Smart Detection Of AC Series Arc Fault On Home Voltage Line Based On Fast Fourier Transform And Artificial Neural Network

2019 ◽  
Author(s):  
Muhammad Alwi Abdulrachman ◽  
Eka Prasetyono ◽  
Dimas Okky Anggriawan ◽  
Anang Tjahjono
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Artificial Neural

scholarly journals Intelligent Automatic V6 and V8 Engine Sound Detection Based on Artificial Neural Network

2019 ◽  
Vol 130 ◽  
pp. 01035 ◽  
Author(s):  
Wenny Vincent ◽  
Astuti Winda ◽  
Mahmud Iwan Solihin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Intelligent System ◽  
Recorded Sound ◽  
Sound Detection ◽  
Testing Accuracy ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The sound of V6 or V8 engines has its own cultural appeal that cannot be replaced by the modern four-cylinder naturally aspirated or turbocharged engines. The identification of the type of engine by the sound is not an easy task, even for the professionals. An intelligent system that can identify V6 to V8 engines from various cars will give an insight of the features in the engine sounds that characterized the two different engines. In this work, an Artificial Neural Network (ANN) approach is applied for identifying cylinder of the engine based on the engine sound identification is proposed. The recorded sound of the engine is then processed in order to get some features which later be used in the proposed system. The Fast Fourir Transform (FFT) is adopted as a feature and later used as input to the Artificial Neural Network (ANN) based identifier. The Experimental results confirm the effectiveness of the proposed intelligent automatic six cylinder and eight cylinder engine based on Fast Fourier Transform (FFT) and Artificial Neural Network (ANN), since it resulting the training and testing accuracy of 100 % and 100 %, respectively.

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

scholarly journals KLASIFIKASI SUARA MANUSIA KE DALAM SOPRAN, MEZZO SOPRAN, ALTO, TENOR, BARITON, BASS DENGAN SELF ORGANIZING MAP

2015 ◽  
Vol 11 (1) ◽  
Author(s):  
Andreas Saputra ◽  
Sri Suwarno ◽  
Lukas Chrisantyo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Unsupervised Learning ◽  
Self Organizing Map ◽  
Artificial Neural ◽  
Self Organizing

Self Organizing Map adalah metode jaringan syaraf tiruan (Artificial Neural Network) yang biasa digunakan untuk melakukan proses klasifikasi dengan sifat unsupervised learning atau pelatihan tak terbimbing. Cluster yang akan digunakan akan ditentukan secara manual hanya saja dalam prosesnya data yang masuk akan dikelompokan secara otomatis tanpa adanya intevensi dari sistem. Penelitian ini menerapkan Self Organizing Map untuk melakukan klasifikasi data berupa rekaman suara dengan format file WAV karena merupakan format audio yang belum terkompresi ke dalam sopran, mezzo sopran, alto, tenor, baritone, dan bass. Dalam pengambilan data untuk input melalui proses preemphasis, frame, blocking, dan windowing sebelum dirubah menjadi sinyal diskrit dengan Fast Fourier Transform. Data berupa rata-rata magnitude menjadi input dalam sistem klasifikasi Self Organizing Map. Dalam penelitian ini hasil yang didapat belum sesuai dengan harapan karena data tidak mengelompok dengan baik.Self Organizing Map adalah metode jaringan syaraf tiruan (Artificial Neural Network) yang biasa digunakan untuk melakukan proses klasifikasi dengan sifat unsupervised learning atau pelatihan tak terbimbing. Cluster yang akan digunakan akan ditentukan secara manual hanya saja dalam prosesnya data yang masuk akan dikelompokan secara otomatis tanpa adanya intevensi dari sistem. Penelitian ini menerapkan Self Organizing Map untuk melakukan klasifikasi data berupa rekaman suara dengan format file WAV karena merupakan format audio yang belum terkompresi ke dalam sopran, mezzo sopran, alto, tenor, baritone, dan bass. Dalam pengambilan data untuk input melalui proses preemphasis, frame, blocking, dan windowing sebelum dirubah menjadi sinyal diskrit dengan Fast Fourier Transform. Data berupa rata-rata magnitude menjadi input dalam sistem klasifikasi Self Organizing Map. Dalam penelitian ini hasil yang didapat belum sesuai dengan harapan karena data tidak mengelompok dengan baik.

Non-Invasive Optical Blood Glucose Measurement based on Discrete Fourier Transform and Fast Artificial Neural Network: Fasting Normal Glucose Participants Case Study

2019 ◽  
Author(s):  
Renan Prasta Jenie ◽  
Evy Damayanthi ◽  
Irzaman Irzaman ◽  
Rimbawan Rimbawan ◽  
Dadang Sukandar ◽  
...  
Keyword(s):  
Neural Network ◽  
Clinical Trial ◽  
Artificial Neural Network ◽  
Fourier Transform ◽  
Blood Glucose ◽  
Discrete Fourier Transform ◽  
Glucose Measurement ◽  
Non Invasive ◽  
Artificial Neural

A prototype non-invasive blood glucose level measurement optical device (NI-BGL-MOD) has been developed. The NI-BGL-MOD uses a discrete Fourier transform (DFT) method and a fast artificial neural network algorithm to optimize device performance. The appropriate light-emitting diode for the sensory module was selected based on near-infrared spectrophotometry of a blood glucose model and human blood. DFT is implemented in an analog-to-digital converter module. An in vitro trial using the blood glucose model along with a clinical trial involving 110 participants were conducted to evaluate the performance of the prototype. The root-mean-square error of the prototype was 10.8 mg/dl in the in vitro trial and 3.64 mg/dl in the clinical trial, which is lower than the ISO-15197:2016 mandated value of 10 mg/dl. In each trial, consensus error grid analysis indicated that the measurement error was within the safe range. The sensitivity and specificity of the prototype were 0.83 (0.36, 1.00) and 0.90 (0.55, 1.00) in the in vitro trial and 0.81 (0.75, 0.85) and 0.83 (0.78, 0.87) in the clinical trial, respectively. In general, the proposed NI-BGL-MOD demonstrated good performance than gold-standard measurement. Key words: Non-invasive blood glucose measurement, optical device, discrete Fourier transform, multi-formulatric regression, fast artificial neural network

Sign in / Sign up

Export Citation Format

Share Document