DISTRIBUTED DYNAMIC INTEGRATION OF OPEN ARCHITECTURE SYSTEMS

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分布式梯度聚合

分布式梯度聚合介绍分布式梯度聚合是一种用于加速机器学习训练的技术。

在大规模数据集上进行训练时，单个机器的计算能力可能不足以快速完成训练任务。

通过将训练任务分布到多台机器上，并利用分布式梯度聚合算法将各个机器上的梯度进行聚合，可以显著提高训练速度和效果。

分布式训练的挑战分布式训练面临着一些挑战，包括通信开销、数据不一致和容错性等问题。

分布式梯度聚合算法旨在解决这些问题，以便在分布式环境下高效地进行训练。

通信开销在分布式训练中，各个机器之间需要进行大量的通信，以传输模型参数和梯度信息。

通信开销可能成为训练的瓶颈，影响整体的训练速度。

分布式梯度聚合算法需要在保证通信效率的前提下，尽量减少通信开销。

数据不一致在分布式训练中，各个机器上的数据可能不完全相同，导致梯度计算的结果不一致。

这种数据不一致会影响梯度聚合的准确性和效果。

分布式梯度聚合算法需要考虑如何解决数据不一致的问题，以保证模型的训练效果。

容错性在分布式训练中，机器之间可能存在故障或者通信中断的情况。

分布式梯度聚合算法需要具备一定的容错性，能够应对机器故障和通信中断等异常情况，保证训练的稳定性和鲁棒性。

分布式梯度聚合算法分布式梯度聚合算法是一种通过将各个机器上的梯度进行聚合，得到全局梯度的方法。

常见的分布式梯度聚合算法包括同步梯度平均（Synchronous Gradient Averaging）和异步梯度聚合（Asynchronous Gradient Aggregation）等。

同步梯度平均同步梯度平均是一种常用的分布式梯度聚合算法。

在同步梯度平均算法中，各个机器在每个训练周期结束时将本地计算的梯度发送给中央服务器。

中央服务器收到所有机器的梯度后，将这些梯度进行求和并取平均，得到全局梯度。

然后，中央服务器将全局梯度发送给各个机器，用于更新模型参数。

同步梯度平均算法的优点是简单易实现，并且能够保证各个机器之间的数据一致性。

然而，由于各个机器需要等待其他机器的梯度，因此同步梯度平均算法可能会受到通信延迟的影响，从而降低训练速度。

桥梁英语词汇

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A3_大规模可再生能源联网规划研究之管见_穆钢

十、组织领导——切实加强组织领导，确保战略目标全面实现
微通电力系统研究室
Magique Power System Research Group
第 22张
穆钢
Magique Power System Research Group
第 21张
穆钢
《能源生产和消费革命战略》主要内容
共10章
一、紧迫性 ——把握能源发展大势，充分认识能源革命紧迫性
二、战略目标——面向全面建设现代化，明确能源革命战略目标
三、消费革命——推动能源消费革命，开创节约高效新局面
第 6张
穆钢
地球如何变热——基本热平衡方程
不计大气层时地表热辐射能量平衡关系式为：
Input S0(1-α)πR2 = 4πR2σT4 Output
地球接收的太阳短波辐射热量
地球向太空散发的红外辐射热量
R
其中， S0=1368Wm-2是太阳常数，即太阳在地球轨道的辐射通量；α=0.3，是地球的行星反照率；R=6370km是地球半径，σ= 5.67*10-8Wm-2K-4是斯特蕃-玻尔兹曼
微通电力系统研究室
Magique Power System Research Group
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穆钢
能源革命的提出
推动能源消费革命：
抑制不合理能源消费。坚决控制能源消费总量，有效落实节能优先方针，坚定调
整产业结构，高度重视城镇化节能，加快形成能源节约型社会。
推动能源供给革命：
建立多元供应体系。大力推进煤炭清洁高效利用，着力发展非煤能源，形成煤、
℃ ★
440
530 < 560
不扭转碳排放增长趋势，限制温升低于2℃难度很大！

含多区域综合能源系统的主动配电网双层博弈优化调度策略

第50卷第1期电力系统保护与控制Vol.50 No.1 2022年1月1日Power System Protection and Control Jan. 1, 2022 DOI: 10.19783/ki.pspc.210303含多区域综合能源系统的主动配电网双层博弈优化调度策略李咸善，马凯琳，程杉(梯级水电站运行与控制湖北省重点实验室(三峡大学)，湖北宜昌 443002)摘要：区域综合能源系统(Regional Integrated Energy System, RIES)通常经电气接口与主动配电网(Active Distribution Network, ADN)相连，参与ADN需求响应调度。

为了提高RIES与ADN的交互效益，提出了含多RIES的ADN 双层博弈优化调度策略。

在RIES内部，以RIES效益最大为目标，建立满足电-气-热负荷需求、响应ADN需求调度的RIES异质能优化协调调度策略。

在此基础上，建立ADN与多RIES联盟的双层博弈调度模型。

上层为ADN与RIES联盟的非合作博弈，ADN通过制定分时购售电价引导RIES联盟制定购售电策略。

下层为RIES联盟成员合作博弈，达到联盟出力在成员之间的最优分配，并基于Shapley值对联盟成员分摊合作利益。

采用粒子群算法求解博弈模型的纳什均衡点，得到最优电价策略及各RIES的最优购售电策略。

算例结果表明，所提策略能够提高ADN削峰填谷能力，保障RIES的经济性及ADN的可靠运行。

关键词：区域综合能源系统；主动配电网；双层博弈；优化调度Dispatching strategy of an active distribution network with multiple regional integrated energysystems based on two-level game optimizationLI Xianshan, MA Kailin, CHENG Shan(Hubei Provincial Key Laboratory of Operation and Control of Cascade Hydropower Stations,China Three Gorges University, Yichang 443002, China)Abstract: A regional integrated energy system (RIES) is usually connected with an active distribution network (ADN) through an electrical interface, and participates in the ADN demand response dispatch. To improve the interaction efficiency of RIES and ADN, a two-level game optimal scheduling strategy for ADN with multiple RIES is proposed. In RIES, a heterogeneous energy optimization and coordination scheduling strategy is established to meet the demands of electricity-gas-heat load of the RIES, and to respond to ADN electricity demand scheduling with the goal of maximizing RIES benefit. A two-layer game scheduling model of ADN and RIES-coalition is established. The upper layer is the non-cooperative game between ADN and RIES-coalition, and the ADN guides RIES-coalition to formulate power purchase and sale strategies responding to the ADN demand scheduling through a time-of-use purchase and sale price policy. The lower layer is RIES-coalition members’ cooperative game to achieve the optimal distribution of coalition trading power among members, and cooperation benefits are shared among coalition members based on the Shapley value.The particle swarm optimization algorithm is used to solve the Nash equilibrium point of the game model, and the optimal electricity price strategy and the optimal electricity purchase and sale strategy of each RIES are obtained. The results of a numerical example show that the proposed strategy can improve the peak shifting and valley filling capacity of ADN, ensure the economy of RIES and the reliable operation of the ADN.This work is supported by the National Natural Science Foundation of China (No. 51607105) and the Natural Science Foundation of Hubei Province (No. 2016CFA097).Key words: regional integrated energy system; active distribution network;two-level game; optimal scheduling0 引言随着化石能源供给匮乏的形势日益严峻，能源基金项目：国家自然科学基金项目资助(51607105)；湖北省自然科学基金项目资助(2016CFA097) 互联及能源高效利用成为当前研究的热点问题[1]。

基于多主体主从博弈的区域综合能源系统低碳经济优化调度

第50卷第5期电力系统保护与控制Vol.50 No.5 2022年3月1日 Power System Protection and Control Mar. 1, 2022 DOI: 10.19783/ki.pspc.210888基于多主体主从博弈的区域综合能源系统低碳经济优化调度王瑞1，程杉1，汪业乔1，代江2，左先旺1(1.智慧能源技术湖北省工程研究中心(三峡大学)，湖北宜昌 443002；2.贵州电网有限责任公司，贵州贵阳 550002)摘要：为解决环境污染以及区域综合能源系统中多市场主体利益冲突的问题，提出一种考虑奖惩阶梯型碳交易机制和双重激励综合需求响应策略的区域综合能源系统多主体博弈协同优化方法。

首先，为充分考虑系统的低碳性，在博弈模型中引入奖惩阶梯型碳交易机制限制各主体碳排放量，并在用户侧提出了基于价格和碳补偿双重激励的综合需求响应策略。

其次，考虑源-荷-储三方主动性和决策能力，以能源管理商为领导者，供能运营商、储能运营商和用户为跟随者，建立了基于碳交易和博弈协同优化的多主体低碳交互机制，并构建了各主体的交易决策模型。

最后，采用结合Gurobi工具箱的自适应差分进化算法对所提模型进行求解。

仿真结果验证了所提模型和方法的有效性，即各主体在低碳框架下可以合理调整自身策略，并兼顾系统经济、环境效益。

关键词：区域综合能源系统；低碳交互；多主体博弈；碳交易；综合需求响应Low-carbon and economic optimization of a regional integrated energy system based ona master-slave game with multiple stakeholdersWANG Rui1, CHENG Shan1, WANG Yeqiao1, DAI Jiang2, ZUO Xianwang1(1. Engineering Center for Intelligent Energy Technology (China Three Gorges University), Yichang 443002, China;2. Guizhou Power Grid Co., Ltd., Guiyang 550002, China)Abstract: To solve the problems of environmental pollution and the conflict of interests of multi-market players in a regional integrated energy system, a multi-agent game collaborative optimization method for a regional integrated energy system considering a reward and punishment ladder carbon trading mechanism and dual incentive integrated demand response is proposed. First, to fully consider the low-carbon nature of the system, a reward and punishment ladder carbon trading mechanism is introduced to limit the carbon emissions of each stakeholder. Then an integrated demand response strategy based on price and carbon compensation is proposed on the user side. Secondly, considering the initiative and decision-making ability of the source, load and storage parties, a multi-agent low-carbon interaction mechanism based on carbon trading and game collaborative optimization is proposed, and the decision-making model of each stakeholder is constructed.Finally, an adaptive differential evolution algorithm combined with the Gurobi toolbox is used to solve the proposed model. The simulation results verify the effectiveness of the proposed model. In a low-carbon framework, each stakeholder can reasonably adjust its own strategies and take into account the economic and environmental benefits of the system.This work is supported by the National Natural Science Foundation of China (No. 51607105).Key words: regional integrated energy system; low-carbon interaction; multi-agent game; carbon trading; integrated demand response0 引言随着能源需求上升及环境污染日益严重，安全高效、低碳清洁已成为能源发展的主流方向[1-2]。

