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Simulation Winter Conference

作者： Anastasia Anagnostou Simon J. E. Taylor Nura Tijjani Abubakar Tamas Kiss James DesLauriers Gregoire Gesmier Gabor Terstyanszky Peter Kacsuk Jozsef Kovacs Modelling & Simulation Group Brunel University London Uxbridge UK Centre for Parallel Computing Faculty of Science and Technology University of Westminster London UK Hungarian Academy of Science Institute for Computer Science and Control (MTA SZTAKI) Budapest HUNGARY

ISBN: (数字)9781728132839

ISBN: (纸本)9781728120522

There is growing number of research efforts in developing auto-scaling algorithms and tools for cloud resources. Traditional performance metrics such as CPU, memory and bandwidth usage for scaling up or down resources are not sufficient for all applications. For example, modeling and simulation experimentation is usually expected to yield results within a specific timeframe. In order to achieve this, often the quality of experiments is compromised either by restricting the parameter space to be explored or by limiting the number of replications required to give statistical confidence. In this paper, we present early stages of a deadline-based simulation experimentation framework using a micro-services auto-scaling approach. A case study of an agent-based simulation of a population physical activity behavior is used to demonstrate our framework.

关键词： Containers Cloud computing Computational modeling Monitoring Measurement Runtime Tools

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Microsecond measurement resolution for process cycle diagnostics of micro-EDM milling

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Measurement: Sensors 2021年 18卷

作者： Zsolt János Viharos Balázs Zsolt Farkas 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 Budapest University of Technology and Economics Budapest Hungary

Ceramics are non-conductive materials, consequently, they cannot be machined directly by any electrical means. If these ceramics contain some small fractions of conductive components, e.g., as pollutant or as glue materials, they perform a certain, really low level, local and accidental flash of some conductivity. The reported research was performed on this border of science, with ceramics that are below the practical and also below the state-of-the-art conductivity level for electrical machining. Even so, micro-Electric Discharge Machining (micro-EDM) for cermaics was developed with varying and many times uncertain success. The paper describes the required, extended measuring system of a micro discharge milling machine equipped with a linear encoder, voltage, and current sensors. With the help of the installed measuring system, the ceramic discharge milling of the Al 2 O 3 –TiC mixture was tested, in particular, the process stability, the geometric transient zones and the edges of the machined grooves.

关键词： Process monitoring Micro-EDM Ceramic machining Machining diagnostic Measurement system

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A Heuristic Approach to Road Environment-type Detection from Traffic Sign Data

A Heuristic Approach to Road Environment-type Detection from...

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Conference on control and Fault-Tolerant Systems (SysTol)

作者： Zoltán Fazekas Gábor Balázs Péter Gáspár Faculty of Transportation Engineering and Vehicle Engineering Budapest University of Technology and Economics (BME) Budapest Hungary Zukunft Mobility GmbH Kösching Germany Institute for Computer Science and Control (MTA SZTAKI) Budapest Hungary

ISBN: (纸本)9781728103815

A heuristic approach for the detection of the road environment-type (RET) that appears around, and sweeps past an ego-car, while the car is being driven in an urban area, is presented in the paper. The proposed RET detection relies on the traffic signs (TSs) encountered and detected along the route. In an expedient implementation, the TS data is gathered and stored by an on-board TS recognition system. The stored TS data is continually updated, aggregated, and evaluated in a multi-scale manner, i.e., over short, medium and longer patches of the road. Heuristic rules are set up to handle various competition situations between RETs put forward by the evaluation programs responsible for the different scales. These programs make use of the current aggregated TS data, as well as the aggregated TS and RET data gathered earlier during data collection drives. The competition situations between RETs are similar to those that arise in group tournaments between sport teams, or athletes. For this reason, labels, comparison and score-calculation techniques - used by sport competition juries - have been borrowed for the purpose. The RET detection results computed in this manner are compared to those acquired via another TS-based detection approach.

