• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.4B
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• pp.559-565
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• 2010

In Mobile IPv6 handover should be followed by RO(Route Optimization) to support direct communication between a MN(Mobile Node) and CN(Correspondent Node). For this RR MIPv6 must perform RR(Return Routability) procedure before BU(Binding Update) to CN. The Fast Handover for MIPv6(FMIPv6) also performs the RR test for MN to communicate with CN directly. However, Return Routability test has long latency resulting in handover delay in MIPv6. This paper proposes the method to reduce the handover deay by reducing RO latency in FastMobile IPv6.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.104-108
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• 2003

Finite element (FE) model updating is a procedure to minimize the differences between analytical and experimental results, which can be usually posed as an optimization problem. This paper aims to introduce a hybrid optimization algorithm (GA-SA), which consists of a Genetic algorithm (GA) stage and an Adaptive Simulated Annealing (ASA) stage, to FE model updating for a shrunk shaft. A good agreement of the first four natural frequencies has been achieved obtained from GASA based updated model (FEgasa) and experiment. In order to prove the validity of GA-SA, comparisons of natural frequencies obtained from the initial FE model (FEinit), GA based updated model (FEga) and ASA based updated model (FEasa) are carried out. Simultaneously, the FRF comparisons obtained from different FE models and experiment are also shown. It is concluded that the GA, ASA, GA-SA are powerful optimization techniques which can be successfully applied to FE model updating, the natural frequencies and FRF obtained from all the updated models show much better agreement with experiment than that obtained from FEinit model. However, FEgasa is proved to be the most reasonable FE model, and also FEasa model is better than FEga model.

• International Journal of Aeronautical and Space Sciences
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• v.9 no.2
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• pp.18-33
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• 2008

This paper presents design optimization of a new Active Twist Rotor (ATR) blade and conducts its aeroelastic analysis in forward flight condition. In order to improve a twist actuation performance, the present ATR blade utilizes a single crystal piezoelectric fiber composite actuator and the blade cross-sectional layout is designed through an optimization procedure. The single crystal piezoelectric fiber composite actuator has excellent piezoelectric strain performance when compared with the previous piezoelectric fiber composites such as Active Fiber Composites (AFC) and Macro Fiber Composites (MFC). Further design optimization gives a cross-sectional layout that maximizes the static twist actuation while satisfying various blade design requirements. After the design optimization is completed successfully, an aeroelastic analysis of the present ATR blade in forward flight is conducted to confirm the efficiency in reducing the vibratory loads at both fixed- and rotating-systems. Numerical simulation shows that the present ATR blade utilizing single crystal piezoelectric fiber composites may reduce the vibratory loads significantly even with much lower input-voltage when compared with that used in the previous ATR blade. However, for an application of the present single crystal piezoelectric actuator to a full scaled rotor blade, several issues exist. Difficulty of manufacturing in a large size and severe brittleness in its material characteristics will need to be examined.

• International Journal of Control, Automation, and Systems
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• v.6 no.4
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• pp.468-476
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• 2008

This paper presents a novel approach to economic dispatch (ED) with nonconvex fuel cost function as combinatorial optimization problems (COP) while most of the conventional researches have been developed as function optimization problems (FOP). One nonconvex fuel cost function can be divided into several convex fuel cost functions, and each convex function can be regarded as a generation type (G-type). In that case, ED with nonconvex fuel cost function can be considered as COP finding the best case among all feasible combinations of G-types. In this paper, a genetic algorithm is applied to solve the COP, and the $\lambda$-P table method is used to calculate ED for the fitness function of GA. The $\lambda$-P table method is reviewed briefly and the GA procedure for COP is explained in detail. This paper deals with three kinds of ED problems, namely ED considering valve-point effects (EDVP), ED with multiple fuel units (EDMF), and ED with prohibited operating zones (EDPOZ). The proposed method is tested for all three ED problems, and the test results show an improvement in solution cost compared to the results obtained from conventional algorithms.

• KIEE International Transactions on Power Engineering
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• v.5A no.3
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• pp.303-308
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• 2005

The restructure of the electrical power industry is accompanied by the extension of the electrical power exchange. One of the key pieces of information used to determine how much power can be transferred through the network is known as available transfer capability (ATC). The traditional ATC deterministic approach is based on the severest case and it involves a complex procedure. Therefore, a novel approach for A TC calculation is proposed using cost optimization in this paper. The Jeju Island interconnected HVDC system has inland KEPCO (Korean Electric Power Corporation) systems, and its demand is increasing at the rate of about $\10[%]$ annually. To supply this increasing demand, the capability of the HVDC system must be enlarged. This paper proposes the optimal transfer capability of the HVDC system between Haenam in the inland and Jeju in Cheju Island through cost optimization. The cost optimization is based on generating cost in Jeju Island, transfer cost through Jeju-Haenam HVDC system and outage cost with one depth (N-1 contingency).

