• Journal of Advanced Navigation Technology
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• v.23 no.6
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• pp.487-494
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• 2019

Recently, interest in and use of drones is increasing. In this study, to provide accurate wind prediction at ultra low altitudes of 150 meters or below, the sensitivity of the physical process parameterization and initial conditions was assessed to select the optimal physical process and initial conditions. For this purpose, GFS and LDAPS data were used as initial and boundary conditions, and 7 experiments were constructed using a combination of PBL schemes such as YSU, RUC, ACM2, and LSM such as Noah, RUC, and Pleim. The experiment conducted for 1 month in April 2018. As a result, the RUC-YSU physical process combination using the GFS initial data showed the best performance. This study is meaningful in establishing an optimal modeling method for ultra low altitude wind prediction through experiments using different initial conditions and combination of physical processes.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.1-23
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• 2014

Market forecasting aims to estimate the sales volume of a product or service that is sold to consumers for a specific selling period. From the perspective of the enterprise, accurate market forecasting assists in determining the timing of new product introduction, product design, and establishing production plans and marketing strategies that enable a more efficient decision-making process. Moreover, accurate market forecasting enables governments to efficiently establish a national budget organization. This study aims to generate a market growth curve for ICT (information and communication technology) goods using past time series data; categorize products showing similar growth patterns; understand markets in the industry; and forecast the future outlook of such products. This study suggests the useful and meaningful process (or methodology) to identify the market growth pattern with quantitative growth model and data mining algorithm. The study employs the following methodology. At the first stage, past time series data are collected based on the target products or services of categorized industry. The data, such as the volume of sales and domestic consumption for a specific product or service, are collected from the relevant government ministry, the National Statistical Office, and other relevant government organizations. For collected data that may not be analyzed due to the lack of past data and the alteration of code names, data pre-processing work should be performed. At the second stage of this process, an optimal model for market forecasting should be selected. This model can be varied on the basis of the characteristics of each categorized industry. As this study is focused on the ICT industry, which has more frequent new technology appearances resulting in changes of the market structure, Logistic model, Gompertz model, and Bass model are selected. A hybrid model that combines different models can also be considered. The hybrid model considered for use in this study analyzes the size of the market potential through the Logistic and Gompertz models, and then the figures are used for the Bass model. The third stage of this process is to evaluate which model most accurately explains the data. In order to do this, the parameter should be estimated on the basis of the collected past time series data to generate the models' predictive value and calculate the root-mean squared error (RMSE). The model that shows the lowest average RMSE value for every product type is considered as the best model. At the fourth stage of this process, based on the estimated parameter value generated by the best model, a market growth pattern map is constructed with self-organizing map algorithm. A self-organizing map is learning with market pattern parameters for all products or services as input data, and the products or services are organized into an $N{\times}N$ map. The number of clusters increase from 2 to M, depending on the characteristics of the nodes on the map. The clusters are divided into zones, and the clusters with the ability to provide the most meaningful explanation are selected. Based on the final selection of clusters, the boundaries between the nodes are selected and, ultimately, the market growth pattern map is completed. The last step is to determine the final characteristics of the clusters as well as the market growth curve. The average of the market growth pattern parameters in the clusters is taken to be a representative figure. Using this figure, a growth curve is drawn for each cluster, and their characteristics are analyzed. Also, taking into consideration the product types in each cluster, their characteristics can be qualitatively generated. We expect that the process and system that this paper suggests can be used as a tool for forecasting demand in the ICT and other industries.

• International journal of advanced smart convergence
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• v.4 no.1
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• pp.45-53
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• 2015

This paper is proposed mathematical load modelling based on system identification approach of energy consumption of residential air conditioning. Due to air conditioning is one of the significant equipment which consumes high energy and cause the peak load of power system especially in the summer time. The demand response is one of the solutions to decrease the load consumption and cutting peak load to avoid the reservation of power supply from power plant. In order to operate this solution, mathematical modelling of air conditioning which explains the behaviour is essential tool. The four type of linear model is selected for explanation the behaviour of this system. In order to obtain model, the experimental setup are performed by collecting input and output data every minute of 9,385 BTU/h air-conditioning split type with $25^{\circ}C$ thermostat setting of one sample house. The input data are composed of solar radiation ($W/m^2$) and ambient temperature ($^{\circ}C$). The output data are power and energy consumption of air conditioning. Both data are divided into two groups follow as training data and validation data for getting the exact model. The model is also verified with the other similar type of air condition by feed solar radiation and ambient temperature input data and compare the output energy consumption data. The best model in term of accuracy and model order is output error model with 70.78% accuracy and $17^{th}$ order. The model order reduction technique is used to reduce order of model to seven order for less complexity, then Kalman filtering technique is applied for remove white Gaussian noise for improve accuracy of model to be 72.66%. The obtained model can be also used for electrical load forecasting and designs the optimal size of renewable energy such photovoltaic system for supply the air conditioning.

