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Transaction transmission model for blockchain channels based on non-cooperative games

Science China(information Sciences) 2023年第1期66卷 97-114页

作者： Peiyun ZHANG Chenxi LI Mengchu ZHOU Wenjun HUANG Abdullah ABUSORRAH Omaimah O.BAMASAG Engineering Research Center of Digital Forensics of Ministry of Education School of Computer and SoftwareNanjing University of Information Science and Technology School of Computer and Information Anhui Normal University Department of Electrical and Computer Engineering New Jersey Institute of Technology Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Faculty of Engineering and K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University Department of Computer Science Faculty of Computing and Information Technology Center of Excellence in Smart Environment Research King Abdulaziz University

In blockchain networks, transactions can be transmitted through channels. The existing transmission methods depend on their routing information. If a node randomly chooses a channel to transmit a transaction, the transmission may be aborted due to insufficient funds(also called balance) or a low transmission rate. To increase the success rate and reduce transmission delay across all transactions, this work proposes a transaction transmission model for blockchain channels based on non-cooperative game *** balance, channel states, and transmission probability are fully considered. This work then presents an optimized channel transaction transmission algorithm. First, channel balances are analyzed and suitable channels are selected if their balance is sufficient. Second, a Nash equilibrium point is found by using an iterative sub-gradient method and its related channels are then used to transmit transactions. The proposed method is compared with two state-of-the-art approaches: Silent Whispers and Speedy Murmurs. Experimental results show that the proposed method improves transmission success rate, reduces transmission delay,and effectively decreases transmission overhead in comparison with its two competitive peers.

关键词： Blockchain channel transaction transmission channel balance channel state channel transmission probability non-cooperative game

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Intelligent Recognition of Geometric Tolerance Symbol Annotation Blocks 4

Intelligent Recognition of Geometric Tolerance Symbol Annota...

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4th International Symposium on Artificial Intelligence and Intelligent Manufacturing, AIIM 2024

作者： Li, Congxin Xu, Yuanping Zhang, Chaolong Kong, Chao Jin, Jin Wang, Weiye Xu, Zhijie Guo, Benjun Tang, Dan School of Software Engineering Chengdu University of Information Technology Chengdu China School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou China

ISBN: (纸本)9798331541729

In previous mechanical design drawings, the geometric tolerance symbol annotation block was directly annotated on the mechanical design drawing by engineers, resulting in the symbol annotation block being non-editable and non-shareable. To achieve digitization, this study proposes an improved Swin_YOLOv5_GE model based on YOLOv5 to realize the positioning of the symbol annotation block. Compared with the original YOLOv5, the accuracy is increased by 4%. Then, image processing is performed on the located and cropped symbol annotation block to achieve denoising and tilt correction of the symbol annotation block. Finally, it is input into CRNN for the recognition of the symbol annotation block. The final experimental results prove that the method in this paper can obtain an overall recognition accuracy of 94%, proving the feasibility of this method. © 2024 IEEE.

关键词： Machine design

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Optimization of multi-state generation problem based on spatial information network topology

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High-Confidence Computing 2023年第1期3卷 58-65页

作者： Peng Yang JiaYing Zhang Shijie Zhou Jinyu Zhou School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054China School of Mathematical Sciences University of Electronic Science and Technology of ChinaChengdu 611731China

Spatial information network is a kind of satellite network with high speed node movement and fast dynamic topology *** the increasing number of low-orbit satellites,the research on the subnets topology and dynamic optimization of space information networks has become an important direction to study the destructibility of spatial information *** this paper,two common objective functions in inter-satellite link assignment,network observation position and network communication factor are studied,and a multi-objective optimization model is *** first search,simulated annealing,NSGA-II and adaptive optimization simulated annealing were used to analyze and solve the *** comparing the solving efficiency of the model through simulation experiments,the difference of the results caused by the four algorithms is verified.

关键词： Spatial information network Simulated annealing NSGA-II

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Joint User Scheduling and UAV Height Control for Smart Wearable Device Charging Network 19th

Joint User Scheduling and UAV Height Control for Smart Wea...

