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检索条件"任意字段=3rd IEEE International Conference on Electrical Engineering, Big Data and Algorithms, EEBDA 2024"

共 317 条记录，以下是71-80 订阅

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Mechanical equipment fault identification based on machine learning algorithm under unbalanced data 3

Mechanical equipment fault identification based on machine l...

引用

3rd ieee international conference on electrical engineering, big data and algorithms, eebda 2024

作者： Xiangxiu, Yao Pingtao, Zeng Weiyi, Ni Zhiyan, Shen Xi'an Eurasia University Xi'an710065 China

ISBN: (纸本)9798350380989

Aiming at the fault diagnosis of mechanical equipment under category imbalance, a combined model XGBoost-SMOTEENN-Decision Tree was constructed to identify mechanical equipment faults. Firstly, SMOTEENN algorithm is used to deal with the unbalanced distribution of 'normal operation' and 'fault' samples in mechanical equipment data;Secondly, the Decision Tree algorithm is used to evaluate the importance of features. Then we compare the three machine learning models XGBoost, AdaBoost and Random Forest by accuracy, precision, recall and F1 value model performance evaluation index, and find that XGBoost model has more significant classification ability, stronger generalization ability, and is more suitable for mechanical equipment fault diagnosis. Finally, the optimal classification model XGBoost is used to compare the classification effect before and after data balancing treatment, and it is found that the fault identification effect of mechanical equipment is better after data balancing treatment. © 2024 ieee.

关键词： Decision Tree mechanical equipment fault identification SMOTEENN XGBoost

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Discovering Named Entities at Scale of a data Mining Perspective on Large Text Corpora 3

Discovering Named Entities at Scale of a Data Mining Perspec...

引用

3rd ieee international conference on electrical engineering, big data and algorithms, eebda 2024

作者： Su, Jian Dongying Vocational Institute Shandong Dongying China

ISBN: (纸本)9798350380989

The need to protect sensitive data is expanding and becoming more and more critical, in part because of the regulations and directives that the European Union has enforced. While efforts to create automated systems are ongoing, human or semi-automated processes are usually used to support them. In this study, we have created a component that can identify and extract sensitive information from unstructured European Portuguese text. Creating a solution that helps companies understand their data and comply with security and legal obligations was the aim. We examined a hybrid approach to the Named Entity Recognition problem in Portuguese. We propose a novel security framework, the entity recognition model, which relies on regular expressions, known-entity dictionaries, randomised conditional fields (RCF), and four feature templates. This model is known as rdF-RCF. To further enhance recognition performance, the RCF-based extractor makes use of the entities found by the rule-based and dictionary-based extractors. Specific and common security entities may be retrieved using the known-entity dictionary. The phrase based on rules is able to match security entities with good accuracy in simpler scenarios. SIGARRA News Corpus, dataSense NER Corpus, and HAREM Golden Collection were the corpora that were used for testing and training. The outcomes of the experiments demonstrate that can outperform cutting-edge techniques in terms of performance. © 2024 ieee.

关键词： European Union Named Entity Recognition Portuguese language's Randomly conditional fields (RCF)

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Flight Safety Research Based on GBDT Regression and Graph Neural Networks 3

Flight Safety Research Based on GBDT Regression and Graph Ne...

引用

3rd ieee international conference on electrical engineering, big data and algorithms, eebda 2024

作者： Chen, Yanan Liang, Yijing Li, Youbang Chaohu University Hefei China Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9798350380989

This paper focuses on an in-depth study of flight safety in the civil aviation transportation industry. Considering the great impact of serious flight accidents on the safety of airlines and passengers, this study first reviewed the public information of the Civil Aviation Administration of China (CAAC) and conducted a reliability study on the relevant data. Firstly, we questioned the problems in the data set through mathematical and statistical methods and found that the 'elevation and pitch rate' is a key factor affecting flight safety using GBDT regression analysis. Secondly, using a graphical neural network model enhanced by cluster analysis and attention mechanism, this paper identifies the main operational factors affecting aircraft flight as stick volume and attitude, and points out that incorrect stick release operation is the cause of abnormal landing G-value. Finally, this paper explores flight overrun events through qualitative and quantitative analysis and finds that most overrun events occur at landing, especially on the takeoff-to-landing route from Airport 68 to Airport 9. Although the chi-square test showed that aircraft number and route had a significant effect on the type of overrun, the effect was small. These findings provide an important basis for understanding and improving flight safety issues. © 2024 ieee.

关键词： data analysis flight safety neural networks overrun events

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Research on Chemical Laboratory Information System under Computer big data Technology 3

Research on Chemical Laboratory Information System under Com...

