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一、外文资料译文：

Java开发2.0：使用Hibernate Shards 进行切分

横向扩展的关系数据库

Andrew Glover，作者兼开发人员，Beacon50

摘要：Sharding并不适合所有网站，但它是一种能够满足大数据的需求方法。对于一些商店来说，切分意味着可以保持一个受信任的RDBMS，同时不牺牲数据可伸缩性和系统性能。在Java 开发 2.0系列的这一部分中，您可以了解到切分何时起作用，以及何时不起作用，然后开始着手对一个可以处理数TB 数据的简单应用程序进行切分。

日期：2010年8月31日

级别：中级

PDF格式：A4和信（64KB的15页）取得Adobe?Reader?软件

当关系数据库试图在一个单一表中存储数TB 的数据时，总体性能通常会降低。索引所有的数据读取，显然是很耗时的，而且其中有可能是写入，也可能是读出。因为NoSQL 数据商店尤其适合存储大型数据，但是NoSQL 是一种非关系数据库方法。对于倾向于使用ACID-ity 和实体结构关系数据库的开发人员及需要这种结构的项目来说，切分是一个令人振奋的选方法。

切分一个数据库分区的分支，不是在本机上的数据库技术，它发生在应用场面上。在各种切分实现，Hibernate Shards 可能是Java? 技术世界中最流行的。这个漂亮的项目可以让您使用映射至逻辑数据库的POJO 对切分数据集进行几乎无缝操作。当你使用Hibernate Shards 时，您不需要将你的POJO 特别映射至切分。您可以像使用Hibernate 方法对任何常见关系数据库进行映射时一样对其进行映射。Hibernate Shards 可以为您管理低级别的切分任务。

迄今为止，在这个系列，我用一个比赛和参赛者类推关系的简单域表现出不同的数据存储技术比喻为基础。这个月，我将使用这个熟悉的例子，介绍一个实际的切分策略，然后在Hibernate实现它的碎片。请注意，切分首当其冲的工作是和Hibernate没有必然关系的，事实上，对Hibernate stards编码部分是容易的。真正难的是搞清楚内容碎片和你的工作方式。。

关于本系列

Java的发展前景已经发生了根本变化，因为Java技术初现端倪。得益于成熟的开源框架和可靠的租金部署基础设施，它现在的组装，测试，运行和维护Java应用开发的速度和成本降低。在这个系列中，Andrew Glover探讨了技术和工具，使这个新的Java开发有尽可能多的典范。

切分简介

数据库切分是一种划分成一些小团体的逻辑数据，可以将一块表的分成不同的小组。例如，如果您正在根据时间戳对一个名为foo的超大型表进行分区，2010 年8 月之前的所有数据都将进入分区A，而之后的数据则全部进入分区B。分区可以加快读写速度，因为它们的目标是单独分区中的较小型数据集。

分区并不总是可用的（MySQL并没有支持它，直到5.1版），而且与商业系统一起做让它的成本可以让人望而却步。更何况，在同一物理机上实现最分区存储数据，所以你仍然受到硬件基础的限制。分区也不能解决可靠性的或硬件不足。因此，聪明的人开始为寻找各种新的方法。

切分基本上是在数据库级别的：而不是分裂的碎片的数据表的行，数据库本身是被分割（通常是在不同的机器）的一些逻辑数据元素，而不是分裂成较小的块表，分割分片成一个完整的数据库小切分基本上是在数据库级别的：而不是分裂的碎片的数据表的行，数据库本身是被分割（通常是在不同的机器）的一些逻辑数据元素，块。

切分典型的例子是基于大型数据库存储划分各地区的全球客户数据：切分 A 用于存储美国的客户信息，切分 B 用户存储亚洲的客户信息，切分 C 欧洲，等。这些切分分别处于不同的计算机上，且每个切分将存储所有相关数据，如客户喜好或订购历史。

对分片（如分区）的好处是它压缩大数据：在每个单独的碎片表，它允许更快的读取和写入，提高了性能。分片是也可以提高想象可靠性，因为即使一碎片意外失败，其他人仍然能够满足数据。而由于分片是在应用层完成，你可以做的数据库在常规下不支持分割它。资金成本也可能降低。

主键

切分利用多个数据库，所有这些都有自主意识的功能，不干涉其他切分。因此，如果你依赖于数据库序列（如主键自动生成），很可能是相同的主键将显示在一个数据库上成立。这是可能的，以协调跨分布式数据库序列，但这样做增加了系统的复杂性。最安全的方式，禁止重复的主键是让你的应用程序（这将是一个sharded管理系统反正）生成密钥。

