• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.9
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• pp.88-93
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• 2003

This Paper described the simulation program development for helicopter. In the design of flight control system to accomplish some special missions like UAV, it is important to minimize the execution time obtaining a linear model from nonlinear model that is used for design of controller. The first step for this kind of purpose is to complete a nonlinear model that contains full dynamic characteristics. The second step is to get the trim values that are obtained from the nonlinear model by solving an algebraic equation. And then stability and control derivatives are derived through hovering to forward flight by numerical perturbation that will be used for linear model for a specified flight condition. The software program(HeliSim) is developed by using MATLAB GUI and will provide easy modeling procedure. The suggested method in this paper is much more simpler than any other method like a fully scale helicopter model. The advantage of our suggested method will reduce the computational time due to simple formula to extract a linear model from nonlinear model that will be beneficially used for flight control system of unmanned helicopter by some reduction of computational load.

• Journal of the Korea Computer Industry Society
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• v.5 no.5
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• pp.721-728
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• 2004

OCSP (Online Certificate Status Protocol) server which checks the certificate status provides the real time status verification in the PKI (Public Key Infrastructure) system which is the essential system of certificate. However, OCSP server need the message authentication with the server and client, so it has some shortcomings that has slow response time for the demands of many clients concurrently and has complexity of the mathematical process in the public encryption system. In this research, simulation model of the certificate status vertification server is constructed of the DEVS (Discrete EVent system Specification) formalism. This sever model is constructed to practice the authentication with hash function when certificate is checked. Simulation results shows the results of increase of the certificate status verification speed and decrease of the response time to the client.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.165-170
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• 1997

In this paper, we consider the aortic sinus baroreceptor, which is the most representative baroreceptors sensing the variance of pressure in the cardiovascular system(CVS), and propose heart activity control model to observe the effect of delay time in heart period and stroke volume under the regulation of baroreflex in arotic sinus. The proposed heart activity baroreflex regulation model contains CVS electric circuit sub-model, baroreflex regulation sub-model and time delay sub-model. In these models, applied electric circuit sub-model is researched by B.C.Choi and the baroreflex regulation sub-model transforms the input, the arotic pressure of CVS electric circuit sub-model, to outputs, heart period and stroke volume by mathematical nonlinear feedback. We constituted the time delay sub-model to observe sensitivity of heart activity baroreflex regulation model by using the variable value to represent the control signal transmission time from the output of baroreflex regulation model to efferent nerve through central nervous system. The simulation object of this model is to observe variability of the CVS by variable value in time delay sub-model. As simulation results, we observe three patterns of CVS variability by the time delay. First, if the time delay is over 2.5 sec, arotic pressure, stroke volume and heart rate is observed nonperiodically and irregularly. Second, if the time delay is from between 0.1 sec and 0.25 sec, the regular oscillation is observed. Finally, if time delay is under 0.1 sec, then heart rate and arotic pressure-heart rate trajectory is maintained in stable state.

• The Transactions of the Korean Institute of Power Electronics
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• v.3 no.3
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• pp.165-172
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• 1998

The electromechanical energy conversion conditioning and processing implementation in USM direct motion control system is generally divided into two power stages: the two-phase high-frequency ac power inversion stage for driving piezoelectric ceramic PZT transducer array off the USM stator and the mechanical thrust power conversion stage based on the frictional force between the piezo electric stator array and the rotary slider of the USM. However, the dynamic and steady-state mathematical modeling of the USM is extremely default from a theoretical point of view because it contains many complicated an nonlinear characteristics dependant on operation temperature. In +2$0^{\circ}C$~3$0^{\circ}C$, the operating characteristics of the USM has represented normal condition. But the other temperature, it has abnormal condition so that driving frequency, current and motor speed will be down. The recent USM has controller without temperature compensation. This study represents the fuzzy controller for speed compensation according to operating temperature by driving frequency.

• Korean journal of applied entomology
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• v.26 no.1 s.70
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• pp.31-36
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• 1987

A piecewise linear regression model able to describe disease progress curves with simplicity and flexibility was developed in this study. The model divides whole epidemic into several pieces of simple linear regression based on changes in pattern of disease progress in the epidemic and then incorporates the pieces of linear regression into a single mathematical function using indicator variables. When twelve epidemic data obtained from the field experiments were fitted to the piecewise linear regression model, logistic model and Gompertz model to compare statistical fit, goodness of fit was greatly improved with piecewise linear regression compared to other two models. Simplicity, flexibility, accuracy and ease in parameter estimation of the piece-wise linear regression model were described with examples of real epidemic data. The result in this study suggests that piecewise linear regression model is an useful technique for modeling plant disease epidemic.

