Supervised Variational Autoencoders for Soft Sensor Modeling With Missing Data

Xie, Ruimin; Jan, Nabil Magbool; Hao, Kuangrong; Chen, Lei; Huang, Biao

doi:10.1109/tii.2019.2951622

Cited by 95 publications

(36 citation statements)

References 30 publications

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“…, where the dimension of the sample is s, then the VAE [19][20][21][22] learns that the hidden layer represents the Gaussian distribution in the space. In the hidden layer representation space, assuming that the training samples conform to the Gaussian distribution, which means that all training samples are clustered into one cluster center, the samples far from the cluster center are abnormal samples.…”

Section: Anomaly Detection Model Based On Vae Given N Normal Training Samplesmentioning

confidence: 99%

“…Unlike these methods above, in this paper, we propose an end-toend deep learning framework for abnormal event detection. e proposed method is based on variational autoencoder (VAE) [19][20][21][22], which can map high-dimensional raw input data to low-dimensional hidden layer representations through deep learning technology. And, it constrains the low-dimensional hidden layer representation to conform to a Gaussian distribution.…”

Section: Introductionmentioning

confidence: 99%

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Abnormal Event Detection in Videos Based on Deep Neural Networks

2021

Scientific Programming

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Abnormal event detection has attracted widespread attention due to its importance in video surveillance scenarios. The lack of abnormally labeled samples makes this problem more difficult to solve. A partially supervised learning method only using normal samples to train the detection model for video abnormal event detection and location is proposed. Assuming that the distribution of all normal samples complies to the Gaussian distribution, the abnormal sample will appear with a lower probability in this Gaussian distribution. The method is developed based on the variational autoencoder (VAE), through end-to-end deep learning technology, which constrains the hidden layer representation of the normal sample to a Gaussian distribution. Given the test sample, its hidden layer representation is obtained through the variational autoencoder, which represents the probability of belonging to the Gaussian distribution. It is judged abnormal or not according to the detection threshold. Based on two publicly available datasets, i.e., UCSD dataset and Avenue dataset, the experimental are conducted. The results show that the proposed method achieves 92.3% and 82.1% frame-level AUC at a speed of 571 frames per second on average, which demonstrate the effectiveness and efficiency of our framework compared with other state-of-the-art approaches.

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Section: Anomaly Detection Model Based On Vae Given N Normal Training Samplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Abnormal Event Detection in Videos Based on Deep Neural Networks

2021

Scientific Programming

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“…Discriminative methods include Multiple Imputation by Chain Equations (MICE) [13], Random Forest-based Imputation (Missforest) [14] and matrix completion [15]. Generative methods consist mostly of techniques based on Deep Learning (DL) e.g Variational Autoencoders (VAE) [16], [17], Neural Networks with Random Weights(NNRW) [18], Denoising Autoencoders (DAE) [19] and Generative Adversarial Networks(GAN) [20], [21].…”

Section: Introductionmentioning

confidence: 99%

Missing Data Imputation on IoT Sensor Networks: Implications for on-site Sensor Calibration

Okafor¹,

Delaney²

2021

Preprint

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IoT sensors are becoming increasingly important supplement to traditional monitoring systems, particularly for in-situ based monitoring. Data collected using IoT sensors are often plagued with missing values occurring as a result of sensor faults, network failures, drifts and other operational issues. Missing data can have substantial impact on in-field sensor calibration methods. The goal of this research is to achieve effective calibration of sensors in the context of such missing data. To this end, two objectives are presented in this paper. 1) Identify and examine effective imputation strategy for missing data in IoT sensors. 2) Determine sensor calibration performance using calibration techniques on data set with imputed values. Specifically, this paper examines the performance of Variational Autoencoder (VAE), Neural Network with Random Weights (NNRW), Multiple Imputation by Chain Equations (MICE), Random forest based imputation (missForest) and K-Nearest Neighbour (KNN) for imputation of missing values on IoT sensors. Furthermore, the performance of sensor calibration via different supervised algorithms trained on the imputed dataset were evaluated. The analysis showed that VAE technique outperforms the others in imputing the missing values at different proportions of missingness on two real-world datasets. Experimental results also showed improved calibration performance with imputed dataset.

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“…Discriminative methods include Multiple Imputation by Chain Equations (MICE) [13], Random Forest-based Imputation (Missforest) [14] and matrix completion [15]. Generative methods consist mostly of techniques based on Deep Learning (DL) e.g Denoising Autoencoders (DAE) [2] [16] and Generative Adversarial Networks(GAN) [17] [18]. GAN learns the latent distribution of a dataset and can generate real samples from a random noise.…”

Section: Introductionmentioning

confidence: 99%

Missing Data Imputation on Iot Sensor Networks: Implications for on-Site Sensor Calibration

Okafor¹

2021

Preprint

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IoT sensors are gaining more popularity in the environmental monitoring space due to their relatively small size, cost of acquisition and ease of installation and operation. They are becoming increasingly important<br>supplement to traditional monitoring systems, particularly for in-situ based monitoring. However, data collection based on IoT sensors are often plagued with missing values usually occurring as a result of sensor faults, network failures, drifts and other operational issues. Several imputation strategies have been proposed for handling missing values in various application domains. This paper examines the performance of different imputation techniques including Multiple Imputation by Chain Equations (MICE), Random forest based imputation (missForest) and K-Nearest Neighbour (KNN) for handling missing values on sensor networks deployed for the quantification of Green House Gases(GHGs). Two tasks were conducted: first, Ozone (O3) and NO2/O3 concentration data collected using Aeroqual and Cairclip sensors respectively over a six months data collection period were corrupted by removing data intervals at different missing periods (p) where p 2 f1day; 1week; 2weeks; 1monthg and also at random points on the dataset at varying proportion (r) where r 2 f5%; 10%; 30%; 50%; 70%g. The missing data were then filled using the different imputation strategies and their imputation accuracy calculated. Second, the performance of sensor calibration by different regression models including Multi Linear Regression (MLR), Decision Tree (DT), Random Forest (RF) and XGBoost (XGB) trained on the different imputed datasets were evaluated. The analysis showed the MICE technique to outperform the others in imputing the missing values on both the O3 and NO2/O3 datasets when missingness was introduced over periods p. MissForest, however, outperformed the rest when missingness was introduced as randomly occuring point errors. While the analysis demonstrated the effects of missing and imputed data on sensor calibration, experimental results showed that a simple model on the imputed dataset can achieve state of-the-art result on in-situ sensor calibration, improving the data quality of the sensor.

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Supervised Variational Autoencoders for Soft Sensor Modeling With Missing Data

Cited by 95 publications

References 30 publications

Abnormal Event Detection in Videos Based on Deep Neural Networks

Abnormal Event Detection in Videos Based on Deep Neural Networks

Missing Data Imputation on IoT Sensor Networks: Implications for on-site Sensor Calibration

Missing Data Imputation on Iot Sensor Networks: Implications for on-Site Sensor Calibration

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