• 대기
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• 제25권1호
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• pp.1-18
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• 2015

The most objective way to overcome the limitation of numerical weather prediction model is to represent the uncertainty of prediction by introducing probabilistic forecast. The uncertainty of the numerical weather prediction system developed due to the parameterization of unresolved scale motions and the energy losses from the sub-scale physical processes. In this study, we focused on the growth of model errors. We performed ensemble forecast to represent model uncertainty. By employing the multi-physics scheme (PHYS) and the stochastic kinetic energy backscatter scheme (SKEBS) in simulating typhoon Rusa (2002), we assessed the performance level of the two schemes. The both schemes produced better results than the control run did in the ensemble mean forecast of the track. The results using PHYS improved by 28% and those based on SKEBS did by 7%. Both of the ensemble mean errors of the both schemes increased rapidly at the forecast time 84 hrs. The both ensemble spreads increased gradually during integration. The results based on SKEBS represented model errors very well during the forecast time of 96 hrs. After the period, it produced an under-dispersive pattern. The simulation based on PHYS overestimated the ensemble mean error during integration and represented the real situation well at the forecast time of 120 hrs. The displacement speed of the typhoon based on PHYS was closest to the best track, especially after landfall. In the sensitivity tests of the model uncertainty of SKEBS, ensemble mean forecast was sensitive to the physics parameterization. By adjusting the forcing parameter of SKEBS, the default experiment improved in the ensemble spread, ensemble mean errors, and moving speed.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 춘계학술대회
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• pp.204-206
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• 2021

실시간 시추매개변수 예측은 시추효율의 극대화 관점에서 상당히 중요한 연구이다. 시추 극대화 방법 중 시추속도를 향상시키는 방법이 일반적인데 이는 굴진율, 시추스트링 회전속도, 비트 하중, 시추이수 유량과 연관관계를 지니고 있다. 본 연구는 실시간 시추매개변수 중 하나인 굴진율을 순환신경망기반 딥러닝 모델을 이용하여 예측하는 방법을 제안하였으며 기존의 물리적 기반의 굴진율 모델과 딥러닝 모델을 이용한 예측 모델을 비교해 보고자 한다.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2003년도 춘계학술대회
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• pp.400-409
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• 2003

Collaborative filtering (CF) recommendation is a knowledge sharing technology for distribution of opinions and facilitating contacts in network society between people with similar interests. The main concerns of the CF algorithm are about prediction accuracy, speed of response time, problem of data sparsity, and scalability. In general, the efforts of improving prediction algorithms and lessening response time are decoupled. We propose a three-step CF recommendation model which is composed of profiling, inferring, and predicting steps while considering prediction accuracy and computing speed simultaneously. This model combines a CF algorithm with two machine learning processes, SOM (Self-Organizing Map) and CBR (Case Based Reasoning) by changing an unsupervised clustering problem into a supervised user preference reasoning problem, which is a novel approach for the CF recommendation field. This paper demonstrates the utility of the CF recommendation based on SOM cluster-indexing CBR with validation against control algorithms through an open dataset of user preference.

• International journal of advanced smart convergence
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• 제13권3호
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• pp.66-79
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• 2024

The model developed in this study holds significant importance in predicting carbon emissions in maritime transport. By utilizing ship data and EEDI (Energy Efficiency Design Index) guidelines, the model presents a highly accurate prediction tool, providing a solid foundation for maximizing operational efficiency and effectively managing carbon emissions in ship operations. The model's accuracy was demonstrated by an R² score of 0.95 and a Mean Absolute Percentage Error (MAPE) of 1.4%. Through SHAP (SHapley Additive exPlanations) and Partial Dependence Plots (PDP), it was identified that Speed Over Ground and relative wind speed are the most significant variables, both showing a positive correlation with increased CO2 emissions. Additionally, environmental factors such as exceeding an average draft of 22(m), a Leeway over 5°, and a current angle exceeding 200° were found to increase emissions significantly. Specific ranges of wind and swell wave angles also notably affected emissions. Conversely, lower pitch, roll, and rudder angle were associated with reduced emissions, indicating that stable ship operation enhances efficiency.

• Journal of Power Electronics
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• 제19권2호
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• pp.497-508
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• 2019

The predictive speed control (PSC) strategy can realize the simultaneous control of speed and current by using one cost function. As a model-based control method, the performance of the PSC is vulnerable to model mismatches such as load torque disturbances and parameter uncertainties. To solve this problem, this paper presents a robust predictive speed control (RPSC) strategy for surface-mounted permanent magnet synchronous motor (SPMSM) drives. The proposed RPSC uses extended state observers (ESOs) to estimate the lumped disturbances caused by load torque changes and parameter mismatches. The observer-based prediction model is then compensated by using the estimated disturbances. The introduction of ESOs can achieve robustness against predictive model uncertainties. In addition, a modified cost function is designed to further suppress load torque disturbances. The performance of the proposed RPSC scheme has been corroborated by experimental results under the condition of load torque changes and parameter mismatches.

