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A Key Factor in Traffic Management - Vehicle Speed Prediction Using Machine Learning

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A Key Factor in Traffic Management - Vehicle Speed Predictio...

International Conference on Computation, Automation and Knowledge Management (ICCAKM)

作者： Ayush Maurya Ashish Dwivedi Saurabh Pratap Suchi Dubey Fuli Zhou Vinod Kumar Shukla Department of Computer Science and Engineering Shambhunath Institute of Engineering and Technology Prayagraj U.P India Jindal Global Business School O.P. Jindal Global University Sonipat India Department of Mechanical Engineering Indian Institute of Technology (BHU) Varanasi India Manipal Academy of Higher Education Dubai United Arab Emirates College of Economics and Management Zhengzhou University of Light Industry Zhengzhou China Department of Engineering and Architecture Amity University Dubai United Arab Emirates

The analysis of transportation data is considered as an essential application for Machine Learning (ML) based-intelligent transportation design and control especially for safety and improving energy management. There is a requirement to identify efficient methods for optimizing transportation operations and enhance traffic management. This study adopts exploratory data analysis to analyse transportation data including year, month, vehicle type, time period, area type, functional classification. Predictive analytics is used to predict vehicle speed as dependent variable. The study adopts four supervised ML models such as Multiple Linear Regression (MLR), Random Forest Regression (RFR), Decision Tree (DT) and Artificial Neural Network (ANN) approach for analysing the speed prediction factors. Speed prediction assists as a foundation of advanced traffic management systems. The results from the findings suggest that ANN and RFR are the commendable model for performance analytics.

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Global solution to sensor network localization: A non-convex potential game approach and its distributed implementation

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arXiv 2024年

作者： Xu, Gehui Chen, Guanpu Hong, Yiguang Fidan, Baris Parisini, Thomas Johansson, Karl H. Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing100190 China The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures StockholmSE-10044 Sweden Department of Control Science and Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China The Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada The Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom The Department of Engineering and Architecture University of Trieste Trieste34127 Italy

Consider a sensor network consisting of both anchor and non-anchor nodes. We address the following sensor network localization (SNL) problem: given the physical locations of anchor nodes and relative measurements among all nodes, determine the locations of all non-anchor nodes. The solution to the SNL problem is challenging due to its inherent non-convexity. In this paper, the problem takes on the form of a multi-player nonconvex potential game in which canonical duality theory is used to define a complementary dual potential function. After showing the Nash equilibrium (NE) correspondent to the SNL solution, we provide a necessary and sufficient condition for a stationary point to coincide with the NE. An algorithm is proposed_ to reach the NE and shown to have convergence rate O(1/√k). With the aim of reducing the information exchange within a network, a distributed algorithm for NE seeking is implemented and its global convergence analysis is provided. Extensive simulations show the validity and effectiveness of the proposed approach to solve the SNL problem. Copyright © 2024, The Authors. All rights reserved.

关键词： Game theory

The Problem with XSD Binary Floating Point Datatypes in RDF

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arXiv 2020年

作者： Keil, Jan Martin Gänßinger, Merle Heinz Nixdorf Chair for Distributed Information Systems Institute for Computer Science Friedrich Schiller University Jena Jena Germany

The XSD binary floating point datatypes are regularly used for precise numeric values in RDF. However, the use of these datatypes for knowledge representation can systematically impair the quality of data and, compared to the XSD decimal datatype, increases the probability of data processing producing false results. We argue why in most cases the XSD decimal datatype is better suited to represent numeric values in RDF. A survey of the actual usage of datatypes on the relevant subset of the September 2020 Web Data Commons dataset, containing 14 778 325 375 literals from real web data, substantiates the practical relevancy of the described problems: 29 %-68 % of binary floating point values are distorted due to the *** Codes 68T30 © 2020, CC BY.

关键词： Knowledge representation

Interlocking interface design in metal-CFRP joints using a monte-carlo simulation approach 22

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Interlocking interface design in metal-CFRP joints using a m...

