• Journal of the Korea Institute of Building Construction
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• v.20 no.2
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• pp.199-212
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• 2020

In evaluating the competitiveness of construction companies and their development strategies, patents are a useful and objective source of technical information. In this study, the cutting-edge technologies of construction industries of China, Japan, and South Korea were investigated based on the data of patent applications filed by a total of 15 construction companies (five companies from each country). The related technologies were classified into six core technology groups based on their keywords. After that, we used four patent analysis methods: time series analysis, IP (Intellectual Property) emergence level analysis, spiral module analysis, and OS (Object-Solution) Matrix analysis, to identify the promising technologies/vacant technologies for global construction companies in China, Japan, and South Korea, and to analyze the technical competitiveness of the three countries. The findings of this study showed that each country can claim a relative technological advantage over the others. Overall, 3D printing and offsite construction technology, data acquisition technology, AR and VR technology are expected to be promising in the Asian region. The present study contributes to the body of knowledge by expanding our understanding of technological innovation for the competitiveness of companies and the technology development strategies pursued by the construction industries of China, Japan, and South Korea.

• Korean Journal of Construction Engineering and Management
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• v.6 no.2 s.24
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• pp.61-68
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• 2005

The analysis described in this paper indicate the existence of a correlationship for housing demand and water supply ratio. Using subjective statistical data for the trend of population on regional area, water supply ratio and the number of households, the paper examines the correlationship of forecasting factors for apartments in the ways in which the tendency of demands for apartments and water supply ratio have been analyzed within small and mediumsized city. Differences in the correlationship on the several scale of a city are also taken into account in the analysis. The summary table of the tendency for housing supplies, population and water supply ratio on each scale of a city was generated using data from LAIB. This study attempted to address certain factors that are measurable within a specified paradigm, in order to investigate the extent to which the expectation of apartment supplies can be estimated from the correlationship of water supply ratio. Therefore, it can be suggested that the limited scale of a city are set to maintain the correlationship for housing demands and water supply ratio.

• Journal of Korean Society of Transportation
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• v.36 no.2
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• pp.141-154
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• 2018

In order to meet the ever-increasing demand for international air travel, several plans are underway to open new airports and expand existing provincial airports. However, existing air demand forecasts have been based on the total air demand in Korea or the air demand among major cities. There is not much forecast of regional air demand considering local characteristics. In this study, the outbound air travel demand in the southeastern region of Korea was analyzed and the fixed-effects model using panel data was proposed as an optimal model that can reflect the inherent characteristics of metropolitan areas which are difficult to observe in reality. The results of model validation show that panel data analysis effectively addresses the spurious regression and unobserved heterogeneity that are difficult to handle in a model using only a few macroeconomic indicators with time series characteristics. Various statistical validation and conformance tests suggest that the fixed-effects model proposed in this study is superior to other econometric models in predicting demand for international demand in the southeastern region.

• Proceedings of the Korea Association of Information Systems Conference
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• 2000.11a
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• pp.1.2-24
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• 2000

The field of neural networks has been a recent surge in activity as a result of progress in developments of efficient training algorithms. For this reason, and coupled with the widespread availability of powerful personal computer hardware for running simulations of networks, there is increasing focus on the potential benefits this field can offer. The neural network may be viewed as an advanced pattern recognition technique and can be applied in many areas such as financial time series forecasting, medical diagnostic expert system and etc.. The intention of this study is to build and implement one simple artificial neural networks hereinafter called ANN. For this purpose, some literature survey was undertaken to understand the structures and algorithms of ANN theoretically. Based on the review of theories about ANN, the system adopted 3-layer back propagation algorithms as its learning algorithm to simulate one case of medical diagnostic model. The adopted ANN algorithm was performed in PC by using turbo PASCAL and many input parameters such as the numbers of layers, the numbers of nodes, the number of cycles for learning, learning rate and momentum term. The system output more or less successful results which nearly agree with goals we assumed. However, the system has some limitations such as the simplicity of the programming structure and the range of parameters it can dealing with. But, this study is useful for understanding general algorithms and applications of ANN system and can be expanded for further refinement for more complex ANN algorithms.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.161-170
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• 2013

The FX (Foreign Exchange) is a form of exchange for the global decentralized trading of international currencies. The simple sense of Forex is simultaneous purchase and sale of the currency or the exchange of one country's currency for other countries'. We can find the consistent rules of trading by comparing the gradient boosting method and the decision trees methods. Methods such as time series analysis used for the prediction of financial markets have advantage of the long-term forecasting model. On the other hand, it is difficult to reflect the rapidly changing price fluctuations in the short term. Therefore, in this study, gradient boosting method and decision tree method are applied to analyze the short-term data in order to make the rules for the revenue structure of the FX market and evaluated the stability and the prediction of the model.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.108-118
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• 2018

