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Three-dimensional CityGML building models in mobile augmented reality: a smartphone-based pose tracking system

International Journal of Digital Earth 2021年第1期14卷 32-51页

作者： Christoph Blut Jörg Blankenbach Geodetic Institute Department of Computing in Civil Engineering and Geo-Information SystemsRWTH Aachen UniversityAachenGermany

Many augmented reality(AR)systems are developed for entertainment,but AR and particularly mobile AR potentially have more application possibilities in other *** example,in civil engineering or city planning,AR could be used in combination with CityGML building models to enhance some typical workflows in planning,execution and operation processes.A concrete example is the geo-referenced on-site visualization of planned buildings or building parts,to simplify planning processes and optimize the communication between the participating *** of the main challenges for the visualization lies in the pose tracking,*** real-time estimation of the translation and rotation of the mobile device to align the virtual objects with *** this paper,we introduce a proof-of-concept fine-grained mobile AR CityGML-based pose tracking system aimed at the mentioned *** system estimates poses by combining 3D CityGML data with information derived from 2D camera images and an inertial measurement unit and is fully self-sufficient and operates without external *** results of our evaluation show that CityGML and low-cost off-the-shelf mobile devices,such as smartphones,already provide performant and accurate mobile pose tracking for AR in civil engineering and city planning.

关键词： Mobile augmented reality pose tracking CityGML 3D city model Android smartphone

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TwinGen: Advanced technologies to automatically generate digital twins for operation and maintenance of existing bridges 14th

TwinGen: Advanced technologies to automatically generate dig...

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14th European Conference on Product and Process Modelling, ECPPM 2022

作者： Vilgertshofer, S. Mafipour, M.S. Borrmann, A. Martens, J. Blut, T. Becker, R. Blankenbach, J. Göbels, A. Beetz, J. Celik, F. Faltin, B. König, M. Computational Modeling and Simulation Technical University of Munich Germany Geodetic Institute and Chair for Computing in Civil Engineering & Geo Information Systems RWTH Aachen University Germany Design Computation RWTH Aachen University Germany Computing in Engineering Ruhr University Bochum Germany

ISBN: (纸本)9781032406732

The high effort of operating and maintaining existing infrastructure facilities resulted in a large stock of structurally deficient bridges in most industrialized countries. Today, the condition assessment of bridges is conducted mostly manually. To relieve effort and costs, the digitization and automation of conventional manual, labour-intensive methods is necessary. We interpret the ambiguously used term "digital twin" (DT) in this study as a semantic-geometric model of an existing asset (here, a bridge) that contains all information required for assessing its current condition. This paper proposes an approach to automatically generate the DT of existing bridges from point cloud data (PCD) and images captured from the structure. The PCD of the bridge is semantically segmented by means of ML techniques, and a digital model is created using parametric modeling. Subsequently, detected damages and data from existing bridge maintenance systems are linked to the model to create a full DT. This paper reports the main results of the TwinGen research project: The digital twinning process of bridges can be automated to a large extent, in order to efficiently support the maintenance process of bridges. © 2023 the Author(s).

关键词： Bridges

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Precise near-ultrasonic tilt determination for smartphones

Precise near-ultrasonic tilt determination for smartphones

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International Conference on Indoor Positioning and Indoor Navigation (IPIN)

作者： Jan Grottke Daniel Grottke Jörg Blankenbach Geodetic Institute and Chair for Computing in Civil Engineering & Geo Information Systems RWTH Aachen University Aachen Germany

ISBN: (数字)9798350366402

ISBN: (纸本)9798350366419

Modern smartphones incorporate numerous sensors that enable relative positioning utilizing the measurement data from the internal inertial measurement unit. Their motion can thus be derived from the magnetometer, gyroscope, and acceleration sensor data. The problem, however, is that the acceleration signals cannot be separated into vertical and horizontal directions during the movement. Therefore, the absolute tilt of the smartphone is often unknown. Nevertheless, this separation is required for many step-based distance determination methods to estimate the length of the detected steps and the acceleration signals measured between the step events for each directional component. Therefore, a near-ultrasound-based distance determination was realized, which applies the Frequency-Modulated Continuous-Wave Radar method (FMCW Radar) to determine the difference between the onboard microphone’s position of the smartphone and the ground (or the nearest reflective surface). These distances allow for a precise inclination determination of the smartphone relative to the ground. The tilt estimation was compared with a camera-based reference position determination. The smartphone’s angle relative to the ground could be determined with a median accuracy of 2.73°, mean accuracy of 3.37°, and 4.56° for the third quartile error.

