Effective volume sampling of solid models using distance measures

1999 ◽  
Author(s):  
Sealy ◽  
Novins
Keyword(s):  
Distance Measures ◽  
Effective Volume ◽  
Solid Models

Transformation Invariant Shape Similarity Comparison of Solid Models

2001 ◽  
Author(s):  
David McWherter ◽  
Mitchell Peabody ◽  
William C. Regli ◽  
Ali Shokoufandeh
Keyword(s):  
Metric Spaces ◽  
Edit Distance ◽  
Database Systems ◽  
Boundary Representation ◽  
Distance Measures ◽  
Dimension Vector ◽  
Graph Theoretic ◽  
Solid Models ◽  
Np Hard Problem ◽  
Media Types

Abstract This paper presents two complementary approaches to comparing the shape and topology of solid models. First, we develop a mapping of solid models to Model Signature Graphs (MSGs) — labeled, undirected graphs that abstract the boundary representation of the model and capture relevant shape and engineering attributes. Model Signature Graphs are then used to define metric spaces over arbitrary sets of solid models. This paper introduces two such metric spaces: first, a mapping of MSGs to a high-dimension vector space where euclidean distance measures are applied; second, a distance computation performed between graph spectra constructed from MSGs using spectral graph theoretic techniques. In practice, exact computation of the edit distance between model signature graphs is believed to be an NP-hard problem. We show that properties of the design signature graph’s spectra, derived from the eigenvalues of its adjacency matrix, can be used as a efficient and tractable approximation of the edit distance. Lastly, we provide empirical results using real test data from the National Design Repository (http://www.designrepository.org) to validate our approach. We argue comparisons among solid models in these metric space are immune to problems caused by inexactness and ambiguity arising from basic modeling transformations (scale, translation, rotation, sheer, etc.). It is our belief that this work contributes to a growing body of techniques for comparing models and indexing CAD media types in database systems.

Influence of Constructive and Technological Parameters at Generated Spiral Parts with DLP 3D Printing Process

2019 ◽  
Vol 56 (4) ◽  
pp. 801-811
Author(s):  
Mircea Dorin Vasilescu
Keyword(s):  
3D Printing ◽  
Great Influence ◽  
Polymerization Process ◽  
Generation Process ◽  
Technological Parameters ◽  
Printing Process ◽  
The Third ◽  
Solid Models ◽  
Specific Factors ◽  
Printing Processes

This work are made for determine the possibility of generating the specific parts of a threaded assembly. If aspects of CAD generating specific elements was analysed over time in several works, the technological aspects of making components by printing processes 3D through optical polymerization process is less studied. Generating the threaded appeared as a necessity for the reconditioning technology or made components of the processing machines. To determine the technological aspects of 3D printing are arranged to achieve specific factors of the technological process, but also from the specific elements of a trapezoidal thread or spiral for translate granular material in supply process are determined experimentally. In the first part analyses the constructive generation process of a spiral element. In the second part are identified the specific aspects that can generation influence on the process of realization by 3D DLP printing of the two studied elements. The third part is affected to printing and determining the dimensions of the analysed components. We will determine the specific value that can influence the process of making them in rapport with printing process. The last part is affected by the conclusions. It can be noticed that both the orientation and the precision of generating solid models have a great influence on the made parts.

Software to Convert Mechanical Desktop Solid Models into Facet Files for Input to Xpatch

1999 ◽  
Author(s):  
Roger Evans ◽  
John G. Bennett ◽  
Jack Jones
Keyword(s):  
Solid Models

Experimental Study and Numerical Simulation for Explosion and Phenomena of RC Slabs

2014 ◽  
Vol 501-504 ◽  
pp. 1096-1103
Author(s):  
Hong Xiao Wu ◽  
Hao Zhe Xing ◽  
Zhi Fang Yan
Keyword(s):  
Reinforced Concrete ◽  
Impact Loading ◽  
Loading Condition ◽  
Damage Mechanism ◽  
Failure Characteristics ◽  
Explosion Pressure ◽  
Concrete Plate ◽  
Solid Models ◽  
Calculation Results ◽  
Rc Slabs

The blast impact dynamic experiment of reinforced concrete rectangular plate with simply supported boundary conditions was performed using explosion pressure simulator. With 3-D FEM software LS-DYNA, the separate solid models of concrete and steel were established and 3-D FEM dynamic analysis of the experiment process was carried out. Compared calculation results to experiment results synthetically, the damage mechanism and failure characteristics of reinforced concrete plate under explosion impact loading condition were got and it is also verified that the H-J-C model can approximately simulate the concrete properties well under explosion impact loading condition.

