1,184 research outputs found

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

    Get PDF
    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    IEOM Society International

    Get PDF
    IEOM Society Internationa

    Gamification of telematics data to enhance operators’ behaviour for improvement of machine productivity in loading cycles

    Get PDF
    Construction industry is suffering from low productivity rate in various projects such as excavation. Although this issue is discussed in literature and several approaches are proposed to address it, productivity rate is still low in construction industry compared to other domains like manufacturing. Three core components directly affect the overall productivity in construction sector, i.e. labour productivity, raw material productivity, and machine or equipment productivity. With a focus on construction machinery, three factors influence productivity at excavation sites; i.e. 1) machine-based productivity and its configuration, 2) site layout and environmental conditions, and 3) operators’ behaviour. Operators’ competence and motivation represent two key parameters that affect their behaviour. On one side, gamification has attracted a growing area of interest both in literature and practice, seeking to place a layer of entertainment and pleasure to the top of serious activities (with a focus on improving the applicant’s motivation and behaviour). On the other side, telematics systems are utilized to collect operational data of the machine, and calculate its productivity rate. Telematics data are presented to operators (via a built-in screen available in the cabin of the machine) to provide real-time feedback about machine performance. In addition, these data can support machine owners to perceive operators’ behaviour on a real-time basis. To conclude, telematics systems are providing real-time data which can be a great input into gamification. A guideline is proposed in this dissertation that helps gamification designers to develop more transparent gamification models. This guideline is utilized to introduce a gamification model that gamifies telematics data with a focus on enhancing operators’ behaviour (machine productivity) in loading and transferring activities. The model was implemented at two sites(one recycling and one mining site) and could encourage operators (who were operating wheel-loaders and dump-trucks) to prevent redundant activities like texting, phoning, and even eating while operating the machine. Subsequently, it enhanced overall machine productivity up to 37% during the site observation. To summarize, a gamified platform in which different operators from different organizations can share their achievements, or can get scored and ranked in a leader-board will potentially lead to a more proper operators’ behaviour at work and subsequently can improve overall productivity rate at construction sites

    Development,Validation, and Integration of AI-Driven Computer Vision System and Digital-twin System for Traffic Safety Dignostics

    Get PDF
    The use of data and deep learning algorithms in transportation research have become increasingly popular in recent years. Many studies rely on real-world data. Collecting accurate traffic data is crucial for analyzing traffic safety. Still, traditional traffic data collection methods that rely on loop detectors and radar sensors are limited to collect macro-level data, and it may fail to monitor complex driver behaviors like lane changing and interactions between road users. With the development of new technologies like in-vehicle cameras, Unmanned Aerial Vehicle (UAV), and surveillance cameras, vehicle trajectory data can be collected from the recorded videos for more comprehensive and microscopic traffic safety analysis. This research presents the development, validation, and integration of three AI-driven computer vision systems for vehicle trajectory extraction and traffic safety research: 1) A.R.C.I.S, an automated framework for safety diagnosis utilizing multi-object detection and tracking algorithm for UAV videos. 2)N.M.E.D.S., A new framework with the ability to detect and predict the key points of vehicles and provide more precise vehicle occupying locations for traffic safety analysis. 3)D.V.E.D.S applied deep learning models to extract information related to drivers\u27 visual environment from the Google Street View (GSV) images. Based on the drone video collected and processed by A.R.C.I.S at various locations, CitySim: a new drone recorded vehicle trajectory dataset that aim to facilitate safety research was introduced. CitySim has vehicle interaction trajectories extracted from 1140- minutes of video recordings, which provide a large-scale naturalistic vehicle trajectory that covers a variety of locations, including basic freeway segments, freeway weaving segments, expressway segments, signalized intersections, stop-controlled intersections, and unique intersections without sign/signal control. The advantage of CitySim over other datasets is that it contains more critical safety events in quantity and severity and provides supporting scenarios for safety-oriented research. In addition, CitySim provides digital twin features, including the 3D base maps and signal timings, which enables a more comprehensive testing environment for safety research, such as autonomous vehicle safety. Based on these digital twin features provided by CitySim, we proposed a Digital Twin framework for CV and pedestrian in-the-loop simulation, which is based on Carla-Sumo Co-simulation and Cave automatic virtual environment (CAVE). The proposed framework is expected to guide the future Digital Twin research, and the architecture we build can serve as the testbed for further research and development

