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Semi-asynchronous personalized federated learning for short-term photovoltaic power forecasting

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Digital Communications and Networks 2023年第5期9卷 1221-1229页

作者： weishan zhang Xiao Chen Ke He Leiming Chen Liang Xu Xiao Wang Su Yang College of Computer Science and Technology China University of Petroleum(East China)China Sichuan Energy Internet Research Institute Tsinghua UniversityChina School of Computer and Communication Engineering Beijing University of Science and TechnologyChina State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science Fudan UniversityChina

Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power *** deep-learning-based methods can perform well if there are sufficient training data and enough computational ***,there are challenges in building models through centralized shared data due to data privacy concerns and industry *** learning is a new distributed machine learning approach which enables training models across edge devices while data reside *** this paper,we propose an efficient semi-asynchronous federated learning framework for short-term solar power forecasting and evaluate the framework performance using a CNN-LSTM *** design a personalization technique and a semi-asynchronous aggregation strategy to improve the efficiency of the proposed federated forecasting *** evaluations using a real-world dataset demonstrate that the federated models can achieve significantly higher forecasting performance than fully local models while protecting data privacy,and the proposed semi-asynchronous aggregation and the personalization technique can make the forecasting framework more robust in real-world scenarios.

关键词： Photovoltaic power forecasting Federated learning Edge computing CNN-LSTM

基于电磁弹式索力传感器的悬索桥短吊索索力检测

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结构工程师 2024年第3期40卷 145-152页

作者：沈伟张伟山魏巍段元锋段元昌湖北交投智能检测股份有限公司武汉430050 浙江大学建筑工程学院杭州310058 杭州健而控科技有限公司杭州310030

采用传统索力测试方法进行短吊索索力测试的精度难以满足工程需求。本文以棋盘洲长江公路大桥悬索桥荷载试验为工程依托,提出基于磁弹原理及磁电效应的电磁弹式索力传感器检测吊索索力的方法,并与有限元分析结果以及频率法、应变法测试... 详细信息

采用传统索力测试方法进行短吊索索力测试的精度难以满足工程需求。本文以棋盘洲长江公路大桥悬索桥荷载试验为工程依托,提出基于磁弹原理及磁电效应的电磁弹式索力传感器检测吊索索力的方法,并与有限元分析结果以及频率法、应变法测试结果进行对比。结果表明,试验标定误差小于±2%,在荷载试验中,传统索力测试方法对吊索的测量误差偏大,采用电磁弹式索力传感器更为准确地测量了短吊索索力,解决了短吊索索力的测量难题。荷载试验的吊索索力测试结果表明,索力增量的测试值与分析值偏差小于±5%,实际索力的测试值与分析值偏差小于±4%。

关键词：电磁弹索力传感器悬索桥吊索荷载试验索力检测

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基于多元评价主体的临床技能自主训练评价系统构建与实践

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中国医学教育技术 2024年第4期38卷 479-483页

作者：陈晓勤洪晓琳许杰州吴凡张伟山汕头大学医学院广东汕头515041

目的完善临床技能自主训练的评价机制,提升技能自主训练的质量和效果。方法基于学评融合的理念,在临床技能自主训练中引入包括训练者本身、多名同伴和教师在内的多元评价主体,自主设计研发临床技能自主训练评价系统。在已完成体格检查... 详细信息

