Plasma image classification using cosine similarity constrained convolutional neural network
Plasma image classification using cosine similarity constrained convolutional neural network
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DOI：
10.1017/s0022377822000940
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发表时间：
2022-05
影响因子：
2.5
通讯作者：
Michael J. Falato;B. Wolfe;Tali M. Natan;Xinhua Zhang;Ryan S. Marshall;Yi Zhou;P. Bellan;
中科院分区：
文献类型：
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作者：
Michael J. Falato;B. Wolfe;Tali M. Natan;Xinhua Zhang;Ryan S. Marshall;Yi Zhou;P. Bellan;
等离子体射流在实验室和自然界都得到了广泛的研究。天体物理物体，如黑洞、活动星系核和年轻恒星物体，通常会发射各种形式的等离子体喷流。由于有了类似于天体物理等离子喷注的等离子体喷注实验的数据，对此类数据的分类不仅可能有助于研究实验的基本物理，而且有助于研究天体物理喷注。在这项工作中，我们使用深度学习来处理来自加州理工学院球面实验的所有实验室等离子体图像，时间跨度为20年。我们发现余弦相似度可以辅助特征选择，通过比较特征向量的方向对图像进行分类，并将其作为AlexNet训练等离子体图像分类的损失函数。我们还开发了一种简单的二类和多类分类的向量方向比较算法。使用我们的算法，我们演示了用于区分不稳定列和稳定列的93%的二进制分类和92%的五向分类，该小的标记数据集包括对应于不同扭结不稳定程度的三个类别。
Plasma jets are widely investigated both in the laboratory and in nature. Astrophysical objects such as black holes, active galactic nuclei and young stellar objects commonly emit plasma jets in various forms. With the availability of data from plasma jet experiments resembling astrophysical plasma jets, classification of such data would potentially aid in not only investigating the underlying physics of the experiments but also the study of astrophysical jets. In this work we use deep learning to process all of the laboratory plasma images from the Caltech Spheromak Experiment spanning two decades. We found that cosine similarity can aid in feature selection, classify images through comparison of feature vector direction and be used as a loss function for the training of AlexNet for plasma image classification. We also develop a simple vector direction comparison algorithm for binary and multi-class classification. Using our algorithm we demonstrate 93 % accurate binary classification to distinguish unstable columns from stable columns and 92 % accurate five-way classification of a small, labelled data set which includes three classes corresponding to varying levels of kink instability.