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Identify information sources with different start times in complex networks based on sparse observers

Chinese Physics B 2024年第11期33卷 467-479页

作者： Yuan-Zhang Deng Zhao-Long Hu Feilong Lin Chang-Bing Tang Hui Wang Yi-Zhen Huang School of Computer Science and Technology Zhejiang Normal UniversityJinhua 321004China School of Physics and Electronic Information Engineering Zhejiang Normal UniversityJinhua 321004China School of Information Engineering Jinhua PolytechnicJinhua 321016China

The dissemination of information across various locations is an ubiquitous occurrence,however,prevalent methodologies for multi-source identification frequently overlook the fact that sources may initiate dissemination at distinct initial *** there are many research results of multi-source identification,the challenge of locating sources with varying initiation times using a limited subset of observational nodes remains *** this study,we provide the backward spread tree theorem and source centrality theorem,and develop a backward spread centrality algorithm to identify all the information sources that trigger the spread at different start *** proposed algorithm does not require prior knowledge of the number of sources,however,it can estimate both the initial spread moment and the spread *** core concept of this algorithm involves inferring suspected sources through source centrality theorem and locating the source from the suspected sources with linear *** experiments from synthetic and real network simulation corroborate the superiority of our method in terms of both efficacy and ***,we find that our method maintains robustness irrespective of the number of sources and the average degree of *** with classical and state-of-the art source identification methods,our method generally improves the AUROC value by 0.1 to 0.2.

关键词： complex networks information spread source identification backward spread centricity

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Feature-preserving color pencil drawings from photographs

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Computational Visual Media 2023年第4期9卷 807-825页

作者： Dong Wang Guiqing Li Chengying Gao Shengwu Fu Yun Liang College of Mathematics and Informatics South China Agricultural UniversityGuangzhouChina School of Computer Science&Engineering South China University of TechnologyGuangzhouChina School of Data and Computer Science Sun Yat-sen UniversityGuangzhouChina

Color pencil drawing is well-loved due to its rich *** paper proposes an approach for generating feature-preserving color pencil drawings from *** mimic the tonal style of color pencil drawings,which are much lighter and have relatively lower saturation than photographs,we devise a lightness enhancement mapping and a saturation reduction *** lightness mapping is a monotonically decreasing derivative function,which not only increases lightness but also preserves input photograph *** saturation is usually related to lightness,so we suppress the saturation dependent on lightness to yield a harmonious ***,two extremum operators are provided to generate a foreground-aware outline map in which the colors of the generated contours and the foreground object are *** experiments show that color pencil drawings generated by our method surpass existing methods in tone capture and feature preservation.

关键词： non-photorealistic rendering pencil drawings image editing feature preservation

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Revolutionizing Network Performance: The Active and Passive Service Path Performance Monitoring Analysis Method 10

Revolutionizing Network Performance: The Active and Passive ...

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Wen, Jigang Chen, Yuxiang Jin, Kai Liu, Chuda Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Hunan University Computer Science and Electronic Engineering Changsha China Changsha Aeronautical Vocation Technical College Changsha China

ISBN: (纸本)9798350312461

The Service Path Performance Monitoring Scheme is an advanced network performance management method that comprehensively monitors every link of the business system. Its primary goal is to quickly detect and resolve issues that affect key businesses, optimizing operational efficiency and fault-handling capabilities within the network. With customer-centric support in mind, the scheme features real-time performance monitoring, fast fault location, and analytical functions to aid operation and maintenance personnel. Furthermore, the scheme includes a centralized system that collects real-time analysis data from every network node, presenting a clear visual representation of the business system's working conditions. This graphical approach allows for faster interpretation and decision-making, maximizing operation and maintenance efficiency. Ultimately, the Service Path Performance Monitoring Scheme ensures the business network's performance and stability, enabling rapid issue detection and resolution to enhance customer satisfaction. © 2023 IEEE.

关键词： Network performance

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TILT ANGLE DETECTION AND CORRECTION OF LICENSE PLATE BASED ON CONVOLUTIONAL NEURAL NETWORK

International Journal of Mechatronics and Applied Mechanics

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International Journal of Mechatronics and Applied Mechanics 2022年第11期2022卷 141-151页

作者： Li, Jianying Yang, Minsheng Huang, Lijuan School of Computer and Electrical Engineering Hunan University of Arts and Science China

In the intelligent traffic field, accurate recognition of license plate information is not only conducive to the handling of traffic accidents, but also beneficial to safety in the autonomous field. However, the identification of license plate information becomes very difficult if there are tilted angles in the license plate image. The objective of the work is to establish a convolutional neural network (CNN)-based license plate tilt angle recognition network realizing the horizontal and vertical tilt angles detection of the license plate accurately;and then, according to the detected angles, the tilt angle detection algorithm is proposed to realize the tilted license plate image correction and character segmentation. Firstly, the YOLOv3 is adopted to locate license plate and classify the clockwise rotation and anticlockwise rotation license plate images. Secondly, the regression CNN is established to detect the horizontal and vertical tilt angle of the cropped license plate image. Thirdly, the tilted license plate correction algorithm is proposed to correct the horizontal and vertical tilt angles of the license plate using image rotation and shearing transformation theory according to the detected horizontal and vertical tilt angles. Subsequently, the bilinear interpolation is used to improve the correction performance. Finally, the characters of the license plate are segmented from the corrected license plate image after removing the upper and lower boundary. The results show that the proposed tilt angle detection network and the correction algorithm have a good performance, which is superior to the current state-of-the-art tilted license plate correction algorithm. © 2022, Cefin Publishing House. All rights reserved.

