检索结果-内蒙古大学图书馆

11th IFAC Symposium on Control of Power and Energy systems, CPES 2022

作者： Jin, Yuhui Wu, Xiao Shen, Jiong Lee, Kwang Y. Key Laboratory of Energy Thermal Conversion and Control Ministry of Education School of Energy and Environment Southeast University Nanjing210096 China Department of Electrical and Computer Engineering Baylor University One Bear Place 97356 WacoTX76798-7356 United States

Due to the strong coupling and multiple constraints of combined heat and power (CHP) generation in an integrated energy system (IES), the model predictive control (MPC) is widely adopted as the lower layer coordination controller. However, with the upgrade of capacity and the expansion of scale, centralized MPC (CMPC) is no longer capable of dealing with heavy-data, high dimensional IES optimization problem. To address this concern, distributed MPC (DMPC) is applied to coordinate the energy supply of CHP-IES. In view of the characteristics of CHP-IES, DMPC should be endowed with the capability of offset-free tracking and disturbance rejection. Hence, the disturbance augmentation method and the modification of objective function have been applied to facilitate each DMPC controller with the required abilities. Through simulation comparison with CMPC on the same theoretical model, the performance of modified cooperative DMPC (CDMPC) is on par with CMPC, while the computational burden is drastically relieved. © 2022 Elsevier B.V.. All rights reserved.

关键词： Disturbance rejection

来源：评论

学校读者我要写书评

暂无评论

Content Moderation and the Formation of Online Communities: A Theoretical Framework

arXiv

引用

arXiv 2023年

作者： Dwork, Cynthia Hays, Chris Kleinberg, Jon Raghavan, Manish Department of Computer Science Harvard University United States Institute for Data Systems and Society Massachusetts Institute of Technology United States Departments of Computer Science and Information Science Cornell University United States Sloan School of Management Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States

We study the impact of content moderation policies in online communities. In our theoretical model, a platform chooses a content moderation policy and individuals choose whether or not to participate in the community according to the fraction of user content that aligns with their preferences. The effects of content moderation, at first blush, might seem obvious: it restricts speech on a platform. However, when user participation decisions are taken into account, its effects can be more subtle - and counter-intuitive. For example, our model can straightforwardly demonstrate how moderation policies may increase participation and diversify content available on the platform. In our analysis, we explore a rich set of interconnected phenomena related to content moderation in online communities. We first characterize the effectiveness of a natural class of moderation policies for creating and sustaining communities. Building on this, we explore how resource-limited or ideological platforms might set policies, how communities are affected by differing levels of personalization, and competition between platforms. Our model provides a vocabulary and mathematically tractable framework for analyzing platform decisions about content moderation. Copyright © 2023, The Authors. All rights reserved.

关键词： Online systems

来源：评论

学校读者我要写书评

暂无评论

GPU-Accelerated Privacy Aggregation Queries of Crowdsensing Spatio-temporal Data

引用

IEEE Transactions on Consumer Electronics 2024年

作者： Li, Yuxi Chen, Jingjing Ji, Dong Zhu, Fa Chen, Xingchi Park, Jong Hyuk Northeastern University School of Computer Science and Engineering Shenyang110819 China Northeastern University National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Nanjing Forestry University College of Information Sciences and Technology College of Artificial Intelligence Nanjing China Pengcheng Laboratory Shenzhen518000 China Department of Computer Science and Engineering 232 Gongneung-ro Seoul Korea Republic of

Addressing the challenges of query privacy leakage and high verification costs in multisource spatio-temporal data queries within crowdsensing environments, we present CrowdPQ-a novel privacy-preserving aggregation query scheme for spatio-temporal crowdsensing data utilizing GPU acceleration. Our approach leverages a robust two-server secure architecture. At the core of our scheme lies a privacy query protocol based on Function Secret Sharing (FSS), ensuring confidentiality while enabling efficient data aggregation queries. Additionally, we propose a non-interactive lightweight verification protocol utilizing Beaver multiplication triples, significantly minimizing resource consumption from invalid query tokens. We provide a comprehensive formal analysis of CrowdPQ in terms of correctness, security, and complexity. Our prototype, implemented in a two-server setting with GPU support, demonstrates superior performance: GPU acceleration reduces query processing time by 4×, server-side communication cost by 25×, and client-side communication by up to 88.89% compared with existing methods. These results highlight the potential of GPU-enabled secure processing for large-scale crowdsensing data aggregation tasks, offering an innovative pathway for privacy-focused data handling in complex environments. © 1975-2011 IEEE.