22nd International Conference on Composite Materials, ICCM 2019

作者： Günther, Fabian Kretzschmar, Vanessa Stommel, Markus Scheuermann, Gerik Chair of Plastics Technology TU Dortmund University Dortmund Germany Institute of Computer Science Leipzig University Leipzig Germany

Experimental single lap joint tests with different interlocking structures in the interface area of carbon fiber reinforced plastic and thermoplastic parts motivates an extended simulative study about the influences of interlocking elements. Due to a lack of information from a small number of experimental test with interlocking elements, conclusions are hard to draw about how to place structures elements in multi material joint interface. A Monte-Carlo simulation approach with a large number of simulations is used to provide more general explanations of the effects and interaction within interlocking interface boundaries. The derived conclusions are a wide distribution of pins, a certain pin density, as well as a staggered positioning of pins to achieve a maximum load capacity and minimum damage initiation in the composite for small displacements. © 2019 International Committee on Composite Materials. All rights reserved.

关键词： Carbon fiber reinforced plastics

Partitioning of bioportal ontologies: An empirical study 12

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Partitioning of bioportal ontologies: An empirical study

12th International Conference on Semantic Web Applications and Tools for Health Care and Life sciences, SWAT4HCLS 2019

作者： Algergawy, Alsayed König-Ries, Birgitta Heinz-Nixdorf Chair for Distributed Information Systems Institute for Computer Science Friedrich Schiller University of Jena Jena Germany

BioPortal is a leading repository of biomedical ontologies developed in different formats such as OWL and OBO. There is an increasing number of ontologies as well as an increasing number of concepts available via this platform. This has sparked a number of studies analyzing different aspects of BioPortal ontologies, such as their quality and reuse. With this paper, we add a new aspect to this body of work: The current version of BioPortal supports the access to whole ontologies;however, often, users are interested to obtain access to subsets of ontologies. This is particularly true for big ontologies. This requires partitioning of ontologies. In this paper, we therefore investigate how suitable BioPortal ontologies are for being partitioned with state of the art tools. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Ontology

Non-uniform ergodic properties of hamiltonian flows with impacts

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arXiv 2020年

作者： Frączek, Krzysztof Rom-Kedar, Vered Faculty of Mathematics and Computer Science Nicolaus Copernicus University ul. Chopina 12/18 Toruń87-100 Poland The Estrin Family Chair of Computer Science and Applied Mathematics Department of Computer Science and Applied Mathematics The Weizmann Institute Rehovot76100 Israel

The ergodic properties of two uncoupled oscillators, a horizontal and vertical one, residing in a class of non rectangular star-shaped polygons with only vertical and horizontal boundaries and impacting elastically from its boundaries are studied. We prove that the iso-energy level sets topology changes non-trivially;the flow on level sets is always conjugated to a translation flow on a translation surface, yet, for some segments of partial energies the genus of the surface is strictly larger than one. When at least one of the oscillators is un-harmonic, or when both are harmonic and non-resonant, we prove that for almost all partial energies, including the impacting ones, the flow on level sets is unique ergodic. When both oscillators are harmonic and resonant, we prove that there exist intervals of partial energies on which periodic ribbons and additional ergodic components co-exist. We prove that for almost all partial energies in such segments the motion is unique ergodic on the part of the level set that is not occupied by the periodic ribbons. This implies that ergodic averages project to piecewise smooth weighted averages in the configuration space. © 2020, CC BY.

关键词： Harmonic analysis

INSTANCE SEGMENTATION AND TEETH CLASSIFICATION IN PANORAMIC X-RAYS

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arXiv 2024年

作者： Budagam, Devichand Ghosh, Sayan Imanbayev, Azamat Zhanatuly Kaplun, Dmitrii Rychenkov, Artem Sinitca, Aleksandr Kumar, Ayush Shrivastav, Anuj Akhmetov, Iskander Rafailovich Antonov, Sergey Cyganov, Gleb Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India Department of Chemical Engineering Indian Institute of Technology Kharagpur Kharagpur India School of IT and Engineering Kazakh-British Technical University Almaty Kazakhstan Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes St. Petersburg Electrotechnical University"LETI" St. Petersburg Russia Centre for Digital Telecommunication Technologies St. Petersburg Electrotechnical University"LETI" St. Petersburg Russia Department of Humanities and Social Sciences Indian Institute of Technology Kharagpur Kharagpur India Department of Architecture and Regional Planning Indian Institute of Technology Kharagpur Kharagpur India Faculty of Digital Transformation ITMO University St. Petersburg Russia

