但是，并非始终都是这种情况，并且可能会出现失效数据异常。
However, this may not always be true, and stale data exceptions might occur.
采样数据异常可能会导致微机保护的不正确动作，需要采取恰当的应对措施。
Unusual sampling data will cause incorrect action of microprocessor-based protection, which needs some reasonable countermeasures.
了解测量过程中的人为干预，人往往是（数据异常）波动的主要（原因）来源。
8226; Beaware of human intervention in a process. Humans are often a large source ofvariation.
通过此策略，由于您未对数据保持读取锁，因此在执行更新操作时遇到失效数据异常的机率会增加。
With this strategy, because you are not holding read locks on data, the chance of getting stale data exceptions when you execute the update operations increases.
远程和命名异常是系统级异常，而应用程序和非法数据异常是业务级异常，因为它们提交更适用的业务信息。
Remote and naming exceptions are system-level exceptions, whereas application and invalid-data exceptions are business-level exceptions, because they deliver more applicable business information.
为降低虚警率，提出嵌入数据异常检测和恢复功能的BIT系统改进模型，并证明了该模型的有效性。
For decreasing FAR, an improved model for BIT systems with embedded abnormal data detecting and renewing functions was proposed, and the effectiveness of the proposed model was proved.
如果您的回答是常见，则面临着极高的失效数据异常风险，那么这个策略对于您的应用程序探查来说是一个很差的选择。
If your answer is that it is common, you run a greater risk of stale data exceptions, making this strategy a poor choice for your application profile.
南加利福尼亚州年夜学的马丁·希尔伯特博士率领了这项研究，他指出该项数据异常巨年夜，但与自然对比饶暌怪是牛之一毛。
Dr Martin Hilbert of the University of South California, who carried out the study, admits that the Numbers involved are too large to comprehend - but that they are dwarfed by nature.
研究提出运用数理统计原理进行检测数据异常性判断，在测区强度最小值和异常值的判定值中，取较大者作为构件强度推定值的取值方法。
The principle of statistics is used to judge abnormality of data, and a better method is further proposed to give appropriate concrete strength based on the minimum strength data and abnormal value.
1994年由克莱门蒂号航天器收集的数据显示，这些月球盆地没有异常的低重力现象，有些甚至有异常高的重力。
Data collected in 1994 by the Clementine spacecraft show that many of these lunar basins have no anomalously low gravity and some even have anomalously high gravity.
佩森确实提出了大量的例子，以及一些令人耳目一新的、易于理解的统计数据，来证明一个异常富有阶级的存在。
Pessen does present a quantity of examples, together with some refreshingly intelligible statistics, to establish the existence of an inordinately wealthy class.
Abeille 可链接LIS系统，自动储存历史数据，并可以做到异常报警。
Abeille is compatible with the LIS system, can automatically store historical data as well as issue alarm for abnormality.
极大地减少用于数据转换和异常处理的繁琐代码，从而专注于核心业务逻辑。
Significant reduction of tedious code for data conversion and exception handling, allowing you to focus on core business logic.
在这个类中，定义服务器操作完成时客户机页面如何处理数据(或异常)。
In this class, you define what the client page does with the data (or the exception) when the server is done.
偏差检测是一项在大型数据集中发现异常数据记录的任务。
Deviation detection is defined as the task of finding unusual data records in large datasets.
务必记住，如果无法在运行时转换该数据，则会产生异常。
Keep in mind that if the data cannot be converted at runtime, you get an exception.
试图使用片段或者试图从不存在的键访问数据就会抛出异常，指出相关的错误。
Trying to use slicing or trying to access data from nonexistent keys throws exceptions, indicating the relevant error.
该数据不包含异常情况。
探索异常数据的第一步只是找到它。
A first step in exploring anomalous data is just finding it.
如果服务器脚本不检查数据，要么数据库抛出异常，要么数据库安全会受到影响。
If the server script does not check the data, either the database will throw an exception or the database will be compromised.
各种异常情况都可能发生；例如，因用户输入错误或缺少可用的业务数据均会造成异常。
Various exceptions can occur; for example, exceptions can result from faulty user input or lack of availability of business data.
这将有助于总结信息，同时允许快速访问相关的异常数据记录。
This will help to summarize information and still allow for a fast access to relevant outlier data records.
目前，如果读线程试图从没有数据的队列读取数据，仅仅会抛出异常并继续执行。
So far, if a reader thread wanted to read data from a queue that had no data, you just threw an exception and moved on.
查找图中显示异常的页面数据。
Spring定义了一致的数据访问异常类层次结构（请参见图12），您可以将其用作服务和DAO 层异常的基础。
Spring defines a coherent data access exception class hierarchy (Figure 12) that you can use as a basis for your service- and DAO-layer exceptions.
Spring定义了一致的数据访问异常类层次结构（请参见图12），您可以将其用作服务和DAO 层异常的基础。
Spring defines a coherent data access exception class hierarchy (Figure 12) that you can use as a basis for your service- and DAO-layer exceptions.
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