With the proliferation of the Internet of Things, a large amount of data is generated constantly by industrial systems, corresponding in many cases to critical tasks. It is particularly important to detect abnormal data to ensure the accuracy of data. Aiming at the problem that the training data are contaminated with anomalies in autoencoder-based anomaly detection, which makes it difficult to distinguish abnormal data from normal data, this paper proposes a data anomaly detection method that combines an isolated forest (iForest) and autoencoder algorithm. In this method (iForest-AE), the iForest algorithm was used to calculate the anomaly score of energy data, and the data with a lower anomaly score were selected for model training. After the test data passed through the autoencoder trained by normal data, the data whose reconstruction error was larger than the threshold were determined as an anomaly. Experiment results on the electricity consumption dataset showed that the iForest-AE method achieved an F1 score of 0.981, which outperformed other detection methods, and a significant advantage in anomaly detection.
To the Editor: The common femoral artery is the most popular puncture access route for surgical intervention. The distal end of the common femoral artery where the deep femoral artery and the superficial femoral artery bifurcate is difficult to identify, especially when vascular surgeons perform antegrade femoral artery puncture to treat ipsilateral lower extremity artery disease. Many studies have reported methods to anatomically localize the femoral head on the X-ray. Here, we introduce an anatomic landmark, Shenton's line, which refers to the arc line connecting the internal lateral margin of the femur neck and the inferior margin of the pubic bone in a normal anteroposterior radiograph of the pelvis.
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