scholarly journals Tetra-Helix Concept Model Based on Vocational Realistic Education (VRE)

2022 ◽  
Author(s):  
Agus A. Putrawan ◽  
Ni W. D. Ayuni
Keyword(s):  
Concept Model ◽  
Model Based

A New Concept Model Based on the RSM

2009 ◽  
Author(s):  
Hai Li ◽  
Zhurong Zhou ◽  
Zhixing Huang ◽  
Yuhui Qiu
Keyword(s):  
Concept Model ◽  
Model Based

Concept model-based unsupervised web image re-ranking

2009 ◽  
Author(s):  
Shenghua Gao ◽  
Xiangang Cheng ◽  
Huan Wang ◽  
Liang-Tien Chia
Keyword(s):  
Concept Model ◽  
Model Based

Parametric Modeling Method for Conceptual Vehicle Design

2005 ◽  
Author(s):  
Jian Wan ◽  
Nanxin Wang ◽  
Gianna Gomez-Levi
Keyword(s):  
Computer Aided Design ◽  
Parametric Modeling ◽  
Vehicle Design ◽  
Early Design ◽  
Concept Model ◽  
Model Based ◽  
Design Cycle ◽  
Adequate Accuracy ◽  
Early Design Stages ◽  
Aided Design

Early conceptual design is one of the most important stages in vehicle product development. At this stage, various iterations of design, analysis, validation, and confirmation need to be carried out with limited and constantly changing vehicle design information. To overcome this difficulty, computer aided design tools are widely used. Various parametric concept models are created and employed to increase the number of design iterations and reduce the design cycle time. However, two of the most common challenges still exist: 1) how to build a parametric model that is flexible and robust while maintaining adequate accuracy, and 2) how to easily manipulate the model based on limited dimensional and geometrical input available at early design stages. In this paper, a parametric modeling and controlling method is presented. It has been developed to generate and manipulate a parametric vehicle concept model for vehicle design at early design stages. This method greatly improves the flexibility and robustness of the parametric concept model, and allows easy modifications of the model based on the limited available input.

scholarly journals Category-Theoretic Formulation of the Model-Based Systems Architecting Cognitive-Computational Cycle

2021 ◽  
Vol 11 (4) ◽  
pp. 1945
Author(s):  
Yaniv Mordecai ◽  
James P. Fairbanks ◽  
Edward F. Crawley
Keyword(s):  
Programming Languages ◽  
Systems Engineering ◽  
System Architecture ◽  
Mathematical Framework ◽  
Conceptual System ◽  
Modeling Languages ◽  
Architecture Model ◽  
Concept Model ◽  
Model Based ◽  
Systems Architecting

We introduce the Concept→Model→Graph→View Cycle (CMGVC). The CMGVC facilitates coherent architecture analysis, reasoning, insight, and decision making based on conceptual models that are transformed into a generic, robust graph data structure (GDS). The GDS is then transformed into multiple views of the model, which inform stakeholders in various ways. This GDS-based approach decouples the view from the model and constitutes a powerful enhancement of model-based systems engineering (MBSE). The CMGVC applies the rigorous foundations of Category Theory, a mathematical framework of representations and transformations. We show that modeling languages are categories, drawing an analogy to programming languages. The CMGVC architecture is superior to direct transformations and language-coupled common representations. We demonstrate the CMGVC to transform a conceptual system architecture model built with the Object Process Modeling Language (OPM) into dual graphs and a stakeholder-informing matrix that stimulates system architecture insight.

scholarly journals Category-Theoretic Formulation of Model-Based Systems Architecting: The Concept-Model-Graph-View-Concept Transformation Cycle

2021 ◽  
Author(s):  
Yaniv Mordecai ◽  
James Fairbanks ◽  
Edward Crawley
Keyword(s):  
Systems Engineering ◽  
System Architecture ◽  
Mathematical Framework ◽  
Conceptual System ◽  
Architecture Model ◽  
Concept Model ◽  
Model Based ◽  
Systems Architecting ◽  
Model Graph ◽  
Transformation Cycle

We introduce the Concept-Model-Graph-View-Concept (CMGVC) transformation cycle. The CMGVC cycle facilitates coherent architecture analysis, reasoning, insight, and decision-making based on conceptual models that are transformed into a common, robust graph data structure (GDS). The GDS is then transformed into multiple views on the model, which inform stakeholders in various ways. This GDS-based approach decouples the view from the model and constitutes a powerful enhancement of model-based systems engineering (MBSE). CMGVC applies the rigorous foundations of Category Theory, a mathematical framework of representations and transformations. The CMGVC architecture is superior to direct transformations and language-coupled common representations. We demonstrate the CMGVC cycle to transform a conceptual system architecture model built with the Object Process Modeling Language (OPM) into dual graphs and a decision support matrix (DSM) that stimulates system architecture insight.

Representation, abstraction, and simple-minded sophisticates

2020 ◽  
Vol 43 ◽  
Author(s):  
Peter Dayan
Keyword(s):  
Decision Theory ◽  
Predictive Coding ◽  
Bayesian Decision Theory ◽  
Bayesian Decision ◽  
Model Based ◽  
Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

Quantitative Model-Based Image Analysis of NuMa Distribution Links Nuclear Organization with Cell Phenotype

2001 ◽  
Vol 7 (S2) ◽  
pp. 578-579
Author(s):  
David W. Knowles ◽  
Sophie A. Lelièvre ◽  
Carlos Ortiz de Solόrzano ◽  
Stephen J. Lockett ◽  
Mina J. Bissell ◽  
...  
Keyword(s):  
Image Analysis ◽  
Mammary Epithelial Cells ◽  
Nuclear Organization ◽  
Critical Role ◽  
Quantitative Model ◽  
Mammary Epithelial ◽  
Cell Phenotype ◽  
Proliferating Cells ◽  
Mitotic Apparatus ◽  
Model Based

The extracellular matrix (ECM) plays a critical role in directing cell behaviour and morphogenesis by regulating gene expression and nuclear organization. Using non-malignant (S1) human mammary epithelial cells (HMECs), it was previously shown that ECM-induced morphogenesis is accompanied by the redistribution of nuclear mitotic apparatus (NuMA) protein from a diffuse pattern in proliferating cells, to a multi-focal pattern as HMECs growth arrested and completed morphogenesis . A process taking 10 to 14 days.To further investigate the link between NuMA distribution and the growth stage of HMECs, we have investigated the distribution of NuMA in non-malignant S1 cells and their malignant, T4, counter-part using a novel model-based image analysis technique. This technique, based on a multi-scale Gaussian blur analysis (Figure 1), quantifies the size of punctate features in an image. Cells were cultured in the presence and absence of a reconstituted basement membrane (rBM) and imaged in 3D using confocal microscopy, for fluorescently labeled monoclonal antibodies to NuMA (fαNuMA) and fluorescently labeled total DNA.

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