• Asian Pacific Journal of Cancer Prevention
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• v.17 no.3
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• pp.1293-1297
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• 2016

Cholangiocarcinoma (CCA) is a serious problem in Thailand, particularly in the northeastern and northern regions. Database of population at risk are need required for monitoring, surveillance, home health care, and home visit. Therefore, this study aimed to develop a geographic information system (GIS) database and Google map of the population at risk of CCA in Mueang Yang district, Nakhon Ratchasima province, northeastern Thailand during June to October 2015. Populations at risk were screened using the Korat CCA verbal screening test (KCVST). Software included Microsoft Excel, ArcGIS, and Google Maps. The secondary data included the point of villages, sub-district boundaries, district boundaries, point of hospital in Mueang Yang district, used for created the spatial databese. The populations at risk for CCA and opisthorchiasis were used to create an arttribute database. Data were tranfered to WGS84 UTM ZONE 48. After the conversion, all of the data were imported into Google Earth using online web pages www.earthpoint.us. Some 222 from a 4,800 population at risk for CCA constituted a high risk group. Geo-visual display available at following www.google.com/maps/d/u/0/edit?mid=zPxtcHv_iDLo.kvPpxl5mAs90&hl=th. Geo-visual display 5 layers including: layer 1, village location and number of the population at risk for CCA; layer 2, sub-district health promotion hospital in Mueang Yang district and number of opisthorchiasis; layer 3, sub-district district and the number of population at risk for CCA; layer 4, district hospital and the number of population at risk for CCA and number of opisthorchiasis; and layer 5, district and the number of population at risk for CCA and number of opisthorchiasis. This GIS database and Google map production process is suitable for further monitoring, surveillance, and home health care for CCA sufferers.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.2
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• pp.209-216
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• 2018

High-resolution satellite imagery is used in diverse fields such as meteorological observation, topography observation, remote sensing (RS), military facility monitoring and protection of cultural heritage. In satellite imagery, low-resolution imagery can take place depending on the conditions of hardware (e.g., optical system, satellite operation altitude, image sensor, etc.) even though the images were obtained from the same satellite imaging system. Once a satellite is launched, the adjustment of the imaging system cannot be done to improve the resolution of the degraded images. Therefore, there should be a way to improve resolution, using the satellite imagery. In this study, a super resolution (SR) algorithm was adopted to improve resolution, using such low-resolution satellite imagery. The SR algorithm is an algorithm which enhances image resolution by matching multiple low-resolution images. In satellite imagery, however, it is difficult to get several images on the same region. To take care of this problem, this study performed the SR algorithm by calibrating geometric changes on images after applying automatic extraction of feature points and projection transform. As a result, a clear edge was found just like the SR results in which feature points were manually obtained.

• Geophysics and Geophysical Exploration
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• v.7 no.2
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• pp.109-116
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• 2004

Petroleum reservoir characterization is a process for quantitatively describing various reservoir properties in spatial variability using all the available field data. Porosity and permeability are the two fundamental reservoir properties which relate to the amount of fluid contained in a reservoir and its ability to flow. These properties have a significant impact on petroleum fields operations and reservoir management. In un-cored intervals and well of heterogeneous formation, porosity and permeability estimation from conventional well logs has a difficult and complex problem to solve by conventional statistical methods. This paper suggests an intelligent technique using fuzzy logic and neural network to determine reservoir properties from well logs. Fuzzy curve analysis based on fuzzy logics is used for selecting the best related well logs with core porosity and permeability data. Neural network is used as a nonlinear regression method to develop transformation between the selected well logs and core analysis data. The intelligent technique is demonstrated with an application to the well data in offshore Korea. The results show that this technique can make more accurate and reliable properties estimation compared with previously used methods. The intelligent technique can be utilized a powerful tool for reservoir characterization from well logs in oil and natural gas development projects.

