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International Joint Conference on Neural Networks (IJCNN)

作者： Kaige Wang Qingyu Xiong Chao Wu Min Gao Yang Yu State Key Laboratory of Power Transmission Equipment and System Security and New Technology School of Big Data and Software Engineering Chongqing University China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Because social networks have become a vital part of people's lives, cyberbullying becomes the most common risk encountered by young people on social networking platforms and raised serious concerns in society. Over the past few decades, most existing work on cyberbullying has focused on text analysis. Yet, the cyberbullying develops into multi-objective, multi-channel, and multi-form. Traditional text analysis methods cannot satisfy the diversity of bullying data in social networks. To deal with the new type of cyberbullying, we propose a multi-modal detection framework that takes into multi-modal information(e.g., image, video, comments, time) on social networks. Specifically, we not only extract textual features but also use the hierarchical attention networks to capture the session feature in social networks and encode several media information(e.g., video, image). Based on these features, we model the multi-modal cyberbullying detection framework to solve the new form of cyberbullying. Experimental analysis on two real-world datasets shows that our framework outperforms several existing state-of-the-art models.

关键词： Social network services Feature extraction Visualization Logic gates Media Encoding Text analysis

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Lowest degree decomposition of complex networks

arXiv

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arXiv 2020年

作者： Yu, Yong Jing, Ming Zhao, Na Zhou, Tao Key Laboratory in Software Engineering of Yunnan Province School of Software Yunnan University Kunming650091 China Electric Power Research Institute of Yunnan Power Grid Kunming650217 China CompleX Lab University of Electronic Science and Technology of China Chengdu611471 China

The heterogeneous structure [1,2] implies that a very few nodes may play the critical role in maintaining structural and functional properties of a large-scale network. Identifying these vital nodes is one of the most important tasks in network science [3,4], which allow us to better conduct successful social advertisements [5], immunize a network against epidemics [6], discover drug target candidates and essential proteins [7], and prevent cascading breakdowns in power grids [8], financial markets [9] and ecological systems [10]. Inspired by the nested nature of real networks [11], we propose a decomposition method where at each step the nodes with the lowest degree are pruned. We have strictly proved that this so-called lowest degree decomposition (LDD) is a subdivision of the famous k-core decomposition [12,13]. Extensive numerical analyses on epidemic spreading, synchronization and nonlinear mutualistic dynamics show that the LDD can more accurately find out the most influential spreaders, the most efficient controllers and the most vulnerable species than k-core decomposition and other well-known indices [14-17]. The present method only makes use of local topological information, and thus has high potential to become a powerful tool for network analysis. Copyright © 2020, The Authors. All rights reserved.

关键词： Electric power transmission networks

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Aerodynamic modeling and transient performance improvement of a free jet altitude test facility 6th

Aerodynamic modeling and transient performance improvement o...

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6th International Conference on Artificial Intelligence and Security,ICAIS 2020

作者： Wu, Feng Gao, Limin Wu, Xinyun Feng, Xudong Leng, Lintao Li, Yaoyu School of Power and Energy Northwestern Polytechnical University Xi’an710072 China The National Key Laboratory of Aerodynamic Design and Research Xi’an710129 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China Key Laboratory of Science and Technology on Aero-Eigine Altitude Simulation AECC Sichuan Gas Turbine Establishment Chengdu610000 China

ISBN: (纸本)9789811581007

An inlet-engine altitude test facility for flight condition simulation is modeled for the system dynamic performance improvement. The system model equations are solved with an improved Euler integral method. To simulate the interaction between the tested engine and the test bench accurately, a turbojet engine module is introduced in the system model. By modeling and simulation, a linear temperature compensation control algorithm based on the control error is validated for feasibility;controller parameters optimization for cabin static pressure stabilizing is also carried out. In this paper, some issues like temperature transient performance and engine operate impact restraint on cabin static pressure are discussed efficiently and economically. With the characteristics of high efficiency and economy, the modeling and simulation tools are suitable for the optimization of such systems. © Springer Nature Singapore Pte Ltd 2020.

