• Journal of The Korean Society of Agricultural Engineers
• /
• v.61 no.1
• /
• pp.61-74
• /
• 2019

In this study, the SPI (Standardized Precipitation Index) of meteorological drought and SDI (Streamflow Drought Index) of hydrological drought for 1, 3, 6, 9, and 12 months duration were estimated to analyse the characteristics of drought using rainfall and dam inflow data for Chungju dam ($6,661.8km^2$) with 31 years (1986-2016) and Boryeong dam ($163.6km^2$) watershed with 19 years (1998-2016) respectively. Using the estimated SPI and SDI, the drought forecasting was conducted using seasonal autoregressive integrated moving average (SARIMA) model for the 5 durations. For 2016 drought, the SARIMA had a good results for 3 and 6 months. For the 3 months SARIMA forecasting of SPI and SDI, the correlation coefficient of SPI3, SPI6, SPI12, SDI1, and SDI6 at Chungju Dam showed 0.960, 0.990, 0.999, 0.868, and 0.846, respectively. Also, for same duration forecasting of SPI and SDI at Boryeong Dam, the correlation coefficient of SPI3, SPI6, SDI3, SDI6, and SDI12 showed 0.999, 0.994, 0.999, 0.880, and 0.992, respectively. The SARIMA model showed the possibility to provide the future short-term SPI meteorological drought and the resulting SDI hydrological drought.

• Proceedings of the Korean Vacuum Society Conference
• /
• 2012.02a
• /
• pp.241-241
• /
• 2012

Semiconductor industry has been taking the advantage of improvements in process technology in order to maintain reduced device geometries and stringent performance specifications. This results in semiconductor manufacturing processes became hundreds in sequence, it is continuously expected to be increased. This may in turn reduce the yield. With a large amount of investment at stake, this motivates tighter process control and fault diagnosis. The continuous improvement in semiconductor industry demands advancements in process control and monitoring to the same degree. Any fault in the process must be detected and classified with a high degree of precision, and it is desired to be diagnosed if possible. The detected abnormality in the system is then classified to locate the source of the variation. The performance of a fault detection system is directly reflected in the yield. Therefore a highly capable fault detection system is always desirable. In this research, time series modeling of the data from an etch equipment has been investigated for the ultimate purpose of fault diagnosis. The tool data consisted of number of different parameters each being recorded at fixed time points. As the data had been collected for a number of runs, it was not synchronized due to variable delays and offsets in data acquisition system and networks. The data was then synchronized using a variant of Dynamic Time Warping (DTW) algorithm. The AutoRegressive Integrated Moving Average (ARIMA) model was then applied on the synchronized data. The ARIMA model combines both the Autoregressive model and the Moving Average model to relate the present value of the time series to its past values. As the new values of parameters are received from the equipment, the model uses them and the previous ones to provide predictions of one step ahead for each parameter. The statistical comparison of these predictions with the actual values, gives us the each parameter's probability of fault, at each time point and (once a run gets finished) for each run. This work will be extended by applying a suitable probability generating function and combining the probabilities of different parameters using Dempster-Shafer Theory (DST). DST provides a way to combine evidence that is available from different sources and gives a joint degree of belief in a hypothesis. This will give us a combined belief of fault in the process with a high precision.

• Korean Journal of Fisheries and Aquatic Sciences
• /
• v.29 no.2
• /
• pp.143-149
• /
• 1996

Forecasts of the monthly catches of anchovy in Korea were carried out by the seasonal Autoregressive Integrated Moving Average (ARIMA) model and spectral analysis. The seasonal ARIMA model is as follows: $$(1-0.431B)(1-B^{12})Z_t=(1-0.882B^{12})e_t$$ where: $Z_t=value$ at month $t;\;B^{p}$ is a backward shift operator, that is, $B^pZ_t=Z_{t-p};$ and $e_t=error$ term at month t, which is to forecast 24 months ahead the anchovy catches in Korea. The prediction error by the Box-Cox transformation on monthly anchovy catches in Korea was less than that by the logarithmic transformation. The equation of the Box-Cox transformation was $Y'=(Y^{0.58}-1)/0.58$. Forecasts of the monthly anchovy catches for $1991\~1992$, which were compared with the actual catches, had an absolute percentage error (APE) range of $1.0\~63.2\%$. Total observed annual catches in 1991 and 1992 were 170,293 M/T and 168,234 M/T respectively, while the predicted catches were 148,201 M/T and 148,834 M/T $(API\;13.0\%\;and\;11.5\%,\;respectively)$. The spectrum analysis of the monthly catches of anchovy showed some dominant fluctuations in the periods of 2.2, 6.1, 10.2 12.0 and 14.7 months. The spectrum analysis was also useful for selecting the ARIMA model.

