• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.36 no.1
• /
• pp.33-39
• /
• 2018

As the retrieval accuracy of PWV estimates from GPS measurements is proportional to the accuracy of water vapor WMT, the WMT model is a significant formulation in the conversion of PWV from the GPS ZWD. The purpose of this study is to develop a MWMT model for the retrieval of highly accurate GPS PWV using the radiosonde measurements from six upper-air observing stations in the region of Korea. The values of 1-hr PWV estimated at four GPS stations during one year are used to evaluate the validity of the MWMT model. It is compared to the PWV obtained from radiosonde data that are located in the vicinity of GPS stations. Intercomparison of radiosonde PWVs and GPS PWVs derived using different WMT models is performed to assess the quality of our MWMT model for Korea. The result in this study indicates that the MWMT model is an effective model to retrieve the enhanced accurate GPS PWV, compared to other GPS PWV derived by Korean annual or global WMT models.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.1500-1503
• /
• 1996

This paper combines the disturbance accommodating control(DAC) and nonlinear model inversion control for a skid-to-turn(STT) missile. The missile autopilot may be designed to be robust with respect to a variety of uncertainties. We proposes the two step control design method. Nonlinear model inversion control is used as the main design method. Due to the model uncertainties and external disturbances, the exact nonlinear model inversion can not be achieved. DAC is designed to detect, to identify, and to compensate these uncertainties. DAC's disturbance observer is linear. Thus it is easy to implement. It does not cause the convergence problem due to coexistence between the modeling uncertainties and external disturbances. 6 DOF simulation results show that the proposed method may improve the missile tracking performance.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.16 no.7
• /
• pp.628-639
• /
• 1991

A new efficient method for GaAs MESFET model parameter extraction is proposed, which is based on the bias dependance of each parameter characteristics derved from the analytic model. The requiremnts of the method are only small-signal S-parameter measurements under the three bias variations. Fixation of the linear model parameter values in the optimization process is made using the sensitivity information of the model parameter obtained by the weighted Broyden update method, it is to improve the uniqueness and reliablility of the solution. The validity of the extracted values of the FET model parameters is confirmed by comparing the simulation results with the experimental data.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2011.11a
• /
• pp.17-18
• /
• 2011

Over the past 20 years, Researchers have tried to develop "Integration Model of Cost and Schedule" in construction industry. They suggested various models and techniques, however, it is still required to develop new methodology for AL Form Work in Public Multi-Housing Projects. Accordingly, this research focused on measuring quantity take-offs of on-going projects and analysing the basic process of using the resources(Labor, Material, Plant) related to cost and time data. There are 3 steps of this research : 1) The literature review of previous studies about Integration Model of Cost and Schedule was conducted. 2) Model for integration between cost and schedule was developed. 3) the accuracy of developed model was verified. The results are expected to improve in integrated managing the cost and schedule data of AL form work.

• Proceedings of the KIEE Conference
• /
• 2001.11c
• /
• pp.215-218
• /
• 2001

To improve the performance of PID controller of high order systems by model reduction, we proposed a new model reduction method in frequency domain. A new model reduction method we proposed, considered four points (${\angle}G(jw)=0$, $-{\pi}/2$, $-{\pi}$, $-3{\pi}/2$) in stead of two points (${\angle}G(jw)=-{\pi}/2$, and $-{\pi}$) in Nyquist curve. And for high order systems that it have not two point (${\angle}G(jw)=-{\pi}/2$, and $-{\pi}$) in Nyquist curve, we proposed a method to annex very small dead time. This method has a annexed very small dead time on the base model for reduction, and we cancel it after to get the reduced model. It is shown that the performance of proposed method is better than any other methods.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2000.05a
• /
• pp.703-707
• /
• 2000

Machine tool errors have to be characterized and predicted to improve machine tool accuracy. A real-time error compensation system has been developed based on volumetric error synthesis model which is composed of machine tool errors. This paper deals with new algorithm about verification of machine tool errors. This new algorithm uses a simplified volumetric error synthesis model. This simplified model is constructed with only main components among the error components of the machines. This main error components are analyzed by three-dimensional helical ball bar test. By substituting result of helical ball bar test fer simplified model, we could find that obtained error components are closed to real error components.

• Journal of the Korean Society for Precision Engineering
• /
• v.20 no.10
• /
• pp.199-207
• /
• 2003

The Timoshenko beam model has been known as the most accurate model for representing beam structures. However, the Timoshenko beam model may give rise to a significant error when it is applied to multi-stepped beam structures. This paper is intended to demonstrate the modeling error of Timoshenko beam based finite element model for multi-stepped beam structures and to suggest a new modeling method to improve the accuracy. A tentative bending spring is introduced into the stepped section to represent the softening effect due to the presence of step. This paper also proposes a finite element modeling method in the light with the tentative bending spring model for the step softening effect. The proposed method rigorously adapts computation results from a commercial finite element code. The validity of the proposed method is demonstrated through a series of simulation and experiment.

• Journal of Mechanical Science and Technology
• /
• v.19 no.spc1
• /
• pp.255-264
• /
• 2005

In this paper, a practical bushing model is proposed to improve the accuracy of the vehicle dynamic analysis. The results of the rubber bushing are used to develop an empirical bushing model with an artificial neural network. A back propagation algorithm is used to obtain the weighting factor of the neural network. Since the output for a dynamic system depends on the histories of inputs and outputs, Narendra algorithm of 'NARMAX' form is employed to consider these effects. A numerical example is carried out to verify the developed bushing model. Then, a full car dynamic model with artificial neural network bushings is simulated to show the feasibility of the proposed bushing model.

• Computers and Concrete
• /
• v.21 no.5
• /
• pp.505-511
• /
• 2018

This paper investigates the potential of a hybrid model which combines the least squares support vector machine (LSSVM) and an improved particle swarm optimization (IMPSO) techniques for prediction of concrete compressive strength. A modified PSO algorithm is employed in determining the optimal values of LSSVM parameters to improve the forecasting accuracy. Experimental data on concrete compressive strength in the literature were used to validate and evaluate the performance of the proposed IMPSO-LSSVM model. Further, predictions from five models (the IMPSO-LSSVM, PSO-LSSVM, genetic algorithm (GA) based LSSVM, back propagation (BP) neural network, and a statistical model) were compared with the experimental data. The results show that the proposed IMPSO-LSSVM model is a feasible and efficient tool for predicting the concrete compressive strength with high accuracy.

• Journal of Information Technology Services
• /
• v.15 no.1
• /
• pp.153-166
• /
• 2016

Ensemble classification is an approach that combines individually trained classifiers in order to improve prediction accuracy over individual classifiers. Ensemble techniques have been shown to be very effective in improving the generalization ability of the classifier. But base classifiers need to be as accurate and diverse as possible in order to enhance the generalization abilities of an ensemble model. Bagging is one of the most popular ensemble methods. In bagging, the different training data subsets are randomly drawn with replacement from the original training dataset. Base classifiers are trained on the different bootstrap samples. In this study we proposed a new bagging variant ensemble model, Randomized Bagging (RBagging) for improving the standard bagging ensemble model. The proposed model was applied to the bankruptcy prediction problem using a real data set and the results were compared with those of the other models. The experimental results showed that the proposed model outperformed the standard bagging model.