毕业论文外文翻译-数据仓库
河北工程大学毕业论文（设计）英文参考文献原文复印件及译文
数据仓库
数据仓库为商务运作提供结构与工具，以便系统地组织、理解和使用数据进行决策。大量组织机构已经发现，在当今这个充满竞争、快速发展的世界，数据仓库是一个有价值的工具。在过去的几年中，许多公司已花费数百万美元，建立企业范围的数据仓库。许多人感到，随着工业竞争的加剧，数据仓库成了必备的最新营销武器——通过更多地了解客户需求而保住客户的途径。“那么”，你可能会充满神秘地问，“到底什么是数据仓库？”
数据仓库已被多种方式定义，使得很难严格地定义它。宽松地讲，数据仓库是一个数据库，它与组织机构的操作数据库分别维护。数据仓库系统允许将各种应用系统集成在一起，为统一的历史数据分析提供坚实的平台，对信息处理提供支持。
按照W. H. Inmon，一位数据仓库系统构造方面的领头建筑师的说法，“数据仓库是一个面向主题的、集成的、时变的、非易失的数据集合，支持管理决策制定”。这个简短、全面的定义指出了数据仓库的主要特征。四个关键词，面向主题的、集成的、时变的、非易失的，将数据仓库与其它数据存储系统（如，关系数据库系统、事务处理系统、和文件系统）相区别。让我们进一步看看这些关键特征。
(1) 面向主题的：数据仓库围绕一些主题，如顾客、供应商、产品和销售组织。数据仓库关注决策者的数据建模与分析，而不是构造组织机构的日常操作和事务处理。因此，数据仓库排除对于决策无用的数据，提供特定主题的简明视图。
(2) 集成的：通常，构造数据仓库是将多个异种数据源，如关系数据库、一般文件和联机事务处理记录，集成在一起。使用数据清理和数据集成技术，确保命名约定、编码结构、属性度量的一致性等。
(3) 时变的：数据存储从历史的角度（例如，过去5-10 年）提供信息。数据仓库中的关键结构，隐式或显式地包含时间元素。
(4) 非易失的：数据仓库总是物理地分离存放数据；这些数据源于操作环境下的应用数据。由于这种分离，数据仓库不需要事务处理、恢复和并行控制机制。通常，它只需要两种数据访问：数据的初始化装入和数据访问。
概言之，数据仓库是一种语义上一致的数据存储，它充当决策支持数据模型的物理实现，并存放企业决策所需信息。数据仓库也常常被看作一种体系结构，通过将异种数据源中的数据集成在一起而构造，支持结构化和启发式查询、分析
报告和决策制定。
“好”，你现在问，“那么，什么是建立数据仓库？”
根据上面的讨论，我们把建立数据仓库看作构造和使用数据仓库的过程。数据仓库的构造需要数据集成、数据清理、和数据统一。利用数据仓库常常需要一些决策支持技术。这使得“知识工人”（例如，经理、分析人员和主管）能够使用数据仓库，快捷、方便地得到数据的总体视图，根据数据仓库中的信息做出准确的决策。有些作者使用术语“建立数据仓库”表示构造数据仓库的过程，而用术语“仓库DBMS”表示管理和使用数据仓库。我们将不区分二者。
“组织机构如何使用数据仓库中的信息？”许多组织机构正在使用这些信息支持商务决策活动，包括:
(1)、增加顾客关注，包括分析顾客购买模式（如，喜爱买什么、购买时间、预算周期、消费习惯）；
(2)、根据季度、年、地区的营销情况比较，重新配置产品和管理投资，调整生产策略；
(3)、分析运作和查找利润源；
(4)、管理顾客关系、进行环境调整、管理合股人的资产开销。
从异种数据库集成的角度看，数据仓库也是十分有用的。许多组织收集了形形色色数据，并由多个异种的、自治的、分布的数据源维护大型数据库。集成这些数据，并提供简便、有效的访问是非常希望的，并且也是一种挑战。数据库工业界和研究界都正朝着实现这一目标竭尽全力。
对于异种数据库的集成，传统的数据库做法是：在多个异种数据库上，建立一个包装程序和一个集成程序（或仲裁程序）。这方面的例子包括IBM 的数据连接程序和Informix的数据刀。当一个查询提交客户站点，首先使用元数据字典对查询进行转换，将它转换成相应异种站点上的查询。然后，将这些查询映射和发送到局部查询处理器。由不同站点返回的结果被集成为全局回答。这种查询驱动的方法需要复杂的信息过滤和集成处理，并且与局部数据源上的处理竞争资源。这种方法是低效的，并且对于频繁的查询，特别是需要聚集操作的查询，开销很大。
对于异种数据库集成的传统方法，数据仓库提供了一个有趣的替代方案。数据仓库使用更新驱动的方法，而不是查询驱动的方法。这种方法将来自多个异种源的信息预先集成，并存储在数据仓库中，供直接查询和分析。与联机事务处理数据库不同，数据仓库不包含最近的信息。然而，数据仓库为集成的异种数据库系统带来了高性能，因为数据被拷贝、预处理、集成、注释、汇总，并重新组织到一个语义一致的数据存储中。在数据仓库中进行的查询处理并不影响在局部源
上进行的处理。此外，数据仓库存储并集成历史信息，支持复杂的多维查询。这样，建立数据仓库在工业界已非常流行。
1.操作数据库系统与数据仓库的区别由于大多数人都熟悉商品
关系数据库系统，将数据仓库与之比较，就容易理解什么是数据仓库。
联机操作数据库系统的主要任务是执行联机事务和查询处理。这种系统称为联机事务处理（OLTP）系统。它们涵盖了一个组织的大部分日常操作，如购买、库存、制造、银行、工资、注册、记帐等。另一方面，数据仓库系统在数据分析和决策方面为用户或“知识工人”提供服务。这种系统可以用不同的格式组织和提供数据，以便满足不同用户的形形色色需求。这种系统称为联机分析处理（OLAP）系统。
OLTP 和OLAP 的主要区别概述如下。
(1) 用户和系统的面向性：OLTP 是面向顾客的，用于办事员、客户、和信息技术专业人员的事务和查询处理。OLAP 是面向市场的，用于知识工人（包括经理、主管、和分析人员）的数据分析。
(2) 数据内容：OLTP 系统管理当前数据。通常，这种数据太琐碎，难以方便地用于决策。OLAP 系统管理大量历史数据，提供汇总和聚集机制，并在不同的粒度级别上存储和管理信息。这些特点使得数据容易用于见多识广的决策。
(3) 数据库设计：通常，OLTP 系统采用实体-联系（ER）模型和面向应用的数据库设计。而OLAP 系统通常采用星形或雪花模型和面向主题的数据库设计。
(4) 视图：OLTP 系统主要关注一个企业或部门内部的当前数据，而不涉及历史数据或不同组织的数据。相比之下，由于组织的变化，OLAP 系统常常跨越数据库模式的多个版本。OLAP 系统也处理来自不同组织的信息，由多个数据存储集成的信息。由于数据量巨大，OLAP 数据也存放在多个存储介质上。
(5)、访问模式：OLTP 系统的访问主要由短的、原子事务组成。这种系统需要并行控制和恢复机制。然而，对OLAP系统的访问大部分是只读操作（由于大部分数据仓库存放历史数据，而不是当前数据），尽管许多可能是复杂的查询。OLTP 和OLAP 的其它区别包括数据库大小、操作的频繁程度、性能度量等。
2.但是，为什么需要一个分离的数据仓库“既然操作数据库存放了大量数据”，你注意到，“为什么不直接在这种数据库上进行联机分析处理，而是另外花费时间和资源去构造一个分离的数据仓库？”分离的主要原因是提高两个系统的性能。操作数据库是为已知的任务和负载设计的，如使用主关键字索引和散列，检索特定的记录，和优化“罐装的”查询。另一方面，数据仓库的查询通常是复杂的，涉及大量数据在汇总级的计算，可能需要特殊的数据组织、存取方法和基于
