• Journal of Preventive Medicine and Public Health
• /
• v.44 no.6
• /
• pp.242-248
• /
• 2011

Objectives: Busan is reported to have the highest mortality rate among 16 provinces in Korea, as well as considerable health inequality across its districts. This study sought to examine overall and cause-specific mortality and deprivation at the town level in Busan, thereby identifying towns and causes of deaths to be targeted for improving overall health and alleviating health inequality. Methods: Standardized mortality ratios (SMRs) for all-cause and four specific leading causes of death were calculated at the town level in Busan for the years 2005 through 2008. To construct a deprivation index, principal components and factor analysis were adopted, using 10% sample data from the 2005 census. Geographic information system (GIS) mapping techniques were applied to compare spatial distributions between the deprivation index and SMRs. We fitted the Gaussian conditional autoregressive model (CAR) to estimate the relative risks of mortality by deprivation level, controlling for both the heterogeneity effect and spatial autocorrelation. Results: The SMRs of towns in Busan averaged 100.3, ranging from 70.7 to 139.8. In old inner cities and towns reclaimed for replaced households, the deprivation index and SMRs were relatively high. CAR modeling showed that gaps in SMRs for heart disease, cerebrovascular disease, and physical injury were particularly high. Conclusions: Our findings indicate that more deprived towns are likely to have higher mortality, in particular from cardiovascular disease and physical injury. To improve overall health status and address health inequality, such deprived towns should be targeted.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.26 no.1
• /
• pp.41-50
• /
• 2008

Recent progress in state-of-the-art geospatial information technologies such as digital mapping, LiDAR(Light Detection And Ranging), and high-resolution satellite imagery provides various data sources fer Digital Elevation Model(DEM). DEMs are major source to extract elements of the hydrological terrain property that are necessary for efficient watershed management. Especially, watersheds extracted from DEM are important geospatial database to identify physical boundaries that are utilized in water resource management plan including water environmental survey, pollutant investigation, polluted/wasteload/pollution load allocation estimation, and water quality modeling. Most of the previous studies related with watershed extraction using DEM are mainly focused on the hydrological elements analysis and preprocessing without considering grid size of the DEMs. This study aims to analyze accuracy of the watersheds extracted from DEMs with various grid sizes generated by LiDAR data and digital map, and appropriate preprocessing methods.

• Smart Structures and Systems
• /
• v.25 no.3
• /
• pp.369-384
• /
• 2020

A wavefield sparse reconstruction technique based on compressed sensing is developed in this work to dramatically reduce the number of measurements. Firstly, a severely underdetermined representation of guided wavefield at a snapshot is established in the spatial domain. Secondly, an optimal compressed sensing model of guided wavefield sparse reconstruction is established based on l₁-norm penalty, where a suite of discrete cosine functions is selected as the dictionary to promote the sparsity. The regular, random and jittered undersampling schemes are compared and selected as the undersampling matrix of compressed sensing. Thirdly, a gradient projection method is employed to solve the compressed sensing model of wavefield sparse reconstruction from highly incomplete measurements. Finally, experiments with different excitation frequencies are conducted on an aluminum plate to verify the effectiveness of the proposed sparse reconstruction method, where a scanning laser Doppler vibrometer as the true benchmark is used to measure the original wavefield in a given inspection region. Experiments demonstrate that the missing wavefield data can be accurately reconstructed from less than 12% of the original measurements; The reconstruction accuracy of the jittered undersampling scheme is slightly higher than that of the random undersampling scheme in high probability, but the regular undersampling scheme fails to reconstruct the wavefield image; A quantified mapping relationship between the sparsity ratio and the recovery error over a special interval is established with respect to statistical modeling and analysis.

• Korean Journal of Remote Sensing
• /
• v.26 no.1
• /
• pp.29-38
• /
• 2010

It is necessary to develop an integration model which can account for various data acquired at different measurement scales in environmental thematic mapping with high-resolution ground survey data and relatively low-resolution remote sensing data. This paper presents and applies a multi-scale geostatistical methodology for downscaling of thematic maps generated from lowresolution remote sensing data. This methodology extends a traditional ordinary kriging system to a block kriging system which can account for both ground data and remote sensing data which can be regarded as point and block data, respectively. In addition, stochastic simulation based on block kriging is also applied to describe spatial uncertainty attached to the downscaling. Two downscaling experiments including SRTM DEM and MODIS Leaf Area Index (LAI) products were carried out to illustrate the applicability of the geostatistical methodology. Through the experiments, multi-scale geostatistics based on block kriging successfully generated relatively high-resolution thematic maps with reliable accuracy. Especially, it is expected that multiple realizations generated from simulation would be effectively used as input data for investigating the effects of uncertain input data on GIS model outputs.

• Journal of Environmental Health Sciences
• /
• v.45 no.5
• /
• pp.487-496
• /
• 2019

Objective: The purpose of this study was to create soil contamination maps using QGIS (Quantum Geographic Information System) and suggest selection methods for soil pollution sources for preferential investigation in a soil contamination survey. Method: Data from soil contamination surveys over five years in Gyeonggi-do Province, South Korea (2013-2017) were used for making soil contamination maps and analyzing the density of survey points. By analyzing points exceeding the concern level of soil contamination, soil pollutant sources for priority management were identified and selection methods for preferred survey points were suggested through a study of the model area. Results: A soil contamination survey was conducted at 1,478 points over five years, with the largest number of surveys conducted in industrial complex and factory areas. Soil contamination maps for copper, zinc, nickel, lead, arsenic, fluoride, and total petroleum hydrocarbons were made, and most of the survey points were found to be below concern level 1 for soil contamination. The density of the survey points is similar to that of densely populated areas and factory areas. The analysis results of points exceeding the criteria showed that soil pollutant sources for priority management were areas where ore and scrap metals were used and stored, traffic-related facilities areas, industrial complex and factory areas, and areas associated with waste and recycling. According to the study of the model area, the preferred survey points were traffic-related facilities with 15 years or more since their construction and factories with a score of 10 or more for soil contamination risk. Conclusion: Soil contamination surveys should use GIS for even regional distribution of survey points and for the effective selection of preferred survey points. This study may be used as guidelines to select points for a soil contamination survey.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.38 no.4
• /
• pp.142-148
• /
• 2015

