Real Distributions Research Articles

MIXED PROBABILITY MODELS FOR ALEATORIC UNCERTAINTY ESTIMATION IN THE CONTEXT OF DENSE STEREO MATCHING

Abstract. The ability to identify erroneous depth estimates is of fundamental interest. Information regarding the aleatoric uncertainty of depth estimates can be, for example, used to support the process of depth reconstruction itself. Consequently, various methods for the estimation of aleatoric uncertainty in the context of dense stereo matching have been presented in recent years, with deep learning-based approaches being particularly popular. Among these deep learning-based methods, probabilistic strategies are increasingly attracting interest, because the estimated uncertainty can be quantified in pixels or in metric units due to the consideration of real error distributions. However, existing probabilistic methods usually assume a unimodal distribution to describe the error distribution while simply neglecting cases in real-world scenarios that could violate this assumption. To overcome this limitation, we propose two novel mixed probability models consisting of Laplacian and Uniform distributions for the task of aleatoric uncertainty estimation. In this way, we explicitly address commonly challenging regions in the context of dense stereo matching and outlier measurements, respectively. To allow a fair comparison, we adapt a common neural network architecture to investigate the effects of the different uncertainty models. In an extensive evaluation using two datasets and two common dense stereo matching methods, the proposed methods demonstrate state-of-the-art accuracy.

A FEM-Based 2D Model for Simulation and Qualitative Assessment of Shot-Peening Processes.

Shot peening is one of the most favored surface treatment processes mostly applied on large-scale engineering components to enhance their fatigue performance. Due to the stochastic nature and the mutual interactions of process parameters and the partially contradictory effects caused on the component’s surface (increase in residual stress, work-hardening, and increase in roughness), there is demand for capable and user-friendly simulation models to support the responsible engineers in developing optimal shot-peening processes. The present paper contains a user-friendly Finite Element Method-based 2D model covering all major process parameters. Its novelty and scientific breakthrough lie in its capability to consider various size distributions and elastoplastic material properties of the shots. Therewith, the model is capable to provide insight into the influence of every individual process parameter and their interactions. Despite certain restrictions arising from its 2D nature, the model can be accurately applied for qualitative or comparative studies and processes’ assessments to select the most promising one(s) for the further experimental investigations. The model is applied to a high-strength steel grade used for automotive leaf springs considering real shot size distributions. The results reveal that the increase in shot velocity and the impact angle increase the extent of the residual stresses but also the surface roughness. The usage of elastoplastic material properties for the shots has been proved crucial to obtain physically reasonable results regarding the component’s behavior.

Spatial Congruence Analysis (SCAN): A method for detecting biogeographical patterns based on species range congruences.

Species with congruent geographical distributions, potentially caused by common historical and ecological spatial processes, constitute biogeographical units called chorotypes. Nevertheless, the degree of spatial range congruence characterizing these groups of species is rarely used as an explicit parameter. Methods conceived for the identification of patterns of shared ranges often suffer from scale bias associated with the use of grids, or the incapacity to describe the full complexity of patterns, from core areas of high spatial congruence, to long gradients of range distributions expanding from these core areas. Here, we propose a simple analytical method, Spatial Congruence Analysis (SCAN), which identifies chorotypes by mapping direct and indirect spatial relationships among species. Assessments are made under a referential value of congruence as an explicit numerical parameter. A one-layered network connects species (vertices) using pairwise spatial congruence estimates (edges). This network is then analyzed for each species, separately, by an algorithm which searches for spatial relationships to the reference species. The method was applied to two datasets: a simulated gradient of ranges and real distributions of birds. The simulated dataset showed that SCAN can describe gradients of distribution with a high level of detail. The bird dataset showed that only a small portion of range overlaps is biogeographically meaningful, and that there is a large variation in types of patterns that can be found with real distributions. Species analyzed separately may converge on similar or identical groups, may be nested in larger chorotypes, or may even generate overlapped patterns with no species in common. Chorotypes can vary from simple ones, composed by few highly congruent species, to complex, with numerous alternative component species and spatial configurations, which offer insights about possible processes driving these patterns in distinct degrees of spatial congruence. Metrics such as congruence, depth, richness, and ratio between common and total areas can be used to describe chorotypes in detail, allowing comparisons between patterns across regions and taxa.

