DATAMINE4.0 · Advanced Data Modeling and Analysis Applied to next Generation Industry 4.0 settings and the Internet of Things
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2018-01-22 → 2020-01-21
- EU contribution
- €170,122
- Participants
- 1
- Scheme
- MSCA-IF
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Results in brief
Advanced Data Modeling and Analysis Applied to next Generation Industry 4.0 settings and the Internet of Things
The goal of the DataMining4.0 project is to identify the most promising data mining techniques and its applications in the context of Industry4.0, where incorporation of “intelligent” techniques in industrial processes is a central theme, and to execute several “data project” case studies. In particular, the project objective is to build calibration and optimisation models using advanced statistical and machine learning methods, for applications in which existing methods have been unsatisfactory. We considered the following applications: 1) food processing - detection of foreign matter in food with help of hyper-spectral imaging, 2) agriculture - forecast of the rice blast disease, and 3) human-robot collaborative application - object detection problem. These applications require very high prediction accuracy, and even a small improvement in the accuracy can have a high impact in a long run (for example, less food waste in food processing application).
Data: CORDIS, © European Union
Project objective
DATAMINE4.0 is an IF for Career Development for Natasa Sarafijanovic-Djukic, PhD in Computer/Communication Sciences obtained from EPFL, Switzerland, a researcher in statistical modelling originally from Serbia, whereby she will work in IRIS, a Spanish R&D and advanced engineering SME. The project will study the modeling and analysis of data coming from complex industrial and organizational applications, in order to identify hidden causistic relationships in order to calibrate production and organizational control systems. It is highly multidisciplinary combining data science, statistics, artificial intelligence, telecommunications, specific engineering and organizational domains and information technology processing competences. In line with new paradigms such as Industry 4.0 and the Internet of Things. Although machine learning paradigms have been in existence for several decades, their penetration in the industrial and public organization setting has been limited. The goal is to build calibration and optimization models using advanced statistical and machine learning methods and other artificial intelligence inspired algorithms, for applications in which existing methods have been unsatisfactory. This will have a big impact on productivity and repeatability of winning formulas for key industrial applications such as customized production, public sector applications (e.g. optimization of urban mobility), and food processing. This is in line with findings published by the European Union. The developed data mining solutions will greatly enhance the current performance of the selected applications, allowing them a competitive advantage over current methods. The work will be done in an applied R&D environment in line with the objective for Dr. Natasa Sarafijanovic-Djukic’s career continuation. An engineering approach will be ideally completed with 7 formal scientific trainings, including two academic secondments, each of 2 months.
Original text from CORDIS.
Participants
- IRIS TECHNOLOGY SOLUTIONS, SOCIEDAD LIMITADA · Cornella de LlobregatCoordinatorSpain
Links
Data: CORDIS, © European Union