• Journal of the Society of Naval Architects of Korea
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• v.47 no.4
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• pp.613-627
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• 2010

Ships and off-shore plants have very long lifecycles. Design data of ships and off-shore plants are used as master data in the subsequent phases after the design phase. Therefore, it is an important issue how to convert native design data generated from commercial shipbuilding CAD systems into neutral data with the use of international industrial data standards. International standard-based exchange of ship outfitting and off-shore plant data needs the construction of a library for specifications data of equipment and materials and the provision of external referencing mechanism for retrieving data stored in the library. This paper proposes an approach to construct a specifications data library with the use of ISO 15926 process plants. This library is used for providing specifications data through external referencing when translating ship outfitting data from TRIBON system into ISO 10303 STEP AP 227-formed data.

• Journal of the Korean Wood Science and Technology
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• v.43 no.5
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• pp.691-699
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• 2015

To evaluate the possibility of charcoal as Green Infrastructure (GI) materials, data such as moisture content, amount of adsorbed water, and amount of evaporation were collected. Some data from previous study were referenced to find out if correlations exist between results in this study and previous study. Only porosity was directly related to moisture content. Two mechanical charcoal had better abilities than traditional charcoal in all three categories. Mechanical black charcoal chips produced by National Forestry Cooperative Federation (NFCFC) adsorbed 333.3% of water in thirty minutes, 297.5% in five minutes, and evaporated around 75% water in four days. This ability is much higher than other five charcoal. Even though results of test showed various degrees and NFCFC was the best as GI materials, data of charcoal were also within acceptable range based on generally accepted characteristics of GI materials.

• Proceedings of the Korean Magnestics Society Conference
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• 2008.12a
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• pp.294.1-294.1
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• 2008

• Korean Journal of Construction Engineering and Management
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• v.24 no.6
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• pp.66-73
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• 2023

The construction industry suffers losses due to failures in demand forecasting due to price fluctuations in construction raw materials, increased user costs due to project cost changes, and lack of forecasting system. Accordingly, it is necessary to improve the accuracy of construction raw material price forecasting. This study aims to predict the price of construction raw materials and verify applicability through the improvement of the Data Refactor technique. In order to improve the accuracy of price prediction of construction raw materials, the existing data refactor classification of low and high frequency and ARIMAX utilization method was improved to frequency-oriented and ARIMA method utilization, so that short-term (3 months in the future) six items such as construction raw materials lumber and cement were improved. ), mid-term (6 months in the future), and long-term (12 months in the future) price forecasts. As a result of the analysis, the predicted value based on the improved Data Refactor technique reduced the error and expanded the variability. Therefore, it is expected that the budget can be managed effectively by predicting the price of construction raw materials more accurately through the Data Refactor technique proposed in this study.

• Proceedings of the Korean Fiber Society Conference
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• 1998.10a
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• pp.280-282
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• 1998

Communications, data storage or computation by manipulating the optical signals have the strong advantages over the traditional electronic circuits in regard to the data handling speed and capacity. Effective manipulation of the optical signals, however, requires materials of large nonlinear coefficients and of fast response time. Polymeric materials are one of the promising materials to meet the requirements for nonlinear optical (NLO) materials. (omitted)

• Journal of Powder Materials
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• v.26 no.4
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• pp.275-281
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• 2019

An artificial neural network (ANN) model is developed for the analysis and simulation of correlation between flake powder metallurgy parameters and properties of AA2024-SiC nanocomposites. The input parameters of the model are AA 2024 matrix size, ball milling time, and weight percentage of SiC nanoparticles and the output parameters are density and hardness. The model can predict the density and hardness of the unseen test data with a correlation of 0.986 beyond the experimental data. A user interface is designed to predict properties at new instances. We have used the model to simulate the individual as well as the combined influence of parameters on the properties. Moreover, we have analyzed the calculated results from the powder metallurgical point of view. The developed model can be used as a guide for further composite development.

• Journal of the Society of Naval Architects of Korea
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• v.55 no.3
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• pp.243-251
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• 2018

In the shipyard, a lot of data is generated, stored, and managed during design, construction, and operation phases to build ships and offshore structures. However, it is difficult to handle such big data efficiently using existing data-handling technologies. As the big data technology is developed, the ship and offshore industries start to focus on the existing big data to find valuable information from it. In this paper, the material requirement estimation method of offshore structure piping materials using big data analysis is proposed. A big data platform for the data analysis in the shipyard is introduced and it is applied to the analysis of material requirement estimation to solve the problems in piping design by a designer. The regression model is developed from the big data of piping materials and verified using the existing data. This analysis can help a piping designer to estimate the exact amount of material requirement and schedule the purchase time.

• Journal of Information Processing Systems
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• v.12 no.4
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• pp.741-753
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• 2016

In the medical fields, many efforts have been made to develop and improve Hospital Information System (HIS) including Electronic Medical Record (EMR), Order Communication System (OCS), and Picture Archiving and Communication System (PACS). However, materials generated and used in medical fields have various types and forms. The current HISs separately store and manage them by different systems, even though they relate to each other and contain redundant data. These systems are not helpful particularly in emergency where medical experts cannot check all of clinical materials in the golden time. Therefore, in this paper, we propose a process to build an integrated data model for medical information currently stored in various HISs. The proposed data model integrates vast information by focusing on medical images since they are most important materials for the diagnosis and treatment. Moreover, the model is disease-specific to consider that medical information and clinical materials including images are different by diseases. Two case studies show the feasibility and the usefulness of our proposed data model by building models about two diseases, acute myocardial infarction (AMI) and ischemic stroke.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.134-137
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• 2020

Recently, there are emphasized to support the maintenance and management system of vessels using acquired data from engine part equipment. But, there are limitations for data exchange and management. To solve the problem, the ISO published ISO 19847 and 19848. In this paper, we analyze the ISO 19848 requirements related to identify data channel ID for ship equipment, and propose the examples for applying encoding techniques. In addition, we suggest the proposed technique for applying of managing the failure and maintenance type of the ship's engine part facilities by examples. If this method is applied, the vessel's equipment can exchange data through the sharing of the code table, and express what response is needed or acted, including where the failure occurred.

• Smart Structures and Systems
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• v.11 no.3
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• pp.261-282
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• 2013

Structural health monitoring (SHM) is an application area of Wireless Sensor Networks (WSNs) which usually needs high data communication rate to transfer a large amount of monitoring data. Traditional sink node can only process data from one communication channel at the same time because of the single radio chip structure. The sink node constitutes a bottleneck for constructing a high data rate SHM application giving rise to a long data transfer time. Multi-channel communication has been proved to be an efficient method to improve the data throughput by enabling parallel transmissions among different frequency channels. This paper proposes an 8-radio integrated sink node design method based on Field Programmable Gate Array (FPGA) and the time synchronization mechanism for the multi-channel network based on the proposed sink node. Three experiments have been performed to evaluate the data transfer ability of the developed multi-radio sink node and the performance of the time synchronization mechanism. A high data throughput of 1020Kbps of the developed sink node has been proved by experiments using IEEE.805.15.4.