Prediction of Automatic Scram during Abnormal Conditions of Nuclear Power Plants Based on Long Short-Term Memory (LSTM) and Dropout

Chen, Hanying; Gao, Puzhen; Tan, Sichao; Yuan, Hongsheng; Guan, Mingxiang

doi:10.1155/2023/2267376

Cited by 2 publications

(1 citation statement)

References 35 publications

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“…Vehicle Classification using ALSTM Model at the final stage, the class labels of the detected vehicles can be identified using the ALSTM model. Hochreiter and Schmidhuber project LSTM as the key to gradient disappearing issue [22]. LSTM was planned to adaptably control learned features' storage length and illustrates optimum outcomes in the Seq2Seq problem.…”

Section: Ceoa Based Hyperparameter Tuningmentioning

confidence: 99%

Vehicle Detection and Classification using Optimal Deep Learning on High-Resolution Remote Sensing Imagery for Urban Traffic Monitoring

Alotaibi,

Nagappan,

et al. 2023

Preprint

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Remote sensing images (RSI), such as aerial or satellite images, produce a large-scale view of the Earth's surface, getting them used to track and monitor vehicles from several settings like border control, disaster response, and urban traffic surveillance. Vehicle detection and classification utilizing RSIs is a vital application of computer vision and image processing. It contains locating and identifying vehicles from the image. It is done using many approaches having object detection approaches, namely YOLO, Faster R-CNN, or SSD, that utilize deep learning (DL) for locating and identifying the image. Also, the vehicle classification from RSIs contains classifying them dependent upon their variety, like trucks, motorcycles, cars, or buses utilizing machine learning (ML) techniques. This article designed and develop an automated vehicle type detection and classification using a chaotic equilibrium optimization algorithm with deep learning (VDTC-CEOADL) on high resolution RSIs. The presented VDTC-CEOADL technique examines the high-quality RSIs for the accurate detection and classification of vehicles. The VDTC-CEOADL technique employs a YOLO-HR object detector with Residual Network as a backbone model to accomplish this. In addition, CEOA based hyperparameter optimizer is de-signed for the parameter tuning of the ResNet model. For the vehicle classification process, the VDTC-CEOADL technique exploits the attention based long short term memory (ALSTM) mod-el. The performance validation of the VDTC-CEOADL technique is validated on high resolution RSI dataset, and the results portrayed the supremacy of the VDTC-CEOADL technique in terms of different measures.

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Section: Ceoa Based Hyperparameter Tuningmentioning

confidence: 99%

Vehicle Detection and Classification using Optimal Deep Learning on High-Resolution Remote Sensing Imagery for Urban Traffic Monitoring

Alotaibi,

Nagappan,

et al. 2023

Preprint

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Research on Fault Prediction of Nuclear Safety-Class Signal Conditioning Module Based on Improved GRU

Chen,

Dai,

Liu

et al. 2024

Energies

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To improve the reliability and maintainability of the nuclear safety-class digital control system (DCS), this paper conducts a study on the fault prediction of critical components in the output circuit of the nuclear safety-class signal conditioning module. To address the issue of insufficient feature extraction for the minor offset fault feature and the low accuracy of fault prediction, a predictive model based on stacked denoising autoencoder (SDAE) feature extraction and an improved gated recurrent unit (GRU) is proposed. Therefore, fault simulation modeling is performed for critical components of the signal output circuit to obtain fault datasets of critical components, and the SDAE model is used to extract fault features. The fault prediction model based on GRU is established, and the number of hidden layers, the number of hidden layer nodes, and the learning rate of the GRU model are optimized using the adaptive gray wolf optimization algorithm (AGWO). The prediction performance evaluation metrics include the root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and absolute error (EA), which are used for evaluating the prediction results of models such as the AGWO-GRU model, recurrent neural network (RNN) model, and long short-term memory network (LSTM). The results show that the GRU model optimized by AGWO has a better prediction accuracy (errors range within 0.01%) for the faults of the circuit critical components, and, moreover, can accurately and stably predict the fault trend of the circuit.

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Prediction of Automatic Scram during Abnormal Conditions of Nuclear Power Plants Based on Long Short-Term Memory (LSTM) and Dropout

Cited by 2 publications

References 35 publications

Vehicle Detection and Classification using Optimal Deep Learning on High-Resolution Remote Sensing Imagery for Urban Traffic Monitoring

Vehicle Detection and Classification using Optimal Deep Learning on High-Resolution Remote Sensing Imagery for Urban Traffic Monitoring

Research on Fault Prediction of Nuclear Safety-Class Signal Conditioning Module Based on Improved GRU

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