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Nano Research 2024年第8期17卷 7068-7076页

作者： Shucheng Liu Yu Shuai Xiaosi Qi Zhao Ding Yi Liu College of big data and Information Engineering Guizhou UniversityGuiyang 550025China College of Physics Guizhou Province Key Laboratory for Photoelectrics Technology and ApplicationGuizhou UniversityGuiyang 550025China

Modulation of metal sites coordination can significantly refine the electronic architecture of catalysts,thereby improving their catalytic *** work successfully developed a core–shell Co@N-doped porous carbon(Co@NC)catalyst by pyrolyzing the COF/MOF(IISERP-COF3/ZIF-67)composite in an inert *** Co@NC catalyst exhibited impressive oxygen evolution reaction(OER)performance,with a small overpotential of 304 mV and a modest Tafel slope of 88.6 mV·dec^(−1)in a 1 M KOH,alongside remarkable stability,maintaining 98.5%of its activity over 13 *** role of IISERP-COF3 was pivotal in preventing Co atom aggregation during the ZIF-67 pyrolysis,which facilitated the creation of mesopores for enhanced mass transport and ***,it effectively modulated the Co-N coordination to fine-tune the electronic structure,thereby optimizing the catalyst's capacity for adsorption of intermediates and boosting its intrinsic *** functional theory(DFT)studies corroborate that the exceptional OER efficiency of Co@NC can be linked to the enhanced Co-N coordination,optimizing the localized electronic structure at the Co active *** study not only proposes an innovative approach for optimizing COF/MOF as effective electrocatalysts but also clears the path for the emergence of affordable,high-performance alternatives to precious metal-based catalysts,marking a significant advancement in sustainable energy technologies.

关键词： covalent organic framework(COF) metal-organic framework(MOF) core-shell structure catalysts oxygen evolution reaction

Method for the classification of tea diseases via weighted sampling and hierarchical classification learning

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International Journal of Agricultural and Biological Engineering 2024年第3期17卷 211-221页

作者： Rujia Li Weibo Qin Yiting He Yadong Li Rongbiao Ji Yehui Wu Jiaojiao Chen Jianping Yang College of big data Yunnan Agricultural UniversityKunming 650201China College of Plant Protection Jilin Agricultural UniversityChangchun 130118China

This study proposed a weighted sampling hierarchical classification learning method based on an efficient backbone network model to address the problems of high costs,low accuracy,and time-consuming traditional tea disease recognition *** method enhances the feature extraction ability by conducting hierarchical classification learning based on the EfficientNet model,effectively alleviating the impact of high similarity between tea diseases on the model’s classification *** better solve the problem of few and unevenly distributed tea disease samples,this study introduced a weighted sampling scheme to optimize data processing,which not only alleviates the overfitting effect caused by too few sample data but also balances the probability of extracting imbalanced classification *** experimental results show that the proposed method was significant in identifying both healthy tea leaves and four common leaf diseases of tea(tea algal spot disease,tea white spot disease,tea anthracnose disease,and tea leaf blight disease).After applying the“weighted sampling hierarchical classification learning method”to train 7 different efficient backbone networks,most of their accuracies have *** EfficientNet-B1 model proposed in this study achieved an accuracy rate of 99.21%after adopting this learning method,which is higher than EfficientNet-b2(98.82%)and MobileNet-V3(98.43%).In addition,to better apply the results of identifying tea diseases,this study developed a mini-program that operates on *** can quickly obtain accurate identification results and corresponding disease descriptions and prevention methods through simple *** intelligent tool for identifying tea diseases can serve as an auxiliary tool for farmers,consumers,and related scientific researchers and has certain practical value.

