Research on Multi-source Data Fusion Technology Under Power Cloud Platform

2019 ◽  
pp. 554-559
Author(s):  
Xiaomin Zhang ◽  
Qianjun Wu ◽  
Xiaolong Wang ◽  
Yuhang Chen
Keyword(s):  
Data Fusion ◽  
Fusion Technology ◽  
Cloud Platform ◽  
Source Data

Technology for Transformer Fault Diagnosis with Detection and Monitoring Data Fusion

2013 ◽  
Vol 860-863 ◽  
pp. 2046-2049
Author(s):  
Lin Niu ◽  
Gui Ming Wang ◽  
Jie Zhan ◽  
Jin Tao Cui ◽  
Jin Xin Huang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Effective Means ◽  
Monitoring Data ◽  
Equipment Failure ◽  
Fusion Technology ◽  
Safe Operation ◽  
Source Data ◽  
Transformer Fault ◽  
The Relationship

Fault diagnosis is an effective means to assure the safe operation of power system. In this paper, a detection and monitoring of transformer fault diagnosis of multi-source data fusion technology is introduced. Based on the correlation distance, to calculate the relationship between detection and monitoring data and equipment failure, using the weight function to set fault indicators, status online monitoring data over the fault indicators, judging the equipment what kind of failure is happened, improving the precision of fault diagnosis.

scholarly journals Lithological Mapping Based on Fully Convolutional Network and Multi-Source Geological Data

2021 ◽  
Vol 13 (23) ◽  
pp. 4860
Author(s):  
Ziye Wang ◽  
Renguang Zuo ◽  
Hao Liu
Keyword(s):  
Deep Learning ◽  
Data Fusion ◽  
Classification Accuracy ◽  
Learning Algorithms ◽  
Geological Mapping ◽  
Fusion Technology ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Geological Features ◽  
Source Data

Deep learning algorithms have found numerous applications in the field of geological mapping to assist in mineral exploration and benefit from capabilities such as high-dimensional feature learning and processing through multi-layer networks. However, there are two challenges associated with identifying geological features using deep learning methods. On the one hand, a single type of data resource cannot diagnose the characteristics of all geological units; on the other hand, deep learning models are commonly designed to output a certain class for the whole input rather than segmenting it into several parts, which is necessary for geological mapping tasks. To address such concerns, a framework that comprises a multi-source data fusion technology and a fully convolutional network (FCN) model is proposed in this study, aiming to improve the classification accuracy for geological mapping. Furthermore, multi-source data fusion technology is first applied to integrate geochemical, geophysical, and remote sensing data for comprehensive analysis. A semantic segmentation-based FCN model is then constructed to determine the lithological units per pixel by exploring the relationships among multi-source data. The FCN is trained end-to-end and performs dense pixel-wise prediction with an arbitrary input size, which is ideal for targeting geological features such as lithological units. The framework is finally proven by a comparative study in discriminating seven lithological units in the Cuonadong dome, Tibet, China. A total classification accuracy of 0.96 and a high mean intersection over union value of 0.9 were achieved, indicating that the proposed model would be an innovative alternative to traditional machine learning algorithms for geological feature mapping.

A multi-source data fusion approach to assess spatial-temporal variability and delineate homogeneous zones: A use case in a table grape vineyard in Greece

2019 ◽  
Vol 684 ◽  
pp. 155-163 ◽  
Author(s):  
Evangelos Anastasiou ◽  
Annamaria Castrignanò ◽  
Konstantinos Arvanitis ◽  
Spyros Fountas
Keyword(s):  
Data Fusion ◽  
Temporal Variability ◽  
Table Grape ◽  
Use Case ◽  
Source Data ◽  
Fusion Approach
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