关键词： Roads Automobiles Data collection Urban areas Safety Feature extraction

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Tight bounds on the convergence rate of generalized ratio consensus algorithms

arXiv

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arXiv 2019年

作者： Gerencsér, Balázs Gerencsér, László MTA Alfréd Rényi Institute of Mathematics Budapest Hungary Eötvös Loránd University Department of Probability and Statistics Budapest Hungary MTA SZTAKI Hungarian Academy of Sciences Institute for Computer Science and Control Budapest Hungary

The problems discussed in this paper are motivated by the ratio consensus problem formulated and solved in the context of the push-sum algorithm proposed in [9], and extended in [3] under the name weighted gossip algorithm. We consider a strictly stationary, ergodic, sequentially primitive sequence of p × p random matrices with non-negative entries (An), n ≥ 1. Let x,w ∈ pdenote a pair of initial vectors, such that w ≥ 0,w ≠ 0. Our objective is to study the asymptotic properties of the ratios (Equation presented) where eiis the unit vector with a single 1 in its i-th coordinate. The main results of the paper, Theorems 13, 15, 17, provide upper bounds for the almost sure exponential convergence rate in terms of the spectral gap associated with (An). It will be shown that these upper bounds are sharp, see Theorem 21. In the final section of the paper we present a variety of connections between the spectral gap of (An) and the Birkhoff contraction coefficient of the product An⋯ A1. Our results complement previous results of [15] and [21]. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Development of Virtual Learning Factory Toolkit for Production Engineering Education

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IOP Conference Series: Materials science and Engineering 2021年第1期1140卷

作者： Kashif Mahmood Tauno Otto Vladimir Kuts Walter Terkaj Marcello Urgo Geza Haidegger Department of Mechanical and Industrial Engineering Tallinn University of Technology Tallinn Estonia STIIMA-CNR Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing National Research Council Milan Italy Manufacturing and Production Systems Division Department of Mechanical Engineering Politecnico di Milano Milan Italy SZTAKI – Institute for Computer Science and Control Budapest Hungary

The advent of industry 4.0 and the continuous digitalization of production ask for the enhancement of human skills and competences in the field of information and communication technology (ICT). Therefore, higher education has to keep pace with the global market needs for the necessary ICT skills and the overall understanding of the complexity of industries in 21st century. This paper focuses on the development and integration of Virtual Learning Factory (VLF) tools that can be used in production management and engineering education. The digital tools integrated in VLF toolkit can help students to exploit enabling technologies like simulation and virtual reality in their manufacturing studies and practical projects with industrial companies.

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Multi-Object Detection in Urban Scenes Utilizing 3D Background Maps and Tracking

Multi-Object Detection in Urban Scenes Utilizing 3D Backgrou...

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International Conference on control, Artificial Intelligence, Robotics & Optimization (ICCAIRO)

作者： Orkény Zováthi Lóránt Kovács Balázs Nagy Csaba Benedek Budapest University of Technology and Economics Budapest Hungary Institute for Computer Science and Control (SZTAKI) Machine Perception Research Laboratory 3in Research Group P??zm??ny P??ter Catholic University Esztergom Hungary University of Debrecen Faculty of Informatics

ISBN: (数字)9781728135724

ISBN: (纸本)9781728135731

In this paper we propose a novel approach for upgrading real time 3D dynamic object detection methods operating on rotating multi-beam (RMB) Lidar measurements using 3D background city maps stored in new generation geographic information systems (GIS) and previously detected dynamic objects propagated by tracking. First, we apply a state-of-the-art object detection method and distinguish the predicted dynamic object candidates and the remaining static regions of the current Lidar measurement. Next we find an optimal transformation between the static part of the RMB Lidar measurements and the background city map using a multimodal point cloud registration algorithm operating in the Hough space. After the accurate alignment, we filter false-positively detected object candidates in the RMB Lidar data based on the map. To find additional objects missed by the object detector on the current measurement, we apply a Kalman-filter based object tracking. Hereby we first predict the current state of the previously detected and tracked objects. Next, we apply a Hungarian matcher based assignment between the tracked and the current objects and update the object list according to the result. For better accuracy, we keep all predictions through a couple of frames. We evaluated our method qualitatively and quantitatively in crowded urban scenes of Budapest, Hungary, and the results showed that with background map based filtering we can achieve a 26,52% improvement detecting vehicles and 9,38% for pedestrians in precision, while via tracking, a 12,84% improvement for vehicles and 14,34% for pedestrians in recall against the state-of-the-art object detection method relying purely on a single Lidar time frame.

