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IEEE International Conference on Power and Energy Systems (ICPES)

作者： Yunwang Chen Xiang Lei Linni Jian Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China School of Mechanical and Electrical Engineering Shenzhen Polytechnic University Shenzhen China

ISBN: (数字)9798350391329

ISBN: (纸本)9798350391336

While blockchain offers inherent security, trust issues among stakeholders in vehicle-to-grid (V2G) applications remain unresolved due to a lack of regulatory frameworks and standardization. Additionally, a tailored decentralized privacypreserved coordination scheme for blockchain in V2G networks is needed to ensure user privacy and efficient energy transactions. This paper proposes a V2G trading and coordination scheme tailored to the decentralized nature of blockchain as well as the interests of stakeholders utilizing smart charging points (SCPs) and Stackelberg game model. Case studies using real-world data from Southern University of Science and Technology demonstrate the efficacy of the proposed scheme in reducing EV charging costs and the potential for supporting auxiliary grid services.

关键词： Vehicle-to-grid Privacy Standardization Pricing Games Power grids Blockchains Stakeholders Security Smart charging

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Bidding Strategy for Distribution System Operator Considering V2G Operation in Electricity Market Based on Two Stage Robust Optimization

Bidding Strategy for Distribution System Operator Considerin...

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IEEE International Conference on Power and Energy Systems (ICPES)

作者： Xiang Lei Jiahao Zhong Yunwang Chen Linni Jian School of Mechanical and Electrical Engineering Shenzhen Polytechnic University Shenzhen China Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350391329

ISBN: (纸本)9798350391336

Distribution system operators (DSOs) occupy a pivotal role in electricity markets, particularly within policydriven environments such as China, attributed to their robust resilience, customer loyalty, and high value-added capabilities. Nevertheless, DSOs have incurred considerable financial losses due to an escalation in aggregate forecast deviations precipitated by demand-side enterprises. To address this issue, a two-stage robust optimization model with a min-max-min structure has been devised to maximize DSO revenue under the uncertainty of conventional load within the electricity market. This model integrates conventional loads, electric vehicles, and incentivebased demand response, with the objective of enhancing the operational efficacy of DSO. A crucial parameter, termed the budget of uncertainty, has been introduced to adjust the conservatism level of the robust solution against modeled uncertainties. The optimization model is decomposed into a master problem and a subproblem, both featuring a mixedinteger linear structure, and is resolved using the column-andconstraint generation algorithm and strong duality theory. Numerical case studies validate the superiority of the proposed method over deterministic optimization approaches, particularly as prediction errors increase. The resulting day-ahead bidding strategy exhibits enhanced robustness and a heightened capacity to mitigate the risks associated with real-time price fluctuations.

关键词： Vehicle-to-grid Uncertainty Fluctuations Optimization models Electricity supply industry Prediction algorithms Robustness Real-time systems Load modeling Resilience

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Stray Lion Optimizer based LSTM Model for Detecting the Fake News

Stray Lion Optimizer based LSTM Model for Detecting the Fake...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Vadakkethil Somanathan Pillai, Sanjaikanth E Kumar Pareek, Piyush Kumar Dhiman, Vinod Zanke, Pankaj Hassan, Muntather Muhsin University of North Dakota School of Electrical Engineering and Computer Science Grand Forks United States Nitte Meenakshi Institute of Technology Department of Aiml Bengaluru India Gavs Technologies Na Inc PrincetonNJ United States Sapiens International Corporation The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq

ISBN: (纸本)9798350360660

Because of the damage it can do to people, communities, and countries, the massive spread of misinformation across many internet stages has develop an urgent issue. In order to address this issue, the scientific community is presently making significant efforts. Fighting disinformation at its earliest stages is difficult since current techniques for detecting fake news overstate the impact of public opinion. In order to address this problematic, the paper suggests a new framework for detecting bogus news using optimizer-based Deep Learning. At first, the d-D feature vectors for the textual data are recovered using the TF-IDF weighting algorithm. The LSTM algorithm is then told to use the retrieved characteristics. To process the parameters of LSTM and extract the n-gram from the text, the Stray Lion Swarm Optimization Algorithm (SLSOA) is used. In order to identify false news, these retrieved traits are then combined. We apply established assessment methods to two real-world datasets of false news and assess the outcomes. Also included are comparisons with other meta-heuristic algorithms and more modern approaches to detecting false news. The outcomes clearly demonstrate that the suggested model outperforms current models on different datasets. LSTM-SLOA attained as 0.9865 and LSTM measure as 0.7414 and then RNN as 0.7525 and also CNN as 0.6381 and then DBN attained as 0.8118 congruently. © 2024 IEEE.

关键词： Deep learning

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A Simple Pre-Estimation Method for Calculating the Inductance of a Variable Inductor

A Simple Pre-Estimation Method for Calculating the Inductanc...

