 Subjects -> ELECTRONICS (Total: 207 journals)
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- Pengkondisi Sinyal RTD Presisi pada Terowongan Angin Indonesian Low-Speed
Tunnel
Authors: Muhamad Muflih, Munawar Agus Riyadi, Ivranza Zuhdi Pane, Franky Surya Parulian
Pages: 44 - 51
Abstract: The temperature of the Indonesian Low-Speed Tunnel (ILST) wind tunnel test section was measured using a Pt100-type Resistance Temperature Detector (RTD) sensor. With the upgrade of the Indonesian Low-Speed Tunnel - Data Acquisition and Reduction System (ILST-DARS) using Ethernet communication, an integrated RTD linearization circuit was designed with the Conditioning Unit (CU) Mk3 to replace the Newport 267B 16-bit parallel and DAS-Hub as the current RTD interface. In this research, the design of the signal conditioner uses the RTD_Linearization_v7.xls program from Texas Instruments, the LTspice simulator software, and the AMP01E precision instrumentation amplifier. Based on the calibration results in the range of 20 – 50 0C, this signal conditioner has an average deviation value of 0.38 0C (1.31%). In the wind tunnel speed variation testing with a range of 30 – 65 m/s, the RTD signal conditioner had an average deviation of 0.41 K (0.14%). The Repeatability Test procedure was carried out at a wind speed of 65 m/s with an angle of attack for the test model from -90 to 200 and data were collected 10 times at each angle. The average deviation of temperature against variations in the angle of attack of the test model in this procedure is 0.25 K (0.08%) and the average deviation of wind speed against variations in the angle of attack of the test model is 0.03 m/s (0.04%).
PubDate: 2023-01-27
DOI: 10.15294/jte.v14i2.39415
Issue No: Vol. 14, No. 2 (2023)
- Performance Degradation Evaluation of a Lithium-Ion Battery from Multiple
SoC Measurements
Authors: Riza Hadi Saputra, Adi Mahmud Jaya Marindra, Muhammad Agung Nursyeha, Dwi Kurnia Agung Fariyani
Pages: 52 - 58
Abstract: Lithium-Ion (Li-ion) battery is essential in today's energy systems and electric vehicles (EVs). Although Li-ion battery can be charged quickly and have a high energy density, it has several drawbacks, including the rapid degradation of battery performance, especially in terms of battery capacity. Therefore, evaluating its performance degradation is necessary to understand its characteristics. In this paper, the performance degradation of a Li-ion battery is monitored and evaluated from multiple SoC measurements. A simple and low-cost experimental setup consisting of sensors, a microcontroller, and a PC is developed to measure and record the real-time data of Li-ion battery voltage and current. Then, the battery state of charge (SoC) is determined using the Coulomb Counting method, which is based on the incoming and outgoing currents of the battery. As a result, this study derives three parameters that indicate the performance degradation of a Li-ion battery, i.e., SoC, battery capacity, and discharge time. From multiple direct measurements with constant load and C20 discharge process, the minimum SoC value increases from 11% to 18%, while battery capacity decreases from 8.8Ah to 8.3 Ah and, discharge time decreases from 16.9 hours to 16.4 hours. All of those parameters indicate a degradation of around 7% in battery performance. Therefore, this research paves the way for finding a solution to mitigate the quick performance degradation of Li-ion batteries.
PubDate: 2023-03-10
DOI: 10.15294/jte.v14i2.40226
Issue No: Vol. 14, No. 2 (2023)
- Classroom Occupancy Monitoring System using IoT Device and the k-Nearest
Neighbors Algorithm
Authors: Yarnish Dwi Sagita Fidarliyan, Agung Budi Prasetijo, Dania Eridani
Pages: 36 - 43
Abstract: The occupancy monitoring system is one of the substantial aspects of building management. Through monitoring the occupancy in the area in a building, the obtained information can be used for building management purposes such as controlling indoor area air quality and improving building security. Some technologies such as video surveillance cameras, Radio Frequency Identification (RFID), and motion sensors have been used in the occupancy monitoring system. However, those technologies pose several disadvantages including privacy concerns and limited information generated. A classroom occupancy monitoring system using an Internet of Things (IoT) device and the k-Nearest Neighbors (k-NN) algorithm was built to monitor classroom occupancy by classifying the number of occupants based on classroom environmental data into occupancy levels by using the k-NN classifier model. By utilizing IoT devices, CO2, temperature, and humidity data in a naturally ventilated classroom were recorded using the MQ-135 and BME280 sensors, as well as WiFi-based NodeMCU, was used to distribute data to the cloud. The collected data were trained and tested by the k-NN algorithm to produce a k-NN classifier model. From the tests conducted, the performance of the k-NN classifier model in classifying the number of occupants into occupancy levels resulted in an accuracy of 88%. In addition, the proposed system also produces a web-based classroom occupancy monitoring application that has been integrated with the k-NN classifier model so the classification can be done for real-time data and monitored directly.
PubDate: 2022-12-30
DOI: 10.15294/jte.v14i2.37141
Issue No: Vol. 14, No. 2 (2022)
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