• Journal of Internet Computing and Services
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• v.7 no.2
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• pp.37-52
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• 2006

A mobile device like ceil phone is the necessity of modern people, of ich con be easily connected to a wireless internet through such a mobile device. The demand for a wireless data communication is growing rapidly. However, agencies have not yet completed standardization of a markup language. Due to the development of the Mobile Device, agencies in this field have provided different data formats with each Mobile Device Platform. Traditionally, contents is hand-tailored to suit the target device. A key problem is that the characteristics and capabilities of the mobile device are too diverse to service the most suitable mobile contents. Because of these problems, the need for a re-usable document description language increases. In this paper, we defined Template file that is common data to service mobile devices. We proposed a method that could be an effective wireless web service though design and the implementation of the Call manager & the XSL Generator. In the methodology, when requesting a wireless internet service, a mobile device finds out markup language and a hardware specification of the mobile device through the Call Manager component supports. The XSL Generator component creates the XSL file dynamically that is the most suitable to a device. Finally, contents is serviced to each device by XSLT. It can generate a wireless page more easily by reusing the existing web contents through such course. Therefore, it can save the time and expense for generating a wireless page.

• Korean Journal of Social Welfare Studies
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• no.37
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• pp.429-455
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• 2008

The purpose of this study is to generate a substantive theory that accounts for the social readaptation of the women who have engaged in prostitution and propose a practical program for them. We used the grounded theory developed by Strauss and Corbin(1990). Raw data were collected through the in-depth interview, documents and observation. We used constant comparative method for the data analysis. The nine women who had the exprience of prostitution have participated in this study. As a result of the data analysis, in open coding were generated 116 concepts, 29 subcategories and 12 categories, In axial coding the outcomes of a paradigm model were as follows. The causal conditions were named 'hostile environment' and 'the self endowed stigma'. The phenomenon turned out to be 'living as an anonymous being'. The contextual conditions were named 'cohesion of family', 'being pushed' and 'shrinked emotion'. The intervening conditions were presented to be 'desire of self restoration as a social being', 'reactionary reconstruction'. The action/interaction strategies turned out to be 'shifting of social status', 'neighbor alliance'. The consequences were presented to be 'psychological wayfarer' and 'tentacular living'. In selective coding we found a core category, 'repetition of leaving and returning from the life world'. Based on the phenomenon, two types of participants life attitudes were found as follows : present hesitating type, self concealing type. Following the adove results, We finally suggested the need to develope a community oriented case management model.

• Nuclear Engineering and Technology
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• v.42 no.4
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• pp.382-393
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• 2010

Thermal mixing by steam jets in a pool is dominantly influenced by a turbulent water jet generated by the condensing steam jets, and the proper prediction of this turbulent jet behavior is critical for the pool mixing analysis. A turbulent jet flow induced by a steam jet discharged through a vertical upward single hole into a subcooled water pool was subjected to computational fluid dynamics (CFD) analysis. Based on the small-scale test data derived under a horizontal steam discharging condition, this analysis was performed to validate a CFD method of analysis previously developed for condensing jet-induced pool mixing phenomena. In previous validation work, the CFD results and the test data for a limited range of radial and axial directions were compared in terms of profiles of the turbulent jet velocity and temperature. Furthermore, the behavior of the turbulent jet induced by the steam jet through a horizontal single hole in a subcooled water pool failed to show the exact axisymmetric flow pattern with regards to an overall pool mixing, whereas the CFD analysis was done with an axisymmetric grid model. Therefore, another new small-scale test was conducted under a vertical upward steam discharging condition. The purpose of this test was to generate the velocity and temperature profiles of the turbulent jet by expanding the measurement ranges from the jet center to a location at about 5% of $U_m$ and 10 cm to 30 cm from the exit of the discharge nozzle. The results of the new CFD analysis show that the recommended CFD model of the high turbulent intensity of 40% for the turbulent jet and the fine mesh grid model can accurately predict the test results within an error rate of about 10%. In this work, the turbulent jet model, which is used to simply predict the temperature and velocity profiles along the axial and radial directions by means of the empirical correlations and Tollmien's theory was improved on the basis of the new test data. The results validate the CFD model of analysis. Furthermore, the turbulent jet model developed in this study can be used to analyze pool thermal mixing when an ellipsoidal steam jet is discharged under a high steam mass flux in a subcooled water pool.

