COMPARISON OF TWO DATA ASSIMILATION METHODS USED TO ACCOUNT FOR OPEN BOUNDARIES IN SEA AREA HYDROTHERMODYNAMICS MODELING

The problems of modeling hydrothermodynamics of particular sea and coastal areas are of current interest, since the results of this modeling are often used in many applications. One of the methods allowing to take into account open boundaries and bring the simulation results closer to real data is the variational assimilation of observational data. In this paper the following approach is considered: it is supposed that there are observational data at a certain moment in time; the problem is considered as an inverse problem, in which the functions of fluxes across the open boundary are treated as additional unknowns. Comparison of methods for reconstructing unknown functions in boundary conditions at an open boundary using sea level and velocity observational data in a number of numerical experiments for a region of a simple shape is carried out.

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Processing and assimilation of observation data for the hydrodynamics model of the Black Sea and the Sea of Azov

Journal of Physics Conference Series ◽

10.1088/1742-6596/2131/2/022010 ◽

2021 ◽

Vol 2131 (2) ◽

pp. 022010

Author(s):

N B Zakharova ◽

T O Sheloput ◽

N R Lezina ◽

V P Shutyaev ◽

E I Parmuzin ◽

...

Keyword(s):

Black Sea ◽

Incomplete Data ◽

Numerical Experiments ◽

The Black Sea ◽

Observation Data ◽

Computational System ◽

Variational Assimilation ◽

The Earth ◽

Marine Data ◽

Sea Area

Abstract This work is aimed at using the marine data of the Shared Use Centre (SUC) “IKI-Monitoring” in the variational assimilation procedures of the Informational Computational System (ICS) “INM RAS - Black Sea”. SUC “IKI - Monitoring” is a tool for obtaining remote sensing observations on the Earth state. In the paper observation data information is given, data processing procedures are described, algorithms for the assimilation of the information received and several specific features of the numerical model used are presented. Results of the variational assimilation of two sets of observation data are presented and discussed. Numerical experiments have confirmed the possibility of using incomplete data from satellites in the problems of modelling the sea area.

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Gas pipeline leakage detection in the presence of parameter uncertainty using robust extended Kalman filter

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331221989117 ◽

2021 ◽

pp. 014233122198911

Author(s):

Mohadese Jahanian ◽

Amin Ramezani ◽

Ali Moarefianpour ◽

Mahdi Aliari Shouredeli

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Parameter Uncertainty ◽

Oil And Gas ◽

Estimation Error ◽

Real Data ◽

Gas Pipeline ◽

Pipeline System ◽

Parameter Uncertainties ◽

Simulation Results

One of the most significant systems that can be expressed by partial differential equations (PDEs) is the transmission pipeline system. To avoid the accidents that originated from oil and gas pipeline leakage, the exact location and quantity of leakage are required to be recognized. The designed goal is a leakage diagnosis based on the system model and the use of real data provided by transmission line systems. Nonlinear equations of the system have been extracted employing continuity and momentum equations. In this paper, the extended Kalman filter (EKF) is used to detect and locate the leakage and to attenuate the negative effects of measurement and process noises. Besides, a robust extended Kalman filter (REKF) is applied to compensate for the effect of parameter uncertainty. The quantity and the location of the occurred leakage are estimated along the pipeline. Simulation results show that REKF has better estimations of the leak and its location as compared with that of EKF. This filter is robust against process noise, measurement noise, parameter uncertainties, and guarantees a higher limit for the covariance of state estimation error as well. It is remarkable that simulation results are evaluated by OLGA software.

