scholarly journals Improving Gravity Estimation Accuracy for the GT-2A Airborne Gravimeter Using Spline-Based Gravity Models

2020 ◽  
Author(s):  
Vadim S. Vyazmin ◽  
Yuri V. Bolotin ◽  
Anton O. Smirnov
Keyword(s):  
Gravity Models ◽  
Estimation Accuracy ◽  
Airborne Gravimeter ◽  
Gravity Estimation

scholarly journals Gravity: Estimation Methods for Gravity Models in R

2018 ◽  
Vol 3 (31) ◽  
pp. 1038
Author(s):  
Anna-Lena Wölwer ◽  
Jan Burgard ◽  
Joshua Kunst ◽  
Mauricio Vargas
Keyword(s):  
Gravity Models ◽  
Estimation Methods ◽  
Gravity Estimation

Determinants of Brazilian Exports by Levels of Technological Intensity: A Gravity Model Analysis Using the PPML Estimator

2019 ◽  
Vol 10 (9) ◽  
pp. 861-879
Author(s):  
Edson Roberto Vieira ◽  
◽  
Daniel Henrique Alves Reis ◽  
Keyword(s):  
International Market ◽  
Positive Influence ◽  
Gravity Models ◽  
Transport Costs ◽  
Consumer Market ◽  
Technology Products ◽  
The Us ◽  
Trading Partners ◽  
Negative Impacts ◽  
Technological Intensity

The objective of this study is to analyze the determinants of Brazilian exports by levels of technological intensity in the period 2000-2015. Gravity models were estimated for total of the exports and for each type of exports by levels of technological intensity, using the PPML-estimator. The study indicates that there is a process of concentration of Brazilian exports in low technology and medium-low technology products, at the same period in which China's share of total Brazilian shipments abroad grew. Estimates of empirical gravity models have shown that the income and size of the consumer market of Brazil’s trading partners seem to have the greatest positive influence on the Brazilian exports. Indications of this study are that the Brazil should continue to diversify its trading partners to minimize the impacts of a possible reduction of the economic growth of large trading partners (such as China and the US) on its exports and increase its exports of products with greater technological intensity. The results also highlight the need for Brazil to make greater efforts to increase its competitiveness in the international market to reduce the negative impacts of transport costs on the final prices of products exported by the country.

Experimental research of indoor navigation system based on broadcast signals

2020 ◽  
Vol 49 (5) ◽  
pp. 49-57
Author(s):  
A. V. Ksendzuk ◽  
E. A. Surmin ◽  
V. V. Kachesov ◽  
S. O. Zhdanov ◽  
K. S. Shakhalov
Keyword(s):  
Navigation System ◽  
Open Space ◽  
Position Estimation ◽  
Indoor Navigation ◽  
Estimation Accuracy ◽  
Satellite Systems ◽  
Indoor Navigation System ◽  
Point To Point ◽  
Navigation Signals ◽  
Navigation Equipment

Results of an experimental study of a local navigation system based on the processing signals from broadcast sources presented. The results of the development of processing algorithms for point-to-point coordinates estimation of the object are presented. The results of the development of algorithms for trajectories estimation are presented. In performed simulation the possibility of obtaining submeter position estimation accuracy in the proposed system is shown. Development results of the navigation module demonstrator are presented. The results of experimental work in difficult navigation conditions, in the presence of shading, reflections and other factors, are presented. It is shown that the developed navigation module allows in the open space near buildings which partially obscuring the satellite systems signals to obtain accuracy higher than the GNSS navigation equipment. In indoor environment in the absence of satellite navigation signals, the developed module shows positioning accuracy not worse than 1.5 meters and provides a measurement rate 1 Hz and better.

scholarly journals Multilayer Soil Moisture Mapping at a Regional Scale from Multisource Data via a Machine Learning Method

2019 ◽  
Vol 11 (3) ◽  
pp. 284 ◽  
Author(s):  
Linglin Zeng ◽  
Shun Hu ◽  
Daxiang Xiang ◽  
Xiang Zhang ◽  
Deren Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Regional Scale ◽  
Remotely Sensed ◽  
Temporal Variations ◽  
Training Data ◽  
Estimation Accuracy ◽  
Learning Approaches ◽  
Remotely Sensed Data ◽  
Deep Soil

Soil moisture mapping at a regional scale is commonplace since these data are required in many applications, such as hydrological and agricultural analyses. The use of remotely sensed data for the estimation of deep soil moisture at a regional scale has received far less emphasis. The objective of this study was to map the 500-m, 8-day average and daily soil moisture at different soil depths in Oklahoma from remotely sensed and ground-measured data using the random forest (RF) method, which is one of the machine-learning approaches. In order to investigate the estimation accuracy of the RF method at both a spatial and a temporal scale, two independent soil moisture estimation experiments were conducted using data from 2010 to 2014: a year-to-year experiment (with a root mean square error (RMSE) ranging from 0.038 to 0.050 m3/m3) and a station-to-station experiment (with an RMSE ranging from 0.044 to 0.057 m3/m3). Then, the data requirements, importance factors, and spatial and temporal variations in estimation accuracy were discussed based on the results using the training data selected by iterated random sampling. The highly accurate estimations of both the surface and the deep soil moisture for the study area reveal the potential of RF methods when mapping soil moisture at a regional scale, especially when considering the high heterogeneity of land-cover types and topography in the study area.

scholarly journals DEVELOPMENT OF THE MONTHLY AVERAGE DAILY SOLAR RADIATION MAP USING A-CBR, FEM, AND KRIGING METHOD

2017 ◽  
Vol 24 (2) ◽  
pp. 489-512 ◽  
Author(s):  
Choongwan KOO ◽  
Taehoon HONG ◽  
Kwangbok JEONG ◽  
Jimin KIM
Keyword(s):  
Solar Radiation ◽  
Decision Makers ◽  
Estimation Accuracy ◽  
Case Based Reasoning ◽  
Final Decision ◽  
Kriging Method ◽  
Pv System ◽  
Fem Model ◽  
Remote Sensors ◽  
Monthly Average

Photovoltaic (PV) system could be implemented to mitigate global warming and lack of energy. To maximize its effectiveness, the monthly average daily solar radiation (MADSR) should be accurately estimated, and then an accurate MADSR map could be developed for final decision-makers. However, there is a limitation in improving the accuracy of the MADSR map due to the lack of weather stations. This is because it is too expensive to measure the actual MADSR data using the remote sensors in all the sites where the PV system would be installed. Thus, this study aimed to develop the MADSR map with improved estimation accuracy using the advanced case-based reasoning (A-CBR), finite element method (FEM), and kriging method. This study was conducted in four steps: (i) data collection; (ii) estimation of the MADSR data in the 54 unmeasured locations using the A-CBR model; (iii) estimation of the MADSR data in the 89 unmeasured locations using the FEM model; and (iv) development of the MADSR map using the kriging method. Compared to the previous MADSR map, the proposed MADSR map was determined to be improved in terms of its estimation accuracy and classification level.

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