摘要
一位新闻记者兼机器人与机器学习的新闻编辑每日新闻-在一份新的报告中讨论了人工智能的研究结果。根据来自中华人民共和国北京的新闻报道，研究国家D的NewsRx记者说，"低语画廊模式(WGM)微腔由于高品质的因子和小的模体积，是一个非常好的对Netra敏感的传感平台"。《新闻编辑》引用了北京邮电大学的一篇研究文章：“然而，传统的跟踪单模变化的传感方法难以充分利用传感信息，限制了测量精度和动态范围，本文在(PMBR)微泡谐振器上演示了一种基于多模传感方法的高精度温度传感器。”采用一种低成本的宽带光谱光源作为探测光，为高精度测量提供了更多的传感模式，利用机器学习方法充分利用了多模光谱信息，精确读出了真实温度，MEA N平方误差(MSE)为0.0138.，检测限比单模传感方法低3倍，可达0.117°C。在25-45°C的测量范围内，预测与真相之间的相关系数(2)高达0.9996.
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on artificial intell igence are discussed in a new report. According to news reporting originating fr om Beijing, People’s Republic of China, by NewsRx correspondents, research state d, “Whispering gallery mode (WGM) microcavities are excellent platforms for ultr a-sensitive sensing due to high-quality factor and small mode volume.” The news editors obtained a quote from the research from Beijing University of P osts and Telecommunications: “However, the conventional sensing method by tracki ng single-mode changes is difficult to fully utilize the sensing information, wh ich limits the measurement precision and dynamical range. Here, we demonstrate a high-precision temperature sensor based on the multimode sensing method in a pa ckaged microbubble resonator (PMBR). Remarkably, a low-cost broadband spectrum s ource is used as probe light to provide more sensing modes for high-precision me asurement. Empowered by a machine learning method, the multimode spectral inform ation are fully utilized, and the true temperature is precisely readout with mea n-squared error (MSE) of 0.0138. The detection limit is lower three times than s ingle-mode sensing method, capable of reaching 0.117 °C. In addition, the correl ation coefficient (R2) between predictions and truth is as high as 0.9996 within the measurement range of 25-45 °C.”
NETL
NSTL