• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.2
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• pp.171-177
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• 2012

In this paper, we proposed a algorithm and an operation boundary for fault detection of a onboard GNSS receiver. After aircraft exchange corrections computed by an aircraft receiver, a faulty aircraft receiver is detected by checking consistency of correction. For this purpose, PRC residual is used as the test statistic for fault detection of the onboard GNSS receiver. And operation boundaries are set by using DGPS position error increase with respect to the distance from a reference station. If the fault detection is performed by using aircraft only in operation boundary, the more accurate fault detection can be possible.

• Journal of the Korea Society of Computer and Information
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• v.26 no.8
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• pp.23-30
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• 2021

In this study, we present a comparative analysis of major autoencoder(AE)-based anomaly detection methods for quality determination in the manufacturing process and a new anomaly discrimination criterion. Due to the characteristics of manufacturing site, anomalous instances are few and their types greatly vary. These properties degrade the performance of an AI-based anomaly detection model using the dataset for both normal and anomalous cases, and incur a lot of time and costs in obtaining additional data for performance improvement. To solve this problem, the studies on AE-based models such as AE and VAE are underway, which perform anomaly detection using only normal data. In this work, based on Convolutional AE, VAE, and Dilated VAE models, statistics on residual images, MSE, and information entropy were selected as outlier discriminant criteria to compare and analyze the performance of each model. In particular, the range value applied to the Convolutional AE model showed the best performance with AUC PRC 0.9570, F1 Score 0.8812 and AUC ROC 0.9548, accuracy 87.60%. This shows a performance improvement of an accuracy about 20%P(Percentage Point) compared to MSE, which was frequently used as a standard for determining outliers, and confirmed that model performance can be improved according to the criteria for determining outliers.