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Proceedings of the Winter Simulation Conference

作者： Dávid Gyulai Júlia Bergmann Attila Lengyel Botond Kádár Dávid Czirkó Institute for Computer Science and Control (SZTAKI) Budapest HUNGARY Western Digital Corporation EPIC InnoLabs Ltd. Budapest HUNGARY

ISBN: (纸本)9781728194998

Digital analytics tools have been at the forefront of innovation in manufacturing industry in recent years. To keep pace with the demands of industrial digitization, companies seek opportunities to streamline processes and enhance overall efficacy, opting to replace conventional engineering tools with data-driven models. In a high-tech factory, detailed data is collected about the products, processes, and assets in near-real time, providing a basis to build trustworthy analytical models. In this paper, a novel discrete-event simulation (DES) model is proposed for the detailed representation of a complex shop-floor logistics system, employing automated robotic vehicles (AGV). The simulation model is applied to test new AGV management policies, involving both vehicle capacity planning and dispatching decisions. In order to illustrate the usefulness of the model and the effectiveness of the selected policy, numerical results of a case-study are presented, in which the selected policy was realized in a real manufacturing environment.

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A probabilistic perspective on nearest neighbor for implicit recommendation

A probabilistic perspective on nearest neighbor for implicit...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Domokos M. Kelen Andras A. Benczúr Institute for Computer Science and Control (SZTAKI) Eötvös Loránd Research Network (ELKH) Budapest Hungary

ISBN: (纸本)9781665424288

Over the past years, the recommender systems community invented several novel approaches that reached better and better prediction accuracy. Remarkably, however, the classic k nearest neighbor algorithm appears to remain competitive, even when compared to much more sophisticated methods. In this paper, we attempt to explain the inner workings of the nearest neighbor using probabilistic tools, treating similarity as conditional probability and presenting a novel model for explaining and removing popularity ***, we provide a probabilistic formulation of similarity and the classic prediction formula. Second, by modeling user behavior as a combination of personal preference and global influence, we are able to explain the presence of popularity bias in the predictions. Finally, we utilize Bayesian inference to construct a theoretically grounded variant of the widely used inverse frequency scaling, which we use to mitigate the effect of popularity bias in the *** replacing the formerly ad hoc choices of nearest neighbor with probabilistically founded counterparts, we are able to improve prediction accuracy over a variety of data sets and gain an increased understanding of the theory behind the method.

关键词： Uncertainty Predictive models Nearest neighbor methods Probabilistic logic Prediction algorithms Data models Bayes methods

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Parallel and Distributed Training of Deep Neural Networks: A brief overview

Parallel and Distributed Training of Deep Neural Networks: A...

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IEEE International Conference on Intelligent Engineering Systems (INES)

作者： Attila Farkas Gábor Kertész Róbert Lovas Institute for Computer Science and Control (SZTAKI) Hungary

ISBN: (数字)9781728110592

ISBN: (纸本)9781728110608

Deep neural networks and deep learning are becoming important and popular techniques in modern services and applications. The training of these networks is computationally intensive, because of the extreme number of trainable parameters and the large amount of training samples. In this brief overview, current solutions aiming to speed up this training process via parallel and distributed computation are introduced. The necessary components and strategies are described from the low-level communication protocols to the high-level frameworks for the distributed deep learning. The current implementations of the deep learning frameworks with distributed computational capabilities are compared and key parameters are identified to help design effective solutions.

