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He received his B.S., M.S., and Ph.D. degrees from the College of Communication Engineering at Chongqing University, Chongqing, China, in 2003, 2006, and 2012, respectively. He joined Chongqing University as an assistant in 2006. Currently, he is an associate professor in the School of Microelectronics and Communication Engineering, Chongqing University. His research interests include artificial olfaction, electronic tongue, and artificial intelligence.
Drift compensation is an important issue in an electronic nose (E-nose) that hinders the development of E-nose’s model robustness and recognition stability. The model-based drift compensation is a typical and popular countermeasure solving the drift problem. However, traditional model-based drift compensation methods have faced “label dilemma” owing to high costs of obtaining kinds of prepared drift-calibration samples. In this study, we have proposed a calibration model for classification utilizing a single category of drift correction samples for more convenient and feasible operations. We constructed a multi-task learning model to achieve a calibrated classifier considering several demands. Accordingly, an associated solution process has been presented to gain a closed-form classifier representation. Moreover, two E-nose drift datasets have been introduced for method evaluation. From the experimental results, the proposed methodology reaches the highest recognition rate in most cases. On the other hand, the proposed methodology demonstrates excellent and steady performance in a wide range of adjustable parameters. Generally, the proposed method can conduct drift compensation with limited one-class calibration samples, accessing the top accuracy among all presented reference methods. It is a new choice for E-nose to counteract drift effect under cost-sensitive conditions.
Xiuxiu Zhu; Tao Liu; Jianjun Chen; Jianhua Cao; Hongjin Wang. One-Class Drift Compensation for an Electronic Nose. Chemosensors 2021, 9, 208 .
AMA StyleXiuxiu Zhu, Tao Liu, Jianjun Chen, Jianhua Cao, Hongjin Wang. One-Class Drift Compensation for an Electronic Nose. Chemosensors. 2021; 9 (8):208.
Chicago/Turabian StyleXiuxiu Zhu; Tao Liu; Jianjun Chen; Jianhua Cao; Hongjin Wang. 2021. "One-Class Drift Compensation for an Electronic Nose." Chemosensors 9, no. 8: 208.
Gas sensor drift is an important issue of electronic nose (E-nose) systems. This study follows this concern under the condition that requires an instant drift compensation with massive online E-nose responses. Recently, an active learning paradigm has been introduced to such condition. However, it does not consider the “noisy label” problem caused by the unreliability of its labeling process in real applications. Thus, we have proposed a class-label appraisal methodology and associated active learning framework to assess and correct the noisy labels. To evaluate the performance of the proposed methodologies, we used the datasets from two E-nose systems. The experimental results show that the proposed methodology helps the E-noses achieve higher accuracy with lower computation than the reference methods do. Finally, we can conclude that the proposed class-label appraisal mechanism is an effective means of enhancing the robustness of active learning-based E-nose drift compensation.
Jianhua Cao; Tao Liu; Jianjun Chen; Tao Yang; Xiuxiu Zhu; Hongjin Wang. Drift Compensation on Massive Online Electronic-Nose Responses. Chemosensors 2021, 9, 78 .
AMA StyleJianhua Cao, Tao Liu, Jianjun Chen, Tao Yang, Xiuxiu Zhu, Hongjin Wang. Drift Compensation on Massive Online Electronic-Nose Responses. Chemosensors. 2021; 9 (4):78.
Chicago/Turabian StyleJianhua Cao; Tao Liu; Jianjun Chen; Tao Yang; Xiuxiu Zhu; Hongjin Wang. 2021. "Drift Compensation on Massive Online Electronic-Nose Responses." Chemosensors 9, no. 4: 78.
Gas-sensor drift is an important issue fading the gas identification performance of an Electronic Nose (E-nose). The most popular drift countermeasure is mathematical-model updating by periodic drift calibrations. Accordingly, drift-calibration-sample formation becomes a challenging issue in online detection because of rare opportunity for collecting drift-calibration samples. However, the class-imbalance problem may occur during such formation. Thus, we proposed an active drift-calibration-sample selection method including a new metric “classifier state” and an associated sample-evaluating procedure. To assess the proposed method, two benchmarks have been adopted. One is a public dataset while the other one is collected from our own E-nose system. Experimental results have demonstrated the superiority of the proposed method in several drift scenarios. Further, we visually explored the behind reason of the proposed method’s high performance. Additionally, a parameter sensitivity analysis was conducted. We conclude that the proposed methodology reduces the negative effect of class-imbalance problem successfully.