关键词： Spatio-temporal data

来源：评论

学校读者我要写书评

暂无评论

Impact of Techno-Economic Parameters on Optimal Sizing of EV Fast Charging Stations

Impact of Techno-Economic Parameters on Optimal Sizing of EV...

引用

IEEE Transportation Electrification Conference and Expo (ITEC)

作者： Anindita Golder Sheldon S. Williamson Department of Electrical and Computer Engineering Smart Transportation Electrification and Energy Research (STEER) Group Advanced Storage Systems and Electric Transportation (ASSET) Laboratory Ontario Tech-Automotive Center of Excellence (ACE) Faculty of Engineering and Applied Science Ontario Tech University Oshawa Canada

ISBN: (数字)9798350317664

ISBN: (纸本)9798350317671

As the adoption for electric vehicles (EV) increases, the need for proper charging infrastructure also increases. As such, it is necessary to ensure proper charging infrastructure to meet the charging demand. Hence, studies concerning the planning of EV charging stations which include the sizing of distributed energy resources (DERs) in the station to reduce the peak demand on the grid. In this paper, EV charging stations were designed keeping in mind factors such as the EV charging load profile (on demand charging versus deferred charging), uncertainties in the load profile as well as the tariff rates. A sensitivity analysis was also carried out in order to examine the impact of several techno-economic parameters on the overall cost of the system.

关键词： Vehicle-to-grid Costs Uncertainty Sensitivity analysis Tariffs Vehicular ad hoc networks Transportation

来源：评论

学校读者我要写书评

暂无评论

Improving Prediction of GNSS Satellite Visibility in Urban Canyon Based on Graph Transformer 36

Improving Prediction of GNSS Satellite Visibility in Urban C...

引用

36th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2023

作者： Zheng, Shaolong Li, Zhenni Wang, Qianming Xie, Kan Liu, Ming Xie, Shengli Polycarpou, Marios School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China Techtotop Microelectronics Technology Co. Ltd. Guangzhou510006 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China Electrical and Computer Engineering University of Cyprus Nicosia Cyprus KIOS Rsearch and Innovation Center of Excellence Nicosia Cyprus

ISBN: (纸本)9780936406350

Signals from global navigation satellite systems (GNSS) suffer from serious multipath errors in urban areas caused by building blockages and reflections. The use of deep neural networks offer great potential for predicting and eliminating complex multipath/non-line-of-sight (NLOS) errors. However, existing methods for predicting the original signals face two remaining challenges. The first is the inability to exploit effectively the irregular GNSS dataset because of inconsistent numbers of visible satellites in different epochs. The second is degradation in the generalization performance of the multipath/NLOS prediction model when using data collected from different locations and periods. To address these challenges, this paper proposes a novel graph transformer neural network for predicting satellite visibility that effectively learns environment representations from the irregular GNSS measurements to both alleviate multipath interference and improve the generalization performance of the multipath prediction model. To learn from the irregular GNSS measurements, a sky satellite graph is constructed as the input to a graph neural network by using the satellites captured in the same epoch, which can represent the spatial relationships between the satellites and enhance the model to enable learning of satellite-related features sufficiently well. To improve generalization ability of our multipath prediction model, a multihead attention mechanism is introduced to aggregate satellite node information by computing the correlation between satellites for extracting the environment representation around the receiver. Based on the constructed sky satellite graph and the multihead attention mechanism, we develop a novel graph transformer neural network (GTNN) for predicting satellite visibility, which can not only handle irregular GNSS measurements but also learn an environment representation via graph attention. Comparative experiments were carried out on real-world GNSS meas

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhanced Memory Performance in Ferroelectric NAND Applications: The Role of Tunnel Dielectric Position for Robust 10-Year Retention

Enhanced Memory Performance in Ferroelectric NAND Applicatio...

