The existing Improved Multi-label Lazy Learning Approach (IMLLA) has the problem that the influence of similarity information is ignored with only the neighbor label correlation information considered when the neighbor labels were used, which may reduce the robustness of the approach. To solve this problem, with firefly method introduced and the combination of similarity information with label information, a Multi-label Lazy Learning Approach based on FireFly method (FF-MLLA) was proposed. Firstly, Minkowski distance was used to measure the similarity between samples to find the neighbor point. Secondly, the label count vector was improved by combining the neighbor point and firefly method. Finally, Singular Value Decomposition (SVD) and kernel Extreme Learning Machine (ELM) were used to realize linear classification. The robustness of the approach was improved due to considering both label information and similarity information. The experimental results demonstrate that the proposed approach improves the classification performance to a great extent compared to other multi-label learning approaches. And the statistical hypothesis testing and stability analysis are used to further illustrate the rationality and effectiveness of the proposed approach.

Aiming at the problem that object segmentation algorithms based on single color information are very sensitive to the changes on lighting, a novel approach to detect target based on the fusion of color and depth information was proposed. Firstly, improved Visual Background Extractor (ViBe) and multiple-frame subtraction algorithm were used to establish models for RGB and depth images which captured by Kinect senor respectively. Then, strategy of Selection Criterion (SC) was used to optimize segmentation results. Lastly, most likely target was labeled by calculating similar degree between foreground and template in the rg chromaticity space. The experimental results demonstrate that the proposed method exhibit a certain degree of resilience to light disturbance and noise, and it can overcome the disadvantages of single RGB based algorithms effectively.

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.