Genetic Optimization Method of Pantograph and Catenary Comprehensive Monitor Status Prediction Model Based on Adadelta Deep Neural Network

Qu, Zhijian; Yuan, Shengao; Chi, Rui; Chang, Liuchen; Zhao, Liang

doi:10.1109/access.2019.2899074

Cited by 44 publications

(21 citation statements)

References 27 publications

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“…We randomly choose values between 0 and 1 for each weight value of the individual feature. For the fitness function, the fitness of each population chromosome is assessed using the quadratic loss function described in [ 39 ]. For the parent selection, we use size 2 tournament selection technique [ 35 ].…”

Section: Methodsmentioning

confidence: 99%

Power system events classification using genetic algorithm based feature weighting technique for support vector machine

Alimi

Ouahada

Abu‐Mahfouz

et al. 2021

Heliyon

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Section: Methodsmentioning

confidence: 99%

Power system events classification using genetic algorithm based feature weighting technique for support vector machine

Alimi

Ouahada

Abu‐Mahfouz

et al. 2021

Heliyon

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“…Liu et al [15] proposed a unified deep learning architecture for the detection of all catenary support components. Qu et al [16] used a genetic optimization method based on an adadelta deep neural network to predict pantograph and catenary comprehensive monitor status. Zhong et al [17] introduced a CNN-based defect inspection method to detect catenary split pins in high-speed railways.…”

Section: A the Ocs Analysis And Dropper Detectionmentioning

confidence: 99%

An Improved Faster R-CNN for High-Speed Railway Dropper Detection

Guo

Liu

et al. 2020

IEEE Access

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Overhead contact systems (OCSs) are the power supply facility of high-speed trains and plays a vital role in the operation of high-speed trains. The dropper is an important guarantee for the suspension system of the OCS. Faults of the dropper, such as slack and breakage, can cause a certain threat to the power supply system. How to use artificial intelligence technologies to detect faults is an urgent technical problem to be solved. Because droppers are very small in whole images, a feasible solution to the problem is to identify and locate the droppers first, then segment them, and then identify the fault type of the segmented droppers. This paper proposes an improved Faster R-CNN algorithm that can accurately identify and locate droppers. The innovations of the method consist of two parts. First, a balanced attention feature pyramid network (BA-FPN) is used to predict the detection anchor. Based on the attention mechanism, BA-FPN performs feature fusion on feature maps of different levels of the feature pyramid network to balance the original features of each layer. After that, a center-point rectangle loss (CR Loss) is designed as the bounding box regression loss function of Faster R-CNN. Through a center-point rectangle penalty term, the anchor box quickly moves closer to the ground-truth box during the training process. We validate the improved Faster R-CNN through extensive experiments on the VOC 2012 and MSCOCO 2014 datasets. Experimental results prove the effectiveness of the proposed network combined with attention feature fusion and center-point rectangle loss. On the OCS dataset, the accuracy using the combination of the improved Faster R-CNN and ResNet-101 reached 86.

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“…The actual accumulation process is implemented using the same concept as in the Momentum (Zeiler, 2012). Also see Qu, Yuan, Chi, Chang, and Zhao (2019). The highlights of AdaDelta algorithm are summarized in the Algorithm 2.…”

Section: Adadeltamentioning

confidence: 99%

Gradient-based Learning Methods Extended to Smooth Manifolds Applied to Automated Clustering

Koudounas¹,

Fiori

2020

jair

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Grassmann manifold based sparse spectral clustering is a classification technique that consists in learning a latent representation of data, formed by a subspace basis, which is sparse. In order to learn a latent representation, spectral clustering is formulated in terms of a loss minimization problem over a smooth manifold known as Grassmannian. Such minimization problem cannot be tackled by one of traditional gradient-based learning algorithms, which are only suitable to perform optimization in absence of constraints among parameters. It is, therefore, necessary to develop specific optimization/learning algorithms that are able to look for a local minimum of a loss function under smooth constraints in an efficient way. Such need calls for manifold optimization methods. In this paper, we extend classical gradient-based learning algorithms on at parameter spaces (from classical gradient descent to adaptive momentum) to curved spaces (smooth manifolds) by means of tools from manifold calculus. We compare clustering performances of these methods and known methods from the scientific literature. The obtained results confirm that the proposed learning algorithms prove lighter in computational complexity than existing ones without detriment in clustering efficacy.

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Genetic Optimization Method of Pantograph and Catenary Comprehensive Monitor Status Prediction Model Based on Adadelta Deep Neural Network

Cited by 44 publications

References 27 publications

Power system events classification using genetic algorithm based feature weighting technique for support vector machine

Power system events classification using genetic algorithm based feature weighting technique for support vector machine

An Improved Faster R-CNN for High-Speed Railway Dropper Detection

Gradient-based Learning Methods Extended to Smooth Manifolds Applied to Automated Clustering

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