One for More: Selecting Generalizable Samples for Generalizable ReID Model

Current training objectives of existing person Re-IDentification (ReID) models only ensure that the loss of the model decreases on selected training batch, with no regards to the performance on samples outside the batch. It will inevitably cause the model to over-fit the data in the dominant position (e.g., head data in imbalanced class, easy samples or noisy samples). The latest resampling methods address the issue by designing specific criterion to select specific samples that trains the model generalize more on certain type of data (e.g., hard samples, tail data), which is not adaptive to the inconsistent real world ReID data distributions. Therefore, instead of simply presuming on what samples are generalizable, this paper proposes a one-for-more training objective that directly takes the generalization ability of selected samples as a loss function and learn a sampler to automatically select generalizable samples. More importantly, our proposed one-for-more based sampler can be seamlessly integrated into the ReID training framework which is able to simultaneously train ReID models and the sampler in an end-to-end fashion. The experimental results show that our method can effectively improve the ReID model training and boost the performance of ReID models.

Computational design of biopolymer aerogels and predictive modelling of their nanostructure and mechanical behaviour

To address the challenge of reconstructing or designing the three-dimensional microstructure of nanoporous materials, we develop a computational approach by combining the random closed packing of polydisperse spheres together with the Laguerre–Voronoi tessellation. Open-porous cellular network structures that adhere to the real pore-size distributions of the nanoporous materials are generated. As an example, κ-carrageenan aerogels are considered. The mechanical structure–property relationships are further explored by means of finite elements. Here we show that one can predict the macroscopic stress–strain curve of the bulk porous material if only the pore-size distributions, solid fractions, and Young’s modulus of the pore-wall fibres are known a priori. The objective of such reconstruction and predictive modelling is to reverse engineer the parameters of their synthesis process for tailored applications. Structural and mechanical property predictions of the proposed modelling approach are shown to be in good agreement with the available experimental data. The presented approach is free of parameter-fitting and is capable of generating dispersed Voronoi structures.

ENTREPRENEURSHIP CHANNELS AND SUSTAINABLE DEVELOPMENT: DIRECTIONS FOR THE ASIAN ECONOMY

Entrepreneurship has been identified as a catalyst for creating opportunities and enhancing productivity. The Asian economy is one of the fastest growing economies and entrepreneurially inclined continents of the world, owing to the phenomenal strides of China’s economic activities. Despite these, the region is still characterized as developing in terms equality, real income growth and welfare distributions. Thus, one wonders the extent the entrepreneurial strides impact on the continent; and, to what extent are the socio-economic structures of the Asian economy relevant for achieving sustainable entrepreneurship development. Thus, by employing the Generalized Method of Moments (GMM), this study assesses the extent to which the current wave of entrepreneurship outcomes can actualize the Global Development Goals slated for 2030. The findings show that improvement in life expectancy and decrease in inequality influenced entrepreneurial outcomes via the basic requirement channel; while higher education which is an efficiency enhancing channel stimulates income than innovations; thus, indicating the need for continuous investments for nascent training; while investments in research and development is an institutional channel that promotes entrepreneurial outcomes. Thus, if the global goals will be actualized, policy makers should strengthen infrastructures and create enabling environments that will improve entrepreneurial outcomes within Asian economies.

