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An efficient Bitcoin network topology discovery algorithm for dynamic display

Blockchain: Research and Applications 2025年第2期6卷

作者： Zening Zhao Jinsong Wang Miao Yang Haitao Wang School of Computer Science and Engineering Tianjin University of Technology Tianjin 300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin 300384 China National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin 300457 China

The Bitcoin network comprises numerous nodes, necessitating users to invest significant network requests and time in comprehending its network topology. In this paper, we propose a Bitcoin network topology discovery algorithm that uses lightweight probe nodes to facilitate rapid transmission of network protocols. Building upon this, we introduce a node layer clustering algorithm based on filtering stable network nodes, enabling parallel discovery of the network topology. Additionally, we present an adaptive method for dynamically displaying the layered structure of the network topology. Experimental results demonstrate that our proposed method reduces communication overhead by approximately 72.16% when achieving a 95% similarity in network topology. Furthermore, the algorithm is applicable for discovering the network topology in other blockchain networks with similar structures.

关键词： Blockchain Network topology discovery Bitcoin Stability coefficient Dynamic updates

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Collaborative Scheduling of computing Tasks for Edge computing

Collaborative Scheduling of Computing Tasks for Edge Computi...

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2021 China Automation Congress, CAC 2021

作者： Liu, Yue Xiong, Gang Zhu, Fenghua Chen, Shichao Zhang, Liguo Liu, Xudong School of Artificial Intelligence and Automation Beijing University of Technology The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation The Cloud Computing Center Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Qingdao Academy of Intelligent Industries Chinese Academy of Sciences Beijing China Faculty of Information and Technology Beijing University of Technology Beijing China

ISBN: (纸本)9781665426473

In the face of the rapid development of the IoT, the traditional cloud computing, while providing services for mobile intelligent devices, also brings problems such as network bandwidth occupation and communication delay. Edge computing can solve these problems to some extent. But too many offloaded tasks may result in an edge server becoming overloaded and execution delay of tasks increasing, the computing resources of idle edge devices are also not fully utilized. In order to make full use of the computing resources of idle edge devices in the local network and reduce the execution delay for computing tasks, this paper takes the average delay time for all end devices as the optimization objective and proposes an end-edge cooperative scheduling (EECS) model to schedule tasks for edge computing. The scheduling algorithm used in EECS model is improved based on MCT algorithm. We use the EdgeCloudSim to verify the EECS model and scheduling algorithm. The simulation results show that the average completion time of computing tasks in EECS model, compared with only single edge server (SEE) task computing model, can be reduced by up to three times. In addition, the system load can be balanced to a certain extent in local network. © 2021 IEEE

关键词： Edge computing

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Calibration of agent-based model using reinforcement learning 1

Calibration of agent-based model using reinforcement learnin...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Song, Bing Xiong, Gang Yu, Songmin Ye, Peijun Dong, Xisong Lv, Yisheng Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing The Cloud Computing Center Chinese Academy of Sciences Beijing China Fraunhofer Institute for Systems and Innovation Research Breslauer Germany

ISBN: (纸本)9781665433372

In the research and application of Agent-Based Models (ABM), parameter calibration is an important content. Based on the existing state transfer equations that link the micro-parameters and macro-states of the multi-agent system, this paper further proposes to introduce Reinforcement Learning when calibrating the parameters. The state transfer of the agent after learning is used to calibrate the micro-parameters of ABM, and the interaction between each agent and multiple other agents is expressed as the parameters of the agent. The application case study of population migration demonstrates that our method can achieve high accuracy and low computational complexity. © 2021 IEEE.

关键词： Autonomous agents

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When measurements fail: using an interactive SLAM solution to fight bad odometry

When measurements fail: using an interactive SLAM solution t...

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IEEE Instrumentation and Measurement Technology Conference

作者： Robert Milijaš Juraj Oršulić Stjepan Bogdan Laboratory for Robotics and Intelligent Control Systems Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

ISBN: (数字)9781728144603

ISBN: (纸本)9781728144610

In this paper we use the underlying pose graph structure of graph SLAM to allow the user to inspect and intervene into the Simultaneous Localization and Mapping (SLAM) process. With post-processing the map by inserting custom constraints into the optimization problem, the user can improve the quality of the map obtained as the final SLAM result. We refer to this inspection and intervention process as interactive SLAM. The mathematical background, which allows such interventions, is laid out, and a software implementation is used to improve the quality of a map created at the Faculty of Electrical engineering and computing, University of Zagreb.

