• 대한기계학회논문집B
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• 제20권1호
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• pp.304-320
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• 1996

A multiple production .epsilon. equation model was developed in the low Reynolds number $\kappa$-$\varepsilon$ model with the aids of DNS data. We derived the model theoretically and avoided the use of empirical correlations as much as possible in order for the model to have generality in the prediction of complex turbulent flow. Unavoidable model constants were, however, optimized with the aids of DNS data. All the production and dissipation models in the $\varepsilon$ equation were modified with damping functions to satisfy the wall limiting behavior. A new $f_{\mu}$ function, turbulent diffusion and pressure diffusion model for the k and .epsilon. equations were also proposed to satisfy the wall limiting behavior. By, computational investigation on the plane channel flows, we found that the multiple production model for .epsilon. equation could improve the near wall turbulence behavior compared with the standard production model without the complicated empirical modification. Satisfication of the wall limiting conditions for each turbulence model term was found to be most important for the accurate prediction of near wall turbulence behaviors.

• 한국시뮬레이션학회논문지
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• 제7권2호
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• pp.63-78
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• 1998

The hierarchical cell structure consists of the macrocell and microcells to increase the system capacity and to achieve broad coverage. The hierarchical cell structure provides services for users in different mobility. In this paper, an analytical queueing model in mobile networks is proposed for the performance evaluation of the hierarchical cell structure. The model for networks with the multiple levels can simplify multi-dimensional ones into one-dimensional queueing model. The computational advantage will be growing as the layers are constructed in multiple levels. The computer simulation is provided for validating the proposed analytical model.

• 한국대기환경학회지
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• 제10권4호
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• pp.224-232
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• 1994

A new neural network model has been developed to predict short-term air pollution concentration. In addition, a multiple regression model widely used in statistical analysis was tested. These models were applied for prediction of daily maximum ozone concentration in Seoul during the summer season of 1991. The time periods between May and September 1989 and 1990 were utilized to train set of learning patterns in neural network model, and to estimate multiple regression model. To evaluate the results of the different models, several Performance indices were used. The results indicated that the multiple regression model tended to underpredict the daily maximum ozone concentration with small r$^{2}$(0.38). Also, large errors were found in this model; 21.1 ppb for RMSE, 0.324 for NMSE, and -0.164 for MRE. On the other hand, the results obtained from the neural network model were very promising. Thus, we can know that this model has a prominent efficiency in the adaptive control for the non-linear multi- variable systems such as photochemical oxidants. Also, when the recent new information was added in the neural network model, prediction accuracy was increased. From the new model, the values of RMSE, NMSE and r$^{2}$ were 13.2ppb, 0.089, 0.003 and 0.55 respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제4권3호
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• pp.358-379
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• 2010

Multiple object association is an important capability in visual surveillance system with multiple cameras. In this paper, we introduce locally initiating line-based object association with the parallel projection camera model, which can be applicable to the situation without the common (ground) plane. The parallel projection camera model supports the camera movement (i.e. panning, tilting and zooming) by using the simple table based compensation for non-ideal camera parameters. We propose the threshold distance based homographic line generation algorithm. This takes account of uncertain parameters such as transformation error, height uncertainty of objects and synchronization issue between cameras. Thus, the proposed algorithm associates multiple objects on demand in the surveillance system where the camera movement dynamically changes. We verify the proposed method with actual image frames. Finally, we discuss the strategy to improve the association performance by using the temporal and spatial redundancy.

• 한국정밀공학회지
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• 제32권8호
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• pp.719-726
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• 2015

This paper presents a velocity-synchronizing control for the multiple axes of an injection unit; based on MBS, a virtual design model has been developed for the multiple-axes servomechanism. Prior to the design of the controller, a linear plant model was derived via open-loop response simulations. To synchronize the motions of the multiple axes, a cross-type synchronizing controller was designed and combined with the PID control to accommodate any parameter mismatches among the multiple axes. From the tracking control simulations, a significant reduction of both velocity-tracking and position-tracking errors was achieved through the use of the proposed control scheme.

• 대한기계학회논문집
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• 제18권8호
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• pp.1997-2004
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• 1994

A model analysis method has been developed in the paper. The method estimates the modal parameters of multiple input-output systems, assesses their quality, and seperates structural modes form computation ones. The modal parameter extraction algorithm is the least squares method with a finite difference model relating input and output time data. The quality of the estimated system model can be assessed in narrow frequency bands by comparing the measured and model predicted responses in time domain with the aid of digital filters. Structural modes can be effectively separated from computational ones using the convergence factor which represents the pole convergence rate. The modal analysis method has been applied to simulated and experimental vibration data to evaluate its utility and limitations.

• 산업기술연구
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• 제28권B호
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• pp.101-109
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• 2008

In general, non-stationary or chaos time series forecasting is very difficult since there exists a drift and/or nonlinearities in them. To overcome this situation, we suggest a new prediction method based on multiple model TS fuzzy predictors combined with preprocessing of time series data, where, instead of time series data, the differences of them are applied to predictors as input. In preprocessing procedure, the candidates of optimal difference interval are determined by using con-elation analysis and corresponding difference data are generated. And then, for each of them, TS fuzzy predictor is constructed by using k-means clustering algorithm and least squares method. Finally, the best predictor which minimizes the performance index is selected and it works on hereafter for prediction. Computer simulation is performed to show the effectiveness and usefulness of our method.

• 대한전기학회논문지:전력기술부문A
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• 제48권9호
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• pp.1119-1125
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• 1999

In this paper, a decentralized multilevel-adaptive controller for a boiler-turbine system is designed by using multiple model adaptive method. It is applied to the drum type boiler-turbine system which is simplified from Boryung T/P #1,2 model. A linearlized model is decomposed into three subsystems by means of linear transformation. Then the DMC based on such subsystem is designed and a Multiple Model Adaptive Control(MMAC) scheme is applied for the purpose of the good tracking to variable load demands of the thermal power plant. The good performance of the designed controller is shown by simulations in various conditions that have the large step and ramp change of power demamd.

• 스마트미디어저널
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• 제9권3호
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• pp.59-70
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• 2020

This paper presents a fully automatic tool to recognize the liver region from CT images based on a deep learning model, namely Multiple Filter U-net, MFUnet. The advantages of both U-net and Multiple Filters were utilized to construct an autoencoder model, called MFUnet for segmenting the liver region from computed tomograph. The MFUnet architecture includes the autoencoding model which is used for regenerating the liver region, the backbone model for extracting features which is trained on ImageNet, and the predicting model used for liver segmentation. The LiTS dataset and Chaos dataset were used for the evaluation of our research. This result shows that the integration of Multiple Filter to U-net improves the performance of liver segmentation and it opens up many research directions in medical imaging processing field.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 D
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• pp.2382-2384
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• 2002

The accuracy in maneuvering target tracking using multiple models is caused by the suitability of each target motion model to be used. The interacting multiple model (IMM) algorithm and the adaptive IMM (AIMM) algorithm require the predefined sub-models and the predetermined acceleration intervals, respectively, in consideration of the properties of maneuvers in order to construct multiple models. In this paper, to solve these problems intelligently, a genetic algorithm (GA) based-IMM method using fuzzy logic is proposed. In the proposed method, the acceleration input is regarded as an additive noise and a sub-model is represented as a set of fuzzy rules to model the time-varying variances of the process noises of a new piecewise constant white acceleration model. The proposed method is compared with the AIMM algorithm in simulations.