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41st computer Graphics International Conference, CGI 2024

作者： Peng, Jiaxin Li, Tao Jiang, Qin Liu, Jie Wang, Ruibo Laboratory of Software Engineering for Complex Systems School of Computer Science National University of Defense Technology Hunan Changsha410073 China Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Hunan Changsha410073 China

ISBN: (纸本)9783031820205

The conventional Levenberg-Marquardt (LM) algorithm is a state-of-the-art trust-region optimization method for solving bundle adjustment problems in the Structure-from-Motion community, which not only takes advantage of the fast convergence of the Gauss-Newton method, but also the stability of the gradient descent method when approaching optimal solutions. However, the damping ratio of LM is simply provided by trial-and-error, which causes slow convergence rate for large-scale problems. This paper proposes the Yamashita-Fukushima LM (YFLM) algorithm to reduce the time complexity for global bundle adjustment, where the damping factor is determined by Yamashita and Fukushima’s method. YFLM dynamically calculates a more reasonable and optimal damping ratio according to the newest reprojection error. The experimental results show that the YFLM algorithm outperforms the conventional LM algorithm for most public bundle adjustment datasets. Besides this, the convergence of the YFLM algorithm is also evaluated with different σ∈(0,2]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Damping

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Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification

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computers, Materials & Continua 2024年第10期81卷 867-883页

作者： Mahesh Thyluru Ramakrishna Kuppusamy Pothanaicker Padma Selvaraj Surbhi Bhatia Khan Vinoth Kumar Venkatesan Saeed Alzahrani Mohammad Alojail Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India School of Computer Science and Engineering VIT-AP UniversityAmaravati522241India Department of Computer Science&Technology Madanapalle Institute of Technology&ScienceMadanapalle517325India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK School of Computer Science Engineering&Information Systems(SCORE) Vellore Institute of Technology(VIT)Vellore632014India Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh11531Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpuraPunjab140401India

Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient *** different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI *** approaches predominantly rely on traditional machine learning and basic deep learning methods for image *** methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI *** the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor *** approach highlights a major advancement in employing sophisticated machine learning techniques within computer science and engineering,showcasing a highly accurate framework with significant potential for healthcare *** model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification *** successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current *** integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider *** research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.

关键词： Deep learning MRI brain tumor cassification EfficientNetB3 computational engineering healthcare technology artificial intelligence in medical imaging tumor segmentation neuro-oncology

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Multimodal Medical Image Fusion based on the VGG19 Model in the NSCT Domain

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Recent Advances in computer science and Communications 2024年第5期17卷 59-70页

作者： Liu, Chunxiang Wang, Yuwei Cheng, Tianqi Guo, Xinping Wang, Lei School of Resources and Environmental Engineering Shandong University of Technology Zibo255000 China School of Computer Science and Technology Shandong University of Technology Zibo255000 China

Aim: To deal with the drawbacks of the traditional medical image fusion methods, such as the low preservation ability of the details, the loss of edge information, and the image distortion, as well as the huge need for the training data for deep learning, a new multi-modal medical image fusion method based on the VGG19 model and the non-subsampled contourlet transform (NSCT) is proposed, whose overall objective is to simultaneously make the full use of the advantages of the NSCT and the VGG19 model. Methodology: Firstly, the source images are decomposed into the high-pass and low-pass sub-bands by NSCT, respectively. Then, the weighted average fusion rule is implemented to produce the fused low-pass sub-band coefficients, while an extractor based on the pre-trained VGG19 model is constructed to obtain the fused high-pass subband coefficients. Result and Discussion: Finally, the fusion results are reconstructed by the inversion transform of the NSCT on the fused coefficients. To prove the effectiveness and the accuracy, experiments on three types of medical datasets are implemented. Conclusion: By comparing seven famous fusion methods, both of the subjective and objective evaluations demonstrate that the proposed method can effectively avoid the loss of detailed feature information, capture more medical information from the source images, and integrate them into the fused images. © 2024 Bentham science Publishers.

关键词： Image fusion

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Modified sepiolite stabilized stearic acid as a form-stable phase change material for thermal energy storage

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International Journal of Minerals,Metallurgy and Materials 2023年第9期30卷 1835-1845页

作者： Chuanchang Li Xinke Peng Jianjun He Jian Chen Key Laboratory of Renewable Energy Electric-Technology of Hunan Province School of Energy and Power EngineeringChangsha University of Science and TechnologyChangsha 410114China