多维视图的实现方法。在操作数据库上处理OLAP 查询，可能会大大降低操作任务的性能。
此外，操作数据库支持多事务的并行处理，需要加锁和日志等并行控制和恢复机制，以确保一致性和事务的强健性。通常，OLAP 查询只需要对数据记录进行只读访问，以进行汇总和聚集。如果将并行控制和恢复机制用于这OLAP 操作，就会危害并行事务的运行，从而大大降低OLTP 系统的吞吐量。
最后，数据仓库与操作数据库分离是由于这两种系统中数据的结构、内容和用法都不相同。决策支持需要历史数据，而操作数据库一般不维护历史数据。在这种情况下，操作数据库中的数据尽管很丰富，但对于决策，常常还是远远不够的。决策支持需要将来自异种源的数据统一（如，聚集和汇总），产生高质量的、纯净的和集成的数据。相比之下，操作数据库只维护详细的原始数据（如事务），这些数据在进行分析之前需要统一。由于两个系统提供很不相同的功能，需要不同类型的数据，因此需要维护分离的数据库。
Data warehousing provides architectures and tools for business executives to sy stematically organize, understand, and use their data to make strategic decisions. A lar ge number of organizations have found that data warehouse systems are valuable tools in today's competitive, fast evolving world. In the last several years, many firms have spent millions of dollars in building enterprise-wide data warehouses. Many people f eel that with competition mounting in every industry, data warehousing is the latest m ust-have marketing weapon —— a way to keep customers by learning more about the ir needs.
“So", you may ask, full of intrigue, “what exactly is a data warehouse?"
Data warehouses have been defined in many ways, making it difficult to formulat e a rigorous definition. Loosely speaking, a data warehouse refers to a database that is maintained separately from an organization's operational databases. Data warehouse s ystems allow for the integration of a variety of application systems. They support info rmation processing by providing a solid platform of consolidated, historical data for a nalysis.
According to W. H. Inmon, a leading architect in the construction of data wareho use systems, “a data warehouse is a subject-oriented, integrated, time-variant, and non volatile collection of data in support of management's decision making process." This short, but comprehensive definition presents the major features of a data warehouse. T he four keywords, subject-oriented, integrated, time-variant, and nonvolatile, distingui sh data warehouses from other data repository systems, such as relational database sys
tems, transaction processing systems, and file systems. Let's take a closer look at each of these key features.
(1).Subject-oriented: A data warehouse is organized around major subjects, such as customer, vendor, product, and sales. Rather than concentrating on the day-to-day o perations and transaction processing of an organization, a data warehouse focuses on t he modeling and analysis of data for decision makers. Hence, data warehouses typical ly provide a simple and concise view around particular subject issues by excluding dat
a that are not useful in the decision support process.
(2) Integrated: A data warehouse is usually constructed by integrating multiple he terogeneous sources, such as relational databases, flat files, and on-line transaction rec ords. Data cleaning and data integration techniques are applied to ensure consistency i n naming conventions, encoding structures, attribute measures, and so on.
