scholarly journals SCRO: A Domain Ontology for Describing Steel Cold Rolling Processes towards Industry 4.0

Information ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 304
Author(s):  
Sadeer Beden ◽  
Qiushi Cao ◽  
Arnold Beckmann
Keyword(s):  
Cold Rolling ◽  
Domain Knowledge ◽  
Detection System ◽  
Data Access ◽  
Data Sets ◽  
Domain Experts ◽  
Data Querying ◽  
Set Up ◽  
Integration Data

This paper introduces the Steel Cold Rolling Ontology (SCRO) to model and capture domain knowledge of cold rolling processes and activities within a steel plant. A case study is set up that uses real-world cold rolling data sets to validate the performance and functionality of SCRO. This includes using the Ontop framework to deploy virtual knowledge graphs for data access, data integration, data querying, and condition-based maintenance purposes. SCRO is evaluated using OOPS!, the ontology pitfall detection system, and feedback from domain experts from Tata Steel.

Improving Quality and Safety Through Use of Secondary Data: Methods Case Study

2016 ◽  
Vol 39 (11) ◽  
pp. 1477-1501 ◽  
Author(s):  
Victoria Goode ◽  
Nancy Crego ◽  
Michael P. Cary ◽  
Deirdre Thornlow ◽  
Elizabeth Merwin
Keyword(s):  
Research Question ◽  
Secondary Data ◽  
Data Access ◽  
Data Sets ◽  
Complex Data ◽  
Management Skills ◽  
Data Set ◽  
Large Numbers ◽  
Need To Evaluate

Researchers need to evaluate the strengths and weaknesses of data sets to choose a secondary data set to use for a health care study. This research method review informs the reader of the major issues necessary for investigators to consider while incorporating secondary data into their repertoire of potential research designs and shows the range of approaches the investigators may take to answer nursing research questions in a variety of context areas. The researcher requires expertise in locating and judging data sets and in the development of complex data management skills for managing large numbers of records. There are important considerations such as firm knowledge of the research question supported by the conceptual framework and the selection of appropriate databases, which guide the researcher in delineating the unit of analysis. Other more complex issues for researchers to consider when conducting secondary data research methods include data access, management and security, and complex variable construction.

scholarly journals Human-Machine Collaborative Optimization via Apprenticeship Scheduling

2018 ◽  
Vol 63 ◽  
pp. 1-49 ◽  
Author(s):  
Matthew Gombolay ◽  
Reed Jensen ◽  
Jessica Stigile ◽  
Toni Golen ◽  
Neel Shah ◽  
...  
Keyword(s):  
Decision Making ◽  
Domain Knowledge ◽  
Resource Constraints ◽  
Optimization Technique ◽  
Collaborative Optimization ◽  
Data Sets ◽  
Scheduling Problems ◽  
Real World Data ◽  
Domain Experts ◽  
Model Free

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the "single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes. We propose a new approach for capturing this decision-making process through counterfactual reasoning in pairwise comparisons. Our approach is model-free and does not require iterating through the state space. We demonstrate that this approach accurately learns multifaceted heuristics on a synthetic and real world data sets. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of schedule optimization. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates optimal solutions up to 9.5 times faster than a state-of-the-art optimization algorithm.

Discovering Cross-Disciplinary Concepts in Multidisciplinary Context Through Collaborative Framework

2020 ◽  
Vol 11 (2) ◽  
pp. 1-19 ◽  
Author(s):  
Akkharawoot Takhom ◽  
Sasiporn Usanavasin ◽  
Thepchai Supnithi ◽  
Mitsuru Ikeda ◽  
Heinz Ulrich Hoppe ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Research Group ◽  
Text Analysis ◽  
Collaborative Research ◽  
Domain Knowledge ◽  
Multidisciplinary Research ◽  
Domain Experts ◽  
Network Text Analysis ◽  
Multiple Domain

When domain experts try to find a business solution, ambiguous terms arise within the context of discussion in a multidisciplinary research group. Different meanings and relationships with various concepts cause ambiguous semantics. This research aims to address complex business problems in a collaborative research group. This approach presents a collaborative framework based on network text analysis for detecting cross-disciplinary concepts in a multidisciplinary context. The framework recognizes ambiguous concepts (common terms presented in multiple domain knowledge), and these terms are visualized as a network. A case study of sustainable development demonstrates the identification of a set of cross-disciplinary concepts and their relationships across different domains. The main contributions are providing a framework to detect essential concepts that contain the cross-disciplinary concepts and recognize the understanding of multidisciplinary knowledge in the discussion context.

scholarly journals Adapting Data-Driven Research to the Fields of Social Sciences and the Humanities

2021 ◽  
Vol 13 (3) ◽  
pp. 59
Author(s):  
Albert Weichselbraun ◽  
Philipp Kuntschik ◽  
Vincenzo Francolino ◽  
Mirco Saner ◽  
Urs Dahinden ◽  
...  
Keyword(s):  
Social Sciences ◽  
Domain Knowledge ◽  
Data Driven ◽  
Media Criticism ◽  
Domain Experts ◽  
Social Sciences And Humanities ◽  
The Social ◽  
Recent Developments ◽  
Selection Of

