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Multi-scale feature fusion for facial expression recognition

Neural Computing and Applications 2025年第17期37卷 11399-11420页

作者： Guo, Jiatao Peng, Junjie Huang, Yansong Chen, Gan Cai, Zesu Tan, Shuhua School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China School of Computer Science and Technology Harbin Institute of Technology Harbin China National Engineering Laboratory for Logistics Information Technology YTO Express Co. Ltd. Shanghai China

As an important computer vision task that can be used in many areas, facial expression recognition (FER) has been widely studied which much progress has been obtained especially when deep learning (DL) approaches have been introduced in FER. However, existing methods are restricted to extracting localized information or utilizing attention mechanism to focus on the region most relevant to the emotion, possibly resulting in errors in facial expression interpretation. Moreover, the inconsistency of image sizes in the wild FER datasets further increases the difficulty of the FER task. To address these issues, we propose a multi-scale feature fusion (MSFF) for FER. Specifically, our method first extracts features from facial images using multi-scale feature extraction to obtain rich multi-dimensional information. Meanwhile, extensive feature extraction at different scales is achieved by expanding the receptive field of the model, which provides the basis for more detailed analysis of facial expressions. Eventually, features at different scales are fused to enhance their synergistic effect. Subsequently, the adaptive extraction of comprehensive and valuable information from the fused features is performed, optimizing the performance of expression recognition. Extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods on in-the-wild datasets. It is promising for FER in realistic and complex scenarios. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Feature extraction

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Probing the role of Fe and Sr co-doped BaZrO3 for enhanced cathode performance in protonic ceramics fuel cells

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International Journal of Hydrogen Energy 2025年 139卷 510-518页

作者： Yin, Yanru Xiao, Qi Zhang, Wenying Li, Kai Yan, Dong Li, Jian Jia, Lichao School of Materials Science and Engineering State Key Laboratory of Materials Processing and Die & Mould Technology Huazhong University of Science and Technology Wuhan430074 China School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan430074 China School of Materials Science and Engineering Xi'an Shiyou University Xi'an710065 China

Cathode materials greatly govern the performance of protonic ceramics fuel cells (PCFCs), and development of suitable cathodes for PCFCs now becomes important. In this study, the traditional proton conductor BaZrO3 is tailored with Sr and Fe elements, aiming to design proper cathodes for PCFCs. The structural, electronic, and catalytic effects of dual-element doping on BaZrO3-based materials are systemically investigated. Experimental and computational analyses reveal that a 75 % iron doping concentration (BaZr0·25Fe0·75O3) markedly enhances oxygen vacancy concentration, reduces proton migration barriers, and elevates electrical conductivity. Notably, moderate strontium doping (50 %) synergistically improves performance, whereas excessive doping (75 %) induces adverse effects on oxygen vacancy concentration, proton migration, and electrical conductivity. The optimized Ba0·5Sr0·5Zr0·25Fe0·75O3 cathode achieves a peak power density of 945 mW cm-2 at 700 °C. These findings elucidate the interplay between dopant elements, doping concentrations, and material properties related to electrochemical performance, offering actionable strategies for designing high-performance PCFC cathodes. © 2025 Hydrogen Energy Publications LLC

关键词： Proton conductivity

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Isolated Bipolar Modular Multilevel DC-DC Converter with Self-balancing Capability for Interconnection of MVDC and LVDC Grids

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CSEE Journal of Power and Energy systems 2023年第1期9卷 365-379页

作者： Jinmu Lai Xin Yin Yaoqiang Wang Lin Jiang Zia Ullah Xianggen Yin School of Electrical and Information EngineeringZhengzhou UniversityZhengzhou 450001China Department of Electrical Engineering and ElectronicsUniversity of LiverpoolLiverpoolUK State Key Laboratory of Advanced Electromagnetic Engineering and TechnologyHuazhong University of Scienceand TechnologyWuhan 430074China

