Influence and Simulation of multibarrier isolation facilities on noise attenuation distribution of transformer

2021 ◽  
Author(s):  
Zhenhuan Liu ◽  
Yulong Chen ◽  
Hao Wan
Keyword(s):  
Noise Attenuation ◽  
Isolation Facilities

scholarly journals Trend of COVID-19 cases and health sector response in Nepal

2020 ◽  
Vol 1 (1) ◽  
pp. 51-57
Author(s):  
Meghnath Dhimal ◽  
Tamanna Neupane ◽  
Samir Kumar Adhikari ◽  
Pradip Gyanwali
Keyword(s):  
Health Sector ◽  
Joint Effort ◽  
Virus Diseases ◽  
Public Places ◽  
Global Trends ◽  
Epidemic Curve ◽  
Global Pandemic ◽  
First Case ◽  
Government Documents ◽  
Isolation Facilities

We are facing global pandemic of novel corona virus diseases COVID-19 which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This paper is aimed to assess trend of COVID-19 cases and health sector response in Nepal. We reviewed WHO databases to observe the global trends and epidemiology of COVID-19 as well as daily situation updated reports of Health Emergency and Operation Centre (HEOC), guidelines, national and international government documents. The first case of COVID was reported in Nepal on 23 January 2020 and number of cases reached 454 on 21 May 2020. In order to address the increasing number of cases of COVID-19, Government of Nepal is adopting various preventive measures like extending lockdown period, setting up quarantine and isolation facilities, sealing borders, suspending flights, closing public places etc. There is need of joint effort by individuals, communities and government to prevent the further spread and flatten epidemic curve in Nepal.

scholarly journals Improved shift-invariant sparse coding for noise attenuation of magnetotelluric data

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Guang Li ◽  
Xiaoqiong Liu ◽  
Jingtian Tang ◽  
Juzhi Deng ◽  
Shuanggui Hu ◽  
...  
Keyword(s):  
Sparse Coding ◽  
Noise Attenuation ◽  
Magnetotelluric Data

Deep learning for denoising

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. V333-V350 ◽  
Author(s):  
Siwei Yu ◽  
Jianwei Ma ◽  
Wenlong Wang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Graphics Processing Unit ◽  
Random Noise ◽  
Stochastic Gradient Descent ◽  
Processing Unit ◽  
Noise Attenuation ◽  
Optimal Parameters ◽  
Training Set ◽  
Unknown Variance

Compared with traditional seismic noise attenuation algorithms that depend on signal models and their corresponding prior assumptions, removing noise with a deep neural network is trained based on a large training set in which the inputs are the raw data sets and the corresponding outputs are the desired clean data. After the completion of training, the deep-learning (DL) method achieves adaptive denoising with no requirements of (1) accurate modelings of the signal and noise or (2) optimal parameters tuning. We call this intelligent denoising. We have used a convolutional neural network (CNN) as the basic tool for DL. In random and linear noise attenuation, the training set is generated with artificially added noise. In the multiple attenuation step, the training set is generated with the acoustic wave equation. The stochastic gradient descent is used to solve the optimal parameters for the CNN. The runtime of DL on a graphics processing unit for denoising has the same order as the [Formula: see text]-[Formula: see text] deconvolution method. Synthetic and field results indicate the potential applications of DL in automatic attenuation of random noise (with unknown variance), linear noise, and multiples.

Noise attenuation by Venturi tubes

1994 ◽  
Vol 95 (5) ◽  
pp. 2933-2933
Author(s):  
A. Selamet
Keyword(s):  
Noise Attenuation
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