A Structured Sparse Subspace Learning Algorithm for Anomaly Detection in UAV Flight Data

He, Yongfu; Yu, Peng; Wang, Shaojun; Liu, Datong; Leong, Philip H. W.

doi:10.1109/tim.2017.2754698

Cited by 33 publications

(11 citation statements)

References 28 publications

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“…For this purpose, they analyzed the behavior of the driver 3 to 10 seconds before the crash as well as online and offline queries posed to detect the abnormal behavior. A structured sparse subspace learning algorithm [32], is developed to detect the anomalous behavior.…”

Section: ) Statistical Methodsmentioning

confidence: 99%

Anomaly Detection, Analysis and Prediction Techniques in IoT Environment: A Systematic Literature Review

2019

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Anomaly detection has attracted considerable attention from the research community in the past few years due to the advancement of sensor monitoring technologies, low-cost solutions, and high impact in diverse application domains. Sensors generate a huge amount of data while monitoring the physical spaces and objects. These huge collected data streams can be analyzed to identify unhealthy behaviors. It may reduce functional risks, avoid unseen problems, and prevent downtime of the systems. Many research methodologies have been designed and developed to determine such anomalous behaviors in security and risk analysis domains. In this paper, we present the results of a systematic literature review about anomaly detection techniques except for these dominant research areas. We focus on the studies published from 2000 to 2018 in the application areas of intelligent inhabitant environments, transportation systems, health care systems, smart objects, and industrial systems. We have identified a number of research gaps related to the data collection, the analysis of imbalanced large datasets, limitations of statistical methods to process the huge sensory data, and few research articles in abnormal behavior prediction in real scenarios. Based on our analysis, researchers and practitioners can acquaint themselves with the existing approaches, use them to solve real problems, and/or further contribute to developing novel techniques for anomaly detection, prediction, and analysis.INDEX TERMS Statistical learning, machine learning, intelligent environments, smart objects, intelligent transportation systems, industrial systems.

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Section: ) Statistical Methodsmentioning

confidence: 99%

Anomaly Detection, Analysis and Prediction Techniques in IoT Environment: A Systematic Literature Review

2019

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“…A structured sparse subspace learning algorithm for the detection of abnormal behaviors was presented by He et al [26]. To ensure the achievement of structured sparsity in the system, a structured norm was imposed on the projection coefficients matrix for efficient identification of anomaly sources.…”

Section: Intelligent Transportation Systemmentioning

confidence: 99%

Toward a Full Exploitation of IoT in Smart Cities: A Review of IoT Anomaly Detection Techniques

Al-amri

Murugesan

Alshari

et al. 2021

Lecture Notes in Networks and Systems

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The world is currently progressing towards a new connectivity era where billions of sensors are connected over a network called the Internet of Things (IoT). IoT enables a wide range of physical objects and devices to be connected and monitored with insufficient spatial and temporal detail. Despite their potential to improve multiple application domains, anomalies in the devices' behaviors pose a significant challenge, especially in the smart city's domain. Many research works have been devoted to determining such anomalous behaviors; however, there is a lack of comprehensive review focusing on anomaly detection techniques using statistical and machine learning methods in the smart cities domain. This work aims to fill this gap by presenting a review of anomaly detection techniques using statistical and machine learning methods. This paper explains the essential contexts related to IoT, followed by a review of the IoT anomaly detection techniques and their challenges, types, and detection modes. The paper then presents a summary of the related works related to smart cities. Finally, the open challenges and future directions were highlighted.

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“…It is possible to cluster data from various bearing states into a specific subspace using the SSC described in Ref. [34], where each cluster represents a different bearing state, and the distance between clusters identifies the defect. The goal of SSC is to find a subspace suitable for segmenting and sorting data with uncertain class labels.…”

Section: Theoretical Basis and Algorithm Introductionmentioning

confidence: 99%

Dimension reduction graph‐based sparse subspace clustering for intelligent fault identification of rolling element bearings

Zhao

Yang

Liu

2021

Int Journal of Mech Sys Dyn

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Sparse subspace clustering (SSC) is a spectral clustering methodology. Since high‐dimensional data are often dispersed over the union of many low‐dimensional subspaces, their representation in a suitable dictionary is sparse. Therefore, SSC is an effective technology for diagnosing mechanical system faults. Its main purpose is to create a representation model that can reveal the real subspace structure of high‐dimensional data, construct a similarity matrix by using the sparse representation coefficients of high‐dimensional data, and then cluster the obtained representation coefficients and similarity matrix in subspace. However, the design of SSC algorithm is based on global expression in which each data point is represented by all possible cluster data points. This leads to nonzero terms in nondiagonal blocks of similar matrices, which reduces the recognition performance of matrices. To improve the clustering ability of SSC for rolling bearing and the robustness of the algorithm in the presence of a large number of background noise, a simultaneous dimensionality reduction subspace clustering technology is provided in this work. Through the feature extraction of envelope signal, the dimension of the feature matrix is reduced by singular value decomposition, and the Euclidean distance between samples is replaced by correlation distance. A dimension reduction graph‐based SSC technology is established. Simulation and bearing data of Western Reserve University show that the proposed algorithm can improve the accuracy and compactness of clustering.

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A Structured Sparse Subspace Learning Algorithm for Anomaly Detection in UAV Flight Data

Cited by 33 publications

References 28 publications

Anomaly Detection, Analysis and Prediction Techniques in IoT Environment: A Systematic Literature Review

Anomaly Detection, Analysis and Prediction Techniques in IoT Environment: A Systematic Literature Review

Toward a Full Exploitation of IoT in Smart Cities: A Review of IoT Anomaly Detection Techniques

Dimension reduction graph‐based sparse subspace clustering for intelligent fault identification of rolling element bearings

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