跨碎片查询

大部分（包括Hibernate碎片）分片的实现不允许跨碎片查询，这意味着你必须去额外的长度，如果你想利用两对来自不同的碎片的数据集。（有趣的是，Amazon的SimpleDB 的还禁止跨域查询。）如果将美国客户信息存储在切分 1 中，还需要将所有相关数据存储在此。如果您尝试将那些数据存储在切分 2 中，情况就会变得复杂，系统性能也可能受影响。这种情况也与先前提出的观点- 如果你有点最终需要做跨碎片连接，你最好的管理方式，消除了重复的可能性管理键！显然，你需要充分考虑分片策略，然后再设置你的数据库。一旦你已经选择了一种特定的方向，你就或多或少地依赖于它- 它很难在走动后，一直sharded数据。

避免过早分片

切分最好采用分片后期。像过早的优化，分片的基础上增长数据的预期可能是一个灾难。分片实施的成功是基于一段时间内适当地了解数据增长的应用程序，并推断未来。一旦你sharded您的数据可能会极其难以走动。

一个策略的例子

由于分片结合你到一个线性数据模型（即，你不能轻易加入不同碎片的数据），你应该从你的数据清楚地了解每个组织碎片是将如何逻辑的。这通常是最容易由一个域的主节点成为重点。在一个电子商务系统的情况下，主节点可以是一个命令或一个客户。因此，如果你选择“客户”作为您的分片策略的基础，然后与客户的所有数据将被转移到各自的碎片，但你还是要选择哪些碎片去移动这些数据。

对客户来说，你可以根据位置碎片（欧洲，亚洲，非洲等），或者你可以在别的东西的碎片。这取决于你。您的碎片战略应当指出，纳入均匀分布的碎片之间的所有数据的一

些方法。分片整体的思路是，打破大套成小的数据，因此，如果某个特定电子商务领域有一个大的欧洲客户在设置和美国比较少，它可能不会基于意义的碎片对客户的位置。

回到比赛——使用切分！

现在让我们回到我经常提到的赛跑应用程序示例中，我可以根据比赛或参赛者进行切分。在本示例中，我将根据比赛进行切分，因为我看到域是根据参加不同比赛的参赛者进行组织的。因此，比赛是域的根。我也将根据比赛距离进行切分，因为比赛应用程序包含不同长度和不同参赛者的多项比赛。

请注意：在进行上述决定时，我已经接受了一个妥协：如果一个参赛者参加了不止一项比赛，他们分属不同的切分，那该怎么办呢？Hibernate Shards （像大多数切分实现一样）不支持跨切分连接。我必须忍受这些轻微不便，允许参赛者被包含在多个切分中—也就是说，我将在参赛者参加的多个比赛切分中重建该参赛者。

为了简便起见，我将创建两个切分：一个用于10 英里以下的比赛；另一个用于10 英里以上的比赛。

实现Hibernate shards

Hibernate stards与现有的Hibernate项目几乎天衣无缝。唯一的缺点是，Hibernate的碎片需要一些具体资料和你的行为。也就是说，它需要一个碎片访问策略，碎片，选择策略，以及碎片，解决策略。这些接口，你必须执行，尽管在某些情况下，你可以使用默认的。我们将在后面的部分逐个了解各个接口。

ShardAccessStrategy

执行查询时，Hibernate Shards 需要一个决定首个切分、第二个切分及后续切分的机制。Hibernate Shards 无需确定查询什么（这是Hibernate Core 和基础数据库需要做的），但是它确实意识到，在获得答案之前可能需要对多个切分进行查询。因此，Hibernate Shards 提供了两种极具创意的逻辑实现方法：一种方法是根据序列机制（一次一个）对切分进行查询，直到获得答案为止；另一种方法是并行访问策略，这种方法使用一个线程模型一次对所有切分进行查询。

我要保持简单，并利用连续的战略，取名为SequentialShardAccessStrategy。我们将很快配置。

ShardSelectionStrategy

当创建一个新的对象（即，当一个新的Race或Runner是通过Hibernate创建），Hibernate Shards需要知道什么碎片相应的数据应该写入。因此，你必须实现这个接口和代码逻辑的分片。如果你想有一个默认的实现，有一个被称为RoundRobinShardSelectionStrategy，它使用了碎片的数据放入循环赛战略。

对于赛跑应用程序，我需要提供根据比赛距离进行切分的行为。因此，我们需要实现ShardSelectionStrategy 接口并提供依据Race 对象的distance 采用selectShardIdForNewObject 方法进行切分的简易逻辑。（我将稍候在Race 对象中展示。）在运行时，当调用是一些保存在我的领域对象类的方法，该接口的行为是在Hibernate 杠杆内心深处的核心。