• Geophysics and Geophysical Exploration
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• v.8 no.1
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• pp.78-85
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• 2005

This study, combining geophysical and environmental approaches, was undertaken to investigate the causes of secondary salinity in the Goondoola basin, in southwestern Queensland. Airborne radiometric, electromagnetic and ground electromagnetic datasets were acquired, along with data on soils and subsurface materials and groundwater. Relationships established between radiometric, elevation data, and measured material properties allowed us to generate predictive maps of surface materials and recharge potential. Greatest recharge to the groundwater is predicted to occur on the weathered bedrock rises surrounding the basin. Electromagnetic data (airborne, ground, and downhote), used in conjunction with soil and drillhole measurements, were used to quantify regolith salt store and to define the subsurface architecture. Conductivity measurements reflect soil salt distribution. However, deeper in the regolith, where the salt content is relatively constant, the AEM signal is influenced by changes in porosity or material type. This allowed the lateral distribution of bedrock weathering zones to be mapped. Salinisation in this area occurs because of local-andintermediate-scale processes, controlled strongly by regolith architecture. The present surface outbreak is the result of evaporative concentration above shallow saline groundwater, discharging at break of slope. The integration of surficial and subsurface datasets allowed the identification of similar landscape settings that are most at risk of developing salinity with groundwater rise. This information is now being used by local land managers to refine management choices that prevent excess recharge and further salt mobilisation.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1647-1656
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• 2021

In order to reduce the spread of infection due to COVID-19, South Korea has established a four-step social distancing standard and implemented it by changing the steps based on the rate of confirmed cases. The implementation of social distancing brought about a change in the amount of activity of citizens by limiting social contact such as movement and gathering of people. One of the data that can intuitively confirm this is Night Time Light (NTL). NTL is a variable that can measure the size of the national economy measured using lights captured by satellites, and can be used to understand people's social activities during the night. The NTL visible data is obtained via the Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band (DNB) onboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite. 1023 of Suomi data from 1 January 2019 until 26 October 2021 were collected to generate time series of NTL radiance change over Seoul to analyze the correlation with social distancing policy. The results show that implementing the level of social distancing generally decreased the NTL radiance both in spatial disparities and temporal patterns. The higher level of policy, limiting human activities combined with the low number of people who have been vaccinated and the closure of various facilities. Because of social distancing, the differences in human activities affected the nighttime light during the COVID-19 pandemic, especially in Seoul, South Korea. Therefore, this study can be used as a reference for the government in evaluating and improving policies related to efforts reducing the transmission of COVID-19.

• The Journal of the Acoustical Society of Korea
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• v.40 no.5
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• pp.417-427
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• 2021

Long-range communication in deep waters must overcome the low data rate due to limited bandwidth. This paper presents the performance of Multiple-Input-Multiple-Output (MIMO) system to increase the data rate. In MIMO system, communication performance is degraded by crosstalk between users and an adaptive passive Time Reversal Processing (TRP) is widely used to eliminate this. In October 2018, long-range underwater acoustic communication experiment was conducted in deep water (1,000 m ~) off the east of Pohang, South Korea. During the experiment, a vertical line array was utilized and communication signals modulated by binary phase shift keying and quadrature phase shift keying with a symbol rate of 512 sps were transmitted. To generate MIMO communication signals, received signals from ranges of 26 km and 30 km is synthesized. Compared to the conventional passive TRP, the adaptive passive TRP eliminates the crosstalk between users and achieves error-free performance with an increase of output signal-to-noise ratio. Therefore, two users separated by 4 km in range achieves an aggregate data rate of 1,024 symbols/s.

• Journal of Korea Water Resources Association
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• v.54 no.10
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• pp.779-793
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• 2021

Water stress and environmental drivers are important factors to explain the variance of gross primary production (GPP). Environmental drivers are used to generate GPP in Moderate Resolution Imaging Spectroradiometer (MODIS) algorithm and process-based model. However, MODIS algorithm only consider the vapor pressure deficit (VPD) data while the process-based biogeochemical model also uses limited data to express water stress. We compared the relationship between environmental drivers and GPP from eddy covariance method, MODIS algorithm, and Community Land Model 4 (CLM 4) simulation in normal years and drought years. To consider water stress specifically, we used VPD and evaporative fraction (EF). We evaluated the effects from environmental drivers and EF towards GPP products using the structural equation modeling (SEM) in South Korea. We found that GPP products from MODIS algorithm and model simulation results were not restricted from VPD data if VPD was underestimated. We also found that in the cropland area, irrigation effects can relieve VPD effects to GPP. However, GPP products derived from MODIS and CLM 4 had limitation to explain the irrigation effects to GPP. Overall, these results will enhance the understanding of GPP products derived from MODIS and CLM 4.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.1931-1942
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• 2021

