In the detection of components with uneven thickness by X-ray, the problems of low contrast or uneven contrast and low illumination often occur, which make it difficult to observe and analyze some details of components in the images obtained. To solve this problem, an X-ray image enhancement algorithm based on gradient field was proposed. The algorithm takes gradient field enhancement as the core and is divided into two steps. Firstly, an algorithm based on logarithmic transformation was proposed to compress the gray range of an image, remove redundant gray information of the image and improve image contrast. Then, an algorithm based on gradient field was proposed to enhance image details, improve local image contrast and image quality, so that the details of components were able to be clearly displayed on the detection screen. A group of X-ray images of components with uneven thickness were selected for experiments, and the comparisons with algorithms such as Contrast Limited Adaptive Histogram Equalization (CLAHE) and homomorphic filtering were carried out. Experimental results show that the proposed algorithm has more obvious enhancement effect and can better display the detailed information of the components. The quantitative evaluation criteria of calculating average gradient and No-Reference Structural Sharpness (NRSS) texture analysis further demonstrate the effectiveness of this algorithm.

Concerning there are a lot of redundant features classified in data which not only affect the classification accuracy, but also reduce classification speed, a feature selection algorithm based on multi-objective Bare-bones Particle Swarm Optimization (BPSO) was proposed to obtain the tradeoff between the number of feature subsets and the classification accuracy. In order to improve the efficiency of the multi-objective BPSO, firstly an external archive was used to guide the update direction of the particle, and then the search space of the particle was improved by a mutation operator. Finally, the multi-objective BPSO was applied to feature selection problems, and the classification performance and the number of selected features of the K Nearest Neighbors ( KNN) classifier were used as feature selection criteria. The experiments were performed on 12 datasets of UCI datasets and gene expression datasets. The experimental results show that the feature subset selected by the proposed algorithm has better classification performance, the maximum error rate of the minimum classification can be reduced by 7.4%, and the maximum execution speed of the classification algorithm can be shortened by 12 s at most.

This paper gave an example of emulational system of the numerical control lathe. It introduced the design and implement of an important module-numerical control kernel. It expatiated the implement of three aspects which included input and display, code translation, intelligent checking error, and it also provided constructing bintree algorithm about language G’s syntax analysis. It realized the numerical control kernel emulation thoroughly using Lingo.