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Improving Kernel-Based Nonasymptotic Simultaneous Confidence Bands

IFAC-PapersOnLine 2023年第2期56卷 10357-10362页

作者： Balázs Csanád Csáji Bálint Horváth Institute for Computer Science and Control (SZTAKI) Eötvös Loránd Research Network (ELKH) 13-17 Kende utca H-1111 Budapest Hungary Institute of Mathematics Eötvös Loránd University (ELTE) 1/C Pázmány Péter sétány H-1117 Budapest Hungary Institute of Mathematics Budapest University of Technology and Economics (BME) 1 Egry József utca H-1111 Budapest Hungary

The paper studies the problem of constructing nonparametric simultaneous confidence bands with nonasymptotic and distribition-free guarantees. The target function is assumed to be band-limited and the approach is based on the theory of Paley-Wiener reproducing kernel Hilbert spaces. The starting point of the paper is a recently developed algorithm to which we propose three types of improvements. First, we relax the assumptions on the noises by replacing the symmetricity assumption with a weaker distributional invariance principle. Then, we propose a more efficient way to estimate the norm of the target function, and finally we enhance the construction of the confidence bands by tightening the constraints of the underlying convex optimization problems. The refinements are also illustrated through numerical experiments.

关键词： nonparametric methods nonlinear system identification statistical data analysis estimation and filtering convex optimization randomized algorithms

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Collision-free trajectory design for dance choreography of virtual drones in hierarchical structure

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IFAC-PapersOnLine 2022年第6期55卷 502-507页

作者： Balázs Németh Attila Lelkó Zoltán Antal Ajtony Csaba Institute for Computer Science and Control (SZTAKI) Eötvös Loránd Research Network (ELKH) Kende u. 13-17 H-1111 Budapest Hungary School of Music University of Victoria 3800 Finnerty Road Victoria BC V8P 5C2 Canada

This paper proposes a collision-free trajectory design for dance choreography of virtual drones using a hierarchical design structure. The trajectory on two levels is designed, i.e., on the level of individual virtual drones and on the level of their centralized coordination. The design on the drone level through reinforcement learning has been carried out, with which dance steps of virtual drones in their motion can be achieved. The coordination level contains a prediction algorithm, which guarantees the avoidance of collision, even at the faultiness of some virtual drones. The effectiveness of the hierarchical design on the example of a Viennese waltz dance choreography in augmented reality has been illustrated.

关键词： trajectory design collision avoidance reinforcement learning tech art

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Graphs with equal Grundy domination and independence number

arXiv

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arXiv 2022年

作者： Bacsó, Gábor Brešar, Boštjan Kuenzel, Kirsti Rall, Douglas F. Institute for Computer Science and Control ELKH Budapest Hungary Faculty of Natural Sciences and Mathematics University of Maribor Slovenia Institute of Mathematics Physics and Mechanics Ljubljana Slovenia Department of Mathematics Trinity College HartfordCT United States Emeritus of Mathematics Furman University GreenvilleSC United States

The Grundy domination number, γgr(G), of a graph G is the maximum length of a sequence (v1, v2, . . ., vk) of vertices in G such that for every i ∈ {2, . . ., k}, the closed neighborhood N[vi] contains a vertex that does not belong to any closed neighborhood N[vj], where j gr(G) and its subclass consisting of graphs G with α(G) = γgr(G). We characterize the latter class of graphs among all twin-free connected graphs, provide a number of properties of these graphs, and prove that the hypercubes are members of this class. In addition, we give several necessary conditions for graphs G with Γ(G) = γgr(G) and present large families of such *** Codes 05C69, 05C75 © 2022, CC BY.

关键词： Graphic methods

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Investigation of ductile/brittle chip formation zone in the context of manufactured geometry with different CAM paths strategies

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IFAC-PapersOnLine 2022年第10期55卷 2300-2305页

作者： László Móricz Zsolt Viharos János University of Pannonia Faculty of Engineering Mechatronic Education and Research Institute Zalaegerszeg Hungary Zalaegerszeg Center of Vocational Training ZSZC Ganz Ábrahám Technical School Institute for Computer Science and Control (SZTAKI) Centre of Excellence in Production Informatics and Control Center of Excellence of the Hungarian Academy of Sciences (HAS) Eötvös Loránd Research Network (ELKH) Budapest Hungary John von Neumann University Kecskemét Hungary

