Quality inspection is essential in preventing defective products from entering the market. Due to the typically low percentage of defective products, it is generally challenging to detect them using algorithms that aim for the overall classification accuracy. To help solve this problem, we propose an ensemble learning classification model, where we employ adaptive boosting (AdaBoost) to cascade multiple backpropagation (BP) neural networks. Furthermore, cost-sensitive (CS) learning is introduced to adjust the loss function of the basic classifier of the BP neural network. For clarity, this model is called a CS-AdaBoost-BP model. To empirically verify its effectiveness, we use data from home appliance production lines from Bosch. We carry out tenfold cross-validation to evaluate and compare the performance between the CS-AdaBoost-BP model and three existing models: BP neural network, BP neural network based on sampling, and AdaBoost-BP. The results show that our proposed model not only performs better than the other models but also significantly improves the ability to identify defective products. Furthermore, based on the mean value of the Youden index, our proposed model has the highest stability.
The infinitely many symmetries with arbitrary functions of timet for the potential modified Kadomtsev-Petviashvilli equation are obtained by using a simple direct method. These symmetries constitute a generalization of the well-knownW algebra. 相似文献
We used a Stark-Optoacoustic cell and hybrid waveguide resonators to perform an Infrared and Far Infrared Stark Spectroscopy study on some transitions of13CD3OH. Different behaviours of the transitions in the presence of a d.c. electric field were observed. The Stark splittings of six FIR laser lines ranging from 34 to 136 MHz/kVcm–1 were determined. The analysis of the behaviour of the IR and FIR transitions in the presence of the external electric fields gives important and exclusive information on the levels involved in the transitions.Work Supported by FAPESP, CNPq, FAEP - Brazil, and CNR - Italy. 相似文献
We give several equivalences of Bloch functions and little Bloch functions. Using these results we obtain the generalized Carleson measure characterization of Bloch functions and the generalized vanishing Carleson measure characterization of little Bloch functions, that is,fB if and only if |Df(z)|p(1-|z|2)p-1dm(z) is a generalized Carleson measure;fB0 if and only if |Df(z)|p(1-|z|2)p-1dm(z) is a generalized vanishing Carleson measure, whereDf( > 0) is the fractional derivative of analytic functionf of order, m denotes the normalised Lebesgue measure.Supported partly by the Young Teacher Natural Science Foundation of Shandong Province. 相似文献