• Proceedings of the Korean Nuclear Society Conference
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• 2005.05a
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• pp.653-654
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• 2005

• Construction Engineering and Management
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• v.18 no.3
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• pp.23-27
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• 2017

• Journal of Power Electronics
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• v.16 no.4
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• pp.1455-1468
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• 2016

This paper presents a grid-connected dual stator-winding induction generator (DWIG) wind power system suitable for wide wind speed ranges. The parallel connection via a unidirectional diode between dc buses of both stator-winding sides is employed in this DWIG system, which can output a high dc voltage over wide wind speed ranges. Grid-connected inverters (GCIs) do not require booster converters; hence, the efficiency of wind energy utilization increases, and the hardware topology and control strategy of GCIs are simplified. In view of the particularities of the parallel topology and the adopted generator control strategy, we propose a novel excitation-capacitor optimization solution to reduce the volume and weight of the static excitation controller. When this excitation-capacitor optimization is carried out, the maximum power tracking problem is also considered. All the problems are resolved with the combined control of the DWIG and GCI. Experimental results on the platform of a 37 kW/600 V prototype show that the proposed DWIG wind power system can output a constant dc voltage over wide rotor speed ranges for grid-connected operations and that the proposed excitation optimization scheme is effective.

• Journal of Power Electronics
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• v.18 no.3
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• pp.841-852
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• 2018

This paper presents a dual-algorithm search method (GWO-GSO) combining grey wolf optimization (GWO) and golden-section optimization (GSO) to realize maximum power point tracking (MPPT) for photovoltaic (PV) systems. First, a modified grey wolf optimization (MGWO) is activated for the global search. In conventional GWO, wolf leaders possess the same impact on decision-making. In this paper, the decision weights of wolf leaders are automatically adjusted with hunting progression, which is conducive to accelerating hunting. At the later stage, the algorithm is switched to GSO for the local search, which play a critical role in avoiding unnecessary search and reducing the tracking time. Additionally, a novel restart judgment based on the quasi-slope of the power-voltage curve is introduced to enhance the reliability of MPPT systems. Simulation and experiment results demonstrate that the proposed algorithm can track the global maximum power point (MPP) swiftly and reliably with higher accuracy under various conditions.

• Journal of Communications and Networks
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• v.14 no.5
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• pp.554-565
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• 2012

In this paper, we study a cross-layer and end-to-end optimization problem for the integrated wireless and wireline network that consists of one wireline core network and multiple wireless access networks. We consider joint end-to-end flow control/distribution at the transport and network layers and opportunistic scheduling at the data link and physical layers. We formulate a single stochastic optimization problem and solve it by using a dual approach and a stochastic sub-gradient algorithm. The developed algorithm can be implemented in a distributed way, vertically among communication layers and horizontally among all entities in the network, clearly showing what should be done at each layer and each entity and what parameters should be exchanged between layers and between entities. Numerical results show that our cross-layer and end-to-end optimization approach provides more efficient resource allocation than the conventional layered and separated optimization approach.

• Journal of Welding and Joining
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• v.26 no.2
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• pp.85-91
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• 2008

In general, the quality of a welding process tends to vary with depending on the work environment or external disturbances. Hence, in order to achieve the desirable quality of welding, we should have the optimal welding condition that is not significantly affected by these changes in the environment or external disturbances. In this study, we used a dual response surface method in consideration of both the mean output variables and the standard deviation in order to optimize the V-groove arc welding process. The input variables for GMA welding process with the dual response surface are welding voltage, welding current and welding speed. The output variables are the welding quality function using the shape factor of bead geometry. First, we performed welding experiment on the interested area according to the central composite design. From the results obtained, we derived the regression model on the mean and standard deviation between the input and output variables of the welding process and then obtained the dual response surface. Finally, using the grid search method, we obtained the input variables that minimize the object function which led to the optimal V-groove arc welding process.

• 한국연소학회:학술대회논문집
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• 2012.04a
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• pp.199-200
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• 2012

An experimental study on geometric optimization was conducted to develop a hybrid/dual swirl jet combustor for a micro-gas turbine. A hybrid concept indicating a combination of swirling jet partially premixed and premixed flames were adopted to achieve high flame stability as well as clean combustion. Location of pilot nozzle, angle and direction of swirl vane were varied as main parameters with a constant fuel flow rate for each nozzle. The results showed that the variation in location of pilot nozzle resulted in significant change in swirl intensity due to the change in flow area near burner exit, and thus, optimized nozzle location was determined on the basis of CO and NOx emissions under conditions of co-swirl flow and swirl $angle=30^{\circ}$. The increase in swirl angle (from $30^{\circ}$ to $45^{\circ}$) enhanced the emission performances, in particular, with a significant reduction of CO emission near lean-flammability limit. It was observed that the CO emission near lean-flammability limit was further reduced through the counter-swirl flow. However, there was not significant change in the NOx emission in the operating conditions (i.e. equivalence ratio of 0.6~0.7) between the co- and the counter-swirl flow.

• Transactions of the Korean Society of Automotive Engineers
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• v.16 no.6
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• pp.65-73
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• 2008

The purpose of this study is development of universal engine model for integrated Hybrid Electric Vehicle (HEV) simulator and a optimization of engine model. The engine model of this study is based on the MATLAB Simulink for universal and include engine fuel economy technologies for HEV. Various engine fuel economy technologies for HEV is estimated by commercial engine 1-D simulation program - WAVE. And, the 1-D simulation model of base version is compared with engine experiment result. The analyzed engine technologies with 1-D simulation are Dual-CVVT, Atkinson-Cycle and Cylinder-Deactivation System. There are improvement of fuel economy and power performance with Dual-CVVT model at part load and full load, pumping loss reduction with Cylinder-Deactivation System at idle and regeneration. Each estimated technologies are analyzed by 1-D simulation on all operation region for base data to converse simulink. The simulink based engine model maintains a signal with ECU for determination of engine operation point.

• Proceedings of the Korean Society for Quality Management Conference
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• 1998.11a
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• pp.556-567
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• 1998

Robust design in industry is an approach to reducing performance variation of quality characteristic value in products and processes. Taguchi has used the signal-to-noise ratio(SN) to achieve the appropriate set of operating conditions where variability around target is low in the Taguchi parameter design. Taguchi has dealt with having constraints on both the mean and variability of a characteristic (the dual response problem) by combining information on both mean and variability into an SN. Many Statisticians criticize the Taguchi techniques of analysis, particularly those based on the SN. In this paper we propose a substantially simpler optimization procedure for robust design to solve the dual response problems without resorting to SN. Two examples illustrate this procedure. in the two different experimental design(product array, combined array) approaches.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.294-297
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• 2004

Mean squared error (MSE) is an effective criterion to combine the mean and the standard deviation responses in dual response surface optimization. The bias and variance components of MSE need to be weighted properly in the given problem situation. This paper proposes a systematic method to determine the relative weights of bias and variance in accordance with a decision maker's prior and posterior preference structure.