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The focus of control research in wind energy has shifted more and more from individual wind turbines to wind farms due to the potential efficiency improvement in the energy production. In this work, the wind farm control concept “wake redirection” is further investigated. More precisely, the concept of lidar-based closed-loop wake redirection is extended. A lidar-based wake center estimation is assumed...
The paper suggests a new methodology of smart management of computing resources based on set-theoretic explicit and implicit clustering. We have performed frame modeling of knowledge — rules of explicit structures on the basis of explicit clustering, and for fuzzy structures, through implicit clustering. A set of modeling software has been developed for multi-criterion fuzzy management of selection...
We herein propose an evolutionary multi-agent system (EMAS for short) to build an ensemble of surrogates for prediction. In our EMAS, we employ six kinds of basic surrogates, including Gaussian process, Kriging model, polynomial response surface, radial basis function, radial basis function neural network, and support vector regression machine. We define each surrogate as one agent and co-evolve parameters...
Images have become the most popular type of multimedia in the Big Data era. Widely used applications like CBIR underscore the importance of image understanding, especially in terms of semantically meaningful information. Typically, high dimensional image descriptors are embedded to a subspace using a simple linear projection. However, semantic information has a complex distribution in feature space...
It is well recognized that effort estimation is an essential part of successful software management. Among many estimation models, the Case-Base Effort Estimation (CBEE) has been intensively used among researchers and practitioners as a promising model for better and accurate effort prediction. The common challenges with this model are: (1) finding the nearest cases to the new case, (2) selecting...
Sparsity and low-rank structures are recently considered as an important property in various signal processing problems. They have been widely applied in image processing, communication, computer vision, pattern recognition, radar, etc. The main purpose of this paper is to provide a review on sparse representation and low-rank approximation, and their applications in sensor signal processing. Three...
Image procession algorithms for compensation of scattered radiation influence in X-ray imaging were proposed, studied and optimized by numerical simulations. The algorithms include scattering estimation by convolution (superposition) technique, estimation of kernel functions by Monte-Carlo (MC) simulations, determination the optimal number and shape of kernel functions and images segmentation. Determination...
This work proposes a new bottom-up approach for on-line estimation of circuit performance loss due to BTI/HCI effects. Built on the top of device-level models, it takes into account all factors that impact global circuit aging, namely, process, topology, workload, voltage and temperature variations. The proposed model is fed by voltage and temperature monitors that on-line track dynamic variations...
MRI parameter quantification has diverse applications, but likelihood-based methods typically require nonconvex optimization due to nonlinear signal models. To avoid expensive grid searches used in prior works, we propose to learn a nonlinear estimator from simulated training examples and (approximate) kernel ridge regression. As proof of concept, we apply kernel-based estimation to quantify six parameters...
When working with multiple independent mobile robots, each has a different knowledge about its environment, based on its available sensors. This paper proposes an approach that allows working with these different views by independently modeling the common logical relationships between the elements in the scene and the meaning of device-specific sensor data. Using these models, for each robot an estimation...
As an improvement of classical PageRank, the personalized PageRank soon became one of the most major ranking algorithm in graph computation. However, it suffers from a severe efficiency issue and there are many studies focus on enhancing its precision and lowering down its complexity, among which the Mento Carlo random approximation estimation performs well in time and the power iteration method handles...
In this search; Bayesian analysis of the two parameter Lomax distribution reliability has been considered, and the estimation has been obtained under logarithm loss function for three different prior distributions (Quasi distribution, Exponential distribution and Gamma distribution). they has been made under complete data analyses. The simulation study has been conducted to compare by mean squared...
We introduce an adaptive version of directed information to estimate an influence graph over nodes with time-varying features. Originally developed as a generalization of the Shannon Mutual Information for quantifying the effect of feedback in a simple communication channel, directed information (DI) measures the amount of causal, time-varying influence that one node's actions have on another node...
The harmonic chirp signal model has only very recently been introduced for modelling approximately periodic signals with a time-varying fundamental frequency. A number of estimators for the parameters of this model have already been proposed, but they are either inaccurate, non-robust to noise, or very computationally intensive. In this paper, we propose a fast algorithm for the harmonic chirp summation...
In the most general case, source localization has to take into account the radiation pattern of the sources of interest. This is particularly important when the sensors surround the sources, and the sources are anisotropic, as is the case in several applications (EEG, speech, musical instruments, etc.). Cramér-Rao bounds for the joint estimation of the position of a source and its radiation pattern...
In this paper, a method for multi-pitch estimation of stereophonic mixtures of multiple harmonic signals is presented. The method is based on a signal model which takes the amplitude and delay panning parameters of the sources in a stereophonic mixture into account. Furthermore, the method is based on the extended invariance principle (EXIP), and a codebook of realistic amplitude vectors. For each...
This contribution focuses, within the ℓ1-Potts model, on the automated estimation of the regularization parameter balancing the ℓ1 data fidelity term and the TVℓ0 penalization. Variational approaches based on total variation gained considerable interest to solve piecewise constant denoising problems thanks to their deterministic setting and low computational cost. However, the quality of the achieved...
The object of this paper is to introduce a new estimation algorithm specifically designed for the latent high-order autoregressive models. It implements the concept of the filter-based maximum likelihood. Our approach is fully deterministic and is less computationally demanding than the traditional Monte Carlo Markov chain techniques. The simulation experiments and real-world data processing confirm...
When distances between microphone pairs are larger than the half-wavelength of signals, source localization methods using cross-correlation such as time-difference-of-arrival (TDOA), steered response power (SRP) are commonly used in practice. We present here a novel model that expresses microphone pairwise cross-correlations as a sum of autocorrelations of source signals shifted by the relative delays...
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