(3).Time-variant: Data are stored to provide information from a historical pers pective (e.g., the past 5-10 years). Every key structure in the data warehouse contains, either implicitly or explicitly, an element of time.
(4)Nonvolatile: A data warehouse is always a physically separate store of data tra nsformed from the application data found in the operational environment. Due to this separation, a data warehouse does not require transaction processing, recovery, and co ncurrency control mechanisms. It usually requires only two operations in data accessi ng: initial loading of data and access of data.
In sum, a data warehouse is a semantically consistent data store that serves as a p hysical implementation of a decision support data model and stores the information on which an enterprise needs to make strategic decisions. A data warehouse is also often viewed as an architecture, constructed by integrating data from multiple heterogeneou s sources to support structured and/or ad hoc queries, analytical reporting, and decisio n making.
“OK", you now ask, “what, then, is data warehousing?"
Based on the above, we view data warehousing as the process of constructing and using data warehouses. The construction of a data warehouse requires data integratio n, data cleaning, and data consolidation. The utilization of a data warehouse often nec essitates a collection of decision support technologies. This allows “knowledge worke rs" (e.g., managers, analysts, and executives) to use the warehouse to quickly and con veniently obtain an overview of the data, and to make sound decisions based on infor mation in the warehouse. Some authors use the term “data warehousing" to refer only
to the process of data warehouse construction, while the term warehouse DBMS is use d to refer to the management and utilization of data warehouses. We will not make thi s distinction here.
“How are organizations using the information from data warehouses?" Many org anizations are using this information to support business decision making activities, in cluding:
(1) increasing customer focus, which includes the analysis of customer buying pa tterns (such as buying preference, buying time, budget cycles, and appetites for spendi ng),
(2) repositioning products and managing product portfolios by comparing the per formance of sales by quarter, by year, and by geographic regions, in order to fine-tune production strategies,
(3) analyzing operations and looking for sources of profit,
(4) managing the customer relationships, making environmental corrections, and managing the cost of corporate assets.
Data warehousing is also very useful from the point of view of heterogeneous dat abase integration. Many organizations typically collect diverse kinds of data and main tain large databases from multiple, heterogeneous, autonomous, and distributed infor mation sources. To integrate such data, and provide easy and efficient access to it is hi ghly desirable, yet challenging.
Much effort has been spent in the database industry and research community tow ards achieving this goal.
The traditional database approach to heterogeneous database integration is to buil d wrappers and integrators (or mediators) on top of multiple, heterogeneous databases . A variety of data joiner and data blade products belong to this category. When a quer y is posed to a client site, a metadata dictionary is used to translate the query into quer ies appropriate for the individual heterogeneous sites involved. These queries are then mapped and sent to local query processors. The results returned from the different sit es are integrated into a global answer set. This query-driven approach requires comple x information filtering and integration processes, and competes for resources with pro cessing at local sources. It is inefficient and potentially expensive for frequent queries, especially for queries requiring aggregations.
Data warehousing provides an interesting alternative to the traditional approach o f heterogeneous database integration described above. Rather than using a query-drive
n approach, data warehousing employs an update-driven approach in which informati on from multiple, heterogeneous sources is integrated in advance and stored in a ware house for direct querying and analysis. Unlike on-line transaction processing database s, data warehouses do not contain the most current information. However, a data ware house brings high performance to the integrated heterogeneous database system since data are copied, preprocessed, integrated, annotated, summarized, and restructured int o one semantic data store. Furthermore, query processing in data warehouses does not interfere with the processing at local sources. Moreover, data warehouses can store an d integrate historical information and support complex multidimensional queries. As a result, data warehousing has become very popular in industry.
1. Differences between operational database systems and data warehouses
Since most people are familiar with commercial relational database systems, it is easy to understand what a data warehouse is by comparing these two kinds of systems .
The major task of on-line operational database systems is to perform on-line trans action and query processing. These systems are called on-line transaction processing ( OLTP) systems. They cover most of the day-to-day operations of an organization, suc h as, purchasing, inventory, manufacturing, banking, payroll, registration, and account ing. Data warehouse systems, on the other hand, serve users or “knowledge workers" i n the role of data analysis and decision making. Such systems can organize and presen t data in various formats in order to accommodate the diverse needs of the different us ers. These systems are known as on-line analytical processing (OLAP) systems.
The major distinguishing features between OLTP and OLAP are summarized as f ollows.
(1). Users and system orientation: An OLTP system is customer-oriented and is u sed for transaction and query processing by clerks, clients, and information technolog y professionals. An OLAP system is market-oriented and is used for data analysis by k nowledge workers, including managers, executives, and analysts.
(2). Data contents: An OLTP system manages current data that, typically, are too detailed to be easily used for decision making. An OLAP system manages large amou nts of historical data, provides facilities for summarization and aggregation, and stores and manages information at different levels of granularity. These features make the d ata easier for use in informed decision making.
(3). Database design: An OLTP system usually adopts an entity-relationship (ER)
data model and an application -oriented database design. An OLAP system typically adopts either a star or snowflake model, and a subject-oriented database design.
(4). View: An OLTP system focuses mainly on the current data within an enterpri se or department, without referring to historical data or data in different organizations. In contrast, an OLAP system often spans multiple versions of a database schema, due to the evolutionary process of an organization. OLAP systems also deal with informat ion that originates from different organizations, integrating information from many da ta stores. Because of their huge volume, OLAP data are stored on multiple storage me dia.
