• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2004.11a
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• pp.317-322
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• 2004

As a construction project in urban area tends to be high-rise and huge, the importance of the project's underground work, in terms of the cost and the schedule, is gradually increasing. It's extremely significant to choose a proper filing method, at the stage of underground work. However, in piling work many change orders have been occurred since a piling method is experientially selected based on uncertain information and many earth factors to consider. It has effects on the cost and the schedule of the project. In this study, we have suggested a decision model for piling method that can be used to determine and verify the suitable piling method in design and pre-construction phase of a project. Based on historical data, a neural network model has already proven to be efficient. The tests of the model for selecting a suitable piling method have progressed exactly with the data of 150 piling works which were done room 2000 to 2004 in Korea. The optimization or the developed neural network model has progressed with the data for teaming. The validity of the neural network model has been verified.

• Korean Journal of Construction Engineering and Management
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• v.14 no.3
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• pp.42-52
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• 2013

The aim of this study is to present a prediction model of construction cost for a bridge that has a high reliability using historical data from the planning phase based on a CBR (Case-Based Reasoning) method in order to overcome limitations of existing construction cost prediction methods, which is linearly estimated. To do this, a reasoning model of bridge construction cost by a spreadsheet template was suggested using complexly both CBR and GA (Genetic Algorithm). Besides, this study performed a case study to verify the suggested cost reasoning model for bridge construction projects. Measuring efficiency for a result of the case study was 8.69% on average. Since accuracy of the suggested prediction cost is relatively high compared to the other analysis methods for a prediction of construction cost, reliability of the suggested model was secured. In the case that information for detailed specifications of each bridge type in an initial design phase is difficult to be collected, the suggested model is able to predict the bridge construction cost within the minimized measuring efficiency with only the representative specifications for bridges as an improved correction method. Therefore, it is expected that the model will be used to estimate a reasonable construction cost for a bridge project.

• 한국지구물리탐사학회:학술대회논문집
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• 2004.08a
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• pp.49-69
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• 2004

One of the important roles of geophysical exploration in archeological survey may be to provide the subsurface information for effective and systematic excavations of historical remains. Ground Penetrating Radar (GPA) can give us images of shallow subsurface structure with high resolution and is regarded as a useful and important technology in archeological exploration. Since the buried cultural relics are the three-dimensional (3-D) objects in nature, the 3-D or areal survey is more desirable in archeological exploration. 3-D GPR survey based on the very dense data in principle, however, might need much higher cost and longer time of exploration than the other geophysical methods, thus it could have not been applied to the wide area exploration as one of routine procedures. Therefore, it is important to develop an effective way of 3-D GPR survey. In this study, we applied 3-D GPR method to investigate the possible historical remains of Baekje Kingdom at Gatap-Ri, Buyeo city, prior to the excavation. The principal purpose of the investigation was to provide the subsurface images of high resolution for the excavation of the surveyed area. Besides this, another purpose was to investigate the applicability and effectiveness of the continuous data acquisition system which was newly devised for the archeological investigation. The system consists of two sets of GPR antennas and the precise measurement device tracking the path of GPR antenna movement automatically and continuously Besides this hardware system, we adopted a concept of data acquisition that the data were acquired arbitrary not along the pre-established profile lines, because establishing the many profile lines itself would make the field work much longer, which results in the higher cost of field work. Owing to the newly devised system, we could acquire 3-D GPR data of an wide area over about $17,000 m^2$ as a result of the just two-days field work. Although the 3-D GPR data were gathered randomly not along the pre-established profile lines, we could have the 3-D images with high resolution showing many distinctive anomalies which could be interpreted as old agricultural lands, waterways, and artificial structures or remains. This case history led us to the conclusion that 3-D GPR method can be used easily not only to examine a small anomalous area but also to investigate the wider region of archeological interests. We expect that the 3-D GPR method will be applied as a one of standard exploration procedures to the exploration of historical remains in Korea in the near future.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.6
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• pp.430-439
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• 2016

Estimation and analysis of traffic jams plays a vital role in an intelligent transportation system and advances safety in the transportation system as well as mobility and optimization of environmental impact. For these reasons, many researchers currently mainly focus on the brilliant machine learning-based prediction approaches for traffic prediction systems. This paper primarily addresses the analysis and comparison of prediction accuracy between two machine learning algorithms: Naïve Bayes and K-Nearest Neighbor (K-NN). Based on the fact that optimized estimation accuracy of these methods mainly depends on a large amount of recounted data and that they require much time to compute the same function heuristically for each action, we propose an approach that applies multi-threading to these heuristic methods. It is obvious that the greater the amount of historical data, the more processing time is necessary. For a real-time system, operational response time is vital, and the proposed system also focuses on the time complexity cost as well as computational complexity. It is experimentally confirmed that K-NN does much better than Naïve Bayes, not only in prediction accuracy but also in processing time. Multi-threading-based K-NN could compute four times faster than classical K-NN, whereas multi-threading-based Naïve Bayes could process only twice as fast as classical Bayes.

