Performance metrics such as mean-bit-error rate and probability of fade for freespace optical communication (FSOC) applications using intensity-modulation direct detection are theoretically calculated based on probabi...
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Performance metrics such as mean-bit-error rate and probability of fade for freespace optical communication (FSOC) applications using intensity-modulation direct detection are theoretically calculated based on probability density functions (PDFs) describing irradiance fluctuations. Theoretical calculations using common PDF models can result in significant errors in prediction of performance metrics. In particular, these models do not consider the change in skewness of the distribution as the aperture size increases, and often positively skewed distributions (right tailed) are used to model scenarios where the true statistics are negatively skewed (left tailed). We evaluate the magnitude of errors in the prediction of bit-error rate and probability of fade based on simulation data for a collimated Gaussian beam in a realistic FSOC scenario for two strengths of turbulence and varying aperture sizes. The PDF models considered are lognormal, gamma-gamma, inverse-Gaussian-gamma, fractional exponential, exponentiated-Weibull, 3-parameter-Weibull, and normal distributions. (C) 2020 Society of Photo-Optical Instrumentation Engineers (SPIE)
One problem with discriminant analysis of microarray data is representation of each sample by a large number of genes that are possibly irrelevant, insignificant or redundant. Methods of variable selection are, theref...
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One problem with discriminant analysis of microarray data is representation of each sample by a large number of genes that are possibly irrelevant, insignificant or redundant. Methods of variable selection are, therefore, of great significance in microarray data analysis. To circumvent the problem, a new gene mining approach is proposed based on the similarity between probability density functions on each gene for the class of interest with respect to the others. This method allows the ascertainment of significant genes that are informative for discriminating each individual class rather than maximizing the separability of all classes. Then one can select genes containing important information about the particular subtypes of diseases. Based on the mined significant genes for individual classes, a support vector machine with local kernel transform is constructed for the classification of different diseases. The combination of the gene mining approach with support vector machine is demonstrated for cancer classification using two public data sets. The results reveal that significant genes are identified for each cancer, and the classification model shows satisfactory performance in training and prediction for both data sets. (C) 2009 Elsevier B.V. All rights reserved.
A tabulated, pseudo-turbulent probability density function (PDF) model for premixed combustion is proposed. The Linear-Eddy Model (LEM) is used to construct the PDFs for a temperature-based progress variable in a prem...
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A tabulated, pseudo-turbulent probability density function (PDF) model for premixed combustion is proposed. The Linear-Eddy Model (LEM) is used to construct the PDFs for a temperature-based progress variable in a premixed, turbulent methane/air V-flame produced by the Cambridge slot burner. As a second case study, the LEM PDFs are similarly compared to PDFs extracted from Direct Numerical Simulations (DNS) of a turbulent premixed flame. LEM demonstrates the ability to reproduce the salient features from experimental and DNS PDFs;moreover, it is able to better capture turbulent effects than previously suggested laminar flamelet PDF models. The Scalar Dissipation Rate (SDR) for premixed combustion is likewise investigated. The stochastic nature of LEM enables it to mimic the overall behaviors of turbulent reactions inexpensively and qualitatively. Crucially, LEM appears to be well suited for the preprocessing tabulation of PDF and SDR models for a number of premixed combustion simulation strategies.
Energy consumption is one of the important issues in wireless sensor network that rely on non chargeable batteries for power. Also, the sensor network has to maintain a desired sensing coverage area along with periodi...
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Energy consumption is one of the important issues in wireless sensor network that rely on non chargeable batteries for power. Also, the sensor network has to maintain a desired sensing coverage area along with periodically sending of the sensed data to the base station. Therefore, coverage and the lifetime are the two important issues that need to be addressed. Effective deployment of wireless sensors is a major concern as the coverage and lifetime of any wireless sensor network depends on it. In this paper, we propose the design of a probability density function (PDF) targeting the desired coverage, and energy efficient node deployment scheme. The suitability of the proposed PDF based node distribution to model the network architecture considered in this work has been analyzed. The PDF divides the deployment area into concentric coronas and provides a probability of occurrence of a node within any corona. Further, the performance of the proposed PDF is evaluated in terms of the coverage, the number of transmissions of packets and the lifetime of the network. The scheme is compared with the existing node deployment schemes based on various distributions. The percentage gain of the proposed PDF based node deployment is 32% more than that when compared with the existing schemes. Thus, the simulation results obtained confirm the schemes superiority over the other existing schemes.
Strict consensus is difficult to be implemented due to the stochastic behavior of multi-agent systems (MASs), so a new concept, distribution consensus, is proposed here to keep the agents' consensus in the stochas...
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Strict consensus is difficult to be implemented due to the stochastic behavior of multi-agent systems (MASs), so a new concept, distribution consensus, is proposed here to keep the agents' consensus in the stochastic sense, i.e., the output errors do not converge to a fixed value but follow a desired distribution function. The appropriate control protocol, with the output error probability density function (PDF) as the target, is designed based on the combination of sliding mode control and PDF compensation. Sliding mode control is the core part to ensure the whole system's stability, and the PDF compensator is used to compensate the random variation and reduce the chattering effect, respectively. In order to realize the complete control in real time, the PDF compensator is modeling by a radial basis function (RBF) neural network and its optimal control law is calculated by the iterative training of RBF network weights. Finally, the effectiveness of the proposed method is verified by MASs simulations with three different communication topologies. The PDF compensator can greatly improve the consensus effect for the nonlinear stochastic MASs. (C) 2020 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
We develop a new sparse kernel density estimator using a forward constrained regression framework, within which the nonnegative and summing-to-unity constraints of the mixing weights can easily be satisfied. Our main ...
