• 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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.3
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• pp.8-14
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• 2019

Most of the data envelopment analysis (DEA) models evaluate the relative efficiency of a decision making unit (DMU) based on the assumption that inputs in a specific period are consumed to produce the output in the same period of time. However, there may be some time lag between the consumption of input resources and the production of outputs. A few models to handle the concept of the time lag effect have been proposed. This paper suggests a new multi-period input DEA model considering the consistent time lag effects. Consistency of time lag effect means that the time delay for the same input factor or output factor are consistent throughout the periods. It is more realistic than the time lag effect for the same output or input factor can vary over the periods. The suggested model is an output-oriented model in order to adopt the consistent time lag effect. We analyze the results of the suggested model and the existing multi period input model with a sample data set from a long-term national research and development program in Korea. We show that the suggested model may have the better discrimination power than existing model while the ranking of DMUs is not different by two nonparametric tests.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.725-731
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• 2009

Construction documents are recognized as an essential component for making a decision and supporting on construction processes. In construction, the management of project document is a complex process due to different factors such as document types, stakeholder involvement, document flow, and document flow processes. Therefore, inappropriate management of project documents can cause several impacts on construction work processes such as delay or poor quality of work. Several information and communication technologies (ICT) were proposed to overcome problems concerning document management practice in construction projects. However, the adoption of ICT may have some limitation on the compatibility of specific document workflow. Lack of understanding on designing document system may cause many problems during the use and implementation phase. Thus, this paper proposes the framework that integrates Soft System Methodology (SSM) concept and Integrated Definition Modeling Technique (IDEF) for analyzing document management system in construction project. Research methodology is classified as the case study. Five main construction building projects are selected as case studies. The qualitative data related to problems and processes are collected by interviewing construction project participants such as main contractors, owners, consultants, and designers. The findings from case study show the benefits of using SSM and IDEF. The use of SSM can help identify the problems in managing construction document in rich picture view whereas IDEF can illustrate the document flow in construction project in details. In addition, the idea of integrating these two concepts can be used to identify the root causes of process problems at the information level. As the results, this idea can be applied to analyze and design web-based document management system in the future.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.442-445
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• 2003

The purpose of this study is to present an improved lift-up & procurement work flow for effectiveness of resources increase in building construction. The current lift-up & procurement work flow has some waste factors; complicated information system, connection lack with process and decision-making delay. Therefore the study applied the value stream mapping methodology for improvement of current lift-up & procurement work flow. The main contents of the study are as follows; 1) A problem of current work flow were analyzed through current state mapping(CSM). 2) An improved work flow was suggested through future state mapping(FSM). 3) An improvement effect analysis of information system and lift-up planning The study recommends that continuous improvement of lift-up & procurement work flow and efficient management of information in building construction.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.91-102
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• 2022

With the evolution of wireless sensor network (WSN) technology, the routing policy has foremost importance in the Internet of Things (IoT). A systematic routing policy is one of the primary mechanics to make certain the precise and robust transmission of wireless sensor networks in an energy-efficient manner. In an IoT environment, WSN is utilized for controlling services concerning data like, data gathering, sensing and transmission. With the advantages of IoT potentialities, the traditional routing in a WSN are augmented with decision-making in an energy efficient manner to concur finer optimization. In this paper, we study how to combine IoT-based deep learning classifier with routing called, Kriging Regressive Deep Belief Neural Learning (KR-DBNL) to propose an efficient data packet routing to cope with scalability issues and therefore ensure robust data packet transmission. The KR-DBNL method includes four layers, namely input layer, two hidden layers and one output layer for performing data transmission between source and destination sensor node. Initially, the KR-DBNL method acquires the patient data from different location. Followed by which, the input layer transmits sensor nodes to first hidden layer where analysis of energy consumption, bandwidth consumption and light intensity are made using kriging regression function to perform classification. According to classified results, sensor nodes are classified into higher performance and lower performance sensor nodes. The higher performance sensor nodes are then transmitted to second hidden layer. Here high performance sensor nodes neighbouring sensor with higher signal strength and frequency are selected and sent to the output layer where the actual data packet transmission is performed. Experimental evaluation is carried out on factors such as energy consumption, packet delivery ratio, packet loss rate and end-to-end delay with respect to number of patient data packets and sensor nodes.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.606-609
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• 2003

