• The Korean Journal of Emergency Medical Services
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• v.22 no.1
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• pp.7-19
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• 2018

Purpose: With the purpose of educating and producing outstanding paramedics by enhancing their competencies, this study aimed to make policy suggestions to re-establish the education system and improve the national examination and the certification scheme. Methods: This study used focus group interviews and questionnaires to collect data. Totally, there were 277 subjects, including experts from the education and field. Data were collected from September 9 to 20, 2016, and analyzed using SPSS 22.0. Results: To strengthen the curriculum of paramedics, this study suggested 27 courses with 94 credits as the standardized curriculum and derived 9 core competencies of paramedics. For the national examination, this study suggested consolidating written test subjects, adding scenario questions to practical tests, and applying critical criteria to simple practical tests that performs a procedure, grading these tests on a pass/fail basis. In addition, this study suggested converting certification into license, reflecting paramedics' healthcare job characteristics. Conclusion: The quality of emergency medical services in Korea will improve when those with core competencies that originated from the standardized curriculum based on the results of this study acquire their certification through the national test scheme, and the certification management system creates a virtuous cycle to further enhance paramedics' professionalism.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.199-208
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• 2019

As the number of available satellites increases and technology advances, image information outputs are becoming increasingly diverse and a large amount of data is accumulating. In this study, we propose a change detection method for high-resolution satellite images that uses transfer learning and a deep learning network to overcome the limit caused by insufficient training data via the use of pre-trained information. The deep learning network used in this study comprises convolutional layers to extract the spatial and spectral information and convolutional long-short term memory layers to analyze the time series information. To use the learned information, the two initial convolutional layers of the change detection network are designed to use learned values from 40,000 patches of the ISPRS (International Society for Photogrammertry and Remote Sensing) dataset as initial values. In addition, 2D (2-Dimensional) and 3D (3-dimensional) kernels were used to find the optimized structure for the high-resolution satellite images. The experimental results for the KOMPSAT-3A (KOrean Multi-Purpose SATllite-3A) satellite images show that this change detection method can effectively extract changed/unchanged pixels but is less sensitive to changes due to shadow and relief displacements. In addition, the change detection accuracy of two sites was improved by using 3D kernels. This is because a 3D kernel can consider not only the spatial information but also the spectral information. This study indicates that we can effectively detect changes in high-resolution satellite images using the constructed image information and deep learning network. In future work, a pre-trained change detection network will be applied to newly obtained images to extend the scope of the application.

• The Journal of Engineering Geology
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• v.32 no.2
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• pp.257-270
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• 2022

This study evaluated the hydraulic factors contributing to the decreasing groundwater levels across Jeju island. Time-series data for groundwater level, precipitation, and groundwater usage and information on land use were acquired, and the correlations among them were analyzed to evaluate the causes of the decreasing groundwater. The effects of precipitation and groundwater usage on the fluctuations of groundwater level were quantified using response surface analysis and sensitivity analysis, and methods for groundwater quantity management by region were proposed. The results showed that the rate of groundwater decrease in the western region was larger than that in the eastern region. For the eastern region, the influence of precipitation was large and the rate of decrease in the groundwater level was relatively small. The geological formation of this part of the island and continuous seawater intrusion suggest that although the absolute amount of groundwater extracted for use was large, the decrease in the groundwater level was not seen to be great due to an increase in pressure by seawater intrusion. Overall, precipitation and groundwater usage had the greatest effect on the amount of groundwater in the western region, and thus their data would be most useful for informing groundwater management, whereas other factors (e.g., sea level and the location of the freshwater-seawater transition zone) must be considered when understanding Jeju's eastern region. As the characteristics of groundwater level fluctuations in the eastern and western regions are distinct, an optimal management plan for each region should be proposed to ensure the efficient management of groundwater quantity.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1607-1622
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• 2022

The purpose of this study is to determine the actual performance of cool roof in preventing absorbed solar radiation. The spatial correlation between surface temperature and absorbed solar radiation is the method by which the performance of a cool roof can be understood and evaluated. The research area of this study is the vicinity of Jangyu Mugye-dong, Gimhae-si, Gyeongsangnam-do, where an actual cool roof is applied. FLIR Vue Pro R thermal infrared sensor, Micasense Red-Edge multi-spectral sensor and DJI H20T visible spectral sensor was used for aerial photography, with attached to the drone DJI Matrice 300 RTK. To perform the spatial correlation analysis, thermal infrared orthomosaics, absorbed solar radiation distribution maps were constructed, and land cover features of roof were extracted based on the drone aerial photographs. The temporal scope of this research ranged over 9 points of time at intervals of about 1 hour and 30 minutes from 7:15 to 19:15 on July 27, 2021. The correlation coefficient values of 0.550 for the normal roof and 0.387 for the cool roof were obtained on a daily average basis. However, at 11:30 and 13:00, when the Solar altitude was high on the date of analysis, the difference in correlation coefficient values between the normal roof and the cool roof was 0.022, 0.024, showing similar correlations. In other time series, the values of the correlation coefficient of the normal roof are about 0.1 higher than that of the cool roof. This study assessed and evaluated the potential of an actual cool roof to prevent solar radiation heating a rooftop through correlation comparison with a normal roof, which serves as a control group, by using high-resolution drone images. The results of this research can be used as reference data when local governments or communities seek to adopt strategies to eliminate the phenomenon of urban heat islands.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.885-894
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• 2006

