We present a joint forecasting-scheduling (JFS) system, to be implemented at an IoT Gateway, in order to alleviate the Massive Access Problem of the Internet of Things. The existing proposals to solve the Massive Access ...

Most of the biological systems including gene regulatory networks can be described well by ordinary differential equation models with rational non-linearities. These models are derived either based on the reaction kinetics ...

Chaotification is the process of making an originally non-chaotic system being chaotic by applying a suitable control input. The aim of the study was to create a chaotic mixing mechanism using a kitchen type mixer and to ...

This paper presents an analysis on searching the optimal values of the system identification and tracking window lengths, and regularization parameter for the online learning NARMA controller algorithm. Both window lengths ...

This paper presents a Learning Feedback Linearization (LFL) based Nonlinear Auto-Regressive Moving Average (NARMA) controller design for a ROTary inverted PENdulum (ROTPEN) plant. The proposed NARMA controller comprises ...

This paper proposes a method to reduce false positive fire alarms by fusing data from different sensors using a specific machine learning model. We design an electronic circuit with 6 sensors to detect 7 physical sensory ...

Microgrids enable the integration of distributed energy resources with high renewable penetration into the main power grid. In this study, a microgrid problem that takes into account the stochastic nature of the net load, ...

This paper presents a novel stable adaptive controller scheme for Furuta Pendulum via nonlinear auto-regressive moving-average based plant identification. During online learning for the developed controller, input-output ...

We present a comparative study of Autoregressive Integrated Moving Average (ARIMA), Multi-Layer Perceptron (MLP), 1-Dimensional Convolutional Neural Network (1-D CNN), and Long-Short Term Memory (LSTM) models on the problem ...

Deep reinforcement learning (DRL) exhibits a promising approach for controlling humanoid robot locomotion. However, only values relating sensors such as IMU, gyroscope, and GPS are not sufficient robots to learn their ...