• 컴퓨터교육학회논문지
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• 제17권1호
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• pp.75-82
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• 2014

본 논문에서는 문제중심학습(Problem-based Learning)에 기반한 새로운 인터넷 윤리 학습 모델을 수업에 적용하고 윤리의식의 변화를 조사하였다. 이 모델은 토론과 글쓰기를 통한 학습자 참여중심의 수업으로 이루어진다. 새로운 모델의 효과를 검증하기 위해 이를 적용한 실험집단과 강의식 수업을 적용한 통제집단을 구성하고 수업을 진행하였다. 수업 후에 두 집단 간의 인터넷 윤리의식의 변화를 통계적으로 분석하였다. 분석 결과, PBL 모델을 적용한 실험집단의 윤리의식 향상이 통계적으로 유의미한 것으로 확인되었다. 특히 인터넷 윤리의식의 4개 영역 중에서 가장 크게 향상된 것은 책임인 것으로 확인되었다.

• 한국시뮬레이션학회논문지
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• 제27권1호
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• pp.25-32
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• 2018

본 논문은 모델 기반 내장형 소프트웨어의 자동 생성 코드에 대한 효율적인 신뢰성 시험 절차와 구체화된 동적 시험 방안에 대해서 제시하고 있다. 모델 정적/동적 시험 각각을 코드 정적/동적 시험 전에 수행함으로서 코드 신뢰성 시험 수행의 이점이 있음을 기술하였다. 또한, 모델과 코드의 신뢰성 시험 상관관계를 모델의 경우 Model Advisor와 Verification and Validation tool, 코드의 경우 Polyspace와 LDRA를 이용하여 살펴보고 제시한 절차대로 수행한 신뢰성 시험의 결과를 보여주고 있다.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.280-283
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• 2015

Many construction projects have used the building information modeling (BIM) extensively considering data interoperability throughout the projects' lifecycles. However, the current approach, which is to collect the data required to support facility maintenance system (FMS) has a significant shortcoming in that there are various individual pieces of information to represent the performance of the facility and the condition of each of the elements of the facility. Since a heterogeneous external database could be used to manage a construction project, all of the conditions related to the building cannot be included in an integrated BIM-based building model for data exchange. In this paper, we proposed an ontology-based facility maintenance information model to integrate multiple, related pieces of information on the construction project using industry foundation classesbased (IFC-based) BIM data. The proposed process will enable the engineers who are responsible for facility management to use a BIM-based model directly in the FMS-based work process without having to do additional data input. The proposed process can help ensure that the management of FMS information is more accurate and reliable.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2023년도 봄 학술논문 발표대회
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• pp.73-74
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• 2023

The purpose of this study is to derive the cause of the quantity difference and present the considerations when take-off rebar quantity based on BIM model by comparing the quantity of rebar based on BIM model with 2D drawing. This research was limited to take-off the quantity of rebars in the building frame work, and after take-off the quantity of rebars by 3D modeling the 2D drawing of the target building with Revit, the quantity difference was compared with 2D-based software. Therefore, when take-off the quantity of rebars based on the BIM model, instead of take-off the existing 2D-based quantity premium proportion, according to general structural consider development length, lap splice length, covering thickness, reinforcing bars and spacing. In the future, this study is expected to contribute to improving the accuracy of BIM-based frame construction quantity take-off.

• 국제학술발표논문집
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• The 3th International Conference on Construction Engineering and Project Management
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• pp.203-211
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• 2009

Korean public owners who order public multi-family housing construction projects have yet to gain access to a model for predicting construction cost. For this reason, their construction cost prediction is mainly dependent upon historic data and experience. In this paper, a cost-prediction model based on Case-Based Reasoning (CBR) in the design phase of public multi-family housing construction projects was developed. The developed model can determine the total construction cost by estimating the different Building, Civil, Mechanical, Electronic and Telecommunication, and Landscaping work costs. Model validation showed an accuracy of 97.56%, confirming the model's excellent viability. The developed model can thus be used to predict the construction cost to be shouldered by public owners before the design is completed. Moreover, any change orders during the design phase can be immediately applied to the model, and various construction costs by design alternative can be verified using this model. Therefore, it is expected that public owners can exercise effective design management by using the developed cost prediction model. The use of such an effective cost prediction model can enable the owners to accurately determine in advance the construction cost and prevent increase or decrease in cost arising from the design changes in the design phase, such as change order. The model can also prevent the untoward increase in the duration of the design phase as it can effectively control unnecessary change orders.

