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International Conference on Computer and Communications (ICCC)

作者： Ying Bai Dongsheng Yang Hongliang Wang Changyi Deng Wei Wang Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on DigitTwin Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on DigitTwin Shenyang China Institute of Software China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

In recent years, the use of skeleton data for human action recognition has received increasing attention in fields such as intelligent monitoring, human-object interaction, and video retrieval. Among them, Modeling Human Skeleton as Spatial-Temporal Graph Convolutional Network (ST-GCN) has made significant progress. However, methods based on Graph Convolution (GCN) generally face two issues: on one hand, the receptive fields of the spatial and temporal graphs are not flexible enough, limiting their feature representation capabilities. On the other hand, the model is overly parameterized, leading to high computational costs. In this paper, we first propose a spatial-temporal transpose convolution module to overcome the inflexibility of the spatial-temporal graph receptive field. Secondly, we introduce 1 *1 convolutions and depth-wise separable convolution layers into the network model, effectively reducing the computational complexity of the model. On the two subsets of the large-scale skeleton action recognition dataset NTU RGB-D, experimental results show that the proposed method achieved Top1 accuracy metrics of 84.6% and 92.5% respectively, while the model size was reduced by 13.48 times compared to the classic ST-GCN method, and the computational complexity was reduced by approximately 17.18 times.

关键词： Hands Accuracy Convolution Computational modeling Benchmark testing Approximation algorithms Skeleton Partitioning algorithms Computational complexity Monitoring

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Seamless Graph Task Scheduling over Dynamic Vehicular Clouds: A Hybrid Methodology for Integrating Pilot and Instantaneous Decisions

arXiv

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arXiv 2025年

作者： Guo, Bingshuo Liwang, Minghui Xia, Xiaoyu Li, Li Jiao, Zhenzhen Hosseinalipour, Seyyedali Wang, Xianbin School of Informatics Xiamen University Fujian China School of Computing Technologies RMIT University MelbourneVIC Australia Department of Control Science and Engineering Tongji University Shanghai China iF-Labs Beijing Teleinfo Technology Co. Ltd. CAICT China Department of Electrical Engineering University at Buffalo-SUNY NY United States Department of Electrical and Computer Engineering Western University ON Canada

Vehicular clouds (VCs) play a crucial role in the Internet-of-Vehicles (IoV) ecosystem by securing essential computing resources for a wide range of tasks. This paPertackles the intricacies of resource provisioning in dynamic VCs for computation-intensive tasks, represented by undirected graphs for parallel processing over multiple vehicles. We model the dynamics of VCs by considering multiple factors, including varying communication quality among vehicles, fluctuating computing capabilities of vehicles, uncertain contact duration among vehicles, and dynamic data exchange costs between vehicles. Our primary goal is to obtain feasible assignments between task components and nearby vehicles, called templates, in a timely manner with minimized task completion time and data exchange overhead. To achieve this, we propose a hybrid graph task scheduling (P-HTS) methodology that combines offline and online decision-making modes. For the offline mode, we introduce an approach called risk-aware pilot isomorphic subgraph searching (RA-PilotISS), which predicts feasible solutions for task scheduling in advance based on historical information. Then, for the online mode, we propose time-efficient instantaneous isomorphic subgraph searching (TE-InstaISS), serving as a backup approach for quickly identifying new optimal scheduling template when the one identified by RA-PilotISS becomes invalid due to changing conditions. Through comprehensive experiments, we demonstrate the superiority of our proposed hybrid mechanism compared to state-of-the-art methods in terms of various evaluative metrics, e.g., time efficiency such as the delay caused by seeking for possible templates and task completion time, as well as cost function, upon considering different VC scales and graph task topologies. Copyright © 2025, The Authors. All rights reserved.

关键词： Cost functions

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Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5520-5537页

作者： Jian Liu Wei Sun Hui Yang Pengchao Deng Chongpei Liu Nicu Sebe Hossein Rahmani Ajmal Mian National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics and the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China Department of Information Engineering and Computer Science University of Trento Trento Italy School of Computing and Communications Lancaster University Lancaster U.K. Department of Computer Science The University of Western Australia Crawley WA Australia

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance.

关键词： Shape Solid modeling Three-dimensional displays Pose estimation Training Robots Grasping Diffusion models Real-time systems Point cloud compression

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An Optimal Pricing Strategy for Ethereum Gasprice Based on Non-Preemptive Priority Queuing Game

SSRN

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SSRN 2023年

作者： Ma, Yu Wang, Xingya Gu, Xin Zhao, Zhihong College of Computer and Information Engineering College of Artificial Intelligence Nanjing Tech University Jiangsu Nanjing211816 China College of Command and Control Engineering Army Engineering University of PLA Jiangsu Nanjing210001 China College of Computing Science and Artificial Intelligence Suzhou City University Jiangsu Suzhou215104 China

