Pub Date : 2022-10-13DOI: 10.1109/PHM-Yantai55411.2022.9942106
Xiaozhen Yan, Ruochen Ding, Qinghua Luo, Chunyu Ju, Di Wu
Because of its superior obstacle avoidance capability, the Dynamic Window Approach (DWA) algorithm has been widely used in local dynamic path planning nowadays. However, in areas with dense obstacles, the DWA algorithm prefers to go around the outside of the dense obstacle area, which increases the total distance. In addition, when encountering a "C" shaped obstacle, the objective cost function will fail and the path will not be found. Therefore, this paper proposes a method to improve the DWA algorithm. Based on the existing constraints, we also propose to score the distance between the current point and the target. In our experiments, we use the traditional DWA algorithm as a reference method and compare the two algorithms in maps with different characteristics. The experimental results demonstrate that the improved DWA algorithm achieves better results in obstacle avoidance.
{"title":"A Dynamic Path Planning Algorithm Based on the Improved DWA Algorithm","authors":"Xiaozhen Yan, Ruochen Ding, Qinghua Luo, Chunyu Ju, Di Wu","doi":"10.1109/PHM-Yantai55411.2022.9942106","DOIUrl":"https://doi.org/10.1109/PHM-Yantai55411.2022.9942106","url":null,"abstract":"Because of its superior obstacle avoidance capability, the Dynamic Window Approach (DWA) algorithm has been widely used in local dynamic path planning nowadays. However, in areas with dense obstacles, the DWA algorithm prefers to go around the outside of the dense obstacle area, which increases the total distance. In addition, when encountering a \"C\" shaped obstacle, the objective cost function will fail and the path will not be found. Therefore, this paper proposes a method to improve the DWA algorithm. Based on the existing constraints, we also propose to score the distance between the current point and the target. In our experiments, we use the traditional DWA algorithm as a reference method and compare the two algorithms in maps with different characteristics. The experimental results demonstrate that the improved DWA algorithm achieves better results in obstacle avoidance.","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123343273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-13DOI: 10.1109/PHM-Yantai55411.2022.9942025
Huiqi Ruan, Xingjian Ma, Qingchuan He, Jun Pan
Electro-Hydrostatic Actuators (EHA) are used extensively to produce displacements and high forces in various industrial applications, such as aircraft and ships. The internal leakage of EHA can lead to economic loss and personal injury. Convolutional neural network (CNN) is a basic method of deep learning, which has strong autonomous learning capability. In this paper, a two-dimensional convolutional neural network (2D-CNN) based fault diagnosis method for EHA internal leakage is proposed. Firstly, the one-dimensional pressure signals collected by sensors are converted into two-dimensional signals, and then these two-dimensional signals are directly fed into a 2D-CNN model, features are extracted through convolution and pooling operations, and the model is optimized using the reset learning rate to improve the fault diagnosis accuracy of the model, and then the diagnostic results are output using a classifier. The results of the study show that the accuracy of this method in diagnosing the internal leakage of EHA reaches 95.75% Compared with the traditional 1D-CNN, the accuracy of this method in fault diagnosis has been improved to a large extent.
