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27th International Conference on Nuclear Engineering: Nuclear Power Saves the World!, ICONE 2019

作者： Huang, Qian Xiong, Furui Bai, Xiaoming Yu, Xiaofei Zheng, Liangang Lu, Xifeng Song, Haiyang Science and Technology on Reactor System Design Technology Laboratory Chengdu Sichuan China

ISBN: (纸本)9784888983051

In the dynamic analysis of reactor system, the key mechanical input parameters are uncertain due to the design change, manufacturing, installation and calculation deviation, which affects the dynamic response, load distribution and final mechanical evaluation results. In order to quantify the influence of the uncertainty on seismic responses and load distribution, the uncertainty quantification of key structural input parameters of reactor system under seismic load is studied. Firstly, according to the fundamental characters of key input parameters (contact stiffness and gap among various assemblies), maximum entropy principle is adopted to construct the probability density function(PDF). Then, Markov Chain Monte Carlo technique is applied to sample according to the PDF, and the input and output data pool is constructed via finite element transient computations. According to the results, probabilistic distributions of dynamic responses caused by uncertainty from inputs parameters are evaluated and the reliability of nominal model is investigated. The studies show that the probabilistic distribution of reactor system dynamic response differs greatly with different output mechanical parameters, degrees of freedom and frequency bands. The uncertainty quantification method and process for reactor system are proposed in this paper. This method can be extended to other equipments easily. Therefore, it is an effective tools for the reliability of dynamic response. Copyright © 2018-2019 by JSME

关键词： Markov processes

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Experimental and Numerical Investigation Integrated with Machine Learning (Ml) for the Prediction Strategy of Dp590/Cfrp Composite Laminates

SSRN

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SSRN 2024年

作者： Hu, Haichao Wei, Qiang Wang, Tianao Ma, Quanjin Jin, Peng Pan, Shupeng Li, Fengqi Wang, Shuxin Yang, Yuxuan Yan, Li School of Materials Science and Engineering Hebei University of Technology Tianjin300401 China School of Mechanical and Engineering Tianjin Sino-German University of Applied Sciences Tianjin300350 China Tianjin Sino-Spanish Machining Tool Vocational Training Center Tianjin Sino-German University of Applied Sciences Tianjin300350 China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China Tianjin University of Science and Technology China

A machine learning (ML)-assisted composite design framework is established as a high-efficiency tool to obtain optimal layup stacking sequence and laminate orientation selections. This paper uses ML, numerical and experimental investigations to predict the mechanical properties of CFRP/metal composite laminates under quasi-static loading. An initial data set was established with the required experimental database and the finite element modelling. A suitable ML data set is established through the data enhancement process. Based on the augmented data set, nine ML models predict and evaluate the mechanical properties of CFRP/metal composite laminates. The comprehensive comparison of nine ML models to predict the tensile strength showed that XGBoost and gradient boosting models provided the best prediction results regarding low prediction error rate and high interpretability. To predict the bending strength, decision trees, K nearest neighbours, and random forest models were shown the accurate prediction data in terms of maximum error prediction. Moreover, XGBoost and decision trees models provided stable performance on multiple performance indicators, which were suitable and precise models. The tree-based models (such as decision tree, random forest, gradient boosting, and XGBoost) showed excellent overall performance, which was mainly attributed to the CFRP/DP590 composite laminates. In addition, this paper aims to discuss the effect of layup sequences on tensile and bending properties of CFRP/DP590 composite laminates based on ML, numerical and experimental methods. The ML-assisted model method provides deeper insights into the complex mechanisms behind material type, which provides an essential tool for designing and optimising fiber/metal composite laminates. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Comparison Between Gramian Angular Fields (GAF) and Markov Transition Field (MTF) Images Data by Using them as Input to the Deep Learning Neural Network Solar Flare Production Platform

Comparison Between Gramian Angular Fields (GAF) and Markov T...

