scholarly journals Inverse Estimation Method of Material Randomness Using Observation

Crystals ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 512 ◽  
Author(s):  
Dae-Young Kim ◽  
Pawel Sikora ◽  
Krystyna Araszkiewicz ◽  
Sang-Yeop Chung
Keyword(s):  
Elastic Modulus ◽  
Estimation Method ◽  
Observation Data ◽  
Structural Factors ◽  
Response Variance ◽  
Actual Material ◽  
Inverse Estimation ◽  
Temporal And Spatial ◽  
Spatial Distribution Characteristic ◽  
Spatial Randomness

This study proposes a method for inversely estimating the spatial distribution characteristic of a material’s elastic modulus using the measured value of the observation data and the distance between the measurement points. The structural factors in the structural system possess temporal and spatial randomness. One of the representative structural factors, the material’s elastic modulus, possesses temporal and spatial randomness in the stiffness of the plate structure. The structural factors with randomness are typically modeled as having a certain probability distribution (probability density function) and a probability characteristic (mean and standard deviation). However, this method does not consider spatial randomness. Even if considered, the existing method presents limitations because it does not know the randomness of the actual material. To overcome the limitations, we propose a method to numerically define the spatial randomness of the material’s elastic modulus and confirm factors such as response variability and response variance.

scholarly journals DEVELOPMENT OF AN INVERSE ESTIMATION METHOD OF SEA SURFACE DRAG COEFFICIENT UNDER STRONG WIND CONDITIONS

2011 ◽  
Vol 1 (32) ◽  
pp. 32 ◽  
Author(s):  
Masaki Yokota ◽  
Noriaki Hashimoto ◽  
Koji Kawaguchi ◽  
Hiroyasu Kawai
Keyword(s):  
Drag Coefficient ◽  
A Priori ◽  
Estimation Method ◽  
Sea Surface ◽  
Observation Data ◽  
Strong Wind ◽  
High Wind ◽  
Surface Drag ◽  
Inverse Estimation ◽  
The Given

For the purpose of clarifying the mechanism of energy transfer from high wind to waves, the ADWAM, a wave prediction model incorporating the data assimilation method, was modified to deduce the sea surface drag coefficients as its control variables. Validity of the model was verified through the identical twin experiment in deep sea conditions. Also, the behavior of the deduced parameter was examined through several experiments. As a result, it was confirmed that the drag coefficient deduced by the model is accurate enough when the number of the given observation data is sufficient compared with the number of the unknown parameter. It was also confirmed that the accuracy of the deduced coefficient can be improved by adding an a priori condition even if the number of the observation data is insufficient.

Temporal and spatial variations of sandstorm and the related meteorological influences over northern China in the 21st century

2021 ◽  
Author(s):  
Jie Yang ◽  
Tianliang Zhao
Keyword(s):  
Land Surface ◽  
Source Region ◽  
Northern China ◽  
Northwest China ◽  
Gobi Desert ◽  
Taklimakan Desert ◽  
Observation Data ◽  
Average Wind Speed ◽  
Modis Ndvi ◽  
Temporal And Spatial

<p>In this study, we used the sandstorm data of 233 meteorological stations in northern China, conventional meteorological observation data and MODIS-NDVI data in the 40 years from 1980 to 2019 to analyze the spatio-temporal variation of sandstorms in northern China and its related meteorological effects in this century.</p><p>The results show that: 1) The average number of sandstorm days in northern China has been fluctuating and decreasing since the beginning of this century, and increasing from 2017 to 2019. Spring is the main season of dust storm, and the springtime proportion of sandstorm days decreases year by year. 2) In the 1980s and 1990s, sandstorms covered almost covered the whole northwest region; Since the beginning of this century, the range of sandstorm days in the whole Northwest China has shown an obvious decadal downward trend. The spatial pattern of sandstorm days in northern China has been shrinking and moving westward since 2000, and the dominant position of the Gobi Desert in the Asian dust source region has been decreasing year by year. The high sandstorm days were located in the Taklimakan Desert with the increasing trend of sandstorm days year by year. 3) The temporal and spatial variation of sandstorm days in northern China is closely related to the increase of vegetation cover with the greenness and wetness of the land surface, the decreases of average wind speed and gale days, and the significant increase of annual precipitation in northern China after 2000.</p>

scholarly journals Analysis and Estimation of Geographical and Topographic Influencing Factors for Precipitation Distribution over Complex Terrains: A Case of the Northeast Slope of the Qinghai–Tibet Plateau