JBL 306P MkII Studio Monitor说明书

Powered 6" Two-Way Studio MonitorYOUR MIX IS ONLY AS GOOD AS YOUR MONITORSJBL 306P MkII has been equipped with acclaimed 3 Series transducers that now perform even better. Hear deep, accurate and tightly controlled bass, thanks to a long-throw 6.5" woofer and the patented JBL Slip Stream™ low-frequency port. Enjoy soaring, immaculately detailed highs, via its woven-composite 1" Neodymium tweeter.JBL engineers took things to the next level with fasterHF transient response through fine-tuned ferrofluid damping, and greater low-frequency linearity and reduced harmonic distortion courtesy of an enhanced woofer design. The result is a studio monitor you can trust—with unmatched performance, stunning imaging and neutral frequency response that’s unbeatable in its class.TAILORED SOUND TO FIT YOUR STUDIOThe dimensions and acoustics of a room can have a major effect on sonic accuracy, and that’s why JBL 306P MkII lets you adjust the response to fit your studio. The new Boundary EQ attenuates the low-end boost that can occur when you place monitors directly on the desktopor near walls. The 3-position HF Trim switch allows youto adjust the high-frequency response of the 306P MkIIto tailor it to room acoustics or personal tastes.PATENTED IMAGE CONTROL WAVEGUIDEReveal impressive detail, ambience and depth in your mixes with JBL 3 Series’ groundbreaking Image Control Waveguide. Originally developed for JBL’s flagshipM2 Master Reference Monitor, this patented innovation ensures an acoustically seamless transition between thelow- and high-frequency transducers and provides an immersive soundstage, with precise imaging. Offering a wide sweet spot and neutral frequency response, JBL 306P MkII delivers a crystal clear representation of your mix, revealing subtle details, even when listening off-axis.BIG SOUND OUT OF THE BOXJBL 306P MkII is ready for the most demanding production styles right out of the box. With 112 watts of total power, the dual, integrated Class-D power amplifiers, custom designed by JBL for each transducer, give you generous dynamic range for any project. From music production and podcasting to cinematic sound design or daily vlogging, enjoy the output and power you need to hear exceptional detail at any volume—even at peak SPL. Simply plug in, power on, and start creating.KEY MESSAGES 6Patented Image Control Waveguidecreates a stunning soundstage with precise imaging and depth, a wide sweet spot and neutral response in any roomNext-generation JBL transducersfor optimized transient response and improved linearityDual Class-D power ampsprovide ample headroom and dynamic rangeNew Boundary EQattenuates the low-end boost that can occur when monitors are placed on a desktop or near walls3-position HF Trim switchallows tailoring of the high-frequency response tothe listening environment or personal taste HIGHLIGHTS 6The next-generation JBL 306P MkII powered studio monitor makes legendary JBL performance available to every studio. With the revolutionary JBL Image Control Waveguide and refined transducers, JBL 306P MkII offers stunning detail, precise imaging, a wide sweet spot and impressive dynamic range that enhances the mix capabilities of any modern workspace. Leveraging patented technologies derived from the JBL 7 Series and M2 Master Reference Monitors and sporting a sleek, modern design, JBL 306P MkII delivers outstanding performance and an enjoyable mix experience at an accessible price.Powered 6" Two-Way Studio Monitor© 2017 HARMAN International Industries, Incorporated. All rights reserved.ORDER SPECIFICATIONS 6BOX DIMS (H x W x D): 16.5" x 11.5" x 13.3"SHIPPING WEIGHT: 15.95 lbsUPC CODE: EU - 691991007828UK - 691991007835JBL Professional 8500 Balboa Blvd. Northridge, CA 91329 USASPECIFICATIONS 6LF DRIVER SIZE:165mm (6.5")HF DRIVER SIZE:25mm (1")HF DRIVER TYPE: Soft domeCROSSOVER:1425Hz 4th order acoustic Linkwitz-Riley OK FOR USE NEAR MAGNETICALLY SENSITIVE EQUIPMENT:Yes INPUT SENSITIVITY (-10dBV INPUT): 92dB / 1m POWER CONFIGURATION: Bi-amplified HF DRIVER POWER AMP: 56W, Class-D LF DRIVER POWER AMP: 56W, Class-D FREQUENCY RESPONSE (±3dB): 47Hz – 20kHz FREQUENCY RANGE (-10dB): 39Hz – 24kHz LOW FREQUENCY EXTENSION (-10dB): 39HzMAXIMUM CONTINUOUS SPL*: 92dB MAXIMUM PEAK SPL**: 110dBMAXIMUM PEAK INPUT LEVEL (-10dBV / +4dBu):+6dBV / +20.3dBuSYSTEM DISTORTION CRITERIA:<10% THD at maximum output with full compressor / limiter engagementELECTRICAL DISTORTION CRITERIA: 0.2% THD @ 1kHz / 2.83VRMS output; <1% THD @ 1kHz, full rated outputSIGNAL TO NOISE RATIO:75dBA (A-Weighted), 70dBr (unweighted), relative to 2.83VRMS output COVERAGE HORIZONTAL x VERTICAL: 120° x 90°ANALOG INPUT TYPES: 1 x XLR female, 1 x TRS female, balanced HF TRIM CONTROL: -2dB, 0dB, +2dBBOUNDARY EQ: LF Shelf @ 50Hz: -3dB, -1.5dB, 0dB AC INPUT VOLTAGE: 100 – 240VAC (±10%), 50 / 60Hz ENCLOSURE TYPE:Ported ENCLOSURE CONSTRUCTION: 15mm MDF ENCLOSURE FINISH: Matte black PVCBAFFLE CONSTRUCTION: Injection-molded structural ABSCABINET DIMENSIONS (H x W x D***): 361 x 224 x 282mm (14.2" x 8.8" x 11.1")DISPLAY CARTON (H x W x D): 408 x 285 x 328mm (16.1" x 11.2" x 12.9")SHIPPING CARTON (H x W x D): 418 x 292 x 338mm (16.5" x 11.5" x 13.3")NET WEIGHT:6.1 kg (13.42 lbs)SHIPPING GROSS WEIGHT:7.25 kg (15.95 lbs)* Measured using full-bandwidth pink noise, C-Weighted ** Measured C-Weighted*** Depth measured without power cord and audio connectors(typical power cord = 2 inches, typical XLR connector = 2.5 inches)FEATURES 6•Patented Image Control Waveguide for detailed imaging and a broad, room-friendly sweet spot •Next-generation JBL transducers for optimized transient response and improved linearity •Patented Slip Stream™ low-frequency port for superior bass performance at all playback levels •Dual integrated, custom Class-D amplifiers provide 112 watts of power for high output and dynamic range •New Boundary EQ settings compensate for low-frequency variants introduced by the environment •HF Trim switch adjusts high-frequency output to room acoustics or personal preferences •Flexible connectivity with balanced XLR and 1/4"TRS inputs, +4dBu / -10dBV input-sensitivity switch and adjustable volume control •Engineered to JBL Linear Spatial Reference design criteria for outstanding accuracy in any working space •Strenuous JBL 100-hour full-power test ensures years of reliability •Sleek, modern design provides a visual upgrade to any studioRRP:£199 / €199。

Multi-source and Heterogeneous Data Integration Model for Big Data Analytics in Power DCS

