Search results for "Domain knowledge"
showing 10 items of 27 documents
Model-based Engineering for the Integration of Manufacturing Systems with Advanced Analytics
2016
To employ data analytics effectively and efficiently on manufacturing systems, engineers and data scientists need to collaborate closely to bring their domain knowledge together. In this paper, we introduce a domain-specific modeling approach to integrate a manufacturing system model with advanced analytics, in particular neural networks, to model predictions. Our approach combines a set of meta-models and transformation rules based on the domain knowledge of manufacturing engineers and data scientists. Our approach uses a model of a manufacturing process and its associated data as inputs, and generates a trained neural network model as an output to predict a quantity of interest. This pape…
Improving programming skills of Mechanical Engineering students by teaching in C# multi-objective optimizations methods
2017
Designing an optimized suspension system that meet the main functions of comfort, safety and handling on poor quality roads is a goal for researchers. This paper represents a software development guide for designers of suspension systems with less programming skills that will enable them to implement their own optimization methods that improve traditional methods by using their domain knowledge.
Recent advances of HCI in decision-making tasks for optimized clinical workflows and precision medicine.
2020
The ever-increasing amount of biomedical data is enabling new large-scale studies, even though ad hoc computational solutions are required. The most recent Machine Learning (ML) and Artificial Intelligence (AI) techniques have been achieving outstanding performance and an important impact in clinical research, aiming at precision medicine, as well as improving healthcare workflows. However, the inherent heterogeneity and uncertainty in the healthcare information sources pose new compelling challenges for clinicians in their decision-making tasks. Only the proper combination of AI and human intelligence capabilities, by explicitly taking into account effective and safe interaction paradigms,…
An introduction to knowledge computing
2014
This paper deals with the challenges related to self-management and evolution of massive knowledge collections. We can assume that a self-managed knowledge graph needs a kind of a hybrid of: an explicit declarative self-knowledge (as knowledge about own properties and capabilities) and an explicit procedural self-knowledge (as knowledge on how to utilize own properties and the capabilities for the self-management).We offer an extension to a traditional RDF model of describing knowledge graphs according to the Semantic Web standards so that it will also allow to a knowledge entity to autonomously perform or query from remote services different computational executions needed. We also introdu…
Towards MKDA: A Knowledge Discovery Assistant for Researches in Medicine
2007
Nowadays doctors are generating a huge amount of raw data. These data, analyzed with data mining techniques, could be sources of new knowledge. Unluckily such tasks need skilled data analysts, and not so much researchers in Medicine are also data mining experts. In this paper we present a web based system for knowledge discovery assistance in Medicine able to advice a medical researcher in this kind of tasks. The user must define only the experiment specifications in a formal language we have defined. The system GUI helps users in their composition. Then the system plans a Knowledge Discovery Process (KDP) on the basis of rules in a knowledge base. Finally the system executes the KDP and pr…
Knowledge Acquisition Based on Semantic Balance of Internal and External Knowledge
1999
This paper presents a strategy to handle incomplete knowledge during acquisition process. The goal of this research is to develop formal tools that benefit the law of semantic balance. The assumption is used that a situation inside the object’s boundary in some world should be in balance with a situation outside it. It means that continuous cognition of an object aspires to a complete knowledge about it and knowledge about internal structure of the object will be in balance with knowledge about relationships of the object with other objects in its environment. It is supposed that one way to discover incompleteness of knowledge about some object is to measure and compare knowledge about its …
Concept Maps for Comprehension and Navigation of Hypertexts
2013
Comprehension and learning with hypertexts are challenging due to the nonlinearity of such digital documents. Processing hypertexts may involve navigation and comprehension problems, leading learners to cognitive overhead. Concept maps have been added to hypertexts to reduce the cognitive requirements of navigation and comprehension. This chapter explores the literature to examine the effects of concept maps on navigation, comprehension, and learning from hypertexts. The literature review aims to elucidate how concept maps may contribute to processing hypertexts and under which conditions. In spite of the variability of concept maps used in hypertexts, some findings converge. Concept maps r…
Ontological supports of knowledge: knowledge creation and analytical knowledge
2011
PurposeThe purpose of this paper is to examine, from the perspective of different theoretical approaches, the relationship that exists between different ontological supports of knowledge and knowledge itself (the way it is created and its characteristics).Design/methodology/approachThe paper proposes two different types of knowledge (knowledge of concrete situations and abstract knowledge) and two approaches (the constructivist view and the cognitive view) and provides a general classification of the different knowledge types. Second, it examines the underlying ontological support‐knowledge creation, characteristics or types of knowledge relationship in different approaches. Finally, conclu…
CNN based Gearbox Fault Diagnosis and Interpretation of Learning Features
2021
Machine learning based fault diagnosis schemes have been intensively proposed to deal with faults diagnosis of rotating machineries such as gearboxes, bearings, and electric motors. However, most of the machine learning algorithms used in fault diagnosis are pattern recognition tools, which can classify given data into two or more classes. The underlined physical phenomena in fault diagnosis are not directly interpretable in machine learning schemes, thus it is usually called black/gray box models. In this study, convolutional neural networks (CNN) machine learning algorithm is proposed to classify gearbox faults, and the learning features of the CNN filters are visualized to understand the…
A Hybrid Machine Learning and Knowledge Based Approach to Limit Combinatorial Explosion in Biodegradation Prediction
2016
One of the main tasks in chemical industry regarding the sustainability of a product is the prediction of its environmental fate, i.e., its degradation products and pathways. Current methods for the prediction of biodegradation products and pathways of organic environmental pollutants either do not take into account domain knowledge or do not provide probability estimates. In this chapter, we propose a hybrid knowledge-based and machine learning-based approach to overcome these limitations in the context of the University of Minnesota Pathway Prediction System (UM-PPS). The proposed solution performs relative reasoning in a machine learning framework, and obtains one probability estimate fo…