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Data-Driven Power Flow Estimation for MVDC Distribution Systems Based on Physics-Embedded FCN

IEEE Transactions on Smart Grid 2025年第4期16卷 2816-2829页

作者： Sun, Pingyang Wu, Rongcheng Shen, Zhiwei Wang, Hongyi Li, Gen Khalid, Muhammad Konstantinou, Georgios School of Electrical Engineering and Telecommunications UNSW Sydney Australia University of Technology Sydney Data Science Institute Australia Aalborg University Department of AAU energy Denmark Energy Technology and Computer Science Section Department of Engineering Technology Ballerup2750 Denmark King Fahd University of Petroleum and Minerals Electrical Engineering Department Interdisciplinary Research Center for Sustainable Energy Systems Dhahran31261 Saudi Arabia

Maintaining high prediction accuracy with varying grid topologies poses a significant challenge to adopting neural network (NN)-based approaches for power flow (PF) estimation in medium-voltage direct current (MVDC) distribution systems. This paper proposes a physics-embedded fully convolutional network (PEFCN) to improve the accuracy. Physics-embedded techniques are incorporated in the proposed PEFCN through 1) architecture reconstruction, and 2) loss function reformulation. Architecture is reconstructed by input channel conversion calculation in a new physics operation layer. This process offers physical connections among the three input matrix channels (voltage, current, and line conductance). Three new physics loss terms are included in the loss function to restrict the outliers violating the limits of converter power ratings and terminal MVDC voltages. The two operations enable the FCN to achieve improved prediction accuracy and strong generalization capabilities. Five MVDC distribution systems, characterized by different dc voltage levels and topology configurations, serve to validate the superiority of the proposed PEFCN over other NNs in the PF estimation for scenarios involving both fixed and varying system structures. Moreover, a modified IEEE 69-bus distribution system is further used to demonstrate the applicability of the proposed PEFCN for larger systems. © 2010-2012 IEEE.

关键词： DC distribution systems

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ULTRON: Unifying Local Transformer and Convolution for Large-Scale Image Retrieval 17th

ULTRON: Unifying Local Transformer and Convolution for Lar...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Kweon, Minseong Park, Jinsun School of Mechanical Engineering Pusan National University Busan Korea Republic of School of Computer Science and Engineering Pusan National University Busan Korea Republic of Center for Artificial Intelligence Research Pusan National University Busan Korea Republic of

ISBN: (纸本)9789819608843

In large-scale image retrieval, the primary goal is to extract discriminative features and embed them into global image representations. Previous methods based on CNNs effectively learn local features and create robust representations, leading to strong performance. Transformers that excel in learning global context, however, often struggle to extract fine details and therefore do not perform well in large-scale landmark recognition. In this paper, we propose a novel hybrid architecture named ULTRON, which combines transformer blocks with local self-attention and a convolution-based encoder. Our local transformer block contains an advanced self-attention mechanism that enhances the spatial context awareness of key features and updates the value features by considering broader information within fixed-size regional windows. In addition, we have designed a channel-wise dilated convolution that adjusts dilation per channel, enabling effective multiscale feature learning while robustly capturing local features. We focus on learning local contexts throughout the entire network and effectively blending these contexts in the attention-based pooling process. This approach generates a powerful global representation that includes local information, relying solely on classification loss without requiring additional modules to capture local features. Experimental results demonstrate that our model outperforms previous works due to effectively embedding local features into a global representation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolution

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Measuring frequency-bin entanglement from a quasi-phase-matched lithium niobate microring

Measuring frequency-bin entanglement from a quasi-phase-matc...

