Feature Recognition for Virtual Environments

2014 ◽  
Vol 610 ◽  
pp. 642-646
Author(s):  
Mong Heng Ear ◽  
Cheng Cheng ◽  
Salem Mostafa Hamdy ◽  
Alhazmi Marwah
Keyword(s):  
Virtual Environments ◽  
Data Exchange ◽  
Feature Recognition ◽  
Virtual Assembly ◽  
Recognition Algorithm ◽  
Boundary Representation ◽  
Adjacency Graph ◽  
Levels Of Detail ◽  
Data Source ◽  
Design Systems

This paper demonstrates methods to recognize 3D designed features for virtual environments and apply them to Virtual assembly. STEP is a standard of Product data Exchange for interfacing different design systems, but it cannot be used as input for virtual environments. In order to use feature data in virtual assembly environments, main data source from a STEP file should be recognized and features should be re-built. First, Attributed Adjacency Graph (AAG) is used to analyze and express the boundary representation; second, a feature-tree of a part is constructed; third, using the AAG and feature-tree as inputs, we analyze and extract of features with a feature recognition algorithm; finally, various levels of detail of object geometric shapes is built and expressed in XML for virtual assembly applications.

Recognition of classroom student state features based on deep learning algorithms and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Hu Jingchao ◽  
Haiying Zhang
Keyword(s):  
Deep Learning ◽  
Feature Recognition ◽  
Subjective Evaluation ◽  
Recognition Algorithm ◽  
Video Data ◽  
State Recognition ◽  
Detection Model ◽  
State Classification ◽  
Intelligent Models ◽  
Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

Handling Blending Features in Form Feature Recognition Using Convex Decomposition

1994 ◽  
Author(s):  
Sreekumar Menon ◽  
Yong Se Kim
Keyword(s):  
Feature Recognition ◽  
Boundary Representation ◽  
Recognition Method ◽  
Geometric Information ◽  
Polyhedral Model ◽  
Curved Boundary ◽  
Convex Decomposition ◽  
Geometric Objects ◽  
Form Features ◽  
Form Feature

Abstract Form features intrinsic to the product shape can be recognized using a convex decomposition called Alternating Sum of Volumes with Partitioning (ASVP). However, the domain of geometric objects to which ASVP decomposition can be applied had been limited to polyhedral solids due to the difficulty of convex hull construction for solids with curved boundary faces. We develop an approach to extend the geometric domain to solids having cylindrical and blending features. Blending surfaces are identified and removed from the boundary representation of the solid, and a polyhedral model of the unblended solid is generated while storing the cylindrical geometric information. From the ASVP decomposition of the polyhedral model, polyhedral form features are recognized. Form feature decomposition of the original solid is then obtained by reattaching the stored blending and cylindrical information to the form feature components of its polyhedral model. In this way, a larger domain of solids can be covered by the feature recognition method using ASVP decomposition. In this paper, handling of blending features in this approach is described.

Geometric Reasoning for the Coding of CAD Models

1995 ◽  
Author(s):  
A. Z. Qamhiyah ◽  
B. Benhabib ◽  
R. D. Venter
Keyword(s):  
Computer Aided Design ◽  
Feature Recognition ◽  
Effective Means ◽  
Boundary Representation ◽  
Cad Model ◽  
Cad System ◽  
Cad Models ◽  
Form Features ◽  
Feature Based Design ◽  
Design Retrieval

Abstract Many of today’s concurrent product-development cycles depend on the utilization of intelligent Computer-Aided Design (CAD) systems. Thus, it would be essential to provide CAD users with effective means for interacting with the CAD system and its database. This paper addresses the development of a boundary-based coding procedure for CAD models. Coding the geometric and processing characteristics of objects, based on their CAD model representation, has been long recognized as an effective approach that allows convenient design retrieval on the one hand and process-planning automation on the other. Our work is based on the assumption that form features are recognizable and extractable from the CAD model by current feature-recognition, feature extraction and feature-based-design approaches. The coding procedure is applicable to the boundary representation of the object and its extracted form features.

scholarly journals Key Feature Recognition Algorithm of Network Intrusion Signal Based on Neural Network and Support Vector Machine

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 380 ◽  
Author(s):  
Kai Ye
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Feature Recognition ◽  
False Positive Rate ◽  
Principal Component ◽  
Recognition Algorithm ◽  
Support Vector ◽  
Data Set ◽  
Network Intrusion ◽  
Key Features

When identifying the key features of the network intrusion signal based on the GA-RBF algorithm (using the genetic algorithm to optimize the radial basis) to identify the key features of the network intrusion signal, the pre-processing process of the network intrusion signal data is neglected, resulting in an increase in network signal data noise, reducing the accuracy of key feature recognition. Therefore, a key feature recognition algorithm for network intrusion signals based on neural network and support vector machine is proposed. The principal component neural network (PCNN) is used to extract the characteristics of the network intrusion signal and the support vector machine multi-classifier is constructed. The feature extraction result is input into the support vector machine classifier. Combined with PCNN and SVM (Support Vector Machine) algorithms, the key features of network intrusion signals are identified. The experimental results show that the algorithm has the advantages of high precision, low false positive rate and the recognition time of key features of R2L (it is a common way of network intrusion attack) data set is only 3.18 ms.