• 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.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.241-254
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• 2011

Financial time-series forecasting is one of the most important issues because it is essential for the risk management of financial institutions. Therefore, researchers have tried to forecast financial time-series using various data mining techniques such as regression, artificial neural networks, decision trees, k-nearest neighbor etc. Recently, support vector machines (SVMs) are popularly applied to this research area because they have advantages that they don't require huge training data and have low possibility of overfitting. However, a user must determine several design factors by heuristics in order to use SVM. For example, the selection of appropriate kernel function and its parameters and proper feature subset selection are major design factors of SVM. Other than these factors, the proper selection of instance subset may also improve the forecasting performance of SVM by eliminating irrelevant and distorting training instances. Nonetheless, there have been few studies that have applied instance selection to SVM, especially in the domain of stock market prediction. Instance selection tries to choose proper instance subsets from original training data. It may be considered as a method of knowledge refinement and it maintains the instance-base. This study proposes the novel instance selection algorithm for SVMs. The proposed technique in this study uses genetic algorithm (GA) to optimize instance selection process with parameter optimization simultaneously. We call the model as ISVM (SVM with Instance selection) in this study. Experiments on stock market data are implemented using ISVM. In this study, the GA searches for optimal or near-optimal values of kernel parameters and relevant instances for SVMs. This study needs two sets of parameters in chromosomes in GA setting : The codes for kernel parameters and for instance selection. For the controlling parameters of the GA search, the population size is set at 50 organisms and the value of the crossover rate is set at 0.7 while the mutation rate is 0.1. As the stopping condition, 50 generations are permitted. The application data used in this study consists of technical indicators and the direction of change in the daily Korea stock price index (KOSPI). The total number of samples is 2218 trading days. We separate the whole data into three subsets as training, test, hold-out data set. The number of data in each subset is 1056, 581, 581 respectively. This study compares ISVM to several comparative models including logistic regression (logit), backpropagation neural networks (ANN), nearest neighbor (1-NN), conventional SVM (SVM) and SVM with the optimized parameters (PSVM). In especial, PSVM uses optimized kernel parameters by the genetic algorithm. The experimental results show that ISVM outperforms 1-NN by 15.32%, ANN by 6.89%, Logit and SVM by 5.34%, and PSVM by 4.82% for the holdout data. For ISVM, only 556 data from 1056 original training data are used to produce the result. In addition, the two-sample test for proportions is used to examine whether ISVM significantly outperforms other comparative models. The results indicate that ISVM outperforms ANN and 1-NN at the 1% statistical significance level. In addition, ISVM performs better than Logit, SVM and PSVM at the 5% statistical significance level.

• Ocean and Polar Research
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• v.33 no.4
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• pp.497-506
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• 2011

The rapid industrial and technological development has made the human activities for the utilization of marine resources more complex. Marine spatial management is a space-based approach. It is a comprehensive and integrated management approach. The ultimate goal of marine spatial management is the "sustainable use" of marine resources. The partial approach is applied in the existing marine spatial management, mainly coastal zones which involves integrated approach. Also this showed various limitations including restricted mostly to coastal zones, and limitation to implementation tools. However, for marine spatial management to have a reasonable approach that attaches importance to the relationship between humans and the holistic ecosystem, it is important to internalize a central principle in marine spatial management that focuses on the sustainable use of marine resources. In the present study, four central principles are proposed that will eventually be applied through marine spatial management planning tools. These principles are 1) the establishment of a cooperative decision making and planning system that is based on stakeholder participation; 2) scientific assessment of the current status and impact on the basis of ecology, sociology, and economics; 3) reasonable and optimal spatial assignment based on the forecasting of future-use characteristics and environmental changes; and 4) ascribing importance to the implementation of the results of rational planning processes.