Multi-source and Heterogeneous Data Integration Model for Big Data Analytics inPower DCSWengang Chen Jincheng Power Supply CompanyJincheng, Chinajcchenwangang@ Ruijie Wang, Runze Wu, Liangrui TangNorth China Electric Power UniversityBeijing, Chinawang_ruijie2015@wurz@tangliangrui@Junli FanBeijing Guodiantong NetworkTechnology Co. Ltd.Beijing, Chinafanjunli1@Abstract—It is the vital sig nificance for the strong and smart g rid that big data analytics technolog ies apply in the power system. The multi-source and heterog eneous data integ ration technolo g y based on bi g data platform is one of the indispensable content. As there are the problems of data heterog eneity and data islands in the dispatching and control system, a multi-source and heterog eneous data integ ration model is proposed for big data analytics. This model exists the data integration layer in the platform of big data analytics. The model can improve the Extract-Transform-Load (ETL) process in the big data platform according to the extracting rules and transform rules, which are made by uniform data model in the panoramic dispatching and control system. Research shows that the integ rating model developed here is efficient to establish panoramic data and can adapt to various data sources by building uniform data model in the power dispatching and control system. With the development of big data technology, it is expected that the data integration model will be improved and used in more electric power applications.Keywords- power dispatching and control system; big data analysis; data integration model; uniform data modelI.I NTRODUCTIONBig data was referenced the earliest by open source project Nutch in Apache Software Foundation, which was used to describe the analysis of a large number of data sets in web search applications [1]. Different industries have some consensus, but have not a unified definition for the big data. In 2012, Gartner updated its definition in [2] as follows: "Big data is high volume, high velocity, and high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization." The representative features of big data are 3Vs: volume, variety, velocity. International Data C orporation thinks that the 4th V of big data is value with sparse density, but IBM regards as veracity is the 4th V [3].As it has the big data characteristics of large numbers, rapid growth and rich type, the data generated in the power system is representative big data [4], [5]. It describes the features of the electric power big data: 3V and 3E in [4]. The 3V is a representative of volume, velocity and variety and the 3E shows electric power data that is energy, exchange and empathy. For the construction of strong and smart grid, the technology research relating big data is desiderated to make electric power big data analytics.Big data in electric power system spans many areas, including power generation, transmission, distribution, utilization, and scheduling, all of which encompass the big data platform of the State Grid Corporation of China [6]. In the operation process of power grid, dispatching and control system (DC S) collects the vast and various data, which is increased rapidly. Hence, the data collected by DCS has big data characteristics and belongs to the big data. However, the big data of dispatching and control system exists problems of data island and information heterogeneity. In order to manage uniformly the data from any application dispatching and control system, the multi-source and heterogeneous data in the system needs to be integrated in the big data platform to build the panoramic data of DCS. It can promote the big data analytics and data mining for the electric power dispatching control system.Extract-Transform-Load (ETL) is one of the more popular approaches to data integration as shown in [7]. Authors of this work have given the working modules in ETL and introduced a framework that uses a workflow approach to design ETL processes. Some groups have used various approaches such as UML and data mapping diagrams for representing ETL processes, quality metrics driven design for ETL, and scheduling of ETL processes [8]-[11]. In order to fuse the heterogeneous data in DC S and build panoramic data, a multi-source and heterogeneous data integration model is proposed to analysis the big data in the power dispatching and control system. The model adopts the improved ETL processes in the platform of the big data analytics. Meanwhile, the improved ETL processes are based on the uniform data model in the panoramic DCS. The build panoramic data will be stored in big data platform in the form of distributed data warehouse, which can provide the data for user’s data query and data analysis.II.B IG D ATA OF DCSA.Uniform Data Model of Panoramic DCSThe data of dispatching and control system distributes in the application systems, like Supervisory C ontrol and Data Acquisition (SCADA), Energy Management System (EMS), Wide Area Measurement System (WAMS), Project Management Information System (PMIS), etc. Some2016 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discoveryproblems are caused, as the structure of data storage and attribute name exist differences in some different application systems. The uniform data model is built based on IEC TC57 CIM, which is reduced, supplied and expanded according to the global ontology of DC S, to solve these problems. C ore data models of panoramic data cover the integrated information from SCADA, WAMS, EMS, etc. Power system logic model, which is one of the core data models, is divided into the two branches: equipment container and equipment, as is shown in Figure 1. Subclasses of equipment includes a variety of conducting equipment and auxiliary equipment. The logical grid topology is formed when conductingFigure 1. Power System Logic ModelAfter getting the uniform data model, mappingrelationships and transform rules need to be establishedbetween the uniform data model of panoramic with the datamodel of any application system, which are convenient forthe data extraction and data transformation.B. the Platform of Big Data Analytics in DCSAs mentions before, there are mass data and the problemof heterogeneous information in the power scheduling andcontrol system. Therefore, the data from different sourcesneed be fused by big data technologies to build panoramicdata of DCS in the big data platform. Then, we can realizedata analysis, data mining and data visualization on the baseof the panoramic data. The architecture of big data platformis shown in Figure 2, which includes seven layers, fivebackground processing processes and two interface layers.This framework elaborates the responsibilities of each layerby a left-right and up-down view approach. The functionrealized by any layer is as follows.• Heterogeneous Data Sources: there are more than 10sets of application system in electric powerdispatching and control center, such as SC ADA,WAMS, EMS, PMIS and operation managementsystem, etc. These systems provide the massiveamounts of original heterogeneous data, whichcannot be directly used for data analysis and data mining in the bid data field.• Data Access Layer: in order to speed up the process of data processing, data integration and data analysisare run in a distributed processing system based on Hadoop. Hence, data access layer supplies a data channel that transmits the data between data sources Distributed Processing System Based on HadoopData ExplorationReportsHive Hbase HDFSData Integration LayerExtract DataTransformLoad DataUniform Data Model of Panoramic DCSData Access LayerHeterogeneous Data Sources···SCADAWAMS EMS PMISSpark and Sparking StreamingData QueriesData AnalysisData miningFigure 2. Architecture of Big Data Platform•Data Integration Layer: in this layer we present our conceptual model by describing ETL processingbased on the uniform data model of panoramic DCS.This layer aims at building the panoramic data of dispatching and control system and is one of the keymodules in the platform of big data analytics.• Data Storage Layer: the panoramic data built in the data integration layer is stored in this layer, which provide the basic data for data queries, data analysis and data mining. The big data platform. The big data platform stores the data in ways of the distributed data warehouse Hive and distributed data storage ways HDFS, HBase. • Data Analytics Layer: this layer realizes some functions, which includes data queries, data analysis and data mining, by processing the data called form data storage layer. Data mining technology is composed of Bayesian method, neural net algorithm, results analysis, etc. • Visual Data Layer: the results of data analysis and data mining is presented by the tabular or graph representation to provide effective decision-making supporting information for dispatchers. • Interactive Interface: the interactive interface can manage the big data platform by accessing the data layers except visual data layer and adjusting the parameters in operation of the platform, which can help the normal and efficient operation of system. From the above, the panoramic data integrated is the data center in the big data analytics of dispatching and controlsystem. Hence, it is the important component that complete and effective panoramic data is built by a rational dataintegration method. For obtaining the panoramic data of DC S, a multi-source and heterogeneous data integrationmodel is proposed to apply to the data integration layer.III.M ULTI-SOURCE AND H ETEROGENEOUS D ATAI NTEGRATION M ODELIn this section, an integration framework for multi-source heterogeneous data is proposed by describing big data ETL process. The proposed uniform data model and data integration ETL process will be used in this framework.A.Integration Model OverviewIn the data integration layer, we present the big data ETL framework BDETL which uses Hadoop to parallelize ETL processes and to load the panoramic data into Hive. BDETL employs Hadoop Distributed File System (HDFS) as the ETL execution platform and Hive as the distributed data warehouse system (see Figure 3). BDETL has a quantity of components, including the application programming interfaces (APIs) used by the user’s or development professionals’ ETL programs, ETL processing engine performing the data extracting and transforming process, and a job manager that controls the execution of the jobs toFigure 3. BDETL ArchitectureThere are three sequential steps in BDETL’s workflow: data extracting process, data transforming process and data loading process. The data warehouse is built by Hive based on the uniform data model of panorama dispatching and control system, therefore, rules of data executions and transformations are specified by users or research staff. So the three steps of BDETL are described as follows:•Data extracting process: this is the first step of ETL that involves data extraction from appropriate datasources. The transformed data must be present inHDFS (including Hive files and HBase files) Whenthe MapReduce (MR) jobs are started (see the left ofFig.3). So the data from heterogeneous sources, suchas SCADA, WAMS and EMS, needs to be uploadedinto HDFS. The source data executed by mappersand reducers is extracted from the HDFS by user-specified rules. Here, a graphical interface isprovided to create a workflow of ETL activities andautomate their execution.•Data transforming process: the data transforming process involves a number of transformationsprocessing data, such as normalizing data, lookups,removing duplicates, checking data integrity andconsistency by constraint from uniform data model,etc. BDETL allows processing of data into multipletables within a job. BDETL’s job manager submitsjobs to Hadoop’s Job-Tracker in sequential order.The jobs for transformation are to be run by thetransforming rules based on uniform data model.•Data loading process: this step involves the propagation of the data into a data warehouse likeHive that serves Big Data. Hive employs HDFS forphysical data storage but presents data in HDFS filesas logical tables. The data can be written directlyinto files which can be used by Hive.B.Executing ProcessAs the ETL process is executed in the big data analysis platform, the heterogeneous data of sources must be update into HDFS. The source data in HDFS is split by Hadoop and assigned to the map tasks. The records from a file split are processed by specified rules in the mappers. A mapper can process data from HDFS that will go to different dimensions. What is more, a dimension must have a key that distinguishes its members. Then, in the shuffle and sort, the mapper output is sent to different reducers by specified rules. The reducer output is written to the HDFS. When the attribute values of a dimension member are overwritten by new values, we also need to update the versions that have already been loaded into Hive.Listing 1 shows pseudocode for the mapper. In this code, Γis a sequence of transformation process as defined by users or designers. r=⊥ says that r is the smallest element. Meanwhile, a transformation can be followed by other transformations. According to the uniform data model, the data in HDFS is extracted into mapper process by the method GetTransformationData() (lines 3). The first transformation defines the schematic information of the data source such as the names of attributes, the data types, and the attributes for sorting of versions.Listing 1 Mapper1 class Mapper2 method Initialize()3 GetTransformation()DataΓ←4 method Map(Record r)5 for all t∈Γ do6 .ProcessRecord()r t r←7 if r=⊥ then8 return9 else10 CreatCompositeKey(,.targetDimension)key r t←11 CreatValue(,.targetDimension)value r t←12 return (,)key value13 End if14 End forspecialized map-only method for big dimensions. The input is automatically grouped by the composite key values and sorted before it is fed to the Reduce method. We keep the dimension values temporarily in a buffer (lines 5–10), assign a sequential number to the key of a new dimension record (line 9), and update the attribute values (line 11), including the validity dates and the version number. The method MakeDimensionRecord extracts the dimension’s business key from the composite key given to the mapper andcombines it with the remaining values. Finally, we write the reduce output with the name of the dimension table as the key, and the dimension data as the value (line 12). Listing 2 Reducer 1 class Reducer2 method Reduce(CompositeKey key, values[0…n ])3 GetNameofDimensionTable()name key ←4 if DimType(name) = TypeList then5 ()L new List ←6 for 0,i n ← do7 MakeDimensionRecord(,[])r key values i ← 8 if []r id =⊥ then9 []GetDimensionId()r id name ← 10 End if 11 .()L add r12 UpdateAttributeValues(L ) 13 End for14 for all r ęL do 15 return (,)name r 16 End for 17 End ifIV. I MPLEMENTATIONIn this section, we show how to use BDETL to build panorama data of dispatching and controlling system in a data analysis platform based on Hadoop. This platform includes a local cluster of three computers: two computers are used as the DataNodes and TaskTrackers, each of them has a quad-core N3700 processor (1.6GHz) and 4GB RAM; one machine is used as the NameNode and JobTracker, which has two quad-core G3260 (3.3GHz) processors and 4GB RAM. All machines are connected via a 20Mbit/s Ethernet switch. We use Hadoop 0.20.0 and Hive 0.13.0. Two map tasks or reduce tasks on each node are configured in the Hadoop platform.Data sets for the running example are updated into HDFS from SC ADA, WAMS, EMS, etc. In the experiments, the data dimension is scaled from 20GB to 80GB. The BDETL implementation consists of four steps: 1) update the source data into HDFS and define the data source on request, 2) setup the transforming rules, a sample model of which is provide in Figure 4, 3) define the target table on the basis of the uniform data model, 4) add the sequence of BUS Table in the Uniform Data ModelSTATION VOLTAGECLASS...Bus Table from SCADADIANYALEIXINGCHANGZHAN ...Bus Table from EMSDIANYADENGJICHANGZHAN ...Figure 4. Standardized Attribute of Heterogeneous DataIn the process of data integration, we compare BDETL with ETLMR in [12] which is a parallel ETL programming framework using MapReduce. The reason that ETLMR is selected is BDETL has the same goal as ETLMR. The performance is studied by comparing running time of the integration processes in the ETL.Figure 5 shows the total time of ETL processes. ETLMR is efficient to process relatively small size data sets, e.g., 20GB, but the time grows fast when the data is scaled up and the total time is up to about 23% higher than the time used by BDETL for 100GB. So BDETL outperforms ETR when the data is bigger.Figure 5. The Total Time of ETL ProcessThe time of initial load is shown in Figure 6. For BDETL, the initial loads are tested using data with and without co-location. The result shows that the performance is improved significantly by data co-location and about 60% more time is used when there is no co-location. This is because the co-located data can be processed by a map-only job to save time. BDETL is better than ETLMR. For example, when 100Gb is used to test, ETLMR uses up to 3.2 times as long for the load. Meanwhile, the processing time used by ETLMR grows faster with the increase of the data.The time of processes in mappers and reducers is tested. The results are shown in Figure 7. The MapReduce process of BDETL is faster than that of ETLMR. The reason is that the transforming rules based on uniform data model can reduce the complexity of mappers and reducers. So ETLMR takes 1.5 times longer than BDETL.Figure 6.The Time of Initial LoadFigure 7. The Time of Processes in Mappers and ReducersV. C ONCLUSIONS AND F UTURE W ORKThe data from various heterogeneous sources in dispatching and control system need to be integrated into uniform data model of panoramic dispatching and control system, which allows intelligent querying, data analysis and data mining in the big data platform. That is an important open issue in the area of Big Data. For the big data of power dispatching and control system, we proposed the architecture of big data analysis platform. As the data in dispatching and control system exist heterogeneous, this paper proposes an efficient ETL framework BDETL to integrate the data form various heterogeneous sources in a distributed environment. This framework uses uniform data model around dataintegration. Meanwhile, the transforming rules based on uniform data model are conducive to reduce the time of ETL process. We conducted a number of experiments to evaluate BDETL and compared with ETLMR. The results showed that BDETL achieves better performance than ETLMR when processing the integration of heterogeneous data.There are numerous future research directions for ETL process in big data platform. For example, it would be better to establish graphical user interface where users or designers can make an ETL flow by using visual transformation operators; and that we plan to make BDETL support more ETL transformations in power systems.R EFERENCES[1] Gartner. Top ten strategic technology trend for 2012 [EB/OL]. (2011-11-05) [2014-08-17]. .[2] Big data, from Wikipedia, the free encyclopedia./wiki/Big data.[3] Xin Luna Dong and Divesh Srivastava. 2013. Big data integration.Proc. VLDB Endow. 6, 11 (Aug 2013), 1188-89.[4] C hinese Society for Electrical Engineering Information C ommittee,"Chinese Electric Power Big Data Development White Paper (2013)," Chinese Society for Electrical Engineering, Beijing, China, 2013.. [5] China Computer Federation Big Data Experts Committee, "China BigData Technology and Industry Development White Paper (2013)," China Computer Federation, Beijing, China, 2013.[6] Y. Huang and X. Zhou, "Knowledge model for electric power bigdata based on ontology and semantic web," in CSEE Journal of Power and Energy Systems, vol. 1, no. 1, pp. 19-27, March 2015..[7] P. Vassiliadis, A. Simitsis, and E. Baikousi, “A Taxonomy of ETLActivities,” in Proceedings of the AC M Twelfth International Workshop on Data Warehousing and OLAP, New York, NY, USA, 2009, pp. 25–32.[8] S. K. Bansal, "Towards a Semantic Extract-Transform-Load (ETL)Framework for Big Data Integration," 2014 IEEE International Congress on Big Data, Anchorage, AK, 2014, pp. 522-529.[9] A. Simitsis, K. Wilkinson, M. C astellanos, and U. Dayal, “QoX-driven ETL design: reducing the cost of ETL consulting engagements,” in Proceedings of the 2009 AC M SIGMOD International Conference on Management of data, 2009, pp. 953–960. [10] Liu, X., Thomsen, C ., and Pedersen, B. T. C loudETL: scalabledimensional ETL for hive. In Proc. of IDEAS, pp. 195–206, 2014.[11] A. Karagiannis, P. Vassiliadis, and A. Simitsis, “Macro-levelScheduling of ETL Workflows,” Submitted for publication, 2009. [12] Liu, X., Thomsen, C., and Pedersen, B. T. The ETLMR MapReduce-based Dimensional ETL Made Easy. PVLDB, 5(12):1882–1885, 2012.。

