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Concept

Web Ontology Language

Related publications (27)

Design ontology for cognitive thread supporting traceability management in model-based systems engineering

Dimitrios Kyritsis, Jinzhi Lu, Yan Yan

Industrial information integration engineering (IIIE) is an interdisciplinary field to facilitate the industrial information integration process. In the age of complex and large-scale systems, model-based systems engineering (MBSE) is widely adopted in ind ...

Elsevier2024

Ontology-based Knowledge Representation for Traditional Martial Arts

Sarah Irene Brutton Kenderdine, Yumeng Hou

Traditional martial arts are treasures of humanity's knowledge and critical carriers of sociocultural memories throughout history. However, such treasured practices have encountered various challenges in knowledge transmission and now feature many entries ...

2024

A semantic model-based systems engineering approach for assessing the operational performance of metal forming process

Dimitrios Kyritsis, Jinzhi Lu

Metal Forming is a basic and essential industrial process to provide materials for constructing complex products. To design an efficient metal forming process, the functional requirements and operational performance are two important aspects to be consider ...

Pergamon-Elsevier Science Ltd2024

Blue Brain Nexus: An open, secure, scalable system for knowledge graph management and data-driven science

Henry Markram, Sean Lewis Hill, Alejandra Garcia Rojas Martinez, Huanxiang Lu, Mohameth François Sy, Anna-Kristin Kaufmann, Samuel Claude Kerrien, Alexander Désiré Ulbrich, Henry Genet, Pierre-Alexandre Fonta, Silvia Rosario Jimenez Tejeda, Michaël Fernand Paul Dupont, Bogdan Roman, Ian Lavriushev, Didac Montero Mendez, Wojciech Adam Wajerowicz, Kenneth William Pirman, Julien Antonin Machon, Jonathan Raël Lurie, Dhanesh Neela Mana, Natalia Stafeeva, Carolina Johanna Elisabeth Lindqvist

Modern data-driven science often consists of iterative cycles of data discovery, acquisition, preparation, analysis, model building and validation leading to knowledge discovery as well as dissemination at scale. The unique challenges of building and simul ...

2023

An Ontology-based Engineering system to support aircraft manufacturing system design

Jinzhi Lu, Xiaochen Zheng

During the conceptual design phase of an aircraft manufacturing system, different industrial scenarios need to be evaluated against performance indicators in a collaborative engineering process. Domain experts' knowledge and the motivations for decision-ma ...

ELSEVIER SCI LTD2023

Get More for Less in Decentralized Learning Systems

Anne-Marie Kermarrec, Rafael Pereira Pires, Rishi Sharma, Akash Balasaheb Dhasade, Milos Vujasinovic

Decentralized learning (DL) systems have been gaining popularity because they avoid raw data sharing by communicating only model parameters, hence preserving data confidentiality. However, the large size of deep neural networks poses a significant challeng ...

IEEE2023

Cognitive twin construction for system of systems operation based on semantic integration and high-level architecture

Dimitrios Kyritsis, Jinzhi Lu

With the increasing complexity of engineered systems, digital twins (DTs) have been widely used to support integrated modeling, simulation, and decision-making of the system of systems (SoS). However, when integrating DTs of each constituent system, it is ...

IOS PRESS2022

A Data-Knowledge Hybrid Driven Method for Gas Turbine Gas Path Diagnosis

Dimitrios Kyritsis, Jinzhi Lu

Gas path fault diagnosis of a gas turbine is a complex task involving field data analysis and knowledge-based reasoning. In this paper, a data-knowledge hybrid driven method for gas path fault diagnosis is proposed by integrating a physical model-based gas ...

MDPI2022

A semantic-driven tradespace framework to accelerate aircraft manufacturing system design

Dimitrios Kyritsis, Jinzhi Lu, Xiaochen Zheng

During the design phase of an aircraft manufacturing system, different industrial scenarios need to be evaluated according to key performance indicators to achieve the optimal system performance. It is a highly complex process involving multidisciplinary s ...

