Instrument Recognition in Transformer Substation Base on Image Recognition Algorithm

Song, Yong; Zhou, Zhenzhen; Xiang, Pengfei; Su, Fang

doi:10.1109/acirs49895.2020.9162619

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…Song et al [31] proposed an instrument recognition system based on image recognition of substation instruments. The system, based on the scale-invariant feature transform (SIFT), constructs a Gaussian difference model and determines the reading method after instrument positioning, enabling the instrument recognition system to read automatically.…”

Section: Research Statusmentioning

confidence: 99%

Natural Gas Consumption Monitoring Based on k-NN Algorithm and Consumption Prediction Framework Based on Backpropagation Neural Network

Hou,

Wang,

Chang

et al. 2024

Buildings

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With increasing consumption of primary energy and deterioration of the global environment, clean energy sources with large reserves, such as natural gas, have gradually gained a higher proportion of the global energy consumption structure. Monitoring and predicting consumption data play a crucial role in reducing energy waste and improving energy supply efficiency. However, owing to factors such as high monitoring device costs, safety risks associated with device installation, and low efficiency of manual meter reading, monitoring natural gas consumption data at the household level is challenging. Moreover, there is a lack of methods for predicting natural gas consumption at the household level in residential areas, which hinders the provision of accurate services to households and gas companies. Therefore, this study proposes a gas consumption monitoring method based on the K-nearest neighbours (KNN) algorithm. Using households in a residential area in Xi’an as research subjects, the feasibility of this monitoring method was validated, achieving a model recognition accuracy of 100%, indicating the applicability of the KNN algorithm for monitoring natural gas consumption data. In addition, this study proposes a framework for a natural gas consumption prediction system based on a backpropagation (BP) neural network.

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Section: Research Statusmentioning

confidence: 99%

Natural Gas Consumption Monitoring Based on k-NN Algorithm and Consumption Prediction Framework Based on Backpropagation Neural Network

Hou,

Wang,

Chang

et al. 2024

Buildings

View full text Add to dashboard Cite

show abstract

“…Recently, inspired by Transformer's great success in the natural language processing domain [20], researchers have tried to bring Transformer into the vision domain [21]. In [22], a vision transformer (ViT) is proposed to perform the image recognition task. Using two-dimensional (2D) image patches with positional embedding as inputs and pertaining on a large dataset, ViT achieved comparable performance with the CNN-based methods.…”

Section: Introductionmentioning

confidence: 99%

Damaged Building Detection with Improved Swin-Unet Model

Cao

et al. 2022

Wireless Communications and Mobile Computing

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Automatic detection of damaged buildings from satellite remote sensing data has become an urgent problem to rescue planners and military personnel. Unfortunately damaged buildings are in different dimensions and shapes with different roofs depending on the type of the material to be painted. In this study, we present an improved Swin-Unet approach that comprises three main operations. First, improved Swin-Unet as a Unet-like pure Transformer is used for multitemporal image segmentation. Second, different multitemporal features are extracted using hyperspectral image classification algorithm. Finally, a binary change map is generated, and evaluation results are obtained. This article takes AIST building change detection scene as the example, and compared with the conventional approaches tested, overall accuracy, mean intersection over union, and separated Kappa in the proposed method were improved by at least 23.36, 0.1725, and 0.202, respectively. Furthermore, different scenes, such as Gaofen-2/Jilin-1 multitemporal optical images and satellite imagery dataset (xBD), have also come to the same conclusion. Thus, it provides advantageous capabilities for monitoring damaged buildings along coastal areas.

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Instrument Recognition in Transformer Substation Base on Image Recognition Algorithm

Cited by 2 publications

References 7 publications

Natural Gas Consumption Monitoring Based on k-NN Algorithm and Consumption Prediction Framework Based on Backpropagation Neural Network

Natural Gas Consumption Monitoring Based on k-NN Algorithm and Consumption Prediction Framework Based on Backpropagation Neural Network

Damaged Building Detection with Improved Swin-Unet Model

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