• Smart Structures and Systems
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• v.12 no.2
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• pp.209-233
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• 2013

With an annual growth rate of about 30%, wind energy systems, such as wind turbines, represent one of the fastest growing renewable energy technologies. Continuous structural health monitoring of wind turbines can help improving structural reliability and facilitating optimal decisions with respect to maintenance and operation at minimum associated life-cycle costs. This paper presents an integrated monitoring system that is designed to support structural assessment and life-cycle management of wind turbines. The monitoring system systematically integrates a wide variety of hardware and software modules, including sensors and computer systems for automated data acquisition, data analysis and data archival, a multiagent-based system for self-diagnosis of sensor malfunctions, a model updating and damage detection framework for structural assessment, and a management module for monitoring the structural condition and the operational efficiency of the wind turbine. The monitoring system has been installed on a 500 kW wind turbine located in Germany. Since its initial deployment in 2009, the system automatically collects and processes structural, environmental, and operational wind turbine data. The results demonstrate the potential of the proposed approach not only to ensure continuous safety of the structures, but also to enable cost-efficient maintenance and operation of wind turbines.

• Industrial Engineering and Management Systems
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• v.15 no.1
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• pp.49-62
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• 2016

In this paper, we propose an integrated data envelopment analysis (DEA) and analytic hierarchy process (AHP) methodology in which the information about the hierarchical structures of input-output data can be reflected in the performance assessment of decision making units (DMUs). Firstly, this can be implemented by extending a traditional DEA model to a three-level DEA model. Secondly, weight bounds, using AHP, can be incorporated in the three-level DEA model. Finally, the effects of incorporating weight bounds can be analyzed by developing a parametric distance model. Increasing the value of a parameter in a domain of efficiency loss, we explore the various systems of weights. This may lead to various ranking positions for each DMU in comparison to the other DMUs. An illustrative example of road safety performance for a set of 19 European countries highlights the usefulness of the proposed approach.

• Journal of Environmental Science International
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• v.22 no.3
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• pp.303-308
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• 2013

This paper proposed a new method for estimating missing values in time series rainfall data. The proposed method integrated the two most widely used estimation methods, general linear model(GLM) and ordinary kriging(OK), by taking a weighted average of covariance matrices derived from each of the two methods. The proposed method was cross-validated using daily rainfall data at thirteen rain gauges in the Hyeong-san River basin. The goodness-of-fit of the proposed method was higher than those of GLM and OK, which can be attributed to the weighting algorithm that was designed to minimize errors caused by violations of assumptions of the two existing methods. This result suggests that the proposed method is more accurate in missing values in time series rainfall data, especially in a region where the assumptions of existing methods are not met, i.e., rainfall varies by season and topography is heterogeneous.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2001.10a
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• pp.101-105
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• 2001

This paper is to illustrate the possibility to use organizational knowledge and data warehouse simultaenously for a decision maker. Organizational knowledge is produced for qualitative decision-making process and data warehouse is used for quantitative decision-making process. However, two things are currently implemented separately in many organizations although being needed for decision makers. This research shows a model for building integrated system and a prototyping system based on the model. And its effectiveness is discussed.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.179-182
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• 1996

Database management systems are now used in almost every organization to maintain and utilize important collections of information. Relational DBMSs have a firm theoretical base. However, it lacks important features needed for representing certain critical aspects of an entity. One alternatives to remedy this problem is to adopt the object-oriented approach to the data model development process. In this paper, we tried to show the advantages of object-oriented data model design process for the development of extended relational database schema needed for the integrated plant information system at H. Heavy Industry Co.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.164-169
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• 2003

In this paper, we propose the integrated Bayesian network framework to reconstruct genetic regulatory networks from genome expression data. The proposed model overcomes the dimensionality problem of multivariate analysis by building coherent sub-networks from confined gene clusters and combining these networks via intermediary points. Gene Shaving algorithm is used to cluster genes that share a common function or co-regulation. Retrieved clusters incorporate prior biological knowledge such as Gene Ontology, pathway, and protein protein interaction information for extracting other related genes. With these extended gene list, system builds genetic sub-networks using Bayesian network with MDL score and Sparse Candidate algorithm. Identifying functional modules of genes is done by not only microarray data itself but also well-proved biological knowledge. This integrated approach can improve there liability of a network in that false relations due to the lack of data can be reduced. Another advantage is the decreased computational complexity by constrained gene sets. To evaluate the proposed system, S. Cerevisiae cell cycle data [1] is applied. The result analysis presents new hypotheses about novel genetic interactions as well as typical relationships known by previous researches [2].

