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Upconversion luminescence and optical thermometry behaviors of Yb3+ and Ho3+ co-doped NaBi(MoO4)2 single crystal

Journal of Molecular Structure 2025年 1343卷

作者： Su, Wenzhi Zhang, Mengyu Zhang, Chuancheng Zou, Yong Liu, Wenpeng Ding, Shoujun School of Microelectronics and Data Science Anhui University of Technology Anhui Maanshan243002 China Anhui Provincial Joint Key Laboratory of Disciplines for Industrial Big Data Analysis and Intelligent Decision Anhui Maanshan243002 China Anhui Institute of Optics and Fine Mechanics Chinese Academy of Sciences Anhui Hefei230031 China

Upconversion (UC) fluorescence intensity ratio (FIR) technology has emerged as a promising approach for optical thermometry due to its ability to provide non-contact, high-precision temperature measurements. Unlike conventional UC phosphor-based systems, single-crystal UC materials offer enhanced responsiveness and sensitivity for thermal detection, thanks to their structural stability and superior thermal conductivity. In this study, we successfully synthesized an Yb3+, Ho3⁺:NaBi(MoO4)2 (NBM) single crystal with exceptional optical quality using the Czochralski (Cz) growth technique. Comprehensive structural characterization was carried out using X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, and scanning electron microscopy (SEM). The results confirmed that Yb3+, Ho3+:NBM crystallizes in a tetragonal structure with the space group I41/a. The emission of different visible light colors under various excitation wavelengths was found to be due to the multiphonon relaxation process. Systematic analysis of power-dependent up-conversion emission spectra conclusively showed that the green and red up-conversion emissions arise from two-photon excitation processes. Moreover, temperature-dependent spectral evolution in the range of 300–500 K was quantitatively analyzed, allowing the calculation of both absolute sensitivity (Sₐ) and relative sensitivity (Sᵣ) parameters to evaluate the material's optical thermometric performance. These findings highlight the promising potential of Yb3+, Ho3+:NBM single crystal for developing high-performance optical thermometry devices. © 2025

关键词： Fourier transform infrared spectroscopy

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Simulation Research on Nonlinear Modeling and Control of Switched Reluctance Motor

Simulation Research on Nonlinear Modeling and Control of Swi...

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第32届中国控制与决策会议

作者： Luan Ru Zou Hongjian Guo Jinru The School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing Key Laboratory of Intelligent Processing for Building Big Data

The model of a switched reluctance motor(SRM) directly influences it's performance and control ***,the doubly salient structure,switching power supply and seriously saturated magnetic circuit,make it difficult to accurately *** to solve this problem,this paper presents a new simulation method based on BP neural network to establish the current and torque models of a *** a great deal of current,flux linkage,torque numerical solution data obtained by a finite element software as the sample data,a kind of BP neural network is trained offline,and all the expected data sets,which form a table,are *** looking-up the table the nonlinear models of flux linkage and torque of the SRM are *** the basis of the nonlinear models,the current closed-loop control is formed by using the torque sharing function and the fuzzy switching angles to reduce the torque *** simulation results show that the proposed nonlinear models not only have a fast calculation speed,but also are high precision and can meet the requirements of real-time *** proposed control method can effectively reduce the torque ripple of the SRM.

关键词： Switched reluctance motor nonlinear modeling neural network fuzzy control torque sharing function

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Argumentation Based Ontology Alignment Extraction 6th

Argumentation Based Ontology Alignment Extraction

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6th International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2021

作者： Wu, Xiaojing Xue, Xingsi Hu, Wenyu Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology 33 Xuefu South Road Fuzhou Fujian350118 China Intelligent Information Processing Research Center Fujian University of Technology Fuzhou Fujian350118 China Institute of Artificial Intelligence Fujian University of Technology Fuzhou Fujian350118 China Fujian Key Lab for Automotive Electronics and Electric Drive Fujian University of Technology Fuzhou Fujian350118 China School of Information Science and Engineering Fujian University of Technology Fuzhou Fujian350118 China

