• 한국지하수토양환경학회지:지하수토양환경
• /
• 제17권5호
• /
• pp.56-67
• /
• 2012

In the present study, an enhanced subsurface prediction algorithm based on a non-parametric geostatistical model and a history matching technique through Gibbs sampler is developed and the iterative prediction improvement procedure is proposed. The developed model is applied to a simple two-dimensional synthetic case where domain is composed of three different hydrogeologic media with $500m{\times}40m$ scale. In the application, it is assumed that there are 4 independent pumping tests performed at different vertical interval and the history curves are acquired through numerical modeling. With two hypothetical borehole information and pumping test data, the proposed prediction model is applied iteratively and continuous improvements of the predictions with reduced uncertainties of the media distribution are observed. From the results and the qualitative/quantitative analysis, it is concluded that the proposed model is good for the subsurface prediction improvements where the history data is available as a supportive information. Once the proposed model be a matured technique, it is believed that the model can be applied to many groundwater, geothermal, gas and oil problems with conventional fluid flow simulators. However, the overall development is still in its preliminary step and further considerations needs to be incorporated to be a viable and practical prediction technique including multi-dimensional verifications, global optimization, etc. which have not been resolved in the present study.

• 한국항해항만학회지
• /
• 제46권6호
• /
• pp.576-584
• /
• 2022

기존 해상 교통안전을 위한 기반 정보는 AIS 시스템을 사용하여 왔으나, IMO의 e-Navigation 도입이 제기된 이후 LTE 통신을 활용한 초고속 해상무선통신시스템(LTE-Maritime, LTE-M)이 세계 최초로 대한민국에 구축되었다. 본 논문에서는 AIS와 LTE-Maritime을 사용하여 수집된 항적 데이터를 비교 분석하고, 두 가지 종류의 항적 데이터를 기반으로 해상 안전사고 예방에 활용될 수 있는 항적 예측을 수행하였다. AIS 대비 LTE-Maritime의 데이터 수집 간격이 조밀하고 균일하여 항적 예측 오차가 작은 것을 확인하였다. LTE-Maritime에서 수집된 데이터의 경우 데이터 송·수신 시간 간격은 AIS 대비 항적 예측 오차에 대한 영향이 약 17% 적은 것을 발견하였다. 본 연구는 AIS와 LTE-Maritime의 항적 데이터와 그 활용을 정량적으로 비교한 최초의 연구라는 점에서 의미가 있다.

• 한국정보시스템학회지:정보시스템연구
• /
• 제31권3호
• /
• pp.197-220
• /
• 2022

Purpose The purpose of this study is to identifies the demand pattern categorization of repair parts of Automotive After-sales Service(A/S) and proposes a demand prediction model for Auto repair parts using Long Short-Term Memory (LSTM) of artificial neural networks (ANN). The optimal parts inventory quantity prediction model is implemented by applying daily, weekly, and monthly the parts demand data to the LSTM model for the Lumpy demand which is irregularly in a specific period among repair parts of the Automotive A/S service. Design/methodology/approach This study classified the four demand pattern categorization with 2 years demand time-series data of repair parts according to the Average demand interval(ADI) and coefficient of variation (CV²) of demand size. Of the 16,295 parts in the A/S service shop studied, 96.5% had a Lumpy demand pattern that large quantities occurred at a specific period. lumpy demand pattern's repair parts in the last three years is predicted by applying them to the LSTM for daily, weekly, and monthly time-series data. as the model prediction performance evaluation index, MAPE, RMSE, and RMSLE that can measure the error between the predicted value and the actual value were used. Findings As a result of this study, Daily time-series data were excellently predicted as indicators with the lowest MAPE, RMSE, and RMSLE values, followed by Weekly and Monthly time-series data. This is due to the decrease in training data for Weekly and Monthly. even if the demand period is extended to get the training data, the prediction performance is still low due to the discontinuation of current vehicle models and the use of alternative parts that they are contributed to no more demand. Therefore, sufficient training data is important, but the selection of the prediction demand period is also a critical factor.