replicatedmergetree+distributed的集群模式

replicatedmergetree+distributed的集群模式1. 引言1.1 概述在当今信息化时代，数据的处理和管理成为了各个行业的重要任务。

随着数据量不断增长，传统的单机方式已经无法满足海量数据的处理需求。

因此，分布式系统逐渐成为了解决大规模数据处理和存储的有效方法。

本文将主要介绍ReplicatedMergeTree算法与Distributed集群模式相结合的集群架构。

ReplicatedMergeTree是一种可靠存储引擎，可以实现高效地对分布式数据进行复制、归并和同步。

而Distributed则是一个基于分片和副本机制的弹性伸缩的分布式数据库系统。

1.2 文章结构本文将依次介绍ReplicatedMergeTree算法以及其原理、数据同步策略和容错机制。

接下来将详细讨论Distributed集群模式，包括分布式架构介绍、集群规模与扩展性以及数据分发与负载均衡等方面内容。

然后将通过实现与应用案例来更加具体地说明集群部署和配置、数据一致性保证措施以及高可用性和故障恢复策略等关键问题。

最后通过总结研究成果和应用价值以及展望未来发展趋势和改进方向，对本文的研究进行归纳总结。

1.3 目的本文的目的是介绍ReplicatedMergeTree与Distributed集群模式相结合的集群架构，并说明其在大规模数据处理和存储领域中的应用价值。

通过深入分析算法原理、数据同步策略和容错机制，读者能够更好地理解ReplicatedMergeTree 算法。

同时，本文将详细讨论Distributed集群模式的架构特点、扩展性以及负载均衡等问题，读者可以了解分布式系统设计和管理的关键考虑因素。

通过实现与应用案例，读者将掌握集群部署和配置、数据一致性保证措施以及高可用性和故障恢复策略等关键技术，从而为日后的实际项目应用提供参考。

最后，通过对研究成果进行总结和展望未来发展趋势与改进方向，使读者在该领域内能够有所启示并有新的思考。

基于J2EE在分布式环境下的底层结构的自动动态配置的应用-外文翻译

Infrastructure for Automatic DynamicDeployment Of J2EE Application inDistributed EnvironmentsAnatoly Akkerman, Alexander Totok, and Vijay Karamcheti1. IntroductionIn recent years, we have seen a significant growth in component-based enterprise application development. These applications are typically deployed on company Intranets or on the Internet and are characterized by high transaction volume, large numbers of users and wide area access. Traditionally they are deployed in a central location, using server clustering with load balancing (horizontal partitioning) to sustain user load. However, horizontal partitioning has been shown very efficient only in reducing application-related overheads of user-perceived response times, without having much effect on network-induced latencies. Vertical partitioning (e.g., running web tier and business tier in separate VMs) has been used for fault isolation and load balancing but it is sometimes impractical due to significant run-time overheads (even if one would keep the tiers on a fast local-area network) related to heavy use of remote invocations. Recent work in the context of J2EE component based applications has shown viability of vertical partitioning in wide-area networks without incurring the aforementioned overheads. The key conclusions from that study can be summarized as follows:• Using properly designed applications, vertical distribution across wide-area networks improves user-perceived latencies.• Wide-area vertical layering requires replication of application components and maintaining consistency between replicas.• Additional replicas may be deployed dynamically to handle new requests.• Different replicas may, in fact, be different implementations of the same component based on usage (read-only, read-write).• New request paths may reuse components from previously deployed paths.Applying intelligent monitoring and AI planning techniques in conjunction with the conclusions of that study, we see a potential for dynamic adaptation in industry-standard J2EE component-based applications in wide area networksThrough deployment of additional application components dynamically based on active monitoring. However, in order to achieve such dynamic adaptation, we need an infrastructure for automating J2EE application deployment in such an environment. This need is quite evident to anyone who has ever tried deploying a J2EE application even on a single application server, which is a task that involves a great deal of configuration of both the system services and application components. For example one has to set up JDBC data sources, messaging destinations and other resource adapters before application components can be configured and deployed. In a wide area deployment that spans multiple server nodes, this proves even more complex, since more system services that facilitate inter-node communications need to be configured and started and a variety of configuration data, like IP addresses, port numbers, JNDI names and others have to be consistently maintained in various configuration files on multiple nodes.This distributed deployment infrastructure must be able to:• address inter-component connectivity specification and define its effects on component configuration and deployment,• address application component dependencies on application server services, their configuration and deployment,• provide simple but expressive abstractions to control adaptation through dynamic deployment and undeployment of components,• enable reuse of services and components to maintain efficient use of network nodes’ resources,• provide these facilities without incurring significant additional design effort on behalf of application programmers.In this paper we propose the infrastructure for automatic dynamic deployment of J2EE applications, which addresses all of the aforementioned issues. The infrastructuredefines architecture description languages (ADL) for component and link description and assembly. The Component Description Language is used to describe application components and links. It provides clear separation of application components from system components. A flexible type system is used to define compatibility of component ports and links. A declaration and expression language for configurable component properties allows for specification of inter-component dependencies and propagation of properties between components. The Component (Replica) Assembly Language allows for assembly of replicas of previously defined components into application paths by connecting appropriate ports via link replicas and specifying the mapping of these component replicas onto target application server nodes. The Component Configuration Process evaluates an application path’s correctness, identifies the dependenciesof application components on system components, and configures component replicas for deployment. An attempt is made to match and reuse any previously deployed replicas in the new path based on their configurations. We implement the infrastructure as a part of the JBoss open source Java application server and test it on several technology sample J2EE applications –Java Pets tore, Rubies and TPC-W-NYU . The infrastructure implementation utilizes the JBoss’s extendable micro-kernel architecture, based on the JMX specification. Componentized architecture of JBoss allows incremental service deployments depending on the needs of deployed applications. We believe that dynamic reconfiguration of application servers through dynamic deployment and undeployment of system services is essential to building a resource-efficient framework for dynamic distributed deployment of J2EE applications. The rest of the paper is organized as follows. Section 2 provides necessary background for understanding the specifics of the J2EE component technology which are relevant to this study. Section 3 gives a general description of the infrastructure architecture, while section 4 goes deeper in describing particularly important and interesting internal mechanisms of the infrastructure. Section 5 describes the implementation of the framework, and related work is discussed in section 6.2. J2EE Background2.1 IntroductionComponent frameworks.A component framework is a middleware system that supports applications consisting of components conforming to certain standards. Application components are “plugged” into the component framework, which establishes their environmental conditions and regulates the interactions between them. This is usually done through containers, component holders, which also provide commonly required support for naming, security, transactions, and persistence. Component frameworks provide an integrated environment for component execution, as a result significantly reduce the effort .it takes to design, implement, deploy, and maintain applications. Current day industry component framework standards are represented by Object Management Group’s CORBA Component Model, Sun Microsystems’ Java 2 Platform Enterprise Edition (J2EE) and Microsoft’s .NET, with J2EE being currently the most popular and widely used component framework in the enterprise arena.J2EE.Java 2 Platform Enterprise Edition (J2EE) is a comprehensive standard for developing multi-tier enterprise Java applications. The J2EE specification among other things defines the following:• Compon ent programming model,• Component contracts with the hosting server,• Services that the platform provides to these components,• Various human roles,• Compatibility test suites and compliance testing procedures.Among the list of services that a compliant application server must provide are messaging, transactions, naming and others that can be used by the application components. Application developed using J2EE adhere to the classical 3-Tier architectures – Presentation Tier, Business Tier, and Enterprise Information System (EIS)Tier (see Fig. 1). J2EE components belonging to each tier are developed adhering to the Specific J2EE standards.1. Presentation or Web tier.This tier is actually subdivided into client and server sides. The client side hosts a web browser, applets and Java applications that communicate with the server side of presentation tier or the business tier. The server side hosts Java Servlet components, Java Server Pages (JSPs) and static web content. These components are responsible for presenting business data to the end users. The data itself is typically acquired from the business tier and sometimes directly from the Enterprise Information System tier. The server side of the presentation tier is typically accessed through HTTP(S) protocol.2. Business or EJB tier.This tier consists of Enterprise Java Beans (EJBs) that model the business logic of the enterprise application. These components provide persistence mechanisms and transactional support. The components in the EJB tier are invoked through remote invocations (RMI), in-JVM invocations or asynchronous message delivery, depending on the type of EJB component. The EJB specification defines several types of components. They differ in invocation style (synchronous vs. asynchronous, local vs. remote) and statefulness: completely stateless (e.g., Message-Driven Bean), stateful non-persistent (e.g., Stateful Session Bean), stateful persistent (e.g., Entity Bean). Synchronously invocable EJB components expose themselves through a special factory proxy object (an EJB Home object, which is specific to a given EJB), which is typically bound in JNDI by the deployer of the EJB. The EJB Home object allows creation or location of an EJB Object, which is a proxy to a particular instance of an EJB 1.3. Enterprise Information System (EIS) or Data tier.This tier refers to the enterprise information systems, like relational databases, ERP systems, messaging systems and the like. Business and presentation tier component communicate with this tier with the help of resource adapters as defined by the Java Connector Architecture. The J2EE programming model has been conceived as adistributed programming model where application components would run in J2EE servers and communicate with each other. After the initial introduction and first server implementations, the technology, most notably, the EJB technology has seen some a significant shift away from purely distributed computing model towards local interactions There were very legitimate performance-related reasons behind this shift, however the Distributed features are still available. The J2EE specification has seen several revisions, the latest stable being version 1.3, while version 1.4 is going through last review phases 3. We shall focus our attention on the former, while actually learning from the latter. Compliant commercial J2EE implementations are widely available from BEA Systems , IBM, Oracle and other vendors. Several open source implementations, including JBoss and JOnAS claim compatibility as well. A Recent addition to the list is a new Apache project Geronimo.2.2 J2EE Component Programming ModelBefore we describe basic J2EE components, let’s first address the issue of defining what a component is a software component is a unit of composition with contractually specified interfaces and explicit context dependencies only. A software component can be deployed independently and is subject to composition by third parties.According to this definition the following entities which make up a typical J2EE application would be considered application components (some exceptions given below):• EJBs (session, entity, message-driven),• Web components (servlets, JSPs),• messaging destinations,• Data sources,EJB and Web components are deployed into their corresponding containers provided by the application server vendor. They have well-defined contracts with their containers that govern lifecycle, threading, persistence and other concerns. Both Web and EJB components use JNDI lookups to locate resources or other EJB components they want to communicate with. The JNDI context in which these lookups are performed is maintainedseparately for each component by its container. Bindings messaging destinations, such as topics and queues, are resources provided by a messaging service implementation. Data sources are resources provided by the application server for data access by business components into the enterprise information services (data) tier, and most commonly are exemplified by JDBC connection pools managed by the applicationServer.A J2EE programmer explicitly programs only EJBs and Web components. These custom-written components interact with each other and system services both implicitly and explicitly. For example, an EJB developer may choose explicit transaction demarcation (i.e., Bean-Managed Transactions) which means that the developer assumes the burden of writing explicit programmatic interaction with the platform’s Transaction Manager Service through well-defined interfaces. Alternatively, the developer may choose Container-Managed transaction demarcation, where transactional behavior of a component is defined through its descriptors and handled completely by the EJB container, thus acting as an implicit dependency of the EJB on the underlying Transaction Manager service.2.3 Links Between Components2.3.1 Remote InteractionsJ2EE defines only three basic inter-component connection types that can cross application server boundaries, in all three cases; communication is accomplished through special Java objects.•Remote EJB invocation: synchronous EJB invocations through EJB Home and EJB Object interfaces.• Java Connector outbound connection: synchronous message receipt, synchronous and asynchronous message sending,Database query using Connection Factory and Connection interfaces.• Java Connector inbound connection: asynchronous message delivery into Message-Driven Beans (MDBs) only, utilizing Activation Spec objects. In the first two cases, an application component developer writes the code that performs lookup of theseobjects in the component’s run-time JNDI context as well as code that issues method invocations or sends and receives messages to and from the remote component. The component’s run-time JNDI context is created for each deployment of the component. Bindings in the context are initialized at component deployment time by the deployed (usually by means of component’s deployment descriptors). These bindings are assumed to be static, since the specification does not provide any contract between the container and the component to inform of any binding changes In the case of Java Connector inbound communication, Activation Spec object lookup and all subsequent interactions with it are done implicitly by the MDB container. The protocol for lookup has not been standardized, though it is reasonable to assume a JMX- or JNDI-based lookup assuming the underlying application server provides facilities to control each step of deployment process, establishment of a link between J2EE components would involve:• Deployment of target component classes (optional for some components, like destinations),• C reation of a special Java object to be used as a target component’s proxy,• B inding of this object with component’s host naming service (JNDI or JMX),• Start of the target component,• Deployment of referencing component classes,• Creation and population of referencing component’s run-time context in its host naming service,• start of the referencing component.However, none of modern application servers allow detailed control of the deployment process for all component types beyond what is possible by limited options in their deployment descriptors 4. Therefore our infrastructure will use a simplified approach that relies on features currently available on most application servers:• Ability to deploy messaging destinations and data sources dynamically,• Ability to create and bind into JNDI special objects to access messaging destinations and data sources,• Ability to specify initial binding of EJB Home objects upon EJB component deployment,• Ability to specify a JNDI reference 5 in the referencing component’s run-time context to point to the EJB Home binding of the referenced EJB component. In our infrastructure which is limited to homogeneous application servers, these options are sufficient to control intercomponent links through simple deployment descriptor manipulation. However, in context of heterogeneous application servers, simple JNDI references and thus simple descriptor manipulation are insufficient due to cross-application-server Classloading issues.2.3.2 Local InteractionsSome interactions between components can occur only between components co-located in the same application server JVM and sometimes only in the same container. In the Web tier, examples of such interactions are servlet-to-servlet request forwarding. In the EJB tier, such interactions are CMP Entity relations and invocations via EJB local interfaces. Such local deployment concerns need not be exposed at the level of a distributed deployment infrastructure other than to ensure collocation. Therefore, the infrastructure treats all components requiring collocation as a single component.2.4 Deployment of J2EE Applications and System Services2.4.1 Deployment of Application ComponentsDeployment and undeployment of standard J2EE components has not yet been standardized (see JSR 88 [10] for standardization effort 6). Therefore, each application server vendor provides proprietary facilities for component deployment and undeployment. And while the J2EE specification does define packaging of standard components which includes format and location of XML-based deployment descriptors within the package, this package is not required to be deployable by an application server without proprietary transformation. Examples of such transformation are• Generation of additional proprietary descriptors that supplement or replace the standard ones,• Code generation of application server-specific classes.In order to proceed with building a dynamic distributed deployment infrastructure capable of deploying in heterogeneous networks, we propose a universal unit of deployment to be a single XML-based deployment descriptor or a set of such,Bundled into an archive. The archive may optionally include Java classes that implement the component and any other resources that the component may need. Alternatively, the deployment descriptors may simply have URL references to codebases. We assume presence of a dynamic deployment/undeployment service on all compliant J2EE servers and a robust application server classloading architecture capable of repeated deployment cycles without undesired classloading-related issues. Most modern application servers(e.g., JBoss and Geronimo) do provide such facilities.2.4.2 Deployment of System Components (Services)While lacking only in the area of defining a clear specification of deployment and undeployment when it comes to application components, the J2EE standard falls much shorter with respect to system services. Not only a standardized deployment facility for system services is not specified, the specification, in fact, places no requirements even on life cycle properties of these services, nor does it address the issue of explicit specification of application component dependencies on the underlying system services. Instead it defines a role of human deploy who is responsible for ensuring that the required services are running based on his/her understanding of dependencies of application components on system services as implied by the nature of components and their deployment descriptors.基于J2EE在分布式环境下的底层结构的自动动态配置的应用Anatoly Akkerman, Alexander Totok, and Vijay Karamcheti1. 前言近几年，我们已经看到基于组件的企业应用开发的显著增加。