• 대한기계학회논문집B
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• 제32권7호
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• pp.549-557
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• 2008

Despite of the laminar-turbulent transition region co-exist with fully turbulence region around the leading edge of an airfoil, still lots of researchers apply to fully turbulence models to predict aerodynamic characteristics. It is well known that fully turbulent model such as standard k-model couldn't predict the complex stall and the separation behavior on an airfoil accurately, it usually leads to over prediction of the aerodynamic characteristics such as lift and drag forces. So, we apply correlation based transition model to predict aerodynamic performance of the NREL (National Renewable Energy Laboratory) Phase IV wind turbine. And also, compare the computed results from transition model with experimental measurement and fully turbulence results. Results are presented for a range of wind speed, for a NREL Phase IV wind turbine rotor. Low speed shaft torque, power, root bending moment, aerodynamic coefficients of 2D airfoil and several flow field figures results included in this study. As a result, the low speed shaft torque predicted by transitional turbulence model is very good agree with the experimental measurement in whole operating conditions but fully turbulent model(${\kappa}-\;{\varepsilon}$) over predict the shaft torque after 7m/s. Root bending moment is also good agreement between the prediction and experiments for most of the operating conditions, especially with the transition model.

• 대한토목학회논문집
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• 제32권3D호
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• pp.223-229
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• 2012

도로 포장면과 타이어 사이의 마찰 저항을 미끄럼 저항이라고 한다. 미끄럼 저항은 도로 안전에 매우 중요한 요소이며, 다양한 요인이 복합적으로 작용한다. 미끄럼 저항 측정법의 한계를 극복하기 위해 컴퓨터 모델링을 이용한 해석 수행 결과, 속도가 증가하거나 수막두께가 두꺼울수록 미끄럼 저항은 감소하였다. 해석 결과를 이용해 수막두께와 속도에 따라 수막 위를 주행하는 타이어에 발생하는 양력을 계산할 수 있으며, IFI(International Friction Index) 미끄럼 저항 예측모델과 실측 미끄럼 저항과의 차이를 줄이기 위해 양력을 반영한 수정 IFI 미끄럼 저항 예측모델을 개발하였다. 예측모델과 실측 데이터의 상관관계 분석 결과, 기존 IFI 예측모델의 $R^2$는 0.49로, 수정 IFI 예측모델의 $R^2$는 0.64로 나타나 수정 IFI 예측모델이 기존모델에 비해 예측 효과가 우수하였다. 포장면의 상태에 따른 수막두께를 수정 예측모델에 반영할 경우 더욱 정확한 예측모델을 얻을 수 있을 것이다.

• 한국도로학회논문집
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• 제15권4호
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• pp.21-29
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• 2013

PURPOSES : The objective of this study is to provide for the overall SPL (Sound Pressure Level) prediction model by using the NCPX (Noble Close Proximity) measurement method in terms of regression equations. METHODS: Many methods can be used to measure the traffic noise. However, NCPX measurement can powerfully measure the friction noise originated somewhere between tire and pavement by attaching the microphone at the proximity location of tire. The overall SPL(Sound Pressure Level) calculated by NCPX method depends on the vehicle speed, and the basic equation form of the prediction model for overall SPL was used, according to the previous studies (Bloemhof, 1986; Cho and Mun, 2008a; Cho and Mun, 2008b; Cho and Mun, 2008c). RESULTS : After developing the prediction model, the prediction model was verified by the correlation analysis and RMSE (Root Mean Squared Error). Furthermore, the correlation was resulted in good agreement. CONCLUSIONS: If the polynomial overall SPL prediction model can be used, the special cautions are required in terms of considering the interpolation points between vehicle speeds as well as overall SPLs.

• 한국항공운항학회지
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• 제32권3호
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• pp.15-28
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• 2024

Wind forecast is one of the key meteorological factors required for safe aircraft takeoff and landing. In this study, we developed an artificial intelligence-based wind compensation method by learning the Korea Air Force Weather Research and Forecast (KAF-WRF) forecast data and the Airfield Meteorological Observation System (AMOS) data at five airports using Support Vector Machine (SVM). The SVM wind prediction models were composed of three types according to the learning period (30 days, 40 days, and 60 days) using seven KAF-WRF variables as training data, and the wind prediction performance at the five airports was evaluated using Root Mean Squared Errors (RMSE). According to the results, the SVM wind prediction model trained using U (east-west) and V (north-south) components performed approximately 18% better than the model trained using wind speed and wind direction. The wind correction of KAF-WRF with AMOS observations via SVM outperformed the conventional KAF-WRF wind predictions in eight out of ten cases, capturing abrupt changes in wind direction and speed with a 25% reduction in RMSE.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2007년도 춘계 학술대회논문집
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• pp.134-137
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• 2007

Aero-Heating phenomenon is one of the severe problems occurring in high speed missile flight. in the high speed flight, not only stagnation point but also aft body parts encounter high temperature related structural problems. But the phenomenon is not easy to predict accurately because unsteady calculation according to a flight trajectory is needed, and takes much time. In this Paper, a fast and precise scheme is introduced, which calculates heat flow and temperature by simple pressure field prediction on a missile.