random noise

简明释义

随机噪声

英英释义

例句

1.The audio recording was filled with random noise 随机噪声, making it difficult to hear the speaker.

音频录音中充满了随机噪声，随机噪声，这使得听到讲者变得困难。

2.To improve image quality, we applied a filter to reduce random noise 随机噪声 in the photographs.

为了提高图像质量，我们应用了一个滤镜来减少照片中的随机噪声。

3.In the experiment, we had to filter out the random noise 随机噪声 to obtain accurate results.

在实验中，我们必须过滤掉随机噪声以获得准确的结果。

4.During the call, there was a lot of random noise 随机噪声 in the background, making it hard to concentrate.

通话期间，背景中有很多随机噪声，这使得很难集中注意力。

5.The engineer noted that the system's performance could be affected by random noise 随机噪声 in the data.

工程师指出，系统性能可能会受到数据中随机噪声的影响。

作文

In the field of science and engineering, the concept of random noise plays a significant role in understanding various phenomena. Random noise refers to the unpredictable fluctuations that occur in any signal or data set. These fluctuations can arise from numerous sources, including environmental factors, equipment limitations, or inherent uncertainties in measurements. Understanding random noise is crucial because it can obscure the true signals we seek to analyze or interpret.For instance, consider the world of audio engineering. When recording sound, engineers must deal with random noise that can interfere with the quality of the audio. This noise might come from electrical interference, mechanical vibrations, or even background sounds that are not intended to be part of the recording. To ensure a clean sound, audio professionals employ various techniques to minimize random noise, such as using soundproof rooms, high-quality microphones, and advanced editing software.In scientific research, random noise can significantly impact the results of experiments. For example, in physics experiments that measure tiny changes in energy levels, random noise can mask the subtle signals that researchers are trying to detect. Scientists often use statistical methods to filter out random noise from their data, allowing them to focus on the meaningful patterns and trends that emerge from their observations. By applying noise reduction techniques, they can improve the accuracy of their findings and draw more reliable conclusions.Moreover, in the realm of data analysis and machine learning, random noise poses a challenge for algorithms that rely on large datasets. If the data contains a significant amount of random noise, it can lead to inaccurate predictions and poor model performance. Data scientists must carefully preprocess their data to identify and reduce random noise, ensuring that the models they build are robust and effective.Despite its challenges, random noise also serves a purpose in certain contexts. In communication systems, for example, random noise can be used to enhance security. By introducing controlled random noise into signals, it becomes more difficult for unauthorized listeners to intercept and decode the information being transmitted. This technique, known as noise masking, is essential for maintaining the confidentiality of sensitive communications.In summary, random noise is an integral aspect of many fields, from audio engineering to scientific research and data analysis. While it can complicate our efforts to obtain clear signals and accurate results, it also offers opportunities for innovation and security. As technology continues to evolve, understanding and managing random noise will remain a critical skill for professionals across various disciplines. By developing effective strategies to mitigate its effects, we can enhance our ability to analyze, interpret, and communicate information in an increasingly complex world.

在科学和工程领域，随机噪声的概念在理解各种现象中发挥着重要作用。随机噪声指的是在任何信号或数据集中发生的不可预测的波动。这些波动可能来自许多来源，包括环境因素、设备限制或测量中的固有不确定性。理解随机噪声至关重要，因为它可能会掩盖我们试图分析或解释的真实信号。例如，考虑音频工程的世界。在录音时，工程师必须处理可能干扰音频质量的随机噪声。这种噪声可能来自电气干扰、机械振动，甚至是录音中不打算包含的背景声音。为了确保清晰的声音，音频专业人士采用各种技术来最小化随机噪声，例如使用隔音房间、高质量麦克风和先进的编辑软件。在科学研究中，随机噪声可能会显著影响实验结果。例如，在测量微小能量变化的物理实验中，随机噪声可能掩盖研究人员试图检测的微弱信号。科学家们通常使用统计方法来过滤掉数据中的随机噪声，使他们能够专注于从观察中出现的有意义的模式和趋势。通过应用降噪技术，他们可以提高研究结果的准确性，并得出更可靠的结论。此外，在数据分析和机器学习领域，随机噪声对依赖大数据集的算法构成了挑战。如果数据中含有大量随机噪声，可能导致不准确的预测和较差的模型性能。数据科学家必须仔细预处理数据，以识别和减少随机噪声，确保他们构建的模型稳健且有效。尽管存在挑战，随机噪声在某些情况下也发挥着作用。例如，在通信系统中，随机噪声可用于增强安全性。通过将受控的随机噪声引入信号中，使得未授权的监听者更难以拦截和解码正在传输的信息。这种技术称为噪声掩蔽，对于保持敏感通信的机密性至关重要。总之，随机噪声是许多领域的一个重要方面，从音频工程到科学研究和数据分析。虽然它可能会使我们获得清晰信号和准确结果的努力变得复杂，但它也为创新和安全提供了机会。随着技术的不断发展，理解和管理随机噪声将仍然是各个学科专业人士的一项关键技能。通过制定有效的策略来减轻其影响，我们可以增强在日益复杂的世界中分析、解释和交流信息的能力。