• Journal of Korea Port Economic Association
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• v.28 no.3
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• pp.27-44
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• 2012

The offshore wind farm is increasingly attractive as one of future energy sources all over the world. In addition, the capacity of an offshore wind turbine gets larger and its physical characteristics are big and heavy. In this regard, a special port is necessary to assemble, store, and transport the offshore wind systems, supporting to form the offshore wind farms. Thus, this study aims to provide a policy maker which evaluation factors can significantly affect to the optimal site selection of a offshore wind port. For this, Fuzzy-AHP method is applied to capture the relative weights. The results of this study can be summarized as follows. Five criteria in level I was defined such as the accumulation factor, the regional factor, the economic factor, the location factor, and the consortium factor. Of these, the accumulation factor(37.4%), the location factor(34.2%), and the economic factor( 24.5%) were analyzed by major factors. In level II, three assessment items of each factor were selected so that total fifteen items were formed. To sum up, the site selection of offshore wind port should consider the density of the wind industry, cargo volume of securing the economic operation of terminals, the development degree of offshore wind related industry, and the proximity to the offshore wind farms. In other words, the construction of offshore wind port should be paid attention to considering not only the proximity to offshore wind farms but also the preference of turbine manufacturing companies.

• Korean Journal of Construction Engineering and Management
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• v.15 no.2
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• pp.112-121
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• 2014

After IMF bailout crisis in Korea, project financing has been employed as a major funding vehicle for the housing development. In 2008, the recession of housing market due to the global financial crisis had an significant impact on the increasing insolvent site of PF based housing development project, resulting in serious impact to whole economy as a chain effect. In order to resolve this vicious circle of bankruptcy, the major construction companies were urged to take over the insolvent sites and invest to them for normal project exit, and finally play a critical role in normalization of market. Therefore, this study aims to define the core factors for decision making to invest to insolvent site and find out differences among constructors, developers, financial lenders. The results from AHP analysis, the profitability was the most important factor to constructors. Moreover, even though the location merit is little less, through competitive price, we can assure that stable profitability is most important factor to decide to invest in insolvent site. In conclusion, the price is cheap, is highly feasible, if the land secured, major construction company will participate in a PF business investment. These findings were verified by the investment case of major construction company.

• Journal of the Korean association of regional geographers
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• v.21 no.1
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• pp.1-20
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• 2015

This paper is to provide some suggestions to complement and extend theory of network city, and to consider preliminarily its applicability for development of the Yeongnam region, exploring its normative implications for urban and regional policy and its significance of empirical research. In order to resolve some limitations and problems of network city theory and of empirical research, we need to reconsider systematically analysis methods, to extend indices of connectivity, to reconfirm normative characters inherent in network city theory, to suggest the constitution of cooperative governance, and to develop policies for embedding functional connectivity into internal community. In a preliminary analysis of Yeongnam region on the basis of network city theory, it is not clear whether the urban system of the region is entirely a type of network city, even though it seems to be close to network city. However, in order for the Yeongnam region to orient towards network city, we can point out importance of policy issues such as expansion of transportation and communication infrastructure, strengthening of economic connectivity, constitution of cooperative governance, and local embeddedness of functional network within the region.

• Journal of Korea Water Resources Association
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• v.53 no.8
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• pp.569-582
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• 2020

Recently, Korea has faced a change in the pattern of water use due to urbanization, which has caused difficulties in understanding the rainfall-runoff process and optimizing the allocation of available water resources. In this perspective, spatially downscaled analysis of the water balance is required for the efficient operation of water resources in the National Water Management Plan and the River Basin Water Resource Management Plan. However, the existing water balance analysis does not fully consider water circulation and availability in the basin, thus, the obtained results provide limited information in terms of decision making. This study aims at developing a novel water circulation analysis model that is designed to support a quasi-real-time assessment of water availability along the river. The water circulation model proposed in this study improved the problems that appear in the existing water balance analysis. More importantly, the results showed a significant improvement over the existing model, especially in the low flow simulation. The proposed modeling framework is expected to provide primary information for more realistic hydrological drought monitoring and drought countermeasures by providing streamflow information in quasi-real-time through a more accurate natural flow estimation approach with highly complex network.

• Journal of KIISE:Databases
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• v.28 no.4
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• pp.732-741
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• 2001

In the relational database systems the join operation is one of the most time-consuming query operations. Many parallel join algorithms have been developed 개 reduce the execution time Multiple hash join algorithm using allocation tree is one of the most efficient ones. However, it may have some delay on the processing each node of allocation tree, which is occurred in tuple-probing phase by the difference between one page reading time of outer relation and the processing time of already read one. This delay problem was solved by using the concept of synchronization of page execution time with we had proposed In this paper the effects of the performance improvements in each node of the allocation tree are extended to the whole allocation tree and the performance evaluation about that is processed. In addition we propose an efficient algorithm for multiple hash joins in limited number of processor environments according to the relationship between the number of input relations in the allocation tree and the number of processors allocated to the tree. Finally. we analyze the performance by building the analytical cost model and verify the validity of it by various performance comparison with previous method.