Tao Liu; Dongqi Li; Jianjun Chen. An active method of online drift-calibration-sample formation for an electronic nose. Measurement 2020, 171, 108748 .
AMA StyleTao Liu, Dongqi Li, Jianjun Chen. An active method of online drift-calibration-sample formation for an electronic nose. Measurement. 2020; 171 ():108748.
Chicago/Turabian StyleTao Liu; Dongqi Li; Jianjun Chen. 2020. "An active method of online drift-calibration-sample formation for an electronic nose." Measurement 171, no. : 108748.
An electronic nose (E-nose) system is regularly composed of a gas sensor array and certain pattern-recognition algorithms. With the use of E-nose, the gas sensors inevitably undergo physical changes, which causes gas-sensor drift to invalid algorithm models of E-noses. In this study, we intend to explore a suitable approach for online E-nose drift calibration. Considering drift calibration samples cannot be obtained directly during continuous odor detection, we have adopted Active Learning (AL) paradigm to select calibration samples from previous tested samples and provide their categories by querying. Further, we deal with the class imbalance problem of drift calibration set caused by traditional AL instance-selection strategy. We propose a new strategy named Dual-Rule Sampling (DRS) to simultaneously measure sample uncertainty and minority-class similarity. The high uncertain instances being close to minority-class are selected for drift calibration when class imbalance occurs. We have used two datasets to evaluate the performance of DRS. The experimental results show that DRS reaches the highest recognition score among all the tested methodologies by emphasizing the minority-class recognition improvement. We can conclude that DRS successfully implements online E-nose drift calibration in continuous odor detection.
Tao Liu; Jianhua Cao; Dongqi Li; Yanbing Chen; Tao Yang; Xiuxiu Zhu. Active instance selection for drift calibration of an electronic nose. Sensors and Actuators A: Physical 2020, 312, 112149 .
AMA StyleTao Liu, Jianhua Cao, Dongqi Li, Yanbing Chen, Tao Yang, Xiuxiu Zhu. Active instance selection for drift calibration of an electronic nose. Sensors and Actuators A: Physical. 2020; 312 ():112149.
Chicago/Turabian StyleTao Liu; Jianhua Cao; Dongqi Li; Yanbing Chen; Tao Yang; Xiuxiu Zhu. 2020. "Active instance selection for drift calibration of an electronic nose." Sensors and Actuators A: Physical 312, no. : 112149.
In this study, we focus on the long-term drift problem of electronic nose (E-nose) systems under an assumption that the calibration samples are gained online with uncertain amount by category and the recognition-learner updating performs based on few calibration samples allowed to query their categories. We utilize active learning (AL) methods to select few valuable instances for drift calibration. Considering traditional AL methods are not designed for handling continuous online data, we have proposed an AL instance-selection strategy on a mixed kernel (ISSMK) to adapt drifted data. For this methodology, we primarily redesign a hybrid sample-evaluation kernel assessing samples comprehensively. Besides, ISSMK adaptively selects the instances on mixed kernel to gather the global drift information. Meanwhile, a ranking method has been introduced to normalize the outputs of kernel. The experimental results indicate that AL combined with ISSMK (AL-ISSMK) has obtained the highest recognition accuracy among several state-of-art drift compensation methods. Further, we explore the parameter sensitivity, labelled instance distribution, computational complexity and labelling efficiency of the proposed methodology. The overall evaluation proves that AL is a suitable manner to handle E-nose recognitions on online drifted data. The proposed AL-ISSMK methodology shows great potential for E-nose drift compensation in online applications.
Tao Liu; Dongqi Li; Yanbing Chen; Mengya Wu; Tao Yang; Jianhua Cao. Online Drift Compensation by Adaptive Active Learning on Mixed Kernel for Electronic Noses. Sensors and Actuators B: Chemical 2020, 316, 128065 .
