This paper analyzes the working principle, advantages and disadvantages of traditional Boost DC-DC converter firstly, and then proposes a new type of high set-up ratio DC-DC converter topology with the coupled-inductor and voltage-doubler structure. In the following parts, it introduces the composition and the equivalent circuit of this topology in detail, and deduces its set-up ratio expression and the voltage stress expression of the switching tubes and diodes. Finally, this paper designs the main parameters and the relevant models of the topology, and makes the experimental platform. The experiment results indicate that this topology could realize high set-up ratio even under usual duty-ratio conditions, and could effectively reduce the voltage stress and switching-off loss of switching tubes, providing favorable conditions for realizing multilevel conversion of the following inverter in the photovoltaic power generation system.
With the continuous development of Computer Vision and a variety of advanced seam imaging equipment, the information contained in the seam image is very rich. It is of great significance for industry automation system. Single image feature is difficult to fully express seam image content. Multi- feature fusion has become a natural way to extract the seam image features. It can comprehensively utilize the seam image information to gain more rapid and accurate understanding of welding images.
From low to high, information fusion can be divided into three levels. The feature-level fusion not only keeps the most original information, but also overcomes the unstable and large characteristics of original data. Fusion feature can be effectively used in seam image recognition.
Firstly, we build the JARI robot system to research the seam tracking from the image identify. Secondly, principal component analysis (PCA) method based on multivariate statistical analysis is used in feature- level fusion. And it is applied in liver B- image recognition. The recognition results are analyzed and compared. Finally, through the gantry robot 9 degree system to verify the logic of the identify V type seam.
The experimental results show that fusion feature can fully and effectively express seam image, which can bring better recognition results. Analyzing and comparing the feature selection results of different sample images, the results show that feature selection is stable and effective. Comparing with the results of direct PCA fusion applications, the recognition effect after feature selection is better, not only improves the average accuracy rate of recognition but also reduces the time complexity of the recognition process. It has better performance, can be more effectively applicated in welding image recognition.