scholarly journals Safety Supervision Method of Power Work Site Based on Computer Machine Learning and Image Recognition

2021 ◽  
Vol 2074 (1) ◽  
pp. 012021
Author(s):  
Jiaxuan Li ◽  
Yiyang Liu ◽  
Hao Wang
Keyword(s):  
Machine Learning ◽  
Power System ◽  
Electric Power ◽  
Image Recognition ◽  
Mobile Application ◽  
Service Level ◽  
Application Technology ◽  
The Sustainable Development ◽  
Power Construction ◽  
The Times

Abstract China’s traditional power system has been unable to meet the needs of society and the development of The Times. Under the background of intelligence, it is necessary to reform the power industry and increase the application of mobile application technology in the power system, so as to realize the precise management of the power system. The application of mobile application technology in the field operation of electric power construction, based on computer machine learning and image recognition, is helpful to realize the sustainable development of electric power enterprises, improve the service level of electric power enterprises and promote the on-site safety supervision.

scholarly journals Multi-Output Regression for Analyzing Power System Random States in Reliability Assessment by the Monte Carlo Method

2021 ◽  
pp. 49
Author(s):  
D. A. Boyarkin
Keyword(s):  
Machine Learning ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Power System ◽  
Electric Power ◽  
Reliability Assessment ◽  
Electric Power System ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
The Monte Carlo Method

Increasing calculation speed of the electric power system (EPS) reliability of is one of the key issues in their operational management and long-term development planning. Analytical methods to assess the EPS reliability seem to be impossible due to large size of the problem and, as a consequence, essentially the only option for assessing is to use the Monte Carlo method. When it is used both the speed and the accuracy of calculation directly depend on the number of randomly generated system states and the complexity of their calculation in the model. Methods aimed at increasing computational efficiency can relate to two directions - reducing the states under consideration and simplifying the computational model for each state. Both options are performed provided that calculation accuracy is retained.The article presents research on using the machine learning methods and, in particular, the multi-output regression method to modernize the reliability assessment technique via the Monte Carlo method. Machine learning methods are used to determine the power deficit (realization of a random variable) for each random EPS state.The use of multi-output regression enables comprehensive determining of values of all the required variables. The experimental studies are based on the two test circuits of electric power systems: three-zone and IEEE RTS-96 with 24 zones of reliability.

Aplicativo móvil para la identificación de canes callejeros haciendo uso de reconocimiento de imágenes basado en aprendizaje automático

2021 ◽  
Vol 39 (1) ◽  
pp. 105-122
Author(s):  
Michelle Stephanie Rojas Miñan ◽  
Juan Jhair Rodríguez Dávila ◽  
Lenis Rossi Wong Portillo
Keyword(s):  
Machine Learning ◽  
Case Studies ◽  
Image Recognition ◽  
Mobile Application ◽  
Stray Dogs ◽  
Research Questions ◽  
Learning Development

The population of stray dogs increases every year, which is why technologies, pro-tocols, and legislative projects have been implemented in different countries to stop it. The objective of this research is to make use of image recognition (machine lear-ning) to optimize the identification of stray dogs that are in a state of loss. The imple-mented methodology consists of 4 phases: approach to the research questions, gene-ration of the model via machine learning, development of the mobile application where the generated model was integrated, and validation of the proposal with 2 case studies. From the results of the validation, it was found that our proposal managed to optimize the time and precision in the identification of lost dogs by 45%, compa-red to other web platforms.

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