Sepiolite(ST) was used as a supporting matrix in compiste phase change materials(PCMs) due to its unique microstructure, good thermal stability, and other raw material advantages. In this paper, microwave acid treatment were innovatively used for the modification of sepiolite. The modified sepiolite(ST_(m)) obtained in different hydrochloric acid concentrations(0.25, 0.5, 0.75, and 1.0 mol·L^(-1)) was added to stearic acid(SA) via vacuum impregnation method. The thermophysical properties of the composites were changed by varying the hydrochloric acid concentration. The SA-ST_(m0.5)obtained by microwave acid treatment at 0.5 mol·L^(-1)hydrochloric acid concentration showed a higher loading capacity(82.63%) than other composites according to the differential scanning calorimeter(DSC) analysis. The melting and freezing enthalpies of SA-ST_(m0.5)were of 152.30 and 148.90 J·g^(-1), respectively. The thermal conductivity of SA-ST_(m0.5)was as high as 1.52 times that of pure SA. In addition, the crystal structure, surface morphology, and microporous structure of ST_(m)were studied, and the mechanism of SAST_(m0.5)performance enhancement was further revealed by Brunauere Emmett Teller(BET) analysis. Leakage experiment showed that SAST_(m0.5)had a good morphological stability. These results demostrate that SA-ST_(m0.5)has a potential application in thermal energy storage.

关键词： sepiolite stearic acid phase change materials thermal energy storage

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Study on Electromagnetic Performance of Permanent Magnet Rotor and Dual Stator Starter Generator for Electric Vehicle Range Extender

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Progress In Electromagnetics Research B 2024年 106卷 39-55页

作者： Gao, Mingling Yu, Zhenhai Jiao, Wenjie Hu, Wenjing Geng, Huihui Liu, Yixin Liu, Shiqiang Liu, Yishuo School of Computer Science and Technology Shandong University of Technology Zibo255000 China School of Transportation and Vehicle Engineering Shandong University of Technology Zibo255000 China

The flywheel-type dual-stator permanent magnet starter generator combines engine flywheel and starter generator rotor into a single unit, which has the advantages of high efficiency, high power density, and compact structure. This paper proposes a new type of dual-stator permanent magnet starter generator topology in which the two stators are concentric and share the same permanent magnet rotor. Equivalent magnetic circuit modeling of the inner stator’s magnetic field, outer stator’s magnetic field, and synthetic magnetic field using the equivalent magnetic circuit method list the system of flux equations and solve the main magnetic flux, leakage flux, and leakage coefficient, and the results show that the equivalent magnetic circuit method has smaller error and higher accuracy than the finite element method. The harmonic electric potential of the starter generator is modeled and analyzed. The permanent magnet rotor and inner and outer stator structures are optimized to obtain the optimal parameters, and the prototype is manufactured and tested. The optimized starter generator no-load induced electromotive force fundamental amplitude is improved. The induced electromotive force harmonic distortion rate is reduced, and the output performance of the whole generator is significantly improved. © (2024), Electromagnetics Academy. All rights reserved.

关键词： Stators

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Leakage Localization of Gas Pipelines Based on Modal Acoustic Emission and Cross-Power Spectrum Peak Optimization

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IEEE Sensors Journal 2025年第11期25卷 19982-19992页

作者： Zhang, Wenbiao Gong, Kezheng Cui, Xiwang North China Electric Power University School of Control and Computer Engineering Beijing102206 China Beijing Information Science and Technology University School of Instrument Science and Opto-Electronics Engineering Beijing100192 China

Leakage accidents in natural gas pipelines bring huge property losses and pose serious safety risks. Therefore, faster and more accurate leakage localization is of great significance. In this article, a new method based on modal acoustic emission (MAE) and peak optimization of cross-power spectrum is proposed to enhance the performance of leakage localization. When a pipeline leakage occurs, the sensors installed on the pipelines receive acoustic emission (AE) signals, which have various modes. Instead of extracting a single mode through complex algorithms, the proposed method directly extracts the cross-power spectrum of a specified mode by choosing the appropriate window function and window parameters to locate the leakage with velocity information from dispersion curves. Gaussian, Hamming, and Blackman window functions are compared in the extraction of the cross-power spectrum of L(0, 1) mode, among which the Blackman window function is found to be the most effective. The artificial fish swarm algorithm combined with the iteration criterion for peak optimization is used to obtain suitable window parameters. Finally, the localization error of the above method is less than 5.2%. In addition, the performance of the iteration criterion for peak optimization of cross-power spectrum combined with other intelligent swarm algorithms is investigated. The results show that the maximum errors of other intelligent methods are still less than 9%, demonstrating the great compatibility of the proposed method. © 2001-2012 IEEE.

关键词： Acoustic emissions

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Assessment of Displacement Measurement Performance Using Phase-based Optical Flow with the HalfOctave Pyramid 11

Assessment of Displacement Measurement Performance Using Pha...

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11th International Symposium on Precision Mechanical Measurements

作者： Peng, Zhaoxin Wang, Xian Liu, Wei Wang, Zhiliang Liu, Menglian School of Mechanical Engineering Hunan University of Science and Technology Hunan Xiangtan411201 China

ISBN: (纸本)9781510680296

The effectiveness of the Complex Steerable Pyramid (CSP) in enhancing subtle structural motions for dynamic facility observation is widely acknowledged. However, further analysis and evaluation are needed to determine the applicability of directly utilizing CSP for displacement measurement in engineering applications. This paper assesses the displacement measurement performance of phase-based optical flow (PBOF) using HalfOctave CSP. Initially, the impact of the CSP's down-sampling rates on the accuracy of displacement measurements in PBOF is assessed by simulating motion to identify suitable configurations. Afterwards, the accuracy of this method is investigated within the effective range of displacement measurement by simulating motion. The results of the simulation experiments indicate that the optimal down-sampling factor for CSP should be set to zero. When CSP is at its optimal down-sampling factor and using filters located in the middle of the effective filter level, PBOF achieves the best comprehensive displacement measurement performance. © 2024 SPIE.

