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      Interactive Timeline Approach for Contextual Spatio-Temporal ECT Data Investigation

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          Abstract

          This paper presents a novel approach to a complex process of electrical capacitance tomography (ECT) measurement data analysis. ECT is frequently employed for non-invasive monitoring of industrial process phenomena. Proposed methodology is based on the premeditated integration of the spatial and temporal relations inherent in the measurement records into the workflow of the analysis procedure. We propose a concept of interactive timeline that enables arranging data visualization according to the user’s current focus along the process of analysis. We evaluated the proposed method using a prototype system in a task-based user study conducted with a group of domain experts. The evaluation is based on gravitational silo flow measurement datasets. Proposed prototype system enables diverse data manipulation in a more natural way allowing the user to switch back and forth between space and time domains along the data analysis trail. Experiments with the prototype system showed that the accuracy and completion times have significantly improved in comparison to the performance measured in the baseline condition. Additionally, the participants reported decreased physical load with improved efficiency measured with NASA task load index. Finally, a short discussion coupled with directions for the future of interactive spatio-temporal ECT measurement data analysis conclude the paper.

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          Most cited references60

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          Nasa-Task Load Index (NASA-TLX); 20 Years Later

          S. G. Hart (2006)
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            A Review on Basic Data-Driven Approaches for Industrial Process Monitoring

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              Data Mining and Analytics in the Process Industry: The Role of Machine Learning

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                25 August 2020
                September 2020
                : 20
                : 17
                : 4793
                Affiliations
                [1 ]Institute of Applied Computer Science, Lodz University of Technology, Stefanowskiego 18/22 str, 90-924 Łódź, Poland; zch@ 123456kis.p.lodz.pl (Z.C.); 210911@ 123456edu.p.lodz.pl (A.K.); mpwozniak@ 123456ubicomp.pl (M.W.); 203151@ 123456edu.p.lodz.pl (A.N.); pkuchars@ 123456kis.p.lodz.pl (P.K.); tjaworski@ 123456iis.p.lodz.pl (T.J.); 217723@ 123456edu.p.lodz.pl (M.M.); kgrudzi@ 123456kis.p.lodz.pl (K.G.)
                [2 ]Institute of Electrical Power Engineering, Lodz University of Technology, Stefanowskiego 18/22 str, 90-924 Łódź, Poland; pawel.rozga@ 123456p.lodz.pl
                Author notes
                [* ]Correspondence: androm@ 123456kis.p.lodz.pl ; Tel.: +48-42-631-27-50
                Author information
                https://orcid.org/0000-0001-5241-0405
                https://orcid.org/0000-0003-4439-0541
                https://orcid.org/0000-0001-6051-2962
                https://orcid.org/0000-0001-8600-3760
                https://orcid.org/0000-0001-8838-072X
                https://orcid.org/0000-0003-4472-8100
                Article
                sensors-20-04793
                10.3390/s20174793
                7506648
                f33c7255-0141-4e61-bbc5-8b1b241a117f
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 07 July 2020
                : 20 August 2020
                Categories
                Article

                Biomedical engineering
                data analysis,visualization,flow investigation tool,ect,spatio-temporal data interpretation

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