关键词： Convolutional neural networks

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A Deep Recurrent Neural Network for Plant Disease Classification

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SN computer science 2024年第8期5卷 1053页

作者： Singh, Divya Kumar, Ashish School of Computer Science Engineering and Technology Bennett University Greater Noida India

Agriculture is important in emerging nations like India, but food security is still a serious problem. Plant diseases, inadequate storage facilities, and poor transportation cause the majority of harvests to be squandered. Since illnesses cause almost 15% of India’s crop yield to be lost, this is a big issue that needs to be addressed. This proposed model is an automated system that can identify the diseases and assist farmers to take the necessary action to cure the crop losses. Farmers have been using the traditional method of using their own eyes to detect plant illnesses, but not all farmers can detect these diseases in the same way. computer vision capabilities must be incorporated into agriculture given the advancements in artificial intelligence. The proposed model uses a convolutional neural network (CNN) with Recurrent Neural Network (RNN) for PlantVillage dataset, the greatest publicly accessible dataset. The proposed model has a 99.37% prediction accuracy for the condition. The proposed approach can identify 14 different plant classes out of the 38 and other moderate in the Plant Village dataset shows how versatile it is. Farmers may decrease crop loss and enhance crop quality and output using this automated and user-friendly technique. In this study, we present the use of a deep recurrent neural network to automatically detect plant diseases. The resulting algorithm is used to identify the bacterial blight of rice during the growing season with a detection accuracy of 99.16%, a classification accuracy of 99.17%, and a sensor-based detection accuracy of 98.98%. Recurrent networks have made great advances in various sequence modeling, such as speech recognition, language modeling, image captioning, and many other applications in recent years. We detect the bacterial blight of rice leaves in this study with a deep recurrent network. We use a stacked LSTM-CNN network to train representations for the radio signal data collected during the lifespan of the rice

关键词： Convolutional neural network Plant disease detection Recurrent neural network Smart agriculture

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Blockchain-Enabled Federated Learning with Differential Privacy for Internet of Vehicles

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computers, Materials & Continua 2024年第10期81卷 1581-1593页

作者： Chi Cui Haiping Du Zhijuan Jia Yuchu He Lipeng Wang School of Information Science and Technology Zhengzhou Normal UniversityZhengzhou450044China School of Electrical Computer and Telecommunications EngineeringUniversity of WollongongWollongongNSW 2500Australia

The rapid evolution of artificial intelligence(AI)technologies has significantly propelled the advancement of the Internet of Vehicles(IoV).With AI support,represented by machine learning technology,vehicles gain the capability to make intelligent *** a distributed learning paradigm,federated learning(FL)has emerged as a preferred solution in *** to traditional centralized machine learning,FL reduces communication overhead and improves privacy *** these benefits,FL still faces some security and privacy concerns,such as poisoning attacks and inference attacks,prompting exploration into blockchain integration to enhance its security *** paper introduces a novel blockchain-enabled federated learning(BCFL)scheme with differential privacy(DP)tailored for *** order to meet the performance demanding IoV environment,the proposed methodology integrates a consortium blockchain with Practical Byzantine Fault Tolerance(PBFT)consensus,which offers superior efficiency over the conventional public *** addition,the proposed approach utilizes the Differentially Private Stochastic Gradient Descent(DP-SGD)algorithm in the local training process of FL for enhanced privacy *** results indicate that the integration of blockchain elevates the security level of FL in that the proposed approach effectively safeguards FL against poisoning *** the other hand,the additional overhead associated with blockchain integration is also limited to a moderate level to meet the efficiency criteria of ***,by incorporating DP,the proposed approach is shown to have the(ε-δ)privacy guarantee while maintaining an acceptable level of model *** enhancement effectively mitigates the threat of inference attacks on private information.

关键词： Blockchain federated learning differential privacy Internet of Vehicles

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Secure Consumer-Centric Demand Response Management in Resilient Smart Grid as Industry 5.0 Application with Blockchain-Based Authentication

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1370-1379页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Shamshad, Salman Saleem, Muhammad Asad Omar, Marwan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan Illinois Institute of Technology Information Technology and Management ChicagoIL60616 United States

The development of the industrial Internet of Things and smart grid networks has emphasized the importance of secure smart grid communication for the future of electric power transmission. However, the current deployment of smart meters and wireless communication methods presents several security concerns that must be addressed to ensure sustainable and resilient smart grid networks for Industry 5.0, with a focus on consumer-centric technologies. In this context, we propose a privacy-focused authentication solution for secure consumer-centric demand response management in smart grid networks, incorporating consumer electronics. Our proposed protocol utilizes blockchain-based authentication to enhance resistance against potential attacks and ensure the integrity of demand response data. Additionally, we have incorporated a PUF to prevent physical attacks. We have conducted informal and formal security analyses to verify the effectiveness of our proposed protocol against various threats and to confirm the session key security. Our comparison study has demonstrated that our protocol provides superior security and added functionalities, with lower computation and communication costs compared to similar protocols. Furthermore, we have measured the computational time for implementing the blockchain component of our protocol while considering the impact of varying numbers of block additions and transactions per block. © 1975-2011 IEEE.

