Analysis of Recurrent Neural Network and Predictions

Park, Jieun; Yi, Dokkyun; Ji, Sangmin

doi:10.3390/sym12040615

Cited by 26 publications

(15 citation statements)

References 24 publications

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“…Deep learning is a deep machine learning model, which is often used in a variety of supervised pattern recognition problems. Deep learning mainly includes CNN ( Lecun et al, 1998 ), deep belief nets (DBN) ( Hinton et al, 2006 ), recurrent neural network (RNN) ( Jieun et al, 2020 ), etc. Different types of deep learning apply to different scenarios.…”

Section: Theme Clustering Analysismentioning

confidence: 99%

Research on the Emotions Based on Brain-Computer Technology: A Bibliometric Analysis and Research Agenda

et al. 2021

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This study conducts a scientific analysis of 249 literature on the application of brain-computer technology in emotion research. We find that existing researches mainly focus on engineering, computer science, neurosciences neurology and psychology. PR China, United States, and Germany have the largest number of publications. Authors can be divided into four groups: real-time functional magnetic resonance imaging (rtfMRI) research group, brain-computer interface (BCI) impact factors analysis group, brain-computer music interfacing (BCMI) group, and user status research group. Clustering results can be divided into five categories, including external stimulus and event-related potential (ERP), electroencephalography (EEG), and information collection, support vector machine (SVM) and information processing, deep learning and emotion recognition, neurofeedback, and self-regulation. Based on prior researches, this study points out that individual differences, privacy risk, the extended study of BCI application scenarios and others deserve further research.

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Section: Theme Clustering Analysismentioning

confidence: 99%

Research on the Emotions Based on Brain-Computer Technology: A Bibliometric Analysis and Research Agenda

et al. 2021

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“…Two applications of artificial neural networks were proposed for the TES installation: as a predictor of the outlet water temperature of the TES Unit and as a predictor of the charge state of the TES Unit. A recurrent neural network [65], along with the predictive controller, was implemented in a Siemens PM1207 PLC.…”

Section: Implementation Of Annmentioning

confidence: 99%

Application of Phase Change Material and Artificial Neural Networks for Smoothing of Heat Flux Fluctuations

et al. 2021

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The paper presents an innovative method for smoothing fluctuations of heat flux, using the thermal energy storage unit (TES Unit) with phase change material and Artificial Neural Networks (ANN) control. The research was carried out on a pilot large-scale installation, of which the main component was the TES Unit with a heat capacity of 500 MJ. The main challenge was to smooth the heat flux fluctuations, resulting from variable heat source operation. For this purpose, a molten salt phase change material was used, for which melting occurs at nearly constant temperature. To enhance the smoothing effect, a classical control system based on PID controllers was supported by ANN. The TES Unit was supplied with steam at a constant temperature and variable mass flow rate, while a discharging side was cooled with water at constant mass flow rate. It was indicated that the operation of the TES Unit in the phase change temperature range allows to smooth the heat flux fluctuations by 56%. The tests have also shown that the application of artificial neural networks increases the smoothing effect by 84%.

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“…In addition, this process will be The word vector obtained through encoding layer is passed to LSTM network. Compared with the traditional recurrent neural network (RNN) [40], LSTM introduces memory module and cell state to control and store information. As shown in Figure 4, the memory module has three gates, including forget gate, input gate, and output gate.…”

Section: The Model Of Natural Language Generation For Chart Descriptionmentioning

confidence: 99%

An Approach for Chart Description Generation in Cyber–Physical–Social System

Chen

Zhao

2021

Symmetry

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There is an increasing use of charts generated by the social interaction environment in manufacturing enterprise applications. To transform these massive amounts of unstructured chart data into decision support knowledge for demand-capability matching in manufacturing enterprises, we propose a manufacturing enterprise chart description generation (MECDG) method, which is a two-phase automated solution: (1) extracting chart data based on optical character recognition and deep learning method; (2) generating chart description according to user input based on natural language generation method and matching the description with extracted chart data. We verified and compared the processing at each phase of the method, and at the same time applied the method to the interactive platform of the manufacturing enterprise. The ultimate goal of this paper is to promote the knowledge extraction and scientific analysis of chart data in the context of manufacturing enterprises, so as to improve the analysis and decision-making capabilities of enterprises.

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Analysis of Recurrent Neural Network and Predictions

Cited by 26 publications

References 24 publications

Research on the Emotions Based on Brain-Computer Technology: A Bibliometric Analysis and Research Agenda

Research on the Emotions Based on Brain-Computer Technology: A Bibliometric Analysis and Research Agenda

Application of Phase Change Material and Artificial Neural Networks for Smoothing of Heat Flux Fluctuations

An Approach for Chart Description Generation in Cyber–Physical–Social System

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