Convolutional Two-Stream Generative Adversarial Network-Based Hyperspectral Feature Extraction

Hyperspectral image processing is faced with difficulties considering its redundant features and complex information. Studies on hyperspectral feature extraction in the deep learning domain have become increasingly popular. The mainstream techniques fully consider the spatial information in local neighborhoods when extracting spectral features by constructing deep neural networks. Deep generative models simulate the intrinsic structure of samples by adequately training, showing their potential values for signal processing. In this article, a convolutional two-stream network (cs <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> GAN-FE) based on the improved Wasserstein generative adversarial network (WGAN) is proposed for unsupervised hyperspectral spatial–spectral feature extraction. The improved WGAN is composed of one generator and one discriminator; the former perceives real data distributions, and the latter determines the attribution of generated data. The designed two-stream strategy is not a simple extension of a one-stream strategy and considers both the static spectral–spatial information and the dynamic spectral reflectance variation in multiple bands. Intrinsic spatial–spectral features are extracted by the trained discriminator considering sample distributions and feature relationships. The loss function is also improved for the unique structure of cs <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> GAN-FE. Various state-of-the-art techniques are chosen for comparison. Experimental results show the feasibility and potential of this network. Besides, experiments with the random split and the disjointed split both show that the proposed method can outperform other comparison techniques.

Linear logic in normed cones: probabilistic coherence spaces and beyond

Abstract Ehrhard et al. (2018. Proceedings of the ACM on Programming Languages, POPL 2, Article 59.) proposed a model of probabilistic functional programming in a category of normed positive cones and stable measurable cone maps, which can be seen as a coordinate-free generalization of probabilistic coherence spaces (PCSs). However, unlike the case of PCSs, it remained unclear if the model could be refined to a model of classical linear logic. In this work, we consider a somewhat similar category which gives indeed a coordinate-free model of full propositional linear logic with nondegenerate interpretation of additives and sound interpretation of exponentials. Objects are dual pairs of normed cones satisfying certain specific completeness properties, such as existence of norm-bounded monotone weak limits, and morphisms are bounded (adjointable) positive maps. Norms allow us a distinct interpretation of dual additive connectives as product and coproduct. Exponential connectives are modeled using real analytic functions and distributions that have representations as power series with positive coefficients. Unlike the familiar case of PCSs, there is no reference or need for a preferred basis; in this sense the model is invariant. PCSs form a full subcategory, whose objects, seen as posets, are lattices. Thus, we get a model fitting in the tradition of interpreting linear logic in a linear algebraic setting, which arguably is free from the drawbacks of its predecessors.

Improving generative adversarial networks with simple latent distributions

Generative Adversarial Networks (GANs) have drawn great attention recently since they are the powerful models to generate high-quality images. Although GANs have achieved great success, they usually suffer from unstable training and consequently may lead to the poor generations in some cases. Such drawback is argued mainly due to the difficulties in measuring the divergence between the highly complicated the real and fake data distributions, which are normally in the high-dimensional space. To tackle this problem, previous researchers attempt to search a proper divergence capable of measuring the departure of the complex distributions. In contrast, we attempt to alleviate this problem from a different perspective: while retaining the information as much as possible of the original high dimensional distributions, we learn and leverage an additional latent space where simple distributions are defined in a low-dimensional space; as a result, we can readily compute the distance between two simple distributions with an available divergence measurement. Concretely, to retain the data information, the mutual information is maximized between the variables for the high dimensional complex distributions and the low dimensional simple distributions. The departure of the resulting simple distributions are then measured in the original way of GANs. Additionally, for simplifying the optimization further, we optimize directly the lower bound for mutual information. Termed as SimpleGAN, we conduct the proposed approach over the several different baseline models, i.e., conventional GANs, DCGAN, WGAN-GP, WGAN-GP-res, and LSWGAN-GP on the benchmark CIFAR-10 and STL-10 datasets. SimpleGAN shows the obvious superiority on these baseline models. Furthermore, in comparison with the existing methods measuring directly the distribution departure in the high-dimensional space, our method clearly demonstrates its superiority. Finally, a series of experiments show the advantages of the proposed SimpleGAN.

