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12th IEEE International Conference on Information and Communication Networks, ICICN 2024

作者： Zhang, Baitao Zhu, Yuanjiao Luo, Tao Chen, Zi Zhou, Jie Xiao, Jing College of Information Science and Technology Shihezi University Shihezi China College of Computer Information and Security Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798350355802

In the realm of Industrial Wireless Sensor Networks (IWSN), formulating an effective duty cycle is a pivotal technological aspect. Considering that the core components of IWSN networks are sensor nodes powered by energy-restricted microsensors, optimizing the duty cycle is essential for prolonging IWSN's operational duration. Nonetheless, devising an optimal duty cycle configuration for IWSNs represents a computationally challenging NP-hard task. Therefore, a new Elite Adaptive Clonal Ant Colony Algorithm (EACACO) is proposed that can find the ideal duty cycle design to extend the network lifetime in IWSN. First, a newly designed adaptive operator exhibits strong capability for adaptive adjustment and migration exploration, which helps in avoiding local optima. Furthermore, the innovative elite and clone operators significantly expand the population solution space, effectively increasing solution diversity and accelerating algorithm convergence. Additionally, this paper develops a new sensor duty cycle model, thoroughly considering sensor longevity and complete coverage of monitoring targets. Based on this, an objective function is proposed to enhance the overall network lifetime of IWSNs. Finally, extensive simulation experiments were conducted, comparing EACACO with other duty cycle optimization algorithms, including Genetic Algorithm (GA), Ant Colony Algorithm (ACO), and Particle Swarm Optimization Algorithm (PSO), across various scenarios. The simulation results demonstrate that compared to GA, ACO, and PSO, EACACO increases the IWSN network lifetime by at least 8.64%, 31.34%, and 35.38%, respectively. © 2024 IEEE.

关键词： Genetic algorithms

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HRMG-EA: Heterogeneous graph neural network recommendation with multi-level guidance based on enhanced-attributes

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Applied Intelligence 2025年第6期55卷 1-22页

作者： Wang, Longtao Yuan, Guiyuan Li, Chao Zhao, Yufei Duan, Hua Zeng, Qingtian College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Electronic and Information Engineering Shandong University of Science and Technology Shandong Qingdao266590 China College of Mathematics and Systems Science Shandong University of Science and Technology Shandong Qingdao266590 China

Heterogeneous Graph Neural Networks are an efficient and powerful tool for modeling graph structure data in recommendation systems. However, existing heterogeneous graph neural networks often fail to model the dependencies between user and item attribute preferences, limiting graph structure optimization and consequently reducing the accuracy of recommendations. To overcome these issues, we propose a Heterogeneous graph neural network Recommendation with Multi-level Guidance based on Enhanced-Attributes (HRMG-EA). First, we design an attribute enhanced gated network to model user-item interaction attribute scenarios and obtain enhanced-attributes by capturing complex attribute dependencies. It effectively avoids the expansion of the graph scale in attribute graph scenarios and further covers personalized attribute relationship distribution characteristics of users and items. Then, we propose a novel multi-level graph structure guidance strategy based on enhanced-attributes. It guides graph structure learning from three optimization levels, optimizing from two perspectives: explicit (heterogeneity and homogeneity) and implicit (contrast enhancement). The former can screen higher-quality heterogeneous neighbor nodes in a direct interaction environment, and filter out redundant or erroneous edges under different similar semantic interest paths to improve the quality of the neighborhood environment. The latter aligns representation embeddings of enhanced-attributes and graph structure in a latent space, explores their potential commonalities, and obtains more comprehensive, fine-grained semantic and beneficial structural information. Finally, on two real-world datasets, HRMG-EA significantly outperforms the state-of-the-art baseline algorithms in both recall and normalized discounted cumulative gain. A large number of ablation experiments and analytical verifications also verify its effectiveness. © The Author(s), under exclusive licence to Springer science+Business Med

关键词： Graph neural networks

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From light sensing to adaptive learning:hafnium diselenide reconfigurable memcapacitive devices in neuromorphic computing

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Light(science & Applications) 2025年第1期14卷 215-226页

作者： Bashayr Alqahtani Hanrui Li Abdul Momin Syed Nazek El-Atab Electrical and Computer Engineering Program Computer Electrical Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)ThuwalSaudi Arabia Electrical Engineering Department College of EngineeringPrincess Nourah Bint Abdulrahman University(PNU)RiyadhSaudi Arabia

Advancements in neuromorphic computing have given an impetus to the development of systems with adaptive behavior,dynamic responses,and energy efficiency *** charge-based or emerging memory technologies such as memristors have been developed to emulate synaptic plasticity,replicating the key functionality of neurons—integrating diverse presynaptic inputs to fire electrical impulses—has remained *** this study,we developed reconfigurable metal-oxide-semiconductor capacitors(MOSCaps)based on hafnium diselenide(HfSe2).The proposed devices exhibit(1)optoelectronic synaptic features and perform separate stimulus-associated learning,indicating considerable adaptive neuron emulation,(2)dual light-enabled charge-trapping and memcapacitive behavior within the same MOSCap device,whose threshold voltage and capacitance vary based on the light intensity across the visible spectrum,(3)memcapacitor volatility tuning based on the biasing conditions,enabling the transition from volatile light sensing to non-volatile optical data *** reconfigurability and multifunctionality of MOSCap were used to integrate the device into a leaky integrate-and-fire neuron model within a spiking neural network to dynamically adjust firing patterns based on light stimuli and detect exoplanets through variations in light intensity.

