A Novel Dictionary Learning Method for Gas Identification With a Gas Sensor Array

He, Anzhi; Wei, Guangsheng; Yu, Jun; Tang, Zhenan; Lin, Zhonghai; Wang, Pingjian

doi:10.1109/tie.2017.2748034

Cited by 32 publications

(12 citation statements)

References 33 publications

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“…Crucially, this conversion process has to be relevant and robust, otherwise data loss and critical errors may occur. On the other side, there are also very rare studies reported in the literature which use onedimensional CNNs for mixture gases recognition and classification [282,283] .…”

Section: 4mentioning

confidence: 99%

Electronic Nose and Its Applications: A Survey

Karakaya

Ulucan

Türkan

2019

Int. J. Autom. Comput.

234

152

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In the last two decades, improvements in materials, sensors and machine learning technologies have led to a rapid extension of electronic nose (EN) related research topics with diverse applications. The food and beverage industry, agriculture and forestry, medicine and health-care, indoor and outdoor monitoring, military and civilian security systems are the leading fields which take great advantage from the rapidity, stability, portability and compactness of ENs. Although the EN technology provides numerous benefits, further enhancements in both hardware and software components are necessary for utilizing ENs in practice. This paper provides an extensive survey of the EN technology and its wide range of application fields, through a comprehensive analysis of algorithms proposed in the literature, while exploiting related domains with possible future suggestions for this research topic.

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Section: 4mentioning

confidence: 99%

Electronic Nose and Its Applications: A Survey

Karakaya

Ulucan

Türkan

2019

Int. J. Autom. Comput.

234

152

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show abstract

“…In order to evaluate the performance of the proposed algorithm, we compare it with other algorithms, such as SRC (used in [27]), the dictionary learning (DL) classifier (proposed in [32]), deep learning (used in [14]) and BP artificial neural network. All experiments in this paper run on a dual-core processor with a CPU main frequency of 2.4 GHz.…”

Section: Comparisons With Other Classifiersmentioning

confidence: 99%

“…It is a simple and effective dictionary learning algorithm. This is our previous work and more details of the algorithm are shown in [32].…”

Section: Comparisons With Other Classifiersmentioning

confidence: 99%

A Novel Sparse Representation Classification Method for Gas Identification Using Self-Adapted Temperature Modulated Gas Sensors

Wei

et al. 2019

Sensors

Self Cite

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A novel sparse representation classification method (SRC), namly SRC based on Method of Optimal Directions (SRC_MOD), is proposed for electronic nose system in this paper. By finding both a synthesis dictionary and a corresponding coefficient vector, the i-th class training samples are approximated as a linear combination of a few of the dictionary atoms. The optimal solutions of the synthesis dictionary and coefficient vector are found by MOD. Finally, testing samples are identified by evaluating which class causes the least reconstruction error. The proposed algorithm is evaluated on the analysis of hydrogen, methane, carbon monoxide, and benzene at self-adapted modulated operating temperature. Experimental results show that the proposed method is quite efficient and computationally inexpensive to obtain excellent identification for the target gases.

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“…However, high installation and maintenance costs restrict their coverage area. With the continuous advancement of sensor technologies, low-cost air monitoring sensors [2], [5], [6] are widely deployed to finely monitor air quality in various regions, whereas their measurements are not accurate enough [7], [8]. In particular, when a sensor is deployed in the field environment, its sensitivity is affected by uncontrollable environmental factors and decay over time [9]- [11].…”

Section: Introductionmentioning

confidence: 99%

MTNet: A Multi-Task Neural Network for On-Field Calibration of Low-Cost Air Monitoring Sensors

Yu,

Geng,

Zhang

et al. 2021

Preprint

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The advances of sensor technology enable people to monitor air quality through widely distributed low-cost sensors. However, measurements from these sensors usually encounter high biases and require a calibration step to reach an acceptable performance in down-streaming analytical tasks. Most existing calibration methods calibrate one type of sensor at a time, which we call single-task calibration. Despite the popularity of this single-task schema, it may neglect interactions among calibration tasks of different sensors, which encompass underlying information to promote calibration performance. In this paper, we propose a multi-task calibration network (MTNet) to calibrate multiple sensors (e.g., carbon monoxide and nitrogen oxide sensors) simultaneously, modeling the interactions among tasks. MTNet consists of a single shared module, and several taskspecific modules. Specifically, in the shared module, we extend the multi-gate mixture-of-experts structure to harmonize the task conflicts and correlations among different tasks; in each taskspecific module, we introduce a feature selection strategy to customize the input for the specific task. These improvements allow MTNet to learn interaction information shared across different tasks, and task-specific information for each calibration task as well. We evaluate MTNet on three real-world datasets and compare it with several established baselines. The experimental results demonstrate that MTNet achieves the state-of-the-art performance.

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A Novel Dictionary Learning Method for Gas Identification With a Gas Sensor Array

Cited by 32 publications

References 33 publications

Electronic Nose and Its Applications: A Survey

Electronic Nose and Its Applications: A Survey

A Novel Sparse Representation Classification Method for Gas Identification Using Self-Adapted Temperature Modulated Gas Sensors

MTNet: A Multi-Task Neural Network for On-Field Calibration of Low-Cost Air Monitoring Sensors

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