scholarly journals Least-squares method and deep learning in the identification and analysis of name-plates of power equipment

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Yerong Zhong ◽  
Guoheng Ruan ◽  
Ehab Abozinadah ◽  
Jiaming Jiang
Keyword(s):  
Deep Learning ◽  
Least Squares ◽  
Least Squares Method ◽  
Feature Fusion ◽  
Learning Algorithm ◽  
Power Equipment ◽  
Practical Application ◽  
Deep Learning Algorithm ◽  
Character Feature ◽  
Histogram Feature

Abstract This article proposes a nameplate recognition method based on the least-squares method and deep learning algorithm character feature fusion. This method extracts the histogram of the edge direction of the character and constructs the histogram feature vector based on the wavelet transform deep learning algorithm. We use classifier training for the text recognition of the nameplate to segment the text into individual characters. Then, we extract the character features to build a template. Experiments prove that the algorithm meets the practical application needs of nameplate identification of power equipment and achieves the design goals.

scholarly journals Feature Fusion of a Deep-Learning Algorithm into Wearable Sensor Devices for Human Activity Recognition

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8294
Author(s):  
Chih-Ta Yen ◽  
Jia-Xian Liao ◽  
Yi-Kai Huang
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Feature Fusion ◽  
Learning Algorithm ◽  
Human Activity Recognition ◽  
The Self ◽  
Wearable Device ◽  
Deep Learning Algorithm ◽  
Recorded Data

This paper presents a wearable device, fitted on the waist of a participant that recognizes six activities of daily living (walking, walking upstairs, walking downstairs, sitting, standing, and laying) through a deep-learning algorithm, human activity recognition (HAR). The wearable device comprises a single-board computer (SBC) and six-axis sensors. The deep-learning algorithm employs three parallel convolutional neural networks for local feature extraction and for subsequent concatenation to establish feature fusion models of varying kernel size. By using kernels of different sizes, relevant local features of varying lengths were identified, thereby increasing the accuracy of human activity recognition. Regarding experimental data, the database of University of California, Irvine (UCI) and self-recorded data were used separately. The self-recorded data were obtained by having 21 participants wear the device on their waist and perform six common activities in the laboratory. These data were used to verify the proposed deep-learning algorithm on the performance of the wearable device. The accuracy of these six activities in the UCI dataset and in the self-recorded data were 97.49% and 96.27%, respectively. The accuracies in tenfold cross-validation were 99.56% and 97.46%, respectively. The experimental results have successfully verified the proposed convolutional neural network (CNN) architecture, which can be used in rehabilitation assessment for people unable to exercise vigorously.

scholarly journals Study on Radar Echo-Filling in an Occlusion Area by a Deep Learning Algorithm

2021 ◽  
Vol 13 (9) ◽  
pp. 1779
Author(s):  
Xiaoyan Yin ◽  
Zhiqun Hu ◽  
Jiafeng Zheng ◽  
Boyong Li ◽  
Yuanyuan Zuo
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
Learning Algorithm ◽  
Weather Radar ◽  
Loss Functions ◽  
Training Dataset ◽  
Echo Intensity ◽  
Common Mean ◽  
Deep Learning Algorithm ◽  
Radar Beam

Radar beam blockage is an important error source that affects the quality of weather radar data. An echo-filling network (EFnet) is proposed based on a deep learning algorithm to correct the echo intensity under the occlusion area in the Nanjing S-band new-generation weather radar (CINRAD/SA). The training dataset is constructed by the labels, which are the echo intensity at the 0.5° elevation in the unblocked area, and by the input features, which are the intensity in the cube including multiple elevations and gates corresponding to the location of bottom labels. Two loss functions are applied to compile the network: one is the common mean square error (MSE), and the other is a self-defined loss function that increases the weight of strong echoes. Considering that the radar beam broadens with distance and height, the 0.5° elevation scan is divided into six range bands every 25 km to train different models. The models are evaluated by three indicators: explained variance (EVar), mean absolute error (MAE), and correlation coefficient (CC). Two cases are demonstrated to compare the effect of the echo-filling model by different loss functions. The results suggest that EFnet can effectively correct the echo reflectivity and improve the data quality in the occlusion area, and there are better results for strong echoes when the self-defined loss function is used.

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