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Reliability modeling for failure correlations to a CNC lathe subsystem based on pagerank algorithm

QUALITY ENGINEERING 2024年第2期36卷 336-349页

作者： Gu, Dongwei Guo, Jin Han, Wenbo Gao, Song Zhao, Xilu Xu, Zhen Changchun Univ Technol Sch Mechatron Engn Changchun Jilin Peoples R China Saitama Inst Technol Fukaya Saitama Japan Shandong Dongyue Polymer Mat Co Ltd Zibo Shandong Peoples R China Changchun Univ Technol Sch Mechatron Engn 2055 Yanan St Changchun 130012 Peoples R China

The modeling of Computer Numerical Control (CNC) lathes has been constrained by various issues, such as the use of single methods, inaccurate model parameters, and inadequate consideration of subsystem failures. To overcome these challenges, this study proposes a reliability modeling method based on the pagerank algorithm for analyzing failure correlations in CNC lathe subsystems. The proposed method involves analyzing the failure correlation between subsystems through failure transfer-directed graph and representing the relationship between subsystems using an adjacency matrix. Based on the fault correlation between subsystem, a reliability model of the CNC lathe subsystem is established, and the pagerank algorithm is utilized to optimize the influence degree of each subsystem. To validate the proposed method, field use failure information of CNC lathe is utilized as an example, and the above theoretical modeling is verified and analyzed. Finally, the reliability model based on failure correlation is verified using the D-test value, relative error method, and accurate factor. In comparison to the reliability model without considering failure correlation, the proposed model results in a small relative error and more accurate value.

关键词： failure correlation pagerank algorithm bootstrap Friedman test subsystem reliability modeling

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PRFold-TNN: Protein Fold Recognition With an Ensemble Feature Selection Method Using pagerank algorithm Based on Transformer

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024年第6期21卷 1740-1751页

作者： Qin, Xinyi Zhang, Lu Liu, Min Liu, Guangzhong Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China Jiangnan Univ Sch Internet Things Engn Wuxi 214063 Jiangsu Peoples R China

Understanding the tertiary structures of proteins is of great benefit to function in many aspects of human life. Protein fold recognition is a vital and salient means to know protein structure. Until now, researchers have successively proposed a variety of methods to realize protein fold recognition, but the novel and effective computational method is still needed to handle this problem with the continuous updating of protein structure databases. In this study, we develop a new protein structure dataset named AT and propose the PRFold-TNN model for protein fold recognition. First, different types of feature extraction methods including AAC, HMM, HMM-Bigram and ACC are selected to extract corresponding features for protein sequences. Then an ensemble feature selection method based on pagerank algorithm integrating various tree-based algorithms is used to screen the fusion features. Ultimately, the classifier based on the Transformer model achieves the final prediction. Experiments show that the prediction accuracy is 86.27% on the AT dataset and 88.91% on the independent test set, indicating that the model can demonstrate superior performance and generalization ability in the problem of protein fold recognition. Furthermore, we also carry out research on the DD, EDD and TG benchmark datasets, and make them achieve prediction accuracy of 88.41%, 97.91% and 95.16%, which are at least 3.0%, 0.8% and 2.5% higher than those of the state-of-the-art methods. It can be concluded that the PRFold-TNN model is more prominent.

关键词： Feature extraction Hidden Markov models Amino acids Protein sequence Predictive models Prediction algorithms Vectors ASTRAL ensemble feature selection method pagerank algorithm protein fold recognition transformer

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Detection approach for unusable shared bikes enabled by reinforcement learning and pagerank algorithm

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Journal of Safety Science and Resilience 2023年第2期4卷 220-227页

作者： Yu Zhou Ran Zheng Gang Kou Colege of Economics and Management Inner Mongolia UniversityHohhot 010021China School of Business Administration Southwestern University of Finance and EconomicsChengdu 611130China

Existing research models can neither indicate the availability of shared bikes nor detect unusable ones owing to a lack of information on bike maintenance and *** improve awareness regarding the availability of shared bikes,we propose an innovative approach for detecting unusable shared bikes based on reinforcement learning and the pagerank *** proposed method identifies unusable shared bikes depending on the local travel data and provides a ranking of the shared bikes according to their availability *** a sliding time window,the value function for the reinforcement learning model was determined by considering the cumulative number of unavailable shared bikes,the proportion of rental cancelations at the same stations,and the mean time between the *** learning was then used to identify shared bikes with the worst *** availability ranking for the shared bikes below the reward threshold was performed using the pagerank *** proposed detection approach was applied to a trip dataset of a real-world bike-sharing system to illustrate the modeling process and its *** detection results of unusable shared bikes in the absence of failure and feedback data can provide essential information to support the maintenance management decisions regarding shared bikes.

