• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.10a
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• pp.577-578
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• 2009

• Journal of the Ergonomics Society of Korea
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• v.30 no.5
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• pp.645-650
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• 2011

Objective: The aim of this study is to design a sampling inspection plan with human error which is changing according to inspection time. Background: Typical sampling inspection plans have been established typically based on an assumption of the perfect inspection without human error. However, most of all inspection tasks include human errors in the process of inspection. Therefore, a sampling inspection plan should be designed with consideration of imperfect inspection. Method: A model for single sampling inspection plans were proposed for the cases that visual inspection error rate is changing according to inspection time. Additionally, a sampling inspection plan for an optimal inspection time was proposed. In order to show an applied example of the proposed model, an experiment for visual inspection task was performed and the inspection error rates were measured according to the inspection time. Results: Inspection error rates changed according to inspection time. The inspection error rate could be reflected on the single sampling inspection plans for attribute. In particular, inspection error rate in an optimal inspection time may be used for a reasonable single sampling plan in a practical view. Conclusion: Human error rate in inspection tasks should be reflected on typical single sampling inspection plans. A sampling inspection plan with consideration of human error requires more sampling number than a typical sampling plan with perfect inspection. Application: The result of this research may help to determine more practical sampling inspection plan rather than typical one.

• Magazine of the Korean Society of Agricultural Engineers
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• v.45 no.2
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• pp.45-57
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• 2003

An artificial neural network model was developed to analyze and forecast Short-term river runoff from the Naju watershed, in Korea. Error back propagation neural networks (EBPN) of hourly rainfall and runoff data were found to have a high performance In forecasting runoff. The number of hidden nodes were optimized using total error and Bayesian information criterion. Model forecasts are very accurate (i.e., relative error is less than 3% and $R^2$is greater than 0.99) for calibration and verification data sets. Increasing the time horizon for application data sets, thus mating the model suitable for flood forecasting. decreases the accuracy of the model. The resulting optimal EBPN models for forecasting hourly runoff consists of ten rainfall and four runoff data(ANN0410 model) and ten rainfall and ten runoff data(ANN1010 model). Performances of the ANN0410 and ANN1010 models remain satisfactory up to 6 hours (i.e., $R^2$is greater than 0.92).

• Advances in Energy Research
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• v.5 no.1
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• pp.79-90
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• 2017

The main objective of load frequency control (LFC) is to keep the frequency value at nominal value and force deviation of the frequency to zero in case of load change. This paper suggests LFC by using a model predictive control (MPC), based on Integral Square Error (ISE) method designed to optimize the damping of oscillations in a two-area power system. The MPC is designed and simulated with a model system in state space, for robust performance in the system response. The proposed MPC is tuned by ISE to achieve superior efficiency. Moreover, its performance has been assessed and compared with the PI and PID conventional controllers. The settling time and overshoot with MPC are extremely minimized as compared with conventional controllers.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.268-275
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• 2003

We consider the Bayesian accelerated failure time model. The error distribution is assigned a skewed normal distribution which is including normal distribution. For noninformative priors of regression coefficients, we show the propriety of posterior distribution. A Markov Chain Monte Carlo algorithm(i.e., Gibbs Sampler) is used to obtain a predictive distribution for a future observation and Bayes estimates of regression coefficients.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.211-216
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• 2004

In this thesis, we present the Takagi-Sugeno-Kang (TSK) fuzzy model based adaptive controller and adaptive identification for a general class of uncertain nonlinear dynamic systems. We use an estimated model for the unknown plant model and use this model for designing the controller. The hybrid adaptive control combined direct and indirect adaptive control based on TSK fuzzy model is constructed. The direct adaptive law can be showed by ignoring the identification errors and fails to achieve parameter convergence. Thus, we propose an TSK fuzzy model based hybrid adaptive (HA) law combined of the tracking error and the model ins error to adjust the parameters. Using a Lyapunov synthesis approach, the proposed hybrid adaptive control is proved. The hybrid adaptive law (HA) is better than the direct adaptive (DA) method without identifying the model ins error in terms of faster and improved tracking and parameter convergence. In order to show the applicability of the proposed method, it is applied to the inverted pendulum system and the performance is verified by some simulation results.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.23 no.1
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• pp.1-6
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• 2014

This paper describes a method of estimating and evaluating the volumetric errors of multi-axis machine tools. The estimation method is based on a generic model that was developed from conventional kinematic error models for the geometric and thermal errors to help predict the volumetric error easily in various configurations. To demonstrate the advantages of the model, an application in the early stages of a five-axis machine tool design is presented as an example. The model was experimentally evaluated for a four-axis machine tool by using the data from ISO230-6 and R-test measurements to compare the estimated and measured volumetric errors.

• Magazine of the Korean Society of Agricultural Engineers
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• v.38 no.2
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• pp.108-122
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• 1996

This study was to introduced estimation model for optimum probabilistic rainfall intensity on hydrological area. Originally, probabilistic rainfall intensity formula have been characterized different coefficient of formula and model following watersheds. But recently in korea rainfall intensity formula does not use unionize applyment standard between administration and district. And mingle use planning formula with not assumption model. Following the number of year hydrological duration adjust areal index. But, with adjusting formula applyment was without systematic conduct. This study perceive the point as following : 1) Use method of excess probability of Iwai to calculate survey rainfall intensity value. 2) And, use method of least squares to calculate areal coefficient for a unit of 157 rain gauge station. And, use areal coefficient was introduced new probabilistic rainfall intensity formula for each rain gauge station. 3) And, use new probabilistic rainfall intensity formula to adjust a unit of fourteen duration-a unit of fifteen year probabilistic rainfall intensity. 4) The above survey value compared with adjustment value. And use three theory of error(absolute mean error, squares mean error, relative error ratio) to choice optimum probabilistic rainfall intensity formula for a unit of 157 rain gauge station.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.3
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• pp.945-959
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• 1996

In this paper, a comprehensive computer model is described which can be used to generate the volumetric error map combining the machine parametric errors and the measurement prove error, for most types of CMMs and axis configurations currently in use.

• Proceedings of the KSRS Conference
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• v.2
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• pp.635-638
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• 2006

Regression of conventional prediction techniques in data mining uses the model which is generated from the training step. This model is applied to new input data without any change. If this model is applied directly to time series, the rate of prediction accuracy will be decreased. This paper proposes an incremental regression for time series prediction like typhoon track prediction. This technique considers the characteristic of time series which may be changed over time. It is composed of two steps. The first step executes a fractional process for applying input data to the regression model. The second step updates the model by using its information as new data. Additionally, the model is maintained by only recent data in a queue. This approach has the following two advantages. It maintains the minimum information of the model by using a matrix, so space complexity is reduced. Moreover, it prevents the increment of error rate by updating the model over time. Accuracy rate of the proposed method is measured by RME(Relative Mean Error) and RMSE(Root Mean Square Error). The results of typhoon track prediction experiment are performed by the proposed technique IMLR(Incremental Multiple Linear Regression) is more efficient than those of MLR(Multiple Linear Regression) and SVR(Support Vector Regression).