interpolation
简明释义
英[ɪnˌtɜːpəˈleɪʃ(ə)n]美[ɪnˌtɜːrpəˈleɪʃn]
n. 插入；篡改；填写；插值
复 数 i n t e r p o l a t i o n s
英英释义
单词用法
插值法；内插法；插补方法 | |
线性插值；线性内插法；直线切削 |
同义词
反义词
例句
1.By several simple aspects of three-dimensional space interpolation, some simple and practical algorithms are developed.
通过介绍三维内插的几种简单情形，导出了一些简单、实用的算法。
2.Linear and circular interpolation are often hard-wired into the control unit.
直线和圆弧插补往往很难连接到控制单元。
3.Specify the -e option and provide an expression to evaluate. Again, wrap the expression in quotation marks to prevent interpolation by the shell.
指定- e选项并提供要运算的表达式。
4.And this is where interpolation and a prediction function can provide a solution.
而这就是插值和预报函数能提供的解决方案。
5.This is the interpolation algorithm to achieve the three functions are relatively wide range of methods of application.
这是三个实现插值算法的函数，都是应用比较广泛的方法。
6.Point-by-point comparing interpolation is one of the contour interpolation algorithms.
逐点比较插补算法是轮廓插补的算法之一。
7.Specify the -e option and provide an expression to evaluate. Again, wrap the expression in quotation marks to prevent interpolation by the shell.
指定- e选项并提供要运算的表达式。
8.The gamestate gets updated 25 times per second, so if you don’t use interpolation in your rendering, frames will also be displayed at this speed.
游戏状态每秒更新25次，因此如果你不在渲染的时候使用插值，帧数也只能以这个速度显示。
9.The weather model uses interpolation to provide accurate forecasts.
天气模型使用插值来提供准确的预测。
10.In computer graphics, interpolation is used to create smooth transitions between colors.
在计算机图形学中，插值用于在颜色之间创建平滑过渡。
11.The algorithm employs interpolation to estimate values at unmeasured points.
该算法使用插值来估算未测量点的值。
12.In statistics, interpolation helps fill in gaps in data sets.
在统计学中，插值帮助填补数据集中的空白。
13.Using interpolation, we can predict future sales based on past data.
通过使用插值，我们可以根据过去的数据预测未来的销售。
作文
Interpolation is a mathematical and statistical technique used to estimate unknown values that fall within a range of known data points. It plays a significant role in various fields, including computer graphics, data analysis, and scientific research. The concept of interpolation (插值) can be simplified as a method of filling in gaps in data by using existing values. For instance, when we have a dataset that includes the temperature readings at specific hours of the day, we can use interpolation (插值) to estimate the temperature at times when we do not have measurements. This is crucial for creating a more complete picture of the data we are analyzing.In computer graphics, interpolation (插值) is used extensively to create smooth transitions between images or frames. When animating characters or objects, artists often rely on interpolation (插值) techniques to generate intermediate frames that blend seamlessly from one pose to another. This not only enhances the visual quality of animations but also makes them more lifelike. Without interpolation (插值), animations would appear choppy and less engaging.Moreover, in data analysis, interpolation (插值) helps researchers make predictions based on incomplete datasets. For example, if scientists are studying climate change and have temperature data for certain years, they can use interpolation (插值) to estimate temperatures for years where data is missing. This allows for a more comprehensive understanding of trends and patterns over time.There are several methods of interpolation (插值), including linear interpolation (线性插值), polynomial interpolation (多项式插值), and spline interpolation (样条插值). Linear interpolation (线性插值) is the simplest form, where a straight line is drawn between two known data points to estimate the value in between. Polynomial interpolation (多项式插值) uses polynomial functions to estimate values and can provide a smoother curve than linear interpolation (线性插值), especially when dealing with non-linear datasets. Spline interpolation (样条插值) is a more advanced technique that uses piecewise polynomials to create a smooth curve that passes through all known data points.In conclusion, interpolation (插值) is a vital tool in mathematics and various scientific disciplines. Its ability to estimate unknown values from known data makes it invaluable for researchers, artists, and analysts alike. By employing different interpolation (插值) methods, we can enhance our understanding of complex datasets, create stunning visual effects in media, and make informed decisions based on incomplete information. As we continue to gather more data in our increasingly digital world, mastering the concept of interpolation (插值) will remain essential for anyone involved in data-driven fields.
插值是一个数学和统计技术，用于估计已知数据点范围内的未知值。它在计算机图形学、数据分析和科学研究等多个领域发挥着重要作用。插值（插值）的概念可以简化为一种通过使用现有值来填补数据中的空白的方法。例如，当我们有一个包含特定时间段温度读数的数据集时，我们可以使用插值（插值）来估计没有测量值的时间的温度。这对于创建我们正在分析的数据的更完整的图景至关重要。在计算机图形学中，插值（插值）被广泛用于在图像或帧之间创建平滑过渡。在动画角色或物体时，艺术家通常依赖于插值（插值）技术生成中间帧，从一个姿势无缝过渡到另一个姿势。这不仅增强了动画的视觉质量，还使其更具生命力。如果没有插值（插值），动画将显得生硬且不够吸引人。此外，在数据分析中，插值（插值）帮助研究人员根据不完整的数据集进行预测。例如，如果科学家正在研究气候变化，并且有某些年份的温度数据，他们可以使用插值（插值）来估计缺失年份的温度。这使得对趋势和模式的全面理解成为可能。有几种插值（插值）方法，包括线性插值（线性插值）、多项式插值（多项式插值）和样条插值（样条插值）。线性插值（线性插值）是最简单的形式，即在两个已知数据点之间绘制一条直线，以估计中间的值。多项式插值（多项式插值）使用多项式函数来估计值，并且在处理非线性数据集时可以提供比线性插值（线性插值）更平滑的曲线。样条插值（样条插值）是一种更高级的技术，使用分段多项式创建一条通过所有已知数据点的平滑曲线。总之，插值（插值）是数学和各种科学学科中的一个重要工具。它从已知数据中估计未知值的能力使其对研究人员、艺术家和分析师来说都具有不可或缺的价值。通过采用不同的插值（插值）方法，我们可以增强对复杂数据集的理解，在媒体中创造惊人的视觉效果，并根据不完整的信息做出明智的决策。随着我们在日益数字化的世界中收集更多数据，掌握插值（插值）的概念将对任何参与数据驱动领域的人来说仍然至关重要。
