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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 321 条记录，以下是61-70 订阅

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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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Ensemble Hardness Harmonize Classifier for Imbalanced Fault Classification 3

Ensemble Hardness Harmonize Classifier for Imbalanced Fault ...

引用

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

作者： Lu, Jinyu Yang, Tong Liu, Xinfeng Pan, Weichao Zhang, Yuyao Yin, Yuyan Shandong Jianzhu University Jinan China

ISBN: (纸本)9798350380989

Fault diagnosis is a crucial step to ensure the normal operation of machines and equipment. However, realworld fault data usually presents an imbalanced distribution and low quality, which makes the classification task difficult. To address this problem, this paper delves into the nature of imbalanced classification and finds that in addition to class imbalance, other difficulties such as noise and class overlap also affect the learning of effective classifiers. Therefore, this paper proposes a new imbalanced classification framework that aims to generate powerful integrated classifiers by downsampling and adaptively adjusting the data difficulty. In this paper, extensive experiments are conducted using fault data from oil pumping machines and data from wind blade machine sheets. The results show that the new framework proposed in this paper not only excels in computational efficiency, but also provides robust performance even with highly overlapping fault classes and extremely skewed data distributions. It is important to note that the method in this paper can be easily adapted to existing learning methods such as C4.5, SVM, GBDT, and neural networks to improve their performance on imbalanced fault diagnosis data. This research provides an effective solution for imbalanced classification for practical applications in the field of fault diagnosis. © 2024 ieee.

关键词： data difficulty adaptation Fault diagnosis imbalanced classification integrated classifiers performance improvement

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Research on the Difficulty Mining Algorithm for the Integration of Multiple Sets of data Platforms Based on big data Analysis in Smart Factories 3

Research on the Difficulty Mining Algorithm for the Integrat...

引用

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

作者： Shen, Lidong Wang, Feiping Zhou, Zengping Xu, Rongzhao Hongta Group Zhaotong Cigarette Factory Zhaotong657000 China

ISBN: (纸本)9798350380989

With the continuous development of intelligent manufacturing technology, smart factories have become an important direction of manufacturing transformation and upgrading. The integration of multiple sets of data platforms in the smart factory is a key part of realizing the smart factory. In this paper, the difficulty mining and automatic algorithm research of multiple data platforms in smart factory integration are discussed. Firstly, this paper analyzes and summarizes several sets of data platforms in the smart factory. The data platform in smart factory includes production data platform, equipment data platform, quality data platform and many other aspects. There are some problems among these data platforms, such as the inconsistency of data format, large amount of data and fast update of data, which brings challenges to the data fusion of multi-platforms. Secondly, this paper puts forward the difficulty mining method of multi-set data platform integration based on big data analysis. By analyzing the problems and faults in the process of data fusion on different platforms, the difficulties of data fusion are found out. The algorithm realizes automatic fusion between data platforms through intelligent analysis and processing of data. Finally, this paper verifies the proposed method and algorithm through a practical case. Through the application of the proposed method and algorithm in the actual smart factory, good results are obtained. The data fusion between data platforms is more efficient and accurate, providing strong support for the production and management of smart factories. © 2024 ieee.

关键词： big data data analysis data mining smart factory

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Spatial Distribution of Electromagnetic Field of 6005 Aluminum Alloy by Low Frequency Electromagnetic Casting with Dual Magnetic Field 3

Spatial Distribution of Electromagnetic Field of 6005 Alumin...

引用

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

作者： Ni, Xianpeng School of Mechanical & Electrical Engineering Shandong Vocational College of Industry Zibo Shandong 256400 China

ISBN: (纸本)9798350380989

The two-dimensional model of low-frequency electromagnetic casting with dual magnetic field for 6005 aluminum alloy was established by using ANSYS finite element software. The influence of current magnitude and current frequency on the magnetic induction intensity of electromagnetic field and the spatial distribution and magnitude of electromagnetic force was studied by using the method of controlling variables. The change law of magnetic induction intensity and electromagnetic force under the condition of single coil working independently and double coils working simultaneously was emphatically studied. The results show that the current is in direct proportion to the magnetic induction intensity and electromagnetic force of the electromagnetic field. For the casting process of 6005 aluminum alloy with a diameter of 200 mm, the current of 150 A-200 A is suitable;The current frequency is inversely proportional to the magnetic induction intensity and directly proportional to the electromagnetic force. However, the penetration of high-frequency current is poor and the effect on the melt core is not obvious. For the casting process of 6005 aluminum alloy with a diameter of 200mm, the frequency of 20Hz-30Hz is appropriate;The simultaneous operation of dual magnetic fields can effectively enhance the range and intensity of electromagnetic field and improve the effect of electromagnetic field. © 2024 ieee.

关键词： 6005 aluminum alloy Ansys dual magnetic field electromagnetic casting

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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...

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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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Graph Neural Networks: Application in Air Pollution Imputation 3

Graph Neural Networks: Application in Air Pollution Imputati...

