Pengaturan Kecepatan Motor Induksi Tanpa Sensor Kecepatan Dengan Metoda Direct Torque Control Menggunakan Observer Recurrent Neural Network

This paper describes about development of sensorless control for three phase induction motor speed which is operated by Direct Torque Control (DTC). Induction motor speed is identified by an Observer. Current supply and Stator Voltage are ruquired by Observer to gain Motor Speed Estimation. Observer for motor speed identification is developed using Artificial Neural Network (ANN) Method and Recurrent Neural Network (RNN) learning algorithm. The simulation results using MathLab/Simulink show that on PI controller with Recurrent Neural Network (RNN) observer, there are the overshoot 7,0224%, rise time 0,0125 second and settling time 0,364 second with reference speed 77,9743 rad./sec

Deep recurrent neural networks for building energy prediction

posterThis poster illustrates the development of a deep recurrent neural network (RNN) model using long-short-term memory (LSTM) cells to predict energy consumption in buildings at one-hour time resolution over medium-to-long term time horizons ( greater than or equal to 1 week)

KLASIFIKASI GENDER BERDASARKAN SUARA MENGGUNAKAN RECURRENT NEURAL NETWORK (RNN)

The information technology field continues to progress rapidly. Technological progress is kept in check with such factors as touch, sight, and sound. Each man with another man has a characteristic difference, one that can be seen is by his voice. The processing of sound is an essential concept to all kinds of systems that require human interaction in its daily activities. One of the techniques used in processing speech is classification, which has a direct effect on speech recognition systems. SimpleRNN and LSTM are models of deep learning that can be used to classify sentiment. It can process data in such a sequence as sound, video, and text. These results provide accuracy 90% of the test data and 95% accuracy to the training data.Bidang teknologi informasi terus menerus terjadi perkembangan yang pesat. Perkembangan teknologi tidak lepas dari beberapa faktor seperti sentuhan, penglihatan maupun suara. Setiap manusia dengan manusia yang lain memiliki perbedaan karakteristik, salah satunya yang dapat dilihat yaitu dari suaranya. Pemrosesan suara adalah konsep yang sangat penting untuk semua jenis sistem yang membutuhkan interaksi manusia dalam aktivitas sehari-hari. Adapun  Salah satu teknik yang digunakan dalam pemrosesan ucapan yaitu klasifikasi, yang berdampak langsung pada sistem pengenalan ucapan. SimpleRNN dan LSTM adalah model deep learning yang bisa dipakai untuk mengklasifikasikan sentimen. Metode ini bisa mengolah data dengan berurutan seperti suara, video, dan teks. Hasil penelitian ini mampu memberikan akurasi 90% pada data uji dan akurasi 95% pada data latih

Folk music style modelling by recurrent neural networks with long short term memory units

We demonstrate two generative models created by training a recurrent neural network (RNN) with three hidden layers of long short-term memory (LSTM) units. This extends past work in numerous directions, including training deeper models with nearly 24,000 high-level transcriptions of folk tunes. We discuss our on-going work