(5). Access patterns: The access patterns of an OLTP system consist mainly of sh ort, atomic transactions. Such a system requires concurrency control and recovery me chanisms. However, accesses to OLAP systems are mostly read-only operations (since most data warehouses store historical rather than up-to-date information), although m any could be complex queries.
Other features which distinguish between OLTP and OLAP systems include data base size, frequency of operations, and performance metrics and so on. 2. But, why ha ve a separate data warehouse?
“Since operational databases store huge amounts of data", you observe, “why not perform on-line analytical processing directly on such databases instead of spending additional time and resources to construct a separate data warehouse?"
A major reason for such a separation is to help promote the high performance of both systems. An operational database is designed and tuned from known tasks and w orkloads, such as indexing and hashing using primary keys, searching for particular re cords, and optimizing “canned" queries. On the other hand, data warehouse queries ar e often complex. They involve the computation of large groups of data at summarized levels, and may require the use of special data organization, access, and implementati on methods based on multidimensional views. Processing OLAP queries in operationa l databases would substantially degrade the performance of operational tasks.
Moreover, an operational database supports the concurrent processing of several t ransactions. Concurrency control and recovery mechanisms, such as locking and loggi ng, are required to ensure the consistency and robustness of transactions. An OLAP qu ery often needs read-only access of data records for summarization and aggregation. Concurrency control and recovery mechanisms, if applied for such OLAP operations, may jeopardize the execution of concurrent transactions and thus substantially reduce
the throughput of an OLTP system.
Finally, the separation of operational databases from data warehouses is based on the different structures, contents, and uses of the data in these two systems. Decision support requires historical data, whereas operational databases do not typically mainta in historical data. In this context, the data in operational databases, though abundant, i s usually far from complete for decision making. Decision support requires consolidat ion (such as aggregation and summarization) of data from heterogeneous sources, resu lting in high quality, cleansed and integrated data. In contrast, operational databases c ontain only detailed raw data, such as transactions, which need to be consolidated bef ore analysis. Since the two systems provide quite different functionalities and require different kinds of data, it is necessary to maintain separate databases.
五分钟搞定5000字毕业论文外文翻译，你想要的工具都在这里!
在科研过程中阅读翻译外文文献是一个非常重要的环节，许多领域高水平的文献都是外文文献，借鉴一些外文文献翻译的经验是非常必要的。由于特殊原因我翻译外文文献的机会比较多，慢慢地就发现了外文文献翻译过程中的三大利器：Google“翻译”频道、金山词霸（完整版本）和CNKI“翻译助手"。
具体操作过程如下：
1.先打开金山词霸自动取词功能，然后阅读文献；
2.遇到无法理解的长句时，可以交给Google处理，处理后的结果猛一看，不堪入目，可是经过大脑的再处理后句子的意思基本就明了了；
3.如果通过Google仍然无法理解，感觉就是不同，那肯定是对其中某个“常用单词”理解有误，因为某些单词看似很简单，但是在文献中有特殊的意思，这时就可以通过CNKI的“翻译助手”来查询相关单词的意思，由于CNKI的单词意思都是来源与大量的文献，所以它的吻合率很高。
另外，在翻译过程中最好以“段落”或者“长句”作为翻译的基本单位，这样才不会造成“只见树木，不见森林”的误导。
四大工具：
1、Google翻译：https://www.360docs.net/doc/2511911184.html,/language_tools
google，众所周知，谷歌里面的英文文献和资料还算是比较详实的。我利用它是这样的。一方面可以用它查询英文论文，当然这方面的帖子很多，大家可以搜索，在此不赘述。回到我自己说的翻译上来。下面给大家举个例子来说明如何用吧
比如说“电磁感应透明效应”这个词汇你不知道他怎么翻译，
首先你可以在CNKI里查中文的，根据它们的关键词中英文对照来做，一般比较准确。
在此主要是说在google里怎么知道这个翻译意思。大家应该都有词典吧，按中国人的办法，把一个一个词分着查出来，敲到google 里，你的这种翻译一般不太准，当然你需要验证是否准确了，这下看着吧，把你的那支离破碎的翻译在google里搜索，你能看到许多相关的文献或资料，大家都不是笨蛋，看看，也就能找到最精确的翻译了，纯西式的！我就是这么用的。
2、CNKI翻译：https://www.360docs.net/doc/2511911184.html,
CNKI翻译助手，这个网站不需要介绍太多，可能有些人也知道的。主要说说它的有点，你进去看看就能发现：搜索的肯定是专业词汇，而且它翻译结果下面有文章与之对应（因为它是CNKI检索提供的，它的翻译是从文献里抽出来的），很实用的一个网站。估计别的写文章的人不是傻子吧，它们的东西我们可以直接拿来用，当然省事了。网址告诉大家，有兴趣的进去看看，你们就会发现其乐无穷！还是很值得用的。https://www.360docs.net/doc/2511911184.html,
3、网路版金山词霸（不到1M）：
https://www.360docs.net/doc/2511911184.html,/6946901637944806
4、有道在线翻译：https://www.360docs.net/doc/2511911184.html,/?keyfrom=fanyi.logo
毕业论文外文翻译模版
吉林化工学院理学院 毕业论文外文翻译English Title（Times New Roman ，三号） 学生学号：08810219 学生姓名：袁庚文 专业班级：信息与计算科学0802 指导教师：赵瑛 职称副教授 起止日期：2012.2.27～2012.3.14 吉林化工学院 Jilin Institute of Chemical Technology
1 外文翻译的基本内容 应选择与本课题密切相关的外文文献（学术期刊网上的），译成中文，与原文装订在一起并独立成册。在毕业答辩前，同论文一起上交。译文字数不应少于3000个汉字。 2 书写规范 2.1 外文翻译的正文格式 正文版心设置为：上边距：3.5厘米，下边距：2.5厘米，左边距：3.5厘米，右边距：2厘米，页眉：2.5厘米，页脚：2厘米。 中文部分正文选用模板中的样式所定义的“正文”，每段落首行缩进2字；或者手动设置成每段落首行缩进2字，字体：宋体，字号：小四，行距：多倍行距1.3，间距：前段、后段均为0行。 这部分工作模板中已经自动设置为缺省值。 2.2标题格式 特别注意：各级标题的具体形式可参照外文原文确定。 1．第一级标题（如：第1章绪论）选用模板中的样式所定义的“标题1”，居左；或者手动设置成字体：黑体，居左，字号：三号，1.5倍行距，段后11磅，段前为11磅。 2．第二级标题（如：1.2 摘要与关键词）选用模板中的样式所定义的“标题2”，居左；或者手动设置成字体：黑体，居左，字号：四号，1.5倍行距，段后为0，段前0.5行。 3．第三级标题（如：1.2.1 摘要）选用模板中的样式所定义的“标题3”，居左；或者手动设置成字体：黑体，居左，字号：小四，1.5倍行距，段后为0，段前0.5行。 标题和后面文字之间空一格（半角）。 3 图表及公式等的格式说明 图表、公式、参考文献等的格式详见《吉林化工学院本科学生毕业设计说明书（论文）撰写规范及标准模版》中相关的说明。
