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arXiv 2020年

作者： Rozenberszki, Dávid Majdik, András L. Machine Perception Research Laboratory MTA SZTAKI Hungarian Academy of Sciences Institute for Computer Science and Control Budapest1111 Hungary

— 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 Lidar-only 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. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical radar

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Procedia computer science 2020年 170卷 59-66页

作者： A. Boulmakoul Z. Fazekas L. Karim P. Gáspár G. Cherradi Computer Sc. Dept. FSTM Hassan II University of Casablanca Morocco Institute for Computer Science and Control (SZTAKI) Budapest Hungary LISA Lab. ENSA Berrechid Hassan I University Morocco

The road environment recognition using embedded technologies has been researched intensively recently. Several papers deal with the detection of the urban road environment-type (RET), such as downtown, residential area, and business/industrial area. These RETs can characterize the road environment around an ego-car. A RET detection approach taking into account relevant traffic signs (TSs) that are visible from the ego-car – along its route – was also proposed. It was assumed that the TS data, namely the type and the location of each detected TS along the route, was made available for the purpose by an on-board TS recognition system (TSR). The TS data is constantly updated, aggregated and evaluated in a multi-scale manner by a RET detection system, so one can produce a probability series of occurrence of each TS type with respect to each of the considered urban RETs. In the present paper, we develop a heuristic for dynamic RET detection using fuzzy similarities on a special graph. We also propose the generic process of this approach, which will be the subject of further development and testing.

关键词： Smart mobility smart road vehicles road environment-type detection fuzzy set theory reactive systems machine learning

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A stochastic approach to calculate assembly cycle times based on spatial shop-floor data stream

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Procedia CIRP 2020年 93卷 1164-1169页

作者： Júlia Bergmann Dávid Gyulai Dávid Morassi József Váncza EPIC Centre of Excellence in Production Informatics and Control Institute for Computer Science and Control (SZTAKI) 13-17 Kende Budapest 1111 Hungary Department of Manufacturing Science and Engineering Budapest University of Technology and Economics Müegyetem rkp. 3 Budapest 1111 Hungary

Indoor positioning systems (IPS) allow assets on the shop-floor to be tracked with a relatively high accuracy. In order to obtain the useful, underlying production information, smart and fast processing algorithms are needed, as IPSs produce an immense amount of data in a very short period. In the paper, a novel approach is presented that offers the near real-time calculation of assembly times, based on the dynamically streamed spatial data stream of assets. The approach relies on probabilistic analytic models, respecting the needs of manufacturing and operations management. The efficiency of the results is presented through an industry-related application case.

关键词： Type your keywords here separated by semicolons

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Innovations in Simulation: Experiences With Cloud-Based Simulation Experimentation

Innovations in Simulation: Experiences With Cloud-Based Simu...

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Simulation Winter Conference

作者： Simon J. E. Taylor Anastasia Anagnostou Nura Tijjani Abubakar Tamas Kiss James DesLauriers Gabor Terstyanszky Peter Kacsuk Jozsef Kovacs Shane Kite Gary Pattison James Petry Modelling & Simulation Group Brunel University London Uxbridge UK Centre for Parallel Computing School of Computer Science and Engineering University of Westminster London UK Institute for Computer Science and Control (SZTAKI) Budapest HUNGARY Saker Solutions Ltd Warwickshire UK

ISBN: (数字)9781728194998

ISBN: (纸本)9781728195001

The amount of simulation experimentation that can be performed in a project can be restricted by time, especially if a model takes a long time to simulate and many replications are required. Cloud Computing presents an attractive proposition to speeding up, or extending, simulation experimentation as computing resources can be hired on demand rather than having to invest in costly infrastructure. However, it is not common practice for simulation users to take advantage of this and, arguably, rather than speeding up simulation experimentation users tend to make compromises by using unnecessary model simplification techniques. This may be due to a lack of awareness of what Cloud Computing can offer. Based on several years' experience of innovation in this area, this article presents our experiences in developing Cloud Computing applications for simulation experimentation and discusses what future innovations might be created for the widespread benefit of our simulation community.

关键词： Technological innovation Cloud computing Computational modeling Optimization

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Simulation-Based Digital Twin of a Complex Shop-Floor Logistics System

Simulation-Based Digital Twin of a Complex Shop-Floor Logist...

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Simulation Winter Conference

作者： Dávid Gyulai Júlia Bergmann Attila Lengyel Botond Kádár Dávid Czirkó Centre of Excellence in Production Informatics and Control (EPIC) Institute for Computer Science and Control (SZTAKI) Budapest HUNGARY Digital Analytics Office Western Digital Corporation San Jose CA USA EPIC InnoLabs Ltd. Budapest HUNGARY

ISBN: (数字)9781728194998

ISBN: (纸本)9781728195001

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.

