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Keyword extraction method based on complex network

International Journal of Information and Communication technology 2024年第4期25卷 323-335页

作者： Yang, Zhen Gong, Huajian Ma, Weiyun Yin, Chunlin Li, Jie Liu, Siyang Zhu, Hua Zhao, Na Electric Power Research Institute of Yunnan Power Grid Kunming650217 China School of Computer Science and Technology Xidian University Xian710000 China Key Laboratory in Software Engineering of Yunnan Province School of Software Yunnan University Kunming650091 China

Keyword extraction has a wide range of applications in the field of natural language processing. Many research results on keyword extraction at present. Among them, keyword extraction methods are based on complex networks do not require a lot of data training in advance and are simple to implement. This paper analyses and realises the application of complex network theory in the Chinese news keyword extraction task. We collected and analysed 2,061 news texts from Sina website as experimental data, and found that the number of keywords extracted affected the performance of keyword extraction method. We also compared the performance of our algorithm with other keyword extraction algorithms. The experiments verify the accuracy and effectiveness of complex networks in keyword extraction of Chinese news texts. Based on PyQt and TextRank, a news text keyword analysis platform is constructed to realise the visualisation of text network and the extraction of Chinese news keywords Copyright © 2024 Inderscience Enterprises Ltd.

关键词： complex network keyword extraction text network TextRank

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Evolution Analysis of Network Attack and Defense Situation Based on Game Theory

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Computers, Materials & Continua 2025年第4期83卷 1475-1494页

作者： Haiyan Sun Chenglong Shao Jianwei Zhang Kun Wang Wanwei Huang College of Software Engineering Zhengzhou University of Light IndustryZhengzhou450007China Intelligent Computing Power Research Department Zhengzhou Xinda Advanced Technology Research InstituteZhengzhou450007China

To address the problem that existing studies lack analysis of the relationship between attack-defense game behaviors and situation evolution from the game perspective after constructing an attack-defense model,this paper proposes a network attack-defense game model(ADGM).Firstly,based on the assumption of incomplete information between the two sides of the game,the ADGM model is established,and methods of payoff quantification,equilibrium solution,and determination of strategy confrontation results are ***,drawing on infectious disease dynamics,the network attack-defense situation is defined based on the density of nodes in various security states,and the transition paths of network node security states are ***,the network zero-day virus attack-defense behaviors are analyzed,and comparative experiments on the attack-defense evolution trends under the scenarios of different strategy combinations,interference methods,and initial numbers are conducted using the NetLogo simulation *** experimental results indicate that this model can effectively analyze the evolution of the macro-level network attack-defense situation from the micro-level attack-defense *** instance,in the strategy selection experiment,when the attack success rate decreases from 0.49 to 0.29,the network destruction rate drops by 11.3%,in the active defense experiment,when the interference coefficient is reduced from 1 to 0.7,the network destruction rate decreases by 7%,and in the initial node number experiment,when the number of initially infected nodes increases from 10 to 30,the network destruction rate rises by 3%.

关键词： Network attack-defense situation evolution zero-day virus NetLogo

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Coronavirus Mask Protection Algorithm:A New Bio-inspired Optimization Algorithm and Its Applications

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Journal of Bionic engineering 2023年第4期20卷 1747-1765页

作者： Yongliang Yuan Qianlong Shen Shuo Wang Jianji Ren Donghao Yang Qingkang Yang Junkai Fan Xiaokai Mu School of Mechanical and Power Engineering Henan Polytechnic UniversityJiaozuo454003China School of Mechanical Engineering Dalian University of TechnologyDalian116024China School of Software Henan Polytechnic UniversityJiaozuo454003China

Nowadays,meta-heuristic algorithms are attracting widespread interest in solving high-dimensional nonlinear optimization *** this paper,a COVID-19 prevention-inspired bionic optimization algorithm,named Coronavirus Mask Protection Algorithm(CMPA),is proposed based on the virus transmission of *** main inspiration for the CMPA originated from human self-protection behavior against *** CMPA,the process of infection and immunity consists of three phases,including the infection stage,diffusion stage,and immune ***,wearing masks correctly and safe social distancing are two essential factors for humans to protect themselves,which are similar to the exploration and exploitation in optimization *** study simulates the self-protection behavior mathematically and offers an optimization *** performance of the proposed CMPA is evaluated and compared to other state-of-the-art metaheuristic optimizers using benchmark functions,CEC2020 suite problems,and three truss design *** statistical results demonstrate that the CMPA is more competitive among these state-of-the-art ***,the CMPA is performed to identify the parameters of the main girder of a gantry *** show that the mass and deflection of the main girder can be improved by 16.44%and 7.49%,respectively.

关键词： Coronavirus Mask Protection Algorithm Bionic algorithm Metaheuristic algorithm Optimization algorithm Truss optimization Parameter identification

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On the Creep Collapse of the Cladding Considering the Irradiation Growth Effect

On the Creep Collapse of the Cladding Considering the Irradi...

