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检索条件"任意字段=Proceedings of the 9th International Conference on Predictive Models in Software Engineering"

共 2518 条记录，以下是201-210 订阅

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Deep Learning Based Ground Penetrating Radar Nondestructive Testing Technology in Highway Maintenance Application 9

Deep Learning Based Ground Penetrating Radar Nondestructive ...

引用

9th Annual international conference on Network and Information Systems for Computers, ICNISC 2023

作者： Yi, Mingwei Jiang, Haoran Lin, Xiang Lyu, Xinpeng Yi, Xiaoming Chen, Zongwu RoadMainT Co. Ltd. Beijing China Key Laboratory of Transport Industry of Road Structure and Material Research Institute of Highway Ministry of Transport Beijing China State Kye Laboratory of Silicate Materials for Architectures Wuhan University of Technology Wuhan China Faculty of Engineering Hubei Wuhan China

ISBN: (纸本)9798350306682

Highways are prone to developing cracks, potholes, sinkholes, and other sur-face and structural layer hidden diseases due to the combined effects of traffic loads, weather conditions, and environmental influences during their ser-vice life. GPR is a widely employed NDT technique for detecting such concealed defects in pavement structures. However, in practical detection applications, GPR mapping relies heavily on experience for recognition and analysis, resulting in low recognition efficiency. this study collected disease map-ping data at a GPR standardized disease test road and utilized simulation analysis software to generate ortho maps. these maps were used to construct an automatic identification dataset containing 900 maps. three target recognition models, SSD, Faster R-CNN, and YOLO v5, were employed to analyze the dataset and compare their effectiveness. the results revealed that YOLO v5 model had the best application effect, with an average recognition accuracy of 90.45%. the YOLO v5 model's backbone was improved based on Swin-Transformer and FE Module, leading to significant improvements in recognition. the improved model achieved an average recognition accuracy of 94.21%, demonstrating its superior performance compared to the other two models. © 2023 IEEE.

关键词： Ground penetrating radar systems

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An industrial benchmark study of automated modal analysis using density-based clustering techniques 31

An industrial benchmark study of automated modal analysis us...

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31st international conference on Noise and Vibration engineering, ISMA 2024 and 10th international conference on Uncertainty in Structural Dynamics, USD 2024

作者： Tavares, A. Di Lorenzo, E. Cornelis, B. Manzato, S. Peeters, B. Desmet, W. Gryllias, K. Siemens Industry Software NV Interleuvenlaan 68 Leuven3001 Belgium KU Leuven Department of Mechanical Engineering Celestijnenlaan 300 Leuven3001 Belgium Flanders Make @ KU Leuven Belgium

ISBN: (纸本)9789082893175

Modal analysis has advanced as a vital technology for studying structural dynamics by capturing complex phenomena through modal parameters. However, manual pole selection from stabilization diagrams can be challenging, especially with complex datasets, necessitating automation for precision and repeatability. this study introduces three Automated Modal Analysis (AMA) methodologies using ML density-based clustering techniques (DBSCAN, OPTICS, HDBSCAN) along with domain knowledge. these methodologies are evaluated against manual selections by engineers with varying levels of expertise across eight industrial datasets, covering diverse structures like metallic specimens, motors, cars, a jet aircraft, and a wind turbine blade. Engineers' proficiency levels are categorized into novices, intermediates, and experts, depending on their expertise in the field. the paper provides a comprehensive comparative analysis, assessing metrics such as the number of selected modes, the quality of modal models, and alignment with expert results. © 2024 proceedings of ISMA 2024 - international conference on Noise and Vibration engineering and USD 2024 - international conference on Uncertainty in Structural Dynamics. All rights reserved.

关键词： Modal analysis

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Determining the Critical Factors of Antenatal Care Follow-Up Using Two-Phase Machine Learning 27

Determining the Critical Factors of Antenatal Care Follow-Up...

引用

27th international conference on Soft Computing and Measurements, SCM 2024

作者： Bessmertny, Igor A. Enkomaryam, Bidru C. Mamuye, Adane L. Itmo University Faculty of Software Engineering and Computer Systems Saint Petersburg Russia University of Gondar Department of Computer Science Gondar Ethiopia

ISBN: (纸本)9798350363708

Determining the critical factors affecting antenatal visits will greatly contribute to reducing maternal and infant mortality. this study, thus attempted to construct a cluster-based predictive model to determine the critical factors of antenatal care follow-up among women in Ethiopia using two-step machine learning techniques. the 5,990 preprocessed obstetric dataset with 13 selected attributes were used that was collected from the Dabat Research Center at the University of Gondar, Ethiopia. To solve the study problem, six cluster models were developed using K-means, Expectation Maximization and Make Density clustering algorithms, which are well-suited for analyzing health datasets, providing scalability, flexibility, robustness to noise, and the ability to handle uneven cluster sizes. K-means out-formers Density based and Expectation Maximization clustering algorithms with minimum number of sum of square errors (17094) and number of iteration (3). then, output of the K-means cluster model was used as input to segment the dataset and build a total of 21 predictive models using the tree-based J48 classifier, PART and JRip rule generators while adjusting their parameters. the model for predicting antenatal care follow-up is constructed by induction of the truncated PART rule (accuracy of 99.93%) and extracts attributes of interest such as antenatal care attendance, antenatal care location, antenatal care frequency, current vaccination, woman's age, and HIV testing to predict antenatal care follow-up in the study area. © 2024 IEEE.