• Journal of the Korea Society for Simulation
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• v.15 no.3
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• pp.39-47
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• 2006

In most of supply chain planning practices, the estimated demands, which are forecasted for each individual period in a forecasting window, are regarded as deterministic. But, in reality, the forecasted demands for the periods of a given horizon are stochastically distributed. Instead of using a safety stock, this study considers a direct control of service level by choosing the demand used in planning from the distributed forecasted demand values for the corresponding period. Using the demand quantile and echelon stock concept, we propose a simple but efficient heuristic algorithm for multi-echelon serial systems under service level constraints. Through a comprehensive simulation study, the proposed algorithm was shown to be very accurate compared with the optimal solutions.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.379-379
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• 2017

과거에는 하천 범람으로 인한 홍수피해가 많았으나 최근에는 도시화로 인한 불투수면적의 증가로 홍수도달시간의 단축 및 노면수의 배수불량으로 인한 내수 홍수피해가 많아졌다. 이러한 변화는 도시하천의 홍수예보에 밀접한 관련이 있으며 관련된 분석 모형 및 연계방안 또한 매우 중요하게 되었다. 일반적으로 하천에 대한 유출해석 모형으로 HEC-RAS((Hydrologic Engineering Center-River Analysis System)가 주로 사용되고 있으나 현재와 같이 도심지 하천에서는 내배수의 특성을 고려한 SWMM(Storm Water Management Model)을 사용한다. 또는 이 두모형의 연계를 통해 유출해석을 진행하기도 한다. 최근 HEC-RAS와 SWMM모형이 최신 버전을 공개하였다. HEC-RAS의 경우 2016년 9월 5.0.3버전을 출시하며 1D뿐만 아닌 2D의 모의도 가능하도록 기능을 개선하였으며 SWMM의 경우 2016년 09월 07일 5.1.011버젼이 공개되었다. 본 연구에서는 공개된 최신 모형을 도림천 지역에 적용하여 도림천 지역에 적합한 모형 및 연계 방법을 찾아보려 한다. 이를 통해 최적의 도시홍수예보 시스템을 구성하기 위한 모형 및 연계방안의 조사와 가장 합리적인 도시홍수 시스템의 구성방안을 제시하고자 한다.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.373-383
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• 2020

In this study we established an operational storm-surge system for the northwestern pacific ocean, based on the NEMO (Nucleus for European Modeling of the Ocean). The system consists of the tide and the surge models. For more accurate storm surge prediction, it can be completed not only by applying more precise depth data, but also by optimal parameterization at the boundaries of the atmosphere and ocean. To this end, we conducted several sensitivity experiments related to the application of available bathymetry data, ocean bottom friction coefficient, and wind stress and air pressure on the ocean surface during August~September 2018 and the case of typhoon SOULIK. The results of comparison and verification are presented here, and they are compared with POM (Princeton Ocean Model) based Regional Tide Surge forecasting Model (RTSM). The results showed that the RTSM_NEMO model had a 29% and 20% decrease in Bias and RMSE respectively compared to the RTSM_POM model, and that the RTSM_NEMO model had a lower overall error than the RTSM_POM model for the case of typhoon SOULIK.

• Journal of Korea Water Resources Association
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• v.32 no.6
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• pp.731-740
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• 1999

Autocorrelation function is widely used as a tool measuring linear dependence of hydrologic time series. However, it may not be appropriate for choosing decorrelation time or delay time ${\tau}_d$ which is essential in nonlinear dynamics domain and the mutual information have recommended for measuring nonlinear dependence of time series. Furthermore, some researchers have suggested that one should not choose a fixed delay time ${\tau}_d$ but, rather, one should choose an appropriate value for the delay time window ${\tau}_d={\tau}(m-1)$, which is the total time spanned by the components of each embedded point for the analysis of chaotic dynamics. Unfortunately, the delay time window cannot be estimated using the autocorrelation function or the mutual information. Basically, the delay time window is the optimal time for independence of time series and the delay time is the first locally optimal time. In this study, we estimate general dependence of hydrologic time series using the C-C method which can estimate both the delay time and the delay time window and the results may give us whether hydrologic time series depends on its linear or nonlinear characteristics which are very important for modeling and forecasting of underlying system.