引用

2025 IEEE International Reliability Physics Symposium, IRPS 2025

作者： Venkatesan, Prasanna Padovani, Andrea Fernandes, Lance Ravikumar, Priyankka Park, Chinsung Tran, Huy Wang, Zekai Jayasankar, Hari Garlapati, Amrit Song, Taeyoung Chen, Hang Chern, Winston Wang, Zheng Kim, Kijoon Woog, Jongho Lim, Suhwan Kim, Kwangsoo Kim, Wanki Ha, Daewon Yu, Shimeng Dattal, Suman Larcher, Luca Thareja, Gaurav Khanl, Asif School of Electrical and Computer Engineering Georgia Institute of Technology GA United States University of Modena and Reggio Emilia Engineering Department 'Enzo Ferrari' Italy Institute of Electronics and Nanotechnology Georgia Institute of Technology GA United States Micron Technology United States Samsung Electronics Advanced Device Research Laboratory Semiconductor Research Center South Korea School of Material Science and Engineering Georgia Institute of Technology GA United States Applied Materials United States

ISBN: (纸本)9798331504779

We report a comprehensive study on memory window (MW) enhancement and retention loss (RL) in ferroelectric (FE) field effect transistors for NAND applications with a dielectric layer inserted within the FE film (next to the gate GBL devices or in the middle of the FE layer TDL devices). Using a temperature-dependent modeling framework, we show that MW and RL properties are controlled respectively by carriers trapping into defects at the dielectric-FE interface and by its fragile balance with FE depolarization. While MW enhancement is achieved whenever the inserted dielectric layer is present, a robust retention is obtained only in TDL FEFETs, thanks to a stabilization of the trapped charge preventing FE depolarization. Simulation results clearly demonstrate that the fragile balance between above phenomena is ultimately controlled by the position of the dielectric layer, which determines i) the field redistribution within the stack, ii) the magnitude of charge de-trapping, and iii) the magnitude of the depolarization field. Our results provide critical guidelines for the improvement of FE 3D-NAND reliability. © 2025 IEEE.

关键词： 3D-NAND FEFET Ginestra™ Retention

来源：评论

学校读者我要写书评

暂无评论

Pano-NeRF: Synthesizing High Dynamic Range Novel Views with Geometry from Sparse Low Dynamic Range Panoramic Images

arXiv

引用

arXiv 2023年

作者： Lu, Zhan Zheng, Qian Shi, Boxin Jiang, Xudong School of Electrical and Electronic Engineering Nanyang Technological University Singapore College of Computer Science and Technology Zhejiang University China The State Key Lab of Brain-Machine Intelligence Zhejiang University China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China National Engineering Research Center of Visual Technology School of Computer Science Peking University China

Panoramic imaging research on geometry recovery and High Dynamic Range (HDR) reconstruction becomes a trend with the development of Extended Reality (XR). Neural Radiance Fields (NeRF) provide a promising scene representation for both tasks without requiring extensive prior data. However, in the case of inputting sparse Low Dynamic Range (LDR) panoramic images, NeRF often degrades with under-constrained geometry and is unable to reconstruct HDR radiance from LDR inputs. We observe that the radiance from each pixel in panoramic images can be modeled as both a signal to convey scene lighting information and a light source to illuminate other pixels. Hence, we propose the irradiance fields from sparse LDR panoramic images, which increases the observation counts for faithful geometry recovery and leverages the irradiance-radiance attenuation for HDR reconstruction. Extensive experiments demonstrate that the irradiance fields outperform state-of-the-art methods on both geometry recovery and HDR reconstruction and validate their effectiveness. Furthermore, we show a promising byproduct of spatially-varying lighting estimation. The code is available at https://***/Lu-Zhan/Pano-NeRF. Copyright © 2023, The Authors. All rights reserved.

关键词： Recovery

来源：评论

学校读者我要写书评

暂无评论

Deep Subspace Encoders for Nonlinear System Identification

arXiv

引用

arXiv 2022年

作者： Beintema, Gerben I. Schoukens, Maarten Toth, Roland Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

Using Artificial Neural Networks (ANN) for nonlinear system identification has proven to be a promising approach, but despite of all recent research efforts, many practical and theoretical problems still remain open. Specifically, noise handling and models, issues of consistency and reliable estimation under minimisation of the prediction error are the most severe problems. The latter comes with numerous practical challenges such as explosion of the computational cost in terms of the number of data samples and the occurrence of instabilities during optimization. In this paper, we aim to overcome these issues by proposing a method which uses a truncated prediction loss and a subspace encoder for state estimation. The truncated prediction loss is computed by selecting multiple truncated subsections from the time series and computing the average prediction loss. To obtain a computationally efficient estimation method that minimizes the truncated prediction loss, a subspace encoder represented by an artificial neural network is introduced. This encoder aims to approximate the state reconstructability map of the estimated model to provide an initial state for each truncated subsection given past inputs and outputs. By theoretical analysis, we show that, under mild conditions, the proposed method is locally consistent, increases optimization stability, and achieves increased data efficiency by allowing for overlap between the subsections. Lastly, we provide practical insights and user guidelines employing a numerical example and state-of-the-art benchmark results. © 2022, CC BY.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Provably Secure and Efficient One-to-Many Authentication and Key Agreement Protocol for Resource-Asymmetric Smart Environments

