A New Statistical Image Watermark Detector in RHFMs Domain using Beta Exponential Distribution

The detection of watermarks can be achieved by statistical approaches. How to select robust modeling object, appropriate statistical model, and decision rules is one of the major issues in statistical image watermark detection. In this paper, we propose a new image watermark detector in robust fast radial harmonic Fourier moments (FRHFMs) magnitudes domain, wherein the Beta exponential distribution model and locally most powerful (LMP) decision rule are used. We first investigate the statistical modeling of the robust FRHFMs magnitudes by the Beta exponential distribution. It is shown that the Beta exponential distribution model fits the empirical data more accurately than the formerly employed statistical distributions, such as the Cauchy, Weibull, BKF and Exponential, do. Motivated by the statistical modeling results, we design a blind image watermark detector in FRHFMs magnitudes domain by using Beta exponential distribution and LMP test. Also, we utilize the Beta-exponential model to derive the closed-form expressions for the watermark detector. We provide comparative experimental results to alternative approaches to demonstrate the advantages of the proposed image watermark detector.

On the Investigation of Effective Factors on Higher Heating Value of Biodiesel: Robust Modeling and Data Assessments

Higher heating value (HHV) is one of the properties of biomass fuels which is essential in investigating their special characteristics and potentialities. In this paper, various techniques based on Gaussian process regression (GPR) were utilized to assess this value for biomass fuels, including several kernel functions, i.e., exponential, Matern, rational quadratic, and squared exponential. An extensive databank was collected from literature. The findings were compared, and the results indicated that Exponential-based model was more accurate, with the coefficient of regression ( R 2 ) of 0.961 and the mean relative error (% MRE) of 3.11 for total data. Compared to former models presented by previous researchers, the model proposed in this study showed a higher ability to predict output values. With various analyses, it can be concluded that the proposed method has a high rate of efficiency in assessing the HHV of various biomass.

Validation of Forced Heave Simulations on a Planing Hull

Advances in nonlinear modeling techniques have created opportunities for more robust modeling of planing hull dynamics than previous techniques relying on linear assumptions. These techniques rely on the imposition of complex, coupled forced motions on a hull. RANSE CFD provides a distinct advantage over experimentation when imposing complicated forced motions because mechanical limitations of the forced motion mechanism and uncertainty in the prescribed motion are eliminated, though the accuracy of the simulations needs to be validated. In this work, a series of sinusoidal forced heave experiments on a planing craft are used to validate the force response predicted by simulation for the same forced motion. The accuracy of the predicted force response is evaluated relative to the experiments with the experimental setup uncertainty considered. Within the experimental setup uncertainty, the force response is predicted well by RANSE CFD and is found to be reasonably accurate. The dynamic trim angle is found to have a major impact on the dynamic force response with variations on the order of half a degree having substantial impacts on the measured forces.

Unified real-time environmental-epidemiological data for multiscale modeling of the COVID-19 pandemic

An impressive number of COVID-19 data catalogs exist. None, however, are optimized for data science applications, e.g., inconsistent naming and data conventions, uneven quality control, and lack of alignment between disease data and potential predictors pose barriers to robust modeling and analysis. To address this gap, we generated a unified dataset that integrates and implements quality checks of the data from numerous leading sources of COVID-19 epidemiological and environmental data. We use a globally consistent hierarchy of administrative units to facilitate analysis within and across countries. The dataset applies this unified hierarchy to align COVID-19 case data with a number of other data types relevant to understanding and predicting COVID-19 risk, including hydrometeorological data, air quality, information on COVID-19 control policies, and key demographic characteristics.