• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1183-1193
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• 2021

Low flow affects various fields such as river water supply management and planning, and irrigation water. A sufficient period of flow data is required to calculate the Flow Duration Curve. However, in order to calculate the Flow Duration Curve, it is essential to secure flow data for more than 30 years. However, in the case of rivers below the national river unit, there is no long-term flow data or there are observed data missing for a certain period in the middle, so there is a limit to calculating the Flow Duration Curve for each river. In the past, statistical-based methods such as Multiple Regression Analysis and ARIMA models were used to predict sulfur in the unmeasured watershed, but recently, the demand for machine learning and deep learning models is increasing. Therefore, in this study, we present the DNN technique, which is a machine learning technique that fits the latest paradigm. The DNN technique is a method that compensates for the shortcomings of the ANN technique, such as difficult to find optimal parameter values in the learning process and slow learning time. Therefore, in this study, the Flow Duration Curve applicable to the unmeasured watershed is calculated using the DNN model. First, the factors affecting the Flow Duration Curve were collected and statistically significant variables were selected through multicollinearity analysis between the factors, and input data were built into the machine learning model. The effectiveness of machine learning techniques was reviewed through statistical verification.

• Journal of Korean Society of Transportation
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• v.24 no.3 s.89
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• pp.113-123
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• 2006

This article evaluates the effects of eight traffic safety policies such as traffic accident reduction campaign, the seat belt law, three strike out driving while intoxicated, rewarding for reporting traffic offenders on the number of accidents and fatalities. Intervention analysis of time series is used to compare the monthly accident and fatalities with the before and after reinforcement. The results indicate that no significant impact of the traffic enforcement measures on reducing the number of accidents and fatalities.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.10b
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• pp.1323-1326
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• 2001

서브네트워크에서 실시간으로 통신 트래픽을 감시하고, 트래픽 정보를 바탕으로 시계열 분석을 이용해 트래픽의 변화추이를 예측할 수 있는 시스템을 설계 및 구현한다. SNMP를 이용한 MIB-II 정보를 바탕으로 하는 분석 방법은 누적 데이터를 기본으로 하는 관리 방법으로 이상 징후의 판단이 실시간 감시에는 적합하지 않은 점이 있다. 따라서, 본 논문에서는 실시간 트래픽 감시를 위해 서브네트워크에 들어오거나 나가는 트래픽의 양을 측정하여 분석하고, 이 정보를 바탕으로 특정 시점 이후의 트래픽 추이를 시계열 분석 방법을 이용하여 미래의 트래픽 양을 예측하는 알고리즘을 시스템으로 구현한다. 예측 알고리즘으로는 AR, MA, ARMA, ARIMA 모델중에 평균 제곱 오차를 최소로 가지는 알고리즘을 선택하여 예측하도록 설계한다. 개발되는 시스템을 망 관리자가 전체 통신 네트워크의 부하 상태를 예상할 수 있게 하여 신속하고 예방적인 대응을 할 수 있다.

• Journal of Electrical Engineering and Technology
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• v.11 no.1
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• pp.227-233
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• 2016

Google Trends provides weekly information on keyword search frequency on the Google search engine. Search volume patterns for the search keyword can also be analyzed based on category and by the location of those making the search. Also, Google provides “Hot searches” and “Top charts” including top and rising searches that include the search keyword. All this information is kept up to date, and allows trend comparisons by providing past weekly figures. In this study, we present a predictive model for TV markets using the searched data in Google search engine (Google Trend data). Using a predictive model for the market and analysis of the Google Trend data, we obtained an efficient and meaningful result for the TV market, and also determined highly ranked countries and cities. This method can provide very useful information for TV manufacturers and others.

• Journal of Korean Society for Quality Management
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• v.36 no.4
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• pp.93-101
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• 2008

The rare metals, used for semiconductors, PDP-LCS and other specialized metal areas necessarily, has been playing a key role for the Korean economic development. Rare metals are influenced by exogenous variables, such as production quantity, price and supplied areas. Nowadays the supply base of rare metals is threatened by the sudden increase in price. For the stable supply of rare metals, a rational demand outlook is needed. In this study, focusing on the domestic demand for chromium, the uncertainty and probability materializing from demand and price is analyzed, further, a demand forecast model, which takes into account various exogenous variables, is suggested, differing from the previously static model. Also, through the OOS(out-of-sampling) method, comparing to the preexistence ARIMA model, ARMAX model, multiple regression analysis model and ECM(Error Correction Mode) model, we will verify the superiority of suggested model in this study.

