automatic selectivity control

简明释义

自动选择性控制

英英释义

例句

1.The new communication system features automatic selectivity control, which optimizes signal reception based on the surrounding environment.

新通信系统具有自动选择性控制功能，可以根据周围环境优化信号接收。

2.In advanced radar systems, automatic selectivity control enhances target detection capabilities.

在先进的雷达系统中，自动选择性控制增强了目标检测能力。

3.With automatic selectivity control, the device can filter out unwanted frequencies, improving audio clarity.

通过自动选择性控制，设备可以过滤掉不需要的频率，从而提高音频清晰度。

4.The automatic selectivity control feature in this radio allows users to enjoy clearer sound without manual tuning.

这款收音机中的自动选择性控制功能使用户在不需手动调谐的情况下享受更清晰的声音。

5.The implementation of automatic selectivity control in our network devices has significantly reduced interference.

在我们的网络设备中实施自动选择性控制显著减少了干扰。

作文

In today's rapidly advancing technological landscape, the concept of automatic selectivity control has emerged as a pivotal element in enhancing the efficiency of various systems. This term refers to the ability of a system to automatically adjust its parameters to selectively respond to specific inputs or conditions while ignoring irrelevant ones. The significance of automatic selectivity control can be observed in numerous fields, including telecommunications, automotive engineering, and even artificial intelligence. To illustrate, consider the role of automatic selectivity control in telecommunications. Modern communication systems must handle vast amounts of data and numerous signals simultaneously. Without effective selectivity control, these systems would struggle to differentiate between relevant signals and background noise. By employing automatic selectivity control, these systems can filter out unwanted signals, ensuring that only the most pertinent information is processed. This not only enhances the clarity of communication but also optimizes bandwidth usage, leading to more efficient data transmission. In the automotive industry, automatic selectivity control plays a crucial role in advanced driver-assistance systems (ADAS). These systems rely on a variety of sensors to gather information about the vehicle's surroundings. However, not all data is equally important; for instance, differentiating between pedestrians, road signs, and other vehicles is vital for safe navigation. Automatic selectivity control enables the vehicle's systems to prioritize certain inputs over others, thereby improving decision-making processes in real time. This capability is essential for the development of fully autonomous vehicles, where safety and reliability are paramount. Furthermore, in the realm of artificial intelligence, automatic selectivity control assists in optimizing machine learning algorithms. AI systems often deal with large datasets containing both relevant and irrelevant information. By implementing automatic selectivity control, these systems can focus on the most significant features of the data, enhancing their learning efficiency and accuracy. This selective approach not only speeds up the training process but also leads to better performance in tasks such as image recognition and natural language processing. Despite its advantages, the implementation of automatic selectivity control is not without challenges. One major concern is the potential for overfitting, where a system becomes too specialized in filtering out certain inputs, potentially missing out on valuable information. Striking the right balance between selectivity and inclusiveness is crucial for the success of any system employing this technology. Moreover, as systems become increasingly complex, developing robust algorithms that can effectively manage automatic selectivity control requires ongoing research and innovation. In conclusion, automatic selectivity control is a transformative concept that significantly impacts various industries by enhancing the efficiency and effectiveness of systems. Its ability to filter and prioritize information allows for improved performance in telecommunications, automotive engineering, and artificial intelligence. As technology continues to evolve, the importance of automatic selectivity control will only grow, making it an essential area of focus for researchers and engineers alike. Understanding and mastering this concept will undoubtedly pave the way for future advancements and innovations across multiple domains.

在当今快速发展的技术环境中，‘自动选择性控制’这一概念已成为提高各种系统效率的重要元素。这个术语指的是一个系统能够自动调整其参数，以选择性地响应特定输入或条件，同时忽略无关的输入。‘自动选择性控制’的重要性可以在多个领域中观察到，包括电信、汽车工程甚至人工智能。例如，考虑一下‘自动选择性控制’在电信中的作用。现代通信系统必须同时处理大量数据和众多信号。如果没有有效的选择性控制，这些系统将难以区分相关信号和背景噪声。通过采用‘自动选择性控制’，这些系统可以过滤掉不必要的信号，确保只有最重要的信息被处理。这不仅提高了通信的清晰度，还优化了带宽使用，从而实现更高效的数据传输。在汽车行业中，‘自动选择性控制’在高级驾驶辅助系统（ADAS）中发挥着至关重要的作用。这些系统依赖各种传感器收集有关车辆周围环境的信息。然而，并非所有数据都是同等重要的；例如，区分行人、交通标志和其他车辆对于安全导航至关重要。‘自动选择性控制’使车辆的系统能够优先考虑某些输入，从而实时改善决策过程。这种能力对于开发完全自动驾驶的车辆至关重要，因为安全性和可靠性是首要考虑因素。此外，在人工智能领域，‘自动选择性控制’有助于优化机器学习算法。AI系统通常处理包含相关和无关信息的大型数据集。通过实施‘自动选择性控制’，这些系统可以专注于数据中最重要的特征，提高学习效率和准确性。这种选择性的方法不仅加快了训练过程，而且在图像识别和自然语言处理等任务中也提高了性能。尽管‘自动选择性控制’具有诸多优势，但其实施并非没有挑战。其中一个主要问题是过拟合的潜在风险，即系统在过滤特定输入时变得过于专业化，可能会错过有价值的信息。在任何采用此技术的系统中，找到选择性与包容性之间的正确平衡至关重要。此外，随着系统变得越来越复杂，开发能够有效管理‘自动选择性控制’的强大算法需要持续的研究和创新。总之，‘自动选择性控制’是一个变革性的概念，显著影响着各个行业，通过提高系统的效率和效果。它过滤和优先处理信息的能力使电信、汽车工程和人工智能的性能得以改善。随着技术的不断发展，‘自动选择性控制’的重要性只会增加，使其成为研究人员和工程师的一个重要关注领域。理解和掌握这一概念无疑将为未来在多个领域的进步和创新铺平道路。