Assets management on electrical grid using Faster-RCNN

Kala, Jules Raymond; Kre, Didier Michael; Gnassou, Armelle N’Guessan; Kala, Jean Robert; Akpablin, Yves Melaine Akpablin; Coulibaly, Tiorna

doi:10.1007/s10479-020-03650-4

Cited by 14 publications

(10 citation statements)

References 27 publications

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“…The structure of Faster-RCNN shown in Fig. 4 includes deep full convolutional network, Region Proposal Networks (RPN), ROI Pooling module, target classification and positioning module [24][25][26] .…”

Section: Methodsmentioning

confidence: 99%

Identification and Area Calculation of the Egg Mass of Spodoptera Frugiperda Based on Faster-RCNN

Liang

Peng

Sheikh

et al. 2021

Preprint

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Spodoptera frugiperda is a migratory and destructive crop pest. The number of eggs is an important method to evaluate the pest situation, which can be estimated by the area of egg mass. The traditional manual method is inefficient, but the new method of egg mass image recognition improves the efficiency of eggs number estimation. In this paper, the optimized Faster-RCNN target detection algorithm was used to recognize the egg mass image of Spodoptera frugiperda. The Maximum Between-Class Variance method (Otsu) was used for threshold segmentation to obtain the position, shape and size of the egg mass and calculate the area of the egg mass. The mean value of the relative error of the egg mass area in the test samples was -0.02032, and the minimum value was -0.00047. The experimental results show that the egg area calculation method proposed in this paper is fast and accurate, which can meet the requirements of egg area measurement.

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Section: Methodsmentioning

confidence: 99%

Identification and Area Calculation of the Egg Mass of Spodoptera Frugiperda Based on Faster-RCNN

Liang

Peng

Sheikh

et al. 2021

Preprint

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“…In all these examples, the deep learning algorithms outperformed the basic machine learning algorithms. Another example of using deep networks in the energy sector is to perform the assets management for the electrical grid companies (Kala et al, 2020), where the authors showed that their proposed algorithm involves the faster regional convolutional neural networks outperformed the human-coding efforts for asset management. For a comprehensive review of deep learning for energy systems and building energy, please see, Forootan et al (2022) and Ardabili et al (2022), respectively.…”

Section: Energy Demand/consumption Forecastingmentioning

confidence: 99%

Incorporating causality in energy consumption forecasting using deep neural networks

2022

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Forecasting energy demand has been a critical process in various decision support systems regarding consumption planning, distribution strategies, and energy policies. Traditionally, forecasting energy consumption or demand methods included trend analyses, regression, and auto-regression. With advancements in machine learning methods, algorithms such as support vector machines, artificial neural networks, and random forests became prevalent. In recent times, with an unprecedented improvement in computing capabilities, deep learning algorithms are increasingly used to forecast energy consumption/demand. In this contribution, a relatively novel approach is employed to use long-term memory. Weather data was used to forecast the energy consumption from three datasets, with an additional piece of information in the deep learning architecture. This additional information carries the causal relationships between the weather indicators and energy consumption. This architecture with the causal information is termed as entangled long short term memory. The results show that the entangled long short term memory outperforms the state-of-the-art deep learning architecture (bidirectional long short term memory). The theoretical and practical implications of these results are discussed in terms of decision-making and energy management systems.

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“…e self-organizing competitive neural network model performs self-organization and self-judgment on the input and finally divides our input into different types [25].…”

Section: Analysis Of Crime Characteristics Based On the Self-organizi...mentioning

confidence: 99%

Juvenile Crime Monitoring and Characteristic Analysis Based on the Internet of Things and Grid Management

Gong

2022

Mobile Information Systems

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Over the past decade, researchers have identified intervention methods and program models that reduce juvenile delinquency and encourage prosocial development. Preventing juvenile delinquency not only saves young lives from being wasted but also prevents the onset of adult criminal careers and thus reduces the burden of crime on its victims and society. Aiming at this social problem, this study combines Internet of Things and grid models to study the attribution and prevention mechanisms of minor campus violence crimes. It realizes the modern network system mode of information management, collaborative operation, and grid integration in the target area through Internet of Things and grid models. In addition, based on consulting-related literature and practical research, the causes of campus violence crimes are analyzed through a self-organizing competitive neural network. Finally, this article combines the judicial practice of the management of campus violence and presents the prevention and control countermeasures of juvenile campus violence crimes to effectively prevent and control the occurrence of juvenile campus violence crimes.

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Assets management on electrical grid using Faster-RCNN

Cited by 14 publications

References 27 publications

Identification and Area Calculation of the Egg Mass of Spodoptera Frugiperda Based on Faster-RCNN

Identification and Area Calculation of the Egg Mass of Spodoptera Frugiperda Based on Faster-RCNN

Incorporating causality in energy consumption forecasting using deep neural networks

Juvenile Crime Monitoring and Characteristic Analysis Based on the Internet of Things and Grid Management

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