关键词： Engines

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Advancing prenatal healthcare by explainable AI enhanced fetal ultrasound image segmentation using U-Net++ with attention mechanisms

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Scientific Reports 2025年第1期15卷 1-18页

作者： Singh, Retinderdeep Gupta, Sheifali Mohamed, Heba G. Bharany, Salil Rehman, Ateeq Ur Ghadi, Yazeed Yasin Hussen, Seada Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of Electrical Engineering College of Engineering Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia School of Computing Gachon University Seongnam-si 13120 South Korea Department of Computer Science and Software Engineering Al Ain University Abu Dhabi United Arab Emirates Department of Electrical Power Adama Science and Technology University Adama 1888 Ethiopia

Prenatal healthcare development requires accurate automated techniques for fetal ultrasound image segmentation. This approach allows standardized evaluation of fetal development by minimizing time-exhaustive processes that perform poorly due to human intervention. This research develops a segmentation framework through U-Net++ with ResNet backbone features which incorporates attention components for enhancing extraction of features in low contrast, noisy ultrasound data. The model leverages the nested skip connections of U-Net++ and the residual learning of ResNet-34 to achieve state-of-the-art segmentation accuracy. Evaluations of the developed model against the vast fetal ultrasound image collection yielded superior results by reaching 97.52% Dice coefficient as well as 95.15% Intersection over Union (IoU), and 3.91 mm Hausdorff distance. The pipeline integrated Grad-CAM++ allows explanations of the model decisions for clinical utility and trust enhancement. The explainability component enables medical professionals to study how the model functions, which creates clear and proven segmentation outputs for better overall reliability. The framework fills in the gap between AI automation and clinical interpretability by showing important areas which affect predictions. The research shows that deep learning combined with Explainable AI (XAI) operates to generate medical imaging solutions that achieve high accuracy. The proposed system demonstrates readiness for clinical workflows due to its ability to deliver a sophisticated prenatal diagnostic instrument that enhances healthcare results. © The Author(s) 2025.

关键词： Attention block Deep learning Fetal head Fetal head segmentation ResNet-34 UNet++

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Ultra-Fast and Ultra-Efficient Removal of Cr (Vi) by the Aqueous Solutions of Monolayer Mxene (Ti3C2Tx)

SSRN

引用

SSRN 2022年

作者： Wang, Hongwei Wu, Fang Wang, Zhiheng Ruan, Haibo Wang, Yang Zhang, Shufang Luo, Haijun Zheng, Zhiqin Zhou, Miao Fang, Liang State Key Laboratory of Power Transmission Equipment & System Safety and New Technology Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials College of Physics Chongqing University Chongqing 400044 China Research Center for Materials Interdisciplinary Sciences Chongqing University of Arts and Sciences Chongqing 402160 China Key Laboratory of Optoelectronic Technology and Systems Education Ministry of China College of Optoelectronic Engineering Chongqing University Chongqing 400044 China College of Software Chongqing College of Electronic Engineering Chongqing 401331 China Key Laboratory on Optoelectronic Functional Materials College of Physics and Electronic Engineering Chongqing Normal University Chongqing 401331 China Fundamental Science on Nuclear Wastes and Environmental Safety Laboratory Southwest University of Science and Technology Sichuan Mianyang621010 China

Chromium (VI) removal is crucial to the safety of water resources, but there is still a lack of effective fast dislodge methods, especially the on-site rapid remediation and emergency removal technology. Herein, the monolayer (ML) Ti3C2Tx nanosheets (NSs) were prepared to remove Cr (VI) from water. The solution sample of the two-dimensional (2D) Ti3C2Tx NSs demonstrate ultrafast desorption kinetics (the concentration decreasing from 300 to 3 mg L-1 in 10 min) and impressively high capacity (1483 mg g-1), which is several times higher than that of the most of the previous reported Cr (VI) adsorption by Ti3C2Tx. The analysis of the adsorption kinetics and isotherm models indicates that the adsorption reaction is endothermic, coexists with physical and chemical adsorption, but mainly ML chemical adsorption. The removal efficiency decreases gradually with pH increasing from 2 to 12, while the maximum adsorption capacity increases with increasing temperature. The joint study of the experimental analysis and theoretical calculation based on the density functional theory (DFT) demonstrates that the extraordinary outstanding removal performance of Cr (VI) is due to the synergetic effect of reduction reaction removal and electrostatic adsorption. It is found that the Cr (VI) is mainly reduced by the OH- terminals on Ti3C2, and the detoxified Cr (III) is anchored on the surface of Ti3C2Tx through complexation reaction with O terminal. The advantages of ultrafast desorption kinetics, extra-high adsorption capacity, and broad working pH range of the single-layered Ti3C2Tx for Cr (VI) removal make it a remarkable candidate for rapid and urgent removal of Cr (VI) wastewater. © 2022, The Authors. All rights reserved.

