Applied Mechanics and Materials Vols. 602-605

Abstract: Pork storage time is relevant to its freshness which influences pork quality. To achieve the rapid and effective discrimination of pork storage time, near infrared spectroscopy was used to collect the near infrared reflectance (NIR) spectra of pork in different storage time. The high-dimensional NIR spectra was firstly compressed by principal component analysis (PCA) and then classified by fuzzy learning vector quantization (FLVQ). PCA plus FLVQ is a completely unsupervised learning algorithm which finds hidden patterns in unlabeled data. Experimental results showed that PCA plus FLVQ could classify pork NIR spectra effectively.

Abstract: Through the software defect prediction can effectively guide the rational distribution of software system development resources, so as to improve the quality of software and software reliability. In order to fully utilize the existing historical data to guide the software development of existing software system development, this paper based on an improved classification and regression tree (Classification and Regression, CART) algorithm software defect prediction models. The paper first principal component analysis of the data predicted correlation dimension (Principle Component Analysis, PCA) between data and reduce the data, and configured according to the theory and optimized CART decision tree algorithm, existing software defect prediction system, and with traditional defect prediction method, the experimental results show that the proposed prediction model has higher prediction accuracy and stability.

Abstract: The essay describes the basic principles and the method of application of prony algorithm, as well as how to use prony algorithm to fit fault signal and extract the corresponding transient parameters. And on this basis, the order of the signal model, the data window length, sampling interval and the parameters such as attenuation factor are determined. Then, the correctness of the prony algorithm is verified by the means of extracting of the small current grounding fault transient component.

Abstract: Traffic volume prediction has been an interesting topic for decades during which various prediction models have been proposed. In this paper, Kalman filtering (KF) model is applied to predict traffic volume because of its significance in continuously updating the state variable as new observations. In order to enhance the prediction accuracy, an improved KF model is developed based on the current and historical data. To validate the improved KF model, empirical analysis is conducted. The results show that the improved KF model has higher accuracy than the traditional one and is more reliable and powerful in traffic volume prediction.

Abstract: Fine arts, in recent years, with the rapid development of information technology, its diverse artistic expression trends to be more obvious, at the same time, the artistic expression based on multimedia has been paid more attention by the fields. This paper firstly analyzes the general characteristics of art image fusion process in multimedia environment. On this basis, the digital-to-analog conversion (DAC) method of multimedia artistic expression is proposed, which takes the Gauss Pyramid as basis. In order to verify the scientificity of this method, this paper adopts the empirical method to carry on the empirical analysis. The results show that multimedia artistic expression behavior of fine arts based on this method is significant, and has strong practicability and operability.