引用

3rd ieee international conference on electrical engineering, big data and algorithms, eebda 2024

作者： Wu, Junwei Liu, Fei Chao, Xitao Tao, Liang Hefei Technology College Chaohu China Jiangsu University of Science and Technology Zhenjiang China

ISBN: (纸本)9798350380989

This paper designs and develops a set of information and equipment management system for chemical laboratory based on big data computer technology. The system mainly includes: user account management system, instrument and equipment management system, chemical management system, scientific research management system and information management system five modules. C/S structure and modular design method are adopted, and each functional module is designed according to the whole process of reagent management. It includes the whole process of reagent management. Then a genetic algorithm for mining chemical pattern classification extraction rules is proposed systematically. The rules of chemical classification extraction are deduced into the rules of population evolution. The fitness function and parameters are also set based on the rules of discrete chemical data and continuous chemical data extraction. Simulation results show that the algorithm has good performance in solving chemical pattern classification problems. At the same time, the system is object-oriented, easy to operate and has perfect functions. © 2024 ieee.

关键词： big data Chemical Laboratory Chemical Pattern Classification Computer Genetic Algorithm Information Management System

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Three-Dimensional Visualization of Multibeam Survey Lines Based on Theoretical Derivation and Simulation 3

Three-Dimensional Visualization of Multibeam Survey Lines Ba...

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3rd ieee international conference on electrical engineering, big data and algorithms, eebda 2024

作者： Wang, Xinran Zhang, Yaxin Niu, Man Wang, Yanqing Nanjing Xiaozhuang University Nanjing China

ISBN: (纸本)9798350380989

This paper studies the modeling and application of multibeam bathymetry. Firstly, considering the influence of slope on the multibeam bathymetric system, mathematical models of coverage width and overlap rate between adjacent strips of multibeam bathymetry are established, and parameters such as seawater depth, coverage width and overlap rate corresponding to the distance of different survey lines from the center point are calculated. Then, the influence of the angle between the direction of the survey line and the projection of the normal direction of the seabed slope in the horizontal plane is taken into account to evaluate the effect of multibeam bathymetry in a more complex three-dimensional environment, and the size of the coverage widths corresponding to the distances at different angles is calculated. Again, a set of survey lines with the shortest measuring length, which can completely cover the whole sea area to be measured and with an overlap rate of 100%20%, are designed according to the parameters of the sea area conditions, and the methods of calculating the coverage width and the spacing of the lines are determined through mathematical models to give the surveying line scheme. Finally, a three-dimensional seabed topographic model was constructed to depict the seabed topographic features in detail. In order to meet the requirements of minimizing the total length of the survey lines, covering the entire sea area to be surveyed, and controlling the overlap rate of adjacent strips to be less than 20%, the optimal direction and spacing of the survey lines were defined, and an iterative method was used to find the optimal direction and spacing of the survey lines. © 2024 ieee.

关键词： 3D visualization iterative methods line design multibeam measurement

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Research on Fast Fault Identification Method for Transmission Line Traveling Wave data Based on Machine Learning algorithms 3

Research on Fast Fault Identification Method for Transmissio...

引用

3rd ieee international conference on electrical engineering, big data and algorithms, eebda 2024

作者： Chengjun, Ren Yuhui, Peng Yingpu, Xie Min, Wu Mengfei, Lei Lin, Zhou Yadi, Zhang Zhuowen, Li Haonan, Zhu Yongzeng, Ji Southwest Branch of State Grid Corporation of China Chengdu China State Grid Electric Power Research Institute Wuhan Nari Limited Liability Company Wuhan China

ISBN: (纸本)9798350380989

The different types of faults on transmission lines seriously threaten the safe and stable operation of the system. Currently, the fault diagnosis of transmission lines involves very little fault information, only focusing on the zero sequence current information in the fault recording. Therefore, this article proposes a fast fault identification method based on machine learning algorithms for transmission line traveling wave data. The method is mainly based on the obtained traveling wave data of distributed online monitoring devices for transmission lines, and integrates the actual effective fault waveform database and labeled information database of transmission lines. The time-domain function, FFT, and wavelet packet analysis are used to extract the time-domain, frequency-domain, and the CNN-LSTM-SE machine learning algorithm is used to complete fault diagnosis of transmission lines based on time-frequency domain fault eigenvalues. And based on historical trip line fault data, the correctness and effectiveness of the method proposed in this paper were verified through practical examples. © 2024 ieee.

关键词： Distributed devices Fault diagnosis machine learning Traveling wave data Wavelet packet

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Multi-view Road Disease Detection Based on Attention Fusion and Distillation

Multi-view Road Disease Detection Based on Attention Fusion ...

引用

ieee international conference on electrical engineering, big data and algorithms (eebda)

作者： Mo, Jiadi Wang, Yue Yu, Zhi Wang, Yangyang Yan, Shoujing Zhejiang Univ Polytech Inst Hangzhou Peoples R China Zhejiang Univ Sch Software Technol Hangzhou Peoples R China Zhejiang Sci Res Inst Transport Key Lab Rd & Bridge Detect & Maintenance Technol Hangzhou Peoples R China

ISBN: (纸本)9781665416061

The radar dataset collected by the threedimensional ground- penetrating radar is presented as multiple views, which is difficult to analyze manually. Disease detection based on multi-view radar maps extremely requires expert experience and knowledge. The high cost of labeling results in a small number of samples, which makes the task more difficult. One solution to this problem is to create a deeper network to extract disease features, but this is not conducive to practical use. Therefore, we propose a two-stage attention fusion and distillation model for multi-view road disease detection, which enables us to make full use of multi-view datasets and improve their practical application in road detection. Experiments show that our model can use fewer parameters and calculations to achieve high accuracy on both original and enhanced datasets.