关键词： Density functional theory

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Machine learning-integrated computational analysis on a blood-based ternary nanofluid flow in a stenosed artery with the artificial neural networks and modified finite difference approach

引用

Chaos, Solitons and Fractals 2025年 199卷

作者： Hussain, Mohib Lin, Du Waqas, Hassan Ali, Bagh Shah, Nehad Ali School of Mathematics and Statistics Northwestern Polytechnical University Xian710072 China MIIT Key Laboratory of Dynamics and Control Complex System Northwestern Polytechnical University Xi'an710072 China School of Naval Architecture Ocean and Energy Power Engineering Wuhan University of Technology Hubei Wuhan430063 China Department of Software Engineering Faculty of Science & Technology ILMA University 75190 Karachi Pakistan Department of Mechanical Engineering Sejong University Seoul05006 Korea Republic of

Heart failure and stroke continue to be the most common cause of global death, with atherosclerosis artery stenosis becoming a significant contributor. Although prior research has progressed in comprehending blood circulation behaviour, however, the incorporation of artificial intelligence (AI) and machine learning (ML) in the examination of ternary nanofluids for stenosed arterial diseases signifies a notable breakthrough in this domain. We proposes an integration of ML technique with computational fluid dynamics (CFD) to analyse the non-linear dynamics of thermal characterization in blood-based tri-hybrid nano-fluid flow, influenced by thermal radiation, variable heat sources and sinks, and aligned magnetic field effects within a blood artery exhibiting cosine stenosis. The investigation is based on AI approach, the Levenberg–Marquardt algorithm (LMA), with back propagation Artificial Neural Network (ANN-BP). The mathematical model is developed in the form of partial differentia equations and transformed into ordinary differential equation by similarity scaling, and then numerically evaluated by a modified finite difference approach, the Keller-Box method. The suggested ANN-LMA accuracy is compared to the ML solution for boundary layer flow. Regression values indicate an excellent fit between the predictions and the real data. It is observed that the inclined magnetic angle affects the drag force and heat transfer rate. There is a 27.9% increase in the heat transfer rate for ternary nano-fluid. Conclusively, the non-linear interaction between the magnetic field and nanofluid flow may significantly enhance heat transfer rates, which could have potential applications in biomedical sciences. © 2025 Elsevier Ltd

关键词： Magnetic levitation

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A TPAA-SBFE Based Partitioning Algorithm for Structural Viscoelastic Dynamic Analysis With Cyclic Symmetry

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International Journal for Numerical Methods in engineering 2025年第11期126卷

作者： Yang Yu Chongshuai Wang Ean Tat Ooi Sundararajan Natarajan Haitian Yang State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment Department of Engineering Mechanics International Research Centre for Computational Mechanics Dalian University of Technology Dalian China College of Civil Engineering Jilin Jianzhu University Changchun China State Key Laboratory of Intelligent Power Distribution Equipment and System School of Electrical Engineering Hebei University of Technology Tianjin China Institute of Innovation Science and Sustainability Federation University Australia Ballarat Australia Integrated Modelling and Simulation Lab Department of Mechanical Engineering Indian Institute of Technology Chennai India

An innovative and efficient reduced order algorithm is proposed for the dynamic analysis of 2-D and 3-D viscoelastic structures featuring cyclic symmetry, which is applicable to complete cyclically symmetric structures with or without common nodes, as well as incomplete cyclically symmetric structures. The proposed algorithm is developed on a recursive platform based on TPAA-SBFEM and inherits all the advantages of TPAA-SBFEM. For complete cyclically symmetric structures with or without common nodes, the global stiffness and mass matrices are proved to be block-circulant and can be generated via a cyclically symmetric part instead of the whole domain. And then a recursive partitioning algorithm is rendered, such that the solution scale and computational cost can be efficiently reduced via solving a series of independent problems with small scales. For incomplete cyclically symmetric structures, the complete cyclic symmetry and Woodbury formula are utilized to reduce computational expense. Four numerical examples are provided to elucidate the effectiveness and efficiency of the proposed approach with satisfactory results.

关键词： cyclically symmetric dynamic analysis partitioning algorithm scaled boundary finite element method (SBFEM) viscoelasticity Woodbury formula

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Estimation of roof photovoltaic potential and carbon reduction potential based on semantic segmentation method

Estimation of roof photovoltaic potential and carbon reducti...

引用

Sustainable power and Energy Conference (iSPEC)

作者： Yan Huang Han Ding Jiaming Chen Yunkang Chen Suchuan Quan Yongkang Zhu School of Software(Suzhou) Southeast University Suzhou China State Grid Wuxi Power Supply Company Wuxi China School of Electrical and Electronic Engineering Southeast University Nanjing China School of information science and technology Nanjing Forestry University Nanjing China School of computer science and technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Electrical and Automation Engineering Nanjing Normal University Nanjing China

