检索结果-内蒙古大学图书馆

A Data-driven Variable Reduction Approach for Transmission-constrained Unit Commitment of Large-scale Systems

Journal of Modern Power Systems and Clean Energy 2023年第1期11卷 254-266页

作者： Yuzhou Zhou Qiaozhu Zhai Lei Wu Moammad Shahidehpour Systems Engineering Institute MOEKLINNS LabXi’an Jiaotong UniversityXi’an 710049China Electrical and Computer Engineering Department Stevens Institute of TechnologyHobokenU.S. Electrical and Computer Engineering Department Illinois Institute of TechnologyChicagoU.S. IEEE

This paper presents a data-driven variable reduction approach to accelerate the computation of large-scale transmission-constrained unit commitment(TCUC).Lagrangian relaxation(LR)and mixed-integer linear programming(MILP)are popular approaches to solving ***,with many binary unit commitment variables,LR suffers from slow convergence and MILP presents heavy computation *** proposed data-driven variable reduction approach consists of offline and online calculations to accelerate computational performance of the MILP-based large-scale TCUC problems.A database including multiple nodal net load intervals and the corresponding TCUC solutions is first built offline via the data-driven and all-scenario-feasible(ASF)approaches,which is then leveraged to efficiently solve new TCUC instances ***/off statuses of considerable units can be fixed in the online calculation according to the database,which would reduce the computation burden while guaranteeing good solution quality for new TCUC instances.A feasibility proposition is proposed to promptly check the feasibility of the new TCUC instances with fixed binary variables,which can be used to dynamically tune parameters of binary variable fixing strategies and guarantee the existence of feasible UC solutions even when system structure *** tests illustrate the efficiency of the proposed approach.

关键词： Unit commitment accelerated algorithm data driven variable reduction

来源：评论

学校读者我要写书评

暂无评论

An Online Spatial-Temporal Graph Trajectory Planner for Autonomous Vehicles

IEEE Transactions on Intelligent Vehicles

引用

IEEE Transactions on Intelligent Vehicles 2024年 1-11页

作者： Samiuddin, Jilan Boulet, Benoit Wu, Di Intelligent Automation Lab of the Centre for Intelligent Machines and the Department of Electrical and Computer Engineering McGill University Montreal QC Canada

The autonomous driving industry is expected to grow by over 20 times in the coming decade and, thus, motivate researchers to delve into it. The primary focus of their research is to ensure safety, comfort, and efficiency. An autonomous vehicle has several modules responsible for one or more of the aforementioned items. Among these modules, the trajectory planner plays a pivotal role in the safety of the vehicle and the comfort of its passengers. The module is also responsible for respecting kinematic constraints and any applicable road constraints. In this paper, a novel online spatial-temporal graph trajectory planner is introduced to generate safe and comfortable trajectories. First, a spatial-temporal graph is constructed using the autonomous vehicle, its surrounding vehicles, and virtual nodes along the road with respect to the vehicle itself. Next, the graph is forwarded into a sequential network to obtain the desired states. To support the planner, a simple behavioral layer is also presented that determines kinematic constraints for the planner. Furthermore, a novel potential function is also proposed to train the network. Finally, the proposed planner is tested on three different complex driving tasks, and the performance is compared with two frequently used methods. The results show that the proposed planner generates safe and feasible trajectories while achieving similar or longer distances in the forward direction and comparable comfort ride. IEEE

关键词： Kinematics

来源：评论

学校读者我要写书评

暂无评论

On Balancing of Dynamic Wireless Power Transfer System in the Presence of Misalignment and Load Variation

On Balancing of Dynamic Wireless Power Transfer System in th...

引用

2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Rastegar, Mohammad Ghaderi, Mohammad Hassan Fard, Milad Bahrami Lab Department of Electrical & Computer Engineering Richardson United States

ISBN: (纸本)9798350376067

Since dynamic wireless power transfer (DWPT) primarily entails integrating segmented embedded charging coils into roads, it is essential to assess system reliability and efficiency, particularly when powered by multiple sources. This paper proposes a reconfigured topology to enhance the efficiency of DWPT systems during charging of multiple electric vehicles (EVs). Tunable switching frequency and phase shift are used to maximize the efficiency in a multi-output DWPT system. Furthermore, an additional leg in the source inverter is used to balance the system when the DC-link voltages stemming from the backup systems are unequal. The proposed topology benefits from the reduced number of switches while guaranteeing maximum efficiency in the presence of misalignment. Simulation results have been obtained to demonstrate the performance and feasibility of the proposed system. © 2024 IEEE.