关键词： Displacement measurement

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Gender-Based Analysis of User Reactions to Facebook Posts

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Big Data Mining and Analytics 2024年第1期7卷 75-86页

作者： Yassine El Moudene Jaafar Idrais Rida El Abassi Abderrahim Sabour Mathematical Engineering and Computer Science Laboratory Faculty of SciencesIbn Zohr UniversityAgadir 8706Morocco Department of Computer Science Mathematical−High School of Technology Ibn Zohr UniversityAgadir 8706Morocco

Online Social Networks(OSNs)are based on the sharing of different types of information and on various interactions(comments,reactions,and sharing).One of these important actions is the emotional reaction to the *** diversity of reaction types available on Facebook(namely FB)enables users to express their feelings,and its traceability creates and enriches the users’emotional identity in the virtual *** paper is based on the analysis of 119875012 FB reactions(Like,Love,Haha,Wow,Sad,Angry,Thankful,and Pride)made at multiple levels(publications,comments,and sub-comments)to study and classify the users’emotional behavior,visualize the distribution of different types of reactions,and analyze the gender impact on emotion *** of these can be achieved by addressing these research questions:who reacts the most?Which emotion is the most expressed?

关键词： profiling knowledge extraction data mining emotion mining social media data crawling Facebook reactions gender

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Deep Energy-Efficient Optimization Network for URLLC Over Cell-Free Massive MIMO

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IEEE Internet of Things Journal 2025年第12期12卷 20973-20987页

作者： Li, Donggen Li, Jingfu Niyato, Dusit Feng, Wenjiang Jiang, Weiheng Chongqing University School of Microelectronics and Communication Engineering Chongqing400044 China Southwest Jiaotong University School of Information Science and Technology Chengdu611756 China Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore

To achieve ultra-reliable and low-latency communication (URLLC) and support high density of wireless connections simultaneously, the sixth-generation industrial Internet of things (6G-IIoT) necessitates an expansion of antenna arrays and broader bandwidths, which suffers from high energy consumption. To address this issue, this paper investigates a cell-free massive multiple-input multiple-output (CF-mMIMO) system and designs an iterative search-based two-stage energy efficiency (EE) optimization algorithm for the uplink communication of the system. The first stage prioritizes reliability to ensure that all users meet the URLLC requirements regarding latency and reliability. The second stage maximizes the EE under URLLC-satisfied conditions. Considering that iterative search algorithms incur a high computational overhead and variable number of iterations, we further propose a convolutional neural network architecture (RACNN) to approximate an optimal resource allocation strategy and to realize the real-time and stable output. This structure extracts deep correlations among users from the global channel features. It takes strategies of multi-task learning and weight loss adaptation to improve the model's convergence speed. Furthermore, we employ deep transfer learning to adjust RACNN parameters to accommodate the potential of dynamic communication scenarios, thereby reducing the demand for training samples and training time overhead. Finally, the efficacy of the proposed algorithm, RACNN, and deep transfer learning is validated through experimental simulations. © 2014 IEEE.

关键词： Convolutional neural networks

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A detection method for ship based on the improved YOLOv7-tiny 11

A detection method for ship based on the improved YOLOv7-tin...

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11th International Symposium on Precision Mechanical Measurements

作者： Cao, Hong Wu, Jigang School of Mechanical Engineering Hunan University of Science and Technology Hunan Xiangtan411201 China

ISBN: (纸本)9781510680296

Aiming at the problems of poor real-time performance, high leakage rate and false detection rate of existing ship detection methods in complex environment, we propose a ship detection method based on improved YOLOv7-tiny algorithm. The clustering algorithm is used to solve the problem that the preset anchor frame is not applicable to the extreme target size detection of ships;the GSConv lightweight convolution module is introduced to enrich the feature information;and the Efficient multi-scale attention (EMA) mechanism is integrated into the backbone feature extraction network to improve the model's ability to perceive the target with large multi-scale differences in the location information. location information. The recognition accuracy of this paper's model in SeaShips dataset is 97.2%, which is 2.9% higher than that before the improvement, and the model size is reduced by 16.3%. This paper's method carries out comparative validation experiments with Faster R-CNN, YOLOv5, YOLOv7, etc., and this paper's method improves by 7.1%, 3.6%, and 1.0% compared to them, respectively, and the experimental results show that this paper's The experimental results show that the algorithm has the advantages of high accuracy and robustness, which provides a theoretical basis for intelligent and efficient ship detection. © 2024 SPIE.

关键词： Information leakage

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