• Journal of Korea Water Resources Association
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• v.36 no.3 s.134
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• pp.345-363
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• 2003

From the General Circulation Models(GCMs), it is known that the increases of concentrations of greenhouse gases will have significant implications for climate change in global and regional scales. The GCM has an uncertainty in analyzing the meteorologic processes at individual sites and so the 'downscaling' techniques are used to bridge the spatial and temporal resolution gaps between what, at present, climate modellers can provide and what impact assessors require. This paper describes a method for assessing local climate change impacts using a robust statistical downscaling technique. The method facilitates the rapid development of multiple, low-cost, single-site scenarios of daily surface weather variables under current and future regional climate forcing. The construction of climate change scenarios based on spatial regression(transfer function) downscaling and on the use of a local stochastic weather generator is described. Regression downscaling translates the GCM grid-box predictions with coarse resolution of climate change to site-specific values and the values were then used to perturb the parameters of the stochastic weather generator in order to simulate site-specific daily weather values. In this study, the global climate change scenarios are constructed using the YONU GCM control run and transient experiments.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.5
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• pp.91-101
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• 2014

In this study, we presented a timing verification method for a passenger car diesel engine management system (EMS) using measurement-based worst-case execution time (WCET) analysis. In order to cope with AUTOSAR-compliant software architecture, a development process model is proposed. In the process model, a runnable is regarded as a test unit and its temporal behavior (i.e. maximum observed execution time, MOET) is obtained along with on-target functionality evaluation results during online unit test. Furthermore, a cost-effective framework for online unit test is proposed. Because the runtime environment layer and the standard calibration environment are utilized to implement test interface, additional resource consumption of the target processor is minimized. Using the proposed development process model and unit test framework, the MOETs of 86 runnables for diesel EMS are obtained with 213 unit test cases. Using the obtained MOETs of runnables, the WCETs of tasks are estimated and the schedulability is evaluated. From the schedulability analysis results, the problems of the initially designed schedule table is recognized and it is fixed by redesigning of the runnable mapping and task offset. Through the various test scenarios, the proposed method is validated.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1621-1621
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• 2001

The quality of sugarcane grown on the NE Australian tropical coast ($16^{\circ}$15'- $18^{\circ}$15' S Lat.) has declined markedly in the past seven years. This has been linked to dilution of mill-supply cane with increasing levels of non mature-stalk material consisting of leaves and sucker culms. The prime research objective was to examine the transition from the pre-harvest, in-field crop to harvested material sent for processing, in terms of quality and crop fraction proportions. A secondary objective was to quantify the effects of preharvest-season crop habit and culm condition on crop quality. Ten quadrat samples from each of 54 random crop sites (17 in 1999 and 37 in 2000), covering a wide range of variables (cultivar, crop class, and edaphic, topographic, climatic, and temporal factors) were collected immediately before harvest. Samples were partitioned into four fractions:- sound and unsound mature stalks (culms), sucker culms, and extraneous matter (leaves). Material harvested from each site was sampled and partitioned into four fractions:- sound and unsound billets (culm pieces), culm-spindle pieces, and leaf. In 2000, before harvest, 14 additional sites were sampled monthly, on three occasions, from March - June. Erect and non-erect culms were divided into sound and unsound classes. All samples were disintegrated and presented to a remote reflectance module of a scanning spectrophotometer using the BSES large cassette module. Near infra-red spectroscopic (NIS) analyses were developed for the rapid determination of quality components (Brix, commercial cane sugar (CCS), fibre, moisture, and polariscope reading). Calibrations for three material groups (culm (n = 639), non-culm (n = 496), and combined) were developed for all components using the 1999 data set. Two sub-sets (n = 178, and 190) of about 10% of the preharvest-season and harvest populations scanned in 2000 also were subjected to full routine laboratory analyses. The 1999 combined calibrations were excellent, but the culm calibrations produced consistently lower standard errors. Non-culm calibrations were marginally better than the combined for only CCS and pol. reading. Analysis of the 2000 culm data with calibrations using all 1999 and 2000 culm data resulted in better predictions relative to the 1999 culm calibrations. This also was true for the combined calibrations. Assessment of quality components in pre- and post-harvest sugarcane using NIS calibrations was more cost effective than using routine laboratory techniques. Outcomes from this NIS-facilitated research will have important economic consequences for the Australian sugarcane industry. Potential CCS present in mature culms is being discounted by dilution with leaves and sucker culms, threatening farm viability. The results question the efficacy of current harvesting technology. The CCS of harvested cane is improved only marginally over that of the in-field crop. Current harvesting technology requires either supplementary, innovative pre-mill processing or a design revolution to improve mill-supply cane quality, and therefore whole of industry economics. NIS-facilitated analyses, before the harvest season, highlighted the benefits of growing erect, sound crops. Loss of CCS then, can be minimized only by a combination of crop improvement and agronomic solutions, applied as part of sound on-farm management.

