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8th International Conference on Energy System, Electricity, and Power, ESEP 2023

作者： Zhu, Zhipeng Zhang, Zhao Wang, Chenyu Zhou, Hongyan School of Computer Science and Software Engineering University of Science and Technology Liaoning 185 Qianshanzhong Road Liaoning Anshan114051 China School of Electronic and Information Engineering University of Science and Technology Liaoning 185 Qianshanzhong Road Liaoning Anshan114051 China

ISBN: (数字)9781510679801

ISBN: (纸本)9781510679795

In this paper, we study the obstacle avoidance problem of second-order nonlinear multi-agent systems (MASs) with directed graph based on event-triggered control. Firstly, the consensus requirement is accomplished by using the event-triggered consensus algorithm, and experiments are given for verification. Secondly, an improved artificial potential field (IAPF) method is proposed to solve the obstacle avoidance problem, and comparison experiments are made, from which the experimental results show that the IAPF method is significantly better than the traditional artificial potential field (TAPF) method under the same conditions. © 2024 SPIE.

关键词： Directed graphs

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Application of a class of density peak clustering algorithm in short-term smart grid 8

Application of a class of density peak clustering algorithm ...

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8th International Conference on Energy System, Electricity, and Power, ESEP 2023

作者： Wang, Chenyu Zhang, Zhao Zhu, Zhipeng Zhou, Hongyan School of Computer Science and Software Engineering University of Science and Technology Liaoning 185 Qianshanzhong Road Liaoning Anshan114051 China School of Electronic and Information Engineering University of Science and Technology Liaoning 185 Qianshanzhong Road Liaoning Anshan114051 China

ISBN: (数字)9781510679801

ISBN: (纸本)9781510679795

The classification of short-term power load data by clustering algorithm can lay a good foundation for the subsequent power load forecasting work and provide a more efficient, safe and reliable direction for the operation of power system. Density peaks clustering algorithm with k-nearest neighbors and weighted similarity can reduce the negative impact of global data on local data, avoid the collateral effect of errors and improve the accuracy of the model. It has better effect on local peak power change and global power forecast. In this paper, the density peaks clustering algorithm with k-nearest neighbors and weighted similarity are compared with DPC clustering method through simulation examples, and the effectiveness of this method is verified. © 2024 SPIE.

关键词： Clustering algorithms

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Resource Load Prediction of Internet of Vehicles Mobile Cloud Computing

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computers, Materials & Continua 2022年第10期73卷 165-180页

作者： Wenbin Bi Fang Yu Ning Cao Russell Higgs School of Computer and Software Dalian Neusoft University of InformationDalian116023China School of Information Engineering Qingdao Bin Hai UniversityQingdaoChina School of Internet of Things and Software Technology Wuxi Vocational College of Science and TechnologyWuxi214028China School of Mathematics and Statistics University College DublinDublinIreland

Load-time series data in mobile cloud computing of Internet of Vehicles(IoV)usually have linear and nonlinear composite *** order to accurately describe the dynamic change trend of such loads,this study designs a load prediction method by using the resource scheduling model for mobile cloud computing of ***,a chaotic analysis algorithm is implemented to process the load-time series,while some learning samples of load prediction are ***,a support vector machine(SVM)is used to establish a load prediction model,and an improved artificial bee colony(IABC)function is designed to enhance the learning ability of the ***,a CloudSim simulation platform is created to select the perminute CPU load history data in the mobile cloud computing system,which is composed of 50 vehicles as the data set;and a comparison experiment is conducted by using a grey model,a back propagation neural network,a radial basis function(RBF)neural network and a RBF kernel function of *** shown in the experimental results,the prediction accuracy of the method proposed in this study is significantly higher than other models,with a significantly reduced real-time prediction error for resource loading in mobile cloud *** with single-prediction models,the prediction method proposed can build up multidimensional time series in capturing complex load time series,fit and describe the load change trends,approximate the load time variability more precisely,and deliver strong generalization ability to load prediction models for mobile cloud computing resources.

关键词： Internet of Vehicles mobile cloud computing resource load predicting multi distributed resource computing scheduling chaos analysis algorithm improved artificial bee colony function

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Research on Facial Landmark Detection Algorithm Based on Improved Attention Mechanism

Research on Facial Landmark Detection Algorithm Based on Imp...

