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IEEE International Conference on Communications (ICC)

作者： Tianheng Xu Yinjun Xu Jingyi Wu Haijun Zhang Ting Zhou Honglin Hu Victor C.M. Leung Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai China Shanghai Frontier lnnovation Research Institute Shanghai China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Microelectronics Shanghai University Shanghai China Department of Electrical and Computer Engineering The University of British Columbia Canada College of Computer and Software Engineering Shenzhen University China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

The upcoming sixth-generation (6G) of ubiquitous connectivity communications systems is driven by the next-generation of Internet of Things (IoT) technology. Accordingly, the demand for spectrum resources is growing exponentially. Spectrum sensing, which dynamically explores spectrum holes, is expected to be crucial in the 6G era. In the meantime, Non-Orthogonal Multi-Access (NOMA), an efficient means of reusing resources, can enable multiple users to share the same frequency band stably. The combination of both technologies holds promise for more effective use of spectrum resources in future commu-nications. In this paper, we present a spectrum sensing method for the uplink communication scenario in NOMA with multi-user interference, which aims to make effective use of both static and dynamic gain on spectrum efficiency. Firstly, the sensing criterion and workflow are outlined. The closed-form solution for the sensing threshold configuration is derived thereafter. The simulation results demonstrate the feasibility of the proposed approach, which can increase the system throughput up to 19.7%, when compared to NOMA without spectrum sensing.

关键词： 6G mobile communication NOMA Closed-form solutions Simulation Throughput Orbits Sensors

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Hybrid BiGRU-CNN Model for Load Forecasting in Smart Grids with High Renewable Energy Integration

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IET Generation, Transmission and Distribution 2025年第1期19卷

作者： Ullah, Kaleem Shakir, Daniyal Abid, Usama Alahmari, Saad Aslam, Sheraz Ullah, Zahid US-Pakistan Centre for Advanced Studies in Energy University of Engineering and Technology Peshawar Peshawar Pakistan Sr. Business Analyst S&P Global Islamabad Islamabad Pakistan Department of Computer Science and Information Technology School of Computing Engineering and Mathematical Sciences La Trobe University MelbourneVIC Australia Department of Computer Science Applied College Northern Border University Arar Saudi Arabia Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Department of Computer Science CTL eurocolege Limassol Cyprus Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Lombardia Italy

Integrating renewable energy sources into smart grids increases supply and demand management because renewable energy sources are intermittent and variable. To overcome this type of challenge, short-term load forecasting (STLF) is essential for managing energy, demand-side flexibility, and the stability of smart grids with renewable energy integration. This paper presents a new model called BiGRU-CNN to improve the operation of STLF in smart grids. The BiGRU-CNN model integrates bidirectional gated recurrent units (BiGRUs) to model temporal dependencies and convolutional neural networks (CNNs) to extract spatial patterns from energy consumption data. The newly developed BiGRU captures past and future contexts through bidirectional processing, and the CNN component extracts high-level features to enhance the accuracy of load demand prediction. The BiGRU-CNN model is compared with two other hybrid models, CNN-LSTM and CNN-GRU, on real-world data from American electric power (AEP) and ISONE datasets. Simulation results show that the proposed BiGRU-CNN outperforms other models, with single-step forecasting yielding root mean square error (RMSE) of 121.43 (AEP) and 123.57 (ISONE), mean absolute error (MAE) of 90.95 (AEP) and 62.97 (ISONE), and mean absolute percentage error (MAPE) of 0.61% (AEP) and 0.41% (ISONE). For multi-step forecasting, the model yields RMSE of 680.02 (AEP) and 581.12 (ISONE), MAE of 481.12 (AEP) and 411.20 (ISONE), and MAPE of 3.27% (AEP) and 2.91% (ISONE). The results show that the BiGRU-CNN model can generate accurate and reliable STLF, which is useful in managing massive renewable energy-integrated smart grids. © 2025 The Author(s). IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Convolutional neural networks

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Insulating materials for realising carbon neutrality:Opportunities,remaining issues and challenges

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High Voltage 2022年第4期7卷 610-632页

