摘要
由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-机器学习的最新研究结果已经发表。根据《来自南京的新闻》，由NewsRx记者报道，研究称，基于机器学习模型的含氧添加剂类型识别方法(est er)异构体，即通过光学诊断的丁酸甲酯、巴豆酸甲酯、丙烯酸乙酯和丙烯酸乙酯)提议。利用光学诊断方法提取火焰特征,建立了三种火焰模型包括随机森林(RF)、人工神经网络(ANN)和支持向量机(SVM)采用电磁脉冲法建立火焰图像与含氧添加剂s之间的关系。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Current study results on Machine Learn ing have been published. According tonews originating from Nanjing, People’s Re public of China, by NewsRx correspondents, research stated,“Based on machine le arning models, an approach for the type recognition of oxygenated additives (est erisomers, i.e., methyl butyrate, methyl crotonate, ethyl acrylate, and ethyl a crylate) via optical diagnosticswas proposed. By utilizing optical diagnostic m ethods flame features were extracted, and three modelsincluding random forest ( RF), artificial neural network (ANN), and support vector machine (SVM), wereemp loyed to establish the relationship between flame images and oxygenated additive s.”
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