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Full-Text Articles in Engineering
Prediction Of Anomalous Events With Data Augmentation And Hybrid Deep Learning Approach, Ahmed Shoyeb Raihan
Prediction Of Anomalous Events With Data Augmentation And Hybrid Deep Learning Approach, Ahmed Shoyeb Raihan
Graduate Theses, Dissertations, and Problem Reports
In this study, we propose a novel anomaly detection framework designed specifically for Multivariate Time Series (MTS) data, addressing the prevalent challenges in analyzing such complex datasets. The detection of anomalies within MTS data is notably difficult due to the complex interplay of numerous variables, temporal dependencies, and the common issue of class imbalance, where one category significantly outnumbers another. Traditional deep learning (DL) approaches often fall short in simultaneously tackling these issues. Our framework is designed to address these challenges through a two-phased approach. Phase I employs Conditional Tabular Generative Adversarial Networks (CTGAN) to create strategic synthetic data, setting …
Spatio-Temporal Deep Learning Approaches For Addressing Track Association Problem Using Automatic Identification System (Ais) Data, Md Asif Bin Syed
Spatio-Temporal Deep Learning Approaches For Addressing Track Association Problem Using Automatic Identification System (Ais) Data, Md Asif Bin Syed
Graduate Theses, Dissertations, and Problem Reports
In the realm of marine surveillance, track association constitutes a pivotal yet challenging task, involving the identification and tracking of unlabelled vessel trajectories. The need for accurate data association algorithms stems from the urge to spot unusual vessel movements or threat detection. These algorithms link sequential observations containing location and motion information to specific moving objects, helping to build their real-time trajectories. These threat detection algorithms will be useful when a vessel attempts to conceal its identity. The algorithm can then identify and track the specific vessel from its incoming signal. The data for this study is sourced from the …
Multivariate Time Series Classification Of Sensor Data From An Industrial Drying Hopper: A Deep Learning Approach, Md Mushfiqur Rahman
Multivariate Time Series Classification Of Sensor Data From An Industrial Drying Hopper: A Deep Learning Approach, Md Mushfiqur Rahman
Graduate Theses, Dissertations, and Problem Reports
In recent years, the advancement of industry 4.0 and smart manufacturing has made a large number of industrial process data attainable with the use of sensors installed in the machineries. This thesis proposes an experimental predictive maintenance framework for an industrial drying hopper so that it can detect any unusual event in the hopper which reduces the risk of erroneous fault diagnosis in the manufacturing shop floor. The experimental framework uses Deep Learning (DL) algorithms in order to classify Multivariate Time Series (MTS) data into two categories- failure or unusual events and regular events, thus formulating the problem as binary …