Improved Sensor System Detecting via Joint Transfer Learning and Array Optimization Design Based on Multiple Phase Feature Extraction

This article designs a lung cancer odor detection system with preconcentration function, which can be used to collect exhaled gas and predict lung cancer. Combining the adsorption characteristics of the preconcentration system, we analyze and evaluate the effectiveness of the multiple phases of the experiment. An unsupervised cross-domain category difference maximization model is proposed to address the common drift problem in sensors for drift compensation. The proposed model considers the consistency of both marginal distribution and conditional distribution, improving the compensation performance of sensor drift by combining domain adaptation (DA) and Bayesian probability to maximize category information. For the information redundancy existing in sensor array, a new evaluation criterion based on mutual information (MI) is proposed, which reduces the amount of features and optimizes the sensor array by vectorizing them. The derived scheme solves the problem of both sensor drift and redundancy of array information simultaneously. The final experimental data indicate the effectiveness of the proposed design. The final experimental data indicate the effectiveness of the proposed design. The final experimental results show that the proposed modified class difference allotrope adaptation algorithm can achieve the recognition accuracy of 0.8644, which is the highest performance among the comparison algorithms, while the unmigrated data can only achieve the recognition accuracy of 0.5254. In terms of sensor optimization, the accuracy of the proposed array optimization method can reach 0.8983 on the data of two features from four sensors, and the optimal results can be achieved in the comparison of array optimization algorithms.