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Novel design of deep learning knowledge-driven recurrent neurostructure for bioconvective Maxwell nanofluid flow model with convective boundary and variable thermal conductivity

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European Physical Journal Plus 2025年第6期140卷 1-21页

作者： Khan, Asma Raja, Muhamad Asif Zahoor Chang, Chuan-Yu Khan, Maryam Pervaiz Khan, Zeshan Aslam Shoaib, Muhammad Shu, Chi-Min Department of Data Science and AI Applications Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Yunlin 64002 Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin 64002 Taiwan Department of Computer Science and Information Engineering Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Yunlin 64002 Taiwan International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin 64002 Taiwan AI Center Yuan Ze University Taoyuan 320 Taiwan Department of Safety Health and Environmental Engineering National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin 64002 Taiwan

The significance of computational fluid dynamics (CFD) has paramount interest to simulate the dynamics of fluid flows, heat transfer, and relative processes extensively applicable in aerospace, energy, automation, and biomedical engineering industries for optimize designs, modeling and productivity. Artificial intelligence knowledge-driven machine learning-approaches can effectively reduce simulation time, improve precision, and make top-notch outcome of CFD simulations. The presented study stochastic numerical paradigm is exploited to determine the bioconvective flow of Maxwell nanofluidic (BCFM-NF) model with impact of thermal conductivity and convective boundary conditions using deep recurrent neural network trained with the Levenberg–Marquardt backpropagation (RNN-LMB). The BCFM-NF model is governed by partial differential equations (PDEs) that are further transformed into nonlinear ordinary differential equations (ODEs) by using similarity transformations. Synthetic dataset acquired for the BCFM-NF model with Adams numerical method by varying Maxwell parameter, and numbers, i.e., Lewis, Hartmann and Prandtl, while fixed values of mean absorption coefficient, Brownian diffusion, variable thermal conductivity etc. The generated dataset is used for execution of RNN-LMB algorithm to optimize the mean square error (MSE)-based objective function in order to get the approximated solutions for each case of all scenarios of BCFM-NF system with negligible error magnitude. The efficacy of RNN-LMB is certified accordingly by the convergence curves on iterative update of MSE, training state dynamics of adaptive controlling indices, frequency pattern in the error histograms, and correlation of error, autocorrelation statistics on exhaustive numerical simulations for BCFM-NF model. © The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2025.

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CDCAT: A Multi-Language Cross-Document Entity and Event Coreference Annotation Tool

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Tsinghua science and Technology 2022年第3期27卷 589-598页

作者： Yang Xu Boming Xia Yueliang Wan Fan Zhang Jiabo Xu Huansheng Ning School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing 100083China Research Institute with Run Technologies Company Ltd.Beijing 100192China School of Information Engineering Xinjiang Institute of EngineeringUrumqi 830091China

A tool for the manual annotation of cross-document entity and event coreferences that helps annotators to label mention coreference relations in text is essential for the annotation of coreference corpora. To the best of our knowledge, CROss-document Main Events and entities Recognition(CROMER) is the only open-source manual annotation tool available for cross-document entity and event coreferences. However, CROMER lacks multi-language support and extensibility. Moreover, to label cross-document mention coreference relations, CROMER requires the support of another intra-document coreference annotation tool known as Content Annotation Tool, which is now unavailable. To address these problems, we introduce Cross-Document Coreference Annotation Tool(CDCAT), a new multi-language open-source manual annotation tool for cross-document entity and event coreference, which can handle different input/output formats, preprocessing functions, languages, and annotation systems. Using this new tool, annotators can label a reference relation with only two mouse clicks. Best practice analyses reveal that annotators can reach an annotation speed of 0.025 coreference relations per second on a corpus with a coreference density of 0.076 coreference relations per word. As the first multi-language open-source cross-document entity and event coreference annotation tool, CDCAT can theoretically achieve higher annotation efficiency than CROMER.

关键词： event coreference entity coreference manual annotation tool natural language processing

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A Novel Learning-Based Approach for Large Outdoor Power Transmission Equipment Detection 14th

A Novel Learning-Based Approach for Large Outdoor Power Tran...

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14th International Conference on Genetic and Evolutionary Computing, ICGEC 2021

作者： Jafari, Fatana Xu, Rui Shyirambere, Gilbert Ndungutse, Jean Bosco Liu, Shi-Jian School of Computer Science and Mathematics Fujian University of Technology Fuzhou China Key Laboratory of Big Data Mining and Applications of Fujian Province Fujian University of Technology Fuzhou China

ISBN: (纸本)9789811684296

Automatic inspection of large outdoor power transmission equipment via Unmanned Arial Vehicle (UAV) is a significant way to be sure to promote electrical reliability and safety. As a fundamental work of automated inspection, detection of power towers and insulators from aerial images are studied in this research using a deep learning method. Firstly, the aerial images of power towers and insulators with complex backgrounds are collected and well prepared. Secondly, a convolutional neural network based on the latest version of YOLO is trained using the prepared dataset. As the result, the trained model can be used to fulfil the automatic detection. Experiments proved that the proposed network can precisely identify towers and insulators simultaneously and has better performance than existed method. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Towers