关键词： Accuracy Magnetic sensors Buildings Estimation Radar Smart phones Floors Video recording Standards Microphones

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Classification of Cacao Pods Ripeness Using Convolutional Neural Networks 16

Classification of Cacao Pods Ripeness Using Convolutional Ne...

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16th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Borromeo, Karl O. Bonotan, Melbert R. Llagas, Archie P. Juan, Joana Marie O. Sanchez, Philip Donald C. Berro, Nathan Kit A. Department of Computer Science College of Computing and Information Sciences Caraga State University Butuan City Philippines Department of Information Technology College of Computing and Information Sciences Caraga State University Butuan City Philippines Center for Resource Assessment Analytics and Emerging Technologies College of Engineering and Geo-Sciences Caraga State University Butuan City Philippines Department of Information Systems College of Computing and Information Sciences Caraga State University Butuan City Philippines

This paper presents methods for the development of Ripeness Identification of Cacao Pods using Convolutional Neural Network (CNN) and precision agriculture employed in a Mobile Application. This research explores if there is a correlation between the color dynamics of cacao pods and its physiological characteristics, and the possibility of using this correlation to develop Artificial Intelligence (AI) model for ripeness and variety identification, which conventional eye estimation resulted to unripe and overripe harvesting of Cacao Pods. Data analysis and correlation was developed, integrating detection and classification models based on the Single Shot Detection (SSD) MobileNet and Visual geometry Group (VGG) 16 or VGG16 layers network architectures, respectively. The mobile application utilizes the TensorFlow template for object detection and incorporates image preprocessing techniques for enhanced accuracy. The successful integration of the model into a mobile application demonstrates its potential for practical applications in the cacao industry. © 2025 Elsevier B.V.. All rights reserved.

关键词： Convolutional neural networks

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Thermal Reality Capturing with the Microsoft Hololens 2 for Energy System Analysis

SSRN

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SSRN 2023年

作者： Kinnen, Tristan Blut, Christoph Effkemann, Christoph Blankenbach, Jörg Geodetic Institute and Chair for Computing in Civil Engineering & Geo Information Systems RWTH Aachen University Germany

Using digital methods to analyze the often highly complex structures of technical building equipment (TBE) holds great potential for identifying energy savings. For digitizing TBE, we evaluated the Microsoft HoloLens 2 (MHL2), since it promises to be a mobile and cost-effective solution and found that the MHL2 can be a suitable instrument for as-built acquisition, depending on the use-case’s accuracy requirements. The depth sensor of the MHL2 operates in a low single-digit centimeter accuracy range, while the position tracking inaccuracy is more significant. Furthermore, we extended the acquisition system with an external thermal camera to generate both, RGB and thermal point clouds. Additionally, we built a custom 3D calibration setup that allows us to calibrate both cameras in parallel and achieve promising accuracies for the level of detail of the resulting RGB and thermal point clouds and thus for the energy system analysis approach. © 2023, The Authors. All rights reserved.

关键词： Cameras

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Enhancing Low-Dose CT Image Quality Through Deep Learning: A DoG-Sharpened U-Net Approach With Attention Mechanism

Enhancing Low-Dose CT Image Quality Through Deep Learning: A...

