• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.4
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• pp.59-64
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• 2020

Occasionally, MODEM test in satellite communication systems are needed. But Rx terminals in satellite communications are designed to obtain high SNR generally, therefore artificial bad conditions and environments are demanded for the test. One of the typical method is satellite output power reduction. Using noise generator can be another method. However, these costs a lot of money, time, and procedures in reality. In order to overcome these problems, this paper proposes an artificial noise generation method for MODEM test in satellite communication systems. First of all, SNR of a general heterodyne Rx terminal is calculated. Based on the calculation, a new model which is including variable attenuator is proposed to increase noise level. Simulation results illustrate the variable attenuator can control SNR, and these show that MODEM test in satellite communication systems be possible.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.7
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• pp.851-856
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• 2013

Recently, as the tactical environment has changedto one of network-centric warfare, where all components are connected through a network, much emphasis has been placed on the use of an artificial satellite for achieving high communication speeds. To provide a high-quality artificial satellite link, stabilization is very important in a platform. Previous stabilization control techniques used PI control, which is commonly used for vessels. However, for ground terminals that require a higher communication speed, the antenna should move faster to track an artificial satellite within a short period of time. Moreover, the terminals must be equipped with proper sensors and algorithms so that they can detect and compensate for external disturbances while tracking the artificial satellite. In this study, through the analysis of the dynamic model of an antenna system, a stabilization algorithm for ground terminals was proposed;this algorithm shows high isolation performance in the low-frequency range and includes $PI^2$ control.

• Journal of Cadastre & Land InformatiX
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• v.53 no.1
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• pp.107-121
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• 2023

In order to acquire a wide range of land that changes in real time and quickly and accurately grasp it, we plan to utilize the recently released high-resolution S.Korea's satellite image data and artificial intelligence (AI). Compared to existing satellite images, the spectral and periodic resolutions of S.Korea's satellite are higher, making them a more suitable data source for periodically monitoring changes in land. Therefore, this study aims to acquire S.Korea's satellite, select 8 types of objects to detect land changes, construct data sets for them, and apply AI models to analyze them. In order to confirm the optimal model and variable conditions for detecting 8 types of objects of various types, several experiments are performed and AI-based image analysis is technically reviewed.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.120-120
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• 2020

Spatial precipitation data is one of the essential components in modeling hydrological problems. The estimation of these data has achieved significant achievements own to the recent advances in remote sensing technology. However, there are still gaps between the satellite-derived rainfall data and observed data due to the significant dependence of rainfall on spatial and temporal characteristics. An effective approach based on the Convolutional Neural Network (CNN) model to correct the satellite-derived rainfall data is proposed in this study. The Mekong River basin, one of the largest river system in the world, was selected as a case study. The two gridded precipitation data sets with a spatial resolution of 0.25 degrees used in the CNN model are APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks). In particular, PERSIANN-CDR data is exploited as satellite-based precipitation data and APHRODITE data is considered as observed rainfall data. In addition to developing a CNN model to correct the satellite-based rain data, another statistical method based on standard deviations for precipitation bias correction was also mentioned in this study. Estimated results indicate that the CNN model illustrates better performance both in spatial and temporal correlation when compared to the standard deviation method. The finding of this study indicated that the CNN model could produce reliable estimates for the gridded precipitation bias correction problem.

• Journal of the Korean earth science society
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• v.31 no.5
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• pp.465-474
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• 2010

Real-time rainfall monitoring is of great practical importance over the highly populated Indochina area, which is prone to natural disasters, in particular in association with rainfall. With the goal of d etermining near real-time half-hourlyrain estimates from satellite, the three-layer, artificial neural networks (ANN) approach was used to train the brightness temperatures at 6.7, 11, and $12-{\mu}m$ channels of the Japanese geostationary satellite MTSAT against passive microwavebased rain rates from Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and TRMM Precipitation Radar (PR) data for the June-September 2005 period. The developed model was applied to the MTSAT data for the June-September 2006 period. The results demonstrate that the developed algorithm is comparable to the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) results and can be used for flood monitoring across the Indochina area on a half-hourly time scale.