目的完善临床技能自主训练的评价机制,提升技能自主训练的质量和效果。方法基于学评融合的理念,在临床技能自主训练中引入包括训练者本身、多名同伴和教师在内的多元评价主体,自主设计研发临床技能自主训练评价系统。在已完成体格检查学习并考核合格的2021级临床医学专业461名学生中进行教学应用实践,包括教师发布训练任务、系统随机抽签项目、学生录制训练视频、系统随机分配视频,由学生自评、同伴互评和教师抽评训练视频、系统检测评价信息生成反馈报告等系列环节。同时发放调查问卷,了解学生对录制评价视频对自身技能的影响和满意度。结果92.1%的学生表示录制和评价视频对自己的技能有积极影响;54.2%的学生对目前自己体格检查技能的掌握情况表示满意;仅有29.2%的学生使用评语评价。结论系统的构建给学生之间、师生之间提供了更多互动机会,随机抽签项目提高了视频拍摄质量,随机分配消除了人际因素对评价的影响。学生在自评中进行了反思、在同伴互评中吸取了不同评价者的意见,教师在抽评中了解到学生训练情况的同时,也尽量避免教师评价对学生评价的消极影响。大部分学生认为该系统对自身技能有积极影响。

关键词：临床技能自主训练评价系统多元评价

Validation of Pervasive Cloud Task Migration with Colored Petri Net

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Tsinghua Science and Technology 2016年第1期21卷 89-101页

作者： Lianzhang Zhu Shouchao Tan weishan zhang Yong Wang Xiwei Xu Department of Software Engineering China University of Petroleum Software Systems Research Group NICTA

Mobile devices are resource-limited, and task migration has become an important and attractive feature of mobile clouds. To validate task migration, we propose a novel approach to the simulation of task migration in a pervasive cloud environment. Our approach is based on Colored Petri Net（CPN）. In this research, we expanded the semantics of a CPN and created two task migration models with different task migration policies： one that took account of context information and one that did not. We evaluated the two models using CPN-based simulation and analyzed their task migration accessibility, integrity during the migration process, reliability, and the stability of the pervasive cloud system after task migration. The energy consumption and costs of the two models were also investigated. Our results suggest that CPN with context sensing task migration can minimize energy consumption while preserving good overall performance.

关键词： colored Petri net task migration pervasive cloud context information validation

Analytical Determination of Interwell Connectivity Based on Interwell Influence

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Tsinghua Science and Technology 2021年第6期26卷 813-820页

作者： Jiangru Yuan Xingjie Zeng Haiyun Wu weishan zhang Jiehan Zhou Bingyang Chen Research Institute of Petroleum Exploration and Development Beijing 100083China College of Computer Science and Technology China University of PetroleumQingdao 266580China Department of Computer and Electronic Engineering University of OuluOulu FI-90014Finland

Interwell connectivity, an important element in reservoir characterization, especially for water flooding,is used to make decisions for better oil production. The existing methods in literature directly use related data of wells to infer interwell connectivity, but they ignore the influence between different wells. The connection of one well to more than two wells(as is often true in the oil field well pattern) will impact the accuracy of the connectivity analysis. To address this challenge, this paper proposes the Particle Swarm Optimization-based CatBoost for Interwell Connectivity(PSOC4IC) based on relative features to analyze interwell connectivity with the combination of joint mutual information maximization-based denoising sparse autoencoder for inter-feature construction and extraction and PSO-based CatBoost(PSO-CatBoost) for connectivity prediction with high-dimensional noise *** experimental results show that the PSOC4IC improves analysis accuracy.

关键词： interwel connectivity interwel influence Particle Swarm Optimization(PSO) CatBoost

An OSGi-based flexible and adaptive pervasive cloud infrastructure

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Science China(Information Sciences) 2014年第3期57卷 90-100页

作者： zhang weishan CHEN LiCheng LIU Xin LU QingHua zhang PeiYing YANG Su Department of Software Engineering China University of PetroleumQingdao College of Computer Fudan University

There is a trend that diferent computing paradigms such as cloud computing,pervasive,and mobile computing are converging with each *** to this convergence,there arise unprecedented complexities,such as huge number of computing devices,flexibilities and adaptation of service infrastructure(infrastructure elasticity)for fitting dynamics of large smart city applications,expectations of powerful computing and storage capabilities on handhold devices,and so ***,a supporting infrastructure is needed,which can flexibly switch services at run time and can be used to enhance capabilities for small devices through component/service *** this paper,we propose an elastic open service gateway initiative(OSGi)-based pervasive cloud(OSGi-PC)infrastructure which can make use of both the cloud computing capabilities and the component flexibilities from ***-PC provides flexible management of component migrations between small devices themselves and powerful nodes in between,which is remaining a critical challenge for enabling mobile *** have evaluated the OSGi-PC in terms of performance for adaptive service provision,and power consumption during service adaptation,performance and power consumption for component migrations in diferent scenarios,which show the usability of OSGi-PC.