关键词： Road Disease Detection 3D-GPR Multi-view Attention Knowledge Distillation

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Electric Field Safety Planning Method under Urban Power Grid based on Improved ELECTRE 3

Electric Field Safety Planning Method under Urban Power Grid...

引用

3rd ieee international conference on electrical engineering, big data and algorithms, eebda 2024

作者： Li, Yipu Wang, Yanzhao Xie, Huichun Xue, Tianliang Ye, Jing College of Electrical and New Energy China Three Gorges University Hubei Yichang China China Electric Power Research Institute National Key Laboratory on Power Grid Environment Protection Hubei Wuhan China

ISBN: (纸本)9798350380989

The distribution of electric fields around transmission lines is one of the key concerns in urban power supply. This paper focuses on the impact of electric fields from transmission lines on sensitive points in urban buildings and houses, and proposes corresponding control methods. The distribution patterns of electric fields near different types of buildings and urban clusters are summarized for both AC and DC transmission lines sharing the same tower structure. Taking into account the electric field requirements at the top and inside sensitive points of buildings, a combined approach using shielding wires and trees for electric field control is proposed. The control results under various methods are quantitatively calculated using COMSOL software. Finally, the ELECTRE method and entropy weight method are employed to select the optimal electric field control planning scheme, demonstrating the effectiveness and practical significance of the proposed method. © 2024 ieee.

关键词： COMSOL ELECTRE Electric field control Electric field layout in multiple buildings Smart city planning

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Computer Virtual Reality Technology in 3D Digital Polymer Material Printing System 3

Computer Virtual Reality Technology in 3D Digital Polymer Ma...

引用

3rd ieee international conference on electrical engineering, big data and algorithms, eebda 2024

作者： Liu, Jing Xie, Bo Dong, Jingjing Xue, Feng Liu, Yanhong High-tech Institute Shandong Qingzhou China

ISBN: (纸本)9798350380989

As a new advanced manufacturing method, 3D (Three Dimensional) printing technology has been widely used in industrial production because of its unique advantages. The rapid development of digital polymer materials has put forward higher-level requirements for virtual reality printing technology. In the actual production and processing process, the computer virtual environment will be used for process design and printing manufacturing. In order to solve the problems of traditional 3D printing such as edge warping, object skew, ripple shadows, and top layer scratches, this article uses digital polymer materials as printing materials, and uses virtual reality technology to analyze and adjust the process of the mechanical products to be printed, thereby improving product quality and reducing or avoiding common problems. After analyzing carbon fiber resin composite materials, this article describes its 3D printing molding process, and compares the performance of the strategy iteration algorithm and the learning algorithm based on simulated annealing through optimization experiments. This article discusses average cost, processing rate, waiting time, print usage and average quality. Experimental data shows that under the simulated annealing learning algorithm, the average cost is -1.84, the processing rate is 0.97, the average waiting time is 1.614, the printing space utilization is 0.752, and the average quality is 0.033. © 2024 ieee.

关键词： Machinery Manufacturing Polymer Materials Printing System Virtual Reality

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Research on Intelligent Recommendation Algorithm of Online Learning Platform Based on Artificial Intelligence 3

Research on Intelligent Recommendation Algorithm of Online L...

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3rd ieee international conference on electrical engineering, big data and algorithms, eebda 2024

作者： Song, Kuangye Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350380989

As science and technology continue to advance, artificial intelligence technology is progressively being applied in diverse fields, among which the development of online learning platform has become a convenient way for people to obtain knowledge. With the theme of ' Research on Intelligent Recommendation Algorithm of online learning Platform based on Artificial Intelligence '', this research is devoted to explore how to improve the user experience of online learning platform through advanced artificial intelligence technology. With the popularity of online learning, the problems of information overload and difficulty in course selection are becoming more and more prominent. In order to solve this problem, we introduce intelligent recommendation algorithm, which aims to provide personalized and efficient learning resource recommendation for each learner through the powerful learning and analysis ability of artificial intelligence. The core idea of this research is to comprehensively analyze learners' historical learning behavior, interest preference and subject level through artificial intelligence technologies such as deep learning and data mining. Based on this information, we built an intelligent recommendation system that can accurately predict the needs of learners and recommend the courses, textbooks and learning resources that best fit the personalized learning trajectory. In our research, we employ advanced algorithms such as deep neural networks to model and train large amounts of learning data. Through these algorithms, we can dig out the potential learning patterns and rules, and tailor the most suitable learning path for each learner. The research results show that the intelligent recommendation algorithm of online learning platform based on artificial intelligence has achieved remarkable results in improving the recommendation accuracy. © 2024 ieee.

关键词： artificial intelligence data mining deep learning intelligent recommendation

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