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CLEO: Fundamental science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Seshadri, Suparna Myilswamy, Karthik V. Ma, Zhao-Hui Huang, Yu-Ping Weiner, Andrew M. School of Electrical and Computer Engineering Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Department of Physics Center for Quantum Science and Engineering Stevens Institute of Technology HobokenNJ07030 United States

We employ phase modulation to measure the phase coherence between 31.75 GHz-spaced frequency bins in a biphoton frequency comb generated from an integrated quasi-phase-matched thin-film lithium niobate microresonator.... 详细信息

ISBN: (纸本)9781957171395

关键词： Microresonators

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Online Privacy Preservation for Camera-Incremental Person Re-Identification 26

Online Privacy Preservation for Camera-Incremental Person Re...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Wu, Sheng Ge, Wenhang Wu, Jiong Meng, Jingke Zhang, Huang School of Computer & Communication Engineering Changsha University of Science & Technology Changsha China China Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia United States School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (纸本)9781643684369

Task-incremental person re-identification aims to train a model with consecutively available cross-camera annotated data in the current task and a small number of saved data in preceding tasks, which may lead to individual privacy disclosure due to data storage and annotation. In this work, we investigate a more realistic online privacy preservation scenario for camera-incremental person re-identification, where data storage in preceding cameras is not allowed, while data in the current camera are intra-camera annotated online by a pedestrian tracking algorithm without cross-camera annotation. In this setup, the missing data of previous cameras not only results in catastrophic forgetting as task-incremental learning, but also makes the cross-camera association infeasible, which further leads to the incapability of person matching across cameras due to the camera-wise domain gap. To solve these problems, we propose an Online Privacy Preservation (OPP) framework based on the generated exemplars of previous cameras by DeepInversion, where generated exemplars used as supplements to alleviate forgetting and enable cross-camera association to be feasible for camera-wise domain shift mitigation, meanwhile further improving the cross-camera matching capability. Specifically, we propose to mine underlying cross-camera positive pairs between samples of the current camera and exemplars of previous cameras by similarity cues. Furthermore, we introduce a mixup learning strategy to handle the domain gap with mixed samples and labels. Finally, intra-camera incremental learning and cross-camera incremental learning are aggregated into the OPP framework. Extensive experiments on Re-ID benchmarks validate the superiority of the OPP framework as compared with state-of-the-art methods. © 2023 The Authors.

关键词： Cameras

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Wearable pressure sensors based on antibacterial and porous chitosan hydrogels for full-range human motion detection

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science China(Technological sciences) 2024年第8期67卷 2475-2484页

作者： ZHANG YingXin LIU HanGuang JIN ShunYu LI YaJuan ZHANG Wan ZHOU Hang NIE ZanXiang HUANG Yuan School of Microelectronics Science and Technology Sun Yat-sen UniversityGuangzhou 510275China Hefei National Research Center for Physical Sciences at the Microscale University of Science and Technology of ChinaHefei 230022China Department of Clinical Laboratory the First Affiliated Hospital of Anhui Medical UniversityHefei 230022China School of Electronic and Computer Engineering Peking University Shenzhen Graduate SchoolShenzhen 518055China Zinergy Shenzhen Ltd. Shenzhen 518109China

Wearable pressure sensors made from conductive hydrogels hold significant potential in health ***,limited pressure range(Pa to hundreds of kPa)and inadequate antibacterial properties restrict their practical applications in diagnostic and health ***,a wearable high-performance pressure sensor was assembled using a facilely prepared porous chitosan-based hydrogel,which was constructed from commercial phenolphthalein particles as a sacrificial *** relationship between the porosity of hydrogels and sensing performance of sensors was systematically ***,the wearable pressure sensor,featuring an optimized porosity of hydrogels,exhibits an ultrawide sensing capacity from 4.83 Pa to 250 k Pa(range-to-limit ratio of 51,760)and high sensitivity throughout high pressure ranges(0.7 kPa~(-1),120–250 kPa).The presence of chitosan endows these hydrogels with outstanding antibacterial performance against *** and ***,making them ideal candidates for use in wearable *** features allow for a practical approach to monitor full-range human motion using a single device with a simple structure.

