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Study on sensor array optimization of Medical Electronic Nose for Wound Infection Detection

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第3期69卷 1867-1871页

作者： Qian, Junhui Lu, Mengchen Tian, Fengchun Liu, Ran Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China Chongqing Univ Chongqing Key Lab Biopercept & Intelligent Inform Chongqing 400044 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China

In order to improve the detection effect and simplify the number of sensors of electronic nose system for wound infection detection, the sensor arrays are optimized based on Hilbert-Schmidt independence criterion (HSIC) in this brief. Specifically, the HSIC optimization algorithm of Gaussian kernel function is exploited. The criterion of Hilbert-Schmidt independence is obtained by empirical estimation of norm of Hilbert-Schmidt cross-covariance operator. The main idea of its application in sensor array optimization is that the larger the empirical norm estimates of Hilbert-Schmidt cross-covariance operator, the stronger the correlation between sensor features and labels, the greater the role of sensor features in classification and recognition. The experimental results show that the HSIC optimization method (Gaussian kernel function) performs better than the linear discriminant analysis (LDA) method and the principal component analysis (PCA).

关键词： sensor arrays Microorganisms Electronic noses optimization Indexes Wounds Support vector machines Electronic nose sensor array optimization HSIC

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A Multi-Objective Intelligent optimization Method for sensor array optimization in Distributed SAR-GMTI Radar Systems

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REMOTE SENSING 2024年第16期16卷 3041页

作者： Li, Xianghai Wang, Rong Liang, Gengchen Yang, Zhiwei Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

The design and optimization of sensor array configurations is a significant challenge for distributed SAR-GMTI radar systems because the system performance of distributed array radar is a comprehensive result of several conflicting evaluation indicators. This paper developed a multi-objective intelligent optimization method to solve the global optimal problem of array configurations in terms of achieving optimal GMTI performance. Firstly, to formulate the relationship between array configuration and GMTI performance, we established three objective functions derived from evaluating indicators of SAR-GMTI performance. Specifically, in the objective functions, we proposed a novel clutter covariance matrix model that added several typical non-ideal factors of the real-world detection environment. This provides a way to build a bridge between the array configuration, environment clutter, and GMTI performance. Then, we proposed an improved multi-objective snake optimization algorithm (IMOSOA) that combined the Pareto optimization mechanism with snake optimization to solve the multi-objective optimization problem while reconciling the conflicts between different objective functions. Meanwhile, some significant improvements were made to speed up convergence. That is, tent chaotic mapping-based initialization, multi-group coevolution, and individual mutation strategies were applied to solve the non-convergence problem of global searching. Finally, in the case of an airborne SAR-GMTI system, numerical experiments demonstrated that the proposed IMOSOA has superior performance than other contrast methods, especially in terms of GMTI applications.

关键词： distributed array radar system sensor array optimization multi-objective optimization ground moving target indication (GMTI)

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Robust Sparse Learning Based sensor array optimization for Multi-feature Fusion Classification 1

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31st International Conference on Artificial Neural Networks (ICANN)

作者： Zhao, Leilei Tian, Fengchun Qian, Junhui Liu, Ran Jiang, Anyan Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China Chongqing Univ Chongqing Key Lab Biopercept & Intelligent Inform Chongqing 400044 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China

ISBN: (数字)9783031159374

ISBN: (纸本)9783031159374;9783031159367

In this paper, we propose a robust sensor array optimization method based on sparse learning for multi-feature fusion data classification. The proposed approach contains three key characteristics. First, it considers the intrinsic group structure among features by combining an l(F,1) norm regularizer design and least squares regression framework. Second, in sensor selection, insignificant feature groups can be eliminated by grouped row sparse coefficients generated by the model, while the epsilon-dragging trick is introduced to improve the classification ability. Third, an efficient alternating iteration algorithm is presented to optimize the convex objective function. The results compared with the other classical methods on gas sensor array data sets demonstrate that the proposed method can effectively reduce the number of sensors with higher classification accuracy.

关键词： sensor array optimization Sparse learning Multiple feature fusion

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Information-Theoretic Ensemble Feature Selection With Multi-Stage Aggregation for sensor array optimization

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IEEE sensorS JOURNAL 2021年第1期21卷 476-489页

作者： Wijaya, Dedy Rahman Afianti, Farah Telkom Univ Sch Appl Sci Bandung 40257 Indonesia Telkom Univ Sch Comp Bandung 40257 Indonesia

