• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제15권4호
• /
• pp.1232-1245
• /
• 2021

In this study, prediction of product sales as they relate to changes in temperature is proposed. This model uses long short-term memory (LSTM), which has shown excellent performance for time series predictions. For verification of the proposed sales prediction model, the sales of short pants, flip-flop sandals, and winter outerwear are predicted based on changes in temperature and time series sales data for clothing products collected from 2015 to 2019 (a total of 1,865 days). The sales predictions using the proposed model show increases in the sale of shorts and flip-flops as the temperature rises (a pattern similar to actual sales), while the sale of winter outerwear increases as the temperature decreases.

• Advances in concrete construction
• /
• 제9권3호
• /
• pp.289-299
• /
• 2020

To investigate the long-term behavior of eccentrically loaded RC columns, which are more realistic in practice than concentrically loaded RC columns, long-term eccentric loading tests were conducted for 10 RC columns. Test parameters included concrete compressive strength, reinforcement ratio, bar yield strength, eccentricity ratio, slenderness ratio, and loading pattern. Test results showed that the strain and curvature of the columns increased with time, and concrete forces were gradually transferred to longitudinal bars due to the creep and shrinkage of concrete. The long-term behavior of the columns varied with the test parameters, and long-term effects were more pronounced in the case of using the lower strength concrete, lower strength steel, lower bar ratio, fewer loading-step, higher eccentricity ratio, and higher slenderness ratio. However, in all the columns, no longitudinal bars were yielded under service loads at the final measuring day. Meanwhile, the numerical analysis modeling using the ultimate creep coefficient and ultimate shrinkage strain measured from cylinder tests gave quite good predictions for the behavior of the columns.

• 충청수학회지
• /
• 제34권2호
• /
• pp.157-168
• /
• 2021

This study uses the benchmark interest rate of the Federal Open Market Committee (FOMC) to predict gold futures prices. For the predictions, we used the support vector machine (SVM) (a machine-learning model) and the long short-term memory (LSTM) deep-learning model. We found that the LSTM method is more accurate than the SVM method. Moreover, we applied the Boruta algorithm to demonstrate that the FOMC benchmark interest rates correlate with gold futures.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2020년도 학술발표회
• /
• pp.123-123
• /
• 2020

The present study is aimed to correcting radar-based mean areal precipitation forecasts to improve urban flood predictions and uncertainty analysis of water levels contributed at each stage in the process. For this reason, a long short-term memory (LSTM) network is used to reproduce three-hour mean areal precipitation (MAP) forecasts from the quantitative precipitation forecasts (QPFs) of the McGill Algorithm for Precipitation nowcasting by Lagrangian Extrapolation (MAPLE). The Gangnam urban catchment located in Seoul, South Korea, was selected as a case study for the purpose. A database was established based on 24 heavy rainfall events, 22 grid points from the MAPLE system and the observed MAP values estimated from five ground rain gauges of KMA Automatic Weather System. The corrected MAP forecasts were input into the developed coupled 1D/2D model to predict water levels and relevant inundation areas. The results indicate the viability of the proposed framework for generating three-hour MAP forecasts and urban flooding predictions. For the analysis uncertainty contributions of the source related to the process, the Bayesian Markov Chain Monte Carlo (MCMC) using delayed rejection and adaptive metropolis algorithm is applied. For this purpose, the uncertainty contributions of the stages such as QPE input, QPF MAP source LSTM-corrected source, and MAP input and the coupled model is discussed.

• 한국지하수토양환경학회지:지하수토양환경
• /
• 제21권4호
• /
• pp.30-41
• /
• 2016

The amount of groundwater related data is drastically increasing domestically from various sources since 2000. To justify the more expansive continuation of the data acquisition and to derive valuable implications from the data, continued employments of sophisticated and state-of-the-arts statistical tools in the analyses and predictions are important issue. In the present study, we employed a well established machine learning technique of Gaussian Process Regression (GPR) model in the trend analyses of groundwater level for the long-term change. The major benefit of GPR model is that the model provide not only the future predictions but also the associated uncertainty. In the study, the long-term predictions of groundwater level from the stations of National Groundwater Monitoring Network located within Han River Basin were exemplified as prediction cases based on the GPR model. In addition, a few types of groundwater change patterns were delineated (i.e., increasing, decreasing, and no trend) on the basis of the statistics acquired from GPR analyses. From the study, it was found that the majority of the monitoring stations has decreasing trend while small portion shows increasing or no trend. To further analyze the causes of the trend, the corresponding precipitation data were jointly analyzed by the same method (i.e., GPR). Based on the analyses, the major cause of decreasing trend of groundwater level is attributed to reduction of precipitation rate whereas a few of the stations show weak relationship between the pattern of groundwater level changes and precipitation.

• 한국컴퓨터정보학회논문지
• /
• 제28권5호
• /
• pp.155-161
• /
• 2023

우리나라 수출의 97.5%, 수입의 87.2%가 해상운송으로 이뤄지며 항만이 한국 경제의 중요한 구성요소이다. 이러한 항만의 효율적인 운영을 위해서는 항만 물동량의 단기 예측을 통해 개선시킬 수가 있으며 과학적인 연구방법이 필요하다. 이전 연구는 주로 장기예측을 기반으로 대규모 인프라투자를 위한 연구에 중점을 두었으며 컨테이너 항만물동량에만 집중한 측면이 크다. 본 연구는 국내 대표적인 석유항만인 울산항의 석유 및 가스화물 물동량에 대한 단기 예측을 수행하였으며 딥러닝 모델인 LSTM(Long Short Term Memory) 모델을 사용하여 RMSE기준으로 예측성능을 확인하였다. 본 연구의 결과는 석유 및 가스화물 물동량 수요 예측의 정확도를 높여 항만 운영의 효율성을 개선하는 근거가 될 수 있을 것으로 기대된다. 또한 기존 연구의 한계로 컨테이너 항만 물동량뿐만 아니라 석유 및 가스화물 물동량 예측에도 LSTM의 활용할 수 있다는 가능성을 확인할 수 있으며 향후 추가 연구를 통해 일반화가 가능할 것으로 기대된다.

