High-power multi-beam klystrons represent a key component to amplify RF to
generate the accelerating field of the superconducting radio frequency (SRF)
cavities at European XFEL. Exchanging these high-power components takes time
and effort, thus it is necessary to minimize maintenance and downtime and at
the same time maximize the device's operation. In an attempt to explore the
behavior of klystrons using machine learning, we completed a series of
experiments on our klystrons to determine various operational modes and conduct
feature extraction and dimensionality reduction to extract the most valuable
information about a normal operation. To analyze recorded data we used
state-of-the-art data-driven learning techniques and recognized the most
promising components that might help us better understand klystron operational
states and identify early on possible faults or anomalies.

为探索通过机器学习研究速调管的行为，我们完成了一系列实验，确定了各种操作模式，并进行了特征提取和降维，以提取有关正常运行的最有价值信息，使用先进的数据驱动学习技术分析记录的数据，识别可能有助于更好地理解速调管运行状态并及早发现故障或异常的最有前途的组件。

欧洲 XFEL 电子管自动异常检测

Automated Anomaly Detection on European XFEL Klystrons

We present a self-supervised learning approach to learning monocular 3D face
reconstruction with a pose guidance network (PGN). First, we unveil the
bottleneck of pose estimation in prior parametric 3D face learning methods, and
propose to utilize 3D face landmarks for estimating pose parameters. With our
specially designed PGN, our model can learn from both faces with fully labeled
3D landmarks and unlimited unlabeled in-the-wild face images. Our network is
further augmented with a self-supervised learning scheme, which exploits face
geometry information embedded in multiple frames of the same person, to
alleviate the ill-posed nature of regressing 3D face geometry from a single
image. These three insights yield a single approach that combines the
complementary strengths of parametric model learning and data-driven learning
techniques. We conduct a rigorous evaluation on the challenging AFLW2000-3D,
Florence and FaceWarehouse datasets, and show that our method outperforms the
state-of-the-art for all metrics.

本研究提出自监督学习的方法，使用姿势引导网络 (PGN) 进行单眼 3D 人脸重建，并通过结合参数模型的学习和数据驱动的学习技术，从完全标记的 3D 地标和无限制的野外面部图像中学习。通过使用包含同一人物多帧嵌入的面部几何信息的自监督学习方案，缓解了从单个图像回归 3D 面部几何的不适定性。在 AFLW2000-3D、Florence 和 FaceWarehouse 数据集上进行了严格评估，并表明我们的方法在所有指标上均优于最先进技术。