关键词： porous hydrogels antibacterial performances wearable pressure sensors full-range human motion chitosan

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An Efficient Gait-generating Method for Electrical Quadruped Robot Based on Humanoid Power Planning Approach

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Journal of Bionic engineering 2021年第6期18卷 1463-1474页

作者： Lelai Zhou Tianfa Li Zaiyang Liu Yibin Li School of Control Science and Engineering Center for RoboticsShandong UniversityJinan250061China Engineering Research Center of Intelligent Unmanned System Ministry of EducationJinan250061China

The research field of legged robots has always relied on the bionic robotic research,especially in locomotion regulating approaches,such as foot trajectory planning,body stability regulating and energy efficiency *** energy consumption and keeping the stability of body are considered as two main characteristics of human *** work devotes to develop an energy-efficient gait control method for electrical quadruped robots with the inspiration of human walking *** on the mechanical power distribution trend,an efficient humanoid power redistribution approach is established for the electrical quadruped *** studying the walking behavior acted by mankind,such as the foot trajectory and change of mechanical power,we believe that the proposed controller which includes the bionic foot movement trajectory and humanoid power redistribution method can be implemented on the electrical quadruped robot *** stability and energy efficiency of the proposed controller are tested by the simulation and the single-leg prototype *** results verify that the humanoid power planning approach can improve the energy efficiency of the electrical quadruped robots.

关键词： Bionic robot Mechanical power Bionic trajectory Quadruped robot

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Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data

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ACM Transactions on Intelligent Systems and Technology 2024年第6期15卷 1-28页

作者： Liu, Ji Jia, Juncheng Zhang, Hong Yun, Yuhui Wang, Leye Zhou, Yang Dai, Huaiyu Dou, Dejing Hithink RoyalFlush Information Network Co. Ltd. Hangzhou China School of Computer Science and Technology Soochow University Suzhou China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Baidu Research Beijing China Key Lab of High Confidence Software Technologies Ministry of Education and Software Institute Peking University Beijing China Department of Computer Science and Software Engineering Auburn University AuburnAL United States Department of Electrical and Computer Engineering North Carolina State University RaleighNC United States Bedi Cloud and School of Computer Science Fudan University Beijing China

Despite achieving remarkable performance, Federated Learning (FL) encounters two important problems, i.e., low training efficiency and limited computational resources. In this article, we propose a new FL framework, i.e., FedDUMAP, with three original contributions, to leverage the shared insensitive data on the server in addition to the distributed data in edge devices so as to efficiently train a global model. First, we propose a simple dynamic server update algorithm, which takes advantage of the shared insensitive data on the server while dynamically adjusting the update steps on the server in order to speed up the convergence and improve the accuracy. Second, we propose an adaptive optimization method with the dynamic server update algorithm to exploit the global momentum on the server and each local device for superior accuracy. Third, we develop a layer-adaptive model pruning method to carry out specific pruning operations, which is adapted to the diverse features of each layer so as to attain an excellent tradeoff between effectiveness and efficiency. Our proposed FL model, FedDUMAP, combines the three original techniques and has a significantly better performance compared with baseline approaches in terms of efficiency (up to 16.9 times faster), accuracy (up to 20.4% higher), and computational cost (up to 62.6% smaller). © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Federated learning

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Machine Learning In Sustainable Development-An Overview 1

Machine Learning In Sustainable Development-An Overview

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Altrjman, Chadi Gupta, Richa Near East University Ai and Robotics Institute Research Center for Ai and IoT Department of Computer Engineering Nicosia Turkey Near East University Ai and Robotics Institute Artificial Intelligence Engineering Dept. Turkey University of Kyrenia Faculty of Engineering Kyrenia Turkey Waterloo University Canada Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350338027

I have studied how artificial intelligence (AI) or machine learning (ML) is applied in a sustainable system called "Eugenie"for my work, project, and thesis. In this project, I get to discuss Eugenie's role in ensuring the sustainability of large environmental business organizations. I also get to discuss how it aids operation managers in making predictions about when their machines will break down days, weeks, and months in advance, allowing them to schedule maintenance cycles more effectively. In other words, Eugenie provides plant operators with data so they can prioritize repairs. Eugenie executes this process by utilizing real-time data from numerous sensors attached to large machinery and equipment in the process industry, metals and mining, oil and gas, and smart cities industries. Two products make up this program;the first is named RAY-FINN and the second is called PAPILLON. Eugenie assists businesses in a variety of ways, including:•Aids in the attainment of operational excellence by process organizations•Always raise overall profitability.•Contributes to a greener and safer future. © 2023 IEEE.

