Tuning a variational autoencoder for data accountability problem in the Mars Science Laboratory ground data system

The Mars Curiosity rover is frequently sending back engineering and science data that goes through a pipeline of systems before reaching its final destination at the mission operations center making it prone to volume loss and data corruption. A ground data system analysis (GDSA) team is charged with the monitoring of this flow of information and the detection of anomalies in that data in order to request a re-transmission when necessary. This work presents $\Delta$-MADS, a derivative-free optimization method applied for tuning the architecture and hyperparameters of a variational autoencoder trained to detect the data with missing patches in order to assist the GDSA team in their mission

Comparing Principal Component Analysis (PCA) and -Variational Autoencoder (ß-VAE) for Anomaly Detection in Selective Laser Melting (SLM) Process Data

The usability of machine learning approaches for the development of in-situ process monitoring, automated anomaly detection and quality assurance for the selective laser melting (SLM) process receives currently increasing attention. For a given set of real machine data we compare two established methods, principal component analysis (PCA) and -variational autoencoder (ß-VAE), for their applicability in exploratory data analysis and anomaly detection. We introduce a PCA-based unsupervised feature extraction algorithm, which allows for root cause analysis of process anomalies. The ß-VAE enables a slightly more compact dimensionality reduction; we consider it an option for automated process monitoring systems

Improved Variational Autoencoder Anomaly Detection in Time Series Data

Uncertainty in observations about the state of affairs is unavoidable, and generally undesirable, so we are motivated to try to minimize its effect on data analysis. Detection of anomalies in data has become an important research area. In this paper, we propose a novel approach to anomaly detection based on the Variational Autoencoder method with a Mish activation function and a Negative Log-Likelihood loss function. The proposed method is validated with ten standard datasets, comparing performance on each of the various activation functions and loss functions. Experimental results show that our proposed method offers an improvement over existing methods. Statistical properties (i.e., F1 score, AUC, and ROC) of the method are also examined in light of the experimental results.2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020), October 11-14, 2020, Toronto, Canada （オンライン開催に変更

Anomaly Detection using Variational Autoencoder with Spectrum Analysis for Time Series Data

Uncertainty is an ever present challenge in life. To meet this challenge in data analysis, we propose a method for detecting anomalies in data. This method, based in part on Variational Autoencoder, identifies spiking raw data by means of spectrum analysis. Time series data are examined in the frequency domain to enhance the detection of anomalies. In this paper, we have used the standard data sets to validate the proposed method. Experimental results show that the comparison of the frequency domain with the original data for anomaly detection can improve validity and accuracy on all criteria. Therefore, analysis of time series data by combining Variational Autoencoder and frequency domain spectrum methods can effectively detect anomalies. Contribution- We have proposed an anomaly detection method based on the time series data analysis by combining Variational Autoencoder and Spectrum analysis, and have benchmarked the method with reference to recent related research.10th International Conference on Informatics, Electronics, and Vision (ICIEV20), 26-29 August, 2020, Kitakyushu, Japa

多変量時系列データの変分オートエンコーダによるロバストな教示なし異常検知

九州工業大学博士学位論文 学位記番号：情工博甲第370号 学位授与年月日：令和4年9月26日1: Introduction|2: Background & Theory|3: Methodology|4: Experiments and Discussion|5: Conclusions九州工業大学令和4年