关键词： Manufacturing industries Technological innovation Tools Production facilities Numerical models Robots Logistics

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Semi-parametric uncertainty bounds for binary classification

arXiv

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arXiv 2019年

作者： Csáji, Balázs Csánad Tamás, Ambrus MTA SZTAKI: The Institute for Computer Science and Control Hungarian Academy of Sciences Budapest Hungary

The paper studies binary classification and aims at estimating the underlying regression function which is the conditional expectation of the class labels given the inputs. The regression function is the key component of the Bayes optimal classifier, moreover, besides providing optimal predictions, it can also assess the risk of misclassification. We aim at building non-asymptotic confidence regions for the regression function and suggest three kernel-based semi-parametric resampling methods. We prove that all of them guarantee regions with exact coverage probabilities and they are strongly *** Codes 62G15, 62H30, 62G09 Copyright © 2019, The Authors. All rights reserved.

关键词： Regression analysis

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Semi-Parametric Uncertainty Bounds for Binary Classification

Semi-Parametric Uncertainty Bounds for Binary Classification

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IEEE Annual Conference on Decision and control

作者： Balazs Csanad Csaji Ambrus Tamas MTA SZTAKI: The Institute for Computer Science and Control Hungarian Academy of Sciences Budapest Hungary

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

The paper studies binary classification and aims at estimating the underlying regression function which is the conditional expectation of the class labels given the inputs. The regression function is the key component of the Bayes optimal classifier, moreover, besides providing optimal predictions, it can also assess the risk of misclassification. We aim at building non-asymptotic confidence regions for the regression function and suggest three kernel-based semi-parametric resampling methods. All of them guarantee confidence regions with exact coverage probabilities and they are strongly consistent.

关键词： Kernel Extraterrestrial measurements Hilbert space Uncertainty Probability distribution

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Gradient representations in ReLU networks as similarity functions

arXiv

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arXiv 2021年

作者： Rácz, Dániel Daróczy, Bálint Zoltán 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-NeuveB-1348 Belgium Institute for Computer Science and Control SZTAKI/ELKH Kende utca 13-17 BudapestH-1111 Hungary

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, CC BY.

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Improving the planning quality in production planning and control with machine learning 16

Improving the planning quality in production planning and co...

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16th IMEKO TC10 Conference 2019 on Testing, Diagnostics and Inspection as a Comprehensive Value Chain for Quality and Safety

作者： Ryback, Thomas Lukas, Lingitz Gaal, Alexander Gallina, Viola Gyulai, Dávid Fraunhofer Austria Research GmbH Theresianumgasse 27 ViennaA-1040 Austria Institute for Computer Science and Control [SZTAKI Hungarian Academy of Sciences [MTA Kende Str. 13-17 BudapestH-1111 Hungary

ISBN: (纸本)9789299008416

There are always deviations between production planning and subsequent execution. These deviations are caused by uncertainties, e.g. inaccurate or insufficient planning data (e.g. data quality and availability), inappropriate planning and control systems or unforeseeable events. Production planners therefore use buffers in the form of inventories or extended transitional periods to create possibilities for implementing corrective measures in production control. Buffers, however, lead to increased coordination and control effort and to negative effects, e.g. on inventory, throughput time and capacity utilization. Furthermore, it was found that the reliability of the production plans and thus the planning quality (PQ) can drop down to 25% in the first three days after plan creation [1]. Potentials for more accurate planning remains largely unexploited. The objective of this paper is to investigate the possibilities to increase planning quality. Two approaches are presented, focusing on reducing gaps between master data and predicted data used during the production planning process. © 2019 International Measurement Confederation (IMEKO). All rights reserved.

关键词： Planning

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Enabling research of cultural heritage and recent history using COURAGE linked data registry 14

Enabling research of cultural heritage and recent history us...

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Posters and Demos Track of the 14th International Conference on Semantic Systems, SEMPDS 2018

作者： Micsik, András Felker, Tamás MTA SZTAKI Institute for Computer Science and Control Hungarian Academy of Sciences Hungary

The COURAGE project aims at preserving the history and heritage of dissent in former socialist countries. The items of so-called cultural opposition are curated in various collections, and COURAGE builds a registry of these collections. The registry is a Linked Data platform, and data input, editing and publishing are all done using RDF and SPARQL. We present our effort of building the registry and introduce our experiences with this unique born-RDF historical dataset. © 2018 CEUR-WS. All Rights Reserved.

关键词： Linked data

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