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A Programmable Interface Circuit for MOS gas sensor array

IEEE sensorS JOURNAL 2024年第16期24卷 25869-25878页

作者： Chen, Dongliang Xie, Dongcheng Geng, Liang Yang, Yujie Wu, Qiuju Rong, Qian Xu, Lei Wu, Feng Univ Sci & Technol China Sch Microelect Hefei 230027 Peoples R China Micro Nano Sensing Hefei Technol Co Ltd Hefei 230031 Peoples R China Univ Sci & Technol China Hefei Natl Lab Phys Sci Microscale Hefei 230027 Peoples R China Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230027 Peoples R China

In this article, a programmable interface circuit for metal-oxide-semiconductors (MOS) gas sensor array is proposed. Integrated with analog and digital circuits, the interface circuit can realize sensor configuration, signal acquisition, data processing, and recognition results output by embedded with timing and algorithm code. A fully integrated and programmable analog front-end (AFE) circuit is introduced for sensor configuration, such as heating voltage generator, measuring voltage generator, and load resistor selector. A 12-bit SAR ADC supporting multiple channels is used for signal acquisition. Data processing and analysis are completed in a Cortex-M0 processor and recognition results can be output via common digital interfaces. The programmable interface circuit is realized in Nexchip 110 nm embedded flash process platform with an area of $2.53 \times 2.73$ mm. By introducing this interface circuit, an e-nose system with virtualized 16 channels is built up based on four MOS gas sensors and applied in safety monitoring of automotive power lithium batteries. With k-nearest neighbor classification algorithm embedded in the processor, the e-nose system identified gas types of thermal runaway process and output the current state of lithium battery pack with high accuracy.

关键词： gas detectors sensors Temperature sensors Generators Voltage measurement sensor arrays Resistors Analog front-end (AFE) gas recognition gas sensor array programmable

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A Novel Calibration Scheme of gas sensor array for a More Accurate Measurement Model of Mixed gases

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ACS sensorS 2024年第11期9卷 6022-6031页

作者： Ma, Yilun Qiu, Xingchang Duan, Zaihua Liu, Lili Li, Juan Wu, Yuanming Yuan, Zhen Jiang, Yadong Tai, Huiling Univ Elect Sci & Technol China UESTC Sch Optoelect Sci & Engn State Key Lab Elect Thin Films & Integrated Device Chengdu 610054 Peoples R China

gas sensor arrays (GSAs) usually encounter challenges due to the cross-contamination of mixed gases, leading to reduced accuracy in measuring gas mixtures. However, with the advent of artificial intelligence, there is a promising avenue for addressing this issue effectively. In pursuit of more accurate mixed gas measurements, we proposed a measurement model leveraging neural networks. Our approach involved employing the encoder of an autoencoder network (AEN) to extract features from experimental data, while fully connected layers were utilized for predicting concentrations of mixed gases. To refine the neural network parameters, we employed a variational autoencoder to generate additional data resembling the distribution of experimental data. Subsequently, we designed a domain difference maximum entropy technique to identify optimal concentration points for the calibration data. These calibration points were instrumental in training the fully connected layers, enhancing the model's accuracy. During practical usage, with the AEN configuration fixed, the model can be fine-tuned by using a small subset of test points across large-scale GSA deployments. Simulation and practical measurement results demonstrated the efficacy of our proposed measurement model, boasting high accuracy, with confidence intervals for relative errors of the four gas measurements below 3% at the 95% confidence level. Besides, the calibration scheme reduced the number of test points compared with traditional methods, reducing the cost of labor and equipment.

关键词： gas sensor array gas concentrationcalibration variational autoencoder maximum entropy cross-contamination transfer learning

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Fault Diagnosis of SF6-Insulated Equipment by Micro gas sensor array

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IEEE TRANSACTIONS ON POWER DELIVERY 2023年第1期38卷 222-230页

作者： Chu, Jifeng Wang, Qiongyuan Liu, Yuyang Pan, Jianbin Yuan, Huan Yang, Aijun Wang, Xiaohua Rong, Mingzhe Xi An Jiao Tong Univ Sch Elect Engn State Key Lab Elect Insulat & Power Equipment Xian 710049 Peoples R China

