Application of cost-sensitive LSTM in water level prediction for nuclear reactor pressurizer

Zhang, Jin; Wang, Xiaolong; Zhao, Chuanqi; Bai, Wei; Shen, Jun; Li, Yang; Pan, Zhisong; Duan, Yexin

doi:10.1016/j.net.2019.12.025

Cited by 29 publications

(15 citation statements)

References 19 publications

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“…The application of the LSTM model in translation has the problem of insufficient learning and training. SCN is a kind of CNN, which can effectively analyze different sentences [ 22 ]. Based on this, the SCN-LSTM (Skip Convolutional Network and Long Short Term Memory) fusion translation model is proposed.…”

Section: Methodsmentioning

confidence: 99%

The use of machine translation algorithm based on residual and LSTM neural network in translation teaching

Ren¹

2020

PLoS ONE

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With the rapid development of big data and deep learning, breakthroughs have been made in phonetic and textual research, the two fundamental attributes of language. Language is an essential medium of information exchange in teaching activity. The aim is to promote the transformation of the training mode and content of translation major and the application of the translation service industry in various fields. Based on previous research, the SCN-LSTM (Skip Convolutional Network and Long Short Term Memory) translation model of deep learning neural network is constructed by learning and training the real dataset and the public PTB (Penn Treebank Dataset). The feasibility of the model’s performance, translation quality, and adaptability in practical teaching is analyzed to provide a theoretical basis for the research and application of the SCN-LSTM translation model in English teaching. The results show that the capability of the neural network for translation teaching is nearly one times higher than that of the traditional N-tuple translation model, and the fusion model performs much better than the single model, translation quality, and teaching effect. To be specific, the accuracy of the SCN-LSTM translation model based on deep learning neural network is 95.21%, the degree of translation confusion is reduced by 39.21% compared with that of the LSTM (Long Short Term Memory) model, and the adaptability is 0.4 times that of the N-tuple model. With the highest level of satisfaction in practical teaching evaluation, the SCN-LSTM translation model has achieved a favorable effect on the translation teaching of the English major. In summary, the performance and quality of the translation model are improved significantly by learning the language characteristics in translations by teachers and students, providing ideas for applying machine translation in professional translation teaching.

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Section: Methodsmentioning

confidence: 99%

The use of machine translation algorithm based on residual and LSTM neural network in translation teaching

Ren¹

2020

PLoS ONE

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“…5 | Latest applications of the data-driven machine learning (DDML) for the fault diagnosis and detection (FDD) of the nuclear power plant (NPP) component. (Baraldi et al, 2013;Zhang et al, 2020), reactor coolant pump (Di et al, 2013;Liu and Zio, 2017), steam generator (Lu and Upadhyaya, 2005;Zhao and Upadhyaya, 2005;Razavi-Far et al, 2009;Li et al, 2012;Ayodeji and Liu, 2018b), control rod (Moshkbar-Bakhshayesh, 2020Oluwasegun and Jung, 2020), turbine generator (Biet, 2012;Zhang et al, 2013), bearing (Ren et al, 2016;Zhao and Wang, 2018;Miki and Demachi, 2020), and sensors (Upadhyaya et al, 2003;Mandal et al, 2017a,b;Choi and Lee, 2020;Nguyen et al, 2020;Yu et al, 2020;Wang et al, 2021) are captured by different modeling techniques. Initially, Baraldi et al (2013) tested the clustering for the FDD in the pressurizer in the NPP.…”

Section: Latest Applications Of Ddml For Fdd In the Npp Systemmentioning

confidence: 99%

“…Initially, Baraldi et al (2013) tested the clustering for the FDD in the pressurizer in the NPP. Later, Zhang et al (2020) applied the LSTM for the water lever prediction of the pressurizer. For the reactor coolant pump, Di et al (2013) conducted the FDD for the reactor coolant pump with the PCA and kernel-based regression method.…”

Section: Latest Applications Of Ddml For Fdd In the Npp Systemmentioning

confidence: 99%

Data-Driven Machine Learning for Fault Detection and Diagnosis in Nuclear Power Plants: A Review

Zhou

Liu

2021

Front. Energy Res.

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Data-driven machine learning (DDML) methods for the fault diagnosis and detection (FDD) in the nuclear power plant (NPP) are of emerging interest in the recent years. However, there still lacks research on comprehensive reviewing the state-of-the-art progress on the DDML for the FDD in the NPP. In this review, the classifications, principles, and characteristics of the DDML are firstly introduced, which include the supervised learning type, unsupervised learning type, and so on. Then, the latest applications of the DDML for the FDD, which consist of the reactor system, reactor component, and reactor condition monitoring are illustrated, which can better predict the NPP behaviors. Lastly, the future development of the DDML for the FDD in the NPP is concluded.

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“…In order to solve these limitations, the LSTM model was proposed and it was used for learning continuously composed data, mainly for purposes such as language translation and speech pattern recognition. In the hydrological studies, the LSTM model was applied for prediction of various hydrological variables such as runoff (Hu et al, 2018;Fan et al, 2020), water level (Zhang et al, 2020), soil properties (Adeyemi et al, 2018), and precipitation (Akbari Asanjan et al, 2018). These previous studies using LSTM model provided desirable predictive performances for hydrological forecasting in various regions.…”

Section: Deep Learning Model: Lstm Modelmentioning

confidence: 99%

Is the deep-learning technique a completely alternative for the hydrological model?: A case study on Hyeongsan River Basin, Korea

Kwak

Han²,

Kim

2021

Stoch Environ Res Risk Assess

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It is no doubt that the reliable runoff simulation for proper water resources management is essential. In the past, the runoff was generally modeled from hydrologic models that analyze the rainfall-runoff relationship of the basin.However, since techniques have developed rapidly, it has been attempted to apply especially deep-learning technique for hydrological studies as an alternative to the hydrologic model. The objective of the study is to examine whether the deep-learning technique can completely replace the hydrologic model and show how to improve the performance of runoff simulation using deep-learning technique. The runoff in the Hyeongsan River basin, South Korea from 2013 to 2020 were simulated using two models, 1) Long Short-Term Memory model that is a deep learning technique widely used in the hydrological study and 2) TANK model, and then we compared the runoff modeling results from both models. The results suggested that it is hard to completely replace the hydrological model with the deep-learning technique due to its simulating behavior and discussed how to improve the reliability of runoff simulation results. Also, a method to improve the efficiency of runoff simulation through a hybrid model which is a combination of two approaches, deep-learning technique and hydrologic model was presented.

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Application of cost-sensitive LSTM in water level prediction for nuclear reactor pressurizer

Cited by 29 publications

References 19 publications

The use of machine translation algorithm based on residual and LSTM neural network in translation teaching

The use of machine translation algorithm based on residual and LSTM neural network in translation teaching

Data-Driven Machine Learning for Fault Detection and Diagnosis in Nuclear Power Plants: A Review

Is the deep-learning technique a completely alternative for the hydrological model?: A case study on Hyeongsan River Basin, Korea

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