我们可以简单的将其设置为一个比较低的值，这就意味着当我们迭代该集合时，数据库将反复查询以获得数据。
We can be lazy and set this to a low value, which means that the database must be repeatedly queried to get data as we iterate over the collection.
然后，for循环反复调用这个迭代器的. next方法，直到它遇到StopIteration异常为止。
The for loop then repeatedly calls the.next method of this iterator until it encounters a StopIteration exception.
这很可能也是一个迭代过程，同样应该使用反复试验的方法。
It will likely be an iterative process as well, using the trial and error approach again.
产品设计是一个反复的迭代过程。
本文给出这类系统的参数递推辨识算法，克服了迭代算法不能在线运行、需反复矩阵求逆的不足。
This paper presents a recursive parameter identification algorithm for the system. Compared with the iterative algorithm, it can avoid the matrix inversion and can be operated on-line.
通过反复迭代进行时反操作，可以明显提高聚焦信号能量；
Repeated time reversal iterations can obviously enhance the focus signal energy.
但是由于其迭代过程需要对所有图像数据反复进行计算，因而其时间效率很低，难以实时应用。
But it needs computing all the data of the image constantly during the iterative course, so it is not suitable for real-time application for its low efficiency.
工程结构的设计过程是一个反复修改的迭代过程。
Design process of an engineering structure is an iterative modification process.
通常，制订项目进度计划是一个反复迭代的过程，该过程确定每个活动和每个里程碑的开工日期和完成日期。
Developing the project schedule is often an iterative process. It determines the planned start and finish dates for project activities and milestones.
出众的用户体验成为关键目标，为此目标，我们定义了一个由外向内驱动，反复迭代的开发流程。
Superior User Experience is a key goal and to achieve this we defined an outside-in driven, iterative development process.
每一方位反复迭代直至尾迹稳定，从而得到其诱导下洗。
Each azimuth is re iterated until its wake is stable and its induced down wash flow is obtained.
用一次二阶矩法分析结构可靠度需要反复迭代求解。
For structural reliability analysis calculation. Rackwitz and Fiessler Method has to employ iteration.
采用基于最大类间方差的遗传分割法通过反复迭代搜索得到较优的分割阈值，实现了憎水性图像的准确分割，可满足后续基于BP神经网络的憎水性等级的判定。
The optimum threshold value was obtained and accurate segmentation of hydrophobic image was realized, which can meet the need of the classification of hydrophobicity levels based on BP neural network.
采用基于最大类间方差的遗传分割法通过反复迭代搜索得到较优的分割阈值，实现了憎水性图像的准确分割，可满足后续基于BP神经网络的憎水性等级的判定。
The optimum threshold value was obtained and accurate segmentation of hydrophobic image was realized, which can meet the need of the classification of hydrophobicity levels based on BP neural network.
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