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We develop a new sparse kernel density estimator using a forward constrained regression framework, within which the nonnegative and summing-to-unity constraints of the mixing weights can easily be satisfied. Our main contribution is to derive a recursive algorithm to select significant kernels one at time based on the minimum integrated square error (MISE) criterion for both the selection of kernels and the estimation of mixing weights. The proposed approach is simple to implement and the associated computational cost is very low. Specifically, the complexity of our algorithm is in the order of the number of training data N, which is much lower than the order of N-2 offered by the best existing sparse kernel density estimators. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with comparable accuracy to those of the classical Parzen window estimate and other existing sparse kernel density estimators. (C) 2013 Elsevier B.V. All rights reserved.
A new method, probability density function (PDF), is proposed for evaluating the state of health (SOH) of electric storage batteries by analyzing the charge/discharge (C/D) data. First, a comparison of the PDF method,...
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A new method, probability density function (PDF), is proposed for evaluating the state of health (SOH) of electric storage batteries by analyzing the charge/discharge (C/D) data. First, a comparison of the PDF method, the cyclic voltammogram (CV), incremental capacity analysis (ICA) and differential voltage analysis (DVA) is provided. The mathematical basis of the four methods are in agreement. Moreover, the PDF method and the ICA/DVA have an equivalence verified by mathematical derivation. Thus the results acquired by the PDF and the ICA/DVA are quite similar. LiFePO4 and LiMn2O4 batteries are tested to demonstrate the PDF method. Coin cells are tested by the PDF and the CV methods. Results show that the PDF curves and the CV curves have similar shapes. In addition, durability tests are conducted on four commercial batteries to analyze the aging regularities using the PDF method. The PDF results show that the height of the peak reduces as the battery capacity fades. Employing the regularity of peak height reduction with battery aging, an algorithm is proposed to evaluate the SOH online. The PDF method extends the application of the ICA/DVA method. The PDF algorithm is promising to be used in the online SOH evaluation of lithium-ion batteries. (C) 2013 Elsevier B.V. All rights reserved.
probability density functions (PDFs) are normally used to describe wind speed distribution for the proper selection of wind turbines in a given location. The identification of a suitable PDF is fundamental for accurat...
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probability density functions (PDFs) are normally used to describe wind speed distribution for the proper selection of wind turbines in a given location. The identification of a suitable PDF is fundamental for accurately assessing the wind energy potential and designing the wind farms. To achieve this objective, the use of a mixture of two truncated normal distributions (MTTND), defined for v >= .0 and obtained by linearly combining two normal distributions with different means and variances, is proposed in this work for the representation of the wind speed PDF. The distribution is a function of five parameters, does not require a high computational burden and allows the representation of wind calm hours (v = 0). The use of the MITND allows an accurate estimation to be obtained of the experimental discrete distribution of the probabilitydensity and cumulative probability, and the characteristic statistical quantities used to estimate the available energy and the performance indicators in the selection of both the site and wind turbine. The validity of the use of the MTIND was verified by comparison with the most widespread PDFs in the scientific literature: Weibull, Rayleigh, lognormal, gamma, inverse Gaussian and Burr. This comparison was developed using experimental wind speed data relating to five Italian locations and a location in Colorado (USA) belonging to the National Renewable Energy Laboratory. For each location, the parameters of each PDF were obtained with the least squares non-linear regression method. The results of the comparisons, in terms of the coefficient of determination R-2 and root mean square error (RMSE) for goodness of fit and in terms of relative error in the calculation of the statistical quantities, show that the use of the MTIND gives rise to greater accuracy than a conventional wind speed PDF. (C) 2017 Elsevier Ltd. All rights reserved.
The effective prediction of remaining useful life is essential to realize system failure diagnosis and health management. The existing researches often assume that the degradation model is constant or the degradation ...
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The effective prediction of remaining useful life is essential to realize system failure diagnosis and health management. The existing researches often assume that the degradation model is constant or the degradation process is measurable. The accurate degradation model, however, usually can not be established, and the parametric variation and measurement error of the degradation process are unavoidable, which makes it hard to obtain the exact value for predicting the remaining useful life. Regarding this problem, on basis of the concept of first failure time, a real-time probability density function is derived for the Wiener degradation process with the uncertainty of parameters, the stochasticity of degradation process and the randomness of measurement error. The main steps are as follows: firstly, the degradation model with three kinds of uncertainties is established, and then the stochastic degradation state and the parameters of the uncertainty model are estimated by fusion Kalman/UFIR filter;then, the analytical expression of the probability density function of remaining useful life is deduced. Finally, the correctness and effectiveness of the proposed method are verified by a group of comparison experiments and Monte Carlo simulations.
In the present work a new version of the Probabilistic Transformation Method (PTM) has been reported for the study of linear systems subjected to static random loads. Even if this application could appear trivial, it ...
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In the present work a new version of the Probabilistic Transformation Method (PTM) has been reported for the study of linear systems subjected to static random loads. Even if this application could appear trivial, it allows to find some exact results, difficulty obtainable by other approaches. In particular, some interesting results have been obtained in the case of uniformly distributed random loads. For a generic vector of random loads this version of the PTM has allowed to obtain the characteristic function (cf) of any response elements in a very simple and effective way. (c) 2013 Elsevier Ltd. All rights reserved.
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