In Ethereum, a game-theoretic relationship exists among transactions competing to be included in blocks. Miners prioritize transactions in the memory pool by their gasPrice, and select higher-ranked transactions for confirmation in blocks firstly. Consequently, from the perspective of transactions, this process of selection and packaging can be likened to a queuing system. To assist users in saving transaction fees and reducing waiting time, this paper utilizes a queuing game model to describe the transaction confirmation competition process and proposes an optimal pricing strategy for Ethereum transaction gasPrice that satisfies the Nash equilibrium. Specifically, the paper first models the queuing competition for transaction confirmation as a non-preemptive queuing model with priority. Then, it analyzes the impact of gasPrice on waiting time, deducing a functional relationship between waiting time and gasPrice, thereby deriving the users’ Nash equilibrium payment strategy. Experimental results demonstrate that compared to two state of the art machine learning-based pricing strategies and one based on Poisson regression, applying the pricing strategy proposed in this paper increases the Packing Rates of transactions being included in the next block by 56.21%, 50.95%, and 39.15%, respectively, and achieves cost savings of 21 times, 297 times, and 3 times for users. The results indicate that the gasPrice optimal pricing strategy proposed in this paper helps users effectively balance waiting time and cost. © 2023, The Authors. All rights reserved.

关键词： Game theory

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Model of decentralized cross-chain energy trading for power systems

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Global Energy Interconnection 2021年第3期4卷 324-334页

作者： Shaomin Zhang Cong Hou School of Control and Computer Engineering North China Electric Power UniversityEngineering Research Center of Intelligent Computing for Complex Energy SystemsMinistry of EducationHebei Baoding 071003P.R.China

With the development of the energy Internet, more distributed generators are connected to the power grid, resulting in numerous heterogeneous energy networks. However, different energy networks cannot perform efficient energy trading in the centralized management mode, this deeply affecting the complementary ability of heterogeneous energy, resulting in the islanded energy phenomenon. In this model, the same energy on the chain is traded within the chain, and the heterogeneous energy on different chains is traded across chains. To trade energy between heterogeneous energy networks more efficiently, the blockchain-based cross-chain model is proposed based on the existing infrastructure. Heterogeneous energy nodes are assigned to different energy sub-chains and cross-chain energy transactions are performed through a relay-chain, which utilizes the improved Boneh–Lynn–Shacham signature scheme consensus algorithm based on the proof-of-stake and practical Byzantine fault tolerance. The experimental simulations on energy trading efficiency, throughput, and security, show its superiority over existing systems. Further, the simulation results provide a reference for the application of cross-chain technology in energy interconnection.

关键词： Energy internet Blockchain Cross-chain transaction Distributed generation Consensus algorithm

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INTELLIGENT TRACKING AND NAVIGATING MOVING OBJECTS IN A SMART ENVIRONMENT USING IOT NETWORK 1

INTELLIGENT TRACKING AND NAVIGATING MOVING OBJECTS IN A SMAR...

引用

1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Parvathi, R. Kumar, T.Ch. Anil Nandhakumar, A. Arshad, Haqqani Singh, Ajith B. Department of Information Technology School of Computing Science Hindustan Institute of Technology and Science Padur Chennai India Department of Mechanical Engineering Vignan's Foundation for Science Technology and Research Vadlamudi Dt. Andhra Pradesh Guntur India Department of Electronics and Communication Engineering Dhaanish Ahmed Institute of Technology Chennai India Department of Mechanical Engineering Yanbu Industrial Collge Yanbu Western Region Saudi Arabia Department- Instrumentation and Control Engineering Sri Krishna College of Technology Coimbatore India

ISBN: (纸本)9781607685395

Internet of things (IoT) constitutes the combinations of sensors, controllers, actuators, connectivity. Tracking and navigating moving objects are crucial tasks for security as well as a secure and smart environment. By using the sensory data, location of objects can be identified then that sensory data will be transferred to controllers for the further processes. One pivot role of the system is to identify the trajectory and location of the object using the speed, velocity, acceleration, maps etc and this can be done by machine learning programs. Analyzing service required, prediction of the trajectory and real-world knowledge of maps or locations, To avoid the loop-carry dependency, service request parameters are dynamically changed to locate relevant virtual objects. In order to track moving items, this study provides an architecture based on IoT self-aware environment technology that facilitates the tracking and allocation of relevant virtual objects. © The Electrochemical Society

关键词： IoT loop-carry dependency machine learning object identification self-aware environment sensory data

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Parallel Systems Framework: A Safe and Efficient Testbed for Unmanned Ships 4

Parallel Systems Framework: A Safe and Efficient Testbed for...