从EDPS 2019年工作报告看欧盟数据保护

从EDPS 2019年工作报告看欧盟数据保护作者：魏书音孙舒扬周千荷来源：《中国计算机报》2020年第32期2020年3月，欧洲数据保护专员公署（EDPS）发布2019年工作报告，总结了执法调查、政策咨询、技术研发、国际合作等各项数据保护工作的进展情况。

EDPS全面深入推进数据保护工作，通过采用将个人数据保护意识和文化融入欧盟社会经济发展事务、实现数据保护与其他个人权益协调发展、引导国际议题和规则等方式，确保欧盟数据保护规则的有效实施和推广。

EDPS数据保护工作经验（一）管理与服务并重，将个人数据保护意识和文化融入欧盟社会经济发展事务。

一是开展专项调查、审查评估、发布临时禁令，及时发现并处置欧盟机构在个人数据安全相关事务处理、使用大型数据系统方面存在的问题，督促整改未经同意的第三方跟踪等问题，探索个人数据保护的最佳实践。

其中，对欧盟机构使用微软产品和服务的调查促成了海牙论坛的建立，推进研究创建标准合同，以拒绝大型IT服务提供商的格式条款，强化了欧盟机构对IT服务和产品的安全控制。

二是参与个人数据安全相关政策制定，提供咨询和指导意见，帮助欧盟机构将个人数据安全、隐私权保护纳入政策设计考量范围，并实现融入立法。

三是鼓励欧盟机构内部发展问责文化，确保各机构不仅遵守数据保护规则，还能证明其遵守行为规范;同时，还要认识到，即使是合法处理个人数据也可能导致侵犯个人权利，需审慎考量。

四是发布政策评估和执法监管工具，深入推进数据保护规则实施。

比如，公布《个人数据保护比例原则指南》，帮助评估拟议中的欧盟政策是否符合《基本权利宪章》中关于隐私和个人数据基本权利的规定;发布关于控制器、处理器和联合控制器概念的指南，澄清控制器、处理器和联合控制人员范围、义务分配和各自责任，帮助欧盟机构工作人员更好地理解其角色并遵守数据保护规则;建立新数据安全规则的多人协作写作系统（Wiki），创建新法律注释版本，鼓励EDPS内部分享并向欧盟机构开放，确保以统一的方法监督和执行欧盟数据保护规则。

工业互联网PaaS平台Predix技术介绍

建立在 Cloud Foundry
*
GE Confidential – Distribution authorized to individuals with need to know only
是一个领先的开源平台由 Cloud Foundry 社区发展, 现由 GE CF 道场为工业用例继续发展
DevOps
*
GE Confidential – Distribution authorized to individuals with need to know only
What is it?
Benefits To Platform Subscribers
Tools and Processes that stress Communication, Collaboration (information sharing and web service usage), Integration, Automation, and Measurement of cooperation between Developers and its Operations
1 Engineering
3 Operations
5 Culture
4 Financials
DevOps CI / CD (continuous integration / continuous delivery) Paired programming (eXtreme programming)
DevOps Op Center BizOps (business operations)
Invent and simplify Be a minimalist Bias for action Cultivate a meritocracy Disagree and commit

分布式计算8

分布式计算系统—分布式数据管理分布式计算系统分布式数据管理
分布式系统中一个重要的问题是数据的复制问题, 分布式系统中一个重要的问题是数据的复制问题, 为了提高可靠性和提高数据的访问性能, 为了提高可靠性和提高数据的访问性能,数据往往有多个副本,分别存放在系统中的不同物理节点上. 个副本,分别存放在系统中的不同物理节点上.在一个大规模的分布计算系统中, 大规模的分布计算系统中,如果数据以单副本的形式集中存放在一个机器上,这个机器就成为整个系统的瓶颈. 中存放在一个机器上,这个机器就成为整个系统的瓶颈. 其他机器上的进程对该文件的访问必须通过网络来完成, 其他机器上的进程对该文件的访问必须通过网络来完成, 势必影响远程进程访问文件的性能. 势必影响远程进程访问文件的性能.如果在不同的机器上有多个副本的话,进程可以就近访问本地的文件副本. 上有多个副本的话,进程可以就近访问本地的文件副本. 与分布式数据管理相关的一个主要问题是如何保持多副本数据的一致性.分布式系统的许多方面和多副本多副本数据的一致性. 数据的一致性有关,如分布式文件系统, 数据的一致性有关,如分布式文件系统,分布式数据库系统和分布式共享存储器等. 系统和分布式共享存储器等.
分布式计算系统—分布式数据管理分布式计算系统分布式数据管理
8.1 一致性模型当数据有多个副本的时候, 当数据有多个副本的时候,一个重要的问题是如何保持多个副本的一致性.也就是说,当一个副本更新后, 持多个副本的一致性.也就是说,当一个副本更新后,我们需要保证其他的副本也同样得到更新.否则的话, 们需要保证其他的副本也同样得到更新.否则的话,两个副本的内容就会不同. 副本的内容就会不同. 一致性模型是进程和数据存储之间的一个基本约定. 一致性模型是进程和数据存储之间的一个基本约定. 也就是说,如果进程对数据的访问遵守特定的规则, 也就是说,如果进程对数据的访问遵守特定的规则,那么数据存储就能够正确地进行.一般地来说, 数据存储就能够正确地进行.一般地来说,一个进程如果对一个数据项施加一个读操作, 对一个数据项施加一个读操作,那么期望读操作的返回值就是最后一次对该数据项进行写所得的结果. 就是最后一次对该数据项进行写所得的结果.由于在分布计算系统中没有一个全局的时钟, 计算系统中没有一个全局的时钟,很难精确地定义哪个写操作是最后一个.为此,需要提供其他一些定义, 操作是最后一个.为此,需要提供其他一些定义,不同的定义对应着不同的一致性模型. 定义对应着不同的一致性模型.