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Iranian Conference on Renewable Energy and Distributed Generation (ICREDG)

作者： Sahand Fardadidokht Seyedamirhossein Talebi Salar Sadeghian Seyed Fariborz Zarei School of Electrical Engineering Iran University of Science and Technology Tehran Iran Department Electrical and Computer Engineering North Carolina State University North Carolina State USA Department of Electrical and Computer Engineering Qom University of Technology (QUT) Qom Iran

ISBN: (数字)9798331525132

ISBN: (纸本)9798331525149

This paper presents a simple method for predicting inductance in applications of variable inductors with ferrite cores. Today, switching converters are widely used for voltage and current level conversion in renewable energy applications. The variation of inductance in switching converters improves the voltage gain range and other converter characteristics. In this paper, through the analysis of core saturation, a simple circuit model is proposed to predict inductance when the core reaches saturation. This model enables designers to predict inductance variations prior to the winding of the core and the construction of the inductor. Two approaches regarding core permeability are analyzed: the well-known Brauer relationship and a proposed alternative. Finally, the proposed method has been validated and tested on two types of cores, includes E42 and E55.

关键词： Ferrites Inductance Switching converters Renewable energy sources Windings Voltage Predictive models Mathematical models Inductors Integrated circuit modeling

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A Subarray-based Approach for Gain Flatness Improvement of Multibeam Antennas

A Subarray-based Approach for Gain Flatness Improvement of M...

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2022 IEEE Region 10 International Conference, TENCON 2022

作者： Zhao, Hanjun Chu, Hui Zhu, Xiaohua Guo, Yongxin School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing China National University of Singapore Department of Electrical and Computer Engineering Singapore

ISBN: (数字)9781665450959

ISBN: (纸本)9781665450959

A novel scheme to improve the gain flatness of multibeam antennas by utilizing reconfigurable subarrays is presented. The reconfigurability of radiation pattern is realized by tuning the internal phase distribution within a subarray, thus achieving different coarse tilted scanning angles for specific scanning states. By optimizing the cost function defined with mean-square error, a premium scanning performance with minimum gain ripple can be achieved for various multibeam antennas, especially for large angle scanning arrays. In the illustrated Butler matrix-fed prototype, a markable gain ripple improvement of 2.4 dB and 2.9 dB is achieved for different cases, where an agreement between calculated and simulated results is obtained for proof of concept. © 2022 IEEE.

关键词： Multibeam antennas

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Custom Over-the-air Scalable mmWave Testbed for Fast TTD-Based Rainbow Beam Training

Custom Over-the-air Scalable mmWave Testbed for Fast TTD-Bas...

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IEEE International Conference on Communications (ICC)

作者： Mohammad Ali Mokri Yen-Chin Wang Ruifu Li Aditya Wadaskar Subhanshu Gupta Deukhyoun Heo Danijela Cabric School of Electrical Engineering and Computer Science Washington State University WA USA Department of Electrical and Computer Engineering University of California Los Angeles CA USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Millimeter-wave (mmWave) systems require a large number of antennas, which makes the beam training challenging and time-consuming for conventional phased arrays. Recently, a true-time-delay (TTD) array-based beam training algorithm has been shown as an effective solution to overcome the training overhead in large arrays. In this paper, we present a custom-built over-the-air (OTA) testbed to study the effects of hardware impairments on the TTD-based beam training and verify its feasibility in a real system. We proposed an orthogonal matching pursuit (OMP) based reconstruction algorithm along with a phase calibration dictionary to combat nonidealities such as strong frequency selectivity and phase misalignment in the received raw IQ signal. Post-processing results showed that with the nonideality effects properly handled, the 3D TTD beam training algorithm can achieve high AOA estimation accuracy.

关键词： Training Phased arrays Three-dimensional displays Wireless networks Matching pursuit algorithms Estimation Reconstruction algorithms

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Accelerating GPU-Based Parallel FDTD with Advanced Operator Fusion

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IEEE Antennas and Wireless Propagation Letters 2025年

作者： Huang, Siyi Cheng, Yu Mittra, Raj Zhang, Xinyue Zhang, Xingqi University of Alberta Department of Electrical and Computer Engineering Canada University College Dublin School of Electrical and Electronic Engineering Ireland University of Central Florida Department of Electrical and Computer Engineering United States

The finite-difference time-domain (FDTD) method is one of the most widely used methods for solving Maxwell's equations, but its efficiency is limited by the Courant–Friedrichs–Lewy (CFL) stability condition. Recent research has extensively explored graphics processing unit (GPU)-based parallel implementations of the FDTD method to enhance computational performance. However, the inherent time-stepping and time-marching nature of the FDTD algorithm leads to frequent kernel launches and low memory efficiency on GPUs, still significantly impacting execution efficiency. This paper proposes a GPU-based FDTD framework enhanced with operator fusion to address this challenge. Within this framework, the FDTD algorithm is represented as a computation graph composed of operators. We classify these operators into different types based on their input and output relationships. Using this type information, a rule-based strategy is developed to merge the operators into larger computational kernels, effectively enhancing GPU execution efficiency. Simulation results demonstrate that the proposed operator fusion framework does not introduce additional errors while achieving a 4× speedup compared to conventional GPU-based implementations. © 2002-2011 IEEE.