关键词： integrate volatile integrating

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Denoising graph neural network based on zero-shot learning for Gibbs phenomenon in high-order DG applications

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Chinese Journal of Aeronautics 2025年第3期38卷 234-248页

作者： Wei AN Jiawen LIU Wenxuan OUYANG Haoyu RU Xuejun LIU Hongqiang LYU College of Aerospace Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China

With the availability of high-performance computing technology and the development of advanced numerical simulation methods, Computational Fluid Dynamics (CFD) is becoming more and more practical and efficient in engineering. As one of the high-precision representative algorithms, the high-order Discontinuous Galerkin Method (DGM) has not only attracted widespread attention from scholars in the CFD research community, but also received strong development. However, when DGM is extended to high-speed aerodynamic flow field calculations, non-physical numerical Gibbs oscillations near shock waves often significantly affect the numerical accuracy and even cause calculation failure. Data driven approaches based on machine learning techniques can be used to learn the characteristics of Gibbs noise, which motivates us to use it in high-speed DG applications. To achieve this goal, labeled data need to be generated in order to train the machine learning models. This paper proposes a new method for denoising modeling of Gibbs phenomenon using a machine learning technique, the zero-shot learning strategy, to eliminate acquiring large amounts of CFD data. The model adopts a graph convolutional network combined with graph attention mechanism to learn the denoising paradigm from synthetic Gibbs noise data and generalize to DGM numerical simulation data. Numerical simulation results show that the Gibbs denoising model proposed in this paper can suppress the numerical oscillation near shock waves in the high-order DGM. Our work automates the extension of DGM to high-speed aerodynamic flow field calculations with higher generalization and lower cost.

关键词： Computational fluid dynamics High-order discon tinuous Galerkin method Gibbs phenomenon Graph neural networks Zero-shot learning

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3D Road Network Modeling and Road Structure Recognition in Internet of Vehicles

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computer Modeling in engineering & sciences 2024年第2期138卷 1365-1384页

作者： Dun Cao Jia Ru Jian Qin Amr Tolba Jin Wang Min Zhu College of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia College of Information Science and Technology Zhejiang Shuren UniversityHangzhou310015China

Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transportationsystem. The movement of vehicles and the three-dimensional (3D) nature of the road network cause the topologicalstructure of IoV to have the high space and time *** modeling and structure recognition for 3Droads can benefit the description of topological changes for IoV. This paper proposes a 3Dgeneral roadmodel basedon discrete points of roads obtained from GIS. First, the constraints imposed by 3D roads on moving vehicles areanalyzed. Then the effects of road curvature radius (Ra), longitudinal slope (Slo), and length (Len) on speed andacceleration are studied. Finally, a general 3D road network model based on road section features is *** paper also presents intersection and road section recognition methods based on the structural features ofthe 3D road network model and the road features. Real GIS data from a specific region of Beijing is adopted tocreate the simulation scenario, and the simulation results validate the general 3D road network model and therecognitionmethod. Therefore, thiswork makes contributions to the field of intelligent transportation by providinga comprehensive approach tomodeling the 3Droad network and its topological changes in achieving efficient trafficflowand improved road safety.

关键词： Internet of vehicles road networks 3D road model structure recognition GIS

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GTE: learning code AST representation efficiently and effectively

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science China(Information sciences) 2025年第3期68卷 393-394页

作者： Yihao QIN Shangwen WANG Bo LIN Kang YANG Xiaoguang MAO College of Computer Science and Technology National University of Defense Technology Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology

With the development of deep learning in recent years, code representation learning techniques have become the foundation of many software engineering tasks such as program classification [1] and defect detection. Earlier approaches treat the code as token sequences and use CNN, RNN, and the Transformer models to learn code representations.

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Study of Braess paradox in a power grid considering node similarity 9

Study of Braess paradox in a power grid considering node sim...