关键词： Authentication

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More than just a name?From magnetic to optical skyrmions and the topology of light

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Light(science & Applications) 2025年第1期14卷 26-29页

作者： Jian Chen Andrew Forbes Cheng-Wei Qiu School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai200093China Department of Electrical and Computer Engineering National University of SingaporeSingapore117583Singapore School of Physics University of the WitwatersrandJohannesburgSouth Africa

Topology is usually perceived intrinsically immutable for a given *** argue that optical topologies do not immediately enjoy such ***'optical skyrmions'as an example,we show that they will exhibit varying textures and topological invariants(skyrmion numbers),depending on how to construct the skyrmion vector when projecting from real to parameter *** demonstrate the fragility of optical skyrmions under a ubiquitous scenario-simple reflection off an optical *** topology is not without benefit,but it must not be assumed.

关键词： numbers space. topology

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A Lightweight Network with Dual Encoder and Cross Feature Fusion for Cement Pavement Crack Detection

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computer Modeling in engineering & sciences 2024年第7期140卷 255-273页

作者： Zhong Qu Guoqing Mu Bin Yuan School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing400065China School of Software Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China

Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning,with convolutional neural networks(CNN)playing an important role in this ***,as the performance of crack detection in cement pavement improves,the depth and width of the network structure are significantly increased,which necessitates more computing power and storage *** limitation hampers the practical implementation of crack detection models on various platforms,particularly portable devices like small mobile *** solve these problems,we propose a dual-encoder-based network architecture that focuses on extracting more comprehensive fracture feature information and combines cross-fusion modules and coordinated attention mechanisms formore efficient feature ***,we use small channel convolution to construct shallow feature extractionmodule(SFEM)to extract low-level feature information of cracks in cement pavement images,in order to obtainmore information about cracks in the shallowfeatures of *** addition,we construct large kernel atrous convolution(LKAC)to enhance crack information,which incorporates coordination attention mechanism for non-crack information filtering,and large kernel atrous convolution with different cores,using different receptive fields to extract more detailed edge and context ***,the three-stage feature map outputs from the shallow feature extraction module is cross-fused with the two-stage feature map outputs from the large kernel atrous convolution module,and the shallow feature and detailed edge feature are fully fused to obtain the final crack prediction *** evaluate our method on three public crack datasets:DeepCrack,CFD,and *** results on theDeepCrack dataset demonstrate the effectiveness of our proposed method compared to state-of-the-art crack detection methods,which achieves Precision(P)87.2%,Recall(R)87.7%,and F-score(F1)87.4%.Thanks to our lightweight cr

关键词： Shallow feature extraction module large kernel atrous convolution dual encoder lightweight network crack detection

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An effective image fusion algorithm based on grey relation of similarity and morphology

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Journal of Ambient Intelligence and Humanized Computing 2023年第11期14卷 14859-14872页

作者： Liu, Caiping Long, Yahui Mao, Jianxu Zhang, Hui Huang, Ruizhi Dai, Yang College of Computer Science and Electronic Engineering Hunan University Changsha410082 China College of Electrical and Information Engineering Hunan University Changsha410082 China College of Electrical and Information Engineering Changsha University of Science and Technology Changsha410012 China

Considering the problems of the limited energy in wireless multi-media sensor networks (WMSNs) and the focused regions discontinuity of the fused image obtained using traditional multi-scale analysis tools (MST)-based methods, an effective multi-focus image fusion algorithm is proposed in this paper. In this method, the original fused image is obtained based on wavelet transform where the low-frequency coefficients are fused by average scheme, whereas the high-frequency coefficients are fused by the proposed merging rule consisting of the grey relation analysis of similarity and local area energy. Then, grey absolute relation analysis is again utilized as measurement indicator to estimate the similarities between the initial fused image and source images, during which the initial map is acquired and then corrected by the mathematical morphological opening and closing. Finally, the fused image is obtained with the guidance of the corrected map, namely the decision map. Experiment results demonstrate that the fused image using the proposed algorithm is more continuous in focused region and more similar to source images in brightness compared with state-of-art multi-focus image fusion algorithms, such as Curvelet transform, lifting stationary wavelet transform (LSWT), non-subsampled contourlet transform (NSCT) and non-subsampled shearlet transform (NSST). Meanwhile, the proposed method shows better superiority in term of the computational complexity. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Computational complexity

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