您已看到，为将xml与sdo配合使用，确实需要一个XML模式文件，XMLDAS将通过此文件初始化类型与属性模型，或许您还会发现这有些令人不快。
You will have seen that to use XML with SDO, you do need a XML schema file from which the XML DAS can initialize the model of types and properties, and perhaps you found that off-putting.
XMLDAS初始化其模型的方法是：读取和解析与准备载入的 XML文档对应的 XML模式定义文件（XSD 文件）。
The XML DAS always initializes its model by reading and parsing an XML schema definition file (an XSD file) that corresponds to the XML instance document it is going to load.
为工作区激活一个百科全书，那么根百科全书自然就会使用结构模型初始化。
You enable an encyclopedia for Workspaces, and the root Workspace is naturally populated with architecture models.
为了初始化并安装这个模型，请运行数据库命令syncdb。
To initialize and install the model, run the synchronize database command, syncdb.
由于该类是由LotusExpeditor框架构造的，所以不能使用我们的数据模型初始化这个特定的实例。
Because this is constructed by the Lotus Expeditor framework, we cannot initialize this specific instance with our data model.
实际的应用程序会从数据库中获得模型的数据，但为了简便起见，OrderBean将由一个静态数组初始化。
A real application would retrieve the model's data from a database, but for simplicity, OrderBean is initialized from a static array.
如果向Bean中添加新字段，则需要添加另外三行代码进行初始化以及在GUI组件和域模型实现双向同步。
If a new field is added to the bean, there are three more lines to add for initialization and synchronization in each direction between the GUI components and domain model.
Sandesha建议的模型要求用户首先为SandeshaContext设置必需的参数，然后对其进行初始化。
The model that Sandesha proposes requires the users to first set the required parameters to the SandeshaContext, and then initialize it.
要创建一个类型为a [m]的一维数组(这里的m是一个已经被定义和初始化的整数变量)，可以在uml模型中在这个数组变量a上运行cpp _type原型。
To create a single dimensional array of the type a [m] (where m is an integer variable already defined and initialized), apply the cpp_type stereotype on the array variable a in the UML model.
TopologyHelper 类中的初始化函数和setupNames() 函数帮助为数据模型设置默认值。
The initialize function on the TopologyHelper class and the setupNames() function help to set up the default values into our data model.
本书中我们使用数据库初始化器在每次模型变化时来删除和重建数据库。
Throughout this book we have used database initializers to drop and recreate the database every time the model changes.
我们还可以为SoaML规范的最新版本SoaML 1.0Beta2添加支持，并使得使用和重用BPMN2过程模型来初始化并指定服务模型变得更加容易。
We also have added support for the latest version of the SoaML specification, 1.0 Beta 2, and we have made it easier to use and reuse BPMN2 process models to initiate and specify service models.
该模型采用二值化水平集方法实现，避免了传统实现方法水平集函数需要重新初始化为符号距离函数，从而导致稳定性差、计算量大、实现复杂等缺点。
The model was implemented by level set method with a binary level set function to reduce the expensive computational cost of re-initialization of the traditional level set function.
该模型利用人工间接初始化轮廓的几点信息后，再利用图象自身的一些特征，能自动地、高效地、准确地识别所需的轮廓。
This model can automatically, efficiently, and accurately identify the interest contours with the some indirect manual information and the features from the image.
为了实现设计重用，提出了一种面向复杂结构零件的初始化设计知识模型构建方法。
To achieve the design reuse of complex structural components, a method for the initialized design knowledge model is investigated.
利用检测过程中人脸区域初始化跟踪窗口，建立肤色的色调信息模型对后续帧进行跟踪。
Firstly, the tracking window based on face region was initialized in detecting process. Then, color hue information model was established to track the follow-up frame.
另外，模型对网络的初始化和运行过程，以及节点间进行交互时的资源查找，信任值查询、更新等算法进行了较为详尽的描述。
The initialization and operation process and the resource searching, trust querying and updating algorithm are also thoroughly described in the model.
MATLAB软体中阵列的初始化与使用，以及使用样本资料集做线性模型。
Initializing and using matrices in MATLAB?. Linear modelling in a sample data set.
系统具有柔性、开放性，可灵活地根据设备的传动关系建模。当模型被初始化后，系统会自动搜寻传动关系并计算各零部件对象的工作频率。
The system with features of opening and flexibility can be used to build models for a mechanical equipment based on its drive connections.
HLA对象模型支持邦联的规划，邦联执行过程中数据交换需求的定义，以及完成rti的初始化。
HLA object models support for federation planning, define requirements for data exchange during execution, and provide a means for initializing the RTI.
该算法主要包括三个特色技术：基于纹线局部走向的分类预测、体现指纹微观纹理的扩展上下文以及基于成像仪器的分类熵编码器概率模型初始化。
There are mainly three distinguishing features in our proposed algorithm:local direction-based prediction, extended context for micro texture and histogram initialization based on imaging apparatus.
该算法主要包括三个特色技术：基于纹线局部走向的分类预测、体现指纹微观纹理的扩展上下文以及基于成像仪器的分类熵编码器概率模型初始化。
There are mainly three distinguishing features in our proposed algorithm:local direction-based prediction, extended context for micro texture and histogram initialization based on imaging apparatus.
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