• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.629-634
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• 2000

Generally, the accuracy of motion control systems is strongly influenced by both the mechanical characteristics and servo characteristics of feed drive systems. In the fed drive systems of machine tools that consist of mechanical parts and electrical parts, a torsional vibration is often generated because of its elastic elements in torque transmission. Especially, a torsional vibration caused by the elasticity of mechanical elements might deteriorate the quick movement of system and lead to shorten the life time of the mechanical transmission elements. So it is necessary to analyze the electromechanical system mathematically to optimize the dynamic characteristics of the feed drive system. In this paper, based on the simplifies feed drive system model, radius errors due to position gain mismatch and servo response characteristic have been developed and an optimal criterion for tuning the gain of speed controller is discussed. The proportional and integral parameter gain of the feed drive controller are optimal design variables for the gain tuning of PI speed controller. Through the optimization problem formulation, both proportional and integral parameter are optimally tuned so as to compensate the radius errors by using the genetic algorithm. As a result, higher performance on circular profile tests has been achieved than the one with standard parameters.

• International journal of advanced smart convergence
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• v.10 no.4
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• pp.59-65
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• 2021

Electricity contributes to the development of the economy. Therefore, forecasting electricity demand plays an important role in the development of the electricity industry in particular and the economy in general. This study aims to provide a precise model for long-term electricity demand forecast in the residential sector by using three independent variables include: Population, Electricity price, Average annual income per capita; and the dependent variable is yearly electricity consumption. Based on the support of Multiple variable regression, the proposed method established a model with variables that relate to the forecast by ignoring variables that do not affect lead to forecasting errors. The proposed forecasting model was validated using historical data from Vietnam in the period 2013 and 2020. To illustrate the application of the proposed methodology, we presents a five-year demand forecast for the residential sector in Vietnam. When demand forecasts are performed using the predicted variables, the R square value measures model fit is up to 99.6% and overall accuracy (MAPE) of around 0.92% is obtained over the period 2018-2020. The proposed model indicates the population's impact on total national electricity demand.

• Korean System Dynamics Review
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• v.8 no.1
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• pp.97-115
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• 2007

In System Dynamics, causal maps are used as a tool for analyzing dynamic problems and discussing the outcome of analyzed problems. However there are some limitations to use causal maps. In the drawing phase of causal maps, the high abstraction of variables that constitutes problems makes it difficult to find out correct information. And principles or rules to check errors on causal maps are not sufficient yet. Moreover, simulation modeling tasks are required to be concerned separately from drawing causal maps because causal maps cannot provide enough information to simulation modeling. In order to overcome these limitations, this study shows ways that ensure validity, increase power of expression of causal maps and improve the connection between causal maps and simulation modeling.

• Journal of Korean Society for Quality Management
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• v.21 no.2
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• pp.17-26
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• 1993

Process fault diagnosis is a complicated matter because quality control problems can result from a variety of causes. These causes include problems with electrical components, mechanical components, human errors, job justification errors, and air conditioning influences. In order to make the system run smoothly with minimum delay, it is necessary to suggest heuristic remedies for the detected faults. Hence, this paper describes a heuristic methodology of fault diagnosis that is performed using statistical patterns generated by quality characteristics The proposed methodology is described briefly as follows: If a sample pattern generated by random variables is similar to the number of prototype patterns, the sample pattern may be matched by any prototype pattern among them to be resembled. This concept is based on the similarity between a sample pattern and the matched prototype pattern. The similarity is calculated as the weighted average of squared deviation, which is expressed as the difference between the relative values of standard normal distribution to be transformed by the observed values of quality characteristics in a sample pattern and the critical values of the corresponding ones in a matched prototype pattern.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1233-1235
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• 1999

This paper is about to the branch topology error identification in electric power systems. Topology errors may cause the state estimators to converge to a wrong solution or in some cases not to converge at all. The branch error identification is carried out as part of the state estimation procedure. The basic idea is that the estimates of these error variables will be insignificant if the branch is modeled correctly and they will be relatively large otherwise. A two step procedure for the identification of faulted branches is proposed.