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Impurity induced scale-free localization

Communications Physics ◽

10.1038/s42005-021-00547-x ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Linhu Li ◽

Ching Hua Lee ◽

Jiangbin Gong

Keyword(s):

Boundary Conditions ◽

Skin Effect ◽

Reciprocal Lattice ◽

Periodic Boundary Conditions ◽

Open Boundary ◽

Open Boundary Conditions ◽

Translational Invariance ◽

Scale Free ◽

Special Cases ◽

Simulation Results

AbstractNon-Hermitian systems have been shown to have a dramatic sensitivity to their boundary conditions. In particular, the non-Hermitian skin effect induces collective boundary localization upon turning off boundary coupling, a feature very distinct from that under periodic boundary conditions. Here we develop a full framework for non-Hermitian impurity physics in a non-reciprocal lattice, with periodic/open boundary conditions and even their interpolations being special cases across a whole range of boundary impurity strengths. We uncover steady states with scale-free localization along or even against the direction of non-reciprocity in various impurity strength regimes. Also present are Bloch-like states that survive albeit broken translational invariance. We further explore the co-existence of non-Hermitian skin effect and scale-free localization, where even qualitative aspects of the system’s spectrum can be extremely sensitive to impurity strength. Specific circuit setups are also proposed for experimentally detecting the scale-free accumulation, with simulation results confirming our main findings.

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Stiffness Analysis to Predict the Spread Out of Fake Information

Future Internet ◽

10.3390/fi13090222 ◽

2021 ◽

Vol 13 (9) ◽

pp. 222

Author(s):

Raffaele D'Ambrosio ◽

Giuseppe Giordano ◽

Serena Mottola ◽

Beatrice Paternoster

Keyword(s):

Numerical Experiments ◽

Real Data ◽

Sir Model ◽

Stiffness Index ◽

Fake News ◽

Stiffness Analysis ◽

Data Support

This work highlights how the stiffness index, which is often used as a measure of stiffness for differential problems, can be employed to model the spread of fake news. In particular, we show that the higher the stiffness index is, the more rapid the transit of fake news in a given population. The illustration of our idea is presented through the stiffness analysis of the classical SIR model, commonly used to model the spread of epidemics in a given population. Numerical experiments, performed on real data, support the effectiveness of the approach.

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Nonparametric estimation of the measure of functional dependence

AIMS Mathematics ◽

10.3934/math.2021782 ◽

2021 ◽

Vol 6 (12) ◽

pp. 13488-13502

Author(s):

Qingsong Shan ◽

◽

Qianning Liu

Keyword(s):

Asymptotic Distribution ◽

Nonparametric Estimation ◽

Functional Dependence ◽

Kernel Estimator ◽

Real Data ◽

High Accuracy ◽

Simulation Results

<abstract><p>In this paper, we propose a beta kernel estimator to measure functional dependence (MFD). The MFD not only can measure the strength of linear or monotonic relationships, but it is also suitable for more complicated functional dependence. We derive the asymptotic distribution of the proposed estimator and then use several simulated examples to compare our estimator with the traditional measures. Our simulation results demonstrate that beta kernel provides high accuracy in estimation. A real data example is also given to illustrate one possible application of the new estimator.</p></abstract>

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Evaluating Effects of Observational Data Assimilation in General Ocean Circulation Model by Ensemble Kalman Filtering: Numerical Experiments with Synthetic Observations

Meteorologiya i Gidrologiya ◽

10.52002/0130-2906-2021-2-50-66 ◽

2021 ◽

pp. 50-66

Author(s):

V. N. Stepanov ◽

◽

Yu. D. Resnyanskii ◽

B. S. Strukov ◽

A. A. Zelen’ko ◽

...

Keyword(s):

Data Assimilation ◽

Observational Data ◽

Ocean Circulation ◽

Numerical Experiments ◽

Turbulent Viscosity ◽

Bottom Topography ◽

Synthetic Data ◽

Circulation Model ◽

Ocean Model ◽

Global Ocean

The quality of simulation of model fields is analyzed depending on the assimilation of various types of data using the PDAF software product assimilating synthetic data into the NEMO global ocean model. Several numerical experiments are performed to simulate the ocean–sea ice system. Initially, free model was run with different values of the coefficients of horizontal turbulent viscosity and diffusion, but with the same atmospheric forcing. The model output obtained with higher values of these coefficients was used to determine the first guess fields in subsequent experiments with data assimilation, while the model results with lower values of the coefficients were assumed to be true states, and a part of these results was used as synthetic observations. The results are analyzed that are assimilation of various types of observational data using the Kalman filter included through the PDAF to the NEMO model with real bottom topography. It is shown that a degree of improving model fields in the process of data assimilation is highly dependent on the structure of data at the input of the assimilation procedure.

