• International Journal of Contents
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• v.4 no.2
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• pp.7-12
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• 2008

In this paper, we present a real-time physical simulation model of water surfaces with a novel method to represent the water mass flow in full three dimensions. In a physical simulation model, the state of the water surfaces is represented by a set of physical values, including height, velocity, and the gradient. The evolution of the velocity field in previous works is handled by a velocity solver based on the Navier-Stokes equations, which occurs as a result of the unevenness of the velocity propagation. In this paper, we integrate the principle of the mass conservation in a fluid of equilateral density to upgrade the height field from the unevenness, which in mathematical terms can be represented by the divergence operator. Thus the model generates waves induced by horizontal velocity, offering a simulation that puts forces added in all direction into account when calculating the values for height and velocity for the next frame. Other effects such as reflection off the boundaries, and interactions with floating objects are involved in our method. The implementation of our method demonstrates to run with fast speed scalable to real-time rates even for large simulation domains. Therefore, our model is appropriate for a real-time and large scale water surface simulation into which the animator wishes to visualize the global fluid flow as a main emphasis.

• Journal of the Korea Society for Simulation
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• v.13 no.2
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• pp.13-21
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• 2004

The thermal-hydraulic model ARTS which was based on the RETRAN-3D code adopted in the domestic full-scope power plant simulator which was provided in 1998 by KEPRI. Since ARTS is a generalized code to model the components with control volumes, the smaller time-step size should be used even if converged solution could not get in a single volume. Therefore, dedicated models which do not force to reduce the time-step size are sometimes more suitable in terms of a real-time calculation and robustness. In the case of PRT(Pressurizer Relief Tank) model, it is consist of subcooled water in bottom and non-condensable gas in top. The sparger merged under subcooled water enhances condensation. The complicated thermal-hydraulic phenomena such as condensation, phase separation with existence of non-condensable gas makes difficult to simulate. Therefore, the PRT volume can limit the time-step size if we model it with a general control volume. To prevent the time-step size reduction due to convergence failure for simulating this component, we developed a dedicated model for PRT. The dedicated model was expected to provide substantially more accurate predictions in the analysis of the system transients. The results were resonable in terms of accuracy, real-time simulation, robustness and education of operators, complying with the ANSI/ANS-3.5-1998 simulator software performance criteria and RETRAN-3D results.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.2
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• pp.53-60
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• 2023

Recently, the demand and traffic volume for various multimedia contents are rapidly increasing through real-time streaming platforms. In this paper, we predict real-time streaming traffic to improve the quality of service (QoS). Statistical models have been used to predict network traffic. However, since real-time streaming traffic changes dynamically, we used recurrent neural network-based deep learning models rather than a statistical model. Therefore, after the collection and preprocessing for real-time streaming data, we exploit vanilla RNN, LSTM, GRU, Bi-LSTM, and Bi-GRU models to predict real-time streaming traffic. In evaluation, the training time and accuracy of each model are measured and compared.

• International Journal of Highway Engineering
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• v.10 no.1
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• pp.155-164
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• 2008

It is critical to develop a core ITS technology such as real-time travel time estimation in order that the efficient use of the ITS implementation can be achieved as the ITS infrastructure and relevant facilities are broadly installed in recent years. The provision of travel time information in real-time allows travellers to make informed decisions and hence not only the traveller's travel utilities but also the road utilization can be maximized. In this paper, a hybrid model is proposed to combine VDS and AVI which have different characteristics in terms of space and time dimensions. The proposed model can incorporate the immediacy of VDS data and the reality of AVI data into one single framework simultaneously. In addition, the solution algorithm is made to have no significant computational burden so that the model can be deployable in real world. A set of real field data is used to analyze the reliability and applicability of the proposed model. The analysis results suggest that the proposed model is very efficient computationally and improves the accuracy of the information provided, which demonstrates the real-time applicability of the proposed model. In particular, the data fusion methodology developed in this paper is expected to be used more widely when a new type of traffic data becomes available.

