• Radiation Oncology Journal
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• v.20 no.3
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• pp.283-293
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• 2002

Purpose : A PC based brachytherapy planning system was developed to display dose distributions on simulation images by 2D isodose curve including the dose profiles, dose-volume histogram and 30 dose distributions. Materials and Methods : Brachytherapy dose planning software was developed especially for the Ir-192 source, which had been developed by KAERI as a substitute for the Co-60 source. The dose computation was achieved by searching for a pre-computed dose matrix which was tabulated as a function of radial and axial distance from a source. In the computation process, the effects of the tissue scattering correction factor and anisotropic dose distributions were included. The computed dose distributions were displayed in 2D film image including the profile dose, 3D isodose curves with wire frame forms and dosevolume histogram. Results : The brachytherapy dose plan was initiated by obtaining source positions on the principal plane of the source axis. The dose distributions in tissue were computed on a $200\times200\;(mm^2)$ plane on which the source axis was located at the center of the plane. The point doses along the longitudinal axis of the source were $4.5\~9.0\%$ smaller than those on the radial axis of the plane, due to the anisotropy created by the cylindrical shape of the source. When compared to manual calculation, the point doses showed $1\~5\%$ discrepancies from the benchmarking plan. The 2D dose distributions of different planes were matched to the same administered isodose level in order to analyze the shape of the optimized dose level. The accumulated dose-volume histogram, displayed as a function of the percentage volume of administered minimum dose level, was used to guide the volume analysis. Conclusion : This study evaluated the developed computerized dose planning system of brachytherapy. The dose distribution was displayed on the coronal, sagittal and axial planes with the dose histogram. The accumulated DVH and 3D dose distributions provided by the developed system may be useful tools for dose analysis in comparison with orthogonal dose planning.

• Journal of Climate Change Research
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• v.1 no.1
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• pp.31-50
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• 2010

The Arctic climate change for the Last Glacial Maximum(LGM) occurred at 21,000 years ago (21ka) was investigated using simulation results of atmosphere-ocean coupled models from the second phase of the Paleoclimate Modelling Intercomparison Program(PMIP2). In the analysis, we used seven models, the NCAR CCSM of USA, ECHAM3-MPIOM of German Max-Planxk Institute, HadCM3M2 of UK Met Office, IPSL-CM4 of France Laplace Institute, CNRM-CM3 of France Meteorological Institute, MIROC3.2 of Japan CCSR at University of Tokyo, and FGOALS of China Institute of Atmospheric Physics. All the seven models reproduces the Arctic climate features found in the present climate at 0ka(pre-industrial time) in a reasonable degree in comparison to observations. During the LGM, the atmospheric $CO_2$ concentration and other greenhouse gases were reduced, the ice sheets were expanded over North America and northern Europe, the sea level was lowered by about 120m, and orbital parameters were slightly different. These boundary conditions were implemented to simulated LGM climate. With the implemented LGM conditions, the biggest temperature reduction by more than $24^{\circ}C$ is found over North America and northern Europe owing to ice albedo feedback and the change in lapse rate by high elevation. Besides, the expansion of ice sheets leads to the marked temperature reduction by more then $10^{\circ}C$ over the Arctic Ocean. The temperature reduction in northern winter is larger than in summer around the Arctic and the annual mean temperature is reduced by about $14^{\circ}C$. Compared to low mid-latitudes, the temperature reduction is much larger in high northern altitudes in the LGM. This results mirror the larger warming around the Artic in recent century. We could draw some information for the future under global warming from the knowledge of the LGM.

• Economic and Environmental Geology
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• v.56 no.4
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• pp.435-445
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• 2023

A delta is a depositional landform that is formed when sediment transported by a river is deposited in a relatively low-energy environment, such as a lake, sea, or a main channel. Among these, a delta formed at the confluence of rivers has a great importance in river management and research because it has a significant impact on the hydraulic and sedimentological characteristics of the river. Recently, the equilibrium state of the confluence area has been disrupted by large-scale dredging and construction of levees in the Nakdong River. However, due to the natural recovery of the river, the confluence area is returning to its pre-dredging natural state through ongoing sedimentation. The time-series data show that the confluence delta has been steadily growing since the dredging, but once it reaches a certain size, it repeats growth and retreat, and the overall size does not change significantly. In this study, we developed a model to explain the sedimentation-erosion processes in the confluence area based on the assumption that the confluence delta reaches a dynamic equilibrium. The model is based on two fundamental principles: sedimentation due to supply from the tributary and erosion due to the main channel. The erosion coefficient that represents the Nakdong River confluence areas, was obtained using data from the tributaries of the Nakdong River. Sensitivity analyses were conducted using the developed model to understand how the confluence delta responds to changes in the sediment and water discharges of the tributary and the main channel, respectively. We then used annual average discharge of the Nakdong River's tributaries to predict the dynamic equilibrium positions of the confluence deltas. Finally, we conducted a simulation experiment on the development of the Gamcheon-Nakdong River delta using recorded daily discharge. The results showed that even though it is a simple model, it accurately predicted the dynamic equilibrium positions of the confluence deltas in the Nakdong River, including the areas where the delta had not formed, and those where the delta had already formed and predicted the trend of the response of the Gamcheon-Nakdong River delta. However, the actual retreat in the Gamcheon-Nakdong River delta was not captured fully due to errors and limitations in the simplification process. The insights through this study provide basic information on the sediment supply of the Nakdong River through the confluence areas, which can be implemented as a basic model for river maintenance and management.

• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.35-44
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• 2024

Research in dam inflow prediction has actively explored the utilization of data-driven machine learning and deep learning (ML&DL) tools across diverse domains. Enhancing not just the inherent model performance but also accounting for model characteristics and preprocessing data are crucial elements for precise dam inflow prediction. Particularly, existing rainfall data, derived from snowfall amounts through heating facilities, introduces distortions in the correlation between snow accumulation and rainfall, especially in dam basins influenced by snow accumulation, such as Soyang Dam. This study focuses on the preprocessing of rainfall data essential for the application of ML&DL models in predicting dam inflow in basins affected by snow accumulation. This is vital to address phenomena like reduced outflow during winter due to low snowfall and increased outflow during spring despite minimal or no rain, both of which are physical occurrences. Three machine learning models (SVM, RF, LGBM) and two deep learning models (LSTM, TCN) were built by combining rainfall and inflow series. With optimal hyperparameter tuning, the appropriate model was selected, resulting in a high level of predictive performance with NSE ranging from 0.842 to 0.894. Moreover, to generate rainfall correction data considering snow accumulation, a simulated snow accumulation algorithm was developed. Applying this correction to machine learning and deep learning models yielded NSE values ranging from 0.841 to 0.896, indicating a similarly high level of predictive performance compared to the pre-snow accumulation application. Notably, during the snow accumulation period, adjusting rainfall during the training phase was observed to lead to a more accurate simulation of observed inflow when predicted. This underscores the importance of thoughtful data preprocessing, taking into account physical factors such as snowfall and snowmelt, in constructing data models.