• Proceedings of the Korean Society of Crop Science Conference
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• 1998.10a
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• pp.415-445
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• 1998

For effective prevention of the spreading and outbreak of crop insects and disease pests, an intensive Pest surveillance system was established to predict their density changes, and distribution. After their initial establishment by either immigration or overwintering, it is necessary to anticipate how they spread out geographically and predict where/when outbreaks are possible. The two major tools, boundary layer atmospheric model (Blayer) and the geographic information system(GIS), have been being developed to facilitate the prediction of pest occurrence in recent days. We are also developing the PeMos (Pest Monitoring System) that is able to manage the pest surveillance data collected from 152 pest monitoring stations in Korea. These three system related to the pest surveillance should be integrated into an internet based comprehensive database management system to facilitate information resources systematically organized and closely linked. Considering various data types and large data size in each system, a new special information management system is suggested. The integrated system should express complex types of information, such as text, multimedia, and other scientific data under the Internet environment. This paper discussed the major three systems, GIS, Blayer, and PeMos, relevant to the crop pest surveillance, then how they can be integrated in a comprehensive system under the Internet environment.

• Korean Journal of Computational Design and Engineering
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• v.3 no.1
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• pp.48-56
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• 1998

Finite element analysis is very important in the design and analysis of mechanical engineering. The process of FEA encompasses shape modeling, mesh generation, matrix solving and post-processing. Some of these processes can be tightly integrated with the current software architectures and data sharing mode. However, complete integration of all the FEA process itself and the integration to the manufacturing processes is almost impossible in the current practice. The barriers to this problem are inconsistent data format and the enterprise-wise software integration technology. In this research, the information model based on STEP AP209 was chosen for handling finite element analysis data. The international standard for the FEA data can bridge the gap between design, analysis and manufacturing processes. The STEP-based FEA system can be further tightly integrated to the distributed software and database environment using CORBA technology. The prototype FEA system DICESS is implemented to verify the proposed concepts.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.1075-1081
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• 2016

The big data analysis has received much attention from the researchers working in various fields because the big data has a great potential in detecting or predicting future events such as epidemic outbreaks and changes in stock prices. Reflecting the current popularity of big data analysis, many authors have proposed methods tracking influenza epidemics based on internet-based information. The recently proposed 'autoregressive model using Google (ARGO) model' (Yang et al., 2015) is one of those influenza tracking models that harness search queries from Google as well as the reports from the Centers for Disease Control (CDC), and appears to outperform the existing method such as 'Google Flu Trends (GFT)'. Although the ARGO predicts well the outbreaks of influenza, this study demonstrates that a classical seasonal autoregressive integrated moving average (SARIMA) model can outperform the ARGO. The SARIMA model incorporates more accurate seasonality of the past influenza activities and takes less input variables into account. Our findings show that the SARIMA model is a functional tool for monitoring influenza epidemics.

• Journal of Ocean Engineering and Technology
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• v.24 no.2
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• pp.1-9
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• 2010

Oscillating Water Column is one of the most widely used converting systems all over the world. The operating performance is influenced by the efficiencies of the two converting stages in the OWC chamber-turbine integrated system. In order to study the effects of the pressure drop induced by the air turbine, the experiments using the impulse turbine and the orifice device are carried out in the wave simulator test rig. The numerical simulation utilizing the orifice and porous media modules is calculated and validated by the corresponding experimental data. The numerical wave tank based on the two-phase VOF model embedded with the above modules is employed to investigate the wave elevation, pressure variation inside the chamber and the air flow velocity in the duct. The effects of the air turbine on the integrated system and interaction among the wave elevation, pressure and air flow velocities variations are investigated, which demonstrates that the present numerical model are more accurate to be employed.