• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1805-1810
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• 1991

This paper derives the upper and the lower bound of the mean cycle time and the mean service time of the class 6 and the class 4, within which the minimum utilization constrain of the class 4 is guaranteed. Also, derived are conditions under which the token bus network is stable or unstable. These bounds and stable conditions are represented in terms of the high priority token hold time, the token rotation time and the arrival rate and the total station number etc. This paper suggest a parameter tuning algorithm in a partially symmetric token bus network with two classes, which maximizes the token rotation time for a suitable high priority token hold time and at the same time meets the stability condition of the network, the real time constraint and the minimum utilization constraint of the class 4.

• Korean Journal of Food Science and Technology
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• v.25 no.2
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• pp.178-184
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• 1993

The procedure using equivalent time at reference temperature has been assessed for the estimation of kinetic parameters with experimental data. Kinetic studies of nonenzymatic browning reaction in model and food system were carried out with linearly increasing temperature method. These kinetic parameters, n, $k_{ref}$ and $E_a$ of the systems were evaluated from original data in one step by nonlinear least square regression. The one step procedure yielded efficiently accurate parameter estimation. Computer simulated data with the kinetic models were well consistent with experimental data (average correlation coefficient=0.96).

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.1
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• pp.62-74
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• 2002

The tracking of a reference temperature trajectory in a polymerization batch reactor is a common problem and has critical importance because the quality control of a batch reactor is usually achieved by implementing the trajectory precisely. In this study, only energy balances around a reactor are considered as a design model for control synthesis, and material balances describing concentration variations of involved components are treated as unknown disturbances, of which the effects appear as time-varying parameters in the design model. For the synthesis of a tracking controller, a method combining the input-output linearization of a time-variant system with the parameter estimation is proposed. The parameter estimation method provides parameter estimates such that the estimated outputs asymptotically follow the measured outputs in a specified way. Since other unknown external disturbances or uncertainties can be lumped into existing parameters or considered as another separate parameters, the method is useful in practices exposed to diverse uncertainties and disturbances, and the designed controller becomes robust. And the design procedure and setting of tuning parameters are simple and clear due to the resulted linear design equations. The performances and the effectiveness of the proposed method are demonstrated via simulation studies.

• Journal of the Korean Data and Information Science Society
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• v.8 no.2
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• pp.183-194
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• 1997

In accelerated life tests, the failure time of an item is observed under a high stress level and based on the time, the failure rates of items we estimated at the normal stress level. In this paper, when the mean of the prior distribution of a parameter is known in Weibull lifetime model with censored failure time data, we study various estimating methods to obtain the empirical Bayes estimator of a parameter from the empirical Bayes approach under the normal stress level by considering the fact that the Bayes estimator is the function of prior parameters and of the acceleration parameter representing the effect of acceleration. And we compare the performance of several empirical Bayes estimators of a parameter in terms of the Bayes risk.

• Proceedings of the KIEE Conference
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• 2007.04c
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• pp.29-33
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• 2007

In this paper, time-varying parameter of passive telemetry RF sensor system is estimated using RLS(Rescursive $\leq$* Square) algorithm. In order to overcome the problems such as power limits and complication that general RF sensor system including IC chip has, the principle of inductive coupling is applied to model sensor system The model parameter is rearranged for applying RLS algorithm based on mathematical model to the derived model using inductive coupling principle. Time variant parameter of rearranged model is estimated using forgetting factor, and in case measured data is contaminated by noise and modelling error, the performance of RLS algorithm characterized by the convergence of squared error sum is verified by simulation.

• Journal of Korean Society on Water Environment
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• v.24 no.3
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• pp.345-353
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• 2008

In order to design storage-based non-point source management facilities, the aspect of statistical features of the entire precipitation time series should be considered since non-point source pollutions are delivered by continuous rainfall runoffs. The 3-parameter mixed exponential probability density function instead of traditional single-parameter exponential probability density function is applied to represent the probabilistic features of long-term precipitation time series and model urban stormwater runoff. Finally, probability density functions of water quality control basin overflow are derived under two extreme intial conditions. The 31-year continuous precipitation time series recorded in Busan are analyzed to show that the 3-parameter mixed exponential probability density function gives better resolution.

• Smart Structures and Systems
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• v.23 no.4
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• pp.359-371
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• 2019

As part of a structural health monitoring system, the relative geometric relationship between a ship and bridge has been recognized as important for bridge authorities and ship owners to avoid ship-bridge collision. This study proposes a novel computer vision method for the real-time geometric parameter identification of moving ships based on a single shot multibox detector (SSD) by using transfer learning techniques and monocular vision. The identification framework consists of ship detection (coarse scale) and geometric parameter calculation (fine scale) modules. For the ship detection, the SSD, which is a deep learning algorithm, was employed and fine-tuned by ship image samples downloaded from the Internet to obtain the rectangle regions of interest in the coarse scale. Subsequently, for the geometric parameter calculation, an accurate ship contour is created using morphological operations within the saturation channel in hue, saturation, and value color space. Furthermore, a local coordinate system was constructed using projective geometry transformation to calculate the geometric parameters of ships, such as width, length, height, localization, and velocity. The application of the proposed method to in situ video images, obtained from cameras set on the girder of the Wuhan Yangtze River Bridge above the shipping channel, confirmed the efficiency, accuracy, and effectiveness of the proposed method.

• Journal of the Korean Society for Nondestructive Testing
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• v.31 no.2
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• pp.165-173
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• 2011

The acoustic emission(AE) technique is a well established method to carry out structural health monitoring(SHM) of large structures. However, the real-time monitoring of the crack growth in the roller coaster support structures is not easy since the vehicle operation produces very large noise as well as crack growth. In this investigation, we present the waveform parameter filtering method to classify acoustic sources in real-time. This method filtrates only the AE hits by the target acoustic source as passing hits in a specific parameter band. According to various acoustic sources, the waveform parameters were measured and analyzed to verify the present filtering method. Also, the AE system employing the waveform parameter filter was manufactured and applied to the roller coaster support structure in an actual amusement park.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.5
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• pp.151-157
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• 2011

This study analyzed the posture balance of time variation for exercising body a period of time. Posture balance measured output values for the posture balance system of body moving in the multi-parameter. Posture moving variation had three methods such as open and closed eye, head moving and upper body moving. There were checked a parameter that measured vision, vestibular, somatosensory, CNS. This system was evaluated a data through the stability. This system has catched a signal for physical condition of body data such as a data acquisition system, data signal processing and feedback system. The output signal was generated Fourier analysis that using frequency of 0.1Hz, 0.1-0.5Hz, 0.5-1Hz and 1Hz over. The posture balance system will be used to support assessment for body moving the posture balance of time variation. It was expected to monitor a physical parameter for health verification system.

• Journal of the Korea Society for Simulation
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• v.20 no.4
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• pp.67-79
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• 2011

The statistical process control (SPC) assumes that observations follow the particular statistical distribution and they are independent to each other. However, the time-series data do not always follow the particular distribution, and most of cases are autocorrelated, therefore, it has limit to adopt the general SPC in tim series process. In this study, we propose a MPBC (Model Parameter Based Control-chart) method for fault detection in time-series processes. The MPBC builds up the process as a time-series model, and it can determine the faults by detecting changes parameters in the model. The process we analyze in the study assumes that the data follow the ARMA (p,q) model. The MPBC estimates model parameters using RLS (Recursive Least Square), and $K^2$-control chart is used for detecting out-of control process. The results of simulations support the idea that our proposed method performs better in time-series process.