关键词： Sustainable development

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A Data Consistency Insurance Method for Smart Contract

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computers, Materials & Continua 2023年第9期76卷 3783-3795页

作者： Jing Deng Xiaofei Xing Guoqiang Deng Ning Hu Shen Su Le Wang Md Zakirul Alam Bhuiyan School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou510006China Information Network Engineering and Reasearch Center South China University of TechnologyGuangzhou510640China Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China Department of Computer and Information Sciences Fordham UniversityNew York10458USA

As one of the major threats to the current DeFi(Decentralized Finance)ecosystem,reentrant attack induces data inconsistency of the victim smart contract,enabling attackers to steal on-chain assets from DeFi projects,which could terribly do harm to the confidence of the blockchain ***,protecting DeFi projects from the reentrant attack is very difficult,since generating a call loop within the highly automatic DeFi ecosystem could be very *** researchers mainly focus on the detection of the reentrant vulnerabilities in the code testing,and no method could promise the non-existent of reentrant *** this paper,we introduce the database lock mechanism to isolate the correlated smart contract states from other operations in the same contract,so that we can prevent the attackers from abusing the inconsistent smart contract *** to the existing resolutions of front-running,code audit,andmodifier,our method guarantees protection resultswith better *** we further evaluate our method on a number of de facto reentrant attacks observed from *** results prove that our method could efficiently prevent the reentrant attack with less running cost.

关键词： Blockchain smart contract data consistency reentrancy attack

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Efficient Optimal Control of Open Quantum Systems 19

Efficient Optimal Control of Open Quantum Systems

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19th Conference on the Theory of Quantum Computation, Communication and Cryptography, TQC 2024

作者： He, Wenhao Li, Tongyang Li, Xiantao Li, Zecheng Wang, Chunhao Wang, Ke Center for Computational Science and Engineering MIT CambridgeMA United States School of Physics Peking University Beijing China Center on Frontiers of Computing Studies School of Computer Science Peking University Beijing China Department of Mathematics Pennsylvania State University University ParkPA United States Department of Computer Science and Engineering Pennsylvania State University University ParkPA United States

ISBN: (纸本)9783959773287

The optimal control problem for open quantum systems can be formulated as a time-dependent Lindbladian that is parameterized by a number of time-dependent control variables. Given an observable and an initial state, the goal is to tune the control variables so that the expected value of some observable with respect to the final state is maximized. In this paper, we present algorithms for solving this optimal control problem efficiently, i.e., having a poly-logarithmic dependency on the system dimension, which is exponentially faster than best-known classical algorithms. Our algorithms are hybrid, consisting of both quantum and classical components. The quantum procedure simulates time-dependent Lindblad evolution that drives the initial state to the final state, and it also provides access to the gradients of the objective function via quantum gradient estimation. The classical procedure uses the gradient information to update the control variables. At the technical level, we provide the first (to the best of our knowledge) simulation algorithm for time-dependent Lindbladians with an 1-norm dependence. As an alternative, we also present a simulation algorithm in the interaction picture to improve the algorithm for the cases where the time-independent component of a Lindbladian dominates the time-dependent part. On the classical side, we heavily adapt the state-of-the-art classical optimization analysis to interface with the quantum part of our algorithms. Both the quantum simulation techniques and the classical optimization analyses might be of independent interest. © Wenhao He, Tongyang Li, Xiantao Li, Zecheng Li, Chunhao Wang, and Ke Wang.

关键词： Optimal control systems

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