基于DDQN的电力传感网资源分配算法

研究不足与展望
实验局限性
虽然该算法在实验中表现出色，但仍然存在一些局限性，例如未考虑复杂的实际环境因素和大规模网络的性能表现。
理论完善
需要进一步深入研究该算法的理论基础和优化方法，以进一步提高算法的性能和泛化能力。
应用前景
随着物联网和智能电网的快速发展，电力传感网的应用前景广阔。基于DDQN的资源分配算法有望在实际应用中发挥重要作用，为智能电网的发展提供新的解决方案。
初始化Q表
使用随机方法初始化Q 表，为每个状态-动作对赋予一个初始的Q值。
训练过程
通过不断迭代，利用Q 表更新每个状态-动作对的Q值，以优化资源分配策略。
测试过程实现
确定测试集
根据实际应用场景，选择具有代表性的测试用例组成测试集。
资源分配策略生成
利用训练好的神经网络模型，为每个测试用例生成相应的资源分配策
算法适用场景
电力传感网资源分配问题
该算法适用于解决电力传感网的资源分配问题，例如在分布式能源调度、电力市场交易等领域中，需要对电力传感网的运行状态进行实时监测和优化控制。
VS
其他资源分配问题
除了电力传感网资源分配问题外，该算法还可以应用于其他类似的资源分配问题中，例如物联网、智能交通等领域中的资源分配问题。
06
结论与展望
研究成果总结ห้องสมุดไป่ตู้
01
算法有效性
通过实验验证，基于DDQN的电力传感网资源分配算法能够在不同场景下实现资源的最优分配，有效提高电力传感网的能效和稳定性。
02
算法通用性
该算法适用于多种电力传感网拓扑结构和资源分配场景，具有较好的通用性和可扩展性。

数据并行的大模型分布式训练-概述说明以及解释

数据并行的大模型分布式训练-概述说明以及解释1.引言1.1 概述数据并行的大模型分布式训练是一种在机器学习和深度学习领域中广泛应用的技术。

随着数据量的不断增长和模型复杂度的提高，传统的单机模型训练已经无法满足对高效、快速训练的需求。

因此，采用分布式训练来加速模型训练过程成为一种趋势。

概括而言，数据并行是一种将大型数据集切分成小的子数据集，在多个计算节点上同时进行训练的策略。

每个计算节点使用相同的模型参数，但操作的是不同的数据子集。

通过在每个节点上分别计算梯度，并进行梯度的聚合，可以快速更新全局的模型参数。

这种数据并行的方式能够显著提高训练速度，解决了单机训练时的瓶颈问题。

大型模型的分布式训练则是指在数据并行的基础上，对大规模模型进行训练的方法。

大型模型具有巨大的网络结构和参数量，因此需要更大的计算资源和存储空间来进行训练。

为了实现大模型的分布式训练，通常需要使用分布式存储系统和计算框架来支持数据的并行处理和模型的训练过程。

大模型的分布式训练不仅可以提高训练速度，还可以有效地利用集群资源，提升模型的性能和泛化能力。

同时，数据并行的优势也体现在其适用的广泛性。

从大规模机器学习任务到深度学习模型的训练，数据并行都发挥着重要的作用。

接下来，本文将从数据并行的概念和原理出发，详细介绍大模型的分布式训练方法。

同时，还将探讨数据并行和大模型分布式训练在实际应用中所面临的挑战，并提出相应的解决方案。

文章结构部分的内容可以如下编写："1.2 文章结构":本文主要分为三个部分，即引言、正文和结论。

在引言部分，将首先概述数据并行的概念和原理，并介绍大模型的分布式训练的背景和意义。

随后，说明本文的目的，即探讨数据并行在大模型分布式训练中的应用和挑战。

接下来的正文部分将详细介绍数据并行的概念和原理。

首先，将阐述数据并行的基本概念以及其实现的原理和方法。

然后，将探讨大模型的分布式训练，从理论和实践两个方面来分析大模型的分布式训练的优势和挑战。

微电网规划设计导则

微电网规划设计导则1 范围本部分提供了微电网规划设计导则。

本部分中的微电网指的是包含中、低压负载和分布式能源（Distributed Energy Resources, DER）的交流电气系统。

本部分不涉及直流微电网。

微电网分为并网型微电网和独立型微电网。

独立型微电网和公用电网没有电气连接；并网型微电网是电力系统的一个受控部分，可以运行于以下两种模式：——并网模式；——孤岛模式。

本部分主要包括以下内容：——微电网应用范围、资源分析、发电预测、负荷预测；——DER规划和微电网电力系统规划；——对于DER、微电网接入配电网、微电网控制、保护和通信系统等的技术要求；——微电网项目的评估。