The purpose of this study is focused on the development of the effective classification technique using ultra multiband of hyperspectral image. This study suggests the classification technique using canonical correlation analysis, one of multivariate statistical analysis in hyperspectral image classification. High accuracy of classification result is expected for this classification technique as the number of bands increase. This technique is compared with Maximum Likelihood Classification(MLC). The hyperspectral image is the EO1-hyperion image acquired on September 2, 2001, and the number of bands for the experiment were chosen at 30, considering the band scope except the thermal band of Landsat TM. We chose the comparing base map as Ground Truth Data. We evaluate the accuracy by comparing this base map with the classification result image and performing overlay analysis visually. The result showed us that in MLC's case, it can't classify except water, and in case of water, it only classifies big lakes. But Canonical Correlation Classification (CCC) classifies the golf lawn exactly, and it classifies the highway line in the urban area well. In case of water, the ponds that are in golf ground area, the ponds in university, and pools are also classified well. As a result, although the training areas are selected without any trial and error, it was possible to get the exact classification result. Also, the ability to distinguish golf lawn from other vegetations in classification classes, and the ability to classify water was better than MLC technique. Conclusively, this CCC technique for hyperspectral image will be very useful for estimating harvest and detecting surface water. In advance, it will do an important role in the construction of GIS database using the spectral high resolution image, hyperspectral data.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1031-1042
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• 2023

In this study, a deep learning model was developed to predict the yield of cabbage and radish, one of the five major supply and demand management vegetables, using satellite images of Landsat 8. To predict the yield of cabbage and radish in Gangwon-do from 2015 to 2020, satellite images from June to September, the growing period of cabbage and radish, were used. Normalized difference vegetation index, enhanced vegetation index, lead area index, and land surface temperature were employed in this study as input data for the yield model. Crop yields can be effectively predicted using satellite images because satellites collect continuous spatiotemporal data on the global environment. Based on the model developed previous study, a model designed for input data was proposed in this study. Using time series satellite images, convolutional neural network, a deep learning model, was used to predict crop yield. Landsat 8 provides images every 16 days, but it is difficult to acquire images especially in summer due to the influence of weather such as clouds. As a result, yield prediction was conducted by splitting June to July into one part and August to September into two. Yield prediction was performed using a machine learning approach and reference models , and modeling performance was compared. The model's performance and early predictability were assessed using year-by-year cross-validation and early prediction. The findings of this study could be applied as basic studies to predict the yield of field crops in Korea.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.3 no.1
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• pp.26-27
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• 1978

It can be expected that intelligence and knowledge will be the core of the post-industrial society in a near future. Accordingly, the age of intelligence shall be accelerated extensively to find ourselves in an age of 'Communication' service enterprise. The communication actions will increase its efficiency and multiply its utility, indebted to its scientic principles and legal idea. The two basic elements of communication action, that is, communication station and communication men are considered to perform their function when they are properly supported and managed by the government administration. Since the communication action itself is composed of various factors, the elements such as communication stations and officials must be cultivated and managed by specialist or experts with continuous and extensive study practices concerned. With the above mind, this study reviewed our public service officials law with a view to improve it by providing some suggestions for communication experts and researchers to find suitable positions in the framework of government administration. In this study, I would like to suggest 'Occupational Group of Communication' that is consisted of a series of comm, management positions and research positions in parallel to the existing series of comm, technical position. The communication specialist or expert is required to be qualified with necessary scientific knowledge and techniques of communication, as well as prerequisites as government service officials. Communication experts must succeed in the first hand to obtain government licence concerned in with the government law and regulation, and international custom before they can be appointed to the official positions. This system of licence-prior-to-appointment is principally applied in the communication management position. And communication research positions are for those who shall engage themselves to the work of study and research in the field of both management and technical nature. It is hopefully expected that efficient and extensive management of communication activities, as well as scientific and continuous study over than communication enterprise will be upgraded at national dimensions.