scholarly journals Series Arc Fault Breaker in Low Voltage Using Microcontroller Based on Fast Fourier Transform

2021 ◽  
Vol 9 (2) ◽  
pp. 239-251
Author(s):  
Dimas Okky Anggriawan ◽  
Audya Elisa Rheinanda ◽  
Muhammad Khanif Khafidli ◽  
Eka Prasetyono ◽  
Novie Ayub Windarko
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Real Time ◽  
High Speed ◽  
Low Voltage ◽  
Resistive Load ◽  
Gas Ionization ◽  
Primary Driver ◽  
Experimental Process ◽  
Ac Arc

Series Arc Fault is one of the disturbances of arcing jump is caused by gas ionization between two ends of damaged conductors or broken wire forming a gap in the insulator. Series arc fault is the primary driver of electrical fire. However, lack of knowledge of the disturbance of series arc fault causes the problem of electrical fire not be mitigated. Magnitude current is not capable to detect of series arc fault. Therefore, this paper proposes fast fourier transform (FFT) to detect series AC arc fault in low voltage using microcontroller ARM STM32F7NGH in real time. A cheap and high speed of microcontroller ARM STM32F7NGH can be used for FFT computation to transform signal in time domain to frequency domain. Moreover, in this paper, protection of series AC arc fault is proposed in the real time mode. In this experimental process, some various experiments are tested to evaluate the reliability of FFT and protection with various load starts from 1 A, 2 A, 3 A, 4 A in resistive load. The result of this experiment shows that series AC arc fault protection with STM32F7 microcontroller and FFT algorithm can be utilized to ensure series AC arc fault properly.

scholarly journals A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 234 ◽  
Author(s):  
Hyun Yoo ◽  
Soyoung Han ◽  
Kyungyong Chung
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Real Time ◽  
Normal Control ◽  
Input Data ◽  
Deep Neural Network ◽  
Pattern Mining ◽  
F Measure

Recently, a massive amount of big data of bioinformation is collected by sensor-based IoT devices. The collected data are also classified into different types of health big data in various techniques. A personalized analysis technique is a basis for judging the risk factors of personal cardiovascular disorders in real-time. The objective of this paper is to provide the model for the personalized heart condition classification in combination with the fast and effective preprocessing technique and deep neural network in order to process the real-time accumulated biosensor input data. The model can be useful to learn input data and develop an approximation function, and it can help users recognize risk situations. For the analysis of the pulse frequency, a fast Fourier transform is applied in preprocessing work. With the use of the frequency-by-frequency ratio data of the extracted power spectrum, data reduction is performed. To analyze the meanings of preprocessed data, a neural network algorithm is applied. In particular, a deep neural network is used to analyze and evaluate linear data. A deep neural network can make multiple layers and can establish an operation model of nodes with the use of gradient descent. The completed model was trained by classifying the ECG signals collected in advance into normal, control, and noise groups. Thereafter, the ECG signal input in real time through the trained deep neural network system was classified into normal, control, and noise. To evaluate the performance of the proposed model, this study utilized a ratio of data operation cost reduction and F-measure. As a result, with the use of fast Fourier transform and cumulative frequency percentage, the size of ECG reduced to 1:32. According to the analysis on the F-measure of the deep neural network, the model had 83.83% accuracy. Given the results, the modified deep neural network technique can reduce the size of big data in terms of computing work, and it is an effective system to reduce operation time.

Acoustic Analysis of Cylindrical, Conical and Worm Gears

2019 ◽  
Vol 957 ◽  
pp. 211-220
Author(s):  
Cornel Cristian Enciu ◽  
Cristian Tarba ◽  
Cristian Barbulescu
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Real Time ◽  
Acoustic Analysis ◽  
Single Channel ◽  
Intensity Level ◽  
Analog Filter ◽  
Transform Method ◽  
Worm Gears ◽  
Electric Drill