Hotel energy rating system using dynamic zone EUI method in Taiwan

This study seeks to develop a predictive method for hotel energy consumption and an energy rating system for existing hotels based on utility bills. It forms part of T-BERS (Taiwan’s Building Energy Rating System) currently being developed by the Architecture and Building Research Institute. T-BERS adopts the dynamic zone EUI (energy use intensity) method and a right-skewed EUI distribution and references the 7-class rating of EP Label used in the EU for public buildings. Rather than using a EUI for an entire hotel building as the rating index, the dynamic zone EUI method takes “dynamic” EUI indices calculated accumulatively from corresponding EUIs for 10 energy zones in a hotel. Thus, a customized EUI rating scale may be established to tailor to the unique scale and spatial composition of each hotel, thereby ensuring a fair assessment. The minimum, median and maximum EUI values for the 10 energy zones are set based on benchmark models simulated with eQUEST. The median EUI value is set based on the minimum criteria in the latest building code, and the maximum and minimum values based on the worst and best conditions in existing hotels in the market. The scoring model is compared and validated against the distribution of actual EUIs for 89 hotels measured by Taiwan's Bureau of Energy. It is shown to be aligned with real distributions of EUIs in the hotel building market and comply with the requirements of scale sensitivity and scale reliability. A modification for energy assessment predicating on utility bills has also been proposed herein. Using yearly occupancy rate YOR as a modifying factor, a hotel with irregular operations can now receive more customized, fair and rational assessment.

Beyond the hubble sequence – exploring galaxy morphology with unsupervised machine learning

ABSTRACT We explore unsupervised machine learning for galaxy morphology analyses using a combination of feature extraction with a vector-quantized variational autoencoder (VQ-VAE) and hierarchical clustering (HC). We propose a new methodology that includes: (1) consideration of the clustering performance simultaneously when learning features from images; (2) allowing for various distance thresholds within the HC algorithm; (3) using the galaxy orientation to determine the number of clusters. This set-up provides 27 clusters created with this unsupervised learning that we show are well separated based on galaxy shape and structure (e.g. Sérsic index, concentration, asymmetry, Gini coefficient). These resulting clusters also correlate well with physical properties such as the colour–magnitude diagram, and span the range of scaling relations such as mass versus size amongst the different machine-defined clusters. When we merge these multiple clusters into two large preliminary clusters to provide a binary classification, an accuracy of $\sim 87{{\ \rm per\ cent}}$ is reached using an imbalanced data set, matching real galaxy distributions, which includes 22.7 per cent early-type galaxies and 77.3 per cent late-type galaxies. Comparing the given clusters with classic Hubble types (ellipticals, lenticulars, early spirals, late spirals, and irregulars), we show that there is an intrinsic vagueness in visual classification systems, in particular galaxies with transitional features such as lenticulars and early spirals. Based on this, the main result in this work is not how well our unsupervised method matches visual classifications and physical properties, but that the method provides an independent classification that may be more physically meaningful than any visually based ones.

High-resolution prediction of the spatial distribution of PM2.5 concentrations in China using a long short-term memory model

The concentration of fine particulate matter (PM2.5) has a significant impact on the environment and human health. However, strong spatial heterogeneity and spatiotemporal dependence increases the difficulty of prediction. Moreover, due to the lag of the update of auxiliary variables at national scale in the prediction application, it is still difficult to achieve the timely nationwide PM2.5 prediction at present. To better model and predict real time concentrations and spatial distributions of PM2.5, this study developed a workflow of future PM2.5 concentrations prediction based on long short-term memory (LSTM) model. Using ground-based station PM2.5 data in 2014–2018, the 1 km Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol optical depth (AOD) product and other auxiliary data to predict PM2.5 concentrations in the next year and generate a high-resolution national PM2.5 concentration spatial distribution map. The LSTM model outperformed random forest (RF) and Cubist approaches for prediction PM2.5 because of its recurrent neural network structure that can capture time dependence and nonlinear relationships among PM2.5 concentrations and other independent variables, and exhibited a stable accuracy with an R2 of 0.83, by applying the annual time series, with an improvement of 0.04–0.09, compared to daily and monthly data. The results indicated that PM2.5 pollution had gradually decreased in 2019 after application of pollution controls, with annual mean PM2.5 concentrations of 27.33 ± 15.56 μg m−3, although there were still some areas with severe pollution, including the North China Plain, parts of the Loess Plateau, and the Taklimakan Desert. The LSTM model makes it possible to predict fine-scale PM2.5 spatial distributions nationwide in the future and may thus be useful for sustainable management and control of air pollution at a national scale.

