Fully Convolutional Variational Autoencoder For Feature Extraction Of Fire Detection System

Nugroho, Herminarto; Susanty, Meredita; Irawan, Ade; Koyimatu, Muhamad; Yunita, Ariana

doi:10.21609/jiki.v13i1.761

Cited by 7 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both PCA and the β-VAE can be used for dimensionality reduction and feature extraction [8,9]. The choice of suitable features, however, requires assessment metrics.…”

Section: Methodsmentioning

confidence: 99%

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

Voigt¹,

Moeckel²

2021

14th WCCM-ECCOMAS Congress

View full text Add to dashboard Cite

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 β-variational autoencoder (β-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 β-VAE enables a slightly more compact dimensionality reduction; we consider it an option for automated process monitoring systems.

show abstract

“…Both PCA and the β-VAE can be used for dimensionality reduction and feature extraction [8,9]. The choice of suitable features, however, requires assessment metrics.…”

Section: Methodsmentioning

confidence: 99%

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

Voigt¹,

Moeckel²

2021

14th WCCM-ECCOMAS Congress

View full text Add to dashboard Cite

show abstract

“…This technique optimizes the distribution parameter while maintaining the ability to randomly sample from that distribution. The challenge of the VAE is to learn a meaningful and generalizable latent space despite having far fewer units in the encoding than the input (Nugroho et al 2020). The architecture of Variational Autoencoder is visualized on Figure 2:…”

Section: Variational Autoencodermentioning

confidence: 99%

Anomaly Detection on Univariate Sensing Time Series Data for Smart Aquaculture Using Deep Learning

Petkovski

Shehu

2023

SEEU Review

View full text Add to dashboard Cite

Aquaculture plays a significant role in both economic development and food production. Maintaining an ecological environment with good water quality is essential to ensure the production efficiency and quality of aquaculture. Effective management of water quality can prevent abnormal conditions and contribute significantly to food security. Detecting anomalies in the aquaculture environment is crucial to ensure that the environment is maintained correctly to meet healthy and proper requirements for fish farming. This article focuses on the use of deep learning techniques to detect anomalies in water quality data in the aquaculture environment. Four deep learning anomaly detection techniques, including Autoencoder, Variational Autoencoder, Long-Short Term Memory Autoencoder, and Spectral-Residual Convolutional Neural Network, were analysed using multiple real-world sensor datasets collected from IoT aquaculture systems. Extensive experiments were conducted for temperature, dissolved oxygen, and pH parameters, and the evaluation analysis revealed that the Long-Short Term Memory Autoencoder anomaly detection method showed promising results in detecting anomalies for the temperature and oxygen datasets, while the Spectral-Residual Convolutional Neural Network demonstrated the best performance on the pH datasets.

show abstract

“…The Model Classifier can be trained and tested using the output. An autoencoder was used to extract the dimensions, after which they were converted into features for 27,26,25,24,23,22,21,20,19,18,14,10, and 6 neurons. The Autoencoder (AE) model is configured with the activation function at the bottleneck, encoder 1, encoder 2, decoder 1, and encoder 2 using Relu, the kernel initializer used is Random Normal with a standard deviation of 0.01, the initializer bias used is Zeros, and linear activation is used on the output layers.…”

Section: Architecture Of the Autoencodermentioning

confidence: 99%

“…[23]. The autoencoder is used to find new input representations without losing too much information and the input can be reconstructed [24]. Inputs in autoencoder can be reconstructed effectively with minimum reconstruction error [25].…”

Section: Introductionmentioning

confidence: 99%

Autoencoder-Based Feature Learning for Predicting Cardiovascular Disease

Giovani,

Pardede,

Subekti

2023

IJCDS

View full text Add to dashboard Cite

Cardiovascular disease is the world's leading cause of death. Some studies have used the machine learning method to predict cardiovascular diseases based on medical records. However, due to high correlation between data in medical records, much needs to be done in the field. Here, we propose to use Autoencoder based feature learning to predict cardiovascular disease, because Autoencoder can process complex, high-dimensional datasets by doing linear and non-linear projections. Thus, we hope the autoencoder can learn about non-linear and complex connections between the medical data being used. We varied the depth of autoencoder in this paper from 3 to 7 layers, and the depth of layer was varied to several neurons at the bottleneck. The results are then input into other classifiers, such as Logistic Regression, Naive Bayes, SVM, KNN, Decision Tree, XGBoost, Random Forest, and Neural Networks. Our experiments show that use of autoencoder-based feature learning can improves the performance of the classifier by 0.75%. However, we see that the depth of the layer does not always enhance performance and needs to be defined empirically.

show abstract

Fully Convolutional Variational Autoencoder For Feature Extraction Of Fire Detection System

Cited by 7 publications

References 14 publications

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

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

Anomaly Detection on Univariate Sensing Time Series Data for Smart Aquaculture Using Deep Learning

Autoencoder-Based Feature Learning for Predicting Cardiovascular Disease

Contact Info

Product

Resources

About