• 한국지반신소재학회논문집
• /
• 제17권4호
• /
• pp.239-249
• /
• 2018

연약점토 지반 개량을 위해 연직배수재 타설 후 선행재하공법이 일반적으로 적용되는데, 현장에서의 시공계획 변경 등으로 인해 연직배수재 타설 후 장기간 방치되는 경우가 종종 발생된다. 따라서 장기간 방치된 조건에서의 연직배수재 열화 현상을 고려하기 위해 구속압으로 적용되는 수온을 각각 30, 35, $40^{\circ}C$를 적용하였다. 그 결과, 시간경과에 따라 배수성능이 급격히 저하되는 경향을 나타냈다. 그리고 현장 원위치 조건, 즉, 점토 구속조건하에서 장기간 통수능 저하 정도를 평가하기 위하여 Miura와 Chai(2000)식을 적용하였다. 그 결과, 온도 변화 조건에서 수행된 통수능 시험결과를 이용한 신뢰성 해석 방법과 Miura와 chai(2000)식을 적용하여 장기 통수능을 평가할 수 있는 것으로 평가되었다.

• 한국공간구조학회논문집
• /
• 제14권3호
• /
• pp.93-100
• /
• 2014

Ethylene Tetrafluoroethylene (ETFE) has been widely used in long-span buildings because of its light weight and high transparency. This paper studies the short and long term creep behaviour of ETFE foil. A series of short-term creep and recovery tests were performed, in which the residual strain was observed. A long-term creep test of the ETFE foil was also performed over 110 days. A viscoelastic-plastic model was then established to describe the short-term creep and recovery behaviour. The model contains a traditional multi-Kelvin part and an added steady-flow component to represent the viscoelastic and viscoplastic behaviour, respectively. The model successfully fit the data for three stresses and six temperatures. Additionally, time-temperature equivalency was adopted to predict the long-term creep behaviour of ETFE foil. Horizontal shifting factors were determined from the process of shifting creep-curves at six temperatures. The long-term creep behaviours at three temperatures were predicted. Finally, the long-term creep test showed that the short-term creep test at identical temperatures insufficiently predicted additional creep behaviour, and the long-term test verified the horizontal shifting factors derived from the time-temperature equivalency.

• 산업공학
• /
• 제16권4호
• /
• pp.400-410
• /
• 2003

Most of the demand forecasting which have been studied is about long-term time series over 15 years demand forecasting. In this paper, we set up the most optimal ARIMA model for the short-term time series demand forecasting and suggest demand forecasting system for short-term time series by appraising suitability and predictability. We are going to use the univariate ARIMA model in parallel with the bivariate transfer function model to improve the accuracy of forecasting. We also analyze the effect of advertisement cost, scale of branch stores, and number of clerk on the establishment of marketing policy by applying statistical methods. After then we are going to show you customer's needs, which are number of buying products. We have applied this method to forecast the annual sales of refrigerator in four branch stores of A company.

• International Journal of Computer Science & Network Security
• /
• 제23권4호
• /
• pp.166-171
• /
• 2023

The Stock price analysis is an increasing concern in a financial time series. The purpose of the study is to analyze the price parameters of date, high, low, and news feed about the stock exchange price. Long short term memory (LSTM) is a cutting-edge technology used for predicting the data based on time series. LSTM performs well in executing large sequence of data. This paper presents the Long Short Term Memory Model has used to analyze the stock price ranges of 10 days and 20 days by exponential moving average. The proposed approach gives better performance using technical indicators of stock price with an accuracy of 82.6% and cross entropy of 71%.

• 한국대기환경학회지
• /
• 제19권5호
• /
• pp.561-568
• /
• 2003

Surface ozone concentrations are highly sensitive to meteorological variability. Therefore, in order to reveal the long-term changes in ozone due to the changes in precursor emissions, we need to remove the effects of meteorological fluctuations on the annual distribution of surface ozone. In this paper, the meteorologically adjusted trends of daily maximum surface ozone concentrations in two major Korean cities (Seoul and Busan) are investigated based on ozone data from 11 (Seoul) and 6 (Busan) sites over the period 1992 ∼ 2000. The original time series consisting of the logarithm of daily maximum ozone concentrations are splitted into long-term, seasonal and short-term component using Kolmogorov-Zurbenko (KZ) filter. Meteorological effects are removed from filtered ozone series using multiple linear regression based on meteorologcial variables. The long-term evolution of ozone forming capability due to changes in precursor emission can be obtained applying the KZ filter to the residuals of the regression. The results indicated that meteorologically adjusted long-term daily maximum ozone concentrations had a significant upward trend (Seoul: ＋ 3.02% yr$^{-1}$ , Busan: ＋ 3.45% yr$^{-1}$ ). These changes of meteorologically adjusted ozone concentrations represent the effects of changing background ozone concentrations as well as the more localized changes in emissions.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2020년도 학술발표회
• /
• pp.101-101
• /
• 2020