As the utility of an optical satellite image with a high spatial resolution (i.e., fine-scale) has been emphasized, recently, various studies of the land surface monitoring using those have been widely carried out. However, the usefulness of fine-scale satellite images is limited because those are acquired at a low temporal resolution. To compensate for this limitation, the spatiotemporal data fusion can be applied to generate a synthetic image with a high spatio-temporal resolution by fusing multiple satellite images with different spatial and temporal resolutions. Since the spatio-temporal data fusion models have been developed for mid or low spatial resolution satellite images in the previous studies, it is necessary to evaluate the applicability of the developed models to the satellite images with a high spatial resolution. For this, this study evaluated the applicability of the developed spatio-temporal fusion models for KOMPSAT-3A and Sentinel-2 images. Here, an Enhanced Spatial and Temporal Adaptive Fusion Model (ESTARFM) and Spatial Time-series Geostatistical Deconvolution/Fusion Model (STGDFM), which use the different information for prediction, were applied. As a result of this study, it was found that the prediction performance of STGDFM, which combines temporally continuous reflectance values, was better than that of ESTARFM. Particularly, the prediction performance of STGDFM was significantly improved when it is difficult to simultaneously acquire KOMPSAT and Sentinel-2 images at a same date due to the low temporal resolution of KOMPSAT images. From the results of this study, it was confirmed that STGDFM, which has relatively better prediction performance by combining continuous temporal information, can compensate for the limitation to the low revisit time of fine-scale satellite images.

• Journal of Food Hygiene and Safety
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• v.34 no.2
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• pp.199-204
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• 2019

This study aimed to generate a probability distribution model based on temperature data of frozen food storage facility as input variables for microbial risk assessment (MRA). We visited 8 food-handling businesses to collect temperature data from their cold storage warehouses. The overall mean temperature inside the storage facilities was $-20.48{\pm}3.08^{\circ}C$, with 20.4% of the facilities having above $-18^{\circ}C$, with minimum and maximum temperature values of -10.3 and $-25.80^{\circ}C$ respectively. Temperature distributions by space locations of natural and forced convection were $-22.57{\pm}0.84$ and $-17.81{\pm}1.47^{\circ}C$, $-22.49{\pm}1.05$ and $-17.94{\pm}1.44^{\circ}C$, and $-22.68{\pm}1.03$ and $-18.08{\pm}1.42^{\circ}C$ in the upper (2.4~4 m), middle (1.5~2.4 m), and lower (0.7~1.5 m) shelves, respectively. Probability distributions from the temperature data were obtained using the program @RISK. Statistical ranking was determined using goodness of fit to determine the probability distribution model. Our results show that a log-normal distribution [5.9731, 3.3483, shift (-26.4281)] is most appropriate for relative MRA conduction.

• Journal of Korea Water Resources Association
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• v.54 no.5
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• pp.301-309
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• 2021

In this study, we tried to improve the performance of the existing U-net-based deep learning rainfall prediction model, which can weaken the meaning of time series order. For this, ConvLSTM2D U-Net structure model considering temporal consistency of data was applied, and we evaluated accuracy of the ConvLSTM2D U-Net model using a RainNet model and an extrapolation-based advection model. In addition, we tried to improve the uncertainty in the model training process by performing learning not only with a single model but also with 10 ensemble models. The trained neural network rainfall prediction model was optimized to generate 10-minute advance prediction data using four consecutive data of the past 30 minutes from the present. The results of deep learning rainfall prediction models are difficult to identify schematically distinct differences, but with ConvLSTM2D U-Net, the magnitude of the prediction error is the smallest and the location of rainfall is relatively accurate. In particular, the ensemble ConvLSTM2D U-Net showed high CSI, low MAE, and a narrow error range, and predicted rainfall more accurately and stable prediction performance than other models. However, the prediction performance for a specific point was very low compared to the prediction performance for the entire area, and the deep learning rainfall prediction model also had limitations. Through this study, it was confirmed that the ConvLSTM2D U-Net neural network structure to account for the change of time could increase the prediction accuracy, but there is still a limitation of the convolution deep neural network model due to spatial smoothing in the strong rainfall region or detailed rainfall prediction.