关键词： Machine learning

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DEVELOPING AN ARTIFICIAL NEURAL NETWORK MODEL thAT PREDICTS thE FUNDAMENTAL PERIOD OF STEEL STRUCTURES USING A LARGE DATASET 9

DEVELOPING AN ARTIFICIAL NEURAL NETWORK MODEL THAT PREDICTS ...

引用

9th ECCOMAS thematic conference on Computational Methods in Structural Dynamics and Earthquake engineering, COMPDYN 2023

作者： van der Westhuizen, Ashley Megan Bakas, Nikolaos Markou, George Department of Civil Engineering University of Pretoria South Africa National Infrastructures for Research and Technology GRNET 7 Kifisias Avenue Athens11523 Greece

the fundamental period of structures is an important parameter to consider when designing structures in seismic-prone areas. Currently, the formulae available in the international literature and design codes fail to capture the true dynamic behaviour of structures, especially when they are founded on soft soils. It is necessary to develop more accurate models for predicting the fundamental period while taking into account the soil-structure interaction (SSI) effect. For the needs of this research, a dataset of 49,154 models (98,308 numerical results) was created for developing a predictive model for calculating the fundamental period of steel structures. the SSI phenomenon was also considered with structures modelled with a soil domain with varying depths. the model used herein is an Artificial Neural Network (ANN). the ANN model was able to predict the fundamental period with a correlation of 99.99% and a mean absolute percentage error (MAPE) of 0.7%. © 2023 COMPDYN proceedings. All rights reserved

关键词： Neural networks

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Improved Business Analysis by Using 3D models 18

Improved Business Analysis by Using 3D Models

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18th international conference on Evaluation of Novel Approaches to software engineering (ENASE)

作者： Kuhlen, David Speck, Andreas IU Int Univ Appl Sci Waterloohain 9 D-22769 Hamburg Germany Christian Albrechts Univ Kiel Hermann Rodewald Str 3 D-24118 Kiel Germany

ISBN: (纸本)9789897586477

the use of 3D objects may enhance the requirements engineering procedures. Different approaches using 3D modelling in requirements engineering, are described in the academic literature. Furthermore, the state of research shows objectives which can be obtained by using 3D modelling. this provides a good basis to formulate a reference model which helps project managers to plan the use of 3D modelling in requirements engineering. In order to support the usage of 3D modelling in requirements engineering, a reference model is designed. this model should help project managers, to plan the usage of 3D modelling. For doing so, project managers need to know why, how and when to use 3D modelling. the findings from literature were used in combination with an experiment, to elaborate a recommendation for using 3D models in requirements engineering. the experiment was combined with a survey in a requirements engineering lecture at IU international University of Applied Sciences. the experiment shows 3D modelling by using LEGO (R) SERIOUS PLAY (R) to be a liked method. the method seems to facilitate the motivation and collaboration. However, a further pre-analysis, before using the regular LEGO (R) SERIOUS PLAY (R) method shows no significant effect, to improve the analysis. A reference model was proposed to guide the usage of 3D modelling in requirements engineering. Especially the phases of requirements elicitation and the solution design benefit from using 3D modelling techniques in this proposal.

关键词： Requirements engineering 3D Modelling software Development

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SEMPDS 2023 - proceedings of the Posters and Demo Track of the 19th international conference on Semantic Systems, co-located with 19th international conference on Semantic Systems, SEMANTiCS 2023

SEMPDS 2023 - Proceedings of the Posters and Demo Track of t...

引用

19th international conference on Semantic Systems, SEMPDS 2023

the proceedings contain 9 papers. the topics discussed include: a framework generate, store, and publish FAIR data in experimental sciences;a mapping lifecycle for public procurement data;a toolset for normative interpretations in FLINT;developing a scalable benchmark for assessing large language models in knowledge graph engineering;enhancing interpretability of machine learning models over knowledge graphs;OntoAnon: an anonymizer for sharing ontology structure without data;SPARQLGEN: one-shot prompt-based approach for SPARQL query generation;and towards assessing FAIR research software best practices in an organization using RDF-star.