• Korean Journal of Optics and Photonics
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• v.20 no.2
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• pp.94-101
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• 2009

A novel method for the reconstruction of 3D shape and texture from elemental images has been proposed. Using this method, we can estimate a full 3D polygonal model of objects with seamless triangulation. But in the triangulation process, all the objects are stitched. This generates phantom surfaces that bridge depth discontinuities between different objects. To solve this problem we need to connect points only within a single object. We adopt a segmentation process to this end. The entire process of the proposed method is as follows. First, the central pixel of each elemental image is computed to extract spatial position of objects by correspondence analysis. Second, the object points of central pixels from neighboring elemental images are projected onto a specific elemental image. Then, the center sub-image is segmented and each object is labeled. We used the normalized cut algorithm for segmentation of the center sub-image. To enhance the speed of segmentation we applied the watershed algorithm before the normalized cut. Using the segmentation results, the subdivision process is applied to pixels only within the same objects. The refined grid is filtered with median and Gaussian filters to improve reconstruction quality. Finally, each vertex is connected and an object-based triangular mesh is formed. We conducted experiments using real objects and verified our proposed method.

• Journal of Environmental Health Sciences
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• v.42 no.1
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• pp.10-18
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• 2016

Objectives: Microcystin (MC) produced during cyanobacterial blooms is a worldwide problem presenting a serious health threats to humans and ecosystems. During July through October of 2013, the Ilwol Reservoir experienced a high biomass of phytoplankton (maximum $211.7mg/m^3$ of Chlorophyll-a) containing the toxigenic cyanobacterium Oscillatoria sp. The aim of this study is to analyze MC concentration in the reservoir water, as well as in representative fish species (Carassius cuvieri, Carassius auratus, Channa argus). We also evaluated the human health risk of exposure to MCs accumulated in the fish. Methods: Concentrations of MCs in the water and fish samples were analyzed by liquid chromatography with a triple quadrupole tandem mass spectrometer (LC/MS/MS) and enzyme-linked immunosorbent assay (ELISA). Results: The total levels of four MC variants, including MC-LR, MC-RR, MC-YR and MC-LA were below the WHO drinking water guideline limit (1 ug MC-LR per liter) both for the dissolved and particulate fraction present in the water samples. The mean MC concentrations in the livers of all species were significantly higher than in the gills (p < 0.01) and muscles (p < 0.05). The values of estimated daily intake of MCs in muscles, the edible part of the fish, would be only $0.005-0.015{\mu}g/kg{\cdot}day$, much lower than WHO's provisional tolerable daily intake of $0.04{\mu}g/kg{\cdot}day$. Conclusion: This study suggests that, owing to the spatial distribution or temporal variation of MC, there is a need for careful monitoring of cyanotoxin in reservoir water and aquatic animals to protect public health.

• Journal of Korean Society for Geospatial Information Science
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• v.12 no.3 s.30
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• pp.69-78
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• 2004

Since 1995, One of the NGIS(National Geographic Information System) projects, the underground utilities project, has been finished in 2003. The 19 cities in korea for this project will use underground utilities management system using underground utilities database focusing on water and sewage system. However, special expert, technical and experience problem in computer environment has not been had in working department for water and sewage management and maintenance for each local government. The management and maintenance by underground utilities database by manual method is getting lowered a data qualify of underground utilities database because of the data omitting, error in writing and securing of the lasted database. Therefore, in this study, Application test for field workers using a mobile tool and environment analysis of wireless communication in Korea and application test of GPS receiver for mobile tools had been processed and made a mobile working environment. The efficient management and maintenance method for underground utilities database has been developed an off-line method for modification and update of database about general work which has a high working frequency because of the limitation of wireless communication environment. Also, the emergency working has been applied for on-line method.

• Journal of Broadcast Engineering
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• v.16 no.3
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• pp.510-519
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• 2011

Intra mode decision is a useful coding tool in Distributed Video Coding (DVC) for improving DVC coding efficiency for video sequences having fast motion. A major limitation associated with the existing intra mode decision methods, however, is that its efficiency highly depends on user-specified thresholds or modeling parameters. This paper proposes an entropy-based method to address this problem. The probabilities of intra and Wyner?Ziv (WZ) modes are determined firstly by examining correlation of pixels in spatial and temporal directions. Based on these probabilities, entropy of the intra and the WZ modes are computed. A comparison based on the entropy values decides a coding mode between intra coding and WZ coding without relying on any user-specified thresholds or modeling parameters. Experimental results show its superior rate-distortion performance of improvements of PSNR up to 2 dB against a conventional Wyner?Ziv coding without intra mode decision. Furthermore, since the proposed method does not require any thresholds or modeling parameters from users, it is very attractive for real life applications.

