• Korean Journal of Construction Engineering and Management
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• v.22 no.3
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• pp.21-30
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• 2021

This study proposes a 5-month rebar price prediction method using the recursive prediction method of deep learning. This approach predicts a long-term point in time by repeating the process of predicting all the characteristics of the input data and adding them to the original data and predicting the next point in time. The predicted average accuracy of the rebar prices for one to five months is approximately 97.24% in the manner presented in this study. Through the proposed method, it is expected that more accurate cost planning will be possible than the existing method by supplementing the systematicity of the price estimation method through human experience and judgment. In addition, it is expected that the method presented in this study can be utilized in studies that predict long-term prices using time series data including building materials other than rebar.

• Communications for Statistical Applications and Methods
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• v.27 no.1
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• pp.15-35
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• 2020

In research on behavioral studies, significant attention has been paid to the stage-sequential process for multiple latent class variables. We now explore the stage-sequential process of multiple latent class variables using the multivariate latent class profile analysis (MLCPA). A latent profile variable, representing the stage-sequential process in MLCPA, is formed by a set of repeatedly measured categorical response variables. This paper proposes the extended MLCPA in order to explain an association between the latent profile variable and the latent group variable as a form of a two-dimensional contingency table. We applied the extended MLCPA to the National Longitudinal Survey on Youth 1997 (NLSY97) data to investigate the association between of developmental progression of depression and substance use behaviors among adolescents who experienced Authoritarian parental styles in their youth.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.6 s.183
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• pp.180-186
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• 2006

To investigate the bone remodeling phenomenon around implant device, 3-D mathematical simulation model was developed. Strain energy density from the finite element method was chosen for the indicator for remodeling process. Recursive calculations continued until converged results between FEM and mathematical model. For a osteo-integration example, bone-remodeling process in a implanted tibia of beagle was adapted. Calculated results indicated that the bone densities around screw pitch were increased which indicates firm fixations between the bone and implant. Screw design parameters have an influence on initial stability of the implant rather than remodeling process.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.344-349
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• 1990

Under the assumption that process input/output data are sufficiently rich to allow reasonable plant identification, a long-range predictive control method for SISO bilinear plant is derived. In order to ensure offset-free behaviour of the control method, a new bilinear CARIMA model with variable dead-time is introduced. Furthermore, to extend the maximum output prediction horizon, the future predicted outputs in the bilinear term are assumed to be equal to the known future set-points. With a classical recursive adaptation algorithm, the proposed control scheme is capable of stable control of bilinear plants with variable parameters, with variable dead-time, and with a model order which changes instantaneously. Several simulation results demonstrate the characteristics of the proposed bilinear model predictive control method.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.191-196
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• 1993

Since the welding process is complex and highly nonlinear, it is very difficult to accurately model the process for real-time control. In this paper, a discrete-time transfer function matrix model for gas metal arc welding process is proposed. Although this linearized model is valid only around the operating point of interest, the adaptation mechanism employed in the control system render this model useful over a wide operating range. A multivariable one-step-ahead adaptive control strategy combined with a recursive least-squares method for on-line parameter estimation is implemented in order to achieve the desired weld bead geometries. Command following and disturbance rejection properties of the adaptive control system for both SISO and MIMO cases are investigated by simulation and experiment.

• Journal of the Korean Data and Information Science Society
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• v.14 no.2
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• pp.187-199
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• 2003

We deal with the problem of testing a sequence of independent normal random variables with constant, known or unknown, variance for no change in mean versus alternatives with a single change-point. Various tests based on the likelihood ratio and recursive residuals, score statistics and cusums are studied. Proposed tests are modified version of Buckley's cusum statistics. A comparison study of various change-point test statistics is done by Monte Carlo simulation with S-plus software.

• Journal of the Korean Statistical Society
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• v.30 no.1
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• pp.31-39
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• 2001

For estimating parameters of possibly nonlinear and/or non-stationary seasonal autoregressive(AR) processes, we introduce a new instrumental variable method which use the direction vector of the regressors in the same period as an instrument. On the basis of the new estimator, we propose new seasonal random walk tests whose limiting null distributions are standard normal regardless of the period of seasonality and types of mean adjustments. Monte-Carlo simulation shows that he powers of he proposed tests are better than those of the tests based on ordinary least squares estimator(OLSE).

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.785-787
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• 1999

In this paper, recursive least square algorithm is presented to estimate the parameters of step motor under low-speed operation. Parameter estimation is important for compensating the input current by calculating the ratio of the motor torque constant and detent torque constant that causes torque-ripple in low-speed applications. On-line parameter estimation process is a preliminary procedure to apply step motor to adaptive control. Computer simulation shows that the estimated parameters converge in finite time.

• Journal of the Korean Mathematical Society
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• v.40 no.2
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• pp.251-272
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• 2003

Multidimensional integrals are expressed in terms of lower dimensional integrals and function evaluations. An iterative process is used where a trapezoidal and three point identities are used as generators for higher dimensional identities. Bounds are obtained utilising the resulting identities. It is demonstrated that earlier Ostrowski type results are obtained as particular instances of the current work.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.16 no.28
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• pp.93-101
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• 1993

In this paper, two adaptive exponentially weighted moving avenge control chart schemes which available for real-time are proposed. The weighting coefficient is estimated using a recursive kalman filter algorithm. Simulated average run lengths indicate the proposed schemes are sensitive to process shifts And their performance is comparable to CUSUM control chart and customary EWMA control chart.