Training technique of electronic nose using labeled and unlabeled samples based on multi-kernel LapSVM

Jia, Pengfei; Meng, Fangzhou; Cao, Huaisheng; Duan, Shukai; Peng, Xiaoyan; Xu, Min

doi:10.1016/j.snb.2019.05.034

Cited by 11 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [52], ANN was used to process electronic nose data. In [53], the authors discussed the training technique of electronic nose by using the labeled and unlabeled samples based on multi-kernel support vector machine (SVM). In [54], the rapid detection approach for enhancing the electronic nose system's performance was verified by using different deep learning models and SVMs, where three deep learning architecture implementations types were used for the classification tasks.…”

Section: The Inherent Technologiesmentioning

confidence: 99%

Using Recurrent Neural Network to Optimize Electronic Nose System with Dimensionality Reduction

Zou

2020

Electronics

View full text Add to dashboard Cite

Electronic nose is an electronic olfactory system that simulates the biological olfactory mechanism, which mainly includes gas sensor, data pre-processing, and pattern recognition. In recent years, the proposals of electronic nose have been widely developed, which proves that electronic nose is a considerably important tool. However, the most recent studies concentrate on the applications of electronic nose, which gradually neglects the inherent technique improvement of electronic nose. Although there are some proposals on the technique improvement, they usually pay attention to the modification of gas sensor module and barely consider the improvement of the last two modules. Therefore, this paper optimizes the electronic nose system from the perspective of data pre-processing and pattern recognition. Recurrent neural network (RNN) is used to do pattern recognition and guarantee accuracy rate and stability. Regarding the high-dimensional data pre-processing, the method of locally linear embedding (LLE) is used to do dimensionality reduction. The experiments are made based on the real sensor drift dataset, and the results show that the proposed optimization mechanism not only has higher accuracy rate and stability, but also has lower response time than the three baselines. In addition, regarding the usage of RNN model, the experimental results also show its efficiency in terms of recall ratio, precision ratio, and F1 value.

show abstract

Section: The Inherent Technologiesmentioning

confidence: 99%

Using Recurrent Neural Network to Optimize Electronic Nose System with Dimensionality Reduction

Zou

2020

Electronics

View full text Add to dashboard Cite

show abstract

“…The electronic nose (e-nose) system obtains the overall gas information of the sample using a cross-sensitive sensor array. It has the advantages of fast detection speed, high precision, strong objectivity and non-destructive tests, and be widely used in food engineering [6,7], environmental monitoring [8,9], industrial testing [10,11] and other fields. In this paper, the e-nose system is used to realize the intelligent acquisition of rice gas information, and the classification of rice from different origins is realized by combining the data decision analysis method, which provides an effective technical method for quality supervision.…”

Section: Introductionmentioning

confidence: 99%

A deep learning method combined with electronic nose to identify the rice origin

Yan

Liu

2022

J. Inst.

View full text Add to dashboard Cite

The rice quality is directly affected by the natural environment in which it is grown. It is common to pass off poor quality as good quality rice sold in rice markets. Therefore, an effective rice quality detection method should be proposed. This work proposes a deep learning method combined with an electronic nose (e-nose) to realize the classification of rice origin, and then realize the detection of rice quality. Firstly, channel-spatial collaborative attention (CSCA) is proposed to adaptively focus on key features of gas information. Secondly, three shallow convolutional neural network (CNN) structures are introduced to achieve gas information classification. Finally, CSCA and three CNN structures are combined to construct a classification network for rice gas recognition. The results show that in the comparison of multi-structure models, LeNet5-CSCA has the best classification performance, with a classification accuracy of 97.70%, a precision of 97.45%, a recall of 97.67%, and a F1-score of 97.56%.

show abstract

“…Luo et al 10 proposed a recognition algorithm of quantum particle swarm optimization to improve the performance of e-nose detection. Jia et al 11 proposed a multi-core support vector machine algorithm to identify the pollutant gas based on e-nose technology. Shi et al 12 tracked the origin of rice based on the multi-recognition technology, and the difference of gas features was visualized.…”

Section: Introductionmentioning

confidence: 99%

A global extended extreme learning machine combined with electronic nose for identifying tea gas information

Wang

Yang

2022

Measurement and Control

View full text Add to dashboard Cite

To enhance the detection performance of electronic nose (e-nose), a recognition method of gas feature based on a global extended extreme learning machine (GEELM) is proposed, which combines the expansion factor and global balance coefficient to expand and balance the difference between categories, and improve the classification performance. Then this method is applied to identify the quality of tea. Firstly, the dragging factor and following matrix are introduced to increase the distance between classes. Secondly, the global identification coefficient is introduced further to increase the feature differences among different types of tea, and improve the classification stability. Finally, under different feature sets, the classification performance of multi-pattern recognition methods is compared to prove the effectiveness of GEELM in e-nose gas feature recognition. The results show that GEELM has the best classification accuracy of 98.20%, F1-score of 0.9871, and Kappa coefficient of 0.9775. In conclusion, GEELM can be an effective technique to identify gas features, and it also provides a new method for tea quality measurement.

show abstract

Training technique of electronic nose using labeled and unlabeled samples based on multi-kernel LapSVM

Cited by 11 publications

References 23 publications

Using Recurrent Neural Network to Optimize Electronic Nose System with Dimensionality Reduction

Using Recurrent Neural Network to Optimize Electronic Nose System with Dimensionality Reduction

A deep learning method combined with electronic nose to identify the rice origin

A global extended extreme learning machine combined with electronic nose for identifying tea gas information

Contact Info

Product

Resources

About