• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.1226-1236
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• 2022

Because students majoring in construction-related fields must develop a broad repository of knowledge and skills, effective transferal of these is the primary focus of most academic programs. While inculcation of this body of knowledge is certainly critical, actual construction projects are complicated ventures that involve levels of risk and uncertainty, such as resistant neighboring communities, unforeseen weather conditions, escalating material costs, labor shortages and strikes, accidents on jobsites, challenges with emerging forms of technology, etc. Learning how to develop a level of discernment about potential ways to handle such uncertainty often takes years of costly trial-and-error in the proverbial "school of hard knocks." There is therefore a need to proactively expedite the development of a sharpened intuition when making decisions. The AGC Education and Research Foundation case study committee was formed to address this need. Since its inception in 2011, 14 freely downloadable case studies have thus far been jointly developed by an academics and industry practitioners to help educators elicit varied responses from students about potential ways to respond when facing an actual project dilemma. AGC case studies are typically designed to focus on a particular concern and topics have thus far included: ethics, site logistics planning, financial management, prefabrication and modularization, safety, lean practices, preconstruction planning, subcontractor management, collaborative teamwork, sustainable construction, mobile technology, and building information modeling (BIM). This session will include an overview of the history and intent of the AGC case study program, as well as lively interactive demonstrations and discussions on how case studies can be used both by educators within a typical academic setting, as well as by industry practitioners seeking a novel tool for their in-house training programs.

• 한국임상수의학회지
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• 제30권4호
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• pp.268-272
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• 2013

The purpose of this study was to assess the effects of reconstruction kernel, and slice thickness on the accuracy of spiral CT-based volume assessment over a range of object sizes typical of synthetic simulated tumor. Spiral CT scanning was performed at various reconstruction kernels (soft tissue, standard, bone), and slice thickness (1, 2, 3 mm) using a phantom made of gelatin and 10 synthetic simulated tumors of different sizes (diameter 3.0-12.0 mm). Three-dimensional volume assessments were obtained using an automated software tool. Results were compared with the reference volume by calculating the percentage error. Statistical analysis was performed using ANOVA and setting statistical significance at P < 0.05. In general, smaller slice thickness and larger sphere diameters produced more accurate volume assessment than larger slice thickness and smaller sphere diameter. The measured volumes were larger than the actual volumes by a common factor depending on slice thickness; in 100HU simulated tumors that had statistically significant, 1 mm slice thickness produced on average 27.41%, 2 mm slice thickness produced 45.61%, 3 mm slice thickness produced 93.36% overestimates of volume. However, there was no statistically significant difference in volume error for spiral CT scans taken with techniques where only reconstruction kernel was changed. These results supported that synthetic simulated tumor size, slice thickness were significant parameters in determining volume measurement errors. For an accurate volumetric measurement of an object, it is critical to select an appropriate slice thickness and to consider the size of an object.

• International journal of advanced smart convergence
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• 제4권1호
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• pp.45-53
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• 2015

This paper is proposed mathematical load modelling based on system identification approach of energy consumption of residential air conditioning. Due to air conditioning is one of the significant equipment which consumes high energy and cause the peak load of power system especially in the summer time. The demand response is one of the solutions to decrease the load consumption and cutting peak load to avoid the reservation of power supply from power plant. In order to operate this solution, mathematical modelling of air conditioning which explains the behaviour is essential tool. The four type of linear model is selected for explanation the behaviour of this system. In order to obtain model, the experimental setup are performed by collecting input and output data every minute of 9,385 BTU/h air-conditioning split type with $25^{\circ}C$ thermostat setting of one sample house. The input data are composed of solar radiation ($W/m^2$) and ambient temperature ($^{\circ}C$). The output data are power and energy consumption of air conditioning. Both data are divided into two groups follow as training data and validation data for getting the exact model. The model is also verified with the other similar type of air condition by feed solar radiation and ambient temperature input data and compare the output energy consumption data. The best model in term of accuracy and model order is output error model with 70.78% accuracy and $17^{th}$ order. The model order reduction technique is used to reduce order of model to seven order for less complexity, then Kalman filtering technique is applied for remove white Gaussian noise for improve accuracy of model to be 72.66%. The obtained model can be also used for electrical load forecasting and designs the optimal size of renewable energy such photovoltaic system for supply the air conditioning.