Bipolar medium-voltage DC(MVDC)and lowvoltage DC(LVDC)grids have the advantages of flexible integration of distributed renewable-energy generation and reliable power *** order to achieve voltage conversion,power transfer,and electrical isolation for bipolar MVDC and LVDC grids,a high-power DC-DC converter is ***,this paper proposes an isolated bipolar modular multilevel DCDC converter(BiMMDC)with power self-balancing capability for interconnection of MVDC and LVDC *** proposed BiMMDC consists of two series connected MMCs in the MV stage to configure a bipolar MVDC interface,and interleaved converters combined with a dual-transformer are designed in the LV stage to configure the bipolar LVDC interface and to provide a self-balancing *** circuits of two series-connected MMCs and a dual-transformer with interleaved converters are *** that,operation principles of the proposed BiMMDC are introduced,considering balanced/unbalanced power transfer of bipolar LVDC grid and monopolar shortcircuit or open-circuit faults at MVDC *** control scheme is also presented for the proposed BiMMDC under different operating ***,a Matlab simulation and controller hardware-in-the-loop(CHIL)evaluation results are provided to validate the feasibility and effectiveness of the proposed typology and its operating performance.

关键词： Bipolar DC-DC converter equivalent circuit modular monopolar operation multilevel self-balancing

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Assessing and prioritizing crucial drivers for CloudIoT-based healthcare adoption: an analytic hierarchy process approach

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International Journal of information technology (Singapore) 2024年 1-18页

作者： Altawaiha, Iyad Atan, Rodziah Yaakob, Razali Bin Abdullah, Rusli Bin Hj Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang 43400 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang 43400 Malaysia

The CloudIoT paradigm has profoundly transformed the healthcare industry, providing outstanding innovation and practical applications. However, despite its many advantages, the adoption of this paradigm in healthcare settings remains limited. There is a lack of comprehensive understanding of the factors that ensure successful implementation. To address this issue, we have used the Analytic Hierarchy Process to prioritize 26 factors that could potentially influence the adoption of CloudIoT-based healthcare. These factors were derived from existing literature and categorized into four main groups: personal-specific, technology-specific, environment-specific, and organization-specific factors. Our findings revealed that performance expectancy, trust, facilitating conditions, task complexity, and perceived security are the top five critical factors influencing CloudIoT-based healthcare adoption. Interestingly, our results indicated that environmental factors were less influential than those in the other categories, with personal, technological, and organizational factors dominating the top ten significant factors. The insights from this study can help policymakers, CloudIoT service providers, healthcare providers, designers, and developers better understand the factors contributing to the successful implementation of CloudIoT-based healthcare. This understanding can guide decision-making and inform the development of strategies to promote the broader adoption of this innovative technology. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Adoption Analytic hierarchy process CloudIoT Factors Healthcare industry

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Comparative Study of Disease Prediction Through Data Mining 2

Comparative Study of Disease Prediction Through Data Mining

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2nd International Conference on Emerging Trends in information technology and engineering, ic-ETITE 2024

作者： Karthikeyan, D. Aameer Arshath, A. Gunasekaran, V. Uthra, B. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350328202

The growing occurrence of persistent liver disorder has been cited in current times. Similarly Diabetes, one of the frequently spreading disease is growing hastily in all of the Nations nowadays. According to WHO, approximately 422 million humans globally have diabetes and 1.5 million deaths are without delay associated with diabetes disorder. So the effective analysis should be made to detect the presence of disease before it kills the patient which reflects a plenty of studies have been made in the field of healthcare. In this paper, we are going to predict these two diseases through one of the data mining techniques called classification. The analysis is completed using Jupyter Notebook. One of the data mining techniques called supervised learning or the classification is applied in which we have used Decision tree, Logistic Regression, Random Forest, K-Nearest Neighbor. The data in this research paper is taken from University of California Irvine repository of machine learning. The main aim of this paper is to provide brief prediction on both diabetes and liver disease and the goal is to provide a better final results when compared to the other algorithms, for the disease prediction. In results we are predicting the presence of diabetes disease with 98% of accuracy with Random forest algorithm and prediction of liver disorder has 81.59% of accuracy with Random Forest algorithm along with accuracy we have attempted to use some parameters like precision, recall, F-Measure for the evaluation. © 2024 IEEE.

