interpolating

简明释义

英[ˌɪntəˈpəʊleɪtɪŋ]美[ˌɪntərˈpoʊleɪtɪŋ]

n. 插值；内插

v. 窜改；加入（额外的事）（interpolate 的现在分词形式）

英英释义

单词用法

同义词

反义词

例句

1.Two classes of general bivariate interpolating frames are established by introducing multiple parameters.

本文通过引进多参数建立了二元插值的一般框架。

2.This new method features relatively high interpolating speed, and easily improving the homogeneity of interpolating instruction impulses.

可使插补速度相对提高，易改善插补指令脉冲的均匀性。

3.In the case of GPU, we can also use interpolating texture units instead of computing some expensive expression.

在GPU情况下，我们也能使用中断处理单元替代高昂的计算处理。

4.A novel encoding scheme with high speed and low power is proposed for folding and interpolating ADC.

提出了一种新的适用于折叠插值型adc的高速低功耗的编码器。

5.At first, the indefinite integral of binary fractal interpolating function generated by IFS is proved, and its IFS is given.

首先证明二元插值函数的不定积分也是由迭代函数系迭代生成的，并得到了其迭代函数系。

6.These filter banks have quincunx sampling form and good interpolating characteristic, which can be applied in image decomposition and coding.

该滤波器具有五株排列的交错采样形式和良好的内插性能，适用于图像分解压缩。

7.Giving the number of segment points, a polygonal approximation method is presented. Then a fast contour interpolating method is introduced.

提出一种给定分段点数的轮廓多边形近似方法，继而提出一种快速断面图象间轮廊插值方法。

8.These filter banks have quincunx sampling form and good interpolating characteristic, which can be applied in image decomposition and coding.

该滤波器具有五株排列的交错采样形式和良好的内插性能，适用于图像分解压缩。

9.In computer graphics, interpolating colors can create smooth transitions.

在计算机图形中，插值颜色可以创建平滑的过渡。

10.The scientist is interpolating the data points to predict future trends.

科学家正在插值数据点以预测未来趋势。

11.When interpolating data, it's important to choose the right method for accuracy.

在插值数据时，选择正确的方法以确保准确性非常重要。

12.By interpolating between the known values, we can estimate the missing information.

通过插值已知值，我们可以估计缺失的信息。

13.The algorithm works by interpolating the images to enhance resolution.

该算法通过插值图像来增强分辨率。

作文

In the field of mathematics and data analysis, the concept of interpolating is crucial for understanding how to estimate unknown values within a range of known values. This technique allows us to create new data points from existing ones, providing insights that would otherwise remain hidden. For instance, when we have a set of measurements taken at specific intervals, we can use interpolating methods to fill in the gaps between these measurements, creating a more complete picture of the data. Consider a simple example of temperature readings taken every hour. If we have temperatures recorded at 1 PM, 2 PM, and 4 PM, we might want to know what the temperature was at 3 PM. By interpolating the known values, we can estimate that the temperature at 3 PM was likely somewhere between the readings at 2 PM and 4 PM. This process not only helps in making educated guesses but also enhances our ability to analyze trends over time.There are various methods available for interpolating data, each with its own advantages and disadvantages. Linear interpolation is one of the simplest forms, where we assume that the change between two known points is constant. However, this method may not always accurately reflect the true nature of the data, especially if the data points are not linear. More sophisticated methods, such as polynomial or spline interpolation, can provide better estimates by fitting curves to the data points rather than straight lines.The importance of interpolating extends beyond mathematics into many real-world applications. In fields such as meteorology, economics, and engineering, professionals often rely on interpolating techniques to make predictions based on incomplete data. For example, meteorologists may use interpolating to predict weather patterns based on temperature and pressure readings from various locations. Similarly, in economics, analysts might interpolating historical data to forecast future market trends, providing valuable insights for investors and businesses.Moreover, the advent of technology has further enhanced our ability to perform interpolating efficiently. With the use of software tools and algorithms, we can now handle large datasets and complex calculations that would have been impossible manually. This capability allows researchers and analysts to visualize data in ways that were previously unimaginable, leading to more informed decision-making processes.In conclusion, interpolating is an essential technique in both theoretical and applied contexts. It enables us to draw meaningful conclusions from incomplete data, facilitating better understanding and predictions across various fields. As we continue to generate vast amounts of data in our increasingly digital world, mastering the art of interpolating will be vital for anyone looking to make sense of the information at their disposal. Whether in science, business, or everyday life, the ability to estimate and predict using interpolating methods will remain a valuable skill for the foreseeable future.

在数学和数据分析领域，插值的概念对于理解如何在已知值范围内估计未知值至关重要。这种技术使我们能够从现有的数据点创建新的数据点，从而提供否则将保持隐藏的洞察。例如，当我们在特定时间间隔内有一组测量值时，我们可以使用插值方法填补这些测量之间的空白，创建更完整的数据图景。考虑一个简单的例子，即每小时记录的温度读数。如果我们在下午1点、下午2点和下午4点记录了温度，我们可能想知道下午3点的温度是多少。通过插值已知值，我们可以估计下午3点的温度很可能在下午2点和下午4点的读数之间。这一过程不仅有助于进行有根据的猜测，还增强了我们分析趋势的能力。有多种方法可用于插值数据，每种方法都有其优缺点。线性插值是最简单的形式之一，我们假设两个已知点之间的变化是恒定的。然而，这种方法可能并不总是准确反映数据的真实性质，尤其是当数据点不是线性时。更复杂的方法，如多项式或样条插值，通过拟合曲线而不是直线来提供更好的估计。插值的重要性超越了数学，延伸到许多现实应用中。在气象学、经济学和工程等领域，专业人员通常依赖于插值技术根据不完整的数据做出预测。例如，气象学家可能利用插值根据来自不同地点的温度和压力读数预测天气模式。同样，在经济学中，分析师可能会插值历史数据来预测未来市场趋势，为投资者和企业提供有价值的洞察。此外，科技的进步进一步增强了我们高效执行插值的能力。借助软件工具和算法，我们现在可以处理大量数据集和复杂计算，这在手动操作时是无法实现的。这种能力使研究人员和分析师能够以前所未有的方式可视化数据，从而促进更明智的决策过程。总之，插值是一种在理论和应用上下文中都至关重要的技术。它使我们能够从不完整的数据中得出有意义的结论，从而促进各个领域的更好理解和预测。随着我们在日益数字化的世界中继续生成大量数据，掌握插值的艺术对于任何希望理解手头信息的人来说都是至关重要的。无论是在科学、商业还是日常生活中，使用插值方法进行估计和预测的能力将在可预见的未来仍然是一项宝贵的技能。