A cloud automatic recognition method for PV power prediction

2015 ◽  
pp. 127-130
Keyword(s):  
Automatic Recognition ◽  
Power Prediction ◽  
Recognition Method

scholarly journals A Practical Method for the Automatic Recognition of Rock Structures in Panoramic Borehole Image during Deep-Hole Drilling Engineering

2021 ◽  
Vol 11 (21) ◽  
pp. 10490
Author(s):  
Xianjian Zou ◽  
Chuanying Wang ◽  
Huajun Zhang ◽  
Shuangyuan Chen
Keyword(s):  
Recognition Rate ◽  
Automatic Recognition ◽  
Characteristic Functions ◽  
Hole Drilling ◽  
Time Cost ◽  
Recognition Method ◽  
Structural Plane ◽  
Drilling Engineering ◽  
Borehole Image ◽  
Borehole Imaging

Digital panoramic borehole imaging technology has been widely used in the practice of drilling engineering. Based on many high-definition panoramic borehole images obtained by the borehole imaging system, this paper puts forward an automatic recognition method based on clustering and characteristic functions to perform intelligent analysis and automatic interpretation researches, and successfully applied to the analysis of the borehole images obtained at the Wudongde Hydropower Station in the south-west of China. The results show that the automatic recognition method can fully and quickly automatically identify most of the important structural planes and their position, dip, dip angle and gap width and other characteristic parameter information in the entire borehole image. The recognition rate of the main structural plane is about 90%. The accuracy rate is about 85%, the total time cost is about 3 h, and the accuracy deviation is less than 4% among the 12 boreholes with a depth of about 50 m. The application of automatic recognition technology to the panoramic borehole image can greatly improve work efficiency, reduce the time cost, and avoid the interference caused by humans, making it possible to automatically recognize the structural plane parameters of the full-hole image.

An automatic recognition method of microseismic signals based on EEMD-SVD and ELM

2019 ◽  
Vol 133 ◽  
pp. 104318 ◽  
Author(s):  
Jinyong Zhang ◽  
Ruochen Jiang ◽  
Biao Li ◽  
Nuwen Xu
Keyword(s):  
Automatic Recognition ◽  
Recognition Method

scholarly journals Automatic Blastomere Recognition from a Single Embryo Image

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Yun Tian ◽  
Ya-bo Yin ◽  
Fu-qing Duan ◽  
Wei-zhou Wang ◽  
Wei Wang ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Automatic Recognition ◽  
Least Square ◽  
Circle Fitting ◽  
Single Image ◽  
Dimensional Model ◽  
Embryo Viability ◽  
Three Dimensional Model ◽  
Recognition Method ◽  
Day 3 Embryos

The number of blastomeres of human day 3 embryos is one of the most important criteria for evaluating embryo viability. However, due to the transparency and overlap of blastomeres, it is a challenge to recognize blastomeres automatically using a single embryo image. This study proposes an approach based on least square curve fitting (LSCF) for automatic blastomere recognition from a single image. First, combining edge detection, deletion of multiple connected points, and dilation and erosion, an effective preprocessing method was designed to obtain part of blastomere edges that were singly connected. Next, an automatic recognition method for blastomeres was proposed using least square circle fitting. This algorithm was tested on 381 embryo microscopic images obtained from the eight-cell period, and the results were compared with those provided by experts. Embryos were recognized with a 0 error rate occupancy of 21.59%, and the ratio of embryos in which the false recognition number was less than or equal to 2 was 83.16%. This experiment demonstrated that our method could efficiently and rapidly recognize the number of blastomeres from a single embryo image without the need to reconstruct the three-dimensional model of the blastomeres first; this method is simple and efficient.

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