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2022 International Conference on Artificial Intelligence, Virtual Reality, and Visualization, AIVRV 2022

作者： Cui, Xi-Shuo Shi, Tian-Wei Cui, Wen-Hua Tao, Ye School of Electronic and Information Engineering University of Science and Technology Liaoning Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning Anshan114051 China

ISBN: (纸本)9781510662988

In recent years, facial landmark detection has assumed an important role in various fields. However, the current facial landmark detection algorithms are still lacking in recognition accuracy. In order to solve the above problem, this paper uses Ghost bottleneck to replace the original bottleneck on the basis of the original model of PFLD model, and adds and improves the CBAM attention mechanism. The improved PFLD model increases the ability of the model to extract facial landmark and improves the accuracy of the algorithm. The improved model has high accuracy and low parametric number and improves the accuracy of facial landmark detection. It also provides a new idea for facial landmark detection task. © 2023 SPIE. All rights reserved.

关键词： Face recognition

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Quantum software Models: Quantum Modules Tomography and Recovery Theorem 35

Quantum Software Models: Quantum Modules Tomography and Reco...

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35th International Conference on software engineering and Knowledge engineering, SEKE 2023

作者： Exman, Iaakov Zvulunov, Ariel School of Computer Science Faculty of Sciences HIT – Holon Institute of Technology Holon Israel Software Engineering The Jerusalem College of Engineering Azrieli Jerusalem Israel

Quantum Tomography partially measures and then recovers the remaining density matrix quantum state, in order to verify that a certain device – processor or detector – indeed outputs the intended quantum state. However, single matrix element measurements rapidly increase in number with the density matrix dimension and are error-prone. This work proposes a novel quantum software viewpoint on quantum tomography. Instead of individual matrix elements, measurements and density matrix recovery are performed on higher-abstraction modules, i.e. semantically meaningful groups of matrix elements. Quantum Modules Tomography potentially reduce the necessary number of explicit measurements, while still allowing recovery of the whole density matrix. A Recovery Theorem is formulated and proved: density matrix diagonal measurements suffice to recover the whole system density matrix, in terms of modules. The recovery procedure is illustrated by a few case studies. © 2023 Knowledge Systems Institute Graduate school. All rights reserved.

关键词： Recovery

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DPFedSAM-Meas: Comparison Of Differential Privacy Federated Learning In Medical Image Classification

DPFedSAM-Meas: Comparison Of Differential Privacy Federated ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Jiang, Shunrong Wang, Zhi He, Zixuan Li, Yonggang Li, Xiaofan Chi, Haotian Du, Xiaojiang China University of Mining and Technology School of Computer Science Xuzhou221116 China Jiangsu Jingchuang Electric CO. LTD China Shanxi University School of Automation and Software Engineering Taiyuan030006 China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States

ISBN: (纸本)9798350351255

Machine learning is widely used in medical image classification tasks. However, medical images often exhibit uneven distribution and high sensitivity to noise. A feasible solution involves using federated learning (FL) with differential privacy (DP), a distributed training method that protects patient privacy. In this work, many studies have proposed improved solutions for localized differential-private federated learning (DP-FL) frameworks. However, these studies are isolated, which would be detrimental to privacy practitioners in designing and using the algorithms. To provide a comprehensive analysis of these algorithms, we propose DPFedSAM-Meas, as a framework for comprehensive utility analysis of DP-FL. In this framework, we employed the state-of-the-art federated learning framework FedSAM. Moreover, we categorize DP algorithms into Laplace DP and Gaussian DP by the underlying DP mechanisms, and into Gradient DP and Parameter DP by the DP position in FL train. DPFedSAM-Meas allows a comparative analysis of these four DP techniques, measuring their model utility, privacy leakage, and overhead when FL uses different network structures. Finally, we evaluate DPFedSAM-Meas on datasets of Pneumonia, Blood, and Path, aiming to investigate the performance of different DP techniques on mainstream deep learning algorithms, including Convolutional Neural Networks (CNN) and Vision Transformers (ViT). © 2024 IEEE.

关键词： Federated learning

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Light field depth estimation:A comprehensive survey from principles to future

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High-Confidence Computing 2024年第1期4卷 92-107页

作者： Tun Wang Hao Sheng Rongshan Chen Da Yang Zhenglong Cui Sizhe Wang Ruixuan Cong Mingyuan Zhao State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Zhongfa Aviation Institute Beihang UniversityHangzhou 311115China Faculty of Applied Sciences Macao Polytechnic UniversityMacao 999078China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Light Field(LF)depth estimation is an important research direction in the area of computer vision and computational photography,which aims to infer the depth information of different objects in threedimensional scenes by capturing LF *** this new era of significance,this article introduces a survey of the key concepts,methods,novel applications,and future trends in this *** summarize the LF depth estimation methods,which are usually based on the interaction of radiance from rays in all directions of the LF data,such as epipolar-plane,multi-view geometry,focal stack,and deep *** analyze the many challenges facing each of these approaches,including complex algorithms,large amounts of computation,and speed *** addition,this survey summarizes most of the currently available methods,conducts some comparative experiments,discusses the results,and investigates the novel directions in LF depth estimation.