清单1。一个简单的碎片，选择策略

import org.hibernate.shards.ShardId;

import org.hibernate.shards.strategy.selection.ShardSelectionStrategy;

public class RacerShardSelectionStrategy implements ShardSelectionStrategy {

public ShardId selectShardIdForNewObject(Object obj) {

if (obj instanceof Race) {

Race rce = (Race) obj;

return this.determineShardId(rce.getDistance());

} else if (obj instanceof Runner) {

Runner runnr = (Runner) obj;

if (runnr.getRaces().isEmpty()) {

throw new IllegalArgumentException("runners must have at least one race");

} else {

double dist = 0.0;

for (Race rce : runnr.getRaces()) {

dist = rce.getDistance();

break;

}

return this.determineShardId(dist);

}

} else {

throw new IllegalArgumentException("a non-shardable object is being created");

}

}

private ShardId determineShardId(double distance){

if (distance > 10.0) {

return new ShardId(1);

} else {

return new ShardId(0);

}

}

}

正如你可以看到清单1，如果该对象被保存的一场Race，那么它的距离确定，因此，而且（因此）选择了一个切分。在这种情况下，有两个切分：0 和1，其中切分 1 中包含10 英里以上的比赛，切分0 中包含所有其他比赛。

如果持久化一个Runner 或其他对象，情况会稍微复杂一些。我已经编码了一个逻辑规则，其中有三个规定：

一名Runner 在没有对应的Race 时无法存在。

如果Runner 被创建时参加了多场Races，这名Runner 将被持久化到寻找到的首场Race 所属的切分中。（顺便说一句，该原则对未来有负面影响。）

如果还保存了其他域对象，现在将引发一个异常。

根据这些你就可以擦你眉头上的汗水,因为大多数的辛勤的工作都做完了。随着比赛应用的增长，我所使用的逻辑可能不灵活,但这行得通为执行本示范!

ShardResolutionStrategy

要找这个对象的关键,Hibernate Stards需要一个办法决定先切分那个。你就用SharedResolutionStrategy接口去引导。

正如我之前所说的,sharding迫使你对基本有敏锐的钥匙,你可以管理之行。幸运的是,已经好Hibernate Stards或UUID生成方面表现良好。因此Hibernate Shards 创造性地提供一个ID 生成器，名为ShardedUUIDGenerator，它可以灵活地将切分ID 信息嵌入到UUID 中。

如果您最后使用ShardedUUIDGenerator 进行键生成（我在本文中也将采取这种方法），那么您也可以使用Hibernate Shards 提供的创新ShardResolutionStrategy 实现，名为AllShardsShardResolutionStrategy，这可以决定依据一个特定对象的ID 搜索什么切分。

配置好Hibernate Shards 工作所需的三个接口后，我们就可以对切分示例应用程序的第二步进行实现了。现在应该启动Hibernate 的SessionFactory 了。

外文原文资料信息

[1] 外文原文作者：

[2] 外文原文所在书名或论文题目：

[3] 外文原文来源：

出版社或刊物名称、出版时间或刊号、译文部分所在页码：网页地址：

二、外文原文资料：

Java development 2.0: Sharding with Hibernate Shards

Horizontal scalability for relational databases

Andrew Glover, Author and developer, Beacon50

Summary:Sharding isn't for everyone, but it's one way that relational systems can meet the demands of big data. For some shops, sharding means being able to keep a trusted RDBMS in place without sacrificing data scalability or system performance. In this installment of the Java development 2.0series, find out when sharding works, and when it doesn't, and then get your hands busy sharding a simple application capable of handling terabytes of data.

Date:31 Aug 2010

Level: Intermediate

PDF:A4 and Letter (64KB | 15 pages)Get Adobe? Reader?

When relational databases attempt to store terabytes of data in single tables, overall performance typically degrades. Indexing all that data is obviously expensive for reads, but also for writes. While NoSQL datastores are particularly suited to storing big data (think Google's Bigtable), NoSQL is a patently non-relational approach. For the developer who prefers the ACID-ity and solid structure of a relational database, or the project that requires it, sharding could be an exciting alternative.

Sharding, an offshoot of database partitioning, isn't a native database technique — it happens at the level of the application. Among various sharding implementations, Hibernate Shards is possibly the most popular in the world of Java? technology. This nifty project lets you work more or less seamlessly with sharded datasets (I will explain the "more or less" part shortly) using POJOs that are mapped to a logical database. When you use Hibernate Shards, you don't have to specifically map your POJOs to shards — you map them as you would any normal relational database in the Hibernate way. Hibernate Shards manages the low-level sharding stuff for you.

