Conflicts classification and solving for collaborative feature modeling

2007 ◽  
Vol 21 (2) ◽  
pp. 211-219 ◽  
Author(s):  
Min Tang ◽  
Shang-Ching Chou ◽  
Jin-Xiang Dong
Keyword(s):  
Feature Modeling

Sketch2Jewelry: Semantic feature modeling for sketch-based jewelry design

2014 ◽  
Vol 38 ◽  
pp. 69-77 ◽  
Author(s):  
Long Zeng ◽  
Yong-jin Liu ◽  
Jin Wang ◽  
Dong-liang Zhang ◽  
Matthew Ming-Fai Yuen
Keyword(s):  
Feature Modeling ◽  
Semantic Feature ◽  
Jewelry Design

An Interactive Graphical Approach to Feature Modeling Using Halfspaces and Geometric Constraints

1990 ◽  
Author(s):  
Maarten J. G. M. van Emmerik
Keyword(s):  
Spatial Relations ◽  
Feature Modeling ◽  
Geometric Constraints ◽  
Boolean Combination ◽  
Graphical Approach ◽  
Geometric Characteristics ◽  
Feature Based ◽  
New Feature ◽  
High Level ◽  
And Control

Abstract Feature modeling enables the specification of a model with standardized high-level shape aspects that have a functional meaning for design or manufacturing. In this paper an interactive graphical approach to feature-based modeling is presented. The user can represent features as new CSG primitives, specified as a Boolean combination of halfspaces. Constraints between halfspaces specify the geometric characteristics of a feature and control feature validity. Once a new feature is defined and stored in a library, it can be used in other objects and positioned, oriented and dimensioned by direct manipulation with a graphics cursor. Constraints between features prevent feature interference and specify spatial relations between features.

MDTP

2021 ◽  
Vol 14 (8) ◽  
pp. 1289-1297
Author(s):  
Ziquan Fang ◽  
Lu Pan ◽  
Lu Chen ◽  
Yuntao Du ◽  
Yunjun Gao
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Real Life ◽  
Feature Modeling ◽  
Traffic Prediction ◽  
Interactive System ◽  
Trajectory Data ◽  
Spatio Temporal ◽  
Prediction Approach

Traffic prediction has drawn increasing attention for its ubiquitous real-life applications in traffic management, urban computing, public safety, and so on. Recently, the availability of massive trajectory data and the success of deep learning motivate a plethora of deep traffic prediction studies. However, the existing neural-network-based approaches tend to ignore the correlations between multiple types of moving objects located in the same spatio-temporal traffic area, which is suboptimal for traffic prediction analytics. In this paper, we propose a multi-source deep traffic prediction framework over spatio-temporal trajectory data, termed as MDTP. The framework includes two phases: spatio-temporal feature modeling and multi-source bridging. We present an enhanced graph convolutional network (GCN) model combined with long short-term memory network (LSTM) to capture the spatial dependencies and temporal dynamics of traffic in the feature modeling phase. In the multi-source bridging phase, we propose two methods, Sum and Concat, to connect the learned features from different trajectory data sources. Extensive experiments on two real-life datasets show that MDTP i) has superior efficiency, compared with classical time-series methods, machine learning methods, and state-of-the-art neural-network-based approaches; ii) offers a significant performance improvement over the single-source traffic prediction approach; and iii) performs traffic predictions in seconds even on tens of millions of trajectory data. we develop MDTP + , a user-friendly interactive system to demonstrate traffic prediction analysis.

scholarly journals Multi-purpose, multi-level feature modeling of large-scale industrial software systems

2016 ◽  
Vol 17 (3) ◽  
pp. 913-938 ◽  
Author(s):  
Daniela Rabiser ◽  
Herbert Prähofer ◽  
Paul Grünbacher ◽  
Michael Petruzelka ◽  
Klaus Eder ◽  
...  
Keyword(s):  
Large Scale ◽  
Feature Modeling ◽  
Software Systems ◽  
Multi Level ◽  
Industrial Software

Visualized Feature Modeling in Software Product Line

2009 ◽  
pp. 299-310
Author(s):  
Li Zheng ◽  
Chao Zhang ◽  
Zhanwei Wu ◽  
Yixin Yan
Keyword(s):  
Software Product Line ◽  
Product Line ◽  
Feature Modeling ◽  
Software Product

scholarly journals Feature Modeling and Variability Modeling Syntactic Notation Comparison and Mapping

2014 ◽  
Vol 02 (02) ◽  
pp. 101-108 ◽  
Author(s):  
Wahyudianto   ◽  
Eko K. Budiardjo ◽  
Elviawaty M. Zamzami
Keyword(s):  
Feature Modeling ◽  
Variability Modeling
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