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Improved deep bidirectional recurrent neural network for learning the cross-sensitivity rules of gas sensor array

sensorS AND ACTUATORS B-CHEMICAL 2024年 401卷

作者： Wang, Zhen Li, Yanhao He, Xiangnan Yan, Rui Li, Zhemin Jiang, Yadong Li, Xian Chinese Acad Agr Sci Agr Informat Inst Beijing 100081 Peoples R China Univ Elect Sci & Technol China Sch Optoelect Sci & Engn State Key Lab Elect Thin Films & Integrated Device Chengdu 610054 Peoples R China Chinese Acad Agr Sci Grad Sch Beijing 100081 Peoples R China

Cross-sensitivity among chemical gas sensors leads to inaccurate identification of mixed gas. Pattern recognition algorithms are usually applied to improve the recognition accuracy. However, the abilities of current methods to process sequence property of response data are not strong enough. Especially, they cannot deal with bidirectional cross-sensitive issue, leading to recognition errors. Bidirectional Recurrent Neural Network (BRNN) could well learn bidirectional association features between word sequences in natural language processing field, which is similar to the bidirectional interaction of cross-sensitivity. In this study, an improved deep BRNN model was constructed to solve the cross-sensitivity problem of chemical gas sensor array. A chemical gas sensor array with four units was fabricated and response data was thoroughly obtained. Data preprocessing methods, model structure hyperparameters and optimizers were studied. Finally, an improved deep BRNN model was developed with 3 layers and 100 hidden_size, training with Adamax optimizer. A recognition accuracy of 98.93% was achieved, attributing to the model's excellent learning ability to the bidirectional cross-sensitivity rules among gas sensors. This improved BRNN model provided a novel idea to eliminate cross-sensitivity, exhibiting good potential for recognizing mixed gas analyte accurately.

关键词： gas sensor array Deep BRNN Bidirectional learning ability Cross-sensitivity Pattern recognition

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A Novel gas Recognition Algorithm for gas sensor array Combining Savitzky-Golay Smooth and Image Conversion Route

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CHEMOsensorS 2023年第2期11卷 96页

作者： Wang, Xi Qian, Chen Zhao, Zhikai Li, Jiaming Jiao, Mingzhi China Univ Min & Technol Natl & Local Joint Engn Lab Internet Applicat Tech Xuzhou 221116 Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Peoples R China

In recent years, the application of Deep Neural Networks to gas recognition has been developing. The classification performance of the Deep Neural Network depends on the efficient representation of the input data samples. Therefore, a variety of filtering methods are firstly adopted to smooth filter the gas sensing response data, which can remove redundant information and greatly improve the performance of the classifier. Additionally, the optimization experiment of the Savitzky-Golay filtering algorithm is carried out. After that, we used the Gramian Angular Summation Field (gasF) method to encode the gas sensing response data into two-dimensional sensing images. In addition, data augmentation technology is used to reduce the impact of small sample numbers on the classifier and improve the robustness and generalization ability of the model. Then, combined with fine-tuning of the GoogLeNet neural network, which owns the ability to automatically learn the characteristics of deep samples, the classification of four gases has finally been realized: methane, ethanol, ethylene, and carbon monoxide. Through setting a variety of different comparison experiments, it is known that the Savitzky-Golay smooth filtering pretreatment method effectively improves the recognition accuracy of the classifier, and the gas recognition network adopted is superior to the fine-tuned ResNet50, Alex-Net, and ResNet34 networks in both accuracy and sample processing times. Finally, the highest recognition accuracy of the classification results of our proposed route is 99.9%, which is better than other similar work.

关键词： gas recognition gas sensor array Savitzky-Golay smooth filter sensor data visualization Deep Neural Network

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Maxwell displacement current induced wireless self-powered gas sensor array

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MATERIALS TODAY PHYSICS 2023年 30卷

作者： Su, Yuanjie Chen, Shuo Liu, Bohao Lu, Haijun Luo, Xiaolan Chen, Chunxu Li, Weixiong Long, Yin Tai, Huiling Xie, Guangzhong Jiang, Yadong Univ Elect Sci & Technol China Sch Optoelect Sci & Engn State Key Lab Elect Thin Films & Integrated Device Chengdu 610054 Peoples R China

