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A deep neural network based surrogate model for damage identification in full-scale structures with incomplete noisy measurements

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Frontiers of Structural and Civil engineering 2024年第3期18卷 393-410页

作者： Tram BUI-NGOC Duy-Khuong LY Tam T TRUONG Chanachai THONGCHOM T.NGUYEN-THOI Laboratory for Computational Mechanics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Civil Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Department of Computer Science Aarhus UniversityAarhus 8000Denmark Thammasat University research unit in structural and foundation engineering Department of Civil EngineeringThammasat UniversityPathumthani 12120Thailand Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Thammasat School of Engineering Thammasat UniversityPathumthani 12120Thailand

The paper introduces a novel approach for detecting structural damage in full-scale structures using surrogate models generated from incomplete modal data and deep neural networks(DNNs).A significant challenge in this field is the limited availability of measurement data for full-scale structures,which is addressed in this paper by generating data sets using a reduced finite element(FE)model constructed by SAP2000 software and the MATLAB programming *** surrogate models are trained using response data obtained from the monitored structure through a limited number of measurement *** proposed approach involves training a single surrogate model that can quickly predict the location and severity of damage for all potential *** achieve the most generalized surrogate model,the study explores different types of layers and hyperparameters of the training algorithm and employs state-of-the-art techniques to avoid overfitting and to accelerate the training *** approach’s effectiveness,efficiency,and applicability are demonstrated by two numerical *** study also verifies the robustness of the proposed approach on data sets with sparse and noisy measured ***,the proposed approach is a promising alternative to traditional approaches that rely on FE model updating and optimization algorithms,which can be computationally *** approach also shows potential for broader applications in structural damage detection.

关键词： vibration-based damage detection deep neural network full-scale structures finite element model updating noisy incomplete modal data

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Divide-and-train: A new approach to improve the predictive tasks of bike-sharing systems

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Mathematical Biosciences and engineering 2024年第7期21卷 6471-6492页

作者： Ali, Ahmed Salah, Ahmad Bekhit, Mahmoud Fathalla, Ahmed Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Higher Future Institute for Specialized Technological Studies Cairo3044 Egypt College of Computing and Information Sciences University of Technology and Applied Sciences Ad-Dhahirah Ibri Oman Zagazig University Department of Computer Science Faculty of Computers and Informatics Sharkeya Zagazig Egypt Sydney Australia Kaplan Business School Sydney Australia Department of Mathematics Faculty of Science Suez Canal University Ismailia Egypt

Bike-sharing systems (BSSs) have become commonplace in most cities worldwide as an important part of many smart cities. These systems generate a continuous amount of large data volumes. The effectiveness of these BSS systems depends on making decisions at the proper time. Thus, there is a vital need to build predictive models on the BSS data for the sake of improving the process of decision-making. The overwhelming majority of BSS users register before utilizing the service. Thus, several BSSs have prior knowledge of the user’s data, such as age, gender, and other relevant details. Several machine learning and deep learning models, for instance, are used to predict urban flows, trip duration, and other factors. The standard practice for these models is to train on the entire dataset to build a predictive model, whereas the biking patterns of various users are intuitively distinct. For instance, the user’s age influences the duration of a trip. This endeavor was motivated by the existence of distinct user patterns. In this work, we proposed divide-and-train, a new method for training predictive models on station-based BSS datasets by dividing the original datasets on the values of a given dataset attribute. Then, the proposed method was validated on different machine learning and deep learning models. All employed models were trained on both the complete and split datasets. The enhancements made to the evaluation metric were then reported. Results demonstrated that the proposed method outperformed the conventional training approach. Specifically, the root mean squared error (RMSE) and mean absolute error (MAE) metrics have shown improvements in both trip duration and distance prediction, with an average accuracy of 85% across the divided sub-datasets for the best performing model, i.e., random forest. © 2024 the Author(s).

