• Proceedings of the Korean Institute of Building Construction Conference
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• 2019.11a
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• pp.182-183
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• 2019

The amount of multi-aged housing more than 15 years since the completion has increased, and the real estate market caused by apartment remodeling has also increased. The domestic mortgage loan regulation on relocation expense caused by remodeling apply to same limit as that for mortgage loan newly-built apartment. The excessive government housing regulation could be an obstacle for the occupants residential stability of remodeling apartment. To suggest methods for government housing regulation of residential mortgage loan related to remodeling, examples for mortgage loan regulation and mortgage products were studied and compared. It is pointed out that regulation on occupants relocation expense caused by remodeling has to be relaxed and mortgage product development are needed to boost remodeling project.

• Annual Conference of KIPS
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• 2013.05a
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• pp.501-503
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• 2013

This project presents a concept of mobile robots using prototypes, computing proposal oriented to embedded systems implementation. We implement our system using GPS module, Ultrasonic sensor(range sensors), H-bridge dual stepper control, DTMF(Dual-tone Multi-Frequency ) and LCD module. In this paper we construct a mechanical simple mobile robot model, which can measure the distance from obstacle with the aid of sensor and should able to control the speed of motor accordingly. Modules were interfaced with FPGA(Field Programmable Gate Array) controller for hardware implementation.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1071-1082
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• 2021

Since the smart factory has been recently recognized as an industrial core requirement, various mechanisms to ensure efficient and stable operation have attracted much attention. This attention is based on the fact that in a smart factory environment where operating processes, such as facility control, data collection, and decision making are automated, the disruption of processes due to problems such as facility anomalies causes considerable losses. Although many studies have considered methods to prevent such losses, few have investigated how to effectively apply the solutions. This study proposes a Kubernetes based system applied in a smart factory providing effective operation and facility management. To develop the system, we employed a useful and popular open source project, and adopted deep learning based anomaly detection model for multi-sensor anomaly detection. This can be easily modified without interruption by changing the container image for inference. Through experiments, we have verified that the proposed method can provide system stability through nondisruptive maintenance, monitoring and non-disruptive updates for anomaly detection models.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1161-1165
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• 2009

Safety of workers and productivity in construction site are affected according to the conflict status of work spaces which are performed at a time. The interference among the work spaces in construction site should be minimized because it becomes a blocking factor that causes construction delay and low productivity. Those factors can be managed by reasonable H&S (Health & Safety) practice. This research suggests the requirements of H&S practice and rules to establish the strategy of H&S management based on the literature reviews related to H&S rule. The suggested H&S rule can be visualized for searching space conflict point if the rule is linked with visual simulation tool. Accordingly the research results can be used for improving and visualizing construction productivity by work space control in 4D/nD CAD system.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1383-1387
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• 2009

As information technologies in construction field get developed, various studies and projects are in progress for improvement of construction industry. Meanwhile, web-basis online system for building materials is tending upward. However, most of the informations about classification system for building materials and specifications are not systematic yet. Most field staffs have some troubles in making full use of the material information, repeating inefficient works from constructional design to the maintenance of it. This study designed auto-categorization system classified by materials, multi-search engines, auto-converting/creating electronic catalog as well as RFID search support to provide standardized building materials information.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1162-1169
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• 2022

As buildings become more and more complicated, the importance of developing and managing facility management information is increasing. In a building fire situation, various information is generated and needed to be quickly shared among participants. However, the current fire response system fails to monitor the relevant information in a real time basis. This study aims to develop a system prototype for fire protection management which can quickly and accurately manage and effectively deliver the pertinent information to the target participants. The research contributes to the efficiency of fire protection endeavors by interpreting both dynamic and static fire information in an appropriate manner.

• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.36.3-37
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• 2020

Recent observation of the neutron star merger event, GW170817, through both gravitational wave (GW) and electromagnetic wave (EM) observations opened a new way of exploring the universe, namely, multi-messenger astronomy (MMA). One of the keys to the success of MMA is a rapid identification of EM counterpart. We will introduce GW follow-up observation project in Korea for hunting GW EM counterpart rapidly and its strategy for prioritization of GW source host galaxy candidates. Our method relies on recent simulation results regarding plausible properties of GW source host galaxies and the low latency localization map from LIGO/Virgo. We will show a test result for both binary neutron star merger events using previous event and describe observing strategy with our facilities for GW events during the ongoing LIGO/Virgo O3 run. Finally, we report the results of optical/NIR follow-up observation of GW190425, the first neutron.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.41-43
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• 2015

Urban Informatics, the application of data science methodologies to the urban development and planning domain, has been increasingly adopted to improve the management and efficiency of cities. This paper introduces state of the art use cases in major cities including New York, London, Seoul and Amsterdam. It also introduces recent advances in using Big Data by multi-lateral institutions for poverty reduction, and startups utilizing open data initiatives to create new value and insights. Preliminary research performed on using Seoul's open data such as building permit data and health code violations are also introduced to demonstrate opportunities in this relatively new but promising area of research.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.52-57
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• 2011

Bridges are essential and valuable elements in road and rail transportation networks. Bridge remediation is a top priority for asset managers, but identifying the nature of true defect deterioration and associated remediation treatments remains a complex task. Nowadays Decision Support Systems (DSS) are used extensively to assist in decision-making across a wide spectrum of unstructured decision environments. In this paper a requirements-driven framework is used to develop a risk based decision support model which has the ability to quantify the bridge condition and find the best remediation treatments using Multi Attribute Utility Theory (MAUT), with the aim of maintaining a bridge within acceptable limits of safety, serviceability and sustainability.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.56-62
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• 2024

To enhance energy efficiency and reduce emissions in prefabricated construction, optimizing the production scheduling of precast concrete is considered an effective approach. Due to the unique characteristics of precast concrete during production, traditional scheduling models are no longer applicable. This present study introduces practical considerations, such as a limited number of molds, buffers, uncertainty of order arrivals and vehicles. Furthermore, to meet the requirements of contemporary industrial development, a mulit-objective optimization scheduling model is formulated by integrating total processing time, on-time delivery rate and work station idle time. A solution based on reinforcement learning algorithm is devised. Results indicate that this method can effectively undergo training and achieve outstanding performance in addressing such issues. The model has the potential to significantly reduce decision-making time in precast production, thereby contributing to the sustainable development of prefabricated construction.