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2022 International Conference on Engineering and MIS, ICEMIS 2022

作者： Nagem, Tarek A M Hamad Alshareef, Sohil Mohamed, Abdelhafid Gihedan, Akram Albargathe, Salma Libyan British University Dept. Computer System Design Benghazi Libya University of Benghazi Dept. Software Engineering Benghazi Libya College of Computer Technology Dept. Software Engineering Benghazi Libya Darna University Dept. Computer Science Algoba Libya

ISBN: (数字)9781665454360

ISBN: (纸本)9781665454360

The prediction of solar storms from real-time satellites data is an essential to protect various aviation, power, and communication infrastructures. For this reason, current research interest is focusing on creating systems that can predict solar fare accurately. This paper combers Gramian Angular Fields (GAF) images and Markov Transition Field (MTF) images that have been generated by converting pre-flare Geostationary Operational Environmental Satellite (GOES) data from 2010 to 2016. After that, the deep learning neural network for the solar flare production platform accepts both MTF and GAF images as input to generate the predictions. Furthermore, this paper;investigated using MTF and GAF images spritely as input to the solar flare production platform. After that, the results of MTF and GAF images were compared using various prediction verification measures. © 2022 IEEE.

关键词： Forecasting

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Enhanced optical imaging and fluorescent labeling for visualizing drug molecules within living organisms

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Acta Pharmaceutica Sinica B 2024年第6期14卷 2428-2446页

作者： Ting Sun Huanxin Zhao Luyao Hu Xintian Shao Zhiyuan Lu Yuli Wang Peixue Ling Yubo Li Kewu Zeng Qixin Chen School of Pharmaceutical Sciences National Key Laboratory of Advanced Drug Delivery SystemMedical Science and Technology Innovation CenterShandong First Medical University&Shandong Academy of Medical SciencesJinan 250062China Departments of Diagnostic Radiology SurgeryChemical and Biomolecular Engineeringand Biomedical EngineeringYong Loo Lin School of Medicine and College of Design and EngineeringNational University of SingaporeSingapore 119074Singapore Institute of Biochemical and Biotechnological Drugs School of Pharmaceutical SciencesCheeloo College of MedicineShandong UniversityJinan 250012China Key Laboratory of Biopharmaceuticals Postdoctoral Scientific Research WorkstationShandong Academy of Pharmaceutical ScienceJinan 250098China State Key Laboratory of Natural and Biomimetic Drugs School of Pharmaceutical SciencesPeking UniversityBeijing 100191China School of Life Sciences Science and Technology Innovation CenterShandong First Medical University&Shandong Academy of Medical SciencesJinan 250062China School of Chinese Materia Medica Tianjin University of Traditional Chinese MedicineTianjin 301617China Tianjin Pharmaceutical DA REN TANG Group Corporation Limited Traditional Chinese Pharmacy Research Institute Tianjin 300457China Key Laboratory of Systems Bioengineering(Ministry of Education) School of Chemistry Engineering and TechnologyTianjin UniversityTianjin 300072China

The visualization of drugs in living systems has become key techniques in modern *** advancements in optical imaging technologies and molecular design strategies have revolutionized drug *** the subcellular level,super-resolution microscopy has allowed exploration of the molecular landscape within individual cells and the cellular response to *** beyond subcellular imaging,researchers have integrated multiple modes,like optical near-infrared II imaging,to study the complex spatiotemporal interactions between drugs and their *** combining these visualization approaches,researchers gain supplementary information on physiological parameters,metabolic activity,and tissue composition,leading to a comprehensive understanding of drug *** review focuses on cutting-edge technologies in drug visualization,particularly fluorescence imaging,and the main types of fluorescent molecules ***,we discuss current challenges and prospects in targeted drug research,emphasizing the importance of multidisciplinary cooperation in advancing drug *** the integration of advanced imaging technology and molecular design,drug visualization has the potential to redefine our understanding of pharmacology,enabling the analysis of drug micro-dynamics in subcellular environments from new perspectives and deepening pharmacological research to the levels of the cell and organelles.