关键词： Forecasting

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Predicting the friction angle of clays using a multi-layer perceptron neural network enhanced by yeo-johnson transformation and coral reefs optimization

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Journal of Rock Mechanics and Geotechnical engineering 2024年第10期16卷 3982-4003页

作者： Libing Yang Trung Nguyen-Thoi Trung-Tin Tran College of Civil Engineering and Architecture Hunan Institute of Science and TechnologyYueyang414006China Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh CityViet Nam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh CityViet Nam Department of Information Technology Greenwich VietnamFPT UniversityDa Nang550000Viet Nam

The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering *** study addresses the importance of estimating the friction angle and presents the development of four soft computing models:YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and *** of all,the Yeo-Johnson(YJ)transformation technique was used to stabilize the variance of data and make it more suitable for parametric statistical models that assume normality and equal *** technique is expected to improve the accuracy of friction angle prediction *** friction angle prediction models then utilized multi-layer perceptron neural networks(MLPnet)and metaheuristic optimization algorithms to further enhance performance,including flower pollination algorithm(FPA),coral reefs optimization(CRO),ant colony optimization continuous(ACOC),and cuckoo search algorithm(CSA).The prediction models without the YJ technique,***-MLPnet,CRO-MLPnet,ACOC-MLPnet,and CSA-MLPnet,were then compared to those with the YJ technique,***-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and *** these,the YJ-CRO-MLPnet model demonstrated superior reliability,achieving an accuracy of up to 83%in predicting the friction angle of clay in practical engineering *** improvement is significant,as it represents an increase from 1.3%to approximately 20%compared to the models that did not utilize the YJ transformation technique.

关键词： Natural hazards Slope stability Friction angle Clay Soft computing models Geotechnical engineering

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A Cautionary Note on Poli's Stability Condition for Particle Swarm Optimization

A Cautionary Note on Poli's Stability Condition for Particle...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Von Eschwege, Daniel H. Engelbrecht, Andries P. Stellenbosch University Department of Industrial Engineering Stellenbosch South Africa Stellenbosch University Department of Industrial Engineering and Computer Science Division Stellenbosch South Africa Gulf University for Science and Technology Center for Applied Mathematics and Bioinformatics Kuwait Kuwait

ISBN: (纸本)9781665430654

Particle swarm optimization (PSO) is a swarm intelligence algorithm that finds candidate solutions by iteratively updating the positions of particles in a swarm. PSO performance depends on the use of a suitable control parameter (CP) configuration, which governs the trade-off between exploration and exploitation in the swarm. Various methods of adapting or tuning CPs exist, but many result in exploding particle velocities and an unstable search process. Poli's stability condition ensures convergence in the mathematical limit, and is often used to inform CP configuration. However, this study shows that since it does not place any practical convergence constraints, it cannot be used to guarantee a stable search process. Velocity explosion occurs nonetheless and can lead to floating-point overflow and numerical instability. The investigation into various CP configurations across diverse functions and measurements of particle velocities provides empirical evidence of velocity explosion, and cautions against the assumption that enforcing Poli's criterion guarantees stability. The findings underline the need for comprehensive understanding of CP tuning and stability conditions in PSO, as well as the crucial role of empirical evidence in evaluating the real-world performance of swarm intelligence algorithms. © 2023 IEEE.

关键词： Particle swarm optimization Poli Stability condition Swarm intelligence

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PARALLEL SCALABILITY OF THREE-LEVEL FROSch PRECONDITIONERS TO 220000 CORES USING THE THETA SUPERcomputer

PARALLEL SCALABILITY OF THREE-LEVEL FROSch PRECONDITIONERS T...

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作者： Heinlein, Alexander Rheinbach, Oliver Rover, Friederike Delft University of Technology Faculty of Electrical Engineering Mathematics & Computer Science Delft Institute of Applied Mathematics Mekelweg 4 Delft2628 CD Netherlands Technische Universität Bergakademie Freiberg Freiberg09596 Germany

