ADMOST: UAV Flight Data Anomaly Detection and Mitigation via Online Subspace Tracking

He, Yongfu; Yu, Peng; Wang, Shaojun; Liu, Datong

doi:10.1109/tim.2018.2863499

Cited by 44 publications

(10 citation statements)

References 33 publications

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“…In the equation: M + is the generalized inverse of matrix M. The earliest learning machine principle was to deal with the faults of the feedback neural network of a single airtight layer, but in the later stage, a large number of work-related personnel extended the principle of extreme learning to problems that are not network neural, which also verified the limit. The applicable conditions of the learning machine are lower than the vector mechanism and the least squares mechanism [16], and this is the case for the extreme learning machine in this article.…”

Section: Target Detection Algorithm Based On Deep Learningmentioning

confidence: 91%

The Role of 5G Network Image Information Based on Deep Learning in UAV Prediction Target Trajectory Tracking

Tang

Chen

Wang

et al. 2021

Wireless Communications and Mobile Computing

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With the development of information technology in the network era and the popularization of the 5G era, UAV-related applications are becoming more and more widely used, which is one of the essential basic technologies. Therefore, the technology has great research value and practical significance, a multiobjective detector based on support vector machine (SVM) is designed based on directional gradient histogram (HOG), and the startup method used with cross-validation methods can improve detector performance. It makes the detector accuracy above 98% and has good resistance to the target scale. A real-time target tracker is designed with its rotation variation and with an improved average displacement algorithm. The algorithm must manually select the target model and suggest the target model to achieve automatic acquisition of the target model. Due to the ambiguity of the target tracking state, several judgment conditions are set to determine whether the tracking has failed and whether the tracker state is correctly verified, with several similar target tracking algorithms. When the system is started, the system detects targets frame by frame. And it will locate a possible target by color segmentation and specify the target to be tracked to recommend the relevant model during the tracking process and open the tracker to determine the target tracking state frame by frame and perform target detection at each frame. Then it will find possible goals and will follow them to achieve a balance of stable and real-time system performance, using the results of the TPD-KCF method. The percentage of correctly tracking images can reach 98%, and the efficiency is significantly improved.

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Section: Target Detection Algorithm Based On Deep Learningmentioning

confidence: 91%

The Role of 5G Network Image Information Based on Deep Learning in UAV Prediction Target Trajectory Tracking

Tang

Chen

Wang

et al. 2021

Wireless Communications and Mobile Computing

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“…In [29], an adaptive OCSVM method was designed to realize online novelty detection for time series scenarios. [30] proposed an online subspace tracking algorithm to supervise the flight data anomalies of the unmanned aerial vehicle system. An online and unsupervised anomaly detection algorithm for streaming data was designed in [31] using an array of sliding windows and the probability density-based descriptors.…”

Section: Related Workmentioning

confidence: 99%

Distributed Online Anomaly Detection for Virtualized Network Slicing Environment

Wang¹,

Liang²,

Chen³

et al. 2022

Preprint

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As the network slicing is one of the critical enablers in communication networks, one anomalous physical node (PN) or physical link (PL) in substrate networks that carries multiple virtual network elements can cause significant performance degradation of multiple network slices. To recover the substrate networks from anomaly within a short time, rapid and accurate identification of whether or not the anomaly exists in PNs and PLs is vital. Online anomaly detection methods that can analyze system data in real-time are preferred. Besides, as virtual nodes and links mapped to PNs and PLs are scattered in multiple slices, the distributed detection modes are required to adapt to the virtualized environment. According to those requirements, in this paper, we first propose a distributed online PN anomaly detection algorithm based on a decentralized one-class support vector machine (OCSVM), which is realized through analyzing real-time measurements of virtual nodes mapped to PNs in a distributed manner. Specifically, to decouple the OCSVM objective function, we transform the original problem to a group of decentralized quadratic programming problems by introducing the consensus constraints. The alternating direction method of multipliers is adopted to achieve the solution for the distributed online PN anomaly detection. Next, by utilizing the correlation of measurements between neighbor virtual nodes, another distributed online PL anomaly detection algorithm based on the canonical correlation analysis is proposed. The network only needs to store covariance matrices and mean vectors of current data to calculate the canonical correlation vectors for real-time PL anomaly analysis. The simulation results on both synthetic and real-world network datasets show the effectiveness and robustness of the proposed distributed online anomaly detection algorithms.

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“…Moreover, the pronounced imbalance between positive and negative samples impedes the application of supervised methods. Contrary to supervised detection methods, specific anomaly detection techniques exploit the inherent distributional attributes gleaned from defect-free examples to identify test data abnormalities [14][15][16][17][18]. Several anomaly detection strategies that have exhibited aptitude in specific applications encompass unsupervised clustering [19], employing high-dimensional space classification [20], methodologies to evaluate reconstruction quality [21,22], adversarial training [23][24][25][26], and application of memory-augmented methods [27,28].…”

Section: Introductionmentioning

confidence: 99%

ISRM: introspective self-supervised reconstruction model for rail surface defect detection and segmentation

Li,

Min,

Yue

2024

Meas. Sci. Technol.

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The problems of intrinsic imbalance of the sample and interference from complex backgrounds limit the performance of existing deep learning methods when applied to the detection and segmentation of rail surface defects. To address these issues, an introspective self-supervised reconstruction model (ISRM) is proposed, which only requires normal samples in the training phase and incorporates the concept of self-supervised learning into an introspective autoencoder. The training framework of ISRM first extracts general features using a pretrained Feature Extractor. Subsequently, a Feature Transformer transfers the features to the target domain. Next, a Synthetic Defect Embedder embeds Bessel-Gaussian random defects into the feature space. Finally, the asymmetric autoencoder reconstructs the rail surface features back into image space. The transformation of pretrained features into target-oriented features helps mitigate domain bias. Since defects exhibit higher commonality in the feature space relative to the image space, embedding synthetic defects into the feature space effectively improves training efficiency. Moreover, the adversarial training architecture enhances the clarity of reconstructed images. The impact of core parameters on the model performance is analyzed through ablation experiments. The results from comparative experiments demonstrate that ISRM achieves 98.5% and 97.2% accuracy on defect detection and segmentation tasks, respectively, reducing the error rate by 11.8% and 3.4% compared to the current state-of-the-art model.

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ADMOST: UAV Flight Data Anomaly Detection and Mitigation via Online Subspace Tracking

Cited by 44 publications

References 33 publications

The Role of 5G Network Image Information Based on Deep Learning in UAV Prediction Target Trajectory Tracking

The Role of 5G Network Image Information Based on Deep Learning in UAV Prediction Target Trajectory Tracking

Distributed Online Anomaly Detection for Virtualized Network Slicing Environment

ISRM: introspective self-supervised reconstruction model for rail surface defect detection and segmentation

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