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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 presents a new algorithm for predicting the deviations for local timescale UTC(k) in relation to the coordinated universal time (UTC) scale by means of group method of data handling (GMDH) neural network (NN). A very important element of this algorithm significantly affecting the quality of the obtained prediction is the block of time series preparation for NN. On the basis of carried out...
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...
In the electricity sector, new sides have emerged with the development of technology and the increasing the electric energy need. Today, electricity has become a product that is bought and sold in the market environment. Forecasting which is the first step of plans and planning have become much more important and have been made mandatory for the market participants by energy market regulators. In...
This paper presents a novel frame-pair based method for visual object tracking. Instead of adopting two-stream Convolutional Neural Networks (CNNs) to represent each frame, we stack frame pairs as the input, resulting in a single-stream CNN tracker with much fewer parameters. The proposed tracker can learn generic motion patterns of objects with much less annotated videos than previous methods. Besides,...
Solvents are used in a large number of industries especially in cleaning and cosmetic. Solvents are known to be harmful to human health. Classification of solvent in a product is important to determine the level of hazard that can people faced. In this study, three different solvents, methanol, acetone, and chloroform, are used to obtain binary gas mixtures in a laboratory environment. A gas sensor...
Passwords are frequently used in data encryption and user authentication. Since people incline to choose meaningful words or numbers as their passwords, lots of passwords are easy to guess. This paper introduces a password guessing method based on Long Short-Term Memory recurrent neural networks. After training our LSTM neural network with 30 million passwords from leaked Rockyou dataset, the generated...
Epileptic seizure source identification involves neurologists combing through a substantial amount of data manually, which sometimes takes weeks per patient. This paper presents a methodology for minimizing the amount of data a neurologist has to analyze to identify the seizure focus. The method keeps the neurologist as the final decision maker and aids in the decision making process. It has to be...
Reconfigurable manufacturing systems are susceptible to disturbances because of the characteristics associated with changeover of machine configuration and functionality. An Artificial Neural Network Driven Decision-Making System can mitigate these disturbances, if applied with extensive knowledge of the manufacturing system. This paper introduces a new concept into the paradigm of agile manufacturing...
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