Monitoring of the Discharge Current by Hall-Effect Sensor

Batinić, Bojan; Rodić, Dragan; Gostimirović, Marin; Kulundzic, Nenad; Lakovic, Nikola

doi:10.24867/jpe-2017-01-039

Cited by 8 publications

(1 citation statement)

References 8 publications

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“…Gambar 1. Prinsip hall element tanpa medan magnetik Akan tetapi apabila terdapat medan magnetik pada sensor maka arus yang mengalir pada penghantar menghasilkan tegangan output atau hall voltage VH (Batinić et al, 2017) sebagaimana tampak pada gambar 2 yang mana VH dapat dihitung dengan menggunakan persamaan (1) berikut ini dimana I adalah arus yang mengalir [Amper] dan B merupakan kerapatan medan magnetik [Weber/m 2 ].…”

Section: Pendahuluan Prinsip Dasar Sensor Hall Effectunclassified

Pengujian Sensitivitas Dan Akurasi Sensor Arus Hall Effect Menggunakan Arduino-Uno

Sampebatu,

Patabang,

Leda

2022

IND TECH

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The sensitivity and accuracy of the hall effect current sensor is an important factor in its application considering that electric current is the main parameter in the study of electrical energy engineering, telecommunications and electronics. However, on the other hand, studies related to the sensitivity and accuracy of Hall effect current sensors are still rarely carried out. The method used in this research is an experimental laboratory study using the ACS712 sensor with a variation rating of 5A, 20A and 30A. The results show that the sensitivity is inversely proportional to the ampere rating of the ACS712 sensor where the greater the current rating of the sensor, the lower the sensitivity. While the accuracy is directly proportional to the ampere rating of the ACS712 sensor where the greater the current rating of the sensor, the more accurate the sensor is

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Section: Pendahuluan Prinsip Dasar Sensor Hall Effectunclassified

Pengujian Sensitivitas Dan Akurasi Sensor Arus Hall Effect Menggunakan Arduino-Uno

Sampebatu,

Patabang,

Leda

2022

IND TECH

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Determination of Isosteric Heat and Entropy of Sorption of Air Dried Sheet Rubber Using Artificial Neural Network Approach

Suntaro

Tirawanichakul

2014

AMM

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Equilibrium moisture contents (EMC) of air dried sheet (ADS) rubber were determined by commonly gravimetric-static method with saturated salt solution among surrounding temperatures of 40-70°C correlated to water activity (aw) ranges between 0.10 and 0.9. The experimental results was analyzed by 5 commonly EMC model. The results showed that equilibrium moisture content of ADS rubber decreased with increase of surrounding temperature at constant water activity and the simulated data using Chung-Pfost model has a good relation to experimental data with R2, RMSE and χ2 equal 0.9565, 0.0235 and 0.0006, respectively. However some physical property of ADS rubber sample affects to evaluate EMC modeling. Due to avoid this effect, thus the aim of this research work was to determine EMC value by using Artificial neural network (ANN) method and also evaluate the isosteric heat of sorption by following the Clausius-Clapeyron equation. The results showed that simulated results using ANN approach has relatively high accuracy compared to common EMC model. Finally determination of isosteric heat of sorption and entropy of sorption of ADS rubber were carried on. The results stated that the enthalpy and entropy of heat sorption was power function and polynomial function of moisture content respectively. These two parameters of ADS rubber can be used for prediction suitable storage condition and drying condition for ADS rubber drying in the near future work.

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Using ANN in Process of Seismic Damage Prediction for Residential Buildings

Dražić

Peško

Mučenski

et al. 2015

AMM

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With the aim of planning preventive measures of seismic protection, estimation of possible damages on building constructions plays an important role. In order to predict the category of damage , a data base was formed out of the data from reports submitted by the commission of the Faculty of Technical Sciences in Novi Sad. The paper presents the research on the influence of the number and structure of input data and the comparative results of prediction of damage category on building constructions for two models of ANN (ANN-1 and ANN-2)

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Monitoring of the Discharge Current by Hall-Effect Sensor

Cited by 8 publications

References 8 publications

Pengujian Sensitivitas Dan Akurasi Sensor Arus Hall Effect Menggunakan Arduino-Uno

Pengujian Sensitivitas Dan Akurasi Sensor Arus Hall Effect Menggunakan Arduino-Uno

Determination of Isosteric Heat and Entropy of Sorption of Air Dried Sheet Rubber Using Artificial Neural Network Approach

Using ANN in Process of Seismic Damage Prediction for Residential Buildings

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