SF6 decomposition products could reflect the running status and inner faults of power equipment, and it's expected to realize a timely warning. In this work, six faults including spark and corona discharge were simulated, and SF6 decomposition products with various types and contents were obtained as well. Different from previous investigations employing precision instruments, such as gas chromatography and infrared spectroscopy, a micro sensor array loaded with three gas-sensitive nanomaterials was used to discriminate fault characteristic gases, performing obvious advantages in small size, high integration, and rapid detection. gas chromatography-mass spectrometry (GCMS) indicated that seven analytes had significant differences in types and contents. Meanwhile, the as-prepared micro gas sensor array also outputted significantly various signals for seven analytes, which provided a basis for gas identification. With the assistance of stacked denoising autoencoder (SDAE)-based discrimination algorithms, the recognition model between the response signals of the array and the discharge faults in power equipment could be established. In comparison with KNN (66.67 %), decision tree (70.47 %), and BPNN (73.33 %), SVM has achieved the highest average accuracy of 75.23 %. Totally, this work provides a promising novel method for rapid on-site inspection of SF6-insulated power equipment.

关键词： Spark discharge corona discharge SF(6 )decomposition products gas sensor array fault diagnosis on-site inspection

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Combination of metal oxide semiconductor gas sensor array and solid-phase microextraction gas chromatography-mass spectrometry for odour classification of brewed coffee

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FLAVOUR AND FRAGRANCE JOURNAL 2023年第6期38卷 451-463页

作者： Hardoyono, Fajar Windhani, Kikin Univ Islam Negeri Prof Kiai Haji Saifuddin Zuhri Fac Islamic Educ & Teaching Sci Lab Phys Purwokerto Jawa Tengah Indonesia Univ Jenderal Soedirman Fac Econ & Business Dept Econ & Dev Studies Agro Econ Res Grp Purwokerto Jawa Tengah Indonesia Univ Islam Negeri Prof Kiai Haji Saifuddin Zuhri Fac Islamic Educ & Teaching Sci Lab Phys Jalan A Yani 40 Purwokerto Jawa Tengah Indonesia

Odour analysis of coffee using low-cost and portable instruments in coffee shops, restaurants and bars is essential to keep the loyalty of coffee consumers. This paper aimed to analyse the performance of a gas sensor array for odour classification of brewed coffee. Five grams of ground coffee sample from five different brands was brewed in 80 mL of hot water at a temperature of 90 & DEG;C. The gas sensor array then measured the sensor's response to the brewed coffee odour. The recorded data were analysed using a principal component analysis (PCA), a hierarchical cluster analysis (HCA) and a support vector machine (SVM). Solid-phase microextraction gas chromatography-mass spectrometry (SPME-GC-MS) was used to identify the five coffee samples' volatile organic compounds (VOCs). The visualisation of the PCA score plot shows that the gas sensor array efficiently classifies the brewed coffee based on different odours. The SVM classification using a polynomial kernel obtained an accuracy of 95.21% using training data sets and an accuracy of 96.94% using testing data sets. Meanwhile, for SVM classification using radial basis function kernel, the SVM obtained an accuracy of 100% for training data sets and 93.06% for testing data sets. The SPME-GC-MS analysis showed that the abundance of 2-furanmethanol;2-methoxy-4-vinyl phenol;phenol, 4-ethyl-2-methoxy- and acetic acid contributed to the separation of the first and the second clusters in the principal components coordinate. Based on data analysis, the gas sensor showed high performance as a low-cost and portable instrument for odour analysis of coffee based on sensory technique.

关键词： coffee gas sensor array odour classification SPME-GC-MS support vector machine volatile organic compounds

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Detection of Drug-Producing Chemicals Based on gas sensor array With Dynamic Temperature Modulation

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IEEE sensorS JOURNAL 2023年第8期23卷 8109-8119页

作者： Meng, Fanli Mi, Chunjin Luan, Xi Ji, Hanyang Zhu, Hongmin Yuan, Zhenyu Northeastern Univ Coll Informat Sci & Engn Natl Frontiers Sci Ctr Ind Intelligence & Syst Opt Minist Educ Shenyang 110819 Peoples R China Northeastern Univ State Key Lab Data Analyt & Optimizat Smart Ind Shenyang 110819 Peoples R China Hebei Key Lab Micronano Precis Opt Sensing & Measu Qinhuangdao 066004 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

This article aims to study how to use gas sensor array detection system to realize the remote contactless identification of drug-producing chemicals in the external environment. The dynamic temperature modulation method of semiconductor sensor array is used to prove the feasibility and practicality of the method. In this article, the influence of heating period on sensor stability under rectangular wave heating condition is studied. The hardware system of gas sensor array is established, and the data are collected by the data acquisition system. In the simulated external environment, acetone, toluene, ether, and 2-butanone are qualitative analysis and identification. The data are dimensionally reduced by principal component analysis (PCA) algorithm and combined with decision tree algorithm to identify the data dynamic temperature modulation and array, which provides a research direction for more accurate qualitative identification and quantitative analysis of drug-producing chemicals.

