• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.20-20
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• 2018

다년동안 지속되는 가뭄현상이 빈번하게 발생하고 있지만, 우리나라에서는 지금까지 장기 가뭄보다 단기 가뭄에 초점을 맞춰 연구가 진행되어 왔다. 다년 가뭄을 반영하지 않고 댐의 저수용량을 평가할 경우, 저수용량이 과소평가될 수 있기 때문에 다년간의 가뭄을 반영한 시계열 모형을 통해 다양한 시나리오를 생성하고 분석해야 한다. 본 연구에서는 2015년부터 2017년까지 장기 가뭄이 발생한 보령댐의 1998년-2017년까지의 관측 월평균 유입량 자료를 바탕으로 Autoregressive Moving Average(ARMA)시계열 모형과 Hurst Coefficient를 추가하여 장기지속성을 반영하도록 개발된 시계열 모형인 Autoregressive Fractionally Integreated Moving Average(ARFIMA)를 사용하여 보령댐 500년 기간의 유입량 자료를 생성하였다. Hurst Coefficient는 Hurst가 제안한 Rescaled Range(R/S)방법 외에도 경험식, 이론식을 모두 사용하여 산정하였다. 생성된 자료가 관측 자료의 장기지속성을 잘 반영하는지에 대한 검증을 위해 관측자료의 누적유입량으로부터 선형 이동평균방법을 사용하여 가뭄기준을 산정하고, 생성한 유입량 자료가 장기가뭄을 반영하고 있는지 판단하였다. 그 결과 가뭄의 장기지속성을 잘 반영하는 시계열 모형을 선정하였으며, 향후 연구를 통해 미래 기후변화 시나리오를 반영한 장기가뭄 분석을 수행할 예정이다.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.29-29
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• 2015

수자원의 지속적인 관리 및 효율적인 활용을 위하여 수문량의 예측과 분석은 필수적인 과정이라 할 수 있으며 이에 따라 다양한 수문 모형이 구축되고 강우, 유량 등 대표적인 수문량의 예측이 수행되어져 왔다. 그 중에서도 수문 시계열 모형은 시간의 흐름에 따라 일정하게 기록되어온 수문 자료를 확률적인 과정을 통하여 모형을 구축하고 이를 바탕으로 미래 수문량을 예측하는 데활용되는 모형으로, 과거에 기록된 수문 패턴이 미래에도 지속된다는 가정 하에 구축된다. 일반적으로 시계열 모형은 하나의 자료계열로 모형을 구축하는 단변량 모형과 원 자료계열 외에 다른 자료계열을 고려하여 모형을 구축하는 다변량 모형이 있으며, 다변량 모형은 원 자료계열에 영향을 미치는 외부변수를 고려함으로써 두 자료계열간의 상관성을 모형에 반영할 수 있는 장점을 가지고 있다. 또한 자료계열의 계절성을 고려하여 시계열 모형을 구축할 경우, 수문 시계열이 가지고 있는 계절적 영향을 잘 반영할 수 있다. 따라서 본 연구에서는 계절성을 고려한 다변량 시계열 모형인 SARIMAX(Seasonal AutoRegressive Integrated Moving Average with eXogenous) 모형을 이용하여 대표적인 수공구조물인 댐의 유입량 예측을 수행하였다. 일반적으로 댐 유입량 예측에는 댐의 유입량과 상관성이 높은 강우가 외부변수로 사용되어져 왔으나, 이 외에도 영향을 미칠 수 있는 지점특성치를 고려하여 모형을 구축한 후 비교하였다.

• Journal of Korea Multimedia Society
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• v.24 no.9
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• pp.1251-1260
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• 2021

This paper discusses missing data processing using simple moving average (SMA) and kalman filter. Also SMA and kalman predictive value are made a comparative study. Time series analysis is a generally method to deals with time series data in photovoltaic field. Photovoltaic system records data irregularly whenever the power value changes. Irregularly recorded data must be transferred into a consistent format to get accurate results. Missing data results from the process having same intervals. For the reason, it was imputed using SMA and kalman filter. The kalman filter has better performance to observed data than SMA. SMA graph is stepped line graph and kalman filter graph is a smoothing line graph. MAPE of SMA prediction is 0.00737%, MAPE of kalman prediction is 0.00078%. But time complexity of SMA is O(N) and time complexity of kalman filter is O(D²) about D-dimensional object. Accordingly we suggest that you pick the best way considering computational power.

• The Korean Journal of Applied Statistics
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• v.35 no.5
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• pp.645-655
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• 2022

In this paper, we investigate automatic functions for time series forecasting in R system and compare their performances. For the exponential smoothing models and ARIMA (autoregressive integrated moving average) models, we focus on the representative time series forecasting functions in R: forecast::ets(), forecast::auto.arima(), smooth::es() and smooth::auto.ssarima(). In order to compare their forecast performances, we use M3-Competiti on data consisting of 3,003 time series and adopt 3 accuracy measures. It is confirmed that each of the four automatic forecasting functions has strengths and weaknesses in the flexibility and convenience for time series modeling, forecasting accuracy, and execution time.

• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.82-91
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• 2022

In this paper, time series prediction method of photovoltaic power is introduced using seasonal autoregressive integrated moving average (SARIMA). In order to obtain the best fitting model by a time series method in the absence of an environmental sensor, this research was used data below 50% of cloud cover. Three samples were extracted by time intervals from the raw data. After that, the best fitting models were derived from mean absolute percentage error (MAPE) with the minimum akaike information criterion (AIC) or beysian information criterion (BIC). They are SARIMA (1,0,0)(0,2,2)14, SARIMA (1,0,0)(0,2,2)28, SARIMA (2,0,3)(1,2,2)55. Generally parameter of model derived from BIC was lower than AIC. SARIMA (2,0,3)(1,2,2)55, unlike other models, was drawn by AIC. And the performance of models obtained by SARIMA was compared. MAPE value was affected by the seasonal period of the sample. It is estimated that long seasonal period samples include atmosphere irregularity. Consequently using 1 hour or 30 minutes interval sample is able to be helpful for prediction accuracy improvement.

• Clean Technology
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• v.28 no.2
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• pp.138-146
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• 2022

Electricity has become a factor that dramatically affects the market economy. The day-ahead system marginal price determines electricity prices, and system marginal price forecasting is critical in maintaining energy management systems. There have been several studies using mathematics and machine learning models to forecast the system marginal price, but few studies have been conducted to develop, compare, and analyze various machine learning and deep learning models based on a data-driven framework. Therefore, in this study, different machine learning algorithms (i.e., autoregressive-based models such as the autoregressive integrated moving average model) and deep learning networks (i.e., recurrent neural network-based models such as the long short-term memory and gated recurrent unit model) are considered and integrated evaluation metrics including a forecasting test and information criteria are proposed to discern the optimal forecasting model. A case study of South Korea using long-term time-series system marginal price data from 2016 to 2021 was applied to the developed framework. The results of the study indicate that the autoregressive integrated moving average model (R-squared score: 0.97) and the gated recurrent unit model (R-squared score: 0.94) are appropriate for system marginal price forecasting. This study is expected to contribute significantly to energy management systems and the suggested framework can be explicitly applied for renewable energy networks.

• The Korean Journal of Applied Statistics
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• v.36 no.1
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• pp.21-32
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• 2023

The amount of money processed in medicine from the Korea Consumer Agency was studied by the various time series models. The medical data set from the Korea Consumer Agency were consisted of counseling, damage relief and conciliation. For the analysis of time series, autoregressive moving average model, vector autoregressive model and the transfer function model were used. We considered the stationarity and cross correlation function for the identification and fitting. As a result, the transfer function model showed a better prediction. Whereas, the vector autoregressive model also provided good information for the degree and duration of the influence of variables.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.658-659
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• 2015

GCM and scenario uncertainties are first investigated for 5 major watersheds (Han River, Paldang dam, Namhan River, Bukhan River and Imjin River watersheds). As a result of this study, it is found that CCSM3-based annual precipitation increases linearly with respect to the 10-year moving average values while CSIRO-based precipitation does not show much of trend. The results from annual DJF mean precipitation show a similar trend with respect to their 10-year moving average values. Both CCSM3- and CSIRO-based annual JJA mean precipitation do not show much of trend toward 21^st century. In general, CCSM3-based precipitation values are slightly higher than CSIRO-based values with respect to their annual and annual JJA mean precipitation values, but CSIRO-based annual DJF mean precipitation values are slightly higher than CCSM3-based values. In case of mean air temperature between CCSM3 and CSIRO during 21^st century, all of results show a clear trend in warming with the passage of time for 5 watersheds. However the upward trends from CCSM3-based values slow down toward end of 21^stcentury while CSRIO-based values increases almost linearly.

• Journal of Labour Economics
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• v.41 no.2
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• pp.61-88
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• 2018

This paper examines the impact of internal migration on wage growth among college graduates using Propensity Score Matching methods. We define migration as moving between Seoul Metropolitan Area (SMA) and non-SMA based on the locations of graduates' first and second jobs. We also take the direction of migration into account for examining the wage premium in SMA. In order to estimate the impact of migration on wage growth, we use the Graduate Occupational Mobility Survey (2010GOMS) coupled with other supplementary data such as College Scholastic Ability Test score and local characteristic variables. The results reveals that graduates moving from SMA to non-SMA do not experience significant wage growth. However, we find that graduates moving from non-SMA to SMA experience an increase in their monthly wage about 170,000~186,000 KRW on average (9.5~10.3% of their monthly wage on the first job).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.5
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• pp.407-412
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• 2014

Concerns and demands for the Location Based Services (LBS) using Global Positioning System (GPS) and Wi-Fi are largely increased in the world in the present. In some experimental results, it was noted that many errors are frequently occurred when the distances between an anchor node and a mobile node acre measured in indoor localization environment of Wireless Personal Area Network (WPAN). In this paper, localization compensation algorithm based on maximum measurement value ($LCA_{MMV}$) for moving objects in WPAN is proposed, and the performance of the algorithm is analyzed by experiments on three scenarios for movement of mobile nodes. From the experiments, it was confirmed that the average localization accuracy of suggested algorithm was more increased than Symmetric Double-Sided Two-Way Ranging (SDS-TWR) and triangulation as average 40.9cm, 77.6cm and 6.3cm, respectively on scenario 1-3.