Teeth segmentation and recognition are critical in various dental applications and dental diagnosis. Automatic and accurate segmentation approaches have been made possible by integrating deep learning models. Although teeth segmentation has been studied in the past, only some techniques were able to effectively classify and segment teeth simultaneously. This article offers a pipeline of two deep learning models, U-Net and YOLOv8, which results in BB-UNet, a new architecture for the classification and segmentation of teeth on panoramic X-rays that is efficient and reliable. We have improved the quality and reliability of teeth segmentation by utilising the YOLOv8 and U-Net capabilities. The proposed networks have been evaluated using the mean average precision (mAP) and dice coefficient for YOLOv8 and BB-UNet, respectively. We have achieved a 3% increase in mAP score for teeth classification compared to existing methods, and a 10-15% increase in dice coefficient for teeth segmentation compared to U-Net across different categories of teeth. A new Dental dataset was created based on UFBA-UESC dataset with Bounding-Box and Polygon annotations of 425 dental panoramic X-rays. The findings of this research pave the way for a wider adoption of object detection models in the field of dental diagnosis. © 2024, CC BY.

关键词： Object detection

Learning-based Online Optimization for Autonomous Mobility-on-Demand Fleet Control

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arXiv 2023年

作者： Jungel, Kai Parmentier, Axel Schiffer, Maximilian Vidal, Thibaut TUM School of Management Technical University of Munich Munich80333 Germany CERMICS École des Ponts Marne-la-Vallée France TUM School of Management Munich Data Science Institute Technical University of Munich Munich80333 Germany CIRRELT & SCALE-AI Chair in Data-Driven Supply Chains Department of Mathematics and Industrial Engineering École Polytechnique de Montréal Montréal Canada Department of Computer Science Pontifical Catholic University of Rio de Janeiro Rio de Janeiro Brazil

Autonomous mobility-on-demand systems are a viable alternative to mitigate many transportation-related externalities in cities, such as rising vehicle volumes in urban areas and transportation-related pollution. However, the success of these systems heavily depends on efficient and effective fleet control strategies. In this context, we study online control algorithms for autonomous mobility-on-demand systems and develop a novel hybrid combinatorial optimization enriched machine learning pipeline which learns online dispatching and rebalancing policies from optimal full-information solutions. We test our hybrid pipeline on large-scale real-world scenarios with different vehicle fleet sizes and various request densities. We show that our approach outperforms state-of-the-art greedy, and model-predictive control approaches with respect to various KPIs, e.g., by up to 17.1% and on average by 6.3% in terms of realized profit. Copyright © 2023, The Authors. All rights reserved.

关键词： Pipelines

Distantly-Supervised Long-Tailed Relation Extraction Using Constraint Graphs

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arXiv 2021年

作者： Liang, Tianming Liu, Yang Liu, Xiaoyan Zhang, Hao Sharma, Gaurav Guo, Maozu The School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China The Department of Electrical and Computer Engineering University of Rochester RochesterNY14627 United States The School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China The Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing100044 China

Label noise and long-tailed distributions are two major challenges in distantly supervised relation extraction. Recent studies have shown great progress on denoising, but paid little attention to the problem of long-tailed relations. In this paper, we introduce a constraint graph to model the dependencies between relation labels. On top of that, we further propose a novel constraint graph-based relation extraction framework(CGRE) to handle the two challenges simultaneously. CGRE employs graph convolution networks to propagate information from data-rich relation nodes to data-poor relation nodes, and thus boosts the representation learning of long-tailed relations. To further improve the noise immunity, a constraint-aware attention module is designed in CGRE to integrate the constraint information. Extensive experimental results indicate that CGRE achieves significant improvements over the previous methods for both denoising and long-tailed relation extraction. © 2021, CC BY.

关键词： Graphic methods