• The KIPS Transactions:PartD
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• v.16D no.4
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• pp.583-594
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• 2009

Most of the SOA solutions applicable to businesses and organizations are taking a top-down methodology. It starts with an analysis of an organization's requirements, followed by definition of business models and identification of candidate services and ends with finding or developing required services. Challenges in adopting SOA while abandoning legacy systems involve time and cost required during the process. Many businesses and organizations want to gradually migrate into SOA while making the most of the existing system. In this paper, we propose A Method for Migration of Legacy System into Web Service(M-LSWS); it allows legacy system to be migrated into web service accessible by SOA and be used as data repositories. M-LSWS defines procedures for migration into reusable web services through analysis of business processes and identification of candidate services based on design specification and code of legacy system. M-LSWS aims to migrate of legacy system into web service appropriate for SOA. The proposed method consists of four steps: analysis of legacy system, elicitation of reusable service and its specification, service wrapping and service registration. Each step has its own process and guideline. The eligibility of the proposed method will be tested by applying the method to book management system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.220-227
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• 2021

PCT (Power Cable Tunnel) and UT (Utility Tunnel), which are non-transport underground infrastructures, are mostly RC (Reinforced Concrete) structures, and their durability decreases due to the deterioration caused by carbonation over time. In particular, since the rate of carbonation varies by use and region, a predictive model based on actual carbonation data is required for individual maintenance. In this study, a carbonation prediction model was developed for non-transport underground infrastructures, such as PCT and UT. A carbonation prediction model was developed using multiple regression analysis and deep neural network techniques based on the actual data obtained from a safety inspection. The structures, region, measurement location, construction method, measurement member, and concrete strength were selected as independent variables to determine the dependent variable carbonation rate coefficient in multiple regression analysis. The adjusted coefficient of determination (Ra2) of the multiple regression model was found to be 0.67. The coefficient of determination (R2) of the model for predicting the carbonation of non-transport underground infrastructures using a deep neural network was 0.82, which was superior to the comparative prediction model. These results are expected to help determine the optimal timing for repair on carbonation and preventive maintenance methodology for PCT and UT.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1143-1153
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• 2018

For TOPS InSAR processing, high-precision image co-registration is required. We propose an image co-registration method suitable for the TOPS mode by comparing the performance of cross correlation method, the geometric co-registration and the enhanced spectral diversity (ESD) matching algorithm based on the spectral diversity (SD) on the Sentinel-1 TOPS mode image. Using 23 pairs of interferometric pairs generated from 25 Sentinel-1 TOPS images, we applied the cross correlation (CC), geometric correction with only orbit information (GC1), geometric correction combined with iterative cross-correlation (GC2, GC3, GC4), and ESD iteration (ESD_GC, ESD_1, ESD_2). The mean of co-registration errors in azimuth direction by cross correlation and geometric matching are 0.0041 pixels and 0.0016 pixels, respectively. Although the ESD method shows the most accurate result with the error of less than 0.0005 pixels, the error of geometric co-registration is reduced to 0.001 pixels by repetition through additional cross correlation matching between the reference and resampled slave image. The ESD method is not applicable when the coherence of the burst overlap areas is low. Therefore, the geometric co-registration method through iterative processing is a suitable alternative for time series analysis using multiple SAR data or generating interferogram with long time intervals.

• The Journal of Bigdata
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• v.6 no.1
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• pp.23-35
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• 2021

In the era of the 4th industrial revolution, smart factories have received great attention, where production and manufacturing technology and ICT converge. With the development of IoT technology and big data, automation of production systems has become possible. In the advanced manufacturing industry, production systems are subject to unscheduled performance degradation and downtime, and there is a demand to reduce safety risks by detecting and reparing potential errors as soon as possible. This study designs a model based on supervised and unsupervised learning for detecting anomalies. The accuracy of XGBoost, LightGBM, and CNN models was compared as a supervised learning analysis method. Through the evaluation index based on the confusion matrix, it was confirmed that LightGBM is most predictive (97%). In addition, as an unsupervised learning analysis method, MD, AE, and LSTM-AE models were constructed. Comparing three unsupervised learning analysis methods, the LSTM-AE model detected 75% of anomalies and showed the best performance. This study aims to contribute to the advancement of the smart factory by combining supervised and unsupervised learning techniques to accurately diagnose equipment failures and predict when abnormal situations occur, thereby laying the foundation for preemptive responses to abnormal situations. do.

• Journal of Practical Engineering Education
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• v.14 no.2
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• pp.267-277
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• 2022

Achievement at university is recognized in a comprehensive sense as the level of qualitative change and development that students have embodied as a result of their experience in university education. Therefore, the academic achievement of university students will be given meaning in cooperation with the historical and social demands for diverse human resources such as creativity, leadership, and global ability, but it is practically an indicator of the outcome of university education. Measurement of academic achievement by such credits involves many problems, but in particular, standardization of academic achievement by credits based on evaluation methods, contents, and university rankings is a very difficult problem. In this study, we present a model that uses machine learning techniques to predict whether or not academic achievement is excellent for D-University graduates. The variables used were analyzed using up to 96 personal information and bachelor's information such as graduation year, department number, department name, etc., but when establishing a future education course, only the data after enrollment works effectively. Therefore, the items to be analyzed are limited to the recommended ability to improve the academic achievement of the department/student. In this research, we implemented an academic achievement prediction model through analysis of core abilities that reflect the philosophy, goals, human resources image, and utilized machine learning to affect the impact of the introduction of the prediction model on academic achievement. We plan to apply the results of future research to the establishment of curriculum and student guidance conducted in the department to establish a basis for improving academic achievement.