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Composite optimization for robust rank one bilinear sensing

Information and Inference A Journal of the IMA ◽

10.1093/imaiai/iaaa027 ◽

2020 ◽

Author(s):

Vasileios Charisopoulos ◽

Damek Davis ◽

Mateo Díaz ◽

Dmitriy Drusvyatskiy

Keyword(s):

Numerical Experiments ◽

Lipschitz Continuity ◽

Blind Deconvolution ◽

Real Data ◽

Local Search Algorithms ◽

Composite Optimization ◽

Statistical Assumptions ◽

Rank One ◽

Initialization Procedure ◽

Initialization Strategy

Abstract We consider the task of recovering a pair of vectors from a set of rank one bilinear measurements, possibly corrupted by noise. Most notably, the problem of robust blind deconvolution can be modeled in this way. We consider a natural nonsmooth formulation of the rank one bilinear sensing problem and show that its moduli of weak convexity, sharpness and Lipschitz continuity are all dimension independent, under favorable statistical assumptions. This phenomenon persists even when up to half of the measurements are corrupted by noise. Consequently, standard algorithms, such as the subgradient and prox-linear methods, converge at a rapid dimension-independent rate when initialized within a constant relative error of the solution. We complete the paper with a new initialization strategy, complementing the local search algorithms. The initialization procedure is both provably efficient and robust to outlying measurements. Numerical experiments, on both simulated and real data, illustrate the developed theory and methods.

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A Novel Searching Method Using Reinforcement Learning Scheme for Multi-UAVs in Unknown Environments

Applied Sciences ◽

10.3390/app9224964 ◽

2019 ◽

Vol 9 (22) ◽

pp. 4964 ◽

Cited By ~ 4

Author(s):

Yue ◽

Guan ◽

Wang

Keyword(s):

Reinforcement Learning ◽

Prior Information ◽

Search Task ◽

Efficiency Function ◽

Awareness Information ◽

Simulation Results ◽

Searching Method ◽

Sea Area ◽

Sensor Detection ◽

Multi Uav

In this paper, the important topic of cooperative searches for multi-dynamic targets in unknown sea areas by unmanned aerial vehicles (UAVs) is studied based on a reinforcement learning (RL) algorithm. A novel multi-UAV sea area search map is established, in which models of the environment, UAV dynamics, target dynamics, and sensor detection are involved. Then, the search map is updated and extended using the concept of the territory awareness information map. Finally, according to the search efficiency function, a reward and punishment function is designed, and an RL method is used to generate a multi-UAV cooperative search path online. The simulation results show that the proposed algorithm could effectively perform the search task in the sea area with no prior information.

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The ROTOR: Rotation of Frames Via Representation of Systematic Differences in Terms of Spherical Functions

International Astronomical Union Colloquium ◽

10.1017/s0252921100046960 ◽

1997 ◽

Vol 165 ◽

pp. 463-474

Author(s):

V. V. Vityazev

Keyword(s):

Least Squares ◽

Observational Data ◽

Numerical Experiments ◽

Reference Frames ◽

Spherical Functions ◽

Radio Sources ◽

Orthogonal Representation ◽

Rotor Rotation ◽

Pure Rotation

AbstractThe paper presents a method to derive rotational angles between two reference frames from the systematic differences represented in terms of spherical functions – hereafter referred to as the ROTOR (ROTation by Orthogonal Representation). It is shown that the ROTOR is preferable over the least-squares technique since it (a) takes into account only the harmonics which correspond to rotation, (b) tests them for pure rotation, and (c) discovers the existence of quasirotational terms which may smear rotation. Due to these properties the ROTOR yields realistic results even in the case when the observational data contain not only noise but other systematic terms that have nothing to do with rotation. Numerical experiments with the FK5 and three catalogs of radio sources are described.

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