• Communications for Statistical Applications and Methods
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• 제22권3호
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• pp.241-253
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• 2015

In this paper we predict the track of typhoons using a Bayesian principal component regression model based on wind field data. Data is obtained at each time point and we applied the Bayesian principal component regression model to conduct the track prediction based on the time point. Based on regression model, we applied to variable selection prior and two kinds of prior distribution; normal and Laplace distribution. We show prediction results based on Bayesian Model Averaging (BMA) estimator and Median Probability Model (MPM) estimator. We analysis 8 typhoons in 2006 using data obtained from previous 6 years (2000-2005). We compare our prediction results with a moving-nest typhoon model (MTM) proposed by the Korea Meteorological Administration. We posit that is possible to predict the track of a typhoon accurately using only a statistical model and without a dynamical model.

• 한국지능시스템학회논문지
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• 제11권7호
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• pp.615-620
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• 2001

This paper addresses the design of sliding model fuzzy-model-based controller using genetic algorithms. In general, the construction of fuzzy logic controllers has difficulties for the lack of systematic design procedure. To release this difficulties, the sliding model fuzzy-model-based controllers was presented by authors. In this proposed method, the fuzzy model, which represents the local dynamic behavior of the given nonlinear system, is utilized to construct the controller. The overall controller consists of the local compensators which compensate the local dynamic linear model and the feed-forward controller which is designed via sliding mode control theory. Although, the stability and the performance is guaranteed by the proposed method, some design parameters have to be chosen by the designer manually. This problem can be solved by using genetic algorithms. The proposed method tunes the parameters of the controller, by which the reasonable accuracy and the control effort is achieved. The validity and the efficiency of the proposed method are verified through simulations.

• 소성∙가공
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• 제22권8호
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• pp.450-455
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• 2013

In the current study, a new dynamic recrystallization model for predicting high temperature flow stress is developed based on a physical model and the mean field theory. In the model, the grain aggregate is assumed as a representative volume element to describe dynamic recrystallization. The flow stress and microstructure during dynamic recrystallization were calculated using three sub-models for work hardening, for nucleation and for growth. In the case of work hardening, a single parameter dislocation density model was used to calculate change of dislocation density and stress in the grains. For modeling nucleation, the nucleation criterion developed was based on the grain boundary bulge mechanism and a constant nucleation rate was assumed. Conventional rate theory was used for describing growth. The flow stress behavior of pure copper was investigated using the model and compared with experimental findings. Simulated results by cellular automata were used for validating the model.

• 지능정보연구
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• 제3권2호
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• pp.33-55
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• 1997

A data model is a diagram that describes the properties of different categories of data and the associations among them within a business or information system. In spite of its importance and usefulness, data modeling activity requires not only a lot of time and effort but also extensive experience and expertise. The data models for similar business areas are analogous to one another. Therefore, it is reasonable to reuse the already-developed data models if the target business area is similar to what we have already analyzed before. In this research, we develop a case-based reasoning system for data model reuse, which we shall call CB-DM Reuser (Case-Based Data Model Reuser). CB-DM Reuse consists of four subsystems : the graphic user interface to interact with end user, the data model management system to build new data model, the case base to store the past data models, and the knowledge base to store data modeling and data model reusing knowledge. We present the functionality of CB-DM Reuser and show how it works on real-life a, pp.ication.

• Journal of information and communication convergence engineering
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• 제17권2호
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• pp.128-134
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• 2019

Based on the concept of Industry 4.0, various sensors are attached to facilities and equipment to collect data in real time and diagnose faults using analyzing techniques. Diagnostic technology continuously monitors faults or performance degradation of facilities and equipment in operation and diagnoses abnormal symptoms to ensure safety and availability through maintenance before failure occurs. In this paper, we propose a model to analyze the data and diagnose the state or failure using machine learning. The diagnosis model is based on a support vector machine (SVM)-based diagnosis model and a self-learning one-class SVM-based diagnostic model. In the future, it is expected that this model can be applied to facilities used in the entire industry by applying the actual data to the diagnostic model proposed in this paper, conducting the experiment, and verifying it through the model performance evaluation index.