89,666 research outputs found
High Capacity Reversible Data Hiding for Encrypted 3D Mesh Models Based on Topology
Reversible data hiding in encrypted domain(RDH-ED) can not only protect the
privacy of 3D mesh models and embed additional data, but also recover original
models and extract additional data losslessly. However, due to the insufficient
use of model topology, the existing methods have not achieved satisfactory
results in terms of embedding capacity. To further improve the capacity, a
RDH-ED method is proposed based on the topology of the 3D mesh models, which
divides the vertices into two parts: embedding set and prediction set. And
after integer mapping, the embedding ability of the embedding set is calculated
by the prediction set. It is then passed to the data hider for embedding
additional data. Finally, the additional data and the original models can be
extracted and recovered respectively by the receiver with the correct keys.
Experiments declare that compared with the existing methods, this method can
obtain the highest embedding capacity
Information hiding through variance of the parametric orientation underlying a B-rep face
Watermarking technologies have been proposed for many different,types of digital media. However, to this date, no viable watermarking techniques have yet emerged for the high value B-rep (i.e. Boundary Representation) models used in 3D mechanical CAD systems. In this paper, the authors propose a new approach (PO-Watermarking) that subtly changes a model's geometric representation to incorporate a 'transparent' signature. This scheme enables software applications to create fragile, or robust watermarks without changing the size of the file, or shape of the CAD model. Also discussed is the amount of information the proposed method could transparently embed into a B-rep model. The results presented demonstrate the embedding and retrieval of text strings and investigate the robustness of the approach after a variety of transformation and modifications have been carried out on the data
Visualization as a guidance to classification for large datasets
Data visualization has gained a lot of attention after the stressing need to make sense of the huge amounts of data that we collect every day. Lower dimensional embedding techniques such as IsoMap, Locally Linear Embedding and t-SNE help us visualize high dimensional data by projecting it on a two or three-dimensional space. t-SNE, or t-Distributed Stochastic Neighbor Embedding proved to be successful in providing lower dimensional data mappings that makes interpreting the underlying structure of data easier for our human brains. We wanted to test the hypothesis that this simple visualization that human beings can easily understand will also simplify the job of the classification models and boost their performance. In order to test this hypothesis, we reduce the dimensionality of a student performance dataset using t-SNE into 2D and 3D and feed the calculated 2D and 3D feature vectors into a classifier to classify students according to their predicted performance. We compare the classifier performance before and after the dimensionality reduction. Our experiments showed that t-SNE helps improve classification accuracy of NN and KNN on a benchmarking dataset as well as a user-curated dataset on performance of students at our home institution. We also visually compared the 2D and 3D mapping of t-SNE and PCA. Our comparison favored t-SNE\u27s visualization over PC\u27s. This was also reflected in the classification accuracy of all classifiers used, scoring higher on t-SNE\u27s mapping than on the PCA\u27s mapping
Graph Neural Network for Stress Predictions in Stiffened Panels Under Uniform Loading
Machine learning (ML) and deep learning (DL) techniques have gained
significant attention as reduced order models (ROMs) to computationally
expensive structural analysis methods, such as finite element analysis (FEA).
Graph neural network (GNN) is a particular type of neural network which
processes data that can be represented as graphs. This allows for efficient
representation of complex geometries that can change during conceptual design
of a structure or a product. In this study, we propose a novel graph embedding
technique for efficient representation of 3D stiffened panels by considering
separate plate domains as vertices. This approach is considered using Graph
Sampling and Aggregation (GraphSAGE) to predict stress distributions in
stiffened panels with varying geometries. A comparison between a
finite-element-vertex graph representation is conducted to demonstrate the
effectiveness of the proposed approach. A comprehensive parametric study is
performed to examine the effect of structural geometry on the prediction
performance. Our results demonstrate the immense potential of graph neural
networks with the proposed graph embedding method as robust reduced-order
models for 3D structures.Comment: 20 pages; 7 figure
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