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The P-V curve of photovoltaic system exhibits multiple peaks under various conditions of function and changes in meteorological conditions which reduce the effectiveness of conventional maximum power point tracking (MPPT) methods. Artificial Neural Network (ANN) is one of soft computing used for learning, modeling, and analyzing a very complicated phenomenon. Furthermore, there is an algorithm based...
This paper presents an artificial neural network (ANN) model based design for Hénon chaotic systems, and its equivalent hardware model for hardware co-simulation using Field Programmable Gate Arrays (FPGA). Chaotic generators can be used for the study of chaotic behaviors of brain activities captured by Electroencephalogram (EEG). The ANN model is designed with different fixed-point data format and...
To understand the behavior of moving entities in a given environment, one should be capable of predicting their motion, that is, to model their dynamics. In a setting where different behaviors can arise, one can assume that each of them corresponds to different motivational states of observed entities. Here, those motivations are understood as goal positions or spots where entities seek to arrive...
Artificial intelligence is a new subject which has been applied in many fields. In recent years, many countries have been promoting quality education, and promotion of the culture of all the students and the overall quality of research and solve problems of practical ability, and multifaceted Intelligent students also advocated the development of a variety of intelligence, and that the goal of quality...
In this paper we evaluate three state-of-the-art neural-network-based approaches for large-scale video classification, where the computational efficiency of the inference step is of particular importance due to the ever increasing amount of data throughput for video streams. Our evaluation focuses on finding good efficiency vs. accuracy tradeoffs by evaluating different network configurations and...
Recent developments in wireless communications have led to numerous applications that require accurate positioning. Further, people are becoming more dependent on location based services. Many of these services are located indoors. Because of the complexity of indoor channels, traditional algorithms cannot accurately estimate the signal attenuation, which degrades positioning accuracy. To overcome...
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...
In advanced wireless communication systems that require spectrally efficient modulation schemes, the modulated signal with a high peak-to-average power ratio (PAPR) drives the power amplifier (PA) to operate near the saturation region and introduces serious nonlinearity of the PA. Digital predistortion (DPD) is one of the most promising techniques for PA linearization. In this paper, we propose a...
This paper proposes to examine the possible uses of Artificial Neural Networks (ANN) to aid the landing of an Unmanned Aerial Vehicle (UAV) on a ship. Three distinct phases are proposed. The dataset required for training and testing was produced by simulating a ship's motion at sea using Unity. Phase 1 converts video images from a UAV on-board camera to numeric data. Phase 2 utilizes Phase 1 data...
The performance conditions of steam turbine regenerative system have important influence on the safety and economy of the units. It is of great significance to doing the research on the performance monitoring of the regenerative system to ensure the safe and economical operation of the whole coal-fired power plants. In view of the shortcomings of the complexity of traditional performance monitoring...
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...
One of the key technologies to take full advantage of wind power is to establish a wind turbine (WT) generator output estimation system with high accuracy. The static feed forward artificial neural network is widely used in previous WT generator output estimation technology. However, this method has many problems such as local minimization, a lack of dynamics, edge effect, and multi-correlation. To...
Due to the finite restricted ground TT&C resources, more and more conflicts appear caused by the increasing TT&C requirements of multi-satellite. The priority determination of TT&C arcs is affected by many factors. It is an important criterion of conflicts elimination for resources scheduling. This paper analyzes the main factors in practice, and put forward a method of using artificial...
This paper proposes a hybrid negative correlation learning in which each individual neural network in an neural network ensemble would either learn a data point by negative correlation learning or learn to be different to the neural network ensemble. The implementation is through randomly splitting the training set into two subsets for each individual neural network in learning. On one subset of the...
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,...
This paper discusses a methodology to construct a synthetic dataset using realistic geophysical data and the L-MEB model to compute synthetic brightness temperatures (Tb's) and to train a Neural Network (NN) for global retrievals of soil moisture (SM). The trained NNs are applied to real Tb's measured by the Soil Moisture and Ocean Salinity (SMOS) satellite (L-MEB NN). The objective is twofold. First,...
An algorithm using in situ measurements for training a neural network (NN) to retrieve soil moisture (SM) from SMOS observations is discussed. The in situ data are measurements of the SM content in the 0–5 cm depth layer from the SCAN, SNOTEL and USCRN networks. It is shown that this approach can be used to retrieve SM at continental scale in North America. The NN retrieval (NNinSitu) is evaluated...
Understanding the generalization properties of deep learning models is critical for their successful usage in many applications, especially in the regimes where the number of training samples is limited. We study the generalization properties of deep neural networks (DNNs) via the Jacobian matrix of the network. Our analysis is general to arbitrary network structures, types of non-linearities and...
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