• 한국해양공학회지
• /
• 제13권4호통권35호
• /
• pp.118-123
• /
• 1999

The mathematical model used in the simulation of ship's maneuvering contains the hydrodynamic coefficients, which are usually evaluated based on PMM model tests in the towing tank and used to predict ship's maneuvering performance when applied to the proto-type ship. The proper mathematical model has to be developed to predict ship's maneuvering motions with hydrodynamic coefficients very well. The mathematical model for PMM model tests is analyzed with identification program and the hydrodynamic coefficients and maneuvering motions by system identification we compared with those obtained directly from PMM model tests and sea trial. The mathematical model for PMM model tests was established and the magnitudes of ship's maneuvering coefficients were determined. When the identified values of coefficients were used to simulate the maneuvers, a very good agreement was obtained between the numerically simulated motion responses and those obtained from PMM model tests.

• 한국농공학회지
• /
• 제35권2호
• /
• pp.57-64
• /
• 1993

A modified kinematic wave model of the overland flow in vegetated filter strips was developed. The model can predict both flow depth and hydraulic radius of the flow. Existing models can predict only mean flow depth. By using the hydraulic radius, erosion, deposition and flow's transport capacity can be more rationally computed. Spacing hydraulic radius was used to compute flow's hydraulic radius. Numerical solution of the model was accomplished by using both a second-order nonlinear scheme and a linear solution scheme. The nonlinear portion of the model ensures convergence and the linear portion of the model provides rapid computations. This second-order nonlinear scheme minimizes numerical computation errors that may be caused by linearization of a nonlinear model. This model can also be applied to golf courses, parks, no-till fields to route runoff and production and attenuation of many nonpoint source pollutants.

• 한국기계기술학회지
• /
• 제13권3호
• /
• pp.17-23
• /
• 2011

To estimate fuel consumption of a vehicle, a car can be tested on chassis dynamometer. In this case, test causes a lot of time and money. To predict the fuel efficiency of vehicles in the design stage or early stage of development, the development of computer simulation model is necessary. Using simulation to predict the fuel consumption, the driving model which consists of time-velocity profile and time-grade profile is necessary In this study, vehicle model is developed in MatLab/simulink to estimate real driving fuel consumption rate with time-velocity profile, time-shift gear profile and time-grade profile. Vehicle model consists of driver model, engine model, power train model, and so on. On-road vehicle tests to verify the vehicle model are carried out for analyzing the result of simulation and comparing with those of the experiments.

• Journal of applied mathematics & informatics
• /
• 제39권1_2호
• /
• pp.239-249
• /
• 2021

This study attempts to predict the price of gold futures, a real financial product, using ARIMA and LSTM. The wavelet analysis was applied to the data to predict the price of gold futures through LSTM and ARIMA. As results, it is confirmed that the prediction performance of the existing model of predict was improved. the case of predict of price of gold futures, we confirmed that the use of a deep learning model that is not affected by the non-stationary series data is suitable and the possibility of improving the accuracy of prediction through wavelet analysis.

• Agribusiness and Information Management
• /
• 제6권1호
• /
• pp.1-11
• /
• 2014

The purpose of this study is to develop the growth prediction model that can predict growth and development information influencing on the production of citrus fruits. The growth model was developed to predict the floral leaf ratio, number of fruit sets, fruit width, and overweight fruits depending on the main period of growth and development by considering the weather factors because the fruit production is influenced by weather depending on the growth and development period. To predict the outdoor-grown citrus fruit production, the investigation result for the standard farms is used as the basic data; in this study, we also understood that the influence of weather factors on the citrus fruit production based on the data from 2004 to 2013 of the outdoor-grown citrus fruit observation report in which the standard farms were targeted by the Agricultural Research Service and suggested the growth and development information prediction model with the weather information as an independent variable to build the observation model. The growth and development model for outdoor-grown citrus fruits was assumed by using the Ordinary Least Square method (OLS), and the developed growth prediction model can make a prediction in advance with the weather factors prior to the observation investigation for the citrus fruit production. To predict the growth and development information of the production of citrus fruits having a great ripple effect as a representative crop in Jeju agriculture, the prediction result regarding the production applying the weather factors depending on growth and development period could be applied usefully.