关键词： tea diseases hierarchical classification learning weighted sampling classification method EfficientNet miniprogram

Expression Recognition Method Based on Convolutional Neural Network and Capsule Neural Network

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Computers, Materials & Continua 2024年第4期79卷 1659-1677页

作者： Zhanfeng Wang Lisha Yao School of Computer Science and Artificial Intelligence Chaohu UniversityHefei238000China School of big data and Artificial Intelligence Anhui Xinhua UniversityHefei230088China

Convolutional neural networks struggle to accurately handle changes in angles and twists in the direction of images,which affects their ability to recognize patterns based on internal feature levels. In contrast, CapsNet overcomesthese limitations by vectorizing information through increased directionality and magnitude, ensuring that spatialinformation is not overlooked. Therefore, this study proposes a novel expression recognition technique calledCAPSULE-VGG, which combines the strengths of CapsNet and convolutional neural networks. By refining andintegrating features extracted by a convolutional neural network before introducing theminto CapsNet, ourmodelenhances facial recognition capabilities. Compared to traditional neural network models, our approach offersfaster training pace, improved convergence speed, and higher accuracy rates approaching stability. Experimentalresults demonstrate that our method achieves recognition rates of 74.14% for the FER2013 expression dataset and99.85% for the CK+ expression dataset. By contrasting these findings with those obtained using conventionalexpression recognition techniques and incorporating CapsNet’s advantages, we effectively address issues associatedwith convolutional neural networks while increasing expression identification accuracy.

关键词： Expression recognition capsule neural network convolutional neural network

Compressed data Separation via ℓ_(q)-Split Analysis with ℓ_(∞)-Constraint

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Acta Mathematica Sinica,English Series 2024年第7期40卷 1655-1673页

作者： Ming Yang Gu Song Li Jun Hong Lin School of Mathematical Science Zhejiang UniversityHangzhou310027P.R.China center for data Science Zhejiang UniversityHangzhou310027P.R.China

In this paper,we study compressed data separation(CDS)problem,i.e.,sparse data separation from a few linear random *** propose the nonconvex ℓ_(q)-split analysis with ℓ_(∞)-constraint and 0 详细信息

In this paper,we study compressed data separation(CDS)problem,i.e.,sparse data separation from a few linear random *** propose the nonconvex ℓ_(q)-split analysis with ℓ_(∞)-constraint and 0

关键词： data separation ℓ_(q)-split analysis Dantzig selector frames restricted isometry property compressed sensing

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A visual modeling method for spatiotemporal and multidimensional features in epidemiological analysis:Applied COVID-19 aggregated datasets

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Computational Visual Media 2024年第1期10卷 161-186页

作者： Yu Dong Christy Jie Liang Yi Chen Jie Hua School of Computer Science University of Technology SydneySydneyNSW2007Australia Beijing Key Laboratory of big data Technology for Food Safety Beijing Technology and Business UniversityBeijing100048China

The visual modeling method enables flexible interactions with rich graphical depictions of data and supports the exploration of the complexities of epidemiological ***,most epidemiology visualizations do not support the combined analysis of objective factors that might influence the transmission situation,resulting in a lack of quantitative and qualitative *** address this issue,we developed a portrait-based visual modeling method called+*** method considers the spatiotemporal features of virus transmission patterns and multidimensional features of objective risk factors in communities,enabling portrait-based exploration and comparison in epidemiological *** applied+msRNAer to aggregate COVID-19-related datasets in New South Wales,Australia,combining COVID-19 case number trends,geo-information,intervention events,and expert-supervised risk factors extracted from local government area-based *** perfected the+msRNAer workflow with collaborative views and evaluated its feasibility,effectiveness,and usefulness through one user study and three subject-driven case *** feedback from experts indicates that+msRNAer provides a general understanding for analyzing comprehension that not only compares relationships between cases in time-varying and risk factors through portraits but also supports navigation in fundamental geographical,timeline,and other factor *** adopting interactions,experts discovered functional and practical implications for potential patterns of long-standing community factors regarding the vulnerability faced by the *** confirmed that+msRNAer is expected to deliver visual modeling benefits with spatiotemporal and multidimensional features in other epidemiological analysis scenarios.