{"title":"2D-CNN-Based Fault Diagnosis of Internal Leakage in Electro-Hydrostatic Actuators","authors":"Huiqi Ruan, Xingjian Ma, Qingchuan He, Jun Pan","doi":"10.1109/PHM-Yantai55411.2022.9942025","DOIUrl":"https://doi.org/10.1109/PHM-Yantai55411.2022.9942025","url":null,"abstract":"Electro-Hydrostatic Actuators (EHA) are used extensively to produce displacements and high forces in various industrial applications, such as aircraft and ships. The internal leakage of EHA can lead to economic loss and personal injury. Convolutional neural network (CNN) is a basic method of deep learning, which has strong autonomous learning capability. In this paper, a two-dimensional convolutional neural network (2D-CNN) based fault diagnosis method for EHA internal leakage is proposed. Firstly, the one-dimensional pressure signals collected by sensors are converted into two-dimensional signals, and then these two-dimensional signals are directly fed into a 2D-CNN model, features are extracted through convolution and pooling operations, and the model is optimized using the reset learning rate to improve the fault diagnosis accuracy of the model, and then the diagnostic results are output using a classifier. The results of the study show that the accuracy of this method in diagnosing the internal leakage of EHA reaches 95.75% Compared with the traditional 1D-CNN, the accuracy of this method in fault diagnosis has been improved to a large extent.","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124891604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-13DOI: 10.1109/PHM-Yantai55411.2022.9941767
Zeng Guo, Jiaying Zhu
Aiming at the problem of indoor optimal path planning, based on the hierarchical characteristics of public building space, a hierarchical optimal path optimization method for public building space based on improved ant colony algorithm is proposed, and the indoor hierarchical optimal path algorithm is implemented. The algorithm regards the road network and floor connections of each floor as an independent structure, and dynamically constructs a structured network model spanning two floors on a floor-by-floor basis according to the floor distribution of the stops. The proposed method uses the network model to analyze the path across floors, thereby obtaining the optimal path traversing all stops in the public building space. The experimental results show that compared with the traditional optimal path algorithm, the time efficiency of this algorithm is significantly improved when the path planning results are more reasonable.
{"title":"Hierarchical Optimal Path Optimization Method for Public Building Space Based on Improved Ant Colony Algorithm","authors":"Zeng Guo, Jiaying Zhu","doi":"10.1109/PHM-Yantai55411.2022.9941767","DOIUrl":"https://doi.org/10.1109/PHM-Yantai55411.2022.9941767","url":null,"abstract":"Aiming at the problem of indoor optimal path planning, based on the hierarchical characteristics of public building space, a hierarchical optimal path optimization method for public building space based on improved ant colony algorithm is proposed, and the indoor hierarchical optimal path algorithm is implemented. The algorithm regards the road network and floor connections of each floor as an independent structure, and dynamically constructs a structured network model spanning two floors on a floor-by-floor basis according to the floor distribution of the stops. The proposed method uses the network model to analyze the path across floors, thereby obtaining the optimal path traversing all stops in the public building space. The experimental results show that compared with the traditional optimal path algorithm, the time efficiency of this algorithm is significantly improved when the path planning results are more reasonable.","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122554597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-13DOI: 10.1109/PHM-Yantai55411.2022.9941954
Youpeng Wan, Wenjin Zhu, Shubin Si
Aeroengine is a complex system consisting of various components, which has high economic benefits and research value. Accurately evaluating the performance status of aero-engines has become a hot issue in current aero-engine research, and it plays an important role in the maintenance and storage of aero-engines. In this paper, a network model is constructed based on the aero-engine sensors data and the correlation coefficient of sensors. A method for predicting the remaining useful life (RUL) of aero-engines based on the change data of average network node strength and similarity is proposed. Through the node strength to analyze the change of the network node correlation, and the change law of the sensors network overall correlation. The RUL of aero-engines can be predicted accurately. It is found that the correlation between sensors generally increases uniformly at the end of the engine life.