关键词： OSGi pervasive cloud component migration elastic infrastructure

Parallel-Data-Based Social Evolution Modeling

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Tsinghua Science and Technology 2021年第6期26卷 878-885页

作者： weishan zhang Zhaoxiang Hou Xiao Wang Zhidong Xu Xin Liu Fei-Yue Wang China University of Petroleum Qingdao 266580China Qingdao Academy of Intelligent Industry Qingdao 266111China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100049China Institute of National Security National Defense UniversityBeijing 100081China

Abnormal or drastic changes in the natural environment may lead to unexpected events,such as tsunamis and earthquakes,which are becoming a major threat to national ***,no effective assessment approach can deduce a situation and determine the optimal response strategy when a natural disaster *** this study,we propose a social evolution modeling approach and construct a deduction model for self-playing,self-learning,and self-upgrading on the basis of the idea of parallel data and reinforcement *** proposed approach can evaluate the impact of an event,deduce the situation,and provide optimal strategies for *** the breakage of a submarine cable caused by earthquake as an example,we find that the proposed modeling approach can obtain a higher reward compared with other existing methods.

关键词： paral el data reinforcement learning decision-making

Guest editorial:Special issue on data intelligence for IoT

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Digital Communications and Networks 2021年第4期7卷 461-462页

作者： weishan zhang Huansheng Ning Paolo Bellavista Liming Chen Jiehan Zhou Chunsheng Zhu Dept of Intelligence ScienceChina University of PetroleumQingdaoChina University of Science and Technology Beijing China University of Bologna School of Computing Ulster University faculty of Information Technology and Electrical Engineering University of Oulu(UOU) Institute of Future Networks at Southern University of Science and Technology in China

The Internet of Things(IoT)is increasingly deployed to enable smart *** types of data are accumulated continuously during the running of these *** and using these IoT data to derive intelligence for making the smart world reality is attracting both industrial and academic *** quite some progress has been made in this area,there is still a need for high data intelligence in IoT applications.

关键词： IoT smart attracting

AccFed:物联网中基于模型分割的联邦学习加速

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电子与信息学报 2023年第5期45卷 1678-1687页

作者：曹绍华陈辉陈舒张汉卿张卫山中国石油大学(华东)计算机科学与技术学院青岛266580

随着物联网(IoT)的快速发展,人工智能(AI)与边缘计算(EC)的深度融合形成了边缘智能(Edge AI)。但由于IoT设备计算与通信资源有限,并且这些设备通常具有隐私保护的需求,那么在保护隐私的同时,如何加速Edge AI仍然是一个挑战。联邦学习(FL... 详细信息

随着物联网(IoT)的快速发展,人工智能(AI)与边缘计算(EC)的深度融合形成了边缘智能(Edge AI)。但由于IoT设备计算与通信资源有限,并且这些设备通常具有隐私保护的需求,那么在保护隐私的同时,如何加速Edge AI仍然是一个挑战。联邦学习(FL)作为一种新兴的分布式学习范式,在隐私保护和提升模型性能等方面,具有巨大的潜力,但是通信及本地训练效率低。为了解决上述难题,该文提出一种FL加速框架AccFed。首先,根据网络状态的不同,提出一种基于模型分割的端边云协同训练算法,加速FL本地训练;然后,设计一种多轮迭代再聚合的模型聚合算法,加速FL聚合;最后实验结果表明,AccFed在训练精度、收敛速度、训练时间等方面均优于对照组。

关键词：边缘智能联邦学习端边云协同模型分割