• Title/Summary/Keyword: 에너지모의실험

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A Study of Dose Stability at Low Monitor Unit Setting for Multiple Irradiated Field (다중 조사면 치료 시 기계적 입력치(MU)에 따른 선량적 안정성에 대한 연구)

  • Kim Joo-Ho;Lee Sang-Gyu;Shin Hyun-Kyung;Lee Suk;Na Soo-Kyung;Cho Jung-Hee;Kim Dong-Wook
    • The Journal of Korean Society for Radiation Therapy
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    • v.17 no.2
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    • pp.155-160
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    • 2005
  • Purpose : Many authors have been introduced field in field technique and 3-D conformal radiotherapy that increased the tumor dose as well as decreased the dose of abutting critical organ. These technique have multiple beam direction and small beam segments even below 10 MU(monitor unit)for each field. we have confirmed the influence of low MU on dose output and beam stability. Materials and Methods : To study the dose output, the dose for each field was always 90MU, but it divided into different segment size: 1, 2, 3, 5, 10, 15 segments, 90, 45, 30, 18, 9, 6 MU the measurements were carried out for X-ray energy 4 MV, 6 MV, 10 MV of three LINAC(Varian 600C, 2100C, 2100C, 2100C/D), in addition each measurement was randomly repeated three times for each energy. To study the field symmetry and flatness, X-omat V films were irradiated. After being developed, films were scanned and analyzed using densitometer. Results : Influence of low MU on dose is slightly more increase output about $1.2{\sim}2.9%$ in cGy/mu than 90MU, but may not changed beam quality(flatness or symmetry), Output stability depends on dose rate(PRF)rather than beam energy, field size. Conclusion : Presented result are under the limits(out put<3%, flatness<${\pm}3%$, symmetry<2%). The 3 accelerators are safe to use and to perform conformal radiotherapy treatments in small segments, small MU around 10MU. but Even if the result presented here under the limits, continuous adjustments and periodic QA should be done for use of small MU

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Dynamic Equilibrium Position Prediction Model for the Confluence Area of Nakdong River (낙동강 합류부 삼각주의 동적 평형 위치 예측 모델: 감천-낙동강 합류점 중심 분석 연구)

  • Minsik Kim;Haein Shin;Wook-Hyun Nahm;Wonsuck Kim
    • 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.

Basic Research on the Possibility of Developing a Landscape Perceptual Response Prediction Model Using Artificial Intelligence - Focusing on Machine Learning Techniques - (인공지능을 활용한 경관 지각반응 예측모델 개발 가능성 기초연구 - 머신러닝 기법을 중심으로 -)

  • Kim, Jin-Pyo;Suh, Joo-Hwan
    • Journal of the Korean Institute of Landscape Architecture
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    • v.51 no.3
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    • pp.70-82
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    • 2023
  • The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.