ERCIM News 122
July 2020
Special theme Solving Engineering Problems with Machine Learning
Guest editors Noémi Friedman (SZTAKI), and Abdel Labbi (IBM Research - Europe)
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by Noémi Friedman (Institute for Computer Science and Control (SZTAKI)) and Abdel Labbi (IBM Research – Europe)
Machine learning (ML) brings many new and innovative approaches to engineering, improving efficiency, flexibility and quality of systems. This special theme of ERCIM News focuses on ML applications in industrial engineering (see keynote by Christopher Ganz), with a focus on civil, environmental, mechanical, chemical, process, agricultural, and transportation engineering.
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by Christopher Ganz (ABB Future Labs)
In recent years AI has rapidly developed across many fields, finding its way into applications where it hasn’t succeeded previously, and reaching into areas that were unthinkable even a few years ago.
AI and machine learning (ML) have found their place in industry. With machine learning showing its particular strength in areas that map an input data set to an output set or conclusions, its predominant applications are becoming those of classification or perception. Current success stories around ML applications often focus on condition monitoring: determining the current health status of an asset, based on a given set of measurements. The inclusion of data that led to a failure not only allows diagnosis of failures, but also prediction of failures, giving the operator a chance to reduce or stop production and resolve the issue.
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by Hamid Asgari, Juha Kortelainen, and Mikko Tahkola (VTT)
Artificial intelligence, machine learning and artificial neural networks are introducing interesting opportunities to engineering design as well as to monitoring and operations of systems and processes. Core and enabling technologies are evolving fast, with great potential for industrial applications. Machine learning, combined with simulation, can enable models that are both fast and sufficiently accurate to be used in new applications.
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by Balázs Németh and Péter Gáspár (SZTAKI Institute for Computer Science and Control)
Several advanced complex control systems can incorporate learning agents, especially in designing functions in automated vehicles. At the same time, an open problem is to find a systematic design method that is guaranteed to satisfy the performance specifications of the system. This paper presents a possible design method based on robust control theory, which has been developed in the Systems and Control Laboratory of SZTAKI.
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by Dishi Liu, Daigo Maruyama and Stefan Görtz (German Aerospace Center)
Within the framework of the project “Uncertainty Management for Robust Industrial Design in Aeronautics” (UMRIDA), funded by the European Union, several machine learning-based predictive models were compared in terms of their efficiency in estimating statistics of aerodynamic performance of aerofoils. The results show that the models based on both samples and gradients achieve better accuracy than those based solely on samples at the same computational costs.
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by Emir Karavelić (Univ. of Sarajevo), Hermann G. Matthies (TU Braunschweig) and Adnan Ibrahimbegovic (Univ. de Technologie de Compiègne)
Bayesian inference can be used in machine learning to provide a reduced order model for multi-scale stochastic plasticity, with parameters as random variables. Machine learning can deliver either the random variables or their probability measure.
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by Truong-Vinh Hoang (RWTH-Aachen University) and Hermann G. Matthies (TU Braunschweig)
Data assimilation is a challenge in many forecasting applications ranging from weather and environmental forecasting to engineering applications such as structural health monitoring and digital twins. A common technique for data assimilation is the ensemble Kalman filter (EnKF). However, it is well-known that the EnKF does not yield consistent estimates for highly nonlinear dynamical systems. Deep learning (DL) techniques can be applied to improve the EnKF in high-dimensional and nonlinear dynamical systems. This article presents an extension of the EnKF using deep neural networks (DNNs) with a focus on the theoretical and numerical aspects.
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by Gianluigi Folino, Massimo Guarascio (ICAR-CNR), Francesco Chiaravalloti (IRPI-CNR) and Salvatore Gabriele (IRPI-CNR)
Accurate rainfall estimates are critical for areas presenting high hydrological risks. We have devised a general machine learning framework based on a deep learning architecture, which also integrates information derived from remote sensing measurements, such as weather radars and satellites. Experimental results conducted on real data from a southern region in Italy, provided by the Department of Civil Protection (DCP), show significant improvements compared to current state-of-the-art methods.
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by João Gante, Gabriel Falcão (University of Coimbra) and Leonel Sousa (INESC-ID)
Despite being available to civilians since the 1980s, the Global Positioning System is still the standard method for positioning. While unquestionably precise enough for most uses, it requires a dedicated antenna and a significant amount of energy from mobile devices. Using 5G’s millimetre wave networks and machine learning, our work shows that we can obtain similar accuracies without these drawbacks.
