Sensor drift is currently the most challenging problem in gas sensing. We propose a novel ensemble method with dynamic weights based on fitting (DWF) to solve the gas discrimination problem, regardless of the gas concentration, with high accuracy over extended periods of time. The DWF method uses a dynamic weighted combination of support vector machine (SVM) classifiers trained by the datasets that are collected at different time periods. In the testing of future datasets, the classifier weights are predicted by fitting functions, which are obtained by the proper fitting of the optimal weights during training. We compare the performance of the DWF method with that of competing methods in an experiment based on a public dataset that was compiled over a period of three years. The experimental results demonstrate that the DWF method outperforms the other methods considered. Furthermore, the DWF method can be further optimized by applying a fitting function that more closely matches the variation of the optimal weight over time.

Sensor drift is the most challenging problem in gas sensing at present. We propose a novel two-dimensional classifier ensemble strategy to solve the gas discrimination problem, regardless of the gas concentration, with high accuracy over extended periods of time. This strategy is appropriate for multi-class classifiers that consist of combinations of pairwise classifiers, such as support vector machines. We compare the performance of the strategy with those of competing methods in an experiment based on a public dataset that was compiled over a period of three years. The experimental results demonstrate that the two-dimensional ensemble outperforms the other methods considered. Furthermore, we propose a pre-aging process inspired by that applied to the sensors to improve the stability of the classifier ensemble. The experimental results demonstrate that the weight of each multi-class classifier model in the ensemble remains fairly static before and after the addition of new classifier models to the ensemble, when a pre-aging procedure is applied.

Bridge is an important part of modern transportation systems and deflection is an important index for bridge’s safety evaluation. In this paper, a method of deflection measurement using Wireless MEMS Inclination Sensor Systems (WMISS) is presented and validated. Firstly, based on various bridge deflection measuring methods, the method of deflection measurement using inclination parameter is introduced. Secondly, a low-power wireless inclination sensor based on 3D-MEMS SCA60 inclinometer is designed using modularization way, and this kind of wireless sensor loaded with ZigBee/IEEE 802.15.4 MAC protocol stack can self-organize wireless sensor network, measure the angle value and send the data to the coordinator. Then the deflection curve is displayed on PC. Finally, deflection measurement experiments are conducted on a bridge model and Beida Bridge. The experimental results show that, the presented deflection measurement method is feasible, practical and reliable; the wireless inclination sensor is easy to operate with no lines, and has extensive and broad application prospects.