• 대한임베디드공학회논문지
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• 제7권5호
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• pp.267-275
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• 2012

In this paper, we present the model-based autonomic computing framework for a cyber-physical system which provides a self-management and a self-adaptation characteristics. A development process using this framework consists of two phases: a design phase in which a developer models faults, normal status constrains, and goals of the CPS, and an operational phase in which an autonomic computing engine operates monitor-analysis-plan-execute(MAPE) cycle for managed resources of the CPS. We design a hierachical architecture for autonomic computing engines and adopt the Model Reference Adaptive Control(MRAC) as a basic feedback loop model to separate goals and resource management. According to the GroundVehicle example, we demonstrate the effectiveness of the framework.

• 대한산업공학회지
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• 제31권2호
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• pp.152-163
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• 2005

The focus of enterprise collaboration of supply chain management, has changed from integrating and exchanging business information to integrating and managing business processes between business partners. However, the collaboration is difficult due to the inherent complexity such as diverse business processes and dynamic business environments. To settle these problems, a reference model based enterprise architecture framework for the collaboration of supply chains is proposed in this paper. The supply chain collaboration framework is composed of three reference models capturing the different views of supply chain collaboration: supply process reference model, service component reference model, and technology and standard reference model. As the framework adapts the OMG's metadata architecture, the processes in the supply chain can be extended and integrated with other value chains, such as design chains, when it is necessary. Using the proposed framework, business managers can rapidly integrate and manage their business processes with their suppliers and customers.

• 한국군사과학기술학회지
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• 제13권1호
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• pp.41-49
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• 2010

In this paper, we propose a hierarchical RAM simulation model framework which are used to analyze the RAM specifications on the concept refinement phase. The hierarchical RAM simulation model framework consists of RAM simulation models, class library and each model's input and output data lists. The hierarchical RAM simulation models are co-operated with 3 kinds of model - type I, II, III. Type I, II models are used to analyze the target operational availability and Type III is used to establish the initial RAM specifications. Each model's input and output data lists are defined by considering each model's purpose of RAM analysis. The class library is arranged with each model's classes for implementing the hierarchical simulation models. The proposed framework may be applied for executing the RAM activities effectively.

• Educational Technology International
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• 제24권2호
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• pp.295-329
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• 2023

The recent development of metaverse-related technology has led to efforts to overcome the limitations of time and space in education by creating a virtual educational environment. To make use of this platform efficiently, applying learning analytics has been proposed as an optimal instructional and learning decision support approach to address these issues by identifying specific rules and patterns generated from learning data, and providing a systematic framework as a guideline to instructors. To achieve this, we employed an inductive, bottom-up approach for framework modeling. During the modeling process, based on the activity system model, we specifically derived the fundamental components of the learning analytics framework centered on learning activities and their contexts. We developed a prototype of the framework through deduplication, categorization, and proceduralization from the components, and refined the learning analytics framework into a 7-stage framework suitable for application in the metaverse through 3 steps of Delphi surveys. Lastly, through a framework model evaluation consisting of seven items, we validated the metaverse learning analytics framework, ensuring its validity.

• 한국컴퓨터정보학회논문지
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• 제22권10호
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• pp.129-135
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• 2017

This research is to identify an easy and effective method of teaching-learning plan. The teaching-learning plan is a blue_print applied for designing effective lessons. However, most of the teachers regard it as a difficult and inefficient job. This study proposed the concrete instructional model framework as a tool to develop the teaching-learning plan easily and effectively. The concrete instructional model framework will represent a decomposed instructional strategy applied for each step of the instructional model developed by educational researchers. This method is applied to develop a computer teaching-learning plan. Therefore, the proposed method will expand an easier teaching-learning plan. Furthermore, the proposed method develops a teaching-learning plan with fluent content in detail based on low-level instruction strategies applied in the concrete instruction model framework.

• 한국IT서비스학회지
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• 제8권4호
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• pp.87-101
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• 2009

The current information system audit merely inspects a web based information system by focusing on checking items that are extracted from structured and information engineering model and object-oriented component model. As a result, the checking item of web contents and design is inadequate. This paper aims to extract audit framework in order to strengthen the audit of web contents and design during the development of the web based information system and to suggest checking items based on audit framework. For this, the web development process and web site evaluation model were studied, compared, and analyzed with the current information system development audit. From a result of the survey, it was found that the adequacy of the suggested audit framework and audit checking items is above the average value. It is believed that the suggested audit framework is helpful for the audit of web based information system.

