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"Production Planning and Control" is one of the major courses in Industrial Engineering, and its cross-cutting, adaptable, and practical characteristics require that the course should well integrate technology and management to meet the evolution of modern manufacturing industry. Through the study of the traditional teaching effectiveness of "Production Planning and Control", this paper pointed out the issues, including insufficient interaction, unappealing content, and lack of systematization of the knowledge. By introducing teaching reform approaches for the course, it proposes to combine various teaching methods, rearrange the teaching material, systematically plan the practical teaching links, and assess the learning effect through a diversified evaluation system, in order to promote students' motivation and learning enthusiasm, meanwhile improve the teaching effect of the course to meet the requirement of modern manufacturing industry for talents.

Four kinds of blueberry beverage from different varieties, a total of 140 samples were acquired and analyzed by applying of spectrum technology. Using Savitzky-Golay spectral smoothing and multiplicative scatter correction (MSC) on the sample data pretreatment, four varieties of blueberry beverage were cluster analyzed by using principal component analysis method (PCA),a three-dimensional score view was achieved by the first 3 principal components of all samples (PC1, PC2 and PC3), which shows an obvious classification effect on the blueberry beverage. The first three principal components of the load diagram analysis, the characteristic bands related with the blueberry beverage varieties were 420-430nm, 490-500nm, 570-580nm and 1350-1365nm. According to the cumulative contribution rate (99.20%) of the first 6 principal components, the first 6 principal components was choosed as the input of multilayer perceptron (MLP) neural network, 100 samples in all the blueberry beverage samples were selected as a training set, and the remaining 40 samples were used as the prediction set. Training set were trained and prediction set were predicted by applying the multilayer perceptron neural network, and the correct rate of prediction were 100%.Research shows, using principal component analysis combined with multilayer perceptron neural network to identify the varieties of blueberry beverage is feasible.

This paper will introduce the method to apply partial differential equations for the decontamination processing of images. It will establish continuous partial differential mathematical models for image information and use specific solving methods to conduct decontamination processing to images during the process of solving partial differential equations, such as image noise reduction, image denoising and image segmentation. This paper will study the uniqueness of solution for the partial differential equations and the monotonicity that functional constrain has on multipliers by making analysis of the ROF model in the partial differential mathematical model.