Simulation and analysis on the ship energy efficiency operational indicator for bulk carriers by Monte Carlo simulation method

The ship energy efficiency management is an important topic in the field of the energy management onboard and the exhaust gases emission nowadays. The advanced model plays a vital role to improve the ship energy efficiency management when considering the variable factors. The establishment of the ship energy efficiency model through energy efficiency operational indicator (EEOI) index has been conducted through Monte Carlo simulation method along with using the operation data of a bulk carrier. A bulk carrier is chosen, namely, M/V NSU JUSTICE 250,000 DWT of VINIC Shipping Transportation Company in Vietnam. This research uses the real operational data to perform a statistical methodology which calculates the various factors used to calculate EEOI. This method is supported by Matlab program through the curve fitting tool. The normal distribution estimation and the kernel density estimation method are used for the parametric curve fitting and non-parametric curve fitting, respectively. The average weather condition (wind speed and wave height) and the fouling condition of hull have been investigated and compared with the research results. The validation of the proposed methods has been conducted through the study of the external factors influencing the research results. The research result shows the optimal operational data for the fuel consumption at each certain voyage. This paper is useful for the ship-owners and the ship-operators in the field of the ship energy efficiency management.

A Learning-Based Formulation of Parametric Curve Fitting for Bioimage Analysis

Parametric curve models are convenient to describe and quantitatively characterize the contour of objects in bioimages. Unfortunately, designing algorithms to fit smoothly such models onto image data classically requires significant domain expertise. Here, we propose a convolutional neural network-based approach to predict a continuous parametric representation of the outline of biological objects. We successfully apply our method on the Kaggle 2018 Data Science Bowl dataset composed of a varied collection of images of cell nuclei. This work is a first step towards user-friendly bioimage analysis tools that extract continuously-defined representations of objects.

Estimating the Tails of Loss Severity Distributions Using Extreme Value Theory

Good estimates for the tails of loss severity distributions are essential for pricing or positioning high-excess loss layers in reinsurance. We describe parametric curve-fitting methods for modelling extreme historical losses. These methods revolve around the generalized Pareto distribution and are supported by extreme value theory. We summarize relevant theoretical results and provide an extensive example of their application to Danish data on large fire insurance losses.