关键词： Optical imaging Drug visualization Fluorophore labeling Therapeutics

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Correction to:Study on the resource utilization of high fluorine-containing organic waste through fluidized incineration

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Waste Disposal and Sustainable Energy 2023年第2期5卷 245-245页

作者： Wenhan Li Zengyi Ma Jianhua Yan Qianming Huang Xingjian Wen Zian Zhai Bochen Huang Shuang Wang Yongqiang Chen Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of ChinaChengdu610213China State Key Laboratory of Clean Energy Utilization Institute for Thermal Power EngineeringZhejiang UniversityHangzhou310027China Zhejiang Xianchuang Energy&Technology Co.Ltd Jinhua321000China

Correction to:Waste Disposal&Sustainable Energy(2022)4:117-129 https://***/10.1007/s42768-022-00101-7 The section Confict of interest'has been amended:'Jian-hua Yan is the Editor in-Chief of Waste Disposal&Sustain-able Energy.'The revised'Confict of interest'is as follows:Jianhua Yan is the Editor-in-Chief of Waste Disposal&Sustainable *** behalf of all authors,the corresponding author states that there is no conflict of interest.

关键词： Sustainable fluorine behalf

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Multimodal Knowledge Graph Completion Method Based on Integrated Modality Adversarial Training and Relation-Enhanced Attention Mechanism

Multimodal Knowledge Graph Completion Method Based on Integr...

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International Conference on Computer Supported Cooperative Work in design

作者： Ruijia Zhang Zheng Dong Jianqiang Zhang Gongpeng Song Qin Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Negative sampling (NS) is widely used in knowledge graph completion (KGC) to generate negative triples for contrastive learning during training. However, existing NS methods are not suitable when multimodal information is incorporated into KGC models. Due to their complex design, these methods are also inefficient. In this paper, we propose the integration of Modality-Aware Adversarial Training (IMAT) to generate higher-quality negative samples for Multimodal Knowledge Graph Completion (MMKGC), and introduce a Relation-Enhanced Cross-modal Attention (RECA) mechanism to evaluate bidirectional attention weights between multimodal features using relational information, thereby improving the model's ability to identify hard negative samples. Our approach represents a joint design of MMKGC models and training strategies, surpassing 16 recent MMKGC methods and achieving new state-of-the-art results on three public MMKGC benchmarks.

关键词： Training Federated learning design methodology Computational modeling Semantics Focusing Knowledge graphs Contrastive learning Sampling methods Real-time systems

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Hierarchical Text Classification Method Driven by Dynamic Hierarchical Fusion and Prompt Embedding

Hierarchical Text Classification Method Driven by Dynamic Hi...

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International Conference on Computer Supported Cooperative Work in design

作者： Yiyun Xing Kaili Zhou Jianqiang Zhang Gongpeng Song Qin Lu Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Computer Science and Technology Shandong Engineering Research Center of Big Data Applied Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Hierarchical Text Classification, as a critical task in natural language processing, has broad applications in scenarios with complex label structures and limited samples. However, existing methods still face challenges in capturing hierarchical label information and text interactions, resulting in suboptimal classification performance. This study proposes a few-shot hierarchical text classification method based on prompt embeddings and dynamically fused hierarchical features. Building on pre-trained language models, the method encodes hierarchical structure information as prompts through an adaptive prompt embedding model and dynamically fuses hierarchical features to generate more expressive text representations. Additionally, a positive sample generation strategy based on contrastive learning is introduced, enabling the model to effectively distinguish hierarchical features across different categories, further enhancing classification performance. Experimental results demonstrate that this method significantly improves classification accuracy on multiple public datasets, exhibiting stronger generalization ability and robustness compared to mainstream methods. This approach offers a novel and effective solution for hierarchical text classification tasks.

关键词： Adaptation models Accuracy Federated learning Computational modeling Text categorization Buildings Contrastive learning Robustness Natural language processing Faces

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Effective parametric optimization for packaging design using Bayesian optimization 21

Effective parametric optimization for packaging design using...

