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• W4312143472 startingPage "109973" @default.
• W4312143472 abstract "The construction of smart grid is inseparable from the appropriate deployment of utility poles. China has many poles; if they are fully utilized, can they bring more power to the smart grid? Fortunately, thanks to advances in technologies such as artificial intelligence and the Internet of Things, can we use Google Earth to recognize utility poles and optimize the deployment of power facilities? Fundamentally, Google Earth generates static images. Faced with many static images, we need further image processing for high-definition recognition of utility poles. In addition to the current chaotic distribution of poles, we also need to optimize the deployment of power facilities. However, due to the resolution of Google Earth and the aerial photography angle, many backlight phenomena are not conducive to the recognition of utility poles. Therefore, this paper proposes a backlight image enhancement algorithm based on convolutional neural networks (CNN) and constructs a novel network architecture that integrates decomposition, restoration, and adjustment to recognize poles in high-definition under backlight. Furthermore, to solve the problems of the overflow of CNN parameters and unclear training effect, particle swarm optimization (PSO), the evolutionary computing-based machine learning (ECML) is used to search CNN parameters automatically and seek the optimal solution to achieve the optimization of the overall model. The experiments prove that the CNN-based image enhancement algorithm effectively recognizes the utility poles under different illumination and backlight. At the same time, the experimental results show that the PSO-based optimization method can optimize CNN parameters obviously, and the classification accuracy is increased." @default.
• W4312143472 created "2023-01-04" @default.
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• W4312143472 date "2023-02-01" @default.
• W4312143472 modified "2023-09-26" @default.
• W4312143472 title "ECML driven geographical location of utility poles in smart grid: Data analysis and high-definition recognition" @default.
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• W4312143472 doi "https://doi.org/10.1016/j.asoc.2022.109973" @default.
• W4312143472 hasPublicationYear "2023" @default.
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