Generative Learning of Heterogeneous Tail Dependence

We propose a multivariate generative model to capture the complex dependence structure often encountered in business and financial data. Our model features heterogeneous and asymmetric tail dependence between all pairs of individual dimensions while also allowing heterogeneity and asymmetry in the tails of the marginals. A significant merit of our model structure is that it is not prone to error propagation in the parameter estimation process, hence very scalable, as the dimensions of datasets grow large. However, the likelihood methods are infeasible for parameter estimation in our case due to the lack of a closed-form density function. Instead, we devise a novel moment learning algorithm to learn the parameters. To demonstrate the effectiveness of the model and its estimator, we test them on simulated as well as real-world datasets. Results show that this framework gives better finite-sample performance compared to the copula-based benchmarks as well as recent similar models

An Investigation of Current and Future Data Systems in Numerical Meteorology

The Advanced Microwave Sounding Unit (AMSU) and the Microwave Humidity Sounder (MHS) constitute the advanced microwave sounding system to be flown on the EOS-PM platform. Similar instruments (the AMSU-A corresponding to the AMSU and the AMSU-B corresponding to the MHS) are scheduled to become operational on the NOAA polar orbiting satellites beginning with NOAA-K. The unique characteristics of the AMSU-MHS instruments, as compared to the capabilities of their infrared and microwave predecessors, introduce new opportunities, and challenges, for operational retrievals of atmospheric structure. Not only will these new data improve present capabilities for the retrieval of atmospheric profiles of temperature and moisture, but they will provide the only opportunity for successfully retrieving atmospheric temperature and humidity profiles in the presence of modest amounts of cloud and precipitation. A complementary opportunity is presented by the potential of the AMSU-MHS to obtain information about the structure of clouds and precipitation. The data sets obtained will contribute to the current knowledge of global water and energy budgets, and provide critical information on the horizontal and vertical distribution of tropospheric water vapor, the spatial and temporal distribution of rain, and the relationship of cloud formation and dissipation to atmospheric dynamics and thermodynamics

Retinal vessel segmentation:An efficient graph cut approach with Retinex and local phase

Our application concerns the automated detection of vessels in retinal images to improve understanding of the disease mechanism, diagnosis and treatment of retinal and a number of systemic diseases. We propose a new framework for segmenting retinal vasculatures with much improved accuracy and efficiency. The proposed framework consists of three technical components: Retinex-based image inhomogeneity correction, local phase-based vessel enhancement and graph cut-based active contour segmentation. These procedures are applied in the following order. Underpinned by the Retinex theory, the inhomogeneity correction step aims to address challenges presented by the image intensity inhomogeneities, and the relatively low contrast of thin vessels compared to the background. The local phase enhancement technique is employed to enhance vessels for its superiority in preserving the vessel edges. The graph cut-based active contour method is used for its efficiency and effectiveness in segmenting the vessels from the enhanced images using the local phase filter. We have demonstrated its performance by applying it to four public retinal image datasets (3 datasets of color fundus photography and 1 of fluorescein angiography). Statistical analysis demonstrates that each component of the framework can provide the level of performance expected. The proposed framework is compared with widely used unsupervised and supervised methods, showing that the overall framework outperforms its competitors. For example, the achieved sensitivity (0:744), specificity (0:978) and accuracy (0:953) for the DRIVE dataset are very close to those of the manual annotations obtained by the second observer

GEO-LEO Reflective Band Inter-Comparison with BRDF and Atmospheric Scattering Corrections

