interpolated
简明释义
英[ˌɪntəˈpəʊleɪtɪd]美[ˌɪntərˈpoʊleɪtɪd]
adj. 以内插值替换的
v. 篡改（interpolate的过去分词）
英英释义
Interpolated refers to the process of estimating or inserting values within a range of data points, often used in mathematical or computational contexts. | 插值是指在一系列数据点之间估算或插入值的过程，通常用于数学或计算机上下文中。 |
单词用法
线性插值 | |
三次插值 | |
在...之间插值 | |
从...中插值 | |
插值结果 | |
插值曲线 | |
插值表面 | |
插值方法 | |
数据被插值 | |
插值时间序列 |
同义词
| 估计的 | 缺失的数据是通过之前的趋势进行估计的。 | ||
| 外推的 | 数值是从现有数据集中外推得出的。 | ||
| 推导的 | 最终结果是从多个实验中推导出来的。 | ||
| 计算的 | 增长率是基于历史数据计算得出的。 |
反义词
| 外推的 | 数据被外推以预测未来趋势。 | ||
| 离散的 | 测量是在离散的时间间隔内进行的。 |
例句
1.First, the testing data must be interpolated and the basic time series must be built.
对经过预处理的测试数据进行三次样条插值，得到基本时间序列。
2.Data can be interpolated to different grid spacings if required.
数据可以插不同的网格间距，如有需要。
3.Some people may ask, and as you say, high-resolution and interpolated resolution is useless.
有人会问了，按你这么一说，高分辨率和插值分辨率是不是没用了。
4.Remember that it's just several Interpolated Noise functions added together.
记住这知识几个插值的噪声函数叠加在一起。
“但为什么？”他插嘴问。
6.Some people may ask, and as you say, high-resolution and interpolated resolution is useless.
有人会问了，按你这么一说，高分辨率和插值分辨率是不是没用了。
7.He interpolated a phrase about the growth of profits into the report.
他在报告中加了一句关于利润增长的话。
8.The artist interpolated colors between the primary shades to create a gradient effect.
艺术家在主要色调之间进行了插值以创建渐变效果。
9.The missing data points were interpolated to provide a smoother curve.
缺失的数据点被插值以提供更平滑的曲线。
10.To fill in the gaps, we interpolated the missing temperature readings.
为了填补空白，我们对缺失的温度读数进行了插值。
11.The software interpolated the images to enhance their resolution.
该软件对图像进行了插值以提高其分辨率。
12.In the graph, values between the known points were interpolated to estimate the trend.
在图表中，已知点之间的值被插值以估计趋势。
作文
In the field of data analysis, one often encounters the term "interpolated". This term refers to the process of estimating unknown values that fall within the range of known data points. For example, when we have a set of measurements taken at specific intervals, but we need to know the value at a point that was not measured, we can use interpolation to fill in that gap. The concept is crucial in various applications, including statistics, computer graphics, and even economics. To illustrate the importance of interpolated (插值) data, consider a scenario where a meteorologist is predicting temperatures for a week. If the temperature was recorded at noon for several days, but we need to estimate what the temperature was at 6 PM, the meteorologist might use interpolation techniques to provide an educated guess based on the known data points. This method allows for more accurate predictions and helps in making informed decisions. Interpolation can be done using various methods, such as linear interpolation, polynomial interpolation, or spline interpolation. Linear interpolation is the simplest form, where a straight line is drawn between two known points to estimate the unknown value. On the other hand, polynomial interpolation involves fitting a polynomial equation to the known data points, which can provide a smoother estimate, especially when dealing with complex datasets. Spline interpolation uses piecewise polynomials to create a smooth curve that passes through all known points, offering a high degree of accuracy. The choice of interpolation method depends on the nature of the data and the required precision. In some cases, simple linear interpolation may suffice, while in others, more sophisticated methods like spline interpolation might be necessary to achieve better results. Regardless of the method used, the goal remains the same: to produce an interpolated (插值) value that closely approximates the true value at the desired point. Moreover, interpolation is not limited to numerical data; it can also be applied to visual data in computer graphics. For instance, when rendering images, algorithms often use interpolated (插值) values to enhance the quality of images by smoothing out pixels and creating a more visually appealing result. This is particularly important in video games and animation, where the realism of graphics significantly impacts user experience. In conclusion, the concept of interpolated (插值) values plays a vital role in many fields, enabling professionals to make estimates and predictions based on existing data. Whether it is in weather forecasting, scientific research, or digital media, understanding how to effectively utilize interpolation techniques can lead to more accurate outcomes and improved decision-making. As technology continues to advance, the ability to interpolate data will remain an essential skill for analysts, scientists, and artists alike.