ISBN: (纸本)9783030697167

The ontology matching technique aims at establishing the correspondence between two ontologies’ entities to solve their heterogeneity problem. In the matching process, the quality of the single matcher’s alignment can not be ensured. Usually, multiple matches are required to work together to increase the evidence of potential matches or mismatches. Selecting, combining, and adjusting these ontology matches to obtain high-quality ontology comparisons is one of the main challenges in ontology matching. This paper proposes an Argumentation Framework (A.F.) based on the ontology extracting technique to address this challenge. First, the proposed approach obtains the alignments obtained by different ontology matches. It extracts the final alignment by debating the disputed entity correspondences with A.F. The experiment is conducted on the Bibliographic track provided by Ontology Alignment Evaluation Initiative (OAEI), and the statistical comparison with the state-of-the-art ontology matching techniques shows the effectiveness of the proposed approach. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Extraction

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Simultaneous Deep Learning of Myocardium Segmentation and T2 Quantification for Acute Myocardial Infarction MRI

arXiv

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arXiv 2024年

作者： Zhou, Yirong Wang, Chengyan Lu, Mengtian Guo, Kunyuan Wang, Zi Ruan, Dan Guo, Rui Zhao, Peijun Wang, Jianhua Wu, Naiming Lin, Jianzhong Chen, Yinyin Jin, Hang Xie, Lianxin Wu, Lilan Zhu, Liuhong Zhou, Jianjun Cai, Congbo Wang, He Qu, Xiaobo Department of Electronic Science Intelligent Medical Imaging R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University Xiamen361104 China Human Phenome Institute Fudan University Shanghai201210 China Xianning437100 China School of Medical Technology Beijing Institute of Technology Beijing100081 China Department of Radiology The First Affiliated Hospital of Xiamen University School of Medicine Xiamen University Xiamen361003 China Department of Radiology Xiamen Cardiovascular Hospital Xiamen University School of Medicine Xiamen University Xiamen361004 China Department of Radiology Zhongshan Hospital Affiliated to Xiamen University School of Medicine Xiamen University Xiamen361004 China Department of Radiology Zhongshan Hospital Shanghai200032 China Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial Intelligence Xiamen361006 China

In cardiac Magnetic Resonance Imaging (MRI) analysis, T2 mapping is a tissue quantification technology to measure water and inflammation levels and has been recognized as an important versatile index for myocardial pathologies. The T2 quantification is a highly relevant task with the myocardial segmentation but they only have been explored separately in deep learning. Here, we proposed a simultaneous dual-task network for myocardial segmentation and T2 quantification, called SQNet. It integrates Transformer and Convolutional Neural Network (CNN) components. SQNet has a T2-refine fusion decoder for quantitative analysis, incorporating global features extracted by the Transformer, and employs a segmentation decoder with multiple local region supervision to enhance segmentation accuracy. Additionally, SQNet proposes a tight coupling module that aligns and fuses CNN and Transformer branch features. The mutual promotion of the two tasks enables the SQNet to focus on the myocardium regions. The dual-task performance is validated on healthy controls (HC, N=17) and acute myocardial infarction patients (AMI, N=12). The segmentation dice of SQNet on HC/AMI is 89.3/89.2, which is higher than that (87.7/87.9) of the compared state-of-the-art segmentation method. The Pearson correlation coefficient of the T2 value on HC/AMI is 0.84 and 0.93 to the label value, indicating a strong linear correlation in myocardial T2 mapping. From the clinical diagnostic perspective, eight experienced radiologists have evaluated the image quality (Score standard: Good is 4.0 and excellent is 5.0) of SQNet on HC/AMI with the segmentation score 4.60/4.58 and the T2 quantification score 4.32/4.42, which is higher than the compared state-of-the-art segmentation score (4.50/4.44) and the T2 quantification score (3.59/4.37). Thus, SQNet provides an accurate simultaneous segmentation and quantification, providing the potentially more accurate diagnosis of cardiac disease, such as AMI. Copyright © 2024, T

关键词： Mapping

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MTSS: multi-path traffic scheduling mechanism based on SDN

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Journal of Systems Engineering and Electronics 2019年第5期30卷 974-984页

作者： XU Xiaolong CHEN Yun HU Liuyun KUMAR Anup Jiangsu Key Laboratory of Big Data Security&Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing 210023China Institute of Big Data Research at Yancheng Nanjing University of Posts and TelecommunicationsYancheng 224000China School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023China Department of Computer Engineering and Computer Science University of LouisvilleLouisville KY40292USA