scholarly journals Deformable Models for Segmentation Based on Local Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Jimena Olveres ◽  
Erik Carbajal-Degante ◽  
Boris Escalante-Ramírez ◽  
Enrique Vallejo ◽  
Carla María García-Moreno
Keyword(s):  
Local Binary Patterns ◽  
Distance Measures ◽  
Local Analysis ◽  
Hermite Transform ◽  
Active Shape Models ◽  
Shape Models ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Image Modality ◽  
New Algorithms

Segmentation tasks in medical imaging represent an exhaustive challenge for scientists since the image acquisition nature yields issues that hamper the correct reconstruction and visualization processes. Depending on the specific image modality, we have to consider limitations such as the presence of noise, vanished edges, or high intensity differences, known, in most cases, as inhomogeneities. New algorithms in segmentation are required to provide a better performance. This paper presents a new unified approach to improve traditional segmentation methods as Active Shape Models and Chan-Vese model based on level set. The approach introduces a combination of local analysis implementations with classic segmentation algorithms that incorporates local texture information given by the Hermite transform and Local Binary Patterns. The mixture of both region-based methods and local descriptors highlights relevant regions by considering extra information which is helpful to delimit structures. We performed segmentation experiments on 2D images including midbrain in Magnetic Resonance Imaging and heart’s left ventricle endocardium in Computed Tomography. Quantitative evaluation was obtained with Dice coefficient and Hausdorff distance measures. Results display a substantial advantage over the original methods when we include our characterization schemes. We propose further research validation on different organ structures with promising results.

scholarly journals A Dynamic Distance Measure of Picture Fuzzy Sets and Its Application

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 436
Author(s):  
Ruirui Zhao ◽  
Minxia Luo ◽  
Shenggang Li
Keyword(s):  
Fuzzy Sets ◽  
Distance Measure ◽  
Distance Measures ◽  
Numerical Comparison ◽  
Mathematical Tool ◽  
Practical Applications ◽  
Fuzzy Point ◽  
Point Operator ◽  
The Difference ◽  
Picture Fuzzy Sets

Picture fuzzy sets, which are the extension of intuitionistic fuzzy sets, can deal with inconsistent information better in practical applications. A distance measure is an important mathematical tool to calculate the difference degree between picture fuzzy sets. Although some distance measures of picture fuzzy sets have been constructed, there are some unreasonable and counterintuitive cases. The main reason is that the existing distance measures do not or seldom consider the refusal degree of picture fuzzy sets. In order to solve these unreasonable and counterintuitive cases, in this paper, we propose a dynamic distance measure of picture fuzzy sets based on a picture fuzzy point operator. Through a numerical comparison and multi-criteria decision-making problems, we show that the proposed distance measure is reasonable and effective.

Identifying longitudinal-growth patterns from infancy to childhood: a study comparing multiple clustering techniques

2021 ◽  
Author(s):  
Paraskevi Massara ◽  
Charles D G Keown-Stoneman ◽  
Lauren Erdman ◽  
Eric O Ohuma ◽  
Celine Bourdon ◽  
...  
Keyword(s):  
Latent Class ◽  
Growth Patterns ◽  
Pattern Detection ◽  
Distance Measures ◽  
Longitudinal Growth ◽  
Important Health ◽  
Modelling Techniques ◽  
Different Populations ◽  
Growth Features ◽  
Time Invariant

Abstract Background Most studies on children evaluate longitudinal growth as an important health indicator. Different methods have been used to detect growth patterns across childhood, but with no comparison between them to evaluate result consistency. We explored the variation in growth patterns as detected by different clustering and latent class modelling techniques. Moreover, we investigated how the characteristics/features (e.g. slope, tempo, velocity) of longitudinal growth influence pattern detection. Methods We studied 1134 children from The Applied Research Group for Kids cohort with longitudinal-growth measurements [height, weight, body mass index (BMI)] available from birth until 12 years of age. Growth patterns were identified by latent class mixed models (LCMM) and time-series clustering (TSC) using various algorithms and distance measures. Time-invariant features were extracted from all growth measures. A random forest classifier was used to predict the identified growth patterns for each growth measure using the extracted features. Results Overall, 72 TSC configurations were tested. For BMI, we identified three growth patterns by both TSC and LCMM. The clustering agreement was 58% between LCMM and TS clusters, whereas it varied between 30.8% and 93.3% within the TSC configurations. The extracted features (n = 67) predicted the identified patterns for each growth measure with accuracy of 82%–89%. Specific feature categories were identified as the most important predictors for patterns of all tested growth measures. Conclusion Growth-pattern detection is affected by the method employed. This can impact on comparisons across different populations or associations between growth patterns and health outcomes. Growth features can be reliably used as predictors of growth patterns.

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