• Korean Chemical Engineering Research
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• v.46 no.3
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• pp.521-528
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• 2008

The micro-porous asymmetric PVDF hollow fiber membranes for gas-liquid contactor were prepared by the dry-jet wet phase inversion process and the characteristics of hollow fiber membranes were evaluated by the gas permeation method and scanning electron microscope. The chemical absorbent for removal of $SO_2$ gas was sodium hydroxide at bench scale hollow fiber membrane contactor. The experiments were performed in a counter-current mode of operation with gas in the shell side and liquid in the fiber lumen of the module to examine the effect of various operating variables such as concentration of absorbent, gas flow rate, L/G ratio and concentration of inlet $SO_2$ gas on the $SO_2$ removal efficiency using PVDF hollow fiber membrane contactor. Membrane mass transfer coefficient($k_m$) was calculated by mathematical modeling. The volumetric overall mass transfer coefficient increased with increasing the concentration of absorbent and L/G ratio. The increase of the absorbent concentration and L/G ratio not only provides more sufficient alkalinity but also decreases liquid phase resistance. The volumetric overall mass transfer coefficient increased with increasing gas flow rate due to decreasing the gas phase resistance.

• Journal of Communications and Networks
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• v.7 no.4
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• pp.401-407
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• 2005

Simulations are currently an essential tool to develop and test wireless sensor networks (WSNs) protocols and to analyze future WSNs applications performance. Researchers often simulate their proposals rather than deploying high-cost test-beds or develop complex mathematical analysis. However, simulation results rely on physical layer assumptions, which are not usually accurate enough to capture the real behavior of a WSN. Such an issue can lead to mistaken or questionable results. Besides, most of the envisioned applications for WSNs consider the nodes to be at the ground level. However, there is a lack of radio propagation characterization and validation by measurements with nodes at ground level for actual sensor hardware. In this paper, we propose to use a low-computational cost, two slope, log-normal path­loss near ground outdoor channel model at 868 MHz in WSN simulations. The model is validated by extensive real hardware measurements obtained in different scenarios. In addition, accurate model parameters are provided. This model is compared with the well-known one slope path-loss model. We demonstrate that the two slope log-normal model provides more accurate WSN simulations at almost the same computational cost as the single slope one. It is also shown that the radio propagation characterization heavily depends on the adjusted model parameters for a target deployment scenario: The model parameters have a considerable impact on the average number of neighbors and on the network connectivity.

• Transactions of the Korean hydrogen and new energy society
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• v.31 no.5
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• pp.436-443
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• 2020

The performance prediction model of a solid oxide fuel cell stack has been developed using deep neural network technique, one of the machine learning methods. The machine learning has been received much interest in various fields, including energy system mo- deling. Using machine learning technique can save time and cost requried in developing an energy system model being compared to the conventional method, that is a combination of a mathematical modeling and an experimental validation. Results reveal that the mean average percent error, root mean square error, and coefficient of determination (R2) range 1.7515, 0.1342, 0.8597, repectively, in maximum. To improve the predictability of the model, the pre-processing is effective and interpolative machine learning and application is more accurate than the extrapolative cases.

• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.153-161
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• 2017

Branching processes which is used for epidemic dispersion as stochastic process model have advantages to estimate parameters by real data. We have to estimate both mean and dispersion parameter in order to use the negative binomial distribution as an offspring distribution on branching processes. In existing studies on biology and epidemiology, it is estimated using maximum-likelihood methods. However, for most of epidemic data, it is hard to get the best precision of maximum-likelihood estimator. We suggest a Bayesian inference that have good properties of statistics for small-sample. After estimating dispersion parameter we modelled the posterior distribution for 2015 Korea MERS cases. As the result, we found that the estimated dispersion parameter is relatively stable no matter how we assume prior distribution. We also computed extinction probabilities on branching processes using estimated dispersion parameters.

• Journal of the Korea Convergence Society
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• v.9 no.6
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• pp.39-44
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• 2018

The application of deep neural networks to finance has received a great deal of attention from researchers because no assumption about a suitable mathematical model has to be made prior to forecasting and they are capable of extracting useful information from large sets of data, which is required to describe nonlinear input-output relations of financial time series. The paper presents a new deep neural network model where single layered autoencoder and 4 layered neural network are serially coupled for stock price forecasting. The autoencoder extracts deep features, which are fed into multi-layer neural networks to predict the next day's stock closing prices. The proposed deep neural network is progressively learned layer by layer ahead of the final learning of the total network. The proposed model to predict daily close prices of KOrea composite Stock Price Index (KOSPI) is built, and its performance is demonstrated.