In an electronic nose (e-nose) system, the gas sensor array is the key component for detecting the volatile profile of a sample. An inappropriate sensor combination can lead to several issues, including overlapping selectivities, high computational overhead, performance degradation, etc. To deal with this problem, typically, a feature selection algorithm (FSA) is utilized to optimize the gas sensor combination in the sensor array. However, the instability of the FSA output is a serious problem in sensor array optimization. An unstable FSA output makes it difficult to conclude the general sensor combination. Hence, in this study, the Information-Theoretic Ensemble Feature Selection (ITEFS) method is proposed to deal with FSA instability. In the experiment, twelve homogeneous e-nose data sets were used that corresponded to twelve types of beef samples. Moreover, the outputs from eleven information-theoretic FSAs were aggregated to determine the general sensor combination. The performance of the selected sensors was validated based on the number of overlapping selectivities, F-measure, and execution time. The results indicated that ITEFS can more effectively reduce overlapping selectivities than other FSAs. The selected sensors also displayed comparable performance in classification tasks to other FSAs even when using fewer sensors, with an average F-measure of more than 99% using k-NN and SVM on all data sets. This indicates that the combination of selected sensors had sufficiently good generalization to detect the various types of beef samples. In addition, the utilization of a lower number of sensors also reduced the execution time in the training and testing processes.

关键词： sensor arrays Gas detectors Feature extraction optimization sensor phenomena and characterization Beef quality monitoring electronic nose ensemble feature selection sensor array optimization stability of feature selection

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Stability Assessment of Feature Selection Algorithms on Homogeneous Datasets: A Study for sensor array optimization Problem

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IEEE ACCESS 2020年 8卷 33944-33953页

作者： Wijaya, Dedy Rahman Afianti, Farah Telkom Univ Sch Appl Sci Bandung 40257 Indonesia Telkom Univ Sch Comp Bandung 40257 Indonesia

A feature selection algorithm (FSA) is used to eliminate redundant and irrelevant features. Obviously, it can reduce dimensionality as well as the complexity of the original problem. Furthermore, the stability of FSA output becomes a major issue in real-world applications. Stability refers to the consistency of its feature preference related to the perturbation of data samples. In sensor array optimization, an FSA is used to find the best sensor combination in a sensor array. Typically, the main objectives of sensor array optimization are reducing data dimensions, electrical consumption, production cost, computational and traffic overhead, etc. Furthermore, the stable outputs of FSA in several observations are necessary to make a firm conclusion of selected sensors. The contribution of this research is to investigate the stability of FSAs in twelve homogeneous datasets in relation to the sensor array optimization problem. In this study, the stability of seventeen filter-based FSAs is compared across twelve homogeneous datasets. These datasets are generated from the electronic nose (e-nose) used to monitor twelve types of beef cuts. In this case, gas sensor array must have good generalization to differentiate all beef types. The experimental results show that a single FSA cannot guarantee stable sensors recommendation in sensor array optimization. Thus, it becomes a caution to researchers and practitioners to use a proper approach when performing sensor array optimization.

关键词： sensor arrays optimization Gas detectors Feature extraction Stability criteria Electronic nose feature selection sensor array optimization stability assessment

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sensor array optimization techniques for exhaled breath analysis to discriminate diabetics using an electronic nose

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ETRI JOURNAL 2018年第6期40卷 802-812页

作者： Jeon, Jin-Young Choi, Jang-Sik Yu, Joon-Boo Lee, Hae-Ryong Jang, Byoung Kuk Byun, Hyung-Gi Kangwon Natl Univ Div Elect Informat & Commun Engn Samcheok South Korea SW & Content Res Lab Elect Informat & Commun Engn Deajeon South Korea Keimyung Univ Dept Internal Med Deagu South Korea

Disease discrimination using an electronic nose is achieved by measuring the presence of a specific gas contained in the exhaled breath of patients. Many studies have reported the presence of acetone in the breath of diabetic patients. These studies suggest that acetone can be used as a biomarker of diabetes, enabling diagnoses to be made by measuring acetone levels in exhaled breath. In this study, we perform a chemical sensor array optimization to improve the performance of an electronic nose system using Wilks' lambda, sensor selection based on a principal component (B4), and a stepwise elimination (SE) technique to detect the presence of acetone gas in human breath. By applying five different temperatures to four sensors fabricated from different synthetic materials, a total of 20 sensing combinations are created, and three sensing combinations are selected for the sensor array using optimization techniques. The measurements and analyses of the exhaled breath using the electronic nose system together with the optimized sensor array show that diabetic patients and control groups can be easily differentiated. The results are confirmed using principal component analysis (PCA).