• Journal of Astronomy and Space Sciences
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• v.14 no.1
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• pp.136-141
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• 1997

To calculate the position and velocity of the artificial satellite precisely, one has to build a mathematical model concerning the perturbations by understanding and analysing the space environment correctly and then quantifying. Due to these space environment model, the total acceleration of the artificial satellite can be expressed as the 2nd order differential equation and we build an orbit propagation algorithm by integrating twice this equation by using the Cowell's method which gives the position and velocity of the artificial satellite at any given time. Perturbations important for the orbits of geostationary spacecraft are the Earth's gravitational potential, the gravitational influences of the sun and moon, and the solar radiation pressure. For precise orbit propagation in Cowell' method, 40 x 40 spherical harmonic coefficients can be applied and the JPL DE403 ephemeris files were used to generate the range from earth to sun and moon and 8th order Runge-Kutta single step method with variable step-size control is used to integrate the the orbit propagation equations.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.497-500
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• 2002

When one is interested in the dynamics of a mechanical system with electric motors, the force generated by the motor is generally considered as only an applied torque or force independent of mechanical state variables such as velocity. For a system operated in non-steady dynamic conditions, however, the usual analysis approach may fail to predict some characteristics in the dynamic behaviors because of electromechanical coupling effects. In this paper, we propose dynamics analysis model in which dc motor dynamics with the electromechanical coupling effects are embedded to mechanical dynamics models. The do motor is modeled based on its equivalent circuit model and included in the dynamics solving algorithm which we developed before, called generalized recursive dynamics formula. The developed dynamic analysis model is effective and realistic for analysis of electromechanical dynamics of a system with do motors. The developed model is evaluated by constructing and simulating the flexible antennas of an artificial satellite driven by do motors.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.1965-1974
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• 2021

Satellite remote sensing approach can be actively used for forest monitoring. Especially, it is much meaningful to utilize Korea multi-purpose satellites, an independently operated satellite in Korea, for forest monitoring of Korea, Recently, several studies have been performed to exploit meaningful information from satellite remote sensed data via machine learning approaches. The forest information produced through machine learning approaches can be used to support the efficiency of traditional forest monitoring methods, such as in-situ survey or qualitative analysis of aerial image. The performance of machine learning approaches is greatly depending on the characteristics of study area and data. Thus, it is very important to survey the best model among the various machine learning models. In this study, the performance of deep neural network to classify artificial or natural forests was analyzed in Samcheok, Korea. As a result, the pixel accuracy was about 0.857. F1 scores for natural and artificial forests were about 0.917 and 0.433 respectively. The F1 score of artificial forest was low. However, we can find that the artificial and natural forest classification performance improvement of about 0.06 and 0.10 in F1 scores, compared to the results from single layered sigmoid artificial neural network. Based on these results, it is necessary to find a more appropriate model for the forest type classification by applying additional models based on a convolutional neural network.

• Journal of the Korean Geographical Society
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• v.46 no.3
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• pp.319-330
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• 2011

In this study, a soil moisture estimation model was proposed using the ground observation data of soil moisture, precipitation, surface temperature, MODIS NDVI and artificial neural networks. The model was calibrated and verified on the Yongdam dam watershed which has reliable ground soil moisture networks. The test statistics of calibration sites, Jucheon, Bugui, Sangjeon, showed that the correlation coefficients between observations and estimations are about 0.9353 and RMSE is about 1.4957%. Also that of the verification site, Cheoncheon2, showed that the correlation coefficient is about 0.8215 and RMSE is about 4.2077%. The soil moisture estimation model was applied to estimate the spatial distribution of soil moisture in the Yongdam dam watershed and results showed improved spatial soil moisture distribution since the model used satellite information of NDVI and artificial neural networks which can represent the nonlinear relationships between data well. The model should be useful to estimate wide range soil moisture information.

• Korean Journal of Remote Sensing
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• v.35 no.3
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• pp.447-455
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• 2019

Since the forest type map in Korea has been mostly constructed every five years, the forest information from the map lacks up-to-date information. Forest research has been carried out by aerial photogrammetry and field surveys, and hence it took a lot of times and money. The vertical structure of forests is an important factor in evaluating forest diversity and environment. The vertical structure is essential information, but the observation of the vertical structure is not easy because the vertical structure indicates the internal structure of forests. In this study, the index map and texture map produced from KOMPSAT-3/3A/5 satellite images and the canopy information generated by the difference between DSM (Digital Surface Model) and DTM (Digital Terrain Model) were classified using the artificial neural network. The vertical structure of forests of single and multi-layer forests was classified to identify 81.59% of the final classification result.