Enabling automated engineering’s project progress measurement by using data flow models and digital twins

Abstract

A significant challenge of managing successful engineering projects is to know their status at any time. This paper describes a concept of automated project progress measurement based on data flow models, digital twins, and machine learning (ML) algorithms. The approach integrates information from previous projects by considering historical data using ML algorithms and current unfinished artifacts to determine the degree of completion. The information required to measure the progress of engineering activities is extracted from engineering artifacts and subsequently analyzed and interpreted according to the project’s progress. Data flow models of the engineering process help understand the context of the analyzed artifacts. The use of digital twins makes it possible to connect plan data with actual data during the completion of the engineering project.

Theater Music Data Acquisition and Genre Recognition Using Edge Computing and Deep Brief Network

“…In addition, after using DBN for feature extraction, the MGC accuracy for each music genre has improved, further showing DBN's superiority in MGC. Ebel et al [32] used data flow models and digital twins to design automation projects. ey determined the degree of completion by considering the historical data using ML algorithms and currently uncompleted artifacts.…”