毕业论文英文参考文献与译文
Inventory management Inventory Control On the so-called "inventory control", many people will interpret it as a "storage management", which is actually a big distortion. The traditional narrow view, mainly for warehouse inventory control of materials for inventory, data processing, storage, distribution, etc., through the implementation of anti-corrosion, temperature and humidity control means, to make the custody of the physical inventory to maintain optimum purposes. This is just a form of inventory control, or can be defined as the physical inventory control. How, then, from a broad perspective to understand inventory control? Inventory control should be related to the company's financial and operational objectives, in particular operating cash flow by optimizing the entire demand and supply chain management processes (DSCM), a reasonable set of ERP control strategy, and supported by appropriate information processing tools, tools to achieved in ensuring the timely delivery of the premise, as far as possible to reduce inventory levels, reducing inventory and obsolescence, the risk of devaluation. In this sense, the physical inventory control to achieve financial goals is just a means to control the entire inventory or just a necessary part; from the perspective of organizational functions, physical inventory control, warehouse management is mainly the responsibility of The broad inventory control is the demand and supply chain management, and the whole company's responsibility. Why until now many people's understanding of inventory control, limited physical inventory control? The following two reasons can not be ignored: First, our enterprises do not attach importance to inventory control. Especially those who benefit relatively good business, as long as there is money on the few people to consider the problem of inventory turnover. Inventory control is simply interpreted as warehouse management, unless the time to spend money, it may have been to see the inventory problem, and see the results are often very simple procurement to buy more, or did not do warehouse departments . Second, ERP misleading. Invoicing software is simple audacity to call it ERP, companies on their so-called ERP can reduce the number of inventory, inventory control, seems to rely on their small software can get. Even as SAP, BAAN ERP world, the field of
概率论毕业论文外文翻译
Statistical hypothesis testing Adriana Albu，Loredana Ungureanu Politehnica University Timisoara,adrianaa@aut.utt.ro Politehnica University Timisoara,loredanau@aut.utt.ro Abstract In this article,we present a Bayesian statistical hypothesis testing inspection, testing theory and the process Mentioned hypothesis testing in the real world and the importance of, and successful test of the Notes. Key words Bayesian hypothesis testing; Bayesian inference;Test of significance Introduction A statistical hypothesis test is a method of making decisions using data, whether from a controlled experiment or an observational study (not controlled). In statistics, a result is called statistically significant if it is unlikely to have occurred by chance alone, according to a pre-determined threshold probability, the significance level. The phrase "test of significance" was coined by Ronald Fisher: "Critical tests of this kind may be called tests of significance, and when such tests are available we may discover whether a second sample is or is not significantly different from the first."[1] Hypothesis testing is sometimes called confirmatory data analysis, in contrast to exploratory data analysis. In frequency probability,these decisions are almost always made using null-hypothesis tests. These are tests that answer the question Assuming that the null hypothesis is true, what is the probability of observing a value for the test statistic that is at [] least as extreme as the value that was actually observed?) 2 More formally, they represent answers to the question, posed before undertaking an experiment,of what outcomes of the experiment would lead to rejection of the null hypothesis for a pre-specified probability of an incorrect rejection. One use of hypothesis testing is deciding whether experimental results contain enough information to cast doubt on conventional wisdom. Statistical hypothesis testing is a key technique of frequentist statistical inference. The Bayesian approach to hypothesis testing is to base rejection of the hypothesis on the posterior probability.[3][4]Other approaches to reaching a decision based on data are available via decision theory and optimal decisions. The critical region of a hypothesis test is the set of all outcomes which cause the null hypothesis to be rejected in favor of the alternative hypothesis. The critical region is usually denoted by the letter C. One-sample tests are appropriate when a sample is being compared to the population from a hypothesis. The population characteristics are known from theory or are calculated from the population.
毕业论文 外文翻译#(精选.)