The parallel performance of the three-level fast and robust overlapping Schwarz (FROSch) preconditioners is investigated for linear elasticity. The FROSch framework is part of the Trilinos software library and contains a parallel implementation of different preconditioners with energy minimizing coarse spaces of generalized Dryja-Smith-Widlund type. The three-level extension is constructed by a recursive application of the FROSch preconditioner to the coarse problem. In this paper, the additional steps in the implementation in order to apply the FROSch preconditioner recursively are described in detail. Furthermore, it is shown that no explicit geometric information is needed in the recursive application of the preconditioner. In particular, the rigid body modes, including the rotations, can be interpolated on the coarse level without additional geometric information. Parallel results for a three-dimensional linear elasticity problem obtained on the Theta supercomputer (Argonne Leadership Computing Facility, Argonne, IL) using up to 220 000 cores are discussed and compared to results obtained on the SuperMUC-NG supercomputer (Leibniz Supercomputing Centre, Garching, Germany). Notably, it can be observed that a hierarchical communication operation in FROSch related to the coarse operator starts to dominate the computing time on Theta, which has a dragonfly interconnect, for 100 000 message passing interface (MPI) ranks or more. The same operation, however, scales well and stays within the order of a second in all experiments performed on SuperMUC-NG, which uses a fat tree network. Using hybrid MPI/OpenMP parallelization, the onset of the MPI communication problem on Theta can be delayed. Further analysis of the performance of FROSch on large supercomputers with dragonfly interconnects will be necessary. © 2022 Alexander Heinlein, Oliver Rheinbach, and Friederike Röver.

关键词： Supercomputers

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Analysis of Nonlinear Processes in the P-I-N Diodes Plasma by the Perturbation Theory Methods 13

Analysis of Nonlinear Processes in the P-I-N Diodes Plasma b...

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13th International Conference on Advanced computer Information Technologies, ACIT 2023

作者： Bomba, Andrii Moroz, Igor National University of Water and Environmental Engineering Department of Computer Science and Applied Mathematics Rivne Ukraine Rivne State University of Humanities Department of Information Technologies and Modeling Rivne Ukraine

A nonlinear singularly perturbed boundary value problem of the electron-hole plasma state predicting in the p-i-n diodes active region is considered. The search for solutions to the problem is carried out by the asymptotic method of boundary corrections. The decomposition of the original problem is carried out, the results are analyzed, and the existence of its asymptotic solutions is proved. © 2023 IEEE.

关键词： Plasma diodes

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Effect of Credit Financing on the Supply Chain for Imperfect Deteriorating Items with Carbon Emission 11

Effect of Credit Financing on the Supply Chain for Imperfect...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Mittal, Mandeep Chauhan, Sudhir Kumar Rastogi, Shivangi School of Computer Science Engineering and Technology Bennett University Department of Mathematics Uttar Pradesh Greater Noida India Amity University Uttar Pradesh Aias Department of Mathematics Noida India Amity Institute of Applied Sciences Amity University Uttar Pradesh Noida India

ISBN: (纸本)9798350350357

The sustainable inventory model is a framework designed to ensure manufacturing processes' sustainability while maintaining profitability. During manufacturing, some items may experience degradation, resulting in imperfect goods. Nevertheless, a screening process is implemented to eliminate such items before they reach the consumers. Therefore, it is critical to scrutinize items prone to deterioration thoroughly. Suppliers prioritize trade credit in payments when supplying retailers, necessitating the development of an inventory model that caters to the needs of retailers dealing with imperfect and deteriorating quality items while considering carbon emissions and trade credit as a payment. The study presents numerical illustrations and sensitivity analysis for a better understanding of the model. © 2024 IEEE.

关键词： Supply chains

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Edge-SAN: An Edge-Prompted Foundation Model for Accurate Nuclei Instance Segmentation in Histology Images

Edge-SAN: An Edge-Prompted Foundation Model for Accurate Nuc...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wu, Xuening Shen, Yiqing Zhao, Qing Kang, Yanlan Hu, Ruiqi Zhang, Wenqiang Fudan University Shanghai Engineering Research Center of Ai & Robotics Academy for Engineering & Technology Shanghai China Johns Hopkins University Department of Computer Science BaltimoreMD United States Hong Kong Polytechnic University Department of Applied Mathematics Hong Kong Fudan University Engineering Research Center of Ai & Robotics Ministry of Education Academy for Engineering & Technology School of Computer Science Shanghai China