Architecture for Symbolic Object Warehouse

2009 ◽  
pp. 648-657
Author(s):  
Sandra Elizabeth González Císaro ◽  
Héctor Oscar Nigro
Keyword(s):  
Decision Making ◽  
Data Warehouse ◽  
Complex Data ◽  
Data Complexity ◽  
Intrinsic Structure ◽  
Levels Of Detail ◽  
Decision Making Processes ◽  
Design Systems ◽  
Central Database ◽  
Different Levels

Much information stored in current databases is not always present at necessary different levels of detail or granularity for Decision-Making Processes (DMP). Some organizations have implemented the use of central database - Data Warehouse (DW) - where information performs analysis tasks. This fact depends on the Information Systems (IS) maturity, the type of informational requirements or necessities the organizational structure and business own characteristic. A further important point is the intrinsic structure of complex data; nowadays it is very common to work with complex data, due to syntactic or semantic aspects and the processing type (Darmont et al., 2006). Therefore, we must design systems, which can to maintain data complexity to improve the DMP.

N-Rep: A Neutral Feature Representation to Support Feature Mapping and Data Exchange Across Applications

2004 ◽  
Author(s):  
Madhu S. Medichalam ◽  
Jami J. Shah ◽  
Roshan D’Souza
Keyword(s):  
Data Exchange ◽  
Feature Recognition ◽  
Data Transfer ◽  
Feature Representation ◽  
Declarative Language ◽  
Feature Mapping ◽  
Topological Relationships ◽  
Feature Based ◽  
Parametric Relationships ◽  
Exchange Medium

The proliferation of different feature based systems has made feature data exchange an important issue. Unlike geometry data exchange, where different representations use the same fundamental concepts; the most popular being B-Rep and CSG [Shah et al. 88], different feature representation schemes use different concepts to represent features corresponding to the application and domain. Therefore, feature data transfer between applications not only involves transfer of instance data but also transformation of feature concepts. This paper presents N-Rep, an application independent declarative language, for feature definition that includes topology, topological relationships, geometry, geometric relationships, parameters and parametric relationships. N-Rep has been designed to serve three roles, viz., (a) to generate feature recognition algorithms for recognizing features from geometry, (b) to generate feature producing procedures to be used in design by feature approaches, and (c) to serve as a neutral feature data exchange medium between representations.

Research on Shape Feature Recognition of Boundary Representation CAD Model

2017 ◽  
Vol 14 (1) ◽  
pp. 477-484
Author(s):  
Jihua Wang
Keyword(s):  
Feature Recognition ◽  
Boundary Representation ◽  
Surface Boundary ◽  
Cad Model ◽  
Shape Feature ◽  
Product Model ◽  
Data Files ◽  
Visual Mechanism ◽  
Human Visual Mechanism ◽  
Visual Shape

B-Rep (Boundary Representation) CAD model is being widely used in representation of industrial product, so its feature recognition has acquired widespread research interests in computer vision and 3D model retrieval fields. We present one approach of feature recognition based on the idea of human visual mechanism. Surfaces as the visual shape features, and solids as well as shells as the topological relations, were extracted from the neutral STEP (Standard for Exchange of Product Model Data) files of B-Rep model. Towards three surface types of NURBS, analytical and poly loop, the properties of surface boundary and region are established based on curvature and other geometric index. So B-Rep CAD model is characterized as the hierarchical tree with solid layer, shell layer and surface layer for object recognition and retrieval, and the corresponding experiments verified the effectiveness of the method of shape feature recognition.

MIVAS: A Multi-Modal Immersive Virtual Assembly System

2004 ◽  
Author(s):  
Huagen Wan ◽  
Shuming Gao ◽  
Qunsheng Peng ◽  
Guozhong Dai ◽  
Fengjun Zhang
Keyword(s):  
Virtual Environments ◽  
Large Scale ◽  
Force Feedback ◽  
User Interaction ◽  
Virtual Assembly ◽  
Research Area ◽  
Optimization Techniques ◽  
Assembly System ◽  
Assembly Operations ◽  
Assembly Constraints

Evaluation and planning of assembly processes in virtual environments have become an active research area in engineering community. However, planning of complex assemblies in virtual environments, especially large-scale virtual environments, is still hindered by limitations like unnatural user interaction, insufficient frame rates, and deficiencies in processing of assembly constraints. In this paper, we present MIVAS, a Multi-modal Immersive Virtual Assembly System. By viewing the virtual assembly system as a finite state machine, we incorporate tracked devices, force feedback dataglove, voice commands, human sounds, fully immersive 4-sided CAVE, together with optimization techniques for both complex assembly models and assembly operations to provide for engineers an intuitive and natural way of assembly evaluation and planning. Testing scenarios on disassembling different components of an intelligent hydraulic excavator are described. Special attention is paid upon such technical issues as interface between CAD packages and the CAVE virtual environment, natural and intuitive user interaction including realistic virtual hand interaction and force feedback, intelligent navigation for assembly operations, and real-time display of complex assemblies.

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