关键词： SLAM Robotics Interactive SLAM supersonic low altitude missile robotics maps software implementation ELECTRICAL engineering odometry

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Ascl: Accelerating Semi-Supervised Learning Via Contrastive Learning

SSRN

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SSRN 2024年

作者： Liu, Haixiong Li, Zuoyong Wu, Jiawei Zeng, Kun Hu, Rong Zeng, Wei Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Data Science Minjiang University Fuzhou350121 China School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University Guangdong Shenzhen518107 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China School of Physics and Mechanical and Electrical Engineering Longyan University Longyan364012 China

SSL(Semi-supervised learning) is widely used in machine learning, which leverages labeled and unlabeled data to improve model performance. SSL aims to optimize class mutual information, but noisy pseudo-labels introduce false class information due to the scarcity of labels. Therefore, these algorithms often need significant training time to refine pseudo-labels for performance improvement iteratively. To tackle this challenge, we propose a novel plug-and-play method named ASCL(Accelerating Semi-Supervised Learning via Contrastive Learning). This method combines contrastive learning with uncertainty-based selection for performance improvement and accelerates the convergence of SSL algorithms. Contrastive learning initially emphasizes the mutual information between samples as a means to decrease dependence on pseudo-labels. Subsequently, it gradually turns to maximizing the mutual information between classes, aligning with the objective of semi-supervised learning. Uncertainty-based selection provides a robust mechanism for acquiring pseudo-labels. The combination of the contrastive learning module and the uncertainty-based selection module forms a virtuous cycle to improve the performance of the proposed model. Extensive experiments demonstrate that ASCL outperforms state-of-the-art methods in terms of both convergence efficiency and performance. In the experimental scenario where only one label is assigned per class in the CIFAR-10 dataset, the application of ASCL to Pseudo-label, UDA (Unsupervised Data Augmentation for Consistency Training), and Fixmatch benefits substantial improvements in classification accuracy. Specifically, the results demonstrate notable improvements in respect of 16.32%, 6.9%, and 24.43% when compared to the original outcomes. Moreover, the required training time is reduced by almost 50%. © 2024, The Authors. All rights reserved.

关键词： Image classification

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Skeleton-prompt: A cross-dataset transfer learning approach for skeleton action recognition

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Pattern Recognition 2026年 169卷

作者： Mingqi Lu Xiaobo Lu Jun Liu School of Automation Southeast University Nanjing 210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing 210096 China School of Computing and Communications Lancaster University UK

This paper presents Skeleton-Prompt, a novel tuning method designed to tackle cross-dataset transfer issues in skeleton action recognition models. Given the scarcity of large-scale 3D skeleton datasets and the variability in keypoint structures across datasets, existing methods often rely on training models from scratch, necessitating extensive labeled data and exhibiting high sensitivity to occlusion. Our approach aims to fine-tune pre-trained models to adapt to limited real-world skeleton data. We use 2D skeletons as inputs and leverage a large human motion dataset for 2D to 3D pose estimation to learn generalizable motion features. A lightweight prompt generator produces instance-level prompts, and we employ dynamic queries with cross-attention to refine the semantic information of the input data. Additionally, we introduce a joint-enhanced multi-stream fusion mechanism based on self-attention to improve robustness against incomplete skeletons. Skeleton-Prompt represents a significant advancement in efficient fine-tuning for skeleton action recognition, effectively addressing cross-dataset generalization challenges in a data-efficient and parameter-efficient manner.

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Network compression via central filter

arXiv

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arXiv 2021年

作者： Duan, Yuanzhi Hu, Xiaofang Zhou, Yue Liu, Qiang Duan, Shukai College of Artificial Intelligence Southwest University China College of Electronics and Information Engineering Southwest University China Brain-inspired Computing & Intelligent Control of Chongqing Key Lab China Harbin Institute of Technology China

Neural network pruning has remarkable performance for reducing the complexity of deep network models. Recent network pruning methods usually focused on removing unimportant or redundant filters in the network. In this paper, by exploring the similarities between feature maps, we propose a novel filter pruning method-Central Filter (CF), which suggests that a filter is approximately equal to a set of other filters after appropriate adjustments. Our method is based on the discovery that the average similarity between feature maps changes very little, regardless of the number of input images. Based on this finding, we establish similarity graphs on feature maps and calculate the closeness centrality of each node to select the Central Filter. Moreover, we design a method to directly adjust weights in the next layer corresponding to the Central Filter, effectively minimizing the error caused by pruning. Through experiments on various benchmark networks and datasets, CF yields state-of-the-art performance. For example, with ResNet-56, CF reduces approximately 39.7% of FLOPs by removing 47.1% of the parameters, with even 0.33% accuracy improvement on CIFAR-10. With GoogLeNet, CF reduces approximately 63.2% of FLOPs by removing 55.6% of the parameters, with only a small loss of 0.35% in top-1 accuracy on CIFAR-10. With ResNet-50, CF reduces approximately 47.9% of FLOPs by removing 36.9% of the parameters, with only a small loss of 1.07% in top-1 accuracy on ImageNet. The codes can be available at https://***/8ubpshLR23/Central-Filter. Copyright © 2021, The Authors. All rights reserved.

