• Atmosphere
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• v.23 no.1
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• pp.113-121
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• 2013

The objective of this study is to develop and evaluate the system to produce the real-time ensemble drought prediction data. Ensemble drought prediction consists of 3 processes (meteorological outlook using the multi-initial conditions, hydrological analysis and drought index calculation) therefore, more processing time and data is required than that of single member. For ensemble drought prediction, data process time is optimized and hardware of existing system is upgraded. Ensemble drought data is estimated for year 2012 and to evaluate the accuracy of drought prediction data by using ROC (Relative Operating Characteristics) analysis. We obtained 5 ensembles as optimal number and predicted drought condition for every tenth day i.e. 5th, 15th and 25th of each month. The drought indices used are SPI (Standard Precipitation Index), SRI (Standard Runoff Index), SSI (Standard Soil moisture Index). Drought conditions were determined based on results obtained for each ensemble member. Overall the results showed higher accuracy using ensemble members as compared to single. The ROC score of SRI and SSI showed significant improvement in drought period however SPI was higher in the demise period. The proposed ensemble drought prediction system can be contributed to drought forecasting techniques in Korea.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2003.11a
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• pp.445-446
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• 2003

대기오염물질은 배출되면 인위적으로 제거하는 과정이 용이하지 않을 뿐만 아니라 인체, 자연생태계 및 재산상에 다방면으로 피해를 주는 특성을 가지고 있다. 그중에서도 호흡성 먼지 또는 미세먼지라고 부르고 있는 PM-10은 대기 중 체류시간이 길며 인체에 대한 위해도를 갖고 있으나 최선의 대처 방법은 외출을 삼가하는 등의 미세먼지에 대한 노출을 사전에 방지 하는 방법밖에 없는 것으로 알려져 있다. 따라서 미세먼지에 대한 노출을 사전에 막고 이에 대한 대처를 위하여 미세먼지농도의 예보 및 경보 시스템 체제를 가동해야 할 필요성이 대두되고 있다. (중략)

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2003.11a
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• pp.135-136
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• 2003

효과적인 대기오염 관리대책을 수립하기 위해서는 각종 오염원으로부터 배출되는 오염물질의 종류와 배출량을 이해하여야 하며 또한 이로부터 야기되는 대기오염도 및 위해성을 과학적인 근거에 의해서 정확하게 산정할 수 있어야 한다. 현재 굴뚝에서 배출되는 대기오염물질 농도를 감시하기 위해 구축중인 굴뚝 TMS망을 실용적인 대기환경 관리시스템으로 전환하기 위해서는 TMS 측정되고 있는 오염물질이 주변 대기환경에 미치는 오염도를 실시간으로 정확히 예측하여 수용체 중심으로 사업장 및 도시ㆍ산단지역의 대기환경을 투명하게 관리하는 선진화된 차세대 대기환경관리 기법을 도입할 필요가 있다. (중략)

• Korean Journal of Hydrosciences
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• v.2
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• pp.99-113
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• 1991

Integrated irrigation management system (IIMS) that is incorporated with a microcomputer-based decision support system (DSS) has been developed and applied to paddy rice irrigation systems management. The system hardwares consist of field data acquisition units, data transmission units, central data processing units, and printing and displaying units. Ridld data to be collected include incremental rainfall, streamflow and reservoir water levels, and water levels at several irrigation canal sections within an irrigation sidtricts. The softwares are to process field data, real-time forecasting, irrigation control data, and decision variables from data-base and simulation model subsystems. And the user-interface subsystems are incorporated to present the water system operators and managers the results from data and model sugsystems. User-friendly menu with animated graphic modules are adopted to help understand irrigation controls for the district. This paper issues the overal descriptions of DSS as applied to Anjuk irrigation district. The details of major model components for the irrigation controls are presented along with real-time data collection systems. The potentials of DSS have been appraised very practical and promising for better irrigation system operation and management.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1037-1051
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• 2020

