Chemical gas sensor drift compensation using classifier ensembles

By Alexander Vergara, Shankar Vembu, Tuba Ayhan, M. Ryan, M. Homer, Ramón Huerta. 2012

712 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Electrical and Electronic Engineering, Insect Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Sanjeev Arora
2. Nadav Cohen
3. David Dapeng Zhang
4. Lei Zhang
5. Elad Hazan
6. Wei Hu
7. Benjamin Goodrich
8. Vivak Patel

In Electrical and Electronic Engineering

1. Sanjeev Arora
2. Noah Golowich
3. Elad Hazan
4. Lei Zhang
5. David Dapeng Zhang

In Insect Science

1. Lei Zhang
2. David Dapeng Zhang
3. Benjamin Goodrich
4. Vivak Patel

Citations in the corpus

• A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks (2018, methodology): “The dataset chosen for the ex-periment was UCI Machine Learning Repository’s “Gas Sensor Array Drift at Different Concen-trations” (Vergara et al., 2012; Rodriguez-Lujan et al., 2014).”
• Contrastive Pessimistic Likelihood Estimation for Semi-Supervised Classification (2015, background): “banknote authentication banknote climate model simulation climate [82] crashes first-order theorem proving first-order [83] gas sensor array drift gas [84] landsat satellite landsat letter recognition letter low resolution spectrometer low magic gamma telescope magic miniboone particle miniboone ide”
• Domain Adaptation Extreme Learning Machines for Drift Compensation in E-Nose Systems (2015, methodology): “We strictly follow the experimental setup in [26] to evaluate our DAELM framework.”
• On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization (2018, methodology): “The dataset chosen was UCI Machine Learning Repository’s “Gas Sensor Array Drift at Different Concentrations” (Ver-gara et al., 2012; Rodriguez-Lujan et al., 2014).”
• On the calibration of sensor arrays for pattern recognition using the minimal number of experiments (2014, methodology): “Additionally, systems based on Metal Oxide (MOX) gas sensors, which still are a common choice for chemical detection applications due to their sensitivity, low cost, ease of operation, ability to detect large number of chemicals, and robustness [8,9], are dynamic systems that show a transient respon”