In this paper, we explore the optimization of metal recycling with a focus on
real-time differentiation between alloys of copper and aluminium. Spectral
data, obtained through Prompt Gamma Neutron Activation Analysis (PGNAA), is
utilized for classification. The study compares data from two detectors, cerium
bromide (CeBr$_{3}$) and high purity germanium (HPGe), considering their energy
resolution and sensitivity. We test various data generation, preprocessing, and
classification methods, with Maximum Likelihood Classifier (MLC) and
Conditional Variational Autoencoder (CVAE) yielding the best results. The study
also highlights the impact of different detector types on classification
accuracy, with CeBr$_{3}$ excelling in short measurement times and HPGe
performing better in longer durations. The findings suggest the importance of
selecting the appropriate detector and methodology based on specific
application requirements.

本研究探讨了金属回收优化，重点关注铜和铝合金的实时区分。通过脉冲中子活化分析（PGNAA）获得的光谱数据用于分类。研究比较了两种探测器（铈溴化物和高纯锗）的数据，考虑了其能量分辨率和灵敏度。我们测试了各种数据生成、预处理和分类方法，最大似然分类器（MLC）和条件变分自编码器（CVAE）取得了最佳结果。研究还强调了不同探测器类型对分类准确性的影响，铈溴化物在短测量时间上表现出色，高纯锗在较长持续时间上表现更佳。研究结果表明根据具体应用要求选择合适的探测器和方法的重要性。

PGNAA 光谱分类铝合金和铜合金的机器学习

PGNAA Spectral Classification of Aluminium and Copper Alloys with  Machine Learning

The pursuit of novel alloys tailored to specific requirements poses
significant challenges for researchers in the field. This underscores the
importance of developing predictive techniques for essential physical
properties of alloys based on their chemical composition and processing
parameters. This study introduces AlloyBERT, a transformer encoder-based model
designed to predict properties such as elastic modulus and yield strength of
alloys using textual inputs. Leveraging the pre-trained RoBERTa encoder model
as its foundation, AlloyBERT employs self-attention mechanisms to establish
meaningful relationships between words, enabling it to interpret human-readable
input and predict target alloy properties. By combining a tokenizer trained on
our textual data and a RoBERTa encoder pre-trained and fine-tuned for this
specific task, we achieved a mean squared error (MSE) of 0.00015 on the Multi
Principal Elemental Alloys (MPEA) data set and 0.00611 on the Refractory Alloy
Yield Strength (RAYS) dataset. This surpasses the performance of shallow
models, which achieved a best-case MSE of 0.00025 and 0.0076 on the MPEA and
RAYS datasets respectively. Our results highlight the potential of language
models in material science and establish a foundational framework for
text-based prediction of alloy properties that does not rely on complex
underlying representations, calculations, or simulations.

使用文本输入预测合金的弹性模量和屈服强度，AlloyBERT 是一种基于变压器编码器的模型，使用 RoBERTa 作为基础，通过自注意机制建立单词之间的关联，实现对合金属性的预测。