关键词： Bike-sharing system Bike availability Maintenance Failure Shared bike Intelligent transportation Reinforcement learning pagerank algorithm Detection

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Visual social network group consensus method with improved pagerank algorithm

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KYBERNETES 2023年第9期52卷 3588-3612页

作者： Fan, Tianmeng Wang, Yuhong Jiangnan Univ Sch Business Wuxi Jiangsu Peoples R China

Purpose The purpose of this study is to build a consensus model of social network group decision-making (SNGDM) based on improved pagerank algorithm. By objectively and fairly measuring the evaluation ability of participants in the decision-making process, the authors can improve the fairness and authenticity of the weight solution of decision-makers (DM) in the decision-making process. This ensures the reliability of the final group consensus results. Design/methodology/approach This study mainly includes six parts: preference expression, calculation of DM's weight, preference aggregation, consensus measurement, opinion adjustment and alternative selection. First, Pythagorean fuzzy expression is introduced to express the preference of DMs, which expands the scope of preference expression of DMs. Second, based on the social network structure among DMs, the process of "mutual judgment" among DMs is increased to measure the evaluation ability of DMs. On this basis, the pagerank algorithm is improved to calculate the weight of DMs. This makes the process of reaching consensus more objective and fair. Third, in order to minimize the evaluation difference between groups and individuals, a preference aggregation model based on plant growth simulation algorithm (PGSA) is proposed to aggregate group preferences. Fourth, the consensus index of DMs is calculated from three levels to judge whether the consensus degree reaches the preset value. Fifth, considering the interaction of DMs in the social network, the evaluation value to achieve the required consensus degree is adjusted according to the DeGroot model to obtain the overall consensus. Finally, taking the group preference as the reference, the ranking of alternatives is determined by using the Pythagorean fuzzy score function. Findings This paper proposes a consensus model of SNGDM based on improved pagerank algorithm to aggregate expert preference information. A numerical case of product evaluation is introduced, and t

关键词： pagerank algorithm PGSA Preference aggregation SNGDM

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Improved Key Node Recognition Method of Social Network Based on pagerank algorithm

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Computers, Materials & Continua 2023年第1期74卷 1887-1903页

作者： Lei Hong Yiji Qian Chaofan Gong Yurui Zhang Xin Zhou Jiangsu Police Institute Nanjing210000China Nanjing Police Station Nanjing210000China System Consulting Pty Ltd. Sydney201101Australia

The types and functions of social networking sites are becoming more abundant with the prevalence of self-media culture,and the number of daily active users of social networking sites represented by Weibo and Zhihu continues to *** are key node users in social *** with ordinary users,their influence is greater,their radiation range is wider,and their information transmission capabilities are *** key node users playimportant roles in public opinion monitoring and hot event prediction when evaluating the criticality of nodes in social networking *** order to solve the problems of incomplete evaluation factors,poor recognition rate and low accuracy of key nodes of social networking sites,this paper establishes a social networking site key node recognition algorithm(SNSKNIS)based on pagerank(PR)algorithm,and evaluates the importance of social networking site nodes in combination with the influence of nodes and the structure of nodes in social *** article takes the Sina Weibo platform as an example,uses the key node identification algorithm system of social networking sites to discover the key nodes in the social network,analyzes its importance in the social network,and displays it visually.

关键词： Social networking site pagerank algorithm key node

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Comprehensive assessment of cable-stayed bridge based on pagerank algorithm

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ADVANCES IN BRIDGE ENGINEERING 2023年第1期4卷 32页

作者： Liu, Ying Wang, Bing Liu, Xiaoling Ningbo Univ Fac Maritime & Transportat Ningbo 315211 Peoples R China Ningbo Univ Sch Civil & Environm Engn & Geog Sci Ningbo 315211 Peoples R China

This paper develops an improved structural health assessment method for cable-stayed bridge to address the issue of neglecting component correlations in existing assessment standards. Firstly, the directed graph of fault transmission between components in the cable-stayed bridge system was constructed. The pagerank algorithm was used to analyze the degree of correlation between these components, and then the influencing degree of and the influenced degree of each component were determined. Secondly, considering the failure rate of individual components and the influenced degree of other component faults, a condition evaluation method with component correlation for cable-stayed bridge was proposed. Finally, the improved assessment method was applied to a super large-span steel cable-stayed bridge as a case study and compared with the relevant assessment specifications. The results show that main girder alignment, cable force and main tower alignment have a greater degree of correlation with other components and are important indicators for bridge health monitoring. Visual inspection of main girder and bridge bearing are the fault appearance components and should be paid attention to in preventive maintenance. The drainage system and electromechanical facilities are the fault source components and must be kept in good condition in daily inspections. The proposed method considers the interrelationships among components more comprehensively and can provide more reliable bridge health assessment results to support bridge maintenance decisions.

