Efficient methods of automatic calibration for rainfall-runoff modelling in the Floreon+ system

Calibration of rainfall-runoff model parameters is an inseparable part of hydrological simulations. To achieve more accurate results of these simulations, it is necessary to implement an efficient calibration method that provides sufficient refinement of the model parameters in a reasonable time frame. In order to perform the calibration repeatedly for large amount of data and provide results of calibrated model simulations for the flood warning process in a short time, the method also has to be automated. In this paper, several local and global optimization methods are tested for their efficiency. The main goal is to identify the most accurate method for the calibration process that provides accurate results in an operational time frame (typically less than 1 hour) to be used in the flood prediction Floreon(+) system. All calibrations were performed on the measured data during the rainfall events in 2010 in the Moravian-Silesian region (Czech Republic) using our in-house rainfall-runoff model.Web of Science27441339

Optimized Superconducting Nanowire Single Photon Detectors to Maximize Absorptance

Dispersion characteristics of four types of superconducting nanowire single photon detectors, nano-cavity-array- (NCA-), nano-cavity-deflector-array- (NCDA-), nano-cavity-double-deflector-array- (NCDDA-) and nano-cavity-trench-array- (NCTA-) integrated (I-A-SNSPDs) devices was optimized in three periodicity intervals commensurate with half-, three-quarter- and one SPP wavelength. The optimal configurations capable of maximizing NbN absorptance correspond to periodicity dependent tilting in S-orientation (90{\deg} azimuthal orientation). In NCAI-A-SNSPDs absorptance maxima are reached at the plasmonic Brewster angle (PBA) due to light tunneling. The absorptance maximum is attained in a wide plasmonic-pass-band in NCDAI_1/2*lambda-A, inside a flat-plasmonic-pass-band in NCDAI_3/4*lambda-A and inside a narrow plasmonic-band in NCDAI_lambda-A. In NCDDAI_1/2*lambda-A bands of strongly-coupled cavity and plasmonic modes cross, in NCDDAI_3/4*lambda-A an inverted-plasmonic-band-gap develops, while in NCDDAI_lambda-A a narrow plasmonic-pass-band appears inside an inverted-minigap. The absorptance maximum is achieved in NCTAI_1/2*lambda-A inside a plasmonic-pass-band, in NCTAI_3/4*lambda-A at inverted-plasmonic-band-gap center, while in NCTAI_lambda-A inside an inverted-minigap. The highest 95.05% absorptance is attained at perpendicular incidence onto NCTAI_lambda-A. Quarter-wavelength type cavity modes contribute to the near-field enhancement around NbN segments except in NCDAI_lambda-A and NCDDAI_3/4*lambda-A. The polarization contrast is moderate in NCAI-A-SNSPDs (~10^2), NCDAI- and NCDDAI-A-SNSPDs make possible to attain considerably large polarization contrast (~10^2-10^3 and ~10^3-10^4), while NCTAI-A-SNSPDs exhibit a weak polarization selectivity (~10-10^2).Comment: 26 pages, 8 figure

State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

Optimization techniques in respiratory control system models

One of the most complex physiological systems whose modeling is still an open study is the respiratory control system where different models have been proposed based on the criterion of minimizing the work of breathing (WOB). The aim of this study is twofold: to compare two known models of the respiratory control system which set the breathing pattern based on quantifying the respiratory work; and to assess the influence of using direct-search or evolutionary optimization algorithms on adjustment of model parameters. This study was carried out using experimental data from a group of healthy volunteers under CO2 incremental inhalation, which were used to adjust the model parameters and to evaluate how much the equations of WOB follow a real breathing pattern. This breathing pattern was characterized by the following variables: tidal volume, inspiratory and expiratory time duration and total minute ventilation. Different optimization algorithms were considered to determine the most appropriate model from physiological viewpoint. Algorithms were used for a double optimization: firstly, to minimize the WOB and secondly to adjust model parameters. The performance of optimization algorithms was also evaluated in terms of convergence rate, solution accuracy and precision. Results showed strong differences in the performance of optimization algorithms according to constraints and topological features of the function to be optimized. In breathing pattern optimization, the sequential quadratic programming technique (SQP) showed the best performance and convergence speed when respiratory work was low. In addition, SQP allowed to implement multiple non-linear constraints through mathematical expressions in the easiest way. Regarding parameter adjustment of the model to experimental data, the evolutionary strategy with covariance matrix and adaptation (CMA-ES) provided the best quality solutions with fast convergence and the best accuracy and precision in both models. CMAES reached the best adjustment because of its good performance on noise and multi-peaked fitness functions. Although one of the studied models has been much more commonly used to simulate respiratory response to CO2 inhalation, results showed that an alternative model has a more appropriate cost function to minimize WOB from a physiological viewpoint according to experimental data.Postprint (author's final draft

An Improved Differential Evolution Algorithm for Maritime Collision Avoidance Route Planning

High accuracy navigation and surveillance systems are pivotal to ensure efficient ship route planning and marine safety. Based on existing ship navigation and maritime collision prevention rules, an improved approach for collision avoidance route planning using a differential evolution algorithm was developed. Simulation results show that the algorithm is capable of significantly enhancing the optimized route over current methods. It has the potential to be used as a tool to generate optimal vessel routing in the presence of conflicts

A Bayesian approach to constrained single- and multi-objective optimization

This article addresses the problem of derivative-free (single- or multi-objective) optimization subject to multiple inequality constraints. Both the objective and constraint functions are assumed to be smooth, non-linear and expensive to evaluate. As a consequence, the number of evaluations that can be used to carry out the optimization is very limited, as in complex industrial design optimization problems. The method we propose to overcome this difficulty has its roots in both the Bayesian and the multi-objective optimization literatures. More specifically, an extended domination rule is used to handle objectives and constraints in a unified way, and a corresponding expected hyper-volume improvement sampling criterion is proposed. This new criterion is naturally adapted to the search of a feasible point when none is available, and reduces to existing Bayesian sampling criteria---the classical Expected Improvement (EI) criterion and some of its constrained/multi-objective extensions---as soon as at least one feasible point is available. The calculation and optimization of the criterion are performed using Sequential Monte Carlo techniques. In particular, an algorithm similar to the subset simulation method, which is well known in the field of structural reliability, is used to estimate the criterion. The method, which we call BMOO (for Bayesian Multi-Objective Optimization), is compared to state-of-the-art algorithms for single- and multi-objective constrained optimization