Maxwell displacement current induced by triboelectric nanogenerators (TENGs) endows an efficient route in wireless power transmission and self-powered sensing technique. Herein, we invented a Maxwell displacement current motivated wireless self-powered gas sensor array (WSA) for wireless and passive respiratory analysis and exhaust emission monitoring. The as-prepared WSA exhibited a maximum response value of 147% toward 100 ppm NH3 and 87% toward 0.5 ppm NO2, respectively. By combining experimental characterization and finite element analysis, the influence of induction distance and electrode arrangement on the transducing and sensing properties of WSA was systematically investigated. Meanwhile, the prepared WSA possessed good repeatability, selectivity, and long-term stability towards NH3 and NO2. Finally, triggered by the mechanical energy from human respiration and object rotation, the as-received WSA is capable of actively distinguishing trace-level ammonia and nitrogen dioxide, illustrating the feasibility in respiratory analysis and air quality monitoring. This work opens up a new paradigm for self-powered wireless gas detection with attribute of cost effectiveness, multivariable, easy fabrication, and environment friendliness.

关键词： Displacement current gas sensor array Self-powered Wireless energy transmission Respiratory analysis

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A Novel Approach for Apple Freshness Prediction Based on gas sensor array and Optimized Neural Network

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sensorS 2023年第14期23卷 6476-6476页

作者： Wang, Wei Yang, Weizhen Li, Maozhen Zhang, Zipeng Du, Wenbin North Univ China Sch Informat & Commun Engn Taiyuan 030051 Peoples R China Brunel Univ London Dept Elect & Elect Engn Uxbridge UB8 3PH England

Apple is an important cash crop in China, and the prediction of its freshness can effectively reduce its storage risk and avoid economic loss. The change in the concentration of odor information such as ethylene, carbon dioxide, and ethanol emitted during apple storage is an important feature to characterize the freshness of apples. In order to accurately predict the freshness level of apples, an electronic nose system based on a gas sensor array and wireless transmission module is designed, and a neural network prediction model using an improved Sparrow Search Algorithm (SSA) based on chaotic sequence (Tent) to optimize Back Propagation (BP) is proposed. The odor information emitted by apples is studied to complete an apple freshness prediction. Furthermore, by fitting the relationship between the prediction coefficient and the input vector, the accuracy benchmark of the prediction model is set, which further improves the prediction accuracy of apple odor information. Compared with the traditional prediction method, the system has the characteristics of simple operation, low cost, reliable results, mobile portability, and it avoids the damage to apples in the process of freshness prediction to realize non-destructive testing.

关键词： gas sensor array freshness prediction chaotic sequence sparrow search

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Research on Improved Quantitative Identification Algorithm in Odor Source Searching Based on gas sensor array

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MICROMACHINES 2023年第6期14卷 1215-1215页

作者： Zhao, Yanru Wang, Dongsheng Huang, Xiaojie Henan Polytech Univ Coll Mech & Power Engn Jiaozuo 454003 Peoples R China

In order to improve the precision of gas detection and develop valid search strategies, the improved quantitative identification algorithm in odor source searching was researched based on the gas sensor array. The gas sensor array was devised corresponding to the artificial olfactory system, and the one-to-one response mode to the measured gas was set up with its inherent cross-sensitive properties. The quantitative identification algorithms were researched, and the improved Back Propagation algorithm was proposed combining cuckoo algorithm and simulated annealing algorithm. The test results prove that using the improved algorithm to obtain the optimal solution -1 at the 424th iteration of the Schaffer function with 0% error. The gas detection system designed with MATLAB was used to obtain the detected gas concentration information, then the concentration change curve may be achieved. The results show that the gas sensor array can detect the concentration of alcohol and methane in the corresponding concentration detection range and show a good detection performance. The test plan was designed, and the test platform in a simulated environment in the laboratory was found. The concentration prediction of experimental data selected randomly was made by the neural network, and the evaluation indices were defined. The search algorithm and strategy were developed, and the experimental verification was carried out. It is testified that the zigzag searching stage with an initial angle of 45 & DEG;is with fewer steps, faster searching speed, and a more exact position to discover the highest concentration point.