So far in this series, I've used a simple domain based on the analogy of races and runners to demonstrate various data storage technologies. This month, I'll use this familiar example to introduce a practical sharding strategy, then implement it in Hibernate Shards. Note that the brunt of the work related to sharding isn't necessarily related to Hibernate; in fact, coding for Hibernate Shards is the easy part. The real work is figuring out how and what you'll shard.

About this series

The Java development landscape has changed radically since Java technology first emerged. Thanks to mature open source frameworks and reliable for-rent deployment infrastructures, it's now possible to assemble, test, run, and maintain Java applications quickly and inexpensively. In this series, Andrew Glover explores the spectrum of technologies and tools that make this new Java development paradigm possible.

Sharding at a glance

Database partitioning is an inherently relational process of dividing a table's rows by some logical piece of data into smaller groups. If you were partitioning a gigantic table named foo based on timestamps, for instance, all the data for August 2010 would go in Partition A, while anything since then would be in Partition B, and so on. Partitioning has the effect of making reads and writes faster because they target smaller datasets in individual partitions.

Partitioning isn't always available (MySQL didn't support it until version 5.1), and the cost of doing it with a commercial system can be prohibitive. What's more, most partitioning implementations store data on the same physical machine, so you're still bound to the limits of your hardware. Partitioning also doesn't resolve the reliability, or lack thereof, of your hardware. Thus, various smart people started looking for new ways to scale.

Sharding is essentially partitioning at the database level: rather than divide a table's rows by pieces of data, the database itself is split up (usually across different machines) by some logical data element. That is, rather than splitting up a table into smaller chunks, sharding splits up an entire database into smaller chunks.

The canonical example for sharding is based on dividing a large database storing worldwide customer data by region: Shard A for customers in the United States, Shard B for Asia, Shard C for Europe, and so on. The shards themselves would live on different machines and each shard would hold all related data, such as customer preferences or order history.

The benefit of sharding (like partitioning) is that it compacts big data: individual tables are smaller in each shard, which allows for faster reads and writes, which increases performance. Sharding also conceivably improves reliability, because even if one shard unexpectedly fails, others are still able to serve data. And because sharding is done at the application layer, you can do it for databases that don't support regular partitioning. The monetary cost is also potentially lower.

Sharding and strategy

Like most technologies, sharding does entail some trade-offs. Because sharding isn't a native database technique — that is, you must implement it in your application — you'll need to map out your sharding strategy before you begin. Both primary keys and cross-shard queries play a major role when sharding, mainly by defining what you can't do.

Primary keys

Sharding leverages multiple databases, all of which function autonomously, without awareness of their peers. As a result, if you rely on database sequences (such as for automatic primary key generation), it's likely that an identical primary key will show up across a set of databases. It's possible to coordinate sequences across a distributed database but doing so increases system complexity. The safest way to prohibit duplicate primary keys is to have your application (which will be managing a sharded system anyway) generate keys.

Cross-shard queries

Most sharding implementations (including Hibernate Shards) don't permit cross-shard querying, which means you have to go to extra lengths if you want to leverage two sets of data from different shards. (Interestingly, Amazon's SimpleDB also prohibits cross-domain queries.) For instance, if you're storing United States customers in Shard 1, you also need to store all of their related data there. If you try to store that data in Shard 2, things will get complicated, and system performance will probably suffer. This situation is also related to the point made earlier — if you somehow end up needing to do cross-shard joins, you had better be managing keys in a way that eliminates the possibility of duplicates!

Clearly, you'll need to fully consider a sharding strategy before you set up your database. And once you've chosen a particular direction, you're more or less tied to it — it's hard to move data around after it's been sharded.

Avoid premature sharding

Sharding is best employed late in the game. Like premature optimization, sharding based on expected data growth could be a recipe for disaster. Successful sharding implementations are based on measurably understanding an application's data growth over time, and then extrapolating to the future. Once you've sharded your data it can be extraordinarily hard to move around.

A strategy example

Because sharding binds you to a linear data model (that is, you can't easily join data in different shards), you should start with a clear picture of how your data will be logically organized per shard. This is usually easiest by focusing on the primary node of a domain. In the case of an

e-commerce system, the primary node could be either an order or a customer. Thus, if you choose "customer" as the basis for your sharding strategy, then all data related to customers will be moved into the respective shards, though you'll still have to choose to which shard to move that data.

For customers, you could shard based on location (Europe, Asia, Africa, etc.), or you could shard based on something else. It's up to you. Your shard strategy should, however, incorporate some means of distributing data evenly among all of your shards. The whole idea of sharding is to break up big data sets into smaller ones; thus, if a particular e-commerce domain had a large set of European customers and relatively few in the United States, it probably wouldn't make sense to shard based on customer location.

