Incomplete Region Estimation and Restoration of 3D Point Cloud Human Face Datasets.

Owing to imperfect scans, occlusions, low reflectance of the scanned surface, and packet loss, there may be several incomplete regions in the 3D point cloud dataset. These missing regions can degrade the performance of recognition, classification, segmentation, or upsampling methods in point cloud datasets. In this study, we propose a new approach to estimate the incomplete regions of 3D point cloud human face datasets using the masking method. First, we perform some preprocessing on the input point cloud, such as rotation in the left and right angles. Then, we project the preprocessed point cloud onto a 2D surface and generate masks. Finally, we interpolate the 2D projection and the mask to produce the estimated point cloud. We also designed a deep learning model to restore the estimated point cloud to improve its quality. We use chamfer distance (CD) and hausdorff distance (HD) to evaluate the proposed method on our own human face and large-scale facial model (LSFM) datasets. The proposed method achieves an average CD and HD results of 1.30 and 21.46 for our own and 1.35 and 9.08 for the LSFM datasets, respectively. The proposed method shows better results than the existing methods.

Thermodynamic analysis of low-GWP blends to replace R410A for residential building air conditioning applications.

The Kyoto Protocol has stipulated array of national policies to combat the climate change. To tackle the global warming, governments embraced Paris Agreement and Kigali Amendment which deal with the reduction of greenhouse gas emission. For example, the European F-gas regulation and the Japan METI now enforce refrigerants below 150 GWP for automobile industry and below 750 GWP for the residential air-conditioning applications. To invent a perfect refrigerant that meets performance requirement, environmental requirements, and safety standards is considered extremely difficult. On the other hand, some existing refrigerants exhibit excellent performance with safe operation but record high-GWP while refrigerants such as R1234yf and R744 possess almost 0 GWP. Thus, these refrigerant blends might serve as urgent solutions with minimum performance compromise. This paper evaluates the performance of binary and ternary blends using several promising refrigerants. Exploiting the excellent performance of R32 as the base refrigerant, R1123, R1234yf, R1234ze(E), and R744 are utilized in the blends. The performance indicators employed here are (i) GWP, (ii) temperature glide, (iii) volumetric capacity, and (iv) coefficient of performance. The advantages to reduce the irreversible heat loss by glide matching and energy saving potential for the blends are also discussed. Results showed that some refrigerant blends considering GWP 200 and 300 could successfully replace the widely used R410A in a residential air conditioner. Thermodynamic cycle and performance of zeotropic blend.