2 术语与定义下列术语和定义适用于本文件。

2.1黑启动 black start电力系统停电后通过内部电源实现启动。

[IEC 60050-617:2009,617-04-24]2.2母线 busbar低阻抗导体，可以在其上分开的各点接入若干个电路。

注：在大多数情况下，母线由杆状导体构成。

[GB/T 2900.83-2008, 151-12-30]2.3变流器 converter改变与电能相关的一个或几个特性的装置。

注1:与电能相关的特性有：例如电压、相数和频率(包括零频率)等。

注2: 改写GB/T 2900.83-2008, 151-13-36。

2.4热电联产 combined heat and power (CHP)在发电的同时可生产有用的热能。

注1:采用热电联产，多余的热可用于民用或工业用途。

注2:改写IEC 60050-602:1983, 602-01-24。

2.5地 earth大地与接地极有电接触的部分，其电位不一定等于零。

注：改写GB/T 2900.73-2008, 195-01-03。

2.6接地配置 earthing arrangement系统、设施和装置的接地所包含的所有电气连接和设备。

注：改写GB/T 2900.73-2008, 195-02-20。

基于平衡概率分布和实例的迁移学习算法

㊀第52卷第3期郑州大学学报(理学版)Vol.52No.3㊀2020年9月J.Zhengzhou Univ.(Nat.Sci.Ed.)Sep.2020收稿日期:2019-09-25基金项目:河南省高校科技创新团队支持计划项目(17IRTSTHN013)㊂作者简介:黄露(1994 ),女,河南驻马店人,硕士研究生,主要从事智能控制理论㊁机器学习研究,E-mail:1751037268@;通信作者:曾庆山(1963 ),男,湖北武汉人,教授,主要从事智能控制理论㊁复杂系统的建模研究,E-mail:huanglulu823@㊂基于平衡概率分布和实例的迁移学习算法黄㊀露,㊀曾庆山(郑州大学电气工程学院㊀河南郑州450001)摘要:在联合匹配边缘概率和条件概率分布以减小源域与目标域的差异性时,存在由类不平衡导致模型泛化性能差的问题,从而提出了基于平衡概率分布和实例的迁移学习算法㊂通过基于核的主成分分析方法将特征数据映射到低维子空间,在子空间中对源域与目标域的边缘分布和条件分布进行联合适配,利用平衡因子动态调节每个分布的重要性,采用加权条件概率分布自适应地改变每个类的权重,同时融合实例更新策略,进一步提升模型的泛化性能㊂在字符和对象识别数据集上进行了多组对比实验,表明该算法有效地提高了图像分类的准确率㊂关键词:迁移学习;平衡分布;类不平衡;实例更新;领域自适应中图分类号:TP3㊀㊀㊀㊀㊀文献标志码:A㊀㊀㊀㊀㊀文章编号:1671-6841(2020)03-0055-07DOI :10.13705/j.issn.1671-6841.20194390㊀引言我们正处在一个飞速发展的大数据时代,每天各行各业都产生海量的图像数据㊂数据规模的不断增大,使得机器学习的模型能够持续不断地进行训练和更新,从而提升模型的性能㊂传统的机器学习和图像处理中,通常假设训练集和测试数据集遵循相同的分布,而在实际视觉应用中相同分布假设很难成立,诸如姿势㊁光照㊁模糊和分辨率等许多因素都会导致特征分布发生改变,而重新标注数据工作量较大,且成本较高,也就形成了大量的不同分布的训练数据,如果弃之不用则会造成浪费㊂如何充分有效地利用这些不同分布的训练数据,成为计算机视觉研究中的一个具有挑战性的问题㊂而迁移学习是针对此类问题的一种有效解决方法,能够将知识从标记的源域转移到目标域,用来自旧域的标记图像来学习用于新域的精确分类器㊂目前,迁移学习已经成为人工智能领域的一个研究热点㊂其基本方法可以归纳为4类[1],即基于特征㊁基于样本㊁基于模型及基于关系的迁移㊂其中基于特征的迁移学习方法是指通过特征变换的方法,来尽可能地缩小源域与目标域之间的分布差异,实现知识跨域的迁移[2-8]㊂文献[2]提出迁移主成分分析(transfercomponent analysis,TCA),通过特征映射得到新的特征表示,以最大均值差异(maximum mean discrepancy,MMD)作为度量准则,将领域间的边缘分布差异最小化㊂由于TCA 仅对域间边缘分布进行适配,故而有较大的应用局限性㊂文献[3]提出的联合分布自适应(joint distribution adaptation,JDA)在TCA 的基础上增加对源域和目标域的条件概率进行适配,联合选择特征和保留结构性质,将域间差异进一步缩小㊂基于样本的迁移方法通常对样本实例进行加权[9-10],以此来削弱源域中与目标任务无关的样本的影响,不足之处是容易推导泛化误差上界,应用的局限性较大㊂基于模型的迁移方法则是利用不同域之间能够共享的参数信息,来实现源域到目标域的迁移㊂而基于关系的迁移学习方法关注的是不同域的样本实例之间的关系,目前相关方面的研究较少㊂本文提出的基于平衡概率分布和实例的迁移学习算法(balanced distribution adaptation and instance basedtransfer learning algorithm,BDAITL)是一种混合算法,结合了上述的基于特征和样本实例这两种基本的迁移算法㊂在多个真实数据集上进行的多组相关实验表明,BDAITL 算法模型泛化性能良好㊂郑州大学学报(理学版)第52卷1㊀问题描述迁移学习就是把源域中学习到的知识迁移到目标域,帮助目标域进行模型训练㊂领域和任务是迁移学习的两个基本概念㊂下面从领域和任务的定义方面,对要解决的问题进行描述[1]㊂定义1㊀领域D 是迁移学习中进行学习的主体,由特征空间χ和边缘概率分布P (X )组成,可以表示为D ={χ,P (X )},其中:特征矩阵X ={x 1,x 2, ,x n }ɪχ㊂领域与领域之间的不同一般有两种情况,特征空间不同或边缘概率分布不同㊂定义2㊀给定一个领域D ,任务T 定义为由类别空间Y 和一个预测函数f (x )构成,表示为T ={Y ,f (x )},其中类别标签y ɪY ㊂问题1㊀给定一个有完整标注的源领域D s ={x i ,y i }nsi =1和源任务T s ㊂一个没有任何标注的目标领域D t ={x j }n tj =1和目标任务T t ㊂假设D s 和D t 有相同的特征空间和类别空间:即χs =χt ㊁Y s =Y t ;以及不同的分布:即边缘概率分布P (X s )ʂP (X t )㊁条件概率分布P (y s /x s )ʂP (y t /x t )㊂迁移学习最终的目标是,迁移D s 和T s 中的知识以帮助D t 和T t 训练预测函数f (x ),提升模型的性能㊂2㊀基于平衡概率分布和实例的迁移学习算法BDAITL 算法从特征和样本实例两个层面进行知识的迁移㊂首先,使用基于核的主成分分析法(Kernelprincipal component analysis,KPCA),采用非线性映射将源域与目标域的高维数据映射到一个低维子特征空间㊂然后,在子空间内采用MMD 方法联合匹配域间的边缘分布和条件分布㊂与JDA 直接忽略两者之间重要性不同的是,BDAITL 算法采用平衡因子来评估每个分布的重要性[4]㊂另外,JDA 在适配条件分布时,由于目标域无标签,无法直接建模,采用了类条件概率来近似㊁隐含地假设每个域中该类的概率是相似的,而实际应用中通常是不成立的㊂而BDAITL 算法在适配条件分布时,充分考虑类不平衡问题,采用加权来平衡每个域的类别比例,得出了更为稳健的近似㊂最后,考虑源域中并不是所有的样本实例都与目标任务的训练有关,采用L 2,1范数将行稀疏性引入变换矩阵A ,选择源域中相关性高的实例进行目标任务模型的训练㊂BDAITL 算法的具体过程在下文介绍㊂2.1㊀问题建模首先,针对源域和目标域特征维数过高的问题,对其进行降维重构,最大限度地最小化领域间的分布差异,从而利于判别信息从源域到目标域的迁移㊂记X =[X s ,X t ]=[x 1,x 2, ,x n ]ɪR m ˑn 表示源域和目标域的所有样本组成的矩阵,中心矩阵表示为H =I -(1/n )1,其中:m 表示样本维数;n =n s +n t 表示样本总数;1ɪR nˑn表示元素全为1的矩阵㊂PCA 的优化目标是找到正交变换V ɪR m ˑq ,使样本的协方差矩阵XHX T最大化,即max tr(V T XHX T V ),s.t .V T V =I ,(1)其中:q 为降维后特征子空间基向量的个数;新的特征表示为Z =V T X ㊂本文使用KPCA 方法对源域和目标域数据降维㊂利用KPCA 方法,应用核映射X ңΨ(X )对PCA 进行非线性推广,获取数据的非线性特征,相应的核矩阵为K =Ψ(t )T Ψ(t )ɪR n ˑn ,对式(1)进行核化后可得max tr(A T KHK T A ),s.t .A T A =I ,(2)其中:A ɪR nˑq是变换矩阵;核化后的特征表示为Z =A T K ㊂其次,平衡概率分布㊂迁移学习需要解决的一个主要问题是减小源域与目标域之间的分布差异,包括边缘分布和条件分布,将不同的数据分布的距离拉近㊂本文采用MMD 方法来最小化源域与目标域之间的边缘分布P (X s )㊁P (X t )以及条件分布P (y s /x s )㊁P (y t /x t )的距离㊂即D (D s ,D t )=(1-μ) P (X s )-P (X t ) +μ P (y s /x s )-P (y t /x t ) =(1-μ)MMD 2H (P (X s ),P (X t ))+μMMD 2H (P (y s /x s ),P (y t /x t )),(3)其中:μɪ[0,1]是平衡因子㊂当μң0时,表示源域和目标域数据本身存在较大的差异性,边缘分布更重65㊀第3期黄㊀露,等:基于平衡概率分布和实例的迁移学习算法要;当μ=0时,即为TCA;当μң1时,表示域间数据集有较高的相似性,条件分布适配更为重要;当μ=0.5时,即为JDA㊂也就是说,平衡因子根据实际数据分布的情况,来动态调节每个分布的重要性㊂源域与目标域边缘概率分布的MMD 距离计算如下,M o 是MMD 矩阵,MMD 2H(P (X s ),P (X t ))= 1n s ðns i =1A T k i -1n t ðn s +nt j =n s +1A Tk j 2H=tr(A T KM o K T A ),(4)M o (i ,j )=1/(n s )2,x i ɪD s ,x j ɪD s ,1/(n t )2,x i ɪD t ,x j ɪD t ,-1/n s n t ,其他㊂ìîíïïïï(5)适配源域与目标域的条件概率分布时,采用加权来平衡每个域的类别比例㊂具体为P (y s x s)-P (y t x t) 2H = P (y s )P (x s )P (x s y s)-P (y t )P (x t )P (x t y t) 2H= αs P (x s y s)-αt P (x t y t) 2H ,(6)其中:αs ㊁αt 表示权值㊂故源域与目标域条件概率分布的MMD 距离计算为MMD 2H(P (y s x s),P (y t x t))=ðCc =1αc sns(c )ðx i ɪD s(c )A Tk i -α(c )tn t(c )ðx j ɪD t(c )A Tk j 2H=ðCc =1tr(ATKM c K T A ),(7)其中:c ɪ(1,2, ,C )表示样本类别;D (c )s ㊁D (c )t 和n (c )s ㊁n (c )t分别表示源域和目标域中属于类别c 的样本集合和样本数;M c 为每一类别的加权MMD 矩阵,M c (i ,j )=P (y (c )s )/n (c )s n (c )s ,x i ɪD (c )s ,x j ɪD (c )s ,P (y (c )t )/n (c )t n (c )t ,x i ɪD (c )t ,x j ɪD (c )t ,-P (y (c )s )P (y (c )t )/n (c )s n (c )t ,x i ɪD (c )s ,x j ɪD (c )t 或x i ɪD (c )t ,x j ɪD (c )s ,0,其他㊂ìîíïïïïïï(8)综合式(2)㊁式(3)㊁式(7)和式(8),可得源域和目标域的平衡概率分布D (D s ,D t )=(1-μ)tr(A TKM o K TA )+μðCc =1tr(A T KM c K T A )=(1-μ)tr(A T KM o K T A )+μtr(A T KW c K T A ),(9)其中:W c =ðCc =1M c㊂最后,实例更新㊂源域中通常会存在一些特殊的样本实例,对于训练目标域的分类模型是没有用的㊂由于变换矩阵A 的每一行都对应一个实例,基于它们与目标实例的相关性,行稀疏性基本上可以促进实例的自适应加权,实现更新学习㊂故本文对变换矩阵中与源域相关的部分A s 引入L 2,1范数约束,同时对与目标域相关的部分A t 施加F 范数约束,以保证模型是良好定义的㊂即A s 2,1+ A t 2F ㊂(10)㊀㊀通过最小化式(10)使得式(2)最大化,与目标实例相关(不相关)的源域实例被自适应地重新加权,在新的特征表示Z =A T K 中具有更大(更少)的重要性㊂综上所述,可得本文的最终优化目标min (1-μ)tr(A T KM o K T A )+μtr(A T KW c K T A )+λ( A s 2,1+ A t 2F )㊀s.t.A T KHK T A =I ,(11)其中:λ是权衡特征匹配和实例重新加权的正则化参数,能够控制模型复杂度并保证模型正定㊂2.2㊀目标优化式(11)所示目标函数是一个带有约束的最优化问题,利用Lagrange 法进行求解,记L =(1-μ)tr(A T KM o K T A )+μtr(A T KW c K T A )+λ( A s 2,1+ A t 2F )-tr((I -A T KHK T A )Φ)为式(11)的Lagrange 函数,Φ为Lagrange 乘子㊂∂L /∂A =(K ((1-μ)M o +μW c )K T +λG )A -KHK T AΦ,令∂L /∂A =0可得,(K ((1-μ)M o +μW c )K T +λG )A =KHK T AΦ㊂由于在零点并不是平滑的,故子梯度的计算为∂( A s 2,1+ A t 2F )/∂A=2GA ,其中:G 是一个子梯度矩阵,且75郑州大学学报(理学版)第52卷G ii =1/(2 a i),x i ɪD s ,a i ʂ0,0,x i ɪD s ,a i =0,1,x i ɪD t ,ìîíïïïï其中:a i 是矩阵A 的第i 行㊂这样将求解变换矩阵A 归结为求解特征分解,得到q 个最小的特征向量㊂3㊀实验结果及分析3.1㊀实验数据集为了研究和测试算法的性能,在不同的数据集上进行测试实验㊂USPS 和MNIST 是包含0~9的手写数字的标准数字识别数据集,分别包含训练图像60000幅和7291幅以及测试图像10000幅和2007幅,示例如图1所示㊂office 由3个对象域组成:amazon (在线电商图像)㊁webcam (网络摄像头拍摄的低解析度图像)㊁DSLR(单反相机拍摄的高清晰度图像),共有4652幅图像,31个类别㊂caltech-256是对象识别的基准数据集,共有30607幅图像,256个类别,示例如图2所示㊂图1㊀MINST 和USPS 数据集图片示例Figure 1㊀Example of MINST and USPSdataset图2㊀office 和caltech-256数据集图片示例Figure 2㊀Example of office and caltech-256dataset㊀㊀本文实验采用文献[5]中的方法预处理数据集MNIST 和USPS,以及文献[6]中方法的预处理数据集office 和caltech-256㊂其统计信息如表1所示,数据子集M 和U 分别作为源域和目标域,可构建M ңU ㊁U ңM 两个跨域迁移学习任务㊂数据子集A ㊁W ㊁D 和C 中任意两个作为源域和目标域,可构建12个跨域迁移学习任务,记为:D ңW ㊁D ңC ㊁㊁A ңC ㊂表1㊀实验数据子集的统计信息Table 1㊀Dataset used in the experiment数据集样本数特征维数类别数子集MNIST 200025610M USPS180025610Uoffice +caltech-256253380010A ,W ,D ,C3.2㊀实验结果分析实验验证环节,将BDAITL 方法与用于图像分类问题的6种相关方法进行了比较,即最近邻算法(nea-rest neighbor,NN)㊁主成分分析法(principal component analysis,PCA)㊁TCA㊁基于核的测地流形法(geodesicflow kernel,GFK)㊁JDA 以及转移联合匹配方法(transfer joint matching,TJM)㊂评价准则是目标域中的样本分类准确率(accuracy ),具体计算为accuracy =x :x ɪD t ɘy ^(x )=y (x )/x :x ɪD t,其中:x 表示目标域中的测试样本;y (x )表示其真实标签;y ^(x )表示其预测标签㊂实验结果如表2所示,分别设置q =40㊁λ=1㊁迭代次数t =10㊂如表2所示,BDAITL 算法的分类准确率相较于传统方法NN 和PCA 有明显的提升㊂与经典迁移学习算法TCA㊁GFK㊁JDA㊁TJM 相比,BDAITL 算法的分类准确率在大部分的跨域学习任务中有较大幅度的提高,85㊀第3期黄㊀露,等:基于平衡概率分布和实例的迁移学习算法㊀㊀表2㊀7种算法在14个迁移任务中的平均准确率Table2㊀Accuracy comparison of7algorithms on14cross-domain tasks数据集平均准确率/%NN PCA TCA GFK JDA TJM BDAITLDңW63.3975.9386.4475.5989.4985.4291.19 DңC26.2729.6532.5030.2830.9931.4333.57 DңA28.5032.0531.5232.0532.2532.6134.76 WңD25.8477.0785.9980.8989.1789.7392.99 WңC19.8626.3627.1630.7231.1730.1933.93 WңA22.9631.0030.6929.7532.7829.2633.51 CңW25.7632.5436.6140.6838.6437.9744.41 CңD25.4838.2245.8638.8545.2243.3146.50 CңA23.7036.9544.8941.0242.9046.6646.87 AңW29.8335.5937.6338.9837.9740.7443.05 AңD25.4827.3931.8536.3139.4942.1746.50 AңC26.0034.7340.7840.2538.3639.4541.23 MңU65.9466.2254.2867.2262.8962.3776.00 UңM44.7044.9552.2046.4557.4552.2563.05其中在任务MңU中较其最佳基准算法(GFK)提高了8.78%,这表明BDAITL算法在适配条件概率时采用加权来平衡每个域的类别比例对算法的性能提升是有效的,是平衡域之间不同类别分布的有效方法㊂同时实例的更新学习也能够削弱一些不相关实例的影响,一定程度上提升了算法的性能㊂3.3㊀参数分析在本文的BDAITL算法的优化模型中,设置了3个参数,即平衡因子μ㊁正则化参数λ以及子空间纬度q㊂实验中通过保持其中两个参数不变,改变第3个参数的值来观察其对算法性能的影响㊂平衡因子μ可以通过分别计算两个领域数据的整体和局部的分布距离来近似给出㊂为了分析μ在不同的取值下对BDAITL算法性能的影响,取μɪ{0,0.1,0.2, ,0.9},实验结果如表3所示㊂从表中可以看出,不同的学习任务对于μ的取值敏感度不完全相同,如DңW㊁WңD㊁CңD㊁MңU㊁UңM分别在0.6㊁0.4㊁0.6㊁0.2㊁0.3时取得最大的分类准确率,μ值越大说明适配条件概率分布越重要㊂它表明在不同的跨领域学习问题中,边缘分布自适应和条件分布自适应并不是同等重要的,而μ起到了很好的平衡作用㊂表3㊀μ的取值对BDAITL算法准确率的影响Table3㊀Influence ofμon the accuracy of the BDAITL algorithm数据集准确率/%μ=0μ=0.1μ=0.2μ=0.3μ=0.4μ=0.5μ=0.6μ=0.7μ=0.8μ=0.9DңW89.1590.8590.5190.5190.1790.5191.1990.8590.8590.53 DңC32.3232.2832.2432.6832.7732.5933.5732.8632.6832.41 DңA32.9933.6134.3434.6634.6634.7634.2433.6133.5133.19 WңD89.8190.4591.0892.3692.9992.3691.7291.0890.4589.17 WңC34.7334.6434.2833.9333.7533.5733.2133.1333.4833.84 WңA31.5232.0532.2531.9432.5732.4632.7833.0933.0933.51 CңW39.3238.6438.9840.3442.0342.3743.7344.4143.3943.05 CңD42.6842.6843.3143.3143.9543.9546.5043.9543.9543.31 CңA45.8245.8245.7245.9346.3546.4546.6646.5646.8746.87 AңW41.6941.3641.3642.0342.7143.0542.3741.0240.6840.00 AңD46.5045.8644.5943.9543.9544.5946.5045.8645.2244.59 AңC41.1441.2340.6941.0541.0540.8740.7840.3440.5240.61 MңU62.1774.6176.0075.2874.7273.8973.4472.5673.1173.28 UңM49.9561.5062.7063.0561.8061.6561.8561.6061.4561.20㊀㊀表4是q分别取20㊁40㊁60㊁80㊁100㊁140㊁180㊁220㊁260㊁300时,BDAITL算法的分类准确率的变化情况㊂从表中可以看出,不同的迁移学习任务在达到最优性能时,所对应的q是不同的,即不同任务的最优子空间纬度是不同的,如DңW㊁WңD㊁CңD㊁MңU㊁UңM的最优子空间纬度分别是80㊁100㊁80㊁60㊁60㊂95郑州大学学报(理学版)第52卷表4㊀q的取值对BDAITL算法准确率的影响Table4㊀Influence of q on the accuracy of the BDAITL algorithm数据集准确率/%q=20q=40q=60q=80q=100q=140q=180q=220q=260q=300DңW89.1591.1992.5492.8892.2090.1789.8389.4989.1589.15 DңC33.0433.5733.2133.6633.3932.3232.8632.5032.0631.97 DңA35.1834.2432.4633.4032.1532.7832.5732.4632.2532.05 WңD89.1791.7291.0889.1792.3691.7291.0890.4588.5487.26 WңC32.9533.2132.5932.5033.3032.5033.2132.7732.0631.52 WңA33.0932.7833.5132.9934.1333.0932.4633.9234.3433.92 CңW41.6942.3740.6840.3439.3239.6639.6640.3440.0039.66 CңD47.7746.5047.1348.4145.8645.2247.1345.2245.2244.59 CңA45.5146.6645.8247.1847.6046.3545.6244.5744.4743.95 AңW46.4442.7139.6638.9839.3237.2936.9535.5936.2735.25 AңD42.6846.5036.3133.7632.4835.0335.6737.5838.2236.94 AңC41.1440.7840.6939.5439.3639.1839.2739.0839.0038.82 MңU73.2273.4475.4475.0674.9475.0675.1175.0075.1775.22 UңM59.8561.8562.1561.9561.7061.6561.9061.8061.2561.50㊀㊀正则化参数λ取值为λɪ{0.001,0.01, ,100}时,对BDAITL算法性能的影响如表5所示㊂可以看出,由于不同的迁移任务中源域与目标域的样本实例相差较大,导致不同的迁移学习任务在λ的不同取值下得到最优分类性能,其中部分任务如DңW㊁WңD㊁CңD㊁MңU㊁UңM分别是在0.1㊁10㊁0.1㊁1㊁1时取得最优性能㊂表5㊀λ的取值对BDAITL算法准确率的影响Table5㊀Influence ofλon the accuracy of the BDAITL algorithm数据集准确率/%λ=0.001λ=0.01λ=0.1λ=1λ=10λ=100DңW84.4187.8092.8892.5490.1790.51 DңC31.6131.9733.3033.2132.1531.52 DңA36.1233.9231.4232.4631.8431.94 WңD82.1785.3588.5491.0892.3689.81 WңC30.2830.9929.3932.5932.4131.88 WңA33.0932.8831.2133.5130.1729.54 CңW30.8534.5837.2940.6841.0240.34 CңD40.7643.9547.7747.1344.5942.04 CңA42.1744.7848.3345.8246.3546.03 AңW34.9236.2737.2939.6640.6840.68 AңD31.8536.3140.7646.5043.9543.31 AңC39.1840.8741.9440.7839.7239.54 MңU72.1771.9473.0675.4474.3367.44 UңM60.0059.8061.1562.1558.2552.754㊀总结本文提出基于平衡概率分布和实例的迁移学习算法,融合了特征选择和实例更新两种策略㊂它采用平衡因子来自适应地调节边缘和条件分布适应的重要性,使用加权条件分布来处理域间的类不平衡问题,然后融合实例更新策略,进一步提升算法的性能㊂在4个图像数据集上的大量实验证明了该方法优于其他几种方法㊂但参数优化方面仍有改进的空间,在下一步的研究中将着重探索多参数优化方法,以期进一步提高算0616㊀第3期黄㊀露,等:基于平衡概率分布和实例的迁移学习算法法的性能㊂未来将继续探索迁移学习中针对类不平衡问题的处理方法,在传递式迁移学习和多源域迁移学习方向进行深入研究㊂参考文献:[1]㊀PAN S J,YANG Q.A survey on transfer learning[J].IEEE transactions on knowledge and data engineering,2010,22(10):1345-1359.[2]㊀PAN S J,TSANG I W,KWOK J T,et al.Domain adaptation via transfer component analysis[J].IEEE transactions on neuralnetworks,2011,22(2):199-210.[3]㊀LONG M S,WANG J M,DING G G,et al.Transfer feature learning with joint distribution adaptation[C]ʊIEEE InternationalConference on Computer Vision.Sydney,2013:2200-2207.[4]㊀WANG J D,CHEN Y Q,HAO S J,et al.Balanced distribution adaptation for transfer learning[C]ʊIEEE International Confer-ence on Data Mining.New Orleans,2017:1129-1134.[5]㊀LONG M S,WANG J M,DING G G,et al.Transfer joint matching for unsupervised domain adaptation[C]ʊIEEE Conferenceon Computer Vision and Pattern Recognition.Columbus,2014:1410-1417.[6]㊀GONG B Q,SHI Y,SHA F,et al.Geodesic flow kernel for unsupervised domain adaptation[C]ʊIEEE Conference on Comput-er Vision and Pattern Recognition.Providence,2012:2066-2073.[7]㊀TAHMORESNEZHAD J,HASHEMI S.Visual domain adaptation via transfer feature learning[J].Knowledge and informationsystems,2017,50(2):585-605.[8]㊀ZHANG J,LI W Q,OGUNBONA P.Joint geometrical and statistical alignment for visual domain adaptation[C]ʊIEEE Confer-ence on Computer Vision and Pattern Recognition.Honolulu,2017:5150-5158.[9]㊀赵鹏,吴国琴,刘慧婷,等.基于特征联合概率分布和实例的迁移学习算法[J].模式识别与人工智能,2016,29(8):717-724.ZHAO P,WU G Q,LIU H T,et al.Feature joint probability distribution and instance based transfer learning algorithm[J].Pattern recognition and artificial intelligence,2016,29(8):717-724.[10]戴文渊.基于实例和特征的迁移学习算法研究[D].上海:上海交通大学,2009:8-23.DAI W Y.Instance-based and feature-based transfer learning[D].Shanghai:Shanghai Jiaotong University,2009:8-23.Balanced Distribution Adaptation and Instance Based TransferLearning AlgorithmHUANG Lu,ZENG Qingshan(College of Electrical Engineering,Zhengzhou University,Zhengzhou450001,China) Abstract:Aim to deal with the poor generalization ability caused by class imbalance of jointly matching the marginal probability and conditional probability distribution to reduce the domain difference,a bal-anced distribution adaptation and instance based transfer learning algorithm was proposed.The feature in-stances were mapped to the subspace with the kernel principal component analysis.In this subspace,the marginal and conditional probability distribution were jointly matched with dynamically adjusting the dif-ferent importance of each distribution by a balance factor and adaptively changing the weight of each class.Thus,the difference between the source domain and target domain was reduced.Meanwhile,the instance update strategy was merged and the generalization ability of the model obtained by transfer learn-ing was improved further.Experimental results on the digital and object recognition datasets demonstrated the validity and efficiency of the proposed algorithm.Key words:transfer learning;balance distribution;class imbalance;instance update;domain adapta-tion(责任编辑:王浩毅)。