• 한국자동차공학회논문집
• /
• 제12권2호
• /
• pp.117-122
• /
• 2004

The fatigue life in wheels was predicted by simulating the experimental method using Finite-Element analysis. Based on a high frequency fatigue property, calculations of the stresses in wheels were performed by simulating the rotating bending fatigue test. Wheels made of an aluminum alloy(A356.2) were tested using a bending fatigue tester. Results from bending fatigue test showed a linear correlation between bending moment and stress amplitude. Consequently, Finite-Element calculations were performed by a linear analysis. In order to find stress-cycles curves, spoke parts of wheel were tested using a rotary bending fatigue tester. Also, highly accurate Finite-Element analysis requires regression lines and confidence intervals from these results. In conclusion, if the fatigue data related to the material and manufacturing procedure are reliable, the prediction on fatigue lift in wheels can be carried out with high accuracy.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2002년도 ITC-CSCC -1
• /
• pp.102-105
• /
• 2002

A noise reduction technique to reduce background noise in corrupted voice is proposed. The proposed method is based on linear prediction and takes advantages of periodicity of voiced speech. A voiced sound is regarded as a periodic stationary signal in short time interval. Therefore, the current voice signal is correlated with the voice signal delayed by a pitch period. A linear predictor can estimate only the current signal correlated with the delayed signal. Therefore, the enhanced voice can be obtained as output of the linear predictor. Simulation results show that the proposed method is able to reduce the background noise.

• 한국태양에너지학회 논문집
• /
• 제37권1호
• /
• pp.81-90
• /
• 2017

We have analyzed the performance of 58 kWp photovoltaic (PV) power systems installed in Jeddah, Saudi Arabia. Performance ratio (PR) of 3 PV systems with 3 desert-type PV modules using monitoring data for 1 year showed 85.5% on average. Annual degradation rate of 5 individual modules achieved 0.26%, the regression model using monitoring data for the specified interval of one year showed 0.22%. Root mean square error (RMSE) of 6 big data analysis models for power output prediction in May 2016 was analyzed 2.94% using a support vector regression model.

• 한국경영과학회:학술대회논문집
• /
• 대한산업공학회/한국경영과학회 1996년도 춘계공동학술대회논문집; 공군사관학교, 청주; 26-27 Apr. 1996
• /
• pp.67-70
• /
• 1996

The values measured at different time and enumerated sequentially by homogenous interval is called time series. Its goal is to predict values in future by analysing the measured values in past. The stastical approach to time series prediction tend to be by a neural approach with difficulties in expressing the reationship among past data. In neural approach, the preblem is the acquisting of the enough training data in advance. The goal of this paper is that such problem is solved by generating another term as virtual term between terms in time series.

• 콘크리트학회논문집
• /
• 제17권6호
• /
• pp.1045-1051
• /
• 2005

For critical structures and application, where a given reliability must be met, it is necessary to account for uncertainties and variability in material properties, structural parameters affecting the corrosion process, in addition to the statistical and decision uncertainties. This paper presents an approach to the fuzzy arithmetic based modeling of the chloride-induced corrosion of reinforcement in concrete structures that takes into account the uncertainties in the physical models of chloride penetration into concrete and corrosion of steel reinforcement, as well as the uncertainties in the governing parameters, including concrete diffusivity, concrete cover depth, surface chloride concentration and critical chloride level for corrosion initiation. The parameters of the models are regarded as fuzzy numbers with proper membership function adapted to statistical data of the governing parameters and the fuzziness of the corrosion time is determined by the fuzzy arithmetic of interval arithmetic and extension principle

• Journal of the Korean Data and Information Science Society
• /
• 제19권4호
• /
• pp.1429-1440
• /
• 2008

In a meta-analysis combining the results from different clinical trials, it is important to consider the possible heterogeneity in outcomes between trials. Such variations can be regarded as random effects. Thus, random-effect models such as HGLMs (hierarchical generalized linear models) are very useful. In this paper, we propose a HGLM framework for analyzing the binominal response data which may have variations in the odds-ratios between clinical trials. We also present the prediction intervals for random effects which are in practice useful to investigate the heterogeneity of the trial effects. The proposed method is illustrated with a real-data set on 22 trials about respiratory tract infections. We further demonstrate that an appropriate HGLM can be confirmed via model-selection criteria.

• Journal of the Korean Statistical Society
• /
• 제17권2호
• /
• pp.101-120
• /
• 1988

A time series model with Laplacian (double-exponential) marginal distribution, NLAR(2), was proposed by Dewald and Lewis (1985). The special cases of NLAR(2) process and their properties are considered. Extensions to the NLAR(p) is discussed. It is shown that the NLAR(1) satisfies the strong-mixing conditions, hence the model-free prediction interval using the sample quantiles can be obtained.