• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.12
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• pp.2582-2589
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• 2011

This Paper proposed the prediction method of fuel consumption from vehicle diagnosis informations through OBD-II Interface. We assumed mass air flow (MAF), shor-term fuel trim (STFT), and long-term fuel trim (LTFT) had a relationship with fuel consumption. We got the output as fuel-consumption from MAF, STFT, and LTFT as input variables. We had modelling as combustion reaction equation with OBD-II data and fuel consumption data supported by automotive company in real. In order to verify the effectiveness of proposed method, 5 km real road-test was performed. The results showed that the proposed method can estimate precisely the fuel consumption from vehicle data.

• Journal of Advanced Navigation Technology
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• v.26 no.2
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• pp.119-124
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• 2022

The international gnss service (IGS) provides real-time service (RTS) orbit and clock correction applicable to the broadcast ephemeris of GNSS satellites. However, since the RTS correction cannot be received if the Internet connection is lost, the RTS correction should be predicted and used when a signal interruption occurs in order to perform stable precise point positioning (PPP). In this paper, PPP was performed by predicting orbit and clock correction using a long short-term memory (LSTM) algorithm in real-time during the signal loss. The prediction performance was analyzed by implementing the LSTM algorithm in RPI (raspberry pi), the processing speed of which is not high. Compared to the polynomial prediction model, LSTM showed excellent performance in long-term prediction.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.423-426
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• 2006

본 논문에서는 GPS와 WAAS를 동시에 수신하고 처리할 수 있는 GPS/WAAS 소프트웨어 수신기를 개발한다. WAAS는 GPS L1과 동일한 반송파 주파수와 C/A 코드, 데이터로 구성되며, 데이터 포맷이 다르므로 WAAS 데이터를 복조하기 위한 블록을 추가하여, 보정 정보와 무결성 정보, WAAS 위성의 위치 정보를 추출 및 적용한다. 본 논문에서는 보정 정보를 처리하는 블록이 추가된 GPS/WAAS 소프트웨어 수신기를 설계하여 수신기의 측위 성능 및 가용성을 높이고, 실제 측정치를 이용한 측위 실험을 통하여 그 성능을 검증한다.

• Korean Journal of Remote Sensing
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• v.26 no.4
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• pp.403-410
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• 2010

NDVI(Normalized Difference Vegetation Index), which is broadly used as short-term data composite, is an important parameter for climate change and long-term land surface monitoring. Although atmospheric correction is performed, NDVI dramatically appears several low peak noise in the long-term time series. They are related to various contaminated sources, such as cloud masking problem and wet ground condition. This study suggests a simple method through harmonic analysis for reducing NDVI noise using SPOT/VGT NDVI 10-day MVC data. The harmonic analysis method is compared with the polynomial regression method suggested previously. The polynomial regression method overestimates the NDVI values in the time series. The proposed method showed an improvement in NDVI correction of low peak and overestimation.

• Journal of Satellite, Information and Communications
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• v.12 no.3
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• pp.22-34
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• 2017

The K-DRUM(K-water hydrologic & hydraulic Distributed RUnoff Model), a distributed rainfall-runoff model of K-water, calculates predicted runoff and water surface level of a dam using precipitation data. In order to obtain long-term hydrometeorological information, K-DRUM requires long-term weather forecast. In this study, we built a system providing long-term hydrometeorological information using predicted rainfall ensemble of GloSea5(Global Seasonal Forecast System version 5), which is the seasonal meteorological forecasting system of KMA introduced in 2014. This system produces K-DRUM input data by automatic pre-processing and bias-correcting GloSea5 data, then derives long-term inflow predictions via K-DRUM. Web-based UI was developed for users to monitor the hydrometeorological information such as rainfall, runoff, and water surface level of dams. Through this UI, users can also test various dam management scenarios by adjusting discharge amount for decision-making.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.4
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• pp.321-329
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• 2013

Despite the advantage of unlimited access, there are insufficient studies for the accuracy and stability of FKP that blocks the spread of the system for various applications. Therefore, we performed a long-term analysis from continuous real-time positioning, and investigated the error characteristics dependent on the size and the surrounding environment. The FKP shows significant changes in the positioning accuracy at different times of day, where the accuracy during daytime is worse than that of nighttime. In addition, the size and deviation of FKP correction may change with the ionospheric conditions, and high correlation between ambiguity resolution rate and the deviation of correction was observed. The receivers continuously request the correction information in order to cope with sudden variability of ionosphere. On the other hand, the correction information was not received up to an hour in case of stable ionospheric condition. It is noteworthy that the outliers of FKP are clustered in their position with some biases. Since several meters of errors can be occurred for kinematic positioning with FKP, therefore, it is necessary to make appropriate preparation for real-time applications.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.6
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• pp.815-821
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• 2021