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent systems, ICETSIS 2024

作者： Zubair, Muhammad Rais, Helmi B Md Ullah, Fasee Yousafzai, Arsalaan Khan Hassan, Farrukh Universiti Teknologi Petronas Department of Computer and Information Sciences Seri Iskandar32610 Malaysia Universiti Teknologi Petronas Department of Civil and Enviromental Engineering Seri Iskandar32610 Malaysia Sunway University Bandar University Sunway Department of Computing and Information Systems School of Engineering and Technology Selangor Darul Ehsan 47500 Malaysia

ISBN: (纸本)9798350372229

Computed Tomography (CT) scans utilize electromagnetic radiation. However, the excessive exposure of a patient's body during CT acquisition poses potential health risks. Subsequently, the integration of low-dose CT scans has resulted in an escalation of noise, artifacts, and a discernible decline in the overall quality of CT imaging, significantly impacting the diagnostic capabilities of Computer-Aided Diagnosis (CAD) system. Removing these noises and artifacts while preserving critical information is a challenging task. Traditional noise reduction algorithms are expensive, produce blurry results, and rely on challenging sinogram data. As a result, deep learning-based image-denoising approaches have emerged. This study introduces a DoG-UNet+ model which incorporates a novel layer called the 'Difference of Gaussians (DoG) Sharpening Layer' into the U-Net architecture. This layer utilizes two distinct convolutional kernels, termed 'fat' and 'skinny,' aimed to capturing diverse scales of features. To increase the clinical detection precision, an attention mechanism has been added to focus on the critical features in CT images. This model has been compared with the most recent algorithms based on the Peak Signal-to-Noise Ratio (PSNR), Root Mean Squared Error (RMSE), and Structural Similarity Index (SSIM). The outcomes showed superior performance of DoG-UNet+ model, showcasing promising and notable results compared to existing methods. © 2024 IEEE.

关键词： Computed Tomography Deep Learning Image Enhancement LDCT Noise Removal

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Voting Classifier and Metaheuristic Optimization for Network Intrusion Detection

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Computers, Materials & Continua 2023年第2期74卷 3183-3198页

作者： Doaa Sami Khafaga Faten Khalid Karim Abdelaziz A.Abdelhamid El-Sayed M.El-kenawy Hend K.Alkahtani Nima Khodadadi Mohammed Hadwan Abdelhameed Ibrahim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt The Department of Civil and Environmental Engineering Florida International UniversityMiamiFLUSA Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

Managing physical objects in the network’s periphery is made possible by the Internet of Things(IoT),revolutionizing human *** attacks and unauthorized access are possible with these IoT devices,which exchange data to enable remote *** attacks are often detected using intrusion detection methodologies,although these systems’effectiveness and accuracy are *** paper proposes a new voting classifier composed of an ensemble of machine learning models trained and optimized using metaheuristic *** employed metaheuristic optimizer is a new version of the whale optimization algorithm(WOA),which is guided by the dipper throated optimizer(DTO)to improve the exploration process of the traditionalWOA *** proposed voting classifier categorizes the network intrusions robustly and *** assess the proposed approach,a dataset created from IoT devices is employed to record the efficiency of the proposed algorithm for binary attack *** dataset records are balanced using the locality-sensitive hashing(LSH)and Synthetic Minority Oversampling Technique(SMOTE).The evaluation of the achieved results is performed in terms of statistical analysis and visual plots to prove the proposed approach’s effectiveness,stability,and *** achieved results confirmed the superiority of the proposed algorithm for the task of network intrusion detection.

关键词： Voting classifier whale optimization algorithm dipper throated optimization intrusion detection internet-of-things

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Vehicle Trajectory-Based Intersection Classification Using Deep Transfer Learning 5

Vehicle Trajectory-Based Intersection Classification Using D...