毕业论文（设计）外文翻译 题目：中国上市公司偏好股权融资：非制度性因素 系部名称：经济管理系专业班级:会计082班 学生姓名：任民学号： 200880444228 指导教师：冯银波教师职称:讲师 年月日
译文： 中国上市公司偏好股权融资：非制度性因素 国际商业管理杂志 2009.10 摘要：本文把重点集中于中国上市公司的融资活动，运用西方融资理论，从非制度性因素方面，如融资成本、企业资产类型和质量、盈利能力、行业因素、股权结构因素、财务管理水平和社会文化，分析了中国上市公司倾向于股权融资的原因，并得出结论，股权融资偏好是上市公司根据中国融资环境的一种合理的选择。最后，针对公司的股权融资偏好提出了一些简明的建议。 关键词：股权融资，非制度性因素，融资成本 一、前言 中国上市公司偏好于股权融资，根据中国证券报的数据显示，1997年上市公司在资本市场的融资金额为95.87亿美元，其中股票融资的比例是72.5％，，在1998年和1999年比例分别为72.6％和72.3％，另一方面，债券融资的比例分别是17.8％，24.9％和25.1％。在这三年，股票融资的比例，在比中国发达的资本市场中却在下跌。以美国为例，当美国企业需要的资金在资本市场上，于股权融资相比他们宁愿选择债券融资。统计数据显示，从1970年到1985年，美日企业债券融资占了境外融资的91.7％，比股权融资高很多。阎达五等发现，大约中国3/4的上市公司偏好于股权融资。许多研究的学者认为，上市公司按以下顺序进行外部融资：第一个是股票基金，第二个是可转换债券，三是短期债务，最后一个是长期负债。许多研究人员通常分析我国上市公司偏好股权是由于我们国家的经济改革所带来的制度性因素。他们认为，上市公司的融资活动违背了西方古典融资理论只是因为那些制度性原因。例如，优序融资理论认为，当企业需要资金时，他们首先应该转向内部资金（折旧和留存收益），然后再进行债权融资，最后的选择是股票融资。在这篇文章中，笔者认为，这是因为具体的金融环境激活了企业的这种偏好，并结合了非制度性因素和西方金融理论，尝试解释股权融资偏好的原因。
毕业论文外文翻译模板
农村社会养老保险的现状、问题与对策研究社会保障对国家安定和经济发展具有重要作用，“城乡二元经济”现象日益凸现，农村社会保障问题客观上成为社会保障体系中极为重要的部分。建立和完善农村社会保障制度关系到农村乃至整个社会的经济发展，并且对我国和谐社会的构建至关重要。我国农村社会保障制度尚不完善，因此有必要加强对农村独立社会保障制度的构建，尤其对农村养老制度的改革，建立健全我国社会保障体系。从户籍制度上看，我国居民养老问题可分为城市居民养老和农村居民养老两部分。对于城市居民我国政府已有比较充足的政策与资金投人，使他们在物质和精神方面都能得到较好地照顾，基本实现了社会化养老。而农村居民的养老问题却日益突出，成为摆在我国政府面前的一个紧迫而又棘手的问题。 一、我国农村社会养老保险的现状 关于农村养老，许多地区还没有建立农村社会养老体系，已建立的地区也存在很多缺陷，运行中出现了很多问题，所以完善农村社会养老保险体系的必要性与紧迫性日益体现出来。 （一）人口老龄化加快 随着城市化步伐的加快和农村劳动力的输出，越来越多的农村青壮年人口进入城市，年龄结构出现“两头大，中间小”的局面。中国农村进入老龄社会的步伐日渐加快。第五次人口普查显示：中国65岁以上的人中农村为5938万，占老龄总人口的67.4%.在这种严峻的现实面前，农村社会养老保险的徘徊显得极其不协调。 （二）农村社会养老保险覆盖面太小 中国拥有世界上数量最多的老年人口，且大多在农村。据统计，未纳入社会保障的农村人口还很多，截止2000年底，全国7400多万农村居民参加了保险，占全部农村居民的11.18%，占成年农村居民的11.59%.另外，据国家统计局统计，我国进城务工者已从改革开放之初的不到200万人增加到2003年的1.14亿人。而基本方案中没有体现出对留在农村的农民和进城务工的农民给予区别对待。进城务工的农民既没被纳入到农村养老保险体系中，也没被纳入到城市养老保险体系中，处于法律保护的空白地带。所以很有必要考虑这个特殊群体的养老保险问题。
大学毕业论文---软件专业外文文献中英文翻译
软件专业毕业论文外文文献中英文翻译 Object landscapes and lifetimes Tech nically, OOP is just about abstract data typing, in herita nee, and polymorphism, but other issues can be at least as importa nt. The rema in der of this sect ion will cover these issues. One of the most importa nt factors is the way objects are created and destroyed. Where is the data for an object and how is the lifetime of the object con trolled? There are differe nt philosophies at work here. C++ takes the approach that con trol of efficie ncy is the most importa nt issue, so it gives the programmer a choice. For maximum run-time speed, the storage and lifetime can be determined while the program is being written, by placing the objects on the stack (these are sometimes called automatic or scoped variables) or in the static storage area. This places a priority on the speed of storage allocatio n and release, and con trol of these can be very valuable in some situati ons. However, you sacrifice flexibility because you must know the exact qua ntity, lifetime, and type of objects while you're writing the program. If you are trying to solve a more general problem such as computer-aided desig n, warehouse man ageme nt, or air-traffic con trol, this is too restrictive. The sec ond approach is to create objects dyn amically in a pool of memory called the heap. In this approach, you don't know un til run-time how many objects you n eed, what their lifetime is, or what their exact type is. Those are determined at the spur of the moment while the program is runnin g. If you n eed a new object, you simply make it on the heap at the point that you n eed it. Because the storage is man aged dyn amically, at run-time, the amount of time required to allocate storage on the heap is sig ni fica ntly Ion ger tha n the time to create storage on the stack. (Creat ing storage on the stack is ofte n a si ngle assembly in structio n to move the stack poin ter dow n, and ano ther to move it back up.) The dyn amic approach makes the gen erally logical assumpti on that objects tend to be complicated, so the extra overhead of finding storage and releas ing that storage will not have an importa nt impact on the creati on of an object .In additi on, the greater flexibility is esse ntial to solve the gen eral program ming problem. Java uses the sec ond approach, exclusive". Every time you want to create an object, you use the new keyword to build a dyn amic in sta nee of that object. There's ano ther issue, however, and that's the lifetime of an object. With Ian guages that allow objects to be created on the stack, the compiler determines how long the object lasts and can automatically destroy it. However, if you create it on the heap the compiler has no kno wledge of its lifetime. In a Ianguage like C++, you must determine programmatically when to destroy the