ISBN: (纸本)9798350386226

Accurate nuclei segmentation is fundamental in histology image analysis, playing an essential role in cancer grading and diagnosis. However, this task remains challenging due to variations in staining protocols, heterogeneity among nuclei types, and the densely clustered nature of nuclei. While SAM exhibits zero-shot generalization capabilities in natural image segmentation, its performance degrades when applied to nuclei segmentation in histology images. Existing adaptations of SAM for medical imaging primarily focus on organ or lesion segmentation, which differs substantially from nuclei segmentation due to the unique characteristics of nuclei - specifically, their sparse distribution combined with dense clustering. To address these challenges, we propose Edge-SAN (Segment Any Nuclei with Edge Prompting), an interactive segmentation foundation model specifically designed for nuclei segmentation. Edge-SAN introduces a novel edge prompting method that enhances the delineation of nuclei boundaries, particularly among densely clustered nuclei, by leveraging edge information to improve segmentation accuracy. We evaluate Edge-SAN on 12 diverse datasets in both few-shot and zero-shot scenarios, demonstrating its effectiveness as a foundation model for nuclei segmentation, achieving 66.81% AJI and 73.13% DSC - improvements of 16.33% and 15.48% over SAM-Med2D, respectively. The code is available at https://***/deep-geo/Edge-SAN. © 2024 IEEE.

关键词： Deep Learning Digital Pathology Foundation Model Nuclei Segmentation Segment Anything Model

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Face Identification with Feature Learning Using Quasilinear Partial Differential Equation 15th

Face Identification with Feature Learning Using Quasilinear ...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Muthukumaran, V. Satheesh Kumar, S. Radhika, E.K. Arpana, G.K. Nalini, R. Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Computer Science School of Applied Sciences REVA University Bangalore India Department of Computer Science Sindhi College Bangalore India Department of ECE East West College of Engineering Bangalore India

ISBN: (纸本)9783031810855

This study suggests a novel soft computing approach for enhancing face recognition performance using the feature learning methodology. The suggested system of Quasi-linear Partial Differential Equations (QPDE) based face recognition model is used to increase the accuracy of facial identification. The webcam first catches the user's image and saves it to the user's computer. The inference model receives the captured image. The input image is pre-processed to enhance the image, and then the image is split into numerous integral images at the inference model. Using speed-up robust features, an intriguing point is constructed for each integral image. The computed interest point is converted into a QPDE based weight matrix by representing the strength of features at all the regions of the face. With the generated weighted matrix of features extracted from the facial image, the method generates a Fast Artificial intelligence (AI) network with many layers. Each layer of the neural network has many neurons with dedicated functions to compute the similarity and feature weight using QPDE. The proposed approach has produced efficient results compared to earlier methods for the Flickr-Faces-HQ database (FFHQ) datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Soft computing

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Orthopedic Walker Fall Detection and Motion Classification Using Bidirectional Gated Recurrent Unit Neural Network 10

Orthopedic Walker Fall Detection and Motion Classification U...

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10th International Conference on Digital Arts, Media and Technology and 8th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Mekruksavanich, Sakorn Hnoohom, Narit Phaphan, Wikanda Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Thailand Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science Bangkok Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798331543273

Accurate fall detection for orthopedic walker users is essential for timely medical intervention and enhancing safety for elderly and mobility-impaired individuals. This paper introduces a new method using a bidirectional gated recurrent unit (BiGRU) neural network to detect falls and classify motion patterns in walker usage. We use the Walker dataset, which includes inertial measurement unit sensor data for four motion classes: idle, motion, step, and fall. The dataset consists of 6-axis IMU readings (3-axis accelerometer and 3-axis gyroscope) from sensors mounted on orthopedic walkers, with around 620 samples per class. Our BiGRU model leverages temporal dependencies in both forward and backward directions to improve classification accuracy. We assess the model's performance in two scenarios: binary classification (fall vs. non-fall) and multi-class classification of all four motion types. The proposed architecture shows strong performance, achieving 99.79% accuracy and a 99.73% F1-score in binary classification, and 98.51% accuracy and a 98.50% F1-score in multi-class classification. It outperforms traditional deep learning models like CNN, LSTM, and GRU. The experimental results confirm the effectiveness of bidirectional processing in capturing temporal motion patterns, indicating significant potential for realtime fall detection in walker-assisted mobility. The high precision and recall values in both scenarios demonstrate the model's reliability for practical healthcare monitoring applications. © 2025 IEEE.

关键词： Recurrent neural networks

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