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Design and Analysis of Optimized Solid Propellant Grain 9th

Design and Analysis of Optimized Solid Propellant Grain

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9th international and 49th National conference on Fluid Mechanics and Fluid Power, FMFP 2022

作者： Akbar, Mohammed Phondekar, Prabhat Dattakumar Department of Mechanical Engineering Medi-Caps University Indore453331 India Department of Space Engineering and Rocketry Birla Institute of Technology Mesra835215 India

ISBN: (纸本)9789819978267

In rocket motors, propellants are utilized to undergo combustion and generate thrust. In the case of solid rocket motors, this is accomplished with solid propellant grain, which is the mixture of solid fuel and/or oxidizer cast and cured inside the combustion chamber to a semi-solid state. For the purpose of achieving various types of combustion, numerous designs have been created. there are four distinct types of combustion: progressive, neutral, regressive, and multi-stage thrust. Although these geometries have been designed since the earliest days of rocket manufacturing, they perform as intended despite not being optimized. this research investigates and analyzes the creation of new and optimized designs with differential calculus method and finite element analysis in order to increase surface area and propellant volume within the combustion chamber. Two models are theorized, designed, and analyzed in software for their various parameters, including internal surface area, volume, and pressure versus time curves. © 2024, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Combustion chambers

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Development of a software Tool for Grounding System Design in Regular and Irregular Shaped Areas 9

Development of a Software Tool for Grounding System Design i...

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9th international Multidisciplinary Moratuwa engineering Research conference, MERCon 2023

作者： Rodrigo, Asanka S. Yohan Harshamal, V.G. Usha Kawmadie, P.R. Vidanapathirana, thanuja S. University of Moratuwa Department of Electrical Engineering Moratuwa Sri Lanka

ISBN: (纸本)9798350345216

In this paper, primary focus is the computation of grounding parameters essential for both regular and irregular grounding grid designs. the key grounding parameters under examination encompass ground resistance, surface voltage, touch voltage, and step voltage. To fulfill this objective, a specialized software tool is introduced, consisting of a computer program. this software is underpinned by mathematical models specifically engineered to facilitate the precise determination of grounding parameters tailored to varying soil structures. Our approach entails a rigorous comparative analysis, wherein the outcomes generated by our methodology are compared with existing methods documented in the literature. © 2023 IEEE.

关键词： Electric grounding

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Queue Length Prediction Method of Short-linked Intersections Based on Long Short-Term Memory 9

Queue Length Prediction Method of Short-linked Intersections...

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9th international conference on Systems and Informatics, ICSAI 2023

作者： Shi, Kang Shi, Kang Yang, Jun Liang, Zi Jun Jun Liang, Zi Li, Yu Qi Wang, Rui Han Tongji University Key Laboratory of Road and Traffic Engineering Ministry of Education Shanghai201804 China Anhui Chaoyuan Information Technology Co. Ltd HeFei230088 China Anhui Province Transportation Big Data Analysis and Application Engineering Laboratory Hefei230601 China Hefei University School of Urban Construction and Transportation Hefei230601 China

ISBN: (纸本)9798350383706

To address traffic congestion and vehicle queue overflow at short-link intersections, it is essential to conduct a predictive analysis of vehicle queue length between these intersections. this helps in proactively implementing appropriate traffic management measures to prevent traffic overflow and congestion. In this paper, we propose a short-link intersection vehicle queue length prediction method based on Long Short-Term Memory (LSTM). First, historical traffic data from the morning peak hours over the past month is collected. After comparing multiple machine learning models, LSTM is chosen for traffic flow prediction. Subsequently, mathematical models are used to predict queue length at future time intervals. through practical validation, we found that the proposed method for predicting queue length between short-link intersections in this paper is effective, with an average prediction error rate of 11.9%. It meets the requirements for predicting vehicle queue length between intersections and proactively controlling them, holding significant practical significance for preventing traffic flow and congestion events. © 2023 IEEE.

关键词： Long short-term memory

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Developing a Digital Twin of the Subsea System 5

Developing a Digital Twin of the Subsea System

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5th international conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Desai, Urvi Kulkarni, Tanvi Jacob, Joella Susan Patil, Nupur Bedekar, Mangesh Dr. Vishwanath Karad MIT World Peace University Department of Computer Science and Technology Maharashtra Pune India

ISBN: (纸本)9798350367171

A digital twin of a physical system models its behaviour in its intended environment. It has found application in testing, monitoring and maintaining crucial systems large and small while avoiding the risk of any actual damage to the physical systems. Subsea laboratories offer a safe, reliable and easy way to study, visualise, monitor and test various components of an offshore oil rig without needing to be physically present or use real equipment. this paper proposes a digital twin for a double stage reciprocating air compressor at the subsea laboratory at MIT World Peace University, Pune - the Centre for Subsea engineering Research (CSER). In this work, we propose a framework for end-to-end functioning of the digital twin, including the usage of Machine Learning tools for modules such as testing and predictive maintenance. the User Interface, 3D Simulation and ML models used are all detailed in the paper, with several machine learning and deep learning models being implemented. the best accuracy was provided by Polynomial Regression for Outlet Temperature and Pressure with a Root Mean Square Error (RMSE) of 0. 0 9 and 0. 0 3 6 respectively, while AdaBoost gave the best accuracy for Air Flow with an RMSE of 0.019. this system will allow virtual access to the physical system for various purposes, with work set to continue on more components from the laboratory, making simple, safe and costefficient testing and monitoring in subsea environments a widely accessible reality. © 2024 IEEE.

关键词： predictive maintenance

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