作者： Chuanyang Li Yang Yang Guoqiang Xu Yao Zhou Mengshuo Jia Shaolong Zhong Yu Gao Chanyeop Park Qiang Liu Yalin Wang Shakeel Akram Xiaoliang Zeng Yi Li Fangwei Liang Bin Cui Junpeng Fang Lingling Tang Yulin Zeng Xingtao Hu Jiachen Gao Giovanni Mazzanti Jinliang He Jianxiao Wang Davide Fabiani Gilbert Teyssedre Yang Cao Feipeng Wang Yunlong Zi The State Key Laboratory of Power System Department of Electrical EngineeringTsinghua UniversityBeijingChina Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong KongChina Simpson Querrey Institute Northwestern UniversityEvanstonIllinoisUSA Department of Materials Science and Engineering The Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA Department of Information Technology and Electrical Engineering ETH ZürichZürichSwitzerland School of Electrical and Information Engineering Tianjin UniversityTianjinChina Department of Electrical and Computer Engineering Mississippi State UniversityMississippi StateMississippiUSA Electrical and Electronic Engineering The University of ManchesterManchesterUK Department of Electrical Engineering School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina Key Laboratory of Control of Power Transmission and Conversion(SJTU) Ministry of EducationShanghaiChina College of Electrical Engineering Sichuan UniversityChengduChina Shenzhen Institute of Advanced Technology Chinese Academy of Sciences ShenzhenShenzhenChina School of Electrical Engineering Wuhan UniversityWuhanChina School of Integrated Circuits Tsinghua UniversityBeijingChina Jiangsu Jinxin Electric Appliance Co.Ltd. YangzhouChina Tai'an Tiancheng Safety Evaluation Co. Ltd.Tai'anChina Department Electrical Electronic and Information EngineeringUniversity of BolognaBolognaItaly National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijingChina Laplace Paul Sabatier Universityand CNRSToulouseFrance Electrical and Computer Engineering University of ConnecticutStorrsConnecticutUSA State Key Laboratory of Power Transmission Equipment&System Security and New Technology School of Electrical EngineeringChongqing UniversityChongqingChina Thrust of Sustainable Energy and Environment The Hong Kong University of Science and Technology(Guangzhou)GuangzhouChina De

The 2050 carbon-neutral vision spawns a novel energy structure revolution,and the construction of the future energy structure is based on equipment *** material,as the core of electrical power equipment and electrified transportation asset,faces unprecedented challenges and *** goal of carbon neutral and the urgent need for innovation in electric power equipment and electrification assets are first *** engineering challenges constrained by the insulation system in future electric power equipment/devices and electrified transportation assets are *** materials,including intelligent insulating material,high thermal conductivity insulating material,high energy storage density insulating material,extreme environment resistant insulating material,and environmental-friendly insulating material,are cat-egorised with their scientific issues,opportunities and challenges under the goal of carbon neutrality being *** the context of carbon neutrality,not only improves the understanding of the insulation problems from a macro level,that is,electrical power equipment and electrified transportation asset,but also offers opportunities,remaining issues and challenges from the insulating material *** is hoped that this paper en-visions the challenges regarding design and reliability of insulations in electrical equipment and electric vehicles in the context of policies towards carbon neutrality *** authors also hope that this paper can be helpful in future development and research of novel insulating materials,which promote the realisation of the carbon-neutral vision.

关键词： neutral insulation extreme

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ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular Carcinoma Using 3D CT with Multiple Instance Learning

ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular ...