关键词： gas sensor array neural network quantitative identification odor source searching

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A Pilot Study for Legionella pneumophila Volatilome Characterization Using a gas sensor array and GC/MS Techniques

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sensorS 2023年第3期23卷 1401-1401页

作者： Capuano, Rosamaria Mansi, Antonella Paba, Emilia Marcelloni, Anna Maria Chiominto, Alessandra Proietto, Anna Rita Gordiani, Andrea Catini, Alexandro Paolesse, Roberto Tranfo, Giovanna Di Natale, Corrado Univ Roma Tor Vergata Dept Elect Engn Via Politecn 1 I-00133 Rome Italy Univ Roma Tor Vergata Interdept Ctr Volatil A DAmico Via Politecn 1 I-00133 Rome Italy Italian Workers Compensat Author INAIL Dept Occupat & Environm Med Epidemiol & Hyg Via Fontana Candida 1 I-00078 Rome Italy Univ Roma Tor Vergata Dept Chem Sci & Technol Via Ric Sci I-00133 Rome Italy

Legionellosis is a generic term describing the pneumonic (Legionnaires' disease, LD) and non-pneumonic (Pontiac fever, PF) forms of infection with bacteria belonging to the genus Legionella. Currently, the techniques used to detect Legionella spp. in water samples have certain limitations and drawbacks, and thus, there is a need to identify new tools to carry out low-cost and rapid analysis. In this regard, several studies demonstrated that a volatolomics approach rapidly detects and discriminates different species of microorganisms via their volatile signature. In this paper, the volatile organic compounds (VOCs) pattern emitted in vitro by Legionella pneumophila cultures is characterized and compared to those produced by other Legionella species and by Pseudomonas aeruginosa, using a gas sensor array and gas chromatograph mass spectrometer (GC-MS). Bacterial cultures were measured at the 3rd and 7th day after the incubation. sensor array data analyzed via the K-nearest neighbours (k-NN) algorithm showed a sensitivity to Legionella pneumophila identification at around 89%. On the other hand, GC-MS identified a bouquet of VOCs, mainly alcohols and ketones, that enable the differentiation of Legionella pneumophila in respect to other waterborne microorganisms.

关键词： gas sensor array volatile organic compounds Legionella pneumophila volatilome

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SWCNTs-based MEMS gas sensor array and its pattern recognition based on deep belief networks of gases detection in oil-immersed transformers

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sensorS AND ACTUATORS B-CHEMICAL 2020年 312卷 127998-000页

作者： Tang, Sirui Chen, Weigen Jin, Lingfeng Zhang, He Li, Yanqiong Zhou, Qu Zen, Wen Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se Chongqing 400030 Peoples R China Chongqing Univ Arts & Sci Sch Elect & Elect Engn Chongqing 400030 Peoples R China Southwest Univ Coll Engn & Technol Chongqing 400715 Peoples R China

MEMS gas sensor arrays and specially designed pattern recognition systems are the main research directions in the field of modern sensing technology in the engineering, especially in the smart sensing and monitoring of faults in large power equipment such as oil-immersed transformers. In this paper, the MEMS sensor array composed by eight SWCNTs-based (pure, -OH functionalized, -COOH functionalized, -NH2 functionalized by ethylenediamine, -NH2 functionalized by aniline, Ni-coated, Pd-doped, ZnO-doped) sensing units was palced in the fault characteristic gases (H-2, CO, and C2H2) of oil-immersed transformers, and their gas-sensing characteristics were tested in single and mixed gas atmosphere. Combined with the DBN-DNN pattern recognition method, the qualitative identification and quantitative analysis of the sensor array in a mixed gas atmosphere was realized, and the accuracy and reliability of the results are higher than the traditional BPNN model.