AMA StyleTao Liu, Dongqi Li, Yanbing Chen, Mengya Wu, Tao Yang, Jianhua Cao. Online Drift Compensation by Adaptive Active Learning on Mixed Kernel for Electronic Noses. Sensors and Actuators B: Chemical. 2020; 316 ():128065.
Chicago/Turabian StyleTao Liu; Dongqi Li; Yanbing Chen; Mengya Wu; Tao Yang; Jianhua Cao. 2020. "Online Drift Compensation by Adaptive Active Learning on Mixed Kernel for Electronic Noses." Sensors and Actuators B: Chemical 316, no. : 128065.
As a kind of intelligent instrument, an electronic tongue (E-tongue) realizes liquid analysis with an electrode-sensor array and certain machine learning methods. The large amplitude pulse voltammetry (LAPV) is a regular E-tongue type that prefers to collect a large amount of response data at a high sampling frequency within a short time. Therefore, a fast and effective feature extraction method is necessary for machine learning methods. Considering the fact that massive common-mode components (high correlated signals) in the sensor-array responses would depress the recognition performance of the machine learning models, we have proposed an alternative feature extraction method named feature specificity enhancement (FSE) for feature specificity enhancement and feature dimension reduction. The proposed FSE method highlights the specificity signals by eliminating the common mode signals on paired sensor responses. Meanwhile, the radial basis function is utilized to project the original features into a nonlinear space. Furthermore, we selected the kernel extreme learning machine (KELM) as the recognition part owing to its fast speed and excellent flexibility. Two datasets from LAPV E-tongues have been adopted for the evaluation of the machine-learning models. One is collected by a designed E-tongue for beverage identification and the other one is a public benchmark. For performance comparison, we introduced several machine-learning models consisting of different combinations of feature extraction and recognition methods. The experimental results show that the proposed FSE coupled with KELM demonstrates obvious superiority to other models in accuracy, time consumption and memory cost. Additionally, low parameter sensitivity of the proposed model has been demonstrated as well.
Tao Liu; Yanbing Chen; Dongqi Li; Tao Yang; Jianhua Cao. Electronic Tongue Recognition with Feature Specificity Enhancement. Sensors 2020, 20, 772 .
AMA StyleTao Liu, Yanbing Chen, Dongqi Li, Tao Yang, Jianhua Cao. Electronic Tongue Recognition with Feature Specificity Enhancement. Sensors. 2020; 20 (3):772.
Chicago/Turabian StyleTao Liu; Yanbing Chen; Dongqi Li; Tao Yang; Jianhua Cao. 2020. "Electronic Tongue Recognition with Feature Specificity Enhancement." Sensors 20, no. 3: 772.
An electronic nose (EN) is a bionic system that relies on an array of gas sensors for effective odor recognition. Since the gas-sensor drift would depress the EN performance, we proposed an adaptive Domain based subspace learning method considering both Maximizing label feature Dependency and Minimizing feature Redundancy (DMDMR) to address the EN based drift issue. Considering the inconsistent data distribution caused by drift, the proposed method learns a time-varying common subspace with similar distribution for both regular and recent drifted EN responses. In order to preserve useful classification and topological structure information simultaneously, the Hilbert-Schmidt independence criterion (HSIC) has been adopted to measure the dependence between features and labels while the feature redundancy is reduced according to the PCA criterion. An EN drift dataset acquired from a 16-gas-sensor EN system over 36 months was adopted in the experiments. To verify the effectiveness of the proposed method, we used the variations of both first and second order statistics to measure the movement of data distribution under drift condition. The results show that the data distribution in the subspace learned by DMDMR has stronger stability than the one in original space. Furthermore, the positive effects of DMDMR have been exhibited versus other state-of-the-art methods in recognition. The performance of DMDMR paradigm has demonstrated obvious superiority to other paradigms. These results prove that the proposed subspace learning approach is suitable for EN based drift counteraction and can be successfully implemented.
Tao Liu; Yanbing Chen; Dongqi Li; Tao Yang; Jianhua Cao; Mengya Wu. Drift Compensation for an Electronic Nose by Adaptive Subspace Learning. IEEE Sensors Journal 2019, 20, 337 -347.
