• 한국해양공학회지
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• 제38권1호
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• pp.18-29
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• 2024

Waves are a complex phenomenon in marine and coastal areas, and accurate wave prediction is essential for the safety and resource management of ships at sea. In this study, three types of machine learning techniques specialized in nonlinear data processing were used to predict the waves of Korea waters. An optimized algorithm for each area is presented for performance evaluation and comparison. The optimal parameters were determined by varying the window size, and the performance was evaluated by comparing the mean absolute error (MAE). All the models showed good results when the window size was 4 or 7 d, with the gated recurrent unit (GRU) performing well in all waters. The MAE results were within 0.161 m to 0.051 m for significant wave heights and 0.491 s to 0.272 s for periods. In addition, the GRU showed higher prediction accuracy for certain data with waves greater than 3 m or 8 s, which is likely due to the number of training parameters. When conducting marine and offshore research at new locations, the results presented in this study can help ensure safety and improve work efficiency. If additional wave-related data are obtained, more accurate wave predictions will be possible.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2003년도 춘계학술대회 논문집
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• pp.133-136
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• 2003

In these days, the reliability analysis and prediction are applied for many industrial products and many products require guaranteeing the quality and efficiency of their products. In this study reliability prediction for core units of machine tools has been performed in order to improve and analyze its reliability. ATC(Automatic Tool Changer) and interface Card of PC-NC that are core component of the machine tools were chosen as the target of the reliability prediction. A reliability analysis tool was used to obtain the reliability data(failure rate database) for reliability prediction. It is expected that the results of reliability prediction be applied to improve and evaluate its reliability. Failure rate, MTBF (Mean Time Between Failure) and reliability for core units of machine tools were evaluated and analyzed in this study.

• 한국복합재료학회:학술대회논문집
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• 한국복합재료학회 2002년도 춘계학술발표대회 논문집
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• pp.181-183
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• 2002

The accurate permeability for preform is critical to model and design the impregnation of fluid resin in the composite manufacturing process. In this study, the in-plane and transverse permeability for a woven fabric are predicted numerically through the coupled flow model which combines microscopic with macroscopic flow. The microscopic and macroscopic flow which are flows within the micro-unit and macro-unit cell, respectively, are calculated by using 3-D CVFEM(control volume finite element method). To avoid checker-board pressure field and improve the efficiency on numerical computation, A new interpolation function for velocity is proposed on the basis of analytic solutions. The permeability of plain woven fabric is measured through unidirectional flow experiment and compared with the permeability calculated numerically. Based on the good agreement of the results, the relationships between the permeability and the structures of preform such as the fiber volume fraction and stacking effect can be understood. The reverse and the simple stacking are taken in account. Unlike past literatures, this study is based on more realistic unit cell and the improved prediction of permeability can be achieved. It is observed that in-plane flow is more dominant than transverse flow in the real flow through preform and the stacking effect of multi-layered preform is negligible. Consequently, the proposed coupled flow model can be applied to modeling of real composite materials processing.

• Journal of Information Processing Systems
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• 제16권4호
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• pp.959-974
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• 2020

High efficiency video coding (HEVC) employs quadtree coding tree unit (CTU) structure to improve its coding efficiency, but at the same time, it also requires a very high computational complexity due to its exhaustive search processes for an optimal coding unit (CU) partition. With the aim of solving the problem, a fast CU size decision optimal algorithm based on neighborhood prediction is presented for HEVC in this paper. The contribution of this paper lies in the fact that we successfully use the partition information of neighborhood CUs in different depth to quickly determine the optimal partition mode for the current CU by neighborhood prediction technology, which can save much computational complexity for HEVC with negligible RD-rate (rate-distortion rate) performance loss. Specifically, in our scheme, we use the partition information of left, up, and left-up CUs to quickly predict the optimal partition mode for the current CU by neighborhood prediction technology, as a result, our proposed algorithm can effectively solve the problem above by reducing many unnecessary prediction and partition operations for HEVC. The simulation results show that our proposed fast CU size decision algorithm based on neighborhood prediction in this paper can reduce about 19.0% coding time, and only increase 0.102% BD-rate (Bjontegaard delta rate) compared with the standard reference software of HM16.1, thus improving the coding performance of HEVC.

• 대한토목학회논문집
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• 제34권2호
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• pp.529-536
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• 2014

점토-시멘트 혼합토의 물리적 특성인 함수비, 비중, 단위중량과 간극비 등은 혼합토의 강도, 압축성, 압밀거동 예측 등에 적용되는 주요한 인자이다. 기존에는 혼합토의 물리적 특성을 복잡한 실내시험 및 시공 후 확인조사를 통해 이루어 졌다. 본 연구는 점토 함수비 90~170%, 시멘트 함유율 5~25%와 재령기간은 3~90일 조건으로 실내시험을 수행하였으며, 양생 후 혼합토 함수비, 비중, 단위중량과 간극비 등에 대한 변화를 분석하였다. 시험결과를 이용하여 원지반 점토 함수비, 시멘트 함유율과 재령기간 등의 역학적 관계를 바탕으로 혼합토의 함수비, 비중과 단위중량에 관한 물성 예측식을 제안하였다. 혼합토의 물성 예측식을 지반공학 분야에서 일반적으로 사용하는 간극비 산출식에 대입하여 혼합토의 간극비 예측식을 도출하였으며, 방콕 점토를 대상으로 간극비에 대한 실험결과와 본 연구에서 제안한 예측식을 검증하였다.

