• Journal of Korea Society of Industrial Information Systems
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• v.11 no.2
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• pp.1-8
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• 2006

In this paper, we propose a distance-based location registration scheme and evaluate it's performance in a mobility model. We compare performance of the distance-based registration scheme to that of zone-based registration scheme at the mobility model. Numerical results show that the registration load of the distance-based registration with call arrival is similar to that of the zone-based registration, and is equally distributed to all cells in a location area. So the proposed scheme can be effectively used in the limited radio resources.

• Phonetics and Speech Sciences
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• v.2 no.3
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• pp.141-148
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• 2010

In this paper, voice activity detection (VAD) for dual-channel noisy speech recognition is proposed in which spatial cues are employed. In the proposed method, a probability model for speech presence/absence is constructed using spatial cues obtained from dual-channel input signal, and a speech activity interval is detected through this probability model. In particular, spatial cues are composed of interaural time differences and interaural level differences of dual-channel speech signals, and the probability model for speech presence/absence is based on a Gaussian kernel density. In order to evaluate the performance of the proposed VAD method, speech recognition is performed for speech segments that only include speech intervals detected by the proposed VAD method. The performance of the proposed method is compared with those of several methods such as an SNR-based method, a direction of arrival (DOA) based method, and a phase vector based method. It is shown from the speech recognition experiments that the proposed method outperforms conventional methods by providing relative word error rates reductions of 11.68%, 41.92%, and 10.15% compared with SNR-based, DOA-based, and phase vector based method, respectively.

• Journal of Fisheries and Marine Sciences Education
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• v.28 no.5
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• pp.1183-1197
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• 2016

The purpose of this study is to develop a Puzzle-Based Computer Science Instruction Model and Learning Program and to confirm the effects. To do so, we selected 2 classes with a similar level of pre-computational thinking in elementary schools in the Jeju Province. After that, from 2 classes, we designated the 5th grade students in 'D' elementary school as group A and designated students of the same grade in 'J' elementary school as group B. In a total of 28 sessions during an 18 week period, a Puzzle-Based Computer Science Learning Program was used with 31 students in group A, and the traditional computer science course was used with 25 students in group B. The results showed that there were significant improvements in computational thinking, which is computational cognition and its creativity, of the students in group A compared to students in group B. Also, this study proved that the Puzzle-Based program correlated with positive changes group A students' Science-Related Affective Domain. In this paper, on the basis of proven effectiveness, we introduce the Puzzle-Based Computer Science Instruction Model and Learning Program as an alternative to traditional, computer science education.

• The Journal of the Korea Contents Association
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• v.11 no.4
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• pp.225-234
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• 2011

The purpose of this study was to survey the intervention model, intervention settings, intervention time, factors influencing selection of intervention model, relationship between intervention model and areas which supporting center for special education based occupational therapist was using. Study data were provided by 46 therapy supporting service professionals through e-mail and analyzed. The findings indicated that first, the therapy supporting service professionals often employed a direct therapy(individual) focusing on performance component of child. Second, they believed that direct therapy is most effective in improving children's performance and raising awareness of the importance of occupational therapy. Third, when choose intervention model, they took into consideration the child's performance components deficits and mind of team chief. Fourth, no correlation between the application time and the perception of effectiveness of intervention model. Also didn't find correlation between applying time of intervention model and intervention area. When putting the various research result together, the model school-based occupational therapists using was similar to medical model. Therefore it is need a study to develop effective intervention model and apply it in school environment.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.2D
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• pp.103-110
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• 2012

This study presents O/D based emission estimation model and methodology under cold- and hot-start conditions. Contrasting with existing link-based model, new model is able to estimate cold-start emissions with actual traffic characteristics. The results of the case study with new model show similar amount of emission with existing model under hot-start conditions, but five times much more than existing model under cold-start conditions. The annual social benefit estimated by this model is 56.2 hundred million won, which is 48% higher than the result from existing model. It means current green transportation policies are undervalued in terms of air quality improvement. Therefore, New model is expected to improve the objectivity of air quality evaluation results regarding green transportation policies and be applied in various transportation-environment policies.

• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.3
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• pp.76-87
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• 2009

Most distributed models have been developed for data interchange between model for hydrological analysis and GIS(Geographic Information System). And some interface systems between them have been developed to operate the model conveniently. This study is about developing integrated system between model and GIS not coupled system based on file interchange or interface system. In this study, HyGIS-GRM which is integrated system between GRM(Grid based Rainfall-runoff Model) which is physically based distributed rainfall-runoff model and HyGIS(Hydro Geographic Information System) have been developed. HyGIS-GRM can carry out all the processes from preparing input data to appling them to model in the same system, and this operation environment can improve the efficiency of running the model and analyzing modeling results. HyGIS-GRM can provide objective modeling environment through establishing the process of integrated operation of GIS and distributed model, and we can obtain fundamental technologies for developing integrated system between GIS and water resources model.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.153-153
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• 2019

