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Multiple-sensor Detection System Design for Tea Identification Based on Mutual Information array optimization Scheme

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IEEE sensorS JOURNAL 2023年第12期23卷 13751-13761页

作者： Qian, Junhui Lu, Mengchen Xu, Peng Liu, Ziyu Lu, Yuanyuan Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China Chongqing Univ Chongqing Key Lab Bio Percept & Intelligent Infor Chongqing 400044 Peoples R China CETC Acad Chips Technol Beijing 100863 Peoples R China

In this article, a multimetal oxide sensor (MOS) detection system is developed for the identification of different grades of tea leaves. To cope with the interference brought by redundant information and further improve the detection capability of the system and reduce the number of sensors, sensor array optimization algorithms are further implemented. The main idea of sensor array optimization is to select sensors according to evaluation index which ranks the importance of sensors. This article optimizes the sensor array based on information theory. Specifically, a weighted modified mutual information (MI) optimization algorithm that simultaneously considers relevancy, redundancy, and complementarity is developed. The experimental results show that the weighted modified MI optimization method can obtain the best system performance with the minimum number of sensors.

关键词： sensor arrays sensors sensor systems Gas detectors Mutual information Costs sensor phenomena and characterization Mutual information (MI) odor detection sensor array optimization tea-grade identification weighted modified MI

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A sensor array optimization method of electronic nose based on elimination transform of Wilks statistic for discrimination of three kinds of vinegars

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JOURNAL OF FOOD ENGINEERING 2014年第Apr.期127卷 43-48页

作者： Yin, Yong Yu, Huichun Chu, Bing Xiao, Yujuan Henan Univ Sci & Technol Sch Food & Bioengn Luoyang 471003 Peoples R China

In order to discriminate three kinds of Chinese vinegars using an electronic nose (E-nose), an E-nose sensor array optimization method based on elimination transform with pivoting of Wilks A-statistic is put forward. With the help of the elimination transform some sensors with high discriminant ability are orderly selected step by step, and an ordered set of the selected sensors is simultaneously obtained by the selected sequence. Although the ordered set is not necessarily optimization array, the optimization array can be easily obtained by further exploring the discrimination results of sub-arrays which are composed by the first two, three, four and more sensors of the ordered set. In the discrimination of three kinds of vinegar samples, 9 sensors were selected in sequence and consisted of the ordered set using the method, and these vinegar samples could be well discriminated by principal component analysis (PCA). Furthermore, the first two sensors were selected and their PCA result was better than that of the 9 sensors. This illuminates that the array optimization method is very suitable and effective. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Electronic nose Vinegar discrimination sensor array optimization Elimination transform Wilks A-statistic

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Quantitative Damage Detection and Sparse sensor array optimization of Carbon Fiber Reinforced Resin Composite Laminates for Wind Turbine Blade Structural Health Monitoring

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sensorS 2014年第4期14卷 7312-7331页

作者： Li, Xiang Yang, Zhibo Chen, Xuefeng Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China

The active structural health monitoring (SHM) approach for the complex composite laminate structures of wind turbine blades (WTBs), addresses the important and complicated problem of signal noise. After illustrating the wind energy industry's development perspectives and its crucial requirement for SHM, an improved redundant second generation wavelet transform (IRSGWT) pre-processing algorithm based on neighboring coefficients is introduced for feeble signal denoising. The method can avoid the drawbacks of conventional wavelet methods that lose information in transforms and the shortcomings of redundant second generation wavelet (RSGWT) denoising that can lead to error propagation. For large scale WTB composites, how to minimize the number of sensors while ensuring accuracy is also a key issue. A sparse sensor array optimization of composites for WTB applications is proposed that can reduce the number of transducers that must be used. Compared to a full sixteen transducer array, the optimized eight transducer configuration displays better accuracy in identifying the correct position of simulated damage (mass of load) on composite laminates with anisotropic characteristics than a non-optimized array. It can help to guarantee more flexible and qualified monitoring of the areas that more frequently suffer damage. The proposed methods are verified experimentally on specimens of carbon fiber reinforced resin composite laminates.