• Korean Management Science Review
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• v.33 no.1
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• pp.35-47
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• 2016

Generation of electricity using wind power has received considerable attention worldwide in recent years mainly due to its minimal environmental impact. However, volatility of wind power production causes additional problems to provide reliable electricity to an electrical grid regarding power system operations, power system planning, and wind farm operations. Those problems require appropriate stochastic models for the electricity generation output of wind power. In this study, we review previous literatures for developing the stochastic model for the wind power generation, and propose a systematic procedure for developing a stochastic model. This procedure shows a way to build an ARIMA model of volatile wind power generation using historical data, and we suggest some important considerations. In addition, we apply this procedure into a case study for a wind farm in the Republic of Korea, Shinan wind farm, and shows that our proposed model is helpful for capturing the volatility of wind power generation.

• Journal of Korean Society of Water and Wastewater
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• v.10 no.2
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• pp.88-103
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• 1996

The objective of this study is to develop stochastic and deterministic models which could be used to synthesize water application time series. Adaptive models using mulitivariate ARIMA(Transfer Function Model) are developed for daily urban water use forecasting. The model considers several variables on which water demands is dependent. The dynamic response of water demands to several factors(e.g. weekday, average temperature, minimum temperature, maximum temperature, humidity, cloudiness, rainfall) are characterized in the model by transfer functions. Daily water use data of Kumi city in 1992 are employed for model parameter estimation. Meteorological data of Seonsan station are utilized to input variables because Kumi has no records about the meteorological factor data.To determine the main factors influencing water use, autocorrelogram and cross correlogram analysis are performed. Through the identification, parameter estimation, and diagnostic checking of tentative model, final transfer function models by each month are established. The simulation output by transfer function models are compared to a historical data and shows the good agreement.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.325-326
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• 2021

LNG는 환경규제에 따라 화석에너지에서 친환경 재생에너지로 전환되는데 중요한 역할을 하는 에너지원이다. UN산하 세계해사기구(IMO)의 MARPOL협약에 따라 선박 황산화물 배출가스규제로 LNG추진 선박에 대한 수요가 증가되고 있을 뿐만 아니라 미국의 쉐일혁명으로 LNG를 수출함에 따라 공급의 변화가 급격하게 이뤄지고 있다. 과거 국가 주도의 프로젝트 성격이 강한 LNG 운송시장은 장기정기용선계약이 대부분이었으나 수요와 공급시장의 급격한 변화로 스팟시장의 중요성이 커지고 있다. 따라서 본 논문은 LNG 운송시장에서 시장참여자들의 스팟거래에 합리적인 의사결정이 이뤄지도록 과학적인 예측방법을 제시하고자 한다. LNG 스팟운임 예측에 기계학습모델 중 인공신경망 모델을 적용할 것이며 기존의 시계열분석 방법인 ARIMA모델과 비교하여 본문에서 제시된 모델의 예측성능의 우수성을 확인하였다. 본 논문은 LNG 스팟운임을 다룬 최초의 연구로서 학문적인 차별성이 기대된다.

• International Journal of Control, Automation, and Systems
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• v.6 no.5
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• pp.639-650
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• 2008

Electricity price forecasting has become an integral part of power system operation and control. In this paper, a wavelet transform (WT) based neural network (NN) model to forecast price profile in a deregulated electricity market has been presented. The historical price data has been decomposed into wavelet domain constitutive sub series using WT and then combined with the other time domain variables to form the set of input variables for the proposed forecasting model. The behavior of the wavelet domain constitutive series has been studied based on statistical analysis. It has been observed that forecasting accuracy can be improved by the use of WT in a forecasting model. Multi-scale analysis from one to seven levels of decomposition has been performed and the empirical evidence suggests that accuracy improvement is highest at third level of decomposition. Forecasting performance of the proposed model has been compared with (i) a heuristic technique, (ii) a simulation model used by Ontario's Independent Electricity System Operator (IESO), (iii) a Multiple Linear Regression (MLR) model, (iv) NN model, (v) Auto Regressive Integrated Moving Average (ARIMA) model, (vi) Dynamic Regression (DR) model, and (vii) Transfer Function (TF) model. Forecasting results show that the performance of the proposed WT based NN model is satisfactory and it can be used by the participants to respond properly as it predicts price before closing of window for submission of initial bids.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.217-224
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• 2019

As the world's population grows, how to maintain the food supply is becoming a bigger problem. Now and in the future, big data will play a major role in decision making in the agriculture industry. The challenge is how to obtain valuable information to help us make future decisions. Big data helps us to see history clearer, to obtain hidden values, and make the right decisions for the government and farmers. To contribute to solving this challenge, we developed the Agriculture Big Data Analysis System. The system consists of agricultural big data collection, big data analysis, and big data visualization. First, we collected structured data like price, climate, yield, etc., and unstructured data, such as news, blogs, TV programs, etc. Using the data that we collected, we implement prediction algorithms like ARIMA, Decision Tree, LDA, and LSTM to show the results in data visualizations.