• Journal of Korean Society for Geospatial Information Science
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• v.13 no.1 s.31
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• pp.89-96
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• 2005

As the number of the municipalities which have finished the phase I of their GIS system development projects increases, it is very natural that GIS system operation and maintenance rather than development itself has been a more important concern for these municipalities. It seems to me that these phenomena will be amplified as time goes by, and this is the right time for a series of proper guidelines regarding the GIS system maintenance to be supplied to the related municipalities. The subject which is called as 'GIS maintenance' is surely a very comprehensive area. It has been also founded that the necessity is widely agreed among the concerned people working for the municipalities which have already finished their first GIS projects. Nevertheless, it has been investigated that the studies done up to now in this area is in a very infant state from the quality and also quantity points of views. This paper, therefore, makes one of the very initial trials for the systematical GIS maintenance. Among the areas of GIS maintenance, the GIS database maintenance has been selected as the first trial area because of its natural characteristics of the basic element composing IS(Information System). For this, the present conditions of a variety of municipalities have been investigated as far as GIS maintenance is concerned, and the results are analyzed to produce a series of useful outcomes in terms of facing problems and accompanied plans. For each problem, a solution is defined and for each solution a detailed action plan (item) is derived. For each of the two kinds of GIS data given which are the fundamental map and the underground facilities map, an effective GIS data maintenance plan is suggested for the concerned municipalities.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.959-971
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• 2019

CNN (Convolutional Neural Network) is one representative deep learning algorithm, which can extract high-level spatial and spectral features, and has been applied for hyperspectral image classification. However, one significant drawback behind the application of CNNs in hyperspectral images is the high dimensionality of the data, which increases the training time and processing complexity. To address this problem, several CNN based hyperspectral image classification studies have exploited PCA (Principal Component Analysis) for dimensionality reduction. One limitation to this is that the spectral information of the original image can be lost through PCA. Although it is clear that the use of PCA affects the accuracy and the CNN training time, the impact of PCA for CNN based hyperspectral image classification has been understudied. The purpose of this study is to analyze the quantitative effect of PCA in CNN for hyperspectral image classification. The hyperspectral images were first transformed through PCA and applied into the CNN model by varying the size of the reduced dimensionality. In addition, 2D-CNN and 3D-CNN frameworks were applied to analyze the sensitivity of the PCA with respect to the convolution kernel in the model. Experimental results were evaluated based on classification accuracy, learning time, variance ratio, and training process. The size of the reduced dimensionality was the most efficient when the explained variance ratio recorded 99.7%~99.8%. Since the 3D kernel had higher classification accuracy in the original-CNN than the PCA-CNN in comparison to the 2D-CNN, the results revealed that the dimensionality reduction was relatively less effective in 3D kernel.

• Journal of Korean Society on Water Environment
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• v.28 no.3
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• pp.347-358
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• 2012

The SWAT (Soil and Water Assessment Tool) should be calibrated and validated with observed data to secure accuracy of model prediction. Recently, the SWAT-CUP (Calibration and Uncertainty Program for SWAT) software, which can calibrate SWAT using various algorithms, were developed to help SWAT users calibrate model efficiently. In this study, three algorithms (GLUE: Generalized Likelihood Uncertainty Estimation, PARASOL: Parameter solution, SUFI-2: Sequential Uncertainty Fitting ver. 2) in the SWAT-CUP were applied for the Soyang-gang dam watershed to evaluate these algorithms. Simulated total streamflow and 0~75% percentile streamflow were compared with observed data, respectively. The NSE (Nash-Sutcliffe Efficiency) and $R^2$ (Coefficient of Determination) values were the same from three algorithms but the P-factor for confidence of calibration ranged from 0.27 to 0.81 . the PARASOL shows the lowest p-factor (0.27), SUFI-2 gives the greatest P-factor (0.81) among these three algorithms. Based on calibration results, the SUFI-2 was found to be suitable for calibration in Soyang-gang dam watershed. Although the NSE and $R^2$ values were satisfactory for total streamflow estimation, the SWAT simulated values for low flow regime were not satisfactory (negative NSE values) in this study. This is because of limitations in semi-distributed SWAT modeling structure, which cannot simulated effects of spatial locations of HRUs (Hydrologic Response Unit) within subwatersheds in SWAT. To solve this problem, a module capable of simulating groundwater/baseflow should be developed and added to the SWAT system. With this enhancement in SWAT/SWAT-CUP, the SWAT estimated streamflow values could be used in determining standard flow rate in TMDLs (Total Maximum Daily Load) application at a watershed.