• Environmental Sciences Bulletin of The Korean Environmental Sciences Society
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• v.1 no.1
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• pp.35-43
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• 1997

Handling the emergency problems such as Chemobyl accident require real time prediction of pollutants dispersion. One-point real time sounding at pollutant source and simple model including turbulent-radiation process are very important to predict dispersion at real time. The stability categories obtained by one-dimensional numerical model (including PBL dynamics and radiative process) are good agreement with observational data (Golder, 1972). Therefore, the meteorological parameters (thermal, moisture and momentum fluxes; sensible and latent heat; Monin-Obukhov length and bulk Richardson number; vertical diffusion coefficient and TKE; mixing height) calculated by this model will be useful to understand the structure of stable boundary layer and to handling the emergency problems such as dangerous gasses accident. Especially, this simple model has strong merit for practical dispersion models which require turbulence process but does not takes long time to real predictions. According to the results of this model, the urban area has stronger vertical dispersion and weaker horizontal dispersion than rural area during daytime in summer season. The maximum stability class of urban area and rural area are 'A' and 'B' at 14 LST, respectively. After 20 LST, both urban and rural area have weak vertical dispersion, but they have strong horizontal dispersion. Generally, the urban area have larger radius of horizontal dispersion than rural area. Considering the resolution and time consuming problems of three dimensional grid model, one-dimensional model with one-point real sounding have strong merit for practical dispersion model.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.1
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• pp.39-42
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• 2002

For the purpose of modeling EEG signal which has nonstationary and nonlinear dynamic characteristics, this paper propose a state feedback real time recurrent neural network model. The state feedback real time recurrent neural network is structured to have memory structure in the state of hidden layers so that it has arbitrary dynamics and ability to deal with time-varying input through its own temporal operation. For the model test, Mackey-Glass time series is used as a nonlinear dynamic system and the model is applied to the prediction of three types of EEG, alpha wave, beta wave and epileptic EEG. Experimental results show that the performance of the proposed model is better than that of other neural network models which are compared in this paper in some view points of the converging speed in learning stage and normalized mean square error for the test data set.

• Proceedings of the KIPE Conference
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• 2016.07a
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• pp.357-358
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• 2016

This paper presents a dynamic, electric-thermal model for a photovoltaic (PV) cell that combines electrical and thermal parameters. In this model, the irradiance and ambient temperature are used to calculate the PV cell temperature based on a five-layer thermal model. The cell temperature is then used in the electrical model to accurately adjust the PV cell output electrical characteristics and power. A custom experimental setup was built to test and verify the electrical and thermal characteristics of the PV cell and its surrounding layers. The electric-thermal model is validated using experimental data in realistic scenarios. This PV model can be scaled up and used to simulate PV systems in wide variety of applications, extreme environmental conditions, and fault conditions in real-time.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.466-470
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• 1988

A tunable reduced-order distillation model is proposed for real-time applications. To develop the model, a binary distillation column with MaCabe-Thiele assumptions was considered first and then the governing equations for the column were reduced to a simplified vector differential equations using the collocation method combined with cubic spline interpolation function. The final reduced-order model has four tuning parameters, relative volatilities and liquid holdups for rectifying and stripping sections, respectively. To assess the applicability of the developed model,the real-time adjustment of the model was tried by recursively updating the tuning parameters using the BKF algorithm. As a result, it was found that the reduced-model follows the simulated distillation process very closely as the parameters are improved.

• Journal of KIISE:Software and Applications
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• v.31 no.6
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• pp.773-783
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• 2004

This paper presents a method for real-time verification of Statecharts. Statecharts has been widely used for real-time reactive systems, and supports two time models: synchronous and asynchronous. However, existing real-time verification methods for them are incompatible with the asynchronous time model or increase state space by introducing new variables to the target models. We solved these problems by extending existing model checking algorithms. The extended algorithms can be used with both time models of Statecharts because they consider time increasing transitions only. In addition, they do not increase target state space since they count those transitions internally without additional variables. We extended an existing model checker, NuSMV, based on the proposed algorithms and conducted some experiments to show their advantage.

• International Journal of Advanced Culture Technology
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• v.11 no.1
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• pp.325-331
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• 2023

In this paper, we propose a mixed neural network structure of CNN and LSTM that can be used to detect or predict odor occurrence, which is most required in manufacturing industry or real life, using odor complex sensors. In addition, the proposed learning model uses a complex odor sensor to receive four types of data such as hydrogen sulfide, ammonia, benzene, and toluene in real time, and applies this data to an inference model to detect and predict odor conditions. The proposed model evaluated the prediction accuracy of the learning model through performance indicators according to accuracy, and the evaluation result showed an average performance of 94% or more.