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2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： Chang, Shun-Cheng Yu, Hsin-Pei Hsieh, Yi-Hsien Wang, Pochuang Wang, Weichung Su, Tung-Hung Kao, Jia-Horng Lin, Che Taipei10617 Taiwan Department of Computer Science and Information Engineering Ntu Taipei10617 Taiwan Institute of Applied Mathematical Sciences Ntu Taipei10617 Taiwan National Taiwan University Hospital Division of Gastroenterology and Hepatology Department of Internal Medicine Taipei10048 Taiwan Department of Electrical Engineering Ntu Taipei10617 Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taipei10617 Taiwan Smart Medicine and Health Informatics Program Ntu Taipei10617 Taiwan

ISBN: (纸本)9798350351552

Deep learning has revolutionized medical imaging, offering advanced methods for accurate diagnosis and treatment planning. The BCLC staging system is crucial for staging Hepatocellular Carcinoma (HCC), a high-mortality cancer. An automated BCLC staging system could significantly enhance diagnosis and treatment planning efficiency. However, we found that BCLC staging, which is directly related to the size and number of liver tumors, aligns well with the principles of the Multiple Instance Learning (MIL) framework. To effectively achieve this, we proposed a new preprocessing technique called Masked Cropping and Padding(MCP), which addresses the variability in liver volumes and ensures consistent input sizes. This technique preserves the structural integrity of the liver, facilitating more effective learning. Furthermore, we introduced Re ViT, a novel hybrid model that integrates the local feature extraction capabilities of Convolutional Neural Networks (CNNs) with the global context modeling of Vision Transformers (ViTs). Re ViT leverages the strengths of both architectures within the MIL framework, enabling a robust and accurate approach for BCLC staging. We will further explore the trade-off between performance and interpretability by employing TopK Pooling strategies, as our model focuses on the most informative instances within each bag. © 2024 IEEE.

关键词： Convolutional neural networks

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Optimal Parameter Estimation of CAPN Model for Li-ion Battery

Optimal Parameter Estimation of CAPN Model for Li-ion Batter...

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computer, Electronics & electrical engineering & their Applications (IC2E3), International Conference on

作者： Soumya Bharti Vikash Kumar Saini Anshul Kumar Yadav Rajesh Kumar Ameena S. Al-Sumaiti Department of Electrical Engineering Malaviya National Institute of Technology Jaipur Center for Energy and Environment Malaviya National Institute of Technology Jaipur Advanced Power and Energy Center Electrical Engineering and Computer Science Khalifa University UAE Department of Electrical Engineering Swami Keshvanand Institute of Technology Jaipur

Recent developments in battery technology and the reduction of battery costs have resulted in battery energy storage systems (BESS) becoming more economically viable for various power sectors and automobile applications. The degradation of BESS when used in conjunction with photovoltaics (PV) and electric vehicles (EVs) brings new challenges in both technical and economic analysis. Consequently, to ensure the reliability and safety of an energy storage system, modeling battery degradation is critical in order to maintain and replace batteries before potential issues arise. This paper presents a hybrid optimal parameter estimation technique for a combination-factor-based semi-empirical approach to evaluate the state of health (SOH) of Li-ion batteries. The parameters of the proposed model are determined using the SCA optimization algorithm, and the numerical results are confirmed by placing them side-by-side with other heuristic algorithms such as GWO, DE, HHO, and GA. The proposed model outperformed the traditional model with a SOH value of 0.9591 as compared to the previous SOH obtained 0.9467 by the traditional model.

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A Logical Cyber Security Enabled Methodology Design for Identifying Distributed Denial of Service Attacks Using Enhanced Learning Principles

A Logical Cyber Security Enabled Methodology Design for Iden...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Karthiga, R. Praveen Kumar, E. Kumari, S. Kala Priyadarshini, G. Sureka, V. Pandi, V. Samuthira R.M.K Engineering College Department of It Tamil Nadu Kavaraipettai India Tamil Nadu Coimbatore India Panimalar Engineering College Department of Information Technology Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India S.A.Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350313987

