Deep learning-based gas identification and quantification with auto-tuning of hyper-parameters

Pareek, Vishakha; Chaudhury, Santanu

doi:10.1007/s00500-021-06222-1

Cited by 16 publications

(5 citation statements)

References 46 publications

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“…For our experiments, we utilized the publicly available dataset that was constructed by Vergara et al [27], which is a well-known dataset used in drift compensation research [7,12,14,15,[28][29][30]. This dataset comprises 13,910 measurements gathered from an E-nose device with 16 chemical sensors that were exposed to the 6 gases of ethanol, ethylene, ammonia, acetaldehyde, acetone, and toluene at various concentrations.…”

Section: Datasetmentioning

confidence: 99%

“…Adaptive drift correction, which is a method that updates a classifier continuously using new samples, has been implemented in various deep learning structures [5]. Fine-tuning has also been carried out for novel deep learning models such as autoencoders [12], restricted Boltzmann machines [13], deep belief networks [14], and augmented convolutional neural networks [15]. Furthermore, online drift compensation methods, which can update trained models with new samples, have been studied.…”

Section: Introductionmentioning

confidence: 99%

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A Sensor Drift Compensation Method with a Masked Autoencoder Module

Kwon,

Park,

Jang

et al. 2024

Applied Sciences

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Deep learning algorithms are widely used for pattern recognition in electronic noses, which are sensor arrays for gas mixtures. One of the challenges of using electronic noses is sensor drift, which can degrade the accuracy of the system over time, even if it is initially trained to accurately estimate concentrations from sensor data. In this paper, an effective drift compensation method is introduced that adds sensor drift information during training of a neural network that estimates gas concentrations. This is achieved by concatenating a calibration feature vector with sensor data and using this as an input to the neural network. The calibration feature vector is generated via a masked-autoencoder-based feature extractor trained with transfer samples, and acts as a prompt to convey sensor drift information. Our method is tested on a 3-year gas sensor array drift dataset, showing that a neural network using our method performs better than other models, including a network with additional fine tuning, demonstrating that our method is efficient at compensating for sensor drift. In this study, the effectiveness of using prompts for network training is confirmed, which better compensates for drifts in new sensor signals than network fine-tuning.

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Section: Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Sensor Drift Compensation Method with a Masked Autoencoder Module

Kwon,

Park,

Jang

et al. 2024

Applied Sciences

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“…Two deep learning-based architectures for gas identification and quantification automatically adapt network hyperparameters for best performance [43]. Both networks' hyperparameters are automatically tuned for optimal performance.…”

Section: Related Workmentioning

confidence: 99%

A New Hybrid Based on Long Short-Term Memory Network with Spotted Hyena Optimization Algorithm for Multi-Label Text Classification

et al. 2022

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An essential work in natural language processing is the Multi-Label Text Classification (MLTC). The purpose of the MLTC is to assign multiple labels to each document. Traditional text classification methods, such as machine learning usually involve data scattering and failure to discover relationships between data. With the development of deep learning algorithms, many authors have used deep learning in MLTC. In this paper, a novel model called Spotted Hyena Optimizer (SHO)-Long Short-Term Memory (SHO-LSTM) for MLTC based on LSTM network and SHO algorithm is proposed. In the LSTM network, the Skip-gram method is used to embed words into the vector space. The new model uses the SHO algorithm to optimize the initial weight of the LSTM network. Adjusting the weight matrix in LSTM is a major challenge. If the weight of the neurons to be accurate, then the accuracy of the output will be higher. The SHO algorithm is a population-based meta-heuristic algorithm that works based on the mass hunting behavior of spotted hyenas. In this algorithm, each solution of the problem is coded as a hyena. Then the hyenas are approached to the optimal answer by following the hyena of the leader. Four datasets are used (RCV1-v2, EUR-Lex, Reuters-21578, and Bookmarks) to evaluate the proposed model. The assessments demonstrate that the proposed model has a higher accuracy rate than LSTM, Genetic Algorithm-LSTM (GA-LSTM), Particle Swarm Optimization-LSTM (PSO-LSTM), Artificial Bee Colony-LSTM (ABC-LSTM), Harmony Algorithm Search-LSTM (HAS-LSTM), and Differential Evolution-LSTM (DE-LSTM). The improvement of SHO-LSTM model accuracy for four datasets compared to LSTM is 7.52%, 7.12%, 1.92%, and 4.90%, respectively.

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“…In the underground coal mining system, it is necessary to monitor harmful, combustible, and noxious gases for the safety of the workers. Instead of using fuzzy, rule-based statistical approaches which are inefficient in complex scenario, Sharma et al and Pareek et al 165,166 approached with 1-D CNN models powered by Dempster Shafer evidence theory that seem to perform in accuracy and the number of training parameters. A multilayer perceptual neural network model was used for natural gas monitoring application by an array of infrared sensors earlier.…”

Section: Evolution Of Managerial Toolsmentioning

confidence: 99%

Review–Modern Data Analysis in Gas Sensors

Sagar

Allison

Jalajamony

et al. 2022

J. Electrochem. Soc.

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Development in the field of gas sensors has witnessed exponential growth with a multitude of applications. The diversity of the applications has led to unexpected challenges. Recent advances in data science have addressed the challenges such as selectivity, drift, aging, limit of detection, and response time. The incorporation of modern data analysis including machine learning techniques have enabled a self-sustaining gas-sensing infrastructure without human intervention. This article provides a birds-eye view on data enabled technologies in the realm of gas sensors. While elaborating the prior developments in gas-sensing related data analysis, this article is intended as an entrant for enthusiasts in the domain of data science and gas sensors.

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Deep learning-based gas identification and quantification with auto-tuning of hyper-parameters

Cited by 16 publications

References 46 publications

A Sensor Drift Compensation Method with a Masked Autoencoder Module

A Sensor Drift Compensation Method with a Masked Autoencoder Module

A New Hybrid Based on Long Short-Term Memory Network with Spotted Hyena Optimization Algorithm for Multi-Label Text Classification

Review–Modern Data Analysis in Gas Sensors

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