A Transfer Learning for Line-Based Portrait Sketch

This paper presents a transfer learning-based framework that produces line-based portrait sketch images from portraits. The proposed framework produces sketch images using a GAN architecture, which is trained through a pseudo-sketch image dataset. The pseudo-sketch image dataset is constructed from a single artist-created portrait sketch using a style transfer model with a series of postprocessing schemes. The proposed framework successfully produces portrait sketch images for portraits of various poses, expressions and illuminations. The excellence of the proposed model is proved by comparing the produced results with those from the existing works

A Transfer Learning for Line-Based Portrait Sketch

This paper presents a transfer learning-based framework that produces line-based portrait sketch images from portraits. The proposed framework produces sketch images using a GAN architecture, which is trained through a pseudo-sketch image dataset. The pseudo-sketch image dataset is constructed from a single artist-created portrait sketch using a style transfer model with a series of postprocessing schemes. The proposed framework successfully produces portrait sketch images for portraits of various poses, expressions and illuminations. The excellence of the proposed model is proved by comparing the produced results with those from the existing works

Multiscale study to investigate nanoparticle agglomeration effect on electrical conductivity of nano-SiC reinforced polypropylene matrix composites

The clustering effect of beta-SiC nanoparticles on the electrical conductivity of polypropylene matrix composites was investigated through a new multiscale modeling framework where density functional theory-based first-principles calculation and electron hopping-based numerical homogenization are integrated. According to parametric studies for particle dispersion states, the electrical conductivity of the nanocomposites clearly depends on the dispersion/agglomeration of the nanoparticles. Due to the work function of beta-SiC, agglomerated particles made a greater contribution to improving electrical conductivity when compared to well-dispersed particles. In addition, the microstructure-conductivity relationship was determined using the clustering density. The proposed framework was validated with the reported experimental literature.N

Improved Finite Element Thermomechanical Analysis of Laminated Composite and Sandwich Plates Using the New Enhanced First-Order Shear Deformation Theory

This paper proposes a simple yet accurate finite element (FE) formulation for the thermomechanical analysis of laminated composites and sandwich plates. To this end, an enhanced first-order shear deformation theory including the transverse normal effect based on the mixed variational theorem (EFSDTM_TN) was employed in the FE implementation. The primary objective of the FE formulation was to systematically interconnect the displacement and transverse stress fields using the mixed variational theorem (MVT). In the MVT, the transverse stress field is derived from the efficient higher-order plate theory including the transverse normal effect (EHOPT_TN), to enhance the solution accuracy, whereas the displacement field is defined by the first-order shear deformation theory including the transverse normal effect (FSDT_TN), to amplify the numerical efficiency. Furthermore, the transverse displacement field is modified by incorporating the components of the external temperature loading, enabling the consideration of the transverse normal strain effect without introducing additional unknown variables. Based on the predefined relationships, the proposed FE formulation can extract the C0-based computational benefits of FSDT_TN, while improving the solution accuracy for thermomechanical analysis. The numerical performance of the proposed FE formulation was demonstrated by comparing the obtained solutions with those available in the literature, including 3-D exact solutions