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by Yous van Halder and Benjamin Sanderse (CWI)
Numerically solving the Navier-Stokes equations is an important tool in a wide range of industry applications involving fluid flow, but accurately solving them is computationally expensive. Many of these numerical solutions need to be computed, and a trade-off between computational time and accuracy needs to be made. At CWI we developed a method that attains a speed-up of up to 100 times when solving these equations, based on intrusive neural networks.
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by Blaž Kurent, Boštjan Brank (University of Ljubljana) and Aleksandar Pavic (University of Exeter)
As the number of tall wooden buildings increases, a good understanding of their dynamic behaviour becomes important. This calls for the collection of empirical data, namely in-situ measured dynamic responses, to enable the calibration of finite element models, the use of surrogates, Bayesian structural identification and uncertainty quantification.
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by Nan Deng (IMSIA, ENSTA Paris, IP Paris & LIMSI, UPSaclay), Luc R. Pastur (IMSIA, ENSTA Paris, IP Paris) and Bernd R. Noack (Harbin Institute of Technology)
Reduced-order modelling and system identification can help us figure out the elementary degrees of freedom and the underlying mechanisms from the high-dimensional and nonlinear dynamics of fluid flow. Machine learning has brought new opportunities to these two processes and is revolutionising traditional methods. We show a framework to obtain a sparse human-interpretable model from complex high-dimensional data using machine learning and first principles.
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by Guy Y. Cornejo Maceda, François Lusseyran (LIMSI, CNRS, Université Paris-Saclay) and Bernd R. Noack (Harbin Institute of Technology
Machine learning control is a model-free method based on artificial intelligence techniques to build optimal control laws exploiting non-linear dynamics in an unsupervised way. It is a game changer in discovering new dynamics for experiments and real-life applications.
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by Henry Muccini (University of L’Aquila) and Karthik Vaidhyanathan (Gran Sasso Science Institute)
Software systems are developed following standard architecting practices but are prone to uncertainties that result in suboptimal behaviour in certain unexpected conditions. We humans learn by making mistakes, by adapting to environments, situations, and conditions. What if our software architectures could automatically learn to handle uncertainties? Just like self-driving cars, self-learnable software architectures have the potential to outperform current software in unanticipated circumstances.
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by Costas Smaragdakis and Michael I. Taroudakis (University of Crete and IACM-FORTH)
The analysis of long time series of measured acoustic or seismic signals may lead to the extraction/determination of specific features that characterise the signals and the information they carry. Two scientific fields that could make extensive use of signal characterisation are acoustical oceanography, where the acoustic signal can be used as a monitoring tool of the marine environment and seismology in which the seismic signals are rich in information about the geological structure of the earth. We are developing alternative tools for signal characterisation based on a time-frequency analysis of the corresponding recordings followed by a probabilistic feature extraction driven by the hidden Markov theory, a well-known machine learning approach for describing sequential data.
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by Ruslan Bernijazov (Fraunhofer IEM), Leon Özcan and Roman Dumitrescu (University of Paderborn)
Artificial Intelligence (AI) is one of the key technologies of the future and can provide substantial efficiency and productivity gains for product creation. However, manufacturing companies often lack sufficient expertise to take advantage of AI’s potential. The AI marketplace will address this challenge by creating an ecosystem for artificial intelligence in product creation.
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by Vladimir Samsonov (Cybernetics Lab IMA & RWTH Aachen University), Mohamed Behery and Gerhard Lakemeyer (RWTH Aachen University)
Continually refined and adjusted methods for production planning are among the cornerstones of manufacturing excellence. Heuristics and metaheuristic methods developed to address these tasks are often hard to deploy or lead to suboptimal results under constantly changing conditions combined with short response times of modern production planning. Within the DFG-funded Cluster of Excellence “Internet of Production”, a team of researchers from RWTH Aachen University is investigating the use of novel deep learning algorithms to facilitate complex decision-making processes along the manufacturing chain.
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by Vassilis Pikoulis, Christos Mavrokefalidis (ISI, ATHENA R.C.), Georgios Keramidas (Think Silicon S.A. and Aristotle University of Thessaloniki, Greece), Michael Birbas (University of Patras) and Nikos Tsafas (University of Patras) and Aris S. Lalos (ISI, ATHENA R.C.)