• Journal of Chest Surgery
• /
• 제57권3호
• /
• pp.302-311
• /
• 2024

Background: Unexpected conversion to thoracotomy during planned video-assisted thoracoscopic surgery (VATS) can lead to poor outcomes and comparatively high morbidity. This study was conducted to assess preoperative risk factors associated with unexpected thoracotomy conversion and to develop a risk scoring model for preoperative use, aimed at identifying patients with an elevated risk of conversion. Methods: A retrospective analysis was conducted of 1,506 patients who underwent surgical resection for non-small cell lung cancer. To evaluate the risk factors, univariate analysis and logistic regression were performed. A risk scoring model was established to predict unexpected thoracotomy conversion during VATS of the lung, based on preoperative factors. To validate the model, an additional cohort of 878 patients was analyzed. Results: Among the potentially significant clinical variables, male sex, previous ipsilateral lung surgery, preoperative detection of calcified lymph nodes, and clinical T stage were identified as independent risk factors for unplanned conversion to thoracotomy. A 6-point risk scoring model was developed to predict conversion based on the assessed risk, with patients categorized into 4 groups. The results indicated an area under the receiver operating characteristic curve of 0.747, with a sensitivity of 80.5%, specificity of 56.4%, positive predictive value of 1.8%, and negative predictive value of 91.0%. When applied to the validation cohort, the model exhibited good predictive accuracy. Conclusion: We successfully developed and validated a risk scoring model for preoperative use that can predict the likelihood of unplanned conversion to thoracotomy during VATS of the lung.

• Journal of information and communication convergence engineering
• /
• 제5권2호
• /
• pp.171-176
• /
• 2007

To give a guarantee a consistently high level of quality and reliability of Telematics traffic service, traffic flow forecasting is very important issue. In this paper, we proposed an adaptable integrated prediction model to predict the traffic flow in the future. Our model combines two methods, short-term prediction model and long-term prediction model with different combining coefficients to reflect current traffic condition. Short-term model uses the Kalman filtering technique to predict the future traffic conditions. And long-term model processes accumulated speed patterns which means the analysis results for all past speeds of each road by classifying the same day and the same time interval. Combining two models makes it possible to predict future traffic flow with higher accuracy over a longer time range. Many experiments showed our algorithm gives a better precise prediction than only an accumulated speed pattern that is used commonly. The result can be applied to the car navigation to support a dynamic shortest path. In addition, it can give users the travel information to avoid the traffic congestion areas.

• 한국항공우주학회지
• /
• 제45권8호
• /
• pp.697-703
• /
• 2017

펄스형 플라즈마 추력기의 성능해석을 위해 Teflon의 승화와 이온화 모델링을 수행하였다. PPT 작동 시 발생하는 방전 전휴의 변화를 예측하기 위해 일차원 lumped circuit model을 적용하였으며 Mickeal Keidar가 제시한 테플론-플라즈마 온도에 따른 테플론 승화 모델을 적용하였다. Saha 방정식을 이용하여 테플론 구성 원소인 탄소와 불소분자의 이온화를 예측하였다. 프로그램 검증을 위해 선행실험 결과와 비교하여 유사함을 확인하였으며 PPT 작동 전압에 따른 전류 변화정도를 고찰하였다.

• 한국안전학회지
• /
• 제30권5호
• /
• pp.67-73
• /
• 2015

In this paper, the simple truss model was modified to predict the punching shear strength of long-span prestressed concrete (PSC) deck slabs under wheel load including the effects of transverse prestressing and long span length between girders. The strength of the compressive zone arounding punching cone was evaluated by the stiffness of inclined strut which was modified by considering aging effective modulus. The stiffness of springs which control lateral displacement of the roller supports consists of the steel reinforcement and prestressing which passed through the punching cone. Initial angle of struts was determined by the experimental observation to compensate for uncertainties in the complexities of the punching shear. The validity of computed punching shear strength by modified simple truss model was shown by comparing with experimental results and the experimental results were also compared with existing punching shear equations to determine level of predictability. The modified simple truss model appeared to better predict the punching shear strength of PSC deck slabs than other available equations. The punching shear strength, which was determined by snap-through critical load of modified simple truss model, can be used effectively to examine punching shear strength of long span PSC deck slabs.

• 반도체디스플레이기술학회지
• /
• 제22권1호
• /
• pp.113-117
• /
• 2023

As sputter equipment becomes more complex, it becomes increasingly difficult to understand the parameters that affect the thickness uniformity of thin metal film deposited by sputter. To address this issue, we verified a deep learning model that can predict complex relationships. Specifically, we trained the model to predict the height of 36 magnets based on the thickness of the material, using Support Vector Machine (SVM), Multilayer Perceptron (MLP), 1D-Convolutional Neural Network (1D-CNN), and 2D-Convolutional Neural Network (2D-CNN) algorithms. After evaluating each model, we found that the MLP model exhibited the best performance, especially when the dataset was constructed regardless of the thin film material. In conclusion, our study suggests that it is possible to predict the sputter equipment source using film thickness data through a deep learning model, which makes it easier to understand the relationship between film thickness and sputter equipment.