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29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2021

作者： Rácz, Dániel Daróczy, Bálint Eötvos Loránd University Institute of Mathematics Pázmány Péter sétány 1/C BudapestH-1117 Hungary Université catholique de Louvain INMA ICTEAM Avenue Georges Lemaître 4 Louvain-la-Neuve B-1348 Belgium Institute for Computer Science and Control SZTAKI ELKH Kende utca 13-17 BudapestH-1111 Hungary

ISBN: (纸本)9782875870827

Feed-forward networks can be interpreted as mappings with linear decision surfaces at the level of the last layer. We investigate how the tangent space of the network can be exploited to refine the decision in case of ReLU (Rectified Linear Unit) activations. We show that a simple Riemannian metric parametrized on the parameters of the network forms a similarity function at least as good as the original network and we suggest a sparse metric to increase the similarity gap. © 2021 ESANN Intelligence and Machine Learning. All rights reserved.

关键词： Machine learning

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Adaptive AGV fleet management in a dynamically changing production environment

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Procedia Manufacturing 2021年 54卷 148-153页

作者： Júlia Bergmann Dávid Gyulai József Váncza EPIC Center of Excellence in Production Informatics and Control Institute for Computer Science and Control (SZTAKI) Eötvös Loránd Research Network (ELKH) Budapest Hungary Doctoral School of Informatics Eötvos Lóránd University Budapest Hungary Department of Manufacturing Science and Engineering Budapest University of Technology and Economics Budapest Hungary

In the era of smart manufacturing, autonomous mobile robots have become affordable for numerous companies, although the fleet management remains a challenging problem. A novel approach is proposed, supporting the solution of vehicle assignment problem. The method relies on adaptive workstation clustering that considers not only complex environment layout, but also the main characteristics of the material flow. The technique combines network analytical and optimization tools with a greedy algorithm of refinement. The implementation is presented, and the impact of clustering techniques on selected performance metrics are analyzed within a series of experiments, taken from an industrial case study.

关键词： internal logistics AGV network modularity MIQP

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Production rescheduling through product quality prediction

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Procedia Manufacturing 2021年 54卷 142-147页

作者： Maik Frye Dávid Gyulai Júlia Bergmann Robert H. Schmitt Fraunhofer Institute for Production Technology (IPT) Steinbachstraße 17 52074 Aachen Germany EPIC Centre of Excellence in Production Informatics and Control Institute for Computer Science and Control (SZTAKI) 13-17 Kende Budapest 1111 Hungary Laboratory for Machine Tools WZL RWTH Aachen University Steinbachstraße 19. 52074 Aachen Germany

In production management, efficient scheduling is key towards smooth and balanced production. Scheduling can be well-supported by real-time data acquisition systems, resulting in decisions that rely on actual or predicted status of production environment and jobs in progress. Utilizing advanced monitoring systems, prediction-based rescheduling method is proposed that can react on in-process scrap predictions, performed by machine learning algorithms. Based on predictions, overall production can be rescheduled with higher efficiency, compared to rescheduling after completion of the whole machining process with realization of scrap. Series of numerical experiments are presented to demonstrate potentials in prediction-based rescheduling, with early-stage scrap detection.

关键词： Machine Learning Production Scheduling Product Quality Prediction Data Quality

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Work in Progress Level Prediction with Long Short-Term Memory Recurrent Neural Network

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Procedia Manufacturing 2021年 54卷 136-141页

作者： Viola Gallina Lukas Lingitz Johannes Breitschopf Elisabeth Zudor Wilfried Sihn Fraunhofer Austria Theresianumgasse 7 Vienna 1040 Austria Institute for Computer Science and Control (SZTAKI) Ëotvös Loránd Research Network (ELKH) Budapest Hungary Kende str. 13-17 H-1111 Budapest Hungary Vienna University of Technology Institute of Management Science Theresianumgasse 27 Vienna 1040 Austria

Since the reliability of production plans drops largely within several days after plan creation, production control faces huge challenges, when trying to foresee the work in progress (WIP) level at bottleneck machines and trying to react appropriately. Whereas several researchers applied artificial intelligence to predict lead times or transition times to improve the planning reliability, only small efforts have been taken on time series prediction in the field of production control, especially on the topic WIP prediction. In this paper univarate times series approaches are used for predicting the work in progress for a bottleneck machine for one and more step ahead. Long short-term memory recurrent neural networks, LSMT models show higher accuracy than classical methods. For more step ahead forecasting four different approaches are investigated. Systematical model tuning and comparison of various error measures are presented for a real industrial use case from the steal manufacturing industry.

关键词： capacity planning WIP prediction time series LSTM

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Real-time analysis of neuronal firing patterns via Hawkes processes

Real-time analysis of neuronal firing patterns via Hawkes pr...

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Asilomar Conference on Signals, Systems & computers

作者： László Gerencsér György Perczel Systems and Control Lab Institute for Computer Science and Control (SZTAKI) Eötvös Loránd Research Network (ELKH) Budapest Hungary Faculty of Information Technology and Bionics Pázmány Péter Catholic University Budapest Hungary

Quick and reliable real-time detection of the onset of epileptic seizures is a key computational component for the development of high quality closed loop neurostimulators. The objective of this paper is the construction of a low-cost algorithm for real-time statistical analysis of EEG signals of epileptic patients. The mathematical methodology is the theory of self-exiting point processes or Hawkes processes, see Truccolo (2016) or Lambert et al. (2018). The main advance is the development of a computationally feasible recursive maximum likelihood method for fitting a Hawkes process the impulse response function of which is a sum of exponential functions. The viability of the method is proven by strong mathematical heuristics and numerical tests on simulated data, based on a priori analysis of human data recorded during both inter-ictal and ictal epochs.

