• Journal of Electrical Engineering and information Science
• /
• v.2 no.4
• /
• pp.79-85
• /
• 1997

A unified, modular and decoupled approach for the simulation of converter fed induction machine systems is presented. The system under consideration could have semiconductor devices connected to the stator or the rotor of the induction machine for the purpose of controlling its performance. The machine model, however is invariant to these aspects. The model spans the circuit and equation domains of description thus allowing he advantages of both these domains of descriptions to be utilized. The results obtained using this machine and switch model for a VSI fed induction machine (stator fed, rotor shorted0 are compared with those from laboratory experiment to establish the validity and accuracy of th approach. Results for a slip energy recovery system are also presented and compared with those of earlier workers to establish the performance of the models and algorithms in he doubly-fed mode of operation of induction machine systems.

• Journal of Korean Association for Spatial Structures
• /
• v.23 no.3
• /
• pp.87-94
• /
• 2023

Chloride is one of the most common threats to reinforced concrete (RC) durability. Alkaline environment of concrete makes a passive layer on the surface of reinforcement bars that prevents the bar from corrosion. However, when the chloride concentration amount at the reinforcement bar reaches a certain level, deterioration of the passive protection layer occurs, causing corrosion and ultimately reducing the structure's safety and durability. Therefore, understanding the chloride diffusion and its prediction are important to evaluate the safety and durability of RC structure. In this study, the chloride diffusion coefficient is predicted by machine learning techniques. Various machine learning techniques such as multiple linear regression, decision tree, random forest, support vector machine, artificial neural networks, extreme gradient boosting annd k-nearest neighbor were used and accuracy of there models were compared. In order to evaluate the accuracy, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used as prediction performance indices. The k-fold cross-validation procedure was used to estimate the performance of machine learning models when making predictions on data not used during training. Grid search was applied to hyperparameter optimization. It has been shown from numerical simulation that ensemble learning methods such as random forest and extreme gradient boosting successfully predicted the chloride diffusion coefficient and artificial neural networks also provided accurate result.

• Korean Management Science Review
• /
• v.29 no.3
• /
• pp.157-168
• /
• 2012

In this study, we consider an order promising problem at parallel machine shops where orders arrive dynamically. We develop methods for the problem, which instantly quote the due-dates of arrived orders. In this study, we first propose methods which can estimate flow times of orders, in which the current and future inventory status as well as the specific scheduling scheme used in the shop are taken into account, and then the due-dates are set by the order promising methods using the estimation results. The quoted due-dates of orders are compared with the actual completion times of those which are obtained from the simulation runs. The series of computational experiments show that the superior performance of the proposed methods in terms of the accuracy of due-date quotation.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1992.10a
• /
• pp.249-254
• /
• 1992

Hydrostatic Bearings have been applied to ultra high precision machine tools and precision instruments, because of their low friction characteristic, high load carrying capacity and high moving accuracy at all range of speed. In regard to realizing the Hydrostatic Bearing, various restrictors such as capillary, orifice, diaphram valve, spool valve, and etc can be used. However, their stiffness and flexibility are not sufficient in practical use for ultra precision machine tool elements. In this study dynamic equations were derived and the dynamic characteristics were simulated for both orifice and flow control servo valve. Simulation was carried out on the condition that static and sinusoidal dynamic loads were applied to the table of CNC jig Boring machine. The simulation results indicate the improvement of the performance of the Bearing system when flow control servo valve has been used as restrictor of Hydrostatic Bearing.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2002.10a
• /
• pp.158-163
• /
• 2002

This paper presents a multiple sensor system for rapid and high-precision coordinate data acquisition in the OMM (On-machine measurement) process. In this research, three sensors (touch probe, laser, and vision sensor) are integrated to obtain more accurate measuring results. The touch-type probe has high accuracy, but is time-consuming. Vision sensor can acquire many point data rapidly over a spatial range but its accuracy is less than other sensors. Also, it is not possible to acquire data for invisible areas. Laser sensor has medium accuracy and measuring speed among the sensors, and can acquire data for sharp or rounded edge and the features with very small holes and/or grooves. However, it has range- constraints to use because of its system structure. In this research, a new optimum sensor integration method for OMM is proposed by integrating the multiple-sensor to accomplish mote effective inspection planning. To verify the effectiveness of the proposed method, simulation and experimental works are performed, and the results are analyzed.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.19 no.1
• /
• pp.1-13
• /
• 1995