Recent developments in the fields of computer science, such as advances in the areas of big data, knowledge extraction, and deep learning, have triggered the application of data-driven research methods to disciplines such as the social sciences and humanities. This article presents a collaborative, interdisciplinary process for adapting data-driven research to research questions within other disciplines, which considers the methodological background required to obtain a significant impact on the target discipline and guides the systematic collection and formalization of domain knowledge, as well as the selection of appropriate data sources and methods for analyzing, visualizing, and interpreting the results. Finally, we present a case study that applies the described process to the domain of communication science by creating approaches that aid domain experts in locating, tracking, analyzing, and, finally, better understanding the dynamics of media criticism. The study clearly demonstrates the potential of the presented method, but also shows that data-driven research approaches require a tighter integration with the methodological framework of the target discipline to really provide a significant impact on the target discipline.

scholarly journals Query and Predicate Emptiness in Ontology-Based Data Access

2016 ◽  
Vol 56 ◽  
pp. 1-59 ◽  
Author(s):  
Franz Baader ◽  
Meghyn Bienvenu ◽  
Carsten Lutz ◽  
Frank Wolter
Keyword(s):  
Computational Complexity ◽  
Domain Knowledge ◽  
Description Logics ◽  
Data Access ◽  
Practical Case ◽  
Query Formulation ◽  
Database Querying

In ontology-based data access (OBDA), database querying is enriched with an ontology that provides domain knowledge and additional vocabulary for query formulation. We identify query emptiness and predicate emptiness as two central reasoning services in this context. Query emptiness asks whether a given query has an empty answer over all databases formulated in a given vocabulary. Predicate emptiness is defined analogously, but quantifies universally over all queries that contain a given predicate. In this paper, we determine the computational complexity of query emptiness and predicate emptiness in the EL, DL-Lite, and ALC-families of description logics, investigate the connection to ontology modules, and perform a practical case study to evaluate the new reasoning services.

scholarly journals A cost-effective IoT strategy for remote deployment of soft sensors – a case study on implementing a soft sensor in a multistage MBBR plant

2020 ◽  
Vol 81 (8) ◽  
pp. 1733-1739 ◽  
Author(s):  
A. M. Nair ◽  
A. Hykkerud ◽  
H. Ratnaweera
Keyword(s):  
Open Source ◽  
Cost Effective ◽  
Data Access ◽  
Quality Parameters ◽  
Soft Sensor ◽  
Biofilm Reactor ◽  
Remote Access ◽  
Soft Sensors ◽  
Set Up

Abstract Model-based soft sensors can enhance online monitoring in wastewater treatment processes. These soft sensor scripts are executed either locally on a programmable logic controller (PLC) or remotely on a system with data-access over the internet. This work presents a cost-effective, flexible, open source IoT solution for remote deployment of a soft sensing algorithm. The system uses low-priced hardware and open-source programming language to set up the communication and remote-access system. Advantages of the new IoT architecture are demonstrated through a case study for remote deployment of an Extended Kalman Filter (EKF) to estimate additional water quality parameters in a multistage moving bed biofilm reactor (MBBR) plant. The soft-sensor results are successfully validated against standardised laboratory measurements to prove their ability to provide real-time estimations.

Ontologies for probabilistic networks: a case study in the oesophageal-cancer domain

2007 ◽  
Vol 22 (1) ◽  
pp. 67-86 ◽  
Author(s):  
EVELINE M. HELSPER ◽  
LINDA C. VAN DER GAAG
Keyword(s):  
Oesophageal Cancer ◽  
Domain Knowledge ◽  
Real Life ◽  
Life Domain ◽  
Probabilistic Networks ◽  
Domain Experts ◽  
Proper Management ◽  
Probabilistic Network

AbstractBuilding a probabilistic network for a real-life domain of application is a hard and time-consuming process, which is generally performed with the help of domain experts. As the scope and, hence, the size and complexity of networks are increasing, the need for proper management of the elicited domain knowledge becomes apparent. To study the usefulness of ontologies for this purpose, we constructed an ontology for the domain of oesophageal cancer, based on a real-life probabilistic network for the staging of cancer of the oesophagus and the knowledge elicited for its construction. In this paper, we describe the various components of our ontology and outline the benefits of using ontologies in engineering probabilistic networks.

scholarly journals Segment-Based Approach for Assessing Hazard Risk of Coastal Highways in Hawai‘i

2019 ◽  
Vol 2673 (1) ◽  
pp. 83-91 ◽  
Author(s):  
Harrison Togia ◽  
Oceana P. Francis ◽  
Karl Kim ◽  
Guohui Zhang
Keyword(s):  
Qualitative Method ◽  
Data Sets ◽  
Regional Environment ◽  
The Road ◽  
Hazard Exposure ◽  
Multiple Indicators ◽  
System Data ◽  
Rural Highway ◽  
Geographic Information System Data

Hazards to roadways and travelers can be drastically different because hazards are largely dependent on the regional environment and climate. This paper describes the development of a qualitative method for assessing infrastructure importance and hazard exposure for rural highway segments in Hawai‘i under different conditions. Multiple indicators of roadway importance are considered, including traffic volume, population served, accessibility, connectivity, reliability, land use, and roadway connection to critical infrastructures, such as hospitals and police stations. The method of evaluating roadway hazards and importance can be tailored to fit different regional hazard scenarios. It assimilates data from diverse sources to estimate risks of disruption. A case study for Highway HI83 in Hawai‘i, which is exposed to multiple hazards, is conducted. Weakening of the road by coastal erosion, inundation from sea level rise, and rockfall hazards require adaptation solutions. By analyzing the risk of disruption to highway segments, adaptation approaches can be prioritized. Using readily available geographic information system data sets for the exposure and impacts of potential hazards, this method could be adapted not only for emergency management but also for planning, design, and engineering of resilient highways.

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