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He received the B.E. degree from School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China, in 2019. He joined Chongqing University as an undergraduate student in 2015. Currently he is an undergraduate student in the School of Microelectronics and Communication Engineering, Chongqing University, China. His research interests include artificial olfaction, pattern recognition, 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.
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.
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.
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.