scholarly journals Research on Transformer Partial Discharge UHF Pattern Recognition Based on Cnn-lstm

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 61 ◽  
Author(s):  
Xiu Zhou ◽  
Xutao Wu ◽  
Pei Ding ◽  
Xiuguang Li ◽  
Ninghui He ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Spatial Information ◽  
Short Term Memory ◽  
Partial Discharge ◽  
Surface Discharge ◽  
Recognition Ability ◽  
Void Defects ◽  
Memory Network ◽  
Local Spatial Information

In view of the fact that the statistical feature quantity of traditional partial discharge (PD) pattern recognition relies on expert experience and lacks certain generalization, this paper develops PD pattern recognition based on the convolutional neural network (cnn) and long-term short-term memory network (lstm). Firstly, we constructed the cnn-lstm PD pattern recognition model, which combines the advantages of cnn in mining local spatial information of the PD spectrum and the advantages of lstm in mining the PD spectrum time series feature information. Then, the transformer PD UHF (Ultra High Frequency) experiment was carried out. The performance of the constructed cnn-lstm pattern recognition network was tested by using different types of typical PD spectrums. Experimental results show that: (1) for the floating potential defects, the recognition rates of cnn-lstm and cnn are both 100%; (2) cnn-lstm has better recognition ability than cnn for metal protrusion defects, oil paper void defects, and surface discharge defects; and (3) cnn-lstm has better overall recognition accuracy than cnn and lstm.

scholarly journals Study on the Correlation between Partial Discharge Energy and SF6 Decomposition Gas Generation

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4655
Author(s):  
Yong Sung Cho ◽  
Tae Yoon Hong ◽  
Young Woo Youn ◽  
Jong Ho Sun ◽  
Se-Hee Lee
Keyword(s):  
Partial Discharge ◽  
Surface Discharge ◽  
Discharge Energy ◽  
The Finite Element Method ◽  
Gas Generation ◽  
Energy Effect ◽  
Sf6 Decomposition ◽  
Maintenance Process ◽  
The Relationship

In this paper, the amount of SF6 decomposition gases due to the partial discharge (PD) was studied in the SF6 gas-insulated transformer. The long-term PD degradation experiment was performed while controlling the discharge magnitude using the surface discharge, and the gas generation amount was measured by using gas chromatography for SO2F2, SOF2, SO2, CO, and CF4. In addition, to investigate the relationship between the partial discharge energy and the decomposed gas generation amount, partial discharge energy was calculated by a data processing program and converted to the unit of joule per mole. With the finite element method (FEM), the electric field distribution and SF6 gas decomposition mechanism were explained for the partial discharge energy effect on the gas generation. This study helps understand the relationship between the partial discharge energy and the decomposed gas generation ratio with the experimental results and can be used for the diagnosis of PD and maintenance process for the gas-insulated transformers.

Deficits in Verbal Long-Term Memory and Learning in Children with Poor Phonological Short-Term Memory Skills

2008 ◽  
Vol 61 (3) ◽  
pp. 474-490 ◽  
Author(s):  
Susan E. Gathercole ◽  
Josie Briscoe ◽  
Annabel Thorn ◽  
Claire Tiffany ◽  
Keyword(s):  
Spatial Information ◽  
Short Term Memory ◽  
Memory Function ◽  
Verbal Material ◽  
Long Term Memory ◽  
Short Term ◽  
Memory And Learning ◽  
Term Memory ◽  
Memory Group

Possible links between phonological short-term memory and both longer term memory and learning in 8-year-old children were investigated in this study. Performance on a range of tests of long-term memory and learning was compared for a group of 16 children with poor phonological short-term memory skills and a comparison group of children of the same age with matched nonverbal reasoning abilities but memory scores in the average range. The low-phonological-memory group were impaired on longer term memory and learning tasks that taxed memory for arbitrary verbal material such as names and nonwords. However, the two groups performed at comparable levels on tasks requiring the retention of visuo-spatial information and of meaningful material and at carrying out prospective memory tasks in which the children were asked to carry out actions at a future point in time. The results are consistent with the view that poor short-term memory function impairs the longer term retention and ease of learning of novel verbal material.

scholarly journals GIS Partial Discharge Pattern Recognition Based on a Novel Convolutional Neural Networks and Long Short-Term Memory

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 774
Author(s):  
Tingliang Liu ◽  
Jing Yan ◽  
Yanxin Wang ◽  
Yifan Xu ◽  
Yiming Zhao
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Short Term Memory ◽  
Partial Discharge ◽  
Series Data ◽  
Short Term ◽  
Term Memory ◽  
Analysis Methods ◽  
Voltage Signal ◽  
Long Short Term Memory

Distinguishing the types of partial discharge (PD) caused by different insulation defects in gas-insulated switchgear (GIS) is a great challenge in the power industry, and improving the recognition accuracy of the relevant models is one of the key problems. In this paper, a convolutional neural network and long short-term memory (CNN-LSTM) model is proposed, which can effectively extract and utilize the spatiotemporal characteristics of PD input signals. First, the spatial characteristics of higher-level PD signals can be obtained through the CNN network, but because CNN is a deep feedforward neural network, it does not have the ability to process time-series data. The PD voltage signal is related to the time dimension, so LSTM saves and analyzes the previous voltage signal information, realizes the modeling of the time dependence of the data, and improves the accuracy of the PD signal pattern recognition. Finally, the pattern recognition results based on CNN-LSTM are given and compared with those based on other traditional analysis methods. The results show that the pattern recognition rate of this method is the highest, with an average of 97.9%, and its overall accuracy is better than that of other traditional analysis methods. The CNN-LSTM model provides a reliable reference for GIS PD diagnosis.