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19th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2023

作者： Ji, Hongjing Wang, Xiaojie Ning, Zhaolong School of Software Engineering Dalian University of Technology Dalian China School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9783031651229

This paper studies the user device time slot scheduling and Unmanned Aerial Vehicle (UAV) height control problem in the UAV-assisted smart wearable charging network. In contrast to traditional studies that only consider UAVs to serve user devices at a fixed height and ignore the flexibility and mobility of UAVs. We consider the UAV coverage and limited battery energy constraints as well as the charging demand of wearable devices;aiming to maximize network throughput. Specifically, first we formulate the problem as a Constrained Markov Decision Process (CMDP), and then propose a Proximal Policy Optimization (PPO)-CMDP algorithm for solving the formulated problem based on the Lagrangian original pairwise strategy optimization. Finally, the results from simulation evaluations show that our proposed algorithm can learn the mobility policy of UAV and the time slot scheduling policy of wearable devices to maximize the throughput of the achieved network while ensuring that the power of the UAV satisfies the constraint, and outperforms the baseline scheme. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Markov processes

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Point Cloud Implicit 3D Reconstruction Based on Adaptive Threshold Optimal Transport 4

Point Cloud Implicit 3D Reconstruction Based on Adaptive Thr...

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4th International Symposium on Artificial Intelligence and Intelligent Manufacturing, AIIM 2024

作者： Li, Qinyun Jin, Jin Xu, Yuanping Kong, Chao Wang, Weiye Xu, Zhijie Zhang, Chaolong Guo, Benjun School of Software Engineering Chengdu University of Information Technology Chengdu China School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou China

ISBN: (纸本)9798331541729

To achieve high precision reconstruction of complex point cloud data, this paper presents a novel method based on an adaptive threshold. Firstly, a pre trained neural network is used to initially segment the point cloud, and an adaptive threshold is introduced into the discrete optimal transport to effectively detect feature regions such as edges and corners based on local geometric information. Subsequently, a zero level set smoothing loss function and a normal alignment loss function are introduced into the implicit neural network to optimize the feature region and the smooth region respectively. Finally, the optimized point cloud is reconstructed through the Marching Cubes algorithm. Experimental results show that compared with DeepSDF and Occupancy Networks, the accuracy of the proposed method on the corresponding data sets is improved by 3.33% and 1.2% respectively. © 2024 IEEE.

关键词： Cloud platforms

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FPGA-based acceleration for binary neural networks in edge computing

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Journal of Electronic Science and technology 2023年第2期21卷 65-77页

作者： Jin-Yu Zhan An-Tai Yu Wei Jiang Yong-Jia Yang Xiao-Na Xie Zheng-Wei Chang Jun-Huan Yang School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu610054China School of Automation Chengdu University of Information TechnologyChengdu610225China State Grid Sichuan Electric Power Research Institute Chengdu610095China Department of Information Sciences and Technology George Mason UniversityFairfaxVA22030USA

As a core component in intelligent edge computing,deep neural networks(DNNs)will increasingly play a critically important role in addressing the intelligence-related issues in the industry domain,like smart factories and autonomous *** to the requirement for a large amount of storage space and computing resources,DNNs are unfavorable for resource-constrained edge computing devices,especially for mobile terminals with scarce energy *** of DNN has become a promising technology to achieve a high performance with low resource consumption in edge ***-programmable gate array(FPGA)-based acceleration can further improve the computation efficiency to several times higher compared with the central processing unit(CPU)and graphics processing unit(GPU).This paper gives a brief overview of binary neural networks(BNNs)and the corresponding hardware accelerator designs on edge computing environments,and analyzes some significant studies in *** performances of some methods are evaluated through the experiment results,and the latest binarization technologies and hardware acceleration methods are *** first give the background of designing BNNs and present the typical types of *** FPGA implementation technologies of BNNs are then *** comparison with experimental evaluation on typical BNNs and their FPGA implementation is further ***,certain interesting directions are also illustrated as future work.

关键词： Accelerator Binarization Field-programmable gate array(FPGA) Neural networks Quantification

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A Line Complex-Based Evolutionary Algorithm for Many-Objective Optimization

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IEEE/CAA Journal of Automatica Sinica 2023年第5期10卷 1150-1167页

作者： Liang Zhang Qi Kang Qi Deng Luyuan Xu Qidi Wu the Department of Control Science and Engineering Tongji UniversityShanghai 201804 the Jiangsu Province Support Software Engineering R&D Center for Modern Information Technology Application in Enterprise Suzhou 215104China the Shanghai Institute of Intelligent Science and Technology Tongji UniversityShanghai 200092China

In solving many-objective optimization problems(MaO Ps),existing nondominated sorting-based multi-objective evolutionary algorithms suffer from the fast loss of selection *** candidate solutions become nondominated during the evolutionary process,thus leading to the failure of producing offspring toward Pareto-optimal front with *** we find a more effective way to select nondominated solutions and resolve this issue?To answer this critical question,this work proposes to evolve solutions through line complex rather than solution points in Euclidean ***,Plücker coordinates are used to project solution points to line complex composed of position vectors and momentum *** position vectors of the solution points,momentum vectors are used to extend the comparability of nondominated solutions and enhance selection ***,a new distance function designed for high-dimensional space is proposed to replace Euclidean distance as a more effective distancebased *** on them,a novel many-objective evolutionary algorithm(MaOEA)is proposed by integrating a line complex-based environmental selection strategy into the NSGAⅢ*** proposed algorithm is compared with the state of the art on widely used benchmark problems with up to 15 *** results demonstrate its superior competitiveness in solving MaOPs.