Recently, agricultural sector apply ICT technology such as Smart Farm to pursue innovation in the changing situation that is emerging as the fourth industrial revolution. However, this innovation requires techniques for forecasting and analyzing in various data bases and spatial information provides such infrastructure data. In this study, the cultivation area of Chinese cabbage, radish, garlic, onion, and red pepper were calculated and analyzed by year. The purpose of this analysis is to cope with sudden changes in vegetable crops and changes in cultivated area caused by weather changes to supply and demand of major vegetables and price instability. As a result of this study, spatial information based on time series information of vegetable complex will be used as efficient agricultural environment observation data, as well as interpretation of various spatial ranges such as the estimation of cultivation area using remote sensing.

• Journal of IKEEE
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• v.23 no.1
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• pp.120-126
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• 2019

Recently, accurate prediction of power consumption based on machine learning techniques in Internet of Energy (IoE) has been actively studied using the large amount of electricity data acquired from advanced metering infrastructure (AMI). In this paper, we propose a deep learning model based on Gated Recurrent Unit (GRU) as an artificial intelligence (AI) network that can effectively perform pattern recognition of time series data such as the power consumption, and analyze performance of the prediction based on real household power usage data. In the performance analysis, performance comparison between the proposed GRU-based learning model and the conventional learning model of Long Short Term Memory (LSTM) is described. In the simulation results, mean squared error (MSE), mean absolute error (MAE), forecast skill score, normalized root mean square error (RMSE), and normalized mean bias error (NMBE) are used as performance evaluation indexes, and we confirm that the performance of the prediction of the proposed GRU-based learning model is greatly improved.

• Atmosphere
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• v.31 no.2
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• pp.215-227
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• 2021

The necessity of the prediction on the Seasonal-to-Subseasonal (S2S) timescale continues to rise. It led a series of studies on the S2S prediction models, including the Global Seasonal Forecasting System Version 5 (GloSea5) of the Korea Meteorological Administration. By extending previous studies, the present study documents sea surface temperature (SST) prediction skill around the Korean peninsula in the GloSea5 hindcast over the period of 1991~2010. The overall SST prediction skill is about a week except for the regions where SST is not well captured at the initialized date. This limited prediction skill is partly due to the model mean biases which vary substantially from season to season. When such biases are systematically removed on daily and seasonal time scales the SST prediction skill is improved to 15 days. This improvement is mostly due to the reduced error associated with internal SST variability during model integrations. This result suggests that SST around the Korean peninsula can be reliably predicted with appropriate post-processing.

• The Journal of Bigdata
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• v.5 no.2
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• pp.111-120
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• 2020

Despite the recent economic contraction caused by the Corona 19 incident, interest in the residential environment is growing as more people live at home due to the increase in telecommuting, thereby increasing demand for remodeling. In addition, the government's real estate policy is also expected to have a visible impact on the sales of the interior and furniture industries as it shifts from regulatory policy to the expansion of housing supply. Accurate demand forecasting is a problem directly related to inventory management, and a good demand forecast can reduce logistics and inventory costs due to overproduction by eliminating the need to have unnecessary inventory. However, it is a difficult problem to predict accurate demand because external factors such as constantly changing economic trends, market trends, and social issues must be taken into account. In this study, LSTM model and 1D-CNN model were compared and analyzed by artificial intelligence-based time series analysis method to produce reliable results for manufacturers producing furniture components.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.261-270
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• 2020

In this study, we identify promising technologies for Piggyback transportation system by analyzing the relevant patent information. In order for this, we first develop the patent database by extracting relevant technology keywords from the pioneering research papers for the Piggyback flactcar system. We then employed textmining to identify the frequently referred words from the patent database, and using these words, we applied the LDA (Latent Dirichlet Allocation) algorithm in order to identify "topics" that are corresponding to "key" technologies for the Piggyback system. Finally, we employ the ARIMA model to forecast the trends of these "key" technologies for technology forecasting, and identify the promising technologies for the Piggyback system. with keyword search method the patent analysis. The results show that data-driven integrated management system, operation planning system and special cargo (especially fluid and gas) handling/storage technologies are identified to be the "key" promising technolgies for the future of the Piggyback system, and data reception/analysis techniques must be developed in order to improve the system performance. The proposed procedure and analysis method provides useful insights to develop the R&D strategy and the technology roadmap for the Piggyback system.