The paper aims to determine the characteristic frequencies of an electric drill, by measuring and analyzing comparatively the variations of the acoustic intensity level, using the Fast Fourier Transform method (FFT). This was applied using a stand which had been specifically developed for the presented work. In two channel and multichannel systems, digital methods have been used for the calculation of cross properties as they were the only practical methods. Using digital techniques has gained considerable ground, being nowadays applied to problems once solved by resorting to analog methods. The increasing use of Fast Fourier Transform methods is found in single channel real time narrow band measurements and the Digital Filtering is replacing the Analog Filter bank which was used as the basis for real time analyzers operating with constant percentage bandwidth.

scholarly journals Aplikasi Tuner Alat Musik Sasando Real-Time Menggunakan Teknik Fast Fourier Transform (FFT) dan Harmonic Product Spectrum (HPS)

2018 ◽  
pp. 31-37
Author(s):  
Amin Ajaib Maggang ◽  
Sarlince O. Manu ◽  
Molina O. Odja
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Real Time ◽  
Product Spectrum

ABSTRAK Tujuan dari penelitian ini adalah untuk mendesain suatu aplikasi tuner sasando yang memiliki tingkat akurasi yang tinggi dalam mendeteksi frekuensi fundamental dari jenis sasando 32 senar menggunakan Algoritma Fast Fourier Transform (FFT) dan Harmonic Product Spectrum (HPS). Tingkat akurasi yang tinggi ditentukan dari resolusi frekuensi yang tinggi. Menurut Lourde, M (2009), kemampuan telinga manusia untuk membedakan bunyi adalah pada resolusi 0.5 Hz. Manusia tidak mampu membedakan bunyi jika resolusi atau selisih dua frekuensi yang berdekatan lebih kecil dari 0.5 Hz. Oleh karena itu, aplikasi yang didesain harus mampu memberikan resolusi 0.5 Hz. Berdasarkan hasil simulasi, Aplikasi Tuner sasando yang telah dibangun mampu memberikan output resolusi 0.5 Hz. Selain itu, aplikasi yang dibangun juga dapat menampilkan spektrum frekuensi dan mampu memberikan informasi apabila frekuensi dari senar yang sedang dituning sudah tepat pada frekuensi fundamentalnya. Tidak hanya itu, apabila frekuensinya masih kurang atau sudah melewati frekuensi dasarnya, maka akan ditampilkan juga pada outputnya.

scholarly journals A High Speed Redundant IO Bus for Energy Power Controller System

2021 ◽  
Vol 2113 (1) ◽  
pp. 012024
Author(s):  
Qinghui Lou ◽  
Liguo Sun ◽  
Haisong Lu ◽  
Weifeng Xu ◽  
Zhebei Wang ◽  
...  
Keyword(s):  
Operating System ◽  
Real Time ◽  
High Throughput ◽  
High Speed ◽  
Low Voltage ◽  
Differential Signal ◽  
Real Time Operating System ◽  
Power Controller ◽  
Data Feedback ◽  
Memory Monitoring

Abstract This paper designs and implements a High Speed Redundant IO Bus for Energy Power Controller System. The physical layer adopts multi-point low-voltage differential signal standard. This bus has the characteristics of high real-time, high throughput and easy expansion. The controller communicates with IO module by A/B bus alternately, monitors link status in real time and collects IO module data. Non real time slots can be used to control non real time messages for IO modules such as time synchronizing and memory monitoring. The controller ARM core runs QNX real-time operating system, and transmits the message needed to communicate with IO modules to the FPGA through DMA. After receiving the message, the FPGA parses the message and automatically fills in the CRC check code and frame end flag at the end of the message. When the FPGA receives the data feedback from the IO module, it performs CRC verification. If the verification passes, it fills the corresponding module receiving buffer. Otherwise, it fills the CRC verification error flag in the register of the corresponding IO module to reduce the load of the arm core.

Real Time Harmonic Load Identification Based on Fast Fourier Transform and Artificial Neural Network

2021 ◽  
Vol 16 (3) ◽  
pp. 220
Author(s):  
Dimas Okky Anggriawan ◽  
Aidin Amsyar ◽  
Aji Akbar Firdaus ◽  
Endro Wahjono ◽  
Indhana Sudiharto ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Real Time ◽  
Harmonic Load ◽  
Load Identification ◽  
Time Harmonic ◽  
Artificial Neural
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