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21st International Conference on Electronic Packaging technology, ICEPT 2020

作者： Dai, Weijing Xue, Ke Wu, Jingshen Liu, Zihao Southern University of Science and Technology School of System Design and Intelligent Manufacturing Shenzhen China

ISBN: (纸本)9781728168265

As different structure, materials and components are widely applied in electronic packaging, complicated responses of these heterogenous but integrated parts subjecting to thermo-mechanical loading during manufacturing and usage are becoming a crucial issue to reliability of packaging. Therefore, how to find optimal engineering design of packaging receives growing attention. For the past, design of Experiment (DoE) combined with statistical analysis is generally adopted. However, the increasing complexity of electronic packaging dramatically enlarges the amount of design parameters, making these traditional methods hard to maintain its efficiency. In current study, we implement Bayesian optimization with Gaussian process (BO-GP) as a framework to search the optimal combination of design parameters to reduce the warpage of a testing packaging vehicle. Upper confidence bound (UCB) method is adopted to define how this framework search in the design space. By employing simulated annealing algorithm, the BO-GP framework in this study can balance the exploration and exploitation within the space and present good convergence towards optimal design parameters. © 2020 IEEE.

关键词： Simulated annealing

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Optimal design of water immersion sensor layout for helicopter emergency flotation system based on the nsgaII algorithm 2

Optimal design of water immersion sensor layout for helicopt...

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2023 2nd International Conference on Aerospace and Control Engineering, ICoACE 2023

作者： Wang, Bingqian Wang, Zhe Sun, Zhi Hou, Bin College of Civil Aviation Nanjing University of Aeronautics and Astronautics Nanjing211106 China Key Laboratory of Aircraft Environment Control and Life Support MIIT Nanjing University of Aeronautics and Astronautics Nanjing210016 China Key Laboratory of Civil Aviation Emergency Science and Technology CAAC Nanjing University of Aeronautics and Astronautics Nanjing211106 China Environment Control System Research Department COMAC Shanghai Aircraft Design & Research Institute Shanghai201210 China

Aiming at the problem of sensor layout in an emergency flotation system, a sensor layout scheme design method based on the NSGAII method is proposed. The ALE method is used to simulate the water landing process of the helicopter on different pitch angles. The logic relationship of the 4/5 voting system is used to improve the sensor module reliability. Further, a mathematical model of layout scheme is established to maximize the detection coverage and minimize the force acting on the water immersion sensors when helicopter emergent water landing as the objective functions. Based on the mathematical model of water immersion sensor layout optimization constructed in this paper, NSGAII is used to solve this problem. The results show that the proposed method can realize the optimization of the layout of the water immersion sensor, the detection coverage of optimized schemes is increased by 3.26% on average compared with the value under the original scheme, and the force acting on the water immersion sensors is reduced by 61.86%. © Published under licence by IOP Publishing Ltd.

关键词： Flotation

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Context-Aware Enhancement and Transformer Network for Image-Text Retrieval

Context-Aware Enhancement and Transformer Network for Image-...

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International Conference on Computer Supported Cooperative Work in design

作者： Zheng Dong Ruijia Zhang Jianqiang Zhang Gongpeng Song Qin Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Computer Science and Technology Shandong Engineering Research Center of Big Data Applied Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Image-text matching is an important and challenging task in the field of multimedia analysis, aimed at bridging the semantic gap between visual content and language descriptions. Although this field has significant implications for enhancing cross-modal interaction, most previous work still faces challenges in accurately aligning images with text descriptions, especially when dealing with images containing rich semantic information. To address this issue, we propose a novel context-aware image-text matching model that extracts and summarizes visual region information aligned with multiple text descriptions from a single image. Specifically, we designed an adaptive context-aware self-attention module to extract representations of visual regions and text words. By controlling the complementary semantic relationships within each modality, our model can adaptively capture contextual information for each modality. We then introduce a Transformer-based encoder layer to extract features at multiple levels and aggregate region-level features into image-level features. Finally, fine-grained word-region alignment is conducted to match image features with corresponding text features. To evaluate the effectiveness of our method, we conducted extensive experiments on two benchmark datasets, Flickr30K and MS-COCO. The experimental results show that our model outper-forms several state-of-the-art baselines in image-text matching tasks, demonstrating the effectiveness and practicality of our model in handline cross-modal retrieval tasks.

关键词： Measurement Visualization Adaptation models Federated learning Semantics Transformers Feature extraction Faces Context modeling Cross modal retrieval

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