• Journal of Korea Port Economic Association
• /
• v.37 no.1
• /
• pp.179-196
• /
• 2021

Port congestion rate at Busan Port has increased for three years. Port congestion causes container reconditioning, which increases the dockyard labor's work intensity and ship owner's waiting time. If congestion is prolonged, it can cause a drop in port service levels. Therefore, this study proposed an anomaly detection method using ARIMA(Autoregressive Integrated Moving Average) model with the daily volume data from 2013 to 2020. Most of the research that predicts port volume is mainly focusing on long-term forecasting. Furthermore, studies suggesting methods to utilize demand forecasting in terms of port operations are hard to find. Therefore, this study proposes a way to use daily demand forecasting for port anomaly detection to solve the congestion problem at Busan port.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.2
• /
• pp.383-403
• /
• 2021

With the development of mobile edge computing (MEC), some late-model application technologies, such as self-driving, augmented reality (AR) and traffic perception, emerge as the times require. Nevertheless, the high-latency and low-reliability of the traditional cloud computing solutions are difficult to meet the requirement of growing smart cars (SCs) with computing-intensive applications. Hence, this paper studies an efficient offloading decision and resource allocation scheme in collaborative vehicular edge computing networks with multiple SCs and multiple MEC servers to reduce latency. To solve this problem with effect, we propose a context-aware offloading strategy based on differential evolution algorithm (DE) by considering vehicle mobility, roadside units (RSUs) coverage, vehicle priority. On this basis, an autoregressive integrated moving average (ARIMA) model is employed to predict idle computing resources according to the base station traffic in different periods. Simulation results demonstrate that the practical performance of the context-aware vehicular task offloading (CAVTO) optimization scheme could reduce the system delay significantly.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.21 no.2
• /
• pp.227-242
• /
• 2019

This paper presents a method to predict ground types ahead of a tunnel face utilizing operational data of the earth pressure-balanced (EPB) shield tunnel boring machine (TBM) when running through soil ground. The time series analysis model which was applicable to predict the mixed ground composed of soils and rocks was modified to be applicable to soil tunnels. Using the modified model, the feasibility on the choice of the soil conditioning materials dependent upon soil types was studied. To do this, a self-organizing map (SOM) clustering was performed. Firstly, it was confirmed that the ground types should be classified based on the percentage of 35% passing through the #200 sieve. Then, the possibility of predicting the ground types by employing the modified model, in which the TBM operational data were analyzed, was studied. The efficacy of the modified model is demonstrated by its 98% accuracy in predicting ground types ten rings ahead of the tunnel face. Especially, the average prediction accuracy was approximately 93% in areas where ground type variations occur.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2021.06a
• /
• pp.326-326
• /
• 2021

장기간에 걸쳐 넓은 지역에 대해 발생하는 가뭄을 예측하기위해 많은 학자들의 기술적, 학술적 시도가 있어왔다. 본 연구에서는 복잡한 시계열을 가진 가뭄을 전망하는 방법 중 시나리오에 기반을 둔 가뭄전망 방법과 실시간으로 가뭄을 예측하는 비시나리오 기반의 방법 등을 이용하여 미래 가뭄전망을 실시했다. 시나리오에 기반을 둔 가뭄전망 방법으로는, 3개월 GCM(General Circulation Model) 예측 결과를 바탕으로 2009년도 PDSI(Palmer Drought Severity Index) 가뭄지수를 산정하여 가뭄심도에 대한 단기예측을 실시하였다. 또, 통계학적 방법과 물리적 모델(Physical model)에 기반을 둔 확정론적 수치해석 방법을 이용하여 비시나리오 기반 가뭄을 예측했다. 기존 가뭄을 통계학적 방법으로 예측하기 위해서 시도된 대표적인 방법으로 ARIMA(Autoregressive Integrated Moving Average) 모델의 예측에 대한 한계를 극복하기위해 서포트 벡터 회귀(support vector regression, SVR)와 웨이블릿(wavelet neural network) 신경망을 이용해 SPI를 측정하였다. 최적모델구조는 RMSE(root mean square error), MAE(mean absolute error) 및 R(correlation Coefficient)를 통해 선정하였고, 1-6개월의 선행예보 시간을 갖고 가뭄을 전망하였다. 그리고 SPI를 이용하여, 마코프 연쇄(Markov chain) 및 대수선형모델(log-linear model)을 적용하여 SPI기반 가뭄예측의 정확도를 검증하였으며, 터키의 아나톨리아(Anatolia) 지역을 대상으로 뉴로퍼지모델(Neuro-Fuzzy)을 적용하여 1964-2006년 기간의 월평균 강수량과 SPI를 바탕으로 가뭄을 예측하였다. 가뭄 빈도와 패턴이 불규칙적으로 변하며 지역별 강수량의 양극화가 심화됨에 따라 가뭄예측의 정확도를 높여야 하는 요구가 커지고 있다. 본 연구에서는 복잡하고 비선형성으로 이루어진 가뭄 패턴을 기상학적 가뭄의 정도를 나타내는 표준강수증발지수(SPEI, Standardized Precipitation Evapotranspiration Index)인 월SPEI와 일SPEI를 기계학습모델에 적용하여 예측개선 모형을 개발하고자 한다.