关键词： Forecasting

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A three-step SEM-Bayesian network approach for predicting the determinants of CloudIoT-based healthcare adoption

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International Journal of information technology (Singapore) 2024年 1-21页

Adopting the CloudIoT-based healthcare paradigm provides various prospects for medical IT and considerably enhances healthcare services. However, compared to the advanced development of CloudIoT-based healthcare systems, their usage is relatively low among healthcare professionals in hospitals. Thus, a study on the healthcare professionals acceptance and adoption of CloudIoT-based healthcare is critical. This study explores factors influencing healthcare professionals adoption of CloudIoT-based healthcare solutions by extending the Unified Theory of Acceptance and Use of technology 2 model. Data was collected from 300 healthcare professionals in Jordan through an online questionnaire developed using the Google^® form application. The Bayesian Network and Structural Equation Modeling techniques were used to analyze and validate the proposed model. The results revealed that the healthcare professional’s behavioral intention is directly affected by seven factors: performance expectancy, effort expectancy, facilitating conditions, habit, trust, privacy, and security. The results also indicate that trust mediates the influence of privacy, facilitating conditions, and performance expectancy on behavioral intention. The research results will aid CloudIoT service providers, healthcare organizations, designers, and developers by offering comprehensive knowledge of the significant factors of CloudIoT-based healthcare adoption. A better understanding of these factors will help these stakeholder groups enhance their services and speed up the adoption of CloudIoT in the healthcare area. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Adoption Bayesian network CloudIoT Healthcare professionals Structural equation modeling UTAUT2

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A Digital Forensic Analysis of Profiling and Avoidance of Websites Disseminating Disinformation 2

A Digital Forensic Analysis of Profiling and Avoidance of We...

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2nd International Conference on Emerging Trends in information technology and engineering, ic-ETITE 2024

作者： Agrawal, Harsh Singh, Shaurya Pratap Dixit, Shashwat Nagdev, Parth Priya, V. Thaseen, Sumaiya School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350328202

The internet is an essential source of data for individuals across the world. However, disinformation has emerged as a significant problem due to its rapid growth on social media and the prevalence of fallacious news. Conventional strategies for detecting disinformation sites, like manual examination, are tedious and time-wasting. As a result, it is necessary to develop an effective method to find such websites. The goal of this research is to find websites that spread false information and prevent its further spread. The methodology is unique and effective for detecting disinformation websites as it makes use of the OSINT tool Maltego and focuses on websites that spread news having a negative impact such as in politics, health, finance, etc. Thus, the outcome of this research helps users to detect the authenticity of news in an easy and user-friendly manner by footprinting mechanisms. © 2024 IEEE.

关键词： Websites

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Potassium ion pre‑intercalated MnO_(2)for aqueous multivalent ion batteries

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Frontiers of Optoelectronics 2023年第4期16卷 81-89页

作者： Zikang Xu Ruiqi Ren Hang Ren Jingyuan Zhang Jinyao Yang Jiawen Qiu Yizhou Zhang Guoyin Zhu Liang Huang Shengyang Dong School of Environmental Science and Engineering School of Chemistry and Materials ScienceNanjing University of Information Science and TechnologyNanjing 210044China Wuhan National Laboratory for Optoelectronics School of Optical and Electronic InformationHuazhong University of Science and TechnologyWuhan 430074China

Manganese dioxide(MnO_(2)),as a cathode material for multivalent ion(such as Mg^(2+)and Al^(3+))storage,is investigated due to its high initial ***,during multivalent ion insertion/extraction,the crystal structure of MnO_(2)partially collapses,leading to fast capacity decay in few charge/discharge ***,through pre-intercalating potassium-ion(K+)intoδ-MnO_(2),we synthesize a potassium ion pre-intercalated MnO_(2),K_(0.21)MnO_(2)·0.31H_(2)O(KMO),as a reliable cathode material for multivalent ion *** as-prepared KMO exhibits a high reversible capacity of 185 mAh/g at 1 A/g,with considerable rate performance and improved cycling stability in 1 mol/L MgSO_(4)*** addition,we observe that aluminum-ion(Al^(3+))can also insert into a KMO *** work provides a valid method for modifcation of manganesebased oxides for aqueous multivalent ion batteries.