The Distributed Denial of Service (DDoS) attack is a common type of cyber-attack that targets computer networks. When a hostile user seeks to prohibit access to a service by overwhelming the system with data that is not intended, this is an example of the distributed denial of service attack, also known as a DDoS attack. Because of this, the provision of services to legitimate users is slowed down. DDoS attacks are launched when a large number of sources, such as a Botnet that controls the devices remotely for malicious reasons, simultaneously launch attacks on a target. DDoS refers to 'distributed denial of service' attacks. In order to detect and prevent Distributed Denial of Service attacks, classification algorithms have been widely employed in research. Attacks using a Distributed Denial of Service may be easily carried out by using the weaknesses of networks as well as making application service demands. The relevance of this strategy cannot be overstated due to the fact that DDoS attacks are difficult to detect and prevent in real time and can have devastating effects. This study led to the development of a deep learning model called the Learning Model to Detect Cyber Attacks (LMDCA). Its purpose is to anticipate application layer DDoS attacks in real time. It is the responsibility of the availability component to guarantee that authorized users will always have access to the system. During the course of this study, we modeled both a denial-of-service attack as well as normal network traffic. Following that, we utilized packet-level analysis in an effort to differentiate between the aforementioned two usage patterns. Then, in order to detect and classify the various DOS attacks, we make use of the LMDCA-based classifier logic that was recommended. © 2023 IEEE.

关键词： Cyber Attack Cyber Security DDoS Deep Learning Distributed Denial of Service Learning Principle LMDCA

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A BCLC Staging System for Hepatocellular Carcinoma using Swin Transformer and CT Imaging 46

A BCLC Staging System for Hepatocellular Carcinoma using Swi...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Chang, Shun-Cheng Wang, Pochuang Wang, Weichung Su, Tung-Hung Kao, Jia-Horng Lin, Che Taipei10617 Taiwan Department of Computer Science and Information Engineering Ntu Taipei10617 Taiwan Institute of Applied Mathematical Sciences Ntu Taipei10617 Taiwan Division of Gastroenterology and Hepatology Department of Internal Medicine National Taiwan University Hospital Taipei10048 Taiwan Department of Electrical Engineering Ntu Taipei10617 Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taipei10617 Taiwan Smart Medicine and Health Informatics Program Ntu Taipei10617 Taiwan

ISBN: (纸本)9798350371499

The Barcelona Clinic Liver Cancer (BCLC) staging system plays a crucial role in clinical planning, offering valuable insights for effectively managing hepatocellular carcinoma. Accurate prediction of BCLC stages can significantly ease the workload on radiologists. However, few datasets are explicitly designed for discerning BCLC stages. Despite the common practice of appending BCLC labels to clinical data within datasets, the inherent imbalance in BCLC distribution is further amplified by the diverse purposes for which datasets are curated. In this study, we aim to develop a BCLC staging system using the advanced Swin Transformer model. Additionally, we explore the integration of two datasets, each originally intended for separate objectives, highlighting the critical challenge of preserving class distribution in practical study designs. This exploration is pivotal for ensuring the applicability of our developed staging system in the designed clinical settings. Our resulting BCLC staging system demonstrates an accuracy of 55.81% (±7.8%), contributing to advancing medical image-based research for predicting BCLC stages. © 2024 IEEE.

关键词： Distribution transformers

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Magnetomechanical Accelerometer Based on Magnetic Tunnel Junctions

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Physical Review Applied 2023年第3期20卷 034003-034003页

作者： Andrea Meo Francesca Garescì Victor Lopez-Dominguez Davi Rodrigues Eleonora Raimondo Vito Puliafito Pedram Khalili Amiri Mario Carpentieri Giovanni Finocchio Department of Electrical and Information Engineering Politecnico di Bari Bari 70125 Italy Department of Engineering University of Messina Messina I-98166 Italy Institute of Advanced Materials (INAM) Universitat Jaume I Castellon 12006 Spain Department of Electrical and Computer Engineering Northwestern University 2145 Sheridan Road Evanston 60208 Illinois USA Department of Mathematical and Computer Sciences Physical Sciences and Earth Sciences University of Messina Messina I-98166 Italy

Accelerometers have widespread applications and are an essential component in many areas, such as automotive, consumer electronics, and industrial applications. Most commercial accelerometers are based on a microelectromechanical system (MEMS) that is limited in downscaling and power consumption. Spintronics-based accelerometers are proposed as alternatives; however, current proposals suffer from design limitations that result in reliability issues and high cost. Here, we propose spintronic accelerometers with magnetic tunnel junctions (MTJs) as building blocks, which map accelerations into a measurable voltage across the MTJ terminals. The device exploits elastic and dipolar coupling as a sensing mechanism and the spintronic diode effect for the direct readout of acceleration. The proposed technology represents a potentially competitive and scalable solution to current capacitive MEMS-based approaches that could lead to a step forward in many of the commercial applications.