• 한국지구물리탐사학회:학술대회논문집
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• 2004.06a
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• pp.3-17
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• 2004

Space-borne Earth observation technique is one of the most cost effective and rapidly advancing Earth science research tools today and the potential field and micro-wave radar applications have been leading the discipline. The traditional optical imaging systems including the well known Landsat, NOAA - AVHRR, SPOT, and IKONOS have steadily improved spatial imaging resolution but increasing cloud covers have the major deterrent. The new Earth observation satellites ENVISAT (launched on March 1 2002, specifically for Earth environment observation), ALOS (planned for launching in 2004 - 2005 period and ALOS stands for Advanced Land Observation Satellite), and RADARSAT-II (planned for launching in 2005) all have synthetic aperture radar (SAR) onboard, which all have partial or fully polarimetric imaging capabilities. These new types of polarimetric imaging radars with repeat orbit interferometric capabilities are opening up completely new possibilities in Earth system science research, in addition to the radar altimeter and scatterometer. The main advantage of a SAR system is the all weather imaging capability without Sun light and the newly developed interferometric capabilities, utilizing the phase information in SAR data further extends the observation capabilities of directional surface covers and neotectonic surface displacements. In addition, if one can utilize the newly available multiple frequency polarimetric information, the new generation of space-borne SAR systems is the future research tool for Earth observation and global environmental change monitoring. The potential field strength decreases as a function of the inverse square of the distance between the source and the observation point and geophysicists have traditionally been reluctant to make the potential field observation from any space-borne platforms. However, there have recently been a number of potential field missions such as ASTRID-2, Orsted, CHAMP, GRACE, GOCE. Of course these satellite sensors are most effective for low spatial resolution applications. For similar objects, AMPERE and NPOESS are being planned by the United States and France. The Earth science disciplines which utilize space-borne platforms most are the astronomy and atmospheric science. However in this talk we will focus our discussion on the solid Earth and physical oceanographic applications. The geodynamic applications actively being investigated from various space-borne platforms geological mapping, earthquake and volcano .elated tectonic deformation, generation of p.ecise digital elevation model (DEM), development of multi-temporal differential cross-track SAR interferometry, sea surface wind measurement, tidal flat geomorphology, sea surface wave dynamics, internal waves and high latitude cryogenics including sea ice problems.

• Journal of Korean Society of Forest Science
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• v.83 no.4
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• pp.441-449
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• 1994

The two reflective channels(red and near infrared spectrum) of advanced very high resolution radiometer(AVHRR) data were used to classify primary vegetation cover types in the Korean Peninsula. From the NOAA-11 satellite data archive of 1991, 27 daytime scenes of relatively minimum cloud coverage were obtained. After the initial radiometric calibration, normalized difference vegetation index(NDVI) was calculated for each of the 27 data sets. Four or five daily NDVI data were then overlaid for each of the six months starting from February to November and the maximum value of NDVI was retained for every pixel location to make a monthly composite. The six bands of monthly NDVI composite were nearly cloud free and used for the computer classification of vegetation cover. Based on the temporal signatures of different vegetation cover types, which were generated by an unsupervised block clustering algorithm, every pixel was classified into one of the six cover type categories. The classification result was evaluated by both qualitative interpretation and quantitative comparison with existing forest statistics. Considering frequent data acquisition, low data cost and volume, and large area coverage, it is believed that AVHRR data are effective for vegetation cover type mapping at regional scale.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.4
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• pp.35-41
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• 2015

Real estate appraisal methods include profit-based, cost-based and comparison-based measures. The purpose of this study is to scientifically quantify the comparison-based method mostly utilized in valuating real estate property among the appraisal methods. The comparison method is to estimate the value of target property from other previously-traded property cases by comparing and adjusting their temporal gap, spatial gap and space-time gap. In appraisal practices, this comparison method is used generally for land property. If based on previous transactions; prices, time point of transaction, region and individual factors were analyzed to valuate. If based on official land values; official value, time point, region and individual factors are analyzed. Of these, the individual factors are an important process of comparing individual characteristics where real estate appraisers' subjective assessment could intervene. Though appraisers, as experts make generally precise assessment, still, it is a subjective judgment open to difference between appraisers themselves, causing disputes from time to time. In this recognition, the study seeks to quantify such a subjective assessment of appraisers by running GIS analysis on individual factor components including street condition, access condition;and plotting condition.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.1
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• pp.83-93
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• 2020