Off to the races — with sharding!

Getting back to the familiar example of my racing application, I can shard by race or by runner. In this case, I'm going to shard by race, because I see the domain being organized by runners who belong to races. So the race is the root of my domain. I'm also going to shard based on race distance, because my racing application holds myriad races of different lengths, along with myriad runners.

Note that in making these decisions, I have already accepted a trade-off: what if a runner participates in more than one race, each of them living in different shards? Hibernate Shards (like most sharding implementations) doesn't support cross-shard joins. I'm going to have to live with this slight inconvenience and allow runners to live in multiple shards — that is, I will recreate each runner in the shards where his or her various races live.

To keep things simple, I'm going to create two shards: one for races less than 10 miles and another for anything greater than 10 miles.

Implementing Hibernate Shards

Hibernate Shards is made to work almost seamlessly with existing Hibernate projects. The only catch is that Hibernate Shards needs some specific information and behavior from you. Namely, it needs a shard-access strategy, a shard-selection strategy, and a shard-resolution strategy. These are interfaces you must implement, though in some cases you can use default ones. We'll look at each interface separately in the following sections.

ShardAccessStrategy

When a query is executed, Hibernate Shards needs a mechanism for determining which shard to hit first, second, and so on. Hibernate Shards doesn't necessarily figure out what a query is looking for (that's for the Hibernate Core and underlying database to do), but it does recognize that a query might need to execute against multiple shards before an answer is obtained. So, Hibernate Shards provides two logical implementations out of the box: one executes a query in a sequential mechanism (one at a time) against shards until an answer is returned, or until all of the shards have been queried. The other implementation is a parallel-access strategy, which uses a threading model to hit all of the shards at once.

I'm going to keep things simple and utilize the sequential strategy, aptly named SequentialShardAccessStrategy. We'll configure it shortly.

ShardSelectionStrategy

When a new object is created (that is, when a new Race or Runner is created via Hibernate), Hibernate Shards needs to know what shard the corresponding data should be written to. Accordingly, you must implement this interface and code the sharding logic. If you want a default implementation, there's one dubbed RoundRobinShardSelectionStrategy, which uses a round-robin strategy for putting data into shards.

For the racing application, I need to provide behavior that shards by race distance. Accordingly, I'll need to implement the ShardSelectionStrategy interface and provide some simple logic that shards based on a Race object's distance in the selectShardIdForNewObject method. (I'll show the Race object shortly.)

At runtime, when a call is made to some save-like method on my domain objects, this interface's behavior is leveraged deep down in Hibernate's core.

Listing 1. A simple shard-selection strategy

import org.hibernate.shards.ShardId;

import org.hibernate.shards.strategy.selection.ShardSelectionStrategy;

public class RacerShardSelectionStrategy implements ShardSelectionStrategy {

public ShardId selectShardIdForNewObject(Object obj) {

if (obj instanceof Race) {

Race rce = (Race) obj;

return this.determineShardId(rce.getDistance());

} else if (obj instanceof Runner) {

Runner runnr = (Runner) obj;

if (runnr.getRaces().isEmpty()) {

throw new IllegalArgumentException("runners must have at least one race");

} else {

double dist = 0.0;

for (Race rce : runnr.getRaces()) {

dist = rce.getDistance();

break;

}

return this.determineShardId(dist);

}

} else {

throw new IllegalArgumentException("a non-shardable object is being created");

}

}

private ShardId determineShardId(double distance){

if (distance > 10.0) {

return new ShardId(1);

} else {

return new ShardId(0);

}

}

}

As you can see in L Listing 1, if the object being persisted is a Race, then its distance is determined and, accordingly, a shard is picked. In this case, there are two shards: 0 and 1, where Shard 1 holds races with a distance greater than 10 miles and Shard 0 holds all others.

If a Runner or some other object is being persisted, things get a bit more involved. I've coded a logical rule that has three stipulations:

? A Runner can't exist without a corresponding Race.

?If a Runner has been created with multiple Races, the Runner will be persisted in the shard for the first Race found. (This rule has negative implications for the future, by the way.)

?If some other domain object is being saved, for now, an exception will be thrown.

With that, you can wipe the sweat from your brow, because most of the hard work is done. The logic I've captured might not be flexible enough as the racing application grows, but it'll work for the purpose of this demonstration!

ShardResolutionStrategy

When searching for an object by its key, Hibernate Shards needs a way of determining which shard to hit first. You'll use the SharedResolutionStrategy interface to guide it.