关键词： Cable-stayed bridge Assessment method Component correlation pagerank algorithm Defect propagation

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pagerank algorithm-Based Recommendation System for Construction Safety Guidelines

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BUILDINGS 2024年第10期14卷 3041-3041页

作者： Lee, Jungwon Ahn, Seungjun Hongik Univ Dept Civil & Environm Engn Seoul 04066 South Korea

The construction industry faces significant challenges with frequent accidents, largely due to the inefficient use of safety guidelines. These guidelines, which are often text and figure heavy, demand substantial human effort to identify the most relevant items for specific tasks and conditions. Additionally, the guidelines contain both central and peripheral elements, and central items are critical yet difficult to identify without extensive domain knowledge. This study proposes a novel recommendation framework to enhance the usability of these safety guidelines. By leveraging natural language processing (NLP) and knowledge graph (KG) modeling techniques, unstructured safety texts are transformed into a structured, interconnected KG. The pagerank and Louvain Clustering algorithm is then employed to rank guidelines by their relevance and importance. A case study on "High-rise Building Construction (General) Safety and Health Guidelines", using 'scaffolding' as the keyword, demonstrates the framework's effectiveness in improving retrieval efficiency and practical application. The analysis highlighted key clusters such as 'fall', 'drop', and 'scaffolding', with critical safety measures identified through their interconnections. This research not only overcomes the fragmentation of safety management documents but also contributes to advancing hazard analysis and risk prevention practices in construction management.

关键词： safety guidelines knowledge graph recommendation system pagerank algorithm

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A Comparative Analysis of Arabic Text Summarization Techniques: Evaluating Word Frequency, K-means Clustering, and pagerank algorithm 4

A Comparative Analysis of Arabic Text Summarization Techniqu...

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4th International Mobile, Intelligent, and Ubiquitous Computing Conference

作者： Karim, Abdelrhman A. Ibrahim, Manar A. Hosny, Mahmoud M. Ibrahim, Abdulrhman H. Mohamed, Reem A. Elbanbawy, Magy Al-Zoghby, Aya M. Foad, Khaled Moawad, Ibrahim Fathy New Mansoura Univ Fac Comp Sci & Engn Artificial Intelligence Engn Dakhlia Egypt New Mansoura Univ Fac Comp Sci & Engn Artificial Intelligence Sci Dakhlia Egypt Mansoura Univ Fac Engn Comp & Syst Dept Dakhlia Egypt New Mansoura Univ Fac Comp Sci & Engn Dakhlia Egypt Damietta Univ Fac Comp & Artificial Intelligence Comp Sci Dept Dumyat Egypt Benha Univ Fac Comp & Artificial Intelligence Informat Syst Banha Egypt Ain Shams Univ Fac Comp & Informat Sci Informat Syst Cairo Egypt Taibah Univ Coll Comp Sci & Engn Dept Artificial Intelligence Medina 42353 Saudi Arabia

ISBN: (纸本)9798350367782;9798350367775

This study explores methods to efficiently summarize extensive Arabic texts, addressing the growing need to condense large volumes of content across various fields. Three primary techniques are evaluated: Word Frequency Analysis, K-means Clustering based on Sentence Proximity, and the pagerank algorithm. The research finds the pagerank algorithm to be the most effective, delivering higher compression ratios while maintaining strong recall and precision metrics. In particular, the pagerank method achieved the highest compression ratio of 0.562 while maintaining a population standard deviation of 2.0, compared to other techniques. Evaluation metrics such as population standard deviation, F1 score, and compression ratio support these findings. The study also examines advanced approaches like fuzzy logic-based methods, transformers, and multi-document summarization, aiming to enhance Arabic text summarization.

关键词： Word frequency-based analysis Arabic text summarization Extractive techniques K-means clustering pagerank algorithm Unsupervised learning techniques

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Business Information Mining Technology of Social Media Platform Based on pagerank algorithm 1

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6th European-Alliance-for-Innovation (EAI) International Conference on Advanced Hybrid Information Processing (ADHIP)

作者： Liu, Ying Jinggangshan Univ Sch Business Jian 343009 Peoples R China

ISBN: (数字)9783031288678

ISBN: (纸本)9783031288661;9783031288678

The existing methods of business information mining are flexible and cannot effectively mine the business information of media platform. In order to summarize and manage the mass business information effectively, a research method of business information mining based on pagerank algorithm for social media platform is proposed. Different from the existing methods, it innovatively optimizes the business information evaluation algorithm of social media platforms, increases the flexibility of information mining, and realizes the business information mining of social media platforms. The experiment proves that the technology of business information mining based on social media platforms can effectively summarize and manage a large amount of business information.

关键词： pagerank algorithm Social media Business information Data mining

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A Novel Feature-based pagerank algorithm for Node Importance Ranking 34

A Novel Feature-based PageRank Algorithm for Node Importance...

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34th Chinese Control and Decision Conference (CCDC)

作者： Xiao, Junyi Yi, Jingwen Chai, Li Wuhan Univ Sci & Technol Engn Res Ctr Met Automat & Measurement Technol Wuhan 430081 Peoples R China

ISBN: (纸本)9781665478960

Node importance ranking is one of key problems in the study of complex networks. The classical pagerank algorithm only focus on the network structure, which lead to inaccurate ranking result. By introducing the features of node attributes and user preference, a novel feature-based pagerank (FBPR) algorithm is proposed to identify the important nodes accurately and efficiently. The weight matrix and the fixed teleportation vector are redesigned by the feature similarities in the FBPR model. For different application scenarios, we can get different ranking results by adjusting the node attributes factor and the user preference factor. Finally, several simulation experiments are presented to verify the effectiveness of the FBPR algorithm.

关键词： pagerank algorithm feature similarity node attribute user preference

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