Methodology for Holistic Reference Modeling in Systems Engineering
- URL: http://arxiv.org/abs/2211.11453v1
- Date: Mon, 21 Nov 2022 13:41:07 GMT
- Title: Methodology for Holistic Reference Modeling in Systems Engineering
- Authors: Dominik Ascher, Erik Heiland, Diana Schnell, Peter Hillmann, Andreas
Karcher
- Abstract summary: This paper presents a holistic approach to describe reference models across different views and levels.
Benefits include an end-to-end traceability of the capability coverage with performance parameters considered already at the starting point of the reference design.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Models in face of increasing complexity support development of new systems
and enterprises. For an efficient procedure, reference models are adapted in
order to reach a solution with les overhead which covers all necessary aspects.
Here, a key challenge is applying a consistent methodology for the descriptions
of such reference designs. This paper presents a holistic approach to describe
reference models across different views and levels. Modeling stretches from the
requirements and capabilities over their subdivision to services and components
up to the realization in processes and data structures. Benefits include an
end-to-end traceability of the capability coverage with performance parameters
considered already at the starting point of the reference design. This enables
focused development while considering design constraints and potential
bottlenecks. We demonstrate the approach on the example of the development of a
smart robot. Here, our methodology highly supports transferability of designs
for the development of further systems.
Related papers
- Bridging Design Gaps: A Parametric Data Completion Approach With Graph Guided Diffusion Models [9.900586490845694]
This study introduces a generative imputation model leveraging graph attention networks and tabular diffusion models for completing missing parametric data in engineering designs.
We demonstrate our model significantly outperforms existing classical methods, such as MissForest, hotDeck, PPCA, and TabCSDI in both the accuracy and diversity of imputation options.
The graph model helps accurately capture and impute complex parametric interdependencies from an assembly graph, which is key for design problems.
arXiv Detail & Related papers (2024-06-17T16:03:17Z) - Constraint based Modeling according to Reference Design [0.0]
Reference models in form of best practices are an essential element to ensured knowledge as design for reuse.
We present a generic approach for the formal description of reference models using semantic technologies and their application.
It is possible to use multiple reference models in context of system of system designs.
arXiv Detail & Related papers (2024-06-17T07:41:27Z) - Generative Design through Quality-Diversity Data Synthesis and Language Models [5.196236145367301]
Two fundamental challenges face generative models in engineering applications: the acquisition of high-performing, diverse datasets, and the adherence to precise constraints in generated designs.
We propose a novel approach combining optimization, constraint satisfaction, and language models to tackle these challenges in architectural design.
arXiv Detail & Related papers (2024-05-16T11:30:08Z) - Beyond development: Challenges in deploying machine learning models for structural engineering applications [2.6415688445750383]
This paper aims to illustrate the challenges of developing machine learning models suitable for deployment through two illustrative examples.
Among various pitfalls, the presented discussion focuses on model overfitting and underspecification, training data representativeness, variable omission bias, and cross-validation.
Results highlight the importance of implementing rigorous model validation techniques through adaptive sampling, careful physics-informed feature selection, and considerations of both model complexity and generalizability.
arXiv Detail & Related papers (2024-04-18T23:40:42Z) - Human as Points: Explicit Point-based 3D Human Reconstruction from
Single-view RGB Images [78.56114271538061]
We introduce an explicit point-based human reconstruction framework called HaP.
Our approach is featured by fully-explicit point cloud estimation, manipulation, generation, and refinement in the 3D geometric space.
Our results may indicate a paradigm rollback to the fully-explicit and geometry-centric algorithm design.
arXiv Detail & Related papers (2023-11-06T05:52:29Z) - Design Space Exploration and Explanation via Conditional Variational
Autoencoders in Meta-model-based Conceptual Design of Pedestrian Bridges [52.77024349608834]
This paper provides a performance-driven design exploration framework to augment the human designer through a Conditional Variational Autoencoder (CVAE)
The CVAE is trained on 18'000 synthetically generated instances of a pedestrian bridge in Switzerland.
arXiv Detail & Related papers (2022-11-29T17:28:31Z) - Extending Process Discovery with Model Complexity Optimization and
Cyclic States Identification: Application to Healthcare Processes [62.997667081978825]
The paper presents an approach to process mining providing semi-automatic support to model optimization.
A model simplification approach is proposed, which essentially abstracts the raw model at the desired granularity.
We aim to demonstrate the capabilities of the technological solution using three datasets from different applications in the healthcare domain.
arXiv Detail & Related papers (2022-06-10T16:20:59Z) - Concept for a Technical Infrastructure for Management of Predictive
Models in Industrial Applications [0.0]
We describe our technological concept for a model management system.
This concept includes versioned storage of data, support for different machine learning algorithms, fine tuning of models, subsequent deployment of models and monitoring of model performance after deployment.
arXiv Detail & Related papers (2021-07-29T08:38:46Z) - Dynamically Grown Generative Adversarial Networks [111.43128389995341]
We propose a method to dynamically grow a GAN during training, optimizing the network architecture and its parameters together with automation.
The method embeds architecture search techniques as an interleaving step with gradient-based training to periodically seek the optimal architecture-growing strategy for the generator and discriminator.
arXiv Detail & Related papers (2021-06-16T01:25:51Z) - Towards a Predictive Processing Implementation of the Common Model of
Cognition [79.63867412771461]
We describe an implementation of the common model of cognition grounded in neural generative coding and holographic associative memory.
The proposed system creates the groundwork for developing agents that learn continually from diverse tasks as well as model human performance at larger scales.
arXiv Detail & Related papers (2021-05-15T22:55:23Z) - A Diagnostic Study of Explainability Techniques for Text Classification [52.879658637466605]
We develop a list of diagnostic properties for evaluating existing explainability techniques.
We compare the saliency scores assigned by the explainability techniques with human annotations of salient input regions to find relations between a model's performance and the agreement of its rationales with human ones.
arXiv Detail & Related papers (2020-09-25T12:01:53Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.