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Comparison of Sound Velocity Estimation and Classification Methods for Ultrasonic Testingof Cheese

机译:奶酪超声波测试的声速估计和分类方法的比较

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摘要

Testing of two methods novel to ultrasonic measurements was carried out on cheese samples to estimate the Time-of-Flight (TOF) parameter. The Short Time Average /Long Time Average (STA/LTA) method and the Autoregressive Akaike Information Criterion Picker (AR-AIC picker) method are used mainly in seismology for earthquake event detection. The STA/LTA method proved to be ineffective with such noise level that is present during ultrasonic measurements, but the AIC picker algorithm yielded reliable results. A new approach for classification was tested on two types of samples, those were matching in composition, but different in treatment and texture. The method used is based on the results of wavelet decomposition, and after retrieving sufficient spectral data, a linear discriminant analysis (DA) resulted in 100% correct classification, which was compared to the DA classification results based on other methods.
机译:对奶酪样品进行了两种超声波测量方法的测试,以估计飞行时间(TOF)参数。短时平均/长时平均(STA / LTA)方法和自回归赤池信息准则选择器(AR-AIC选择器)方法主要用于地震学中的地震事件检测。事实证明,STA / LTA方法对于这种在超声波测量过程中出现的噪声水平无效,但是AIC Picker算法产生了可靠的结果。在两种类型的样品上测试了一种新的分类方法,它们的成分匹配,但处理和质地不同。所使用的方法基于小波分解的结果,并且在检索到足够的光谱数据之后,线性判别分析(DA)得出100%正确的分类,并将其与基于其他方法的DA分类结果进行了比较。

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