关键词： Vehicle dynamics Three-dimensional displays Laser radar Current measurement Object detection Heuristic algorithms Urban areas

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Distribution-Free Uncertainty Quantification for Kernel Methods by Gradient Perturbations

arXiv

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arXiv 2018年

作者： Csáji, Balázs Csanád Kis, Krisztián Balázs EPIC Centre of Excellence MTA SZTAKI: Institute for Computer Science and Control Hungarian Academy of Sciences Budapest Hungary

We propose a data-driven approach to quantify the uncertainty of models constructed by kernel methods. Our approach minimizes the needed distributional assumptions, hence, instead of working with, for example, Gaussian processes or exponential families, it only requires knowledge about some mild regularity of the measurement noise, such as it is being symmetric or exchangeable. We show, by building on recent results from finite-sample system identification, that by perturbing the residuals in the gradient of the objective function, information can be extracted about the amount of uncertainty our model has. Particularly, we provide an algorithm to build exact, non-asymptotically guaranteed, distribution-free confidence regions for ideal, noise-free representations of the function we try to estimate. For the typical convex quadratic problems and symmetric noises, the regions are star convex centered around a given nominal estimate, and have efficient ellipsoidal outer approximations. Finally, we illustrate the ideas on typical kernel methods, such as LS-SVC, KRR, Ε-SVR and kernelized LASSO. Copyright © 2018, The Authors. All rights reserved.

关键词： Support vector machines

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Street object classification via LIDARs with only a single or a few layers

Street object classification via LIDARs with only a single o...

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International Image Processing, Applications and Systems Conference (IPAS)

作者： Zoltan Rozsa Tamas Sziranyi Hungarian Academy of Sciences Institute for Computer Science and Control (MTA SZTAKI) and Budapest University of Technology and Economics Budapest Hungary

ISBN: (纸本)9781728102481;9781728102474

LIDAR sensors are part of the sensor system of several intelligent vehicles and transportation systems providing both object and free-space detection capabilities. In this paper a recognition method is proposed for LIDARs with only a few detection planes. Our method is especially useful in the case when angular resolution of the scan is sufficient, but in the vertical direction the planes are far from each other. The proposed method uses new features including Fourier based descriptor, deep learning classification and exploits additional 3D information if it is available. We tested the method on ten thousands of samples from a large public database. This paper gives an effective solution for a hard problem of LIDAR based recognition problems, namely the far-object detection in case of mobile LIDARs of limited or poor vertical resolution.

关键词： Laser radar Sensors Three-dimensional displays Two dimensional displays Databases Automobiles Conferences

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LPV model reduction methods for aeroelastic structures

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IFAC-PapersOnLine 2017年第1期50卷 6344-6349页

作者： Lipták G. Luspay T. Péni T. Takarics B. Vanek B. Institute for Computer science and Control (SZTAKI) Kende u. 13-17Budapest H-1111 Hungary

The aeroservoelastic modeling of flexible structures leads in general to large dimensional linear parameter varying (LPV) models. These models are intractable by the most analysis and control synthesis algorithms. Therefore, efficient model order reduction methods are needed. This paper investigates two approaches. Both are based on decoupling the large-scale model into smaller dynamical components, that are easier to reduce by balanced reduction techniques. While the first approach exploits the a-priori known, specific structure of the model, the second method algorithmically generates the low-level subsystems by performing a modal transformation and clustering the modes of similar dynamical behavior. The properties of the methods are analyzed via the reduction of the large-scale aeroelastic model of the BAH jet transport wing. © 2017

关键词： Aerospace Linear parameter-varying systems Model reduction

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Online lead time prediction supporting situation-aware production control

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Procedia CIRP 2018年 78卷 190-195页

作者： Dávid Gyulai András Pfeiffer Júlia Bergmann Viola Gallina Centre of Excellence in Production Informatics and Control (EPIC) Institute for Computer Science and Control (SZTAKI) Hungarian Academy of Sciences (MTA) Kende 13-17 H-1111 Budapest Hungary Fraunhofer Austria Research GmbH Theresianumgasse 27 A-1040 Wien Austria

Cyber-Physical Production Systems (CPPS) appeared already in recent manufacturing environments, and they are capable of providing detailed data about the products, processes and resources in near real-time. Various analytics techniques are available to exploit such technology related data in decision making, however, these tools typically act in the fields of maintenance and quality. Only a few approaches target production control, while the effectiveness of related processes is of crucial importance from overall performance’s viewpoint. In the paper, a new production data analytics tool is presented, applying machine learning techniques to proactively predict manufacturing lead times to make decisions by implementing a closed-loop production control. The proposed method applies regression techniques, and based on that, it supports job priorization to be utilized in dispatching decisions. The efficiency of the proposed method is analyzed and presented by numerical results of a case study.

关键词： production control machine learning data analytics

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