可分级的

The degree of agreement of judgement by the generated neural networks model is about 84% compared with human inspection. The applicability of the neural networks to judge the grade of pimento is confirmed in this report.

利用所建立的类神经网路模型，以购自市场已分级的样本测试后，与人工分级结果比对有84％的相符性，显示类神经网路在青椒分级上的可应用性。

An improved scalable video coding scheme is proposed in this paper.

摘要该文提出了一种改进的可分级视频编码方法。

Such as new typed air classifier can meet the requirements of submicron classification.

新设计的气流分级机可达到对颗粒进行亚微米分级的要求。

Traditional scalable encoding technique can only decompose stream into limited layers, and it is lack of flexibility, while video transcoding can provide more flexible changement of bit rate for compressed video.

传统的可分级编码技术层数有限，且不够灵活，而视频转换编码则可提供码率在更宽范围内变化的压缩视频流。

According to the existing standard, criterion and the checkable data, classification and weight confirming of each parameter have been done by using analytic hierarchy process.

根据现有的标准、规范和可查的资料对每个指标进行分级，采用层次分析法确定了各指标的权重。

The properties of various opposites are explored in detail with reference to their semantic characteristics, with emphasis paid on gradable opposite and its properties like comparison, committedness and markedness.

本文将不同类型对立关系所呈现的语义特征一一分析，具体分析了可分级对立词的特征如比较性、偏袒性和标记性。

The test was based on lines concordanced in Web-based CCL corpus, the result shows that the gradable and non-gradable adjectival opposites differ only in two minor textual functions.

结果证明，在汉语中可分级性标准在语篇功能方面的区别性也是很微小的，仅限于两个低频语篇功能。

According to constant distortion criterion, sliding window rate allocation method is proposed for fine granularly coded bitstream.

根据恒定失真准则，本文首次提出了适合精细可分级编码码流的滑动窗口码率分配方法(SlidingWindow Rate Allocation，SWRA)。

The experimental results sgow thow that the proposed algorithm can make the single bit srteam produce better qualities at various data rates.

指出了码率可分级编码算法不仅要考虑误差扩散问题，还要考虑帧间依赖关系，并提出了一种新的算法。

A test on the basis of concordances retrieved from Brown Corpus was made on English to check whether or not gradability is of that critical distinguishing power in textual environments.

然而，当我们把语篇功能因素考虑在内的时候，可分级性仍然那么重要吗？

Graf, without question, is the greatest women's player in history.

格拉芙，现在，毫无疑问是女子网坛历史上最伟大的选手。

If not, you will have to look around at all your important data.

如果没有，您将需要检查所有的重要数据。