关键词： Aqueous batteries Multivalent ion batteries Magnesium ion Aluminum ion MnO_(2)

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Super-resolution reconstruction of single image for latent features

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Computational Visual Media 2024年第6期10卷 1219-1239页

作者： Xin Wang Jing-Ke Yan Jing-Ye Cai Jian-Hua Deng Qin Qin Yao Cheng School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610000 China State Key Laboratory of Rail Transit Vehicle System Southwest Jiaotong UniversityChengdu 610000China School of Marine Engineering Guilin University of Electronic TechnologyGuilin 541000China School of Computer and Information Security Guilin University of Electronic TechnologyGuilin 541000China

Single-image super-resolution(SISR)typically focuses on restoring various degraded low-resolution(LR)images to a single high-resolution(HR)***,during SISR tasks,it is often challenging for models to simultaneously maintain high quality and rapid sampling while preserving diversity in details and texture *** challenge can lead to issues such as model collapse,lack of rich details and texture features in the reconstructed HR images,and excessive time consumption for model *** address these problems,this paper proposes a Latent Feature-oriented Diffusion Probability Model(LDDPM).First,we designed a conditional encoder capable of effectively encoding LR images,reducing the solution space for model image reconstruction and thereby improving the quality of the reconstructed *** then employed a normalized flow and multimodal adversarial training,learning from complex multimodal distributions,to model the denoising *** so boosts the generative modeling capabilities within a minimal number of sampling *** comparisons of our proposed model with existing SISR methods on mainstream datasets demonstrate that our model reconstructs more realistic HR images and achieves better performance on multiple evaluation metrics,providing a fresh perspective for tackling SISR tasks.

关键词： image superresolution reconstruction denoising diffusion probabilistic model normalized flow adversarial neural network variational auto-encoder

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Low frequency error analysis and calibration for multiple star sensors system of GaoFen7 satellite

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地球空间信息科学学报（英文版） 2024年第1期27卷 82-94页

作者： Yanli Wang Mi Wang Ying Zhu Xiaoxiang Long The College of Geodesy and Geomatics Shandong University of Science and TechnologyQingdaoChina The State Key Laboratory of Information Engineering in Surveying Mappingand Remote SensingWuhan UniversityWuhanChina School of Electrical and Information Engineering Wuhan Institute of TechnologyWuhanChina Value-added Service Department China Center for Resources Satellite Data and ApplicationBeijingChina

The GaoFen7(GF7)optical satellite is the first Chinese civilian sub-meter high-resolution stereo mapping satellite and is equipped with a double linear array camera and laser altimeter to achieve large-scale topographic *** improve the accuracy of attitude determination,an attitude determination system comprised of four star sensors is *** to the measurement accuracy and steady performance,the star sensors 1a and 1b is usually used together for satellite attitude calculation,which is called the conventional mode of attitude ***,the combination of star sensors 2a and 2b is called the unconventional mode of attitude *** by variations in the incident angle of sunlight and solar radiation,thermal deformation occurs in the body and installation structure of the star sensor,which causes Attitude Low-Frequency Error(ALFE)and seriously influences the con-sistency of attitude determination results of different combination modes for multiple star sensors *** study proposes an ALFE analysis and calibration approach for the multiple star sensors system of GF7 satellite to ensure the consistency of attitude determination results of different combination *** on the statistical characteristics of the angles of the three axes,the installation parameters of the four star sensors are first *** analyzing the characteristics of the optical axis angles within 1420 orbit periods over 135 days,the segmented ALFE compensation model between the unconventional and conven-tional modes is proposed based on the Fourier series model and input parameter of *** on the on-orbit installation parameters and the ALFE model,the precise attitude determination results of the unconventional mode are *** results show that the attitude determination consistency after compensation is better than 2".Moreover,the reliable application time range of the compensation model is 30 days to satisfy the re

关键词： Attitude Low Frequency Error(ALFE) multiple star sensors system GaoFen7 attitude determination consistency Fourier series

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