关键词： visual modeling epidemiological analysis spatiotemporal multidimensional COVID-19

A New Encrypted Traffic Identification Model Based on VAE-LSTM-DRN

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Computers, Materials & Continua 2024年第1期78卷 569-588页

作者： Haizhen Wang Jinying Yan Na Jia College of Computer and Control Engineering Qiqihar UniversityQiqihar161006China Heilongjiang Key Laboratory of big data Network Security Detection and Analysis Qiqihar UniversityQiqiharChina

Encrypted traffic identification pertains to the precise acquisition and categorization of data from traffic datasets containing imbalanced and obscured *** extraction of encrypted traffic attributes and their subsequent identification presents a formidable *** existing models have predominantly relied on direct extraction of encrypted traffic data from imbalanced datasets,with the dataset’s imbalance significantly affecting the model’s *** the present study,a new model,referred to as UD-VLD(Unbalanced dataset-VAE-LSTM-DRN),was proposed to address above *** proposed model is an encrypted traffic identification model for handling unbalanced *** encoder of the variational autoencoder(VAE)is combined with the decoder and Long-short term Memory(LSTM)in UD-VLD model to realize the data enhancement processing of the original unbalanced *** enhanced data is processed by transforming the deep residual network(DRN)to address neural network gradient-related ***,the data is classified and *** UD-VLD model integrates the related techniques of deep learning into the encrypted traffic recognition technique,thereby solving the processing problem for unbalanced *** UD-VLD model was tested using the publicly available Tor dataset and VPN *** UD-VLD model is evaluated against other comparative models in terms of accuracy,loss rate,precision,recall,F1-score,total time,and ROC *** results reveal that the UD-VLD model exhibits better performance in both binary and multi classification,being higher than other encrypted traffic recognition models that exist for unbalanced ***,the evaluation performance indicates that the UD-VLD model effectivelymitigates the impact of unbalanced data on traffic *** can serve as a novel solution for encrypted traffic identification.

关键词： data enhancement LSTM deep residual network VAE

Recent Advances in Asymmetric Structural Composites for Excellent Electromagnetic Interference Shielding: A Review

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Chinese Journal of Polymer Science 2024年第6期42卷 693-710页

作者： Ying Zhou Bai Xue Lan Xie Chang-Mei Wu Qiang Zheng Department of Polymer Materials and Engineering College of Materials and Metallurgy Guizhou University National Engineering Research center for Compounding and Modification of Polymer Materials National and Local Joint Engineering Research Center for Functional Polymer Membrane Materials and Membrane Processes State Key Laboratory of Public big data Guizhou University Department of Polymer Science and Engineering Zhejiang University

Since electromagnetic pollution is detrimental to human health and the environment, numerous efforts have been successively made to achieve excellent electromagnetic interference shielding effectiveness(EMI SE) via designing the hierarchical structures for electromagnetic interference(EMI) shielding polymer composites. Among the plentiful structures, the asymmetric structures are currently a hot spot, principally categorizing into multi-layered, porous, fibrous, and segregated asymmetric structures, which endows the high EMI shielding performance for polymer composites incorporated with magnetic, conductive, and/or dielectric micro/nano-fillers, due to the “absorption-reflection-reabsorption” shielding mechanism. Therefore, this review provides the retrospection and summary of the efforts with respect to abundant asymmetric structures and multifunctional micro/nano-fillers for enhancing EMI shielding properties, which is conducive to the booming development of polymeric EMI shielding materials for the promising prospect in modern electronics and 5-generation(5G) technology.

关键词： Asymmetric structure Microano functional filler Polymer composite Electromagnetic interference shielding

Improving Federated Learning through Abnormal Client Detection and Incentive

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 383-403页

作者： Hongle Guo Yingchi Mao Xiaoming He Benteng Zhang Tianfu Pang Ping Ping College of Computer and Information Hohai UniversityNanjing211100China Key Laboratory ofWater big data Technology of Ministry ofWater Resources Hohai UniversityNanjing211100China

