Abstract Anomaly detection addresses the problem of finding unexpected values in data sets. Often, these anomalies, also known as outliers, discordant values, or exceptions, describe patterns in the behavior of the data. Anomaly detection is important because it frequently involves significant and critical information in many application domains. In the case of nuclear fusion, there is a wide variety of anomalies that could be related to plasma behaviors, such as disruptions or low-high (L-H) transitions. In this context, there are known and unknown anomalies, where unknown anomalies represent the largest proportion of the total that can be found in nuclear fusion. This paper presents a study of the application of deep learning and architecture called Autoencoder to detect anomalies predicting (encode-decode) in a discharge.

中文翻译：

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应用深度学习自动识别放电异常模式

摘要 异常检测解决了在数据集中发现意外值的问题。通常，这些异常（也称为异常值、不一致值或异常）描述了数据行为的模式。异常检测很重要，因为它经常涉及许多应用领域中的重要和关键信息。在核聚变的情况下，有各种各样的异常可能与等离子体行为有关，例如中断或低-高 (LH) 转变。在这种情况下，有已知和未知异常，其中未知异常占核聚变中可发现的总数的最大比例。本文介绍了深度学习和称为 Autoencoder 的架构在检测放电中的异常预测（编码-解码）方面的应用研究。