• 한국건설관리학회:학술대회논문집
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• 한국건설관리학회 2007년도 정기학술발표대회 논문집
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• pp.607-612
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• 2007

사업 초기 단계에서의 정확한 공사비 예측은 각각의 대안을 비교하여 향후 공사비에 대한 정보를 제공함으로써 효율적인 예산수립을 가능하게 한다. 하지만 사업 초기 단계에는 공사비 산정 기준이 모호하고 가용 정보가 부족함에 따라 공사비 예측에 한계가 나타난다. 더욱이 현행 공사비 산정모델이 단위 길이당 공사비를 활용한 선형적이고 단순한 모델을 활용함에 따라 예측의 정확도에 한계를 가지고 있다. 따라서 본 연구에서는 기존 공사비 산정모델의 한계를 개선하고 사업 초기 단계에서 가용한 데이터를 활용할 수 있는 공사비 산정 모델의 Framework를 구축하고자 한다. 이를 위하여 본 연구에서는 국내외 개략공사비 산정 모델을 분석하였으며, 기존 도로공사의 공사비 분석자료를 토대로 노선선정 등 사업초기 단계에서 활용 가능한 도로공사 개략공사비 산정모델의 Framework를 제시하였다.

• International journal of advanced smart convergence
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• 제12권1호
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• pp.53-58
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• 2023

With the fast development of artificial intelligence day by day, users are demanding explanations about the results of algorithms and want to know what parameters influence the results. In this paper, we propose a model for bankruptcy prediction with interpretability using the SHAP framework. SHAP (SHAPley Additive exPlanations) is framework that gives a visualized result that can be used for explanation and interpretation of machine learning models. As a result, we can describe which features are important for the result of our deep learning model. SHAP framework Force plot result gives us top features which are mainly reflecting overall model score. Even though Fully Connected Neural Networks are a "black box" model, Shapley values help us to alleviate the "black box" problem. FCNNs perform well with complex dataset with more than 60 financial ratios. Combined with SHAP framework, we create an effective model with understandable interpretation. Bankruptcy is a rare event, then we avoid imbalanced dataset problem with the help of SMOTE. SMOTE is one of the oversampling technique that resulting synthetic samples are generated for the minority class. It uses K-nearest neighbors algorithm for line connecting method in order to producing examples. We expect our model results assist financial analysts who are interested in forecasting bankruptcy prediction of companies in detail.

• Smart Structures and Systems
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• 제32권1호
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• pp.49-59
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• 2023

A bridge transportation network supplies products from various source nodes to destination nodes through bridge structures in a target region. However, recent frequent earthquakes have caused damage to bridge structures, resulting in extreme direct damage to the target area as well as indirect damage to other lifeline structures. Therefore, in this study, a surrogate model-based comprehensive framework to estimate the seismic resilience of bridge transportation networks is proposed. For this purpose, total system travel time (TSTT) is introduced for accurate performance indicator of the bridge transportation network, and an artificial neural network (ANN)-based surrogate model is constructed to reduce traffic analysis time for high-dimensional TSTT computation. The proposed framework includes procedures for constructing an ANN-based surrogate model to accelerate network performance computation, as well as conventional procedures such as direct Monte Carlo simulation (MCS) calculation and bridge restoration calculation. To demonstrate the proposed framework, Pohang bridge transportation network is reconstructed based on geographic information system (GIS) data, and an ANN model is constructed with the damage states of the transportation network and TSTT using the representative earthquake epicenter in the target area. For obtaining the seismic resilience curve of the Pohang region, five epicenters are considered, with earthquake magnitudes 6.0 to 8.0, and the direct and indirect damages of the bridge transportation network are evaluated. Thus, it is concluded that the proposed surrogate model-based framework can efficiently evaluate the seismic resilience of a high-dimensional bridge transportation network, and also it can be used for decision-making to minimize damage.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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• pp.176-176
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• 2018

This study presents a regional, probabilistic framework for estimating streamflow via spatial scaling in the Great Lakes basin, which is the largest lake system in the world. The framework follows a two-fold strategy including (1) a quadratic-programming based optimization model a priori to explore the model structure, and (2) a time-varying hierarchical Bayesian model based on insights found in the optimization model. The proposed model is developed to explore three innovations in hierarchical modeling for reconstructing historical streamflow at ungaged sites: (1) information of physical characteristics is utilized in spatial scaling, (2) a time-varying approach is introduced based on climate information, and (3) heteroscedasticity in residual errors is considered to improve streamflow predictive distributions. The proposed model is developed and calibrated in a hierarchical Bayesian framework to pool regional information across sites and enhance regionalization skill. The model is validated in a cross-validation framework along with four simpler nested formulations and the optimization model to confirm specific hypotheses embedded in the full model structure. The nested models assume a similar hierarchical Bayesian structure to our proposed model with their own set of simplifications and omissions. Results suggest that each of three innovations improve historical out-of-sample streamflow reconstructions although these improvements vary corrsponding to each innovation. Finally, we conclude with a discussion of possible model improvements considered by additional model structure and covariates.