Large-scale and diverse businesses based on the cloud computing platform bring the heavy network traffic to cloud data ***,the unbalanced workload of cloud data center network easily leads to the network congestion,the low resource utilization rate,the long delay,the low reliability,and the low *** order to improve the utilization efficiency and the quality of services(QoS)of cloud system,especially to solve the problem of network congestion,we propose MTSS,a multi-path traffic scheduling mechanism based on software defined networking(SDN).MTSS utilizes the data flow scheduling flexibility of SDN and the multi-path feature of the fat-tree structure to improve the traffic balance of the cloud data center network.A heuristic traffic balancing algorithm is presented for MTSS,which periodically monitors the network link and dynamically adjusts the traffic on the heavy link to achieve programmable data forwarding and load *** experimental results show that MTSS outperforms equal-cost multi-path protocol(ECMP),by effectively reducing the packet loss rate and *** addition,MTSS improves the utilization efficiency,the reliability and the throughput rate of the cloud data center network.

关键词： cloud data center software defined networking(SDN) load balancing multi-path transmission OpenFlow

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Graph Convolutional Neural Network with Inter-layer Cascade Based on Attention Mechanism

Graph Convolutional Neural Network with Inter-layer Cascade ...

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IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)

作者： Lu Wei Yiting Liu Kaiyuan Feng Jianzhao Li Kai Sheng Yue Wu Key Laboratory of Big Data and Intelligent Vision School of Computer Science and Technology Xidian University Xi’an China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Electronic Engineering Xidian University Xi’an China Academy of Advanced Interdisciplinary Research Xidian University Xi’an China Dongfeng USharing Technology Co. Ltd. Dongfeng Motor Corporation Wuhan China

ISBN: (纸本)9781665441506

In recent years, graph data in the non-Euclidean space has been widely used, and the methods and techniques for learning graph data in many deep learning fields have been continuously developed, such as the Graph neural network (GNN). The structural characteristics of graph data, the aggregation mode and representation mode of node information, and the node neighbor information are the core issues of GNN. However, most of the existing graph convolutional neural networks have an excessive smoothing problem, which limits the learning ability of the model. Aiming at the over-smoothing problem of the current algorithm, this paper enhances the learning of graph data by improving the expression ability of local information and global features. This paper constructs a cascade structure between graph convolutional layers. This kind of network structure realizes the dense connection of convolutional layers, makes the local feature information is effectively used, and further enhances the graph representation ability. Introduce self-attention and TopK into the Readout module, selectively aggregate and express feature information, and use graph-level information more efficiently. Graph classification is a downstream task to verify the performance of the proposed model. Experimental results prove that this densely structured graph convolutional network can effectively aggregate local node information and global graph-level information.

关键词： Training Deep learning Smoothing methods Aggregates Conferences Graph neural networks data models

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FABind: fast and accurate protein-ligand binding 23

FABind: fast and accurate protein-ligand binding

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Qizhi Pei Kaiyuan Gao Lijun Wu Jinhua Zhu Yingce Xia Shufang Xie Tao Qin Kun He Tie-Yan Liu Rui Yan Gaoling School of Artificial Intelligence Renmin University of China and Engineering Research Center of Next-Generation Intelligent Search and Recommendation Ministry of Education School of Computer Science and Technology Huazhong University of Science and Technology Microsoft Research AI4Science School of Information Science and Technology University of Science and Technology of China Gaoling School of Artificial Intelligence Renmin University of China Gaoling School of Artificial Intelligence Renmin University of China and Beijing Key Laboratory of Big Data Management and Analysis Methods

Modeling the interaction between proteins and ligands and accurately predicting their binding structures is a critical yet challenging task in drug discovery. Recent advancements in deep learning have shown promise in addressing this challenge, with sampling-based and regression-based methods emerging as two prominent approaches. However, these methods have notable limitations. Sampling-based methods often suffer from low efficiency due to the need for generating multiple candidate structures for selection. On the other hand, regression-based methods offer fast predictions but may experience decreased accuracy. Additionally, the variation in protein sizes often requires external modules for selecting suitable binding pockets, further impacting efficiency. In this work, we propose FABind, an end-to-end model that combines pocket prediction and docking to achieve accurate and fast protein-ligand binding. FABind incorporates a unique ligand-informed pocket prediction module, which is also leveraged for docking pose estimation. The model further enhances the docking process by incrementally integrating the predicted pocket to optimize protein-ligand binding, reducing discrepancies between training and inference. Through extensive experiments on benchmark datasets, our proposed FABind demonstrates strong advantages in terms of effectiveness and efficiency compared to existing methods. Our code is available at Github https://***/QizhiPei/FABind.