关键词： MEMS gas sensor array SWCNTs Pattern recognition

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Rapid evaluation of chicken meat freshness using gas sensor array and signal analysis considering total volatile basic nitrogen

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INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2020年第1期23卷 297-305页

作者： Tang, Xuxiang Yu, Zhi Zhejiang Gongshang Univ Sch Management & E Business Hangzhou Peoples R China Zhejiang Gongshang Univ Ctr Networking & Informat Hangzhou 310018 Peoples R China

Total volatile basic nitrogen (TVB-N) level rapid evaluation on chicken meat based on gas sensor array (GSA) technique was studied in this paper. GSA responses to chicken meat stored at 4 degrees C were examined for 5 days. TVB-N content was synchronously measured by chemical examination. Principal component analysis (PCA) and non-linear double-layered cascaded serial stochastic resonance (DCSSR) were utilized for measurement data analysis. TVB-N examination results suggested that chicken meat stored for more than 3 days was not fresh. PCA showed poor discrimination abilities, while DCSSR signal-to-noise ratio (SNR) quantitatively characterized the freshness of all samples. Chicken meat TVB-N forecasting model was developed by non-linear fitting between SNR eigenvalues and TVB-N values. The predicting model was constructed. Validation experiment results demonstrated that the forecasting accuracy of the developed model reached 93.3%.

关键词： Chicken meat total volatile basic nitrogen rapid prediction gas sensor array stochastic resonance

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Model-based calibration of a gas sensor array for on-line monitoring of ethanol concentration in Saccharomyces cerevisiae batch cultivation

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BIOSYSTEMS ENGINEERING 2020年 198卷 198-209页

作者： Yousefi-Darani, Abdolrahim Babor, Majharulislam Paquet-Durand, Olivier Hitzmann, Bernd Univ Hohenheim Dept Proc Analyt & Cereal Sci Stuttgart Germany

The ethanol concentration in batch cultivation with the yeast Saccharomyces cerevisiae was predicted on-line using a gas sensor array. Headspace samples were pumped past the gas sensors array for 10 s every five minutes and the voltage changes of the sensors were measured. For the calibration procedure no off-line sampling was used. Instead, a theoretical model of the process has been applied to simulate the ethanol production at any given time. However, the kinetic parameters of the simulation model are unknown at the beginning of the calibration. It is demonstrated that these kinetic parameters of the theoretical process model can be acquired from the response of the gas sensor array alone. The calculated parameters result in a simulation model that is at least as accurate as a model whose parameters have been acquired by least squares fitting to off-line measurements. The root mean square error of calibration as well as the percentage error for validation sets were below 0.2 g L-1 and 7%, respectively. The obtained results indicate that the model-based calibrated gas sensor array can be a cheap alternative to other tools, such as spectroscopy-based methods, that are used for monitoring yeast cultivations. (C) 2020 IAgrE. Published by Elsevier Ltd. All rights reserved.

关键词： gas sensor array Model-based calibration Ethanol prediction Yeast cultivation

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The New Approach to a Pattern Recognition of Volatile Compounds: The Inflammation Markers in Nasal Mucus Swabs from Calves Using the gas sensor array

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CHEMOsensorS 2021年第6期9卷 116页

作者： Kuchmenko, Tatiana Shuba, Anastasiia Umarkhanov, Ruslan Lvova, Larisa Voronezh State Univ Engn Technol VSUET Dept Phys & Analyt Chem Revolutsii Ave 19 Voronezh 394036 Russia Univ Tor Vergata Dept Chem Sci & Technol Via Ric Sci 1 I-00133 Rome Italy

This paper discusses the application of two approaches (direct and inverse) to the identification of volatile substances by means of a gas sensor array in a headspace over nasal mucus swab samples taken from calves with differing degrees of respiratory damage. We propose a unique method to visualize sensor array data for quality analysis, based on the spectra of cross mass sensitivity parameters. The traditional method, which requires an initial sensor array trained on the vapors of the individual substances (database accumulation)-with their further identification in the analyzed bio-samples through the comparison of the analysis results to the database-has shown unsatisfactory performance. The proposed inverse approach is more informative for the pattern recognition of volatile substances in the headspace of mucus samples. The projection of the calculated parameters of the sensor array for individual substances in the principal component space, acquired while processing the sensor array output from nasal swab samples, has allowed us to divide animals into groups according to the clinical diagnosis of their lung condition (healthy respiratory system, bronchitis, or bronchopneumonia). The substances detected in the gas phase of the nasal swab samples (cyclohexanone, butanone-2,4-methyl-2-pentanone) were correlated with the clinical state of the animals, and were consistent with the reference data on disease markers in exhaled air established for destructive organism processes.

关键词： gas sensor array principal component analysis volatile metabolites nasal swabs pattern recognition

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