关键词： Magnetic tunnel junctions Micromechanical devices Landau-Lifschitz-Gilbert equation Micromagnetic modeling

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Entanglement in Quantum Dots: Insights from Dynamic Susceptibility and Quantum Fisher Information

arXiv

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arXiv 2024年

作者： Abouie, Jahanfar Vashaee, Daryoosh Department of Physics Institute for Advanced Studies in Basic Sciences Zanjan45137-66731 Iran Department of Materials Science & Engineering North Carolina State University RaleighNC27606 United States Department of Electrical & Computer Engineering North Carolina State University RaleighNC27606 United States

This study investigates the entanglement properties of quantum dots (QDs) under a universal Hamiltonian where the Coulomb interaction between particles (electrons or holes) decouples into charging energy and exchange coupling terms. Although this formalism typically decouples the charge and spin components, confinement-induced energy splitting can induce unexpected entanglement within the system. By analyzing the dynamic susceptibility and quantum Fisher information (QFI), we uncover significant behaviors influenced by exchange constants, temperature variations, and confinement effects. In QDs with Ising exchange interactions, far below the Stoner instability (SI) point, where the QD is in a disordered paramagnetic phase, temperature reductions lead to decreased entanglement, challenging conventional expectations. Our findings demonstrate that for QDs with small exchange interactions, the responses of easy-plane (Jz ⊥) and easy-axis (Jz > J⊥) configurations are similar, with increased anisotropy broadening susceptibility and shifting its maximum to higher frequencies. For large exchange interactions, the susceptibility differences between easy-plane and easy-axis QDs become significant, with easy-plane QDs exhibiting a higher susceptibility magnitude. Additionally, the study reveals that temperature variations affect the dynamic response functions differently in easy-axis and easy-plane QDs. In easy-plane QDs, QFI consistently decreases with increasing temperature, whereas in easy-axis QDs, QFI behavior is highly dependent on the strengths of Jz and J⊥, showing either an increase or decrease with temperature based on specific coupling conditions. Conversely, at low temperatures, anisotropic Heisenberg models exhibit enhanced entanglement near isotropic points. Overall, this work contributes to advancing our understanding of entanglement in QDs and its potential applications in quantum technologies. Copyright © 2024, The Authors. All rights reserved.

关键词： Anisotropy

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Robust Domain Generalization for Multi-modal Object Recognition

arXiv

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arXiv 2024年

作者： Qiao, Yuxin Li, Keqin Lin, Junhong Wei, Rong Jiang, Chufeng Luo, Yang Yang, Haoyu Department of Computer Information Technology Northern Arizona University FlagstaffAZ United States Department of Computer Science AMA University Quezon Philippines Electrical Engineering & Computer Science Department Massachusetts Institute of Technology CambridgeMA United States Academy for Advanced Interdisciplinary Studies Peking University Beijing China Department of Computer Science The University of Texas at Austin AustinTX United States Department of Computer Science University of Southern California Los AngelesCA United States College of Computing Georgia Institute of Technology AtlantaGA United States

In multi-label classification, machine learning encounters the challenge of domain generalization when handling tasks with distributions differing from the training data. Existing approaches primarily focus on vision object recognition and neglect the integration of natural language. Recent advancements in vision-language pre-training leverages supervision from extensive visual-language pairs. This allows learning across diverse domains and enhances recognition in multi-modal scenarios, showcasing superior transfer learning capabilities in methods like CLIPood. However, CLIPood has several limitations: differences in the utilized loss, loss of generality in evaluating only a single backbone, and neglect of class-aware visual fusion. To address these, we propose this paper that infers the actual loss based on the implementation, broadens evaluations to larger vision-language backbones, and introduces Mixup-CLIPood with a novel mix-up loss for enhanced class-aware visual fusion. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual languages

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