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International Conference on Water Reactor Fuel Performance Meeting, WRFPM 2023

作者： Zhang, Ming Luo, Yayun Lu, Yong Zhu, Yanan Liu, Xiaohan Li, Jinggang Miao, Xinying China Nuclear Power Technology Research Institute Co. Ltd. Reactor Engineering Software Research Institute Shenzhen China

ISBN: (纸本)9789819971565

Creep collapse of the cladding is one of the most important performance to be verified at the fuel rod’s design stage to guarantee safe operation. The irradiation growth effect should be considered to improve the conservatism. In this study, finite element software ABAQUS was utilized to model the creep and irradiation growth of the cladding. The creep collapse criterion was established. The results show that irradiation growth can promote creep collapse of the cladding tube. Thus, the irradiation growth should be considered in the design of fuel rods in order to increase conservatism and safety. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Irradiation

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power Load Forecasting based on TCN-BIGRU Neural Network with IGJO Optimized 24

Power Load Forecasting based on TCN-BIGRU Neural Network wit...

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2024 International Conference on Intelligent Perception and Pattern Recognition, IPPR 2024

作者： Bai, Yaxin Liu, Qiuming Zhou, Yang School of Software Engineering Jiangxi University of Science and Technology China Information and Communication Branch State Grid Jiangxi Elettric Power Co China

ISBN: (纸本)9798400707315

As the level of intelligence in power systems continues to increase, accurate load forecasting is crucial for ensuring the safe and stable operation of the grid. Recent advances in deep learning have greatly improved load forecasting, especially with the application of Temporal Convolutional Network (TCN) and Bidirectional Gated Recurrent Unit (BIGRU) models. However, the selection of hyperparameters for these models greatly impacts prediction accuracy. Traditional manual tuning methods are inefficient and ineffective. To address this issue, this paper proposes a power load forecasting model based on an improved golden jackal optimization algorithm (IGJO) and a temporal convolutional network-bidirectional gated recurrent unit (TCN-BiGRU). By constructing the electricity consumption features and meteorological features of the sample load data, the deep correlations between variables are explored. The TCN is used to perform multi-layer convolution and dimensionality reduction on the constructed feature sequences. This retains global temporal features while removing redundant information. The BiGRU is then used to model the reduced-dimensional features, enabling accurate power load forecasting. © 2024 Copyright held by the owner/author(s).

关键词： Dimensionality reduction

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Fast resistivity imaging of transient electromagnetic using an extreme learning machine

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Soft Computing 2025年第2期29卷 1121-1131页

作者： Li, Ruiyou Zhang, Yong Li, Guang Li, Ruiheng Hu, Jia Li, Min School of Software and Internet of Things Engineering Jiangxi University of Finance and Economics NanChang330013 China School of Geophysics and Measurement Control Technology East China University of Technology NanChang330013 China School of Information Engineering Hubei University of Economics Wuhan430205 China The State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing University Chongqing400044 China

The transient electromagnetic method (TEM) is widely used in geophysical exploration. In TEM data interpretation, nonlinear inversion plays an important role. However, traditional TEM nonlinear inversion adopts the OCCAM imaging method, which merely presents the approximate shape of the stratum model, with poor inversion accuracy and much iteration time. To solve the above problems, a novel nonlinear inversion approach based on extreme learning machine (ELM) is proposed in this paper. The ELM is required to establish the input–output mapping relationship of the inversion network only once through the analytical method, which is different from the traditional neural network method that demands iterative gradient learning and is prone to fall into the local optimum. Moreover, the ELM inversion network by randomly assigning the hidden layer parameters is capable of mapping the observed TEM data and quickly producing resistivity images, which avoids time-consuming iteration and inversion calculations. The presented approach is applied to both synthetic and field examples. The results show that compared with the traditional nonlinear inversion algorithms (BP and OCCAM), the proposed method achieves better inversion accuracy and significantly reduces the calculation time, which verifies the effectiveness of the ELM algorithm for TEM data interpretation. Additionally, the research provides a new method and technology for TEM data inversion. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Electromagnetic transients

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A Transformer-based Time Series Prediction Model with Contrastive Learning for Nuclear power System State Monitoring 15

A Transformer-based Time Series Prediction Model with Contra...