East Asia, which includes China, Japan, Korea, and Mongolia, is highly impacted by hydroclimate extremes such drought, flood, and typhoon recent year. In 2017, more than 18.5 million hectares of crops have been damaged in China, and Korea has suffered economic losses as a result of severe drought. Satellite-derived rainfall products are becoming more accurate as space and time resolution become increasingly higher, and provide an alternative means of estimating ground-based rainfall. In this study, we verified the availability of rainfall products by comparing widely used satellite images such as Climate Hazards Groups InfraRed Precipitation with Station (CHIRPS), Global Precipitation Climatology Centre (GPCC), and Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) with ground stations in East Asia. Also, the satellite-based rainfall products were used to calculate the Standardized Precipitation Index (SPI). The temporal resolution is based on monthly images and compared with the past 30 years data from 1989 to 2018. The comparison between rainfall data based on each satellite image products and the data from weather station-based weather data was shown by the coefficient of determination and showed more than 0.9. Each satellite-based rainfall data was used for each grid and applied to East Asia and South Korea. As a result of SPI analysis, the RMSE values of CHIRPS were 0.57, 0.53 and 0.47, and the MAE values of 0.46, 0.43 and 0.37 were better than other satellite products. This satellite-derived rainfall estimates offers important advantages in terms of spatial coverage, timeliness and cost efficiency compared to analysis for drought assessment with ground stations.

• Korean Journal of Remote Sensing
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• v.11 no.3
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• pp.101-116
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• 1995

The fact that the demands on traffic data collection are imposed by economic and safety considerations raisese the question of the potential for complementing existing traffic data collection programs with satellite data. Evaluating and monitoring traffic characteristics is becoming increasingly important as worsening congestion, declining economic situations, and increasing environmental sensitivies are forcing the government and municipalities to make better use of existing roadway capacities. The present system of using automatic counters at selected points on highways works well from a temporal point of view (i.e., during a specific period of time at one location). However, the present system does not cover the spatial aspects of the entire road system (i.e., for every location during specific periods of time); the counters are employed only at points and only on selected highways. This lack of spatial coverage is due, in part, to the cost of the automatic counters systems (fixed procurement and maintenance costs) and of the personal required to deploy them. The current procedure is believed to work fairly well in the aggregate mode, at the macro level. However, at micro level, the numbers are more suspect. In addition, the statistics only work when assuming a certain homogenity among characteristics of highways in the same class, an assumption that is impossible to test whn little or no data is gathered on many of the highways for a given class. In this paper, a remote sensing system as complement of the existing system is considered and implemented. Since satellite imagery with high resolution is not available, digitized panchromatic imagery acquired from an aircraft platform is utilized for initial test of the feasibility and performance capability of remote sensing data. Different levels of imagery resolutions are evaluated in an attempt to determine what vehicle types could be classified and counted against a background of pavement types, which might be expected in panchromatic satellite imagery. The results of a systematic study with three different levels of resolutions (1m, 2m and 4m) show that the panchromat ic reflectances of vehicles and pavements would be distributed so similarly that it would be difficult to classify systematically and analytically remotely sensing vehicles on pavement within panchromatic range. Anaysis of the aerial photographs show that the shadows of the vehicles could be a cue for vehicle detection.

• Korean Journal of Remote Sensing
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• v.15 no.2
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• pp.147-158
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• 1999

Remote sensing technique has offered better understanding of our environment for the decades by providing useful level of information on the landcover. In many applications using the remotely sensed data, digital image processing methodology has been usefully employed to characterize the features in the data and develop the models. Random field models, especially Markov Random Field (MRF) models exploiting spatial relationships, are successfully utilized in many problems such as texture modeling, region labeling and so on. Usually, remotely sensed imagery are very large in nature and the data increase greatly in the problem requiring temporal data over time period. The time required to process increasing larger images is not linear. In this study, the methodology to reduce the computational cost is investigated in the utilization of the Markov Random Field. For this, multiresolution framework is explored which provides convenient and efficient structures for the transition between the local and global features. The computational requirements for parameter estimation of the MRF model also become excessive as image size increases. A Bayesian approach is investigated as an alternative estimation method to reduce the computational burden in estimation of the parameters of large images.

• Journal of the Korean Society of Hazard Mitigation
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• v.11 no.3
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• pp.229-235
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• 2011

In order to manage a sewer system effectively, flow conditions such as flux, water quality, Infiltration and Inflow (I/I), Combined Sewer Overflows (CSOs), etc need to be monitored on a regular base. Therefore, in sewer networks, a monitoring is so important to prevent the river disaster. Monitoring all nodes of an entire sewer system is not necessary and cost-prohibitive. Water quality monitoring points that can represent a sewer system should be selected in a economical manner. There is no a standard for the selection of monitoring points and the quantitative analysis of the observed data has not been applied in sewer system. In this study, the entropy method was applied for a sewer network to evaluate and determine the optimal water quality monitoring points using genetic algorithm. The entropy method allows to analyze the observed data for the pattern and magnitude of temporal water quality change. Since water quality measurement usually accompanies with flow measurement, a set of installation locations of flowmeters was chosen as decision variables in this study.