{"title":"Prediction of Remaining Useful Life of Aero-engine Based on Network and Similarity","authors":"Youpeng Wan, Wenjin Zhu, Shubin Si","doi":"10.1109/PHM-Yantai55411.2022.9941954","DOIUrl":"https://doi.org/10.1109/PHM-Yantai55411.2022.9941954","url":null,"abstract":"Aeroengine is a complex system consisting of various components, which has high economic benefits and research value. Accurately evaluating the performance status of aero-engines has become a hot issue in current aero-engine research, and it plays an important role in the maintenance and storage of aero-engines. In this paper, a network model is constructed based on the aero-engine sensors data and the correlation coefficient of sensors. A method for predicting the remaining useful life (RUL) of aero-engines based on the change data of average network node strength and similarity is proposed. Through the node strength to analyze the change of the network node correlation, and the change law of the sensors network overall correlation. The RUL of aero-engines can be predicted accurately. It is found that the correlation between sensors generally increases uniformly at the end of the engine life.","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125595713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-13DOI: 10.1109/PHM-Yantai55411.2022.9942033
Chengcheng Lv
Based on the interval non-probabilistic model, this paper presents a new reliability index expressed by upper, lower limits and length of the interval. By discussing the relationship between the structure failure state function, failure criterion is determined. At the same time, calculating formulas of reliability and sensitivity index are derived under explicit failure function. The cases analysis shows the feasibility of proposed method in engineering application.
{"title":"A New Method of Interval Non-probabilistic Reliability Calculation","authors":"Chengcheng Lv","doi":"10.1109/PHM-Yantai55411.2022.9942033","DOIUrl":"https://doi.org/10.1109/PHM-Yantai55411.2022.9942033","url":null,"abstract":"Based on the interval non-probabilistic model, this paper presents a new reliability index expressed by upper, lower limits and length of the interval. By discussing the relationship between the structure failure state function, failure criterion is determined. At the same time, calculating formulas of reliability and sensitivity index are derived under explicit failure function. The cases analysis shows the feasibility of proposed method in engineering application.","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129850975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-13DOI: 10.1109/phm-yantai55411.2022.9942128
X. An, Huixing Meng, Xuan Liu
Due to insufficient knowledge of rare accidents, it is essential to transfer knowledge from source systems with sufficient cases into target systems with limited cases. In this study, a knowledge transfer-based methodology is proposed to evaluate emergency schemes from the perspective of emergency risk in presence of limited accident cases. By considering dynamic evolution and operational characteristics of accidents, a hybrid model integrating dynamic Bayesian networks (DBN) and program evaluation and review technique (PERT) is introduced. In the integrated model, we transferred graph structures and parameters to obtain emergency schemes based on the similarities between the source systems and target systems. On the one hand, to design the operation of emergency schemes, we utilized PERT to judge the logical relationships and the response-time requirement of the emergency procedures. On the other hand, to evaluate and prevent emergency risk, we employed DBN to conduct the dynamic risk assessment of emergency operations.
{"title":"A Knowledge Transfer-based Methodology for Risk Assessment of Emergency Schemes","authors":"X. An, Huixing Meng, Xuan Liu","doi":"10.1109/phm-yantai55411.2022.9942128","DOIUrl":"https://doi.org/10.1109/phm-yantai55411.2022.9942128","url":null,"abstract":"Due to insufficient knowledge of rare accidents, it is essential to transfer knowledge from source systems with sufficient cases into target systems with limited cases. In this study, a knowledge transfer-based methodology is proposed to evaluate emergency schemes from the perspective of emergency risk in presence of limited accident cases. By considering dynamic evolution and operational characteristics of accidents, a hybrid model integrating dynamic Bayesian networks (DBN) and program evaluation and review technique (PERT) is introduced. In the integrated model, we transferred graph structures and parameters to obtain emergency schemes based on the similarities between the source systems and target systems. On the one hand, to design the operation of emergency schemes, we utilized PERT to judge the logical relationships and the response-time requirement of the emergency procedures. On the other hand, to evaluate and prevent emergency risk, we employed DBN to conduct the dynamic risk assessment of emergency operations.","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130016645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-13DOI: 10.1109/phm-yantai55411.2022.9941917
Qinghua Luo, Xiaozhen Yan, Di Wu, Ruochen Ding
Task allocation modeling plays an important role in unmanned surface vehicles (USV) collaborative systems. In order to adapt to the complex environment, a cooperative multi-task assignment problem (CMTAP) model suitable for multi-USV, multi-target, and multi-task is designed. The article first clarifies the advantages of collaboration, then based on the traditional genetic algorithm (GA), the crossover and mutation operators are optimized to be more suitable for the current environment. This method utilizes the strong global search ability of GA to optimize the result of cooperative task assignment of USV. Simulation experiments demonstrate the effectiveness of the method.
