scholarly journals Reuse of Physical System Models by means of Semantic Knowledge Representation: A Case Study applied to Modelica

2015 ◽  
Author(s):  
Elena Gallego ◽  
Jose María Alvarez Rodríguez ◽  
Juan Llorens
Keyword(s):  
Knowledge Representation ◽  
Physical System ◽  
Semantic Knowledge ◽  
System Models

scholarly journals UML knowledge model for measurement process including uncertainty of measurement

2021 ◽  
Vol 12 ◽  
pp. 26
Author(s):  
Priyanka Bharti ◽  
QingPing Yang ◽  
Alistair B. Forbes ◽  
Yacine Koucha
Keyword(s):  
Knowledge Representation ◽  
Manufacturing Industry ◽  
Object Oriented ◽  
Semantic Knowledge ◽  
Measurement Process ◽  
Measurement Technology ◽  
Measurement Systems ◽  
Calibration Uncertainty ◽  
Measurement Planning

Measurement technology has made an enormous progress in the last decade. With the advent of knowledge representation, various object-oriented models for measurement systems have been developed in the past. Most common limitations of all these models were not incorporating the uncertainty in the measurement process. In this paper, we proposed an object-oriented model depicting the information and knowledge flow in the measurement process, including the measurement uncertainty. The model has three major object classes, namely measurement planning, measurement system and analysis & documentation. These are further classified into sub-classes and relationships amongst them. Attributes and operations are also defined within the classes. This gives a practical and conceptual view of knowledge in the form of object-model for measurement processes. A case study is presented which evaluates the uncertainty of the measurement of a 100 mm gauge block, using both Type A and Type B evaluation methods of the GUM approach.This case study is very similar to the evaluation of calibration uncertainty of CMM. This model can be converted into semantic knowledge representation such as ontology of measurement process domain. Other use of this model is to support the quality engineering in manufacturing industry and research.

Interval Algebra and Region Connection Calculus for Ontological Spatiotemporal Assembly Product Motion Knowledge Representation

2021 ◽  
pp. 1-18
Author(s):  
Md Tarique Hasan Khan ◽  
Frédéric Demoly ◽  
Kyoung Yun Kim
Keyword(s):  
Knowledge Representation ◽  
Semantic Knowledge ◽  
Added Value ◽  
Topological Spaces ◽  
Product Knowledge ◽  
Interval Algebra ◽  
Assembly Design ◽  
Knowledge Models ◽  
Region Connection Calculus

Over the last decades, noticeable efforts have been made to construct design knowledge during the detailed geometric definition phase systematically. However, physical products exhibit functional behaviors, which explain that they evolve over space and time. Hence, there is a need to extend assembly product knowledge towards the spatiotemporal dimension to provide more realistic knowledge models in assembly design. Systematic semantic knowledge representation via ontology enables designers to understand the anticipated product’s behavior in advance. In this article, Interval Algebra (IA) and Region Connection Calculus (RCC) are investigated to formalize and construct ontological spatiotemporal assembly product motion knowledge. IA is commonly used to represent the temporality between two entities, while RCC is more appropriate to represent the ‘part-to-part’ relationships of two topological spaces. This paper discusses the roles of IA and RCC and presents a case study of a nutcracker assembly model’s behavior. The assembly product motion ontology with the aid of IA and RCC is evaluated using a task-based approach. The evaluation shows the added value of the developed ontology compared to others published in the literature.

scholarly journals VeriPhy: verified controller executables from verified cyber-physical system models

2018 ◽  
Vol 53 (4) ◽  
pp. 617-630 ◽  
Author(s):  
Brandon Bohrer ◽  
Yong Kiam Tan ◽  
Stefan Mitsch ◽  
Magnus O. Myreen ◽  
André Platzer
Keyword(s):  
Physical System ◽  
Cyber Physical System ◽  
System Models

scholarly journals The Physical Basis of Analogies in Physical System Models

Mechatronics ◽  
2005 ◽  
pp. 8-1-8-10
Author(s):  
Neville Hogan ◽  
Peter Breedveld
Keyword(s):  
Physical System ◽  
Physical Basis ◽  
System Models

An Ontology-based approach to enable data-driven research in the field of NDT in Civil Engineering

2021 ◽  
Author(s):  
Benjamin Moreno-Torres ◽  
Christoph Völker ◽  
Sabine Kruschwitz
Keyword(s):  
Knowledge Representation ◽  
Civil Engineering ◽  
Common Knowledge ◽  
Measurement Data ◽  
Data Access ◽  
Semantic Knowledge ◽  
Data Sources ◽  
Data Sets ◽  
Extensive Literature ◽  
Distributed Data

<div> <p>Non-destructive testing (NDT) data in civil engineering is regularly used for scientific analysis. However, there is no uniform representation of the data yet. An analysis of distributed data sets across different test objects is therefore too difficult in most cases.</p> <p>To overcome this, we present an approach for an integrated data management of distributed data sets based on Semantic Web technologies. The cornerstone of this approach is an ontology, a semantic knowledge representation of our domain. This NDT-CE ontology is later populated with the data sources. Using the properties and the relationships between concepts that the ontology contains, we make these data sets meaningful also for machines. Furthermore, the ontology can be used as a central interface for database access. Non-domain data sources can be integrated by linking them with the NDT ontology, making them directly available for generic use in terms of digitization. Based on an extensive literature research, we outline the possibilities that result for NDT in civil engineering, such as computer-aided sorting and analysis of measurement data, and the recognition and explanation of correlations.</p> <p>A common knowledge representation and data access allows the scientific exploitation of existing data sources with data-based methods (such as image recognition, measurement uncertainty calculations, factor analysis or material characterization) and simplifies bidirectional knowledge and data transfer between engineers and NDT specialists.</p> </div>

Semantic and Formal Representation of Cognitive Models for the Metacognitive Architecture CARINA

2021 ◽  
pp. 54-72
Author(s):  
Alba J. Jerónimo ◽  
María P. Barrera ◽  
Manuel F. Caro ◽  
Adán A. Gómez
Keyword(s):  
Knowledge Representation ◽  
Semantic Memory ◽  
Cognitive Model ◽  
Semantic Knowledge ◽  
Cognitive Models ◽  
Formal Representation ◽  
Object Properties ◽  
Internal Information ◽  
Processing Mechanisms ◽  
The Brain

A cognitive model is a computational model of internal information processing mechanisms of the brain for the purposes of comprehension and prediction. CARINA metacognitive architecture runs cognitive models. However, CARINA does not currently have mechanisms to store and learn from cognitive models executed in the past. Semantic knowledge representation is a field of study which concentrates on using formal symbols to a collection of propositions, objects, object properties, and relations among objects. In CARINA Beliefs are a form of represent the semantic knowledge. The aim of this chapter is to formally describe a CARINA-based cognitive model through of denotational mathematics and to represent these models using a technique of semantic knowledge representation called beliefs. All the knowledge received by CARINA is stored in the semantic memory in the form of beliefs. Thus, a cognitive model represented through beliefs will be ready to be stored in semantic memory of the metacognitive architecture CARINA. Finally, an illustrative example is presented.

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