The accurate monitoring and forecasting of the intensity of tropical cyclones (TCs) are able to effectively reduce the overall costs of disaster management. In this study, we proposed a multi-task learning (MTL) based deep learning model for real-time TC intensity estimation and forecasting with the lead time of 6-12 hours following the event, based on the fusion of geostationary satellite images and numerical forecast model output. A total of 142 TCs which developed in the Northwest Pacific from 2011 to 2016 were used in this study. The Communications system, the Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) data were used to extract the images of typhoons, and the Climate Forecast System version 2 (CFSv2) provided by the National Center of Environmental Prediction (NCEP) was employed to extract air and ocean forecasting data. This study suggested two schemes with different input variables to the MTL models. Scheme 1 used only satellite-based input data while scheme 2 used both satellite images and numerical forecast modeling. As a result of real-time TC intensity estimation, Both schemes exhibited similar performance. For TC intensity forecasting with the lead time of 6 and 12 hours, scheme 2 improved the performance by 13% and 16%, respectively, in terms of the root mean squared error (RMSE) when compared to scheme 1. Relative root mean squared errors(rRMSE) for most intensity levels were lessthan 30%. The lower mean absolute error (MAE) and RMSE were found for the lower intensity levels of TCs. In the test results of the typhoon HALONG in 2014, scheme 1 tended to overestimate the intensity by about 20 kts at the early development stage. Scheme 2 slightly reduced the error, resulting in an overestimation by about 5 kts. The MTL models reduced the computational cost about 300% when compared to the single-tasking model, which suggested the feasibility of the rapid production of TC intensity forecasts.

• Industrial Engineering and Management Systems
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• v.14 no.3
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• pp.318-324
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• 2015

This paper proposes a dynamic short-term load forecasting method that utilizes a new sequential learning algorithm based on Relevance Vector Machine (RVM). The method performs general optimization of weights and hyperparameters using the current relevance vectors and newly arriving data. By doing so, the proposed algorithm is trained with the most recent data. Consequently, it extends the RVM algorithm to real-time and nonstationary learning processes. The results of application of the proposed algorithm to prediction of electrical loads indicate that its accuracy is comparable to that of existing nonparametric learning algorithms. Further, the proposed model reduces computational complexity.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.03a
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• pp.649-658
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• 2009

The Aim of this development is the safety management network of embankment facilities using forecasting analysis algorism. Using this algorithm it is possible to predict a failure of embankment facilities in advance. therefore, it is necessary for making plans of a safety countermove. In this development we have researched the analysis method which could operate effectively the embankment facilities using real-time monitoring data from a remote sensing system and the safety managerial program using the algorithm from the analysis method developed.

• Proceedings of the KIEE Conference
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• 2003.11a
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• pp.438-440
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• 2003

This paper resents an application of an Artificial Neural Network(ANN) technique to forecast the short-term system marginal price(SMP). The forecasting of SMP is a very important factor in an electricity market for the optimal biddings of market participants as well as for the market stabilization of regulatory bodies. The proposed neural network scheme is composed of three layers. In this process, input data are set up to reflect market conditions. And the $\lambda$ that is the coefficient of activation function is modified in order to give a proper signal to each neuron and improve the adaptability for a neural network. The reposed techniques are trained validated and tested with the historical real-world data from korea Power Exchange(KPX).

• Journal of Korea Water Resources Association
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• v.42 no.2
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• pp.131-139
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• 2009

The weather accidents by global warming effect are increasing rapidly whole world. Flood forcasting system and hydrological database are operated by almost all the countries in the world. An objective of this study is to research revised methods of missing rainfall data and find more effective revised method for this operating system. 194 rainfall data of the Han river basin is used. Arithmetic average method, coefficient of correlation weighting method and inverse distance weighting method are compared to estimate revised methods. The result from the analysis shows that coefficient of correlation weighting method is best quantitatively among the 3 methods.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.