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5th International Conference on Intelligent computing, Communication, Networking and Services, ICCNS 2024

作者： Kased, Abanoub Rabee, Rana Fahmy, Akram Mohamed, Hussein Yacoub, Marco Elhenawy, Mohammed Ashqar, Huthaifa I. Alsobeh, Anas Masoud, Mahmoud Hassan, Abdallah A. Minia University Faculty of Engineering Computers and Systems Department Minia Egypt Australia Arab American University Civil Engineering Department Jenin Palestine School of Computing Southern Illinois University Carbondale CarbondaleIL United States Center for Smart Mobility and Logistics King Fahd University of Petroleum and Minerals Department of Information Systems and Operations Management Dhahran Saudi Arabia

ISBN: (纸本)9798350354690

This document is a model and instructions for M-TEX. The accelerated expansion of global road networks necessitates the availability of accurate and up-to-date cartographic data for connected and automated vehicles. Conventional mapping techniques, such as satellite imagery and field surveys, are inher-ently time-consuming and labor-intensive. This study proposes a novel approach that makes use of the ubiquity of GPS-embedded devices to facilitate the efficient updating of road maps. We present a classifier that identifies the types of intersections from vehicle trajectories, which represents a crucial initial step in the generation of automated maps. The methodology employs five distinct convolutional deep neural network architectures for the detection and classification of road intersection types. The experimental results, which were obtained using three real-world vehicle trajectory datasets, demonstrate classification accuracy levels that range from 84 % to 91 %. It is noteworthy that these results were achieved using less than 10% of the data that is typically required for comparable accuracy levels, which highlights the efficiency of the feature extraction approach that was employed. This research contributes to the advancement of intelligent transportation systems by offering a more efficient and scalable method for road network mapping and updating. © 2024 IEEE.

关键词： Convolutional neural networks

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Experiment the Effect of Providing Monetary Incentives and Safety Patrols on Work Safety Behavior in Construction Project Implementation 2nd

Experiment the Effect of Providing Monetary Incentives and S...

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2nd International Conference of Construction, Infrastructure, and Materials, ICCIM 2021

作者： Harianto, Feri Anwar, Nadjadji Wiguna, I Putu Artama Suryani, Erma Department of Civil Engineering Faculty of Civil Planning and Geo Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Information Systems Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (纸本)9789811679483

The process of implementing a construction project has a high risk of work accidents. The risk of work accidents is due to the limited project implementation time, low human resources and weak work safety management system. For this reason, efforts should be made to minimize the risk of accidents. Therefore, the role of workplace safety management is essential. One form of policy in occupational safety management is patrols and the provision of financial incentives for work safety. This study aims to determine the effect of safety patrols and the provision of financial incentives on safety behavior. This research method is quasi-experimental with a 2 × 2 factorial design, with the respondents being workers. The number of samples is 20 respondents. Sampling using purposive sampling. The treatment of this research is the provision of financial incentives and safety patrols. The analysis in this study used a two-way ANOVA. The result of this study is that there is a significant effect between safety patrol and the provision of financial incentives (together) on work safety behavior. On the other hand, what is quite impactful is the provision of financial incentives. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Finance

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Conceptual System Model Dynamic OSH Performance Improvement of Building Construction Projects 2nd

Conceptual System Model Dynamic OSH Performance Improvement ...

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2nd International Conference of Construction, Infrastructure, and Materials, ICCIM 2021

ISBN: (纸本)9789811679483

In Indonesia, construction projects have a high risk of work accidents. The human factor is the cause of work accidents. For example, most workers have low safety awareness. Therefore, the workforce has a significant role in reducing the risk of work accidents. This study aims to build a conceptual model for improving the performance of Occupational Safety and Health (OSH) building construction projects based on behavior and understanding of workplace safety. This research method uses a dynamic system, choosing a dynamic system because this method can explain complex systems over time, time delays and looping between variables. OSH behavior, understanding and performance variables form the dynamic system model to improve OSH performance. Concept scenarios for developing a dynamic system model are financial incentives, OSH training and patrol frequency. This research was conducted in a high-rise building construction project in Surabaya. The results of this study are the dynamic system modeling for improving safety performance into a sub-model of understanding construction OSH, sub-model of building OSH behavior and sub-model of building OSH performance. Meanwhile, the scenario for improving OSH performance is providing financial incentives, training and increasing the frequency of safety patrols. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Tall buildings

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