{"title":"Unmanned Surface Vehicle Cooperative Task Assignment Based on Genetic Algorithm","authors":"Qinghua Luo, Xiaozhen Yan, Di Wu, Ruochen Ding","doi":"10.1109/phm-yantai55411.2022.9941917","DOIUrl":"https://doi.org/10.1109/phm-yantai55411.2022.9941917","url":null,"abstract":"Task allocation modeling plays an important role in unmanned surface vehicles (USV) collaborative systems. In order to adapt to the complex environment, a cooperative multi-task assignment problem (CMTAP) model suitable for multi-USV, multi-target, and multi-task is designed. The article first clarifies the advantages of collaboration, then based on the traditional genetic algorithm (GA), the crossover and mutation operators are optimized to be more suitable for the current environment. This method utilizes the strong global search ability of GA to optimize the result of cooperative task assignment of USV. Simulation experiments demonstrate the effectiveness of the method.","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129724496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-13DOI: 10.1109/phm-yantai55411.2022.9941984
Yanhui Bai, Honghui Li, Sen Zhao, Ning Zhang
The running conditions of wheels of Heavy-duty Railway Train are complex, and the real-time running state data is Multi-Dimension and Time-Sequence. Aiming at the problems that the traditional deep learning models have weak learning ability, cannot extract different scale information and gradient explosion in the prediction of remaining useful life (RUL), this paper proposes a multi-scale deep long short-term memory (MDLSTM) network model, which extracts time-series features of different scales through different number of hidden layer units of LSTM networks. In order to obtain more robust features under the premise of reducing the loss of original information and better to predict RUL of wheels, A Dual Channel Multi-scale Deep convolutional Multi-scale Deep long short-term memory (DC-MDCNN-MDLSTM) is proposed which combined the CNN and LSTM to extract multi-scale feature of wheels under different conditions and extract the different time step features of wheels from time series data. Using the actual wheels data to experiments. The results show that DC-MDCNN-MDLSTM network model is effective in predicting the degradation state of the wheels and provides technical support for repairing on condition of Heavy- duty Railway Train.
{"title":"Remaining Useful Life Prediction of Wheel of Heavy-duty Railway Train based on Dual Channel Multi-scale Deep convolution Multi-scale Deep Long Short-Term Memory network","authors":"Yanhui Bai, Honghui Li, Sen Zhao, Ning Zhang","doi":"10.1109/phm-yantai55411.2022.9941984","DOIUrl":"https://doi.org/10.1109/phm-yantai55411.2022.9941984","url":null,"abstract":"The running conditions of wheels of Heavy-duty Railway Train are complex, and the real-time running state data is Multi-Dimension and Time-Sequence. Aiming at the problems that the traditional deep learning models have weak learning ability, cannot extract different scale information and gradient explosion in the prediction of remaining useful life (RUL), this paper proposes a multi-scale deep long short-term memory (MDLSTM) network model, which extracts time-series features of different scales through different number of hidden layer units of LSTM networks. In order to obtain more robust features under the premise of reducing the loss of original information and better to predict RUL of wheels, A Dual Channel Multi-scale Deep convolutional Multi-scale Deep long short-term memory (DC-MDCNN-MDLSTM) is proposed which combined the CNN and LSTM to extract multi-scale feature of wheels under different conditions and extract the different time step features of wheels from time series data. Using the actual wheels data to experiments. The results show that DC-MDCNN-MDLSTM network model is effective in predicting the degradation state of the wheels and provides technical support for repairing on condition of Heavy- duty Railway Train.","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128434830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-13DOI: 10.1109/PHM-Yantai55411.2022.9941765
P. Jiang, Xiaodong Wang, Dian Zhang, Jianjun Qi
Reliability acceptance tests are used to qualify product’s reliability, which decides whether the product could be accepted. For highly reliable systems, conventional reliability acceptance tests in standards are not preferred, as the test plans either require long test durations or induce high risks for both producer and consumer, which results from the facts that the methods only use the system test data. Meanwhile, some related reliability data, such as data from subsystem test, are often neglected. To make use of the subsystem data, this paper proposes a reliability acceptance test plan derivation method, to derive system test plans with short test durations while keeping producer and consumer risks low, compared with the conventional RAT plans. A case study is provided to illustrate that when using subsystem test data in deriving system test plans, our proposed method has the potential to reduce the risks and shorten the test duration as well.