• Journal of the Society of Naval Architects of Korea
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• v.43 no.1 s.145
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• pp.96-102
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• 2006

In this study, an optimization routine with genetic algorithms is coupled for the selection of free variables for the production of a control signal for the motion of wave board in the numerical wave tank. An excitation function for the controlling of the wave board is formulated on basis of amplitude modulation for the generation of nonlinear wave packets. The found variables by the optimization serve for the determination of wave board motion both with the computation and with the experiment. The breaking criterion of the water waves is implemented as boundary condition for the optimization procedure. With the analysis of the time registration on the local position in the wave tank the optimization routine is accomplished until the given design wave with defined surface elevation is found. Water surface elevation and associated fields of velocity and pressure are numerically computed.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.6
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• pp.319-325
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• 2023

In this study, we proposed a method for hyperparameter optimization in the building and training of a deep learning model designed to process point cloud data collected by a millimeter-wave radar system. The primary aim of this study is to facilitate the deployment of a baseline model in resource-constrained IoT devices. We evaluated a RadHAR baseline deep learning model trained on a public dataset composed of point clouds representing five distinct human activities. Additionally, we introduced a coarse-to-fine hyperparameter optimization procedure, showing substantial potential to enhance model efficiency without compromising predictive performance. Experimental results show the feasibility of significantly reducing model size without adversely impacting performance. Specifically, the optimized model demonstrated a 3.3% improvement in classification accuracy despite a 16.8% reduction in number of parameters compared th the baseline model. In conclusion, this research offers valuable insights for the development of deep learning models for resource-constrained IoT devices, underscoring the potential of hyperparameter optimization and model size reduction strategies. This work contributes to enhancing the practicality and usability of deep learning models in real-world environments, where high levels of accuracy and efficiency in data processing and classification tasks are required.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.20 no.1
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• pp.9-18
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• 2007

This study shows an implementation of partial holes in an initial design domain in order to improve convergences of topology optimization algorithms. The method is associated with a bubble method as introduced by Eschenauer et al. to overcome slow convergence of boundary-based shape optimization methods. However, contrary to the bubble method, initial holes are only implemented for initializations of optimization algorithm in this approach, and there is no need to consider a characteristic function which defines hole's deposition during every optimization procedure. In addition, solid and void regions within the initial design domain are not fixed but merged or split during optimization Procedures. Since this phenomenon activates finite changes of design parameters without numerically calculating movements and positions of holes, convergences of topology optimization algorithm can be improved. In the present study, material topology optimization designs of Michell-type beam utilizing the initial design domain with initial holes of varied sizes and shapes is carried out by using SIMP like a density distribution method. Numerical examples demonstrate the efficiency and simplicity of the present method.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.6
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• pp.568-583
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• 2015

In this paper, the conceptual design procedure and results of Earth observation satellite through Multidisciplinary Design Optimization (MDO) are described. The conceptual design equations for major parameters are developed based on the established database of Earth observation satellite so far. The MDO conceptual design tool for Earth observation satellite was developed by applying the Collaborative Optimization (CO) architecture amongst several MDO architecture techniques available today. The objective for this research was set to minimize the total mass of satellite as well as satisfy all design constraints by utilizing the Sequential Quadratic Programming (SQP) algorithm. Eventually the effectiveness of MDO conceptual design tool was verified through proposing a comparison between the conceptual design results with MDO applied and the design specification of ASNARO-1 & IKONOS-2 Earth observation satellite.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.9
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• pp.39-45
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• 2009

When Mobile IPv6 is deployed in commercial network, a mobile node needs AAA services for an authentication, authorization and accounting. AAA and Mobile IPv6 are protocols which are operated independently. Then schemes which merge these protocols have been emerged. These schemes can enable a mobile node to establish a security association between the mobile node and a home agent and to perform a binding update for the home agent using AAA authentication request. But these schemes introduce many signal messages and long handover latency during the handover, since Route Optimization mode for Mobile Ipv6 is performed using Return Routability procedure. To solve this problem, we propose a scheme for Route Optimization mode that the home agent performs the binding update for a correspondent node via the AAA infrastructure between the home agent and the correspondent node instead of Return Routability procedure. For performance evaluation, we analyze signal message transmission costs and handover latencies during handover. We show performance improvement of the proposed scheme which reduces handover latency as 61% compared with the existing scheme.