关键词： Training Machine learning Graphics processing units Biological neural networks Neurons Open source software

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Kinetic discretization of one-dimensional nonlocal flow models

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IFAC-PapersOnLine 2022年第20期55卷 67-72页

作者： Mihály A. Vághy Mihály Kovács Gábor Szederkényi Faculty of Information Technology and Bionics Pázmány Péter Catholic University H-1444 Budapest Hungary Department of Mathematical Sciences Chalmers University of Technology and University of Gothenburg SE-41296 Gothenburg Sweden Systems and Control Laboratory Institute for Computer Science and Control (SZTAKI) H-1111 Budapest Hungary

We show that one-dimensional nonlocal flow models in PDE form with Lighthill-Whitham-Richards flux supplemented with appropriate in- and out-flow terms can be spatially discretized with a finite volume scheme to obtain formally kinetic models with physically meaningful reaction graph structure. This allows the utilization of the theory of chemical reaction networks, as demonstrated here via the stability analysis of a flow model with circular topology. We further propose an explicit time discretization and a Courant-Friedrichs-Lewy condition ensuring many advantageous properties of the scheme. Additional characteristics, including monotonicity and the total variation diminishing property are also discussed.

关键词： control of hyperbolic systems conservation laws kinetic modeling modeling for control stability of distributed parameter systems stability of nonlinear systems

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Design of learning-based control with guarantees for autonomous vehicles in intersections

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IFAC-PapersOnLine 2021年第2期54卷 210-215页

作者： Balázs Németh Péter Gáspár Systems and Control Laboratory Institute for Computer Science and Control (SZTAKI) Eötvös Loránd Research Network (ELKH) Kende u. 13-17 H-1111 Budapest Hungary

This paper proposes a design method for the coordination of velocity profiles of autonomous vehicles in non-signalized intersection scenarios. The coordination is motivated by the avoidance of vehicle collision and the minimization of their energy loss resulted by stop and go maneuvers in the intersection. Therefore, the coordinated design is formed as an optimal control problem, which is solved through two optimization tasks. A quadratic optimization task with online solution is formed, which provides guarantees on the avoidance of the collision. Moreover, a reinforcement-learning-based optimization task with offline solution is formed, which is able to improve the economy performances of the autonomous vehicles. The optimization tasks are interconnected, i.e. the quadratic optimization with the vehicle model is used as an environment during the training process. The effectiveness of the proposed coordinated control through simulation examples with three number of autonomous vehicles is illustrated.

关键词： automated vehicles intersection reinforcement learning collision avoidance

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A tracking performance analysis method for autonomous systems with neural networks ⁎

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IFAC-PapersOnLine 2021年第1期54卷 696-701页

作者： Attila Lelkó Balázs Németh Péter Gáspár Systems and Control Laboratory Institute for Computer Science and Control (SZTAKI) Eötvös Loránd Research Network (ELKH) Kende u. 13-17 H-1111 Budapest Hungary

Intelligent manufacturing and automated systems several complex control tasks must be carried out. A possible way for improving the performance level of the systems is the application of machine-learning-based agents, e.g. neural networks in the control loop. A novel challenge of these complex systems is to provide analysis and synthesis methods, with which their performance levels can be assessed. The paper proposes analysis method for tracking control systems, whose control loop contains feed-forward neural networks. Through the method the asymptotic stability and the tracking performance through decay rate are assessed. The proposed method is based on the linear approximation of the closed-loop system, and thus, a polytopic set of linear systems is resulted. Using the resulted polytopic system an analysis method based on an optimization is formed, whose result approximates the decay rate of the system. The effectiveness of the method is illustrated through a benchmark example, i.e. the torque control of a one-degree-of-freedom robotic arm.

关键词： performance analysis neural network control systems robotic arm

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A simultaneous localization and mapping algorithm for sensors with low sampling rate and its application to autonomous mobile robots

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Procedia Manufacturing 2021年 54卷 154-159页

作者： Krisztián Balázs Kis János Csempesz Balázs Csanád Csáji SZTAKI: Institute for Computer Science and Control Ëotvö s Loránd Research Network Kende u. 13-17. Budapest H-1111 Hungary

In this paper we suggest a Simultaneous Localization and Mapping (SLAM) algorithm for Autonomous Mobile Robots (AMRs) which have LiDAR (light detection and ranging) type planar sensors with low sampling rate, e.g., less than 1 Hz. The proposed method uses 2-dimensional point clouds for its internal occupancy map representation and applies Point Set Registration (PSR) algorithms for mapping and localization. The approach is validated on both synthetic and real-world data. The results demonstrate that the proposed method is efficient, even when the observations are imprecise as well as the difference between consecutive measurements is high in terms of position and orientation.