关键词： Inductive power transmission

来源：评论

学校读者我要写书评

暂无评论

NEXUS: A 28nm 3.3pJ/SOP 16-Core Spiking Neural Network With a Diamond Topology for Real-Time Data Processing

引用

IEEE Transactions on Biomedical Circuits and Systems 2025年第3期19卷 523-535页

作者： Sadeghi, Maryam Rezaeiyan, Yasser Khatiboun, Dario Fernandez Eissa, Sherif Corradi, Federico Augustine, Charles Moradi, Farshad Aarhus University Department of Electrical and Computer Engineering Aarhus8000 Denmark Eindhoven University of Technology Department of Electrical Engineering Eindhoven5612 AP Netherlands Intel Circuit Research Lab HillsboroOR97124 United States

The realization of brain-scale spiking neural networks (SNNs) is impeded by power constraints and low integration density. To address these challenges, multi-core SNNs are utilized to emulate numerous neurons with high energy efficiency, where spike packets are routed through a network-on-chip (NoC). However, the information can be lost in the NoC under high spike traffic conditions, leading to performance degradation. This work presents NEXUS, a 16-core SNN with a diamond-shaped NoC topology fabricated in 28-nm CMOS technology. It integrates 4096 leaky integrate-and-fire (LIF) neurons with 1M 4-bit synaptic weights, occupying an area of 2.16 mm2. The proposed NoC architecture is scalable to any network size, ensuring no data loss due to contending packets with a maximum routing latency of 5.1 μs for 16 cores. The proposed congestion management method eliminates the need for FIFO in routers, resulting in a compact router footprint of 0.001 mm2. The proposed neurosynaptic core allows for increasing the processing speed by up to 8.5× depending on input sparsity. The SNN achieves a peak throughput of 4.7 GSOP/s at 0.9 V, consuming a minimum energy per synaptic operation (SOP) of 3.3 pJ at 0.55 V. A 4-layer feed-forward network is mapped onto the chip, classifying MNIST digits with 92.3% accuracy at 8.4K-classification/s and consuming 2.7-μJ/classification. Additionally, an audio recognition task mapped onto the chip achieves 87.4% accuracy at 215-μJ/classification. © 2007-2012 IEEE.

关键词： Spatio-temporal data

来源：评论

学校读者我要写书评

暂无评论

Self-Supervised Representation Learning for Knee Injury Diagnosis From Magnetic Resonance Data

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1613-1623页

作者： Manna, Siladittya Bhattacharya, Saumik Pal, Umapada Indian Statistical Institute Computer Vision and Pattern Recognition Unit Kolkata700108 India Indian Institute of Technology Kharagpur Department of Electronics and Electrical Communication Engineering Kharagpur721302 India

In medical image analysis, the cost of acquiring high-quality data and annotation by experts is a barrier in many medical applications. Most of the techniques used are based on a supervised learning framework and require a large amount of annotated data to achieve satisfactory performance. As an alternative, in this article, we propose a self-supervised learning approach for learning the spatial anatomical representations from the frames of magnetic resonance (MR) video clips for the diagnosis of knee medical conditions. The pretext model learns meaningful context-invariant spatial representations. The downstream task in our article is a class-imbalanced multilabel classification. Different experiments show that the features learned by the pretext model provide competitive performance in the downstream task. Moreover, the efficiency and reliability of the proposed pretext model in learning representations of minority classes without applying any strategy toward imbalance in the dataset can be seen from the results. To the best of our knowledge, this work is the first of its kind in showing the effectiveness and reliability of self-supervised learning algorithms in imbalanced multilabel classification tasks on MR scans. © 2020 IEEE.

关键词： Supervised learning

来源：评论

学校读者我要写书评

暂无评论

A survey on cross-user federated recommendation

引用

Science China(Information Sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

来源：评论

学校读者我要写书评

暂无评论

Bidding Strategy for Hybrid PV-BESS Plants via Knowledge-data-complementary Learning

引用

Journal of Modern Power Systems and Clean Energy 2025年第1期13卷 365-378页

作者： Xiaoge Huang Zhenhuan Ding Zhao Liu Tianqiao Zhao Pei Zhang Xiaojun Wang Department of Electrical and Computer Engineering State University of New York at BinghamtonBinghamtonUSA School of Artificial Intelligence Anhui UniversityHefei 230601China School of Electrical Engineering Beijing Jiaotong UniversityBeijing 100044China Brookhaven National Lab UptonUSA School of Electrical Automation and Information Engineering Tianjin UniversityTianjin 300072China

The hybrid photovoltaic(PV)-battery energy storage system(BESS)plant(HPP)can gain revenue by performing energy arbitrage in low-carbon power ***,multiple operational uncertainties challenge the profitability and reliability of HPP in the day-ahead *** paper proposes two coherent models to address these ***,a knowledge-driven penalty-based bidding(PBB)model for HPP is established,considering forecast errors of PV generation,market prices,and under-generation ***,a data-driven dynamic error quantification(DEQ)model is used to capture the variational pattern of the distribution of forecast *** role of the DEQ model is to guide the knowledgedriven bidding ***,the DEQ model aims at the statistical optimum,but the knowledge-driven PBB model aims at the operational *** two models have independent optimizations based on misaligned *** address this,the knowledge-data-complementary learning(KDCL)framework is proposed to align data-driven performance with knowledge-driven objectives,thereby enhancing the overall performance of the bidding strategy.A tailored algorithm is proposed to solve the bidding *** proposed bidding strategy is validated by using data from the National Renewable Energy laboratory(NREL)and the New York Independent System Operator(NYISO).

