• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2008.11a
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• pp.855-858
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• 2008

The lightweight bubble mixture soil are lightweight soft ground rear, which is used with the material filling. However, comparing with the general soil, it is not valuably useful from domestic. The utilization of the general soil which initial public corporation holds mainly few. The overlay method of general soil decreasing the number of layers increases according to use research study. From the research which consequently, BOX mechanical behavior materials rear executed LCC analyses the general soil which is a material filling and lightweight bubble mixture soil, discussed two kind alternatives and presents the analysis will be able to support the decision-making which is rational from the economics. The expense, which results from the resultant of lightweight bubble mixture soil maintenance, was fewer and was then analyzed with the fact that, will be able to secure an economical efficiency within 6 years.

• Korean Business Review
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• v.6
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• pp.135-152
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• 1993

The optional market segmentation pricing policy for rooms of hotels are investigated under the assumption of a linear demand function, and for four different situations: (1) single price market, (2) optimal segmentation of the unused capacity of a single-price-maeket, (3) optimal segmantation for all rooms, and (4) opimal segmentation for infiltration from higher priced to adjacent lower priced segments. The purpose of tis study is th show that with proper pricing policy, it would be possible to increase profits considerably. Such a profit increase might be achived by market segmentation coupled with product differentiation, where the different market segments are identified, sperated, and in each segment a different price per room is called for. The different prices are determined based on the specific price elasticity typical for each market segment and the relavant costs. The pricing model implied in this study is based on basic economic pricing theory and optimization techniques. While somewhat complex in its mathmatical solution, it can be easily programmed for use by practitioners, avoiding the need to cope with the technical aspects of the solution. In section II-1, the optimal single-market Single-price policy is evaluated. The optimal strategy under the constraint that only the previously unutilized rooms are segmented is analysed in section II-2, while the optimal strategy without this constraint is determined in section II-3. In section II-4, the optimal market-segmentation pricing policy is derived for the case in which market seperation is allowed for all the rooms under the assumption of custtomer infiltration from each market segment to the adjacent lower priced segment Finally, some considerations relating to the practicality of the model as a decision support tool and the requirements for its implementation are discussed in section III.

• Korean Journal of Construction Engineering and Management
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• v.13 no.5
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• pp.135-143
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• 2012

Constructing super-tall buildings is significantly different from constructing general ones in every technological and managerial aspects. Especially lift-car operations planning and management is one of core parts among various management techniques required during the course of the whole construction process of the super-tall buildings because vertical movements of physical resources enormously affect the efficiency of the construction processes. However, discrepancy between lifting plans and actual lifting operations causes serious efficiency problems. As an effort to solve the problem, this research suggests an improved method of estimating resource-based lifting load. The computing model developed as a result of this research facilitates more accurate computation of the total operation time and the maximum lifting capacity of the lift-cars. Further, this research can be developed as a decision support system for the total lift-car operations management.

• The Journal of Fisheries Business Administration
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• v.34 no.2
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• pp.165-183
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• 2003

The effect of red tide on the marine ecological system is so severe that many researches on the diverse subjects related to it have been conducted. Notwithstanding the enormous efforts and inputs the results of the past researches show no clear ways to deal with the HAB problems. As many researches are being conducted, the efficient and appropriate research project management systems as one of the critical factors for successful research are also needed as well as the fund and the capabilities of the researchers. It is assumed that the development of the evaluation and management systems for red tide research projects is so important and critical to enhance the researches and to utilize efficiently the physical and human resources for research. In this respect this study aims to present the evaluation and management scheme for the red tide researches that can not only decide the priority of the research subjects and tell the desirable research directions, but also support to develop the useful managerial policies and guidelines for the policy maker. The main subjects dealt with in this study are as follows : the characteristics of the HAB researches, the basic attributes and criterion of the research evaluation systems, the structure and design of the evaluation systems, and the development of the managerial policies by the type of the evaluation system. The conceptual scheme developed in this study is expected to be applied to the related areas and can suggest to the policy makers so many implications for identifying and setting the proper policy objects and management techniques. This study has a couple of weak points. It suggests only the conceptual scheme but not the applications so that the researches focusing on the applications in practical perspectives are needed to follow.

• The Korean Journal of Community Living Science
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• v.16 no.2
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• pp.61-73
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• 2005

This study aims to examine the socio-demographic heterogeneity of community participation in rural Korea. Data was collected through interviews with 1,870 rural householders and housewives who have lived in Up or Myen as an administrative unit of rural communities, and analyzed by the SPSS/PC Win V.10 program. The statistical techniques used for this study were frequency and percentile. The major findings of this study were as follows. Firstly, the extent to which rural people have participated in community organizations were: cooperative groups, $80.8\%$; religious groups, $20.6\%$; learning groups, $12.7\%$; political groups, $9.8\%;$ civil groups $6.7\%$; and voluntary groups, $5.3\%$. Whereas the numbers were high for community participation in groups related to agricultural production, participation in civil and voluntary groups were lower. Secondly, it showed that people who lived in urbanized and high population density areas were more likely to participate in community groups. The diversity of community organizations was different according to the level of rurality. Thirdly, farm householders were more likely to participate in religious, civil and voluntary groups than non-farm householders. Fourthly, people with higher education, females, those in the 40 to 50 age groups were more likely to participate in community organizations. Fifthly, even though men are more likely to participate in political parties, women were more likely then men to agree that women should participate in political parties. This empirical study could support the results of Sundeen (1988) and Wilson and Musick (1997) in that education was related positively to community participation. In addition, we concluded that community participation in a rural development process has two main considerations: philosophical and pragmatic. This implies that there is room for government to enable and facilitate 'true' community participation. That can be done through policy reform which creates a permissive environment for community decision-making and input, in addition to simply supporting community development through financial assistance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1700-1709
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• 2019