The inter-comparison of the reflective solar bands (RSB) between the instruments onboard a geostationary orbit satellite and a low Earth orbit satellite is very helpful in assessing their calibration consistency. Himawari-8 was launched 7 October 2014 and GOES-R was launched on 19 November 2016. Unlike previous GOES instruments, the Advanced Himawari Imager (AHI) on Himawari-8 and the Advanced Baseline Imager (ABI) on GOES-R have onboard calibrators for the RSB. Independent assessment of calibration is nonetheless important to enhance their product quality. MODIS (Moderate Resolution Imaging Spectroradiometer) and VIIRS (Visible Infrared Imaging Radiometer Suite) can provide good references for sensor calibration. In this work, the inter-comparison between AHI and VIIRS is performed over a pseudo-invariant target. The use of stable and uniform calibration sites provides comparison with accurate adjustment for band spectral difference, reduction of impact from pixel mismatching, and consistency of BRDF (Bidirectional Reflectance Distribution Function) and atmospheric correction. The site used is the Strzelecki Desert in Australia. Due to the difference in solar and view angles, two corrections must be applied in order to compare the measurements. The first is the atmospheric scattering correction applied to the top of atmosphere reflectance measurements. The second correction is applied to correct the BRDF effect. The atmospheric correction is performed using a vector version of the Second Simulation of a Satellite Signal in the Solar Spectrum (6SV) model and the BRDF correction is performed using a semi-empirical model. Our results show that AHI band 1 (0.47 microns) has a good agreement with VIIRS band M3 within 0.15 percent. AHI band 5 (1.61 microns) shows the largest difference (5.09 percent) with VIIRS band M10, while AHI band 5 shows the least difference (1.87 percent) in comparison with VIIRS band I3. The methods developed in this work can also be directly applied to assess GOES-16/ABI (Geostationary Operational Environment Satellite16 / Advanced Baseline Imager) calibration consistency, a topic we will address in the future

Load flow calculation for droop-controlled islanded microgrids based on direct Newton-Raphson method with step size optimisation

Load flow calculation for droop-controlled islanded microgrids (IMGs) is different from that of transmission or distribution systems due to the absence of slack bus and the variation of frequency. Meanwhile considering the common three-phase imbalance condition in low-voltage systems, a load flow algorithm based on the direct Newton-Raphson (NR) method with step size optimisation for both three-phase balanced and unbalanced droop-controlled IMGs is proposed in this study. First, the steady-state models for balanced and unbalanced droop-controlled IMGs are established based on their operational mechanisms. Then taking frequency as one of the unknowns, the non-linear load flow equations are solved iteratively by the NR method. Generally, iterative load flow algorithms are faced with challenges of convergence performance, especially for unbalanced systems. To tackle this problem, a step-size-optimisation scheme is employed to improve the convergence performance for three-phase unbalanced IMGs. In each iteration, a multiplier is deduced from the sum of higher-order terms of Taylor expansion of the load flow equations. Then the step size is optimised by the multiplier, which can help smooth the iterative process and obtain the solutions. The proposed method is performed on several balanced and unbalanced IMGs. Numerical results demonstrate the correctness and effectiveness of the proposed algorithm

Palmprint Texture Analysis Using Derivative of Gaussian Filters

This paper presents a novel approach of palmprint tex-ture analysis based on the derivative of gaussian filter. In this approach, the palmprint image is respectively prepro-cessed along horizontal and vertical direction using deriva-tive of gaussian (DoG) Filters. And then the palmprint is encoded according to the sign of the value of each pixel of the filtered images. This code is called DoGCode of the palmprint. The size of DoGCode is 256 bytes. The simi-larity of two DoGCode is measured using their Hamming distance. This approach is tested on the PolyU Palmprint Database, which containing 7605 samples from 392 palms, and the EER is 0.19%, which is comparable with the exist-ing palmprint recognition methods. 1

Automated Personal Authentication Using Both Palmprints

Abstract. To satisfy personal interests, different entertainment computing should be performed for different people (called personal entertainment computing). For personal entertainment computing, the personal identity should be first automatically authenticated. This paper proposes a novel approach for automated personal authentication by using both palmprints. The experimental results show that the fusion of the information of both palmprints can dramatically improve the authentication accuracy

Otterbein Aegis May 1909

https://digitalcommons.otterbein.edu/aegis/1182/thumbnail.jp