainty and both binary and continuous decisions of quickstart generation units within the intraday dispatch,we develop a Wasserstein-metric-based distributionally robust optimization model for the day-ahead network-constrained unit commitment(NCUC)problem with mixed-integer *** propose two feasible frameworks for solving the optimization *** approximates the continuous support of random wind power with a finite number of events,and the other leverages the extremal distributions *** solution frameworks rely on the classic nested column-and-constraint generation(C&CG)*** is shown that due to the sparsity of L_(1)-norm Wasserstein metric,the continuous support of wind power generation could be represented by a discrete one with a small number of events,and the rendered extremal distributions are sparse as *** this reduction,the distributionally robust NCUC model with complicated mixed-integer recourse problems can be efficiently handled by both solution *** studies are carried out,demonstrating that the model considering quick-start generation units ensures unit commitment(UC)schedules to be more robust and cost-effective,and the distributionally robust optimization method captures the wind uncertainty well in terms of out-of-sample tests.

关键词： Unit commitment distributional robustness mixed-integer recourse nested column-and-constraint generation(C&CG) sparsity Wasserstein metric

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AUTONOMOUS CONTROL MANAGEMENT OF SYNCHRONOUS GENERATORS IN MICROGRID SYSTEM

AUTONOMOUS CONTROL MANAGEMENT OF SYNCHRONOUS GENERATORS IN M...

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2025 International Conference on Energy, Power, Environment, Control and Computing, ICEPECC 2025

作者： Qamar, Hafiz Ghulam Murtza Ashraf, Adil Ahmad, Fareed Riaz, Ayesha Imran, Muhammad Yousaf, Muhammad Zain Khan, Imran School of Electrical Engineering Yanshan University Qinhuangdao066004 China Department of Electrical Electronics and Computer Systems University of Sargodha Pakistan Center for Renewable Energy and Microgrids Huanjiang Laboratory Zhejiang University Zhejiang Zhuji311816 China

ISBN: (纸本)9781837243235

Decentralized excitation control based on local asynchronous clock feedback measurement leads to the problem of decentralized incompatibility of excitation controllers, which reduces the ability of excitation control design to improve the stability of power systems. Therefore, the problem of decentralized compatible excitation control is proposed, and the effect of excitation control on the dynamic characteristics of the system under compatible and incompatible modes is compared and analyzed, and the shortcomings of decentralized incompatible excitation control and the advantages of decentralized compatible excitation control are given. © The Institution of engineering & Technology 2025.

关键词： Decentralized control

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MEMS-Silicon Chip Bonding Verification using GHz Pulse-echo Simulation and Measurements

MEMS-Silicon Chip Bonding Verification using GHz Pulse-echo ...

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2023 Joint Conference of the European Frequency and Time Forum and IEEE International Frequency Control Symposium, EFTF/IFCS 2023

作者： Trivedi, Shyam Yeo, Yi Xuan Sarafianou, Mantalena Wai, Eva Leong Ching Sharma, Jaibir Chen, Daniel Ssu-Han Lal, Amit Chai, Kevin Tshun Chuan Institute of Microeletronics Agency for Science Singapore Singapore Cornell University SonicMEMS Laboratory School of Electrical and Computer Engineering IthacaNY United States

ISBN: (纸本)9798350311426

This research presents a novel method to differentiate the bonding quality of a MEMS chip to a silicon substrate by using bulk acoustic wave (BAW) transducer arrays to determine the GHz ultrasonic wave transmission through the bonding interface. Prior to bonding, one-port S-parameter measurements were conducted on the array device at the wafer level to determine the mean resonance frequency (1.487 GHz) and standard deviation (33.8 MHz). The MEMS chip was then bonded to a silicon substrate using Au-AuSn bonding at a temperature 70%, due to constructive interference of ultrasonic waves reflected from the bonding interface and the substrate bottom. For fully bonded cases, the fourth echo became larger or comparable to the second echo, and first echo levels diminish by at least 20%. Electro-acoustic measurements were performed before and after bonding to confirm these distinctive signatures and categorize the devices as fully bonded, partially bonded, or unbonded. © 2023 IEEE.

关键词： Acoustic Transmission AlN Piezoelectric BAW Transducers GHz Gold-Tin Bonding Pulse-echo Ultrasonic Wavefront

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Stochastic Physics-Informed Deep Generative Network Scenario Generation: Application on Responsive Residential Load Management

Stochastic Physics-Informed Deep Generative Network Scenario...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Afrasiabi, Mousa Afrasiabi, Shahabodin Allahmoradi, Sarah Aghaei, Jamshid Chung, C.Y. University of Saskatchewan Department of Electrical and Computer Engineering Saskatoon Canada Lappeenranta University of Technology School of Energy Systems Department of Electrical Engineering Lappeenranta Finland Central Queensland University School of Engineering and Technology Department of Electrical Engineering Rockhampton Australia The Hing Kong Polytechnic University Department of Electrical Engineering Hong Kong

ISBN: (纸本)9781665471640

This paper introduces a stochastic model for the optimal residential responsive loads power scheduling considering participants' satisfaction. In this context, firstly, a physical-informed based generative adversarial network (PI-GAN) network is designed for scenario generation with a high correlation with the actual data. In this network, conventional GANs are improved to learn spatial-temporal features of the residential loads and physics-informed concepts. To realize the spatial feature of the complex and highly nonlinear time series like residential loads, a residual convolutional neural network (Res-CNN) is considered to learn the spatial features, while the fully temporal features are realized by gated recurrent neural networks (GNN). Then, generated scenarios are used to cover the uncertainty associated with residential loads and provide the optimal results for responsive loads, including shiftable and curtailable loads. The numerical results on actual data in London, England, verify the effectiveness of the proposed stochastic framework and superiority by comparison with conditional GAN and improved version of GAN in scenario generations impact of stochastic demand response program. © 2023 IEEE.