• 한국정밀공학회지
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• 제29권12호
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• pp.1279-1284
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• 2012

Micro structures which are widely used at various fields are commonly fabricated by lithograph, etching and laser methods. Recently, with the emergence of micro tools and ultra-precision machine tools, fabrication of the micro structures obtained using end-milling are studied. However, there are some problems due to the diameter of the micro end-mill getting smaller below $100{\mu}m$. The micro run-out resulted from miniaturization of end-mills have influence seriously on accuracy of micro structures. The error of run-out with a tooling jig showed a decrease of about $9.3{\mu}m$. Furthermore, micro structures with width of $30{\mu}m$ could be applied through experiments of slot machining obtained using 30 and $50{\mu}m$ end-mill. Also, narrow angle structures with $30^{\circ}$ angle could be applied through analysis of machining acute angle structures. Based on basic experiments, micro fluidics channels and spiral patterns for air bearing were machined.

• 대기
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• 제22권4호
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• pp.415-427
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• 2012

$CO_2$ is the most important trace gas related to climate change. Therefore, understanding surface carbon sources and sinks is important when seeking to estimate the impact of $CO_2$ on the environment and climate. CarbonTracker, developed by NOAA, is an inverse modeling system that estimates surface carbon fluxes using an ensemble Kalman filter with atmospheric $CO_2$ measurements as a constraint. In this study, to investigate the capability of CarbonTracker as an analysis tool for estimating surface carbon fluxes in Asia, an experiment with a nesting domain centered in Asia is performed. In general, the results show that setting a nesting domain centered in Asia region enables detailed estimations of surface carbon fluxes in Asia. From a rank histogram, the prior ensemble spread verified at observational sites located in Asia is well represented with a relatively flat rank histogram. The posterior flux in the Eurasian Boreal and Eurasian Temperate regions is well analyzed with proper seasonal cycles and amplitudes. On the other hand, in tropical regions of Asia, the posterior flux does not differ greatly from the prior flux due to fewer $CO_2$ observations. The root mean square error of the model $CO_2$ calculated by the posterior flux is less than the model $CO_2$ calculated by the prior flux, implying that CarbonTracker based on the ensemble Kalman filter works appropriately for the Asia region.

• 전기전자학회논문지
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• 제24권1호
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• pp.25-32
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• 2020

본 논문에서는 OLED 패널에 영상을 디스플레이 하면서 패널 불량 검사 및 광학 측정을 이용하여 색 좌표 및 휘도를 보상할 수 있는 OLED 패널 테스트를 위한 영상 발생기를 제안한다. 제안된 영상 발생기는 영상 발생 과정과 광학 측정을 이용한 색 좌표 및 휘도를 보상하는 과정 등의 2가지 과정으로 구성된다. 영상 발생 과정은 패널을 구동하기 위한 패널의 정보를 셋팅하고, 패널 정보에 맞게 영상 발생기의 출력 셋팅을 조절하여 영상을 출력한다. 영상의 출력 형태는 디지털 RGB 방식으로 구성된다. 영상발생기 내부의 패턴 발생 알고리즘은 패널의 해상도에 맞게 동기 신호를 기준으로 24비트 데이터 라인에 색데이터를 전송하는 방식으로 칼라 및 그레이 계열 영상 데이터를 출력한다. 광학 측정을 이용한 색 좌표 및 휘도를 보상하는 과정은 영상 발생기에서 영상을 OLED 패널에 출력하고, 광학 모듈로 측정한 색 좌표 및 휘도 데이터를 기준 데이터보다 차이나는 부분을 보상한다. 본 논문에서 제안된 OLED 패널 테스트를 위한 영상 발생기의 정확성을 평가하기 위해서 Xilinx 사의 Spartan 6 계열의 XC6SLX25-FG484 FPGA를 사용하였고 설계 툴은 ISE 14.5를 사용하였다. 영상 발생 과정의 출력은 오실로스코프를 이용한 디지털 RGB 출력에 대하여 목표로 한 설정 값과 시뮬레이션 결과 값이 일치함을 확인 할 수 있었다. 광학 측정을 이용한 색 좌표 및 휘도를 보상한 데이터는 패널 제조업체에서 제시한 오차율 이내의 정확도를 나타내었다.

