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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111-120 订阅

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Detection and Analysis of Thermal Runaway Acoustic Signal Characteristics of Energy Storage Lithium Battery

Detection and Analysis of Thermal Runaway Acoustic Signal Ch...

引用

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

作者： Song Xu Tao Wan Junjie Wu Fanglin Zha Yufeng Cai Yiyi Liu State Grid Hunan Electrical Power Company Limited Research Institute Changsha China

Acoustic signal is commonly generated in the thermal runaway process of lithium energy storage batteries. In order to understand the acoustic information of the lithium batteries, an experimental platform is designed to test the thermal runaway sound signals of different type of lithium blade batteries. The sound variance process of thermal runaway is recorded. Time-and-frequency-domain methods are used to analyze the acoustic characteristics of the batteries. It is found that thermal runaway can be detected with sound test method. The research result validates that acoustic method could be used as a new technology for early and ultra-early safety warning of lithium batteries, which is of great significance to the safe operation of energy storage stations.

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Research on Distributed Parallelization of Improved Spectral Clustering Algorithm for big data

Research on Distributed Parallelization of Improved Spectral...

引用

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

作者： Han Yang Tianjin University of Technology Tianjin China

In the field of data mining, clustering algorithms play a key role in extracting valuable insights from vast datasets without incorporating learning mechanisms. One such classical clustering approach is the spectral clustering algorithm. This algorithm effectively converts a clustering challenge into the segmentation of an undirected graph, enabling it to handle intricate non-convex datasets adeptly and avoid getting trapped in local optimization pitfalls. Nevertheless, the conventional spectral clustering technique relies on the Gaussian kernel function, which uses Euclidean distance to determine sample similarities. This method proves overly sensitive to the Gaussian kernel's parameters and fails to accurately represent inter-sample relationships. To address the drawbacks related to similarity measurement and the computational inefficiencies inherent in the traditional spectral clustering method, enhancements have been made to refine the clustering outcomes. The enhanced spectral clustering algorithm has been redesigned to be distributed and parallelized, a strategic move intended to bolster the processing ability when handling enormous datasets.

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Secure Sharing Method of big data Based on ECC Encryption Algorithm and Casual Transmission 3

Secure Sharing Method of Big Data Based on ECC Encryption Al...

引用

3rd international conference on Cloud Computing, big data Application and Software engineering, CBASE 2024

作者： Wang, Shuang Li, Yang Xu, Guang Chen, Wei Gao, Ningning He, Zeyang R&D Center of Travelsky Technology Limited China Shunyi District Tianbei Rd Beijing101310 China

ISBN: (纸本)9798331506582

Due to the large amount of computation required for authentication and matching in the data sharing phase, there is a certain delay in the real-time performance of the corresponding efficiency. Therefore, this paper proposes the research on the security sharing method of big data based on ECC encryption algorithm and casual transmission. After encrypting the shared data by using the point operation and group algorithm in ECC pairing, a digital signature for the shared data is designed;OT (3-out-of-l OT) mechanism is introduced into the sharing protocol to realize the matching of wildcard patterns between two security parties. In the test results, the increase in attribute vector length did not have a significant impact on the computational cost of the data sharing stage. The specific computational cost remained stable within 2.0 ∗ 103 byts, and the sharing time was within 35.0ms. Compared with the control group, it had a significant advantage. © 2024 ieee.

关键词： Encryption algorithms

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Design of Marketing Analysis System Based on data Mining Technology

Design of Marketing Analysis System Based on Data Mining Tec...

引用

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

作者： Jie Fu Shanghai University Shanghai China

This paper designs a power marketing data analysis system based on data mining technology. Then the software and hardware of the scheme are optimized respectively. The main contents of this paper include: data collection, data mining and data processing. Then a new multi-level clustering method based on implicit Markov pattern is proposed. The method converts the time series of a time series into a likelihood space in which the likelihood value is identified by a symmetric KL distance. A symmetric KL migration matrix is constructed, and the access modes of users are divided into different levels by the idea of hierarchical clustering. Then a complete power grid enterprise information system is established, which is an important part of the power grid marketing system. Online analysis technology is used to ensure the whole logic input, so as to ensure the normal operation of the power marketing management system. The experiment shows that this method can realize the comprehensive processing of all kinds of electricity information, and will not cause large fluctuations.

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Optimization of Dual-Blockchain Smart Grid data Sharing Strategy Based on Reverse Auction

Optimization of Dual-Blockchain Smart Grid Data Sharing Stra...

引用

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

作者： Qijie Lai Rongchang Xie Zhifei Yang Guibin Wu Zechao Hong Department of Infrastructure Guangdong Power Grid Corporation Guangzhou China Yunfu Power Supply Bureau Guangdong Power Grid Corporation Yunfu China

In order to overcome the payment delay and the difficulty of verifying frontline worker information, caused by the subcontract and the rapid flow of workers in power infrastructure projects, we propose a power infrastructure payroll system framework based on dual blockchain. The framework consists of an identity authentication chain and a data sharing chain, which communicates through smart contracts. The efficient operation of the framework depends on the timely and accurate reporting of various personnel information of workers and enterprise credit information from subcontractors, thus, we construct a data sharing scheme between the frontline subcontractors and power general contractors in smart grids using the reverse auction theory. Simulation results demonstrate that the proposed dual blockchain framework and data sharing scheme can promote the data sharing efficiency of frontline subcontractors, and the security and efficiency of payroll payment in the power infrastructure system are also improved.