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Deep Learning Driven - Pest Classification and Sustainable Agriculture 3

Deep Learning Driven - Pest Classification and Sustainable A...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kumar, M. Sirish Usha, R. Reddy, B. Rama Kantha Ramachandra, S. Prakash, P. Jaya Swetha, B. Mohan Babu University School of Computing Dept. of Data Science India Madanapalle Institute of Technology and Science Madanapalle India Sri Venkateswara College of Engineering Tirupati India Mother Theresa Institute of Engineering and Technology Palamaner India Sri Venkateswara College of Engineering Department of It Tirupati India Mohan Babu University Dept. of Master of Computer Applications India

ISBN: (纸本)9798331509675

This study introduces a system designed to identify pests in crops and classify them as either beneficial or harmful. The project begins by providing a comprehensive overview of various pest identification methods, analyzing their strengths and limitations. To address the problem, a categorization approach is proposed that incorporates convolutional neural networks (CNN) to build the pest identification and classification model. The model was trained using a dataset containing images of different pest species. Extensive testing was conducted on a large dataset, and the system's performance was validated against multiple traditional classification models. The proposed framework achieved a classification accuracy of 90%, outperforming conventional methods. This research highlights agriculture's vital role in the economy and underscores the potential of advanced technologies in supporting sustainable farming practices. © 2025 IEEE.

关键词： Agriculture Sustainability Classification Accuracy Classification Model Convolutional Neural Network (CNN) Pest Classification

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Automatic type Identification of Sub - Axial Vertebral Column Fractures using Deep learning Technique 2

Automatic type Identification of Sub - Axial Vertebral Colum...

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2nd International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2023

作者： Sindhura, D.N. Pai, Radhika M Bhat, Shyamasunder N Manohara Pai, M.M. Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Data Science and Department of Orthopaeadics Kasturba Medical College Manipal Manipal Academy of Higher Education Manipal India Department of Data Science Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

Traumas like falls, sports injuries, or vehicle accidents are the main causes of Vertebral Column Fracture(VCFs) or spine fracture. The symptoms of VCF include back pain, swelling, numbness, and height decrease. VCF causes extreme pain, paralysis, difficulty in movement, etc. CT scans are inexpensive and effective in providing precise and quick VCF type detection. The determination of type VCF, however subject to inter-observer variability. To address this limitation this work introduces an automatic system for the detection of VCF type based on an ensemble of deep fine tuned models. This can assist the orthopaedicians in type identification of VCF which decreases the image interpretation time and increases the patient care time. For type identification, a total of two fine-tuned CNN architectures are used. Then, to further enhance the type identification performance, we developed an ensemble model of fine tuned VGG16 and ResNet50 deep learning models, which average the outputs of the models during final prediction. We used a dataset of 2820 CT images from eight different VCF types that were collected from Kasturba Medical college, Manipal Academy of Higher education, Manipal. The results shows that the ensemble model performs well compare to individual fine-tuned models with accuracy of 82%, precision of 0.84, recall of 0.82 and F1-score of 0.81 for VCF type identification from CT scans. This study shows that type identification of VCF in CT scans may be successfully accomplished using an ensemble deep transfer learning system with various fine tuned CNN architectures. © Published under licence by IOP Publishing Ltd.

关键词： Deep learning

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A Study on Intelligent Optimization Algorithms for Capacity Allocation of Production Networks 17th

A Study on Intelligent Optimization Algorithms for Capacity ...

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17th Chinese Intelligent Systems Conference, CISC 2021

作者： Dong, Zhenyu Wang, Chia-Hung Zhao, Qigen Wei, Yi Chen, Shumeng Yang, Qipeng College of Computer Science and Mathematics Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China

ISBN: (纸本)9789811663239

Artificial intelligence contributes to many potential applications in the production networks. How to share resources flexibly and effectively in the production network to bring greater economic benefits has become an important issue. In this paper, we formulate the capacity allocation problem of the production network as an optimization model, and apply three heuristic algorithms to solve it with Matlab software. We also conduct the data analysis in the numerical experiments for comparing the optimal solutions and convergence speeds of the presented three algorithms at several examples. Simulation experiments show that the newly proposed GAPSO algorithm has better optimization efficiency. The main contribution of this paper is to apply genetic screening rules to the particle swarm optimization (PSO) algorithm, improve the convergence speed of the algorithm, avoid the algorithm from falling into the local optimal solution, and solve the capacity allocation problem in the production network. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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Tensor Extended Kalman Filter and its Application to Traffic Prediction

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IEEE Transactions on Intelligent Transportation Systems 2023年第12期24卷 13813-13829页

作者： Chang, Shih Yu Wu, Hsiao-Chun Kao, Yi-Chih San Jose State University Department of Applied Data Science San JoseCA95192 United States Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for Ai Applications Taoyuan32003 Taiwan National Yang Ming Chiao Tung University Information Technology Service Center Hsinchu30010 Taiwan