• Journal of KIISE
• /
• v.44 no.9
• /
• pp.954-965
• /
• 2017

A stable power supply is very important for the maintenance and operation of the power infrastructure. Accurate power consumption prediction is therefore needed. In particular, a university campus is an institution with one of the highest power consumptions and tends to have a wide variation of electrical load depending on time and environment. For this reason, a model that can accurately predict power consumption is required for the effective operation of the power system. The disadvantage of the existing time series prediction technique is that the prediction performance is greatly degraded because the width of the prediction interval increases as the difference between the learning time and the prediction time increases. In this paper, we first classify power data with similar time series patterns considering the date, day of the week, holiday, and semester. Next, each ARIMA model is constructed based on the classified data set and a daily power consumption forecasting method of the university campus is proposed through the time series cross-validation of the predicted time. In order to evaluate the accuracy of the prediction, we confirmed the validity of the proposed method by applying performance indicators.

• The Korean Journal of Applied Statistics
• /
• v.32 no.1
• /
• pp.69-81
• /
• 2019

We consider bivariate long range dependent (LRD) time series forecasting using a deep learning method. A long short-term memory (LSTM) network well-suited to time series data is applied to forecast bivariate time series; in addition, we compare the forecasting performance with bivariate fractional autoregressive integrated moving average (FARIMA) models. Out-of-sample forecasting errors are compared with various performance measures for functional MRI (fMRI) data and daily realized volatility data. The results show a subtle difference in the predicted values of the FIVARMA model and VARFIMA model. LSTM is computationally demanding due to hyper-parameter selection, but is more stable and the forecasting performance is competitively good to that of parametric long range dependent time series models.

• Journal of Preventive Medicine and Public Health
• /
• v.44 no.1
• /
• pp.2-8
• /
• 2011

Objectives: Public release of and feedback (here after public release) on institutional (clinics and hospitals) cesarean section rates has had the effect of reducing cesarean section rates. However, compared to the isolated intervention, there was scant evidence of the effect of repeated public releases (RPR) on cesarean section rates. The objectives of this study were to evaluate the effect of RPR for reducing cesarean section rates. Methods: From January 2003 to July 2007, the nationwide monthly institutional cesarean section rates data (1 951 303 deliveries at 1194 institutions) were analyzed. We used autoregressive integrated moving average (ARIMA) time-series intervention models to assess the effect of the RPR on cesarean section rates and ordinal logistic regression model to determine the characteristics of the change in cesarean section rates. Results: Among four RPR, we found that only the first one (August 29, 2005) decreased the cesarean section rate (by 0.81 percent) and continued to have an impact period through the last observation in May 2007. Baseline cesarean section rates (OR, 4.7; 95% CI, 3.1 to 7.1) and annual number of deliveries (OR, 2.8; 95% CI, 1.6 to 4.7) of institutions in the upper third of each category at before first intervention had a significant contribution to the decrease of cesarean section rates. Conclusions: We could not found the evidence that RPR has had the significant effect of reducing cesarean section rates. Institutions with upper baseline cesarean section rates and annual number of deliveries were more responsive to RPR.