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SGBCD: A data Discretization Algorithm Based on Granular-ball Computing and Attribute Significance

SGBCD: A Data Discretization Algorithm Based on Granular-bal...

引用

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

作者： Weiqi Chen Chongqing Vocational College of Transportation Chongqing China

As an important method in data preprocessing, discretization can effectively reduce the size of data, generate concise semantic representation, obtain valuable knowledge and information contained in big data, which is of great significance in the fields of data mining and machine learning. Nevertheless, most of the traditional discretization algorithms don't consider the distribution of sample and difficulty in setting parameters during interval partitioning, which leads to a decrease in efficiency and classification accuracy. A data discretization algorithm based on granular-ball computing and attribute significance is proposed in this paper, which is a multi-granularity method. Firstly, by introducing the granular-ball computing, the difficulty of parameter optimization is reduced and the efficiency of the algorithm is improved. At the same time, interval partitioning adaptively fits the original data distribution, reducing the occurrence of uneven interval partitioning and improving classification accuracy. Then, the randomness of attribute selection leads to instability in interval partitioning results. By introducing attribute significance and prioritizing the selection of attributes with high importance, this paper further improves the classification accuracy. Compared to other excellent discretization algorithms in the experiments, the proposed algorithm shows an ideal performance.

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Phishing Web Page Detection Based on data Enhancement and Feature engineering

Phishing Web Page Detection Based on Data Enhancement and Fe...

引用

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

作者： Anran Zhu Ping Wu Xiangyu Hu School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu Sichuan China

Phishing, a deceptive cyberattack technique, poses a significant threat to online users by tricking them into disclosing personal or financial information through counterfeit web pages that impersonate legitimate ones. This paper explores data enhancement and feature engineering techniques as a means to improve the performance of phishing web page detection methods. data enhancement involves methods like data augmentation, oversampling, and undersampling, which aim to balance class distribution and enhance data diversity and quality. These techniques are essential to mitigate the data imbalance issue often encountered in phishing detection datasets. Feature engineering techniques, including feature selection, extraction, and transformation, are introduced to reduce data dimensionality and enhance the discriminative power of features. The proposed approach is experimentally validated, providing a valuable resource for improving online security in the face of evolving cyber threats. This work lays the foundation for future research in the domain of phishing detection, addressing a critical issue in cybersecurity.

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A Detection Method for Typical Component of Space Aircraft Based on YOLOv3 Algorithm

A Detection Method for Typical Component of Space Aircraft B...

引用

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

作者： Bian He Cao Jianzhong Li Cheng Dong Junpeng Ruan Zhongling Mei Chao Xi'an Institute of Optics and Precision Mechanics of CAS Xi'an China University of Chinese Academy of Sciences Beijing China Xi'an Key Laboratory of Spacecraft Optical Imaging and Measurement Technology Xi'an China

A solar panel recognition method based on YOLOv3 deep learning algorithm is proposed to address issues such as inaccurate recognition of traditional algorithms in space solar panel detection. First, this paper scales the dataset images to $\mathbf{416} \times \mathbf{416}$ , then uses Labelme to annotate the data and transform the bounding box position information, and finally uses the YOLOv3 algorithm framework for model training. The results show that the recall, F1 score and accuracy of YOLOv3 algorithm are all above 80%. The YOLOv3 deep learning algorithm meets the requirements for real-time detection of solar panels in terms of accuracy.

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Research on data lakehouse architecture for grid business data 3

Research on data lakehouse architecture for grid business da...

引用

3rd international conference on Energy, Power and electrical Technology, ICEPET 2024

作者： Meng, Yu Li, Qiang Wang, Ziheng State Grid Information and Telecommunication Group Co. Ltd. Beijing100052 China

ISBN: (纸本)9798350352658

With the development of national informatization construction and big data technology, the traditional database design can no longer support the efficient storage and real-time analysis of massive data, and it is difficult to adapt to increasingly complex business scenarios with high reliability and timeliness. Therefore, to build a high-performance and low-cost big data processing system, combined with the characteristics of the grid business data, this paper proposes an optimized big data architecture based on data lakehouse, using the pre-computing and ETL capabilities of the Doris database instead of the original complex technology stacks, and designing a Doris table structure that meets the statistical analysis requirements of the five-level organizational structure of State Grid to support data analysis result query. The lightweight architecture supports high concurrency, sub-second latency, and multi-dimensional analysis. Finally, a big data analysis system for power grid business operation is realized with less development cost and lower deployment difficulty, which can quickly dig deep information of application and user data and improve platform operation capability. © 2024 ieee.

关键词： data lakehouse data processing database

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Design of On-Board data Download and Application Platform of EMU Based on 5G

Design of On-Board Data Download and Application Platform of...

引用

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

作者： Chunhui Yang Weijiao Zhang Kai Cheng Chaoxu Li Institute of Computing Technologies China Academy of Railway Sciences Corporation Limited Beijing China

The on-board data of EMU is the parameter information collected by the sensor during the operation of EMU, which plays an important role in evaluating the operating state of EMU and analyzing the cause of fault in depth. By analyzing the situation of vehicle data and combining the characteristics of 5G technology, this paper puts forward a method of vehicle data download based on 5G, designs an EMU vehicle data download and application platform, and adopts key technologies such as massive data processing and monitoring, batch efficient data analysis and large amount of data storage. To achieve vehicle data collection and transmission, data monitoring, data analysis, data sharing and comprehensive display and other functions. The realization of the platform is helpful to further promote the application of on-board data of EMU, which is of great significance to promote the fault analysis of EMU and the application of state evaluation and prediction.

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