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Jianjun Chen
Chongqing Key Laboratory of Bio-Perception &Intelligent Information Processing, No. 174 Shazheng Street, Shapingba District, Chongqing 400044, China

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Short Biography

He received the B.S from College of Physics and Information Technology at Chongqing University in 2001, M.S. degrees from School of science at Xi'an University of Electronic Science and technology in 2003, and Ph.D. degree from the College of Communication Engineering at Chongqing University in 2009. He joined Chongqing University as an assistant in 2004. Currently he is an associate professor in the School of Microelectronics and Communication Engineering, Chongqing University. His research interests include signal processing and artificial olfaction.

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Journal article
Published: 04 August 2021 in Chemosensors
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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.

ACS Style

Xiuxiu Zhu; Tao Liu; Jianjun Chen; Jianhua Cao; Hongjin Wang. One-Class Drift Compensation for an Electronic Nose. Chemosensors 2021, 9, 208 .

AMA Style

Xiuxiu Zhu, Tao Liu, Jianjun Chen, Jianhua Cao, Hongjin Wang. One-Class Drift Compensation for an Electronic Nose. Chemosensors. 2021; 9 (8):208.

Chicago/Turabian Style

Xiuxiu Zhu; Tao Liu; Jianjun Chen; Jianhua Cao; Hongjin Wang. 2021. "One-Class Drift Compensation for an Electronic Nose." Chemosensors 9, no. 8: 208.

Regular research paper
Published: 12 April 2021 in Memetic Computing
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Multi-objective optimization problem (MOP) denotes the optimization problem involving more than one objective function to be optimized simultaneously. In the literature, to solve MOP, evolutionary algorithm has been recognized as an effective approach. Over the years, a number of multi-objective evolutionary algorithms (MOEAs) have been developed. In this paper, we present a study on multiform multi-objective evolutionary optimization. In contrast to existing MOEAs, which only focus on the optimization of a single MOP, the proposed new paradigm considers to construct multiple forms of a given MOP, which may contain different useful information for solving the MOP. The evolutionary search is then performed on both the given MOP and the constructed forms concurrently. By transferring useful traits found along the evolutionary search across the given MOP and the built problem forms, enhanced multi-objective optimization performance can be obtained. To the best of our knowledge, there is no existing work that considers the multiform optimization for solving MOP. To evaluate the performance of the proposed multiform paradigm for multi-objective optimization, comprehensive empirical studies with commonly used MOP benchmarks using different existing MOEAs as the basic MOP solvers are conducted and analyzed.

ACS Style

Liangjie Zhang; Yuling Xie; Jianjun Chen; Liang Feng; Chao Chen; Kai Liu. A study on multiform multi-objective evolutionary optimization. Memetic Computing 2021, 13, 307 -318.

AMA Style

Liangjie Zhang, Yuling Xie, Jianjun Chen, Liang Feng, Chao Chen, Kai Liu. A study on multiform multi-objective evolutionary optimization. Memetic Computing. 2021; 13 (3):307-318.

Chicago/Turabian Style

Liangjie Zhang; Yuling Xie; Jianjun Chen; Liang Feng; Chao Chen; Kai Liu. 2021. "A study on multiform multi-objective evolutionary optimization." Memetic Computing 13, no. 3: 307-318.

Journal article
Published: 11 April 2021 in Chemosensors
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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.

ACS Style

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 Style

Jianhua 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 Style

Jianhua 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.

Journal article
Published: 19 August 2019 in Sensors
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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.

ACS Style

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 Style

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 (16):3601.

Chicago/Turabian Style

Tao 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.

Journal article
Published: 19 November 2018 in Sensors
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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.

ACS Style

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 Style

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 (11):4028.

Chicago/Turabian Style

Tao 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.