scholarly journals Deep learning network based lifetime analysis of energy - fed traction power supply converter

2022 ◽  
Vol 355 ◽  
pp. 02021
Author(s):  
Zeshu Li ◽  
Mingchao Xia ◽  
Qifang Chen
Keyword(s):  
Deep Learning ◽  
Power Unit ◽  
Network Architecture ◽  
Life Prediction ◽  
Prediction Method ◽  
Coupling Model ◽  
Junction Temperature ◽  
Learning Network ◽  
Deep Learning Network ◽  
Deep Learning Neural Network

This paper presents a life prediction method based on the parameters of the actual operation history data collected by the existing converter power unit sensors. Firstly, the characteristics of junction temperature curves of forced air-cooled radiator and power unit are extracted, and the deep learning neural network architecture is constructed based on the characteristics. Then the thermoelectric coupling model of power unit based on thermal resistance calculation theory is established, and the cumulative loss is obtained from the measured data. The deep learning network is trained and the model prediction is verified. Finally, the power unit loss distribution under different setting temperature thresholds and the correlation analysis with radiator parameters are obtained, which provides a feasible scheme for parameter setting and life prediction.

scholarly journals Toxicity Prediction Method Based on Multi-Channel Convolutional Neural Network

Molecules ◽  
2019 ◽  
Vol 24 (18) ◽  
pp. 3383 ◽  
Author(s):  
Yuan ◽  
Wei ◽  
Guan ◽  
Jiang ◽  
Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Prediction Method ◽  
Toxicity Prediction ◽  
Learning Network ◽  
Machine Learning Methods ◽  
Molecular Information ◽  
Molecular Toxicity ◽  
Deep Learning Network

Molecular toxicity prediction is one of the key studies in drug design. In this paper, a deep learning network based on a two-dimension grid of molecules is proposed to predict toxicity. At first, the van der Waals force and hydrogen bond were calculated according to different descriptors of molecules, and multi-channel grids were generated, which could discover more detail and helpful molecular information for toxicity prediction. The generated grids were fed into a convolutional neural network to obtain the result. A Tox21 dataset was used for the evaluation. This dataset contains more than 12,000 molecules. It can be seen from the experiment that the proposed method performs better compared to other traditional deep learning and machine learning methods.

scholarly journals 1058 Modeling Self-reported Sleep Duration And Hypertension Using Deep Learning Network: Analysis Of The National Health And Nutrition Examination Survey Data

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A402-A402
Author(s):  
S Williams ◽  
A Seixas ◽  
G Avirappattu ◽  
R Robbins ◽  
L Lough ◽  
...  
Keyword(s):  
Deep Learning ◽  
Sleep Duration ◽  
National Health ◽  
Network Architecture ◽  
Nutrition Examination Survey ◽  
Public Health Importance ◽  
Learning Network ◽  
Representative Study ◽  
Health And Nutrition ◽  
Deep Learning Network

Abstract Introduction Epidemiologic data show strong associations between self-reported sleep duration and hypertension (HTN). Modeling these associations is suboptimal when utilizing traditional logistic regressions. In this study, we modeled the associations of sleep duration and HTN using Deep Learning Network. Methods Data were extracted from participants (n=38,540) in the National Health and Nutrition Examination Survey (2006-2016), a nationally representative study of the US civilian non-institutionalized population. Self-reported demographic, medical history and sleep duration were determined from household interview questions. HTN was determined as SBP ≥ 130 mmHg and DBP ≥ 80 mmHg. We used a deep neural network architecture with three hidden layers with two input features and one binary output to model associations of sleep duration with HTN. The input features are the hours of sleep (limited to between 4 and 10 hours) and its square; and the output variable HTN. Probability predictions were generated 100 times from resampled (with replacement) data and averaged. Results Participants ranged from 18 to 85 years old; 51% Female, 41% white, 22% black, 26% Hispanic, 46% married, and 25% < high school. The model showed that sleeping 7 hours habitually was associated with the least observed HTN probabilities (P=0.023%). HTN probabilities increased as sleep duration decreased (6hrs=0.05%; 5hrs=0.110%; 4hrs=0.16%); HTN probabilities for long sleepers were: (8hrs=0.027; 9hrs=0.024; 10hrs=0.022). Whites showed sleeping 7hrs or 9hrs was associated with lowest HTN probabilities (0.008 vs. 0.005); blacks showed the lowest HTN probabilities associated with sleeping 8hrs (0.07), and Hispanics showed the lowest HTN probabilities sleeping 7hrs (0.04). Conclusion We found that sleeping 7 hours habitually confers the least amount of risk for HTN. Probability of HTN varies as a function of individual’s sex and race/ethnicity. Likewise, the finding that blacks experience the lowest HTN probability when they sleep habitually 8 hours is of great public health importance. Support This study was supported by funding from the NIH: R01MD007716, R01HL142066, R01AG056531, T32HL129953, K01HL135452, and K07AG052685.

scholarly journals UAVs in rail damage image diagnostics supported by deep-learning networks

2021 ◽  
Vol 11 (1) ◽  
pp. 339-348
Author(s):  
Piotr Bojarczak ◽  
Piotr Lesiak
Keyword(s):  
Deep Learning ◽  
Head Width ◽  
Learning Networks ◽  
Image Brightness ◽  
Learning Network ◽  
Rail Head ◽  
Image Recording ◽  
Python Language ◽  
Deep Learning Network ◽  
Efficiency Rate

Abstract The article uses images from Unmanned Aerial Vehicles (UAVs) for rail diagnostics. The main advantage of such a solution compared to traditional surveys performed with measuring vehicles is the elimination of decreased train traffic. The authors, in the study, limited themselves to the diagnosis of hazardous split defects in rails. An algorithm has been proposed to detect them with an efficiency rate of about 81% for defects not less than 6.9% of the rail head width. It uses the FCN-8 deep-learning network, implemented in the Tensorflow environment, to extract the rail head by image segmentation. Using this type of network for segmentation increases the resistance of the algorithm to changes in the recorded rail image brightness. This is of fundamental importance in the case of variable conditions for image recording by UAVs. The detection of these defects in the rail head is performed using an algorithm in the Python language and the OpenCV library. To locate the defect, it uses the contour of a separate rail head together with a rectangle circumscribed around it. The use of UAVs together with artificial intelligence to detect split defects is an important element of novelty presented in this work.

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