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Evangelos Rozos, Demetris Koutsoyiannis and Alberto Montanari
Uncertainty is inherent in the modelling of any physical processes. Regarding hydrological modelling, the uncertainty has multiple sources including the measurement errors of the stresses (the model inputs), the measurement errors of the hydrological pro...
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Volker Hoffmann, Bendik Nybakk Torsæter, Gjert Hovland Rosenlund and Christian Andre Andresen
With the advancing integration of fluctuating renewables, a more dynamic demand-side, and a grid running closer to its operational limits, future power system operators require new tools to anticipate unwanted events. Advances in machine learning and ava...
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Giovanni Tardioli, Ricardo Filho, Pierre Bernaud and Dimitrios Ntimos
The estimation of indoor thermal comfort and the associated occupant feedback in office buildings is important to provide satisfactory and safe working environments, enhance the productivity of personnel, and to reduce complaints. The assessment of therm...
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Nikola Andelic, Sandi Baressi ?egota, Ivan Lorencin, Igor Poljak, Vedran Mrzljak and Zlatan Car
In this paper, the publicly available dataset for the Combined Diesel-Electric and Gas (CODLAG) propulsion system was used to obtain symbolic expressions for estimation of fuel flow, ship speed, starboard propeller torque, port propeller torque, and tota...
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Matevz Resman, Jernej Protner, Marko Simic and Niko Herakovic
A digital twin of a manufacturing system is a digital copy of the physical manufacturing system that consists of various digital models at multiple scales and levels. Digital twins that communicate with their physical counterparts throughout their lifecy...
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Laslo ?ere?, Ljubica Dokic, Bojana Ikonic, Dragana ?oronja-Simovic, Miljana Djordjevic, ?ana ?aranovic, Nikola Maravic
Pág. 114 - 122
Cross-flow microfiltration using ceramic tubular membrane was applied for treatment of steepwater from corn starch industry. Experiments are conducted according to the faced centered central composite design at three different transmembrane pressures (1,...
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Shailesh Kumar Singh, Sharad K. Jain and András Bárdossy
Artificial Neural Networks (ANNs) are classified as a data-driven technique, which implies that their learning improves as more and more training data are presented. This observation is based on the premise that a longer time series of training samples w...
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Margherita Vestoso
While securing personal data from privacy violations, the new General Data Protection Regulation (GDPR) explicitly challenges policymakers to exploit evidence from social data-mining in order to build better policies. Against this backdrop, two issues be...
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Margherita Vestoso
While securing personal data from privacy violations, the new General Data Protection Regulation (GDPR) explicitly challenges policymakers to exploit evidence from social data-mining in order to build better policies. Against this backdrop, two issues be...
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Gabriele Vozza, Domenica Costantino, Massimiliano Pepe and Vincenzo Saverio Alfio
The aim of the paper was the implementation of low-cost smart sensors for the collection of bathymetric data in shallow water and the development of a 3D modelling methodology for the reconstruction of natural and artificial aquatic scenarios. To achieve...
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Aikaterini Kasimati, George Papadopoulos, Valentina Manstretta, Marianthi Giannakopoulou, George Adamides, Damianos Neocleous, Vassilis Vassiliou, Savvas Savvides and Andreas Stylianou
Addressing the urgent sustainability challenges in the wine industry, this study explores the efficacy of sustainability-oriented innovations (SOIs) and smart farming technologies (SFTs) across wine value chains in Cyprus and Italy. Utilising a mixed-met...
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Higuatzi Moreno and Alexander Schaum
Batteries are complex systems involving spatially distributed microscopic mechanisms on different time scales whose adequate interplay is essential to ensure a desired functioning. Describing these phenomena yields nonlinearly coupled partial differentia...
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Ming-Yen Lin, Jia-Sin Liu, Tzu-Yang Huang, Ping-Hsun Wu, Yi-Wen Chiu, Yihuang Kang, Chih-Cheng Hsu, Shang-Jyh Hwang and Hsing Luh
(1) Background: A disease prediction model derived from real-world data is an important tool for managing type 2 diabetes mellitus (T2D). However, an appropriate prediction model for the Asian T2D population has not yet been developed. Hence, this study ...
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Bishwajit Roy, Maheshwari Prasad Singh, Mosbeh R. Kaloop, Deepak Kumar, Jong-Wan Hu, Radhikesh Kumar and Won-Sup Hwang
Rainfall-runoff (R-R) modelling is used to study the runoff generation of a catchment. The quantity or rate of change measure of the hydrological variable, called runoff, is important for environmental scientists to accomplish water-related planning and ...
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Mahmood Mahmoodian, Jairo Arturo Torres-Matallana, Ulrich Leopold, Georges Schutz and Francois H. L. R. Clemens
In this study, applicability of a data-driven Gaussian Process Emulator (GPE) technique to develop a dynamic surrogate model for a computationally expensive urban drainage simulator is investigated. Considering rainfall time series as the main driving fo...
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Thimo F. Schindler, Simon Schlicht and Klaus-Dieter Thoben
Within the integration and development of data-driven process models, the underlying process is digitally mapped in a model through sensory data acquisition and subsequent modelling. In this process, challenges of different types and degrees of severity ...
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Giuliano Cornacchia and Luca Pappalardo
Modelling human mobility is crucial in several areas, from urban planning to epidemic modelling, traffic forecasting, and what-if analysis. Existing generative models focus mainly on reproducing the spatial and temporal dimensions of human mobility, whil...
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Amzar Omairi and Zool Hilmi Ismail
In this work, a comprehensive review on how data-driven modelling and machine learning for error compensation in additive manufacturing is conferred.
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Sitalakshmi Venkatraman and Ramanathan Venkatraman
In the past decades, a number of methodologies have been proposed to innovate and improve business processes that play an important role in enhancing the operational efficiency of an organisation in order to attain business competitiveness. Traditional b...
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John Fulcher
The focus of this paper is twofold: firstly we make a case for the use of Computational Intelligence (CI) techniques in the modelling and/or prediction of global weather, CO2 emissions, climate change and similar endeavours. CI exploits processes found i...
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