引用

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

作者： Zhang, Jingshuai University of Minnesota Twin Cities Minneapolis United States

ISBN: (纸本)9798350380989

The issue of complete air pollution data has been receiving increasing attention in recent years. Imputing missing air pollution data is a crucial challenge in modern life. Existing methods typically rarely consider spatial correlation data attained by air pollution monitoring stations and use less additional information in each station such as meteorological data and geographic location data. In this paper, we propose a more advanced model for imputing missing data that leverages graph neural networks (GNNs). We formulate Matrix Completion on Graph structure by using Delaunay triangulations to construct clustering graph structure based on the geographic location data of different air pollution monitoring station, where the each edge between different nodes represents the similarity between two station. In terms of the neural network, we take the Graph Convolutional Networks (GCNs) to complete this task, which takes advantage of the connectivity information between nodes in a graph to update their feature representations. Furthermore, we compare it with conventional imputation models, such as convolutional neural network (CNN) and random forest. The lower Mean Squared Error (MSE) in our model, specifically 0.22 for SO2 and 0.0074 for CO, demonstrates the robustness of our method in imputing missing pollution data. © 2024 ieee.

关键词： Air pollution data imputation Convolutional network Delaunay triangulations Graph neural network Graph structure

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Research on Campus Digital Teaching System of Smart u Based on data Mining Algorithm 3

Research on Campus Digital Teaching System of Smart u Based ...

引用

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

作者： Wang, Guoyun Baise University Guangxi Baise533000 China

ISBN: (纸本)9798350380989

With the continuous development of data mining technology, the informatization level of colleges and universities is gradually improving. However, the traditional digital teaching system has many problems such as low utilization rate of data resources, non-sharing of information and data between systems, and it is difficult to meet the emerging new needs of colleges and universities. Therefore, it is of great significance to study the smart u campus digital teaching system based on data mining. Aiming at the problems of many links, serious information islands and low data utilization rate in traditional university teaching, this paper studies the smart u campus digital teaching system based on data mining. The core module of the system is the decision analysis module, which is based on the C4.5 algorithm framework, reduces the decision number of threshold points according to the Fayyad boundary point decision principle, and uses the equivalent infinitesimal principle to simplify the calculation of information entropy, overcoming the disadvantages of C4.5 algorithm. The module generates decision tree, generates classification rules through IF-THEN form, and realizes achievement prediction. The experimental data in this paper is the teaching data of a university. The experimental results show that the designed system can effectively realize data preprocessing, complete the learning effect prediction, provide personalized guidance for students, and realize the digital teaching work of smart u campus. © 2024 ieee.

关键词： C4.5 Algorithm data mining Digital teaching Wisdom u Campus

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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...

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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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Application of an Ensemble Method Based on XGBoost and Transformer in Predicting Sugarcane and Dragon Fruit Yields in Guangxi Region 3

Application of an Ensemble Method Based on XGBoost and Trans...

引用

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

作者： Jing, Long Bingxiang, Mo Nanning University Guangxi Nanning China

ISBN: (纸本)9798350380989

This study proposes an ensemble method specifically designed for predicting sugarcane and dragon fruit yields in the Guangxi region, based on XGBoost and Transformer. After exploring the complex relationship between meteorological factors (such as temperature, humidity, and precipitation) and the yields of sugarcane and dragon fruit, we developed this innovative predictive model. By combining the strong predictive power of XGBoost with the temporal pattern recognition ability of Transformer-based models, the ensemble model demonstrated excellent accuracy, stability, and convergence during both training and testing stages. We also conducted extensive data preprocessing and feature engineering to further enhance the model's performance. Practical applications demonstrate that this method is not only applicable to the Guangxi region but also widely used in other aspects of agricultural planning, risk management, and sustainable development. Moreover, compared to a single model, the flexibility and robustness of the ensemble method have been improved. Future research directions will include the assessment of model generalization ability, the increase of interpretability, and the possibility of extending this method to more crops and regions. Overall, this study provides not only an advanced and effective method for climate-driven crop yield prediction but also robust support for cross-disciplinary research in agricultural science and economics. © 2024 ieee.

关键词： dragon fruit yield forecast Guangxi integrated learning sugarcane yield forecast Transformer weather data XGBoost

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High-Fidelity StyleGAN Inversion and Attribute Editing for Low Light Images 3

High-Fidelity StyleGAN Inversion and Attribute Editing for L...

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

作者： Wu, Ling Zhang, Jun School of Electrical Engineering and Automation Anhui University Hefei China School of Artificial Intelligence Anhui University Hefei China

ISBN: (纸本)9798350380989

Recently, many studies have been explored the latent space of Generative Adversarial Networks (GANs), especially the W-space of StyleGAN. Real-world image manipulation has made great progress, and most of them follow the 'invert first, edit later' methodology. These methods use well light images as input, but it is challenging for low light image captured in extremely weak illumination conditions. Therefore, applying the existing StyleGAN inversion techniques under such conditions leads to serious image distortion. In this study, an embedding module is proposed for image inversion and editing of dark faces in the real world. The module can be embedded in popular StyleGAN inversion techniques to enhance low-quality dark face images and provide high-quality input for subsequent inversion editing. It is called the SE module. The structure of the SE module is designed by combining the U-N et structure with the CBAM attention module. This has two benefits. First, it facilitates capturing complete information from the images. Second, the incorporation of the attention mechanism enables the network model to focus more on the crucial features of the images. This generalized embedding module effectively alleviates the shortcomings of dark face image inversion, achieve high-quality inversion of dark images. The experiments demonstrate the superiority of the module. © 2024 ieee.

关键词： deep learning GAN inversion image manipulation

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