SPRINGER2022

Ontology-based system to support industrial system design for aircraft assembly

Dimitrios Kyritsis, Jinzhi Lu

The development of an aircraft industrial system is a complex process which faces the challenge of digital discontinuity in multidisciplinary engineering due to various interfaces between different digital tools, leading to extra development time and costs ...

ELSEVIER2022

The Neuron Phenotype Ontology: A FAIR Approach to Proposing and Classifying Neuronal Types

Sean Lewis Hill, Mohameth François Sy

The challenge of defining and cataloging the building blocks of the brain requires a standardized approach to naming neurons and organizing knowledge about their properties. The US Brain Initiative Cell Census Network, Human Cell Atlas, Blue Brain Project, ...

HUMANA PRESS INC2022

Co-simulation of complex engineered systems enabled by a cognitive twin architecture

Dimitrios Kyritsis, Jinzhi Lu

Since the complex engineered system involves multi-disciplinary, co-simulation is the key technique to the performance analysis. However, the co-simulation is hindered by heterogeneous sub-systems and ununified environments. In this paper, a Cognitive Twin ...

TAYLOR & FRANCIS LTD2022

Digital Twin-Enabled Decision Support Services in Industrial Ecosystems

Dimitrios Kyritsis, Jinzhi Lu

The goal of this paper is to further elaborate a new concept for value creation by decision support services in industrial service ecosystems using digital twins and to apply it to an extended case study. The aim of the original model was to design and int ...

MDPI2021

Design Ontology Supporting Model-Based Systems Engineering Formalisms

Dimitrios Kyritsis, Jinzhi Lu

Model-based systems engineering (MBSE) provides an important capability for managing the complexities of system development. MBSE empowers the formalism of system architectures for supporting model-based requirement elicitation, specification, design, deve ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Interpreting deep learning models for epileptic seizure detection on EEG signals

David Atienza Alonso, Leila Cammoun, Marina Zapater Sancho, Tomas Teijeiro Campo, Valentin Alexandre Guy Gabeff

While Deep Learning (DL) is often considered the state-of-the art for Artificial Intelligence-based medical decision support, it remains sparsely implemented in clinical practice and poorly trusted by clinicians due to insufficient interpretability of neur ...

2021

Deep Learning with Convolutional Neural Network for Proportional Control of Finger Movements from surface EMG Recordings

Silvestro Micera, Fiorenzo Artoni, Vincent Alexandre Mendez, Leonardo Pollina

The control of robotic prosthetic hands (RPHs) for upper limb amputees is far from optimal. Simultaneous and proportional finger control of a RPH based on EMG signals is still challenging. Based on EMG and kinematics recordings of subjects following a pre- ...

IEEE2021

The organisation of science: topics, incentives and funding

Omar Ballester Gonzalez

Ever since the links between the development of new technologies and economic growth became evident, researchers have attempted to study how the creation of knowledge fosters progress. If pushing the frontier of knowledge has an impact on progress and well ...

EPFL2020

Towards semantics-driven modelling and simulation of context-aware manufacturing systems

Damiano Nunzio Arena

Systems modelling and simulation are two important facets for thoroughly and effectively analysing manufacturing processes. The ever-growing complexity of the latter, the increasing amount of knowledge, and the use of Semantic Web techniques adhering meani ...

EPFL2019

An ontological approach to representing the product life cycle

Dimitrios Kyritsis

The ability to access and share data is key to optimizing and streamlining any industrial production process. Unfortunately, the manufacturing industry is stymied by a lack of interoperability among the systems by which data are produced and managed, and t ...

IOS PRESS2019

Graph-based image representation learning

Renata Khasanova

Though deep learning (DL) algorithms are very powerful for image processing tasks, they generally require a lot of data to reach their full potential. Furthermore, there is no straightforward way to impose various properties, given by the prior knowledge a ...

EPFL2019