引用

IEEE Internet of Things Journal 2025年

作者： Li, Long Hsu, Chingfang Cui, Jianqun Au, Man Ho Harn, Lein Li, Quanrun Central China Normal University Computer School Wuhan430079 China Central China Normal University Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Wuhan430079 China Department of Computing Hong Kong Polytechnic University 25809 Hong Kong University of Missouri-Kansas City Department of Computer Science Electrical Engineering MO64110 United States Zhejiang Sci-Tech University School of Cyber Science and Engineering Hangzhou310020 China

The smart environment is a crucial application of the Internet of Things(IoT). Due to its growing security and efficiency needs, recent years have seen the proposal of numerous authentication and key agreement (AKA) protocols. Unfortunately, most of existing AKA protocols only support one-to-one AKA and rely on the elliptic curve cryptosystem, resulting in huge overhead. In addition, these protocols fail to consider the resource-asymmetric characteristics of this scenario. That is, the resources on the gateway side are abundant, while the resources on user sides and device sides are limited. In order to achieve efficient and secure one-to-many AKA establishment in this scenario, where one-to-many means that users can realize key agreements with multiple smart devices at the same time. For the first time, this paper uses the one-to-many computing structure of the Chinese Remainder Theorem (CRT) to design an efficient one-to-many AKA establishment, which is perfectly adapted to resource-asymmetric allocation in smart environments. Compared with existing solutions, this solution has the following advantages. Firstly, our protocol is suitable for resource-asymmetric environments, where the gateway acts as an intermediate node and uses rich resources to integrate multiple AKA requests. Secondly, the solution supports users to negotiate session keys with multiple smart devices at the same time. Thirdly, we prove the protocol’s security under the Real-or-Random (ROR) model. In addition, we perform formal security verification of the protocol using the Automated Validation of Internet Security Protocols and Applications(AVISPA) tool. Finally, the security and efficiency of this solution are superior to similar solutions. Specifically, our solution can meet 18 security and functionality requirements. Compared with the latest similar scheme, assuming that the number of smart devices is 10, our scheme reduces the computational cost by 75.75%. At the same time, in terms of comm

关键词： Authentication Protocol

来源：评论

学校读者我要写书评

暂无评论

IRS Channel Estimation in Cell-free MIMO Network: A Coalition Formation Guided Federated Learning Approach

IRS Channel Estimation in Cell-free MIMO Network: A Coalitio...

引用

IEEE Conference on Wireless Communications and Networking

作者： Haoxuan Liu Nan Qi Xiaojie Li Alexandros A. A. Boulogeorgos Theodoros A. Tsiftsis Ming Xiao Juha Röning Nanjing University of Aeronautics and Astronautics Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing China Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Department of Informatics and Telecommunications University of Thessaly Lamia Greece Department of Electrical and Electronic Engineering University of Nottingham Ningbo China Ningbo China School of Electrical Engineering of KTH Royal Institute of Technology Stockholm Sweden Biomimetics and Intelligent Systems Group University of Oulu Finland

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The downlink channel estimation is currently a critical bottleneck for IRS-assisted cell-free multiple input multiple output communication. Conventionally, most studies have employed deep learning methods to estimate the high-dimensional, complex cascaded channels generated by IRS, necessitating data collection from all users for centralized model training, which results in excessively large overheads, and data privacy problems. To tackle this challenge, a federated learning (FL)-based channel estimation framework incorporates coalition formation to guide the formation of FL user groups. We propose a coalition formation-enabled federated learning framework for channel estimation, utilizing a deep reinforcement learning (DRL) approach to intelligently group users into multiple coalitions, thereby improving channel estimation accuracy. Moreover, considering that nodes with similar distances to the base station and similar received signal power have a strong likelihood that they experience similar channel fading, we designed a transfer learning method that incorporates both received reference signal power and distance similarity metrics. The transfer learning technique is designed to accelerate the convergence of DRL-federated learning process. Simulations reveal that the proposed algorithms significantly reduce communication overhead for local users and improve data privacy while maintaining commendable channel estimation accuracy.

关键词： Training Data privacy Accuracy Federated learning Transfer learning Channel estimation Deep reinforcement learning Data models Surface treatment Convergence

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：