• 콘크리트학회논문집
• /
• 제26권5호
• /
• pp.615-625
• /
• 2014

고층 건물에서 많이 사용되는 장스팬 플랫 플레이트에서 과도한 시공 하중의 작용과 그에 따른 슬래브의 장기 처짐은 콘크리트 슬래브 디자인에 큰 영향을 미칠 수 있다. 본 연구에서는 플랫 플레이트의 장기처짐에서 슬래브의 조기 균열을 유발하는 시공하중의 영향을 이론적으로 연구하였다. 연구 결과를 바탕으로 플랫 플레이트의 장기처짐 산정법을 개발하였다. 제안한 방법에서는 슬래브 균열에 의한 즉시처짐 증가와 크리프 및 건조수축 효과에 의한 장기처짐 증가를 고려한다. 시공하중의 영향을 평가하기 위하여 실제 시공중인 플랫플레이트 건물에서 시공단계부터 슬래브의 장기처짐을 계측하였다. 계측결과, 시공하중에 의한 조기재령 슬래브의 즉시처짐은 플랫 플레이트의 장기처짐을 크게 증가시켰다. 슬래브 장기처짐 제안법은 계측된 슬래브의 장기처짐과 비교하여 검증하였으며, 제안모델은 시공하중에 의한 플랫 플레이트의 장기처짐을 비교적 잘 예측하는 것으로 나타났다.

• 산경연구논집
• /
• 제9권10호
• /
• pp.83-89
• /
• 2018

Purpose - The purpose of this study examines when the optimism impact on financial asset price forecasting and the boundary condition of optimism in the financial asset price forecasting. People generally tend to optimistically forecast their future. Optimism is a nature of human beings and optimistic forecasting observed in daily life. But is it always observed in financial asset price forecasting? In this study, two factors were focused on considering whether the optimism that people have applied to predicting future performance of financial investment products (e.g., mutual fund). First, this study examined whether the degree of optimism varied depending on the direction of the prior price trend. Second, this study examined whether the degree of optimism varied according to the forecast period by dividing the future forecasted by people into three time horizon based on forecast period. Research design, data, and methodology - 2 (prior price trend: rising-up trend vs falling-down trend) × 3 (forecast time horizon: short term vs medium term vs long term) experimental design was used. Prior price trend was used between subject and forecast time horizon was used within subject design. 169 undergraduate students participated in the experiment. χ² analysis was used. In this study, prior price trend divided into two types: rising-up trend versus falling-down trend. Forecast time horizon divided into three types: short term (after one month), medium term (after one year), and long term (after five years). Results - Optimistic price forecasting and boundary condition was found. Participants who were exposed to falling-down trend did not make optimistic predictions in the short term, but over time they tended to be more optimistic about the future in the medium term and long term. However, participants who were exposed to rising-up trend were over-optimistic in the short term, but over time, less optimistic in the medium and long term. Optimistic price forecasting was found when participants forecasted in the long term. Exposure to prior price trends (rising-up trend vs falling-down trend) was a boundary condition of optimistic price forecasting. Conclusions - The results indicated that individuals were more likely to be impacted by prior price tends in the short term time horizon, while being optimistic in the long term time horizon.

• Computers and Concrete
• /
• 제12권6호
• /
• pp.739-754
• /
• 2013

Creep and shrinkage have pronounced effects on the long-term deflection of prestressed concrete members. Under repeated loading, the rate of creep in prestressed concrete members is often accelerated. In this paper, an iterative computational procedure based on the well known Model B3 for creep and shrinkage was developed to predict the time-dependent deflection of partially prestressed concrete members. The developed model was validated using the experimental observed deflection behavior of a simply supported partially prestressed concrete beam under repeated loading. The validated model was then employed to make predictions of the long-term deflection of the prestressed beams under a variety of conditions (e.g., water cement ratio, relatively humidity and time at drying). The simulation results demonstrate that ignoring creep and shrinkage could lead to significant underestimation of the long-term deflection of a prestressed concrete member. The model will prove useful in reducing the long-term deflection of the prestressed concrete members via the optimal selection of a concrete mix and prestressing forces.

• Smart Structures and Systems
• /
• 제29권3호
• /
• pp.433-444
• /
• 2022

Concrete is a most utilized material in the construction industry that have main components. The strength of concrete can be improved by adding some admixtures. Evaluating the impact of fly ash (FA) and silica fume (SF) on the long-term compressive strength (CS) of concrete provokes to find the significant parameters in predicting the CS, which could be useful in the practical works and would be extensible in the future analysis. In this study, to evaluate the effective parameters in predicting the CS of concrete containing admixtures in the long-term and present a fitted equation, the multivariate adaptive regression splines (MARS) method has been used, which could find a relationship between independent and dependent variables. Next, for optimizing the output equation, biogeography-based optimization (BBO), particle swarm optimization (PSO), and hybrid PSOBBO methods have been utilized to find the most optimal conclusions. It could be concluded that for CS predictions in the long-term, all proposed models have the coefficient of determination (R²) larger than 0.9243. Furthermore, MARS-PSOBBO could be offered as the best model to predict CS between three hybrid algorithms accurately.