The need of high-rise building is increased, because population is concentrated and the building site is limited in the city. Owing to this request buildings in downtown become more high rise. Therefore, we must develop the design, structure, high technology and materials. The core wall construction is one of the method of construction preferred because it cut down the cost and decrease the schedule. According to research in this study, we found that the selection process of core wall system form is focused on the schedule and cost. the construability is relatively failed to notice. As a result, the problem of construability is happened under the construction, it lead to delay the schedule and increase the cost. The purpose of this study is suggest to decision making process of core wall system form considered improving construction productivity.

• Korean Journal of Construction Engineering and Management
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• v.5 no.1 s.17
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• pp.133-139
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• 2004

The purpose of this study is to present an improved lift-up &procurement work flow for effective resource utilization in building construction. The current lift-up &procurement work flow has some waste factors; complicated information system, connection lack with process and decision-making delay. Therefore the study applied the value stream mapping methodology for improvement of current lift-up &procurement work flow. The main contents of the study are as follows; 1) Problems of current work flow were analyzed through current state mapping(CSM). 2) An improved work flow was suggested through future state mapping(FSM). 3) An improvement effect analysis of information system and lift-up planning was investigated. The study recommends continuous improvement of lift-up &procurement work flow and efficient management of information in building construction as a future research.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.1
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• pp.109-126
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• 2014

The level of service (LOS) of the Highway Capacity Manual (KHCM) has been used as a basic criterion at decision making processes for signalized intersections in Korea. The KHCM provides five steps for the signalized intersection analysis. Among them, lane grouping, which is the third step, significantly influence the final LOS. The current method presented in the KHCM, however, classifies a shared lane as a de facto turning lane group, even though the turning traffic of the shared lane is few. Thus, this research was initiated to provide an alternative. To this end, three alternatives were suggested, including the method based on the lane grouping presented in the U.S. Highway Capacity Manual, the method using turning ratio of shared turning lane, and the method using a threshold traffic volume in lane grouping. The feasibilities of the three methods were evaluated using a calibrated CORSIM model. Conclusively, the method using a threshold traffic volume in lane grouping outperformed.

• Journal of KIISE
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• v.44 no.9
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• pp.966-975
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• 2017

In Internet of Things (IoT), the aim of the nodes (called 'Things') is to exchange information with each other, whereby they gather and share information with each other through self decision-making. Therefore, we cannot apply existing aggregation algorithms of Wireless sensor networks that aim to transmit information to only a sink node or a central server, directly to the IoT environment. In addition, since existing algorithms aggregate information from all sensor nodes, problems can arise including an increasing number of transmissions and increasing transmission delay and energy consumption. In this paper, we propose the clustering and property based data exchange method for energy efficient information sharing. First, the proposed method assigns the properties of each node, including the sensing data and unique resource. The property determines whether the node can respond to the query requested from the other node. Second, a cluster network is constructed considering the location and energy consumption. Finally, the nodes communicate with each other efficiently using the properties. For the performance evaluation, TOSSIM was used to measure the network lifetime and average energy consumption.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.3
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• pp.31-39
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• 2014

In KSLV-I launch mission, real-time data from the tracking stations are acquired, processed and distributed by the Mission Control System to the user group who needed to monitor processed data for safety and flight monitoring purposes. The processed trajectory data by the mission control system is sent to each tracking system for target designation in case of tracking failure. Also, the processed data are used for decision making for flight termination when anomalies occur during flight of the launch vehicle. In this paper, we propose the processing mechanism of slaving data which plays a key role of launch vehicle tracking mission. The best position data is selected by predefined logic and current status after every available position data are acquired and pre-processed. And, the slaving data is distributed to each tracking stations through time delay is compensated by extrapolation. For the accurate processing, operation timing of every procesing modules are triggered by time-tick signal(25ms period) which is driven from UTC(Universial Time Coordinates) time. To evaluate the proposed method, we compared slaving data to the position data which received by tracking radar. The experiments show the average difference value is below 0.01 degree.