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[Objectives] To deal with uncertainties in the quality evaluation of alternative navy vessel designs,the cloud barycenter evaluation method is introduced.[Methods] The basic concept of cloud theory is expounded upon and the evaluation principle and basic process of the cloud barycenter method are analyzed. The existing cloud barycenter algorithm cannot effectively express the matching degree between quality characteristics and user requirement. The improvement of the cloud barycenter algorithm is realized by defining the quality matching degree. Due to the small amount of data,the accuracy of the evaluation express is insufficient. The evaluation data can be effectively extended through the cloud generator so as to better express the evaluation results. On this basis,the method is analyzed according to alternative vessel design choices. [Results] The results show that the improved method has good adaptability to the problems,and the evaluation results are in accordance with the quality evaluation requirements of the alternative naval vessel design.[Conclusions] The findings of this paper can provide valuable references for the application of the cloud barycenter method and new ideas for the selection of alternative navy vessel design.

As an unsupervised learning method, stochastic competitive learning is commonly used for community detection in social network analysis. Compared with the traditional community detection algorithms, it has the advantage of realizing the time-series community detection by simulating the community formation process. In order to improve the accuracy and solve the problem that several parameters in stochastic competitive learning need to be pre-set, the author improves the algorithms and realizes improved stochastic competitive learning by particle position initialization, parameter optimization and particle domination ability self-adaptive. The experiment result shows that each improved method improves the accuracy of the algorithm, and the F1 score of the improved algorithm is 9.07% higher than that of original algorithm. [ABSTRACT FROM AUTHOR]

Feature point matching plays an important role in feature-based image registration such as the scale-invariant feature transform algorithm. Feature-based image registration is widely used in visual simultaneous localisation and mapping, augmented reality, self-driving etc. The most meaningful study on feature matching is to improve the accuracy and efficiency and this study pays attention to improving the accuracy by removing the mismatching feature points. Since most of the existed feature-based image registration algorithms are not so strong and efficient enough in mismatch removing, in this study, the authors propose a novel mismatch removal algorithm by incorporating depth prediction into feature matching to improve the performance. In this approach, the depth maps are predicted in pixel-wise through the given red–green–blue images using a deep learning algorithm. Experimental results show that their method outperforms conventional ones in mismatch removing. [ABSTRACT FROM AUTHOR]

VR content is a key link in building a VR ecosystem, but the extreme lack of high-quality content has become the core shortcoming restricting the development of the VR industry, so in the medium and long term, the VR industry will shift from hardware technology upgrades to high-quality content-oriented, and is expected to usher in a new round of growth driven by business model innovation and content explosion. With 3D reconstruction, users can experience virtual scenes visually and audibly. The development of 3D reconstruction technology will bring great changes to existing players, and also greatly promote the rapid development of metaverse content Through continuous algorithm improvement, 3D reconstruction continues to be applied to all aspects of life. [ABSTRACT FROM AUTHOR]