• International conference on construction engineering and project management
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• 2020.12a
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• pp.485-492
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• 2020

Information on the energy consumption of buildings that can be obtained through conventional methods is limited. Therefore, this study aims to develop a model that can support decision making about building facility management through digital transformation technologies. Through the IoT sensor, the building's energy data and indoor air quality data are collected, and the monitored data is visualized through the ELK Stack and produced as a dashboard. In addition, the target building is photographed with a 360-degree camera and maps using a tool to create a 360-degree tour. Using such digital transformation technologies, users of buildings can obtain various information in real time without visiting buildings directly. This can lead to changes in actions or actions for building management, supporting facility management decisions, and consequently reducing building energy consumption.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.129-132
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• 2013

Determining on a particular construction method is typically decided in the initial phases of a project. However, changing conditions during actual construction may require a different method or technology to be employed. Providing an option for project managers to change construction provides flexibility that can increase value to the overall project. This research provides the ability to modify construction methods as a real option, which allows its value to be modeled. The research also formalizes a way to integrate a binomial lattice model with the Earned Value Method's S-curve. The integrated model provides a decision support tool that planners can use to determine whether to exercise the option depending on the status metrics provided by EVM.

• Korean Management Science Review
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• v.16 no.1
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• pp.51-61
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• 1999

Pipeline is an important transportation mode for ail products. The popeline transportation and storage rates affect the popeline usage, and the popeline usage also affects the transportation revenue and operating costs of the popeline. The purpose of our study is to develop a decision support system simulating popeline transportation and storage rates for maximizing the utilization and profitability of the oil pipeline and apply it to the real situation. To do this, a simulation model to help the decision maker decide the rates of the oil pipeline is first proposed. Second, a simulation program is developed, which enables the user to evaluate the various scenarios of oil transportation and storage rates. Finally, this program is applied to the case study of oil industry in korea.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.693-694
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• 2015

This paper present a decision method of middle ear disease which is developed in children and adults. In the proposed method, features are extracted from the middle ear disease images and normal images using HOG(histogram of oriented gradient) descriptor and the extracted features are learned by SVM(support vector machine) classifier. Input images are classified by SVM classifier based on the model of learning features. Experimental results show that the method yields accuracy of over 90% in decision.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1999.10c
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• pp.783-788
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• 1999

In this study, a decision support system (DSS) was developed to calculate optimal wastetreatment cost, treatment level and treatment quantity of various pollutants for applying for in rural basin. The DSS includes a gegraphic informatino system (GIS), relational database system (RDBS), water quality models(Loading function , WASP5), watershed pollution load calculation module(SPLC), optimal water quality management plan to satisfy the water quality regulations. The system can be modified by user to trace the optimal condition for decision. The effort was conducted to apply the developed DSS to select the for optimal water quality management plan small rural basin called Kwanri Stream.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1134-1139
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• 2009

Haul routes for earthmoving operation need to be carefully selected because the decision on the haul routes could make a significant difference in the project's cost and time. This paper proposes a decision support system for improving productivity of earthmoving operation that helps construction managers choose the best haul routes of trucks based on time slots. Also, a methodology for optimal selection of haul routes considering traffic conditions and topographic conditions of the routes is explained. Raster data model is used to find an available shortest path based on cost weighted distance. A system was developed on a geographic information system environment for efficient database management and easy manipulation of graphical data. A real-world case study was conducted to verify the applicability of the proposed system.

• Research in Mathematical Education
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• v.24 no.3
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• pp.137-173
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• 2021

