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Detecting Anomaly in Chemical Sensors via L1-Kernels based Principal Component Analysis
We propose a kernel-PCA based method to detect anomaly in chemical sensors.
Discrete Cosine Transform Based Causal Convolutional Neural Network for Drift Compensation in Chemical Sensors
Our results show that we can have an accurate and smooth drift estimate even when the observed sensor signal is very noisy.
Detecting Gas Vapor Leaks Using Uncalibrated Sensors
Chemical and infra-red sensors generate distinct responses under similar conditions because of sensor drift, noise or resolution errors.
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