ISBN: (纸本)9781665414401

Since industrialization, the global surface temperature has been on an upward trend, and so far it has had a serious impact on the global ecosystem and socio-economic environment. Therefore, it is necessary to reduce carbon emissions through global joint efforts, maintain the stability of the surface temperature not exceeding 2°C, and achieve global carbon neutrality as soon as possible. Solar energy is the cleanest renewable energy and has good prospects for sustainable development in the future. Installing solar photovoltaic (PV) systems on the roofs of buildings has become the most extensive method of utilizing solar energy. In this study, we used the semantic segmentation method to estimate the solar photovoltaic potential based on identifying roofs from remote sensing images, and then combined the photovoltaic module parameters derived from building features with solar radiation data to evaluate the solar photovoltaic potential. We did experiments in Haizhou Economic Development Zone, Lianyungang City, Jiangsu Province. The results show that the number of available roofs of the photovoltaic system is 671, the available roof area is 925,810 square meters, and the annual photovoltaic power generation potential of the study area is 119,300.97 MW$\cdot$h/year, which has a large solar photovoltaic potential. The method to perform precise calculation of specific rooftop solar PV potential developed in this study will guide the formulation of energy policy for solar PV in the future.

关键词： Photovoltaic systems Image segmentation Semantics Buildings Urban areas Solar energy Carbon dioxide

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Unified resilience model using deep learning for assessing power system performance

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Heliyon 2025年第4期11卷 e42802页

作者： Artemchuk, Volodymyr Garbuz, Iurii Jamil Alsayaydeh, Jamil Abedalrahim Shkarupylo, Vadym Oliinyk, Andrii Bin Yusof, Mohd Faizal Herawan, Safarudin Gazali Department of Mathematical and Econometric Modelling G.E. Pukhov Institute for Modelling in Energy Engineering of the NAS of Ukraine Kyiv Ukraine Department of Environmental Protection Technologies and Radiation Safety Center for Information-analytical and Technical Support of Nuclear Power Facilities Monitoring of the NAS of Ukraine Kyiv Ukraine Department of Іnformation Systems in Economics Kyiv National Economic University Named After Vadym Hetman Kyiv Ukraine Department of Computerized Control System National Aviation University Kyiv Ukraine Department of Engineering Technology Fakulti Teknologi Dan Kejuruteraan Elektronik Dan Komputer (FTKEK) Universiti Teknikal Malaysia Melaka (UTeM) Melaka 76100 Malaysia Department of Computer Systems Networks and Cybersecurity of the National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Department of Software Tools Faculty of Computer Science and Technology Zaporizhzhia Polytechnic National University Zaporizhzhia Ukraine Research Section Faculty of Resilience Rabdan Academy Abu Dhabi United Arab Emirates Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta 11480 Indonesia

Energy resilience in renewable energy sources dissemination components such as batteries and inverters is crucial for achieving high operational fidelity. Resilience factors play a vital role in determining the performance of power systems, regardless of their operating environment and interruptions. This article introduces a Unified Resilience Model (URM) using Deep Learning (DL) to enhance power system performance. The proposed model analyzes environmental factors impacting the resilience of batteries and energy storage devices. This deep learning approach trains performance-impacting factors using previously known low resilience drain data. The learning output is utilized to augment various strengthening factors, thereby improving resilience. Drain mitigation and performance improvements are combined for direct impact verification. This process validates the model's fidelity in enhancing power system performance, with a specific focus on the impact of weather factors. © 2025 The Authors

关键词： Deep learning Energy resilience Fidelity Weather impact

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Ultra-short-term wind power prediction based on hybrid denoising with improved CEEMD decomposition

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Renewable Energy 2025年 251卷

作者： Gao, JiaJing Xing, HongMei Wang, YongSheng Liu, GuangChen Cheng, Bo Zhang, DeLong College of Data Science and Application Inner Mongolia University of Technology Hohhot010080 China Software Service Engineering Technology Research Center Inner Mongolia Autonomous Region Hohhot010080 China College of Electric Power Inner Mongolia University of Technology Hohhot010080 China Information Construction and Management Center Inner Mongolia University of Technology Hohhot010080 China Meteorological Data Centre Inner Mongolia Autonomous Region Hohhot010051 China

Ultra-short-term wind power prediction plays a crucial role in grid stability and energy management. To tackle the complexity of wind power data, this paper presents a Grey Relation Analysis-Improved Complete Ensemble Empirical Mode Decomposition (GCEEMD) method, integrating advanced denoising techniques and improvements. First, the Discrete Fourier Transform (DFT) and Discrete Wavelet Transform (DWT) are applied to analyze the data across multiple scales in both the frequency and time domains. This effectively extracts periodic features, removes noise, and preserves the dynamic characteristics of wind power. Next, Grey Relation Analysis (GRA) is introduced to optimize the CEEMD decomposition process by prioritizing key variables, which enhances the model's responsiveness to major trends and patterns. Finally, a Generative Adversarial Network (GAN) is combined with the NSGA-II algorithm to optimize component integration, significantly enhancing prediction accuracy and stability. Experimental results confirm the effectiveness of the proposed method when applied to real wind farm data. © 2025 The Authors

关键词： Image reconstruction

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