    Hands-on Science. Celebrating Science and Science Education

    Get PDF
    The book herein aims to contribute to the improvement of Science Education in our schools and to an effective implementation of a sound widespread scientific literacy at all levels of society

    PhD students´day FMST 2023

    Get PDF
    The authors gave oral presentations of their work online as part of a Doctoral Students’ Day held on 15 June 2023, and they reflect the challenging work done by the students and their supervisors in the fields of metallurgy, materials engineering and management. There are 82 contributions in total, covering a range of areas – metallurgical technology, thermal engineering and fuels in industry, chemical metallurgy, nanotechnology, materials science and engineering, and industrial systems management. This represents a cross-section of the diverse topics investigated by doctoral students at the faculty, and it will provide a guide for Master’s graduates in these or similar disciplines who are interested in pursuing their scientific careers further, whether they are from the faculty here in Ostrava or engineering faculties elsewhere in the Czech Republic. The quality of the contributions varies: some are of average quality, but many reach a standard comparable with research articles published in established journals focusing on disciplines of materials technology. The diversity of topics, and in some cases the excellence of the contributions, with logical structure and clearly formulated conclusions, reflect the high standard of the doctoral programme at the faculty.Ostrav

    Machine Learning Use-Cases in C-ITS Applications

    Get PDF
    In recent years, the development of Cooperative Intelligent Transportation Systems (C-ITS) have witnessed significant growth thus improving the smart transportation concept. The ground of the new C-ITS applications are machine learning algorithms. The goal of this paper is to give a structured and comprehensive overview of machine learning use-cases in the field of C-ITS. It reviews recent novel studies and solutions on CITS applications that are based on machine learning algorithms. These works are organised based on their operational area, including self-inspection level, inter-vehicle level and infrastructure level. The primary objective of this paper is to demonstrate the potential of artificial intelligence in enhancing C-ITS applications

    Cyber-Human Systems, Space Technologies, and Threats

    Get PDF
    CYBER-HUMAN SYSTEMS, SPACE TECHNOLOGIES, AND THREATS is our eighth textbook in a series covering the world of UASs / CUAS/ UUVs / SPACE. Other textbooks in our series are Space Systems Emerging Technologies and Operations; Drone Delivery of CBNRECy – DEW Weapons: Emerging Threats of Mini-Weapons of Mass Destruction and Disruption (WMDD); Disruptive Technologies with applications in Airline, Marine, Defense Industries; Unmanned Vehicle Systems & Operations On Air, Sea, Land; Counter Unmanned Aircraft Systems Technologies and Operations; Unmanned Aircraft Systems in the Cyber Domain: Protecting USA’s Advanced Air Assets, 2nd edition; and Unmanned Aircraft Systems (UAS) in the Cyber Domain Protecting USA’s Advanced Air Assets, 1st edition. Our previous seven titles have received considerable global recognition in the field. (Nichols & Carter, 2022) (Nichols, et al., 2021) (Nichols R. K., et al., 2020) (Nichols R. , et al., 2020) (Nichols R. , et al., 2019) (Nichols R. K., 2018) (Nichols R. K., et al., 2022)https://newprairiepress.org/ebooks/1052/thumbnail.jp

    University of Windsor Undergraduate Calendar 2023 Spring

    Get PDF
    https://scholar.uwindsor.ca/universitywindsorundergraduatecalendars/1023/thumbnail.jp
    corecore