This study addresses on the design of performance monitoring system for the time synchronization service of the enhanced long-range navigation (eLoran) system, which has a representative ground-wave radio broadcast system capable of providing positioning, navigation, timing and data (PNT&D) services. The limitations of time-synchronized systems due to the signal vulnerabilities of the global navigation satellite system (GNSS) are explained, and the performance monitoring system for the eLoran timing service as a backup to the GNSS is proposed. The time synchronization service using eLoran system as well as system configurations and the user requirements in the differential Loran (dLoran) system are described to monitor the time synchronization performance. The results of the designed system are presented for long-term operation in the eLoran testbed environment. As the results of time performance monitoring, we were able to verify the time synchronization precision within 43.71 ns without corrections, 22.52 ns with corrections. Based on these results, the eLoran system can be utilized as a precise time synchronization source for GPS timing backup.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.201-217
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• 2022

The United States is one of the largest producers of major crops such as wheat, maize, and soybeans, and is a major exporter of these crops. Therefore, it is important to estimate the crop production of the country in advance based on reliable long- term weather forecast information for stable crops supply and demand in Korea. The purpose of this study is to improve the seasonal predictability of the agro-climatic indices over the United States by using regional-scale daily temperature. For long-term numerical weather prediction, a dynamical downscaling is performed using Weather Research and Forecasting (WRF) model, a regional climate model. As the initial and lateral boundary conditions of WRF, the global hourly prediction data obtained from the Pusan National University Coupled General Circulation Model (PNU CGCM) are used. The integration of WRF is performed for 22 years (2000-2021) for period from June to December of each year. The empirical quantile mapping, one of the bias correction methods, is applied to the timeseries of downscaled daily mean, minimum, and maximum temperature to correct the model biases. The uncorrected and corrected datasets are referred WRF_UC and WRF_C, respectively in this study. The daily minimum (maximum) temperature obtained from WRF_UC presents warm (cold) biases over most of the United States, which can be attributed to the underestimated the low (high) temperature range. The results show that WRF_C simulates closer to the observed temperature than WRF_UC, which lead to improve the long- term predictability of the temperature- based agro-climatic indices.

• Korean Journal of Remote Sensing
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• v.37 no.5_2
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• pp.1235-1243
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• 2021

Geostationary Ocean Color Imager 2 (GOCI-II) on Geo-KOMPSAT-2 (GK2B)satellite was developed as a mission successor of GOCI on COMS which had been operated for around 10 years since launch in 2010 to observe and monitor ocean color around Korean peninsula. GOCI-II on GK2B was successfully launched in February of 2020 to continue for detection, monitoring, quantification, and prediction of short/long term changes of coastal ocean environment for marine science research and application purpose. GOCI-II had already finished IAC and IOT including early in-orbit calibration and had been handed over to NOSC (National Ocean Satellite Center) in KHOA (Korea Hydrographic and Oceanographic Agency). Radiometric calibration was periodically conducted using on-board solar calibration system in GOCI-II. The final calibrated gain and offset were applied and validated during IOT. And three video parameter sets for one day and 12 video parameter sets for a year was selected and transferred to NOSC for normal operation. Star measurement-based INR (Image Navigation and Registration) navigation filtering and landmark measurement-based image geometric correction were applied to meet the all INR requirements. The GOCI2 INR software was validated through INR IOT. In this paper, status and results of IOT, radiometric calibration and INR of GOCI-II are analysed and described.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.4
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• pp.111-124
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• 2010

To monitor the environment of land surface change is considered as an important research field since those parameters are related with land use, climate change, meteorological study, agriculture modulation, surface energy balance, and surface environment system. For the change detection, many different methods have been presented for distributing more detailed information with various tools from ground based measurement to satellite multi-spectral sensor. Recently, using high resolution satellite data is considered the most efficient way to monitor extensive land environmental system especially for higher spatial and temporal resolution. In this study, we use two different spatial resolution satellites; the one is SPOT/VEGETATION with 1 km spatial resolution to detect coarse resolution of the area change and determine objective threshold. The other is Landsat satellite having high resolution to figure out detailed land environmental change. According to their spatial resolution, they show different observation characteristics such as repeat cycle, and the global coverage. By correlating two kinds of satellites, we can detect land surface change from mid resolution to high resolution. The K-mean clustering algorithm is applied to detect changed area with two different temporal images. When using solar spectral band, there are complicate surface reflectance scattering characteristics which make surface change detection difficult. That effect would be leading serious problems when interpreting surface characteristics. For example, in spite of constant their own surface reflectance value, it could be changed according to solar, and sensor relative observation location. To reduce those affects, in this study, long-term Normalized Difference Vegetation Index (NDVI) with solar spectral channels performed for atmospheric and bi-directional correction from SPOT/VEGETATION data are utilized to offer objective threshold value for detecting land surface change, since that NDVI has less sensitivity for solar geometry than solar channel. The surface change detection based on long-term NDVI shows improved results than when only using Landsat.