摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-机器学习的新研究是一篇报告的主题。根据NewsR X编辑在意大利都灵的新闻报道，研究表明，“在食品工业中，生产线上自动化工艺的使用越来越多，导致在食品包装中发现污染物的可能性越来越高。在将产品推向市场之前检测这些污染物已经成为至关重要的必要。”这项研究的财政支持来自法意大学。我们的新闻记者引用了都灵理工大学的一篇研究报告，“本文提出了一个开创性的实时检测食品和饮料中污染物的系统，它将微波(MW)传感技术与机器学习(ML)工具相结合，并考虑到水和油作为许多食品和饮料中主要成分的流行，”该方法对微波传感系统进行了全面的研究,从选择合适的频带到表征天线近场区域的特性,最后通过计算散射参数来创建数据集,从而实现了微波传感系统的设计。然后利用支持向量机(SVM)学习算法对两类数据进行分类,包括复数和振幅数据的两类数据进行二进制和多类ss分类.
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting out of Turin, Italy, by NewsR x editors, research stated, "In the food industry, the increasing use of automat ic processes in the production line is contributing to the higher probability of finding contaminants inside food packages. Detecting these contaminants before sending the products to market has become a critical necessity." Financial support for this research came from Franco-Italian University. Our news journalists obtained a quote from the research from Polytechnic Univers ity Torino, "This paper presents a pioneering real-time system for detecting con taminants within food and beverage products by integrating microwave (MW) sensin g technology with machine learning (ML) tools. Considering the prevalence of wat er and oil as primary components in many food and beverage items, the proposed t echnique is applied to both media. The approach involves a thorough examination of the MW sensing system, from selecting appropriate frequency bands to characte rizing the antenna in its near-field region. The process culminates in the colle ction of scattering parameters to create the datasets, followed by classificatio n using the Support Vector Machine (SVM) learning algorithm. Binary and multicla ss classifications are performed on two types of datasets, including those with complex numbers and amplitude data only."
NETL
NSTL