Rolling bearings are one of the essential components in rotating machinery. Efficient bearing fault diagnosis is necessary to ensure the regular operation of the mechanical system. Traditional fault diagnosis methods usually rely on a complex artificial feature extraction process, which requires a lot of human expertise. Emerging deep learning methods can reduce the dependence of the feature extraction process on manual intervention effectively. However, its training requires a large number of fault signals, which is difficult to obtain in actual engineering. In this paper, a rolling bearing fault diagnosis method based on Convolutional Neural Network and Support Vector Machine is proposed to solve the above problems. Firstly, the Continuous Wavelet Transform is used to convert one-dimensional original vibration signals into two-dimensional time-frequency images. Secondly, the obtained time-frequency images are input for training the constructed model. Finally, the diagnosis of the fault location and severity is completed. The method is verified on the CWRU data set and the MFPT data set. The results demonstrate that the proposed method achieves higher diagnostic accuracy and stability than other advanced techniques.INDEX TERMS Convolutional neural network, continuous wavelet transform, fault diagnosis, rolling bearing, support vector machine.

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Combining Fourier transform infrared spectroscopy (FTIR) with endoscopy, it is expected that noninvasive, rapid detection of colorectal cancer can be performed in vivo in the future. In this study, Fourier transform infrared spectra were collected from 88 endoscopic biopsy colorectal tissue samples (41 colitis and 47 cancers). A new method, viz., entropy weight local-hyperplane k-nearest-neighbor (EWHK), which is an improved version of K-local hyperplane distance nearest-neighbor (HKNN), is proposed for tissue classification. In order to avoid limiting high dimensions and small values of the nearest neighbor, the new EWHK method calculates feature weights based on information entropy. The average results of the random classification showed that the EWHK classifier for differentiating cancer from colitis samples produced a sensitivity of 81.38% and a specificity of 92.69%.

In data collected from virtual learning environments (VLEs), item response theory (IRT) models can be used to guide the ongoing measurement of student ability. However, such applications of IRT rely on unbiased item parameter estimates associated with test items in the VLE. Without formal piloting of the items, one can expect a large amount of nonignorable missing data in the VLE log file data, and this is expected to negatively affect IRT item parameter estimation accuracy, which then negatively affects any future ability estimates utilized in the VLE. In the psychometric literature, methods for handling missing data have been studied mostly around conditions in which the data and the amount of missing data are not as large as those that come from VLEs. In this article, we introduce a semisupervised learning method to deal with a large proportion of missingness contained in VLE data from which one needs to obtain unbiased item parameter estimates. First, we explored the factors relating to the missing data. Then we implemented a semisupervised learning method under the two-parameter logistic IRT model to estimate the latent abilities of students. Last, we applied two adjustment methods designed to reduce bias in item parameter estimates. The proposed framework showed its potential for obtaining unbiased item parameter estimates that can then be fixed in the VLE in order to obtain ongoing ability estimates for operational purposes.