关键词： Benchmarking

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A high-bandwidth compact micro/nano-positioning stage based on a double parallelogram compliant mechanism

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Smart Materials and Structures 2025年第6期34卷 065015-065015页

作者： Tao Zhang Wantao Zhu Donglong Bian Xingxiang Li Rui Ma Wanyun Ding Yuhang Wang Faculty of Mechanical and Electrical Engineering Northeast Forestry University Harbin Heilongjiang 150000 People’s Republic of China Faculty of Computing and Control Northeast Forestry University Harbin Heilongjiang 150000 People’s Republic of China

This paper introduces a novel piezoelectric actuator (PEA)-driven two-degree-of-freedom micro/nano-positioning stage for high-precision adjustments in small-space situations. To enhance stage compactness and dynamic performance while significantly reducing parasitic motions, the composite leaf-type double parallelogram mechanism and the leaf-type double parallelogram mechanism are presented for implementation in guided and decoupled mechanisms, respectively. The composite bridge arm structure and compact double parallelogram design enable the amplification mechanism to provide significant lateral rigidity and safeguard the *** orthogonal arrangement of the composite bridge type amplification mechanism and the dual parallelogram decoupling guiding mechanism are integrated to achieve full decoupling of the stage at both the input and output ends. The compliance matrix method and Lagrange's equation were employed to establish mathematical analytical models of the static and dynamic characteristics, which were subsequently validated through finite element analysis. The stage's static and dynamic performances were thoroughly assessed through experiments. The closed-loop control of the PEA utilizing integral strain gauges effectively eliminates the nonlinear hysteresis issue on the stage and exhibits proficient trajectory tracking capability. The experimental findings indicate that the motion range of the precision positioning stage is 51.71 × 49.56 µm², the output coupling displacement error is under 1%, the displacement resolution is 12 nm, and the natural frequencies of the X-axis and Y-axis are 429 Hz and 438 Hz, respectively.

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FaSTrack:A Modular Framework for Real-Time Motion Planning and Guaranteed Safe Tracking

FaSTrack:A Modular Framework for Real-Time Motion Planning a...

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作者： Chen, Mo Herbert, Sylvia L. Hu, Haimin Pu, Ye Fisac, Jaime Fernandez Bansal, Somil Han, Soojean Tomlin, Claire J. School of Computing Science Simon Fraser University BurnabyBCV5A 1S6 Canada Department of Mechanical and Aerospace Engineering University of California San Diego San DiegoCA92122 United States Department of Electrical Engineering Princeton University PrincetonNJ08536 United States Department of Electrical and Electronic Engineering University of Melbourne ParkvilleVIC3010 Australia Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA94704 United States Control and Dynamical Systems Program California Institute of Technology Los AngelesCA90019 United States

Real-time, guaranteed safe trajectory planning is vital for navigation in unknown environments. However, real-time navigation algorithms typically sacrifice robustness for computation speed. Alternatively, provably safe trajectory planning tends to be too computationally intensive for real-time replanning. We propose FaSTrack, Fast and Safe Tracking, a framework that achieves both real-time replanning and guaranteed safety. In this framework, real-time computation is achieved by allowing any trajectory planner to use a simplified planning model of the system. The plan is tracked by the system, represented by a more realistic, higher dimensional tracking model. We precompute the tracking error bound (TEB) due to mismatch between the two models and due to external disturbances. We also obtain the corresponding tracking controller used to stay within the TEB. The precomputation does not require prior knowledge of the environment. We demonstrate FaSTrack using Hamilton-Jacobi reachability for precomputation and three different real-time trajectory planners with three different tracking-planning model pairs. © 1963-2012 IEEE.

关键词： Real time systems

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Numerical Simulation of Lowering a Payload into Waves with a Heave Compensation System Using Openfoam

SSRN

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SSRN 2022年

作者： Wang, Kai Ma, Xin Bai, Wei Qian, Ling Lin, Zaibin School of Control Science and Engineering Shandong University Shandong China Centre for Mathematical Modelling and Flow Analysis Department of Computing and Mathematics Manchester Metropolitan University ManchesterM1 5GD United Kingdom School of Engineering University of Aberdeen AberdeenAB24 3UE United Kingdom 17923 Jingshi Road. Shandong Jinan250061 China

In heavy lifting and lowering marine operations, heave compensation is wildly used to decouple the vessel heave motion from the payload and reduce the dynamic cable tension. This paper proposes a novel OpenFOAM-based numerical model to study the process of lowering payloads into waves by an offshore crane with a heave compensation system. First, an elastic cable module and electric winch system are established in a three-dimensional Numerical Wave Tank (NWT) model to achieve the crane vessel lifting and lowering operation. A correction method for the cable spring force is proposed to reduce the calculation error of the cable system. Second, a heave compensation is designed to compensate the vessel motion while taking hydrodynamic forces on payloads during the lowering operation into account. A physical experiment of lowering an aluminum cuboid into a water tank by the crane platform is conducted to verify the developed model. Finally, the process of lowering payloads into waves is simulated to analyze the dynamic character of the cable force and the payload motion using the developed numerical model. A PID controller is applied to stabilize the payload motion by controlling the winch velocity, reducing the dynamic cable tension and the risk of cable slack. © 2022, The Authors. All rights reserved.

关键词： Numerical models

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