• 대한방사선치료학회지
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• 제16권2호
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• pp.33-41
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• 2004

목적 : 선형가속기의 정도관리 항목 중 광조사야와 방사선조사야의 일치성 검사는 기존에 필름을 이용하여 측정하였다. 하지만 필름을 이용한 측정은 측정관찰자에 따라 오차가 크게 발생하여 이를 개선하기 위해 BIS(Beam Image System) 영상장치를 이용하여 필름과 조사야 일치성을 비교평가 하고자 하였다. 대상 및 방법 : 선형가속기를 이용하여 필름과 BIS 영상장치에 6, 15MV의 광자선을 조사하여 측정하였다. 조사야는 각각 $50{\times}50,\;100{\times}100,\;200{\times}200mm^2$지었고, 갠트리 각도는 필름을 사용시 $0^{\circ}$에서 측정하였고, BIS 영상장치를 사용시에는 $0^{\circ}$와 $270^{\circ}$에서 측정하였다. 그리고 조사야 일치성 측정은 필름을 사용시 눈금자와 필름 스캐너를 이용하였고, BIS 영상장치에서는 스캔된 광조사야와 방사선조사야의 폭을 X축과 Y축으로 각각 측정하여 그 오차값을 구하였다. 결과 : 필름을 이용한 관찰자의 시각적인 측정에서는 광조사야보다 방사선조사야가 더 크게 측정되었으며, 최대 1.9mm의 오차값을 보였다. 필름 스캐너를 이용한 측정에서도 방사선조사야가 더 크게 측정되었으며, 평균 오차값의 크기는 더 작은 값을 보였다. BIS 영상장치를 이용한 측정에서는 필름 사용시와는 반대로 갠트리 $0^{\circ}$와 $270^{\circ}$에서 광조사야가 방사선조사야보다 더 크게 측정되었고, 최대 0.96mm의 오차값을 보였다. ${\Delta}X$, Y축 모두 허용오차의 범위는 $<{\pm}2mm$로 나타났다. 측정된 평균 ${\Delta}X$, Y 오차값은 시각적인 필름측정, 필름스캐너 측정, BIS 순으로 작게 나타났다. 결론 : 본 연구의 측정결과, 필름을 이용한 시각적인 조사야 일치성 평가시에는 관찰자에 따른 측정오차의 값이 크게 나타날 수 있으므로 정확한 정도관리를 위해서는 세심한 주의로 측정할 것과 필요하다면 다른 영상장치의 도움이나 측정도구의 개발로 정확한 측정이 필요하다 하겠다.

• 한국의학물리학회지:의학물리
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• 제21권2호
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• pp.192-200
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• 2010