关键词： structural health monitoring sensor array optimization composite laminates second generation wavelet Lamb wave wind turbine blade

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Design and optimization of Electronic Nose sensor array for Real-Time and Rapid Detection of Vehicle Exhaust Pollutants

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CHEMOsensorS 2022年第12期10卷 496页

作者： Tong, Jin Song, Chengxin Tong, Tianjian Zong, Xuanjie Liu, Zhaoyang Wang, Songyang Tan, Lidong Li, Yinwu Chang, Zhiyong Jilin Univ Coll Biol & Agr Engn Changchun 130022 Peoples R China Jilin Univ Key Lab Engn Bio Minist Educ Changchun 130022 Peoples R China Iowa State Univ Agr & Biosyst Engn Ames IA 50010 USA Zibo Municipal Transport Management Serv Ctr Zibo 255000 Peoples R China China FAW Grp CO Ltd Intelligent Connected Vehicle Dev Inst Digital Intelligent Cockpit Dept Changchun 130013 Peoples R China Jilin Univ Coll Transportat Changchun 130022 Peoples R China Jilin Univ Weihai Inst Bio Weihai 264401 Peoples R China

Traditional vehicle exhaust pollutant detection methods, such as bench test and remote sensing detection, have problems such as large volume, high cost, complex process, long waiting time, etc. In this paper, according to the main components of vehicle exhaust pollutants, an electronic nose with 12 gas sensors was designed independently for real-time and rapid detection of vehicle exhaust pollutants. In order to verify that the designed electronic nose based on machine learning classification method can accurately identify the exhaust pollutants from different engines or different concentration levels from the same engine. After feature extraction of the collected data, Random Forest (RF) was used as the classifier, and the average classification accuracy reached 99.92%. This result proved that the designed electronic nose combined with RF method can accurately and sensitively judge the concentration level of vehicle exhaust pollutants.. Then, in order to enable the electronic nose to be vehicle-mounted and to achieve real-time and rapid detection of vehicle exhaust pollutants. We used Recursive Feature Elimination with Cross Validation (RFECV), Random Forest Feature Selector (RFFS) and Principal Component Analysis (PCA) to optimize the sensor array. The results showed that these methods can effectively simplify the sensor array while ensuring the RF classifier's classification recognition rate. After using RFECV and RFFS to optimize the sensor array, the RF classifier's classification recognition rate of the optimized sensor arrays for vehicle exhaust pollutants reached 99.77% and 99.44%, respectively. The numbers of sensors in the optimized sensor arrays were six and eight respectively, which achieved the miniaturization and low-cost of the electronic nose. With the limitation of six sensors, RFECV is the best sensor array optimization method among the three methods.

关键词： vehicle exhaust pollutants electronic nose sensor array optimization feature extraction feature selection

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A sensor array optimization Method Based on Variance Difference for Machine Olfaction

A Sensor Array Optimization Method Based on Variance Differe...

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International Conference on Advanced Nano-Technology and Biomedical Material (ANTBM)

作者： Chen, Rong-rong Luo, De-han Sun, Yu Sun, Yun-long H-GholamHossini Guangdong Univ Technol Sch Informat Engn Guangzhou Guangdong Peoples R China Zhaoqing Univ Fac Elect Informat & Mech Elect Engn Zhaoqing Peoples R China Auckland Univ Technol Sch Engn Auckland New Zealand

ISBN: (纸本)9783038352044

In machine olfaction or electronic nose, sensor optimization is important to enhance pattern recognition efficiency and reduce redundant information. Highly correlated response of one sensor to two different odors implies less contribution of this sensor to the classification of these two odors. Variance difference is a significant index to measure the similarity of sensor responses. A sensor optimization method based on variance difference is proposed in this paper;both the average value of variance difference and cluster analysis of variance difference matrix were considered to identify several possible sensor subsets. Six Chinese herbal medicines and linear discrimination analysis (LDA) were applied to test the classification results in order to determine the best subset. LDA results indicated that the optimized sensor subset performed well in classification of the six Chinese medicines. The proposed sensor array optimization method could be applied to other kinds of odors classification as a novel method.