AMA StyleTao Liu, Yanbing Chen, Dongqi Li, Tao Yang, Jianhua Cao, Mengya Wu. Drift Compensation for an Electronic Nose by Adaptive Subspace Learning. IEEE Sensors Journal. 2019; 20 (1):337-347.
Chicago/Turabian StyleTao Liu; Yanbing Chen; Dongqi Li; Tao Yang; Jianhua Cao; Mengya Wu. 2019. "Drift Compensation for an Electronic Nose by Adaptive Subspace Learning." IEEE Sensors Journal 20, no. 1: 337-347.
Drift correction is an important concern in Electronic noses (E-nose) for maintaining stable performance during continuous work. A large number of reports have been presented for dealing with E-nose drift through machine-learning approaches in the laboratory. In this study, we aim to counter the drift effect in more challenging situations in which the category information (labels) of the drifted samples is difficult or expensive to obtain. Thus, only a few of the drifted samples can be used for label querying. To solve this problem, we propose an innovative methodology based on Active Learning (AL) that selectively provides sample labels for drift correction. Moreover, we utilize a dynamic clustering process to balance the sample category for label querying. In the experimental section, we set up two E-nose drift scenarios-a long-term and a short-term scenario-to evaluate the performance of the proposed methodology. The results indicate that the proposed methodology is superior to the other state-of-art methods presented. Furthermore, the increasing tendencies of parameter sensitivity and accuracy are analyzed. In addition, the Label Efficiency Index (LEI) is adopted to measure the efficiency and labelling cost of the AL methods. The LEI values indicate that our proposed methodology exhibited better performance than the other presented AL methods in the online drift correction of E-noses.
Tao Liu; Dongqi Li; Jianjun Chen; Yanbing Chen; Tao Yang; Jianhua Cao. Active Learning on Dynamic Clustering for Drift Compensation in an Electronic Nose System. Sensors 2019, 19, 3601 .
AMA StyleTao Liu, Dongqi Li, Jianjun Chen, Yanbing Chen, Tao Yang, Jianhua Cao. Active Learning on Dynamic Clustering for Drift Compensation in an Electronic Nose System. Sensors. 2019; 19 (16):3601.
Chicago/Turabian StyleTao Liu; Dongqi Li; Jianjun Chen; Yanbing Chen; Tao Yang; Jianhua Cao. 2019. "Active Learning on Dynamic Clustering for Drift Compensation in an Electronic Nose System." Sensors 19, no. 16: 3601.
Gas sensors are the key components of an electronic nose (E-nose) in violated odour analysis. Gas-sensor drift is a kind of physical change on a sensor surface once an E-nose works. The perturbation of gas-sensor responses caused by drift would deteriorate the performance of the E-nose system over time. In this study, we intend to explore a suitable approach to deal with the drift effect in an online situation. Considering that the conventional drift calibration is difficult to implement online, we use active learning (AL) to provide reliable labels for online instances. Common AL learning methods tend to select and label instances with low confidence or massive information. Although this action clarifies the ambiguity near the classification boundary, it is inadequate under the influence of gas-sensor drift. We still need the samples away from the classification plane to represent drift variations comprehensively in the entire data space. Thus, a novel drift counteraction method named AL on adaptive confidence rule (AL-ACR) is proposed to deal with online drift data dynamically. By contrast with conventional AL methods selecting instances near the classification boundary of a certain category, AL-ACR collects instances distributed evenly in different categories. This action implements on an adjustable rule according to the outputs of classifiers. Compared with other reference methods, we adopt two drift databases of E-noses to evaluate the performance of the proposed method. The experimental results indicate that the AL-ACR reaches higher accuracy than references on two E-nose databases, respectively. Furthermore, the impact of the labelling number is discussed to show the trend of performance for the AL-type methods. Additionally, we define the labelling efficiency index (LEI) to assess the contribution of certain labelling numerically. According to the results of LEI, we believe AL-ACR can achieve the best effect with the lowest cost among the AL-type methods in this work.
Tao Liu; Dongqi Li; Jianjun Chen; Yanbing Chen; Tao Yang; Jianhua Cao. Gas-Sensor Drift Counteraction with Adaptive Active Learning for an Electronic Nose. Sensors 2018, 18, 4028 .
