Unsupervised Possibilistic Clustering Based on Kernel Methods

AbstractA new kernel based unsupervised clustering algorithm has been proposed. The proposed algorithm is called unsupervised kernel possibilistic clustering algorithm (UKPC), which is an extension of the previously proposed clustering algorithm of unsupervised possibilistic clustering algorithm (UPC). In UKPC, the sample points are mapped into the feature space by the introduced kernel function, and the final clustering partition is obtained by optimizing the objective function of UKPC, which adopts the same clustering rule with UPC clustering model. UKPC has the ability of revealing the non-convex cluster structure because the input data are mapped implicitly into a high-dimensional feature space where the nonlinear pattern now appears linear. The contrast experimental results with UPC and other typical fuzzy clustering algorithms show the better performance of the proposed algorithm

Face Detection and Location System Based on Software and Hardware Co-design

Face localization was an important research direction of face recognition technology. Its aim was to segment the face from the background of the detecting image. This technology was widely used in many areas of research, such as identity verification, HMI, visual communication, virtual reality, public files, etc. In this paper, we firstly constructed skin model and calculated the similarity of the image data, then calculated the face boundaries based space projection. Importantly, in order to improve the real-time, we utilized Xilinx high-level synthesis tool Vivado HLS (AutoESL) to achieve a hardware from a C program, which was based on Zynq platform. And the hardware module is used to realize the face location greatly improved the computing speed. The simulation results show that the proposed method worked well and the efficiency increased by 80

Soybean Variety Identification Based on Improved ResNet18 Hyperspectral Image

Abstract Aiming at the problems of insufficient feature extraction, slow speed and low accuracy of traditional machine learning methods, a soybean variety identification method based on improved ResNet18 hyperspectral image was proposed. This method extracts more effective detail features by decomposing the large convolution kernel, and changes the connection of residual structure and introduces the BN layer optimization network to make the feature extraction more sufficient. The perception of soybean hyperspectral image recognition is enhanced by adding multi-scale feature extraction module. The experimental results show that the recognition accuracy of this method reaches 97.36 %, which is higher than Nasnet large and Resnet18 models, and the robustness of the model is further enhanced, which can provide reference for soybean variety recognition.</jats:p

Method of 3D Coating Accumulation Modeling Based on Inclined Spraying

In the process of repairing the surface of products in aviation, aerospace, and other fields by spraying, accurate 3D cumulative-coating modeling is an important research issue in spraying-process simulation. The approach to this issue is a 3D cumulative-coating model based on inclined spraying. Firstly, an oblique spraying layer cumulative model was established, which could quickly collect the coating thickness distribution data of different spray distances. Secondly, 3D cumulative-coating modeling was conducted with the distance between the measuring point and the axis of the spray gun and the spraying distance between the measuring points as the input parameters, and the coating thickness of the measuring point as the output parameter. The experimental results show that the mean relative error of the cumulative model of the oblique spraying layer is less than 4.1% in the case of a 170~290 mm spraying distance and that the model is applicable in the range of −80~80 mm, indicating that the data on the oblique spraying coating proposed in this paper is accurate and fast. The accuracy of the 3D cumulative-coating model proposed in this paper is 1.2% and 21.5% higher than that of the two similar models, respectively. Therefore, the approach of 3D cumulative-coating modeling based on inclined distance spraying is discovered, demonstrating the advantages of fast and accurate modeling and enabling accurate 3D cumulative-coating modeling for spraying process simulation