关键词： sensors sensor arrays Temperature sensors gas detectors Chemical sensors Modulation Chemicals Decision tree drug-producing chemicals dynamic temperature modulation gas sensor array

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A Hybrid Machine Learning Algorithm for Detection of Simulated Expiratory Markers of Diabetic Patients Based on gas sensor array

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IEEE sensorS JOURNAL 2023年第3期23卷 2940-2947页

作者： Zhu, Hongyin Liu, Chao Zheng, Yao Zhao, Jing Li, Lei Changchun Univ Technol Dept Elect & Elect Engn Changchun 130012 Peoples R China

The method for breath detection using gas sensor array is gaining popularity. It was found that acetone can be used as breath marker in diabetic patients. In this article, seven metal oxide gas sensors were used to collect acetone and ethanol gas, which are used to simulate the exhaled breath of diabetic patients to obtain multidimensional response data. The kernel principal component analysis (KPCA) algorithm is used to extract the characteristics from the data collected by the sensor array. The kind of gases is qualitatively identified using the adaptive boosting (AdaBoost) algorithm, and the grid search (GS) method is used to automatically optimize the parameters of AdaBoost algorithm. The quantitative analysis of gas concentration is performed using multivariate relevance vector machine (MVRVM) and it is also trained using the gas sensor array drift dataset at different concentrations from the University of California (UCI) database. The experimental results show that the accuracy of the algorithm in the qualitative identification of acetone and ethanol gas reaches 99.722%, and the root-mean-square errors (RMSE) for quantification of acetone and ethanol gases are 0.027 and 0.030, respectively. The algorithm is used for qualitative identification on the gas sensor array drift dataset at different concentrations with an accuracy of 94.55%, and the RMSE for quantification of acetone and ethanol gases are 11.59 and 8.72, respectively.

关键词： sensors Diabetes Ethanol sensor arrays gas detectors Prediction algorithms Liquids Acetone diabetes gas sensor array machine learning prediction

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Evaluation of Pregnacy-Induced Modification of Urinary Volatilome by a gas sensor array 22nd

Evaluation of Pregnacy-Induced Modification of Urinary Volat...

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22nd National Conference on sensors and Microsystems

作者： Capuano, Rosamaria Nicolai, Eleonora Catini, Alexandro Paolesse, Roberto Bernardini, Sergio Rizzo, Giuseppe Di Natale, Corrado Univ Roma Tor Vergata Dept Elect Engn Rome Italy Univ Roma Tor Vergata Interdept Ctr Volatil AD Amico Rome Italy Univ Roma Tor Vergata Dept Expt Med Rome Italy Univ Roma Tor Vergata Dept Chem Sci & Technol Rome Italy Univ Roma Tor Vergata Dept Biomed & Prevent Rome Italy Univ Roma Tor Vergata Dept Obstet & Gynecol Fdn Policlin Tor Vergata Rome Italy

ISBN: (纸本)9783031820786;9783031820762;9783031820755

This study investigated changes in the volatile organic compound (VOC) pattern induced by pregnancy, focusing on urine samples. Pregnancy leads to significant alterations in maternal anatomy and physiology to meet metabolic demands and support fetal development. Understanding these changes is crucial for distinguishing between normal and pathological conditions. Metabolic variations in the body result in modifications to VOC patterns in biological fluids. Urine VOCs from 30 pregnant women (at the end of the first trimester) and 21 non-pregnant women were analyzed using a porphyrinoids QMB-based gas sensor array and gas chromatography coupled with ion mobility spectrometry (GC-IMS) to characterize the volatilome of pregnancy under physiological conditions. Kruskal-Wallis rank test on GC-IMS data identified 27 compounds that differentiated pregnant from non-pregnant women (p < 0.05). Considering those data, Linear Discriminant Analysis (LDA) achieved an accuracy of over 80% in testing. The gas sensor array had an accuracy of 71% in the LDA model test, a slightly lower compared to the analytical method.