关键词： B4 (based on principal component) diabetes E-nose principal component analysis (PCA) sensor array optimization stepwise elimination (SE) Wilks' lambda

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sensor array optimization of Electronic Nose for Detection of Bacteria in Wound Infection

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2017年第9期64卷 7350-7358页

作者： Sun, Hao Tian, Fengchun Liang, Zhifang Sun, Tong Yu, Bin Yang, Simon X. He, Qinghua Zhang, Longlong Liu, Xiangmin Chongqing Univ Dept Commun Engn Chongqing 400030 Peoples R China Univ Guelph Sch Engn Guelph ON N1G 2W1 Canada Third Mil Med Univ Inst Field Surg Chongqing 400010 Peoples R China

In order to identify the bacteria in wound infection, an electronic nose system with a sensor array of 34 sensors was designed. Eight kinds of samples were detected, i.e., culture medium, Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa and their mixture with different concentration. Using support vector machine as the classifier and without sensor array optimization, the recognition rate is up to 86.54%. To simplify the sensor array and improve the recognition rate for bacteria samples, Wilks' lambda statistic (Wilks'.statistic), Mahalanobis distance, principal component analysis (PCA), linear discriminant analysis (LDA), and genetic algorithm are used to optimize the sensor array. It is shown that the sensor array optimization may be realized efficiently by these methods except PCA. After sensor array optimization by Wilks' A-statistic and LDA, both of their recognition rates are the highest and up to 96.15%, while the numbers of sensors in optimized sensor arrays are 22 and 20, respectively. Under the limitation of ten sensors, the recognition rate optimized by Wilks' A-statistic and LDA may still reach 95.19%.

关键词： Bacteria electronic nose (E-nose) linear discriminant analysis (LDA) sensor array optimization Wilks' lambda statistic

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Study of bamboo shoots quality by utilizing electronic nose sensor array and its optimization

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INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2019年第1期22卷 1548-1561页

作者： Ying, Xiaoguo Deng, Shanggui Zhu, Bowei Zhang, Feixiang Mao, Xinyi Tu, Jiayun Ruan, Xiaorong Yi, Xiaomei Li, Jian Gao, Yuanyuan Zhejiang Ocean Univ Coll Food & Pharm Zhoushan 316022 Peoples R China Zhejiang A&F Univ Key Lab Forestry Intelligent Monitoring & Informa Key Lab Forestry Sensing Technol & Intelligent Eq Sch Informat EngnDept Forestry Linan 311300 Peoples R China

Bamboo shoots quality determination method using EN and M.O.S sensor array optimization was studied in this paper. EN responses to winter bamboo shoots samples under different storage time were measured. sensor loading analysis results demonstrated that sensor array optimization improved principal component analysis discrimination degree of shoots samples in different quality. Non-linear stochastic resonance was utilized for optimized EN measurement data analysis. SR signal-to-noise ratio quantitatively discriminated all samples. Winter bamboo shoots quality determination model was developed by linear fitting regression of SR signal-to-noise ratio eigen values. Validating experiments results indicated that the developed model presented a quality-predicting accuracy of 83.3% for shoots samples.

关键词： Winter bamboo shoots quality electronic nose non-linear sensor array optimization

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A Multiple sensor Detection Design via Embedded array optimization and Modified Chebyshev Polynomial Weighting Scheme

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Qian, Junhui Zhang, Chao Xu, Peng Liu, Jiheng Huang, Yulong Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China Jianghuai Adv Technol Ctr Hefei 230000 Peoples R China Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China Harbin Engn Univ Dept Automat Harbin 150001 Peoples R China

In order to meet the demand for odor detection in herbal medicine, a multisensor detection system consisting of various types is implemented. With the consideration of possible imbalance data distribution existing in the herbal medicine datasets, a classifier based on Chebyshev polynomial weighting (CPW) is considered. Besides, the discriminant density is taken into consideration to overcome the negative impact on the calibration recognition result due to the unbalanced number of samples of different classes in the dataset. Moreover, considering the redundancy contained in the sensor data, a sensor array optimization algorithm is explored based on an embedded Hilbert scheme with an area under the curve (AUC) measure. The odor data of five classes of herbal medicine are collected by our system. The effectiveness of the multiple sensor detection system and the proposed algorithm framework show the potential of the system to be applied in the intelligent identification of herbal medicine.

关键词： Herbal medicine detection imbalance classifier design multiple sensor detection system sensor array optimization Herbal medicine detection imbalance classifier design multiple sensor detection system sensor array optimization

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An Enhanced Multi-sensor Detection Scheme With Drift Calibration and array optimization

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第8期71卷 4030-4034页

作者： Qian, Junhui Xu, Peng Lu, Yuanyuan Liu, Ziyu Wang, Shiyuan Chongqing Univ Sch Microelect & Commun Engn Chongqing 400030 Peoples R China Jianghuai Adv Technol Ctr Hefei 230000 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China

In this brief, a multi-metal oxide sensor (MOS) detection system aims at identifying herbal medicines is proposed. To improve the long-term stability of the system and reduce the number of sensors, we derive a combined scheme with drift calibration and array optimization design. Specifically, a subspace projection matrix considering multiple constraints is first optimized to compensate the sensor drift. Subsequence, a sensor array optimization via an embedded joint mutual information framework is developed to remove redundant information in the target domain. Finally, the experimental results show that compared with the existing approaches, the proposed calibration framework can obtain the best system performance with the minimum number of sensors.

关键词： sensor arrays Feature extraction optimization Calibration Time series analysis System performance Mutual information Detection system sensor array calibration sensor array optimization

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