• 대기
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• 제25권3호
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• pp.529-541
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• 2015

Korea Institute of Atmospheric Prediction System (KIAPS) has been developing a global numerial prediction model and data assimilation system. We has implemented LETKF (Local Ensemble Transform Kalman Filter, Hunt et al., 2007) data assimilation system to NCAR CAM-SE (National Center for Atmospheric Research Community Atmosphere Model with Spectral Element dynamical core, Dennis et al., 2012) that has cubed-sphere grid, known as the same grid system of KIAPS Integrated Model (KIM) now developing. In this study, we have assimilated Global Positioning System Radio Occultation (GPS-RO) bending angle measurements in addition to conventional data within ensemble-based data assimilation system. Before assimilating bending angle data, we performed a vertical unit conversion. The information of vertical localization for GPS-RO data is given by the unit of meter, but the vertical localization method in the LETKF system is based on pressure unit. Therefore, with a clever conversion of the vertical information, we have conducted experiments to search for the best vertical localization scale on GPS-RO data under the Observing System Simulation Experiments (OSSEs). As a result, we found the optimal setting of vertical localization for the GPS-RO bending angle data assimilation. We plan to apply the selected localization strategy to the LETKF system implemented to KIM which is expected to give better analysis of GPS-RO data assimilation due to much higher model top.

• 전기전자학회논문지
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• 제23권1호
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• pp.120-126
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• 2019

최근 에너지 인터넷에서 지능형 원격검침 인프라를 이용하여 확보된 대량의 전력사용데이터를 기반으로 효과적인 전력수요 예측을 위해 다양한 기계학습기법에 관한 연구가 활발히 진행되고 있다. 본 연구에서는 전력량 데이터와 같은 시계열 데이터에 대해 효율적으로 패턴인식을 수행하는 인공지능 네트워크인 Gated Recurrent Unit(GRU)을 기반으로 딥 러닝 모델을 제안하고, 실제 가정의 전력사용량 데이터를 토대로 예측 성능을 분석한다. 제안한 학습 모델의 예측 성능과 기존의 Long Short Term Memory (LSTM) 인공지능 네트워크 기반의 전력량 예측 성능을 비교하며, 성능평가 지표로써 Mean Squared Error (MSE), Mean Absolute Error (MAE), Forecast Skill Score, Normalized Root Mean Squared Error (RMSE), Normalized Mean Bias Error (NMBE)를 이용한다. 실험 결과에서 GRU기반의 제안한 시계열 데이터 예측 모델의 전력량 수요 예측 성능이 개선되는 것을 확인한다.

• 콘크리트학회논문집
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• 제27권2호
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• pp.169-176
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• 2015

현재까지 발표된 크기효과법칙은 보통중량 콘크리트에 기반하고 있어 파괴특성이 다른 경량골재 콘크리트에서는 그 활용성이 의문시되고 있다. 따라서 이 연구에서는 콘크리트의 기건단위질량이 압축강도의 크기효과에 미치는 영향을 예측할 수 있는 모델을 개발하고 기존 연구결과들을 모아 데이터베이스화하였다. 그리고 비선형 파괴역학에 근거한 Ba${\check{z}}$ant와 Kim and Eo의 예측모델 및 이 연구에서 제안한 식에 대한 실험상수들을 결정한 후, 상호 비교 분석하였다. 그 결과, 콘크리트의 기건단위질량을 고려한 본 연구의 예측모델이 Ba${\check{z}}$ant와 Kim and Eo의 예측모델보다 경량골재 콘크리트에 대한 실험결과를 더 잘 예측하고 있음을 알 수 있었다.

• 한국농공학회논문집
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• 제64권5호
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• pp.27-39
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• 2022

The smart farm is recognized as a solution for future farmers having positive effects on the sustainability of the poultry industry. Intelligent microclimate control can be a key technology for broiler production which is extremely vulnerable to abnormal indoor air temperatures. Furthermore, better control of indoor microclimate can be achieved by accurate prediction of indoor air temperature. This study developed predictive models for internal air temperature in a mechanically-ventilated broiler house based on the data measured during three rearing periods, which were different in seasonal climate and ventilation operation. Three machine learning models and a mechanistic model based on thermal energy balance were used for the prediction. The results indicated that the all models gave good predictions for 1-minute future air temperature showing the coefficient of determination greater than 0.99 and the root-mean-square-error smaller than 0.306℃. However, for 1-hour future air temperature, only the mechanistic model showed good accuracy with the coefficient of determination of 0.934 and the root-mean-square-error of 0.841℃. Since the mechanistic model was based on the mathematical descriptions of the heat transfer processes that occurred in the broiler house, it showed better prediction performances compared to the black-box machine learning models. Therefore, it was proven to be useful for intelligent microclimate control which would be developed in future studies.

• 전산구조공학
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• 제29권3호
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• pp.17-23
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• 2016