关键词： sensor array optimization Variance Difference Machine Olfaction LDA

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optimization of Electronic Nose sensor array for Tea Aroma Detecting Based on Correlation Coefficient and Cluster Analysis

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CHEMOsensorS 2021年第9期9卷 266页

作者： Wang, Jin Zhang, Cheng Chang, Meizhuo He, Wei Lu, Xiaohui Fei, Shaomei Lu, Guodong Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Peoples R China

The electronic nose system is widely used in tea aroma detecting, and the sensor array plays a fundamental role for obtaining good results. Here, a sensor array optimization (SAO) method based on correlation coefficient and cluster analysis (CA) is proposed. First, correlation coefficient and distinguishing performance value (DPV) are calculated to eliminate redundant sensors. Then, the sensor independence is obtained through cluster analysis and the number of sensors is confirmed. Finally, the optimized sensor array is constructed. According to the results of the proposed method, sensor array for green tea (LG), fried green tea (LF) and baked green tea (LB) are constructed, and validation experiments are carried out. The classification accuracy using methods of linear discriminant analysis (LDA) based on the average value (LDA-ave) combined with nearest-neighbor classifier (NNC) can almost reach 94.44 similar to 100%. When the proposed method is used to discriminate between various grades of West Lake Longjing tea, LF can show comparable performance to that of the German PEN2 electronic nose. The electronic nose SAO method proposed in this paper can effectively eliminate redundant sensors and improve the quality of original tea aroma data. With fewer sensors, the optimized sensor array contributes to the miniaturization and cost reduction of the electronic nose system.

关键词： tea aroma detection electronic nose sensor array optimization correlation analysis discriminating performance value cluster analysis

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A sensor array optimization Method Based on Variance Difference for Machine Olfaction

A Sensor Array Optimization Method Based on Variance Differe...

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2014 International Conference on Advanced Nano-Technology and Biomedical Material(ANTBM 2014)

作者： Rong-rong Chen De-han Luo Yu Sun Yun-long Sun H-GholamHossini School of Information Engineering Guangdong University of Technology Faculty of Electronic Information and Mechanical Electrical Engineering Zhaoqing University School of Engineering Auckland University of Technology

In machine olfaction or electronic nose, sensor optimization is important to enhance pattern recognition efficiency and reduce redundant information. Highly correlated response of one sensor to two different odors implies less contribution of this sensor to the classification of these two odors. Variance difference is a significant index to measure the similarity of sensor responses. A sensor optimization method based on variance difference is proposed in this paper;both the average value of variance difference and cluster analysis of variance difference matrix were considered to identify several possible sensor subsets. Six Chinese herbal medicines and linear discrimination analysis(LDA) were applied to test the classification results in order to determine the best subset. LDA results indicated that the optimized sensor subset performed well in classification of the six Chinese medicines. The proposed sensor array optimization method could be applied to other kinds of odors classification as a novel method.

关键词： sensor array optimization Variance Difference Machine Olfaction LDA

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Improving Multisensor Detection System Performance via Subspace Projection and Embedded Hilbert Framework

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Qian, Junhui Liu, Jiheng Xu, Peng Jia, Fengde Qian, Guobing Wang, Shiyuan Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China Donghua Univ Sch Informat Sci & Technol Shanghai 150080 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China

In recent years, multisensor detection systems have gained widespread attention in the fields of environmental monitoring and food quality assessment, etc. However, the problems of sensor drift, redundant data, and high cost of the system have seriously restricted their long-term stability and detection accuracy. To cope with these challenges, this article designs a low-cost multisensor detection system for the identification of different kinds of herbal medicines. On the basis of the system, considering the similarity between drift-free data and drift data, we derive a drift calibration framework via subspace projection to improve the long-term stability of the designed system. Furthermore, considering the redundancy feature in the source domain may be highly possibility to be redundant in the target domain, a sensor array optimization named embedded conditional Hilbert scheme is developed to reduce the number of sensors, which considers the redundancy between features and features, features and labels. Finally, the experimental results show that the proposed calibration framework can achieve the best performance using the least sensors.