In order to investigate the effect of liquid balancer in washing machine, we identify the vibration characteristics of suspension system of washing machine and formulate the 4 D. O. F. system dynamic equations. As the washing machine rotates higher speed, it is emphasized to reduce the ecentric force due to unbalanced mass. Nowadays, the most effective cancelling method of eccentric force is known as the usage of liquid balancer. To determine the liquid distribution in liquid balancer, the fluid statics is considered. The system dynamic equations are solved by Runge-Kutta method and represent the good characteristics of real washing machine in X-Y plane. The accuracy of the numerical solution was examined by experiments. The simulation results show that the unbalanced mass has so much influence on vibration magnitude and the rotating shape of spin-basket. But the effect of mass reduction due to the dehydration of the spin-basket has little influence on transient vibration.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1997.10a
• /
• pp.184-187
• /
• 1997

Coordinate measuring machines(CMMs) are used to obtain the dimensional information with micron accuracy. This paper is concerned with the development of the horizontal arm type coordinate measuring machine using open architecture controller. The coordinate measuring machine considered in this paper consists of three orthogonal axes in the x, y and z directions. Open architecture controller IS used to implement a measuring system which can be fulfill to various needs of endusers of coordinate measuring machines. The open architecture controller presented here is embodied in personal computers. The programs and man-machine interfaces(MM1) are developed for various measuring conditions. Through the computer simulation based on the mathematical models of the coordinate measuring machine, control parameters are optimally tuned.

• Journal of the Korean Society for Precision Engineering
• /
• v.15 no.9
• /
• pp.56-67
• /
• 1998

This paper introduces a friction compensation fuzzy logic controller, which utilizes a rule-based approach. The paper explains the algorithm of the proposed controller and compares it with a conventional PID controller in simulations and experiments. For the experiments, the two control algorithms were implemented on a 3-axis milling machine in contour milling. These simulation and experimental analyses show that the proposed fuzzy logic controller has superior performance over conventional PID controllers In terms of part contour accuracy.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1991.11a
• /
• pp.101-104
• /
• 1991

Bal1 screw systems has been used for positioning elements of machine tools. In order to maintain high rigidity and accuracy, preload is applied between nut and screw. However, large amount of preload increases frictional heat. Temperature rises remarkably at high speed notion, Thermal expansion degrades positioning accuracy, In this paper, finite differance method is applied to compute temperature distributions and thermal expansions of ball screw systems according to preload condition and rotational steed. Some simulation results show that the developed methodology is good to study thermal expansion of ball screw systems.

• Journal of Wetlands Research
• /
• v.25 no.3
• /
• pp.167-175
• /
• 2023

Precipitation data is one of the essential input datasets used in various fields such as wetland management, hydrological simulation, and water resource management. In order to efficiently manage water resources using precipitation data, it is essential to secure as much data as possible by minimizing the missing rate of data. In addition, more efficient hydrological simulation is possible if precipitation data for ungauged areas are secured. However, missing precipitation data have been estimated mainly by statistical equations. The purpose of this study is to propose a new method to restore missing precipitation data using machine learning algorithms that can predict new data based on correlations between data. Moreover, compared to existing statistical methods, the applicability of machine learning techniques for restoring missing precipitation data is evaluated. Representative machine learning algorithms, Artificial Neural Network (ANN) and Random Forest (RF), were applied. For the performance of classifying the occurrence of precipitation, the RF algorithm has higher accuracy in classifying the occurrence of precipitation than the ANN algorithm. The F1-score and Accuracy values, which are evaluation indicators of the classification model, were calculated as 0.80 and 0.77, while the ANN was calculated as 0.76 and 0.71. In addition, the performance of estimating precipitation also showed higher accuracy in RF than in ANN algorithm. The RMSE of the RF and ANN algorithms was 2.8 mm/day and 2.9 mm/day, and the values were calculated as 0.68 and 0.73.