• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.1118-1120
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• 1997

This paper presents a simple method for evaluating of voltage stability using the line flow equation. Line flow equations ($P_{ij}$, $Q_{ij}$) are comprised of state variable, $V_i$, ${\delta}_i$, $V_j$ and ${\delta}_j$, and line parameter, r and x. Using the feature of polar coordinate, these becomes one equation with two variables, $V_i$ and $V_j$. Moreover, if bus j is slack or generater bus, which is specified voltage magnitude, it becomes one equation with one variable $V_i$, that is, may be formulated with the second-order equation for $V_i^2$. Therefore, multiple load flow solutions may be obtained with simple computation, and the formulated equation used for approximately evaluating of voltage stability limit considering line flow sensitivity. The proposed method was validated to 2-bus and IEEE 6-bus system.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.2
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• pp.101-108
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• 2004

Recently, a semi-active shock absorber has been taking interest because of its low cost and simple structure than the active one. CFD analysis has been conducted to investigate the continuous and variable damping characteristics of the semi-active shock absorber. Also, the flow resistance characteristics of a spool valve has been examined to identify individual parameters(namely, exponent and discharge coefficient) of pressure-flow rate relation needed for the accurate valve modeling. The flow field in the damping valve was simulated using the commercial code, CFX-5.3. The numerical results showed reasonable agreement with the experimental outputs. The pressure distribution with the variation of spool opening length and volume flow rate were discussed in detail. And the continuous and variable damping performance was found clearly. The individual parameters of spool valve were obtained as a function of orifice area. The exponent and discharge coefficient were fitted in with the first and the third polynomial respectively.

• Journal of Power Electronics
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• v.12 no.1
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• pp.164-171
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• 2012

Variable step size maximum power point trackers (MPPTs) are widely used in photovoltaic (PV) systems to extract the peak array power which depends on solar irradiation and array temperature. One essential factor which judges system dynamics and steady state performances is the scaling factor (N), which is used to update the controlling equation in the tracking algorithm to determine a new duty cycle. This paper proposes a novel stability study of variable step size incremental resistance maximum power point tracking (INR MPPT). The main contribution of this analysis appears when developing the overall small signal model of the PV system. Therefore, by using linear control theory, the boundary value of the scaling factor can be determined. The theoretical analysis and the design principle of the proposed stability analysis have been validated using MATLAB simulations, and experimentally using a fixed point digital signal processor (TMS320F2808).

• Journal of Power Electronics
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• v.14 no.1
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• pp.156-164
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• 2014

The power-voltage (P-V) characteristic of a photovoltaic (PV) array is nonlinear and time varying with the change in atmospheric conditions. As a result, the maximum power point tracking (MPPT) technique must be applied in PV systems to maximize the generated energy. The incremental conductance (INC) algorithm, one of the MPPT strategies, is widely used for its high tracking accuracy, good adaptability to rapidly changing atmospheric conditions, and easy implementation. This paper presents a modified asymmetrical variable step size INC MPPT method that is based on the asymmetrical feature of the P-V curve. Compared with conventional fixed or variable step size method, the proposed method can effectively improve tracking accuracy and speed. The theoretical foundation and design principle of the proposed approach are validated by the simulation and experimental results.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.5
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• pp.314-326
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• 2016

The purpose of variable selection techniques is to select a subset of relevant variables for a particular learning algorithm in order to improve the accuracy of prediction model and improve the efficiency of the model. We conduct an empirical analysis to evaluate and compare seven well-known variable selection techniques for multiple linear regression model, which is one of the most commonly used regression model in practice. The variable selection techniques we apply are forward selection, backward elimination, stepwise selection, genetic algorithm (GA), ridge regression, lasso (Least Absolute Shrinkage and Selection Operator) and elastic net. Based on the experiment with 49 regression data sets, it is found that GA resulted in the lowest error rates while lasso most significantly reduces the number of variables. In terms of computational efficiency, forward/backward elimination and lasso requires less time than the other techniques.

• Communications for Statistical Applications and Methods
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• v.22 no.1
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• pp.41-54
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• 2015

In this article, we propose nonconcave penalties on a reduced-rank regression model to select variables and estimate coefficients simultaneously. We apply HARD (hard thresholding) and SCAD (smoothly clipped absolute deviation) symmetric penalty functions with singularities at the origin, and bounded by a constant to reduce bias. In our simulation study and real data analysis, the new method is compared with an existing variable selection method using $L_1$ penalty that exhibits competitive performance in prediction and variable selection. Instead of using only one type of penalty function, we use two or three penalty functions simultaneously and take advantages of various types of penalty functions together to select relevant predictors and estimation to improve the overall performance of model fitting.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.1383-1388
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• 2007

Accuracy and processing time are very important factors when the desired shape is fabricated with Selective Laser Sintering (SLS), one of Solid Freeform Fabrication (SFF) systems. In a conventional SLS process, laser spot size is fixed during laser exposing on the sliced figure. Therefore, it is difficult to accurately and rapidly fabricate the desired shape. In this paper, to deal with those problems an SFF system having ability of changing spot size is developed. The system provides high accuracy and optimal processing time. Specifically, a variable beam expander is employed to adjust spot size for different figures on a sliced shape. Finally, Design and performance estimation of the SFF system employing a variable beam expander are achieved and the mechanism will be addressed to measure the real spot size generated from the variable beam expander.

• Transactions of the Korean Society of Automotive Engineers
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• v.1 no.1
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• pp.90-100
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• 1993

This paper presents some inherent characteristics of a semi-active variable damper featuring electro-rheological (ER) fluid. The damping force of the damper can be selectively adjusted or controlled by employing electric field to the ER fluid domain. This is possible owing to the pressure drop across the piston occured by field-dependent variable yield stress of the ER fluid. This is fundamentally different than the performance of a conventional adjustable viscous damper. To demonstrate the effectiveness and superiority over the conventional one, the proposed damper is incorporated with a suspension system. A quarter car model with the suspension system is formulated and represented by a state equation. By choosing numerical values based on realistic package size, power requirements and suitable ER properties, the performance characteristics of the suspension system are obtained and evaluated in both frequency and time domains. The effects of constant electric field and on-off controlled electric field which relates to the damping force are also examined.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.382-386
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• 2009

Online or sequential learning is one of the most basic and powerful method to train neuron network, and it has been widely used in disease detection, weather prediction and other realistic classification problem. At present, there are many algorithms in this area, such as MRAN, GAP-RBFN, OS-ELM, SVM and SMC-RBF. Among them, SMC-RBF has the best performance; it has less number of hidden neurons, and best efficiency. However, all the existing algorithms use signal normal distribution as kernel function, which means the output of the kernel function is same at the different direction. In this paper, we use multi-variable normal distribution as kernel function, and derive EKF learning formulas for multi-variable normal distribution kernel function. From the result of the experience, we can deduct that the proposed method has better efficiency performance, and not sensitive to the data sequence.

• International Journal of Fluid Machinery and Systems
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• v.1 no.1
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• pp.47-56
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• 2008

The flow behind the variable area nozzle which corresponds to the flow at the leading edge of the impeller was measured with a 3-hole yaw probe and calculated with CFD. Two nozzle throat-areas were investigated. One is the smallest and the other is the largest opening for the variable nozzle. Test results agreed with the calculated results qualitatively. The leakage flow through the tip clearance of the nozzle vane significantly affected the flow field downstream of the nozzle vane with the smallest opening. However, the effect on leakage flow on the flow field downstream of the nozzle vane with the largest opening was very weak and the effect of wake is dominant.