Evapotranspiration (ET) is an important component of hydrological processes. Accurate estimates of ET variation are of vital importance for natural hazard adaptation and water resource management. This study first developed a soil water index (SWI)-based Priestley-Taylor algorithm (SWI-PT) based on the enhanced vegetation index (EVI), SWI, net radiation, and temperature. The algorithm was then compared with a modified satellite-based Priestley-Taylor ET model (MS-PT). After examining the performance of the two models at 10 flux tower sites in different land cover types over East Asia and Australia, the daily estimates from the SWI-PT model were closer to observations than those of the MS-PT model in each land cover type. The average correlation coefficient of the SWI-PT model was 0.81, compared with 0.66 in the original MS-PT model. The average value of the root mean square error decreased from $36.46W/m^2$ to $23.37W/m^2$ in the SWI-PT model, which used different variables of soil moisture and vegetation indices to capture soil evaporation and vegetative transpiration, respectively. By using the EVI and SWI, uncertainties involved in optimizing vegetation and water constraints were reduced. The estimated ET from the MS-PT model was most sensitive (to the normalized difference vegetation index (NDVI) in forests) to net radiation ($R_n$) in grassland and cropland. The estimated ET from the SWI-PT model was most sensitive to $R_n$, followed by SWI, air temperature ($T_a$), and the EVI in each land cover type. Overall, the results showed that the MS-PT model estimates of ET in forest and cropland were weak. By replacing the fraction of soil moisture ($f_{sm}$) with the SWI and the NDVI with the EVI, the newly developed SWI-PT model captured soil evaporation and vegetation transpiration more accurately than the MS-PT model.

• Journal of Information Technology Services
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• v.21 no.3
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• pp.63-72
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• 2022

The extinction crisis of local cities, caused by a population density increase phenomenon in capital regions, directly causes the increase of vacant houses in local cities. According to population and housing census, Gunsan-si has continuously shown increasing trend of vacant houses during 2015 to 2019. In particular, since Gunsan-si is the city which suffers from doughnut effect and industrial decline, problems regrading to vacant house seems to exacerbate. This study aims to provide a foundation of a system which can predict and deal with the building that has high risk of becoming vacant house through implementing a data driven vacant house prediction machine learning model. Methodologically, this study analyzes three types of machine learning model by differing the data components. First model is trained based on building register, individual declared land value, house price and socioeconomic data and second model is trained with the same data as first model but with additional POI(Point of Interest) data. Finally, third model is trained with same data as the second model but with excluding water usage and electricity usage data. As a result, second model shows the best performance based on F1-score. Random Forest, Gradient Boosting Machine, XGBoost and LightGBM which are tree ensemble series, show the best performance as a whole. Additionally, the complexity of the model can be reduced through eliminating independent variables that have correlation coefficient between the variables and vacant house status lower than the 0.1 based on absolute value. Finally, this study suggests XGBoost and LightGBM based machine learning model, which can handle missing values, as final vacant house prediction model.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.152-152
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• 2022

Only employing historical data limits the estimation of the full distribution of probable Tropical Cyclone (TC) risk due to the insufficiency of samples. Addressing this limitation, this study introduces a semi-physical TC rainfall model that produces spatially and temporally resolved TC rainfall data to improve TC risk assessments. The model combines a statistical-based track model based on the Markov renewal process to produce synthetic TC tracks, with a physics-based model that considers the interaction between TC and the atmospheric environment to estimate TC rainfall. The simulated data from the combined model are then fitted to a probability distribution function to compute the spatially heterogeneous risk brought by landfalling TCs. The methodology is employed in South Korea as a case study to be able to implement a country-scale-based vulnerability inspection from damaging TC impacts. Results show that the proposed model can produce TC tracks that do not only follow the spatial distribution of past TCs but also reveal new paths that could be utilized to consider events outside of what has been historically observed. The model is also found to be suitable for properly estimating the total rainfall induced by landfalling TCs across various points of interest within the study area. The simulated TC rainfall data enable us to reliably estimate extreme rainfall from higher return periods that are often overlooked when only the historical data is employed. In addition, the model can properly describe the distribution of rainfall extremes that show a heterogeneous pattern throughout the study area and that vary per return period. Overall, results show that the proposed approach can be a valuable tool in providing sufficient TC rainfall samples that could be an aid in improving TC risk assessment.

• Journal of Science Education
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• v.42 no.2
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• pp.132-147
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• 2018

The goal of this research is to suggest a theoretical process-centered assessment model based on Learning Progressions of key competencies in the context of STEAM instructions. The "Process-Products Combined Module-type (P2CM) STEAM Assessment Model (P2CM STEAM Assessment Model, hereafter) can be used both as an instructional model and as an assesment model, applicable for various STEAM topics and instructional types. consists of 3 axes. The first X axis stands for 4C competencies that should be emphasized through STEAM instruction. The second Y axis stands for the types and the hierarchy of STEAM instructions. The third Z axis stands for the assessment standards based on LP. We also exemplified an assessment module combined creativity competency with creativity-based instruction based on . Based on the research results, we suggested elaboration of assessment models based on Korean LP research outcomes, development and supply of formative assessment models through field-based in-depth research, modification of formative assessment models with the participation of teacher communities and in-service teachers, and the necessity of further research on assessment models for tracking LP.