关键词： gas sensor array volatile organic compounds volatilome pregnancy gas chromatography

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High Accuracy Real-Time Multi-gas Identification by a Batch-Uniform gas sensor array and Deep Learning Algorithm

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ACS sensorS 2022年第2期7卷 430-440页

作者： Kang, Mingu Cho, Incheol Park, Jaeho Jeong, Jaeseok Lee, Kichul Lee, Byeongju Henriquez, Dionisio Del Orbe Yoon, Kukjin Park, Inkyu Korea Adv Inst Sci & Technol KAIST Dept Mech Engn Daejeon 34141 South Korea

Semiconductor metal oxide (SMO) gas sensors are attracting great attention as next-generation environmental monitoring sensors. However, there are limitations to the actual application of SMO gas sensors due to their low selectivity. Although the electronic nose (E-nose) systems based on a sensor array are regarded as a solution for the selectivity issue, poor accuracy caused by the nonuniformity of the fabricated gas sensors and difficulty of real-time gas detection have yet to be resolved. In this study, these problems have been solved by fabricating uniform gas sensor arrays and applying the deep learning algorithm to the data from the sensor arrays. Nanocolumnar films of metal oxides (SnO2, In2O3, WO3, and CuO) with a high batch uniformity deposited through glancing angle deposition were used as the sensing materials. The convolutional neural network (CNN) using the input data as a matrix form was adopted as a learning algorithm, which could conduct pattern recognition of the sensor responses. Finally, real-time selective gas detection for CO, NH3, NO2, CH4, and acetone (C3H6O) gas was achieved (minimum response time of 1, 8, 5, 19, and 2 s, respectively) with an accuracy of 98% by applying preprocessed response data to the CNN.

关键词： gas sensor array electronic nose glancing angle deposition deep learning real-time gas identification

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Tilapia freshness prediction utilizing gas sensor array system combined with convolutional neural network pattern recognition model

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INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2022年第1期25卷 2066-2072页

作者： Sun, Yiqin Zhang, Xianfei HangZhou Dianzi Univ Key Lab RF Circuits & Syst Minist Educ Hangzhou Peoples R China HangZhou Dianzi Univ Key Lab Large Scale Integrated Design Hangzhou Peoples R China

Freshness of tilapia stored under cold storage was studied by utilizing gas sensor array combined with convolutional neural network (CNN) pattern recognition model in this paper. Total volatile basic nitrogen (TVBN) index was conducted to supply a freshness mark for tilapia sample. A portable electronic nose was designed and fabricated. The sensor array responses to tilapia sample were recorded. Principal component analysis (PCA) was used for gas sensor array data treatment, and this method could not discriminate all the samples. CNN model was optimized. The structure of CNN model had one input layer, four convolutional layers, two pooling layers, one Dropout layer, one fully connected layer and one output layer. The predicting accuracy of optimized CNN was 92.31%. The method investigated in this paper presented some advantages including rapid, easy operation, high sensitivity, high precision, etc. This method can be promising in aquatic food quality evaluating applications.

关键词： Tilapia freshness gas sensor array Principal component analysis Convolutional neural network

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gas sensor array Fault Diagnosis Based on Multi-Dimensional Fusion, an Attention Mechanism, and Multi-Task Learning

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sensorS 2023年第18期23卷 7836-7836页

作者： Huang, Pengyu Wang, Qingfeng Chen, Haotian Lu, Geyu Jilin Univ Coll Elect Sci & Engn State Key Lab Integrated Optoelect Changchun 130012 Peoples R China

With the development of gas sensor arrays and computational technology, machine olfactory systems have been widely used in environmental monitoring, medical diagnosis, and other fields. The reliable and stable operation of gas sensing systems depends heavily on the accuracy of the sensors outputs. Therefore, the realization of accurate gas sensor array fault diagnosis is essential to monitor the working status of sensor arrays and ensure the normal operation of the whole system. The existing methods extract features from a single dimension and require the separate training of models for multiple diagnosis tasks, which limits diagnostic accuracy and efficiency. To address these limitations, for this study, a novel fault diagnosis network based on multi-dimensional feature fusion, an attention mechanism, and multi-task learning, MAM-Net, was developed and applied to gas sensor arrays. First, feature fusion models were applied to extract deep and comprehensive features from the original data in multiple dimensions. A residual network equipped with convolutional block attention modules and a Bi-LSTM network were designed for two-dimensional and one-dimensional signals to capture spatial and temporal features simultaneously. Subsequently, a concatenation layer was constructed using feature stitching to integrate the fault details of different dimensions and avoid ignoring useful information. Finally, a multi-task learning module was designed for the parallel learning of the sensor fault diagnosis to effectively improve the diagnosis capability. The experimental results derived from using the proposed framework on gas sensor datasets across different amounts of data, balanced and unbalanced datasets, and different experimental settings show that the proposed framework outperforms the other available methods and demonstrates good recognition accuracy and robustness.

关键词： gas sensor array fault diagnosis deep learning attention mechanism multi-task learning

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