关键词： Demand response gated recurrent neural network physics-informed based generative adversarial network residual convolutional neural network scenario-generation

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Masked Generative Story Transformer with Character Guidance and Caption Augmentation

arXiv

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arXiv 2024年

作者： Papadimitriou, Christos Filandrianos, Giorgos Lymperaiou, Maria Stamou, Giorgos Artificial Intelligence and Learning Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Greece

Story Visualization (SV) is a challenging generative vision task, that requires both visual quality and consistency between different frames in generated image sequences. Previous approaches either employ some kind of memory mechanism to maintain context throughout an auto-regressive generation of the image sequence, or model the generation of the characters and their background separately, to improve the rendering of characters. On the contrary, we embrace a completely parallel transformer-based approach, exclusively relying on Cross-Attention with past and future captions to achieve consistency. Additionally, we propose a Character Guidance technique to focus on the generation of characters in an implicit manner, by forming a combination of text-conditional and character-conditional logits in the logit space. We also employ a caption-augmentation technique, carried out by a Large Language Model (LLM), to enhance the robustness of our approach. The combination of these methods culminates into state-of-the-art (SOTA) results over various metrics in the most prominent SV benchmark (Pororo-SV), attained with constraint resources while achieving superior computational complexity compared to previous arts. The validity of our quantitative results is supported by a human survey1 © 2024, CC BY.

关键词： Visualization

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Evaluation of the Electric Stress on an Insulating Down-Conductor Caused by Lightning Strikes through ATP-EMTP Simulations

Evaluation of the Electric Stress on an Insulating Down-Cond...

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2023 IEEE Industry Applications Society Annual Meeting, IAS 2023

作者： Samaras, Pavlos K. Staikos, Evangelos T. Datsios, Zacharias G. Mikropoulos, Pantelis N. Tsovilis, Thomas E. Kokkinos, Nikolaos D. School of Electrical & Computer Engineering Aristotle University of Thessaloniki High Voltage Laboratory Thessaloniki54124 Greece Elemko Sa Athens14452 Greece

ISBN: (纸本)9798350320169

electrically insulated lightning protection systems, LPS, utilize insulating stand-offs and insulating downconductors to maintain the required separation distance from grounded objects. The equivalent separation distance offered by these insulating components can be determined through comparative tests, such as those suggested in IEC TS 625618:2018, where crossed-conductors air gaps under negative standard lightning impulse voltages are used as reference. However, in service the insulating components of an electrically insulated LPS are stressed by non-standard fast-front overvoltages related to lightning currents. In this study the electric stress experienced by the insulating components of an electrically insulated LPS due to direct lightning strikes has been assessed through ATP-EMTP simulations. For the evaluated electrically insulated LPS critical values of separation distance for air-insulated configurations and of dielectric strength for the insulating down-conductor ensuring withstand against the overvoltages estimated for upper limits of lightning currents have been determined. © 2023 IEEE.

关键词： Electric insulation

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Power System Transient Security Assessment using Unsupervised Probabilistic Deep Bayesian Neural Network

Power System Transient Security Assessment using Unsupervise...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Afrasiabi, Shahabodin Allahmoradi, Sarah Liang, Xiaodong Afrasiabi, Mousa Aghaei, Jamshid Chung, C.Y. University of Saskatchewan Department of Electrical and Computer Engineering Saskatoon Canada Lappeenranta University of Technology School of Energy Systems Department of Electrical Engineering Lappeenranta Finland Central Queensland University School of Engineering and Technology Department of Electrical Engineering Rockhampton Australia The Hing Kong Polytechnic University Department of Electrical Engineering Hong Kong

ISBN: (纸本)9781665471640

This paper introduces an unsupervised deep Bayesian network, built upon normalizing flow and deep Bayesian network principles, to precisely evaluate the transient security condition of a power system. The proposed approach can capture locational and temporal features using an imbalanced dataset, is noise-model-free, and can handle unlabeled data. It can learn interdependencies between different signals and understand high-dimensional signals in power systems. To validate its effectiveness, the proposed method is studied using the New England power system and shows accuracy and reliability in comparison with state-of-the-art deep networks (convolutional neural network (CNN) and long short-term memory (LSTM)) and shallow networks (support vector machine (SVM) and artificial neural network (ANN)). © 2023 IEEE.

关键词： Bayesian network imbalanced dataset noise-model free normalizing flow transient security assessment unsupervised deep learning

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