data sharing and privacy protection are made possible by federated learning,which allows for continuous model parameter sharing between several clients and a central *** reliable and high-quality clients must participate in practical applications for the federated learning global model to be accurate,but because the clients are independent,the central server cannot fully control their *** central server has no way of knowing the correctness of the model parameters provided by each client in this round,so clients may purposefully or unwittingly submit anomalous data,leading to abnormal behavior,such as becoming malicious attackers or defective *** reduce their negative consequences,it is crucial to quickly detect these abnormalities and incentivize *** this paper,we propose a Federated Learning framework for Detecting and Incentivizing Abnormal Clients(FL-DIAC)to accomplish efficient and security federated *** build a detector that introduces an auto-encoder for anomaly detection and use it to perform anomaly identification and prevent the involvement of abnormal clients,in particular for the anomaly client detection *** them,before the model parameters are input to the detector,we propose a Fourier transform-based anomaly data detectionmethod for dimensionality reduction in order to reduce the computational ***,we create a credit scorebased incentive structure to encourage clients to participate in training in order tomake clients actively *** training models(CNN,MLP,and ResNet-18)and three datasets(MNIST,Fashion MNIST,and CIFAR-10)have been used in *** to theoretical analysis and experimental findings,the FL-DIAC is superior to other federated learning schemes of the same type in terms of effectiveness.

关键词： Federated learning abnormal clients incentive credit score abnormal score detection

Enhancing action discrimination via category-specific frame clustering for weakly-supervised temporal action localization

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Frontiers of Information Technology & Electronic Engineering 2024年第6期25卷 809-823页

作者： Huifen XIA Yongzhao ZHAN Honglin LIU Xiaopeng REN School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang 212013China Jiangsu Engineering Research center of big data Ubiquitous Perception and Intelligent Agricultural Applications Zhenjiang 212013China Changzhou Vocational Institute of Mechatronic Technology Changzhou 213164China

Temporal action localization (TAL) is a task of detecting the start and end timestamps of action instances and classifying them in an untrimmed video. As the number of action categories per video increases, existing weakly-supervised TAL (W-TAL) methods with only video-level labels cannot provide sufficient supervision. Single-frame supervision has attracted the interest of researchers. Existing paradigms model single-frame annotations from the perspective of video snippet sequences, neglect action discrimination of annotated frames, and do not pay sufficient attention to their correlations in the same category. Considering a category, the annotated frames exhibit distinctive appearance characteristics or clear action ***, a novel method to enhance action discrimination via category-specific frame clustering for W-TAL is proposed. Specifically,the K-means clustering algorithm is employed to aggregate the annotated discriminative frames of the same category, which are regarded as exemplars to exhibit the characteristics of the action category. Then, the class activation scores are obtained by calculating the similarities between a frame and exemplars of various categories. Category-specific representation modeling can provide complimentary guidance to snippet sequence modeling in the mainline. As a result, a convex combination fusion mechanism is presented for annotated frames and snippet sequences to enhance the consistency properties of action discrimination,which can generate a robust class activation sequence for precise action classification and localization. Due to the supplementary guidance of action discriminative enhancement for video snippet sequences, our method outperforms existing single-frame annotation based methods. Experiments conducted on three datasets (THUMOS14, GTEA, and BEOID) show that our method achieves high localization performance compared with state-of-the-art methods.

关键词： Weakly supervised Temporal action localization Single-frame annotation Category-specific Action discrimination

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Asymptotic normality of error density estimator in stationary and explosive autoregressive models

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Applied Mathematics(A Journal of Chinese Universities) 2024年第1期39卷 140-158页

作者： WU Shi-peng YANG Wen-zhi GAO Min HU Shu-he School of Mathematical Sciences&School of big data and Statistics Anhui UniversityHefei 230601China School of Statistics Shanxi University of Finance and EconomicsTaiyuan030006China

In this paper,we consider the limit distribution of the error density function estima-tor in the rst-order autoregressive models with negatively associated and positively associated random *** mild regularity assumptions,some asymptotic normality results of the residual density estimator are obtained when the autoregressive models are stationary process and explosive *** order to illustrate these results,some simulations such as con dence intervals and mean integrated square errors are provided in this *** shows that the residual density estimator can replace the density\estimator"which contains errors.

关键词： explosive autoregressive models residual density estimator asymptotic distribution association sequence

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