As I mentioned earlier, sharding forces you to be keenly aware of primary keys, as you'll manage them yourself. Luckily, Hibernate is already good at providing key or UUID generation. Consequently, out of the box, Hibernate Shards provides an ID generator dubbed ShardedUUIDGenerator, which has the smarts to embed shard ID information in the UUID itself.

If you end up using ShardedUUIDGenerator for key generation (as I will for this article), then you can can also use the Hibernate Shards out-of-the-box ShardResolutionStrategy implementation dubbed AllShardsShardResolutionStrategy, which can determine what shard to search based on a particular object's ID.

Having configured the three interfaces required for Hibernate Shards to work properly, we're ready for the next step in sharding the example application. It's time to launch Hibernate's SessionFactory.

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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 customer’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 customer’s operation demand. Keywords: Infrared dray；Code；Decoding；LM386；Red outside transceiver 1 Introduction 1.1 research the background and significance Infrared Data Communication Technology is the world wide use of a wireless connection technology, by the many hardware and software platforms supported. Is a data through electrical pulses and infrared optical pulse switch between the wireless data transceiver technology.

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5G无线通信网络中英文对照外文翻译文献

5G无线通信网络中英文对照外文翻译文献(文档含英文原文和中文翻译)

翻译： 5G无线通信网络的蜂窝结构和关键技术 摘要 第四代无线通信系统已经或者即将在许多国家部署。然而，随着无线移动设备和服务的激增，仍然有一些挑战尤其是4G所不能容纳的，例如像频谱危机和高能量消耗。无线系统设计师们面临着满足新型无线应用对高数据速率和机动性要求的持续性增长的需求，因此他们已经开始研究被期望于2020年后就能部署的第五代无线系统。在这篇文章里面，我们提出一个有内门和外门情景之分的潜在的蜂窝结构，并且讨论了多种可行性关于5G无线通信系统的技术，比如大量的MIMO技术，节能通信，认知的广播网络和可见光通信。面临潜在技术的未知挑战也被讨论了。 介绍 信息通信技术（ICT）创新合理的使用对世界经济的提高变得越来越重要。无线通信网络在全球ICT战略中也许是最挑剔的元素，并且支撑着很多其他的行业，它是世界上成长最快最有活力的行业之一。欧洲移动天文台（EMO）报道2010年移动通信业总计税收1740亿欧元,从而超过了航空航天业和制药业。无线技术的发展大大提高了人们在商业运作和社交功能方面通信和生活的能力无线移动通信的显著成就表现在技术创新的快速步伐。从1991年二代移动通信系统(2G)的初次登场到2001年三代系统（3G）的首次起飞，无线移动网络已经实现了从一个纯粹的技术系统到一个能承载大量多媒体内容网络的转变。4G无线系统被设计出来用来满足IMT-A技术使用IP面向所有服务的需求。在4G系统中，先进的无线接口被用于正交频分复用技术（OFDM），多输入多输出系统（MIMO）和链路自适应技术。4G无线网络可支持数据速率可达1Gb/s的低流度，比如流动局域无线访问，还有速率高达100M/s的高流速，例如像移动访问。LTE系统和它的延伸系统LTE-A，作为实用的4G系统已经在全球于最近期或不久的将来部署。 然而，每年仍然有戏剧性增长数量的用户支持移动宽频带系统。越来越多的

变电站_外文翻译_外文文献_英文文献_变电站的综合概述

英文翻译 A comprehensive overview of substations Along with the economic development and the modern industry developments of quick rising, the design of the power supply system become more and more completely and system. Because the quickly increase electricity of factories, it also increases seriously to the dependable index of the economic condition, power supply in quantity. Therefore they need the higher and more perfect request to the power supply. Whether Design reasonable, not only affect directly the base investment and circulate the expenses with have the metal depletion in colour metal, but also will reflect the dependable in power supply and the safe in many facts. In a word, it is close with the economic performance and the safety of the people. The substation is an importance part of the electric power system, it is consisted of the electric appliances equipments and the Transmission and the Distribution. It obtains the electric power from the electric power system, through its function of transformation and assign, transport and safety. Then transport the power to every place with safe, dependable, and economical. As an important part of power’s transport and control, the transformer substation must change the mode of the traditional design and control, then can adapt to the modern electric power system, the development of modern industry and the of trend of the society life. Electric power industry is one of the foundations of national industry and national economic development to industry, it is a coal, oil, natural gas, hydropower, nuclear power, wind power and other energy conversion into electrical energy of the secondary energy industry, it for the other departments of the national economy fast and stable development of the provision of adequate power, and its level of development is a reflection of the country's economic development an important indicator of the level. As the power in the industry and the importance of the national economy, electricity transmission and distribution of electric energy used in these areas is an indispensable component.。Therefore, power transmission and distribution is critical. Substation is to enable superior power plant power plants or power after adjustments to the lower load of books is an important part of power transmission. Operation of its functions, the capacity of a direct impact on the size of the lower load power, thereby affecting the industrial production and power consumption.Substation system if a link failure, the system will protect the part of action. May result in power outages and so on, to the production and living a great disadvantage. Therefore, the substation in the electric power system for the protection of electricity reliability,