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15th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Beijing 2024

作者： Gou, Pengfei Zheng, Daiwei Tang, Lei Liu, Mengjuan School of Information and Software Engineering University of Electronic Science and Technology of China UESTC Chengdu China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China NPIC Chengdu China

ISBN: (纸本)9798350354010

Prognostics and Health Management (PHM) is currently the mainstream solution for the condition monitoring and maintenance of industrial systems. In PHM, the system condition is monitored by monitoring the operational status parameters of the system to diagnose equipment faults, predict the remaining useful life, etc. Utilizing sequential operational status parameters to complete prediction and classification tasks is usually modeled as a time series processing task. Therefore, a high-performance time series prediction model is crucial for PHM. This paper focuses on the use of time series prediction models to achieve the prediction of key state parameters of the nuclear power system. However, there are still some challenges when directly applying existing time series prediction models to industrial systems. First, obtaining operational data on the nuclear power system is relatively difficult, resulting in a small amount of training data, which causes many models to face the risk of insufficient training or overfitting. Second, most of the existing time series prediction models are composed of high- and low-frequency decoupling modules, while the operational data of nuclear power systems often have fewer high-frequency components, making it difficult for many existing SOTA models to achieve good results. In order to address these challenges, we have developed a Transformer-based time series prediction model with contrastive learning for monitoring the status of nuclear power systems. We evaluated the new model on three nuclear power simulation datasets and four power transformer datasets. The experimental results show that the new model's performance is superior to baseline models. We also proved the effectiveness of our improvement through ablation experiments. © 2024 IEEE.

关键词： Prediction models

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Multi-feature fusion friend recommendation algorithm based on complex network

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International Journal of Information and Communication technology 2023年第4期23卷 401-423页

作者： Pan, Kan Chen, Hailong Liu, Qian Wang, Jian Pu, Yingming Yin, Chunlin Yang, Zheng Zhao, Na Electric Power Research Institute Yunnan Power Grid Co. Ltd. Kunming650217 China School of Software Yunnan University Kunming650504 China College of Information Engineering and Automation Kunming University of Science and Technology Kunming650504 China

At present, one of the problems of friend recommendation algorithms used in most social networks is that these networks often rely on a single index for recommendation. To solve this problem, multi-feature fusion (MFF) algorithm, a social network friend recommendation algorithm based on complex network theory, is proposed. The recommendation algorithm works by firstly divides the existing social networks into different communities. The importance of nodes in a social network is then calculated through the fusion of nodes’ importance information. Lastly, by integrating node importance information, friend number information and the shortest path information features are comprehensively evaluated, so as to generate final friend recommendation list. Simulation shows that with the increase of network nodes, the MFF algorithm outperforms common friend (CF) algorithm and friend similarity (FS) algorithm over all evaluation indicators including P-value, R-value and F1-value. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Complex networks

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Accurate Reliability Analysis Methods for Approximate Computing Circuits

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Tsinghua Science and technology 2022年第4期27卷 729-740页

作者： Zhen Wang Guofa Zhang Jing Ye Jianhui Jiang Fengyong Li Yong Wang School of the Computer Science and Technology Shanghai University of Electric PowerShanghai 200090China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Software Engineering Tongji UniversityShanghai 200092China

In recent years,Approximate Computing Circuits(ACCs)have been widely used in applications with intrinsic tolerance to *** the increased availability of approximate computing circuit approaches,reliability analysis methods for assessing their fault vulnerability have become highly *** this study,two accurate reliability evaluation methods for approximate computing circuits are *** reliability of approximate computing circuits is calculated on the basis of the iterative Probabilistic Transfer Matrix(PTM)*** the calculation,the correlation coefficients are derived and combined to deal with the correlation problem caused by fanout *** accuracy and scalability of the two methods are verified using three sets of approximate computing circuit instances and more circuits in Evo Approx8 b,which is an approximate computing circuit open source *** results show that relative to the Monte Carlo simulation,the two methods achieve average error rates of 0.46%and 1.29%and time overheads of 0.002%and 0.1%.Different from the existing approaches to reliability estimation for approximate computing circuits based on the original PTM model,the proposed methods reduce the space overheads by nearly 50%and achieve time overheads of 1.78%and 2.19%.

关键词： Approximate Computing Circuit(ACC) correlation coefficient iterative Probabilistic Transfer Matrix(PTM) reliability

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Global Registration Based On Feature Remapping 7

Global Registration Based On Feature Remapping

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7th Asian Conference on Artificial Intelligence technology, ACAIT 2023

作者： Fu, Guiduo Huang, Min Zhang, Bo South China University of Technology School of Software Engineering Guangzhou China Guangzhou Bureau of Csg Ehv Power Transmission Compan Guangzhou China

ISBN: (纸本)9798350359145

Point cloud has been widely used in various industrial areas such as high-precision maps for autonomous driving, structure from motion, and simultaneously localization and mapping, among which registration is a key section for subsequent processing. Traditional registration normally involves two stages, global alignment and local refinement. Global alignment offers an initial pose for local refinement to optimize. However, for point cloud segments containing noise and being partially overlapped, traditional methods are hard to meet the reasonable speed and accuracy standard. To address these defects, this paper presents an enhanced feature-matching-based registration pipeline, consisting of one novel matches filtration module and two improvements. The filtration module suppresses false positive matches in the initial matching set by using metric learning to distinguish false positive matches, hence the closed-form pose solver can provide a more globally accurate solution. The other two improvements include a scatter constraint that suppresses registration falling into local optimal and a matches sampling algorithm reducing the time cost to sample a set of valid matches. © 2023 IEEE.

关键词： feature remapping metric learning point-cloud registration

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