scaler

简明释义

英[ˈskeɪlə(r)]美[ˈskeɪlər]

n. 攀登者；[电子] 定标器；刮器

英英释义

单词用法

同义词

反义词

例句

1.The overall design idea, gross structure, the structure characteristics and selection of circuits of an automatic scaler are described.

介绍了自动定标器的设计思想、总体结构以及各部分电路的结构特点和选取，并简要说明了软件实现的功能。

2.Cruise control commands can be calculated based on the adaptive scaler values.

巡航控制指令可基于所述自适应比例因子的值来计算。

3.The overall design idea, gross structure, the structure characteristics and selection of circuits of an automatic scaler are described. The functions fulfilled by software are also presented.

介绍了自动定标器的设计思想、总体结构以及各部分电路的结构特点和选取，并简要说明了软件实现的功能。

4.Use a Towner U-15 scaler here. Be sure to reach under the particle of calculus and scrape upward.

用着一把“汤纳u –15 ”刮锄器。一定要刮到齿石粒的下面，然后往上刮。

5.Adaptive scaler values can be determined based on a cylinder deactivation signal and calibrated scaler values.

自适应比例因子的值可基于汽缸停用信号和标定比例因子的值来确定。

6.It was worked out to use two scaler potentials method for calculating magnetic field in aluminium reduction cell.

本文采用双标量位法计算铝电解槽的磁场。

7.Concerning the complexity of verification for the Scaler chip's operating at high frequencies, many problems about the setup of the verifying environment need careful treatment.

实际工作频率要求较高的定标器芯片的逻辑验证具有一定的复杂程度，整个逻辑验证环境的搭建需要考虑诸多问题。

8.The other stain is hard. It has to be scraped away with a scaler.

其他染色的部分硬的，要用刮除器把他刮掉。

9.Use a coupling compatible with this scaler.

使用的连接耦合不适合。

10.A data scaler can help improve the performance of machine learning models.

数据缩放器可以帮助提高机器学习模型的性能。

11.The software engineer implemented a scaler to adjust the size of images dynamically.

软件工程师实现了一个缩放器，以动态调整图像的大小。

12.The dentist used a scaler to remove plaque from my teeth.

牙医使用刮牙器来去除我牙齿上的牙菌斑。

13.In climbing, a scaler helps to assess the best route up the rock face.

在攀岩中，攀登者帮助评估最佳的岩壁攀爬路线。

14.During the maintenance check, the technician used a scaler to clean the equipment.

在维护检查期间，技术人员使用清洁器来清洁设备。

作文

In the world of technology and data science, the term scaler (缩放器) is often used to describe a tool or method that adjusts the size of data inputs. This concept is crucial in various fields, including machine learning, where raw data must be transformed into a suitable format for analysis. When working with large datasets, it is essential to ensure that the data is scaled appropriately so that algorithms can function effectively. For example, if we consider a dataset containing features with vastly different ranges, a scaler (缩放器) can help normalize these values, allowing the model to learn more efficiently. One common type of scaler (缩放器) is the Min-Max Scaler, which transforms features by scaling them to a given range, typically between 0 and 1. This method is particularly useful when the distribution of data is not Gaussian. Another popular method is the Standard Scaler, which standardizes features by removing the mean and scaling to unit variance. This approach assumes that the data follows a Gaussian distribution and is often effective in many machine learning applications. Using a scaler (缩放器) is not just about improving the performance of machine learning models; it also plays a critical role in data visualization. When visualizing data, having features on a similar scale makes it easier to interpret patterns and relationships. Without proper scaling, some features may dominate the visual representation, leading to misleading conclusions. Moreover, in the realm of image processing, a scaler (缩放器) can refer to techniques used to resize images. Image scaling is an essential aspect of computer graphics, where images need to be adjusted to fit different display sizes without losing quality. Techniques such as bilinear and bicubic interpolation are commonly used scaling methods that help maintain image clarity while changing dimensions. In conclusion, the concept of a scaler (缩放器) extends beyond mere numerical adjustments in data science; it encompasses various applications across technology and data visualization. Understanding how to implement and utilize different types of scalers (缩放器) is vital for anyone looking to work in fields that rely heavily on data analysis and interpretation. As we continue to generate and analyze massive amounts of data, mastering the use of scalers (缩放器) will remain an essential skill for data scientists and engineers alike, ensuring that they can derive meaningful insights from complex datasets.

在技术和数据科学的世界中，术语scaler（缩放器）通常用来描述一种调整数据输入大小的工具或方法。这个概念在多个领域中至关重要，包括机器学习，在这些领域中，原始数据必须转换为适合分析的格式。当处理大型数据集时，确保数据被适当地缩放是至关重要的，以便算法能够有效地运行。例如，如果我们考虑一个包含范围差异巨大的特征的数据集，scaler（缩放器）可以帮助规范化这些值，从而使模型能够更高效地学习。一种常见的scaler（缩放器）是最小-最大缩放器，它通过将特征缩放到给定范围（通常在0到1之间）来转换特征。这种方法在数据分布不是高斯分布时特别有用。另一种流行的方法是标准缩放器，它通过去除均值并缩放到单位方差来标准化特征。这种方法假设数据遵循高斯分布，并且在许多机器学习应用中通常有效。使用scaler（缩放器）不仅仅是为了提高机器学习模型的性能；它在数据可视化中也起着关键作用。当可视化数据时，具有相似规模的特征使得解释模式和关系变得更加容易。如果没有适当的缩放，一些特征可能会主导视觉表现，导致误导性的结论。此外，在图像处理领域，scaler（缩放器）可以指用于调整图像大小的技术。图像缩放是计算机图形学中的一个重要方面，其中图像需要调整以适应不同的显示尺寸，而不会失去质量。双线性和双立方插值等技术是常用的缩放方法，可以在改变尺寸的同时保持图像的清晰度。总之，scaler（缩放器）的概念不仅仅局限于数据科学中的数值调整；它涵盖了技术和数据可视化中的各种应用。理解如何实施和利用不同类型的scalers（缩放器）对于任何希望在依赖数据分析和解释的领域工作的人来说都是至关重要的。随着我们继续生成和分析大量数据，掌握使用scalers（缩放器）将仍然是数据科学家和工程师的重要技能，确保他们能够从复杂的数据集中提取有意义的见解。