Selecting Classification Methods for Small Samples of Next-Generation Sequencing Data.

Next-generation sequencing has emerged as an essential technology for the quantitative analysis of gene expression. In medical research, RNA sequencing (RNA-seq) data are commonly used to identify which type of disease a patient has. Because of the discrete nature of RNA-seq data, the existing statistical methods that have been developed for microarray data cannot be directly applied to RNA-seq data. Existing statistical methods usually model RNA-seq data by a discrete distribution, such as the Poisson, the negative binomial, or the mixture distribution with a point mass at zero and a Poisson distribution to further allow for data with an excess of zeros. Consequently, analytic tools corresponding to the above three discrete distributions have been developed: Poisson linear discriminant analysis (PLDA), negative binomial linear discriminant analysis (NBLDA), and zero-inflated Poisson logistic discriminant analysis (ZIPLDA). However, it is unclear what the real distributions would be for these classifications when applied to a new and real dataset. Considering that count datasets are frequently characterized by excess zeros and overdispersion, this paper extends the existing distribution to a mixture distribution with a point mass at zero and a negative binomial distribution and proposes a zero-inflated negative binomial logistic discriminant analysis (ZINBLDA) for classification. More importantly, we compare the above four classification methods from the perspective of model parameters, as an understanding of parameters is necessary for selecting the optimal method for RNA-seq data. Furthermore, we determine that the above four methods could transform into each other in some cases. Using simulation studies, we compare and evaluate the performance of these classification methods in a wide range of settings, and we also present a decision tree model created to help us select the optimal classifier for a new RNA-seq dataset. The results of the two real datasets coincide with the theory and simulation analysis results. The methods used in this work are implemented in the open-scource R scripts, with a source code freely available at https://github.com/FocusPaka/ZINBLDA.

Social Wasps (Vespidae: Polistinae) in an Urban Park from Piauí, Brazil

Studies of social wasps (Vespidae: Polistinae) in Piauí State are scarce and only two have been carried out. Due to the lack of studies, we surveyed the wasp species in the Zoobotanical State Park in the urban area of Teresina, Piauí, Brazil. From December 2017 to May 2018, 10 expeditions were carried out using active collection with entomological nets and attractive traps with guava juice. A total of 282 individuals were collected distributed in six genera and 12 species. Parachartergus fraternus Gribodo, 1892 and Synoeca surinama (Linnaeus, 1767) are new records for Piauí, which now has 12 genera and 24 species registered for the state. Such data reveals the need to continue studying Polistinae in Piauí, in order to determine their real species distributions, help conserve natural areas and accurately manage these sites.

KMap.py: A Python program for simulation and data analysis in photoemission tomography