电子信息工程专业毕业论文外文翻译中英文对照翻译
本科毕业设计（论文）中英文对照翻译 院（系部）电气工程与自动化 专业名称电子信息工程 年级班级 04级7班 学生姓名 指导老师
Infrared Remote Control System Abstract Red outside data correspondence the technique be currently within the scope of world drive extensive usage of a kind of wireless conjunction technique,drive numerous hardware and software platform support. Red outside the transceiver product have cost low, small scaled turn, the baud rate be quick, point to point SSL, be free from electromagnetism thousand Raos etc.characteristics, can realization information at dissimilarity of the product fast, convenience, safely exchange and transmission, at short distance wireless deliver aspect to own very obvious of advantage.Along with red outside the data deliver a technique more and more mature, the cost descend, red outside the transceiver necessarily will get at the short distance communication realm more extensive of application. The purpose that design this system is transmit cu stomer’s operation information with infrared rays for transmit media, then demodulate original signal with receive circuit. It use coding chip to modulate signal and use decoding chip to demodulate signal. The coding chip is PT2262 and decoding chip is PT2272. Both chips are made in Taiwan. Main work principle is that we provide to input the information for the PT2262 with coding keyboard. The input information was coded by PT2262 and loading to high frequent load wave whose frequent is 38 kHz, then modulate infrared transmit dioxide and radiate space outside when it attian enough power. The receive circuit receive the signal and demodulate original information. The original signal was decoded by PT2272, so as to drive some circuit to accomplish
毕业论文外文资料翻译
毕业论文外文资料翻译题目(宋体三号，居中) 学院(全称，宋体三号，居中) 专业(全称，宋体三号，居中) 班级(宋体三号，居中) 学生(宋体三号，居中) 学号(宋体三号，居中) 指导教师(宋体三号，居中) 二〇一〇年月日(宋体三号，居中，时间与开题时间一致)
（英文原文装订在前）
Journal of American Chemical Society, 2006, 128(7): 2421-2425. (文献翻译必须在中文译文第一页标明文献出处：即文章是何期刊上发表的，X年X 卷X期，格式如上例所示，四号，右对齐，杂志名加粗。) [点击输入译文题目-标题1，黑体小二] [点击输入作者，宋体小四] [点击输入作者单位，宋体五号] 摘要[点击输入，宋体五号] 关键词[点击输入，宋体五号] 1[点击输入一级标题-标题2，黑体四号] [点击输入正文，宋体小四号，1.25倍行距] 1.1[点击输入二级标题-标题3，黑体小四] [点击输入正文，宋体小四，1.25倍行距] 1.1.1[点击输入三级标题-标题4，黑体小四] [点击输入正文，宋体小四，1.25倍行距] 说明： 1.外文文章必须是正规期刊发表的。 2.翻译后的中文文章必须达到2000字以上，并且是一篇完整文章。 3.必须要有外文翻译的封面，使用学校统一的封面； 封面上的翻译题目要写翻译过来的中文题目； 封面上时间与开题时间一致。 4.外文原文在前，中文翻译在后； 5.中文翻译中要包含题目、摘要、关键词、前言、全文以及参考文献，翻译要条理
清晰，中文翻译要与英文一一对应。 6.翻译中的中文文章字体为小四，所有字母、数字均为英文格式下的，中文为宋体， 标准字符间距。 7.原文中的图片和表格可以直接剪切、粘贴，但是表头与图示必须翻译成中文。 8.图表必须居中，文章段落应两端对齐、首行缩进2个汉字字符、1.25倍行距。 例如： 图1. 蛋白质样品的PCA图谱与8-卟啉识别排列分析(a)或16-卟啉识别排列分析(b)。为了得到b 的 数据矩阵，样品用16-卟啉识别排列分析来检测，而a 是通过捕获首八卟啉接收器数据矩阵从 b 中 萃取的。
本科毕业设计外文翻译(原文)
Real-time interactive optical micromanipulation of a mixture of high- and low-index particles Peter John Rodrigo, Vincent Ricardo Daria and Jesper Glückstad Optics and Plasma Research Department, Ris? National Laboratory, DK-4000 Roskilde, Denmark jesper.gluckstad@risoe.dk http://www.risoe.dk/ofd/competence/ppo.htm Abstract: We demonstrate real-time interactive optical micromanipulation of a colloidal mixture consisting of particles with both lower (n L < n0) and higher (n H > n0) refractive indices than that of the suspending medium (n0). Spherical high- and low-index particles are trapped in the transverse plane by an array of confining optical potentials created by trapping beams with top-hat and annular cross-sectional intensity profiles, respectively. The applied method offers extensive reconfigurability in the spatial distribution and individual geometry of the optical traps. We experimentally demonstrate this unique feature by simultaneously trapping and independently manipulating various sizes of spherical soda lime micro- shells (n L≈ 1.2) and polystyrene micro-beads (n H = 1.57) suspended in water (n0 = 1.33). ?2004 Optical Society of America OCIS codes: (140.7010) Trapping, (170.4520) Optical confinement and manipulation and (230.6120) Spatial Light Modulators. References and links 1. A. Ashkin, “Optical trapping and manipulation of neutral particles using lasers,” Proc. Natl. Acad. Sci. USA 94, 4853-4860 (1997). 