Skeletonization via Local Separators

We propose a new algorithm for curve skeleton computation that differs from previous algorithms by being based on the notion of local separators . The main benefits of this approach are that it is able to capture relatively fine details and that it works robustly on a range of shape representations. Specifically, our method works on shape representations that can be construed as spatially embedded graphs. Such representations include meshes, volumetric shapes, and graphs computed from point clouds. We describe a simple pipeline where geometric data are initially converted to a graph, optionally simplified, local separators are computed and selected, and finally a skeleton is constructed. We test our pipeline on polygonal meshes, volumetric shapes, and point clouds. Finally, we compare our results to other methods for skeletonization according to performance and quality.

A New Geometric Data Perturbation Method for Data Anonymization Based on Random Number Generators

With the technology’s rapid development and its involvement in all areas of our lives, the volume and value of data have become a significant field of study. Valuation of the data to this extent has produced some consequences in terms of people’s knowledge. Data anonymization is the most important of these issues in terms of the security of personal data. Much work has been done in this area and continues to being done. In this study, we proposed a method called RSUGP for the anonymization of sensitive attributes. A new noise model based on random number generators has been proposed instead of the Gaussian noise or random noise methods, which are being used conventionally in geometric data perturbation. We tested our proposed RSUGP method with six different databases and four different classification methods for classification accuracy and attack resistance; then, we presented the results section. Experiments show that the proposed method was more successful than the other two classification accuracy, attack resistance, and runtime.

Determination of the Reference Wing Area of Aircraft from Various Aircraft Manufacturers

Purpose - To check whether the reference wing areas of Boeing, Fokker and McDonnell Douglas (MD) were calculated using the methods specified by the manufacturers for calculating the reference wing areas. ---Methodology - Different aircraft from the three manufacturers are selected. The publicly available three-view drawings and the reference wing area published by the manufacturer are used. The areas determined with the three methods are then compared with the given areas. ---Results - With the Boeing 747 and the aircraft from Fokker and MD it could be shown with sufficient certainty that the reference wing area was also determined with the corresponding method of the manufacturer. This could not be shown on the rest of the Boeing aircraft. This could be explained in two ways: It is indicated that Boeing changed the method for determining the reference wing area (hence a "wrong" method may have been used in the calculation), or the information available in the form of drawings and geometric data contained errors. ---Limits of applicability - Slight data variations can easily results in differences in the wing area of one percent. The difference in the values for the reference wing area when comparing the methods with each other is often less than one percent for some aircraft and methods. This shows the difficulties associated with inferring the method used from a recalculation of the reference wing area. ---Value - It is well known that reference wing areas are calculated using different equations depending on the aircraft manufacturer. So far, the manufacturer's information on the reference wing area has been accepted uncritically. This is possible because the final aerodynamic results do not depend on the choice of the reference wing area. Here details have been checked despite the fact that any value for the reference wing area can be used to normalize aerodynamic data.