Yigang Wang¹, Hong Li¹, Wanbin Pan^1,*, Weijuan Cao¹, Jie Miao¹, Xiaofei Ai¹, Enya Shen²

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 859-878, 2023, DOI:10.32604/cmes.2023.027320 - 23 April 2023

Abstract Kinematic semantics is often an important content of a CAD model (it refers to a single part/solid model in this work) in many applications, but it is usually not the belonging of the model, especially for the one retrieved from a common database. Especially, the effective and automatic method to reconstruct the above information for a CAD model is still rare. To address this issue, this paper proposes a smart approach to identify each assembly interface on every CAD model since the assembly interface is the fundamental but key element of reconstructing kinematic semantics. First,… More >

Anil Kumar Yadav¹, R. K. Pateriya², Nirmal Kumar Gupta³, Punit Gupta^4,*, Dinesh Kumar Saini⁴, Mohammad Alahmadi⁵

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2697-2712, 2022, DOI:10.32604/cmc.2022.023052 - 29 March 2022

Abstract Face recognition systems have enhanced human-computer interactions in the last ten years. However, the literature reveals that current techniques used for identifying or verifying faces are not immune to limitations. Principal Component Analysis-Support Vector Machine (PCA-SVM) and Principal Component Analysis-Artificial Neural Network (PCA-ANN) are among the relatively recent and powerful face analysis techniques. Compared to PCA-ANN, PCA-SVM has demonstrated generalization capabilities in many tasks, including the ability to recognize objects with small or large data samples. Apart from requiring a minimal number of parameters in face detection, PCA-SVM minimizes generalization errors and avoids overfitting problems More >

Yan Zhou¹, Qingwei Ma^*

CMES-Computer Modeling in Engineering & Sciences, Vol.115, No.2, pp. 131-147, 2018, DOI:10.3970/cmes.2018.00249

Abstract An identification technique for sharp interface and penetrated isolated particles is developed for simulating two-dimensional, incompressible and immiscible two-phase flows using meshless particle methods in this paper. This technique is based on the numerically computed density gradient of fluid particles and is suitable for capturing large interface deformation and even topological changes such as merging and breaking up of phases. A number of assumed particle configurations will be examined using the technique, including these with different level of randomness of particle distribution. The tests will show that the new technique can correctly identify almost all More >