Recently, many researches have been under way to develop systems (services) to support the safety navigation of ships, and in these studies, common difficulties have been encountered in assessing the usefulness and effectiveness of the developed system. To solve these problems, we propose the DEVS-based ship navigation modeling and simulation technique. Following the preceding study, we analyze the COLREG rules and reflected to officer and helmsman agent models for decision making. Also we propose estimation and interpolation techniques to adopt the motion characteristics of the actual vessel to simulation. In addition, we implement the navigation simulation system to reflect the designed proposed methods, and we present five-scenarios to verify the developed simulation system. And we conduct simulations according to each scenario and the results were reconstructed. The simulation results confirm that the components modelled in each scenario enable to operate according to the navigation relationships.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.148-162
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• 2023

Classification systems can significantly assist the medical sector by allowing for the precise and quick diagnosis of diseases. As a result, both doctors and patients will save time. A possible way for identifying risk variables is to use machine learning algorithms. Non-surgical technologies, such as machine learning, are trustworthy and effective in categorizing healthy and heart-disease patients, and they save time and effort. The goal of this study is to create a medical intelligent decision support system based on machine learning for the diagnosis of heart disease. We have used a mixed feature creation (MFC) technique to generate new features from the UCI Cleveland Cardiology dataset. We select the most suitable features by using Least Absolute Shrinkage and Selection Operator (LASSO), Recursive Feature Elimination with Random Forest feature selection (RFE-RF) and the best features of both LASSO RFE-RF (BLR) techniques. Cross-validated and grid-search methods are used to optimize the parameters of the estimator used in applying these algorithms. and classifier performance assessment metrics including classification accuracy, specificity, sensitivity, precision, and F1-Score, of each classification model, along with execution time and RMSE the results are presented independently for comparison. Our proposed work finds the best potential outcome across all available prediction models and improves the system's performance, allowing physicians to diagnose heart patients more accurately.

• Journal of the Korea Society of Computer and Information
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• v.14 no.4
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• pp.171-178
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• 2009

Recently, a combination of Business Process Management (BPM) and Service Oriented Architecture (SOA) is being recommended as the best approach for automating large business systems. And the need to create meaningful information from daily operational data is increased today. In this paper, we propose a methodology for automating business processes based on the BPM-SOA convergence trend and verify the methodology by implementing the project management business process. BPM-SOA convergence provides higher extensibility and productivity due to the loosely coupled system construction and maintenance. The system has good properties for frequent process changes and reuse of duplicate processes. We then analyze extensibility of the system as new business processes are added to the existing system. We finally analyze the data generated by BPM by using SAP business intelligence to support management's decision making and strategy. Business intelligence provides not only useful data for business decisions but also chance to optimize the business processes.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.823-830
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• 2022

The risk of project execution increases due to the enlargement and complexity of Engineering, Procurement, and Construction (EPC) plant projects. In the fourth industrial revolution era, there is an increasing need to utilize a large amount of data generated during project execution. The design is a key element for the success of the EPC plant project. Although the design cost is about 5% of the total EPC project cost, it is a critical process that affects the entire subsequent process, such as construction, installation, and operation & maintenance (O&M). This study aims to develop a system using machine-learning (ML) techniques to predict risks and support decision-making based on big data generated in an EPC project's design and construction stages. As a result, three main modules were developed: (M1) the design cost estimation module, (M2) the design error check module, and (M3) the change order forecasting module. M1 estimated design cost based on project data such as contract amount, construction period, total design cost, and man-hour (M/H). M2 and M3 are applications for predicting the severity of schedule delay and cost over-run due to design errors and change orders through unstructured text data extracted from engineering documents. A validation test was performed through a case study to verify the model applied to each module. It is expected to improve the risk response capability of EPC contractors in the design and construction stage through this study.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.408-417
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• 2022

Although traditionally perceived as being a visualization and asset management resource, the relatively rapid rate of improvement of computing power, coupled with the proliferation of cloud and edge computing and the IoT has seen the expanded functionality of modern Digital Twins (DTs). These technologies, when applied to buildings, are now providing users with the ability to analyse and predict their energy consumption, implement building controls and identify faults quickly and efficiently, while preserving acceptable comfort and well-being levels. Furthermore, when these building DTs are linked together to form a community DT, entirely new and novel energy management techniques, such as demand side management, demand response, flexibility and local energy markets can be unlocked and analysed in detail, creating circularity in the economy and making ordinary building occupants active participants in the energy market. Through the EU Horizon 2020 funded TwinERGY project, three different levels of DT (consumer - building - community) are being created to support the creation of local energy markets while optimising building performance for real-time occupant preferences and requirements for their building and community. The aim of this research work is to demonstrate the development of this new, interrelated, multi-level DT that can be used as a decision-making tool, helping to determine optimal scenarios simultaneously at consumer, building and community level, while enhancing and successfully supporting the community's management plan implementation.