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9th International Conference on Energy Materials and Electrical engineering, ICEMEE 2023

作者： Zhang, Shaoze Zou, Yanli College of Computer Science Guangdong University of Science and Technology Dongguan China College of Electronic and Information Engineering Guangxi Normal University Guilin China

ISBN: (纸本)9781510672765

In order to explore the influence of the similarity between nodes on Braess paradox in a power grid, combined with the actual power grid, a distribution index considering the distance and power of network nodes is proposed, and the relative entropy is used to measure the difference in distribution between nodes in a power grid, and transmission lines are added between nodes in the power grid for dynamic simulation. The research shows that the probability of Braess paradox is related to the similarity between nodes. Adding transmission lines between the nodes in a power grid, the more dissimilar the nodes are, the more likely the Braess paradox will occur. The research in this paper has important reference value and practical significance for the optimization of power grid topology structure. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Power grids Distance measurement engineering Information theory Modeling Numerical simulations Complex systems

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A Novel Trustworthiness Measurement Model Based on Weight and User Feedback

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Chinese Journal of Electronics 2022年第4期31卷 612-625页

作者： ZHOU Wei MA Yanfang PAN Haiyu School of Computer Science and Technology Huaibei Normal University School of Computer Science and Information Engineering Changzhou Institute of Technology School of Computer Science and Information Security Guilin University of Electronic Technology

Software trustworthiness is an essential criterion for evaluating software quality. In componentbased software, different components play different roles and different users give different grades of trustworthiness after using the software. The two elements will both affect the trustworthiness of software. When the software quality is evaluated comprehensively, it is necessary to consider the weight of component and user feedback. According to different construction of components, the different trustworthiness measurement models are established based on the weight of components and user feedback. Algorithms of these trustworthiness measurement models are designed in order to obtain the corresponding trustworthiness measurement value automatically. The feasibility of these trustworthiness measurement models is demonstrated by a train ticket purchase system.

关键词： Component-based software Trustworthiness Weight User feedback Measurement

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Chestnut shell-like N-doped carbon coated NiCoP hollow microspheres for hybrid supercapacitors with excellent electrochemical performance

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Frontiers of Materials science 2022年第1期16卷 141-155页

作者： Liangshuo LI Lin QIN Xin FAN Xinyu LI Guangxi Key Laboratory of Optical and Electronic Materials and Devices College of Materials Science and EngineeringGuilin University of TechnologyGuilin 541004China College of Science Guilin University of TechnologyGuilin 541004China

In this work,transition metal phosphides(TMPs)were reinforced by a solvothermal synthesis method and in situ polymerization in dopamine with one-step phosphating and carbonizing process to form chestnut shell-like N-doped carbon coated NiCoP(NiCoP@N-C)hollow *** morphologic structure is still reflected in NiCoP@N-C,which is suitable for rapid electron and electrolyte *** from the excellent structure,the coating of N-doped carbon,and the synergistic effect of Ni and Co,NiCoP@N-C reveals excellent electrochemical properties(high specific capacitance of 1660 F·g^(-1)(830 C·g^(-1))at 1 A·g^(-1)).In addition,a NiCoP@N-C//carbonization HKUST-1(HC)achieves high specific energy of 51.8 Wh·kg^(-1),ultrahigh specific power of 21.63 kW·kg^(-1),and excellent cycling stability up to 10000 cycles(a capacitance retention of 96.7%).The results show that the NiCoP@N-C electrode material has a wide application in supercapacitors and other energy storage devices.

关键词： chestnut shell-like sphere hybrid supercapacitor NiCoP@N-C N-doped carbon

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Research on numerical simulation method for 3D marine direct current resistivity

Research on numerical simulation method for 3D marine direct...

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2023 International Conference on Frontiers of Ocean science and technology, ICFOST 2023

作者： Ling, Jiaxuan Deng, Wei Liu, Siqin Chen, Qingrui Wei, Shiwei Li, Mengmeng Wei, Shuliu College of Computer Science and Engineering Guilin University of Aerospace Technology GUANGXI Guilin541004 China College of Meteorology and Oceanography National University of Defense Technology HUNAN Changsha410073 China

As one of the most important methods in marine electromagnetic exploration, the marine direct current (DC) resistivity method has been widely used in the exploration of marine minerals, oil and gas, as well as in large-scale engineered geological exploration and other fields. Efficient and high-precision forward algorithms are the foundation for an accurate quantitative interpretation of the marine DC resistivity method. We have implemented an efficient and high-precision forward modeling approach for 3D marine DC resistivity. First, the wave number domain anomalous potential control equations are obtained by performing a 2D Fourier transform along the horizontal direction based on the differential control equations satisfied by the marine DC anomalous potential. Instead of directly solving the 3D numerical simulation problem, we transform the 3D numerical simulation problem into multiple 1D numerical simulation problems in the wavenumber domain by dimensionality reduction for computational efficiency. Second, the boundary conditions of the governing equations are given to obtain the corresponding boundary value problems, and the anomalous potential is solved using the 1D finite element method in the wavenumber domain. Next, we perform a 2D inverse Fourier transform on the wave-number domain anomalous potential to obtain the spatial domain anomalous potential. Furthermore, compact operators are used to iteratively modify the potential and obtain high-precision numerical solutions. Finally, we demonstrate the correctness of the proposed algorithm's solution strategy by using a hierarchical ocean model, and the efficiency of the proposed algorithm by using a spherical model. © Published under licence by IOP Publishing Ltd.

关键词： Computational efficiency

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