土木工程外文文献翻译

专业资料 学院： 专业：土木工程 姓名： 学号： 外文出处：Structural Systems to resist (用外文写) Lateral loads 附件：1.外文资料翻译译文；2.外文原文。

附件1：外文资料翻译译文 抗侧向荷载的结构体系 常用的结构体系 若已测出荷载量达数千万磅重，那么在高层建筑设计中就没有多少可以进行极其复杂的构思余地了。确实，较好的高层建筑普遍具有构思简单、表现明晰的特点。 这并不是说没有进行宏观构思的余地。实际上，正是因为有了这种宏观的构思，新奇的高层建筑体系才得以发展，可能更重要的是：几年以前才出现的一些新概念在今天的技术中已经变得平常了。 如果忽略一些与建筑材料密切相关的概念不谈，高层建筑里最为常用的结构体系便可分为如下几类： 1.抗弯矩框架。 2.支撑框架，包括偏心支撑框架。 3.剪力墙，包括钢板剪力墙。 4.筒中框架。 5.筒中筒结构。 6.核心交互结构。 7. 框格体系或束筒体系。 特别是由于最近趋向于更复杂的建筑形式，同时也需要增加刚度以抵抗几力和地震力，大多数高层建筑都具有由框架、支撑构架、剪力墙和相关体系相结合而构成的体系。而且，就较高的建筑物而言，大多数都是由交互式构件组成三维陈列。 将这些构件结合起来的方法正是高层建筑设计方法的本质。其结合方式需要在考虑环境、功能和费用后再发展，以便提供促使建筑发展达到新高度的有效结构。这并

不是说富于想象力的结构设计就能够创造出伟大建筑。正相反，有许多例优美的建筑仅得到结构工程师适当的支持就被创造出来了，然而，如果没有天赋甚厚的建筑师的创造力的指导，那么，得以发展的就只能是好的结构，并非是伟大的建筑。无论如何，要想创造出高层建筑真正非凡的设计，两者都需要最好的。 虽然在文献中通常可以见到有关这七种体系的全面性讨论，但是在这里还值得进一步讨论。设计方法的本质贯穿于整个讨论。设计方法的本质贯穿于整个讨论中。 抗弯矩框架 抗弯矩框架也许是低，中高度的建筑中常用的体系，它具有线性水平构件和垂直构件在接头处基本刚接之特点。这种框架用作独立的体系，或者和其他体系结合起来使用，以便提供所需要水平荷载抵抗力。对于较高的高层建筑，可能会发现该本系不宜作为独立体系，这是因为在侧向力的作用下难以调动足够的刚度。 我们可以利用STRESS，STRUDL 或者其他大量合适的计算机程序进行结构分析。所谓的门架法分析或悬臂法分析在当今的技术中无一席之地，由于柱梁节点固有柔性，并且由于初步设计应该力求突出体系的弱点，所以在初析中使用框架的中心距尺寸设计是司空惯的。当然，在设计的后期阶段，实际地评价结点的变形很有必要。 支撑框架 支撑框架实际上刚度比抗弯矩框架强，在高层建筑中也得到更广泛的应用。这种体系以其结点处铰接或则接的线性水平构件、垂直构件和斜撑构件而具特色，它通常与其他体系共同用于较高的建筑，并且作为一种独立的体系用在低、中高度的建筑中。