关键词： Light field Depth estimation Deep learning Sub-aperture image Epipolar-plane image

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Distributed Secure Storage Scheme Based on Sharding Blockchain

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computers, Materials & Continua 2022年第3期70卷 4485-4502页

作者： Jin Wang Chenchen Han Xiaofeng Yu Yongjun Ren R.Simon Sherratt School of Computer Science and Mathematics Fujian University of TechnologyFuzhou350118China School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410004China School of Business Nanjing UniversityNanjing210093China School of Computer School of SoftwareSchool of Cyberspace SecurityEngineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science&TechnologyNanjing210044China Department of Biomedical Engineering University of ReadingRG66AYUK

Distributed storage can store data in multiple devices or servers to improve data ***,in today’s explosive growth of network data,traditional distributed storage scheme is faced with some severe challenges such as insufficient performance,data tampering,and data lose.A distributed storage scheme based on blockchain has been proposed to improve security and efficiency of traditional distributed *** this scheme,the following improvements have been made in this *** paper first analyzes the problems faced by distributed *** proposed to build a new distributed storage blockchain scheme with sharding *** proposed scheme realizes the partitioning of the network and nodes by means of blockchain sharding technology,which can improve the efficiency of data verification between *** addition,this paper uses polynomial commitment to construct a new verifiable secret share scheme called *** new scheme is one of the foundations for building our improved distributed storage blockchain *** with the previous scheme,our new scheme does not require a trusted third party and has some new features such as homomorphic and batch *** security of VSS can be further *** comparisons show that the proposed scheme significantly reduces storage and communication costs.

关键词： Blockchain distributed storage verifiable secret share polynomial commitment

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Crack Detection Method for Asphalt Pavement Combining Multi-scale Features and Attention Mechanism 4

Crack Detection Method for Asphalt Pavement Combining Multi-...

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4th International Conference on Advances in Electrical engineering and computer Applications, AEECA 2023

作者： Xu, Zijian Wang, Lili Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing China Chongqing University of Posts and Telecommunications School of Software Engineering Chongqing China

ISBN: (纸本)9798350308082

Crack, as one of the common diseases of asphalt pavements, seriously affects the health of asphalt pavements. To cope with the demand of crack detection in the context of complex pavements, an improved network model with an encoder-decoder structure is proposed. First, dense long and short connections are combined with full-scale jump connections, and the jump connection structure yields fullscale feature information to each node of the decoding layer. Second, spatial and channel attention modules are incorporated into the proposed network. The former is used at the low level of the network to improve the ability to capture crack detail information, and the latter is applied at the high level of the proposed network to obtain the semantic information. Finally, improve network performance by building deep supervision network. The proposed network is compared with on three datasets, DeepCrack, CFD, and Crack500, and the F-score reaches 86.79%. In this paper, the network is effective in crack detection and plays a certain role in maintaining road safety. © 2023 IEEE.

关键词： Decoding

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Tri-PANet: Triplanar Ensemble Network with Parallelly Aggregating Global and Local Information for Brain Tumor Segmentation

Tri-PANet: Triplanar Ensemble Network with Parallelly Aggreg...

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2023 International Conference on Machine Vision, Image Processing and Imaging technology, MVIPIT 2023

作者： Zhou, Li Fang Li, Jun Chongqing University of Posts and Telecommunications School of Software Engineering Chongqing China Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing China

ISBN: (纸本)9798350306545

Accurate segmentation of brain tumor images holds great importance in the clinical diagnosis and effective treatment of brain tumors diseases. Nonetheless, tumors exhibit extensive variations in their characteristics, both in terms of their visual appearance and their location within brain MR images. This poses a significant challenge in achieving robust tumor segmentation. Furthermore, sample labeling bias problem also limits the segmentation accuracy of current methods. In this work, we propose a innovation approach named Tri-PANet for brain tumor segmentation. On the one hand, Tri-PANet learns both local features and global dependencies of images to better obtain shape and location information from a global perspective. On the other hand, Tri-PANet constrains tumor regions that are prone to mislabeling by a triplanar ensemble framework, with an independent tumour core prediction module. We evaluate our method using publicly accessible brain tumor segmentation datasets BraTS2019 and BraTS2020. The experimental results demonstrate that our propose model effective segmentation the tumor region and consistently outperform state-of-the-art methods. © 2023 IEEE.

关键词： Tumors

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