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Hyper-Parameter-Free Latent Factor Analysis via Adaptive Moment Estimation-Incorporated Particle Swarm Optimization

Hyper-Parameter-Free Latent Factor Analysis via Adaptive Mom...

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Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： Jiufang Chen Ye Yuan Chunlin He School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Chinese Academy of Sciences Chongqing Institute of Green and Intelligent Technology Chongqing China Chongqing School University of Chinese Academy of Sciences Chongqing China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing China School of Computer Science China West Normal University Nanchong Sichuan China

ISBN: (纸本)9781665426220

A latent factor analysis (LFA) model can efficiently address a high-dimensional and sparse (HiDS) matrix with a stochastic gradient descent (SGD) algorithm. However, an SGD-based LFA model's performance depends heavily on its hyper-parameters. The popular method is based on grid-search, which fails because of expensive computation and time-consuming. Aiming at implementing a hyper-parameter-free LFA model, this study proposes an adaptive moment estimation-incorporated particle swarm optimization (Adam-PSO) algorithm that efficiently addresses the premature issues in a PSO algorithm. With achieved the hyper-parameter adaptation in an SGD-based LFA model, an Adam-PSO-based LFA (APL) model possessing hyper-parameter-free training is further implemented. Empirical studies on four HiDS matrices indicate that compared with state-of-the-art models with hyper-parameter adaptation settings, an APL model achieves the most efficient hyper-parameter-free training and highly competitive prediction accuracy for missing data of an HiDS matrix. Hence, it fits the need of real applications with high scalability and efficiency.

关键词： Training Adaptation models Computational modeling Stochastic processes Predictive models Prediction algorithms data models

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FortisAVQA and MAVEN: a Benchmark dataset and Debiasing Framework for Robust Multimodal Reasoning

arXiv

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arXiv 2025年

作者： Ma, Jie Gao, Zhitao Chai, Qi Liu, Jun Wang, Pinghui Tao, Jing Su, Zhou Ministry of Education of Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China Shannxi Provincial Key Laboratory of Big Data Knowledge Engineering School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China Guangdong Guangzhou510000 China

Audio-Visual Question Answering (AVQA) is a challenging multimodal reasoning task requiring intelligent systems to answer natural language queries based on paired audio-video inputs accurately. However, existing AVQA approaches often suffer from overfitting to dataset biases, leading to poor robustness. Moreover, current datasets may not effectively diagnose these methods. To address these challenges, we first introduce a novel dataset, FortisAVQA, constructed in two stages: (1) rephrasing questions in the test split of the public MUSIC-AVQA dataset and (2) introducing distribution shifts across questions. The first stage expands the test space with greater diversity, while the second enables a refined robustness evaluation across rare, frequent, and overall question distributions. Second, we introduce a robust Multimodal Audio-Visual Epistemic Network (MAVEN) that leverages a multifaceted cycle collaborative debiasing strategy to mitigate bias learning. Experimental results demonstrate that our architecture achieves state-of-the-art performance on FortisAVQA, with a notable improvement of 7.81%. Extensive ablation studies on both datasets validate the effectiveness of our debiasing components. Additionally, our evaluation reveals the limited robustness of existing multimodal QA methods. We also verify the plug-and-play capability of our strategy by integrating it with various baseline models across both datasets. Our dataset and code are available at https://***/reml-group/fortisavqa. Copyright © 2025, The Authors. All rights reserved.

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Erratum to “ECMER: Edge-Cloud Collaborative Personalized Multimodal Emotion Recognition Framework in the Internet of Vehicles”

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IEEE Network 2024年第3期38卷 292-292页

作者： Puning Zhang Miao Fu Rongjian Zhao Dapeng Wu Hongbin Zhang Zhigang Yang Ruyan Wang School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China and the Chongqing Key Laboratory of Ubiquitous Sensing and Networking Chongqing China Chongqing Innovation Center of Industrial Big-Data Company Ltd. Chongqing China

This addresses errors in [1] . There is incorrect author affiliation information in this article.

关键词： Emotion recognition Collaboration Internet of Vehicles

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