scholarly journals Hyperspectral Image Classification via a Novel Spectral–Spatial 3D ConvLSTM-CNN

2021 ◽  
Vol 13 (21) ◽  
pp. 4348
Author(s):  
Ghulam Farooque ◽  
Liang Xiao ◽  
Jingxiang Yang ◽  
Allah Bux Sargano
Keyword(s):  
Spatial Information ◽  
Short Term Memory ◽  
Hyperspectral Image ◽  
Fast Learning ◽  
Spatial Features ◽  
Learning Capabilities ◽  
Benchmark Datasets ◽  
3D Cnn ◽  
Local Feature Extraction

In recent years, deep learning-based models have produced encouraging results for hyperspectral image (HSI) classification. Specifically, Convolutional Long Short-Term Memory (ConvLSTM) has shown good performance for learning valuable features and modeling long-term dependencies in spectral data. However, it is less effective for learning spatial features, which is an integral part of hyperspectral images. Alternatively, convolutional neural networks (CNNs) can learn spatial features, but they possess limitations in handling long-term dependencies due to the local feature extraction in these networks. Considering these factors, this paper proposes an end-to-end Spectral-Spatial 3D ConvLSTM-CNN based Residual Network (SSCRN), which combines 3D ConvLSTM and 3D CNN for handling both spectral and spatial information, respectively. The contribution of the proposed network is twofold. Firstly, it addresses the long-term dependencies of spectral dimension using 3D ConvLSTM to capture the information related to various ground materials effectively. Secondly, it learns the discriminative spatial features using 3D CNN by employing the concept of the residual blocks to accelerate the training process and alleviate the overfitting. In addition, SSCRN uses batch normalization and dropout to regularize the network for smooth learning. The proposed framework is evaluated on three benchmark datasets widely used by the research community. The results confirm that SSCRN outperforms state-of-the-art methods with an overall accuracy of 99.17%, 99.67%, and 99.31% over Indian Pines, Salinas, and Pavia University datasets, respectively. Moreover, it is worth mentioning that these excellent results were achieved with comparatively fewer epochs, which also confirms the fast learning capabilities of the SSCRN.

Shared long-term and short-term memory representational formats in occipital and parietal cortex

2021 ◽  
Author(s):  
Vy A. Vo ◽  
David W. Sutterer ◽  
Joshua J. Foster ◽  
Thomas C. Sprague ◽  
Edward Awh ◽  
...  
Keyword(s):  
Parietal Cortex ◽  
Spatial Information ◽  
Short Term Memory ◽  
Memory Task ◽  
Memory Systems ◽  
Long Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Sensory Evoked

AbstractCurrent theories propose that the short-term retention of information in working memory (WM) and the recall of information from long-term memory (LTM) are supported by overlapping neural mechanisms in occipital and parietal cortex. Both are thought to rely on reinstating patterns of sensory activity evoked by the perception of the remembered item. However, the extent of the shared representations between WM and LTM are unclear, and it is unknown how WM and LTM representations may differ across cortical regions. We designed a spatial memory task that allowed us to directly compare the representations of remembered spatial information in WM and LTM. Critically, we carefully matched the precision of behavioral responses in these tasks. We used fMRI and multivariate pattern analyses to examine representations in (1) retinotopic cortex and (2) lateral parietal cortex (LPC) regions previously implicated in LTM. We show that visual memories were represented in a sensory-like code in both tasks across retinotopic regions in occipital and parietal cortex. LPC regions also encoded remembered locations in both WM and LTM, but in a format that differed from the sensory-evoked activity. These results suggest a striking correspondence in the format of WM and LTM representations across occipital and parietal cortex. On the other hand, we show that activity patterns in nearly all parietal regions, but not occipital regions, contained information that could discriminate between WM trials and LTM trials. Our data provide new evidence for theories of memory systems and the representation of mnemonic content.

Precipitation Nowcasting using Deep Neural Network

2020 ◽  
Author(s):  
Mohamed Chafik Bakey ◽  
Mathieu Serrurier
Keyword(s):  
Traffic Management ◽  
Prediction Models ◽  
Short Term Memory ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Proposed Model ◽  
Anticipation Time ◽  
Memory Network ◽  
Convolutional Encoder

<p>Precipitation nowcasting is the prediction of the future precipitation rate in a given geographical region with an anticipation time of a few hours at most. It is of great importance for weather forecast users, for activitites ranging from outdoor activities and sports competitions to airport traffic management. In contrast to long-term precipitation forecasts which are traditionally obtained from numerical weather prediction models, precipitation nowcasting needs to be very fast. It is therefore more challenging to obtain because of this time constraint. Recently, many machine learning based methods had been proposed. In this work, we develop an original deep learning approach. We formulate precipitation nowcasting issue as a video prediction problem where both input and prediction target are image sequences. The proposed model combines a Long Short-Term Memory network (LSTM) with a convolutional encoder-decoder network (U-net). Experiments show that our method captures spatiotemporal correlations and yields meaningful forecasts</p>

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