White Noise Windows: Data Augmentation for Time Series

Goubeaud, Maxime; Jousen, Philipp; Gmyrek, Nicolla; Ghorban, Farzin; Kummert, Anton

doi:10.1109/icoa51614.2021.9442656

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…Generating synthetic data, which is also known as data augmentation, is a solution for overcoming the problem of insufficient data samples [ 30 ] or compensating for datasets with imbalanced classes [ 31 ]. Data augmentation helps to increase the generalization capability of an ML prediction model and improve the model’s performance by increasing the variability of the training and validation data samples [ 32 , 33 ].…”

Section: Methodsmentioning

confidence: 99%

Predicting Analyte Concentrations from Electrochemical Aptasensor Signals Using LSTM Recurrent Networks

et al. 2022

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Nanomaterial-based aptasensors are useful devices capable of detecting small biological species. Determining suitable signal processing methods can improve the identification and quantification of target analytes detected by the biosensor and consequently improve the biosensor’s performance. In this work, we propose a data augmentation method to overcome the insufficient amount of available original data and long short-term memory (LSTM) to automatically predict the analyte concentration from part of a signal registered by three electrochemical aptasensors, with differences in bioreceptors, analytes, and the signals’ lengths for specific concentrations. To find the optimal network, we altered the following variables: the LSTM layer structure (unidirectional LSTM (LSTM) and bidirectional LSTM (BLSTM)), optimizers (Adam, RMSPROP, SGDM), number of hidden units, and amount of augmented data. Then, the evaluation of the networks revealed that the highest original data accuracy increased from 50% to 92% by exploiting the data augmentation method. In addition, the SGDM optimizer showed a lower performance prediction than that of the ADAM and RMSPROP algorithms, and the number of hidden units was ineffective in improving the networks’ performances. Moreover, the BLSTM nets showed more accurate predictions than those of the ULSTM nets on lengthier signals. These results demonstrate that this method can automatically detect the analyte concentration from the sensor signals.

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Section: Methodsmentioning

confidence: 99%

Predicting Analyte Concentrations from Electrochemical Aptasensor Signals Using LSTM Recurrent Networks

et al. 2022

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“…In this study, we employed two distinct noise injection methods, such as white noise and environmental noise, each serving a specific purpose. White noise is a fundamental source of randomness, characterized by equal energy distribution across all frequencies and a flat power spectral density [28]. Using this method allows us to convey the unpredictability that is present in real-world audio recordings into our audio samples.…”

Section: ) Data Augmentationmentioning

confidence: 99%

Acoustic-Based Online Monitoring of Cooling Fan Malfunction in Air-Forced Transformers Using Learning Techniques

Nematirad,

Behrang,

Pahwa

2024

IEEE Access

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Cooling fans are one of the critical components of air-forced dry-type transformers for regulating internal temperatures. Therefore, effective malfunction detection is crucial to maintain the transformer temperature within an acceptable range and prevent overheating. Current malfunction detection of cooling fans in certain types of transformers relies on complementary indicators, such as top-oil temperature, oil convection, dissolved gas, and oil quality. However, these conventional indicators are not directly applicable to air-forced transformers, which primarily use cooling fans as their cooling system. To overcome this challenge, this study utilizes cooling fan audio records as indicators. The audio signals are classified into normal and malfunctioning classes using advanced learning algorithms, including convolutional neural networks and random forests. Learning algorithms require transforming recorded audio data into proper formats. Accordingly, convolutional neural networks are trained based on spectrogram images derived from audio signals. For random forests, various time-frequency feature extraction methods are used to derive meaningful representations from audio signals. Besides, multiple data augmentation techniques are employed to enhance the dataset size and diversity. Algorithmic performance is optimized through hyperparameter tuning and classifier threshold adjustment. To further validate the model, a test is conducted on another dataset to evaluate the fitted learning model applicability in real-world applications. Simulations reveal that convolutional neural networks outperform random forests, whereas the latter provides superior interpretability of acoustic features compared to the former.

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Network security AIOps for online stream data monitoring

Nguyen,

Dlugolinsky,

Tran

et al. 2024

Neural Comput & Applic

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In cybersecurity, live production data for predictive analysis pose a significant challenge due to the inherently secure nature of the domain. Although there are publicly available, synthesized, and artificially generated datasets, authentic scenarios are rarely encountered. For anomaly-based detection, the dynamic definition of thresholds has gained importance and attention in detecting abnormalities and preventing malicious activities. Unlike conventional threshold-based methods, deep learning data modeling provides a more nuanced perspective on network monitoring. This enables security systems to continually refine and adapt to the evolving situation in streaming data online, which is also our goal. Furthermore, our work in this paper contributes significantly to AIOps research, particularly through the deployment of our intelligent module that cooperates within a monitoring system in production. Our work addresses a crucial gap in the security research landscape toward more practical and effective secure strategies.

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White Noise Windows: Data Augmentation for Time Series

Cited by 5 publications

References 7 publications

Predicting Analyte Concentrations from Electrochemical Aptasensor Signals Using LSTM Recurrent Networks

Predicting Analyte Concentrations from Electrochemical Aptasensor Signals Using LSTM Recurrent Networks

Acoustic-Based Online Monitoring of Cooling Fan Malfunction in Air-Forced Transformers Using Learning Techniques

Network security AIOps for online stream data monitoring

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