monitoring system
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2022 ◽  
Vol 50 ◽  
pp. 101745
Author(s):  
F. Frota de Albuquerque Landi ◽  
C. Fabiani ◽  
A. D’Alessandro ◽  
F. Ubertini ◽  
A.L. Pisello

2022 ◽  
Vol 24 (3) ◽  
pp. 1-26
Author(s):  
Nagaraj V. Dharwadkar ◽  
Anagha R. Pakhare ◽  
Vinothkumar Veeramani ◽  
Wen-Ren Yang ◽  
Rajinder Kumar Mallayya Math

This paper presents design and experiments for a production line monitoring system. The system is designed based on an existing production line which mapping to the smart grid standards. The Discrete wavelet transform (DWT) and regression neural network (RNN) are applied to the operation modes data analysis. DWT used to preprocess the signals to remove noise from the raw signals. The output of DWT energy distribution has given as an input to the GRNN model. The neural network GRNN architecture involves multi-layer structures. Mean Absolute Percentage Error (MAPE) loss has used in the GRNN model, which is used to forecast the time-series data. Current research results can only apply to the single production line but in future, it will used for multiple production lines.


2022 ◽  
Vol 24 (3) ◽  
pp. 0-0

This paper presents design and experiments for a production line monitoring system. The system is designed based on an existing production line which mapping to the smart grid standards. The Discrete wavelet transform (DWT) and regression neural network (RNN) are applied to the operation modes data analysis. DWT used to preprocess the signals to remove noise from the raw signals. The output of DWT energy distribution has given as an input to the GRNN model. The neural network GRNN architecture involves multi-layer structures. Mean Absolute Percentage Error (MAPE) loss has used in the GRNN model, which is used to forecast the time-series data. Current research results can only apply to the single production line but in future, it will used for multiple production lines.


2022 ◽  
Vol 242 ◽  
pp. 106789
Author(s):  
Matthew A. Goodwin ◽  
Daniel L. Chester ◽  
Richard Britton ◽  
Ashley V. Davies ◽  
Joshua Border

2022 ◽  
Vol 175 ◽  
pp. 112992
Author(s):  
YoungHwa An ◽  
Boseong Kim ◽  
Haewon Shin ◽  
Yeonjung Kim ◽  
Changrae Seon ◽  
...  

2022 ◽  
Vol 193 ◽  
pp. 106620
Author(s):  
Tianyi Wang ◽  
Robert G. Hardin IV ◽  
Jason K. Ward ◽  
John D. Wanjura ◽  
Edward M. Barnes
Keyword(s):  

Author(s):  
Umang Deogade

Abstract: The most significant system for monitoring solar systems is the solar parameters monitoring system. Solar energy is a renewable energy source produced by solar panels. Solar energy is a renewable energy source produced by solar panels. Voltage, light intensity, and temperature are the parameters that the system measures. An Arduino Uno microcontroller board is used in the suggested monitoring system. Solar panel, LDR Sensor, LM 35, Arduino microcontroller, and resistors are used in the system. Light. LDR sensor is used to detect light intensity, L35 is used to measure temperature, and a voltage divider circuit is used to monitor voltage in this system. Keywords: Solar Panel, Monitoring, Renewable Energy, Solar Panel, Arduino Uno.


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