论文标题
“及时伽马中子激活分析（PGNAA）”使用深度学习方法的金属光谱分类
"Prompt-Gamma Neutron Activation Analysis (PGNAA)" Metal Spectral Classification using Deep Learning Method
论文作者
论文摘要
市场需求紧迫，以最大程度地减少迅速伽玛中子激活分析（PGNAA）光谱测量机的测试时间，以便它可以充当即时材料分析仪，例如立即对废物样品进行分类，并根据检测到的样品的构成确定最佳的回收方法。 本文介绍了深度学习分类的新开发，并旨在减少PGNAA机器的测试时间。我们提出了随机抽样方法和类激活图（CAM），以生成“缩小”样品并连续训练CNN模型。随机采样方法（RSM）旨在减少样品中的测量时间，而类激活图（CAM）用于滤除缩小样品的不太重要的能量范围。 我们将总PGNAA测量时间缩短到2.5秒，同时确保我们的数据集的精度约为96.88％，该数据集使用12种不同的物质。与分类不同的材料分类相比，对于具有相同元素的物质以归档良好的精度，它需要更多的测试时间（样品计数率）。例如，铜合金的分类需要将近24秒的测试时间才能达到98％的精度。
There is a pressing market demand to minimize the test time of Prompt Gamma Neutron Activation Analysis (PGNAA) spectra measurement machine, so that it could function as an instant material analyzer, e.g. to classify waste samples instantaneously and determine the best recycling method based on the detected compositions of the testing sample. This article introduces a new development of the deep learning classification and contrive to reduce the test time for PGNAA machine. We propose both Random Sampling Methods and Class Activation Map (CAM) to generate "downsized" samples and train the CNN model continuously. Random Sampling Methods (RSM) aims to reduce the measuring time within a sample, and Class Activation Map (CAM) is for filtering out the less important energy range of the downsized samples. We shorten the overall PGNAA measuring time down to 2.5 seconds while ensuring the accuracy is around 96.88 % for our dataset with 12 different species of substances. Compared with classifying different species of materials, it requires more test time (sample count rate) for substances having the same elements to archive good accuracy. For example, the classification of copper alloys requires nearly 24 seconds test time to reach 98 % accuracy.