关键词： Finite-difference time-domain (FDTD) graphics processing unit (GPU) operator fusion

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Learning Stereo Depth Estimation with Bio-Inspired Spike Cameras

Learning Stereo Depth Estimation with Bio-Inspired Spike Cam...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Wang, Yixuan Li, Jianing Zhu, Lin Xiang, Xijie Huang, Tiejun Tian, Yonghong School of Electronic and Computer Engineering Peking University China School of Computer Science Peking University China Department of Electrical and Computer Engineering Shanghai Jiao Tong University China Peng Cheng National Laboratory China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Bio-inspired spike cameras, offering high temporal resolution spike streams, have brought a new perspective to address common challenges (e.g.,high-speed motion blur) in depth estimation tasks. In this paper, we propose a novel problem setting, spike-based stereo depth estimation, which is the first trail that explores an end-to-end network to learn stereo depth estimation with transformers for spike cameras, named Spike-based Stereo Depth Estimation Transformer (SSDEFormer). We first build a hybrid camera platform and provide a new stereo depth estimation dataset (i.e.,PKU-Spike-Stereo) with spatiotemporal synchronized labels. Then, we propose a novel spike representation to effectively exploit spatiotemporal information from spike streams. Finally, a transformer-based network is designed to generate dense depth maps without a fixed-disparity cost volume. Empirically, it shows that our approach is extremely effective on both synthetic and real-world datasets. The results verify that spike cameras can perform robust depth estimation even in cases where conventional cameras and event cameras fail in fast motion scenarios. © 2022 IEEE.

关键词： Cameras

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Evaluating Automated Information Retrieval Techniques for Improved Data Accessibility

Evaluating Automated Information Retrieval Techniques for Im...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Mishra, Vaishali A. Srivastava, Manish Nithya, M.S. Mishra, Smita Kannimuthu, S. Divya, N. Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Department of Computer Science and Information Technology Karnataka Bangalore India Maharishi School of Business Management Maharishi University of Information Technology UP India Karpagam College of Engineering Department of Information Technology Coimbatore641032 India Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India

ISBN: (纸本)9798350329773

This looks at targets to assess numerous automated fact retrieval strategies with appreciation for their potential to provide advanced data accessibility. The diverse methods might be evaluated by exploring how nicely they offer green statistics access, person-friendly systems, and the potential to handle massive datasets. The unique techniques include SEO (SEO), herbal language processing (NLP), content-primarily based retrieval, and disbursed databases. The impact of every method in the records accessibility procedure may be evaluated based on the extent of personal pleasure, typical performance, and scalability of the gadget. Sooner or later, the results can be used to offer pointers for improving data accessibility. © 2023 IEEE.

关键词： Scalability

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Performance Optimization Across the Edge-Cloud Continuum: A Multi-agent Rollout Approach for Cloud-Native Application Workload Placement

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SN computer Science 2024年第3期5卷 318页

作者： Soumplis, Polyzois Kontos, Georgios Kokkinos, Panagiotis Kretsis, Aristotelis Barrachina-Muñoz, Sergio Nikbakht, Rasoul Baranda, Jorge Payaró, Miquel Mangues-Bafalluy, Josep Varvarigos, Emmanuel School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Institute of Communication and Computer Systems Athens Greece Department of Digital Systems University of Peloponnese Sparta Greece Services as Networks (SaS) Centre Tecnològic Telecomunicacions CTTC/CERCA) Catalunya Spain

The advancements in virtualization technologies and distributed computing infrastructures have sparked the development of cloud-native applications. This is grounded in the breakdown of a monolithic application into smaller, loosely connected components, often referred to as microservices, enabling enhancements in the application’s performance, flexibility, and resilience, along with better resource utilization. When optimizing the performance of cloud-native applications, specific demands arise in terms of application latency and communication delays between microservices that are not taken into consideration by generic orchestration algorithms. In this work, we propose mechanisms for automating the allocation of computing resources to optimize the service delivery of cloud-native applications over the edge-cloud continuum. We initially introduce the problem’s Mixed Integer Linear Programming (MILP) formulation. Given the potentially overwhelming execution time for real-sized problems, we propose a greedy algorithm, which allocates resources sequentially in a best-fit manner. To further improve the performance, we introduce a multi-agent rollout mechanism that evaluates the immediate effect of decisions but also leverages the underlying greedy heuristic to simulate the decisions anticipated from other agents, encapsulating this in a Reinforcement Learning framework. This approach allows us to effectively manage the performance–execution time trade-off and enhance performance by controlling the exploration of the Rollout mechanism. This flexibility ensures that the system remains adaptive to varied scenarios, making the most of the available computational resources while still ensuring high-quality decisions. © The Author(s) 2024.

关键词： Cloud edge continuum Cloud native applications Multi-agent rollout Reinforcement learning Resource allocation

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