![]() ![]() A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.įirst Author et al. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:Īs the author, you retain the copyright to your Work. ![]() A Creative Commons license does not relinquish the author’s copyright rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. This work is licensed under a Creative Commons Attribution 3.0 Unported License. Finally, we demonstrate the significance of the proposed method using real flight data from JFK to LAX. ![]() For anomaly prediction, we will build up a point-wise prediction framework based on the Hidden Markov Model and Convectional LSTM to predict the probability that the pilot would deviate from the flight plan. For anomaly diagnostics, we would like to link the entire anomalous trajectory sequences with the convective weather data and identify the important weather impact factors base on XGBoost and time-series feature engineering. For anomaly detection, we propose to apply the CUSUM chart to detect the abnormal trajectory point which differs from the flight plan. Our approach considers the problem of trajectory deviation as the "anomaly" and builds up an analytics pipeline for anomaly detection, anomaly diagnostics, and anomaly prediction. However, the complexity of the weather information and various human factors make it hard to build up an accurate trajectory prediction framework. In order to solve this problem, most of the existing research attempt to build up a stochastic trajectory prediction model to capture the influence of the weather. Such uncertainty will result in an inappropriate decision for flight management. However, there are various impact factors which will cause a large deviation between the actual flight and the original flight plan. In addition, we design a toy dataset to prove that our model can better balance the learning ability to adapt to different detection demands.With ahead-of-time aircraft management, we are able to reduce aircraft collision and improve air traffic capacity. We conduct experiments on three benchmarks and perform extensive analysis, and the results demonstrate that our method performs comparablely to the state-of-the-art methods. Since the anomaly set is complicated and unbounded, our STHA can adjust its detection ability to adapt to the human detection demands and the complexity degree of anomaly that happened in the history of a scene. Thus, STHA can provide various representation learning abilities by expanding or contracting hierarchically to detect anomalies of different degrees. Considering the multisource knowledge of videos, we also model the spatial normality of video frames and temporal normality of RGB difference by designing two parallel streams consisting of stacks. Then, we stack blocks according to the complexity degrees with both intra-stack and inter-stack residual links to learn hierarchical normality gradually. Specifically, we design several auto-encoder-based blocks that possess varying capacities for extracting normal patterns. The comprehensive structure of the STHA is delineated into a tripartite hierarchy, encompassing the following tiers: the stream level, the stack level, and the block level. Unlike previous unsupervised VAD methods that adopt a fixed structure to learn normality without considering different detection demands, we design a spatial-temporal hierarchical architecture (STHA) as a configurable architecture to flexibly detect different degrees of anomaly. Video anomaly detection (VAD) is a vital task with great practical applications in industrial surveillance, security system, and traffic control. ![]()
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