Research and national standards, such as the Next Generation Science Standards (NGSS) in the United States, promote the development and implementation of K-12 interdisciplinary curricula integrating the disciplines of science, technology, engineering, mathematics, and computer science (STEM+CS). However, little research has explored how teachers provide epistemic support in interdisciplinary contexts or the factors that inform teachers' epistemic support in STEM+CS activities. The goal of this paper is to articulate how interdisciplinary instruction complicates epistemic knowledge and resources needed for teachers' instructional decision-making. Toward these ends, this paper builds upon existing models of teachers' instructional decision-making in individual STEM+CS disciplines to highlight specific challenges and opportunities of interdisciplinary approaches on classroom epistemic supports. First, we offer considerations as to how teachers can provide epistemic support for students to engage in disciplinary practices across mathematics, science, engineering, and computer science. We then support these considerations using examples from our studies in elementary classrooms using integrated STEM+CS curriculum materials. We focus on an elementary school context, as elementary teachers necessarily integrate disciplines as part of their teaching practice when enacting NGSS-aligned curricula. Further, we argue that as STEM+CS interdisciplinary curricula in the form of NGSS-aligned, project-based units become more prevalent in elementary settings, careful attention and support needs to be given to help teachers not only engage their students in disciplinary practices across STEM+CS disciplines, but also to understand why and how these disciplinary practices should be used. Implications include recommendations for the design of professional learning experiences and curriculum materials.

• Korean Journal of Construction Engineering and Management
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• v.15 no.1
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• pp.51-61
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• 2014

Preventive maintenance of buildings has increased the importance of interest in that it is able to maintain the performance building has and to prevent a problem occurred in future. For improved preventive maintenance work, it should be performed to select works order clearly and preceded the accurate measurement for the state of works order. when measuring the conditions, measurement of the state of work order considering the various criteria is more effective than to measure by only criterion. But, there are something hard to evaluate exactly between the criteria because of decision-maker's subjective judgments. To solve these problems, this research proposes decision making support model to determine preventive maintenance works order using Fuzzy-sets. By using Fuzzy-sets when measuring state of work objects, it can be reduced vagueness of judgments by decision-makers. This model can be used as a tool for objective evaluation of preventive maintenance work orders and offer the guideline to perform decision-making.

• Nuclear Engineering and Technology
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• v.53 no.8
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• pp.2534-2546
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• 2021

Nuclear emergency preparedness and response is an essential part to ensure the safety of nuclear power plant (NPP). Key support technologies of nuclear emergency decision-making usually consist of accident diagnosis, source term estimation, accident consequence assessment, and protective action recommendation. Source term estimation is almost the most difficult part among them. For example, bad communication, incomplete information, as well as complicated accident scenario make it hard to determine the reactor status and estimate the source term timely in the Fukushima accident. Subsequently, it leads to the hard decision on how to take appropriate emergency response actions. Hence, this paper aims to develop a method for rapid source term estimation to support nuclear emergency decision making in pressurized water reactor NPP. The method aims to make our knowledge on NPP provide better support nuclear emergency. Firstly, this paper studies how to build a Bayesian network model for the NPP based on professional knowledge and engineering knowledge. This paper presents a method transforming the PRA model (event trees and fault trees) into a corresponding Bayesian network model. To solve the problem that some physical phenomena which are modeled as pivotal events in level 2 PRA, cannot find sensors associated directly with their occurrence, a weighted assignment approach based on expert assessment is proposed in this paper. Secondly, the monitoring data of NPP are provided to the Bayesian network model, the real-time status of pivotal events and initiating events can be determined based on the junction tree algorithm. Thirdly, since PRA knowledge can link the accident sequences to the possible release categories, the proposed method is capable to find the most likely release category for the candidate accidents scenarios, namely the source term. The probabilities of possible accident sequences and the source term are calculated. Finally, the prototype software is checked against several sets of accident scenario data which are generated by the simulator of AP1000-NPP, including large loss of coolant accident, loss of main feedwater, main steam line break, and steam generator tube rupture. The results show that the proposed method for rapid source term estimation under nuclear emergency decision making is promising.

• Korean Journal of Construction Engineering and Management
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• v.10 no.1
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• pp.3-15
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• 2009

As building construction projects have been more higher and bigger in scale, the needs for cost reduction, productivity improvement, and reducing of work terms have rapidly increased in recent years. There has been, accordingly, a great emphasis on the necessity of logistics and progress management by well-organized system developed based on the appliance of different management theories. Although highly developed IT technology has contributed to the efficiency and effectiveness in information research and project management, RFID has been merely applied to a single progress or a few types of materials in current management studies, not to the overall process of the projects. This research proposes a consistent and systemized approach for decision making in adopting RFID technology in a construction project to support construction logistics and progress management. With a decision making model that consists of process model and template developed in this research, risks in cost, time, and error in building RFID-based construction logistics and progress management could be minimized.