Titlebook: Electronic Nose: Algorithmic Challenges; Lei Zhang,Fengchun Tian,David Zhang Book 2018 Springer Nature Singapore Pte Ltd. 2018 Electronic

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Other inorganic electrolytic processes, constructed for correction. Finally, an effective signal correction method was employed for E-nose data. Experimental results in the real case-studies demonstrate the effectiveness of the presented model in E-nose based on MOS gas sensors array.

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得意牛

Domain Adaptation Guided Drift Compensationin classifier with drift compensation. Experiments on the popular sensor drift data of multiple batches clearly demonstrate that the proposed DAELM significantly outperforms existing drift compensation methods.

祝賀

Domain Regularized Subspace Projection Method and anti-drift is manifested with a well-solved projection matrix in real application. Experiments on synthetic data and real datasets demonstrate the effectiveness and efficiency of the proposed anti-drift method in comparison to state-of-the-art methods.

Yag-Capsulotomy

Pattern Recognition-Based Interference Reduction constructed for correction. Finally, an effective signal correction method was employed for E-nose data. Experimental results in the real case-studies demonstrate the effectiveness of the presented model in E-nose based on MOS gas sensors array.

Nonconformist

Introductionduring the past two decades. Then, we propose to address these key challenges in E-nose, which are sensor induced and sensor specific. This chapter is closed by a statement of the objective of the research, a brief summary of the work, and a general outline of the overall structure of this book.

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Heuristic and Bio-inspired Neural Network Model using a multi-sensor system. The estimation accuracy in actual application is concerned too much by manufacturers and researchers. This chapter analyzes the application of different bio-inspired and heuristic techniques to improve the concentration estimation in experimental electronic nose applica