{"title":"A Subsystem Data Based Reliability Acceptance Test Plan Derivation Method","authors":"P. Jiang, Xiaodong Wang, Dian Zhang, Jianjun Qi","doi":"10.1109/PHM-Yantai55411.2022.9941765","DOIUrl":"https://doi.org/10.1109/PHM-Yantai55411.2022.9941765","url":null,"abstract":"Reliability acceptance tests are used to qualify product’s reliability, which decides whether the product could be accepted. For highly reliable systems, conventional reliability acceptance tests in standards are not preferred, as the test plans either require long test durations or induce high risks for both producer and consumer, which results from the facts that the methods only use the system test data. Meanwhile, some related reliability data, such as data from subsystem test, are often neglected. To make use of the subsystem data, this paper proposes a reliability acceptance test plan derivation method, to derive system test plans with short test durations while keeping producer and consumer risks low, compared with the conventional RAT plans. A case study is provided to illustrate that when using subsystem test data in deriving system test plans, our proposed method has the potential to reduce the risks and shorten the test duration as well.","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"405 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124803948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-13DOI: 10.1109/PHM-Yantai55411.2022.9942090
Lijuan Zhang
Aiming at the difficulty of users obtaining preschool education network resources, a precise recommendation algorithm of preschool education network resources based on improved decision tree is proposed. The categories of effective information of preschool education network resources are adjusted by improved decision tree, combined with the reconstruction of preschool education network resources, Extract the effective information of preschool education network resources, set the weight threshold of preschool education network resources similarity through the analysis of the similarity of different preschool education network resources in the data set, calculate the weight value, obtain the similarity between preschool education network resources in the data set, obtain the distribution of the similarity between preschool education network resources, and complete the calculation of the similarity value between preschool education network resources, Through user interest modeling, an accurate recommendation algorithm for preschool education network resources is designed. The experimental results show that the accurate recommendation algorithm of preschool education network resources based on improved decision tree has good effect and performance on preschool education network resources recommendation.
{"title":"Accurate Recommendation Algorithm of Preschool Education Network Resources Based on Improved Decision Tree","authors":"Lijuan Zhang","doi":"10.1109/PHM-Yantai55411.2022.9942090","DOIUrl":"https://doi.org/10.1109/PHM-Yantai55411.2022.9942090","url":null,"abstract":"Aiming at the difficulty of users obtaining preschool education network resources, a precise recommendation algorithm of preschool education network resources based on improved decision tree is proposed. The categories of effective information of preschool education network resources are adjusted by improved decision tree, combined with the reconstruction of preschool education network resources, Extract the effective information of preschool education network resources, set the weight threshold of preschool education network resources similarity through the analysis of the similarity of different preschool education network resources in the data set, calculate the weight value, obtain the similarity between preschool education network resources in the data set, obtain the distribution of the similarity between preschool education network resources, and complete the calculation of the similarity value between preschool education network resources, Through user interest modeling, an accurate recommendation algorithm for preschool education network resources is designed. The experimental results show that the accurate recommendation algorithm of preschool education network resources based on improved decision tree has good effect and performance on preschool education network resources recommendation.","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125075135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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