• Journal of Information Technology Applications and Management
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• v.14 no.3
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• pp.95-113
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• 2007

To minimize IT operational risks and the opportunity cost for lost business hours. it is necessary to have preparedness in advance and mitigation activities for minimization of a loss due to the business discontinuity. There are few cases that banks have a policy on systematic management, system recovery and protection activities against system failure. and most developers and system administrators response based on their experience and the instinct. This article focuses on the mitigation model development for minimizing the incidents of disk unit in IT operational risks. The model will be represented by a network model which is composed of the three items as following: (1) the risk factors(causes, attributes and indicators) of IT operational risk. (2) a periodic time interval through an analysis of historical data. (3) an index or an operational regulations related to the examination of causes of an operational risk. This article will be helpful when enterprise needs to hierarchically analyze risk factors from various fields of IT(information security, information telecommunication, web application servers and so on) and develop a mitigation model. and it will also contribute to the reduction of operational risks on information systems.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.3
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• pp.76-82
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• 2017

Recently, the material industry in the world has started appreciating the value of new materials that can overcome the limitation of steel material. In particular, new materials are expected to play a very important role in the future industry, demonstrating superior performance compared to steel in lightweight materials and ability to maintain in high temperature environments. Carbon materials have recently increased in value due to excellent physical properties such as high strength and ultra lightweight compared to steel. However, they have not overcome the limitation of productivity and price. The carbon materials are classified into various composites depending on the purpose of use and the performance required. Typical composites include carbon-glass, carbon-carbon, and carbon-plastic composites. Among them, carbon-carbon composite technology is a necessary technology in aviation and space, and can be manufactured with high investment cost and technology. In this paper, in order to find the optimal conditions to achieve productivity improvement and cost reduction of carbon material densification process, the correlation between each process parameters and results of densification is first analyzed. The main process parameters of the densification process are selected by analyzing the correlation results. And then a certain linear relationship between major process variables and density of carbon materials is derived by performing a regression analysis based on the historical production result data. Using the derived casualty, the optimal management range of major process variables is suggested. Effective process operation through optimal management of variables will have a great effect on productivity improvement and manufacturing cost reduction by shortening the lead time.

• The Journal of Society for e-Business Studies
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• v.24 no.3
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• pp.99-110
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• 2019

As the 4th industrial revolution progressing, manufacturers are trying to apply intelligent information technologies such as IoT(internet of things) and machine learning. In the semiconductor/LCD/tire manufacturing process, schedule plan that minimizes setup change and due date violation is very important in order to ensure efficient production. Therefore, in this paper, we suggest the deep learning based scheduling generation model minimizes setup change and due date violation in parallel machines. The proposed model learns patterns of minimizing setup change and due date violation depending on considered order using the amount of historical data. Therefore, the experiment results using three dataset depending on levels of the order list, the proposed model outperforms compared to priority rules.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.123-130
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• 2002

Project plan and control is a highly skilled task which is vital for business success, and Project control systems are important for successful project execution. To support construction process management effectively, an integrated cost and schedule control function is required to collect quality data in a timely manner and to provide quality historical data for future planning of new projects. In Korea construction industry, the development and implementation cases of a project control system which to efficiently support construction project planning and controlling processes, have been increased to ensure a project success and the profitability of a construction company. To contribute to improving practical effectiveness of a project control system, the paper reviewed and analyzed the current trends and functional features of current project control systems. And it also described the pending issues and their solutions faced on previous project control systems.

• Korean Journal of Construction Engineering and Management
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• v.5 no.1 s.17
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• pp.168-174
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• 2004

Project plan and control is a highly skilled task which is vital for business success, and Project control systems are important for successful project execution. To support construction process management effectively, an integrated cost and schedule control function is required to collect quality data in a timely manner and to provide quality historical data for future planning of new projects. In Korea construction industry, the development and implementation cases of a project control system which to efficiently support construction project planning and controlling processes, have been increased to ensure a project success and the profitability of a construction company. To contribute to improving practical effectiveness of a project control system, the paper reviewed and analyzed the current trends and functional features of current project control systems. And it also described the pending issues and their solutions faced on previous project control systems.

• Korean Journal of Construction Engineering and Management
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• v.20 no.1
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• pp.141-150
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• 2019

In the overseas plant construction market, the domain construction firms' construction capability has been greatly improved, but the capability of project management is evaluated to be insufficient compared to the technical aspect. Project management capabilities from the initial planning stage of project execution are regarded as the core competence of advanced construction companies. Among them, it is urgent to improve the capacity of conceptual estimate for domestic companies. In this study, the researchers surveyed and analyzed the methodology of estimating project cost in the planning phase of the plant project and developed an estimation method by conducting a case study analysis. Based on the logic of the cost index and parametric estimation method among the existing estimation methodology, the estimation tool was developed by deriving the input and output variables tailored to the plant project. The validity of the proposed methodology was evaluated by comparing the accuracy between the project estimate amount of the case project and the actual project amount. In order to increase the utilization of the developed conceptual estimate methodology,for plant construction project, it is necessary to systematize the data of the historical project data. Increasing the accuracy of future project cost estimates is directly related to increasing project award and profitability of the domestic construction company.