The DEEP-EVIoT project focuses on providing tools to help execute deep multimodal algorithms for scene analysis on embedded heterogeneous platforms (consisting of commercial embedded GPUs as well as dedicated hardware accelerators).
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by Alkiviadis Savvopoulos, Christos Alexakos and Athanasios Kalogeras (Industrial Systems Institute ATHENA Research Center)
Peak residential energy demand does not always coincide with peak production times. This energy imbalance is known as the “duck curve”. Variational recurrent autoencoders can normalise the duck curve, optimise consumption profile clustering, and acquire useful insights for managing energy demand.
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by Enrique Garcia-Ceja, Åsmund Hugo, Brice Morin (SINTEF) and Per Olav Hansen (Unger)
Process optimisation within industry can reduce production times, as well as material and energy consumption, which translates to more efficient use of resources and money. In the chemical production industry, soft-sensing and machine learning technologies can help to optimise processes.
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by Sebastian Raubitzek and Thomas Neubauer (Vienna University of Technology)
Machine learning has found its way into agricultural science for analysis and predictions, e.g., of yield or nitrogen status. Results are encouraging, but predictions in agricultural sciences are still tricky because agriculture is a highly complex system, with outcomes depending on a multitude of complex phenomena, such as weather, irrigation and soil properties. We propose future machine learning research in this sector to consider complex systems (chaos theory) and improve machine learning approaches.
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by Théophile Gaudin, Oliver Schilter, Federico Zipoli and Teodoro Laino (IBM Research Europe)
In many material manufacturing processes nowadays a large amount of data is created and stored, often without utilizing them to the full potential because of their complexity. Applying state of the art deep learning techniques can be a powerful tool to extract knowledge out of them allowing to get useful insights. In this work we present autoencoder-based machine learning models to find links among composition, properties and processes applied to two prototypical industrial applications.
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by Nikhil Kumar Jha, Sebastian von Enzberg and Michael Hillebrand (Fraunhofer IEM)
The use of deep learning algorithms is largely restricted to application domains where a large amount of labelled data is readily available, e.g., computer vision. Thus, applications of deep learning in autonomous systems for Industry 4.0 are rare. The application of deep learning to anomaly detection within autonomous systems for Industry 4.0 is a current research topic at Fraunhofer IEM. Our latest studies deliver some promising anomaly detection models as well as automated configuration of model hyperparameters.
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by Leonardo Gutiérrez-Gómez (LIST), Alexandre Bovet and Jean-Charles Delvenne (UCLouvain)
Anomaly detection is an important problem in data mining with diverse applications in multiple domains. Anomalies, also known as outliers, can be defined as individual objects with patterns or behaviours that differ starkly from a background property. Examples of applications include fraud detection in finance, detection of faults in manufacturing, identifying fake news in social media, or web spam detection. Anomalies in real problems may lead to enormous economic, social, or political costs and are often difficult to find, mainly because they are scarce and unknown a priori. Therefore, efficient detection of anomalies may bring significant value to people, companies, and authorities.
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by Péter Földesy, Imre Jánoki, Ákos Zarándy (SZTAKI and Péter Pázmány Catholic University, Budapest)
A camera and machine learning based system, developed at SZTAKI and by Péter Pázmány at Catholic University Budapest [L1], enables continuous non-contact measurement of respiration and pulse of premature infants. It also performs high precision monitoring, immediate apnoea warnings and logging of motion activity and caring events.
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by Bekir Sahin and Ahmet Soylu (NTNU)
Various global factors - including, variability in maritime regulations, technological progress, and ecological and environmental problems - have been converging, pointing to the importance of sustainability in the maritime industry. To reduce maritime accidents and the loss of life and property, sustainability needs to be factored into the design of autonomous ships. During the transition from conventional to autonomous ships, all past experience should be transferred to new systems. An anomaly detection system integrated with big data analysis, inference systems and cloud systems can become quite sensitive to maritime accidents.
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by Angelica Lo Duca (IIT-CNR) and Andrea Marchetti (IIT-CNR)
The National Research Council in Pisa has been implementing a ship route prediction algorithm based on multiclass classification. The algorithm was developed within the OSIRIS project [1], which aimed to build a decision support system for maritime surveillance.