关键词： SLAM AMR LiDAR Point Set Registration Point Cloud

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Fault-tolerant control of semi-active suspension in case of oil leakage of magnetorheological damper

Fault-tolerant control of semi-active suspension in case of ...

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European control Conference (ECC)

作者： Hakan Basargan Filip Jeniš András Mihály Péter Gáspár Department of Control for Transportation and Vehicle Systems Budapest University of Technology and Economics Stoczek u. 2 Budapest Hungary Faculty of Mechanical Engineering Institute of Machine and Industrial Designs Brno University of Technology Technicka 2 Brnos Czech Republic Systems and Control Laboratory Institute for Computer Science and Control (SZTAKI) Eötvös Loránd Research Network (ELKH) Kende u. 13-17 Budapest Hungary

The paper presents a reconfigurable fault-tolerant control strategy for a semi-active suspension using magnetorheological (MR) damper. The aim of the control reconfiguration is to handle the adverse behaviour of the MR damper due to oil leakage induced by the wear of the suspension component. The proposed method relies on the data driven model of the MR damper, using an estimation procedure to quantify the healthiness of the damper and to estimate the performance degradation due to the oil leakage. The reconfiguration control strategy is founded on the Linear Parameter Varying (LPV) framework, where a scheduling variable is defined to represent the healthiness level of the MR damper. By the scaling of the control action through the scheduling variable, the performance degradation of the MR damper can be compensated to match the behaviour of the healthy dampers. The proposed method is demonstrated through simulations, comparing the performance of the fault-tolerant LPV control to conventional semi-active control methods

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Fault detection in GPU-enabled Cloud Systems – An Overview

Fault detection in GPU-enabled Cloud Systems – An Overview

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International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Farida Asadova Gá bor Kerté sz Ró bert Lovas Sá ndor Szé ná si Doctoral School of Applied Informatics and Applied Mathematics &#x00D3 buda University Hungary Institute for Computer Science and Control (SZTAKI) E&#x00F6 tv&#x00F6 s Lor&#x00E1 nd Research Network (ELKH) Hungary John von Neumann Faculty of Informatics &#x00D3

Fault detection and handling are crucial tasks in cloud systems. As these infrastructures are growing and evolving, manual monitoring and interaction have become less feasible. To deal with this issue, monitoring systems are developed to track the behavior of the various components (e.g. nodes) in cloud systems, as well as the served applications in the virtual environment. Nowadays, most cloud environments provide graphics accelerators for their users leading to different problems. However, the application of GPUs in deep learning could also help the detection of incorrect behavior. In this paper, a short overview of cloud monitoring and fault detection methods is given focusing on GPU-enabled nodes.

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Vision-based motion estimation for vehicles on test track via cone markers

Vision-based motion estimation for vehicles on test track vi...

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International Conference on Cognitive Infocommunications (CogInfoCom)

作者： Máté Fazekas Péter Gáspár Balázs Németh SZTAKI Institute for Computer Science and Control Budapest Hungary

ISBN: (数字)9781728182131

ISBN: (纸本)9781728182148

In this paper, a vision-based motion estimation algorithm based on the ORB feature detector is presented. The mono camera is mounted facing forwards and the method is not utilized any aprior motion model. Supplementing the general detection-match-pose estimation steps, an iterative loop is proposed to eliminate the problems of the vision-based technique and increase the accuracy. The method operates with vehicle dynamic based assumptions since the sensor is mounted to a car-like ground vehicle. The motion estimation results can be used for orientation smoothing, sideslip estimation, or sensor calibration.

关键词： Smoothing methods Motion estimation Computational modeling Estimation Feature extraction Calibration Vehicle dynamics

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