Tokamak Design as One Sustainable System

Abstract

Nowadays, remote sensing technology is being used as an essential tool for monitoring and detecting oil spills to take precautions and to prevent the damages to the marine environment. As an important branch of remote sensing, satellite based synthetic aperture radar imagery (SAR) is the most effective way to accomplish these tasks. Since a marine surface with oil spill seems as a dark object because of much lower backscattered energy, the main problem is to recognize and differentiate the dark objects of oil spills from others to be formed by oceanographic and atmospheric conditions. In this study, Radarsat-1 images covering Lebanese coasts were employed for oil spill detection. For this purpose, a powerful classifier, Artificial Neural Network Multilayer Perceptron (ANN MLP) was used. As the original contribution of the paper, the network was trained by a novel heuristic optimization algorithm known as Artificial Bee Colony (ABC) method besides the conventional Backpropagation (BP) and Levenberg-Marquardt (LM) learning algorithms. A comparison and evaluation of different network training algorithms regarding reliability of detection and robustness show that for this problem best result is achieved with the Artificial Bee Colony algorithm (ABC).

Investigation of Stabilities and Instabilities at Tokamak Plasma Behavior and Machine Learning with Big Data

“…When estimating the spline coefficients, no information on levels or trends during the observation period was exchanged if it was possible [23]. In the case of changes in plasma evolution it has been shown that with Bayesian learning and maximum of fuzzy entropy can be reached all probabilistic distributions, including non-Gibbsians distributions [24,25]. As reference papers concerning the diagnostic of high-temperature plasma conditions in tokamak reactors under the effect of plasmawall interaction see [26][27][28][29].…”

Nonuniformly Sampled Data Processing Using LSTM Networks

“…Even though there exist several nonlinear modeling approaches to process the sequential data [9], [10], neuralnetwork-based methods are more practical in general because of their capability of modeling highly nonlinear and complex underlying relations [11]. Especially, recurrent neural networks (RNNs) are employed to process the sequential data since they are able to identify sequential patterns and learn temporal behavior, thanks to their internal memory exploiting past information.…”

An adaptive PID neural network for complex nonlinear system control

“…PID control technique has been widely used in the real control system for its advantages such as simple mechanism and clear physical conception. It is hard to get a precise control performance even while researchers focus on other intelligent control methods, including adaptive control [1,2], fuzzy control [3,4,5], neural network control [6,7,8] and decoupling control [9,10,11] etc.. And then, some mixed control methods are emerging, such as PID neural network. Due to the characteristics of self-learning, self-organizing and self-adaptation, PID neural network would automatically identify the controlled system parameters and automatically adjust the parameters according to the changes in the process parameters.…”