Traditional stochastic simulation of hydroclimatological variables often underestimates the variability and correlation structure of larger timescale due to the difficulty in preserving long-term memory. However, the Long Short-Term Memory (LSTM) model illustrates a remarkable long-term memory from the recursive hidden and cell states. The current study, therefore, employed the LSTM model in stochastic generation of hydrologic and climate variables to examine how much the LSTM model can preserve the long-term memory and overcome the drawbacks of conventional time series models such as autoregressive (AR). A trigonometric function and the Rössler system as well as real case studies for hydrological and climatological variables were tested. Results presented that the LSTM model reproduced the variability and correlation structure of the larger timescale as well as the key statistics of the original time domain better than the AR and other traditional models. The hidden and cell states of the LSTM containing the long-memory and oscillation structure following the observations allows better performance compared to the other tested conventional models. This good representation of the long-term variability can be important in water manager since future water resources planning and management is highly related with this long-term variability.

• 응용통계연구
• /
• 제32권1호
• /
• pp.69-81
• /
• 2019

본 논문에서는 딥러닝을 이용한 이변량 장기종속시계열(long-range dependent time series) 예측을 고려하였다. 시계열 데이터 예측에 적합한 LSTM(long short-term memory) 네트워크를 이용하여 이변량 장기종속시계열을 예측하고 이를 이변량 FARIMA(fractional ARIMA) 모형인 FIVARMA 모형과 VARFIMA 모형과의 예측 성능을 실증 자료 분석을 통해 비교하였다. 실증 자료로는 기능적 자기공명 영상(fMRI) 및 일일 실현 변동성(daily realized volatility) 자료를 이용하였으며 표본외 예측(out-of sample forecasting) 오차 비교를 통해 예측 성능을 측정하였다. 그 결과, FIVARMA 모형과 VARFIMA 모형의 예측값에는 미묘한 차이가 존재하며, LSTM 네트워크의 경우 초매개변수 선택으로 복잡해 보이지만 계산적으로 더 안정되면서 예측 성능도 모수적 장기종속시계열과 뒤지지 않은 좋은 예측 성능을 보였다.

• 응용통계연구
• /
• 제23권2호
• /
• pp.249-261
• /
• 2010

Long-term forecasting of seasonal time series is critical in many applications such as planning business strategies and resolving possible problems of a business company. Unlike the traditional approach that depends solely on dynamic models, Li and Hinich (2002) introduced a combination of stochastic dynamic modeling with filter bank approach for forecasting seasonal patterns using highly coherent(High-C) waveforms. We modify the filter selection and forecasting procedure on wavelet domain to be more feasible and compare the resulting predictor with one that obtained from the wavelet variance estimation method. An improvement over other seasonal pattern extraction and forecasting methods based on such as wavelet scalogram, Holt-Winters, and seasonal autoregressive integrated moving average(SARIMA) is shown in terms of the prediction error. The performance of the proposed method is illustrated by a simulation study and an application to the real stock price data.

• 한국실내디자인학회논문집
• /
• 제3호
• /
• pp.91-99
• /
• 1994

The purpose of this study was to determine the long term effect of a pleasantly designed interior on pro-saptial behavior. For pleasantly designed interior, the existing interior was remodeled through the change of finishing materials for major architectural elements such as wall, floor and ceiling, and changes of furniture and it's arrangement . Pro-spatial behavior was operationalized as seat arranging behavior and measured through the arranged condition and observable arranging behavior. Time-series design, one of quasi-experimental design was used. The data in this study were extracted from an existing field experimental research. Five hundred survey video tapes record during 2 years period were used. In conclusion, the pleasantly designed environment has a long term effect on the pro-spatial behavior change . While self-centered pro-spatial was improved continuously and even reinforced , altruistic pro-spatial behavior was improved but diminished as time passed. There were no differences in the effect between male and female children. The result of the research provide scientific background of an answer to why Interior Design.

• 한국수자원학회논문집
• /
• 제42권10호
• /
• pp.809-824
• /
• 2009

한정된 기간의 짧은 유출량 기록을 갖는 댐 유역에서의 수자원 시스템 거동예측은 수문학적 지속성여부에 대한 판단이 선행 되어야 하며 가용한 시계열자료에 대한 추계학적 분석을 통하여 실시하여야 한다. 본 연구에서는 계절형 ARIMA모형을 통하여 안동댐 유역의 강우량, 증발량 및 유출량 시계열자료로 월별 수문시스템 거동을 예측하였으며, 예측된 결과를 토대로 TANK모형과 ARIMA+TANK결합모형에 의한 장기유출모의를 실시하였다. 분석결과 관측자료의 특성을 비교적 잘 반영 하였으며, 댐 유입량 예측을 위한 추계학적 결합모형의 적용가능성을 검토하였다. 이는 상대적으로 유출량자료의 보유년한이 짧은 대상유역의 시계열 수문인자 예측을 통한 유출모의의 적용으로 수자원의 중 장기 전략수립에 도움이 되리라 사료된다.