Atmosphere ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 349 ◽  
Author(s):  
Weicheng Liu ◽  
Qiang Zhang ◽  
Zhao Fu ◽  
Xiaoyan Chen ◽  
Hong Li
Keyword(s):  
Complex Terrain ◽  
Multivariate Regression ◽  
Estimation Method ◽  
Precipitation Forecast ◽  
Tibet Plateau ◽  
Mean Annual Precipitation ◽  
Observation Data ◽  
Precipitation Distribution ◽  
Precipitation Estimation ◽  
Qinghai Tibet Plateau

Due to the complex terrain, sparse precipitation observation sites, and uneven distribution of precipitation in the northeastern slope of the Qinghai–Tibet Plateau, it is necessary to establish a precipitation estimation method with strong applicability. In this study, the precipitation observation data from meteorological stations in the northeast slope of the Qinghai–Tibet Plateau and 11 geographical and topographic factors related to precipitation distribution were used to analyze the main factors affecting precipitation distribution. Based on the above, a multivariate linear regression precipitation estimation model was established. The results revealed that precipitation is negatively related to latitude and elevation, but positively related to longitude and slope for stations with an elevation below 1700 m. Meanwhile, precipitation shows positive correlations with both latitude and longitude, and negative correlations with elevation for stations with elevations above 1700 m. The established multivariate regression precipitation estimating model performs better at estimating the mean annual precipitation in autumn, summer, and spring, and is less accurate in winter. In contrast, the multivariate regression mode combined with the residual error correction method can effectively improve the precipitation forecast ability. The model is applicable to the unique natural geographical features of the northeast slope of the Qinghai–Tibet Plateau. The research results are of great significance for analyzing the temporal and spatial distribution pattern of precipitation in complex terrain areas.

Estimation of Propagation Direction of Earthquake Ground Motion near Ground Surface Based on Array Observation Data

2011 ◽  
Vol 71-78 ◽  
pp. 1354-1359
Author(s):  
Zhong Yuan Yang
Keyword(s):  
Ground Motion ◽  
Structural Model ◽  
Three Dimensional ◽  
Estimation Method ◽  
Ground Surface ◽  
Horizontal Layer ◽  
Earthquake Ground Motion ◽  
Observation Data ◽  
Three Dimension ◽  
Layer Velocity

The three-dimensional propagation directions of earthquake ground motion near ground surface are estimated for eight earthquakes. By the NIOM method, three dimension (3-D) oriental angles are resulted in determining the arrival time of an incident wave and a reflectiveed wave. In this study, the estimation method of 3-D propagation directions is developed rationally and confirmed reliably with the independent data of Chiba array in Japan. The propagation directions are equivalent numerically to the geographical position of plural spots in horizontal and vertical, and the theoretical result obtained by the structural model of global horizontal layer velocity.

scholarly journals Overcoming the Existence of Extreme Outlier Data by Using Robust MM Method Based on The Objective Function of Tukey bisquare

2018 ◽  
Vol 1 (1) ◽  
pp. 022-032
Author(s):  
Science Nature
Keyword(s):  
High Efficiency ◽  
Robust Regression ◽  
Weighting Function ◽  
Estimation Method ◽  
Breakdown Point ◽  
Error Component ◽  
Least Square ◽  
Model Parameters ◽  
Observation Data ◽  
Extreme Outlier

A widely used estimation method in estimating regression model parameters is the ordinary least square (OLS) that minimizes the sum of the error squares. In addition to the ease of computing, OLS is a good unbiased estimator as long as the error component assumption ()  in the given model is met. However, in the application, it is often encountered violations of assumptions. One of the violation types is the violation of distributed error assumption which is caused by the existence of the outlier on observation data. Thus, a solid method is required to overcome the existence of outlier, that is Robust Regression. One of the Robust Regression methods commonly used is robust MM method. Robust MM method is a combination of breakdown point and high efficiency. Results obtained based on simulated data generated using SAS software 9.2, shows that the use of objective weighting function tukey bisquare is able to overcome the existence of extreme outlier. Furthermore, it is determined that the value of tuning constant c with Robust MM method is 4.685 and it is obtained95% of efficiency. Thus, the obtained breakdown point is 50%.    

scholarly journals Parameter estimation in a Black Scholes

2018 ◽  
Vol 22 (Suppl. 1) ◽  
pp. 117-122
Author(s):  
Mustafa Bayram ◽  
Buyukoz Orucova ◽  
Tugcem Partal
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Parametric Method ◽  
Estimation Method ◽  
Parametric Estimation ◽  
Likelihood Method ◽  
Observation Data ◽  
Unknown Parameters ◽  
Black Scholes ◽  
Non Parametric

In this paper we discuss parameter estimation in black scholes model. A non-parametric estimation method and well known maximum likelihood estimator are considered. Our aim is to estimate the unknown parameters for stochastic differential equation with discrete time observation data. In simulation study we compare the non-parametric method with maximum likelihood method using stochastic numerical scheme named with Euler Maruyama.

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