Towards seamless integration of N-version programming in model-based design

2011 ◽

Vol 383-390 ◽

pp. 2484-2491

Author(s):

Jun Fan

Keyword(s):

Data Structure ◽

Data Model ◽

Object Oriented ◽

Geological Structure ◽

Solid Model ◽

Topological Relations ◽

Seamless Integration ◽

Computer Data ◽

Model Based ◽

Geometric Primitives

In the long evolution of the earth formation often form a complex geological structure, modeling for these complex geological entities (such as thinning-out, bifurcation, reverse, etc.) still require in-depth 3D modeling study. Because of discontinuity, complexity and uncertainty of distribution of 3D geo-objects, some models only are suitable for regular, continuous and relatively simple spatial objects, and some are suitable for discontinue, complex and uncertain geo-objects, but some improvements on these models, such as, updating of model, maintenance of topological and seamless integration between models, are still to be made. OO-Solid model, put forward by writer in 2002, is an object- oriented topological model based on sections. The OO-Solid Model is an object-oriented 3D topologic data model based on component for geology modeling with fully considering the topological relations between geological objects and its geometric primitives, Comparatively, it accords with the actual requirements of three-dimensional geological modeling . The key issue of 3D geology modeling is the 3D data model. Some data models are suitable for discontinue, complex and uncertain geo-objects, but the OO-Solid model is an object-oriented 3D topologic data model based on component for geology modeling with fully considering the topological relations between geological objects and its geometric primitives. OO-Solid model and data structure are designed. At last, 3D complex geological entities modeling based on OO-Solid are studied in this paper. These study is important and one of the core techniques for the 3DGM.

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Towards seamless integration of functional safety and model-based systems engineering

Tag des Systems Engineering ◽

10.3139/9783446443761.006 ◽

2014 ◽

pp. 53-62

Author(s):

Inga Binder

Keyword(s):

Systems Engineering ◽

Functional Safety ◽

Seamless Integration ◽

Model Based ◽

Model Based Systems Engineering

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XMapDB-Sim: Performance Evalaution on Model-based XML to Relational Database Mapping Choices

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v7.i2.pp551-566 ◽

2017 ◽

Vol 7 (2) ◽

pp. 551 ◽

Cited By ~ 1

Author(s):

Haw Su-Cheng ◽

Emyliana Song ◽

Nur Amirah Azhar ◽

Aisyah Amin

Keyword(s):

Relational Databases ◽

Mapping Technique ◽

The Internet ◽

Related Area ◽

Seamless Integration ◽

Model Based ◽

Simulation Engine ◽

Unified View ◽

Mapping Techniques ◽

Future Direction

<span>XML has emerged as the standard for information representation over the Internet. However, most enterprises today have long secured the use of relational databases. Thus, it is crucial to map XML data into relational data to provide seamless integration between these database infrastructures. Many mapping techniques have been proposed, yet, none has provides a unified view on these techniques. Ultimately, understanding how these techniques work is important especially if one needs to decide which technique to adopt in their organization. This paper (i) reviews on some existing model-based mapping schemes focusing on how the mapping technique works, the advantages and the disadvantages, (ii) present the simulation engine to evaluate the performance of selected mapping schemes, (iii) highlight the future direction of the related area.</span>

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Model-Based Diagnosis for Cyber-Physical Production Systems Based on Machine Learning and Residual-Based Diagnosis Models

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33012727 ◽

2019 ◽

Vol 33 ◽

pp. 2727-2735 ◽

Cited By ~ 4

Author(s):

Andreas Bunte ◽

Benno Stein ◽

Oliver Niggemann

Keyword(s):

Machine Learning ◽

Production Systems ◽

Quantitative Model ◽

Machine Learning Algorithms ◽

Seamless Integration ◽

Model Based ◽

Novel Approach ◽

Qualitative Models ◽

Easy Integration ◽

Cyber Physical Production Systems

This paper introduces a novel approach to Model-Based Diagnosis (MBD) for hybrid technical systems. Unlike existing approaches which normally rely on qualitative diagnosis models expressed in logic, our approach applies a learned quantitative model that is used to derive residuals. Based on these residuals a diagnosis model is generated and used for a root cause identification. The new solution has several advantages such as the easy integration of new machine learning algorithms into MBD, a seamless integration of qualitative models, and a significant speed-up of the diagnosis runtime. The paper at hand formally defines the new approach, outlines its advantages and drawbacks, and presents an evaluation with real-world use cases.

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Representation, abstraction, and simple-minded sophisticates

Behavioral and Brain Sciences ◽

10.1017/s0140525x19002942 ◽

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.

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

Microscopy and Microanalysis ◽

10.1017/s1431927600028968 ◽

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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