关键词： sensor arrays Feature extraction optimization Calibration Humidity Filtering Costs Circuit stability Transfer learning Training Conditional Hilbert-Schmidt independence criterion (HSIC) multisensor detection system sensor array optimization sensor drift calibration

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A smartphone-integrated paper-based colorimetric sensor array: Real-time detection and classification of flavonoids

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TALANTA 2025年 293卷 128030页

作者： Liang, Zhenwu Qin, Yuelian Zhong, Xinyu Ma, Xiaolong Deng, Lijun Zou, Ziwei Feng, Linlin Pan, Ziping Pan, Shihan Li, Mei Su, Zhiheng Wu, Jinxia Guangxi Med Univ Pharmaceut Coll Nanning 530021 Peoples R China Guangxi Med Univ Inst Life Sci Nanning 530021 Peoples R China Guangxi Key Lab Bioact Mol Res & Evaluat Nanning 530021 Peoples R China Guangxi Beibu Gulf Marine Biomed Precis Dev & High Nanning 530021 Peoples R China Guangxi Hlth Commiss Key Lab Basic Res Antigeriatr Nanning 530021 Peoples R China

Here, a smartphone-integrated paper-based colorimetric sensor array (PBCSA) was developed for distinguishing flavonoid-rich Citrus herbal products (FRCHPs): Citri reticulatae pericarpium, Aurantii fructus immaturus, Aurantii fructus, Citri grandis exocarpium, Citri reticulatae pericarpium viride, and Citri sarcodactylis fructus. Based on the strategy of indicator displacement assay induced by flavonoids, a 3 x 3 PBCSA with a hydrophobic barrier was constructed using inkjet printing technology. The PBCSA can accurately distinguished different species or concentrations flavonoids, and FRCHPs, demonstrating its broad applicability. After optimization with Genetic Algorithm, the Support Vector Machine (SVM) reduced the number of PBCSA sensor units from nine to five while maintaining an accuracy of 100.00 %, significantly improving the efficiency and accuracy of detection. Furthermore, the optimized SVM was integrated into a self-developed Quick Viewer app for real-time detection, greatly enhancing its practicability. This study not only presents a novel strategy for optimizing sensor arrays but also introduces a simple, economical, and real-time approach for analyzing FRCHPs.

关键词： Flavonoid detection Paper-based colorimetric sensor array sensor array optimization Real-time analysis

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Electronic sensing combined with machine learning models for predicting soil nutrient content

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2024年 221卷

作者： Liu, Shuyan Chen, Xuegeng Xia, Xiaomeng Jin, Yvhan Wang, Gang Jia, Honglei Huang, Dongyan Jilin Univ Minist Educ Key Lab Bionics Engn Changchun 130022 Peoples R China Jilin Univ Coll Biol & Agr Engn Changchun 130022 Peoples R China Shihezi Univ Coll Mech & Elect Engn Shihezi 832003 Peoples R China

Traditional methods for detecting soil nutrient content usually involve laborious and time-consuming experimental procedures, hindering the efficiency of soil analysis and making them less suitable for large-scale soil testing. Therefore, there exists a pressing need to develop innovative solutions. This study aimed to investigate the potential relationship between soil pyrolysis gas and soil nutrient content by establishing a model that combined electronic sensing and machine learning to predict soil nutrient content. The initial data sets were derived from utilizing pyrolysis gas response curves of soil samples acquired from 10 distinct gas sensors. Afterward, we innovatively proposed a sensor array optimization (SAO) method based on the dynamic feature importance (DFI) of the Random Forest-Pearson Correlation Coefficient (RF-PCC) to identify the optimal sensor combinations for four soil nutrients. Furthermore, Support Vector Machine (SVM), Random Forest (RF), Multilayer Perceptron (MLP), and Multilayer Perceptron-Random Forest (MLP-RF) models were employed to predict the soil nutrient content based on the electronic sense to address the challenges associated with developing a unified model. The MLP-RF models showed superior performance compared with the other three models, with the coefficient of determination (R2) for the four soil nutrients ranging from 0.54 to 0.94. Overall, our research not only provides approximately quantitative predictive outcomes but also contributes to a better understanding of a new approach for detecting soil nutrient content.

关键词： Soil nutrient content Electronic sensing Machine learning sensor array optimization Dynamic feature importance

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