사이버나이프 치료에서 사용하는 소조사면은 전자평형의 부재와 급격한 선량 경사도(Steep dose gradients), 그리고 광자와 전자들의 스펙트럼 변화 요인으로 인하여 소조사면 광자선 선량 측정은 일반적인 치료의 측정보다 좀더 어렵고 복잡하다. 본 연구에서는 다이오드 검출기를 이용한 측정값과 GEANT4를 이용한 계산값을 비교하고 정확한 선량 전달을 위한 측정 선량의 검증 도구의 한 종류로 GEANT4의 유용성을 입증하고자 한다. 사이버나이프 몬테카를로 모델을 개발하는데 있어 두 단계로 진행하였다. 첫 번째 단계는 선형가속기 헤드(treatment head) 시뮬레이션과 이를 통한 광자 에너지 스펙트럼의 계산이었고, 두 번째 단계는 5, 10, 20, 30, 50, 60 mm의 여섯 개 원형 조사면에 대한 물팬텀속에서의 깊이선량율의 계산이었다. 그리고 출력인수(Relative output factors)에 대한 계산은 5 mm부터 60 mm까지 총 12가지 조사면에 대해 수행되었으며 그 결과를 다이오드 검출기를 이용한 측정값과 비교하였다. 가로선량분포(Profiles)의 경우 5, 10, 20, 30, 50, 60 mm의 6가지 조사면에 대해 계산이 이루어졌고 깊이는 1.5, 10, 20 cm의 세 가지 깊이에 대해 수행되었다. 깊이선량율의 계산값을 측정값과 비교한 결과 평균 2% 미만의 오차를 보여 임상에서 허용 가능한 결과를 얻었다. 조사면 출력인자의 경우에 조사면 직경 7.5 mm 이상에서 3% 이내의 오차를 보였으나 직경 5 mm 조사면에서는 6.9%로 높은 오차를 보였다. 가로선량분포에서 20 mm 이상의 조사면에서는 2% 미만의 오차를 보였고 그 이하의 조사면에서는 3.5% 미만의 오차를 보였다. 본 연구에서는 소조사면 사이버나이프 측정을 위한 선량분포 계산을 GEANT4 코드를 사용하여 다이오드 측정 결과와 비교하였다. 다이오드와의 측정 비교 결과 5 mm 조사면을 제외한 나머지 조사면들에 대해 오차 0.2~0.6% 내의 만족할만한 결과를 얻었다. 향후 소조사면에서 정확성을 가지는 Gafchromic 필름 등 다른 측정기와 비교를 통하여 그 정확성이 평가된다면 이 GEANT4의 선량분포 계산 방법은 소조사면을 이용하는 사이버나이프 방사선치료에서 정확한 선량 전달을 위한 측정 선량의 검증 도구의 한 종류로 사용할 수 있을 것으로 예상한다.

• Journal of Construction Engineering and Project Management
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• 제5권1호
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• pp.11-19
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• 2015

This paper presents optimized artificial neural networks (ANNs) claims prediction and decision awareness framework that guides owner organizations in their pre-bid construction project decisions to minimize claims. The framework is composed of two genetic optimization ANNs models: a Claims Impact Prediction Model (CIPM), and a Decision Awareness Model (DAM). The CIPM is composed of three separate ANNs that predict the cost and time impacts of the possible claims that may arise in a project. The models also predict the expected types of relationship between the owner and the contractor based on their behavioral and technical decisions during the bidding phase of the project. The framework is implemented using actual data from international projects in the Middle East and Egypt (projects owned by either public or private local organizations who hired international prime contractors to deliver the projects). Literature review, interviews with pertinent experts in the Middle East, and lessons learned from several international construction projects in Egypt determined the input decision variables of the CIPM. The ANNs training, which has been implemented in a spreadsheet environment, was optimized using genetic algorithm (GA). Different weights were assigned as variables to the different layers of each ANN and the total square error was used as the objective function to be minimized. Data was collected from thirty-two international construction projects in order to train and test the ANNs of the CIPM, which predicted cost overruns, schedule delays, and relationships between contracting parties. A genetic optimization backward analysis technique was then applied to develop the Decision Awareness Model (DAM). The DAM combined the three artificial neural networks of the CIPM to assist project owners in setting optimum values for their behavioral and technical decision variables. It implements an intelligent user-friendly input interface which helps project owners in visualizing the impact of their decisions on the project's total cost, original duration, and expected owner-contractor relationship. The framework presents a unique and transparent hybrid genetic algorithm-ANNs training and testing method. It has been implemented in a spreadsheet environment using MS Excel$^{(R)}$ and EVOLVERTM V.5.5. It provides projects' owners of a decision-support tool that raises their awareness regarding their pre-bid decisions for a construction project.