Traffic prediction is a very important mechanism in intelligent transportation systems for applications including routing planning and traffic control. In order to infer multifarious traffic information, one/two-relational traffic data in the vector/matrix form need to be expanded to multi-relational traffic data in an arbitrary tensor form. However, none of the existing approaches is capable of performing traffic prediction by characterizing and tracking the inherent nonlinear dynamics which are often encountered in realistic time-series analysis. Although the extended Kalman filter (EKF) has been proven to be quite promising in inferring nonlinear dynamics from time series, but the current EKF approach still suffers, unfortunately, from a serious drawback that state variables have to be represented in vector form. In fact, the characteristics of multi-relational states in practice can never been manifested accurately in practice and the performance of an EKF would be greatly restricted thereby. In this work, we introduce a new tensor extended Kalman filter (TEKF) approach to accommodate arbitrary input, output, and state variables all in arbitrary tensor forms. We also propose a new tensor-based expectation-maximization (EM) algorithm to estimate the nonlinear state-transition and observation-model mappings. The computational and memory complexities of the proposed TEKF approach are also studied in this paper. Finally, numerical experiments are conducted to evaluate the traffic prediction performance of the proposed new TEKF approach over the simulated and realworld traffic datasets in comparison with three other existing deep-learning prediction methods. © 2000-2011 IEEE.

关键词： Tensors

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MPC-based Motion Control of Underwater Vehicle with Fixed Depth 4

MPC-based Motion Control of Underwater Vehicle with Fixed De...

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2022 4th International Conference on Electronic Engineering and Informatics, EEI 2022

作者： Yu, Wenjie Liang, Quan Xiong, Neng Hong, Chuanbo School of Computer Science and Mathematics Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China

ISBN: (纸本)9783800759231

Aiming at the underactuated and nonlinear system of underwater vehicle, this paper studies the control of depth-fixing motion. The first is to analyze the kinematics and dynamics of the underwater vehicle, so as to establish the corresponding depth-fixing motion control model. Secondly, the controller based on the model predictive control algorithm is designed in the Simulink platform to realize the depth-fixing motion control and anti-disturbance test of the underwater vehicle. At the same time, the PID algorithm is introduced to compare the step response of the two. Finally, according to the simulation results, it can be concluded that the controller based on the MPC algorithm can effectively reduce the overshoot of the underwater vehicle, the control system can converge to the target value faster, and has strong anti-interference ability. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Controllers

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Semi-global Stereo Matching Algorithm Based on Census Transform 4

Semi-global Stereo Matching Algorithm Based on Census Transf...

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2022 4th International Conference on Electronic Engineering and Informatics, EEI 2022

作者： Hu, Jinjing Liang, Quan Yu, Wenze School of Computer Science and Mathematics Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China

ISBN: (纸本)9783800759231

Aiming at the problem that the center point of traditional Census transform window is vulnerable to the noise of outer boundary environment and the area of partial depth discontinuous area has low matching precision in stereo matching, a semi-global stereo matching algorithm based on anti-noise Census transform is proposed. Parallax graphs with a higher degree of match can be output. Firstly, by preprocessing the image, more information of the image can be retained and noise interference can be removed. Meanwhile, the Census algorithm is improved to overcome the dependence on the center pixel of the window and enhance the anti-interference of the initial generation value against noise. Secondly, in the cost aggregation stage, Sobel edge detection is used for cost aggregation. Then, the initial aberration is determined according to the rule that the winner is king. The last is the parallax optimization, through the left-right consistency detection strategy to reduce the false match points, improve the parallax accuracy of occlusion points, and finally get a fine parallax map. Compared with other stereo matching algorithms on Middlebury Vision website, the experimental results show that the proposed algorithm is superior to other stereo matching algorithms. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Geometrical optics

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A Matrix-Analytic Solution to Three-Level Multi-Server Queueing Model with a Shared Finite Buffer 14th

A Matrix-Analytic Solution to Three-Level Multi-Server Queue...

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14th International Conference on Genetic and Evolutionary Computing, ICGEC 2021

作者： Zhao, Qigen Wang, Chia-Hung Dong, Zhenyu Chen, Shumeng Yang, Qipeng Wei, Yi College of Computer Science and Mathematics Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China

ISBN: (纸本)9789811684296

A computing method is presented in this paper for three-level multi-server queues with a shared finite buffer. We derive a matrix-analytic method to determine the steady-state probability for the studied queueing model, which is formulated as a state-dependent Quasi-Birth-and-Death (QBD) process. In addition, a solving algorithm is also developed to calculate the expected waiting time and average queue size. In our numerical experiments, it illustrates the relationship between the system performance and model parameters of interest. The findings in our work could reveal the managerial insights into the stochastic process of a class of queueing systems with three-type heterogeneous servers and a common buffer. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Stochastic systems

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