摘要
机器人与机器学习的新闻编辑每日新闻-机器学习的新研究是一篇报道的主题。根据News Rx编辑在加纳库马西的新闻报道，研究表明，“柴油掺假是包括加纳在内的大多数发展中国家的主要担忧，尽管采取了许多监管计划。加纳目前使用的溶剂示踪分析方法多年来受到了一些限制，影响了该计划的总体实施。”我们的新闻记者从夸梅·恩克鲁玛科技大学的研究中获得了一句话，“因此，需要替代或补充工具来帮助检测汽车燃料的掺假。在此，我们描述了一种结合核磁共振波谱和机器学习算法来检测柴油掺假的两级分类方法。机器学习算法训练中使用的训练集包含20-40%w/w掺假，这一水平通常在加纳发现。在第一级，建立了柴油样品的分类模型,将柴油样品分为纯柴油样品和掺假柴油样品,成年柴油样品进入第二阶段,第二阶段由第二个分类模型识别掺假柴油样品的类型(煤油、石脑油、汽油用核磁共振氢谱对样本进行分析，所得数据用于构建和验证支持向量机(SVM)分类模型，在1级，SVM模型对所有200个样本进行分类，分类误差仅为2.5%，开发的2级分类模型对煤油和柴油预混没有分类误差。对掺石脑油样品的分类误差为2.5%。尽管所提出的方案性能良好，但由于两种模型的训练SE都含有20%w/w以上的掺杂物，因此掺杂物水平低于20%w/w的预测结果明显不稳定。
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 Kumasi, Ghana, by News Rx editors, research stated, “Adulteration of diesel fuel poses a major concern in most developing countries including Ghana despite the many regulatory schemes adopted. The solvent tracer analysis approach currently used in Ghana has over the years suffered several limitations which affect the overall implementation o f the scheme.” Our news journalists obtained a quote from the research from the Kwame Nkrumah U niversity of Science and Technology, “There is therefore a need for alternative or supplementary tools to help detect adulteration of automotive fuel. Herein we describe a two-level classification method that combines NMR spectroscopy and m achine learning algorithms to detect adulteration in diesel fuel. The training s ets used in training the machine learning algorithms contained 20-40% w/w adulterant, a level typically found in Ghana. At the first level, a classifi cation model is built to classify diesel samples as neat or adulterated. Adulter ated samples are passed on to the second stage where a second classification mod el identifies the type of adulterant (kerosene, naphtha, or premix) present. Sam ples were analyzed by H NMR spectroscopy and the data obtained were used to buil d and validate support vector machine (SVM) classification models at both levels . At level 1, the SVM model classified all 200 samples with only 2.5% classification errors after validation. The level 2 classification model develop ed had no classification errors for kerosene and premix in diesel. However, 2.5% classification error was recorded for samples adulterated with naphtha. Despite the great performance of the proposed schemes, it showed significantly erratic p redictions with adulterant levels below 20% w/w as the training se ts for both models contained adulterants above 20% w/w.”
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