• Journal of Preventive Medicine and Public Health
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• v.47 no.5
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• pp.273-280
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• 2014

Objectives: To evaluate the occurrence of patient adverse events in Korean hospitals as perceived by nurses and examine the correlation between patient adverse events with the nurse practice environment at nurse and hospital level. Methods: In total, 3096 nurses working in 60 general inpatient hospital units were included. A two-level logistic regression analysis was performed. Results: At the hospital level, patient adverse events included patient falls (60.5%), nosocomial infections (51.7%), pressure sores (42.6%) and medication errors (33.3%). Among the hospital-level explanatory variables associated with the nursing practice environment, 'physician-nurse relationship' correlated with medication errors while 'education for improving quality of care' affected patient falls. Conclusions: The doctor-nurse relationship and access to education that can improve the quality of care at the hospital level may help decrease the occurrence of patient adverse events.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.20 no.6
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• pp.703-713
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• 2011

In this study, to minimize trial and error in the design and manufacturing processes of a high-precision large-surface micro-grooving machine which is able to fabricate the molds for 42 inch LCD light guide panels, the effects of the structural deformation of the micro-grooving machine according to the positions of the X-axis, Y-axis and Z-axis feed systems were examined on the tool tip displacement errors associated with the machining accuracy. The virtual prototype (finite element model) of the micro-grooving machine was constructed to include the joint stiffnesses of the hydrostatic bearings, hydrostatic guideways and linear motors, and then the tool tip displacement errors were measured from the virtual prototype. Especially, to establish the prediction model of the tool tip displacement errors, which was constructed using the positions of the X-axis, Y-axis and Z-axis feed systems as independent variables, the response surface method based on the central composite design was introduced. The reliability of the prediction model was verified by the fact that the tool tip displacement errors obtained from the prediction model coincided well those measured from the virtual prototype. And the causes of the tool tip displacement errors were identified through the analysis of interactions between the positions of the X-axis, Y-axis and Z-axis feed systems.

• Nuclear Engineering and Technology
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• v.44 no.4
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• pp.355-362
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• 2012

The aim of this study was to develop a bundle position-wise linear model (BPLM) to predict Pressure Tube (PT) diametral creep employing the previously measured PT diameters and operating conditions. There are twelve bundles in a fuel channel, and for each bundle a linear model was developed by using the dependent variables, such as the fast neutron fluences and the bundle coolant temperatures. The training data set was selected using the subtractive clustering method. The data of 39 channels that consist of 80 percent of a total of 49 measured channels from Units 2, 3, and 4 of the Wolsung nuclear plant in Korea were used to develop the BPLM. The data from the remaining 10 channels were used to test the developed BPLM. The BPLM was optimized by the maximum likelihood estimation method. The developed BPLM to predict PT diametral creep was verified using the operating data gathered from Units 2, 3, and 4. Two error components for the BPLM, which are the epistemic error and the aleatory error, were generated. The diametral creep prediction and two error components will be used for the generation of the regional overpower trip setpoint at the corresponding effective full power days. The root mean square (RMS) errors were also generated and compared to those from the current prediction method. The RMS errors were found to be less than the previous errors.

• Communications for Statistical Applications and Methods
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• v.28 no.6
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• pp.595-610
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• 2021

Recently a few articles have derived robust first-order rotatable and D-optimal designs for the lifetime response having distributions gamma, lognormal, Weibull, exponential assuming errors that are correlated with different correlation structures such as autocorrelated, intra-class, inter-class, tri-diagonal, compound symmetry. Practically, a first-order model is an adequate approximation to the true surface in a small region of the explanatory variables. A second-order model is always appropriate for an unknown region, or if there is any curvature in the system. The current article aims to extend the ideas of these articles for second-order models. Invariant (free of the above four distributions) robust (free of correlation parameter values) second-order rotatable designs have been derived for the intra-class and inter-class correlated error structures. Second-order rotatability conditions have been derived herein assuming the response follows non-normal distribution (any one of the above four distributions) and errors have a general correlated error structure. These conditions are further simplified under intra-class and inter-class correlated error structures, and second-order rotatable designs are developed under these two structures for the response having anyone of the above four distributions. It is derived herein that robust second-order rotatable designs depend on the respective error variance covariance structure but they are independent of the correlation parameter values, as well as the considered four response lifetime distributions.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.3
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• pp.211-217
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• 2000

A main contribution of this paper is the development of a Hopfield network-based algorithm for the fault diagnosis of the actuators in linear system with uncertainties. An unknown input decoupling approach is introduced to the design of an adaptive observer so that the observer is insensitive to uncertainties. As a result, the output observation error equation does not depend on the effect of uncertainties. Simultaneous energy minimization by the Hopfield network is used to minimize the least mean square of errors of errors of estimates of output variables. The Hopfield network provides an estimate of the gains of the actuators. When the system dynamics changes, identified gains go through a transient period and this period is used to detect faults. The proposed scheme is demonstrated through its application to a simulated second-order system.