CutPaste-Based Anomaly Detection Model using Multi Scale Feature Extraction in Time Series Streaming Data

doi:10.3837/tiis.2022.08.018

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…In addition, the LSTM uses multiple gates such as update gate, input gate, and output gate. Still, GRU uses only an update gate and a reset gate, so the operation is relatively simpler than the LSTM model [31]. Thus, it is evident that while the LSTM model offers higher accuracy, it does so at the expense of slower computational speed than the GRU model.…”

Section: Comparison Of Computational Speeds Of Lstm Gru Tcn Modelsmentioning

confidence: 99%

Time-Series Data Augmentation for Improving Multi-Class Classification Performance

Kim,

Ahn

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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This paper proposes a new approach to classify and evaluate defects in concrete structures automatically. To overcome the limitations of defect detection methods that traditionally relied on expert visual observation, the reflection signal of electromagnetic pulses is extracted as time-series data and used to analyze the propagation characteristics of each defect. This study uses deep learning models to analyze these time-series data and classify defects. Since anomaly detection data has more normal data than anomaly data, data augmentation methods such as Time Warping, Noise Injection, Smoothing, Trend Shifting, etc., were applied to solve the problem of data imbalance and overfitting. Among them, Noise Injection showed the best performance. The generalization performance of the proposed method was evaluated through performance evaluation using LSTM, GRU, and TCN models, and LSTM models showed the highest performance. The study results show that the proposed method effectively classifies defect types in concrete structures and can solve the limitations of existing methods by automatic classification through deep learning models. In addition, it was confirmed that the model's performance could be improved by improving the amount and diversity of data by selecting and applying appropriate data augmentation methods. The contribution of the research is to present a new approach that automates the defect detection and classification task of concrete structures and provides high accuracy and efficiency.

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Section: Comparison Of Computational Speeds Of Lstm Gru Tcn Modelsmentioning

confidence: 99%

Time-Series Data Augmentation for Improving Multi-Class Classification Performance

Kim,

Ahn

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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show abstract

“…Especially for pyramid-structured stratum images, high-level semantic information and low-level detail features are often difficult to consider simultaneously [16][17][18]. Additionally, the uneven distribution of samples in object detection also poses challenges to model training [19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Recognition and Feature Extraction of Stratum Images Based on Deep Learning

Wang,

Yan,

Yuan

et al. 2023

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Accurate identification and feature extraction of stratum images play a crucial role in geological exploration, resource prospecting, and mining operations. Traditional methods of stratum image identification largely rely on human experience and manual operations, which are inefficient and prone to errors. In recent years, deep learning technology has provided new methods for the identification and feature extraction of stratum images, but existing deep learning models still face challenges in computational efficiency, multi-scale feature extraction, and uneven sample distribution. This paper proposes a stratum image feature extraction network based on the pyramid model and constructs a lightweight stratum identification model for real-time recognition. By introducing a classification-regression network structure and anchor-based sample supervision rules, this study aims to improve the accuracy and efficiency of the model, providing an effective solution for real-time recognition of stratum images.

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Variational auto encoder fused with Gaussian process for unsupervised anomaly detection

Guan,

Xu,

et al. 2023

J Supercomput

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CutPaste-Based Anomaly Detection Model using Multi Scale Feature Extraction in Time Series Streaming Data

Cited by 4 publications

References 22 publications

Time-Series Data Augmentation for Improving Multi-Class Classification Performance

Time-Series Data Augmentation for Improving Multi-Class Classification Performance

Real-Time Recognition and Feature Extraction of Stratum Images Based on Deep Learning

Variational auto encoder fused with Gaussian process for unsupervised anomaly detection

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