关键词： Maximum likelihood estimation Maximum likelihood detection Statistical analysis Firing Fitting Reliability theory Real-time systems

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Analysis of asset location data to support decisions in production management and control

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Procedia CIRP 2020年 88卷 197-202页

作者： Dávid Gyulai András Pfeiffer Júlia Bergmann EPIC Centre of Excellence in Production Informatics and Control at Institute for Computer Science and Control (SZTAKI) Hungarian Academy of Sciences (MTA) 13-17 Kende Street Budapest 1111 Hungary

In the era of cyber-physical environments, indoor asset tracking systems enable to monitor and control production in a smarter way than ever before, as they are capable of providing data about the location of various equipment on the shop-floor in near real time. The right use of this data contributes to the improvement of production control and management processes, however, utilization of the related information often requires novel methods. In the paper, decision-making approaches are presented that rely on advanced data analytics for asset location systems. The efficiency of the results are presented through an industry related use-case.

关键词： Indoor positioning system Data analytics Production management

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Assessment of hypertensive patients' lipid profile using data mining methods

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Atherosclerosis 2024年 395卷 117895-117895页

作者： Ágnes Diószegi Beáta Kovács Ákos Németh Bálint Daróczy László Maroda Zsolt Karányi Denes Pall Mariann Harangi Department Of Internal Medicine University of Debrecen Faculty of Medicine Debrecen Hungary Institute For Computer Science And Control (sztaki) Hungarian Research Network Budapest Hungary Medical Clinical Pharmacology University of Debrecen Debrecen Hungary

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Tumor model parameter estimation for therapy optimization using artificial neural networks

Tumor model parameter estimation for therapy optimization us...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Melánia Puskás Dániel András Drexler Physiological Research Center Research and Innovation Center of Óbuda University Óbuda University Budapest Hungary Institute for Computer Science and Control (SZTAKI) Eötvös Loránd Research Network (ELKH) Budapest Hungary

ISBN: (纸本)9781665442084

Therapy optimization and personalization in cancer treatment requires reliable mathematical models. A key issue in personalization is the identification of the model parameters. We employ artificial neural networks to identify the model parameters based on few measurements using a priori information about the range of the parameters. The trainig data are generated in silico on known parameter intervals, taking into consideration the experimental setup we use to validate our results. The estimated parameters can be used to track the change of the parameters and can also be used as initial guesses for identification algorithms using local search.

关键词： Training Estimation error Parameter estimation Heuristic algorithms Training data Artificial neural networks Mathematical models

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LOL: Lidar-only Odometry and Localization in 3D point cloud maps*

LOL: Lidar-only Odometry and Localization in 3D point cloud ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Dávid Rozenberszki András L. Majdik Machine Perception Research Laboratory MTA SZTAKI Hungarian Academy of Sciences Institute for Computer Science and Control Budapest Hungary

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

In this paper we deal with the problem of odometry and localization for Lidar-equipped vehicles driving in urban environments, where a premade target map exists to localize against. In our problem formulation, to correct the accumulated drift of the Lidar-only odometry we apply a place recognition method to detect geometrically similar locations between the online 3D point cloud and the a priori offline map. In the proposed system, we integrate a state-of-the-art Lidaronly odometry algorithm with a recently proposed 3D point segment matching method by complementing their advantages. Also, we propose additional enhancements in order to reduce the number of false matches between the online point cloud and the target map, and to refine the position estimation error whenever a good match is detected. We demonstrate the utility of the proposed LOL system on several Kitti datasets of different lengths and environments, where the relocalization accuracy and the precision of the vehicle's trajectory were significantly improved in every case, while still being able to maintain real-time performance.

关键词： Three-dimensional displays Laser radar Sensors Iterative closest point algorithm Image color analysis Trajectory Real-time systems

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Optimal Wildfire Escape Route Planning for Drones under Dynamic Fire and Smoke

Optimal Wildfire Escape Route Planning for Drones under Dyna...

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International IEEE Conference on Signal-Image Technologies and Internet-Based System

作者： Chang Liu Tamas Sziranyi Department of Networked Systems and Services Budapest University of Technology and Economics Műegyetem rkp. 3H-1111 Budapest Hungary Machine Perception Research Laboratory of HUN-REN Institute for Computer Science and Control (SZTAKI) H-1111 Budapest Kende u. 13-17 Hungary Faculty of Transportation Engineering and Vehicle Engineering Budapest University of Technology and Economics (BME-KJK) Műegyetem rkp. 3 Budapest Hungary

In recent years, the increasing prevalence and intensity of wildfires have posed significant challenges to emergency response teams. The utilization of unmanned aerial vehicles (UAVs), commonly known as drones, has shown promise in aiding wildfire management efforts. This work focuses on the development of an optimal wildfire escape route planning system specifically designed for drones, considering dynamic fire and smoke models. First, the location of the source of the wildfire can be well located by information fusion between UAV and satellite, and the road conditions in the vicinity of the fire can be assessed and analyzed using multichannel remote sensing data. Second, the road network can be extracted and segmented in real time using UAV vision technology, and each road in the road network map can be given priority based on the results of road condition classification. Third, the spread model of dynamic fires calculates the new location of the fire source based on the fire intensity, wind speed and direction, and the radius increases as the wildfire spreads. Smoke is generated around the fire source to create a visual representation of a burning fire. Finally, based on the improved A* algorithm, which considers all the above factors, the UAV can quickly plan an escape route based on the starting and destination locations that avoid the location of the fire source and the area where it is spreading. By considering dynamic fire and smoke models, the proposed system enhances the safety and efficiency of drone operations in wildfire environments.

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