关键词： Hybrid power plant(HPP) photovoltaic(PV) battery energy storage system(BESS) optimization uncertainty bidding strategy error quantification knowledge-data-complementary learning

来源：评论

学校读者我要写书评

暂无评论

Active Beam Learning for Full-Duplex Wireless Systems 58

Active Beam Learning for Full-Duplex Wireless Systems

引用

58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Kong, Jeong Min Roberts, Ian P. Ucla Wireless Lab Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350354058

In this paper, we present a novel active beam learning method for in-band full-duplex wireless systems, that aims to design transmit and receive beams which suppress self-interference and maximize the sum spectral efficiency. Rather than rely on explicit estimation of the downlink, uplink, and/or self-interference channels like in most existing work, our method instead actively probes all three channels through measurements of SNR and INR over a fixed number of time slots. Then, once this probing concludes, all collected probing measurements are used to design transmit and receive beams which serve downlink and uplink in a full-duplex fashion. We realize this active beam learning scheme through a network of LSTMs and DNNs, which learns to design each probing beam pair and subsequently extract and record valuable information from each probing measurement such that near-optimal serving beams can be designed following the probing stage. Simulation indicates that our method reliably suppresses self-interference while delivering near-maximal SNR on the downlink and uplink with merely 3-10 probing time slots, while exhibiting robustness to measurement noise and the structure of the self-interference channel. © 2024 IEEE.

关键词： Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Multi-satellite image alignment over large areas with featureless regions

Multi-satellite image alignment over large areas with featur...

引用

2023 Annual Conference, ASPRS 2023

作者： Roros, C.J. Deshmukh, R. Kak, A.C. Robot Vision Lab Purdue University School of Electrical and Computer Engineering West LafayetteIN United States

There is a great deal of interest in fusing together the information provided by different satellites for real-Time change detection on the surface of the earth. For detecting important types of changes on the ground, it is necessary to inject geometry into the data provided by low-res satellites using multi-view imaging satellites that record each point on the ground from multiple perspectives. Combining these perspectives and generating a DSM (Digital Surface Model) gives us the geometry needed for a more meaningful analysis of satellite data. Before such an analysis can be carried out, it is necessary to align the images from all available satellites. Automatic image alignment, however, requires features on the ground that can be identified and correctly matched across different images using computer vision algorithms. While such features are common in urban areas, that is not always the case in predominantly rural areas that present a more-or-less uniform texture to the sensors. In this paper we present methods for automatic identification and alignment of featureless regions. Featureless regions are identified using point spread maps, which are a byproduct of DSM generation. The subsequent strategy for aligning featureless regions depends on the proportion of featureless regions to feature-rich regions. If most of the AOI (Area of Interest) is feature-rich, we ignore featureless regions when estimating inter-satellite image alignment parameters and apply those parameters to the entire AOI. Finally, we present a technique to propagate and fuse parameters from feature-rich regions to featureless regions. © 2023 International Society for Photogrammetry and Remote Sensing. All rights reserved.

关键词： Satellites

来源：评论

学校读者我要写书评

暂无评论

Formulations and Approximations of Branch Flow Model for General Power Networks

引用

Journal of Modern Power Systems and Clean Energy 2022年第5期10卷 1110-1126页

作者： Zhao Yuan Electrical Power Systems Laboratory(EPS-Lab) Department of Electrical and Computer EngineeringUniversity of IcelandReykjavikIceland IEEE

The formulations and approximations of the branch flow model for general(radial and mesh) power networks(General-BranchFlow) are given in this paper. Using different sets of the power flow equations, six formats of the exact General-BranchFlow model are listed. The six formats are mathematically equivalent with each other. Linear approximation and second-order cone programming(SOCP) are then used to derive the six formats of the convex General-BranchFlow model. The branch ampacity constraints considering the shunt conductance and capacitance of the transmission line Π-model are derived. The key foundation of deriving the ampacity constraints is the correct interpretation of the physical meaning of the transmission line Π-model. An exact linear expression of the ampacity constraints of the power loss variable is derived. The applications of the General-BranchFlow model in deriving twelve formats of the exact optimal power flow(OPF) model and twelve formats of the approximate OPF model are formulated and analyzed. Using the Julia programming language, the extensive numerical investigations of all formats of the OPF models show the accuracy and computational efficiency of the General-BranchFlow model. A penalty function based approximation gap reduction method is finally proposed and numerically validated to improve the AC-feasibility of the approximate General-BranchFlow model.

关键词： Branch flow linear approximation second-order cone programming ampacity constraint optimal power flow radial network mesh network

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：