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Article is devoted to the system development allowing to restore the volume of the left ventricle of heart, to estimate final systolic and diastolic volumes on the basis of the sequence of MRT-images from a parasternal position of a short axis in the automatic mode. The realized system was built on convolutional neural networks, 500 patients were used for training, for testing 200.
This paper presents a nearest neighbor partitioning method designed to improve the performance of a neural-network classifier. For neural-network classifiers, usually the number, positions, and labels of centroids are fixed in partition space before training. However, that approach limits the search for potential neural networks during optimization; the quality of a neural network classifier is based...
We propose herein a data-driven dead-zone (DZ) compensation strategy using a model-free Virtual Reference Feedback Tuning (VRFT) approach. The VRFT tuning scheme is accommodated for two controller structures: the first one which explicitly includes a model of the DZ inverse to be identified and the second one which uses a Neural Network (NN) to model the controller to be identified. The main question...
This paper proposes a neural network (NN) approach for demodulating output signals of a nonlinear channel with memory. The feed-forward neural network is trained to learn the appropriate mapping between nonlinear input patterns and source bits. The simulation results provide some evidence that neural networks can learn the effect of nonlinear channels with memory and demodulate the output signal of...
Artificial intelligence is widely used in image processing. Neural networks (NN) were successful used for solving complicated issues due to their capacity of generalization and learning from examples. In this paper some aspects of image compression using artificial neural networks are discussed. The network is used in the feedback loop of the visual servoing system, which aims to control a wheeled...
This paper summarizes the AAIA'17 Data Mining Challenge: Helping AI to Play Hearthstone which was held between March 23, and May 15, 2017 at the Knowledge Pit platform. We briefly describe the scope and background of this competition in the context of a more general project related to the development of an AI engine for video games, called Grail. We also discuss the outcomes of this challenge and...
This project explored fundamental methods to find the factors that can be used in classifying and detecting the type of wood. Whereas, the literatures have been reviewed to determine the algorithms developed. Some experiments have been conducted to analyze the model and system. The experiments are based on artificial neural network (ANN) algorithm that used back propagation and conjugate gradient...
We present detection of various fdters using neural networks usable for our Long wave infrared (LWIR) hyperspectral detection system (HDES). Some reduction techniques are shown, for our aim of the small neural network with small computing requirements. In addition, the filter measurement is usable for calibration and verification of the HDES properties.
Training of artificial neural networks (ANNs) using reinforcement learning (RL) techniques is being widely discussed in the robot learning literature. The high model complexity of ANNs along with the model-free nature of RL algorithms provides a desirable combination for many robotics applications. There is a huge need for algorithms that generalize using raw sensory inputs, such as vision, without...
We report our recent studies on the use of Neural Networks to process the measured Brillouin gain spectrum (BGS) from Brillouin Optical Time Domain Analyzer (BOTDA) and extract temperature information along fiber under test (FUT). Artificial Neural Network (ANN) is trained with ideal Lorentizian BGS before it is used for temperature extraction. Its performance is evaluated by comparison to conventional...
This paper addresses neural network (NN) control of a lower limb exoskeleton for rehabilitation. Both the interaction between human and exoskeleton and external disturbances are considered. The controller is developed based on a combined scheme of repetitive learning control (RLC) and neural networks (NN), where RLC is used to learn periodic uncertainties (the interaction between human and exoskeleton)...
In this paper, we propose a new approach to estimate the gain and the noise figure of EDFAs. This is an important tool for solving the adaptive control of operating point (ACOP) problem in optical amplifiers. The proposal uses an artificial neural network to enable a quick estimation of both amplifiers features requiring a small amount of memory. Results show that the neural network estimator is 80...
This paper examines the application of a deep learning approach to converting night-time images to day-time images. In particular, we show that a convolutional neural network enables the simulation of artificial and ambient light on images. In this paper, we illustrate the design of the deep neural network and some preliminary results on a real indoor environment and two virtual environments rendered...
This paper addresses the band selection of a hyperspectral image. Considering a binary classification, we devise a method to choose the more discriminating bands for the separation of the two classes involved, by using a simple algorithm: single-layer neural network. After that, the most discriminative bands are selected, and the resulting reduced data set is used in a more powerful classifier, namely,...
Early detection of small faults in closed-loop systems is a challenging issue in the fault diagnosis literature. The effect of faults in closed-loop systems will be obscured by a robust feedback control, especially when the controller is coupled with nonlinear uncertainty. In this paper, an approach for rapid detection for small faults in a class of closed-loop uncertain systems is proposed based...
A fast, yet accurate nanoscale IC energy estimation is a design-time desideratum for area-delay-power-reliability optimized circuits and architectures. This paper introduces an IC energy estimation approach, which instead of sequentially propagating workload vectors throughout the circuit, relies on an one time propagation of the workload statistics. To this end, the basic gates need be SPICE pre-characterized...
We present the control system synthesis for the multilink redundant manipulator. Our control system is based on the unique algorithm that includes the novel hybrid method for solving the inverse kinematics problem. This method combines ANFIS-network and iterative refinement. As a result, the control system has high integrative capabilities and is easy to modify for another construction. The manipulator...
Accurate and robust risk prediction methods are of critical importance in calculating insurance costs. In the present paper, we study the case of vehicle insurance and develop a computational intelligence based method for obtaining risk estimates based on the data provided by the client to the insurance company. The method is based on analyzing the contracts, processing the input data, applying classification,...
Various optimal power flow (OPF) algorithms have been utilized in power networks primarily for cost reduction, profit optimization and/or system loss minimization. However, these optimization solvers are either computationally demanding or sensitive to initialization settings and do not guarantee a global optimal. In order to overcome these challenges, this paper proposes an artificial intelligent...
Chemical Reactors, such as continuous stir tank reactor (CSTR) and polymerization reactors (PR) are nonlinear dynamics in nature. Moreover, in practice, uncertainties and disturbances always exist in operation of such kind of reactors. Therefore, the smooth tracking of these reactors is still a challenging task for the researchers. As a core contribution, a prediction interval (PI)-based controller...
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