软件工程论文参考文献

软件工程论文参考文献 [1] 杜献峰 . 基于三层 B/S 结构的档案管理系统开发 [J]. 中原工学院学报，2009:19-25 [2]林鹏，李田养. 数字档案馆电子文件接收管理系统研究及建设[J].兰台世界，2008:23-25 [3]汤星群.基于数字档案馆建设的两点思考[J].档案时空，2005:23-28 [4]张华丽.基于 J2EE 的档案管理系统设计与实现[J].现代商贸工业. 2010:14-17 [5] 纪新.转型期大型企业集团档案管理模式研究[D].天津师范大学，2008：46-57. [6] 周玉玲.纸质与电子档案共存及网络环境电子档案管理模式[J].中国科技博览，2009：44-46. [7] 张寅玮.甘肃省电子档案管理研究[D]. 兰州大学，2011:30-42 [8] 惠宏伟.面向数字化校园的档案信息管理系统的研究与实现[D]. 电子科技大学，2006:19-33 [9] 刘冬立.基于 Web 的企业档案管理系统的设计与实现[D].同济大学，2007:14-23 [10]钟瑛.浅议电子文件管理系统的功能要素[J]. 档案学通讯，2006:11-20 [11] 刘洪峰，陈江波.网络开发技术大全[M].人民邮电出版社，2005：119-143. [12] 程成，陈霞.软件工程[M].机械工业出版社，2003：46-80. [13] 舒红平.Web 数据库编程-Java[M].西安电子科技大学出版社，2005：97-143. [14] 徐拥军.从档案收集到知识积累[M].是由工业出版社，2008：6-24. [15]Gary P Johnston，David V. Bowen.he benefits of electronic recordsmanagement systems: a general review of published and some unpublishedcases. RecordsManagement Journal，2005:44-52 [16]Keith Gregory.Implementing an electronic records management system: Apublic sector case study. Records Management Journal，2005:17-21 [17]Duranti Luciana.Concepts，Principles，and Methods for the Management of Electronic RecordsR[J].Information Society，2001：57-60.

通信工程移动通信中英文对照外文翻译文献

中英文翻译 附件1：外文资料翻译译文 通用移动通信系统的回顾 1.1 UMTS网络架构 欧洲/日本的3G标准，被称为UMTS。 UMTS是一个在IMT-2000保护伞下的ITU-T 批准的许多标准之一。随着美国的CDMA2000标准的发展，它是目前占主导地位的标准，特别是运营商将cdmaOne部署为他们的2G技术。在写这本书时，日本是在3G 网络部署方面最先进的。三名现任运营商已经实施了三个不同的技术：J - PHONE 使用UMTS，KDDI拥有CDMA2000网络，最大的运营商NTT DoCoMo正在使用品牌的FOMA（自由多媒体接入）系统。 FOMA是基于原来的UMTS协议，而且更加的协调和标准化。 UMTS标准被定义为一个通过通用分组无线系统（GPRS）和全球演进的增强数据

技术（EDGE）从第二代GSM标准到UNTS的迁移，如图。这是一个广泛应用的基本原理，因为自2003年4月起，全球有超过847万GSM用户，占全球的移动用户数字的68％。重点是在保持尽可能多的GSM网络与新系统的操作。 我们现在在第三代（3G）的发展道路上，其中网络将支持所有类型的流量：语音，视频和数据，我们应该看到一个最终的爆炸在移动设备上的可用服务。此驱动技术是IP协议。现在，许多移动运营商在简称为2.5G的位置，伴随GPRS的部署，即将IP骨干网引入到移动核心网。在下图中，图2显示了一个在GPRS网络中的关键部件的概述，以及它是如何适应现有的GSM基础设施。 SGSN和GGSN之间的接口被称为Gn接口和使用GPRS隧道协议（GTP的，稍后讨论）。引进这种基础设施的首要原因是提供连接到外部分组网络如，Internet或企业Intranet。这使IP协议作为SGSN和GGSN之间的运输工具应用到网络。这使得数据服务，如移动设备上的电子邮件或浏览网页，用户被起诉基于数据流量，而不是时间连接基础上的数据量。3G网络和服务交付的主要标准是通用移动通信系统，或UMTS。首次部署的UMTS是发行'99架构，在下面的图3所示。 在这个网络中，主要的变化是在无线接入网络（RAN的）CDMA空中接口技术的引进，和在传输部分异步传输模式作为一种传输方式。这些变化已经引入，主要是为了支持在同一网络上的语音，视频和数据服务的运输。核心网络保持相对不变，主要是软件升级。然而，随着目前无线网络控制器使用IP与3G的GPRS业务支持节点进行通信，IP协议进一步应用到网络。 未来的进化步骤是第4版架构，如图4。在这里，GSM的核心被以语音IP技术为基础的IP网络基础设施取代。 海安的发展分为两个独立部分：媒体网关（MGW）和MSC服务器（MSS）的。这基本上是打破外连接的作用和连接控制。一个MSS可以处理多个MGW，使网络更具有扩展性。 因为现在有一些在3G网络的IP云，合并这些到一个IP或IP/ ATM骨干网是很有意义的（它很可能会提供两种选择运营商）。这使IP权利拓展到整个网络，一直到BTS(基站收发信台)。这被称为全IP网络，或推出五架构，如图五所示。在HLR/ VLR/VLR/EIR被推广和称为HLR的子系统（HSS）。 现在传统的电信交换的最后残余被删除，留下完全基于IP协议的网络运营，并