AMA StyleTao Liu, Dongqi Li, Jianjun Chen, Yanbing Chen, Tao Yang, Jianhua Cao. Gas-Sensor Drift Counteraction with Adaptive Active Learning for an Electronic Nose. Sensors. 2018; 18 (11):4028.
Chicago/Turabian StyleTao Liu; Dongqi Li; Jianjun Chen; Yanbing Chen; Tao Yang; Jianhua Cao. 2018. "Gas-Sensor Drift Counteraction with Adaptive Active Learning for an Electronic Nose." Sensors 18, no. 11: 4028.
Electronic noses (e-nose) are composed of an appropriate pattern recognition system and a gas sensor array with a certain degree of specificity and broad spectrum characteristics. The gas sensors have their own shortcomings of being highly sensitive to interferences which has an impact on the detection of target gases. When there are interferences, the performance of the e-nose will deteriorate. Therefore, it is urgent to study interference suppression techniques for e-noses. This paper summarizes the sources of interferences and reviews the advances made in recent years in interference suppression for e-noses. According to the factors which cause interference, interferences can be classified into two types: interference caused by changes of operating conditions and interference caused by hardware failures. The existing suppression methods were summarized and analyzed from these two aspects. Since the interferences of e-noses are uncertain and unstable, it can be found that some nonlinear methods have good effects for interference suppression, such as methods based on transfer learning, adaptive methods, etc.
Zhifang Liang; Fengchun Tian; Simon X. Yang; Ci Zhang; Hao Sun; Tao Liu. Study on Interference Suppression Algorithms for Electronic Noses: A Review. Sensors 2018, 18, 1179 .
AMA StyleZhifang Liang, Fengchun Tian, Simon X. Yang, Ci Zhang, Hao Sun, Tao Liu. Study on Interference Suppression Algorithms for Electronic Noses: A Review. Sensors. 2018; 18 (4):1179.
Chicago/Turabian StyleZhifang Liang; Fengchun Tian; Simon X. Yang; Ci Zhang; Hao Sun; Tao Liu. 2018. "Study on Interference Suppression Algorithms for Electronic Noses: A Review." Sensors 18, no. 4: 1179.
A discrete wavelet transform (DWT) extracts meaningful information in a time-frequency domain and is a favorable feature extraction approach from pulse-like responses in large pulse voltammetry (LAPV) electronic tongues (e-tongue). A regular DWT generates lots of coefficients to describe signal details and approximations at different scales. Thus, coefficient selection is necessary to reduce the feature size. However, the common DWT-based feature selection follows a passive mode: manipulation through human experience or exhaustive trials. It is subjective, time consuming, and barely works in nonlaboratory conditions. In this paper, we present an active feature selection strategy consisting of a dispersion ratio computation and optimal searching search. To evaluate the performance of the proposed method, we prepared several beverage samples and performed experiments with a LAPV e-tongue. Meanwhile, the features of raw response, peak-inflection point, referenced DWT method, and our proposed method were presented to indicate the effects of the refined features of the proposed method. Furthermore, we utilized several classifiers such as the k-nearest neighbor (k-NN), support vector machine (SVM), and random forest (RF) to evaluate the improvement of recognition by the refined features. Compared with other regular feature extraction methods, the proposed method can automatically explore high-quality features with an acceptable feature size. Moreover, the highest average accuracy was achieved by the proposed method for each classifier. It is an alternative feature extraction approach for a LAPV e-tongue without any manipulation in real applications.
Tao Liu; Yanbing Chen; Dongqi Li; Mengya Wu. An Active Feature Selection Strategy for DWT in Artificial Taste. Journal of Sensors 2018, 2018, 1 -11.
AMA StyleTao Liu, Yanbing Chen, Dongqi Li, Mengya Wu. An Active Feature Selection Strategy for DWT in Artificial Taste. Journal of Sensors. 2018; 2018 ():1-11.
Chicago/Turabian StyleTao Liu; Yanbing Chen; Dongqi Li; Mengya Wu. 2018. "An Active Feature Selection Strategy for DWT in Artificial Taste." Journal of Sensors 2018, no. : 1-11.