Due to advancements in technology and manufacturing capability, it is not uncommon that life tests yield no or few failures at low stress levels. In these situations it is difficult to analyse lifetime data and make meaningful inferences about product or system reliability. For some products or systems whose performance characteristics degrade over time, a failure is said to have occurred when a performance characteristic crosses a critical threshold. The measurements of the degradation characteristic contain much useful and credible information about product or system reliability. Degradation measurements of the performance characteristics of an unfailed unit at different times can directly relate reliability measures to physical characteristics. Reliability prediction based on physical performance measures can be an efficient and alternative method to estimate for some highly reliable parts or systems. If the degradation process and the distance between the last measurement and a specified threshold can be established, the remaining useful life is predicted in advance. In turn, this prediction leads to just in time maintenance decision to protect systems. In this paper, we describe techniques for mapping product or system which has degrading performance parameter to the associated classical reliability measures in the performance domain. This paper described a general modeling and analysis procedure for reliability prediction based on one dominant degradation performance characteristic considering pseudo degradation performance life trend model. This pseudo degradation trend model is based on probability modeling of a failure mechanism degradation trend and comparison of a projected distribution to pre-defined critical soft failure point in time or cycle.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.39 no.5
• /
• pp.279-288
• /
• 2021

In the era of the 4th industrial revolution, smart construction, in which new technologies such as UAV (Unmanned Aerial Vehicle) are fused, is attracting attention in the construction field. However, the method of estimating earthwork volume using DEM generated by UAV survey according to practical regulations such as construction design guidelines or standard product counting is not officially recognized and needs to be improved. In this study, different types of UAV were measured and DEM was obtained using this data. The DEM (Digital Elevation Model) thus obtained was analyzed for changes in the amount of earthworks according to the size of the GSD (Ground Sample Distance). In addition, the amount of earthwork by DEM and the amount of earthwork by existing design drawings were compared and analyzed. As a result of the study, it was suggested that images with a GSD of 5cm or less are effective to generate a high-quality DEM. Next, as a result of comparing the earthwork volume calculation method using DEM and the earthwork volume based on the existing 2D design drawings, a difference of about 1% was shown. In addition, when the design earthwork amount calculated by the double-section averaging method was compared with the designed earthwork amount using DEM data by UAV survey, a difference of about 1% was found. Therefore, it is suggested that the method of calculating the amount of earthworks using UAV is an efficient method that can replace the existing method.

• The Korean Journal of Archival Studies
• /
• no.40
• /
• pp.225-277
• /
• 2014

This essay focused on mapping out the next archiving model during 15 years. This metaphorical 'JINGU PROJECT' is titled from Shinto relation in Japan rebuilding every 20 years. We need some kinds of self sustainable growth archiving model. It means that it's time to say about our archiving experiences in field for archiving technics and knowledges. We should know the fact that a few thousands archivists have been acting ih fields. Since 1999, we have tried to build up the best practices in public offices. So far, So good. However, we need another wing for better flying from now on. I believe in the power of silences in field because they will speak up their voices.

• Korean Journal of Agricultural Science
• /
• v.47 no.4
• /
• pp.995-1010
• /
• 2020

In this study, a multivariate analysis model of partial least square regression (PLSR) was developed to predict the moisture content of green peppers using hyperspectral imaging (HSI). In HSI, illumination is essential for high-quality image acquisition and directly affects the analytical performance of the visible near-infrared hyperspectral imaging (VIS/NIR-HSI) system. When green pepper images were acquired using a direct lighting system, the specular reflection from the surface of the objects and their intensities fluctuated with time. The images include artifacts on the surface of the materials, thereby increasing the variability of data and affecting the obtained accuracy by generating false-positive results. Therefore, images without glare on the surface of the green peppers were created using a polarization filter at the front of the camera lens and by exposing the polarizer sheet at the front of the lighting systems simultaneously. The results obtained from the PLSR analysis yielded a high determination coefficient of 0.89 value. The regression coefficients yielded by the best PLSR model were further developed for moisture content mapping in green peppers based on the selected wavelengths. Accordingly, the polarization filter helped achieve an uniform illumination and the removal of gloss and artifact glare from the green pepper images. These results demonstrate that the HSI technique with a polarized lighting system combined with chemometrics can be effectively used for high-throughput prediction of moisture content and image-based visualization.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.20 no.4
• /
• pp.71-82
• /
• 2021

In this study, a novel method for monitoring road pavements using the Mobile Mapping System (MMS) and a deep learning crack detection system was presented. Furthermore, an optimal maintenance method through economic analysis was presented targeting the pavement section of Sejong City. As a result of monitoring the pavement conditions, it was confirmed that the pavement ratings were good in the order of national highways, municipal roads, and roads of provinces. In addition, economic analysis using the pavement deterioration model showed that micro-surfacing, one of the preventive maintenance methods, is the most economical in terms of maintenance costs and user benefits. The results of this study are expected to be used as fundamental reference for local governments' infrastructure management plans.