Distributed Programming for the Cloud: Models, Challenges, and Analytics Engines

2014 ◽  
pp. 1-38 ◽  
Author(s):  
Mohammad Hammoud ◽  
Majd Sakr
Keyword(s):  
Distributed Programming ◽  
Cloud Models

scholarly journals OICP: An Online Fast Registration Algorithm Based on Rigid Translation Applied to Wire Arc Additive Manufacturing of Mold Repair

Materials ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1563
Author(s):  
Ruibing Wu ◽  
Ziping Yu ◽  
Donghong Ding ◽  
Qinghua Lu ◽  
Zengxi Pan ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Point Cloud ◽  
Point Clouds ◽  
Iterative Closest Point ◽  
Multiple Point ◽  
Icp Algorithm ◽  
Registration Algorithm ◽  
Cloud Models ◽  
Comparison Results ◽  
Wire Arc Additive Manufacturing

As promising technology with low requirements and high depositing efficiency, Wire Arc Additive Manufacturing (WAAM) can significantly reduce the repair cost and improve the formation quality of molds. To further improve the accuracy of WAAM in repairing molds, the point cloud model that expresses the spatial distribution and surface characteristics of the mold is proposed. Since the mold has a large size, it is necessary to be scanned multiple times, resulting in multiple point cloud models. The point cloud registration, such as the Iterative Closest Point (ICP) algorithm, then plays the role of merging multiple point cloud models to reconstruct a complete data model. However, using the ICP algorithm to merge large point clouds with a low-overlap area is inefficient, time-consuming, and unsatisfactory. Therefore, this paper provides the improved Offset Iterative Closest Point (OICP) algorithm, which is an online fast registration algorithm suitable for intelligent WAAM mold repair technology. The practicality and reliability of the algorithm are illustrated by the comparison results with the standard ICP algorithm and the three-coordinate measuring instrument in the Experimental Setup Section. The results are that the OICP algorithm is feasible for registrations with low overlap rates. For an overlap rate lower than 60% in our experiments, the traditional ICP algorithm failed, while the Root Mean Square (RMS) error reached 0.1 mm, and the rotation error was within 0.5 degrees, indicating the improvement of the proposed OICP algorithm.

Overlapping Allografts Provide Superior and More Reliable Surface Topography Matching Than Oblong Allografts: A Computer-Simulated Model Study

2021 ◽  
pp. 036354652110030
Author(s):  
Hailey P. Huddleston ◽  
Atsushi Urita ◽  
William M. Cregar ◽  
Theodore M. Wolfson ◽  
Brian J. Cole ◽  
...  
Keyword(s):  
Articular Cartilage ◽  
Surface Topography ◽  
Osteochondral Allograft ◽  
Cartilage Defects ◽  
Radius Of Curvature ◽  
Cartilage Surface ◽  
3 Dimensional ◽  
Cloud Models ◽  
And Cartilage ◽  
Osteochondral Allografts

Background: Osteochondral allograft transplantation is 1 treatment option for focal articular cartilage defects of the knee. Large irregular defects, which can be treated using an oblong allograft or multiple overlapping allografts, increase the procedure’s technical complexity and may provide suboptimal cartilage and subchondral surface matching between donor grafts and recipient sites. Purpose: To quantify and compare cartilage and subchondral surface topography mismatch and cartilage step-off for oblong and overlapping allografts using a 3-dimensional simulation model. Study Design: Controlled laboratory study. Methods: Human cadaveric medial femoral hemicondyles (n = 12) underwent computed tomography and were segmented into cartilage and bone components using 3-dimensional reconstruction and modeling software. Segments were then exported into point-cloud models. Modeled defect sizes of 17 × 30 mm were created on each recipient hemicondyle. There were 2 types of donor allografts from each condyle utilized: overlapping and oblong. Grafts were virtually harvested and implanted to optimally align with the defect to provide minimal cartilage surface topography mismatch. Least mean squares distances were used to measure cartilage and subchondral surface topography mismatch and cartilage step-off. Results: Cartilage and subchondral topography mismatch for the overlapping allograft group was 0.27 ± 0.02 mm and 0.80 ± 0.19 mm, respectively. In comparison, the oblong allograft group had significantly increased cartilage (0.62 ± 0.43 mm; P < .001) and subchondral (1.49 ± 1.10 mm; P < .001) mismatch. Cartilage step-off was also found to be significantly increased in the oblong group compared with the overlapping group ( P < .001). In addition, overlapping allografts more reliably provided a significantly higher percentage of clinically acceptable (0.5- and 1-mm thresholds) cartilage surface topography matching (overlapping: 100% for both 0.5 and 1 mm; oblong: 90% for 1 mm and 56% for 0.5 mm; P < .001) and cartilage step-off (overlapping: 100% for both 0.5 and 1 mm; oblong: 86% for 1 mm and 12% for 0.5 mm; P < .001). Conclusion: This computer simulation study demonstrated improved topography matching and decreased cartilage step-off with overlapping osteochondral allografts compared with oblong osteochondral allografts when using grafts from donors that were not matched to the recipient condyle by size or radius of curvature. These findings suggest that overlapping allografts may be superior in treating large, irregular osteochondral defects involving the femoral condyles with regard to technique. Clinical Relevance: This study suggests that overlapping allografts may provide superior articular cartilage surface topography matching compared with oblong allografts and do so in a more reliable fashion. Surgeons may consider overlapping allografts over oblong allografts because of the increased ease of topography matching during placement.

scholarly journals What Polarimetric Weather Radars Offer to Cloud Modelers: Forward Radar Operators and Microphysical/Thermodynamic Retrievals

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 362 ◽  
Author(s):  
Alexander V. Ryzhkov ◽  
Jeffrey Snyder ◽  
Jacob T. Carlin ◽  
Alexander Khain ◽  
Mark Pinsky
Keyword(s):  
Cloud Model ◽  
Weather Prediction ◽  
Primary Source ◽  
Polarimetric Radar ◽  
Microphysical Process ◽  
Weather Radars ◽  
Cloud Models ◽  
Cloud Modeling ◽  
Bin Microphysics ◽  
The Impact

The utilization of polarimetric weather radars for optimizing cloud models is a next frontier of research. It is widely understood that inadequacies in microphysical parameterization schemes in numerical weather prediction (NWP) models is a primary cause of forecast uncertainties. Due to its ability to distinguish between hydrometeors with different microphysical habits and to identify “polarimetric fingerprints” of various microphysical processes, polarimetric radar emerges as a primary source of needed information. There are two approaches to leverage this information for NWP models: (1) radar microphysical and thermodynamic retrievals and (2) forward radar operators for converting the model outputs into the fields of polarimetric radar variables. In this paper, we will provide an overview of both. Polarimetric measurements can be combined with cloud models of varying complexity, including ones with bulk and spectral bin microphysics, as well as simplified Lagrangian models focused on a particular microphysical process. Combining polarimetric measurements with cloud modeling can reveal the impact of important microphysical agents such as aerosols or supercooled cloud water invisible to the radar on cloud and precipitation formation. Some pertinent results obtained from models with spectral bin microphysics, including the Hebrew University cloud model (HUCM) and 1D models of melting hail and snow coupled with the NSSL forward radar operator, are illustrated in the paper.

An overview of the Arjuna distributed programming system

IEEE Software ◽  
1991 ◽  
Vol 8 (1) ◽  
pp. 66-73 ◽  
Author(s):  
S.K. Shrivastava ◽  
G.N. Dixon ◽  
G.D. Parrington
Keyword(s):  
Distributed Programming ◽  
Programming System
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