scholarly journals Application of Multiattention Mechanism in Power System Branch Parameter Identification

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhiwei Wang ◽  
Liguo Weng ◽  
Min Lu ◽  
Jun Liu ◽  
Lingling Pan
Keyword(s):  
Deep Learning ◽  
Parameter Identification ◽  
Power Grid ◽  
Grid Data ◽  
Global Features ◽  
Learning Methods ◽  
Single Target ◽  
Topology Information ◽  
Model Training ◽  
Local Feature Extraction

Maintaining accuracy and robustness has always been an unsolved problem in the task of power grid branch parameter identification. Therefore, many researchers have participated in the research of branch parameter identification. The existing methods of power grid branch parameter identification suffer from two limitations. (1) Traditional methods only use manual experience or instruments to complete parameter identification of single branch characteristics, but they are only used to identify a single target and cannot make full use of the historical information of power grid data. (2) Deep learning methods can complete model training through historical data, but these methods cannot consider the constraints of power grid topological structure, which is equivalent to identifying connected power grid branches separately. To overcome these limitations, we propose a novel multitask Graph Transformer Network (GTN), which combines a graph neural network and a multiattention mechanism to construct our model. Specifically, we input the global features and topology information of branch nodes into our GTN model. In the process of parameter identification, the multihead attention mechanism is used to fuse the branch feature information of different subspaces, which highlights the importance of different branches and enhances the ability of local feature extraction. Finally, the fitting and prediction of each branch feature are completed through the decoding layer. The experiment shows that our proposed GTN is superior to other machine learning methods and deep learning methods and can still realize accurate branch parameter identification under various noise conditions.

scholarly journals MAT-Net: Medial Axis Transform Network for 3D Object Recognition

2019 ◽  
Author(s):  
Jianwei Hu ◽  
Bin Wang ◽  
Lihui Qian ◽  
Yiling Pan ◽  
Xiaohu Guo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Representation ◽  
Nearest Neighbor ◽  
Medial Axis ◽  
Fixed Number ◽  
Learning Performance ◽  
Medial Axis Transform ◽  
K Nearest Neighbor ◽  
Global Features ◽  
Local Feature Extraction

3D deep learning performance depends on object representation and local feature extraction. In this work, we present MAT-Net, a neural network which captures local and global features from the Medial Axis Transform (MAT). Different from K-Nearest-Neighbor method which extracts local features by a fixed number of neighbors, our MAT-Net exploits effective modules Group-MAT and Edge-Net to process topological structure. Experimental results illustrate that MAT-Net demonstrates competitive or better performance on 3D shape recognition than state-of-the-art methods, and prove that MAT representation has excellent capacity in 3D deep learning, even in the case of low resolution.

scholarly journals MCN-CPI: Multiscale Convolutional Network for Compound–Protein Interaction Prediction

Biomolecules ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1119
Author(s):  
Shuang Wang ◽  
Mingjian Jiang ◽  
Shugang Zhang ◽  
Xiaofeng Wang ◽  
Qing Yuan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Protein Interaction ◽  
The Novel ◽  
Global Features ◽  
Learning Methods ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Different Types ◽  
Protein Interaction Prediction ◽  
Local And Global Features

In the process of drug discovery, identifying the interaction between the protein and the novel compound plays an important role. With the development of technology, deep learning methods have shown excellent performance in various situations. However, the compound–protein interaction is complicated and the features extracted by most deep models are not comprehensive, which limits the performance to a certain extent. In this paper, we proposed a multiscale convolutional network that extracted the local and global features of the protein and the topological feature of the compound using different types of convolutional networks. The results showed that our model obtained the best performance compared with the existing deep learning methods.

scholarly journals Identifying Patient–Ventilator Asynchrony on a Small Dataset Using Image-Based Transfer Learning

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4149
Author(s):  
Qing Pan ◽  
Mengzhe Jia ◽  
Qijie Liu ◽  
Lingwei Zhang ◽  
Jie Pan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Life Support ◽  
Ventilatory Support ◽  
Model Performance ◽  
Learning Methods ◽  
Discriminative Ability ◽  
Two Dimensional Image ◽  
Ventilation Modes ◽  
Model Training