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4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Fu, Long Shen, Zhen Dong, Xisong Han, Yunjun Yang, Shuguang Xiong, Gang Wang, Fei-Yue University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Beijing100190 China Chinese Academy of Sciences Guangdong Engineering Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Dongguan523808 China Shandong Jiaotong University Schoool of Rail Transportation Jinan250399 China Chinese Academy of Sciences State Key Laboratory for Management and Control for Complex Systems Institute of Automation Beijing100190 China

ISBN: (纸本)9798350349252

In this study, unmanned ships test platform based on the parallel systems framework is proposed to improve the test efficiency and accuracy of unmanned ships in complex ocean environment. The parallel intelligence theory and ACP approach, the efficient interaction between virtual and real environment is achieved, which provides more comprehensive and accurate data for unmanned ships testing. Future research will further expand the scope of virtual environment simulation and improve systems accuracy and response speed to a wider range of unmanned ships test requirements. © 2024 IEEE.

关键词： Virtual environments

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Analysis of fast prototyping of microcontroller-based ML software for acoustic signal classification

Analysis of fast prototyping of microcontroller-based ML sof...

引用

Signal Processing Algorithms, Architectures, Arrangements and Applications (SPA)

作者： Filip Marciniak Wojciech Marciniak Tomasz Marciniak Faculty of Computing and Telecommunications Poznan University of Technology Poznań Poland DECYBEL Student Science Club Poznan University of Technology Poznań Poland Faculty of Automatic Control Robotics and Electrical Engineering Institute of Automatic Control and Robotics Division of Electronic Systems and Signal Processing Poznan University of Technology Poznań Poland

The paper analyzes the preparation of software for acoustic signal classification with machine learning techniques for microcontrollers. The design process was tested for three types of devices: Nordic Thingy:53, *** and Arduino Nano 33 BLE Sense Lite. The classifier training process was carried out using the Edge Impulse platform. Experimental studies were carried out for the process of classifying sound signals generated by the vacuum cleaner motor. The results of the training and the model test were presented for different configurations.

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Protecting Personalized Trajectory with Differential Privacy under Temporal Correlations

arXiv

引用

arXiv 2024年

作者： Cao, Mingge Zhu, Haopeng Min, Minghui Li, Yulu Li, Shiyin Zhang, Hongliang Han, Zhu School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of CyberScience and Engineering Wuhan University Wuhan430072 China School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States

Location-based services (LBSs) in vehicular ad hoc networks (VANETs) offer users numerous conveniences. However, the extensive use of LBSs raises concerns about the privacy of users’ trajectories, as adversaries can exploit temporal correlations between different locations to extract personal information. Additionally, users have varying privacy requirements depending on the time and location. To address these issues, this paper proposes a personalized trajectory privacy protection mechanism (PTPPM). This mechanism first uses the temporal correlation between trajectory locations to determine the possible location set for each time instant. We identify a protection location set (PLS) for each location by employing the Hilbert curve-based minimum distance search algorithm. This approach incorporates the complementary features of geo-indistinguishability and distortion privacy. We put forth a novel Permute-and-Flip mechanism for location perturbation, which maps its initial application in data publishing privacy protection to a location perturbation mechanism. This mechanism generates fake locations with smaller perturbation distances while improving the balance between privacy and quality of service (QoS). Simulation results show that our mechanism outperforms the benchmark by providing enhanced privacy protection while meeting user’s QoS requirements. Copyright © 2024, The Authors. All rights reserved.

关键词： Location based services

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Soil-Available Potassium Content Measurement Based on Nernst Informed Neural Network With Electrical Signal

IEEE Transactions on AgriFood Electronics

引用

IEEE Transactions on AgriFood Electronics 2025年

作者： Sun, Mingchun Zhao, Rongqiang Liu, Hongfang Jin, Jing Liu, Jie Harbin Institute of Technology Faculty of Computing Harbin150001 China National Key Laboratory of Smart Farm Technologies and Systems Harbin150001 China Heilongjiang Academy of Agriculture Science Jiamusi154000 China Department of Control Science and Engineering Harbin Institute of Technology Harbin150001 China

Soil-available potassium content is essential for agricultural crop management. The electrochemical measurement method exhibits advantages of high speed, low cost, and simple operation. However, it encounters challenges, such as poor signal reproducibility, limited sample accessibility, and restrictions on measurement accuracy. To address these issues, the Nernst informed neural network (NINN)-based electrochemical measurement of soil-available potassium content is proposed. The framework for the electrochemical soil-available potassium content measurement is established. The proposed method is developed to exploit the nonlinear relationship between the electrical signal and the soil-available potassium content end-to-end with a single potassium ions-selective electrode. The NINN in the proposed method is developed to improve the measurement accuracy on the dataset with a limited number of samples by integrating Nernst mechanism into the network. The potassium-content dynamic estimation in the proposed method is developed to overcome the uncertainties of the dilution factor and influence of interfering ions. The practical measurement exhibits the mean absolute percentage error in a low range and the time within 10 min. Experimental results demonstrate that the proposed method improves the accuracy compared to purely data-driven models and reduces the electrochemical measurement costs. Furthermore, the proposed method also exhibits the superiority in terms of measurement speed;operational complexity compared to the commercial products relied on spectroscopy. The proposed method can contribute to the advancement of the measurement of soil nutrients. © 2023 IEEE.

关键词： Crops

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