Ultra-violet photoemission spectroscopy is a widely-used experimental technique to investigate the valence electronic structure of surfaces and interfaces. When detecting the intensity of the emitted electrons not only as a function of their kinetic energy, but also depending on their emission angle, as is done in angle-resolved photoemission spectroscopy (ARPES), extremely rich information about the electronic structure of the investigated sample can be extracted. For organic molecules adsorbed as well-oriented ultra-thin films on metallic surfaces, ARPES has evolved into a technique called photoemission tomography (PT). By approximating the final state of the photoemitted electron as a free electron, PT uses the angular dependence of the photocurrent, a so-called momentum map or k-map, and interprets it as the Fourier transform of the initial state’s molecular orbital, thereby gaining insights into the geometric and electronic structure of organic/metal interfaces.In this contribution, we present kMap.py which is a Python program that enables the user, via a PyQt-based graphical user interface, to simulate photoemission momentum maps of molecular orbitals and to perform a one-to-one comparison between simulation and experiment. Based on the plane wave approximation for the final state, simulated momentum maps are computed numerically from a fast Fourier transform (FFT) of real space molecular orbital distributions, which are used as program input and taken from density functional calculations. The program allows the user to vary a number of simulation parameters, such as the final state kinetic energy, the molecular orientation or the polarization state of the incident light field. Moreover, also experimental photoemission data can be loaded into the program, enabling a direct visual comparison as well as an automatic optimization procedure to determine structural parameters of the molecules or weights of molecular orbitals contributions. With an increasing number of experimental groups employing photoemission tomography to study molecular adsorbate layers, we expect kMap.py to serve as a helpful analysis software to further extend the applicability of PT. Program summaryProgram Title:kMap.pyCPC Library link to program files:https://doi.org/10.17632/tnrm9jcccc.1Developer’s respository link:https://github.com/brands-d/kMap/Code Ocean capsule:https://codeocean.com/capsule/5788845Licensing provisions: GPLv3Programming language: Python 3.xNature of problem: Photoemission tomography (PT) has evolved as a powerful experimental method to investigate the electronic and geometric structure of organic molecular films [1]. It is based on valence band angle-resolved photoemission spectroscopy and seeks an interpretation of the angular dependence of the photocurrent, a so-called momentum map, from a given initial state in terms of the spatial structure of molecular orbitals. For this purpose, PT heavily relies on a simulation platform which is capable of efficiently predicting momentum maps for a variety of organic molecules, which allows for a convenient way of treating the effect of molecular orientations, and which also accounts for other experimental parameters such as the geometrical setup and nature of the incident photon source. Thereby, PT has been used to determine molecular geometries, gain insight into the nature of the surface chemistry, unambiguously determine the orbital energy ordering in molecular homo- and heterostructures and even reconstruct the orbitals of adsorbed molecules [1–4].Solution method:kMap.py is a Python program that enables the user, via a PyQt-based graphical user interface, to simulate photoemission momentum maps of molecular orbitals and to perform a one-to-one comparison between simulation and experiment. Based on the plane wave approximation for the final state, simulated momentum maps are computed numerically from a fast Fourier transform (FFT) of real space molecular orbital distributions [2] which are used as program input and which are usually obtained from density functional calculations. The user can vary a number of simulation parameters such as the final state kinetic energy, the molecular orientation or the polarization state of the incident light field. Moreover, also experimental photoemission data can be loaded into the program, enabling a direct visual comparison as well as an automatic optimization procedure to minimize the difference between simulated and measured momentum maps. Thereby, structural parameters of the molecules [2] and the weights of molecular orbitals to experimentally observed emission features can be determined [3].

Applying Quantum Mechanics for Extreme Value Prediction of VaR and ES in the ASEAN Stock Exchange

The advantage of quantum mechanics to shift up the ability to econometrically understand extreme tail losses in financial data has become more desirable, especially in cases of Value at Risk (VaR) and Expected Shortfall (ES) predictions. Behind the non-novel quantum mechanism, it does interestingly connect with the distributional signals of humans’ brainstorms. The highlighted purpose of this article is to devise a quantum-wave distribution methodically to analyze better risks and returns for stock markets in The Association of Southeast Asian Nations (ASEAN) countries, including Thailand (SET), Singapore (STI), Malaysia (FTSE), Philippines (PSEI), and Indonesia (PCI). Data samples were observed as quarterly trends between 1994 and 2019. Bayesian statistics and simulations were applied to present estimations’ outputs. Empirically, quantum distributions are remarkable for providing “real distributions”, which computationally conform to Bayesian inferences and crucially contribute to the higher level of extreme data analyses in financial economics.

On controlled invariance of regular distributions

This paper considers the problem of controlled invariance of involutive regular distribution, both for smooth and real analytic cases. After a review of some existing work, a precise formulation of the problem of local and global controlled invariance of involutive regular distributions for both affine control systems and affine distributions is introduced. A complete characterization for local controlled invariance of involutive regular distributions for affine control systems is presented. A geometric interpretation for this characterization is provided. A result on local controlled invariance for real analytic affine distribution is given. Then, we investigate conditions that allow passages from local controlled invariance to global controlled invariance, for both smooth and real analytic affine distributions. We clarify existing results in the literature. Finally, for manifolds with a symmetry Lie group action, the problem of global controlled invariance is considered.