2. K. Svoboda and S. M. Block, “Biological applications of optical forces,” Annu. Rev. Biophys. Biomol. Struct. 23, 247-285 (1994). 3. D. G. Grier, “A revolution in optical manipulation,” Nature 424, 810-816 (2003). 4. M. P. MacDonald, G. C. Spalding and K. Dholakia, “Microfluidic sorting in an optical lattice,” Nature 426, 421-424 (2003). 5. J. Glückstad, “Microfluidics: Sorting particles with light,” Nature Materials 3, 9-10 (2004). 6. A. Ashkin, “Acceleration and trapping of particles by radiation-pressure,”Phys. Rev. Lett. 24, 156-159 (1970). 7. A. Ashkin, J. M. Dziedzic, J. E. Bjorkholm and S. Chu, “Observation of a single-beam gradient force optical trap for dielectric particles,” Opt. Lett. 11, 288-290 (1986). 8. K. Sasaki, M. Koshioka, H. Misawa, N. Kitamura, and H. Masuhara, “Optical trapping of a metal particle and a water droplet by a scanning laser beam,” Appl. Phys. Lett. 60, 807-809 (1992). 9. K. T. Gahagan and G. A. Swartzlander, “Trapping of low-index microparticles in an optical vortex,” J. Opt. Soc. Am. B 15, 524-533 (1998). 10. K. T. Gahagan and G. A. Swartzlander, “Simultaneous trapping of low-index and high-index microparticles observed with an optical-vortex trap,” J. Opt. Soc. Am. B 16, 533 (1999). 11. M. P. MacDonald, L. Paterson, W. Sibbett, K. Dholakia, P. Bryant, “Trapping and manipulation of low-index particles in a two-dimensional interferometric optical trap,” Opt. Lett. 26, 863-865 (2001). 12. R. L. Eriksen, V. R. Daria and J. Glückstad, “Fully dynamic multiple-beam optical tweezers,” Opt. Express 10, 597-602 (2002), https://www.360docs.net/doc/2511911184.html,/abstract.cfm?URI=OPEX-10-14-597. 13. P. J. Rodrigo, R. L. Eriksen, V. R. Daria and J. Glückstad, “Interactive light-driven and parallel manipulation of inhomogeneous particles,” Opt. Express 10, 1550-1556 (2002), https://www.360docs.net/doc/2511911184.html,/abstract.cfm?URI=OPEX-10-26-1550. 14. V. Daria, P. J. Rodrigo and J. Glückstad, “Dynamic array of dark optical traps,” Appl. Phys. Lett. 84, 323-325 (2004). 15. J. Glückstad and P. C. Mogensen, “Optimal phase contrast in common-path interferometry,” Appl. Opt. 40, 268-282 (2001). 16. S. Maruo, K. Ikuta and H. Korogi, “Submicron manipulation tools driven by light in a liquid,” Appl. Phys. Lett. 82, 133-135 (2003). #3781 - $15.00 US Received 4 February 2004; revised 29 March 2004; accepted 29 March 2004 (C) 2004 OSA 5 April 2004 / Vol. 12, No. 7 / OPTICS EXPRESS 1417
电气专业毕业论文外文翻译分析解析
本科毕业设计 外文文献及译文 文献、资料题目：Designing Stable Control Loops 文献、资料来源：期刊 文献、资料发表（出版）日期：2010.3.25 院（部）：信息与电气工程学院 专班姓学业：电气工程与自动化级： 名： 号： 指导教师：翻译日期：2011.3.10
外文文献： Designing Stable Control Loops The objective of this topic is to provide the designer with a practical review of loop compensation techniques applied to switching power supply feedback control. A top-down system approach is taken starting with basic feedback control concepts and leading to step-by-step design procedures,initially applied to a simple buck regulator and then expanded to other topologies and control algorithms. Sample designs are demonstrated with Math cad simulations to illustrate gain and phase margins and their impact on performance analysis. I. I NTRODUCTION Insuring stability of a proposed power supply solution is often one of the more challenging aspects of the design process. Nothing is more disconcerting than to have your lovingly crafted breadboard break into wild oscillations just as its being demonstrated to the boss or customer, but insuring against this unfortunate event takes some analysis which many designers view as formidable. Paths taken by design engineers often emphasize either cut-and-try empirical testing in the laboratory or computer simulations looking for numerical solutions based on complex mathematical models.While both of these approach a basic understanding of feedback theory will usually allow the definition of an acceptable compensation network with a minimum of computational effort. II. S TABILITY D EFINED Fig. 1.Definition of stability Fig. 1 gives a quick illustration of at least one definition of stability. In its simplest terms, a system is stable if, when subjected to a perturbation from some source, its response to that