关键词： Environmental selection line complex many-objective optimization problems(MaOPs) Plücker coordinate

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An Uncertainty Quantization-Based Method for Anti-UAV Detection in Infrared Images

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Computers, Materials & Continua 2025年第4期83卷 1439-1458页

作者： Can Wu Wenyi Tang Yunbo Rao Yinjie Chen Hui Ding Shuzhen Zhu Yuanyuan Wang School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu610054China State Key Laboratory of Air Traffic Management System Nanjing210000China National Airspace Management Center Beijing100094China

Infrared unmanned aerial vehicle(UAV)target detection presents significant challenges due to the inter-play between small targets and complex *** methods,while effective in controlled environments,often fail in scenarios involving long-range targets,high noise levels,or intricate backgrounds,highlighting the need for more robust *** address these challenges,we propose a novel three-stage UAV segmentation framework that leverages uncertainty quantification to enhance target *** framework incorporates a Bayesian convolutional neural network capable of generating both segmentation maps and probabilistic uncertainty *** utilizing uncer-tainty predictions,our method refines segmentation outcomes,achieving superior detection ***,this marks the first application of uncertainty modeling within the context of infrared UAV target *** evaluations on three publicly available infrared UAV datasets demonstrate the effectiveness of the proposed *** results reveal significant improvements in both detection precision and robustness when compared to state-of-the-art deep learning *** approach also extends the capabilities of encoder-decoder convolutional neural networks by introducing uncertainty modeling,enabling the network to better handle the challenges posed by small targets and complex environmental *** bridging the gap between theoretical uncertainty modeling and practical detection tasks,our work offers a new perspective on enhancing model interpretability and *** codes of this work are available openly at https://***/general-learner/UQ_Anti_UAV(acceessed on 11 November 2024).

关键词： Object segmentation uncertainty quantification bayesian convolutional neural network

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Multi-layer composite autoencoders for semi-supervised change detection in heterogeneous remote sensing images

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Science China(information Sciences) 2023年第4期66卷 124-125页

作者： Jiao SHI Tiancheng WU Hanwen YU A.K.QIN Gwanggil JEON Yu LEI School of Electronics and Information Northwestern Polytechnical University School of Resources and Environment University of Electronic Science and Technology of China Department of Computer Science and Software Engineering Swinburne University of Technology Department of Embedded Systems Engineering Incheon National University

With the increasing complexity of application scenarios, the fusion of different remote sensing data types has gradually become a trend, which can greatly improve the utilization of massive remote sensing *** the problem of change detection for heterogeneous remote images can be much more complicated than the traditional change detection for homologous remote sensing images,

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IEARACDA: Predicting circRNA-disease associations based on information enhancement and arctangent rank approximation

IEARACDA: Predicting circRNA-disease associations based on i...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Pang, Shanchen Song, Zheqi Liu, Tiyao Wu, Wenhao Zhang, Yuanyuan Wang, Shudong College of Computer Science and Technology Qingdao Institute of Software Qingdao China Qingdao University of Technology School of Information and Control Engineering Qingdao China

ISBN: (纸本)9798350386226

Circular RNAs (circRNAs) are non-coding RNA molecules that play a significant role in cell regulation and disease occurrence. In recent years, the use of computational methods to predict circRNAs associated with diseases has become a research focus. However, these methods are often constrained by the noise interference and an insufficient exploration of the underlying information in known associations. Therefore, this paper proposes an information enhancement and arctangent rank approximation based method to predict potential circRNA-disease associations (IEARACDA). Firstly, information enhancement methods are used to make circRNA and disease similarity information more complete and to reduce the sparsity of known association. Subsequently, the arctangent rank approximation method is employed to mitigate the potential bias issues that may arise from the nuclear norm, and optimization is carried out using the inexact augmented Lagrange multiplier (IALM) method. Finally, potential associations are predicted through heterogeneous graph inference method. The experimental results demonstrate that IEARACDA exhibits superior performance compared to existing methods. Furthermore, case studies for hepatocellular carcinoma and lung cancer provide additional confirmation of the accuracy and practical significance of the model predictions. © 2024 IEEE.

关键词： Lung cancer

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