Bit Security Estimation Using Various Information-Theoretic Measures

In this paper, we propose various quantitative information-theoretic bit security reduction measures that correlate the statistical difference between two probability distributions with the security level gap for two cryptographic schemes. We derive tighter relations between information-theoretic measures for quantifying the precision and guarantee the security level of the cryptographic scheme implemented over a precision-restricted environment. Further, the generalized versions of previous security reductions are devised by relaxing the constraints on the upper bounds of the information-theoretic measures. This makes it possible to estimate bit security more reliable and improves the security level. We also estimate the effects on the security level when the $\kappa $ -bit secure original scheme is implemented on a $p$ -bit precision system. In previous studies, $p$ was fixed as $\frac {\kappa }2$ ; however, the proposed schemes are generalized such that the security level $\kappa $ and precision $p$ can vary independently. This results in a significant difference. Moreover, previous results cannot provide the exact lower bound of the security level for $p\ne {\frac {\kappa }{2}}$ . However, the proposed results can provide the exact lower bound of the estimation value of the security level as long as the precision $p$ satisfies certain conditions. We provide diverse types of security reduction formulas for the six types of information-theoretic measures. The proposed schemes can provide information-theoretic guidelines regarding the difference between the security levels of two identical cryptographic schemes when extracting randomness from two different probability distributions. In particular, the proposed schemes can be used to quantitatively estimate the effect of the statistical difference between the ideal and real distributions on the security level.

Enhanced Multiplicity on Shaped Patterns by Introducing Symmetric Pure Real Distributions: Taylor Linear and Circular Sources

The techniques on the generation of multiple solutions in shaped-beam pattern synthesis are standardly focused on the use of patterns with complex nature as input. Otherwise, in order to derive a symmetric pure real distribution from the canonical pattern synthesis techniques, a generation of a pure-real pattern has to be imposed. In the present work, the exploitation of the multiplicity of the shaped pattern generated by this symmetric pure real distribution is proposed, without constraining the solutions to necessarily meet the pure-real pattern requirement. Therefore, an increase on the degrees of freedom is produced and a greater number of continuous distributions (presenting different natures) is achieved, by omitting the restrictions found in the state-of-the-art methodologies. Thus, a general multiplicity of solutions can be reached and the design protocol can increase its number of alternatives for facing different feeding network structures. In such a way, this article is devoted to illustrate the improvements in terms of number of feasible solutions reached by the general method, including alternative symmetric pure real distributions as input within the procedure. In this manner, two different approaches, constraining the pattern to present the same number of ripples or a similar main beam width, are discussed. Examples of both Taylor distributions linear and circular are illustrated.

Proposal of a novel GPU-accelerated lifetime optimization method for onshore wind turbine dampers under real wind distribution

In recent years, the criticality of fore-aft vibrations induced by winds on wind turbine towers has increased; these vibrations are generally evaluated using equivalent fatigue load (EFL). This is the first study to adopt wind speed histories from large eddy simulations as input for dynamic analysis of wind turbines, and to evaluate EFL against real wind distribution. Tuned mass damper (TMD) and rotational inertial double tuned mass damper (RIDTMD) were employed to control these vibrations. Parametric analysis of the damper parameters was conducted. An innovative global optimization tool was developed based on a radial basis function neural network and genetic algorithm. Moreover, for the first time, GPU acceleration technologies were adopted to enable the optimizations of dampers through massive cases. Numerical results show that damper optimizations under real wind distributions are essential, and that optimized dampers reduce 44% EFL. The performance of RIDTMD is better than TMD but has a narrower system control bandwidth. The optimized dampers are significantly affected by wind speed; however, they are least affected by wind direction. The developed GPU-based codes can run 2001 times faster than the CPU-based ones, and the optimization tool can further reduce 85% computational time, which is open to other researchers.