Mechanical ventilation is an essential life-support treatment for patients who cannot breathe independently. Patient–ventilator asynchrony (PVA) occurs when ventilatory support does not match the needs of the patient and is associated with a series of adverse clinical outcomes. Deep learning methods have shown a strong discriminative ability for PVA detection, but they require a large number of annotated data for model training, which hampers their application to this task. We developed a transfer learning architecture based on pretrained convolutional neural networks (CNN) and used it for PVA recognition based on small datasets. The one-dimensional signal was converted to a two-dimensional image, and features were extracted by the CNN using pretrained weights for classification. A partial dropping cross-validation technique was developed to evaluate model performance on small datasets. When using large datasets, the performance of the proposed method was similar to that of non-transfer learning methods. However, when the amount of data was reduced to 1%, the accuracy of transfer learning was approximately 90%, whereas the accuracy of the non-transfer learning was less than 80%. The findings suggest that the proposed transfer learning method can obtain satisfactory accuracies for PVA detection when using small datasets. Such a method can promote the application of deep learning to detect more types of PVA under various ventilation modes.

scholarly journals Fully automatic image colorization based on semantic segmentation technology

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259953
Author(s):  
Min Xu ◽  
YouDong Ding
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Global Feature ◽  
Global Features ◽  
Image Colorization ◽  
Complex Scenes ◽  
Fully Automatic ◽  
Color Prediction ◽  
Local Feature Extraction

Aiming at these problems of image colorization algorithms based on deep learning, such as color bleeding and insufficient color, this paper converts the study of image colorization to the optimization of image semantic segmentation, and proposes a fully automatic image colorization model based on semantic segmentation technology. Firstly, we use the encoder as the local feature extraction network and use VGG-16 as the global feature extraction network. These two parts do not interfere with each other, but they share the low-level feature. Then, the first fusion module is constructed to merge local features and global features, and the fusion results are input into semantic segmentation network and color prediction network respectively. Finally, the color prediction network obtains the semantic segmentation information of the image through the second fusion module, and predicts the chrominance of the image based on it. Through several sets of experiments, it is proved that the performance of our model becomes stronger and stronger under the nourishment of the data. Even in some complex scenes, our model can predict reasonable colors and color correctly, and the output effect is very real and natural.

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
Learning Methods ◽  
Comprehensive Review

Sequence-Based Deep Learning Frameworks on Enhancer-Promoter Interactions Prediction

2020 ◽  
Vol 26 ◽  
Author(s):  
Xiaoping Min ◽  
Fengqing Lu ◽  
Chunyan Li
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Dna Sequences ◽  
Experimental Methods ◽  
Learning Methods ◽  
Comprehensive Review ◽  
Genomic Features ◽  
Disease Mechanisms ◽  
Evaluation Strategies ◽  
Learning Frameworks

: Enhancer-promoter interactions (EPIs) in the human genome are of great significance to transcriptional regulation which tightly controls gene expression. Identification of EPIs can help us better deciphering gene regulation and understanding disease mechanisms. However, experimental methods to identify EPIs are constrained by the fund, time and manpower while computational methods using DNA sequences and genomic features are viable alternatives. Deep learning methods have shown promising prospects in classification and efforts that have been utilized to identify EPIs. In this survey, we specifically focus on sequence-based deep learning methods and conduct a comprehensive review of the literatures of them. We first briefly introduce existing sequence-based frameworks on EPIs prediction and their technique details. After that, we elaborate on the dataset, pre-processing means and evaluation strategies. Finally, we discuss the challenges these methods are confronted with and suggest several future opportunities.

scholarly journals A Review of Deep Learning-Based Contactless Heart Rate Measurement Methods

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3719
Author(s):  
Aoxin Ni ◽  
Arian Azarang ◽  
Nasser Kehtarnavaz
Keyword(s):  
Heart Rate ◽  
Deep Learning ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Video Camera ◽  
Measurement Methods ◽  
Rate Measurement ◽  
Learning Methods ◽  
The Public ◽  
Heart Rate Measurement

The interest in contactless or remote heart rate measurement has been steadily growing in healthcare and sports applications. Contactless methods involve the utilization of a video camera and image processing algorithms. Recently, deep learning methods have been used to improve the performance of conventional contactless methods for heart rate measurement. After providing a review of the related literature, a comparison of the deep learning methods whose codes are publicly available is conducted in this paper. The public domain UBFC dataset is used to compare the performance of these deep learning methods for heart rate measurement. The results obtained show that the deep learning method PhysNet generates the best heart rate measurement outcome among these methods, with a mean absolute error value of 2.57 beats per minute and a mean square error value of 7.56 beats per minute.

scholarly journals Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4595
Author(s):  
Parisa Asadi ◽  
Lauren E. Beckingham
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Ct Images ◽  
Ct Imaging ◽  
Learning Method ◽  
Learning Methods ◽  
X Ray ◽  
Machine Learning Methods ◽  
Filtering Techniques

X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.

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