For maintenance conform processing of brittle materials (ceramics, glass), the resulting chips are not continuous, as in the case of soft and tough materials, it is called a brittle chip removal range. In this machining zone, the quality of the produced surface may decline (appears the cracks and pitting of the material) and the tool life also decreases. When defining the cutting technology (technological parameters, tool edges, tool cooling / lubrication, etc.), chip removal must be carried out in the ductile range. However, due to tool wear, this range may cross the transition and then it can work in the brittle chip formation zone. In this paper, the machinability of zirconia oxid ceramics has been investigated. The aim of the research is to find out the correlation between the wear of the cutting tool, the volume change of the manufactured pockets and the transitions between the chip formation ranges measured by cracks and pitting deformations on the edge of the machined material. It was observed that they overlap to a large extent, consequently, these observed phenomena can be easily applied for tool wear and quality monitoring for realising condition-based maintenance, it is the main scientific contribution of the paper.

关键词： Ductile/brittle chip formation zone ceramics micro-milling indirect tool wearing monitoring

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Tool failure recognition using inconsistent data

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Procedia CIRP 2022年 107卷 1204-1209页

作者： Júlia Bergmann Klaudia Éva Zeleny József Váncza Andrea Kő EPIC Center of Excellence in Production Informatics and Control Institute for Computer Science and Control (SZTAKI) Eötvös Loränd Research Network (ELKH) Budapest Hungary Eötvös Loränd University Doctoral School of Informatics Budapest Hungary EPIC InnoLabs Nonprofit Ltd. Budapest Hungary Corvinus University of Budapest Budapest Hungary Budapest University of Technology and Economics Dept. of Manufacturing Science and Technology Budapest Hungary

Data is everything - at least this is one of the main messages of the ongoing industrial revolution. Manufacturing companies all over the world are expanding their digital infrastructure and knowledge on data analysis in the hope of increasing their KPIs with the help of artificial intelligence (AI). Although several well-designed data-driven solutions are available, the most crucial part, data preparation is still not fully supported. In this paper a framework is presented for processing sensor data of machining processes with variable cycle times in an unstable environment. Traditional and novel AI algorithms are tested on the data of a vulcanization process from the automotive industry, namely from tire manufacturing’s curing phase. The process in question consists of several subprocesses, and the quality of curing is mostly dependent of the status of a specific type of machine tool. Conventional methods (e.g., examining the cured product manually) are currently used for failure recognition, however the examination is only feasible after a long delay due to the extreme level of heat, which leads to unnecessary and unwanted scrap production. Therefore, a more sophisticated and complex approach is required to increase quality score. A combination of mathematical methods is proposed combining t-SNE feature representation, convolutional neural network, and linear programming optimization. The model highly relies on the tool’s continuous degradation characteristics. The threshold for the given binary classification is set by maximizing the accuracy of the detection model. The main contribution of the research is the method of inconsistent sensor data manipulation which supports a unique combination of AI models for early failure recognition.

关键词： Type your keywords here separated by semicolons artificial intelligence failure detection data preparation t-SNE deep insight linear programming

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LPV-Fuzzy control approach for road adaptive semi-active suspension system

LPV-Fuzzy control approach for road adaptive semi-active sus...

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IEEE Symposium on Intelligent Vehicle

作者： Hakan Basargan Andrá s Mihá ly Pé ter Gá spá r Oliver Sename Department of Control for Transportation and Vehicle Systems BME Stoczek u. 2 Budapest Hungary Systems and Control Laboratory Institute for Computer Science and Control (SZTAKI) E&#x00F6 tv&#x00F6 s Lor&#x00E1 nd Research Network (ELKH) Kende utca 13-17 Budapest Hungary GIPSA-lab INPG Universit&#x00E9 Grenoble Alpes 11 Rue des Math&#x00E9 matiques Grenoble France

This paper proposed a road adaptive semi-active suspension control method, where a trade-off between driving comfort and vehicle stability/road-holding is accomplishable in order to achieve desirable performance results at different road irregularities and velocities by modifying the scheduling variable that is designed by Fuzzy Logic control. The proposed semi-active controller is founded on the Linear Parameter-Varying framework. Hungarian highway route data has been implemented into the TruckSim simulation environment based on real geographical data having road irregularities in order to compare the proposed adaptive method with a non-adaptive scenario. Simulation results show that all performances have been improved with the proposed method in different road irregularities and velocities.

关键词： Fuzzy logic Suspensions (mechanical systems) Adaptation models Adaptive systems Intelligent vehicles Roads Simulation

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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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Gradient representations in ReLU networks as similarity functions 29

Gradient representations in ReLU networks as similarity func...

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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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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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