scalings

简明释义

英[ˈskeɪlɪŋz]美[ˈskeɪlɪŋz]

落下的石块

英英释义

单词用法

同义词

反义词

例句

1.Motion is specified by translations, rotations, and nonuniform scalings of the local coordinate system of each moving object.

运动通过平移，旋转，以及每一个移动对象的局部坐标系统的非一致缩放来实现。

2.Motion is specified by translations, rotations, and nonuniform scalings of the local coordinate system of each moving object.

运动通过平移，旋转，以及每一个移动对象的局部坐标系统的非一致缩放来实现。

3.Slag, dross (other than granulated slag), scalings and other waste from the manufacture of iron or steel.

冶炼钢铁所产生的熔渣、浮渣(粒状熔渣除外)、氧化皮及其他废料。

4.The engineer proposed several scalings 缩放 for the product line to increase efficiency.

工程师提出了几种产品线的缩放以提高效率。

5.During the presentation, we discussed the scalings 缩放 needed for our marketing strategy.

在演示中，我们讨论了我们的营销策略所需的缩放。

6.In machine learning, scalings 缩放 of data features can significantly impact model performance.

在机器学习中，数据特征的缩放可以显著影响模型性能。

7.The software is designed to handle multiple scalings 缩放 of the database to accommodate user growth.

该软件旨在处理数据库的多次缩放以适应用户增长。

8.The architect implemented various scalings 缩放 in the design to fit different site conditions.

建筑师在设计中实施了各种缩放以适应不同的场地条件。

作文

In the realm of mathematics and science, the concept of scalings plays a crucial role in understanding various phenomena. Scalings refer to the process of adjusting the size or dimensions of an object, function, or dataset in a proportional manner. This can be particularly useful in fields such as physics, engineering, and computer graphics, where precise measurements and representations are essential.For instance, when creating a model for architectural designs, architects often use scalings to ensure that their blueprints accurately reflect the intended dimensions of the final structure. By applying scalings, they can create a smaller version of the building that maintains the same proportions as the actual construction. This allows for easier visualization and communication of ideas to clients and stakeholders.In the world of data analysis, scalings are equally significant. When working with datasets that vary drastically in size, applying scalings can help normalize the data, making it easier to analyze and interpret. For example, in machine learning, features with different ranges can lead to biased predictions. To mitigate this issue, data scientists often apply scalings such as min-max normalization or standardization, ensuring that each feature contributes equally to the model's performance.Moreover, scalings are not limited to physical dimensions or numerical data; they also extend to abstract concepts such as time and space. In physics, the principle of scalings can be observed in the study of fractals, where patterns repeat at different scales. This self-similarity is a fascinating aspect of nature, revealing how scalings can manifest across various levels of complexity.In the field of computer graphics, scalings are integral to rendering images and animations. Artists and developers use scalings to manipulate the size of objects within a scene, ensuring that they appear realistic and proportionate to one another. For example, when designing a video game, developers might apply scalings to characters and environments to create a cohesive visual experience.Furthermore, understanding scalings can enhance our comprehension of social dynamics and economic models. In sociology, researchers might examine how social networks expand through scalings of connections among individuals. Similarly, economists may analyze market trends by applying scalings to various indicators, allowing them to predict future behaviors based on past data.In conclusion, the concept of scalings is multifaceted and applicable across numerous disciplines. Whether in mathematics, science, art, or social sciences, scalings provide a framework for understanding relationships, proportions, and changes. By mastering the idea of scalings, we can improve our analytical skills and enhance our ability to interpret the world around us. As we continue to explore and innovate, the significance of scalings will undoubtedly remain a fundamental aspect of our intellectual pursuits.

在数学和科学领域，scalings的概念在理解各种现象中起着至关重要的作用。Scalings指的是以比例方式调整物体、函数或数据集的大小或维度的过程。这在物理学、工程学和计算机图形学等领域尤其有用，因为精确的测量和表示至关重要。例如，在创建建筑设计模型时，建筑师常常使用scalings来确保他们的蓝图准确反映最终结构的预期尺寸。通过应用scalings，他们可以创建一个较小版本的建筑，保持与实际建筑相同的比例。这使得更容易可视化和与客户及利益相关者沟通想法。在数据分析的世界中，scalings同样重要。当处理大小差异极大的数据集时，应用scalings可以帮助规范化数据，使其更易于分析和解读。例如，在机器学习中，不同范围的特征可能导致偏见预测。为了缓解这一问题，数据科学家通常会应用如最小-最大规范化或标准化等scalings，确保每个特征对模型的性能贡献均等。此外，scalings不仅限于物理尺寸或数值数据；它们还扩展到时间和空间等抽象概念。在物理学中，scalings的原则可以在分形研究中观察到，其中模式在不同的尺度上重复。这种自相似性是自然界的一个迷人方面，揭示了scalings如何在各种复杂性水平中表现出来。在计算机图形学领域，scalings是渲染图像和动画不可或缺的一部分。艺术家和开发人员使用scalings来操纵场景中对象的大小，确保它们看起来真实且彼此成比例。例如，在设计视频游戏时，开发人员可能会对角色和环境应用scalings，以创建一致的视觉体验。此外，理解scalings可以增强我们对社会动态和经济模型的理解。在社会学中，研究人员可能会通过分析个人之间的连接的scalings来研究社交网络如何扩展。同样，经济学家可能会通过对各种指标应用scalings来分析市场趋势，从而根据过去的数据预测未来的行为。总之，scalings的概念是多方面的，并适用于众多学科。无论是在数学、科学、艺术还是社会科学中，scalings都提供了理解关系、比例和变化的框架。通过掌握scalings的思想，我们可以提高我们的分析能力，增强我们解释周围世界的能力。随着我们继续探索和创新，scalings的重要性无疑将始终是我们智力追求的一个基本方面。